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Record W4412572427 · doi:10.1128/mbio.01735-25

ACE-2-like enzymatic activity in COVID-19 convalescents with persistent pulmonary symptoms associated with immunoglobulin

2025· article· en· W4412572427 on OpenAlexaff
Yufeng Song, Frances Mehl, Lyndsey M. Muehling, Glenda Canderan, Jie Sun, Michael T. Yin, Sarah J. Ratcliffe, Jeffrey M. Wilson, Alexandra Kadl, Judith A. Woodfolk, Steven L. Zeichner

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsColumbia College
FundersNational Institute of Allergy and Infectious DiseasesBen and Catherine Ivy FoundationAlpha-1 Foundation
KeywordsAntibodyCoronavirus disease 2019 (COVID-19)Angiotensin-converting enzyme 2PathogenesisImmunologyMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ChemistryVirologyInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Many difficult-to-understand clinical features characterize COVID-19 and post-acute sequelae of COVID-19 (PASC or long COVID [LC]). These can include blood pressure instability, hyperinflammation, coagulopathies, and neuropsychiatric complaints. The pathogenesis of these features remains unclear. The SARS-CoV-2 Spike protein receptor-binding domain (RBD) binds angiotensin converting enzyme 2 (ACE2) on the surface of host cells to initiate infection. We hypothesized that some people convalescing from COVID-19 may produce anti-RBD antibodies that resemble ACE2 sufficiently to have ACE2-like catalytic activity, that is, they are ACE2-like proteolytic abzymes that may help mediate the pathogenesis of COVID-19 and LC. In previous work, we showed that some people with acute COVID-19 had immunoglobulin-associated ACE2-like proteolytic activity, suggesting that some people with COVID-19 indeed produced ACE2-like abzymes. However, it remained unknown whether ACE2-like abzymes were seen only in acute COVID-19 or whether ACE2-like abzymes could also be identified in people convalescing from COVID-19. Here, we show that some people convalescing from COVID-19 attending a clinic for people with persistent pulmonary symptoms also have ACE2-like abzymes and that the presence of ACE2-like catalytic activity correlates with alterations in blood pressure in an exercise test. IMPORTANCE: Patients who have had COVID-19 can sometimes have troublesome symptoms, termed post-acute sequelae of COVID-19 (PASC) or long COVID (LC), which can include problems with blood pressure regulation, gastrointestinal problems, inflammation, blood clotting, and symptoms like "brain fog." The proximate causes for these problems are not known, which makes these problems difficult to treat definitively. We previously found that some acute COVID-19 patients make antibodies against SARS-CoV-2, the virus that causes COVID-19, that act like an enzyme, angiotensin converting enzyme 2 (ACE2). ACE2 normally helps regulate blood pressure and serves as the receptor for SARS-CoV-2 in the body. We show that patients convalescing from COVID-19 also make antibodies that act like ACE2 and that the presence of those antibodies correlates with problems in blood pressure regulation. The findings provide a new opening to potentially understanding the causes of LC, and so provide direction for the development of new treatments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.277
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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