Cytokine priming of naïve CD8 <sup>+</sup> T lymphocytes modulates chromatin accessibility that partially overlaps with changes induced by antigen simulation
Bibliographic record
Abstract
Abstract Background Naïve CD8 + T lymphocytes undergo antigen non-specific proliferation following exposure to certain synergistic combination of inflammatory (IL-6, IL-21) and homeostatic (IL-7, IL-15) cytokines. Such cytokine-stimulated naïve CD8 + T cells display increased T cell antigen receptor (TCR) sensitivity, allowing them to respond to limiting concentrations of cognate antigenic peptides and altered peptide ligands of lower affinity towards the TCR. The purpose of this study is to gain insight into the molecular mechanisms of such ‘cytokine priming’. Methods Naïve CD8 + T lymphocytes expressing the PMEL-1 transgenic TCR were stimulated with IL-15 and IL-21, and chromatin accessibility was assessed using the assay for transposase-accessible chromatin (ATAC) sequencing. Cells stimulated by the cognate antigenic peptide mgp100 25-33 were used as controls. Results Compared to naïve cells, cytokine-primed cells showed 212 opening and 484 closing peaks, whereas antigen-stimulated cells showed 12087 opening and 6982 closing peaks. However, a significant fraction of the opening (33%) and closing (63%) peaks of cytokine-primed cells overlapped with those of the antigenic stimulated cells. Chromatin accessibility peaks modulated in cytokine-primed cells were strongly represented in gene ontology pathways for T cell signaling, activation, regulation and effector functions. Many of the transcription factor binding motifs located close to the opening and closing peaks of cytokine-primed cells also occurred in antigen-stimulated cells. Conclusions Our data suggest that by modulating the gene expression programs involved in TCR signaling, cytokine priming induces a poised state that lowers the TCR signaling threshold in naïve CD8 + T cells and increases their antigen responsiveness.
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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".