RelB-deficient autoinflammatory pathology presents as interferonopathy, but in mice is interferon-independent
Bibliographic record
Abstract
BACKGROUND: Autoimmune diseases are leading causes of ill health and morbidity and have diverse etiology. Two signaling pathways are key drivers of autoimmune pathology, interferon and nuclear factor-κB (NF-κB)/RelA, defining the 2 broad labels of interferonopathies and relopathies. Prior work has established that genetic loss of function of the NF-κB subunit RelB leads to autoimmune and inflammatory pathology in mice and humans. OBJECTIVE: We sought to characterize RelB-deficient autoimmunity by unbiased profiling of the responses of immune sentinel cells to stimulus and to determine the functional role of dysregulated gene programs in the RelB-deficient pathology. METHODS: Transcriptomic profiling was performed on fibroblasts and dendritic cells derived from patients with RelB deficiency and knockout mice, and transcriptomic responses and pathology were assessed in mice deficient in both RelB and the type I interferon receptor. RESULTS: We found that loss of RelB in patient-derived fibroblasts and mouse myeloid cells results in elevated induction of hundreds of interferon-stimulated genes. Removing hyperexpression of the interferon-stimulated gene program did not ameliorate the autoimmune pathology of RelB knockout mice. Instead, we found that RelB suppresses a different set of inflammatory response genes in a manner that is independent of interferon signaling but associated with NF-κB binding motifs. CONCLUSION: Although transcriptomic profiling would describe RelB-deficient autoimmune disease as an interferonopathy, the genetic evidence indicates that the pathology in mice is interferon-independent.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".