Plant Physiology

Minorsky, Peter V.

doi: 10.1104/pp.104.900190pmid: N/A

Minorsky, Peter V.

doi: 10.1104/pp.104.900190pmid: N/A

Xanthan Prevents Callose Block to Black Rot Disease The phytopathogenic bacterium Xanthomonas campestris pv campestris (Xcc) is the causal agent of black rot disease in crucifers. Xcc produces xanthan, an extracellular polysaccharide that is essential for pathogenesis, particularly during the early stages of Xcc infection in leaf mesophyll tissue. Other than its appearance during and necessity for bacterial pathogenesis, little is known about the precise functional role of xanthan in Xanthomonas pathogenesis. Yun et al. (pp. 178–187) show that, in contrast to wild-type Xcc, two mutant strains, one lacking xanthan and the other producing a truncated form of xanthan, fail to cause disease in either Nicotiana benthamiana or Arabidopsis plants. Unlike the wild-type Xcc strain, both of the xanthan-deficient mutant strains induce callose deposition in Nicotiana and Arabidopsis plants. Callose deposition appears to be a key factor in plant resistance to Xcc infection. Treatment with an inhibitor of callose deposition prior to infection induces susceptibility to the two xanthan-deficient mutant strains. Moreover, treatment with xanthan but not truncated xanthan suppresses the accumulation of callose and enhances the susceptibility of both Nicotiana and Arabidopsis plants to infection by the two mutant strains. Thus, xanthan's suppressive effect on callose deposition is critical for Xanthomonas infectivity. Calcium Oxalate Crystals Defend against Chewing Insects Because of their known qualities as irritants to humans and their formidable needle-like appearance, raphide crystals of calcium oxalate in plants have long been suggested to be physical deterrents to herbivore feeding. In the barrel medic Medicago truncatula, prismatic calcium oxalate crystals accumulate predominantly in a sheath surrounding secondary veins of leaves. Korth et al. (pp. 188–195) have employed mutants of M. truncatula with decreased levels of calcium oxalate crystals to assess the defensive role of this mineral against insects. They report that caterpillar larvae of the beet armyworm Spodoptera exigua show a clear feeding preference for tissues from mutants with reduced levels of calcium oxalate crystals as compared to wild-type plants. Larvae that fed upon wild-type plants suffer reduced growth and increased mortality compared with those that feed on the mutant plants. The induction of wound-responsive genes is normal in the crystal-poor mutants, indicating that these lines are not deficient in induced insect defenses. Electron micrographs of insect mouthparts indicate that the prismatic crystals in M. truncatula leaves act as physical abrasives during feeding. Food utilization measurements show that calcium oxalate also interferes with the conversion of plant material into insect biomass during digestion. In marked contrast to their detrimental effects on a chewing insect species, calcium oxalate crystals have no effect on the performance of the pea aphid Acyrthosiphon pisum, a sap-feeding insect with piercing-sucking mouthparts. The results confirm a long-held hypothesis for the defensive function of these crystals, and point to the potential value of understanding the genes involved in controlling crystal formation and localization in crop plants. Transcriptome of Desmid Sexual Reproduction Charophycean green algae and land plants share many distinctive characteristics with respect to cellular structures and metabolism. The unicellular desmid Closterium peracerosum-strigosum-littorale complex is the closest unicellular relative of land plants and also is the best-characterized charophycean alga with respect to the process of sexual reproduction (Fig. 1 Figure 1. Open in new tabDownload slide Transcriptome analyses of the closest unicellular relative of land plants, the unicellular desmid C. peracerosum-strigosum-littorale complex, should provide important insights into the evolution of land plants. (Photo from Microbial Culture Collection, National Institute for Environmental Studies, Japan.) Figure 1. Open in new tabDownload slide Transcriptome analyses of the closest unicellular relative of land plants, the unicellular desmid C. peracerosum-strigosum-littorale complex, should provide important insights into the evolution of land plants. (Photo from Microbial Culture Collection, National Institute for Environmental Studies, Japan.) ). Heterothallic strains of Closterium have two morphologically indistinguishable sexes: mating-type plus (mt+) and mating-type minus (mt−). Sexual reproduction is easily induced when cells of these two sexes are cultured together in nitrogen-depleted medium under light. To elucidate the molecular mechanism of intercellular communication during sexual reproduction, Sekimoto et al. (pp. 271–279) classified 3,236 expressed sequence tags into 1,615 nonredundant groups and generated a Closterium cDNA microarray. Candidate genes for key factors involved in fertilization, such as those that encode putative receptor-like protein kinase, Leu-rich-repeat receptor-like protein, and sex pheromone homologs, were up-regulated during sexual reproduction and/or by the addition of the purified sex pheromones. There also were mating strain-specific differences in the expression of genes coding for aquaporin-related proteins. This first transcriptome profile of Closterium may provide important clues as to the mechanism and evolution of intercellular communication between the egg and sperm cells of land plants. The Closterium microarray resource is also a potentially useful tool for the exploration of genes that are regulated in response to environmental changes. One can easily monitor gene expression changes caused by environmental modifications without influences from other tissues and organs since Closterium is a unicellular plant. Calmodulin-Like Proteins in the Symbiosome Space of Root Nodules Signaling between rhizobia and legumes initiates the development of root nodules. During this process, the bacteria are endocytosed by the plant and become surrounded by a plant membrane, thereby forming a symbiosome. Between this membrane and the encased bacteria, there exists a matrix-filled space (the symbiosome space) that is thought to contain a mixture of plant- and bacteria-derived proteins. Changes in intracellular Ca2+ and signaling via Ca2+ are well-documented features of legume-rhizobia interactions and root nodule development. Liu et al. (pp. 167–177) report upon the occurrence in the model legume M. truncatula of a novel family of six calmodulin (CaM)-like proteins (CaMLs) that are expressed specifically in root nodules and that are localized within the symbiosome space. All six nodule-specific CaML genes are clustered in the M. truncatula genome along with two other nodule-specific genes, nodulin-22 and nodulin-25. Sequence comparisons and phylogenetic analysis suggest that an unequal recombination event occurred between nodulin-25 and a nearby CaM, giving rise to the first CaML, and the gene family evolved by tandem duplication and divergence. Thus, an ancestral CaM gene appears to have been co-opted and recruited for root nodule symbiosis. Although the specific functions of nodule-specific CaMLs are not yet known, based upon their location in the symbiosome space and the fact that Ca2+ flux affects anion channel gating, the authors believe they are integrally related to symbiosome function. Abscisic Acid Utilizes Tissue-Specific G-Protein Pathways G proteins are involved in a multitude of plant processes, including cell division, ion channel regulation, seed germination, biotic and abiotic stress responses, and blue-light-mediated responses. Abscisic acid (ABA) signaling in both seeds and guard cells also involves components of the heterotrimeric G-protein complex. To assess the roles of the Arabidopsis (Arabidopsis thaliana) Gα subunit (GPA1), the Gβ subunit (AGB1), and a candidate G-protein-coupled receptor (GCR1) in ABA signaling during germination and early seedling development, Pandey et al. (pp. 243–256) utilized knockout mutants lacking one or more of these components. Their data reveal that GPA1, AGB1, and GCR1 negatively regulate ABA signaling in seed germination and early seedling development. Contrary to the case in guard cells, where GCR1 and GPA1 have opposite effects on ABA signaling during stomatal opening, GCR1 acts in concert with GPA1 and AGB1 in ABA signaling during germination and early seedling development. Their data afford an excellent example of cell- and tissue-specific differences in the G-protein-mediated signal transduction pathway of a single primary messenger, ABA. Transcriptome Analysis of Cold Acclimation in Chloroplast Mutants Cold acclimation is a complex process characterized by the coordinated regulation of hundreds of genes. The signal transduction pathways leading to the expression of cold-regulated genes involve a regulatory network where only a few regulatory genes control many genes in the cold response. The C-repeat binding factor (Cbf) genes, regulators of the cold-induced transcriptional cascade, have been shown to increase freezing tolerance when overexpressed in transgenic plants. The ability of plants to develop a frost-resistant phenotype, however, also is affected by the presence of light and photosynthetic activity during cold acclimation. An increased PSII excitation pressure (the relative reduction state of Qa, the first stable electron acceptor of PSII) is one of the primary stimuli promoting expression of cold-regulated genes. Consequently, exposure to cold in the absence of light reduces the induction of several cold-regulated genes. Previously, it has been shown that barley (Hordeum vulgare) plants carrying a mutation preventing chloroplast development are impaired in the expression of several cold-regulated genes and completely frost susceptible. The recent commercial availability of a barley microarray provides a powerful new tool for studying the role of the chloroplast in cold acclimation. Svensson et al. (pp. 257–270) investigated four chloroplast barley mutants and the corresponding wild type using a barley DNA microarray to assess the effect of the chloroplast on the expression of cold-regulated genes. Their results highlight the major role of the chloroplast in the molecular adaptation to cold. Their description of the cold response in wild-type barley and in four independent chloroplast mutants allowed the identification of three main pathways containing more than 80% of the wild-type cold-regulated genes: (1) cold-regulated genes unaffected by any mutations, including Cbf genes and many genes known to be under Cbf control; (2) cold-regulated genes constitutively induced, although to different levels, in all mutants, including those activated in response to photooxidative stress; and (3) cold-regulated genes belonging to a signaling pathway(s) disrupted in all mutants, whose expression consequently was not, or was only marginally responsive to cold. Since only a minor portion of cold-regulated genes belongs to the same regulatory pathway as Cbf, the authors conclude that other factors deriving from the chloroplast in addition to Cbf also are required to promote the full suite of molecular changes associated with cold acclimation. Author notes www.plantphysiol.org/cgi/doi/10.1104/pp.104.900190. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Calderon-Villalobos, Luz Irina A.; Kuhnle, Carola; Li, Hanbing; Rosso, Mario; Weisshaar, Bernd; Schwechheimer, Claus

doi: 10.1104/pp.106.078097pmid: 16684932

Abstract Proper plant growth and development strongly rely on the plant's ability to respond dynamically to signals and cues from the intra- and extracellular environment. Whereas many of these responses require specific changes at the level of gene expression, in recent years it has become increasingly clear that many plant responses are at least in part also controlled at the level of protein turnover. It is a challenge for signal transduction research to understand how distinct incoming signals are integrated to generate specific changes at the transcript or protein level. The activity of luciferase (LUC) reporters can be detected in nondestructive qualitative and quantitative assays in vivo. Therefore,z LUC reporters are particularly well suited for the detection of changes at the transcript and protein level. To the best of our knowledge, the number of plant transformation vectors for LUC fusions is very limited. In this article, we describe the LucTrap plant transformation vectors that allow generation of targeted and random transcriptional and translational fusions with the modified firefly LUC reporter LUC+. We demonstrate that LucTrap-based fusions can be used to monitor rapid changes in gene expression and protein abundance in vivo. Plants are sessile organisms that need to respond quickly to signals and cues from their intra- and extracellular environment. Many of these responses require the transcription of specific subsets of downstream genes (Schwechheimer and Bevan, 1998; Schmid et al., 2005). At the level of the individual gene, the spatial and temporal control of gene expression is mediated by transcriptional activators and repressors that regulate promoters and enhancers, and the integration of these activities determines the resulting gene expression changes. To understand how these changes are brought about and how individual signaling pathways modulate gene expression at the level of the individual gene is a big challenge for signal transduction research. Gene expression can be monitored at the level of the individual gene by northern blotting or reverse transcription (RT)-PCR, or at the genomic level using microarrays (Hennig et al., 2003; Zhu, 2003; Schmid et al., 2005). In recent years, microarray data originating from hundreds of experiments conducted with the plant model species Arabidopsis (Arabidopsis thaliana) have been obtained and collected in specific databases so that an overview of a gene's expression pattern can now be gained by simple database analysis (Schmid et al., 2005; Zimmermann et al., 2005). Nevertheless, the comparatively high cost of a microarray experiment adds restrictions to the number of experimental conditions that can be tested in such studies. Therefore, these techniques cannot be used extensively to understand the expression of a single gene of interest, its transcriptional regulation over time, and its responses to complex signaling events. In these cases, transgenic plants expressing transcriptional or translational fusions between the promoter of the gene of interest and the reporter proteins β-glucuronidase (GUS), green fluorescent protein (GFP) and its derivatives, and the luciferases (LUCs) are suitable alternatives (for review, see de Ruijter et al., 2003). Because each reporter has specific advantages and disadvantages, the goal of the specific experiment generally determines the choice of the reporter. Transcriptional and translational fusions of a promoter or gene of interest to the GUS reporter allow assay of gene expression in a quantitative and qualitative manner (Jefferson, 1987). GUS activity can be quantified in protein extracts in fluorometric assays and tissue-specific and, in some cases, subcellular GUS activity can be assayed using chromogenic assays (Jefferson et al., 1987; von Arnim et al., 1997). Due to its relatively long half-life and its property of forming stable aggregates in vivo, the GUS reporter is being considered a stable and reliable reporter protein. Conversely, and for the same reasons, the GUS reporter is not well suited to follow transcriptional repression and protein degradation events because loss of reporter protein activity cannot be detected against the background of aggregated GUS (Gray et al., 2001). GFP and other fluorescent reporter proteins can easily be monitored in vivo in a noninvasive manner by fluorescence microscopy (Haseloff and Amos, 1995; Shaner et al., 2005). These proteins are generally best suited for determination of the subcellular localization of a protein of interest and, in combination with other fluorescent protein-tagged proteins, these reporters are ideal tools for studying protein colocalization as well as in vivo protein-protein interactions (Haseloff and Amos, 1995; Shaner et al., 2005). However, the quantification of fluorescent protein levels, in absolute or relative terms, as is required for some applications, such as fluorescent resonance energy transfer, can only be achieved by the skilled user of sophisticated software (Haseloff, 1999; Shaner et al., 2005). In addition, it has been observed that the folding of GFP is temperature dependent and that many fluorescent proteins photobleach during analysis (Shaner et al., 2005). Therefore, it is difficult to correctly quantify the amount of fluorescent protein that is produced or present within a cell. In contrast, LUCs can be detected and quantified in vivo in a highly sensitive manner using photomultipliers or highly sensitive cameras. The reaction with the LUC substrates luciferin, ATP, and oxygen causes the release of a photon at 592 nm in 90% of the catalytic cycles (DeLuca and McElroy, 1986). Luciferin can be supplied to plants as a media supplement or a luciferin-containing solution can be sprayed or painted onto the plant material for imaging. One interesting and important feature of LUC is that it is inactivated after the LUC reaction has taken place. For this reason, LUC activity only reveals the amount of de novo synthesized protein rather than the amount of protein that has accumulated over time (Millar et al., 1992; de Ruijter et al., 2003). Therefore, transcriptional or translational LUC fusions are excellent tools to monitor dynamic changes in transcript or protein abundance. To the best of our knowledge, the number of plant transformation vectors for LUC fusions is very limited. In this article, we report on the LucTrap vectors and describe their use for the analysis of plant response mechanisms that lead to changes in transcript and protein abundance. The LucTrap and LucTrap-3(GW) vectors are designed for the cloning of transcriptional and translational LUC fusions. Using selected examples, we demonstrate that these vectors serve to monitor and quantify positive and negative changes in gene expression as well as changes in protein abundance in planta. We also describe and characterize a collection of 700 Arabidopsis lines that we generated with the gene trap vector LucTrap-2, and we demonstrate that these lines can serve to uncover novel regulatory mechanisms that, in our specific case, are controlled by unstable regulators. RESULTS The LucTrap Vector for in Vivo Gene Expression Analyses To obtain a LUC reporter vector suitable for transcriptional and translational fusions, we constructed the LucTrap vector, which is a derivative of the previously described plant transformation vector pGREEN0029-II (Fig. 1A Figure 1. Open in new tabDownload slide The plant transformation vector LucTrap allows generation of transcriptional and translational LUC reporter fusions. A, Schematic representation of the LucTrap vector T-DNA. Black triangles mark the T-DNA right border (RB) and left border (LB), respectively. The Nco1 site of the LucTrap MCS overlaps with the ATG start codon of the modified firefly LUC gene (LUC+). CaMV 35S terminator, TCaMV; nopaline synthase (NOS) promoter, PNOS; NOS terminator, TNOS; neomycin phosphotransferase II/Kanamycin resistance gene, KANR. The GenBank accession number of LucTrap is DQ073044. B, GH3-2:LucTrap carries a GH3-2 (At4g37390) gene fragment corresponding to the 800 bp upstream of the predicted GH3-2 start codon. C, Typical result of an auxin-induction experiment with 5-d-old seedlings of a selected transgenic Arabidopsis GH3-2:LucTrap line. White squares, LUC activity without induction; black squares, LUC activity following induction with 5 μm 2,4D. LUC activity at t = 0 min of the untreated sample was set as 1. D, GH3-2 gene expression following induction with 5 μm 2,4D as monitored by semiquantitative RT-PCR. ACTIN was used as an internal standard for cDNA amounts used in the experiment. E, Quantification of the RT-PCR results. GH3-2 expression at t = 0 min was set as 1. Figure 1. Open in new tabDownload slide The plant transformation vector LucTrap allows generation of transcriptional and translational LUC reporter fusions. A, Schematic representation of the LucTrap vector T-DNA. Black triangles mark the T-DNA right border (RB) and left border (LB), respectively. The Nco1 site of the LucTrap MCS overlaps with the ATG start codon of the modified firefly LUC gene (LUC+). CaMV 35S terminator, TCaMV; nopaline synthase (NOS) promoter, PNOS; NOS terminator, TNOS; neomycin phosphotransferase II/Kanamycin resistance gene, KANR. The GenBank accession number of LucTrap is DQ073044. B, GH3-2:LucTrap carries a GH3-2 (At4g37390) gene fragment corresponding to the 800 bp upstream of the predicted GH3-2 start codon. C, Typical result of an auxin-induction experiment with 5-d-old seedlings of a selected transgenic Arabidopsis GH3-2:LucTrap line. White squares, LUC activity without induction; black squares, LUC activity following induction with 5 μm 2,4D. LUC activity at t = 0 min of the untreated sample was set as 1. D, GH3-2 gene expression following induction with 5 μm 2,4D as monitored by semiquantitative RT-PCR. ACTIN was used as an internal standard for cDNA amounts used in the experiment. E, Quantification of the RT-PCR results. GH3-2 expression at t = 0 min was set as 1. ; Hellens et al., 1999). In LucTrap, a unique Nco1 restriction site is positioned at the start codon of the modified firefly LUC+ gene, and this site can be used to generate transcriptional and translational LUC+ fusions. To test the performance of LucTrap, we inserted an 800-bp GH3-2 (At4g37390) promoter fragment into the vector to obtain GH3-2:LucTrap (Fig. 1B). Several members of the GH3 gene family, including GH3-2, have previously been shown to be induced by auxin (Tian et al., 2003). We therefore tested auxin-induced LUC expression in transgenic Arabidopsis seedlings containing GH3-2:LucTrap. Whereas no significant LUC activity was detected in the absence of auxin, 18 of 20 transgenic lines showed LUC expression as early as 45 min following induction with the synthetic auxin 2,4-dichlorophenoxyacetic acid (2,4D; Fig. 1C). To confirm that LUC+ activity driven by GH3-2 correlates with the expression of the endogenous GH3-2 gene, we analyzed GH3-2 mRNA accumulation by semiquantitative RT-PCR (Fig. 1, D and E). In these experiments, auxin-induced GH3-2 mRNA accumulation was detected as early as 15 min after auxin induction and the overall kinetics of auxin-induced GH3-2 expression were found to be comparable between the RT-PCR analysis and the LUC assays. Because the detection of the GH3-2 transcript by RT-PCR precedes the detection of the active LUC+ protein by approximately 30 min, we suggest that this delay corresponds to the time required for transcript maturation and protein biosynthesis (Fig. 1, C–E). We therefore conclude that the LucTrap vector can serve to faithfully report on the presence and absence of a gene product and its accumulation over time. The LucTrap-3(GW) Vector for Gateway-Compatible LUC Fusions To generate a vector that is compatible with the increasingly popular Gateway cloning technology, we inserted the Gateway cassette (rfB) upstream of the LUC+ open reading frame of LucTrap to obtain LucTrap-3(GW) (Fig. 2 Figure 2. Open in new tabDownload slide The Gateway destination vector LucTrap-3(GW) for plant transformation. Scheme of the LucTrap-3(GW) T-DNA with the attR1 and attR2 recombination sites of the Gateway rfB cassette. ccdB, Escherichia coli DNA gyrase for negative selection; CmR, chloramphenicol resistance gene for positive selection. The first amino acid of LUC+ is underlined. For other abbreviations, refer to Figure 1 legend. The GenBank accession number of LucTrap-3(GW) is AY968054. Figure 2. Open in new tabDownload slide The Gateway destination vector LucTrap-3(GW) for plant transformation. Scheme of the LucTrap-3(GW) T-DNA with the attR1 and attR2 recombination sites of the Gateway rfB cassette. ccdB, Escherichia coli DNA gyrase for negative selection; CmR, chloramphenicol resistance gene for positive selection. The first amino acid of LUC+ is underlined. For other abbreviations, refer to Figure 1 legend. The GenBank accession number of LucTrap-3(GW) is AY968054. ). We subsequently tested LucTrap-3(GW) with five different entry clones and achieved full cloning efficiency in all cases, suggesting that LucTrap-3(GW) is a fully functional Gateway vector (data not shown). Next, we examined whether translational fusions obtained with LucTrap-3(GW) can be used to determine protein abundance in vivo. To this end, we generated transgenic Arabidopsis lines that carry the construct REPRESSOR-OF-ga1-3 (RGA):RGA:LUC. RGA:RGA:LUC lines express a fusion protein of Arabidopsis RGA with LUC+ under the control of a 2-kb RGA promoter fragment. RGA is a predominantly nuclear-localized downstream regulator of the gibberellic acid (GA3) signaling pathway and it is known to be degraded by the 26S proteasome in response to GA3 (Silverstone et al., 2001; Dill et al., 2004). We therefore tested whether GA3 and the GA3 biosynthesis inhibitor paclobutrazol (PAC) have an effect on RGA:LUC abundance in vivo. We found that light-grown RGA:RGA:LUC seedlings show moderate expression of the RGA:LUC fusion protein, whereas RGA:LUC abundance is increased after PAC application, a finding that may be explained by the expected stabilization of RGA:LUC (Fig. 3A Figure 3. Open in new tabDownload slide Translational LUC+ fusions with the RGA protein allow detection of protein degradation events. A, Representative result of a transgenic line expressing the LUC gene fused to the RGA open reading frame under the control of a 2-kb RGA promoter fragment (RGA:RGA:LUC). LUC activity was measured in 5-d-old seedlings (untreated) and, after 12-h treatment with 100 μm of the GA3 biosynthesis inhibitor PAC, 100 μm PAC, and 100 μm GA3 (PAC + GA3), as well as 100 μm of the 26S proteasome inhibitor MG132, as indicated. n = 4. B, Fluorescence microscopy and Nomarski images of root cells of 5-d-old Arabidopsis seedlings expressing the RGA:GFP:RGA fusion protein. Treatments were as described in A. Figure 3. Open in new tabDownload slide Translational LUC+ fusions with the RGA protein allow detection of protein degradation events. A, Representative result of a transgenic line expressing the LUC gene fused to the RGA open reading frame under the control of a 2-kb RGA promoter fragment (RGA:RGA:LUC). LUC activity was measured in 5-d-old seedlings (untreated) and, after 12-h treatment with 100 μm of the GA3 biosynthesis inhibitor PAC, 100 μm PAC, and 100 μm GA3 (PAC + GA3), as well as 100 μm of the 26S proteasome inhibitor MG132, as indicated. n = 4. B, Fluorescence microscopy and Nomarski images of root cells of 5-d-old Arabidopsis seedlings expressing the RGA:GFP:RGA fusion protein. Treatments were as described in A. ). In turn, RGA:LUC stabilization could be reversed by the concomitant application of GA3, a treatment that counteracts the reduction in endogenous GA3 resulting from PAC treatment. Finally, and in line with the notion that RGA:LUC requires proteasomal activity for its degradation, we were also able to stabilize RGA:LUC by application of the 26S proteasome inhibitor MG132 (Fig. 3A). RGA protein abundance has so far almost exclusively been studied using transgenic Arabidopsis lines that contain RGA:GFP:RGA (Silverstone et al., 2001). RGA:GFP:RGA lines express a fusion protein between RGA and the reporter GFP under the control of a RGA promoter fragment. As a control experiment, we therefore subjected RGA:GFP:RGA lines to the same treatment we had applied to the RGA:RGA:LUC lines. We found that treatments with the inhibitors PAC and MG132, as well as treatments with GA3, had the same effect on the previously established GFP:RGA reporter as on RGA:LUC (Fig. 3B). We therefore propose that LucTrap-3(GW) allows generation of LUC+ fusions that can serve to detect changes in protein abundance in vivo. The LucTrap-1 and LucTrap-2 Vectors for Promoter and Gene Trapping Promoter, enhancer, or gene traps are genomic tools to generate untargeted reporter gene fusions (Evans et al., 1997; Durick et al., 1999; Springer, 2000). Promoter, enhancer, or gene traps are designed in a way that the random insertion of a promoterless reporter gene in a gene (gene trap) or in the proximity of a promoter or enhancer element (promoter or enhancer trap) will lead to the detectable expression of the reporter either as a result of a transcriptional (enhancer or promoter trap) or a translational (gene trap) fusion. In Arabidopsis, such unbiased trapping approaches have been successfully used for the discovery of genes and reporter lines that are expressed in specific tissues, in specific developmental stages, or in response to specific signals, as well as for the discovery of proteins that localize to specific subcellular structures (Kertbundit et al., 1991; Sundaresan et al., 1995; Campisi et al., 1999; Parinov et al., 1999; Cutler et al., 2000; Geisler et al., 2002; Birnbaum et al., 2003; Yamamoto et al., 2003; Alvarado et al., 2004; Tian et al., 2004; Nakayama et al., 2005). We generated LucTrap-1 as a vector for promoter and gene trapping in plants. LucTrap-1 contains a modified intron of the Arabidopsis G-protein α-subunit gene (Gα; At2g26300) that was inserted between the T-DNA right border and the LUC+ open reading frame (Fig. 4A Figure 4. Open in new tabDownload slide LucTrap-1 and LucTrap-2 plant transformation vectors for promoter and gene trapping. A, Schematic representation of the LucTrap-1 T-DNA. The intron of the Gα subunit gene was placed between the T-DNA right border (RB) and the LUC+ gene. The artificial splice donor (D) and three splice acceptor sites (A1, A2, and A3) flanking the Gα intron are indicated. The A1, A2, and A3 sites are spaced in a manner that will permit the formation of three alternatively spliced products, one of which will be in frame with the LUC+ reporter and will therefore generate productive LUC+ fusions. The first amino acids of the LUC+ protein are underlined. The GenBank accession number of LucTrap-1 is AY944581. B, Schematic representation of the LucTrap-2 T-DNA. The vector is identical to LucTrap-1, except that the LUC+ start codon was deleted. The initial amino acids of the LUC+ protein are underlined. The right border (RB) sequence and the adjacent Gα intron sequence lack stop codons in any of the three reading frames to avoid premature chain termination during translation. The GenBank accession number of LucTrap-2 is AY944582. C, Rationale of the LucTrap-1 promoter trap vector where LucTrap-1 T-DNA insertions in transcriptionally active regions will result in the formation of LUC+ fusion mRNAs. D, Rationale of the LucTrap-2 gene trap vector where forward LucTrap-2 T-DNA insertions in an exon (top section) or intron (bottom section) will result in the formation of productive LUC+ fusions as indicated by the line drawing. Figure 4. Open in new tabDownload slide LucTrap-1 and LucTrap-2 plant transformation vectors for promoter and gene trapping. A, Schematic representation of the LucTrap-1 T-DNA. The intron of the Gα subunit gene was placed between the T-DNA right border (RB) and the LUC+ gene. The artificial splice donor (D) and three splice acceptor sites (A1, A2, and A3) flanking the Gα intron are indicated. The A1, A2, and A3 sites are spaced in a manner that will permit the formation of three alternatively spliced products, one of which will be in frame with the LUC+ reporter and will therefore generate productive LUC+ fusions. The first amino acids of the LUC+ protein are underlined. The GenBank accession number of LucTrap-1 is AY944581. B, Schematic representation of the LucTrap-2 T-DNA. The vector is identical to LucTrap-1, except that the LUC+ start codon was deleted. The initial amino acids of the LUC+ protein are underlined. The right border (RB) sequence and the adjacent Gα intron sequence lack stop codons in any of the three reading frames to avoid premature chain termination during translation. The GenBank accession number of LucTrap-2 is AY944582. C, Rationale of the LucTrap-1 promoter trap vector where LucTrap-1 T-DNA insertions in transcriptionally active regions will result in the formation of LUC+ fusion mRNAs. D, Rationale of the LucTrap-2 gene trap vector where forward LucTrap-2 T-DNA insertions in an exon (top section) or intron (bottom section) will result in the formation of productive LUC+ fusions as indicated by the line drawing. ). In the context of a similar arrangement, this Gα intron had previously been used successfully for promoter and gene trapping in Arabidopsis with the GUS reporter (Sundaresan et al., 1995). LucTrap-1 also contains one splice donor site (D) located directly adjacent to the T-DNA right border as well as three splice acceptor sites (A1, A2, and A3) located upstream of the LUC+ gene (Fig. 4A). The acceptor sites are spaced in the three different forward reading frames and this spacing should result in the formation of alternatively spliced transcripts between a splice donor site of the trapped gene or the LucTrap-1 D site and the LucTrap-1 acceptors A1, A2, and A3 (Fig. 4C). Hence, LucTrap-1 is designed such that insertion of its T-DNA will result in the expression of LUC+ or a LUC+ fusion transcript under the spatial and temporal control of the trapped promoter (Fig. 4C). Furthermore, we generated LucTrap-2, which has all the features of LucTrap-1, but lacks the LUC+ ATG start codon (Fig. 4B). Because the start codon for the initiation of translation needs to be provided by the trapped gene, we reason that such an arrangement will favor the identification of in-gene T-DNA insertions and therefore LUC+ protein fusions (Fig. 4D). Characterization of a LucTrap-2 Collection To test the performance of LucTrap-2, we generated and analyzed a collection of 700 transgenic Arabidopsis lines carrying LucTrap-2. The segregation of the kanamycin resistance trait in the T2 progeny of these lines indicated that the vast majority of lines have single locus insertions. We then tested 5-d-old light-grown seedlings for LUC+ expression. In this analysis, we found 90 lines (12.8%) to express LUC+ at levels that are at least 2-fold above the levels detected in nontransgenic control plants (Fig. 5A Figure 5. Open in new tabDownload slide Quantitative analysis of 700 Arabidopsis LucTrap-2 gene trap lines identifies 90 LUC-expressing lines. A, Distribution of LUC activity in the 90 LUC-expressing LucTrap-2 lines. The average and sd of four replicate measurements is shown. Background (BG) activity in this particular experiment was 14 relative light units. Please note the logarithmic scale of the graph. B, Absolute number of LucTrap-2 lines with LUC expression levels above specified BG activities. Figure 5. Open in new tabDownload slide Quantitative analysis of 700 Arabidopsis LucTrap-2 gene trap lines identifies 90 LUC-expressing lines. A, Distribution of LUC activity in the 90 LUC-expressing LucTrap-2 lines. The average and sd of four replicate measurements is shown. Background (BG) activity in this particular experiment was 14 relative light units. Please note the logarithmic scale of the graph. B, Absolute number of LucTrap-2 lines with LUC expression levels above specified BG activities. ). This group included 46 lines (6.6%) that express LUC+ at levels at least 10 times above that detected in nontransgenic seedlings (Fig. 5, A and B). This shows that the LUC+ gene of LucTrap-2 is functional in Arabidopsis in the context of genomic insertions. We then adopted previously established strategies for the amplification and identification of LucTrap-2 flanking sequence tags (FSTs; Table I Table I. Primers for the amplification of LucTrap-2 T-DNA flanking sequences RB, Right border; LB, left border. Name . Sequence . Vectorette Primers     TopL 5′-CGAATCGTAACCGTTCGTACGAGAATTCGTACGAGAATCGCTGTCCTCTCCAACGAGCCAAGG-3′     BamHI 5′-GATCCCTTGGCTCGTTTTTTTTTGCAAAAA-3′     VEC1 5′-CGAATCGTAACCGTTCGTACGAGAA-3′     VEC2 5′-TCGTACGAGAATCGCTGTCCTCTCC-3′ LucTrap-2 Right Border Primers     LucR1 5′-CAATCAATTTTCCTTGTGGACTTGG-3′     LucR2 5′-GTTTTCATGTGTGATTTTACCGAAC-3′     LucR3 5′-GGTTCCCAGTCCGATTTCGACAGG-3′ LucTrap-2 Left Border Primers     LucL1 5′-CGATAGAAAACAAAATATAGCGCGC-3′     LucL2 5′-CTAGGATAAATTATCGCGCGCGG-3′     LucL3 5′-CTAGATCGACCGGCATGCAAGC-3′ Name . Sequence . Vectorette Primers     TopL 5′-CGAATCGTAACCGTTCGTACGAGAATTCGTACGAGAATCGCTGTCCTCTCCAACGAGCCAAGG-3′     BamHI 5′-GATCCCTTGGCTCGTTTTTTTTTGCAAAAA-3′     VEC1 5′-CGAATCGTAACCGTTCGTACGAGAA-3′     VEC2 5′-TCGTACGAGAATCGCTGTCCTCTCC-3′ LucTrap-2 Right Border Primers     LucR1 5′-CAATCAATTTTCCTTGTGGACTTGG-3′     LucR2 5′-GTTTTCATGTGTGATTTTACCGAAC-3′     LucR3 5′-GGTTCCCAGTCCGATTTCGACAGG-3′ LucTrap-2 Left Border Primers     LucL1 5′-CGATAGAAAACAAAATATAGCGCGC-3′     LucL2 5′-CTAGGATAAATTATCGCGCGCGG-3′     LucL3 5′-CTAGATCGACCGGCATGCAAGC-3′ Open in new tab Table I. Primers for the amplification of LucTrap-2 T-DNA flanking sequences RB, Right border; LB, left border. Name . Sequence . Vectorette Primers     TopL 5′-CGAATCGTAACCGTTCGTACGAGAATTCGTACGAGAATCGCTGTCCTCTCCAACGAGCCAAGG-3′     BamHI 5′-GATCCCTTGGCTCGTTTTTTTTTGCAAAAA-3′     VEC1 5′-CGAATCGTAACCGTTCGTACGAGAA-3′     VEC2 5′-TCGTACGAGAATCGCTGTCCTCTCC-3′ LucTrap-2 Right Border Primers     LucR1 5′-CAATCAATTTTCCTTGTGGACTTGG-3′     LucR2 5′-GTTTTCATGTGTGATTTTACCGAAC-3′     LucR3 5′-GGTTCCCAGTCCGATTTCGACAGG-3′ LucTrap-2 Left Border Primers     LucL1 5′-CGATAGAAAACAAAATATAGCGCGC-3′     LucL2 5′-CTAGGATAAATTATCGCGCGCGG-3′     LucL3 5′-CTAGATCGACCGGCATGCAAGC-3′ Name . Sequence . Vectorette Primers     TopL 5′-CGAATCGTAACCGTTCGTACGAGAATTCGTACGAGAATCGCTGTCCTCTCCAACGAGCCAAGG-3′     BamHI 5′-GATCCCTTGGCTCGTTTTTTTTTGCAAAAA-3′     VEC1 5′-CGAATCGTAACCGTTCGTACGAGAA-3′     VEC2 5′-TCGTACGAGAATCGCTGTCCTCTCC-3′ LucTrap-2 Right Border Primers     LucR1 5′-CAATCAATTTTCCTTGTGGACTTGG-3′     LucR2 5′-GTTTTCATGTGTGATTTTACCGAAC-3′     LucR3 5′-GGTTCCCAGTCCGATTTCGACAGG-3′ LucTrap-2 Left Border Primers     LucL1 5′-CGATAGAAAACAAAATATAGCGCGC-3′     LucL2 5′-CTAGGATAAATTATCGCGCGCGG-3′     LucL3 5′-CTAGATCGACCGGCATGCAAGC-3′ Open in new tab ; Devon et al., 1995; Strizhov et al., 2003). Because our main interest lies in the identification of FSTs from LUC+-expressing lines, we preferentially determined FSTs from these lines and, consequently, our sample is not necessarily representative of the entire collection. Furthermore, we would like to point out that, using this strategy, we were unable to identify FSTs for several LucTrap-2 lines, including the three lines LT028, LT032, and LT095 described in more detail below. This may be due to the absence of the appropriate restriction sites in the proximity of the insertion site, and alternative enzyme-primer combinations may have to be used for the successful identification of FSTs from some LucTrap-2 lines (Devon et al., 1995; Strizhov et al., 2003). We were successful in identifying FSTs from 49 lines and we analyzed these using BLASTN searches (Table II Table II. Insertion sites identified in LucTrap-2 lines LucTrap-2 lines with FSTs identify genomic insertions. The E-values obtained in BLASTN searches using FST reads and primers used for FST identification are indicated. Productive LUC fusions are expected in 27 lines (lines expected to give rise to LUC+ fusions), nonproductive LUC fusions are predicted in 22 lines (lines not expected to give rise