<i>Prunella vulgaris</i> extract and suramin block SARS-coronavirus <i>2</i> virus Spike protein D614 and G614 variants mediated receptor association and virus entry in cell culture system
Bibliographic record
Abstract
Abstract Until now, no approved effective vaccine and antiviral therapeutic are available for treatment or prevention of SARS-coronavirus 2 (SCoV-2) virus infection. In this study, we established a SCoV-2 Spike glycoprotein (SP), including a SP mutant D614G, pseudotyped HIV-1-based vector system and tested their ability to infect ACE2-expressing cells. This study revealed that a C-terminal 17 amino acid deletion in SCoV-2 SP significantly increases the incorporation of SP into the pseudotyped viruses and enhanced its infectivity, which may be helpful in the design of SCoV2-SP-based vaccine strategies. Moreover, based on this system, we have demonstrated that an aqueous extract from the Chinese herb Prunella vulgaris (CHPV) and a compound, suramin, displayed potent inhibitory effects on both wild type and mutant (G614) SCoV-2 SP pseudotyped virus (SCoV-2-SP-PVs)-mediated infection. The 50% inhibitory concentration (IC50) for CHPV and suramin on SCoV-2-SP-PVs are 30, and 40 μg/ml, respectively. To define the mechanisms of their actions, we demonstrated that both CHPV and suramin are able to directly interrupt SCoV-2–SP binding to its receptor ACE2 and block the viral entry step. Importantly, our results also showed that CHPV or suramin can efficiently reduce levels of cytopathic effect caused by SARS-CoV-2 virus (hCoV-19/Canada/ON-VIDO-01/2020) infection in Vero cells. Furthermore, our results demonstrated that the combination of CHPV/suramin with an anti-SARS-CoV-2 neutralizing antibody mediated more potent blocking effect against SCoV2-SP-PVs. Overall, this study provides evidence that CHPV and suramin has anti-SARS-CoV-2 activity and may be developed as a novel antiviral approach against 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 | 0.000 |
| 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".