Vitamin C is an efficient natural product for prevention of SARS-CoV-2 infection by targeting ACE2 in both cell and in vivo mouse models
Bibliographic record
Abstract
SUMMARY ACE2 is a major receptor for cell entry of SARS-CoV-2. Despite advances in targeting ACE2 to inhibit SARS-CoV-2’s binding, how to efficiently and flexibly control ACE2 levels for prevention of SARS-CoV-2 infection has not been explored. Here, we revealed Vitamin C (VitC) administration as an effective strategy to prevent SARS-CoV-2 infection. VitC reduced ACE2 protein levels in a dose-dependent manner, while partial reduction of ACE2 can greatly restrict SARS-CoV-2 infection. Further studies uncovered that USP50 is a crucial regulator of ACE2 protein levels, and VitC blocks the USP50-ACE2 interaction, thus promoting K48-linked polyubiquitination at Lys788 and degradation of ACE2, without disrupting ACE2 transcriptional expression. Importantly, VitC administration reduced host ACE2 and largely blocked SARS-CoV-2 infection in mice. This study identified an in vivo ACE2 balance controlled by both USP50 and an essential nutrient VitC, and revealed a critical role and application of VitC in daily protection from SARS-CoV-2 infection. Highlights VitC reduces ACE2 protein levels in a dose-dependent manner VitC and USP50 regulate K48-linked ubiquitination at Lys788 of ACE2 VitC blocks the interaction between USP50 and ACE2 VitC administration lowers host ACE2 and prevents SARS-CoV-2 infection in vivo Abstract Figure The deubiquitinase USP50 controls ACE2 protein stability and levels, while Vitamin C blocks the USP50-ACE2 interaction and therefore results in ACE2 degradation, offering a flexible and efficient approach to protection of the host from SARS-CoV-2 infection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".