to LUC+ fusions). RLUs and sd as detected in 5-d-old seedlings are indicated. n ≥ 4. LT, LucTrap-2; RLU, relative light units; BG, background activity. LT No. . Locus . Position . E-Value . Primer . Fusion . RLUs . LucTrap-2 Lines Expected to Give Rise to LUC+ Fusions     LT001 At4g21750 (ML1-specific homeobox gene) First intron 3.00E-73 LUCR3 Yes 4,368 ± 1,170     LT005 At5g40730 (arabinogalactan-protein AGP24) 3′-UTR 0 LUCR3 Yes BG     LT033 At5g45775 (60S ribosomal protein L11) Third intron 0 LUCR3 Yes 150 ± 42     LT042 At3g08810 (F-box family protein) 3′-UTR 1.00E-105 LUCR3 Yes BG     LT055 At4g32450 (pentatricopeptide repeat-containing protein) Unique exon 0 LUCR3 Yes BG     LT134 At3g23260 (F-box family protein) Unique exon 1.00E-18 LUCR3 Yes BG     LT136 At2g44260 (expressed protein) Second exon 8.00E-26 LUCR3 Yes BG     LT140 At1g77440 (20S proteasome β-subunit PBC2) Fifth intron 3.00E-26 LUCR3 Yes BG     LT155 At5g57399 (UbiE/COq5 methyl transferase) Third exon 3.00E-15 LUCL3 Yes 105 ± 39     LT171 At4g18570 (Pro-rich family protein) First intron 2.00E-68 LUCL3 Yes 221 ± 70     LT174 At3g19510 (homeobox protein HAT3.1) Sixth intron 1.00E-102 LUCR3 Yes BG     LT179 At3g02820 (zinc knuckle [CCHC-type] family protein) Fourth exon 1.00E-139 LUCR3 Yes BG     LT184 At3g11580 (B3 domain transcription factor) First exon 1.00E-21 LUCR3 Yes 240 ± 0     LT186 At1g05630 (At5PTase 13 inositol 5-P) Fifth intron 3.00E-126 LUCR3 Yes BG     LT188 At1g65365 (putative protein kinase, pseudogene) Unique exon 0 LUCR3 Yes BG     LT189 At5g10520 (protein kinase) Seventh intron 2.00E-78 LUCR3 Yes BG     LT200 At5g67420 (LOB domain protein 37) Third exon 1.00E-48 LUCR3 Yes BG     LT206 At1g75840 (Rac-like GTP-binding protein ARAC5) 3′-UTR 4.00E-43 LUCR3 Yes BG     LT210 At1g73230 (NPAC BTF3 transcription factor) 3′-UTR 1.00E-109 LUCR3 Yes BG     LT301 At5g65110 (Acyl-CoA oxidase ACX2) 5′-UTR 4.00E-32 LUCL3 Yes 467 ± 195     LT316 At4g33620 (Ulp1 protease family SUMO protease) Seventeenth exon 1.00E-124 LUCR3 Yes BG     LT332 At1g48900 (SRP-54C signal recognition particle) Seventh exon 1.00E-101 LUCR3 Yes 241 ± 58     LT334 At3g02470 (S-adenosylmethionine decarboxylase) First intron 7.00E-57 LUCR3 Yes 26,391 ± 6,355     LT348 At1g21065 (expressed protein) First intron 6.00E-22 LUCL3 Yes 490 ± 122     LT368 At1g49880 (Erv1/Air family protein) Fourth exon 8.00E-40 LUCL3 Yes 225 ± 48     LT430 At4g20410 (γ-SNAP) First intron 3.00-E83 LUCR3 Yes 940 ± 382     LT649 At5g48560 (basic helix-loop-helix transcription factor) Fifth exon 3.00E-70 LUCR3 Yes 72 ± 26 LucTrap-2 Lines Not Expected to Give Rise to LUC+ Fusions     LT004 At2g18700 and At2g18690 Intergenic region 1.00E-21 LUCR3 No BG     LT037 At2g44260 (expressed protein) Second exon 1.00E-34 LUCR3 No BG     LT046 At5g10980 (expressed protein) 5′-UTR 1.00E-167 LUCR3 No BG     LT062 At5g40270 and At5g40260 Intergenic region 0 LUCR3 No BG     LT104 At3g58500 (Ser/Thr protein phosphatase subunit) Seventh intron 4.00E-71 LUCR3 No BG     LT117 At5g38200 and unannotated open reading frame Intergenic region 6.00E-69 LUCR3 No BG     LT173 At5g40260 and At5g40270 Intergenic region 6.00E-67 LUCR3 No BG     LT178 At1g47600 (thioglucosidase) Thirteenth exon 9.00E-61 LUCL3 No 3,270 ± 951     LT190 At3g23900 (RNA recognition motif-containing protein) Sixth intron 2.00E-45 LUCR3 No BG     LT196 At1g04830 and At1g04840 Intergenic region 6.00E-101 LUCR3 No BG     LT221 At3g53450 (decarboxylase) Fourth intron 2.00E-06 LUCL3 No 51 ± 25     LT224 At1g13260 (DNA-binding protein RAV1) 5′-UTR 1.00E-93 LUCR3 No BG     LT263 At4g25620 and At4g25630 Intergenic region 6.00E-56 LUCR3 No 200 ± 75     LT278 At5g15460 (expressed protein with ubiquitin domain) Second exon 2.00E-14 LUCR3 No 61 ± 30     LT297 At5g34960 and At5g34965 Intergenic region 3.00E-10 LUCL3 No 154 ± 59     LT303 At5g53570 (RabGAP/TBC domain-containing protein) Fifth exon 2.00E-28 LUCR3 No BG     LT322 At4g33520 (metal-transporting P-type ATPase) Fifteenth exon 0.002 LUCR3 No 112 ± 46     LT340 At4g38730 and At4g38740 Intergenic region 4.00E-11 LUCR3 No 70 ± 50     LT414 At4g38710 (glycine-rich protein cylicin II) First exon 6.00E-05 LUCR3 No 9,730 ± 3,000     LT510 At1g79430 and At1g79440 Intergenic region 6.00E-43 LUCR3 No 122 ± 29     LT516 At3g03700 (expressed protein) 3′-UTR 5.00E-83 LUCR3 No 42 ± 16 LT No. . Locus . Position . E-Value . Primer . Fusion . RLUs . LucTrap-2 Lines Expected to Give Rise to LUC+ Fusions     LT001 At4g21750 (ML1-specific homeobox gene) First intron 3.00E-73 LUCR3 Yes 4,368 ± 1,170     LT005 At5g40730 (arabinogalactan-protein AGP24) 3′-UTR 0 LUCR3 Yes BG     LT033 At5g45775 (60S ribosomal protein L11) Third intron 0 LUCR3 Yes 150 ± 42     LT042 At3g08810 (F-box family protein) 3′-UTR 1.00E-105 LUCR3 Yes BG     LT055 At4g32450 (pentatricopeptide repeat-containing protein) Unique exon 0 LUCR3 Yes BG     LT134 At3g23260 (F-box family protein) Unique exon 1.00E-18 LUCR3 Yes BG     LT136 At2g44260 (expressed protein) Second exon 8.00E-26 LUCR3 Yes BG     LT140 At1g77440 (20S proteasome β-subunit PBC2) Fifth intron 3.00E-26 LUCR3 Yes BG     LT155 At5g57399 (UbiE/COq5 methyl transferase) Third exon 3.00E-15 LUCL3 Yes 105 ± 39     LT171 At4g18570 (Pro-rich family protein) First intron 2.00E-68 LUCL3 Yes 221 ± 70     LT174 At3g19510 (homeobox protein HAT3.1) Sixth intron 1.00E-102 LUCR3 Yes BG     LT179 At3g02820 (zinc knuckle [CCHC-type] family protein) Fourth exon 1.00E-139 LUCR3 Yes BG     LT184 At3g11580 (B3 domain transcription factor) First exon 1.00E-21 LUCR3 Yes 240 ± 0     LT186 At1g05630 (At5PTase 13 inositol 5-P) Fifth intron 3.00E-126 LUCR3 Yes BG     LT188 At1g65365 (putative protein kinase, pseudogene) Unique exon 0 LUCR3 Yes BG     LT189 At5g10520 (protein kinase) Seventh intron 2.00E-78 LUCR3 Yes BG     LT200 At5g67420 (LOB domain protein 37) Third exon 1.00E-48 LUCR3 Yes BG     LT206 At1g75840 (Rac-like GTP-binding protein ARAC5) 3′-UTR 4.00E-43 LUCR3 Yes BG     LT210 At1g73230 (NPAC BTF3 transcription factor) 3′-UTR 1.00E-109 LUCR3 Yes BG     LT301 At5g65110 (Acyl-CoA oxidase ACX2) 5′-UTR 4.00E-32 LUCL3 Yes 467 ± 195     LT316 At4g33620 (Ulp1 protease family SUMO protease) Seventeenth exon 1.00E-124 LUCR3 Yes BG     LT332 At1g48900 (SRP-54C signal recognition particle) Seventh exon 1.00E-101 LUCR3 Yes 241 ± 58     LT334 At3g02470 (S-adenosylmethionine decarboxylase) First intron 7.00E-57 LUCR3 Yes 26,391 ± 6,355     LT348 At1g21065 (expressed protein) First intron 6.00E-22 LUCL3 Yes 490 ± 122     LT368 At1g49880 (Erv1/Air family protein) Fourth exon 8.00E-40 LUCL3 Yes 225 ± 48     LT430 At4g20410 (γ-SNAP) First intron 3.00-E83 LUCR3 Yes 940 ± 382     LT649 At5g48560 (basic helix-loop-helix transcription factor) Fifth exon 3.00E-70 LUCR3 Yes 72 ± 26 LucTrap-2 Lines Not Expected to Give Rise to LUC+ Fusions     LT004 At2g18700 and At2g18690 Intergenic region 1.00E-21 LUCR3 No BG     LT037 At2g44260 (expressed protein) Second exon 1.00E-34 LUCR3 No BG     LT046 At5g10980 (expressed protein) 5′-UTR 1.00E-167 LUCR3 No BG     LT062 At5g40270 and At5g40260 Intergenic region 0 LUCR3 No BG     LT104 At3g58500 (Ser/Thr protein phosphatase subunit) Seventh intron 4.00E-71 LUCR3 No BG     LT117 At5g38200 and unannotated open reading frame Intergenic region 6.00E-69 LUCR3 No BG     LT173 At5g40260 and At5g40270 Intergenic region 6.00E-67 LUCR3 No BG     LT178 At1g47600 (thioglucosidase) Thirteenth exon 9.00E-61 LUCL3 No 3,270 ± 951     LT190 At3g23900 (RNA recognition motif-containing protein) Sixth intron 2.00E-45 LUCR3 No BG     LT196 At1g04830 and At1g04840 Intergenic region 6.00E-101 LUCR3 No BG     LT221 At3g53450 (decarboxylase) Fourth intron 2.00E-06 LUCL3 No 51 ± 25     LT224 At1g13260 (DNA-binding protein RAV1) 5′-UTR 1.00E-93 LUCR3 No BG     LT263 At4g25620 and At4g25630 Intergenic region 6.00E-56 LUCR3 No 200 ± 75     LT278 At5g15460 (expressed protein with ubiquitin domain) Second exon 2.00E-14 LUCR3 No 61 ± 30     LT297 At5g34960 and At5g34965 Intergenic region 3.00E-10 LUCL3 No 154 ± 59     LT303 At5g53570 (RabGAP/TBC domain-containing protein) Fifth exon 2.00E-28 LUCR3 No BG     LT322 At4g33520 (metal-transporting P-type ATPase) Fifteenth exon 0.002 LUCR3 No 112 ± 46     LT340 At4g38730 and At4g38740 Intergenic region 4.00E-11 LUCR3 No 70 ± 50     LT414 At4g38710 (glycine-rich protein cylicin II) First exon 6.00E-05 LUCR3 No 9,730 ± 3,000     LT510 At1g79430 and At1g79440 Intergenic region 6.00E-43 LUCR3 No 122 ± 29     LT516 At3g03700 (expressed protein) 3′-UTR 5.00E-83 LUCR3 No 42 ± 16 Open in new tab Table II. Insertion sites identified in LucTrap-2 lines LucTrap-2 lines with FSTs identify genomic insertions. The E-values obtained in BLASTN searches using FST reads and primers used for FST identification are indicated. Productive LUC fusions are expected in 27 lines (lines expected to give rise to LUC+ fusions), nonproductive LUC fusions are predicted in 22 lines (lines not expected to give rise to LUC+ fusions). RLUs and sd as detected in 5-d-old seedlings are indicated. n ≥ 4. LT, LucTrap-2; RLU, relative light units; BG, background activity. LT No. . Locus . Position . E-Value . Primer . Fusion . RLUs . LucTrap-2 Lines Expected to Give Rise to LUC+ Fusions     LT001 At4g21750 (ML1-specific homeobox gene) First intron 3.00E-73 LUCR3 Yes 4,368 ± 1,170     LT005 At5g40730 (arabinogalactan-protein AGP24) 3′-UTR 0 LUCR3 Yes BG     LT033 At5g45775 (60S ribosomal protein L11) Third intron 0 LUCR3 Yes 150 ± 42     LT042 At3g08810 (F-box family protein) 3′-UTR 1.00E-105 LUCR3 Yes BG     LT055 At4g32450 (pentatricopeptide repeat-containing protein) Unique exon 0 LUCR3 Yes BG     LT134 At3g23260 (F-box family protein) Unique exon 1.00E-18 LUCR3 Yes BG     LT136 At2g44260 (expressed protein) Second exon 8.00E-26 LUCR3 Yes BG     LT140 At1g77440 (20S proteasome β-subunit PBC2) Fifth intron 3.00E-26 LUCR3 Yes BG     LT155 At5g57399 (UbiE/COq5 methyl transferase) Third exon 3.00E-15 LUCL3 Yes 105 ± 39     LT171 At4g18570 (Pro-rich family protein) First intron 2.00E-68 LUCL3 Yes 221 ± 70     LT174 At3g19510 (homeobox protein HAT3.1) Sixth intron 1.00E-102 LUCR3 Yes BG     LT179 At3g02820 (zinc knuckle [CCHC-type] family protein) Fourth exon 1.00E-139 LUCR3 Yes BG     LT184 At3g11580 (B3 domain transcription factor) First exon 1.00E-21 LUCR3 Yes 240 ± 0     LT186 At1g05630 (At5PTase 13 inositol 5-P) Fifth intron 3.00E-126 LUCR3 Yes BG     LT188 At1g65365 (putative protein kinase, pseudogene) Unique exon 0 LUCR3 Yes BG     LT189 At5g10520 (protein kinase) Seventh intron 2.00E-78 LUCR3 Yes BG     LT200 At5g67420 (LOB domain protein 37) Third exon 1.00E-48 LUCR3 Yes BG     LT206 At1g75840 (Rac-like GTP-binding protein ARAC5) 3′-UTR 4.00E-43 LUCR3 Yes BG     LT210 At1g73230 (NPAC BTF3 transcription factor) 3′-UTR 1.00E-109 LUCR3 Yes BG     LT301 At5g65110 (Acyl-CoA oxidase ACX2) 5′-UTR 4.00E-32 LUCL3 Yes 467 ± 195     LT316 At4g33620 (Ulp1 protease family SUMO protease) Seventeenth exon 1.00E-124 LUCR3 Yes BG     LT332 At1g48900 (SRP-54C signal recognition particle) Seventh exon 1.00E-101 LUCR3 Yes 241 ± 58     LT334 At3g02470 (S-adenosylmethionine decarboxylase) First intron 7.00E-57 LUCR3 Yes 26,391 ± 6,355     LT348 At1g21065 (expressed protein) First intron 6.00E-22 LUCL3 Yes 490 ± 122     LT368 At1g49880 (Erv1/Air family protein) Fourth exon 8.00E-40 LUCL3 Yes 225 ± 48     LT430 At4g20410 (γ-SNAP) First intron 3.00-E83 LUCR3 Yes 940 ± 382     LT649 At5g48560 (basic helix-loop-helix transcription factor) Fifth exon 3.00E-70 LUCR3 Yes 72 ± 26 LucTrap-2 Lines Not Expected to Give Rise to LUC+ Fusions     LT004 At2g18700 and At2g18690 Intergenic region 1.00E-21 LUCR3 No BG     LT037 At2g44260 (expressed protein) Second exon 1.00E-34 LUCR3 No BG     LT046 At5g10980 (expressed protein) 5′-UTR 1.00E-167 LUCR3 No BG     LT062 At5g40270 and At5g40260 Intergenic region 0 LUCR3 No BG     LT104 At3g58500 (Ser/Thr protein phosphatase subunit) Seventh intron 4.00E-71 LUCR3 No BG     LT117 At5g38200 and unannotated open reading frame Intergenic region 6.00E-69 LUCR3 No BG     LT173 At5g40260 and At5g40270 Intergenic region 6.00E-67 LUCR3 No BG     LT178 At1g47600 (thioglucosidase) Thirteenth exon 9.00E-61 LUCL3 No 3,270 ± 951     LT190 At3g23900 (RNA recognition motif-containing protein) Sixth intron 2.00E-45 LUCR3 No BG     LT196 At1g04830 and At1g04840 Intergenic region 6.00E-101 LUCR3 No BG     LT221 At3g53450 (decarboxylase) Fourth intron 2.00E-06 LUCL3 No 51 ± 25     LT224 At1g13260 (DNA-binding protein RAV1) 5′-UTR 1.00E-93 LUCR3 No BG     LT263 At4g25620 and At4g25630 Intergenic region 6.00E-56 LUCR3 No 200 ± 75     LT278 At5g15460 (expressed protein with ubiquitin domain) Second exon 2.00E-14 LUCR3 No 61 ± 30     LT297 At5g34960 and At5g34965 Intergenic region 3.00E-10 LUCL3 No 154 ± 59     LT303 At5g53570 (RabGAP/TBC domain-containing protein) Fifth exon 2.00E-28 LUCR3 No BG     LT322 At4g33520 (metal-transporting P-type ATPase) Fifteenth exon 0.002 LUCR3 No 112 ± 46     LT340 At4g38730 and At4g38740 Intergenic region 4.00E-11 LUCR3 No 70 ± 50     LT414 At4g38710 (glycine-rich protein cylicin II) First exon 6.00E-05 LUCR3 No 9,730 ± 3,000     LT510 At1g79430 and At1g79440 Intergenic region 6.00E-43 LUCR3 No 122 ± 29     LT516 At3g03700 (expressed protein) 3′-UTR 5.00E-83 LUCR3 No 42 ± 16 LT No. . Locus . Position . E-Value . Primer . Fusion . RLUs . LucTrap-2 Lines Expected to Give Rise to LUC+ Fusions     LT001 At4g21750 (ML1-specific homeobox gene) First intron 3.00E-73 LUCR3 Yes 4,368 ± 1,170     LT005 At5g40730 (arabinogalactan-protein AGP24) 3′-UTR 0 LUCR3 Yes BG     LT033 At5g45775 (60S ribosomal protein L11) Third intron 0 LUCR3 Yes 150 ± 42     LT042 At3g08810 (F-box family protein) 3′-UTR 1.00E-105 LUCR3 Yes BG     LT055 At4g32450 (pentatricopeptide repeat-containing protein) Unique exon 0 LUCR3 Yes BG     LT134 At3g23260 (F-box family protein) Unique exon 1.00E-18 LUCR3 Yes BG     LT136 At2g44260 (expressed protein) Second exon 8.00E-26 LUCR3 Yes BG     LT140 At1g77440 (20S proteasome β-subunit PBC2) Fifth intron 3.00E-26 LUCR3 Yes BG     LT155 At5g57399 (UbiE/COq5 methyl transferase) Third exon 3.00E-15 LUCL3 Yes 105 ± 39     LT171 At4g18570 (Pro-rich family protein) First intron 2.00E-68 LUCL3 Yes 221 ± 70     LT174 At3g19510 (homeobox protein HAT3.1) Sixth intron 1.00E-102 LUCR3 Yes BG     LT179 At3g02820 (zinc knuckle [CCHC-type] family protein) Fourth exon 1.00E-139 LUCR3 Yes BG     LT184 At3g11580 (B3 domain transcription factor) First exon 1.00E-21 LUCR3 Yes 240 ± 0     LT186 At1g05630 (At5PTase 13 inositol 5-P) Fifth intron 3.00E-126 LUCR3 Yes BG     LT188 At1g65365 (putative protein kinase, pseudogene) Unique exon 0 LUCR3 Yes BG     LT189 At5g10520 (protein kinase) Seventh intron 2.00E-78 LUCR3 Yes BG     LT200 At5g67420 (LOB domain protein 37) Third exon 1.00E-48 LUCR3 Yes BG     LT206 At1g75840 (Rac-like GTP-binding protein ARAC5) 3′-UTR 4.00E-43 LUCR3 Yes BG     LT210 At1g73230 (NPAC BTF3 transcription factor) 3′-UTR 1.00E-109 LUCR3 Yes BG     LT301 At5g65110 (Acyl-CoA oxidase ACX2) 5′-UTR 4.00E-32 LUCL3 Yes 467 ± 195     LT316 At4g33620 (Ulp1 protease family SUMO protease) Seventeenth exon 1.00E-124 LUCR3 Yes BG     LT332 At1g48900 (SRP-54C signal recognition particle) Seventh exon 1.00E-101 LUCR3 Yes 241 ± 58     LT334 At3g02470 (S-adenosylmethionine decarboxylase) First intron 7.00E-57 LUCR3 Yes 26,391 ± 6,355     LT348 At1g21065 (expressed protein) First intron 6.00E-22 LUCL3 Yes 490 ± 122     LT368 At1g49880 (Erv1/Air family protein) Fourth exon 8.00E-40 LUCL3 Yes 225 ± 48     LT430 At4g20410 (γ-SNAP) First intron 3.00-E83 LUCR3 Yes 940 ± 382     LT649 At5g48560 (basic helix-loop-helix transcription factor) Fifth exon 3.00E-70 LUCR3 Yes 72 ± 26 LucTrap-2 Lines Not Expected to Give Rise to LUC+ Fusions     LT004 At2g18700 and At2g18690 Intergenic region 1.00E-21 LUCR3 No BG     LT037 At2g44260 (expressed protein) Second exon 1.00E-34 LUCR3 No BG     LT046 At5g10980 (expressed protein) 5′-UTR 1.00E-167 LUCR3 No BG     LT062 At5g40270 and At5g40260 Intergenic region 0 LUCR3 No BG     LT104 At3g58500 (Ser/Thr protein phosphatase subunit) Seventh intron 4.00E-71 LUCR3 No BG     LT117 At5g38200 and unannotated open reading frame Intergenic region 6.00E-69 LUCR3 No BG     LT173 At5g40260 and At5g40270 Intergenic region 6.00E-67 LUCR3 No BG     LT178 At1g47600 (thioglucosidase) Thirteenth exon 9.00E-61 LUCL3 No 3,270 ± 951     LT190 At3g23900 (RNA recognition motif-containing protein) Sixth intron 2.00E-45 LUCR3 No BG     LT196 At1g04830 and At1g04840 Intergenic region 6.00E-101 LUCR3 No BG     LT221 At3g53450 (decarboxylase) Fourth intron 2.00E-06 LUCL3 No 51 ± 25     LT224 At1g13260 (DNA-binding protein RAV1) 5′-UTR 1.00E-93 LUCR3 No BG     LT263 At4g25620 and At4g25630 Intergenic region 6.00E-56 LUCR3 No 200 ± 75     LT278 At5g15460 (expressed protein with ubiquitin domain) Second exon 2.00E-14 LUCR3 No 61 ± 30     LT297 At5g34960 and At5g34965 Intergenic region 3.00E-10 LUCL3 No 154 ± 59     LT303 At5g53570 (RabGAP/TBC domain-containing protein) Fifth exon 2.00E-28 LUCR3 No BG     LT322 At4g33520 (metal-transporting P-type ATPase) Fifteenth exon 0.002 LUCR3 No 112 ± 46     LT340 At4g38730 and At4g38740 Intergenic region 4.00E-11 LUCR3 No 70 ± 50     LT414 At4g38710 (glycine-rich protein cylicin II) First exon 6.00E-05 LUCR3 No 9,730 ± 3,000     LT510 At1g79430 and At1g79440 Intergenic region 6.00E-43 LUCR3 No 122 ± 29     LT516 At3g03700 (expressed protein) 3′-UTR 5.00E-83 LUCR3 No 42 ± 16 Open in new tab ). Based on the position and orientation of the LucTrap-2 T-DNA, we predict that 27 of the 49 lines will give rise to productive fusions between the trapped gene and LUC+ (Table II). Indeed, the lines predicted to produce LUC+ fusions include 12 lines that we had identified as LUC-expressing lines, suggesting that the trapped genes are expressed during the seedling stage. We also found that the genes that are trapped in 10 of the remaining 14 lines had been reported to be expressed only at low levels during the seedling stage, a finding that may explain the absence of LUC activity in our assays (Zimmermann et al., 2005). Whereas the lack of LUC activity in three remaining lines (LT140, LT200, and LT210) cannot be explained without further analysis, we noticed that line LT005 carries the LucTrap-2 insertion in the 3′-untranslated (UTR) region of At5g40730, and that this gene is composed of a single exon. Because the insertion in LT005 is not in the gene's coding region and because At5g40730 does not contain any introns, this insertion is not expected to result in the formation of productive LUC fusions due to the absence of a splice donor site. In addition, we cannot rule out that the lack of LUC activity in these lines is the result of a LUC+ fusion transcript or LUC+ fusion protein instability or an impairment of enzymatic activity in the fusion protein context. Our FST analysis also identified 22 LucTrap-2 lines that we do not predict to give rise to productive LUC+ fusions (Table II). Nevertheless, two lines (LT178 and LT414) display very strong LUC activity, whereas the remaining eight lines have comparatively low LUC levels. This may indicate that the expression of LUC+ can also be driven from cryptic promoters and cryptic open reading frames, which we would expect to provide the ATG start codon that had been deleted from the LUC+ gene in LucTrap-2. Alternatively, it may be envisioned that these lines have a duplicated T-DNA insertion in the respective locus so that the right border of the second insertion is oriented such that productive LUC+ fusions can be formed. Such more complex T-DNA insertion events have frequently been reported for T-DNA insertions (De Neve et al., 1997; Forsbach et al., 2003; Lechtenberg et al., 2003; Windels et al., 2003). In summary, we suggest that LucTrap-2 can be used as a gene trap vector that will allow generation of random LUC fusion proteins. However, we also have to conclude that LUC expression does not necessarily correlate with the apparent occurrence of such fusion events. Unstable Negative and Positive Regulators Control Auxin-Induced Gene Expression In recent years, it has become increasingly clear that many signaling events are controlled by unstable regulators that are degraded by the ubiquitin-proteasome system (Schwechheimer and Calderon-Villalobos, 2004). Signal transduction in response to auxin is currently one of the best characterized cases for proteolysis-dependent signaling in plants. Genetic and biochemical studies have led to the identification of the AUXIN/INDOLE ACETIC ACID (AUX/IAA) proteins as transcriptional regulators that repress gene expression in the absence of auxin (Gray et al., 2001; Tiwari et al., 2001). In response to auxin, AUX/IAA degradation is promoted by the activity of the E3 ubiquitin ligase SCFTIR1 whose F-box protein subunit TIR1 also functions as an auxin receptor (Gray et al., 2001; Dharmasiri et al., 2005; Kepinski and Leyser, 2005). Following AUX/IAA degradation, AUXIN RESPONSE FACTOR transcription can activate auxin-induced gene expression (Tiwari et al., 2001). Auxin response is subsequently turned off when de novo synthesized AUX/IAA proteins are available for the repression of AUXIN RESPONSE FACTOR activity (Abel et al., 1994, 1995; Tian and Reed, 1999). The role of protein degradation in auxin-induced gene expression is nicely illustrated in our GH3-2:LucTrap lines, where the application of the 26S proteasome inhibitor MG132 together with auxin results in decreased auxin induction (Fig. 6A Figure 6. Open in new tabDownload slide MG132 proteasome inhibitor treatments reveal the role of unstable repressors and activators in controlling auxin-induced gene expression. Relative LUC expression of GH3-2:LucTrap (A), LT028 (B), LT095 (C), and LT032 (D) as detected over time in 5-d-old seedlings (white squares), after auxin induction (5 μm 2,4D; black squares), after proteasomal inhibition (100 μm MG132; white triangles), and after auxin induction (5 μm 2,4D) with concomitant proteasomal inhibition (100 μm MG132; black triangles). The result of a typical induction experiment is shown. The data for GH3-2:LucTrap, uninduced, and auxin treated are identical to those shown in Figure 1C. LUC activity at t = 0 min of the untreated sample was set as 1. Figure 6. Open in new tabDownload slide MG132 proteasome inhibitor treatments reveal the role of unstable repressors and activators in controlling auxin-induced gene expression. Relative LUC expression of GH3-2:LucTrap (A), LT028 (B), LT095 (C), and LT032 (D) as detected over time in 5-d-old seedlings (white squares), after auxin induction (5 μm 2,4D; black squares), after proteasomal inhibition (100 μm MG132; white triangles), and after auxin induction (5 μm 2,4D) with concomitant proteasomal inhibition (100 μm MG132; black triangles). The result of a typical induction experiment is shown. The data for GH3-2:LucTrap, uninduced, and auxin treated are identical to those shown in Figure 1C. LUC activity at t = 0 min of the untreated sample was set as 1. ). We attribute this effect to a MG132-dependent stabilization of AUX/IAA repressors such as SHORT HYPOCOTYL2 (SHY2)/IAA3, which had previously been shown to control GH3-2 expression (Tian et al., 2003). To examine whether the LucTrap-2 collection contains auxin-induced genes, we examined the effect of auxin on LUC gene expression in all 700 LucTrap-2 lines. In this analysis, we identified three LucTrap-2 lines, namely, LT028, LT032, and LT095, whose LUC expression was activated in response to 2,4D (Fig. 6, B–D). We then went on to study the effect of MG132 application on auxin-induced gene expression in these lines. In agreement with a model where MG132 causes the stabilization of transcriptional repressors such as the AUX/IAAs, we found that auxin-induced gene expression is impaired in LT028 and LT095 following MG132 application (Fig. 6, B–C). In contrast, our studies indicate that auxin-induced gene expression in LT032 may be governed by a different mechanism. Whereas MG132 alone does not have an effect on the expression of LUC+ in LT028 or LT095, MG132 is sufficient to induce LUC+ expression in the absence of auxin in LT032 (Fig. 6D). Furthermore, MG132 superinduces the expression of LUC+ in LT032 when applied together with auxin (Fig. 6D). Such a result cannot be explained by the activity of unstable transcriptional repressors, but rather points to the activity of an unstable transcriptional activator that is stabilized in response to auxin. Such an unstable activator could be stabilized independently by auxin and by inhibition of proteasomal activity and, in combination, these treatments may then lead to the observed superinduction. In all four cases examined, auxin-induced LUC+ expression was followed by negative feedback regulation (Fig. 6, A–D). Such a negative feedback mechanism may be due to the activity of de novo synthesized AUX/IAA repressors whose transcription is known to be promoted by auxin (Abel et al., 1994, 1995; Tian and Reed, 1999). Interestingly, the auxin induction in line LT032, which we hypothesize to be under the control of an unstable activator, is also subject to negative feedback regulation. Therefore, this gene expression mechanism may be negatively controlled by AUX/IAAs or by other as yet unknown repressors. Alternatively, it may be envisioned that the expression of the hypothetical and as yet unidentified activator is down-regulated in response to auxin. As far as we are aware, an auxin-induction mechanism as reported here for LT032 has not been described as yet. LT032 may now be used for the isolation of mutants that show altered LUC+ expression and that carry defects in genes whose gene products are required for auxin- and proteasome-dependent gene expression in this line. DISCUSSION Dynamic Detection of Transcript and Protein Abundance Using the LucTrap Vectors In this article, we introduce the LucTrap vectors that make use of the modified firefly LUC+ as a reporter for regulated gene expression and protein abundance. Using transgenic Arabidopsis lines that express a promoter fragment of the auxin-inducible GH3-2 gene, we demonstrate that LucTrap is well suited to follow gene expression patterns in a dynamic and time-resolved manner (Fig. 1). Furthermore, we show that a protein fusion between the unstable GA pathway regulator RGA and LUC+ expressed from LucTrap-3(GW) responds to changes in GA levels and that these changes can be quantified in transgenic lines expressing the fusion protein (Fig. 3; Dill et al., 2001). Furthermore, analysis of a collection of 700 transgenic Arabidopsis lines harboring the vector LucTrap-2 revealed that this vector can be used to generate random LUC+ fusions (Figs. 4 and 5). In 12 of 22 LUC-expressing lines, we were able to provide evidence for LucTrap-2 insertions that are predicted to give rise to productive LUC+ fusions as judged by the T-DNA insertion position and orientation (Table II). Taken together, we provide strong evidence that LucTrap vectors are functional vectors and that LucTrap-based LUC fusions can be used to follow changes in gene expression and protein abundance in vivo. In comparison to other reporter proteins, such as GFP and GUS, LUC reporters including LUC+ offer the important advantage that they can report on changes in reporter abundance in a time-resolved manner. A number of specific features of LUC contribute to this important advantage. First, LUC reactions are not toxic to the organism under investigation. Furthermore, none of our experiments suggest that the amount of luciferin, its penetration into the plant tissue, and its distribution within the plant are rate-limiting steps in in vivo experiments. For example, in our experiments, we have been able to measure LUC activities as early as 2 min after luciferin application to the plant. Since LUCs including LUC+ are inactivated after the first LUC reaction has taken place, LUC activity measurements report on the current synthesis of LUC rather than on its accumulation over time. Several of our experiments clearly demonstrate that LUC measurements allow the detection of positive and negative changes in LUC synthesis rates at minute intervals when luciferin is continuously supplied. In contrast, the detection of protein degradation events, as exemplified in our case with the RGA:LUC fusion protein, requires measurements of absolute LUC activities and therefore single-point LUC activity measurements (e.g. a comparison of untreated and treated samples). In the same context, we would like to point out that these measurements can be made in a high-throughput manner with seedlings grown in microtiter plates with extremely short measurement times (<1 s). Whereas the dynamic nature of LUC expression and the ease of its quantification in a high-throughput manner are certainly great advantages of the LUC reporters, LUCs cannot be used to detect changes in the subcellular localization of a LUC fusion protein. Therefore, whereas LUCs may be optimally suited to detect changes in gene expression rates or protein abundance, they may only allow understanding of some aspects of gene expression or protein behavior. Protein Degradation as a Regulatory Mechanism The analysis of the Arabidopsis genome sequence allows the prediction that plant growth and development is regulated to a large extent at the level of protein degradation (Schwechheimer and Calderon-Villalobos, 2004). The identity of the vast majority of protein degradation-dependent processes, however, remains to be uncovered. The control of transcription in response to auxin is one of the best understood plant-signaling processes, and auxin response has been shown to be dependent on the degradation of the AUX/IAA transcriptional repressors (Gray et al., 2001; Dharmasiri et al., 2005). Through the application of the 26S proteasome inhibitor MG132, we demonstrate that auxin-inducible gene expression in GH3-2:LucTrap as well as in two LucTrap-2 gene trap lines is protein degradation dependent (Fig. 6, A–C). This effect can best be explained through the stabilization of AUX/IAA proteins following proteasomal inhibition with MG132 (Worley et al., 2000; Ramos et al., 2001; Kepinski and Leyser, 2005). In the case of GH3-2:LUC regulation, this hypothesis is also supported by the previously published observations that GH3-2 expression is negatively regulated in the Arabidopsis shy2 mutant, which expresses a stabilized form of the AUX/IAA protein IAA3, as well as in mutants of the COP9 signalosome, a protein complex required for proper AUX/IAA degradation (Schwechheimer et al., 2001; Tian et al., 2003; Dohmann et al., 2005). Interestingly, we also discovered one LucTrap-2 line, LT032, where the inhibition of proteasomal activity by MG132 was sufficient to induce gene expression and where MG132 treatment resulted in a superinduction of auxin-induced gene expression (Fig. 6D). The induction kinetics of the single and the combined treatments strongly suggest that the induction is direct and that both substances act on the same protein. Such induction kinetics cannot be explained through the activity of an unstable repressor, but may best be explained through the activity of an unstable activator that is stabilized by MG132 and stabilized or activated by auxin. As far as we are aware, such a regulatory mechanism for auxin-induced gene expression has not been described as yet and LT032 may now be used for genetic screens that aim at isolation of the factors that control gene expression in LT032. CONCLUSION In this article, we introduce the four LucTrap plant transformation vectors. We provide evidence that transcriptional and translational LUC fusions expressed from the LucTrap vectors allow the monitoring of changes in gene expression and protein abundance in vivo. We also demonstrate that LUC measurements can be used to quantify changes in transcript and fusion protein abundance in response to proteasomal inhibition. The Arabidopsis genome encodes for hundreds of proteins with clear homology to known components of the ubiquitin-proteasome pathway (Bachmair et al., 2001; Gagne et al., 2002). The vast majority of processes that require proteasomal activity remain to be identified. The detailed analysis of the already-identified protein degradation-dependent pathways, as well as that of the many as yet unidentified ones, will require novel or complementary tools for the quantification of transcripts and proteins in vivo. We propose that LucTrap vectors will be an essential part of this tool kit. MATERIALS AND METHODS Biological Material Arabidopsis (Arabidopsis thaliana) ecotype Columbia was used for all plant transformations described in this study. Arabidopsis transformation was performed using the floral-dip method (Desfeux et al., 2000). LucTrap Vector Cloning To generate LucTrap-1, the intron sequence of the Gα (At2g26300) was PCR amplified from the previously published CD126 vector using the primers intron-FW, 5′-AGATCTAGGCCTGTCGAAATCGGACGG-3′ and intron-RV, 5′-CCATGGACCTGCATATAACCTG-3′ (Sundaresan et al., 1995). The intron fragment was cloned into pGEM-T (Promega), sequence verified, and inserted as a BglII/Nco1 fragment upstream of the LUC+ gene in pSP-LUC+ (Promega). Subsequently, the cauliflower mosaic virus (CaMV) 35S terminator (TER) sequence was obtained by PCR from the vector pCAMBIA-1391Z with the primers CaMV TER-FW, 5′-GAATTCCAGATAAGGGAATTAG-3′ and CaMV TER-RV, 5′-CCATGGCAACCACTTTGTACAAGA-3′, cloned into pGEM-T (Promega), sequence verified, and subcloned as Xba1/EcoR1 fragment into the Gα intron containing pSP-LUC+. The resulting LUC+ gene cassette was then inserted as a Stu1/EcoR1 fragment adjacent to the T-DNA right border of previously published plant transformation vector pGREEN0029-II (Hellens et al., 1999). The resulting vector was designated LucTrap-1 (GenBank accession no. AY944581). LucTrap-2 (GenBank accession no. AY944582) is derived from LucTrap-1 and was obtained by religation of the Nco1-digested and S1 nuclease-treated LucTrap-1 vector. The presence of the desired 4-bp deletion, including the ATG start codon of LUC+, was confirmed by DNA sequencing. LucTrap (GenBank accession no. DQ073044) is derived from LucTrap-1 and was obtained by insertion of the phosphorylated and annealed oligonucleotides LucTrap multiple cloning sites (MCS)-FW, 5′-CCTGGATCCTGCAGAGCTCACTAGTC-3′ and LucTrap MCS-RV, 5′-CATGGACTAGTGAGCTCTGCAGGATCCAGG-3′ into the Stu1/Nco1-digested LucTrap-1 vector. LucTrap-3(GW) (GenBank accession no. AY968054) was obtained by insertion of a modified rfB Gateway selection cassette (Invitrogen) into LucTrap-1. To this end, the Gateway rfB cassette was PCR amplified using the primers attR1-StuI, 5′-AGGCCTATCAACAAGTTTGTACAAAAAAG-3′ and attR2-NcoI, 5′-CCATGGCAACCACTTTGTACAAGA-3′, cloned into pCR-TOPO (Invitrogen), sequence verified, and subsequently subcloned as a Stu1/Nco1 fragment into LucTrap-1. LucTrap-3(GW) confers resistance to kanamycin in Escherichia coli, and therefore LucTrap-3(GW) works best in combination with the Gentamycin-resistant donor vector pDONR207 (Invitrogen). Because all LucTrap vectors are based on the previously published pGreen0029-II vector, plant transformation requires the presence of the helper plasmid pSOUP (Hellens et al., 1999). LucTrap-Derived Constructs To generate GH3-2:LucTrap, an 800-bp GH3-2 (At4g37390) fragment was PCR amplified from Arabidopsis genomic DNA using the primers GH3-1, 5′-CCATGGTTGTTTTTTTTTCTAAAAGAAAAAGTG-3′ and GH3-2, 5′-AGATCTGTCGACATGCTATAGATTGATATAAGAAAAAAG-3′. The resulting PCR fragment was cloned into pGEM-T (Promega), sequence verified, and subcloned as a NcoI/StuI fragment into LucTrap-1. Twenty independent transgenic lines that harbor GH3-2:LucTrap were generated and analyzed. For RGA:RGA:LUC, a 3,600-bp genomic fragment that comprises the RGA (At2g01570) open reading frame and a 2,000-bp promoter fragment were amplified from genomic DNA of Arabidopsis ecotype Columbia with the primers RGA-FW, 5′-AGGCCTTTTATGTTTTCGATGGCTGAGCTTC-3′ and RGA-RV, 5′-CCATGGGCGCCGCCGTCGAGAGTTTCCAAGCGGA-3′. The resulting fragment was inserted into pENTR/D-TOPO (Invitrogen), sequence verified, and subcloned into LucTrap-3(GW). Ten transgenic lines that harbor RGA:RGA:LUC were generated and analyzed. LUC Activity Measurements LUC activity was measured using 5-d-old seedlings that had been grown on moist filter paper in 96-well microtiter plates in continuous light (Thermo LabSystems). Seedlings were assayed in a Berthold Mithras LB940 luminometer in the presence of 80 μL Murashige and Skoog medium (Duchefa), supplemented with 5 mmd-luciferin (PJK), 2,4D, or GA3 (Duchefa) or the inhibitors PAC (Duchefa) and MG132 (Sigma-Aldrich) as indicated. For gene expression experiments, seedlings were incubated with luciferin and LUC activity was measured at regular intervals over the course of the experiments. Changes in LUC+ fusion protein levels were quantified in single-point measurements from samples that had been subjected to the respective treatments for 12 h. The result of one typical experiment is shown in each case. Fluorescence Microscopy Transgenic seedlings expressing RGA:GFP:RGA were treated for 12 h with GA3, PAC (Duchefa), and MG132 (Sigma-Aldrich) as indicated and then imaged using a Leica TCS SP2 confocal microscope. Representative images are shown in each case. Identification of LucTrap-2 Flanking Sequences For the determination of flanking sequences from LucTrap-2 transgenic lines, previously established procedures were adapted (Devon et al., 1995; Strizhov et al., 2003). In brief, genomic DNA was digested using the restriction enzymes BamHI, BglII, or BclI. Subsequently, an asymmetric adaptor obtained by annealing the TopL and phosphorylated BamHI primers was ligated to the digested genomic DNA (Devon et al., 1995). LucTrap-2-specific fragments were amplified in two or three PCR rounds with the nested vectorette primers VEC1 and VEC2 in combination with LucTrap-2-specific primers. Amplification products were sequenced using LucR3 or LucL3. Sequence reads were analyzed using the BLASTN algorithm at http://www.arabidopsis.org/blast. All primer sequences are provided in Table I. RT-PCR Analysis Auxin-induced GH3-2 (At4g37390) gene expression was examined by semiquantitative RT-PCR. Total RNA was prepared using the RNeasy kit (Qiagen) from 5-d-old seedlings that had been treated with 5 μm 2,4D. One microgram of total RNA was used in combination with the oligo(dT) adaptor primer 5′-GACTCGAGTCGACATCGA(17xT)-3′ for RT as previously described and GH3-2 transcription was examined by PCR (28 cycles) using the GH3-2 gene-specific primers GH3-2-FW, 5′-GTTTCAGCGACGACTTCTGAGAAAGATGT-3′, and GH3-2-RV, 5′-TCTTCGCTCATAAGAGCATTGCT-3′ (Frohman et al., 1988). RT-PCR results were quantified using ImageJ software available at http://rsb.info.nih.gov. Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers AY944581, AY944582, AY968054, and DQ073044. 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Plant J 35 : 273 –283 Zhu T ( 2003 ) Global analysis of gene expression using GeneChip microarrays. Curr Opin Plant Biol 6 : 418 –425 Zimmermann P, Hennig L, Gruissem W ( 2005 ) Gene-expression analysis and network discovery using Genevestigator. Trends Plant Sci 10 : 407 –409 Author notes 1 This work was supported by the Deutsche Forschungsgemeinschaft (grant nos. SCHW751/4–1 and SCHW751/4–2) as part of the Arabidopsis Functional Genomics Network Schwerpunktprogramm. * Corresponding author; e-mail [email protected]; fax 49–7071–295135. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Claus Schwechheimer ([email protected]). www.plantphysiol.org/cgi/doi/10.1104/pp.106.078097. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Brewer, Marin Talbot; Lang, Lixin; Fujimura, Kikuo; Dujmovic, Nancy; Gray, Simon; van der Knaap, Esther

