Integration of stress-regulated transcription, mRNA turnover and translation in plants

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Plants thrive in highly variable environments because of their ability to modulate growth and reproduction in response to environmental factors including light, temperature, oxygen, moisture, and nutrient availability. When environmental conditions are sub-optimal, for example during a drought or flood, plant switch on or off a subset of their genes to control cellular metabolism and adjust development. The molecular processes involved in this switching are not well-understood. This project will use high-resolution genomic technologies, including ones developed for this study, to define the coordinated regulation of networks of genes in the model plant Arabidopsis thaliana that function under low oxygen stress, a condition associated with flooding. The results will provide a greater understanding of how plants sense and respond effectively to short periods of low oxygen and floods. The genetic resources, methods and datasets spanning multiple steps of gene activity will be a valuable community resource for plant biologists. The research will be carried out at a Hispanic Serving institution where postdoctoral, graduate, and undergraduate researchers will receive interdisciplinary training in biology and bioinformatics. Trainees will also gain experience in teaching and mentoring, and will participate in science communication. The project will develop a lab module for a biology course at a Hispanic-serving community college and engage undergraduates and 5th graders in exciting hands-on research related to agriculture.Plant responses to external stimuli range from rapid temporal changes in cellular metabolism to seasonal adjustments in development. These changes are induced by toggling up and down of gene expression at multiple steps, including transcription, mRNA decay, and translation. This project will examine the hypothesis that low oxygen-activated gene transcription fast-tracks mRNAs for translation, limiting their destruction. The study will focus on specific classes of proteins that promote transcription under low oxygen stress or facilitate the turnover of mRNAs. A suite of genomics datasets--including measurements of chromatin accessibility, transcription, mRNA degradation, and translation efficiency--will be collected and analyzed by predictive modeling to obtain an integrated, systems-level view of how plants respond to low-oxygen conditions.