Trupti Joshi

Genome sequence of the palaeopolyploid soybean

Soybean (Glycine max) is one of the most important crop plants for seed protein and oil content, and for its capacity to fix atmospheric nitrogen through symbioses with soil-borne microorganisms. We sequenced the 1.1-gigabase genome by a whole-genome shotgun approach and integrated it with physical and high-density genetic maps to create a chromosome-scale draft sequence assembly. We predict 46,430 protein-coding genes, 70% more than Arabidopsis and similar to the poplar genome which, like soybean, is an ancient polyploid (palaeopolyploid). About 78% of the predicted genes occur in chromosome ends, which comprise less than one-half of the genome but account for nearly all of the genetic recombination. Genome duplications occurred at approximately 59 and 13 million years ago, resulting in a highly duplicated genome with nearly 75% of the genes present in multiple copies. The two duplication events were followed by gene diversification and loss, and numerous chromosome rearrangements. An accurate soybean genome sequence will facilitate the identification of the genetic basis of many soybean traits, and accelerate the creation of improved soybean varieties.

An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants.

Soybean (Glycine max L.) is a major crop providing an important source of protein and oil, which can also be converted into biodiesel. A major milestone in soybean research was the recent sequencing of its genome. The sequence predicts 69,145 putative soybean genes, with 46,430 predicted with high confidence. In order to examine the expression of these genes, we utilized the Illumina Solexa platform to sequence cDNA derived from 14 conditions (tissues). The result is a searchable soybean gene expression atlas accessible through a browser (http://digbio.missouri.edu/soybean_atlas). The data provide experimental support for the transcription of 55,616 annotated genes and also demonstrate that 13,529 annotated soybean genes are putative pseudogenes, and 1736 currently unannotated sequences are transcribed. An analysis of this atlas reveals strong differences in gene expression patterns between different tissues, especially between root and aerial organs, but also reveals similarities between gene expression in other tissues, such as flower and leaf organs. In order to demonstrate the full utility of the atlas, we investigated the expression patterns of genes implicated in nodulation, and also transcription factors, using both the Solexa sequence data and large-scale qRT-PCR. The availability of the soybean gene expression atlas allowed a comparison with gene expression documented in the two model legume species, Medicago truncatula and Lotus japonicus, as well as data available for Arabidopsis thaliana, facilitating both basic and applied aspects of soybean research.

Inferring gene regulatory networks from multiple microarray datasets

MOTIVATION Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points. RESULTS This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis. AVAILABILITY The software is available from http://zhangorup.aporc.org/bioinfo/grninfer/, http://digbio.missouri.edu/grninfer/ and http://intelligent.eic.osaka-sandai.ac.jp or upon request from the authors.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Background:Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.Results:In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.Conclusion:We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Epigenome-wide inheritance of cytosine methylation variants in a recombinant inbred population

Cytosine DNA methylation is one avenue for passing information through cell divisions. Here, we present epigenomic analyses of soybean recombinant inbred lines (RILs) and their parents. Identification of differentially methylated regions (DMRs) revealed that DMRs mostly cosegregated with the genotype from which they were derived, but examples of the uncoupling of genotype and epigenotype were identified. Linkage mapping of methylation states assessed from whole-genome bisulfite sequencing of 83 RILs uncovered widespread evidence for local methylQTL. This epigenomics approach provides a comprehensive study of the patterns and heritability of methylation variants in a complex genetic population over multiple generations, paving the way for understanding how methylation variants contribute to phenotypic variation.

Transcriptional and physiological responses of Bradyrhizobium japonicum to desiccation-induced stress

