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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".