doi: 10.1104/pp.106.077867pmid: 16684933

Abstract The domestication and improvement of fruit-bearing crops resulted in a large diversity of fruit form. To facilitate consistent terminology pertaining to shape, a controlled vocabulary focusing specifically on fruit shape traits was developed. Mathematical equations were established for the attributes so that objective, quantitative measurements of fruit shape could be conducted. The controlled vocabulary and equations were integrated into a newly developed software application, Tomato Analyzer, which conducts semiautomatic phenotypic measurements. To demonstrate the utility of Tomato Analyzer in the detection of shape variation, fruit from two F2 populations of tomato (Solanum spp.) were analyzed. Principal components analysis was used to identify the traits that best described shape variation within as well as between the two populations. The three principal components were analyzed as traits, and several significant quantitative trait loci (QTL) were identified in both populations. The usefulness and flexibility of the software was further demonstrated by analyzing the distal fruit end angle of fruit at various user-defined settings. Results of the QTL analyses indicated that significance levels of detected QTL were greatly improved by selecting the setting that maximized phenotypic variation in a given population. Tomato Analyzer was also applied to conduct phenotypic analyses of fruit from several other species, demonstrating that many of the algorithms developed for tomato could be readily applied to other plants. The controlled vocabulary, algorithms, and software application presented herein will provide plant scientists with novel tools to consistently, accurately, and efficiently describe two-dimensional fruit shapes. Domestication of plant species was accompanied by profound changes in overall plant and organ morphology (Smith, 1997; Frary and Doganlar, 2003; Paris et al., 2003; Doebley, 2004). Domesticated lines of crops that were selected for improved fruit characters typically carry fruit that are more variable in shape, size, and color than their wild relatives (Grandillo et al., 1999; Paris et al., 2003). We are interested in understanding the genetic and molecular mechanisms that contribute to this variation in morphology with a specific focus on tomato (Solanum lycopersicum) fruit shape. Large-scale gene expression studies conducted on developing tomato fruit (Alba et al., 2005; Lemaire-Chamley et al., 2005) in addition to genetic analyses will permit the identification of genes controlling fruit ontogeny and provide insight into developmental pathways regulating fruit formation. Furthermore, the identification of genes underlying traits characteristic of domesticated varieties may reveal patterns of selection. Tomato is an excellent model for fruit development and domestication studies owing to the tremendous genetic and genomic resources available for this species (Mueller et al., 2005). International efforts are under way to determine the sequence of the euchromatic portion of its genome, further improving the genomic toolbox for tomato. Tomato fruit is classified according to 10 shape categories such as rounded, high rounded, ellipsoid, or pyriform (International Plant Genetic Resources Institute, 1996; International Union for the Protection of New Varieties and Plants, 2001). Additionally, the distal end of the fruit is categorized as indented, flat, or pointed, whereas the proximal end of the fruit is categorized as flat or indented (International Union for the Protection of New Varieties and Plants, 2001; International Plant Genetic Resources Institute, 1996). While these classifications are useful to group tomato varieties and describe cultivars, the classification scheme cannot be utilized to conduct precise quantitative measurements in a reliable and systematic manner. In addition, the terminology of fruit shape attributes is not described in sufficient detail and tends to be taxon specific. While the taxon-specific terminology may not pose a problem for intraspecies comparisons, cross-species comparisons may be hampered by the lack of agreed-upon terms describing common attributes. Thus, querying information across databases and facilitating retrieval would require development of a structured controlled vocabulary that is arranged in ontologies. Trait ontology provides a structured framework to describe and quantify plant phenotypes (Bruskiewich et al., 2002; Yamazaki and Jaiswal, 2005). Objective phenotypic evaluation can then be conducted for comparative purposes and genetic analysis of quantitative trait loci (QTL). Therefore, a first step toward a comprehensive fruit trait ontology database requires development of agreed-upon terms and associated descriptions for each attribute or trait. To date, most phenotypic analyses consist of time-consuming manual measurements and subjective scoring of traits that limit the detection of underlying genes. Software-aided measurements of attributes such as height, width, area, and perimeter are most commonly conducted with ImageJ, a public domain program developed at the U.S. National Institutes of Health (program created by W.S. Rasband; http://rsb.info.nih.gov/ij/). ImageJ is a versatile program that allows the user to make minor adjustments to the image. However, objective measurements such as height and width are neither automated nor exported efficiently, and extensive and detailed phenotypic analyses that describe subtle differences in shape such as degree of circular shape and the slope along the boundary of the fruit require development of novel algorithms for the attributes. In addition, many of the descriptors necessary to characterize shape cannot be scored objectively without proper software tools. For example, the degree of distal end indentation would be difficult to rate on a scale, and the scoring of this trait would be inconsistent between years, plots, and persons. Moreover, precise phenotypic measurements are necessary to sufficiently characterize loci and the underlying genes that contribute to shape variation. Thus, an accurate and objective method for conducting phenotypic analyses combined with a concise and detailed set of descriptors and terms for fruit shape attributes is necessary. In previous work, algorithms for some of the shape attributes were defined and shown to have a genetic basis (Van der Knaap and Tanksley, 2003). Here, additional trait terms and mathematical descriptors of shape attributes were developed to further improve phenotypic analyses. The terms were proposed for broader acceptance in the community and future incorporation into a trait ontology database. The mathematical descriptors were implemented in a software application, Tomato Analyzer. This program performs semiautomatic, objective, and quantitative measurements of attributes that will accelerate phenotypic analyses and eliminate subjective scoring of many fruit shape traits. Consequently, Tomato Analyzer can provide more accurate, consistent, and objective results and measure shape attributes that are impossible or impractical to determine manually. To validate that Tomato Analyzer functions properly and to show its use for fruit other than tomato, we used the software to collect phenotypic data on tomato fruit from segregating F2 populations as well as on a sample of fruit from other species. RESULTS Trait Ontology Terms and Mathematical Descriptors An accurate description of fruit shape requires the development of a common vocabulary that encompasses a range of plant species. The structure of the terms needs to follow the True Path Rule that states that the pathway from a child term all the way up to the parent term must be accurate (Bruskiewich et al., 2002). As an example, Figure 1 Figure 1. Open in new tabDownload slide Trait ontology terms. Generic terms and their relationship as instance of other terms are indicated by the arrows. The synonyms are tomato-specific terms of the generic terms. The acronyms are used in QTL analyses. The Image column lists the Figure 2 section that displays how the mathematical descriptor for each term or acronym is measured. na, Not applicable. Figure 1. Open in new tabDownload slide Trait ontology terms. Generic terms and their relationship as instance of other terms are indicated by the arrows. The synonyms are tomato-specific terms of the generic terms. The acronyms are used in QTL analyses. The Image column lists the Figure 2 section that displays how the mathematical descriptor for each term or acronym is measured. na, Not applicable. displays the child term distal end fruit shape angle as an instance of distal end fruit shape. In turn, distal end fruit shape is an instance of fruit end shape and follows the path fruit trait, fruit shape, and fruit end shape. The trait terms and path relationships that were developed are listed in Figure 1. Instances of fruit trait are fruit shape and fruit size. Fruit shape is a parent term of fruit shape index, fruit shape triangle, fruit shape eccentric, and fruit end shape. Fruit shape eccentric and fruit end shape are parent terms of additional child terms (Fig. 1). The remaining fruit shape terms, fruit shape circular, ellipsoid, heart, and rectangular, describe how well the fruit surface depicts a circle, ellipse, etc. and determine the uniformity and homogeneity of objects. Instances of fruit size are fruit mass, fruit area, fruit height, fruit perimeter, and fruit width (Fig. 1). Tomato-specific synonyms for some of the terms are listed as well as the acronyms for each term. The acronyms will be used for detailed QTL analyses pertaining to fruit shape. The development of a definition of each term and an associated mathematical descriptor would permit objective measurements of fruit shape attributes. Several terms and equations were adopted from previous published work on tomato fruit shape. These included fruit shape index, blockiness, and fruit shape triangle (Van der Knaap and Tanksley, 2003), fruit height, and fruit width (Lippman and Tanksley, 2001). Most acronyms applied in previous studies also remained the same. These included fs for fruit shape index, fl for fruit height, and fd for fruit width (Grandillo et al., 1999; Lippman and Tanksley, 2001; Van der Knaap and Tanksley, 2003). Terms and acronyms for distal and proximal end blockiness, as well as triangle (dblk, pblk, and tri), were renamed based on previously used blossom and stem end blockiness and heart shape terms, respectively (Van der Knaap and Tanksley, 2003). The remaining acronyms were newly developed for attributes first described and measured here. The definitions and mathematical descriptors for each trait term are presented below. A single equation was developed for each term or acronym. Fruit Shape Index Shape index is defined as the ratio of height to width (Fig. 2A Figure 2. Open in new tabDownload slide Descriptors of fruit morphology traits. A, Fruit shape index I: ratio of maximum height to width, H/W. Fruit shape index II: ratio of mid height to mid width, Hm/Wm. B, Fruit shape triangle: the ratio of proximal width to distal width, w1/w2. C, Fruit shape eccentric obovoid: the position of the widest width of the fruit, 4 × (y − 0.5) if y > 0.5; 0 otherwise. D, Fruit shape eccentric horizontal asymmetry, (Σ|n − ni|)/number of columns L. E, Fruit shape eccentric vertical asymmetry, (Σ|m − mi|)/number of rows L. F, Distal fruit end shape angle at position 5% above the tip from the fruit. G, Distal fruit end shape angle at position 5% above the tip from the fruit. H, Distal fruit end blockiness: ratio of fruit width at the distal end to mid width, w2/Wm. I, Distal end indentation area relative to total fruit area. J, Proximal fruit end shape angle. K, Proximal fruit end blockiness: ratio of fruit width at the proximal end to mid width, w1/Wm. L, Proximal fruit end indentation: shoulder height, (h1 + h2)/2H. M, Proximal fruit end indentation: area, indentation area relative to total fruit area. N, Fruit shape circular, fitting precision R2. O, Fruit shape ellipsoid, fitting precision R2. P, Fruit shape heart: taperness function, 1 − w2/W + w1/W. w2 = average width below widest width W; w1 = average width above widest width W. Q, Fruit shape rectangular: the ratio of maximum area inscribing the rectangle to the minimum area of the enclosing rectangle, Sin/Sout. Figure 2. Open in new tabDownload slide Descriptors of fruit morphology traits. A, Fruit shape index I: ratio of maximum height to width, H/W. Fruit shape index II: ratio of mid height to mid width, Hm/Wm. B, Fruit shape triangle: the ratio of proximal width to distal width, w1/w2. C, Fruit shape eccentric obovoid: the position of the widest width of the fruit, 4 × (y − 0.5) if y > 0.5; 0 otherwise. D, Fruit shape eccentric horizontal asymmetry, (Σ|n − ni|)/number of columns L. E, Fruit shape eccentric vertical asymmetry, (Σ|m − mi|)/number of rows L. F, Distal fruit end shape angle at position 5% above the tip from the fruit. G, Distal fruit end shape angle at position 5% above the tip from the fruit. H, Distal fruit end blockiness: ratio of fruit width at the distal end to mid width, w2/Wm. I, Distal end indentation area relative to total fruit area. J, Proximal fruit end shape angle. K, Proximal fruit end blockiness: ratio of fruit width at the proximal end to mid width, w1/Wm. L, Proximal fruit end indentation: shoulder height, (h1 + h2)/2H. M, Proximal fruit end indentation: area, indentation area relative to total fruit area. N, Fruit shape circular, fitting precision R2. O, Fruit shape ellipsoid, fitting precision R2. P, Fruit shape heart: taperness function, 1 − w2/W + w1/W. w2 = average width below widest width W; w1 = average width above widest width W. Q, Fruit shape rectangular: the ratio of maximum area inscribing the rectangle to the minimum area of the enclosing rectangle, Sin/Sout. ). The software contains two acronyms for fruit shape index. The first acronym, fs I, is the ratio of maximum height to maximum width (H/W). The second acronym, fs II, is the ratio of the height at mid width to the width at mid height (Hm/Wm; Fig. 2A). For both descriptors, a value greater than 1 indicates an elongated fruit, equal to 1 indicates a round fruit, and less than 1 indicates a squat fruit. Typically, the results of these two measurements are very similar and the user can select which one describes the shape of a particular object most appropriately. Fruit Shape Triangle The software defines triangle as the ratio of the proximal end width to distal end width, w1/w2 (Fig. 2B). The width is measured at user-defined distances from the proximal end. A fruit shape triangle value greater than 1 indicates that the proximal end of the fruit is wider than the distal end of the fruit, while a value less than 1 indicates that the distal end of the fruit is wider. Fruit Shape Eccentric The ovoid and obovoid functions describe how top or bottom heavy a fruit is, respectively. Thus, the fruit displayed in Figure 2C is considered obovoid shaped. The degree of ovoid or obovoid is calculated by identifying the position of the widest section, y, of the fruit (Fig. 2C). The following formula is then used to describe obovoid: 4 × (y − 0.5) if y > 0.5; 0 otherwise, indicating the fruit is not obovoid. The following formula describes ovoid: −4 × (y − 0.5) if y < 0.5; 0 otherwise, indicating the fruit is not ovoid. Horizontal asymmetry and vertical asymmetry describe how asymmetric a fruit is when divided along a horizontal or vertical axis, respectively. The horizontal or vertical axes that divide the fruit are termed n and m, respectively (Fig. 2, D and E). The position of the horizontal axis, n, is determined by finding the topmost and bottommost points of the fruit and dividing by two to find the center. Likewise, the position of the vertical axis, m, is determined by finding the leftmost and rightmost points of the fruit and dividing in half to find the center. To compute horizontal asymmetry, each column of pixels, termed Li, is determined, and the midpoint of the column, ni, is found (Fig. 2D). Next, the difference between ni and n is calculated and recorded. Once every column is examined, the sum of the differences is determined and divided by the number columns. Thus, the general formula for horizontal asymmetry is (Σ|n − ni|)/number of columns. For vertical asymmetry, each row of pixels, termed Li, is determined, and the midpoint of the column, mi, is found (Fig. 2E). Next, the difference between mi and m is calculated and recorded. Once every row is examined, the sum of the differences is determined and divided by the number rows. Thus, the formula for vertical asymmetry is (Σ|m − mi|)/number of rows. Vertical and horizontal asymmetry values of 0 signify a perfectly symmetric shape. Horizontal asymmetry ovoid is defined by the general formula for horizontal asymmetry if there is more area above the horizontal axis n than below it; otherwise, horizontal asymmetry ovoid equals 0. Similarly, horizontal asymmetry obovoid is defined by the general formula for horizontal asymmetry if there is more area below the horizontal axis n than below it; otherwise, horizontal asymmetry obovoid equals 0. Distal Fruit End Shape The angle of the distal fruit tip refers to the intersection of two lines where the slope is measured via regression along the boundary of the fruit on both sides at a user-defined distance from the distal end of the fruit (Fig. 2, F and G). The slope is determined by the regression measured at ±5% when the user-defined positions are between 5% and 50% from the distal end (macro setting), whereas the slope is determined by the regression measured at ±2% when the user-defined positions are between 2% and 10% from the distal end (micro setting). The angle is measured at the point where the lines intersect and is expressed in degrees, where 180° is flat, greater than 180° is indented (Fig. 2F), and less than 180° is pointed (Fig. 2G). The term blockiness is referred to as squared or box-like shapes. Blockiness is calculated as the ratio of the width at a user-selected proportion of the height closest to the distal end of the fruit to the mid width, w2/Wm (Fig. 2H). Indentation refers to the area of indentation at the distal end of the fruit (Fig. 2I). The distal indentation area is determined by finding the most indented distal end position, the lowest distal end on both sides of the indented position, and by the boundary of the fruit along the indented area. The computational method is described in more detail below (shoulder height; Fig. 2L). The distal end indentation area is the ratio of indentation area to total fruit area. Proximal Fruit End Shape The angle of the proximal fruit end refers to the angle from the shoulder points to the site of pedicel attachment or the proximal end, where 180° is flat and greater than 180° is concave (Fig. 2J). Blockiness is calculated as the ratio of the width at a user-selected proportion of the height from the top of the fruit to the mid width, w1/Wm (Fig. 2K). Indentation is measured by two methods, either as the “shoulder height, psh” (Fig. 2L) or as the “indentation area, piar” (Fig. 2M). Shoulder height is calculated by first locating the most indented point (P) at the proximal end. A straight vertical line from point P to the center of gravity is drawn. Next, a line perpendicular to the vertical line is drawn through the boundary, selecting the intersection points A and B. Finally, shoulder height points h1 and h2 are defined to be the maximal distance from the arc A-P and B-P to the line AB, respectively. Shoulder height is defined as (h1 + h2)/2H (Fig. 2L). The larger the shoulder height value is, the more indented the fruit at the proximal end. The indentation area is calculated by finding the area between the two shoulder points, the lowest position P, and the boundary. The “proximal end indentation area, piar” is the ratio of the indentation area to the total fruit area. Fruit Shapes Circular, Ellipsoid, Heart, and Rectangular These functions are related to homogeneity and uniformity, that is, similarity of the object to the common shapes circle, ellipse, heart, and rectangle. The method developed for circular and ellipsoid determines the fitting precision R2. This value represents the coefficient of determination and reflects how well the actual shape fits a circle or ellipse based on regression (Fig. 2, N and O). The closer the value to 1, the more similar the fruit is to a circle or ellipse. Heart shape is a function of three object characteristics: the location of the maximum width y (Fig. 2C), the shoulder height (Fig. 2L), and the taperness (Fig. 2P). To calculate heart, the location of the maximum width is described as 1 − y, where y is the widest point. The shoulder height is described as (h1 + h2)/2H (see above). The taperness is described as 1 − w2/W + w1/W. w1 and w2 are the average width above and below the widest width, respectively. The weight of these individual components is expressed as 0.25 × [(1 − y) × (1 − w2/W + w1/W)] + 20 × [(h1 + h2)/2H] and is returned as the heart shape value. Rectangular is calculated as the ratio of Sin/Sout where Sout is the minimum area of an enclosing rectangle and Sin is the maximum area of the inscribing rectangle (Fig. 2Q). Thus, the closer the value is to 1, the more rectangular the shape of the object. Fruit Size Fruit size measurements calculated by Tomato Analyzer include area, width, height, and perimeter. For width, two descriptors are available, the widest width (fd I, largest horizontal cross section) and the width measured at the midpoint of the height (fd II; Fig. 2A). There are also two descriptors for height available: the highest height (fl I, largest vertical cross section) and the height measured at the midpoint of the width (fl II; Fig. 2A). Tomato Analyzer Application The Tomato Analyzer application requires digital images of cut fruit saved in jpeg format. When loaded into the application, the entire image appears in the left viewing window (Fig. 3 Figure 3. Open in new tabDownload slide Tomato Analyzer application. Figure 3. Open in new tabDownload slide Tomato Analyzer application. ). The software is designed to recognize objects of a certain size and image resolution, measured in dots (pixels) per inch (dpi). Generally, the smaller the object, the higher the resolution required to provide accurate analysis. The implementation of the equations with the software relies on obtaining the x and y coordinates of a pixel in a jpeg image of the fruit objects. The software automatically determines the boundaries of fruit in a scanned image. The object boundary is extracted through contour tracing, which results in a list of adjacent points describing the border of an object in an image. All phenotypic measurements are calculated based on the boundaries. Prior to phenotypic analysis, if fruit are positioned at an angle or if an attached object distorts the boundary, the position of individual fruit can be manually adjusted using the software. Occasionally, the distal and proximal ends of the fruit are not correctly identified, resulting in aberrant angle and indentation values. Therefore, Tomato Analyzer also contains a function to manually adjust the distal and proximal end points of the fruit. In addition, objects can be deselected or selected. The units used for the attribute values can be selected as pixels, centimeters, millimeters, or inches. As most of the measurements are ratios, selecting the appropriate dpi setting consistent with the resolution of original jpeg image is only required for the size measurements including perimeter, area, height, and width. The measurements saved setting allows the user to select which attribute values to compute for display and export. Individual attributes or an entire measurement cluster can be selected or deselected. Shape attributes are divided into several clusters in Tomato Analyzer application and largely follow the grouping listed in Figure 1. Basic Measurements group comprises all the fruit size traits; Fruit Shape Index comprises fruit shape index I and II; Homogeneity comprises circular, ellipsoid, and rectangular; Distal Fruit End Shape comprises macro and micro angle, indentation, and protrusion; Proximal Fruit End Shape comprises angle, shoulder height, and indentation area; Eccentricity comprises all eccentricity attributes in addition to fruit shape heart; and Blockiness comprises distal and proximal fruit end shape blockiness and fruit shape triangle. By selecting the corresponding group tab in the lower right corner of Tomato Analyzer, results for the selected group are displayed (Fig. 3). Some attributes allow the user to select settings that maximize phenotypic diversity. User-defined settings are offered for upper and lower blockiness positions. The upper position is used for calculating proximal end blockiness. The lower position is used to calculate distal end blockiness. In addition, these new values will affect fruit shape triangle. The values for the blockiness positions equal the percentage of the height from the top of the fruit. These values can be changed to any number as long as they are both between 0 and 1 and lower position is greater than upper position. There are two settings to calculate distal end angles, referred to as macro and micro. The setting for macro level determines the percentage of the perimeter from the bottom where the angle will be measured, ranging from 5% to 50%. The micro level setting determines where the proximal angle is measured, ranging from 2% to 10% from the tip of the fruit. The save function allows the user to save the manual adjustments and analyzed fruit shape attributes. Subsequently, when a user opens the original image file, the saved file will be opened and will display all of the adjustments. There are two methods to export data obtained by Tomato Analyzer. In the first, one image is analyzed and the data for the shape attributes of individual fruit is exported to a .csv file suitable for loading into a spreadsheet or statistical analysis package. The second method is called batch mode and allows more than one image to be loaded and analyzed. In this scenario, the data for each attribute is exported to a .csv file as an average of all fruit in a single image. This method of export is most useful when conducting QTL analyses. Phenotypic Analyses Conducted by Tomato Analyzer To validate the accuracy and demonstrate the utility of the Tomato Analyzer software, phenotypic and genetic analyses were conducted on two F2 populations derived from crosses between one of the extreme-shaped S . lycopersicum cultivars, Howard German or Banana Legs, and a small, round, wild relative, Solanum pimpinellifolium LA1589 (Fig. 4, A–C Figure 4. Open in new tabDownload slide Phenotypic variation within the Howard German and Banana Legs populations. The fruit images in A, B, and C are depicted to scale. A, Image of S. lycopersicum cv Howard German. B, Image of S. lycopersicum cv Banana Legs. C, Image of S. pimpinellifolium accession LA1589. D, Graphical display of PC 1, 2, and 3 from analysis of fruit morphology traits in the Howard German (HG) × LA1589, and Banana Legs (BL) × LA1589 F2 populations. Clouds represent the means and se of the first three PCs. The variation described by each of the PCs is listed in parentheses. Figure 4. Open in new tabDownload slide Phenotypic variation within the Howard German and Banana Legs populations. The fruit images in A, B, and C are depicted to scale. A, Image of S. lycopersicum cv Howard German. B, Image of S. lycopersicum cv Banana Legs. C, Image of S. pimpinellifolium accession LA1589. D, Graphical display of PC 1, 2, and 3 from analysis of fruit morphology traits in the Howard German (HG) × LA1589, and Banana Legs (BL) × LA1589 F2 populations. Clouds represent the means and se of the first three PCs. The variation described by each of the PCs is listed in parentheses. ). Phenotypic data were collected for fruit from both populations using Tomato Analyzer. Principal components analysis (PCA) was conducted to determine the major sources of variation among the morphological traits within and between these populations (Fig. 4D). The variation within each of the two F2 populations could be explained by the first three principal components (PC), which combined represented 77.4% of the total variability (Fig. 4D). In addition, PC 3 demonstrated significant differences between the Howard German and Banana Legs F2 populations, whereas PC 1 and PC 2 were not significantly different (Fig. 4D; Supplemental Table I). PC 1, representing 35.4% of the variation, was predominantly affected by attributes that evaluate the tapered shape of fruit, including the traits fruit shape triangle, proximal end blockiness, and horizontal asymmetry ovoid (loading values for contributing traits are listed in Supplemental Table II). PC 2, representing 24.9% of the variation, was primarily controlled by traits that contribute to fruit elongation such as fruit shape index, proximal end shape features, and distal end angle, as well as the fruit shape uniformity and homogeneity features circular, rectangular, and heart shape (Supplemental Table II). PC 3, representing 17.1% of the variation, was influenced by fruit size, homogeneity, and eccentricity characteristics, as well as distal end blockiness and indentation area (Supplemental Table II). To identify regions of the genome responsible for the observed fruit morphology variation, QTL analysis was performed using the first three PCs as traits in the Banana Legs and Howard German F2 populations. A genetic map was constructed with molecular markers using MAPMAKER (Lander et al., 1987), and subsequent QTL analyses of each of the three PCs were conducted using QTL Cartographer (created in 2001 by S. Wang, C.J. Basten, and Z.B. Zeng [Department of Statistics, North Carolina State University]). In both populations, PC 1 was controlled by similarly located QTL on chromosomes 2, 3, and 7 (Table I Table I. Regions of the genome responsible for fruit shape variation in two F2 populations as detected by PCA and subsequent QTL analysis Population . QTLa . LOD . Most Significant Markerb . Ac . Dd . R2 . Howard German PC1.2 6.38 TG337 2.02* −1.92* 0.19 PC1.3 4.64 TG242 1.08 1.40 0.14 PC1.7 4.31 CD57 2.01* 0.41 0.14 PC2.7 22.01 COS103 −4.02* 0.33* 0.52 PC3.7 7.09 TG342 3.55* −3.31* 0.25 PC3.10 4.10 CT234 1.37* −0.25 0.12 Banana Legs PC1.1 4.95 CT191 1.47* −0.47 0.11 PC1.2 8.89 TG337 2.16* −0.95 0.22 PC1.3 6.47 TG246 1.60* −0.78 0.17 PC1.7 7.86 COS103 2.61* −0.34 0.18 PC2.7 12.02 COS103 −2.47* −0.11 0.30 PC2.9 6.41 TG551 −1.54* −0.05 0.16 PC3.10 4.16 CT234 0.46 −1.07* 0.11 PC3.11 4.74 TG546 1.10* 0.03 0.18 Population . QTLa . LOD . Most Significant Markerb . Ac . Dd . R2 . Howard German PC1.2 6.38 TG337 2.02* −1.92* 0.19 PC1.3 4.64 TG242 1.08 1.40 0.14 PC1.7 4.31 CD57 2.01* 0.41 0.14 PC2.7 22.01 COS103 −4.02* 0.33* 0.52 PC3.7 7.09 TG342 3.55* −3.31* 0.25 PC3.10 4.10 CT234 1.37* −0.25 0.12 Banana Legs PC1.1 4.95 CT191 1.47* −0.47 0.11 PC1.2 8.89 TG337 2.16* −0.95 0.22 PC1.3 6.47 TG246 1.60* −0.78 0.17 PC1.7 7.86 COS103 2.61* −0.34 0.18 PC2.7 12.02 COS103 −2.47* −0.11 0.30 PC2.9 6.41 TG551 −1.54* −0.05 0.16 PC3.10 4.16 CT234 0.46 −1.07* 0.11 PC3.11 4.74 TG546 1.10* 0.03 0.18 a QTL acronym reflects the PC for which it was detected (first number) and the chromosome where it was located (second number). b The map location of these markers can be found on the Solanaceae Genomics Network Web site (http://www.sgn.cornell.edu). c An asterisk indicates a significant additive effect. A negative value indicates that an increase in the value of the attribute is due to S. pimpinellifolium allele, and a positive value indicates that an increase in the value of the trait is due to S. lycopersicum allele. d An asterisk indicates a significant dominant effect. A negative value indicates that the S. pimpinellifolium allele is dominant and a positive value indicates that the S. lycopersicum allele is dominant. Open in new tab Table I. Regions of the genome responsible for fruit shape variation in two F2 populations as detected by PCA and subsequent QTL analysis Population . QTLa . LOD . Most Significant Markerb . Ac . Dd . R2 . Howard German PC1.2 6.38 TG337 2.02* −1.92* 0.19 PC1.3 4.64 TG242 1.08 1.40 0.14 PC1.7 4.31 CD57 2.01* 0.41 0.14 PC2.7 22.01 COS103 −4.02* 0.33* 0.52 PC3.7 7.09 TG342 3.55* −3.31* 0.25 PC3.10 4.10 CT234 1.37* −0.25 0.12 Banana Legs PC1.1 4.95 CT191 1.47* −0.47 0.11 PC1.2 8.89 TG337 2.16* −0.95 0.22 PC1.3 6.47 TG246 1.60* −0.78 0.17 PC1.7 7.86 COS103 2.61* −0.34 0.18 PC2.7 12.02 COS103 −2.47* −0.11 0.30 PC2.9 6.41 TG551 −1.54* −0.05 0.16 PC3.10 4.16 CT234 0.46 −1.07* 0.11 PC3.11 4.74 TG546 1.10* 0.03 0.18 Population . QTLa . LOD . Most Significant Markerb . Ac . Dd . R2 . Howard German PC1.2 6.38 TG337 2.02* −1.92* 0.19 PC1.3 4.64 TG242 1.08 1.40 0.14 PC1.7 4.31 CD57 2.01* 0.41 0.14 PC2.7 22.01 COS103 −4.02* 0.33* 0.52 PC3.7 7.09 TG342 3.55* −3.31* 0.25 PC3.10 4.10 CT234 1.37* −0.25 0.12 Banana Legs PC1.1 4.95 CT191 1.47* −0.47 0.11 PC1.2 8.89 TG337 2.16* −0.95 0.22 PC1.3 6.47 TG246 1.60* −0.78 0.17 PC1.7 7.86 COS103 2.61* −0.34 0.18 PC2.7 12.02 COS103 −2.47* −0.11 0.30 PC2.9 6.41 TG551 −1.54* −0.05 0.16 PC3.10 4.16 CT234 0.46 −1.07* 0.11 PC3.11 4.74 TG546 1.10* 0.03 0.18 a QTL acronym reflects the PC for which it was detected (first number) and the chromosome where it was located (second number). b The map location of these markers can be found on the Solanaceae Genomics Network Web site (http://www.sgn.cornell.edu). c An asterisk indicates a significant additive effect. A negative value indicates that an increase in the value of the attribute is due to S. pimpinellifolium allele, and a positive value indicates that an increase in the value of the trait is due to S. lycopersicum allele. d An asterisk indicates a significant dominant effect. A negative value indicates that the S. pimpinellifolium allele is dominant and a positive value indicates that the S. lycopersicum allele is dominant. Open in new tab ). A QTL on chromosome 1 also controlled PC 1, but only in the Banana Legs population. PC 2 was controlled by a highly significant QTL on chromosome 7 in both populations. In addition, a QTL on chromosome 9 was present for PC 2 in the Banana Legs population. PC 3 differentiated the two populations, and showed one common QTL on chromosome 10. However, a QTL on the top of chromosome 7 controlled PC 3 only in the Howard German population, and a QTL on chromosome 11 affected PC 3 in only the Banana Legs population. These different QTL may underlie the subtle differences in fruit shape of the parental plants (Fig. 4, A and B). The PC 2 QTL located on chromosome 7 coincides with the sun locus that has been shown to control fruit shape index (Van der Knaap and Tanksley, 2001; Van der Knaap et al., 2004). The following analysis was conducted with the attribute distal fruit end shape angle to demonstrate the flexibility of Tomato Analyzer. The user can select the location of the slope for the distal end angle measurement. Mapping of distal end angle at various distances from the tip of the fruit, from 2% to 20%, showed that the most significant QTL is associated with the angle measurement at 20% above the tip and that this QTL decreases in significance with angle measurements taken at positions closer to the tip of the fruit (Fig. 5 Figure 5. Open in new tabDownload slide LOD curves for distal fruit end angle measurements on chromosome 7. Graphical depiction of the LOD curves for distal fruit end angle measurements at the 2%, 5%, 10%, and 20% above the distal tip of the fruit. The results were derived from the Banana Legs F2 population. The x axis displays the molecular markers along the chromosome; the y axis displays the LOD score. Figure 5. Open in new tabDownload slide LOD curves for distal fruit end angle measurements on chromosome 7. Graphical depiction of the LOD curves for distal fruit end angle measurements at the 2%, 5%, 10%, and 20% above the distal tip of the fruit. The results were derived from the Banana Legs F2 population. The x axis displays the molecular markers along the chromosome; the y axis displays the LOD score. ). A similar trend was noted in the Howard German population at the same chromosomal location (data not shown). The results of the distal end angle measurements demonstrate that user-defined settings of Tomato Analyzer application permit phenotypic analyses that are optimized for each population or tailored to specific research questions. In addition, adjustment of settings is efficiently applied to all fruit in a population, while such a change would be labor intensive if manual measurements were taken. To demonstrate a wider utility of Tomato Analyzer in shape analysis, we evaluated its performance on fruit of different species (Fig. 6 Figure 6. Open in new tabDownload slide Variety of fruit that were subjected to phenotypic evaluations by Tomato Analyzer. A, Butternut squash. B, Yellow squash. C, Large jalapeno. D, Banana pepper. E, Chili pepper. F, Jalapeno. G, Grape. H, Strawberry (Fragaria spp.). I, Bartlett pear. Figure 6. Open in new tabDownload slide Variety of fruit that were subjected to phenotypic evaluations by Tomato Analyzer. A, Butternut squash. B, Yellow squash. C, Large jalapeno. D, Banana pepper. E, Chili pepper. F, Jalapeno. G, Grape. H, Strawberry (Fragaria spp.). I, Bartlett pear. ). The software could accurately determine the boundaries of fruit from as small as grape (Vitis vinifera) to as large as butternut squash (Cucurbita moschata) and of fruit of different colors. The output values provided by Tomato Analyzer were consistent with visual observations and manual measurements (Table II Table II. Output from Tomato Analyzer for select phenotypic traits on a variety of fruit Images of these fruit appear in Figure 6. Fruit . Fruit Shape Index Ia . Area . Distal End Angle, 5%b . Distal End Angle, 20%b . Obovoidc . Triangle, 10%d . cm2 Degrees Degrees A, butternut squash 1.67 122.8 190 80 0.99 0.60 B, yellow squash 3.55 53.0 72 22 0.78 0.99 C, large jalapeno 2.04 70.5 83 48 0 2.21 D, banana pepper 4.00 25.1 28 12 0 2.04 E, chili pepper 2.51 12.0 47 17 0 2.75 F, jalapeno 2.04 18.5 98 35 0 1.71 G, grape 0.98 2.4 168 103 0 1.28 H, strawberry 1.13 13.9 158 87 0 1.43 I, Bartlett pear 1.12 46.6 228 125 0.73 0.59 Fruit . Fruit Shape Index Ia . Area . Distal End Angle, 5%b . Distal End Angle, 20%b . Obovoidc . Triangle, 10%d . cm2 Degrees Degrees A, butternut squash 1.67 122.8 190 80 0.99 0.60 B, yellow squash 3.55 53.0 72 22 0.78 0.99 C, large jalapeno 2.04 70.5 83 48 0 2.21 D, banana pepper 4.00 25.1 28 12 0 2.04 E, chili pepper 2.51 12.0 47 17 0 2.75 F, jalapeno 2.04 18.5 98 35 0 1.71 G, grape 0.98 2.4 168 103 0 1.28 H, strawberry 1.13 13.9 158 87 0 1.43 I, Bartlett pear 1.12 46.6 228 125 0.73 0.59 a Fruit shape index I was measured as the ratio of the maximum height of the fruit to the maximum width. b Distal end angle was measured as the slope of two lines that were drawn at 5% or 20% distance, respectively, along boundary from the tip of the fruit. c Obovoid is a measure of pear shape. d Triangle, was measured as the ratio of the width 10% of the height from the proximal end of the fruit to the width 10% from the distal end of the fruit. Open in new tab Table II. Output from Tomato Analyzer for select phenotypic traits on a variety of fruit Images of these fruit appear in Figure 6. Fruit . Fruit Shape Index Ia . Area . Distal End Angle, 5%b . Distal End Angle, 20%b . Obovoidc . Triangle, 10%d . cm2 Degrees Degrees A, butternut squash 1.67 122.8 190 80 0.99 0.60 B, yellow squash 