| Autor: |
Dillard LR; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA., Glass EM; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA., Lewis AL; Department of Obstetrics and Gynecology, University of California-San Diego, La Jolla, California, USA., Thomas-White K; Evvy, New York, New York, USA., Papin JA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, USA.; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
MSystems [mSystems] 2023 Feb 23; Vol. 8 (1), pp. e0068922. Date of Electronic Publication: 2022 Dec 13. |
| DOI: |
10.1128/msystems.00689-22 |
| Abstrakt: |
Gardnerella is the primary pathogenic bacterial genus present in the polymicrobial condition known as bacterial vaginosis (BV). Despite BV's high prevalence and associated chronic and acute women's health impacts, the Gardnerella pangenome is largely uncharacterized at both the genetic and functional metabolic levels. Here, we used genome-scale metabolic models to characterize in silico the Gardnerella pangenome metabolic content. We also assessed the metabolic functional capacity in a BV-positive cervicovaginal fluid context. The metabolic capacity varied widely across the pangenome, with 38.15% of all reactions being core to the genus, compared to 49.60% of reactions identified as being unique to a smaller subset of species. We identified 57 essential genes across the pangenome via in silico gene essentiality screens within two simulated vaginal metabolic environments. Four genes, gpsA , fas , suhB , and psd , were identified as core essential genes critical for the metabolic function of all analyzed bacterial species of the Gardnerella genus. Further understanding these core essential metabolic functions could inform novel therapeutic strategies to treat BV. Machine learning applied to simulated metabolic network flux distributions showed limited clustering based on the sample isolation source, which further supports the presence of extensive core metabolic functionality across this genus. These data represent the first metabolic modeling of the Gardnerella pangenome and illustrate strain-specific interactions with the vaginal metabolic environment across the pangenome. IMPORTANCE Bacterial vaginosis (BV) is the most common vaginal infection among reproductive-age women. Despite its prevalence and associated chronic and acute women's health impacts, the diverse bacteria involved in BV infection remain poorly characterized. Gardnerella is the genus of bacteria most commonly and most abundantly represented during BV. In this paper, we use metabolic models, which are a computational representation of the possible functional metabolism of an organism, to investigate metabolic conservation, gene essentiality, and pathway utilization across 110 Gardnerella strains. These models allow us to investigate in silico how strains may differ with respect to their metabolic interactions with the vaginal-host environment. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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