Browsing by Author "Krishnan, Arjun"
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- Coordinated regulation of photosynthesis in rice increases yield and tolerance to environmental stressAmbavaram, Madana M. R.; Basu, Supratim; Krishnan, Arjun; Ramegowda, X.; Batlang, Utlwang; Rahman, L.; Baisakh, Niranjan; Pereira, Andy (Nature Publishing Group, 2014-12-01)Improving photosynthetic efficiency to increase crop yield is an important goal of plant breeders. Here, Ambavaram et al. identify a transcription factor that is a key regulator of photosynthetic carbon metabolism in rice and show that its overexpression enhances grain yield.
- RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress ResponseKrishnan, Arjun; Gupta, Chirag; Ambavaram, Madana M. R.; Pereira, Andy (2017-09-20)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.
- Systems analysis of stress response in plantsKrishnan, Arjun (Virginia Tech, 2010-09-08)The response of plants to environmental stress spans several orders of magnitude in time and space, causing system-wide changes. These changes comprise of both protective responses and adverse reactions in the plant. Stresses like water deficit or drought cause a drastic effect in crop yield, while concomitantly agriculture consumes 1/3rd of the fresh water available to us and there is widespread water scarcity around the world. It is, hence, a fundamental goal of modern biology and applied biotechnology to unravel this complex stress response in laboratory model plants like Arabidopsis and crop models like rice. Such an understanding, especially at the cellular level, will aid in informed engineering of stress tolerance in plants. We have developed and used integrative functional genomics approaches to characterize environmental stress response at various levels of organization including genes, modules and networks in Arabidopsis and rice. We have also applied these methods in problems concerning bioenergy. Since the poor knowledge of the cellular roles of a large portion of plant genes remains a fundamental barrier to using such approaches, we have further explored the problem of 'gene function prediction'. And, finally, as a contribution to the community, we have curated a large mutant resource for the crop model, rice, and established a web resource for exploratory analysis of abiotic stress in this model. All together, this work presents insights into several facets of stress response, offers numerous novel predictions for experimental validation, and provides principled analysis frameworks for systems level analysis of environmental stress response in plants.