3.55 53.0 72 22 0.78 0.99 C, large jalapeno 2.04 70.5 83 48 0 2.21 D, banana pepper 4.00 25.1 28 12 0 2.04 E, chili pepper 2.51 12.0 47 17 0 2.75 F, jalapeno 2.04 18.5 98 35 0 1.71 G, grape 0.98 2.4 168 103 0 1.28 H, strawberry 1.13 13.9 158 87 0 1.43 I, Bartlett pear 1.12 46.6 228 125 0.73 0.59 Fruit . Fruit Shape Index Ia . Area . Distal End Angle, 5%b . Distal End Angle, 20%b . Obovoidc . Triangle, 10%d . cm2 Degrees Degrees A, butternut squash 1.67 122.8 190 80 0.99 0.60 B, yellow squash 3.55 53.0 72 22 0.78 0.99 C, large jalapeno 2.04 70.5 83 48 0 2.21 D, banana pepper 4.00 25.1 28 12 0 2.04 E, chili pepper 2.51 12.0 47 17 0 2.75 F, jalapeno 2.04 18.5 98 35 0 1.71 G, grape 0.98 2.4 168 103 0 1.28 H, strawberry 1.13 13.9 158 87 0 1.43 I, Bartlett pear 1.12 46.6 228 125 0.73 0.59 a Fruit shape index I was measured as the ratio of the maximum height of the fruit to the maximum width. b Distal end angle was measured as the slope of two lines that were drawn at 5% or 20% distance, respectively, along boundary from the tip of the fruit. c Obovoid is a measure of pear shape. d Triangle, was measured as the ratio of the width 10% of the height from the proximal end of the fruit to the width 10% from the distal end of the fruit. Open in new tab ). For example, the software accurately measured distal end angle values from extremely pointed fruit, like the peppers (Capsicum annuum), to very rounded fruit, like the grape and Bartlett pear (Pyrus communis). In addition, the butternut and yellow squash (Cucurbita pepo) and pear had obovoid values, indicating that the largest width of the fruit was well below the midpoint in those fruit. Lastly, triangle shape at the 10% setting indicated that the peppers were the most triangular in shape, while butternut squash and Bartlett pear were the least triangular in shape. Thus, the Tomato Analyzer application is not limited to tomato fruit but can be applied to fruit morphology analyses of other plants. DISCUSSION The development of structured, controlled vocabularies arranged in ontologies would provide great benefit to plant scientists (Bruskiewich et al., 2002). Currently, extensive trait ontologies are being developed for rice (Oryza sativa; Bruskiewich et al., 2002; Yamazaki and Jaiswal, 2005) and maize (Zea mays; Vincent et al., 2003). However, these species are monocotyledonous, and thus the present ontologies do not incorporate traits common to fruit typical of dicot species. Here, controlled vocabulary terms were developed to describe fruit shape traits. The terms must be defined, well structured, and biologically accurate to assist in information retrieval and provide meaningful comparative studies in synteny and homology (Bruskiewich et al., 2002; Vincent et al., 2003). The controlled vocabulary terms presented here were defined to facilitate consistent use within and across taxa. Additionally, mathematical descriptors were developed, which make the definitions even more robust. These terms are presented for acceptance by the plant scientific community and eventual incorporation into a trait ontology. Software programs and computational methods have been developed to categorize and classify plant organ shapes such as fruit (Morimoto et al., 2000; Beyer et al., 2002) and seeds (Sako et al., 2001a, 2001b). Many of these applications were developed to describe shape uniformity and overall quality of fruit and seed lots. Despite the usefulness of categorizing objects for quality purposes, these methods could neither measure specific shape features, such as the degree of indentation of the proximal fruit end, nor be applied to quantitative genetic analyses. Recently, a method was employed to describe leaf shape by calculating the distance between coordinates along the leaf surface and subjecting the measurements to PCA to determine the greatest sources of variation (Langlade et al., 2005). In addition, the PCs were considered as traits in QTL analyses, and loci were identified contributing to the variation in leaf morphology (Langlade et al., 2005). The described method identifies overall changes in leaf shape but cannot identify individual features of shape, for instance the angle along the boundary or the position of the widest width of the leaf. Consequently, it is difficult to discern which locus controls a specific shape feature and vice versa which attribute is controlled by a specific locus. Tomato Analyzer, on the other hand, analyzes specific shape traits, which were combined and subjected to PCA and subsequent QTL analyses. Individual shape attributes that most significantly contribute to each PC and underlying QTL could be discerned (Supplemental Table II). One prime example of coincidence of individual trait QTL and PC QTL is offered by PC 2 and the attributes that most significantly contribute to this component. The major PC 2 QTL is located on chromosome 7 near marker cos103. This result suggests that the attributes comprising PC 2 are controlled, at least in part, by the same locus. Indeed, when we map the individual traits such as fruit shape index, distal fruit end shape angles, and the proximal fruit end shape features, which comprise PC 2, we identify QTL for these traits at a similar location on chromosome 7 (Fig. 5; data not shown). In addition, these QTL coincide with the sun locus that is known from our previous studies to control fruit shape index (Van der Knaap and Tanksley, 2001; Van der Knaap et al., 2004). Another interesting finding is that PC 3 is controlled in part by traits affecting fruit size (Supplemental Table II). The major size QTL are typically located on chromosomes 1, 2, 3, 7, and 11 (Lippman and Tanksley, 2001; Van der Knaap and Tanksley, 2003), whereas the QTL controlling PC 3 in both populations is located on chromosome 10 and not known to play a major role in controlling fruit size in tomato. This result suggests that employing PCA on several traits may lead to identification of novel QTL and/or improve significance of minor QTL. In all, Tomato Analyzer is a flexible and comprehensive application that provides intuitive descriptors and output that facilitate the analysis of fruit morphology. Furthermore, our efforts to combine controlled vocabulary with mathematical descriptors into one software application make this a very useful tool for several applications. Tomato Analyzer allows for accurate and objective measurements of fruit shape attributes in a high-throughput manner and of traits that are nearly impossible to quantify manually. The application is specifically developed to analyze fruit shape QTL in tomato but could readily be applied to fruit of other species and other plant organs such as seed and leaves. MATERIALS AND METHODS Software Implementation The software application has been implemented in C++ using Visual Studio 6.0 and runs on the Windows operating system. The image processing library Computer Vision and Image Processing (CVIP) 3.7c is used for image I/O. Modifications to the software were done using Visual Studio 2003 with source code control provided by SourceSafe. The source code cross indexer LXR was used to create an online indexable and searchable version of the software code. The program is free for academic purposes and can be downloaded from our laboratory Web site: http://www.oardc.ohio-state.edu/vanderknaap/. Plant Material Two F2 populations were constructed from crosses between one of two Solanum lycopersicum cultivars (Banana Legs or Howard German) and a wild species, Solanum pimpinellifolium accession LA1589. The Banana Legs population consisted of 99 plants, whereas the Howard German population contained 130 plants. Both populations were grown simultaneously in the same greenhouse in the summer of 2003. Up to eight fruits were harvested from each plant. Fruit were weighed, cut longitudinally, and scanned at 300 dpi resolution. The images were saved as jpeg image files for phenotypic analyses. Each image contained fruit from only one plant. If eight fruit per plant were harvested at once, these fruits were scanned and saved as one image. If fewer than eight fruit per plant were harvested at once and additional fruits were harvested later, both images were combined using Adobe Photoshop 7.0 (Adobe Systems) prior to analysis with Tomato Analyzer. Phenotypic Analyses Tomato Analyzer was used to conduct the phenotypic analyses. Manual adjustments, including modification of fruit boundaries and rotation of individual fruits, were made to the images, if necessary. If boundaries were distorted, for example by an attached seed, they would be modified using the software. If fruit were scanned at an angle, manual rotation was required to properly align them. Adjusted images and associated results were saved as a separate file with the extension .tmt. Batch analyses allowed analysis and exportation of selected shape attribute data for at least 100 images at once. The values for each attribute were averaged for all fruit per image and exported into .csv file. Genotypic and Statistical Analysis Total genomic DNA was isolated from young leaves as described by Bernatzky and Tanksley (1986) and Fulton et al. (1995). The genetic map was constructed with a combination of RFLP and PCR-based markers. Additional information on RFLP and PCR-based markers, including map location and primer information, can be found on the Solanaceae Genomics Network Web site (http://www.sgn.cornell.edu). In addition, marker cos103 is the same marker as Lp103E7-R in Van der Knaap et al. (2004). A molecular linkage map was constructed with MAPMAKER v2.0 and the Kosambi mapping function (Kosambi, 1944; Lander et al., 1987). PCA and analysis of variance were conducted with SAS V8 (SAS Institute). QTL analysis was performed by composite interval mapping (Zeng, 1993, 1994) using model six with five marker cofactors selected by forward regression and a 10-cm window size, as implemented in Windows QTL Cartographer v2.0. Log of the odds (LOD) scores greater than 4.0 were considered significant. Additive, dominance (d) effects and percent of phenotypic variance explained by the QTL (R2) were estimated with Windows QTL Cartographer at highest probability peaks. ACKNOWLEDGMENTS Drs. R. Pratt and D.M. Francis are acknowledged for their critical reading of the manuscript, and Dr. P. Jaiswal for insightful comments and suggestions on the trait ontology terms. Also, we acknowledge J. Moyseenko for excellent plant care and assistance in genotyping, and B. Stricker and R. Drushal for their contributions on Tomato Analyzer. 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Trends Plant Sci 8 : 517 –520 Yamazaki Y, Jaiswal P ( 2005 ) Biological ontologies in rice databases: an introduction to the activities in Gramene and Oryzabase. Plant Cell Physiol 46 : 63 –68 Zeng ZB ( 1993 ) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci USA 90 : 10972 –10976 Zeng ZB ( 1994 ) Precision mapping of quantitative trait loci. Genetics 136 : 1457 –1468 Author notes 1 This work was supported by the National Science Foundation (grant no. DBI 0227541). * Corresponding author; e-mail [email protected]; fax 330–263–3887. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Esther van der Knaap ([email protected]). [W] The online version of this article contains Web-only data. www.plantphysiol.org/cgi/doi/10.1104/pp.106.077867. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

McNally, Kenneth L.; Bruskiewich, Richard; Mackill, David; Buell, C. Robin; Leach, Jan E.; Leung, Hei

doi: 10.1104/pp.106.077313pmid: 16684934

The International Rice Functional Genomics Consortium (IRFGC) has initiated a project to provide the rice research community with access to extensive information on genetic variation present within and between diverse rice cultivars and landraces, as well as the genetic resources to exploit that information. Among crop plants, rice is uniquely positioned to achieve this goal due to the release of a high-quality, whole-genome sequence; advances in the use of high-density arrays to compare complex genomes; and the availability of large collections of genetic materials rich in trait variation. In this project, the international rice research community will collaborate with Perlegen Sciences to identify a large fraction of the single nucleotide polymorphisms (SNPs) present in cultivated rice through whole-genome comparisons of 21 rice genomes, including cultivars, germplasm lines, and landraces. The SNP data will be entirely public (www.oryzasnp.org) and can be used to identify a collection of SNPs for undertaking whole-genome scans. Initial funding for this effort has been provided by the International Rice Research Institute (IRRI), the Generation Challenge Program, and the U.S. Department of Agriculture's Cooperative State Research, Education and Extension Service. In this communication, we wish to inform the research community about this project, to mobilize the research community to participate in detailed phenotyping of these lines, and to provide the opportunity to nominate additional candidate lines for a potential extension of this study. THE IMPORTANCE OF SNPs DNA sequence variation accounts for a large fraction of observed differences between plant individuals or varieties, including plant development, yield, stress tolerance, and nutritional quality. The bulk of natural genetic variation in organisms is represented by SNPs or small insertions or deletions. The potentially large number of SNPs in the genomes of individuals within a population or species (Schafer and Hawkins, 1998; Cargill et al., 1999; Kwok, 2001; Syvanen, 2001) provides the foundation for novel approaches to genomic mapping of quantitative trait loci. In humans, SNP variation is approximately 0.5% per nucleotide site (Cargill et al., 1999), while in maize (Zea mays) the variation is closer to 1% to 2% per site (Tenaillon et al., 2001). In the rice genome, SNP occurrences are estimated at approximately three to four SNPs per 1,000 bases, depending on the examined chromosomal regions (Feng et al., 2002; Yu et al., 2005). Recent estimates for SNP variation in rice, based on the indica and japonica genome sequence data, are less than 0.4% (Feltus et al., 2004). On an applied level, the very high densities of SNPs in a genome have made them a preferred molecular marker for fine-mapping studies (Rafalski, 2002). More fundamentally, SNPs are the basic units of genomic diversity, and understanding the evolutionary dynamics of plant genomes involves, in part, assessing the levels, patterning, and distribution of these SNPs (Aquadro, 1992). Studies of SNPs provide a framework for examining how population history, breeding system, and selection affect variation at genetic loci, and delineate the mechanisms that lead to evolutionary diversification of genomes (Nordborg and Innan, 2002; Palaisa et al., 2004). For example, molecular population genomics uses SNP data to probe the levels and patterning of nucleotide polymorphisms within and between loci to test whether specific genes are evolving under selection or in a neutral manner (Bergelson et al., 1998; Purugganan and Suddith, 1999; Bustamante et al., 2002; Palaisa et al., 2004). Of particular utility for whole-genome scans will be the identification of tag SNPs, SNPs that define haplotype regions (Johnson et al., 2001) and can be used to track these regions across populations for testing associations (Gonzalez-Neira et al., 2006). From a practical perspective, SNP discovery is valuable for rice improvement in two fundamental ways. First, it reveals DNA variation among varieties, thus providing the tools for selection in breeding programs (Rafalski, 2002). Second, it provides the “ultimate anchor” to relate all forms of polymorphisms, including biochemical, metabolic, physiological, and phenotypic performance. Due to the availability of extensive SNP datasets, the theory and practice of using SNP data to identify causal genetic factors of phenotypes have largely come from human and mouse research (Botstein and Risch, 2003; Frazer et al., 2004; Hirschhorn and Daly, 2005; Wang et al., 2005). These systems provide excellent examples where relationships between SNPs and phenotypes in case versus control studies or in well-characterized inbred mice lines (Pletcher et al., 2004; Guenet, 2005) have been established. Aranzana et al. (2005) have shown in Arabidopsis (Arabidopsis thaliana) that association mapping in a selfing species is possible through identification of previously known flowering-time and pathogen-resistance alleles; however, in their study, a high level of false positives were found for all traits, indicating the need for appropriate genomic controls for association studies. Eliminating spurious genotype-phenotype associations could be possible by adopting the novel approach of Yu et al. (2006) that includes tests of both the population structure and kinship relationships. Considering the wide experimental options possible with plants using natural populations, historical breeding pedigrees, and specially designed segregating populations (Rafalski and Morgante, 2004), we expect great utility of genome-wide association analysis for gene discovery in rice. With the exception of Arabidopsis (Bevan and Walsh, 2005; Nordborg et al., 2005), as yet no other plant species has an extensive, genome-wide SNP dataset. A HIGH-THROUGHPUT TECHNOLOGY TO IDENTIFY RELEVANT SNPs IN COMPLEX GENOMES The IRFGC will collaborate with Perlegen Sciences to obtain a rich resource of rice SNPs through the “resequencing” of the nonrepetitive portions of the genomes in multiple rice lines using a high-density microarray technology pioneered at Perlegen Sciences (http://www.perlegen.com; Patil et al., 2001). Using the high-quality whole rice genome sequence as a template, Perlegen will design SNP-discovery arrays that contain oligomers designed to include all possible SNP variations with multiple levels of redundancy. When used in hybridizations with “challenge” genomes, sequence differences between the regions in common to the two genomes will be revealed, and through comparison a sequence for these regions of the new “challenge” genome can be deduced. By comparing the information from 24 human genomes, Perlegen discovered and developed assays for approximately 1.5 million SNPs distributed throughout the human genome (Patil et al., 2001; Hinds et al., 2005). Projects are currently in progress to identify SNPs in the mouse and Arabidopsis genomes (K. Frazer, Perlegen Sciences, personal communication). RICE IS AN EXCELLENT PLANT MODEL FOR CONNECTING WHOLE-GENOME VARIATION WITH PHENOTYPE Rice is ideally positioned to exploit the Perlegen technology because of the availability of a high-quality, whole-genome sequence in combination with a large store of genetic materials exhibiting extensive trait variation. A high-quality, finished sequence of the japonica subspecies (var Nipponbare) was recently published by the International Rice Genome Sequencing Project (2005), and a draft sequence (approximately 6× sequence coverage) of the indica subspecies (var 93-11; Yu et al., 2005) generated by the Beijing Genomics Institute also is available (Sasaki and Burr, 2000; Barry, 2001; Goff et al., 2002; Yu et al., 2005). In addition, high-quality, uniform annotation of the rice genome is ongoing at the structural and functional levels (Yuan et al., 2005). Furthermore, rice functional genomic resources for assessing gene function on a genome-wide scale are well established or ongoing in rice. At the transcript level, serial analysis of gene expression projects are under way for rice (Matsumura et al., 1999; Gowda et al., 2004). Serial analysis of gene expression data, coupled with the approximately 32,000 full-length cDNAs (Kikuchi et al., 2003) and approximately 400,000 expressed sequence tags in GenBank, provide a rich resource for transcript structure and expression patterns in rice. In addition, a large, public massively parallel signature sequencing project has commenced in rice (http://mpss.udel.edu/rice/) to more deeply sample the transcriptome. A collection of microarray platforms is available for genome-wide expression studies, including a publicly available long oligonucleotide array (www.ricearray.org), an Affymetrix expression array (http://www.affymetrix.com/products/arrays/specific/rice.affx), and an Agilent expression array (http://www.chem.agilent.com/Scripts/PDS.asp?lPage=12133). These are complemented by a project to develop an atlas of expression in a panel of rice tissues throughout development (http://plantgenomics.biology.yale.edu/riceatlas). Collections of tagged lines are available in rice using Tos17, Ac/Ds, and T-DNA (Hirochika et al., 2004; http://orygenesdb.cirad.fr/) and provide induced variation. Of critical importance for future application of the SNP data to plant breeding is the availability of rice germplasm that contains a wealth of trait diversity (Rafalski and Morgante, 2004). The International Rice GenBank Collection (IRGC) at IRRI comprises the largest collection of rice germplasm held in trust for the world community, with more than 102,547 accessions from the Asian cultivated rice (Oryza sativa), 1,651 accessions from the African cultivated rice (Oryza glaberrima), and 4,508 accessions from 22 related wild relatives. IRRI maintains records of breeding pedigrees of all modern rice varieties derived from mating of traditional varieties (International Rice Information System; http://www.iris.irri.org/). The power of the genetic resources in rice is that they allow a detailed characterization of important traits, such as tolerance to biotic and abiotic stresses, yield, nutrition, and grain quality. The deep collection also enables analysis of traits undergoing selection in the course of domestication. These existing diverse germplasm collections are “gold mines” for analysis of allelic diversity of all rice genes. Furthermore, application of the SNP data will also depend on the extent of linkage disequilibrium (LD) present in rice. Garris et al. (2003) showed that at the xa5 locus, LD was found to be significant across 100 kb. Another study indicates that for other loci, LD extends up to 200 kb or more (M. Purugganan, personal communication). If the lower estimate is used, haplotype blocks of 100 kb imply that about 4,000 tag SNPs—SNPs representative of the haplotype within a region—would be adequate for whole-genome scans in rice. While SNP data from 21 varieties may not be sufficient for robust inferences of association depending on the magnitude of phenotypic differences for the trait, tag SNPs identified from the data would be the entry point to genotype sufficient additional varieties with contrasting phenotypes for improving the power of association tests. Rice, like other plants, offers an advantage for genetic analysis that is not possible (as in humans) or straightforward in animal species (e.g. in mouse). Genetic crosses can be readily performed in plants to produce segregating populations. Efforts are under way to develop genetic stocks (i.e. mapping populations, such as recombinant inbred lines) using the rice lines nominated for resequencing to facilitate application of the SNP and phenotyping data to exploit genetic diversity for crop improvement. CANDIDATE RICE LINES FOR SNP DISCOVERY After consultation with the international rice research community, a list of varieties was developed that will be used for SNP discovery (Table I Table I. Potential candidate varieties for the SNP discovery projecta Variety . Origin . Type (VG)b . Sourcec . Agronomic Attributes . Co 39 India Indica (VG I) IRGC 51231 Parent in a mapping population, good yield in aerobic drought screening trials; blast susceptible, very short duration. Fedearroz 50 Colombia Indica (VG I) CIAT-FLAR BCF 1530 Popular variety in several countries, stay green, quality traits, disease tolerance, progenitor of many breeding lines. IR64 The Philippines Indica (VG I) IRGC 66970 Quality traits, multiple stress tolerance (diseases and insects), progenitor of many breeding and mapping populations, 40,000 chemical- and irradiation-induced mutants available, widely grown for many years. Pokkali India Indica (VG I) IRGC 108921 Salt tolerance, parent of multiple mapping populations, microarray gene expression data available. Sadu-cho Korea Indica (VG I) IRGC 2243 Korean landrace with long grain and indica-type endosperm. Shan-Huang Zhan-2 (SHZ-2) China Indica (VG I) IRTP 19808 Disease-resistant, high-yielding variety. Demonstrated source of at least five superior defense gene alleles for durable resistance to blast. In the pedigrees of many varieties in south China. Swarna India Indica (VG I) IRTP 12715 High yield potential, wide adaptability. Widely planted variety in India. Dular India Aus/boro (VG II) IRGC 32561 Possible β-carotene donor. Red pericarp. FR13 A India Aus/boro (VG II) IRGC 6144 Submergence tolerance, carrying the effective Sub1 allele. Red pericarp. N 22 India Aus/boro (VG II) IRGC 4819 Iron, red pericarp. Heat tolerant. Considered drought tolerant. Good yield in managed drought trials. Drought-response expressed sequence tag collection. Aswina Bangladesh Deep-water III (VG III) IRGC 26289 Deep-water rice. Rayada Bangladesh Deep-water IV (VG IV) IRGC 77210 Deep-water rice. Dom Sufid Iran Aromatic (VG V) IRGC 12880 Basmati plant type, aromatic rice. Azucena The Philippines Trop japonica (VG VI) IRGC 328 Grain-iron quantitative trait loci. Quality traits. Parent of two mapping populations. Near-isogenic lines with high yield under water stress. Deep root distribution. Cypress United States Trop japonica (VG VI) IRTP 19532 Good grain quality; cold tolerant. IAC 165 South America Trop japonica (VG VI) IRGC 82275 Parent in mapping population, deep roots. Inia Tacuari Uruguay Trop japonica (VG VI) CIAT-FLAR BCF 828 Popular early variety, good adaptation and stability, excellent grain quality. Moroberekan Guinea Trop japonica (VG VI) IRGC 12048 Source of blast quantitative resistance traits and drought-tolerance factors. Multiple advanced breeding populations. Functional evidence for drought tolerance observed in mapping populations and backcross progeny. Gerdeh Iran Temp japonica (VG VI) IRGC 32301 Near East origin. Li-Jiang-Xin-Tuan-Hei-Gu (LTH) China Temp japonica (VG VI) IRGC 59323 High disease susceptibility, cold tolerant. M 202 United States Temp japonica (VG VI) U.S. Department of Agriculture; IRGC 77142 Popular variety. Patbyeo Korea Temp japonica (VG VI) IRGC 55607 Traditional Korean variety. Variety . Origin . Type (VG)b . Sourcec . Agronomic Attributes . Co 39 India Indica (VG I) IRGC 51231 Parent in a mapping population, good yield in aerobic drought screening trials; blast susceptible, very short duration. Fedearroz 50 Colombia Indica (VG I) CIAT-FLAR BCF 1530 Popular variety in several countries, stay green, quality traits, disease tolerance, progenitor of many breeding lines. IR64 The Philippines Indica (VG I) IRGC 66970 Quality traits, multiple stress tolerance (diseases and insects), progenitor of many breeding and mapping populations, 40,000 chemical- and irradiation-induced mutants available, widely grown for many years. Pokkali India Indica (VG I) IRGC 108921 Salt tolerance, parent of multiple mapping populations, microarray gene expression data available. Sadu-cho Korea Indica (VG I) IRGC 2243 Korean landrace with long grain and indica-type endosperm. Shan-Huang Zhan-2 (SHZ-2) China Indica (VG I) IRTP 19808 Disease-resistant, high-yielding variety. Demonstrated source of at least five superior defense gene alleles for durable resistance to blast. In the pedigrees of many varieties in south China. Swarna India Indica (VG I) IRTP 12715 High yield potential, wide adaptability. Widely planted variety in India. Dular India Aus/boro (VG II) IRGC 32561 Possible β-carotene donor. Red pericarp. FR13 A India Aus/boro (VG II) IRGC 6144 Submergence tolerance, carrying the effective Sub1 allele. Red pericarp. N 22 India Aus/boro (VG II) IRGC 4819 Iron, red pericarp. Heat tolerant. Considered drought tolerant. Good yield in managed drought trials. Drought-response expressed sequence tag collection. Aswina Bangladesh Deep-water III (VG III) IRGC 26289 Deep-water rice. Rayada Bangladesh Deep-water IV (VG IV) IRGC 77210 Deep-water rice. Dom Sufid Iran Aromatic (VG V) IRGC 12880 Basmati plant type, aromatic rice. Azucena The Philippines Trop japonica (VG VI) IRGC 328 Grain-iron quantitative trait loci. Quality traits. Parent of two mapping populations. Near-isogenic lines with high yield under water stress. Deep root distribution. Cypress United States Trop japonica (VG VI) IRTP 19532 Good grain quality; cold tolerant. IAC 165 South America Trop japonica (VG VI) IRGC 82275 Parent in mapping population, deep roots. Inia Tacuari Uruguay Trop japonica (VG VI) CIAT-FLAR BCF 828 Popular early variety, good adaptation and stability, excellent grain quality. Moroberekan Guinea Trop japonica (VG VI) IRGC 12048 Source of blast quantitative resistance traits and drought-tolerance factors. Multiple advanced breeding populations. Functional evidence for drought tolerance observed in mapping populations and backcross progeny. Gerdeh Iran Temp japonica (VG VI) IRGC 32301 Near East origin. Li-Jiang-Xin-Tuan-Hei-Gu (LTH) China Temp japonica (VG VI) IRGC 59323 High disease susceptibility, cold tolerant. M 202 United States Temp japonica (VG VI) U.S. Department of Agriculture; IRGC 77142 Popular variety. Patbyeo Korea Temp japonica (VG VI) IRGC 55607 Traditional Korean variety. a Initial selection of 22 rice lines based on geographic representation, diversity, traits, and usage. Final list for resequencing may differ pending on suitable nominations by the community. b Type refers to the variety group (VG) as determined by isozyme analysis. For the japonica group, lines are designated as either tropical (trop) or temperate (temp). c Source designations are given as accession numbers from the IRGC, the Latin Fund for Irrigated Rice genebank at the International Center for Tropical Agriculture (CIAT-FLAR BCF), or the International Network for Genetic Evaluation of Rice (previously the International Rice Testing Program [IRTP]). Open in new tab Table I. Potential candidate varieties for the SNP discovery projecta Variety . Origin . Type (VG)b . Sourcec . Agronomic Attributes . Co 39 India Indica (VG I) IRGC 51231 Parent in a mapping population, good yield in aerobic drought screening trials; blast susceptible, very short duration. Fedearroz 50 Colombia Indica (VG I) CIAT-FLAR BCF 1530 Popular variety in several countries, stay green, quality traits, disease tolerance, progenitor of many breeding lines. IR64 The Philippines Indica (VG I) IRGC 66970 Quality traits, multiple stress tolerance (diseases and insects), progenitor of many breeding and mapping populations, 40,000 chemical- and irradiation-induced mutants available, widely grown for many years. Pokkali India Indica (VG I) IRGC 108921 Salt tolerance, parent of multiple mapping populations, microarray gene expression data available. Sadu-cho Korea Indica (VG I) IRGC 2243 Korean landrace with long grain and indica-type endosperm. Shan-Huang Zhan-2 (SHZ-2) China Indica (VG I) IRTP 19808 Disease-resistant, high-yielding variety. Demonstrated source of at least five superior defense gene alleles for durable resistance to blast. In the pedigrees of many varieties in south China. Swarna India Indica (VG I) IRTP 12715 High yield potential, wide adaptability. Widely planted variety in India. Dular India Aus/boro (VG II) IRGC 32561 Possible β-carotene donor. Red pericarp. FR13 A India Aus/boro (VG II) IRGC 6144 Submergence tolerance, carrying the effective Sub1 allele. Red pericarp. N 22 India Aus/boro (VG II) IRGC 4819 Iron, red pericarp. Heat tolerant. Considered drought tolerant. Good yield in managed drought trials. Drought-response expressed sequence tag collection. Aswina Bangladesh Deep-water III (VG III) IRGC 26289 Deep-water rice. Rayada Bangladesh Deep-water IV (VG IV) IRGC 77210 Deep-water rice. Dom Sufid Iran Aromatic (VG V) IRGC 12880 Basmati plant type, aromatic rice. Azucena The Philippines Trop japonica (VG VI) IRGC 328 Grain-iron quantitative trait loci. Quality traits. Parent of two mapping populations. Near-isogenic lines with high yield under water stress. Deep root distribution. Cypress United States Trop japonica (VG VI) IRTP 19532 Good grain quality; cold tolerant. IAC 165 South America Trop japonica (VG VI) IRGC 82275 Parent in mapping population, deep roots. Inia Tacuari Uruguay Trop japonica (VG VI) CIAT-FLAR BCF 828 Popular early variety, good adaptation and stability, excellent grain quality. Moroberekan Guinea Trop japonica (VG VI) IRGC 12048 Source of blast quantitative resistance traits and drought-tolerance factors. Multiple advanced breeding populations. Functional evidence for drought tolerance observed in mapping populations and backcross progeny. Gerdeh Iran Temp japonica (VG VI) IRGC 32301 Near East origin. Li-Jiang-Xin-Tuan-Hei-Gu (LTH) China Temp japonica (VG VI) IRGC 59323 High disease susceptibility, cold tolerant. M 202 United States Temp japonica (VG VI) U.S. Department of Agriculture; IRGC 77142 Popular variety. Patbyeo Korea Temp japonica (VG VI) IRGC 55607 Traditional Korean variety. Variety . Origin . Type (VG)b . Sourcec . Agronomic Attributes . Co 39 India Indica (VG I) IRGC 51231 Parent in a mapping population, good yield in aerobic drought screening trials; blast susceptible, very short duration. Fedearroz 50 Colombia Indica (VG I) CIAT-FLAR BCF 1530 Popular variety in several countries, stay green, quality traits, disease tolerance, progenitor of many breeding lines. IR64 The Philippines Indica (VG I) IRGC 66970 Quality traits, multiple stress tolerance (diseases and insects), progenitor of many breeding and mapping populations, 40,000 chemical- and irradiation-induced mutants available, widely grown for many years. Pokkali India Indica (VG I) IRGC 108921 Salt tolerance, parent of multiple mapping populations, microarray gene expression data available. Sadu-cho Korea Indica (VG I) IRGC 2243 Korean landrace with long grain and indica-type endosperm. Shan-Huang Zhan-2 (SHZ-2) China Indica (VG I) IRTP 19808 Disease-resistant, high-yielding variety. Demonstrated source of at least five superior defense gene alleles for durable resistance to blast. In the pedigrees of many varieties in south China. Swarna India Indica (VG I) IRTP 12715 High yield potential, wide adaptability. Widely planted variety in India. Dular India Aus/boro (VG II) IRGC 32561 Possible β-carotene donor. Red pericarp. FR13 A India Aus/boro (VG II) IRGC 6144 Submergence tolerance, carrying the effective Sub1 allele. Red pericarp. N 22 India Aus/boro (VG II) IRGC 4819 Iron, red pericarp. Heat tolerant. Considered drought tolerant. Good yield in managed drought trials. Drought-response expressed sequence tag collection. Aswina Bangladesh Deep-water III (VG III) IRGC 26289 Deep-water rice. Rayada Bangladesh Deep-water IV (VG IV) IRGC 77210 Deep-water rice. Dom Sufid Iran Aromatic (VG V) IRGC 12880 Basmati plant type, aromatic rice. Azucena The Philippines Trop japonica (VG VI) IRGC 328 Grain-iron quantitative trait loci. Quality traits. Parent of two mapping populations. Near-isogenic lines with high yield under water stress. Deep root distribution. Cypress United States Trop japonica (VG VI) IRTP 19532 Good grain quality; cold tolerant. IAC 165 South America Trop japonica (VG VI) IRGC 82275 Parent in mapping population, deep roots. Inia Tacuari Uruguay Trop japonica (VG VI) CIAT-FLAR BCF 828 Popular early variety, good adaptation and stability, excellent grain quality. Moroberekan Guinea Trop japonica (VG VI) IRGC 12048 Source of blast quantitative resistance traits and drought-tolerance factors. Multiple advanced breeding populations. Functional evidence for drought tolerance observed in mapping populations and backcross progeny. Gerdeh Iran Temp japonica (VG VI) IRGC 32301 Near East origin. Li-Jiang-Xin-Tuan-Hei-Gu (LTH) China Temp japonica (VG VI) IRGC 59323 High disease susceptibility, cold tolerant. M 202 United States Temp japonica (VG VI) U.S. Department of Agriculture; IRGC 77142 Popular variety. Patbyeo Korea Temp japonica (VG VI) IRGC 55607 Traditional Korean variety. a Initial selection of 22 rice lines based on geographic representation, diversity, traits, and usage. Final list for resequencing may differ pending on suitable nominations by the community. b Type refers to the variety group (VG) as determined by isozyme analysis. For the japonica group, lines are designated as either tropical (trop) or temperate (temp). c Source designations are given as accession numbers from the IRGC, the Latin Fund for Irrigated Rice genebank at the International Center for Tropical Agriculture (CIAT-FLAR BCF), or the International Network for Genetic Evaluation of Rice (previously the International Rice Testing Program [IRTP]). Open in new tab ). The varieties were selected in terms of their value in breeding and genetic studies and relative diversity to each other. Figure 1 Figure 1. Open in new tabDownload slide Dendrogram of potential candidate varieties showing between- and within-variety diversity. For each variety, four to seven individual plants were genotyped using 49 simple sequence repeat markers distributed across the genome. Pairwise distances were calculated using the Dice similarity coefficient for 500 bootstrap replicates and subjected to clustering by unweighted pair group method with arithmetic means. Numbers on branches indicate the percentage of times that group occurred in the strict majority-rule consensus tree. Variety types are annotated as ind (indica), jap (japonica), aro (aromatic), aus (aus/boro), dp3 (deep-water III), and dp4 (deep-water IV). shows a dendrogram derived by simple sequence repeat fingerprinting. It depicts the genetic relationship of these lines and also illustrates some degree of genetic heterogeneity within some lines. The population structure in the dendrogram is similar to that observed by Garris et al. (2005) in their study of a larger collection of germplasm varieties, some of which are in common. Together with the two already sequenced varieties (Nipponbare and 93-11), the selected lines span the genetic diversity from major rice varietal groups and include important breeding lines and varieties with a wealth of derived pedigrees, mapping populations, mutants, and introgression lines. All varieties to be resequenced will be subjected to single-seed descent purification. DETAILED PHENOTYPING OF THE RICE LINES Application of the SNP data generated by this effort for association genetics will require detailed and comprehensive phenotyping of the rice lines for multiple traits, such as tolerance to abiotic and biotic stresses, grain quality, and nutrition. Currently, through collaborations among the IRFGC, phenotyping of some of these traits is planned. Yet, additional phenotyping in a range of environments and conditions will be necessary before the SNP data can be fully utilized. Hence, we are seeking experts in physiology and biochemistry willing to collaborate on the phenotyping of these rice lines for traits of interest. We encourage you to visit our project Web site at http://www.oryzasnp.org. At this site, you can nominate lines for inclusion into a potential second phase to extend the coverage of the SNP data across additional varieties and indicate your interest to participate in phenotyping of the nominated rice lines. Comments are also welcome through e-mail with the corresponding author. 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Hajduch, Martin; Casteel, Jill E.; Hurrelmeyer, Katherine E.; Song, Zhao; Agrawal, Ganesh Kumar; Thelen, Jay J.

