Journal of Experimental Botany

Liu, Haiyang; Song, Song; Xing, Yongzhong

doi: 10.1093/jxb/ery408pmid: 30590675

Barley, Brachypodium distachyon, fertility, FLOWERING LOCUS T-like (FT-like), FLOWERING LOCUS T2, flowering time, Ghd genes, Photoperiod-1 (Ppd-1), spike development, wheat Development of the grain-bearing organ, or spike (inflorescence) is critical to cereal grain development and yield. Shaw et al. (2018) found that FT2, the closet paralogue of florigen candidate FT1 in wheat, had a minor effect on heading date in wheat but a significant contribution to spike development, including regulation of the number of spikelets per spike and sterility in Brachypodium distachyon, barley and tetraploid wheat. Not only does this increase our understanding of the regulation of flowering time and grain yield control, but suggests FT2 is a good candidate for breeding high yield cultivars. Flowering time is one of the most crucial target traits in crop breeding programmes due to its high correlation with final grain yield. Flowering plants respond to environmental cues to flower at a suitable time to maximize reproductive success, and so by optimizing this process breeders maximize grain yield (Jung and Muller, 2009). It is a highly complex trait that is determined by both environmental and endogenous factors. Integration of various environmental cues triggers the expression of florigen genes in leaves, in turn activating the expression of floral meristem identity genes in the shoot apical meristem and initiation of reproductive growth (Andres and Coupland, 2012). Over the past two decades, numerous pleiotropic genes that regulate both flowering time and grain yield have been characterized in crops, such as the Grain number, plant height, and heading date (Ghd) gene series, Ghd7, Ghd8, Ghd7.1 and Ghd6 in rice and Photoperiod-1 (Ppd-H1) in barley (Yan et al., 2013; Boden et al., 2015; Zhang et al., 2017). Interestingly, all these genes regulate heading time – the number of days from sowing to emergence of the grain-bearing organ – by suppressing the expression of FLOWERING LOCUS T-like (FT-like) florigen genes and increasing grain yield. The findings of Shaw et al. (2018) add important new information to this body of work, helping show how FT-like genes regulate grain yield in cereals. Diverse functions of FT-like genes The phosphatidylethanolamine binding protein (PEBP) gene family is involved in regulation of flowering time, seed dormancy and panicle/spike development. It can be divided into three subfamilies: MOTHER OF FT AND TFL1-like (MFT-like), TERMINAL FLOWER1-like (TFL1-like) and FT-like. The MFT-like subfamily, the proposed ancestor of the other two subfamilies, regulates seed germination, flowering time and spikelets per panicle. The duplication and diversification of MFT-like genes eventually resulted in the FT-like and TFL1-like subfamilies (Wickland and Hanzawa, 2015). Comparison of the phenotypes donated by FT-like and TFL1-like genes in cereals (Table 1) shows that FT-like genes mainly induce flowering, while TFL1-like genes mainly possess anti-florigen activity which represses flowering. Table 1. FT2 and its homologue(s) in rice, maize, barley and wheat Species . Clade . Genes . Flowering time . Floret number . Reference . Rice FT-like Hd3a/OsFTL2 Earlier flowering Decrease Kojima et al., 2002 FT-like RFT1/OsFTL3 Earlier flowering Decrease Zhao et al., 2015; Zhu et al., 2017 FT-like OsFTL1 Earlier flowering Decrease Izawa et al., 2002 TFL1-like RCN1-4 Later flowering Increase Kaneko-Suzuki et al., 2018 Maize TFL1-like ZCN1 Later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN2 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN3 Unchanged Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN4 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN5 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN6 Unchanged Increase (in tassel) Danilevskaya et al., 2010 FT-like ZCN8 Earlier flowering Decrease Danilevskaya et al., 2011; Meng et al., 2011 Barley FT-like HvFT1/VRN3 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT2 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT3/Ppd-H2 Earlier flowering Unchanged in LD but aborted in SD Kikuchi et al., 2009; Mulki et al., 2018 TFL1-like HvCEN Later flowering Increased yield Comadran et al., 2012 Wheat FT-like TaFT1/VRN3 Earlier flowering Decrease Lv et al., 2014 FT-like TaFT2 Slightly earlier flowering Increase Shaw et al., 2018 FT-like TaFT3 Earlier flowering Unknown Zikhali et al., 2017 Species . Clade . Genes . Flowering time . Floret number . Reference . Rice FT-like Hd3a/OsFTL2 Earlier flowering Decrease Kojima et al., 2002 FT-like RFT1/OsFTL3 Earlier flowering Decrease Zhao et al., 2015; Zhu et al., 2017 FT-like OsFTL1 Earlier flowering Decrease Izawa et al., 2002 TFL1-like RCN1-4 Later flowering Increase Kaneko-Suzuki et al., 2018 Maize TFL1-like ZCN1 Later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN2 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN3 Unchanged Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN4 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN5 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN6 Unchanged Increase (in tassel) Danilevskaya et al., 2010 FT-like ZCN8 Earlier flowering Decrease Danilevskaya et al., 2011; Meng et al., 2011 Barley FT-like HvFT1/VRN3 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT2 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT3/Ppd-H2 Earlier flowering Unchanged in LD but aborted in SD Kikuchi et al., 2009; Mulki et al., 2018 TFL1-like HvCEN Later flowering Increased yield Comadran et al., 2012 Wheat FT-like TaFT1/VRN3 Earlier flowering Decrease Lv et al., 2014 FT-like TaFT2 Slightly earlier flowering Increase Shaw et al., 2018 FT-like TaFT3 Earlier flowering Unknown Zikhali et al., 2017 Open in new tab Table 1. FT2 and its homologue(s) in rice, maize, barley and wheat Species . Clade . Genes . Flowering time . Floret number . Reference . Rice FT-like Hd3a/OsFTL2 Earlier flowering Decrease Kojima et al., 2002 FT-like RFT1/OsFTL3 Earlier flowering Decrease Zhao et al., 2015; Zhu et al., 2017 FT-like OsFTL1 Earlier flowering Decrease Izawa et al., 2002 TFL1-like RCN1-4 Later flowering Increase Kaneko-Suzuki et al., 2018 Maize TFL1-like ZCN1 Later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN2 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN3 Unchanged Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN4 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN5 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN6 Unchanged Increase (in tassel) Danilevskaya et al., 2010 FT-like ZCN8 Earlier flowering Decrease Danilevskaya et al., 2011; Meng et al., 2011 Barley FT-like HvFT1/VRN3 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT2 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT3/Ppd-H2 Earlier flowering Unchanged in LD but aborted in SD Kikuchi et al., 2009; Mulki et al., 2018 TFL1-like HvCEN Later flowering Increased yield Comadran et al., 2012 Wheat FT-like TaFT1/VRN3 Earlier flowering Decrease Lv et al., 2014 FT-like TaFT2 Slightly earlier flowering Increase Shaw et al., 2018 FT-like TaFT3 Earlier flowering Unknown Zikhali et al., 2017 Species . Clade . Genes . Flowering time . Floret number . Reference . Rice FT-like Hd3a/OsFTL2 Earlier flowering Decrease Kojima et al., 2002 FT-like RFT1/OsFTL3 Earlier flowering Decrease Zhao et al., 2015; Zhu et al., 2017 FT-like OsFTL1 Earlier flowering Decrease Izawa et al., 2002 TFL1-like RCN1-4 Later flowering Increase Kaneko-Suzuki et al., 2018 Maize TFL1-like ZCN1 Later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN2 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN3 Unchanged Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN4 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN5 Much later flowering Increase (in tassel) Danilevskaya et al., 2010 TFL1-like ZCN6 Unchanged Increase (in tassel) Danilevskaya et al., 2010 FT-like ZCN8 Earlier flowering Decrease Danilevskaya et al., 2011; Meng et al., 2011 Barley FT-like HvFT1/VRN3 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT2 Earlier flowering Unknown Kikuchi et al., 2009 FT-like HvFT3/Ppd-H2 Earlier flowering Unchanged in LD but aborted in SD Kikuchi et al., 2009; Mulki et al., 2018 TFL1-like HvCEN Later flowering Increased yield Comadran et al., 2012 Wheat FT-like TaFT1/VRN3 Earlier flowering Decrease Lv et al., 2014 FT-like TaFT2 Slightly earlier flowering Increase Shaw et al., 2018 FT-like TaFT3 Earlier flowering Unknown Zikhali et al., 2017 Open in new tab Lv et al. (2014) reported FT1 as a florigen candidate promoting flowering in wheat. Shaw et al. (2018) add to this with their characterization of FT2, the closest paralogue of FT1, in the tetraploid wheat variety Kronos. The ft2 mutant shows slightly delayed heading time but greatly increased number of spikelets per spike and florets per spikelet. Although FT2 is highly expressed in leaves and under the control of VRN1, VRN2 and Ppd-1, the null mutant only causes a delay in heading time of 2–4 days; this compares with the null mutant of FT1 which causes a delay of more than 20 days. Therefore, FT2 is not primarily acting as a florigen despite functional FT2 being expressed in leaves. The double null mutant of ft1 and ft2 has a comparable flowering time to the ft1 single mutant, which indicates that the FT2 effect is independent of FT1, and other florigen genes exist besides FT1 in wheat. Unlike other FT-like and TFL1-like genes, most of which have major effects on heading time, FT2 shows a major effect on spikelets per spike and fertility but minor effect on heading time. Besides being highly expressed in leaves, FT2 is also highly expressed in the distal part of the spike. The ft2 mutant greatly increases the floret number and dramatically reduces the fertility of florets on the spike (Shaw et al., 2018). Thus, FT2 mainly appears to function in spike development. Notably, sterility is observed not only in the ft2 mutant in wheat, but also in transgenic knockdown B. distachyon and barley, indicating for the first time that FT2 control of fertility may be a conserved function in temperate cereals. It is clear that FT-like subfamily genes also exhibit some degree of sub-functionalization in cereals. Spikelets per spike and heading time Florigen interacts with the bZIP transcription factor FD and 14-3-3 proteins in the shoot apical meristem to form the florigen activation complex. This activates floral meristem identity genes such as OsMADS15 in rice or VRN1 in wheat (Li and Dubcovsky, 2008; Taoka et al., 2011), which initiates the transition from the vegetative to the reproductive phase. Next, the shoot apical meristem begins to generate the inflorescence meristem. RICE CENTRORADIALIS (RCN) belongs to the TFL1-like subfamily. RCNs are expressed in the vasculature and move to the shoot apical meristem, where they compete with Hd3a for FD and 14-3-3 to form the florigen repression complex and regulate inflorescence development (Kaneko-Suzuki et al., 2018). Members of the PEBP family seem to interact with FD and 14-3-3 to form a complex. Different complexes and their functions in heading time control and later panicle/spike development are still to be confirmed. Overexpression of RCN greatly delays heading time and generates a larger inflorescence meristem, which produces a long and dense panicle in rice (Kyozuka et al., 2014; Kaneko-Suzuki et al., 2018). The heading date genes upstream of florigen genes such as Ghd7 in rice probably induce a larger inflorescence meristem by greatly delaying heading date, and finally significantly increase grain yield. Moreover, it seems that plants with a reasonable delay of heading time produce more spikelets per panicle and more grain yield (Zhang et al., 2015). FT2 represents another type of FT-like genes that greatly increase number of spikelets per spike (spike size) but with minor effects on flowering time. FT2 is mainly expressed in the inflorescence meristem to increase spikelets per spike after phase transition. This working model is similar to the scenario that OsMFT1 mainly functions in inflorescence meristem to down-regulate the expression of floral identity genes (e.g. FZP, OsMADS1) as floral organ determinants (Song et al., 2018). To this end, the spikelets per panicle trait controlled by floral identity genes such as FZP is usually independent of flowering time (Bai et al., 2017). Potential breeding applications Negative effects of FT2 on fertility limits its potential for breeding high yield wheat cultivars without significantly delaying heading date (Shaw et al., 2018). Low fertility of both FT2 knockout mutants in wheat and RNAi mutants in barley indicates that non-functional or very low expression FT2 alleles are unlikely to be widely distributed. However, promoter variation could enable a moderate change in FT2 transcript level. Thus, investigation of natural variation of FT2 in wheat germplasm should be encouraged to seek regulatory elements which can generate a favourable FT2 allele to maximize grain yield by balancing the trade-off between spikelets per spike and fertility. This potential FT2 allele would be more valuable in developing high yield cultivars regardless of FT1 background. Alternatively, a series of FT2 RNAi plants with varied interference are worth generating by transformation: the new germplasm with optimized transcript level of FT2 would be screened in terms of yield performance from these RNAi plants. As suggested by the authors, the Ft-B1-Hope allele could probably compensate for the negative effect on fertility of FT2 (Shaw et al., 2018). Identification of its downstream genes by ChIP sequencing and screening FT2-interaction proteins would help to separate the positive effects on spikelet per spike and negative effects on fertility. To conclude, the features of FT2 provide us new opportunities to understand the mechanism of FT-like gene in flowering time and grain yield control. If its negative effects on fertility can be resolved, FT2 is a good candidate for manipulation in breeding high yield cultivars. References Andres F , Coupland G 2012 . The genetic basis of flowering responses to seasonal cues . Nature Reviews Genetics 13 , 627 – 639 . Google Scholar Crossref Search ADS PubMed WorldCat Bai X , Huang Y, Hu Y, Liu H, Zhang B, Smaczniak C, Hu G, Han Z, Xing Y 2017 . Duplication of an upstream silencer of FZP increases grain yield in rice . Nature Plants 3 , 885 – 893 . Google Scholar Crossref Search ADS PubMed WorldCat Boden SA , Cavanagh C, Cullis BR, Ramm K, Greenwood J, Jean Finnegan E, Trevaskis B, Swain SM 2015 . Ppd-1 is a key regulator of inflorescence architecture and paired spikelet development in wheat . Nature Plants 1 , 14016 . 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Bouwmeester, Harro

doi: 10.1093/jxb/ery407pmid: 30590674

Epithelial cells, laser-assisted dissection, loblolly pine, mesophyll cell, oleoresin, pine, Pinus spp, resin duct, resinous ducts, terpenoids Oleoresin, a mixture of terpenoids produced by pine trees, protects the tree against insects but is also exploited by humans for a variety of industrial applications. Turner et al. (2018) quite literally dissected the machinery that is responsible for the highly specialized production of these green chemicals in loblolly pine. Not only does this further reveal the incredible metabolic specialization in the secretory cells of pine trees, but it will help in unravelling the mechanisms underlying the formation of these defensive chemicals and could inspire new engineering approaches for the production of renewable, green chemicals. Pine trees are grown all over the world as a source of many different products. There is wood for timber and cellulose for paper, but also a wide variety of chemical products based on the oleoresin that can be extracted. This includes turpentine and rosin, produced from oleoresin by the naval stores industry, which serve as the basis for the production of many chemical feedstocks. Together these represent a multibillion-dollar industry (see Box 1). The importance of this industry is increasing as it represents a durable alternative for replacing petroleum‐derived chemicals and fuels (da Silva Rodrigues-Corrêa et al., 2013). Because they fix carbon dioxide, pine forests also contribute to mitigation of the greenhouse effect (Rodrigues-Corrêa et al., 2012). Box 1. Oleoresin and its uses Oleoresin consists of a varying mixture of terpenoids, especially monoterpenoids, sequiterpenoids and diterpenoids. All pine trees produce this oleoresin with the composition differing between species (Rodrigues-Corrêa et al., 2012). The oleoresin plays an important role in the defence of pine trees, especially against insects. This is reflected in the fact that upon attack by insects (or treatment with the insect herbivory-mimicking jasmonic acid) trees develop resinous ducts and/or expression of oleoresin biosynthetic genes is strongly upregulated (Miller et al., 2005). However, the green chemistry that the oleoresin is composed of also represents a multitude of industrial products, including turpentine (Rodrigues-Corrêa et al., 2012). Also the individual mono-, sequi- and diterpenoids that can be isolated from the oleoresin can be used for a multitude of industrial applications. Examples of monoterpenoids and their applications include α-pinene and β-pinene, used in the fragrance industry, and as solvents, plasticizers and alternative biofuels; limonene, used as a fragrance; myrcene, used as a feedstock for the production of polymers; verbenone, used as an anti-aggregation pheromone; and myrtenol, used as a flavouring ingredient. Similar examples of sesquiterpenoids are isolongifolene and β-caryophyllene, both used as fragrance ingredients. Diterpenoids include abietic acid and levopimaric acid, used for the production of adhesives, coatings, ink and surfactants. Further information: https://www.manufacturing.net/blog/2015/01/pine-chemicals-engine-economic-growth-and-sustainability. Working on needles of loblolly pine, Turner et al. (2018) describe the laser-assisted dissection of epithelial cells of the resinous ducts as well as adjacent mesophyll cells. The non-photosynthetic epithelial cells are anticipated to be involved in the biosynthesis of oleoresin compounds and their subsequent secretion into the resin duct. The photosynthetic mesophyll cells serve as a control in which oleoresin biosynthesis should not occur. Subsequently, they analyzed the transcriptomes and modelled the metabolic flux of these two cell types, based on the transcriptomes they obtained and the approximate metabolite composition. Their analysis suggests that an enormous level of specialization has occurred, turning the epithelial cells into highly specialized chemical factories that produce oleoresin, while the mesophyll cells fix CO2 in photosynthesis and provide the sucrose, required for the energy-costly oleoresin biosynthesis, to the epithelial cells. Why is specialization needed? The primary biological role of these oleoresins is to protect the tree against insect and pathogen attack (Miller et al. 2005; Vickers et al., 2014). The oleoresin is therefore composed of terpenoid compounds with high bioactivity that are, however, often also phytotoxic. Therefore, these compounds are stored in specialized structures that prevent them damaging the plant itself (Lange, 2015). Examples of structures in other plant species that are similar to the resinous ducts in pine trees, as described by Turner et al., are the glandular trichomes that occur in species such as tomato (Balcke et al., 2017) and mint (Johnson et al., 2017), laticifers and oil ducts (Lange, 2015). The formation of the oleoresin constituents is not only limited to certain tissue types – to prevent toxicity – but also strictly regulated to take place only where and when they are really needed, such as under herbivore attack (Celedon et al., 2017). This regulation is important from a fitness perspective, as the biosynthesis of these compounds is energetically costly for plants (Gershenzon, 1994) and thus goes at the expense of growth and reproduction. Is specialization absolute? With their laser microdissection and models Turner et al. (2018) have divided metabolism in loblolly pine needles into what is traditionally called primary and secondary metabolism. Primary metabolism, the generation of sucrose, occurs in the mesophyll cells while secondary metabolism, the generation of specialized defence compounds, occurs in the epithelial cells lining the resin ducts. However, the story is not entirely as straightforward as this suggests. The high expression of a large proportion of the genes involved in specialized metabolism in the mesophyll cells is puzzling. The authors explain this by assuming contamination of the mesophyll cells with (parts of) epithelial cells (Turner et al., 2018). Indeed, it could be expected that some cross-contamination of the different cell types occurs. However, their wonderfully detailed images suggest that contamination would then be more likely to occur in the epithelial cell sample as the mesophyll cell sample is larger and much easier to separate from the epithelial cells than the other way around. In their discussion, they quantify the contamination by calculating the ratio between transcript levels in mesophyll and epithelial cells for the transcripts pertaining to diterpene biosynthesis. For the enzyme catalysing the first step in this pathway, abieatadiene/levopimaradiene synthase, and two cytochrome P450s probably catalysing subsequent steps, the ‘contamination’ amounts to 28, 33 and 29%, respectively. However, ‘contamination’ of the mesophyll cells with the supposed epithelial monoterpene synthase transcripts, which can be calculated from the transcripts per kilobase million (TPM) levels provided, is much lower [8 and 6% for (–)-α-pinene synthase and (+)-α-pinene synthase, respectively] making contamination a less likely explanation. Also the expression of the genes encoding the precursor supplying 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway in the mesophyll cells is much higher than would be expected based on the predicted flux (Turner et al., 2018). The authors agree that this cannot be explained by cross-contamination and suggest that the discrepancy must be due to modes of regulation other than transcription, notably post-transcriptional regulation, which has been demonstrated to occur in the MEP pathway (Banerjee and Sharkey, 2014). One alternative possibility is that neighbouring mesophyll cells help their neighbours by providing more dedicated isoprenoid precursors in addition to sucrose. The high level of expression of diterpenoid biosynthetic genes in the mesophyll cells noted above may suggest that this even includes advanced precursors, although this clearly requires more detailed study. Along a similar line, the contamination with photosynthesis-related transcripts in the epithelial cells might be because it is more difficult to obtain these cells in a pure form, so is due to contamination with mesophyll cell tissue. However, it appears that the TPM levels of some photosynthetic genes are even higher in the epithelial cells compared with the mesophyll cells, which cannot be caused by contamination alone (see supplementary tables in Turner et al., 2018). The enrichment of chlorophyll breakdown-related transcripts in the epithelial cells further adds to the confusion about the extent to which specialization has occurred. It makes sense that these genes are not expressed in the mesophyll but why chlorophyll would first be synthesized and then actively degraded again in the epithelial cells remains a conundrum, as also concluded by the authors. Biological implications Despite lack of clarity about the involvement of photosynthesis, a picture is emerging in which the plant cells involved in specialized metabolism employ dramatically altered metabolism to fuel the production of these high energy-requiring metabolites (Balcke et al., 2017; Turner et al., 2018). For example, in tomato trichomes, which are photosynthetic, the energy required for the production of specialized metabolites mostly originates from sugars imported from the photosynthetic tissue in the leaf (Balcke et al., 2017). Turner et al. also suggest that the energy for specialized metabolism in loblolly pine is not generated in the epithelial cells itself but is imported from elsewhere, probably the mesophyll. In the epithelial cells, glycolysis and the oxidative pentose phosphate pathway use this sucrose to generate the ATP required for specialized metabolism. Industrial implications Oleoresin from pine trees represents an interesting renewable chemical feedstock and, with new breeding tools, it is possible to select for trees with the highest production (da Silva Rodrigues-Corrêa et al., 2013) (see Box 1). Turner et al. have certainly increased our understanding of the underlying mechanisms of this production. This will help us identify the right selection traits if we want to improve oleoresin production/composition for pine tree resistance to insects or for the production of more desirable compounds for industrial applications through either selection for desirable alleles in populations or directed genetic modification. Ultimately this could result in robust pine tree genotypes that can compete with classical oil-based chemistry for the production of green chemicals through forest plantations. References Balcke GU , Bennewitz S, Bergau N, Athmer B, Henning A, Majovsky P, Jiménez-Gómez JM, Hoehenwarter W, Tissier A 2017 . Multi-omics of Tomato glandular trichomes reveals distinct features of central carbon metabolism supporting high productivity of specialized metabolites . The Plant Cell 29 , 960 – 983 . Google Scholar Crossref Search ADS PubMed WorldCat Banerjee A , Sharkey TD 2014 . Methylerythritol 4-phosphate (MEP) pathway metabolic regulation . 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Pine oleoresin: tapping green chemicals, biofuels, food protection, and carbon sequestration from multipurpose trees . Food and Energy Security 1 , 81 – 93 . Google Scholar Crossref Search ADS WorldCat Turner GW , Parrish AN, Zager JJ, Fischedick JT, Lange BM 2018 . Assessment of flux through oleoresin biosynthesis in epithelial cells of loblolly pine resin ducts . Journal of Experimental Botany 70 , 227 – 240 . Google Scholar OpenURL Placeholder Text WorldCat Vickers CE , Bongers M, Liu Q, Delatte T, Bouwmeester H 2014 . Metabolic engineering of volatile isoprenoids in plants and microbes . Plant, Cell & Environment 37 , 1753 – 1775 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: [email protected]

Evans, John, R;Clarke, Victoria, C

doi: 10.1093/jxb/ery366pmid: 30357381

Abstract Global food security depends on three main cereal crops (wheat, rice and maize) achieving and maintaining high yields, as well as increasing their future yields. Fundamental to the production of this biomass is photosynthesis. The process of photosynthesis involves a large number of proteins that together account for the majority of the nitrogen in leaves. As large amounts of nitrogen are removed in the harvested grain, this needs to be replaced either from synthetic fertilizer or biological nitrogen fixation. Knowledge about photosynthetic properties of leaves in natural ecosystems is also important, particularly when we consider the potential impacts of climate change. While the relationship between nitrogen and photosynthetic capacity of a leaf differs between species, leaf nitrogen content provides a useful way to incorporate photosynthesis into models of ecosystems and the terrestrial biosphere. This review provides a generalized nitrogen budget for a C3 leaf cell and discusses the potential for improving photosynthesis from a nitrogen perspective. Bioenergetics, chlorophyll protein complexes, fertilizer, leaf traits, light capture, photosynthetic electron transport, Rubisco Introduction Just over a century has passed since the discovery of the Haber–Bosch method to reduce atmospheric dinitrogen and produce ammonia, which paved the way for large-scale production of nitrogenous fertilizer. There is a close correlation between the production of nitrogenous fertilizer and the production of the three key cereals that dominate the human diet (wheat, rice and maize; http://www.fao.org/faostat). Crop production reflects photosynthesis integrated over the life of the crop. The process of photosynthesis requires a system that is comprised of many proteins, and which accounts for the majority of nitrogen in any plant. It is this large nitrogen requirement to construct a photosynthetic system that results in the need for nitrogenous fertilizer by highly productive crops. The photosynthetic rate and other leaf attributes have been measured for an extensive number of species. By combining two attributes, nitrogen content and the leaf dry mass, both expressed per unit leaf area, it is possible to predict the photosynthetic capacity. This has proved a useful way of parameterizing photosynthesis over the large areas of natural ecosystem that is necessary for global models (Rogers et al., 2017a). There are differences between species in the relationship between photosynthesis and leaf nitrogen content (Kattge et al., 2011). These reflect underlying differences in the allocation of nitrogen between proteins and the properties of those proteins, or they are a consequence of anatomical differences. Nitrogen and photosynthesis are central to each of these interrelated topics (Box 1), which are considered in this review. Box 1. Key developments in relating photosynthesis with nitrogen Leaf nitrogen budget: a trade-off is apparent between nitrogen allocated to Rubisco versus cell walls amongst plant functional types In a meta-analysis of C3 species, Onoda et al. (2017) showed that with increasing leaf dry mass per unit area, the fraction of leaf nitrogen allocated to Rubisco declined while that allocated to cell wall material increased. Short-lived leaves have greater photosynthetic rates per unit leaf N. Scaling to the ecosystem: Rubisco capacity per unit leaf N Rubisco capacity (Vcmax) is commonly derived from gas-exchange measurements, but this does not always equate to Rubisco protein. For tropical rainforest trees (Bahar et al., 2017) and Arctic tundra (Rogers et al., 2017b) new field data has improved ecosystem models. Fertilizer, photosynthesis, food security: rising atmospheric CO2 reduces grain protein concentration Achieving and maintaining high cereal yields requires the use of nitrogen fertilizers, yet rising atmospheric CO2 is diminishing the grain quality (Zhu et al., 2018). How can we diminish the negative impact of fertilizer use while maintaining protein? Engineering photosynthesis: protein targets that increase photosynthesis and biomass Increasing a photosystem-II protein and two enzymes that interconvert carotenoids to regulate energy dissipation has led to increased biomass production in field trials (Kromdijk et al., 2016). There are a growing number of candidate genes being investigated to enhance photosynthesis. Leaf nitrogen budget It is timely to revisit the nitrogen budget of a leaf. Firstly, X-ray crystallography of protein complexes reveals atomic resolution, providing accurate pigment-to-protein stoichiometries. Secondly, a vast number of proteins and their relative abundance can now be determined using mass spectrometry (MS). Dividing nitrogen between different pools can take several directions. At a cellular level, soluble and membrane fractions can be separated from a cell-wall pool. Alternatively, nitrogen can be partitioned between different organelles. These two approaches rely on different methodologies and generally no approach accounts for all of the nitrogen. Consequently, melding together these disparate pieces of information requires adjustments to reach an average total. This average may not apply to a particular leaf due to effects of age, environment, and species, but it provides a useful common starting point for C3 species. Using MS, thousands of proteins and their relative abundance in a range of organisms have been measured. The PaxDb resource (Wang et al., 2015) provides estimates of protein abundance derived from spectral counts across many experiments and tissue types. The Arabidopsis thaliana database comprises 46 datasets, covering 76% of the expected proteome. More than 90% of protein is accounted for by the 1000 most-abundant proteins. However, protein quantification by MS has an inherent bias, over-representing more-abundant proteins when low-abundance proteins fall below the instrument detection limits. Identification of proteins by MS can also be biased due to a range of factors affecting peptide detection, such as peptide solubility, enzymatic digestion efficacy, and differing ion efficiencies (reviewed in Lundgren et al., 2010). Consequently, the PaxDb values cannot be taken at face value (Li et al., 2017). For example, the abundance of Rubisco large subunits outnumbers that of the small subunit by more than 8-fold. One would expect that the amounts of these two subunits should be similar as the mature Rubisco enzyme contains eight large and eight small subunits. Rubisco represents about 40% of soluble protein (Eckardt et al., 1997), or 20% of leaf nitrogen (Evans and Seemann, 1984), which equates to about 119000 ppm for each subunit (see Supplementary Data S1 at JXB online). Because Rubisco is such an abundant protein, this potentially introduces a significant bias unless it is corrected (Li et al., 2017). Further, the stoichiometry in PaxDb of proteins within and between complexes does not necessarily match expectations, perhaps reflecting the fact that not all proteins are quantitatively captured during tissue preparation and subsequent measurement. However, the data available from MS allows a deeper understanding of nitrogen distribution between proteins than previous techniques have afforded. Moving forward, new data-independent acquisition proteomic techniques, such as SWATH MS (Law and Lim, 2013) will allow greater accuracy and a much finer resolution of leaf nitrogen allocation between proteins within leaves. Thylakoid nitrogen costs Within the chloroplast, protein complexes in the thylakoid membranes are involved with light-capture, photosynthetic electron transport from water to NADP, and ATP synthesis. The relative abundance of these protein complexes varies in response to growth irradiance, which also changes the electron transport capacity per unit of chlorophyll. It is convenient to divide thylakoid nitrogen between two pools: light-capture and bioenergetics. The reaction centres of both photosystem II and I capture light and perform electron transport, but under unstressed conditions neither determine the electron transport capacity. Consequently, it is appropriate to place them in the pool associated with light-capture, together with the light-harvesting chlorophyll a/b complexes (LHCs). The distribution of chlorophyll between these complexes can be used to estimate the nitrogen associated with each, if the chlorophyll-to-protein stoichiometry is known (Table 1). The majority of chlorophyll is associated with the LHCs (56%), each of which binds 14 chlorophyll molecules (Liu et al., 2004). Photosystem I with its four associated LHCs accounts for 30% of leaf chlorophyll in complexes that bind 156 chlorophyll molecules (Scheller et al., 2001; Caspy and Nelson, 2018). Photosystem II with the antenna proteins CP26 and CP29 bind 63 chlorophyll molecules (Wei et al., 2016) and account for the remaining 14% of chlorophyll. Putting these three fractions together results in an average nitrogen cost for light-capture of 37.3 mol N mol−1 Chl (Table 1). Table 1. Nitrogen cost of light-harvesting complexes Complex Molecular weight (kDa) No. of Chl molecules per complex Protein N/Chl (mol N mol−1 Chl)* % total Chl† N cost/Chl (mol N mol−1 Chl)‡ LHC 28.8 14 23.5 56 13.2 PSI-LHCI 388 156 28.4 30 8.5 PSII 456 63 82.7 14 11.6 Chl 4 Total nitrogen cost of light harvesting 37.3 Complex Molecular weight (kDa) No. of Chl molecules per complex Protein N/Chl (mol N mol−1 Chl)* % total Chl† N cost/Chl (mol N mol−1 Chl)‡ LHC 28.8 14 23.5 56 13.2 PSI-LHCI 388 156 28.4 30 8.5 PSII 456 63 82.7 14 11.6 Chl 4 Total nitrogen cost of light harvesting 37.3 * Protein nitrogen cost per chlorophyll in the complex. † Percentage of the total chlorophyll associated with each complex. ‡ Nitrogen cost of each complex weighted by the percentage of total chlorophyll associated with it. Updated from Evans and Seemann (1989). View Large Table 1. Nitrogen cost of light-harvesting complexes Complex Molecular weight (kDa) No. of Chl molecules per complex Protein N/Chl (mol N mol−1 Chl)* % total Chl† N cost/Chl (mol N mol−1 Chl)‡ LHC 28.8 14 23.5 56 13.2 PSI-LHCI 388 156 28.4 30 8.5 PSII 456 63 82.7 14 11.6 Chl 4 Total nitrogen cost of light harvesting 37.3 Complex Molecular weight (kDa) No. of Chl molecules per complex Protein N/Chl (mol N mol−1 Chl)* % total Chl† N cost/Chl (mol N mol−1 Chl)‡ LHC 28.8 14 23.5 56 13.2 PSI-LHCI 388 156 28.4 30 8.5 PSII 456 63 82.7 14 11.6 Chl 4 Total nitrogen cost of light harvesting 37.3 * Protein nitrogen cost per chlorophyll in the complex. † Percentage of the total chlorophyll associated with each complex. ‡ Nitrogen cost of each complex weighted by the percentage of total chlorophyll associated with it. Updated from Evans and Seemann (1989). View Large The second thylakoid nitrogen pool, bioenergetics, is associated with photosynthetic electron transport and ATP synthesis. The relative abundances of the cytochrome b6f and ATP synthase complexes covary depending on the growth irradiance, and are directly correlated with the electron transport capacity (Evans, 1987; Yamori et al., 2011). Consequently, the cytochrome f content provides a way to link photosynthetic performance to the nitrogen cost of these complexes. As quantitative measures of ATP synthase were lacking when the thylakoid nitrogen budget was first assembled, a ratio of cyt f:ferredoxin-NADP+ reductase (FNR):ATP synthase of 1:1:1.2 was assumed, which resulted in a nitrogen cost for bioenergetics of 8.85 mol N mmol–1 cyt f (Evans and Seemann, 1989). Now, with the availability of PaxDb (Wang et al., 2015; Li et al., 2017), we have reassessed this assumption (Supplementary Data S2) and obtained a ratio of cyt f:FNR:ATP synthase of 1:0.85:1.35, which leads to a revised cost for bioenergetics of 10.86 mol N mmol−1 cyt f. The actual ratio assumed for ATP synthase makes a significant impact on the total nitrogen cost of bioenergetics as it represents about 80% of this pool. The nitrogen cost of thylakoids with respect to their electron transport capacity can be represented graphically. In Box 2, cytochrome f content per unit chlorophyll, which is directly proportional to the electron transport capacity per unit chlorophyll, varies along the x-axis; the total thylakoid nitrogen cost per unit chlorophyll is presented on the y-axis. The symbols represent actual measurements taken from spinach and pea leaves that were grown under different irradiances, as well as several C4 species where mesophyll and bundle sheath cells were analysed separately (Evans, 1987; Terashima and Evans, 1988; Evans and Seemann, 1989; Ghannoum et al., 2005). The rectangular area labelled ‘light capture’ represents the average nitrogen cost associated with LHCs and the two photosystem complexes (37.3 mol N mol−1 Chl). For simplicity, the minor variation in chlorophyll distribution between pigment protein complexes has been ignored here (Leong and Anderson, 1984). The triangular area labelled ‘bioenergetics’ represents the increasing cost of nitrogen as the electron transport capacity increases per unit chlorophyll. Two upper bounds are shown depending on the nitrogen cost assumed for bioenergetics (8.85 and 10.86 mol N mmol−1 cyt f being the original and revised estimates, respectively). On average for a leaf growing in sunlight, there are about 55 mol N mol−1 Chl in chloroplast thylakoid membranes. Box 2. The nitrogen cost of thylakoids in relation to their electron transport capacity Photosynthetic electron transport capacity is directly proportional to the cytochrome f content: 155 mol electrons mol−1 cyt f s−1 (Evans, 1988; Niinemets and Tenhunen, 1997). A constant nitrogen cost associated with pigment protein complexes of 37.3 mol N mol−1 Chl is assumed (rectangle labelled ‘Light capture’). Thylakoid nitrogen associated with photosynthetic electron transport (triangle labelled ‘Bioenergetics’) is shown for two different assumed costs (red lines). BS, bundle sheath; M, mesophyll. Data from: Evans (1987); Terashima and Evans (1988); Evans and Seemann (1989); Ghannoum et al. (2005). Nitrogen distribution within the cell To establish the relative distribution of nitrogen between the cellular organelles, it is necessary to juggle different sources of information as none provide the complete picture. An average distribution for mature leaves of C3 plants is chloroplast 75%, mitochondria 5%, peroxisomes 2.5%, cytosol 7.5%, and cell walls 10% (Makino and Osmond, 1991; Wang et al., 2015; Li et al., 2017; Onoda et al., 2017). Alternatively, the nitrogen distribution can be grouped by function and this can be superimposed onto the organellar structure, as shown in Box 3. The relative size of each pool related to photosynthesis has been scaled to represent the fraction of leaf nitrogen associated with it, in total accounting for 54% of the total. In the case of the photorespiratory cycle, this occurs across three organelles. Within chloroplasts, about 16% of the nitrogen is associated with other proteins and molecules not directly associated with photosynthesis and protein synthesis. For the remainder of the cell, another 13% is left in the ‘other’ category, which includes the nucleus, cytosol, and non-photorespiratory mitochondrial processes. Box 3. Nitrogen budget for a C3 leaf cell The coloured shapes are scaled relative to their proportion of leaf nitrogen. The distribution of nitrogen between different organelles is shown to the right of the figure (see Supplementary Data S3). LHC, light-harvesting chlorophyll a/b complex; PSII, photosystem II reaction centre; PSI-LHCI, photosystem I reaction centre with its light-harvesting chlorophyll a/b complex, ATPase, ATP synthase; Cyt f, cytochrome b6f Rieske iron–sulphur complex; RCA, Rubisco activase; CA, carbonic anhydrase; PCR, enzymes of the photosynthetic carbon reduction cycle excluding Rubisco; PCO, enzymes in the photosynthetic carbon oxidation cycle; Protein synth., nitrogen associated with protein synthesis including amino acids. Scaling to the ecosystem Given the diversity of plant species and ecosystems, it is a challenge to represent them through generalizations. Leaf dry mass and nitrogen contents per unit area have been determined for samples collected in the field for many species, and for those leaves that also had photosynthetic attributes measured in the field, relationships have emerged. Linear relationships between photosynthetic capacity and leaf nitrogen content per unit area exist for different plant types (Kattge et al., 2009) although, perhaps surprisingly, nitrogen-fixing legumes overlap with non-leguminous dicotyledonous crop species (Adams et al., 2018). Since there are many more measurements of leaf nitrogen than photosynthesis on field-grown material, these relationships are widely embedded into ecosystem and global models. However, given the variability in the slope relating photosynthetic capacity to leaf nitrogen content per unit area between plant types, ground-truthing is still required, for example in arctic biomes (Rogers et al., 2017b). Field gas-exchange measurements can establish the relationship between Rubisco capacity and leaf nitrogen content, although this may not reflect the actual allocation of nitrogen in Rubisco (Bahar et al., 2017). Improvements in remote sensing capability are increasing our ability to estimate plant characteristics from reflectance spectra (Martin et al., 2018). Whether it is possible to use hyperspectral reflectance to derive estimates of Rubisco capacity directly (Serbin et al., 2015; Yendrek et al., 2017; Silva-Perez et al., 2018) or indirectly by first predicting nitrogen content (Dechant et al., 2017) is currently an active area of research. An analysis of multiple publications has revealed four features associated with increasing leaf mass per unit area between species (Onoda et al., 2017). Firstly, there is an apparent decrease in nitrogen allocated to Rubisco. Secondly, there is a decrease in mesophyll conductance per unit of mesophyll cell surface exposed to intercellular airspace. Thirdly, the draw-down in CO2 partial pressure between intercellular airspaces and the sites of carboxylation inside chloroplasts during photosynthesis increases with increasing leaf mass per unit area (LMA). Fourthly, there is an increase in the fraction of leaf nitrogen associated with the cell wall. The combination of these features reduces photosynthetic capacity per unit of leaf N in species with greater LMA. Given that LMA is associated with leaf lifespan, rather than achieving an instantaneous high photosynthetic rate per unit leaf nitrogen, species with high LMA may instead achieve greater lifetime photosynthetic return from a given investment of nitrogen into a leaf. Fertilizer—photosynthesis—food In the forty years 1962–2002, the combined global production of wheat, rice, and maize increased from 682 to 1752 Mt year−1 and nitrogen fertilizer production increased from 13.6 to 88.2 Mt year−1 (http://www.fao.org/faostat/en/#data). There was a close linear relationship between the two, with 13.8 tonnes of grain produced per tonne of nitrogen fertilizer. Assuming an average grain nitrogen content for wheat, rice, and maize of 1.9% (Uribelarrea et al., 2008; Jaksomsak et al., 2017; Rapp et al., 2018), harvested grain accounts for one quarter of global nitrogen fertilizer. This is remarkable given that the fertilizer is not only applied to these three crops, that the harvested grain represents only part of the nitrogen in the crop at maturity, that there are losses of nitrogen from leaching, erosion, and denitrification, and that there is some residual nitrogen left in the soil. However, the environmental costs associated with nitrogen escape are a growing cause for concern and there are pressing demands for improving the efficiency in the use of nitrogen applied in agriculture in order to reduce environmental damage, economic cost, and atmospheric greenhouse gas consequences both during the production of fertilizer and NOx emissions from fields. Plants need to balance carbon gain with the synthesis of organic nitrogen compounds. As a consequence of the oxygenation reaction catalysed by Rubisco, the photorespiratory pathway recycles two molecules of phosphoglycolate to produce one phosphoglycerate (PGA). At the same time, one molecule of ammonia is released in mitochondria and is refixed by Gln synthetase and glutamine oxoglutarate aminotransferase (GS/GOGAT). The widely used Farquhar, von Caemmerer, and Berry biochemical model of C3 photosynthesis (Farquhar et al., 1980) assumes complete recycling, although this may not always be the case (Abadie et al., 2017; Bloom and Lancaster, 2018; Busch et al., 2018). At 25 °C and current atmospheric CO2 concentrations, approximately six carbon atoms are fixed per ammonia recycled (see Supplementary Data S4). By comparison, new biomass requires 33 carbon to be fixed for each new nitrogen, assuming the plant contains 2% N, 40% C, and respires 30% of daily carbon fixed during the production of this new biomass. Incorporation of ammonia during photorespiration or de novo incorporation in leaves uses the same GS/GOGAT enzymatic pathway. Therefore, for plants converting inorganic nitrogen into organic compounds in their leaves, 85% of the GS/GOGAT flux is dealing with photorespiration on average. At any given instant, this proportion would change as it is affected by temperature, irradiance, and CO2 concentration. One consequence of rising atmospheric CO2 concentrations is that the C:N balance of plant tissue is changing. Elevated CO2 reduces photorespiration and, with the exception of legumes that can fix atmospheric nitrogen symbiotically, plants grown under elevated atmospheric CO2 have lower nitrogen concentrations (Feng et al., 2015). This translates into lower grain protein concentrations, which may have dietary implications in the future (Myers et al., 2014; Zhu et al., 2018). Engineering photosynthesis to improve crop yield Our detailed knowledge of photosynthesis has led to the identification of many proteins that can be targeted to increase carbon gain. A selection of targets that have been identified are presented in Box 4. In some cases, initial proof of concept has been obtained with transformed model plants (Kromdijk et al., 2016; Driever et al., 2017; Lopez-Calcagno et al., 2018; Salesse-Smith et al., 2018). Field trials with crop plants are underway and their outcome is eagerly awaited. Given the central importance of Rubisco in determining the rates of CO2 assimilation and photorespiration, and because it accounts for so much of leaf nitrogen, much attention is focussed on ways to improve its performance. Approaches fall into two categories: firstly, those where the catalytic properties of Rubisco are altered, for example from C4 species or other organisms (Orr et al., 2016), and secondly, those where the CO2 partial pressure around Rubisco is increased, for example CO2-concentrating mechanisms such as carboxysomes (Hanson et al., 2016; Rae et al., 2017; Long et al., 2018), greater mesophyll conductance (Groszmann et al., 2017), or photorespiratory bypass (Peterhansel and Maurino, 2011). While some variation in kinetic properties of Rubisco between wheat relatives has been identified (Prins et al., 2016), detailed crop modelling is needed to assess the impact and cost/benefit from engineering an alternative form into elite wheat. While there are several crop models available (Song et al., 2017; Yin and Struik, 2017; Wu et al., 2018), it is a complex task to deal with plant functions that are not necessarily well represented or fully parameterized. The perennial debate about whether plant growth and yield are determined by source photosynthesis or sink demand continues. In the case of rice, increasing sink capacity has led to a dramatic increase in yield (Ashikari et al., 2005). The current focus on improving photosynthesis is because the gains in harvest index (grain yield/above-ground biomass) associated with the introduction of dwarfing genes have been largely maximized, but maintaining or increasing both sink strength and harvest index is also crucial. Box 4. Targets for improving photosynthesis Many proteins have been identified that could potentially increase carbon gain, and a selection is shown. The numbering order reflects the nitrogen cost of adding additional proteins, beginning with the greatest nitrogen requirement for Rubisco or ATP synthase. The protein cost associated with increased expression of targets 3–8 is likely to be small. In the case of the light-harvesting complex, a reduction in chlorophyll content per unit area frees up nitrogen that could be invested in other more rate-limiting photosynthetic proteins. If a plant could be engineered to fix more carbon per unit of nitrogen associated with photosynthesis, then unless de novo incorporation of nitrogen was also enhanced, there would be a lowering of the nitrogen concentration of the plant and most likely also the protein content of the grain. An increase in carbon gain per unit photosynthetic N could free up nitrogen for investment in new tissues elsewhere and increase growth. This is observed when plants are grown under elevated atmospheric CO2 (Ainsworth and Long, 2005). However, unless additional organic nitrogen is incorporated into other tissues, the conversion of that increased growth into greater yield would result in lower grain protein. If the additional organic nitrogen incorporated elsewhere in the plant could not provide any improvement above that gained from greater photosynthesis per unit of photosynthetic N, what is the benefit from raising photosynthetic rate per unit N? A second concern is that for cereal crops, nitrogen is remobilized from leaves and stems during grain filling. At maturity, the grain can account for 80–90% of above-ground nitrogen (Barraclough et al., 2010; Gaju et al., 2014). For a crop yielding 10 t ha–1 with a 2.5% N concentration in the grain, this represents 250 kg N ha−1. To contain this within a crop canopy with a leaf area index of 7 (Shearman et al., 2005), the leaf nitrogen content would need to be 3.6 g m−2. This is close to the maximum leaf nitrogen content that is observed (Silva-Perez et al., 2018). If increasing photosynthesis per unit N resulted in lower nitrogen contents per unit leaf area, then a greater fraction of this remobilizable nitrogen would need to be present in the sheath and stem fractions. In the case of wheat, the ear can also make a substantial photosynthetic contribution to the grain (Maydup et al., 2012; Zhou et al., 2016). While these tissues can contribute to canopy photosynthesis, the relative efficiency of leaf and stem needs to be investigated in order to assess the consequences. The point is, that to increase yield while maintaining grain protein concentration requires increasing both photosynthetic carbon gain and de novo nitrogen incorporation. In addition, the crop canopy has to be capable of holding the vast majority of that nitrogen in its leaves to enable its relocation into developing grain. An alternative is to continue de novo nitrogen incorporation during grain filling, which requires continued root growth, nitrogen uptake (perhaps associated with a late application of fertilizer), and incorporation into protein while leaves are senescing. Future work Given that Rubisco constitutes the largest fraction of nitrogen in leaves of C3 plants, it justifiably attracts great attention. In the absence of complete kinetic information to describe the performance of Rubisco from different species, the default has frequently been to assume kinetic values of tobacco Rubisco (Bernacchi et al., 2002). However, the kinetic properties of Rubisco from diverse species need to be determined. Some of the variation between species in the apparent Rubisco activity per unit leaf N might be associated with variation in kinetic properties, but other factors could also be involved, such as different allocation of nitrogen towards Rubisco and different activation states. With improved quantification of relative protein abundance, the extent to which variation in nitrogen allocation to pigment protein complexes is associated with Rubisco performance will be revealed. The limited number of species for which thylakoid nitrogen cost has been quantified should be expanded. In particular, the nitrogen allocated to ATP synthase needs attention, given its apparent significant cost. Supplementary data Supplementary data are available at JXB online. Data S1. Rescaling PaxDb to account for Rubisco abundance. Data S2. Nitrogen cost of bioenergetics. Data S3. Nitrogen distribution within the cell. Data S4. Nitrogen fixed per carbon assimilated. Acknowledgements This work was supported by the Australian Research Council Centre of Excellence for Translational Photosynthesis (CE140100015) and the Grains Research Development Corporation (ANU00025). Thanks to Harvey Millar and Nic Taylor from UWA for proteomics information, and Christine Raines for encouragement. References Abadie C , Lothier J , Boex-Fontvieille E , Carroll A , Tcherkez G . 2017 . Direct assessment of the metabolic origin of carbon atoms in glutamate from illuminated leaves using 13C-NMR . New Phytologist 216 , 1079 – 1089 . Google Scholar Crossref Search ADS PubMed Adams MA , Buckley TN , Salter WT , Buchmann N , Blessing CH , Turnbull TL . 2018 . Contrasting responses of crop legumes and cereals to nitrogen availability . New Phytologist 217 , 1475 – 1483 . Google Scholar Crossref Search ADS PubMed Ahmad R , Bilal M , Jeon JH , Kim HS , Park YI , Shah MM , Kwon SY . 2016 . 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For permissions, please email: [email protected] 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)

