Mutant tumor necrosis factor receptor associated with tumor necrosis factor receptor–associated periodic syndrome is altered antigenically and is retained within patients' leukocytes
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of mutations in tumor necrosis factor receptor superfamily member 1A (TNFRSF1A) in TNFR-associated periodic syndrome (TRAPS) on the binding of anti-TNFRSF1A monoclonal antibodies (mAb), and to investigate the subcellular distribution of mutant versus wild-type (WT) TNFRSF1A in patients with TRAPS. METHODS: HEK 293 cells transfected with WT and/or mutant TNFRSF1A were used to investigate the interaction of anti-TNFRSF1A mAb with the WT and mutant proteins. Monoclonal antibodies that differentially bound to C33Y TNFRSF1A were used to investigate the distribution of WT and mutant TNFRSF1A in TRAPS patients with the C33Y mutation. RESULTS: We identified a mAb whose binding to TNFRSF1A was completely abolished by the C33Y or C52F TRAPS-associated mutations, whereas other mutations (T50M, C88Y, R92Q) had lesser effects on the binding of this mAb. A different mAb was found to bind efficiently to all of the mutant forms of TNFRSF1A examined as well as to the WT receptor. Exploitation of the differential binding properties of these mAb indicated that mutant (as distinct from WT) TNFRSF1A showed abnormal intracellular retention in the neutrophils of TRAPS patients with the C33Y mutation, with little if any expression of mutant TNFRSF1A on the cell surface or as soluble receptor in plasma. CONCLUSION: TRAPS-associated mutant TNFRSF1A has an antigenically altered structure and shows abnormal retention in the leukocytes of patients with TRAPS, which is consistent with previous findings from in vitro and transgenic model systems. This is consistent with a misfolded protein response contributing to the pathophysiology of TRAPS.
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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) | 0.001 | 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".