CD39+Foxp3+ Regulatory T Cells Suppress Pathogenic Th17 Cells and Are Impaired in Multiple Sclerosis
Bibliographic record
Abstract
Despite the fact that CD4(+)CD25(+)Foxp3(+) regulatory T cells (Treg cells) play a central role in maintaining self-tolerance and that IL-17-producing CD4(+) T cells (Th17 cells) are pathogenic in many autoimmune diseases, evidence to date has indicated that Th17 cells are resistant to suppression by human Foxp3(+) Treg cells. It was recently demonstrated that CD39, an ectonucleotidase which hydrolyzes ATP, is expressed on a subset of human natural Treg cells. We found that although both CD4(+)CD25(high)CD39(+) and CD4(+)CD25(high)CD39(-) T cells suppressed proliferation and IFN-gamma production by responder T cells, only the CD4(+)CD25(high)CD39(+), which were predominantly FoxP3(+), suppressed IL-17 production, whereas CD4(+)CD25(high)CD39(-) T cells produced IL-17. An examination of T cells from multiple sclerosis patients revealed a normal frequency of CD4(+)CD25(+)CD127(low)FoxP3(+), but interestingly a deficit in the relative frequency and the suppressive function of CD4(+)CD25(+)CD127(low)FoxP3(+)CD39(+) Treg cells. The mechanism of suppression by CD39(+) Treg cells appears to require cell contact and can be duplicated by adenosine, which is produced from ATP by the ectonucleotidases CD39 and CD73. Our findings suggest that CD4(+)CD25(+)Foxp3(+)CD39(+) Treg cells play an important role in constraining pathogenic Th17 cells and their reduction in multiple sclerosis patients might lead to an inability to control IL-17 mediated autoimmune inflammation.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".