SARS-CoV-2 3CLpro Main Protease Drives Cytoskeletal Reorganization and Tunnelling Nanotube Formation for Stealth Intercellular Infection
Bibliographic record
Abstract
Abstract To identify and characterize roles for SARS-CoV-2 3CLpro main protease in promoting viral infection and dissemination, we employed Inactive Catalytic Domain Capture and proteomics to identify host cell interactors and substrates in the interactome of 3CLpro. Of 259 3CLpro interactors, 145 were associated with the cytoskeleton and its organisation. We determined enzyme kinetic specificity constants for 139 3CLpro cut-sites in 43 interactors using a multiplex assay, identifying 29 efficiently-cleaved substrates and validating 13 as substrates in vitro, in SARS-CoV-2-infected human lung cells, and in COVID-19 post-mortem lungs. 3CLpro cleavage of adherens junction proteins initiated cytoskeletal rearrangement, activated zonular signalling, removed subcellular localization motifs to trigger translocation of nuclear TRIM28 and NUMA1 to adherens junctions, and YAP1 in the hippo pathway, to the nucleus. These cytoskeletal remodelling events rapidly generated tunnelling nanotubes connecting lung epithelial cells and containing virus colocalized with 3CLpro substrates for stealth trafficking of SARS-CoV-2 to distant cells.
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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) | 0.002 | 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 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".