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Record W4411204376 · doi:10.2337/db24-1122

Autoreactive T Cells and Cytokine Stress Drive β-Cell Senescence Entry and Accumulation in Type 1 Diabetes

2025· article· en· W4411204376 on OpenAlexafffund
Jasmine Pipella, Roozbeh Akbari Motlagh, Nayara Rampazzo Morelli, Peter J. Thompson

Bibliographic record

VenueDiabetes · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersInstitute of Nutrition, Metabolism and DiabetesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaManitoba Medical Service FoundationResearch ManitobaBeckman Research Institute, City of Hope
KeywordsSenescenceCytokineImmunologyCell biologyDiabetes mellitusBiologyType 2 diabetesMedicineEndocrinology

Abstract

fetched live from OpenAlex

Type 1 diabetes (T1D) results from a complex dialogue between the immune system and islets characterized by T cell–mediated autoimmune destruction of pancreatic β-cells. In this dialogue, β-cell stress responses have emerged as drug targets for slowing T1D progression, including a subpopulation of senescent β-cells that accumulate during T1D in humans and nonobese diabetic (NOD) mice. However, the mechanisms that cause β-cells to activate senescence in T1D are not known. Here, we show that β-cell senescence entry and accumulation are driven by damage inflicted by autoreactive CD4+ and CD8+ T cells in the late presymptomatic stages of T1D. Genetically immune-deficient NOD strains showed reduced frequencies of senescent β-cells, and adoptive transfer of diabetogenic splenocytes was sufficient to activate β-cell senescence in immune-deficient mice. Modulation of antigen-specific CD4+ T cells using an intermittent paradigm of CD3 antibody in immune-competent wild-type NOD mice led to reduced senescence, but did not affect other responses, concomitantly with slowing disease progression. Depletion of CD4+ or CD8+ T cells phenocopied the effect of CD3 antibody on β-cell senescence. CD3 antibody and senolytic ABT-199 had a complementary effect in reducing senescent β-cell burden, consistent with these agents acting in different pathways. Mechanistically, exposure to T1D-related inflammatory cytokines recapitulated stable phenotypes of senescence in human islets and β-cells. Our results demonstrate that β-cell senescence is a stress response that depends on progressive autoreactive CD4+ and CD8+ T-cell damage in T1D and suggests a novel mechanism of action for CD3 immunotherapy in limiting the accumulation of senescent β-cells. ARTICLE HIGHLIGHTS Senescence is a β-cell stress response in type 1 diabetes (T1D), the origins of which are not understood. We wanted to determine the role of the T cell–mediated autoimmune process in β-cell senescence during T1D. In the nonobese diabetic mouse model, β-cell senescence largely depended on damage inflicted by autoreactive CD4+ and CD8+ T cells during the development of T1D. Chronic exposure to sublethal doses of proinflammatory cytokines associated with the diabetogenic process was sufficient to elicit stable senescence phenotypes in human islets in culture. Our findings suggest that autoreactive T cells trigger not only β-cell death but also β-cell senescence, potentially via cytokine-dependent mechanisms in T1D. This finding has implications for understanding the mechanisms of action and beneficial impacts of immunotherapy using CD3 antibodies in T1D.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.228
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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