Stortenbeker,, Niek;Bemer,, Marian

doi: 10.1093/jxb/ery332pmid: 30239806

Abstract The family of small auxin up-regulated RNA (SAUR) genes is a family of auxin-responsive genes with ~60–140 members in most higher plant species. Despite the early discovery of their auxin responsiveness, their function and mode of action remained unknown for a long time. In recent years, the importance of SAUR genes in the regulation of dynamic and adaptive growth, and the molecular mechanisms by which SAUR proteins act are increasingly well understood. SAURs play a central role in auxin-induced acid growth, but can also act independently of auxin, tissue specifically regulated by various other hormone pathways and transcription factors. In this review, we summarize recent advances in the characterization of the SAUR genes in Arabidopsis and other plant species. We particularly elaborate on their capacity to fine-tune growth in response to internal and external signals, and discuss the breakthroughs in understanding the mode of action of SAURs in relation to their complex regulation. Acid growth, auxin, brassinosteroids, cell elongation, growth adaptation, PIFs, PP2C.D, small auxin up-regulated RNA, SAUR Introduction The first discovery of small transcripts that rapidly responded to auxin dates back to 1987 from experiments with elongating soybean hypocotyls (McClure and Guilfoyle, 1987). In the years thereafter, these small auxin up-regulated RNAs (SAURs) were also identified in tobacco, Arabidopsis, and maize (Gil et al., 1994; Newman et al., 1993; Knauss et al., 2003), all showing a rapid induction after auxin treatment. Both the transcript and protein half-lives were found to be very short (McClure and Guilfoyle, 1989; Newman et al., 1993; Knauss et al., 2003), indicating that SAUR activity can be quickly reduced after removal of the auxin stimulus, allowing very dynamic responses. Because the transcripts were identified in elongating hypocotyls and induced by the growth hormone auxin, which had been proposed to induce cell elongation via acid growth (Rayle and Cleland, 1970; Rayle and Cleland, 1980), a link between auxin, SAUR gene expression, and cell elongation was apparent. However, the genetic evidence demonstrating the role of SAURs in auxin-induced cell elongation remained elusive for a long time. It was the renewed interest in SAUR gene function, in combination with a large increase in the availability of genetic and molecular tools and resources, which recently allowed linking the SAURs to auxin-induced growth in correspondence with the acid growth theory. First, different SAUR genes were found to induce cell elongation in Arabidopsis when overexpressed (Chae et al., 2012; Spartz et al., 2012; Stamm and Kumar, 2013; Li et al., 2015) and, secondly, Spartz et al. (2014) made a major contribution to the field by showing that SAURs can interact with PP2C.D phosphatases to inhibit their activity. This inhibition prevents membrane H+-ATPases from being dephosphorylated, which increases their activity and induces cell wall acidification. Thus, SAURs indeed induce plant growth by regulating cell wall acidification. In addition to induction by auxin, SAUR genes can be regulated by a plethora of other upstream factors, thereby regulating growth dynamically in response to internal as well as environmental cues (e.g. Kodaira et al., 2011; Oh et al., 2014; Favero et al., 2017; van Mourik et al., 2017; Hu et al., 2018). Because SAUR overexpression is sufficient to induce growth (Fendrych et al., 2016; Spartz et al., 2017), other upstream factors may regulate SAUR-mediated growth independently of the auxin pathway. SAURs have thus been unveiled as growth factors that are essential for both normal plant development and adaptation to environmental conditions. In the last few years, SAUR studies from species other than Arabidopsis have also been emerging, broadening our view on the importance of SAUR genes in the plant kingdom. Here, we review recent advances in the characterization of SAUR genes in Arabidopsis as well as in other plant species, and discuss their conservation and divergence in the plant kingdom. We will summarize the novel insights into the molecular function of SAURs, and in particular elaborate on the different mechanisms of upstream and downstream regulation of SAUR activity, which allow the plant to fine-tune growth in a tissue-specific manner under different environmental conditions. SAUR gene evolution in the plant kingdom The SAUR genes form a plant-specific gene family, with the most basic members described in the moss Physcomitrella patens, which contains 18 SAUR genes (Rensing et al., 2008). Notably, Aux/IAA-ARF-mediated auxin signalling is also present from moss lineages to higher plants (Lau et al., 2009), suggesting that SAUR genes have been important for the output of the auxin response from the beginning of land plant evolution onwards. Thanks to recent advances in genome sequencing, SAUR families could be described in a large number of species. Besides Arabidopsis, which contains 79 SAUR genes (Ren and Gray, 2015), most higher plant species contain between 60 and 140 SAUR genes in their genomes, which are often arranged in clusters (Jain et al., 2006; Wang et al., 2010; Wu et al., 2012; Chen et al., 2014; Li et al., 2017; Hu et al., 2018). This high level of tandem and segmental duplications is remarkable, but may to some extent be explained by the small size of the SAURs, permitting duplication of the complete gene without loss of essential regions. SAUR genes are generally intronless, with ORFs predicted to encode proteins of between 7 kDa and 20 kDa (~60–180 amino acids) (Jain et al., 2006; Wang et al., 2010; Wu et al., 2012; Chen et al., 2014). These proteins have a conserved core of ~60 residues, whereas the homology at the N- and C-termini is rather low (Jain et al., 2006; Park et al., 2007; Ren and Gray, 2015). Within this core region, Wu et al. (2012) identified four highly conserved motifs, present in the vast majority of the SAUR proteins. The presence of these highly conserved motifs suggests that the SAUR proteins all share a conserved basic function (see below). However, their variable N- and C-termini also hint at distinct roles. For example, intracellular localization has been found to be different for the SAUR proteins (e.g. Park et al., 2007; Spartz et al., 2012; Markakis et al., 2013; Qiu et al., 2013; Ma et al., 2017), and may thus be encoded by the less conserved N- or C-terminus. In addition, histidine-rich regions in the N- and C-termini of some Arabidopsis, sorghum, tomato, and potato SAURs were suggested to allow metal binding (Wu et al., 2012), and some maize, Arabidopsis (SAUR70), and soybean SAURs have been shown to bind calmodulin via their N-terminus (Yang and Poovaiah, 2000; Popescu et al., 2007), while many more are expected to have this capacity (Ren and Gray, 2015). The presence of the divergent N- and C-termini thus suggests functional divergence amongst the SAUR proteins. Kodaira et al. (2011) published a phylogenetic tree of the Arabidopsis SAURs, in which three distinct SAUR clades could be recognized (indicated as clades I–III). To discuss the conservation and divergence of the SAURs in a broader perspective and evaluate the position of the Arabidopsis clades, we used the protein sequences from Arabidopsis, Physcomitrella, potato, tomato, rice, and sorghum SAURs to construct a phylogenetic tree of the SAUR family. Based on this analysis, the plant SAUR family can be divided into three subfamilies, which all contain both monocot and eudicot sequences (see Fig. 1 for an overview and see Supplementary Fig. S1 at JXB online for the complete tree). However, all Physcomitrella SAURs group together in one clade of subfamily A (green in Fig. 1), which consists of two Physcomitrella subclades that are sister to a third subclade containing SAURs from sorghum, rice, potato, and Arabidopsis. These ancestral SAURs have sequences that are quite divergent from the other SAURs (see Supplementary Dataset S1). The other two subfamilies, B and C, have only evolved after the divergence of the mosses. These subfamilies contain clades that are lineage specific for either higher plants, monocots, eudicots, Arabidopsis, or Solanum. This reveals that a considerable number of recent gene duplication events have taken place throughout the evolutionary history of the SAUR family, and that the duplicates have often been retained. This retention may be explained by the advantage that a higher number of SAUR genes offers the plant. The increasing complexity of higher land plants and their capability of colonizing different habitats probably also caused a higher demand for growth adaptation in response to environmental factors such as herbivory, shade, and drought. The retention of duplicated SAUR clusters in many different plant lineages suggests that SAUR copies are in general beneficial for the plant’s fitness, probably enhancing the plant’s options to regulate growth. Fig. 1. View largeDownload slide Phylogenetic tree of the SAUR family. The unrooted tree was constructed from a hand-adjusted BioEdit alignment of all SAURs from Arabidopsis (www.Arabidopsis.org), Physcomitrella (Rensing et al., 2008), potato (Wu et al., 2012), tomato (Wu et al., 2012), rice (Jain et al., 2006), and sorghum (Wang et al., 2010) (Supplementary data) using the maximum likelihood method in the MEGA software (Hall, 2013). The colours of the triangles indicate the species represented in that clade. Green, all species (including Physcomitrella); blue, eudicot and monocot; yellow, monocot; orange, eudicot; white, Arabidopsis; purple, Solanum. In some cases, the separation of the clades is uncertain and supported by low bootstrap values (Supplementary Fig. S1). Some characterized SAURs have been listed alongside the clades. The clade division from Kodaira et al. (2011) is indicated in dark green. Fig. 1. View largeDownload slide Phylogenetic tree of the SAUR family. The unrooted tree was constructed from a hand-adjusted BioEdit alignment of all SAURs from Arabidopsis (www.Arabidopsis.org), Physcomitrella (Rensing et al., 2008), potato (Wu et al., 2012), tomato (Wu et al., 2012), rice (Jain et al., 2006), and sorghum (Wang et al., 2010) (Supplementary data) using the maximum likelihood method in the MEGA software (Hall, 2013). The colours of the triangles indicate the species represented in that clade. Green, all species (including Physcomitrella); blue, eudicot and monocot; yellow, monocot; orange, eudicot; white, Arabidopsis; purple, Solanum. In some cases, the separation of the clades is uncertain and supported by low bootstrap values (Supplementary Fig. S1). Some characterized SAURs have been listed alongside the clades. The clade division from Kodaira et al. (2011) is indicated in dark green. Interestingly, proteins classified in clades I and II by Kodaira et al. (2011) were recovered in two clades of subfamily C, most distantly related to the ancestral SAURs, while clade III SAURs are dispersed over many clades. The Arabidopsis SAUR63 clade, placed into clade II by Kodaira et al., can be found back as a separate clade (‘clade IV’) in our analysis. The SAURs from clades I and II appear to possess functions distinct from those of clade III SAURs, as many are responsive to abscisic acid (ABA) (Kodaira et al., 2011) and regulate cell elongation in seedlings (Sun et al., 2016) (see next section). This raises the intriguing possibility that these functions have evolved more recently and are particularly important for the growth of higher plants. SAUR function and mode of action Cell elongation and growth The long period between the discovery of auxin up regulated RNAs and their functional characterization can be ascribed to the fact that single SAUR knock-outs rarely give a mutant phenotype due to redundancy. In addition, distinct overexpression phenotypes could often only be observed after stabilization of the protein through fusion with, for example, green fluorescent protein (GFP) (Chae et al., 2012; Spartz et al., 2012). The first functional data therefore originated from overexpression of fusion proteins or simultaneous down-regulation of a group of paralogous genes using artificial miRNA (amiRNA) silencing. The majority of these studies showed that overexpression of SAUR genes can induce cell elongation in Arabidopsis (Franklin et al., 2011; Chae et al., 2012; Spartz et al., 2012; Kong et al., 2013; Stamm and Kumar, 2013; Bemer et al., 2017b; van Mourik et al., 2017). Recently, Sun et al. (2016) used a comprehensive approach to show that light-regulated seedling growth in Arabidopsis is controlled by a group of 32 redundantly acting SAUR genes. These so-called lirSAUR (light-induced in cotyledons and/or repressed in hypocotyls) genes are responsible for auxin-induced hypocotyl elongation in the dark and/or for the expansion of cotyledons upon exposure to light. Phytochrome-interacting factors (PIFs) are important for this regulation in both tissues, but, surprisingly, their breakdown upon exposure to light reduces SAUR expression in the hypocotyls, while inducing it in the cotyledons (Sun et al., 2016). The mechanisms behind this opposite effect remain to be resolved, but different cofactors probably play a role (Sun et al., 2016). Although the function of the SAURs has thus far been mainly studied in Arabidopsis seedlings, there is increasing evidence that their cell-elongating function goes far beyond the juvenile stage, regulating growth in many different tissues. In addition to expression data, which show plant-wide SAUR gene activity in various species (Jain et al., 2006; Wu et al., 2012; Xie et al., 2015; van Mourik et al., 2017; Hu et al., 2018), overexpression studies revealed that SAUR activity can induce growth in leaves, stems, and floral organs (Chae et al., 2012; Spartz et al., 2012; van Mourik et al., 2017). Interestingly, the specific expression of a SAUR50-like gene from sunflower on the east side of the stem correlates with the diurnal bending of the apex towards the sun (Atamian et al., 2016), and there is evidence that the Arabidopsis SAUR10 gene, which is up-regulated in shaded conditions, affects the degree of branch bending (Bemer et al., 2017b). This indicates that SAURs can also regulate light responses in the adult phase in different plant species. In conclusion, the majority of SAUR genes probably play a role in the induction of growth via cell elongation. Auxin-induced cell elongation has been hypothesized to occur according to the acid growth theory, based on the observation that a low pH induces cell wall loosening (Rayle and Cleland, 1970) and that H+ excretion takes place in response to auxin application (Rayle and Cleland, 1980). Recently, the mechanism by which acid growth occurs via auxin and SAURs was elucidated step by step. First, Chen et al. (2010) showed that auxin induces phosphorylation of the plasma membrane H+-ATPase AHA1 in vitro. Plasma membrane H+-ATPases, of which AHA1 and AHA2 have the highest expression (Ren and Gray, 2015), require phosphorylation of the C-terminal Thr947 residue and subsequent binding of a 14-3-3 protein for activation (Fuglsang et al., 1999). Takahashi et al. (2012) then demonstrated that auxin treatment increases the phosphorylation levels and 14-3-3 binding in planta, without changing the amount of H+-ATPases. The localization of SAUR19-clade proteins to the plasma membrane prompted Spartz et al. (2014) to investigate whether SAURs could regulate the H+-ATPases, thereby discovering the link between auxin and cell membrane acidification, and achieving a major breakthrough in the understanding of SAUR function. In their study, Spartz et al. showed that SAUR proteins can interact with protein phosphatases of the PP2C.D family to inhibit their function. This prevents dephosphorylation of the H+-ATPases, resulting in increased H+-ATPase activity and induced membrane acidification (Fig. 2A). Cell growth is subsequently probably achieved by activation of cell wall expansins due to the low apoplastic pH, as well as an increase of osmotic water flow due to plasma membrane hyperpolarization (Spartz et al., 2017). Arabidopsis SAURs from different clades were tested for their ability to reduce PP2C.D activity in vitro, and they all exhibited this capacity (Spartz et al., 2014; Sun et al., 2016), suggesting that repression of PP2C.D activity is the general mechanism by which SAURs induce cell elongation. The Arabidopsis PP2C.D subfamily consists of nine members, of which three (D2, D5, and D6) are located at the plasma membrane. In a recent study, Ren et al. (2018) showed that the three plasma membrane-localized PP2C.D members are the primary regulators of AHA activity in planta, although small contributions of the other PP2C.Ds, some of which can also interact in vitro with SAUR19, cannot be excluded. The phenotype of the d2d5d6 triple mutant is similar to that of SAUR overexpression lines, with increased cell elongation in seedlings, leaves, stem, and floral organs (Ren et al., 2018), suggesting that the SAUR-induced cell elongation is regulated via interaction with these PP2C.Ds throughout the plant. Fig. 2. View largeDownload slide Schematic model of the putative molecular functions of SAURs in different cell compartments. (A) In the plasma membrane, SAURs interact with PP2C.D2/5/6, thereby repressing dephosphorylation of the H+-ATPases AHA1/2 and inducing cell elongation. (B) In the cytosol, SAURs can probably interact with SSPP (PP2C.D1), thereby repressing dephosphorylation of AtSARK and inducing senescence. (C) In the nucleus, the function of SAURs is still unclear, but they may interact with the nuclear-localized PP2C.D1/3/4. Fig. 2. View largeDownload slide Schematic model of the putative molecular functions of SAURs in different cell compartments. (A) In the plasma membrane, SAURs interact with PP2C.D2/5/6, thereby repressing dephosphorylation of the H+-ATPases AHA1/2 and inducing cell elongation. (B) In the cytosol, SAURs can probably interact with SSPP (PP2C.D1), thereby repressing dephosphorylation of AtSARK and inducing senescence. (C) In the nucleus, the function of SAURs is still unclear, but they may interact with the nuclear-localized PP2C.D1/3/4. SAUR function in other processes Interestingly, the overexpression lines of some SAUR genes were reported to display phenotypes other than increased cell elongation, indicating that SAUR family genes may perform additional functions. Some of these functions can probably be related to their interaction with PP2C.Ds, while the mechanisms underlying other observed phenotypes may rely on different factors. In this section, we briefly discuss the involvement of SAURs in other processes based on the different phenotypes that have been reported. An early senescence phenotype has been observed in overexpression lines of SAUR10, SAUR36, and the rice gene OsSAUR39 (Kant et al., 2009; Hou et al., 2013; Bemer et al., 2017b), while saur36 knock-out mutants exhibited a delayed leaf senescence phenotype (Hou et al., 2013). Thus, SAURs appear to induce senescence, a function that may be regulated by interaction with a PP2C.D phosphatase, as Xiao et al. (2015) identified the PP2C.D phosphatase SENESCENCE-SUPPRESSED PROTEIN PHOSPHATASE (SSPP) as an important negative regulator of leaf senescence. SSPP (PP2C.D7 according to TAIR, but designated PP2C.D1 in Ren et al., 2018), which is mainly localized in the cytosol, dephosphorylates the senescence-inducing receptor-like kinase AtSARK, localized at the plasma membrane (Fig. 2B) (Xiao et al., 2015). SAURs may thus interact with SSPP in the cytosol, thereby repressing its activity and activating AtSARK and leaf senescence. Several other studies reported SAUR overexpression phenotypes not related to cell elongation. In particular, the few studies that reported nuclear-localized SAURs revealed overexpression phenotypes different from cell elongation. Overexpression of SAUR32, the first characterized Arabidopsis SAUR gene, leads to reduced hypocotyl growth and abolished apical hook formation in the dark. The gene does not respond to auxin or light (Park et al., 2007; Sun et al., 2016) and is localized to the nucleus, suggesting that it does not interact with the plasma membrane PP2C.Ds. Overexpression of SAUR76, which is predominantly localized in the nucleus, does not promote cell elongation either, but affects the meristematic activity of the tissues, with fewer cells in the leaves and more cells in the roots (Markakis et al., 2013). Both genes thus appear to have a function in the nucleus that may be unrelated to interaction with PP2C.Ds, or which involves nuclear-localized PP2C.(D)s. Interestingly, Ma et al. (2017) reported that the cassava MeSAUR1 protein, also localized to the nucleus, can bind to and regulate the promoter of the ADP glucose pyrophosphorylase subunit MeAGPs1a, and would thus act as a transcription factor (TF). MeSAUR1 contains a specific N-terminus conserved in a clade of monocot and eudicot SAURs, among which are the Arabidopsis SAUR10 and SAUR50 proteins (Fig. 1). It is not very likely that this N-terminus provides DNA binding activity however, as both SAUR10 and SAUR50 exhibit canonical cell elongation phenotypes upon overexpression. A more thorough in vivo analysis of MeSAUR1 and other SAURs in the future is required to determine whether some SAURs can act as TFs and to unveil the role of SAURs in the nucleus. SAUR overexpression can also have an effect on auxin levels, polar auxin transport, and/or expression of auxin pathway genes (Kant et al., 2009; Chae et al., 2012; Spartz et al., 2012; Kong et al., 2013; Ren and Gray, 2015; Xu et al., 2017). Overexpression of growth-inducing SAUR genes (SAUR19, SAUR41, and SAUR63) results in increased auxin transport, while overexpression of growth-inhibiting SAUR genes (OsSAUR39 and OsSAUR45) has a repressive effect (Kant et al., 2009; Xu et al., 2017). These effects on the auxin pathway can be indirect, because the increase in H+-ATPase activity probably leads to an increased plasma membrane potential, expected to induce auxin transport (Ren and Gray, 2015). However, since polar auxin transport is regulated by phosphorylation of the PIN auxin efflux carriers via PP2C.A phosphatases (Ballesteros et al., 2012), one could also speculate that some SAURs might interact with other PP2C phosphatases, thereby acting directly on polar auxin transport. Another plausible explanation for the effect on polar auxin transport is the putative calmodulin binding capacity of many SAURs, because polar auxin transport depends on calcium signalling (Vanneste and Friml, 2013; Ren and Gray, 2015). These examples show that SAUR function is not restricted to the promotion of cell elongation. Other observed functions, such as senescence, are probably also regulated via the interaction with PP2C.Ds, while other functions may depend on other mechanisms and be more clade specific. The presence of specific N- or C-termini could enable calmodulin binding, metal binding (Wu et al., 2012), interaction with ethylene receptors (SAUR76–SAUR78; Li et al., 2015), or even DNA binding capacity. The clade-specific presence of conserved N- or C-termini suggests that different subclades can have distinct functions. Interestingly, the Arabidopsis SAURs that can induce cell elongation and were reported by Sun et al. (2016) to be regulated during seedling morphogenesis practically all fall into clades I and II defined by Kodaira et al. (2011), while most clade III SAURs are either not expressed in the hypocotyl/cotyledon, or do not exhibit differential expression upon transfer to the light (except for SAUR41, SAUR49, and SAUR52) (Sun et al. 2016). This could mean that the ability to induce cell elongation, probably linked to plasma membrane localization, is recorded in the protein sequence. Likewise, the ability to perform functions other than cell elongation may also depend on specific protein motifs. The future elucidation of protein motifs responsible for localization and protein–protein interactions will provide more insight into the possible presence of clade-specific functions. In conclusion, the main function of SAUR proteins is the plant-wide induction of cell elongation, by repression of PP2C.D activity, in accordance with the acid growth theory. The growth-inducing function appears to be executed by plasma membrane-localized SAURs interacting with PP2C.D2, D5, and D6. Furthermore, some SAURs probably perform roles in processes other than cell elongation, such as leaf senescence or cell division. In agreement with this, a number of SAURs (including MeSAUR1, OsSAUR39, OsSAUR45, SAUR32, SAUR36, SAUR40, SAUR41, SAUR55, and SAUR71) do not localize to the plasma membrane, but to the cytosol or nucleus (Park et al., 2007; Kant et al., 2009; Narsai et al., 2011; Kong et al., 2013; Qiu et al., 2013; Xu et al., 2017). These SAURs can possibly interact with other PP2C.Ds, which are localized to other cell compartments (Ren et al., 2018) (see Fig. 2), or even with PP2Cs from other classes. Interestingly, only a few rice and sorghum sequences group together with the clade I and II Arabidopsis proteins, while the majority of the monocot sequences are closest to the clade III Arabidopsis proteins, the function of which appears less restricted to cell elongation. This may imply that the abundance of SAUR proteins involved in cell elongation has evolved in the eudicots, while the majority of the monocot SAURs display other functions. Regulation of the different SAUR genes is highly diverse In contrast to their general role in cell elongation, the regulation of different SAUR genes is highly diverse (see Fig. 3 for a graphical summary). In recent years, reports from Arabidopsis as well as other species have unveiled that SAUR genes show tissue-specific expression patterns and can be regulated by a plethora of upstream factors. Although many SAUR genes can be induced by auxin (~70% in Arabidopsis; van Mourik et al., 2017), there is also a group of SAUR genes, named class II SAUR genes by van Mourik et al. (2017), which is not responsive to auxin. At least one of these SAUR genes (SAUR8), however, can induce cell elongation when overexpressed (van Mourik et al., 2017), indicating that class II SAURs can promote growth by repressing PP2C.D activity in response to stimuli other than auxin. Fig. 3. View largeDownload slide Regulation of SAUR genes by developmental-, environmental-, and clock-controlled factors. The different tissues where SAUR genes play a role are indicated, as well as some upstream tissue-specific regulators (in yellow). Environmental signals (dark red) are transduced via hormones (orange). Most pathways converge at the level of the ARF–BZR–PIF complex (purple), while others act directly on the upstream region of SAUR genes or affect transcript stability. The black lines indicate direct or indirect activation or repression. The circadian regulation is indicated with a clock symbol. Fig. 3. View largeDownload slide Regulation of SAUR genes by developmental-, environmental-, and clock-controlled factors. The different tissues where SAUR genes play a role are indicated, as well as some upstream tissue-specific regulators (in yellow). Environmental signals (dark red) are transduced via hormones (orange). Most pathways converge at the level of the ARF–BZR–PIF complex (purple), while others act directly on the upstream region of SAUR genes or affect transcript stability. The black lines indicate direct or indirect activation or repression. The circadian regulation is indicated with a clock symbol. Factors that can up- or down-regulate SAUR expression have been identified in different species. Characterization of the SAUR family in tomato (Wu et al., 2012), cotton (Li et al., 2017), poplar (Hu et al., 2018), citrus (Xie et al., 2015), watermelon (Zhang et al., 2017), maize (Chen et al., 2014), and Arabidopsis (e.g. van Mourik et al., 2017) all revealed that the different SAUR genes exhibit specific expression patterns throughout plant development. Moreover, the expression of different sets of SAUR genes can be positively or negatively regulated by many different hormones, including auxin (summarized in Ren and Gray, 2015; van Mourik et al., 2017), cytokinin (van Mourik et al., 2017), gibberellic acid (GA) (Bai et al., 2012; Stamm and Kumar, 2013), brassinosteroids (e.g. Oh et al., 2014; van Mourik et al., 2017; Wiesel et al., 2015), ethylene (only SAUR76–SAUR78; Li et al., 2015), ABA (Kodaira et al., 2011; Nemhauser et al., 2006), jasmonate (JA) (Nemhauser et al., 2006), as well as by different light conditions (e.g. OuYang et al., 2015; Roig-Villanova et al., 2007; Sun et al., 2016; van Mourik et al., 2017), cold (Wu et al., 2012; Hu et al., 2018), drought (Wu et al., 2012; Guo et al., 2018), high temperature (Franklin et al., 2011), and high salt conditions (Wu et al., 2012; Guo et al., 2018) in different plant species. In general, SAUR genes are up-regulated in response to hormones and conditions that are known to induce growth, such as auxin, brassinosteroids, gibberellin, and decreased red:far-red (R:FR) ratios, but down-regulated in response to ABA, JA, and stress conditions, such as drought, cold, and high salt. This stress-induced down-regulation of growth is probably compensating the plant’s investment in resistance mechanisms. β-Glucuronidase (GUS) reporter analysis revealed that the response of SAUR genes to environmental and hormonal stimuli occurs mainly in the tissue where they are already expressed (Markakis et al., 2013; van Mourik et al., 2017). This suggests that the tissue-specific expression of SAUR genes is determined by upstream TFs that may be mainly developmentally regulated, while the amplitude of their expression in these tissues depends on their response to various environmental and hormonal stimuli. Plants thus contain an extensive toolbox to regulate growth dynamically in different tissues in accordance with environmental conditions. The idea that tissue-specific SAUR gene expression is regulated by upstream developmentally regulated TFs is supported by large-scale ChIP-seq data, revealing frequent binding events of key developmental regulators such as LEAFY (LFY), APETALA 1 (AP1), APETALA 2 (AP2), SEPALLATA 3 (SEP3), and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) (van Mourik et al., 2017). Induced activity of the TCP (TEOSINTE BRANCHED 1/CYCLOIDEA/PROLIFERATING CELL FACTOR 1) TFs TCP4 and TCP20 can also rapidly up-regulate a set of SAUR genes (Danisman et al., 2012; Challa et al., 2016) but, in the case of TCP4, this occurs probably indirectly via the ARF–BZR pathway (discussed below) by direct induction of the auxin biosynthesis gene YUCCA5 (Challa et al., 2016). Only the binding to and regulation of the MADS-domain TF FRUITFULL (FUL) by the SAUR10 locus, involved in the repression of its stem-specific expression (Bemer et al., 2017b), and the direct repression of SAUR19 clade genes by the AHL transcription factor SUPPRESSOR OF PHYTOCHROME B4-#3 (SOB3) in hypocotyls (Favero et al., 2016) have been characterized in more detail so far. The factors involved in the regulation of tissue-specific SAUR expression thus largely await further investigation. Regulation by ARF–BZR–PIF The mechanisms controlling auxin-, brassinosteroid-, GA-, and light-regulated SAUR expression have been largely elucidated in recent years. Oh et al. (2014) showed that ARF6, BZR1, and PIF4 can physically interact with each other in hypocotyls and have largely overlapping target gene sets, including a large number of SAUR genes. This points to a major role for an ARF–BZR–PIF complex in the regulation of SAUR gene expression. In line with this, SAUR genes can be synergistically up-regulated by combined addition of auxin and brassinosteroids (Walcher and Nemhauser, 2012; Bemer et al., 2017b; van Mourik et al., 2017), and are abundantly present in target lists of ARF5, ARF7, ARF8, and ARF19 (Nagpal et al., 2005; Okushima et al., 2005; Schlereth et al., 2010) and downstream of different PIFs (Sun et al., 2016; van Mourik et al., 2017). Several other studies have provided additional evidence for the role of a ARF–BZR–PIF complex in the SAUR-induced growth response. Sun et al. (2016) showed direct binding of PIFs to the lirSAUR genes, which induces their expression in dark-grown hypocotyls; Miyazaki et al. (2016) reported that the hypocotyl elongation phenotype of LOV KELCH PROTEIN 2 (LKP2) overexpression is accompanied by SAUR gene up-regulation and depends on both auxin and PIFs; and Favero et al. (2017) found that both brassinolide and auxin treatment enhanced transcript accumulation of SAUR19 subfamily genes in hypocotyls and that blocking polar auxin transport could attenuate the growth responses of SOB3 mutants to exogenous brassinolide. Moreover, family-wide in silico analysis of the regulatory regions of the Arabidopsis SAUR genes revealed that inverted repeats of two AuxRE elements, bound by ARFs (Boer et al., 2014), are enriched in auxin-induced class I SAURs, in combination with BZR- and PIF5-binding motifs (van Mourik et al., 2017). GA also plays a role in the ARF–BZR–PIF signalling module, as the growth-inhibiting DELLA proteins interact with BZR1 and with ARF6 (Bai et al., 2012; Oh et al., 2014; Bemer et al., 2017a), thereby preventing their binding to DNA. In the presence of GA, DELLAs are degraded and the ARF–BZR–PIF complex can induce SAUR expression. DELLAs can also interact with PIFs, thus controlling the activity of the inducing complex even more (De Bruyne et al., 2014). In line with this, GA-induced hypocotyl elongation requires both BZR1 and PIFs (Bai et al., 2012). These data indicate that at least in Arabidopsis, there is a distinct group of SAUR genes that can be induced by auxin, brassinosteroids, gibberellin, and light through ARF–BZR–PIF complexes (van Mourik et al., 2017). The light response of SAUR genes is regulated via the PIFs, which are degraded by the phytochromes in the active Pfr state when the R:FR ratio is high (Castillon et al., 2007). In low light conditions or at low R:FR ratios (in the shade), PIFs are active and induce SAUR expression. SAUR genes thus both regulate growth downstream of photomorphogenesis and contribute to the shade avoidance response (Ren and Gray, 2015; Sun et al., 2016; van Mourik et al., 2017). Also warm temperature-induced SAUR up-regulation in Arabidopsis is mediated by the PIFs (Franklin et al., 2011). Research in other species has also linked brassinosteroid and light signalling to SAUR gene expression. Mutations in the Medicago brassinosteroid receptor MtBRI1 affected the expression of a set of SAUR genes (Cheng et al., 2017), and a potato SAUR gene was identified as a marker for induction of the brassinosteroid pathway (Wiesel et al., 2015), SAUR genes were identified downstream of PIFs in rice (Kudo et al., 2017), and light treatments in Norway spruce also induced SAUR gene expression (OuYang et al., 2015). In conclusion, the cell elongation capacity and light response of seedlings appears to be regulated to a large extent by the ARF–BZR–PIF complex. Other factors involved in SAUR regulation The regulatory networks involved in the repression of SAUR genes upon stress conditions such as cold, drought, and increased salinity have been less well characterized. However, Kodaira et al. (2011) showed that the cold- and high salt-inducible TFs ARABIDOPSIS ZINC-FINGER 1 (AZF1) and AZF2, which function in the ABA response pathway, can repress 15 SAUR genes. EMSAs also showed that both TFs can bind to the upstream region of SAUR20 and SAUR63, indicating that the regulation of the SAUR genes by AZF1/2 occurs via direct binding. The repressive effect of JA is probably transduced via the ARF–BZR–PIF complex, because JAZ proteins can interact with the DELLA proteins, thereby inhibiting the interaction of the DELLA proteins with the PIFs. In the presence of JA, JAZ proteins are degraded, resulting in increased DELLA-mediated inhibition of ARF–BZR–PIF (Yang et al., 2012). The fact that the pifq mutant is impaired in JA-induced growth inhibition (Yang et al., 2012) confirms this dependency of JA signalling upon the ARF–BZR–PIF complex. SAUR transcript levels are also regulated in a circadian manner. The sunflower SAUR50-like gene, for example, is particularly highly expressed in the morning on the east side of the stem (Atamian et al., 2016), while the circadian movement of waterlily flowers is under control of auxin, associated with daytime-dependent expression of 25 SAUR homologues in the petals (Ke et al., 2018). In Arabidopsis hypocotyls, SAUR genes are induced by PIFs (Oh et al., 2014; Sun et al., 2016), which accumulate at dawn in short-day (SD) seedlings (Soy et al., 2014). This suggests that SAUR transcripts may also be most abundant around dawn, at least in SD conditions, in agreement with the timing of maximum hypocotyl elongation (Soy et al., 2014). Indeed, SAUR63 subfamily genes were revealed to be diurnally expressed, with the highest expression in the early morning (Chae et al., 2012). The clock genes PSEUDO-RESPONSE REGULATOR 5 (PRR5) and PRR7 are negative regulators of hypocotyl growth expressed during the course of the day, and act as transcriptional repressors (Nakamichi et al., 2010). Both factors can directly bind to many Arabidopsis SAUR genes (van Mourik et al., 2017), thereby probably repressing their expression in the afternoon. Thus, the majority of the SAUR genes may be more highly expressed in the early morning and repressed in the afternoon through the upstream control of clock genes. Family-wide temporal expression analyses are required, however, to validate this circadian expression pattern. In addition to upstream regulation of SAUR gene transcription, post-transcriptional and post-translational regulation of SAUR activity also contributes considerably to the SAUR-mediated dynamic growth control. SAUR overexpression gives a much more severe phenotype when fused to a tag such as GFP, which probably stabilizes the protein which has a very short half-life (Knauss et al., 2003; Chae et al., 2012; Ren and Gray, 2015). Besides the rapid protein decay, which has not been further investigated so far, several studies have shown that SAUR transcript levels quickly drop after removal of the inducer (e.g. auxin) (Markakis et al., 2013; van Mourik et al., 2017). This post-transcriptional regulation is at least in part regulated by an ~40 nucleotide downstream (DST) element in the 3'-untranslated region (UTR) of a number of SAUR genes. This region was initially characterized in a few SAUR genes from soybean, mung bean, and Arabidopsis (McClure and Guilfoyle, 1989; Newman et al., 1993), and confers mRNA instability (Newman et al., 1993). Sullivan and Green (1996) identified two functionally important conserved regions within the DST element (ATAGAT and GTA) by mutational analysis in tobacco. The DST element, more precisely defined as GGA(N)xATAGAT(N)xGTA, is present in 30 of the 79 Arabidopsis SAUR genes (Ren and Gray, 2015). Overexpression of Arabidopsis SAUR genes including the DST element resulted in a much less severe phenotypes than when the element was excluded (Hou et al., 2013; van Mourik et al., 2017). Putative DST elements were also identified in SAUR genes from rice (Jain et al., 2006) and tomato (Wu et al., 2012). The DST element has been associated with circadian control of mRNA, because several other transcripts with a DST sequence, which are up-regulated in the dst1 and dst2 EMS (ethyl methanesulphonate) mutants, are regulated in a circadian manner (Pérez-Amador et al., 2001). However, more recently, oxidative stress was also found to induce transcript degradation via 3'-UTR DST sequences (Ravet et al., 2012), suggesting that several upstream cues can induce DST-mediated transcript degradation. Which upstream factors regulate SAUR mRNA decay remains to be investigated. Identification of the loci causal for the dst1 and dst2 molecular phenotypes would certainly contribute to the elucidation of DST-controlled SAUR mRNA decay. The data summarized in this section illustrate the complex regulation of the SAUR genes via developmental-, environmental-, and clock-controlled pathways at the transcriptional and post-transcriptional levels (see Fig. 3). Despite the high level of complexity, many regulatory modules appear to converge at the ARF–BZR–PIF complex, which integrates various upstream cues. In addition, tissue-specific TFs and other upstream regulators also contribute significantly to the dynamics of SAUR activity, and also determine the expression of the SAUR genes that are not regulated via ARF–BZR–PIF. Concluding remarks Land plants need constantly to adapt their growth to the environmental circumstances in accordance with their developmental stage. To achieve this, they evolved dynamic growth factors that can rapidly induce growth in response to a wide range of internal and environmental stimuli. These growth factors, the SAUR proteins, generally share a common function in repression of PP2C.D phosphatases, but their genes exhibit a great regulatory region diversity, allowing tissue-specific and stimuli-specific expression patterns. This provides the plant with an extensive toolbox for growth adaptation. The high retention of SAUR genes after duplication indicates that expansion of this toolbox delivers an evolutionary advantage. In Arabidopsis, ~70% of the SAUR genes are responsive to auxin and probably regulated by the ARF–BZR–PIF complex. The majority of these SAUR genes regulate cell elongation, at least in the seedling (Sun et al., 2016), which is linked to interaction of their proteins with the plasma membrane-localized PP2C.Ds (D2, D5, and D6) (Ren et al., 2018). Plasma membrane-localized SAURs are presumably the main determinants of cell elongation, at least in part regulated via the ARF–BZR–PIF module. SAURs that are localized to the cytosol, on the other hand, could repress the cytosolic PP2C.D PPSL, thereby inducing senescence. Several SAURs, such as SAUR10 and SAUR36 (Hou et al., 2013; Bemer et al., 2017b), can induce both cell elongation and senescence, and are thus expected to localize to both the plasma membrane and the cytosol. Interestingly, several SAURs exhibit nuclear localization (Park et al., 2007; Narsai et al., 2011), and can possibly target the nuclear-localized PP2C.Ds: D1, D3, and D4 (Ren et al., 2018). The nuclear-localized SAUR32, which has been characterized in detail (Park et al., 2007), inhibits cell elongation and is not responsive to auxin, suggesting that nuclear-localized SAURs may fulfil a function different from promoting cell elongation, possibly linked to induction by other factors, such as cytokinin. SAUR51, expressed in meristematic cells, is also non-responsive to auxin, but is up-regulated by cytokinin (van Mourik et al., 2017). Future experiments will have to elucidate whether the localization of SAUR proteins is indeed predictive for their function, and whether this also correlates with their response to particular stimuli. Other outstanding questions regarding SAUR regulation and molecular function (see Table 1) will hopefully be resolved in future studies. Table 1. Outstanding questions • Which protein motifs determine the intracellular localization of the SAUR proteins? • Can SAURs also interact with other PP2C clades? • Which protein motifs are required for the interaction with PP2C.Ds? • Is the effect on senescence regulated via the interaction with PPSL? • Are only plasma membrane-localized SAURs involved in cell elongation? • What is the biological function of the calmodulin-binding SAURs? • Does the predicted metal-binding capacity of some SAURs have a biological function? • Which TFs are involved in tissue-specific SAUR expression? • Which SAURs act redundantly in the different tissues? • Which pathways are involved in the response of SAUR genes to abiotic stresses? • Is the response to ARF–BZR–PIF linked to plasma membrane localization? • How is the DST-mediated SAUR mRNA decay regulated? • What is the reason for the short half-life of SAUR proteins? • Which protein motifs determine the intracellular localization of the SAUR proteins? • Can SAURs also interact with other PP2C clades? • Which protein motifs are required for the interaction with PP2C.Ds? • Is the effect on senescence regulated via the interaction with PPSL? • Are only plasma membrane-localized SAURs involved in cell elongation? • What is the biological function of the calmodulin-binding SAURs? • Does the predicted metal-binding capacity of some SAURs have a biological function? • Which TFs are involved in tissue-specific SAUR expression? • Which SAURs act redundantly in the different tissues? • Which pathways are involved in the response of SAUR genes to abiotic stresses? • Is the response to ARF–BZR–PIF linked to plasma membrane localization? • How is the DST-mediated SAUR mRNA decay regulated? • What is the reason for the short half-life of SAUR proteins? View Large Table 1. Outstanding questions • Which protein motifs determine the intracellular localization of the SAUR proteins? • Can SAURs also interact with other PP2C clades? • Which protein motifs are required for the interaction with PP2C.Ds? • Is the effect on senescence regulated via the interaction with PPSL? • Are only plasma membrane-localized SAURs involved in cell elongation? • What is the biological function of the calmodulin-binding SAURs? • Does the predicted metal-binding capacity of some SAURs have a biological function? • Which TFs are involved in tissue-specific SAUR expression? • Which SAURs act redundantly in the different tissues? • Which pathways are involved in the response of SAUR genes to abiotic stresses? • Is the response to ARF–BZR–PIF linked to plasma membrane localization? • How is the DST-mediated SAUR mRNA decay regulated? • What is the reason for the short half-life of SAUR proteins? • Which protein motifs determine the intracellular localization of the SAUR proteins? • Can SAURs also interact with other PP2C clades? • Which protein motifs are required for the interaction with PP2C.Ds? • Is the effect on senescence regulated via the interaction with PPSL? • Are only plasma membrane-localized SAURs involved in cell elongation? • What is the biological function of the calmodulin-binding SAURs? • Does the predicted metal-binding capacity of some SAURs have a biological function? • Which TFs are involved in tissue-specific SAUR expression? • Which SAURs act redundantly in the different tissues? • Which pathways are involved in the response of SAUR genes to abiotic stresses? • Is the response to ARF–BZR–PIF linked to plasma membrane localization? • How is the DST-mediated SAUR mRNA decay regulated? • What is the reason for the short half-life of SAUR proteins? View Large Supplementary data Supplementary data are available at JXB online. Fig. S1. Full version of the phylogenetic tree displayed in Fig. 1. Dataset S1. Alignment of all SAUR proteins used to generate the phylogenetic tree. Acknowledgements We thank Gerco Angenent for his useful comments on the manuscript. The work of MB has been supported by the Dutch Organization for Scientific research (NWO) (NWO-Veni/ALWOP.199). References Atamian HS , Creux NM , Brown EA , Garner AG , Blackman BK , Harmer SL . 2016 . Circadian regulation of sunflower heliotropism, floral orientation, and pollinator visits . Science 353 , 587 . Google Scholar Crossref Search ADS PubMed Bai MY , Shang JX , Oh E , Fan M , Bai Y , Zentella R , Sun TP , Wang ZY . 2012 . Brassinosteroid, gibberellin and phytochrome impinge on a common transcription module in Arabidopsis . Nature Cell Biology 14 , 810 – 817 . 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Wang,, Liangsheng;Apel,, Klaus