doi: 10.1104/pp.105.075390pmid: 16543413

Abstract Brassica napus (cultivar Reston) seed proteins were analyzed at 2, 3, 4, 5, and 6 weeks after flowering in biological quadruplicate using two-dimensional gel electrophoresis. Developmental expression profiles for 794 protein spot groups were established and hierarchical cluster analysis revealed 12 different expression trends. Tryptic peptides from each spot group were analyzed in duplicate using matrix-assisted laser desorption ionization time-of-flight mass spectrometry and liquid chromatography-tandem mass spectrometry. The identity of 517 spot groups was determined, representing 289 nonredundant proteins. These proteins were classified into 14 functional categories based upon the Arabidopsis (Arabidopsisthaliana) genome classification scheme. Energy and metabolism related proteins were highly represented in developing seed, accounting for 24.3% and 16.8% of the total proteins, respectively. Analysis of subclasses within the metabolism group revealed coordinated expression during seed filling. The influence of prominently expressed seed storage proteins on relative quantification data is discussed and an in silico subtraction method is presented. The preponderance of energy and metabolic proteins detected in this study provides an in-depth proteomic view on carbon assimilation in B. napus seed. These data suggest that sugar mobilization from glucose to coenzyme A and its acyl derivative is a collaboration between the cytosol and plastids and that temporal control of enzymes and pathways extends beyond transcription. This study provides a systematic analysis of metabolic processes operating in developing B. napus seed from the perspective of protein expression. Data generated from this study have been deposited into a web database (http://oilseedproteomics.missouri.edu) that is accessible to the public domain. Brassica napus (also known as rape and oilseed rape) is the third largest oilseed crop in the world, providing approximately 13% of the world's supply of vegetable oil. B. napus seeds produce oil and protein as the main storage compounds out of the three principal storage reserves (proteins, oil [triacylglycerols], and carbohydrates [starch]) found in plant seeds (Norton and Harris, 1975; Murphy et al., 1989; King et al., 1997; Schwender and Ohlrogge, 2002). These compounds support early seedling growth, and in nature the relative proportions of these compounds vary dramatically among different plant seeds. Seeds of legume species such as soybean (Glycine max) and pea (Pisum sativum) produce seeds with 20% to as much as 40% protein and 20% oil, while B. napus produces seeds with approximately 40% oil and 15% protein (Gunstone et al., 1995). Biochemical and molecular studies are beginning to define the biosynthetic pathways responsible for accumulation of these storage components in B. napus seed (Rawsthorne, 2002; Schwender and Ohlrogge, 2002; Hill et al., 2003; Schwender et al., 2003; Goffman et al., 2004; Kubis et al., 2004; Ruuska et al., 2004; Schwender et al., 2004a, 2004b; Chia et al., 2005; Goffman et al., 2005). Recently, a study of B. napus developing embryos demonstrated that Rubisco acts without the Calvin cycle to increase the efficiency of carbon use during triacylglycerol production (Schwender et al., 2004a). Despite these advancements, little is known about the regulation, both translational and posttranslational, of proteins during seed development. It is quite evident from the available studies that seed-filling processes are highly complex and that many genes encoding enzymes of the respective pathways are tightly coordinated for fine-tuned regulation of each storage component. Since storage components are of nutritional and economical importance, understanding the regulatory mechanisms responsible for their synthesis has become an important challenge. To this end, it may be necessary to apply systematic and multiparallel approaches to study the association of metabolic networks with seed filling on a global scale, rather than studying an isolated enzyme or pathway using conventional biochemical and molecular approaches. Impressive achievements in genome and cDNA sequencing have yielded a wealth of information for many model organisms, including the flowering plants Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa; Arabidopsis Genome Initiative, 2000; Goff et al., 2002; Seki et al., 2002; Yu et al., 2002). As a consequence, there have been tremendous advances in high-throughput technologies, such as transcriptomics and proteomics to profile genome-wide expression of genes at the mRNA and protein levels, respectively. These technologies have provided a unique opportunity to study the biological systems on a genome-wide scale. Arabidopsis was the first plant that was used for genome-wide analysis of seed filling at the transcriptome level (Girke et al., 2000; Ruuska et al., 2002). These studies cataloged temporal changes in gene expression and provided insight into the primary transcriptional networks that coordinate the genome-wide response to seed developmental programs and lead to the distribution of carbon among oil, protein, and carbohydrate reserves. Nevertheless, it should be noted that cellular signaling and metabolic events are also driven by protein-protein interactions, posttranslational protein modifications, and enzymatic activities that cannot be predicted accurately or described by transcriptional profiling approaches alone. Therefore, to better understand gene function and to address biochemical and physiological questions, it is imperative to include proteomics in any systems biological approach (Aebersold and Mann, 2003; Huber, 2003). Improvements in high-resolution two-dimensional gel electrophoresis (2-DE), imaging and analysis, development of protein and nucleotide databases, and methods for protein identification using modern mass spectrometry (MS), such as matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) and liquid chromatography-tandem MS (LC-MS/MS), have made the large-scale profiling and identification of proteins a dynamic new area of research in plant biology (Aebersold and Mann, 2003; Gorg et al., 2003; Agrawal et al., 2005). In recent years, proteomics has been applied to investigate seed germination and seed development using a proteomics workflow including Medicago truncatula (Gallardo et al., 2003), pea (Schiltz et al., 2004), and soybean (Hajduch et al., 2005). Soybean represents the most systematic study conducted to date; the high-density reference map established in the study includes the developmental expression profiles of 679 protein spots and identification of 422 protein spots representing 216 nonredundant proteins and 14 protein functional classes. Here we have conducted an in-depth analysis of B. napus (cv Reston) during five sequential stages of seed filling using 2-DE coupled with MS (MALDI-TOF and LC-MS/MS), with the main objective to characterize metabolic networks. We present high-resolution reference maps of pH 3 to 10 and pH 4 to 7, along with expression profiles of 794 protein spots that reveal 12 principal expression trends during seed development. Using both MALDI-TOF and LC-MS/MS, the identity of 517 protein spots were obtained representing 289 nonredundant proteins. One of the surprising findings of this proteomic study is the preponderance of proteins related to metabolism and energy production. We present and discuss the regulation of these metabolic networks at the protein level, by mapping the protein components and their expression profiles on to the pathways of carbon assimilation. Data generated from this study have been deposited into a database (http://oilseedproteomics.missouri.edu) that is accessible to the public domain. RESULTS Characterization of Developing B. napus Seed Developing B. napus (var. Reston) seeds were staged precisely at 2, 3, 4, 5, and 6 weeks after flowering (WAF), the period when major metabolic changes occur within the embryo. At each developmental stage, seed fresh weight and protein content were measured (Fig. 1 Figure 1. Open in new tabDownload slide Characterization of B. napus seeds during seed filling. Sampling was carried out at 2, 3, 4, 5, and 6 WAF. The top section shows seed stages. Seed fresh weight, FA content, and total seed protein were measured and presented graphically in the bottom sections, respectively. Fresh weight values are the average of 10 seeds, FA and protein determinations are the average of three biological replicates. sd is denoted by error bars. Figure 1. Open in new tabDownload slide Characterization of B. napus seeds during seed filling. Sampling was carried out at 2, 3, 4, 5, and 6 WAF. The top section shows seed stages. Seed fresh weight, FA content, and total seed protein were measured and presented graphically in the bottom sections, respectively. Fresh weight values are the average of 10 seeds, FA and protein determinations are the average of three biological replicates. sd is denoted by error bars. ). At 2 WAF the liquid endosperm dominates while the embryo accounts for a small portion of the seed. The embryo begins to take significance within the seed at 3 WAF. This can be estimated by the pale green appearance of seeds at 2 and 3 WAF with increasing darker portion representing the developing embryo. At 4, 5, and 6 WAF, the embryo accounts for most of the seed mass and the seed coat begins to senesce, as visualized by the change in color, when approaching the desiccation stage (Bewley and Black, 1994). Seed fresh weight increased from 2 until 5 WAF followed by a decrease in weight at 6 WAF, indicating that developing seeds enter into the desiccation phase after 5 WAF. This is in agreement with previous studies on B. napus seed development where the authors concluded that the aqueous soluble fraction increased to a maximum at 5 WAF and then declined (Norton and Harris, 1975). After this period, protein content increased dramatically, reaching approximately 10% of fresh seed weight at 6 WAF, also in agreement with a previous report (Norton and Harris, 1975). Measurements of seed fresh weight and protein content at five different stages indicate that selected time points are representative of the seed-filling phase of development. To further characterize developing B. napus seed, seed fatty acids (FAs) were quantified by gas chromatography (GC; Table I Table I. FA composition in developing B. napus cv Reston seeds Twelve principal B. napus seed FAs were quantified in biological triplicate from 2, 3, 4, 5, and 6 WAF seed. The seed FAs were derivatized to FA methyl esters for analysis by gas chromatography (GC). Identification of FAs was performed by on-line mass spectral analysis while quantification was performed by flame ionization. The amount of each (and total) FAs is expressed in microgram per milligram of seed dry weight. Values are the average of biological triplicates and sd is shown. ND, Not detected. FA . WAF . . . . . . 2 . 3 . 4 . 5 . 6 . 16:0 3.5 ± 0.4 9.5 ± 5.0 9.2 ± 3.4 17.3 ± 1.8 19.2 ± 5.3 18:0 1.9 ± 0.3 4.5 ± 4.1 4.4 ± 3.3 9.3 ± 0.8 10.6 ± 2.4 18:1Δ9 0.6 ± 0.1 13.0 ± 2.9 36.7 ± 9.7 49.7 ± 16.4 34.6 ± 0.9 18:1Δ11 0.6 ± 0.1 7.0 ± 2.0 6.4 ± 1.0 10.0 ± 2.0 8.1 ± 1.3 18:2 12.1 ± 1.7 19.4 ± 2.1 19.5 ± 2.0 31.9 ± 6.4 36.1 ± 9.0 18:3 4.6 ± 0.7 6.5 ± 0.5 9.2 ± 1.1 13.7 ± 2.3 16.1 ± 2.7 20:0 0.2 ± 0.1 0.5 ± 0.1 0.8 ± 0.4 1.5 ± 0.7 1.3 ± 0.4 20:1Δ11 ND 3.4 ± 1.9 11.9 ± 9.1 22.7 ± 7.4 15.2 ± 1.9 20:1Δ13 ND 0.9 ± 0.6 1.6 ± 0.9 2.5 ± 0.4 3.7 ± 0.6 22:0 ND 0.9 ± 1.3 1.2 ± 1.0 2.1 ± 0.3 2.5 ± 1.3 22:1Δ13 ND 2.3 ± 2.8 33.6 ± 11.8 36.0 ± 10.2 61.4 ± 13.6 22:1Δ15 ND 0.4 ± 0.1 1.5 ± 0.3 1.3 ± 0.2 4.2 ± 1.0 Total FA content 23.2 ± 3.4 68.0 ± 15.6 135.9 ± 43.0 197.9 ± 45.1 213.1 ± 38.6 FA . WAF . . . . . . 2 . 3 . 4 . 5 . 6 . 16:0 3.5 ± 0.4 9.5 ± 5.0 9.2 ± 3.4 17.3 ± 1.8 19.2 ± 5.3 18:0 1.9 ± 0.3 4.5 ± 4.1 4.4 ± 3.3 9.3 ± 0.8 10.6 ± 2.4 18:1Δ9 0.6 ± 0.1 13.0 ± 2.9 36.7 ± 9.7 49.7 ± 16.4 34.6 ± 0.9 18:1Δ11 0.6 ± 0.1 7.0 ± 2.0 6.4 ± 1.0 10.0 ± 2.0 8.1 ± 1.3 18:2 12.1 ± 1.7 19.4 ± 2.1 19.5 ± 2.0 31.9 ± 6.4 36.1 ± 9.0 18:3 4.6 ± 0.7 6.5 ± 0.5 9.2 ± 1.1 13.7 ± 2.3 16.1 ± 2.7 20:0 0.2 ± 0.1 0.5 ± 0.1 0.8 ± 0.4 1.5 ± 0.7 1.3 ± 0.4 20:1Δ11 ND 3.4 ± 1.9 11.9 ± 9.1 22.7 ± 7.4 15.2 ± 1.9 20:1Δ13 ND 0.9 ± 0.6 1.6 ± 0.9 2.5 ± 0.4 3.7 ± 0.6 22:0 ND 0.9 ± 1.3 1.2 ± 1.0 2.1 ± 0.3 2.5 ± 1.3 22:1Δ13 ND 2.3 ± 2.8 33.6 ± 11.8 36.0 ± 10.2 61.4 ± 13.6 22:1Δ15 ND 0.4 ± 0.1 1.5 ± 0.3 1.3 ± 0.2 4.2 ± 1.0 Total FA content 23.2 ± 3.4 68.0 ± 15.6 135.9 ± 43.0 197.9 ± 45.1 213.1 ± 38.6 Open in new tab Table I. FA composition in developing B. napus cv Reston seeds Twelve principal B. napus seed FAs were quantified in biological triplicate from 2, 3, 4, 5, and 6 WAF seed. The seed FAs were derivatized to FA methyl esters for analysis by gas chromatography (GC). Identification of FAs was performed by on-line mass spectral analysis while quantification was performed by flame ionization. The amount of each (and total) FAs is expressed in microgram per milligram of seed dry weight. Values are the average of biological triplicates and sd is shown. ND, Not detected. FA . WAF . . . . . . 2 . 3 . 4 . 5 . 6 . 16:0 3.5 ± 0.4 9.5 ± 5.0 9.2 ± 3.4 17.3 ± 1.8 19.2 ± 5.3 18:0 1.9 ± 0.3 4.5 ± 4.1 4.4 ± 3.3 9.3 ± 0.8 10.6 ± 2.4 18:1Δ9 0.6 ± 0.1 13.0 ± 2.9 36.7 ± 9.7 49.7 ± 16.4 34.6 ± 0.9 18:1Δ11 0.6 ± 0.1 7.0 ± 2.0 6.4 ± 1.0 10.0 ± 2.0 8.1 ± 1.3 18:2 12.1 ± 1.7 19.4 ± 2.1 19.5 ± 2.0 31.9 ± 6.4 36.1 ± 9.0 18:3 4.6 ± 0.7 6.5 ± 0.5 9.2 ± 1.1 13.7 ± 2.3 16.1 ± 2.7 20:0 0.2 ± 0.1 0.5 ± 0.1 0.8 ± 0.4 1.5 ± 0.7 1.3 ± 0.4 20:1Δ11 ND 3.4 ± 1.9 11.9 ± 9.1 22.7 ± 7.4 15.2 ± 1.9 20:1Δ13 ND 0.9 ± 0.6 1.6 ± 0.9 2.5 ± 0.4 3.7 ± 0.6 22:0 ND 0.9 ± 1.3 1.2 ± 1.0 2.1 ± 0.3 2.5 ± 1.3 22:1Δ13 ND 2.3 ± 2.8 33.6 ± 11.8 36.0 ± 10.2 61.4 ± 13.6 22:1Δ15 ND 0.4 ± 0.1 1.5 ± 0.3 1.3 ± 0.2 4.2 ± 1.0 Total FA content 23.2 ± 3.4 68.0 ± 15.6 135.9 ± 43.0 197.9 ± 45.1 213.1 ± 38.6 FA . WAF . . . . . . 2 . 3 . 4 . 5 . 6 . 16:0 3.5 ± 0.4 9.5 ± 5.0 9.2 ± 3.4 17.3 ± 1.8 19.2 ± 5.3 18:0 1.9 ± 0.3 4.5 ± 4.1 4.4 ± 3.3 9.3 ± 0.8 10.6 ± 2.4 18:1Δ9 0.6 ± 0.1 13.0 ± 2.9 36.7 ± 9.7 49.7 ± 16.4 34.6 ± 0.9 18:1Δ11 0.6 ± 0.1 7.0 ± 2.0 6.4 ± 1.0 10.0 ± 2.0 8.1 ± 1.3 18:2 12.1 ± 1.7 19.4 ± 2.1 19.5 ± 2.0 31.9 ± 6.4 36.1 ± 9.0 18:3 4.6 ± 0.7 6.5 ± 0.5 9.2 ± 1.1 13.7 ± 2.3 16.1 ± 2.7 20:0 0.2 ± 0.1 0.5 ± 0.1 0.8 ± 0.4 1.5 ± 0.7 1.3 ± 0.4 20:1Δ11 ND 3.4 ± 1.9 11.9 ± 9.1 22.7 ± 7.4 15.2 ± 1.9 20:1Δ13 ND 0.9 ± 0.6 1.6 ± 0.9 2.5 ± 0.4 3.7 ± 0.6 22:0 ND 0.9 ± 1.3 1.2 ± 1.0 2.1 ± 0.3 2.5 ± 1.3 22:1Δ13 ND 2.3 ± 2.8 33.6 ± 11.8 36.0 ± 10.2 61.4 ± 13.6 22:1Δ15 ND 0.4 ± 0.1 1.5 ± 0.3 1.3 ± 0.2 4.2 ± 1.0 Total FA content 23.2 ± 3.4 68.0 ± 15.6 135.9 ± 43.0 197.9 ± 45.1 213.1 ± 38.6 Open in new tab ). Accumulation of total FAs increased significantly during seed development (Fig. 1) and by 6 WAF accounted for over 20% of the seed dry mass. Compositional analysis revealed major fluctuations in the individual FA species (Table I). Twelve different FAs accounted for nearly all of the acyl chains present in developing B. napus seed. Interestingly, the accumulation of oleic acid (18:1) and eicosenoic acid (20:1) showed maximum abundance at 5 WAF, while erucic acid (22:1) peaked later at 6 WAF. This is not surprising, given that 18:1 is the precursor for elongation to 22:1, the prominent FA in B. napus Reston. Medium-Range Isoelectric Focusing Is Necessary for Reduction of Spot Overlap in Two-Dimensional Gels of Developing B. napus Seed Initial 2-DE analysis of proteins from B. napus seed was performed using an isoelectric focusing (IEF) range of pH 3 to 10. As seen in Figure 2A Figure 2. Open in new tabDownload slide Two-dimensional gel electrophoresis analysis of proteins (1 mg) isolated from B. napus seeds at 2, 3, 4, 5, and 6 WAF. A, Protein analysis using wide-range IPG strips with pH range from 3 to 10. B, Protein analysis using medium-range IPG strips with pH range from 4 to 7. Reference gel is a composite of all five investigated stages obtained by pooling of 0.2 mg of protein from each stage. Figure 2. Open in new tabDownload slide Two-dimensional gel electrophoresis analysis of proteins (1 mg) isolated from B. napus seeds at 2, 3, 4, 5, and 6 WAF. A, Protein analysis using wide-range IPG strips with pH range from 3 to 10. B, Protein analysis using medium-range IPG strips with pH range from 4 to 7. Reference gel is a composite of all five investigated stages obtained by pooling of 0.2 mg of protein from each stage. , the region between pH 4 to 7 is protein rich and overlapping spots are prevalent. Use of medium pH gradients is one effective way to reduce spot overlap (Campostrini et al., 2005). Therefore, additional 2-DE with pH 4 to 7 immobiline pH gradient (IPG) strips was performed to ameliorate this problem (Fig. 2B). Visual inspection of 2-DE gels revealed highly dynamic changes in the seed proteome. Each 2-DE gel contained approximately 1,200 to 1,300 Coomassie-stained protein spots with a dynamic range in spot volume ranging from 1 to 4.5 × 106. However, spots below a volume of 7 × 103 were difficult to quantify, therefore, the quantitative, dynamic range varied from 7 × 103 to 4.5 × 106. Protein Quantification Established Developmental Expression Profiles for 794 Protein Spots For accurate determination of protein expression, the five developmental stages of B. napus seed development were analyzed in biological quadruplicate (Fig. 3A Figure 3. Open in new tabDownload slide Proteomics experimental design used in this study. A, Each investigated seed stage was analyzed in quadruplicate by 2-DE. Only those spots present in at least three gels were considered for analysis. Relative quantification and expression profiles were determined using ImageMaster 2-DE Platinum software version 5.0. Spots from analyzed gels were matched to a reference gel and relative volumes were calculated for each group. Average relative volumes and sds for matched spots were calculated and data were plotted onto a line graph. Expression profiles were then deposited into a Web-based data repository (http://oilseedproteomics.missouri.edu). B, Protein spots with expression profiles were excised from reference (pooled) gels of pH ranges of 3 to 10 and 4 to 7. Each spot was trypsin digested and peptides were analyzed by MALDI-TOF and LC-MS/MS. Spectra were searched against NCBI and TIGR databases using MS Fit program of Protein Prospector for MALDI-TOF spectra and SEQUEST for data acquired by LC-MS/MS. At this point, four independent protein assignments for each spot were obtained: (1) MALDI-TOF data searched against NCBI; (2) MALDI-TOF searched against TIGR; (3) LC-MS/MS searched against NCBI; and (4) LC-MS/MS searched against TIGR. Two-step data consolidation approach was applied (see “Materials and Methods”) that resulted in 517 highly confident protein assignments. Figure 3. Open in new tabDownload slide Proteomics experimental design used in this study. A, Each investigated seed stage was analyzed in quadruplicate by 2-DE. Only those spots present in at least three gels were considered for analysis. Relative quantification and expression profiles were determined using ImageMaster 2-DE Platinum software version 5.0. Spots from analyzed gels were matched to a reference gel and relative volumes were calculated for each group. Average relative volumes and sds for matched spots were calculated and data were plotted onto a line graph. Expression profiles were then deposited into a Web-based data repository (http://oilseedproteomics.missouri.edu). B, Protein spots with expression profiles were excised from reference (pooled) gels of pH ranges of 3 to 10 and 4 to 7. Each spot was trypsin digested and peptides were analyzed by MALDI-TOF and LC-MS/MS. Spectra were searched against NCBI and TIGR databases using MS Fit program of Protein Prospector for MALDI-TOF spectra and SEQUEST for data acquired by LC-MS/MS. At this point, four independent protein assignments for each spot were obtained: (1) MALDI-TOF data searched against NCBI; (2) MALDI-TOF searched against TIGR; (3) LC-MS/MS searched against NCBI; and (4) LC-MS/MS searched against TIGR. Two-step data consolidation approach was applied (see “Materials and Methods”) that resulted in 517 highly confident protein assignments. ). Four sample harvesting events were followed by four independent protein extractions. This naturally resulted in both biological and technical variation in the quantification results that is noted by the sds presented in Supplemental Tables I and II. It was noticed that low abundance spots, as well as those spots present in only two biological replicates, typically exhibited the highest level of variation. To select only high-quality protein spots for expression profiling, the following threshold criteria were applied. Each protein spot was present in at least three biological replicate gels and detected in at least two developmental stages. Using this approach, a total of 794 protein spot groups were matched, manually validated, and quantified (Supplemental Table I). Moreover, coefficient of variation (CV) value was calculated to allow direct comparison of significance in acquired quantification data (Supplemental Table I). Seed Storage Proteins Can Be Subtracted in Silico to Eliminate the Bias of Highly Expressed Proteins Seed storage proteins (SSPs) are highly abundant at 4, 5, and 6 WAF. As these proteins accumulate, the relative volume of other spots concomitantly decrease, which may bias composite and individual expression profiles. To test this hypothesis, all spots corresponding to SSPs were subtracted in silico and expression of the remaining spots were renormalized (Supplemental Table II). To view possible influence of in silico SSP subtraction, the composite expression profiles (discussed below) were created using both original and SSP-subtracted datasets (Fig. 6). Although the shape of expression trends was not greatly affected, slight changes were observed. For instance, maximum accumulation of photosynthetic proteins occurred at 3 WAF. After SSP removal, the peak moved to 4 WAF. Combination of MALDI-TOF MS and LC-MS/MS Resulted in 517 High-Confidence Protein Assignments In theory, B. napus is a nonideal organism for proteomic analyses, due to the lack of comprehensive sequence databases. The National Center for Biotechnology Information (NCBI) nonredundant protein database contained only 2,207 entries for Brassica species, as of March, 2005. The largest database for B. napus is available at The Institute for Genomic Research (TIGR), containing 5,568 tentative consensus entries. Due to these limitations, we decided to acquire MS data in duplicate using two different mass spectrometers and to perform comprehensive data mining using both NCBI and TIGR databases. Figure 3B gives an overview of data acquisition and data mining strategy. Altogether 794 spots were excised from 2-DE gels of pH 4 to 7 and pH 3 to 10 (pH 7–10 region only), digested with trypsin, and analyzed by MALDI-TOF MS and LC-MS/MS in parallel. Peptide mass fingerprint (PMF) analysis using MALDI-TOF MS resulted in 175 protein assignments, while LC-MS/MS resulted in 479 assignments. Protein assignments by MALDI-TOF and LC-MS/MS were integrated to reveal 137 identical and 46 nonidentical assignments. In total, 517 assignments were made (65.1% identification efficiency), of which 289 were nonredundant proteins (Supplemental Table III). LC-MS/MS Yielded Higher Percentage of Protein Assignments Than MALDI-TOF PMF It was previously reported that MALDI-TOF and LC-MS/MS are equally efficient at protein identification when comprehensive databases are available (Lim et al., 2003; Person et al., 2003). With plant species such as B. napus, for which database resources are limited, application of both MS methods may improve the identification rate. The protein identification workflow (Fig. 3B) used here allows for direct comparison of these two ubiquitous MS methods for protein identification. Interrogation of MALDI-TOF data against two databases (nonredundant NCBInr and TIGR) resulted in 175 protein assignments out of 794 protein spots, representing a 22.0% identification frequency. When compared to results obtained with other plants such as soybean (Herman et al., 2003; Mooney et al., 2004; Hajduch et al., 2005), Arabidopsis (Gallardo et al., 2001, 2002a, 2002b), or Medicago truncatula (Gallardo et al., 2003; Watson et al., 2003) the identification efficiency is low. In contrast, searching LC-MS/MS data against the same databases resulted in 479 assignments (60.3% identification frequency). These data support the notion that LC-MS/MS is superior to MALDI-TOF for protein identification. However, our results indicate that parallel MS identification strategies can improve the overall identification. In this study, the identification rate increased from 60.3% using LC-MS/MS alone to 65.1% using a combination of MALDI-TOF and LC-MS/MS. From the 517 identified proteins, 38 (7.3%) were identified exclusively by MALDI-TOF, 342 by LC-MS/MS only (66.1%), and 137 by both methods (26.5%). Although LC-MS/MS clearly produced a higher number of assignments, identification by MALDI-TOF may be more successful for some proteins. Manual Validation of Protein Annotation Improves Data Analysis In general, database annotations infrequently provide information about intracellular localization. Since enzymes may have different functional roles depending upon subcellular location, proteins that may potentially have multiple intracellular isoforms were analyzed by three different algorithms to predict subcellular targeting. For example, in this study, six protein spots were identified simply as malate dehydrogenase (MDH). In this case, MDH can be located either in the cytosol where it is a part of gluconeogenesis, in the mitochondria where it is involved in the TCA cycle, or in the plastids where it is involved in redox shuttling or the photosynthetic C4 cycle. Applying subcellular localization algorithms, it was suggested that four spots (884, 5,218, 5,222, and 5,225) are cytosolic MDH, and the remaining two spots (5,185 and 5,292) are mitochondrial MDH isoforms. However, it should be noted that subcellular assignments based upon localization algorithms are at best predictive, experimental evidence is required for confirmation. A large percentage of proteins identified in this study were annotated as unknown. At an early stage of this investigation, unknown proteins accounted for as much as 38% of all the identified proteins (data not shown). Although some databases provide current annotation information about sequence homology/similarity in the identifier tag, many do not. Therefore, all assignments lacking this information were subjected to homology search using BLASTP against the NCBI nonredundant database. This manual validation strategy reduced the percentage of unknown proteins from 38% to 2.1%. Proteins Associated with Energy and Metabolism Are Prevalent in Developing B. napus Seed All identified proteins were classified into functional classes as originally established by Bevan et al. (1998) for the Arabidopsis genome project. However, classes were slightly modified to be more suitable for a seed development study. With respect to the metabolism class, a polysaccharide catabolism subclass was added and the lipid and sterols subclass of metabolism was separated into individual subclasses. An additional subclass, seed maturation, was added in the cell growth and division class. Supplemental Table III provides a list of all identified proteins sorted into plant functional classes with details of protein assignments. On a relative spot volume basis, proteins associated with protein destination and storage are the most abundant class followed by proteins associated with energy production and metabolism (Fig. 4 Figure 4. Open in new tabDownload slide Functional classification of identified B. napus developing seed proteins according to scheme by Bevan et al. (1998). Out of a total of 517 identified proteins, 289 had nonredundant function. Black bars show distribution of all 517 proteins and gray bars show distribution of 289 nonredundant proteins into functional classes. Figure 4. Open in new tabDownload slide Functional classification of identified B. napus developing seed proteins according to scheme by Bevan et al. (1998). Out of a total of 517 identified proteins, 289 had nonredundant function. Black bars show distribution of all 517 proteins and gray bars show distribution of 289 nonredundant proteins into functional classes. ). However, when isoform redundancy is taken into account, proteins associated with energy are the most represented followed by metabolism-related proteins. Hierarchical Clustering of 794 Quantified Spot Groups Resulted in 12 Cluster Groups Hierarchical clustering reduced 794 expression profiles into 12 expression cluster groups (Fig. 5 Figure 5. Open in new tabDownload slide Hierarchical clustering of 794 protein spot expression profiles using SAS statistical software. Relative quantification data without SSP removal were used for these analyses (Supplemental Table I). Expression trend, number of clustered expression profiles, and functional composition are shown for each of the 12 established clusters. Vertical lines indicate inverse degree of relatedness of clusters. Figure 5. Open in new tabDownload slide Hierarchical clustering of 794 protein spot expression profiles using SAS statistical software. Relative quantification data without SSP removal were used for these analyses (Supplemental Table I). Expression trend, number of clustered expression profiles, and functional composition are shown for each of the 12 established clusters. Vertical lines indicate inverse degree of relatedness of clusters. ). Visual inspection of these expression groups suggests diverse and complex patterns of regulation. The two most abundant groups, 3 and 11, had negative slopes, indicating decreasing abundance with seed age. These groups were largely composed of proteins involved in energy and protein destination and storage, representing 21.2% and 20.1% of proteins, respectively (Fig. 5). The third most abundant group, cluster 10, representing 13.7% of proteins, showed a positive slope during seed filling; the majority of these proteins were involved in protein destination and storage. Figure 5 also revealed that the distribution of plant functional classes in protein expression clusters is not homogenous. For instance, proteins involved in protein destination and storage were mainly grouped in clusters 10 and 11 with negative and positive expression slopes, respectively. This is due to the heterogeneous composition of this functional class that contains SSPs, which increase during seed fill, but also proteins involved in folding and stability that decreased with seed age (Supplemental Table III). Composite Expression Profiles of Functional Subclasses Revealed System-Wide Trends To characterize cellular activities during seed filling, composite expression profiles were established for individual subclasses. For statistical reasons, only those functional subclasses containing 10 or more proteins were considered for analysis. Based upon these criteria, 384 of 517 identified proteins (74.7%) were grouped into 13 functional subclasses (Fig. 6 Figure 6. Open in new tabDownload slide Chronological characterization of processes during seed filling in B. napus. The composite expression profiles (pooled relative volumes) for all functional subclasses of proteins (according to classification scheme by Bevan et al., 1998) containing 10 or more proteins are shown. Profiles were generated using relative two quantification datasets: total protein spot groups (full line) and spot groups minus in silico subtracted SSPs (dashed line). Name of functional subclass, number of unique proteins (in parentheses), and expression trend with maximum y axis values are shown. Figure 6. Open in new tabDownload slide Chronological characterization of processes during seed filling in B. napus. The composite expression profiles (pooled relative volumes) for all functional subclasses of proteins (according to classification scheme by Bevan et al., 1998) containing 10 or more proteins are shown. Profiles were generated using relative two quantification datasets: total protein spot groups (full line) and spot groups minus in silico subtracted SSPs (dashed line). Name of functional subclass, number of unique proteins (in parentheses), and expression trend with maximum y axis values are shown. ). The most prevalent subclass of proteins in terms of relative spot volume, storage proteins (71 proteins), gradually increased in abundance beginning at 3 WAF. The second most abundant subclass, amino acid metabolism (42 proteins), decreased in abundance from 2 WAF until midpoint of seed filling and remained at a constant level thereafter. Relative abundance of detoxification proteins, the third most abundant group with 36 proteins, rapidly increased beginning at 5 WAF. Proteins related to photosynthesis are the fourth abundant cluster and revealed a gradual increase in relative abundance until 3 WAF, followed by a slight decrease. Collectively, these functional subclasses exhibited three different expression trends. The first group included proteins expressed mainly at early stages of seed filling. These proteins are involved in glycolysis, respiration, metabolism of sugars, signal transduction, metabolism of amino acids, proteolysis, and defense (Fig. 6). Proteins of the second group, involved in photosynthesis and lipid metabolism, exhibited highest expression at midpoint of seed filling. Finally, detoxification, seed maturation, and SSPs were highly abundant at later stages of development (Fig. 6). Although perhaps overly simplistic, the expression trends agree with previous observations that seed filling in B. napus begins with sugar mobilization, and is followed by sequential surges in amino acid, lipid, and storage protein synthesis (Fig. 6). DISCUSSION Within the larger goal of profiling protein expression globally in developing B. napus seeds, the aim of this study was to characterize the metabolic pathways operating during seed filling. The major economic value of B. napus lies in its oil, and any insight into the regulation of oil accumulation during seed filling would be useful. Therefore, we focus our discussion on the pathways leading to de novo FA synthesis. However, it is important to mention that this study has also found protein components for a number of other pathways (Supplemental Table III). For instance, 8% and 5% of the total identified proteins are involved in amino acid metabolism and proteolysis, respectively. Interestingly, the majority of proteins identified here were represented by multiple isoelectric forms, suggestive of posttranslational modification. Thus, as genome resources of Brassica crops improve, the high-resolution 2-DE maps reported here can be used as a predictive tool to search for unexpected isoelectric species to begin uncovering posttranslation regulation. As demonstrated in this study, the main advantage of 2-DE is the detection of different isoelectric species of various proteins. For example, 17 isoelectric species were detected for the glycolytic enzyme Fru bisphosphate aldolase (FBA). This is an unusually high number of isoelectric species, which is strongly suggestive of posttranslational modification(s). This type of information cannot be acquired using transcriptomic or metabolomic approaches. Although adept at resolving isoelectric species, the technique of 2-DE is somewhat restricted at quantifying low abundance proteins. For instance, Glc 6-P dehydrogenase, transketolase, transaldolase, ribulose 5-P epimerase, and Rib 5-P isomerase were analyzed in a recent study of enzyme activities of oxidative pentose phosphate pathway in developing B. napus embryos (Hutchings et al., 2005). However, this proteomics study did not detect these proteins, presumably due to their low abundance. Despite the known limitations of current 2-DE methodology at detecting underrepresented proteins, the expression and identity of 517 protein species expressed during seed filling of B. napus were characterized in this investigation, representing the largest integrated dataset for any oilseed. Rubisco Is Highly Expressed throughout Seed Filling Eleven Rubisco large subunits were detected and can be divided into two groups, based on their peak of accumulation. The first group showed maximum abundance at early stages of seed filling (2 or 3 WAF) and includes seven protein spots (511, 520, 536, 4,885, 4,931, 4,937, and 5,009). The second group (spots 4,919, 4,922, and 4,924) with higher protein abundance at midpoint of seed filling (4 WAF), showed bell-shape expression profiles, very similar to the data acquired for pyruvate dehydrogenase (PDH; spots 5,148 and 5,811; Fig. 7 Figure 7. Open in new tabDownload slide Pathways for carbon assimilation during seed filling in B. napus. Proteins involved in sugar breakdown are displayed on the corresponding metabolic pathways. Graph shows expression of protein spots during seed filling. Expression profiles were generated using the total protein spot dataset after in silico SSP subtraction. Number above each graph shows maximum value y axis (relative volume). Dashed arrows are used when no protein was detected. Abbreviations for metabolites: UDP-G, UDP-Glc; G-1-P, Glc 1 phosphate; G-6-P, Glc 6 phosphate; F-6-P, Fru 6 phosphate; F-1,6bisP, Fru 1,6 bis phosphate; GAP, glyceraldehyde 3-P; DHAP, dihydroxyacetone phosphate; 1,3-bis PGA, 1,3 bis phosphoglyceric acid; 3-PGA, 3 phosphoglyceric acid; 2-PGA, 2 phosphoglyceric acid; PEP, phosphoenolpyruvate. Abbreviations for enzymes: SuSy, Suc synthase; UGP, UDP-Glc pyrophosphorylase; PGM, phosphoglucomutase; PGI, phosphoglucose isomerase; PFK, pyrophosphate-dependent phosphofructokinase; FBA, Fru bisphosphate aldolase; GAPDH, glyceraldehyde 3-P dehydrogenase; TPI, triose-phosphate isomerase; PGK, phosphoglycerate kinase; iPGAM, 2,3-bisphosphoglycerate-independent phosphoglycerate mutase; PRK, phosphoribulokinase; Rubisco, ribulose-1,5-bisphosphate carboxylase; PK, pyruvate kinase; PDH, pyruvate dehydrogenase. Figure 7. Open in new tabDownload slide Pathways for carbon assimilation during seed filling in B. napus. Proteins involved in sugar breakdown are displayed on the corresponding metabolic pathways. Graph shows expression of protein spots during seed filling. Expression profiles were generated using the total protein spot dataset after in silico SSP subtraction. Number above each graph shows maximum value y axis (relative volume). Dashed arrows are used when no protein was detected. Abbreviations for metabolites: UDP-G, UDP-Glc; G-1-P, Glc 1 phosphate; G-6-P, Glc 6 phosphate; F-6-P, Fru 6 phosphate; F-1,6bisP, Fru 1,6 bis phosphate; GAP, glyceraldehyde 3-P; DHAP, dihydroxyacetone phosphate; 1,3-bis PGA, 1,3 bis phosphoglyceric acid; 3-PGA, 3 phosphoglyceric acid; 2-PGA, 2 phosphoglyceric acid; PEP, phosphoenolpyruvate. Abbreviations for enzymes: SuSy, Suc synthase; UGP, UDP-Glc pyrophosphorylase; PGM, phosphoglucomutase; PGI, phosphoglucose isomerase; PFK, pyrophosphate-dependent phosphofructokinase; FBA, Fru bisphosphate aldolase; GAPDH, glyceraldehyde 3-P dehydrogenase; TPI, triose-phosphate isomerase; PGK, phosphoglycerate kinase; iPGAM, 2,3-bisphosphoglycerate-independent phosphoglycerate mutase; PRK, phosphoribulokinase; Rubisco, ribulose-1,5-bisphosphate carboxylase; PK, pyruvate kinase; PDH, pyruvate dehydrogenase. ). Interestingly, expression profiles of the second group (spots 4,919, 4,922, and 4,924) and PDH (spots 5,148 and 5,811) are also very similar to the composite expression profile of enzymes involved in lipid metabolism (Fig. 5). A similar increase in the Rubisco small subunit was also reported in Arabidopsis by microarray analysis (Ruuska et al., 2002). The high abundance of Rubisco subunits is in contrast with low abundance of other enzymes of the Calvin cycle, many of which were below the detection limit of this proteomics study. A possible explanation for this disparity in protein abundance may be found in a recent stable isotope labeling study of B. napus embryos (Schwender et al., 2004a). The Calvin cycle is the cyclic regeneration of ribulose-1,5-bisphosphate from 3-phosphoglyceric acid (3-PGA), which results in carbon dioxide distribution into all carbon positions of the cycle's intermediates, including 3-PGA (Bassham et al., 1954). The labeling experiment showed that 13C was incorporated mainly into the C1 carbon position of 3-PGA, while FAs derived from C2 and C3 of 3-PGA were labeled at extremely low levels (Schwender et al., 2004a). Based upon these data the authors concluded a possible role of Rubisco in carbon dioxide recycling, apart from the Calvin cycle. Glycolytic Reactions during Seed Filling Are Principally Cytosolic An important component of carbon assimilation in developing seeds is glycolysis. Although this ubiquitous pathway was first elucidated in the 1940's (Meyerhof and Junowicz-Kocholaty, 1943; Meyerhof, 1945), relatively little is known about the regulation and control of this pathway. This is particularly true in plants due to the added complexity of parallel pathways in both the cytosol and plastids (Plaxton, 1996; Fernie et al., 2004). This study localized several protein spots corresponding to numerous different glycolytic enzymes both in the cytosol and plastids. Based upon quantification data acquired during seed development, it is possible to examine the apparent redundancy of glycolytic pathways between the cytosol and plastids. Suc synthase (SuSy) catalyzes the initial release of sugar for glycolysis by converting Suc into UDP-Glc and Fru. A total of three SuSy spots (202, 204, and 4,628) were identified (Fig. 7). The overall abundance of all three detected protein spots is similar although their expression profiles differ. While two SuSy spots (spots 202 and 204) shared almost identical expression profile with maximum abundance at 3 WAF, the abundance of a third form (spot 4,628) reached a maximum at 5 WAF followed by a dramatic decrease thereafter. This suggests the presence of two types of SuSy that are perhaps active during early (type I) and late (type II) phases of seed filling. UDP-Glc pyrophoshorylase (UGP) catalyzes the reversible production of Glc-1-P from UDP-Glc. In Arabidopsis there are two homologous UGP genes located on two different chromosomes (Kleczkowski et al., 2004). We identified their homologs in B. napus, spot 557 (homolog to At5g17310) and spot 4,932 (homolog to At3g03250). Despite 92% amino acid homology sequence, their expression profiles during seed filling are slightly different (Fig. 7). Spot 4,932 was found to be highly expressed at 2 WAF after which its relative abundance declined in a gradual manner reaching a minimum at 6 WAF. Spot 557, which is about 4.4 times less abundant than spot 4,932, peaked in abundance at 3 WAF and decreased thereafter. These expression profiles suggest that the abundant UGP (spot 4,932) may play a typical role in glycolysis, because its profile is the same as the composite expression profiles of glycolytic enzymes (Fig. 7). On the other hand, protein spot 557 may also have a role outside of glycolysis during seed filling. In addition to glycolysis, UGP can be involved in cell wall biogenesis because a product of UGP, UDP-Glc, is used in the biosynthesis of cell wall polysaccharides and serves as a precursor for cell wall biogenesis (Gibeaut, 2000). Phosphoglucomutase (PGM) catalyzes the interconversion of Glc-1-P and Glc-6-P. The Arabidopsis genome contains two cytosolic and one plastidial form of PGM (Caspar et al., 1985). The plastidial form of PGM has been characterized in B. napus (Harrison et al., 2000). We have identified six protein spots as cytosolic PGM matching to three different sequences in the database (Supplemental Table III). Spot 337 matched to cytosolic PGM from Populus tomentosa, three others (4,724, 4,731, and 4,740) to B. napus homologs of cytoplasmic PGM from Arabidopsis, and two (4,705 and 4,741) matching to another cytoplasmic PGM from Arabidopsis. The overall expression trend of each PGM was found to be similar, higher expression at early stages of seed filling and lower at 6 WAF (Fig. 7). However, differences between protein abundances at the early stages were observed. For instance, spots 4,740 and 4,741 were most abundant at 2 WAF and spots 337, 4,705, 4,724, and 4,731 at 3 WAF. In Arabidopsis two isozymes of phosphoglucose isomerase (PGI) exist, one in the plastids and the other in the cytosol (Caspar et al., 1985). We identified two protein spots as cytosolic PGI, that had similar expression trends, but one (4,883) with maximum abundance at 3 WAF and the second (4,875) at 2 and 4 WAF. Pyrophosphate-dependent phosphofructokinase (PFK) catalyzes conversion of F-6-P and F-1,6-bisP, and in plants is regulated by Fru-2,6 bisphosphate (Huber, 1986; Stitt, 1990; Nielsen et al., 2004). We identified two cytosolic isoelectric species of PFK (spots 374 and 4,801; Fig. 7) and a putative organellar form (spot 4,844). Both cytosolic and organellar PFKs are abundant, but the cytosolic spot 374 is about 3 times more abundant than organellar PFK. Cytosolic and organellar PFKs also differ in their developmental expression trends. The two detected cytosolic spots of PFK share similar expression profiles: high abundance at 2 WAF followed by dramatic decrease, and undetectable at 5 and 6 WAF. On the other hand, the organellar PFK is detectable throughout seed filling showing a maximum abundance at 4 WAF. FBA catalyzes the aldol cleavage of Fru 1,6-bisP to glyceraldehyde 3-P (GAP) and dihydroxyacetone phosphate (DHAP). Surprisingly, a relatively large number of cytosolic and plastidial FBA spots were identified (Fig. 7). Most of the nine cytosolic spots and eight plastidial spots shared similar expression profiles, high abundance at early stages followed by rapid decrease between 3 and 4 WAF. However, two differences between cytosolic and plastidial FBA can be noted; the cytosolic forms were generally more abundant and only two cytosolic FBA (spots 5,157 and 5,189) peaked in expression at 4 WAF. The high number of protein spots suggests these activities may be posttranslationally modified. Triose-P isomerase (TPI) catalyzes the interconversion of GAP and DHAP. This reaction is reversible although the equilibrium favors DHAP. Seven cytosolic spots of TPI (spots 1,156, 5,515, 5,520, 5,524, 5,525, 5,528, and 5,530) were identified, but no plastidial TPI spot could be detected (Fig. 7). Interestingly, one protein spot (spot 5,515) showed maximum abundance at 6 WAF (Fig. 7). Glyceraldehyde 3-P dehydrogenase (GAPDH) reversibly catalyzes the conversion of GAP into 1,3-bis PGA. Five cytosolic spots (spots 821, 1,427, 5,184, 5,186, and 5,206) and one plastidial (spot 820) GAPDH were identified. The cytosolic and plastidial GAPDH were almost equally abundant during seed filling and shared very similar expression profiles (Fig. 7). Two spots of cytosolic phosphoglycerate kinase (PGK; spots 5,144 and 5,145) and two plastidial PGK (5,054 and 5,055) were identified. Like TPI, only cytosolic forms of 2,3-bisphosphoglycerate-independent phosphoglycerate mutase (iPGAM) and enolase were identified (Fig. 7). Expression profile of iPGAM (spot 4,747) was present in abundance at 2 WAF. Expression profiles of enolases (spots 4,898 and 4,903) were also high at 2 WAF, but they accumulated strongly until 4 WAF followed by a rapid decrease in abundance. The detection of multiple isoelectric species for cytosolic and plastidial glycolytic enzymes and strong similarities in their expression profiles suggest possible posttranslational modifications as well as coordination between cytosolic and plastidial glycolysis during seed filling. Proteomics Data Suggest That Phosphoenolpyruvate Is a Direct Precursor for de Novo FA Synthesis in Plastids The current model of metabolite flux between cytosol and plastids has established that either phosphoenolpyruvate (PEP) or pyruvate is transported into plastids for further processing into acetyl-CoA (Weber, 2004; Weber et al., 2005). Moreover, a previous microarray analysis of Arabidopsis developing seeds has indicated that plastid uptake of cytosolic PEP is a more likely possibility than the uptake of cytosolic pyruvate during seed development (Ruuska et al., 2002), particularly since a plastid pyruvate translocator has yet to be identified. Furthermore, a flux model for central carbon metabolism of developing B. napus embryos constructed based on stable isotope labeling of sugars has also suggested that the main carbon flux into FAs is through plastid uptake of cytosolic PEP (Schwender et al., 2003; Kubis et al., 2004). In this study, we identified almost all enzymes involved in cytosolic and many for plastid glycolysis. The notable exceptions are cytosolic pyruvate kinase, plastid iPGAM, plastid enolase, and plastid TPI (Fig. 7). One possible explanation is low expression levels, which is supported by the observation that plastidial iPGAM and enolase were previously determined to have low specific activities (Eastmond and Rawsthorne, 2000). Since we did not detect plastid iPGAM and enolase, 3-PGA produced by plastidial glycolysis, and more importantly by Rubisco bypass, would need to be transported into the cytosol by triose phosphate/phosphate translocator, converted to PEP, then transported back into plastids by PEP translocator before conversion to acetyl-CoA for FA synthesis. However, the absence of these plastid glycolytic enzymes in this proteomic study do not preclude the potential for low level expression, due to the limited dynamic range of this analysis. FA Biosynthesis Machinery Expressed Prominently at Midpoint of Seed Filling Conversion of acetyl-CoA into malonyl-CoA is catalyzed by acetyl-CoA carboxylase. This plastid complex is comprised of four subunits, the biotin carboxylase, biotin carboxyl carrier protein, and carboxyltransferase subunits (α and β; Shorrosh et al., 1996). Detection of two spots corresponding to biotin carboxylase (spots 4,940 and 4,952) indicates high expression from 3 to 5 WAF. Previous investigations showed that acetyl-CoA carboxylase is highly expressed during embryo development in B. napus (Elborough et al., 1996; Thelen et al., 2001). The peak of protein accumulation for malonyl-CoA transacylase suggests that the reversible conversion of malonyl-CoA into malonyl-acyl-carrier protein (ACP) is highly active at 3 WAF. Expression profiles of enzymes involved in remaining reactions toward FA synthesis are highly expressed at 4 or 5 WAF. Ketoacyl-ACP synthetase I, the enzyme that catalyzes the condensation of malonyl-ACP into 3-ketoacyl-ACP, reached high abundance at 3 WAF and remained high through 4 WAF. Four detected spots of enoyl-ACP reductase shared similar expression trends. Surprisingly, we detected seven spots corresponding to stearoyl-ACP desaturase. Four proteins (spots 5,166, 5,193, 5,194, and 5,198) are highly abundant and share almost identical expression profiles, with peak of protein abundance at 4 WAF. Two low abundant protein spots (spots 5,169 and 5,180) are expressed differently during seed filling. Spot 5,169 was found to be highly abundant only at 4 WAF, whereas spot 5,180 was constitutively and highly expressed from 2 until 4 WAF. The abundance of stearoyl-ACP desaturase during seed filling suggests low catalytic turnover of this enzyme as well as the importance of 18:1 export from the plastids. In summary, this investigation represents a systematic proteomics study of whole-seed proteins expressed during seed filling in B. napus. Multiple categories of proteins were observed, although protein storage, energy, and metabolism associated proteins were most abundant. The preponderance of metabolic proteins presented a unique opportunity to map activities (and isoelectric species therein) for carbon assimilation. Surprisingly, carbon flow from Suc to acetyl-CoA could be entirely predicted based upon the representation of proteins for each enzymatic step. The expression levels of cytosolic pyruvate kinase, plastid enolase, and most of the enzymes of the Calvin cycle were below the detection limit of this proteomics study, except Rubisco and phosphoribulokinase that were both highly expressed. Thus, carbon flow from Suc appears to primarily follow a cytosolic glycolytic track until PEP, at which point carbon is likely imported into plastids and converted into pyruvate and acetyl-CoA for de novo FA synthesis. MATERIALS AND METHODS Plant Material and Growth Conditions B. napus (cv Reston) was grown in a growth chamber (16-h light/8-h dark cycle, 23°C day/20°C night, 50% humidity and light intensity of 8,000 LUX). Flowers were tagged upon opening and the developing seeds were collected at precisely 2, 3, 4, 5, and 6 WAF, in the middle of a light cycle. The dry weight and total protein content were measured at each developmental stage. Total protein was quantified using the dye-binding Coomassie protein assay using chicken γ-globulin as the standard (Bio-Rad). FA Analysis Developing seeds of B. napus at 2, 3, 4, 5, and 6 WAF were divided to three test glass tubes per stage (5–10 seeds per tube) and dried at 80°C overnight. After dry weight determination, 1 mL of 14% boron trifluoride was added to each tube along with 17:0 FA standard in toluene (0.5% of dry mass exactly). Total volume of toluene was brought to 150 μL and samples were incubated at 95°C for 90 min, with vortexing every 10 min. After incubation, samples were cooled to room temperature. To each tube, 1 mL of water and 3 mL of hexane were added. Tubes were vortexed and centrifuged at 3,000 rpm for 5 min. Top phase was removed and transferred to a new conical glass tube. Samples were reextracted with additional 3 mL of hexane, dried under nitrogen stream, and resuspended in 400 μL of hexane before analysis by GC. Analysis of FA was carried on Agilent Technologies model 689N Network GC system gas chromatograph with a DB-23 column (30 m × 0.25 mm; film thickness 0.25 μm; Agilent 122–2,332). The GC conditions were: injector temperature and flame ionization detector temperature, 250°C; running temperature program, 150°C for 1 min, then increasing at 2°C/min to 200°C followed by a 5 min hold at 200°C. Protein Isolation and 2-DE Total protein was isolated from developing seed and subjected to 2-DE as described previously (Hajduch et al., 2005). Briefly, the protein pellet was resuspended in IEF sample extraction buffer (8 m urea, 2 m thiourea, 2% (w/v) CHAPS, 2% (v/v) Triton X-100, and 50 mm dithiothreitol) with vortex mixing for 30 min at room temperature followed by centrifugation for 15 min at 14,000g to remove insoluble material. Protein quantification was performed in triplicate using the Coomassie dye binding assay (Bio-Rad) against standard curve of chicken γ-globulin. One milligram of total protein was mixed with 2.25 μL of appropriate IPG buffer (Amersham Biosciences) in a total volume of 450 μL and subjected to IEF followed by SDS-PAGE, as described previously (Hajduch et al., 2005). Image Acquisition, Analysis, and in Silico SSP Subtraction Coomassie G-250 (colloidal) stained gels were imaged by scanning densitometry. Digitized 2-DE images (300 dpi, 16-bit grayscale pixel depth) of five developmental stages in biological quadruplicate were analyzed using ImageMaster 2-D Platinum software (version 5.0, GE Healthcare) as described previously (Hajduch et al., 2005). A two-step data normalization method was employed to obtain an integrated dataset for the entire investigation. First, protein abundance was expressed as relative volume according to the normalization method provided by ImageMaster software that compensates for slight variations in sample loading, gel staining, and destaining. Second, relative spot volumes were adjusted using correction constants, as described previously (Hajduch et al., 2005), to allow direct data comparison between the two gel datasets: pH 4 to 7 and pH 3 to 10 sets. To enable direct spot-to-spot comparison of significance levels of acquired protein spot quantifications, coefficient of variation (CV) for each protein spot was calculated using following formula: \[\mathrm{CV}{=}\left(\frac{\sqrt{\frac{{\sum_{\mathrm{i}{=}1}^{\mathrm{n}}}(x_{\mathrm{i}}{-}{\bar{x}})}{n{-}1}}}{{\bar{x}}}\right)\mathrm{{\times}100},\] where \({\bar{x}}\) is the average of relative volumes (x) of spots in biological quadruplicate analysis and n is the sample size (four in case of biological quadruplicate). To subtract SSP in silico, spot volumes were calculated for each of 794 protein spots using ImageMaster 2-D Platinum software. In total, 71 identified SSP were removed from the dataset and two-step normalization approach was applied as described above. Hierarchical Cluster Analysis of Expression Profiles For cluster analysis of expression profiles, hierarchical clustering was performed using SAS statistical software (SAS Institute). The procedure contained two steps. First, the program established the number of classes that is best for a present dataset. The CLUSTER keyword was used with options STANDARD METHOD=AVERAGE CCC PSEUDO as the command for step 1, in which STANDARD means to normalize the variables; AVERAGE means a certain clustering method in contrast to the other 10 methods that are included in SAS IDE; CCC and PSEUDO are both options for calculating some statistical variables that are used to determine the class number. Second, the program clustered expression profiles into each of the established classes. Expression profile data were normalized in two steps. In the first step, any zero between two nonzero points was replaced with the average of two neighbor values. In the second step, a linear transformation was used to normalize expression profiles of different spots to uniform scale. The SAS program used the procedure FASTCLUS for the real clustering and the maximum number of clusters established earlier. For each spot, the variable distance parameter was generated by SAS. Protein Identification by MS Arraying of 2-DE gel spot, in-gel digestion, and C18 microbed chromatograph were each performed as described previously (Hajduch et al., 2005). Mass spectral analysis of trypsin-digested protein samples were carried out on a Voyager-DE Pro MALDI-TOF mass spectrometer (Applied Biosystems) and on a linear ion trap tandem mass spectrometer (ProteomeX LTQ, Thermo-Finnigan) using LC and nano-spray ionization. The MALDI-TOF instrument was operated as described previously (Hajduch et al., 2005). The LC-MS/MS was operated according to manufacturer's instructions for high-throughput protein identification. Briefly, on-line capillary LC included two polymeric sample traps (2 μg capacity each) and a fast-equilibrating C18 capillary column (Micro-Tech Scientific; 150 μm i.d. × 10 cm). The method alternated between loading/equilibration and elution using the two peptide traps (one trap is being equilibrated while the other is being eluted) to reduce time required for each on-line LC-MS/MS. For analysis, 10 μL of sample in 0.1% (v/v) formic acid was loaded. For sample elution, a 15 min gradient with 40% of solution A (0.1% formic acid in water) and 60% of solution B (0.1% formic acid in acetonitrile) was followed by a 5 min gradient with 20% solution A and 80% solution B. The column was reset for 2 min and reequilibrated for 10 min with 100% of solution A before sample previously absorbed onto the second trap was eluted. Eluted tryptic peptides were directly analyzed by LC-MS/MS using 75 μm i.d., 360 μm, o.d. 15 μm tip needles (New Objective) with a 1.7 kV nano-spray voltage. Manufacturer's recommended scan method for high-throughput protein identification consisted of double-play analysis mode, a full MS scan (400–1,600 mass-to-charge ratio), followed by data-dependent triggered MS/MS scan for the most intense ion. Database Searching with Spectral Data and Uploading to the Oilseed Proteome Database Searches against the NCBI (ftp://ftp.ncbi.nih.gov/blast/) nonredundant database (as of March, 2005) and TIGR tentative consensus database for B. napus (http://www.tigr.org/tigr-scripts/tgi/T_index.cgi?species=oilseed_rape) were independently performed using a two-step approach to mine maximum information from MALDI-TOF MS and LC-MS/MS. PMF-based protein identification was performed on local copy of version 3.2.1 of the MS-Fit program of Protein Prospector (http://prospector.ucsf.edu; Clauser et al., 1999) as previously described (Hajduch et al., 2005). Analysis of LC-MS/MS data was performed on a local license copy of SEQUEST software (Eng et al., 1994; Yates et al., 1995) as part of the BioWorks 3.1SR1 software suite. Search parameters were set as follows: enzyme, trypsin; number of internal cleavage sites, 2; mass range, 400 to 1,600; threshold, 500; minimum ion count, 35; peptide mass tolerance, 1.50; variable modifications, oxidation (M); static modification, carboxyamidomethylation (C). Matching peptides were filtered according to correlation scores (XCorr at least 1.5, 2.0, and 2.5 for +1, +2, and +3 charged peptides, respectively). For all protein assignments, a minimum of two unique peptides was required. Proteins with three or more unique peptides matching to protein sequence were automatically considered as a positive identification. In a situation where assignments with two unique peptides matched to the sequence, the difference between theoretical and experimental MW/pI should not exceed ±25% variance to be considered a match. This approach resulted in four independent sets of protein identification data: (1) MALDI-TOF, TIGR search; (2) MALDI-TOF, NCBI search; (3) LC-MS/MS, TIGR search; and (4) LC-MS/MS, NCBI search. To reach consensus in protein assignments, a two-step data reduction strategy was employed. The first step combined the search results from TIGR and NCBI databases for each MS method. If two different protein assignments for one protein spot were noted, the one with the highest number of matching peptides was taken. If number of matching peptides was the same, the assignment with the highest coverage was taken. The second step combined the integrated protein assignments assigned by MALDI-TOF and MS/MS (from step 1). If two different protein identifications were assigned, preference was given to MS/MS-based protein assignment. Assignments annotated as unknown and without specific homology/similarity descriptions in identifier tag were BLASTP searched against the NCBI nonredundant database (as of March, 2005) to further query their homology. This study uses terminology as follows: homology for results, where E-value of BLAST search was 0.0; in all other cases similarity is used. Subcellular localizations of assigned proteins were predicted using three independent programs: TargetP (http://www.cbs.dtu.dk/services/TargetP/; Emanuelsson et al., 2000), iPSORT (http://hc.ims.u-tokyo.ac.jp/iPSORT/; Bannai et al., 2002), and Predotar v. 1.03 (http://genoplante-info.infobiogen.fr/predotar/predotar.html; Small et al., 2004). Information about subcellular localization was incorporated into protein description if at least two programs predicted the same subcellular destination. All data from this investigation are available from the oilseed proteomics server (http://oilseedproteomics.missouri.edu). Programming for the web database was performed, as described previously (Hajduch et al., 2005). Data are viewable through 2-DE gels and a protein identification table. The spots on 2-DE gel and protein numbers in the protein table are hyperlinked to display expression profile and protein identification data. Expression profiles of all proteins except SSP were generated using relative abundance data with in silico SSP subtraction (Supplemental Table II). However, expression profiles of individual SSPs may represent valuable data due to the number of different isoelectric species identified in this study. For this purpose, expression profiles of 71 identified SSPs were generated using the original dataset (Supplemental Table I). Thus, online database represents compromise, where two independent datasets of expression profiles can be viewed together. 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Science 296 : 79 –92 Author notes 1 This work was supported by the National Science Foundation-Plant Genome Research Program Young Investigator Award (grant no. DBI–0332418). 2 Present address: Institute of Plant Genetics and Biotechnology, Slovak Academy of Sciences, 95007 Nitra, Slovak Republic. * Corresponding author; e-mail [email protected]; fax 573–884–9676. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Jay J. Thelen ([email protected]). [W] The online version of this article contains Web-only data. Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.105.075390. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Ma, Changle; Haslbeck, Martin; Babujee, Lavanya; Jahn, Olaf; Reumann, Sigrun

