Construction of a Bicistronic Lentiviral Vector for Efficient Transduction and Expression of Multiple Therapeutic Genes in Adult Stem Cells
Bibliographic record
Abstract
Adult stem cells (ASC) have emerged as a potentially useful substrate for neovascularization and repair of ischemic myocardium and injured blood vessels. Pseudotyped retroviral vectors efficiently transduce ASC, providing long‐term stable expression of therapeutic genes. However, the cytotoxicity of these vectors is increased in conditions where multiple transductions may be required to simultaneously express more than one gene. In order to minimize the toxicity associated with multiple retroviral transductions we designed a bicistronic lentiviral vector for simultaneous expression of two genes. To construct the bicistronic vector we cloned a modified internal ribosome entry sequence (IRES) from pIRES2AcGFP1 between two reporter genes (Green Fluorescent protein, AcGFP1 and Firefly Luciferase, Luc). To test the strength of translation of the IRES we generated two complementary vectors, the first with GFP and Luc cloned 5′ and 3′ of the IRES sequence, respectively; the second vector with the reporter genes cloned in the reverse order with respect to the IRES. Luciferase activity was determined using the Bright‐Glo Luciferase Assay System. The first construct was packaged into lentiviral particles by four plasmid transfection into HEK 293FT cells, using VSG‐V for viral pseudotyping. Analyis of reporter gene expression in transduced HEK 293 cells showed that the bicistronic expresses both reporter genes with minimal attenuation of expression by the IRES sequence. Transduction efficiency of cells was consistently > 40% with no signs of cytotoxicity. Supported by grants from CIHR and HSFO to L.G. Melo and C.A. Ward
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".