The papain-like protease of coronaviruses cleaves ULK1 to disrupt host autophagy
Bibliographic record
Abstract
The ongoing pandemic of COVID-19 alongside the outbreaks of SARS in 2003 and MERS in 2012 underscore the significance to understand betacoronaviruses as a global health challenge. SARS-CoV-2, the etiological agent for COVID-19, has infected over 50 million individuals’ worldwide with more than ∼1 million fatalities. Autophagy modulators have emerged as potential therapeutic candidates against SARS-CoV-2 but recent clinical setbacks urge for better understanding of viral subversion of autophagy. Using MHV-A59 as a model betacoronavirus, time-course infections revealed significant loss in the protein level of ULK1, a canonical autophagy-regulating kinase, and the concomitant appearance of a possible cleavage fragment. To investigate whether virus-encoded proteases target ULK1, we conducted in-vitro and cellular cleavage assays and identified ULK1 as a novel bona fide substrate of SARS-CoV-2 papain-like protease (PLpro). Mutagenesis studies discovered that ULK1 is cleaved at a conserved PLpro recognition sequence (LGGG) after G499, separating its N-terminal kinase domain from a C-terminal substrate recognition region. Over-expression of SARS-CoV-2 PLpro is sufficient to impair starvation-induced autophagy and disrupt formation of ULK1-ATG13 complex. Finally, we demonstrated a dual role for ULK1 in MHV-A59 replication, serving a pro-viral functions during early replication that is inactivated at late stages of infection. In conclusion, our study identified a new mechanism by which PLpro of betacoronaviruses induces viral pathogenesis by targeting cellular autophagy.
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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".