A 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 which has been proposed to contribute to atherosclerosis. Moreover, recent large scale genome wide association studies have linked common genetic variants within the FN1 gene to coronary artery disease (CAD) risk. Methods Public databases were analyzed by two-Sample Mendelian Randomization. Expression constructs encoding short FN1 reporter constructs and full-length plasma FN1 , differing in the polymorphism, were designed and introduced in various cell models. Secreted and cellular levels were then analyzed and quantified by SDS-PAGE and fluorescence approaches. Mass spectrometry and glycosylation analyses were performed to probe possible post-transcriptional differences. Results Higher FN1 protein levels in plasma associates with a reduced risk of cardiovascular disease. Moreover, common CAD risk SNPs in the FN1 locus associate with circulating levels of FN1. This region is shown to encompass a L15Q polymorphism within the FN1 signal peptide. The presence of the minor allele that predisposes to CAD, corresponding to the Q15 variant, alters glycosylation and reduces FN1 secretion in a direction consistent with the bioinformatic analyses. Conclusion In addition to providing novel functional evidence implicating FN1 as a protective force in cardiovascular disease, 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) | 0.000 | 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".