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The Capital Region of Denmark - a part of Copenhagen University Hospital
E-pub ahead of print

Integrated univariate, multivariate and correlation-based network analyses reveal metabolite-specific effects on bacterial growth and biofilm formation in necrotizing soft tissue infections

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  3. Molecular profiling of tissue biopsies reveals unique signatures associated with streptococcal necrotizing soft tissue infections

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Necrotizing soft-tissue infections (NSTIs) have multiple causes, risk factors, anatomical locations, and pathogenic mechanisms. In patients with NSTI, circulating metabolites may serve as substrate having impact on bacterial adaptation at the site of infection. Metabolic signatures associated with NSTI may reveal potential be useful as diagnostic and prognostic markers, as well as novel targets for therapy. This study used untargeted metabolomics analyses of plasma from NSTI patients (n=34) and healthy (non-infected) controls (n=24) to identify the metabolic signatures and connectivity patterns among metabolites associated with NSTI. Metabolite-metabolite association networks were employed to compare the metabolic profiles of NSTI patients and non-infected surgical controls. Out of 97 metabolites detected, the abundance of 33 was significantly altered in NSTI patients. Analysis of metabolite-metabolite association networks showed a more densely connected network: specifically, 20 metabolites differentially connected between NSTI and controls. A selected set of significantly altered metabolites were tested in vitro to investigate potential influence on NSTI group A streptococcal strain growth and biofilm formation. Using chemically defined media supplemented with the selected metabolites, ornithine, ribose, urea and glucuronic acid, revealed metabolite-specific effects on both bacterial growth and biofilm formation. This study identifies for the first time an NSTI specific metabolic signature with implications for optimized diagnostics and therapies.

Original languageEnglish
JournalJournal of Proteome Research
ISSN1535-3893
DOIs
Publication statusE-pub ahead of print - 2020

ID: 58863173