Dataset Information

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response.

ABSTRACT: Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g., response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource - RECoN - that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 abiotic stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

SUBMITTER: Krishnan A

PROVIDER: S-EPMC5611544 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response.

Krishnan Arjun A Gupta Chirag C Ambavaram Madana M R MMR Pereira Andy A

Frontiers in plant science 20170920

Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g., response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource - RECoN - that relies on a network-based approach to go beyond currently lim ...[more]

PMID: 28979289

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Project description:BackgroundMacronutrients are pivotal elements for proper plant growth and development. Although extensive gene expression profiling revealed a large number of genes differentially expressed under various nutrient deprivation, characterization of these genes has never been fully explored especially in rice. Coexpression network analysis is a useful tool to elucidate the functional relationships of genes based on common expression. Therefore, we performed microarray analysis of rice shoot under nitrogen (N), phosphorus (P), and potassium (K) deficiency conditions. Moreover, we conducted a large scale coexpression analysis by integrating the data with previously generated gene expression profiles of organs and tissues at different developmental stages to obtain a global view of gene networks associated with plant response to nutrient deficiency.ResultsWe statistically identified 5400 differentially expressed genes under the nutrient deficiency treatments. Subsequent coexpression analysis resulted in the extraction of 6 modules (groups of highly interconnected genes) with distinct gene expression signatures. Three of these modules comprise mostly of downregulated genes under N deficiency associated with distinct functions such as development of immature organs, protein biosynthesis and photosynthesis in chloroplast of green tissues, and fundamental cellular processes in all organs and tissues. Furthermore, we identified one module containing upregulated genes under N and K deficiency conditions, and a number of genes encoding protein kinase, kinase-like domain containing protein and nutrient transporters. This module might be particularly involved in adaptation to nutrient deficiency via phosphorylation-mediated signal transduction and/or post-transcriptional regulation.ConclusionsOur study demonstrated that large scale coexpression analysis is an efficient approach in characterizing the nutrient response genes based on biological functions and could provide new insights in understanding plant response to nutrient deficiency.

Project description:BackgroundWhen plants are subjected to cold stress, they undergo a series of molecular and physiological changes to protect themselves from injury. Indica cultivars can usually withstand only mild cold stress in a relatively short period. Hormone-mediated defence response plays an important role in cold stress. Weighted gene co-expression network analysis (WGCNA) is a very useful tool for studying the correlation between genes, identifying modules with high phenotype correlation, and identifying Hub genes in different modules. Many studies have elucidated the molecular mechanisms of cold tolerance in different plants, but little information about the recovery process after cold stress is available.ResultsTo understand the molecular mechanism of cold tolerance in rice, we performed comprehensive transcriptome analyses during cold treatment and recovery stage in two cultivars of near-isogenic lines (9311 and DC907). Twelve transcriptomes in two rice cultivars were determined. A total of 2509 new genes were predicted by fragment splicing and assembly, and 7506 differentially expressed genes were identified by pairwise comparison. A total of 26 modules were obtained by expression-network analysis, 12 of which were highly correlated with cold stress or recovery treatment. We further identified candidate Hub genes associated with specific modules and analysed their regulatory relationships based on coexpression data. Results showed that various plant-hormone regulatory genes acted together to protect plants from physiological damage under short-term low-temperature stress. We speculated that this may be common in rice. Under long-term cold stress, rice improved the tolerance to low-temperature stress by promoting autophagy, sugar synthesis, and metabolism.ConclusionThrough WGCNA analysis at the transcriptome level, we provided a potential regulatory mechanism for the cold stress and recovery of rice cultivars and identified candidate central genes. Our findings provided an important reference for the future cultivation of rice strains with good tolerance.

Dataset Information

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response.

Publications

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response.

Similar Datasets

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