Candidate Genes in Patients with Autoinflammatory Syndrome Resembling Tumor Necrosis Factor Receptor-associated Periodic Syndrome Without Mutations in the <i>TNFRSF1A</i> Gene
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor (TNF) receptor-associated periodic syndrome (TRAPS) is an autosomal-dominant multisystemic autoinflammatory condition. Patients display different mutations of the TNF receptor superfamily 1A gene (TNFRSF1A), coding for a nearly ubiquitous TNF receptor (TNFR1). No TNFRSF1A mutation has been identified in a proportion of patients with TRAPS-like phenotype. METHODS: We investigated mechanisms downregulating the TNF-induced inflammatory response such as (1) receptor shedding, producing a secreted form acting as a TNF inhibitor; (2) receptor internalization with subsequent induction of apoptosis; and (3) negative regulation of nuclear factor-κB (NF-κB) transcription. We analyzed the sequence of genes known to play a pivotal role in these pathways, in 5 patients with TRAPS symptoms and showing shedding and/or apoptosis defects, but without mutations of the TNFRSF1A gene. RESULTS: Sequence analysis of 3 genes involved in TNFR1 shedding (ERAP1, NUCB2, RBMX) and 3 genes involved in negative regulation of NF-κB signaling (TNFAIP3, CARP-2) or NF-κB transcription (ZFP36) revealed only a few unreported variants, apparently neutral. CONCLUSION: Our study rules out any involvement in the pathogenesis of TRAPS of some of the genes known to regulate TNFR1 shedding and TNF-induced NF-κB signaling and transcription. Gene(s) responsible for TRAPS-like syndrome remain to be investigated among currently unidentified genes likely involved in these pathways, or by applying the genome-wide function-free sequencing approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".