{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jenior ML"],"funding":["HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)","HHS | NIH | National Center for Complementary and Integrative Health","NCCIH NIH HHS","HHS | NIH | National Institute of Diabetes and Digestive and Kidney Diseases","NIDDK NIH HHS","NIAID NIH HHS","HHS | NIH | National Institute of Allergy and Infectious Diseases","HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID)","HHS | NIH | National Center for Complementary and Integrative Health (NCCIH)"],"pagination":["e0091921"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8547418"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(5)"],"pubmed_abstract":["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 deletio"],"journal":["mSystems"],"pubmed_title":["Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis."],"pmcid":["PMC8547418"],"funding_grant_id":["F32 DK124048","R01 AI152477","R01AI124214","T32 AI007496","R01Al143638","R01 AI124214","R01 AI143638","F32DK124048","R01AT010253","R01 AT010253"],"pubmed_authors":["Powers DA","Papin JA","Leslie JL","Dickenson ME","Garrett EM","Tamayo R","Petri WA","Walker KA","Jenior ML"],"additional_accession":[]},"is_claimable":false,"name":"Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.","description":"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 deletio","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2026-06-17T06:24:26.356Z","creation":"2025-04-06T12:43:41.891Z"},"accession":"S-EPMC8547418","cross_references":{"pubmed":["34609164"],"doi":["10.1128/mSystems.00919-21"]}}