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From genome to gene: a new epoch for wheat research? Meng Wang, Shubin Wang, and Guangmin Xia The Key Laboratory of Plant Cell Engineering and Germplasm Innovation, Ministry of Education, School of Life Sciences, Shandong University, Jinan 250100, P.R. China
Genetic research for bread wheat (Triticum aestivum), a staple crop around the world, has been impeded by its complex large hexaploid genome that contains a high proportion of repetitive DNA. Recent advances in sequencing technology have now overcome these challenges and led to genome drafts for bread wheat and its progenitors as well as high-resolution transcriptomes. However, the exploitation of these data for identifying agronomically important genes in wheat is lagging behind. We review recent wheat genome sequencing achievements and focus on four aspects of strategies and future hotspots for wheat improvement: positional cloning, ‘omics approaches, combining forward and reverse genetics, and epigenetics. Progress towards the acquisition of the wheat genome sequence Since the release of the Arabidopsis (Arabidopsis thaliana) genome sequence in 2000 an ever-growing number of plant genomes have been sequenced, and these have revolutionized our understanding of plant biology and facilitated the improvement of crops [1–3]. By contrast, the acquisition of the genome sequence of bread wheat (Triticum aestivum) has been hampered by three major features distinguishing it from the genomes of other plants such as Arabidopsis: (i) the 17 Gb giant size, which is approximately sixfold larger than the human genome and 125-fold larger than that of Arabidopsis, means that initial attempts of genome sequencing were costly; (ii) the extensive stretches of repetitive DNA (>80%) make sequence assembly challenging, not only because they contain multiple highly homologous sequences but also because the transposable element (TE) bursts create numerous non-colinear genes relative to grass models, hindering their anchoring to the ‘backbone’; (iii) bread wheat (2n = 6x = 42) is a hexaploid species with an AABBDD genome, derived from two amphiploidization events: the first hybridization forming the tetraploid wheat species (2n = 4x = 28, genome AABB) was between the Triticum urartu (2n = 2x = 14, genome AA) and presumably Aegilops speltoides, belonging to the section Sitopsis (2n = 2x = 14, genome SS); the second hybridization was between the tetraploid wheat and Corresponding author: Xia, G. ([email protected]). Keywords: wheat; whole-genome sequence; agronomically important genes; strategies for gene identification. 1360-1385/ ß 2015 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.tplants.2015.03.010
Aegilops tauschii (2n = 2x = 14, genome DD) [4,5]. This hexaploid nature of bread wheat leads to problems in differentiating and assigning the highly conserved homeologous genes originating from the A, B, and D subgenomes. Further complicating sequence assembly is the fact that during the course of evolution several inter-chromosome translocations (such as between the 4AL and 5AL, 7BS) have occurred [6]. With the emergence of next-generation sequencing (NGS) technologies, whole-genome shotgun sequencing became faster and cheaper, and provided a feasible approach for the bread wheat genome. Based on this approach, low-coverage (5), relatively long-read (454) shotgun sequences of model hexaploid cultivar Chinese Spring were produced using the crude sequences of diploid progenitor genomes as a guide for the assembly of thousands of small contigs [7]. This led to the first database covering the hexaploid wheat genome, and this formed a framework for further sequencing of bread wheat, accelerated marker development, and gave a rough estimation of gene content with 96 000 genes. Nevertheless, reliable assembly of these sequences in such a complicated genome has proved to be impossible [8]. A more systematic approach is focused on acquiring the genome sequences of three diploid ancestors of wheat (T. urartu, Ae. spletoides and Ae. tauschii), which separates the hexaploid genome into A, B, and D genomes. Currently, draft genome sequences of the A and D genome progenitors (T. urartu and Ae. tauschii) have been acquired using shotgun sequencing, and these provide a new basis for a comparative analysis with the bread wheat genome and insight into evolutionary aspects [9,10]. However, both the progenitor genomes as well as the shotgun sequencing of hexaploid wheat (described above) are fragmentary and incomplete because the raw data were acquired from short reads. Subsequently, a 4 Gb physical map of Ae. tauschii using the SNaPshot BAC fingerprinting technology has been generated [11] and the construction of its reference sequence is on the agenda [12]. Based on the earlier construction of cytogenetic stocks of Chinese Spring, such as telosomic and ditelosomic lines that carry chromosome arms as telocentric chromosomes (telosomes) [13], and the technological advances of highthroughput chromosome isolation using flow cytometry by the group of J. Dolezˇel in Olomouc (Czech Republic) [14], a chromosome-by-chromosome strategy has proved to be the optimal approach to obtain the bread wheat genome. This strategy can reduce the sample size and complexity by sequencing a single chromosome/chromosome arm, avoid Trends in Plant Science xx (2015) 1–8
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the confounding effects of homeologous sequences, and allows international cooperation to sequence the individual chromosome parts simultaneously in multiple labs. Consequently, the wheat research community formed the International Wheat Genome Sequencing Consortium (IWGSC) aiming to construct the physical map and to obtain a high-quality reference sequence in bread wheat via the chromosome-based approach [15]. The first chromosome to be successfully sorted was the largest of the chromosome complement, namely chromosome 3B. The DNA from a purified preparation of this chromosome was successfully used to construct the BAC library [16] and to produce the physical map [17]. Recently, another paper released the pseudomolecule of chromosome 3B by sequencing the minimal tiling path of the chromosome physical map and ordering 1358 scaffolds along the chromosome (93% of the complete sequence), which shed light on the partitioning pattern of bread wheat chromosome 3B [18]. In addition to the high-resolution sequence of 3B, a chromosome-based draft of the bread wheat genome was presented by IWGSC in which a summary and preliminary analysis of the chromosome-based sequencing were given [19]. Until now the survey sequences of all chromosomes,
the physical maps of 16 chromosomes, and the reference sequence of 3B are available; at the same time the assembly of physical maps of the other five chromosomes and the acquisition of the complete reference sequence of bread wheat are ongoing (see http://www.wheatgenome.org/ and Table 1). Genotyping and phenotyping technologies Along with the progress in genome sequencing, another milestone in wheat genomics is the emergence of highthroughput SNP-based genotyping technologies. Compared with the traditional markers, SNP can be more abundant and polymorphic, which ensures that sufficient numbers of polymorphic loci can be defined among most genomes. Therefore SNP discovery has been one major task in wheat genetics, but has been progressing slowly for a long time. Early approaches for SNP discovery in wheat were achieved by analyzing sequence variations of cDNAs/expressed sequence tags (ESTs) among wheat lines with PCR and Sanger sequencing (see http://wheat.pw. usda.gov/SNP/new/index.shtml) but were costly and complicated. Recent application of NGS significantly improved the efficiency and throughput of SNP discovery. As a result,
Table 1. Online resources for wheat research Web The International Maize and Wheat Improvement Center (CIMMYT) The USDA NSGC
Description Ordering different accessions of wheat germplasms and stocks
