Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

Abstract

Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors.

Coordinated modulation of multiple processes through phase variation of a c-di-GMP phosphodiesterase in Clostridioides difficile

“…While we cannot exclude the possibility that phase variation occurred during experimentation, we ensured that the starting switch orientations in each strain did not explain the observed phenotypes. Future work will examine the potential selective pressures imposed in these growth conditions on phase variation in C. difficile [46].…”

Transcriptome guided metabolic network analysis reveals rearrangements of carbon flux distribution inNeisseria gonorrhoeaeduring neutrophil co-culture

“…To accomplish this goal, we applied the RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) algorithm (45), which uses RNA-seq data to identify the most cost-effective usage of metabolism while also reflecting the organism's transcriptional investment. RIPTiDE has been used successfully with models of Pseudomonas aeruginosa and Clostridioides difficile to uncover metabolic contributors to virulence in the context of mucin degradation, biofilm formation, murine infection 13 models, and co-culture with other microbes (19,21,46). We reasoned this approach would generate context-specific models of the metabolism of Gc when grown with and without PMN co-culture and would identify those reactions that are likely to be differentially active in each condition.…”

“…The copyright holder for this preprint this version posted December 20, 2022. ; https://doi.org/10.1101/2022.12. 19.521143 doi: bioRxiv preprint small repertoire of virulence factors compared to other pathogenic bacteria, and it has no known exotoxins (2). Instead, the success of Gc during human infection is related to its unique physiology, in particular its ability to exploit the resources in the host environment.…”

“…The copyright holder for this preprint this version posted December 20, 2022. ; https://doi.org/10.1101/2022.12. 19.521143 doi: bioRxiv preprint engineering that would otherwise be time-consuming and labor intensive to conduct (18). More recently, these tools have been used for the integration and interpretation of multi-omics data and applied to studies of human health and disease, including modeling of the metabolism of prominent human pathogens including Mycobacterium tuberculosis, Staphylococcus aureus, Pseudomonas aeruginosa, Clostridioides difficile, and Salmonella typhimurium (19)(20)(21).…”

“…GENREs enable large-scale, in silico manipulations of bacterial metabolism and have been used in a variety of applications including genome wide-knockout screens, synthetic lethal studies, and metabolic engineering that would otherwise be time-consuming and labor intensive to conduct (18). More recently, these tools have been used for the integration and interpretation of multi-omics data and applied to studies of human health and disease, including modeling of the metabolism of prominent human pathogens including Mycobacterium tuberculosis, Staphylococcus aureus, Pseudomonas aeruginosa, Clostridioides difficile, and Salmonella typhimurium (19)(20)(21). In contrast, there is no published model of Gc metabolism; while there is a GENRE for the related N. meningitidis (22), these two species are known to have key differences in their metabolism, for instance in sugar utilization (23).…”