Harnessing autophagy for cell fate control gene therapy
Bibliographic record
Abstract
We hypothesized that rapamycin, through induction of autophagy and promotion of an antiapoptotic phenotype, would permit lentiviral (LV)-based transgene delivery to human T-Rapa cells, which are being tested in phase II clinical trials in the setting of allogeneic hematopoietic cell transplantation. Manufactured T-Rapa cells were exposed to supernatant enriched for a LV vector encoding a fusion protein consisting of truncated CD19 (for cell surface marking) and DTYMK/TMPKΔ, which provides "cell-fate control" due to its ability to phosphorylate (activate) AZT prodrug. LV-transduction in rapamycin-treated T-Rapa cells: (1) resulted in mitochondrial autophagy and a resultant antiapoptotic phenotype, which was reversed by the autophagy inhibitor 3-MA; (2) yielded changes in MAP1LC3B and SQSTM1 expression, which were reversed by 3-MA; and (3) increased T-Rapa cell expression of the CD19-DTYMKΔ fusion protein, despite their reduced proliferative status. Importantly, although the transgene-expressing T-Rapa cells expressed an antiapoptotic phenotype, they were highly susceptible to cell death via AZT exposure both in vitro and in vivo (in a human-into-mouse xenogeneic transplantation model). Therefore, rapamycin induction of T cell autophagy can be used for gene therapy applications, including the CD19-DTYMKΔ cell-fate control axis to improve the safety of T cell immuno-gene therapy.
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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".