http://www.ars.usda.gov/main/docs.htm? docid=2884 http://www.ipk-gatersleben.de/en/dept-genebank/ http://www.dpi.nsw.gov.au/research/centres/ tamworth/specialist-research http://www.shigen.nig.ac.jp/wheat/komugi/ http://www.cgris.net http://www.nationalgenebank.org/en/ http://www.vir.nw.ru/
The IPK genebank The Australian Winter Cereal Collection KOMUGI wheat genetic resources database Chinese crop germplasm resources The N.I. Vavilov Research Institute of Plant Industry TriticeaeCAP
The IWGSC Wheat genomics JCVI wheat genome database
TriFLDB PlaNet DFCI gene index GrainGenes
URGI
CMap Wheat iSelect SNP microarray wDBTF PlantCare
2
URLs http://www.cimmyt.org/en/
Genotyping and phenotyping different accessions of wheat germplasms and stocks Genome sequence and SNP assay Genome sequence Gene annotation Wheat–rice comparative maps Gene annotation EST blast Protein domains Gene annotation EST blast QTL markers Transcriptome Gene annotation EST blast Physical and genetic maps Markers and QTL Genetic map Wheat SNP array Wheat transcription factor database A database of plant cis-acting regulatory elements
http://www.triticeaecap.org/
http://www.wheatgenome.org/ http://www.cerealsdb.uk.net/cerealgenomics/ Index_Home.html http://www.jcvi.org/wheat/index.php
http://trifldb.psc.riken.jp/v3/index.pl http://aranet.mpimp-golm.mpg.de/ http://compbio.dfci.harvard.edu/tgi/plant.html http://wheat.pw.usda.gov/GG2/index.shtml
http://wheat-urgi.versailles.inra.fr/
http://ccg.murdoch.edu.au/cmap/ccg-live/ http://129.130.90.211/snp/ http://wwwappli.nantes.inra.fr:8180/wDBFT/ http://bioinformatics.psb.ugent.be/webtools/ plantcare/html/
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thousands of SNP loci have been uncovered from both the bread wheat transcriptomes [20,21] and exomes [22]. A large number of identified SNPs are now used in working assay platforms to allow genotyping large numbers of loci in parallel. Current SNP assay platforms, in particular the Illumina GoldenGate/Infinium and the Affymetrix Axiom (Table 1), have massively driven down the per-unit assay time and cost. Furthermore, a large percent of SNPs in each platform are mapped by combining different mapping studies [20–23]. This makes these commercial services widely used in genome-wide association studies (GWAS) and other population genetics analysis where the order of markers is essential. However, when applied to analyze genetic diversity, population structure, or linkage disequilibrium in related species [e.g., emmer (T. dicoccum)], such commercial platforms may introduce ascertainment bias because the original SNP discovery panels only harbor a small sample of wheat lines, in particular bread wheat and durum wheat (T. durum). In some cases, the choice has been made to dispense with indirect methods of SNP detection and to rely instead on resequencing. In these cases it is necessary to first reduce the level of genomic complexity by digesting the template with a restriction enzyme(s) and size-selecting the fragments [24]. One such strategy is genotyping-by-sequencing (GBS), which has been used with some success in both hexaploid [25,26] and tetraploid wheat [27]. GBS provides a much higher marker density, but its application in wheat should be further improved – for example, user friendly pipelines for SNP calling and haplotype maps are expected to enhance its convenience and accuracy [26]. GWAS, which can identify markers linked to agronomic traits based on linkage
disequilibrium (LD) in natural germplasm, is fast becoming a focal point in the application of new marker systems [28]. Although the low rate of LD decay in wheat limits the resolution of GWAS, it makes GWAS efficient to detect marker-trait association with a low coverage of genetic markers, and these can further guide marker-assisted selection (MAS) or genetic mapping [28,29]. Coupled with the headway in building up genomic information, phenotyping data in wheat are also rapidly increasing. Thus GWAS, as a bridge between the large-scale phenotype data and the genomic information, will promote our understanding of the genetic basis of complex traits in wheat [30,31]. Current status of positional cloning in wheat Positional cloning has succeeded in identifying several Mendelian genes in wheat, mostly conferring disease resistance (Table 2). However, the absence of a reference genome sequence remains an impediment for efficient positional cloning. First, construction of a contig covering the causal gene is still an essential step, but this is timeconsuming and complicated in wheat. Second, the lack of high-density ordered sequences hinders marker development for localized high-resolution mapping. In most successful cases, marker development was guided by the sequenced genomes of rice or Brachypodium distachyon through comparative analysis, but this is not always effective. Although waiting for the whole-genome reference sequence of bread wheat requires patience, increasing resources of genome sequences with varying degrees of assembly have recently provided some details. Obviously,
Table 2. Agronomically important wheat genes isolated to date Strategy
Gene
Positional cloning
TaLr21 TaVRN1 TaLr10 TaPm3b TaVRN2 TaQ TaVRN3 TaGPC
Chromosome map 1DS 5AL 1AS 1AS 5A 5AL 7BS 6BS
TaPh1
5BL
TaYr36 TaLr34 TaTsn1 TaSr33 TaSr35 TaHKT1;5-D TmHKT1;5-A TmHKT1;4-A TaSRO1 TaAMLT1 TaMATE1 TaStpk-v
6BS 7DS 5BL 1D 3AL 4DL 4AL/5AL 2AL 5AL 4DL 4BL 6AL
TaMFT TaBo1 and TaBo4
3AS 7BL and 4AL
Combining forward and reverse genetics (relative to QTL)
Description of function
Refs
Wheat leaf rust resistance gene Wheat vernalization gene Wheat leaf rust resistance gene Wheat powdery mildew resistance gene A flowering repressor regulated by vernalization The wheat domestication gene Wheat vernalization gene A NAC gene regulating senescence improves grain protein, zinc, and iron Content A gene ensuring the correct pairing of homologous chromosomes Wheat stripe rust resistance gene Wheat resistance gene Wheat disease resistance gene Wheat stem rust resistance gene Wheat stem rust resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat aluminium resistance gene Wheat aluminium resistance gene A candidate for the powdery mildew resistance gene Pm21 A candidate for a seed dormancy QTL Wheat boron resistance genes
[74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [60] [59] [57] [49] [88] [89] [41] [48] [56] 3
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Review the finished chromosome 3B sequence [18] can be expected to be heavily relied upon to support the isolation of genes located both on this chromosome and on its homeologs 3A and 3D. Recent sequence enrichments possess valuable features to complement some weaknesses of the previously wide-used ESTs. Combined with chromosome sorting, bread wheat survey sequences [19] offer a greater sequence length and the ability to discriminate between homeologs; this is important because it is frequently necessary to design allele-specific PRC primers. Extron–intron patterns are another important source of information for marker development because introns are considered to be more polymorphic and can therefore be more easily defined with new sequence resources. Emergence of high-density maps is another outstanding advance because the ordered sequences are very valuable for positional cloning. A variety of high-density maps have become available by genetically mapping a large number of markers from SNP assays [2021,23] and GBS [26]. With the advance of wheat genome sequencing, a comparative approach (‘genome zipper’), which combines chromosome sorting, sequencing, array hybridization, and the exploitation of synteny, has also been used to good effect to generate the necessary ordered sequences [10,18]. As a result, extensive use has been made of the synteny retained between the Pooidae genomes, which provide both a (albeit imperfect) physical framework akin to a BAC-based contig and a supply of gene-based markers likely to map within the target region. Gene discovery through the use of ‘omics technologies Biological methodologies have in recent years been transformed by the development of ‘omics technologies, first applied for the analysis of DNA (genomics), then extended to RNA (transcriptomics), protein (proteomics), and small metabolites (metabolomics). Transcriptomic data, in particular, have become a mainstay of gene discovery in wheat. For example, cDNA arrays [32] were used for the identification of the three salinity tolerance associated genes CHP, OPR1, and AOC1 [33–35]. However, numerous candidate genes in wheat were omitted by this means in the past due largely to: (i) low sensitivity, leading to false negatives with respect to genes transcribed at a low level; (ii) incomplete coverage of the transcriptome on the array, leading to detection escapes; and (iii) the inability to distinguish between homeologs. The rapidly improving technical capacity of NGS and the genomic information enrichments of the bread wheat and its progenitors now offer a more direct means of sampling the transcriptome, which overcomes some of these problems. A series of techniques (including highthroughput RNA-seq for genome-based and de novo transcriptomic assembly) have been implemented for analyzing the stress response in wheat [19,36–38], and new trends for gene discovery in wheat are emerging: (i) the assembly and annotation of the transcripts is based on information from other grass genomes (such as Brachypodium) [32,39] as well as wheat genome survey sequences [19,38]. (ii) Using the genome sequencing information as a reference, homeologs from the three sub-genomes and alternative splicing variants can be distinguished in these transcriptomic resources, and this will allow the precise identification of 4
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candidate genes or previously unidentified transcripts/isoforms. For instance, profiling of individual homeologous transcripts in the developing grain has now become possible, giving a better resolution [38] than previous studies [39–41]. (iii) As additional wheat transcriptomes from different tissues at different developmental stages and for different growth conditions are deciphered [18,19], there is great potential to utilize these ‘omics and genomic resources for gene discovery. For example, genome sequencing results demonstrated that TE activity can generate duplicated gene fragments, which are non-syntenic genes compared with other grasses [18,19], in particular, multiplying the representation in multigene families such as NBS-LRR (involved in disease resistance) and CP450 (biotic and abiotic stress response) [9,10,19]. Compared with the original syntenic genes, transcriptomic analysis indicates that these duplicated genes are mostly expressed non-constitutively, suggesting that these genes are neofunctionalized in wheat [18]. By contrast, combining the synteny analysis based on the genomic information with tissue- or stress condition-specific expression scanning in the transcriptome resources, can uncover novel agronomically important genes, possibly with functions uniquely found in wheat. Combining forward and reverse genetics Both forward genetics and reverse genetics strategies have been refined in wheat over the years. Gene function can be efficiently validated by transformation in wheat, ectopic expression in model organisms, and single-cell transient expression [42]. Gene knockout/down by combining mutagenesis and TILLING [43], RNAi [44], VIGS [45], and gene editing by TALEN and CRISPR-Cas9 [46] are also available in wheat. Together with the advances in wheat genomics, including NGS and its application in genome sequencing, SNP identification, and ‘omics, gene isolation in wheat has been significantly accelerated, although it still has not reached the efficiency of genome analysis in fully sequenced plants. A recent trend in isolating agronomically important genes in wheat is to combine complementary strategies to track favorable alleles. The longterm goal is to exploit the power of genetic engineering to manipulate the regulation and expression of these important genes to tailor the plant to specific environments or end-uses. The contribution of ‘omics technologies to positional cloning Positional cloning remains a challenge in wheat, and moreover fails in some cases, for example when the target lies in a genomic region where recombination is rare (such as around the centromere or within a chromosome segment introduced from a wild relative). One solution for identifying candidate genes in a stagnant or slowly progressing genetic mapping is to combine the ‘omics platforms. For instance, a candidate gene (TRIP1) for the quantitative trait locus (QTL) QLRO-B1, which contributes substantially to variation in primary root length, was identified via a proteomic analysis of two isogenic lines [47]. A similar approach based on transcriptome analysis revealed the sequences responsible for Pm21 (conferring resistance to
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Review powdery mildew) [42], MFT (a candidate for the seed dormancy locus QPhs.ocs-3A.1) [48] and SRO1 (a candidate for an abiotic tolerance gene) [49] (Table 2). A key feature of such studies is that the phenotypic and physiological characterization is both relevant and exhaustive, but is essential to confirm the complicated results from ‘omics analysis. Particularly, for some complex traits such as drought tolerance and salinity tolerance, component contributory traits show a higher heritability and provide more in-depth information than the overall trait itself [31]. Thus, for example, the endogenous reactive oxygen species (ROS)-related biochemical pathway was considered to be responsible for the abiotic tolerance in bread wheat variety Shanrong No. 3 after an extensive physiological survey, and this matched a variety of transcriptomics analyses. Furthermore, the analysis confirmed the selection of SRO1 as the candidate key regulator because its homologs are known to be involved in the production and scavenging of ROS [49]. Finally, some materials widely used in genetic analysis or breeding are essential for ‘omicbased comparisons, such as isogenic lines and introgression lines, which share a common genetic background but differ at and around the target locus. The candidate gene approach Some biochemical or signaling pathways have been thoroughly characterized in rice and/or A. thaliana. Using this information to identify candidate genes has proved to be a very productive strategy in wheat. One of the earliest examples concerned the ‘Green Revolution’ semi-dwarfing Rht genes, which were identified through their homology with the A. thaliana dwarfing gene GAI [50]. The same approach has been exploited to derive genes associated with grain weight [51–53], nitrogen utilization [54,55], and tolerance to excessive soil boron [56] or salinity [57,58]. Compared with other studies which focused on the functional validation through reverse genetic method, these recent studies paid more attention to systematic analysis of genetic effect, sequence diversity, artificial selection, and their relation to corresponding traits. All these results have potential to enhance the application of these genes in wheat breeding. One crucial issue in this method is to identify a candidate favorable allele. Several orthologs or paralogs are obtained by initially sequence alignment and, particularly in bread wheat, one candidate represents three homeologs, in most cases not all of which are functional. Thus, functional validation of a randomly selected allele cannot provide a reliable result. To identify the candidate alleles for Bo1 and Bo4, two major QTLs for boron tolerance in wheat, several putative boron transporter coding sequences were genetically mapped, and two of these, Bot-B5/D5 colocalizing with Bo1 and Bo4, were selected for further analysis [56]. Colinearity can also be a useful criterion for clarifying the relationship between QTL and candidate genes. Thus, the two salinity tolerance QTLs Nax2 and Kna1, although they are located on 5AL and 4DL respectively, could be shown to be homologs and both conferred a HKT1;5-like gene [58–60]. In addition, haplotype polymorphisms generate an altered protein or an altered gene expression profile. This variation is exploited in genotype–phenotype association analyses.