doi: 10.1093/jxb/ery343pmid: 30272237

Abstract In plants, highly reactive singlet oxygen (1O2) is known to inhibit photosynthesis and to damage the cell as a cytotoxin. However, more recent studies have also proposed 1O2 as a signal. In plants under stress, not only 1O2 but also other reactive oxygen species (ROS) are generated simultaneously, thus making it difficult to link a particular response to the release of 1O2 and establish a signaling role for this ROS. This obstacle has been overcome by the identification of conditional mutants of Arabidopsis thaliana that selectively generate 1O2 and trigger various 1O2-mediated responses. In chloroplasts of these mutants, chlorophyll or its biosynthetic intermediates may act as a photosensitizer and generate 1O2. These 1O2-mediated responses are not only dependent on the dosage of 1O2 but also are determined by the timing and suborganellar localization of its production. This spatial- and temporal-dependent variability of 1O2-mediated responses emphasizes the importance of 1O2 as a highly versatile and short-lived signal that acts throughout the life cycle of a plant. Chloroplast, photosynthesis, reactive oxygen species, signaling, singlet oxygen, stress Introduction Plants performing oxygenic photosynthesis must cope with photo-oxidative stresses throughout their life cycle. They utilize light as a primary energy source and synthesize and accumulate large amounts of chlorophyll (Chl) in chloroplasts to absorb light energy. If this light energy cannot be dissipated through photosynthetic electron transport, the excited Chl may turn into a potent photosensitizer that generates highly reactive singlet oxygen (1O2) (Apel and Hirt, 2004; Krieger-Liszkay et al., 2008; Foyer and Noctor, 2009; Triantaphylidès and Havaux, 2009). Plants have evolved various protection mechanisms to alleviate photodynamic damage caused by 1O2 (Foyer and Noctor, 2009; Li et al., 2009; Dogra et al., 2018). However, if this balance is disturbed when plants are subjected to environmental stresses, 1O2 is generated. For a long time, 1O2 had been recognized as detrimental to plants because of its oxidative damage effects, but more recent findings also suggest 1O2 as a signal (Li et al., 2009; Triantaphylidès and Havaux, 2009; Fischer et al., 2013; Laloi and Havaux, 2015; Noctor and Foyer, 2016; Foyer et al., 2017). Another emerging concept is that all types of oxidative modification/damage are involved in signaling (Foyer et al., 2017). For various reasons it has been difficult to assess and verify such a signaling capacity of 1O2. (i) Besides 1O2, there are other chemically distinct reactive oxygen species (ROS) such as hydrogen peroxide (H2O2), superoxide (O2·−), and hydroxyl radical (·OH), whose concentrations in plants under stress increase almost simultaneously with those of 1O2, thus making it difficult to define the biological effects of 1O2 and to link it to a particular cellular response (Apel and Hirt, 2004; Foyer and Noctor, 2009; Noctor et al., 2018). (ii) 1O2 may be formed at different intracellular and intraorganellar sites, and its biological impact may vary depending on where it is generated (Apel and Hirt, 2004; Noctor and Foyer, 2016). (iii) 1O2 interacts with proteins, lipids, carbohydrates, and nucleic acids (Sies and Menck, 1992; Davies, 2003; Triantaphylidès et al., 2008) and in this way may irreversibly inactivate and/or destroy the target (Elstner, 1982). It is not known yet how such a highly reactive molecule may initiate signaling rather than causing oxidative damage. (iv) Plants are exposed to diverse environmental conditions whose impact may rapidly change. (v) Even though the activity of 1O2 can be studied under steady-state conditions, it is difficult to separate primary from secondary effects of 1O2. To overcome these obstacles, one needs to induce the production of 1O2 non-invasively (Apel and Hirt, 2004). In the meantime, experimental approaches have been developed that overcome most of these obstacles. Using these approaches, 1O2 has been recently demonstrated to act as a highly versatile signal that induces a wide range of stress responses throughout the life cycle of a plant (Fischer et al., 2013; Laloi and Havaux, 2015; Foyer et al., 2017; Noctor et al., 2018). Formation of singlet oxygen Ground state triplet molecular oxygen (3O2) may be converted to the highly reactive 1O2 either metabolically or photochemically (Halliwell and Gutteridge, 1999; Apel and Hirt, 2004; Krieger-Liszkay et al., 2008; Triantaphylidès and Havaux, 2009; Fischer et al., 2013; Laloi and Havaux, 2015). Knowledge about the former reaction is still scarce (Kanofsky and Axelrod, 1986; Steinbeck et al., 1992; Khan and Kasha, 1994), whereas photochemical formation of 1O2 has been the subject of intense research (Krieger-Liszkay et al., 2008; Foyer and Noctor, 2009; Triantaphylidès and Havaux, 2009; Fischer et al., 2013; Laloi and Havaux, 2015). Energy transfer by an excited photosensitizer reverses the spin direction of one of the two outermost valence electrons of triplet oxygen that occupy separate orbitals with parallel spins, and allows the pairing of these electrons (Fig. 1). This spin reversal and pairing of electrons dramatically enhances the reactivity of oxygen (Fig. 1). In plants, 1O2 is primarily generated in chloroplasts due to the photosensitizing activity of tetrapyrroles. The syntheses of these porphyrins share a common pathway up to the formation of protoporphyrin IX (ProtoIX), when metals are inserted (Tanaka and Tanaka, 2007). Afterwards, the pathway diverges into two major branches, with the Fe branch being directed to the synthesis of hemes and phycobilins, and the Mg branch leading to the formation of Chls. These porphyrins have the potential to act as photosensitizers and transfer the excitation energy directly to ground state triplet oxygen, leading to the formation of singlet oxygen (Fig. 1) (Apel and Hirt, 2004; Krieger-Liszkay et al., 2008; Triantaphylidès and Havaux, 2009). Fig. 1. View largeDownload slide Formation of singlet oxygen (1O2). The biradical 3O2 has two unpaired electrons with parallel spins. To oxidize a non-radical molecule, 3O2 needs to react with a partner that provides a pair of electrons with parallel spins. Pairs of electrons usually have opposite spins and thus restrict the ability of 3O2 to react with other molecules. Energy transfer from an excited photosensitizer (e.g. 3Chl) transforms 3O2 into 1O2 by reversing the spin direction of one of the two unpaired electrons and allowing their pairing. Fig. 1. View largeDownload slide Formation of singlet oxygen (1O2). The biradical 3O2 has two unpaired electrons with parallel spins. To oxidize a non-radical molecule, 3O2 needs to react with a partner that provides a pair of electrons with parallel spins. Pairs of electrons usually have opposite spins and thus restrict the ability of 3O2 to react with other molecules. Energy transfer from an excited photosensitizer (e.g. 3Chl) transforms 3O2 into 1O2 by reversing the spin direction of one of the two unpaired electrons and allowing their pairing. Chl, heme, and phycobilin are usually bound to proteins and, in this state, may use various quenching mechanisms to dissipate excess absorbed light energy. Thus, formation of 1O2 by these porphyrins is usually compromised under non-stressful conditions. Their biosynthetic intermediates, however, occur mostly in a free form and are potentially much more destructive when illuminated (Mochizuki et al., 2010). To avoid extensive photo-oxidative damage by these intermediates, plants prevent their accumulation by strictly controlling tetrapyrrole biosynthesis and catabolism. One important element of this regulation is the negative feedback inhibition of the first step of tetrapyrrole biosynthesis, formation of δ-aminolevulinic acid (ALA), by two effecter molecules, the FLU protein and heme, that both interact with different parts of Glu-tRNA reductase (Vothknecht et al., 1998; Meskauskiene et al., 2001; Goslings et al., 2004; Levicán et al., 2007), the first enzyme committed to tetrapyrrole synthesis (Vothknecht et al., 1998; Meskauskiene et al., 2001; Cornah et al., 2003). In the dark, the Mg branch leads only to the formation of protochlorophyllide (Pchlide), the immediate precursor of chlorophyllide (Chlide). The subsequent step from Pchlide to Chlide requires light (Griffiths, 1978; Apel et al., 1980). Once a critical level of Pchlide has been reached in the dark, ALA synthesis slows down and accumulation of Pchlide stops. Only after re-exposure to light, when Pchlide is photoreduced to Chlide, does Chl biosynthesis resume. Pchlide has been implicated in activating the FLU-dependent suppression of ALA formation, thereby allowing the Mg branch to regulate the initial step of tetrapyrrole biosynthesis, whereas the Fe branch controls this step via heme (Vothknecht et al., 1998; Goslings et al., 2004). When plants are exposed to severe environmental stresses that interfere with photosynthetic electron transport, 1O2 production may also be initiated in chloroplasts by the photosensitizing activity of protein-bound Chl (Krieger-Liszkay et al., 2008; Li et al., 2009; Triantaphylidès and Havaux, 2009; Fischer et al., 2013; Laloi and Havaux, 2015 ). Upon light absorption, Chl changes from a ground state to the singlet excited state, 1Chl. The fate of the excitation energy in this Chl may vary. Usually the absorbed light energy is transferred from Chl of the light-harvesting antenna complexes to the reaction center (RC) Chl where it drives photosynthetic electron transport. However, under high light, excessive excitation energy can also be dissipated as heat when the excited light-harvesting Chl interacts with carotenoids, or it can decay via formation of the excited triplet state of Chl (3Chl) (Krieger-Liszkay et al., 2008; Li et al., 2009). 3Chl has a longer life time than 1Chl (Krieger-Liszkay, 2005), thereby allowing its excitation energy to be transferred to ground-state 3O2 that produces 1O2 if no efficient quenchers are close enough to compete for this energy (Krieger-Liszkay et al., 2008). In the antenna, the 3Chl is in the close vicinity of various carotenoids that are able to quench 3Chl directly, and generation of 1O2 is usually suppressed. In contrast, in the RC of PSII the special Chl, P680, is not in close contact with carotenoids, and the β-carotene associated with the RC of PSII is unable to quench the excitation energy of 3P680; thus, generation of 1O2 is favored (Trebst, 1999; Umena et al., 2011) (Fig. 2A). Even though most of Chl is found in the antenna, 1O2 is mainly produced in the RC of PSII (Krieger-Liszkay et al., 2008). Whenever the electron acceptor of PSII remains reduced and is unable to accept electrons originating from excited P680 (e.g. under high light, low temperature, or drought conditions) (Fig. 2A), the excited triplet state of P680 may then act as a photosensitizer and produces 1O2. Under the stress conditions, 3Chl can also be generated in the PSII RC by charge recombination reactions (back-flow of electron transfer and charge separation reactions) and acts as a photosensitizer that produces 1O2 (Krieger-Liszkay et al., 2008). This 1O2 is believed to interact primarily with its nearest target, the D1 protein of PSII RC that binds the P680 Chl, thereby inactivating this protein and inhibiting PSII (Keren et al., 1997; Szilárd et al., 2005; Ohad et al., 2011; Vass, 2012; Kale et al., 2017) (Fig. 2A). Fig. 2. View largeDownload slide Schematic diagrams that illustrate how the PSII reaction center (RC) either in its active state in the grana core (A, B) or during its repair in grana margins (B, C) is thought to generate 1O2. (A) Light energy absorbed by PSII is trapped within the PSII RC whenever the electron acceptor of PSII remains reduced under various stress conditions and is unable to accept electrons which originated from the excited P680 Chl of the RC. This favors the transformation of the short-lived singlet state P680 into the more stable triplet state P680 that may then act as a photosensitizer and generate 1O2. This 1O2 is believed to interact primarily with its nearest target, the D1 protein of the PSII RC that binds the P680 Chl. At higher concentrations, such as under high light stress, 1O2 may also interact with β-carotene that is associated with PSII. Some of the oxidative breakdown products of β-carotene may act as a signal. (B) Schematic diagram showing the different regions of thylakoid membranes. Active PSII is localized in the grana core region, whereas the repair of damaged PSII takes place within the grana margin. (C) During the repair of PSII, the damaged D1 protein is degraded and replaced by newly synthesized D1 polypeptides. During the reassembly of active PSII, the insertion of D1 and Chl needs to be strictly co-ordinated to avoid photo-oxidative damage caused by the photosensitizing activity of unbound Chl. The Chl may be derived from the damaged PSII and/or is synthesized de novo within the grana margin region. A perturbation of this reconstitution is expected to allow a transient accumulation of either free Chl or its precursors (e.g. Pchlide or ProroIX) that may generate 1O2. PSII within the grana margin co-localizes with the FtsH protease, EX1, FLU, and enzymes of Chl synthesis. The onset of 1O2-mediated and EX1-dependent signaling strictly depends on an FtsH-dependent decline of the EX1 protein. Fig. 2. View largeDownload slide Schematic diagrams that illustrate how the PSII reaction center (RC) either in its active state in the grana core (A, B) or during its repair in grana margins (B, C) is thought to generate 1O2. (A) Light energy absorbed by PSII is trapped within the PSII RC whenever the electron acceptor of PSII remains reduced under various stress conditions and is unable to accept electrons which originated from the excited P680 Chl of the RC. This favors the transformation of the short-lived singlet state P680 into the more stable triplet state P680 that may then act as a photosensitizer and generate 1O2. This 1O2 is believed to interact primarily with its nearest target, the D1 protein of the PSII RC that binds the P680 Chl. At higher concentrations, such as under high light stress, 1O2 may also interact with β-carotene that is associated with PSII. Some of the oxidative breakdown products of β-carotene may act as a signal. (B) Schematic diagram showing the different regions of thylakoid membranes. Active PSII is localized in the grana core region, whereas the repair of damaged PSII takes place within the grana margin. (C) During the repair of PSII, the damaged D1 protein is degraded and replaced by newly synthesized D1 polypeptides. During the reassembly of active PSII, the insertion of D1 and Chl needs to be strictly co-ordinated to avoid photo-oxidative damage caused by the photosensitizing activity of unbound Chl. The Chl may be derived from the damaged PSII and/or is synthesized de novo within the grana margin region. A perturbation of this reconstitution is expected to allow a transient accumulation of either free Chl or its precursors (e.g. Pchlide or ProroIX) that may generate 1O2. PSII within the grana margin co-localizes with the FtsH protease, EX1, FLU, and enzymes of Chl synthesis. The onset of 1O2-mediated and EX1-dependent signaling strictly depends on an FtsH-dependent decline of the EX1 protein. Signaling versus damaging: the role of singlet oxygen For a long time, photo-oxidative damage of plant cells had been considered to be the only biological effect of 1O2 production (Elstner, 1982; Davies, 2003; Krieger-Liszkay et al., 2008). Thus, it came as a surprise when 1O2 was also shown to act as a signal that triggered specific nuclear gene expression changes and greatly impacted the phenotype of the affected plant (Kim et al., 2008; Krieger-Liszkay et al., 2008; Triantaphylidès and Havaux, 2009; Fischer et al., 2013; Laloi and Havaux, 2015; Wang and Apel, 2016; Foyer et al., 2017). A signaling role for 1O2 was first demonstrated in the conditional fluorescent (flu) mutant of Arabidopsis (op den Camp et al., 2003; Wagner et al., 2004). FLU acts as a negative regulator of tetrapyrrole biosynthesis, and flu seedlings lacking this regulator are unable to restrict the accumulation of the Chl precursor Pchlide in the dark. When these seedlings are transferred to the light, they rapidly bleach and die due to the photosensitizing activity of excess amounts of free Pchlide (Meskauskiene et al., 2001; op den Camp et al., 2003) (Fig. 3A–D). In the flu mutant, the amount of 1O2 generated is proportional to the amount of Pchlide and thus to the duration of dark treatment (Laloi and Havaux, 2015). When grown under continuous light, the flu mutant looks exactly like the wild-type plant as Pchlide is immediately photoreduced to Chlide. These properties make the flu mutant an ideal tool for generation of 1O2 and study of 1O2-induced stress responses. In the flu mutant, shortly (~30 min) after the release of 1O2, the chloroplast starts to lose its integrity (the chloroplast becomes leaky as the stromal protein appears in the cytosol), and afterwards rupture of the central vacuole occurs followed by final collapse of the cell (Kim et al., 2012). However, all these drastic phenotypic changes disappear in the flu/ex1 double mutant although it overaccumulates Pchlide in the dark and generates a similar amount of 1O2 to that generated in the parental flu line during re-illumination (Wagner et al., 2004; Kim et al., 2012) (Fig. 3). In addition to EX1, an EX1-like protein, dubbed EX2, was identified that, unlike EX1, did not impact the 1O2-mediated cell death and growth inhibition responses of the flu mutant but greatly affected 1O2-mediated nuclear gene expression changes (Lee et al., 2007). Fig. 3. View largeDownload slide Dose-dependent 1O2-mediated responses of the flu mutant. Various concentrations of 1O2 were reached by either changing the duration of the dark treatment (A–D) or using different light intensities during illumination of etiolated seedlings (E). Seven-day-old light-grown seedlings of the wild type (WT), flu, and flu/ex1 were grown for 7 d under continuous light (90 μmol photons m−2 s−1) at 22 °C in soil, and put in the dark for different periods of time (A–D) and re-exposed to light for 24 h (A–C). With increasing duration of the dark period, the amount of the photosensitizer Pchlide (indicated by arrows) increased (D). Up to an 8 h dark period 1O2-mediated growth inhibition (A), photoinhibition of PSII as revealed by transient Chl fluorescence changes (B), and cell death as shown by trypan blue staining (C) in flu were abrogated in flu/ex1 seedlings, suggesting that these responses of the flu mutant were triggered by EX1-dependent signaling. The EX1 dependency of these responses was gradually lost in flu plants that were kept in the dark for 16 h or 24 h. R.F.U., relative fluorescence unit. (E) Four-day-old etiolated seedlings grown on 1/2 Murashige and Skoog (MS) (0.8% agar, 0.5% sucrose) were exposed for 2 d to different light intensities as indicated at the bottom of the figure. 1O2-mediated cell death was revealed by trypan blue staining. At the highest light intensity, flu but also flu/ex1 seedlings initiated a cell death response, whereas at an intermediate light intensity this response was abrogated in flu/ex1 but not in flu. At a very low light intensity, none of these seedlings showed a cell death response, and flu and flu/ex1 seedlings looked similar to green wild-type control seedlings. Images of transient fluorescence were taken with a FluorCam 800MF system (Photon Systems Instruments) following manuals provided by the manufacturer. Trypan blue staining of dead cells and determination of Pchlide was performed as described by op den Camp et al. (2003). Fig. 3. View largeDownload slide Dose-dependent 1O2-mediated responses of the flu mutant. Various concentrations of 1O2 were reached by either changing the duration of the dark treatment (A–D) or using different light intensities during illumination of etiolated seedlings (E). Seven-day-old light-grown seedlings of the wild type (WT), flu, and flu/ex1 were grown for 7 d under continuous light (90 μmol photons m−2 s−1) at 22 °C in soil, and put in the dark for different periods of time (A–D) and re-exposed to light for 24 h (A–C). With increasing duration of the dark period, the amount of the photosensitizer Pchlide (indicated by arrows) increased (D). Up to an 8 h dark period 1O2-mediated growth inhibition (A), photoinhibition of PSII as revealed by transient Chl fluorescence changes (B), and cell death as shown by trypan blue staining (C) in flu were abrogated in flu/ex1 seedlings, suggesting that these responses of the flu mutant were triggered by EX1-dependent signaling. The EX1 dependency of these responses was gradually lost in flu plants that were kept in the dark for 16 h or 24 h. R.F.U., relative fluorescence unit. (E) Four-day-old etiolated seedlings grown on 1/2 Murashige and Skoog (MS) (0.8% agar, 0.5% sucrose) were exposed for 2 d to different light intensities as indicated at the bottom of the figure. 1O2-mediated cell death was revealed by trypan blue staining. At the highest light intensity, flu but also flu/ex1 seedlings initiated a cell death response, whereas at an intermediate light intensity this response was abrogated in flu/ex1 but not in flu. At a very low light intensity, none of these seedlings showed a cell death response, and flu and flu/ex1 seedlings looked similar to green wild-type control seedlings. Images of transient fluorescence were taken with a FluorCam 800MF system (Photon Systems Instruments) following manuals provided by the manufacturer. Trypan blue staining of dead cells and determination of Pchlide was performed as described by op den Camp et al. (2003). Many genes are up-regulated in response to the release of 1O2 in the flu mutant (op den Camp et al., 2003; Gadjev et al., 2006; Dogra et al., 2017). The genes that are up-regulated prior to chloroplast leakage are directly affected by the release of 1O2. In contrast, the genes whose expression changed after chloroplast leakage are only indirectly affected as the loss of chloroplast integrity seems to enhance the photosensitizing activity of membrane-bound Chl and to amplify drastically the production of 1O2 that subsequently causes massive non-enzymatic lipid peroxidation and photo-oxidative damage (Kim and Apel, 2013). For most 1O2-responsive genes, the up-regulation is first seen shortly after the loss of chloroplast integrity (Kim and Apel, 2013). A large number of these genes have been annotated as being under hormonal control (Baruah et al., 2009a). As the concentrations of phytohormones such as jasmonic acid (JA), ethylene, and salicylic acid (SA) increase soon after the onset of 1O2 production (op den Camp et al., 2003; Danon et al., 2005), the enhanced expression of the majority of 1O2-responsive genes seems to be triggered by these phytohormones (Kim and Apel, 2013). Nevertheless, the 1O2-mediated transcriptional changes rely on the presence of EX1/EX2 protein. Without these proteins, all subsequent 1O2 signaling-mediated stress responses of the flu mutant are abolished (Wagner et al., 2004; Kim et al., 2012). EX1-dependent signaling operates only in response to rather minor increase in 1O2 production, when light-grown flu plants are transferred to the dark for up to 8 h, and are re-exposed to light (Wagner et al., 2004; Wang et al., 2016) (Fig. 3A–D). If the duration of the dark treatment is prolonged to 16 h or longer, flu seedlings accumulate more Pchlide and, upon re-illumination, generate higher amount of 1O2. Under these latter conditions, 1O2-mediated responses become EX independent (Fig. 3), thus re-emphasizing the fact that EX-dependent signaling in flu mutants occurs only at low, non-toxic concentrations of 1O2. Because of its high reactivity and short half-life, primary reactions of 1O2 that initiate signaling should be localized near the site of 1O2 formation. Thus, in the case of EX1-dependent signaling, 1O2 should be generated close to where EX1 is localized. EX1 is present in thylakoid membranes and is restricted to the non-appressed margins of grana stacks (Wang et al., 2016) (Fig. 2B, C). Grana margins play an important role during repair of damaged PSII (Khatoon et al., 2009; Puthiyaveetil et al., 2014; Yoshioka-Nishimura et al., 2014). Active PSII is present in the core of tightly compressed grana membranes (Aro et al., 1993). Following inactivation, PSII needs to be translocated to the grana margins where it is disassembled and the damaged D1 and—to a lesser extent—also the D2 protein of the PSII RC are cleaved by the FtsH protease (Fig. 2C) (Lindahl et al., 2000; Bailey et al., 2002; Dogra et al., 2018). During the reassembly of active PSII, these proteins are replaced by D1 and D2 polypeptides synthesized on membrane-bound ribosomes attached to non-appressed stroma thylakoids (Mullet et al., 1990). In grana margins, EX1 forms part of a larger physical unit that contains PSII, the FtsH protease, the NADPH-Pchlide oxidoreductases (PORs) B and C that catalyze the light-dependent reduction of Pchlide to Chlide in light-grown plants, and two elongation factors (Wang et al., 2016). This localization of EX1 suggests that 1O2 is produced close to where the disassembly and reassembly of PSII take place. Most probably this 1O2 is generated by the photosensitizing activity of Chl or some of its intermediates that are used for the reconstitution of PSII (Fig. 2C). Turnover of Chl in light-adapted plants is confined to the core of PSII (Feierabend and Dehne, 1996), suggesting that newly synthesized Chl in green plants is primarily used for the assembly of active PSII. Enzymes of Chl biosynthesis and the FLU protein are highly enriched in grana margins and co-localize with the site of PSII repair (Wang et al., 2016). The recycling and de novo synthesis of Chl must be tightly controlled and synchronized with the synthesis of the PSII RC proteins D1 and D2 to minimize the disruptive effect of unbound, photoreactive Chl and its intermediates ( Mullet et al., 1990; Müller and Eichacker, 1999). A slight disturbance of this co-ordinated assembly process is likely to enhance the level of unbound Chl or its intermediates and to generate 1O2 (Fig. 2C). As shown by the example of the flu mutant, this 1O2 may trigger EX-dependent signaling (Wagner et al., 2004; Kim et al., 2012; Wang et al., 2016). In the flu mutant, the spatial distribution of the photosensitizer does not exactly match that of the EX1 protein. Whereas EX1 is confined to grana margins, Pchlide is evenly distributed in margin and core regions of grana stacks and is also found in stroma lamellae (Wang et al., 2016). This difference in the distribution of photosensitizer and EX1 re-emphasizes the notion that the amounts of 1O2 generated in the flu mutant after an 8 h dark–light shift are too low to cause apparent photo-oxidative damage to a plant. As the biological activity of 1O2 depends on the presence of EX1, this protein seems to act as a sensor of 1O2 that amplifies its potential signaling effect. With the onset of 1O2-mediated signaling in the flu mutant, there is a rapid decline of EX1 that depends on the FtsH protease (Wang et al., 2016; Dogra et al., 2017). Generation of 1O2 without a decline of EX1 is not sufficient to trigger 1O2 signaling (Fig. 2C). As FtsH also cleaves the two PSII RC proteins D1 and D2 (Lindahl et al., 2000; Bailey et al., 2002; Kato et al., 2009), EX1-dependent signaling seems not only spatially but also functionally linked to the repair of PSII (Fig. 2C). The simultaneous onset of 1O2 production and decline of EX1 indicates that EX1 by interacting with 1O2 becomes susceptible to proteolytic attack by FtsH. It seems conceivable that EX1 acts as a negative regulator that needs to be removed by proteolytic cleavage to activate the 1O2-dependent signaling pathway. However, experimental evidence does not support such a notion. In flu/ex1 plants, 1O2-mediated signaling is only active after EX1 is expressed in the complemented mutant line (Wang et al., 2016; Dogra et al., 2017). At present, it is not known how FtsH modifies EX1 and whether proteolytic breakdown products of EX1 interact with downstream signaling components within the plastid or outside in the extraplastidic cytoplasm or in the nucleus. Both cytotoxic and signaling effects of 1O2 can result in plant cell death, and which of the two effects prevails depends on the amount of 1O2. When the amount of 1O2 increases only slightly or moderately, its signaling effects prevails. In contrast, cytotoxic effects become dominant if the amount of 1O2 increases drastically. The loss of chloroplast integrity and rupture of the central vacuole can be mediated by signaling effects of 1O2 (Kim et al., 2012; Woodson et al., 2015). It is likely that these processes can also be induced by the cytotoxic effects of 1O2, but evidence is needed. To distinguish the signaling and cytotoxic effects of 1O2, two hallmarks are generally used: the impact of EX1 mutation and the prevalence of either enzymatic or non-enzymatic lipid peroxidation (Przybyla et al., 2008). If the 1O2-induced stress responses can be suppressed by EX1 mutation, and/or the enzymatic but not non-enzymatic lipid peroxidation occurs predominantly, these responses are generally attributed to signaling effects of 1O2, and otherwise to its cytotoxic effects. 13-HOT and 13-HOD are representatives of enzymatic lipid peroxidation, while 10-HOT, 10-HOD, 12-HOD, and 15-HOD are typical products of non-enzymatic lipid peroxidation (Przybyla et al., 2008). The etiolated flu and flu/ex1 seedlings accumulate ~3 times more Pchlide compared with seedlings that are grown under continuous light and treated with 8 h darkness. Thus, upon illumination, the etiolated flu and flu/ex1 seedlings generate a much higher amount of 1O2 that exceeds the threshold concentration of 1O2 which induces EX1-dependent signaling. In the etiolated seedlings of both flu and flu/ex1, non-enzymatic lipid peroxidation prevails and the EX1 mutation is unable to suppress the cell death response. In contrast, in 8 h dark-treated flu seedlings, lipid peroxidation occurs almost exclusively enzymatically, and both the lipid peroxidation and the cell death response can be suppressed by EX1 mutation (Przybyla et al., 2008). Different roles of 1O2: quality control versus induction of cell death Once a signaling role for 1O2 had been established in the flu mutant, interest in studying the biological activity of 1O2 markedly increased. Other experimental systems have now also been established that non-invasively induce generation of 1O2. One of them is the ferrochelatase2 (fc2) mutant of Arabidopsis. In the fc2 mutant, the photosensitizing activity of the tetrapyrrole intermediate, ProtoIX, generates 1O2 (Woodson et al., 2015). Similarly, down-regulation of a tobacco ferrochelatase by RNAi increases the level of ProtoIX that generates 1O2 and leads to formation of necrotic leaves in the transformed tobacco (Papenbrock et al., 2001). Moreover, when plants are treated with peroxidizing herbicides that inhibit Protox, the enzyme that catalyzes the oxidization of Protogen to ProtoIX, they start to overaccumulate ProtoIX and show severe photo-oxidative damage (Sandmann and Boger, 1988; Becerril and Duke, 1989; Watanabe et al., 1998). Studies of the fc2 mutant have implicated the release of 1O2 with activation of a ubiquitin-dependent quality control pathway that has been proposed to remove damaged chloroplasts selectively from plant cells (Woodson et al., 2015). The fc2 mutant of Arabidopsis lacks one of two ferrochelatases that catalyze formation of protoheme by inserting Fe2+ into ProtoIX. While fc2 mutant plants grown under continuous light turn green like the wild type, they become pale and form abnormally small leaves under an 8 h light/16 h dark regime (Woodson et al., 2015). This phenotypic change had been attributed to the generation of 1O2 by the photosensitizing activity of ProtoIX (Scharfenberg et al., 2015; Woodson et al., 2015). Upon transfer to dark, the fc2 mutant starts to overaccumulate ProtoIX and after 50 min of darkness reaches an ~10-fold higher maximum than the wild type, but that declines afterwards over the next 30 min. The concentration of ProtoIX at the end of the 16 h dark period has not been reported and thus it is not known whether generation of 1O2 in fc2 mutants grown under an 8 h light/16 h dark cycle is indeed caused by increased levels of ProtoIX. In addition, the transformed tobacco leaves with reduced expression of a tobacco ferrochelatase accumulate ProtoIX under light but not in the dark (Papenbrock et al., 2001). Thus, it is necessary to determine the ProtoIX concentration of the fc2 mutant at the end of the dark period and at selected time points during the light period. In etiolated fc2 seedlings, ProtoIX reaches the same low level as in wild-type control plants (Scharfenberg et al., 2015), whereas the concentration of Pchlide is higher than in the wild type but not as high as in similarly treated flu plants (Scharfenberg et al., 2015; Woodson et al., 2015). Thus, as pointed out by Scharfenberg et al. (2015), the fc2 mutant displays a weak flu phenotype when grown in the dark, suggesting that generation of 1O2 in this mutant is due to the photosensitizing activity of Pchlide. This interpretation is supported by the fact that a 1O2-mediated loss of chloroplast integrity and a subsequent collapse of the affected cell appears not only in the flu mutant but also in the fc2 mutant (Kim et al., 2012; Woodson et al., 2015). Among second-site mutations of fc2 that restore the ability to green when grown under a dark/light regime, the PUB4 gene encoding a E3 ubiquitin ligase has been identified (Woodson et al., 2015). When grown under non-permissive dark/light conditions, the fc2/pub4 double mutant generates 1O2 and its chloroplasts appear stressed with a distorted membrane system, but unlike chloroplasts of the parental fc2 line they remain intact and are not degraded. Thus, ubiquitination of chloroplast proteins seems to be an important step during 1O2-mediated dismantling and subsequent degradation of chloroplasts. Whether in fc2 mutants this ubiquitin-dependent breakdown of chloroplasts signifies activation of a quality control pathway that is mediated by 1O2 is difficult to judge. As the release of 1O2 in flu and fc2 mutants induces chloroplast leakage followed by the disruption of the central vacuole and the collapse of the cell (Kim et al., 2012; Woodson et al., 2015), it sounds impossible to identify a quality control pathway that selectively removes individual damaged chloroplasts in a collapsed cell. However, in fc2 plants grown under continuous light that are not expected to overaccumulate ProtoIX and Pchlide, evidence for the operation of a ubiquitin-dependent quality control pathway has been obtained (Woodson et al., 2015). These plants show, relative to wild-type plants, a reduced growth and slightly impaired photosynthetic electron transport (Scharfenberg et al., 2015). These differences between the mutant and wild type may explain why in fc2 the number of damaged chloroplasts is higher than in the wild type. In the fc2/pub4 double mutant, the number of damaged chloroplasts returns to the wild-type level, suggesting that ubiquitination is indeed involved in recognizing damaged chloroplasts and subsequently allowing their degradation to proceed. However, so far it remains unclear whether activation of this pathway and/or damage of chloroplasts in these plants are caused by 1O2. Even though fc2 resembles flu, the biological effects of 1O2 in these two mutants show a remarkable difference. In flu, 1O2-mediated phenotypic changes depend on EX1 and are completely abrogated in the flu/ex1 double mutant. Whereas in fc2, 1O2-induced changes are not affected by the absence of EX1 (Woodson et al., 2015). At the moment, it is difficult to explain this difference. Since fc2 mutants accumulate less Pchlide in the dark than flu mutants (Scharfenberg et al., 2015), they are not expected to release a higher amount of 1O2 that surpasses the signaling capacity of EX1, as shown in Fig. 3. It is not known whether FC2 is localized in the grana margin region close to EX1. As the intraorganellar location of 1O2 formation is expected to influence the biological effects of 1O2, the release of 1O2 at a different site away from EX1 may explain why activation of 1O2-mediated responses in the fc2 mutant does not depend on EX1. 1O2-induced cell death under severe high light stress The third experimental system used to induce generation of 1O2 non-invasively and to study its signaling activity is the Chl b-deficient chlorina1 (ch1) mutant of Arabidopsis that is devoid of PSII antenna complexes (Dall’Osto et al., 2010; Ramel et al., 2013; Shumbe et al., 2016). Without these antenna complexes, the mutant lacks light-scavenging capacity and is highly sensitive to high light. In contrast to wild-type controls, ch1 plants exposed to a combination of high light (1000 µmol photons m−2 s−1) and low temperature (10 °C) suffer from severe photoinhibition of PSII and photo-oxidative damage due to an enhanced production of 1O2 in the RC of PSII (Ramel et al., 2013). The excitation energy trapped within the PSII RC favors the transformation of the short-lived singlet state of P680 into the more stable triplet state that allows the excited Chl to act as a photosensitizer that generates 1O2 (Fig. 2A). 1O2 oxidizes various chloroplast membrane constituents such as proteins (Davies, 2003), polyunsaturated fatty acids (Przybyla et al., 2008; Triantaphylidès et al., 2008), and carotenoids (Ramel et al., 2012a), causing photo-oxidative damage that ultimately may lead to the collapse of cells. This cell death response of the ch1 mutant to high light stress is not exclusively due to the toxicity of 1O2 but also involves genetically controlled stress-responsive components that are activated by the release of 1O2, and depends on the OXIDATIVE SIGNAL INDUCIBLE 1 (OXI1) Ser/Thr kinase (Shumbe et al., 2016; Foyer et al., 2017; Dogra et al., 2018). Expression of the OXI1 gene in the high-light-stressed ch1 mutant is strongly induced. Inactivation of OXI1 reduces the extent of photo-oxidative damage and cell death in the high-light-stressed ch1/oxi1 double mutant even though these plants generate similar amounts of 1O2 to those generated by the parental ch1 mutant (Shumbe et al., 2016). OXI1 had been shown earlier to be involved in H2O2-mediated signaling that controlled root hair growth and plant–pathogen interactions (Rentel et al., 2004). As OXI1-dependent responses triggered by H2O2 differ from those induced by 1O2, both ROS seem to activate different signaling pathways that converge on OXI1 but otherwise operate via distinct mechanisms and lead to different physiological responses of the affected plants. At first glance, the 1O2-mediated cell death responses of ch1 and flu seem to be very similar. In both mutants, the release of 1O2 not only leads to cell death but also affects similar sets of 1O2-responsive nuclear genes, and these responses occur independently of H2O2-dependent signaling. However, initiation of the high-light-induced cell death in ch1 results primarily from photo-oxidative damage, whereas in flu the cell death response is under genetic control and is triggered by a rapid but minor increase of 1O2 that is too low to damage the cell directly (Kim et al., 2012; Shumbe et al., 2016; Wang et al., 2016). In ch1, the 1O2 level rises more gradually, with a production lasting as long as the light stress is maintained and the PSII RCs are still intact. During this period, increases of various hormones such as JA and ethylene form an integral part of the cell death-inducing mechanism (Shumbe et al., 2016). Also in flu, 1O2-mediated hormone changes have been reported (op den Camp et al., 2003; Danon et al., 2005; Przybyla et al., 2008). However, in flu, the enhanced hormone production and expression changes of most of the 1O2-responsive genes occur later than the 1O2-mediated loss of chloroplast integrity (Kim et al., 2012). Hence, these 1O2-responsive genes seem to be only indirectly affected by 1O2 and are probably activated during the loss of cellular integrity by hormones such as JA and ethylene (Kim and Apel, 2013). Generation of 1O2 in ch1 has been reported to occur within grana stacks of PSII (Laloi and Havaux, 2015), whereas in flu1O2 formation that induces cell death responses takes place within grana margins close to the repair site of damaged PSII (Wang et al., 2016). In flu, the 1O2-mediated rapid loss of chloroplast integrity and the subsequent cell death strictly depend on the two EX proteins that are localized within the grana margins, whereas in ch1 the high-light-induced cell death response is not affected by the absence of these proteins (Shumbe et al., 2016). Thus, even though the cell death responses of flu and ch1 are both triggered by 1O2, they are controlled by different mechanisms. Under moderate light stress (400 µmol photons m−2 s−1/20 °C), 1O2 induces stress acclimation in ch1 that attenuates the 1O2-mediated cell death response during a subsequent severe high light stress (Shumbe et al., 2016). 1O2-mediated signaling under moderate light stress seems different from OXI1-dependent signaling in high-light-treated plants. Under severe light stress, the 1O2-mediated cell death is preceded by an enhanced expression of the OXI1 gene, whereas under moderate light stress expression of this gene is suppressed. At the same time, an enhanced expression of acclimation-specific genes under moderate light contrasts with the down-regulation of other 1O2-responsive genes that are activated under high light stress (Shumbe et al., 2016). Some of these latter genes are known to control the biosynthesis of JA. In high-light-treated ch1 plants, up-regulation of these genes correlates with high levels of JA, whereas in pre-acclimated ch1 plants this JA accumulation during high light treatment is suppressed (Shumbe et al., 2016). Exogenously applied JA restores the high light stress-induced cell death response in pre-acclimated ch1 plants. On the other hand, a genetic block of JA biosynthesis significantly reduces the extent of photo-oxidative damage and cell death during high light stress in a JA-deficient delayed-dehiscence2 (dde2)/ch1 double mutant (Ramel et al., 2013). Collectively, these results suggest that JA plays a dual role by promoting a cell death response and suppressing the effect of stress acclimation in high-light-treated ch1 mutant plants. 1O2-mediated dose-dependent and spatially resolved responses So far, different 1O2-mediated reactions of plants have been attributed to different dose-dependent effects of 1O2. In high-light-treated ch1 plants, 1O2-mediated signaling at higher 1O2 concentrations contributes to the cell death response and induces an up-regulation of OXI1 gene expression, whereas under moderate light stress a lower level of 1O2 correlates with stress acclimation, suppression of OXI1 gene expression, and reduced photodamage (Shumbe et al., 2016). In light-grown flu seedlings transferred to the dark for up to 8 h and re-exposed to light, a lower amount of 1O2 activates EX1-dependent signaling and triggers a cell death response without obvious photo-oxidative damage (Kim et al., 2012; Wang et al., 2016). An extension of the duration of the dark period in the flu mutant enhances the accumulation of the photosensitizer Pchlide that upon re-illumination generates a higher amount of 1O2 and causes photo-oxidative damage (Fig. 3A–D). Under these latter conditions, the toxicity of 1O2 prevails and superimposes the EX1-dependent signaling induced by 1O2 (Fig. 3). It might be interesting to test the behavior of EX1-overproducing plants under these latter conditions. During illumination, the amounts of 1O2 produced in etiolated flu and flu/ex1 are also affected by light intensities. Under a higher light intensity (100 μmol photons m−2 s−1), a higher amount of 1O2 is produced. As a result, the cytotoxic effect of 1O2 prevails and both flu and flu/ex1 seedlings initiate a cell death response. However, under a low light intensity (10 μmol photons m−2 s−1), only a moderate amount of 1O2 is generated; the cell death response is abrogated in flu/ex1 but not in flu. At a very low light intensity (1 μmol photons m−2 s−1), the amount of 1O2 produced is too low to lead to a cell death response even in flu (Fig. 3E). Since in flu a cell death response is triggered by a minor transient increase of 1O2 whereas in ch1 a cell death response is seen under a higher 1O2 production, 1O2-mediated cell death responses do not seem to be only dose dependent. In flu and ch1, 1O2 is generated at different sites within the chloroplast, suggesting that the spatial distribution of 1O2 production also influences the reaction of the plant. Because of its high reactivity and short half-life (~4 µs in water), the primary reaction of 1O2 is restricted to a small suborganellar area adjacent to the site of 1O2 generation (Redmond and Kochevar, 2006; Laloi and Havaux, 2015). Spatially resolved 1O2-initiated responses have been proposed to depend on a spatial resolution of the photosensitizer distribution (Redmond and Kochevar, 2006). However, in the case of the EX1-mediated cell death, this response of the flu mutant strictly depends on the presence of EX1 within grana margins and not on the distribution of the photosensitizer Pchlide (Wang et al., 2016). EX1 seems to operate as a sensor of minor increases of 1O2 concentrations that amplifies the potential biological activity of 1O2 and reveals its signaling capacity. Thus, it is the location of EX1 but not the spatial distribution of the photosensitizer that defines the site where the primary photo-oxidation by 1O2 and initiation of 1O2-mediated and EX1-dependent signaling occur (Wang et al., 2016). With the onset of 1O2 formation in the flu mutant, there is a rapid decline of the EX1 protein in grana margins that depends on the metalloprotease FtsH (Wang et al., 2016). Hence, the proteolytically modified EX1 or its breakdown products may be expected to be directly involved in 1O2-mediated signaling and to interact with downstream signaling components. In ch1, 1O2 formation in grana stacks has been implicated in damaging active PSII and inhibiting photosynthesis (Shumbe et al., 2016). Under these severe high light stress conditions, 1O2 generates non-enzymatically a wide range of oxidation products, some of which may disseminate within the cell and act as a second messenger that triggers stress responses. For instance, oxidation of lipids has been shown to produce multiple reactive derivatives with strong electrophilic properties (Farmer and Mueller, 2013) that may activate redox-sensitive transcription factors. Oxidation of β-carotene by 1O2 in the PSII RC generates various oxidative breakdown products, some of which, such as β-cyclocitral (Ramel et al., 2012a, b) and dihydroactinidolide (Shumbe et al., 2014), are biologically active (Fig. 2A). Intriguingly, these carotenoid oxidation products do not trigger cell death but induce stress defense responses and acclimation. Spatially resolved responses may be triggered not only by 1O2 generated in grana margins and grana stacks. There are also other sites within the chloroplast that upon stress accumulate tetrapyrroles and release 1O2. The chloroplast envelope contains Pchlide (Pineau et al., 1986). In cotyledons, the import of PORA into plastids depends on Pchlide that interacts with the PORA precursor polypeptide during its uptake (Kim and Apel, 2004). In the outer plastid envelope protein16-1 (oep16-1) mutant, this import of PORA is impaired and free Pchlide starts to accumulate (Samol et al., 2011). Upon light exposure, it generates 1O2 and induces a cell death response that differs from that of the flu mutant (Samol et al., 2011). As mentioned above, the fc2 mutant displays a weak flu phenotype when grown under non-permissive dark/light conditions and induces a cell death response that unlike flu is not affected by the absence of EX1 (Woodson et al., 2015). As the intraorganellar location of 1O2 formation is expected to influence the biological effects of 1O2, the release of 1O2 at a site other than the grana margin could explain why the 1O2-mediated cell death responses in the oep16-1 and fc2 mutants differ from the cell death response of the flu mutant. In addition to the location and the extent of 1O2 formation, the developmental stage at which 1O2 is produced also influences the specificity of 1O2-mediated responses. A development-dependent specification of 1O2-mediated responses was first documented in the flu mutant. At the seedling stage, the release of 1O2 induced a rapid bleaching of flu seedlings (op den Camp et al., 2003), in more mature flu plants the release of 1O2 triggered lesion formation in leaves (Wang et al., 2016), whereas in plants ready to bolt generation of 1O2 led to an immediate cessation of growth (op den Camp et al., 2003; Przybyla et al., 2008). At all three developmental stages, 1O2-mediated responses were dependent on EX1 and were abolished in the flu/ex1 double mutant (Wagner et al., 2004; Wang et al., 2016). In Arabidopsis wild-type plants, 1O2-mediated and EX-dependent signaling was shown also to be active during late embryogenesis prior to the onset of seed dormancy and to affect plastid development after seed germination (Kim et al., 2009). Etioplasts and chloroplasts of seedlings are derived from undifferentiated progenitors in embryos named proplastids that descend from maternal plastids (Possingham, 1980). During the transition from the morphogenic phase with rapid cell division to the maturation phase of embryogenesis, proplastids may either differentiate into functional photoheterotrophic chloroplasts or remain undifferentiated (Vicente-Carbajosa and Carbonero, 2005). With the onset of seed desiccation, thylakoid membranes of these photoheterotrophic chloroplasts disintegrate and release their Chl. At the same time, 1O2-mediated and EX-dependent signaling is initiated that pre-determines the fate of plastid differentiation by recruiting absisic acid that later on acts as a positive regulator of plastid formation in etiolated and light-grown seedlings. In wild-type seedlings lacking EX1 and EX2, chloroplast development in cotyledons is severely impaired. This is reflected in a reduced Chl and protein content and a much smaller size of chloroplasts in cotyledons of ex1/ex2 seedlings that resemble undifferentiated proplastids (Kim et al., 2009). Outlook As the biological effects of 1O2 are influenced not only by the location and extent of 1O2 production within chloroplasts but also change during plant development, 1O2 is expected to act as a highly versatile and short-lived signal throughout the life cycle of a plant and to give rise to a surprising variety of different signaling pathways. So far little is known about what distinguishes these different signaling pathways from each other. In the flu mutant, second-site genetic screens aimed at identifying constituents involved in 1O2-mediated signaling have only led to the discovery of the plastid-localized EX1 protein but failed to identify other signaling components (Wagner et al., 2004; Baruah et al., 2009a; Meskauskiene et al., 2009). In the fc2 mutant, similar second-site mutant screens have led to the identification of the E3 ubiquitin ligase that is required for the breakdown of chloroplasts but is unlikely to take part in cell death-inducing signaling (Woodson et al., 2015). In ch1, OXI1 acts as an enhancer of high-light-induced cell death rather than being an obligatory component of a 1O2-dependent cell death-inducing signaling pathway (Shumbe et al., 2016). These results suggest that the proposed 1O2-dependent signals are not transferred to the nucleus via a single linear signaling pathway that can easily be blocked genetically but rather through a more complex signaling network that is difficult to analyze by introducing only single gene mutations. Unlike the flu, fc2, and ch1 mutant plants, in wild-type plants 1O2-mediated signaling does not operate alone but interacts with other signaling pathways that converge with 1O2-dependent signaling and delay or modify some of the 1O2-mediated responses seen in the three mutant lines (Baruah et al., 2009b; Kim and Apel, 2013). For instance, perturbations of cellular homeostasis prior to 1O2 production confer an enhanced stress resistance by activating acclimation that suppresses 1O2-mediated cell death responses without blocking 1O2-mediated expression changes of 1O2-responsive genes (Baruah et al., 2009b; Coll et al., 2009; Meskauskiene et al., 2009; Šimková et al., 2012). 1O2 itself may also modify consequences of higher 1O2 concentrations through an autoregulatory feedback control that induces acclimation (Ledford et al., 2007; Kim and Apel, 2013; Shumbe et al., 2016). Finally, hydrogen peroxide may antagonize 1O2-mediated signaling. Overexpression of a thylakoid-bound ascorbate peroxidase (tAPX, an H2O2 scavenger) in the flu mutant increases the expression of most of the 1O2-induced genes and enhances the 1O2-mediated cell death and growth inhibition phenotypes compared with the flu parental line (Laloi et al., 2007). Other recent examples of 1O2-mediated signaling in wild-type plants have indicated that it is not only restricted to light stress, but may also occur during wounding, pathogen attack, senescence, and drought stress (Mur et al., 2010; Vellosillo et al., 2010; Alboresi et al., 2011; Nomura et al., 2012; González-Pérez et al., 2011; Gutiérrez et al., 2014; Mor et al., 2014; Uberegui et al., 2015). 1O2 that triggers these responses may not only be generated photochemically in chloroplasts, but in some cases has been suggested to be formed metabolically in the absence of light and to emanate also from other subcellular compartments (Mor et al., 2014; Noctor and Foyer, 2016; Foyer et al., 2017). Collectively, these data emphasize the complexity of signaling events that must be dissected before the biological significance of 1O2-mediated signaling in wild-type plants can be fully understood. Acknowledgements During the revision of this manuscript, my excellent supervisor, and dear friend, Professor Klaus Apel (18 November 1942 to 30 June 2017) left us forever. Professor Apel was a pioneer of photosynthesis research and an authority on plant molecular genetics. He pioneered research on singlet oxygen and proved that besides its cytotoxicity singlet oxygen could also be a signal. This work was supported by the National Institutes of Health Grant R01-GM085036. I thank Dr Tatjana Kleine and Dr Belén Naranjo for careful reading and advice on modifying this manuscript. I apologize to those authors whose manuscripts are not cited in this review due to limited space. References Alboresi A , Dall’Osto L , Aprile A , Carillo P , Roncaglia E , Cattivelli L , Bassi R . 2011 . Reactive oxygen species and transcript analysis upon excess light treatment in wild-type Arabidopsis thaliana vs a photosensitive mutant lacking zeaxanthin and lutein . BMC Plant Biology 11 , 62 . 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Pereira, Jorge, F;Ryan, Peter, R

