Inactive disease in lupus patients is linked to autoantibodies to type-I interferons that normalize blood IFNα and B cell subsets
Bibliographic record
Abstract
Abstract Systemic Lupus Erythematosus (SLE) is characterized by a prominent increase in expression of type-I interferon (IFN)-regulated genes in 50-75% of patients. Here we investigate the presence of autoantibodies (auto-Abs) against type I IFN in SLE patients and their possible role in controlling disease severity. We report that out of 491 SLE patients, 66 had detectable anti-IFNα-auto-Abs. The presence of neutralizing anti-IFNα-auto-Abs correlates with lower levels of circulating IFNα protein, inhibition of IFN down-stream signalling molecules and gene signatures and with an inactive global disease score. Previously reported B cell frequency abnormalities, found to be involved in SLE pathogenesis, including increased levels of immature, double negative and plasmablast B cell populations were partially normalized in patients with neutralising anti-IFNα-auto-Abs compared to other patient groups. We also show that sera from SLE patients with neutralising anti-IFNα-auto-Abs biases in vitro B cell differentiation towards classical memory phenotype, while sera from patients without anti-IFNα-Abs drives plasmablasts differentiation. Our findings support a role for neutralising anti-IFNα-auto-Abs in controlling SLE pathogenesis and highlight their potential efficacy as novel therapy.
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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) | 0.004 | 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".