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In some cases, haplotype variation can be associated with geographical provenance, and can be used to recognize the effect of anthropogenic selection or adaptation to particular environments. An example in wheat is provided by TaSus1 and TaSus2, two genes which encode sucrose synthase. In this case, haplotype variation was associated with provenance and with grain weight, and showed clear signals of selection [61]. The impact of epigenetic variation The polyploid nature of bread wheat complicates genomic analysis, but also can be taken advantage of to examine gene dosage and the effects of polyploidization on gene expression, in which epigenetics plays an important role [62,63]. It has been known for some time that newly synthesized wheat allopolyploids undergo substantial reprogramming of cytosine methylation, with knock-on effects on gene silencing and activation [62,64]. Variation with respect to transcription behavior affects about one third of single-copy gene homeoalleles [65]. Epiallelic variation has been implicated in this phenomenon because at least some of the observed silencing can be shown to be reversible. With advances in bisulfite sequencing, the effect of variable cytosine methylation on homeologous transcription has since been confirmed fragmentarily for some genes [66–68]. As more information on the wheat genome and a higherresolution transcriptome which can differentiate bread wheat homeologous genes become available, multiple wheat genes of known agronomic importance, HKT1;5 (the candidate gene of salinity tolerance QTL, Kna1) [59], pinB (component of the grain hardness locus), SPA (the storage protein activator) [39], and others, have demonstrated divergent expression patterns between homeologs. Further study on wheat epigenetics will focus not only on its contribution to the gene silencing and expression-interaction among homeologs but also on its potential to improve wheat profound traits via an ‘epigenetic modification reprogramming – expression alteration of vital genes – wheat improvement’ model. For example, suppressing the expression of DEMETER, a gene encoding a 5methylcytosine DNA glycosylase responsible for transcriptional derepression of gliadins and low molecular weight glutenins (LMWgs) by active demethylation of their promoters in the bread wheat endosperm, via RNA interference, results in a reduction in the amount of immunogenic prolamins [69]. However, the reprogramming of cytosine methylation effected by DNA methyltransferases/demethylases or the DNA methyltransferase inhibitor 5-azacytidine [70] is nontargeted and uncontrollable (because most epigenetic mutants in Arabidopsis are deficient in phenotype). Small non-coding RNAs have been proven involved in expression regulation both at the transcriptional (RNA-directed DNA methylation pathway) and post-transcriptional levels by binding to the targeted genes [71]. The sequencing of the bread wheat genome [19] and those of its progenitors [9,10] has identified large numbers of these RNAs. With the assistance of published A and D genome sequences, the small RNA transcriptome has been successfully analyzed, providing insights into small RNA-mediated dynamic 5
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homeologs regulation in hexaploid wheat [72]. A putative miRNA172 binding site in the exon 10 points to a possible role of miRNA in regulation of the domestication Q gene expression in bread wheat [73]. Therefore, there is potential to regulate small non-coding RNA leading to altered expression of its targeted gene or even of a specific homeologous allele for wheat improvement. Concluding remarks Because the core of wheat improvement is the recognition and then deployment of elite genes (or alleles of genes), the focus of many wheat research teams has shifted away from genetic analysis to the exploitation of agronomically important genes. However, the lack of sequence information for the extremely intricate genome of wheat has led to slow progress. Since 2012, genome sequence drafts of bread
wheat and its progenitors have been acquired via shotgun and chromosome-based approaches, and this represents a milestone for wheat improvement. Currently the survey sequences of all chromosomes, the physical maps of 16 chromosomes, and the reference sequence of 3B have been achieved in bread wheat. Based on these achievements and advances in NGS, high-resolution transcriptome and genotype maps are now available, providing extensive resources. Consequently, a new epoch of wheat gene research is opening up (Figure 1). In the future, positional cloning will be greatly accelerated on chromosome 3B, and even on 3A or 3D. Although the reference sequences of other chromosomes are still being constructed, high-density consensus maps generated by SNP-based genotyping will expedite the process of genetic mapping. Moreover, higherresolution transcription data at the level of individual
Large
He xa plo id
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Hi
Wheat genome
Shotgun sequencing
Chromosomebased sequencing
SNP-based genotyping
Phenotyping
GWAS
Highresoluon transcriptome
Progenitor sequencing
Precise genome engineering
Posional cloning
Synteny analysis with other plants
Reverse genecs
Candidate gene
Haplotype analysis
Funcon validaon
Evoluonary/ homeologous analysis
Epigenecs
TRENDS in Plant Science
Figure 1. How wheat whole-genome sequence information can expedite the identification of agronomically important genes. Abbreviation: GWAS, genome-wide association study.
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Review homeologs will promote the use of reverse genetics-based strategies. The level of information on the wheat genome sequence is still trailing behind that for the genomes of rice and A. thaliana to support positional cloning. Therefore, approaches linking physiology, ‘omics, genetic mapping, and haplotype analysis will continue to play a predominant role in gene identification in wheat. Another significant trend in wheat research is the increasing high-throughput and high-quality of phenotyping. Therefore, an integrative strategy combining the large-scale phenotype data with the genomic information and the ‘omics information will also be crucial to deciphering the genetic basis of complex traits in wheat. In addition, unraveling the regulation of traits in wheat spans understanding sequence diversity to epigenetic modifications. In summary, current progress for the exploitation of agronomically important genes in wheat is promising. Acknowledgments We apologize to authors whose relevant work we could not cite owing to space limitations. We are also grateful to Prof. Jaroslav Dolezˇel for providing further data. This work is supported by funds of the Major Program of the Natural Science Foundation of China (No.31430060) and the National Transgenic Project (Grants 2013ZX08002002).
