TLR5 as an Anti-Inflammatory Target and Modifier Gene in Cystic Fibrosis
Bibliographic record
Abstract
New treatments are needed to improve the health of people with cystic fibrosis (CF). Reducing lung-damaging inflammation is likely to be beneficial, but specific anti-inflammatory targets have not been identified. By combining cellular immunology with a population-based genetic modifier study, we examined TLR5 as an anti-inflammatory target and modifier gene in CF. Using two pairs of human CF and control airway epithelial cells, we demonstrated that the TLR5-flagellin interaction is a major mediator of inflammation following exposure to Pseudomonas aeruginosa. To validate TLR5 as an anti-inflammatory target, we analyzed the disease modifying effects of the TLR5 c.1174C>T single nucleotide polymorphism (rs5744168) in a large cohort of CF patients (n = 2219). rs5744168 encodes a premature stop codon and the T allele is associated with a 45.5-76.3% reduction in flagellin responsiveness (p < 0.0001). To test the hypothesis that reduced TLR5 responsiveness would be associated with improved health in CF patients, we examined the relationship between rs5744168 and two clinical phenotypes: lung function and body weight. Adults with CF carrying the TLR5 premature stop codon (CT or TT genotype) had a higher body mass index than did CF patients homozygous for the fully functional allele (CC genotype) (p = 0.044); however, similar improvements in lung function associated with the T allele were not statistically significant. Although follow-up studies are needed to confirm the impact of TLR5 on nutritional status, this translational research provides evidence that genetic variation in TLR5 resulting in reduced flagellin responsiveness is associated with improved health indicators in adults with CF.
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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 | 0.001 | 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".