Elucidating Role of Ezh2 in Tolerogenic Function of NOD Bone Marrow-Derived Dendritic Cells Expressing Constitutively Active Stat5b.
Bibliographic record
Abstract
Tolerogenic dendritic cells are crucial to control development of autoreactive T cell responses and prevention of autoimmunity. We have reported that NOD.CD11cStat5b-CA transgenic mice expressing a constitutively active form of Stat5b under the control of CD11c promoter are protected from diabetes and that Stat5b-CA-expressing DCs are tolerogenic and halt ongoing diabetes in NOD mice. However, the molecular mechanisms by which Stat5b-CA modulates DC tolerogenic function is not fully understood. Here, we used bone marrow-derived DCs from NOD.CD11cStat5b-CA transgenic mice (Stat5b-CA.BMDC) and found that Stat5b-CA.BMDC displayed high levels of MHC class II, CD80, CD86, PD-L1 and PD-L2 and produced elevated amounts of TGFβ but low amounts of TNF and IL-23. Stat5b-CA.BMDCs upregulated Irf4 and downregulated Irf8 genes and protein expression and promoted CD11c+CD11b+ DC2 subset differentiation. Interestingly, we found that the histone methyltransferase Ezh2 interacted with Stat5b-CA complex that bound GAS sequences in the Irf8 enhancer whereas Ezh2 did not interact with GAS sequences in the case of the Irf4 promoter. Injection of Stat5b-CA.BMDCs to prediabetic NOD mice halted progression of islet inflammation and protected against diabetes. Importantly, inhibition of Ezh2 in tolerogenic Stat5b-CA.BMDCs reduced their ability to prevent diabetes development in NOD recipient mice. Taken together, our data suggest that the active form of Stat5b induces tolerogenic DC function by modulating IRF4 and IRF8 expression through recruitment of Ezh2 and highlight the fundamental role of Ezh2 in Stat5b-mediated induction of tolerogenic DCs function.
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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) | 0.000 | 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) | 0.001 | 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".