Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks. In our recent publication (Zhu et al., 2012; see publication list) we describe the development of an integrated set of computational methods for analysis of differential gene expression data, including gene clustering, gene network inference, gene function prediction, and DNA motif identification. These tools automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses. We recently submitted a modification of these methods that is suitable for use starting from RNA-seq data and will post this information when available.
The initial steps in the rhizobia-root hair infection process are known to involve specific receptor kinases and subsequent kinase cascades. In our recent publication (Nguyen et al., 2012; see publication list) we characterized the phosphoproteome of the root hairs and the corresponding stripped roots (i.e., roots from which root hairs were removed) during rhizobial colonization and infection to gain insight into the molecular mechanism of root hair cell biology. Phosphopeptides derived from root hairs and stripped roots, mock inoculated or inoculated with the soybean-specific rhizobium Bradyrhizobium japonicum, were labeled with the isobaric tag 8-plex ITRAQ, enriched using Ni-NTA magnetic beads and subjected to nRPLC-MS/MS analysis using HCD and decision tree guided CID/ETD strategy. A total of 1,625 unique phosphopeptides, spanning 1,659 non-redundant phosphorylation sites, were detected from 1,126 soybean phosphoproteins. Among them, 273 phosphopeptides corresponding to 240 phosphoproteins were found to be significantly regulated (>1.5 fold abundance change) in response to inoculation with B. japonicum. The data reveal unique features of the soybean root hair phosphoproteome, including root hair and stripped root-specific phosphorylation suggesting a complex network of kinase-substrate and phosphatase-substrate interactions in response to rhizobial inoculation. Full details are available in our publication. The phosphorylation site data is available at the Plant Protein Phosphorylation Database via the link below. Note that this link works well with Google Chrome and Firefox only. Here is the link: http://digbio.missouri.edu/p3db/
Over the last 100 years, the atmospheric concentration of carbon dioxide has dramatically increased, in major part due to the burning of fossil fuels, recent rapid industrialization, and land use changes. The predicted effects of continued climate change are complex but include effects on air and surface temperature, with coincident effects on water availability. Soil temperature can influence root growth, cell elongation, root length and extension, initiation of new lateral roots and root hairs, and root branching. These effects are likely manifestations of the variety of physiological effects brought about by temperature on plant roots; including changes in root respiration, nutrient uptake, as well as physicochemical effects on the soil environment (e.g., changes in nitrogen mineralization). Ambient temperature changes also affects other parts of the plant (e.g., photosynthetic rates), which also affects below ground growth and physiology. When we include in this discussion issues of plant genetic variation, as well as the effects of temperature on water availability, the full complexity of the effects of climate change on the plant root environment becomes clear.
A hallmark of modern biology is large-scale -omics data. These data are massive and often very complex in nature; thereby generating the need for extensive storage, detailed computational analyses, fast retrieval and efficient integration, for better understanding of the data and hypothesis generation for the underlying biological system. Our efforts to study the systems biology of the soybean root hair cell have generated very large datasets. In addition, other laboratories are now applying these methods to soybean to address a variety of biological questions. To address the need for web resources capable of handling the complex task of integrating soybean -omics data and to provide data annotation (e.g. the pathway information), we developed the Soybean Knowledge Base. .
The Soybean Knowledge Base (SoyKB) is a comprehensive all-inclusive web resource for soybean. SoyKB is designed to handle the storage and integration of the genomics, microarray, transcriptomics, proteomics and metabolomics data along with the function and pathway information. It has four modules including the main mySQL database module at the back end that incorporates and integrates all the soybean genomics and -omics data from various sources. It is designed to contain information on four different entities namely genes, miRNAs, metabolites and SNPs. The other three front-end modules are web interface, genome browser and pathway integration.
SoyKB has four tiers of registration, which control the access to the public and private experimental datasets. Users can add comments, download data for multiple genes as well as submit their own datasets. Tools like protein 3D-structure and pathway viewers, gene family browsers and BLAST sequence similarity tool are all part of key features of SoyKB.
SoyKB can be accessed at http://soykb.org/.