Patients with Inflammatory Bowel Disease Exhibit Dysregulated Responses to Microbial DNA
Bibliographic record
Abstract
BACKGROUND: A critical role for the gut epithelium lies in its ability to discriminate between pathogens and commensals and respond appropriately. Dysfunctional interactions between microbes and epithelia are believed to have a role in inflammatory bowel disease (IBD). In this study, we analyzed microbiota and gene expression in IBD patients and examined responses of mucosal biopsies to bacterial DNA. METHODS: Biopsies were taken from non-inflamed areas of the colon in healthy controls (HC) and Crohn's disease (CD) and ulcerative colitis (UC) patients in remission. Biopsies were snap-frozen or cultured with DNA from Lactobacillus plantarum (LP) or Salmonella dublin (SD). Gene expression was analyzed under basal conditions and in response to DNA. Gene networks were analyzed using Ingenuity Pathways software. Mucosal-associated microbiota was analyzed using terminal restriction fragment length polymorphism. Frequency of single nucleotide polymorphisms in NOD2 and TLR9 was assessed. RESULTS: Patients with IBD had altered microbiota, enhanced expression of inflammatory genes, and increased correlations between specific gene expression and microbes. Principle component analysis showed CD and UC patients to cluster independently from healthy controls in both gene expression and microbial analysis. DNA from LP stimulated anti-inflammatory pathways in controls and UC patients, but induced an upregulation of IL17A in CD patients. There were no differences in SNP frequencies of TLR9 or NOD2 in the groups. CONCLUSIONS: Patients with Crohn's disease exhibit altered responses to bacterial DNA. These findings suggest that the gut response to bacterial DNA may depend not only on the specific type of bacterial DNA, but also on the host.
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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) | 0.001 | 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".