Calreticulin Regulates SARS-CoV-2 Spike Protein Turnover and Modulates SARS-CoV-2 Infectivity
Bibliographic record
Abstract
Cardiovascular complications are major clinical hallmarks of acute and post-acute coronavirus disease 2019 (COVID-19). However, the mechanistic details of SARS-CoV-2 infectivity of endothelial cells remain largely unknown. Here, we demonstrate that the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) protein shares a similarity with the proline-rich binding ena/VASP homology (EVH1) domain and identified the endoplasmic reticulum (ER) resident calreticulin (CALR) as an S-RBD interacting protein. Our biochemical analysis showed that CALR, via its proline-rich (P) domain, interacts with S-RBD and modulates proteostasis of the S protein. Treatment of cells with the proteasomal inhibitor bortezomib increased the expression of the S protein independent of CALR, whereas the lysosomal/autophagy inhibitor bafilomycin 1A, which interferes with the acidification of lysosome, selectively augmented the S protein levels in a CALR-dependent manner. More importantly, the shRNA-mediated knockdown of CALR increased SARS-CoV-2 infection and impaired calcium homeostasis of human endothelial cells. This study provides new insight into the infectivity of SARS-CoV-2, calcium hemostasis, and the role of CALR in the ER-lysosome-dependent proteolysis of the spike protein, which could be associated with cardiovascular complications in COVID-19 patients.
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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".