Common Polymorphism That Protects From Cardiovascular Disease Increases Fibronectin Processing and Secretion
Bibliographic record
Abstract
Background: Fibronectin ( FN1 ) is an essential regulator of homodynamic processes and tissue remodeling that have been proposed to contribute to atherosclerosis. Moreover, recent large-scale genome-wide association studies (GWAS) have linked common genetic variants within the FN1 gene to coronary artery disease risk. Methods: Public databases were analyzed by 2-Sample Mendelian Randomization. Expression constructs encoding short FN1 reporter constructs and full-length plasma FN1 variants were introduced in various cell models. Secreted and cellular levels were then analyzed and quantified by SDS-PAGE and fluorescence microscopy. Mass spectrometry and glycosylation analyses were performed to probe possible posttranscriptional differences. Results: Bioinformatic analyses revealed that common coronary artery disease risk single nucleotide polymorphisms in the FN1 locus associate with circulating levels of FN1 and that higher FN1 (fibronectin 1) protein levels in plasma are linked to lower coronary artery disease risk. The coronary artery disease-associated FN1 locus encompasses a common polymorphism that translates a L15Q variant situated within the FN1 signal peptide. Introduction of FN1 reporter constructs, differing at position 15, revealed differences in secretion, with the FN1 Q15 variant being less well secreted. Moreover, the L15Q polymorphism was found to alter glycosylation in some cell models but not in human plasma. Conclusions: In addition to providing novel functional evidence implicating FN1 in cardioprotection, these findings demonstrate that a common variant within a secretion signal peptide regulates protein function.
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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) | 0.001 | 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".