Helios is a marker, not a driver, of human Treg stability
Bibliographic record
Abstract
Abstract Treg therapy holds promise as a potentially curative approach to establish immune tolerance in transplantation and autoimmune disease. An outstanding question is whether therapeutic Tregs have the potential to transdifferentiate into effector T‐cells and, thus, exacerbate rather than suppress immune responses. In mice, the transcription factor Helios is thought to promote Treg lineage stability in a range of inflammatory contexts. In humans, the role of Helios in Tregs is less clear, in part, due to the inability to enrich and study subsets of Helios‐positive versus Helios‐negative Tregs. Using an in vitro expansion system, we found that loss of high Helios expression and emergence of an intermediate Helios (Helios mid )‐expressing population correlated with Treg destabilization. We used CRISPR/Cas9 to genetically ablate Helios expression in human naive or memory Tregs and found that Helios‐KO and unedited Tregs were equivalent in their suppressive function and stability in inflammation. Thus, high Helios expression is a marker, but not a driver, of human Treg stability in vitro. These data highlight the importance of monitoring Helios expression in therapeutic Treg manufacturing and provide new insight into the biological function of this transcription factor in human T‐cells.
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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 | 0.001 | 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".