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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".