Specific pharmacological dimerization of KDR in lentivirally transduced human hematopoietic cells activates antiapoptotic and proliferative mechanisms
Bibliographic record
Abstract
Selective and regulatable expansion of transduced cells could augment gene therapy for many disorders. The activation of modified growth factor receptors via synthetic chemical inducers of dimerization allows for the coordinated growth of transduced cells. This system can also provide information on specific receptor-mediated signaling without interference from other family members. Although several receptor subunits have been investigated in this context, little is known about the precise molecular events associated with dimerizer-initiated signaling. We have constructed and expressed an AP20187-regulated KDR chimeric receptor in human TF1 cells and analyzed activation of this gene switch using functional, biochemical, and microarray analyses. When deprived of natural ligands, GM-CSF, interleukin-3, or erythropoietin, AP20187 prevented apoptosis of transduced TF1 cells, induced dose-dependent proliferation, and supported long-term growth. In addition, AP20187 stimulation activated the signaling molecules associated with mitogen-activated protein kinase and phosphatidyl-inositol 3-kinase/Akt pathways. Microarray analysis determined that a number of transcripts involved in a variety of cellular processes were differentially expressed. Notably, mRNAs affiliated with heat stress, including Hsp70 and Hsp105, were up-regulated. Functional assays showed that Hsp70 and Hsp105 protected transduced TF1 cells from apoptosis and premature senescence, in part through regulation of Akt. These observations delineate specific roles for kinase insert domain-containing receptor, or KDR, signaling and suggest strategies to endow genetically modified cells with a survival advantage enabling the generation of adequate cell numbers for therapeutic outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".