Shifting FcγRIIA-ITAM from activation to inhibitory configuration ameliorates arthritis
Bibliographic record
Abstract
Rheumatoid arthritis-associated (RA-associated) inflammation is mediated through the interaction between RA IgG immune complexes and IgG Fc receptors on immune cells. Polymorphisms within the gene encoding the human IgG Fc receptor IIA (hFcγRIIA) are associated with an increased risk of developing RA. Within the hFcγRIIA intracytoplasmic domain, there are 2 conserved tyrosine residues arranged in a noncanonical immunoreceptor tyrosine-based activation motif (ITAM). Here, we reveal that inhibitory engagement of the hFcγRIIA ITAM either with anti-hFcγRII F(ab')2 fragments or intravenous hIgG (IVIg) ameliorates RA-associated inflammation, and this effect was characteristic of previously described inhibitory ITAM (ITAMi) signaling for hFcαRI and hFcγRIIIA, but only involves a single tyrosine. In hFcγRIIA-expressing mice, arthritis induction was inhibited following hFcγRIIA engagement. Moreover, hFcγRIIA ITAMi-signaling reduced ROS and inflammatory cytokine production through inhibition of guanine nucleotide exchange factor VAV-1 and IL-1 receptor-associated kinase 1 (IRAK-1), respectively. ITAMi signaling was mediated by tyrosine 304 (Y304) within the hFcγRIIA ITAM, which was required for recruitment of tyrosine kinase SYK and tyrosine phosphatase SHP-1. Anti-hFcγRII F(ab')2 treatment of inflammatory synovial cells from RA patients inhibited ROS production through induction of ITAMi signaling. These data suggest that shifting constitutive hFcγRIIA-mediated activation to ITAMi signaling could ameliorate RA-associated inflammation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".