Emerging research suggested that the gut microbiota would play a critical role in defining the efficacy and toxicity of cancer chemotherapy. Here we show how single changes in the diet or the composition of the microbiota can increase chemotherapeutic toxicity a hundredfold. We use metabolic modeling of the microbiome, coupled to E. coli and C. elegans reverse genetics and metabolomics to elucidate the complex network of molecular interactions between diet, microbes, and the host that determine the level of toxicity of commonly used anti-cancer therapies.
Inferring the activity of metabolic enzymes based on transcriptomics data is challenging. In the absence of validated tools, this can be inaccurate or even misleading. In this study, we propose a thresholding method, StanDep, for functionally qualifying a metabolic reaction to be active. We show that our thresholding method improves the coverage of functions required for cellular maintenance. We also validated and compared models built with our approach against those with existing approaches using CRISPR-Cas9 essentiality screens. Overall, our study provides novel insights into how cells may deal with context-specific and ubiquitous functions.