Journal of Experimental Botany

Baucher, Marie;Jaziri, Mondher El;Vandeputte, Olivier

doi: 10.1093/jxb/erm185pmid: 17898423

Abstract Vascular tissue differentiation is essential to enable plant growth and follows well-structured and complex developmental patterns. Based on recent data obtained from Arabidopsis and Populus, advances in the understanding of the molecular basis of vascular system development are reviewed. As identified by forward and/or reverse genetics, several gene families have been shown to be involved in the proliferation and identity of vascular tissues and in vascular bundle patterning. Although the functioning of primary meristems, for example the shoot apical meristem (SAM), is well documented in the literature, the genetic network that regulates (pro)cambium is still largely not deciphered. However, recent genome-wide expression analyses have identified candidate genes for secondary vascular tissue development. Of particular interest, several genes known to regulate the SAM have also been found to be expressed in the vascular cambium, highlighting possible overlapping regulatory mechanisms between these two meristems. Arabidopsis, cambium, phloem, Populus, primary growth, secondary growth, vascular system development, xylem Introduction Higher plants have acquired unique developmental mechanisms enabling them to cope with their sessile status. The mature plant embryo comprises structures required for seedling outgrowth: the cotyledon(s), the embryonic axis, the shoot apical meristem (SAM), the root apical meristem (RAM), and a network of procambial cells. The development of tissues and organs required for further plant growth occurs post-embryonically through the activity of both primary (SAM and RAM) and secondary meristems (phellogen and vascular cambium) (reviewed by Jürgens, 2001). Meristems, the driving forces of plant growth, are niches of cells responding to the two criteria defining stem cells, self-maintenance and the ability to give rise to daughter cells capable of differentiating into at least one specialized cell type (Laux, 2003; Sablowski, 2004; Scheres, 2007). Stem cells in the SAM divide infrequently and produce either daughter stem cells or cells that will divide more rapidly and undergo differentiation when displaced at a certain distance from the stem cells (Stewart and Dermen, 1970; Grandjean et al., 2004; Reddy et al., 2004). The indeterminate growth resulting from the activity of these primary meristems allows the spatial deployment of both aerial and underground organs, thereby providing plants with an efficient photosynthetic activity and a functional translocation of water, nutrient, and signalling molecules throughout their life. However, as plants grow, the expansion of their assimilation surfaces requires adapted growth features, including the implementation of appropriate supporting and conducting tissues. This developmental adaptation is of particular importance for perennial species such as trees since it contributes to their longevity and robustness. During evolution, these requirements were largely met by the acquisition of fundamental changes in cell wall ultrastructure and plant architecture, ensuring long-distance conduction and mechanical support. Firstly, the incorporation of lignin, resulting from the polymerization of monolignols into the cell wall of specialized cells (such as fibres and tracheary elements), has strengthened the supporting system of plants. Lignin is considered to be absent from algae, but the presence of lignin-like compounds has been reported in Coleochaete, an algal model for land plant ancestry (Delwiche et al., 1989). The Coleochaete lignin-related structures were identified by a positive Maüle reaction and by the resistance of cell walls of fertile thalli to acetolysis degradation. As suggested by Delwiche et al. (1989), this lignin ancestor could have played a role as an antimicrobial agent that predated its function as a structural cell wall component. Secondly, the implementation of the phellogen and the vascular cambium, at the origin of protective and secondary vascular tissues respectively, led to the enlargement of the girth of plant axes. Secondary growth occurs in gymnosperms and in most dicotyledonous species, but not in monocotyledons nor in ferns. The appearance of lignification in the Lower Devonian (−409 to −386 My) (Ewbank et al., 1996), followed by vascular cambium emergence during the Middle Devonian (-386 to −377 My) (Rowe and Speck, 2005), represent key innovations for vascular plants as they allowed plants to adapt both their size and their growth forms to environmental land conditions. During the last decade, significant progress has been achieved in the knowledge of the molecular mechanisms that are involved in the establishment of the vascular system. In this review, the focus is on data collected mainly from Arabidopsis, which is widely used to study primary growth, and Populus, which is a model plant to study secondary growth. Advances in the identification of molecular determinants for vascular development are presented. Finally, based mainly on expression data, emphasis is laid on the hypothesis that some regulatory mechanisms acting on SAM are co-opted by the vascular cambium. Molecular determinants of vascular development during primary growth phase In Arabidopsis, procambium precursor cells can be discerned during the transition from the globular to the heart stage of embryogenesis (West and Harada, 1993). In the Arabidopsis embryo, no mature or differentiating vascular elements have been identified and the vascular system is composed of a continuous network of procambial cells distributed along the hypocotyl–root axis and the cotyledons (Busse and Evert, 1999a). After seed germination and during primary growth of the stem, the procambium produces xylem centripetally and phloem centrifugally, leading to the formation of vascular bundles that are arranged along a ring passing through the ground tissue (Esau, 1960) (Fig. 1B). Xylem is composed of different cell types including tracheary elements, fibres, and parenchyma cells. Phloem is also a complex tissue consisting of sieve elements, companion cells, fibres, and parenchyma cells. Several genes have been found to be involved in the setting up of the vascular system. Their expression pattern and the consequences of their mutation or overexpression on vascular tissue development are described in the following sections and summarized in Table 1. Table 1. Molecular determinants for vascular development in Arabidopsis: expression patterns and phenotypic alterations upon modification of gene expression Gene family  Gene name  Post-embryonic expression in vascular tissues  Loss-of-function phenotype  Gain-of-function phenotype  Downregulation  Overexpression  References  1. HD-ZIP III (adaxial specification)    ATHB8  Procambium, parenchyma cells surrounding vessels, induced by wounding and IAA  No phenotype  No phenotype  No phenotype  Wider VB, more xylary procambial cells, more SXy production and more Ph fibres, lignin in the pith  Baima et al., 1995, 2001; Prigge et al., 2005; Pineau et al., 2005    PHV  Pith  No phenotype  Amphivasal VB  No phenotype  –  McConnell et al., 2001; Prigge et al., 2005    PHB  Vascular tissues  No phenotype  Amphivasal VB  No phenotype  –  McConnell and Barton, 1998; Prigge et al., 2005; Williams et al., 2005    CNA  Procambium  VB less well distributed  Reduced lignified tissue, reduced number of VB  Dwarf plants, stem fasciation, increased Xy production, increased number of VB  No phenotype  Ohashi-Ito and Fukuda, 2003; Prigge et al., 2005; Kim et al., 2005; Green et al., 2005    REV  IF, VB  Pendent stems, no IF fibres, reduced cell number in VB, reduced SXy differentiation, reduced PAT  Stem fasciation, amphivasal VB, ectopic VB  No phenotype  No phenotype  Emery et al., 2003; Zhong et al., 1997, 1999; Zhong and Ye, 2001, 2004; Prigge et al., 2005;1999, Talbert et al., 1995  Double and triple mutants in HD-ZIP III genes    cna phv  NA  Ectopic VB  NA  NA  NA  Green et al., 2005    rev phb/rev phv  NA  Enhanced rev phenotype  NA  NA  NA  Prigge et al., 2005    cna phb phv  NA  Ectopic amphivasal VB  NA  NA  NA  Green et al., 2005; Prigge et al., 2005    rev phb phv  NA  Amphicribal VB  NA  NA  NA  Emery et al., 2003    rev cna athb8  NA  Reduced rev phenotype  NA  NA  NA  Prigge et al., 2005  2. miRNA (abaxial specification?)    miR166  Adaxial region of cotyledons and provascular tissues in embryos  –  Stem fasciation, disturbed radial patterning, ectopic amphivasal VB in stem  –  Stem fasciation  Kidner and Martienssen, 2004; Kim et al., 2005; Jung and Park, 2007; Williams et al., 2005    miR165  Detected throughout the entire embryo  –  –  –  Stem fasciation, reduced leaf venation, abnormal leaf polarity, affected SAM formation, anthocyanin accumulation, pendent stems, reduced cell number in VB, reduced number of IF  Li et al., 2005; Zhou et al., 2007, Jung and Park, 2007  3. KANADI (abaxial specification)    KAN1  Limited to root Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001    KAN2  Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001    KAN3  Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001  Double and triple mutants in KANADI genes    kan1 kan2  NA  Amphivasal VB  NA  NA  NA  Eshed et al., 2004; Emery et al., 2003    kan1 kan2 kan3  NA  Amphivasal VB  NA  NA  NA  Eshed et al., 2004; Emery et al., 2003  4. Histidine kinase/Cytokinin receptor (procambial cell division)    WOL  Procambium  Reduction in number of procambial cells, absence of Ph in root and lower part of Hy  NA  NA  NA  Scheres et al., 1995; Mähönen et al., 2000  5. MYB (phloem identity)    APL  Ph  Ectopic formation of Xy in position of Ph  –  –  Repressed Xy differentiation (WOL promoter)  Bonke et al., 2003  6. NAC (vessel cell fate)    VND6  Metaxylem in root  No phenotype  –  No phenotype  Ectopic metaxylem  Kubo et al., 2005    VND7  Immature protoxylem in root, vascular cells in shoot  No phenotype  –  No phenotype  Ectopic protoxylem  Kubo et al., 2005  7. COV1 (ordered patterning of vascular bundles)    COV1  Not described  Stunted growth and wrinkled leaves, increased vascular development in place of IF tissue, loss of defined VB  –  –  –  Parker et al., 2003  8. Spermine synthase (polyamine biosynthesis pathway)    ACL5/TKV  Procambium, VB  Dwarf plants, reduction in internode length, increase in Xy and Ph, reduced PAT, increased HD-ZIP III transcript level  –  Semi-dwarf plants  –  Hanzawa et al., 1997, 2000; Clay and Nelson, 2005  9. KNOX1 (lignin biosynthesis pathway)    BP/KNAT1  Cortex adjacent to vascular cells, Ph occasionally  Short internodes and pedicels, VB closer to each other, premature lignin deposition in IF tissue, less lignin in VB, defect in VB organization, gaps of non-lignified cells  –  –  Delayed lignindeposition  Lincoln et al., 1994Venglat et al., 2000Douglas et al., 2002Smith and Hake, 2003 Douglas and Riggs, 2005  10. Sterol methyltransferase (sterol biosynthesis pathway)    CVP1(SMT2)  Cotyledon vascular cells  Defects in venation, more Xy and lignified sclerenchyma  –  –  No phenotype  Carland et al., 1999, 2002  11. Brassinosteroid receptor (Xy/Ph patterning)    BRL1  Procambium  More Ph, less Xy  –  –  –  Caño-Delgado et al., 2004    BRL3  Ph in leaves  No phenotype  –  –  –  Caño-Delgado et al., 2004  Gene family  Gene name  Post-embryonic expression in vascular tissues  Loss-of-function phenotype  Gain-of-function phenotype  Downregulation  Overexpression  References  1. HD-ZIP III (adaxial specification)    ATHB8  Procambium, parenchyma cells surrounding vessels, induced by wounding and IAA  No phenotype  No phenotype  No phenotype  Wider VB, more xylary procambial cells, more SXy production and more Ph fibres, lignin in the pith  Baima et al., 1995, 2001; Prigge et al., 2005; Pineau et al., 2005    PHV  Pith  No phenotype  Amphivasal VB  No phenotype  –  McConnell et al., 2001; Prigge et al., 2005    PHB  Vascular tissues  No phenotype  Amphivasal VB  No phenotype  –  McConnell and Barton, 1998; Prigge et al., 2005; Williams et al., 2005    CNA  Procambium  VB less well distributed  Reduced lignified tissue, reduced number of VB  Dwarf plants, stem fasciation, increased Xy production, increased number of VB  No phenotype  Ohashi-Ito and Fukuda, 2003; Prigge et al., 2005; Kim et al., 2005; Green et al., 2005    REV  IF, VB  Pendent stems, no IF fibres, reduced cell number in VB, reduced SXy differentiation, reduced PAT  Stem fasciation, amphivasal VB, ectopic VB  No phenotype  No phenotype  Emery et al., 2003; Zhong et al., 1997, 1999; Zhong and Ye, 2001, 2004; Prigge et al., 2005;1999, Talbert et al., 1995  Double and triple mutants in HD-ZIP III genes    cna phv  NA  Ectopic VB  NA  NA  NA  Green et al., 2005    rev phb/rev phv  NA  Enhanced rev phenotype  NA  NA  NA  Prigge et al., 2005    cna phb phv  NA  Ectopic amphivasal VB  NA  NA  NA  Green et al., 2005; Prigge et al., 2005    rev phb phv  NA  Amphicribal VB  NA  NA  NA  Emery et al., 2003    rev cna athb8  NA  Reduced rev phenotype  NA  NA  NA  Prigge et al., 2005  2. miRNA (abaxial specification?)    miR166  Adaxial region of cotyledons and provascular tissues in embryos  –  Stem fasciation, disturbed radial patterning, ectopic amphivasal VB in stem  –  Stem fasciation  Kidner and Martienssen, 2004; Kim et al., 2005; Jung and Park, 2007; Williams et al., 2005    miR165  Detected throughout the entire embryo  –  –  –  Stem fasciation, reduced leaf venation, abnormal leaf polarity, affected SAM formation, anthocyanin accumulation, pendent stems, reduced cell number in VB, reduced number of IF  Li et al., 2005; Zhou et al., 2007, Jung and Park, 2007  3. KANADI (abaxial specification)    KAN1  Limited to root Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001    KAN2  Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001    KAN3  Ph  No phenotype  –  –  Lack of vasculature  Emery et al., 2003; Eshed et al., 2001; Kerstetter et al., 2001  Double and triple mutants in KANADI genes    kan1 kan2  NA  Amphivasal VB  NA  NA  NA  Eshed et al., 2004; Emery et al., 2003    kan1 kan2 kan3  NA  Amphivasal VB  NA  NA  NA  Eshed et al., 2004; Emery et al., 2003  4. Histidine kinase/Cytokinin receptor (procambial cell division)    WOL  Procambium  Reduction in number of procambial cells, absence of Ph in root and lower part of Hy  NA  NA  NA  Scheres et al., 1995; Mähönen et al., 2000  5. MYB (phloem identity)    APL  Ph  Ectopic formation of Xy in position of Ph  –  –  Repressed Xy differentiation (WOL promoter)  Bonke et al., 2003  6. NAC (vessel cell fate)    VND6  Metaxylem in root  No phenotype  –  No phenotype  Ectopic metaxylem  Kubo et al., 2005    VND7  Immature protoxylem in root, vascular cells in shoot  No phenotype  –  No phenotype  Ectopic protoxylem  Kubo et al., 2005  7. COV1 (ordered patterning of vascular bundles)    COV1  Not described  Stunted growth and wrinkled leaves, increased vascular development in place of IF tissue, loss of defined VB  –  –  –  Parker et al., 2003  8. Spermine synthase (polyamine biosynthesis pathway)    ACL5/TKV  Procambium, VB  Dwarf plants, reduction in internode length, increase in Xy and Ph, reduced PAT, increased HD-ZIP III transcript level  –  Semi-dwarf plants  –  Hanzawa et al., 1997, 2000; Clay and Nelson, 2005  9. KNOX1 (lignin biosynthesis pathway)    BP/KNAT1  Cortex adjacent to vascular cells, Ph occasionally  Short internodes and pedicels, VB closer to each other, premature lignin deposition in IF tissue, less lignin in VB, defect in VB organization, gaps of non-lignified cells  –  –  Delayed lignindeposition  Lincoln et al., 1994Venglat et al., 2000Douglas et al., 2002Smith and Hake, 2003 Douglas and Riggs, 2005  10. Sterol methyltransferase (sterol biosynthesis pathway)    CVP1(SMT2)  Cotyledon vascular cells  Defects in venation, more Xy and lignified sclerenchyma  –  –  No phenotype  Carland et al., 1999, 2002  11. Brassinosteroid receptor (Xy/Ph patterning)    BRL1  Procambium  More Ph, less Xy  –  –  –  Caño-Delgado et al., 2004    BRL3  Ph in leaves  No phenotype  –  –  –  Caño-Delgado et al., 2004  Hy, hypocotyl; IF, interfascicular; NA, not applicable; PAT, polar auxin transport; Ph, phloem; SXy, secondary xylem; VB, vascular bundle; Xy, xylem; -, not described; ? not confirmed by Li et al. (2005). View Large Genes involved in adaxial/abaxial specification The class III homeodomain-leucine zipper (HD-ZIP III) gene family comprises five members in Arabidopsis, ATHB8, PHABULOSA/ATHB14 (PHB), PHAVOLUTA/ATHB9 (PHV), CORONA/ATHB15 (CNA), and REVOLUTA/INTERFASCICULAR FIBERLESS1 (REV/IFL). These genes have been associated with several developmental processes including embryo patterning, meristem initiation, meristem regulation, organ polarity, and vascular development (Talbert et al., 1995; McConnell and Barton, 1998; McConnell et al., 2001; Emery et al., 2003; Prigge et al., 2005) (Fig. 1). During Arabidopsis embryogenesis, ATHB8 expression is restricted to procambial cells of torpedo stage embryos and of developing organs, as demonstrated by in situ mRNA localization (Baima et al., 1995). In Arabidopsis seedlings, the expression of an ATHB8 promoter–GUS fusion (pATHB8::GUS) was localized in the region where the vasculature will be formed in cotyledons and leaflets as well as in the procambium of cotyledons and roots (Baima et al., 1995). Ohashi-Ito and Fukuda (2003) showed, by promoter–GUS analysis, that CNA had an expression pattern similar to the one of ATHB8 in young leaves and in roots. By in situ mRNA localization, the expression of PHV, PHB, REV, and CNA was detected in the adaxial region of embryos including procambial cells starting from the heart/torpedo stage (McConnell et al., 2001; Otsuga et al., 2001; Emery et al., 2003; Prigge et al., 2005; Williams et al., 2005). Fig. 1. View largeDownload slide Schematic outline of relevant molecular determinants involved in the specification of adaxial/abaxial patterning during the early organ differentiation from the Arabidopsis SAM and during vascular bundle formation. For the references, see text. (A) In a functional SAM, the stem cell fate is maintained by the CLV–WUS feedback loop. STM, which is expressed throughout the meristem, is down-regulated in founder cells and is proposed to restrict differentiation by repressing AS genes. The AS gene products maintain the repressed state of BP and KNAT2 during leaf development. HD-ZIP III gene activity maintains adaxial/xylem differentiation and their expression is repressed by miR165/166 expressed in the abaxial domain of developing leaves. KANADI activity promotes abaxial/phloem tissue differentiation. (B) Stem development stages in angiosperms. A cross-section beneath the SAM shows procambial strands within ground tissue that represents an early stage in primary stem development. At completion of primary growth, procambial cells produce xylem and phloem forming the vascular bundles. The transition from primary to secondary growth is associated with the formation and functioning of vascular cambium originating from procambium within the vascular bundles and from parenchyma cells in the interfascicular region. (C) Patterns of phloem and xylem within vascular bundles. Collateral bundles are formed in WT and amphivasal and amphicribal bundles in mutants of several genes that are involved in abaxial and adaxial specification. Fig. 1. View largeDownload slide Schematic outline of relevant molecular determinants involved in the specification of adaxial/abaxial patterning during the early organ differentiation from the Arabidopsis SAM and during vascular bundle formation. For the references, see text. (A) In a functional SAM, the stem cell fate is maintained by the CLV–WUS feedback loop. STM, which is expressed throughout the meristem, is down-regulated in founder cells and is proposed to restrict differentiation by repressing AS genes. The AS gene products maintain the repressed state of BP and KNAT2 during leaf development. HD-ZIP III gene activity maintains adaxial/xylem differentiation and their expression is repressed by miR165/166 expressed in the abaxial domain of developing leaves. KANADI activity promotes abaxial/phloem tissue differentiation. (B) Stem development stages in angiosperms. A cross-section beneath the SAM shows procambial strands within ground tissue that represents an early stage in primary stem development. At completion of primary growth, procambial cells produce xylem and phloem forming the vascular bundles. The transition from primary to secondary growth is associated with the formation and functioning of vascular cambium originating from procambium within the vascular bundles and from parenchyma cells in the interfascicular region. (C) Patterns of phloem and xylem within vascular bundles. Collateral bundles are formed in WT and amphivasal and amphicribal bundles in mutants of several genes that are involved in abaxial and adaxial specification. In cross-sections of Arabidopsis inflorescence stems, Zhong and Ye (1999) showed by both in situ mRNA localization and promoter–GUS analysis that REV is expressed in interfascicular fibres and in vascular bundles. Pineau et al. (2005) reported that pATHB8::GUS is expressed in parenchyma cells surrounding vessel elements with only a trace amount of expression in the fascicular cambium. In longitudinal sections of Arabidopsis inflorescence stems, in situ mRNA localization revealed that each of the HD-ZIP III genes is expressed in vascular tissues, although PHV expression was only weakly detected in pith cells (Prigge et al., 2005). Forward and reverse genetic studies of HD-ZIP III function in Arabidopsis have indicated their possible role in the development of the vascular system. Single loss-of-function mutants have been analysed for each family member and, whereas no phenotype has been observed for athb8, phb, and phv (Baima et al., 2001; Emery et al., 2003; Prigge et al., 2005), alterations of vascular differentiation were observed for cna and rev mutants. In single cna mutants, vascular bundles are less well distributed around the stem periphery and expanded lignified tissues are observed (Prigge et al., 2005). A similar phenotype was obtained in antisense CNA transgenic Arabidopsis (Kim et al., 2005). Mutation of REV not only abolished the differentiation of interfascicular fibres but also reduced the number of fibres and vessels in vascular bundles as well as secondary xylem differentiation (Talbert et al., 1995; Zhong et al., 1997; Zhong and Ye, 1999; Prigge et al., 2005). According to Zhong and Ye (2001), the polar auxin transport (PAT) was reduced by 70% in the rev mutant. PAT is known to be essential for the patterning of procambium and vascular tissues (Sachs, 1981; Mattsson et al., 2003; reviewed by Scarpella and Meijer, 2004). In Arabidopsis, several potential PAT candidate genes were shown to be essential for vascularization such as PIN-FORMED (PIN1) encoding a putative auxin efflux carrier (Gälweiler et al., 1998) and GNOM encoding a guanine-nucleotide exchange factor for ADP-ribosylation factor G protein known to be responsible for the polar localization of PIN1 (Steinmann et al., 1999; Geldner et al., 2003). Mutation in either gene hampers normal basipetal transport of auxin and results, in addition to the failure to develop lateral organs, in dramatic alterations of vascular differentiation, such as localized proliferation of vascular tissue and the formation of discontinuous vascular bundles (Gälweiler et al., 1998; Steinmann et al., 1999; Koizumi et al., 2000). Similar alterations have been observed in plants in which PAT was chemically inhibited (Mattsson et al., 1999) or in plants that overproduce auxin (Klee et al., 1987; Uggla et al., 1996) supporting a role for auxin gradients in the specification of vascular differentiation sites during plant development. PHB and PHV gain-of-function mutants (McConnell and Barton, 1998; McConnell et al., 2001) present amphivasal bundles in leaves, with xylem surrounding the phloem (Fig. 1C). Similarly, the REV gain-of-function mutant, also called amphivasal vascular bundle1 (avb1), is characterized by amphivasal bundles in leaves as well as in stems where, in addition to the ring-like arrangement of vascular bundles, extra ectopic vascular bundles are present in the pith (Zhong et al., 1999; Emery et al., 2003; Zhong and Ye, 2004) (Fig. 1C). These three gain-of-function mutations are located in a microRNA target sequence (recognized by miR165 or miR166), indicating that HD-ZIP III genes are under the control of microRNA regulation through mRNA cleavage (McConnell et al., 2001; Mallory et al., 2004; Zhong and Ye, 2004). By in situ hybridization using pre-miR165 and pre-miR166 antisense probes, miR165 and miR166 have been shown to be expressed in the abaxial domain of developing leaves, suggesting that the occurrence of these microRNAs could specify abaxial fate (Juarez et al., 2004; Kidner and Martienssen, 2004) (Fig. 1A). However, by using a 4-concatenate miR165 and a pre-miR165 antisense probe, Li et al. (2005) revealed a non-polar miR165/166 distribution pattern in leaf primordia. These authors suggested that miR165/166 may be required for the development of the entire leaf and not only for the abaxial domain. These divergent data, probably due to the occurrence of several members of miR165/166, should be clarified by localizing their expression by other methods, such as pmiRNA-reporter gene fusions (Jung and Park, 2007). Overexpression of a miR166-resistant CNA cDNA resulted in a reduced proportion of lignified tissues (Kim et al., 2005). These results indicate a role for CNA, as negative regulator, in the production of xylem (Kim et al., 2005). Moreover, since the overexpression of ATHB8 resulted in an increased production of xylem (Baima et al., 1995, 2001), Kim et al. (2005) suggested that ATHB8 and CNA could have antagonistic roles during xylem development. In the miR166a gain-of-function mutant men1 (meristem enlargement 1) (Kim et al., 2005) and in the miR166g gain-of-function mutant jabba-1D (jba-1D) (Williams et al., 2005), levels of CNA, PHV, and PHB transcripts were reduced and the mutants had fasciated inflorescence stems with disrupted radial vascular patterning, including amphivasal ectopic bundles within the pith (Fig. 1C). The overexpression of miR165 led to a reduction of the transcript level of the five HD-ZIP III genes (Zhou et al., 2007) (Fig. 1A). These transgenic lines were affected in SAM formation, had impaired vein development and alteration in organ polarity. In cross-sections of inflorescence stems of miR165 overexpressors, a reduced number of cells was observed in the vascular bundles and fewer interfascicular fibres developed between the vascular bundles when compared with the wild type (WT), a phenotype reminiscent of the rev mutant (Zhou et al., 2007). As suggested by these authors, miR165 and miR166 seem to differentially regulate the expression of HD-ZIP III genes, probably as a consequence of distinct effectiveness of miR165 and miR166 on the cleavage of their target genes and/or different specificities in tissue expression. The REV gene seems to be the principal determinant in vascular development since a single mutation of this gene confers a pendent stem phenotype, probably due to the lack of interfascicular fibres (Zhong and Ye, 1999). Moreover, since rev phb or rev phv double mutants have phenotypes similar to each other, corresponding to enhanced vascular defects of the rev single mutants (Prigge et al., 2005), and rev phb phv triple mutant displays amphicribal vascular bundles with phloem surrounding the xylem (Emery et al., 2003), it can be assumed that these three genes interact in a complex and not-yet-elucidated regulation network acting on vascular system development. cna phb phv triple loss-of-function mutants display ectopic amphivasal bundles within the pith (Prigge et al., 2005; Green et al., 2005), indicating that CNA and REV have distinct roles in vascular development and/or patterning. The KANADI gene family, belonging to the GARP family of transcription factors, has been shown to promote the differentiation of abaxial tissues (Eshed et al., 2001; Kerstetter et al., 2001; Emery et al., 2003) (Fig. 1A). By using promoter–GUS fusions, Emery et al. (2003) showed that KAN2 and KAN3 are expressed in the developing phloem along the entire plant whereas KAN1 expression is limited to the root phloem. kan1 kan2 double mutants (Eshed et al., 2004) and kan1 kan2 kan3 triple mutants (Emery et al., 2003) exhibit amphivasal bundles, a phenotype also observed for several HD-ZIP III mutants (Table 1; Fig. 1C). Ectopic expression of KAN1, KAN2, or KAN3 using the constitutive CaMV 35S (35S) promoter resulted in a lack of vasculature formation in the cotyledons and the hypocotyl (Eshed et al., 2001; Kerstetter et al., 2001). In conclusion, KAN activity seems to promote differentiation of abaxial/phloem tissue differentiation whereas REV, PHB, and PHV activity is required to maintain the adaxial/xylem differentiation (Fig. 1A). Identification of other vascular development-related genes The wooden leg (wol) mutant is characterized by a reduction in the number of procambial cells in the root and these cells differentiate into xylem but not into phloem (Scheres et al., 1995). These authors observed a similar phenotype in the lower part of the hypocotyl of wol seedlings but not in the upper part of the hypocotyl where phloem differentiation occurs. The absence of phloem in the primary root of wol mutant was apparently due to a deficiency in procambial cell division during embryogenesis (Scheres et al., 1995; Mähönen et al., 2000). In addition, the narrow vascular cylinder of the wol primary root consists solely of protoxylem whereas the WT vascular cylinder is made of both proto- and metaxylem (Caño-Delgado et al., 2000). By in situ mRNA localization in Arabidopsis embryos, WOL was found to be expressed in the precursors of the procambium and in the procambium (Mähönen et al., 2000). WOL [also known as CYTOKININ RESPONSE 1 (CRE1) and ARABIDOPSIS HISTIDINE KINASE 4 (AHK4)] encodes a putative histidine kinase believed to function as a cytokinin receptor (Inoue et al., 2001; Suzuki et al., 2001). ALTERED PHLOEM DEVELOPMENT (APL), belonging to the MYB coiled-coil-type transcription factor family, has been shown to be involved in the determination of phloem identity in Arabidopsis (Bonke et al., 2003). The mutation of APL results in the formation of xylem in the phloem position in both roots and aerial organs (Bonke et al., 2003). When APL was expressed throughout the procambium, under the control of the WOL promoter, cells close to the root tip that normally differentiate into protoxylem remained undifferentiated and metaxylem elements in the vascular cylinder of the root differentiated later than in the WT plants. These data indicate that APL promotes phloem differentiation but also represses xylem differentiation in phloem poles. In mature embryos, APL expression was detected in the prospective phloem tissue of cotyledons and hypocotyls. At later stages of plant development, APL was shown to be expressed in phloem of all organs (Bonke et al., 2003). As suggested by these authors, APL could be required for the asymmetric division of sieve element mother cells as well as for the differentiation of the derived sieve elements and companion cells. The VASCULAR-RELATED NAC-DOMAIN PROTEIN 6 and 7 (VND6 and VDN7), encoding NAC transcription factors, have been shown to be regulators of vessel cell fate (Kubo et al., 2005). These genes were identified by microarray analysis as being up-regulated during in vitro xylem vessel element differentiation in Arabidopsis suspension cell cultures. Expression of pVND7::YFP-NLS was detected in the immature protoxylem vessels just above the root meristem and expression of pVND6::YFP-NLS was restricted to the metaxylem vessels in the middle part of the root (Kubo et al., 2005). When overexpressed in Arabidopsis or poplar under the control of the 35S promoter, these authors showed that VND7 and VND6 induced transdifferentiation of various cells into protoxylem- and metaxylem-like elements, respectively. No phenotype was noticed for vnd6 and vnd7 loss-of-function mutants or for transgenic plants carrying antisense VND6 and VND7. By contrast, the overexpression of the translational fusion of VND7 and VND6 to the SRDX strong repression domain inhibited metaxylem and protoxylem formation, respectively, suggesting that these two VND transcription factors control the expression of specific target genes required for the differentiation of each type of vessel element (Kubo et al., 2005). The continuous vascular ring (cov1) mutant is characterized by an increased vascular development in the stem in place of the interfascicular tissue resulting in a continuous ring-like pattern of xylem and phloem and the loss of defined vascular bundles (Parker et al., 2003). Although the function of COV1 is still unknown, this predicted integral membrane protein, possibly involved in signalization or transport, may play a role in the maintenance or in the initiation of a defined pattern of vascular bundles within the stem (Parker et al., 2003). An increased number of vascular cells has been detected in the Arabidopsis acaulis5 (ACL5)/thickvein (tkv) mutant (Hanzawa et al., 2000; Clay and Nelson, 2005). The acl5 mutants exhibit severe reduction in the internode length of inflorescence stems compared with WT plants (Hanzawa et al., 1997, 2000). Cross-sections of veins or inflorescence stems of acl5 mutants showed a slight increase in the number of cells involved in the vascular system, including xylem, phloem, and cambial-like cells (Hanzawa et al., 1997; Clay and Nelson, 2005). By both in situ mRNA localization and promoter–GUS analysis, ACL5 expression was detected during embryogenesis, starting from early globular embryos and persisting until the bent cotyledon-staged embryos in which the ACL5 expression was limited to procambial cells (Clay and Nelson, 2005). This procambial specific expression pattern was also noticed during primary root development and at early leaf development as well as in axillary buds. In cross-sections of inflorescence stems, GUS expression was detected in vascular bundles. The acl5 mutant exhibited a 34% reduction in PAT, as measured by the basipetal transport of 14C-IAA in excised inflorescence stems (Clay and Nelson, 2005), and ACL5 is induced by auxin (Hanzawa et al., 2000). ACL5 encodes a spermine synthase involved in polyamine biosynthesis (Hanzawa et al., 2000; Clay and Nelson, 2005). Interestingly, the transcript level of the five HD-ZIP III genes, and particularly the one of ATHB8, was increased in acl5 mutants, suggesting that the defect in PAT in the acl5 mutant may cause a local increase in auxin levels which, in turn, results in the induction of HD-ZIP III expression and in the increased number of vascular cells (Imai et al., 2006). Mutants in the homeobox gene BREVIPEDICELLUS (BP), also named KNAT1 and belonging to the class-1 KNOTTED1-like homeobox (KNOX1) family, show defects in the organization of the vascular bundles in inflorescence stems (Smith and Hake, 2003). bp mutants are characterized by short internodes and pedicels due to reduced cell division compared with the WT (Douglas et al., 2002; Venglat et al., 2002). The vascular bundles in the bp mutants were closer to each other compared with the WT and the continuous ring of lignified cells was interrupted in the bp mutants by gaps of non-lignified cells. Epidermal and cortical cells adjacent to these gaps were lignified, suggesting a switch in cell fate (Douglas et al., 2002; Venglat et al., 2002; Mele et al., 2003; Smith and Hake, 2003). By in situ mRNA localization (Lincoln et al., 1994) and promoter–GUS analysis (Venglat et al., 2002; Douglas and Riggs, 2005), BP expression was localized in cortical tissue peripheral to the vascular bundle and occasionally in phloem cells adjacent to the stem cortex. Comparison of the genome expression in bp mutants and WT revealed differences in the expression of genes related to cell wall biosynthesis, and particularly those involved in the lignin biosynthetic pathway (Mele et al., 2003). As shown by these authors, BP seems to be involved in the regulation of lignin biosynthesis since a premature deposition of lignin was observed in the interfascicular region of the inflorescence stem basis in bp mutants. In addition, lignin deposition was delayed in BP overexpressing transgenic plants as compared to the WT. The mutation of COTYLEDON VASCULAR PATTERN 1 (CVP1) results in defects in the normal pattern of vascular bundles in cotyledon (Carland et al., 1999, 2002). Histological analyses of stem cross-sections revealed an increased amount of xylem and lignified sclerenchyma in affected internodes of the mutant (Carland et al., 1999). Since CVP1 encodes a sterol methyltransferase (SMT2), a role for sterols in vascular differentiation and or patterning can be suggested, but their precise function is still not elucidated. In Arabidopsis, BRASSINOSTEROID-INSENSITIVE LIKE 1 (BRL1) and BRL3, encoding two brassinosteroid (BR) receptors, are specifically expressed in vascular tissues, as shown by promoter–GUS analyses (Caño-Delgado et al., 2004). These authors revealed that, in mature Arabidopsis stems, pBRL1::GUS expression was associated with the procambial cells of the vascular bundles whereas pBRL3::GUS expression was localized in the phloem of cotyledons and leaves but not in stems. Besides, the brl1 mutant displays an increased number of phloem cells and a decreased xylem differentiation in the vascular bundles, suggesting that BRL1 plays a role in phloem/xylem patterning (Caño-Delgado et al., 2004). BRs involvement in vascular development is further supported by the findings that inhibition of BRs biosynthesis causes an increased phloem/xylem ratio in Arabidopsis and prevents the differentiation of procambium-like cells into tracheary elements in the Zinnia elegans system (reviewed by Fukuda, 2004). Besides, BRs have been shown to up-regulate the expression of ZeHB-12, a Zinnia REV homologue (Ohashi-Ito et al., 2002). The expression of BRL3 and BRI-ASSOCIATED RECEPTOR KINASE 1-like were shown to be up-regulated in Arabidopsis plants overexpressing ZeHB-12, reinforcing the role of BR in the regulation of xylem differentiation (Ohashi-Ito et al., 2005). Molecular determinants of vascular development during secondary growth Secondary growth is of great economical importance as it results in the production of wood, which is a valuable renewable source of energy and is a raw material for pulping and construction purposes. This developmental process takes place when the vascular cambium initials differentiate from procambium within the vascular bundles (fascicular cambium) and from parenchyma in the interfascicular regions (interfascicular cambium) (Esau, 1965) (Fig. 1B). Cambium activity results in the production of xylem and phloem, in its self-maintenance, in intercellular signal transmission as well as in stem radial expansion (Savidge, 2001). In Arabidopsis, the first histological evidence of vascular cambium formation, a periclinal division of the procambial cells, has been detected in the hypocotyl–root axis and the cotyledonary node of 6-d-old seedlings (Busse and Evert, 1999b). As shown by these authors, secondary growth was clearly visible after 14 d of growth. Recently, the Arabidopsis mutant high cambial activity (hca) was shown to exhibit alteration in cambium activity resulting in dramatic increase in vascular tissue development, but the genetic origin of this mutant is not known (Pineau et al., 2005). The vascular cambium has typically two morphologically distinct types of initials: the axillary elongated fusiform initials that will lead to the formation of the axial system (including tracheids, vessel elements, fibres, axial parenchyma cells, sieve elements, and companion cells) and the smaller isodiametrical ray initials giving rise to the radially orientated parenchymatous rays (Iqbal and Gouse, 1990). The identification of putative regulatory genes associated with cell type identity within the vascular cambium is of particular interest since they are probably specific to cambium and their characterization could help in understanding how cambium works and how secondary vascular tissue develops. For instance, an aspen gene encoding a member of the RING-H2 protein family, called PtaRHE1, has been shown to be expressed in the ray initials and derivatives within the cambial zone, but not in their fusiform counterparts, suggesting a potential role for this gene in the determination and/or the maintenance of cambial cell identity (van Raemdonck et al., 2005). Rays are determinant for secondary growth in plants because they ensure the translocation of nutrients between the phloem and the xylem and the transmission of messenger molecules (Lachaud et al., 1999). The process of secondary growth has been studied in A. thaliana (Lev-Yadun, 1994; Altamura et al., 2001). Although Chaffey et al. (2002) reported the formation of secondary vascular tissues in the hypocotyls of short-day-grown Arabidopsis plants, they noticed a lack of rays, suggesting that there may be some developmental processes characteristic to wood formation in trees that cannot be approached in the Arabidopsis system. A typical organization of secondary vascular tissues is shown in Fig. 2. Anticlinal divisions of the cambium initials cause enlargement of the circumference of the cambial cylinder, whereas periclinal divisions produce phloem or xylem mother cells, called derivatives, leaving initial cells in the meristem. Cambium derivatives may divide several times before differentiating into vascular tissues (Lachaud et al., 1999; Dengler, 2001; Mellerowicz et al., 2001). Because most anatomical criteria are often not sufficient to discriminate between cambial initials and derivatives, the term cambial zone is used to denote these cell types (reviewed by Samuels et al., 2006). However, differences in cell wall composition (Catesson et al., 1994), ultrastructural characteristics (Arend and Fromm, 2003), or in transcriptome profiles (Schrader et al., 2004) between cells within the cambial zone suggest that the differentiation process may occur early in cambial derivatives. Determination of the number and the location of anticlinal divisions within the cambial zone of aspen in the growing season allowed the vascular cambial stem cells to be located on the phloem side of the cambial zone (Schrader et al., 2004). Fig. 2. View largeDownload slide Cross-section of a 6-month-old aspen stem showing the organization of the secondary vascular tissues. The term cambium refers to one or several layers of initial cells composed of fusiform and ray initials. These initials divide periclinally inwards to xylem mother cells and outwards to phloem mother cells. The mother cells, also called derivatives, can, in turn, divide several times before undergoing differentiation. They are collectively called the cambial zone. The axillary elongated fusiform initials will lead to the formation of the axial system (including tracheids, vessel elements, fibres, axial parenchyma cells, sieve elements, and companion cells) and the smaller isodiametrical ray initials giving origin to the radially orientated parenchymatous rays. f, xylem fibre; r, parenchymatous ray; v, vessel. Scale bar=100 μM. Fig. 2. View largeDownload slide Cross-section of a 6-month-old aspen stem showing the organization of the secondary vascular tissues. The term cambium refers to one or several layers of initial cells composed of fusiform and ray initials. These initials divide periclinally inwards to xylem mother cells and outwards to phloem mother cells. The mother cells, also called derivatives, can, in turn, divide several times before undergoing differentiation. They are collectively called the cambial zone. The axillary elongated fusiform initials will lead to the formation of the axial system (including tracheids, vessel elements, fibres, axial parenchyma cells, sieve elements, and companion cells) and the smaller isodiametrical ray initials giving origin to the radially orientated parenchymatous rays. f, xylem fibre; r, parenchymatous ray; v, vessel. Scale bar=100 μM. The strategies used to study molecular processes associated with vascular secondary growth are based on the identification of candidate genes expressed in particular tissues. Transcript profiling either across aspen cambial zones (Schrader et al., 2004) and along the vertical stem segments of a hybrid aspen tree (Prassinos et al., 2005; van Raemdonck et al., 2005) or loblolly pine (Lorenz and Dean, 2002) provided molecular support for candidate genes involved in the setting up of secondary growth and cambium functioning in woody plants. Other strategies such as gene and enhancer trap tagging of vascular expressed genes in poplar have been used to identify genes expressed in the cambial zone (Johansson et al., 2003) and during secondary vascular development (Groover et al., 2004). Emerging from transcriptomic analyses in Arabidopsis and poplar, some genes associated with vascular development during primary growth have been found to be expressed during secondary growth as well. For instance, Populus tremula×P. tremuloides PttKAN1, a homologue of AtKAN1, was shown to have a higher expression toward the phloem side of the cambial zone (Schrader et al., 2004). In Arabidopsis, KAN2 and KAN3 expressions were higher in the phloem or in non-vascular tissues than in the secondary xylem, whereas KAN1 expression was not detected in the root–hypocotyl junction, where secondary growth takes place (Zhao et al., 2005). APL expression was detected in the phloem and cambial zone, but not in the secondary xylem of Arabidopsis (Zhao et al., 2005). Besides, HD-ZIP III genes were shown to be expressed in the cambium region of aspen (Schrader et al., 2004; Ko et al., 2006). P. tremula×P. tremuloides homologues of PHV, PHB (PttHB9), CNA (PttHB15), and AtHB8 (PttHB8) are up-regulated in the aspen cambium xylem side as compared to the phloem side (Hertzberg et al., 2001; Schrader et al., 2004). In P. tremula×P. alba, the expression level of PtaHB1, a poplar REV homologue, was found to increase in the stem segment where primary to secondary growth transition occurs and to accumulate preferentially on the xylem side of the cambium (Ko et al., 2006). These authors showed that the transcript level of PtaHB1 was inversely correlated with that of Pta-miR166. Similarly, in Arabidopsis stems undergoing secondary growth, ATHB8 expression was increased (Ko and Han, 2004; Ko et al., 2004) and the five HD-ZIP III genes were up-regulated in the secondary xylem of Arabidopsis compared to the phloem and cambium (Oh et al., 2003; Zhao et al., 2005). Members of the HD-ZIP III gene family have been shown to play key roles in the establishment of SAM (Otsuga et al., 2001; McHale and Koning, 2004; Green et al., 2005; Prigge et al., 2005) (Fig. 1A). For instance, rev phb (Prigge et al., 2005) and rev phb phv (Emery et al., 2003) mutants lack a functional SAM. Although SAM and vascular cambium produce different structures, all these observations suggest that several regulatory mechanisms may be conserved in these two meristems. Overlapping molecular mechanisms associated with SAM and vascular cambium functioning The functional organization of the SAM relies on three main developmental states (i) maintenance of the stem cells, (ii) prevention from premature cell differentiation, and (iii) initiation of tissues and organs (Baürle and Laux, 2003). The number of stem cells within the SAM is maintained by a balance between cell division and cell differentiation. WUSCHEL (WUS) and CLAVATA (CLV) have been identified as key actors in the control of the size of stem cell population (Fig. 1A). WUS, encoding a putative homeodomain transcription factor, has been shown to confer stem cell identity (Mayer et al., 1998). In the Arabidopsis wus mutant, the SAM is formed during embryogenesis but is not maintained because the stem cells appear to undergo differentiation (Laux et al., 1996). By contrast, Arabidopsis mutants in any of the three CLV1–3 genes have a phenotype opposite to the wus phenotype as they formed enlarged meristems due to the generation of an excess number of cells in the SAM (Clark et al., 1993, 1995; Kayes and Clark, 1998). CLV1 encodes a leucine-rich repeat receptor kinase (Clark et al., 1997), CLV2 a similar protein without the kinase domain (Jeong et al., 1999) and CLV3 a secreted and processed polypeptide (Fletcher et al., 1999; Rojo et al., 2002; Kondo et al., 2006; Ni and Clark, 2006) belonging to the CLV3/EMBRYO-SURROUNDING REGION (ESR) related (CLE) protein family (Cock and McCormick, 2001). The signalling between CLVs and WUS is not totally understood, but it is presumed that CLV3 activates, as a ligand, a CLV1/CLV2 receptor complex which restricts WUS expression (Brand et al., 2000; Schoof et al., 2000). clv mutants, in addition to their enlarged SAM producing an increased number of lateral organs, exhibit also enlarged, flattened stems that contain increased numbers of stem veins (Brand et al., 2000). A comparable phenotype was reported for the cna phb phv triple mutant (Prigge et al., 2005) and the jba-1D mutant (Williams et al., 2005). The clv cna double mutant developed massively enlarged apices compared with the clv meristems (Green et al., 2005). On this basis, Green et al. (2005) suggested that HD-ZIP III genes and the CLV pathway contribute to the regulation of meristem size in a parallel manner. The analysis of WUS expression led Williams et al. (2005) to propose that, in WT plants, PHV, PHB, and CNA restrict SAM activity by down-regulating WUS transcription. In Arabidopsis, other major genes involved in the maintenance of stem cells in an indeterminate state in the SAM are the KNOX1 genes SHOOT MERISTEMLESS (STM) (Barton and Poethig, 1993), BP (considered to be partially redundant to STM; Long et al., 1996; Byrne et al., 2002), and KNAT6 (Belles-Boix et al., 2006). Strong stm mutants fail in the establishment of the SAM indicating that STM is required to specify the SAM cells of the embryo and weaker stm mutants have a lower amount of undifferentiated cells in the SAM, revealing a role for this gene in the maintenance of the SAM as well (Barton and Poethig, 1993; Endrizzi et al., 1996; Long et al., 1996). BP and KNAT6 have been shown to contribute with STM to SAM maintenance (Byrne et al., 2002; Belles-Boix et al., 2006). The down-regulation of KNOX1 genes (STM, BP, KNAT2, and KNAT6) is considered as a key step in leaf initiation and organogenesis (reviewed by Hake et al., 2004; Scofield and Murray, 2006). STM, which is expressed throughout the meristem but down-regulated in organ founder cells (Long et al., 1996; Long and Barton, 2000), has been proposed to restrict differentiation by repressing genes like ASYMMETRIC LEAVES (AS1), encoding a R2R3-MYB protein, and AS2, encoding a cysteine-repeat rich and leucine zipper protein (Byrne et al., 2000, 2002; Iwakawa et al., 2002). The products of AS1 and AS2, expressed in the primordial founder cells and in the primordia, are thought to maintain the repressed state of BP and KNAT2 expression during leaf development (Byrne et al., 2000; Ori et al., 2000; Lenhard et al., 2002) (Fig. 1A). Alternatively, STM and AS1 may competitively regulate common target genes involved in meristem functioning (Scofield and Murray, 2006). The aspen putative orthologues of CLV3 (PttCLV3) and WUS (PttWUS) are expressed in the aspen apex, but their expression was not detected in the vascular cambium (Schrader et al., 2004). In accordance, neither WUS nor CLV3 transcripts were detected in Arabidopsis stems undergoing secondary growth (Ko and Han, 2004; Zhao et al., 2005), but a mechanism similar to the SAM CLV–WUS signalling pathway regulating the functioning of the vascular cambium cannot be excluded. Indeed, the expression of PttHB3 and PttRLK3, two aspen genes respectively related to WUS and CLV1, was detected within the cambial zone and was found to be higher in the xylem than in the phloem. The putative poplar CLV1 orthologue, PttCLV1, was shown to have a higher expression in the phloem than in the cambial zone and the xylem (Schrader et al., 2004). Two other genes encoding CLE proteins, PttCLE;1 and PttCLE;3, had a higher expression in the phloem than in the cambial zone and the xylem (Schrader et al., 2004). In Arabidopsis, a gene related to CLV1 and two members of the CLE family, CLE6 and CLE26, were found to be more expressed in the phloem/cambial zone than in the xylem in the root–hypocotyl junction of Arabidopsis (Zhao et al., 2005). The GFP expression driven by the CLV1 promoter has been localized in the cambial zone and in secondary phloem of the root–hypocotyl junction of Arabidopsis (Zhao et al., 2005). Finally, CLV1 was up-regulated in Arabidopsis stems undergoing secondary growth (Ko and Han, 2004) and CLV2 expression was also detected in the root–hypocotyl junction of Arabidopsis (Zhao et al., 2005). The Populus putative orthologue of STM [PttSTM, Schrader et al., 2004; ARBORKNOX1 (ARK1), Groover et al., 2006] is expressed in shoot apices and in the cambial zone suggesting that similar mechanisms prevent cells from premature differentiation in both SAM and cambium. Accordingly, STM is expressed in Arabidopsis stems undergoing secondary growth (Ko and Han, 2004). The overexpression of either Arabidopsis STM or poplar ARK1 in aspen resulted in a similar bushy and highly branched phenotype, reflecting the formation and the outgrowth of ectopic meristem during primary growth (Groover et al., 2006). Both 35S::STM and 35S::ARK1 had thin stems and the onset of secondary growth in the stem was delayed compared with WT plants. Although a continuous ring of lignified secondary xylem was present at the stem base of 6-month-old trees, the boundary between the cambium and secondary xylem was wavy compared with WT and there were almost no lignified phloem fibres (Groover et al., 2006). Transcriptomic analysis of ARK1 overexpressors showed that 42% of the genes that were up-regulated are involved in extracellular matrix-linked functions including genes encoding fasciclin class arabinogalactan proteins, glycosyl hydrolases, and proteins involved in secondary cell wall biosynthesis (Groover et al., 2006). The poplar KNOX1 genes PttKNOX1, PttKNOX2, and PttKNOX6 were found to be highly expressed in both aspen SAM and the cambial zone, where their expression level was slightly higher on the phloem side than on the xylem side (Schrader et al., 2004). As suggested by these authors, these data are compatible with a regulation model analogous in both the cambium and the SAM of aspen, in which STM expression in the cambial zone would repress the AS1-2 homologue. If xylem and phloem differentiation is regulated by a mechanism similar to the one controlling organ formation in the SAM, it can be expected that a balance exists between AS1-2 and STM homologue gene expression within the cambial zone to promote vascular tissue differentiation. The expression of the closest homologue of AS1 was not detected in the poplar cambial zone (Schrader et al., 2004), suggesting that the differentiation of secondary vascular tissues involves repressor genes different from known AS acting in leaf primordia initiation. Concluding remarks Vascular development is essential for plant growth and relies on a tight integration of cell proliferation, cell-fate determination, cell differentiation, and patterning, leading to xylem and phloem formation. In the last decade, several molecular determinants involved in the regulatory mechanisms controlling these developmental processes in Arabidopsis have been uncovered, thanks to reverse and forward genetics. For instance, the pendent phenotype of the rev mutants, the absence of phloem in the primary root of the wol mutant, the production of xylem in place of phloem in the apl mutant, the increased production of phloem in the brl1 mutant, or the opposite roles of the HD-ZIP III and KAN transcription factors illustrate the complexity of vascular system development in plants. Beside these genetic controls, integration of hormone signals (particularly auxin and BRs) appears to be equally important for the appropriate continuity and patterning of procambium and vascular tissues as manifested by the phenotype of Arabidopsis plants with reduced PAT. Considerations on the functioning of primary (SAM) and secondary (vascular cambium) meristems suggest overlapping regulatory mechanisms between them, but also specific characteristics for each of these two plant meristems (Schrader et al., 2004; Groover, 2005). On the one hand, studies on Arabidopsis and Populus have shown that SAM and vascular cambium both have an indeterminate cell fate, and several genes such as STM, CLV1, KANADI, and members of the HD-ZIP III gene family are probably involved in the genetic regulation of both meristems, suggesting the occurrence of evolutionary conserved processes in the functioning of plant meristems. Important regulatory genes for the maintenance of the stem cells in the SAM, such as WUS and CLV3, have been found to be expressed in the SAM but not in the vascular cambium. Although homologue genes have been reported to be expressed in the cambium (Schrader et al., 2004), their precise role in the cambium still has to be demonstrated. On the other hand, SAM and vascular cambium have several specific features. One of the major differences between the SAM and the cambium is the ability of vegetative SAM to become determinate in order to ensure flower production. By contrast, the indeterminate state of the cambium is critical to ensure perennial growth. Although significant progress has been made in the identification of genes involved in the biosynthesis of cell wall components, including lignin (Boerjan et al., 2003), cellulose (Somerville, 2006), and hemicellulose (Farrokhi et al., 2006; Lerouxel et al., 2006), until now, the molecular determinants for vascular cambium initiation, maintenance, and functioning have not yet been identified, probably because existing developmental genetic analyses are difficult to perform on trees due to their large size and long generation time. The recent availability of the Populus genome sequence (Tuskan et al., 2006) should greatly facilitate the study of aspects linked to woody species, such as secondary growth. The comparison of Arabidopsis and Populus genomes revealed that the relative frequency of protein domains is similar in both genomes (Tuskan et al., 2006). However, these authors showed that Populus has more protein-coding genes than Arabidopsis and that some genes are overrepresented in Populus compared with the Arabidopsis genome, such as genes associated with meristem development and cell wall biosynthesis. Therefore, dissimilarities in growth patterns amongst taxa could result from variations in gene expression levels, but also from the expression of various sets of genes at different stages of growth. Functional approaches including silencing and/or over-expression of candidate genes should be achieved in the near future to take advantage of the results obtained from the transcriptomic analysis towards vascular cambium development and to identify those genes that concern more specifically the vascular cambium versus other plant meristems. The authors would like to warmly thank Sylvia Burssens and Anne-Marie Catesson for critical reading of the manuscript, Pierre Martens for designing Fig. 1 and Sophie Vandeputte for correcting the manuscript. MB is a Research Associate from the ‘Fonds National de la Recherche Scientifique’ (FNRS—Belgian National Funds for the Scientific Research). This work received partial financial support from the ‘Fonds de la Recherche Fondamentale Collective’ (No. 2.4574.06) and from the France–Belgium Tournesol program (2007). 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Castells, Enric;Casacuberta, Josep M.

doi: 10.1093/jxb/erm226pmid: 17951602

Abstract The structure of plant receptor-like kinases (RLKs) is similar to that of animal receptor tyrosine kinases (RTKs), and consists of an extracellular domain, a transmembrane span, and a cytoplasmic domain containing the conserved kinase domain. The mechanism by which animal RTKs, and probably plant RLKs, signal includes the dimerization of the receptor, their intermolecular phosphorylation, and the phosphorylation of downstream signalling proteins. However, atypical RTKs with a kinase-dead domain that signal through phosphorylation-independent mechanisms have also been described in animals. In the last few years, some atypical RLKs have also been reported in plants. Here these examples and their possible signalling mechanisms are reviewed. Plant genomes contain a much larger number of genes coding for receptor kinases than other organisms. The prevalence of atypical RLKs in plants is analysed here. A sequence analysis of the Arabidopsis kinome revealed that 13% of the kinase genes do not retain some of the residues that are considered as invariant within kinase catalytic domains, and are thus putatively kinase-defective. This percentage rises to close to 20% when analysing RLKs, suggesting that phosphorylation-independent mechanisms mediated by atypical RLKs are particularly important for signal transduction in plants. Atypical kinases, phosphorylation, RLK, signalling Receptor protein kinases and signal transduction: the importance of phosphorylation All living organisms receive and process information at the cellular level through various classes of receptors, which recognize signals from the environment or from neighbouring cells and activate downstream signalling cascades. A particular type of receptors are receptor kinases (RKs), which are found in metazoans and plants. RKs are characterized by the presence of an extracellular domain, which specifically recognizes the ligand, linked by a transmembrane region to a cytoplasmic kinase domain. RK activation has been extensively studied in animals, where it has been shown to be a phosphorylation-dependent mechanism which generally involves two steps: first, ligand binding and receptor oligomerization resulting in the intracellular kinase domain activation; and secondly, upon activation, intermolecular autophosphorylation and conformational changes allowing the receptor to bind and activate downstream signalling proteins (Pawson and Nash, 2000). Two classes of RKs are found in animals, the receptor tyrosine kinases (RTKs) and the serine/threonine kinase receptors (STRKs). The best known example of STRK is the transforming growth factor (TGF)–β receptor complex, which is formed by a heteromeric complex of two different receptors. The type II receptor (also known as the primary receptor) binds the ligand and this triggers the phosphorylation of the type I receptor (also known as the transducer), which cannot bind the ligand in the absence of type II receptors. Phosphorylation of the transducer allows further signalling to downstream cascades (Massagué, 1996). The second class of animal RKs, the RTKs, act as ligand-activated homodimers or heterodimers of two related RTKs. Autophosphorylation in the activation loop of the kinase domain results in stimulation of the kinase activity. This allows its subsequent tyrosine phosphorylation that generates docking sites to recruit downstream signalling components, which may also be activated by phosphorylation triggering signalling cascades (Ulrich and Schlessinger, 1990; Hubbard and Till, 2000). The structure of plant receptor-like kinases (RLKs) is similar to that of animal RKs, being composed of an extracellular domain, a transmembrane span, and a cytoplasmic domain containing the conserved kinase domain. Nevertheless, in contrast to animal RKs, which in most cases are tyrosine kinases, all reported plant RLKs have serine/threonine kinase specificity. Different studies suggest that the mechanism of activation of plant RLKs may be similar to the mechanism of activation of animal RTKs (Shiu and Bleecker, 2001a; Cock et al., 2002; Morris and Walker, 2003; Johnson and Ingram, 2005). RLK autophosphorylation seems to be a common step in plants since a large number of RLKs have been described to autophosphorylate. For some of them, experimental evidence suggests that autophosphorylation is particularly important for their function. For instance, the Brasssica SRK, the self-incompatibility receptor, is autophosphorylated only when pollinated with incompatible pollen (Cabrillac et al., 2001). On the other hand, biochemical analyses revealed that the intracellular domain of the resistance protein Xa21 is autophosphorylated in Ser686, Thr688, and Ser689 (Xu et al., 2006), and the substitution of these residues by an alanine destabilizes the protein and compromises Xa21-mediated pathogen resistance (Xu et al., 2006). The activation of the Brassinosteroid receptor (BRI1) is one of the best characterized mechanisms of RLK activation in plants (Belkhadir and Chory, 2006). It has been demonstrated that BRI1 binds the brassinolide (Kinoshita et al., 2005) and that brassinolide treatment results in BRI1 autophosphorylation and activation (Wang et al., 2005a). In the absence of its ligand, BRI1 is inhibited by its C-terminal tail (Wang et al., 2005b) and by BRI1 kinase inhibitor 1 (BKI1) (Wang and Chory, 2006). This inhibition is released upon binding of the brassinolide to the BRI1 oligomer and consequent autophosphorylation of the activation loop of the receptor (Wang and Chory, 2006), and the subsequent phosphorylation allows the formation of the putative active complex BRI1–BAK1 (Wang et al., 2005a). On the other hand, the analysis of mutants of two receptors, CLV1, which is involved in apical meristem development, and FLS2, involved in pathogen-triggered defence responses, suggested that kinase activity may also be required for ligand binding. Indeed, mutated constructs of CLV1 and FLS2 lacking kinase activity do not interact with CLV3 or flagellin, their respective ligands (Trotochaud et al., 1999; Gómez-Gómez and Boller, 2000). Furthermore, kinase activity seems to be essential for RLK signal transduction since severe phenotypes are obtained when introducing mutations that disrupt kinase activity in BRI1 (Friedrichsen et al., 2000), CLV1 (Clark et al., 1997), FLS2 (Gómez-Gómez et al., 2001), SRK (Stahl et al., 1998), and also in ERECTA, an RLK regulating organ formation (Lease et al., 2001; Shpack et al., 2003), and SYMRK/NORK, RLKs that participate in nodulation (Stracke et al., 2002). These observations indicate that kinase activity is required at different steps of RLK activation and suggest that RLK signal transduction is a phosphorylation-dependent mechanism in plants, as has been shown in animal systems. Atypical receptor kinases: transduction without phosphorylation While, as reviewed above, the general RK activation mechanism is phosphorylation-dependent, kinase-defective atypical RKs transducing signals by phosphorylation-independent mechanisms have also been described in animals and very recently in plants. Catalytic kinase domains consist of 250–300 residues subdivided into 12 conserved subdomains (Hanks et al., 1988). The domain forms a two-lobed structure joined by subdomain V. The small N-terminal lobe (i.e. subdomains I–IV) participates in anchoring and orienting the ATP molecule, while the large C-terminal lobe (i.e. subdomains VIa–XI) binds the protein substrate and initiates the phosphotransfer. Each of the 12 subdomains contains conserved residues thought to be essential for the catalytic activity (Hanks and Hunter, 1995). This is the case for the aspartic acid of subdomain VIb which is part of the kinase active site (Knighton et al., 1993; Taylor et al., 1995), or the DFG motif of subdomain VII, involved in cation binding and orientation of the ATP gamma phosphate for phosphate transfer (Knighton et al., 1993; Hanks and Hunter, 1995; Huse and Kuriyan, 2002). Several atypical RKs that do not present some of the conserved residues of their kinase domains have been described in animals. These proteins include the human CCK-4 (Mossie et al., 1995), H-Ryk (Hovens et al., 1992; Katso et al., 1999), and ErbB-3 (Guy et al., 1994; Sierke et al., 1997), and the Drosophila DNT proteins (Savant-Bhonsale et al., 1999). The ErbB3 receptor belongs to the EGFR family of RTKs, which includes ErbB1/EGF, ErbB2/neu/HER2, ErbB3, and ErbB4 (Stein and Staros, 2000). The ErbB3 sequence contains substitutions of some of the highly conserved residues within the kinase-like domain, particularly the aspartic acid of subdomain VIb, and it has been demonstrated that its kinase activity is significantly impaired (Kim et al., 1998). In spite of this fact, ErbB3 is essential for proper signalling since mouse ErbB3 knockouts are lethal at embryonic stages (Riethmacher et al., 1997). ErbB3 forms heterodimers with other members of the EGFR family that phosphorylate it (Kim et al., 1998). Phosphorylated residues of the intracellular domain of ErbB3 act as docking sites, allowing the interaction with downstream signalling proteins, including phosphatidylinositol 3-kinase and SHC (Prigent et al., 1994), which are effector proteins responsible for mitogen-activated protein kinase (MAPK) cascade activation (Citri et al., 2003) (Fig. 1). The human CCK-4 RTK (Mossie et al., 1995) and its orthologous proteins, the chicken Klg (Chou and Hayman, 1991), the hydra Lemon (Miller and Steele, 2000), and the Drosophila Drtk (Pulido et al., 1992), also contain substitutions of some of the highly conserved residues within the kinase-like domain. In particular these proteins do not present the DFG motif of subdomain VII and also lack kinase activity. Their mechanism of activation is unknown, but the fact that their sequences contain elements that could mediate protein–protein interactions suggests that CCK-4 members may interact with kinase-active partners and signal in a way similar to that of the human ErbB3 atypical RTK. Fig. 1. View largeDownload slide Signalling through kinase-active and kinase-defective RTKs. (A) Scheme of the typical transduction mechanism of RLKs. After ligand (filled square) binding to the extracellular receptor domains (open and grey circles), the RTK oligomer is activated through autophosphorylation in the activation loops (open small circle) of their kinase domain (open and grey squares), allowing its subsequent phosphorylation at other residues that serve as docking sites for downstream signalling proteins. The plasma membrane is shown by a double line, and the extracellular (E) and intracellular (I) media are indicated. (B) Scheme of the transduction mechanism mediated by the ErbB3 atypical RTK. After ligand binding by a heterodimer of ErbB3 (shown in grey) and another member of the EGFR family, the kinase-inpaired intracellular domain of ErbB3 (shown by an X in its activation loop) is phosphorylated by its EGFR partner and becomes a docking site for downstream signalling proteins. (C) Signal transduction through the MARK atypical RLK. Ligand (filled square) binding to the receptor domain of MARK (shown by a grey circle) could induce a conformational change allowing its kinase-impaired domain (grey square) to interact with MIK. The C-terminal domain of MIK (shown by an open rectangle) inhibits MIK kinase activity, and its interaction with the intracellular domain of MARK induces a conformational change of MIK, releasing its autoinhibition, and activating its kinase activity. Fig. 1. View largeDownload slide Signalling through kinase-active and kinase-defective RTKs. (A) Scheme of the typical transduction mechanism of RLKs. After ligand (filled square) binding to the extracellular receptor domains (open and grey circles), the RTK oligomer is activated through autophosphorylation in the activation loops (open small circle) of their kinase domain (open and grey squares), allowing its subsequent phosphorylation at other residues that serve as docking sites for downstream signalling proteins. The plasma membrane is shown by a double line, and the extracellular (E) and intracellular (I) media are indicated. (B) Scheme of the transduction mechanism mediated by the ErbB3 atypical RTK. After ligand binding by a heterodimer of ErbB3 (shown in grey) and another member of the EGFR family, the kinase-inpaired intracellular domain of ErbB3 (shown by an X in its activation loop) is phosphorylated by its EGFR partner and becomes a docking site for downstream signalling proteins. (C) Signal transduction through the MARK atypical RLK. Ligand (filled square) binding to the receptor domain of MARK (shown by a grey circle) could induce a conformational change allowing its kinase-impaired domain (grey square) to interact with MIK. The C-terminal domain of MIK (shown by an open rectangle) inhibits MIK kinase activity, and its interaction with the intracellular domain of MARK induces a conformational change of MIK, releasing its autoinhibition, and activating its kinase activity. On the other hand, members of the Ryk family in Caenorhabditis elegans (Halford et al., 1999), Drosophila (Savant-Bhonsale et al., 1999), and vertebrates (Hovens et al., 1992) also contain substitutions of the DFG motif, and also lack kinase activity (Katso et al., 1999). H-Ryk also forms heterodimers with other kinase-active RTKs, although in this case the interaction does not result in phosphorylation of the inactive kinase (Trivier and Ganesan, 2002). A chimeric receptor approach showed that the ligand stimulation of H-Ryk results in the activation of a MAPK pathway (Katso et al., 1999), suggesting that the activated H-Ryk can interact with and activate other downstream signalling proteins. Catalytically impaired kinases belonging to classes other than RKs have also been reported. Those atypical kinases, which are also essential for signal transduction, are proposed to function as scaffolds or docking platforms. The kinase suppressor of Ras (KSR) lacks the conserved lysine in the ATP-binding domain (Therrien et al., 1995), and biochemical experiments suggested that it does not exhibit kinase activity. More importantly, it has been reported that KSR constructs containing mutations which usually disrupt kinase activity rescued the KSR loss-of-function phenotype. This observation indicates that KSR mutants can be restored by a kinase-independent mechanism (Stewart et al., 1999). It is suggested that KSR acts as a scaffolding protein (Morrison, 2001) that interacts with Raf, MEK, and ERK, and co-ordinates their membrane localization, facilitating mitogen-activated kinase activation (Ritt et al., 2005). Similarly, kinase-impaired constructs of integrin-like kinase (ILK), which is essential for integrin-mediated adhesion of muscles in C. elegans and Drosophila, can rescue null mutations of ILK (Zervas et al., 2001). This points to a phosphorylation-independent role for ILK and it has been suggested that the ILK kinase domain might function as a platform for protein–protein interactions (Zervas and Brown, 2002). In summary, atypical RKs signal through phosphorylation-independent mechanisms involving regulated protein–protein interactions mediated by their intracellular kinase-like domains (Kroiher et al., 2001). The importance of protein–protein interactions for signalling through these proteins probably explains why all these atypical RKs have maintained during evolution the general structure of their kinase domains in spite of their lack of kinase activity (Stein and Staros, 2000). In the last few years, atypical plant RLKs that could transduce signals by phosphorylation-independent mechanisms have also been described in plants (Llompart et al., 2003; Cao et al., 2005; Chevalier et al., 2005). MARK (maize atypical receptor kinase) contains alterations of some conserved amino acids within the kinase domain (Llompart et al., 2003) (Fig. 2). The MARK sequence lacks the conserved aspartic acid of subdomain VIb, and the aspartic acid and phenylalanine within the DFG motif of subdomain VII. All those alterations suggest that the intracellular domain of MARK could be a kinase-dead domain. Moreover, recombinant MARK fails to auto- and transphosphorylate in vitro (Llompart et al., 2003). It has been demonstrated that the intracellular domain of MARK interacts with the C-terminal domain of a maize GCK-like kinase named MIK, and this interaction results in the activation of MIK kinase activity (Llompart et al., 2003). As the C-terminal domain of MIK inhibits its own kinase activity (Castells et al., 2006), MARK probably activates MIK by inducing conformational changes that release MIK autoinhibition (Castells et al., 2006) (Fig. 1C). Those findings showed for the first time in plants that atypical RLKs can transduce signals by means of phosphorylation-independent mechanisms. Another putative kinase-dead RLK, the Arabidopsis TMKL1 protein, had been described earlier (Valon et al., 1993) but its kinase activity has not been analysed. The sequence of TMKL1 contains alterations of several conserved residues within the kinase domain: in particular, the glycine-rich domain of subdomain I, the invariant lysine of subdomain II, the invariant glutamic acid of subdomain II, the aspartic acid of subdomain VIb, and the aspartic acid of subdomain VII (Fig. 2), suggesting that TMKL1 may be an atypical RLK with a kinase-dead domain. Fig. 2. View largeDownload slide Sequence comparison of the atypical RLKs described in plants and the BRI1 RLK. The sequences of subdomains II, VIb, VII, and VIII are shown. The residues conserved in active kinases are designated with an asterisk on the top, and the consensus amino acid is shown on the bottom. Fig. 2. View largeDownload slide Sequence comparison of the atypical RLKs described in plants and the BRI1 RLK. The sequences of subdomains II, VIb, VII, and VIII are shown. The residues conserved in active kinases are designated with an asterisk on the top, and the consensus amino acid is shown on the bottom. Recently, the analysis of two additional atypical RLKs has been reported. The Arabidopsis Strubbelig (SUB) RLK plays an essential role in Arabidopsis organ development, since sub mutants show defects in ovule development. SUB contains a kinase domain containing two substitutions of conserved amino acids within the catalytic loop (Fig. 2). The conserved aspartic acid of subdomain VIb is substituted by an asparagine, and the conserved asparagine of the same domain by a lysine. Biochemical approaches demonstrated that SUB lacks kinase activity, and, remarkably, genetic experiments showed that the catalytic activity is not essential for in vivo SUB function (Chevalier et al., 2005). Nothing is known on the possible signalling mechanism of SUB, but it has been suggested that its kinase-dead domain could have maintained the ability to interact with downstream effectors requiring the typical three-dimensional configuration of a kinase domain (Chevalier et al., 2005). The second reported analysis on atypical RLKs refers to the Arabidopsis ATCRR1 and ATCRR2 receptors, both related to the maize CRINKLY4 receptor which is implicated in maize development. Arabidopsis ATCRR1 and ATCRR2 have a deletion of subdomain VIII (Fig. 2) and display significantly attenuated kinase activity in vitro. It has been shown that ATCRR2 can be phosphorylated by ACR4, the Arabidopsis CRINKLY4 homologue, in vitro, suggesting that these proteins could signal through ATCRR2–ACR4 heterodimerization and subsequent transphosphorylation of ATCRR2, a mechanism reminiscent of that of the human ErbB3 RTK (Cao et al., 2005). Interestingly, a recent report shows that, although ACR4 is an RLK with an active kinase domain, its kinase activity may not be required for protein function (Gifford et al., 2005). Indeed, an ACR4 kinase-dead mutant can complement the acr4 mutant phenotype, suggesting that at least part of ACR4 signalling may pass via a route independent of its kinase activity (Gifford et al, 2005). Prevalence of atypical RLKs The human genome contains 518 genes coding for protein kinases (Manning et al., 2002). An analysis of the human kinome revealed that 50 human kinase domains lack the conserved lysine of subdomain II, the aspartic acid of subdomain VIb, or the aspartic acid of subdomain VII, suggesting that 10% of the predicted human kinase proteins are putatively enzymatically inactive (Manning et al., 2002) (Table 1). The mouse kinome shows an almost perfect conservation of the predicted inactive kinases (Caenepeel et al., 2004), suggesting that most of these proteins fulfil a cellular role and that their corresponding genes are not merely pseudogenes. Table 1. Percentage of atypical kinases in different organisms   Human  S. cerevisiae  A. thaliana  A. thaliana RLK/Pelle family  Predicted kinases  518 (518)  118 (94)  1000 (911)  610 (610)  % atypical kinases  10%  6%  13%  20%    Human  S. cerevisiae  A. thaliana  A. thaliana RLK/Pelle family  Predicted kinases  518 (518)  118 (94)  1000 (911)  610 (610)  % atypical kinases  10%  6%  13%  20%  The number of analysed sequences is shown in parentheses. View Large A general survey of 911 sequences of the Arabidopsis kinome has been performed and it was found that 13% of the putative kinases lack the conserved lysine of subdomain II, the aspartic acid of subdomain VIb, or the aspartic acid of subdomain VII, and are putatively enzymatically inactive (Table 1). This percentage is slightly higher than that found in mammals, although both plants and mammals have a higher percentage of putative defective kinases than yeast, where only six out of the 94 kinase-encoding genes analysed lack those conserved residues (Table 1). Intriguingly, when analysing the RLK/Pelle family, which include the RLKs and the receptor-like cytoplasmic kinases, or RLCKs, this percentage rises to 20% (Table 1). A detailed sequence analysis of the Arabidopsis RLK/Pelle family members was performed and it was found that 121 out of the 610 analysed sequences lack the conserved aspartic acid of subdomain VIb or the DFG motif of subdomain VII. More precisely, 77 sequences lack the aspartic acid of subdomain VIb, 17 lack the DFG motif of subdomain VII, and 27 lack both motifs. These proteins are predicted to be defective kinases, as all these mutations have been reported to disrupt the kinase catalytic activity. Plant genomes contain a much larger number of genes coding for protein kinases than mammalian genomes (Champion et al., 2004; Krupa et al., 2004). For instance, the Arabidopsis genome comprises ∼1000 genes coding for kinases (Champion et al., 2004), and the RLK/Pelle gene family, with some 600 genes (Shiu and Bleeker, 2001b), is one of the largest gene families in Arabidopsis and in the plant kingdom in general. Different hypotheses have been formulated to explain the high number of plant RLKs, and plant kinases in general, compared with animals. Plants, as sessile organisms, may perceive and integrate more signals to adapt their morphogenesis to the changing environment. Alternatively, the mechanisms of signalling may have diverged between plants and animals and, as a consequence, plants might need a larger number of receptors. Finally, the low frequency of alternative splicing leading to different isoforms from a single gene in plants compared with animals (Ner-Gaon et al., 2004) could be compensated by an increase in the number of kinase genes. It has been suggested that tandem duplications and segmental/whole-genome duplications are the major mechanisms for the expansion of the RLK family in Arabidopsis (Shiu and Bleecker, 2003). The high expansion of the RLK gene family through gene duplication could have allowed the maintenance of mutations affecting kinase activity and the subsequent evolution of new functions for these atypical RLKs, which could explain the particular prevalence of defective kinases within plant RLKs. Most atypical RLKs belong to a few of the previously defined subfamilies of the RLK/Pelle family (Shiu and Bleecker, 2001b), the subfamilies LRRIII, LRRIV, LRRV, LRRVI, LRRVII, RLCKI, RLCKII, and RLCKIII. In some of these subfamilies, most of the sequences lack the aspartic acid of subdomain VIb. For instance, eight out of the nine members of the LRRV subfamily lack the aspartic acid of subdomain VIb (shown in red in Fig. 3). A phylogenetic analysis by Neighbor–Joining and maximum-likelihood approaches suggests that the aspartate loss may have occurred after At5g06820 gene duplication, the mutated gene giving rise to eight new members with the same mutation (Fig. 3). A mutation in a single sequence whose amplification has given rise to the whole subfamily is also one of the most parsimonious hypotheses for the formation of the LRRIV subfamily, where all the sequences contain the same inactivating mutation (see Supplementary Fig. S1A, B at JXB online). In other cases, the phylogenetic relationships of the different mutated genes suggest that some atypical members may have experienced a second mutation restoring the conserved amino acid. This is probably the case for the LRRIII subfamily, where most proteins have the aspartate mutation to arginine in subdomain VIb while five of them (shown in black in Fig. 4) have the conserved aspartate, and this is probably also the case for the group formed by the LRRVI and RLCKI subfamilies where two sequences do not present this inactivating mutation (see Supplementary Fig. S1C, D at JXB online). On the other hand, most of the subfamilies also contain sequences that have an additional substitution in the DGF motif of subdomain VII. This is the case for the LRRIII (Fig. 4), LRRIV (see Supplementary Fig. S1A, B at JXB online), and LRRVI (see Supplementary Fig. S1C, D at JXB online) subfamilies where the phylogenetic analysis is compatible with mutations of subdomain VII occurring in some already inactivated proteins. This could indicate that after a kinase-inactivating substitution the selective pressure to maintain invariable the amino acids important for kinase activity diminishes, and a second inactivating mutation is more frequently allowed. On the other hand, the mutation of subdomain VII could also arise in some cases independently or even prior to the mutation in subdomain VIb, as could be suggested to explain the phylogenetic relationships of the proteins of the LRRVII, RLCKII, and RLCKIII subfamilies (see Supplementary Fig. S1E, F; G, H; and I, J at JXB online). In the case of RLCKII, both the Neighbor–Joining and the maximum-likelihood analyses suggest that the mutation of the DFG motif of subdomain VII may have occurred in the ancestor of the group, while two independent mutations of the aspartate of subdomain VIb occurred later in evolution to give rise to the sequences having mutations in both subdomains. Fig. 3. View largeDownload slide Phylogenetic trees of the LRRV subfamily of RLKs. The amino acid sequences of the kinase domain of LRRV subfamily members were aligned using ClustalW (version 1.5; Thompson et al., 1994) together with that of the P0A3Y5 protein that was used as an outgroup for the analysis. Phylogenetic trees were constructed using Neighbor–Joining (A) and maximum-likelihood methods (B) using MEGA (Kumar et al., 2004) and PHYML programs (Guindon et al., 2003), respectively. For the maximum-likelihood, the JTT model of evolution with four rate categories was used. The root of the tree was placed in the mid-point. Sequences containing the conserved aspartate of subdomain VIb are shown in black, whereas those not containing the conserved residue are shown in red. One possible parsimonious reconstruction of mutations inactivating the conserved amino acid is given. Mutations are schematized by a red arrow. Bootstrap values were obtained from 100 replicates. Values >50% are shown. The scale bar represents the number of mutations per site. Fig. 3. View largeDownload slide Phylogenetic trees of the LRRV subfamily of RLKs. The amino acid sequences of the kinase domain of LRRV subfamily members were aligned using ClustalW (version 1.5; Thompson et al., 1994) together with that of the P0A3Y5 protein that was used as an outgroup for the analysis. Phylogenetic trees were constructed using Neighbor–Joining (A) and maximum-likelihood methods (B) using MEGA (Kumar et al., 2004) and PHYML programs (Guindon et al., 2003), respectively. For the maximum-likelihood, the JTT model of evolution with four rate categories was used. The root of the tree was placed in the mid-point. Sequences containing the conserved aspartate of subdomain VIb are shown in black, whereas those not containing the conserved residue are shown in red. One possible parsimonious reconstruction of mutations inactivating the conserved amino acid is given. Mutations are schematized by a red arrow. Bootstrap values were obtained from 100 replicates. Values >50% are shown. The scale bar represents the number of mutations per site. Fig. 4. View largeDownload slide Phylogenetic trees of the LRRIII subfamily of RLKs. The amino acid sequence of the kinase domain of the LRRIII subfamily members were aligned and phylogenetic trees constructed using Neighbor–Joining (A) and maximum-likelihood methods (B) as described in Fig. 3. Sequences containing substitution of conserved residues in subdomain VIb alone or in both subdomains VIb and VII are shown in red and purple, respectively. One possible parsimonious reconstruction of mutations inactivating and restoring the conserved amino acids is given. Inactivating and restoring mutations are represented by inward and outward pointing arrowheads, respectively. Mutations in subdomain VIb and VII are indicated by red and blue arrowheads, respectively. Bootstrap values were obtained from 500 replicates. Values >50% are shown. Fig. 4. View largeDownload slide Phylogenetic trees of the LRRIII subfamily of RLKs. The amino acid sequence of the kinase domain of the LRRIII subfamily members were aligned and phylogenetic trees constructed using Neighbor–Joining (A) and maximum-likelihood methods (B) as described in Fig. 3. Sequences containing substitution of conserved residues in subdomain VIb alone or in both subdomains VIb and VII are shown in red and purple, respectively. One possible parsimonious reconstruction of mutations inactivating and restoring the conserved amino acids is given. Inactivating and restoring mutations are represented by inward and outward pointing arrowheads, respectively. Mutations in subdomain VIb and VII are indicated by red and blue arrowheads, respectively. Bootstrap values were obtained from 500 replicates. Values >50% are shown. Thus, the phylogenetic analysis of the LRR subfamilies containing atypical receptor kinases suggests that atypical RLKs had arisen independently multiple times and, more importantly, that different atypical RLKs had been maintained and expanded through evolution. Moreover, the phylogenetic analysis of RLKs, already published by Shiu and Bleecker (2001b), shows that the eight subgroups that contain atypical RLKs and that have been analysed here are not phylogenetically related, reinforcing the idea that the mutations of the atypical RLKs do not have a monophyletic origin. In summary, close to 20% of Arabidopsis RLKs present substitutions in highly conserved amino acids within the kinase domain, being putative atypical kinase receptors. This observation suggests that phosphorylation-independent mechanisms mediated by atypical RLKs are important in signal transduction in plants, as they have been shown to be in animal systems. Supplementary material The phylogenetic analysis of the RLK subfamilies LRRIV, LRRVI and LRCKI, LRRVII, LRCKII, and LRCKIII are presented in Supplementary Fig. S1A, B; C, D; E, F; G, H; and I, J, respectively, available at JXB online. We are grateful to Paloma Mas, Soraya Pelaz, and Jaume Martínez-García for their critical reading of the manuscript, and to José Castresana for his help with the phylogenetic analysis. This work was partially funded by Centre de Referència en Biotecnologia (CeRBa) from the Generalitat de Catalunya. 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Samakovli, Despina; Thanou, Aggeliki; Valmas, Charalampos; Hatzopoulos, Polydefkis

