Natural variation in Arabidopsis thaliana: Molecular genetic architecture of stress tolerance under water deficit

Abstract only availableThe functional genomics tools available for studying Arabidopsis thaliana are a great resource for researchers trying to characterize and understand the genetic basis of natural variation. Genome wide transcript profiling can simultaneously monitor the gene expression programs regulated by growth and development and signal transduction pathways in response to environmental stress conditions. The responses of plants to water deficit depend on the extent and rate of water loss and its timing and duration. As a physical stress, water deficit triggers biochemical responses through a cascade that includes stress perception, signal transduction and regulation of gene expression. Arabidopsis accessions differ largely in their adaptation to stress tolerance. To understand the genetic basis of this intra-specific variation we analyzed five accessions under gradual water deficit leading to severe stress conditions. The changes in the gene expression profiles under water deficit conditions were studied using functional genomics tools, microarray and quantitative real time PCR and the regulatory roles of stress induced and developmental related transcripts will be discussed.Plant Genomics Internships @ M

Constructing proteome and metabolome maps for genetic improvement of energy-related traits in soybean [abstract]

Only abstract of poster available.Track V: BiomassAlthough the genetic blueprint of soybean is represented by the genome, its phenotype is a product of that blueprint manifested as the production of proteins and metabolites influencing growth characteristics, stress responses, seed composition, and yield. We are using various tools of genomics and molecular breeding with an aim towards development of value-added soybeans that will help United States farmers to maintain their competitiveness and expand utilization of soybean crops (e.g. functional foods, industrial uses, biodiesel, etc). Profiling soybean gene products will lay the foundation for a systems biology approach to key processes such as seed development, which will lead to the genetic improvement of yield and seed composition. Being one of the major bio-energy crops, building a comprehensive map of proteins and metabolites for soybean will help make connections between regulatory or metabolic pathways not previously characterized. Another major benefit from these studies is the discovery of energy related traits including plant productivity and seed compositional traits for the genetic improvement of soybean. It is well known that environmental cues influence developmental phenotypes in plants. Different biotic stresses such as fungal diseases and abiotic stresses, such as drought and flooding, also elicit phenotypic responses from the genome. Thus, by studying the gene products, a direct correlation between response and specific peptides/metabolites can be made. This will lead to crop improvement either through breeding or transgenic efforts. Major objectives of this study are: a) to identify key soybean seed, leaf, and root proteins involved in development and biotic and abiotic stress responses; b) to establish a comprehensive set of chemical standards for soybean metabolites moving toward construction of a metabolome map with a focus on seed and drought effects on seed development and, c) to compile a database linking proteomic and metabolite information and associate this information to value-added soybean traits and markers for assisted breeding. We are utilizing GC/MS, LC/MS, and NMR approaches to identify key molecules for further characterization

Soybean transcription factor ORFeome associated with drought resistance: a valuable resource to accelerate research on abiotic stress resistance

Tissue/organ expression pattern of TF genes. The expression of soybean TF-ORFeome candidates in seven soybean organs including root, root tip, leaf, shoot apical meristem (SAM), nodule, flower and green pod were based on published RNA-Seq data [26]. The color scale indicates the degree of gene expression levels (yellow, low expression level; red, high expression level)

SoyDB: a knowledge database of soybean transcription factors

<p>Abstract</p> <p>Background</p> <p>Transcription factors play the crucial rule of regulating gene expression and influence almost all biological processes. Systematically identifying and annotating transcription factors can greatly aid further understanding their functions and mechanisms. In this article, we present SoyDB, a user friendly database containing comprehensive knowledge of soybean transcription factors.</p> <p>Description</p> <p>The soybean genome was recently sequenced by the Department of Energy-Joint Genome Institute (DOE-JGI) and is publicly available. Mining of this sequence identified 5,671 soybean genes as putative transcription factors. These genes were comprehensively annotated as an aid to the soybean research community. We developed SoyDB - a knowledge database for all the transcription factors in the soybean genome. The database contains protein sequences, predicted tertiary structures, putative DNA binding sites, domains, homologous templates in the Protein Data Bank (PDB), protein family classifications, multiple sequence alignments, consensus protein sequence motifs, web logo of each family, and web links to the soybean transcription factor database PlantTFDB, known EST sequences, and other general protein databases including Swiss-Prot, Gene Ontology, KEGG, EMBL, TAIR, InterPro, SMART, PROSITE, NCBI, and Pfam. The database can be accessed via an interactive and convenient web server, which supports full-text search, PSI-BLAST sequence search, database browsing by protein family, and automatic classification of a new protein sequence into one of 64 annotated transcription factor families by hidden Markov models.</p> <p>Conclusions</p> <p>A comprehensive soybean transcription factor database was constructed and made publicly accessible at <url>http://casp.rnet.missouri.edu/soydb/</url>.</p

Sequencing the USDA core soybean collection reveals gene loss during domestication and breeding

The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). Here we have assembled and analyzed a soybean (Glycine spp.) pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding. We identified 3,765 genes that are absent from the Lee reference genome assembly and assessed the presence/absence of all genes across this population. In addition to a loss of genetic diversity, we found a significant reduction in the average number of protein-coding genes per individual during domestication and subsequent breeding, though with some genes and allelic variants increasing in frequency associated with selection for agronomic traits. This analysis provides a genomic perspective of domestication and breeding in this important oilseed crop

Evaluation of genetic variation among Brazilian soybean cultivars through genome resequencing.

Soybean [Glycine max (L.) Merrill] is one of the most important legumes cultivated worldwide, and Brazil is one of the main producers of this crop. Since the sequencing of its reference genome, interest in structural and allelic variations of cultivated and wild soybean germplasm has grown. To investigate the genetics of the Brazilian soybean germplasm, we selected soybean cultivars based on the year of commercialization, geographical region and maturity group and resequenced their genomes. We resequenced the genomes of 28 Brazilian soybean cultivars with an average genome coverage of 14.8X. A total of 5,835,185 single nucleotide polymorphisms (SNPs) and 1,329,844 InDels were identified across the 20 soybean chromosomes, with 541,762 SNPs, 98,922 InDels and 1,093 CNVs that were exclusive to the 28 Brazilian cultivars. In addition, 668 allelic variations of 327 genes were shared among all of the Brazilian cultivars, including genes related to DNA-dependent transcription-elongation, photosynthesis, ATP synthesis-coupled electron transport, cellular respiration, and precursors of metabolite generation and energy. A very homogeneous structure was also observed for the Brazilian soybean germplasm, and we observed 41 regions putatively influenced by positive selection. Finally, we detected 3,880 regions with copy-number variations (CNVs) that could help to explain the divergence among the accessions evaluated. The large number of allelic and structural variations identified in this study can be used in marker-assisted selection programs to detect unique SNPs for cultivar fingerprinting. The results presented here suggest that despite the diversification of modern Brazilian cultivars, the soybean germplasm remains very narrow because of the large number of genome regions that exhibit low diversity. These results emphasize the need to introduce new alleles to increase the genetic diversity of the Brazilian germplasm