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Novel 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-...

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Autores principales: Jenior, Matthew L., Leslie, Jhansi L., Powers, Deborah A., Garrett, Elizabeth M., Walker, Kimberly A., Dickenson, Mary E., Petri, William A., Tamayo, Rita, Papin, Jason A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547418/
https://www.ncbi.nlm.nih.gov/pubmed/34609164
http://dx.doi.org/10.1128/mSystems.00919-21
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author Jenior, Matthew L.
Leslie, Jhansi L.
Powers, Deborah A.
Garrett, Elizabeth M.
Walker, Kimberly A.
Dickenson, Mary E.
Petri, William A.
Tamayo, Rita
Papin, Jason A.
author_facet Jenior, Matthew L.
Leslie, Jhansi L.
Powers, Deborah A.
Garrett, Elizabeth M.
Walker, Kimberly A.
Dickenson, Mary E.
Petri, William A.
Tamayo, Rita
Papin, Jason A.
author_sort Jenior, Matthew L.
collection PubMed
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 in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE 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. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.
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spelling pubmed-85474182021-10-27 Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis Jenior, Matthew L. Leslie, Jhansi L. Powers, Deborah A. Garrett, Elizabeth M. Walker, Kimberly A. Dickenson, Mary E. Petri, William A. Tamayo, Rita Papin, Jason A. mSystems Research Article 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 in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE 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. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets. American Society for Microbiology 2021-10-05 /pmc/articles/PMC8547418/ /pubmed/34609164 http://dx.doi.org/10.1128/mSystems.00919-21 Text en Copyright © 2021 Jenior et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Jenior, Matthew L.
Leslie, Jhansi L.
Powers, Deborah A.
Garrett, Elizabeth M.
Walker, Kimberly A.
Dickenson, Mary E.
Petri, William A.
Tamayo, Rita
Papin, Jason A.
Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title_full Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title_fullStr Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title_full_unstemmed Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title_short Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis
title_sort novel drivers of virulence in clostridioides difficile identified via context-specific metabolic network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547418/
https://www.ncbi.nlm.nih.gov/pubmed/34609164
http://dx.doi.org/10.1128/mSystems.00919-21
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