http://orcid.org/0000-0001-9144-6072
Figures
Abstract
Bread wheat (AABBDD) originated from the diploid progenitor Triticum urartu (AA), a relative of Aegilops speltoides (BB), and Ae. tauschii (DD). The DREB1 transcriptional factor plays key regulatory role in low-temperature tolerance. The modern breeding strategies resulted in serious decrease of the agricultural biodiversity, which led to a loss of elite genes underlying abiotic stress tolerance in crops. However, knowledge of this gene’s natural diversity is largely unknown in the genome donor species of wheat. We characterized the dehydration response element binding protein 1 (DREB1) gene-diversity pattern in Ae. speltoides, Ae. tauschii, T. monococcum and T. urartu. The highest nucleotide diversity value was detected in Ae. speltoides, followed by Ae. tauschii and T. monococcum. The lowest nucleotide diversity value was observed in T. urartu. Nucleotide diversity and haplotype data might suggest no reduction of nucleotide diversity during T. monococcum domestication. Alignment of the 68 DREB1 sequences found a large-size (70 bp) insertion/deletion in the accession PI486264 of Ae. speltoides, which was different from the copy of sequences from other accessions of Ae. speltoides, suggesting a likely existence of two different ancestral Ae. speltoides forms. Implication of sequences variation of Ae. speltoides on origination of B genome in wheat was discussed.
Citation: Xu Y, Sun F-Y, Ji C, Hu Q-W, Wang C-Y, Wu D-X, et al. (2019) Nucleotide diversity patterns at the DREB1 transcriptional factor gene in the genome donor species of wheat (Triticum aestivum L). PLoS ONE 14(5): e0217081. https://doi.org/10.1371/journal.pone.0217081
Editor: Aimin Zhang, Institute of Genetics and Developmental Biology Chinese Academy of Sciences, CHINA
Received: February 25, 2019; Accepted: May 5, 2019; Published: May 28, 2019
Copyright: © 2019 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data is provided as supplementary file.
Funding: This project was supported in part by the National Key Research and Development Program of China (2017YFD0301304), a Research Foundation for Talented Scholars from Anhui Agricultural University and the introduced leading talent research team for Universities in Anhui Province, the Natural Sciences and Engineering Research Council of Canada (grant No: RGPIN-2017-05817 and RGPIN-2018-05433) and a Senate Research Grant at Saint Mary’s University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: This manuscript or substantial parts of it, submitted to the journal has not be under consideration by any other journal. No material submitted as part of a manuscript infringes existing copyrights, or the rights of a third party. All authors have approved the manuscript. The authors have declared that no competing interests exist.
Introduction
Frequent changes in climate, such as sudden low temperature, high temperature, and flooding, have caused serious damage to crop growth and development [1, 2], while, gradually, drought and salinity under increased agricultural pressure have constrained the yield and geographical distribution of global crops, resulting in a 70% reduction in their potential yield [3] and caused irreversible damage to field ecology [4]. Unreasonable practices will continue to increase the soil salinity [5]. The global drought problem may gradually increase in the foreseeable future [6]. Under the influence of plant growth and development [1], the mechanism of tolerance and adaptation of plant to abiotic stress has been a research hotspot.
In response to stress, plants respond with variety of complex processes of molecular regulation. When plant cells perceive external pressure, through signal transduction, plants turn on metabolic pathways to tolerate abiotic stress and synthesize a wide variety of stress-responsive proteins. These stress-responsive proteins include changes not only on the level of enzymes at the metabolic level, but also in composition at the transcriptional and translational levels.
The ethylene response element binding factor (AP2/ERF) family is a group of plant-specific transcriptional factors. Studies have shown that AP2/ERF transcriptional factors are involved in plant development and stress pathways [7]. Their main features contain at least an AP2 binding domain, a typical three-dimensional structure consisting of approximately 60 amino acid residues in the form of three β-sheets and one α-helix [8]. The AP2/ERF family is divided into 4 subfamilies based on their domains, namely the AP2 subfamily, the RAV subfamily, the ERF subfamily, and the dehydration response element binding protein (DREB) subfamily [9]. The AP2 subfamily contains two AP2 domains that are highly similar and repeat in tandem. The AP2/ERF transcriptional factor containing one AP2 domain and one B3 domain was named as the RAV subfamily. Both the DREB subfamily and the ERF (ethylene response transcription factor) subfamily contain only one AP2 domain, but their main differences are the fourteenth and nineteenth amino acid residues of the AP2 domain. The fourteenth amino acid in the DREB subfamily is proline, the nineteenth is glutamic acid, the fourteenth amino acid in the ERF subfamily is alanine, and the nineteenth is aspartic acid [9].
