Thymic Stromal Lymphopoietin and Thymic Stromal Lymphopoietin–Conditioned Dendritic Cells Induce Regulatory T-Cell Differentiation and Protection of NOD Mice Against Diabetes
Bibliographic record
Abstract
OBJECTIVE: Autoimmune diabetes in the nonobese diabetic (NOD) mouse model results from a breakdown of T-cell tolerance caused by impaired tolerogenic dendritic cell development and regulatory T-cell (Treg) differentiation. Re-establishment of the Treg pool has been shown to confer T-cell tolerance and protection against diabetes. Here, we have investigated whether murine thymic stromal lymphopoietin (TSLP) re-established tolerogenic function of dendritic cells and induced differentiation and/or expansion of Tregs in NOD mice and protection against diabetes. RESEARCH DESIGN AND METHODS: We examined the phenotype of TSLP-conditioned bone marrow dendritic cells (TSLP-DCs) of NOD mice and their functions to induce noninflammatory Th2 response and differentiation of Tregs. The functional relevance of TSLP and TSLP-DCs to development of diabetes was also tested. RESULTS: Our results showed that bone marrow dendritic cells of NOD mice cultured in the presence of TSLP acquired signatures of tolerogenic dendritic cells, such as an absence of production of pro-inflammatory cytokines and a decreased expression of dendritic cell costimulatory molecules (CD80, CD86, and major histocompatibility complex class II) compared with LPS-treated dendritic cells. Furthermore, TSLP-DCs promoted noninflammatory Th2 response and induced the conversion of naïve T-cells into functional CD4(+)CD25(+)Foxp3(+) Tregs. We further showed that subcutaneous injections of TSLP for 6 days or a single intravenous injection of TSLP-DCs protected NOD mice against diabetes. CONCLUSIONS: Our study demonstrates that TSLP re-established a tolerogenic immune response in NOD mice and protects from diabetes, suggesting that TSLP may have a therapeutic potential for the treatment of type 1 diabetes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".