PHACTR1 splicing isoforms and eQTLs in atherosclerosis-relevant human cells
Bibliographic record
Abstract
BACKGROUND: Genome-wide association studies (GWAS) have identified a variant (rs9349379) at the phosphatase and actin regulator 1 (PHACTR1) locus that is associated with coronary artery disease (CAD). The same variant is also an expression quantitative trait locus (eQTL) for PHACTR1 in human coronary arteries (hCA). Here, we sought to characterize PHACTR1 splicing pattern in atherosclerosis-relevant human cells. We also explored how rs9349379 modulates the expression of the different PHACTR1 splicing isoforms. METHODS: We combined rapid amplification of cDNA ends (RACE) with next-generation long-read DNA sequencing to discover all PHACTR1 transcripts in many human tissues and cell types. We measured PHACTR1 transcripts by qPCR to identify transcript-specific eQTLs. RESULTS: We confirmed a brain-specific long transcript, a short transcript expressed in monocytes and four intermediate transcripts that are different due to alternative splicing of two in-frame exons. In contrast to a previous report, we confirmed that the PHACTR1 protein is present in vascular smooth muscle cells. In 158 hCA from our collection and the GTEx dataset, rs9349379 was only associated with the expression levels of the intermediate PHACTR1 transcripts. CONCLUSIONS: Our comprehensive transcriptomic profiling of PHACTR1 indicates that this gene encodes six main transcripts. Five of them are expressed in hCA, where atherosclerotic plaques develop. In this tissue, genotypes at rs9349379 are associated with the expression of the intermediate transcripts, but not the immune-specific short transcript. This result suggests that rs9349379 may in part influence CAD by modulating the expression of intermediate PHACTR1 transcripts in endothelial or vascular smooth muscle cells found in hCA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".