The DREB subfamily includes two subgroups, DREB1 and DREB2 [10]. DREB transcriptional factor specifically binds to the DRE/CRT cis-element in the promoter and can activate the expression of many stress-inducible genes, thereby enhancing plant tolerance to stress. Arabidopsis DREB1A and DREB2A transcriptional factor genes were induced by low temperature or drought and high salinity, and DREB1A and DREB2 transcription factors were generated to regulate rd17, kin1, cor6.6, cor15a, erd10, which are related to low temperature and high salt stress tolerance, respectively [10]. HvDREB1 was isolated from barley and found to bind as a transcriptional factor to DRE/CRT elements. Overexpression of HvDREB1 increased Arabidopsis resistance to salt stress [11]. Under drought and high temperature stress, DREB2s binds to DRE/CRT elements as transcriptional factors in Arabidopsis to induce downstream gene expression and increase plant resistance to abiotic stresses [12].
Hexaploid Triticum aestivum L. (AABBDD) (2n = 6x = 42) is derived from the three homologous genomes, A, B, and D, with an approximate genome size of 16–17 Gb [13–15]. The progenitor of the A genome contains Triticum urartu Thum ex Gand (genome Au) [16] and Triticum monococcum Linn (genome Am). T. urartu has been considered as the A-genome donor to tetraploid and hexaploid wheat species [17, 18]. The origin of the B genome is still under debate, in spite of a large number of attempts to identify the parental species [17], but Ae. speltoides (S genome) has been suggested as the most likely progenitor of the B genome [19, 20]. Ae. tauschii Coss (genome DD) has been documented as the D genome progenitor of T. aestivum [17].
The modern breeding strategies resulted in serious decrease of agricultural biodiversity, which led to a loss of elite genes underlying abiotic stress tolerance in crop [21]; therefore, exploitation of the genetic resources of wild relatives is a widely used strategy to increase biodiversity for crop improvement. In recent years, new introgression lines between commercial cultivars and wild relatives have been generated in many crops. As an example, the genes from wheat wild relatives Aegilops umbellulata and Elytrigia elongata [22–24] have been introgressed into wheat to significantly improve abiotic stress tolerance traits in wheat.
DREB1genes in wheat were located on chromosomes 3A, 3B and 3D [25], and can be induced by low temperature, abscisic acid (ABA), salinity and drought [26]. The nucleotide diversity π and θ values of wheat DREB gene on 1A chromosome have been characterized [27]. However, the nucleotide diversity of DREB1gene in wheat genome donor species is uncharacterized. In order to efficiently use of wild relatives for improving wheat tolerance to abiotic stress, we analyzed the nucleotide diversity of DREB1 gene among the genomes of Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu. The haplotype diversity and evolutionary factors combined with the relationship between DREB1 transcriptional factors and stress resistance further explore the evolution and origin of the wheat-tribe genome.
Materials and methods
Plant materials
Thirteen accessions of Aegilops speltoides (S genome), 12 accessions of Ae. tauschii (D genome), 24 accession of Triticum monococcum (Am genome) and 19 accessions of T. urartu (Au genome) were sampled (Table 1). The seeds were provided by USDA (United States Department of Agriculture). Germinated seeds were transplanted to a sand-peat mixture, and the plants maintained in a greenhouse. Twenty-three sequences from other Triticeae species and Brachypodium distachyon downloaded from NCBI website were included in phylogenetic analysis.
DNA extraction, amplification and sequencing
Leaf tissue samples were collected and frozen in liquid nitrogen. DNA was isolated using the GeneJet Plant Genomic DNA Purification Mini Kit according to the manufacture’s instruction (Thermo Scientific). The isolated genomic DNA was stored at -20°C for use.
The primers for amplifying the DREB1 sequence were designed based on Triticum aestivum AP2-containing protein (DREB1) mRNA (AF303376.1). The forward primer sequence was 5’-GAAGAAAGTGCGCAGGAGAAG-3’ (Dreb1F), reversed primer was 5’- TCCCTATTGCTCCGCATGAC-3’(Dreb1R). The sequence was amplified in a 15 μl reaction containing 20 ng template DNA, 0.25 mM dNTP, 2.0 mM MgCL2, 0.25 μM of each primer and 2.0 U Taq polymerase (TransGen, Beijing, China). The amplification profile was as follows: an initial denaturation at 95°C for 5 min and 36cycles of 95°C for 45 sec, 58°C for 50 sec, 72°C for 150 sec. The cycling ended with 72°C for 10 min. PCR products were purified using the EasyPure Quick Gel Extraction Kit (TransGen, Beijing, China) according to manufacturer’s instruction.
PCR products were commercially sequenced by the Shanghai Sangon Biological Engineering & Technology Service Ltd (Shanghai, China). To enhance the sequence quality, both forward and reverse strands were sequenced independently. To avoid any error which would be induced by Taq DNA polymerase during PCR amplification, each sample was independently amplified twice and sequenced.
Data analysis
Automated sequence outputs were visually inspected with chromatographs. Multiple sequence alignments were performed using ClustalX with default parameters. Maximum-parsimony (MP) method was used to perform phylogenetic analysis using computer program PAUP* ver. 4 beta 10 [28]. All characters were specified as unweighted and unordered. The most parsimonious trees were constructed by performing a heuristic search using the Tree Bisection-Reconnection (TBR) with the following parameters: MulTrees on and ten replications of random addition sequences with the stepwise addition option. A strict consensus tree was generated from multiple parsimonious trees. The consistency index (CI) and the retention index (RI) were used to estimate the overall character congruence. Bootstrap (BS) values with 1,000 replications [29] that was calculated by performing a heuristic search using the TBR option with Multree on were used to test the robustness of clades.
