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Impaired Neutrophil Extracellular Trap (NET) Degradation in Rheumatoid Arthritis (RA) and Preclinical RA Is Mediated by Anti-NET Antibodies

2025· article· en· W4411846629 on OpenAlexaffvenue
Jeba Atkia Maisha, А. В. Семененко, Jun Kim, Mario Navarrete, Xiaobo Meng, Hani El‐Gabalawy, Liam J. O’Neil

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsUniversity of WinnipegHealth Sciences CentreManitoba HealthCalgary Laboratory ServicesUniversity of Manitoba
Fundersnot available
KeywordsNeutrophil extracellular trapsMedicineRheumatoid arthritisImmunologyAntibodyArthritisTrap (plumbing)Inflammation

Abstract

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Objectives Rheumatoid Arthritis (RA), a systemic autoimmune arthritis, begins after a prolonged preclinical phase which is marked by the development of RA antibodies, typically against citrullinated proteins (ACPA). The production of neutrophil extracellular traps (NETs), a form of cell death undertaken by neutrophils, leads to the extrusion of self-DNA decorated in citrullinated proteins, predominantly histones and proteases.[1] Increased NET abundance has been observed in the blood and at mucosal surfaces in established and preclinical RA, but it is not known if dysregulated NET production or impaired NET degradation, or both, is involved. This study aimed to evaluate NET degradation in a well-established longitudinal cohort of first-degree relatives (FDR) of RA patients, individuals at-risk to develop future RA. Methods Total NET complexes (citrullinated Histone-3-dsDNA complexes) were measured in 10% plasma using a custom in-house ELISA. A plate-based NET degradation assay was performed based on past publications. In brief, neutrophils (1.0×106 cells) were cultured in the presence of A23187 (1uM) to form NETs, which are detected by fluorescence (SYTOX green). Data was used to calculate the % of NETs degraded in quadruplicates. Poor/good NET degraders were determined using group sample median. Anti-NET antibodies were determined by ELISA, using plates coated with isolated NET complexes. DNAse-1 activity was assessed in plasma via fluorescence (abcam). Results Using samples from RA (n=30), ACPA- FDR (n=16), ACPA+ FDR (n=16) we observed higher NET complexes (citH3-dsDNA) in the plasma of ACPA+ FDR (p<0.0001) and RA patients (p<0.0001) compared to ACPA- FDR. NET degradation (using healthy control plasma as positive control, n=10), was impaired in ACPA+ FDR (p=0.005) and RA (p=0.001), but not in ACPA- FDR. There was no association between NET complexes and NET degradation capacity. Anti-NET antibodies were detected in ACPA+ FDR (p<0.0001) and RA (p<0.0001) and anti-NET antibody levels were higher in individuals who were labeled as poor NET degraders (p=0.04). DNAse-1 activity was no different between any of the sample groups and not reduced in samples considered poor degraders. Total IgG was isolated from poor NET degraders with high anti-NET IgG (n=3). Pre-incubation of IgG (10% total IgG) reduced the degradation of NETs by HC plasma (p=0.003) (Figure). Conclusion We show for the first time that NET degradation is impaired in RA and preclinical RA and that this observation is not related to reduced DNAse-1 activity. Our data suggests that anti-NET antibodies, likely ACPA, reduce NET degradation by binding to protein-DNA complexes, potentially shielding these complexes from local DNAses. [1.] O’Neil LJ. Science Adv 2020;6(44):eabd2688. Best Abstract by a Post-Graduate Research Trainee Award

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0020.001

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 designBench or experimental
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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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