doi: 10.1093/jxb/erm191pmid: 18057034

Abstract Stochastic processes are intrinsic phenomena that perturb developmental processes. However, the canalization process restricts the magnitude of perturbation and hence the magnitude of morphological variation during development. Heat-shock protein 90 (Hsp90) chaperones are a class of proteins stabilizing a network of ‘client’ proteins that are involved in diverse signal transduction pathways affecting development. Here it is reported that a reduction of Hsp90 gene dose creates canalization perturbations that affect many aspects of Arabidopsis development and results in a plethora of morphological alterations. Hence, Hsp90 restricts stochastic phenomena by minimizing perturbations, thereby canalizing development. It is also shown that morphogenesis is determined by three mutually inter-related parameters: genotype, environment, and time. Hsp90 is involved in the interaction of these three parameters which ultimately affect developmental processes. The amount of phenotypic variation upon the reduction of Hsp90 function could be perceived as adaptive and could have an impact on the evolutionary process. Arabidopsis, genetic analysis, Hsp90, phenotypic variation, stochastic development Introduction Stochastic mechanisms during developmental processes of multicellular organisms result in polymorphisms (Gartner, 1990; Mc Adams and Arkin, 1997). This condition is distinct from genetic variation. In a given inbred population genetic variation tends towards equilibrium and hence phenotype uniformity (Powell, 1997). Subtle genetic variations do not usually substantiate phenotypic variants (Kitano, 2004). Stochastic development, being more flexible, allows a better adaptation to environmental changes during the life span of an organism and might generate another platform for evolution. However, radical stochastic mechanisms are restricted by a buffering system that canalizes canonical development (Waddington, 1942; Schmahausen, 1986). Harsh environmental conditions can reveal new phenotypic polymorphisms presumably because of perturbation of this buffering capacity. These new phenotypic polymorphisms are heritable and genetically assimilated even though sometimes genetic polymorphisms are not present (Waddington, 1953, 1956; Sollars et al., 2003). The chaperone heat-shock protein 90 (Hsp90) can function as a buffering capacitor, thus operating as a periphery restricting individual polymorphisms. This phenomenon was apparent in evolutionarily distant organisms (Rutherford and Lindquist, 1998; Queitsch et al., 2002; Sollars et al., 2003). When the function of Hsp90 was disturbed by mutation, pharmacological inhibition, or environmental stress in an obligatory outcrossing species such as Drosophila, a plethora of polymorphic phenotypes was uncovered for almost every part of the body structure or developmental pathways (Rutherford and Lindquist, 1998). Consequently, the chaperone Hsp90 system creates a network buffering the canalization during development and ultimately suppresses individual polymorphisms. Interestingly, when multiple polymorphisms were enriched by selection, these features were heritable even after Hsp90 functional restoration (Sollars et al., 2003). In the inbreeding plant species Arabidopsis thaliana, the function of Hsp90 was challenged only by pharmacological inhibition while the phenotypic traits were measured and characterized during a restricted time period of the plant life, e.g. in seedlings (Queitsch et al., 2002). Reduced Hsp90 function generated an array of seedling morphological phenotypes apparently based on underlying genetic variation of different genetic backgrounds. This indicates that the Hsp90 chaperone system might influence morphogenetic responses to environmental cues and buffer normal development from destabilizing effects of stochastic processes (Queitsch et al., 2002; Sangster et al., 2004). Hsp90 is a ubiquitous highly conserved molecular chaperone found in all organisms induced by heat stress. In all eukaryotes examined, Hsp90 is an abundant protein at normal temperatures. Under normal conditions, the dynamic interaction of Hsp90 with its client proteins is diverse, but nevertheless highly selective (Buchner, 1999; Young et al., 2001; Zhao et al., 2005). Recent work in yeast provided evidence that almost 10% of the genetic pool acts as client proteins to Hsp90, interacting physically, genetically, or chemically, thus creating a dynamic network in signal transduction pathways and cellular activities (Zhao et al., 2005). Typically, Hsp90 in an ATP-dependent mode retains these metastable proteins at a dynamic status quo before they are stabilized by conformational changes (Buchner, 1999; Mayer and Bukau, 1999; Young et al., 2001; Picard, 2002). Heterozygocity is an important parameter in maintaining genetic variability and phenotypic robustness (Thoday, 1953). However, since heterozygocity per individual in Arabidopsis is extremely low (Abbott and Gomes, 1989; Kuittinen et al., 1997) and becomes even lower in self-fertilizing species, it is unclear whether Hsp90 action restricts variations resulting from heterozygocity at multiple loci or whether it predominantly restricts bona fide stochastic mechanisms. Here, using a genetic approach, it is demonstrate that Hsp90 restricts stochastic processes during developmental canalization rather than releasing cryptic genetic variations. Moreover, it is shown that Hsp90 plays a key role in the manifestation of phenotype plasticity and buffers development against environmental perturbations. The present data also indicate that the accumulation of stochastic phenomena over developmental time has a major influence on development and that Hsp90 canalizes development by counteracting these effects. The multiplicity of Hsp90 genes in Arabidopsis provides a fine tuning in the understanding of canalization and stochastic mechanisms during development that could lead to adaptation and evolution. Materials and methods Plant material and normal or stress growth conditions SALK collections seeds from the Arabidopsis Biological Resource Center (ABRC) and the Nottingham Arabidopsis Stock Center (NASC) were used: 007614 for AtHsp90-1 (At5g52640); 013240 for AtHsp90-2 (At5g56010); 038646 for AtHsp90-3 (At5g56030), and 084059 for AtHsp90-4 (At5g56000). Col seeds were laboratory stocks. Accessions were self-fertilized and their progeny (F1–F5) were scored. Surface-sterilized seeds were plated and left to grow under standard conditions, at 27 °C or 32 °C. Seeds and seedlings were heat stressed for appropriate periods at 27, 32, or 37 °C and left to grow under normal conditions or the indicated stress conditions. In order to provide objective evaluations, Col seeds were plated independently but left to grow under the same conditions and observed simultaneously with the mutant plants. Growth variations were eliminated by letting mutants and Col plants grow for longer, and phenotypes were observed at different time points. Digital images were taken at different time points of growth under different magnifications as indicated. Seeds were placed in 20% glycerol, dissected, and embryos were observed and photographed using Olympus scopes. Molecular techniques Genomic DNA was isolated using the standard cetyltrimethyl ammonium bromide (CTAB) method. In order to determine the T-DNA insertion point and the genes that were disrupted, pairs of primers specific for the insertion and for the Hsp90 genes were used. For the T-DNA insertion, the following primers were used: primer nosf 5′-GCATGACGTTATTTATGAGATGGG-3′, primer nosR 5′-GACACCGCGCGCGATAATTTATCC-3′; and for the NPTII gene, primer npt1 5′-TCTGTTGTGCCCAGTCATAGCCGAATAG-3′, primer npt3 5′-CATCTTGTTCAATCATGCGAAACGATCC-3′, and primer npst 5′-TCAAGACCGACCTGTCCGGTGCCCTGAA-3′. For the Hsp90 genes, the following primers were used: for AtHsp90-1 primer 81revA 5′-CGTTGGCTGCAGCCATAAGAGCAATTTCTTCATCTC-3′; for AtHsp90-2 primer 5utr812 5′-TTTCCGATCAACGAGAATGG-3′, primer 812ex3 5′-TCCTCATCCTTCTTCYCYTCCTCC-3′, and primer 2intr812 5′-TCAGATCTGAACCTTGG-3′; for AtHsp90-3 primer Sma813a 5′-AGTACCCGGGCCGATCAACGAGAATGGCGGA-3′ and primer Pst813a 5′-CTAGCTGCAGCGCAAACCATAGTCTTATAACACCGCT-3′, and for AtHsp90-4 primer f814 5′-CTTTTCATTCATATCATTCCG-3′ and primer h814 5′-TTTGTGCAATATTTAGTCG-3′. RNA was isolated using the SDS/phenol method (Haralampidis et al. 2002). Total RNA was then treated with RNase-free DNase I (Invitrogen) and about 800 ng were used as template in first-strand cDNA synthesis using Superscript™ II RNase H– Reverse Transcriptase (Invitrogen), according to the manufacturer's protocol. Unless otherwise stated, the first-strand cDNA was primed by the poly(A) tail with the reverse transcription primer T17XHO (5′-GTCGACCTCGAGTTTTTTTTTTTTTTTTT-3′). Transcript analysis was performed using specific primers (for AtHsp90-1 forward 81FORA 5′-TTAATGGATCCAAGTTCGTTGCGATGGCGGATG-3′ and reverse 81REVA 5′-CGTTGGCTGCAGCCATAAGAGCAATTTCTTCATCTC-3′; for AtHsp90-2 forward f812 5′-AGAGCTCTTCATTCACATC-3′ and reverse h812 5′-TCTGTGTGATAATTTAGTCA-3′; for AtHsp90-3 forward Sma813a 5′-AGTACCCGGGCCGATCAACGAGAATGGCGGA-3′ and reverse Pst813a 5′-CTAGCTGCAGCGCAAACCATAGTCTTATAACACCGCT-3′; and for AtHsp90-4 forward f814 5′-CTTTTCATTCATATCATTCCG-3′ and reverse h814 5′-TTTGTGCAATATTTAGTCG-3′) and reverse transcription–PCRs (RT-PCRs). We found that either the Hsp90 genes were not transcribed at all or the transcript length was decreased. Results Hsp90 is required for pattern formation The Arabidopsis cytosolic group comprises four Hsp90 members (Milioni and Hatzopoulos, 1997). To verify subtle or extreme phenotypes at any stage of development and to determine the effect of the Hsp90 system on developmental pathways or morphogenesis, >25 000 seeds, embryos, seedlings, and Arabidopsis plants were scored for at least four generations of all four hsp90 mutants. Homozygous plants mutated at any of the four cytosolic members are not lethal, although a noticeable percentage (an average of 40% for the Athsp90-1 mutant and 15% for the other three Athsp90 mutants) resulted in non-germinated seeds. For Col plants, an average of 1.5% resulted in non-germinating seeds. Athsp90-1 mutant seeds showed the most pronounced phenotypes, being shrivelled and having various shapes, while a few seeds were almost dry, indicating the absence of embryo or endosperm tissue (Fig. 1). Embryos were dissected out from the abnormal non-germinating seeds of all four hsp90 mutants and showed defective root integrity and morphology, an unorganized central cylinder, deficiency in pattern formation and cell fate, and Siamese twin-like single cotyledon embryos (Fig. 1) Fig. 1. Open in new tabDownload slide Embryo and seed morphology of hsp90 mutants. (A) Wild-type Arabidopsis Col embryo. (B–G) hsp90-1 mutant phenotypes. (B) Shrivelled seeds, seed coat, and highly malformed seed shape. (C) The root is starting to split in the central cylinder (arrow). (D) Anomalous central cylinder organization (arrow). (E) Cellular organization is highly abnormal. (F) The root tip is split. (G) Higher magnification of (F). (H–L) hsp90-2 mutant phenotypes. (H) A second root starts to grow from the primary root initial. (I) The root is split at the central cylinder. (J) The splitting creates almost a double root. (K) Two primary roots develop from the embryo root initial. (L) Higher magnification of (K). An undifferentiated cell mass adheres to the double root, while the deformation of the root tip is obvious. (M–Q) hsp90-3 mutant phenotypes. (M) Single cotyledon embryo. (N) A constriction is formed at the root–hypocotyl junction. (O) Ectopic vascular bundle formation at the periphery. (P) Embryos lacking a vascular system. (Q) A highly deformed embryo. (R–V) hsp90-4 mutant phenotypes. (R) A twin primary root is almost detached while cotyledons start to split. (S) A single cotyledon embryo. (T) A hole in the root. (U) Cotyledons are extended abnormally. (V) Siamese twin-like single cotyledon arrested embryos. Scale bar: 0.5 mm except 0.25 mm for G. The phenotypes of the Athsp90-1 mutant defective embryos were most prominent in the root system in which the central cylinder was either split, interrupted, or misplaced. All the scored Athsp90-2 mutant defective embryos had a pronounced and aberrant root system (e.g. a second primary root emerged, a split at the central cylinder). For the Athsp90-3 mutants, the aberrant phenotypes were obvious mainly in cotyledons. They showed an ectopic pattern formation of the phloem and xylem, single cotyledon embryos, a split root, and loss of cell fate. The Athsp90-4 mutant defective embryos were severely affected in cotyledon and root morphogenesis. In most cases the split in the root or cotyledon was more prominent, while in other cases Siamese twin-like single cotyledon embryos were scored. Disorganization or misplacement of cotyledon development was also scored (Fig. 1). In many cases hsp90 mutants showed a phenotypic appearance reminiscent of homeotic gene defects. There is, at least partly, functional overlap of the individual members of the Hsp90 cytosolic gene family; nevertheless, each mutant member had a certain class of phenotype. The mutant phenotypes observed here corroborate recent work showing that AtHsp90-1 is expressed mainly in embryo root tissues, while AtHsp90-3:GUS activity was intense in cotyledons (Haralampidis et al., 2002; Prasinos et al., 2005). The germinated seeds from all four mutants produced mainly healthy seedlings, although a noticeable percentage (30% on average for all four mutants) exhibited a number of phenotypes ranging from weak to strong (Fig. 2). Aberrant mutant seedlings showed morphological defects affecting cotyledon number, morphology, shape, and development; shape and length of hypocotyls; appearance and growth of the first pair of leaves; and root presence or architecture. Few seedlings were etiolated or had an ethylene-like phenotype (Fig. 2 and data not shown). A spectrum of seedling phenocopies was also observed when AtHsp90 function was pharmacologically inhibited (Queitsch et al., 2002; Sangster et al., 2004). Fig. 2. Open in new tabDownload slide Peculiar seedling phenotypes of Athsp90 mutants. (A) A wild-type Col seedling. (B–D) Athsp90-1 mutants. (B) S-shaped cotyledons. (C) Uneven growth of cotyledons. (D) Triple cotyledon (c) seedling. (E–G) Athsp90-2 mutants. (E) Uneven and malformed cotyledons. (F) Epinasty phenomenon. (G) Malformed cotyledons and high anthocyanin accumulation. (H–J) Athsp90-3 mutants. (H) Uneven growth of cotyledons and long hypocotyl. (I) Formation of a hole in one cotyledon. (J) Abnormal cotyledon bending, long hypocotyl. (K and L) Athsp90-4 mutants. (K) Rootless seedling with malformed and twisted cotyledons. (L) Long hypocotyl with triple cotyledons. Scale bar: 2.5 mm for A–G; 1.25 mm for H–L. Even though there was an overlap in the spectrum of phenotypes for the four mutants, certain mutants had distinct phenotypes (Fig. 2). Root morphological abnormalities were found mostly in Athsp90-4 mutants, while leaf defects were more profound in Athsp90-1 mutants. The most severe phenotypes (e.g. lack of root), representing a small proportion, failed to prosper, while the majority eventually advanced to, more or less, wild type-like phenotypes acquiring a normal developmental process. Col seedlings showed a narrow range of subtle phenotypes. This phenomenon emphasizes that Hsp90 restricts developmental plasticity. The results also show that there is a degree of overlap in function among the members of the Hsp90 cytosolic gene family. Perturbation of phenotypic morphs upon reduction of Hsp90 activity Hsp90 has a strategic role in the developmental dynamics that govern early morphogenesis. To determine whether the process towards adult development was also affected, about 1000 seedlings from each hsp90 mutant line, irrespective of phenotypic appearance, were randomly collected, transferred to soil, and allowed to grow under standard conditions. A small proportion of the seedlings were unable to cope with this abrupt environmental change and died almost immediately. Five distinct phenotypic classes (A–E) were observed when the AtHsp90-1 gene was mutated. Each class represented a group of plants showing a more or less similar phenotype (Fig. 3). Class A comprised seedlings growing for up to 10 d on plates having epinasty-like phenotypes with highly deformed leaves, and they eventually died. Class B comprised mature plants characterized by a bushy rosette while the inflorescence was either absent or grew up to 4 cm. In some cases the inflorescence appeared normal, the mutant plant had fewer inflorescences than the wild type, and bolting was delayed. Class C comprised plants having a small sized rosette with fewer leaves. Class D contained dwarf mature plants, while class E were wild-type-like plants (Fig. 3). Fig. 3. Open in new tabDownload slide Phenotypic classes of hsp90 40-day-old adult mutant plants. Athsp90-1 mutants (90-1). (A) Ten-day-old ‘plate’ plants with deformed or epinasty-like cotyledons and unorganized apical meristem. (B) Plants with a bushy rosette and short (4 cm) or no inflorescence. (C) Plants with a small rosette and few leaves. (D) Dwarf-like plants. (E) Col-like plants. Athsp90-2 mutants (90-2). (A) Plants with a small rosette and long inflorescence. (B) Bushy plants with no or a Sshort inflorescence and saw-edged leaves. (C) Similar to (B) but larger in size, all with a short inflorescence and absence of apical dominance. (D) Plants having rosettes with many leaves. (E) Col-like plants. Athsp90-3 mutants (90-3). (A) Plants having few rosette leaves. The inflorescence emerged after 40 d. (B) Bushy rosette and abnormal inflorescence growth. (C) Asymmetric rosette growth at different levels. (D) Bushy rosette, many inflorescences, and absence of apical dominance. (un) Unclassified peculiar phenotypes. Athsp90-4 mutants (90-4). (A) Plants with a small rosette and delayed bolting. (B, C, and D) Plants like D, C, and B of Athsp90-3, respectively. (un) Unclassified peculiar phenotypes. Scale bar: Athsp90-1: 2.0 mm for A; 1.725 cm for B–E; 4 mm for Athsp90-2; Athsp90-3: 1.725 cm for C1; 4 mm for A–D and un3; 9 mm for (un1); 5 mm for (un2); 4 mm for Athsp90-4. Five distinct phenotypic classes were also observed when the AtHsp90-2 gene was mutated. Class A comprised small plants having a small sized rosette and the inflorescence was short. Class B contained bushy plants some with short or no inflorescence at all, and had saw-edged leaves. Class C comprised plants similar to class B. However, they were generally taller with many but short inflorescences implying the absence of apical dominance. Class D contained plants with altered morphology and a higher number of rosette leaves, while class E plantts had a wild-type-like phenotype (Fig. 3). Athsp90-3 mutant plants showed four distinct classes. Class A contained plants with fewer rosette leaves and a delay in flowering. Class B was composed of plants with a bushy rosette appearance while the inflorescences crawled and did not grow from the centre of the rosette. Class C showed a defect in rosette architecture that grew at different levels, while class D comprised plants with an absence of apical dominance. Also, Athsp90-4 mutant plants could be divided into four distinct classes. Class A contained plants with delayed bolting and small sized rosettes. Class B plants showed absence of apical dominance. Class C plants showed defects in rosette architecture. Class D plants had a bushy rosette appearance while inflorescences crawled and did not grow from the centre of the rosette (Fig. 3). Col plants had uniform wild-type phenotypes when grown under the same growth conditions. Within each mutant plant line peculiar phenotypes were recovered. These phenotypes were not grouped in any of the above classes and represented a rather low proportion of nevertheless distinctive phenotypic appearance (Fig. 3). Rosette leaves were highly deformed and developed at different levels (reminiscent of homeoitic mutants), developmental arrest was apparent, and seed production was minimal. All these plants are inbred lines and therefore any heterozygocity that may emerge will depend solely on a single self-fertilizing cross. Hence heterozygocity should be minimal, indicating that any divergence from the norm depends exclusively on the hsp90 mutation. Although there were certain classes of different mutants showing a similar phenotype (e.g. class B or D of Athsp90-3 mutants to class D or B of Athsp90-4 mutants, respectively), other classes (such as class B of Athsp90-2 mutants, and class A or D of Athsp90-1 mutants) showed a rather distinctive phenotype (Fig. 3). The results showed that besides the overlapping function of the individual Hsp90 cytosolic members, there are specific aspects of developmental processes affected mainly by a single gene or a subgroup of genes. Hsp90 determines developmental plasticity The predisposition of the four different mutants to show different phenotypic classes illustrates the buffering capacity of the Hsp90 function in plant development. Similarly, the buffering capacity of Hsp90 was observed in Drosophila (Rutherford and Lindquist, 1998). It also suggests that the Hsp90 chaperones are engaged in stochastic mechanisms during development. To verify this concept and to examine whether any mutant phenotypic class is imprinted at any level of cellular activity or differentiation process, seeds were collected independently from each class of all four mutants after self-fertilization. About 100 seeds were grown to maturity. Surprisingly, each class of any mutant gave rise to phenotypes characteristic of all classes (Fig. 4). The percentage of each offspring class was almost distinctive and showed no clear pattern or type of direction towards a particular phenotype. Again peculiar unclassified phenotypes were recovered (data not shown). This phenomenon was repeated during the next generations. These results show that any mutant phenotypic class was partially penetrant and that the navigation of the chaperone network channels development through stochastic mechanisms. Fig. 4. Open in new tabDownload slide Navigation of the Hsp90 system through stochastic mechanisms. Percentages of phenotypic classes (A–E) from the parental (P) plants and from the offspring of individual classes. (A) Athsp90-1 mutant, (B) Athsp90-2, (C) Athsp90-3, and (D) Athsp90-4. The percentage of phenotypic classes derived from each mutant class is presented. Blue, class A; yellow, class B; green, class C; red, class D; and black, class E for Athsp90-1 or -2 mutants or unclassified for Athsp90-3 or -4. For parental lines n=600–1000 plants, while for each mutant offspring n=30–200 plants. Hsp90 influences morphogenetic effects of environmental cues In yeast and Drosophila if the Hsp90 activity is lowered from a threshold point, then it leads to abnormal development, more peculiar phenotypes, and finally to lethality, as in the case of homozygous mutants (Rutherford and Lindquist, 1998; Sollars et al., 2003). To test vigorously if this also holds for plants, the content of Hsp90 available for client proteins was diluted, thus increasing the demand for Hsp90. It is known that the highest expression of Arabidopsis Hsp90 genes occurs upon exposure at 37 °C for 3 h (Milioni and Hatzopoulos, 1997). All four mutants were grown at 22 °C for 10 d and then were subjected to short but severe heat stress (27 °C and/or 37 °C). Under these harsh environmental conditions, Col plants showed defects in development. However, extreme variegated phenotype appearances were observed in all mutants. Almost every individual plant of all hsp90 mutants had a distinct phenotype, and basic patterns of plant development were affected (Fig. 5). Therefore, many morphogenetic pathways were affected and the phenotype of each mutant plant was not strictly defined. However, mutant plants that were stressed at 37 °C and then transferred to 22 °C were more vigorous than the wild-type plants, as has been also observed in seedlings using geldanamycin (GDA; Queitsch et al., 2002; Sangster et al., 2004). Fig. 5. Open in new tabDownload slide Disturbed morphs on short but severe heat stress. (A, B, E, F, J, K, N, and Q–S) 10-day-old seedlings were stressed for 1.5 h at 27 °C followed by an additional stress for 1.5 h at 37 °C. (C, D, G–I, L, M, O, P, and T–V) 10-day-old seedlings were stressed for 3 h at 37 °C and then left to grow at 22 °C for up to approximately 40 d. (A–D) Col plants showing defects in organ development and pattern formation. (E–I) Athsp90-1 mutants showing multiple pattern formation defects, and abnormal growth and architecture. (J–M) Athsp90-2 mutants having a disturbed appearance of body growth and rosettes growing at multiple levels. (N–P) Athsp90-3 mutants showing variegation in growth, arrest of growth, and anomalous leaf pattern formation. (Q–V) Athsp90-4 mutants showing malformation and variegation in growth with rudimentary floral organs. Scale bar: 4 mm. Development is coordinated by time, environment, and genotype, and proceeds through stochastic mechanisms buffered by Hsp90 To investigate the interplay of environment and time in the stochastic processes and its dependence on the Hsp90 system, we artificially incorporated the parameter time during the course of development by placing all four Arabidopsis hsp90 mutants to grow under moderate (27 °C or 32 °C) stress conditions, for different lengths of time. If stochastic mechanisms contribute to multiple polymorphic traits, then time or environment might affect the proportion of each phenotypic class arbitrarily. Also, if Hsp90 restricts and buffers stochastic mechanisms, the distorted Hsp90 genes would increase phenotypic polymorphism. The earlier or the greater the stress to which the Arabidopsis Col plants were subjected, the higher the frequencies of subtle altered phenotypes observed (∼30%, compared with 2% at 22 °C). In the distorted hsp90 plants, the frequency of wild-type-like phenotypes decreased dramatically (by almost 15% for the Athsp90-1 mutant), while new and extreme phenotypes were detected (e.g. formation of multiple buds). The rest of the classes of Athsp90-1 mutants and all classes of the other three mutants showed a shift towards severe phenotypes when plants were grown at 27 °C (Fig. 6 and see Supplementary Fig. S1 available at JXB online). Mutant seeds or seedlings growing in a more severe environment at 32 °C showed even more extreme seedling phenotypes than plants grown at 27 °C. When mutant seeds were placed at 32 °C there was an abrupt reduction in the germination rate, ranging from 80% for the Athsp90-1 mutant to an average of 40% for the other three mutants. Again, the earlier the stress, the more extreme the phenotypes recovered. When 10-day-old Athsp90-1 or -2 mutant seedlings were transferred to 32 °C, they revealed a rather wide range of polymorphic phenotypes and grew up to the stage of the third pair of true leaves. Athsp90-3 and Athsp90-4 mutants survived longer, reverted to class A or C (dwarf), formed inflorescences, and eventually developed distorted and dried buds. Col plants showed fewer polymorphisms, formed inflorescences, and eventually dried. Fig. 6. Open in new tabDownload slide Effect of time and environment on developmental canalization. Percentages of phenotypic classes when Athsp90-1 mutants were transferred at different time points [seeds (0), 5-, 10-, or 20-day-old seedlings (dos)] of development at 27 °C (A) or when the progeny of these plants that set seeds at 27 °C were grown at 22 °C (B). Classes A, B, C, D, and E are represented by blue, yellow, green, red, or black colours, respectively. The percentages of novel (nov) phenotypes are also shown in brown. c, percentages of mutant plants which set seeds at 22° C. For each treatment for parental or offspring lines, n=40–140. When 20-day-old Athsp90-1 mutant plants were placed at 32 °C, about 50% survived, showing the polymorphic classes B–E, while additional phenotypes were scored (e.g. large rosette leaves, chlorotic petioles, and dwarf phenotypes). Classes B–E of Athsp90-1 or D and E of Athsp90-2 formed chlorotic inflorescences, developed buds, and eventually died. Athsp90-3 or -4 mutant plants reverted to class A 20 d later. Novel phenotypes were also observed. New rosette leaves began to develop when Athsp90-3 mutants remained for 30 d at 32 °C. This phenomenon was less apparent for the Athsp90-4 mutant. Col plants were variegated at the rosette level, and showed a large petiole and green leaves with a small blade. No mutant or wild-type plant was able to set seeds at 32 °C, and most plants died. The diversity of the proportion of the phenotypic classes was not due mainly to reduced survival rates. To investigate whether environmental changes influence multiple polymorphic traits and whether these are further affected by the Hsp90 buffering system, seeds from wild-type and hsp90 mutant plants set at 27 °C were collected in pools relative to the time that seeds, seedlings, or plants were placed at 27 °C and left to grow at 22 °C. The progeny of all hsp90 mutants showed defects in most aspects of development or body architecture, resulting in severe phenotypes. Col seedlings were also affected, though the variance or the percentage of peculiar phenotypes was less profound (see Supplementary Fig. S2 at JXB online). The longer the mutant parental plants remained at 27 °C the greater was the shift towards severe phenotypes and the higher the level of polymorphism of the offspring grown at 22 °C (Fig. 6; see Supplementary Fig. S1 and S2 at JXB online). However, the germination rates of the Athsp90-1 offspring were surprisingly diverse. When parental seeds, 5-, 10-, or 20-day-old mutants were transferred to 27 °C, the germination rates were 88, 75, 66, or 35%, respectively. For the other mutants or Col, the germination rates of the offspring were stable at an average of 77% or 95%, respectively. Therefore, the earlier the parents were placed at 27 °C the better the adaptation of the offspring, i.e. the greater were the survival rates of the Athsp90-1 mutant offspring. These results also show that the functional activity of the members of the Hsp90 gene family is not completely overlapping, and particular aspects of development are undertaken by specific members. Adult mutant offspring plants also showed new and distinct traits that were similar to those observed in the previous generation, supporting previous results showing that the Hsp90 system is also involved in epigenetic phenomena (Sollars et al., 2003; Zhao et al., 2005). Discussion The Arabidopsis Hsp90 gene family is divided into two groups (Milioni and Hatzopoulos, 1997; Krishna and Gloor, 2001). The organelle-type Hsp90 group comprises three members that play important roles in developmental pathways related to the organelles (Lin and Cheng, 1997; Cao et al., 2000, 2003). The cytosolic group comprises four members: AtHsp90-1, -2, -3, and -4. Even though organelles such as chloroplasts are vital components during the normal developmental process of a plant (Leech, 1984; Budziszewski et al., 2001; Tetlow et al., 2004), this study focused on the cytosolic members to avoid any interference of the organelle function in relation to the development and differentiation of a multicellular organism. All four cytosolic proteins are very similar; however, three of them have the highest homology in amino acid sequence as well as in gene structure (Milioni and Hatzopoulos, 1997). Therefore, overlapping functions could be plausible. Since there are four Hsp90 genes, any single mutation in these members will not manifest a lethal phenotype as has been found in other organisms containing a single gene (Rutherford and Lindquist, 1998). If this overlap in expression and/or function was complete, a mutation in any given member of the cytosolic Hsp90 gene family would not necessarily be noticeable. However, mutations in an individual Hsp90 cytosolic member confer sensitivity to plant pathogens (Hubert et al., 2003; Takahashi et al., 2003). A variety of seedling phenotypes were revealed when the Hsp90 protein function was decreased by pharmacological application (GDA) to progeny of Arabidopsis outcrosses (Queitsch et al., 2002; Sangster et al., 2004). Nevertheless, there are crucial limitations on the use of GDA. The drug is inactivated by light, therefore the effective selective pressure varies and consequently the phenocopies are displayed and scored only during a limited time period, e.g. at the seedling level. The present results based on genetic analyses of nearly isogenic lines and observations at different developmental stages of Arabidopsis growth revealed that Hsp90 strongly affects developmental plasticity. Hsp90 mutations affect almost every part of the seed and, more intensively, every part of the embryo. Consequently, Hsp90 proteins are crucial determinants in embryo morphogenesis and pattern formation, as has also been described in Drosophila and zebra fish (Ding et al., 1993; Krone et al., 2003). Since the acquired phenotypes cover most norms of seedling growth, it is plausible that Hsp90 chaperone activity has a vital role in the progression of seedling development. Hsp90 also affects adult body formation and is essential in most developmental pathways. Therefore, the data presented herein showed that Hsp90 functions as a networking system in a wide range of developmental pathways affecting many aspects of morphogenesis at different stages. The nature or the degree to which the plasticity of morphs increases depends mainly on individual defective Hsp90 cytosolic gene. However, overlapping functions among the individual members are not excluded. Evidently, the Hsp90 system affects the likelihood that a particular trait might appear, thus resulting in polymorphism. Otherwise a single mutation or a nearly isogenic inbred line could fix a particular phenotype. Therefore, it is suggested that Hsp90 buffers the plasticity, while a decrease by mutation will provide an avenue through which populations can evolve different phenotypic states. It is known that the function of the chaperone Hsp90 is to assist metastable ‘client’ proteins by conformational stabilization, therefore triggering their ability to be activated in a proper manner, transiently and spatially. The diverse class of ‘client’ proteins also encompasses regulators of the multicomplex network of development (Buchner, 1999; Young et al., 2001; Picard, 2002; Zhao et al., 2005). It is conceivable that a shortage of the Hsp90 system could provoke an interruption in the continuity of the pathway. Since Hsp90 cross-talks with many components of the regulatory pathway (Zhao et al., 2005), the lack of a particular member of the Hsp90 family could potentially interfere stochastically with a specific networking pathway. The random divergence of the adult phenotypes from the seedling phenotype appearance of all hsp90 mutants draws a direct line between stochastic mechanisms in development and Hsp90. It also indicates that the Hsp90 system actually restricts the magnitude of perturbations in development, a prerequisite in polymorphism of morphogenesis of multicellular organisms besides the genetic variation, and buffers the canalization of development. Morphogenetic appearances of all four hsp90 mutants were developmentally stochastic and independently resulted in classes of phenotypic appearance. Each hsp90 mutant showed this classification of phenotypes reminiscent of each other. Nevertheless, discrete phenotypic categories in each mutant line indicated a fine tuning of stochastic mechanisms in specific aspects of development. The inhibition of Hsp90 should be modest in order to uncover these diverse and specific phenotypic classes, i.e. mutation in one of the four genes or allowing a threshold in the networking of chaperone activity as was observed in Drosophila (Rutherford and Lindquist, 1998). If reduction of Hsp90 activity passes a certain threshold, then it could interfere stochastically with a particular developmental process that is dependent on Hsp90 action. Therefore, each individual plant from any mutant might represent a distinct phenotypic class (as those that were not classified). However, the canalization process during development and most plausibly the intrinsic capacity of the Hsp90 system and a degree of overlap in function of the four genes restrict this phenomenon, and consequently these mutant morphs are clustered. Partial fixation of certain phenotypes was also observed. Thus, Hsp90 normally restricts the stochastic mechanisms that are potentially capable of producing changes in phenotypic traits within the dynamic network of signals and pathways of the developmental processes. Misfolding of proteins which are also provoked by stress conditions induces Hsp90. When Hsp90 competence to sustain a functional pathway is exceeded by increased demand, mutation, and heat stress, then these pathways become highly discontinuous, resulting in a diverse range of developmental defects and phenotypes at any point during the course of development. The more severe the stress was, the broader the range of polymorphic traits observed. Heat stress on Col plants led to the emergence of polymorphic traits. The severity of phenotypes was directly related to the level of stress. Even more peculiar phenotypes were apparent when the Hsp90 system was additionally challenged by mutation, meaning that the erroneous switch could be almost anywhere or at almost any point in the developmental process, and multiple polymorphic morphs will emerge when Hsp90 is strongly challenged. This again indicates that the Hsp90 chaperone is networking developmental plasticity and therefore stochastic mechanisms. The phenotypes recovered through the hsp90 mutant lines did not emerge from general loss of vigour since they were more robust than Col plants under short but severe stress. Hence, environmental stress intensifies stochastic mechanisms in hsp90 mutant backgrounds as it reinforces the demand for Hsp90 activity in regulating gene networks. The phenotypic classes of the individual hsp90 mutants were varied in relation to the time for which the stress was applied. There was not a clear direction towards a particular proportion of phenotypic class when the stress was applied for different time periods, and morphogenesis changed arbitrarily. The earlier the Hsp90 was challenged the greater the perturbation events during development, thus the broader the magnitude of polymorphic phenotypes. Given that development and morphogenetic appearance are influenced by time and environment, and virtually by the genetic background, it was envisaged that development passes through a dynamic cone in which any point in space represents a distinct morphogenetic appearance at a particular time frame of development. If the phenotype at a specific time of development could be represented as a point in space, then the next position or phenotype appearance cannot be predicted precisely, apparently due to stochastic mechanisms in development. However, there is a given frame or restricted space for possible phenotypes to emerge. The present results show that this cone, made up from these points in space representing phenotypes at a particular developmental stage, is directly related to genome, environment, and time. The smaller the volume of the cone, the less the polymorphism. The width of the cone, representing the range of the morphs, is restricted by the Hsp90 chaperone system. The more or the earlier in development the Hsp90 was challenged, the wider the angle of the cone, the more the perturbation of stochastic events, and the higher the number of variants of morphs uncovered (Fig. 7). When the Hsp90 system was additionally challenged by elevated temperature at any given time point of development, more polymorphism and peculiar phenotypes were detected. Therefore, abrupt environmental alterations broaden the angle of the cone, and therefore diverse morphs appear (Fig. 7). This dynamic scheme symbolizes how the Hsp90 challenge allows more freedom in stochastic development. According to this model, it was envisaged that if the challenge of the Hsp90 system is complete, the angle of the cone might become wide enough to be represented eventually by a straight line, meaning lethality. Therefore, the canalization process is manifested by the Hsp90 system through the interaction with ‘client’ proteins via their activation in proper time and space, in order to exhibit proper developmental pathways and normal or canalized morphogenesis. Fig. 7. Open in new tabDownload slide Canalization of morphogenetic processes. (A) Phenotype polymorphisms are restricted within the cone. Each space point represents a morph of an embryo (white), seedling (light-green), plant (green), or during the reproductive period and flower development (yellow) that is stochastically linked to any space point during the course of development. Perimetrical circles are considered as the Hsp90-restricted borders. (B) If the restricted circles become broader by a decrease in Hsp90 function, then the diameter becomes larger, allowing more developmental perturbations to occur and thus a higher magnitude of polymorphisms appear. (C) When the environment changes abruptly at a certain time point of development, then the diameter becomes broader and the canalization changes direction. This eventually allows more perturbations during development and produces novel polymorphic traits. The magnitude of this phenomenon is striking when the Hsp90 function is decreased, and eventually the restricted borders become broader and developmental perturbations greater. ‘Mild’ traits or noise are manifested in isogenic Col plants, though in an extremely low proportion, since the canalization process orchestrates canonical development. However, when Col plants are stressed, phenotypes having defects in development appear. Consequently, Hsp90 restricts polymorphic appearances by controlling stochastic mechanisms in development that otherwise occur when the functional status of a protein is continuously challenged in the dynamic state of cell differentiation through the rigorous interplay of genome, environment, and time. Crucially, ‘phenocopying’ mechanisms under a suitable environment uncover polymorphisms, as in the case of Hsp90 challenge. These apparent polymorphisms could be perceived as adaptive forms and a rapid response of a population to sudden unforeseen environmental events without the intervening period of reduced fitness. Most polymorphisms could be deleterious and may be periodically eliminated from the population, as in the case of highly deformed embryos, seedlings, or plants of all hsp90 mutants due to selective abortions of residuals with high noise loads. Curiously, certain polymorphisms can uncover advantageous phenotypes. The enlargement of the angle of the cone of morphogenetic appearances (Fig. 7) allows more perturbations or adaptive peak shifts to occur in populations, without passing an adaptive valley. When the parents were subjected to mild stress, the offspring were less polymorphic and adaptive while the longer the stress, the more the polymorphism and adaptability in the next progeny. Consequently, there is a positive correlation between polymorphism and adaptability. Hence, from the evolutionary point of view, polymorphisms based on perturbations due to protein activation/inactivation in a developmental pathway could be crucial determinants for the adaptive peak. The more sensitive a system is, the more responsive to environmental changes, and hence it is evolvable. AtHsp90-1 appears to be the most responsive to abrupt environmental changes, while its expression level is rather low at 22 °C (Haralampidis et al., 2002). Evidently, these advantageous, but nevertheless highly polymorphic, phenotypes were recovered when the Athsp90-1 mutant was challenged by heat stress at early stages of development, permitting higher germination rates and therefore better adaptation in the next generation. Supplementary material The following supplementary material is available at JXB on line. Supplementary Data. Effect of time and environment on developmental canalization. Supplementary Data. Seedling polymorphisms from seeds set at 27 °C. The authors thank the Arabidopsis Biological Resource Center (ABRC) and Nottingham Arabidopsis Stock Center (NASC). We are grateful to G Banilas for his critical comments. This research was partly supported by a grant to P.H. from the GSRT, Greece (PENED 01/148) and Pythagoras I. References Abbott RJ , Gomes MF . 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Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved. For Permissions, please e-mail: [email protected]

Sheoran, Inder S.; Ross, Andrew R. S.; Olson, Douglas J. H.; Sawhney, Vipen K.