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67 Hu, Z. et al. (2013) Epigenetic modification contributes to the expression divergence of three TaEXPA1 homoeologs in hexaploid wheat (Triticum aestivum). New Phytol. 197, 1344–1352 68 Sun, H. et al. (2014) DNA methylation pattern of Photoperiod-B1 is associated with photoperiod insensitivity in wheat (Triticum aestivum). New Phytol. 204, 682–692 69 Wen, S. et al. (2012) Structural genes of wheat and barley 5methylcytosine DNA glycosylases and their potential applications for human health. Proc. Natl. Acad. Sci. U.S.A. 109, 20543–20548 70 Wang, M. et al. (2014) Induced and constitutive DNA methylation in a salinity tolerant wheat introgression line. Plant Cell Physiol. 55, 1354–1365 71 Vazquez, F. et al. (2010) The biosynthetic pathways and biological scopes of plant small RNAs. Trends plant Sci. 15, 337–345 72 Li, A. et al. (2014) mRNA and small RNA transcriptomes reveal insights into dynamic homoeolog regulation of allopolyploid heterosis in nascent hexaploid wheat. Plant Cell 26, 1878–1900 73 Zhang, Z. et al. (2011) Duplication and partitioning in evolution and function of homoeologous Q loci governing domestication characters in polyploid wheat. Proc. Natl. Acad. Sci. U.S.A. 108, 18737–18742 74 Huang, L. et al. (2003) Map-based cloning of leaf rust resistance gene Lr21 from the large and polyploid genome of bread wheat. Genetics 164, 655–664 75 Yan, L. et al. (2003) Positional cloning of the wheat vernalization gene VRN1. Proc. Natl. Acad. Sci. U.S.A. 100, 6263–6268 76 Feuillet, C. et al. (2003) Map-based isolation of the leaf rust disease resistance gene Lr10 from the hexaploid wheat (Triticum aestivum L.) genome. Proc. Natl. Acad. Sci. U.S.A. 100, 15253–15258 77 Yahiaoui, N. et al. (2004) Genome analysis at different ploidy levels allows cloning of the powdery mildew resistance gene Pm3b from hexaploid wheat. Plant J. 37, 528–538 78 Yan, L. et al. (2004) The wheat VRN2 gene is a flowering repressor down-regulated by vernalization. Science 303, 1640–1644 79 Faris, J.D. et al. (2003) A bacterial artificial chromosome contig spanning the major domestication locus Q in wheat and identification of a candidate gene. Genetics 164, 311–321 80 Yan, L. et al. (2006) The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proc. Natl. Acad. Sci. U.S.A. 103, 19581–19586 81 Uauy, C. et al. (2006) A NAC gene regulating senescence improves grain protein, zinc, and iron content in wheat. Science 314, 1298–1301 82 Griffiths, S. et al. (2006) Molecular characterization of Ph1 as a major chromosome pairing locus in polyploid wheat. Nature 439, 749–752 83 Fu, D. et al. (2009) A kinase-START gene confers temperaturedependent resistance to wheat stripe rust. Science 323, 1357–1360 84 Krattinger, S.G. et al. (2009) A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323, 1360–1363 85 Faris, J.D. et al. (2010) A unique wheat disease resistance-like gene governs effector-triggered susceptibility to necrotrophic pathogens. Proc. Natl. Acad. Sci. U.S.A. 107, 13544–13549 86 Periyannan, S. et al. (2013) The gene Sr33, an ortholog of barley Mla genes, encodes resistance to wheat stem rust race Ug99. Science 341, 786–788 87 Saintenac, C. et al. (2013) Identification of wheat gene Sr35 that confers resistance to Ug99 stem rust race group. Science 341, 783–786 88 Sasaki, T. et al. (2004) A wheat gene encoding an aluminum-activated malate transporter. Plant J. 37, 645–653 89 Ryan, P.R. et al. (2009) A second mechanism for aluminum resistance in wheat relies on the constitutive efflux of citrate from roots. Plant Physiol. 149, 340–351
Review
From genome to gene: a new epoch for wheat research? Meng Wang, Shubin Wang, and Guangmin Xia The Key Laboratory of Plant Cell Engineering and Germplasm Innovation, Ministry of Education, School of Life Sciences, Shandong University, Jinan 250100, P.R. China
Genetic research for bread wheat (Triticum aestivum), a staple crop around the world, has been impeded by its complex large hexaploid genome that contains a high proportion of repetitive DNA. Recent advances in sequencing technology have now overcome these challenges and led to genome drafts for bread wheat and its progenitors as well as high-resolution transcriptomes. However, the exploitation of these data for identifying agronomically important genes in wheat is lagging behind. We review recent wheat genome sequencing achievements and focus on four aspects of strategies and future hotspots for wheat improvement: positional cloning, ‘omics approaches, combining forward and reverse genetics, and epigenetics. Progress towards the acquisition of the wheat genome sequence Since the release of the Arabidopsis (Arabidopsis thaliana) genome sequence in 2000 an ever-growing number of plant genomes have been sequenced, and these have revolutionized our understanding of plant biology and facilitated the improvement of crops [1–3]. By contrast, the acquisition of the genome sequence of bread wheat (Triticum aestivum) has been hampered by three major features distinguishing it from the genomes of other plants such as Arabidopsis: (i) the 17 Gb giant size, which is approximately sixfold larger than the human genome and 125-fold larger than that of Arabidopsis, means that initial attempts of genome sequencing were costly; (ii) the extensive stretches of repetitive DNA (>80%) make sequence assembly challenging, not only because they contain multiple highly homologous sequences but also because the transposable element (TE) bursts create numerous non-colinear genes relative to grass models, hindering their anchoring to the ‘backbone’; (iii) bread wheat (2n = 6x = 42) is a hexaploid species with an AABBDD genome, derived from two amphiploidization events: the first hybridization forming the tetraploid wheat species (2n = 4x = 28, genome AABB) was between the Triticum urartu (2n = 2x = 14, genome AA) and presumably Aegilops speltoides, belonging to the section Sitopsis (2n = 2x = 14, genome SS); the second hybridization was between the tetraploid wheat and Corresponding author: Xia, G. ([email protected]). Keywords: wheat; whole-genome sequence; agronomically important genes; strategies for gene identification. 1360-1385/ ß 2015 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.tplants.2015.03.010
Aegilops tauschii (2n = 2x = 14, genome DD) [4,5]. This hexaploid nature of bread wheat leads to problems in differentiating and assigning the highly conserved homeologous genes originating from the A, B, and D subgenomes. Further complicating sequence assembly is the fact that during the course of evolution several inter-chromosome translocations (such as between the 4AL and 5AL, 7BS) have occurred [6]. With the emergence of next-generation sequencing (NGS) technologies, whole-genome shotgun sequencing became faster and cheaper, and provided a feasible approach for the bread wheat genome. Based on this approach, low-coverage (5), relatively long-read (454) shotgun sequences of model hexaploid cultivar Chinese Spring were produced using the crude sequences of diploid progenitor genomes as a guide for the assembly of thousands of small contigs [7]. This led to the first database covering the hexaploid wheat genome, and this formed a framework for further sequencing of bread wheat, accelerated marker development, and gave a rough estimation of gene content with 96 000 genes. Nevertheless, reliable assembly of these sequences in such a complicated genome has proved to be impossible [8]. A more systematic approach is focused on acquiring the genome sequences of three diploid ancestors of wheat (T. urartu, Ae. spletoides and Ae. tauschii), which separates the hexaploid genome into A, B, and D genomes. Currently, draft genome sequences of the A and D genome progenitors (T. urartu and Ae. tauschii) have been acquired using shotgun sequencing, and these provide a new basis for a comparative analysis with the bread wheat genome and insight into evolutionary aspects [9,10]. However, both the progenitor genomes as well as the shotgun sequencing of hexaploid wheat (described above) are fragmentary and incomplete because the raw data were acquired from short reads. Subsequently, a 4 Gb physical map of Ae. tauschii using the SNaPshot BAC fingerprinting technology has been generated [11] and the construction of its reference sequence is on the agenda [12]. Based on the earlier construction of cytogenetic stocks of Chinese Spring, such as telosomic and ditelosomic lines that carry chromosome arms as telocentric chromosomes (telosomes) [13], and the technological advances of highthroughput chromosome isolation using flow cytometry by the group of J. Dolezˇel in Olomouc (Czech Republic) [14], a chromosome-by-chromosome strategy has proved to be the optimal approach to obtain the bread wheat genome. This strategy can reduce the sample size and complexity by sequencing a single chromosome/chromosome arm, avoid Trends in Plant Science xx (2015) 1–8
