Genetic variability of human angiotensin‐converting enzyme 2 (hACE2) among various ethnic populations
Bibliographic record
Abstract
BACKGROUND: There appears to be large regional variation for susceptibility, severity, and mortality for COVID-19 infections. Numerous potential factors could explain the wide variability in the number of infections and death among the countries. We examined genetic differences in the human angiotensin-converting enzyme 2 (hACE2) gene, as its receptor serves as a cellular entry for SARS-CoV-2. At present, there is a paucity of data regarding the differences for ACE2 polymorphisms and expression levels between ethnicities. METHODS: We compared the allele frequency of mutations between European and East Asians. Molecular dynamic simulation were performed to investigate the influences of significant mutant on protein structure. The binding free energies were calculated between S protein and hACE2. We also examined hACE2 gene expression in eight global populations from HapMap3. RESULTS: Four missense mutations showed significant minor allele frequency difference between Asians and Caucasians. Molecular dynamic demonstrated that two of these variants (K26R and I468V) may affect binding characteristics between S protein of the virus and hACE2 receptor. We also noted marginal differences in gene expression for some populations in HapMap3 as compared to the Chinese population. CONCLUSION: Our studies reveal subtle changes in the genetics of hACE2 between human populations, but the magnitude of the difference was small and the significance is not clear in the absence of further in vitro and functional studies.
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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 | 0.000 |
| 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) | 0.002 | 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".