Long Chain N3-PUFA Decreases ACE2 Protein Levels and Prevents SARS-CoV-2 Cell Entry
Bibliographic record
Abstract
Angiotensin-converting enzyme 2 (ACE2) is a target of interest for both COVID-19 and cardiovascular disease management. Even though lower ACE2 levels may be beneficial in SARS-CoV-2 infectivity, maintaining the ACE1/ACE2 balance is also crucial for cardiovascular health. So far, reports describing conditions capable of altering ACE2 protein levels, especially via dietary components, are limited. In this study, the effects of omega-3 polyunsaturated fatty acids (n3-PUFA) on the protein levels of ACE1 and ACE2 in rodent tissues, human endothelial and kidney cell lines, and human plasma were examined. The ability of n3-PUFA to affect the entry of the SARS-CoV-2 pseudovirus into cells was also tested. Docosahexaenoic acid (DHA), and in some cases eicosapentaenoic acid (EPA), but not α-linoleic acid (ALA), reduced both ACE1 and ACE2 (non-glycosylated p100 and glycosylated p130 forms) in the heart, aorta, and kidneys of obese rats, as well as in human EA.hy926 endothelial and HEK293 kidney cells. Dietary supplementation with either DHA or ALA had no effect on plasma soluble ACE2 levels in humans. However, treatment of HEK293 cells with 80 and 125 µM DHA for 16 h inhibited the entry of the SARS-CoV-2 pseudovirus. These results strongly suggest that DHA treatment may reduce the ability of SARS-CoV-2 to infect cells via a mechanism involving a decrease in the absolute level of ACE2 protein as well as its glycosylation. Our findings warrant further evaluation of long-chain n3-PUFA supplements as a novel option for restricting SARS-CoV-2 infectivity in the general population.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".