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Comparative chemical-genomic profiling of plant-based hydrolysate toxins enables predictive assessment of responses to complex mixtures

ABSTRACT: Many toxins and stressors found in hydrolysates inhibit microbial metabolism and product formation, requiring mitigation strategies including strain engineering. To identify mechanisms of toxicity and targets for genetic engineering, we used a chemical genomics approach with a library of S. cerevisiae deletion mutants cultured anaerobically in dozens of individual compounds found in different types of hydrolysates, to explore shared and divergent gene requirements across inhibitors. Relationships in chemical-genomic profiles identified classes of toxins that provoked similar cellular responses, spanning inhibitor relationships that were not expected from chemical classification. Our results also revealed widespread antagonistic effects across inhibitors, such that the same gene deletions are beneficial for surviving some toxins but detrimental for others. As a proof of principle, we used the gene-deletion responses to single inhibitors to successfully predict strains whose fitness was improved in complex inhibitor mixes found in synthetic hydrolysates. We discuss the implications for strain engineering and the potential of this rich dataset for identifying engineering targets.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE186866 | GEO | 2021/11/01

REPOSITORIES: GEO

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Project description:<p>We have used a "chemistry first" approach to discover druggable acquired vulnerabilities that arised in the pathogenesis of non-small cell lung cancer (NSCLC). We screened chemical libraries (~200,000 compounds) for chemical toxins that killed subsets of NSCLC but not normal human lung epithelial cells (HBECs). We first screened a panel of 12 NSCLC lines that represented a variety of known oncogenotypes and identified chemicals with large Z scores and appropriate properties including re-supply, chemistry, and reproducible drug response phenotypes. This was then narrowed down to a list of 202 chemicals and 18 drugs with known targeting (henceforth called "Precision Oncology Probe Set", or POPS). These, and a panel of 30 clinically available drugs, targeted therapies, and drug combinations, already in use or in trials for NSCLC treatment, were then tested on a panel of 96 NSCLC lines for their drug response phenotypes in 12-point dose response curves. This information was analyzed using scanning ranked KS (Kolmogorov-Smirnov) and elastic net biostatistics approaches to identify molecular biomarkers (mutations, mRNA expression, copy number variation, protein expression, and metabolomics) which could predict for sensitivity or resistance to a particular chemical toxin or treatment regimen. From this we have discovered that: our approach identifies already known molecular biomarker of drug sensitivities (e.g. EGFR mutations and EGFR TK inhibitors); many clinically available chemotherapy agents have molecular biomarkers predicting preclinical model drug responses; the POP set of chemical toxins provides novel drug response phenotype patterns in the large NSCLC panel different from those found with clinically available agents including a therapeutic window; many of the POP toxins only hit a small percentage (~5%) of the NSCLC panel but the POP set as a whole provides "coverage" of the entire NSCLC panel; there are simple, one or 2 component molecular biomarkers (mutations, mRNA expression) that predict responses to the different chemical toxins in the NSCLC panel; and that the molecular biomarkers provide some information on the targets and pathways involved in response to the chemical toxins. Thus, we have identified a group of chemical toxins with selectivity for subsets of NSCLC and associated tumor molecular biomarkers to facilitate their development for precision medicine, and also, in some cases, information on the targets and pathways interdicted by these chemical compounds. In addition, we have discovered NSCLC predictive biomarkers for clinically available agents.</p>

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Comparative chemical-genomic profiling of plant-based hydrolysate toxins enables predictive assessment of responses to complex mixtures

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