Associations of NOD2 polymorphisms with Erysipelotrichaceae in stool of in healthy first degree relatives of Crohn’s disease subjects
Bibliographic record
Abstract
BACKGROUND: Genetic analyses have identified many variants associated with the risk of inflammatory bowel disease (IBD) development. Among these variants, the ones located within the NOD2 gene have the highest odds ratio of all IBD genetic risk variants. Also, patients with Crohn's disease (CD) have been shown to have an altered gut microbiome, which might be a reflection of inflammation itself or an effect of other parameters that contribute to the risk of the disease. Since NOD2 is an intracellular pattern recognition receptor that senses bacterial peptidoglycan in the cytosol and stimulates the host immune response (Al Nabhani et al., PLoS Pathog 13:e1006177, 2017), it is hypothesized that NOD2 variants represent perfect candidates for influencing host-microbiome interactions. We hypothesized that NOD2 risk variants affect the microbiome composition of healthy first degree relative (FDR) of CD patients and thus potentially contribute to an altered microbiome state before disease onset. METHODS: Based on this, we studied a large cohort of 1546 healthy FDR of CD patients and performed a focused analysis of the association of three major CD SNPs in the coding region of the NOD2 gene, which are known to confer a 15-40-fold increased risk of developing CD in homozygous or compound heterozygous individuals. RESULTS: Our results show that carriers of the C allele at rs2066845 was significantly associated with an increase in relative abundance in the fecal bacterial family Erysipelotrichaceae. CONCLUSIONS: This result suggests that NOD2 polymorphisms contribute to fecal microbiome composition in asymptomatic individuals. Whether this modulation of the microbiome influences the future development of CD remains to be assessed.
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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) | 0.002 | 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".