doi: 10.1093/jxb/ery357pmid: 30325439

Abstract Aluminium (Al) toxicity can severely reduce root growth and consequently affect plant development and yield. A mechanism by which many species resist the toxic effects of Al relies on the efflux of organic anions (OAs) from the root apices via OA transporters. Several of the genes encoding these OA transporters contain transposable elements (TEs) in the coding sequences or in flanking regions. Some of the TE-induced mutations impact Al resistance by modifying the level and/or location of gene expression so that OA efflux from the roots is increased. The importance of genomic modifications for improving the adaptation of plants to acid soils has been raised previously, but the growing number of examples linking TEs with these changes requires highlighting. Here, we review the role of TEs in creating genetic modifications that enhance the adaptation of plants to acid soils by increasing the release of OAs from the root apices. We argue that TEs have been an important source of beneficial mutations that have co-opted OA transporter proteins with other functions to perform this role. These changes have occurred relatively recently in the evolution of many species and likely facilitated their expansion into regions with acidic soils. Acid soils, cis elements, citrate transporter, malate transporter, mutation, transposon Introduction Transposable elements (TEs) or transposons are found in the genomes of most species and often represent a large proportion of the genome in plants (Bennetzen, 2000; Tenaillon et al., 2010; Negi et al., 2016). They comprise a large group of mobile genetic elements showing a diverse range of sequences and open reading frames. TEs are characterized by their ability to change their positions in the genome or transpose. Transposition requires conserved sequences to be present and a specific set of proteins that are provided either by the TE itself (autonomous elements) or by similar elements encoded elsewhere (trans activation of non-autonomous elements). TEs generate different types of mutations as they either multiply across the genome or excise from one place in the genome and insert into another. In addition to transposition, TEs can induce other genetic rearrangements such as translocations, inversions and duplications as they recombine throughout the genome (Hua-Van et al., 2011; Lisch, 2013). TEs are an important source of spontaneous mutations (Paquin and Williamson, 1986; Green, 1988; Bennetzen, 2000) and their expression and activity can also increase in response to different biotic and abiotic stresses (Capy et al., 2000; Makarevitch et al., 2015). On occasions these mutations can be beneficial to the organism, and specific TEs have now been associated with the adaptation of plants to a range of environmental stresses (Vitte et al., 2014; Negi et al., 2016). Aluminum (Al) toxicity is a major abiotic constraint limiting root growth in acid soils. Al is common in most soils and generally harmless to plants when pH is above 5.0. However, when the soil becomes more acidic, the concentration of soluble Al cations increases and the molar fraction of the highly toxic trivalent cation species (Al3+) becomes predominant. Many plant species are detrimentally affected by prolonged exposure to these Al cations with the inhibition of root growth being a key symptom (Singh et al., 2017). Stunted root systems limit the capacity for water and nutrient uptake (Kochian et al., 2015; Lynch and Wojciechowski, 2015). Plants have evolved different mechanisms to adapt to acid soils and most of the important crop species show a significant genotypic variation in Al resistance. Resistance mechanisms can be broadly divided into those that exclude Al from plant tissues, especially the root apices that are critical to root growth, and those mechanisms that enable plants to better tolerate the Al that is absorbed by the cells. Comprehensive reviews of these resistance mechanisms are available elsewhere (Taylor, 1991; Matsumoto, 2000; Hiradate et al., 2007; Kochian et al., 2015). When plants are treated with toxic concentrations of Al, the activity of certain TEs is enhanced (Milla et al., 2002; Mao et al., 2004; Yang et al., 2007; Zhen et al., 2007; Mattiello et al., 2010; Chen et al., 2011; Guo et al., 2017). TEs have been detected in the transcribed regions of some genes associated with Al resistance or in their flanking regions. While the role of genomic changes in enhancing acid-soil tolerance has been discussed previously (Magalhaes, 2010; Ryan and Delhaize, 2010; Delhaize et al., 2012), the growing number of examples implicating TEs with these changes deserves a more thorough review. This article examines the evidence linking TE activity with increased tolerance to acid soils by increasing organic anion efflux from roots. The increased expression of certain organic anion transporters is linked with an important mechanism of Al resistance in plants A widespread mechanism of Al resistance in plants that excludes Al from the sensitive root apices involves the release (or efflux) of organic anions (OAs) from the root apices (Fig. 1). The OAs commonly released by crop plants are citrate and malate (Ryan et al., 2011). The current model proposes that these anions bind the toxic Al cations in the apoplast, which reduces Al uptake into the cells and minimizes other damaging interactions in the cell wall. By protecting the sensitive growing zone, the OA efflux helps to maintain root growth in acid soil. OA efflux from the root apices is facilitated by specific transport proteins in the plasma membrane. The expression of genes encoding these transporters and the activity of the transporter proteins are often increased by exposure to Al. In species such as wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), OA efflux is the major mechanism of Al resistance and the relative expression of the genes involved is closely correlated with OA efflux and Al resistance (Delhaize et al., 2012). In other species, OA efflux is one of several mechanisms contributing to resistance and the relationship between OA efflux and resistance is weaker. Fig. 1. View largeDownload slide Aluminium (Al) resistance based on the efflux of organic anions by the roots. In acid soils, toxic aluminium cations (Al3+) impact root growth. The Al toxicity is reduced when organic anions (citrate and/or malate) released by the roots bind to Al3+. Two families of transporters (ALMT and MATE) facilitate the transport of organic anions to the outside of root cells and higher expression of the genes encoding these transporters is associated with higher efflux of citrate and/or malate and greater Al resistance. Three-dimensional structures for ALMT and MATE transporters were built by SWISS-MODEL (Biasini et al., 2014) based on sequences of TaALMT1 (GenBank DQ072260) and HvAACT1 (GenBank KX278713). Fig. 1. View largeDownload slide Aluminium (Al) resistance based on the efflux of organic anions by the roots. In acid soils, toxic aluminium cations (Al3+) impact root growth. The Al toxicity is reduced when organic anions (citrate and/or malate) released by the roots bind to Al3+. Two families of transporters (ALMT and MATE) facilitate the transport of organic anions to the outside of root cells and higher expression of the genes encoding these transporters is associated with higher efflux of citrate and/or malate and greater Al resistance. Three-dimensional structures for ALMT and MATE transporters were built by SWISS-MODEL (Biasini et al., 2014) based on sequences of TaALMT1 (GenBank DQ072260) and HvAACT1 (GenBank KX278713). The first genes encoding malate and citrate transporters associated with Al resistance were identified in wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and sorghum (Sorghum bicolor L.) (Sasaki et al., 2004; Furukawa et al., 2007; Magalhaes et al., 2007; Wang et al., 2007). More than 30 additional genes encoding OA transporters have been linked with Al resistance in other species including most important crops (Table 1). These OA transporters are encoded by two gene families: the Al-activated malate transporter (ALMT) family encodes anion channels that release malate from cells, and the multidrug and toxic compound extrusion (MATE) family encodes co-transporters that release citrate from cells (Takanashi et al., 2014; Palmer et al., 2016; Sharma et al., 2016). Some of the genes in Table 1 have a strong genetic and physiological association with Al resistance, but for others, the links remain correlative and further confirmation is required. Table 1. List of plant genes/candidate genes encoding organic anion (OA) transporters, which are involved in Al resistance, and the presence of transposons (TE) insertions near or within them Genea OA Species TE insertionb Reference Genes where the upstream regions (at least 1.5 kb) have been sequencedc  TaALMT1 Malate Triticum aestivum No Sasaki et al. (2006)  SbMATE Citrate Sorghum bicolor Upstream region Magalhaes et al. (2007)  AtALMT1 Malate Arabidopsis No Kobayashi et al. (2007)  ScALMT1 Malate Secale cereale Inside intron Collins et al. (2008)  AtMATE Citrate Arabidopsis No Liu et al. (2009)  AetALMT1 Malate Aegilops tauschii No Ryan et al. (2010)  HvAACT1 Citrate Hordeum vulgare Upstream region Fujii et al. (2012)  ScAACT1 Citrate Secale cereale Downstream region Silva-Navas et al. (2012)  HlALMT1 Malate Holcus lanatus No Chen et al. (2013)  ZmMATE1 Citrate Zea mays Flanking regions Maron et al. (2013)  TaMATE1B Citrate Triticum aestivum Upstream region Tovkach et al. (2013)  BdALMT1 Malate Brachypodium distachyon Upstream regiond Contreras et al. (2014)  GmMATE75 Citrate Glycine max No Liu et al. (2016a)  VuMATE1 Citrate Vigna umbellata No Liu et al. (2016b)  OsFRDL4 Citrate Oryza sativa Upstream region Yokosho et al. (2016)  SlALMT9 Malate Solanum lycopersicum Inside intron Ye et al. (2017)  CcMATE1 Citrate Cajanus cajan No Daspute et al. (2018)  VuMATE2 Citrate Vigna umbellata No Liu et al. (2018)  HvAACT1 Citrate Hordeum vulgare Upstream region Kashino-Fujii et al. (2018) Genes whose flanking regions were not sequenced  BnALMT1 Malate Brassica napus Not in coding region Ligaba et al. (2006)  BnALMT2 Malate Brassica napus Not in coding region Ligaba et al. (2006)  ZmMATE2 Citrate Zea mays No Maron et al. (2010)  ScFRDL2 Citrate Secale cereale Not in coding region Yokosho et al. (2010)  MsALMT1 Malate Medicago sativa Not in coding region Chen et al. (2011)  CgALMTse Malate Citrus grandis Not in coding region Yang et al. (2012); Guo et al. (2017)  CsALMTse Malate Citrus sinensis Not in coding region Yang et al. (2012); Guo et al. (2017)  GmALMT1 Malate Glycine max Not in coding region Liang et al. (2013)  EcMATE1 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  EcMATE3 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  BsALMT1 Malate Brachypodium stacei No Contreras et al. (2014)  BdMATE1 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BdMATE2 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BoMATE Citrate Brassica oleracea Not in coding region Wu et al. (2014)  CsMATEse Citrate Citrus sinensis Not in coding region Guo et al. (2017)  BoALMT1 Malate Brassica oleracea Not in coding region Zhang et al. (2018) Genea OA Species TE insertionb Reference Genes where the upstream regions (at least 1.5 kb) have been sequencedc  TaALMT1 Malate Triticum aestivum No Sasaki et al. (2006)  SbMATE Citrate Sorghum bicolor Upstream region Magalhaes et al. (2007)  AtALMT1 Malate Arabidopsis No Kobayashi et al. (2007)  ScALMT1 Malate Secale cereale Inside intron Collins et al. (2008)  AtMATE Citrate Arabidopsis No Liu et al. (2009)  AetALMT1 Malate Aegilops tauschii No Ryan et al. (2010)  HvAACT1 Citrate Hordeum vulgare Upstream region Fujii et al. (2012)  ScAACT1 Citrate Secale cereale Downstream region Silva-Navas et al. (2012)  HlALMT1 Malate Holcus lanatus No Chen et al. (2013)  ZmMATE1 Citrate Zea mays Flanking regions Maron et al. (2013)  TaMATE1B Citrate Triticum aestivum Upstream region Tovkach et al. (2013)  BdALMT1 Malate Brachypodium distachyon Upstream regiond Contreras et al. (2014)  GmMATE75 Citrate Glycine max No Liu et al. (2016a)  VuMATE1 Citrate Vigna umbellata No Liu et al. (2016b)  OsFRDL4 Citrate Oryza sativa Upstream region Yokosho et al. (2016)  SlALMT9 Malate Solanum lycopersicum Inside intron Ye et al. (2017)  CcMATE1 Citrate Cajanus cajan No Daspute et al. (2018)  VuMATE2 Citrate Vigna umbellata No Liu et al. (2018)  HvAACT1 Citrate Hordeum vulgare Upstream region Kashino-Fujii et al. (2018) Genes whose flanking regions were not sequenced  BnALMT1 Malate Brassica napus Not in coding region Ligaba et al. (2006)  BnALMT2 Malate Brassica napus Not in coding region Ligaba et al. (2006)  ZmMATE2 Citrate Zea mays No Maron et al. (2010)  ScFRDL2 Citrate Secale cereale Not in coding region Yokosho et al. (2010)  MsALMT1 Malate Medicago sativa Not in coding region Chen et al. (2011)  CgALMTse Malate Citrus grandis Not in coding region Yang et al. (2012); Guo et al. (2017)  CsALMTse Malate Citrus sinensis Not in coding region Yang et al. (2012); Guo et al. (2017)  GmALMT1 Malate Glycine max Not in coding region Liang et al. (2013)  EcMATE1 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  EcMATE3 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  BsALMT1 Malate Brachypodium stacei No Contreras et al. (2014)  BdMATE1 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BdMATE2 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BoMATE Citrate Brassica oleracea Not in coding region Wu et al. (2014)  CsMATEse Citrate Citrus sinensis Not in coding region Guo et al. (2017)  BoALMT1 Malate Brassica oleracea Not in coding region Zhang et al. (2018) aHvAACT1 is shown twice due to the identification of independent TE insertions. bAbsence of TEs is based on the available sequences (promoter and/or gene or only coding region—cDNA). cFor AetALMT1 and CcMATE1, less than 1.5 kb of the upstream region was sequenced. dProbably. eMore than one sequence has been found for these transporters. View Large Table 1. List of plant genes/candidate genes encoding organic anion (OA) transporters, which are involved in Al resistance, and the presence of transposons (TE) insertions near or within them Genea OA Species TE insertionb Reference Genes where the upstream regions (at least 1.5 kb) have been sequencedc  TaALMT1 Malate Triticum aestivum No Sasaki et al. (2006)  SbMATE Citrate Sorghum bicolor Upstream region Magalhaes et al. (2007)  AtALMT1 Malate Arabidopsis No Kobayashi et al. (2007)  ScALMT1 Malate Secale cereale Inside intron Collins et al. (2008)  AtMATE Citrate Arabidopsis No Liu et al. (2009)  AetALMT1 Malate Aegilops tauschii No Ryan et al. (2010)  HvAACT1 Citrate Hordeum vulgare Upstream region Fujii et al. (2012)  ScAACT1 Citrate Secale cereale Downstream region Silva-Navas et al. (2012)  HlALMT1 Malate Holcus lanatus No Chen et al. (2013)  ZmMATE1 Citrate Zea mays Flanking regions Maron et al. (2013)  TaMATE1B Citrate Triticum aestivum Upstream region Tovkach et al. (2013)  BdALMT1 Malate Brachypodium distachyon Upstream regiond Contreras et al. (2014)  GmMATE75 Citrate Glycine max No Liu et al. (2016a)  VuMATE1 Citrate Vigna umbellata No Liu et al. (2016b)  OsFRDL4 Citrate Oryza sativa Upstream region Yokosho et al. (2016)  SlALMT9 Malate Solanum lycopersicum Inside intron Ye et al. (2017)  CcMATE1 Citrate Cajanus cajan No Daspute et al. (2018)  VuMATE2 Citrate Vigna umbellata No Liu et al. (2018)  HvAACT1 Citrate Hordeum vulgare Upstream region Kashino-Fujii et al. (2018) Genes whose flanking regions were not sequenced  BnALMT1 Malate Brassica napus Not in coding region Ligaba et al. (2006)  BnALMT2 Malate Brassica napus Not in coding region Ligaba et al. (2006)  ZmMATE2 Citrate Zea mays No Maron et al. (2010)  ScFRDL2 Citrate Secale cereale Not in coding region Yokosho et al. (2010)  MsALMT1 Malate Medicago sativa Not in coding region Chen et al. (2011)  CgALMTse Malate Citrus grandis Not in coding region Yang et al. (2012); Guo et al. (2017)  CsALMTse Malate Citrus sinensis Not in coding region Yang et al. (2012); Guo et al. (2017)  GmALMT1 Malate Glycine max Not in coding region Liang et al. (2013)  EcMATE1 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  EcMATE3 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  BsALMT1 Malate Brachypodium stacei No Contreras et al. (2014)  BdMATE1 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BdMATE2 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BoMATE Citrate Brassica oleracea Not in coding region Wu et al. (2014)  CsMATEse Citrate Citrus sinensis Not in coding region Guo et al. (2017)  BoALMT1 Malate Brassica oleracea Not in coding region Zhang et al. (2018) Genea OA Species TE insertionb Reference Genes where the upstream regions (at least 1.5 kb) have been sequencedc  TaALMT1 Malate Triticum aestivum No Sasaki et al. (2006)  SbMATE Citrate Sorghum bicolor Upstream region Magalhaes et al. (2007)  AtALMT1 Malate Arabidopsis No Kobayashi et al. (2007)  ScALMT1 Malate Secale cereale Inside intron Collins et al. (2008)  AtMATE Citrate Arabidopsis No Liu et al. (2009)  AetALMT1 Malate Aegilops tauschii No Ryan et al. (2010)  HvAACT1 Citrate Hordeum vulgare Upstream region Fujii et al. (2012)  ScAACT1 Citrate Secale cereale Downstream region Silva-Navas et al. (2012)  HlALMT1 Malate Holcus lanatus No Chen et al. (2013)  ZmMATE1 Citrate Zea mays Flanking regions Maron et al. (2013)  TaMATE1B Citrate Triticum aestivum Upstream region Tovkach et al. (2013)  BdALMT1 Malate Brachypodium distachyon Upstream regiond Contreras et al. (2014)  GmMATE75 Citrate Glycine max No Liu et al. (2016a)  VuMATE1 Citrate Vigna umbellata No Liu et al. (2016b)  OsFRDL4 Citrate Oryza sativa Upstream region Yokosho et al. (2016)  SlALMT9 Malate Solanum lycopersicum Inside intron Ye et al. (2017)  CcMATE1 Citrate Cajanus cajan No Daspute et al. (2018)  VuMATE2 Citrate Vigna umbellata No Liu et al. (2018)  HvAACT1 Citrate Hordeum vulgare Upstream region Kashino-Fujii et al. (2018) Genes whose flanking regions were not sequenced  BnALMT1 Malate Brassica napus Not in coding region Ligaba et al. (2006)  BnALMT2 Malate Brassica napus Not in coding region Ligaba et al. (2006)  ZmMATE2 Citrate Zea mays No Maron et al. (2010)  ScFRDL2 Citrate Secale cereale Not in coding region Yokosho et al. (2010)  MsALMT1 Malate Medicago sativa Not in coding region Chen et al. (2011)  CgALMTse Malate Citrus grandis Not in coding region Yang et al. (2012); Guo et al. (2017)  CsALMTse Malate Citrus sinensis Not in coding region Yang et al. (2012); Guo et al. (2017)  GmALMT1 Malate Glycine max Not in coding region Liang et al. (2013)  EcMATE1 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  EcMATE3 Citrate Eucalyptus camaldulensis Not in coding region Sawaki et al. (2013)  BsALMT1 Malate Brachypodium stacei No Contreras et al. (2014)  BdMATE1 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BdMATE2 Citrate Brachypodium distachyon Not in coding region Contreras et al. (2014)  BoMATE Citrate Brassica oleracea Not in coding region Wu et al. (2014)  CsMATEse Citrate Citrus sinensis Not in coding region Guo et al. (2017)  BoALMT1 Malate Brassica oleracea Not in coding region Zhang et al. (2018) aHvAACT1 is shown twice due to the identification of independent TE insertions. bAbsence of TEs is based on the available sequences (promoter and/or gene or only coding region—cDNA). cFor AetALMT1 and CcMATE1, less than 1.5 kb of the upstream region was sequenced. dProbably. eMore than one sequence has been found for these transporters. View Large The founder member of the ALMT family is TaALMT1 from wheat. This gene encodes an anion channel and controls the major Al resistance mechanism by facilitating the Al-activated efflux of malate from roots (Sasaki et al., 2004). Other members of this gene family have now been characterized in detail, but only a few contribute to Al resistance in a similar fashion to TaALMT1. The remainder encode anion channel proteins with diverse functions including the regulation of stomatal aperture, anion homeostasis and fruit development (Palmer et al., 2016; Sharma et al., 2016). Some ALMTs are permeable to γ-aminobutyric acid (GABA) and might have other functions transducing stress signals via GABA concentrations (Ramesh et al., 2018). ALMT proteins are usually 350–500 amino acids long with five to seven transmembrane domains (Delhaize et al., 2007; Dreyer et al., 2012; Sharma et al., 2016). At least some of the ALMT transporters function as tetramers (Zhang et al., 2013). MATE transporters were first identified in prokaryotic cells, where they facilitate the efflux of a variety of secondary compounds including xenobiotics (Putman et al., 2000; Moriyama et al., 2008). MATE proteins are 400–700 amino acids long with 12 membrane-spanning domains. They are divided into three phylogenetic families and 14 small subfamilies with the plant members in subfamily 2B (Moriyama et al., 2008). The MATEs characterized in plants transport a diverse range of compounds involved in different functions including mineral nutrition, transport of secondary metabolites, and hormone signalling (Takanashi et al., 2014). The first MATE genes linked with Al resistance were the SbMATE in sorghum (Magalhaes et al., 2007) and the aluminum-activated citrate transporter (HvAACT1) in barley (Furukawa et al., 2007; Wang et al., 2007). ALMT and MATE proteins share no sequence homology, indicating that these transporters have evolved in a convergent manner to perform similar functions (Delhaize et al., 2007; Ryan and Delhaize, 2010). In other words, the same phenotype (Al resistance) is achieved by similar mechanisms (OA efflux from roots) via transporters from different families. In some species only one of these transporters contributes to Al resistance (e.g. barley) whereas in other species members of both families contribute to Al resistance in the same plant (see Table 1). Why should members from distinct transporter families evolve a role in Al resistance? The two key features common to the ALMT and MATE transporters are, firstly, that they facilitate OA efflux from cells and, secondly, that their substrates (malate and citrate) form stable complexes with Al3+. Both are essential requirements if the OAs are to protect the roots by reducing the concentration of free Al ions in the apoplast. Many other substrates, including certain secondary metabolites and peptides, can bind Al strongly as well (Kidd et al., 2001, Poschenrieder et al., 2008), but these more complex compounds require extra energy and resources for their synthesis and transport. Malate and citrate anions, by contrast, are small, energetically cheap to synthesize and prevalent in living cells. Because they are so common, many genes are likely to encode transporters that move these substrates across cellular membranes (Ryan and Delhaize, 2010). Spontaneous mutations that increase the expression of specific MATE or ALMT genes in the apical tissues of roots have the potential to increase OA release from those cells and enhance Al resistance. Plant transposons: balancing between parasitism and beneficial genetic change Many different types of TEs have been described in living cells. Two broad classes represent the first division in TE taxonomy, and further subdivisions into subclasses, orders, superfamilies, families, and subfamilies depend on the sequence similarity (DNA and protein) and phylogenetic data of the elements (Wicker et al., 2007; Lisch, 2013; Gozukirmizi et al., 2016). Plant genomes contain representatives from both classes, but the majority belong to Class I, which require an RNA intermediate for transposition. During transposition, Class I TEs remain in the same position and a copy produced by reverse transcription inserts elsewhere in the genome. This explains why some Class I TEs, such as the long terminal repeat (LTR) retrotransposons, are very prevalent in plants (Vitte et al., 2014). TEs that do not require RNA intermediates are grouped in Class II, and these elements excise from one site in the genome and then insert at another site. Class II includes the Mutator superfamily, the CACTA superfamily and the miniature inverted-repeat transposable elements (MITEs) among others. Some TEs may have singular characteristics from both classes. Helitrons, for instance, are DNA transposons that transpose via a ‘rolling circle’ mechanism. Because they replicate by a copy-and-paste mechanism, Helitrons are similar to Class I elements. However, the ‘rolling circle’ mechanism does not require an RNA intermediate (Wicker et al., 2007; Lisch, 2013; Gozukirmizi et al., 2016). The association between TE transposition and the generation of mutant phenotypes in plants was reported nearly 70 years ago (McClintock, 1950). The hypothesis that segments of DNA could change their position in the genome challenged the status quo that then held genes were in a stable arrangement along the chromosome (Ravindran, 2012). TEs were initially seen as unimportant or ‘junk DNA’ and their importance was not fully realized for decades. It took almost 40 years to characterize the TEs in maize (Zea mays L.) and understand the molecular basis of the Dissociation (Ds) and Activator (Ac) loci that explained the chromosomal changes that caused sectored kernel pigmentation (Fedoroff et al., 1983; Lazarow et al., 2013). The current view is that mutations generated by TE activity are important drivers of genetic change and adaptation (Bennetzen, 2000; Bennetzen and Wang, 2014). Indeed, more than 50 TE-induced phenotypic changes have been directly linked with domestication or diversification of cultivated plants (Vitte et al., 2014). The mutations generated by TEs can be divided into three main groups: transposition (mutations that occur from an insertion or excision), recombination (where similar TEs dispersed throughout the genome cause inter- or intra-chromosomal rearrangements and sequence duplications) and exaptation (where sequences of the TEs are co-opted to perform functions that generate new phenotypes). The insertions or excisions of TEs that occur inside coding regions or introns can disrupt transcription or cause frameshifts that affect splicing or protein function. For example, TE excision generates a chromosomal break that can lead to point mutations or frameshifts if a small number of bases (called footprints) are added or deleted as the DNA reconnects. Indeed, the imperfect repair of DNA after TE excision is associated with a large number of mutations with evolutionary significance in rice, maize, wheat, and barley (Wicker et al., 2016). TE-dependent recombinations of sequences can be so severe that in some cases they disrupt the gene collinearity between related plant species (Morgante et al., 2005). TE insertions do not necessarily occur randomly in the genome. For instance, Class II TEs in maize and sorghum are more frequently found in low-copy regions and the sequences flanking genes rather than in the coding regions themselves (Zhang et al., 2000; Lisch, 2002; Wei et al., 2016), while LTR retrotransposons are primarily found inserted within each other (SanMiguel et al., 1996). Furthermore, Class II and low-copy-number Class I TEs are enriched in the upstream promoter sequences of all categories of plant genes, including stress-induced genes in maize and rice (Baucom et al., 2009; Naito et al., 2009; Bennetzen and Wang, 2014; Makarevitch et al., 2015). TEs that insert in the promoter regions of genes have the potential for altering gene expression in a number of ways. They can either up- or down-regulate expression of the genes or change their tissue-specific expression (Selinger and Chandler, 2001; Lisch, 2013; Dhadi et al., 2015). The ability of some TEs to increase gene expression is related to them containing transcription start sites or binding sites for transcription factors that would not normally influence the neighbouring genes (Thornburg et al., 2006; Fujii et al., 2012). Remarkably, TEs can even impact the expression of genes located more than 50 kb away from their insertion sites (Studer et al., 2011), although this is quite rare in plants (Bennetzen and Wang, 2018). Since many of the mutations caused by TEs are detrimental to the host, organisms have evolved mechanisms to reduce their activity. Epigenetic mechanisms, such as DNA methylation and demethylation, can moderate TE activity and suppress their parasitic-like behaviour (Kim and Zilberman, 2014; Bewick et al., 2016; Bennetzen and Park, 2018). Sometimes these epigenetic processes function imperfectly and affect the expression of neighbouring, non-target genes (Hollister and Gaut, 2009; Lisch and Bennetzen, 2011; Le et al., 2014). Therefore, a balance is required between the activation and inactivation of TEs which minimizes their detrimental effects without losing the benefits that sometimes flow from the genetic variation they create. Transposons alter the expression of organic anion transporters involved in Al resistance Figure 2 classifies the TEs that have been detected inside or near specific OA transporters associated with Al resistance. While the coding regions are known for most of these genes, the sequence from the upstream flanking regions (usually ≥1.5 kb) is only available for 18 of them (Table 1) and fewer have sequence from the downstream regions. In nine of these genes, TEs have been detected upstream, downstream, or in the transcribed regions. In the case of the barley gene HvAACT1, two independent insertions have been reported (Fig. 3). Most of these TEs enhance Al resistance by increasing OA efflux from the roots, but others appear to have no effect or even decrease Al resistance. Fig. 2. View largeDownload slide Taxonomy of the elements found to be associated with organic anion transporter genes that contribute to Al resistance in plants. The multi-retrotransposon-like (MRL) sequence upstream of HvAACT1 (Kashino-Fujii et al., 2018) was not considered because it contains a mix of LTR elements from different superfamilies. INT, integrase; LTR, long terminal repeat; ORF, open reading frame; PR, protease; RH, RNase H; RT, reverse transcriptase; TIR, terminal inverted repeat; TSD, target site duplication. Fig. 2. View largeDownload slide Taxonomy of the elements found to be associated with organic anion transporter genes that contribute to Al resistance in plants. The multi-retrotransposon-like (MRL) sequence upstream of HvAACT1 (Kashino-Fujii et al., 2018) was not considered because it contains a mix of LTR elements from different superfamilies. INT, integrase; LTR, long terminal repeat; ORF, open reading frame; PR, protease; RH, RNase H; RT, reverse transcriptase; TIR, terminal inverted repeat; TSD, target site duplication. Fig. 3. View largeDownload slide Schematic representation of transposable elements that are associated with organic anion transporters genes/candidate genes. Putative cis elements discussed in the text are shown in (A–E). In (B), MRL indicates a multi-retrotransposon-like sequence. In (E), the numbers of cis elements for ART1 and WRKY22 in the solo LTR near OsFRDL4 are different from the ones reported previously (Yokosho et al., 2016; Li et al., 2018). Here, we consider a larger range of affinity levels for ART1 and the sequence (T/C)TGAC(T/C) and its reverse complement for WRKY22. Fig. 3. View largeDownload slide Schematic representation of transposable elements that are associated with organic anion transporters genes/candidate genes. Putative cis elements discussed in the text are shown in (A–E). In (B), MRL indicates a multi-retrotransposon-like sequence. In (E), the numbers of cis elements for ART1 and WRKY22 in the solo LTR near OsFRDL4 are different from the ones reported previously (Yokosho et al., 2016; Li et al., 2018). Here, we consider a larger range of affinity levels for ART1 and the sequence (T/C)TGAC(T/C) and its reverse complement for WRKY22. TE insertions near OA transporters that increase Al resistance TE insertions have been detected near Al-resistance genes encoding OA transporters in barley, wheat, rice, sorghum, Brachypodium distachyon, and rye (Secale cereale L.) (Magalhaes et al., 2007; Fujii et al., 2012; Silva-Navas et al., 2012; Tovkach et al., 2013; Yokosho et al., 2016; Kashino-Fujii et al., 2018). All of these transporters except one are MATE-type transporters that facilitate citrate efflux. The exception is an ALMT-type transporter in Brachypodium distachyon. These TEs vary in classification (Fig. 2) and are positioned at a range of distances from the coding regions (Fig. 3) as detailed below. Al resistance in barley is controlled by a single major gene, HvAACT1 on chromosome 4H (Minella and Sorrells, 1997; Furukawa et al., 2007; Wang et al., 2007). HvAACT1 encodes a MATE transporter that facilitates the Al-activated efflux of citrate from the root apices. Al-resistant cultivars of barley show a constitutively higher level of HvAACT1 expression in the apices than sensitive cultivars. In some resistant cultivars this is caused by a 1023-bp CACTA-like transposon (Class II) located in the 5′ untranslated region (UTR) approximately 4.8 kb upstream of the HvAACT1 start codon (Fujii et al., 2012). Among a range of barley genotypes tested, those with this TE grew better in Al3+-toxic conditions than others without the TE (Fujii et al., 2012; Ferreira et al., 2018). More recently, another independent insertion affecting Al resistance was detected 6.6 kb upstream of HvAACT1 (Kashino-Fujii et al., 2018). This is a 15.3-kb-long multi-retrotransposon-like (MRL) sequence which includes LTRs. It is referred to as ‘an insertion’ but it may be a mix of more than one element. Cultivated barley accessions containing this other insertion also showed greater HvAACT1 expression in the root apices and improved resistance to Al stress as long as the HvAACT1 promoter region is demethylated (Kashino-Fujii et al., 2018). In wheat, the TaMATE1B gene on chromosome 4B controls the constitutive release of citrate from root apices. Relatively few genotypes show this phenotype (Ryan et al., 2009) but those that do possess an 11.1-kb TE inserted 25 bp upstream of the TaMATE1B start codon. This insert contains a 3.9-kb Sukkula-like TE (Class I) (Tovkach et al., 2013) that is linked with greater TaMATE1B expression. The association between this TE and Al resistance among different genotypes is not strong because most of the variation in Al resistance is determined by malate efflux via TaALMT1 (Garcia-Oliveira et al., 2014; Aguilera et al., 2016). Nevertheless, many of the most Al-resistant cultivars from Brazil (e.g. cv Carazinho, cv IAC5-Maringá and cv Toropi) combine the superior alleles of TaALMT1 and TaMATE1B (Pereira et al., 2015; Aguilera et al., 2016; Pereira, 2018). In rice, Al resistance is a complex trait involving multiple mechanisms of Al exclusion and Al tolerance (Yamaji et al., 2009). Most of the 30 or more genes that regulate these mechanisms are induced by Al via a C2H2 zinc finger-type transcription factor called Aluminium Resistance Transcription Factor1 (ART1) (Yamaji et al., 2009; Tsutsui et al., 2011). Transcription factors from this family in other species, including Arabidopsis (e.g. AtSTOP1), have also been implicated in the regulation of Al-resistance genes (Sawaki et al., 2009, 2014; Chen et al., 2013; Liu et al., 2016a,b, 2018; Daspute et al., 2018). One of the genes induced by ART1 in most japonica rice lines is Ferric Reductase Defective3-like 4 (OsFRDL4), which encodes a MATE-type transporter. Part of the phenotypic variation for Al resistance in rice is explained by greater OsFRDL4 expression and citrate efflux, which are directly linked with a 1213-bp solo LTR inserted 615 bp upstream of the OsFRDL4 transcription start site (TSS) (Yokosho et al., 2011, 2016). In sorghum, Al resistance is controlled by a major QTL on chromosome 3 (AltSB), which contains a MATE gene called SbMATE (Magalhaes et al., 2007). In the absence of Al toxicity, SbMATE is expressed equally in the root apices of Al-resistant and sensitive genotypes of sorghum but no citrate efflux occurs. When the plants are treated with Al, SbMATE expression increases over several days and the increase in expression is closely correlated with citrate efflux and enhanced Al resistance (Magalhaes et al., 2007). The polymorphisms in SbMATE between resistant and sensitive genotypes occur in a ~6-kb region encompassing part of the transcribed region and an insert ~1.4 kb upstream of the predicted TATA box (Caniato et al., 2014). This insert harbours a Class II TE called a Tourist-like MITE and the number of copies of the MITE and flanking sequences is positively correlated with Al resistance. However these MITEs only account for part of the variation in expression because important SNPs were detected in intron 2. Other trans-acting elements may affect SbMATE expression as well because the integration of the AltSB locus into different genetic backgrounds generates variable phenotypes (Melo et al., 2013). TEs could be implicated in Al resistance in two final examples (Brachypodium spp. and rye) but further experiments are required to directly demonstrate their impact. Contreras et al. (2014) found that the significant variation in Al resistance among a range of diploid (B. distachyon; 2n=10) and allotetraploid (B. hybridum; 2n=30) Brachypodium accessions was correlated with malate efflux from the roots. The Al-resistant genotypes showed greater expression of the BdALMT1 gene than sensitive accessions and all resistant genotypes possessed a large insert ~1789 bp upstream of the first ATG. The insert has not been fully sequenced and a complete description is not available, but there is a LTR at that position in the Brachypodium genome (Contreras et al., 2014). Nevertheless, these findings do establish a link between the insertion adjacent to BdALMT1, greater BdALMT1 expression, and increased malate efflux. In rye, both ALMT- and MATE-type transporters contribute to Al resistance by controlling malate and citrate efflux, respectively (Fontecha et al., 2007; Collins et al., 2008; Yokosho et al., 2010; Silva-Navas et al., 2012). One of the candidate MATE genes involved, ScAACT1, resides on chromosome 7RS (Silva-Navas et al., 2012) and its expression was greater in a resistant line than a sensitive line. MITE insertions (Stowaway family) were detected downstream of the coding region in the resistant and sensitive lines but the sequences and their insertion sites were different. Additional work is required to determine whether these differences can account for the contrasting levels of ScAACT1 expression and Al resistance. How do TEs increase the expression of Al-resistance genes? The preceding discussion provided examples of where TE insertions appear to improve the Al resistance of plants by enhancing the expression of genes in the root apices. Several mechanisms can explain this behaviour and Fig. 4 provides a stylized explanation for one mechanism. The expression of genes can be increased if the sequence of the TE contains transcriptional enhancer activity that alters the spatial or temporal expression pattern of adjacent genes. This mechanism has been attributed to the enhanced expression of several stress-responsive genes in maize (Makarevitch et al., 2015) and it also may explain the impact of TEs on the expression of OA transporter genes. For example, in Al-sensitive cultivars of barley, expression of HvAACT1 is greater in the vascular bundle of roots than in the cortical cells and root apices. This localization supports the proposed function of HvAACT1, which is to export citrate from the xylem parenchyma into xylem to accompany iron movement to the shoots (Fujii et al., 2012). Indeed, other MATE transporters have been ascribed similar functions in Arabidopsis, rice and perhaps wheat (Durrett et al., 2007; Yokosho et al., 2009; Tovkach et al., 2013). However, in Al-resistant barley genotypes that possess the 1023-bp CACTA-like TE insertion, HvAACT1 expression is expanded to include cells at the root apices. The TE sequence has promoter activity and contains additional TSSs both of which increase gene transcription. This was confirmed in transgenic studies that used promoters derived from Al-resistant and -sensitive barley to drive expression of HvAACT1 or green fluorescent protein in transgenic barley plants (Fujii et al., 2012). Promoter studies of this kind have also been used to confirm the effect of TEs on TaMATE1B expression in wheat and OsFRDL4 expression in rice (Tovkach et al., 2013; Yokosho et al., 2016). A second example in barley is the MRL insertion located farther upstream of HvAACT1 because it also contains multiple TSSs that increase HvAACT1 expression (Kashino-Fujii et al., 2018). Fig. 4. View largeDownload slide An illustration of how transposable elements (TEs) can change the expression of organic anion (OA) transporter genes in plants. As an example, the TE represented here is a Class II element and the OA transporter gene belongs to the MATE family. Fig. 4. View largeDownload slide An illustration of how transposable elements (TEs) can change the expression of organic anion (OA) transporter genes in plants. As an example, the TE represented here is a Class II element and the OA transporter gene belongs to the MATE family. TEs can also affect the expression of neighbouring genes if their sequences contain cis-regulatory elements that recognize certain transcription factors. For instance, the solo LTR inserted near OsFRDL4 in an Al-resistant rice (cv Nipponbare) contains nine cis-acting elements that recognize the ART1 transcription factor. This compares with only two cis-acting elements in a sensitive cultivar (cv Kasalath) that lack the insert (Yokosho et al., 2016). It is proposed that the greater number of cis elements causes more interaction between ART1 and the promoter, which increases OsFRDL4 expression and citrate efflux (Yamaji et al., 2009; Yokosho et al., 2016). The core motif of the cis-acting elements of ART1 is GGN(T/g/a/C)V(C/A/g)S(C/G), where the bases shown in lowercase show weaker ART1-biding affinity when compared with the bases shown in uppercase (Tsutsui et al., 2011). In the same way, cis elements that recognize another transcription factor, OsWRKY22, are present in the solo LTR upstream from OsFRDL4 (Li et al., 2018). OsWRKY22 belongs to the WRKY family of transcription factors which bind with W-box sequences (T/C)TGAC(T/C). The 25-fold induction of OsWRKY22 expression under Al treatment and the large number of cis elements in the TE near OsFRDL4 is consistent with these elements increasing OsFRDL4 expression under Al stress (Li et al., 2018). Since the core sequences for the transcription factors ART1 and OsWRKY22 are relatively short, other cis-acting elements might be required to control the specificity between these transcription factors (Delhaize et al., 2012). As part of this review, we analysed the TE insertions upstream of SbMATE, HvAACT1, TaMATE1B, and OsFRDL4 to determine whether putative cis elements for ART1/STOP1- and WRKY22-like transcription factors are present. We also used the PlantCARE database (Lescot et al., 2002) to detect other cis elements that could affect gene expression (Fig. 3; Supplementary Fig. S1; Supplementary Tables S1–S5 at JXB online). This analysis used the following sequences: the two TE insertions upstream of HvAACT1 in barley (the 1023-bp CACTA-like transposon in cv Murasakimochi and the first 1 kb of the MRL insertion in cv FM404), the first 2.3 kb of the Sukkula-like transposon upstream of TaMATE1B in wheat (cv Carazinho), the 455 pb region containing the MITE insertion upstream of SbMATE in sorghum (line TX430), and the 1213-bp solo LTR upstream of OsFRDL4 in rice (cv Nipponbare) (Magalhaes et al., 2007; Fujii et al., 2012; Tovkach et al., 2013; Yokosho et al., 2016; Kashino-Fujii et al., 2018). Possible biding sites for ART1/STOP1 and WRKY22 were searched manually while cis elements registered in the Plant CARE database were detected by submitting the sequences to the program (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). This in silico analysis identified common cis-acting elements in promoter and enhancer regions as well as the TSS in all the TEs. It also detected five cis-acting elements of WRKY22 in the 1023-bp TE insertion upstream of HvAACT1 and four cis-acting elements in the solo LTR upstream of OsFRDL4, but no cis elements were present in the TEs near SbMATE, TaMATE1B or in the first 1 kb of the MRL insertion upstream of HvAACT1. We found possible binding sites for ART1 in the solo LTR upstream of OsFRDL4, as identified by Yokosho et al. (2016), and similar ART1/STOP1 binding sites in the TEs near HvAACT1 (first 1 kb of the MRL insertion and the 1023 bp TE) and in the MITE near SbMATE. An ART1/STOP1-like protein reportedly contributes to the Al resistance of sorghum (Huang et al., 2018) but a direct interaction between that protein and these cis elements in the MITE has not been demonstrated. Similarly, the role of these putative cis elements upstream of HvAACT1 is unclear but it is important to note that HvAACT1 expression is not induced by exposure to Al (Furukawa et al., 2007). We also found that the number of cis elements for ART1/STOP1-like transcription factors was reduced by the insertion of the Sukkula-type TE upstream of TaMATE1B in wheat compared with the first 2.3 kb upstream of TaMATE1B without the TE insertion (Tovkach et al., 2013), but another motif with possible transcriptional activity (the 5′UTR pyrimidine-rich sequence TTTCTTCTCT; Daraselia et al., 1996) was detected 369 bp upstream from the coding region but only in the Sukkula-type TE insertion. Whether this motif is important for increasing TaMATE1B transcription is uncertain but it is consistent with Tovkach et al. (2013), who showed that a promoter derived from 1.5 kb of the 5′ end of that insertion generated stronger expression than a promoter without this region. Finally, multiple copies of another motif (CCGTCC-box) previously associated with expression in meristematic cells (Chaubet et al., 1996) was detected in the TEs near SbMATE, OsFRDL4, and HvAACT1 (both the 1023-bp insertion and the first 1 kb of the MRL insertion). While this finding is consistent with the expression of these genes in the root apices (which contain meristematic tissue) additional promoter studies are required to determine whether any of these putative cis elements are indeed affecting gene expression. Enhanced Al resistance through sequence duplication: a role for TEs? Repeated blocks of sequence can be generated by TEs and some of these have resulted in gene duplications that have beneficial phenotypes. For instance, a Copia-like retrotransposon named Rider contributed to the domestication of tomato (Solanum lycopersicum L.) by altering fruit shape. The Rider TE caused this change by inducing a 24.7 kb duplication that increased expression of the IQD12 gene encoding an IQ67 domain-containing protein (Xiao et al., 2008). The duplication arose when the TE failed to stop reverse-transcribing its own 3′ LTR and continued to produce a ~25-kb fragment that included the gene (Xiao et al., 2008; Flagel and Wendel, 2009). Another example is the R-r complex that controls anthocyanin production. The R-r locus in maize contains a series of homologous repeats and the CACTA-like TEs present at the breakpoints thus implicated these TEs in the chromosomal rearrangements (Walker et al., 1995). Finally, Helitrons are another group of TEs that can duplicate sequences. These elements encode proteins with the rolling-circle replication initiator domain and a DNA helicase domain, which are required for transposition. Helitrons have been an important source of genetic diversity in maize (Morgante et al., 2005) and all other investigated angiosperms. These types of mutations have also been implicated in the adaptation of plants to acid soils as illustrated by the following examples. Sequence duplications have been implicated in the mechanisms of Al resistance in maize and wheat. Al resistance in maize is a polygenic trait, but a major QTL on chromosome 6 contains two MATE genes, ZmMATE1 and ZmMATE2 (Maron et al., 2010). ZmMATE1 encodes a citrate transporter whose expression was induced by Al in resistant lines more than in sensitive lines. This difference was not associated with polymorphisms in the gene or in cis-regulatory regions (Maron et al., 2013). Instead, variation in gene expression was linked with the number of copies of ZmMATE1 such that Al-sensitive lines (e.g. L53) have a single copy and Al-resistant lines (e.g. Al237) have three copies (Maron et al., 2013). Each ZmMATE1 copy is flanked by different TEs that are highly conserved between the copies (Maron et al., 2013) except for ZmMATE1-2, which has a Gypsy-type retrotransposon (Class I) in its upstream region. It is possible the TEs flanking ZmMATE1 were involved in the duplication events in the resistant line which then enhanced ZmMATE1 expression. TaALMT1 is the major gene controlling Al resistance in wheat and the higher constitutive expression of TaALMT1 in resistant lines is caused by tandemly repeated blocks of sequence (31–803 bp long) upstream of the coding region (Sasaki et al., 2006; Raman et al., 2008). These repeats occur in different arrangements but the number of repeats is generally correlated with the level of gene expression and malate efflux. Furthermore, promoter studies in transgenic plants have demonstrated that these tandem repeats do increase transcriptional activity (Ryan et al., 2010). While the origins of these repeats in wheat are unknown, it has been previously proposed that the rolling-circle DNA replication machinery used by Helitrons could generate tandem repeats of this kind (Delhaize et al., 2007). The repeats could result from the inaccurate repair of damaged double-stranded DNA (Vaughn and Bennetzen, 2014) and from inaccurate DNA replication due to simply repeated sequences. Not all transposons associated with OA transporters increase Al resistance The cases highlighted previously illustrate the beneficial role of TEs to Al resistance, but not all TE insertions will have this affect. In tomato, for example, the SlALMT9 gene encodes a tonoplast-localized ALMT transporter that contributes to malate content in the fruit and resistance to Al stress (Ye et al., 2017). A LTR transposon (superfamily Copia) was detected in the second intron of SlALMT9, but Al resistance is not controlled by this insertion. Instead, those phenotypes were linked to a 3-bp deletion in the promoter that disrupted the binding site of a WRKY transcription repressor (Ye et al., 2017). This is an example of a TE inserted in the non-coding region of an OA transporter that has no apparent effect on the Al-resistance phenotype. In rye, Collins et al. (2008) showed that the copy number and expression level of the ScALMT1 gene on chromosome 7RS differed in Al-resistant and -sensitive genotypes. The Al-resistant haplotype (M39A-1–6) had five copies of ScALMT1 of which two were induced by Al treatment, whereas the Al-sensitive haplotype (M77A-1) had two copies of ScALMT1 of which only one was induced by Al (Collins et al., 2008). The sequence obtained of the ScALMT1 transcripts revealed that the Al-sensitive haplotype had a much greater proportion of splice variants, which may be caused by a 400-bp insertion in intron 2 harboring a 227-bp MITE (Collins et al., 2008). Therefore, not only does the Al-sensitive rye have fewer copies of ScALMT1, but a TE may cause a further reduction in functional transcripts. The MITE insertion in ScALMT1 is a possible example of a TE negatively affecting Al resistance. These examples suggest that the positive effect of TEs on Al resistance mainly come from their insertions into regions upstream of OA transporter genes rather than the transcribed regions. TE-dependent improvements to Al resistance are evolutionarily recent events The TE-dependent mutations associated with Al resistance are relatively recent events in the evolution of those species and may have facilitated their wider expansion into regions with acid soils. The mutations near HvAACT1 in barley, OsFRDL4 in rice, TaALMT1 and TaMATE1B in wheat, and SbMATE1 in sorghum, as well as the multiple copies of ZmMATE1 in maize, all occur rarely in those populations and most have not been detected in their progenitor species. So why should these genetic changes be recent events when Al has been prevalent in the earth’s crust for a large part of the planet’s history? One explanation is that the TE-dependent mutations may have occurred in a different ‘aluminium environment’ from the one during which higher lifeforms began evolving. Exley (2009) proposed that the concentration of ‘biologically reactive’ Al has increased through evolutionary time as the levels of silicic acid decreased. Silicic acid decreases the biological availability of Al by forming hydroxyaluminosilicates. As silicic acid levels fell, plants were gradually exposed to greater levels of Al stress (Exley, 2009). In this new ‘aluminium environment’ any TE-dependent mutations that increased the resistance of plants to biologically reactive Al would be selected for. This is discussed further below. Al resistance in barley is likely to have had a single origin during its expansion from the Near East and Fertile Crescent to the Far East where acid soils are more common (Fujii et al., 2012). The 1023-bp TE insertion upstream of HvAACT1 has only been detected in resistant genotypes from China, Japan, and Korea, but not in any of the wild barley or progenitor species (Fujii et al., 2012; Ferreira et al., 2018). Novel alleles do occur near the same insertion site in a Chinese six-rowed barley, which suggests that the locus is prone to genetic rearrangements (Ma et al., 2016). Tibetan wild barley (Hordeum spontaneum) diverged early from the Near East genotypes and could represent an additional centre of diversity and domestication (Wang et al., 2015). When barley cultivation expanded towards Europe, the second TE insertion, named MRL, appears to have contributed to acid soil tolerance (Kashino-Fujii et al., 2018). The MRL insertion is also rare since it was detected in only two out of 289 wild barley accessions screened and in 26 out of 274 cultivated barley genotypes. Interestingly, wild barley accessions with the MRL insertion do not have the same elevated levels of HvAACT1 expression that the modern cultivars show. The reason for this was that the promoter of HvAACT1 was heavy methylated in the wild barley accessions. As stated previously, DNA methylation plays an important role in reducing TE activity (Bewick et al., 2016). Consequently, for the MRL sequence to enhance HvAACT1 expression and improve Al resistance in cultivated barley material, the promoter had to be demethylated at some stage. Kashino-Fujii et al. (2018) argued that demethylation likely occurred during the early domestication of barley when natural hybridizations transferred the MRL sequence from the wild barley to domesticated barley accessions. In rice, the solo LTR found in the promoter of OsFRDL4 is more prevalent in japonica than in indica varieties but absent in six wild species examined (Oryza rufipogon, O. barthii, O. glumaepatula, O. meridionalis, O. australiensis, and O. punctata). This led Yokosho et al. (2016) to conclude that the insertion event happened at the initial stage of domestication of the japonica subspecies. A similar pattern exists for maize. In a diversity panel of almost 200 maize inbreds, founders and teosinte, only two inbred lines possessed multiple copies of ZmMATE1, indicating that it is a rare allele (Maron et al., 2013). Both of those lines were resistant to Al stress and shared similar geographic origins in tropical South American where acid soils are prevalent. The tandem repeats described upstream of the TaALMT1 gene in wheat could not be detected in 29 accessions of Aegilops tauschii, the D genome donor of hexaploid wheat. This also suggests that those mutations occurred after hybridization of the diploid and tetraploid ancestors (Ryan et al., 2010). Similarly, wheat genotypes with the Sukkula-like TE upstream of TaMATE1B have only been detected in a few older Portuguese landraces and cultivars from Brazil (Tovkach et al., 2013; Garcia-Oliveira et al., 2014; Pereira et al., 2015; Aguilera et al., 2016). It is proposed that the Portuguese immigrants introduced these varieties to Brazil and the selection pressure posed by the highly Al-toxic soils in Brazil increased the frequency of the TaMATE1B allele with the TE insertion (Aguilera et al., 2016). In sorghum, the Al-resistant alleles of SbMATE in the AltSB locus are rare and non-randomly distributed across the range of species diversity (Caniato et al., 2011). A large analysis of the haplotype network indicated that Al resistance likely had a single and recent origin (Caniato et al., 2014). Resistance was most prevalent in guinea-type accessions and material from West and South/East Africa (Caniato et al., 2011). The mutations to SbMATE associated with Al resistance possibly appeared after the initial migration from the origin of domestication and after the guinea race differentiated from the progenitor bicolor types (Caniato et al., 2011). Concluding remarks TE activity can generate genetic changes in all living organisms containing these elements. Occasionally the TE-induced mutations result in beneficial changes that will facilitate adaptation to biotic and abiotic stresses. The first suggestions that TE-induced mutations contributed to Al resistance in plants were reported in wheat and sorghum. Similar roles have now been proposed in many other species. Most of these TEs are inserted upstream of OA transporter genes where they increase gene expression in the root apices. This increases organic anion release from those cells and protects the growing tissues from Al toxicity. TEs can also enhance expression by duplicating sequences or increasing gene copy number. However, not all TE insertions are beneficial. Those that reduce the expression of a key gene, affect splicing patterns, or alter protein function can decrease tolerance to acid soils. These might be under-represented in any germplasm analysis because the greater sensitivity to Al would often be selected against in both wild and domesticated accessions. TEs are an important factor in cereal domestication. Their activity near OA transporter genes appears to have had a major impact on the evolution of Al resistance in acid soils in many major crops species. These changes likely facilitated their selection and wider distribution to regions with acidic soils. There remains much to learn about the impact of TEs on plant adaptation to stress. Further characterization of the sequences flanking ALMT and MATE genes, together with expression studies and analysis of cis-acting elements, will provide further insights into the genetics of this important trait. Supplementary data Supplementary data are available at JXB online. Table S1. Putative cis elements detected at the 1023-bp insertion (CACTA-like transposon) upstream of HvAACT1 that may be associated with altered gene expression in comparison with the allele not containing the insertion. Table S2. Putative cis elements detected at the first 1 kb of the MRL insertion upstream of HvAACT1 (bases −6601 to 7600 upstream of the first ATG) that may be associated with altered gene expression in comparison with the allele not containing the insertion. Table S3. Putative cis elements detected at the first 2.3 kb upstream of TaMATE1B (containing the Sukkula-like transposon insertion) that may be associated with altered gene expression in comparison with the allele not containing the insertion. Table S4. Putative cis elements detected at one MITE insertion upstream of SbMATE that may be associated with altered gene expression in comparison to the allele not containing the insertion. Table S5. Putative cis elements detected at the 1.2 kb solo LTR upstream of OsFRDL4 that may be associated with altered gene expression in comparison with the allele not containing the insertion. Fig. S1. Putative cis-acting elements of ART1/STOP1 and WRKY22 manually detected in the transposon insertions upstream of HvAACT1, TaMATE1B, SbMATE, and OsFRDL4. Abbreviations: Abbreviations: ALMT aluminum-activated malate transporter LTR long terminal repeat MATE multidrug and toxic compound extrusion MITE miniature inverted-repeat transposable element MRL multi-retrotransposon-like OA organic anion TE transposable element TSS transcription start site Acknowledgements We thank Emmanuel Delhaize, Ming-Bo Wang (CSIRO Agriculture and Food) and Jeff Bennetzen (University of Georgia) for valuable comments and discussions. Author contributions JFP performed the in silico analysis and illustrated the artwork. JFP and PRR contributed to the discussion and wrote the manuscript. References Aguilera JG , Minozzo JAD , Barichello D , Fogaça CM , da Silva JP Jr , Consoli L , Pereira JF . 2016 . Alleles of organic acid transporter genes are highly correlated with wheat resistance to acidic soil in field conditions . Theoretical and Applied Genetics 129 , 1317 – 1331 . 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Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: [email protected] 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)

