Autoantibodies targeting angiotensin‐converting enzyme 2 are prevalent and not induced by <scp>SARS</scp> ‐ <scp>CoV</scp> ‐2 infection
Bibliographic record
Abstract
Clinical outcomes resulting from SARS-CoV-2 infection vary widely, ranging from asymptomatic cases to the development of mild to severe respiratory illness, and in some instances, chronic lingering disease and mortality. The underlying biological mechanisms driving this wide spectrum of pathogenicity among certain individuals and demographics remain elusive. Autoantibodies have emerged as potential contributors to the severity of COVID-19. Although preliminary reports have suggested the induction of antibodies targeting Angiotensin-Converting Enzyme II (ACE2) post-infection, this assertion lacks confirmation in large-scale studies. In this study, our objective is to comprehensively characterize and quantify the prevalence and expression levels of autoantibodies directed against ACE2 in a sizable cohort (n = 464). Our findings reveal that ACE2-reactive IgM antibodies are the most prevalent, with an overall seroprevalence of 18.8%, followed by IgG at 10.3% and IgA at 6.3%. Longitudinal analysis of individuals with multiple blood draws showed stable ACE2 IgG and IgA levels over time. Upon stratifying individuals based on molecular testing for SARS-CoV-2 or serological evidence of past infection, no significant differences were observed between groups. Functional assessment of ACE2 autoantibodies demonstrated that they are non-neutralizing and failed to inhibit spike-ACE2 interaction or affect the enzymatic activity of ACE2. Our results highlight that ACE2 autoantibodies are prevalent in the general population and were not induced by SARS-CoV-2 infection in our cohort. Notably, we found no substantiated evidence supporting a direct role for ACE2 autoantibodies in SARS-CoV-2 pathogenesis.
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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".