Microbiota-sensitive epigenetic signature predicts inflammation in Crohn’s disease
Bibliographic record
Abstract
Altered response to the intestinal microbiota strongly associates with inflammatory bowel disease (IBD); however, how commensal microbial cues are integrated by the host during the pathogenesis of IBD is not understood. Epigenetics represents a potential mechanism that could enable intestinal microbes to modulate transcriptional output during the development of IBD. Here, we reveal a histone methylation signature of intestinal epithelial cells isolated from the terminal ilea of newly diagnosed pediatric IBD patients. Genes characterized by significant alterations in histone H3-lysine 4 trimethylation (H3K4me3) showed differential enrichment in pathways involving immunoregulation, cell survival and signaling, and metabolism. Interestingly, a large subset of these genes was epigenetically regulated by microbiota in mice and several microbiota-sensitive epigenetic targets demonstrated altered expression in IBD patients. Remarkably though, a substantial proportion of these genes exhibited H3K4me3 levels that correlated with the severity of intestinal inflammation in IBD, despite lacking significant differential expression. Collectively, these data uncover a previously unrecognized epigenetic profile of IBD that can be primed by commensal microbes and indicate sensitive targets in the epithelium that may underlie how microbiota predispose to subsequent intestinal inflammation and disease.
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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 | 0.000 |
| 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) | 0.001 | 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".