429640 Constructing a General Bioinformatics Pipeline to Identify Regulatory Mechanisms That Improve Ethanol Tolerance in Zymomonas Mobilis

Thursday, November 12, 2015: 1:10 PM
150D/E (Salt Palace Convention Center)
Seung Hee Cho, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, Katie Haning, McKetta Department of Chemical engineering, University of Texas at Austin, Austin, TX, Chen-Hsun Tsai, McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX and Lydia M. Contreras, McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX

Zymomonas mobilis has been identified as a promising cellular factory for biofuels due to its efficient, natural production of and tolerance to ethanol. Recent discovery of ethanol-responsive small regulatory RNAs (sRNAs) in Z. mobilis suggested the potential of exploiting these elements for strain engineering. As global controllers of gene expression, sRNAs represent powerful tools for engineering complex phenotypes. However, mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery; this makes it difficult to understand how to efficiently identify sRNAs that could be used to engineer a phenotype of interest. In this study, we use a forward systems approach to first predict sRNAs that impact ethanol tolerance in Z. mobilis using large-scale transcriptomics and proteomics profiles of Z. mobilis under ethanol stress. The effect of the bioinformatically predicted candidates were experimentally characterized by building overexpression strains and this led to the successful uncovering of several sRNAs that could be manipulated to enhance ethanol tolerance. Using these ethanol-related sRNAs, we then performed traditional genetic and biochemical approaches to identify a variety of mRNA targets and pathways that were being regulated under conditions of enhanced tolerance. In this way, we have demonstrated application of a novel bioinformatics pipeline to accelerate the discovery of specific pathways and extract insightful regulatory mechanisms that could be further optimized to enhance a given complex phenotype. This work represents the first application of a de novo sRNA engineering strategy in non-model Z. mobilis that is of relevance to biofuel technologies.

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