IgG Immunocomplexes Sensitize Human Monocytes for Inflammatory Hyperactivity via Transcriptomic and Epigenetic Reprogramming in Rheumatoid Arthritis
Bibliographic record
Abstract
Prevalence of circulating immunocomplexes (ICs) strongly correlates with rheumatoid arthritis (RA) in humans. Deposits of IgG-ICs are abundant in affected joints of patients, yet molecular mechanisms for the pathogenic roles of such ICs are not fully understood. In this study, we present evidence that IgG-ICs precipitated from RA sera sensitized human monocytes for a long-lasting inflammatory functional state, characterized by a strong TNF-α response to cellular proteins representing damage-associated molecular patterns and microbe-derived pathogen-associated molecular patterns. Importantly, plate-coated human IgG (a mimic of deposited IC without Ag restriction) exhibited a similarly robust ability of monocyte sensitization in vitro. The plate-coated human IgG-induced functional programming is accompanied by transcriptomic and epigenetic modification of various inflammatory cytokines and negative regulator genes. Moreover, macrophages freshly isolated from synovia of patients with RA, but not sera-negative arthropathy, displayed a signature gene expression profile highly similar to that of IC-sensitized human monocytes, indicative of historical priming events by IgG-ICs in vivo. Thus, the ability of IgG-ICs to drive sustainable functional sensitization/reprogramming of monocytes and macrophages toward inflammation may render them key players in the development of RA.
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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 | 0.000 |
| 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) | 0.001 | 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".