Inhibition of glycogen synthase kinase-3-beta (GSK3β) blocks nucleocapsid phosphorylation and SARS-CoV-2 replication
Bibliographic record
Abstract
GSK3β has been proposed to have an essential role in Coronaviridae infections. Screening of a targeted library of GSK3β inhibitors against both SARS-CoV-2 and HCoV-229E to identify broad-spectrum anti-Coronaviridae inhibitors resulted in the identification of a high proportion of active compounds with low toxicity to host cells. A selected lead compound, T-1686568, showed low micromolar, dose-dependent activity against SARS-CoV-2 and HCoV-229E. T-1686568 showed efficacy in viral-infected cultured cells and primary 2D organoids. T-1686568 also inhibited SARS-CoV-2 variants of concern Delta and Omicron. Importantly, while inhibition by T-1686568 resulted in the overall reduction of viral load and protein translation, GSK3β inhibition resulted in cellular accumulation of the nucleocapsid protein relative to the spike protein. Following identification of potential phosphorylation sites of Coronaviridae nucleocapsid, protein kinase substrate profiling assays combined with Western blotting analysis of nine host kinases showed that the SARS-CoV-2 nucleocapsid could be phosphorylated by GSK3β and PKCa. GSK3β phosphorylated SARS-CoV-2 nucleocapsid on the S180/S184, S190/S194 and T198 phospho-sites, following previous priming in the adjacent S188, T198 and S206, respectively. Such inhibition presents a compelling target for broad-spectrum anti-Coronaviridae compound development, and underlies the mechanism of action of GSK3β host-directed therapy against this class of obligate intracellular pathogens.
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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".