Bayesian analysis was used to for phylogeny analysis of haplotypes. The jModelTest 2.1.10 [30] was used to calculate the best-fitting model of sequence evolution using default parameters. The Maximum likelihood value (-LnL), Akaike information criterion (AIC) [31] and Bayesian Information Criterion (BIC) [32] were estimated. The model test showed the TrN+I substitution model led to best BIC and AIC scores, therefore, the TrN+I model was used in the Bayesian analysis using MrBayes 3.1 [33]. MrBayes 3.1 was run with the program’s standard setting of two analyses in parallel, each with four chains, and estimates of convergence of results were determined by calculating standard deviation of split frequencies between analyses. 689,000 generations were run to make the standard deviation of split frequencies < 0.01. Samples were taken every 1000 generations. The first 25% of samples from each run were discarded as burn-in to ensure the stationary of the chains. Bayesian posterior probability (PP) values were used to test the robustness of clades.
Nucleotide diversity values of Tajima’s π [34] and Watterson’s θ [35] as well as tests of neutral evolution [36] were performed using the software program DnaSP 4.0 [37].
The analysis of protein domain and conserved motif of sequences was characterized using Pfam (https://pfam.xfam.org/search) and Multiple Em for Motif Elicitation (MEME) software [38].
Results
Sequence analysis
The DNA from 13 accessions of Aegilops speltoides (S genome), 12 accessions of Ae. tauschii (D genome); 24 accession of Triticum monococcum (Am genome); and 19 accessions of T. urartu (Au genome) were amplified using the Dreb1F/R primer pair. The size of amplified products from these DNA was approximately 850 bp. Complete alignment of the 68 DREB1 sequences detected a large-size (70 bp) insertion/deletion in the accession PI486264 of Ae. speltoides (Fig 1). BLAST search against NCBI found that this sequence shared 100% identity with T. aestivum DREB transcription factor 6 (DREB6) mRNA (AY781361.1); the sequence on T. aestivum chromosome 3B (LS992087); and T. aestivum genome B dehydration-responsive element-binding protein (DREB1) gene, partial cds (DQ195069.1). BLASTX search found that protein sequence of this accession matched with DREB transcription factor 6 (AAX13289.1) of T. aestivum with 100% identity, while it lost “KDESESPPSLISNAPTAALHRSDA” when compared with AP2-containing protein in T. aestivum (AAL01124.1).
Note that a large size (70 bp) insertion/deletion in the sequence from the accession PI486264 of Ae. speltoides.
The haplotypes of DREB1 sequences from Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu were calculated. A total of 19 haplotypes were identified in the 68 accessions of these four species. Seven, 7, 10, and 4 haplotypes were detected from 13 sequences of Ae. speltoides, 12 sequences of Ae. tauschii, 24 sequences of T. monococcum, and 19 sequences of T. urartu, respectively (Table 2). Twenty-six out of 68 accessions belonged to the Hap 2, and 15 belonged to the Hap 7. For each species, most Ae. speltoides sequences belonged to Hap 2 with frequency of 0.538. The Hap 2, Hap 7, and Hap 2 showed the highest frequency in Ae. tauschii, T. monococcum, and T. urartu, respectively. Thirteen haplotypes were species-specific, while two haplotypes (Hap 1 and Hap 7) were shared by Ae. speltoides, Ae. tauschii, and T. monococcum; Hap 8 and Hap 10 was shared by Ae. speltoides and T. monococcum, and by Ae. tauschii and T. monococcum, respectively. Only one haplotype (Hap 2) was commonly detected among four species (Table 2).
Nucleotide diversity
Tajima’s π and Watterson’s θ were used to determine nucleotide diversity for each species studied here (Table 3). The highest nucleotide diversity values (π and θ) were detected in Ae. speltoides with π = 0.00456 and θ = 0.00685, followed by Ae. tauschii (π = 0.00320 and θ = 0.00281), and T. monococcum (π = 0.00301 and θ = 0.00325). The lowest nucleotide diversity values (π and θ) were observed in T. urartu with π = 0.00146 and θ = 0.00173. Tajima and Fu and Li’s D statistics were also calculated for each species. Tajima’s D values for Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu were -1.40161, 0.53752, -0.23968 and -0.49107, respectively, which were all not significant. Fu and Li’s D values and Fu and Li’s F test for these four species were also not significant (Table 3).
