Mechanisms of human FoxP3+ Treg cell development and function in health and disease
Bibliographic record
Abstract
Summary Regulatory T (Treg) cells represent an essential component of peripheral tolerance. Given their potently immunosuppressive functions that is orchestrated by the lineage-defining transcription factor forkhead box protein 3 (FoxP3), clinical modulation of these cells in autoimmunity and cancer is a promising therapeutic target. However, recent evidence in mice and humans indicates that Treg cells represent a phenotypically and functionally heterogeneic population. Indeed, both suppressive and non-suppressive Treg cells exist in human blood that are otherwise indistinguishable from one another using classical Treg cell markers such as CD25 and FoxP3. Moreover, murine Treg cells display a degree of plasticity through which they acquire the trafficking pathways needed to home to tissues containing target effector T (Teff) cells. However, this plasticity can also result in Treg cell lineage instability and acquisition of proinflammatory Teff cell functions. Consequently, these dysfunctional CD4+FoxP3+ T cells in human and mouse may fail to maintain peripheral tolerance and instead support immunopathology. The mechanisms driving human Treg cell dysfunction are largely undefined, and obscured by the scarcity of reliable immunophenotypical markers and the disregard paid to Treg cell antigen-specificity in functional assays. Here, we review the mechanisms controlling the stability of the FoxP3+ Treg cell lineage phenotype. Particular attention will be paid to the developmental and functional heterogeneity of human Treg cells, and how abrogating these mechanisms can lead to lineage instability and Treg cell dysfunction in diseases like immunodysregulation polyendocrinopathy enteropathy X-linked (IPEX) syndrome, type 1 diabetes, rheumatoid arthritis and cancer.
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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".