Type I IFN‐mediated Inhibition of Inflammatory Th cell Responses by a Subset of SLE Patient Sera
Bibliographic record
Abstract
Inflammatory, IFN‐γ‐secreting Th1 cells are implicated in mediating serious forms of systemic lupus erythematosus (SLE), including nephritis and CNS lupus. In SLE, triggering of plasmacytoid DC (pDC) Toll‐like receptors (TLRs) by circulating anti‐nucleic acid‐containing autoimmune complexes stimulates pDC secretion of high levels of type I interferon (IFN) (IFN‐α/β). Study of both human and murine lupus disease strongly implicates these IFNs as key disease effectors. However, the role of pDC‐derived type I IFN in regulating the function of inflammatory Th cells in SLE is unknown. We and others have shown that, although classically considered to promote Th1 cell mediated inflammation, type I IFN can also function as a potent inhibitor of both inflammatory Th1 and Th17 responses. We employed the pan‐type I IFN neutralizing reagent, B18R, to investigate how type I IFN regulate Th cell responses in SLE. Recent observations indicate a subset of SLE patient sera that inhibits IFN‐γ secretion by superantigen‐stimulated healthy donor PBMC in a type I IFN‐dependent manner. This effect positively correlates with PBMC secretion of the Th cell inflammatory cytokines, LT and IL‐17, while negatively correlating with IL‐10 secretion. Remarkably, the ability of SLE patient serum to inhibit IFN‐γ secretion in a type I IFN dependent manner correlates with positive serum Sm and RNP titers. Our findings suggest that in SLE patients with significant serum type I IFN activity, type I IFN blockade may exacerbate Th cell‐mediated autoimmune 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".