Phylogenetic analysis
The phylogenetic relationship of 90 DREB1 sequences from Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu along with DREB1 sequences from other Triticeae species was analyzed using the maximum parsimony. The sequence from Brachypodium distachyon was used as an outgroup. The maximum parsimony analysis resulted in 236 most parsimonious trees (642 constant characters, 110 parsimony-uninformative characters, 77 parsimony-informative characters, CI excluding uninformative characters = 0.864; RI = 0.935). The strict consensus phylogenetic tree yielded obvious Aegilops + Triticum and Hordeum species group (Fig 2) with highly supported bootstrap values (78% and 100%, respectively). All sequences from Aegilops and Triticum species studied here were grouped into the Aegilops + Triticum except the sequences of T. aestivum from B genome and Ae. speltoides accession PI 486264, which formed a group with 97% bootstrap support. Within the Aegilops + Triticum clade, the sequence DQ195070 encoding dehydration responsive element binding protein (DREB1) on A genome of T. aestivum was grouped with the DQ022952, dehydration responsive element binding protein W73 mRNA (87% bootstrap support), and was nested within the most sequences from Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu (56% bootstrap support). The sequence DQ195068 encoding dehydration-responsive element binding protein on D genome of T. aestivum was grouped with the sequence AF303376, AP2-containing protein (DREB1) mRNA (80% bootstrap value), and was sister to the largest group containing sequences from Ae. speltoides, Ae. tauschii, T. monococcum, T. urartu, and A genome of T. aestivum.
Numbers above are bootstrap values from maximum parsimony. Brachypodium distachyon was used as an outgroup. Consistency index (CI) = 0.864, retention index (RI) = 0.935.
The relationship of the 19 haplotypes from accessions of Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu was revealed by Bayesian analysis, and shown in Fig 3 with Bayesian posterior probability (PP) values above branch. Haplotypes of Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu were grouped into different clades (Fig 3). The Hap 4 and Hap 5 showed a close relationship with a well-supported value (PP = 0.99).
The value above branch was Bayesian posterior probability (PP) values. The full list of haplotype was placed aside the tree.
Pfam analysis showed that all sequences contain AP2 domain structure. The conserved motif analysis of the DREB proteins found that the haplotype 19 of DREB sequence did not have motif “PPSLISNGPTAALHRSDAKDESESAGTVARK VKKEVSNDLRSTHEEHKTL”, the haplotype 5, 9, 13, and 16 did not have motif “KKVRRRSTGPDSVAETIKKWKEENQKLQQENGSRKAPAKGS” (Fig 4).
Each motif was represented in boxes with different colors.
Discussion
Nucleotide diversity in Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu
Previous studies have provided evidence that crop domestication and modern breeding strategies resulted in serious reduction of genetic diversity on various species [39, 40], which led to a loss of elite genes underlying abiotic stress tolerance in crop [21]; therefore, exploitation of the genetic resources of wild relatives is widely used strategy to increase biodiversity for crop improvement. Characterization of genetic diversity not only has significant effect on genetic improvement and resistance research, but also provides new direction for the conservation and utilization of genetic resources in germplasm gene banks [41]. The nucleotide diversity of Ae. speltoides, Ae. tauschii, T. monococcum, and T. urartu was examined here.
The genomes of T. urartu Thum ex Gand (genome Au) and T. monococcum Linn (genome Am) have similar genome size and gene content [42]. Triticum urartu, the wild diploid wheat from the Fertile Crescent region, has long been considered as the A-genome donor to tetraploid and hexaploid wheat species [17, 43]. The diploid wheat T. monococcum was among the first domesticated crops in the Fertile Crescent 10,000 years ago [44]. Our results showed that both the number of haplotypes and nucleotide diversity values of T. monococcum were much higher than those of T. urartu, which might suggest “no reduction of nucleotide diversity during T. monococcum domestication” made from a study of 18 loci in 321 wild and 92 domesticated lines of Triticum species [44], but was not consistent with the Qi et al. [45]. That higher variability of DREB1 in T. monococcum than in T. urartu could be due to DREB1 regulating role in response to abiotic stress, as this kind of gene might be experienced elevated rates of mutations and adaptation. This was evidenced by the highest number of haplotypes detected in T. monococcum among the four species analyzed here. Indirectly, this variation generated in T. monococcum might be a source to wheat breeding to improve the resistance to abiotic stress.
Higher nucleotide diversity value of DREB1 in Ae. speltoides than that in Ae. tauschii; T. monococcum; and T. urartu was expected, and might agree well with previous studies [45, 46]. This might attribute to the mating system of these species. Aegilops speltoides is an outcrossing species, while Ae. tauschii, T. monococcum, and T. urartu are inbreeding species. Mating system is one of the major factors controlling molecular diversity [44, 47, 48]. It was reported that averaged π value (0.01323) of Acc-1 gene in the genomes of outcrossing species was two-fold of the value (0.005664) in the genomes of selfer in Triticeae species [49].
In general, genetic bottlenecks acting on neutrally evolving loci during either the domestication process or subsequent breeding, or both, are sufficient to account for reduced diversity [50]. In domesticated forms, this reduction is evident in a shift toward more positive values of Tajima’s D in the domesticated relative to wild species population [51, 52]. Domesticated T. monococcum showed negative Tajima’s D values (Table 3), suggesting that there might be no genetic bottlenecks effects on or a signature of a recent population expansion of T. monococcum.
