Establishing the phenotypic basis of adherent-invasive <i>Escherichia coli</i> (AIEC) pathogenicity in intestinal inflammation
Bibliographic record
Abstract
Abstract Background & Aims Adherent-invasive Escherichia coli (AIEC) are enriched in ileal Crohn’s disease patients and implicated in disease etiology. However, AIEC pathogenesis is poorly understood, and it is unclear if the expansion of these organisms contributes to inflammatory bowel disease (IBD). Questions also remain as to what extent the various in vitro phenotypes used to classify AIEC are pathologically relevant. Methods We utilized a combination of in vitro phenotyping and a murine model of intestinal inflammation to systematically relate AIEC phenotypes to pathogenicity for 30 mucosa-associated human-derived E. coli strains. In vitro assays used included survival/replication in and TNF-α production by J774 macrophages as well as invasion/replication in Caco2 intestinal epithelial cells. Results AIEC do not form a phenotypic group that is clearly separated from non-AIEC. However, E. coli strains displaying in vitro AIEC phenotypes caused, on average, more severe intestinal inflammation. Survival/replication of strains in J774 and Caco2 cells were positively correlated with disease in vivo , while adherence to Caco2 cells and TNF-α production by J774 cells were not. Importantly, co-colonization with adherent non-AIEC strains ameliorated AIEC-mediated disease. Conclusion Our findings do not support the existence of an AIEC pathovar that can be clearly separated from commensal E. coli . However, intracellular survival/replication phenotypes do contribute to murine intestinal inflammation, suggesting that the AIEC overgrowth observed in human IBD makes a causal contribution to disease. The ability to differentiate pathologically-relevant AIEC phenotypes from those that are not provides an important foundation for developing strategies to predict, diagnose and treat human IBD through characterizing and modulating patient E. coli populations.
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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 | 0.001 | 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".