doi: 10.1093/jxb/erm199pmid: 17921476

Abstract In flowering plants, pollen grains are produced in the anther and released to the external environment with the primary function of delivering sperm cells to the female gametophyte. This study was conducted to identify proteins in tomato pollen and to analyse their roles in relation to pollen function. Tomato is an important crop which is grown worldwide and is an excellent experimental system. Proteins were extracted from pollen, separated by two-dimensional gel electrophoresis (2-DE), and identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and peptide mass fingerprinting. Of the 960 spots observed on Colloidal Coomassie Blue (CCB)-stained 2-DE gels, 190 were selected for analysis. Of these, 158 spots, representing 133 distinct proteins, were identified by searching the NCBInr and Expressed Sequence Tag databases. The identified proteins were classified based on designated functions and the majority included those involved in defence mechanisms, energy conversions, protein synthesis and processing, cytoskeleton formation, Ca2+ signalling, and as allergens. A number of proteins in tomato pollen were similar to those reported in the pollen of other species; however, several additional proteins with roles in defence mechanisms, metabolic processes, and hormone signalling were identified. The potential roles of the identified proteins in the survival strategy of the small, independent, two-celled pollen grain of tomato, and subsequently in pollen germination and tube growth are discussed. Lycopersicon esculentum, MALDI-TOF MS, pollen, proteomics, Solanaceae, tomato, two-dimensional gel electrophoresis Introduction In angiosperms, pollen grains (male gametophytes) are the dispersal agents of sperm cells and are vital for successful sexual reproduction and subsequent seed and fruit production. After release from an anther, pollen grains are carried by insects, wind, or other agents to the stigma of a carpel, where they germinate and deliver sperm cells to the female gametophyte via the formation of pollen tubes. The development of pollen, microsporogenesis and microgametogenesis, involves the co-ordinated expression of several genes in different tissues of an anther (Koltunow et al., 1990; McCormick, 2004; Ma, 2005), and pollen grains at maturity contain a large number of transcripts with designated roles in cell wall metabolism, cytoskeleton formation, cell signalling, and vesicle transport (Becker et al., 2003; Honys and Twell, 2003, 2004; Pina et al., 2005). Rapid advances in proteomic technologies, along with completion of the Arabidopsis and rice genome sequence projects and the availability of comprehensive public sequence databases, have provided tremendous impetus to plant proteomics research (Hirano et al., 2004; Rose et al., 2004; Rossignol et al., 2006). Proteomic analyses of various plant reproductive processes have been conducted, including the identification of sporophytic and gametophytic proteins in normal microspore development (Kerim et al., 2003; Miki-Hirosige et al., 2004), changes in anther proteins due to cold stress (Imin et al., 2006), proteins in relation to pollen germination and tube growth (Dai et al., 2006, 2007), and self-incompatibility (Kalinowski et al., 2002). The proteome analyses of mature Arabidopsis and rice pollen have also been conducted, and many proteins identified correspond to the known transcripts in pollen, in addition to several other proteins in each of these studies (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). The objective of this study was to analyse the proteome of tomato pollen. Tomato is an important crop grown around the world (Rick, 1980) and is also known to have significant effects on human health (Willcox et al., 2003; Omoni and Aluko, 2005). Tomato pollen grains are bi-cellular with a large vegetative cell and a small generative cell; the latter divides to form two sperm cells during pollen germination and tube growth, unlike the Arabidopsis and rice pollen which are tri-cellular. Pollen development in tomato has been studied extensively at both the light microscope and ultrastructural levels (Sawhney and Bhadula, 1988; Polowick and Sawhney, 1993a, b), and a number of male-sterile mutants are available in tomato which makes it an excellent model system for genetic and molecular investigations on pollen development, and in hybrid seed programmes (Sawhney, 1994; Gorman and McCormick, 1997). By combining two-dimensional gel electrophoresis (2-DE) with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), and by using the available databases for tomato and other species, as well as tomato expressed sequence tags (ESTs), a comprehensive analysis of the tomato pollen proteome has been performed. Many of the proteins identified in this study have designated roles in defence mechanisms, energy conversion, pollen germination, and pollen tube growth, and some possibly in sperm cell formation. To our knowledge, this is the first proteomic study on tomato pollen, and several of the proteins reported here have not been identified in the pollen of other species. Materials and methods Plant growth Seeds of tomato (cv. Rutgers) were germinated in 16 cm plastic pots containing Tera-lite Redi-earth mix. Young seedlings and plants were subsequently grown in a growth chamber at 26/23 °C (day/night) and under 16/8 h light/dark conditions. Illumination was provided by fluorescent tubes (F72T12/CW/VHO; Sylvania, USA) and incandescent bulbs at a photon flux density of 100–200 μmol m−2 s−1. Pollen collection Pollen grains were collected from freshly open flowers by shaking the anthers on a glass slide, checked under a dissecting microscope, and any debris removed with a needle. Pollen samples were pooled in an Eppendorf tube and the purity of pollen was again determined under a light microscope. Each pooled sample represented pollen from approximately 400 flowers. Pollen was either used immediately or stored at –80 °C until further use. The viability of each pollen sample was tested using an in vitro germination test (Shivanna and Sawhney, 1995) and pollen germination was in the range of 70–75%. Three separate batches of pooled pollen samples were used for protein extraction. Protein extraction The pollen samples (∼50 mg each, collected from 150–200 flowers) were ground to a fine powder in a pestle and mortar in liquid nitrogen, and extracted with acetone containing 10% TCA and 1% DTT. The samples were kept at –20 °C for 2 h and centrifuged at 25 000 g for 20 min at 4 °C. The resulting pellet was washed by suspending in acetone containing 1% DTT, incubated at –20 °C for 2 h, and centrifuged; it was re-suspended in acetone, sonicated (3×15 s), and centrifuged at 25 000 g. The pellet was vacuum-dried and then dissolved in urea buffer comprising 8 M urea, 20 mM DTT, 4% CHAPS, and 2% ampholyte (pH 3–10). The solution was vortexed extensively for 1 h at room temperature, centrifuged at 20 °C for 20 min at 25 000 g, and the supernatant collected. The resulting pellets were re-extracted with urea buffer and the supernatant combined with that collected earlier. The resulting protein samples were centrifuged again for 20 min at 25 000 g, quantified (3–3.5 mg per 50 mg pollen) using the Bio-Rad DC protein Assay Kit (Bio-Rad, Hercules, CA, USA), and either used immediately or stored at –80 °C for later use. Two-dimensional gel electrophoresis (2-DE) 2-DE was carried out as previously described (Sheoran et al., 2005, 2006). Isoelectric focusing (IEF) was performed using the Multiphor II horizontal electrophoresis system (Amersham Biosciences, Uppsala, Sweden) and 18 cm Immobiline Dry Strips of 4–7 or 3–10 linear pH gradients (Bio-Rad, Hercules, CA, USA). The strips were rehydrated overnight in a solution containing 8 M urea, 2% CHAPS, 20 mM DTT, 0.002% bromophenol blue, 2% IPG buffer (pH 3–10), and 600 μg of the protein sample. IEF was carried out by applying a voltage of 250 V for 1 h, increasing to 3500 V over 2 h, and holding at 3500 V until a total of 90 kVh was obtained. Following IEF, the strips were equilibrated for 15 min in an equilibration buffer containing 0.05 M TRIS-HCl (pH 8.8), 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS, and 20 mM DTT, followed by another 15 min equilibration in the same buffer containing 125 mM iodoacetamide without DTT. The equilibrated strips were applied to vertical SDS–polyacrylamide gels (12.5% resolving 5% stacking) and sealed with 0.5% agarose in SDS buffer containing bromophenol blue. Electrophoresis was performed for 30 min at 15 mA gel−1, and then at 20 mA gel−1 until the dye front reached the bottom of the gel, in an SDS electrophoresis buffer containing 25 mM TRIS base, 192 mM glycine, and 0.1% SDS, pH 8.3 in a PROTEAN II XL multi-cell (Bio-Rad, USA). Gel staining and image analysis Gels were fixed overnight in 50% (v/v) ethanol with 10% (v/v) orthophosphoric acid, washed with water (3×20 min), and stained with Colloidal Coomassie Blue G-250 (CCB) as described earlier (Sheoran et al., 2006). After washing with water, gels were scanned, annotated, and analysed for spot number using Phoretix 2D Image analysis software (UBI, Canada). Two replicate gels (for both pH 4–7 and 3–10) were run for each of three different pooled pollen samples collected from different batches of plants. Mass spectrometry and protein identification Of the spots observed consistently on CCB-stained 2-DE gels, 190 were selected for mass spectrometric analysis from both pH 4–7 and 3–10 gels, as previously described (Sheoran et al., 2005, 2006). Excised protein spots were automatically de-stained, dehydrated, reduced with DTT, alkylated with iodoacetamide, and digested with trypsin using a MassPREP protein digest station (Micromass, Manchester, UK) according to the recommended procedure. The resulting tryptic digests were concentrated and desalted using C18 ZipTips (Millipore Corporation, Bedford, MA, USA) according to the manufacturer's instructions. Samples were then analysed by MALDI-TOF MS on a Voyager-DE STR instrument (Applied Biosystems, Framingham MA, USA) operating in the positive ion and reflectron modes as described earlier (Sheoran et al., 2005, 2006). Spectra were acquired in the 700–3000 m/z range, processed with Mascot Distiller 2.0.0 (www.matrixscience.com), and the resulting peak lists used to identify the corresponding proteins in NCBInr (non-redundant) and Swiss-Prot databases by peptide mass fingerprinting (PMF) using the Mascot (www.matrixscience.com) search engine. Searches were performed using the following parameters: trypsin as the proteolytic enzyme, allowing for one missed cleavage; carbamidomethylation of cysteine as a fixed modification; oxidation of methionine as a variable modification. Proteins identified with a Mowse score greater than 66 (significant at 95% confidence interval) are reported. Because of the limited availability of tomato protein sequence information, database searches were also performed using the NCBI tomato EST database. Results and discussion The soluble proteins extracted from mature tomato pollen, separated by 2-DE on pH 4–7 and 3–10 IPG strips, and stained with CCB are shown in Fig. 1A and B, respectively. A total of 960 reproducible protein spots were detected using the pH 4–7 IPG strips (Fig. 1A) and 870 using the pH 3–10 strips (Fig. 1B), indicating better resolution on pH 4–7 gels, as previously observed for rice pollen (Dai et al., 2006). The number of protein spots observed in this study is comparable with that of our proteome analysis of Arabidopsis pollen (Sheoran et al., 2006) and is substantially higher than that reported in two other studies on Arabidopsis pollen (Holmes-Davis et al., 2005; Noir et al., 2005). Fig. 1. Open in new tabDownload slide Colloidal Coomassie Blue-stained 2-DE gels of tomato mature pollen protein extract (600 μg). (A) Proteins separated on pH 4–7 IPG strips. (B) Proteins separated on pH 3–10 NL IPG strips. Molecular masses (kDa) are shown on the left and pI ranges at the top corners of each figure. The numbered spots were analysed by MALDI-TOF MS, and the identified proteins listed in Table 1. Fig. 1. Open in new tabDownload slide Colloidal Coomassie Blue-stained 2-DE gels of tomato mature pollen protein extract (600 μg). (A) Proteins separated on pH 4–7 IPG strips. (B) Proteins separated on pH 3–10 NL IPG strips. Molecular masses (kDa) are shown on the left and pI ranges at the top corners of each figure. The numbered spots were analysed by MALDI-TOF MS, and the identified proteins listed in Table 1. Table 1. Tomato (Lycopersicon esculentum) pollen proteins separated by 2-DE (Fig. 1A, B) and identified using MALDI-TOF-MS Spot no. Gene index (gi) Speciesa Protein identity Mw/pIb NPM/Cov.c (%) Mowse scored Proteins in pollen of other speciese Defence-/stress-related proteins 7 300265 LE HSP68=68 kDa heat stress DnaK homologue 62.5/5.2 10/21 85 10 7671443 AT Cytochrome P450-like protein 64.6/6.2 11/31 91 22 1532049 SO Monodehydroascorbate reductase 54.0/6.7 14/30 124 AT 24 5759320 LE Copper/zinc superoxide dismutase 32.8/6.5 13/25 176 AT, OS 38 444340 AT Catalase 57.2/6.6 24/34 211 AT 41* 5891529 LE EST276332 (callus) similar to gi15236375 52.0/6.8 7/23 94 Serine hydroxymethyltransferase 4 56 50252724 OS Putative glutathione transferase 37.7/5.9 14/38 130 AT, OS 62 12231300 LE Ripening-regulated protein DDTFR10 22.2/4.7 9/38 99 68 21039134 LE Ascorbate peroxidase 42.4/8.9 17/41 144 AT, OS 78 62526498 LE Ascorbate peroxidase 27.0/5.9 16/42 140 AT, OS 81 70913175 LP Pto-disease resistance protein 34.7/5.4 10/44 110 82 73761753 LE Cytosolic ascorbate peroxidase 2 27.5/6.0 13/42 153 AT, OS 90 62526498 LE Ascorbate peroxidase 27.6/5.9 14/47 127 AT, OS 93 77641257 ST I2 (disease resistance protein) 25.3/5.3 6/32 71 95 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 7/36 91 96 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 13/60 124 104 28170732 LE Coat protein (ToMV) 17.9/4.9 7/55 89 105 229181 LE Coat protein (ToMV) 17.7/4.9 7/54 93 118 115465191 OS Putative group3 LEA protein 20.5/5.9 11/51 101 AT 119 15778360 LE Coat protein (ToMV) 17.9/4.9 6/52 123 120 854248 LE Cytosolic Cu, Zn superoxide dismutase 15.3/5.6 5/35 76 AT, OS 134 15054759 SS Putative Pto-like serine/threonine kinase 14.6/6.8 6/51 85 135 15054741 SB Pto-like serine/threonine kinase 19.7/6.1 10/60 111 142 3850778 LE Glutaredoxin 11.5/8.8 6/57 79 AT 158 438247 ST Glycine hydroxymethyltransferase 57.2/8.5 26/46 209 180 54261837 ST Hypothetical protein PGEC13.19 20.0/9.2 8/50 103 Energy-related 4 758340 ST 76 kDa mitochondrial complex I subunit 81.0/5.9 13/23 93 8 4582924 ST Phosphoglycerate mutase 61.0/5.4 19/34 134 AT, OS 9 4582924 ST Phosphoglycerate mutase 61.0/5.4 12/21 78 AT, OS 14 410634 ST Cytochrome c reductase-processing peptide 59.5/6.3 16/31 89 15 10444388 ST Dihydrolipoamide dehydrogenase precursor 53.4/6.4 14/26 93 OS 19 19685 NP ATP synthase beta subunit 59.9/5.9 20/43 141 AT, OS 21 8415909 AT ATP binding/H-exporting ATPase 59.8/6.2 18/39 154 AT, OS 23 19281 LE Enolase 48.0/5.7 12/36 121 AT, OS 33 56784992 OS ATP synthase beta subunit 45.2/5.3 15/34 101 OS 34 20465305 AT Putative hexokinase 54.5/5.5 13/22 88 OS 42* 6533434 LE EST298561 (leaf) similar to gi3850999 pyruvate dehydrogenase E1 beta subunit isoform I (Zea mays) 48 52139816 LE Mitochondrial MDH 36.2/8.7 15/57 143 AT, OS 51 21388550 ST Mitochondrial MDH 36.4/8.5 12/43 128 AT, OS 52 19281 LE Enolase 48.0/5.7 18/37 136 AT, OS 53 75221385 LE Fructokinase-2 35.0/5.8 14/39 110 OS 54 75221385 LE Fructokinase-2 35.0/5.8 20/57 193 OS 58 37991922 OS Cytochrome c oxidase subunit 6b-1 19.1/4.3 6/41 94 59 1161573 LE Enolase 35.3/6.3 19/50 205 AT, OS 60 1161573 LE Enolase 35.3/6.3 11/36 122 AT, OS 61 21388550 ST Mitochondrial MDH 36.4/8.5 11/33 122 AT, OS 63 19281 LE Enolase 48.0/5.7 10/39 103 AT, OS 65 19281 LE Enolase 48.0/5.7 11/28 112 AT, OS 67 1161573 LE Enolase 35.3/6.3 15/44 205 AT, OS 85 1915974 LE Fructokinase 35.0/5.8 11/38 140 OS 86 1915974 LE Fructokinase 35.0/5.8 13/48 156 OS 89 38112662 SC Triose phosphate isomerase cytosolic isoform 27.0/5.7 11/37 103 AT, OS 94 48209968 SD Mitochondrial ATP synthase D-chain 19.8/5.3 18/64 186 AT, OS 111 1915974 LE Fructokinase (fragment) 35.0/5.8 8/30 139 129* 58236622 LE EST/BP893151 (fruit) similar to gi82623399 cytochrome c oxidase family protein-like 133 50916028 LE Putative vacuolar ATP synthase subunit F 14.4/5.6 8/55 120 AT, OS 140 21360507 AT Cytochrome c oxidase 09.5/5.3 6/45 87 155 23321340 LE Dihydrolipoamide dehydrogenase precursor 53.1/6.9 15/24 123 AT, OS 165 8328399 ST Fructose-bisphosphate aldolase-like protein 39.0/7.5 17/47 216 AT, OS 166 77745438 ST Unknown (fructose-bisphosphate aldolase) 40.0/8.3 14/44 135 AT, OS 167 312179 ZM Glyceraldehyde 3-P dehydrogenase 36.6/6.4 8/24 75 AT, OS 168 3059140 PS NAD-dependent G3PDH 39.3/9.0 14/34 131 AT, OS 169 52139818 LE Cytosolic MDH 36.2/6.5 11/25 129 AT, OS 178* 5273558 LE EST256617 (leaf) similar to ATP-synthase 27.2/9.6 21/67 187 delta chain oligomycin sensitivity conferral protein (gi4774163) Protein synthesis and processing 1 18390588 AT Cell division cycle protein 48-related 134.5/5.6 16/14 86 AT, OS 2 1346172 LE Luminal-binding protein precursor 73.5/5.1 18/25 143 AT, OS 3 2654208 SO Heat shock70 protein 76.3/5.2 15/23 159 AT, OS 5 587564 ST Mitochondrial processing peptidase-like 59.5/6.2 11/18 95 OS 11 16221 AT Chaperonin hsp60 61.7/5.2 18/32 110 AT, OS 12 12546 C Chaperonin 60 61.5/6.3 21/39 140 AT, OS 13 587566 ST Mitochondrial processing peptidase-like 60.1/6.2 10/28 76 OS 16 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 6/18 76 OS 17 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 12/22 89 OS 28 7268689 AT Protein kinase-like protein 48.2/5.8 11/25 89 31 30025966 NT Heat shock protein 70 71.3/5.2 19/23 130 AT, OS 44 21493 ST Mitochondrial processing peptidase 55.0/5.7 12/26 105 OS 66 48209911 ST Putative elongation factor 1-beta 24.6/4.6 12/50 143 OS 71 19805 NT Luminal-binding protein 32.1/4.6 10/28 91 72 729617 NT 78 kDa glucose-regulated protein homologue 1 32.1/4.6 15/43 134 76 77416969 ST Unknown (proteosome alpha type-2) 25.6/5.4 7/48 101 77 77999303 ST Proteasome-like protein alpha subunit-4 27.3/5.6 12/41 131 83 4539545 NT (PRCI) Proteasome subunit alpha type 6 27.0/5.9 11/38 115 OS 100 49425163 LE Translationally controlled tumour protein-like 19.0/4.6 7/32 77 AT, OS 107 15778156 NT 14-3-3 protein 23.0/5.6 7/35 88 AT, OS 112 78191460 ST Ubiquitin-conjugating enzyme 16.7/6.2 8/54 88 OS 138 6671194 LE Cystatin 10.4/5.8 6/67 88 145 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 156 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 162 3986110 SG Heat shock protein 70 cognate 45.6/5.2 18/41 149 181 118103 LE Peptidylprolyl isomerase (PPI) (cyclophilin) 18.2/8.8 14/83 165 AT Ca2+ binding and signalling 18 1419088 NP Calreticulin 47.7/4.4 15/26 138 AT, OS 50 7960742 AT Calcium-binding protein (annexin 7) 36.6/6.4 17/45 172 106 115447273 OS Temperature stress-induced lipocalin 22.3/5.2 10/47 87 124 48209896 SD Putative calmodulin 16.6/4.9 9/62 113 AT 183 2388889 LE Calmodulin 13.3/4.1 5/23 87 AT 184 228408 AT Calmodulin-1 15.5/4.2 8/40 99 AT Cytoskeleton (cell organization and biogenesis) 20 2499814 LE Profilin 1 14.5/5.0 9/40 99 AT, OS 25 32527831 PT UDP-glucose pyrophosphorylase 52.0/5.7 12/25 88 AT, OS 40 48478827 LE UDP-glucose: protein transglucosylase-like 41.6/5.8 15/34 142 43 38194918 PV Reversibly glycosylated protein 40.7/5.8 9/ 33 94 AT, OS 45 21594350 AT dTDP-glucose 4-6-dehydratase 44.2/5.6 10/29 82 AT, OS 4 21599 ST UTP-glucose-1-phosphate uridylyltransferase 52.0/5.4 12/19 84 AT 73 50355625 UP Actin 42.0/5.2 12/43 112 AT, OS 113 15229001 AT Pectin methylesterase inhibitor 39.1/6.3 9/23 90 AT, OS 125 1399496 LE Profilin 14.5/5.0 8/57 107 AT, OS 130 2499814 LE Profilin-1 14.5/5.0 8/ 40 87 AT, OS 131 2499814 LE Profilin-1 14.5/5.0 5/33 67 AT, OS 136 7441438 LE Profilin-1 14.5/5.0 7/52 83 AT, OS 152 15667247 LE Pectin methylesterase 64.0/9.3 15/20 125 AT, OS 157 5931765 NT Phragmoplastin 68.5/7.7 18/23 100 Hormone metabolism and signalling 26 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 10/24 96 AT, OS 29 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 13/41 114 AT, OS 30 1084408 LE S-adenosyl-L-methionine synthetase 43.0/5.8 16/39 148 AT, OS 36 15225278 AT GPA1 (G protein alpha subunit1) 44.9/6.0 17/47 137 69 52353464 OS Aminocyclopropane-1-carboxylate oxidase 34.7/5.1 10/22 68 114 15218243 AT IAA5; transcription factor 18.7/6.4 9/53 92 187 15226486 AT Auxin-responsive calmodulin binding 11.9/8.8 8/52 87 Glycine-rich proteins 97* 58247573 LE EST/BP904102 (leaf) similar to GRP-2 108 82623423 ST Glycine-rich RNA-binding protein 17.6/5.6 7/61 154 AT 121 799015 ST Putative glycine-rich RNA-binding protein 17.6/5.6 7/39 124 AT 141 8272390 PP Glycine-rich protein 10.7/6.3 4/23 69 AT Nucleic acid metabolism 55 15229589 AT Nucleotide-binding protein 36.2/6.7 8/22 83 57 15229589 AT Nucleotide-binding protein 36.2/6.7 14/27 126 87 15230956 AT DNA binding (MAD2) 23.9/4.8 12/40 130 127 575953 LE Nucleotide diphosphate kinase 15.5/6.8 6/44 93 147 6681343 AT Putative transitional endoplasmic ATPase 90.1/5.1 19/25 161 148 18414193 AT ATP binding/ATPase/CDC48 90.0/5.1 17/24 136 185 998712 SO Nucleotide diphosphate kinase type III 17.1/8.1 11/54 116 Other metabolism 37 30687061 AT ATP binding/protein kinase 58.3/6.1 13/26 97 39 27803873 LE Succinyl CoA ligase beta subunit 44.8/5.9 18/48 132 AT 46 1419094 NT Glutamine synthetase 39.4/5.4 11/32 103 OS 47a 2243118 BJ Glutathione synthetase 60.0/6.6 15/29 116 47b 1419094 NT Glutamine synthetase 39.4/5.4 8/17 66 OS 64 29169309 LE Biotin carboxylase carrier protein 30.4/9.0 11/47 103 70 75280142 LE Aspartate carbamoyltransferase (fragment) 30.0/6.0 10/43 114 79* 7337866 LE EST313668 (radicle) tomato similar to 23.0/5.6 10/39 113 gi30690243 uridylate kinase 88 11994278 AT Cysteine synthase 32.4/5.8 13/41 126 103* 5276857 LE EST259695 (leaf) similar to gi18399910 3-hydroxyacyl-acyl-carrier protein dehydratase 149 8439545 ST Methionine synthase 84.9/5.9 16/19 132 AT 150 8439545 ST Methionine synthase 84.9/5.9 17/24 141 AT 151 8439545 ST Methionine synthase 84.9/5.9 19/22 146 AT 160 438254 ST Aminomethyltransferase (T-protein) 45.0/9.1 15/43 140 AT 161 21537268 AT Putative acetyl-CoA acyltransferase 47.2/9.0 17/40 153 AT Membrane transport 6 27883932 LE Vacuolar H+-ATPase A1 subunit isoform 68.8/5.2 13/24 78 AT, OS 75 534916 ST Soluble inorganic pyrophosphatase 24.4/5.6 10/27 114 AT, OS 91 51854284 OS GTP-binding protein 27.2/6.4 8/39 66 170 515358 ST 36 kDa porin I 29.4/7.8 12/39 132 171 30689271 AT Unknown protein (Fascin domain) 34.5/7.0 15/44 114 172 516166 ST 34 kDa porin 30.0/8.9 9/37 103 173 515360 ST 36 kDa porin II 29.4/7.8 9/29 102 175 77999247 ST POM30-like protein (Porin) 29.3/8.9 10/38 98 Unknown function 74 21593492 AT Unknown protein (mitochondrial glycoprotein) 25.0/6.0 7/60 145 84 15219092 AT Protein binding/unknown 38.1/5.8 12/30 94 116 295812 LE Anther-specific protein LAT 52 17.7/4.9 5/56 83 122 47496869 OS Hypothetical protein 18.8/6.6 8/51 100 153 9755453 AT Hypothetical protein 93.9/9.5 15/11 98 159 17978713 AT Unknown protein 58.0/9.3 16/24 115 163* 9292405 LE EST355772 (flower bud at anthesis), no homology with any protein 179 30678969 AT Unknown protein 22.3/10.0 11/37 84 Spot no. Gene index (gi) Speciesa Protein identity Mw/pIb NPM/Cov.c (%) Mowse scored Proteins in pollen of other speciese Defence-/stress-related proteins 7 300265 LE HSP68=68 kDa heat stress DnaK homologue 62.5/5.2 10/21 85 10 7671443 AT Cytochrome P450-like protein 64.6/6.2 11/31 91 22 1532049 SO Monodehydroascorbate reductase 54.0/6.7 14/30 124 AT 24 5759320 LE Copper/zinc superoxide dismutase 32.8/6.5 13/25 176 AT, OS 38 444340 AT Catalase 57.2/6.6 24/34 211 AT 41* 5891529 LE EST276332 (callus) similar to gi15236375 52.0/6.8 7/23 94 Serine hydroxymethyltransferase 4 56 50252724 OS Putative glutathione transferase 37.7/5.9 14/38 130 AT, OS 62 12231300 LE Ripening-regulated protein DDTFR10 22.2/4.7 9/38 99 68 21039134 LE Ascorbate peroxidase 42.4/8.9 17/41 144 AT, OS 78 62526498 LE Ascorbate peroxidase 27.0/5.9 16/42 140 AT, OS 81 70913175 LP Pto-disease resistance protein 34.7/5.4 10/44 110 82 73761753 LE Cytosolic ascorbate peroxidase 2 27.5/6.0 13/42 153 AT, OS 90 62526498 LE Ascorbate peroxidase 27.6/5.9 14/47 127 AT, OS 93 77641257 ST I2 (disease resistance protein) 25.3/5.3 6/32 71 95 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 7/36 91 96 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 13/60 124 104 28170732 LE Coat protein (ToMV) 17.9/4.9 7/55 89 105 229181 LE Coat protein (ToMV) 17.7/4.9 7/54 93 118 115465191 OS Putative group3 LEA protein 20.5/5.9 11/51 101 AT 119 15778360 LE Coat protein (ToMV) 17.9/4.9 6/52 123 120 854248 LE Cytosolic Cu, Zn superoxide dismutase 15.3/5.6 5/35 76 AT, OS 134 15054759 SS Putative Pto-like serine/threonine kinase 14.6/6.8 6/51 85 135 15054741 SB Pto-like serine/threonine kinase 19.7/6.1 10/60 111 142 3850778 LE Glutaredoxin 11.5/8.8 6/57 79 AT 158 438247 ST Glycine hydroxymethyltransferase 57.2/8.5 26/46 209 180 54261837 ST Hypothetical protein PGEC13.19 20.0/9.2 8/50 103 Energy-related 4 758340 ST 76 kDa mitochondrial complex I subunit 81.0/5.9 13/23 93 8 4582924 ST Phosphoglycerate mutase 61.0/5.4 19/34 134 AT, OS 9 4582924 ST Phosphoglycerate mutase 61.0/5.4 12/21 78 AT, OS 14 410634 ST Cytochrome c reductase-processing peptide 59.5/6.3 16/31 89 15 10444388 ST Dihydrolipoamide dehydrogenase precursor 53.4/6.4 14/26 93 OS 19 19685 NP ATP synthase beta subunit 59.9/5.9 20/43 141 AT, OS 21 8415909 AT ATP binding/H-exporting ATPase 59.8/6.2 18/39 154 AT, OS 23 19281 LE Enolase 48.0/5.7 12/36 121 AT, OS 33 56784992 OS ATP synthase beta subunit 45.2/5.3 15/34 101 OS 34 20465305 AT Putative hexokinase 54.5/5.5 13/22 88 OS 42* 6533434 LE EST298561 (leaf) similar to gi3850999 pyruvate dehydrogenase E1 beta subunit isoform I (Zea mays) 48 52139816 LE Mitochondrial MDH 36.2/8.7 15/57 143 AT, OS 51 21388550 ST Mitochondrial MDH 36.4/8.5 12/43 128 AT, OS 52 19281 LE Enolase 48.0/5.7 18/37 136 AT, OS 53 75221385 LE Fructokinase-2 35.0/5.8 14/39 110 OS 54 75221385 LE Fructokinase-2 35.0/5.8 20/57 193 OS 58 37991922 OS Cytochrome c oxidase subunit 6b-1 19.1/4.3 6/41 94 59 1161573 LE Enolase 35.3/6.3 19/50 205 AT, OS 60 1161573 LE Enolase 35.3/6.3 11/36 122 AT, OS 61 21388550 ST Mitochondrial MDH 36.4/8.5 11/33 122 AT, OS 63 19281 LE Enolase 48.0/5.7 10/39 103 AT, OS 65 19281 LE Enolase 48.0/5.7 11/28 112 AT, OS 67 1161573 LE Enolase 35.3/6.3 15/44 205 AT, OS 85 1915974 LE Fructokinase 35.0/5.8 11/38 140 OS 86 1915974 LE Fructokinase 35.0/5.8 13/48 156 OS 89 38112662 SC Triose phosphate isomerase cytosolic isoform 27.0/5.7 11/37 103 AT, OS 94 48209968 SD Mitochondrial ATP synthase D-chain 19.8/5.3 18/64 186 AT, OS 111 1915974 LE Fructokinase (fragment) 35.0/5.8 8/30 139 129* 58236622 LE EST/BP893151 (fruit) similar to gi82623399 cytochrome c oxidase family protein-like 133 50916028 LE Putative vacuolar ATP synthase subunit F 14.4/5.6 8/55 120 AT, OS 140 21360507 AT Cytochrome c oxidase 09.5/5.3 6/45 87 155 23321340 LE Dihydrolipoamide dehydrogenase precursor 53.1/6.9 15/24 123 AT, OS 165 8328399 ST Fructose-bisphosphate aldolase-like protein 39.0/7.5 17/47 216 AT, OS 166 77745438 ST Unknown (fructose-bisphosphate aldolase) 40.0/8.3 14/44 135 AT, OS 167 312179 ZM Glyceraldehyde 3-P dehydrogenase 36.6/6.4 8/24 75 AT, OS 168 3059140 PS NAD-dependent G3PDH 39.3/9.0 14/34 131 AT, OS 169 52139818 LE Cytosolic MDH 36.2/6.5 11/25 129 AT, OS 178* 5273558 LE EST256617 (leaf) similar to ATP-synthase 27.2/9.6 21/67 187 delta chain oligomycin sensitivity conferral protein (gi4774163) Protein synthesis and processing 1 18390588 AT Cell division cycle protein 48-related 134.5/5.6 16/14 86 AT, OS 2 1346172 LE Luminal-binding protein precursor 73.5/5.1 18/25 143 AT, OS 3 2654208 SO Heat shock70 protein 76.3/5.2 15/23 159 AT, OS 5 587564 ST Mitochondrial processing peptidase-like 59.5/6.2 11/18 95 OS 11 16221 AT Chaperonin hsp60 61.7/5.2 18/32 110 AT, OS 12 12546 C Chaperonin 60 61.5/6.3 21/39 140 AT, OS 13 587566 ST Mitochondrial processing peptidase-like 60.1/6.2 10/28 76 OS 16 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 6/18 76 OS 17 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 12/22 89 OS 28 7268689 AT Protein kinase-like protein 48.2/5.8 11/25 89 31 30025966 NT Heat shock protein 70 71.3/5.2 19/23 130 AT, OS 44 21493 ST Mitochondrial processing peptidase 55.0/5.7 12/26 105 OS 66 48209911 ST Putative elongation factor 1-beta 24.6/4.6 12/50 143 OS 71 19805 NT Luminal-binding protein 32.1/4.6 10/28 91 72 729617 NT 78 kDa glucose-regulated protein homologue 1 32.1/4.6 15/43 134 76 77416969 ST Unknown (proteosome alpha type-2) 25.6/5.4 7/48 101 77 77999303 ST Proteasome-like protein alpha subunit-4 27.3/5.6 12/41 131 83 4539545 NT (PRCI) Proteasome subunit alpha type 6 27.0/5.9 11/38 115 OS 100 49425163 LE Translationally controlled tumour protein-like 19.0/4.6 7/32 77 AT, OS 107 15778156 NT 14-3-3 protein 23.0/5.6 7/35 88 AT, OS 112 78191460 ST Ubiquitin-conjugating enzyme 16.7/6.2 8/54 88 OS 138 6671194 LE Cystatin 10.4/5.8 6/67 88 145 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 156 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 162 3986110 SG Heat shock protein 70 cognate 45.6/5.2 18/41 149 181 118103 LE Peptidylprolyl isomerase (PPI) (cyclophilin) 18.2/8.8 14/83 165 AT Ca2+ binding and signalling 18 1419088 NP Calreticulin 47.7/4.4 15/26 138 AT, OS 50 7960742 AT Calcium-binding protein (annexin 7) 36.6/6.4 17/45 172 106 115447273 OS Temperature stress-induced lipocalin 22.3/5.2 10/47 87 124 48209896 SD Putative calmodulin 16.6/4.9 9/62 113 AT 183 2388889 LE Calmodulin 13.3/4.1 5/23 87 AT 184 228408 AT Calmodulin-1 15.5/4.2 8/40 99 AT Cytoskeleton (cell organization and biogenesis) 20 2499814 LE Profilin 1 14.5/5.0 9/40 99 AT, OS 25 32527831 PT UDP-glucose pyrophosphorylase 52.0/5.7 12/25 88 AT, OS 40 48478827 LE UDP-glucose: protein transglucosylase-like 41.6/5.8 15/34 142 43 38194918 PV Reversibly glycosylated protein 40.7/5.8 9/ 33 94 AT, OS 45 21594350 AT dTDP-glucose 4-6-dehydratase 44.2/5.6 10/29 82 AT, OS 4 21599 ST UTP-glucose-1-phosphate uridylyltransferase 52.0/5.4 12/19 84 AT 73 50355625 UP Actin 42.0/5.2 12/43 112 AT, OS 113 15229001 AT Pectin methylesterase inhibitor 39.1/6.3 9/23 90 AT, OS 125 1399496 LE Profilin 14.5/5.0 8/57 107 AT, OS 130 2499814 LE Profilin-1 14.5/5.0 8/ 40 87 AT, OS 131 2499814 LE Profilin-1 14.5/5.0 5/33 67 AT, OS 136 7441438 LE Profilin-1 14.5/5.0 7/52 83 AT, OS 152 15667247 LE Pectin methylesterase 64.0/9.3 15/20 125 AT, OS 157 5931765 NT Phragmoplastin 68.5/7.7 18/23 100 Hormone metabolism and signalling 26 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 10/24 96 AT, OS 29 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 13/41 114 AT, OS 30 1084408 LE S-adenosyl-L-methionine synthetase 43.0/5.8 16/39 148 AT, OS 36 15225278 AT GPA1 (G protein alpha subunit1) 44.9/6.0 17/47 137 69 52353464 OS Aminocyclopropane-1-carboxylate oxidase 34.7/5.1 10/22 68 114 15218243 AT IAA5; transcription factor 18.7/6.4 9/53 92 187 15226486 AT Auxin-responsive calmodulin binding 11.9/8.8 8/52 87 Glycine-rich proteins 97* 58247573 LE EST/BP904102 (leaf) similar to GRP-2 108 82623423 ST Glycine-rich RNA-binding protein 17.6/5.6 7/61 154 AT 121 799015 ST Putative glycine-rich RNA-binding protein 17.6/5.6 7/39 124 AT 141 8272390 PP Glycine-rich protein 10.7/6.3 4/23 69 AT Nucleic acid metabolism 55 15229589 AT Nucleotide-binding protein 36.2/6.7 8/22 83 57 15229589 AT Nucleotide-binding protein 36.2/6.7 14/27 126 87 15230956 AT DNA binding (MAD2) 23.9/4.8 12/40 130 127 575953 LE Nucleotide diphosphate kinase 15.5/6.8 6/44 93 147 6681343 AT Putative transitional endoplasmic ATPase 90.1/5.1 19/25 161 148 18414193 AT ATP binding/ATPase/CDC48 90.0/5.1 17/24 136 185 998712 SO Nucleotide diphosphate kinase type III 17.1/8.1 11/54 116 Other metabolism 37 30687061 AT ATP binding/protein kinase 58.3/6.1 13/26 97 39 27803873 LE Succinyl CoA ligase beta subunit 44.8/5.9 18/48 132 AT 46 1419094 NT Glutamine synthetase 39.4/5.4 11/32 103 OS 47a 2243118 BJ Glutathione synthetase 60.0/6.6 15/29 116 47b 1419094 NT Glutamine synthetase 39.4/5.4 8/17 66 OS 64 29169309 LE Biotin carboxylase carrier protein 30.4/9.0 11/47 103 70 75280142 LE Aspartate carbamoyltransferase (fragment) 30.0/6.0 10/43 114 79* 7337866 LE EST313668 (radicle) tomato similar to 23.0/5.6 10/39 113 gi30690243 uridylate kinase 88 11994278 AT Cysteine synthase 32.4/5.8 13/41 126 103* 5276857 LE EST259695 (leaf) similar to gi18399910 3-hydroxyacyl-acyl-carrier protein dehydratase 149 8439545 ST Methionine synthase 84.9/5.9 16/19 132 AT 150 8439545 ST Methionine synthase 84.9/5.9 17/24 141 AT 151 8439545 ST Methionine synthase 84.9/5.9 19/22 146 AT 160 438254 ST Aminomethyltransferase (T-protein) 45.0/9.1 15/43 140 AT 161 21537268 AT Putative acetyl-CoA acyltransferase 47.2/9.0 17/40 153 AT Membrane transport 6 27883932 LE Vacuolar H+-ATPase A1 subunit isoform 68.8/5.2 13/24 78 AT, OS 75 534916 ST Soluble inorganic pyrophosphatase 24.4/5.6 10/27 114 AT, OS 91 51854284 OS GTP-binding protein 27.2/6.4 8/39 66 170 515358 ST 36 kDa porin I 29.4/7.8 12/39 132 171 30689271 AT Unknown protein (Fascin domain) 34.5/7.0 15/44 114 172 516166 ST 34 kDa porin 30.0/8.9 9/37 103 173 515360 ST 36 kDa porin II 29.4/7.8 9/29 102 175 77999247 ST POM30-like protein (Porin) 29.3/8.9 10/38 98 Unknown function 74 21593492 AT Unknown protein (mitochondrial glycoprotein) 25.0/6.0 7/60 145 84 15219092 AT Protein binding/unknown 38.1/5.8 12/30 94 116 295812 LE Anther-specific protein LAT 52 17.7/4.9 5/56 83 122 47496869 OS Hypothetical protein 18.8/6.6 8/51 100 153 9755453 AT Hypothetical protein 93.9/9.5 15/11 98 159 17978713 AT Unknown protein 58.0/9.3 16/24 115 163* 9292405 LE EST355772 (flower bud at anthesis), no homology with any protein 179 30678969 AT Unknown protein 22.3/10.0 11/37 84 The proteins are listed under broad functional categories; however, many of the proteins have more than one function. a Abbreviations used for various species: AT, Arabidopsis thaliana; C, Cucurbita Sp; LE, Lycopersicon esculentum LP, Lycopersicon peruvianum; NP, Nicotiana plumbaginifolia; NT, Nicotiana tabacum; OS, Oryza sativa; PP, Pyrus pyrifolia; PS, Pinus sylvestris; PV, Phaseolus vulgaris; SB, Solanum berthaultii; SC, Solanum chacoense; SG, Salix gilgiana; SO, Spinacia oleracea; SS, Solanum sucrense; ST, Solanum tuberosum; UP, Ulva pertusa; ZM, Zea mays. b Theoretical molecular weight and pI of the identified proteins. c NPM, number of peptides matched and percentage of protein sequence coverage. d Probability-based protein scores greater than 66 are considered significant (P <0.05). e AT, Arabidopis thaliana; OS,Oryza sativa. * Identified using the EST database. No significant matches were found for the spot numbers not listed in the table. Open in new tab Table 1. Tomato (Lycopersicon esculentum) pollen proteins separated by 2-DE (Fig. 1A, B) and identified using MALDI-TOF-MS Spot no. Gene index (gi) Speciesa Protein identity Mw/pIb NPM/Cov.c (%) Mowse scored Proteins in pollen of other speciese Defence-/stress-related proteins 7 300265 LE HSP68=68 kDa heat stress DnaK homologue 62.5/5.2 10/21 85 10 7671443 AT Cytochrome P450-like protein 64.6/6.2 11/31 91 22 1532049 SO Monodehydroascorbate reductase 54.0/6.7 14/30 124 AT 24 5759320 LE Copper/zinc superoxide dismutase 32.8/6.5 13/25 176 AT, OS 38 444340 AT Catalase 57.2/6.6 24/34 211 AT 41* 5891529 LE EST276332 (callus) similar to gi15236375 52.0/6.8 7/23 94 Serine hydroxymethyltransferase 4 56 50252724 OS Putative glutathione transferase 37.7/5.9 14/38 130 AT, OS 62 12231300 LE Ripening-regulated protein DDTFR10 22.2/4.7 9/38 99 68 21039134 LE Ascorbate peroxidase 42.4/8.9 17/41 144 AT, OS 78 62526498 LE Ascorbate peroxidase 27.0/5.9 16/42 140 AT, OS 81 70913175 LP Pto-disease resistance protein 34.7/5.4 10/44 110 82 73761753 LE Cytosolic ascorbate peroxidase 2 27.5/6.0 13/42 153 AT, OS 90 62526498 LE Ascorbate peroxidase 27.6/5.9 14/47 127 AT, OS 93 77641257 ST I2 (disease resistance protein) 25.3/5.3 6/32 71 95 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 7/36 91 96 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 13/60 124 104 28170732 LE Coat protein (ToMV) 17.9/4.9 7/55 89 105 229181 LE Coat protein (ToMV) 17.7/4.9 7/54 93 118 115465191 OS Putative group3 LEA protein 20.5/5.9 11/51 101 AT 119 15778360 LE Coat protein (ToMV) 17.9/4.9 6/52 123 120 854248 LE Cytosolic Cu, Zn superoxide dismutase 15.3/5.6 5/35 76 AT, OS 134 15054759 SS Putative Pto-like serine/threonine kinase 14.6/6.8 6/51 85 135 15054741 SB Pto-like serine/threonine kinase 19.7/6.1 10/60 111 142 3850778 LE Glutaredoxin 11.5/8.8 6/57 79 AT 158 438247 ST Glycine hydroxymethyltransferase 57.2/8.5 26/46 209 180 54261837 ST Hypothetical protein PGEC13.19 20.0/9.2 8/50 103 Energy-related 4 758340 ST 76 kDa mitochondrial complex I subunit 81.0/5.9 13/23 93 8 4582924 ST Phosphoglycerate mutase 61.0/5.4 19/34 134 AT, OS 9 4582924 ST Phosphoglycerate mutase 61.0/5.4 12/21 78 AT, OS 14 410634 ST Cytochrome c reductase-processing peptide 59.5/6.3 16/31 89 15 10444388 ST Dihydrolipoamide dehydrogenase precursor 53.4/6.4 14/26 93 OS 19 19685 NP ATP synthase beta subunit 59.9/5.9 20/43 141 AT, OS 21 8415909 AT ATP binding/H-exporting ATPase 59.8/6.2 18/39 154 AT, OS 23 19281 LE Enolase 48.0/5.7 12/36 121 AT, OS 33 56784992 OS ATP synthase beta subunit 45.2/5.3 15/34 101 OS 34 20465305 AT Putative hexokinase 54.5/5.5 13/22 88 OS 42* 6533434 LE EST298561 (leaf) similar to gi3850999 pyruvate dehydrogenase E1 beta subunit isoform I (Zea mays) 48 52139816 LE Mitochondrial MDH 36.2/8.7 15/57 143 AT, OS 51 21388550 ST Mitochondrial MDH 36.4/8.5 12/43 128 AT, OS 52 19281 LE Enolase 48.0/5.7 18/37 136 AT, OS 53 75221385 LE Fructokinase-2 35.0/5.8 14/39 110 OS 54 75221385 LE Fructokinase-2 35.0/5.8 20/57 193 OS 58 37991922 OS Cytochrome c oxidase subunit 6b-1 19.1/4.3 6/41 94 59 1161573 LE Enolase 35.3/6.3 19/50 205 AT, OS 60 1161573 LE Enolase 35.3/6.3 11/36 122 AT, OS 61 21388550 ST Mitochondrial MDH 36.4/8.5 11/33 122 AT, OS 63 19281 LE Enolase 48.0/5.7 10/39 103 AT, OS 65 19281 LE Enolase 48.0/5.7 11/28 112 AT, OS 67 1161573 LE Enolase 35.3/6.3 15/44 205 AT, OS 85 1915974 LE Fructokinase 35.0/5.8 11/38 140 OS 86 1915974 LE Fructokinase 35.0/5.8 13/48 156 OS 89 38112662 SC Triose phosphate isomerase cytosolic isoform 27.0/5.7 11/37 103 AT, OS 94 48209968 SD Mitochondrial ATP synthase D-chain 19.8/5.3 18/64 186 AT, OS 111 1915974 LE Fructokinase (fragment) 35.0/5.8 8/30 139 129* 58236622 LE EST/BP893151 (fruit) similar to gi82623399 cytochrome c oxidase family protein-like 133 50916028 LE Putative vacuolar ATP synthase subunit F 14.4/5.6 8/55 120 AT, OS 140 21360507 AT Cytochrome c oxidase 09.5/5.3 6/45 87 155 23321340 LE Dihydrolipoamide dehydrogenase precursor 53.1/6.9 15/24 123 AT, OS 165 8328399 ST Fructose-bisphosphate aldolase-like protein 39.0/7.5 17/47 216 AT, OS 166 77745438 ST Unknown (fructose-bisphosphate aldolase) 40.0/8.3 14/44 135 AT, OS 167 312179 ZM Glyceraldehyde 3-P dehydrogenase 36.6/6.4 8/24 75 AT, OS 168 3059140 PS NAD-dependent G3PDH 39.3/9.0 14/34 131 AT, OS 169 52139818 LE Cytosolic MDH 36.2/6.5 11/25 129 AT, OS 178* 5273558 LE EST256617 (leaf) similar to ATP-synthase 27.2/9.6 21/67 187 delta chain oligomycin sensitivity conferral protein (gi4774163) Protein synthesis and processing 1 18390588 AT Cell division cycle protein 48-related 134.5/5.6 16/14 86 AT, OS 2 1346172 LE Luminal-binding protein precursor 73.5/5.1 18/25 143 AT, OS 3 2654208 SO Heat shock70 protein 76.3/5.2 15/23 159 AT, OS 5 587564 ST Mitochondrial processing peptidase-like 59.5/6.2 11/18 95 OS 11 16221 AT Chaperonin hsp60 61.7/5.2 18/32 110 AT, OS 12 12546 C Chaperonin 60 61.5/6.3 21/39 140 AT, OS 13 587566 ST Mitochondrial processing peptidase-like 60.1/6.2 10/28 76 OS 16 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 6/18 76 OS 17 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 12/22 89 OS 28 7268689 AT Protein kinase-like protein 48.2/5.8 11/25 89 31 30025966 NT Heat shock protein 70 71.3/5.2 19/23 130 AT, OS 44 21493 ST Mitochondrial processing peptidase 55.0/5.7 12/26 105 OS 66 48209911 ST Putative elongation factor 1-beta 24.6/4.6 12/50 143 OS 71 19805 NT Luminal-binding protein 32.1/4.6 10/28 91 72 729617 NT 78 kDa glucose-regulated protein homologue 1 32.1/4.6 15/43 134 76 77416969 ST Unknown (proteosome alpha type-2) 25.6/5.4 7/48 101 77 77999303 ST Proteasome-like protein alpha subunit-4 27.3/5.6 12/41 131 83 4539545 NT (PRCI) Proteasome subunit alpha type 6 27.0/5.9 11/38 115 OS 100 49425163 LE Translationally controlled tumour protein-like 19.0/4.6 7/32 77 AT, OS 107 15778156 NT 14-3-3 protein 23.0/5.6 7/35 88 AT, OS 112 78191460 ST Ubiquitin-conjugating enzyme 16.7/6.2 8/54 88 OS 138 6671194 LE Cystatin 10.4/5.8 6/67 88 145 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 156 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 162 3986110 SG Heat shock protein 70 cognate 45.6/5.2 18/41 149 181 118103 LE Peptidylprolyl isomerase (PPI) (cyclophilin) 18.2/8.8 14/83 165 AT Ca2+ binding and signalling 18 1419088 NP Calreticulin 47.7/4.4 15/26 138 AT, OS 50 7960742 AT Calcium-binding protein (annexin 7) 36.6/6.4 17/45 172 106 115447273 OS Temperature stress-induced lipocalin 22.3/5.2 10/47 87 124 48209896 SD Putative calmodulin 16.6/4.9 9/62 113 AT 183 2388889 LE Calmodulin 13.3/4.1 5/23 87 AT 184 228408 AT Calmodulin-1 15.5/4.2 8/40 99 AT Cytoskeleton (cell organization and biogenesis) 20 2499814 LE Profilin 1 14.5/5.0 9/40 99 AT, OS 25 32527831 PT UDP-glucose pyrophosphorylase 52.0/5.7 12/25 88 AT, OS 40 48478827 LE UDP-glucose: protein transglucosylase-like 41.6/5.8 15/34 142 43 38194918 PV Reversibly glycosylated protein 40.7/5.8 9/ 33 94 AT, OS 45 21594350 AT dTDP-glucose 4-6-dehydratase 44.2/5.6 10/29 82 AT, OS 4 21599 ST UTP-glucose-1-phosphate uridylyltransferase 52.0/5.4 12/19 84 AT 73 50355625 UP Actin 42.0/5.2 12/43 112 AT, OS 113 15229001 AT Pectin methylesterase inhibitor 39.1/6.3 9/23 90 AT, OS 125 1399496 LE Profilin 14.5/5.0 8/57 107 AT, OS 130 2499814 LE Profilin-1 14.5/5.0 8/ 40 87 AT, OS 131 2499814 LE Profilin-1 14.5/5.0 5/33 67 AT, OS 136 7441438 LE Profilin-1 14.5/5.0 7/52 83 AT, OS 152 15667247 LE Pectin methylesterase 64.0/9.3 15/20 125 AT, OS 157 5931765 NT Phragmoplastin 68.5/7.7 18/23 100 Hormone metabolism and signalling 26 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 10/24 96 AT, OS 29 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 13/41 114 AT, OS 30 1084408 LE S-adenosyl-L-methionine synthetase 43.0/5.8 16/39 148 AT, OS 36 15225278 AT GPA1 (G protein alpha subunit1) 44.9/6.0 17/47 137 69 52353464 OS Aminocyclopropane-1-carboxylate oxidase 34.7/5.1 10/22 68 114 15218243 AT IAA5; transcription factor 18.7/6.4 9/53 92 187 15226486 AT Auxin-responsive calmodulin binding 11.9/8.8 8/52 87 Glycine-rich proteins 97* 58247573 LE EST/BP904102 (leaf) similar to GRP-2 108 82623423 ST Glycine-rich RNA-binding protein 17.6/5.6 7/61 154 AT 121 799015 ST Putative glycine-rich RNA-binding protein 17.6/5.6 7/39 124 AT 141 8272390 PP Glycine-rich protein 10.7/6.3 4/23 69 AT Nucleic acid metabolism 55 15229589 AT Nucleotide-binding protein 36.2/6.7 8/22 83 57 15229589 AT Nucleotide-binding protein 36.2/6.7 14/27 126 87 15230956 AT DNA binding (MAD2) 23.9/4.8 12/40 130 127 575953 LE Nucleotide diphosphate kinase 15.5/6.8 6/44 93 147 6681343 AT Putative transitional endoplasmic ATPase 90.1/5.1 19/25 161 148 18414193 AT ATP binding/ATPase/CDC48 90.0/5.1 17/24 136 185 998712 SO Nucleotide diphosphate kinase type III 17.1/8.1 11/54 116 Other metabolism 37 30687061 AT ATP binding/protein kinase 58.3/6.1 13/26 97 39 27803873 LE Succinyl CoA ligase beta subunit 44.8/5.9 18/48 132 AT 46 1419094 NT Glutamine synthetase 39.4/5.4 11/32 103 OS 47a 2243118 BJ Glutathione synthetase 60.0/6.6 15/29 116 47b 1419094 NT Glutamine synthetase 39.4/5.4 8/17 66 OS 64 29169309 LE Biotin carboxylase carrier protein 30.4/9.0 11/47 103 70 75280142 LE Aspartate carbamoyltransferase (fragment) 30.0/6.0 10/43 114 79* 7337866 LE EST313668 (radicle) tomato similar to 23.0/5.6 10/39 113 gi30690243 uridylate kinase 88 11994278 AT Cysteine synthase 32.4/5.8 13/41 126 103* 5276857 LE EST259695 (leaf) similar to gi18399910 3-hydroxyacyl-acyl-carrier protein dehydratase 149 8439545 ST Methionine synthase 84.9/5.9 16/19 132 AT 150 8439545 ST Methionine synthase 84.9/5.9 17/24 141 AT 151 8439545 ST Methionine synthase 84.9/5.9 19/22 146 AT 160 438254 ST Aminomethyltransferase (T-protein) 45.0/9.1 15/43 140 AT 161 21537268 AT Putative acetyl-CoA acyltransferase 47.2/9.0 17/40 153 AT Membrane transport 6 27883932 LE Vacuolar H+-ATPase A1 subunit isoform 68.8/5.2 13/24 78 AT, OS 75 534916 ST Soluble inorganic pyrophosphatase 24.4/5.6 10/27 114 AT, OS 91 51854284 OS GTP-binding protein 27.2/6.4 8/39 66 170 515358 ST 36 kDa porin I 29.4/7.8 12/39 132 171 30689271 AT Unknown protein (Fascin domain) 34.5/7.0 15/44 114 172 516166 ST 34 kDa porin 30.0/8.9 9/37 103 173 515360 ST 36 kDa porin II 29.4/7.8 9/29 102 175 77999247 ST POM30-like protein (Porin) 29.3/8.9 10/38 98 Unknown function 74 21593492 AT Unknown protein (mitochondrial glycoprotein) 25.0/6.0 7/60 145 84 15219092 AT Protein binding/unknown 38.1/5.8 12/30 94 116 295812 LE Anther-specific protein LAT 52 17.7/4.9 5/56 83 122 47496869 OS Hypothetical protein 18.8/6.6 8/51 100 153 9755453 AT Hypothetical protein 93.9/9.5 15/11 98 159 17978713 AT Unknown protein 58.0/9.3 16/24 115 163* 9292405 LE EST355772 (flower bud at anthesis), no homology with any protein 179 30678969 AT Unknown protein 22.3/10.0 11/37 84 Spot no. Gene index (gi) Speciesa Protein identity Mw/pIb NPM/Cov.c (%) Mowse scored Proteins in pollen of other speciese Defence-/stress-related proteins 7 300265 LE HSP68=68 kDa heat stress DnaK homologue 62.5/5.2 10/21 85 10 7671443 AT Cytochrome P450-like protein 64.6/6.2 11/31 91 22 1532049 SO Monodehydroascorbate reductase 54.0/6.7 14/30 124 AT 24 5759320 LE Copper/zinc superoxide dismutase 32.8/6.5 13/25 176 AT, OS 38 444340 AT Catalase 57.2/6.6 24/34 211 AT 41* 5891529 LE EST276332 (callus) similar to gi15236375 52.0/6.8 7/23 94 Serine hydroxymethyltransferase 4 56 50252724 OS Putative glutathione transferase 37.7/5.9 14/38 130 AT, OS 62 12231300 LE Ripening-regulated protein DDTFR10 22.2/4.7 9/38 99 68 21039134 LE Ascorbate peroxidase 42.4/8.9 17/41 144 AT, OS 78 62526498 LE Ascorbate peroxidase 27.0/5.9 16/42 140 AT, OS 81 70913175 LP Pto-disease resistance protein 34.7/5.4 10/44 110 82 73761753 LE Cytosolic ascorbate peroxidase 2 27.5/6.0 13/42 153 AT, OS 90 62526498 LE Ascorbate peroxidase 27.6/5.9 14/47 127 AT, OS 93 77641257 ST I2 (disease resistance protein) 25.3/5.3 6/32 71 95 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 7/36 91 96 30841938 LE Thioredoxin peroxidase 1 17.5/5.2 13/60 124 104 28170732 LE Coat protein (ToMV) 17.9/4.9 7/55 89 105 229181 LE Coat protein (ToMV) 17.7/4.9 7/54 93 118 115465191 OS Putative group3 LEA protein 20.5/5.9 11/51 101 AT 119 15778360 LE Coat protein (ToMV) 17.9/4.9 6/52 123 120 854248 LE Cytosolic Cu, Zn superoxide dismutase 15.3/5.6 5/35 76 AT, OS 134 15054759 SS Putative Pto-like serine/threonine kinase 14.6/6.8 6/51 85 135 15054741 SB Pto-like serine/threonine kinase 19.7/6.1 10/60 111 142 3850778 LE Glutaredoxin 11.5/8.8 6/57 79 AT 158 438247 ST Glycine hydroxymethyltransferase 57.2/8.5 26/46 209 180 54261837 ST Hypothetical protein PGEC13.19 20.0/9.2 8/50 103 Energy-related 4 758340 ST 76 kDa mitochondrial complex I subunit 81.0/5.9 13/23 93 8 4582924 ST Phosphoglycerate mutase 61.0/5.4 19/34 134 AT, OS 9 4582924 ST Phosphoglycerate mutase 61.0/5.4 12/21 78 AT, OS 14 410634 ST Cytochrome c reductase-processing peptide 59.5/6.3 16/31 89 15 10444388 ST Dihydrolipoamide dehydrogenase precursor 53.4/6.4 14/26 93 OS 19 19685 NP ATP synthase beta subunit 59.9/5.9 20/43 141 AT, OS 21 8415909 AT ATP binding/H-exporting ATPase 59.8/6.2 18/39 154 AT, OS 23 19281 LE Enolase 48.0/5.7 12/36 121 AT, OS 33 56784992 OS ATP synthase beta subunit 45.2/5.3 15/34 101 OS 34 20465305 AT Putative hexokinase 54.5/5.5 13/22 88 OS 42* 6533434 LE EST298561 (leaf) similar to gi3850999 pyruvate dehydrogenase E1 beta subunit isoform I (Zea mays) 48 52139816 LE Mitochondrial MDH 36.2/8.7 15/57 143 AT, OS 51 21388550 ST Mitochondrial MDH 36.4/8.5 12/43 128 AT, OS 52 19281 LE Enolase 48.0/5.7 18/37 136 AT, OS 53 75221385 LE Fructokinase-2 35.0/5.8 14/39 110 OS 54 75221385 LE Fructokinase-2 35.0/5.8 20/57 193 OS 58 37991922 OS Cytochrome c oxidase subunit 6b-1 19.1/4.3 6/41 94 59 1161573 LE Enolase 35.3/6.3 19/50 205 AT, OS 60 1161573 LE Enolase 35.3/6.3 11/36 122 AT, OS 61 21388550 ST Mitochondrial MDH 36.4/8.5 11/33 122 AT, OS 63 19281 LE Enolase 48.0/5.7 10/39 103 AT, OS 65 19281 LE Enolase 48.0/5.7 11/28 112 AT, OS 67 1161573 LE Enolase 35.3/6.3 15/44 205 AT, OS 85 1915974 LE Fructokinase 35.0/5.8 11/38 140 OS 86 1915974 LE Fructokinase 35.0/5.8 13/48 156 OS 89 38112662 SC Triose phosphate isomerase cytosolic isoform 27.0/5.7 11/37 103 AT, OS 94 48209968 SD Mitochondrial ATP synthase D-chain 19.8/5.3 18/64 186 AT, OS 111 1915974 LE Fructokinase (fragment) 35.0/5.8 8/30 139 129* 58236622 LE EST/BP893151 (fruit) similar to gi82623399 cytochrome c oxidase family protein-like 133 50916028 LE Putative vacuolar ATP synthase subunit F 14.4/5.6 8/55 120 AT, OS 140 21360507 AT Cytochrome c oxidase 09.5/5.3 6/45 87 155 23321340 LE Dihydrolipoamide dehydrogenase precursor 53.1/6.9 15/24 123 AT, OS 165 8328399 ST Fructose-bisphosphate aldolase-like protein 39.0/7.5 17/47 216 AT, OS 166 77745438 ST Unknown (fructose-bisphosphate aldolase) 40.0/8.3 14/44 135 AT, OS 167 312179 ZM Glyceraldehyde 3-P dehydrogenase 36.6/6.4 8/24 75 AT, OS 168 3059140 PS NAD-dependent G3PDH 39.3/9.0 14/34 131 AT, OS 169 52139818 LE Cytosolic MDH 36.2/6.5 11/25 129 AT, OS 178* 5273558 LE EST256617 (leaf) similar to ATP-synthase 27.2/9.6 21/67 187 delta chain oligomycin sensitivity conferral protein (gi4774163) Protein synthesis and processing 1 18390588 AT Cell division cycle protein 48-related 134.5/5.6 16/14 86 AT, OS 2 1346172 LE Luminal-binding protein precursor 73.5/5.1 18/25 143 AT, OS 3 2654208 SO Heat shock70 protein 76.3/5.2 15/23 159 AT, OS 5 587564 ST Mitochondrial processing peptidase-like 59.5/6.2 11/18 95 OS 11 16221 AT Chaperonin hsp60 61.7/5.2 18/32 110 AT, OS 12 12546 C Chaperonin 60 61.5/6.3 21/39 140 AT, OS 13 587566 ST Mitochondrial processing peptidase-like 60.1/6.2 10/28 76 OS 16 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 6/18 76 OS 17 82621176 ST Mitochondrial processing peptidase-like 58.2/5.8 12/22 89 OS 28 7268689 AT Protein kinase-like protein 48.2/5.8 11/25 89 31 30025966 NT Heat shock protein 70 71.3/5.2 19/23 130 AT, OS 44 21493 ST Mitochondrial processing peptidase 55.0/5.7 12/26 105 OS 66 48209911 ST Putative elongation factor 1-beta 24.6/4.6 12/50 143 OS 71 19805 NT Luminal-binding protein 32.1/4.6 10/28 91 72 729617 NT 78 kDa glucose-regulated protein homologue 1 32.1/4.6 15/43 134 76 77416969 ST Unknown (proteosome alpha type-2) 25.6/5.4 7/48 101 77 77999303 ST Proteasome-like protein alpha subunit-4 27.3/5.6 12/41 131 83 4539545 NT (PRCI) Proteasome subunit alpha type 6 27.0/5.9 11/38 115 OS 100 49425163 LE Translationally controlled tumour protein-like 19.0/4.6 7/32 77 AT, OS 107 15778156 NT 14-3-3 protein 23.0/5.6 7/35 88 AT, OS 112 78191460 ST Ubiquitin-conjugating enzyme 16.7/6.2 8/54 88 OS 138 6671194 LE Cystatin 10.4/5.8 6/67 88 145 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 156 29893543 AT Putative elongation factor 74.7/6.9 15/24 119 AT 162 3986110 SG Heat shock protein 70 cognate 45.6/5.2 18/41 149 181 118103 LE Peptidylprolyl isomerase (PPI) (cyclophilin) 18.2/8.8 14/83 165 AT Ca2+ binding and signalling 18 1419088 NP Calreticulin 47.7/4.4 15/26 138 AT, OS 50 7960742 AT Calcium-binding protein (annexin 7) 36.6/6.4 17/45 172 106 115447273 OS Temperature stress-induced lipocalin 22.3/5.2 10/47 87 124 48209896 SD Putative calmodulin 16.6/4.9 9/62 113 AT 183 2388889 LE Calmodulin 13.3/4.1 5/23 87 AT 184 228408 AT Calmodulin-1 15.5/4.2 8/40 99 AT Cytoskeleton (cell organization and biogenesis) 20 2499814 LE Profilin 1 14.5/5.0 9/40 99 AT, OS 25 32527831 PT UDP-glucose pyrophosphorylase 52.0/5.7 12/25 88 AT, OS 40 48478827 LE UDP-glucose: protein transglucosylase-like 41.6/5.8 15/34 142 43 38194918 PV Reversibly glycosylated protein 40.7/5.8 9/ 33 94 AT, OS 45 21594350 AT dTDP-glucose 4-6-dehydratase 44.2/5.6 10/29 82 AT, OS 4 21599 ST UTP-glucose-1-phosphate uridylyltransferase 52.0/5.4 12/19 84 AT 73 50355625 UP Actin 42.0/5.2 12/43 112 AT, OS 113 15229001 AT Pectin methylesterase inhibitor 39.1/6.3 9/23 90 AT, OS 125 1399496 LE Profilin 14.5/5.0 8/57 107 AT, OS 130 2499814 LE Profilin-1 14.5/5.0 8/ 40 87 AT, OS 131 2499814 LE Profilin-1 14.5/5.0 5/33 67 AT, OS 136 7441438 LE Profilin-1 14.5/5.0 7/52 83 AT, OS 152 15667247 LE Pectin methylesterase 64.0/9.3 15/20 125 AT, OS 157 5931765 NT Phragmoplastin 68.5/7.7 18/23 100 Hormone metabolism and signalling 26 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 10/24 96 AT, OS 29 429108 LE S-adenosyl-L-methionine synthetase 42.6/5.8 13/41 114 AT, OS 30 1084408 LE S-adenosyl-L-methionine synthetase 43.0/5.8 16/39 148 AT, OS 36 15225278 AT GPA1 (G protein alpha subunit1) 44.9/6.0 17/47 137 69 52353464 OS Aminocyclopropane-1-carboxylate oxidase 34.7/5.1 10/22 68 114 15218243 AT IAA5; transcription factor 18.7/6.4 9/53 92 187 15226486 AT Auxin-responsive calmodulin binding 11.9/8.8 8/52 87 Glycine-rich proteins 97* 58247573 LE EST/BP904102 (leaf) similar to GRP-2 108 82623423 ST Glycine-rich RNA-binding protein 17.6/5.6 7/61 154 AT 121 799015 ST Putative glycine-rich RNA-binding protein 17.6/5.6 7/39 124 AT 141 8272390 PP Glycine-rich protein 10.7/6.3 4/23 69 AT Nucleic acid metabolism 55 15229589 AT Nucleotide-binding protein 36.2/6.7 8/22 83 57 15229589 AT Nucleotide-binding protein 36.2/6.7 14/27 126 87 15230956 AT DNA binding (MAD2) 23.9/4.8 12/40 130 127 575953 LE Nucleotide diphosphate kinase 15.5/6.8 6/44 93 147 6681343 AT Putative transitional endoplasmic ATPase 90.1/5.1 19/25 161 148 18414193 AT ATP binding/ATPase/CDC48 90.0/5.1 17/24 136 185 998712 SO Nucleotide diphosphate kinase type III 17.1/8.1 11/54 116 Other metabolism 37 30687061 AT ATP binding/protein kinase 58.3/6.1 13/26 97 39 27803873 LE Succinyl CoA ligase beta subunit 44.8/5.9 18/48 132 AT 46 1419094 NT Glutamine synthetase 39.4/5.4 11/32 103 OS 47a 2243118 BJ Glutathione synthetase 60.0/6.6 15/29 116 47b 1419094 NT Glutamine synthetase 39.4/5.4 8/17 66 OS 64 29169309 LE Biotin carboxylase carrier protein 30.4/9.0 11/47 103 70 75280142 LE Aspartate carbamoyltransferase (fragment) 30.0/6.0 10/43 114 79* 7337866 LE EST313668 (radicle) tomato similar to 23.0/5.6 10/39 113 gi30690243 uridylate kinase 88 11994278 AT Cysteine synthase 32.4/5.8 13/41 126 103* 5276857 LE EST259695 (leaf) similar to gi18399910 3-hydroxyacyl-acyl-carrier protein dehydratase 149 8439545 ST Methionine synthase 84.9/5.9 16/19 132 AT 150 8439545 ST Methionine synthase 84.9/5.9 17/24 141 AT 151 8439545 ST Methionine synthase 84.9/5.9 19/22 146 AT 160 438254 ST Aminomethyltransferase (T-protein) 45.0/9.1 15/43 140 AT 161 21537268 AT Putative acetyl-CoA acyltransferase 47.2/9.0 17/40 153 AT Membrane transport 6 27883932 LE Vacuolar H+-ATPase A1 subunit isoform 68.8/5.2 13/24 78 AT, OS 75 534916 ST Soluble inorganic pyrophosphatase 24.4/5.6 10/27 114 AT, OS 91 51854284 OS GTP-binding protein 27.2/6.4 8/39 66 170 515358 ST 36 kDa porin I 29.4/7.8 12/39 132 171 30689271 AT Unknown protein (Fascin domain) 34.5/7.0 15/44 114 172 516166 ST 34 kDa porin 30.0/8.9 9/37 103 173 515360 ST 36 kDa porin II 29.4/7.8 9/29 102 175 77999247 ST POM30-like protein (Porin) 29.3/8.9 10/38 98 Unknown function 74 21593492 AT Unknown protein (mitochondrial glycoprotein) 25.0/6.0 7/60 145 84 15219092 AT Protein binding/unknown 38.1/5.8 12/30 94 116 295812 LE Anther-specific protein LAT 52 17.7/4.9 5/56 83 122 47496869 OS Hypothetical protein 18.8/6.6 8/51 100 153 9755453 AT Hypothetical protein 93.9/9.5 15/11 98 159 17978713 AT Unknown protein 58.0/9.3 16/24 115 163* 9292405 LE EST355772 (flower bud at anthesis), no homology with any protein 179 30678969 AT Unknown protein 22.3/10.0 11/37 84 The proteins are listed under broad functional categories; however, many of the proteins have more than one function. a Abbreviations used for various species: AT, Arabidopsis thaliana; C, Cucurbita Sp; LE, Lycopersicon esculentum LP, Lycopersicon peruvianum; NP, Nicotiana plumbaginifolia; NT, Nicotiana tabacum; OS, Oryza sativa; PP, Pyrus pyrifolia; PS, Pinus sylvestris; PV, Phaseolus vulgaris; SB, Solanum berthaultii; SC, Solanum chacoense; SG, Salix gilgiana; SO, Spinacia oleracea; SS, Solanum sucrense; ST, Solanum tuberosum; UP, Ulva pertusa; ZM, Zea mays. b Theoretical molecular weight and pI of the identified proteins. c NPM, number of peptides matched and percentage of protein sequence coverage. d Probability-based protein scores greater than 66 are considered significant (P <0.05). e AT, Arabidopis thaliana; OS,Oryza sativa. * Identified using the EST database. No significant matches were found for the spot numbers not listed in the table. Open in new tab One hundred and ninety protein spots were selected throughout the molecular mass and isoelectric point (pI) ranges of pH 4–7 and 3–10 gels and analysed by MALDI-TOF MS. Of these, 158 spots representing 133 distinct proteins were successfully analysed. Despite the limited availability of tomato protein sequence data, it was possible to identify 83% of the selected spots. This was achieved by concentrating and desalting the tryptic digests using C18 ZipTips, and by processing the MS data with Mascot Distiller 2.0.0. The identified proteins, along with the gene index (gi) number and Mowse score, are listed in Table 1. Some of the identified proteins were present as multiple spots on 2-DE gels. These may correspond to multiple isoforms, which could play an important role in pollen development and germination by diversifying the functions of proteins in the haploid genome. Multiple spots corresponding to the same protein have been reported in other proteomic studies (Kerim et al., 2003; Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). The predicted molecular masses and pIs for the majority of the identified proteins were generally consistent with the experimental data, as judged from the location of spots on 2-D gels; however, there were some exceptions. For example, spots 31, 47a, 65, 111, and 113 had an apparent molecular mass greater than the corresponding identified protein, whereas spots 5, 20, 24, 58, and 145 had a molecular mass lower than the predicted value. These deviations in molecular mass and pI, as well as multiple spots for the same protein, could be due to various factors, including post-translational modifications, protein degradation, and partial synthesis of proteins during pollen maturation, protein translation from alternatively spliced mRNAs, or protein homologues that may be unique to pollen (Sheoran et al., 2006). Functional grouping of identified proteins The identified proteins were categorized into 12 functional groups (Fig. 2) based on predicted protein function and defined criteria (Berardini et al., 2004; Sheoran et al., 2006). More than half of the identified proteins were in three major groups, i.e. energy (19%), defence-related (18%), and protein synthesis and processing (18%). The other groups included proteins involved in cytoskeleton (cell biogenesis and organization), membrane transport, hormone metabolism and signalling, Ca2+ binding and signalling, pollen allergens, other metabolism, and those of unknown function (Fig. 2). Proteins associated with hormone metabolism and signalling, Ca2+ binding and signalling, pollen allergens, and glycine-rich proteins (GRPs) were grouped separately in Fig. 2 because of their established roles in pollen function. In Arabidopsis and rice pollen also, proteins belonging to most, but not all, of these groups were identified and the majority of them were associated with energy, defence, and protein synthesis and processing (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). For comparison purposes, proteins in tomato pollen which are common with Arabidopsis and/or rice pollen are indicated in the last column in Table 1. Fig. 2. Open in new tabDownload slide Functional categorization of proteins identified from mature tomato pollen. Fig. 2. Open in new tabDownload slide Functional categorization of proteins identified from mature tomato pollen. Each group of proteins in tomato pollen and their potential roles in pollen function are discussed separately below. Defence-related proteins Proteins included in this group are associated with both biotic and abiotic stresses. The biotic stress-related proteins included the Pto-disease resistance protein, Pto-like serine/threonine kinase, I2 (disease resistance protein), tomato mosaic viral (ToMV) coat protein, and a hypothetical protein PGEC 13.19. Plants expressing the Pto gene encoding a serine/threonine kinase protein were shown to be resistant to both bacterial and fungal pathogens (Tang et al., 1999; Pedley and Martin, 2003), and the Pto-mediated resistance has been used to control the bacterial speck disease in different tomato cultivars (Martin, et al., 2003). The presence of ToMV coat protein in tomato pollen could provide resistance to viruses, since the constitutive expression of viral coat protein genes is known to be effective in producing virus-resistant plants (Koo et al., 2004). The Pto-like serine/threonine kinase and ToMV coat protein were not reported in rice and Arabidopsis pollen (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006) although they were present in the embryo and endosperm of tomato seed (Sheoran et al., 2005). Pollen grains are free-floating structures and are subject to various abiotic stresses, including drought and extreme temperatures. Plants produce reactive oxygen species (ROS) in responses to abiotic and biotic stresses. Although ROS are known to serve as second messengers in many developmental processes (Foyer and Noctor, 2005), the excessive production of ROS causes oxidative damage to cellular components (Apel and Hirt, 2004; Gechev et al., 2006). Plants have evolved a strategy to combat the ROS by inducing various protective enzymes. Many enzymes known to play a role in the detoxification of ROS were identified in tomato pollen, including superoxide dismutase, thioredoxin peroxidase, ascorbate peroxidase, mono-dehydroascorbate reductase, glutathione transferase, glutaredoxin, and catalase (Table 1). These proteins were also reported in rice and Arabidopsis pollen (Table 1; Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). Heat shock proteins (HSPs) and luminal-binding proteins play key roles in defence mechanisms, in addition to their roles as molecular chaperones in protein processing. In tomato pollen, HSP 60, HSP 70, HSP 70-cognate, chaperonin 60, and luminal-binding proteins were identified. HSPs are known to act as protectants of protein function (Vierling, 1991; Wang et al., 2004) and their accumulation in response to heat stress has been reported in developing pollen (Mascarenhas and Crone, 1996). Other proteins present in tomato pollen, such as temperature stress-induced lipocalin, ripening-regulated protein DDTFR10, and the heat stress DnaK homologue, might also have a role in combating abiotic stresses. One spot representing LEA proteins, which are known to have a role in desiccation tolerance (Park et al., 2005), was identified in tomato pollen, whereas 4–7 LEA protein spots were observed in Arabidopsis pollen (Noir et al., 2005; Sheoran et al., 2006). The defence-related proteins identified in tomato pollen could be part of the survival strategy of these small two-celled structures, which are independent of the parental tissues and, therefore, particularly susceptible to biotic and abiotic stresses. Energy-related proteins The presence of a high percentage (19%) of proteins related to energy metabolism correlates well with the large number of mitochondria observed in mature tomato pollen (Polowick and Sawhney, 1993b). These include proteins associated with glycolysis, for example phosphoglucomutase, glyceraldehyde 3-P dehydrogenase, triose phosphate isomerase, enolase, and fructokinase; with the TCA cycle, for example MDH, succinyl CoA ligase, dihydrolipoamide dehydrogenase; and with the electron transport chain, for example cytochrome c oxidase and reductase, and various subunits of ATP synthase (Table 1). Cytochrome c oxidase and reductase were identified only in tomato pollen, and a relatively high number of spots was observed for fructokinase and enolase in tomato compared with Arabidopsis and rice pollen. However, most of the energy-related proteins in tomato were also reported in rice and Arabidopsis pollen (Table 1; Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). Pollen germination and tube growth are high-energy-requiring processes and it seems that most of the proteins required for these events are in place in mature tomato pollen. Although the transcriptome of tomato pollen is not yet available, in Arabidopsis pollen, the transcripts of energy-related proteins are under-represented (Becker et al., 2003; Honys and Twell, 2003, 2004; Pina et al., 2005). Indeed, Holmes-Davis et al. (2005) showed an inverse relationship of high abundance energy-related proteins between and the corresponding mRNA in Arabidopsis pollen. Protein synthesis and processing Three spots corresponding to a putative elongation factor and elongation factor-1β involved in protein synthesis were identified in tomato pollen. Proteins involved in proper protein folding, assembly, and localization, including chaperonin 60, cyclophilin, HSPs, luminal-binding proteins, 78 kDa glucose-regulated protein homologue 1, and HSP68 heat stress DnaK homologue, were also identified (Table 1). In addition, a large number of proteins involved in protein degradation such as cystatin, transitional endoplasmic ATPase, mitochondrial processing peptidase-like, proteasome subunits, ubiquitin-conjugating enzyme, cell division cycle protein 48-related, and translationally controlled tumour protein-like were identified (Table 1). There were relatively few proteins involved in protein synthesis compared with those in protein processing and degradation in tomato pollen, as in pollen of other species (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). This is consistent with the relatively small number of polysomes observed in mature tomato pollen (Polowick and Sawhney, 1993b). However, the mature pollen has abundant stored mRNA (Schrauwen et al., 1990; Honys et al., 2000) and translational apparatus (Mascarenhas, 1989), indicating rapid protein synthesis at the onset of pollen germination and tube growth. Cell biogenesis and organization Actin cytoskeleton is an essential component of pollen tube growth as it transports new cell wall materials to the growing tip region (Drobak et al., 2004). Actin was identified in tomato pollen (Table 1), as in the pollen of other species (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). In addition, profilin, a major actin-binding protein involved in pollen tube growth (Taylor and Hepler, 1997), was also identified in tomato pollen (Table 1). Cell wall loosening and synthesis are essential for pollen germination and rapid pollen tube growth, and various proteins associated with these processes were identified in tomato pollen, including pectin methylesterase (PME), pectin methylesterase inhibitor (PMEI), UTP-glucose-1-phosphate uridylyltransferase, UDP-glucose pyrophosphorylase, UDP-glucose:protein transglucosylase-like, microtubule-associated dTDP-glucose 4–6-dehydratase, and reversibly glycosylated protein. The PME enzyme catalyses the demethylesterification of homogalacturonans and plays an important role in pollen tube growth (Bosch et al., 2005; Chen and Ye, 2007). The post-translational modulation of PME activity is regulated by the enzyme PMEI, and both these enzymes were also reported in rice and Arabidopsis pollen (Table 1). Three GRPs were identified in tomato pollen, and the cell wall GRPs are suggested to have a structural function, probably acting as a scaffold or agglutinating agent for the deposition of cell wall constituents (Mousavi and Hotta, 2005). The presence of GRPs in tomato pollen could reflect their requirement during germination and tube growth. Phragmoplastin, which is known to function in cell division and cell plate formation (Hong et al., 2003), was also identified in tomato pollen. The presence of phragmoplastin in mature tomato pollen could be related to its role in generative cell division, i.e. sperm cell formation, during pollen germination. Phragmoplastin was not reported in tri-cellular Arabidopsis and rice pollen. Ca2+ binding and signalling Ca2+ binding and signalling proteins such as annexin, calreticulin, and calmodulin were identified in tomato pollen, as in rice and Arabidopsis pollen (Table 1; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). Calcium and Ca2+-binding proteins play important roles in pollen germination and tube growth (Taylor and Hepler, 1997; Golovkin and Reddy, 2003; Rato et al., 2004), and the presence of such proteins in mature pollen is indicative of the ready availability of Ca2+ required for these processes. Hormone metabolism and signalling The proteins aminocyclopropane-1-carboxylate (ACC) oxidase, IAA5 transcription factor, auxin-responsive calmodulin binding, and α-G protein (involved in hormone metabolism and signalling) were identified in tomato pollen (Table 1). ACC oxidase is one of the key enzymes in ethylene biosynthesis, and IAA5 transcription factor and auxin-responsive calmodulin-binding proteins play a significant role in auxin signalling. Both ethylene and auxin are known to regulate cell elongation, and these proteins could be required for pollen tube growth. Ethylene and auxin, or their precursors, have been reported in mature pollen (Singh and Sawhney, 1992; Holden et al., 2003). G proteins, which are heterotrimeric with α, β, and γ subunits, have a role in several plant developmental processes (Perfus-Barbeoch et al., 2005; Pandey et al., 2006) including modulation of cell division (Ullah et al., 2001; Chen et al., 2006). The presence of GPA1 in tomato pollen could be another factor involved in generative cell division. G proteins were not identified in Arabidopsis and rice pollen. Pollen allergens Pollen grains have been studied for allergen proteins in a number of species because of their allergenic activity toward humans (Mohapatra et al., 2005; Radauer and Breiteneder, 2006). A number of proteins in tomato pollen, such as profilins, luminal-binding proteins, and some of the Ca2+-binding proteins discussed earlier, are also known to act as allergens. Allergenic proteins were also reported in rice and Arabidopsis pollen (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). Other proteins A number of proteins associated with nucleic acid, amino acid, lipid, and various other metabolic processes were identified. For example, six spots representing porins, which are localized in the outer mitochondrial membrane in various organisms and play a crucial role in the transport of metabolites between mitochondria and cytoplasm (Benz, 1994), were identified (Table 1). Seven spots were identified as hypothetical/unknown proteins with no well-defined function, as in other pollen proteomic studies (Holmes-Davis et al., 2005; Noir et al., 2005; Dai et al., 2006; Sheoran et al., 2006). An anther-specific protein, LAT52, with an established role in pollen development (Muschietti et al., 1994; McCormick, 2004), was also identified in tomato pollen. Conclusions This study has shown that tomato pollen contain various proteins with designated roles in defence mechanisms, energy metabolism, protein synthesis and processing, cytoskeleton formation, Ca2+ binding and signalling, and hormone signalling. The presence of these proteins in mature pollen is reflective both of the survival strategies of this small, two-celled independent structure and of the requirement for, and participation in, subsequent pollen germination and tube growth. Although no proteins specific for sperm cell formation were identified in mature tomato pollen, phragmoplastin and the α-G-protein may have a role in this process. Several new proteins not reported in the pollen of other species were identified in tomato pollen, including Pto-like serine/threonine kinase, coat protein (ToMV), HSP68 heat stress DnaK homologue, proteasome subunit-4, cystatin, IAA5 transcription factor, phragmoplastin, and α-G protein. 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Han, Hyun-Hee.; Coutand, Catherine; Cochard, Hervé; Trottier, Catherine; Lauri, Pierre-Éric

doi: 10.1093/jxb/erm200pmid: 18057035

Abstract The aim of this work was to study the variability of physiological responses to bending and the relationship with hydraulic conductance of the sap pathway to the laterals for five apple genotypes. The study focuses on the fate of the laterals. The genetic variability of bending can have two sources: a genetic variability of stem geometry which can lead to differences in mechanical state; and a genetic variability of sensitivity to bending. Since the aim was to check if some genetic variability of sensitivity to bending exists, the genetic variability of shoot geometry was taken into account. To do so, bending was controlled by imposing different bending intensities using guides of different curvature conferring a similar level of deformation to the five genotypes. Bending was done either in the proximal zone or in the distal zone of shoots, in June and in the following winter, respectively. A Principal Component Analysis comparing upright and bent shoots revealed that bending in the proximal zone stimulated vegetative growth of buds which would otherwise stay latent. A second Principal Component Analysis restricted to bent shoots revealed that bending increased the abortion of laterals in the lower face of the shoots. The abortion phenomenon was to the detriment of sylleptic laterals or of inflorescence, depending on the genotype. There was a strong effect of position around the shoot on within-shoot hydraulics. Hydraulic conductance was significantly decreased in the lower face of the shoot bent in winter. This result suggested a causal relationship between this phenomenon and lateral abortion. Apple, bending, biomechanics, hydraulic conductance, lateral type, longitudinal strain, radial location, shoot tapering, topological location Introduction The control of growth and branching of a fruit tree is monitored at two levels, at the whole-tree scale via the initial choice of rootstock and the yearly management of irrigation and fertilization, and at the branch or shoot scale via physical manipulations such as pruning and bending. Although plant growth regulators (PGRs) have been advocated partly to control branching density and flowering, their use is turning into an important societal and environmental problem in the context of sustainable horticulture. This topic re-updates the question of to what extent the use of classical environmentally neutral physical manipulations, i.e. based on a better knowledge of the genetic variability of shoot architecture, could be effective in order to monitor branching and flowering with precision. Bending is addressed here, which deserves more attention in some innovative fruit tree training systems but still remains based on empirical rules (Lauri and Laurens, 2005). The main concepts in shoot architecture (e.g. apical dominance, acrotony) are well illustrated by the branching pattern of shoots in an upright (orthotropic) position (Champagnat, 1954a, 1965; Brown et al., 1967; Crabbé, 1985; Powell, 1995; Cook et al., 1998; Lauri and Térouanne, 1998; Guédon et al., 2001; Costes and Guédon, 2002). Moving the shoot from the vertical to any other direction and especially to a strictly horizontal position by leaning or bending changes the initial branching pattern (Champagnat, 1961; Salisbury, 1993). Wareing and Nasr (1961) proposed the term gravimorphism to refer to the biomechanical effects related to both gravity and mechanical manipulation. From an architectural viewpoint, a review of the studies on gravimorphism means that some general trends can be stated. First, branching topology is changed with a shift from acrotony toward mesotony or basitony (Wareing and Nasr, 1961; Champagnat and Crabbé, 1974; Lakhoua and Crabbé, 1975a; Lauri and Lespinasse, 2001). Second, branching frequency is increased on a bent shoot compared with an upright one (Naor et al., 2003; Hampson et al., 2004). Third, lateral type frequencies may also be modified with a controversial effect on flowering, i.e. either an increase in, or no consistent effect on, flowering (Longman et al., 1965; Mullins, 1965; Tromp, 1970; Wareing, 1970). Eventually, an increased flowering precocity has been noticed on a bent shoot compared with an upright one (Meilan, 1997; Ito et al., 1999). It is probable that the effects of bending on branch architecture are partly genotype dependent and also depend on the time of manipulation (Lauri and Lespinasse, 2001). In this latter case, the response of the branch to re-orientation may be fast, as shown by the increase of fruit set in apple branches trained to the horizontal during flowering (in this case fruit set enhancement is related to an increase in the proportion of healthy ovules; Robbie et al., 1993). Changes in hormone levels in shoot and lateral buds as a reaction to bending have been shown (e.g. increase of zeatin-type cytokinins in the bent shoot; Ito et al., 1999). Moreover, the stimulated bud growth in bent shoots is related to the increased sink capacity of the bud relative to the adjacent shoot tissues. This is suggested by the enhancement of the activities of several enzymes involved in sugar metabolism in the lateral bud, NAD-dependent sorbitol dehydrogenase (NAD-SDH), NADP-dependent SDH (NADP-SDH), and acid invertase (AI) (Ito et al., 2004). Although the relatively poor vegetative and fruiting development of laterals located on the underside of the bent shoot has been noticed in apple (Crabbé, 1969; Champagnat and Crabbé, 1974; Lakhoua and Crabbé, 1975a, b; Rom, 1992) and rose (Zieslin and Halevy, 1978), there is a lack of quantitative analysis of the effect of position around the bent shoot, hereafter referred to as radial location, on lateral development. Indeed, the relative part played by bud latency and lateral abortion has not yet been investigated. Some authors noticed a reduced water transport in the bent shoot compared with the upright shoot of annuals (Helianthus annuus, Smith and Ennos, 2003), as well as for woody plants (apple, Cristoferi and Giachi, 1964; Vitis vinifera, Schubert et al., 1995). A more severe reduction was also noticed at the bending point compared with the other portions of the downward or horizontal portions of the shoots (Schubert et al., 1995). In rose, it has been suggested that the reduced water conduction of the bent shoot may be, in part, responsible for the lower net photosynthesis, transpiration, and stomatal conductance of water vapour of the leaves projecting downward (Kim et al., 2004). This phenomenon can be related to reaction wood (i.e. xylem fibres with a thick extra G-layer at the inner side of the secondary wall) differentiation which reduces water conduction (Woodrum et al., 2003; Pilate et al., 2004). On the other hand, on a 30-year-old trunk of Pinus taeda, it was found that bending did not affect hydraulic conductivity (Fredericksen et al., 1994). As far as is known, the effect of bending on water transport just beneath the bud and depending on radial location is not documented. This study was carried out on apple for which shoot architecture is well documented (Lauri and Térouanne, 1998; Guédon et al., 2001; Costes and Guédon, 2002; Renton et al., 2006). Generally speaking, the various lateral types follow an orderly sequence from the bottom to the top of the annual growth unit with a predominance of latent buds and vegetative laterals in the proximal zone, and a predominance of vegetative and flowering laterals in the distal zone (Renton et al., 2006). Sylleptic laterals (i.e. which develop in the same year as the parent shoot) are usually found in a medial position (Champagnat, 1954a; Costes and Guédon, 1997). The objectives of this study were therefore (i) to document across a range of genotypes the change in frequency of lateral types on bent shoots compared with upright ones taking into account the effect both of the topological zone on which bending was applied and of the radial location, and (ii) to analyse the relationships with pre-bud burst hydraulic conductance (kLAT) of the vascular system immediately beneath the bud. Materials and methods Plant material and determination of zone and time of bending Five genotypes with a range of 1-year-old shoot dimensions (length, diameter) and shape (slenderness) were chosen: Ariane, Braeburn, Fuji, Gala, and Granny Smith (Table 1). One-year-old trees, grafted on Pajam rootstock, were planted in two adjacent rows in February 2004 in the INRA experimental field in Montpellier, France. Trees were pruned at planting to leave 3–5 buds at the bottom of the scion. The most vigorous 2004 shoot was then selected after bud burst for the experiment. Table 1 Length and basal diameter (mean ±SE) of shoots at time of bending, i.e. spring for proximal zone and winter for distal zone, according to the genotype Genotype Bending in proximal zone n Bending in distal zone N Shoot length (cm) Shoot basal diameter (mm) Shoot length (cm) Shoot basal diameter (mm) Ariane 37.9±1.1 b 7.3±0.1 a 11 143.8±9.7 a 17.7±0.9 b 13 Braeburn 39.9±1.8 b 7.0±0.2 a 16 124.7±4.1 b 20.3±0.8 ab 21 Fuji 54.4±1.6 a 7.4±0.2 a 22 149.6±3.6 a 22.0±0.6 a 22 Gala 38.6±1.7 b 6.1±0.2 b 12 131.6±4.3 b 18.9±0.7 b 12 Granny Smith 37.3±1.6 b 7.2±0.3 a 11 131.9±4.7 b 19.7±0.8 ab 12 Genotype Bending in proximal zone n Bending in distal zone N Shoot length (cm) Shoot basal diameter (mm) Shoot length (cm) Shoot basal diameter (mm) Ariane 37.9±1.1 b 7.3±0.1 a 11 143.8±9.7 a 17.7±0.9 b 13 Braeburn 39.9±1.8 b 7.0±0.2 a 16 124.7±4.1 b 20.3±0.8 ab 21 Fuji 54.4±1.6 a 7.4±0.2 a 22 149.6±3.6 a 22.0±0.6 a 22 Gala 38.6±1.7 b 6.1±0.2 b 12 131.6±4.3 b 18.9±0.7 b 12 Granny Smith 37.3±1.6 b 7.2±0.3 a 11 131.9±4.7 b 19.7±0.8 ab 12 ANOVA is performed to separate the effects of genotype. Within the same column, different letters indicate significant differences at P=0.05, Duncan multiple means comparison test. n is the number of shoots. Open in new tab Table 1 Length and basal diameter (mean ±SE) of shoots at time of bending, i.e. spring for proximal zone and winter for distal zone, according to the genotype Genotype Bending in proximal zone n Bending in distal zone N Shoot length (cm) Shoot basal diameter (mm) Shoot length (cm) Shoot basal diameter (mm) Ariane 37.9±1.1 b 7.3±0.1 a 11 143.8±9.7 a 17.7±0.9 b 13 Braeburn 39.9±1.8 b 7.0±0.2 a 16 124.7±4.1 b 20.3±0.8 ab 21 Fuji 54.4±1.6 a 7.4±0.2 a 22 149.6±3.6 a 22.0±0.6 a 22 Gala 38.6±1.7 b 6.1±0.2 b 12 131.6±4.3 b 18.9±0.7 b 12 Granny Smith 37.3±1.6 b 7.2±0.3 a 11 131.9±4.7 b 19.7±0.8 ab 12 Genotype Bending in proximal zone n Bending in distal zone N Shoot length (cm) Shoot basal diameter (mm) Shoot length (cm) Shoot basal diameter (mm) Ariane 37.9±1.1 b 7.3±0.1 a 11 143.8±9.7 a 17.7±0.9 b 13 Braeburn 39.9±1.8 b 7.0±0.2 a 16 124.7±4.1 b 20.3±0.8 ab 21 Fuji 54.4±1.6 a 7.4±0.2 a 22 149.6±3.6 a 22.0±0.6 a 22 Gala 38.6±1.7 b 6.1±0.2 b 12 131.6±4.3 b 18.9±0.7 b 12 Granny Smith 37.3±1.6 b 7.2±0.3 a 11 131.9±4.7 b 19.7±0.8 ab 12 ANOVA is performed to separate the effects of genotype. Within the same column, different letters indicate significant differences at P=0.05, Duncan multiple means comparison test. n is the number of shoots. Open in new tab Each tree was dedicated to bear either an upright (control) or a bent shoot. In the latter case, following previous observations (data not shown), bending was carried out on two shoot zones with, presumably, the most contrasting branching patterns: proximal zone (P) on growing shoots of approximately one-third of the final length (38–54 cm depending on the genotype), and distal zone (D) on fully grown shoots (125–150 cm depending on the genotype) (Table 1; Fig. 1). These shoots will hereafter be referred to as P- and D-shoots, respectively. For each treatment, there were about 20 trees for Fuji and Braeburn, and about 10 trees for Ariane, Gala, and Granny Smith (Table 1) in a completely randomized design. The two bending treatments were done at two different times, during active growth (June 2004) for P-shoots, and during dormancy (January 2005) for D-shoots. In the former case bending was done during active organogenesis and would potentially lead to a change in lateral bud development. In the latter case bending was done during dormancy on already pre-formed buds and would potentially lead only to post-organogenesis processes. This system made it difficult to separate the effects of the topological zone along the shoot and time of year, because the proximal part of a shoot always developed before the distal part. First, bending in June could only be done on the proximal zone at a time when the distal zone was not yet fully elongated. Second, bending of the proximal zone during dormancy, i.e. at the time when bending of the distal zone was done, could not be done because of a high risk of breakage due to the large diameter of the bottom part of the shoots. All shoots were kept during the whole of the 2005 growing season for morphological observations. Fig. 1. Open in new tabDownload slide Quantitative control of bending and determination of the bent portion in shoots bent in the proximal zone in spring (A) and in shoots bent in the distal zone in the following winter (B). Bending is done in order to place the apical bud in a vertical position towards the ground. The bent zone corresponds to the portion of the shoot rolled on the guide. The uppermost bud is located at the middle of the bent zone. The three faces around the bent shoot are illustrated in (C). Fig. 1. Open in new tabDownload slide Quantitative control of bending and determination of the bent portion in shoots bent in the proximal zone in spring (A) and in shoots bent in the distal zone in the following winter (B). Bending is done in order to place the apical bud in a vertical position towards the ground. The bent zone corresponds to the portion of the shoot rolled on the guide. The uppermost bud is located at the middle of the bent zone. The three faces around the bent shoot are illustrated in (C). Bending treatment and biomechanics The variability of reaction to bending can come from two sources: a genetic variability of shoot diameter that will lead to a variability of the mechanical state of the bent branch if bending is the same (Brüchert and Gardiner, 2006); and a genetic variability of reaction to the mechanical state imposed by bending. Until now, the mechanical state of the bent shoot has been poorly controlled: angle of the tip (Lauri and Lespinasse, 2001), natural shoot and fruit load (Alméras et al., 2002, 2004), and weight of artificial mass added (Barritt, 1992). Because of intraspecific variability of shoot tapering, the same tip angle or the same mass added to the shoot can lead to a very different mechanical state of the bent shoot. In order to decorrelate the two genetic variabilities concerning bending, all bent shoots were set in a similar mechanical state (see below). That way, if differences between genotypes were observed they would indicate a genetic variability of shoot sensitivity to mechanical state imposed by bending. In order to do that, a quantification of bending was required. Studies have demonstrated that the mechanical variable which is sensed by the plant submitted to mechanical constraint is the level of strain and not the applied force (Coutand and Moulia, 2000). In this study, the rationale was to take into account the variability of shoot geometry and tapering, and to adapt the intensity of bending to each genotype in order to impose the same average mechanical strain on the different genotypes. From a mechanical point of view, as shoots are slender structures, they can be considered as beams. The level of maximal longitudinal strain at a point located at the stem periphery and at a distance i from the stem base (εLL, i) is given by the product of the imposed curvature (Ci) and the radius of the stem (ri) at point i: So, in order to get the same level of strain, the stoutest shoots have to be curved less than the most slender ones. Therefore, to set the genotypes at the same average level of strain, different bending must be done. A study of shoot tapering between genotypes was done and showed, first, a linear evolution of diameter from the apex for P- as well as for D-shoots, and, second, a significant variability between genotypes: statistical tests on differences between slopes clustered the genotypes into three groups for P-shoots: (i) Ariane, Granny Smith; (ii) Braeburn and Gala; (iii) Fuji (Fig. 2A). For D-shoots, the same procedure also led to three groups: (i) Ariane, (ii) Granny Smith, (iii) Braeburn, Fuji, and Gala (data not shown). Fig. 2. Open in new tabDownload slide Geometry and state of strain of shoots. The example of shoots bent in spring. In (A) change in diameter of shoots from the apex. Each symbol corresponds to a genotype. The tapering of the shoots was well fitted by a linear equation. The equation and determination coefficient are given for each genotype. In (B) imposed deformation along the shoots for the five genotypes. Taking the geometry of shoots into account in bending resulted in a similar imposed strain state. Fig. 2. Open in new tabDownload slide Geometry and state of strain of shoots. The example of shoots bent in spring. In (A) change in diameter of shoots from the apex. Each symbol corresponds to a genotype. The tapering of the shoots was well fitted by a linear equation. The equation and determination coefficient are given for each genotype. In (B) imposed deformation along the shoots for the five genotypes. Taking the geometry of shoots into account in bending resulted in a similar imposed strain state. In practical terms metallic guides were designed to control the level of applied curvature and longitudinal strain. The shoot was rolled on the guide (beginning from the apex toward the stem base) and then attached to wires behind the shoot in order to maintain the shoot at the imposed bending and to set the guide free for another shoot. The control of the curvature applied is given by the curvature of the guide. This rationale was used for bending on both proximal and distal zones. (i) For P-shoots, as Fuji tapering was very close to those of Braeburn and Gala, the same metallic guide (137 mm in radius) was used for the three genotypes. The guide for Ariane and Granny Smith was 162 mm in radius. The use of a circular guide led to a longitudinal gradient of strain, but all the genotypes were set at the same average level of strain (Fig. 2B). (ii) For D-shoots, the 1-year-old shoot was stouter than in the previous case and a stronger tapering led to the building of other guides. As the taper exhibited differences compared with shoots bent in the proximal zone, setting the same strain state meant designing new guides. The use of circular guides in spring led to a strain gradient along the shoots. Setting the shoots bent in winter at the same state of strain as the shoots bent in spring (i.e. with respect to the imposed gradient of strain) required the guides to be non-circular. Three guides were used according to the analysis of slopes (as for P-shoots, see above): (i) Ariane; (ii) Granny Smith; and (iii) Braeburn, Fuji, and Gala. Description of shoot architecture In the spring of 2005, the bent zone of the bent shoots was first determined on P-shoots including all nodes from the grafting point upwards to the uppermost node, and the same number of nods downwards (Fig. 1A). The same number of nodes was then determined on the bent zone of D-shoots (Fig. 1B). To compare the branching patterns of the bent zone of P- and D-shoots with their topological counterparts on upright shoots, the mean number of nodes from the bottom bounding the P and D zones on the bent shoots was then compared with upright shoots (Fig. 1A, B). Each lateral was characterized by its type and radial location. Five types of laterals were considered: sylleptic (S), latent bud (L), vegetative bud (V), inflorescence (I), and aborted lateral (AL). AL types were seen in both situations: 2004 sylleptic lateral, usually short, whose terminal bud failed to grow in 2005; and a bud which began to grow in spring 2005 and soon died. The radial location was considered by dividing the cross-section circumference of the shoot into four quarters. The two lateral quarters were merged, determining three faces hereafter referred to as upper (U), lower (L), and side (S) faces (Fig. 1C). Hydraulic studies Studies were carried out on two genotypes chosen from the five genotypes previously studied for architecture, Fuji and Braeburn. Since hydraulic measurements were destructive and had to be done before bud break, they were done on a separate shoot sample. Eleven and nine 1-year-old shoots exhibiting similar lengths to the shoots used for architectural studies were selected for Braeburn and Fuji, respectively. In these samples, eight and six shoots for Braeburn and Fuji, respectively, were bent in December 2005 using the same methodology as for the D-shoots in the architectural study, and three shoots per genotype were left as controls. In March 2006, 10–15 d before the estimated bud burst, all shoots belonging to the two genotypes were cut off in the field, with their cut end immediately immersed in water, and transported to the laboratory. For bent shoots, the cord linking the upward and the downward portion of the shoots was maintained in order to avoid possible passive uprighting. Hydraulic conductance (kLAT) of the sap pathway to the different buds was measured using the High Pressure Flow Meter apparatus (HPFM, Dynamax, USA; Tyree et al., 1995; Salleo et al., 2002) which is based on the perfusion of deionized and filtered water at a given pressure at the bottom of the cut shoot (P, MPa) and the measurement of the rate of water exudation (F; mmol s−1) at the base of each lateral bud just below bud scars (Cochard et al., 2005). Buds could be in a strictly axillary position or ending a sylleptic lateral. The buds were excised with a razor, permitting water to exude, and F was measured by using a weighed piece of dry cotton applied for 1 min on the cut surface of the shoot where the bud had been removed. The difference in weight before and just after measurement gave the amount of water exuded. In a preliminary work, the strong positive relationship (r2≈0.99) between P and F of a sample of excised buds was assessed for a range of water pressure of 0.1–0.5 MPa. To avoid any possible effect of bud removal on F of the other buds, all studied buds of a shoot were removed at the beginning of each shoot study (Cochard et al., 2005). On all shoots, kLAT was measured for every two buds within zone D of both bent and upright shoots. On the latter shoots, radial location (U, L, S) was noted as for the architectural analysis. Data analysis Three types of analysis were carried out. A first analysis aimed at modelling the effects of genotype (GEN), zone of branching along the shoot (ZON), and bending status (BST) on the proportion of lateral types (LAT). For this, multinomial models were constructed using the canonical logarithmic link (a linear predictor combining factors is used to explain the ratio of probabilities of each lateral type to one reference type category, here L). The effects of the three factors and of interactions between factors up to order 3 were considered. A backward model construction strategy was adopted, beginning from the richest model containing all effects and order 2 and 3 interactions, and removing step by step non-significant elements by testing embedded models. Finally, since this analysis revealed a highly significant order 3 interaction (Table 2; Model 0), a Principal Component Analysis (PCA), based on a covariance matrix, was carried out on fitted values in order to help the interpretation of the model obtained. Table 2. Effects of genotype (GEN), zone along the shoot (ZON), and bending status (BST) on proportion of lateral types (LAT) of Malus×domestica Models, factors, and interactions Model structure Deviance test P Model 0 – LAT∼GEN*ZON*BST Model 1 – LAT∼GEN+ZON+BST+GEN:ZON+GEN:BST+ZON:BST M1 ⊂ M0 90.16 2.34×10−12 Models, factors, and interactions Model structure Deviance test P Model 0 – LAT∼GEN*ZON*BST Model 1 – LAT∼GEN+ZON+BST+GEN:ZON+GEN:BST+ZON:BST M1 ⊂ M0 90.16 2.34×10−12 The multinomial model is constructed by selection of factors and interactions. For each model, ‘∼’ separates the dependent variable on the left from the list (‘+’) of dependent variables on the right; an ‘asterisk’ indicates the proper effect of each factor and interactions between them; ‘:’ indicates interaction between two variables. Open in new tab Table 2. Effects of genotype (GEN), zone along the shoot (ZON), and bending status (BST) on proportion of lateral types (LAT) of Malus×domestica Models, factors, and interactions Model structure Deviance test P Model 0 – LAT∼GEN*ZON*BST Model 1 – LAT∼GEN+ZON+BST+GEN:ZON+GEN:BST+ZON:BST M1 ⊂ M0 90.16 2.34×10−12 Models, factors, and interactions Model structure Deviance test P Model 0 – LAT∼GEN*ZON*BST Model 1 – LAT∼GEN+ZON+BST+GEN:ZON+GEN:BST+ZON:BST M1 ⊂ M0 90.16 2.34×10−12 The multinomial model is constructed by selection of factors and interactions. For each model, ‘∼’ separates the dependent variable on the left from the list (‘+’) of dependent variables on the right; an ‘asterisk’ indicates the proper effect of each factor and interactions between them; ‘:’ indicates interaction between two variables. Open in new tab A second analysis using the same modelling tools and following the same strategy was carried out on bent shoots only. Here, the effects on the proportion of lateral types (LAT) of the three factors: genotype (GEN), zone of branching along the shoot (ZON), and radial location (RAD) were considered. As for the first analysis, the model was obtained by removing the order 3 interaction and the order 2 interaction between genotype and radial location (GEN:RAD) (Table 3; Model 4′), and was then interpreted by a PCA on fitted values. Table 3. Effects of genotype (GEN), zone along the shoot (ZON), and radial location (RAD) on proportion of lateral types (LAT) of bent shoots of Malus×domestica Models, factors, and interactions Model structure Deviance test P Model 0’ – LAT∼GEN*ZON*RAD Model 1’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD+ZON:RAD M1′ ⊂ M0′ 48.65 0.03 Model 2’ – LAT∼GEN+ZON+RAD+GEN:RAD+ZON:RAD M2′ ⊂ M1′ 361.61 <10−12 Model 3’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD M3′ ⊂ M1′ 23.24 3×10−3 Model 4’ – LAT∼GEN+ZON+RAD+GEN:ZON+ZON:RAD M4′ ⊂ M1′ 30.35 0.55 Models, factors, and interactions Model structure Deviance test P Model 0’ – LAT∼GEN*ZON*RAD Model 1’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD+ZON:RAD M1′ ⊂ M0′ 48.65 0.03 Model 2’ – LAT∼GEN+ZON+RAD+GEN:RAD+ZON:RAD M2′ ⊂ M1′ 361.61 <10−12 Model 3’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD M3′ ⊂ M1′ 23.24 3×10−3 Model 4’ – LAT∼GEN+ZON+RAD+GEN:ZON+ZON:RAD M4′ ⊂ M1′ 30.35 0.55 The multinomial model is constructed by a selection of factors and interactions. For each model, ‘∼’ separates the dependent variable on the left from the list (‘+’) of dependent variables on the right; an ‘asterisk’ indicates the proper effect of each factor and interactions between them; ‘:’ indicates interaction between two variables. Open in new tab Table 3. Effects of genotype (GEN), zone along the shoot (ZON), and radial location (RAD) on proportion of lateral types (LAT) of bent shoots of Malus×domestica Models, factors, and interactions Model structure Deviance test P Model 0’ – LAT∼GEN*ZON*RAD Model 1’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD+ZON:RAD M1′ ⊂ M0′ 48.65 0.03 Model 2’ – LAT∼GEN+ZON+RAD+GEN:RAD+ZON:RAD M2′ ⊂ M1′ 361.61 <10−12 Model 3’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD M3′ ⊂ M1′ 23.24 3×10−3 Model 4’ – LAT∼GEN+ZON+RAD+GEN:ZON+ZON:RAD M4′ ⊂ M1′ 30.35 0.55 Models, factors, and interactions Model structure Deviance test P Model 0’ – LAT∼GEN*ZON*RAD Model 1’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD+ZON:RAD M1′ ⊂ M0′ 48.65 0.03 Model 2’ – LAT∼GEN+ZON+RAD+GEN:RAD+ZON:RAD M2′ ⊂ M1′ 361.61 <10−12 Model 3’ – LAT∼GEN+ZON+RAD+GEN:ZON+GEN:RAD M3′ ⊂ M1′ 23.24 3×10−3 Model 4’ – LAT∼GEN+ZON+RAD+GEN:ZON+ZON:RAD M4′ ⊂ M1′ 30.35 0.55 The multinomial model is constructed by a selection of factors and interactions. For each model, ‘∼’ separates the dependent variable on the left from the list (‘+’) of dependent variables on the right; an ‘asterisk’ indicates the proper effect of each factor and interactions between them; ‘:’ indicates interaction between two variables. Open in new tab Eventually, analyses on hydraulic conductance were performed with Duncan's Multiple Range Test, at the 5% level of confidence. Results The effect of bending on shoot architecture The effects of GEN, ZON, and BST on the probabilities of lateral types were clearly interacting, resulting in a highly significant order 3 interaction (Model 1, P=2.34×10−12; Table 2). This prevented any simple and general interpretations. The only general and consistent trend across the genotypes was observed for inflorescences (I) which were in a higher proportion in the distal zone compared with the proximal zone (36–68% versus 0–2%; Fig. 3). The same picture was not observed for the other lateral types whose proportions varied according to genotype, zone, and bending status, for example, sylleptic laterals which were higher in the proximal (Granny Smith) or in the distal (Ariane) zones, or aborted laterals which were increased (Gala) or decreased (Braeburn) by bending the same zone (Fig. 3). Fig. 3. Open in new tabDownload slide Observed proportions of the lateral types on distal and proximal zones of upright (Up) and bent (Be) shoots for the five genotypes. For legibility only three types are shown (inflorescence, sylleptic lateral, and aborted lateral). Fig. 3. Open in new tabDownload slide Observed proportions of the lateral types on distal and proximal zones of upright (Up) and bent (Be) shoots for the five genotypes. For legibility only three types are shown (inflorescence, sylleptic lateral, and aborted lateral). The PCA on fitted values revealed that 78.8% of all variability was taken into account by the first two factors (64.1% and 14.7% for factors 1 and 2, respectively; Fig. 4A). Factor 1 was essentially explained by I, whereas factor 2 opposed L and V. S and AL had little influence on these two factors (grey symbols; Fig. 4A). Factor 1 clearly opposed the proximal zone on the right part of the graph, i.e. with low I, to the distal zone on the left part of the graph, i.e. with high I (Fig. 4B). Factor 2 discriminated between upright and bent shoots, with more L in the former and high V in the latter whatever the genotype (grey arrows; Fig. 4B). This effect was higher in the P zone compared with the D zone. Fig. 4. Open in new tabDownload slide Principal Component Analysis of fitted values of lateral probabilities in Model 0. Projection of the variables (A) and of individuals (B). In projection of the variables, the labels refer to the five lateral types (AL, aborted lateral; I, inflorescence; L, latent; S, sylleptics; V, vegetative). Black and grey symbols indicate variables with a high versus a low link with factor 2, respectively. In projection of the individuals, each label is composed of the following items: genotype [GEN, Ariane (Ar); Braeburn (Br); Fuji (Fu); Gala (Ga); and Granny Smith (Gr)], and bending status [BST, upright (Up), bent (Be)]. Within the same zone (proximal, distal), the same symbols belong to the same genotype. Within the proximal zone, grey arrows link the upright to the bent status of the same genotype. Fig. 4. Open in new tabDownload slide Principal Component Analysis of fitted values of lateral probabilities in Model 0. Projection of the variables (A) and of individuals (B). In projection of the variables, the labels refer to the five lateral types (AL, aborted lateral; I, inflorescence; L, latent; S, sylleptics; V, vegetative). Black and grey symbols indicate variables with a high versus a low link with factor 2, respectively. In projection of the individuals, each label is composed of the following items: genotype [GEN, Ariane (Ar); Braeburn (Br); Fuji (Fu); Gala (Ga); and Granny Smith (Gr)], and bending status [BST, upright (Up), bent (Be)]. Within the same zone (proximal, distal), the same symbols belong to the same genotype. Within the proximal zone, grey arrows link the upright to the bent status of the same genotype. The asymmetric effect of bending on lateral type distribution around the shoot The effect of GEN, ZON, and RAD on the proportion of lateral types was studied on bent shoots only. There was only a weak order 3 interaction (+48.65, P=0.03; Table 3) compared with order 2 interactions between ZON and RAD, and ZON and GEN which were highly significant (+23.24, P=3×10−3 and +361.61, P <10−12, respectively; Table 3). There was no significant order 2 interaction between GEN and RAD (+30.35, P=0.55; Table 3). The PCA on fitted values revealed that 83.5% of all variability was taken into account by the first two factors (61.3% and 22.2% for factors 1 and 2, respectively; Fig. 5A). Factor 1 was strongly explained by I, whereas factor 2 opposed S and AL with a lower impact of L and V on these two factors (grey symbols; Fig. 5A). Moving from U to L consistently increased AL (grey arrows; Fig. 5B). However, this was to the main detriment of S for Granny Smith and Gala, whereas it was to the main detriment of I for Braeburn and Ariane (vertical and oblique grey arrows, respectively; Fig. 5B). Fuji was in an intermediate position. Fig. 5. Open in new tabDownload slide Principal Component Analysis of fitted values of lateral probabilities in Model 4′. Projection of the variables (A) and of individuals (B). In projection of the variables, labels refer to the five lateral types (AL, aborted lateral; I, inflorescence; L, latent; S, sylleptics; V, vegetative). Black and grey symbols indicate variables with a high versus a low link with factor 2, respectively. In projection of the individuals, each label is composed of the following items: genotype [GEN, Ariane (Ar); Braeburn (Br); Fuji (Fu); Gala (Ga); and Granny Smith (Gr)], zone along the shoot [ZON, proximal (P); distal (D)], and radial location [RAD, upper (U); lower (L)]. The same symbols belong to the same combination of genotype–zone. For legibility, laterals in the side faces are not labelled (grey symbols). Grey arrows link the upper and lower faces of the same genotype–zone combination. Fig. 5. Open in new tabDownload slide Principal Component Analysis of fitted values of lateral probabilities in Model 4′. Projection of the variables (A) and of individuals (B). In projection of the variables, labels refer to the five lateral types (AL, aborted lateral; I, inflorescence; L, latent; S, sylleptics; V, vegetative). Black and grey symbols indicate variables with a high versus a low link with factor 2, respectively. In projection of the individuals, each label is composed of the following items: genotype [GEN, Ariane (Ar); Braeburn (Br); Fuji (Fu); Gala (Ga); and Granny Smith (Gr)], zone along the shoot [ZON, proximal (P); distal (D)], and radial location [RAD, upper (U); lower (L)]. The same symbols belong to the same combination of genotype–zone. For legibility, laterals in the side faces are not labelled (grey symbols). Grey arrows link the upper and lower faces of the same genotype–zone combination. Hydraulic measurements There was a strong effect of the genotype on kLAT, with Fuji values 2- to 6-fold those observed in Braeburn (Table 4). kLAT was not significantly influenced by bending for Fuji and Braeburn. There was no significant interaction between genotype and bending (Table 4). For both genotypes, kLAT varied significantly according to radial location: laterals situated in U of the bent shoot had kLAT values about 4-fold higher than laterals situated in L, with intermediate values for laterals in S (Fig. 6). For both genotypes, this asymmetric distribution of kLAT on bent shoots was significantly affected by the re-uprighting of shoots, resulting in equivalent kLAT in faces which were previously in the U, L, and S positions (Fig. 6). Table 4. Hydraulic conductance of the vascular system connected to the lateral (kLAT; mmol s−1MPa−1; mean ±SE) of upright and bent shoots for the two genotypes, Braeburn and Fuji Genotype Treatment n Hydraulic conductance Braeburn Bending 52 0.092±0.021 ab Upright 20 0.024±0.010 b Fuji Bending 35 0.166±0.030 a Upright 24 0.130±0.037 a Genotype effect F 9.644 P 0.0023 Treatment effect F 3.239 P 0.074 Genotype×Treatment F 0.321 P 0.572 Genotype Treatment n Hydraulic conductance Braeburn Bending 52 0.092±0.021 ab Upright 20 0.024±0.010 b Fuji Bending 35 0.166±0.030 a Upright 24 0.130±0.037 a Genotype effect F 9.644 P 0.0023 Treatment effect F 3.239 P 0.074 Genotype×Treatment F 0.321 P 0.572 ANOVA is performed to separate the effects of genotype and bending treatment. Within the same column, different letters indicate significant differences at P=0.05, Duncan multiple mean comparison test. n is the number of laterals. Open in new tab Table 4. Hydraulic conductance of the vascular system connected to the lateral (kLAT; mmol s−1MPa−1; mean ±SE) of upright and bent shoots for the two genotypes, Braeburn and Fuji Genotype Treatment n Hydraulic conductance Braeburn Bending 52 0.092±0.021 ab Upright 20 0.024±0.010 b Fuji Bending 35 0.166±0.030 a Upright 24 0.130±0.037 a Genotype effect F 9.644 P 0.0023 Treatment effect F 3.239 P 0.074 Genotype×Treatment F 0.321 P 0.572 Genotype Treatment n Hydraulic conductance Braeburn Bending 52 0.092±0.021 ab Upright 20 0.024±0.010 b Fuji Bending 35 0.166±0.030 a Upright 24 0.130±0.037 a Genotype effect F 9.644 P 0.0023 Treatment effect F 3.239 P 0.074 Genotype×Treatment F 0.321 P 0.572 ANOVA is performed to separate the effects of genotype and bending treatment. Within the same column, different letters indicate significant differences at P=0.05, Duncan multiple mean comparison test. n is the number of laterals. Open in new tab Fig. 6 Open in new tabDownload slide Effects of radial location (upper, side, and lower faces) on bent and re-uprighted shoots, on hydraulic conductance of the vascular system connected to the bud (kLAT; mmol s−1 MPa−1; mean ±SE) for the two genotypes, Braeburn (A) and Fuji (B). Within the same side part of each graph different letters indicate significant differences (P < 0.05, Duncan multiple means comparison test). Fig. 6 Open in new tabDownload slide Effects of radial location (upper, side, and lower faces) on bent and re-uprighted shoots, on hydraulic conductance of the vascular system connected to the bud (kLAT; mmol s−1 MPa−1; mean ±SE) for the two genotypes, Braeburn (A) and Fuji (B). Within the same side part of each graph different letters indicate significant differences (P < 0.05, Duncan multiple means comparison test). Discussion This study compared branching on shoots of five apple genotypes either in an upright position or in a bent position. In the latter case the same level of mechanical strain was applied to all shoots, taking into account the mean geometrical properties of each cultivar. As a method for unravelling the respective effects of position along the parent shoot and bending on the fate of the lateral shoots, the data analyses developed here, i.e. multinomial modelling followed by a PCA, appeared to be efficient. In the first step of this study, the architectural analysis of upright and bent shoots confirmed on the five genotypes that the proximal and distal zones were characterized by contrasting branching patterns. Two main conclusions could be drawn. First, an ontogenetic effect was indicated by the first factor of the PCA, discriminating between the proximal and the distal zones by the proportion of inflorescences. These results confirmed existing literature on shoot architecture (see Introduction). Second, an effect of bending was shown by the second factor of the PCA opposing latent buds and vegetative laterals. It is shown here that bending could change the original branching patterns by stimulating the growth of latent buds giving rise to vegetative laterals. This result agrees with the literature, showing a global increase of lateral shoot development in response to bending (Naor et al., 2003; Hampson et al., 2004) probably related to an increase of cytokinin in buds (Ito et al., 1999). According to classical hypotheses, axillary bud outgrowth is determined by a balance among several hormones, in particular the basipetal flow of auxin and the locally and/or root-derived cytokinin (Salisbury, 1993; Shimizu-Sato and Mori, 2001; Bennett and Leyser, 2006). In our experiment, the increase in vegetative lateral outgrowth was observed mainly in the proximal part and to a far lesser extent in the distal part. This could be explained by the fact that, in our experimental setting, bending in the proximal zone in spring was able to affect bud organogenesis during the rest of the growing season whereas bending in the distal zone during winter dormancy was unable to alter the course of bud organogenesis which was already set. It should also be noticed that within the proximal zone, bending affected all the genotypes in the same way, i.e. an increased number of vegetative buds to the detriment of latent buds. It may be that this one-way trend was found because of taking care to apply a similar mechanical strain to the five genotypes. It would be of interest to carryout the same type of study, but to compare the effect of controlled bending versus uncontrolled bending on bud fate. As mentioned earlier, the effect of bending on flowering is still controversial. In the present study flowering was not increased by bending. This could be related to shoot type, i.e. a vertical trunk directly stemming from the graft point in this study versus oblique branches on fruiting trees in previous studies. Furthermore, it may be suggested from our results that the zone along the shoot interacting with time of bending may play a role in the effect of bending on bud fate and flowering in particular. Future studies should then investigate if other possible changes in lateral type (e.g. latent to inflorescence, vegetative to inflorescence) may be related to various combinations of these two traits. The second step of the analysis was done on bent shoots only. It took the radial location of the lateral into account. It clearly showed that in all cases bending increased the probability of aborted laterals in the lower face of the parent shoot. However, a genotypic effect was evident with a concomitant decrease in number of inflorescences for Ariane and Braeburn, and a concomitant decrease in sylleptic laterals for Gala and Granny Smith. This phenomenon was observed whatever the zone along the shoot (i.e. the same trend was observed for proximal and distal zones) except for Fuji which resembled the first two genotypes for the distal zone, and resembled the latter two genotypes for the proximal zone. Although, in the former case, abortion occurred mostly on buds which would otherwise give rise to an inflorescence, in the latter case abortion could appear on already existing sylleptic laterals (usually short; data not shown) corresponding to the death of the terminal buds. Our results, therefore, showed that lateral abortion and not bud latency played a consistent role in the asymmetric branching patterns of the bent shoot. To the best of our knowledge this point is not documented in the literature. The fact that bud abortion was enhanced in the lower face of shoots bent in spring, as well as in shoots bent in the following winter, suggests that the abortion mechanism may intervene at various moments in the growth cycle. In shoots bent in the proximal zone in spring, abortion may occur during bud organogenesis or later, namely during dormancy. In shoots bent in the distal zone in winter, it can only occur post-organogenesis on already completely pre-formed lateral buds. Tromp (1970) suggested that the enhancement of branching frequency on the bent shoot may be, in part, related to a reduced bud abortion. It is shown here that this phenomenon is true only at a local level. Indeed, vegetative branching was generally enhanced by bending in the proximal zone to the detriment of latent buds without any effect of AL on the first two factors of the PCA (first analysis). However, there was a clear increase of AL in the lower face of the bent shoot compared with the upper face, as seen by the strong impact of AL on factor 2 in PCA on bent shoots (second analysis). Our study on kLAT did not agree with the findings of Cristoferi and Giachi (1964) and Schubert et al. (1995), that showed a reduction of shoot hydraulic conductance in the bent shoot compared with the upright one. However, this study revealed a strong asymmetry between the opposite two faces of the shoot. Indeed, mirroring the effects of bending on AL probabilities, kLAT was significantly decreased in the lower face compared with the upper face of the bent shoot. Although the reduced leaf traits noticed by Kim et al. (2004) and the higher lateral abortion (present results) could be attributed to the anisotropy of the physical environment (e.g. possible lower light irradiance on the lower face of the bent shoot compared with the upper face), the present study strongly suggested a causal relationship between the reduced kLAT and the increased AL probabilities. The fact that the asymmetrical kLAT distribution could be reversed by returning the shoot to an upright position could show that the relationships between hydraulics and lateral fate, especially abortion, could be overcome by shoot re-orientation. These results should, however, be restricted to the effects of bending without secondary growth, which was the case in the present hydraulic study. In this case the mechanical constraints experienced by the shoot (e.g. extended versus compressed wood on the upper and lower face, respectively) may satisfactorily explain the results. To what extent secondary growth occurring on a bent shoot is able to change the radial distribution of hydraulic conductance remains to be documented. Indeed, both the local reduction of hydraulic conductance in tension wood, i.e. in the upper face (Woodrum et al., 2003; Pilate et al., 2004), and the higher number of vessels in the opposite wood, i.e. in the lower face, compared with tension wood (Pruyn et al., 2000; Ruelle et al., 2006) suggest that, on shoots kept in a bent position during a growing season and developing tension wood, hydraulic conductance is enhanced in the lower face of the bent shoot compared with the upper face. In our experiment, therefore, the increase of aborted laterals in the lower face of shoots bent in the spring could not be caused by a reduction of hydraulic conductance and could be better related to environmental factors. A comparative study of branching patterns and hydraulics in response to bending at various times of the year and of different durations would permit the respective effects of mechanical strain (with bending, one side is compressed and one side is set under tension), biomechanical reaction of the bent shoot (when secondary growth occurs tension wood is produced on the upper side), and environment anisotropy (light for example) to be disentangled. Furthermore, the temporal variability of herbaceous plant sensitivity to mechanical stress has been described by Lefèvre et al. (1994) and Beyl and Mitchell (1977). It may be suggested that the sensitivity of the shoot to bending varies during the year. It would be interesting to carryout a factorial experiment in order to assess the effect both of the intensity of bending and of the time at which bending is applied. The effect of bending has often been addressed in a horticultural context, i.e. focusing on the intensity of vegetative growth, and flowering and fruiting (Lakhoua and Crabbé, 1975a; Robbie et al., 1993; Ito et al., 1999; Lauri and Lespinasse, 2001). It was shown here that bending could also affect branching frequency through lateral abortion. Indeed, shoot and bud mortality, i.e. cladoptosis, is usually described as an adaptation to environmental stresses (shade, drought; Bell, 1991; Davis et al., 2002). Compared with other cases of cladoptosis that have been described, the lateral abortion observed in our experiment presented two possible conclusions. First, it might be induced in a relatively short time: bending applied a few weeks before bud burst triggered significant lateral mortality in the following spring. In this case lateral mortality would probably be caused by a significant reduction in hydraulic conductance. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2007 The Author(s).

