Differential effect of DJ-1/PARK7 on development of natural and induced regulatory T cells
Bibliographic record
Abstract
Regulatory T cells (Tregs) are essential for maintaining an effective immune tolerance and a homeostatic balance of various other immune cells. To manipulate the immune response during infections and autoimmune disorders, it is essential to know which genes or key molecules are involved in the development of Tregs. Transcription factor Foxp3 is required for the development of Tregs and governs most of the suppressive functions of these cells. Inhibited PI3K/AKT/mTOR signalling is critical for Foxp3 stability. Previous studies have suggested that DJ-1 or PARK7 protein is a positive regulator of the PI3K/AKT/mTOR pathway by negatively regulating the activity of PTEN. Thus, we hypothesised that a lack of DJ-1 could promote the development of Tregs. As a result, loss of DJ-1 decreased the total CD4(+) T cell numbers but increased the fraction of thymic and peripheral nTregs. In contrast, Foxp3 generation was not augmented following differentiation of DJ-1-deficient naïve CD4(+) T cells. DJ-1-deficient-iTregs were imperfect in replication, proliferation and more prone to cell death. Furthermore, DJ-1 deficient iTregs were less sensitive to pSmad2 and pStat5 signalling but had activated AKT/mTOR signalling. These observations reveal an unexpected differential role of DJ-1 in the development of nTregs and iTregs.
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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".