Comparison of nucleotide diversity of DREB1 gene with other species
Since DREB1/CBF (dehydration responsive element binding/C-repeat binding factor) encoding genes in abiotic stress have important roles in responding to abiotic stress, nucleotide diversity of DREB1 gene has been characterized in several plants [53–57]. In 126 wheat lines, the nucleotide diversity π and θ values of wheat DREB gene on 1A chromosome were 0.180 and 0.392, respectively [27], which were much higher than the values detected in A genome T. monococcum and T. urartu. DREB1A nucleotide diversity was calculated from the 126 wheat lines that were developed by the International Maize and Wheat Improvement Center from entries in the elite spring wheat yield trial, semiarid wheat yield trial, and high temperature wheat yield trial [57]. These lines, during the breeding procedure, might suffer different natural selection pressure, resulting in the wide range of diversity of this gene. Speculatively, the significant Tajima’s D value of DREB1A in these wheat lines might be an indicator of presence of selection footprints, while the Tajima’s D value of DREB1 in T. monococcum and T. urartu accessions studied here did not reach significant level.
The haplotype (gene) diversity of DREB1 gene among the 10 promising upland and lowland cultivars rice was 0.756 [53], which is comparable to the haplotype diversity detected in Ae. speltoides and T. monococcum, but higher than that in T. urartu. The nucleotide diversity (π) of DREB1 in 191 chickpea was 0.0011 [54], which is similar to the values detected in this study, while the nucleotide diversity in C. canephora CDS region was π = 0.0101, θ = 0.0080) [56], which was much higher than that in our study. This might be attributed to the nature of species.
Conserved motif of DREB proteins
The conserved motif analysis of the DREB proteins found that some sequences did not have “PPSLISNGPTAALHRSDAKDESESAGTVARKVKKEVSNDLRST HEEHKTL”, and motif “KKVRRRSTGPDSVAETIKKWKEENQKLQQENGS RKAPAKGS”. All sequences contain AP2 domain structure, suggesting the structural diversity and functional similarity of the DREB gene in these species. Allele mining across DREB1A and DREB1B in diverse rice genotypes also found indels across DREB1A and DREB1B [55]. Since DREBs are important transcriptional factors regulating stress-responsive gene expression, the highly conserved domains in these genes are essential for their specific biological functions. Further correlated the SNPs and indels in the DREB1 with its genotype responding to stress will enhance our understanding the role played by this gene.
Implication of sequences variation of Ae. speltoides on origination of B genome in wheat
Overwhelming evidences have suggested that the diploid ancestor of the B genome of tetraploid and hexaploid wheat species is closely related to the S genome of Aegilops speltoides in the Sitopsis section (SS, 2n = 14) [19, 42, 43, 58–60]. However, none of the presently known species in this group have all properties of the B-genome [60]. A study on transposable elements (TEs) suggested that the S genome of Ae. speltoides has diverged very early from the progenitor of the B genome which remains to be identified [58]. Analysis of the Pgk-1 gene among the Ae. speltoides accessions revealed an 89 bp indel in the intron of the Pgk-1 gene, indicating that likely existence of two different ancestral Ae. speltoides forms, which gave rise to two evolutionarily close lineages of polyploid wheats [61]. The Wcor15 results suggested that Ae. speltoides might be the direct donor of the Wcor15-2B in tetraploid and hexaploid wheat varieties [42]. Our study here also revealed two forms of DREB1 sequences in Ae. speltoides, suggesting “likely existence of two different ancestral Ae. speltoides forms” [61]. The form in the accession PI486264 shared 100% identity with the sequences on T. aestivum chromosome 3B, which might be more likely the B donor genome of wheat.
Recent studies suggested that mono-or polyphyletic B subgenome origin cannot explain entirely the observed accumulation of mutations during evolution in shaping the modern bread wheat B subgenome. The consequences of a differential evolutionary plasticity of the B subgenome was proposed as an alternative scenario where the increased divergence of the B subgenome in the hexaploid wheat compared to Ae. speltoides at the sequences level [62]. Phylogenetic analysis routinely applied to test evolutionary questions and to trace the origin of polyploidy is based on assumptions that intraspecifc variation is smaller than interspecific variation, and that within and between species, sample sizes are sufficiently large enough to capture variation at both levels [63]. When sampling a single individual per species or treating each individual or haplotype as a separate terminal taxon could delineate the potential risk of bias [64]. Intraspecific variation is abundant in all types of systematic characters which could cause bias in the phylogenetic analyses [65], such as in Ae. speltoides. Our results suggested that, in order to reveal the origination of B subgenome in the modern bread, it is critical to include wide range of accessions of Ae. speltoides in phylogenetic analysis.
