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Record W4387661838 · doi:10.1080/19490976.2023.2267189

Quorum sensing modulates bacterial virulence and colonization dynamics of the gastrointestinal pathogen <i>Citrobacter rodentium</i>

2023· article· en· W4387661838 on OpenAlexafffund
Jorge Peña-Díaz, Sarah E. Woodward, Anna Creus-Cuadros, Antonio Serapio-Palacios, Stephanie Ortiz-Jiménez, Wanyin Deng, B. Brett Finlay

Bibliographic record

VenueGut Microbes · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health ResearchAlliance de recherche numérique du CanadaMitacs
KeywordsCitrobacter rodentiumVirulenceBiologyQuorum sensingPathogenMicrobiologyCitrobacterColonizationContext (archaeology)PopulationGastrointestinal tractHuman pathogenEscherichia coliBacteriaEnterobacteriaceaeGeneticsGene

Abstract

fetched live from OpenAlex

Quorum Sensing (QS) is a form of cell-to-cell communication that enables bacteria to modify behavior according to their population density. While QS has been proposed as a potential intervention against pathogen infection, QS-mediated communication within the mammalian digestive tract remains understudied. Using an LC-MS/MS approach, we discovered that Citrobacter rodentium, a natural murine pathogen used to model human infection by pathogenic Escherichia coli, utilizes the CroIR system to produce three QS-molecules. We then profiled their accumulation both in vitro and across different gastrointestinal sites over the course of infection. Importantly, we found that in the absence of QS capabilities the virulence of C. rodentium is enhanced. This highlights the role of QS as an effective mechanism to regulate virulence according to the pathogen’s spatio-temporal context to optimize colonization and transmission success. These results also demonstrate that inhibiting QS may not always be an effective strategy for the control of virulence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.197
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes2
Has abstractyes

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