Multi-Stress Rice Transcriptome Analysis Using Network-based Approach

Abstract

Rice is a fascinating and complex plant. Consumed by more than half of the world’s population, it is important to have a comprehensive understanding of the organism to advance crop engineering and breeding strategies. Abiotic stresses like drought, high temperature, salinity and flood have affected its growth and productivity. Furthermore, global climate change has added to the severity of these stresses, suggesting the need for varieties with improved stress tolerance for sustainable crop production. Improving stress tolerance requires an in-depth understanding of the biological processes, transcriptional pathways and hormone signaling involved in stress response. With the surge in omics data, it has paved the way for deciphering the biological information underlying complex traits. However, dealing with such large datasets calls for the development of powerful bioinformatics methods for a thorough transcriptome analysis. A popular approach is the construction and analysis of co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. Using prior knowledge and data integration further enhances the elucidation of gene regulatory relationships in this network. With this objective we have developed NetREx, a Network based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available mRNAseq data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in preconstructed tissue-specific networks, allowing users to view the fold-change, module memberships, gene annotations and analysis of their neighborhood genes and associated pathways. The webserver also allows querying of orthologous from Arabidopsis, wheat, maize, barley, and sorghum. Here we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Available at: https://bioinf.iiit.ac.in/netrex/. In the second part of the thesis, we present a meta-analytic study using co-expressed modules to understand the biological functions associated with different abiotic stresses in the root tissue. The osmotic stress condition is an extremely severe stress condition involving the effects of multiple stresses like drought, salinity and ionic stress and is discussed in detail. The early responsive modules are analyzed and a causal flow of mechanisms and signaling pathways is established.