Koch, Marcus, A

doi: 10.1093/jxb/ery340pmid: 30260404

Abstract Arabidopsis thaliana is one of the prevailing plant model systems used for most facets of plant molecular, cell, and evolutionary biology. There are good reasons for that, such as easy cultivation, short generation time, a selfing reproductive system, and a wide geographic distribution with easy access to respective germplasm material. For the last two decades, the entire genus Arabidopsis with its various species has emerged as a model system allowing the study of additional traits and characters not found in A. thaliana. These species grow naturally under very different environmental conditions and mostly underwent independent evolutionary histories. Accordingly, consideration of their respective biogeographic and phylogenetic (taxonomic) context is important for any study aiming to explore fully the potential of comparative studies. Here, we review current understanding of spatio-temporal patterns of Arabidopsis evolutionary history and introduce the various taxa in more detail, including those traits well studied by the scientific community. The significance of polyploidization and interspecies gene flow is also discussed. Arabidopsis, biogeography, evolutionary history, phylogeny, speciation, taxonomy, trait evolution Introduction The taxonomy of Arabidopsis as an emerging study and model system The genus Arabidopsis, and in particular Arabidopsis thaliana (Brassicaceae), is among the few prevailing plant model species for studying fundamental biological processes from molecule to population level (Weigel, 2012; Provart et al., 2016). An excellent historical overview of how A. thaliana found its way into thousands of labs of plant molecular biologists has been provided by Koornneef and Meinke (2010). Its career as a model system started with Laibach’s work using the species as a genetic system (Laibach, 1943), followed by renewed interest in a ‘renaissance period’ in the late 1970s and 1980s (reviewed in Meyerowitz, 2001; Koornneef and Meinke, 2010), and accumulated into >55 000 indexed papers having Arabidopsis in the title, abstract, or keywords (Provart et al., 2016). During most of this time, systematics and the evolutionary history of the genus Arabidopsis were not an issue and they were not much taken into account until recently. Consequently, there is a parallel and often confusing taxonomic history regarding species and genus delimitations. Here, a benchmark was set upon entering the new millennium when a comprehensive new circumscription of the entire genus was introduced (reviewed in Koch et al., 2008). This had some serious consequences for >60 Arabidopsis species (excluding A. thaliana) recognized at that time, most of which have been transferred to very different genera (Al-Shehbaz et al., 1999). On the other hand, the newly circumscribed genus Arabidopsis gained species because species shown by molecular markers to be the closest relatives of A. thaliana (O’Kane and Al-Shehbaz, 2003; Koch et al., 2008) were incorporated. This taxonomic history—changes of names on the genus and species level following distinct rules provided by the ‘Code of nomenclature’ (Turland et al., 2018)—is recapitulated here in in some detail, and it should be kept in mind that previous work published under species names such as Arabidopsis pumila, Arabidopsis griffithiana, or Arabidopsis wallichii (to name just a few) is in fact focused on species that are only distantly related to A. thaliana, and belong to different genera such as Olimarabidopsis or Crucihimalya (Kiefer et al., 2014; Koch et al., 2018). The confusion that can arise from assuming close relatedness based on genus names alone emphasizes the fact that reliable taxonomy and systematics are key to the interpretation of biological, especially evolutionary, information (Koch and German, 2013; Koch et al., 2018). The taxonomic confusion surrounding A. thaliana started soon after 1872. Many species were transferred into a genus Arabidopsis based on a few simple morphological characters (latiseptate siliques terete to quadrangular in cross section; and branched trichomes) and, therefore, some 60 species were recognized in Arabidopsis (German and Ebel, 2005). With the pioneering work of O’Kane and Al-Shehbaz (1997) and Al-Shehbaz et al. (1999), this concept was completely changed and nine ‘new’ Arabidopsis species with several subspecies, mostly from the former genus Cardaminopsis, were recognized together with A. thaliana. This taxonomic revision was based on initial phylogenetic analysis using the nuclear-encoded internal transcribed spacer of rDNA (O’Kane et al., 1996) and later confirmed with a broader taxon sampling (O’Kane and Al-Shehbaz, 2003). It has to be noted that earlier, Hedge (1968) proposed a closer relationship between Arabidopsis and Arabis, while recognizing that the two genera differ only in the position of the cotyledons relative to the radicle in the seeds. Meanwhile we know from a series of phylogenetic studies that Arabis is also not monophyletic, and it was at that time that species from the whole Brassicaceae family (Karl and Koch, 2013) were resampled. It is curious to see that Hedge (1968) claimed that the Himalayan species A. wallichii (now Crucihimalya wallichii) is essentially an intermediate between the two genera. However, as molecular data were not available, Hedge and others could not have been aware that, in fact, the genus Arabis at that time was comprised of various unrelated evolutionary lineages (see Koch et al., 1999, 2000, 2001; Bailey et al., 2006). Nowadays the historical concept of a monophyletic genus Arabis no longer exists (Karl and Koch, 2013). Because taxa from both genera, Arabidopsis and Arabis, share this taxonomic history, they frequently exhibit related taxonomic nomenclatural problems with numerous misleading phylogenetic implications. In a review entitled ‘Arabidopsis and its poorly known relatives’, it is >10 years ago that the closest relatives of A. thaliana were introduced in more detail (Clauss and Koch, 2006),with some particular focus on Arabidopsis lyrata, with >600 papers published using this species between 2000 and 2018, and >1350 papers published between 2000 and 2018 dealing with any of the ‘true wild relatives’ (Web of Science, accessed 7 July 2018). The new concept of the genus Arabidopsis was presented 15 years ago (O’Kane and Al-Shehbaz, 1997, 2003) paralleled by a contribution from Koch et al. (1999) in which some taxonomical problems including both Arabis and Arabidopsis have been unravelled. Some species and subspecies were added later, either because of a transfer of taxa previously never associated with Arabidopsis (Warwick et al., 2006) or because of the description of a new species (Kadota, 2007). Changes since 1997 were mostly due to changing the rank of taxa as subspecies or recognizing synonyms (Dorofeyev, 2002; Marhold et al., 2003; Shimizu et al., 2005; Kolník and Marhold, 2006; Kadota, 2007; Elven and Murray, 2008). As a result, depending on the applied species concept, Arabidopsis can be considered as a genus comprising at least nine species and six subspecies (O’Kane and Al-Shehbaz, 1997); additionally, 15 species and 16 subspecies are presented with the latest and most up-to-date taxonomic status report (e.g. summarized in Koch et al., 2008; Hohmann et al., 2014) (Table 1). Table 1. The genus Arabidopsis and its various taxa Arabidopsis thaliana L. 2x Native range almost all Europe to Central Asia Arabidopsis arenosa group A. arenosa (L.) Lawalréea (2x) 4x subsp. borbasii (Zapał.) O’Kane & Al-Shehbaz Mountain ranges in Central and Western Europe subsp. arenosa Central and Western Europe, Scandinavia at lower altitudes A. carpatica nom. prov. 2x Carpathian Mountains in mountain ranges on calcareous bedrock A. neglecta (Schult.) O’Kane & Al-Shehbaz Carpathian Mountains subsp. robusta nom. prov. 4x Alpine regions and only occasionally at lower altitudes subsp. neglecta nom. prov. 2x Alpine regions on siliceous bedrock types A. nitida nom. prov. 2x Carpathian Mountains A. petrogena (A. Kern.) V.I. Dorof. Carpathian Mountains subsp. petrogena nom. prov. 2x Carpathians and Pannonian lowland subsp. exoleta nom. prov. 4x Carpathians at lower altitudes Arabidopsis halleri group 2x A. halleri (L.) O’Kane & Al-Shehbaz subsp. dacica (Heuff.) Kolník Carpathians, Romania subsp. gemmifera (Matsum.) O’Kane & Al-Shehbaz Russia Far East, NE China, Korea, Japan, and Taiwan subsp. halleri Europe subsp. ovirensis (Wulfen) O’Kane & Al-Shehbaz Austria (taxon and its distribution is unclear) subsp. tatrica (Pawł.) Kolník Tatra mountains, Slovakia A. umezawana Kadota Japan, Hokkaido (alpine zone of Mt. Rishirizin) Arabidopsis lyrata group A. lyrata (L.) O’Kane & Al-Shehbaz subsp. lyrata 2x (4x) Alaska, Canada, USA subsp. petraea (L.) O’Kane & Al-Shehbaz 2x Europe subsp. umbrosa nom. prov. 2x Arctic NE Asia, Siberia, Alaska, Canada subsp. septentrionalis nom. prov. 4x Arctic NE Europe, European Russia to Siberia (may be admixed populations between subsp. lyrata and umbrosa) A. arenicola (Richardson) Al-Shehbaz et al. 2x Arctic Canada and Greenland Endemic diploid taxa A. croatica (Schott) O’Kane & Al-Shehbaz 2x Bosnia, Croatia A. cebennensis (DC.) O′Kane & Al-Shehbaz 2x SE France A. pedemontana (Boiss.) O’Kane & Al-Shehbaz 2x NW Italy Allopolyploid taxa A. suecica (Fr.) Norrl. 4x Fennoscandinavia and the Baltic region A. kamchatica (DC.) K. Shimizu & Kudoh subsp. kamchatica 4x Boreal Alaska, Canada, E Siberia, Russian Far East, Korea, Japan, Taiwan A. kamchatica subsp. kawasakiana (Makino) K. Shimizu & Kudoh 4x Japan, winter annual (coastal, lowland) Arabidopsis thaliana L. 2x Native range almost all Europe to Central Asia Arabidopsis arenosa group A. arenosa (L.) Lawalréea (2x) 4x subsp. borbasii (Zapał.) O’Kane & Al-Shehbaz Mountain ranges in Central and Western Europe subsp. arenosa Central and Western Europe, Scandinavia at lower altitudes A. carpatica nom. prov. 2x Carpathian Mountains in mountain ranges on calcareous bedrock A. neglecta (Schult.) O’Kane & Al-Shehbaz Carpathian Mountains subsp. robusta nom. prov. 4x Alpine regions and only occasionally at lower altitudes subsp. neglecta nom. prov. 2x Alpine regions on siliceous bedrock types A. nitida nom. prov. 2x Carpathian Mountains A. petrogena (A. Kern.) V.I. Dorof. Carpathian Mountains subsp. petrogena nom. prov. 2x Carpathians and Pannonian lowland subsp. exoleta nom. prov. 4x Carpathians at lower altitudes Arabidopsis halleri group 2x A. halleri (L.) O’Kane & Al-Shehbaz subsp. dacica (Heuff.) Kolník Carpathians, Romania subsp. gemmifera (Matsum.) O’Kane & Al-Shehbaz Russia Far East, NE China, Korea, Japan, and Taiwan subsp. halleri Europe subsp. ovirensis (Wulfen) O’Kane & Al-Shehbaz Austria (taxon and its distribution is unclear) subsp. tatrica (Pawł.) Kolník Tatra mountains, Slovakia A. umezawana Kadota Japan, Hokkaido (alpine zone of Mt. Rishirizin) Arabidopsis lyrata group A. lyrata (L.) O’Kane & Al-Shehbaz subsp. lyrata 2x (4x) Alaska, Canada, USA subsp. petraea (L.) O’Kane & Al-Shehbaz 2x Europe subsp. umbrosa nom. prov. 2x Arctic NE Asia, Siberia, Alaska, Canada subsp. septentrionalis nom. prov. 4x Arctic NE Europe, European Russia to Siberia (may be admixed populations between subsp. lyrata and umbrosa) A. arenicola (Richardson) Al-Shehbaz et al. 2x Arctic Canada and Greenland Endemic diploid taxa A. croatica (Schott) O’Kane & Al-Shehbaz 2x Bosnia, Croatia A. cebennensis (DC.) O′Kane & Al-Shehbaz 2x SE France A. pedemontana (Boiss.) O’Kane & Al-Shehbaz 2x NW Italy Allopolyploid taxa A. suecica (Fr.) Norrl. 4x Fennoscandinavia and the Baltic region A. kamchatica (DC.) K. Shimizu & Kudoh subsp. kamchatica 4x Boreal Alaska, Canada, E Siberia, Russian Far East, Korea, Japan, Taiwan A. kamchatica subsp. kawasakiana (Makino) K. Shimizu & Kudoh 4x Japan, winter annual (coastal, lowland) Species-level taxonomy refers to Al-Shehbaz et al. (1999), Koch et al. (2008), and Hohmann et al. (2014). The most recent treatment of subspecies within A. halleri has been proposed by Kolnik and Marhold (2006). Arabidopsis arenicola has been introduced by Warwick et al. (2006) on the species level. Ploidal levels (diploid=2x, tetraploid=4x) are summarized from Hohmann et al. (2014) and Kolár et al. (2016b). Base chromosome number is n=8 (2n=2x=16), with A. suecica (2n=4x=26) and A. thaliana (2n=2x=10) as the only exceptions. a There is another variety still treated under the old genus name Cardaminopsis: C. arenosa var. intermedia (Neilr.) Hayek from high alpine regions in the south-eastern Austrian Alps (Seckauer Tauern). View Large Table 1. The genus Arabidopsis and its various taxa Arabidopsis thaliana L. 2x Native range almost all Europe to Central Asia Arabidopsis arenosa group A. arenosa (L.) Lawalréea (2x) 4x subsp. borbasii (Zapał.) O’Kane & Al-Shehbaz Mountain ranges in Central and Western Europe subsp. arenosa Central and Western Europe, Scandinavia at lower altitudes A. carpatica nom. prov. 2x Carpathian Mountains in mountain ranges on calcareous bedrock A. neglecta (Schult.) O’Kane & Al-Shehbaz Carpathian Mountains subsp. robusta nom. prov. 4x Alpine regions and only occasionally at lower altitudes subsp. neglecta nom. prov. 2x Alpine regions on siliceous bedrock types A. nitida nom. prov. 2x Carpathian Mountains A. petrogena (A. Kern.) V.I. Dorof. Carpathian Mountains subsp. petrogena nom. prov. 2x Carpathians and Pannonian lowland subsp. exoleta nom. prov. 4x Carpathians at lower altitudes Arabidopsis halleri group 2x A. halleri (L.) O’Kane & Al-Shehbaz subsp. dacica (Heuff.) Kolník Carpathians, Romania subsp. gemmifera (Matsum.) O’Kane & Al-Shehbaz Russia Far East, NE China, Korea, Japan, and Taiwan subsp. halleri Europe subsp. ovirensis (Wulfen) O’Kane & Al-Shehbaz Austria (taxon and its distribution is unclear) subsp. tatrica (Pawł.) Kolník Tatra mountains, Slovakia A. umezawana Kadota Japan, Hokkaido (alpine zone of Mt. Rishirizin) Arabidopsis lyrata group A. lyrata (L.) O’Kane & Al-Shehbaz subsp. lyrata 2x (4x) Alaska, Canada, USA subsp. petraea (L.) O’Kane & Al-Shehbaz 2x Europe subsp. umbrosa nom. prov. 2x Arctic NE Asia, Siberia, Alaska, Canada subsp. septentrionalis nom. prov. 4x Arctic NE Europe, European Russia to Siberia (may be admixed populations between subsp. lyrata and umbrosa) A. arenicola (Richardson) Al-Shehbaz et al. 2x Arctic Canada and Greenland Endemic diploid taxa A. croatica (Schott) O’Kane & Al-Shehbaz 2x Bosnia, Croatia A. cebennensis (DC.) O′Kane & Al-Shehbaz 2x SE France A. pedemontana (Boiss.) O’Kane & Al-Shehbaz 2x NW Italy Allopolyploid taxa A. suecica (Fr.) Norrl. 4x Fennoscandinavia and the Baltic region A. kamchatica (DC.) K. Shimizu & Kudoh subsp. kamchatica 4x Boreal Alaska, Canada, E Siberia, Russian Far East, Korea, Japan, Taiwan A. kamchatica subsp. kawasakiana (Makino) K. Shimizu & Kudoh 4x Japan, winter annual (coastal, lowland) Arabidopsis thaliana L. 2x Native range almost all Europe to Central Asia Arabidopsis arenosa group A. arenosa (L.) Lawalréea (2x) 4x subsp. borbasii (Zapał.) O’Kane & Al-Shehbaz Mountain ranges in Central and Western Europe subsp. arenosa Central and Western Europe, Scandinavia at lower altitudes A. carpatica nom. prov. 2x Carpathian Mountains in mountain ranges on calcareous bedrock A. neglecta (Schult.) O’Kane & Al-Shehbaz Carpathian Mountains subsp. robusta nom. prov. 4x Alpine regions and only occasionally at lower altitudes subsp. neglecta nom. prov. 2x Alpine regions on siliceous bedrock types A. nitida nom. prov. 2x Carpathian Mountains A. petrogena (A. Kern.) V.I. Dorof. Carpathian Mountains subsp. petrogena nom. prov. 2x Carpathians and Pannonian lowland subsp. exoleta nom. prov. 4x Carpathians at lower altitudes Arabidopsis halleri group 2x A. halleri (L.) O’Kane & Al-Shehbaz subsp. dacica (Heuff.) Kolník Carpathians, Romania subsp. gemmifera (Matsum.) O’Kane & Al-Shehbaz Russia Far East, NE China, Korea, Japan, and Taiwan subsp. halleri Europe subsp. ovirensis (Wulfen) O’Kane & Al-Shehbaz Austria (taxon and its distribution is unclear) subsp. tatrica (Pawł.) Kolník Tatra mountains, Slovakia A. umezawana Kadota Japan, Hokkaido (alpine zone of Mt. Rishirizin) Arabidopsis lyrata group A. lyrata (L.) O’Kane & Al-Shehbaz subsp. lyrata 2x (4x) Alaska, Canada, USA subsp. petraea (L.) O’Kane & Al-Shehbaz 2x Europe subsp. umbrosa nom. prov. 2x Arctic NE Asia, Siberia, Alaska, Canada subsp. septentrionalis nom. prov. 4x Arctic NE Europe, European Russia to Siberia (may be admixed populations between subsp. lyrata and umbrosa) A. arenicola (Richardson) Al-Shehbaz et al. 2x Arctic Canada and Greenland Endemic diploid taxa A. croatica (Schott) O’Kane & Al-Shehbaz 2x Bosnia, Croatia A. cebennensis (DC.) O′Kane & Al-Shehbaz 2x SE France A. pedemontana (Boiss.) O’Kane & Al-Shehbaz 2x NW Italy Allopolyploid taxa A. suecica (Fr.) Norrl. 4x Fennoscandinavia and the Baltic region A. kamchatica (DC.) K. Shimizu & Kudoh subsp. kamchatica 4x Boreal Alaska, Canada, E Siberia, Russian Far East, Korea, Japan, Taiwan A. kamchatica subsp. kawasakiana (Makino) K. Shimizu & Kudoh 4x Japan, winter annual (coastal, lowland) Species-level taxonomy refers to Al-Shehbaz et al. (1999), Koch et al. (2008), and Hohmann et al. (2014). The most recent treatment of subspecies within A. halleri has been proposed by Kolnik and Marhold (2006). Arabidopsis arenicola has been introduced by Warwick et al. (2006) on the species level. Ploidal levels (diploid=2x, tetraploid=4x) are summarized from Hohmann et al. (2014) and Kolár et al. (2016b). Base chromosome number is n=8 (2n=2x=16), with A. suecica (2n=4x=26) and A. thaliana (2n=2x=10) as the only exceptions. a There is another variety still treated under the old genus name Cardaminopsis: C. arenosa var. intermedia (Neilr.) Hayek from high alpine regions in the south-eastern Austrian Alps (Seckauer Tauern). View Large An introduction to the biological system The phylogenetic context The genus Arabidopsis belongs to tribe Camelineae, and among its closest sister groups is the genus Capsella. The name is derived from Arabis (a genus of Brassicaceae, from which it cannot be distinguished by any unique character) and Greek opsis, in reference to this similarity. Members are mostly perennial and rarely annual or biennial. Multicellular glands are absent and trichomes are simple and mixed with stalked 1–3(or 4)-forked trichomes. Petals are white, pink, or purple, and nectar glands are confluent and subtending bases of all stamens. Fruits are dehiscent and formed as capsular, linear siliques. Other closely related genera are Neslia and Camelina. All three of them, Capsella, Neslia, and Camelina, are characterized by silicules. Recent phylogenetic data indicated incongruencies among gene trees comparing plastome and nuclear genome data and questioning the monophyly of tribe Camelineae and the sister relationship of Arabidopsis to a clade consisting of Neslia–Camelina–Capsella (Hohmann et al., 2015; Huang et al., 2016). However, Beilstein and co-workers have shown that the Arabidopsis versus Neslia–Camelina–Capsella relationship is the most likely sister relationship scenario, and this might be masked because of ancient gene flow among lineages pre-dating most of the species considered (Forsythe et al., 2017, Preprint). In our current circumscription, tribe Camelineae is species poor (~50 species) (Koch et al., 2018), comprised of nearly 50% of neopolyploids, and its average 1 Cx (monoploid) genome size is ~0.36 pg (equals 300 Mbp, compared with 0.16 pg in A. thaliana representing ~135 Mbp) (Hohmann et al., 2015). The base chromosome number of Arabidopsis is n=8, with A. thaliana showing a derived situation with n=5 chromosomes (Table 1). Within the genus Arabidopsis, phylogenetic analyses have recognized seven major lineages, with all of them also comprising diploid taxa: Arabidopsis halleri lineage, Arabidopsis lyrata lineage, Arabidopsis arenosa lineage, Arabidopsis pedemontana, Arabidopsis cebennensis, Arabidopsis croatica, and A. thaliana (Koch and Matschinger, 2007; Hohmann et al., 2014; Novikova et al., 2016) (Fig. 1, Table 1). Although substantial ancient [million(s) of years ago] and contemporary gene flow (past and interglacial) has been demonstrated (e.g. Jørgensen et al., 2011; Arnold et al., 2015; Novikova et al., 2016; Hohmann and Koch, 2017), these major lineages are well defined, genetically, ecologically, and taxonomically. Also it becomes evident that this source of natural variation including A. thaliana itself may serve as an excellent system to study evolutionary processes and ecological strategies within an environmental context (reviewed for A. thaliana by Takou et al., 2018). Fig. 1. View largeDownload slide Schematic phylogenetic relationships among lineages of relatives of Arabidopsis thaliana. Arabidopsis thaliana split from this entire group during Early Pliocene/Late Miocene ~5.8 million years ago (mya). Divergence times are summarized from Hohmann et al. (2015), Arnold et al. (2015), Hohmann (2016), and Hohmann and Koch (2017). Fig. 1. View largeDownload slide Schematic phylogenetic relationships among lineages of relatives of Arabidopsis thaliana. Arabidopsis thaliana split from this entire group during Early Pliocene/Late Miocene ~5.8 million years ago (mya). Divergence times are summarized from Hohmann et al. (2015), Arnold et al. (2015), Hohmann (2016), and Hohmann and Koch (2017). Species and subspecies in Arabidopsis are taxonomic units and are defined by morphological descriptions including information on cytology, ecology, or distribution. Accordingly, any validly described taxon—which refers to species but also to subspecies and varieties—following the rules of botanical nomenclature can serve as accepted taxon. As a consequence, there is no general agreement or taxonomic concept when a subspecies should be considered on species rank or vice versa. Obviously, this needs to be kept in mind for the genus Arabidopsis, too. In some lineages, such as A. halleri, taxonomic subgrouping has mostly been introduced on a subspecies level, but in A. arenosa taxomomic grouping of the entire complex was done with preference for a species-level concept. However, most important is that the taxa are defined, on either a species or a subspecies level, with unambiguous names linked with all information on the respective taxon (Koch et al., 2018) Arabidopsis halleri: metals and soils Arabidopsis halleri is a perennial, outcrossing, diploid, creeping species and its various subspecies are widely distributed in Europe and Eastern Asia. Subspecies definition is often based on morphology, ecology, and distribution, and a discussion of initial evidence for correlation with genetic data is summarized below. A first draft genome was recently published (Briskine et al., 2017). This species grows on metal-rich soils and exhibits high accumulation of zinc, cadmium, and lead. The species is one among few which are heavy metal hypertolerant (Ernst, 1974) and are able to colonize metalliferous soils. Furthermore, the species also shows the rare trait of metal hyperaccumulation (e.g. Hanikenne et al., 2008, and references therein), and it has been shown that the complex interactions of plants with contaminated soils are not only environment dependent but also geographically structured (Stein et al., 2017). However, the entire distribution range in montane to alpine environments is also characterized by non-metalliferous soils, and large-scale phylogeographic analysis and ecological niche modelling indicate complex post-glacial recolonization scenarios (Wasowicz et al., 2016). Since metallicolous populations have been founded separately all over the distribution range, this trait seems to have arisen multiple times from distinct populations that do not exhibit that trait (Pauwels et al., 2005). Among the other Arabidopsis species, initial work on bedrock adaptation has been done on North American A. lyrata from serpentine soils (Turner et al., 2008, 2010), or A. arenosa from serpentine stands in Eastern Austria (Arnold et al., 2016). Within A. halleri, up to five subspecies have been recognized (for details, refer to Koch et al., 2008) with two of them, A. halleri subsp. gemmifera and A. halleri subsp. ovirensis, accepted by some authors as separate species, namely A. gemmifera and A. ovirensis, respectively (Iljinska et al., 2007; Kadota, 2007). In Central Europe three subspecies can be differentiated (Kolnik and Marhold, 2006). The most widely distributed taxon is A. halleri subsp. halleri found from Poland to Serbia and from France to Western Ukraine. The second taxon, A.halleri subsp. tatrica, is a Western Carpathian endemic species. In contrast to these two subspecies, which are substrate indifferent, the third subspecies, A. halleri subsp. dacica, from the Eastern and Southern Carpathians is an alpine and rarely montane species from acidic bedrock types. The Eastern Asian subspecies A. halleri subsp. gemmifera is geographically and genetically fully isolated (Koch et al., 2008); and A. halleri subsp. ovirensis described as endemic to the East Austrian high mountain range at Mount Obir, in Carinthia, might not represent a genetically defined taxon but rather an isolated population from within the A. halleri subsp. halleri gene pool (Koch and Matschinger, 2007; Koch et al., 2008). There are some first phylogeographic studies, and it has been shown that one major centre of genetic diversity is located in the Eastern Austrian Alps (Koch and Matschinger, 2007; Pauwels et al., 2012). A comprehensive phylogeographic scenario (Pauwels et al., 2012) also demonstrated high levels of genetic diversity found in the eastern region of the European Alps and was explained convincingly by admixture and secondary contact of different European gene pools. The evolutionary scenario of A. halleri is best placed among Pleistocene glaciation and deglaciation cycles (Koch and Matschinger, 2007; Wasowicz et al, 2016). In a detailed evolutionary study, Roux et al. (2011) suggested the onset of radiation within A. halleri subsp. halleri to be ~335 000 years ago but, because this study does not consider other subspecies, a much deeper evolutionary split is likely, as indicated more recently (Novikova et al., 2016). Generally, the number of genetic–evolutionary studies centred around A. halleri is high (e.g. Van Rossum et al., 2004; Meyer et al., 2009; Heidel et al., 2010), and various ecological studies consider traits such as herbivory (Kawagoe and Kudoh, 2010; Sato and Kudoh, 2017), flowering time (Shimizu et al., 2011), or reproduction (Llaurens et al., 2008). Arabidopsis lyrata: breeding system and evolutionary biology Arabidopsis lyrata is a perennial and mostly outbreeding (self-incombatible) species with five taxa in total. Its close relationship to A. thaliana and a genetically diverse sporophytic self-incompatibility system fostered its early establishment as a model system to study breeding system evolution and its genetic and molecular components (e.g. Nasrallah et al., 2004; Guo et al., 2011; Vekemans et al., 2014). Various aspects of trait evolution in a perennial plant have also been studied intensively, such as thermal stress response (Wos and Willi, 2018), seed biology (Hämälä et al., 2017), or general fitness and survival (Leinonen et al., 2011). More details and earlier ecological work have been summarized by Clauss and Koch (2006). The species is one of the model systems used to study local adaptation in the context of trait evolution. One of those traits is adaptation in response to heavy metal such as nickel, thereby linking this species with A. halleri (e.g. Turner et al., 2008, 2010; Veatch-Blohm et al., 2017); other traits are herbivory response (Vergeer and Kunin, 2011), flowering time (Hämälä et al., 2018), or more generally pathogen-mediated selection (Buckley et al., 2018). Studies of molecular evolution have been fostered by the publication of the A. lyrata genome (Hu et al., 2011) and have focused on various features including transposons (Bonchev and Willi, 2018), transcriptional changes (Wos and Willi, 2018), or accumulation of mutational load (Willi et al., 2018). Research on populations in North America at the Great Lakes shows that some of them are undergoing a loss of a functional self-incompatible (SI) system (Mable et al., 2005), which allows the study of causes and genomic consequences of mating shifts (e.g. Mable et al., 2005, 2017; Griffin and Willi, 2014) and to compare those with largely inbreeding A. thaliana. Artifical interspecific hybrids of A. lyrata with Arabidopsis were created to study the expression pattern of transposable elements (TEs) in the hybrid genomic context and showing that TE expression is globally robust (Göbel et al., 2018). An artificial interspecific hybrid of A. lyrata with A. halleri elucidated the role of cis-acting modifications in cold adaption (He et al., 2016) The rosette-forming plant has a circumpolar distribution. The two main distribution centres are formed by subsp. petraea in Central and Northern Europe and subsp. lyrata in Northern America. This disjunct distribution is mediated by subsp. umbrosa and subsp. septentrionalis (Table 1). The fifth taxon, Arabidopsis arenicola, was originally described as Arabis and later transferred to Arabidopsis as a separate species. Arabidopsis arenicola is an arctic and self-compatible (MAK, unpublished results) species from Canada and Greenland of very recent and probably post-glacial origin from subsp. lyrata (Hohmann et al., 2014). Therefore, it might be best treated in future as another subspecies of A. lyrata. The species complex is diploid with two exceptions: A. lyrata subsp. sepentrionalis probably represents a tetraploid taxon; it might have arisen via secondary contact between the two main subsp. lyrata and subsp. petraea gene pools and may represent an ‘allo’tetraploid (Hohmann et al., 2014). The other exceptions are tetraploid populations from the northeastern limestone Forealps and pannonical dryland regions close to the Danube river in Austria. Those originated from adaptive introgression between two species (A. lyrata and A. arenosa) and are discussed separately below (Schmickl and Koch, 2011; Hohmann and Koch, 2017). Studying six populations, Ross-Ibara et al. (2008) presented an estimate of the split time between the European and North American distribution ranges of ~35 000 years based on demographic models and assuming a mutation rate of 1.5 × 10–8 (following Koch et al., 2000; see also the section on A. arenosa for application of different mutational rates). This was also compared with FST-based comparisons which result instead in an estimate of ~90 000–170 000 years ago. A recent study estimated a split time between subsp. petraea and subsp. lyrata at 190 000–310 000 years ago (Mattila et al., 2017). This much better fits presented comprehensive phylogeographic scenarios covering the entire distribution range. It also considers the amphiberingean region (which means that the range is centred on what was formerly Beringia and that the species occurs on both sides of the Bering Strait) as a melting pot of lineages coming into secondary contact during the last glaciation and deglaciation cycles (e.g. Schmickl et al., 2008, 2010; Hohmann et al., 2014). A phylogeographic study focusing on North American subsp. lyrata was not able to show any clear structure of genetic variation (Tedder et al., 2010), tentatively because of a very recent post-glacial biogeography. Similarly, Hämälä et al. (2018) demonstrated post-glacial evolutionary demographic scenarios in Scandinavia. Central European populations might have diverged from direct ancestors of Scandinavian populations ~28 000 generations ago. This equals ~56 000 years ago considering the life cycle of this plant which flowers latest in its second year. Arabidopsis arenosa: polyploid evolution and interspecies gene flow The A. arenosa complex is definitely the most complex and diverse group of taxa. Although its overall distribution is smaller than that of A. lyrata and A. halleri, the taxa occupy a wide range of habitats from alpine regions to sandy coastal sites and dune vegetation. The species complex consists of short-lived perennials, and the original rosette often dies after flowering. Daughter rosettes are frequently formed. At the moment, five species with various subspecies (Table 1) are recognized. However, in various cases, the respective names are provisional, such as A. nitida. There are tetraploid and diploid taxa, and it was only recently that a single origin of the tetraploids was demonstrated (Arnold et al., 2015). This may be surprising since a large-scale genetic screen using microsatellite variation demonstrated that tetraploids are genetically very diverse and mask the primary centre of diversity of diploids in the Carpathians (Schmickl et al., 2012). One explanation could be that tetraploid A. arenosa and its tetraploid subspecies originated from within large populations carrying a substantial amount of genetic variation. Furthermore, at least some inter- and intraspecies level gene flow might have occurred after polyploidization, even crossing ploidy levels (Jørgensen et al., 2011; Arnold et al., 2015) and thereby contributing substantial genetic variation. Intensive genetic interplay of A. arenosa has also been demonstrated with A. lyrata (Schmickl and Koch, 2011) and A. halleri (Hohmann, 2011; Hohmann et al., 2014) (Fig. 2). Fig. 2. View largeDownload slide Examples for genetic admixture between the three more widely distributed species groups in Central Europe. Genetic admixture is shown (a) for A. lyrata–A. arenosa and (b) for A. halleri–A. arenosa. Genetic admixture data are taken and redrawn as pie charts from earlier published microsatellite data (Hohmann, 2011; Hohmann et al., 2014). (Genetic assignment analysis, number of genetic clusters: K=2 each, using Structure; Falush et al., 2007.) Glacial border from the Last Glacial Maximum (~25 000 years ago) are drawn as red lines based on Gibbard (2002) and Zasadni and Klapyta (2014). Fig. 2. View largeDownload slide Examples for genetic admixture between the three more widely distributed species groups in Central Europe. Genetic admixture is shown (a) for A. lyrata–A. arenosa and (b) for A. halleri–A. arenosa. Genetic admixture data are taken and redrawn as pie charts from earlier published microsatellite data (Hohmann, 2011; Hohmann et al., 2014). (Genetic assignment analysis, number of genetic clusters: K=2 each, using Structure; Falush et al., 2007.) Glacial border from the Last Glacial Maximum (~25 000 years ago) are drawn as red lines based on Gibbard (2002) and Zasadni and Klapyta (2014). Arabidopsis arenosa contributed as paternal hybridization partner to give rise to A. suecica (Koch and Matschinger, 2007; Novikova et al., 2017). Meanwhile A. suecica served as a model for interspecies A. thaliana hybrids (Säll et al., 2003), and studies using artificial hybrids between A. arenosa and Arabidopsis have been used (Nasrallah et al., 2000) to study gene silencing (e.g. Comai et al., 2000) or post-zygotic hybridization barriers (Bushell et al., 2003). More recently, with Yant et al. (2013) and the overview given by Bomblies and Madlung (2014), A. arenosa was introduced as a system to study polyploidization. It also serves as a model system to study the molecular basis and evolution of meiosis in polyploids (e.g. Bomblies et al., 2015; Yant and Bomblies, 2017). This work also hints at interactive effects with important environmental factors such as temperature (Wright et al., 2015; Morgan et al., 2017). Arabidopsis lyrata as well as A. arenosa only rarely occur on metalliferous soils, the exceptions being serpentine (e.g. in Austria), and it has been shown that parallel adaptation of different populations occurred via post-glacial adaptive introgression of genes from populations from the same species but also from A. lyrata (Arnold et al., 2016). This is another example of interspecies and interploidal gene flow since in this area A. arenosa is exclusively tetraploid and A. lyrata is diploid. The species split time between A. lyrata and A. arenosa was calculated as ~400 000 generations, which corresponds to ~800 000 years ago. This is in line with previous assumptions of a Mid- to Late-Pleistocene species split (Hohmann et al., 2014; Novikova et al., 2016). However, plastome data indicate a species divergence, which is younger and of ~550 000 years ago (Hohmann and Koch, 2017). A single origin of all tetraploids in A. arenosa (Arnold et al., 2015) was estimated to ~11 000–33 000 generations ago and, considering phylogenetic relationships among tetraploid lineages, the centre of origin is best placed within the Carpathian Mountains. Geographically this matches the centre of species diversity of the A. arenosa lineage (Koch and Matschinger, 2007) as well as the distribution centre of diploids within the A. arenosa lineage (Kolár et al., 2016b). Extensive interglacial, glacial, or post-glacial range expansion of diploids has not been demonstrated (Kolár et al., 2016a), but a few diploids have migrated post-glacially towards the North into the Baltic. This also matches spatio-temporally with post-glacial hybrid speciation of Fennoscandinavian A. suecica (Novikova et al., 2017). However, it should be mentioned that the age estimate of tetraploid A. arenosa (Arnold et al., 2015) was based on a mutation rate of 3.7 × 10–8. This is five times higher than the rates measured in A. thaliana in laboratory-based experiments (Ossowski et al., 2010); and applying those rates for the origin of tetraploid A. arenosa would result in a five times higher age estimate of ~110 000–330 000 years ago (compared with 11 000–33 000 generations with the 2 year life cycle equalling 22 000–66 000 years). This does correlate well with a spatio-temporal scenario of introgression of tetraploid A. arenosa into A. lyrata in Eastern Austria after the Riss glaciation (130 000 years ago) (Hohmann and Koch, 2017). The diploid and local endemics: A. cebennensis, A. pedemontana, and A. croatica—from the French volcanic Massif Central to the Croatian Karst mountains There are three species with a particularly narrow distribution. All of them are also phylogenetically separated from the other major lineages (Fig. 1). Arabidopsis cebennensis and A. pedemontana represent sister species and have the smallest distribution range in the whole genus (Koch et al., 2008). Arabidopsis cebennensis is restricted to the mountainous regions in Southern France at elevations ranging from 900 m to 1500 m above sea level. The highly disjunct populations occur in the Massif Central, the Cevennes, and the Ardeche regions. The outcrossing and perennial species is the tallest among all Arabidopsis relatives and can reach a size of up to 1 m. It grows in a narrow ecological niche in riverine systems often close to natural springs and characterized by continuously running cold mineral-rich water. Arabidopsis pedemontana is restricted to a range of 50 km2 in the Piedmont region of the north-western Italian Alps, at altitudes ranging from 1300 m to 2200 m above sea level. Also A. pedemontana is presumably self-incombatible, with a strong tendency for vegetative reproduction by clonal growth (Hohmann et al., 2014), but it resembles a growth type more similar to A. halleri (Fig. 1). Both species demonstrate very strong inter-regional genetic differentiation indicating severe genetic bottlenecks due to past glaciations (Jacquemin et al., 2016, Preprint). Drastic reductions of effective population sizes have been demonstrated and placed those events with a maximum of 4000 and 40 000 years ago for A. cebennensis and A. pedemontana, respectively (Jacquemin et al., 2016, Preprint). Because the same authors questioned the dimension of the timing results considering their calculated and highly contradictory species split time of 160 000 years ago (see Fig. 1, compared ith 800 000 years ago), it is worth considering five times higher estimates. This would place the drastic bottleneck events for both species close to the glaciation maxima 25 000 years ago (Würm glaciation, A. cebennensis) and 130 000 years ago (Riss glaciation, A. pedemontana). Both species are genetically isolated from any other Arabidopsis species, and they also do not exhibit significant signatures of past gene flow among different species as was demonstrated for the other taxa (Koch and Matchinger, 2007; Hohmann et al., 2014; Novikova et al., 2016). However, ancient gene flow between the common ancestor of A. pedemontana and A. cebennensis with A. halleri from its western distribution range is nonetheless likely (Novikova et al., 2016). Arabidopsis croatica is distributed along the Dinaric Mountains on rocky outcroppings and fissures in Croatia. The thermophylous diploid species is perennial and self-incompatible, and it is restricted to calcareous bedrock. Phylogenetic analyses placed this taxon as a sister species basal to A. lyrata and A. arenosa (Novikova et al., 2016; sequence data from re-sequenced nuclear genomes), and maternal signatures from entire plastome sequences even placed this taxon basal to all three species complexes (A. halleri, A. lyrata, and A. arenosa). However, only very few individuals of A. croatica have been analysed so far, and a more comprehensive sampling is needed to explain the incongruency which might reflect incomplete lineage sorting, secondary gene flow, or ancestral shared polymorphisms. It is interesting to see that genetic admixture analysis assuming five genetic clusters among perennial Arabidopsis relatives shows A. croatica sharing substantial genetic variation with A. cebennensis and A. pedemontana (Novikova et al., 2016), which cannot be explained by (unlikely) gene flow during the last 130 000 years and which may indeed indicate massive shared ancestral genetic variation. All three species are endangered, and A. croatica and A. pedemontana are strictly protected according to national law and listed as ‘critically endangered’ according to the IUCN definition. Arabidopsis cebennensis is most often found in protected nature reserve areas, and thereby also receives full protection. Hence, major efforts should be undertaken to cultivate these species and provide germplasm resources, with the scientific community following the regulations of the Convention on Biological Diversity and the Nagoya protocol. Allopolyploid systems: whole-genome duplications in A. kamchatica and A. suecica There are two contrasting allopolyploid species systems, which have been nicely introduced in a recent review (Novikova et al., 2018), thereby highlighting the origin of these polyploid taxa around recent glaciation maxima. The Brassicaceae family as a whole is characterized by a high percentage of polyploid taxa. More than 43% of the species are neopolyploids (Hohmann et al., 2015), and actually 10 mesopolyploidization events pre-dating the evolution of tribes (monophyletic assemblages of genera) have been described (Lysak et al., 2005, 2007; Haudry et al., 2013; Mandákova et al., 2017). A family-wide summary of genome size and ploidy level evolution is provided in Hohmann et al. (2015) and indicates tremendous evolutionary dynamics of the genomes in size and structure, and continuous and rapid genome downsizing after polyploidization events (Lysak et al., 2009; Mandákova and Lysak, 2018). Therefore, polyploidization in Brassicaceae can be regarded as one of the driving mechanisms of adaptation to rapidly changing environments and is often able to explain the high speciation rates found (Jordon-Thaden et al., 2013). On the family level, there is also a significant correlation of lower 1 Cx (monoploid) genome size with annual life cycle in Arabidopsis (Hohmann et al., 2015) compared with its perennial wild relatives. Arabidopsis suecica (2n=4x=26) had an origin with A. thaliana (2n=2x=10) as maternal partner providing an unreduced gamete and tetraploid A. arenosa providing the parental n=16 gamete. This plastid (i.e. maternally inherited) molecular signature was demonstrated more than two decades ago (Mummenhoff and Hurka, 1995; O’Kane et al., 1996). Chloroplast and nuclear markers suggest that A. suecica arose just once (Säll et al., 2003; Jakobsson et al., 2006). An analysis of haplotype variation in the A. thaliana portion of the A. suecica genome was recently used to argue that there were multiple origins (Novikova et al., 2016). The time of origin was estimated to be ~16 000 years ago in Eastern Europe or Central Eurasia, with subsequent migration into the Fennoscandinavian region while glaciers were retreating (Novikova et al., 2017). Whether it has a single or multiple origins, A. suecica is certainly of a more regional origin and carries limited genetic variation. Showing high levels of genetic diversity compared with A. suecica, another allopolyploid, A. kamchatica (2n=4x=32), originated via hybridization and polyploidization of outcrossing diploid A. lyrata and diploid A. halleri subsp. gemmifera in Eastern Asia, presumably around Japan, north-eastern China, and eastern Siberia (Shimizu-Inatsugi et al., 2009; Schmickl et al., 2010). A post-glacial origin ~20 000 years ago is likely (Tsuchimatsu et al., 2012), but there is still some uncertainty because of different mutation rates applicable to the analyses. Both A. suecica and A. kamchatica are selfing species. In A. suecica, the self-incombatible system seems to have been overcome just once at the beginning of its evolutionary history (Novikova et al., 2017), whereas A. kamchatica underwent transitions to selfing several times via degradation of male specificity genes (Tsuchimatsu et al., 2012; Shimizu and Tsuchimatsu, 2015). Arabidopsis suecica is an annual plant like its maternal parent, A. thaliana, while the amphiberingean A. kamchatica subsp. kamchatica, which occurs on both sides of the Bering Strait, is a short-lived perennial, though in Japan the subspecies kawasakiana is also annual, in association with its adaptation to highly disturbed sandy seashore habitats at Lake Biwa in Japan (Higashi et al., 2012). The combination of two different genomes in natural allopolyploids attracts research to study various aspects of genomic interactions. Both species allow study of the interplay of genomes which diverged nearly 6 million years ago (A. suecica: A. thaliana versus A. arenosa) and ~600 000 years ago (A. kamchatica: A. lyrata versus A. halleri) with a similar time span of allopolyploid evolutionary history of ~20 000 years. These studies focus on gene expression (e.g. Tian et al., 2014; Miller et al., 2015; Paape et al., 2016), epigenetic regulation (Costa-Nunes et al., 2010; Pontvianne et al., 2012), retention of homologues (Chang et al., 2010; Paape et al., 2018, Preprint), or heterosis effects (Solhaug et al., 2016). Furthermore, ecological aspects of adaptation are addressed, such as cold tolerance (Armstrong et al., 2015), flowering biology (Kenta et al., 2011), trichome production and reproductive trade-off evolution (Steets et al., 2010), and also the ability to cope with heavy metal-rich soils (Paape et al., 2016) by retaining the ability to hyperaccumulate zinc from A. halleri. Arabidopsis thaliana: evolutionary history beyond the 1001 genomes Since the first phylogeographic studies on A. thaliana were published more than a decade ago (Sharbel et al., 2000; Beck et al., 2008), the 1001 Arabidopsis genomes consortium provided the first comprehensive insights into A. thaliana evolutionary history. These indicate glacial refuge areas on the Iberian Peninsula, the Canary and Cape Verde Islands, Sicily, and the Levant region (1001 Genomes Consortium, 2016). A very dynamic post-glacial spatio-temporal diversification pattern all over Eurasia was shown, with strong footprints of selection correlated with post-glacially changing climate and environment. This study highlighted the importance of the Last Glacial Maximum in structuring the present-day A. thaliana populations, and the detected spatio-temporal signature collapsed ~130 000 years ago, which exactly matches the preceding glacial maximum of the Mindel glacial. This highlights again, as shown for wild allopolyploid relatives (Novikova et al., 2018), that diversification processes in space and time are often set into the context of glacial maxima and the subsequent early phase of the generally shorter interglacial intervals. More detailed studies on A. thaliana (Brennan et al., 2014; Durvasula et al., 2017; Zou et al., 2017; Fulgione et al., 2018) elucidated multiple colonizations of the Iberian Peninsula via Central Europe and via Africa during the last glaciation (45 000 and 90 000 years ago, respectively) but pre-dating the Last Glacial Maximum, and large-scale range expansions in Africa at ~120 000–90 000 years ago were demonstrated. Africa was shown to be one primary cradle of genetic diversity (Durvasula et al., 2017) of present-day A. thaliana, with the transition to selfing occurring in North-western Africa. It was concluded that African populations best represent the early history of the species (Fulgione and Hancock, 2018). However, this conclusion does have some restrictions, because the presented modelling of nuclear genomic data does not reveal deeper evolutionary splits than 130 000 years ago and thereby points to the last common ancestor of present-day A. thaliana populations. Furthermore, there are no Eurasian A. thaliana populations older than any African lineages. However, there are at least a few indirect hints allowing tracing of even deeper evolutionary trajectories in A. thaliana. S-locus haplotype variation suggests a geographic localization of the transition to selfing in Africa (Durvasula et al., 2017), and comparative studies among various Arabidopsis species estimated this transition to have occurred 413 000 years ago (Bechsgaard et al., 2006) or even ~1 million years ago (Tang et al., 2007). Assuming that these older estimates are reliable, the African history of A. thaliana is indeed nearly 1 million years old. However, A. thaliana split from its relatives >5 million years ago (e.g. Hohmann et al., 2015; Huang et al., 2016; Guo et al., 2017) and, therefore, more >4 million years of evolutionary history remains unclear. It is worth mentioning that for several genera of Brassicaceae there is a phylogenetic sister relationship of a species-poor clade of annuals (often with one single species only) and a predominantly perennial clade. Environmental changes (e.g. increasing temperature or drought) led to the realization of two different strategies: (i) transition to an annual life cycle with later transition to selfing, rapid colonization of low-elevation habitats; and (ii) migration to the North and in particular towards higher elevations, thereby favouring niche differentiation, allopatric speciation, and increased speciation (Karl and Koch, 2013; Koch et al., 2016). Accordingly, it may be hypothesized that ~1 million years ago two perennial lineages evolved rapidly and independently. One lineage may have successfully colonized the African continent with a transition towards selfing and an annual life cycle. The other lineage underwent a complex evolutionary history leading to speciation in Eurasia and the amphiberingean region and leaving deep footprints of its development within its plastid genomes (Novikova et al., 2016; Hohmann and Koch, 2017). This scenario is in agreement with the finding of ancient gene flow between A. thaliana and A. lyrata but not between A. thaliana and A. arenosa or A. halleri (Novikova et al., 2016). However, most probably the detailed biogeographical context will remain a mystery, because we have no data for paleoenvironments from 1 million years ago and present distribution ranges do not allow comparative phylogeographic conclusions in deeper times. The temporal context might be solved in much more detail in the future. Estimation of a temporally resolved evolutionary scenario largely depends on assumptions on the underlying mutational rate of DNA. Here it is important to distinguish between the mutation rate, which is the rate at which genomes change due to DNA damage, faulty repair, gene conversion, and replication errors, and the substitution rate, which is the rate at which mutations accumulate within a given gene pool (Exposito-Alonso et al., 2018). Therefore, it is important that any rate used is critically chosen, set into the right context, and compared carefully with other studies. The following examples apply for the nuclear genome. Studies spanning hundreds of millions of years and analysing various plant families including Brassicaceae used, for example, synonymous substitution rates of 6–7 × 10–9 substitutions per site per year (Lynch, 2010; De la Torre et al., 2017), data sets focusing on the Brassicaceae family and spanning a few million years assumed 3.83–8.56 × 10–9 (Huang et al., 2012) or 8.22 × 10–9 (Kagale et al., 2014) synonymous mutations per site per year, whereas in a study focusing on Capsella and Arabidopsis, a substitution rate per site per year of 7 × 10–9 was used (Wu et al., 2017). Other studies aiming to resolve time spans of a few hundreds or thousands years have elaborated substitution rates of 2.7 × 10–9 substitutions per site and generation for Arabidopsis (Exposito-Alonso et al., 2018), which was translated into 2–5 × 10–9 substitutions per site per year as A. thaliana is an annual. A mutation rate of A. thaliana under ‘lab conditions’ was estimated and introduced with 7 × 10–9 mutations per site per year per generation (Ossowski et al., 2010). Finally, Arnold et al. (2015) simulated a rate for their analyses in A. arenosa of 3.7 × 10–8 substitutions per site per year. In summary, there is a great demand carefully to document and explain the nature of the chosen rates and compare results with those obtained using alternative rates and thereby allow cross-study comparisons. Summary: bridging Arabidopsis thaliana and its relatives This review highlighted that A. thaliana and its relatives do not just share a common ancestor at ~6 million years. After this split, the various species shared a spatio-temporal context to varying degrees. Severe environmental changes, such as via oscillating climate change during the entire Pleistocene, affected some of the lineages. This did not only lead to differing patterns and processes, but some are similar among them. This makes the genus Arabidopsis as a whole a suitable system to conduct comparative and across-species studies given that reliable spatio-temporal evolutionary scenarios are available. It is also summarized that secondary genetic contact is often triggered by glaciation maxima and subsequent shorter warming phases, and has affected any lineage including A. thaliana. There are many open questions such as the ‘deep history’ of A. thaliana or more detailed analyses differentiating between contemporary and past gene flow among A. lyrata, A. arenosa, and A. halleri. So far, most often comparative studies have involved only two species but, with the knowledge of putative gene flow among all species, future studies might consider multi-species approaches. Comment on germplasm accessibility It has to be highlighted that for some of the perennial relatives of Arabidopsis, access to natural populations requires not only considering local/regional regulations (e.g. protected areas such as nature reserves or national parks) and international regulations of benefit sharing (Convention of Biological Diversity, Nagoya protocol from 2014), but some species are actually under severe threat and are categorized according to IUCN protection definition categories as ‘critically endangered’, such as A. croatica and A. pedemontana. Others are locally endangered, and some populations of A. lyrata, in particular in Eastern Austria, are regionally endangered and may even be ‘oversampled’. Acknowledgements I thank Nora Hohmann for her help with data export and preparation of Fig. 2. Markus Kiefer, Kirsten Bomblies, Angela Hancock, and two anonymous reviewers are greatly acknowledged for their very helpful comments. 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Liu, Mengyu; Zhang, Cunjia; Duan, Lixin; Luan, Qianqian; Li, Jialin; Yang, Aigang; Qi, Xiaoquan; Ren, Zhonghai

