T cell protein tyrosine phosphatase attenuates T cell signaling to maintain tolerance in mice
Bibliographic record
Abstract
Many autoimmune diseases exhibit familial aggregation, indicating that they have genetic determinants. Single nucleotide polymorphisms in PTPN2, which encodes T cell protein tyrosine phosphatase (TCPTP), have been linked with the development of several autoimmune diseases, including type 1 diabetes and Crohn's disease. In this study, we have identified TCPTP as a key negative regulator of TCR signaling, which might explain the association of PTPN2 SNPs with autoimmune disease. We found that TCPTP dephosphorylates and inactivates Src family kinases to regulate T cell responses. Using T cell-specific TCPTP-deficient mice, we established that TCPTP attenuates T cell activation and proliferation in vitro and blunts antigen-induced responses in vivo. TCPTP deficiency lowered the in vivo threshold for TCR-dependent CD8(+) T cell proliferation. Consistent with this, T cell-specific TCPTP-deficient mice developed widespread inflammation and autoimmunity that was transferable to wild-type recipient mice by CD8(+) T cells alone. This autoimmunity was associated with increased serum levels of proinflammatory cytokines and anti-nuclear antibodies, T cell infiltrates in non-lymphoid tissues, and liver disease. These data indicate that TCPTP is a critical negative regulator of TCR signaling that sets the threshold for TCR-induced naive T cell responses to prevent autoimmune and inflammatory disorders arising.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".