Identification of the VERNALIZATION 4 gene reveals the origin of spring growth habit in ancient wheats from South Asia.

Wheat varieties with a winter growth habit require long exposures to low temperatures (vernalization) to accelerate flowering. Natural variation in four vernalization genes regulating this requirement has favored wheat adaptation to different environments. The first three genes (VRN1-VRN3) have been cloned and characterized before. Here we show that the fourth gene, VRN-D4, originated by the insertion of a ∼290-kb region from chromosome arm 5AL into the proximal region of chromosome arm 5DS. The inserted 5AL region includes a copy of VRN-A1 that carries distinctive mutations in its coding and regulatory regions. Three lines of evidence confirmed that this gene is VRN-D4: it cosegregated with VRN-D4 in a high-density mapping population; it was expressed earlier than other VRN1 genes in the absence of vernalization; and induced mutations in this gene resulted in delayed flowering. VRN-D4 was found in most accessions of the ancient subspecies Triticum aestivum ssp. sphaerococcum from South Asia. This subspecies showed a significant reduction of genetic diversity and increased genetic differentiation in the centromeric region of chromosome 5D, suggesting that VRN-D4 likely contributed to local adaptation and was favored by positive selection. Three adjacent SNPs in a regulatory region of the VRN-D4 first intron disrupt the binding of GLYCINE-RICH RNA-BINDING PROTEIN 2 (TaGRP2), a known repressor of VRN1 expression. The same SNPs were identified in VRN-A1 alleles previously associated with reduced vernalization requirement. These alleles can be used to modulate vernalization requirements and to develop wheat varieties better adapted to different or changing environments.

Sequencing chromosome 5D of Aegilops tauschii and comparison with its allopolyploid descendant bread wheat (Triticum aestivum).

Flow cytometric sorting of individual chromosomes and chromosome-based sequencing reduces the complexity of large, repetitive Triticeae genomes. We flow-sorted chromosome 5D of Aegilops tauschii, the D genome donor of bread wheat and sequenced it by Roche 454 GS FLX platform to approximately 2.2x coverage. Repetitive sequences represent 81.09% of the survey sequences of this chromosome, and Class I retroelements are the prominent type, with a particular abundance of LTR/Gypsy superfamily. Nonrepetitive sequences were assembled to cover 17.76% of the total chromosome regions. Up to 6188 nonrepetitive gene loci were predicted to be encoded by the 5D chromosome. The numbers and chromosomal distribution patterns of tRNA genes suggest abundance in tRNA(L) (ys) and tRNA(M) (et) species, while the nonrepetitive assembly reveals tRNA(A) (la) species as the most abundant type. A comparative analysis of the genomic sequences of bread wheat and Aegilops chromosome 5D indicates conservation of gene content. Orthologous unique genes, matching Aegilops 5D sequences, numbered 3730 in barley, 5063 in Brachypodium, 4872 in sorghum and 4209 in rice. In this study, we provide a chromosome-specific view into the structure and organization of the 5D chromosome of Ae. tauschii, the D genome ancestor of bread wheat. This study contributes to our understanding of the chromosome-level evolution of the wheat genome and presents a valuable resource in wheat genomics due to the recent hybridization of Ae. tauschii genome with its tetraploid ancestor.

Capturing Wheat Phenotypes at the Genome Level.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Dissecting miRNAs in Wheat D Genome Progenitor, Aegilops tauschii.

As the post-transcriptional regulators of gene expression, microRNAs or miRNAs comprise an integral part of understanding how genomes function. Although miRNAs have been a major focus of recent efforts, miRNA research is still in its infancy in most plant species. Aegilops tauschii, the D genome progenitor of bread wheat, is a wild diploid grass exhibiting remarkable population diversity. Due to the direct ancestry and the diverse gene pool, A. tauschii is a promising source for bread wheat improvement. In this study, a total of 87 Aegilops miRNA families, including 51 previously unknown, were computationally identified both at the subgenomic level, using flow-sorted A. tauschii 5D chromosome, and at the whole genome level. Predictions at the genomic and subgenomic levels suggested A. tauschii 5D chromosome as rich in pre-miRNAs that are highly associated with Class II DNA transposons. In order to gain insights into miRNA evolution, putative 5D chromosome miRNAs were compared to its modern ortholog, Triticum aestivum 5D chromosome, revealing that 48 of the 58 A. tauschii 5D miRNAs were conserved in orthologous T. aestivum 5D chromosome. The expression profiles of selected miRNAs (miR167, miR5205, miR5175, miR5523) provided the first experimental evidence for miR5175, miR5205 and miR5523, and revealed differential expressional changes in response to drought in different genetic backgrounds for miR167 and miR5175. Interestingly, while miR5523 coding regions were present and expressed as pre-miR5523 in both T. aestivum and A. tauschii, the expression of mature miR5523 was observed only in A. tauschii under normal conditions, pointing out to an interference at the downstream processing of pre-miR5523 in T. aestivum. Overall, this study expands our knowledge on the miRNA catalog of A. tauschii, locating a subset specifically to the 5D chromosome, with ample functional and comparative insight which should contribute to and complement efforts to develop drought tolerant wheat varieties.

A Comprehensive Prescription for Plant miRNA Identification.

microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts "SUmirFind" and "SUmirFold" and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, "SUmirPredictor" and "SUmirLocator," and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The "SUmirPredictor" and "SUmirLocator" scripts are freely available together with a reference high-confidence plant miRNA list.