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Accession IconSRP059270

Transcriptome Engineering Promotes a Fermentative Transcriptional State

Organism Icon Saccharomyces cerevisiae
Sample Icon 83 Downloadable Samples
Technology Badge IconIllumina Genome Analyzer IIx, Illumina HiSeq 2000, Illumina HiSeq 2500

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Purpose: The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed a novel algorithm, NetSurgeon, which utilizes genome-wide gene regulatory networks to identify interventions that force a cell toward a desired expression state. Results: We used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in S. cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional states of cells utilizing xylose toward the fermentative state typical of cells that are producing ethanol rapidly (while utilizing glucose) might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. Conclusions: We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future metabolic engineering efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism. Overall design: Mutant and wildtype S. cerevisiae cells were put into 48 hour aerobic batch fermentations of synthetic complete medium supplmented with 2% glucose and 5% xylose and culture samples were taken at 4 hours and 24 hours for transcriptional profiling performed by RNA-Seq analysis. In addition, wildtype S. cerevisiae cells were grown in various single carbon sources for 12 hours and culture samples were taken for transcriptional profiling performed by RNA-Seq analysis.
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