Variants of the <i>IFI16</i> Gene Affecting the Levels of Expression of mRNA Are Associated with Susceptibility to Behçet Disease
Bibliographic record
Abstract
OBJECTIVE: Behçet disease (BD) is a multifactorial disease in which infectious agents have been proposed as triggers in genetically predisposed individuals. The aim of our study was to investigate the role of innate immunity receptors, specifically the nucleic acid sensors, in susceptibility to BD. METHODS: Seventy-four tag single nucleotide polymorphisms (tSNP) selected in 9 candidate genes (DDX58, IFIH1, TLR3, TLR7, TLR8, AIM2, IFI16, ZBP1, and TLR9) were genotyped in 371 patients and 854 controls. Assays of mRNA expression and allele-specific transcript quantification (ASTQ) were performed in 110 and 50 controls, respectively. RESULTS: Patients and controls were genotyped and 2 tSNP (rs6940 in IFI16 and rs855873 in AIM2) were associated with BD. To confirm this association, these tSNP were genotyped in 850 additional controls, and the total cohort was randomly divided into 2 cohorts. The association of these 2 tSNP with the disease remained in both cohorts. One haplotype (rs6940T-rs855873G) was identified as a risk factor (OR 1.41, 95% CI 1.06-1.86, p = 0.015), and another (rs6940A-rs855873A) as a protective factor (OR 0.65, 95% CI 0.47-0.90, p = 0.009). Samples with the risk haplotype had lower IFI16 expression levels than samples with the protective (0.99 ± 0.29 vs 1.23 ± 0.50, p = 0.022). Consistently, in the ASTQ assays performed with the nonsynonymous rs6940 SNP, the risk allele had lower IFI16 expression levels than the protective (p = 0.027). CONCLUSION: Our findings suggest association of IFI16, a cytosolic sensor of dsDNA and mediator of the AIM2 inflammasome-dependent pathway, in susceptibility to BD. Differences genetically determined in the levels of this molecule could be the cause of this association.
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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 | 0.000 | 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) | 0.003 | 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".