Novel metabolic drivers of virulence in Clostridioides difficile identified through genome-scale metabolic network analysis
The bacterial pathogen Clostridioides difficile causes a toxin-mediated diarrheal illness and is now the leading cause of hospital-acquired infection in the US. Due to growing threats of antibiotic resistance and recurrent infection, targeting components of C. difficile metabolism presents a novel approach to combat this infection. Bacterial genome-scale metabolic network reconstructions (GENREs) have been used to identify new therapeutic targets and uncover properties that drive cellular behaviors. With this approach we constructed highly-curated C. difficile GENREs for a hyper-virulent isolate (str. R20291) as well as a historic strain (str. 630), and validated these models with both in vitro and in vivo datasets. Growth simulations of carbon source usage revealed significant correlations with experimentally measured values (p-values < 0.001, PPV ≥ 92.7%), and single-gene deletion analysis showed accuracies of >89.0% compared with transposon mutant libraries. Next, we utilized each GENRE to identify potential metabolic drivers of multiple virulence-related phenotypes including sporulation and biofilm formation. Through contextualization of these models using transcriptomic data we generated from in vitro and infection conditions, we discovered both greater reliance on the Pentose Phosphate Pathway as well as significantly increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced. These novel relationships were subsequently validated experimentally. Collectively, our results highlight the ability of GENREs to identify novel metabolite signals in bacterial pathogenesis.
project
|- README # description of content
|- LICENSE # the license for this project
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|- doc/ # additional documents associated with the study
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|- data/ # raw and primary data
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|- code/ # all programmatic code (python & R)
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|- results/ # all output from workflows and analyses
| |- figures/ # manuscript figures
| +- tables/ # supplementary tables
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|- notebooks/ # jupyter notebooks for the analyses performed during this study