Gastric acid and escape to systemic circulation represent major bottlenecks to host infection by <i>Citrobacter rodentium</i>
Bibliographic record
Abstract
Abstract The gastrointestinal (GI) environment plays a critical role in shaping enteric infections. Host environmental factors create bottlenecks, restrictive events that reduce the genetic diversity of invading bacterial populations. However, the identity and impact of bottleneck events on bacterial infection are largely unknown. We used Citrobacter rodentium infection of mice, a model of human pathogenic Escherichia coli infections, to examine bacterial population dynamics and quantify bottlenecks to host colonization. Using Sequence Tag-based Analysis of Microbial Populations (STAMP) we characterized the founding population size (Nb′) and relatedness of C. rodentium populations at relevant tissue sites during early- and peak-infection. We demonstrate that the GI environment severely restricts the colonizing population, with an average Nb′ of only 12–43 lineages (of 2,000+ inoculated) identified regardless of time or biogeographic location. Passage through gastric acid and escape to the systemic circulation were identified as major bottlenecks during C. rodentium colonization. Manipulating such events by increasing gastric pH dramatically increased intestinal Nb′. Importantly, removal of the stomach acid barrier had downstream consequences on host systemic colonization, morbidity, and mortality. These findings highlight the capability of the host GI environment to limit early pathogen colonization, controlling the population of initial founders with consequences for downstream infection outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".