Major Facilitator Superfamily Domain Containing 5 Inhibition Reduces Lipoprotein(a) Uptake and Calcification in Valvular Heart Disease
Bibliographic record
Abstract
BACKGROUND: High circulating levels of Lp(a) (lipoprotein[a]) increase the risk of atherosclerosis and calcific aortic valve disease, affecting millions of patients worldwide. Although atherosclerosis is commonly treated with low-density lipoprotein–targeting therapies, these do not reduce Lp(a) or risk of calcific aortic valve disease, which has no available drug therapies. Targeting Lp(a) production and catabolism may provide therapeutic benefit, but little is known about Lp(a) cellular uptake. METHODS: Here, unbiased ligand-receptor capture mass spectrometry was used to identify MFSD5 (major facilitator superfamily domain containing 5) as a novel receptor/cofactor involved in Lp(a) uptake. RESULTS: Reducing MFSD5 expression by a computationally identified small molecule or small interfering RNA suppressed Lp(a) uptake and calcification in primary human valvular endothelial and interstitial cells. MFSD5 variants were associated with aortic stenosis ( P =0.027 after multiple hypothesis testing) with evidence suggestive of an interaction with plasma Lp(a) levels. CONCLUSIONS: MFSD5 knockdown suppressing human valvular cell Lp(a) uptake and calcification, along with meta-analysis of MFSD5 variants associating with aortic stenosis, supports further preclinical assessment of MFSD5 in cardiovascular diseases, the leading cause of death worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".