In summary, the highest DREB1 gene diversity was detected in Ae. speltoides, followed by Ae. tauschii and T. monococcum. The lowest nucleotide diversity value was observed in T. urartu. Both the number of haplotypes and nucleotide diversity values of T. monococcum were much higher than those of T. urartu, which likely supports no reduction of nucleotide diversity during T. monococcum domestication [44]. Our study here revealed two forms of DREB1 sequences in Ae. speltoides. The form in the accession PI486264 shared 100% identity with the sequences on T. aestivum chromosome 3B, which might be more likely the B donor genome of wheat. Our results suggested that, in order to reveal the origination of B subgenome in the modern bread, it is critical to include wide range of accessions of Ae. speltoides in phylogenetic analysis. Stress tolerance study such as drought on these materials will be conducted to make possibly link of the haplotype with gene expression in future.
Supporting information
S1 Table. Sequences used in phylogenetic analysis.
https://doi.org/10.1371/journal.pone.0217081.s001
(DOCX)
References
- 1. Chinnusamy V, Schumaker K, Zhu JK (2004) Molecular genetic perspectives on cross-talk and specificity in abiotic stress signaling in plants. J Exp Bot 55: 225–236. pmid:14673035
- 2. Heidarvand L, Amiri RM (2010) What happens in plant molecular responses to cold stress. Acta Physiol Plant 32: 419–431.
- 3. Agarwal PK, Agarwal P, Reddy MK, Sopory SK (2006) Role of DREB transcription factors in abiotic and biotic stress tolerance in plants. Plant Cell Rep 25: 1263–1274. pmid:16858552
- 4.
Bray EA, Bailey-Serres J, Weretilnyk E (2000) Responses to abiotic stresses. In: Buchanan BB, Gruissem W, Jones RL (eds) Biochemistry and molecular biology of plants. American Society of Plant Biologists. Rockville, MD, pp1158–1203.
- 5.
FAO (2004) FAO production yearbook. Food and Agriculture Organization of the United Nations, Rome.
- 6. Burke EJ, Brown SJ, Christidis N (2006) Modelling the recent evolution of global drought and projections for the twenty-first century with the hadley centre climate model. J Hydromet 7: 1113–1125.
- 7. Charu L, Manoj P (2011) Role of DREBs in regulation of abiotic stress responses in plants. J Exp Bot 62(14):4731–4748. pmid:21737415
- 8. Allen MD, Yamasaki K, Takagi M, Tateno M, Suzuki M (1998) A novel mode of DNA recognition by a bea-sheet revealed by the solution structure of the GCC-box binding domain in complex with DNA. The EMBO J 17 (18):5484–5496. pmid:9736626
- 9. Sakuma Y, Liu Q, Dubouzet JG, Abe H, Shinozaki K, Yamaguchi-Shinozaki K (2002) DNA-binding specificity of the ERF/AP2 domain of Arabidopsis DREBs, transcription factors involved in dehydration- and cold-inducible gene expression. Biochem Biophys Res Commun 290: 998–1009 pmid:11798174
- 10. Liu Q, Kasuga M, Sakuma Y, Abe H, Miura S, Yamaguchi-Shinozaki K, Shinozaki K (1998) Two transcription factors, DREB1 and DREB2, with an EREBP/AP2 DNA binding domain separate two cellular signal transduction pathways in drought- and low-temperature-responsive gene expression, respectively, in Arabidopsis. Plant Cell 10: 1391–406. pmid:9707537
- 11. Xu ZS, Ni ZY, Li ZY, Li YC (2009) Isolation and functional characterization of HvDREB1-a gene encoding a dehydration-responsive element binding protein in Hordeum vulgare. J Plant Res 122 (1): 121–130. pmid:19067111
- 12. Matsukura S, Mizoi J, Yoshida T, Todaka D, Ito Y, Maruyama K, et al (2010) Comprehensive analysis of rice DREB2-type genes that encode transcription factors involved in the expression of abiotic stress-responsive genes. Mol Genet Genom 283(2): 185–196.
- 13. Bennett MD, Smith JB (1976) Nuclear DNA amounts in angiosperms. Phil Transact Royal Soc London Series B, Biol Sci 274: 227–274.
- 14. Devos KM, Gale MD (2000) Genome relationships: The grass model in current research. Plant cell 12: 637–646. pmid:10810140
- 15. Hirosawa S et al (2004) Chloroplast and nuclear DNA variation in common wheat: insight into the origin and evolution of common wheat. Genes Genet Syst 79: 271–282. pmid:15599057
- 16. Gulbitti-Onarici SELMA, Sumer S, Ozcan S (2007) Determination of phylogenetic relationships between some wild wheat species using amplified fragment length polymorphism (AFLP) markers. Bot J Linnean Soc 153: 67–72.
- 17. Huang S, Sirikhachornkit A, Su X, Faris J, Gill B, Haselkorn R, et al (2002) Genes encoding plastid acetyl-CoA carboxylase and 3-phosphoglycerate kinase of the Triticum/Aegilops complex and the evolutionary history of polyploid wheat. Proc Natl Acad Sci USA 99: 8133–8138. pmid:12060759
- 18. Luo G, Zhang X, Zhang Y, Yang W, Li Y, Sun J, et al (2015) Composition, variation, expression and evolution of low-molecular-weight glutenin subunit genes in Triticum urartu. BMC Plant Biol 15: 68. pmid:25849991
- 19. Dvorak J, Zhang HB (1990) Variation in repeated nucleotide sequences sheds light on the phylogeny of the wheat B and G genomes. Proc Natl Acad Sci UAS 87: 9640–9644.
