FAM13A as potential therapeutic target in modulating TGF-β-induced airway tissue remodeling in COPD
Bibliographic record
Abstract
Genome-wide association studies have shown that a gene variant in the Family with sequence similarity 13, member A (FAM13A) is strongly associated with reduced lung function and the appearance of respiratory symptoms in patients with chronic obstructive pulmonary disease (COPD). A key player in smoking-induced tissue injury and airway remodeling is the transforming growth factor-β1 (TGF-β1). To determine the role of FAM13A in TGF-β1 signaling, FAM13A−/− airway epithelial cells were generated using CRISPR-Cas9, whereas overexpression of FAM13A was achieved using lipid nanoparticles. Wild-type (WT) and FAM13A−/− cells were treated with TGF-β1, followed by gene and/or protein expression analyses. FAM13A−/− cells augmented TGF-β1-induced increase in collagen type 1 (COL1A1), matrix metalloproteinase 2 (MMP2), expression compared with WT cells. This effect was mediated by an increase in β-catenin (CTNNB1) expression in FAM13A−/− cells compared with WT cells after TGF-β1 treatment. FAM13A overexpression was partially protective from TGF-β1-induced COL1A1 expression. Finally, we showed that airway epithelial-specific FAM13A protein expression is significantly increased in patients with severe COPD compared with control nonsmokers, and negatively correlated with lung function. In contrast, β-catenin (CTNNB1), which has previously been linked to be regulated by FAM13A, is decreased in the airway epithelium of smokers with COPD compared with non-COPD subjects. Together, our data showed that FAM13A may be protective from TGF-β1-induced fibrotic response in the airway epithelium via sequestering CTNNB1 from its regulation on downstream targets. Therapeutic increase in FAM13A expression in the airway epithelium of smokers at risk for COPD, and those with mild COPD, may reduce the extent of airway tissue remodeling.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".