Type I Interferon-Dependent and -Independent TRIF Signaling are Required for Autoantibody Generation in an Induced Model of Systemic Lupus Erythematosus
Bibliographic record
Abstract
Abstract Systemic lupus erythematosus (SLE) is an autoimmune condition characterized by the production of autoantibodies to cellular and nuclear self-antigens, with damage to multiple organs. Immunization with the SLE autoantigen β2-glycoprotein I (β2GPI) and TLR4 ligand lipopolysaccharide (LPS) induces a murine model of SLE characterized by the generation of autoantibodies to multiple SLE antigens. LPS induces both inflammatory cytokines and type I interferons (IFNs) by signaling through the adaptor proteins MyD88 and TRIF, respectively. We investigated the mechanisms of LPS-dependent signaling on the induction of murine SLE following β2GPI/LPS immunization. Immunization of mice deficient in MyD88 or TRIF with β2GPI/LPS revealed that LPS-dependent TRIF signaling preferentially promoted autoantibody production in this model, compared to MyD88 signaling. Furthermore, SLE-specific autoantibody production was induced by TLR3 or TLR4 agonists, in combination with β2GPI, but not by agonists of TRIF-independent TLRs. RNA-sequencing of dendritic cells and macrophages isolated from wildtype (WT) and TRIF-deficient mice immunized with β2GPI/LPS revealed differential expression of both type I IFN-dependent and -independent genes. Moreover, type I IFN receptor (IFNaR)-deficient mice immunized with β2GPI/LPS showed diminished autoantibody production compared to WT mice, but to a lesser extent than TRIF-deficient mice. We conclude that LPS-dependent TRIF signaling is required for the generation of autoantibody production in our induced SLE model, both through type I IFN-dependent and - independent effects. Supported by grants from CIHR (????)
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".