Genome-scale metabolic reconstruction analysis of Clostridioides difficile identifies conserved patterns of virulence-related metabolic reprogramming

Clostridioides difficile is a Gram-positive, sporulating anaerobe that has become the leading cause of hospital-acquired infections. Over the previous decade, many studies have demonstrated the importance of metabolism in numerous aspects of C. difficile biology from initial colonization to regulation of virulence factors. Additionally, due to growing threats of antibiotic resistance and recurrent infection, targeting components of metabolism presents a novel possible approach to combat this infection. In the past, genome-scale metabolic network analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that potentially contribute to downstream phenotypes as well as prediction of outcome from perturbations to these pathways. These predictions ultimately create a platform for high-throughput identification and screening of metabolic targets prior to laboratory testing. To accomplish these goals in C. difficile, we constructed highly-curated genome-scale metabolic network reconstructions (GENREs) for a well-studied laboratory strain of the pathogen (str. 630) as well as a more recently characterized hyper-virulent isolate (str. R20291). These computational modeling platforms account for key components of C. difficile core metabolism and nutrient acquisition systems to recapitulate metabolic behaviors within the complex milieu of the gut. Simulating the impact of single-gene deletions resulted in accuracies of ~89.9% for both GENREs compared with transposon mutant libraries. Further analysis of both strains also revealed significant correlations between in silico and experimentally measured growth in carbon source utilization screens (p-values [≤] 0.002), with positive predictive values of ~95.0%. Subsequently, we generated context-specific models by integrating transcriptomic data from C. difficile grown in vitro or during in vivo infection. Simulations also predicted the consistent inverse patterns of carbohydrate and amino acid catabolism that corresponded with differential virulence factor expression measured experimentally. Collectively, our results indicate that GENRE-based analyses of C. difficile are an effective means for gaining novel insight into metabolism as it relates to pathogenesis and provides a platform for the identification of novel therapeutic targets.