Epigenome accessibility changes before and after activation reveal distinct and progressive differentiation for human memory T cell subsets
Bibliographic record
Abstract
Abstract Memory T cells (MTC), an indispensable part of adaptive immune memory, have historically been categorized by the cell surface proteins CCR7 and CD45RA into specialized subtypes (TCM, TEM, and TEMRA), each with unique functions. However, the epigenetic characteristics that distinguish the MTC subsets as well as the gene regulatory networks governing each MTC subsets’ response to activation remain poorly understood. Here we define and categorize the transcriptional and epigenetic differences of MTC and their respective primary subsets found in human blood, both in a resting state and after ex-vivo stimulation. Resting TCM were found to be relatively more similar to naïve cells in both CD4 and CD8 lineages, while TEM exhibited greater numbers of differentially expressed genes (DEG) and alterations to chromatin accessibility. Differentially accessible regions (DAR) discerning memory subsets contained binding motifs for factors thought to regulate memory formation from the bZIP, T-box and HMG families, as well as sites for novel bHLH factors MSC and AHR that may function in MTC development. Examining the effect of stimulation on MTC gene expression and chromatin identified unique DAR and DEG modules, some of which may have initially been primed by previous activation events in naive progenitors. Primed DAR were correlated with augmented expression of important genes in MTC after stimulation, suggesting an epigenetic mechanism of regulation. Ultimately these results describe the accumulation of epigenetic alterations during primary activation and during memory development that distinguish MTC from naïve T cells, enable differentiation into distinct subsets, and influence how MTC respond to secondary activation. Supported by grants from NIH (RO1 AI113021,T32 GM0008490)
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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 | 0.000 |
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".