- 20. Wang GZ, Miyashita NT, Tsunewaki K(1997) Plasmon analyses of Triticum (wheat) and Aegilops: PCR-single-strand conformational polymorphism (PCR-SSCP) analyses of organellar DNAs. Proc Natl Acad Sci 94: 14570–14577. pmid:9405654
- 21. Dwivedi SL, Scheben A, Edwards D, Spillane C, Ortiz R (2017) Assessing and exploiting functional diversity in germplasm pools to enhance abiotic stress adaptation and yield in cereals and food Legumes. Front Plant Sci. 8:1461. pmid:28900432
- 22. Ganeva G, Georgieva V, Panaĭotova M, Stoilova T, Balevska P(2000) The transfer of genes for Brown Rust resistance from Aegilops umbellulata Eig. to wheat (Triticum aestivum L.) genome. Genetika 36: 71–76. pmid:10732282
- 23. Li H, Deal KR, Luo MC, Ji W, Distelfeld A, Dvorak J (2017). Introgression of the Aegilops speltoides Su1-Ph1 suppressor into Wheat. Front Plant Sci.8:2163. pmid:29326749
- 24. Colmer TD. Flowers TJ. Munns R (2006). Use of wild relatives to improve salt tolerance in wheat. J Exp Bot 57: 1059–1078. pmid:16513812
- 25. Wei B, Jing R, Wang C, Chen J, Mao X, Chang X (2009) DREB1, genes in wheat (Triticum aestivum. L): development of functional markers and gene mapping based on SNPs. Mol Breed 23:13–22.
- 26. Kurahashi Y, Terashima A, Takumi S (2009) Variation in dehydration tolerance, ABA sensitivity and related gene expression patterns in D-genome progenitor and synthetic hexaploid wheat lines. Int J Mol Sci10:2733–2751. pmid:19582226
- 27. Edae EA, Byrne PF, Manmathan H, Haley SD, Moragues M, Lopes MS, et al (2013) Association mapping and nucleotide sequence variation in five drought tolerance candidate genes in spring wheat. The Plant Genome 6(2).
- 28.
Swofford DL (2003) PAUP*. Phylogenetic analysis using parsimony (*and other methods). Version 4. Sunderland, MA, USA: Sinauer Associates.
- 29. Felsenstein J (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39: 783–791. pmid:28561359
- 30. Darriba D, Taboada GL, Doallo R and Posada D (2012) jModelTest 2: more models, new heuristics and parallel computing. Nat Methods 9(8): 772
- 31.
Akaike H (1973) Information theory and an extension of maximum likelihood principle. In: Petrov BN, Csaki F(eds) Second International Symposium on Information Theory. Akademiai Kiado, Budapest, pp 267–281.
- 32. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6: 461–464.
- 33.
Ronquist F, Huelsenbeck JP (2005) Bayesian analysis of molecular evolution using MrBayes. In: Nielsen R (ed) Statistical methods in molecular evolution. Springer-Verlag Press, pp183–232.
- 34. Tajima F (1989) Statistical method for testing the neutral mutation of hypothesis by DNA polymorphism. Genetics 123: 585–595. pmid:2513255
- 35. Watterson GA (1975) On the number of segregation sites in genetic models without recombination. Theor Popul Biol 7: 256–276. pmid:1145509
- 36. Fu YX, Li WH (1997) Estimating ancestral population parameters. Genetics 133: 693–709.
- 37. Librado P, Rozas J (2009) DnaSP v4: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451–1452. pmid:19346325
- 38. Bailey TL, Bodén M, Buske FA, Frith M, Grant CE, Clementi L, et al (2009) "MEME SUITE: tools for motif discovery and searching". Nucl Acids Res 37: W202–W208 pmid:19458158
- 39. Munoz-Amatriain M, Xiong Y, Schmitt MR, Bilgic H, Budde A, Chao S, et al (2010) Transcriptome analysis of a barley breeding program examines gene expression diversity and reveals target genes for malting quality improvement. BMC Genom 11:653/1471-2164/11/653.
- 40. Wang S, Wong D, Kerrie F, Allen AM (2014) Characterization of polyploid wheat genomic diversity using a high-density 90000 single nucleotide polymorphism array. Biotech J 12(6): 787–796
- 41. Varshney RK, Chabane K, Hendre PS, Aggarwal RK, Graner A (2007) Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci 173: 638–649.
- 42. Özkan H, Willcox G, Graner A, Salamini F, Kilian B (2010) Geographic distribution and domestication of wild emmer wheat (Triticum dicoccoides). Genet Resour Crop Evol 58:11–53.
