TRAF1 in regulation of T cell responses and inflammation
Bibliographic record
Abstract
Abstract TNFR associated factor one (TRAF1) is a signaling adaptor that links a subset of tumor necrosis factor receptors to downstream survival signaling through NF-kB and MAP kinases. TRAF1 is critical for survival signaling downstream of 4-1BB but its role downstream of TNFR2 has been controversial. Genome-wide association studies have identified a single nucleotide polymorphism (SNP), rs3761847, in an intronic segment of the TRAF1 gene as contributing to susceptibility to and severity of Rheumatoid arthritis. However, the effect of this SNP on TRAF1 expression or T cell biology have not been examined to date. To avoid the complications of chronic inflammation, we chose to examine the effect of this common SNP on TRAF1 levels and function in healthy donors. Samples were collected from 80 healthy donors and TRAF1 levels and cytokines measured by intracellular flow cytometry on T cells from resting and anti-CD3/CD28 treated PBMC. Individuals homozygous for the risk polymorphism (GG) exhibited significantly lower TRAF1 protein levels in T cells than donors with the disease resistant (AA) genotype. However, the frequency of naïve, memory and regulatory T cell subsets was indistinguishable between AA and GG donors. T cells from the disease susceptible donor T cells produced less IFNγ, TNFα and IL-2 upon stimulation. Work is in progress to investigate how TRAF1 specifically impacts TNFR2 signaling in T cells. Taken together, our data suggest that the TRAF1 SNP results in lower TRAF1 protein and lower cytokine production by T cells, supporting the evidence that TRAF1 has a net positive role in T cell cytokine production. However, these data also present a paradox of how lower TRAF1 protein levels contribute to increased inflammatory disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".