doi: 10.1093/jxb/ery336pmid: 30256979

Abstract Spine colour is an important fruit quality trait that influences the commercial value of cucumber (Cucumis sativus). However, little is known about the metabolites and the regulatory mechanisms of their biosynthesis in black spine varieties. In this study, we determined that the pigments of black spines are flavonoids, including flavonols and proanthocyanidins (PAs). We identified CsMYB60 as the best candidate for the previously identified B (Black spine) locus. Expression levels of CsMYB60 and the key genes involved in flavonoid biosynthesis were higher in black-spine inbred lines than that in white-spine lines at different developmental stages. The insertion of a Mutator-like element (CsMULE) in the second intron of CsMYB60 decreased its expression in a white-spine line. Transient overexpression assays indicated that CsMYB60 is a key regulatory gene and Cs4CL is a key structural gene in the pigmentation of black spines. In addition, the DNA methylation level in the CsMYB60 promoter was much lower in the black-spine line compared with white-spine line. The CsMULE insert may decrease the expression level of CsMYB60, causing hindered synthesis of flavonols and PAs in cucumber fruit spines. CsMYB60, cucumber, epigenetic alteration, flavonols, fruit spine, Mutator-like element, proanthocyanidins Introduction Cucumber (Cucumis sativus) is an economically important global vegetable crop (Xu et al., 2015b). The visual appearance of the fruit is a highly important commercial trait (Chen et al., 2016), and the colour of the fruit spines (specialized trichomes) is one of the characteristics that determines the economic value (Li et al., 2013). Genetic analysis has indicated that the black fruit-spine trait is controlled by a single gene, B (Black or brown spine 1) and it is dominant over the white trait (Walters et al., 2001). Li et al. (2013) mapped the B gene to a 50-kb region of the short arm of cucumber chromosome 4 (chr.4) based on linkage analysis using 2001 F2 plants from a cross between two inbred lines, WI7200 (black-spine, orange mature fruit) and WI7201 (white-spine, cream-coloured mature fruit). In this 50-kb region, a gene encoding a R2R3-MYB transcription factor, later named CsMYB60 (Zhao et al., 2014), was considered as the best candidate for B, because of the presence of a 1-bp deletion in the third intron and a lower expression level of this R2R3-MYB gene in both immature and mature fruit in WI7201 compared with WI7200 (Li et al., 2013). However, more data are needed to confirm that CsMYB60 is the B locus. In addition, the composition of pigments and the mechanisms regulating their biosynthesis in black-spine plants also need to be clarified. Flavonoids constitute a large family of plant secondary metabolites that are synthesized via the phenylpropanoid pathway, and they can be categorized into chalcones, flavonols, flavanols, anthocyanins, flavones, and proanthocyanidins. They are ubiquitous in the plant kingdom and are beneficial as physiologically active compounds, stress-protecting agents, attractants, and feeding deterrents, as well as playing a general and significant role in plant resistance to pathogens, herbivores, and environmental stress (Treutter, 2006). Moreover, many flavonoids are active principles of medicinal plants and exhibit pharmacological effects (Marles et al., 2003; Yilmaz and Toledo, 2004). Flavonols are present in fruit and vegetables, and they are consumed in considerable amounts in the human diet (Leo and Woodman, 2015). Flavonols have blood pressure-lowering activity (Dauchet et al., 2009). The blood pressure-lowering effects of quercetin were first reported in spontaneously hypertensive rats (SHRs) (Perez-Vizcaino et al., 2001) and subsequently in other animal models of hypertension (Dauchet et al., 2009). In addition to such activity, flavonols modulate tissue antioxidant status by direct scavenging of free radicals (Qin et al., 2008). In some studies, Kaempferol has been shown to improve the survival and function of β-cells and human islets cultured in the presence of high glucose, leading to enhanced insulin secretion (Zhang et al., 2013). Quercetin protects β-cell function and viability, and reduces oxidative damage induced by inflammatory cytokines (Dai et al., 2013). Thus, flavonols may be used as novel anti-diabetic agents (Leo and Woodman, 2015). Proanthocyanidins (PAs; also called condensed tannins) are polymeric end-products of the flavonoid biosynthetic pathway, and their monomeric building blocks, catechin and epicatechin, have cardioprotective (Serafini et al., 2003), anti-cancer (Ahmad et al., 2000), and anti-inflammatory benefits as antioxidants. Moderate levels of PAs in forage crops also improve nitrogen nutrition, reduce urinary nitrogen excretion, and help counter intestinal parasites in the animals that consume them (Aerts et al., 1999). MYB proteins belong to a large family of transcription factors. Certain MYB proteins are key regulators of flavonoid biosynthesis (Liu et al., 2015). MtPAR is a MYB family transcription factor that positively regulates PA biosynthesis in Medicago truncatula (Verdier et al., 2012). Three R2R3-MYB proteins (MYB11, MYB12, and MYB111) activate the early flavonoid biosynthetic genes to regulate flavonol biosynthesis, whilst the later flavonoid biosynthetic genes are activated by the MYB-bHLH-WD40 (MBW) complex to control the production of anthocyanins and PAs (Li, 2014). As a key enzyme, 4-coumarate:CoA ligase (4CL) provides the precursors for flavonoid and lignin biosynthesis (Sun et al., 2015). Studies in the fields of genomics and epigenetics have recently demonstrated the importance of transposable elements in genome function and evolution (Hirsch and Springer, 2017; Song and Cao, 2017). As transposable elements propagate in plant genomes and attract epigenetic marks, their neo-insertions can lead to the formation of new, heritable epigenetic variants of genes in their vicinity and impact on the host gene regulatory networks (Bucher et al., 2012). For example, a transposon-induced epigenetic change leads to sex determination in melon (Martin et al., 2009). In this case, the transition from male to female flowers in gynoecious lines results from epigenetic changes in the promoter of a transcription factor, CmWIP1, an inhibitor of female flower development. This natural and heritable epigenetic change results from the insertion of a transposon, which is required for the initiation and maintenance of spreading of DNA methylation to the CmWIP1 promoter. The transposon Mutator was first identified in maize and was later found in other plants, bacteria, fungi, and protozoans (Robertson, 1978; Wang et al., 2016). The typical structure of a Mutator-like transposable element (MULE) includes terminated inverted repeats (TIRs; usually 100–500 bp) flanking an internal sequence and one target site duplication (TSD; usually 8–11 bp) flanking each TIR (Wicker et al., 2009; Ferguson et al., 2013). MULEs are especially prevalent in higher plant genomes. For example, the Arabidopsis genome contains at least 200 MULEs (van Leeuwen et al., 2007) and the Oryza sativa genome contains more than 10000 copies (Jiang et al., 2004, 2011; Ferguson et al., 2013). MULEs contribute to the evolution of gene innovation and novelty in plant genomes by capturing ectopic genomic sequences and changing DNA methylation (Jiang et al., 2004; Wang et al., 2016). Because MULEs are one of the most active mobile elements in plants, they have been widely employed as a mutagenizing system (Meeley and Briggs, 1995). To date, little is known regarding the functions of MULEs in the evolution of gene innovation and novelty in the genome of cucumber. In this study, by combining transcriptomic and metabolic profiles, we determined that the pigments of the black fruit spines in cucumber are composed of flavonoids, primarily flavonols and PAs. We revealed the key roles of CsMYB60, the best candidate for the B gene, and Cs4CL in the colouration of black spines, and we found that the decrease in expression of CsMYB60 in white-spine plants is caused by an insertion of a Mutator-like element, which might function in an epigenetic manner. Materials and methods Plant material and growth conditions Twenty-two inbred lines of cucumber (Cucumis sativus L.; 2n=2x=14) were used. Plants were grown for two generations each year in a solar greenhouse at the experimental field at Shandong Agricultural University. Standard commercial management was performed during the cultivation period. All spines on fruit from between nodes 8 and 18 of cucumber plants were sampled using a scalpel, and immediately put into liquid nitrogen. The lines RNS8 (white spines) and RNS9 (black spines) were selected for detailed analysis. Unless otherwise stated, the following methods and the results refer to these two lines. Imaging of fruit spines Fruit spines of the lines RNS8 and RNS9 were examined under a dissection microscope (Leica M165 FC) from 2 d before anthesis (DBA) to 12 d after anthesis (DAA), when the fruit were ~35–40 mm long and ~300–350 mm long, respectively. NMR experimental procedures To obtain purified samples for NMR, column chromatography was carried out using a Sephadex LH-20 (GE Healthcare), and HPLC chromatography was performed on an Alltech instrument (426-HPLC pump, Alltech UV-vis-200 detector) equipped with Kromasil semi-preparative (10 μm, ODS, 10 × 250 mm) and YMC-Pack C8 (5 μm, 10 × 250 mm) columns. The purified samples were subjected to NMR analysis. 1H and 13C NMR and 2D NMR spectra were recorded on an AVANCE 400 FT 400 MHz NMR spectrometer (Bruker) using tetramethylsilane (TMS) as the internal standard. Chemical shifts (δ) are expressed in parts per million (ppm), and coupling constants (J) are reported in Hz. Electrospray ionization (ESI)-MS was conducted using a Bruker APEX IV instrument. Black spines (2.15 g) from RNS9 fruit were homogenized in liquid nitrogen and then extracted with water. After evaporation of the water by rotavapor, the residue (300 mg) was partitioned by water. Samples were then subjected to Sephadex LH-20 column chromatography and eluted with MeOH:H2O (1:1) to obtain six fractions (F1–F6). F1 mainly contained sugar (determined by TLC), while F2–F4 contained flavonoid glycoside (detected by UV and 1H NMR). F5 and F6 were mainly low polarity components and only contained trace flavonoid glycosides. F2 was separated by semi-HPLC with CH3CN-H2O (7%) to obtain purified compound A (5.6 mg), F3 was separated by semi-HPLC with CH3CN-H2O (8%) to obtain compound B (5.6 mg) and compound C (4.3 mg), and F4 was separated by semi-HPLC with CH3CN-H2O (10%) to obtain compound D (1.8 mg) and compound E (1.4 mg). LC-MS analysis In order to compare the metabolic profiles of black and white spines, a metabolomics approach was performed. An Agilent 1290 Series UHPLC system was coupled online with a hybrid quadrupole time-of-flight (Q-TOF) mass spectrometer (6540, Agilent) equipped with a jet-stream ion-focus source for the UHPLC-MS analysis The UHPLC system consisted of a degasser, two binary pumps, and an autosampler. Samples of 20 mg for each of the black and white spine samples were extracted by water. Aliquots of 3 µl of filtered extract were applied to a reversed-phase column (SB-C18 RRHD, 2.1 × 100 mm, 1.8 µm; Agilent) with an in-line filter (1290 infinity in-line filter; Agilent). The system was operated in positive ion mode at a flow rate of 0.4 ml min−1 using solvent A (water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). The gradient started from 15% B for 3 min, followed by 15% to 19% B in 6 min, 19 % to 25% B in 2 min, 25% to 40% B in 4 min, from 40% to 100% B in 15 min, and held for 4 min, then returned to the starting condition in 1 min, keeping the re-equilibration at 15% B for 5 min. Data were collected in positive ESI mode in separate runs on a Q-TOF (Agilent 6540) operated in full-scan mode from 50 to 1000 m/z. During the analysis two reference masses (121.0509 m/z for C5H4N4, and 922.0098 m/z for C18H18O6N3P3F24) were continuously measured to allow constant mass correction, and to obtain accurate values. The capillary voltage was 4000 V with a scan rate of 4 s–1; the nebulizer gas flow rate was 12 l min−1; the drying gas flow was 10 l min−1; the gas temperature was 350 ºC, and the skimmer voltage was 65 V. Construction of RNA libraries for comparative transcriptomic analysis Transcriptomic profiling was conducted using cucumber fruit spines at 2 DAA from the RNS8 and RNS9 lines and from the F2 population of RNS8×RNS9. Three biological replicate samples, each containing 0.2g spines from different plants, were collected. All of the spines were stored in liquid nitrogen until RNA isolation. Total RNA samples were extracted using a TIANGEN kit (DP441) according to manufacturer’s manual (Tiangen, China). The single nucleotide polymorphisms (SNPs) of the RNA-seq data for the white and black spine pools of the F2 population of RNS8×RNS9 were used for association analysis to map the B locus. mRNA was enriched using Dynabeads oligo(dT) (Dynal; NEB). After the addition of adenine, the resulting cDNAs were linked to adapters (P5, P7; NEB) and purified by gel electrophoresis. Finally, PCR products were purified (AMPure XP system) and the quality of mRNA including purity, quantity and integrity was tested using a Nanodrop, Qubit, and Agilent Bioanalyzer 2100 system based on optical density values (OD230, OD260, OD280), fluorescence dye binding specifically to RNA, and visual imaging. All libraries were sequenced on an Illumina HiSeq platform at Novogene (Beijing, China). Bioinformatics analysis of RNA-Seq data Adaptor sequences and low-quality sequences were removed from the raw reads. Clean reads were aligned to reference genome sequences of the Cucurbit Genomics Database (http://cucurbitgenomics.org/organism/6) using Hisat2 v2.0.5. FeatureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene, and then the FPKM of each gene was calculated based on the length of the gene and the read counts mapped to the gene. Gene expression differences in the different sample pairs were detected using the DESeq2 R package (1.16.1). DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg approach for controlling the false discovery rate (FDR). The thresholds were set using an FDR≤0.05 to determine significant differences in gene expression. GATK2 (v3.7) software was used to perform SNP calling. Raw vcf files were filtered using the GATK standard filter method and other parameters (cluster:3; WindowSize:35; QD<2.0 o; FS>30.0; DP<10), and the SnpEff software was used for variant annotation. KEGG enrichment analysis of differentially expressed genes The Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) was used to identify differentially expressed genes (DEGs) in the spines of RNS8 and RSN9 lines. We used the clusterProfiler R package to test the statistical enrichment of DEGs in the KEGG pathways. qPCR analysis Total RNA was prepared from spines at different developmental stages using a RNAprep pure Plant Kit (TIANGEN, Beijing, China), according to the manufacturer’s instructions. The extracted RNA was treated with RNase-free DNase I (Fermentas, Harrington, Canada) to eliminate genomic DNA contamination, according to the manufacturer’s instructions. cDNA synthesis was carried out using a RevertAid First Strand Synthesis Kit (ThermoFisher Scientific). An UltraSYBR Green Mixture qPCR kit (CWBIO) was used in the qPCR reaction to determine the expression of relevant genes using an iCycler iQ™ multicolour real-time PCR detection system (Bio-Rad). Expression of genes was normalised to that of the cucumber actin gene. The primers used are listed in Supplementary Table S3 at JXB online. DMACA staining The spines were stained with 1% p-dimethylaminocinnamaldehyde (DMACA) solution (1% w/v DMACA in 1:1 ethanol:concentrated HCl) for 1 h. The stained spines were then washed in 70% (v v−1) ethanol for 1 h and observed under a Leica M165C microscope (Germany). Transient transformation in cucumber cotyledons and tobacco leaves CsMYB60 driven by 35S promoter was recombined into the pCAMBIA1300 vector. The promoter of Cs4CL was fused with GUS (β-glucuronidase), and then recombined into pCAMBIA1300. The primers used are listed in Supplementary Table S3. The construct was separately transformed into Agrobacterium tumefaciens LBA4404. After cultivation, cells were harvested by centrifugation and resuspended in 10 mM MES buffer containing 10 mM MgCl2 and 200 μM acetosyringone. The OD600 of the Agrobacterium suspension was optimized at 0.6–0.8. The Agrobacterium suspension was then infiltrated into cotyledons of 8-d-old cucumber seedlings (Shang et al., 2014) or leaves of 5-week-old tobacco (Nicotiana benthamiana) seedlings using a needleless syringe. After 2–3 d, the samples were harvested for GUS staining or RNA extraction. GUS histochemical assays Samples were immersed into GUS staining solution [1 mM X-Gluc (5-bromo-4-chloro-3-indolyl β-d-glucuronide), 100 mM phosphate buffer pH 8.5, 0.1% v/v Triton X-100, 0.5 mM K3Fe(CN)6, 0.5 mM K4Fe(CN)6, 10 mM EDTA). After a vacuum treatment of 3 min to facilitate the penetration of the staining solution, samples were kept for 16 h in the dark at 37 °C before being destained with 70% (v/v) ethanol (Jefferson et al., 1987). GUS staining was observed under a Leica M165C microscope (Germany). Particle bombardment The constructed plasmids (pro35S::CsMYB60, pro35S::Cs4CL) were extracted using a PurePlasmid Midi Kit (CWBIO, China). Samples of 1 µg of the prepared plasmids were mixed with 10 µl of sterile water, 12.5 µl of 2.5 M CaCl2, and 5 µl of 0.1 M spermidine, and then coated onto gold particles (0.12 mg). Fruit of RNS8 at anthesis with intact white spines were subjected to particle bombardment using a biolistic PDS1000/He Particle Gene Gun System (Bio-Rad). Each target was bombarded at least three times. The bombarded fruits were placed on moisturized filter paper in glass dishes for 6 d. McrBC-based methylation assays Genomic DNA samples (500 ng each) were digested for 2 h at 37 °C with 30 U of McrBC enzyme (NEB). A mock digestion was performed in parallel with no enzyme. Then, 5 ng DNA from each sample was used for PCR analysis. DNA was extracted using a Plant Genomic DNA Kit (TIANGEN, China). Bisulphite sequencing Samples of ~2 μg of genomic DNA were used for bisulphate conversion. Bisulphite modification and desulfonation of genomic DNA were performed using a DNA Bisulfite Conversion kit (TIANGEN) according to the manufacturer’s instructions. The bisulphite-treated DNA was amplified using Taq DNA polymerase. The thermal cycling program was 95 °C for 4 min followed by 35 cycles of 95 °C for 30 s, annealing for 30 s, and extension at 72 °C for 45 s, ending with a 10 min extension at 72 °C. PCR products were cloned into the pMD18-T vector (TaKaRa), and 10 individual clones were sequenced. DNA cytosine methylation in the CG, CHG, and CHH contexts was analysed and displayed using the CyMATE software (http://www.cymate.org/). Generation of alignment files CyMATE does not align sequences itself but reads pre-aligned sequence data. As all sequences must be of equal length (‘blunt-ended’ alignment), leading and trailing gaps should be inserted if necessary. Alignments containing the range between the primers in the master sequence followed by the sequences of individual clones in the desired order were generated using CLUSTAL version 1.83 (http://www.ebi.ac.uk/clustalw). The alignments were saved in sequential (standard FASTA) and interleaved formats (standard CLUSTAL). Results Phenotypic characteristics of white and black spines The colour of the fruit spine is an important quality trait for cucumber. To explore the pigments in black spines, we used two inbred lines with a similar plant stature and fruit shape, RNS8 with white spines and RNS9 with black (the colours can be distinguished easily on the developing young fruit after anthesis). The pigments in black spines exhibited a tissue-specific pattern and could also be observed on the fruit surface and in trichomes of receptacles and pedicels (Fig. 1A–D, Supplementary Fig. S1). Fig. 1. Open in new tabDownload slide Phenotypic comparison of organs with accumulated pigments between white-spine (RNS8) and black-spine (RNS9) cucumber inbred lines. (A) Pedicel, (B) Receptacle, (C) white spines on the fruit skin, and (D) black spines on the fruit skin. (E) No pigmentation of white spines from 2 d before anthesis (DBA) to 12 d after anthesis (DAA) in RNS8. (F) Pigmentation time course of black spines from 2 DBA to 12 DAA in RNS9. Scale bars are 0.7 mm. The black colour appeared on the very top cells of the spines at approximately 2 d before anthesis (DBA). The colouration then gradually moved downward from 4–12 days after anthesis (DAA). The whole spine became completely black at 12 DAA (Fig. 1F). In contrast, white spines showed no significant changes in colour (Fig. 1E). Previous studies have indicated that an orange colour of mature fruit is associated with black spine colour, and both of these traits are controlled by a single dominant gene, B (Li et al., 2013). This association was also observed in the present study (Supplementary Fig. S1). Gross comparisons of metabolite profiles between white and black spines Abundant metabolites that distinguished between white and black spines could be recognized by visual inspection of LC-MS chromatograms and through the use of statistical methods such as principal component analysis. As shown in Fig. 2, slight differences in metabolites began to emerge between the white (RNS8 line) and black (RNS9 line) spines at 2 DAA, and these had become significant by 12 DAA. The results thus indicated that the metabolites were mainly synthesized in the late developmental stage. A total of 115 different metabolites were detected between the white and black spines at 2 DAA, and 135 different metabolites at 12 DAA (Supplementary Fig. S2, Supplementary Table S1). Fig. 2. Open in new tabDownload slide Principal component analysis (PCA) and Partial Least Squares Discrimination Analysis (PLS-DA) of metabolite profiling data for comparison of white spines (line RNS8) and black spines (line RNS9) in cucumber. (A) PLS-DA three-dimensional stereogram, (B) PCA plane figure, (C) PLS-DA plane figure. W2, white spine at 2 DAA; B2, black spine at 2 DAA; W12, white spine at 12 DAA; B12, black spine at 12 DAA. Isolation and identification of flavonols in black spines To determine the secondary metabolites in black spines, we performed chromogenic reactions on water extracts using different chemical agents (Supplementary Fig. S3). The results indicated that no anthocyanins were present in black spines because none of the relevant colour alterations occurred when the pH value was changed through the addition of acid or alkali to water extracts from black spines. Consistently, no anthocyanins were detected in the metabolic analysis (Supplementary Table S1). However, the chromogenic reaction with FeCl3 showed an accumulation of phenolic compounds in the black spines (Supplementary Fig. S3). Using HPLC for flavonols, several distinct metabolites were separated from black spines that were not present in white spines (Fig. 3A). Five of these (designated as A–E) were present in sufficient quantity to be purified for parsing of their molecular structures using NMR analysis. The results indicated that the metabolites were quercetin-3-O-rutinoside-7-O-glucose (A), kempferol-3-O-rutinoside-7-O-glucoside (B), isorhamnetin-3-O-rutinoside-7-O-glucoside (C), kaempferol-3-O-rutinnoside (D), and isorhamnetin-3-O-rutinoside (E) (Fig. 4A). The physicochemical and spectral data are given in Supplementary Fig. S5. The NMR results were also verified by data obtained by high-resolution MS (Supplementary Fig. S6). In agreement with the progression of colouration, these five flavonols accumulated gradually as the spines developed from 2 DBA to 20 DAA in black spines (Fig. 3B, Supplementary Fig. S4). At 12 DAA, isorhamnetin-3-O-rutinoside was present at the highest concentration, followed by quercetin-3-O-rutinoside-7-O-glucose, isorhamnetin-3-O-rutinoside-7-O-glucoside, kempferol-3-O-rutinoside rutinoside-7-O-glucoside, and kaempferol-3-O-rutinnoside. Fig. 3. Open in new tabDownload slide Composition and content of flavonols in black spines of the cucumber line RNS9 compared with the white spines of line RNS8. (A) Flavonol HPLC signals were recorded in black spines but not in white spines. The peaks A–E indicate five different metabolites that could be purified for subsequent NMR. (B) The contents of metabolites A–E in black spines at different developmental stages. Data are means (±SD) (n=3). DBA, days before anthesis; DAA, days after anthesis. Fig. 4. Open in new tabDownload slide Molecular formulae and structures of the five flavonols identified by NMR, and identification of PAs in black spines of line RNS9. (A) The formulae and structures of A, quercetin-3-O-rutinoside -7-O-glucose; B, kempferol 3-O-rutinoside 7-O-glucoside; C, isorhamnetin -3-O-rutinoside-7-O-glucoside; D, kaempferol -3-O- rutinoside; and E, isorhamnetin- 3-O-rutinoside. The NMR physicochemical and spectral data are listed in Supplementary Fig. S5. (B) DMACA (p-dimethylaminocinnamaldehyde) staining of white and black spines (white spines of the RNS8 line). The images show single stained spines (top) and many stained spines (bottom). Scale bars are 1 mm. (C) Confirmation of proanthocyanidins in black spines as determined by tandem MS using catechin as a control. The MS ion peak signal for the catechin monomer is shown (m/z 289.0718 ± 5 ppm). Identification of proanthocyanidins in black spines Proanthocyanidins (PAs; also called condensed tannins) are oligomers of flavan-3-ol units (catechin and epicatechin) and are prominent compounds in seed coats, leaves, fruit, flowers, and bark of many plant species (Dixon et al., 2005). DMACA staining is a rapid and effective method for histological analysis of PAs (Xie and Dixon, 2003; Peng et al., 2012), and it indicated that black spines had high accumulation of PAs, whereas white ones did not (Fig. 4B). The presence of PAs in black spines was further confirmed by MS ion peak signals for catechin monomers (m/z, 289.0718 ± 5 ppm) (Fig. 4C). Transcriptomic profiles of fruit spines To investigate the molecular basis underlying the difference in colour between white and black spines, a comparative transcriptomic analysis was performed using spines from the lines RNS8 (white) and RNS9 (black) at 2 DAA. High-throughput RNA sequencing (RNA-Seq) produced 52, 56, and 54, million reads for the three white-spine samples and 57, 71, and 48 million reads for the black-spine samples (Supplementary Table S2). In addition, SNPs with high confidence were identified using GATK2 (v3.7). Through SNP association analysis, the B gene was located on the distal region of the short arm of chromosome 4, which contains a R2R3-MYB gene, CsMYB60 (Zhao et al., 2014) (Supplementary Fig. S7). This result is consistent with that of a previous study (Li et al., 2013). We found 743 differentially expressed genes (DEGs) (Supplementary Table S4) between white and black spines, of which 447 were up-regulated and 296 were down-regulated in the white spines compared with the black (Fig. 5A, B). KEGG analysis using a hyper-geometric distribution test revealed pathways that were closely associated with the metabolic process and showed significant differences between the white and black spines. The significantly enriched pathways included ‘Flavonoid biosynthesis’, ‘Phenylpropanoid biosynthesis’ and ‘Phenylalanine metabolism’ (Fig. 5C), which was in accordance with our other results. Fig. 5. Open in new tabDownload slide Transcriptomic analysis of cucumber fruit spines. (A) Volcano plot showing the differentially expressed genes (DEGs) between RNS8 (white spines) and RNS9 (black spines) (P<0.05). (B) Cluster analysis of DEGs. Genes with high or weak expression in the RNS8 vs. RNS9 group fruit spines are shown in red and blue, respectively. (C) Significantly enriched KEGG pathways (P <0.05) in the black fruit spines compared with white spines at 2 d after anthesis. Polymorphisms in the DNA sequence of CsMYB60 in different cucumber varieties Li et al (2013) mapped the B gene to a 50-kb region containing a R2R3-MYB gene on cucumber chr.4. This result was verified by SNP association in this study (Supplementary Fig. S7). The R2R3-MYB gene, CsMYB60 (Zhao et al., 2014), is considered as the best candidate for the B locus (Li et al., 2013). To understand the evolutionary relationship between CsMYB60 and Arabidopsis R2R3-MYB proteins, phylogenetic analysis was also performed using the neighbor-joining method (Supplementary Fig. S8). This showed that CsMYB60 is closely related to AtMYB111, AtMYB11, and AtMYB12, which regulate the early biosynthetic genes of flavonoids in Arabidopsis, suggesting that CsMYB60 may be involved in flavonoid biosynthesis in cucumber. We compared the sequences of the two CsMYB60 alleles from the inbred lines RNS8 (white spines) and RNS9 (black spines), and found a 6994-bp insertion at the 1492-bp position after the transcription start site in the second intron of CsMYB60 in RNS8 compared with RNS9. To determine whether this R2R3-MYB gene was conserved in different varieties, we tested 20 other cucumber inbred lines from different geographical regions that were available in our laboratory (Zhang et al., 2016); 14 of the 22 lines have white spines and eight have black spines. The results of the sequencing analysis indicated that the CsMYB60 alleles from the lines with black spines (CNS14, CNS20, RNS5, RNS12, and RNS13) had the same genomic sequence as that in RNS9. In addition, the sequence of CsMYB60 in wild cucumber in the Cucurbit Genomics Database (http://cucurbitgenomics.org/organism/6) is similar to that of RNS9, with the exception of several SNPs that do not affect the ORF. The 14 inbred lines with white fruit spines could be classified into two groups based on the variation type: one group was formed of CNS13, CNS16, CNS17, CNS18, and CNS23 and had a SNP (C to T), which leads to a premature stop codon yielding a truncated protein product, in the second exon of CsMYB60 in comparison to RNS9; the other group, was formed of CNS5, CNS10, CNS15, CNS22, RNS2, RNS3, RNS8, RNS10, and ‘Chinese long’ 9930, and had a 6994-bp insertion at the 1492-bp position after the transcription start site in the second intron of CsMYB60. Interestingly, the lines RNS14 and RNS15 had two inserts (a 2759-bp fragment at 853 bp and a 6994-bp fragment at 1492 bp after the transcription start site) in the second intron of CsMYB60, but still displayed the black spine phenotype (Table 1). Table 1. Polymorphism of the DNA sequence of CsMYB60 in different cucumber lines relative to black-spined RNS9. Lines . Colour of spine . Polymorphism of DNA . CNS5 White 6994-bp insertion in second intron at 1492bp after TSS CNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS15 White 6994-bp insertion in second intron at 1492bp after TSS CNS22 White 6994-bp insertion in second intron at 1492bp after TSS Chinese long 9930 White 6994-bp insertion in second intron at 1492bp after TSS RNS2 White 6994-bp insertion in second intron at 1492bp after TSS RNS3 White 6994-bp insertion in second intron at 1492bp after TSS RNS8 White 6994-bp insertion in second intron at 1492bp after TSS RNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS13 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS16 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS17 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS18 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS23 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS14 Black No change CNS20 Black No change RNS5 Black No change RNS12 Black No change RNS13 Black No change RNS14 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS RNS15 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS Lines . Colour of spine . Polymorphism of DNA . CNS5 White 6994-bp insertion in second intron at 1492bp after TSS CNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS15 White 6994-bp insertion in second intron at 1492bp after TSS CNS22 White 6994-bp insertion in second intron at 1492bp after TSS Chinese long 9930 White 6994-bp insertion in second intron at 1492bp after TSS RNS2 White 6994-bp insertion in second intron at 1492bp after TSS RNS3 White 6994-bp insertion in second intron at 1492bp after TSS RNS8 White 6994-bp insertion in second intron at 1492bp after TSS RNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS13 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS16 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS17 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS18 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS23 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS14 Black No change CNS20 Black No change RNS5 Black No change RNS12 Black No change RNS13 Black No change RNS14 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS RNS15 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS TSS, transcription start site. Open in new tab Table 1. Polymorphism of the DNA sequence of CsMYB60 in different cucumber lines relative to black-spined RNS9. Lines . Colour of spine . Polymorphism of DNA . CNS5 White 6994-bp insertion in second intron at 1492bp after TSS CNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS15 White 6994-bp insertion in second intron at 1492bp after TSS CNS22 White 6994-bp insertion in second intron at 1492bp after TSS Chinese long 9930 White 6994-bp insertion in second intron at 1492bp after TSS RNS2 White 6994-bp insertion in second intron at 1492bp after TSS RNS3 White 6994-bp insertion in second intron at 1492bp after TSS RNS8 White 6994-bp insertion in second intron at 1492bp after TSS RNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS13 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS16 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS17 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS18 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS23 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS14 Black No change CNS20 Black No change RNS5 Black No change RNS12 Black No change RNS13 Black No change RNS14 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS RNS15 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS Lines . Colour of spine . Polymorphism of DNA . CNS5 White 6994-bp insertion in second intron at 1492bp after TSS CNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS15 White 6994-bp insertion in second intron at 1492bp after TSS CNS22 White 6994-bp insertion in second intron at 1492bp after TSS Chinese long 9930 White 6994-bp insertion in second intron at 1492bp after TSS RNS2 White 6994-bp insertion in second intron at 1492bp after TSS RNS3 White 6994-bp insertion in second intron at 1492bp after TSS RNS8 White 6994-bp insertion in second intron at 1492bp after TSS RNS10 White 6994-bp insertion in second intron at 1492bp after TSS CNS13 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS16 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS17 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS18 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS23 White SNP (C to T) at 617 bp after TSS, Arg to stop codon CNS14 Black No change CNS20 Black No change RNS5 Black No change RNS12 Black No change RNS13 Black No change RNS14 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS RNS15 Black 2759-bp insertion in second intron at 853bp after TSS 6994-bp insertion in second intron at 1492bp after TSS TSS, transcription start site. Open in new tab Decreased expression level of CsMYB60 and the key genes involved in flavonoid biosynthesis in white fruit spines The 6994-bp insertion at the 1492-bp position did not knock out CsMYB60 but dramatically decreased its expression level at different developmental stages of spines in white-spined RNS8. Moreover, the expression levels of the key structural genes in the synthetic pathway of flavonoids, such as CsPAL, Cs4CL, CsCHS, and CsF3H, also decreased in at least one of the three spine developmental stages tested (8, 4, and 0 DBA) in RNS8 compared with black-spined RNS9 (Fig. 6A, Supplementary Fig. S9A). Fig. 6. Open in new tabDownload slide Expression of CsMYB60 and key genes involved in flavonoid biosynthesis in cucumber fruit spines of the lines RNS8 (white spines) and RNS9 (black spines). All data are means (±SD) of three technical replicates. (A) Expression of genes in spines at 8 d before anthesis (DBA), 4 DBA, and 0 DBA. Expression values are relative to that of the cucumber actin gene. Different capital letters indicate significant differences at P<0.01and different lowercase letters indicate differences at P<0.05. The other two biological repeats are shown in Supplementary Fig. S9A. (B) The expression of CsMYB60 in spines at 0 DAA from different inbred lines. The other two biological repeats are shown in Supplementary Fig. S8B. Different letters indicate significant differences at P<0.01 Significant differences were determined according to a multiple statistical analysis system using Data Processing System (DPS) statistics version 7.05 software. To further test the importance of CsMYB60 in spine pigmentation, we selected five additional white spine lines (Chinese long 9930, RNS1, CNS5, CNS16, CNS18) and four black spine lines (RNS5, RNS14, RNS15, CNS14) with different CsMYB60 alleles (Table 1) for expression level analysis. The results indicated that the gene expression level of CsMYB60 was much higher in the black spine lines than in the white spine lines (Fig. 6B, Supplementary Fig. S9B). Overexpression of CsMYB60 and Cs4CL induces pigmentation of white spines If CsMYB60 is the B locus, then increasing its expression level in white spines should lead to pigment accumulation. We tested this hypothesis using a transient expression system, and found that overexpression of CsMYB60 could indeed transform white spines into black ones. The presence of PAs in the spines overexpressing CsMYB60 was further confirmed by MS analysis (Fig. 7A). In addition, using this transient expression system, we also assessed the limiting structural gene in the synthetic pathway of flavonoids. We examined the CsPAL, CsCHS, Cs4CL, CsF3H, and CsGST genes involved in the flavonoid pathway. Interestingly, at least under our experimental conditions, only Cs4CL could change white spines into black ones, in which the presence of PAs was also confirmed by tandem MS (Fig. 7B). Fig. 7. Open in new tabDownload slide Overexpression of CsMYB60 and Cs4CL induces pigmentation of white spines in cucumber. (A) CsMYB60 transformation mediated by particle bombardment in white spines of the line RNS8, with confirmation of proanthocyanidins (PAs) in the transgenic spines as determined by tandem MS. (B) Cs4CL transformation mediated by particle bombardment in white spines of RNS8, with confirmation of PAs in the transgenic spines as determined by tandem MS. The MS ion peak signals for the catechin monomer are m/z 289.0718 ± 5 ppm. All scale bars are 0.7 mm. CsMYB60 can indirectly activate Cs4CL expression In our study, overexpression of CsMYB60 or Cs4CL could lead to the synthesis of PAs and flavonols in white spine plants (Fig. 7). Therefore, we used Agrobacterium-mediated transient transformation of cucumber cotyledons and tobacco leaves to clarify their relationship. GUS staining showed that ectopic expression of CsMYB60 could activate the Cs4CL promoter in cucumber cotyledons, but not in tobacco leaves (Fig. 8A). The gene expression level of GUS was significantly higher in the cucumber cotyledons that were co-transfected with 35S::CsMYB60 and proCs4CL::GUS than in those transfected with only proCs4CL::GUS (Fig. 8B, Supplementary Fig. S10). The results indicated that CsMYB60 could indirectly activate Cs4CL expression in cucumber. Thus, as a key regulatory gene, CsMYB60 might cooperate with other factors to indirectly activate the expression of Cs4CL, leading to the synthesis of PAs and flavonols in black spines. Fig. 8. Open in new tabDownload slide Expression of Cs4CL can be activated indirectly by overexpressed CsMYB60 in cucumber cotyledons. (A) GUS staining assays in Nicotiana benthamiana leaves and cucumber cotyledons for proCs4CL::GUS transfection, and pro35S::CsMYB60 and proCs4CL::GUS co-transfection. Scale bars are 0.25 mm. (B) Relative expression of GUS in cucumber cotyledons transfected with proCs4CL::GUS, and pro35S::CsMYB60 plus proCs4CL::GUS. Data are means (±SD) of three technical replicates. The other two biological repeats are shown in Supplementary Fig. S10. Different letters indicate significant differences at P<0.01. Significant differences were determined according to a multiple statistical analysis system using Data Processing System (DPS) statistics version 7.05 software. Identification of a cucumber Mutator-like element Transposable elements (TEs) play vital roles in generating genomic novelty and diversity in plants (Song and Cao, 2017). We were interested in whether the 6994-bp insertion in the second intron of CsMYB60 in RNS8 was a TE (Fig. 9A) and, if so, of what type. We therefore analysed the fragment and found that it contained a pair of terminal inverted repeats (TIRs; left 117-bp TIR, right 115-bp TIR) and a pair of 9-bp target site duplications (TSDs) flanking each of the two TIRs, resulting in a total length of 7003 bp (Fig. 9B). The blastn search results showed that the insertion also included a MuRA-like transposase gene. Phylogenetic analysis of CsMudrA homologues from several species revealed that CsMudrA shares the highest similarity with CuMudrA (Supplementary Fig. S11). These results indicated that this insertion in CsMYB60 from white-spined RNS8 is a Mutator-like element (MULE), which we named CsMULE (GenBank: MG558001). Fig. 9. Open in new tabDownload slide The inserted fragment in CsMYB60 is a Mutator-like element (MULE) in the white-spined cucumber line RNS8. (A) Gene structure of CsMYB60 alleles from RNS9 (black spines) and RNS8 (white spines). (B) Schematic diagram of CsMULE with MURA-like transposase, terminal inverted repeats (TIRs), and target site duplications (TSDs). (C) Sequence alignment of the CX2CX4HX4(or 6)C motif of the mudrA-like protein in CsMULE. (D) Sequence alignment of the DX18DX15E motif of the mudrA-like protein in CsMULE. Zm, Zea mays; Cs, Cucumis sativus; Cu, Cucumis melo; At, Arabidopsis thaliana; Os, Oryza sativa. The GenBank accession numbers of the proteins used are listed in Supplementary Fig. S11. A conserved motif, CX2CX4HX4(or6)C (where X represents any amino acid), characteristic of DNA binding domains, has been identified at the C-terminal end of MuRA from Arabidopsis (Yu et al., 2000). We also found this conserved motif in the MudrA-like gene in CsMULE (Fig. 9C). The transposons of eukaryotic organisms generally have a characteristic triad of acidic amino acids, D-D-E, in the Mutator-like transposase region. The motif conforms to DX18DX15E, with 35 amino acids intervening between the first D and E (Rossi et al., 2004). Functional analyses have shown that mutations in these conserved amino acids prevent mobilization of the element, demonstrating that these residues are part of the transposase active site (Rossi et al., 2004). We again found the characteristic motif of DX18DX15E in the MudrA-like gene in CsMULE (Fig. 9D). These results further confirmed that the insertion in CsMYB60 was the MULE in cucumber. Insertion of CsMULE decreases CsMYB60 expression levels in an epigenetic manner Transposons can lead to DNA methylation that alters gene expression (Martin et al., 2009). Since the insertion in CsMYB60 in RNS8 was a Mutator-like transposon, we were interested to determine whether the decrease in CsMYB60 expression was caused by this CsMULE in an epigenetic manner. Therefore, we analysed the DNA methylation status of the CsMYB60 promoter using a McrBC-sensitive PCR analysis and observed a much higher methylation level of the –6362 to –4578 promoter region of CsMYB60 in white-spined RNS8 than in black-spined RNS9 (Fig. 10A). Next, we divided the region into nine sub-sections and performed PCR analysis with genomic DNA treated with McrBC as a template (Fig. 10B). We then selected two regions of the CsMYB60 promoter for methylation analysis using bisulphite sequencing (Supplementary Fig. S12). The results indicated that several sub-sections had higher methylation in RNS8 than in RNS9. Thus, the insertion of CsMULE might cause greater methylation of the CsMYB60 promoter, thus decreasing its expression level in white-spined RNS8. Fig. 10. Open in new tabDownload slide DNA methylation status of the CsMYB60 promoter in cucumber fruit with white and black spines. (A) McrBC-sensitive PCR analysis of the promoter of CsMYB60 in RNS8 (white spines) and RNS9 (black spines). + and – indicate whether or not the genomic DNA was treated with McrBC before PCR amplification. (B) McrBC-sensitive PCR analysis of the region from –6468 to about –3006 before the translational start codon (ATG) of CsMYB60 in RNS8 and RNS9. The absence of a PCR product in McrBC-treated samples indicates methylated DNA. Discussion The visual appearance of cucumber fruit is a highly important trait for varietal improvement (Chen et al., 2016). The spine colour is a fruit-related characteristic and is considered an important factor for the economic value of the fruit (Li et al., 2013). Cultivated cucumber was domesticated from its wild form Cucumis sativus var. hardwickii, which still grows in the Himalayan foothills in India, Myanmar, north and west Thailand, and southwest China (Qi et al., 2013; Renner, 2017). Most cultivated cucumbers, especially slicing cucumbers, have white spines, but the wild cucumber has black spines (Qi et al., 2013; Renner, 2017). In this study, we determined that the pigments of black spines mainly comprise flavonols and PAs (Fig. 4A, C). A previous study showed that epidermal flavonoids, which absorb UV-radiation, protect the internal tissues of leaves and stems (Treutter, 2006). In addition, an inheritance study using groundnut provided good evidence for quercetin and rutin having a significant negative impact on the development and mortality of neonate larvae of Spodoptera litura in interspecific derivatives of Arachis species (Mallikarjuna et al., 2004). PAs that are present in the fruit, bark, leaves, and seeds of many plants can provide protection against predation and disease (Dixon et al., 2005). Moreover, both the traits of orange skin in mature fruit and black spines are controlled by a single dominant gene (Li et al., 2013) (Supplementary Fig. S1). Thus, as a result of natural selection, the flavonoid pigments in black spines and fruit skin should provide protection for the seeds of wild cucumber. Flavonoids in various plants are beneficial not only for the plant itself as physiologically active compounds or as stress-protecting agents (Treutter, 2006) but also for humans as antioxidants (Qin et al., 2008). Therefore, our present findings might provide the impetus for a new direction of breeding to obtain increasing flavonoids in the fruit endocarp of cucumber to benefit human health. Genetic analysis has indicated that the black fruit-spine trait is controlled by a single gene, B, and is dominant over the white trait (Walters et al., 2001; Li et al., 2013). A R2R3-MYB gene on cucumber chr.4 has been considered the best candidate for the B locus (Li et al., 2013), and this gene was named CsMYB60 (Zhao et al., 2014). In our present study, through SNP association, we also mapped B to the same region of chr.4 containing CsMYB60 (Supplementary Fig. S7), and we found that all the tested inbred lines with white spines had an altered allele and reduced expression of CsMYB60 (Table 1; Fig. 6B). Finally, overexpression of CsMYB60 could transform white spines into black spines (Fig. 7A). These findings indicate that CsMYB60 can be identified as the B locus. Control of the flavonoid pathway is an ancestral function of the MYB-bHLH-WD40 (MBW) complex in plants (Xu et al., 2015a). In Arabidopsis, the MBW complex activates late flavonoid biosynthetic genes to control the production of anthocyanins and PAs, whereas early flavonoid biosynthetic genes are activated by three R2R3-MYB proteins (MYB11, MYB12, and MYB111) (Li, 2014). Flavonoid MBW proteins might be conserved in cucumber, but the mechanisms regulated by the MBW complex and/or by MYB proteins may differ from those in Arabidopsis and other plants (Li, 2014; Xu et al., 2015a) because our data showed that Cs4CL, the last structural gene in general phenylpropanoid metabolism, is the limiting gene for flavonoid biosynthesis in cucumber and is activated indirectly by CsMYB60 (Fig. 7B; Fig. 8). To our knowledge, this kind of regulatory mechanism has not been reported before. Further studies are needed to clarify the detailed mechanisms responsible for flavonoid biosynthesis. Mutator transposons are the most ubiquitous DNA transposable elements in plant genomes and play a special role in gene and genome evolution (Diao and Lisch, 2006). Mutator activity can result in a wide range of changes, including changes in gene expression (Raizada et al., 2001). In our study, the insertion in the second intron of CsMYB60 in white-spined RNS8 was found to be a CsMULE based on its typical Mutator characteristics (Fig. 9C, D). The decreased expression level of CsMYB60 might have been caused by this CsMULE in an epigenetic manner because the methylation level of a particular region of the CsMYB60 promoter was much higher in the white-spine inbred line than in the black (Fig. 10). This result is in agreement with a previous study of the transition from male to female flowers in gynoecious melon, where the natural and heritable epigenetic change resulted from the insertion of a transposon that is required for the initiation and maintenance of the spreading of DNA methylation to the CmWIP1 promoter (Martin et al., 2009). Transposon-mediated epigenetic regulation in gene expression also occurs in rice (Song and Cao, 2017). The transposon Mutator has been recognized as being one of the most active mobile elements in plants and has been widely employed as a mutagenizing system (van Leeuwen et al., 2007). For instance, RescueMu has been used for high-throughput gene mutation and cloning (Raizada et al., 2001; John et al., 2004). Through the Mutator transposon system, many genes have been successfully cloned, such as An1, Zag1, and Fl1 in maize (May et al., 2003; Raizada, 2003). Thus, the CsMULE identified in our present study might also be used in the future as a mutagenizing tool for functional genomics in cucumber. In conclusion, we determined that the pigments of black fruit spines in cucumber are primarily composed of flavonols and PAs. CsMYB60 is a key regulatory gene, and Cs4CL is activated indirectly by CsMYB60 as a limiting structural gene in the flavonoid biosynthesis pathway. In addition, the CsMULE insertion might result in decreased CsMYB60 expression in an epigenetic manner. These results provide a theoretical base for breeding cucumber varieties with high flavonoid contents and provide a possible tool for discovering functional genes in cucumber. Supplementary data Supplementary data are available at JXB online. Fig. S1. Comparison of fruit skin colour between white-spined RNS8 and black-spined RNS9 at different developmental stages. Fig. S2. Different metabolites between black and white spines at 2 DAA and 12 DAA. Fig. S3. Preliminary chromatic test of the compounds in black spines using various chemical agents. Fig. S4. Accumulation of flavonols in black spines at different developmental stages. Fig. S5. NMR physicochemical and spectral data for the metabolites initially labelled as substances A–E. Fig. S6. UV absorption spectrum, HPLC, and high-resolution MS data for the five metabolites A–E. Fig. S7. SNP association at the B locus. Fig. S8. Phylogenetic analysis of CsMYB60. Fig. S9. Expression analysis of CsMYB60 and key genes involved in flavonoid biosynthesis in cucumber spines. Fig. S10. Additional biological repeats to determine the relative expression of GUS in transiently transformed cucumber cotyledons. Fig. S11. Phylogenetic analysis of CsMudrA homologues in four species. Fig. S12. DNA methylation status of the CsMYB60 promoter in white-spined RNS8 and black-spined RNS9 Table S1. List of different metabolites between black spine and white spine. Table S2. Throughput of RNA-Seq. Table S3. Primers used for PCR reactions. Table S4. Differentially expressed genes between white and black cucumber spines. Acknowledgements This work was supported by funding from the National Natural Science Foundation of China (31672170 and 31222048), the Natural Science Foundation of Shandong Province (JQ201309), the Shandong ‘Double Tops’ Program (SYL2017YSTD06), and the ‘Taishan Scholar’ Foundation of the People’s Government of Shandong Province (ts20130932). 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Genome-wide identification and characterization of R2R3-MYB family in Solanum lycopersicum . Molecular Genetics and Genomics: MGG 289 , 1183 – 1207 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes These authors contributed equally to this work. © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Cao,, Jie;Gulyás,, Zsolt;Kalapos,, Balázs;Boldizsár,, Ákos;Liu,, Xinye;Pál,, Magda;Yao,, Yingyin;Galiba,, Gábor;Kocsy,, Gábor

