Gut Microenvironment and Bacterial Invasion in Paediatric Inflammatory Bowel Diseases
Bibliographic record
Abstract
OBJECTIVES: Host-microbial relationship is disrupted in inflammatory bowel diseases (IBD). We hypothesized that altered gut luminal microenvironment can impact microbial virulence in IBD, leading to disruption of homeostasis and disease. We investigated the relationship between gut microenvironment and microbial virulence. METHODS: Intestinal aspirates were collected from 10 non-IBD controls, 9 Crohn disease, and 10 ulcerative colitis paediatric patients during endoscopy. In vitro invasion of bacteria isolated from the duodenum and terminal ileum (TI) was quantified using gentamicin protection assays. Intestinal epithelial cells were infected in vitro by known Escherichia coli strains with patient intestinal aspirates added. Nuclear magnetic resonance spectroscopy (NMR) analysis was conducted on intestinal aspirates to identify metabolites associated with invasion; these metabolites were then introduced to the infection model. RESULTS: There was no difference in in vitro invasion of bacteria obtained from intestinal aspirates of non-IBD and IBD patients. Incubation of laboratory E coli strains with TI aspirates from IBD patients increased their invasion into epithelial cells in vitro. NMR analysis revealed intestinal metabolites that correlated with bacterial invasion; succinate present in the intestinal aspirates correlated positively, whereas acetate and formate related negatively with invasion. Addition of exogenous succinate increased invasion of E coli in vitro. CONCLUSIONS: Alterations in the gut microenvironment in IBD can affect bacterial invasion. Succinate is associated with increased bacterial invasion and can alter bacterial virulence in IBD. This highlights the interaction between specific metabolites and bacteria that could be instrumental in propagating or suppressing inflammation in paediatric IBD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".