Cernusak, Lucas A.; Winter, Klaus; Aranda, Jorge; Turner, Benjamin L.; Marshall, John D.

doi: 10.1093/jxb/erm201pmid: 18057036

Abstract The response of whole-plant water-use efficiency, termed transpiration efficiency (TE), to variation in soil fertility was assessed in a tropical pioneer tree, Ficus insipida Willd. Measurements of stable isotope ratios (δ13C, δ18O, δ15N), elemental concentrations (C, N, P), plant growth, instantaneous leaf gas exchange, and whole-plant water use were used to analyse the mechanisms controlling TE. Plants were grown individually in 19 l pots with non-limiting soil moisture. Soil fertility was altered by mixing soil with varying proportions of rice husks, and applying a slow release fertilizer. A large variation was observed in leaf photosynthetic rate, mean relative growth rate (RGR), and TE in response to experimental treatments; these traits were well correlated with variation in leaf N concentration. Variation in TE showed a strong dependence on the ratio of intercellular to ambient CO2 mole fractions (ci/ca); both for instantaneous measurements of ci/ca (R2=0.69, P <0.0001, n=30), and integrated estimates based on C isotope discrimination (R2=0.88, P <0.0001, n=30). On the other hand, variations in the leaf-to-air humidity gradient, unproductive water loss, and respiratory C use probably played only minor roles in modulating TE in the face of variable soil fertility. The pronounced variation in TE resulted from a combination of the strong response of ci/ca to leaf N, and inherently high values of ci/ca for this tropical tree species; these two factors conspired to cause a 4-fold variation among treatments in (1–ci/ca), the term that actually modifies TE. Results suggest that variation in plant N status could have important implications for the coupling between C and water exchange in tropical forest trees. Carbon isotope, oxygen isotope, soil fertility, transpiration efficiency, tropical tree Introduction Water-use efficiency at the whole-plant level, often referred to as transpiration efficiency (TE), is defined as the rate of biomass production of a plant relative to the rate of transpiration (Bacon, 2004). Although ecophysiologists frequently assess water-use efficiency at the leaf level, relatively few measurements of TE have been reported, largely due to the logistical challenges involved in obtaining such data. Nonetheless, the whole plant is clearly a meaningful organizational level at which to analyse controls over growth, CO2 exchange, and water use (McCree, 1986; Meinzer and Goldstein, 1996). The TE effectively describes the coupling between whole-plant C and water exchange in terrestrial vegetation. Thus, a mechanistic understanding of the controls over TE is relevant to studies of plant competition, ecosystem function, and plant responses to climate change. In tropical forests, it was recently reported that seasonal drought significantly impacts tree community dynamics (Engelbrecht et al., 2007), suggesting that TE could play an important role in determining the performance and distribution of tropical tree species. Leaf-level water-use efficiency generally increases in response to increasing leaf N concentration in C3 plants (Wong, 1979; Toft et al., 1989; Duursma and Marshall, 2006). This is because more leaf N is usually associated with more photosynthetic capacity, which allows for a greater photosynthetic rate at a given rate of water loss. At the whole-plant scale, the TE of trees has also generally been observed to increase with increasing N availability (Guehl et al., 1995; Syvertsen et al., 1997; Livingston et al., 1999; Ripullone et al., 2004), although not always (Guehl et al., 1995; Hobbie and Colpaert, 2004). This trend also appears to apply for tropical trees: TE of Ficus insipida Willd., a fast-growing tropical pioneer tree capable of high rates of photosynthesis (Zotz et al., 1995), increased in response to fertilizer application (Winter et al., 2001); and a linear relationship was observed between TE and leaf N concentration in an experiment involving seven tropical tree species (Cernusak et al., 2007). Although there appears to be general agreement among experiments regarding the direction of response of tree TE to variation in soil fertility (Raven et al., 2004), the physiological mechanisms modulating the response are less well understood. For example, it was suggested that variation in respiratory C use (Guehl et al., 1995), or in the amount of unproductive water loss (Hobbie and Colpaert, 2004), could play important roles in determining the response of TE to soil fertility, in addition to the effects associated with leaf-level photosynthesis. Since the revelation that C isotope fractionation correlates positively with the ratio of intercellular to ambient CO2 mole fractions in C3 plant leaves (Farquhar et al., 1982), analysis of 13C/12C in plant organic material has played an important role in water-use efficiency research (Bacon, 2004). It was later suggested that analysis of 18O/16O in plant organic material could also aid investigations of water-use efficiency by providing complementary information to that obtained from C isotope analyses (Farquhar et al., 1989; Sternberg et al., 1989). In this study, measurements of C and O isotope ratios were combined with measurements of plant elemental composition, growth, instantaneous leaf gas exchange, and whole-plant water use to analyse the mechanisms controlling the response of TE of the tropical pioneer tree Ficus insipida to variation in soil fertility. Theory At the leaf level, photosynthetic water-use efficiency can be expressed as the quotient of the diffusive fluxes of CO2 and water vapour into and out of the leaf, respectively, during photosynthesis (Farquhar and Richards, 1984): (1) where A is net photosynthesis (μmol CO2 m−2 s−1), E is leaf transpiration (mmol H2O m−2 s−1), ca and ci are atmospheric and intercellular CO2 mole fractions (μmol mol−1), 1.6 is the ratio of diffusivities for water vapour and CO2 in air, and v is the leaf-to-air water vapour mole fraction difference (mmol mol−1). A list of symbols used in the text is given in Table 1. Equation (1) can be scaled to the whole-plant level by taking into account respiratory C use and water loss not associated with photosynthesis (Farquhar and Richards, 1984; Hubick and Farquhar, 1989). Thus, whole-plant transpiration efficiency (TE) can be defined as (2) where TE is mmol C fixed in plant biomass mol−1 H2O transpired by the plant; ϕc is the proportion of C fixed during photosynthesis that is subsequently lost by respiration from roots and stems during the day, and from roots, stems, and leaves during the night; and ϕw is the proportion of unproductive water loss relative to that associated with C uptake, i.e. nocturnal transpiration through partially open stomata and cuticular water loss by leaves and stems during the day and night. Table 1. Symbols used in the text A Net photosynthetic rate (μmol CO2 m−2 s−1) a 13C/12C discrimination during diffusion through the stomatal pore ab 13C/12C discrimination during diffusion through the leaf boundary layer al 13C/12C discrimination during liquid phase diffusion b 13C/12C discrimination by carboxylating enzymes during C3 photosynthesis C Molar concentration of water (mol m−3) ca Ambient CO2 mole fraction (μmol mol−1) ci Intercellular CO2 mole fraction (μmol mol−1) D Diffusivity of H218O in water (m2 s−1) d 13C/12C discrimination caused by processes other than a and b during C3 photosynthesis E Transpiration rate (mmol H2O m−2 s−1) Egrav Plant transpiration determined gravimetrically (mmol H2O m−2 s−1) Etot Cumulative transpiration over the course of an experiment (mol H2O) e 13C/12C discrimination during dark respiration es 13C/12C discrimination during dissolution of CO2 into water f 13C/12C discrimination during photorespiration gb Boundary layer conductance to CO2 (mol m−2 s−1) gc Total conductance to CO2 of stomata plus boundary layer (mol m−2 s−1) gi Mesophyll conductance to CO2 (mol m−2 s−1) I Intercept of the linear relationship between TE and Δ13C k Carboxylation efficiency during C3 photosynthesis (mol CO2 m−2 s−1) L Scaled effective path length in relation to 18O advection/diffusion (m) LA Leaf area (m2) MTR Mean transpiration rate (mol H2O m−2 d−1) m Slope of the linear relationship between TE and Δ13C Narea Leaf N concentration expressed on an area basis (mmol N m−2) N/P Mass ratio of N to P in leaf dry matter PFD Photosynthetic photon flux density (μmol m−2 s−1) Parea Leaf P concentration expressed on an area basis (mmol P m−2) pex Proportion of O atoms exchanging with local water during cellulose synthesis px Proportion of unenriched source water in developing plant tissues Rd Leaf dark respiration rate (μmol CO2 m−2 s−1) RGR Mean relative growth rate (mg g−1 d−1) rb Boundary layer resistance (m2 s mol−1) rs Stomatal resistance (m2 s mol−1) T Leaf temperature in K TE Transpiration efficiency (mmol C mol−1 H2O) TEN Transpiration efficiency of N acquisition (μmol N mol−1 H2O) TL Leaf temperature in C t Number of days in experiment v Leaf-to-air water vapour mole fraction difference (mmol mol−1) wa Water vapour mole fraction of ambient air (mmol mol−1) wi Water vapour mole fraction in the intercellular air spaces (mmol mol−1) Δ13C Photosynthetic 13C/12C discrimination Δ13CL 13C/12C discrimination in leaf dry matter Δ13CS 13C/12C discrimination in stem dry matter Δ13CR 13C/12C discrimination in root dry matter Δ13Cwp 13C/12C discrimination in whole plant dry matter Δ18O 18O/16O enrichment relative to source water Δ18Oc 18O/16O enrichment of plant cellulose relative to source water Δ18Oe 18O/16O enrichment at the evaporative sites in leaves relative to source water Δ18OL 18O/16O enrichment of leaf mesophyll water relative to source water Δ18Op 18O/16O enrichment of plant dry matter relative to source water Δ18Ov 18O/16O enrichment of atmospheric water vapour relative to source water δ13C 13C/12C of a sample expressed relative to the Pee Dee Belemnite standard δ13Ca 13C/12C of atmospheric CO2 expressed relative to the Pee Dee Belemnite standard δ13Cp 13C/12C of plant material expressed relative to the Pee Dee Belemnite standard δ15N 15N/14N of a sample expressed relative to that of air δ18O 18O/16O of a sample expressed relative to that of Vienna Standard Mean Ocean Water δ18Op δ18O of leaf dry matter δ18Os δ18O of source water εcp δ18O difference between plant cellulose and plant dry matter εk 18O/16O fractionation during water vapour diffusion through stomata and boundary layer εwc Equilibrium 18O/16O fractionation between organic O and medium water ε+ Equilibrium 18O/16O fractionation between liquid water and vapour ϕc Proportion of fixed C respired to the atmosphere ϕw Ratio of unproductive to productive water loss Γ* CO2 compensation point of C3 photosynthesis in the absence of Rd ℘ Péclet number A Net photosynthetic rate (μmol CO2 m−2 s−1) a 13C/12C discrimination during diffusion through the stomatal pore ab 13C/12C discrimination during diffusion through the leaf boundary layer al 13C/12C discrimination during liquid phase diffusion b 13C/12C discrimination by carboxylating enzymes during C3 photosynthesis C Molar concentration of water (mol m−3) ca Ambient CO2 mole fraction (μmol mol−1) ci Intercellular CO2 mole fraction (μmol mol−1) D Diffusivity of H218O in water (m2 s−1) d 13C/12C discrimination caused by processes other than a and b during C3 photosynthesis E Transpiration rate (mmol H2O m−2 s−1) Egrav Plant transpiration determined gravimetrically (mmol H2O m−2 s−1) Etot Cumulative transpiration over the course of an experiment (mol H2O) e 13C/12C discrimination during dark respiration es 13C/12C discrimination during dissolution of CO2 into water f 13C/12C discrimination during photorespiration gb Boundary layer conductance to CO2 (mol m−2 s−1) gc Total conductance to CO2 of stomata plus boundary layer (mol m−2 s−1) gi Mesophyll conductance to CO2 (mol m−2 s−1) I Intercept of the linear relationship between TE and Δ13C k Carboxylation efficiency during C3 photosynthesis (mol CO2 m−2 s−1) L Scaled effective path length in relation to 18O advection/diffusion (m) LA Leaf area (m2) MTR Mean transpiration rate (mol H2O m−2 d−1) m Slope of the linear relationship between TE and Δ13C Narea Leaf N concentration expressed on an area basis (mmol N m−2) N/P Mass ratio of N to P in leaf dry matter PFD Photosynthetic photon flux density (μmol m−2 s−1) Parea Leaf P concentration expressed on an area basis (mmol P m−2) pex Proportion of O atoms exchanging with local water during cellulose synthesis px Proportion of unenriched source water in developing plant tissues Rd Leaf dark respiration rate (μmol CO2 m−2 s−1) RGR Mean relative growth rate (mg g−1 d−1) rb Boundary layer resistance (m2 s mol−1) rs Stomatal resistance (m2 s mol−1) T Leaf temperature in K TE Transpiration efficiency (mmol C mol−1 H2O) TEN Transpiration efficiency of N acquisition (μmol N mol−1 H2O) TL Leaf temperature in C t Number of days in experiment v Leaf-to-air water vapour mole fraction difference (mmol mol−1) wa Water vapour mole fraction of ambient air (mmol mol−1) wi Water vapour mole fraction in the intercellular air spaces (mmol mol−1) Δ13C Photosynthetic 13C/12C discrimination Δ13CL 13C/12C discrimination in leaf dry matter Δ13CS 13C/12C discrimination in stem dry matter Δ13CR 13C/12C discrimination in root dry matter Δ13Cwp 13C/12C discrimination in whole plant dry matter Δ18O 18O/16O enrichment relative to source water Δ18Oc 18O/16O enrichment of plant cellulose relative to source water Δ18Oe 18O/16O enrichment at the evaporative sites in leaves relative to source water Δ18OL 18O/16O enrichment of leaf mesophyll water relative to source water Δ18Op 18O/16O enrichment of plant dry matter relative to source water Δ18Ov 18O/16O enrichment of atmospheric water vapour relative to source water δ13C 13C/12C of a sample expressed relative to the Pee Dee Belemnite standard δ13Ca 13C/12C of atmospheric CO2 expressed relative to the Pee Dee Belemnite standard δ13Cp 13C/12C of plant material expressed relative to the Pee Dee Belemnite standard δ15N 15N/14N of a sample expressed relative to that of air δ18O 18O/16O of a sample expressed relative to that of Vienna Standard Mean Ocean Water δ18Op δ18O of leaf dry matter δ18Os δ18O of source water εcp δ18O difference between plant cellulose and plant dry matter εk 18O/16O fractionation during water vapour diffusion through stomata and boundary layer εwc Equilibrium 18O/16O fractionation between organic O and medium water ε+ Equilibrium 18O/16O fractionation between liquid water and vapour ϕc Proportion of fixed C respired to the atmosphere ϕw Ratio of unproductive to productive water loss Γ* CO2 compensation point of C3 photosynthesis in the absence of Rd ℘ Péclet number Open in new tab Table 1. Symbols used in the text A Net photosynthetic rate (μmol CO2 m−2 s−1) a 13C/12C discrimination during diffusion through the stomatal pore ab 13C/12C discrimination during diffusion through the leaf boundary layer al 13C/12C discrimination during liquid phase diffusion b 13C/12C discrimination by carboxylating enzymes during C3 photosynthesis C Molar concentration of water (mol m−3) ca Ambient CO2 mole fraction (μmol mol−1) ci Intercellular CO2 mole fraction (μmol mol−1) D Diffusivity of H218O in water (m2 s−1) d 13C/12C discrimination caused by processes other than a and b during C3 photosynthesis E Transpiration rate (mmol H2O m−2 s−1) Egrav Plant transpiration determined gravimetrically (mmol H2O m−2 s−1) Etot Cumulative transpiration over the course of an experiment (mol H2O) e 13C/12C discrimination during dark respiration es 13C/12C discrimination during dissolution of CO2 into water f 13C/12C discrimination during photorespiration gb Boundary layer conductance to CO2 (mol m−2 s−1) gc Total conductance to CO2 of stomata plus boundary layer (mol m−2 s−1) gi Mesophyll conductance to CO2 (mol m−2 s−1) I Intercept of the linear relationship between TE and Δ13C k Carboxylation efficiency during C3 photosynthesis (mol CO2 m−2 s−1) L Scaled effective path length in relation to 18O advection/diffusion (m) LA Leaf area (m2) MTR Mean transpiration rate (mol H2O m−2 d−1) m Slope of the linear relationship between TE and Δ13C Narea Leaf N concentration expressed on an area basis (mmol N m−2) N/P Mass ratio of N to P in leaf dry matter PFD Photosynthetic photon flux density (μmol m−2 s−1) Parea Leaf P concentration expressed on an area basis (mmol P m−2) pex Proportion of O atoms exchanging with local water during cellulose synthesis px Proportion of unenriched source water in developing plant tissues Rd Leaf dark respiration rate (μmol CO2 m−2 s−1) RGR Mean relative growth rate (mg g−1 d−1) rb Boundary layer resistance (m2 s mol−1) rs Stomatal resistance (m2 s mol−1) T Leaf temperature in K TE Transpiration efficiency (mmol C mol−1 H2O) TEN Transpiration efficiency of N acquisition (μmol N mol−1 H2O) TL Leaf temperature in C t Number of days in experiment v Leaf-to-air water vapour mole fraction difference (mmol mol−1) wa Water vapour mole fraction of ambient air (mmol mol−1) wi Water vapour mole fraction in the intercellular air spaces (mmol mol−1) Δ13C Photosynthetic 13C/12C discrimination Δ13CL 13C/12C discrimination in leaf dry matter Δ13CS 13C/12C discrimination in stem dry matter Δ13CR 13C/12C discrimination in root dry matter Δ13Cwp 13C/12C discrimination in whole plant dry matter Δ18O 18O/16O enrichment relative to source water Δ18Oc 18O/16O enrichment of plant cellulose relative to source water Δ18Oe 18O/16O enrichment at the evaporative sites in leaves relative to source water Δ18OL 18O/16O enrichment of leaf mesophyll water relative to source water Δ18Op 18O/16O enrichment of plant dry matter relative to source water Δ18Ov 18O/16O enrichment of atmospheric water vapour relative to source water δ13C 13C/12C of a sample expressed relative to the Pee Dee Belemnite standard δ13Ca 13C/12C of atmospheric CO2 expressed relative to the Pee Dee Belemnite standard δ13Cp 13C/12C of plant material expressed relative to the Pee Dee Belemnite standard δ15N 15N/14N of a sample expressed relative to that of air δ18O 18O/16O of a sample expressed relative to that of Vienna Standard Mean Ocean Water δ18Op δ18O of leaf dry matter δ18Os δ18O of source water εcp δ18O difference between plant cellulose and plant dry matter εk 18O/16O fractionation during water vapour diffusion through stomata and boundary layer εwc Equilibrium 18O/16O fractionation between organic O and medium water ε+ Equilibrium 18O/16O fractionation between liquid water and vapour ϕc Proportion of fixed C respired to the atmosphere ϕw Ratio of unproductive to productive water loss Γ* CO2 compensation point of C3 photosynthesis in the absence of Rd ℘ Péclet number A Net photosynthetic rate (μmol CO2 m−2 s−1) a 13C/12C discrimination during diffusion through the stomatal pore ab 13C/12C discrimination during diffusion through the leaf boundary layer al 13C/12C discrimination during liquid phase diffusion b 13C/12C discrimination by carboxylating enzymes during C3 photosynthesis C Molar concentration of water (mol m−3) ca Ambient CO2 mole fraction (μmol mol−1) ci Intercellular CO2 mole fraction (μmol mol−1) D Diffusivity of H218O in water (m2 s−1) d 13C/12C discrimination caused by processes other than a and b during C3 photosynthesis E Transpiration rate (mmol H2O m−2 s−1) Egrav Plant transpiration determined gravimetrically (mmol H2O m−2 s−1) Etot Cumulative transpiration over the course of an experiment (mol H2O) e 13C/12C discrimination during dark respiration es 13C/12C discrimination during dissolution of CO2 into water f 13C/12C discrimination during photorespiration gb Boundary layer conductance to CO2 (mol m−2 s−1) gc Total conductance to CO2 of stomata plus boundary layer (mol m−2 s−1) gi Mesophyll conductance to CO2 (mol m−2 s−1) I Intercept of the linear relationship between TE and Δ13C k Carboxylation efficiency during C3 photosynthesis (mol CO2 m−2 s−1) L Scaled effective path length in relation to 18O advection/diffusion (m) LA Leaf area (m2) MTR Mean transpiration rate (mol H2O m−2 d−1) m Slope of the linear relationship between TE and Δ13C Narea Leaf N concentration expressed on an area basis (mmol N m−2) N/P Mass ratio of N to P in leaf dry matter PFD Photosynthetic photon flux density (μmol m−2 s−1) Parea Leaf P concentration expressed on an area basis (mmol P m−2) pex Proportion of O atoms exchanging with local water during cellulose synthesis px Proportion of unenriched source water in developing plant tissues Rd Leaf dark respiration rate (μmol CO2 m−2 s−1) RGR Mean relative growth rate (mg g−1 d−1) rb Boundary layer resistance (m2 s mol−1) rs Stomatal resistance (m2 s mol−1) T Leaf temperature in K TE Transpiration efficiency (mmol C mol−1 H2O) TEN Transpiration efficiency of N acquisition (μmol N mol−1 H2O) TL Leaf temperature in C t Number of days in experiment v Leaf-to-air water vapour mole fraction difference (mmol mol−1) wa Water vapour mole fraction of ambient air (mmol mol−1) wi Water vapour mole fraction in the intercellular air spaces (mmol mol−1) Δ13C Photosynthetic 13C/12C discrimination Δ13CL 13C/12C discrimination in leaf dry matter Δ13CS 13C/12C discrimination in stem dry matter Δ13CR 13C/12C discrimination in root dry matter Δ13Cwp 13C/12C discrimination in whole plant dry matter Δ18O 18O/16O enrichment relative to source water Δ18Oc 18O/16O enrichment of plant cellulose relative to source water Δ18Oe 18O/16O enrichment at the evaporative sites in leaves relative to source water Δ18OL 18O/16O enrichment of leaf mesophyll water relative to source water Δ18Op 18O/16O enrichment of plant dry matter relative to source water Δ18Ov 18O/16O enrichment of atmospheric water vapour relative to source water δ13C 13C/12C of a sample expressed relative to the Pee Dee Belemnite standard δ13Ca 13C/12C of atmospheric CO2 expressed relative to the Pee Dee Belemnite standard δ13Cp 13C/12C of plant material expressed relative to the Pee Dee Belemnite standard δ15N 15N/14N of a sample expressed relative to that of air δ18O 18O/16O of a sample expressed relative to that of Vienna Standard Mean Ocean Water δ18Op δ18O of leaf dry matter δ18Os δ18O of source water εcp δ18O difference between plant cellulose and plant dry matter εk 18O/16O fractionation during water vapour diffusion through stomata and boundary layer εwc Equilibrium 18O/16O fractionation between organic O and medium water ε+ Equilibrium 18O/16O fractionation between liquid water and vapour ϕc Proportion of fixed C respired to the atmosphere ϕw Ratio of unproductive to productive water loss Γ* CO2 compensation point of C3 photosynthesis in the absence of Rd ℘ Péclet number Open in new tab The ratio of intercellular to ambient CO2 mole fractions (ci/ca), shown in equation (2), also relates independently to C isotope discrimination (Δ13C). The Δ13C for C3 photosynthesis can be defined as (Farquhar et al., 1982; Farquhar and Richards, 1984; Hubick et al., 1986) (3) where a is the 13C/12C fractionation caused by gaseous diffusion through stomata (4.4‰), and b is the fractionation caused by Rubisco, the primary carboxylating enzyme in C3 plants (29‰). The term d summarizes collectively the fractionations caused by dissolution of CO2 and liquid phase diffusion, photorespiration, and dark respiration (Farquhar et al., 1989). The effects of fractionations associated with these processes on the overall Δ13C are small compared with that caused by Rubisco, but nonetheless significant (Brugnoli and Farquhar, 2000; Ghashghaie et al., 2003). The d in equation (3) substitutes for the following terms in the full model of Δ13C for C3 plants (Farquhar et al., 1989) (4) where gb and gi are conductances to CO2 (mol m−2 s−1) of the leaf boundary layer and between the intercellular air spaces and sites of carboxylation, respectively. The ab is the discrimination against 13CO2 during diffusion through the leaf boundary layer (2.8‰), es is that during dissolution into water (1.1‰), and al is that during liquid phase diffusion (0.7‰). The Rd is day respiration (μmol CO2 m−2 s−1), e is the 13C discrimination associated with day respiration, k is the carboxylation efficiency (mol CO2 m−2 s−1), f is the discrimination against 13C associated with photorespiration, and Γ* is the CO2 compensation point in the absence of Rd (μmol mol−1). The Δ13C in equation (3) is defined with respect to atmospheric CO2 as Δ13C=Ra/Rp–1, where Ra is 13C/12C of atmospheric CO2 and Rp is 13C/12C of plant material (Farquhar and Richards, 1984). In practice, Δ13C is calculated from measured δ13C values as (5) where δ13Ca is δ13C of CO2 in air, and δ13Cp is that of plant material. For convenience, Δ13C and δ13C values are typically expressed as per mil (‰), meaning that they have been multiplied by the scaling factor 1000. Equations (2) and (3) suggest that TE and Δ13C share a mutual dependence on ci/ca. Combining the two equations yields (Hubick and Farquhar, 1989) (6) which, in turn, can be rearranged as (7) Equation (7) presents a linear relationship between TE and Δ13C with slope –m and intercept m(b–d): (8) where m=ca(1–ϕc)/[1.6v(1+ϕw)(b–a)]. Thus, as demonstrated previously (Hubick et al., 1986), the coefficients of a linear regression equation between TE and Δ13C can be used to make inferences about parameters in equation (7) that are difficult to determine experimentally. Namely, the term d can be calculated as d=b–(I/m), where I is the intercept and m is the negative slope of the relationship between TE and Δ13C, and ϕc can be calculated as ϕc=1–1.6vm(1+ϕw)(b–a)/ca. Note that these calculations assume that the terms for which m and I substitute in equation (7) are invariant over the range of Δ13C for which the regression equation is fitted. It was previously suggested that measurements of 18O/16O of plant organic material could prove useful in water-use efficiency studies by providing a means for making integrated estimates of v (Farquhar et al., 1989; Sternberg et al., 1989). The v is defined as wi–wa, where wi is the water vapour mole fraction in the leaf intercellular air spaces and wa is that in the surrounding atmosphere. The suggestion was that the following set of equations, describing the processes contributing to the 18O/16O of plant organic material, could be inverted to solve for wi. The terms wa and wi relate to steady-state leaf water 18O enrichment at the sites of evaporation in leaves (Δ18Oe) in the following way (Craig and Gordon, 1965; Dongmann et al., 1974; Farquhar and Lloyd, 1993) (9) where ε+ is the equilibrium fractionation that occurs during the phase change from liquid water to vapour, εk is the kinetic fractionation that occurs during water vapour diffusion through stomatal pores and the leaf boundary layer, and Δ18Ov is the 18O enrichment of atmospheric water vapour with respect to water taken up by the roots (source water). The 18O enrichment (Δ18O) is defined with respect to source water as Δ18O=R/Rs–1, where R is 18O/16O of the sample of interest and Rs is that of source water. The equilibrium fractionation, ε+, can be calculated as follows (Bottinga and Craig, 1969): (10) where T is leaf temperature in K. The kinetic fractionation, εk, can be calculated as (Farquhar et al., 1989b) (11) where rs and rb are stomatal and boundary layer resistances to water vapour diffusion (m2 s mol−1), and 32 and 21 are associated fractionation factors scaled to per mil (Cappa et al., 2003). The Δ18O of leaf mesophyll water (Δ18OL), the signature most relevant to production of plant organic material (Cernusak et al., 2003), can be related to Δ18Oe as (Farquhar and Lloyd, 1993; Farquhar and Gan, 2003) (12) The ℘ is a Péclet number, defined as EL/(CD), where E is transpiration rate (mol m−2 s−1), L is a scaled effective path length (m), C is the molar concentration of water (mol m−3), and D is the diffusivity of H218O in water (m2 s−1). The D can be calculated as (Cuntz et al., 2007) (13) where T is leaf temperature in K. The Δ18OL can in turn be related to the 18O enrichment of plant cellulose (Δ18Oc) according to the following equation (Barbour and Farquhar, 2000): (14) where pex is the proportion of O atoms that exchange with local water in the developing plant tissue during cellulose synthesis, px is the proportion of unenriched source water in the developing tissue, and εwc is the equilibrium fractionation between organic O and medium water. Finally, the 18O enrichment of plant dry matter (Δ18Op) can be related to that of plant cellulose by adding an additional fractionation factor (εcp) to account for the δ18O difference between the two (Barbour and Farquhar, 2000); (15) Materials and methods Plant material and experimental treatments The experiment took place at the Smithsonian Tropical Research Institute, Santa Cruz Experimental Field Facility, Gamboa, Republic of Panama. The site is located at 9°07’ N latitude, 79°42’ W longitude, at an altitude of 28 m above sea level. Seeds of Ficus insipida Willd. (Moraceae) were collected from mature trees growing in the Panama Canal watershed. Seeds were germinated in trays containing a commercial potting soil in July 2005. Following germination, three seedlings each were transplanted into 40 pots, each of volume 19 l. Upon transplanting, a handful of soil was added to each pot together with roots taken from the base of the palm tree Attalea butyracea (Mutis ex L.f.) Wess. Boer. as a source of mycorrhizal inoculant. The pots were placed under a translucent rain shelter on plastic tables; they were elevated approximately 0.8 m above the concrete surface below the shelter. The shelter reduced incoming photon flux density (PFD) by approximately 20%. After an adjustment period of about 1 month, two seedlings were removed from each pot, leaving a uniform population of seedlings, with one seedling per pot. Among the 40 pots, five treatments were deployed, yielding eight pots per treatment. For each treatment, two pots were selected to serve as controls without plants; seedlings were removed from these pots. The discarded seedlings were used to measure seedling dry mass at the beginning of the experiment, estimated as 0.27 g. The five soil fertility treatments consisted of varying mixtures of homogenous, dark topsoil and rice husks, with the high fertility treatment additionally receiving a one-time application of slow-release fertilizer. It was expected that the addition of rice husks to the soil mixture would reduce the soil fertility in two ways; both by diluting the nutrient content of the pot, and by adding a high C/N substrate that would tend to immobilize N and other nutrients, leading to greater deficiencies as the proportion of rice husks increased. The treatments were as follows, given as the volumetric percentage of air-dried topsoil in the topsoil/rice-husk mixture: 20, 40, 60, 80, 80+N. For the 80+N treatment, approximately 13 g of Osmocote-Plus controlled-release fertilizer (Scotts-Sierra, Maryville, OH, USA) was added to each pot. Due to the difference in density between the topsoil and rice husks, the dry mass of the topsoil/rice-husk mixture required to fill each 19 l pot varied by treatment: 5.4, 8.9, 12.4, 15.4, and 15.4 kg were placed in each pot for treatments 20, 40, 60, 80, and 80+N, respectively. The amount of water required to bring the pots to field capacity also varied slightly: 4.0, 4.5, 5.0, 5.0, and 5.0 kg of water were added to treatments 20, 40, 60, 80, and 80+N, respectively. 1.5 kg of gravel was added to the soil surface of each pot to reduce soil evaporation. Plant water use The pots were weighed at regular intervals from 23 August 2005 until plant harvest on 4 November 2005, a period of approximately 10 weeks. Pot weights were determined with a 64 kg capacity balance (Sartorius QS64B, Thomas, Swedesboro, NJ, USA). The pots were initially weighed once per week, but the frequency was increased to as much as three times per week when plant stature and water use increased toward the end of the experiment. Pot water loss in the interval between weight measurements did not exceed 2.5 kg; this value was only approached near the very end of the experiment and only for the largest plants. Woody tree seedlings typically do not show a reduction in daily transpiration rate until soil water content falls to approximately one-third the value at field capacity (Sinclair et al., 2005), so it is assumed that transpiration was not limited by soil water content at any time during the experiment. After weighing each pot, water was added until the initial weight at field capacity was restored. Plant transpiration over the course of the experiment was calculated as the difference between cumulative pot water loss and the mean water loss of the control pots for each treatment. Leaves, stems, and roots were oven-dried at 70 °C after harvest and weighed separately for each plant. Abscised leaves were collected during the experiment and their dry weight added to the plant dry weight for TE calculations. Leaf area at plant harvest was determined with an LI-3100 Leaf-Area Meter (Li-Cor Inc., Lincoln, NE, USA). Meteorological conditions during the experiment were recorded every 15 min using an automated weather station (Campbell Scientific, Logan, UT, USA), as described previously (Winter et al., 2001, 2005). The mean daytime temperature, calculated between the h of sunrise and sunset, was 27.3 °C; mean daytime relative humidity was 81.5%; mean PFD was 670 μmol m−2 s−1; and mean daytime wind speed was 0.4 m s−1. For three days prior to plant harvest, daily and nightly water use of each plant was measured. Pots were weighed prior to sunrise (05.30 h) and again following sunset (18.00 h). Control pots were also weighed and control water loss subtracted from that of pots with plants to calculate plant water loss. Mean daily and nightly transpiration rates were expressed on a leaf area basis by dividing by the leaf area determined at plant harvest, which followed the third cycle of day/night measurements. The term ϕw for each plant was calculated as night-time plant water use divided by daytime plant water use. Leaf gas exchange measurements Gas exchange of the youngest, fully-expanded leaf of each plant was measured under light-saturating conditions (PFD >800 μmol m−2 s−1) at both morning and midday on 20 October 2005 with an Li-6400 portable photosynthesis system (Li-Cor Inc., Lincoln, NE, USA). Leaves were illuminated during measurements by natural sunlight. The mean PFD at the leaf surface during morning measurements was 1094±225 μmol m−2 s−1 (mean ±1 SD), whereas that during midday measurements was 1333±121 μmol m−2 s−1. The mean v during morning measurements was 12.8±1.6 mmol mol−1, and that during midday measurements was 17.2±1.4 mmol mol−1. Mean leaf temperatures (TL) during morning and midday measurements were 33.0±1.0 °C, and 36.0±0.5 °C, respectively. Dark respiration was measured on the youngest, fully-expanded leaf of each plant on 3 November 2005 between 19.30 h and 22.00 h. Mean leaf temperature during measurements was 25.9±0.2 °C. Isotopic and elemental analyses Leaf, stem, and root dry matter were ground to a fine powder for elemental and isotopic analyses. The 13C/12C and 15N/14N isotope ratios of leaf, stem, and root dry matter were measured at the Idaho Stable Isotopes Laboratory at the University of Idaho, Moscow, ID, USA; the 18O/16O of leaf dry matter was measured at the Stable Isotope Core Laboratory, Washington State University, Pullman, WA, USA. For 13C/12C and 15N/14N analyses, samples of approximately 3 mg were combusted in an NC2500 elemental analyser (CE Instruments, Milan, Italy), then swept by a helium carrier gas, via a continuous flow interface, into a Delta Plus isotope ratio mass spectrometer (Finnigan MAT, Bremen, Germany). In addition to 13C/12C and 15N/14N, the C and N elemental concentrations of the sample material were determined from peak areas obtained from mass spectrometric measurements. The 18O/16O of leaf dry matter was measured on samples of approximately 1 mg on a Delta XP isotope ratio mass spectrometer (Finnigan MAT, Bremen, Germany), following pyrolysis in a high-temperature furnace (Thermoquest TC/EA, Finnigan MAT, Bremen, Germany). The C, N, and O stable isotope ratios were obtained in delta notation relative to standards of Pee Dee Belemnite, air, and Vienna Standard Mean Ocean Water, respectively. Whole-plant δ13C and δ15N compositions were calculated by mass balance using the dry mass of each plant organ (leaves, stems, and roots), the C or N mass fraction, and the δ13C or δ15N composition. The Δ13C was calculated from δ13C values according to equation (5). The δ13Ca was assumed to be –8‰, consistent with observed daytime δ13Ca near Panama City, Panama (Winter and Holtum, 2002). The leaf Δ18Op was expressed with respect to the δ18O of irrigation water (–4.0‰) according to the equation Δ18Op=(δ18Op–δ18Os)/(1+δ18Os), where δ18Op is the δ18O of leaf dry matter, and δ18Os is that of irrigation (source) water (Barbour et al., 2004). In addition, elemental concentrations of P, K, Ca, Mg, Mn, and Zn were quantified in leaf dry matter. Approximately 200 mg of finely ground, oven-dried leaf material were digested at 380 °C in sulphuric acid and lithium sulphate with a selenium catalyst and hydrogen peroxide. Elemental concentrations were then determined on an inductively-coupled plasma optical-emission spectrometer (Perkin Elmer Inc., Wellesley, MA, USA). Leaf temperature and leaf-to-air humidity gradient Three different methods were used to estimate average values for TL and v over the course of the experiment for the five soil fertility treatments. In the first method, a leaf energy balance model was used, details of which have been recently described (Barbour et al., 2000; Cernusak et al., 2003a). In the model, mean daytime air temperature, relative humidity, irradiance, and wind speed were used over the course of the experiment along with the transpiration rates measured gravimetrically just prior to plant harvest. The transpiration rates used in the analysis were those measured on 2 November 2005, when the mean daily PFD and mean daily air water vapour mole fraction deficit matched very closely the average values recorded over the course of the experiment (684 μmol m−2 s−1 and 6.6 mmol mol−1 versus 670 μmol m−2 s−1 and 6.6 mmol mol−1, respectively). It was assumed that the incoming PFD was reduced by 20% by the translucent rain shelter covering the plants, and that the mean intercepted PFD for each plant was further reduced by 25% by self shading and non-horizontal leaf orientation. Thus, the PFD used in the leaf energy balance analysis was 400 μmol m−2 s−1. The mean surface area of individual leaves for each treatment was used to calculate boundary layer conductance. The leaf energy balance model was used to predict mean TL for each treatment, and v was calculated as the difference between the saturation vapour mole fraction at TL and the average daytime air vapour mole fraction. In the second method for estimating TL and v, equation (2) was used, along with measured values of TE, ci/ca (based on measurements of Δ13C), and ϕw. It was assumed that ca was constant at 375 μmol mol−1, and that ϕc was constant at 0.4. Equation (2) for v was then solved. Average wa was used to calculated wi, and TL was calculated as the dew point temperature at wi. The third method for estimating TL and v was based on measurements of Δ18Op for leaf dry matter. Equations (9) to (15) were inverted to solve for wi starting with values of Δ18Op. The εcp was assumed to be –6.8‰ (Cernusak et al., 2004); εwc was assumed to be 27‰ (Sternberg and DeNiro, 1983); the term pexpx was assumed to be 0.38 (Cernusak et al., 2005); the L was assumed to be 0.015 m (Cernusak et al., 2002, 2005); the D was calculated to be 2.46×10−9 m2 s−1; the rs values were taken from leaf gas exchange measurements; the rb was calculated from mean leaf surface area and mean wind speed, as for the leaf energy balance analysis; the ε+ was calculated to be 8.9‰; and the Δ18Ov was assumed equal to –ε+ (Farquhar et al., 2007). Although the parameters D and ε+ are dependent on leaf temperature, estimates of v are relatively insensitive to small changes in these parameters. For example, calculating these parameters with a TL of 25 °C versus 30 °C would shift our mean estimate of v from 8.1 to 8.0 mmol mol−1 in the case of D, and from 7.9 to 8.0 mmol mol−1 in the case of ε+. Therefore, D and ε+ were calculated assuming an approximate TL of 28 °C. Final TL was calculated from wi as described above. Statistical analysis Ordinary least squares regression was used to evaluate the relationships between leaf isotopic characteristics, gas exchange characteristics, and leaf elemental concentrations. These analyses were used to determine the ability of an explanatory variable to predict variation in a response variable. However, to determine the regression coefficients for the relationship between TE and Δ13C for estimations of d and ϕc, geometric mean regression was used. In this case, we were interested in the functional relationship between TE and Δ13C rather than a causal, predictive relationship, the values of the regression coefficients were the primary focus of the analysis, and both parameters were measured with error, thereby indicating the use of geometric mean regression (Sokal and Rohlf, 1995). Analysis of variance was used to test for variation among treatments in physiological and morphological parameters. Tukey's method for pair-wise comparisons was used to test for significant differences between individual treatments. Results Plant growth and morphology Plant growth and morphology differed among the soil fertility treatments (Table 2). Mean plant dry mass at the time of harvest ranged from 2.6 to 80.4 g from the lowest to the highest soil fertility treatment. Corresponding mean relative growth rates (RGR) ranged from 30.4 to 78.0 mg g−1 d−1, respectively (Table 2). Differences in RGR among treatments were statistically significant for all pairs of treatments, except 40% and 60% soil. There was significant variation in root:shoot ratio, leaf area ratio, and specific leaf area among treatments, but this variation did not appear to be systematically related to the soil fertility treatments (Table 2). Table 2. Morphological and physiological parameters for Ficus insipida plants according to soil fertility treatment Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Plant dry mass at harvest (g) 2.6±1.0 a 11.8±3.3 a 7.8±2.0 a 27.4±5.2 b 80.4±12.3 c Leaf area at harvest (cm2) 247±92 a 1086±248 b 826±224 a,b 1885±285 c 7449±750 d Root/shoot ratio (g g−1) 0.68±0.28 a 0.45±0.06 a,b 0.43±0.05 b 0.58±0.07 a,b 0.46±0.07 a,b Leaf area ratio (m2 kg−1) 9.5±2.3 a 9.4±1.4 a 10.6±1.0 a 6.9±0.3 b 9.4±0.9 a Specific leaf area (m2 kg−1) 22.5±1.6 a 20.2±1.3 b,c 22.2±1.3 a,c 19.1±1.4 b 21.4±1.0 a,c Mean relative growth rate (mg g−1 d−1) 30.4±5.5 a 51.4±4.2 b 45.8±3.5 b 63.3±2.7 c 78.0±2.2 d Gravimetric daytime transpiration (mmol m−2 s−1) 1.92±0.18 a 1.58±0.09 b 1.70±0.09 b 1.32±0.08 c 1.14±0.13 c Gravimetric night-time transpiration (mmol m−2 s−1) 0.30±0.10 a 0.18±0.03 b,c 0.23±0.03 a,c 0.14±0.02 b 0.13±0.01 b Ratio night-time/daytime transpiration (ϕw) 0.15±0.05 a 0.11±0.02 a,b 0.13±0.02 a,b 0.11±0.01 b 0.11±0.01 a,b Net photosynthesis (μmol CO2 m−2 s−1) 11.0±2.2 a 15.1±1.5 b 16.4±1.7 b 15.4±1.3 b 22.4±1.7 c Stomatal conductance to H2O (mol m−2 s−1) 0.59±0.06 a 0.72±0.08 a,b 0.76±0.10 b 0.75±0.08 b 0.81±0.09 b Instantaneous transpiration (mmol m−2 s−1) 8.4±0.5 a 9.1±0.4 a,b 8.6±1.0 a 9.0±0.8 a,b 9.9±0.5 b Dark respiration rate (μmol CO2 m−2 s−1) 0.71±0.15 a 1.07±0.29 a,b 1.08±0.25 a,b 0.98±0.15 a,b 1.34±0.33 b Ratio dark respiration/net photosynthesis 0.07±0.02 a 0.07±0.02 a 0.07±0.01 a 0.06±0.01 a 0.06±0.01 a Whole-plant N isotope ratio (δ15N, ‰) 2.7±0.8 a 3.2±0.5 a 2.4±0.4 a 3.4±0.7 a 2.8±0.6 a Transpiration efficiency: N (μmol N mol−1 H2O) 17.8±4.3 a 35.8±3.9 b 39.5±4.0 b 41.8±4.6 b 107.0±18.5 c Whole-plant C isotope discrimination (Δ13Cwp; ‰) 24.1±0.6 a 23.1±0.3 b 23.1±0.3 b 22.4±0.4 c 21.5±0.3 d Leaf C isotope discrimination (Δ13CL; ‰) 24.4±0.7 a 23.3±0.3 b 23.5±0.3 b 23.0±0.4 b 22.1±0.3 c Stem C isotope discrimination (Δ13CS; ‰) 24.5±0.6 a 23.1±0.3 b 23.0±0.3 b,c 22.4±0.5 c 21.5±0.5 d Root C isotope discrimination (Δ13CR; ‰) 23.7±0.5 a 22.9±0.3 a,b 22.7±0.6 b 21.8±0.3 c 20.4±0.5 d Integrated ci/ca calculated from whole-plant Δ13C 0.96±0.02 a 0.92±0.01 b 0.92±0.01 b 0.89±0.01 c 0.86±0.01 d Instantaneous ci/ca from gas exchange 0.88±0.02 a 0.86±0.01 a 0.86±0.01 a 0.86±0.01 a 0.82±0.01 b Transpiration efficiency: C (mmol C mol−1 H2O) 0.74±0.13 a 1.25±0.15 b 1.20±0.07 b 1.81±0.16 c 2.69±0.11 d Leaf dry matter O isotope enrichment (Δ18Op; ‰) 24.9±0.3 a 24.8±0.6 a 24.3±0.2 a 24.4±0.3 a 23.6±0.4 b Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Plant dry mass at harvest (g) 2.6±1.0 a 11.8±3.3 a 7.8±2.0 a 27.4±5.2 b 80.4±12.3 c Leaf area at harvest (cm2) 247±92 a 1086±248 b 826±224 a,b 1885±285 c 7449±750 d Root/shoot ratio (g g−1) 0.68±0.28 a 0.45±0.06 a,b 0.43±0.05 b 0.58±0.07 a,b 0.46±0.07 a,b Leaf area ratio (m2 kg−1) 9.5±2.3 a 9.4±1.4 a 10.6±1.0 a 6.9±0.3 b 9.4±0.9 a Specific leaf area (m2 kg−1) 22.5±1.6 a 20.2±1.3 b,c 22.2±1.3 a,c 19.1±1.4 b 21.4±1.0 a,c Mean relative growth rate (mg g−1 d−1) 30.4±5.5 a 51.4±4.2 b 45.8±3.5 b 63.3±2.7 c 78.0±2.2 d Gravimetric daytime transpiration (mmol m−2 s−1) 1.92±0.18 a 1.58±0.09 b 1.70±0.09 b 1.32±0.08 c 1.14±0.13 c Gravimetric night-time transpiration (mmol m−2 s−1) 0.30±0.10 a 0.18±0.03 b,c 0.23±0.03 a,c 0.14±0.02 b 0.13±0.01 b Ratio night-time/daytime transpiration (ϕw) 0.15±0.05 a 0.11±0.02 a,b 0.13±0.02 a,b 0.11±0.01 b 0.11±0.01 a,b Net photosynthesis (μmol CO2 m−2 s−1) 11.0±2.2 a 15.1±1.5 b 16.4±1.7 b 15.4±1.3 b 22.4±1.7 c Stomatal conductance to H2O (mol m−2 s−1) 0.59±0.06 a 0.72±0.08 a,b 0.76±0.10 b 0.75±0.08 b 0.81±0.09 b Instantaneous transpiration (mmol m−2 s−1) 8.4±0.5 a 9.1±0.4 a,b 8.6±1.0 a 9.0±0.8 a,b 9.9±0.5 b Dark respiration rate (μmol CO2 m−2 s−1) 0.71±0.15 a 1.07±0.29 a,b 1.08±0.25 a,b 0.98±0.15 a,b 1.34±0.33 b Ratio dark respiration/net photosynthesis 0.07±0.02 a 0.07±0.02 a 0.07±0.01 a 0.06±0.01 a 0.06±0.01 a Whole-plant N isotope ratio (δ15N, ‰) 2.7±0.8 a 3.2±0.5 a 2.4±0.4 a 3.4±0.7 a 2.8±0.6 a Transpiration efficiency: N (μmol N mol−1 H2O) 17.8±4.3 a 35.8±3.9 b 39.5±4.0 b 41.8±4.6 b 107.0±18.5 c Whole-plant C isotope discrimination (Δ13Cwp; ‰) 24.1±0.6 a 23.1±0.3 b 23.1±0.3 b 22.4±0.4 c 21.5±0.3 d Leaf C isotope discrimination (Δ13CL; ‰) 24.4±0.7 a 23.3±0.3 b 23.5±0.3 b 23.0±0.4 b 22.1±0.3 c Stem C isotope discrimination (Δ13CS; ‰) 24.5±0.6 a 23.1±0.3 b 23.0±0.3 b,c 22.4±0.5 c 21.5±0.5 d Root C isotope discrimination (Δ13CR; ‰) 23.7±0.5 a 22.9±0.3 a,b 22.7±0.6 b 21.8±0.3 c 20.4±0.5 d Integrated ci/ca calculated from whole-plant Δ13C 0.96±0.02 a 0.92±0.01 b 0.92±0.01 b 0.89±0.01 c 0.86±0.01 d Instantaneous ci/ca from gas exchange 0.88±0.02 a 0.86±0.01 a 0.86±0.01 a 0.86±0.01 a 0.82±0.01 b Transpiration efficiency: C (mmol C mol−1 H2O) 0.74±0.13 a 1.25±0.15 b 1.20±0.07 b 1.81±0.16 c 2.69±0.11 d Leaf dry matter O isotope enrichment (Δ18Op; ‰) 24.9±0.3 a 24.8±0.6 a 24.3±0.2 a 24.4±0.3 a 23.6±0.4 b Values are given as mean ±1 standard deviation. For each treatment, n=6. Values within a row followed by different letters are significantly different at P <0.05. Open in new tab Table 2. Morphological and physiological parameters for Ficus insipida plants according to soil fertility treatment Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Plant dry mass at harvest (g) 2.6±1.0 a 11.8±3.3 a 7.8±2.0 a 27.4±5.2 b 80.4±12.3 c Leaf area at harvest (cm2) 247±92 a 1086±248 b 826±224 a,b 1885±285 c 7449±750 d Root/shoot ratio (g g−1) 0.68±0.28 a 0.45±0.06 a,b 0.43±0.05 b 0.58±0.07 a,b 0.46±0.07 a,b Leaf area ratio (m2 kg−1) 9.5±2.3 a 9.4±1.4 a 10.6±1.0 a 6.9±0.3 b 9.4±0.9 a Specific leaf area (m2 kg−1) 22.5±1.6 a 20.2±1.3 b,c 22.2±1.3 a,c 19.1±1.4 b 21.4±1.0 a,c Mean relative growth rate (mg g−1 d−1) 30.4±5.5 a 51.4±4.2 b 45.8±3.5 b 63.3±2.7 c 78.0±2.2 d Gravimetric daytime transpiration (mmol m−2 s−1) 1.92±0.18 a 1.58±0.09 b 1.70±0.09 b 1.32±0.08 c 1.14±0.13 c Gravimetric night-time transpiration (mmol m−2 s−1) 0.30±0.10 a 0.18±0.03 b,c 0.23±0.03 a,c 0.14±0.02 b 0.13±0.01 b Ratio night-time/daytime transpiration (ϕw) 0.15±0.05 a 0.11±0.02 a,b 0.13±0.02 a,b 0.11±0.01 b 0.11±0.01 a,b Net photosynthesis (μmol CO2 m−2 s−1) 11.0±2.2 a 15.1±1.5 b 16.4±1.7 b 15.4±1.3 b 22.4±1.7 c Stomatal conductance to H2O (mol m−2 s−1) 0.59±0.06 a 0.72±0.08 a,b 0.76±0.10 b 0.75±0.08 b 0.81±0.09 b Instantaneous transpiration (mmol m−2 s−1) 8.4±0.5 a 9.1±0.4 a,b 8.6±1.0 a 9.0±0.8 a,b 9.9±0.5 b Dark respiration rate (μmol CO2 m−2 s−1) 0.71±0.15 a 1.07±0.29 a,b 1.08±0.25 a,b 0.98±0.15 a,b 1.34±0.33 b Ratio dark respiration/net photosynthesis 0.07±0.02 a 0.07±0.02 a 0.07±0.01 a 0.06±0.01 a 0.06±0.01 a Whole-plant N isotope ratio (δ15N, ‰) 2.7±0.8 a 3.2±0.5 a 2.4±0.4 a 3.4±0.7 a 2.8±0.6 a Transpiration efficiency: N (μmol N mol−1 H2O) 17.8±4.3 a 35.8±3.9 b 39.5±4.0 b 41.8±4.6 b 107.0±18.5 c Whole-plant C isotope discrimination (Δ13Cwp; ‰) 24.1±0.6 a 23.1±0.3 b 23.1±0.3 b 22.4±0.4 c 21.5±0.3 d Leaf C isotope discrimination (Δ13CL; ‰) 24.4±0.7 a 23.3±0.3 b 23.5±0.3 b 23.0±0.4 b 22.1±0.3 c Stem C isotope discrimination (Δ13CS; ‰) 24.5±0.6 a 23.1±0.3 b 23.0±0.3 b,c 22.4±0.5 c 21.5±0.5 d Root C isotope discrimination (Δ13CR; ‰) 23.7±0.5 a 22.9±0.3 a,b 22.7±0.6 b 21.8±0.3 c 20.4±0.5 d Integrated ci/ca calculated from whole-plant Δ13C 0.96±0.02 a 0.92±0.01 b 0.92±0.01 b 0.89±0.01 c 0.86±0.01 d Instantaneous ci/ca from gas exchange 0.88±0.02 a 0.86±0.01 a 0.86±0.01 a 0.86±0.01 a 0.82±0.01 b Transpiration efficiency: C (mmol C mol−1 H2O) 0.74±0.13 a 1.25±0.15 b 1.20±0.07 b 1.81±0.16 c 2.69±0.11 d Leaf dry matter O isotope enrichment (Δ18Op; ‰) 24.9±0.3 a 24.8±0.6 a 24.3±0.2 a 24.4±0.3 a 23.6±0.4 b Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Plant dry mass at harvest (g) 2.6±1.0 a 11.8±3.3 a 7.8±2.0 a 27.4±5.2 b 80.4±12.3 c Leaf area at harvest (cm2) 247±92 a 1086±248 b 826±224 a,b 1885±285 c 7449±750 d Root/shoot ratio (g g−1) 0.68±0.28 a 0.45±0.06 a,b 0.43±0.05 b 0.58±0.07 a,b 0.46±0.07 a,b Leaf area ratio (m2 kg−1) 9.5±2.3 a 9.4±1.4 a 10.6±1.0 a 6.9±0.3 b 9.4±0.9 a Specific leaf area (m2 kg−1) 22.5±1.6 a 20.2±1.3 b,c 22.2±1.3 a,c 19.1±1.4 b 21.4±1.0 a,c Mean relative growth rate (mg g−1 d−1) 30.4±5.5 a 51.4±4.2 b 45.8±3.5 b 63.3±2.7 c 78.0±2.2 d Gravimetric daytime transpiration (mmol m−2 s−1) 1.92±0.18 a 1.58±0.09 b 1.70±0.09 b 1.32±0.08 c 1.14±0.13 c Gravimetric night-time transpiration (mmol m−2 s−1) 0.30±0.10 a 0.18±0.03 b,c 0.23±0.03 a,c 0.14±0.02 b 0.13±0.01 b Ratio night-time/daytime transpiration (ϕw) 0.15±0.05 a 0.11±0.02 a,b 0.13±0.02 a,b 0.11±0.01 b 0.11±0.01 a,b Net photosynthesis (μmol CO2 m−2 s−1) 11.0±2.2 a 15.1±1.5 b 16.4±1.7 b 15.4±1.3 b 22.4±1.7 c Stomatal conductance to H2O (mol m−2 s−1) 0.59±0.06 a 0.72±0.08 a,b 0.76±0.10 b 0.75±0.08 b 0.81±0.09 b Instantaneous transpiration (mmol m−2 s−1) 8.4±0.5 a 9.1±0.4 a,b 8.6±1.0 a 9.0±0.8 a,b 9.9±0.5 b Dark respiration rate (μmol CO2 m−2 s−1) 0.71±0.15 a 1.07±0.29 a,b 1.08±0.25 a,b 0.98±0.15 a,b 1.34±0.33 b Ratio dark respiration/net photosynthesis 0.07±0.02 a 0.07±0.02 a 0.07±0.01 a 0.06±0.01 a 0.06±0.01 a Whole-plant N isotope ratio (δ15N, ‰) 2.7±0.8 a 3.2±0.5 a 2.4±0.4 a 3.4±0.7 a 2.8±0.6 a Transpiration efficiency: N (μmol N mol−1 H2O) 17.8±4.3 a 35.8±3.9 b 39.5±4.0 b 41.8±4.6 b 107.0±18.5 c Whole-plant C isotope discrimination (Δ13Cwp; ‰) 24.1±0.6 a 23.1±0.3 b 23.1±0.3 b 22.4±0.4 c 21.5±0.3 d Leaf C isotope discrimination (Δ13CL; ‰) 24.4±0.7 a 23.3±0.3 b 23.5±0.3 b 23.0±0.4 b 22.1±0.3 c Stem C isotope discrimination (Δ13CS; ‰) 24.5±0.6 a 23.1±0.3 b 23.0±0.3 b,c 22.4±0.5 c 21.5±0.5 d Root C isotope discrimination (Δ13CR; ‰) 23.7±0.5 a 22.9±0.3 a,b 22.7±0.6 b 21.8±0.3 c 20.4±0.5 d Integrated ci/ca calculated from whole-plant Δ13C 0.96±0.02 a 0.92±0.01 b 0.92±0.01 b 0.89±0.01 c 0.86±0.01 d Instantaneous ci/ca from gas exchange 0.88±0.02 a 0.86±0.01 a 0.86±0.01 a 0.86±0.01 a 0.82±0.01 b Transpiration efficiency: C (mmol C mol−1 H2O) 0.74±0.13 a 1.25±0.15 b 1.20±0.07 b 1.81±0.16 c 2.69±0.11 d Leaf dry matter O isotope enrichment (Δ18Op; ‰) 24.9±0.3 a 24.8±0.6 a 24.3±0.2 a 24.4±0.3 a 23.6±0.4 b Values are given as mean ±1 standard deviation. For each treatment, n=6. Values within a row followed by different letters are significantly different at P <0.05. Open in new tab Elemental composition Whole-plant C concentration varied among treatments, but over a rather narrow range of 0.40–0.42 g g−1 (Table 3). Whole-plant N concentration, on the other hand, showed more pronounced variation among treatments, ranging from 11.6 to 19.7 mg g−1 (Table 3). Leaf N concentration per unit leaf area (Narea) increased from 53.9 mmol m−2 to 90.4 mmol m−2 from the lowest to the highest soil fertility treatment (Table 3). Leaf P concentration showed an opposite trend to leaf N concentration, decreasing from the lowest to the highest soil fertility treatment (Table 3). Leaf Mg, Mn, and Zn concentrations followed similar trends to leaf P, decreasing from the lowest to the highest soil fertility (Table 3). Leaf K and Ca concentrations were less variable among treatments, although they showed weak tendencies to decrease (K) or increase (Ca) from the lowest to the highest soil fertility (Table 3). Table 3. Elemental composition of the dry matter of Ficus insipida plants according to soil fertility treatment Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Whole-plant C concentration (g g−1) 0.40±0.01 a 0.42±0.01 b,c 0.41±0.01 a,c 0.42±0.01 b,c 0.42±0.01 b,c Whole-plant N concentration (mg g−1) 11.9±2.0 a 14.2±1.6 a,b 16.0±1.6 b 11.6±1.0 a 19.7±3.7 c Whole-plant C/N ratio (g g−1) 34.3±5.5 a,b 29.7±3.7 b,c 26.0±2.4 c,d 36.7±2.7 a 21.9±3.0 d Leaf N/P ratio (g g−1) 4.7±0.9 a 6.0±0.8 a 5.9±1.0 a 7.9±0.7 b 16.4±1.1 c Leaf N per unit area (mmol m−2) 53.9±6.6 a 68.9±6.4 b 67.7±6.9 b 62.0±4.4 a,b 90.4±10.8 c Leaf P per unit area (mmol m−2) 5.34±1.07 a 5.22±0.44 a 5.22±0.49 a 3.55±0.38 b 2.49±0.20 c Leaf N concentration (mg g−1) 16.9±2.1 a 19.4±2.0 a,b 21.0±2.3 b 16.5±1.2 a 27.1±3.7 c Leaf P concentration (mg g−1) 3.75±0.90 a 3.27±0.47 a 3.58±0.31 a 2.09±0.20 b 1.65±0.14 b Leaf K concentration (mg g−1) 27.4±2.1 a,b 26.2±1.9 a,b 29.1±1.8 b 25.1±3.1 a 24.5±2.6 a Leaf Ca concentration (mg g−1) 14.4±1.4 a,b 13.6±0.6 b 14.7±2.0 a,b 16.8±1.7 a 15.4±2.0 a,b Leaf Mg concentration (mg g−1) 2.89±0.37 a 2.18±0.28 b,c 2.64±0.44 a,b 2.25±0.27 b,c 2.04±0.18 c Leaf Mn concentration (μg g−1) 154±7 a 134±14 a,b 133±16 a,b 132±28 a,b 118±15 b Leaf Zn concentration (μg g−1) 28.4±3.2 a 21.9±2.9 b 23.1±4.5 a,b 16.6±4.6 b,c 15.1±3.5 c Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Whole-plant C concentration (g g−1) 0.40±0.01 a 0.42±0.01 b,c 0.41±0.01 a,c 0.42±0.01 b,c 0.42±0.01 b,c Whole-plant N concentration (mg g−1) 11.9±2.0 a 14.2±1.6 a,b 16.0±1.6 b 11.6±1.0 a 19.7±3.7 c Whole-plant C/N ratio (g g−1) 34.3±5.5 a,b 29.7±3.7 b,c 26.0±2.4 c,d 36.7±2.7 a 21.9±3.0 d Leaf N/P ratio (g g−1) 4.7±0.9 a 6.0±0.8 a 5.9±1.0 a 7.9±0.7 b 16.4±1.1 c Leaf N per unit area (mmol m−2) 53.9±6.6 a 68.9±6.4 b 67.7±6.9 b 62.0±4.4 a,b 90.4±10.8 c Leaf P per unit area (mmol m−2) 5.34±1.07 a 5.22±0.44 a 5.22±0.49 a 3.55±0.38 b 2.49±0.20 c Leaf N concentration (mg g−1) 16.9±2.1 a 19.4±2.0 a,b 21.0±2.3 b 16.5±1.2 a 27.1±3.7 c Leaf P concentration (mg g−1) 3.75±0.90 a 3.27±0.47 a 3.58±0.31 a 2.09±0.20 b 1.65±0.14 b Leaf K concentration (mg g−1) 27.4±2.1 a,b 26.2±1.9 a,b 29.1±1.8 b 25.1±3.1 a 24.5±2.6 a Leaf Ca concentration (mg g−1) 14.4±1.4 a,b 13.6±0.6 b 14.7±2.0 a,b 16.8±1.7 a 15.4±2.0 a,b Leaf Mg concentration (mg g−1) 2.89±0.37 a 2.18±0.28 b,c 2.64±0.44 a,b 2.25±0.27 b,c 2.04±0.18 c Leaf Mn concentration (μg g−1) 154±7 a 134±14 a,b 133±16 a,b 132±28 a,b 118±15 b Leaf Zn concentration (μg g−1) 28.4±3.2 a 21.9±2.9 b 23.1±4.5 a,b 16.6±4.6 b,c 15.1±3.5 c Values are given as the mean ±1 standard deviation. For each treatment, n=6. Values within a row followed by different letters are significantly different at P <0.05. Open in new tab Table 3. Elemental composition of the dry matter of Ficus insipida plants according to soil fertility treatment Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Whole-plant C concentration (g g−1) 0.40±0.01 a 0.42±0.01 b,c 0.41±0.01 a,c 0.42±0.01 b,c 0.42±0.01 b,c Whole-plant N concentration (mg g−1) 11.9±2.0 a 14.2±1.6 a,b 16.0±1.6 b 11.6±1.0 a 19.7±3.7 c Whole-plant C/N ratio (g g−1) 34.3±5.5 a,b 29.7±3.7 b,c 26.0±2.4 c,d 36.7±2.7 a 21.9±3.0 d Leaf N/P ratio (g g−1) 4.7±0.9 a 6.0±0.8 a 5.9±1.0 a 7.9±0.7 b 16.4±1.1 c Leaf N per unit area (mmol m−2) 53.9±6.6 a 68.9±6.4 b 67.7±6.9 b 62.0±4.4 a,b 90.4±10.8 c Leaf P per unit area (mmol m−2) 5.34±1.07 a 5.22±0.44 a 5.22±0.49 a 3.55±0.38 b 2.49±0.20 c Leaf N concentration (mg g−1) 16.9±2.1 a 19.4±2.0 a,b 21.0±2.3 b 16.5±1.2 a 27.1±3.7 c Leaf P concentration (mg g−1) 3.75±0.90 a 3.27±0.47 a 3.58±0.31 a 2.09±0.20 b 1.65±0.14 b Leaf K concentration (mg g−1) 27.4±2.1 a,b 26.2±1.9 a,b 29.1±1.8 b 25.1±3.1 a 24.5±2.6 a Leaf Ca concentration (mg g−1) 14.4±1.4 a,b 13.6±0.6 b 14.7±2.0 a,b 16.8±1.7 a 15.4±2.0 a,b Leaf Mg concentration (mg g−1) 2.89±0.37 a 2.18±0.28 b,c 2.64±0.44 a,b 2.25±0.27 b,c 2.04±0.18 c Leaf Mn concentration (μg g−1) 154±7 a 134±14 a,b 133±16 a,b 132±28 a,b 118±15 b Leaf Zn concentration (μg g−1) 28.4±3.2 a 21.9±2.9 b 23.1±4.5 a,b 16.6±4.6 b,c 15.1±3.5 c Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Whole-plant C concentration (g g−1) 0.40±0.01 a 0.42±0.01 b,c 0.41±0.01 a,c 0.42±0.01 b,c 0.42±0.01 b,c Whole-plant N concentration (mg g−1) 11.9±2.0 a 14.2±1.6 a,b 16.0±1.6 b 11.6±1.0 a 19.7±3.7 c Whole-plant C/N ratio (g g−1) 34.3±5.5 a,b 29.7±3.7 b,c 26.0±2.4 c,d 36.7±2.7 a 21.9±3.0 d Leaf N/P ratio (g g−1) 4.7±0.9 a 6.0±0.8 a 5.9±1.0 a 7.9±0.7 b 16.4±1.1 c Leaf N per unit area (mmol m−2) 53.9±6.6 a 68.9±6.4 b 67.7±6.9 b 62.0±4.4 a,b 90.4±10.8 c Leaf P per unit area (mmol m−2) 5.34±1.07 a 5.22±0.44 a 5.22±0.49 a 3.55±0.38 b 2.49±0.20 c Leaf N concentration (mg g−1) 16.9±2.1 a 19.4±2.0 a,b 21.0±2.3 b 16.5±1.2 a 27.1±3.7 c Leaf P concentration (mg g−1) 3.75±0.90 a 3.27±0.47 a 3.58±0.31 a 2.09±0.20 b 1.65±0.14 b Leaf K concentration (mg g−1) 27.4±2.1 a,b 26.2±1.9 a,b 29.1±1.8 b 25.1±3.1 a 24.5±2.6 a Leaf Ca concentration (mg g−1) 14.4±1.4 a,b 13.6±0.6 b 14.7±2.0 a,b 16.8±1.7 a 15.4±2.0 a,b Leaf Mg concentration (mg g−1) 2.89±0.37 a 2.18±0.28 b,c 2.64±0.44 a,b 2.25±0.27 b,c 2.04±0.18 c Leaf Mn concentration (μg g−1) 154±7 a 134±14 a,b 133±16 a,b 132±28 a,b 118±15 b Leaf Zn concentration (μg g−1) 28.4±3.2 a 21.9±2.9 b 23.1±4.5 a,b 16.6±4.6 b,c 15.1±3.5 c Values are given as the mean ±1 standard deviation. For each treatment, n=6. Values within a row followed by different letters are significantly different at P <0.05. Open in new tab The mass ratio of leaf N to P (N/P) increased across treatments from a mean of 4.7 for the lowest soil fertility to 16.4 for the highest soil fertility (Table 3). Variation in TE was closely correlated with variation in N/P (Fig. 1A). The relationship between the two parameters was slightly non-linear, such that the natural logarithm of N/P explained 90% of variation in TE, whereas N/P explained 87%. The relationship between Ln(N/P) and TE was TE=1.42Ln(N/P)–1.28 (R2=0.90, P <0.0001, n=30). The RGR showed a non-linear response to variation in N/P; it increased up to N/P of about 15, then decreased slightly over the small range of values above 15 (Fig. 1B). Variation in RGR and TE was closely correlated (Fig. 1C). Again the relationship was slightly non-linear, such that the relationship between RGR and Ln(TE) was slightly stronger (R2=0.95, P <0.0001, n=30) than that between RGR and TE (R2=0.92, P <0.0001, n=30). Fig. 1. Open in new tabDownload slide Transpiration efficiency (A) and mean relative growth rate (B) plotted against leaf N/P mass ratio, and mean relative growth rate plotted against transpiration efficiency (C) for Ficus insipida plants subject to varying soil fertility. Fig. 1. Open in new tabDownload slide Transpiration efficiency (A) and mean relative growth rate (B) plotted against leaf N/P mass ratio, and mean relative growth rate plotted against transpiration efficiency (C) for Ficus insipida plants subject to varying soil fertility. Leaf Parea showed a strong positive, linear dependence on mean daytime transpiration rate, also expressed on a leaf area basis, with Egrav explaining 74% of variation in leaf Parea (Fig. 2A). Similar positive, linear dependencies on Egrav were also observed for leaf Zn per unit area (R2=0.45, P <0.0001, n=29) and leaf Mg per unit area (R2=0.23, P <0.005, n=29). Fig. 2. Open in new tabDownload slide Leaf P per unit area (A) and leaf dry matter 18O enrichment (B) plotted against mean daytime transpiration rate for Ficus insipida plants. Transpiration was determined gravimetrically for whole plants on 1–3 November 2005; plant leaf area was determined after harvest on 4 November 2005. Fig. 2. Open in new tabDownload slide Leaf P per unit area (A) and leaf dry matter 18O enrichment (B) plotted against mean daytime transpiration rate for Ficus insipida plants. Transpiration was determined gravimetrically for whole plants on 1–3 November 2005; plant leaf area was determined after harvest on 4 November 2005. Leaf gas exchange Photosynthesis of the youngest, fully-expanded leaf of each plant at saturating irradiance increased with increasing soil fertility; mean treatment values ranged from 11.0 to 22.4 μmol CO2 m−2 s−1 from the lowest to the highest soil fertility treatment (Table 2). These values represent averages of measurements made during morning and midday. Leaf dark respiration also increased from low to high soil fertility, with treatment means ranging from 0.71 to 1.34 μmol CO2 m−2 s−1 (Table 2). The ratio of leaf dark respiration to leaf net photosynthesis, on the other hand, was invariant among treatments (Table 2). Leaf net photosynthesis, leaf dark respiration, and whole-plant relative growth rate were linearly related to leaf Narea (Fig. 3). The ratio of leaf dark respiration to leaf net photosynthesis, in contrast, showed no correlation with Narea (P=0.67, n=30). Fig. 3. Open in new tabDownload slide Light-saturated photosynthesis (A), leaf dark respiration (B), and mean relative growth rate (C) plotted against leaf N per unit area for plants of Ficus insipida subject to varying soil fertility. Leaf photosynthesis was measured during morning and midday on 20 October 2005; mean photon flux density was ∼1200 μmol m−2 s−1. Leaf dark respiration was measured on 3 November 2005; mean leaf temperature was ∼26 °C. Gas-exchange measurements were made on the youngest fully-expanded leaf for each plant. Fig. 3. Open in new tabDownload slide Light-saturated photosynthesis (A), leaf dark respiration (B), and mean relative growth rate (C) plotted against leaf N per unit area for plants of Ficus insipida subject to varying soil fertility. Leaf photosynthesis was measured during morning and midday on 20 October 2005; mean photon flux density was ∼1200 μmol m−2 s−1. Leaf dark respiration was measured on 3 November 2005; mean leaf temperature was ∼26 °C. Gas-exchange measurements were made on the youngest fully-expanded leaf for each plant. Gravimetric transpiration Mean daytime transpiration rates, determined gravimetrically, varied among soil fertility treatments. Mean treatment daytime Egrav ranged from 1.92 to 1.14 mmol m−2 s−1, and generally decreased from the lowest soil fertility treatment to the highest (Table 2). Mean night-time Egrav followed a similar pattern (Table 2). The term ϕw, describing night-time transpiration as a proportion of daytime transpiration, ranged from 0.15 to 0.11 from low to high soil fertility treatments, respectively (Table 2). Mean daytime Egrav showed a negative, linear dependence on Narea. The equation describing the relationship was Egrav= –0.01Narea+2.3 (R2=0.29, P=0.001, n=29). Stable isotope composition Mean values of Δ13CL, Δ13CS, Δ13CR, and Δ13Cwp for the five soil fertility treatments are shown in Table 2. All Δ13C values decreased from lowest to highest soil fertility (Table 2). The general pattern among the different plant tissues was Δ13CL>Δ13CS>Δ13CR (Table 2). Average whole-plant δ15N values spanned a relatively narrow range from 2.4‰ to 3.4‰, and did not differ significantly among treatments (Table 2). Leaf Δ18Op was significantly lower for the highest soil fertility treatment than for the other treatments, and tended to decrease with increasing soil fertility (Table 2). The Δ18Op was negatively correlated with mean daytime Egrav across all treatments (Fig. 2B). Leaf temperature and leaf-to-air humidity gradient Estimates of TL and v generated by the three different methods are summarized in Table 4. The leaf energy balance model estimated a variation of 1.5 °C in TL across the soil fertility treatments. Values ranged from 27.1 °C to 28.6 °C from the lowest to the highest soil fertility treatment; corresponding values for v ranged from 6.2 mmol mol−1 to 9.5 mmol mol−1 (Table 4). The second method that was used to estimate TL and v, which assumed a constant value of 0.4 for ϕc, predicted less variation among treatments; values ranged from 26.9 °C to 27.8 °C and 5.8 mmol mol−1 to 7.8 mmol mol−1, respectively. The third method, based on measurements of Δ18Op, predicted a similar range of values to the leaf energy balance model, but with values trending in the opposite direction across treatments; i.e., decreasing from the lowest to the highest soil fertility. For the Δ18Op method, values ranged from 28.4 °C to 27.0 °C for TL and from 9.0 mmol mol−1 to 6.0 mmol mol−1 for v (Table 4). Table 4. Estimates for average leaf temperature (TL) and average leaf to air water vapour mole fraction difference (v) over the course of the experiment for the five soil fertility treatments Method of estimation Parameter Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Leaf energy balance TL (°C) 27.1 27.7 27.5 28.4 28.6 v (mmol mol−1) 6.2 7.5 7.1 9.0 9.5 Constant ϕc TL (°C) 26.9 27.7 27.8 27.7 27.3 v (mmol mol−1) 5.8 7.6 7.8 7.4 6.7 Leaf 18O enrichment TL (°C) 28.4 28.4 27.9 27.9 27.0 v (mmol mol−1) 9.0 9.1 7.9 8.0 6.0 Method of estimation Parameter Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Leaf energy balance TL (°C) 27.1 27.7 27.5 28.4 28.6 v (mmol mol−1) 6.2 7.5 7.1 9.0 9.5 Constant ϕc TL (°C) 26.9 27.7 27.8 27.7 27.3 v (mmol mol−1) 5.8 7.6 7.8 7.4 6.7 Leaf 18O enrichment TL (°C) 28.4 28.4 27.9 27.9 27.0 v (mmol mol−1) 9.0 9.1 7.9 8.0 6.0 Three different methods were used to estimate TL and v. The first method employed a leaf energy balance model. The second method relied on an assumption of constant ϕc across treatments. The third method was based on leaf dry matter Δ18Op. Open in new tab Table 4. Estimates for average leaf temperature (TL) and average leaf to air water vapour mole fraction difference (v) over the course of the experiment for the five soil fertility treatments Method of estimation Parameter Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Leaf energy balance TL (°C) 27.1 27.7 27.5 28.4 28.6 v (mmol mol−1) 6.2 7.5 7.1 9.0 9.5 Constant ϕc TL (°C) 26.9 27.7 27.8 27.7 27.3 v (mmol mol−1) 5.8 7.6 7.8 7.4 6.7 Leaf 18O enrichment TL (°C) 28.4 28.4 27.9 27.9 27.0 v (mmol mol−1) 9.0 9.1 7.9 8.0 6.0 Method of estimation Parameter Soil/rice-husk mixture (v/v) 20% Soil 40% Soil 60% Soil 80% Soil 80% Soil plus fertilizer Leaf energy balance TL (°C) 27.1 27.7 27.5 28.4 28.6 v (mmol mol−1) 6.2 7.5 7.1 9.0 9.5 Constant ϕc TL (°C) 26.9 27.7 27.8 27.7 27.3 v (mmol mol−1) 5.8 7.6 7.8 7.4 6.7 Leaf 18O enrichment TL (°C) 28.4 28.4 27.9 27.9 27.0 v (mmol mol−1) 9.0 9.1 7.9 8.0 6.0 Three different methods were used to estimate TL and v. The first method employed a leaf energy balance model. The second method relied on an assumption of constant ϕc across treatments. The third method was based on leaf dry matter Δ18Op. Open in new tab Transpiration efficiency The TE varied strongly among soil fertility treatments (Table 2). Variation in TE was closely correlated with variation in Δ13Cwp (Fig. 4). The equation relating TE to Δ13Cwp, determined by geometric mean regression, was TE= –0.72Δ13Cwp+18.0. These coefficients were similar to those determined by ordinary least-squares regression (–0.68 and 17.0 for m and I, respectively). The d calculated from m and I values of –0.72 and 18.0 was 4.0‰. Long-term, integrated ci/ca estimated from Δ13Cwp, using d=4.0‰, varied strongly among treatments (Table 2). Treatment means ranged from 0.96 to 0.86 from low to high soil fertility. The Δ13Cwp was well correlated with ci/ca determined from instantaneous gas exchange measurements (Fig. 5), as was TE (R2=0.69, P <0.0001, n=30). Instantaneous ci/ca was lower than Δ13Cwp-based ci/ca for all treatments (Table 2). Instantaneous ci/ca, Δ13Cwp, and TE each correlated well with leaf Narea (Fig. 6A, B, C). These parameters also correlated strongly with the light-saturated photosynthetic rate of the youngest, fully-expanded leaf of each plant (Fig. 6D, E, F). There were weaker correlations between stomatal conductance and ci/ca, Δ13Cwp, and TE; however, these correlations were opposite in sign to what would be expected if variation in stomatal conductance were controlling variation in these parameters (Table 2). Fig. 4. Open in new tabDownload slide Transpiration efficiency (TE) plotted as a function of whole-plant C isotope discrimination (Δ13Cwp) for Ficus insipida plants subject to varying soil fertility. The Δ13Cwp was calculated from measurements of δ13C and C mass in leaves, stems, and roots. The δ13C of ambient air was assumed to be –8‰. Fig. 4. Open in new tabDownload slide Transpiration efficiency (TE) plotted as a function of whole-plant C isotope discrimination (Δ13Cwp) for Ficus insipida plants subject to varying soil fertility. The Δ13Cwp was calculated from measurements of δ13C and C mass in leaves, stems, and roots. The δ13C of ambient air was assumed to be –8‰. Fig. 5. Open in new tabDownload slide Whole-plant C isotope discrimination (Δ13Cwp) plotted as a function of the ratio of intercellular to ambient CO2 mole fractions (ci/ca) for plants of Ficus insipida subject to varying soil fertility. The ci/ca was calculated from instantaneous leaf gas exchange measurements made during the morning and at midday on 20 October 2005. The Δ13Cwp was calculated from measurements of δ13C and C mass in leaves, stems, and roots. The δ13C of ambient air was assumed to be –8‰. Fig. 5. Open in new tabDownload slide Whole-plant C isotope discrimination (Δ13Cwp) plotted as a function of the ratio of intercellular to ambient CO2 mole fractions (ci/ca) for plants of Ficus insipida subject to varying soil fertility. The ci/ca was calculated from instantaneous leaf gas exchange measurements made during the morning and at midday on 20 October 2005. The Δ13Cwp was calculated from measurements of δ13C and C mass in leaves, stems, and roots. The δ13C of ambient air was assumed to be –8‰. Fig. 6. Open in new tabDownload slide Ratio of intercellular to ambient CO2 mole fractions, ci/ca, determined from instantaneous gas-exchange measurements (A), whole-plant C isotope discrimination, Δ13Cwp (B), and transpiration efficiency, TE (C) plotted against leaf N per unit area. Similarly, ci/ca (D), Δ13Cwp (E), and TE (F) plotted against light-saturated net photosynthetic rate of the youngest fully-expanded leaf. Fig. 6. Open in new tabDownload slide Ratio of intercellular to ambient CO2 mole fractions, ci/ca, determined from instantaneous gas-exchange measurements (A), whole-plant C isotope discrimination, Δ13Cwp (B), and transpiration efficiency, TE (C) plotted against leaf N per unit area. Similarly, ci/ca (D), Δ13Cwp (E), and TE (F) plotted against light-saturated net photosynthetic rate of the youngest fully-expanded leaf. The transpiration efficiency of N acquisition (TEN), calculated as whole-plant N increment divided by cumulative plant water loss, increased from 17.8 μmol N mol−1 H2O for the lowest soil fertility treatment to 107.0 μmol N mol−1 H2O for the highest soil fertility treatment (Table 2). The TE increased linearly as a function of TEN according to the equation TE=0.020TEN+0.58 (R2=0.84, P <0.0001, n=30). A sensitivity analysis is shown in Table 5 illustrating the predicted effect of changing the input parameters in equation (2) on TE. The ranges of input values used in the analysis were based on observations for the five soil fertility treatments for the parameters ci/ca, v, and ϕw; for the parameters ϕc and ca, the selected ranges represent best guesses at likely limits of their variation. Table 5 suggests that the variation that was observed in TE was largely driven by variation in ci/ca. Table 5. A sensitivity analysis of the dependence of transpiration efficiency (TE) on ca, ϕc, ci/ca, v, and ϕw Parameter Range of values Change in TE (mmol C mol−1 H2O) ca (μmol mol−1) 370 380 –0.04 ϕc 0.35 0.45 0.25 ci/ca 0.86 0.96 1.66 v (mmol mol−1) 6.0 9.0 0.62 ϕw 0.11 0.15 0.05 Parameter Range of values Change in TE (mmol C mol−1 H2O) ca (μmol mol−1) 370 380 –0.04 ϕc 0.35 0.45 0.25 ci/ca 0.86 0.96 1.66 v (mmol mol−1) 6.0 9.0 0.62 ϕw 0.11 0.15 0.05 Symbols are defined as follows: ca, ambient CO2 mole fraction; ϕc, the proportion of net photosynthesis used for respiration; ci/ca, the ratio of intercellular to ambient CO2 mole fractions; v, the leaf-to-air water vapour mole fraction difference; and ϕw, unproductive water loss as a proportion of productive water loss. Values of the input parameters were varied over their ranges one at a time, and the change in TE calculated according to equation (2). Parameters that were not being varied during each calculation were fixed at the median value in the given range. Open in new tab Table 5. A sensitivity analysis of the dependence of transpiration efficiency (TE) on ca, ϕc, ci/ca, v, and ϕw Parameter Range of values Change in TE (mmol C mol−1 H2O) ca (μmol mol−1) 370 380 –0.04 ϕc 0.35 0.45 0.25 ci/ca 0.86 0.96 1.66 v (mmol mol−1) 6.0 9.0 0.62 ϕw 0.11 0.15 0.05 Parameter Range of values Change in TE (mmol C mol−1 H2O) ca (μmol mol−1) 370 380 –0.04 ϕc 0.35 0.45 0.25 ci/ca 0.86 0.96 1.66 v (mmol mol−1) 6.0 9.0 0.62 ϕw 0.11 0.15 0.05 Symbols are defined as follows: ca, ambient CO2 mole fraction; ϕc, the proportion of net photosynthesis used for respiration; ci/ca, the ratio of intercellular to ambient CO2 mole fractions; v, the leaf-to-air water vapour mole fraction difference; and ϕw, unproductive water loss as a proportion of productive water loss. Values of the input parameters were varied over their ranges one at a time, and the change in TE calculated according to equation (2). Parameters that were not being varied during each calculation were fixed at the median value in the given range. Open in new tab Discussion Large variation in growth and whole-plant water-use efficiency of a tropical pioneer tree in response to variation in soil fertility was observed under non-limiting soil moisture conditions. Analyses of elemental concentrations in dry matter of the experimental plants indicated that treatment differences in RGR and TE resulted largely from variation in N availability. Leaf photosynthesis and dark respiration rates were well correlated with leaf Narea, as was RGR (Fig. 3). Variation in TE was linearly correlated with variation in ci/ca, both for instantaneous measurements of ci/ca, and for integrated estimates based on Δ13Cwp (Figs 4, 5). The ci/ca, Δ13Cwp, and TE, in turn, were well correlated with variation in leaf Narea and leaf photosynthesis (Fig. 6). The response of TE to soil fertility was largely caused by variation in ci/ca; on the other hand, variations in v, ϕc and ϕw probably played only minor roles in modulating TE in response to soil fertility (Table 5). The variation in ci/ca resulted from variation in photosynthetic capacity caused by variation in leaf Narea, rather than from variation in stomatal conductance (Table 2; Fig. 6). Elemental composition Of the mineral elements quantified in the leaves of the experimental plants, leaf N showed the strongest positive relationship with RGR. The only other element to be positively correlated with RGR was Ca. However, leaf Ca per unit area explained only 13% of variation in RGR, whereas leaf N per unit area explained 46%. Thus, it is concluded that plant growth was primarily constrained by N availability. Of the other measured elements, P, Mg, and Zn showed positive linear correlations with Egrav, suggesting that these elements were absorbed in relatively constant proportion to the water flux into the roots. The relationship with leaf P was particularly striking, with variation in Egrav explaining 74% of variation in leaf Parea (Fig. 2A). This relationship was surprising, given that P is generally thought to be relatively insoluble in soils. Supply of a source of mycorrhizal inoculant at planting and the high organic matter content of the soil due to the addition of rice husks may have played some role in enabling the relationship between leaf Parea and Egrav in our experiment. In contrast to the relationship between leaf Parea and Egrav, there was a negative correlation between leaf Narea and Egrav across the soil fertility treatments. Variation in leaf Narea explained 29% of variation in Egrav. Increases in transpiration in response to low N availability have also been observed in other tree species (Guehl et al., 1995; Livingston et al., 1999), and transpirational control of N accumulation was previously demonstrated in a mistletoe/tree complex (Marshall et al., 1994). A non-linear response of RGR to leaf N/P was observed (Fig. 1B). The maximum RGR occurred near N/P of 15, which agrees well with the prediction that N and P should be in balanced supply at N/P between 14 and 16 (Koerselman and Meuleman, 1996). Koerselman and Meuleman (1996) suggested that at N/P <14, growth would be N limited, whereas at N/P >16, growth would be P limited. This further reinforces the notion that variation in RGR and TE in our experiment was primarily caused by differential N availability. It was found that N/P was an excellent predictor of variation in TE (Fig. 1A); the natural logarithm of N/P explained 90% of variation in TE. For comparison, Δ13Cwp explained 88% of variation in TE. The close correlation between N/P and TE stemmed from the relationships between leaf N and photosynthetic rate (Fig. 3A) and leaf P and transpiration rate (Fig. 2A). Whether such a relationship will hold up more generally outside our experimental conditions is unknown. C isotope discrimination and ci/ca Estimating ci/ca from Δ13C according to equation (3) requires an estimate of d. The method described in the theory section for estimating d, based on the coefficients of a regression analysis between TE to Δ13C, assumes that the terms for which the slope and intercept coefficients substitute are invariant over the range of the analysis. This assumption was not strictly met in our experiment; for example, there were probably subtle variations in v and ϕw among treatments (Tables 2, 4). However, if the assumption were strongly violated, one would expect either to see curvature in the relationship between TE and Δ13Cwp, or a large degree of scatter. In fact, the relationship that was observed appeared linear with little scatter: variation in Δ13Cwp explained 88% of variation in TE in a least-squares linear regression (Fig. 4). There were, however, two data points that appeared to depart slightly from the linear trend; these two points had the highest Δ13Cwp values in the dataset (Fig. 4). Repeating the analysis with these two data points excluded would result in an estimate for d of 4.4‰, whereas the estimate for the full data set was 4.0‰. Although this difference is not large, it nonetheless highlights the sensitivity of our method for estimating d to variations in the values of the regression coefficients. Few direct estimates of d exist in the literature, although assessment of d is implicit in the determination of mesophyll conductance from instantaneous measurements of Δ13C and ci/ca (Evans et al., 1986). Hubick et al. (1986) estimated d to be approximately 3‰, based on the simultaneous measurements in wheat of Δ13C and ci/ca (Evans et al., 1986). The d was later estimated to be near zero for barley (Hubick and Farquhar, 1989), and approximately 1‰ for peanut (Hubick, 1990). Thus, our estimate of 4.0‰ for d in Ficus insipida is slightly higher than values previously reported for crop plants. In the method used here to calculate d [i.e., d=b–I/m from equation (8)], the value of d clearly depends on the assumed value of b. Fortunately, any change in the assumed value of b has only a minor effect on Δ13C-based estimates of ci/ca. This is because any change in the assumed value of b will be offset by a similar change in the calculated value of d. Thus, changing the assumed value of b from 29‰ to 27‰ in our analysis would only change the mean Δ13C-based estimate of ci/ca from 0.913 to 0.906. It is clear from equation (4) that d is a complex parameter, and the general trend in the literature has been to drop it from equation (3) when making long-term, integrated estimates of ci/ca from measurements of Δ13C in plant tissues. However, based on our analysis, it is suggested that it may not be prudent to omit d from equation (3). Assuming b=29‰, excluding d from the calculations would shift the mean Δ13Cwp-based estimate of ci/ca in our experiment from 0.91 to 0.75. Whereas if we assumed b=27‰, the mean Δ-based estimate of ci/ca would shift from 0.91 to 0.82 if d were omitted. If these ci/ca estimates were then used to predict TE from equation (2), the shift caused by omitting d would equate to an approximately 3-fold increase in predicted TE at b=29‰, and a doubling of predicted TE at b=27‰. It was observed that instantaneous measurements of ci/ca were consistently lower than Δ13Cwp-based estimates across treatments (Table 2). The mean instantaneous estimate for all treatments combined was 0.85, whereas the mean Δ13Cwp-based estimate was 0.91. This discrepancy may have partly resulted from differences between v and PFD averaged over the course of the experiment, as compared with values in the cuvette during instantaneous measurements (8 mmol mol−1 versus 15 mmol mol−1 for v, respectively; and 400 μmol m−2 s−1 versus 1200 μmol m−2 s−1 for PFD, respectively). However, at least part of the discrepancy between instantaneous ci/ca and Δ13Cwp-based ci/ca relates to the fact that ci/ca is calculated differently from instantaneous gas exchange measurements than from isotopic measurements, as described by GD Farquhar (unpublished presentation, BASIN meeting, Marshall, USA, 2004). The equations used to calculate ci from instantaneous measurements are based on a ternary system of gases: CO2, water vapour, and air (Jarman, 1974). The ci is calculated as ci=[(gc–E/2)ca–A]/(gc+E/2), where gc is the total conductance to CO2 of stomata plus boundary layer (Caemmerer and Farquhar, 1981). This calculation takes into account not only collisions between CO2 and air, but also collisions between CO2 and water vapour. By contrast, the equations presented in the theory section of this paper describing the relationship between ci/ca and Δ13C do not take into account collisions between CO2 and water vapour. Instead, ci is simply defined as ci=ca–A/gc. If the data from the instantaneous measurements are recalculated using this latter definition of ci, a mean value for ci/ca of 0.88 is obtained, which cuts in half the observed difference between instantaneous and Δ13Cwp-based estimates of ci/ca. In our analysis of the relationship between TE and Δ13C, the whole-plant Δ13C was used rather than that of a single tissue, such as leaves. Using Δ13CL in place of Δ13Cwp has only a small effect on our results. It would shift our estimate of d from 4.0‰ to 3.8‰, and would shift the mean Δ13C-based estimate of ci/ca from 0.91 to 0.92. Unproductive water loss The unproductive water loss described by the term ϕw in equation (2) comprises non-stomatal water loss during the day and night, and stomatal water loss at night. Of the two, the expectation was that transpiration at night through partially open stomata would dominate. Conductances of leaf cuticles are on the order of 1–2 mmol m−2 s−1 (Kerstiens, 1996), as are surface conductances of branches and stems (Cernusak and Marshall, 2000; Cernusak et al., 2001). In contrast, it is not uncommon to observe nocturnal stomatal conductances ranging from 20 to more than 100 mmol m−2 s−1 (Donovan et al., 1999; Snyder et al., 2003; Bucci et al., 2004; Barbour et al., 2005; Seibt et al., 2007). The mean nocturnal stomatal conductance that was observed during the dark respiration measurements was 124±43 mmol m−2 s−1 (mean ±1 SD). However, because nocturnal v is typically low (mean value of 1.3 mmol mol−1 in our experiment), as are nocturnal wind speeds, nocturnal transpiration is still likely to be small in comparison to daytime transpiration. A mean ϕw of 0.12 was observed in our experiment, indicating that nocturnal transpiration was equal to 0.12 times daytime transpiration. Expressed as a percentage of total transpiration, nocturnal transpiration was 11% on average. This can be compared to observations of nocturnal transpiration as a percentage of total transpiration of 5% for Eucalyptus grandis (Benyon, 1999), 10% for Betula papyrifera (Daley and Phillips, 2006), and 13–28% for woody species in Brazilian savanna (Bucci et al., 2004, 2005). The mean ϕw of 0.12 that was observed is similar to the value of 0.18 cited by Hubick and Farquhar (1989). Data reviewed above suggest maximum likely values for ϕw of about 0.4. By contrast, values for ϕw ranging from 3.3 to 5.6 were reported for an experiment with seedlings of Pinus sylvestris (Hobbie and Colpaert, 2004). This would suggest that nocturnal transpiration was 3.3–5.6 times greater than daytime transpiration. The possibility is suggested that these surprisingly high estimates of ϕw may have resulted from omitting d from equation (3), which was used to calculate ci/ca from measurements of Δ13C. Mean ci/ca for the Pinus sylvestris seedlings was estimated to be 0.65. This value can be compared to the mean Δ13C-based ci/ca estimate of 0.91 for our experiment with Ficus insipida. The Pinus sylvestris seedlings were grown under similar conditions to those in our experiment: low irradiance (300 μmol photons m−2 s−1), low v (7.3 mmol mol−1), and low N availability. The relative insensitivity of equation (2) to variations in ϕw, as shown in Table 5, means that if the equation is inverted to solve for ϕw, as was done in the experiment with Pinus sylvestris, the predicted values of ϕw can be highly sensitive to any bias in terms such as ci/ca. Although significant variation in ϕw was observed among treatments in our experiment, the range of variation was small, with treatment means ranging from 0.11 to 0.15 (Table 1). This narrow range of values occurred over a large gradient in soil fertility, as evidenced by the large variation among treatments in RGR (Table 2). Compared with other terms in equation (2), such as ci/ca and v, the ϕw played a very minor role in our experiment in modulating TE (Table 5). Leaf-to-air humidity gradient Three different methods were employed to estimate v for the experimental treatments. Whereas the three methods yielded a similar range of results, there were some differences in the direction of variation proceeding from low soil fertility to high soil fertility (Table 4). In using the leaf energy balance model to estimate v, it was necessary to assume a relationship between the mean intercepted irradiance at the leaf level and the irradiance measured outside the canopy. It was assumed that the former would be 0.75 times the latter for all treatments. This assumption is probably not realistic because the increasing plant size and leaf area going from low to high soil fertility (Table 2) would have been accompanied by increased self shading, and increasingly non-horizontal leaf orientation. Therefore, the mean intercepted irradiance at the leaf surface was likely less at high soil fertility than at low soil fertility. Thus, the leaf energy balance model may have overestimated v, especially for the highest soil fertility treatment, where the leaf area per plant was substantially greater than in the other treatments (Table 2). The Δ18Op method, based on equations (9) to (15), provides a means for estimating v that could have considerable advantages over the other two. Namely, it provides an integrated measurement over the life of the plant, based on a single isotopic analysis of plant organic material. The disadvantage of this method is that it requires assumed values for a large number of parameters. Some of these parameters, such as L and εcp, have been measured only for a small number of species, so the extent to which they can be generalized is largely unknown (Barbour, 2007). Nonetheless, it is encouraging that it was possible to obtain estimates of v that agreed rather well with the other two methods of estimation (Table 4). The final method that was used to estimate v was one that involved assuming a constant value for ϕc. It has been suggested that the term ϕc should be a relatively conservative parameter in terrestrial plants (McCree and Troughton, 1966; Gifford, 1994, 2003; Dewar et al., 1999; Thornley and Cannell, 2000). Estimates of ϕc for individual plants are typically in the range of about 0.35 to 0.45 (McCree, 1986; Gifford, 2003). The slope of the regression between TE and Δ suggests a mean ϕc for our experiment near the midrange value of 0.4. Using the slope coefficient and the relationship with ϕc described in the theory section, a mean ϕc of 0.4 would correspond to a mean v across treatments of 7.0 mmol mol−1, close to the mean values for v estimated by the leaf energy balance model and the Δ18Op method (Table 4). Although using the slope coefficient in this way does not allow us to test for variation among treatments in ϕc, no significant variation was observed among treatments at the leaf level in the ratio of dark respiration/net photosynthesis, and systematic variation across treatments in leaf area ratio was not observed (Table 2). Thus, it is suggested that it is possible that there was relatively little variation in ϕc in our experiment. Farquhar et al. (1989) noted that v in equation (2) should actually be weighted by conductance, because a particular v results in greater water loss when conductance is large than when it is small. This consideration suggests an added complexity to v that may not be well represented in the leaf energy balance or Δ18Op models. Solving equation (2) for v, using measured data, and assuming ca of 375 μmol mol−1 and ϕc of 0.40, resulted in a smaller range of values across treatments than the other two methods (Table 4). Although this method of estimating v requires assuming values for ca and ϕc, the possible ranges of these two parameters are reasonably well constrained. Therefore, we suggest that v predictions based on this method probably provided the most reliable estimates under our experimental conditions. O isotope enrichment A positive correlation was observed between Δ18Op and mean daytime Egrav (Fig. 2B). Sheshshayee et al. (2005) recently observed similar positive correlations for different genotypes of groundnut and rice. In their experiments, plant transpiration was measured gravimetrically and expressed as mean transpiration rate (MTR), where MTR=Etot/[(LA1+LA2)0.5t], where Etot is cumulative transpiration over the course of the experiment, LA1 and LA2 are leaf area at the beginning and end of the experiment, and t is number of days in the experiment. If our transpiration data are expressed as MTR and Δ18Op plotted against it, a positive linear relationship is also observed, in which Δ18Op=0.017MTR+23.2 (R2=0.45, P <0.0001, n=30). Farquhar et al. (2007) recently reviewed theory underlying steady-state leaf water enrichment, and posed the following question: as E increases, Δ18O should also increase, true or false? Equations (9) through (13) suggest the following response: when the source of variation in E is evaporative demand, Δ18O should increase with increasing E; conversely, when the source of variation in E is stomatal, Δ18O should decrease with increasing E (Farquhar et al., 2007). At first glance, data presented in Fig. 2B appear to be in disagreement with this generalized response. This is because the plants were grown side by side during the same time period, so that the evaporative demand of the bulk atmosphere was the same for all plants. However, closer examination of the data in Table 2 reveals that the source of variation in Egrav among treatments was not stomatal. By contrast, increasing stomatal conductance across soil fertility treatments was actually associated with decreasing Egrav. The decreasing Egrav was probably associated with decreasing v and increasing canopy boundary layer resistance at high soil fertility as compared to low soil fertility. The increased leaf area per plant with increasing soil fertility would have caused both of these factors, through increased self-shading, and therefore lower canopy-averaged TL, and increased canopy boundary layer development. As seen in equations (9) and (11), these processes would also result in decreasing Δ18Oe, due to greater wa/wi and lower εk. The decrease in Δ18Oe accompanying lower E would be countered by a relative increase in Δ18OL, as seen in equation (12); however, in our data set, this relatively subtle shift caused by the Péclet effect was apparently not sufficient to overcome the effect of lower Δ18Oe, such that the net result was a decrease in Δ18Op with decreasing Egrav. Uncoupling is commonly observed between cuvette-based measurements of stomatal conductance and transpiration measured at the whole plant level for tropical trees (Meinzer et al., 1996). In our experiment, cuvette-based measurements of E were 5-fold higher than corresponding values for Egrav (Table 2). The discrepancy can be partly accounted for by variation in v, which was 2-fold greater in the cuvette than the average value in ambient air during Egrav measurments. The remaining variation relates to differences in leaf boundary layer resistance between the cuvette and the ambient environment, and possibly to variation in stomatal conductance between the youngest, fully-expanded leaf, on which gas exchange measurements were made, and the integrated average over the whole canopy. This uncoupling, in addition to the surprising result of the positive correlation between Δ18Op and Egrav, demonstrates the importance of measuring physiological processes at the whole-plant scale, in addition to the leaf scale. Conclusions It was observed that ci/ca was the primary control over variation in TE in response to soil fertility in a pioneer tree grown under tropical field conditions (Table 5). It is suggested that ci/ca may be a particularly important control over whole-plant water-use efficiency in fast-growing tropical trees, where ci/ca may generally be high, as evidenced by low δ13C (Guehl et al., 1998; Martinelli et al., 1998; Bonal et al., 2000a, b; Holtum and Winter, 2005). When ci/ca is high, the impact of any change in ci/ca on TE is amplified. This is because the relevant term in equation (2) is not actually ci/ca, but rather (1–ci/ca). For example, a change in ci/ca from 0.95 to 0.90, while seemingly trivial, will cause a doubling of TE, all else being equal. The strong response of ci/ca and TE to leaf Narea in tropical trees, as observed here and previously (Cernusak et al., 2007), could have important implications for the management of tropical forest plantations, particularly where managers aim to maximize biomass production relative to stand water use; in addition, it could have important implications for the coupling of C and water cycles of tropical vegetation subject to anthropogenic alterations in N availability. We thank Milton Garcia, Rodrigo Nuñez, Tania Romero, and Aurelio Virgo for technical assistance, and Bob Brander, Ben Miller, and Ben Harlow for assistance with isotopic analyses. We thank two anonymous reviewers for helpful comments on the manuscript. This research was funded by the AW Mellon Foundation and the Smithsonian Tropical Research Institute. LAC was supported by a Smithsonian Institution Postdoctoral Fellowship. 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Liu, Huai-Feng; Génard, Michel; Guichard, Soraya; Bertin, Nadia

