Identification of quantitative trait loci influencing inflammation‐mediated alveolar bone loss: insights into polygenic inheritance of host–biofilm disequilibria in periodontitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The relative contribution of genetic and environmental factors to the onset and progression of periodontitis is inconclusive. Despite the high prevalence, phenotypic heterogeneity and significant local and systemic implications of this disease, early detection and individualized therapy are problematic. Using a murine model of periodontitis in a panel of 17 recombinant inbred mice, the current study addressed the heritability of, and oral dysbiosis associated with, inflammation-mediated alveolar bone loss (iABL), the hallmark of periodontitis. MATERIAL AND METHODS: Quantitative trait locus (QTL) genomics and quantitative PCR for over 99% of known murine oral microbiota were used. RESULTS: It was found that iABL is a polygenic trait with 32.7% heritability. One suggestive QTL, nicknamed inflammation-mediated alveolar bone loss locus (iABLL), was identified on chromosome 2. Eleven genes involved in innate immune responses and bone metabolism, particularly related to macrophage and osteoblast function, namely Etl4, Pdss1, Cobll1, 9330158F14Rik, Xirp2, Stk39, Mettl5, Metapl1, Itga6, Pdk1 and Sp3, were found in the iABLL using cis expression QTL and nonsynonymous single nucleotide polymorphism analyses. Specific oral microbiome shifts in saliva and tongue mucosa are associated with disease in this model. CONCLUSION: Our results indicate that complex host-biofilm interactions generate pathogenic states that extend beyond subgingival biofilms and periodontal tissues. Although no temporal relationship between the onset of iABL and microbiome changes were established, our findings suggest that host factors may be responsible for pathogenic shifts in subgingival biofilms when persistent and undisturbed.
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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 | 0.001 | 0.000 |
| 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".