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the confounding effects of homeologous sequences, and allows international cooperation to sequence the individual chromosome parts simultaneously in multiple labs. Consequently, the wheat research community formed the International Wheat Genome Sequencing Consortium (IWGSC) aiming to construct the physical map and to obtain a high-quality reference sequence in bread wheat via the chromosome-based approach [15]. The first chromosome to be successfully sorted was the largest of the chromosome complement, namely chromosome 3B. The DNA from a purified preparation of this chromosome was successfully used to construct the BAC library [16] and to produce the physical map [17]. Recently, another paper released the pseudomolecule of chromosome 3B by sequencing the minimal tiling path of the chromosome physical map and ordering 1358 scaffolds along the chromosome (93% of the complete sequence), which shed light on the partitioning pattern of bread wheat chromosome 3B [18]. In addition to the high-resolution sequence of 3B, a chromosome-based draft of the bread wheat genome was presented by IWGSC in which a summary and preliminary analysis of the chromosome-based sequencing were given [19]. Until now the survey sequences of all chromosomes,
the physical maps of 16 chromosomes, and the reference sequence of 3B are available; at the same time the assembly of physical maps of the other five chromosomes and the acquisition of the complete reference sequence of bread wheat are ongoing (see http://www.wheatgenome.org/ and Table 1). Genotyping and phenotyping technologies Along with the progress in genome sequencing, another milestone in wheat genomics is the emergence of highthroughput SNP-based genotyping technologies. Compared with the traditional markers, SNP can be more abundant and polymorphic, which ensures that sufficient numbers of polymorphic loci can be defined among most genomes. Therefore SNP discovery has been one major task in wheat genetics, but has been progressing slowly for a long time. Early approaches for SNP discovery in wheat were achieved by analyzing sequence variations of cDNAs/expressed sequence tags (ESTs) among wheat lines with PCR and Sanger sequencing (see http://wheat.pw. usda.gov/SNP/new/index.shtml) but were costly and complicated. Recent application of NGS significantly improved the efficiency and throughput of SNP discovery. As a result,
Table 1. Online resources for wheat research Web The International Maize and Wheat Improvement Center (CIMMYT) The USDA NSGC
Description Ordering different accessions of wheat germplasms and stocks
http://www.ars.usda.gov/main/docs.htm? docid=2884 http://www.ipk-gatersleben.de/en/dept-genebank/ http://www.dpi.nsw.gov.au/research/centres/ tamworth/specialist-research http://www.shigen.nig.ac.jp/wheat/komugi/ http://www.cgris.net http://www.nationalgenebank.org/en/ http://www.vir.nw.ru/
The IPK genebank The Australian Winter Cereal Collection KOMUGI wheat genetic resources database Chinese crop germplasm resources The N.I. Vavilov Research Institute of Plant Industry TriticeaeCAP
The IWGSC Wheat genomics JCVI wheat genome database
TriFLDB PlaNet DFCI gene index GrainGenes
URGI
CMap Wheat iSelect SNP microarray wDBTF PlantCare
2
URLs http://www.cimmyt.org/en/
Genotyping and phenotyping different accessions of wheat germplasms and stocks Genome sequence and SNP assay Genome sequence Gene annotation Wheat–rice comparative maps Gene annotation EST blast Protein domains Gene annotation EST blast QTL markers Transcriptome Gene annotation EST blast Physical and genetic maps Markers and QTL Genetic map Wheat SNP array Wheat transcription factor database A database of plant cis-acting regulatory elements
http://www.triticeaecap.org/
http://www.wheatgenome.org/ http://www.cerealsdb.uk.net/cerealgenomics/ Index_Home.html http://www.jcvi.org/wheat/index.php
http://trifldb.psc.riken.jp/v3/index.pl http://aranet.mpimp-golm.mpg.de/ http://compbio.dfci.harvard.edu/tgi/plant.html http://wheat.pw.usda.gov/GG2/index.shtml
http://wheat-urgi.versailles.inra.fr/
http://ccg.murdoch.edu.au/cmap/ccg-live/ http://129.130.90.211/snp/ http://wwwappli.nantes.inra.fr:8180/wDBFT/ http://bioinformatics.psb.ugent.be/webtools/ plantcare/html/
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thousands of SNP loci have been uncovered from both the bread wheat transcriptomes [20,21] and exomes [22]. A large number of identified SNPs are now used in working assay platforms to allow genotyping large numbers of loci in parallel. Current SNP assay platforms, in particular the Illumina GoldenGate/Infinium and the Affymetrix Axiom (Table 1), have massively driven down the per-unit assay time and cost. Furthermore, a large percent of SNPs in each platform are mapped by combining different mapping studies [20–23]. This makes these commercial services widely used in genome-wide association studies (GWAS) and other population genetics analysis where the order of markers is essential. However, when applied to analyze genetic diversity, population structure, or linkage disequilibrium in related species [e.g., emmer (T. dicoccum)], such commercial platforms may introduce ascertainment bias because the original SNP discovery panels only harbor a small sample of wheat lines, in particular bread wheat and durum wheat (T. durum). In some cases, the choice has been made to dispense with indirect methods of SNP detection and to rely instead on resequencing. In these cases it is necessary to first reduce the level of genomic complexity by digesting the template with a restriction enzyme(s) and size-selecting the fragments [24]. One such strategy is genotyping-by-sequencing (GBS), which has been used with some success in both hexaploid [25,26] and tetraploid wheat [27]. GBS provides a much higher marker density, but its application in wheat should be further improved – for example, user friendly pipelines for SNP calling and haplotype maps are expected to enhance its convenience and accuracy [26]. GWAS, which can identify markers linked to agronomic traits based on linkage
disequilibrium (LD) in natural germplasm, is fast becoming a focal point in the application of new marker systems [28]. Although the low rate of LD decay in wheat limits the resolution of GWAS, it makes GWAS efficient to detect marker-trait association with a low coverage of genetic markers, and these can further guide marker-assisted selection (MAS) or genetic mapping [28,29]. Coupled with the headway in building up genomic information, phenotyping data in wheat are also rapidly increasing. Thus GWAS, as a bridge between the large-scale phenotype data and the genomic information, will promote our understanding of the genetic basis of complex traits in wheat [30,31]. Current status of positional cloning in wheat Positional cloning has succeeded in identifying several Mendelian genes in wheat, mostly conferring disease resistance (Table 2). However, the absence of a reference genome sequence remains an impediment for efficient positional cloning. First, construction of a contig covering the causal gene is still an essential step, but this is timeconsuming and complicated in wheat. Second, the lack of high-density ordered sequences hinders marker development for localized high-resolution mapping. In most successful cases, marker development was guided by the sequenced genomes of rice or Brachypodium distachyon through comparative analysis, but this is not always effective. Although waiting for the whole-genome reference sequence of bread wheat requires patience, increasing resources of genome sequences with varying degrees of assembly have recently provided some details. Obviously,
Table 2. Agronomically important wheat genes isolated to date Strategy
Gene
Positional cloning
TaLr21 TaVRN1 TaLr10 TaPm3b TaVRN2 TaQ TaVRN3 TaGPC
Chromosome map 1DS 5AL 1AS 1AS 5A 5AL 7BS 6BS
TaPh1
5BL
TaYr36 TaLr34 TaTsn1 TaSr33 TaSr35 TaHKT1;5-D TmHKT1;5-A TmHKT1;4-A TaSRO1 TaAMLT1 TaMATE1 TaStpk-v
6BS 7DS 5BL 1D 3AL 4DL 4AL/5AL 2AL 5AL 4DL 4BL 6AL
TaMFT TaBo1 and TaBo4
3AS 7BL and 4AL
Combining forward and reverse genetics (relative to QTL)
Description of function
Refs
Wheat leaf rust resistance gene Wheat vernalization gene Wheat leaf rust resistance gene Wheat powdery mildew resistance gene A flowering repressor regulated by vernalization The wheat domestication gene Wheat vernalization gene A NAC gene regulating senescence improves grain protein, zinc, and iron Content A gene ensuring the correct pairing of homologous chromosomes Wheat stripe rust resistance gene Wheat resistance gene Wheat disease resistance gene Wheat stem rust resistance gene Wheat stem rust resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat salinity resistance gene Wheat aluminium resistance gene Wheat aluminium resistance gene A candidate for the powdery mildew resistance gene Pm21 A candidate for a seed dormancy QTL Wheat boron resistance genes
[74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [60] [59] [57] [49] [88] [89] [41] [48] [56] 3
