microRNA-185 Inhibits SARS-CoV-2 Infection through the Modulation of the Host’s Lipid Microenvironment
Bibliographic record
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there has been an urgent need for the development of fast-acting antivirals, particularly in dealing with different variants of concern (VOC). SARS-CoV-2, like other RNA viruses, depends on host cell machinery to propagate and misregulate metabolic pathways to its advantage. Herein, we discovered that the immunometabolic microRNA-185 (miR-185) restricts SARS-CoV-2 propagation by affecting its entry and infectivity. The antiviral effects of miR-185 were studied in SARS-CoV-2 Spike protein pseudotyped virus, surrogate virus (HCoV-229E), as well as live SARS-CoV-2 virus in Huh7, A549, and Calu-3 cells. In each model, we consistently observed microRNA-induced reduction in lipid metabolism pathways-associated genes including SREBP2, SQLE, PPARG, AGPAT3, and SCARB1. Interestingly, we also observed changes in angiotensin-converting enzyme 2 (ACE2) levels, the entry receptor for SARS-CoV-2. Taken together, these data show that miR-185 significantly restricts host metabolic and other pathways that appear to be essential to SAR-CoV-2 replication and propagation. Overall, this study highlights an important link between non-coding RNAs, immunometabolic pathways, and viral infection. miR-185 mimics alone or in combination with other antiviral therapeutics represent possible future fast-acting antiviral strategies that are likely to be broadly antiviral against multiple variants as well as different virus types of potential pandemics.
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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) | 0.001 | 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".