doi: 10.1104/pp.105.073841pmid: 16531488

Abstract Small heat-shock proteins (sHsps) are widespread molecular chaperones for which a peroxisomal localization has not yet been reported. The Arabidopsis (Arabidopsis thaliana) genome encodes two sHsps with putative peroxisomal targeting signals type 1 or 2 (PTS1 or PTS2). As demonstrated by double-labeling experiments using full-length fusion proteins with enhanced yellow fluorescent protein and deletion constructs lacking the putative targeting domains, AtHsp15.7 (At5g37670) and AtAcd31.2 (At1g06460) are targeted to the peroxisome matrix by a functional PTS1 (SKL>) and a functional PTS2 (RLx5HF), respectively. The peroxisomal localization of AtAcd31.2 was further confirmed by isolation of leaf peroxisomes from Arabidopsis by two successive sucrose density gradients, protein separation by one- and two-dimensional gel electrophoresis, and mass spectrometric protein identification. When AtHsp15.7 and AtAcd31.2 were heterologously expressed in yeast (Saccharomyces cerevisiae) and directed to the cytosol by deletion of the PTSs, both sHsps were able to complement the morphological phenotype of yeast mutants deficient in the cytosolic homologs ScHsp42 or ScHsp26. According to expression studies by reverse transcription-PCR, AtAcd31.2 is constitutively expressed, whereas AtHsp15.7 is hardly expressed under normal conditions but strongly induced by heat and oxidative stress, the latter of which was triggered by the catalase inhibitor 3-aminotriazole or the herbicide methyl viologen applied by watering of whole plants or infiltration of rosette leaves. Thus, plants are exceptional among eukaryotes in employing sHsps in the peroxisome matrix to prevent unspecific aggregation of partially denatured proteins under both physiological and stress conditions. In eukaryotes, many hydrogen peroxide (H2O2)-generating oxidases have been compartmentalized in peroxisomes, where the toxic by-product H2O2 can immediately be degraded by catalase (CAT) at the site of production. Further antioxidative enzymes of plant peroxisomes, such as superoxide dismutase, membrane-bound ascorbate peroxidase, and monodehydroascorbate reductase, play auxiliary roles in the detoxification of reactive oxygen species (ROS; Lisenbee et al., 2005; for review, see del Rio et al., 2002). Depending on their tissue specificity and specialization on further metabolic pathways, several variants of microbodies occur in higher plants, including the two main types, leaf peroxisomes of mesophyll cells involved in the recycling of P-glycolate formed during photosynthesis (Reumann, 2002), and glyoxysomes of triacylglyceride-storing tissues like endosperm and cotyledons, which mediate fatty acid β-oxidation during seed germination (Beevers, 1979). Apart from the enzymes of these well-known metabolic pathways, few peroxisomal matrix proteins have been characterized to date at the molecular level, mainly because biochemical methods are generally not suitable for the identification of low-abundance proteins and polypeptides encoded by inducible genes. Recently, however, some evidence has emerged for the existence of nonenzymatic proteins like molecular chaperones in peroxisomes (Wimmer et al., 1997; Diefenbach and Kindl, 2000). Heat-shock proteins (Hsps) are expressed in response to increased temperature and other forms of abiotic stress (Young et al., 2003; Wang et al., 2004) and facilitate as chaperones the folding of newly synthesized or the refolding of partially denatured polypeptides. Two homologs of different Hsp classes have been associated with plant peroxisomes. An Hsp70 homolog from Citrullus vulgaris was shown to be targeted to both chloroplasts and peroxisomes (Wimmer et al., 1997), and a DnaJ (Hsp40) homolog from Cucumis sativus was found to be attached to the glyoxysomal membrane (Diefenbach and Kindl, 2000). Small Hsps (sHsps) are widespread and powerful molecular chaperones that prevent the aggregation of nascent and stress-accumulated misfolded proteins (Narberhaus, 2002; Haslbeck et al., 2005). These chaperones are characterized by a small polypeptide chain (16–42 kD) and contain a conserved C-terminal α-crystallin domain of about 90 amino acid residues, which is homologous to α-crystallin proteins of the vertebrate eye lens (DeJong et al., 1998). The subunits assemble into oligomeric structures with varying degrees of order and a substantial divergence in size (4–50 subunits; Haslbeck et al., 1999, 2004; van Montfort et al., 2001; Sun et al., 2002). As exemplified by Arabidopsis (Arabidopsis thaliana), plants house an exceptionally large family of 19 closely related sHsp homologs plus 25 more distantly related proteins containing the same α-crystallin domain (Vierling, 1991; Scharf et al., 2001). Homologs of sHsps have been localized to several subcellular compartments, including the cytosol, plastids, mitochondria, and the endoplasmic reticulum (Banzet et al., 1998; Härndahl et al., 1999; Scharf et al., 2001); however, experimental evidence for targeting of sHsp paralogs to peroxisomes has not been provided for any organism previously, to our knowledge. Peroxisomal matrix proteins are nuclear-encoded, synthesized on free cytoplasmic ribosomes, and directed to their destination by peroxisome targeting signals (PTS) in a conserved protein targeting pathway. Most of the known peroxisomal matrix proteins contain a PTS1, the C-terminal so-called SKL motif, or a PTS2, which is an N-terminal cleavable nonapeptide of the prototype RLx5HL (for review, see Johnson and Olsen, 2001). Both targeting motifs have been specified for plant peroxisomes by experimental and bioinformatics-based strategies (Reumann, 2004 and refs. therein). To identify novel low-abundance proteins of plant peroxisomes, we screened the Arabidopsis genome for genes encoding proteins with putative PTSs (Arabidopsis Genome Initiative, 2000; Reumann et al., 2004) and identified two putative sHsps, namely AtHsp15.7 and AtAcd31.2 (At5g37670 and At1g06460, respectively; Scharf et al., 2001). In the course of this study, the corresponding cDNAs were cloned, and subcellular protein targeting, gene expression, and functional complementation of yeast (Saccharomyces cerevisiae) sHsp knockout mutants were analyzed to gain first insights into the function of the putative chaperones in plant peroxisomes. RESULTS Subcellular Localization Studies of Putative Peroxisomal sHsps The Arabidopsis genome encodes two predicted proteins possessing both an α-crystallin domain (Hsp20, Pfam00011) and putative targeting signals for plant peroxisomes (Reumann et al., 2004). The smaller homolog, referred to as AtHsp15.7 throughout this study (At5g37670, previously AtHsp15.7-CI for cytosolic class I; Scharf et al., 2001), carries the putative major PTS1 SKL> with high peroxisome targeting probability and a predicted mitochondrial presequence (Reumann, 2004; Fig. 1A Figure 1. Open in new tabDownload slide Domain structure and predicted targeting signals of AtHsp15.7 and AtAcd31.2. A, For both putative sHsps from plant peroxisomes, the presence of a conserved Hsp20/α-crystallin domain (pfam00011) and several subcellular targeting signals were predicted. B, For representative members of the Arabidopsis family of sHsps and Acd proteins a multiple sequence alignment of the most conserved regions of the α-crystallin domain, consensus I and II, and the E values of the Hsp20 domain are presented (HMMER, hmmer.wustl.edu). The Hsp20/α-crystallin domain is located in the C-terminal end of AtHsp15.7 (previously AtHsp15.7-CI, residues 18–134, score 105.0, E value = 3.2 × 10−24) and AtAcd31.2 (residues 182–285, score 26.0, E = 5.8 × 10−6). Notably, the E value of the Hsp20 domain of some sHsps like AtHsp18.5-CI and AtHsp14.7-P is higher, reflecting lower sequence conservation, than that of some Acd proteins including AtAcd31.2 (see also Supplemental Table II). For AtHsp15.7, a mitochondrial presequence (TargetP, 0.82; DBSubLoc, 91%; Predotar, 0.565; Mitoprot, 0.569) and a putative PTS1 are predicted. AtAcd31.2 contains both a putative PTS2 and PTS1 (Reumann et al., 2004; AraPerox, www.araperox.uni-goettingen.de). The PTS1 and the PTS2 of AtHsp15.7 and AtAcd31.2, respectively, were experimentally shown to be functional, but not the mitochondrial presequence of AtHsp15.7 nor the predicted PTS1 of AtAcd31.2 (see Fig. 2). Light and dark gray shading indicate the localization of the α-crystallin domain and the putative mitochondrial presequence, respectively. Figure 1. Open in new tabDownload slide Domain structure and predicted targeting signals of AtHsp15.7 and AtAcd31.2. A, For both putative sHsps from plant peroxisomes, the presence of a conserved Hsp20/α-crystallin domain (pfam00011) and several subcellular targeting signals were predicted. B, For representative members of the Arabidopsis family of sHsps and Acd proteins a multiple sequence alignment of the most conserved regions of the α-crystallin domain, consensus I and II, and the E values of the Hsp20 domain are presented (HMMER, hmmer.wustl.edu). The Hsp20/α-crystallin domain is located in the C-terminal end of AtHsp15.7 (previously AtHsp15.7-CI, residues 18–134, score 105.0, E value = 3.2 × 10−24) and AtAcd31.2 (residues 182–285, score 26.0, E = 5.8 × 10−6). Notably, the E value of the Hsp20 domain of some sHsps like AtHsp18.5-CI and AtHsp14.7-P is higher, reflecting lower sequence conservation, than that of some Acd proteins including AtAcd31.2 (see also Supplemental Table II). For AtHsp15.7, a mitochondrial presequence (TargetP, 0.82; DBSubLoc, 91%; Predotar, 0.565; Mitoprot, 0.569) and a putative PTS1 are predicted. AtAcd31.2 contains both a putative PTS2 and PTS1 (Reumann et al., 2004; AraPerox, www.araperox.uni-goettingen.de). The PTS1 and the PTS2 of AtHsp15.7 and AtAcd31.2, respectively, were experimentally shown to be functional, but not the mitochondrial presequence of AtHsp15.7 nor the predicted PTS1 of AtAcd31.2 (see Fig. 2). Light and dark gray shading indicate the localization of the α-crystallin domain and the putative mitochondrial presequence, respectively. ). The second sHsp homolog AtAcd31.2 (At1g06460) contains a longer N-terminal extension and an α-crystallin domain of lower E value (E value = 5.8 × 10−6) compared to AtHsp15.7 (E = 3.2 × 10−24), indicating a higher sequence divergence of AtAcd31.2 from the defined Hsp20 motif (HMMER, hmmer.wustl.edu; Fig. 1, A and B). Phylogenetic analysis of sHsp homologs from diverse eukaryotes shows that several subclades of Arabidopsis sHsps and proteins containing one or several α-crystallin domains (Acd proteins) diverged early in evolution (Scharf et al., 2001; Supplemental Fig. 1). The homolog AtAcd31.2 carries both a predicted PTS2 nonapeptide (RLx5HF) and a predicted PTS1 tripeptide (PKL>; Fig. 1A), both of which have been defined as minor PTS peptides and presumably indicate peroxisome targeting with moderate probability (Reumann, 2004). A search of expressed sequence tag (EST) databases for plant homologs that share high sequence similarity with these proteins demonstrated that the putative PTS1 of AtHsp15.7 and the putative PTS2 of AtAcd31.2 in particular are largely conserved, suggesting that these proteins represent orthologs and are generally targeted to peroxisomes in higher plants (Supplemental Fig. 2). The cDNAs of AtHsp15.7 and AtAcd31.2 were cloned by reverse transcription (RT)-PCR from flowers and cold-stressed rosette leaves, respectively, and fused in frame to either of both ends of enhanced yellow fluorescent protein (EYFP) in a plant expression vector under the control of a 2-fold 35S promoter of the Cauliflower mosaic virus (CaMV; Fulda et al., 2002). Onion (Allium cepa) epidermal cells were transformed biolistically, and subcellular protein targeting of the fusion proteins was analyzed upon transient gene expression by fluorescence microscopy. The fusion protein EYFP:AtHsp15.7 with accessible C-terminal tripeptide SKL> was targeted to small punctate structures that moved quickly along cytoplasmic strands in living cells in single transformants (Fig. 2A). Figure 2. Open in new tabDownload slide Subcellular targeting analysis of AtHsp15.7 and AtAcd31.2 in onion epidermal cells. The cDNAs of AtHsp15.7 and AtAcd31.2 were fused at the N- or the C-terminal end to EYFP under the control of a double 35S CaMV promoter, and subcellular protein targeting was analyzed in onion epidermal cells by fluorescence microscopy. In single transformants, the fusion protein EYFP:AtHsp15.7 was targeted to punctate cell structures (A) that coincided with peroxisomes, as shown by double labeling with the peroxisomal ECFP fusion protein CsgMDH:ECFP in double transformants using appropriate filter sets (B and C). The deletion construct EYFP:AtHsp15.7ΔPTS1 (D) and the inverse fusion protein AtHsp15.7:EYFP with accessible predicted mitochondrial presequence (E) remained in the cytosol. The fusion protein AtAcd31.2:EYFP with accessible PTS2 (RLx5HF) was targeted to peroxisomes (F–H), whereas site-directed mutagenesis of the PTS2 from RLx5HF to RLx5DF abolished peroxisome targeting (I). The inverse fusion protein EYFP:AtAcd31.2 was targeted to peroxisomes as well (J and K). In this case, however, deletion (EYFP:AtAcd31.2ΔPTS1; L and M) or mutagenesis of the C-terminal tripeptide (PKL→PEL; N and O) did not abolish peroxisome targeting. For imaging, either EYFP- (A, B, D–G, I, J, L, and N) or ECFP-specific filters were used (C, H, K, M, and O). The bar represents 20 μm. Figure 2. Open in new tabDownload slide Subcellular targeting analysis of AtHsp15.7 and AtAcd31.2 in onion epidermal cells. The cDNAs of AtHsp15.7 and AtAcd31.2 were fused at the N- or the C-terminal end to EYFP under the control of a double 35S CaMV promoter, and subcellular protein targeting was analyzed in onion epidermal cells by fluorescence microscopy. In single transformants, the fusion protein EYFP:AtHsp15.7 was targeted to punctate cell structures (A) that coincided with peroxisomes, as shown by double labeling with the peroxisomal ECFP fusion protein CsgMDH:ECFP in double transformants using appropriate filter sets (B and C). The deletion construct EYFP:AtHsp15.7ΔPTS1 (D) and the inverse fusion protein AtHsp15.7:EYFP with accessible predicted mitochondrial presequence (E) remained in the cytosol. The fusion protein AtAcd31.2:EYFP with accessible PTS2 (RLx5HF) was targeted to peroxisomes (F–H), whereas site-directed mutagenesis of the PTS2 from RLx5HF to RLx5DF abolished peroxisome targeting (I). The inverse fusion protein EYFP:AtAcd31.2 was targeted to peroxisomes as well (J and K). In this case, however, deletion (EYFP:AtAcd31.2ΔPTS1; L and M) or mutagenesis of the C-terminal tripeptide (PKL→PEL; N and O) did not abolish peroxisome targeting. For imaging, either EYFP- (A, B, D–G, I, J, L, and N) or ECFP-specific filters were used (C, H, K, M, and O). The bar represents 20 μm. These punctate structures coincided with peroxisomes labeled with a control fusion protein between the PTS2 domain of glyoxysomal malate dehydrogenase from C. sativus (CsgMDH) and enhanced cyan fluorescent protein (CsgMDH:ECFP; Fulda et al., 2002), as shown for double transformants expressing simultaneously EYFP:AtHsp15.7 and CsgMDH:ECFP (Fig. 2, B and C). By contrast, mitochondria labeled with a fusion protein comprising the presequence of mitochondrial cytochrome c oxidase from yeast in frame to the N-terminal end of ECFP (ScCOX:ECFP; Fulda et al., 2002) differed from the organelles labeled with EYFP:AtHsp15.7 (data not shown). Upon deletion of the putative C-terminal targeting domain from AtHsp15.7, the shortened fusion protein EYFP:AtHsp15.7ΔPTS1 remained in the cytosol (Fig. 2D; Supplemental Table I), demonstrating that the C-terminal domain is necessary for targeting of AtHsp15.7 to peroxisomes and that SKL> is the PTS1 of the protein. The inversely arranged fusion protein AtHsp15.7:EYFP with accessible N-terminal end was detected in the cytosol, strongly suggesting that AtHsp15.7 lacks a functional mitochondrial presequence (Fig. 2E). The C-terminal fusion protein of AtAcd31.2, referred to as AtAcd31.2:EYFP, with accessible N-terminal PTS2 was also targeted to peroxisomes, as shown by double labeling (Fig. 2, F–H). The deletion construct AtAcd31.2ΔPTS2:EYFP lacking the 29 most N-terminal residues including the putative PTS2 (RLx5HF, position 11–19) was no longer targeted to peroxisomes. In line with a moderately confident prediction of a transit peptide in the N-terminal end of this deletion construct (e.g. TargetP: 0.70), however, this fusion protein entered plastids, as indicated by the larger size of the organelles and their characteristic stromuli extensions (data not shown). Conclusive evidence that the N-terminal domain of AtAcd31.2 possesses a functional PTS2 was obtained by site-directed mutagenesis of the putative PTS2 nonapeptide. When the strictly conserved His residue at position 8 of the putative PTS2 (Kato et al., 1998; Reumann, 2004) was mutated to Asp (RLx5HF→RLx5DF), the full-length fusion protein remained cytosolic (Fig. 2I). To investigate if AtAcd31.2 contains in addition to the PTS2 a functional PTS1, EYFP was fused to its N-terminal end allowing the C-terminal tripeptide PKL> of AtAcd31.2 to be recognized by the cytosolic PTS1 receptor, Pex5p. The full-length fusion protein EYFP:AtAcd31.2 was likewise targeted to peroxisomes labeled with ECFP (Fig. 2, J and K). However, deletion of the C-terminal tripeptide (EYFP:AtAcd31.2ΔPTS1) did not abolish peroxisome targeting (Fig. 2, L and M). Likewise, a mutated construct, in which the essential basic residue of the putative PTS1 was exchanged to Glu (PKL→PEL), remained peroxisomal (Fig. 2, N and O). In summary, these results demonstrated that AtHsp15.7 and AtAcd31.2 are both peroxisomal proteins that are targeted to the matrix by a functional PTS1 and PTS2, respectively, and that the putative PTS1 of AtAcd31.2 is not required for peroxisome targeting. To provide a second independent line of evidence for targeting of these novel sHsps to plant peroxisomes, a method was established to isolate leaf peroxisomes from Arabidopsis. In a first Suc density gradient a significant quantity of intact leaf peroxisomes was efficiently separated from chloroplasts, thylakoids, and mitochondria and enriched near the bottom of the gradient, as indicated by the activities of appropriate organelle marker enzymes (Fig. 3A Figure 3. Open in new tabDownload slide Purification of leaf peroxisomes from Arabidopsis. Leaf peroxisomes from Arabidopsis were enriched by differential centrifugation and purified by a first (A), followed by a second Suc density gradient (B). The concentrations of protein and chlorophyll and the activities of appropriate marker enzymes for different cell compartments were analyzed to determine the purity of leaf peroxisomes (leaf peroxisomes, hydroxypyruvate reductase, HPR; chloroplast stroma, NADP-dep. glyceraldehyde dehydrogenase, NADP-GAPDH; mitochondria, fumarase). Note that the scale between the first and the second gradient is identical for HPR but reduced by a factor of five for NADP-dep. GAPDH, fumarase, and chlorophyll. Due to the high BSA concentration, the protein concentration was not determined in the first gradient. Figure 3. Open in new tabDownload slide Purification of leaf peroxisomes from Arabidopsis. Leaf peroxisomes from Arabidopsis were enriched by differential centrifugation and purified by a first (A), followed by a second Suc density gradient (B). The concentrations of protein and chlorophyll and the activities of appropriate marker enzymes for different cell compartments were analyzed to determine the purity of leaf peroxisomes (leaf peroxisomes, hydroxypyruvate reductase, HPR; chloroplast stroma, NADP-dep. glyceraldehyde dehydrogenase, NADP-GAPDH; mitochondria, fumarase). Note that the scale between the first and the second gradient is identical for HPR but reduced by a factor of five for NADP-dep. GAPDH, fumarase, and chlorophyll. Due to the high BSA concentration, the protein concentration was not determined in the first gradient. ). A second Suc density gradient was added to achieve flotation of residual contaminating plastids and mitochondria to the upper fractions and concentration of leaf peroxisomes near the bottom of the gradient (Fig. 3B). Leaf peroxisomal proteins isolated from standard Arabidopsis plants were separated by one- or two-dimensional gel electrophoresis. Candidate protein bands and spots in the lower Mr and basic isoelectric point (IEP) range corresponding to the predicted size and IEP of AtAcd31.2 (AtAcd31.2: MW = 31.2, IEP = 9.82) were in-gel digested by trypsin and subsequently identified by mass spectrometry. In line with the constitutive expression of AtAcd31.2 (see below), a protein band of an apparent molecular mass of about 30 kD, as well as a distinct protein spot of the same approximate molecular mass and an IEP of about 9.5, was identified as AtAcd31.2 from at least two independent one- and two-dimensional gels, respectively (see Fig. 4, A and B, for representative gel images). Figure 4. Open in new tabDownload slide Identification of AtAcd31.2 in isolated Arabidopsis leaf peroxisomes. Leaf peroxisomes were isolated from Arabidopsis by two successive Suc density gradients and the proteins separated by SDS-PAGE (A) or by isoelectric focusing (nonlinear IPG strip, pH 3–10) followed by denaturing SDS-PAGE (B). In one-dimensional electrophoresis (A), the protein profile of leaf peroxisomes enriched by one Suc density gradient (F1) was compared to that of the upper (F2, about fractions 12 and 13, Fig. 4) and lower fractions (F3, about fractions 14 and 15, Fig. 4) of the second density gradient of lower and higher specific HPR activity, respectively, loading 200 μg protein per lane. For two-dimensional electrophoresis, about 50 μg protein of leaf peroxisomes enriched by two Suc density gradients (F3) were analyzed. Candidate protein bands and spots in the lower Mr and basic IEP range corresponding to the predicted size and pI (IEP) of AtAcd31.2 (MW = 31.2, IEP = 9.82) and several leaf peroxisomal control proteins (GOX, glycolate oxidase, At3g14420; pMDH, peroxisomal MDH, At5g09660; pAPX3, peroxisomal ascorbate peroxidase, At4g35000) were identified by mass spectrometry. One protein band (A) and a single protein spot (B) were identified as AtAcd31.2 (At1g06460, gi 15221505). Figure 4. Open in new tabDownload slide Identification of AtAcd31.2 in isolated Arabidopsis leaf peroxisomes. Leaf peroxisomes were isolated from Arabidopsis by two successive Suc density gradients and the proteins separated by SDS-PAGE (A) or by isoelectric focusing (nonlinear IPG strip, pH 3–10) followed by denaturing SDS-PAGE (B). In one-dimensional electrophoresis (A), the protein profile of leaf peroxisomes enriched by one Suc density gradient (F1) was compared to that of the upper (F2, about fractions 12 and 13, Fig. 4) and lower fractions (F3, about fractions 14 and 15, Fig. 4) of the second density gradient of lower and higher specific HPR activity, respectively, loading 200 μg protein per lane. For two-dimensional electrophoresis, about 50 μg protein of leaf peroxisomes enriched by two Suc density gradients (F3) were analyzed. Candidate protein bands and spots in the lower Mr and basic IEP range corresponding to the predicted size and pI (IEP) of AtAcd31.2 (MW = 31.2, IEP = 9.82) and several leaf peroxisomal control proteins (GOX, glycolate oxidase, At3g14420; pMDH, peroxisomal MDH, At5g09660; pAPX3, peroxisomal ascorbate peroxidase, At4g35000) were identified by mass spectrometry. One protein band (A) and a single protein spot (B) were identified as AtAcd31.2 (At1g06460, gi 15221505). On the basis of the peptide mass fingerprinting data, the protein was identified as At1g06460 (gi 15221505) with sequence coverages greater than 40%. The protein identification was further confirmed by tandem mass spectrometry (MS/MS) and sequence analysis of at least two tryptic peptides that had been assigned to AtAcd31.2 in the fingerprint analysis. In an analogous manner, leaf peroxisomal control proteins (Fig. 4, A and B) were identified by a combination of peptide mass fingerprint and MS/MS data. Yeast Complementation Studies Activity analyses of putative sHsps generally rely on in vitro refolding assays using recombinant sHsps and denatured citrate synthase as model substrate (Lee, 1995; Buchner et al., 1998). Haslbeck et al. (2004) recently reported a morphological phenotype of heat-stressed yeast knockout mutants that are deficient in cytosolic sHsps and that now allow functional complementation studies of heterologously expressed sHsp genes from other organisms. Yeast expresses two sHsps, referred to as ScHsp26 and ScHsp42 (Supplemental Fig. 1), both of which are localized in the cytosol and show a broad and unspecific substrate specificity (Petko and Lindquist, 1986; Wotton et al., 1996; Haslbeck et al., 1999, 2004). At late logarithmic phase and when subjected to a heat shock, the morphology of the deletion strains changes dramatically compared to the wild type, resembling wrinkled cells undergoing dehydration or aging, as observed by scanning electron microscopy (SEM). Based on further lines of evidence including increased protein aggregation in the deletion strains (see Haslbeck et al., 2004), the altered cell morphology is thought to reflect a general disturbance in proteome homeostasis in the sHsp-deficient mutants. To investigate whether yeast and plant peroxisomal sHsps have a conserved chaperone function and can mutually complement a functional defect, the cDNAs of AtHsp15.7 and AtAcd31.2 were expressed by deletion of the PTSs as presumably cytosolic proteins from a Gal-inducible promoter in single or double deletion mutants, and the change in cell morphology was analyzed by SEM. Except for AtAcd31.2ΔPTS1 expressed in Δhsp26, both plant peroxisomal sHsps were able to complement the morphological defects of the single mutants deficient in ΔScHsp42 or ΔScHsp26 and the double mutant lacking both sHsps (Fig. 5 Figure 5. Open in new tabDownload slide Complementation studies of single and double deletion strains of yeast deficient in ScHsp42 and ScHsp26 with plant peroxisomal sHsps. The yeast deletion strains Δhsp42, Δhsp26, and Δhsp42/26 were transformed with AtHsp15.7ΔPTS1, AtAcd31.2ΔPTS1, or AtAcd31.2ΔPTS1+PTS2 under the control of a GAL1 promoter, and yeast cells were imaged by SEM. After induction (Gal) for the expression of the Arabidopsis sHsps, the yeast cells were heat shocked for 1 h at 43°C prior to SEM analysis. As a negative control, yeast cells of equally treated deletion strains were transformed with the empty vector pYES2 and exhibited the wrinkly phenotype typical of the mutant. Yeast deletion strains and complementing Arabidopsis sHsps are indicated. The bar represents 10 μm. Figure 5. Open in new tabDownload slide Complementation studies of single and double deletion strains of yeast deficient in ScHsp42 and ScHsp26 with plant peroxisomal sHsps. The yeast deletion strains Δhsp42, Δhsp26, and Δhsp42/26 were transformed with AtHsp15.7ΔPTS1, AtAcd31.2ΔPTS1, or AtAcd31.2ΔPTS1+PTS2 under the control of a GAL1 promoter, and yeast cells were imaged by SEM. After induction (Gal) for the expression of the Arabidopsis sHsps, the yeast cells were heat shocked for 1 h at 43°C prior to SEM analysis. As a negative control, yeast cells of equally treated deletion strains were transformed with the empty vector pYES2 and exhibited the wrinkly phenotype typical of the mutant. Yeast deletion strains and complementing Arabidopsis sHsps are indicated. The bar represents 10 μm. ). In addition, expression of AtHsp15.7ΔPTS1 and AtAcd31.2 ΔPTS1+2 led to a reduction of unspecific protein aggregation in the complemented yeast deletion strains after heat stress (Fig. 6 Figure 6. Open in new tabDownload slide Reduction of protein aggregation in Δhsp42 and Δhsp42 deletion strains of yeast by plant peroxisomal sHsps. Yeast deletion strains Δhsp42 and Δhsp26 were transformed with the empty vector pYES 2 (lane 1) or the vector containing AtAcd31.2ΔPTS1, AtAcd31.2ΔPTS1+2, or AtHsp15.7ΔPTS1 (lanes 2–4), gene expression induced by galatose, and insoluble protein aggregates in lysates were analyzed by Coomassie-stained SDS-PAGE. Yeast cells were grown and heat stressed as for SEM analysis. An equal number of cells were lysed and insoluble proteins enriched by centrifugation (Haslbeck et al., 2004). Figure 6. Open in new tabDownload slide Reduction of protein aggregation in Δhsp42 and Δhsp42 deletion strains of yeast by plant peroxisomal sHsps. Yeast deletion strains Δhsp42 and Δhsp26 were transformed with the empty vector pYES 2 (lane 1) or the vector containing AtAcd31.2ΔPTS1, AtAcd31.2ΔPTS1+2, or AtHsp15.7ΔPTS1 (lanes 2–4), gene expression induced by galatose, and insoluble protein aggregates in lysates were analyzed by Coomassie-stained SDS-PAGE. Yeast cells were grown and heat stressed as for SEM analysis. An equal number of cells were lysed and insoluble proteins enriched by centrifugation (Haslbeck et al., 2004). ). These results demonstrate that not only AtHsp15.7, but also AtAcd31.2, which notably belongs to a novel family of largely unknown Acd proteins with a weakly conserved α-crystallin domain (Fig. 1B; Supplemental Table II), is capable of suppressing the aggregation of a broad variety of cytosolic substrate proteins under heat stress conditions in yeast. It was concluded that both Arabidopsis proteins play a role as chaperones in the peroxisome matrix similar to yeast sHsps (Haslbeck et al., 2004; Cashikar et al., 2005). Expression Analysis of Peroxisomal sHsps Small Hsps play an important role in plant stress tolerance because they assist in refolding of proteins that have been denatured under abiotic stress conditions such as high temperature, drought, high salt concentration, or elevated light intensity (Lee et al., 1995; Härndahl et al., 1999; Sun et al., 2002). Because sHsps are generally regulated at the transcriptional level (Scharf et al., 2001), isoform-specific expression data may indicate their involvement in stress tolerance. According to publicly available expression data retrieved using GENEVESTIGATOR (www.genevestigator.ethz.ch; Zimmermann et al., 2004), AtHsp15.7 is rather weakly expressed at most stages of plant development and in different organs, showing highest mRNA levels in roots, seeds, and suspension-cultured cells (Supplemental Fig. 3, A and B). In contrast, AtAcd31.2 is overall highly expressed at levels that exceed those of AtHsp15.7 in seedlings, leaves, flowers, and siliques about 5- to 20-fold (Supplemental Fig. 3, A and B), suggesting a constitutive expression of AtAcd31.2 under physiological conditions. To investigate the expression of both peroxisomal sHsps in more detail, the effects of various abiotic stress conditions on sHsp expression in leaves were analyzed by RT-PCR using gene-specific oligonucleotide primers. When soil-grown Arabidopsis plants were transferred from ambient (22°C) to elevated (37°C) or cold temperature (5°C), the expression of AtHsp15.7 was hardly detectable under normal growth conditions or upon cold treatment but was quickly induced after 30 min heat shock (Fig. 7A Figure 7. Open in new tabDownload slide Semiquantitative expression analysis of AtHsp15.7 and AtAcd31.2 by RT-PCR. Expression analysis of AtHsp15.7 and AtAcd31.2 by heat and cold stress (A), standard and high light intensity (B), and oxidative stress applied by plant watering with inhibitor solution (C) or leaf infiltration (D). For oxidative stress analysis, soil-grown plants were either watered with 5 mm 3-AT or 100 μm methyl viologen (paraquat; C) or rosette leaves were infiltrated with 100 μm 3-AT or 10 μm methyl viologen (paraquat; D) and further incubated under standard light intensity (100 μE m−2 s−1). Gene expression was analyzed in leaves by RT-PCR using appropriate oligonucleotide primers and ubiquitin as a control for equal cDNA concentration. All experiments were performed three times and showed similar changes in transcript levels in each case. Figure 7. Open in new tabDownload slide Semiquantitative expression analysis of AtHsp15.7 and AtAcd31.2 by RT-PCR. Expression analysis of AtHsp15.7 and AtAcd31.2 by heat and cold stress (A), standard and high light intensity (B), and oxidative stress applied by plant watering with inhibitor solution (C) or leaf infiltration (D). For oxidative stress analysis, soil-grown plants were either watered with 5 mm 3-AT or 100 μm methyl viologen (paraquat; C) or rosette leaves were infiltrated with 100 μm 3-AT or 10 μm methyl viologen (paraquat; D) and further incubated under standard light intensity (100 μE m−2 s−1). Gene expression was analyzed in leaves by RT-PCR using appropriate oligonucleotide primers and ubiquitin as a control for equal cDNA concentration. All experiments were performed three times and showed similar changes in transcript levels in each case. ). The pronounced induction of AtHsp15.7 expression was not due to stress-induced peroxisome proliferation and overall enhanced gene expression of peroxisomal proteins (Lopez-Huertas et al., 2000), because the expression level of house-keeping genes of leaf peroxisomal enzymes, such as peroxisomal malate dehydrogenase 1 (AtpMDH1, At5g09660; Fig. 7A), was not altered by increased temperature, demonstrating that AtHsp15.7 was specifically induced by heat stress among plant peroxisomal matrix proteins. By contrast, AtAcd31.2 was constitutively expressed under standard growth conditions and not further induced by heat (Fig. 7A). Under elevated light and temperature conditions, the ratio of oxygenation-to-carboxylation increases (Sharkey, 1988), and ROS production is enhanced both in chloroplasts (mainly O2−• and H2O2) and peroxisomes (mainly H2O2), possibly stimulating expression of AtHsp15.7. During the long-day light period at either standard (about 100 μE m−2 s−1) or a maximum light intensity of 450 μE m−2 s−1, at which the temperature could still be controlled at about 23°C, no obvious induction of AtHsp15.7 was observed (Fig. 7B), suggesting that elevated ROS levels were efficiently reduced by CAT and the auxiliary antioxidative enzymes. To investigate whether a stronger ROS production affected the expression of peroxisomal sHsps, the CAT inhibitor 3-amino-1,2,4-triazole (3-AT) and the herbicide methyl viologen, which inhibits the D1 protein of PSII and leads to the production of O2−• in chloroplasts, were applied to soil-grown plants by watering to induce ROS production within the peroxisomal matrix or in chloroplasts, respectively. After 12 h of 3-AT application, AtHsp15.7 was strongly induced (Fig. 7C). Interestingly, expression of AtAcd31.2 seemed to decline concomitantly to AtHsp15.7 induction (Fig. 7C; see also Fig. 7, A and D). As for the temperature dependence of gene expression, the drastic increase of AtHsp15.7 expression was gene specific and not found for AtpMDH1 (Fig. 7C). Application of 100 μm methyl viologen had a comparable but minor effect on AtHsp15.7 expression with maximum levels toward the end of the light period (12 h; Fig. 7C). Because uptake of ROS-producing agents from the soil by the plants is difficult to control, gene expression of peroxisomal sHsps was confirmed in an alternative experimental system, i.e. by infiltrating Arabidopsis leaves with the same chemicals. Leaf infiltration with 100 μm 3-AT induced expression of AtHsp15.7 after 3 h with constant levels up to the end of the light period (12 h) and lower levels in the beginning of the light period of the second day (24 h; Fig. 7D). Control plants infiltrated with solution lacking any inhibitor showed a minor induction of AtHsp15.7 expression similar to standard plants (Fig. 7B), suggesting that leaf wounding itself did not alter AtHsp15.7 expression to a considerable extent. Likewise, infiltration with 10 μm methyl viologen induced expression of AtHsp15.7 within about 3 h (Fig. 7D). In both experimental systems, the expression of AtAcd31.2 was not induced by any of the effectors applied. Degenerative symptoms such as leaf bleaching were not observed during the time period of analysis (data not shown). In summary, the inducible gene expression of AtHsp15.7 by elevated temperature and ROS-generating chemicals compared to the constant mRNA levels of AtAcd31.2 supported a stress-inducible and general house-keeping function of AtHsp15.7 and AtAcd31.2 in plant peroxisomes, respectively. DISCUSSION Experimental Validation of Peroxisome Targeting We identified two members of the expanded superfamily of sHsp-related proteins with putative targeting signals for peroxisomes in the Arabidopsis genome. The presence of a PTS peptide is a strong but not conclusive indication for peroxisome targeting of unknown proteins, as outlined previously (Neuberger et al., 2004; Reumann, 2004; Reumann et al., 2004). Experimental subcellular targeting analyses are thus required to verify predicted peroxisome targeting of unknown proteins. As shown by fluorescence microscopy using full-length and deletion constructs with EYFP, AtHsp15.7 was targeted to peroxisomes in onion epidermal cells by the C-terminal tripeptide SKL>. The double-labeling results excluded the possibility that the fluorescent punctate structures represented mitochondria or heat-shock granules, i.e. aggregates of sHsps, reported to occur in plants (Löw et al., 2000). Because a mitochondrial presequence was also predicted for AtHsp15.7 (Fig. 1A) and putative PTS1 signals can be overruled in vivo by N-terminal nonperoxisomal targeting signals (Neuberger et al., 2004), subcellular targeting of the inversely arranged fusion protein with accessible N-terminal end was investigated as well. Cytosolic targeting of AtHsp15.7:EYFP provided strong evidence for an exclusively peroxisomal localization of AtHsp15.7 in vivo. The PTS1 of AtHsp15.7 was conserved as major PTS1 in AtHsp15.7 homologs assembled from overlapping ESTs of various plant species including monocotyledons (Supplemental Fig. 2A), allowing the general conclusion that AtHsp15.7 orthologs are widespread in higher plants and targeted to peroxisomes as well. The second sHsp AtAcd31.2 was rather unusual in containing two predicted minor PTSs (RLx5HF and PKL>; Reumann, 2004). Thorough sequence analysis pointed toward the PTS2 representing the functional PTS. First, additional Arg and Pro residues surrounded the PTS2 peptide (PTS2, RRRLAAFAA HFPA; PTS1, GILRIVI PKL>), as often observed in plant PTS2 proteins (Reumann, 2004). Second, plant ESTs homologous to the PTS2 of AtAcd31.2 showed a high degree of sequence variation, most of which were defined as PTS2 peptides (RLx5HF, RIx5H[LVF], RTx5HL, and R[MV]x5HF; Reumann, 2004; Supplemental Fig. 2B). By contrast, the C-terminal tripeptide of AtAcd31.2 homologs was mutated to PTS1-related peptides (e.g. PKI>, PKV>, PFI>, or AHM>; Supplemental Fig. 2B) that have not (yet) been defined as PTS1 peptides and the peroxisome targeting function of which is questionable (Reumann, 2004). Third, the PTS2 is located in the long presumably flexible N-terminal domain of AtAcd31.2 (Fig. 1), whereas the close proximity of PKL> to the tightly folded β-sheet sandwich of the α-crystallin domain (van Montfort et al., 2001) is likely to prohibit interaction with Pex5p and PTS1 tripeptide optimization by point mutations (Supplemental Fig. 2). Our experimental analyses demonstrated that both fusion proteins AtAcd31.2:EYFP and EYFP:AtAcd31.2 were targeted to peroxisomes. Nonperoxisomal targeting of the deletion construct AtAcd31.2ΔN:EYFP and the version with mutated PTS2 (RLx5HF→RLx5DF) demonstrated that RLx5HF is a functional PTS2 and argued against the presence of an additional internal PTS similar to those described for CAT and a few other proteins (Kamigaki et al., 2003). Subcellular targeting of AtAcd31.2ΔN:EYFP to plastids was in line with computer prediction for this fusion protein but is not thought to be of physiological significance, because a second putative alternative translational start codon is lacking in front of the predicted transit peptide and because dual targeting of the full-length fusion protein AtAcd31.2:EYFP to both peroxisomes and plastids was not observed. Peroxisome targeting of EYFP:AtAcd31.2 argued in favor of the presence of a second PTS, i.e. the putative PTS1 PKL>. Deletion of this C-terminal tripeptide or exchange of the essential basic residue Lys to Glu (PKL→PEL), however, did not abolish peroxisome targeting of the fusion protein nor increased noticeably cytosolic EYFP fluorescence. We interpret these results that the putative PTS1 is not essential for peroxisome targeting. Whether an internal PTS targets EYFP:AtAcd31.2 to peroxisomes needs to be investigated in more detail in future studies. The N-terminal targeting domains of several plant PTS2 proteins are proteolytically removed by a specific yet unknown processing peptidase upon import into the peroxisome matrix (Kato et al., 1998). A peptide with a mass-to-charge ratio (m/z) corresponding to the N-terminal tryptic peptide of the full-length open reading frame of AtAcd31.2 (MEHESITARR, amino acids 1–10, Mcalc = 1228.61) was indeed detected in the protein band/spot of AtAcd31.2 by peptide mass fingerprinting (Fig. 4, A and B; data not shown). However, sequence analysis by MS/MS revealed that this peptide did not represent the N terminus but an internal tryptic peptide of AtAcd31.2 (QASSAQGFFMR, amino acids 99–109, Mcalc = 1228.57). Taken together with the finding that none of the other most probable N-terminal tryptic peptides (MEHESITAR, amino acids 1–9, Mcalc = 1072.50; MEHESITARRR, amino acids 1–11, Mcalc = 1384.71) was detected by peptide mass fingerprinting, these data may indicate that the PTS2 of AtAcd31.2 is proteolytically removed in vivo. To provide independent support for the localization of both sHsps in plant peroxisomes, we established a method to isolate peroxisomes from mature leaves of Arabidopsis. Previous methods published for Arabidopsis cotyledons or leaves from spinach (Spinacia oleracea; Yu and Huang, 1986; Fukao et al., 2002, 2003) were not suitable due to an extreme fragility of peroxisomes isolated from mature Arabidopsis rosette leaves, which is probably caused by the high concentration of secondary metabolites and proteases in this tissue. Moreover, we noticed a pronounced adherence between peroxisomes, mitochondria, and plastids in Brassicaceae. Gentle sedimentation of the leaf peroxisomes onto a Suc cushion during differential centrifugation and addition of a second Suc density gradient allowed a significant organelle enrichment. Even though marker enzyme activities of contaminating organelles were hardly detectable in the final peroxisome fraction (Fig. 3), partial comigration of residual plastids and mitochondria was observed, and the purity of Arabidopsis leaf peroxisomes remained lower as compared to that of model plants like spinach (data not shown). As a result, dominant proteins of plastids and mitochondria, but notably no cytosolic proteins, were still detected in minor concentration in the fraction of enriched Arabidopsis leaf peroxisomes (data not shown). Nevertheless, AtAcd31.2 was successfully identified in several independent leaf peroxisomal protein preparations from Arabidopsis by using one- and two-dimensional gels in combination with different mass spectrometric techniques. Considering the absence of a predicted mitochondrial presequence or plastidic transit peptide in AtAcd31.2 (Fig. 1), these data provide further support for targeting of this chaperone to Arabidopsis peroxisomes in vivo. To provide supplementary support for targeting of AtHsp15.7 to peroxisomes, we tried to isolate leaf peroxisomes from Arabidopsis plants that had been subjected to heat stress. Although leaf peroxisomes could indeed be isolated in a quantity and quality comparable to that obtained for control plants (S. Reumann, unpublished data), we did not succeed in identifying this chaperone from polyacrylamide gels so far. Whether this failure in protein identification is due to biological effects such as a high rate of protein turnover or technical limitations often observed when small and basic proteins (MW = 15.7, IEP = 7.9) are analyzed by gel-based proteomics approaches remains to be determined. Toward an Elucidation of the Function of Peroxisomal sHsps To investigate whether AtHsp15.7 and AtAcd31.2 play a physiological role as sHsps, we tried to complement the morphological phenotype of knockout mutants of yeast that are deficient in one of two cytosolic sHsps (Haslbeck et al., 2004). The PTSs were deleted from AtHsp15.7 and AtAcd31.2 to redirect the sHsps from peroxisomes to the cytosol in yeast. Except for AtAcd31.2ΔPTS1 expressed in Δhsp26, both peroxisomal plant Hsps were able to complement the single and the Δhsp26/42 double mutant (Fig. 5). Complementation by AtAcd31.2ΔPTS1 was generally less effective than that of the fusion protein lacking also the PTS2; it was observed in only about 70% of the experiments (data not shown). Therefore, insufficient complementation of AtAcd31.2ΔPTS1 was most likely caused by protein targeting to yeast peroxisomes by the PTS2 rather than the cytosol and not due to the higher sequence divergence of AtAcd31.2 from the consensus pattern of the α-crystallin domain (Fig. 1B). The analysis of cytosolic yeast extracts upon heat stress (Fig. 6) demonstrated that overexpression of AtHsp15.7ΔPTS1 and AtAcd31.2ΔPTS1+2 reduced the extent of unspecific protein aggregation in the deletion strains of yeast. From these data it can be concluded that AtHsp15.7 and AtAcd31.2 are involved in protein homeostasis and presumably exhibit a conserved sHsp function in vivo. Because AtHsp15.7 clusters with Arabidopsis sHsps of the cytosolic class I and those of the endoplasmic reticulum class, which comprise some well-characterized heat-inducible sHsps (Supplemental Fig. 1; Scharf et al., 2001), a conserved chaperone function was expected for AtHsp15.7. By contrast, AtAcd31.2 belongs to a novel family of Acd proteins, only a few members of which have been analyzed experimentally to date and the function of none of which has been associated with molecular chaperones (Scharf et al., 2001 and refs. therein). Several clades of Arabidopsis Acd proteins branch deeply in the phylogenetic tree of various eukaryotic sHsps (Supplemental Fig. 1), suggesting a diverse evolutionary origin. It needs to be stressed that the previous classification of Arabidopsis proteins into sHsps and Acd proteins (Scharf et al., 2001) is not based on functional studies or Acd conservation (see Supplemental Table II), but generally refers to sequence similarity with known heat-inducible sHsp, whereas the clades of Acd proteins represent largely unknown proteins. Hence, AtAcd31.2 is the first Arabidopsis Acd protein shown to have a chaperone function in vivo, strongly suggesting that other yet unknown Acd proteins represent molecular chaperones as well. To gain first insights into the role of both peroxisomal sHsps in plant peroxisomes, we analyzed their expression patterns in rosette leaves of plants that had been subjected to various forms of abiotic or biotic stress conditions. Leaf peroxisomes play a major role not only in photosynthesis but also in fatty acid β-oxidation. Our expression analyses at the mRNA and the protein level indicate that AtAcd31.2 is constitutively expressed at significant levels, whereas AtHsp15.7 expression is not detectable under standard conditions and strongly induced by heat and oxidative stress conditions. The heat inducibility of AtHsp15.7 is further supported by an extended cluster of heat-shock element motifs in the gene's promoter (Scharf et al., 2001). In two different experimental systems, i.e. inhibitor application by watering or infiltration, and using two alternative ROS-producing effectors (3-AT and methyl viologen), an induction of AtHsp15.7 expression by ROS was observed. For a subset of sHsps, including two located in the cytosol and one each in mitochondria and plastids, a similar induction by oxidative stress has previously been determined (Banzet et al., 1998; Lee and Vierling, 2000; for review, see Sun et al., 2002). The induction of AtHsp15.7 by oxidative stress conditions is of particular interest because peroxisomes are one of the two major subcellular compartments in which ROS are produced during photosynthesis. At elevated temperature, the ratio of carboxylation to oxygenation reaches a value of 1:0.5 (Sharkey, 1988). Thus, fixation of 2 mol of CO2 is accompanied by the production of 1 mol of glycolate and, by the activity of glycolate oxidase, 1 mol of H2O2. Under standard conditions, the high concentration of CAT is thought to detoxify most H2O2 produced during photorespiration. However, CAT is easily inactivated by light and has a rather low turnover rate (Feierabend and Engel, 1986; Grotjohann et al., 1997). Under CAT-inactivating conditions, the ROS concentration in the peroxisome matrix may reach such a high level that matrix proteins are oxidized, hydrophobic polypeptide patches exposed, and enzymes inactivated. The induction of AtHsp15.7 by oxidative stress conditions may therefore indicate that AtHsp15.7 plays an important role in minimizing oxidative damage on leaf peroxisomal metabolism. A constitutive expression as determined for AtAcd31.2 is atypical for sHsps and has, to the best of our knowledge, not been described yet for any Arabidopsis or tomato (Lycopersicon esculentum) sHsp. In a previous study, AtAcd31.2 was shown to be negatively regulated by floral induction and gibberellins (Chandler and Melzer, 2004). Interestingly, in contrast to the typical heat inducibility of sHsps, several yet unknown Arabidopsis Acd genes are not heat inducible (Zimmermann et al., 2004; Supplemental Fig. 4). The constitutive and high expression level of AtAcd31.2 (Fig. 7; Supplemental Figs. 3 and 4) suggests that this chaperone and possibly other Acd proteins play an important role even under normal physiological conditions. The analysis of Arabidopsis knockout mutants deficient in AtHsp15.7 or AtAcd31.2 may reveal the precise function of both sHsps in plant peroxisomal metabolism and their mode of action in the future. Hsps Acting in Concert with Peroxisomal sHsps The localization of two distinct sHsps to the matrix of plant peroxisomes raises new questions and hypotheses. Because sHsps lack ATP-hydrolyzing activity, the chaperone function of sHsps appears to be limited to binding and maintaining the solubility of unfolded proteins, without promoting their refolding directly and actively in an ATP-dependent manner. Therefore, substrate renaturation by sHsps is generally thought to require their interaction with other Hsps (Forreiter et al., 1997; Lee and Vierling, 2000). Targeting of additional Hsps, mainly a Hsp70 or Hsp100 homolog (Mogk et al., 2003; Cashikar et al., 2005), needs to be postulated to act in concert with AtHsp15.7 and AtAcd31.2. Because of its membrane topology facing the cytosolic side, the DnaJ ATPase chaperone of the glyoxysomal membrane of C. sativus (Diefenbach and Kindl, 2000) is not expected to mediate protein folding in the peroxisome matrix. An Hsp70 homolog from Citrullus lanatus was shown to be targeted to both chloroplasts and the peroxisome matrix by the use of two alternative translation start codons, leading to the translation of either a transit peptide or a PTS2 at the N-terminal end (Wimmer et al., 1997). Two Hsp70 isoforms have also been detected in highly purified glyoxysomes from C. sativus (Diefenbach and Kindl, 2000). Members of the Hsp70 family with putative PTSs, however, have not been detected in the Arabidopsis genome to date (Reumann et al., 2004). In one of two Arabidopsis Hsp70 homologs, the second Met of the peroxisomal Hsp70 homolog from Cucumis is indeed conserved but not obviously followed by a putative PTS2. More research is required to investigate dual targeting of Arabidopsis Hsp70 homologs to plastids and peroxisomes. Considering the small number of cloned cDNAs of plant peroxisomal proteins (Reumann, 2004) and the current limits in predicting alternative splice and translational variants, currently unknown PTS are likely to be unraveled in the ongoing postgenomic era and further peroxisomal Arabidopsis proteins to be identified, possibly including peroxisomal Hsp70 and Hsp100 homologs. If neither a matrix-targeted Hsp70 nor an Hsp100 homolog is involved in protein refolding in plant peroxisomes, an alternative mechanism can be envisioned, in which sHsp-substrate complexes are transported from the peroxisome matrix back to the cytosol for proper refolding and the renatured polypeptides subsequently reimported into the peroxisome matrix. CONCLUSION This study shows that low-abundance regulatory proteins from plant peroxisomes can indeed be identified by screening the Arabidopsis genome for genes encoding proteins with putative PTSs. Arabidopsis is thus the first organism shown to contain sHsps in the matrix of peroxisomes. In addition to the previously defined six classes of plant sHsps (Scharf et al., 2001; Sun et al., 2002), we identified a seventh class for peroxisomes and suggest to change the acronyms of these proteins from AtHsp15.7-CI (cytosolic class I; Scharf et al., 2001) to AtHsp15.7-Px and AtAcd31.2-Px (Px, peroxisome). The characterization of AtAcd31.2 as a constitutively expressed sHsp suggests that plant sHsps function not only under stress conditions but also assist in protein refolding under standard physiological conditions. The localization of sHsps to plant peroxisomes and the detection of Acd homologs with putative PTS1s in other organisms (Supplemental Fig. 1) indicate that homologs of larger sHsp families may be targeted to peroxisomes in other eukaryotes as well. Regarding higher eukaryotes, however, plants may be the prototypical organisms that require peroxisomal sHsps due to their sessile nature in combination with their permanent subjection to quickly and drastically changing environmental conditions. MATERIALS AND METHODS Plant Growth Standard Arabidopsis (Arabidopsis thaliana) ecotype Columbia plants were grown for about 4 weeks in a 16-h-light/8-h-dark cycle at 22°C under a light intensity of 100 to 150 μE m−2 s−1. All the stress treatments were initiated after 3 h of light. For heat and cold stress experiments, plants were incubated in the dark at 37°C and 5°C, respectively, whereas the control plants were incubated at 22°C in the dark. For high light stress, the light intensity was raised to 450 μE m−2 s−1 while keeping the temperature constant at about 23°C. Control plants grown at the same temperature, but under normal light, were analyzed in parallel. For the oxidative stress experiments, soil-grown plants were either watered with 5 mm 3-AT or 100 μm methyl viologen (about 50 mL/9-cm pot and day), or rosette leaves were infiltrated with 100 μm 3-AT or 10 μm methyl viologen (in water) using a syringe and floated on inhibitor solution. Rosette leaves infiltrated with water were used as a mock control. Gene Cloning and Semiquantitative RT-PCR Total RNA was isolated from different tissues of Arabidopsis ecotype Columbia using the Invisorb Spin plant mini kit (Invitek). Full-length cDNAs for AtHsp15.7 (At5g37670) and AtAcd31.2 (At1g06460) were isolated from flowers and cold-treated rosette leaves, respectively, using appropriate oligonucleotide primers (Supplemental Table I). Total RNA was converted to single-strand cDNA by reverse transcriptase (Superscript III, Invitrogen) and used as template for PCR using a proof-reading DNA polymerase (Thermozyme, Invitrogen). Amplified products were subcloned into pGEMT using the pGEM-T Easy Vector system (Promega) and sequenced. Amplification errors that resulted in amino acid exchanges were not observed. Semiquantitative RT-PCR was performed using a First-Strand cDNA Synthesis kit (MBI, Fermentas) according to the manufacturer's instruction. For PCR, standard parameter and an appropriate number of cycles were used (AtHsp15.7 and ubiquitin, 30 cycles; AtAcd31.2, 26 cycles; and AtpMDH1, 24 cycles). Complete removal of residual genomic DNA by enzymatic digestion was verified for the intronless gene of AtHsp15.7 using control samples lacking reverse transcriptase. The specificity of AtHsp15.7 amplification was confirmed by restriction endonuclease digest of the RT-PCR products. All RT-PCR experiments were repeated at least three times using independent plant material. Subcellular Localization Studies Targeting prediction was performed as described earlier (Reumann et al., 2004). Fusion proteins with N- or C-terminally located EYFP were generated by PCR (Supplemental Table I) to investigate the function of C-terminal and N-terminal targeting signals, respectively, and subcloned in frame into the plant expression vectors pCAT-EYFP-Nfus and pCAT-EYFP-Cfus (Fulda et al., 2002) under control of a double 35S CaMV promoter. The C-terminal deletion construct EYFP:AtHsp15.7ΔPTS1 lacked the C-terminal 14 residues including the putative PTS1 SKL>. The deletion constructs AtAcd31.2ΔPTS2:EYFP and EYFP:AtAcd31.2ΔPTS1 lacked the N-terminal 29 residues including the putative PTS2 (RLx5HF, residues 11–19) or the C-terminal tripeptide PKL>, respectively (Supplemental Table I). Site-directed mutagenesis (PTS2 of AtAcd31.2, RLx5HF to RLx5DF; PTS1, PKL> to PEL>) was performed using PfuUltra high-fidelity DNA polymerase for mutagenic primer-directed replication of both plasmid strands of AtAcd31.2:EYFP in pCAT-EYFP-Cfus and EYFP:AtAcd31.2 in pCAT-EYFP-Nfus using the Quick-Change II site-directed mutagenesis kit (Stratagene; Supplemental Table I). Onion (Allium cepa) epidermal cells were transformed biolistically as described (Biolistic PDS 1000/He Biolistic Particle Delivery system, Bio-Rad; Fulda et al., 2002) using 1,100 psi rupture discs and a vacuum of 0.1 bar. All subcellular analyses were reproduced at least three times in independent experiments. Yeast Complementation Studies Deletion constructs of AtHsp15.7 or AtAcd31.2 lacking the C-terminal three residues and/or the N-terminal PTS2 (residues 1–29), referred to as AtHsp15.7ΔPTS1, AtAcd31.2ΔPTS1, and AtAcd31.2ΔPTS1+2, were generated to target the proteins to the yeast (Saccharomyces cerevisiae) cytosol and subcloned without any terminal tags into the pYES2.1-topo cloning vector under the control of a GAL1 promoter and containing the URA3 gene for selection of transformants (pYES2.1 TOPO TA expression kit, Invitrogen). After transformation, yeast deletion strains deficient in ScHsp42 and/or ScHsp26 (Haslbeck et al., 2004) and expressing Arabidopsis sHsps were first selected on complete supplement media lacking uracil (CSM-URA) and containing Glc. Prior to induction of the expression of the peroxisomal sHsps, mid logarithmic phase cells cultivated at 30°C were transferred to CSM-URA media containing raffinose for 2 h. Next, the cells were transferred to CSM-URA media containing Gal for induction. After 4 h of induction, the cultures were heat shocked for 1 h at 43°C and subsequently analyzed by SEM. As negative control, equally treated cells transformed with the empty vector pYES2 were used. To determine the total amount of aggregated protein in the complemented yeast strains, 2 × 108 yeast cells were collected subsequently after heat shock and lysed using a Basic Z cell disrupter at 2.5 kbar (Constant Systems). After separation of cellular fragments by gentle centrifugation at 500g for 10 min, insoluble protein aggregates were sedimented by centrifugation (10 min, 13,000g at 4°C) and analyzed by SDS-PAGE. Microscopy Analysis of onion epidermal cells was performed using a fluorescence microscope (Olympus BX51) with the following filter sets: EYFP (F41-028; excitation filter HQ500/20, barrier HQ535/30), and ECFP (F31-044; excitation filter D436/20, barrier D480/40). Digital images were captured using a CCD camera (ColorViewII) with analySIS3.1 Imaging software (Soft imagine system GMDH). For analysis of yeast morphology, cells were fixed and prepared as described by Spector et al. (1998). SEM was performed with a JEOL 5900 LV microscope. Pictures were taken at a constant voltage of 20 kV and a spot size of 20 nm at a magnification of 5,500×. Isolation of Leaf Peroxisomes Leaves were ground in grinding buffer (170 mm Tricine-KOH, pH 7.5, 1.0 m Suc, 1% (w/v) bovine serum albumin (BSA), 2 mm EDTA, 5 mm dithiothreitol, 10 mm KCl, and 1 mm MgCl2) in the presence of protease inhibitors using a mortar and a pestle, the suspension filtered, and chloroplasts were sedimented at 5,000g (1 min). Leaf peroxisomes were sedimented onto a Suc cushion of 50% (w/w) by centrifugation at 20,000g for 5 min. The resuspended organelles were homogenized using a Potter-Elvehjem homogenizer, loaded onto a Suc density gradient prepared in TE buffer (10 mm Tricine-KOH, pH 7.5, 1 mm EDTA) supplemented with 0.5% (w/v) BSA (from the top to the bottom: 2 mL 30% [w/w], 3 mL 35% [w/w], linear gradient of 2× 7.5 mL 40% to 52% [w/w], 3 mL 52% [w/w], 5 mL 60% [w/w]), and centrifuged for 2 h at 80,000g (Beckman SW28 rotor). For analytical purpose, the gradient was fractionated in 2-mL fractions. For preparative purposes, the peroxisome fraction located at the interface between 52% and 60% (w/w) Suc was harvested, combined from several gradients, diluted to 48% (w/w), and loaded onto a second Suc density gradient (linear part of 6 mL each 48% and 60% [w/w], 3 mL 60% [w/w] in TE buffer lacking BSA) by diluting the peroxisome fraction in a gradient mixer with 40% (w/w) in TE buffer. For two-dimensional gel electrophoresis, BSA was omitted in all Suc solutions of a density higher than 40% (w/w). The proteins were precipitated according to Wessel and Flügge (1984), dissolved in urea buffer (7 m urea, 2 m thiourea, 4% [w/v] CHAPS, 0.5% immobilized pH gradient (IPG) buffer, and 3 mg/mL dithiothreitol) and subjected to isoelectric focusing (nonlinear IPG strip, pH 3–10). For SDS-PAGE and the second dimension, the proteins were separated on a large 7.5% to 15% acrylamide gradient gel under denaturing conditions and stained with silver or colloidal Coomassie Blue. Chlorophyll and protein were determined according to Arnon (1949) and Lowry et al. (1951), respectively. The activities of marker enzymes (hydroxypyruvate reductase [HPR] for leaf peroxisomes, fumarase for mitochondria, and NADP-GAPDH for chloroplasts) were determined as described earlier (Reumann et al., 1995). Protein Identification Excised spots were subjected to automated in-gel digest with prior alkylation according to standard protocols supplied with the ProTeam Advanced Digest system (Tecan). Mass spectrometry grade trypsin was purchased from Promega. For the acquisition of peptide mass fingerprints by matrix-assisted laser-desorption ionization time of flight-mass spectrometry, the peptides extracted from the gel plugs were applied to a prestructured sample support (AnchorChip target; Bruker Daltonics) coated with a thin layer of α-cyano-4-hydroxy-cinnamic acid (Gobom et al., 2001) using the same liquid handling system as for the in-gel digest. The target was inserted into a Bruker Ultraflex TOF/TOF instrument (Suckau et al., 2003) and submitted to an automated analysis loop using external calibration. Database searches in the NCBInr primary sequence database restricted to the taxonomy Arabidopsis were performed using the Mascot Software 2.0 (Matrix Science) with carboxamidomethylation of Cys as fixed and oxidation of Met as variable modification, respectively. The monoisotopic mass tolerance was set to 100 ppm and one missed cleavage was allowed. For confirmation of the peptide mass fingerprinting results, the samples were analyzed by MS/MS using the LIFT technology of the Ultraflex TOF/TOF instrument (Suckau et al., 2003) to obtain sequence information of selected peptides. Database searches using combined peptide mass fingerprint and MS/MS datasets were performed as described above with the fragment mass tolerance set to 0.7 D. Sequence data from this article can be found in the EMBL/GenBank data libraries under accession numbers DQ403190 (AtHsp15.7, At5g37670) and DQ403189 (AtAcd31.2, At1g06460). ACKNOWLEDGMENTS We are grateful for practical assistance in yeast complementation studies by B. Richter, for the initial SEM and proteome analyses performed by Dr. A. Olbrich and Dr. H. Kratzin, respectively, for stimulating discussions with Dr. K.D. Scharf and M. Siddique, and for provision of the pCAT plant expression vectors by Dr. M. Fulda. We thank K. Pawlowski, I. Heilmann, and O. 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RE1304/2 to S.R.), by Fonds der Chemischen Industrie (to M.H.), and by the government of Lower Saxony (a Dorothea-Erxleben stipend to S.R.). 2 Present address: Department of Plant Pathology, Faculty of Agriculture, Shizuoka University, Shizuoka 422–8529, Japan. * Corresponding author; e-mail [email protected]; fax 49–551–395749. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Sigrun Reumann ([email protected]). [W] The online version of this article contains Web-only data. Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.105.073841. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Chatterjee, Mithu; Sharma, Pooja; Khurana, Jitendra P.

