Purging Exhausted Virus-Specific CD8 T Cell Phenotypes by Somatic Cell Reprogramming
Bibliographic record
Abstract
Cytotoxic T cells are critical in controlling virus infections. However, continuous antigen stimulation and negative regulatory factors cause CD8 T cells to enter a dysfunctional state (T cell exhaustion), resulting in viral persistence. We hypothesized that the exhausted T cell state could be molecularly rejuvenated using a somatic cell reprogramming technology, which is technically able to convert any types of cells to induced pluripotent stem cells (iPSCs), to regenerate functional T cells capable of purging chronic infection. We generated a new mouse line (B6/129 OKSM ) in which every somatic cell contains four doxycycline-inducible reprogramming genes ( Oct4, Klf4, Sox2, and c-Myc : OKSM), and infected them with lymphocytic choriomeningitis virus (LCMV) clone 13 to establish chronic infection. Exhausted LCMV-specific T cells isolated by flow sorting were successfully reprogrammed ex vivo into iPSCs in the presence of doxycycline. Upon injection into blastocysts and subsequent transfer into foster females, the reprogrammed cells differentiated into functional naive T cells that maintained their original antigen specificity. These results provide proof of concept that somatic cell reprogramming of exhausted T cells into iPSCs can erase imprints of their previous exhausted state and in turn regenerate functional virus-specific T cells.
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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 | 0.001 |
| 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".