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Review the finished chromosome 3B sequence [18] can be expected to be heavily relied upon to support the isolation of genes located both on this chromosome and on its homeologs 3A and 3D. Recent sequence enrichments possess valuable features to complement some weaknesses of the previously wide-used ESTs. Combined with chromosome sorting, bread wheat survey sequences [19] offer a greater sequence length and the ability to discriminate between homeologs; this is important because it is frequently necessary to design allele-specific PRC primers. Extron–intron patterns are another important source of information for marker development because introns are considered to be more polymorphic and can therefore be more easily defined with new sequence resources. Emergence of high-density maps is another outstanding advance because the ordered sequences are very valuable for positional cloning. A variety of high-density maps have become available by genetically mapping a large number of markers from SNP assays [2021,23] and GBS [26]. With the advance of wheat genome sequencing, a comparative approach (‘genome zipper’), which combines chromosome sorting, sequencing, array hybridization, and the exploitation of synteny, has also been used to good effect to generate the necessary ordered sequences [10,18]. As a result, extensive use has been made of the synteny retained between the Pooidae genomes, which provide both a (albeit imperfect) physical framework akin to a BAC-based contig and a supply of gene-based markers likely to map within the target region. Gene discovery through the use of ‘omics technologies Biological methodologies have in recent years been transformed by the development of ‘omics technologies, first applied for the analysis of DNA (genomics), then extended to RNA (transcriptomics), protein (proteomics), and small metabolites (metabolomics). Transcriptomic data, in particular, have become a mainstay of gene discovery in wheat. For example, cDNA arrays [32] were used for the identification of the three salinity tolerance associated genes CHP, OPR1, and AOC1 [33–35]. However, numerous candidate genes in wheat were omitted by this means in the past due largely to: (i) low sensitivity, leading to false negatives with respect to genes transcribed at a low level; (ii) incomplete coverage of the transcriptome on the array, leading to detection escapes; and (iii) the inability to distinguish between homeologs. The rapidly improving technical capacity of NGS and the genomic information enrichments of the bread wheat and its progenitors now offer a more direct means of sampling the transcriptome, which overcomes some of these problems. A series of techniques (including highthroughput RNA-seq for genome-based and de novo transcriptomic assembly) have been implemented for analyzing the stress response in wheat [19,36–38], and new trends for gene discovery in wheat are emerging: (i) the assembly and annotation of the transcripts is based on information from other grass genomes (such as Brachypodium) [32,39] as well as wheat genome survey sequences [19,38]. (ii) Using the genome sequencing information as a reference, homeologs from the three sub-genomes and alternative splicing variants can be distinguished in these transcriptomic resources, and this will allow the precise identification of 4
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candidate genes or previously unidentified transcripts/isoforms. For instance, profiling of individual homeologous transcripts in the developing grain has now become possible, giving a better resolution [38] than previous studies [39–41]. (iii) As additional wheat transcriptomes from different tissues at different developmental stages and for different growth conditions are deciphered [18,19], there is great potential to utilize these ‘omics and genomic resources for gene discovery. For example, genome sequencing results demonstrated that TE activity can generate duplicated gene fragments, which are non-syntenic genes compared with other grasses [18,19], in particular, multiplying the representation in multigene families such as NBS-LRR (involved in disease resistance) and CP450 (biotic and abiotic stress response) [9,10,19]. Compared with the original syntenic genes, transcriptomic analysis indicates that these duplicated genes are mostly expressed non-constitutively, suggesting that these genes are neofunctionalized in wheat [18]. By contrast, combining the synteny analysis based on the genomic information with tissue- or stress condition-specific expression scanning in the transcriptome resources, can uncover novel agronomically important genes, possibly with functions uniquely found in wheat. Combining forward and reverse genetics Both forward genetics and reverse genetics strategies have been refined in wheat over the years. Gene function can be efficiently validated by transformation in wheat, ectopic expression in model organisms, and single-cell transient expression [42]. Gene knockout/down by combining mutagenesis and TILLING [43], RNAi [44], VIGS [45], and gene editing by TALEN and CRISPR-Cas9 [46] are also available in wheat. Together with the advances in wheat genomics, including NGS and its application in genome sequencing, SNP identification, and ‘omics, gene isolation in wheat has been significantly accelerated, although it still has not reached the efficiency of genome analysis in fully sequenced plants. A recent trend in isolating agronomically important genes in wheat is to combine complementary strategies to track favorable alleles. The longterm goal is to exploit the power of genetic engineering to manipulate the regulation and expression of these important genes to tailor the plant to specific environments or end-uses. The contribution of ‘omics technologies to positional cloning Positional cloning remains a challenge in wheat, and moreover fails in some cases, for example when the target lies in a genomic region where recombination is rare (such as around the centromere or within a chromosome segment introduced from a wild relative). One solution for identifying candidate genes in a stagnant or slowly progressing genetic mapping is to combine the ‘omics platforms. For instance, a candidate gene (TRIP1) for the quantitative trait locus (QTL) QLRO-B1, which contributes substantially to variation in primary root length, was identified via a proteomic analysis of two isogenic lines [47]. A similar approach based on transcriptome analysis revealed the sequences responsible for Pm21 (conferring resistance to
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Review powdery mildew) [42], MFT (a candidate for the seed dormancy locus QPhs.ocs-3A.1) [48] and SRO1 (a candidate for an abiotic tolerance gene) [49] (Table 2). A key feature of such studies is that the phenotypic and physiological characterization is both relevant and exhaustive, but is essential to confirm the complicated results from ‘omics analysis. Particularly, for some complex traits such as drought tolerance and salinity tolerance, component contributory traits show a higher heritability and provide more in-depth information than the overall trait itself [31]. Thus, for example, the endogenous reactive oxygen species (ROS)-related biochemical pathway was considered to be responsible for the abiotic tolerance in bread wheat variety Shanrong No. 3 after an extensive physiological survey, and this matched a variety of transcriptomics analyses. Furthermore, the analysis confirmed the selection of SRO1 as the candidate key regulator because its homologs are known to be involved in the production and scavenging of ROS [49]. Finally, some materials widely used in genetic analysis or breeding are essential for ‘omicbased comparisons, such as isogenic lines and introgression lines, which share a common genetic background but differ at and around the target locus. The candidate gene approach Some biochemical or signaling pathways have been thoroughly characterized in rice and/or A. thaliana. Using this information to identify candidate genes has proved to be a very productive strategy in wheat. One of the earliest examples concerned the ‘Green Revolution’ semi-dwarfing Rht genes, which were identified through their homology with the A. thaliana dwarfing gene GAI [50]. The same approach has been exploited to derive genes associated with grain weight [51–53], nitrogen utilization [54,55], and tolerance to excessive soil boron [56] or salinity [57,58]. Compared with other studies which focused on the functional validation through reverse genetic method, these recent studies paid more attention to systematic analysis of genetic effect, sequence diversity, artificial selection, and their relation to corresponding traits. All these results have potential to enhance the application of these genes in wheat breeding. One crucial issue in this method is to identify a candidate favorable allele. Several orthologs or paralogs are obtained by initially sequence alignment and, particularly in bread wheat, one candidate represents three homeologs, in most cases not all of which are functional. Thus, functional validation of a randomly selected allele cannot provide a reliable result. To identify the candidate alleles for Bo1 and Bo4, two major QTLs for boron tolerance in wheat, several putative boron transporter coding sequences were genetically mapped, and two of these, Bot-B5/D5 colocalizing with Bo1 and Bo4, were selected for further analysis [56]. Colinearity can also be a useful criterion for clarifying the relationship between QTL and candidate genes. Thus, the two salinity tolerance QTLs Nax2 and Kna1, although they are located on 5AL and 4DL respectively, could be shown to be homologs and both conferred a HKT1;5-like gene [58–60]. In addition, haplotype polymorphisms generate an altered protein or an altered gene expression profile. This variation is exploited in genotype–phenotype association analyses.