doi: 10.1104/pp.105.076323pmid: 16531484

Abstract Cryptochromes are blue/ultraviolet-A light sensing photoreceptors involved in regulating various growth and developmental responses in plants. Investigations on the structure and functions of cryptochromes in plants have been largely confined to Arabidopsis (Arabidopsis thaliana), tomato (Lycopersicon esculentum), and pea (Pisum sativum). We report here the characterization of the cryptochrome 1 gene from Brassica napus (BnCRY1), an oilseed crop, and its functional validation in transgenics. The predicted BnCRY1 protein sequence shows a high degree of sequence identity (94%) to Arabidopsis CRY1. A semiquantitative reverse transcription-polymerase chain reaction and the western-blot analysis revealed that blue light up-regulates its transcript and protein levels in young seedlings. The BnCRY1 promoter harbors conventional light-responsive cis-acting elements, which presumably impart light activation to the GUS (β-glucuronidase) reporter gene expressed in Arabidopsis. Although the BnCRY1 transcript could be detected in all the tissues examined, its protein was virtually undetectable in mature leaves and the root, indicating a tissue-specific translational control or protein turnover. The antisense-BnCRY1 Brassica transgenic seedlings accumulated negligible levels of CRY1 protein and displayed an elongated hypocotyl when grown under continuous white or blue light (but not under red or far-red light); the accumulation of anthocyanins was also reduced significantly. The adult transformants were also found to be tall when grown under natural light environment in a containment facility without any artificial illumination. These data provide functional evidence for a role of blue light up-regulated cry1 in controlling photomorphogenesis in Brassica species. Plants have evolved sophisticated sensory photoreceptors, which coordinately judge the quality, quantity, direction, and duration of light, to regulate diverse photomorphogenic responses throughout their life cycle (Gyula et al., 2003; Sullivan and Deng, 2003; Franklin and Whitelam, 2004). These sensory photoreceptors have been classified broadly into three groups based on the wavelength of light they perceive. Phytochromes, which are best characterized and extensively studied, comprise a small family of red/far-red (600–750 nm) sensing photoreceptors (Khurana et al., 1998, 2004; Quail, 2002; Chen et al., 2004). Cryptochromes and phototropins perceive the blue/UV-A (320–500 nm) part of the solar spectrum (Briggs and Olney, 2001; Khurana, 2001; Cashmore, 2003; Lin and Shalitin, 2003; Chen et al., 2004; Banerjee and Batschauer, 2005). The photoreceptors responsible for perceiving UV-B radiation (280–320 nm), however, remain elusive (Bharti and Khurana, 1997; Frohnmeyer and Staiger, 2003). The first cryptochrome gene was cloned through the molecular analysis of T-DNA insertion mutant allele of hy4 (Ahmad and Cashmore, 1993). The HY4 gene encodes a protein of 681 amino acid residues, with a high degree of sequence match to photolyase, a DNA repair enzyme activated by blue light. Later, HY4 was designated as cryptochrome 1, cry1 (Lin et al., 1995). The second member of the cryptochrome gene family, At-PHH1 or CRY2, was isolated subsequently by screening an Arabidopsis (Arabidopsis thaliana) cDNA library using CRY1 as a probe (Hoffman et al., 1996; Lin et al., 1996b). The AtCRY1 and AtCRY2 proteins show approximately 58% identity within the N-terminal region, whereas the C-terminal extension shows only approximately 14% identity (Hoffman et al., 1996; Lin et al., 1998). Cryptochromes have now been identified from diverse species, including Chlamydomonas reinhardtii (Small et al., 1995), Physcomitrella patens (Imaizumi et al., 1999, 2002), Adiantum capillus-veneris (Kanegae and Wada, 1998; Imaizumi et al., 2000), rice (Oryza sativa; Kumar, 2000; Matsumoto et al., 2003), tomato (Lycopersicon esculentum; Ninu et al., 1999; Perrotta et al., 2000), pea (Pisum sativum; Platten et al., 2005a, 2005b), and a nonphotosynthetic holoparasitic plant, Orobanche minor (Okazawa et al., 2005). Using a random PCR approach, various cryptochrome members from angiosperms like melon (Cucumis melo), banana (Musa spp.), and barley (Hordeum vulgare) were isolated (Perrotta et al., 2001); however, their function remains unknown. Cryptochromes have also been identified and functionally characterized from Drosophila, zebrafish, mouse, and human (van der Spek et al., 1996; Emery et al., 1998; Kobayashi et al., 1998, 2000). Animal cryptochromes, in most cases, play a role in entrainment of circadian clock and act as components of the central oscillator (Cashmore, 2003; Sancar, 2004). In plants, cryptochromes (cry1 and cry2) participate in many aspects of photomorphogenesis, such as inhibition of hypocotyl elongation (Ahmad and Cashmore, 1993; Lin et al., 1998; Lin, 2002), accumulation of anthocyanins (Ahmad et al., 1995), and cotyledon expansion (Botto et al., 2003). In addition, cryptochromes also regulate flowering time (Guo et al., 1998; Mockler et al., 1999; Giliberto et al., 2005) and circadian clock (Devlin and Kay, 1999, 2000; Millar, 2003). The processes like deetiolation, flowering, and circadian entrainment are in fact coordinately regulated by the combined action of phytochromes and cryptochromes (Casal, 2000; Sullivan and Deng, 2003). In dark, cry1 is localized in the nucleus and detected primarily in the cytoplasm on exposure to light, whereas cry2 is confined to the nucleus in both dark and light (Guo et al., 1999; Yang et al., 2000). Light induced activity of cry1 and cry2 is mediated through its C-terminal (CCT) domain (Yang et al., 2000). The activation of CCT1 (of cry1) most likely is mediated through the blue light-dependent alteration in the dimerized N terminal of cry1 (Sang et al., 2005). The C-terminal domain was also shown to interact with the master regulator COP1 to control photomorphogenesis (Wang et al., 2001; Yang et al., 2001). Besides COP1, only a few more signaling components (e.g. SUB1, PP7, HFR1, OBP3, HRB1, and AtMYC2) involved in cryptochrome-mediated blue light signaling have been identified (Guo et al., 2001; Duek and Fankhauser, 2003; Moller et al., 2003; Kang et al., 2005; Ward et al., 2005; Yadav et al., 2005). Only recently, an insight into the primary photochemistry underlying the photoactivation of cry1 has been gained. It involves intraprotein electron transfer from conserved residues (Trp and Tyr) to the excited flavin adenine dinucleotide (FAD), which stimulates the autophosphorylation of cry1 and is responsible for its biological activity (Giovani et al., 2003; Zeugner et al., 2005). Among higher plants, cryptochromes have been well studied and characterized only in Arabidopsis, tomato, and pea. To learn more about cryptochromes, we have initiated the characterization and functional analysis of the cryptochrome gene family from an agronomical important crop plant, Brassica napus, a close relative of Arabidopsis. The CRY1 gene was isolated from a variety ISN-706, which is cultivated in northern and cooler regions of India and is valued for oilseed. The BnCRY1 gene is represented as a single copy in the genome of B. napus, an allotetraploid, and its expression is up-regulated by light, both in terms of transcript abundance and the translational product. The analysis of anti-BnCRY1 transgenics has substantiated the role of CRY1 in regulating plant height and anthocyanin accumulation. RESULTS Gene Encoding CRY1 Protein in B. napus The full-length BnCRY1 gene from B. napus was isolated by screening a genomic library using AtCRY1 gene as a probe. Two of the strongly hybridizing clones were verified by sequence analysis and found to be identical. The larger clone was processed for sequencing by primer walking. The genomic sequence thus obtained was used to design primers and the corresponding cDNA clone amplified by reverse transcription (RT)-PCR and completed by 5′ RACE and 3′ RACE. The sequence of the genomic and cDNA clones of BnCRY1 is available in the EMBL Nucleotide Sequence Database (accession nos. AJ344565 [gene] and AJ704628 [cDNA]). A comparative analysis of cDNA and genomic sequences revealed that BnCRY1 contains three introns and four exons (Fig. 1A Figure 1. Open in new tabDownload slide A, Schematic diagram representing the alignment of BnCRY1 cDNA with the corresponding gene. Exon borders are indicated with a line connecting the cDNA and the exons. Numbers depict the size of UTR and exons. Restriction sites for EcoRI (EI), HindIII (H), SalI (S), and XbaI (X), which have been used for Southern analysis, are indicated on the horizontal bar representing gene structure. B, Amino acid sequence alignment of five representative plant cryptochromes using ClustalW. Black-boxed and gray-boxed letters represent residues that are identical in all or most cryptochromes, respectively. # and • symbols indicate the residues interacting with FAD and MTHF, respectively. Lines above the sequences mark the DAS domain present in the C-terminal region. The predicted secondary structure of BnCRY1 as determined using SOPM is shown below the alignment data and consists of α-helices (h), extended β-sheets (e), and coil (c) regions. Figure 1. Open in new tabDownload slide A, Schematic diagram representing the alignment of BnCRY1 cDNA with the corresponding gene. Exon borders are indicated with a line connecting the cDNA and the exons. Numbers depict the size of UTR and exons. Restriction sites for EcoRI (EI), HindIII (H), SalI (S), and XbaI (X), which have been used for Southern analysis, are indicated on the horizontal bar representing gene structure. B, Amino acid sequence alignment of five representative plant cryptochromes using ClustalW. Black-boxed and gray-boxed letters represent residues that are identical in all or most cryptochromes, respectively. # and • symbols indicate the residues interacting with FAD and MTHF, respectively. Lines above the sequences mark the DAS domain present in the C-terminal region. The predicted secondary structure of BnCRY1 as determined using SOPM is shown below the alignment data and consists of α-helices (h), extended β-sheets (e), and coil (c) regions. ). The third intron spans 188 bp and is followed immediately by a 261 bp 3′ untranslated region (UTR), which makes up the fourth exon. The stop codon (TAA) is generated by splicing of the third and fourth exons. The BnCRY1 cDNA contains a 5′ noncoding region of 55 nucleotides and a coding region of 2,040 nucleotides (680 amino acids, 76.7 kD). It harbors a polyadenylation signal (AATAAA) at position 2,269 to 2,274 bp just before the polyA tail. The Kyte-Doolittle hydropathy plot analysis does not show any hydrophobic region (data not shown), suggesting that BnCRY1 is a soluble protein consistent with the earlier reports on AtCRY1 (Ahmad and Cashmore, 1993) and other cryptochromes. Using the ClustalW algorithm (Thompson et al., 1994), the deduced amino acid sequence of BnCRY1 was aligned with AtCRY1, LeCRY1, and OsCRY1 (Ahmad and Cashmore, 1993; Perrotta et al., 2000; Matsumoto et al., 2003). When compared to other cryptochromes, including dicot and monocot representatives, a high percentage of sequence identity was observed in the N-terminal PHR (photolyase-related) domain of BnCRY1 (Fig. 1B). In the C-terminal region, although overall similarity is low, all three hallmark motifs are conserved. Collectively, these three motifs are known as the DAS domain and comprise DQXVP (function unknown), an acidic (short stretch represented by E and D), and STAESSSS (implicated in interaction with phytochrome A [phyA]) motifs (Ahmad et al., 1998b; Kanegae and Wada, 1998). However, the traditional STAESSSS motif present in dicots is not conserved in OsCRY1 (Matsumoto et al., 2003). Like Type I photolyases, AtCRY1 associates with two cofactors, the light-harvesting cofactor (MTHF) and a catalytic cofactor (FAD; Lin et al., 1995; Malhotra et al., 1995). All 13 amino acids, predicted to interact with FAD in AtCRY1, were found to be conserved in BnCRY1. The TGYP motif was also observed at the 337- to 340-amino acid position, which is conserved in all the Type I photolyases and forms a part of the FAD-binding domain (Malhotra et al., 1992). Six out of seven identical amino acid residues (His at position 52 is replaced by Gln), known to interact with the light-harvesting cofactor (MTHF), are also conserved in BnCRY1 (Fig. 1B). The secondary structure of BnCRY1 was solved by the self-optimized prediction method (SOPM; Geourjon and Deleage, 1994). The SOPM results indicate that BnCRY1 consists of the α-helix (37.10%), β-strand (15.10%), and random coil (39.74%; Fig. 1B). The software did not provide the percentage of 310 helix, which plays a major role in the structural configuration of both photolyases and AtCRY1 (Brautigam et al., 2004). The α-helices and β-strands were randomly distributed throughout the BnCRY1 polypeptide and not organized into any specific domain. Relationship with Other Cryptochromes The phylogenetic analysis of 30 plant and near-plant cryptochromes representing 12 diverse species was carried out using the Dnastar MegAlign program by the Clustal method (Fig. 2 Figure 2. Open in new tabDownload slide Phylogram of plant cryptochromes. The amino acid sequences of 30 plant and near-plant genes included in the analysis were obtained from the National Center for Biotechnology Information (NCBI) database. The alignment was conducted by Dnastar MegAlign program using Clustal method under default options. The abbreviations used are as follows: At, Arabidopsis; Ac, A. capillus-veneris; Ar, Armoracia rusticana; Bn, B. napus; Le, tomato; Om, O. minor; Os, rice; Pp, P. patens; Ps, pea; Sb, Sorgham bicolor; Cr, C. reinhardtii; and Ns, Nicotiana sylvestris. Figure 2. Open in new tabDownload slide Phylogram of plant cryptochromes. The amino acid sequences of 30 plant and near-plant genes included in the analysis were obtained from the National Center for Biotechnology Information (NCBI) database. The alignment was conducted by Dnastar MegAlign program using Clustal method under default options. The abbreviations used are as follows: At, Arabidopsis; Ac, A. capillus-veneris; Ar, Armoracia rusticana; Bn, B. napus; Le, tomato; Om, O. minor; Os, rice; Pp, P. patens; Ps, pea; Sb, Sorgham bicolor; Cr, C. reinhardtii; and Ns, Nicotiana sylvestris. ). The BnCRY1 grouped under dicot CRY1 clade and showed maximum similarity with AtCRY1, which again reflects a close evolutionary relationship between Arabidopsis and Brassica. A distinct coevolution of cryptochromes along with the hierarchy of plant taxons from algae to angiosperms was also apparent. Duplication of the CRY gene into CRY1 and CRY2 predates the dicot-monocot divergence as these genes were found in both dicots and monocots. Interestingly, the presence of only CRY1-like genes in Adiantum and Physcomitrella suggests that the gene duplication that gave rise to CRY1 and CRY2 major lineages occurred after the divergence of lower plants and seed plants (Spermatophyta). Seed plants are believed to have evolved in the late Paleozoic era about 360 million years ago (Mya), whereas monocots and dicots diverged around 170 Mya (Sanderson et al., 2004). Thus, based on this analysis, we hypothesize that the split between the CRY1-like and CRY2-like lineages occurred between 170 Mya and 360 Mya. BnCRY1 Is Represented as a Single Copy Gene on the Genome of an Allotetraploid B. napus is a natural allotetraploid (2n = 38, AACC) derived from interspecific hybridization of the two diploid (A and C) genomes of Brassica rapa and Brassica oleracea, respectively, followed by spontaneous chromosome doubling (Song et al., 1988; Lakshmikumaran et al., 2003). Thus, two copies of BnCRY1 were expected. Southern-blot hybridization carried out with a gene probe harboring the entire coding region of BnCRY1, particularly under high (42°C) stringency conditions (Fig. 3 Figure 3. Open in new tabDownload slide Southern analysis of BnCRY1 under low and high stringency conditions. Numbers marked in the middle of the two sections represent the size of HindIII-digested λ DNA. Restriction enzymes used for digesting genomic DNA have been indicated on the top of the sections. Figure 3. Open in new tabDownload slide Southern analysis of BnCRY1 under low and high stringency conditions. Numbers marked in the middle of the two sections represent the size of HindIII-digested λ DNA. Restriction enzymes used for digesting genomic DNA have been indicated on the top of the sections. ), however, showed that BnCRY1 is represented as a single copy in the B. napus genome. The restriction map of BnCRY1 shows one site each for EcoRI, SalI, XbaI, and two sites for the HindIII restriction enzyme (Fig. 1A). The XhoI enzyme does not restriction digest BnCRY1. The in silico restriction profile matches well with the Southern profile obtained under high stringency conditions (Fig. 3). A few additional (but mostly faint) bands observed under both high and low stringency conditions may represent nonspecific hybridization with one or more copies of as yet uncharacterized CRY2 gene(s). It will be interesting to establish the genomic (A or C genome) localization of the BnCRY1 characterized in this study. Light-Dependent and Spatial/Temporal Expression Profile Gene Expression Analysis To examine whether transcript levels of BnCRY1 are regulated by light and developmental cues and display tissue specificity, its expression was examined by semiquantitative RT-PCR, using gene-specific primers. The BnCRY1 transcript levels were found to be higher in seedlings grown in white light (70 μmol m−2 s−1) in comparison to the dark-grown seedlings (Fig. 4A Figure 4. Open in new tabDownload slide Comparative RT-PCR analysis to show light and developmental regulation, and tissue-specific expression of BnCRY1. A shows the transcript levels in 6-d-old dark- and light-grown seedlings. B and C show the BnCRY1 transcript levels at different developmental stages and in various tissues/organs, respectively. The inflorescence tissue consisted of inflorescence meristem and young emerging floral buds. ACTIN transcript was used as internal control. Figure 4. Open in new tabDownload slide Comparative RT-PCR analysis to show light and developmental regulation, and tissue-specific expression of BnCRY1. A shows the transcript levels in 6-d-old dark- and light-grown seedlings. B and C show the BnCRY1 transcript levels at different developmental stages and in various tissues/organs, respectively. The inflorescence tissue consisted of inflorescence meristem and young emerging floral buds. ACTIN transcript was used as internal control. ). However, the transcript levels were more or less similar in seedlings grown in light for various durations (Fig. 4B). The BnCRY1 transcript was present ubiquitously, to a detectable level, in all the organs examined, including stem, leaf, root, inflorescence, floral bud, flower, and silique (Fig. 4C); it was relatively more abundant in the stem, inflorescence (consisting of inflorescence meristem and emerging floral buds), young silique, and, surprisingly, in the root. Immunoblot Analysis for Protein Profile The BnCRY1 protein of approximately 76 kD could be detected (by immunoblot assay) in the extracts of whole seedlings grown in dark or white light (70 μmol m−2 s−1) for various durations (Fig. 5 Figure 5. Open in new tabDownload slide Comparative analysis of BnCRY1 protein levels in Brassica seedlings grown in light (A) and dark (B) for various durations. Note that the blot in B was exposed for a longer duration to amplify signals. C shows BnCRY1 expression in 6-d-old light- and dark-grown Brassica seedlings. The effect of blue light on BnCRY1 levels in 6-d-old dark-grown seedlings irradiated with 36 h blue light is displayed in D. The dark-grown seedlings of the same developmental stage were taken as control; the blot in D was exposed for longer duration than the one shown in C. E shows the tissue-specific expression of BnCRY1. For western analysis, anti-6×His∷CT-BnCRY1 primary antibody was used. Figure 5. Open in new tabDownload slide Comparative analysis of BnCRY1 protein levels in Brassica seedlings grown in light (A) and dark (B) for various durations. Note that the blot in B was exposed for a longer duration to amplify signals. C shows BnCRY1 expression in 6-d-old light- and dark-grown Brassica seedlings. The effect of blue light on BnCRY1 levels in 6-d-old dark-grown seedlings irradiated with 36 h blue light is displayed in D. The dark-grown seedlings of the same developmental stage were taken as control; the blot in D was exposed for longer duration than the one shown in C. E shows the tissue-specific expression of BnCRY1. For western analysis, anti-6×His∷CT-BnCRY1 primary antibody was used. , A and B). Apart from the predominant 76-kD polypeptide, a fast-migrating polypeptide was always detected in the extracts of the light-grown tissue. This additional polypeptide may represent an altered phosphorylation status of CRY1 (Shalitin et al., 2002). The level of BnCRY1 was found to increase significantly in seedlings grown in light from 4 to 10 d. In comparison, however, the level of protein remained nearly constant in dark-grown seedlings, except some increase on day 6. The level of BnCRY1 was quite low in the dark-grown seedlings, in comparison to those grown in white light (70 μmol m−2 s−1) continuously for 6 d (Fig. 5C); in fact, a long exposure had to be given to obtain signals for the dark-grown samples (Fig. 5B). To study the effect of blue light on the accumulation of the BnCRY1 protein, 6-d-old etiolated Brassica seedlings were exposed to blue light (10 μmol m−2 s−1) for 36 h (Fig. 5D). On irradiation of seedlings with blue light, the BnCRY1 levels increased severalfold (the gel blot in Fig. 5D was exposed little longer than in Fig. 5C). The western-blot analysis revealed the presence of BnCRY1 in cotyledons, stems, buds, flowers, and siliques (Fig. 5E). The expression was particularly higher in cotyledons, stems, and siliques. However, despite repeated attempts, the BnCRY1 protein could not be detected in roots as well as mature leaves under the given conditions; note that the BnCRY1 transcript could be detected in both leaves and roots (Fig. 4C). The distribution pattern of BnCRY1 appears to be largely consistent with the role cry1 plays in regulating various growth and developmental processes in plants. The BnCRY1 Promoter Imparts Light Regulation to β-Glucuronidase in Transgenic Arabidopsis The core regulatory elements like TATA box and CAAT box were identified at positions −29 (AATATA) and −122 (TCCAAA), respectively. To demonstrate that BnCRY1 promoter is indeed light regulated, the 1,124-bp region upstream of BnCRY1 translational start site was analyzed using PLACE (plant cis-acting regulatory elements; http//www.dna.affrc.go.jp/htdocs/place; Higo et al., 1999). This search revealed the presence of light regulatory elements, like GT1 and GATA boxes (Terzaghi and Cashmore, 1995; Guilfoyle, 1997; Tyagi and Gaur, 2003), along with some circadian clock-regulated elements, like CIACADIANLELHC and CCA1ATLHCB1 (Wang et al., 1997; Piechulla et al., 1998; Table I Table I. Light and clock regulatory elements of the BnCRY1 promoter cis-Regulatory Elements . Nucleotide Position . CIACADIANLELHC −982 CCA1ATLHCB1 −144 GATA BOX −68, −73, −80, −546, −877, −884 GT1 BOX −149, −817, −822 I BOX −65, −71 cis-Regulatory Elements . Nucleotide Position . CIACADIANLELHC −982 CCA1ATLHCB1 −144 GATA BOX −68, −73, −80, −546, −877, −884 GT1 BOX −149, −817, −822 I BOX −65, −71 Open in new tab Table I. Light and clock regulatory elements of the BnCRY1 promoter cis-Regulatory Elements . Nucleotide Position . CIACADIANLELHC −982 CCA1ATLHCB1 −144 GATA BOX −68, −73, −80, −546, −877, −884 GT1 BOX −149, −817, −822 I BOX −65, −71 cis-Regulatory Elements . Nucleotide Position . CIACADIANLELHC −982 CCA1ATLHCB1 −144 GATA BOX −68, −73, −80, −546, −877, −884 GT1 BOX −149, −817, −822 I BOX −65, −71 Open in new tab ), in the BnCRY1 promoter upstream region. To determine if the transcription of BnCRY1 is light inducible, the functional analysis of its promoter was carried out in stably transformed Arabidopsis plants. Two constructs, one of 1.1 kb (CRY1P1∷GUS [β-glucuronidase]) and the other harboring 348 bp (CRY1P2∷GUS) upstream region from the translational start site, were designed. The smaller fragment bears most of the well-known light regulatory elements. Both of these constructs were mobilized into Arabidopsis via Agrobacterium-mediated root explant transformation. Light Activation of GUS Reporter by the BnCRY1 Promoter The T2 progeny seedlings of five independent transgenic events (for each construct) were grown in dark for 8 d and another set of 7-d-old dark-grown seedlings exposed to white light for 24 h. The analysis of both CRY1P1∷GUS and CRY1P2∷GUS harboring seedlings revealed that the GUS activity was higher in dark-grown seedlings irradiated with white light for 24 h, as compared to the dark control (Fig. 6 Figure 6. Open in new tabDownload slide Light induction of BnCRY1 promoter fused to GUS reporter gene. For light induction assay, the T2 transgenic Arabidopsis seedlings representing five independent lines for each construct (CRY1P1 and CRY1P2) were grown for 8 d in dark (D) or 7 d in dark followed by white light irradiation for 24 h (D + L). The data presented represent mean ± sd of GUS activity measured in seedlings of five independent lines for each construct. Figure 6. Open in new tabDownload slide Light induction of BnCRY1 promoter fused to GUS reporter gene. For light induction assay, the T2 transgenic Arabidopsis seedlings representing five independent lines for each construct (CRY1P1 and CRY1P2) were grown for 8 d in dark (D) or 7 d in dark followed by white light irradiation for 24 h (D + L). The data presented represent mean ± sd of GUS activity measured in seedlings of five independent lines for each construct. ). The increased GUS activity in seedlings exposed to light (for only 24 h) indicates that BnCRY1 promoter may be regulated by light. This study further provides evidence that the smaller deletion construct harboring several light regulatory elements may be sufficient for driving GUS expression in a light-dependent manner. Spatial Expression of the BnCRY1 Promoter-Driven GUS The GUS activity was determined histochemically to analyze the pattern of GUS expression and infer the promoter activity of the endogenous gene. The CRY1P1∷GUS construct, harboring 1.1-kb promoter, transcribed in all the organs like root, stem, leaf, floral bud, flower, and silique (Fig. 7, A–F Figure 7. Open in new tabDownload slide Tissue-specific expression analysis of BnCRY1 gene promoter fused to the GUS reporter gene. A and B show the localization of CRY1P1:GUS in 15-d-old transgenic Arabidopsis seedlings. C to F show the GUS expression in leaf, inflorescence buds, flower, and silique of CRY1P1 (T2) transgenic Arabidopsis plants. Figure 7. Open in new tabDownload slide Tissue-specific expression analysis of BnCRY1 gene promoter fused to the GUS reporter gene. A and B show the localization of CRY1P1:GUS in 15-d-old transgenic Arabidopsis seedlings. C to F show the GUS expression in leaf, inflorescence buds, flower, and silique of CRY1P1 (T2) transgenic Arabidopsis plants. ). The GUS activity was distinctly high in the cotyledons of the 15-d-old transgenic plants in comparison to the first pair of leaves. A low level of GUS expression with nonuniform pattern was also observed in the roots (Fig. 7, A and B). Thus, the GUS reporter construct exhibits a regulation essentially similar to that of the endogenous gene as observed by RT-PCR (Fig. 4). Stem Elongation and Decreased Anthocyanin Accumulation in Brassica Transgenics with Reduced BnCRY1 Levels To study the in vivo function of Brassica cry1, the antisense transgenic approach was adopted. However, instead of B. napus, Brassica juncea was selected because of its amenability in tissue cultures and higher transformation efficiency. The C-terminal region of BnCRY1 was amplified and cloned in the reverse orientation between the 35S promoter and nopaline synthase (NOS) polyadenylation site as terminator in a modified pCAMBIA 2310 vector (Fig. 8A Figure 8. Open in new tabDownload slide A, Diagrammatic representation depicting cloning strategy of the C terminus of BnCRY1 in modified pCAMBIA2301 vector for antisense construct. B, Western-blot analysis for quantitation of CRY1 in wild type and five different antisense transgenic lines (T2) of B. juncea (AsCRY1-1 to AsCRY1-5). C, Comparison of hypocotyl length between 8-d-old AsCRY1 and wild-type seedlings grown in dark or irradiated with white, blue, red, or far-red light. The phenotype of 45-d-old AsCRY1 and wild-type adult plants grown under field conditions during winter season in a containment facility is shown in the top right. AsCRY1, Antisense-CRY1 seedlings/plants. Please note that scale in different sections in C may not be same, although within the section the seedlings/plants are of same magnification. Figure 8. Open in new tabDownload slide A, Diagrammatic representation depicting cloning strategy of the C terminus of BnCRY1 in modified pCAMBIA2301 vector for antisense construct. B, Western-blot analysis for quantitation of CRY1 in wild type and five different antisense transgenic lines (T2) of B. juncea (AsCRY1-1 to AsCRY1-5). C, Comparison of hypocotyl length between 8-d-old AsCRY1 and wild-type seedlings grown in dark or irradiated with white, blue, red, or far-red light. The phenotype of 45-d-old AsCRY1 and wild-type adult plants grown under field conditions during winter season in a containment facility is shown in the top right. AsCRY1, Antisense-CRY1 seedlings/plants. Please note that scale in different sections in C may not be same, although within the section the seedlings/plants are of same magnification. ) and introduced into B. juncea via Agrobacterium-mediated transformation of hypocotyl segments. The transgenic plants were allowed to grow and the T1 seeds harvested for at least 10 independent plants. To check for the phenotype (hypocotyl growth) of the antisense-BnCRY1 (AsCRY1) transgenics, the hypocotyl length of 15-d-old T1 seedlings was measured. Under continuous white light (70 μmol m−2 s−1), all the seedlings examined showed elongated hypocotyl and petioles when compared to the wild type (Figs. 8C and 9A Figure 9. Open in new tabDownload slide Comparison of the hypocotyl growth response of the wild-type and AsCRY1 Brassica seedlings grown under white light (70 μmol m−2 s−1; A) and blue light (10 μmol m−2 s−1; B). Histograms represent the hypocotyl growth of 10-d-old wild-type and antisense (T1) seedlings developed from independent transformation events. The data presented represent mean ± sd of 10 seedlings for each transgenic line. Figure 9. Open in new tabDownload slide Comparison of the hypocotyl growth response of the wild-type and AsCRY1 Brassica seedlings grown under white light (70 μmol m−2 s−1; A) and blue light (10 μmol m−2 s−1; B). Histograms represent the hypocotyl growth of 10-d-old wild-type and antisense (T1) seedlings developed from independent transformation events. The data presented represent mean ± sd of 10 seedlings for each transgenic line. ). On illumination with continuous blue light (10 μmol m−2 s−1), all the transgenic lines showed decreased inhibition of hypocotyl elongation (Figs. 8C and 9B); because of shortage of seeds, the AsCRY1-2 lines could not be tested for hypocotyl growth inhibition assay under blue light. In comparison to seedlings grown under white light, the hypocotyl elongation growth was greater under blue light. This may be due to the inhibitory effect of far-red and red light present in the white light, which act in a combinatorial manner with blue light for complete realization of the hypocotyl/stem growth inhibition response (Folta and Spalding, 2001). However, the hypocotyl growth of the AsCRY1 transgenic seedlings was not affected differentially (vis-à-vis wild type) by either red or far-red light (Fig. 8C), indicating that impairment in Brassica cry1 function does not affect red or far-red response. The T1 plants were grown during the winter under a short photoperiod (November to April) in a containment facility. At the adult stage too, the AsCRY1 plants were distinctly taller (Fig. 8C). In addition, leaves too were relatively large and the stem diameter greater in the AsCRY1 plants. Whether cry1 in Brassica plays a direct role in regulating these traits or adversely affects the function of some other sensory photoreceptor will be our endeavor to examine. In addition to controlling plant height, cry1 also regulates anthocyanin accumulation. Earlier studies with Arabidopsis have shown that anthocyanin levels have an overriding effect of developmental cues, and are optimal in 3- to 4-d-old light-grown seedlings and decline thereafter (Feinbaum and Ausubel, 1988; Bharti and Khurana, 2003). The anthocyanin content was thus checked in two of the transgenic lines (AsCRY1-1 and AsCRY1-2) grown in blue light (10 μmol m−2 s−1) for various durations. In B. juncea too, anthocyanin levels were high in the 3-d-old seedlings and declined subsequently both in the wild type and the transgenic lines. However, the anthocyanin content was relatively lower in the transgenic lines, on any given day, with the effect being more pronounced in the line AsCRY1-1, particularly on days 3 and 5 (Fig. 10 Figure 10. Open in new tabDownload slide Comparison of the anthocyanin content in the wild-type and AsCRY1 Brassica seedlings at different stages of development. For experimental details, see “Materials and Methods.” Figure 10. Open in new tabDownload slide Comparison of the anthocyanin content in the wild-type and AsCRY1 Brassica seedlings at different stages of development. For experimental details, see “Materials and Methods.” ). To substantiate whether the long-hypocotyl phenotype and reduced anthocyanin accumulation in AsCRY1 transgenic seedlings was indeed due to reduced CRY1 levels, immunoblot analysis was performed with wild-type and transgenic seedlings. The CRY1 protein could not be detected or was considerably reduced in all five AsCRY1 transgenic lines examined (Fig. 8B). The copy number of AsCRY1 insert(s) was checked by Southern analysis, and one to three insertions in independent transgenic lines were detected (data not shown). A strict correlation between plant height and anthocyanin content, and gene dosage effect, will be possible only when a more detailed analysis of the homozygous lines of antisense-BnCRY1 transgenics becomes available. DISCUSSION As expected, owing to the genomic relatedness among Arabidopsis and Brassica, the BnCRY1 gene showed similar structural organization as AtCRY1. Sequence analysis of BnCRY1 revealed 94% similarity with the gene encoding HY4 flavin-type blue light photoreceptor (AF361588). The BLASTP analysis confirmed the sequence match of BnCRY1 with the other known cryptochromes, such as AtCRY2, LeCRY1, LeCRY2, OsCRY1, OsCRY2, CPH1, PpCRY1, AcCRY1, and SaPHR. A low percentage identity (31%–43%) was also observed with DNA photolyases (CPD photolyase [AE005817] and 6-4 photolyase [AB042254]). Although the length of introns varied, the intron and exon boundaries were conserved between BnCRY1 and AtCRY1 genes. The secondary structure of BnCRY1 consists mainly of α-helices and β-strands, which are randomly distributed throughout the primary amino acid sequence and thus do not cluster into groups like αβ domain and helical domain that are present in the secondary structure of photolyases (Brudler et al., 2003). Based on the evolutionary history and ancient duplication events, angiosperm cryptochromes have been grouped into two classes, CRY1 and CRY2 (Perrotta et al., 2000), with a recent addition of a novel class, CRY-DASH (Kleine et al., 2003). BnCRY1 showed a close relationship with AtCRY1. The CRY1 species belonging to the dicot family are more closely related to the monocot CRY1 rather than to the dicot CRY2. The fern (A. capillus-veneris) cryptochromes can be classified into three groups, indicating three duplication events; AcCRY1/AcCRY2 and AcCRY3/AcCRY4 were grouped in pairs, demonstrating recent duplication events. Similar phylogenetic analyses of cryptochromes were also performed by various groups (Imaizumi et al., 2000; Perrotta et al., 2000, 2001). The apparent presence of only CRY1-like genes in lower plants suggests that the CRY1 and CRY2 duplication is specific to the spermatophytes, which were estimated to have evolved approximately 360 Mya. To date, we do not have sequence information of cryptochromes from gymnosperms; therefore, the sequence of more cryptochromes from diverse groups including gymnosperms would further refine this picture. In a database search, various homologs of OsCRY1 and OsCRY2 were observed with a minor percentage of mismatches at the nucleotide level. In fact, OsCRY1 (1b) sequence has been characterized as OsCRY2 by Matsumoto et al. (2003), despite the fact that overall similarity between OsCRY1 and the renamed OsCRY2 is 78.8%; such high similarity is usually not observed between the two classes of cryptochromes. The phylogenetic analysis also grouped OsCRY2 under the monocot CRY1 class. Moreover, we have identified and sequenced an OsCRY2 gene (accession no. AJ298877) from rice with 38% and 39% amino acid identity with the two OsCRY species identified earlier (D. Kumar, P. Sharma, A.K. Tyagi, and J.P. Khurana, unpublished data). Along with ploidy, the chromosomal rearrangements like duplications and deletions play a major role in evolution. The copy number of CRY1 varies from species to species; for example, AtCRY1 is represented as a single copy in the Arabidopsis genome, whereas both tomato and barley harbor two copies of CRY1. However, despite the fact that B. napus is an amphidiploid (AACC), this study shows that the BnCRY1 gene in B. napus genome is most probably represented as a single copy. The genetic analysis of B. napus indicates that the genome of this amphidiploid is in a state of flux, and a large scale rearrangement due to duplication, deletion, and inversions or translocations of genetic segments has occurred (Sharpe et al., 1995). The loss of the other copy of CRY1 is an example of a secondary loss and supports the theory of major deletions in the C genome of B. napus, although it remains to be validated experimentally. The BnCRY1 transcript abundance, as well as protein levels, is regulated by light and developmental cues. Severalfold induction in the level of BnCRY1 protein was observed on illumination with white or blue light. The expression profile, along with in vivo promoter∷GUS fusion analysis in transgenic Arabidopsis, indicates the abundance of CRY1 in young and meristematic tissues like cotyledonary leaves, emerging inflorescence buds with inflorescence meristem, and young siliques with developing embryos. Although the CRY1 transcript could be detected in the root tissue by RT-PCR analysis, no protein could be detected. Thus, the tissue-specific expression of BnCRY1 may also be regulated at the translational level and/or protein degradation. It is interesting to note here that the BnCRY1 protein was undetectable in leaves under our experimental conditions, whereas in Arabidopsis a substantial amount of CRY1 accumulates (Lin et al., 1996a). The transcript abundance of BnCRY1 is associated with its role in cotyledon expansion, inhibition of stem growth, initiation of flowering, and early stages of silique formation. The promoter∷GUS fusion analysis in transgenic Arabidopsis demonstrated the contribution of BnCRY1 putative light-responsive elements in light-regulated expression, and indicates that the promoter fragment −348 bp upstream from the transcription start site may be sufficient to confer up-regulation by light. The activation of BnCRY1 promoter by light is consistent with the observation by Toth et al. (2001), who demonstrated that the AtCRY1 promoter-driven luciferase gene expression is up-regulated by light. However, earlier reports claim that the CRY1 protein levels do not change on exposure of Arabidopsis seedlings to light (Lin et al., 1996a; Shalitin et al., 2003), although it undergoes blue light-induced protein phosphorylation (Shalitin et al., 2003). In contrast to the light-independent expression of AtCRY1, light caused down-regulation of CRY1 levels in tomato and tobacco in a manner essentially similar to AtCRY2 (Ahmad et al., 1998a). On the other hand, this study provides evidence that BnCRY1 levels are rather up-regulated by white and blue light under the given experimental conditions. There is a possibility that regulation occurs both at the level of protein accumulation/stability and at the transcript level, such that one compensates for the other. The analysis of CRY1 from diverse species may divulge more on the molecular mechanism of light regulation for CRY1 itself. The decrease in growth inhibition of the AsCRY1 transgenics of Brassica under field conditions and also at the seedling stage provides evidence for the in vivo function of cry1 in regulating stem growth. The effect of cry1 in regulating plant height appears to be more pronounced in Brassica than reported for tomato and pea (Ninu et al., 1999; Platten et al., 2005a). This may either be a species-specific response or due to interplay with other sensory receptors involved in regulating plant height, as observed in case of pea in particular (Platten et al., 2005a). In the moss P. patens too, the analysis of the disruptants of two cryptochromes, PpCRY1a and PpCRY1b, revealed that they act redundantly to induce side branching in protonema and leaf growth in gametophores but cause inhibition of stem growth of gametophores specifically in response to blue light (Imaizumi et al., 2002), thus drawing a parallel between higher plants and the moss system with respect to cry1 responses. It was further shown that cryptochromes regulate moss development by repressing auxin signals usually involved in cell elongation and/or cell division. In higher plants like Arabidopsis, tomato, and Brassica too, the loss of inhibition of hypocotyl growth by light in the antisense plants may be due to altered expression of genes regulating cell division and cell wall expansion. In addition, cryptochrome modulates stem growth by repressing GA3 and auxin levels (Folta et al., 2003). The microarray analysis of mRNA isolated from blue light-treated wild-type and cry1 mutant seedlings revealed that CRY1 activates genes in the GA3 biosynthetic pathway. On illumination with blue light, GA20 oxidase and gibberellin β-hydroxylase are activated in cry1, which increases the active GA4. Thus, it is conceivable that all these factors may regulate hypocotyl and stem growth in antisense Brassica plants as well. The AsCRY1 transgenics also display reduced accumulation of anthocyanins and increased internode length and early flowering in at least some transgenic lines (data not shown), implicating the role of BnCRY1 in regulating these blue light-mediated responses. Although cry2 plays a more predominant role in controlling flowering time (Guo et al., 1998), in some studies cry1 too has been shown to influence flowering in Arabidopsis and pea, mostly through its interaction with other sensory receptors, including cry2, phyA, and phyB (Yang et al., 2000; Platten et al., 2005a). In fact, cry1 has a small inhibitory effect on flowering, especially in the absence of functional phyA (Platten et al., 2005a), and our preliminary finding with AsCRY1 lines of Brassica may represent a similar scenario. Besides analyzing the antisense BnCRY1 transgenics in more detail in the above context, it will be our endeavor to also raise the CRY1 overexpression lines and analyze the performance of both types of transgenics in the field (in a containment facility) to examine if their yield is not compromised due to altered photosensitivity to blue light. MATERIALS AND METHODS Plant Materials and Growth Conditions Seeds of Brassica napus var. ISN-706 and Brassica juncea var. RLM-198 were obtained from the Indian Agricultural Research Institute, New Delhi. Seeds were washed thoroughly and soaked overnight in running tap water. The imbibed seeds were spread on cotton saturated with reverse osmosis water. Plants were grown either in dark or light (16-h photoperiod) for desired duration in a culture room/growth chamber maintained at 24°C ± 1°C. Library Construction, Isolation, and Sequencing of the Genomic BnCRY1 Clone Total plant DNA was extracted from 8-d-old dark-grown Brassica seedlings following the procedure of Dellaporta et al. (1983). A genomic library of the high Mr DNA partially digested with MboI restriction enzyme was constructed in a Lambda Dash II replacement vector (Stratagene) according to manufacturer's instructions. A total of 4 × 105 recombinant plaques were screened under low stringency conditions (at 55°C) with an [α-32P]dATP-labeled (Megaprime DNA Labeling system) 2.3-kb AtCRY1 gene as a probe; it was amplified by PCR using a primer pair 5′-ATGTCTGGTTCTGTATCTGGTTGTG-3′ and 5′-TTACCCGGTTTGTGAAAGCCGTC-3′. For details of hybridization and washings, see Kulshreshtha et al. (2005). After three successive rounds of screening, phage DNA was isolated from the putative clones following the protocol of Santos (1991) and subjected to Southern analysis after digestion with desired restriction enzymes. One of the positive clones was sequenced using the Thermosequenase Dye Terminator Cycle sequencing kit (Amersham International) and a DNA sequencer (ABI Prism 377). Amplification of the BnCRY1 cDNA The cDNA was amplified by RT-PCR using primer pair (5′-CCATCGATATGTCTAATTCATGTTCAGGTG-3′ and 5′-GTCTCGAGGTGACAGCCGTCTCCA-3′) designed based upon BnCRY1 gene sequence obtained. Using 1 μg total RNA isolated from 4-d-old light-grown seedlings (Nagy et al., 1988), RT-PCR was carried out with Titan One Tube RT-PCR system (Roche). The PCR conditions were: 30 min at 50°C; 2 min at 94°C; 10 cycles (30 s at 94°C; 30 s at 55°C; 1 min at 68°C); 15 cycles (30 s at 94°C; 30 s at 55°C; 1 min at 68°C); and 5 s extension every cycle. The amplified cDNA was cloned in pBluescript SK+ and sequenced by primer walking. The 5′ UTR and 3′ UTR of BnCRY1 mRNA were completed using the Smart RACE cDNA amplification kit (BD Biosciences), according to manufacturer's protocol using gene-specific primers: 5′-GTGGAGAAAGGAACGAGGTTGTGGCACTG-3′ and 5′-CATGAGGCACTCTCGCAGATGTGGCAAC-3′. The PCR products were cloned into the pGEM-T Easy vector (Promega) and sequenced. Southern Blot and RT-PCR Analysis An aliquot of 15 μg of the plant DNA was digested independently with EcoRI, HindIII, SalI, XbaI, and XhoI restriction enzymes (Roche Molecular Biolabs) and Southern analysis performed as described earlier (Thakur et al., 2003). The full-length BnCRY1 gene labeled with [α-32P]dATP was used as a probe. Hybridization was carried out at 37°C or 42°C in hybridization buffer containing 50% formamide, 5× SSC, 5× Denhard's solution, 50 mm sodium phosphate, pH 6.5, and 250 μg/mL of denatured herring sperm DNA. The filters were washed at ambient temperature (25°C ± 1°C) with the following buffers in sequence: 5× SSC and 0.1% SDS for 10 min, 2× SSC and 0.1% SDS for 15 min, and 1× SSC and 0.1% SDS for 5 min. The filters were exposed to Kodak X-OMAT film with an intensifying screen at −80°C for the desired duration depending upon the counts retained on the filter. For expression analysis, total RNA was isolated from various tissues (frozen in liquid nitrogen) using a LiCl method (Nagy et al., 1988). To avoid DNA contamination, all samples were treated with DNase I (Roche Applied Science). RT-PCR was performed using primer pair 5′-GGCACCAGAGGAAGAAGGGCACT-3′, 5′-CATGGTGGTTCTGCAAGTAGC-3′, and PCR conditions were: 30 min at 50°C for RT; 2 min at 94°C; 10 cycles (30 s at 94°C; 30 s at 55°C; 1 min at 68°C); 15 cycles (30 s at 94°C; 30 s at 55°C; 1 min at 68°C); and 5 s extension every cycle. ACTIN served as the internal control. Antibody Production The BnCRY1 gene was restriction digested with BamHI and SalI enzymes, which have the internal sites present in the third exon, and the 350-bp fragment thus generated cloned into pQE-30 vector in the same reading frame as 6×His affinity tag. The fusion protein was expressed in Escherichia coli strain M15 and purified using a Ni-NTA affinity column (Qiagen). Immunizations were done by subcutaneous injection of 20 μg of emulsified protein per mouse followed by two booster doses with 15 μg of protein after every 2 weeks. Serum collected was stored at −80°C for later use. Immunoblot Analysis The total protein from plant tissue was extracted following the procedure of Zivy et al. (1983). Aliquots of the samples were denatured in the presence of SDS-PAGE sample buffer (15.5 mm Tris-Cl, pH 6.8, 720 mm 2-mercaptoethanol, 10% glycerol, 3% SDS). Equal amount (100 μg) of protein samples was resolved using 12.5% SDS-PAGE and subjected to western blotting (Towbin et al., 1979). The blots were probed with anti-6×His∷CT-BnCRY1 (1:1,000) antibody. The rabbit anti-mouse IgG (1:10,000) conjugated to horseradish peroxidase (Sigma) was used as secondary antibody. Proteins were detected using the ECL Plus Chemiluminescence kit (Amersham) according to manufacturer's instructions. Promoter Deletion Constructs and Transformation of Arabidopsis The 1.1-kb and 348-bp genomic fragments upstream of translation start site were PCR amplified using primers 5′-GCTCTAGACATGAGTTGGAATCAGTT-3′, 5′-GCTCTAGAATACATGTGCGGAGGTACG-3′, and 5′-CCTCTAGACTCAATCTTAAAGCTCTTAC-3′. The amplified promoter fragments were cloned in pBI101 vector (Jefferson et al., 1987) and mobilized to Agrobacterium tumefaciens strain GV3101 by chemical transformation (An et al., 1988). These deletion constructs were then transferred to Arabidopsis (Arabidopsis thaliana) using Agrobacterium-mediated root transformation protocol (Valvekens et al., 1992). The primary transformants were denoted as T0 and seeds (T1) obtained from various independent lines were analyzed for single copy insertion. The kanamycin-resistant lines segregating in 3:1 ratio were selected and allowed to self-fertilize. The T2/T3 seedlings were utilized for promoter analysis. Histochemical and Quantitative Analysis of GUS Activity Arabidopsis seedlings harboring the transgene promoter∷GUS fusions were stained overnight at 37°C in GUS assay buffer (1 m NaHPO4 buffer, pH 7.0, 50 mm EDTA, pH 8.0, 0.5 mm potassium ferrocyanide, 0.5 mm potassium ferricyanide, 0.1% Triton X-100, 1 mg/mL 5-bromo-4-chloro-3-indolyl-β-glucuronic acid). After overnight staining, destaining was done with 70% ethanol and chlorophyll removed by washing at least two to three times. The samples were photographed employing an epifluorescence microscope (Nikon EFD-3). For quantitative analysis, GUS activity was measured as described by Jefferson et al. (1987), using the substrate 4-methylumbelliferyl glucuronide. For each promoter∷GUS construct, several independent insertion lines were analyzed. Brassica Transformation for Raising Antisense-BnCRY1 Transgenics The BnCRY1 cDNA was amplified by PCR: 5 min at 94°C for 30 cycles (94°C for 30 s, 65°C for 30 s, and 70°C for 45 s), followed by incubation at 70°C for 7 min, using the primer pair 5′CRYSacI GGGAGCTCGAAGAAGGACTTGGCGAT and 3′CRYBamHI CGCGGATCCAACTATTTCATGGTGGTTC. The antisense fragment (corresponding to the C terminus of BnCRY1) thus obtained was cloned in the modified pCAMBIA 2310 vector using BamHI and SacI restriction sites and introduced in B. juncea var. RLM-198 hypocotyl sections via Agrobacterium. For regeneration and shoot formation from putative transgenics, the hypocotyl sections cocultivated with Agrobacterium were placed on agar-gelled Murashige and Skoog medium containing 1 mg/L naphthylacetic acid, 1 mg/L benzylaminopurine, 3.4 mg/L AgNO3, 250 mg/L cefotaxime, and 50 mg/L kanamycin. The callus or regenerating plantlets were subcultured on fresh medium after every 15 d, for two to three times, until the shootlets appeared. The healthy shoots were transferred to the rooting medium containing 0.1 mg/L naphthylacetic acid and 50 mg/L kanamycin. As soon as a small root mass was observed, the plantlets were transferred to earthen pots containing garden soil. The T0 plants raised in a growth room were allowed to set seed at 24°C ± 1°C, under continuous light (100 μmol m−2 s−1). The adult (T1) transgenic plants were grown under field conditions in a containment facility. Hypocotyl Elongation Assay and Anthocyanin Estimation For hypocotyl elongation growth assay, seeds were germinated in clay pots containing garden soil and irradiated with white light, blue light, red light, or far-red light in the cabinets kept in a growth room. After 10 d of growth, the hypocotyl length of 10 seedlings each of wild type and antisense-BnCRY1 line was measured and averaged. The experiments were repeated at least once with essentially similar results, and, thus, the data of only a representative experiment are presented. For anthocyanin estimation, the wild-type and antisense seedlings were grown on Murashige and Skoog medium supplemented with 2% Suc and 0.8% agar under continuous blue light (10 μmol m−2 s−1) for 3 to 5 d. The anthocyanins from three seedlings of each line were extracted independently overnight in 3 mL of acidic (1% HCl) methanol in a dark chamber. To the acidic methanol extract, 2 mL of water and 3 mL of chloroform were added and mixed thoroughly. The absorbance of aqueous phase was determined at 530 nm as a measure of anthocyanin levels on a per-seedling basis. Light Source and Energy Measurements For irradiation of Brassica seedlings with monochromatic lights, blue, red, and far-red light sources were custom designed. The blue and red light sources consist of 31 × 12 array of light-emitting diodes (LED) selected for their spectral quality. Each LED (λmax 465 nm for blue and λmax 652 nm for red light) was powered by a variable voltage source capable of 10 mA forward current. For far-red irradiation, four epoxy lens type infrared illuminators (LED735-66-60; Roithner Lasertechnik), comprising 60 high efficiency diode chips each (λmax 735 nm), were mounted to give uniform illumination. Each LED was powered by 9-V, 1,000-mA regulator with heat sink mounted series pass transistor. The forward current and the height of the source from the plant material were adjusted to yield uniform irradiation of blue (10 μmol m−2 s−1), red (8 μmol m−2 s−1), and far-red light (2.5 μmol m−2 s−1), respectively, as measured by the LI-189 radiometer (LI-COR) over an area measuring 40 cm × 30 cm. White light (70 μmol m−2 s−1) was provided from a bank of Cool Daylight fluorescent lamps (Philips, TL 5800 K). Sequence data from this article can be found in the GenBank/EMBL data libraries under the following accession numbers: AtCRY, Q43125; LeCRY1, AAD44161; PsCRY1, AAO23970; OmCRY1, AAR08429; LeCRY1B, AAL02092; OsCRY1b, BAB70688; SbCRY2, AAN37909; OsCRY1a, BAB70686; AcCRY2, BAA32808; AcCRY1, BAA32807; PpCRY1a, BAA83338; PpCRY1b, BAB70665; AcCRY3, BAA32809; AcCRY4, BAA88423; OsCRY2, BAC78798; LeCRY2, AAF72556; AcRY5, BAA88424; AtCRY2, AAL16379; ArCRY2-3, BAC67178; ArCRY2-4, BAC67179; ArCRY2-1, BAC67176; PsCRY2b AAO23972; OsCRY1, BAA82885; ArCRY2-2, BAC67177; PsCRY2a, AAO23971; OsCRY2 (indica var.), CAC82538; CrCPH1, AAC37438; AtCRYDASH, NP_568461; and SaPHR, X72019. ACKNOWLEDGMENTS We sincerely thank Drs. Margaret Ahmad and Akhilesh K. Tyagi for useful suggestions, Dibyendu Kumar for assistance in phylogenetic analysis, and Dr. Anil K. Tyagi for providing facilities and assistance in raising anti-BnCRY1 antibodies. 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The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Jitendra P. Khurana ([email protected]). Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.105.076323. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Sauret-Güeto, Susanna; Botella-Pavía, Patricia; Flores-Pérez, Úrsula; Martínez-García, Jaime F.; San Román, Carolina; León, Patricia; Boronat, Albert; Rodríguez-Concepción, Manuel

doi: 10.1104/pp.106.079855pmid: 16531478

Roig-Villanova, Irma; Bou, Jordi; Sorin, Céline; Devlin, Paul F.; Martínez-García, Jaime F.

