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Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress

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  • Rinku Sharma
  • Garima Singh
  • Sudeepto Bhattacharya
  • Ashutosh Singh

Abstract

Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as—‘photosynthesis’, ‘respiratory burst’, ‘response to hormone’, ‘signal transduction’, ‘metabolic process’, ‘response to water deprivation’, which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.

Suggested Citation

  • Rinku Sharma & Garima Singh & Sudeepto Bhattacharya & Ashutosh Singh, 2018. "Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0203266
    DOI: 10.1371/journal.pone.0203266
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    1. Peter Langfelder & Rui Luo & Michael C Oldham & Steve Horvath, 2011. "Is My Network Module Preserved and Reproducible?," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-29, January.
    2. Zhijin Wu & Rafael A. Irizarry & Robert Gentleman & Francisco Martinez-Murillo & Forrest Spencer, 2004. "A Model-Based Background Adjustment for Oligonucleotide Expression Arrays," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 909-917, December.
    3. Zhijin Wu & Rafael Irizarry & Robert Gentleman & Francisco Martinez Murillo & Forrest Spencer, 2004. "A Model Based Background Adjustment for Oligonucleotide Expression Arrays," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1001, Berkeley Electronic Press.
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