Quercetin inhibits SARS-CoV-2 infection and prevents syncytium formation by cells co-expressing the viral spike protein and human ACE2
Bibliographic record
Abstract
Abstract Background Several in silico studies have determined that quercetin, a plant flavonol, could bind with strong affinity and low free energy to SARS-CoV-2 proteins involved in viral entry and replication, suggesting it could block infection of human cells by the virus. In the present study, we examined the ex vivo ability of quercetin to inhibit of SARS-CoV-2 replication and explored the mechanisms of this inhibition. Methods Green monkey kidney Vero E6 cells and in human colon carcinoma Caco-2 cells were infected with SARS-CoV-2 and incubated in presence of quercetin; the amount of replicated viral RNA was measured in spent media by RT-qPCR. Since the formation of syncytia is a mechanism of SARS-CoV-2 propagation, a syncytialization model was set up using human embryonic kidney HEK293 co-expressing SARS-CoV-2 Spike (S) protein and human angiotensin converting enzyme 2 (ACE2), [HEK293(S + ACE2) cells], to assess the effect of quercetin on this cytopathic event by microscopic imaging and protein immunoblotting. Results Quercetin inhibited SARS-CoV-2 replication in Vero E6 cells and Caco-2 cells in a concentration-dependent manner with a half inhibitory concentration (IC50) of 166.6 and 145.2 µM, respectively. It also inhibited syncytialization of HEK293(S + ACE2) cells with an IC50 of 156.7 µM. Spike and ACE2 co-expression was associated with decreased expression, increased proteolytic processing of the S protein, and diminished production of the fusogenic S2’ fragment of S. Furin, a proposed protease for this processing, was inhibited by quercetin in vitro with an IC50 of 116 µM. Conclusion These findings suggest that at low 3-digit micromolar concentrations of quercetin could impair SARS-CoV-2 infection of human cells partly by blocking the fusion process that promotes its propagation.
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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".