Identification of an Evolutionarily Conserved Transcriptional Signature of CD8 Memory Differentiation That Is Shared by T and B Cells
Bibliographic record
Abstract
After Ag encounter, naive lymphocytes differentiate into populations of memory cells that share a common set of functions including faster response to Ag re-exposure and the ability to self-renew. However, memory lymphocytes in different lymphocyte lineages are functionally and phenotypically diverse. It is not known whether discrete populations of T and B cells use similar transcriptional programs during differentiation into the memory state. We used cross-species genomic analysis to examine the pattern of genes up-regulated during the differentiation of naive lymphocytes into memory cells in multiple populations of human CD4, CD8, and B cell lymphocytes as well as two mouse models of memory development. We identified and validated a signature of genes that was up-regulated in memory cells compared with naive cells in both human and mouse CD8 memory differentiation, suggesting marked evolutionary conservation of this transcriptional program. Surprisingly, this conserved CD8 differentiation signature was also up-regulated during memory differentiation in CD4 and B cell lineages. To validate the biologic significance of this signature, we showed that alterations in this signature of genes could distinguish between functional and exhausted CD8 T cells from a mouse model of chronic viral infection. Finally, we generated genome-wide microarray data from tetramer-sorted human T cells and showed profound differences in this differentiation signature between T cells specific for HIV and those specific for influenza. Thus, our data suggest that in addition to lineage-specific differentiation programs, T and B lymphocytes use a common transcriptional program during memory development that is disrupted in chronic viral infection.
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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".