doi: 10.1104/pp.105.076331pmid: 16565297

Abstract The phytochrome (phy) photoreceptors modulate plant development after perception of light. Upon illumination of etiolated seedlings, phys initiate a transcriptional cascade by directly transducing light signals to the promoters of genes encoding regulators of morphogenesis. In light-grown plants, however, little is known about the transcriptional cascade modulated by phys in response to changes in light. The phy entry points in this cascade are completely unknown. We are particularly interested in the shade avoidance syndrome (SAS). Here we describe a subset of six genes whose expression is rapidly modulated by phys during both deetiolation and SAS in Arabidopsis (Arabidopsis thaliana). Using cycloheximide, we provide evidence that four of these phy rapidly regulated (PAR) genes are direct targets of phy signaling during SAS, revealing these genes as upstream components of the transcriptional cascade. Promoter-β-glucuronidase fusions confirmed that PAR genes are photoregulated at the transcriptional level. Analysis of gene expression in light signal transduction mutants showed that COP1 and DET1 (but not DET2 or HY5) play a role in modulating PAR expression in response to shade in light-grown seedlings. Moreover, genetic analyses showed that one of the genes identified as a direct target of phy signaling was phy-interacting factor 3-like-1 (PIL1). PIL1 has previously been implicated in SAS in response to transient shade, but we show here that it also plays a key role in response to long-term shade. The action of PIL1 was particularly apparent in a phyB background, suggesting an important negative role for PIL1 under dense vegetation canopies. Light regulates different aspects of plant growth and development, such as seed germination, stem elongation, and flowering time. Photoreceptors perceive light and transduce the signal to physiological responses. The red (R) and far-red (FR) light-absorbing phytochromes (phys) play a major role in controlling many of the aforementioned responses. Phys exist in two photointerconvertible forms. After synthesis of the R-absorbing form (Pr; λmax 666 nm), photoconversion to the active FR-absorbing form (Pfr; λmax 730 nm) is required for all responses. FR irradiation can subsequently reconvert Pfr to the Pr form. In Arabidopsis (Arabidopsis thaliana), phys are encoded by a small gene family of five members (PHYA–PHYE). PhyA is exclusively responsible for controlling seedling deetiolation under continuous FR (FRc) and phyB has the major role in this response under continuous R (Rc; Quail, 2002; Schäfer and Bowler, 2002; Chen et al., 2004). In light-grown plants, phyB, phyD, and phyE coregulate other responses, such as those known as the shade avoidance syndrome (SAS; Smith and Whitelam, 1997). In dark-grown seedlings, phyA and phyB are cytosolic, inactive proteins that migrate to the nucleus upon light activation (Quail, 2002; Schäfer and Bowler, 2002). Both Pfr formation and nuclear translocation are necessary for phyB signaling activity (Huq et al., 2003). In the nucleus, phy-interacting factor-3 (PIF3), a basic helix-loop-helix (bHLH) protein, binds preferentially to the Pfr forms of phyA and phyB (Ni et al., 1998). PIF3 simultaneously binds to Pfr and a G-box motif located in the promoter region of several genes (Martínez-García et al., 2000) and exhibits phy-modulated transcriptional activity at target promoters (Ni et al., 1998; Martínez-García et al., 2000; Kim et al., 2003). This is moderated by rapid phy-induced degradation in the nucleus (Bauer et al., 2004; Monte et al., 2004). Further genomic analyses expanded this view and led to the proposal that, during deetiolation, light might implement the photomorphogenic program by regulating a complex transcriptional cascade, probably initiated by direct phy regulation of gene expression of a master set of transcriptional regulators via different PIFs (Tepperman et al., 2001). Indeed, different PIFs or PIF-likes (PILs), all belonging to the bHLH class of transcription factors (TFs), play important roles in phy signal transduction, very likely participating in the early steps of this transcriptional cascade (Huq and Quail, 2002; Kim et al., 2003; Bauer et al., 2004; Huq et al., 2004; Oh et al., 2004). Most of the known phyA and phyB signaling components have been identified by genetic approaches based on analysis of seedling deetiolation. These screens have yielded two major classes of mutants: the cop/det/fus class of global regulators and a miscellaneous group, including components that appear to be specific for either phyA, phyB, or both phy signals. Null mutants of the COP/DET/FUS family of nuclear-localized factors display constitutive deetiolation in darkness. COP1 encodes a repressor shown to be part of a large protein complex and to have E3 ubiquitin ligase activity toward some TFs (Saijo et al., 2003; Seo et al., 2003). In dark-grown seedlings, COP1 accumulates in the nucleus, where it interacts with TFs that trigger deetiolation, such as HY5, HYH, LAF1, and HFR1 (Holm et al., 2002; Seo et al., 2003; Duek et al., 2004), targeting them for proteasome-mediated degradation with the involvement of the COP9 signalosome and COP10, an E2 ubiquitin-conjugating enzyme variant (Suzuki et al., 2002). Soon after illumination, rapid changes in both gene expression (over the first hour of light treatment) and protein abundance (within 2 h) of these TFs initiate deetiolation. In the longer term (several hours), the slow light-mediated nuclear depletion of COP1 relieves the repression of the TFs, eventually resulting in seedling photomorphogenesis (Osterlund et al., 1999, 2000; Hardtke and Deng, 2000). DET1 and DDB1, a DET1-interacting factor (Schroeder et al., 2002), have been shown to form a complex with COP10, called the CDD complex, which interacts with the COP1 complex (Yanagawa et al., 2004). Therefore, it has been suggested that COP1 and DET1 act together to regulate ubiquitin proteasome-mediated degradation of photomorphogenesis-promoting TFs in darkness (Yanagawa et al., 2004). PhyA signaling is also directly regulated during deetiolation by light-induced degradation of the phyA photoreceptor itself, and by COP1 E3 activity in a process that implicates the proteasome-mediated degradation machinery (Seo et al., 2004). An important gap exists in our understanding of phy action because the functioning of phys in established light-grown plants is very poorly understood. Under these conditions, phyB, rather than phyA, is most abundant; the photoequilibrium between the Pfr and Pr forms is already established; phys are already nuclear (Kircher et al., 2002); and the amount of nuclear COP1 is low (although it is still sufficient to modulate development; von Arnim et al., 1997). There are also many other differences between light-grown and etiolated seedlings, such as large changes in gene expression patterns (Tepperman et al., 2001; Ma et al., 2003). We have focused on the analysis of SAS, one of the best-characterized phy-dependent responses in light-grown plants. SAS refers to a set of responses (which affect hypocotyl and/or stem elongation, cotyledon expansion, petiole length, flowering time, etc.) triggered by a reduction in the R to FR ratio associated with the proximity of neighboring vegetation (Smith, 1982; Smith and Whitelam, 1997). Changes in the R to FR ratio are detected by plants as a change in the relative proportions of Pr and Pfr. Although phyB is the major phy controlling SAS, genetic and physiological analyses have shown that other phys act in conjunction with phyB in the control of some aspects of SAS-driven development, like flowering time (phyD and phyE), petiole elongation (phyD and phyE), and internode elongation between rosette leaves (phyE; Devlin et al., 1998, 1999). Downstream of the phys, information about the components involved in the SAS control is limited. Previous work showed that expression of three genes, ATHB2/HAT4 (hereafter ATHB2), ATHB4, and PIL1, is quickly and reversibly regulated by simulated shade (Carabelli et al., 1993, 1996; Salter et al., 2003). Genetic approaches have demonstrated roles for ATHB2 and PIL1 in the SAS response (Steindler et al., 1999; Salter et al., 2003). ATHB2 has also been shown to affect morphology throughout the life history of Arabidopsis (Schena et al., 1993). A role for PIL1 has, thus far, only been demonstrated in the responses of hypocotyls of young seedlings to transient exposure to shade (Salter et al., 2003). Very recently, another gene, HFR1, has been shown to be rapidly up-regulated by simulated shade and to negatively regulate SAS responses, likely contributing to a fitting response to canopy shade in nature (Sessa et al., 2005). Genomic analyses have also identified dozens of additional SAS-regulated genes (Devlin et al., 2003), suggesting that the SAS program is implemented by phy regulation of a complex transcriptional cascade, as is postulated for deetiolation. However, very little is known about how phy perception is translated into changes in gene expression, what the cellular factors or biochemical activities involved are, and whether the large-scale changes in gene expression after simulated shade are necessary for implementing the morphological and physiological modifications that result in the measured SAS responses. Ultimately, these plastic responses are initiated by the proximity of neighboring plants and evoke appropriate competitive or survival reactions by which the plant attempts to overgrow or to accelerate flowering and early seed production. By exploring available genomic and molecular information in Arabidopsis, we have identified in this work a subset of genes whose expression is rapidly regulated by phys after SAS induction by simulated shade. Pharmacological evidence strongly suggests that some of these phy rapidly regulated (PAR) genes are primary targets of phy action during SAS. Promoter-β-glucuronidase (GUS) fusions confirmed that PAR genes are photoregulated at the transcriptional level, with COP1 and DET1 (but not HY5) playing a role in modulating their expression during SAS. Finally, we show that one of the PAR genes, PIL1, controls SAS responses in addition to the previously reported effect on hypocotyl elongation upon transient exposure to shade. RESULTS Early Phy-Regulated Genes during Both Deetiolation and SAS as Candidates for Primary Targets of Phy Action We aimed to identify primary target genes of phy signaling within the transcriptional cascade operating after induction of SAS in light-grown Arabidopsis plants. We reasoned that at least some of these genes should be rapidly regulated by phys in other physiological contexts, such as seedling deetiolation. Indeed, we observed that some of the Arabidopsis genes known to be rapidly up-regulated by simulated shade in light-grown plants (ATHB2, ATHB4, and PIL1) were also rapidly down-regulated after seedling deetiolation. Although light regulates the expression of these genes during deetiolation and SAS in opposite directions (repression and activation, respectively), in both cases their expression is down-regulated by phy action. To identify other PAR genes showing this pattern of expression during both processes, we first looked for genes that were rapidly down-regulated during seedling deetiolation under FRc (Tepperman et al., 2001). Besides ATHB2, ATHB4, and PIL1, we identified genes encoding an unknown factor (At2g42870; hereafter PAR1), a putative pectinesterase (At4g25260; RIP), a β-expansin (At2g20750; β-EXP), and three TFs originally classified as late repressed, but nonetheless showing a clear down-regulation only 1 h after illumination: SCL21 (At2g04890), HAT2 (At5g47370), and HAT7 (At5g15150; Tepperman et al., 2001). Subsequent microarray experiments showed that some of these genes were also rapidly up-regulated by simulated shade (Devlin et al., 2003). To confirm the microarray data, expression of the selected PAR genes was evaluated by RNA-blot analysis in seedlings grown under continuous white light (W) before and 1 h after illumination with W enriched with FR (W + FR, simulated shade). As expected (Carabelli et al., 1993, 1996; Salter et al., 2003), expression of ATHB2, ATHB4, and PIL1 was up-regulated by simulated shade in the three different ecotypes used (data not shown). W + FR also induced expression of HAT2, PAR1, and RIP (Fig. 1 Figure 1. Open in new tabDownload slide Expression of PAR genes in response to simulated shade. A, Experimental configuration used to study the effect of different R to FR ratios on PAR expression. No-0 seedlings grown for 7 d (d7) under continuous white light (W; white box), were treated for 1 h with W enriched in FR applied laterally. The resulting R to FR ratios were 0.07 (a), 0.09 (b), 0.13 (c), 0.20 (d), and 0.31 (e). B, RNA analysis of expression of PAR genes in seedlings grown as indicated in A. Figure 1. Open in new tabDownload slide Expression of PAR genes in response to simulated shade. A, Experimental configuration used to study the effect of different R to FR ratios on PAR expression. No-0 seedlings grown for 7 d (d7) under continuous white light (W; white box), were treated for 1 h with W enriched in FR applied laterally. The resulting R to FR ratios were 0.07 (a), 0.09 (b), 0.13 (c), 0.20 (d), and 0.31 (e). B, RNA analysis of expression of PAR genes in seedlings grown as indicated in A. ). The up-regulated expression of these genes was sustained in seedlings left for up to 3 h under simulated shade (data not shown). Changes in the R to FR ratio, however, did not affect HAT7 and SCL21 expression, whereas β-EXP was undetectable in the light-grown seedlings used in this study (data not shown). The six PAR genes demonstrated to be rapidly up-regulated by simulated shade (ATHB2, ATHB4, HAT2, PAR1, PIL1, and RIP) were selected for further study. Changes in the R to FR Ratio and Phy Levels Impair PAR Gene Expression To further substantiate the dependence on light quality of the observed changes in PAR gene expression, seedlings were exposed to different R to FR ratios for 1 h (Fig. 1A). The level of simulated shade-induced up-regulation ranged from a maximal response for the two lowest R to FR ratios (0.07–0.09) to a lesser effect for the highest R to FR ratio (0.31) in all genes (Fig. 1B). The results indicate that the observed rapid up-regulation of ATHB2, ATHB4, HAT2, PAR1, PIL1, and RIP is truly dependent on simulated shade and proportional to the degree of shading, consistent with physiological SAS responses (Smith, 1982). The role of phys in controlling the expression of the identified PAR genes in Arabidopsis was confirmed using transgenic lines overexpressing oat (Avena sativa) phyA (AOX; Boylan and Quail, 1991) or Arabidopsis phyB (ABO; Wagner et al., 1991). Seedlings grown in W were either maintained in W or transferred to W + FR for 24 h. Wild-type seedlings under simulated shade showed elongated hypocotyls relative to those maintained in W, which is evidence of an active SAS response (Fig. 2A Figure 2. Open in new tabDownload slide Effect of increased levels of phyA (AOX) and phyB (ABO) on PAR expression and hypocotyl length induced by simulated shade. A, Changes in hypocotyl length in response to simulated shade were analyzed in wild-type (No-0), AOX, and ABO seedlings. Seedlings grown for 7 d under W were either maintained in W (white bars) or transferred to W + FR (gray bars) for 24 h, after which mean (±se) hypocotyl lengths were measured. B, RNA analysis of PAR gene expression in Arabidopsis wild-type, AOX, and ABO seedlings harvested at 0, 0.5, and 1 h after W + FR treatment. Figure 2. Open in new tabDownload slide Effect of increased levels of phyA (AOX) and phyB (ABO) on PAR expression and hypocotyl length induced by simulated shade. A, Changes in hypocotyl length in response to simulated shade were analyzed in wild-type (No-0), AOX, and ABO seedlings. Seedlings grown for 7 d under W were either maintained in W (white bars) or transferred to W + FR (gray bars) for 24 h, after which mean (±se) hypocotyl lengths were measured. B, RNA analysis of PAR gene expression in Arabidopsis wild-type, AOX, and ABO seedlings harvested at 0, 0.5, and 1 h after W + FR treatment. ). As expected, such a response was significantly attenuated in phy-overexpressing seedlings, with ABO seedlings displaying stronger inhibition of the response than AOX seedlings (Fig. 2A). PAR gene expression was also affected in both AOX and ABO lines because reduced PAR transcript levels were detected before and after simulated shade treatment compared to wild-type seedlings (Fig. 2B). Again, ABO seedlings displayed the strongest effect. Together, the results indicate that high phy levels maintain a strong repression of PAR expression in light-grown seedlings. The Rapid Phy-Regulated Expression of Some PAR Genes Does Not Require de Novo Protein Synthesis To address whether any of the identified PAR genes might be a primary phy target, we used the protein synthesis inhibitor cycloheximide (CHX). The rationale behind this experiment was that the light response of primary phy target genes would be unaffected by CHX because protein synthesis would not be required. To validate our experimental conditions, we used the previously characterized LhGR-N(4c) line in which the GUS reporter gene is a direct target gene of the TF LhGR (Craft et al., 2005). Nuclear translocation (hence, transcriptional activity) of LhGR is dependent on treatment with dexamethasone (DEX), a synthetic glucocorticoid. In the absence of CHX, seedlings exhibited strong DEX-dependent GUS staining, as expected (Fig. 3A Figure 3. Open in new tabDownload slide Identification of phy primary target genes within the PAR genes. A, GUS activity in seedlings of the DEX-inducible LhGR-N(4c) line 24 h after simultaneous ±CHX and ±DEX treatment, as shown in the top diagram. B, GUS activity in seedlings of the DEX-inducible LhGR-N(4c) line 2 h after ±CHX treatment and 3 h after ±DEX treatment, as schematized in the top diagram. C, Analysis of the effect of CHX on light-regulated PAR gene expression. Two hours before altering light quality, 7-d-old No-0 seedlings were treated without CHX (−CHX) or with CHX (+CHX). W-grown seedlings were irradiated for 1 h with W + FR and then transferred to W for 1 h, as schematized in the top diagram. Plant material was harvested immediately before (0 h; white circle), 1 h (triangle), and 2 h (gray circle) after beginning light treatments. RNA-blot analyses of PAR expression in these samples, as well as the normalized relative levels of expression for one representative experiment, are shown. Figure 3. Open in new tabDownload slide Identification of phy primary target genes within the PAR genes. A, GUS activity in seedlings of the DEX-inducible LhGR-N(4c) line 24 h after simultaneous ±CHX and ±DEX treatment, as shown in the top diagram. B, GUS activity in seedlings of the DEX-inducible LhGR-N(4c) line 2 h after ±CHX treatment and 3 h after ±DEX treatment, as schematized in the top diagram. C, Analysis of the effect of CHX on light-regulated PAR gene expression. Two hours before altering light quality, 7-d-old No-0 seedlings were treated without CHX (−CHX) or with CHX (+CHX). W-grown seedlings were irradiated for 1 h with W + FR and then transferred to W for 1 h, as schematized in the top diagram. Plant material was harvested immediately before (0 h; white circle), 1 h (triangle), and 2 h (gray circle) after beginning light treatments. RNA-blot analyses of PAR expression in these samples, as well as the normalized relative levels of expression for one representative experiment, are shown. ). However, when CHX was coapplied with DEX, it completely blocked GUS activity after 24 h (Fig. 3A), indicating that CHX treatment efficiently inhibited de novo synthesis of the GUS protein. The inhibition of GUS synthesis by CHX was observed as early as 2 h after coapplication of CHX and DEX (data not shown). However, the application of DEX 1 h before CHX treatment resulted in much more reproducible results (Fig. 3B) and confirmed that treatment of seedlings with CHX for 2 h efficiently blocked protein synthesis. In subsequent experiments, we treated seedlings with CHX for 2 h before initiating simulated shade treatments for target-gene analysis in planta. Seedlings grown under W were transferred to W + FR for 1 h and then returned to W for an additional hour. In the absence of CHX (−CHX), the levels of PAR mRNAs increased after simulated shade and decreased upon transferring the seedlings back to W (Fig. 3C), confirming that PAR gene expression is, indeed, rapidly and reversibly regulated by changes in light quality. In the presence of CHX (+CHX), the expression levels of a number of PAR genes were altered even before the simulated shade treatment (Fig. 3C). The strongest effect was a clear increase in the expression of HAT2, a gene previously shown to be induced by CHX treatment (Sawa et al., 2002). Transcript levels of HAT2 and RIP were unaltered by simulated shade in CHX-treated seedlings. By contrast, up-regulation of ATHB2 and ATHB4 transcript levels by simulated shade was dramatically increased in CHX-treated seedlings, whereas a weaker up-regulation was observed for PAR1 and PIL1 compared to mock-treated seedlings. Most significantly, the reversible and photoregulated response of these latter four genes was qualitatively independent of the CHX treatment. We concluded that the shade-mediated up-regulation of a subset of PAR genes (ATHB2, ATHB4, PAR1, and PIL1) does not require de novo protein synthesis, consistent with these being direct targets of phy action. RIP can be considered as a secondary target of phy action. The high sensitivity of HAT2 expression to CHX does not allow us to ascertain whether this is a phy primary target. SAS-Associated Changes in PAR Gene Expression Are Impaired in cop1 Mutants COP1, a master integrator of light signaling during seedling deetiolation, has also been shown to participate in shade-induced hypocotyl elongation (McNellis et al., 1994) and to regulate the abundance of HFR1 (Duek et al., 2004), a TF encoded by a gene recently identified to be rapidly up-regulated by simulated shade (Sessa et al., 2005). To investigate whether COP1 might also have a role in the regulation of the PAR genes identified here as direct targets of phy signaling during SAS, we used the nonlethal loss-of-function alleles cop1-4 and cop1-6 (Deng et al., 1992). Mutant and wild-type seedlings grown in W were transferred to W + FR and samples were collected at 0-, 0.5-, and 1-h time points. RNA-blot analysis showed that PAR1 and PIL1 displayed reduced photomodulation in both cop1 mutants compared to that observed in wild-type seedlings (Fig. 4A Figure 4. Open in new tabDownload slide Role of COP1 on PAR expression induced by simulated shade or after deetiolation under FRc. Only PAR genes identified here to be direct targets of phy action are analyzed. A, RNA-blot analysis of PAR expression in Arabidopsis wild-type (Col-0), cop1-4, and cop1-6 seedlings harvested at 0, 0.5, and 1 h after W + FR treatment. B, RNA-blot analysis of PAR expression in Arabidopsis wild-type and cop1-6 seedlings harvested at 0 and 1 h after deetiolation. Figure 4. Open in new tabDownload slide Role of COP1 on PAR expression induced by simulated shade or after deetiolation under FRc. Only PAR genes identified here to be direct targets of phy action are analyzed. A, RNA-blot analysis of PAR expression in Arabidopsis wild-type (Col-0), cop1-4, and cop1-6 seedlings harvested at 0, 0.5, and 1 h after W + FR treatment. B, RNA-blot analysis of PAR expression in Arabidopsis wild-type and cop1-6 seedlings harvested at 0 and 1 h after deetiolation. ). The same was true for ATHB2 and ATHB4, but the effect was weaker (Fig. 4A). These results reveal that COP1 has a role in regulating expression of the identified phy primary target genes in response to simulated shade. To evaluate whether phy-regulated expression of these PAR genes is also affected by COP1 during deetiolation, wild-type and cop1-6 seedlings were grown for 4 d in the dark and then illuminated with FRc. Samples were harvested before (0 h) and 1 h after FRc treatment and used for RNA-blot analysis (Fig. 4B). As expected, all four genes were rapidly and strongly down-regulated after light treatment in the wild type. In etiolated cop1-6 seedlings, PAR1 mRNA levels were higher than those in wild-type seedlings both before and after treatment, whereas minor differences were observed for ATHB2 and PIL1. Transcript levels of ATHB4 in mutant seedlings were hardly detectable in cop1 mutants under any conditions (Fig. 4B). Most importantly, cop1-6 seedlings showed clear phy-mediated repression of ATHB2, PAR1, and PIL1, as was observed in wild-type seedlings (Fig. 4B). The low levels of ATHB4 mRNAs made it difficult to draw any conclusion as to the role of COP1 in the phy-mediated changes of this gene during FRc-induced deetiolation. These results indicate that, unlike the situation observed for SAS, COP1 does not play a major role in the early repression of at least three of the analyzed PAR genes during FRc-mediated deetiolation. Photomodulation of Phy Primary Target Genes Is Attenuated in det1, But Not in det2 or hy5 Mutants COP1 directly interacts with HY5, another photomorphogenic regulator with a role in seedling deetiolation (Oyama et al., 1997; Ang et al., 1998). Because HY5 is a TF, we aimed to investigate whether it might also participate in controlling the expression of the identified direct target genes of phy signaling during SAS. Mutant hy5-1 (a null allele) and wild-type seedlings were grown in W and then transferred to W + FR. After collecting samples at 0-, 0.5-, and 1-h time points, RNA-blot analyses were performed. The hy5-1 mutation did not dramatically affect expression of the analyzed PAR genes under W or their photomodulated expression after simulated shade (Fig. 5A Figure 5. Open in new tabDownload slide Role of HY5 on hypocotyl length and PAR expression induced by simulated shade. A, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Landsberg erecta) and hy5-1 seedlings. Only PAR genes identified here to be direct targets of phy action are analyzed. B, Changes in hypocotyl length in response to simulated shade in wild-type and hy5-1 seedlings. Figure 5. Open in new tabDownload slide Role of HY5 on hypocotyl length and PAR expression induced by simulated shade. A, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Landsberg erecta) and hy5-1 seedlings. Only PAR genes identified here to be direct targets of phy action are analyzed. B, Changes in hypocotyl length in response to simulated shade in wild-type and hy5-1 seedlings. ). These results suggest that, unlike COP1, HY5 is not required for transducing shade-triggered signals to early molecular SAS responses. Regarding physiological SAS responses, transfer of mutant hy5-1 seedlings to W + FR for 24 h still resulted in a significant response (Fig. 5B), suggesting that HY5 is not needed for hypocotyl elongation in response to simulated shade. Unlike the loss of HY5 function, the loss of COP1 function results in a strong pleitropic phenotype that might somehow be responsible for the observed effects on PAR expression after simulated shade. To evaluate this possibility, photomodulated PAR expression was analyzed in two further mutants of the same constitutively photomorphogenic class as cop1: det1-1 (Pepper et al., 1994) and det2-1 (Li et al., 1996). When grown in the dark, both cop and det mutants exhibit an obvious photomorphogenic phenotype (inhibition of hypocotyl growth, expansion of cotyledons, development of primary leaves, and accumulation of anthocyanins). However, the molecular lesions involved affect very different biochemical and physiological processes. The nuclear DET1 protein has been suggested to participate with COP1 in the degradation of positive regulators of photomorphogenesis via the proteasome system (Yanagawa et al., 2004), whereas DET2 is an enzyme involved in brassinosteroid biosynthesis (Li et al., 1996). A similar experiment to that described for cop1 (Fig. 4A) and hy5 (Fig. 5A) mutants was carried out using det1-1 and det2-1 seedlings (Fig. 6 Figure 6. Open in new tabDownload slide Role of DET1 and DET2 on PAR expression during simulated shade. A, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Col-0) and det1-1 seedlings. B, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Col-0) and det2-1 seedlings. Only PAR genes identified here to be direct targets of phy action are analyzed. Figure 6. Open in new tabDownload slide Role of DET1 and DET2 on PAR expression during simulated shade. A, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Col-0) and det1-1 seedlings. B, RNA-blot analysis of the expression of PAR genes in Arabidopsis wild-type (Col-0) and det2-1 seedlings. Only PAR genes identified here to be direct targets of phy action are analyzed. ). The reduced photomodulation observed for PAR1 and PIL1 and, to a lesser extent, for ATHB2 and ATHB4 in cop1 seedlings compared to the wild type (Fig. 4A), was also observed in det1-1 seedlings (Fig. 6A). By contrast, det2-1 seedlings displayed wild-type (PAR1 and PIL1) or slightly increased (ATHB2 and ATHB4) photomodulation of PAR expression (Fig. 6B), confirming that the attenuated photoregulated PAR expression in cop1 and det1 is not a secondary effect of the constitutively photomorphogenic phenotype, but a direct effect of the molecular lesions in the latter mutants. Together, these results show that not all of the factors genetically identified to have a role in seedling deetiolation participate in the regulation of SAS responses. Furthermore, those that do participate, such as COP1 and DET1, appear to target a different set of primary genes of phy action. The Promoter Regions of ATHB2 and PAR1 Confer Simulated Shade Responsiveness to a Reporter Gene To address whether the observed changes in transcript levels were the result of altered promoter activity (transcriptional regulation), transgenic plants expressing a GUS reporter gene driven by the 1-kb promoter region of ATHB2 and PAR1 were generated. These genes were selected because they represented both types of responses to simulated shade observed in our pharmacological (Fig. 3C) and genetic (Figs. 4A and 6A) experiments. The resulting transgenic plants were referred to as ProATHB2:GUS and ProPAR1:GUS lines. As a control, we also analyzed Pro35S:GUS plants. Several independent transgenic lines were obtained for each construct. In the T2 generation, GUS histochemical assays were performed and lines displaying GUS activity in seedlings were selected for further analysis. GUS transcript levels were quantified before and 1 h after simulated shade treatment in four to six selected lines. Although the analyzed lines displayed variable levels of basal GUS expression (i.e. before transferring the seedlings to simulated shade; data not shown), all the lines but the Pro35S:GUS controls showed a clear photoregulated expression of the GUS reporter (Fig. 7 Figure 7. Open in new tabDownload slide Photoresponse activity of the 5′ promoter regions of ATHB2, PAR1, and 35S. Analyses of GUS expression were performed in 7-d (d7) seedlings using four (Pro35S) or five (ProATHB2 and ProPAR1) independent transgenic lines for each construct. GUS:25S expression levels are shown as fold induction 1 h after simulated shade treatment. Figure 7. Open in new tabDownload slide Photoresponse activity of the 5′ promoter regions of ATHB2, PAR1, and 35S. Analyses of GUS expression were performed in 7-d (d7) seedlings using four (Pro35S) or five (ProATHB2 and ProPAR1) independent transgenic lines for each construct. GUS:25S expression levels are shown as fold induction 1 h after simulated shade treatment. ). ProATHB2:GUS lines displayed the highest degree of shade-induced up-regulation of GUS mRNA levels (Fig. 7). In all cases, the expression of the endogenous PAR gene analyzed was normally photoregulated (data not shown). These data showed that the selected PAR promoter regions are sufficient to confer simulated shade responsiveness to an unrelated reporter gene. Long-Term SAS Responses Are Impaired in a pil1 Mutant From the four PAR genes identified in this work as direct targets of phy signaling, only ATHB2 and PIL1 have been shown to be instrumental in implementing SAS responses (Steindler et al., 1999; Salter et al., 2003). In the case of PIL1, however, the phenotype observed in a loss-of-function pil1 mutant is more subtle than might be expected for a primary gene within the transcriptional cascade modulating SAS responses. Despite extensive phenotypic characterization, PIL1 has only been shown to affect hypocotyl elongation in response to transient simulated shade (Salter et al., 2003; Yamashino et al., 2003). However, our analysis of hypocotyl elongation after prolonged (5 d) simulated shade treatment revealed that the novel pil1-4 mutant, a T-DNA insertion allele that we characterized from the public Salk collection (Fig. 8A Figure 8. Open in new tabDownload slide Role of PIL1 on SAS seedling responses. A, Schematic representation of PIL1 (At2g46970) genomic sequence and T-DNA insertion in pil1-4. Introns (white), exons (light gray), and the bHLH domain (dark gray) are indicated with boxes. B, Changes in hypocotyl length in response to simulated shade in wild-type (Col-0), pil1-4, phyB-9, and pil1-4 phyB-9 double-mutant seedlings. Seedlings grown for 2 d under W were either maintained in W (white bars) or transferred to W + FR (gray bars; R to FR ratio 0.05) for 5 d, after which mean (±se) lengths were measured. Figure 8. Open in new tabDownload slide Role of PIL1 on SAS seedling responses. A, Schematic representation of PIL1 (At2g46970) genomic sequence and T-DNA insertion in pil1-4. Introns (white), exons (light gray), and the bHLH domain (dark gray) are indicated with boxes. B, Changes in hypocotyl length in response to simulated shade in wild-type (Col-0), pil1-4, phyB-9, and pil1-4 phyB-9 double-mutant seedlings. Seedlings grown for 2 d under W were either maintained in W (white bars) or transferred to W + FR (gray bars; R to FR ratio 0.05) for 5 d, after which mean (±se) lengths were measured. ), displayed a subtle, but significantly stronger, response compared to the wild type (Fig. 8B), suggesting that PIL1 may play a role in moderating this shade avoidance response. PIL1 expression still responds strongly to simulated shade in a phyB mutant background in which the expression of this PAR gene is promoted (Devlin et al., 2003; Salter et al., 2003). We examined the effect of loss of PIL1 function in a phyB-9 mutant background in the hope that this might more clearly show the role for PIL1 in prolonged SAS responses. As known, the phyB mutation resulted in a long hypocotyl phenotype in both single and double phyB-9 pil1-4 seedlings. In response to W + FR, the long phyB hypocotyls showed less elongation than under W, in agreement with previous findings (Devlin et al., 2003). This was concluded to be the result of a moderating (i.e. negative) factor only apparent in the absence of phyB (Devlin et al., 2003). By contrast, this reduction in hypocotyl elongation in response to simulated shade was not apparent in phyB-9 pil1-4 seedlings (Fig. 8B), confirming a role for PIL1 in this moderation of hypocotyl elongation in response to shade. Together, these data indicate that the pil1 mutation impairs long-term SAS responses, such as hypocotyl elongation, in addition to the previously observed effect on the response to transient shade conditions (Salter et al., 2003). DISCUSSION SAS generally refers to a broad set of physiological and developmental changes in light-grown plants in response to shade perceived by the phys (Smith, 1982; Smith and Whitelam, 1997). Simulated shade also results in up-regulation of PAR genes in Arabidopsis (Fig. 1), which can be considered as an authentic SAS response. The inverse correlation between Pfr (R to FR ratio) and PAR transcript levels (Fig. 1) supports the idea that the observed up-regulation of PAR expression by simulated shade is actually a release of repression by the active Pfr form of the phys. Consistently, high phy levels in AOX and ABO lines result in low PAR transcript levels both before and after simulated shade treatment (Fig. 2B). In this work, we additionally show that some of the selected PAR genes are direct targets of phy action and unveil a role for COP1 and DET1 in regulating their expression during SAS. Furthermore, we report that one of the identified primary target genes, PIL1, affects long-term SAS responses. As proposed for seedling deetiolation, it was expected that phys transduce light signals to implement SAS responses by rapidly modulating a transcriptional cascade. The genes directly targeted by phy signaling, however, are unknown. Here we report that four PAR genes are authentic direct targets of phy signaling in light-grown seedlings based on four main lines of evidence: (1) the negative correlation between Pfr levels and their expression (Figs. 1 and 2); (2) the rapid kinetics (min) of their light-dependent regulation; (3) the fast responsiveness of their promoters to simulated shade, indicative of a transcriptional control (Fig. 7); and (4) the CHX-independent pattern of their photoregulated expression (Fig. 3). A common strategy to identify primary target genes of a TF is to control its transcriptional activity by regulating the DEX-dependent nuclear translocation of TF-glucocorticoid receptor fusions combined with CHX treatments to block de novo protein synthesis. When CHX is applied together with DEX, only transcript levels of the TF primary targets are affected: Expression of the immediate targets is therefore CHX independent, whereas expression of downstream targets is CHX dependent. Although this approach has most often been used for TFs (Sablowski and Meyerowitz, 1998; Wagner et al., 1999; Ohgishi et al., 2001; Sawa et al., 2002), it has also given successful results for proteins without known DNA-binding domains that need to be nuclear for signaling activity, like CONSTANS (Samach et al., 2000). The experimental configuration presented in this article to identify primary target genes of phys in vivo incorporates a novel aspect: Transcriptional activity is not controlled by nuclear translocation, but by simulated shade, which modulates phy photoequilibrium and, subsequently, its binding ability to different PIFs (the shade signal would eventually also regulate nuclear translocation of the phys, of course, but this would not be the primary factor regulating activity of the light-stable phys over the short time scale involved here [Kircher et al., 2002]). These PIFs are TFs whose transcriptional activity has been hypothesized to be regulated by Pfr action (Quail, 2002). Therefore, in the presence of CHX, we are monitoring the immediate (translation independent) effects of the shade-triggered disappearance of Pfr-PIF complexes on PAR gene expression. By using this experimental design, we observed that the rapid photoregulated response of ATHB2, ATHB4, PAR1, and PIL1 was CHX independent (Fig. 3), strongly suggesting that these genes are primary targets of phy action in light-grown seedlings. Our results do not discriminate whether the active Pfr form binds to the promoters of these genes via specific PIFs, prevents other PIFs from accessing these promoters, or requires additional biochemical steps to transduce light signals to changes in PAR expression. Although we have not directly investigated whether ATHB2, ATHB4, PAR1, and PIL1 are also primary targets of phy action during seedling deetiolation, their similar pattern of phy-mediated regulation during both SAS and deetiolation supports this possibility in both physiological contexts. This is in agreement with the notion of functional gene cassettes, which was developed in animal systems after observing that groups of genes with functions in a given developmental process were also used to serve similar functions in other stages of development (Jan and Jan, 1993). The possible existence of a functional PAR gene cassette working in both seedling deetiolation and SAS, however, does not necessarily imply that all the molecular mechanisms required for its photoregulation are fully conserved. For instance, nuclear COP1 participates in the photoregulation of ATHB2, ATHB4, PAR1, and PIL1 by simulated shade, whereas it does not play a major role in the early repression of ATHB2, PAR1, and PIL1 during deetiolation (Fig. 4). Genetic screens of Arabidopsis seedling deetiolation have originated the concept of early and late, phy-specific or common, light-signaling intermediates. The constitutive deetiolated cop/det/fus class of mutants, shown to be mostly epistatic to all the photoreceptor mutants, has been proposed to participate in the later stages of light signaling (for review, see Quail, 2002; Schäfer and Bowler, 2002; Chen et al., 2004). More recent data indicate, however, that COP1 regulates at least three different and consecutive processes during early phy signaling: (1) accumulation of phy-interacting factors like PIF3 in etiolated seedlings (Bauer et al., 2004); (2) simulated shade-dependent changes in the expression of genes directly regulated by phy action in light-grown plants (Fig. 4A); and (3) degradation of their encoded gene products, such as HFR1 (Duek et al., 2004; Sessa et al., 2005). The multilevel participation of COP1 in early phy signaling implies that some phy-mediated responses may be COP1 independent. Indeed, control of seed germination by phys is unaffected by cop1 mutations (Deng et al., 1992) and phyB is epistatic to cop1 for the reverse cotyledon angle response during seedling deetiolation under both R and FR (Boccalandro et al., 2004). Also, our data consistently show that COP1 is differentially required for early PAR gene expression during SAS and deetiolation (Fig. 4). Our conclusions, however, do not exclude the possibility that COP1 might also act at downstream steps of phy signaling. DET1, which acts together with COP1 in regulating proteolysis of TFs involved in light signaling (Yanagawa et al., 2004), also participates in the photoregulation of ATHB2, ATHB4, PAR1, and PIL1 by simulated shade (Figs. 4A and 6A). A corollary of these observations is that cop1 and det1 mutants sense the differences between various light conditions (dark versus light and light versus shade), although they are clearly impaired in transducing this information to regulate growth and development. On the other hand, other factors, such as DET2 or HY5, have little or no effect on the photoregulation of the selected PAR genes (Figs. 5A and 6B). HY5 is not required for hypocotyl elongation in response to simulated shade (Fig. 5B). By contrast, defective physiological SAS responses have been observed in brassinosteroid-deficient mutants det2-1 (data not shown) and eve1/dwf1 (Luccione et al., 2002). This suggests that brassinosteroids affect SAS responses by mechanisms other than the regulation of PAR gene expression (likely acting downstream of the identified genes). One of the proposed mechanisms of phy signaling is the activation of transcriptional cascades by both PIF3-dependent and -independent pathways (Tepperman et al., 2001). PIF3 belongs to the large and complex bHLH family of DNA-binding proteins, many of which have been shown to bind to the G-box motif GAGCTC in vitro (Martínez-García et al., 2000; Huq and Quail, 2002; Huq et al., 2003). The functional relevance of the G-box motif for the activity of PIF3 and related bHLHs in planta, however, has not been demonstrated yet. In addition to gene activation pathways, microarray data suggest the existence of at least another pathway initiated by early repression of the transcriptional cascade (Tepperman et al., 2001), as exemplified by the PAR genes. It is likely that the PIFs involved in this repression pathway were different from those acting as transcriptional activators, as initially postulated for PIF3 (Ni et al., 1998). Alternatively, the same PIFs may function as either transcriptional activators or repressors, depending on the specific promoters (Kim et al., 2003). The 1-kb region upstream of the ATG codon of ATHB2 and PAR1 is sufficient to confer rapid photoregulation to a reporter gene (Fig. 7). Neither these nor the corresponding upstream regions of ATHB4 and PIL1 contain G-box elements, supporting the proposal that PIF3-like factors are not involved in the early repression of these PAR genes by phys. A relevant contribution of our work is the demonstration that direct target genes of phy action, such as PIL1, can negatively regulate shade-induced hypocotyl elongation in response to sustained (5 d) reductions in the R to FR ratio (Fig. 8B) in addition to the previously reported transient (2 h) response (Salter et al., 2003). The negative role of PIL1 is also consistent with previous data from wild-type plants in which transient low R to FR treatment at subjective dawn results in a maximal increase in PIL1 transcript levels and inhibition of elongation. Conversely, the same signal given at dusk results in a lower increase in PIL1 transcripts and in maximal elongation promotion (Salter et al., 2003). It is also interesting that pil1 mutant seedlings show hyposensitivity to both Rc and FRc at lower fluence rates (Salter et al., 2003). This observation might be ecologically relevant because reductions in both the R to FR ratio and light quantity occur in nature under vegetation canopies (Smith, 1982). Simulated shade also rapidly induces HFR1 expression, another SAS negative regulator (Sessa et al., 2005) that encodes for a bHLH protein (Fairchild et al., 2000). All PIFs and PILs tested so far can bind in vitro to the core G-box motif, except HFR1, which appears to be a non-DNA-binding variant (Fairchild et al., 2000; Huq and Quail, 2002; Huq et al., 2004; Khanna et al., 2004). Furthermore, there is evidence that closely related Arabidopsis bHLH members can form heterodimers such as HFR1-PIF3 and PIF3-PIF4 (Fairchild et al., 2000; Toledo-Ortiz et al., 2003). By these various mechanisms, shade-induced PIL1 and HFR1 transcript changes can, in theory, rapidly feed back into the phy-regulated network of bHLH proteins and alter shade-induced changes in gene expression. In summary, our work has identified candidate factors potentially representing entry points for the phy signal into the shade-modulated transcriptional cascade and has uncovered functions for one of them, PIL1. MATERIALS AND METHODS Plant Material and Growth Conditions AOX (Boylan and Quail, 1991) and ABO (Wagner et al., 1991) lines are in the Arabidopsis (Arabidopsis thaliana) Nossen (No-0) ecotype; cop1-4, cop1-6, det1-1, det2-1, pil1-4 (SALK_043937; Alonso et al. 2003), and phyB-9 mutants are in the Columbia (Col-0) background; and hy5-1 is in the Landsberg erecta background. The SALK_043937 line was named pil1-4 to distinguish it from three previously described mutants, pil1-1 and pil1-2 (Yamashino et al., 2003; Kazusa collection), and garlic line 438c01 (Salter et al., 2003). The pil1-4 mutant allele contains a T-DNA insertion at position 1,102 in the middle of the bHLH domain (position 1 corresponds to the first nucleotide of the starting ATG codon; Fig. 8A). This mutant expressed truncated transcripts of PIL1 (data not shown), as has been described for other alleles (Yamashino et al., 2003). A pil1-4 phyB-9 double mutant was generated by crossing the two single mutants, allowing the F1 progeny to self fertilize, and then selecting the F2 plants. Seedling mutants for phyB, selected as those displaying long hypocotyls under W, were subsequently genotyped for pil1-4 homozygous by using specific oligos (JO287, 5′-ATGGAAGCAAAACCCTTAGCATC-3′; JO288, 5′-TTAGTTTGGCGAGCGATAATAAC-3′; and LBb1, 5′-GCGTGGACCGCTTGCTGCAACT-3′) and standard PCR analysis. Different oligo combinations were used to discriminate between the wild type (JO287 + JO288) and the mutant (JO287 + LBb1) alleles of the PIL1 gene. For analyses of gene expression, seeds were surface sterilized and sown on top of a filter paper circle deposited on growth medium (GM; Valvekens et al., 1988) without Suc (GM−). For the simulated shade treatments, after stratification at 4°C for 2 to 5 d in the dark, seeds were germinated under W (40 μmol m−2 s−1; R to FR ratio of 3.2–4.5) at 22°C. On day 7 after germination, seedlings were given a light treatment (W supplemented with FR, W + FR; R to FR ratio of 0.03–0.12, unless otherwise stated), harvested, frozen in liquid nitrogen, and stored at −80°C until processing. For the deetiolation treatments, after stratification as before, seeds were induced to germinate by a brief (0.5–3 h) W treatment and then transferred to the dark at 22°C. On day 4, etiolated seedlings were given a 1-h FR treatment (8 μmol m−2 s−1), harvested under a green safelight, frozen in liquid nitrogen, and stored at −80°C until processing. For analyses of the hypocotyl elongation response in Figures 2 and 5, plant material was prepared and sown as indicated elsewhere (Devlin et al., 2003). For analyses of hypocotyl elongation to long-term simulated shade treatment (Fig. 8), 50 seeds were individually sown directly onto the GM−; on day 2, seedlings were either maintained under W or transferred to W + FR for 5 additional days. For measuring hypocotyl lengths, seedlings were laid out flat on agar plates. Hypocotyl lengths were measured by using National Institute of Health (NIH) IMAGE software to analyze the digital images of these seedlings. Data represent the mean (±se) of at least 15 seedlings for each treatment. Experiments were repeated at least twice and a representative one is shown. Light Sources and Treatments W was provided by two cool-white fluorescent tubes (36 W, Sylvania standard; R to FR ratio of 3.2–4.5). Supplementary FR light was provided by QB1310CS-670-735, LED hybrid lamps (Quantum Devices). Plants were harvested immediately before (0 h) and after 0.5 and 1 h of W + FR treatment. The fluence rates were measured using a quantum radiometer photometer (188b; LI-COR), fitted with a quantum sensor (Li-190 SB) for R and a near-infrared sensor (Li-220 SB) for FR. CHX and DEX Treatments CHX (Sigma-Aldrich) was dissolved at 50 mm in 50% (v/v) ethanol and DEX (Sigma-Aldrich) was dissolved at 5 mm in 100% (v/v) ethanol and kept at −20°C until use. Fifty micromolar CHX and/or 5 μm DEX in water were prepared prior to the treatments. Day 7 seedlings growing on filter paper circles were transferred to new plates containing 4 mL of different combinations of ±CHX and ±DEX. In Figure 3A, ±CHX and ±DEX were applied at the same time and seedlings were assayed for GUS activity 24 h later. In Figure 3B, ±DEX was applied 1 h before ±CHX and seedlings were assayed for GUS activity 2 h after ±CHX application. In Figure 3C, ±CHX was applied 2 h before light treatments. Seedlings were kept in these conditions during the light treatments until harvesting. Construction of ProATHB2:GUS and ProPAR1:GUS Promoter Fusion Lines The binary vector pCAMBIA1304 (GenBank accession no. AF234300; Pro35S:green fluorescent protein-GUS) was used to subclone all promoter fusions. This plasmid confers hygromycin resistance to transgenic plants. We selected 1,000 bp located 5′ of the translation start. To get the corresponding promoter sequences, specific oligos were designed after the available sequence databases: JO281 (5′-GGAAGCTTTCAACCGTTTTTGTTTAGTTCTTC-3′); JO280 (5′-GTCGGATCCACCATCTTCTGTTGAACTTTCTCAAG-3′); JO301 (5′-GGAAGCTTACCAGGCACCACCCGAATGGC-3′); and JO302 (5′-CGGATCCACCATTGAAAGAAAGAGAGAGATG-3′). From standard Col-0 DNA preparation as template, different combination of oligos were used for PCR of ATHB2 (JO281 + JO280) and PAR1 (JO301 + JO302) promoters. These oligos generated fragments containing 1,000 bp of the corresponding promoter flanked by a HindIII site in the 5′ end and a BamHI site after the translation start. The resulting PCR fragments were directly subcloned into a pGemT-Easy (Promega) plasmid, generating pJF278 and pJF297, and sequenced. The HindIII-BamHI fragments from these plasmids were subcloned into pCAMBIA1304 digested with HindIII-BglII (this digestion removes the original 35S promoter that drives the expression of the GUS reporter) to generate pJF279 and pJF299, respectively. pJF279 corresponds to the 1,000-bp ATHB2 promoter driving expression of GUS (ProATHB2:GUS) and pJF299 corresponds to the 1,000-bp PAR1 promoter driving expression of GUS (ProPAR1:GUS). The binary vectors pCAMBIA1303 (GenBank accession no. AF234299; Pro35S: GUS-green fluorescent protein), pJF279, and pJF299 were introduced in Agrobacterium tumefaciens strain C58C1 (pGV2260) by electroporation, and transformed colonies were selected in kanamycin (50 μg mL−1). Arabidopsis (Col-0) was transformed by floral dipping (Clough and Bent, 1998) and transgenic plant selection (T1 generation) was done in GM plates containing hygromycin (30 μg mL−1). The presence of the transgene in the selected T1 plants was verified by PCR analysis using specific transgene primers on plant genomic DNA isolated from young leaves (Edwards et al., 1991). Promoter activity was verified by GUS histochemical assays of the T2 hygromycin-resistant seedlings. GUS Assays Histochemical GUS assays were performed essentially as described (Craft et al., 2005). Seedlings were cleared with 70% (v/v) ethanol washes to improve contrast. Finally, whole-mount preparations were made in 50% (v/v) glycerol to visualize GUS activity using a Leica MZFLIII stereoscopic microscope and a Leica DC200 digital camera (Leica Microsistemas). RNA Isolation and Northern Analysis Total RNA was isolated from the frozen tissue essentially as described (Rodríguez-Concepción and Gruissem, 1999). Ten micrograms of total RNA were separated on 1.2% (w/v) agarose denaturing formaldehyde gels and transferred onto Hybond N nylon membranes. Hybridization was carried out as described (Martínez-García et al., 2002). The probes for the RNA blot were made by amplifying Col-0 genomic DNA with specific primers: JO282 (5′-CAGAAGATGATGTTCGAGAAAGAC-3′) and JO283 (5′-AAAGACTTAGGACCTAGGACGAAG-3′) for ATHB2; JO284 (5′-AGGACAATGGGGGAAAGAGATGAT-3′) and JO285 (5′-CCTTCCCTAGCGACCTGATTTTTG-3′) for ATHB4; JO289 (5′-TCAATGGAAGAAACTCTAGCCAC-3′) and JO290 (5′-TCAACCTCCGAACTTCATGTCTTC-3′) for PAR1; RO3 (5′-AACATGATGATGGGCAAAGAAG-3′) and RO4 (5′-AAATCACGATCGTGGACGCAAGGC-3′) for HAT2; JO287 (5′-ATGGAAGCAAAACCCTTAGCATC-3′) and JO288 (5′-TTAGTTTGGCGAGCGATAATAAC-3′) for PIL1; and JO293 (5′-ATGGCTAGAAATTTCGAGCTT-3′) and JO294 (5′-TCAATGCTTGGAAGCAAAGTC-3′) for RIP. PCR products were subcloned into pGemT-Easy (Promega) or PTZ57T/R (Fermentas) to give pJF281, pJF282, pJF285, pIR4, pJF284, and pJF290, respectively. Inserts were sequenced for identity confirmation. A partial fragment of 1 kb corresponding to the GUS coding region was PCR amplified using specific oligos (GUS-upper, 5′-CAACGAACTGAACTGGCAGA-3′; GUS-lower, 5′-GGCACAGCACATCAAAGAGA-3′) and pCAMBIA1304 as a template. DNA inserts, isolated by restriction digestion or by PCR using specific primers, were radioactively labeled with [α32P]dCTP by using a random primed DNA-labeling kit (Roche Molecular Biochemicals), and a purified trough Sephadex G-50 column (Amersham). Images were visualized by using a molecular imager FX (Bio-Rad), and band intensities were quantified by using QUANTITY ONE (Bio-Rad) software. Expression levels were calculated relative to the lowest value of each set of samples after normalization with the 25S rRNA signal. 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BIO20002–00298 and BIO2005–00154 to J.F.M.-G.); the Consejo Superior de Investigaciones Cientificas and the Royal Society (grant nos. 2004GB00016 to J.F.M.-G. and 2004/R1–EU to P.F.D.); and the University of London Central Research Fund (grant no. AR/CRF/B to P.F.D.). I.R.-V. is the recipient of a predoctoral fellowship from the Spanish Ministerio de Educación y Ciencia (reference no. BES–2003–1873); J.B. and C.S. are funded by postdoctoral contracts from the Departament d'Universitats Recerca i Societat de la Informació, Generalitat de Catalunya (reference nos. 2004 CRED 10003 and 2004 CRED 00058, respectively). We are part of the Grup de Recerca Emergent (grant no. 2005SGR 00284). * Corresponding author; e-mail [email protected]; fax 34–93–204–5904. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Jaime F. Martínez-García ([email protected]). Article, publication date, and citation information can be found at www.plantphysiol.org/cgi/doi/10.1104/pp.105.076331. © 2006 American Society of Plant Biologists This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)