Suppressor of Cytokine Signaling-1 Overexpression Protects Pancreatic β Cells from CD8+ T Cell-Mediated Autoimmune Destruction
Bibliographic record
Abstract
In type 1 diabetes, cytokine action on beta cells potentially contributes to beta cell destruction by direct cytotoxicity, inducing Fas expression, and up-regulating class I MHC and chemokine expression to increase immune recognition. To simultaneously block beta cell responsiveness to multiple cytokines, we overexpressed suppressor of cytokine signaling-1 (SOCS-1). This completely prevented progression to diabetes in CD8(+) TCR transgenic nonobese diabetic (NOD) 8.3 mice without affecting pancreas infiltration and partially prevented diabetes in nontransgenic NOD mice. SOCS-1 appeared to protect at least in part by inhibiting TNF- and IFN-gamma-induced Fas expression on beta cells. Fas expression was up-regulated on beta cells in vivo in prediabetic NOD8.3 mice, and this was inhibited by SOCS-1. Additionally, IFN-gamma-induced class I MHC up-regulation and TNF- and IFN-gamma-induced IL-15 expression by beta cells were inhibited by SOCS-1, which correlated with suppressed 8.3 T cell proliferation in vitro. Despite this, 8.3 T cell priming in vivo appeared unaffected. Therefore, blocking beta cell responses to cytokines impairs recognition by CD8(+) T cells and blocks multiple mechanisms of beta cell destruction, but does not prevent T cell priming and recruitment to the islets. Our findings suggest that increasing SOCS-1 expression may be useful as a strategy to block CD8(+) T cell-mediated type 1 diabetes as well as to more generally prevent cytokine-dependent tissue destruction in inflammatory diseases.
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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 | 0.000 | 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 | 0.001 |
| 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".