Ten-Eleven Translocation (TET) Enzymes Modulate the Activation of Dendritic Cells in Allergic Rhinitis
Bibliographic record
Abstract
Background: The prevalence of allergic rhinitis (AR) has increased in recent decades. Accumulating evidence indicates that aberrant DNA demethylation modulated by enzymes of ten-eleven translocation (TET) promotes an imbalanced immune response. Objective: This study aimed to explore TETs on the activation of dendritic cells (DCs) in AR. Methods: The levels of TETs in peripheral blood mononuclear cells (PBMCs), peripheral myeloid DCs (mDCs) and plasmacytoid DCs (pDCs) from house dust mite (HDM)-sensitive AR patients and healthy volunteers (HC) were evaluated by qPCR and flow cytometry. The levels of 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) in PBMCs were determined by DNA-5hmC and DNA-5mC ELISA. The major HDM allergen, Der p 1, was used to stimulate atopic monocyte-derived DCs (moDCs) to assess its effect on the TETs. TET1 knockdown effect on the activation of non-atopic and atopic moDCs was investigated. Results: TETs and global 5hmC were higher in PBMCs of AR than HC. So was TET1 in peripheral mDCs and pDCs of AR. In vitro, TET1 in atopic moDCs was significantly decreased by allergen challenge. Knockdown of TET1 in moDCs tended to induce CD86, CD80, and CD40 in AR but not in HC. TET1-knockdown moDCs significantly decreased the differentiation of activated regulatory T cells in AR. Conclusion: DCs from AR patients express higher TET1 and are susceptible to be activated by TET1 decrease, which can be triggered by allergen challenge. Collectively, this suggests a role for TET in the pathogenesis of AR and potential for novel TET1-related, preventive, and therapeutic targets.
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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".