doi: 10.1093/jxb/ery339pmid: 30260414

Abstract Reactive oxygen species and antioxidants have an important role in the regulation of plant growth and development under both optimal and stress conditions. In this study, we investigate a possible redox control of miRNAs in wheat (Triticum aestivum ssp. aestivum). Treatment of seedlings with 10 mM H2O2 via the roots for 24 h resulted in decreased glutathione content, increased half-cell reduction potential of the glutathione disulphide/glutathione redox pair, and greater ascorbate peroxidase activity compared to the control plants. These changes were accompanied by alterations in the miRNA transcript profile, with 70 miRNAs being identified with at least 1.5-fold difference in their expression between control and treated (0, 3, 6 h) seedlings. Degradome sequencing identified 86 target genes of these miRNAs, and 6722 possible additional target genes were identified using bioinformatics tools. The H2O2-responsiveness of 1647 target genes over 24 h of treatment was also confirmed by transcriptome analysis, and they were mainly found to be related to the control of redox processes, transcription, and protein phosphorylation and degradation. In a time-course experiment (0–24 h of treatment) a correlation was found between the levels of glutathione, other antioxidants, and the transcript levels of the H2O2-responsive miRNAs and their target mRNAs. This relationship together with bioinformatics modelling of the regulatory network indicated glutathione-related redox control of miRNAs and their targets, which allows the adjustment of the metabolism to changing environmental conditions. Ascorbate, glutathione, hydrogen peroxide, miRNAs, redox regulation, wheat Introduction Reactive oxygen species (ROS) and antioxidants have an important role in the regulation of growth and development both under optimal and stress conditions (Kocsy et al., 2013; Considine and Foyer, 2014; Locato et al., 2018). Hydrogen peroxide, H2O2, is the most stable ROS, which facilitates its use for long-distance signalling and control of various metabolic processes at the level of gene expression and protein activity (Foyer et al., 1997; Neill et al., 2002; Hossain et al., 2015). Consistent with this, increased H2O2 in catalase-deficient Arabidopsis mutants results in the induction of genes related to the regulation of stress responses, metabolism, development, and energy homeostasis (Vandenabeele et al., 2004). In H2O2-treated Arabidopsis, altered expression of genes encoding proteins involved in transcription, signal transduction, protein transport, energy homeostasis, cellular organisation, and defence processes has been observed by microarray analysis (Desikan et al., 2001). Similar to Arabidopsis, genes involved in cell defence, signal transduction, and metabolism (carbohydrates and lipids) are also affected by H2O2 treatment in wheat (Li et al., 2011). The expression of redox homeostasis- and photosynthesis-related genes is also changed in wheat. Using a proteomic approach, the effect of exogenous H2O2 on most of these processes has also been shown in rice (Wan and Liu, 2008). In addition to its effect on the total amount of proteins, H2O2 may also regulate their activity as a result of oxidation of the Cys residues. The amount of H2O2 is regulated by the ascorbate–glutathione (AsA-GSH) cycle, which is composed of both enzymatic and non-enzymatic components that indirectly affect the H2O2-dependent physiological processes (Roach et al., 2018). The direct regulatory roles of AsA and GSH have been shown to modify the development of reproductive organs and to affect tolerance to low temperature through their effects on the redox system and gene expression (Gulyás et al., 2014). MicroRNAs (miRNAs) are also important in the control of development (Kidner and Martienssen, 2005; Rubio-Somoza and Weigel, 2011) and stress responses (Phillips et al., 2007; Khraiwesh et al., 2012; Rajwanshi et al., 2014). They can control the expression level of their target genes transcriptionally by DNA methylation and post-transcriptionally by cleavage or translational inhibition of target mRNAs. Many of the target genes of miRNAs encode transcription factors; therefore, one miRNA is able to indirectly regulate a whole set of genes. miRNAs have a pleiotropic effect in the control of development, but one target gene may also be regulated by several miRNAs (Kidner and Martienssen, 2005). They are components of regulatory networks that coordinate gene expression programs and ensure developmental plasticity (Rubio-Somoza and Weigel, 2011). Such networks have been described for developmental phase transitions, leaf senescence, cell proliferation, and leaf polarity; furthermore, interconnection of miRNA-dependent regulatory networks has been suggested to exist under both biotic and abiotic stress conditions (Rajwanshi et al., 2014). In wheat, many miRNAs related to development and stress responses have been discovered (Yao and Sun, 2012), either using computational approaches (Dryanova et al., 2008) or by next-generation sequencing (Sun et al., 2014). Although miRNAs are evolutionarily conserved, several monocot- or wheat-specific miRNAs have been described. By surveying miRNA profiles in 11 different tissues, 323 novel miRNAs (belonging to 276 families) and 524 targets for 124 miRNAs have been identified in a study of wheat (Sun et al., 2014). When the redox regulation of miRNAs was studied in rice, seven H2O2-responsive miRNAs were identified that were involved in transcriptional regulation, nutrient transport, auxin homeostasis, cell proliferation, and programmed cell death (Li et al., 2011). In Brachypodium distachyon, Lv et al. (2016) found 61 H2O2-responsive miRNAs, the targets of which were related to development, reproduction, responses to stress, secondary metabolism, catabolic processes, nucleic acid metabolism, and cellular component organisation. The aim of the present study was to determine whether the effect of H2O2-induced oxidative stress on miRNAs and their target genes is mediated by GSH and other antioxidants in wheat. To achieve this, we identified H2O2-reponsive miRNA and target mRNA profiles together with regulatory networks. The proposed relationships between the various antioxidants and the levels of several miRNAs and their targets were verified in a time-course experiment. Materials and methods Plant material and treatments Seeds of the wheat variety Triticum aestivum L. ssp. aestivum cv. Chinese Spring (CS) were germinated in Petri dishes (1 d at 25 °C, 3 d at 4 °C, 2 d at 25 °C). Seedlings were grown in pots (diameter: 10 cm, height: 8 cm; 22 seedlings per pot) containing 500 ml of half-strength modified Hoagland solution, with a photoperiod of 16 h at 260 µmol m–2 s–1, 22 °C and 75% relative humidity in a growth chamber (Conviron PGV-15; Controlled Env., Ltd., Winnipeg, Canada) (Kocsy et al., 2000). After 10 d growth (2-leaf developmental stage), 10 mM H2O2 was added to the nutrient solution and sampling was conducted after treatment for 0, 1, 3, 6, 9, 12, and 24 h. The treatment and first sampling was performed in the middle of the 16-h light period in order to exclude the possibility of rapid changes that might occur in certain parameters as a result of the growth chamber lights being switched on. Thus, the 0–6 h and 24 h samples were taken during the light period and the 9–12 h samples were taken during the dark period. In addition to the collection of leaf samples (2nd leaf from the base of the stem) for biochemical and molecular biological analysis, the fresh and dry weights of the shoots and roots were also determined. Measurement of H2O2 content H2O2 content of the leaves was measured by the FOX-1 method using a spectrophotometer in a colorimetric reaction as described by Kellos et al. (2008). During this reaction ferrous ions are oxidised to ferric ions by H2O2, and the ferric ions are detected by xylenol orange. Determination of AsA Leaf samples of 500 mg fresh weight were ground with liquid nitrogen in a mortar and extracted with 3 ml of 5% meta-phosphoric acid. In the supernatant, reduced and total AsA (the latter reduced by dithiothreitol) contents were determined by HPLC using an Alliance 2690 system equipped with a W996 photodiode array detector (Waters, Milford, MA, USA). The concentration of dehydroascorbate (DHA), a two-electron oxidised form of AsA was estimated by subtracting the reduced portion from the total AsA pool (Szalai et al., 2014). Analysis of cysteine and glutathione Leaves were ground with liquid nitrogen in a mortar, after which 1 ml of 0.1 M HCl was added to 200 mg sample. The total cysteine and glutathione pools (reduced + oxidised forms) were determined after reduction with dithiothreitol and derivatisation with monobromobimane (Kocsy et al., 2000). For the detection of cystine and glutathione disulphide (GSSG), cysteine and GSH were blocked with N-ethylmaleimide, after which the excess of N-ethylmaleimide was removed with toluol (Kranner and Grill, 1996). Cystine and GSSG were reduced and derivatised as described for the total cysteine and glutathione pools. The two thiols were analysed using an Alliance 2690 HPLC system using a W474 scanning fluorescence detector (Waters). The amount of reduced thiols was calculated as the difference between the amount of total and oxidised thiols. The half-cell reduction potential of the thiol redox couples was calculated using the Nernst equation (Schafer and Buettner, 2001). Analysis of activity of antioxidant enzymes The activities of catalase (CAT, EC 1.11.1.6; measured on the basis of reduction of H2O2), ascorbate peroxidase (APX, EC 1.11.1.11; reduction of H2O2 by AsA), glutathione reductase [GR, EC 1.6.4.2; reduction of GSSG by NADPH and reaction of the produced GSH with 5-5′-dithio-bis(2-nitrobenzoic acid)], and glutathione S-transferase (GST, EC 2.5.1.18; reaction of GSH with 1-chloro-2,4-dinitrobenzene) were determined in the leaves with a spectrophotometer using a colorimetric method as described previously (Soltész et al., 2011). The extraction buffer contained 1 mM AsA in order to avoid the inactivation of chloroplastic APX as suggested by Noctor et al. (2016).The protein content was measured using Bradford reagent (Bradford, 1976) according to Soltész et al. (2011). Small RNA sequencing Total RNA was isolated from the leaves of plants treated with 10 mM H2O2 for 0, 3, or 6 h using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. All small RNA libraries prepared for this study according to Sun et al. (2014) were sequenced with an Illumina Hiseq 2000, generating approximately 10 M data for each sample. Low-quality reads and portions of reads were removed using the sickle program (https://github.com/najoshi/sickle) with the parameters ‘-q 20 -f sanger -l 20’. The cutadapt program (Martin, 2011) was used to trim the 3′ adaptors from reads (parameters ‘- CTGTAGGCACCATCAATCAG - match-read-wildcards -m15’), and only the reads ranging from 18–30 nucleotides were collected. Reads were then aligned to the Rfam 10.0 RNA family database (Griffiths-Jones et al., 2003, 2005; Nawrocki et al., 2015) with bowtie2 (Langmead and Salzberg, 2012), and known cellular structural RNAs such as rRNAs, tRNAs, snoRNAs, and snRNAs were removed based on their alignments using an in-house Perl script. The remaining reads were mapped to wheat microRNAs (Sun et al., 2014), collected, and characterised by blastn allowing no mismatches. The miRNA frequency was normalised as ‘transcripts per million’ (TPM), and the expression was set to 0.01 for miRNAs that were not expressed in one of the samples after normalisation. Following the first filtering for reliability, the differentially expressed miRNAs were obtained by a Bayes-based Poisson Distribution Test (Audic and Claverie, 1997) with difference >1.5 times and sequencing reads >10 in at least one sample. Analysis of miRNAs by qRT-PCR Total RNA was isolated from frozen leaves using Trizol (Invitrogen). A Mir-X miRNA First-Strand Synthesis Kit (Clontech Laboratories, Inc) and SYBR Premix EX Taq II (TaKaRa, Dalian, China) were used for miRNA reverse transcription and qRT-PCR according to the manufacturers’ instructions. qRT-PCR was performed on a CFX96 Real Time System (Bio-Rad, USA) with the following program: denaturation at 95 °C for 3 min, and then subjected to 40 cycles of 95 °C for 15 s, 60 °C for 15 s, 72 °C for 10 s. The entire sequence of the mature miRNA (21–23 nt) was used as a miRNA-specific 5′ primer. The 3′ primer for the qPCR was the mRQ 3′ primer supplied with the kit. The relative expression of miRNA was calculated using the 2–ΔΔCT method normalised to the wheat ACTIN gene CT value. For each sample, the PCR amplification was repeated three times, and the mean values of 2–ΔΔCT were used to determine differences of gene expression using Student’s t-test. Three biological replications were performed with similar results and one replicate is shown in the figures. Degradome sequencing Total RNAs obtained from leaves taken after 0, 3, and 6 h H2O2 treatment were mixed equally for degradome sequencing to see which miRNA targets were cleaved at any of the time points. Briefly, the degradome library was constructed as follows: annealing of approximately 150 ng poly(A)-enriched RNA with Biotinylated Random Primers; Strapavidin capture of RNA fragments through Biotinylated Random Primers; 5′PARE adaptor ligation to only those containing 5-monophosphates; first-strand cDNA was generated from the ligated sequence after reverse transcription using random hexamer 3′ primers; a number of DNA products were produced by PCR amplification. The library was single-end sequenced using an Illumina Hiseq2500 platform at the LC-BIO (Hangzhou, China) following the vendor’s recommended protocol. CleaveLand 3.0 (Addo-Quaye et al., 2009) was used for analysing the sequencing data. The raw data of the degradome sequencing have been submitted to the NCBI SRA datasets under the accession number SRP127561. Investigation of the target mRNAs of miRNAs by qRT-PCR Total RNA was extracted from the leaves with TRI Reagent (Sigma) according to the manufacturer’s instructions, and the samples were treated with DNase I enzyme (Promega). Reverse transcription was performed using M-MLV Reverse Transcriptase and Oligo(dT) 15 primer (Promega) according to the manufacturer’s instructions. The expression level of the target genes was determined by real-time RT-PCR using the primers listed in Supplementary Table S1A at JXB online. The reactions were run on a CFX96 Real-Time PCR instrument (Bio-Rad) and the relative fold-change (FC) values were calculated according to Boldizsár et al. (2016). Computational prediction and analysis of miRNA targets The targets of the H2O2-induced miRNA collection were predicted using the psRNATarget tool (https://plantgrn.noble.org/psRNATarget/). To confirm the degradome sequencing results and to determine additional target genes, the EnsemblPlants 31 release of wheat nucleotide sequences (Bolser et al., 2015) was used as a query cDNA library and the default scoring schema were used with the following parameters: (1) # of top targets=200; (2) Penalty for G:U pair=0.5; (3) Extra weight in seed region=1.5; (4) # of mismatch allowed in seed region=2; (5) Allow bulge (gap) on target=enabled; (6) Penalty for opening gap=2; (7) Calculate target accessibility=disabled; (8) Translation inhibition rate=10–11 NT; (9) Expectation=5; (10) Penalty for other mismatches=1; (11) Seed region=2–13 NT; (12) HSP size=19; and (13) Penalty for extending gap=0.5. MapMan (Thimm et al., 2004) (https://mapman.gabipd.org/) and the KEGG (http://www.genome.jp/kegg/) pathway database were used for annotation of the miRNA target genes. In addition, custom blastx searches were performed against the UniProt protein database (http://www.uniprot.org/downloads) and the predicted miRNA targets using the Geneious software version 9.8.1 (Biomatters, New Zealand; http://www.geneious.com). The functional annotations were extended with gene expression data using an oligonucleotide-based microarray (E-MTAB-6627: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6627/). Preparation of Cy5- and Cy3-labelled cDNA using RNA isolated from the control and H2O2-treated samples, respectively, and microarray hybridisation to a stress-specific 15k wheat oligonucleotide microarray (Szucs et al., 2010) were performed as described by Szécsényi et al. (2013). An Agilent scanner was employed for microarray scanning and data collection as described previously (Kalapos et al., 2016). The validation of microarray was done by qRT-PCR as described for the target mRNAs of miRNAs, and the primers are listed in Supplementary Table S1B. Pathway maps of the miRNAs and their target genes were built and visualised using the yEd graph editor version 3.18.0.2 (yWorks, Germany; https://www.yworks.com/products/yed). Statistical analysis Biochemical data from three independent experiments involving three biological replicates each were evaluated. Statistical analysis was conducted by one-way ANOVA and a least-significant difference (LSD) test or a Dunnett T3 non-parametic test (if any condition had not been fulfilled) using SPSS. The homogeneity of variances was tested by Levene’s test. The relationships between the various parameters were checked by correlation analysis (MS Excel). Results Growth parameters Treatment with H2O2 resulted in a transient wilting and rolling of the leaves after 1 h (Fig. 1). After 2 h of treatment, the leaves of the treated plants recovered and were similar to the controls. The fresh and dry weights and the dry/fresh weight ratio of the shoots and roots were not affected by the H2O2 treatment (Supplementary Fig. S1 at JXB online). Fig. 1. View largeDownload slide Effect on wheat plants of 1 h of treatment with 10 mM H2O2 applied to the roots. Fig. 1. View largeDownload slide Effect on wheat plants of 1 h of treatment with 10 mM H2O2 applied to the roots. Effects of H2O2 on the non-enzymatic components of the AsA-GSH cycle Despite the visible effects on the plants after 1 h, there was no difference in H2O2 content between the control and treated plants (Fig. 2). Subsequently a slight and gradual increase was observed in the H2O2 content during the 24-h course of the experiment in the leaves of both untreated and H2O2-treated plants, which resulted in significant differences at some sampling points compared to the starting value. Fig. 2. View largeDownload slide Effect of H2O2 treatment applied to the roots of wheat plants on the endogenous concentration in leaves. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using Dunnett’s T3 test (P<0.05). The white and black bars on the horizontal axis indicate the light and dark periods, respectively. Fig. 2. View largeDownload slide Effect of H2O2 treatment applied to the roots of wheat plants on the endogenous concentration in leaves. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using Dunnett’s T3 test (P<0.05). The white and black bars on the horizontal axis indicate the light and dark periods, respectively. The amount of DHA and the DHA/AsA ratio greatly decreased in the control and treated leaves (to 50% or lower compared to the starting value) while the amount of AsA and the redox potential of the DHA/AsA redox couple did not change during the experiment, except for AsA in the control leaves after 24 h (Fig. 3). Fig. 3. View largeDownload slide Effects of H2O2 treatment on the size and redox state of the ascorbate pool in the leaves of wheat plants. (A) Concentrations of ascorbic acid (AsA) and dehydroascorbate (DHA), and the percentage of DHA compared to ASA (numbers above columns). (B) Half-cell reduction potential of the DHA/AsA couple (EDHA/AsA). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. C, control; T, treated. Fig. 3. View largeDownload slide Effects of H2O2 treatment on the size and redox state of the ascorbate pool in the leaves of wheat plants. (A) Concentrations of ascorbic acid (AsA) and dehydroascorbate (DHA), and the percentage of DHA compared to ASA (numbers above columns). (B) Half-cell reduction potential of the DHA/AsA couple (EDHA/AsA). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. C, control; T, treated. The concentration of GSH greatly increased compared to the starting value after 3 h and generally remained at or near this level in control plants; however, in the leaves of the treated seedlings the concentration decreased after 6 h, resulting in significant differences being observed (Fig. 4A). Minimum values of GSH were detected during the dark period in treated seedlings. The concentration of GSSG significantly increased compared to the starting value at all sampling points under control conditions and at after 3 h H2O2 treatment. The GSSG/GSH ratio was greater than its initial value both in the control and treated seedlings throughout the experiment. The half-cell reduction potential of the GSSG/2GSH pair showed a significant increase after 6 h of H2O2 treatment and thereafter (Fig. 4B). In contrast to GSH, the amount and redox state of its precursor, cysteine was not, or was only slightly affected by H2O2 (Supplementary Fig. S2). The cysteine concentration was greater only in the control seedlings after 24 h. The cystine content increased compared to the starting value in untreated leaves and remained unchanged in the treated ones except for the sampling at 3 h. The cystine/cysteine ratio varied between 17.2 and 25.5%, and the half-cell reduction potential of this redox couple did not change during the whole experiment in either group of plants. Fig. 4. View largeDownload slide Effects of H2O2 treatment on the size and redox state of the glutathione pool in the leaves of wheat plants. (A) Concentrations of glutathione (GSH) and glutathione disulphide (GSSG), and the percentage of GSSG compared to GSH (numbers above columns). (B) Half-cell reduction potential of the GSSG/2GSH couple (EGSSG/2GSH). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. C, control; T, treated. Fig. 4. View largeDownload slide Effects of H2O2 treatment on the size and redox state of the glutathione pool in the leaves of wheat plants. (A) Concentrations of glutathione (GSH) and glutathione disulphide (GSSG), and the percentage of GSSG compared to GSH (numbers above columns). (B) Half-cell reduction potential of the GSSG/2GSH couple (EGSSG/2GSH). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. C, control; T, treated. Effects of H2O2 on the activity of antioxidant enzymes The activities of the antioxidant enzymes are given on a per protein basis. The concentration of total proteins did not change during the course of the experiment and was not affected by the H2O2 treatment (Supplementary Fig. S3). The activity of CAT, which is involved in the degradation of H2O2, increased in both the control and treated seedlings during the experiment; however, it returned to the starting value after 24 h in the control seedlings but remained high in the treated ones (Fig. 5A). The activity of APX, which removes H2O2 in the AsA-GSH cycle, increased after 1 h of treatment and was significantly greater compared to the starting value throughout the experiment (Fig. 5B), in contrast to control plants where differences were only observed at 3 h and 12 h. The activity of GR, which is also an enzymatic component of the AsA-GSH cycle, increased by 50% in both group of plants during the experiment (Fig. 5C); at 24 h it returned to the initial value in the control seedlings but not in the treated ones. The activity of GST, which is involved in the detoxification of xenobiotics and peroxides through catalysing their conjugation with GSH, exhibited a similar pattern of changes to GR (Fig. 5D), although the differences compared with the starting point were about 2-fold higher at most sampling points. There was also a considerable difference between the control and treated plants at 24 h. Fig. 5. View largeDownload slide Effects of H2O2 on the activities of antioxidant enzymes in the leaves of wheat plants. (A) Catalase (CAT), (B) ascorbate peroxidase (APX), (C) glutathione reductase (GR), and (D) glutathione S-transferase (GST). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Fig. 5. View largeDownload slide Effects of H2O2 on the activities of antioxidant enzymes in the leaves of wheat plants. (A) Catalase (CAT), (B) ascorbate peroxidase (APX), (C) glutathione reductase (GR), and (D) glutathione S-transferase (GST). Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Determination of H2O2-responsive miRNAs Small RNA sequencing was performed with samples collected after 0, 3, and 6 h of treatment repeated in two parallel experiments, making six sets of sequencing in total. After removing the low-quality and contaminant reads (length less than 18 nt or more than 30 nt reads), 63.4 million reads were obtained in total and among them 11.6 million were mapped as unique (Supplementary Table S2). The length distribution of the various small RNAs was similar in the different samples (Supplementary Fig. S4). The proportion of redundant reads was highest (25%) for the 21- and 24-nt sequences and the proportion of unique sequences was highest (60%) for the 24-nt sequences. We determined the differential expression of known wheat miRNAs (Sun et al., 2014) between H2O2-treated and control seedlings. A total of 70 miRNAs had a minimum 1.5-fold difference and they formed seven groups on the basis of the changes in their expression after 3 h and 6 h treatment compared to the starting value (Fig. 6). A transient increase was detected in the expression of six miRNAs at 3 h (group I), while the transcript levels of 18 miRNAs were increased at 3 h and remained high at 6 h (group V). The expression of seven miRNAs was decreased more at 3 h than at 6 h (group II), whilst that of six miRNAs was equally low at 6 h (group VII). A considerable number of miRNAs exhibited either no or slight changes during the first 3 h of treatment, and of these the expression of 21 (group III) and 10 (group IV) increased and decreased at 6 h, respectively. Interestingly, the transcript levels of two miRNAs were lower at 3 h and higher at 6 h compared to the value detected before the H2O2 treatment (group VI). Fig. 6. View largeDownload slide Expression of H2O2-responsive miRNAs in leaves of wheat plants as determined by comparative transcriptome profiling. Changes in expression were determined at 3 h after treatment with H2O2 compared with 0 h, and at 6 h after treatment compared with 0 h. The expression changes of the eight miRNAs highlighted by boxes were validated by qRT-PCR (Fig. 7). Fig. 6. View largeDownload slide Expression of H2O2-responsive miRNAs in leaves of wheat plants as determined by comparative transcriptome profiling. Changes in expression were determined at 3 h after treatment with H2O2 compared with 0 h, and at 6 h after treatment compared with 0 h. The expression changes of the eight miRNAs highlighted by boxes were validated by qRT-PCR (Fig. 7). Based on the sequencing results, the transcript levels of eight H2O2-responsive miRNAs with a minimum 2-fold changes in their expression after 3 h and/or 6 h compared to the starting value (Supplementary Table S3) were further examined using qRT-PCR in a time-course experiment with seven sampling points (Fig. 7). The tendency of the H2O2-induced alterations after 3 h and 6 h of treatment observed using next-generation sequencing was confirmed by these measurements (except for miR3168b), and tae-miR2007a, tae-miR3147a, and tae-miR3523a were found to already be induced after 1 h of H2O2 treatment. Without treatment, the expression of the selected miRNAs exhibited daily patterns: in several cases with higher levels during the light period and lower ones during the night (Fig. 7A, C, F, H). This pattern was modified by H2O2 treatment. The levels of miRNAs were at least 2-fold greater after 24 h of H2O2 treatment compared to the control values, except for miR3106a that had a 50% decrease in its expression. For tae-miR2007a, tae-miR818h, and tae-miR3074a the difference was 9-fold or greater. The miRNA levels were a minimum 4-fold greater for miR3106a after 3 h and 12 h, and for miR3074a after 1 h in control seedlings compared to the treated ones at the same sampling point. Fig. 7. View largeDownload slide Time-course of the expression changes of selected miRNAs in the leaves of wheat plants during 24 h of treatment with H2O2. Expression was determined relative to that of the ACTIN gene at 0 h. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Fig. 7. View largeDownload slide Time-course of the expression changes of selected miRNAs in the leaves of wheat plants during 24 h of treatment with H2O2. Expression was determined relative to that of the ACTIN gene at 0 h. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Identification of the target genes of H2O2-responsive miRNAs In order to determine those target genes of the H2O2-responsive miRNAs whose products were cleaved under our experimental system, RNAs obtained from samples taken at 0, 3, and 6 h of treatment were mixed equally for degradome sequencing. A total of 13.5 million raw reads were obtained and after the data analysis 160466 covered cDNA sequences could be identified (Supplementary Table S3). Based on the degradome sequencing, 86 unique target sequences of 28 H2O2-responsive miRNAs could be identified. For 29 target genes, the H2O2-responsiveness was also shown by microarray analysis (validated by qRT-PCR, r2=0.68; Supplementary Fig. S5), and these genes were related to transcription, redox regulation, and protein phosphorylation and degradation (Supplementary Table S3). Two targets of tae-miR3493b, namely the genes encoding peroxidase 52 and a thioredoxin-like protein, and one target of tae-miR3513a, encoding thioredoxin H8, are components of the antioxidant system. Both miRNAs were induced by H2O2 (Fig. 6, group III) and the expression of their targets was repressed, as shown by microarray analysis (Supplementary Table S3). The time-course of changes in the expression of target genes of the eight selected H2O2-responsive miRNAs shown in Fig. 7 was also investigated. Similar to the miRNAs, the expression of the targets genes also exhibited a daily rhythm in the untreated seedlings, which was altered by H2O2 (Fig. 8). For several of the targets the direction of change was opposite in the control and treated plants, which resulted in significant differences between the transcript levels at particular sampling points (Fig. 8A, C, D, H). Expression was already influenced by the treatment at 3 h, and the difference between the control and treated seedlings was a minimum of 2-fold, but sometimes as much as 10-fold, for at least one sampling point for all the target genes. Five of the targets were already induced at 1 h or 3 h of treatment (Fig. 8B, C, F–H), one was induced only at 24 h (Fig. 8E), and two were repressed (Fig. 8A, D). Moderate negative correlations (r ranging from –0.38 to –0.42) between the expressions of the following miRNA and target pairs were found after H2O2 treatment: tae-miR2007a versus ribulose-1.5-bisphosphate carboxylase activase; tae-miR3106 versus beta-carotene isomerase; and tae-miR3523a versus glycerol-3-phosphate dehydrogenase. For three pairs low negative correlations were observed and for two pairs low positive correlations were observed. Fig. 8. View largeDownload slide Expression patterns of miRNA target genes during 24 h of treatment with H2O2. (A) Peroxisomal membrane protein (PMP, TC458412), (B) ribulose-1,5-bisphosphate carboxylase activase (RA, CK215494), (C) UDP-glucuronate epimerase (UGE, CA697618), (D) ubiquitin carboxyl-terminal hydrolase 7 (UCTH7, CA612693), (E) beta-carotene isomerase (β-CI, Ta#S52543088), (F) auxin-responsive protein (IAA6, Ta#S61781874), (G) ADP, ATP carrier protein 1 (ACP, CA665835), and (H) glycerol-3-phosphate dehydrogenase (GPDH, TC402657). Expression was determined relative to that of the ACTIN gene at 0 h. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The correlation coefficients between the expression of miRNAs and their targets are given for the H2O2-treated samples. The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Fig. 8. View largeDownload slide Expression patterns of miRNA target genes during 24 h of treatment with H2O2. (A) Peroxisomal membrane protein (PMP, TC458412), (B) ribulose-1,5-bisphosphate carboxylase activase (RA, CK215494), (C) UDP-glucuronate epimerase (UGE, CA697618), (D) ubiquitin carboxyl-terminal hydrolase 7 (UCTH7, CA612693), (E) beta-carotene isomerase (β-CI, Ta#S52543088), (F) auxin-responsive protein (IAA6, Ta#S61781874), (G) ADP, ATP carrier protein 1 (ACP, CA665835), and (H) glycerol-3-phosphate dehydrogenase (GPDH, TC402657). Expression was determined relative to that of the ACTIN gene at 0 h. Data are means (±SD) of three independent experiments involving three biological replicates each. Significant differences from the value at 0 h were determined using a least-significant difference (LSD) test (P<0.05). The correlation coefficients between the expression of miRNAs and their targets are given for the H2O2-treated samples. The white and black bars on the horizontal axes indicate the light and dark periods, respectively. Using bioinformatics tools, 6808 unique target sequences (of which 86 were identified by degradome sequencing) of 70 H2O2-responsive miRNAs were found (Supplementary Table S3). The following targets were related to the components of the AsA-GSH system or to other antioxidants: tae-miR3369a, a phosphomannomutase involved in AsA biosynthesis; tae-miR3513a, a monodehydroascorbate reductase; tae-miR3506b, a peroxidase; tae-miR506b, GST; tae-miR3064a and tae-miR3510a, both thioredoxin. For 1647 targets the H2O2-responsiveness was also demonstrated by microarray analysis (Supplementary Table S3). Based on their targets, two types of KEGG analysis of H2O2-responsive miRNAs were conducted using the database available for Brachypodium, a close relative to wheat (Supplementary Table S5, Supplementary Fig. S6). In the first analysis, the number of H2O2-responsive miRNAs was compared to the total number of the miRNAs in the individual categories in which the miRNAs were grouped based on their targets. The greatest number of such miRNAs were related to ‘plant–pathogen interaction’ (bdi04626), ‘protein processing in endoplasmatic reticulum’ (bdi04141), ‘carbon metabolism’ (bdi01200), ‘biosynthesis of amino acids’ (bdi01230), ‘purine metabolism’ (bdi00230), processes in ‘spliceosome’ (bdi03040), and ‘plant hormone signal transduction’ (bdi04075). In the second analysis, the number of H2O2-responsive target genes was examined (Supplementary Fig. S6). The two analyses gave different results since one miRNA may have several target genes and one target gene may be controlled by several miRNAs. While the abundance of the H2O2-responsive miRNAs in relation to the whole miRNA set in wheat was a maximum 10% in the various KEGG pathway categories (first analysis approach), this ratio was at least 30% for 45% of the target genes (second approach; Supplementary Fig. S6). Considering the seven largest groups of categories, the same ones were selected by both approaches except for ‘purine metabolism’ (bdi00230) by grouping of miRNAs and ‘ribosome-related processes’ (bdi03010) by grouping of targets. The enrichment of targets of H2O2-responsive miRNAs in the categories related to AsA and GSH metabolism, peroxisome, and proteasome varied between 20–35%. The possible interactions of the 70 identified H2O2-responsive miRNAs with their 6808 targets were also analysed (Supplementary Fig. S7). This network had 9620 connections and showed that most miRNAs had several targets, and most of the targets were controlled only by one miRNA. However, several of them were regulated by two or more miRNAs, and thus the miRNAs and their targets formed a complex network. Located in the central part of this network are tae-miR818b, tae-miR818c, tae-miR818m, tae-miR818k, tae-miR818h, tae-miR3369a, tae-miR3523a, and tae-miR3506b (Fig. 9), which between them have 233 targets with 1437 connections. In this part of the network, tae-miR3369a and members of the tae-miR818 family control the expression of many genes at the translational level, while tae-miR3506b and tae-miR3523a do so by cleavage of their target mRNAs (Supplementary Table S3). Most targets of these eight miRNAs are involved in transcriptional regulation, and protein phosphorylation and degradation (Supplementary Table S6A). Based on the KEGG categories, they were mainly (i.e. 3–8 targets per category) related to processes in the spliceosome, biosynthesis of amino acids, protein processing in the endoplasmic reticulum, and carbon, purine, starch, and sucrose metabolism (Supplementary Table S6B). Using a microarray analysis, the level and direction of the expression changes of 1647 H2O2-responsive target genes were also determined (Table S3). Of the targets of tae-miR3369a, 25 were present on the array and among them the expression of 12 and six genes increased and decreased, respectively. It is worth noting that for miR3493b, 16 of the 35 targets, and for miR156a, four of the 17 targets were also identified by degradome sequencing. Fig. 9. View largeDownload slide miR818 family members in the central part of the network of H2O2-responsive miRNAs and their target genes in leaves of wheat plants. The complete network was determined using degradome sequencing or bioinformatics tools and is shown in Supplementary Fig. S7. Squares, miRNAs; triangles, target genes identified by both degradome analysis and bioinformatics tools; circles, target genes identified by bioinformatics tools. Blue shading indicates a decrease and red shading indicates an increase in the target gene expression based on microarray data (shown on the basis of fold-change, FC); grey shading indicates lack of expression data. Continuous lines indicate cleavage of the target, dashed lines indicate translational inhibition. The colour of the lines indicates the strength of the interaction based on the expected value (E-value). Fig. 9. View largeDownload slide miR818 family members in the central part of the network of H2O2-responsive miRNAs and their target genes in leaves of wheat plants. The complete network was determined using degradome sequencing or bioinformatics tools and is shown in Supplementary Fig. S7. Squares, miRNAs; triangles, target genes identified by both degradome analysis and bioinformatics tools; circles, target genes identified by bioinformatics tools. Blue shading indicates a decrease and red shading indicates an increase in the target gene expression based on microarray data (shown on the basis of fold-change, FC); grey shading indicates lack of expression data. Continuous lines indicate cleavage of the target, dashed lines indicate translational inhibition. The colour of the lines indicates the strength of the interaction based on the expected value (E-value). Discussion Effects of H2O2 on the redox environment in the leaves Induction of oxidative stress is a common consequence of abiotic stresses. This effect has previously been successfully simulated by H2O2 treatment in wheat and maize as evidenced by modification of the redox environment (Kellos et al., 2008; Gulyás et al., 2014). Although the effect of H2O2 on the miRNA profile has been investigated in rice and Brachypodium, the possible involvement of the AsA-GSH cycle and other antioxidants in the control of miRNA levels was not examined in these studies (Li et al., 2011; Lv et al., 2016). In our present study, H2O2 treatment efficiently modified the GSH-dependent redox environment and activated related protective mechanisms in the leaves of wheat seedlings, and thus growth was not affected, as shown by the fresh and dry weight data (Supplementary Fig. S1). The transient wilting and rolling of the leaves, as a part of the protection (Fig. 1), may have been due to a rapid loss of water content by the opening of stomata induced by transient local changes in H2O2 concentration, since H2O2 signaling is involved in the control of stomatal movement (Hua et al., 2012). Alterations in the GSH-dependent redox environment were indicated by a decrease in GSH content and an increase in the half-cell reduction potential (EGSSG/2GSH) compared to the untreated control plants (Fig. 4). After treatment for 3 h, when the first sampling for miRNA sequencing was prepared, the GSH content was by 30% lower in the treated seedlings than in the controls. In contrast to GSH, the AsA concentration and EDHA/AsA value did not change (Fig. 3), which can be explained by the fact that the AsA pool was three times greater than that of GSH. APX, which had increased activity throughout the H2O2 treatment (Fig. 5), could have successfully decomposed the excess H2O2 taken up by the treated plants. Thus, no changes in the endogenous H2O2 concentration were detected in leaf tissue extracts of treated plants compared to controls (Fig. 2), although this does not exclude local changes in concentration in specific cells (stomata) and organelles (chloroplasts). However, during the removal of H2O2 in the AsA-GSH cycle, the amount and redox state of GSH changed in leaf extracts because of its oxidation by GR, which had increased activity in treated plants relative to controls after 24 h of treatment (Fig. 5). In addition, the greater use of GSH by GST for the detoxification of peroxides in the H2O2-treated plants after 24 h would also have reduced its concentration (Fig. 5). CAT also could have significantly contributed to the degradation of H2O2 since its activity was 60% higher in the treated seedlings compared to the control ones after 24 h (Fig. 5). The correlations between the expression levels of miRNAs, their target mRNAs, and antioxidants (GSH, APX, GST, CAT) in the treated seedlings showed the closeness and direction of their relationships (Supplementary Table S7), The control of miRNAs by GSH is in agreement with results obtained for miR395 in sulphate-deprived Arabidopsis, as its expression was modified after addition of exogenous GSH and in GSH-deficient mutants (Jagadeeswaran et al., 2014). In addition, we found tae-miR395a to be H2O2-responsive in wheat (Fig. 6), which is in agreement with previous results in Brachypodium (Lv et al., 2016). Overall, the data indicate that the effect of H2O2 on miRNAs is mediated by certain components of the AsA-GSH cycle, which has a central role in the redox regulation (Foyer and Noctor, 2011). Effects of H2O2 on miRNA profiles The H2O2-induced modification of the GSH-dependent redox environment had a significant effect on the miRNA profile in wheat since a minimum 1.5-fold change was observed in the expression of 70 miRNAs as shown by the sequencing results (Fig. 6). Far more miRNAs were up-regulated (44) than down-regulated (22). Four miRNAs were first inhibited (at 3 h of treatment) and induced (after a further 3 h of treatment). Similar to wheat, a large number of miRNAs (61) have been found to be affected by H2O2 in Brachypodium (Lv et al., 2016), but only seven are affected in rice (Li et al., 2011). The low number of H2O2-responsive miRNAs in rice is surprising, since the developmental stage of the seedlings examined (2–3 leaves), the concentration of H2O2 applied (10–20 mM), and the duration of treatment (2–6 h) were similar in all three studies. These results indicate the greater sensitivity of the miRNA-related regulatory system to oxidative stress in wheat and Brachypodium compared to the more phylogenetically distant rice. The ratio of H2O2-responsive miRNAs compared to their total number was very similar (mostly 1:10) within the various KEGG categories in wheat (Supplementary Fig. S6). Among the categories with the greatest number of H2O2-responsive miRNAs were carbon metabolism and peroxisome-associated reactions, including the glyoxylate pathway. These processes are related to the chloroplasts and peroxisomes (Slesak et al., 2007), the major organelles responsible for the production of H2O2, which explains the large number of H2O2-responsive miRNAs in the metabolic pathways occurring in them. In addition to H2O2 formation, peroxisomes and chloroplasts are also connected to the redox system through the formation of glycine and γ-glutamylcysteine, respectively, since these compounds are precursors of GSH. Two other interesting KEGG categories with high numbers of H2O2-responsive miRNAs in relation to our experimental system were the ‘protein processing in the endoplasmatic reticulum’ (bdi04141) and the ‘proteasome-related degradation of proteins’ (bdi03050). The endoplasmatic reticulum is the main source of H2O2 in the cytosol (Slesak et al., 2007), and proteasomes are protein complexes where H2O2-mediated S-glutathionylation of proteins takes place if the cellular redox state shifts (Jung et al., 2014). In this process, the H2O2-dependent alteration in the ratio of GSH/GSSG may affect the metabolism of proteins via the involvement of miRNAs. Although several H2O2-responsive miRNAs are found in wheat (a total of 70; Fig. 6) and Brachypodium (a total of 61; Lv et al., 2016), only two of them, tae-miR160b and tae-miR395a, were common between the two species (Supplementary Table S8). tae-miR395a is involved in sulphate reduction and therefore indirectly in the formation of GSH through cysteine, and tae-miR160b has auxin-related functions. Thus, basic regulatory processes such as hormonal and redox regulation are conserved between these two species. While no overlap of H2O2-responsive miRNAs was observed between wheat and rice, miR169d, miR827-3p, miR397a, and miR408-5p were affected by H2O2 in both Brachypodium and rice (a total of seven H2O2-responsive miRNAs; Li et al., 2011; Lv et al., 2016), which regulate genes encoding HAP2-like transcription factors, SPX-domain proteins (regulation of phosphate homeostasis), laccase (lignin biosynthesis), and a monosaccharide transport protein, respectively. The limited overlap in H2O2-related miRNAs indicates their specificity between the three species during responses to environmental changes. Ozone-induced oxidative stress affects members of the miR156 family (controlling flowering, yield, and leaf initiation) in Arabidopsis (Iyer et al., 2012), and we also found developmental effects in wheat (Supplementary Table S8). However, the members of the other 21 ozone-responsive miRNA families were not influenced by H2O2 in wheat. There is also only one common oxidative stress-responsive miRNA family between rice and Arabidopsis, namely miR169 (targeting HAP2-like transcription factor), which is involved in stress responses (Li et al., 2011; Iyer et al., 2012). Members of this family are also induced by H2O2 in Brachypodium (Lv et al., 2016). miR160a (targeting auxin response factor 22) and miR164 (targeting phytoene dehydrogenase) were induced by oxidative stress in both Arabidopsis and Brachypodium. These data indicate that different sets of miRNAs are involved in the responses to various oxidants such as H2O2 and O3, and that response of the members of the same miRNA family to various abiotic stresses generally also differ in different species. On the other hand, some similarities can also be found in the miRNA sets induced by oxidative and various abiotic stresses based on the results obtained in Arabidopsis (Barciszewska-Pacak et al., 2015; Zhang, 2015). Hence, it is difficult to describe the involvement of individual miRNAs in stress responses with a general model. The differences between species may be due to the complex regulatory networks of miRNAs, in which the role of the individual miRNAs varies between species. When we examined the effects of H2O2 on miRNAs using qRT-PCR in a time-course experiment over 24 h, light-dependent daily changes were observed in their levels even in the untreated seedlings (Fig. 7), which corresponds with the light-responsiveness of miRNAs that has been demonstrated in Brassica rapa (Zhou et al., 2016). Redox control of these light-dependent daily variations can be supposed since they were modified by H2O2 in wheat. The tendency of changes in the expression of the miRNAs detected by sequencing after 3 h and 6 h treatment with H2O2 were confirmed by qRT-PCR. However, after 9, 12, and 24 h exposure to H2O2, the initial groupings based on the changes in transcription at 3 h and 6 h (Fig. 6) can be no longer applied since the expression levels varied within the groups (Fig. 7). Only two miRNAs that showed an increase after 6 h of H2O2 treatment (tae-miR3147a and tae-miR3074a) exhibited similar time-courses of expression from 9–24 h. The large increases in expression of these two miRNAs that were observed during the first 6 h of treatment were probably due to the additive effect of H2O2 and light, since the transcript levels had decreased substantially only 1 h into the dark period. In contrast, the amount of tae-mir3106a was still high after 1 h in the dark, and it decreased only after 4 h of darkness in the treated plants. The effect of light on H2O2-responsive miRNAs was further supported by the fact that the expression of five of them increased again once the lights in the growth cabinet came back on. Functions of selected miRNAs based on their target genes Using bioinformatics tools, we could determine 6808 possible targets for the 70 H2O2-responsive miRNAs that we had identified (Supplementary Table S3). However, when we used degradome sequencing, only 86 targets of 28 of them could be determined, which indicated that only this smaller set was actually affected by miRNAs after 0, 3, or 6 h of treatment in our experimental system. This large difference could derive from possible temporal and spatial shifts in the expression changes in miRNAs and their targets, and from the existence of a complex regulatory network that includes opposite miRNA regulators of the same target (Kawashima et al., 2009; Liang et al., 2014). Thus, a temporal shift in expression was found for tae-miR3106 and its target, beta-carotene isomerase (Fig. 8E). Among the target genes determined by degradome sequencing, several were related to transcriptional regulation (targets of tae-miR156a, tae-miR3294b, tae-miR3369a, tae-miR3493aa), protein phosphorylation (targets of tae-miR3319b, tae-miR3332a), protein degradation (targets of tae-miR3369a, tae-miR818b, tae-miR2001a, tae-miR399b), and redox regulation (targets of tae-miR3493a, tae-miR3513a), and the core part of our regulatory network model was also associated with these processes (Fig. 9). The H2O2 responsiveness of these target genes in wheat was also confirmed by microarray analysis (Supplementary Table S3). In addition, KEGG analysis confirmed the enrichment of the target genes of H2O2-responsive miRNAs in the categories related to protein metabolism and redox processes (Supplementary Fig. S6). Although the degradation of only a few target mRNAs has been examined in Brachypodium and rice (Li et al., 2011; Lv et al., 2016), the effect of H2O2 has been shown for several redox-related and protein decomposition-related proteins in these species (Wan and Liu, 2008; Bian et al., 2015). The results of these studies are in accordance with those that we obtained in wheat using degradome analysis as shown by the occurrence of common functional categories such as redox regulation and protein degradation. The results of our study indicate feed-back regulation between the redox system and the miRNAs since the H2O2-induced modification of GSH levels and APX activity affected several miRNAs whose targets control the levels of redox compounds in wheat (Fig. 10). Thus, two miRNAs were found by degradome sequencing (tae-miR3493b that targets the genes for peroxidase 52 and a thioredoxin-like protein, and tae-miR3513a that targets the gene for thioredoxin H8) and six were found by bioinformatics tools. This hypothesis was also corroborated by the case of miR395 that controls the synthesis of the GSH precursor, cysteine, which has also been shown to be H2O2-responsive in Brachypodium (Lv et al., 2016) and its expression depends on the availability of GSH in Arabidopsis (Jagadeeswaran et al., 2014). Besides indirect control of miRNAs through various antioxidants, H2O2 may also affect them directly, as has been suggested for heavy metals (Yang and Chen, 2013). Instead of linear relationships, a network of mutual interactions can be presumed among H2O2, other redox compounds, miRNAs, and metabolism that allows a continuous redox-dependent adjustment of miRNA levels and their related metabolic processes (Fig. 10). Fig. 10. View largeDownload slide A proposed regulatory network of H2O2, various components of the redox system, miRNAs, their target genes and related metabolic processes. Fig. 10. View largeDownload slide A proposed regulatory network of H2O2, various components of the redox system, miRNAs, their target genes and related metabolic processes. Although the first 6 h were very important in the responses of miRNAs and their targets to H2O2 in wheat, similar to Brachypodium and rice (Li et al., 2011; Lv et al., 2016), subsequent changes were also determinative for the reaction to stress, as indicated by the observations for eight miRNAs and their targets over a 24-h time-course experiment (Figs 7, 8). Similar to the eight miRNAs selected for qRT-PCR analysis, their target genes also exhibited a light-dependent daily rhythm in untreated seedlings. This observation is not surprising since natural light intensity changes during the day, which affects the possible formation of ROS in the photosynthetic electron transport chain, and consequently the redox-sensitive miRNAs and their targets will be influenced. In our experimental approach we were probably detecting these light-responsive miRNAs and their targets through modifying the amount of ROS after the addition of H2O2 to the nutrient solution of the plants. Although for certain miRNA-target pairs we observed the expected negative correlation, it was not detected for all of them (Fig. 8). The lack of such a relationship can be explained by the function of miRNAs as mobile signalling elements during the H2O2-dependent regulation of gene expression, for which a model has been established in Arabidopsis (Liang et al., 2014). According to this model miRNAs can act on their target genes in different cells, tissues, or organs. Consistent with this hypothesis, in the case of miR395 and its target involved in sulphur assimilation, a positive temporal but negative spatial correlation has been found in Arabidopsis (Kawashima et al., 2009). In addition to the dynamic changes in miRNA levels, similar changes also occur in ROS levels because of the compartmentalisation of ROS production and removal (Noctor et al., 2018), which allows an even more complex redox-dependent regulation of miRNA targets. A further possible explanation for a lack of the expected negative correlation between the level of miRNAs and their targets is the existence in wheat of regulatory networks in which one target gene is regulated by several miRNAs, and one miRNA controls several targets. In addition, a positive post-transcriptional regulation of gene expression is also possible, as has been described for miR171b controlling arbuscular mycorrhizal symbiosis in Medicago trunculata (Couzigou et al., 2017). Thus, in our network the result of the negative and positive regulatory effects could be to produce very fine regulation with small changes. In this network, tae-miR33506b, tae-miR3523a, tae-miR3369a, and members of the tae-miR818 family have a central position with many targets, through which they are interconnected with other miRNAs (Fig. 9). A similar regulatory network of H2O2-responsive miRNAs and their targets has also been proposed in Brachypodium, in which a large number of target genes are also controlled by one miRNA (Lv et al., 2016). This system allows coordinated regulation of a large set of genes. It is probable that only a small number of the many possible targets of a miRNA are regulated at any given time-point, depending on the organ, developmental stage, environmental effects, and regulatory interactions of various signalling pathways, as indicated by the large difference that we found in wheat between the number of targets detected by degradome sequencing and the number predicted by bioinformatics tools. Conclusions H2O2 can modify the redox state in wheat through its effect on GSH, APX, and other antioxidants, which in turn influences the expression of miRNAs and their target genes, and hence affects metabolism. A network of interactions between the components of this model can be proposed. Of the 70 H2O2-responsive miRNAs that we selected, we could identify the degraded targets (86) only for 28 of them by degradome analysis. They were related to transcriptional regulation, protein phosphorylation, protein degradation, and redox regulation. Based on our bioinformatics analysis, we identified a regulatory network of these 70 miRNAs with 6808 unique targets, enabling fine adjustment of a large set of redox-responsive genes. Supplementary data Supplementary data are available at JXB online. Fig. S1. Effect of H2O2 treatment on the fresh and dry weights, and the dry/fresh weight ratio of shoots and roots. Fig. S2. Effect of H2O2 treatment on the size and redox state of the cysteine pool. Fig. S3. Effect of H2O2 treatment on the total protein content. Fig. S4. Proportion of redundant and unique reads in the miRNA transcriptome. Fig. S5. Regression analysis of gene expression results obtained by microarray and qRT-PCR. Fig. S6. KEGG-analysis of H2O2-responsive miRNAs and their targets. Fig. S7. The whole network of the H2O2-responsive miRNAs and their targets determined by degradome sequencing and bioinformatics tools. Table S1. Primers used for the qRT-PCR analysis of miRNAs target genes and the validation of microarray data. Table S2. Results of small RNA sequencing. Table S3. H2O2-responsive miRNAs and their target genes. Table S4. Summary of degradome sequencing. Table S5. KEGG categories of the targets of H2O2-responsive miRNAs. Table S6. Genes and their regulator miRNAs in the core network, and the distribution of core network miRNAs and their target genes in different KEGG metabolic pathways. Table S7. Correlations between the time-course changes during 24-h H2O2 treatment in the level of antioxidants, selected miRNAs, and their target mRNAs as determined by qRT-PCR. Table S8. miRNAs responsive to oxidative stress in two or more species out of wheat, Brachypodium, rice, and Arabidopsis. Abbreviations Abbreviations AsA ascorbic acid APX ascorbate peroxidase CAT catalase GR glutathione reductase GSH glutathione GSSG glutathione disulphide GST glutathione S-transferase ROS reactive oxygen species Acknowledgements The authors thank Apollónia Horváth and Mónika Fehér for their help in plant cultivation and treatments, and Csilla Terézia Nagy and Mohamed Ahres for the biochemical measurements. This work was funded by the National Research, Development and Innovation Office, Hungary (grants: TÉT_12_CN-1-2012-0002 and ANN 117949) and the National Key Research and Development Program of China (2016YFD0101004). References Addo-Quaye C , Miller W , Axtell MJ . 2009 . CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets . Bioinformatics 25 , 130 – 131 . Google Scholar Crossref Search ADS PubMed Audic S , Claverie JM . 1997 . The significance of digital gene expression profiles . Genome Research 7 , 986 – 995 . 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Zhou, Wenguan; Chen, Feng; Zhao, Sihua; Yang, Caiqiong; Meng, Yongjie; Shuai, Haiwei; Luo, Xiaofeng; Dai, Yujia; Yin, Han; Du, Junbo; Liu, Jiang; Fan, Gaoqiong; Liu, Weiguo; Yang, Wenyu; Shu, Kai

