ArticlemSystems2021
Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis.
Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 37 citations in OpenAlex.
- A programmed reset mechanism primesScience advances · 2026Article
- Phase variable colony morphotypes ofInfection and immunity · 2026Article
- Article
- Phase variable colony morphotypes ofbioRxiv : the preprint server for biology · 2026Article
- Non-targeted metabolomics reveals metabolic signatures associated withFrontiers in microbiology · 2026Article
- Phase variation of Clostridioides difficile colony morphology occurs via modulation of cell division.PLoS pathogens · 2025Article
- Unravelling three-way interactions betweenJournal of medical microbiology · 2025Review
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Article
- Article
- Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens.PLoS biology · 2024Article
- Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes.Nature communications · 2024Article
- Emerging tools and best practices for studying gut microbial community metabolism.Nature metabolism · 2024Review
- Article
- Elucidating human gut microbiota interactions that robustly inhibit diversebioRxiv : the preprint server for biology · 2024Article
- Article
- Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations.Briefings in bioinformatics · 2023Review
- Inferring microbial interactions with their environment from genomic and metagenomic data.PLoS computational biology · 2023Article
- Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution inmSystems · 2023Article
- Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.Antibiotics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.