- 43. Liu FF, Si HQ, Wang CC, Sun GL, Zhou ET, Chen C, et al (2016) Molecular evolution of Wcor15 gene enhanced our understanding of the origin of A, B and D genomes in Triticum aestivum. Sci Rep 6:31706 pmid:27526862
- 44. Kilian B, Ozkan H, Deusch O, Effgen S, Brandolini A, Khol J, et al (2007) Independent wheat B and G genome origins in outcrossing Aegilops progenitor haplotypes. Mol Biol Evol 24: 217–227. pmid:17053048
- 45. Qi PF, Wei YM, Ouellet T, Chen Q, Tan X, Zheng YL (2009) The γ-gliadin multigene family in common wheat (Triticum aestivum) and its closely related species. BMC Genom 10:168 pmid:19383144
- 46. Sasanuma T, Chabane K, Endo TR, Valkoun J (2002) Genetic diversity of wheat wild relatives in the Near East detected by AFLP. Euphytica 127: 81–93.
- 47. Charlesworth D, Wright SI (2001) Breeding systems and genome evolution. Cur Opin Genet Develop 11: 685–690.
- 48. Glémin S, Bazin E, Charlesworth D (2006) Impact of mating systems on patterns of sequence polymorphism in flowering plants. Proc Royal Soc B 273: 3011–3019.
- 49. Wu DX, Sun GL, Yang L, Hu QW (2014) Comparison of Acetyl-CoA carboxylase 1 (Acc-1) gene diversity among different Triticeae genomes. Gene 546:11–15. pmid:24865934
- 50. Badr A, El-Shazly H (2012) Molecular approaches to origin, ancestry and domestication history of crop plants: Barley and clover as examples. J Genet Eng Biotechnol 10:1–12.
- 51. Hufford MB, Xu X, Van Heerwaarden J, Pyhäjärvi T, Chia JM, Cartwright RA, et al (2012) Comparative population genomics of maize domestication and improvement. Nat Genet 44: 808–811. pmid:22660546
- 52. Morrell PL, Gonzales AM, Meyer KK, Clegg MT (2013) Resequencing data indicate a modest effect of domestication on diversity in barley: a cultigen with multiple origins. J Hered 105: 253–264. pmid:24336926
- 53. Filiz E, Tombuloglu H (2014) In silico analysis of Dreb transcription factor genes and proteins in grasses. Appl Biochem Biotechnol 174: 1272–1285. pmid:25104001
- 54. Jadhao KR, Samal KC, Pradhan SK, Rout GR (2014) Studies on molecular characterization of Dreb gene in Indica Rice (Oryza sativa L.) Hered Genet 3: 3
- 55. Roorkiwal M, Nayak SN, Thudi M, Upadhyaya HD, Brunel D, Mournet P, et al (2014) Allele diversity for abiotic stress responsive candidate genes in chickpea reference set using gene based SNP markers. Front Plant Sci 5: 248, pmid:24926299
- 56. Challam C, Ghosh T, Rai M, Tyagi W (2015) Allele mining across Dreb1A and Dreb1B in diverse rice genotypes suggest a highly conserved pathway inducible by low temperature. J Genet 94: 231–238. pmid:26174670
- 57. Alves GSC, Torres LF, de Aquino SO, Reichel T, Freire LP, Vieira NG, et al (2018) Nucleotide diversity of the coding and promoter regions of DREB1D, a candidate gene for drought tolerance in Coffea Species. Tropic Plant Biol 11:31–48.
- 58. Salse J, Chagué V, Bolot S, Magdelenat G, Huneau C, Pont C, et al (2008) New insights into the origin of the B genome of hexaploid wheat: Evolutionary relationships at the SPA genomic region with the S genome of the diploid relative Aegilops speltoides. BMC Genom 9: 555. http://doi.org/10.1186/1471-2164-9-555
- 59. Salina EA, Lim KY, Badaeva ED, Shcherban AB, Adonina IG, Amosova AV, et al (2006) Phylogenetic reconstruction of Aegilops section Sitopsis and the evolution of tandem repeats in the diploids and derived wheat polyploids. Genome 49: 1023–1035. pmid:17036077
- 60. Haider N (2013) The origin of the B-genome of bread wheat (Triticum aestivum L.). Genetika 49: 303–314. pmid:23755530
- 61. Golovnina KA, Glushkov SA, Blinov AG, Mayorov VI, Adkison LR, Goncharov NP (2007) Molecular phylogeny of the genus Triticum L. Plant Syst Evol 264: 195–216.
- 62. Baidouri ME, Murat F, Veyssiere M, Molinier M, Flores R, Burlot L, et al (2017) Reconciling the evolutionary origin of bread wheat (Triticum aestivum). New Phytol 213: 1477–1486. pmid:27551821
- 63. Garamszegi LZ, Møller AP (2010) Effects of sample size and intraspecific variation in phylogenetic comparative studies: a meta-analytic review. Biol Rev pmid:20148861
- 64. Wiens JJ, Servedio MR (1998) Phylogenetic analysis and intraspecific variation: performance of parsimony, likelihood, and distance methods. Syst Biol 47: 228–253. pmid:12064228
- 65. Harmon LJ, Losos JB (2005) The effect of intraspecific sample size on type I and type II error rates in comparative studies. Evolution 59: 2705–2710. pmid:16526516