Inflammatory Cytokines That Enhance Antigen Responsiveness of Naïve CD8+ T Lymphocytes Modulate Chromatin Accessibility of Genes Impacted by Antigen Stimulation
Bibliographic record
Abstract
Naïve CD8+ T lymphocytes exposed to certain inflammatory cytokines undergo proliferation and display increased sensitivity to antigens. Such ‘cytokine priming’ can promote the activation of potentially autoreactive and antitumor CD8+ T cells by weak tissue antigens and tumor antigens. To elucidate the molecular mechanisms of cytokine priming, naïve PMEL-1 TCR transgenic CD8+ T lymphocytes were stimulated with IL-15 and IL-21, and chromatin accessibility was assessed using the assay for transposase-accessible chromatin (ATAC) sequencing. PMEL-1 cells stimulated by the cognate antigenic peptide mgp10025-33 served as controls. Cytokine-primed cells showed a limited number of opening and closing chromatin accessibility peaks compared to antigen-stimulated cells. However, the ATACseq peaks in cytokine-primed cells substantially overlapped with those of antigen-stimulated cells and mapped to several genes implicated in T cell signaling, activation, effector differentiation, negative regulation and exhaustion. Nonetheless, the expression of most of these genes was remarkably different between cytokine-primed and antigen-stimulated cells. In addition, cytokine priming impacted the expression of several genes following antigen stimulation in a synergistic or antagonistic manner. Our findings indicate that chromatin accessibility changes in cytokine-primed naïve CD8+ T cells not only underlie their increased antigen responsiveness but may also enhance their functional fitness by reducing exhaustion without compromising regulatory controls.
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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".