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

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.