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Analysis of Functional Genetic Screens for Genome-Wide Metabolic Engineering of Microbial Bioproduction Hosts
- Trivedi, Varun
- Advisor(s): Wheeldon, Ian
Abstract
Microorganisms found in the environment around us exhibit a multitude of desirable characteristics that make them suitable hosts for the industrial production of biochemicals and biofuels. A common strategy to engineer microbes is using rational design approaches that entail manipulation of genes involved in one or more native metabolic pathways to improve biochemical synthesis or other relevant phenotypes. The advent of synthetic biology tools such as CRISPR-Cas systems for gene editing, and next-generation sequencing technologies has, however, made it possible to elucidate pangenome-wide targets for strain engineering by performing experiments at a genomic scale, such as genome-wide pooled CRISPR knockout screens, and analyzing the resulting high-throughput data using bioinformatic methods. A crucial challenge in evaluating the outcomes of pooled CRISPR screens is accounting for the variability in sgRNA knockout efficiency, as low-activity guides can potentially mask screening hits to result in false negatives. Towards that end, we developed an analysis method, acCRISPR, that processes NGS data from pooled CRISPR knockout screens, and provides an activity correction to accurately call statistically significant genes for the phenotype under study. We applied acCRISPR to CRISPR-Cas9 and CRISPR-Cas12a screening datasets from the oleaginous yeast Yarrowia lipolytica to identify a high-confidence set of essential genes for growth on glucose, as well as genes important for providing tolerance to high salt stress conditions. We further used the experimental sgRNA activity profiles from these screens to determine in silico sgRNA activity prediction accuracy of deep learning-based models trained on balanced and imbalanced experimental datasets, and improve prediction power with imbalanced training datasets by augmenting them with synthetic sgRNA. In another study, we sought to identify genetic targets responsible for phenazine biosynthesis in the bacterium Pseudomonas chlororaphis by employing a population genomics approach. We sequenced 34 Pseudomonas isolates using short- and long-read sequencing technologies, characterized them for phenazine production, and performed a microbial genome-wide association study (mGWAS) on the genomic-phenotypic data to elucidate the most influential phenazine biosynthesis targets across the pangenome. Overall, this work demonstrates the utility of high-throughput experimental-computational frameworks for identifying microbial strain engineering targets at a genomic scale and establishing novel genotype-phenotype relationships.
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