doi: 10.1093/jxb/erm202pmid: 18057037

Abstract This work proposed a model of tomato growth adapted from the Fishman and Génard model developed to predict carbon and water accumulation in peach fruit. The main adaptations relied on the literature on tomato and mainly concerned: (i) the decrease in cell wall extensibility coefficient during fruit development; (ii) the increase in the membrane reflection coefficient to solute from 0 to 1, which accounted for the switch from symplasmic to apoplasmic phloem unloading, and (iii) the negative influence of the initial fruit weight on the maximum rate of active carbon uptake based on the assumption of higher competition for carbon among cells in large fruits containing more cells. A sensitivity analysis was performed and the model was calibrated and evaluated with satisfaction on 17 experimental datasets obtained under contrasting environmental (temperature, air vapour pressure deficit) and plant (plant fruit load and fruit position) conditions. Then the model was used to analyse the variations in the main fluxes involved in tomato fruit growth and accumulation of carbon in response to virtual carbon and water stresses. The conclusions are that this model, integrating simple biophysical laws, was able to simulate the complex fruit behaviour in response to external or internal factors and thus it may be a powerful tool for managing fruit growth and quality. Carbon flux, cell expansion, fruit growth, humidity, Lycopersicon esculentum, model, Solanum lycopersicum, temperature, tomato, water flux Introduction Size, water, and the content of carbon compounds are the main criteria for assessing the quality for fresh fruits. Although abundant knowledge is available about the processes involved in growth and primary metabolism, the genetic and environmental improvement of fruit quality remains a complex task due to the antagonism between quality traits, for instance, size and composition. For cultivated tomato, fruit concentration in carbon compounds can be enhanced by cultivation management, but this improvement is often paralleled with an undesirable reduction in yield, mainly due to the decrease of mean fruit size and the increasing incidence of growth disorders (Ho, 2003a). Many studies demonstrated that these effects resulted from alterations in the water and carbon fluxes into the fruit (Ho et al., 1987; Ho and Adams, 1994; Guichard et al., 2001). The development of ecophysiological models of fruit growth seems a good opportunity to manage and optimize fruit quality as they enable us to integrate our understanding of carbon and water fluxes in response to endogenous and external factors (Struik et al., 2005). Fruit volume increase and accumulation of carbon compounds results from a number of processes such as sugar unloading and metabolism, water import, and cell wall expansion, which are intimately connected at the fruit level and regulated by several steps during fruit development. In their model, Fishman and Génard (1998) proposed an integrative approach of the main processes involved in fruit growth and carbon and water accumulation in peach fruit. This model relies on a biophysical description of water and carbohydrate transport coupled with the stimulation of cell wall extension driven by the influx of water and the turgor pressure (Lockhart, 1965; Zonia and Munnik, 2007). This is a seldom model of fruit growth in which quality is concerned (Bertin et al., 2006). It has been successfully used to analyse the effect of climate fluctuations and orchard management on peach yield and quality (Lescourret and Génard, 2005). Yet no model of fruit growth coupling carbon and water fluxes has been developed for tomato fruit. Indeed, current tomato models either focus on water import (Bussières, 1994, 2002) or on carbon import (Heuvelink and Bertin, 1994), the dry matter content being empirically deduced. Objectives of this work were to adapt the Fishman and Génard model to tomato fruit in order to predict the influence of environmental conditions on fruit growth in terms of dry and fresh masses. Tomato fruit growth follows a sigmoid curve and mature fruits contain about 95% of water at maturity. Most of the fruit weight is accumulated during the period of rapid growth which starts about 2 weeks after anthesis and lasts for about 3–5 weeks (Ho and Hewitt, 1986). In tomato, about 80–85% of the water is imported by the phloem tissue (Ho et al., 1987; Guichard et al., 2005) together with carbon which is mainly imported as sucrose. During fruit development, carbon unloading progressively shifts from the symplasmic to the apoplasmic pathway between 15 d and 35 days after anthesis (daa) according to the different authors (Damon et al., 1988; Ruan and Patrick, 1995; Nguyen-Quoc and Foyer, 2001; Ho, 2003b). Fruits from most of the cultivated cultivars accumulate mainly fructose and glucose and, to a lesser extent, sucrose. The tomato fruit osmotic pressure is stable throughout fruit development (Ho et al., 1987; Mitchell et al., 1991). Under non-stressed conditions, hexoses, inorganic ions, and organic acids account for, respectively, about 52%, 32%, and 16% of total fruit osmotic potential (Mitchell et al., 1991). In the following sections, the main equations of the Fishman and Génard model, which drive the water and carbon accumulation during fruit development and the modifications made according to the abundant literature on the regulation of tomato fruit growth are presented. The model was then calibrated on 17 experimental datasets obtained under fluctuating conditions on the same tomato cultivar. A cross validation of the model and a sensitivity analysis were performed. Finally, the model could be applied to analyse the effect of virtual carbon and water stresses on the main fluxes involved in fruit growth and the accumulation of carbon compounds. Model presentation In the Fishman and Génard model (1998), the fruit is assimilated to one big cell separated from the exterior (xylem or phloem tissue) by a composite membrane. The variations in fruit fresh and dry masses are determined by carbon and water flows across this membrane, which are described by thermodynamic equations involving the hydraulic conductivity of the membrane, the differences in hydraulic and osmotic pressures on both sides of it, and the impermeability of the membrane to solutes. The simulation mainly covers the period of rapid fruit growth, and thus starts about 10 daa, as cell proliferation almost ceased. Input variables are the temperature and air humidity, stem water potential, and phloem sugar concentration. In the following section, only the main equations governing fruit growth or those which were changed to account for tomato specificities are described. Model parameters fitted from the literature on tomato are listed in Table 1. Table 1. List of parameters estimated from literature data and input in the tomato model Parameter Symbol and value Unit Reference (species) Growth respiration coefficient qg = 0.22 – Gary et al., 1998 (tomato fruit) Maintenance respiration coefficient qm = 0.00042 g CH2O g−1 DM h−1 Bertin and Heuvelink, 1993 (tomato fruit) Effect of T °C on qm Q10 = 1.4 – Bertin and Heuvelink, 1993 (tomato fruit) Permeation coefficient of fruit surface to water vapour ρ = 0.162 g cm−2 h−1 MPa−1 Leonardi et al., 1999 (tomato fruit) Phloem hydraulic conductivity for water Lp = 0.15 g cm−2 MPa−1 h−1 Maggio and Joly, 1995; Zwieniecki and Boersma, 1997 (tomato root; mean value) Michaelis constant for active transport of sugar Km = 0.08 Milner et al., 1995 (tomato tonoplast sucrose transport) Proportion of soluble sugars in fruit dry matter Z = 0.52 g g−1 S Guichard unpublished data (tomato fruit) Membrane permeability for sugar diffusion Ps = 3.6×10−5 g cm−2 h−1 Ruan and Patrick, 1995 (tomato pericarp phloem membrane) Threshold hydrostatic pressure for cell growth Y = 0.1 MPa Grange, 1995 (tomato pericarp) Cell wall extensibility Φmax = 0.2 MPa−1 h−1 Cosgrove, 1985 (pea stem) Water density Dw = 1 g cm−3 Parameter Symbol and value Unit Reference (species) Growth respiration coefficient qg = 0.22 – Gary et al., 1998 (tomato fruit) Maintenance respiration coefficient qm = 0.00042 g CH2O g−1 DM h−1 Bertin and Heuvelink, 1993 (tomato fruit) Effect of T °C on qm Q10 = 1.4 – Bertin and Heuvelink, 1993 (tomato fruit) Permeation coefficient of fruit surface to water vapour ρ = 0.162 g cm−2 h−1 MPa−1 Leonardi et al., 1999 (tomato fruit) Phloem hydraulic conductivity for water Lp = 0.15 g cm−2 MPa−1 h−1 Maggio and Joly, 1995; Zwieniecki and Boersma, 1997 (tomato root; mean value) Michaelis constant for active transport of sugar Km = 0.08 Milner et al., 1995 (tomato tonoplast sucrose transport) Proportion of soluble sugars in fruit dry matter Z = 0.52 g g−1 S Guichard unpublished data (tomato fruit) Membrane permeability for sugar diffusion Ps = 3.6×10−5 g cm−2 h−1 Ruan and Patrick, 1995 (tomato pericarp phloem membrane) Threshold hydrostatic pressure for cell growth Y = 0.1 MPa Grange, 1995 (tomato pericarp) Cell wall extensibility Φmax = 0.2 MPa−1 h−1 Cosgrove, 1985 (pea stem) Water density Dw = 1 g cm−3 Open in new tab Table 1. List of parameters estimated from literature data and input in the tomato model Parameter Symbol and value Unit Reference (species) Growth respiration coefficient qg = 0.22 – Gary et al., 1998 (tomato fruit) Maintenance respiration coefficient qm = 0.00042 g CH2O g−1 DM h−1 Bertin and Heuvelink, 1993 (tomato fruit) Effect of T °C on qm Q10 = 1.4 – Bertin and Heuvelink, 1993 (tomato fruit) Permeation coefficient of fruit surface to water vapour ρ = 0.162 g cm−2 h−1 MPa−1 Leonardi et al., 1999 (tomato fruit) Phloem hydraulic conductivity for water Lp = 0.15 g cm−2 MPa−1 h−1 Maggio and Joly, 1995; Zwieniecki and Boersma, 1997 (tomato root; mean value) Michaelis constant for active transport of sugar Km = 0.08 Milner et al., 1995 (tomato tonoplast sucrose transport) Proportion of soluble sugars in fruit dry matter Z = 0.52 g g−1 S Guichard unpublished data (tomato fruit) Membrane permeability for sugar diffusion Ps = 3.6×10−5 g cm−2 h−1 Ruan and Patrick, 1995 (tomato pericarp phloem membrane) Threshold hydrostatic pressure for cell growth Y = 0.1 MPa Grange, 1995 (tomato pericarp) Cell wall extensibility Φmax = 0.2 MPa−1 h−1 Cosgrove, 1985 (pea stem) Water density Dw = 1 g cm−3 Parameter Symbol and value Unit Reference (species) Growth respiration coefficient qg = 0.22 – Gary et al., 1998 (tomato fruit) Maintenance respiration coefficient qm = 0.00042 g CH2O g−1 DM h−1 Bertin and Heuvelink, 1993 (tomato fruit) Effect of T °C on qm Q10 = 1.4 – Bertin and Heuvelink, 1993 (tomato fruit) Permeation coefficient of fruit surface to water vapour ρ = 0.162 g cm−2 h−1 MPa−1 Leonardi et al., 1999 (tomato fruit) Phloem hydraulic conductivity for water Lp = 0.15 g cm−2 MPa−1 h−1 Maggio and Joly, 1995; Zwieniecki and Boersma, 1997 (tomato root; mean value) Michaelis constant for active transport of sugar Km = 0.08 Milner et al., 1995 (tomato tonoplast sucrose transport) Proportion of soluble sugars in fruit dry matter Z = 0.52 g g−1 S Guichard unpublished data (tomato fruit) Membrane permeability for sugar diffusion Ps = 3.6×10−5 g cm−2 h−1 Ruan and Patrick, 1995 (tomato pericarp phloem membrane) Threshold hydrostatic pressure for cell growth Y = 0.1 MPa Grange, 1995 (tomato pericarp) Cell wall extensibility Φmax = 0.2 MPa−1 h−1 Cosgrove, 1985 (pea stem) Water density Dw = 1 g cm−3 Open in new tab The temporal variations in fruit fresh mass (w, g) and dry mass (s, g) result from the balance between in and out flows: (1) where Ux and Up are the xylem and phloem water inflows (g h−1) and Tf is the fruit transpiration outflow (g h−1), which depends on the fruit area Af (cm2), on the permeation coefficient (ρ, g cm−2 h−1 MPa−1) of the fruit surface to water vapour, and on air temperature and humidity. Af was deduced from tomato fresh weight (Wf) by the empirical equation Af =5.9436Wf0.6641 experimentally determined. (2) where Us is the phloem sugar input (g h−1) and Rf is the fruit respiration rate (g h−1). Rf is the sum of (i) growth respiration which is proportional to the growth respiration coefficient (qg) and to the dry mass increment, and (ii) maintenance respiration which is proportional to the maintenance coefficient (qm) and to the fruit dry mass and depends on temperature through a Q10 parameter. Ux and Up are calculated from non-equilibrium thermodynamic equations as: (3) (4) where subscripts x, p, and f refer to xylem, phloem, and fruit, respectively. Ax and Ap, the surface (cm2) of exchange between the vascular networks entering the fruit and the fruit compartment, are assumed to be proportional to the fruit surface area (Af) according to a constant non-dimensional coefficient a (Ax=Ap=aAf). Lx and Lp are hydraulic conductivity (g cm−2 MPa−1 h−1), σx and σp are solute reflection coefficients of the membrane separating the fruit from the conducting tissues. P and π are the hydrostatic and osmotic pressures (MPa). As first approximations, σx=1 and πx=0 (Fishman and Génard, 1998). The fruit and phloem osmotic pressures are calculated from the molar concentrations of osmotically active compounds according to Nobel (1974). It was assumed that a constant proportion Z of accumulated sugars remains in soluble forms, thus contributing to the osmotic pressure, whereas the rest was converted into structural material. The contribution to the fruit osmotic potential of osmotically active substances other than carbohydrates (such as amino acids, inorganic ions. and organic acids) was assumed to be constant during fruit development (Ho et al., 1987; Mitchell et al., 1991). This contribution was calculated at the initial stage (10 daa) as equal to the contribution of soluble sugars. In equation 4, the reflection coefficient σp varies from 0 (fully permeable membrane, for instance for symplasmic pathway) to 1 (fully impermeable membrane, for instance in the absence of symplasmic connection). It accounts for the different pathways of sugar transport from the phloem to the sink cells, and it was considered as constant in the original model. As in grape berry (Zhang et al., 2006), the transport of sugar into the sink cells of tomato fruit progressively shifts from the symplasmic to the apoplasmic pathway during fruit development (Damon et al., 1988; Ruan and Patrick, 1995; Brown et al., 1997). This shift of phloem sugar unloading from the symplasmic to the apoplasmic pathway was represented by a gradual temporal increase of σp empirically expressed as: (5) where t is simulation time in hours (t=0 corresponds to 10-d-old fruits), and τ a constant parameter. Now as detailed in the original model, carbohydrates can be transported from the phloem to the fruit by active transport (Ua), by mass flow and by passive diffusion across the membrane: (6) where vm is a kinetic constant (g sucrose g−1 DW h−1), Km is the Michaelis constant, Cp and Cf are the sugar concentrations in the phloem and in the fruit, respectively (g g−1), Cs is the mean of these two concentrations, and ps is the solute permeability coefficient (g cm−2 h−1). The second term of the denominator of Ua accounts for an inhibitory effect which increases with fruit age or time (t, hour). In the original model this inhibitory effect exponentially increased with time after a given developmental stage. In the case of tomato, this inhibitory effect was linked to the initial dry weight of the fruit s0 through two parameters δ and f. Indeed, as the weight of young tomato fruits is positively correlated with the number of cells (Bertin et al., 2003a), the competition for carbon among individual cells may be higher in large fruit as suggested by previous studies (Bertin, 2005), which may reduce the uptake of carbon by each individual cell. As defined here, the maximum uptake rate of sucrose decreased with fruit age: These equations representing the different fluxes involved in water and carbon balance in the fruit could be combined as follows with the Lockart (1965) equation relating the variations in fruit/cell volume (dV/dt) to the hydrostatic pressure Pf, to the threshold value Y (MPa) above which irreversible extension occurs, and to the cell wall extensibility (Φ MPa−1 h−1): (7) where Dw and Ds are, respectively, the water and sugar densities (g cm−3), and Pf ≥Y. In a first approximation the second term of the right side in equation 7 is relatively small and may be neglected. Whereas Φ was constant in the original model, it was assumed here, as for mango fruit (Lechaudel et al., 2007), that the cell wall extensibility exponentially decreased during tomato fruit growth according to two parameters Φmax and k: (8) This decrease is consistent with the decreasing activity observed in tomato epidermis and pericarp of xyloglucan-specific enzymes (Thompson et al., 1998) involved in the mechanical changes underlying cell-wall mechanical properties and growth processes (Thompson, 2001; Cosgrove, 1993). Moreover, the extensive cutinization of epidermal cell wall and thickening of the cuticle during ripening of tomato fruit (Bargel and Neinhuis, 2005) may also contribute to the decline of Φ. Finally, the hydrostatic pressure Pf could be deduced from equation 7 as a function of other variables. Assuming that xylem and phloem water potentials are equal to the stem water potential Ψw, it becomes possible, by combining the different equations, to calculate the sugar and water fluxes in the fruit and to integrate them over time. The diurnal course of the stem water potential in greenhouse tomato presents maximum values around −0.05 MPa at predawn and minimum values in the afternoon depending on air VPD (Guichard et al., 2005). An experimental regression between air VPD (kPa) and stem water potential (MPa) established on different days in two greenhouse compartments at low and high air VPD (S Guichard, unpublished data) was applied in the model: (9) Px and Pp (equations 3 and 4) could then be calculated from the Nobel (1974) equation Ψ=P–π, assuming σx=1 and πx=0, and calculating πp from the phloem sap concentration in sugars. Model parameterization Model parameters were either estimated from experimental data as described in the Materials and methods, or taken from the literature. The latter were almost all specific for tomato fruit (Table 1). The xylem hydraulic conductivity (Lx) was assumed to be proportional to the phloem conductivity (Lp): Lx=0.22Lp, on the basis of known proportions of water transported by xylem and phloem tissues (18% and 82%, respectively) (Ho et al., 1987; Plaut et al., 2004; Guichard et al., 2005). The phloem sucrose concentration (Cp, equation 6) was assumed to be constant. It was independently calculated on the 17 datasets from the increase in fruit dry and fresh weights, and from the amounts of sugar and water lost by respiration and transpiration, respectively (parameters given in Table 1). Considering that the phloem flux accounts for 85% of the total water influx, then the sucrose concentration of the phloem sap was estimated as the ratio between sugar and water amounts imported by the phloem tissue. A mean value of 0.11 g g−1 was found which is fairly consistent with the range of values reported for tomato in the literature. Ho et al. (1987) estimated that the phloem sap concentration decreased during fruit development, from 7.1% to 2.9% dry matter at a salinity of 2 mS cm−1 and from 12.5% to 7.8% at a salinity of 17 mS cm−1. Plaut et al. (2004) found a phloem sap concentration in organic compounds ranging from 5% to 8% which was rather stable during fruit development. Six other parameters were estimated by fitting the simulated curves of fresh and dry weight increases to the experimental curves: τ involved in the shift from symplasmic to apoplasmic carbon unloading (equation 5), vm, δ, and f intervening in the active transport of carbon (equation 6), k determining the cell wall extensibility fluctuations during fruit development (equation 8) and a=Ax/Af=Ap/Af the ratio between the surface of vascular networks and the fruit area (equations 3 and 4). Materials and methods Experimental conditions Seventeen datasets were collected from three experiments performed at INRA Avignon (south of France) in 1998, 2000, and 2001 with the same tomato cultivar (Lycopersicon esculentum Mill. cv. ‘Raïssa’). The 1998 experiments were conducted from spring to summer in two greenhouse compartments under high (VPD+) or low (VPD–) air vapour pressure deficit (VPD). For each humidity regime, inflorescences were pruned either to three flowers (3F) or to six flowers (6F) (detailed in Guichard et al., 2005). Fruit growth parameters were measured once in spring and once in summer, so that this experiment represented eight datasets. In 2000, the experiment was carried out in growth climatic chambers under three controlled day/night air temperature regimes: 20/20 °C, 25/25 °C or 25/20 °C. A 12 h photoperiod was applied at a photosynthetic photon flux density of about 500 μmol m−2 s−1PAR above the canopy. For each temperature regime, inflorescences were pruned to two (2F) or five (5F) fruits (detailed in Bertin, 2005). The 2001 experiment was carried out in a growth chamber with a controlled day/night air temperature of 25/20 °C and a 12 h photoperiod at a light intensity of about 500 μmol m−2 s−1PAR above the canopy (detailed in Bertin et al., 2003b). The first truss was pruned to six flowers (6F) and the second truss was pruned to two flowers (2F). Plants were stopped two leaves above the second truss. The fruit position influence was considered in the 2000-5F and 2001 experiments. Within the inflorescence, fruits were classified as proximal (first and second fruits: F1F2), median (third and fourth fruits: F3F4), and tip (fifth and sixth fruits: F5F6) fruits. Air temperature and humidity were recorded hourly in each experiment and input in the model as external signals. The 17 datasets and corresponding experimental conditions are summed up in Table 2. Tomato fruit diameter, fresh weight, and dry weight were measured weekly in the 1998 experiment and every 2–5 d in the 2000 and 2001 experiments. The 10 daa (start of simulation) dry and fresh weights were estimated by exponential interpolation considering the experimental data from 7 daa to 14 daa. Table 2. Mean environmental conditions and mean fruit dry and fresh masses at 10 d after anthesis (initial weights for the model) Experiment Treatments Day/night temperature Day/night humidity Day/night VPD 10 daa dry mass 10 daa fresh mass (°C) (%) (kPa) (g) (g) 1998 VPD+3Fspring 26/19 47/70 1.8/0.7 0.47 6.14 1998 VPD+6Fspring 0.3 3.84 1998 VPD-3Fspring 25/18 62/73 1.2/0.6 0.35 5 1998 VPD-6Fspring 0.21 2.14 1998 VPD+3Fsummer 28/21 49/75 1.9/0.6 0.46 6.61 1998 VPD+6Fsummer 0.2 2.47 1998 VPD–3Fsummer 27/20 62/77 1.3/0.6 0.63 6.94 1998 VPD–6Fsummer 0.19 1.46 2000 20°C-2F 20/20 70 0.7 0.37 4.97 2000 25°C-2F 25/25 0.9 1.23 16.76 2000 2520°C-2F 25/20 0.9/0.7 0.77 10.38 2000 2520°C-5F-F1 25/20 70 0.9/0.7 0.75 9.73 2000 2520°C-5F-F3 0.55 7.62 2000 2520°C-5F-F5 0.22 12.74 2001 2520°C-6F-F1F2 25/20 75 0.8/0.6 0.53 6.3 2001 2520°C-6F-F3F4 0.47 5.9 2001 2520°C-6F-F5F6 0.4 4.62 Average 0.48 6.1 Experiment Treatments Day/night temperature Day/night humidity Day/night VPD 10 daa dry mass 10 daa fresh mass (°C) (%) (kPa) (g) (g) 1998 VPD+3Fspring 26/19 47/70 1.8/0.7 0.47 6.14 1998 VPD+6Fspring 0.3 3.84 1998 VPD-3Fspring 25/18 62/73 1.2/0.6 0.35 5 1998 VPD-6Fspring 0.21 2.14 1998 VPD+3Fsummer 28/21 49/75 1.9/0.6 0.46 6.61 1998 VPD+6Fsummer 0.2 2.47 1998 VPD–3Fsummer 27/20 62/77 1.3/0.6 0.63 6.94 1998 VPD–6Fsummer 0.19 1.46 2000 20°C-2F 20/20 70 0.7 0.37 4.97 2000 25°C-2F 25/25 0.9 1.23 16.76 2000 2520°C-2F 25/20 0.9/0.7 0.77 10.38 2000 2520°C-5F-F1 25/20 70 0.9/0.7 0.75 9.73 2000 2520°C-5F-F3 0.55 7.62 2000 2520°C-5F-F5 0.22 12.74 2001 2520°C-6F-F1F2 25/20 75 0.8/0.6 0.53 6.3 2001 2520°C-6F-F3F4 0.47 5.9 2001 2520°C-6F-F5F6 0.4 4.62 Average 0.48 6.1 Treatment names indicate climatic treatments (high or low air vapour pressure deficit: VPD+ and VPD–; day/night air temperature °C, inflorescence size (2F, 3F, 5F or 6F) and fruit position within the inflorescence when it was considered (F1 to F6). In the 1998 experiments fruits were sampled for measurements once in spring and once in summer. 1998 experiments took place in greenhouse, whereas 2000 and 2001 experiments were performed in growth chambers. Open in new tab Table 2. Mean environmental conditions and mean fruit dry and fresh masses at 10 d after anthesis (initial weights for the model) Experiment Treatments Day/night temperature Day/night humidity Day/night VPD 10 daa dry mass 10 daa fresh mass (°C) (%) (kPa) (g) (g) 1998 VPD+3Fspring 26/19 47/70 1.8/0.7 0.47 6.14 1998 VPD+6Fspring 0.3 3.84 1998 VPD-3Fspring 25/18 62/73 1.2/0.6 0.35 5 1998 VPD-6Fspring 0.21 2.14 1998 VPD+3Fsummer 28/21 49/75 1.9/0.6 0.46 6.61 1998 VPD+6Fsummer 0.2 2.47 1998 VPD–3Fsummer 27/20 62/77 1.3/0.6 0.63 6.94 1998 VPD–6Fsummer 0.19 1.46 2000 20°C-2F 20/20 70 0.7 0.37 4.97 2000 25°C-2F 25/25 0.9 1.23 16.76 2000 2520°C-2F 25/20 0.9/0.7 0.77 10.38 2000 2520°C-5F-F1 25/20 70 0.9/0.7 0.75 9.73 2000 2520°C-5F-F3 0.55 7.62 2000 2520°C-5F-F5 0.22 12.74 2001 2520°C-6F-F1F2 25/20 75 0.8/0.6 0.53 6.3 2001 2520°C-6F-F3F4 0.47 5.9 2001 2520°C-6F-F5F6 0.4 4.62 Average 0.48 6.1 Experiment Treatments Day/night temperature Day/night humidity Day/night VPD 10 daa dry mass 10 daa fresh mass (°C) (%) (kPa) (g) (g) 1998 VPD+3Fspring 26/19 47/70 1.8/0.7 0.47 6.14 1998 VPD+6Fspring 0.3 3.84 1998 VPD-3Fspring 25/18 62/73 1.2/0.6 0.35 5 1998 VPD-6Fspring 0.21 2.14 1998 VPD+3Fsummer 28/21 49/75 1.9/0.6 0.46 6.61 1998 VPD+6Fsummer 0.2 2.47 1998 VPD–3Fsummer 27/20 62/77 1.3/0.6 0.63 6.94 1998 VPD–6Fsummer 0.19 1.46 2000 20°C-2F 20/20 70 0.7 0.37 4.97 2000 25°C-2F 25/25 0.9 1.23 16.76 2000 2520°C-2F 25/20 0.9/0.7 0.77 10.38 2000 2520°C-5F-F1 25/20 70 0.9/0.7 0.75 9.73 2000 2520°C-5F-F3 0.55 7.62 2000 2520°C-5F-F5 0.22 12.74 2001 2520°C-6F-F1F2 25/20 75 0.8/0.6 0.53 6.3 2001 2520°C-6F-F3F4 0.47 5.9 2001 2520°C-6F-F5F6 0.4 4.62 Average 0.48 6.1 Treatment names indicate climatic treatments (high or low air vapour pressure deficit: VPD+ and VPD–; day/night air temperature °C, inflorescence size (2F, 3F, 5F or 6F) and fruit position within the inflorescence when it was considered (F1 to F6). In the 1998 experiments fruits were sampled for measurements once in spring and once in summer. 1998 experiments took place in greenhouse, whereas 2000 and 2001 experiments were performed in growth chambers. Open in new tab Goodness-of-fit and predictive quality of the model Model solving and calibration were performed using R language (R Development Core Team, 2005). Fresh and dry matter growth curves were fitted by minimizing the weighed (variance) mean squared error using the Nonlinear Least Squares Regression function. The goodness of fit of the model was evaluated through the relative root mean squared error (RRMSE) (Kobayashi and Us Salam, 2000), which is a common criterion to quantify the mean difference between simulation and measurement: (10) where N is the number of sample dates over the fruit growth period, ni is the number of repetitions at date i, yimod is the fruit dry or fresh mass calculated by the model at date i, idata is the mean value measured at date i, and is the mean of all measured values. The smaller the RRMSE in comparison to measurements, the better is the goodness-of-fit. The predictive quality of the model, which evaluates the validity of the model over a range of datasets, was calculated with a cross validation approach (Thorp et al., 2005) by splitting the 17 experimental datasets into eight situations, i.e. spring VPD+ data, spring VPD– data, summer VPD+ data, summer VPD– data, 20/20 °C data, 25/20 °C data, 25/25 °C data, and 2001 year data. The cross validation requires eight successive calibrations of the model by alternatively leaving out one situation or dataset. Then the model was evaluated using the fitted parameters to simulate the left out dataset. The criterion was the relative root mean squared error of prediction (RRMSEP). Averaging the RRMSEP over all the situations gives the overall estimation of the prediction error. Sensitivity analysis A sensitivity analysis was performed to identify the most influential factors on the model response. The investigated factors were temperature, humidity, phloem sugar concentration, stem water potential, initial fruit dry mass (s0), and fresh mass (w0). The values tested for these factors are in the range of those experimentally observed or reported in literature (Table 3). For the initial weights, it was considered that the percentage of dry matter was constant (7.5%) and thus only pairwise variations of s0 and w0 were tested. The sensitivity of the model to the given variation of one factor was quantified by the normalized sensitivity coefficient, defined as the ratio between the variation of fruit dry or fresh mass at the end of the simulation (ΔW) relative to its average value (⁠⁠) and the variation of the input values for the factor (ΔP) relative to its average value (⁠⁠). Sensitivity coefficients were calculated for each individual factor considering stepwise increases of this factor (as defined in Table 3), all other factors being at standard values: (11) Then the mean normalized sensitivity coefficients for the fresh and dry weights were calculated over the whole range of variation for each factor. Table 3. Range of values for the different factors considered in the model sensitivity analysis Open in new tab Table 3. Range of values for the different factors considered in the model sensitivity analysis Open in new tab Results Global estimation of parameters, goodness-of-fit, and predictive quality of the model The average dry mass (s0) and fresh mass (w0) of 10 daa fruits were input as the initial values of simulation for each dataset (Table 2). The six parameters mentioned above were globally estimated on the 17 experimental datasets: Dynamics of Vmax, σp, and Φ are depicted in Fig. 1. The reflection coefficient σp showed a sigmoid evolution and the apoplasmic sugar unloading went up at about 11 daa (σp >0). The cell wall extensibility sharply decreased during the first 15 d of simulation and reached zero around 45 daa. The maximum rate of carbon uptake (Vmax) exhibited a 2–3-fold decrease over the growth period, depending on s0 which mainly affected the initial Vmax; the initial value of Vmax increased from 0.0028 to 0.0042 and 0.0062 g sucrose g−1 DW h−1 when s0 decreased from 1.23 to 0.48 and 0.20 g. At fruit maturity (about 60 daa), Vmax ranged between 0.0014 and 0.0020 g sucrose g−1 DW h−1. Fig. 1. Open in new tabDownload slide Temporal dynamics of (A) the solute reflection coefficient for phloem unloading σp (equation 5), the cell wall extensibility Φ (equation 8), and (B) the maximum rate of carbon uptake from phloem (equation 6) for three values of the 10 daa fruit dry weight (s0max=1.23 g, s0mean=0.48 g, and s0min=0.20 g which are the maximum, average, and minimum values experimentally observed; Table 2). Fig. 1. Open in new tabDownload slide Temporal dynamics of (A) the solute reflection coefficient for phloem unloading σp (equation 5), the cell wall extensibility Φ (equation 8), and (B) the maximum rate of carbon uptake from phloem (equation 6) for three values of the 10 daa fruit dry weight (s0max=1.23 g, s0mean=0.48 g, and s0min=0.20 g which are the maximum, average, and minimum values experimentally observed; Table 2). The model simulations obtained with the global set of parameters were applied to experimental measurements of dry and fresh masses for each experiment (Figs 2, 3, 4) and RRMSE are given in Table 4. The mean RRMSE was 0.23 for the fruit dry mass and 0.25 for the fruit fresh mass. The goodness-of-fit was satisfying for the 1998 experiment both in dry (0.03 to 0.22) and fresh masses (0.05 to 0.30), and it was on average lower (higher RRMSE) for the 2000 and 2001 datasets (0.23 to 0.59 for dry weight and 0.24 to 0.55 for fresh weight). In spring (Fig. 2) the model accurately simulated fruit growth on the 6F-plants despite a slight underestimation of the fresh weight at high VPD, but underestimated the fresh and dry weights on the 3F-plants at both low and high VPD. In summer, the simulations adequately fitted the experimental growth curves on both 3F- and 6F-plants at low VPD, but at high VPD under stressed conditions the dry weight was slightly underestimated by the model for both 3F- and 6F-plants. The 2000 dataset comparing 2-F plants at three temperature regimes (Fig. 3) was well simulated by the model except an underestimation at 20 °C. At this temperature the growth rate was reduced, but the final fruit size was not affected due to a longer period of growth. The fruit position effect was considered in the 2000 and 2001 datasets (Fig. 4). The reduction in fruit growth at the tip position within the inflorescence was accurately simulated by the model until about 35 daa, so that the final fresh and dry weights were underestimated. Table 4. Goodness-of-fit (RRMSE) and quality of prediction (RRMSEP) of the model for tomato fruit dry (DM) and fresh (FM) masses Treament RRMSE DM RRMSE FM RRMSEP DM RRMSEP FM VPD+3Fspring 0.21 0.28 0.17 0.25 VPD+6Fspring 0.03 0.17 0.02 0.14 VPD-3Fspring 0.22 0.30 0.09 0.19 VPD-6Fspring 0.03 0.06 0.25 0.36 VPD+3Fsummer 0.10 0.07 0.05 0.04 VPD+6Fsummer 0.14 0.05 0.12 0.05 VPD-3Fsummer 0.12 0.14 0.07 0.11 VPD-6Fsummer 0.07 0.06 0.32 0.72 20°C-2F 0.32 0.39 0.33 0.40 25°C-2F 0.23 0.24 0.35 0.37 2025°C-2F 0.25 0.29 0.30 0.31 2025°C-5F-F1 0.23 0.25 0.26 0.27 2025°C-5F-F3 0.58 0.55 0.67 0.60 2025°C-5F-F5 0.59 0.48 0.76 0.56 2025°C-6F-F1F2 0.24 0.25 0.23 0.23 2025°C-6F-F3F4 0.23 0.31 0.19 0.26 2025°C-6F-F5F6 0.31 0.31 0.33 0.28 Mean 0.23 0.25 0.27 0.30 Treament RRMSE DM RRMSE FM RRMSEP DM RRMSEP FM VPD+3Fspring 0.21 0.28 0.17 0.25 VPD+6Fspring 0.03 0.17 0.02 0.14 VPD-3Fspring 0.22 0.30 0.09 0.19 VPD-6Fspring 0.03 0.06 0.25 0.36 VPD+3Fsummer 0.10 0.07 0.05 0.04 VPD+6Fsummer 0.14 0.05 0.12 0.05 VPD-3Fsummer 0.12 0.14 0.07 0.11 VPD-6Fsummer 0.07 0.06 0.32 0.72 20°C-2F 0.32 0.39 0.33 0.40 25°C-2F 0.23 0.24 0.35 0.37 2025°C-2F 0.25 0.29 0.30 0.31 2025°C-5F-F1 0.23 0.25 0.26 0.27 2025°C-5F-F3 0.58 0.55 0.67 0.60 2025°C-5F-F5 0.59 0.48 0.76 0.56 2025°C-6F-F1F2 0.24 0.25 0.23 0.23 2025°C-6F-F3F4 0.23 0.31 0.19 0.26 2025°C-6F-F5F6 0.31 0.31 0.33 0.28 Mean 0.23 0.25 0.27 0.30 Open in new tab Table 4. Goodness-of-fit (RRMSE) and quality of prediction (RRMSEP) of the model for tomato fruit dry (DM) and fresh (FM) masses Treament RRMSE DM RRMSE FM RRMSEP DM RRMSEP FM VPD+3Fspring 0.21 0.28 0.17 0.25 VPD+6Fspring 0.03 0.17 0.02 0.14 VPD-3Fspring 0.22 0.30 0.09 0.19 VPD-6Fspring 0.03 0.06 0.25 0.36 VPD+3Fsummer 0.10 0.07 0.05 0.04 VPD+6Fsummer 0.14 0.05 0.12 0.05 VPD-3Fsummer 0.12 0.14 0.07 0.11 VPD-6Fsummer 0.07 0.06 0.32 0.72 20°C-2F 0.32 0.39 0.33 0.40 25°C-2F 0.23 0.24 0.35 0.37 2025°C-2F 0.25 0.29 0.30 0.31 2025°C-5F-F1 0.23 0.25 0.26 0.27 2025°C-5F-F3 0.58 0.55 0.67 0.60 2025°C-5F-F5 0.59 0.48 0.76 0.56 2025°C-6F-F1F2 0.24 0.25 0.23 0.23 2025°C-6F-F3F4 0.23 0.31 0.19 0.26 2025°C-6F-F5F6 0.31 0.31 0.33 0.28 Mean 0.23 0.25 0.27 0.30 Treament RRMSE DM RRMSE FM RRMSEP DM RRMSEP FM VPD+3Fspring 0.21 0.28 0.17 0.25 VPD+6Fspring 0.03 0.17 0.02 0.14 VPD-3Fspring 0.22 0.30 0.09 0.19 VPD-6Fspring 0.03 0.06 0.25 0.36 VPD+3Fsummer 0.10 0.07 0.05 0.04 VPD+6Fsummer 0.14 0.05 0.12 0.05 VPD-3Fsummer 0.12 0.14 0.07 0.11 VPD-6Fsummer 0.07 0.06 0.32 0.72 20°C-2F 0.32 0.39 0.33 0.40 25°C-2F 0.23 0.24 0.35 0.37 2025°C-2F 0.25 0.29 0.30 0.31 2025°C-5F-F1 0.23 0.25 0.26 0.27 2025°C-5F-F3 0.58 0.55 0.67 0.60 2025°C-5F-F5 0.59 0.48 0.76 0.56 2025°C-6F-F1F2 0.24 0.25 0.23 0.23 2025°C-6F-F3F4 0.23 0.31 0.19 0.26 2025°C-6F-F5F6 0.31 0.31 0.33 0.28 Mean 0.23 0.25 0.27 0.30 Open in new tab Fig. 2. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the VPD and fruit load treatment detailed in Table 2. (A) VDP-3Fspring (open symbols, thin line) and VPD-6Fspring (closed symbols, bold line); (B) VDP-3Fsummer (open symbols, thin line) and VPD-6Fsummer (closed symbols, bold line); (C) VDP+3Fspring (open symbols, thin line) and VPD+6Fspring (closed symbols, bold line); (D) VDP+3Fsummer (open symbols, thin line) and VPD+6Fsummer (closed symbols, bold line). Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. Fig. 2. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the VPD and fruit load treatment detailed in Table 2. (A) VDP-3Fspring (open symbols, thin line) and VPD-6Fspring (closed symbols, bold line); (B) VDP-3Fsummer (open symbols, thin line) and VPD-6Fsummer (closed symbols, bold line); (C) VDP+3Fspring (open symbols, thin line) and VPD+6Fspring (closed symbols, bold line); (D) VDP+3Fsummer (open symbols, thin line) and VPD+6Fsummer (closed symbols, bold line). Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. Fig. 3. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the temperature treatment at low fruit load detailed in Table 2. (A) 25°C-2F treatment; (B) 20°C-2F treatment; (C) 2025°C-2F treatment. Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. Fig. 3. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the temperature treatment at low fruit load detailed in Table 2. (A) 25°C-2F treatment; (B) 20°C-2F treatment; (C) 2025°C-2F treatment. Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. Fig. 4. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the fruit position effect detailed in Table 2. (A) 2520°C-5F-F1 treatment (open symbols, thin line) and 2520°C-6F-F1F2 treatment (closed symbols, bold line); (B) 2520°C-5F-F3 treatment (open symbols, thin line) and 2520°C-6F-F3F4 treatment (closed symbols, bold line); (C) 2520°C-5F-F5 treatment (open symbols, thin line) and 2520°C-6F-F5F6 treatment (closed symbols, bold line). Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. Fig. 4. Open in new tabDownload slide Dynamics of measured (symbols) and simulated (lines) fruit fresh (circles, solid line) and dry (squares, dotted line) weight during fruit ageing for the fruit position effect detailed in Table 2. (A) 2520°C-5F-F1 treatment (open symbols, thin line) and 2520°C-6F-F1F2 treatment (closed symbols, bold line); (B) 2520°C-5F-F3 treatment (open symbols, thin line) and 2520°C-6F-F3F4 treatment (closed symbols, bold line); (C) 2520°C-5F-F5 treatment (open symbols, thin line) and 2520°C-6F-F5F6 treatment (closed symbols, bold line). Simulations started 10 daa. Each point is an average of 5–10 measurements and vertical bars indicate standard deviations. The cross validation allowed the predictive quality of the model to be evaluated in the different situations. RRMSEP values ranged from 0.02 to 0.76 for the dry weight (mean value=0.27) and from 0.04 to 0.72 for the fresh weight (mean value=0.30). The 1998 experiments were better predicted by the model, whereas the tip fruit positions in the 2000 dataset were predicted less accurately, but the high RRMSEP may be due to high scattering of this dataset. Sensitivity analysis of the model To understand the response of the model to fluctuations of the fruit environment, a sensitivity analysis to temperature, humidity, stem water potential, phloem sugar concentration, and initial fruit dry and fresh weights was performed. The mean normalized sensivity coefficients for the fresh and dry fruit weight and their range of variation are shown in Fig. 5. The model was mainly sensitive to the phloem sucrose concentration (Cp) involved in the control of fruit sugar import (equation 6). Concerning the fruit dry weight, the model sensitivity to the initial weight, to temperature and to stem water potential was low and in the same range (0.19 to 0.26 in absolute values), whereas the sensitivity to humidity was almost nil. Concerning the fresh weight, the model was hardly sensitive to temperature, and moderately sensitive to the initial dry or fresh weight, to stem water potential and to humidity (0.21 to 0.39). The model sensitivity to a given factor went up as this factor increased in the range described in Table 3, except for the stem water potential (Fig. 5). The largest range of variation concerned the sensitivity to Cp. Fig. 5. Open in new tabDownload slide Mean normalized sensivity coefficients (bars) calculated for the dry (A) and fresh (B) weights according to given variations in phloem sucrose concentration (Cp), initial dry and fresh weights (s0 and w0), air temperature, stem water potential, and air humidity. Circles indicate the range of variation of each coefficient calculated for the minimum (closed circles) and maximum (open circles) value of each factor (see Table 3). Fig. 5. Open in new tabDownload slide Mean normalized sensivity coefficients (bars) calculated for the dry (A) and fresh (B) weights according to given variations in phloem sucrose concentration (Cp), initial dry and fresh weights (s0 and w0), air temperature, stem water potential, and air humidity. Circles indicate the range of variation of each coefficient calculated for the minimum (closed circles) and maximum (open circles) value of each factor (see Table 3). The model sensitivity to interactions between Cp and other factors is shown in Fig. 6 for the highest sensitivity coefficients. As Cp increased from 0.04 to 0.20 g sucrose g−1, the sensitivity of the model to Cp increased 2–3-fold for the dry and fresh weights, respectively. No additive effects and only very small interactions among variables or parameters were observed except a slight additive interaction between Cp and initial weights. Indeed for both fresh and dry weights, the model sensitivity to Cp decreased 1.9-fold at a low Cp value and 1.5-fold at a high Cp value as the initial weights increased. For other variables, only small interactions could be observed (for instance between humidity and temperature, or between stem water potential and initial weights) and in very low ranges of sensitivity (not shown). Fig. 6. Open in new tabDownload slide Sensivity coefficients to the phloem sugar concentration (Cp) in interaction with the initial dry weight (A), initial fresh weight (B), stem water potential (C, D), air temperature (E), and humidity (F), calculated for the fruit dry weight (left graphs) and/or fresh weight (right graphs). Arrows indicate the range and course of variations from minimum to maximum values as indicated in Table 3. Fig. 6. Open in new tabDownload slide Sensivity coefficients to the phloem sugar concentration (Cp) in interaction with the initial dry weight (A), initial fresh weight (B), stem water potential (C, D), air temperature (E), and humidity (F), calculated for the fruit dry weight (left graphs) and/or fresh weight (right graphs). Arrows indicate the range and course of variations from minimum to maximum values as indicated in Table 3. Analysis of main components of fruit growth under situations of carbon and water stresses Responses of the main fluxes and factors involved in the control of fruit growth (phloem and xylem fluxes, transpiration and respiration rates, carbon transport pathways, osmotic and turgor pressures) to given variations in Cp, in air temperature and humidity, and in stem water potential were analysed with the model. Carbon stress was virtually applied by reducing the phloem carbon concentration (Cp) from 0.12 to 0.06 g g−1. Water stress was simulated first by decreasing the stem water potential from –0.22 to –0.6 MPa at constant air humidity (70%), and then by decreasing the air humidity from 70% to 40% at constant stem water potential (–0.22 MPa). All other model parameters were those globally estimated on the 17 experimental datasets. The carbon stress induced a decrease in the final dry and fresh weights of, respectively, 71% and 51%. This response was related to a decrease of all fluxes: both xylem and phloem water influx decreased by about 50%, whereas the respiration and transpiration rates dropped by, respectively, 68% and 31% over the growth period compared with the standard conditions. The reduction of Cp directly decreased the active transport of carbon (equation 6) and thus the accumulation of carbon compounds in the fruit, resulting in a drop and then in the stabilization of the fruit osmotic potential πf (–16% on average over the growth period). The consequent reduction of xylem and phloem fluxes induced a decrease of the fruit turgor pressure (–16% on average over the period), which then resulted in a lowered volume or fresh weight increase. The mass flow transport of carbon was also reduced, especially at the beginning of the growth period (not shown) due to the reduction of the phloem influx. At the end of the simulation period the final fruit dry matter content was reduced by 41% (3.69 against 6.24% in standard conditions). Low air humidity affected only the fruit fresh weight (–9%) through a 2-fold higher transpiration rate. This loss of water concentrated the fruit carbon compounds and increased the fruit osmotic pressure. For this reason the xylem and, to a lesser extent, the phloem water imports were slightly increased during the second period of simulation. However, this could not compensate for the loss of water by transpiration. Finally, the fruit dry matter content was increased by 8% (6.77% against 6.24% in standard conditions). Reducing the stem water potential from –0.22 MPa to –0.6 MPa reduced the final fresh and dry fruit weight by, respectively, 44% and 29% compared with the standard situation. This firstly resulted from the reduction of the xylem-to-fruit and phloem-to-fruit gradient of water potential, which lessened the xylem and phloem water imports by, respectively, 67% and 48% over the simulation period. Transpiration and respiration rates were reduced by, respectively, 35% and 31% over the growth period. Due to the lower influx of water, the fruit osmotic pressure was increased by 31% over the growth period which only very slightly compensated for the drop of xylem and phloem fluxes, and indeed the fruit turgor pressure was reduced by 22% over the growth period. The final fruit dry matter content was increased by 27% (7.89% against 6.24% in standard conditions). The relative proportions of the different pathways of carbon transport were affected, neither by air humidity nor by stem water potential (not shown). Discussion The need for ecophysiological models of fruit quality has recently been emphasized (Struik et al., 2005; Génard et al., 2007), as models are powerful tools to understand complex system behaviour and to point out key-processes and/or key developmental stages involved in the control of complex traits, such as quality. In horticulture, the Fishman and Génard (1998) model pioneered the modelling of fruit quality by combining carbon and water fluxes and allowing the prediction of both fruit size and dry matter content. Since its development on peach fruit, the generic character of this model has been evaluated on mango fruit with only minor modifications (Léchaudel et al., 2005, 2007) and it was also able to predict the intraspecies variability of peach growth in a heterogeneous population (Quilot et al., 2005a, b). The present adaptation to tomato fruit confirmed the generic quality of the model and its suitability for fleshy berry fruit. Only a few modifications have been made, which have focused on three particular points in agreement with the literature on tomato: (i) the reflection coefficient (σ in equation 5) accounting for the cell wall permeability to sugars rose during fruit development representing a shift from symplasmic to apoplasmic transport of sugars. This shift had already appeared around 11 daa and apoplasmic unloading prevailed after 30 daa (Fig. 1A), in accordance with the literature (Ho, 2003b). (ii) The decrease of the cell wall extensibility during fruit growth. (iii) The inhibitor accumulation driving the decline of Ua (rate of active carbon uptake) depended on the initial fruit size (equation 6), whereas it was only developmentally controlled in the original model. According to the function described in equation 6, the maximum uptake of sucrose (Vmax) was 0.0042 g g−1 DW h−1 at the beginning of the simulation (t=10 daa) and 0.0017 g g−1 DW h−1 at maturity (t=60 daa) for the average initial fruit weight (Fig. 1B). Ruan et al. (1997) estimated the maximum hexose uptake rate (Vmax) of 20–25 daa tomato fruit between 0.0054 and 0.0075 g g−1 DW h−1. On the whole, the model was able to simulate reasonably well several contrasting experimental situations with a common set of parameters. Moreover, despite a high sensitivity of the model to Cp (phloem concentration in sugar), a constant value could be applied to simulate the whole range of experimental data. The few situations in which the goodness-of-fit was reduced seemed to be mainly related to the inaccurate estimation of the initial fruit weight. For instance the 2F treatments were accurately predicted by the model at 25 °C and 20/25 °C but not at 20 °C (Fig. 3) for which the initial weight was quite low compared with the two other temperature treatments (Table 2). Although the mean sensitivity coefficient to s0 or w0 was moderate (0.25 and 0.39 for the dry and fresh weights, respectively; Fig. 5), a slight underestimation of s0, for instance 0.25 g instead of 0.4 g, would lead to an underestimation of the final fruit fresh and dry weights of 63 g and 2.1 g, respectively. This range of underestimations is exactly that observed in case of the 20°C-2F treatment (Fig. 3B). As large variations in fruit size occur in the early period of fruit development in response to plant and environmental factors, the accurate estimation or measurement of the initial fruit weight appears as essential for the model initialization. This is confirmed in case of the two experimental datasets taking into account the fruit position (Fig. 4). In the case of the 2520°C-5F treatment, despite a global underestimation, the model predicted well the large differences between proximal and distal fruits only on the basis of different initial weights. In the case of the 2520°C-6F treatment, these differences were low (Table 2) as fruits were picked on the first truss of topped plants, that is in conditions of low competition among sinks. In this study, the model could be used to analyse fruit functioning and to integrate the complex responses to plant or environmental factors. Under the standard situation defined in Table 3, the amount of water imported by the fruit via the phloem and xylem tissues peaked around 5 g d−1 fruit−1 and 0.5 g d−1 fruit−1, respectively. These values are in the range of values reported in the literature for tomato fruit (Ho et al., 1987; Plaut et al., 2004; Guichard et al., 2005). As reported by Ho et al. (1987) and Mitchell et al. (1991), the fruit osmotic pressure was relatively stable during fruit development, except in the case of carbon stress and the simulated fruit water potential was in the range of values reported by different authors (Johnson et al., 1992; Guichard et al., 2005). By contrast, fruit turgor was higher than that measured on cell or pericarp pieces, which remains under 0.3 MPa (Ehret and Ho, 1986a; Johnson et al., 1992; Grange, 1995; Thompson et al., 1998) or around 0.4 MPa (S Guichard, unpublished data). However, Grange (1995) showed that growth rate of tomato pericarp slices is maximum for a turgor pressure around 0.4–0.5 MPa, which is the range of values predicted by the model for the period of rapid growth (Fig. 7). Moreover measurements of tissue turgor are currently performed on isolated cells or pericarp pieces without the dominant epidermis constraint (Thompson et al., 1998), and thus they may underestimate the actual pressure. In the model, the fruit turgor integrates all the constraints as no tissue compartmentation is considered, and the increase in turgor pressure until ripening probably resulted from the decrease in cell/fruit plasticity induced by the drop of cell wall extensibility (Fig. 1A). Fig. 7. Open in new tabDownload slide Fruit growth, carbon and water fluxes, and osmotic and turgor pressures simulated under standard (bold lines), carbon stress (black circles), stem water potential stress (grey circles), and humidity stress (white circles). (A) Fruit fresh weight (g), (B) fruit dry weight (g), (C) respiration rate (g C d−1 fruit−1), (D) transpiration rate (g H2O d−1 fruit−1), (E) xylem water import (g H2O d−1 fruit−1), (F) phloem water import (g H2O d−1 fruit−1), (G) fruit osmotic potential (MPa), and (H) fruit turgor pressure (MPa). Carbon stress was applied by reducing the phloem carbon concentration from 0.12 g g−1 to 0.06 g g−1. Water stress was simulated by decreasing the stem water potential from – 0.22 MPa to – 0.6 MPa at constant air humidity (70%), and the air humidity stress corresponded to a drop from 70% to 40% at constant stem water potential (– 0.22 MPa). Fig. 7. Open in new tabDownload slide Fruit growth, carbon and water fluxes, and osmotic and turgor pressures simulated under standard (bold lines), carbon stress (black circles), stem water potential stress (grey circles), and humidity stress (white circles). (A) Fruit fresh weight (g), (B) fruit dry weight (g), (C) respiration rate (g C d−1 fruit−1), (D) transpiration rate (g H2O d−1 fruit−1), (E) xylem water import (g H2O d−1 fruit−1), (F) phloem water import (g H2O d−1 fruit−1), (G) fruit osmotic potential (MPa), and (H) fruit turgor pressure (MPa). Carbon stress was applied by reducing the phloem carbon concentration from 0.12 g g−1 to 0.06 g g−1. Water stress was simulated by decreasing the stem water potential from – 0.22 MPa to – 0.6 MPa at constant air humidity (70%), and the air humidity stress corresponded to a drop from 70% to 40% at constant stem water potential (– 0.22 MPa). The analysis of the fruit functioning under virtual carbon or water stress situations (Fig. 7) outlined interesting reactions, thanks to interactions and feedback effects among the different fruit components which were difficult to anticipate beforehand. These unexpected properties generated by ecophysiological models are so-called emergent properties (Génard et al., 2007) and they may be very informative for understanding and controlling fruit growth better. For instance any modification of the fruit osmotic pressure, related to low water or carbon input, influenced the xylem and phloem influx and thus the turgor pressure which, in turn, affected the growth. Compensating effects occurred, as for instance the increase of fruit osmotic potential at low air humidity, or the reduction of respiration and transpiration rates in case of low stem water potential. All applied stresses led to smaller fruits with large variations in dry matter concentration (+8% at low humidity, +27% at low stem water potential, and –41% at low phloem sugar concentration). These effects are consistent with experiments on water or salinity stresses or on factors reducing the leaf carbon supply (Ehret and Ho, 1986b; Ho, 2003a, b; Plaut et al., 2004; Guichard et al., 2005). Thus model may be used to perform virtual experiments and it should help in quantifying and optimizing the impact of fluctuating environment on fruit growth and dry matter concentration. It might also be easily applied to test virtual mutants affected on particular traits, for instance, on the transport of sugars, on the cuticular permeability to water, or on specific enzymes involved in the regulation of cell wall extensibility during fruit development. Conclusion The tomato model proposed in this work is based on simple biophysical laws and includes a relatively low number of parameters. It was able to mimic the fruit behaviour and to analyse the interactions and feedback regulations among the fruit system components, for instance, between fruit osmotic and turgor regulation and water and phloem fluxes in response to environmental and plant factors. Thus, it provides for scientists a tool to address genetic and agronomic questions concerning the control of quality and, in particular, the antagonism between size and composition. This work was supported by a post-doc grant from INRA. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2007 The Author(s).