The growth and persistence of rhizobia and bradyrhizobia in soils are negatively impacted by drought conditions. In this study, we used genome-wide transcriptional analyses to obtain a comprehensive understanding of the response of Bradyrhizobium japonicum to drought. Desiccation of cells resulted in the differential expression of 15 to 20% of the 8,453 [corrected] B. japonicum open reading frames, with considerable differentiation between early (after 4 h) and late (after 24 and 72 h) expressed genes. While 225 genes were universally up-regulated at all three incubation times in response to desiccation, an additional 43 and 403 up-regulated genes were common to the 4/24- and 24/72-h incubation times, respectively. Desiccating conditions resulted in the significant induction (>2.0-fold) of the trehalose-6-phosphate synthetase (otsA), trehalose-6-phosphate phosphatase (otsB), and trehalose synthase (treS) genes, which encode two of the three trehalose synthesis pathways found in B. japonicum. Gene induction was correlated with an elevated intracellular concentration of trehalose and increased activity of trehalose-6-phosphate synthetase, collectively supporting the hypothesis that this disaccharide plays a prominent and important role in promoting desiccation tolerance in B. japonicum. Microarray data also indicated that sigma(54)- and sigma(24)-associated transcriptional regulators and genes encoding isocitrate lyase, oxidative stress responses, the synthesis and transport of exopolysaccharides, heat shock response proteins, enzymes for the modification and repair of nucleic acids, and the synthesis of pili and flagella are also involved in the response of B. japonicum to desiccation. Polyethylene glycol-generated osmotic stress induced significantly fewer genes than those transcriptionally activated by desiccation. However, 67 genes were commonly induced under both conditions. Taken together, these results suggest that B. japonicum directly responds to desiccation by adapting to changes imparted by reduced water activity, such as the synthesis of trehalose and polysaccharides and, secondarily, by the induction of a wide variety of proteins involved in protection of the cell membrane, repair of DNA damage, stability and integrity of proteins, and oxidative stress responses.

SoyDB: a knowledge database of soybean transcription factors.

BackgroundTranscription 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.DescriptionThe 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.ConclusionsA comprehensive soybean transcription factor database was constructed and made publicly accessible at http://casp.rnet.missouri.edu/soydb/.

Systems Analysis of Seed Filling in Arabidopsis: Using General Linear Modeling to Assess Concordance of Transcript and Protein Expression

Previous systems analyses in plants have focused on a single developmental stage or time point, although it is often important to additionally consider time-index changes. During seed development a cascade of events occurs within a relatively brief time scale. We have collected protein and transcript expression data from five sequential stages of Arabidopsis (Arabidopsis thaliana) seed development encompassing the period of reserve polymer accumulation. Protein expression profiling employed two-dimensional gel electrophoresis coupled with tandem mass spectrometry, while transcript profiling used oligonucleotide microarrays. Analyses in biological triplicate yielded robust expression information for 523 proteins and 22,746 genes across the five developmental stages, and established 319 protein/transcript pairs for subsequent pattern analysis. General linear modeling was used to evaluate the protein/transcript expression patterns. Overall, application of this statistical assessment technique showed concurrence for a slight majority (56%) of expression pairs. Many specific examples of discordant protein/transcript expression patterns were detected, suggesting that this approach will be useful in revealing examples of posttranscriptional regulation.

SNP discovery by high-throughput sequencing in soybean.

BackgroundWith the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.ResultsA total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.ConclusionsWe have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.

Establishment of a Protein Reference Map for Soybean Root Hair Cells

Root hairs are single tubular cells formed from the differentiation of epidermal cells on roots. They are involved in water and nutrient uptake and represent the infection site on leguminous roots by rhizobia, soil bacteria that establish a nitrogen-fixing symbiosis. Root hairs develop by polar cell expansion or tip growth, a unique mode of plant growth shared only with pollen tubes. A more complete characterization of root hair cell biology will lead to a better understanding of tip growth, the rhizobial infection process, and also lead to improvements in plant water and nutrient uptake. We analyzed the proteome of isolated soybean (Glycine max) root hair cells using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and shotgun proteomics (1D-PAGE-liquid chromatography and multidimensional protein identification technology) approaches. Soybean was selected for this study due to its agronomic importance and its root size. The resulting soybean root hair proteome reference map identified 1,492 different proteins. 2D-PAGE followed by mass spectrometry identified 527 proteins from total cell contents. A complementary shotgun analysis identified 1,134 total proteins, including 443 proteins that were specific to the microsomal fraction. Only 169 proteins were identified by the 2D-PAGE and shotgun methods, which highlights the advantage of using both methods. The proteins identified are involved not only in basic cell metabolism but also in functions more specific to the single root hair cell, including water and nutrient uptake, vesicle trafficking, and hormone and secondary metabolism. The data presented provide useful insight into the metabolic activities of a single, differentiated plant cell type.