Differences in IgG Sialylation Distinguish Asymptomatic From Symptomatic Antinuclear Antibody–Positive Individuals
Bibliographic record
Abstract
Objective The transition from asymptomatic antinuclear antibody (ANA) positivity to systemic autoimmune rheumatic disease (SARD) is associated with increased production of proinflammatory factors such as tumor necrosis factor α (TNFα). Here we investigate whether the relative absence of inflammation in asymptomatic ANA + individuals (ANA + NS) results from a lack of circulating immune complexes (ICs) or from changes in the characteristics of the IgG autoantibodies produced. Methods Flow cytometry was used to characterize circulating microparticles (MPs) in 18 healthy controls (ANA − HC), 31 ANA + NS, and 51 symptomatic ANA + patients. Differences in the ability of the total MPs, purified IgG‐coated MPs, or aggregated IgG to elicit inflammation were investigated by coculture with ANA − HC monocytes or monocyte‐derived dendritic cells (moDCs), measuring cytokines in the supernatants. IgG sialylation was quantified by enzyme‐linked immunosorbent assay or lectin blotting using Sambucus nigra agglutinin, a sialic acid‐binding lectin. Results All ANA + individuals had higher numbers of total and IgG‐coated MPs than ANA − HC. IgG sialylation was significantly reduced in individuals with SARD compared to ANA + NS and ANA − HC and in ANA + NS who clinically progressed in the next two years compared to those who did not. moDCs stimulated with IgG‐coated MPs or aggregated IgG from patients with systemic lupus erythematosus produced significantly more TNFα than those from ANA + NS. The levels of TNFα produced in culture supernatants and serum demonstrated a negative correlation with IgG sialylation. Conclusion The absence of proinflammatory factors in ANA + NS does not result from a lack of circulating ICs but instead may reflect differences in the extent of IgG sialylation in the ICs from ANA + NS as compared to those with SARD.
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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 | 0.001 | 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".