Potent Induction of IFN-α and Chemokines by Autoantibodies in the Cerebrospinal Fluid of Patients with Neuropsychiatric Lupus
Bibliographic record
Abstract
Neuropsychiatric disease in systemic lupus erythematosus (NPSLE) is a poorly understood, but potentially fatal, disease manifestation. A pathogenetic role for autoantibodies is suspected, but the mechanism is unclear. Since immune complexes in SLE can stimulate IFN-alpha and there is strong evidence in humans and in mice that IFN-alpha can cause neuropsychiatric manifestations, we asked whether NPSLE patient serum and/or cerebrospinal fluid (CSF) contain abnormally high IFN-alpha-inducing activity. In a bioassay containing plasmacytoid dendritic cells and a source of Ag, NPSLE CSF induced significantly higher IFN-alpha compared with CSF from patients with multiple sclerosis or other autoimmune disease controls. When normalized for IgG concentration, NPSLE CSF was 800-fold more potent at inducing IFN-alpha compared with paired serum due to inhibitors present in serum. Analysis of Ig-deficient patient serum, depletion of IgG from normal serum, as well as addition of purified IgG to NPSLE CSF and serum in the bioassays revealed that one inhibitor was contained within the IgG fraction itself. In addition to IFN-alpha, immune complexes formed by CSF autoantibodies produced significantly increased levels of IFN-gamma-inducible protein 10 (IP-10/CXCL), IL-8, and MCP-1, all of which have been reported to be elevated in CSF from NPSLE patients. Taken together, these findings are consistent with a two-step model of NPSLE whereby CSF autoantibodies bind to Ags released by neurocytotoxic Abs or other brain cell injury, and the resulting immune complexes stimulate IFN-alpha and proinflammatory cytokines and chemokines.
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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) | 0.001 | 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".