Conserved Virulence-Linked Metabolic Reprogramming in <i>Clostridioides Difficile</i> Identified Through Genome-Scale Metabolic Network Analysis

Abstract

The bacterial pathogen Clostridioides difficile causes a toxin-mediated diarrheal illness and is now the leading cause of hospital-acquired infection in the US. Due to growing threats of antibiotic resistance and recurrent infection, targeting components of metabolism presents a novel approach to combat this infection. Analyses of bacterial genome-scale metabolic network reconstructions (GENREs) have identified new therapeutic targets and helped uncover properties that drive cellular behaviors. We sought to leverage this approach and thus constructed highly-curated C. difficile GENREs for a hyper-virulent isolate (R20291) as well as a historic strain (630). Growth simulations of carbon source usage revealed significant correlations between in silico and experimentally measured values ( p -values ≤ 0.002, PPV ≈ 95%), and single-gene deletion analysis showed accuracies of >89% compared with transposon mutant libraries. Contextualizing these models with in situ omics datasets revealed conserved patterns of elevated proline, leucine, and valine fermentation that corresponded with significant increases in expression of multiple virulence factors during infection. Collectively, our results support that C. difficile utilizes distinct metabolic programs as infection progresses and highlights that GENREs can reveal the underpinnings of bacterial pathogenesis.