Bravo, León A.; Saavedra-Mella, Felipe A.; Vera, Felipe; Guerra, Alexi; Cavieres, Lohengrin A.; Ivanov, Alexander G.; Huner, Norman P. A.; Corcuera, Luis J.

doi: 10.1093/jxb/erm206pmid: 18057038

Abstract The effects of cold acclimation of two ecotypes (Antarctic and Andes) of Colobanthus quitensis (Kunth) Bartl. Caryophyllaceae on their photosynthetic characteristics and performance under high light (HL) were compared. Non-acclimated plants of the Antarctic ecotype exhibited a higher (34%) maximal rate of photosynthesis than the Andes ecotype. In cold-acclimated plants the light compensation point was increased. Dark respiration was significantly increased during the exposure to 4 °C in both ecotypes. Cold-acclimated Antarctic plants showed higher ΦPSII and qP compared with the Andes ecotype. In addition, the Antarctic ecotype exhibited higher heat dissipation (NPQ), especially in the cold-acclimated state, which was mainly associated with the fast relaxing component of non-photochemical quenching (NPQF). By contrast, the Andes ecotype exhibited a lower NPQF and a significant increase in the slowly relaxing component (NPQs) at low temperature and HL, indicating higher sensitivity to low temperature-induced photoinhibition. Although the xanthophyll cycle was fully operational in both ecotypes, cold-acclimated Antarctic plants exposed to HL exhibited higher epoxidation state of the xanthophyll cycle pigments (EPS) compared with the cold-acclimated Andes ecotype. Thus, the photosynthetic apparatus of the Antarctic ecotype operates more efficiently than that of the Andes one, under a combination of low temperature and HL. The ecotype differences are discussed in relation to the different climatic conditions of the two Colobanthus. Antarctic plants, heat dissipation, low temperature, non-photochemical quenching, photoinhibition, photosynthesis Introduction Excess irradiance may be harmful for plants that are unable to balance the absorbed/utilized energy ratio (Huner et al., 1998). This may be even worse when plants are exposed simultaneously to high light and low temperatures which decrease carbon and other enzymatic assimilation processes, creating a greater imbalance because light absorption is largely temperature insensitive (Huner et al., 1998). However, cold acclimation decreases susceptibility to photoinhibition (Krause, 1994) by causing several metabolic alterations and producing changes at the chloroplast level that may restore the energy balance. A widely accepted hypothesis is that cold acclimation may improve the ability of plants to maintain metabolism at low temperature by keeping QA, the primary quinone electron acceptor, more oxidized (Huner et al., 1998; Melis, 1999). Other plants use different strategies. For instance, cold-acclimated Pinus contorta L. partially loses PSII reaction centres, reduces needle chlorophyll per unit area, and reduces its daily carbon gain (Savitch et al., 2002). All these changes are accompanied by an increased and sustained capacity for heat dissipation through non-photochemical quenching. It is also known that xanthophyll levels increase during cold acclimation and the half-time to develop qE decreases (Krause, 1994). All these cold acclimation-induced changes may help to restore the energy balance and hence reduce the incidence of low-temperature-induced photodamage. Colobanthus quitensis (Kunth) Bartl. Caryophyllaceae extends from the Maritime Antarctic and along the Andes Mountains to Ecuador, with one site in Mexico (Lewis Smith, 2003). It usually grows above 2500 masl in the Andes Mountains. The Antarctic C. quitensis plants have been described as morphologically and physiologically adapted to succeed in these cold environments (Mantovani and Vieira, 2000; Perez-Torrez et al., 2004). For instance, its photosynthetic machinery is well adapted to low temperature (Xiong et al., 1999, 2000). The photosynthetic responses of a C. quitensis population from the Andes of Central Chile have been recently studied in the field (Casanova-Katny et al., 2006) and ecotypic differentiation of the Antarctic and Andes populations has been proposed (Gianoli et al., 2004; Sierra-Almeida et al., 2007). Antarctic and Andean environments can differ significantly in solar radiation and temperature during the summer. PFD in the Antarctic summer can go up to 1500 μmol photons m−2 s−1 on sunny days; however, sunny days are infrequent, accounting for less than 20% in summer (Xiong et al., 1999; Xiong and Day, 2001). Average temperature in the Antarctic summer is about 3 °C (Alberdi et al., 2002). In the Andes mountains, PFD in summer is frequently as high as 2500 μmol photons m−2 s−1 and the average temperature at 2600 masl is about 13 °C (Cavieres and Arroyo, 1999). These differences in PFD and temperature regime during the growing season could have resulted in selection for altered plasticity of the photosynthetic apparatus to cope with high light and low temperature in Andes and Antarctic ecotypes, respectively. In order to test this hypothesis, the two ecotypes of C. quitensis described above (Gianoli et al., 2004) were grown, one from Antarctica (sea level) and the other from the Andes (2700 masl), under the same controlled laboratory conditions. The purpose of this study was to assess the effect of cold acclimation on the capacity of these ecotypes of C. quitensis to cope with excess irradiance and low temperature-induced photoinhibition. Materials and methods Plant material Antarctic plants of C. quitensis were collected on King George Island, Maritime Antarctic (sea level; 62°10′ S; 58°29’ W) and transported to the laboratory. Plants of C. quitensis, ecotype Andes were collected on the slopes of Cerro La Parva (2650 masl; 33°19′ S; 70°17′ W). Both ecotypes were reproduced vegetatively in plastic pots, using a soil:peat mixture (3:1 v/v) and maintained at 13–15 °C in a growth chamber (Forma Scientific Inc.) with a photon flux density (PFD) of 120±20 μmol photons m−2 s−1 at the top of the canopy and a 16/8 h light/dark period. The light source consisted of cool-white fluorescent tubes F40CW (General Electric). Plants were fertilized with Phostrogen® (Solaris) using 0.2 g l−1 once every two weeks. One group of both Antarctic and Andes plants was cold-acclimated at 4 °C for 21 d. This treatment reduced the LT50 from –7 °C to –10 °C and from –7 °C to –14.5 °C in Andes and Antarctic ecotypes, respectively (Gianoli et al., 2004). Mature pre-existing leaves were sampled for each analysis. Photoinhibitory treatment Cold-acclimated and non-acclimated plants were subjected to high light treatment (HL) 1600±50 μmol photons m−2 s−1 and low light treatment (LL) 120±20 μmol m−2 s−1 both at low temperature (4 °C) for 2 h. Low and high PFD were provided by 1000 W halogen lamps. Pigment composition was monitored at the end of each treatment. Net photosynthesis Photosynthetic oxygen evolution was measured in detached leaves with a gas phase oxygen electrode unit, using an Oxylab and an oxygen electrode chamber (Model LD2/3 Hansatech Instruments Ltd., King's Lynn, Norfolk, UK). Measurements were performed at either 4 °C and/or 15 °C under saturating CO2 and irradiances over the range of 0–800 μmol m−2 s−1 given by an array of red light-emitting diodes, (Model LH36/2R, Hansatech Instruments Ltd., King's Lynn, Norfolk, UK). Detached leaves were adapted for 10 min to each temperature. Quantum yield of oxygen evolution (⁠ ΦO2 ⁠), maximum rate of net photosynthesis (Pnmax), and light compensation point (LCP) were determined on the bases of incident light measured with the quantum sensor (QSRED, Hansatech). Fluorescence measurements C. quitensis, possess narrow and short leaves, especially the Antarctic ecotype. This makes the fluorescence measurements in attached leaves difficult to perform. For this reason, in order to conduct measurements in the same way for both ecotypes, fully developed detached leaves from control and HL-treated cold-acclimated and non-acclimated plants were aligned parallel and immobilized using transparent tape and then dark-adapted for 30 min using the instrument leaf-clips to obtain open PSII centres to ensure maximum photochemical efficiency. Chlorophyll fluorescence recordings and calculations were performed by a pulse-amplitude modulated fluorimeter (FMS 2, Hansatech, Instruments Ltd., Norfolk, UK) according to Schreiber et al. (1986). The fibre-optic and its adapter were fixed to a ring located over the clip at about 10 mm from the sample and the different light pulses (see below) were applied following the standard routines programmed within the instrument. Minimal fluorescence (Fo) with all PSII reaction centres in the open state was determined by applying a weak modulated light (0.4 μmol m−2 s−1). Maximal fluorescence (Fm) with all PSII reaction centres in the closed state was induced by a 0.8 s saturating pulse of white light (9000 μmol m−2 s−1). After 10 s, the actinic light was turned on and the same saturating pulse described previously was applied every 20 s, until steady-state photosynthesis was reached in order to obtain Fs and Fm′ ⁠. Finally, Fo′ was measured after turning the actinic light off and applying a 2 s far red light pulse. Definitions of fluorescence parameters (qP, Fv′/Fm′ ⁠, and ΦPSII) were used as described by van Kooten and Snel (1990), Non-photochemical quenching (NPQ) was calculated according to Walters and Horton (1991) Fluorescence measurements were performed at different actinic light intensities which was controlled by the light source of the FMS 2 apparatus and applied through an optic fibre. Light intensity at the leaf surface was calibrated using a LI-250 light meter (Li-Cor). Determination of NPQ components The components of non-photochemical quenching (NPQ) were determined at 4 °C and 15 °C in leaves of cold-acclimated and non-acclimated plants from both the Antarctic and Andes ecotypes of C. quitensis. NPQ was resolved into slow (NPQs) and fast (NPQF) components (equivalent to qI and qE, respectively) essentially as described by Walters and Horton (1991) by analysing the kinetics of Fm recovery after actinic light has been turned off. NPQs=(Fm–Fmr)/Fmr and NPQF=(Fm– Fm′ ⁠)–(Fm–Fmr). Fmr (the value of Fm that would have been attained if only slowly relaxing quenching had been present) was obtained by extrapolation in a semi-logarithmic plot of maximum fluorescence yield versus time of data points recorded toward the end of the relaxation back to the time where the actinic light was removed. Pigments C. quitensis leaves were cut and placed immediately in a cold mortar. A tip of spatula (approximately 1 mg) of CaCO3 was added before grinding in 100% (v/v) acetone at 4 °C under dim light. The supernatant was filtered through a 0.22 μm syringe filter and samples were stored at –80 °C until analysed. Pigments were separated and quantified by HPLC analysis as described previously (Ivanov et al., 1995) with some modifications. The HPLC system consisted of the Beckman System Gold programmable solvent module 126, a diode array detector module 168 (Beckman Instruments, San Ramon, California, USA), CSC-Spherisorb ODS-1 reverse-phase column (5 μm particle size, 25×0.46 cm ID) with an Upchurch Perisorb A guard column (both columns from Chromatographic Specialties Inc., Concord, Ontario, Canada). Pigments were eluted isocratically for 6 min with acetonitrile:methanol:0.1 M TRIS–HCl (pH 8.0), (72:8:3.5, by vol.), followed by a 2 min linear gradient to 100% methanol:hexane (4:1, v/v), which continued isocratically for 4 min with a flow rate of 2 ml min−1. Absorbance was monitored at 440 nm. Retention times and response factors of Chl a, Chl b, lutein, and β-carotene were determined by injection of known amounts of pure standards purchased from Sigma (St Louis, MO, USA). The retention times of zeaxanthin, antheraxanthin, violaxanthin and neoxanthin were determined by using pigments purified by thin-layer chromatography as described by Diaz et al. (1990). Epoxidation state (EPS) of the pigments pool was estimated as: EPS=(0.5A+V)/(V+A+Z), where A is antheraxanthin, V is violaxanthin, and Z is zeaxanthin. Statistics Differences in parameters extracted from light response curves of net photosynthesis were statistically evaluated using three-way ANOVA (level of significance was P <0.05) using growth condition, ecotype, and measurement temperature as factors. Fluorescence parameters and pigment contents were statistically evaluated using three-way ANOVA (level of significance was P <0.05) with growth condition, ecotype, and light intensity as factors. Tukey post-hoc tests were used to identify those means with significant differences. Statistical analyses were performed using SigmaStat 3.1 (Systat Software, Inc. Richmond CA, USA). Results Net photosynthesis Light response curves of net photosynthesis (Pn) were performed in cold-acclimated and non-acclimated plants of both Antarctic and Andes ecotypes (Fig. 1). Pn was measured at 4 °C and 15 °C (Table 1). The Antarctic ecotype a exhibited higher maximum rate of net photosynthesis (Pnmax) value than the Andes ecotype, regardless of the measuring and/or growth temperature. The highest net photosynthesis was registered in non-acclimated Antarctic plants exposed to 15 °C, reaching 8.95 μmol O2 m−2 s−1. Cold acclimation did not significantly affect Pnmax of either ecotype (P >0.05) (Table 1). However, measuring temperature had a significant and differential effect on Pn and higher Pnmax values were observed at 15 °C than at 4 °C. This effect depended on the ecotype and the increase of Pnmax in cold-acclimated Andes plants exposed to 15 °C was 51%, with respect to 4 °C, while in the Antarctic plants this increase was significantly higher (73%) (Table 1). This clearly implies an increased capacity for photosynthesis in the cold-acclimated Antarctic ecotype. Table 1. Photosynthetic parameters in both Andes and Antarctic ecotypes of C. quitensis Parameters Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C ΦO2 (mol O2 mol−1 photons) 0.04±0.01 a 0.07±0.01 ab 0.07±0.02 ab 0.12±0.01 bc 0.04±0.01 a 0.18±0.03 c 0.04±0.01 a 0.08±0.01 b Pnmax (μmol O2 m−2 s−1) 3.90±0.44 a 5.89±0.47 b 4.31±0.02 a 7.43±0.85 bc 3.11±0.18 a 6.67±0.62 b 4.14±0.20 a 8.95±0.41 c LCP (μmol photons m−2 s−1) 55±17 a 29±6 c 35±4 c 19±3 b 32±6 c 7±1 d 19±3 b 13±3 b Rd (μmol O2 m−2s−1) –4.0±0.7 a –4.1±0.6 a –4.0±0.2 a –3.3±0.4 a –1.8±0.2 b –1.3±0.1 c –0.9±0.3 d –1.2±0.3 d Parameters Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C ΦO2 (mol O2 mol−1 photons) 0.04±0.01 a 0.07±0.01 ab 0.07±0.02 ab 0.12±0.01 bc 0.04±0.01 a 0.18±0.03 c 0.04±0.01 a 0.08±0.01 b Pnmax (μmol O2 m−2 s−1) 3.90±0.44 a 5.89±0.47 b 4.31±0.02 a 7.43±0.85 bc 3.11±0.18 a 6.67±0.62 b 4.14±0.20 a 8.95±0.41 c LCP (μmol photons m−2 s−1) 55±17 a 29±6 c 35±4 c 19±3 b 32±6 c 7±1 d 19±3 b 13±3 b Rd (μmol O2 m−2s−1) –4.0±0.7 a –4.1±0.6 a –4.0±0.2 a –3.3±0.4 a –1.8±0.2 b –1.3±0.1 c –0.9±0.3 d –1.2±0.3 d These parameters were obtained from the analysis of light response curves (Fig. 1). Cold-acclimated (4 °C) and non-acclimated (15 °C) plants of both ecotypes were measured at 4 °C and 15 °C, respectively. (⁠ ΦO2 ⁠, quantum yield of oxygen evolution; Pnmax, maximal rate of net photosynthesis; LCP, light compensation point; Rd, dark respiration rate. Different letters indicate statistically significant differences within each parameter. Results are means ±SE; n=3. Open in new tab Table 1. Photosynthetic parameters in both Andes and Antarctic ecotypes of C. quitensis Parameters Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C ΦO2 (mol O2 mol−1 photons) 0.04±0.01 a 0.07±0.01 ab 0.07±0.02 ab 0.12±0.01 bc 0.04±0.01 a 0.18±0.03 c 0.04±0.01 a 0.08±0.01 b Pnmax (μmol O2 m−2 s−1) 3.90±0.44 a 5.89±0.47 b 4.31±0.02 a 7.43±0.85 bc 3.11±0.18 a 6.67±0.62 b 4.14±0.20 a 8.95±0.41 c LCP (μmol photons m−2 s−1) 55±17 a 29±6 c 35±4 c 19±3 b 32±6 c 7±1 d 19±3 b 13±3 b Rd (μmol O2 m−2s−1) –4.0±0.7 a –4.1±0.6 a –4.0±0.2 a –3.3±0.4 a –1.8±0.2 b –1.3±0.1 c –0.9±0.3 d –1.2±0.3 d Parameters Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C 4 °C 15 °C ΦO2 (mol O2 mol−1 photons) 0.04±0.01 a 0.07±0.01 ab 0.07±0.02 ab 0.12±0.01 bc 0.04±0.01 a 0.18±0.03 c 0.04±0.01 a 0.08±0.01 b Pnmax (μmol O2 m−2 s−1) 3.90±0.44 a 5.89±0.47 b 4.31±0.02 a 7.43±0.85 bc 3.11±0.18 a 6.67±0.62 b 4.14±0.20 a 8.95±0.41 c LCP (μmol photons m−2 s−1) 55±17 a 29±6 c 35±4 c 19±3 b 32±6 c 7±1 d 19±3 b 13±3 b Rd (μmol O2 m−2s−1) –4.0±0.7 a –4.1±0.6 a –4.0±0.2 a –3.3±0.4 a –1.8±0.2 b –1.3±0.1 c –0.9±0.3 d –1.2±0.3 d These parameters were obtained from the analysis of light response curves (Fig. 1). Cold-acclimated (4 °C) and non-acclimated (15 °C) plants of both ecotypes were measured at 4 °C and 15 °C, respectively. (⁠ ΦO2 ⁠, quantum yield of oxygen evolution; Pnmax, maximal rate of net photosynthesis; LCP, light compensation point; Rd, dark respiration rate. Different letters indicate statistically significant differences within each parameter. Results are means ±SE; n=3. Open in new tab Fig. 1. Open in new tabDownload slide Light response curve of photosynthetic oxygen evolution of C. quitensis in cold-acclimated (A) and non-acclimated (B) Antarctic (squares) and Andes (circles) ecotypes at either 4 °C and/or 15 °C under saturating CO2. Results are means ±SE; n=3. Fig. 1. Open in new tabDownload slide Light response curve of photosynthetic oxygen evolution of C. quitensis in cold-acclimated (A) and non-acclimated (B) Antarctic (squares) and Andes (circles) ecotypes at either 4 °C and/or 15 °C under saturating CO2. Results are means ±SE; n=3. Cold acclimation significantly increased LCP in both ecotypes (Table 1), which is associated with an increase in dark respiration observed in cold-acclimated plants. At either temperature, LCP was higher in the Andes ecotype, except in non-acclimated plants exposed to 15 °C, where the Andes ecotype exhibited a lower LCP (7 μmol photons m−2 s−1) than the Antarctic ecotype (13 μmol photons m−2 s−1). Low measuring temperature increased LCP in both ecotypes independently of growth temperature, with the exception observed in the non-acclimated Antarctic ecotype which exhibited non-significant statistical differences between LCP at 4 °C and 15 °C. The effect of cold acclimation on ΦO2 depends on the ecotype and measuring temperatures. The ΦO2 was higher in non-acclimated plants of the Andes ecotype at 15 °C and it was reduced upon cold acclimation. On the other hand, the Antarctic ecotype exhibited a tendency to increase ΦO2 upon cold acclimation at both measuring temperatures, although no statistically significant differences (P >0.05) were observed in this ecotype (Table 1). Effect of light and growth temperature on steady state fluorescence yield Light response curves of quantum yield of PSII (ΦPSII), in the Andes and Antarctic ecotypes of C. quitensis under non-acclimated and cold-acclimated conditions, showed a decline of ΦPSII with increasing PFD (Fig. 2A). Under cold-acclimated conditions, the Antarctic ecotype exhibited a significantly higher (P <0.05) quantum yield of PSII, compared with the Andes plants, except at the highest PFD (Fig. 2A). Under non-acclimated conditions, the Andes ecotype showed a slower decrease of ΦPSII than the Antarctic ecotype from 200 to 700 μmol photons m−2 s−1 of PFD (Fig. 2B). The Antarctic ecotype also had a higher proportion of open reaction centres measured as Fv′/Fm′ in the cold-acclimated state (Fig. 2C), while it showed no mayor differences with the Antarctic ecotype under non-acclimated conditions (Fig. 2D). Fig. 2. Open in new tabDownload slide Light response of fluorescence parameters in cold-acclimated (A, C, E) and non-acclimated (B, D, F) plants of both ecotypes of C. quitensis. Quantum yield of PSII (ΦPSII) (A, B), open reaction centres (⁠ Fv′/Fm′ ⁠) (C, D), and photochemical quenching (qP) (E, F) were measured at 15 °C. Mean values ±SE were calculated from five independent experiments. Fig. 2. Open in new tabDownload slide Light response of fluorescence parameters in cold-acclimated (A, C, E) and non-acclimated (B, D, F) plants of both ecotypes of C. quitensis. Quantum yield of PSII (ΦPSII) (A, B), open reaction centres (⁠ Fv′/Fm′ ⁠) (C, D), and photochemical quenching (qP) (E, F) were measured at 15 °C. Mean values ±SE were calculated from five independent experiments. Cold-acclimated Antarctic plants demonstrated significantly higher (P <0.05) photochemical quenching (qP) compared with the Andes ecotype (Fig. 2E). The opposite effect was observed under non-acclimated conditions, where the Andes ecotype had higher values of qP than the Antarctic one in the range of 200–800 μmol photons m−2 s−1 (Fig. 2F). Growth and measuring temperature and light effects on NPQ components NPQ and its fast and slow relaxation components were studied in both ecotypes of C. quitensis under cold-acclimated and non-acclimated conditions at different PFD and at two different temperatures, 15 °C and 4 °C, which are the optimum temperature for photosynthesis and the temperature used for cold acclimation, respectively. In general, cold-acclimated plants exhibited higher NPQ values at lower PFD than non-acclimated ones when measured at 15 °C (Fig. 3B), while no major differences were observed at 4 °C (Fig. 3A). Cold-acclimated Antarctic plants exposed to the 15 °C measuring temperature exhibited the highest capacity for NPQ, reaching values over 5.0 at high PFD (Fig. 3A). Non-acclimated leaves of both C. quitensis ecotypes exposed to the low measuring temperature (4 °C) showed a greater capacity for NPQ at lower actinic light and a lower increase with increasing the light intensity than at 15 °C, reaching similar NPQ values at high PFD under both growth temperature regimes (Fig. 3B). Fig. 3. Open in new tabDownload slide Analyses of fast (NPQF) and slow (NPQs) relaxing components of NPQ in C. quitensis. NPQ components were determined at 4 °C (empty symbols) and 15 °C (solid symbols) using non-acclimated and cold-acclimated leaves of both ecotypes of C. quitensis. Results are means ±SE; n=5. Fig. 3. Open in new tabDownload slide Analyses of fast (NPQF) and slow (NPQs) relaxing components of NPQ in C. quitensis. NPQ components were determined at 4 °C (empty symbols) and 15 °C (solid symbols) using non-acclimated and cold-acclimated leaves of both ecotypes of C. quitensis. Results are means ±SE; n=5. The fast relaxing component of the non-photochemical quenching (NPQF), which is associated with the energy-dependent NPQ (qE), reached the highest values at the 15 °C measuring temperature, being higher in the Antarctic ecotype at both measuring and growth temperatures at PFDs higher than 600 μmol photons m−2 s−1 (Fig. 3C, D). The cold-acclimated Andes ecotype measured at 4 °C exhibited little NPQF, which was saturated at the lowest light intensity of about 100 μmol photons m−2 s−1. In non-acclimated plants, NPQF was lower when measured at 4 °C than at 15 °C (Fig. 3D) and was similar to that of cold-acclimated plants of the Antarctic ecotype measured at 4 °C. Cold-acclimated plants exhibited minimal differences in NPQs regardless of the measuring temperature (Fig. 3E). No significant differences in NPQs were observed between the two non-acclimated ecotypes measured at 15 °C, compared with cold-acclimated plants (Fig. 3F). By contrast, there was a significantly higher NPQs in both ecotypes when the measuring temperature was 4 °C, with respect to 15 °C. The highest NPQ values were observed in non-acclimated Andes plants at high PFD (Fig. 3F). Pigments and xanthophyll cycle under photoinhibitory conditions Chlorophylls and carotenoids were measured in non-inhibited control (LL) and photoinhibited (HL) at 4 °C leaves of both Antarctic and Andes ecotypes of C. quitensis under non-acclimated and cold-acclimated growth conditions. In general, plants of the Andes ecotype showed lower total chlorophyll (Chl a+b) and carotenoid contents than the Antarctic ecotype independently of light and temperature treatments (Table 2). Cold-acclimated plants of both ecotypes also exhibited lower chlorophyll and carotenoid contents, the lowest content of pigments being observed in HL-treated cold-acclimated Andean plants, which also showed the lowest Chl/Car ratio (Table 2). Chl a/Chl b ratios were similar for both ecotypes in all treatments. Cold-acclimated and non-acclimated plants of both ecotypes showed an increase of chlorophylls and carotenoids under HL compared with LL treatment, and this increase was smaller in cold-acclimated plants (Table 2). This effect was not observed for the total pool of xanthophyll cycle pigments (VAZ). On the contrary, significantly higher levels of the VAZ pool (P <0.05) were observed in either cold-acclimated or non-acclimated C. quitensis plants of both ecotypes under HL conditions. The highest VAZ content was observed in the non-acclimated HL-treated Antarctic ecotype (Fig. 4). These results clearly indicate that some de novo synthesis of xanthophyll cycle pigments occurs during the HL treatments in both ecotypes, especially in non-acclimated plants. The epoxidation state (EPS) of the xanthophyll pool, which represents the inverse of the efficiency of violaxanthin conversion to zeaxanthin via antheraxanthin (Demmig-Adams and Adams III, 1996) was about 0.9 for both ecotypes at LL with no significant effect of cold-acclimation (Fig. 4). As expected, HL treatment caused a significant decrease in EPS reaching, in both non-acclimated ecotypes and in cold-acclimated Andes plants, values around 0.4 (Fig. 4B). Interestingly, cold-acclimated Antarctic plants showed a significantly (P <0.05) higher EPS (0.62) corresponding to less efficient conversion of violaxanthin to zeaxanthin under HL exposure than non-acclimated ones (Fig. 4). Table 2. Effects of low temperature-induced photoinhibition on pigments composition of C. quitensis ecotypes Pigments (μg g−1 FW) Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic LL HL LL HL LL HL LL HL Chl a 478±30 a 416±29 a 538±23 ab 601±41 bc 643±33 c 719±25 c 731±6 d 831±25 e Chl b 176±2 a 153±1 b 177±5 a 204±9 c 229±4 d 259±4 e 250±4 f 287±6 g Chl a/Chl b 2.7±0.1 a 2.7±0.2 a 3.0±0.1 a 2.9±0.1 a 2.8±0.2 a 2.8±0.1 a 2.9±0.1 a 2.9±0.1 a β-Carotene 43±3 a 41±2 a 53±2 b 56±4 b 58±3 bc 61±1 c 66±2 d 70±2 d Lutein 66±3 a 71.2±0.8 ab 70±1 a 80±4 b 83±1 b 100.1±0.5 c 86±4 b 98±1 c Neoxanthin 10.4±0.6 a 10.2±0.4 a 14.5±0.7 b 14.4±0.5 b 14±1 b 17.8±0.8 c 16.8±0.8 c 18.2±0.5 c Chl/Car 4.6±0.1 a 3.7±0.1 b 4.3±0.1 a 4.4±0.1 a 4.8±0.2 a 4.5±0.1 a 4.8±0.1 a 4.8±0.1 a Pigments (μg g−1 FW) Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic LL HL LL HL LL HL LL HL Chl a 478±30 a 416±29 a 538±23 ab 601±41 bc 643±33 c 719±25 c 731±6 d 831±25 e Chl b 176±2 a 153±1 b 177±5 a 204±9 c 229±4 d 259±4 e 250±4 f 287±6 g Chl a/Chl b 2.7±0.1 a 2.7±0.2 a 3.0±0.1 a 2.9±0.1 a 2.8±0.2 a 2.8±0.1 a 2.9±0.1 a 2.9±0.1 a β-Carotene 43±3 a 41±2 a 53±2 b 56±4 b 58±3 bc 61±1 c 66±2 d 70±2 d Lutein 66±3 a 71.2±0.8 ab 70±1 a 80±4 b 83±1 b 100.1±0.5 c 86±4 b 98±1 c Neoxanthin 10.4±0.6 a 10.2±0.4 a 14.5±0.7 b 14.4±0.5 b 14±1 b 17.8±0.8 c 16.8±0.8 c 18.2±0.5 c Chl/Car 4.6±0.1 a 3.7±0.1 b 4.3±0.1 a 4.4±0.1 a 4.8±0.2 a 4.5±0.1 a 4.8±0.1 a 4.8±0.1 a Cold-acclimated and non-acclimated plants of Andes and Antarctic ecotypes were exposed to high light (HL, 1600 μmol photons m−2 s−1) and low light intensity (LL, 100 μmol photons m−2 s−1).at 4 °C for 2 h. Different letters indicate statistically significant differences within each parameter. Results were means ±SE; n=5. Open in new tab Table 2. Effects of low temperature-induced photoinhibition on pigments composition of C. quitensis ecotypes Pigments (μg g−1 FW) Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic LL HL LL HL LL HL LL HL Chl a 478±30 a 416±29 a 538±23 ab 601±41 bc 643±33 c 719±25 c 731±6 d 831±25 e Chl b 176±2 a 153±1 b 177±5 a 204±9 c 229±4 d 259±4 e 250±4 f 287±6 g Chl a/Chl b 2.7±0.1 a 2.7±0.2 a 3.0±0.1 a 2.9±0.1 a 2.8±0.2 a 2.8±0.1 a 2.9±0.1 a 2.9±0.1 a β-Carotene 43±3 a 41±2 a 53±2 b 56±4 b 58±3 bc 61±1 c 66±2 d 70±2 d Lutein 66±3 a 71.2±0.8 ab 70±1 a 80±4 b 83±1 b 100.1±0.5 c 86±4 b 98±1 c Neoxanthin 10.4±0.6 a 10.2±0.4 a 14.5±0.7 b 14.4±0.5 b 14±1 b 17.8±0.8 c 16.8±0.8 c 18.2±0.5 c Chl/Car 4.6±0.1 a 3.7±0.1 b 4.3±0.1 a 4.4±0.1 a 4.8±0.2 a 4.5±0.1 a 4.8±0.1 a 4.8±0.1 a Pigments (μg g−1 FW) Cold-acclimated Non-acclimated Andes Antarctic Andes Antarctic LL HL LL HL LL HL LL HL Chl a 478±30 a 416±29 a 538±23 ab 601±41 bc 643±33 c 719±25 c 731±6 d 831±25 e Chl b 176±2 a 153±1 b 177±5 a 204±9 c 229±4 d 259±4 e 250±4 f 287±6 g Chl a/Chl b 2.7±0.1 a 2.7±0.2 a 3.0±0.1 a 2.9±0.1 a 2.8±0.2 a 2.8±0.1 a 2.9±0.1 a 2.9±0.1 a β-Carotene 43±3 a 41±2 a 53±2 b 56±4 b 58±3 bc 61±1 c 66±2 d 70±2 d Lutein 66±3 a 71.2±0.8 ab 70±1 a 80±4 b 83±1 b 100.1±0.5 c 86±4 b 98±1 c Neoxanthin 10.4±0.6 a 10.2±0.4 a 14.5±0.7 b 14.4±0.5 b 14±1 b 17.8±0.8 c 16.8±0.8 c 18.2±0.5 c Chl/Car 4.6±0.1 a 3.7±0.1 b 4.3±0.1 a 4.4±0.1 a 4.8±0.2 a 4.5±0.1 a 4.8±0.1 a 4.8±0.1 a Cold-acclimated and non-acclimated plants of Andes and Antarctic ecotypes were exposed to high light (HL, 1600 μmol photons m−2 s−1) and low light intensity (LL, 100 μmol photons m−2 s−1).at 4 °C for 2 h. Different letters indicate statistically significant differences within each parameter. Results were means ±SE; n=5. Open in new tab Fig. 4. Open in new tabDownload slide Xanthophyll cycle pigment contents and epoxidation state (numbers on top of the bars) in cold-acclimated (A) and non-acclimated (B) plants of both ecotypes of C. quitensis exposed to high light, (HL) 1600 μmol photons m−2 s−1 and low light, (LL) 100 μmol photons m−2 s−1 at 4 °C for 2 h. Results are means ±SE; n=5. Fig. 4. Open in new tabDownload slide Xanthophyll cycle pigment contents and epoxidation state (numbers on top of the bars) in cold-acclimated (A) and non-acclimated (B) plants of both ecotypes of C. quitensis exposed to high light, (HL) 1600 μmol photons m−2 s−1 and low light, (LL) 100 μmol photons m−2 s−1 at 4 °C for 2 h. Results are means ±SE; n=5. Discussion Concomitant with earlier studies, net O2 evolution measured in laboratory-grown C. quitensis plants was higher in the Antarctic ecotype compared with the Andes one (Table 1) and the values for Pnmax were similar to those observed in field experiments. For instance, Amax values of 8 μmol CO2 m−2 s−1 have been reported in the Antarctic plants (Xiong et al., 1999), while lower net photosynthesis (5.0 μmol CO2 m−2 s−1) was measured in plants from the Andes under field conditions (Casanova-Katny et al., 2006). It has been suggested that the moderately higher rate of net photosynthesis in the Antarctic ecotype may be associated with the thicker leaves of this ecotype (Gianoli et al., 2004). This is consistent with the higher chlorophyll content in the Antarctic ecotype on fresh weight bases (Table 2) and the lack of significant differences between the Antarctic and Andes ecotypes when net photosynthesis was expressed on chlorophyll bases (data not shown). The higher net photosynthesis and lower LCP at low temperature demonstrates the ability of the Antarctic ecotype to maximize its photosynthetic performance, which optimizes the energy allocated for growth and reproduction in a short period with favourable temperature and very unstable light supply for photosynthesis (Xiong and Day, 2001). It is interesting to note that in cold-acclimated plants of C. quitensis dark respiration was enhanced (Table 1). This confirms a previous observation of CO2 uptake in the cold-acclimated Antarctic ecotype of C. quitensis measured at low temperature (Pérez-Torres et al., 2006). The increase in dark respiration caused by cold-acclimation may be associated with the ability of this species to survive several months covered by snow. In fact, it has been shown that, in over-wintering winter wheat, the activity of respiratory enzymes is increased during the autumn to maintain the adenylate energy charge (Sagisaka et al., 1991). Lower growth temperature exacerbates the alternative oxidase pathway (Vanlerberghe and McIntosh, 1992). Overexpression of alternative oxidase has been shown to alleviate oxidative stress in transgenic A. thaliana under low temperature (Sugie et al., 2006). Furthermore, it has recently been reported that up-regulation of mitochondrial alternative oxidase occurs concomitantly with chloroplast over-reduction by excess light in A. thaliana (Yoshida et al., 2007). These authors suggest that the alternative pathway can dissipate the excess reducing equivalents, which are transported from the chloroplasts, and serve in efficient photosynthesis. It is not yet clear whether this increased dark respiration in cold-acclimated C. quitensis is due to alternative oxidase up-regulation. Steady-state fluorescence yield and fluorescence quenching analyses demonstrated that the photosynthetic apparatus of both non-acclimated C. quitensis ecotypes responded similarly to light intensity. However, cold acclimation induced differential responses of PSII photochemical performance measured as ΦPSII, Fv′/Fm′ ⁠, and qP in these two ecotypes. Overall, growth at low temperature (cold acclimation) increased photochemical performance of the Antarctic ecotype showing higher values of ΦPSII, Fv′/Fm′ ⁠, and qP, while PSII photochemistry in cold-acclimated Andes plants was slightly suppressed (Fig. 2A–F). In both ecotypes, heat dissipation of absorbed excess light energy (NPQ) was mostly associated with the fast relaxing component, or NPQF (Fig. 3) related to the ΔpH- and zeaxanthin-dependent energy quenching (qE) within the LHCII antenna (Walters and Horton, 1991; Xu et al., 2000). Cold acclimation significantly modified the xanthophyll cycle activity in the Antarctic ecotype where a higher EPS was observed during exposure to HL (Fig. 4) as compared to non-acclimated plants and the Andes ecotype at both growth conditions. This unexpected result is quite intriguing, because the cold-acclimated Antarctic ecotype exhibited the highest NPQ and almost 90% of it was associated with NPQF (Fig. 3C, E). One possible explanation would be a greater contribution of an additional quenching process independent of zeaxanthin-mediated NPQ. The existence of such an additional quenching mechanism is consistent with earlier observations that significant levels of NPQ can occur independent of zeaxanthin (Hurry et al., 1997; Demmig-Adams et al., 1999) and cannot be accounted for by antenna quenching (Kramer, 2004). It has been proposed recently that dissipation of excess light energy via PSII reaction centre quenching might serve as such an additional quenching mechanism in cold-acclimated plants when the enzymatic conversion of violaxanthin to zeaxanthin within the xanthophyll cycle is thermodynamically restricted by low temperatures (Ivanov et al., 2003). Indeed, reaction centre quenching of excess light was suggested to play a substantial role in supplementing the antenna-based NPQ in cold-acclimated Scots pine (Ivanov et al., 2002) and cold-hardened Arabidopsis (Sane et al., 2003) and barley plants (Ivanov et al., 2006). Alternatively, the apparent uncoupling of NPQ from the EPS levels observed in the cold-acclimated Antarctic ecotype could also be due to the fact that only a few molecules of zeaxanthin are required for a fully active heat dissipation mechanism (Bukhov et al., 2001). Another interesting observation is that both ecotypes exposed to low temperature (4 °C), regardless of their acclimation state, exhibited higher values of NPQF even at very low PFD compared with plants exposed or acclimated to 15 °C (Fig. 3C, D). At very low PFD, electron transport rates would be too low to create and sustain a stable ΔpH within the magnitude required for the conversion of violaxanthin to zeaxanthin. In addition, no zeaxanthin and/or antheraxantin are present in neither ecotype at low PFD (Fig. 4). Considering these two facts, it appears plausible to suggest that under very low light intensities non-radiative energy dissipation within PSII reaction centres may be induced prior to the detection of antenna quenching as proposed earlier (Finazzi et al., 2004). Moreover, decreased photosynthetic capacity and increased dark respiration was observed in cold-acclimated ecotypes (Table 1). The enhanced rate of dark respiration has been reported earlier in cold-hardened plants such as cereals (Hurry et al., 1995) and conifers (Krivosheeva et al., 1996). It has been suggested that enhanced dark respiration, coupled to an enhanced activity of NADP-malate dehydrogenase, may contribute electrons for non-photochemical reduction of PQ (Savitch et al., 2000). This implies that low temperature may favour a light-independent process that can maintain a trans-thylakoid proton gradient at low light, possibly chlororespiration (Field et al., 1998). It has been suggested that the contribution of the chlororespiratory electron flux involving the NDH-complex and PTOX to total electron flow in the chloroplast and its photoprotective role as an alternative electron sink is rather limited under optimal growth conditions (Ort and Baker, 2002; Rosso et al., 2006). However, chlororespiration has been demonstrated to play an important photoprotective role in the high alpine plant species Ranunculus glacialis acclimated to low temperature (Streb et al., 2005). Furthermore, up-regulation of PTOX and the chloroplast NDH-complex have been reported in oat plants subjected to heat and high light stresses (Quiles, 2006). These data support the role of chlororespiration as an alternative electron sink in alleviating over-reduction of the PQ pool under unfavourable environmental conditions, which might be important for C. quitensis plants acclimated to the harsh environment of the Andes and Antarctica. The increased contribution of the slow relaxing component (NPQs) of NPQ, which corresponds to photoinhibitory damage of PSII (Walters and Horton, 1991) in non-acclimated plants of both ecotypes exposed to 4 °C and high light compared to cold-acclimated plants (Fig. 3E, F), indicates that growth at low temperatures stabilizes the photosynthetic apparatus. The highest NPQs values were observed in non-acclimated Andes plants exposed to 4 °C and high light. This indicates that this ecotype is more susceptible to low temperature induced photoinhibition of PSII. In the field, under clear sky days the Andes ecotype is often under high irradiances (2500 μmol photons m−2 s−1), where temperature close to the soil can approach to 25 °C. Conversely, the Antarctic ecotype rarely experiences 1500 μmol photons m−2 s−1 and air temperature close to the soil is near 10 °C in snow free areas of the Maritime Antarctic. Therefore, the Andes ecotype is prepared to cope with high irradiances at relatively high temperatures, whereas the Antarctic ecotype can withstand low temperature and high irradiances better than the Andes one. Furthermore, freezing resistance of these ecotypes is consistent with the behaviour of their photosynthetic machineries. While the Antarctic ecotype is more freezing tolerant, reaching an LT50 of about –15 °C after 21 d of cold acclimation, the Andes ecotype only reaches an LT50 of –10 °C after the same acclimation time (Gianoli et al., 2004). It is suggested here that different selective pressure imposed by constant low temperature, shorter growing season, and a variable and sometimes limited light resource in the Antarctic relative to the Andes (Xiong et al., 1999; Xiong and Day, 2001) have genetically conditioned the Antarctic ecotype to an improved disposition for cold acclimation and, in this state, to maximize its photosynthetic performance. Abbreviations Abbreviations A antheraxanthin EPS epoxidation state of the xanthophyll cycle pigments Fo instantaneous (dark) chlorophyll fluorescence at open PSII centres in dark-adapted samples Fm maximal fluorescence at closed PSII centres Fv variable fluorescence HL high light intensity LCP light compensation point LL low light intensity NPQ non-photochemical quenching NPQF, NPQs fast and slow relaxing component of the NPQ; respectively Pnmax maximum rate of net photosynthesis ΦPSII quantum yield of PSII ΦO2 quantum yield of oxygen evolution qE energy-dependent quenching of chlorophyll fluorescence qI photoinhibitory quenching qP photochemical quenching Rd dark respiration VAZ pool of the xanthophyll cycle pigments V violaxanthin Z zeaxanthin The authors gratefully thank Fondecyt 1010899 and DI 205.111.042–1S for funding. LA Bravo thanks MECESUP UCO-9906 for supporting a research stay in London, ON, Canada. N Huner is grateful for the support of NSERC. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2007 The Author(s).

Jiang, Yuanqing; Yang, Bo; Harris, Neil S.; Deyholos, Michael K.

