Deciphering the role of Klf2 in CD8 T cell differentiation and exhaustion during chronic infection
Bibliographic record
Abstract
Abstract T cell exhaustion, a state of dysfunction occurring during chronic infections and cancer, is marked by a progressive loss of effector functions and increased expression of inhibitory receptors. In chronic infection, CD8 T cells differentiate into TCF1+ progenitor (TPRO), CX3CR1+ effector (TEFF), and terminally exhausted (TEXH) subsets. The differentiation pathways are governed by complex transcription factor (TF) networks. Employing regulon analysis enabled by single-cell RNA+ATAC multiome sequencing, we dissected the TF networks orchestrating the fate of exhausted CD8 T cell populations. Our integrative analysis, complemented by single-cell CRISPR screening, highlighted Krüppel-like factor 2 (KLF2) as a pivotal TF necessary for maintaining TPRO cells and generating TEFF cells. Our research also revealed that KLF2 regulates the distinct distribution patterns of CD8 T cell subsets across organs. More intriguingly, multiomic analysis uncovered a novel function of Klf2 in limiting T cell exhaustion. Klf2-deficient CD8 T cells exhibited a marked increase in exhaustion signature genes. ATAC-seq and CUT&TAG assays further revealed different chromatin accessibility and histone modifications within Klf2 and its enhancers across CD8 T cell subsets. These insights underscore the differential transcriptional and epigenetic regulation of Klf2, presenting a novel mechanism that influences the cell fate decision of CD8 T cells during chronic infections.
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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) | 0.001 | 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".