Identification of a novel lineage bat SARS-related coronaviruses that use bat ACE2 receptor
Bibliographic record
Abstract
Severe respiratory disease coronavirus-2 (SARS-CoV-2) has been the most devastating disease COVID-19 in the century. One of the unsolved scientific questions of SARS-CoV-2 is the animal origin of this virus. Bats and pangolins are recognized as the most probable reservoir hosts that harbour highly similar SARS-CoV-2 related viruses (SARSr-CoV-2). This study identified a novel lineage of SARSr-CoVs, including RaTG15 and seven other viruses, from bats at the same location where we found RaTG13 in 2015. Although RaTG15 and the related viruses share 97.2% amino acid sequence identities with SARS-CoV-2 in the conserved ORF1b region, it only shows less than 77.6% nucleotide identity to all known SARSr-CoVs at the genome level, thus forming a distinct lineage in the Sarbecovirus phylogenetic tree. We found that the RaTG15 receptor-binding domain (RBD) can bind to ACE2 from Rhinolophus affinis, Malayan pangolin, and use it as an entry receptor, except for ACE2 from humans. However, it contains a short deletion and has different key residues responsible for ACE2 binding. In addition, we showed that none of the known viruses in bat SARSr-CoV-2 lineage discovered uses human ACE2 as efficiently as the pangolin-derived SARSr-CoV-2 or some viruses in the SARSr-CoV-1 lineage. Therefore, further systematic and longitudinal studies in bats are needed to prevent future spillover events caused by SARSr-CoVs or to understand the origin of SARS-CoV-2 better.
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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".