doi: 10.1093/jxb/erm207pmid: 17916636

Abstract NaCl stress is a major abiotic stress limiting the productivity and the geographical distribution of many plant species. Roots are the primary site of salinity perception. To understand better NaCl stress responses in Arabidopsis roots, a comparative proteomic analysis of roots that had been exposed to 150 mM NaCl for either 6 h or 48 h was conducted. Changes in the abundance of protein species within roots were examined using two-dimensional electrophoresis. Among the >1000 protein spots reproducibly detected on each gel, the abundance of 112 protein spots decreased and 103 increased, at one or both time points, in response to NaCl treatment. Through liquid-chromatography–tandem mass spectrometry, identity was assigned to 86 of the differentially abundant spots. The proteins identified included many previously characterized stress-responsive proteins and others related to processes including scavenging for reactive oxygen species; signal transduction; translation, cell wall biosynthesis, protein translation, processing and degradation; and metabolism of energy, amino acids, and hormones. At the resolution of individual genes and proteins, poor statistical correlation (6 h, r= –0.13; 48 h, r=0.11) of these protein expression data with previous microarray results was detected, supporting the concept that post-transcriptional regulation plays an important role in stress-responsive gene expression, and highlighting the need for combined transcriptomic and proteomic analyses. Arabidopsis, 2-DGE, LC-MS/MS, NaCl stress, proteome, root Introduction Soil salinity is a prevalent abiotic stress that limits the productivity and geographical distribution of plants. Natural phenomena and human practices such as irrigation can cause salts to accumulate in soil (Wiebe et al., 2007). Excess NaCl in the soil solution interferes with mineral nutrition and water uptake, and leads to accumulation of toxic ions in plants. To reduce these detrimental effects, plants use several strategies, including the regulated expression of specific proteins, which leads to the re-establishment of proper cellular ion and osmotic homeostasis with other concomitant processes of repair and detoxification (Chinnusamy et al., 2005). Roots are a site of perception and injury for several types of stress, including salinity, nutrient deficiency, and heavy metals. In many circumstances, it is the stress sensitivity of the root that limits the productivity of the entire plant (Atkin et al., 1973; Steppuhn and Raney, 2005). An improved understanding of molecular responses of roots to NaCl treatment may therefore facilitate the development of crops with increased tolerance to NaCl and other stresses. To build a useful description of the molecular mechanisms active in the response of roots to NaCl treatment, it is necessary to characterize the components of these mechanisms, including proteins. Proteomic profiles have been produced for various stresses and species, including NaCl-treated roots of pea (Pisum sativum L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.), as well as drought-treated poplar (Populus trichocarpa Torr. & A.Gray), and cadmium- or arsenic-treated maize (Zea mays L.), rice, and Arabidopsis thaliana (L.) Heynh. (Majoul et al., 2000; Kav et al., 2004; Requejo and Tena, 2005, Yan et al., 2005; Aina et al., 2006; Plomion et al., 2006; Roth et al., 2006). Recently, a technology based on two-dimensional gel electrophoresis (2-DGE) was employed to identify NaCl- and osmotic-responsive proteins in Arabidopsis cell suspension and root microsomal fraction (Lee et al., 2004; Ndimba et al., 2005). In the present study, a moderate NaCl stress was applied to hydroponic-cultured Arabidopsis roots to identify proteins that are responsive to NaCl treatment. Transcriptome profiling, a widely used technique to identify NaCl-responsive genes, has contributed to our understanding of salinity stress in species including Arabidopsis and rice (Kawasaki et al., 2001; Kreps et al., 2002; Seki et al., 2002; Rabbani et al., 2003; Chao et al., 2005; Jiang and Deyholos, 2006). However, transcriptome profiling has some limitations because mRNA levels are not always correlated to those of corresponding proteins, due in part to post-transcriptional regulation. Only poor or moderate correlation between changes in the levels of specific mRNAs and their corresponding proteins has been reported previously in studies involving yeast (Saccharomyces cerevisiae), animals, or Arabidopsis (Gygi et al., 1999; Tian et al., 2004; Mooney et al., 2006). Furthermore, post-translational modifications, such as phosphorylation and glycosylation, can result in a dramatic increase in proteome complexity without a concomitant increase in gene expression (Jensen, 2004; Rose et al., 2004). These biological realities motivated us to perform an analysis of NaCl stress responses at the proteome level, and to compare these results with previous, microarray-based studies of similarly treated tissues (Jiang and Deyholos, 2006). Materials and methods Plant materials and stress treatment Arabidopsis plants (wild-type, ecotype Col-0) were cultured hydroponically as described previously (Jiang and Deyholos, 2006). At 18 d post-germination (dpg), the hydroponic solution was changed to fresh, half-strength Murashige and Skoog (MS) medium, either with or without 150 mM NaCl, and was maintained for 6 h or 48 h, with roots harvested from control or NaCl-treated plants in parallel at each time point, flash frozen in liquid nitrogen, and stored at –80 °C. Three biologically independent replicates were prepared at separate times. Physiological analyses For root elongation assays, 5 dpg, wild-type Arabidopsis seedlings, grown vertically on half-strength MS medium supplemented with 1% sucrose and 0.8% Phytablend (Caisson), were transferred onto half-strength MS plates supplemented with 1% sucrose and 0, 50, 100, 150, 200, or 250 mM NaCl in square Petri dishes. Root lengths were measured after 7 d. For ion concentration determination, hydroponic-cultured Arabidopsis roots and shoots (18 dpg) were dried for 2 d in an oven at 65 °C. Dry root or leaf samples (100–500 mg) were digested according to the EPA 3050B method (http://www.epa.gov/SW-846/pdfs/3050b.pdf) with modifications. Na and K concentrations were determined by flame emission spectroscopy (AAnalyst700; PerkinElmer). For the relative electrolyte leakage (REL) assay, ∼150 mg of hydroponic-cultured seedlings, 18 dpg old, were rinsed with ddH2O, placed in test tubes containing 10 ml of ddH2O, and incubated at room temperature for 2 h, with the electrical conductivity of the solution (C1) measured using a conductivity meter (Orion 115Aplus; ThermoElectron). Then, the tubes were boiled for 15 min and cooled to room temperature, and the electrical conductivity (C2) was measured again. The REL was calculated by the formula C1/C2×100% (Cao et al., 2007). Protein extraction and quantification Total protein extracts were prepared essentially according to the method described by Tsugita and Kamo (1999) with modifications. In brief, ∼1 g of control and NaCl-treated roots were homogenized separately to a fine powder in liquid nitrogen, and were transferred into three 2 ml tubes. One millilitre of 10% (w/v) trichloroacetic acid/0.07% dithiothreitol (DTT) in acetone was added to each tube and incubated at −20 °C for 1 h. Afterwards, tubes were centrifuged at 18 000 g for 15 min and the supernatants discarded. The pellets were washed by resuspension in ice-cold acetone containing 0.07% DTT and centrifuged as described above. This wash was repeated three times, with the pellets dried at room temperature in a SpeedVac for 15 min and resuspended in 300 μl of lysis buffer (30 mM TRIS-HCl, 7 M urea, 2 M thiourea, 4% CHAPS, pH 8.5). The samples were mixed vigorously, incubated overnight at 4 °C, and centrifuged at room temperature for 15 min at 18 000 g, with the supernatants collected into fresh tubes. The protein extracts were cleaned up using 2-D Cleanup kit (GE Healthcare), dissolved in 200 μl of Destreak rehydration buffer containing 2% pH 3–10 immobilized pH gradient (IPG) buffer (GE Healthcare), and quantified by using a 2-D Quant kit (GE Healthcare) using bovine serum albumin as the standard. Isoelectric focusing (IEF) and sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) Two-dimensional electrophoresis of protein extracts was performed using a GE Healthcare 2-DGE system according to the manufacturer's manuals. Briefly, IPG strips (pH 3–10 NL, 24 cm) were rehydrated in 450 μl of Destreak rehydration buffer (containing 2% pH 3–10 IPG buffer) overnight (∼15 h). IEF was performed using an IGPhor isoelectric focusing unit with 400 μg of protein samples loaded by cup-loading. The voltage and duration used were as follows: first step and hold: 300 V, 3 h; second gradient: 1000 V, 6 h; third gradient: 8000 V, 3 h; fourth step and hold: 8000 V, 4 h 40 min. Prior to second dimension separation, the strips were incubated first in an equilibration buffer (6 M urea, 30% v/v glycerol, and 2% SDS in 0.05 M TRIS-HCl, pH 8.8) containing 15 mM DTT for 15 min, then in an equilibration buffer containing 2.5% iodoacetamide (GE Healthcare) for another 15 min, followed by brief equilibration in 1× SDS–TRIS–glycine running buffer for 5 min. The second dimension separation of proteins was performed on SDS–PAGE gel (12.5% acrylamide, Bio-Rad) using the Ettan Dalt Six apparatus (GE Healthcare) with protein markers (Cat#SM0661, Fermentas) loaded at the left-most side. The electrophoresis was carried out at ∼25 °C and 2.5 w/gel for 30 min and then 17 w/gel for ∼5 h 40 min until the bromophenol blue dye front arrived at the bottom of the gels. Following SDS–PAGE, gels were washed in ddH2O three times for 15 min and proteins were detected by a modified collodial Coomassie brilliant blue staining-blue silver protocol, which was assessed to have sensitivity comparable with silver staining (Candiano et al., 2004). After three washes in ddH2O, the 2-D gels were scanned immediately using a Fuji FLA-5000 scanner (Fujifilm) with a resolution of 100 μm and 16 bit greyscale pixel depth. A total of 12 gels were analysed, i.e. three gels (biologically independent replicates) for each of two treatments (0 mM or 150 mM NaCl) at each of two time points (6 h or 48 h). This experimental design was balanced with respect to all conditions, and the use of three gels per condition is consistent with recommendations based on previous statistical analyses of protein gel electrophoresis (Hunt et al., 2005). For isoelectric point and molecular weight calibrations, the 2-D internal standards (Cat#161-0320; Bio-Rad) were used with IEF, SDS–PAGE, staining, and scanning performed as above. Image and statistical analysis 2-D gel images were analysed using ImageMaster 2-D Platinum 6.0 (GE Healthcare). To verify the autodetected results, all spots were manually inspected and edited as necessary. After spot detection, quantification, and background subtraction, each gel analysed was matched individually to the reference gel, and matched spots were grouped into subclasses. To compensate for subtle differences in sample loading, gel staining, and destaining, the volume of each spot (i.e. spot abundance) was normalized as relative volume. This normalization method, provided by ImageMaster 2D Platinum 6.0 software, divides each spot volume value by the sum of total spot volume values to obtain individual relative spot volumes. Class reports were generated for spots of interest. The differences in expression between control and treatment were analysed by Student's t-test with P ≤0.05 considered significant. The molecular masses of proteins on gels were determined by co-electrophoresis of standard protein markers (Fermentas) and internal 2-D internal standards (Bio-Rad) according to the software manual. For the figures shown, spot IDs were renumbered, using the annotation tool in ImageMaster Platinum 6.0, and image brightness and contrast were adjusted, and the protein marker sizes were added using Photoshop CS (Adobe). In-gel digestion Protein spots showing at least a 1.5-fold difference in abundance between control and treatment at one or both time points with P <0.05 were selected and excised manually into 1.5 ml microtubes. The selection of a 1.5-fold change as an arbitrary threshold allowed us to focus on the most responsive proteins for subsequent characterization, and is consistent with thresholds used previously in other microarray studies (Jiang and Deyholos, 2006, and references therein). Gel piece treatment and in-gel digestion of protein spots were performed following Jensen et al. (1999) with modifications. Briefly, gel pieces were first washed with 150 μl of HPLC-grade water (Fisher Scientific), dehydrated with 50 μl of 100% acetonitrile (ACN), then destained with 100 μl of 50 mM NH4HCO3/50% ACN for 2 h or longer until colourless. After dehydration with ACN again, gel pieces were reduced in 30 μl of 10 mM DTT/0.1 M NH4HCO3 at 56 °C for 30 min, dehydrated, and alkylated in 30 μl of 55 mM iodoacetamide/0.1 M NH4HCO3 at room temperature for 20 min in the dark, followed by rinsing with 200 μl of 0.1 M NH4HCO3, dehydration with ACN, and drying in a SpeedVac for 10 min. Afterwards, 20 μl of trypsin solution 0.02 μg μl−1 Trypsin Gold (Promega) in 40 mM NH4HCO3/10% ACN was added and incubated on ice for 1 h, then at 37 °C overnight (∼14 h). Finally, 3 μl of 2% formic acid (FA) was added to stop the digestion reactions, and the supernatant was collected into fresh tubes, followed by two extractions of peptides with 15 μl of 50% ACN/0.1% FA with the extracts (∼50 μl) mixed well and stored at –20 °C before use. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) LC-MS/MS analysis of digested peptide mixtures was performed using an Agilent 1100 LC/MSD Trap XCT (Agilent Technologies). Briefly, an autosampler was used to inject 20 μl of each tryptic digest onto the first of two C-18 columns. This short 5 μm enrichment column, Zorbax 300SB-C18, 5 μm, 5×0.3 mm, served to trap and concentrate the samples. Next, the sample was eluted onto the next C-18 column (Zorbax 300SB-C18, 5 μm, 150×0.3 mm), which was used in conjunction with a solvent gradient to separate the peptides. The peptide-separation gradient started at 85% solvent A (0.1% FA in H2O) and ended at 55% solvent B (0.1% FA, 5% H2O in ACN) over a 42 min span. This was followed by a 10 min period of 90% solvent B to cleanse the columns before returning to 97% solvent A for the next sample. The ion trap mass spectrometer collected information by first running an MS 300–2200 m/z scan and followed that with an MS/MS analysis of the most intense ions. In addition to the most intense ion for each scan, the software was set to exclude this ion after two spectra and gather MS/MS information on the next most intense ion(s). Raw spectral data were processed into Mascot Generic File (.mgf) format using the default method in the ChemStation Data Analysis module. The MS/MS ion search was performed using MASCOT (http://www.matrixscience.com) searching the NCBInr database and taking Arabidopsis as the taxonomy. The parameters for searching were: an MS/MS tolerance of ±0.8 Da, one missed cleavage site, enzyme of trypsin, fixed modifications of carbamidomethyl, peptide tolerance of ±2 Da, peptide charge of 1+ 2+ 3+, monoisotopic, and ESI-TRAP instrument. Only significant hits, as defined by the MASCOT probability analysis (P <0.05), were accepted. In case multiple significant hits were found for a protein, only the highest scoring hit was listed in Table 1, with all significant hits listed in Supplementary Supplementary Data available at JXB online. Table 1. Differentially expressed proteins identified by LC-MS/MS Spot ID AGI # Putative identity Molecular weight (kDa) (theoretical/experimental) Isoelectric point (theoretical/experimental) Scorea PMb C (%)c NCBI Acc # Fold change 6 h 48 h Energy metabolism 18* At3g52930 Fructose-bisphosphate aldolase 38.9/37.9 6.05/5.95 711 27 54 NP_190861 0.91±0.12 0.65±0.07 11 At1g65930 Isocitrate dehydrogenase (NADP+)/oxidoreductase 46.1/43.0 6.13/5.93 1433 42 62 NP_176768 0.4±0.10 0.69±0.13 19† At3g04120 Glyceraldehyde-3-phosphate dehydrogenase C subunit (GAPC) 37.0/37.0 6.62/6.62 852 41 52 NP_187062 0.55±0.08 0.46±0.11 43† At3g55440 Triose phosphate isomerase (TPI) 27.4/24.2 5.24/5.39 620 16 49 2009415A 0.62±0.15 0.75±0.09 12* At2g47510 Fumarase (FUM1)/fumarate hydratase 53.5/43.3 8.01/6.79 1004 27 47 NP_182273 0.63±0.20 0.88±0.16 22 At1g04410 Malate dehydrogenase/oxidoreductase 35.9/35.9 6.11/5.92 787 55 58 NP_171936 0.55±0.14 0.9±0.21 25† At1g53240 Malate dehydrogenase (NAD), mitochondrial 37.2/33.4 8.54/5.95 726 31 47 AAF69549 1.08±0.12 1.52±0.23 47 At3g27890 NADPH:quinone oxidoreductase (NQR) 21.5/22.4 6.84/6.51 308 10 30 AAD37373 0.45±0.23 1.55±0.17 33† At5g20080 Cytochrome-b5 reductase/oxidoreductase 36.1/27.6 8.76/6.72 463 16 32 NP_568391 0.92±0.16 0.58±0.14 2* At2g05710 Cytoplasmic aconitate hydratase 98.7/103.2 5.79/5.83 791 28 28 AAD25640 0.96±0.18 0.59±0.11 24 At1g22450 Cytochrome c oxidase subunit (COX6B) 21.4/33.9 4.31/5.01 83 5 20 AAM63485 0.44±0.16 NDT 8 At2g36530 Phosphopyruvate hydratase, enolase (LOS2) 48.0/48.0 5.54/5.54 1038 60 64 NP_181192 0.83±0.28 0.65±0.14 73* At1g78900 Vacuolar ATP synthase (VHA-A) subunit A 69.2/73.6 5.11/5.21 1483 37 52 NP_001031299 1.24±0.19 0.43±0.19 48 At5g47030 ATP synthase delta chain, mitochondrial 21.5/21.4 6.2/5.2 127 6 16 NP_199514 1.53±0.26 1.39±0.30 31† At4g11150 V-type proton-ATPase (TUF) 26.3/28.8 6.04/5.86 782 34 56 CAA63086 1.48±0.22 0.62±0.11 76* At3g03250 UDP-glucose pyrophosphorylase (UGP) 51.9/47.0 5.8/5.83 1128 29 54 NP_186975 0.49±0.19 0.66±0.15 10* At3g02360 Phosphogluconate dehydrogenase 53.9/46.4 7.02/6.91 741 22 35 NP_850502 0.68±0.14 0.57±0.21 57 At4g09320 Nucleoside-diphosphate kinase (NDPK1) 16.3/14.2 7.03/6.01 309 14 49 S31444 3.43±0.77 1.01±0.35 80† At1g47260 Mitochondrial gamma carbonic anhydrase 30.2/29.0 6.71/6.79 602 17 46 NP_175159 0.63±0.12 0.7±0.10 ROS scavenging and defence 67 At1g02930 Gluthatione S-transferase (ATGST1) 23.5/23.8 5.8/5.84 344 14 41 CAA72413 0.68±0.14 2.15±0.81 42 At4g02520 Glutathione S-transferase (ATGSTF2) 24.1/24.5 5.92/5.87 672 25 59 AAC78264 1.02±0.13 1.95±0.18 41* At4g02520 Glutathione S-transferase (ATGSTF2) 24.0/24.3 5.93/5.98 734 32 70 1BX9_A 0.79±0.25 1.71±0.20 44* At2g47730 Glutathione S-transferase (GST6) 24.1/23.2 6.09/5.93 582 18 55 AAC63629 0.78±0.10 1.5±0.17 58* At1g02920 Glutathione S-transferase (GST11) 23.6/24.0 6.31/6.19 468 17 48 CAA74639 0.61±0.11 2.06±0.50 36† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.4 5.72/5.82 681 50 54 CAA42168 0.62±0.13 1.51±0.07 61† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.8 5.72/5.65 387 13 51 NP_172267 1.08±0.25 1.58±0.10 35† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.1 5.72/5.71 473 16 54 CAA42168 0.64±0.15 0.64±0.11 21* At2g38390 Peroxidase23 (PER23) 38.6/35.4 8.33/9.19 328 18 33 NP_181373 NDT 1.89±0.45 64* At2g38380 Peroxidase22 (PER22) 38.7/39.6 5.95/5.87 352 16 20 AAA32842 1.05±0.31 2.15±0.58 52 At4g11600 Glutathione peroxidase 6 (ATGPX6) 18.8/20.3 6.59/5.96 239 7 62 BAA24226 0.4±0.17 2.61±1.14 15† At1g77120 Alcohol dehydrogenase (ADH1) 41.9/41.9 5.83/5.80 789 29 43 CAA54911 0.57±0.20 0.6±0.12 69 At3g10920 Manganese superoxide dismutase (MSD1) 25.5/22.9 8.47/5.95 470 16 58 NP_187703 1.33±0.35 3.71±1.08 60 At1g51980 Metalloendopeptidase 54.6/45.7 5.94/5.47 1126 33 44 NP_175610 0.35±0.18 0.91±0.23 49 At3g56240 Copper homeostasis factor 13.1/21.3 4.91/5.17 374 12 53 CAB87423 0.56±0.17 1.41±0.25 13† At5g03630 Monodehydroascorbate reductase (ATMDAR2) 47.5/43.3 5.24/5.25 1231 43 70 NP_568125 0.88±0.22 0.58±0.13 46 At4g11650 Osmotin-like protein (AtOSM34) 27.5/22.2 6.26/6.86 160 6 19 AAM61750 1.16±0.20 1.91±0.28 Protein translation, processing, and degradation 23* At3g09200 Ribosomal protein L10 34.2/34.6 5/5.17 411 38 42 NP_187531 0.92±0.15 0.48±0.18 27* At3g53870 Ribosomal protein S3a 27.5/32.6 9.57/9.13 786 31 56 CAB88349 0.83±0.17 0.64±0.07 55 At1g15930 Ribosomal protein S12 (RPS12A) 15.7/17.2 5.38/5.11 79 2 16 NP_173045 0.52±0.14 0.29±0.22 7† At4g34110 Poly(A)-binding protein 68.8/67.3 8.19/7.56 901 24 30 CAB80128 0.71±0.10 0.48±0.09 32* At3g12390 Nascent polypeptide-associated complex alpha chain 22.0/29.0 4.3/5.0 405 17 59 NP_187845 1.65±0.21 0.79±0.11 75* At1g77510 Protein disulphide isomerase (ATPDIL1-2) 56.6/67.3 4.9/5.16 1204 29 54 NP_177875 NDT NDT 63† At1g21750 Protein disulphide isomerase (ATPDIL1-1) 55.9/64.3 4.54/5.13 1480 40 53 NP_173594 1.5±0.37 0.33±0.19 5* At5g09590 mtHSC70-2 (heat shock protein 70) 73.2/73.6 5.63/5.34 1301 35 40 NP_196521 0.62±0.20 0.32±0.24 40* At4g31300 20S proteasome beta subunit A (PBA1) 25.2/24.5 5.31/5.58 669 16 49 CAA74028 0.67±0.30 1.5±0.21 29 At5g23540 26S proteasome subunit RPN11 34.4/32.9 6.3/6.19 544 17 41 AAP86672 0.53±0.17 0.4±0.20 17† At5g43060 Cysteine-type endopeptidase 52.4/39.6 5.86/5.11 528 18 24 NP_568620 0.53±0.10 0.85±0.11 38† At1g56450 Endopeptidase 27.7/24.7 6.09/7.01 522 13 52 NP_176040 2.31±0.59 0.58±0.12 78† At2g46280 Eukaryotic translation initiation factor 3 (eIF3I1/TRIP-1) 36.7/35.7 6.5/6.7 800 18 53 NP_182151 0.55±0.09 0.72±0.08 Cell wall-related 6* At1g47600 Glycosyl hydrolase family 1 protein 58.1/58.8 8.34/7.96 976 42 38 NP_175191 1.92±0.40 0.88±0.12 77* At1g66280 Glycosyl hydrolase family 1 protein 60.2/57.5 6.74/6.77 1177 35 37 NP_176802 0.56±0.10 0.51±0.13 86* At3g09260 Glycosyl hydrolase family 1 protein 60.4/62.9 6.95/6.69 925 23 39 AAB38783 0.6±0.09 1.59±0.17 65* At3g09260 Glycosyl hydrolase family 1 protein 60.3/62.9 6.95/6.56 629 23 24 AAB38783 0.54±0.11 3.04±1.01 62 At4g16260 Glycosyl hydrolase family 17 protein 37.7/32.4 6.43/8.33 100 4 22 AAL36038 NDC 1.63±0.12 26 At4g16260 Glycosyl hydrolases family 17 protein 37.7/32.6 6.43/6.95 793 54 67 AAL36038 NDC 1.59±0.23 56* At2g21660 Glycine-rich RNA-binding protein 7 (GRP7) 16.9/16.0 5.85/5.44 642 27 76 AAM62447 1.51±0.07 0.85±0.09 45 At4g14630 Germin-like protein (GLP9) 23.2/23.2 5.82/5.84 113 8 11 AAD00509 0.92±0.10 1.85±0.21 39 At5g38940 Oxalate oxidase (germin protein)-like protein 23.8/24.2 8.62/7.76 146 13 13 BAB08650 1.83±0.19 1.58±0.15 66* At3g02230 Reversibly glycosylated polypeptide (RGP1) 41.1/36.5 5.61/5.54 996 31 66 NP_186872 1.24±0.12 1.87±0.22 Hormone-related 81† At1g62380 ACC oxidase (ACO2) 36.4/38.3 4.97/5.1 490 12 31 AAC27484 1.31±0.21 0.66±0.09 59† At1g02500 S-Adenosylmethionine synthetase (SAM1) 43.6/45.1 5.5/5.52 740 25 41 AAA32868 0.67±0.12 0.51±0.11 85† At3g25780 Allene oxide cyclase 2 (AOC2) 28.5/22.4 9.19/7.6 141 6 27 NP_566777 NDC NDC 68† At3g16470 Jasmonate-inducible protein (JR1) 48.6/45.1 5.12/5.24 898 27 32 BAB01146 0.83±0.13 1.66±0.15 Signal transduction 83 At1g56340 Calreticulin 1 (CRT1) 48.7/61.5 4.46/5.04 807 20 40 NP_176030 0.57±0.10 1.53±0.10 84* At1g09210 Calreticulin 2 (CRT2) 48.4/53.7 4.37/5.02 956 38 43 NP_172392 0.61±0.17 1.62±0.19 28 At1g62480 Vacuolar calcium-binding protein-related 16.6/32.6 4.05/4.95 57 2 30 NP_564795 0.79±0.20 NDT 34 At5g20010 Small Ras-like GTP-binding protein (Ran-1) 25.6/27.2 6.38/6.45 237 7 35 AAA32851 0.43±0.15 2.69±1.10 Amino acid metabolism 16* At5g07440 Glu dehydrogenase 2 (GDH2) 45.0/41.4 6.07/5.96 915 32 46 NP_196361 0.63±0.14 1.61±0.18 20* At1g66200 Gln synthetase (GS) 47.5/39.6 5.97/5.11 499 14 36 1804333C 0.53±0.10 1.53±0.12 71* At5g14200 3-Isopropylmalate dehydrogenase (AtIMD1) 44.3/42.1 5.75/5.35 1051 24 47 AAU90074 1.02±0.35 0.57±0.07 3* At5g17920 Cobalamine-independent Met synthase 84.3/84.3 6.02/6.03 1319 41 35 1U1H_A 0.72±0.10 0.36±0.24 Cytoskeleton 14 At1g49240 Actin 8 42.1/42.7 5.37/5.4 670 17 36 AAC49523 0.38±0.26 0.75±0.16 70* At4g14960 Tubulin alpha-6 chain 50.2/47.6 4.93/5.13 682 17 35 CAB10275 1.74±0.21 1.44±0.19 74* At5g62690 Putative tubulin beta-2/beta-3 chain 51.4/48.0 4.7/5.05 1064 39 52 BAC42096 0.54±0.11 0.63±0.04 Transcription 50 At1g73230 (NAC) domain-containing protein 18.0/20.8 5.91/5.66 193 3 30 AAM61406 1.68±0.23 1.89±0.31 53 At1g17880 (NAC) domain-containing protein 17.9/18.9 6.62/6.25 402 12 63 NP_173230 1.05±0.19 1.76±0.20 Other metabolism 79 At1g53580 Hydroxyacylglutathione hydrolase, putative 27.0/28.8 5.45/5.26 348 11 34 2GCU_D 0.65±0.10 0.53±0.13 4 At3g60750 Transketolase-like protein 81.9/84.3 5.8/5.46 698 20 35 CAB82679 0.83±0.12 0.54±0.11 30† At5g09650 Inorganic pyrophosphatase (AtPPA6) 33.7/31.4 5.71/5.17 592 15 33 AAS57950 0.65±0.10 0.61±0.05 Unclassified and unknown 37† At2g43090 Aconitase C-terminal domain-containing protein 27.1/27.6 6.33/5.19 558 15 54 NP_181837 1.31±0.19 2.46±0.71 82 At4g23670 Major latex protein-related 17.6/15.4 5.91/5.86 277 17 42 NP_194098 1.55±0.10 NDC 1 At3g15950 Unknown protein 85.2/115.0 4.61/5.09 1094 31 32 NP_188216 0.73±0.12 0.23±0.15 9* At1g03220 Unknown protein 46.4/45.4 8.97/8.79 837 42 47 NP_171821 0.57±0.17 0.59±0.10 51* At3g52300 Putative protein 19.6/20.1 5.09/5.07 888 23 67 CAC07921 1.25±0.18 0.61±0.12 54 At5g10860 Unknown protein 22.8/18.2 9.1/7.01 396 9 37 NP_196647 0.75±0.16 1.52±0.11 72† At2g20360 Hypothetical protein 44.0/35.9 9.26/8.45 920 25 42 AAT68351 1.77±0.22 0.81±0.14 Spot ID AGI # Putative identity Molecular weight (kDa) (theoretical/experimental) Isoelectric point (theoretical/experimental) Scorea PMb C (%)c NCBI Acc # Fold change 6 h 48 h Energy metabolism 18* At3g52930 Fructose-bisphosphate aldolase 38.9/37.9 6.05/5.95 711 27 54 NP_190861 0.91±0.12 0.65±0.07 11 At1g65930 Isocitrate dehydrogenase (NADP+)/oxidoreductase 46.1/43.0 6.13/5.93 1433 42 62 NP_176768 0.4±0.10 0.69±0.13 19† At3g04120 Glyceraldehyde-3-phosphate dehydrogenase C subunit (GAPC) 37.0/37.0 6.62/6.62 852 41 52 NP_187062 0.55±0.08 0.46±0.11 43† At3g55440 Triose phosphate isomerase (TPI) 27.4/24.2 5.24/5.39 620 16 49 2009415A 0.62±0.15 0.75±0.09 12* At2g47510 Fumarase (FUM1)/fumarate hydratase 53.5/43.3 8.01/6.79 1004 27 47 NP_182273 0.63±0.20 0.88±0.16 22 At1g04410 Malate dehydrogenase/oxidoreductase 35.9/35.9 6.11/5.92 787 55 58 NP_171936 0.55±0.14 0.9±0.21 25† At1g53240 Malate dehydrogenase (NAD), mitochondrial 37.2/33.4 8.54/5.95 726 31 47 AAF69549 1.08±0.12 1.52±0.23 47 At3g27890 NADPH:quinone oxidoreductase (NQR) 21.5/22.4 6.84/6.51 308 10 30 AAD37373 0.45±0.23 1.55±0.17 33† At5g20080 Cytochrome-b5 reductase/oxidoreductase 36.1/27.6 8.76/6.72 463 16 32 NP_568391 0.92±0.16 0.58±0.14 2* At2g05710 Cytoplasmic aconitate hydratase 98.7/103.2 5.79/5.83 791 28 28 AAD25640 0.96±0.18 0.59±0.11 24 At1g22450 Cytochrome c oxidase subunit (COX6B) 21.4/33.9 4.31/5.01 83 5 20 AAM63485 0.44±0.16 NDT 8 At2g36530 Phosphopyruvate hydratase, enolase (LOS2) 48.0/48.0 5.54/5.54 1038 60 64 NP_181192 0.83±0.28 0.65±0.14 73* At1g78900 Vacuolar ATP synthase (VHA-A) subunit A 69.2/73.6 5.11/5.21 1483 37 52 NP_001031299 1.24±0.19 0.43±0.19 48 At5g47030 ATP synthase delta chain, mitochondrial 21.5/21.4 6.2/5.2 127 6 16 NP_199514 1.53±0.26 1.39±0.30 31† At4g11150 V-type proton-ATPase (TUF) 26.3/28.8 6.04/5.86 782 34 56 CAA63086 1.48±0.22 0.62±0.11 76* At3g03250 UDP-glucose pyrophosphorylase (UGP) 51.9/47.0 5.8/5.83 1128 29 54 NP_186975 0.49±0.19 0.66±0.15 10* At3g02360 Phosphogluconate dehydrogenase 53.9/46.4 7.02/6.91 741 22 35 NP_850502 0.68±0.14 0.57±0.21 57 At4g09320 Nucleoside-diphosphate kinase (NDPK1) 16.3/14.2 7.03/6.01 309 14 49 S31444 3.43±0.77 1.01±0.35 80† At1g47260 Mitochondrial gamma carbonic anhydrase 30.2/29.0 6.71/6.79 602 17 46 NP_175159 0.63±0.12 0.7±0.10 ROS scavenging and defence 67 At1g02930 Gluthatione S-transferase (ATGST1) 23.5/23.8 5.8/5.84 344 14 41 CAA72413 0.68±0.14 2.15±0.81 42 At4g02520 Glutathione S-transferase (ATGSTF2) 24.1/24.5 5.92/5.87 672 25 59 AAC78264 1.02±0.13 1.95±0.18 41* At4g02520 Glutathione S-transferase (ATGSTF2) 24.0/24.3 5.93/5.98 734 32 70 1BX9_A 0.79±0.25 1.71±0.20 44* At2g47730 Glutathione S-transferase (GST6) 24.1/23.2 6.09/5.93 582 18 55 AAC63629 0.78±0.10 1.5±0.17 58* At1g02920 Glutathione S-transferase (GST11) 23.6/24.0 6.31/6.19 468 17 48 CAA74639 0.61±0.11 2.06±0.50 36† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.4 5.72/5.82 681 50 54 CAA42168 0.62±0.13 1.51±0.07 61† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.8 5.72/5.65 387 13 51 NP_172267 1.08±0.25 1.58±0.10 35† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.1 5.72/5.71 473 16 54 CAA42168 0.64±0.15 0.64±0.11 21* At2g38390 Peroxidase23 (PER23) 38.6/35.4 8.33/9.19 328 18 33 NP_181373 NDT 1.89±0.45 64* At2g38380 Peroxidase22 (PER22) 38.7/39.6 5.95/5.87 352 16 20 AAA32842 1.05±0.31 2.15±0.58 52 At4g11600 Glutathione peroxidase 6 (ATGPX6) 18.8/20.3 6.59/5.96 239 7 62 BAA24226 0.4±0.17 2.61±1.14 15† At1g77120 Alcohol dehydrogenase (ADH1) 41.9/41.9 5.83/5.80 789 29 43 CAA54911 0.57±0.20 0.6±0.12 69 At3g10920 Manganese superoxide dismutase (MSD1) 25.5/22.9 8.47/5.95 470 16 58 NP_187703 1.33±0.35 3.71±1.08 60 At1g51980 Metalloendopeptidase 54.6/45.7 5.94/5.47 1126 33 44 NP_175610 0.35±0.18 0.91±0.23 49 At3g56240 Copper homeostasis factor 13.1/21.3 4.91/5.17 374 12 53 CAB87423 0.56±0.17 1.41±0.25 13† At5g03630 Monodehydroascorbate reductase (ATMDAR2) 47.5/43.3 5.24/5.25 1231 43 70 NP_568125 0.88±0.22 0.58±0.13 46 At4g11650 Osmotin-like protein (AtOSM34) 27.5/22.2 6.26/6.86 160 6 19 AAM61750 1.16±0.20 1.91±0.28 Protein translation, processing, and degradation 23* At3g09200 Ribosomal protein L10 34.2/34.6 5/5.17 411 38 42 NP_187531 0.92±0.15 0.48±0.18 27* At3g53870 Ribosomal protein S3a 27.5/32.6 9.57/9.13 786 31 56 CAB88349 0.83±0.17 0.64±0.07 55 At1g15930 Ribosomal protein S12 (RPS12A) 15.7/17.2 5.38/5.11 79 2 16 NP_173045 0.52±0.14 0.29±0.22 7† At4g34110 Poly(A)-binding protein 68.8/67.3 8.19/7.56 901 24 30 CAB80128 0.71±0.10 0.48±0.09 32* At3g12390 Nascent polypeptide-associated complex alpha chain 22.0/29.0 4.3/5.0 405 17 59 NP_187845 1.65±0.21 0.79±0.11 75* At1g77510 Protein disulphide isomerase (ATPDIL1-2) 56.6/67.3 4.9/5.16 1204 29 54 NP_177875 NDT NDT 63† At1g21750 Protein disulphide isomerase (ATPDIL1-1) 55.9/64.3 4.54/5.13 1480 40 53 NP_173594 1.5±0.37 0.33±0.19 5* At5g09590 mtHSC70-2 (heat shock protein 70) 73.2/73.6 5.63/5.34 1301 35 40 NP_196521 0.62±0.20 0.32±0.24 40* At4g31300 20S proteasome beta subunit A (PBA1) 25.2/24.5 5.31/5.58 669 16 49 CAA74028 0.67±0.30 1.5±0.21 29 At5g23540 26S proteasome subunit RPN11 34.4/32.9 6.3/6.19 544 17 41 AAP86672 0.53±0.17 0.4±0.20 17† At5g43060 Cysteine-type endopeptidase 52.4/39.6 5.86/5.11 528 18 24 NP_568620 0.53±0.10 0.85±0.11 38† At1g56450 Endopeptidase 27.7/24.7 6.09/7.01 522 13 52 NP_176040 2.31±0.59 0.58±0.12 78† At2g46280 Eukaryotic translation initiation factor 3 (eIF3I1/TRIP-1) 36.7/35.7 6.5/6.7 800 18 53 NP_182151 0.55±0.09 0.72±0.08 Cell wall-related 6* At1g47600 Glycosyl hydrolase family 1 protein 58.1/58.8 8.34/7.96 976 42 38 NP_175191 1.92±0.40 0.88±0.12 77* At1g66280 Glycosyl hydrolase family 1 protein 60.2/57.5 6.74/6.77 1177 35 37 NP_176802 0.56±0.10 0.51±0.13 86* At3g09260 Glycosyl hydrolase family 1 protein 60.4/62.9 6.95/6.69 925 23 39 AAB38783 0.6±0.09 1.59±0.17 65* At3g09260 Glycosyl hydrolase family 1 protein 60.3/62.9 6.95/6.56 629 23 24 AAB38783 0.54±0.11 3.04±1.01 62 At4g16260 Glycosyl hydrolase family 17 protein 37.7/32.4 6.43/8.33 100 4 22 AAL36038 NDC 1.63±0.12 26 At4g16260 Glycosyl hydrolases family 17 protein 37.7/32.6 6.43/6.95 793 54 67 AAL36038 NDC 1.59±0.23 56* At2g21660 Glycine-rich RNA-binding protein 7 (GRP7) 16.9/16.0 5.85/5.44 642 27 76 AAM62447 1.51±0.07 0.85±0.09 45 At4g14630 Germin-like protein (GLP9) 23.2/23.2 5.82/5.84 113 8 11 AAD00509 0.92±0.10 1.85±0.21 39 At5g38940 Oxalate oxidase (germin protein)-like protein 23.8/24.2 8.62/7.76 146 13 13 BAB08650 1.83±0.19 1.58±0.15 66* At3g02230 Reversibly glycosylated polypeptide (RGP1) 41.1/36.5 5.61/5.54 996 31 66 NP_186872 1.24±0.12 1.87±0.22 Hormone-related 81† At1g62380 ACC oxidase (ACO2) 36.4/38.3 4.97/5.1 490 12 31 AAC27484 1.31±0.21 0.66±0.09 59† At1g02500 S-Adenosylmethionine synthetase (SAM1) 43.6/45.1 5.5/5.52 740 25 41 AAA32868 0.67±0.12 0.51±0.11 85† At3g25780 Allene oxide cyclase 2 (AOC2) 28.5/22.4 9.19/7.6 141 6 27 NP_566777 NDC NDC 68† At3g16470 Jasmonate-inducible protein (JR1) 48.6/45.1 5.12/5.24 898 27 32 BAB01146 0.83±0.13 1.66±0.15 Signal transduction 83 At1g56340 Calreticulin 1 (CRT1) 48.7/61.5 4.46/5.04 807 20 40 NP_176030 0.57±0.10 1.53±0.10 84* At1g09210 Calreticulin 2 (CRT2) 48.4/53.7 4.37/5.02 956 38 43 NP_172392 0.61±0.17 1.62±0.19 28 At1g62480 Vacuolar calcium-binding protein-related 16.6/32.6 4.05/4.95 57 2 30 NP_564795 0.79±0.20 NDT 34 At5g20010 Small Ras-like GTP-binding protein (Ran-1) 25.6/27.2 6.38/6.45 237 7 35 AAA32851 0.43±0.15 2.69±1.10 Amino acid metabolism 16* At5g07440 Glu dehydrogenase 2 (GDH2) 45.0/41.4 6.07/5.96 915 32 46 NP_196361 0.63±0.14 1.61±0.18 20* At1g66200 Gln synthetase (GS) 47.5/39.6 5.97/5.11 499 14 36 1804333C 0.53±0.10 1.53±0.12 71* At5g14200 3-Isopropylmalate dehydrogenase (AtIMD1) 44.3/42.1 5.75/5.35 1051 24 47 AAU90074 1.02±0.35 0.57±0.07 3* At5g17920 Cobalamine-independent Met synthase 84.3/84.3 6.02/6.03 1319 41 35 1U1H_A 0.72±0.10 0.36±0.24 Cytoskeleton 14 At1g49240 Actin 8 42.1/42.7 5.37/5.4 670 17 36 AAC49523 0.38±0.26 0.75±0.16 70* At4g14960 Tubulin alpha-6 chain 50.2/47.6 4.93/5.13 682 17 35 CAB10275 1.74±0.21 1.44±0.19 74* At5g62690 Putative tubulin beta-2/beta-3 chain 51.4/48.0 4.7/5.05 1064 39 52 BAC42096 0.54±0.11 0.63±0.04 Transcription 50 At1g73230 (NAC) domain-containing protein 18.0/20.8 5.91/5.66 193 3 30 AAM61406 1.68±0.23 1.89±0.31 53 At1g17880 (NAC) domain-containing protein 17.9/18.9 6.62/6.25 402 12 63 NP_173230 1.05±0.19 1.76±0.20 Other metabolism 79 At1g53580 Hydroxyacylglutathione hydrolase, putative 27.0/28.8 5.45/5.26 348 11 34 2GCU_D 0.65±0.10 0.53±0.13 4 At3g60750 Transketolase-like protein 81.9/84.3 5.8/5.46 698 20 35 CAB82679 0.83±0.12 0.54±0.11 30† At5g09650 Inorganic pyrophosphatase (AtPPA6) 33.7/31.4 5.71/5.17 592 15 33 AAS57950 0.65±0.10 0.61±0.05 Unclassified and unknown 37† At2g43090 Aconitase C-terminal domain-containing protein 27.1/27.6 6.33/5.19 558 15 54 NP_181837 1.31±0.19 2.46±0.71 82 At4g23670 Major latex protein-related 17.6/15.4 5.91/5.86 277 17 42 NP_194098 1.55±0.10 NDC 1 At3g15950 Unknown protein 85.2/115.0 4.61/5.09 1094 31 32 NP_188216 0.73±0.12 0.23±0.15 9* At1g03220 Unknown protein 46.4/45.4 8.97/8.79 837 42 47 NP_171821 0.57±0.17 0.59±0.10 51* At3g52300 Putative protein 19.6/20.1 5.09/5.07 888 23 67 CAC07921 1.25±0.18 0.61±0.12 54 At5g10860 Unknown protein 22.8/18.2 9.1/7.01 396 9 37 NP_196647 0.75±0.16 1.52±0.11 72† At2g20360 Hypothetical protein 44.0/35.9 9.26/8.45 920 25 42 AAT68351 1.77±0.22 0.81±0.14 The Arabidopsis Genomics Initiative (AGI) locus name retrieved from TAIR (www.arabidopsis.org) by running the Blast tool is shown for each protein. Where peptides from a single spot matched proteins from more than one AGI locus (scorea of >100), only the highest scoring match is shown, with the additional matches listed in Supplementary Supplementary Data available at JXB online, and the symbol (*) or (†) is appended to the spot ID for multiple matches within the same (*) or different (†) gene families. Fold change is expressed as a ratio of the vol% between 150 mM NaCl-treated/control roots, and each value represents the mean value ±SD of three biologically independent measurements. For some spots, fold change cannot be accurately calculated because of a complete absence of the spot in either treated or control samples; this is noted by the abbreviation NDC (not detected in control) or NDT (not detected in treatment). * Multiple protein matches in the same gene family as the best match shown; † multiple protein matches in different gene families from the best match shown. a Probability-based molecular weight search (Mowse) score. b Number of peptides matched. c Sequence coverage percentage. Open in new tab Table 1. Differentially expressed proteins identified by LC-MS/MS Spot ID AGI # Putative identity Molecular weight (kDa) (theoretical/experimental) Isoelectric point (theoretical/experimental) Scorea PMb C (%)c NCBI Acc # Fold change 6 h 48 h Energy metabolism 18* At3g52930 Fructose-bisphosphate aldolase 38.9/37.9 6.05/5.95 711 27 54 NP_190861 0.91±0.12 0.65±0.07 11 At1g65930 Isocitrate dehydrogenase (NADP+)/oxidoreductase 46.1/43.0 6.13/5.93 1433 42 62 NP_176768 0.4±0.10 0.69±0.13 19† At3g04120 Glyceraldehyde-3-phosphate dehydrogenase C subunit (GAPC) 37.0/37.0 6.62/6.62 852 41 52 NP_187062 0.55±0.08 0.46±0.11 43† At3g55440 Triose phosphate isomerase (TPI) 27.4/24.2 5.24/5.39 620 16 49 2009415A 0.62±0.15 0.75±0.09 12* At2g47510 Fumarase (FUM1)/fumarate hydratase 53.5/43.3 8.01/6.79 1004 27 47 NP_182273 0.63±0.20 0.88±0.16 22 At1g04410 Malate dehydrogenase/oxidoreductase 35.9/35.9 6.11/5.92 787 55 58 NP_171936 0.55±0.14 0.9±0.21 25† At1g53240 Malate dehydrogenase (NAD), mitochondrial 37.2/33.4 8.54/5.95 726 31 47 AAF69549 1.08±0.12 1.52±0.23 47 At3g27890 NADPH:quinone oxidoreductase (NQR) 21.5/22.4 6.84/6.51 308 10 30 AAD37373 0.45±0.23 1.55±0.17 33† At5g20080 Cytochrome-b5 reductase/oxidoreductase 36.1/27.6 8.76/6.72 463 16 32 NP_568391 0.92±0.16 0.58±0.14 2* At2g05710 Cytoplasmic aconitate hydratase 98.7/103.2 5.79/5.83 791 28 28 AAD25640 0.96±0.18 0.59±0.11 24 At1g22450 Cytochrome c oxidase subunit (COX6B) 21.4/33.9 4.31/5.01 83 5 20 AAM63485 0.44±0.16 NDT 8 At2g36530 Phosphopyruvate hydratase, enolase (LOS2) 48.0/48.0 5.54/5.54 1038 60 64 NP_181192 0.83±0.28 0.65±0.14 73* At1g78900 Vacuolar ATP synthase (VHA-A) subunit A 69.2/73.6 5.11/5.21 1483 37 52 NP_001031299 1.24±0.19 0.43±0.19 48 At5g47030 ATP synthase delta chain, mitochondrial 21.5/21.4 6.2/5.2 127 6 16 NP_199514 1.53±0.26 1.39±0.30 31† At4g11150 V-type proton-ATPase (TUF) 26.3/28.8 6.04/5.86 782 34 56 CAA63086 1.48±0.22 0.62±0.11 76* At3g03250 UDP-glucose pyrophosphorylase (UGP) 51.9/47.0 5.8/5.83 1128 29 54 NP_186975 0.49±0.19 0.66±0.15 10* At3g02360 Phosphogluconate dehydrogenase 53.9/46.4 7.02/6.91 741 22 35 NP_850502 0.68±0.14 0.57±0.21 57 At4g09320 Nucleoside-diphosphate kinase (NDPK1) 16.3/14.2 7.03/6.01 309 14 49 S31444 3.43±0.77 1.01±0.35 80† At1g47260 Mitochondrial gamma carbonic anhydrase 30.2/29.0 6.71/6.79 602 17 46 NP_175159 0.63±0.12 0.7±0.10 ROS scavenging and defence 67 At1g02930 Gluthatione S-transferase (ATGST1) 23.5/23.8 5.8/5.84 344 14 41 CAA72413 0.68±0.14 2.15±0.81 42 At4g02520 Glutathione S-transferase (ATGSTF2) 24.1/24.5 5.92/5.87 672 25 59 AAC78264 1.02±0.13 1.95±0.18 41* At4g02520 Glutathione S-transferase (ATGSTF2) 24.0/24.3 5.93/5.98 734 32 70 1BX9_A 0.79±0.25 1.71±0.20 44* At2g47730 Glutathione S-transferase (GST6) 24.1/23.2 6.09/5.93 582 18 55 AAC63629 0.78±0.10 1.5±0.17 58* At1g02920 Glutathione S-transferase (GST11) 23.6/24.0 6.31/6.19 468 17 48 CAA74639 0.61±0.11 2.06±0.50 36† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.4 5.72/5.82 681 50 54 CAA42168 0.62±0.13 1.51±0.07 61† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.8 5.72/5.65 387 13 51 NP_172267 1.08±0.25 1.58±0.10 35† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.1 5.72/5.71 473 16 54 CAA42168 0.64±0.15 0.64±0.11 21* At2g38390 Peroxidase23 (PER23) 38.6/35.4 8.33/9.19 328 18 33 NP_181373 NDT 1.89±0.45 64* At2g38380 Peroxidase22 (PER22) 38.7/39.6 5.95/5.87 352 16 20 AAA32842 1.05±0.31 2.15±0.58 52 At4g11600 Glutathione peroxidase 6 (ATGPX6) 18.8/20.3 6.59/5.96 239 7 62 BAA24226 0.4±0.17 2.61±1.14 15† At1g77120 Alcohol dehydrogenase (ADH1) 41.9/41.9 5.83/5.80 789 29 43 CAA54911 0.57±0.20 0.6±0.12 69 At3g10920 Manganese superoxide dismutase (MSD1) 25.5/22.9 8.47/5.95 470 16 58 NP_187703 1.33±0.35 3.71±1.08 60 At1g51980 Metalloendopeptidase 54.6/45.7 5.94/5.47 1126 33 44 NP_175610 0.35±0.18 0.91±0.23 49 At3g56240 Copper homeostasis factor 13.1/21.3 4.91/5.17 374 12 53 CAB87423 0.56±0.17 1.41±0.25 13† At5g03630 Monodehydroascorbate reductase (ATMDAR2) 47.5/43.3 5.24/5.25 1231 43 70 NP_568125 0.88±0.22 0.58±0.13 46 At4g11650 Osmotin-like protein (AtOSM34) 27.5/22.2 6.26/6.86 160 6 19 AAM61750 1.16±0.20 1.91±0.28 Protein translation, processing, and degradation 23* At3g09200 Ribosomal protein L10 34.2/34.6 5/5.17 411 38 42 NP_187531 0.92±0.15 0.48±0.18 27* At3g53870 Ribosomal protein S3a 27.5/32.6 9.57/9.13 786 31 56 CAB88349 0.83±0.17 0.64±0.07 55 At1g15930 Ribosomal protein S12 (RPS12A) 15.7/17.2 5.38/5.11 79 2 16 NP_173045 0.52±0.14 0.29±0.22 7† At4g34110 Poly(A)-binding protein 68.8/67.3 8.19/7.56 901 24 30 CAB80128 0.71±0.10 0.48±0.09 32* At3g12390 Nascent polypeptide-associated complex alpha chain 22.0/29.0 4.3/5.0 405 17 59 NP_187845 1.65±0.21 0.79±0.11 75* At1g77510 Protein disulphide isomerase (ATPDIL1-2) 56.6/67.3 4.9/5.16 1204 29 54 NP_177875 NDT NDT 63† At1g21750 Protein disulphide isomerase (ATPDIL1-1) 55.9/64.3 4.54/5.13 1480 40 53 NP_173594 1.5±0.37 0.33±0.19 5* At5g09590 mtHSC70-2 (heat shock protein 70) 73.2/73.6 5.63/5.34 1301 35 40 NP_196521 0.62±0.20 0.32±0.24 40* At4g31300 20S proteasome beta subunit A (PBA1) 25.2/24.5 5.31/5.58 669 16 49 CAA74028 0.67±0.30 1.5±0.21 29 At5g23540 26S proteasome subunit RPN11 34.4/32.9 6.3/6.19 544 17 41 AAP86672 0.53±0.17 0.4±0.20 17† At5g43060 Cysteine-type endopeptidase 52.4/39.6 5.86/5.11 528 18 24 NP_568620 0.53±0.10 0.85±0.11 38† At1g56450 Endopeptidase 27.7/24.7 6.09/7.01 522 13 52 NP_176040 2.31±0.59 0.58±0.12 78† At2g46280 Eukaryotic translation initiation factor 3 (eIF3I1/TRIP-1) 36.7/35.7 6.5/6.7 800 18 53 NP_182151 0.55±0.09 0.72±0.08 Cell wall-related 6* At1g47600 Glycosyl hydrolase family 1 protein 58.1/58.8 8.34/7.96 976 42 38 NP_175191 1.92±0.40 0.88±0.12 77* At1g66280 Glycosyl hydrolase family 1 protein 60.2/57.5 6.74/6.77 1177 35 37 NP_176802 0.56±0.10 0.51±0.13 86* At3g09260 Glycosyl hydrolase family 1 protein 60.4/62.9 6.95/6.69 925 23 39 AAB38783 0.6±0.09 1.59±0.17 65* At3g09260 Glycosyl hydrolase family 1 protein 60.3/62.9 6.95/6.56 629 23 24 AAB38783 0.54±0.11 3.04±1.01 62 At4g16260 Glycosyl hydrolase family 17 protein 37.7/32.4 6.43/8.33 100 4 22 AAL36038 NDC 1.63±0.12 26 At4g16260 Glycosyl hydrolases family 17 protein 37.7/32.6 6.43/6.95 793 54 67 AAL36038 NDC 1.59±0.23 56* At2g21660 Glycine-rich RNA-binding protein 7 (GRP7) 16.9/16.0 5.85/5.44 642 27 76 AAM62447 1.51±0.07 0.85±0.09 45 At4g14630 Germin-like protein (GLP9) 23.2/23.2 5.82/5.84 113 8 11 AAD00509 0.92±0.10 1.85±0.21 39 At5g38940 Oxalate oxidase (germin protein)-like protein 23.8/24.2 8.62/7.76 146 13 13 BAB08650 1.83±0.19 1.58±0.15 66* At3g02230 Reversibly glycosylated polypeptide (RGP1) 41.1/36.5 5.61/5.54 996 31 66 NP_186872 1.24±0.12 1.87±0.22 Hormone-related 81† At1g62380 ACC oxidase (ACO2) 36.4/38.3 4.97/5.1 490 12 31 AAC27484 1.31±0.21 0.66±0.09 59† At1g02500 S-Adenosylmethionine synthetase (SAM1) 43.6/45.1 5.5/5.52 740 25 41 AAA32868 0.67±0.12 0.51±0.11 85† At3g25780 Allene oxide cyclase 2 (AOC2) 28.5/22.4 9.19/7.6 141 6 27 NP_566777 NDC NDC 68† At3g16470 Jasmonate-inducible protein (JR1) 48.6/45.1 5.12/5.24 898 27 32 BAB01146 0.83±0.13 1.66±0.15 Signal transduction 83 At1g56340 Calreticulin 1 (CRT1) 48.7/61.5 4.46/5.04 807 20 40 NP_176030 0.57±0.10 1.53±0.10 84* At1g09210 Calreticulin 2 (CRT2) 48.4/53.7 4.37/5.02 956 38 43 NP_172392 0.61±0.17 1.62±0.19 28 At1g62480 Vacuolar calcium-binding protein-related 16.6/32.6 4.05/4.95 57 2 30 NP_564795 0.79±0.20 NDT 34 At5g20010 Small Ras-like GTP-binding protein (Ran-1) 25.6/27.2 6.38/6.45 237 7 35 AAA32851 0.43±0.15 2.69±1.10 Amino acid metabolism 16* At5g07440 Glu dehydrogenase 2 (GDH2) 45.0/41.4 6.07/5.96 915 32 46 NP_196361 0.63±0.14 1.61±0.18 20* At1g66200 Gln synthetase (GS) 47.5/39.6 5.97/5.11 499 14 36 1804333C 0.53±0.10 1.53±0.12 71* At5g14200 3-Isopropylmalate dehydrogenase (AtIMD1) 44.3/42.1 5.75/5.35 1051 24 47 AAU90074 1.02±0.35 0.57±0.07 3* At5g17920 Cobalamine-independent Met synthase 84.3/84.3 6.02/6.03 1319 41 35 1U1H_A 0.72±0.10 0.36±0.24 Cytoskeleton 14 At1g49240 Actin 8 42.1/42.7 5.37/5.4 670 17 36 AAC49523 0.38±0.26 0.75±0.16 70* At4g14960 Tubulin alpha-6 chain 50.2/47.6 4.93/5.13 682 17 35 CAB10275 1.74±0.21 1.44±0.19 74* At5g62690 Putative tubulin beta-2/beta-3 chain 51.4/48.0 4.7/5.05 1064 39 52 BAC42096 0.54±0.11 0.63±0.04 Transcription 50 At1g73230 (NAC) domain-containing protein 18.0/20.8 5.91/5.66 193 3 30 AAM61406 1.68±0.23 1.89±0.31 53 At1g17880 (NAC) domain-containing protein 17.9/18.9 6.62/6.25 402 12 63 NP_173230 1.05±0.19 1.76±0.20 Other metabolism 79 At1g53580 Hydroxyacylglutathione hydrolase, putative 27.0/28.8 5.45/5.26 348 11 34 2GCU_D 0.65±0.10 0.53±0.13 4 At3g60750 Transketolase-like protein 81.9/84.3 5.8/5.46 698 20 35 CAB82679 0.83±0.12 0.54±0.11 30† At5g09650 Inorganic pyrophosphatase (AtPPA6) 33.7/31.4 5.71/5.17 592 15 33 AAS57950 0.65±0.10 0.61±0.05 Unclassified and unknown 37† At2g43090 Aconitase C-terminal domain-containing protein 27.1/27.6 6.33/5.19 558 15 54 NP_181837 1.31±0.19 2.46±0.71 82 At4g23670 Major latex protein-related 17.6/15.4 5.91/5.86 277 17 42 NP_194098 1.55±0.10 NDC 1 At3g15950 Unknown protein 85.2/115.0 4.61/5.09 1094 31 32 NP_188216 0.73±0.12 0.23±0.15 9* At1g03220 Unknown protein 46.4/45.4 8.97/8.79 837 42 47 NP_171821 0.57±0.17 0.59±0.10 51* At3g52300 Putative protein 19.6/20.1 5.09/5.07 888 23 67 CAC07921 1.25±0.18 0.61±0.12 54 At5g10860 Unknown protein 22.8/18.2 9.1/7.01 396 9 37 NP_196647 0.75±0.16 1.52±0.11 72† At2g20360 Hypothetical protein 44.0/35.9 9.26/8.45 920 25 42 AAT68351 1.77±0.22 0.81±0.14 Spot ID AGI # Putative identity Molecular weight (kDa) (theoretical/experimental) Isoelectric point (theoretical/experimental) Scorea PMb C (%)c NCBI Acc # Fold change 6 h 48 h Energy metabolism 18* At3g52930 Fructose-bisphosphate aldolase 38.9/37.9 6.05/5.95 711 27 54 NP_190861 0.91±0.12 0.65±0.07 11 At1g65930 Isocitrate dehydrogenase (NADP+)/oxidoreductase 46.1/43.0 6.13/5.93 1433 42 62 NP_176768 0.4±0.10 0.69±0.13 19† At3g04120 Glyceraldehyde-3-phosphate dehydrogenase C subunit (GAPC) 37.0/37.0 6.62/6.62 852 41 52 NP_187062 0.55±0.08 0.46±0.11 43† At3g55440 Triose phosphate isomerase (TPI) 27.4/24.2 5.24/5.39 620 16 49 2009415A 0.62±0.15 0.75±0.09 12* At2g47510 Fumarase (FUM1)/fumarate hydratase 53.5/43.3 8.01/6.79 1004 27 47 NP_182273 0.63±0.20 0.88±0.16 22 At1g04410 Malate dehydrogenase/oxidoreductase 35.9/35.9 6.11/5.92 787 55 58 NP_171936 0.55±0.14 0.9±0.21 25† At1g53240 Malate dehydrogenase (NAD), mitochondrial 37.2/33.4 8.54/5.95 726 31 47 AAF69549 1.08±0.12 1.52±0.23 47 At3g27890 NADPH:quinone oxidoreductase (NQR) 21.5/22.4 6.84/6.51 308 10 30 AAD37373 0.45±0.23 1.55±0.17 33† At5g20080 Cytochrome-b5 reductase/oxidoreductase 36.1/27.6 8.76/6.72 463 16 32 NP_568391 0.92±0.16 0.58±0.14 2* At2g05710 Cytoplasmic aconitate hydratase 98.7/103.2 5.79/5.83 791 28 28 AAD25640 0.96±0.18 0.59±0.11 24 At1g22450 Cytochrome c oxidase subunit (COX6B) 21.4/33.9 4.31/5.01 83 5 20 AAM63485 0.44±0.16 NDT 8 At2g36530 Phosphopyruvate hydratase, enolase (LOS2) 48.0/48.0 5.54/5.54 1038 60 64 NP_181192 0.83±0.28 0.65±0.14 73* At1g78900 Vacuolar ATP synthase (VHA-A) subunit A 69.2/73.6 5.11/5.21 1483 37 52 NP_001031299 1.24±0.19 0.43±0.19 48 At5g47030 ATP synthase delta chain, mitochondrial 21.5/21.4 6.2/5.2 127 6 16 NP_199514 1.53±0.26 1.39±0.30 31† At4g11150 V-type proton-ATPase (TUF) 26.3/28.8 6.04/5.86 782 34 56 CAA63086 1.48±0.22 0.62±0.11 76* At3g03250 UDP-glucose pyrophosphorylase (UGP) 51.9/47.0 5.8/5.83 1128 29 54 NP_186975 0.49±0.19 0.66±0.15 10* At3g02360 Phosphogluconate dehydrogenase 53.9/46.4 7.02/6.91 741 22 35 NP_850502 0.68±0.14 0.57±0.21 57 At4g09320 Nucleoside-diphosphate kinase (NDPK1) 16.3/14.2 7.03/6.01 309 14 49 S31444 3.43±0.77 1.01±0.35 80† At1g47260 Mitochondrial gamma carbonic anhydrase 30.2/29.0 6.71/6.79 602 17 46 NP_175159 0.63±0.12 0.7±0.10 ROS scavenging and defence 67 At1g02930 Gluthatione S-transferase (ATGST1) 23.5/23.8 5.8/5.84 344 14 41 CAA72413 0.68±0.14 2.15±0.81 42 At4g02520 Glutathione S-transferase (ATGSTF2) 24.1/24.5 5.92/5.87 672 25 59 AAC78264 1.02±0.13 1.95±0.18 41* At4g02520 Glutathione S-transferase (ATGSTF2) 24.0/24.3 5.93/5.98 734 32 70 1BX9_A 0.79±0.25 1.71±0.20 44* At2g47730 Glutathione S-transferase (GST6) 24.1/23.2 6.09/5.93 582 18 55 AAC63629 0.78±0.10 1.5±0.17 58* At1g02920 Glutathione S-transferase (GST11) 23.6/24.0 6.31/6.19 468 17 48 CAA74639 0.61±0.11 2.06±0.50 36† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.4 5.72/5.82 681 50 54 CAA42168 0.62±0.13 1.51±0.07 61† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.8 5.72/5.65 387 13 51 NP_172267 1.08±0.25 1.58±0.10 35† At1g07890 L-Ascorbate peroxidase (APX1) 27.8/26.1 5.72/5.71 473 16 54 CAA42168 0.64±0.15 0.64±0.11 21* At2g38390 Peroxidase23 (PER23) 38.6/35.4 8.33/9.19 328 18 33 NP_181373 NDT 1.89±0.45 64* At2g38380 Peroxidase22 (PER22) 38.7/39.6 5.95/5.87 352 16 20 AAA32842 1.05±0.31 2.15±0.58 52 At4g11600 Glutathione peroxidase 6 (ATGPX6) 18.8/20.3 6.59/5.96 239 7 62 BAA24226 0.4±0.17 2.61±1.14 15† At1g77120 Alcohol dehydrogenase (ADH1) 41.9/41.9 5.83/5.80 789 29 43 CAA54911 0.57±0.20 0.6±0.12 69 At3g10920 Manganese superoxide dismutase (MSD1) 25.5/22.9 8.47/5.95 470 16 58 NP_187703 1.33±0.35 3.71±1.08 60 At1g51980 Metalloendopeptidase 54.6/45.7 5.94/5.47 1126 33 44 NP_175610 0.35±0.18 0.91±0.23 49 At3g56240 Copper homeostasis factor 13.1/21.3 4.91/5.17 374 12 53 CAB87423 0.56±0.17 1.41±0.25 13† At5g03630 Monodehydroascorbate reductase (ATMDAR2) 47.5/43.3 5.24/5.25 1231 43 70 NP_568125 0.88±0.22 0.58±0.13 46 At4g11650 Osmotin-like protein (AtOSM34) 27.5/22.2 6.26/6.86 160 6 19 AAM61750 1.16±0.20 1.91±0.28 Protein translation, processing, and degradation 23* At3g09200 Ribosomal protein L10 34.2/34.6 5/5.17 411 38 42 NP_187531 0.92±0.15 0.48±0.18 27* At3g53870 Ribosomal protein S3a 27.5/32.6 9.57/9.13 786 31 56 CAB88349 0.83±0.17 0.64±0.07 55 At1g15930 Ribosomal protein S12 (RPS12A) 15.7/17.2 5.38/5.11 79 2 16 NP_173045 0.52±0.14 0.29±0.22 7† At4g34110 Poly(A)-binding protein 68.8/67.3 8.19/7.56 901 24 30 CAB80128 0.71±0.10 0.48±0.09 32* At3g12390 Nascent polypeptide-associated complex alpha chain 22.0/29.0 4.3/5.0 405 17 59 NP_187845 1.65±0.21 0.79±0.11 75* At1g77510 Protein disulphide isomerase (ATPDIL1-2) 56.6/67.3 4.9/5.16 1204 29 54 NP_177875 NDT NDT 63† At1g21750 Protein disulphide isomerase (ATPDIL1-1) 55.9/64.3 4.54/5.13 1480 40 53 NP_173594 1.5±0.37 0.33±0.19 5* At5g09590 mtHSC70-2 (heat shock protein 70) 73.2/73.6 5.63/5.34 1301 35 40 NP_196521 0.62±0.20 0.32±0.24 40* At4g31300 20S proteasome beta subunit A (PBA1) 25.2/24.5 5.31/5.58 669 16 49 CAA74028 0.67±0.30 1.5±0.21 29 At5g23540 26S proteasome subunit RPN11 34.4/32.9 6.3/6.19 544 17 41 AAP86672 0.53±0.17 0.4±0.20 17† At5g43060 Cysteine-type endopeptidase 52.4/39.6 5.86/5.11 528 18 24 NP_568620 0.53±0.10 0.85±0.11 38† At1g56450 Endopeptidase 27.7/24.7 6.09/7.01 522 13 52 NP_176040 2.31±0.59 0.58±0.12 78† At2g46280 Eukaryotic translation initiation factor 3 (eIF3I1/TRIP-1) 36.7/35.7 6.5/6.7 800 18 53 NP_182151 0.55±0.09 0.72±0.08 Cell wall-related 6* At1g47600 Glycosyl hydrolase family 1 protein 58.1/58.8 8.34/7.96 976 42 38 NP_175191 1.92±0.40 0.88±0.12 77* At1g66280 Glycosyl hydrolase family 1 protein 60.2/57.5 6.74/6.77 1177 35 37 NP_176802 0.56±0.10 0.51±0.13 86* At3g09260 Glycosyl hydrolase family 1 protein 60.4/62.9 6.95/6.69 925 23 39 AAB38783 0.6±0.09 1.59±0.17 65* At3g09260 Glycosyl hydrolase family 1 protein 60.3/62.9 6.95/6.56 629 23 24 AAB38783 0.54±0.11 3.04±1.01 62 At4g16260 Glycosyl hydrolase family 17 protein 37.7/32.4 6.43/8.33 100 4 22 AAL36038 NDC 1.63±0.12 26 At4g16260 Glycosyl hydrolases family 17 protein 37.7/32.6 6.43/6.95 793 54 67 AAL36038 NDC 1.59±0.23 56* At2g21660 Glycine-rich RNA-binding protein 7 (GRP7) 16.9/16.0 5.85/5.44 642 27 76 AAM62447 1.51±0.07 0.85±0.09 45 At4g14630 Germin-like protein (GLP9) 23.2/23.2 5.82/5.84 113 8 11 AAD00509 0.92±0.10 1.85±0.21 39 At5g38940 Oxalate oxidase (germin protein)-like protein 23.8/24.2 8.62/7.76 146 13 13 BAB08650 1.83±0.19 1.58±0.15 66* At3g02230 Reversibly glycosylated polypeptide (RGP1) 41.1/36.5 5.61/5.54 996 31 66 NP_186872 1.24±0.12 1.87±0.22 Hormone-related 81† At1g62380 ACC oxidase (ACO2) 36.4/38.3 4.97/5.1 490 12 31 AAC27484 1.31±0.21 0.66±0.09 59† At1g02500 S-Adenosylmethionine synthetase (SAM1) 43.6/45.1 5.5/5.52 740 25 41 AAA32868 0.67±0.12 0.51±0.11 85† At3g25780 Allene oxide cyclase 2 (AOC2) 28.5/22.4 9.19/7.6 141 6 27 NP_566777 NDC NDC 68† At3g16470 Jasmonate-inducible protein (JR1) 48.6/45.1 5.12/5.24 898 27 32 BAB01146 0.83±0.13 1.66±0.15 Signal transduction 83 At1g56340 Calreticulin 1 (CRT1) 48.7/61.5 4.46/5.04 807 20 40 NP_176030 0.57±0.10 1.53±0.10 84* At1g09210 Calreticulin 2 (CRT2) 48.4/53.7 4.37/5.02 956 38 43 NP_172392 0.61±0.17 1.62±0.19 28 At1g62480 Vacuolar calcium-binding protein-related 16.6/32.6 4.05/4.95 57 2 30 NP_564795 0.79±0.20 NDT 34 At5g20010 Small Ras-like GTP-binding protein (Ran-1) 25.6/27.2 6.38/6.45 237 7 35 AAA32851 0.43±0.15 2.69±1.10 Amino acid metabolism 16* At5g07440 Glu dehydrogenase 2 (GDH2) 45.0/41.4 6.07/5.96 915 32 46 NP_196361 0.63±0.14 1.61±0.18 20* At1g66200 Gln synthetase (GS) 47.5/39.6 5.97/5.11 499 14 36 1804333C 0.53±0.10 1.53±0.12 71* At5g14200 3-Isopropylmalate dehydrogenase (AtIMD1) 44.3/42.1 5.75/5.35 1051 24 47 AAU90074 1.02±0.35 0.57±0.07 3* At5g17920 Cobalamine-independent Met synthase 84.3/84.3 6.02/6.03 1319 41 35 1U1H_A 0.72±0.10 0.36±0.24 Cytoskeleton 14 At1g49240 Actin 8 42.1/42.7 5.37/5.4 670 17 36 AAC49523 0.38±0.26 0.75±0.16 70* At4g14960 Tubulin alpha-6 chain 50.2/47.6 4.93/5.13 682 17 35 CAB10275 1.74±0.21 1.44±0.19 74* At5g62690 Putative tubulin beta-2/beta-3 chain 51.4/48.0 4.7/5.05 1064 39 52 BAC42096 0.54±0.11 0.63±0.04 Transcription 50 At1g73230 (NAC) domain-containing protein 18.0/20.8 5.91/5.66 193 3 30 AAM61406 1.68±0.23 1.89±0.31 53 At1g17880 (NAC) domain-containing protein 17.9/18.9 6.62/6.25 402 12 63 NP_173230 1.05±0.19 1.76±0.20 Other metabolism 79 At1g53580 Hydroxyacylglutathione hydrolase, putative 27.0/28.8 5.45/5.26 348 11 34 2GCU_D 0.65±0.10 0.53±0.13 4 At3g60750 Transketolase-like protein 81.9/84.3 5.8/5.46 698 20 35 CAB82679 0.83±0.12 0.54±0.11 30† At5g09650 Inorganic pyrophosphatase (AtPPA6) 33.7/31.4 5.71/5.17 592 15 33 AAS57950 0.65±0.10 0.61±0.05 Unclassified and unknown 37† At2g43090 Aconitase C-terminal domain-containing protein 27.1/27.6 6.33/5.19 558 15 54 NP_181837 1.31±0.19 2.46±0.71 82 At4g23670 Major latex protein-related 17.6/15.4 5.91/5.86 277 17 42 NP_194098 1.55±0.10 NDC 1 At3g15950 Unknown protein 85.2/115.0 4.61/5.09 1094 31 32 NP_188216 0.73±0.12 0.23±0.15 9* At1g03220 Unknown protein 46.4/45.4 8.97/8.79 837 42 47 NP_171821 0.57±0.17 0.59±0.10 51* At3g52300 Putative protein 19.6/20.1 5.09/5.07 888 23 67 CAC07921 1.25±0.18 0.61±0.12 54 At5g10860 Unknown protein 22.8/18.2 9.1/7.01 396 9 37 NP_196647 0.75±0.16 1.52±0.11 72† At2g20360 Hypothetical protein 44.0/35.9 9.26/8.45 920 25 42 AAT68351 1.77±0.22 0.81±0.14 The Arabidopsis Genomics Initiative (AGI) locus name retrieved from TAIR (www.arabidopsis.org) by running the Blast tool is shown for each protein. Where peptides from a single spot matched proteins from more than one AGI locus (scorea of >100), only the highest scoring match is shown, with the additional matches listed in Supplementary Supplementary Data available at JXB online, and the symbol (*) or (†) is appended to the spot ID for multiple matches within the same (*) or different (†) gene families. Fold change is expressed as a ratio of the vol% between 150 mM NaCl-treated/control roots, and each value represents the mean value ±SD of three biologically independent measurements. For some spots, fold change cannot be accurately calculated because of a complete absence of the spot in either treated or control samples; this is noted by the abbreviation NDC (not detected in control) or NDT (not detected in treatment). * Multiple protein matches in the same gene family as the best match shown; † multiple protein matches in different gene families from the best match shown. a Probability-based molecular weight search (Mowse) score. b Number of peptides matched. c Sequence coverage percentage. Open in new tab Results and discussion Plant growth response to NaCl stress Arabidopsis thaliana is a glycophyte and is sensitive to NaCl exposure. To find an appropriate concentration of NaCl to use for treatment of plants prior to proteomic profiling, a root elongation dose response assay was performed. The present results demonstrated that 100 mM and 150 mM NaCl inhibited root elongation by 53% and 78%, respectively (Fig. 1A). However, concentrations higher than 200 mM NaCl almost completely inhibited root growth and led to death of almost all seedlings. In a post-stress recovery assay, 18 dpg plants were treated by 150 mM NaCl for 6, 24, or 48 h, and were transferred into fresh media to recover for 1 week. Almost all of the NaCl-treated plants recovered and resumed normal growth (Fig. 1C, D). Therefore, 150 mM was selected as the treatment concentration to be used in the present study, because it induced visible signs of stress including retarded growth rate and loss of turgor. This concentration of NaCl has been used in several previous gene expression studies, because it induces a moderate stress response and is not acutely lethal (Jiang and Deyholos, 2006; Ma et al., 2006). Higher concentrations of NaCl appear to lead to plasmolysis and lethality (Munns, 2005), although other previous Arabidopsis studies have used an NaCl treatment concentration of 250, 300, or even 600 mM (Seki et al., 2002; Ndimba et al., 2005). Fig. 1. Open in new tabDownload slide Physiological responses induced by NaCl treatment. (A) Effect of increasing [NaCl] on root elongation of seedlings (n=9). (B) Relative electrolyte leakage from roots of control (filled circles) or NaCl-treated (open circles) roots following 6, 24, or 48 h exposure to 150 mM NaCl (n=5). Vertical bars indicate standard deviation (SD). (C, D) Post-stress recovery of NaCl-treated plants. Plants were treated with 150 mM NaCl for 6 h (C) or 48 h (D), then returned to normal growth medium for 1 week before being photographed (scale bar=1 cm, diameter of rafts=13.4 cm). REL is an indicator of membrane damage caused by NaCl stress. Stress-induced changes were measured in plants treated with 150 mM NaCl. As shown in Fig. 1B, the REL of seedlings treated by 150 mM NaCl for 6 h was 8.6%. After 24 h treatment, the REL increased to 18% and further increased to 26% after 48 h of NaCl treatment. When compared with the control plants, the REL values of NaCl-treated seedlings were 1.7, 2.8, and 4.4 times higher at 6, 24, and 48 h, respectively, which indicated that stress-induced cellular damage accumulated throughout the duration of the experiment. To establish further the physiological status of plants that were to be subjected to proteome analysis, next the concentrations of K and Na were quantified in both leaves and roots of control and 150 mM-treated plants (Fig. 2). Na and K concentrations significantly increased and decreased, respectively, in both leaves and roots under 150 mM NaCl treatment. Moreover, the Na concentrations in roots treated by 150 mM NaCl for 6, 24, and 48 h were 115-, 167-, and 132-fold higher than those of the control roots at comparable time points. The concentration of Na in roots reached a maximum after 24 h (Fig. 2A); however, leaves continued to accumulate more Na after the 24 h time point (Fig. 2B), presumably due to transpiration-driven flux. The K:Na ratios in both leaves and roots decreased as the NaCl treatment progressed. Part of the basis of Na+ toxicity in plants is that high Na+ concentrations in the cells increase the Na:K ratio, which is adverse for most metabolic processes (Chinnusamy et al., 2005). Thus, the quantitative ion analysis was consistent with the REL data in describing the progressive accumulation of stress symptoms throughout the 48 h duration of the treatments. Fig. 2. Open in new tabDownload slide Changes in Na and K ion concentrations following NaCl exposure. Na (closed symbols) and K (open symbols) concentrations (mg g−1 dry weight) were determined by flame emission spectroscopy in roots (A) or rosette leaves (B) of hydroponically grown Arabidopsis plants, following 6, 24, or 48 h of exposure to media supplemented with either 0 mM NaCl (control, circles) or 150 mM NaCl (treated, triangles). Na concentrations in control samples are very low (<0.4 mg g−1). Data are from three biological replicates, and vertical bars indicate ±SD. 2-DGE analysis of NaCl-responsive proteins in Arabidopsis roots To investigate the temporal changes of protein profiles during NaCl stress, 2-DGE analysis of the total proteins in Arabidopsis roots from three biologically independent replicate experiments was carried out. A representative gel is shown in Fig. 3. Approximately 1000 protein spots were detected on Coomassie brilliant blue-stained gels and about 600 protein spots were matched between six control gels and six treatment gels. Quantitative image analysis revealed a total of 215 protein spots that changed their abundance (vol%) significantly (P ≤0.05) by <1.5-fold at one or two time points. A 1.5-fold threshold value was selected in order to focus protein identification efforts on the most responsive proteins and for consistency with previous microarray experiments (Jiang and Deyholos, 2006). Essentially arbitrary threshold values ranging from 1.3- to 2.0-fold have been used in previous proteomics studies (Casati et al., 2005; Amme et al., 2006; Parker et al., 2006; Yan et al., 2006). It was noted that some protein spots also demonstrated qualitative changes in intensity. For example, spots 21, 24, 28, and 75 were absent in the NaCl-treated gels at one or more time points while spots 26, 62, 82, and 85 were absent in control gels at one or more time points. Fig. 3. Open in new tabDownload slide Representative 2-DE gels of Arabidopsis root proteins. Eighty-six of the spots showing at least a 1.5-fold change under NaCl treatment at least at one time point with P <0.05 were analysed by LC-MS/MS. LC-MS/MS identification and classification of NaCl-responsive proteins Eighty-nine of the 215 differentially expressed spots described above were arbitrarily selected and excised for tryptic digestion and analysis by LC-MS/MS. From 89 gel plugs excised, 86 proteins were successfully identified (Table 1 and Supplementary Supplementary Data available at JXB online), of which 81 were unique. Some NaCl-responsive spots were not excised because of their low abundance. It was found that at least 23 (26%) of these spots contained peptides that matched proteins from unrelated families (Table 1 and Supplementary Supplementary Data available at JXB online), indicating the presence of multiple proteins in some spots. Conversely, it was found that four proteins were identified in more than one spot, although they were excised from the same gel (Table 1). For example, L-ascorbate peroxidase (APX1) was identified from three spots (35, 36, and 61), glutathione S-transferase (AtGSTF2) was identified in two spots (41 and 42), glycosyl hydrolase family 17 protein was identified in two spots (26 and 62), and glycosyl hydrolase family 1 protein was identified twice (65 and 86). Further examination of electrophoresis patterns indicated that the inferred mass or isoelectric point values of these spots differed, due perhaps to post-translational modification or degradation. Post-translational modifications such as glycosylation, phosphorylation, etc. can change the molecular weight and/or charge of proteins. Alternatively, proteins that were present in multiple spots could result from being translated from alternatively spliced mRNAs (Ishikawa et al., 1997). This phenomenon was also reported previously (Holmes-Davis et al., 2005; Ndimba et al., 2005). Proteomic studies have also shown that some proteins may be degraded during abiotic stress. For example, the Rubisco large subunit was detected as 19 different fragments plus the intact protein in NaCl-treated rice roots (Yan et al., 2006). Similar phenomena have also been reported in pea mitochondrial proteome under chilling stress (Taylor et al., 2005). It is possible that reactive oxygen species (ROS) may also contribute to the degradation of proteins under stress conditions (Desimone et al., 1996; Kingston-Smith and Foyer, 2000). The proteins identified were classified into 11 categories similar to the convention used by Ndimba et al. (2005) (Fig. 4). Proteins implicated in energy metabolism (e.g. glycolysis, citrate cycle, electron transport), ROS scavenging and defence, and protein metabolism (e.g. translation, processing, and degradation) comprised 52% of the proteins identified. Further examination showed that after 6 h of stress, the abundance of most NaCl-responsive proteins had decreased. By contrast, after 48 h of treatment, it was observed that the number of proteins that had increased in abundance was approximately equal to the number of proteins that had decreased in abundance (Fig. 4). This suggests that during the initial (6 h) phase of NaCl stress, the synthesis of many proteins was inhibited and/or their degradation increased, while after 48 h, plant roots began to adapt to water deficit and ionic accumulation by synthesizing selected stress-response proteins. This is generally consistent with previous observations of NaCl-treated roots, in which the abundance of transcripts for almost all ribosomal proteins decreased after 6 h, while transcripts for >30 peptidases increased at the same time point (Jiang and Deyholos, 2006). However, in the previous microarray analysis, transcripts for ribosomal proteins remained at decreased levels between 6 h and 48 h, suggesting that the increased expression of selected stress proteins reported here may involve specialized mechanisms of translation that are not reflected in the bulk transcript level of all cellular ribosomal proteins. Fig. 4. Open in new tabDownload slide Functional classification of NaCl-responsive proteins. The number of proteins that either (A) increased or (B) decreased by at least 1.5-fold in 11 functional categories is shown for roots exposed to 150 mM NaCl for 6 h (grey bars) or 48 h (black bars). Functional categories are named according to the major metabolic substrate, except for the categories representing processes of signal transduction, transcriptional regulation (transcription), and unclassified or unknown (unclass. and unk.) proteins. Energy metabolism Under NaCl stress, plants decrease energy metabolism rates to conserve energy and limit further generation of ROS (Moller, 2001). Previously it had been reported that the transcript abundance of components of the glycolytic, citrate cycle, mitochondrial respiration, and pentose phosphate pathways generally decreased in NaCl-treated Arabidopsis roots (Jiang and Deyholos, 2006). Therefore, it was not surprising to observe in the present study that the abundance of 11 proteins involved in glycolysis, citrate circle, pentose phosphate pathways, and electron transport decreased at one or both time points after NaCl treatment (Table 1). For example, aconitase (At2g05710), isocitrate dehydrogenase (At1g65930), fumarase (At2g47510), and malate dehydrogenase (At1g04410) are four enzymes of the citrate cycle that each also decreased in abundance following NaCl treatment. By contrast, the abundance of nucleoside diphosphate kinase 1 (NDPK1, At4g09320), an enzyme converting GTP to ATP, and a mitochondrial malate dehydrogenase (At1g53240) increased at the early phase (6 h) and late phase (48 h) following NaCl treatment, respectively. Differential regulation of structurally related transcripts and proteins has been reported previously in NaCl-treated roots, and may reflect sub-functionalization of related enzymes for optimal activity in different cells or cellular microenvironments (Jiang and Deyholos, 2006). It was noted that phosphopyruvate hydratase/enolase (LOS2, At2g36530), which catalyses the formation of high-energy phosphoenol pyruvate from 2-phosphoglycerate in the glycolytic pathway, decreased after both 6 h and 48 h of stress. Previous reports have shown that mutation in the LOS2 locus results in repression of cold-responsive genes and therefore it acts as a positive regulator of cold-responsive genes (Lee et al., 2002). The decreased abundance of LOS2 protein that was observed indicates that NaCl-induced perturbation of metabolic flux in glycolysis may be different from that of cold stress. Other energy-related proteins, including three proton-transporting ATPases, two vacuolar ATP synthases (At1g78900 and At4g11150), and a mitochondrial ATP synthase delta chain (At5g47030) were found to be responsive to NaCl stimulus. Vacuolar H+-ATPase can generate a proton electrochemical gradient, which is the driving force utilized by the tonoplast Na+/H+ antiporter, AtNHX1, to compartmentalize Na+ into the vacuole (Chinnusamy et al., 2005). Vacuolar sequestration of Na+ is an important and cost-effective strategy for osmotic adjustment that also reduces the Na+ concentration in the cytosol in plants. ROS scavenging and detoxifying enzymes Abiotic stresses induce the production of ROS, which, on one hand can cause damage to cellular components, and on the other hand, can act as signalling molecules for stress responses (Apel and Hirt, 2004). Plants can regulate the ROS level through complex mechanisms such as scavenging them with ascorbate peroxidase (APX), glutathione peroxidase (GPX), glutathione S-transferase (GST), and superoxide dismutase (SOD), of which nine proteins were identified in this study (Table 1). In general, the abundance of these nine identified proteins was increased upon NaCl treatment. It is proposed that some members of APX, GPX, GST, and SOD families are part of the antioxidant system employed by plants (Apel and Hirt, 2004) and, previous microarray results demonstrated that members of them are responsive to various stresses including NaCl, osmotica, drought, and cold (Kreps et al., 2002; Seki et al., 2002; Jiang and Deyholos, 2006). The increased abundance of GST, peroxidase, and SOD proteins following NaCl is consistent with the presence of oxidative stress in NaCl-stressed roots. The alleviation of oxidative damage and increased resistance to environmental stresses is correlated with an efficient anti-oxidative system (Smirnoff, 1998). Overexpression of some SOD, APX, and GST genes has been shown to improve oxidative stress tolerance in transgenic plants (Allen, 1995; Roxas et al., 1997; Mittler, 2002). GSTs are abundant proteins which are encoded by a highly divergent, ancient gene family and have protective functions such as detoxification of herbicides, and the reduction of organic hydroperoxides formed during oxidative stress. Recent studies have also implicated GSTs as components of ultraviolet-inducible cell signalling pathways and as potential regulators of apoptosis (Dixon et al., 2002). Previous microarray analysis indicated that transcripts for at least 19 GST genes increased in abundance in NaCl-treated Arabidopsis roots (Jiang and Deyholos, 2006). Here, proteins of four plant-specific GSTs—AtGST1/GSTF6 (At1g02930), AtGSTF2/GST4 (At4g02520), AtGST6/GSTF8 (AT2g47730), and AtGST11/GSTF7 (AT1g02920)—were identified, which all ultimately increased in abundance after 48 h of NaCl treatment, although several of them slightly decreased in abundance at the initial time point sampled (6 h). Previous studies showed that AtGST1 was up-regulated by a variety of treatments, while AtGSTF2 and AtGST6 each showed a selective spectrum of inducibility to different stresses, indicating that regulation of gene expression in this super-family is controlled by multiple mechanisms (Wagner et al., 2002). APX and GPX can directly detoxify H2O2 to H2O, and previous studies showed that APX1 is a central component of the reactive oxygen gene network of Arabidopsis (Davletova et al., 2005). In Arabidopsis, a family of seven related proteins named AtGPX1–AtGPX7 was identified and several AtGPX genes were up-regulated coordinately in response to abiotic stresses (Milla et al., 2003). AtGPX6 possibly encodes mitochondrial and cytosolic isoforms by alternative initiation, and AtGPX6 transcript showed the strongest responses under most abiotic stresses tested, thus supporting an important role for it in protection against oxidative damage (Milla et al., 2003). In the present study, an NaCl-dependent increase in protein abundance for two class III plant peroxidases—PER22 (At2g38380) and PER23 (At2g38390)—was observed. Class III peroxidases are plant-specific oxidoreductases that are implicated in various physiological processes such as H2O2 detoxification, auxin catabolism, liginfication, suberization, stress response (wounding, pathogen attack, NaCl), and senescence (Hiraga et al., 2001; Passardi et al., 2005). Previously it had been reported that the majority of class III peroxidases are responsive, at the transcript level, to NaCl treatment in Arabidopsis roots (Jiang and Deyholos, 2006). The diverse functions of class III peroxidases are, in part, due to two possible catalytic cycles, peroxidative and hydroxylic, involving the consumption or release of H2O2 and ROS (Passardi et al., 2005). Although some functions of these peroxidases appear to be paradoxical, the whole process is probably regulated by a fine-tuning that has yet to be elucidated (Passardi et al., 2005). SODs catalyse the dismutation of superoxide into oxygen and H2O2, and constitute the first line of defence against ROS within a cell (Alscher et al., 2002). Surprisingly, in our previous microarray analyses, transcripts for all three detectable SOD genes decreased in response to NaCl treatment (Jiang and Deyholos, 2006). Among these was manganese superoxide dismutase, MSD1 (At3g10920), a protein which was observed here to increase in abundance by 3.7-fold at 48 h post-NaCl treatment (Table 1). The contrast in protein and transcript abundance in similarly treated tissues for MSD1 highlights the importance of an integrated proteomic and transcriptomic analysis of gene expression. Ascorbate is a major antioxidant and free-radical scavenger in plants. Monodehydroascorbate reductase is crucial for ascorbate regeneration and essential for maintaining a reduced pool of ascorbate. Surprisingly, it was found that the abundance of one monodehydroascorbate reductase (AtMDAR2, At5g03630) was down-regulated by NaCl stress, suggesting that although plants require reduced ascorbate to remove free radicals, the fine tuning of the levels of various antioxidants is also an important consideration in stress responses (Lisenbee et al., 2005). Protein translation, processing, and degradation Regulation of gene expression is achieved at several levels, i.e. transcriptional, post-transcriptional, translational, and post-translational. Thirteen proteins implicated in protein translation, processing, and degradation were identified in the present study. A decrease in bulk de novo protein synthesis following NaCl treatment has been detected in Arabidopsis (Ndimba et al., 2005), and previous microarray data also demonstrated down-regulation of the majority of transcripts for almost all cytosolic and plastidic ribosomal proteins (Jiang and Deyholos, 2006). Similarly, microarray profiling of Arabidopsis seedlings under hypoxia indicated a repression of bulk protein synthesis followed by selective translation of specific transcripts (Branco-Price et al., 2005). Under dehydration conditions, >90% of the Arabidopsis mRNAs showing a strong decrease in abundance as detected by microarray displayed reduced polysomal association, indicating a decreased translation of those transcripts (Kawaguchi et al., 2004). Consistent with these observations, three ribosomal proteins (At1g15930, At3g09200, and At3g53870), whose abundance decreased following NaCl treatment, were identified, suggesting that short-term NaCl stress represses protein synthesis in vivo (Table 1). The expression level of a eukaryotic translation initiation factor 3 subunit protein (eIF3I1, At2g46280), which is a homologue of mammalian TGF-beta receptor-interacting protein, was also found to have decreased in this study. Previous studies showed that eIF3I1/TGF-beta receptor-interacting protein 1 is involved in brassinosteroid-regulated plant growth and development, thereby revealing a putative link between a developmental signalling pathway and the control of protein translation (Jiang and Clouse, 2001). Several proteins that promote the proper folding of proteins and/or prevent the aggregation of nascent or damaged proteins were detected. Two protein disulphide isomerase-like (PDIL) proteins (PDIL1-1, At1g21750; PDIL1-2, At1g77510), putative nascent polypeptide-associated complex alpha chain protein (At3g12390), and mitochondrial HSC70-2 (70 kDa heat-shock cognate, At5g09590) all ultimately decreased in protein abundance in NaCl-treated tissues as compared with untreated controls after 48 h, although At3g12390 showed transient increases in abundance after 6 h of NaCl treatment. Members of HSC70 proteins are often involved in assisting the folding of de novo synthesized polypeptides and the import/translocation of precursor proteins (Wang et al., 2004). It was proposed that HSC70 might be used as a motor for transporting the precursor protein through the membranes by interacting with the signal peptides (Zhang and Glaser, 2002). The decreased abundance of MtHSC70-2 after NaCl treatment suggests a decrease in the transportation of newly synthesized peptides into mitochondria, due partly to a decreased de novo protein synthesis under saline conditions. Cell wall-related proteins Moderate NaCl stress reduces water availability and leads to the inhibition of plant growth by increasing the threshold pressure for wall yielding in expanding cells or inducing hydraulic limitations to water uptake (Neumann et al., 1994; Steudle, 2000). In the current study, four glycosyl hydrolase (GH) family proteins, three of which belong to GH family 1 (At1g47600, At1g66280, At3g09260) and one to GH family 17 (At4g16260), were identified (Table 1). GH1 and GH17 protein families include β-glucosidases and β-1,3-glucanases, respectively, which play important roles in many physiological processes in plants, including cell wall remodelling (Bray, 2004; Xu et al., 2004). Each of the four GH proteins identified had a distinctive temporal expression pattern: two of the GH1s decreased in abundance after 6 h, while the third GH1, At1g47600, increased at 6 h; however, after 48 h, At3g09260 (spots 65 and 86) showed increased abundance compared with controls. The two GH17 protein spots identified (spots 26 and 62) were more abundant in treated tissues than controls at both time points. The diversity of expression patterns suggested that several different physiological processes were represented by these results. Further experiments on the substrate specificity, localization of the enzymes with respect to potential substrates, and the activities of the substrates and hydrolysis products are required to determine the roles of these enzymes in root responses to NaCl. Glycine-rich proteins (GRPs) containing >60% glycine have been found in the cell walls of many higher plants and form a group of structural protein components of the wall in addition to extensins and proline-rich proteins (Ringli et al., 2001). GRPs play roles in post-transcriptional regulation of gene expression in plants under various stress conditions and, in most cases, they are accumulated in the vascular tissues and their synthesis is part of the plant's defence mechanism (Mousavi and Hotta, 2005). It was observed that salinity caused a transient increase in the abundance of AtGRP7 (At2g21660) (Table 1). Previous studies showed that AtGRP7 transcript level was repressed by ABA, high NaCl, and mannitol (Cao et al., 2006). More recent studies suggest that AtGRP7 exhibits RNA chaperone activity and can promote the cold adaptation process in Escherichia coli (Kim et al., 2007b). Also identified were two germin-like proteins (GLPs): GLP9 (At4g14630) and oxalate oxidase-like protein (At5g38940), whose abundance increased in Arabidopsis roots subjected to NaCl treatment (Table 1). GLPs exhibit sequence and structural similarity to cereal germins and may be associated with the cell wall (Membre et al., 2000). Although GLPs mostly lack oxalate oxidase activity, some GLPs have SOD activity. GLPs are thought to play a significant role both during embryogenesis and in biotic and abiotic stress conditions. For example, GLP expression was detected in barley roots after exposure to NaCl (Hurkman et al., 1994). A reversibly glycosylated polypeptide (RGP1, At3g02230) was also found to be induced by NaCl treatment. RGP1 is possibly involved in plant cell wall synthesis (Dhugga et al., 1997). Cell wall rigidification, the formation of a physical barrier, and a process of class III peroxidase-mediated cross-linking of several compounds (Passardi et al., 2004), would protect plant roots from further dehydration under water deficit. Hormone-related proteins Ethylene and jasmonic acid (JA) are hormones whose activity has been correlated previously with environmental stress (Chen et al., 2005; Devoto and Turner, 2005). The majority of ethylene- and JA-related transcripts detected in previous microarray experiments were responsive to NaCl treatment (Jiang and Deyholos, 2006). 1-Aminocyclopropane-1-carboxylic acid oxidase (ACO) catalyses the conversion of 1-aminocyclopropane-1-carboxylic acid to ethylene. ACOs are encoded by a small gene family. It was found that ACO2 (At1g62380) increased in abundance at the early (6 h) time point but later decreased in abundance (Table 1). S-Adenosylmethionine synthetase catalyses the production of S-adenosyl-L-methionine (SAM) from L-methionine and ATP. SAM serves as a methyl group donor in numerous transmethylation reactions and is the precursor for the biosynthesis of polyamines and ethylene among other metabolites. In plants, SAM synthetases are encoded by small gene families that contain members that are differentially regulated by NaCl stress (Espartero et al., 1994). Here SAM1 (At1g02500) decreased in abundance following NaCl treatment, which is consistent with previously reported microarray results (Jiang and Deyholos, 2006). JA is involved in a wide range of stress, defence, and developmental processes (Devoto and Turner, 2005). One enzyme implicated in JA biosynthesis, allene oxide cyclase 2 (AOC2, At3g25780), whose abundance increased upon NaCl challenge, was identified, indicating that increased JA biosynthesis may also be associated with NaCl responses in Arabidopsis roots. One jacalin lectin family protein (JR1, At3g16470), which is similar to myrosinase-binding protein, was found to be positively regulated by NaCl. JR1 transcript was strongly induced by wounding and JA (Leon et al., 1998), supporting the concept of cross-talk between various abiotic stresses. Signal transduction network involved in NaCl stress responses An increase in NaCl concentration in the extracellular space can be perceived by putative sensors in the cell membrane of Arabidopsis and transmitted to the cellular machinery to regulate gene expression (Chinnusamy et al., 2005). Some proteins involved in signal transduction were identified in this study (Table 1), i.e. two calcium ion-binding proteins (CRT1, At1g56340; CRT2, At1g09210), a vacuolar calcium-binding protein-related (At1g62480), and a small Ras-like GTP-binding protein (Ran-1, At5g20010). In plant cells, Ca2+ is a ubiquitous intracellular second messenger involved in numerous signalling pathways. Modulation of intracellular Ca2+ levels is partly regulated by calcium-binding proteins, which, after activation, induce specific kinases. Calreticulin (CRT) is a multifunctional protein mainly localized to the endoplasmic reticulum in eukaryotic cells. Plants have three CRT isoform groups (CRT1, CRT2, and CRT3) and Arabidopsis has 18 CRT proteins, and members of the different isoform groups respond differently to applied external stimuli (Persson et al., 2003). The CRT1 and CRT2 identified in this study support that they are the major isoforms, possibly due to an enhanced Ca2+-binding efficiency, and play important roles in Ca2+ homeostasis under osmotic stress. Ran is an evolutionarily conserved eukaryotic GTPase, which is likely to be involved in nuclear translocation of proteins and cell cycle progression (Yang, 2002). However, little is known about the function of Ran in plant response to stresses. It was found that the abundance of Ran-1 increased after 48 h NaCl treatment, suggesting that Ran could also play a specific role under saline conditions. Amino acid metabolism The amount of Pro and certain other amino acids is reported to increase following NaCl treatment (Fougère et al., 1991; Di Martino et al., 2003). It was observed that the abundance of four amino acid biosynthesis-related enzymes was influenced by NaCl (Table 1). 3-Isopropylmalate dehydrogenase (AtIMD1, At5g14200), which is involved in Leu biosynthesis, and cobalamine-independent methionine synthase (ATCIMS/AtMetE, At5g17920) decreased in abundance following NaCl treatment. However, glutamate dehydrogenase 2 (GDH2, At5g07440) and glutamine synthetase (GS, At1g66200) both decreased in abundance at the 6 h time point, but increased at the 48 h time point. GS functions as the major assimilatory enzyme for ammonia, and GDH works as a link between carbon and nitrogen metabolism as it can aminate 2-oxoglutarate into glutamate (biosynthetic reaction) or deaminate glutamate into ammonium and 2-oxoglutarate (catabolic reaction). GS and GDH, together with a number of other enzymes, play key roles in maintaining the balance of carbon and nitrogen (Miflin and Habash, 2002). A recent study showed that a salinity-generated ROS signal induces α-GDH subunit expression, and the anionic iso-GDHs assimilate ammonia, acting as antistress enzymes in ammonia detoxification and production of Glu for Pro synthesis (Skopelitis et al, 2006). Cytoskeleton Actin and tubulin dynamics have important functions in cellular homeostasis. The cytoskeleton is rapidly remodelled by various endogenous and external stimuli such as hormones, low temperature, aluminium, and NaCl (Abdrakhamanova et al., 2003; Dhonukshe et al., 2003; Sivaguru et al., 2003). For example, the transverse orientation of cortical microtubule arrays in tobacco BY-2 cells was remodelled to a more random arrangement after treatment with 150 mM NaCl for 15 min (Dhonukshe et al., 2003). More recent research suggests that NaCl stress compromises the organization of cortical microtubule arrays, in which SPR1 is involved, and inhibits anisotropic growth (Shoji et al., 2006). It was found that one actin protein, ACT8 (At1g49240), and one tubulin β-chain (At5g62690) decreased in abundance following NaCl treatment, while tubulin α-6 chain (TUA6) was induced by NaCl (Table 1). These observations are consistent with previously reported microarray results (Seki et al., 2002; Jiang and Deyholos, 2006) and, although their mechanistic significance is not fully clear, the stress-responsiveness of these common, cytoskeletal proteins calls into question their designation sometimes as housekeeping genes. Transcription-related proteins Transcriptional control of the expression of stress-responsive genes is a crucial part of the plant response to various abiotic and biotic stresses. Nascent polypeptide-associated complex (NAC) is a heterodimeric complex (α- and β-NAC) that can reversibly bind to eukaryotic ribosomes. Rospert et al. (2002) suggested that NAC is a negative regulator of translocation into the endoplasmic reticulum and a positive regulator of translocation into the mitochondria. Previous studies found that the α chain of NAC in osteoblasts functions as a transcriptional coactivator (Yotov et al., 1998). Here, two NAC domain-containing proteins (At1g17880 and At1g73230) were identified, which are similar to human transcription factor BTF3 (RNA polymerase B transcription factor 3) and whose abundance increased following NaCl treatment (Table 1). Interestingly, transcripts for these proteins significantly decreased in abundance in the previous microarray study of Jiang and Deyholos (2006). Furthermore, proteomic evidence showed that a rice α-NAC was down-regulated by NaCl and cold stresses (Yan et al., 2005, 2006). Based on previous microarray results, it is likely that hundreds of other transcription factors changed in abundance in the tissues examined, but that these proteins fell below the sensitivity threshold of analysis, and were therefore not detected in the protein gels (Seki et al., 2002; Jiang and Deyholos, 2006). Correlation analysis of mRNA and proteins levels The relationship between gene expression measured at the mRNA level and the corresponding protein level has not been well characterized in plant roots under abiotic stresses. To evaluate the correlation between mRNA and the corresponding protein levels, the differentially expressed protein levels were compared with previous oligonucleotide microarray data (Jiang and Deyholos, 2006). Tissues for both types of analyses were grown and treated under nearly identical conditions to minimize the effects of experimental variation on the results. The oligonucleotide probes for two proteins identified in the proteomic study were not represented in the microarray: ACO2 (At1g62380) and putative tubulin β-2/β-3 chain (At5g62690). Another 32 genes were filtered out from the microarray data as their signal intensities did not pass the threshold background intensity and the statistical analysis. Altogether, 54 genes/proteins were obtained for the comparison (Fig. 5). For the proteins whose abundance decreased at one or more time points following NaCl treatment, mRNA of most genes (85%, 22 out of 26) also decreased. However, for the 28 proteins that increased in abundance after NaCl treatment, only 10 genes (36%) also increased at the mRNA level (Fig. 5; the other 22 spots showed different expression patterns between mRNA and protein at least at one time point). Interestingly, a similar pattern was reported in a comparison of 2-DGE and qRT-PCR data for chilling stress in rice: 88% (15 out of 17) of proteins and their corresponding transcripts decreased in parallel following stress, while only 19% (5 of 27) of proteins that increased in abundance following stress had cognate transcripts that also increased in abundance (Yan et al., 2006). Together, these studies suggest that transcript abundance may be more directly relevant to expression of genes that are down-regulated following stress than those genes that are up-regulated. However, further study is required to demonstrate the generality of this pattern across a larger sample of proteins, treatments, and measurement techniques. Fig. 5. Open in new tabDownload slide Comparison of NaCl-induced changes in mRNA and cognate protein abundance. The relative change in abundance (treated/untreated) is shown in log2 scale for proteins extracted from roots treated for (A) 6 h or (B) 48 h, as compared with previously reported (Jiang and Deyholos, 2006) changes in mRNA abundance levels in tissues grown under identical conditions. In quantitative terms, the correlation between the expression ratios (i.e. treated/control) observed for proteins in the present experiment and the transcript in previous microarray experiments was low at both 6 h (r= –0.13) and 48 h (r=0.11). These results support the conclusion of other authors that, in statistical terms, measurements of mRNA are not well correlated with protein abundance (Gygi et al., 1999; Tian et al., 2004; Noir et al., 2005; Mooney et al. 2006; Yan et al., 2006). Conclusion In this study, a proteomic analysis of Arabidopsis roots subjected to non-lethal NaCl treatment for 6 h or 48 h, with physiologically defined responses, was performed (Fig. 1). Symptoms of stress, such as electrolyte leakage and Na concentration, continued to increase until at least 48 h, even though considerable remodelling of the proteome had apparently occurred before this time point (Figs 1, 2). Eighty-one different NaCl-responsive proteins were identified by LC-MS/MS (Table 1). The proteins identified were implicated in a wide range of physiological processes, i.e. energy metabolism, ROS scavenging and detoxification, protein translation, processing, and degradation, signal transduction, hormone and amino acid metabolism, cell wall modifications, as well as cytoskeleton remodelling, which might work cooperatively to re-establish cellular homeostasis under water deficiency and ionic toxicity. Some of the proteins identified here were also identified in previous microarray profiling of Arabidopsis response to NaCl stress as well as in 2-DGE analysis of Arabidopsis cell suspension cultures (Kreps et al., 2002; Seki et al., 2002; Ndimba et al., 2005; Jiang and Deyholos, 2006). The proteins identified in this study represent only a small part of the Arabidopsis proteome responsive to NaCl treatment, and many other NaCl-responsive proteins still need to be identified. Considering the limitations of a proteomic study based on 2-D gels, i.e. inability to resolve membrane proteins and detect low-abundant proteins, complementary strategies at the transcript, protein, and metabolite levels should be used to gain more insight into the intricate network of plant response to high salinity. Such approaches will include use of two-dimensional high-performance liquid chromatography, sub-proteomics study, or other alternative approaches (Lee et al., 2004; Peck, 2005; Baginsky and Gruissem, 2006; Kim et al., 2007a). The identification of novel NaCl-responsive proteins provides not only new insights into NaCl stress responses but also a good starting point for further dissection of their functions using genetic and other approaches. Supplementary material Supplementary Data. Peptide sequence data for the 86 Arabidopsis root proteins identified by LC-MS/MS. Supplementary Data. Significant hits to protein spots as analysed by MASCOT with a Mowse score of more than 100 and number of matched peptides more than 3. Abbreviations Abbreviations ACN acetonitrile ACO ACC oxidase APX ascorbate peroxidase CHAPS 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulphonate CRT calreticulin 2-DGE two-dimensional gel electrophoresis dpg days post-germination DTT dithiothreitol FA formic acid GDH glutamate dehydrogenase GLP germin-like protein GPX glutathione peroxidase GRP glycine-rich protein GS glutamine synthetase GST glutathione S-transferase IEF isoelectric focusing IPG immobilized pH gradient JA jasmonic acid LC-MS/MS liquid chromatography coupled to tandem mass spectrometry NAC nascent polypeptide-associated complex REL relative electrolyte leakage ROS reactive oxygen species SAM S-adenosyl-l-methionine SOD superoxide dismutase SDS–PAGE sodium dodecyl sulphate–polyacrylamide gel electrophoresis We are grateful to Dr Anthony Cornish in the Molecular Biology Service Unit (MBSU), University of Alberta for help in 2-DGE facility and Agilent 1100 system use, and to Manjeet Kumari, Naomi Hotte, and Matt Bryman for providing methods or technical assistance. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2007 The Author(s).

