Adherent-invasive Escherichia coli Exacerbates Antibiotic-associated Intestinal Dysbiosis and Neutrophil Extracellular Trap Activation
Bibliographic record
Abstract
BACKGROUND: Antibiotic-associated disruption of the gut microbiota is a known risk factor for Crohn's disease. This chronic inflammatory disorder results from aberrant host immune responses to subsets of the gut microbiota, and is characterized by intense neutrophil recruitment to the lamina propria, surface and crypt epithelium. Importantly, adherent-invasive Escherichia coli (AIEC) is abundant in ileal biopsies, highlighting a possible etiological role. In this study, we investigated the impact of antibiotics and AIEC challenge on murine intestinal dysbiosis and neutrophil extracellular trap activation, which is a critical component of the neutrophil antimicrobial repertoire. METHODS: Male C57BL/6 mice were administered vancomycin and gentamicin (once daily, 3 days), and subsequently challenged with AIEC strain LF82 (once daily, 2 days). Perturbation of the gut microbiota was monitored using a combination of molecular and phylogenetic analyses. The impact of commensal and dysbiotic gut bacterial communities on neutrophil extracellular trap mobilization and intestinal redox balance was also quantified. RESULTS: Exposure of neutrophils to murine commensal gut microbial communities activated neutrophil extracellular trap formation. The capacity of neutrophils to cast these web-like structures was exacerbated following antibiotic and AIEC-associated intestinal dysbiosis, highlighting the possible overgrowth of immune-activating intestinal pathobionts. Intestinal dysbiosis was associated with an elevated capacity of the cultivated gut bacteria to produce reactive oxygen species in vitro, and increased colonic oxidative stress in vivo. CONCLUSIONS: Together, these data provide new insights into the detrimental effects of antibiotics on the gut microbiota, with clinically relevant implications for intestinal dysbiosis on neutrophil function and intestinal redox balance.
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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) | 0.001 | 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 | 0.001 | 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".