Mitochondrial gene variant contributing to coronary artery disease
Bibliographic record
Abstract
Genome‐wide association studies (GWAS) have identified genetic variants that are associated with the risk for coronary artery disease (CAD), but the functional significance of these loci has remained obscure. We identified a genetic variant associated with CAD risk that changes the sequence of a mitochondrial protease called paraplegin (SPG7) and confirmed this association by meta‐analysis of 14 GWAS including 22,000 CAD cases and 60,000 controls. The variant changes an arginine residue at position 688 to a glutamine residue in the protease domain of SPG7. Most mutations in SPG7 cause a loss of protease function leading to spastic paraplegia. Although the Gln688 variant was considered benign in terms of spastic paraplegia, we have evidence that it is in fact a protease gain‐of‐function variant. We observed a strong correlation between the presence of the variant allele and the level of mature active SPG7 in peripheral lymphocytes, as well in primary cultures of human aortic smooth muscles. Consistent with increased SPG7 protease activity, increased production of cytochrome c oxidase subunits, ATP synthesis, cell proliferation and ROS production were seen in cells stably expressing this variant. Our data suggest that this gain of function may also be detrimental to mitochondrial function with aging. Our study is the first to link a mitochondrial matrix AAA protease variant to the risk of CAD. (Supported by CIHR)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".