biostudies-literatureJenior MLHHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)HHS | NIH | National Center for Complementary and Integrative HealthNCCIH NIH HHSHHS | NIH | National Institute of Diabetes and Digestive and Kidney DiseasesNIDDK NIH HHSNIAID NIH HHSHHS | NIH | National Institute of Allergy and Infectious DiseasesHHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID)HHS | NIH | National Center for Complementary and Integrative Health (NCCIH)e0091921https://www.ebi.ac.uk/biostudies/studies/S-EPMC8547418biostudies-literatureUnknown6(5)The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with <i>in vitro</i> and <i>in vivo</i> data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletiomSystemsNovel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.PMC8547418F32 DK124048R01 AI152477R01AI124214T32 AI007496R01Al143638R01 AI124214R01 AI143638F32DK124048R01AT010253R01 AT010253Powers DAPapin JALeslie JLDickenson MEGarrett EMTamayo RPetri WAWalker KAJenior MLfalseNovel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with <i>in vitro</i> and <i>in vivo</i> data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletio2021-01-01T00:00:00Z2021 Oct2026-06-17T06:24:26.356Z2025-04-06T12:43:41.891ZS-EPMC85474183460916410.1128/mSystems.00919-21