Klinghammer, Michaela; Tenhaken, Raimund

doi: 10.1093/jxb/erm209pmid: 18057039

Abstract Arabidopsis cell walls contain large amounts of pectins and hemicelluloses, which are predominantly synthesized via the common precursor UDP-glucuronic acid. The major enzyme for the formation of this nucleotide-sugar is UDP-glucose dehydrogenase, catalysing the irreversible oxidation of UDP-glucose into UDP-glucuronic acid. Four functional gene family members and one pseudogene are present in the Arabidopsis genome, and they show distinct tissue-specific expression patterns during plant development. The analyses of reporter gene lines indicate gene expression of UDP-glucose dehydrogenases in growing tissues. The biochemical characterization of the different isoforms shows equal affinities for the cofactor NAD+ (∼40 μM) but variable affinities for the substrate UDP-glucose (120–335 μM) and different catalytic constants, suggesting a regulatory role for the different isoforms in carbon partitioning between cell wall formation and sucrose synthesis as the second major UDP-glucose-consuming pathway. UDP-glucose dehydrogenase is feedback inhibited by UDP-xylose. The relatively (compared with a soybean UDP-glucose dehydrogenase) low affinity of the enzymes for the substrate UDP-glucose is paralleled by the weak inhibition of the enzymes by UDP-xylose. The four Arabidopsis UDP-glucose dehydrogenase isoforms oxidize only UDP-glucose as a substrate. Nucleotide-sugars, which are converted by similar enzymes in bacteria, are not accepted as substrates for the Arabidopsis enzymes. Cell wall precursor, gene expression, hemicellulose, nucleotide-sugar, UDP-glucose dehydrogenase Introduction Plant cells are surrounded by a rigid but often flexible cell wall to counterbalance the high osmotic pressure inside the cells. Therefore, plant growth requires extensive synthesis of cell wall material during development. The principal composition of Arabidopsis cell walls, analysed from leaves of 4–5-week-old plants, was determined previously (Zablackis et al., 1995). This study indicates a high amount of pectic polymers and hemicelluloses, together forming the matrix polysaccharides, in which cellulose fibrils are embedded along with cell wall structural proteins. Matrix polysaccharides are synthesized in the Golgi apparatus by polymer synthases, which require nucleotide-sugars as glycosyl donors. Excellent reviews of the complex nucleotide-sugar interconversion pathways have been published recently (Gibeaut, 2000; Reiter and Vanzin, 2001; Seifert, 2004). Based on the study by Zablackis et al. (1995), one can calculate that ∼50% of the cell wall biomass is derived from the precursor UDP-glucuronic acid (UDP-GlcA). This nucleotide-sugar is the direct precursor of UDP-galacturonic acid after epimerization (Mølhøj et al., 2004; Usadel et al., 2004), UDP-xylose and UDP-apiose after decarboxylation (Kobayashi et al., 2002), and UDP-arabinose derived from UDP-xylose by an epimerase (Burget et al., 2003). Plants have evolved two independent pathways for the synthesis of UDP-GlcA; this fact underlines the importance of this nucleotide-sugar for plant growth. One pathway involves the direct oxidation of UDP-glucose (UDP-Glc) into UDP-GlcA by the enzyme UDP-glucose dehydrogenase (UDP-α-D-glucose:NAD+ oxidoreductase; EC 1.1.1.22; UGD) (Tenhaken and Thulke, 1996). Alternatively, UDP-GlcA can be formed in a more complex reaction via ring cleavage of myo-inositol into glucuronic acid, followed by subsequent activation of the sugar to UDP-GlcA (Loewus et al., 1962; Seitz et al., 2000; Kanter et al., 2005). Because of the unique entry enzyme of the pathway, myo-inositol oxygenase (MIOX), this route is often referred to as the MIOX pathway for UDP-GlcA formation. The UGD enzyme uses UDP-Glc, available from photosynthesis assimilates, as a substrate. The major competing alternative pathway for the consumption of UDP-Glc is the synthesis of sucrose-6-phosphate by the enzyme sucrose-6-phosphate synthase (SPS), which provides the precursor for the major phloem metabolite sucrose (Winter and Huber. 2000). In addition, UDP-Glc may also be used directly as a glycosyl donor for cellulose synthase or for the formation of callose at the plasma membrane. UGD oxidizes the C6 carbon of glucose from an alcohol to a carbonic group in two subsequent oxidation reactions with no release of intermediates (Ge et al., 2004). The overall reaction is energetically irreversible, with the consequence that UDP-GlcA can be used exclusively for the synthesis of cell wall matrix polysaccharides, glucuronylation of secondary compounds, and post-translational modification of glycoproteins. The separation of the nucleotide-sugars into a pool of mostly cell wall-specific precursors (UDP-GlcA, UDP-GalA, UDP-Ara, UDP-Xyl, and UDP-apiose) and a pool used for the synthesis of storage compounds (sucrose) and cell walls (UDP-Glc and UDP-Gal) raises the question of which UDP-sugar(s) feed into the cell wall precursor pool (Seifert, 2004). Whereas UDP-Glc is undoubtedly the major substrate for UGDs, it is discussed controversially whether UDP-galactose could be a direct precursor of UDP-galacturonic acid for pectic polymers (Stewart and Copeland, 1998). The first gene for a eukaryotic UGD was cloned from soybean (Tenhaken and Thulke, 1996). From measuring the enzymatic activity and analysing public DNA databases, it is evident that UGD genes are present in almost all organisms, with the exception of a few with a secondarily reduced genome like the yeast Saccharomyes cerevisiae. Often several isoforms of UGD are present in plants, a finding which has only been studied in maize so far (Karkonen et al., 2005). Most papers on plant UGDs have either ignored the existence of isoforms or performed a combined analysis of several isoforms simultaneously. Here the analysis of the UGD gene family of Arabidopsis is reported to give a comprehensive overview of the gene expression of different isoforms and their biochemical properties. Materials and methods Bioinformatics To identify all UGD-like genes from Arabidopsis the public databases in GenBank were searched. All expressed sequence tag (EST) sequences with high similarity to UGD could be assigned to one of the four UGD genes present in the sequenced Arabidopsis genome. A fifth UGD gene with a weaker similarity was detected at the top of chromosome 3. A detailed analysis suggested a partial sequence of an additional UGD gene (UGD5). To rule out any assembling error in the genome project, the genomic situation for this region was verified by PCR. The primers GCCGGAACAGGATTAGGCTT and CTAGAGGAGACGCCTGTAAC from the flanking neighbouring genes amplified a product of the predicted size (1381 bp) according to the data in the genome project. Reporter gene analysis The promotor sequences of UGD1, 2, 3, and 4 were amplified by PCR using the primer combinations given in Table 1. Genomic DNA from Arabidopsis thaliana was used as template. PCR products were cloned in front of the uidA gene of vector pBI101 (Clontech MountainView, CA, USA) (UGD1, 2, and 3) or pGreen (http://www.pgreen.ac.uk) (UGD4) via the relevant restriction cleavage sites. Table 1. Primer sequences used for constructs and mRNA amplification Gene Primer Application UGD1 5′-GCCTCGAGATTAGACGGTTTTAAATACGC UGD1 promotor cloning 5′-TTGGATCCTTCTGATTTTCAAAACGTCTCCTGTT-3′ 5′-GTGATGGCTCTTAAGTGTCCTG-3′ Cloning expression vector pQEUGD1 5′-ATGGTACCGTTGGCACCTTCATGCCAC-3′ 5′-ATGGATCCAATGGTGAAGATATGCTGCATAG-3′ Cloning expression vector pET21aUGD1 5′-GTCTCGAGCAATGCCACAGCAGGCATA-3′ 5′-TGAAGATATGCTGCATAGGAGCTGGTTAT-3′ Real-time PCR 5′-ATCCTTGAGCCATGAATCAAGCGGTTTAC-3′ UGD2 HindIII/EcoRV fragment (18 000 bp) from a genomic library UGD2 promotor cloning 5′-GCACTTAAGTGTCCAGACGTTGAAGTAG-3′ Cloning expression vector pQEUGD2 5′-ACGGTACCTGTCGAATACAAGTCCTCTT-3′ 5′-AACACACCGACTAAGACTAGAG-3′ Real-time PCR 5′-TAGCTTTTGCAGATTCATAATGTTTC-3′ UGD3 5′-ACGTAAGCTTACTATGAATGGACATTGACGCACAG UGD3 promotor cloning 5′-ACGTGGATCCTTGTAAACTGAATCACCTCCTGTG 5′-GCTCTTAAGTGTCCATCTGTTGAAGTAG-3′ Cloning expression vector pQEUGD3 5′-ACGGTACCACCCAAGGTACATAATTACC-3′ 5′-GTCCAACCATGGCTGTCATTGCTCTAAAG-3′ Real-time PCR 5′-GGTCCAATGGCTTACCAATGGAGTAAACA-3′ UGD4 5′-ACCTCGAGACGATATTGCCCATGTCT-3′ UGD4 promotor cloning 5′-ATCCCGGGTCCAGCTCCAATACAACAG-3′ 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Cloning expression vector pQEUGD4 5′-GTTTTCCCAGTCACGACGTTGTA-3′ 5′-GGGTCAAGTGGCTTACCAAT-3′ Real-time PCR 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Gene Primer Application UGD1 5′-GCCTCGAGATTAGACGGTTTTAAATACGC UGD1 promotor cloning 5′-TTGGATCCTTCTGATTTTCAAAACGTCTCCTGTT-3′ 5′-GTGATGGCTCTTAAGTGTCCTG-3′ Cloning expression vector pQEUGD1 5′-ATGGTACCGTTGGCACCTTCATGCCAC-3′ 5′-ATGGATCCAATGGTGAAGATATGCTGCATAG-3′ Cloning expression vector pET21aUGD1 5′-GTCTCGAGCAATGCCACAGCAGGCATA-3′ 5′-TGAAGATATGCTGCATAGGAGCTGGTTAT-3′ Real-time PCR 5′-ATCCTTGAGCCATGAATCAAGCGGTTTAC-3′ UGD2 HindIII/EcoRV fragment (18 000 bp) from a genomic library UGD2 promotor cloning 5′-GCACTTAAGTGTCCAGACGTTGAAGTAG-3′ Cloning expression vector pQEUGD2 5′-ACGGTACCTGTCGAATACAAGTCCTCTT-3′ 5′-AACACACCGACTAAGACTAGAG-3′ Real-time PCR 5′-TAGCTTTTGCAGATTCATAATGTTTC-3′ UGD3 5′-ACGTAAGCTTACTATGAATGGACATTGACGCACAG UGD3 promotor cloning 5′-ACGTGGATCCTTGTAAACTGAATCACCTCCTGTG 5′-GCTCTTAAGTGTCCATCTGTTGAAGTAG-3′ Cloning expression vector pQEUGD3 5′-ACGGTACCACCCAAGGTACATAATTACC-3′ 5′-GTCCAACCATGGCTGTCATTGCTCTAAAG-3′ Real-time PCR 5′-GGTCCAATGGCTTACCAATGGAGTAAACA-3′ UGD4 5′-ACCTCGAGACGATATTGCCCATGTCT-3′ UGD4 promotor cloning 5′-ATCCCGGGTCCAGCTCCAATACAACAG-3′ 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Cloning expression vector pQEUGD4 5′-GTTTTCCCAGTCACGACGTTGTA-3′ 5′-GGGTCAAGTGGCTTACCAAT-3′ Real-time PCR 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Open in new tab Table 1. Primer sequences used for constructs and mRNA amplification Gene Primer Application UGD1 5′-GCCTCGAGATTAGACGGTTTTAAATACGC UGD1 promotor cloning 5′-TTGGATCCTTCTGATTTTCAAAACGTCTCCTGTT-3′ 5′-GTGATGGCTCTTAAGTGTCCTG-3′ Cloning expression vector pQEUGD1 5′-ATGGTACCGTTGGCACCTTCATGCCAC-3′ 5′-ATGGATCCAATGGTGAAGATATGCTGCATAG-3′ Cloning expression vector pET21aUGD1 5′-GTCTCGAGCAATGCCACAGCAGGCATA-3′ 5′-TGAAGATATGCTGCATAGGAGCTGGTTAT-3′ Real-time PCR 5′-ATCCTTGAGCCATGAATCAAGCGGTTTAC-3′ UGD2 HindIII/EcoRV fragment (18 000 bp) from a genomic library UGD2 promotor cloning 5′-GCACTTAAGTGTCCAGACGTTGAAGTAG-3′ Cloning expression vector pQEUGD2 5′-ACGGTACCTGTCGAATACAAGTCCTCTT-3′ 5′-AACACACCGACTAAGACTAGAG-3′ Real-time PCR 5′-TAGCTTTTGCAGATTCATAATGTTTC-3′ UGD3 5′-ACGTAAGCTTACTATGAATGGACATTGACGCACAG UGD3 promotor cloning 5′-ACGTGGATCCTTGTAAACTGAATCACCTCCTGTG 5′-GCTCTTAAGTGTCCATCTGTTGAAGTAG-3′ Cloning expression vector pQEUGD3 5′-ACGGTACCACCCAAGGTACATAATTACC-3′ 5′-GTCCAACCATGGCTGTCATTGCTCTAAAG-3′ Real-time PCR 5′-GGTCCAATGGCTTACCAATGGAGTAAACA-3′ UGD4 5′-ACCTCGAGACGATATTGCCCATGTCT-3′ UGD4 promotor cloning 5′-ATCCCGGGTCCAGCTCCAATACAACAG-3′ 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Cloning expression vector pQEUGD4 5′-GTTTTCCCAGTCACGACGTTGTA-3′ 5′-GGGTCAAGTGGCTTACCAAT-3′ Real-time PCR 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Gene Primer Application UGD1 5′-GCCTCGAGATTAGACGGTTTTAAATACGC UGD1 promotor cloning 5′-TTGGATCCTTCTGATTTTCAAAACGTCTCCTGTT-3′ 5′-GTGATGGCTCTTAAGTGTCCTG-3′ Cloning expression vector pQEUGD1 5′-ATGGTACCGTTGGCACCTTCATGCCAC-3′ 5′-ATGGATCCAATGGTGAAGATATGCTGCATAG-3′ Cloning expression vector pET21aUGD1 5′-GTCTCGAGCAATGCCACAGCAGGCATA-3′ 5′-TGAAGATATGCTGCATAGGAGCTGGTTAT-3′ Real-time PCR 5′-ATCCTTGAGCCATGAATCAAGCGGTTTAC-3′ UGD2 HindIII/EcoRV fragment (18 000 bp) from a genomic library UGD2 promotor cloning 5′-GCACTTAAGTGTCCAGACGTTGAAGTAG-3′ Cloning expression vector pQEUGD2 5′-ACGGTACCTGTCGAATACAAGTCCTCTT-3′ 5′-AACACACCGACTAAGACTAGAG-3′ Real-time PCR 5′-TAGCTTTTGCAGATTCATAATGTTTC-3′ UGD3 5′-ACGTAAGCTTACTATGAATGGACATTGACGCACAG UGD3 promotor cloning 5′-ACGTGGATCCTTGTAAACTGAATCACCTCCTGTG 5′-GCTCTTAAGTGTCCATCTGTTGAAGTAG-3′ Cloning expression vector pQEUGD3 5′-ACGGTACCACCCAAGGTACATAATTACC-3′ 5′-GTCCAACCATGGCTGTCATTGCTCTAAAG-3′ Real-time PCR 5′-GGTCCAATGGCTTACCAATGGAGTAAACA-3′ UGD4 5′-ACCTCGAGACGATATTGCCCATGTCT-3′ UGD4 promotor cloning 5′-ATCCCGGGTCCAGCTCCAATACAACAG-3′ 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Cloning expression vector pQEUGD4 5′-GTTTTCCCAGTCACGACGTTGTA-3′ 5′-GGGTCAAGTGGCTTACCAAT-3′ Real-time PCR 5′-GCACTTAAGTGTCCAGATATTGAAGTGGC-3′ Open in new tab Cloning products were verified by DNA sequencing (Seqlab, Göttingen, Germany) and plasmids were transferred into Agrobacterium tumefaciens GV 3101. Subsequently, A. thaliana Col-0 plants were transformed by the floral dip method developed by Clough and Bent (1998). Several independent transformants were stained for β-glucuronidase (GUS) activity and a typical line was chosen for detailed analysis. For reporter gene analysis, seedlings were grown sterile on 0.5× MS medium (#M0245, Duchefa Biochemie, Haarlem, The Netherlands), pH 5.7 (KOH) with 0.5% (w/v) sucrose and 0.25% (w/v) Phytagel™ (Sigma-Aldrich, Munich, Germany) or on soil in growth chambers (23 °C, 50% relative humidity). Plants were cultured either with an 8 h light period (fluorescent bulbs ∼100 μE m−2 s−1) or in the dark. Seedlings of different developmental stages and distinct plant tissues were collected and stained with X-Gluc for GUS activity for 5 min–16 h, using the protocol of Jefferson (1987). Plants were photographed with a Leica stereo microscope (Leica MZFL III, Solms, Germany), equipped with a digital camera (Canon PowerShot S40). Pictures were assembled in Adobe PhotoshopCS 8.0.1. Total RNA isolation and real-time PCR For total RNA isolation, seedlings were grown sterile on MS plates in growth chambers for 6 d with 8 h light periods or in the dark as described above. About 100 mg of plant material (light-grown seedlings, etiolated seedlings, roots, and cotyledons/hypocotyl of seedlings) were collected, frozen in liquid nitrogen, and homogenized by a ball mill (Retsch MM200, 3×30 s, frequency 30 Hz). Total RNA was isolated by the acid phenol/guanidinium thiocyanate method (Chomczynski and Sacchi, 1987). First-strand cDNA was synthesized by using a RevertAid™ M-MuLV Reverse Transcriptase Kit (Fermentas GmbH), according to the supplier's protocol, and 3 μg of total RNA. Real-time PCR was performed using 1 μl of a 1/20 (v/v) dilution of first-strand cDNA reaction, 1× reaction buffer [10 mM TRIS-HCl pH 8.5, 50 mM KCl, 0.15% Triton X-100, 2.5 mM MgCl2 (Karsai et al., 2002)], 200 μM dNTPs, 200 nM of each primer, SYBR green (Roche, Mannheim, Germany) diluted to a 1:200 000 concentration, and 1.5 U of Taq (total reaction volume 30 μl) using a Stratagene Mx3000P QPCR system (Statagene, La Jolla, CA, USA). For primer oligonucleotide sequences, see Table 1. PCR was conducted using the following amplification conditions: 94 °C for 3 min, 40× [94 °C for 30 s, 65 °C (UGD1 and 3), 57 °C (UGD2), or 58 °C (UGD4) for 45 s, 72 °C for 1 min], 95 °C for 1 min, 65 °C for 30 s. Each primer pair amplified a single product, as indicated by the melting curve of the amplicons. The resulting CT values were normalized to the average of the Ct values of the transcript of the housekeeping gene ubiquitin-5 (At3g62250) (Karsai et al., 2002) amplified under the following conditions: 94 °C for 3 min, 40× (94 °C for 15 s, 56 °C for 20 s, 72 °C for 20 s), 95 °C for 1 min, 65 °C for 30 s. Expression vector constructs For cloning into expressions vectors, the open reading frame (ORF) of each UGD was amplified by PCR (Phusion High-Fidelity DNA Polymerase Kit, New England Biolabs) using the primer combinations listed in Table 1 and full-length EST clones as templates: UGD1, M77J01; UGD2, AV43959; UGD3, 43C9T7; and UGD4, 105N9T7 (Arabidopsis Stock Center). The PCR products were cloned into pQE31 (Qiagen, Hilden, Germany; UGD2–4) or into pET21a (UGD1) (Novagen, Darmstadt, Germany) via the relevant restriction cleavage sites. Each construct was confirmed by DNA sequencing (Seqlab, Göttingen, Germany). The expression vector constructs were co-transformed with pGroESL (Amrein et al., 1995) into the Escherichia coli expression strain Origami™ (Novagen, Darmstadt, Germany). Protein expression and purification The E. coli expression strains were routinely grown in LB medium containing 100 μg ml−1 ampicillin, 34 μg ml−1 chloramphenicol, 20 μg ml−1 tetracyclin and 50 μg ml−1 kanamycin at 37 °C overnight, inoculated at 1/100 dilution in LB medium (antibiotics as above), and cultured to an OD600 of ∼0.4 under vigorous shaking. After cooling the cultures for 15 min at room temperature, protein expression was induced by addition of 500 μM isopropyl-β-D-thiogalactopyranoside (IPTG). The cultures were grown at 23 °C for a further 20 h. After cooling the cultures by shaking for 15 min on ice, cells were harvested by centrifugation (10 min at 4500 g and 4 °C) and frozen in liquid nitrogen after discarding the supernatant. Subsequently, cells were thawed in 10 ml g−1 FW chilled disruption buffer [50 mM sodium phosphate, 10 mM TRIS-HCl pH 8.0, 10% (v/v) glycerol, 2 mM MgCl2, 2 mM 2-mercaptoethanol, 1 mM NAD+, 0.2 mM phenylmethylsulphonyl fluoride (PMSF, disolved in isopropanol)] by vigorous vortexing. Lysozyme at 200 μg ml−1 and 1% (v/v) Nonidet P-40 were added and shaken slowly on ice for 45 min to disrupt bacterial cells gently. Bacterial debris was removed by centrifugation for 10 min at 14 500 g and 4 °C. The supernatant was transfered into a new tube; 2.4 U ml−1 benzonase nuclease HC (Novagen, Darmstadt, Germany) was added and incubated for 15 min by shaking slowly on ice. The clear supernatant was applied to a Ni-NTA-agarose column (Qiagen, Hilden, Germany) equilibrated with NTA-1 buffer [50 mM sodium phosphate, 10 mM TRIS-HCl pH 8.0, 250 mM NaCl, 10% (v/v) glycerol, 0.5 mM NAD+], after addition of 250 mM NaCl. The column was washed with 5 vols of NTA-1 buffer and 5 vols of NTA-2 buffer (NTA-1 buffer with 20 mM imidazole) to remove all weakly bound proteins. UGD proteins were eluted by addition of 2.5 vols of NTA-3 buffer (NTA-1 buffer with 250 mM imidazole). The enzymes were immediately transfered into storage buffer [20 mM TRIS-HCl pH 8.7, 50 mM KCl, 10% (v/v) glycerol, 0.5 mM NAD+] by gel filtration on a PD10 column (Amersham Bioscience, Freiburg, Germany). The enzymes could be stored at –80 °C (>6 months) after being frozen in liquid nitrogen without any reduction in activity. UGD protein purification was verified by SDS–PAGE. The yeast strain Toy4, expressing a His-tagged version of Arabidopsis UGD1, was a kind gift of Dr Y Jigami. UGD1 was expressed and purified from a yeast extract according to Oka and Jigami (2006). Enzyme assays and kinetic analysis The enzyme activity of UGD was determined photometrically at 340 nm (Beckmann photometer DU640) by the increase of NADH. The assays were performed for 1–10 min at room temperature in assay buffer [40 mM TRIS-HCl pH 8.7, 0.8 mM EDTA, 16% (v/v) glycerol, 0.8 mM NaN3]. For the determination of kinetic parameters, (i) saturated concentrations of NAD+ (500 μM) and various concentrations of UDP-glucose (0.01–1.5 mM) were used; or (ii) various NAD+ concentrations (0.01–1.5 mM) and a constant UDP-glucose concentration (2 mM) were used. The amount of the UGD added was based on enzymatic activity and was set to 0.03 OD340 units change per minute. The final reaction volumn was set to 1 ml. Triplicate values were obtained for each measurement, and data were plotted with Microcal Origin 6.0G Professional. The Km values were calculated from the hyperbolic curve using the least-square algorithm of the Origin-software. Product analysis The substrates and products of UGD enzyme assays were analysed by high-performance liquid chromatography (HPLC; Dionex U3000 system) using ion-pair chromatography on an RP18-column (Prontosil 120 C18 AQ-Plus 150×3 mm). Separation was performed in buffer A (25 mM tetraethylammonium acetate; pH 6) for 8 min, followed by a linear gradient to 25% buffer B (buffer A plus 20% acetonitrile) for 10 min using a flow rate of 0.5 ml min−1. UV spectra were recorded from 240 nm to 300 nm and plotted for the wavelength 260 nm. The reference compounds UDP-Glc was from MP-Biomedical; UDP-Gal, UDP-glucuronic acid, and UMP were purchased from Sigma. Results Identification of the UGD genes in Arabidopsis In Arabidopsis, the UGD gene family is represented by four transcribed members (UGD1–4) and one pseudogene (UGD1, At1g26570; UGD2, AT3g29360; UGD3, At5g15490; and UGD4, At5g39320). The Arabidopsis UGD described earlier (Seitz et al. 2000) is termed UGD2 herein. The four UGDs encode very similar proteins of 480–481 amino acids. The difference (including conserved exchanges) in the amino acid sequence is <10% between the four isoforms (Fig. 1). The schematic structure of the (pre)-mRNA is shown in Fig. 1. All of the four UGDs contain a single intron of variable length in the 5’ untranslated region whereas the full ORF is not disrupted by further introns. The amino acid sequence variations between the four isoforms are not uniformly distributed along the whole sequence and between all isoforms. Clustering of amino acid exchanges occurs between different pairs of UGDs, indicating that a simple recent gene duplication event does not account for the four UGD isoforms in Arabidopsis. Fig. 1. Open in new tabDownload slide Schematic structure of primary RNA transcript protein sequences for UGD genes. (A) All four UGD genes contain a single intron in the 5’ untranslated region, represented by small boxes. The larger boxes represent the ORFs of UGD1–4. Each single amino acid change from the consensus sequence is represented by a black line in the ORF. The double-headed arrow above the sequences shows the NAD+-binding site. The downward pointing arrows above the sequences indicate the cysteine residue essential for catalyis in all UGDs. The upward pointing arrows below the bars indicate the position of all amino acids involved in glucose binding of UDP-Glc, which are positionally conserved between the UGDs from Arabidopsis and the UGD from Streptococcus pyogenes, for which a crystal structure is available (Campbell et al., 2000). (B) The table shows the percentage amino acid identity (left lower triangle) or similarity (right upper triangle) between the four different UGD isoforms. The sequences of UGD2, 3, and 4 are highly similar, but UGD1 differs significantly from the other sequences. (C) Aligment of some plant UGD sequences with ClustalX. The UGD1 from Arabidopsis clusters together with the two poplar sequences, distinct from the other Arabidopsis branch [At-UGD1-4 (this paper); Co-UGD1, Cinnamomum osmophleum gi|40317278; Glycine max Gm-UGD1, gi|6136119; Nicotiana tabaccum Nt-UGD1, gi|48093457, Nicotiana tabaccum Nt-UGD2, gi|48093459; Ps-UGD1, Pinus taeda Unigene Pta.24139; Ps-UGD2, Pinus taeda Unigene Pta.8150; Oryza sativa Os-UGD1, Os03g31210; Oryza sativa Os-UGD2, Os03g40720; Oryza sativa Os-UGD3, Os03g55070; Oryza sativa Os-UGD4, Os12g25690; Oryza sativa Os-UGD5, Os12g25700; Pt-UGD1, Populus trichocarpa eugene3.00041110; Populus trichocarpa Pt-UGD2, eugene3.00101501). Fig. 1. Open in new tabDownload slide Schematic structure of primary RNA transcript protein sequences for UGD genes. (A) All four UGD genes contain a single intron in the 5’ untranslated region, represented by small boxes. The larger boxes represent the ORFs of UGD1–4. Each single amino acid change from the consensus sequence is represented by a black line in the ORF. The double-headed arrow above the sequences shows the NAD+-binding site. The downward pointing arrows above the sequences indicate the cysteine residue essential for catalyis in all UGDs. The upward pointing arrows below the bars indicate the position of all amino acids involved in glucose binding of UDP-Glc, which are positionally conserved between the UGDs from Arabidopsis and the UGD from Streptococcus pyogenes, for which a crystal structure is available (Campbell et al., 2000). (B) The table shows the percentage amino acid identity (left lower triangle) or similarity (right upper triangle) between the four different UGD isoforms. The sequences of UGD2, 3, and 4 are highly similar, but UGD1 differs significantly from the other sequences. (C) Aligment of some plant UGD sequences with ClustalX. The UGD1 from Arabidopsis clusters together with the two poplar sequences, distinct from the other Arabidopsis branch [At-UGD1-4 (this paper); Co-UGD1, Cinnamomum osmophleum gi|40317278; Glycine max Gm-UGD1, gi|6136119; Nicotiana tabaccum Nt-UGD1, gi|48093457, Nicotiana tabaccum Nt-UGD2, gi|48093459; Ps-UGD1, Pinus taeda Unigene Pta.24139; Ps-UGD2, Pinus taeda Unigene Pta.8150; Oryza sativa Os-UGD1, Os03g31210; Oryza sativa Os-UGD2, Os03g40720; Oryza sativa Os-UGD3, Os03g55070; Oryza sativa Os-UGD4, Os12g25690; Oryza sativa Os-UGD5, Os12g25700; Pt-UGD1, Populus trichocarpa eugene3.00041110; Populus trichocarpa Pt-UGD2, eugene3.00101501). Based on the crystal structure of a UGD from Streptococcus pyogenes (Campbell et al., 2000), all of the amino acid residues involved in substrate binding and catalysis are absolutely conserved between the enzyme from bacteria and plants. The residues involved in binding the UDP-glucose are highlighted schematically in Fig. 1. Several plant UGD sequences were aligned using ClustalX to generate a bootstrapped Neighbor–Joining tree (Fig. 1C). The tree indicates a close proximity of Arabidopsis UGD2, 3, and 4, which cluster together with a UGD from soybean, but puts Arabidopsis UGD1 on a different branch. UGDs from rice cluster into groups of the same branch. Similarly, the two sequences from tobacco, Pinus tadea, and Populus each group together, suggesting gene duplication events after speciation. Further UGD sequences of EST libraries were not included because in many cases the algorithm for generating UNIGENE sequences in GenBank puts sequences from different isoforms into a single data set (e.g. tested for soybean; data not shown). The pseudogene is lacking about two-thirds of the coding sequence including the NAD+-binding site, which is essential for catalytic activity. The chromosomal location at the very beginning of chromosome 3 suggests a segmental gene duplication during evolution, as indicated by the doubling of 37 recognized ORFs (At3g01010–At3g02020) matching a highly similar region on chromosome 5 (At5g15510–At5g14060). Expression pattern of UGD genes in Arabidopsis Promoter::GUS fusion constructs were used in stably transformed Arabidopsis plants to compare gene expression patterns of UGD1–4. The homozygous transgenic lines were analysed for each construct and a typical line was selected for a detailed analysis of the reporter gene activity. The pattern of the most abundantly active reporter UGD2::GUS is shown in Fig. 2 (compare also Fig. 4). UGD2::GUS activity is seen first during the germination process in 1-d-old seedlings, when the radicle breaks through the seed coat (Fig. 2a). In seedlings up to 5 d old, the activity of UGD2::GUS is restricted to the primary root (Fig. 2b). In particular, UGD2::GUS activity can be detected in roots tips, in young root hairs, and in the calyptra. In further growth phases, cotyledons show an even but low activity of the UGD2::GUS reporter gene, which is still highest in the roots (Fig. 2c, d). This pattern remains similar for the vegetative phase of the life cycle (Fig. 2e). Growth of seedlings in the dark leads to etiolated and elongated hypocotyls showing a strong UGD2::GUS activity in the hypocotyl (Fig. 2f). This reporter gene activity is absent in light-grown seedlings (Fig. 2b). In roots of etiolated seedlings a similar expression pattern to that of light-grown seedlings can be detected. During germination and the vegetative phase of the life cycle, a close correlation between growth, requiring UDP sugars for the synthesis of matrix polysaccharides, and the activity of the UGD2::GUS reporter is generally seen. The more complex pattern of UGD2::GUS activity in flowers and siliques is shown in Fig. 2g–k. In young flowers, UGD2::GUS activity can only be detected in the pistil. At later development stages, sepals and petals also show reporter gene activity (Fig. 2g). Also, UGD2::GUS activity is found in staminiferous and mature pollen (Fig. 2h). Siliques show UGD2::GUS activity in the abscission zone at the base and close to the top (Fig. 2k). No UGD2::GUS activity was observed in developing embryos or seeds. Fig. 2. Open in new tabDownload slide Reporter gene expression of UGD2::GUS in transgenic Arabidopsis thaliana plants. Different tissues and development stages are shown. Seedlings were grown in light (a–e, g–k) or dark conditions (f): (a) 1-d-old seedlings; (b) 3-d-old seedling; (c) cotyledons of a 7-d-old seedling; (d) root tip; (e) 2-week-old Arabidopsis plant; (f) etiolated 4-d-old seedling; (g) buds and young flowers; (h) pollen sacs containing mature pollen; (i) older flower; (j) pollinated flower with developing silique; (k) different silique stages. Fig. 2. Open in new tabDownload slide Reporter gene expression of UGD2::GUS in transgenic Arabidopsis thaliana plants. Different tissues and development stages are shown. Seedlings were grown in light (a–e, g–k) or dark conditions (f): (a) 1-d-old seedlings; (b) 3-d-old seedling; (c) cotyledons of a 7-d-old seedling; (d) root tip; (e) 2-week-old Arabidopsis plant; (f) etiolated 4-d-old seedling; (g) buds and young flowers; (h) pollen sacs containing mature pollen; (i) older flower; (j) pollinated flower with developing silique; (k) different silique stages. In general, the expression patterns of UGD2, 3, and 4 are very similar. However, UGD1 shows an expression pattern which is distinct from that of the other isoforms. A comparison of the activity of the different UGD reporter gene constructs is shown in Fig. 3. In seedlings up to 4 d post-germination, UGD2, 3, and 4::GUS activity can only be detected in roots (Fig. 3a). This is in contrast to an almost inverse organ-specific pattern seen for UGD1::GUS (Fig, 3a). At 5 d post-germination this effect disappears. Furthermore, in young leaves, UGD1 and UGD4::GUS show a cell type-specific activity in guard cells and in basal cells surrounding each trichome (Fig. 3e, f). All isoforms exhibit reporter gene activity in 3–4-week-old leaves at a low level, and differences in the activity pattern become visible again in the reproductive phase. Activity of all UGD::GUS reporter genes is seen in the stigma, the filaments, and the mature pollen (Fig. 3b, c). However, the activity of UGD1::GUS is limited to these tissues. UGD3 and 4::GUS reporter are also active in the flower bases. Only UGD2::GUS shows a strong activity in sepals and petals, and in pollen sacks (Fig. 3b, c). In developing siliques, the vascular system shows UGD1, 2, and 3::GUS activity (Fig. 3e, f), and UGD2, 3, and 4::GUS are strongly expressed in the abscission zone at the base of siliques (Fig. 3d). Fig. 3. Open in new tabDownload slide UGD::GUS reporter gene expression in transgenic Arabidopsis thaliana plants reveals differential expression patterns for each UGD isoform (UGD1–4). (a) Two-day-old seedlings; (b) older flowers; (c) pollen sacs containing mature pollen; (d) base of siliques; (e, f) inside of siliques (manually opened; UGD1, 2, 3::GUS plants) and cotyledons with stained stomata (UGD1, 4::GUS plants). Fig. 3. Open in new tabDownload slide UGD::GUS reporter gene expression in transgenic Arabidopsis thaliana plants reveals differential expression patterns for each UGD isoform (UGD1–4). (a) Two-day-old seedlings; (b) older flowers; (c) pollen sacs containing mature pollen; (d) base of siliques; (e, f) inside of siliques (manually opened; UGD1, 2, 3::GUS plants) and cotyledons with stained stomata (UGD1, 4::GUS plants). Further analyses of expression of UGD genes in Arabidopsis via real-time PCR indicate that UGD2 is usually expressed at the highest level of all UGD genes in seedlings (Fig. 4, upper panel). In roots of seedlings, UGD2 and 3 are expressed at a very similar high level, while UGD4 is expressed only weakly and no UGD1 transcripts can be detected. Furthermore, in cotyledons and hypocotyl, UGD2 expression dominates again, in addition to lower levels of UGD3. Publicly available microarray data from AtGenexpress were also analysed. The RNA for the microarray hybridization was from 7-d-old hypocotyls and 17-d-old roots. The relative transcript amounts for each UGD gene are similar to our own data shown in the upper panel (Fig. 4, lower panel). As seen before with the reporter gene constructs, UGD1 expression can be demonstrated in cotyledons and hypocotyl. In etiolated seedlings, UGD2 and UGD3 are predominantly expressed. Fig. 4. Open in new tabDownload slide Quantitative expression analysis (real-time PCR) of UGD transcripts in 6-d-old seedlings of Arabidosis thaliana. Upper panel: seedlings were grown under two conditions (light or dark) on half-strength MS medium with 0.5% sucrose. Data are presented as relative expression values normalized to the average of ubiquitin-5 mRNA, which was set to 1. Values represent the mean ±SD of three measurements. Lower panel: data from AtGenexpress microarrays were analysed for UGD expression. The sum of all UGD transcripts was set as 1 and the fraction for each isoform is shown in the bars. RNA from 7-d-old hypocotyls and 17-d-old roots was used in the experiments. Fig. 4. Open in new tabDownload slide Quantitative expression analysis (real-time PCR) of UGD transcripts in 6-d-old seedlings of Arabidosis thaliana. Upper panel: seedlings were grown under two conditions (light or dark) on half-strength MS medium with 0.5% sucrose. Data are presented as relative expression values normalized to the average of ubiquitin-5 mRNA, which was set to 1. Values represent the mean ±SD of three measurements. Lower panel: data from AtGenexpress microarrays were analysed for UGD expression. The sum of all UGD transcripts was set as 1 and the fraction for each isoform is shown in the bars. RNA from 7-d-old hypocotyls and 17-d-old roots was used in the experiments. Expression of recombinant UGD in E. coli UGD converts UDP-Glc into UDP-GlcA and is located at a critical partitioning step for carbohydrates between the storage compound sucrose via the enzyme SPS and building blocks for matrix polysaccharides via the enzyme UGD. To obtain a deeper insight into the biochemical properties of the different UGD isoforms, the individual enzymes were expressed as recombinant proteins in E. coli by cloning the ORF of each UGD isoform into a His-tag expression vector. Though UGDs from different sources have been investigated as recombinant proteins, it was found to be necessary to optimize thoroughly the expression conditions for each of the highly homologous isoforms. Modifications of the procedure for soybean UGD expression in E. coli (Hinterberg et al., 2002) produced an adequate amount of active UGD2, 3, and 4 enzymes. An SDS–PAGE of the purified recombinant proteins, used for enzymatic analysis, is shown in Fig. 5. Several preparations of the recombinant proteins were analysed, which gave very similar data for the enzymatic activity. Expression of UGD1 could be obtained in E. coli but results in an inactive enzyme (data not shown). Several variations in E. coli culturing and protein purification conditions did not result in active recombinant UGD1 enzyme. Very recently Oka and Jigami (2006) published the expression of recombinant Arabidopsis UGD1 in yeast. Fig. 5. Open in new tabDownload slide SDS–PAGE analysis of recombinant UGD. Open reading frames of UGD2–4 were cloned into His-tagged expression vectors and transformed into E. coli. (a) Enzyme purification of active UGD3 (lanes 1–7) on SDS–PAGE (for purification details, see Materials and methods). Crude E. coli extract before IPTG induction (1) or 20 h after IPTG induction (2); (3) cell debris after centrifugation of disrupted E. coli cells; (4) column flow through; (5) flow through of washing step one; (6) flow through of washing step two; (7) purified recombinant UGD3 enzyme; lanes (8) and (9) show purification products of recombinant UGD2 and UGD3 enzyme; purification steps were carried out accordingly. Fig. 5. Open in new tabDownload slide SDS–PAGE analysis of recombinant UGD. Open reading frames of UGD2–4 were cloned into His-tagged expression vectors and transformed into E. coli. (a) Enzyme purification of active UGD3 (lanes 1–7) on SDS–PAGE (for purification details, see Materials and methods). Crude E. coli extract before IPTG induction (1) or 20 h after IPTG induction (2); (3) cell debris after centrifugation of disrupted E. coli cells; (4) column flow through; (5) flow through of washing step one; (6) flow through of washing step two; (7) purified recombinant UGD3 enzyme; lanes (8) and (9) show purification products of recombinant UGD2 and UGD3 enzyme; purification steps were carried out accordingly. Enzyme kinetics The affinity of the UGD isoforms for the cofactor NAD+ does not differ between UGD2, 3, and 4. All enzymes exhibit typical hyperbolic reaction kinetics, with a Km for NAD+ of ∼40–45 μM (Table 2). The high affinity of the enzyme for NAD+ suggests that UGDs are not limited by the NAD+ supply under physiological conditions. Table 2. Kinetic analysis of UGD2, 3, and 4 from Arabidopsis thaliana Isoform Km UDP-Glc (μM) Km NAD+ (μM) kcat (s−1) UGD2 123±9 43±6 1.92 UGD3 335±16 42±7 2.52 UGD4 171±9 44±7 1.17 Isoform Km UDP-Glc (μM) Km NAD+ (μM) kcat (s−1) UGD2 123±9 43±6 1.92 UGD3 335±16 42±7 2.52 UGD4 171±9 44±7 1.17 Kinetic parameters were determined for the substrates UDP-glucose and NAD+. Enzyme activity was measured as conversion of NAD+ to NADH detected by the absorbance at 340 nm. Purified recombinant enzyme was incubated for 1 min with increasing concentrations of UDP-glucose (0.01–1.5 mM) in the presence of saturating NAD+ (500 μM) or with increasing concentrations of NAD+ (0.01–1.5 mM) in the presence of saturating UDP-glucose (1500 μM). Values represent the mean ±SD of three measurements. Open in new tab Table 2. Kinetic analysis of UGD2, 3, and 4 from Arabidopsis thaliana Isoform Km UDP-Glc (μM) Km NAD+ (μM) kcat (s−1) UGD2 123±9 43±6 1.92 UGD3 335±16 42±7 2.52 UGD4 171±9 44±7 1.17 Isoform Km UDP-Glc (μM) Km NAD+ (μM) kcat (s−1) UGD2 123±9 43±6 1.92 UGD3 335±16 42±7 2.52 UGD4 171±9 44±7 1.17 Kinetic parameters were determined for the substrates UDP-glucose and NAD+. Enzyme activity was measured as conversion of NAD+ to NADH detected by the absorbance at 340 nm. Purified recombinant enzyme was incubated for 1 min with increasing concentrations of UDP-glucose (0.01–1.5 mM) in the presence of saturating NAD+ (500 μM) or with increasing concentrations of NAD+ (0.01–1.5 mM) in the presence of saturating UDP-glucose (1500 μM). Values represent the mean ±SD of three measurements. Open in new tab In contrast to almost identical Km values for NAD+, the kinetic constants for UDP-Glc are highly dissimilar. UGD2 shows the highest affinity (of the UGDs studied here) for the substrate UDP-Glc, with a Km of 123 μM, followed by UGD4 (171 μM) and UGD3 (335 μM) (Table 2). The catalytic constant kcat was determined for the different isoforms with a value between 1.17 s−1 (UGD4) and 2.52 s−1 (UGD3) (see Table 2). Thus the different isoforms differ in the turnover rate of UDP-GlcA formation. In some bacteria the activity of UGDs is modulated by phosphorylation on Tyr10, a conserved residue within the NAD-binding site (Mijakovic et al., 2004). Recombinant UGDs were incubated with alkaline phosphatase, which can dephosphorylate the bacterial UGD (Mijakovic et al., 2003, 2004), but no difference in the enzyme activity was found. Fine tuning of UGD activity was reported to be mediated by feedback inhibition of the enzyme by UDP-xylose, a product obtained from UDP-GlcA after decarboxylation by the enzyme UDP-xylose synthase (Neufeld and Hall, 1965; Hinterberg et al., 2002). The Ki value for UDP-xylose was determined for each isoform in the presence of different concentrations of UDP-Glc. The inhibition is competitive to UDP-Glc and therefore is seen mostly at low concentrations of UDP-Glc in the assays. UGD2 is more sensitively inhibited by UDP-xylose (Ki ∼83 μM) compared with UGD3 and 4, which exhibit a Ki of ∼160 μM and 220 μM, respectively (Fig. 6a–c). Fig. 6. Open in new tabDownload slide Inhibitory effect of UDP-xylose on UGD2, 3, and 4 from Arabidopsis thaliana. Saturation curves of UDP-glucose at various inhibitor concentrations (25–350 μM) are shown. Additional plots represent the apparent Km at different inhibitor concentrations revealing the Ki value. (a) UGD2; (b) UGD3; (c) UGD4. Fig. 6. Open in new tabDownload slide Inhibitory effect of UDP-xylose on UGD2, 3, and 4 from Arabidopsis thaliana. Saturation curves of UDP-glucose at various inhibitor concentrations (25–350 μM) are shown. Additional plots represent the apparent Km at different inhibitor concentrations revealing the Ki value. (a) UGD2; (b) UGD3; (c) UGD4. The superfamily of nucleotide-sugar dehydrogenases is quite conserved, and the substrate specificity cannot readily be predicted by bioinformatic tools. Therefore, different nucleotide-sugars were tested to determine if they are accepted as substrates for UGDs from Arabidopsis (Table 3). Unlike UDP, activated forms of glucose (ADP-Glc and TDP-Glc) are not converted into the corresponding NDP-GlcA derivatives, suggesting no direct interference with the starch biosynthesis pathway. In contrast to previous studies (Stewart and Copeland, 1998), no evidence for a direct oxidation of UDP-galactose into UDP-galacturonic acid was found. Products of the enzyme assay were separated by HPLC (Fig. 7). A time-dependent increase of the product UDP-GlcA was observed, directly correlating to the increase in NADH in the spectophotometric assay. The product analysis for the substrate specificity assays is shown in Fig. 7 using recombinant UGD1. This isoform has the highest number of amino acid changes from the UGD consensus sequences (compare Fig. 1) and was thus considered to be the most likely candidate for a nucleotide-sugar dehydrogenase accepting substrates other than UDP-Glc. None of the four UGDs from Arabidopsis accepted UDP-galactose as a substrate (Fig. 7). In bacteria, members of the NDP-sugar dehydrogenase family use nucleotide-sugars such as GDP-mannose, UDP-galactose, UDP-N-acetylglucosamine or UDP-N-acetylgalactosamine as substrates. None of these potential substrates is accepted by the Arabidopsis UGDs. In summary, the data indicate that Arabidopsis has only true UGDs which accept UDP-Glc as their only substrate. The UGDs clearly prefer NAD+ as a cofactor. Exchanging NAD+ for NADP+ greatly reduces the enzyme activity to ∼20% (Table 3). Table 3. Substrate specifity of UGD1, 2, 3. and 4 from Arabidopsis thaliana Nucleotide-sugar Cofactor Enzyme activity of isoforms (% of control) UGD1 UGD2 UGD3 UGD4 UDP-glucose NAD+ 100% 100% 100% 100% UDP-glucose NADP+ Not tested 20% 20% 23% ADP-glucose NAD+ n.d.a n.d. n.d. n.d. TDP-glucose NAD+ n.d. n.d. n.d. n.d. UDP-galactose NAD+ n.d. n.d. n.d. n.d. GDP-mannose NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylglucosamine NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylgalactosamine NAD+ n.d. n.d. n.d. n.d Nucleotide-sugar Cofactor Enzyme activity of isoforms (% of control) UGD1 UGD2 UGD3 UGD4 UDP-glucose NAD+ 100% 100% 100% 100% UDP-glucose NADP+ Not tested 20% 20% 23% ADP-glucose NAD+ n.d.a n.d. n.d. n.d. TDP-glucose NAD+ n.d. n.d. n.d. n.d. UDP-galactose NAD+ n.d. n.d. n.d. n.d. GDP-mannose NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylglucosamine NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylgalactosamine NAD+ n.d. n.d. n.d. n.d In standard enzyme assays either UDP-glucose or NAD+ was substituted by alternative substrates. All nucleotide-sugars and cofactors were at a concentration of 1 mM. The detection limit is ∼2–3% of the control. a n.d., not detected. Open in new tab Table 3. Substrate specifity of UGD1, 2, 3. and 4 from Arabidopsis thaliana Nucleotide-sugar Cofactor Enzyme activity of isoforms (% of control) UGD1 UGD2 UGD3 UGD4 UDP-glucose NAD+ 100% 100% 100% 100% UDP-glucose NADP+ Not tested 20% 20% 23% ADP-glucose NAD+ n.d.a n.d. n.d. n.d. TDP-glucose NAD+ n.d. n.d. n.d. n.d. UDP-galactose NAD+ n.d. n.d. n.d. n.d. GDP-mannose NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylglucosamine NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylgalactosamine NAD+ n.d. n.d. n.d. n.d Nucleotide-sugar Cofactor Enzyme activity of isoforms (% of control) UGD1 UGD2 UGD3 UGD4 UDP-glucose NAD+ 100% 100% 100% 100% UDP-glucose NADP+ Not tested 20% 20% 23% ADP-glucose NAD+ n.d.a n.d. n.d. n.d. TDP-glucose NAD+ n.d. n.d. n.d. n.d. UDP-galactose NAD+ n.d. n.d. n.d. n.d. GDP-mannose NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylglucosamine NAD+ n.d. n.d. n.d. n.d. UDP-N-acetylgalactosamine NAD+ n.d. n.d. n.d. n.d In standard enzyme assays either UDP-glucose or NAD+ was substituted by alternative substrates. All nucleotide-sugars and cofactors were at a concentration of 1 mM. The detection limit is ∼2–3% of the control. a n.d., not detected. Open in new tab Fig. 7. Open in new tabDownload slide Product analysis of UGD enzyme assays. (A) Standard compounds relevant for the enzyme assay (1, UMP; 2, UDP-Gal; 3, UDP-Glc; 4, UDP-GlcA). (B) UGD assay with recombinant UGD2, in which the substrate UDP-Glc is converted into UDP-GlcA. The minor peak in trace B corresponding to compound 1 represents UMP, a breakdown product of UDP-Glc hydrolysis. (C) Control assay in which the enzyme UGD2 was omitted. No UDP-GlcA product is formed. (D) Control assay in which UDP-Glc was omitted. The small peak #5 contains an unknown impurity. (E) Enzyme assays with recombinant UGD1 and substrate UDP-Glc, which is converted to UDP-GlcA by UGD1. (F) Enzyme assay with recombinant UGD1 and substrate UDP-Gal. UDP-Gal is not converted to an oxidized product but remains unchanged in the assay, indicating that UDP-Gal is not a substrate of UGD1. Fig. 7. Open in new tabDownload slide Product analysis of UGD enzyme assays. (A) Standard compounds relevant for the enzyme assay (1, UMP; 2, UDP-Gal; 3, UDP-Glc; 4, UDP-GlcA). (B) UGD assay with recombinant UGD2, in which the substrate UDP-Glc is converted into UDP-GlcA. The minor peak in trace B corresponding to compound 1 represents UMP, a breakdown product of UDP-Glc hydrolysis. (C) Control assay in which the enzyme UGD2 was omitted. No UDP-GlcA product is formed. (D) Control assay in which UDP-Glc was omitted. The small peak #5 contains an unknown impurity. (E) Enzyme assays with recombinant UGD1 and substrate UDP-Glc, which is converted to UDP-GlcA by UGD1. (F) Enzyme assay with recombinant UGD1 and substrate UDP-Gal. UDP-Gal is not converted to an oxidized product but remains unchanged in the assay, indicating that UDP-Gal is not a substrate of UGD1. Discussion The cell wall of Arabidopsis contains large amounts of hemicelluloses and pectic polymers, which are predominantly derived from the common precursor UDP-GlcA (Zablackis et al., 1995). Therefore, biochemical pathways for the formation of UDP-GlcA are of great importance for the supply of glycosyl donors for polymer synthases and glycosyl transferases in the Golgi apparatus. As the nucleotide-sugars derived from UDP-GlcA are almost exclusively used for the synthesis of cell wall material, the entry point of nucleotide-sugars into a pool for cell wall synthesis is tightly controlled. Previous studies have shown a close correlation between UGD transcripts and enzyme activity in Arabidopsis (Seitz et al., 2000). Furthermore, Gahan et al. (1997) and Johansson et al. (2002) have regarded UGD as a marker enzyme for developing xylem cells from cambium meristems in trees, because of a tight correlation between cell division, growth, and UGD enzyme activity. These studies have been extended by analysing the whole gene family of UGD genes from Arabidopsis. The available sequence data from the genome project as well as the sequenced EST libraries suggest four highly similar members of the UGD gene family in Arabidopsis (UGD1–4) in addition to a pseudogene (partial sequence). In the rice genome, at least five sequences for putative UGD genes can be identified (compare the tree in Fig. 1C). The presence of isoforms for UGDs was ignored in previous studies (Tenhaken and Thulke, 1996; Stewart and Copeland, 1998; Seitz et al., 2000; Turner and Botha, 2002). The main reason seems to be highly conserved amino acid sequences, which result in proteins with similar chromatographic properties. Nucleotide-sugar dehydrogenases represent a large family of quite well conserved proteins, which oxidize the primary alcohol group at C6 of various sugars into the corresponding uronic acid (Roychoudhury et al., 1989). In bacteria, diverse substrates are converted by different family members. Based on multiple protein sequence alignments, it is likely that some of the annotations regarding the substrates are falsely assigned (data not shown). The PFAM database (http://www.sanger.ac.uk/Software/Pfam/) for patterns in proteins annotated the UGD-like genes from Arabidopsis and rice, and also plant EST sequences as ‘UDP-glucose/GDP-mannose dehydrogenase family’ (PF03721). This suggests that the substrate specificity of enzymes from this family cannot be predicted accurately by bioinformatics but needs experimental support. By expressing the proteins UGD1, 2, 3, and 4 as recombinant proteins, it has been shown here that they are true UGDs. In the light of the separate nucleotide-sugar pools for cell wall synthesis and for sucrose synthesis, it is important to know whether UDP-Glc is the only entry point of nucleotide-sugars into the cell wall pool. Previously Stewart and Copeland (1998) reported that one of the soybean UGDs also accepts UDP-galactose as a substrate, suggesting that the pectin precursor UDP-galacturonic acid may be directly derived from UDP-galactose. This possibility is excluded for the Arabidopsis UGDs based on the enzyme activity measurements with different potential substrates, which clearly indicate UDP-Glc as the only convertible substrate. As the measurement for UDP-Glc dehydrogenase activity in the study of Stewart and Copeland (1998) was based on an increase of NADH in the assay without analysis of the product, it seems possible either that the substrate UDP-galactose contained some residual UDP-Glc or that the enzyme preparation was contaminated with residual UDP-glucose-4-epimerase, which could have converted part of the UDP-galactose into UDP-Glc. The recent cloning of the genes encoding UDP-GlcA-4-epimerase (Mølhøj et al., 2004; Usadel et al., 2004), which produces UDP-galacturonic acid as glycosyl donor for pectic polymers, further supports this conclusion. Stewart and Copeland (1998) have purified a UDP-Glc dehydrogenase from soybean nodules, but the presence of isoforms was not considered. To our knowledge, only a single UGD from soybean has been characterized biochemically in more detail (Hinterberg et al., 2002), but evidence for the conversion of UDP-galactose was also not found in this study. The biochemical data for the UGD isoforms from Arabidopsis showing a graded affinity for UDP-Glc and substrate turnover numbers suggest a role in the regulation of carbon fluxes into nucleotide-sugar pools for cell walls. During most of the life cycle of Arabidopsis, UGD2, 3, and 4 are co-expressed in the same tissues and thus different affinities for UDP-Glc and substrate turnover numbers might limit the use of too much UDP-Glc for cell wall polymers. In contrast, UGD1 has a high affinity for UDP-Glc (Oka and Jigami, 2006) but is expressed at low levels. Seitz et al. (2000) demonstrated a histochemical UGD activity stain in whole seedlings, which shows only a minor UGD activity in the hypocotyl compared with the primary root. In these seedlings, UGD1 is the major expressed isoform (compare Fig. 3). Kinetic constants for UGDs from different organisms vary over a wide range. One of the soybean UGDs, which has previously been characterized, has a high affinity for UDP-Glc (Km ∼21 μM; Hinterberg et al. 2002) similar to a UGD from sugarcane (Km ∼19 μM; Turner and Botha, 2002), whereas UGDs from maize (Km=380 μM and 950 μM) show much higher Km values (Kärkönen et al. 2005). The Arabidopsis UGDs have an intermediate Km for UDP-Glc, ranging from 123 μM to 335 μM. Oka and Jigami (2006) determined a high affinity Km (15.3 μM) for UDP-Glc for UGD1 from Arabidopsis. The high affinity of UGDs for UDP-Glc is correlated with a strong feedback inhibition by UDP-xylose (∼10 μM in soybean; 17 μM in sugarcane) but with much higher Ki values for the Arabidopsis UGD2, 3, and 4 (Ki 80–220 μM) as indicated in Table 2. Interestingly, Oka and Jigami (2006) determined the Ki of UDP-xylose for UGD1 to be 4.9 μM. This particular isoform also has a high affinity for the substrate UDP-Glc (15.3 μM), suggesting a structural modification of the enzyme substrate-binding pocket which increases the affinity for both the substrate UDP-Glc and the inhibitor UDP-Xyl. The kcat values indicate a similar substrate conversion rate by the different isoforms, ranging from 1.17 s−1 for the slowest enzyme UGD4 to 2.52 s−1 for the fastest enzyme UGD3, and intermediate values for UGD2. These turnover rates agree well with data for the UGD from S. pyogenes (kcat=1.8 s−1) (Ge et al., 2004) and the enzyme purified from bovine liver (kcat= 2.92 s−1; calculated on the basis of 50 kDa per subunit) (Zalitis and Feingold, 1969). In contrast, Bar-Peled et al. (2004) reported a much lower value for the UGD from Cryptococcus neoformans (kcat=0.27 s−1). The 2-fold difference in the turnover number between the Arabidopsis isoforms may well be important. UGD3 has the lowest affinity for UDP-Glc but the highest turnover number, indicating that the flux of UDP-sugars into UDP-GlcA by UGD3 is significant under conditions of a non-limited supply of UDP-Glc. Though the exact concentration of UDP-Glc in Arabidopsis leaves is not known and probably depends on environmental conditions as well, it can be assumed to be in the range of 1 mM. Dancer et al. (1990) estimated 3–4  mM UDP-Glc for spinach leaves. Farré et al. (2001) calculated 0.83 mM for the UDP-Glc concentration in potato tubers. The main competitor enzyme of UGDs for the substrate UDP-Glc is SPS, utilizing UDP-Glc for the biosynthesis of sucrose. The Km values of SPSs for UDP-Glc are usually slightly higher than the Km of UGDs reported here (Avigad, 1982). In addition, cellulose is synthesized from UDP-Glc or sucrose cleaved via membrane-bound isoforms of sucrose synthase into UDP-Glc and fructose. For example, cotton antisense plants for sucrose synthase have impaired cellulose trichomes, indicating an essential role for sucrose synthase in providing UDP-Glc for cellulose biosynthesis (Ruan et al., 2003). The same mechanism may also apply to other β-glucan synthases (Buckeridge et al., 1999; Konishi et al., 2004). These findings suggest a supply of UDP-Glc from sucrose for various β-glucan synthases, which is presumably independent of the soluble UDP-Glc pool. Whether UGD uses UDP-Glc from cleaved sucrose to a larger extent is not known. However, strong evidence for this use is lacking, as indicated by the analysis of single and double knockout mutants in sucrose synthase, which show no cell wall mutant phenotypes (Bieniawska et al., 2007). The flux of UDP-Glc into either sucrose or UDP-GlcA (for cell wall hemicelluloses) will therefore depend on several factors including the Km for UDP-Glc of the enzymes, enzyme substrate turnover numbers, amount of enzyme, and post-translational regulation of activity. In a recent paper by Park et al. (2007) the authors report on transgenic tobacco plants overexpressing an SPS gene, which have a reduced amount of arabinose and xylose in their cell wall. This indicates that the flux of UDP-Glc into hemicellulose material via UGD was displaced by favouring sucrose formation. Taking the data from reporter gene expression, real-time PCR, and knockout mutants (R Reboul, M Klinghammer, T Tenhaken, unpublished data), into account, it is concluded that UGD2 and UGD3 are the major contributing enzymes for the flux from UDP-Glc into UDP-GlcA in Arabidopsis. We thank Christoph Klos and Beate Hinterberg for helpful suggestions. 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