Both pathogen and host dynamically adapt pH responses along the intestinal tract during enteric bacterial infection
Bibliographic record
Abstract
Enteric pathogens navigate distinct regional microenvironments within the intestine that cue important adaptive behaviors. We investigated the response of Citrobacter rodentium, a model of human pathogenic Escherichia coli infection in mice, to regional gastrointestinal pH. We found that small intestinal pH (4.4-4.8) triggered virulence gene expression and altered cell morphology, supporting initial intestinal attachment, while higher pH, representative of C. rodentium's replicative niches further along the murine intestine, supported pathogen growth. Gastric pH, a key barrier to intestinal colonization, caused significant accumulation of intra-bacterial reactive oxygen species (ROS), inhibiting growth of C. rodentium and related human pathogens. Within-host adaptation increased gastric acid survival, which may be due to a robust acid tolerance response (ATR) induced at colonic pH. However, the intestinal environment changes throughout the course of infection. We found that murine gastric pH decreases postinfection, corresponding to increased serum gastrin levels and altered host expression of acid secretion-related genes. Similar responses following Salmonella infection may indicate a protective host response to limit further pathogen ingestion. Together, we highlight interlinked bacterial and host adaptive pH responses as an important component of host-pathogen coevolution.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".