doi: 10.1093/jxb/ery247pmid: 29982626

Abstract Soybean seeds contain higher concentrations of oil (triacylglycerol) and fatty acids than do cereal crop seeds, and the oxidation of these biomolecules during seed storage significantly shortens seed longevity and decreases germination ability. Here, we report that diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, increases germination and seedling establishment from aged soybean seeds by increasing fatty acid metabolism and glycometabolism. Phenotypic analysis showed that DA-6 treatment markedly promoted germination and seedling establishment from naturally and artificially aged soybean seeds. Further analysis revealed that DA-6 increased the concentrations of soluble sugars during imbibition of aged soybean seeds. Consistently, the concentrations of several different fatty acids in DA-6-treated aged seeds were higher than those in untreated aged seeds. Subsequently, quantitative PCR analysis indicated that DA-6 induced the transcription of several key genes involved in the hydrolysis of triacylglycerol to sugars in aged soybean seeds. Furthermore, the activity of invertase in aged seeds, which catalyzes the hydrolysis of sucrose to form fructose and glucose, increased following DA-6 treatment. Taken together, DA-6 promotes germination and seedling establishment from aged soybean seeds by enhancing the hydrolysis of triacylglycerol and the conversion of fatty acids to sugars. Aged soybean seed, DA-6, fatty acid, germination, glycometabolism, seedling establishment Introduction The legume species soybean (Glycine max L.) originated in East Asia (Kuroda et al., 2006; Lam et al., 2010), and is now widely grown, being the primary oilseed crop in the world, with the USA, Brazil, Argentina, India, and China being the main soybean-growing countries (Zhou et al., 2015; Schulte et al., 2017). In China, where soybean production depends on maize–soybean intercropping, the area under which soybean is grown has increased significantly in recent years (Yang et al., 2014, 2015; Chen et al., 2017; Liu et al., 2017). However, it is noteworthy that, despite this, China is currently the major soybean-importing country globally; to meet the increasing demand for plant protein, oil, and food, further improvements in soybean production are essential. Seed germination is one of the most important stages of the plant life cycle, contributing to the distribution of wild species, and to increased yield and quality of cultivated crop plants (Shu et al., 2013, 2015, 2016b, c; Kan et al., 2016; Rubio de Casas et al., 2017). Generally, the emergence of the radicle indicates the completion of seed germination (Bewley, 1997; Shu et al., 2016c). Immediately following seed germination, seedling establishment is another key developmental stage, whereby the seedling transitions from the heterotrophic to the autotrophic state (Eastmond, 2006; Quettier et al., 2008; Chen and Thelen, 2010; Theodoulou and Eastmond, 2012; Eastmond et al., 2015). Consequently, both seed germination and seedling establishment are essential for subsequent plant development. It is worth noting that both processes are powered by the energy that is stored in the seed itself (Eastmond, 2004, 2006; Quettier et al., 2008; Chen and Thelen, 2010). The mitochondrial FAD-dependent glycerol-3-P dehydrogenase:ubiquinone oxidoreductase (FAD-GPDH) pathway has been proposed to be involved in the breakdown of fatty acids and glycerol in plant seeds (Huang, 1975). Several elegant studies demonstrated that the FAD-GPDH cascade is particularly important in oilseed plants, by which the hydrolysis of triacylglycerol releases free fatty acids and glycerol, with the fatty acids and glycerol then being converted to sugars, which support the seed germination and seedling establishment processes (Eastmond, 2004, 2006; Quettier et al., 2008; Theodoulou and Eastmond, 2012). The Arabidopsis SDP6 (Sugar-Dependent 6) gene encodes FAD-dependent glycerol-3-P dehydrogenase (FAD-G3P), and, although sdp6 mutant seeds are able to germinate, the seedlings exhibit a marked arrested growth phenotype in the absence of exogenous sucrose during the seedling establishment stage (Quettier et al., 2008). This means that the transition from glycerol and fatty acids to sucrose is significantly impaired in sdp6 seeds, with the exogenous supply of sucrose fully rescuing the arrested growth phenotype of the sdp6 mutant (Quettier et al., 2008). Another key gene, SDP1 (Sugar-Dependent 1), encoding a patatin-like domain-containing triacylglycerol lipase, also plays a key role during post-germinative growth (Eastmond, 2006). In sdp1, the hydrolysis of triacylglycerol is blocked, resulting in sdp1 seeds also exhibiting an arrested growth phenotype in the absence of sucrose, mimicking the sdp6 phenotype (Eastmond, 2006). Altogether, both the hydrolysis of triacylglycerol and the conversion of fatty acids and glycerol to sugars are important for the successful powering of seed germination and seedling establishment, with the sugar supply allowing the young seedlings to achieve the photosynthetic autotrophic state (Graham, 2008; Kelly et al., 2011). Numerous studies have demonstrated that, compared with the cereal crop seeds, including rice, wheat, and maize, soybean seeds contain much higher oil and fatty acid contents (Schmidt et al., 2011; Lestari et al., 2013; Dhakal et al., 2014; Liu et al., 2014; Li et al., 2017; Teng et al., 2017). During storage, seed respiration, a catabolic reaction, utilizes glucose and other biomolecules (principally oils and fatty acids), and, as a consequence, significantly shortens seed longevity and decreases the rates of seed germination and seedling establishment, even causing soybean seeds which had been stored for long periods to be incapable of germination (Barros et al., 2017; Munz et al., 2017). The ability of aged soybean seed to germinate decreases markedly as storage time increases; interestingly, the loss of germination potential also correlates with a decline in RNA integrity in soybean seeds following prolonged storage (Fleming et al., 2017). Another study demonstrated that, during natural aging processes, phospholipase Dα (PLDα) affected the soybean seed phospholipid and triacylglycerol profiles, suggesting that suppression of PLDα activity in soybean seed has the potential to improve seed quality during long-term storage (Lee et al., 2012). A decline in the germination ability of aged soybean seed significantly constrains soybean production, as it results in poor germination and seedling emergence from farm-saved seeds in the field. Therefore, it would be worthwhile developing an efficient method to promote the germination and seedling establishment of aged soybean seeds. Moreover, dissection of the precise physiological and molecular mechanisms underlying the reduced germination and seedling emergence capabilities of aged seeds will help us to better understand the soybean seed germination and seedling establishment processes. Diethyl aminoethyl hexanoate (DA-6) is a novel artificial plant growth regulator, which can increase leaf chlorophyll content, and increase the photosynthetic rate and the rates of carbon and oxygen metabolism in plants (Yokoyama et al., 1982; Zhang et al., 2008; Jiang et al., 2012). In agriculture, DA-6 has been registered for use on a range of crops, including cabbage, pakchoi, cotton, tomato, soybean, peanut, and maize (Jiang et al., 2012). Furthermore, DA-6 also increased microalgal growth and simultaneously improved the quality and quantity of microalgal lipid for biodiesel production (Salama et al., 2014; Jiang et al., 2015), while treatment with DA-6, in combination with EDTA, appeared to be optimal for the remediation efficiency of Lolium perenne L. (perennial ryegrass) on lead-contaminated soil (He et al., 2013). However, to date, minimal information is available on the role of DA-6 in seed science research especially on seed germination and early seedling establishment from aged soybean seeds. Here, we report that DA-6 promotes germination and seedling establishment in naturally and artificially aged soybean seeds by increasing triacylglycerol hydrolysis, fatty acid metabolism, and glycometabolism. Several types of physiological and biochemical analyses and quantitative PCR (qPCR) assays demonstrated that DA-6 increased the hydrolysis of triacylglycerol and the conversion of fatty acids to sugars during imbibition of aged soybean seeds, and, consequently, increased seed germination and seedling establishment from aged soybean seeds. We believe that this effective treatment will significantly expand the potential applications of DA-6 in agricultural systems, especially in countries where farm-saved seed is at risk of deterioration during storage. Materials and methods Plant materials and growth condition The prevailing soybean cultivar in Southwestern China, Nandou-12 (ND-12), was employed in this study. The seeds were grown in the modern agricultural research and development base of Sichuan Agricultural University (Chengdu, China), and were harvested at the same time. The elite soybean seeds were used for dry storage for different periods as described in the Results section. All the soybean seeds in our lab are stored in a closed container box at room temperature, and the humidity is <5%. Silicon dioxide was added to the box to maintain the dry conditions. Controlled deteriorate treatment assay The assay of controlled deterioration treatment (CDT) was performed according to the protocol described elsewhere, with modifications (Chen et al., 2012, 2016). Those studies investigated the seed longevity in Arabidopsis (Chen et al., 2012, 2016), while this study focused on soybean seeds, thus some procedures are modified. Briefly, the soybean seeds were put into warm water (58 °C) for 20 min, and then the treated seeds were dried for 2 d at room temperature. Finally, the dried seeds were chosen for further germination analyses. Seed germination and seedling establishment Soybean seeds were incubated in 9 cm Petri dishes on two layers of medium-speed qualitative filter paper. Twenty-five seeds were placed in each Petri dish and 20 ml of sterile water or 200 μM DA-6 solution was added. The dishes were incubated in a box at 25 °C (Sanyo Versatile Environmental Test Chamber MLR-350H, made in Japan) under dark conditions. The germination experiments were performed at 25 °C and 60% relative humidity under dark conditions; the germination rates under dark conditions were recorded using a green safety light, according to a previous assay (Barrero et al., 2014). Radicle emergence was scored at the indicated time points. For each germination test, ≥75 seeds per type of soybean seed were used, and three experimental replications were performed. Post-germination growth data including radicle length and fresh weight of germinated seeds were quantified 2–3 d after imbibition according to the particular experiment. For each germination test, the average germination percentage ±SE of experiments was calculated. All the germinated and non-germinated soybean seeds (25 seeds per Petri dish) were transferred into soil and grown in greenhouses under 25 °C with 16 h light and 8 h dark conditions. Subsequently, after 2 weeks, the rates of seedling establishment, plant height, dry weight of seedlings, and total chlorophyll content were quantified according to the requirements of the experiments. Gene expression analysis Total RNA preparation, first-strand cDNA synthesis, and the qPCR assay were performed as in our previously described protocol (Shu et al., 2016a). In detail, DNase I-treated total RNA (2 μg) was denatured and then subjected to reverse transcription using Moloney murine leukemia virus reverse transcriptase (200 U per reaction; Promega Corporation). Gene expression was quantified in the logarithmic phase using the expression of the housekeeping GmTubulin RNA as an internal control. Three biological replicates were performed for each experiment. Quantitative PCR was performed on a QuantStudio 6 Flex Real-Time PCR System (Thermo Fisher Scientific, USA), with a real-time detection system according to the manufacturer’s instructions with Vazyme™ AceQ qPCR SYBR Green Master mix, and data were calculated using the comparative CT method (Schmittgen and Livak, 2008). Primer sequence for qPCR are shown in Supplementary Table 1 at JXB online. Quantification of total chlorophyll About 1 cm2 of leaves (avoiding the thicker veins) were sampled with a puncher, and then were cut into filaments of ~5 mm in length and ~1 mm in width for further analysis. Subsequently, the filaments were placed in a graduated tube containing 5 ml of of 80% acetone, and the tubes were placed under dark conditions until the filaments had turned completely white (overnight). To compensate for the possible losses due to volatilization, the extraction can be made up to 5 ml with 80% acetone, and the solution in the tube was gently poured into the cuvette, and finally the level of total chlorophyll was analyzed according to the protocol described elsewhere (Xie et al., 2007). The SpectraMax i3x Multi-Mode microplate reader (Molecular Devices, LLC, USA) was employed. Quantification of various sugars Samples were taken at different time points (0, 12, 24, 36, and 48 h) during imbibition. After 15 min at 105 °C in an oven, they were dried at 75 °C until a constant weight was detected and were then ground in a clean mortar and put into an Eppendorf tube. A 50 mg aliquot of sample was put into a 10 ml graduated centrifuge tube with addition of 3 ml of 80% ethanol, and placed in a 80 °C water bath with constant stirring for 40 min. Next, the supernatant was collected after centrifugation at 5000 g for 10 min. The extracted solution was diluted to 50 ml with 80% ethanol after extracting three times for the sugar content analysis. Sucrose content was measured by using the resorcinol method and estimated on the basis of the absorbance at a wavelength of 480 nm (Shi et al., 2016). Fructose content was quantified according to the method published elsewhere (Cai et al., 2016). Total soluble sugar analysis was performed by using the anthrone sulfuric acid method (Lei et al., 2014). The SpectraMax i3x Multi-Mode microplate reader (Molecular Devices, LLC, USA) was employed. Fatty acid extraction and measurements Soybean seeds were ground with liquid nitrogen and quantified by using a freeze drying system. Fatty acids were extracted from soybean seed powder according to our previously published protocol (Yang et al., 2017). Briefly, 2 ml of n-hexane was added to the ground soybean seeds (50 mg per tube), followed by 15 min ultrasonic extraction (40 kHz), and then the samples were kept at room temperature for 3 h. Subsequently, the solution was centrifuged at 10000 rpm and 4 °C for 10 min. Next, the supernatant was mixed and 3 ml of 0.4 M methanolic potassium hydroxide solution (Me-OH) was added, with vortex oscillation for 30 s, and then kept at room temperature for 1 h. Next, we transferred the upper liquid layer to a 5 ml capacity bottle and added n-hexane up to 5 ml, and further injected the extract into the GC-MS system through a 0.45 μm organic phase filter. A total of 37 fatty acid methyl ester (FAME) standard mixtures including common fatty acids (C4–C24) were purchased from Nu-chek-prep Inc. (USA). Identification and quantification of each fatty acid were carried using the methods described previously (Yang et al., 2017). Three biological replications were performed. To explore the relationship among the contents of fatty acids in different samples, a heat map was created by the Illustrator software. Measurement of soluble invertase activity A 100 mg aliquot of soybean seeds was ground in a chilled mortar with 8 ml of ice-cold sterile water, and subsequently it was transferred into a 10 ml bottle. Next, it was put into a refrigerator at 4 °C for 3 h, and then the solution was centrifuged at 4000 rpm and 4 °C for 10 min. Next, the standard curve determination and quantification of soluble invertase activity in different types of soybean seeds under imbibition were measured according to a previously published procedure (Tang et al., 1996). Three biological replications were performed. Statistical analysis The data, including germination rates, fresh weight, and radicle length of germinated seeds, and fatty acid and sugar quantification results, were analyzed using Student’s t-test (SPSS 19.0). Image J software was used to measure the length of radicles. Results Natural aging significantly decreases soybean seed germination and seedling establishment Soybean seed germination and seedling establishment were assessed in seeds subjected to different periods (i.e. 5, 10, 22, and 34 months) of dry storage. Seed germination analysis clearly showed that the germination rates of soybean seeds subjected to short-term storage (5 and 10 months) were significantly higher than those under long-term storage treatments (22 and 34 months) (Fig. 1A–E). The radicle length and fresh weight data of germinated seeds also supported the germination findings (Supplementary Fig. S1A, B). The final germination rate was nearly 100% and 85% for seeds stored for 5 and 10 months, respectively; however, the final germination rate of seeds stored for 22 months was only 30% (Fig. 1A–E), while almost all of the soybean seeds which had been stored for 34 months failed to germinate (Fig. 1D, E). Fig. 1. Open in new tabDownload slide Natural aging significantly decreases soybean seed germination and seedling establishment abilities. (A–D) Representative photographs of naturally aged soybean seeds during the imbibition process (60 h after sowing). Soybean seeds were stored for 5, 10, 22, or 34 months after harvest and then subjected to analysis. Scale bar=10 mm. (E) The quantitative analysis of final germination rates of different samples (A–D) are shown (72 h after sowing). (F) The early seedling establishment phenotype of different samples (A–D) immediately following germination (2 weeks after sowing). Scale bar=100 mm. (G) The quantitative analysis of the seedling establishment rates of different samples are shown (2 weeks after sowing). Percentages are the average of three repeats ±SE. The germination experiments were performed under 25 °C and 60% relative humidity conditions, while the seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.0 by Student’s t-test analysis. Fig. 1. Open in new tabDownload slide Natural aging significantly decreases soybean seed germination and seedling establishment abilities. (A–D) Representative photographs of naturally aged soybean seeds during the imbibition process (60 h after sowing). Soybean seeds were stored for 5, 10, 22, or 34 months after harvest and then subjected to analysis. Scale bar=10 mm. (E) The quantitative analysis of final germination rates of different samples (A–D) are shown (72 h after sowing). (F) The early seedling establishment phenotype of different samples (A–D) immediately following germination (2 weeks after sowing). Scale bar=100 mm. (G) The quantitative analysis of the seedling establishment rates of different samples are shown (2 weeks after sowing). Percentages are the average of three repeats ±SE. The germination experiments were performed under 25 °C and 60% relative humidity conditions, while the seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.0 by Student’s t-test analysis. Next, we investigated the effect of soybean seed storage time on the early seedling establishment process. The same number of soybean seeds which were subjected to each of several different periods of storage were sown in soil, and then the percentage establishment of seedlings was scored. The results revealed that the seedling establishment rates from soybean seeds stored for short periods (5 or 10 months) were significantly higher than those from seeds stored for long periods (22 or 34 months) (Fig. 1F, G). Furthermore, it was noted that the seedling establishment rate after 14 d from seeds stored for 10 months (nearly 70%) was lower than their final germination rate (nearly 85%), and that a similar trend was also detected for seeds stored for 22 months (Fig. 1E, G). In particular, there was no seedling emergence from the seeds stored for 34 months, although their germination rate was ~10% (Fig. 1E, G). Taken together, these analyses confirmed the previous conclusion that the natural aging process significantly decreased the ability of soybean seeds to germinate and for seedlings to establish. DA-6 increases germination and seedling establishment from aged soybean seeds but not from fresh seeds We had previously screened several plant growth regulators from those frequently employed in agriculture for their ability to promote germination and seedling establishment from aged soybean seeds (data not shown). Under our experimental conditions, we found that DA-6 increased germination and seedling establishment for both naturally and artificially aged soybean seeds. The results showed that DA-6 significantly increased the germination of naturally aged soybean seeds (stored for 12 months) (Fig. 2A, B). DA-6-treated naturally aged soybean seeds germinated more quickly than did the aged seeds without DA-6 treatment (Fig. 2A, B), and the data on the effects of DA-6 on radicle lengths and the fresh weights of germinated seedlings were consistent with the effects on germination (Supplementary Fig. S2A, B). Furthermore, DA-6 also promoted seedling establishment in naturally aged soybean seeds (Fig. 2C, D), with the seedling establishment rate of DA-6-treated aged seeds (60%) being twice that of seeds without DA-6 treatment (30%) (Fig. 2D). Fig. 2. Open in new tabDownload slide DA-6 promotes germination and seedling establishment from naturally aged soybean seeds. (A) Representative photographs of naturally aged soybean seeds (stored for 12 months) during the imbibition process (60 h after sowing), with or without DA-6 treatment. Scale bar=10 mm. (B) The quantitative analysis of final germination rates of seeds stored for 12 months in the absence or presence of DA-6 treatment. (C) The early seedling establishment phenotype of seeds stored for 12 months with or without DA-6 application. Scale bar=100 mm. (D) The statistical data of early seedling establishment for (C) are shown (15 d after sowing). Percentages are the average of three repeats ±SE. The germination experiments were performed under 25 °C and 60% relative humidity conditions, while the seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Fig. 2. Open in new tabDownload slide DA-6 promotes germination and seedling establishment from naturally aged soybean seeds. (A) Representative photographs of naturally aged soybean seeds (stored for 12 months) during the imbibition process (60 h after sowing), with or without DA-6 treatment. Scale bar=10 mm. (B) The quantitative analysis of final germination rates of seeds stored for 12 months in the absence or presence of DA-6 treatment. (C) The early seedling establishment phenotype of seeds stored for 12 months with or without DA-6 application. Scale bar=100 mm. (D) The statistical data of early seedling establishment for (C) are shown (15 d after sowing). Percentages are the average of three repeats ±SE. The germination experiments were performed under 25 °C and 60% relative humidity conditions, while the seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Given that DA-6 increased germination of naturally aged soybean seeds (Fig. 2), we then investigated the effects of DA-6 on the germination of fresh soybean seeds. Soybean seeds which had been stored for 1 month (‘fresh seeds’) were employed in this experiment. The results showed that no effect of DA-6 on the germination or seedling establishment from fresh soybean seeds was detected (Supplementary Fig. S3A–D). These findings demonstrated that DA-6 promotes the germination of only aged soybean seeds, but not fresh seeds (Fig. 2; Supplementary Fig. S3). In order to control precisely the experimental conditions, we employed the CDT method, which is the prevailing artificial aging method used in seed longevity research (Bhattacharyya et al., 1985; Das and Sen-Mandi, 1992; Chen et al., 2012, 2016). Similar to the effect of DA-6 on germination and early seedling establishment from naturally aged soybean seeds (Fig. 2; Supplementary Fig. S2), the results showed that DA-6 also increased germination in artificially aged soybean seeds (Fig. 3A–D), while the radicle lengths and fresh weights of germinated seeds were also in line with the germination findings (Fig. 3E, F). Fig. 3. Open in new tabDownload slide DA-6 enhances the germination ability of artificially aged soybean seeds. (A–C) Representative photographs of different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) during imbibition (48 h after sowing). Scale bar=10 mm. (D) The quantitative analysis of final germination rates of different samples (A–C) is shown. (E and F) The radical length (E) and fresh weight (F) of germinated soybean seeds were analyzed (48 h after sowing). The germination experiments were performed under 25 °C and 60% relative humidity with dark conditions; the germination rates under dark conditions were recorded using a green safety light, according to a previous assay (Barrero et al., 2014). The average percentages of three repeats ±SE are shown. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Fig. 3. Open in new tabDownload slide DA-6 enhances the germination ability of artificially aged soybean seeds. (A–C) Representative photographs of different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) during imbibition (48 h after sowing). Scale bar=10 mm. (D) The quantitative analysis of final germination rates of different samples (A–C) is shown. (E and F) The radical length (E) and fresh weight (F) of germinated soybean seeds were analyzed (48 h after sowing). The germination experiments were performed under 25 °C and 60% relative humidity with dark conditions; the germination rates under dark conditions were recorded using a green safety light, according to a previous assay (Barrero et al., 2014). The average percentages of three repeats ±SE are shown. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. A positive effect of DA-6 on seedling establishment from artificially aged soybean seeds was also detected (Fig. 4A, B). The germination and seedling establishment rates of CDT-aged soybean seeds were lower than those from seeds without CDT, in both the presence and absence of DA-6 (Figs 3, 4). DA-6 also promoted the growth of seedlings from artificially aged soybean seeds, as illustrated by the corresponding values for the heights and dry weights of seedlings from aged seeds in the presence or absence of DA-6 (Fig. 4C, D; Supplementary Fig. S4A). DA-6 treatment also increased the total chlorophyll concentration in seedling true leaves (Supplementary Fig. S4B), a result which was consistent with findings from previous studies (Jiang et al., 2012; Jiang et al., 2015). Furthermore, given the finding that DA-6 resulted in similar positive effects on germination and seedling establishment from both naturally and artificially aged soybean seeds, subsequent experiments employed only artificially aged seeds. Fig. 4. Open in new tabDownload slide DA-6 positively regulates the seedling establishment of artificially aged soybean seeds. (A) The early seedling establishment phenotype of different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment). Scale bar=100 mm. (B) The statistical data of early seedling establishment for (A) are shown (15 d after sowing). (C) Representative images of the height of soybean seedlings. Scale bar=100 mm. (D) The quantitative analysis of plant height for (C). Percentages are the average of three repeats ±SE. The seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was employed. Fig. 4. Open in new tabDownload slide DA-6 positively regulates the seedling establishment of artificially aged soybean seeds. (A) The early seedling establishment phenotype of different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment). Scale bar=100 mm. (B) The statistical data of early seedling establishment for (A) are shown (15 d after sowing). (C) Representative images of the height of soybean seedlings. Scale bar=100 mm. (D) The quantitative analysis of plant height for (C). Percentages are the average of three repeats ±SE. The seedlings were grown under 25 °C and 16 h light with 8 h dark conditions. The asterisk (*) indicates a significant difference at P<0.05 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was employed. DA-6 treatment increases the concentration of soluble sugars in aged soybean seeds during imbibition During seed germination and early seedling establishment, the energy supporting these biological processes comes primarily from carbon reserves stored in the seed itself, with sucrose and fructose being the main forms of these carbon reserves (Eastmond, 2004, 2006; Theodoulou and Eastmond, 2012). To investigate further the physiological and molecular mechanisms underlying the positive effects of DA-6 on germination and seedling establishment from aged soybean seeds, we next analyzed the concentrations of soluble sugars, namely sucrose and fructose, during imbibition of aged soybean seeds in the presence or absence of DA-6. During soybean seed imbibition, the concentration of total soluble sugars increased initially, before decreasing (Fig. 5A). The concentration of total soluble sugars in DA-6-treated CDT-aged soybean seeds was higher than that of aged seeds that had not been DA-6 treated, especially after 12 h and 24 h imbibition (Fig. 5A). The soluble sugars in seeds are predominantly sucrose and fructose, so we then investigated the effect of DA-6 on these two sugars. The results revealed that, after exogenous DA-6 treatment, the concentrations of fructose and sucrose in CDT-aged soybean seeds were higher than those in CDT-aged seeds without DA-6 treatment (Fig. 5B, C). It is noted that the CDT+DA-6 seeds showed a higher fructose content than both control and CDT-aged seeds at some time points (Fig. 5B). These results suggested that DA-6 promoted the germination and seedling establishment of aged soybean seeds by increasing the concentrations of soluble sugars, principally sucrose. Fig. 5. Open in new tabDownload slide DA-6 treatment increases the concentration of soluble sugars during imbibition by aged soybean seeds. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. (A) Soluble sugar quantification analysis. (B) Fructose concentration quantification. (C) Sucrose concentration analysis. The average percentages of four repeats ±SE are shown. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Fig. 5. Open in new tabDownload slide DA-6 treatment increases the concentration of soluble sugars during imbibition by aged soybean seeds. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. (A) Soluble sugar quantification analysis. (B) Fructose concentration quantification. (C) Sucrose concentration analysis. The average percentages of four repeats ±SE are shown. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. DA-6 increases the concentrations of several fatty acids during imbibition of aged soybean seeds It is well known that, during the germination of and early seedling establishment from seeds of oilseed crops, hydrolysis of triacylglycerol (oil) releases fatty acids and glycerol, which, in turn, are converted by gluconeogenesis to produce different types of soluble sugar (Eastmond, 2004, 2006; Quettier and Eastmond, 2009; Theodoulou and Eastmond, 2012). To better understand the causes of the increase in soluble sugar concentrations in CDT-aged soybean seeds after DA-6 treatment, we quantified the levels of several fatty acids during soybean seed imbibition. GC-MS analysis showed that, after 24 h imbibition, the concentrations of most of the fatty acids in untreated soybean seeds increased (Fig. 6A). In seeds artificially aged by CDT, this increase in fatty acid concentrations after 24 h imbibition was largely absent (Fig. 6A). However, exogenous DA-6 application to CDT-aged seeds fully restored the fatty acid profile, with the concentrations of most of the fatty acids in DA-6-treated CDT seeds being higher than those in seeds without DA-6 treatment after both 12 h and 24 h imbibition (Fig. 6A). The concentrations of total, unsaturated, and saturated fatty acids in the DA-6-treated CDT-aged soybean seeds were all higher than the corresponding levels in CDT seeds in the absence of DA-6 treatment at most of the time points (Fig. 6B–D). It is noted that the level of unsaturated fatty acids in CDT+H2O seeds is significantly lower than that in seeds without CDT after 24 h imbibition (Fig. 6C). Similar to this, the level of total fatty acids in CDT+H2O seeds also decreased compared with the seeds without CDT, although the difference was not significant (Fig. 6B). Finally, we investigated the effects of DA-6 treatment on the concentration of each of the primary types of fatty acids in soybean seeds, namely palmitic, stearic, oleic, linoleic, arachidic, and linolenic acids. The results showed that DA-6 treatment increased the concentration of each fatty acid in aged soybean seeds during imbibition (Supplementary Fig. S5). Taken together, these findings indicated that DA-6 positively regulates the conversion of triacylglycerol to fatty acids, which are important precursors for the production of soluble sugars. Fig. 6. Open in new tabDownload slide DA-6 treatment increases the concentration of total fatty acids in aged soybean seeds during imbibition. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. (A) The heat map analysis of the contents of several types of fatty acids during the soybean seed imbibition process with the time-course. The heat map was created by the Illustrator software. The fatty acid level from low (L) to high (H) indicates the minimum and maximum in the entire database. (B) The total fatty acid concentration in different types of soybean seeds during imbibition. (C) The unsaturated fatty acid concentration in soybean seeds during imbibition. (D) The saturated fatty acid concentration in soybean seeds during imbibition. The average percentages of four repeats ±SE are shown. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was employed. Fig. 6. Open in new tabDownload slide DA-6 treatment increases the concentration of total fatty acids in aged soybean seeds during imbibition. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. (A) The heat map analysis of the contents of several types of fatty acids during the soybean seed imbibition process with the time-course. The heat map was created by the Illustrator software. The fatty acid level from low (L) to high (H) indicates the minimum and maximum in the entire database. (B) The total fatty acid concentration in different types of soybean seeds during imbibition. (C) The unsaturated fatty acid concentration in soybean seeds during imbibition. (D) The saturated fatty acid concentration in soybean seeds during imbibition. The average percentages of four repeats ±SE are shown. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was employed. DA-6 increases the transcription of several key genes involved in the conversion of triacylglycerol to fatty acids and sugars in aged soybean seeds during imbibition We had observed that DA-6 treatment increased the concentrations of several fatty acids and soluble sugars in aged soybean seeds during imbibition (Figs 5, 6). Subsequently, we further analyzed the effects of DA-6 on increasing the conversion of triacylglycerol to fatty acids and sugars in aged soybean seeds during imbibition by studying the effects on the transcription pattern of key genes involved in the conversion of triacylglycerol to fatty acids and sugars. The results of qPCR analysis showed that the transcript levels of the key genes involved in the triacylglycerol hydrolysis pathway, namely GmSDP1 (Fig. 7A), GmSDP6 (Fig. 7B), GmACX2 (Fig. 7C), GmPCK1 (Fig. 7D), GmMFP2 (Fig. 7E), GmMDAR4 (Fig. 7F), and GmCOMATOSE (Fig. 7G), were all up-regulated (to varying degrees) in DA-6-treated aged soybean seeds during imbibition, compared with aged seeds without DA-6 application. Fig. 7. Open in new tabDownload slide Positive effect of DA-6 on the transcription of several key genes which are involved in hydrolysis of triacylglycerol in aged soybean seeds during imbibition. Gene expression was investigated by qPCR assay during the course of the imbibition process. Different types of soybean seeds (CDT seeds with H2O and CDT seeds with DA-6 treatment) were employed. These different types of soybean seeds were employed for mRNA extraction, and three replications were performed. GmSDP6 and GmSDP1 encode FAD-G3P dehydrogenase and patatin-like domain-containing triacylglycerol lipase, and GmACX2 and GmPCK1 encode acyl-CoA oxidase and phosphoenolpyruvate carboxykinase, respectively. Those genes are all involved in the pathways by which the triacylglycerol was transferred to fatty acids and sugars during imbibition. (A) GmSDP1; (B) GmSDP6; (C) GmACX2; (D) GmPCK1; (E) GmMFP2; (F) GmMDAR4; (G) GmCOMATOSE. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Fig. 7. Open in new tabDownload slide Positive effect of DA-6 on the transcription of several key genes which are involved in hydrolysis of triacylglycerol in aged soybean seeds during imbibition. Gene expression was investigated by qPCR assay during the course of the imbibition process. Different types of soybean seeds (CDT seeds with H2O and CDT seeds with DA-6 treatment) were employed. These different types of soybean seeds were employed for mRNA extraction, and three replications were performed. GmSDP6 and GmSDP1 encode FAD-G3P dehydrogenase and patatin-like domain-containing triacylglycerol lipase, and GmACX2 and GmPCK1 encode acyl-CoA oxidase and phosphoenolpyruvate carboxykinase, respectively. Those genes are all involved in the pathways by which the triacylglycerol was transferred to fatty acids and sugars during imbibition. (A) GmSDP1; (B) GmSDP6; (C) GmACX2; (D) GmPCK1; (E) GmMFP2; (F) GmMDAR4; (G) GmCOMATOSE. Asterisks (*) and (**) indicate a significant difference at P<0.05 and P<0.01, respectively, by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. GmSDP6 and GmSDP1 encode FAD-G3P dehydrogenase and patatin-like domain-containing triacylglycerol lipase (Eastmond, 2004, 2006; Quettier et al., 2008), respectively, which are the key enzymes in the hydrolysis of triacylglycerol. The expression levels of GmSDP1 and GmSDP6 in DA-6-treated aged soybean seeds were twice to three times those in seeds without DA-6 treatment (Fig. 7A, B). Similarly, the levels of GmACX2 and GmPCK1 transcripts detected in DA-6-treated aged soybean seeds were higher than in the corresponding aged seeds without DA-6 application (Fig. 7C, D). GmACX2 and GmPCK1 encode acyl-CoA oxidase and phosphoenolpyruvate carboxykinase, respectively, which are also involved in the process of β-oxidation of fatty acids (Li-Beisson et al., 2010; Penfield et al., 2012; Eastmond et al., 2015). GmPCK1 catalyzes the conversion of oxaloacetate (OAA) to phosphoenolpyruvate (PEP), the rate-limiting step in the metabolic pathway (Penfield et al., 2012). The accumulation of the transcripts of GmMFP2, GmMDAR4, and GmCOMATOSE after DA-6 treatment was also detected (Fig. 7E–G). These three genes are also involved in β-oxidation pathways (Rylott et al., 2006; Eastmond, 2007; Kunz et al., 2009). The accumulation of transcripts of these genes induced by DA-6 treatment might be expected to cause an increase in the production of soluble sugars. Altogether, the qPCR results suggested that DA-6 treatment of aged seeds increased the hydrolysis of triacylglycerol, as well as promoting the conversion of fatty acids to sugars during seed imbibition, by up-regulating expression of key genes involved in the respective pathways. The end-products of triacylglycerol hydrolysis, namely sucrose and fructose, provide the energy required to support the DA-6-induced germination and early seedling establishment from aged soybean seeds. DA-6 increases invertase activity in aged soybean seeds during imbibition Invertase is the key enzyme catalyzing the hydrolysis of sucrose to give equimolar amounts of glucose and fructose (Jameson et al., 2016; Wei et al., 2016). We showed that, during germination of aged soybean seeds, DA-6 treatment increased the concentration of soluble sugars, including sucrose and fructose (Fig. 5). A previous study had demonstrated that suppressed invertase activity was associated with a delayed seed germination phenotype in Arabidopsis (Su et al., 2016). To explore the relationship between DA-6 treatment, sucrose hydrolysis, and the germination ability of aged soybean seeds further, we quantified invertase activity during soybean seed germination. The results revealed that invertase activity was significantly repressed during imbibition of aged soybean seeds, compared with the activity in imbibed fresh seeds; while DA-6 treatment of aged seeds markedly increased invertase activity, compared with the seeds which had not been treated with DA-6 (Fig. 8). This finding was consistent with the positive effect of DA-6 on the germination and early seedling establishment of aged soybean seeds (Figs 2–4), and was in agreement with the increase in fructose concentration in DA-6-treated aged soybean seeds during imbibition (Fig. 5). Fig. 8. Open in new tabDownload slide DA-6 treatment increases the invertase activity in aged soybean seeds during imbibition. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. The invertase enzyme activity in these distinct types of seeds was analyzed during imbibition. The average percentages of three repeats ±SE are shown. Asterisks (**) indicate a significant difference at P<0.01 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Fig. 8. Open in new tabDownload slide DA-6 treatment increases the invertase activity in aged soybean seeds during imbibition. Different types of soybean seeds (healthy seeds without CDT, CDT seeds with H2O, and CDT seeds with DA-6 treatment) were employed. The invertase enzyme activity in these distinct types of seeds was analyzed during imbibition. The average percentages of three repeats ±SE are shown. Asterisks (**) indicate a significant difference at P<0.01 by Student’s t-test analysis. A 200 μM concentration of exogenous DA-6 was used. Discussion In the present study, phenotypic analysis, quantification of fatty acid and sugar concentrations, and analysis of gene expression and enzyme activity have demonstrated that the plant growth regulator DA-6 reverses the reduced germination and early seedling establishment rates exhibited by aged soybean seeds. The physiological and molecular mechanisms responsible for this positive effect are proposed to be as follows: in the aged soybean seeds, DA-6 promotes the hydrolysis of triacylglycerol to fatty acids and glycerol, and also enhances the conversion of fatty acids and glycerol to soluble sugars, which supply the energy needed during the soybean seed germination and early seedling establishment processes. This study also demonstrated that aged soybean seeds actually contain sufficient energy for germination, stored in the seeds as different food reserves, namely triacylglycerol, fatty acids, glycerol, and sugars. However, the aged seeds cannot use the storage energy, except for soluble sugars, because the conversion of triacylglycerol to soluble sugars appears to be blocked, resulting in the failure of seed germination and early seedling establishment. DA-6 has a positive effect on germination and early seedling establishment in aged soybean seeds Numerous elegant studies have demonstrated that DA-6, an artificial plant growth regulator, has a range of biological functions which can impact on field crop production (Zhang et al., 2008; Jiang et al., 2012; He et al., 2013; Salama et al., 2014; Jiang et al., 2015). The present investigation extended the practical uses of DA-6, demonstrating that DA-6 can reverse the loss of germination and seedling establishment activities associated with aging in soybean seeds. Seed germination and subsequent seedling establishment are vital steps during the plant life cycle, contributing to the determination of plant distribution in wild species, and of yield in crops. Research from the laboratory, including the current study, has demonstrated that the decrease in seed germination ability as the seed ages is positively associated with the duration of the storage period (Yin et al., 2014; Nguyen et al., 2015; Yin et al., 2015; Fleming et al., 2017). In tillage agriculture, farm-saved seeds are stored by the farmer for varying periods of time, often under less than ideal conditions, resulting in seed aging and a decline in germination characteristics. Consequently, enhancement of the germination and seedling establishment from aged seeds is a worthwhile target in both basic and applied aspects of plant biology and crop production research. Interestingly, a previous study had reported that hydrated graphene ribbon treatment could promote the germination of aged wheat seeds by increasing the concentrations of carbohydrate, amino acids, and fatty acids, and ensuring cell membrane integrity (Hu and Zhou, 2014). Ultrasonic treatment also improved seedling growth from aged grass seeds, including Festuca arundinacea Schreb. (tall fescue) and Psathyrostachys juncea L. (Russian wildrye), but the underlying mechanisms need further investigation (Liu et al., 2016). A more recent study revealed that phytosynthesized silver nanoparticles increased the germination of aged rice seeds, with the mechanisms underlying this positive effect being suggested to include enhanced water uptake, rebooting of the reactive oxygen species (ROS) antioxidant systems in seeds, generation of hydroxyl radicals to achieve cell wall loosening, and nanocatalysis to accelerate starch hydrolysis (Mahakham et al., 2017). However, most of these studies focused only on germination but not seedling establishment from aged seeds, and identified that there were diverse mechanisms underlying the effects promoting germination of aged seeds. DA-6 enhances the hydrolysis of triacylglycerol to sugars in aged soybean seeds It is noted that the process of seed germination is distinct from that of seedling establishment, especially in aged soybean seeds. Germination analysis showed that percentage seedling establishment was generally lower than that of seed germination, especially in the soybean seeds stored for 10, 22, or 34 months (Fig. 1). For the soybean seeds stored for 34 months, the germination rate reached ~10%, but there was zero emergence (Fig. 1). These data suggested that the level of releasable energy stored in the seed itself is important for both processes (seed germination and seedling establishment); if the energy reserves were exhausted in the first stage (seed germination), then the germinated seed could not complete the second process (seedling establishment). It is well known that the primary energy sources used by seeds during germination and seedling establishment are the soluble sugars, principally sucrose and fructose (Eastmond, 2004, 2006; Quettier et al., 2008; Theodoulou and Eastmond, 2012). In oilseed species, including soybean and Arabidopsis, the hydrolysis of triacylglycerol produces fatty acids and glycerol, with the fatty acids and glycerol both being further converted into sucrose (Theodoulou and Eastmond, 2012). Consequently, these biochemical reactions, converting triacylglycerol to fatty acids and glycerol, and fatty acids and glycerol to sucrose, are vital for generating the energy to complete the seed germination and seedling establishment processes. In the present investigation, we found that the germination ability of aged soybean seeds (following long-term storage) was lower than that of seeds stored for a shorter period (Fig. 1; Supplementary Fig. S1), and that DA-6 treatment significantly increased the germination and seedling establishment from aged soybean seeds (Figs 2–4; Supplementary Figs S2, S4). Subsequent research indicated that the conversion of seed storage oil (triacylglycerol) into sucrose during imbibition was insufficient to power germination of the aged soybean seeds in the absence of exogenous DA-6 application. Several lines of evidence supported this conclusion. DA-6 treatment markedly increased the levels of different types of fatty acids and soluble sugars (Figs 5, 6; Supplementary Fig. S5). In line with this, qPCR analysis further showed that DA-6 treatment increased the transcription of several key genes involved in the triacylglycerol hydrolysis pathway (Fig. 7). Taken together, the findings from the present investigation demonstrated that, during imbibition of aged soybean seeds, DA-6 increases the rate of hydrolysis of triacylglycerol to fatty acids and glycerol, and also the conversion of fatty acids and glycerol to sucrose and fructose by promoting the transcription of the key genes and the activity of the enzymes involved in those pathways (Fig. 9). On one hand, this study extended the range of agronomic and physiological crop processes identified as being affected by DA-6. On the other hand, the present investigation also demonstrated that the conversion of triacylglycerol to soluble sugars is important for the germination of aged seeds of oilseed crops. Fig. 9. Open in new tabDownload slide The proposed working model through which DA-6 promotes germination and seedling establishment from aged soybean seeds. In the aged soybean seeds, the transition from triacylglycerol to fatty acid and glycerol and then to the soluble sugars was blocked in the absence of DA-6. The application of exogenous DA-6 treatment in aged soybean seeds promoted the transcription of several key genes and elevated the activity of invertase which is involved in this pathway. Taken together, this model revealed that DA-6 promotes the germination and seedling establishment from aged soybean seeds by increasing the hydrolysis of triacylglycerol and the conversion of fatty acids and glycerol to sugars. Fig. 9. Open in new tabDownload slide The proposed working model through which DA-6 promotes germination and seedling establishment from aged soybean seeds. In the aged soybean seeds, the transition from triacylglycerol to fatty acid and glycerol and then to the soluble sugars was blocked in the absence of DA-6. The application of exogenous DA-6 treatment in aged soybean seeds promoted the transcription of several key genes and elevated the activity of invertase which is involved in this pathway. Taken together, this model revealed that DA-6 promotes the germination and seedling establishment from aged soybean seeds by increasing the hydrolysis of triacylglycerol and the conversion of fatty acids and glycerol to sugars. Given the importance of the phytohormones abscisic acid (ABA) and gibberellins (GAs) in the regulation of seed germination (Shu et al., 2013, 2015, 2016c, 2018), the relationship between DA-6 and the biosynthesis and signaling pathways of ABA and GA during germination of aged soybean seeds needs further investigation. For instance, does DA-6 regulate the biosynthesis and/or signal transduction pathways of ABA and GA? DA-6 is an artificial plant hormone, so the dissection of its receptor(s) will help us better understand the action of DA-6. Furthermore, the relationship between the DA-6 receptor(s) and ABA and GA also needs further exploration. Future studies should address whether DA-6 is also involved in the regulation of germination and early seedling establishment in aged seeds of the cereal crops (such as wheat, rice, and maize). Supplementary data Supplementary data are available at JXB online. Fig. S1. Natural aging significantly decreases soybean seed germination ability. Fig. S2. DA-6 promotes germination ability of natural aged soybean seeds Fig. S3. DA-6 has no effect on germination and seedling establishment of fresh soybean seeds. Fig. S4. DA-6 treatment increases the total chlorophyll content and dry weight in seedlings which germinated from CDT-aged soybean seeds. Fig. S5. DA-6 treatment increases the concentration of several types of fatty acids in aged soybean seeds during imbibition. Table S1. Primer sequences used in this study. Acknowledgements We would like to thank Dr Huhui Chen (State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resource, School of Life Sciences, Sun Yat-sen University, Guangzhou) for critical reading and valuable comments. This work was supported by the National Natural Science Foundation of China (31872804, 31701064), the National Key Research and Development Program of China (2017YFD0201306), and Sichuan Science and Technology Program (2018JZ0060). Author contributions KS conceived and designed this study. W-GZ, FC, S-HZ, C-QY, Y-JM, H-WS, X-FL, Y-JD, HY, J-BD, JL, G-QF, and W-GL performed the experiments. KS and W-YY analyzed the data. KS wrote and revised the paper. References Barrero JM , Downie AB, Xu Q, Gubler F. 2014 . A role for barley CRYPTOCHROME1 in light regulation of grain dormancy and germination . The Plant Cell 26 , 1094 – 1104 . 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Published by Oxford University Press on behalf of the Society for Experimental Biology.