Human ACE2 receptor polymorphisms predict SARS-CoV-2 susceptibility
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease (COVID-19) that has resulted in a global pandemic. It is a highly contagious positive strand RNA virus and its clinical presentation includes severe to critical respiratory disease that appears to be fatal in ∼3-5% of the cases. The viral spike (S) coat protein engages the human angiotensin-converting enzyme2 (ACE2) cell surface protein to invade the host cell. The SARS-CoV-2 S-protein has acquired mutations that increase its affinity to human ACE2 by ∼10-15-fold compared to SARS-CoV S-protein, making it highly infectious. In this study, we assessed if ACE2 polymorphisms might alter host susceptibility to SARS-CoV-2 by affecting the ACE2 S-protein interaction. Our comprehensive analysis of several large genomic datasets that included over 290,000 samples representing >400 population groups identified multiple ACE2 protein-altering variants, some of which mapped to the S-protein-interacting ACE2 surface. Using recently reported structural data and a recent S-protein-interacting synthetic mutant map of ACE2, we have identified natural ACE2 variants that are predicted to alter the virus-host interaction and thereby potentially alter host susceptibility. In particular, human ACE2 variants S19P, I21V, E23K, K26R, T27A, N64K, T92I, Q102P and H378R are predicted to increase susceptibility. The T92I variant, part of a consensus NxS/T N-glycosylation motif, confirmed the role of N90 glycosylation in immunity from non-human CoVs. Other ACE2 variants K31R, N33I, H34R, E35K, E37K, D38V, Y50F, N51S, M62V, K68E, F72V, Y83H, G326E, G352V, D355N, Q388L and D509Y are putative protective variants predicted to show decreased binding to SARS-CoV-2 S-protein. Overall, ACE2 variants are rare, consistent with the lack of selection pressure given the recent history of SARS-CoV epidemics, however, are likely to play an important role in altering susceptibility to CoVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".