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In some cases, haplotype variation can be associated with geographical provenance, and can be used to recognize the effect of anthropogenic selection or adaptation to particular environments. An example in wheat is provided by TaSus1 and TaSus2, two genes which encode sucrose synthase. In this case, haplotype variation was associated with provenance and with grain weight, and showed clear signals of selection [61]. The impact of epigenetic variation The polyploid nature of bread wheat complicates genomic analysis, but also can be taken advantage of to examine gene dosage and the effects of polyploidization on gene expression, in which epigenetics plays an important role [62,63]. It has been known for some time that newly synthesized wheat allopolyploids undergo substantial reprogramming of cytosine methylation, with knock-on effects on gene silencing and activation [62,64]. Variation with respect to transcription behavior affects about one third of single-copy gene homeoalleles [65]. Epiallelic variation has been implicated in this phenomenon because at least some of the observed silencing can be shown to be reversible. With advances in bisulfite sequencing, the effect of variable cytosine methylation on homeologous transcription has since been confirmed fragmentarily for some genes [66–68]. As more information on the wheat genome and a higherresolution transcriptome which can differentiate bread wheat homeologous genes become available, multiple wheat genes of known agronomic importance, HKT1;5 (the candidate gene of salinity tolerance QTL, Kna1) [59], pinB (component of the grain hardness locus), SPA (the storage protein activator) [39], and others, have demonstrated divergent expression patterns between homeologs. Further study on wheat epigenetics will focus not only on its contribution to the gene silencing and expression-interaction among homeologs but also on its potential to improve wheat profound traits via an ‘epigenetic modification reprogramming – expression alteration of vital genes – wheat improvement’ model. For example, suppressing the expression of DEMETER, a gene encoding a 5methylcytosine DNA glycosylase responsible for transcriptional derepression of gliadins and low molecular weight glutenins (LMWgs) by active demethylation of their promoters in the bread wheat endosperm, via RNA interference, results in a reduction in the amount of immunogenic prolamins [69]. However, the reprogramming of cytosine methylation effected by DNA methyltransferases/demethylases or the DNA methyltransferase inhibitor 5-azacytidine [70] is nontargeted and uncontrollable (because most epigenetic mutants in Arabidopsis are deficient in phenotype). Small non-coding RNAs have been proven involved in expression regulation both at the transcriptional (RNA-directed DNA methylation pathway) and post-transcriptional levels by binding to the targeted genes [71]. The sequencing of the bread wheat genome [19] and those of its progenitors [9,10] has identified large numbers of these RNAs. With the assistance of published A and D genome sequences, the small RNA transcriptome has been successfully analyzed, providing insights into small RNA-mediated dynamic 5
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homeologs regulation in hexaploid wheat [72]. A putative miRNA172 binding site in the exon 10 points to a possible role of miRNA in regulation of the domestication Q gene expression in bread wheat [73]. Therefore, there is potential to regulate small non-coding RNA leading to altered expression of its targeted gene or even of a specific homeologous allele for wheat improvement. Concluding remarks Because the core of wheat improvement is the recognition and then deployment of elite genes (or alleles of genes), the focus of many wheat research teams has shifted away from genetic analysis to the exploitation of agronomically important genes. However, the lack of sequence information for the extremely intricate genome of wheat has led to slow progress. Since 2012, genome sequence drafts of bread
wheat and its progenitors have been acquired via shotgun and chromosome-based approaches, and this represents a milestone for wheat improvement. Currently the survey sequences of all chromosomes, the physical maps of 16 chromosomes, and the reference sequence of 3B have been achieved in bread wheat. Based on these achievements and advances in NGS, high-resolution transcriptome and genotype maps are now available, providing extensive resources. Consequently, a new epoch of wheat gene research is opening up (Figure 1). In the future, positional cloning will be greatly accelerated on chromosome 3B, and even on 3A or 3D. Although the reference sequences of other chromosomes are still being constructed, high-density consensus maps generated by SNP-based genotyping will expedite the process of genetic mapping. Moreover, higherresolution transcription data at the level of individual
Large
He xa plo id
ve
pe
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ly-
gh
Hi
Wheat genome
Shotgun sequencing
Chromosomebased sequencing
SNP-based genotyping
Phenotyping
GWAS
Highresoluon transcriptome
Progenitor sequencing
Precise genome engineering
Posional cloning
Synteny analysis with other plants
Reverse genecs
Candidate gene
Haplotype analysis
Funcon validaon
Evoluonary/ homeologous analysis
Epigenecs
TRENDS in Plant Science
Figure 1. How wheat whole-genome sequence information can expedite the identification of agronomically important genes. Abbreviation: GWAS, genome-wide association study.
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Review homeologs will promote the use of reverse genetics-based strategies. The level of information on the wheat genome sequence is still trailing behind that for the genomes of rice and A. thaliana to support positional cloning. Therefore, approaches linking physiology, ‘omics, genetic mapping, and haplotype analysis will continue to play a predominant role in gene identification in wheat. Another significant trend in wheat research is the increasing high-throughput and high-quality of phenotyping. Therefore, an integrative strategy combining the large-scale phenotype data with the genomic information and the ‘omics information will also be crucial to deciphering the genetic basis of complex traits in wheat. In addition, unraveling the regulation of traits in wheat spans understanding sequence diversity to epigenetic modifications. In summary, current progress for the exploitation of agronomically important genes in wheat is promising. Acknowledgments We apologize to authors whose relevant work we could not cite owing to space limitations. We are also grateful to Prof. Jaroslav Dolezˇel for providing further data. This work is supported by funds of the Major Program of the Natural Science Foundation of China (No.31430060) and the National Transgenic Project (Grants 2013ZX08002002).
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