Reduced tumor necrosis factor signaling in primary human fibroblasts containing a tumor necrosis factor receptor superfamily 1A mutant
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor receptor-associated periodic syndrome (TRAPS) is an autoinflammatory syndrome associated with mutations in the gene that encodes tumor necrosis factor receptor superfamily 1A (TNFRSF1A). The purpose of this study was to describe a novel TNFRSF1A mutation (C43S) in a patient with TRAPS and to examine the effects of this TNFRSF1A mutation on tumor necrosis factor alpha (TNFalpha)-induced signaling in a patient-derived primary dermal fibroblast line. METHODS: TNFRSF1A shedding from neutrophils was measured by flow cytometry and enzyme-linked immunosorbent assay (ELISA). Primary dermal fibroblast lines were established from the patient with the C43S TRAPS mutation and from healthy volunteers. Activation of NF-kappaB and activator protein 1 (AP-1) was evaluated by electrophoretic mobility shift assays. Cytokine production was measured by ELISA. Cell viability was measured by alamar blue assay. Apoptosis was measured by caspase 3 assay in the fibroblasts and by annexin V assay in peripheral blood mononuclear cells. RESULTS: Activation-induced shedding of the TNFRSF1A from neutrophils was not altered by the C43S TRAPS mutation. TNFalpha-induced activation of NF-kappaB and AP-1 was decreased in the primary dermal fibroblasts with the C43S TNFRSF1A mutation. Nevertheless, the C43S TRAPS fibroblasts were capable of producing interleukin-6 (IL-6) and IL-8 in response to TNFalpha. However, TNFalpha-induced cell death and apoptosis were significantly decreased in the samples from the patient with the C43S TRAPS mutation. CONCLUSION: The C43S TNFRSF1A mutation results in decreased TNFalpha-induced nuclear signaling and apoptosis. Our data suggest a new hypothesis, in that the C43S TRAPS mutation may cause the inflammatory phenotype by increasing resistance to TNFalpha-induced apoptosis.
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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) | 0.002 | 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".