Human Th1 and Th17 Cells Exhibit Epigenetic Stability at Signature Cytokine and Transcription Factor Loci
Bibliographic record
Abstract
The linear model of Th cell lineage commitment is being revised due to reports that mature Th cells can trans-differentiate into alternate lineages. This ability of Th cells to reprogram is thought to be regulated by epigenetic mechanisms that control expression of transcription factors characteristic of opposing lineages. It is unclear, however, to what extent this new model of Th cell plasticity holds true in human Th cell subsets that develop under physiological conditions in vivo. We isolated in vivo-differentiated human Th1 and Th17 cells, as well as intermediate Th1/17 cells, and identified distinct epigenetic signatures at cytokine (IFNG and IL17A) and transcription factor (TBX21, RORC, and RORA) loci. We also examined the phenotypic and epigenetic stability of human Th17 cells exposed to Th1-polarizing conditions and found that although they could upregulate TBX21 and IFN-γ, this occurred without loss of IL-17 or RORC expression, and resulted in cells with a Th1/17 phenotype. Similarly, Th1 cells could upregulate IL-17 upon enforced expression of RORC2, but did not lose expression of IFN-γ or TBX21. Despite alterations in expression of these signature genes, epigenetic modifications were remarkably stable aside from the acquisition of active histone methylation marks at cytokine gene promoters. The limited capacity of human Th17 and Th1 cells to undergo complete lineage conversion suggests that the bipotent Th1/17 cells may arise from Th1 and/or Th17 cells. These data also question the broad applicability of the new model of Th cell lineage plasticity to in vivo-polarized human Th cell subsets.
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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) | 0.002 | 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".