miR-24-3p Is Antiviral Against SARS-CoV-2 by Downregulating Critical Host Entry Factors
Bibliographic record
Abstract
Despite all the progress in treating SARS-CoV-2, escape mutants to current therapies remain a constant concern. Promising alternative treatments for current and future coronaviruses are those that limit escape mutants by inhibiting multiple pathogenic targets, analogous to the current strategies for treating HCV and HIV. With increasing popularity and ease of manufacturing of RNA technologies for vaccines and drugs, therapeutic microRNAs represent a promising option. In the present work, miR-24-3p was identified to inhibit SARS-CoV-2 entry, replication, and production; furthermore, this inhibition was retained against common mutations improving SARS-CoV-2 fitness. To determine the mechanism of action, bioinformatic tools were employed, identifying numerous potential effectors promoting infection targeted by miR-24-3p. Of these targets, several key host proteins for priming and facilitating SARS-CoV-2 entry were identified: furin, NRP1, NRP2, and SREBP2. With further experimental analysis, we show that miR-24-3p directly downregulates these viral entry factors to impede infection when producing virions and when infecting the target cell. Furthermore, we compare the findings with coronavirus, HCoV-229E, which relies on different factors strengthening the miR-24-3p mechanism. Taken together, the following work suggests that miR-24-3p could be an avenue to treat current coronaviruses and those likely to emerge.
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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) | 0.001 | 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 | 0.001 |
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".