Pre-print journal club review of BioRxiv article: Functional assessment of cell entry and receptor usage for lineage B β-coronaviruses, including 2019-nCoV
Bibliographic record
Abstract
Letko and Munster report a new functional viromics platform whereby receptor binding domains (RBDs) from different lineage B betacoronaviruses were cloned into a codon-optimized gene for SARS CoV spike protein which was then incorporated into pseudotyped VSV particles for functional assays.Entry was indicated by luciferase reporter activity.This screen facilitated rapid identification of RBDreceptor interactions, with less expense than previous methods.The authors confirmed previous findings that only RBDs belonging to clade-1 of the B-lineage of beta-coronaviruses use the ACE2 receptor.Furthermore, the authors showed that for a variety of B-lineage coronaviruses, protease treatment prior to infection enhances entry into different cell types from different species.They confirmed that protease treatment enhanced receptor-dependent viral entry.By introducing 14 amino acids known to contact the ACE2 receptor into clade-2 and clade-3 RBDs, the authors confirmed that these AAs are important for ACE2 recognition.They also determined that the surrounding AA sequence context is important for ACE2 recognition.Finally, they showed that the new 2019-nCoV coronavirus (now known as SARS-CoV-2 according to the ICTV) is related to Clade-1 betacoronaviruses, similar to SARS, and also utilizes the ACE2 entry receptor. OVERALL ASSESSMENT:STRENGTHS: Overall, we conclude that this is a scientifically sound and well-written article by Letko and Munster.The authors' conclusions are generally well-supported by the data.The authors report a screen that is rapid, effective, and cost-efficient compared to previous methods to screen coronavirus receptor usage.The authors were also the first to show that SARS-CoV-2 uses the ACE2 receptor similar to SARS.This demonstrates the effectiveness and rapidity of their screen.The authors confirmed results from previous studies that protease treatment aids viral entry but is not sufficient to promote viral entry into cells that lack cognate receptors.Overall this a strong manuscript that provides important information relevant to the current SARS-CoV-2 outbreak.WEAKNESSES: Some improvements could be made to strengthen the manuscript and provide better support
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.061 |
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".