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Improved Suicide Gene Therapy: Lentiviral Gene Transfer of Equine Herpes Virus Type 4 Thymidine Kinase into Target Cells.

2005· article· en· W2561619633 on OpenAlexaff
Takeya Sato, Anton Neschadim, Vanessa I. Rasaiah, M. Konrad, Daniel H. Fowler, Arnon Lavie, Jeffrey A. Medin

Bibliographic record

VenueBlood · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSuicide geneThymidine kinaseGenetic enhancementGanciclovirViral vectorBiologyVirologyCD19Cancer researchHerpes simplex virusGeneHuman cytomegalovirusVirusCellGeneticsRecombinant DNA

Abstract

fetched live from OpenAlex

Abstract Herpes virus type 1 thymidine kinase (HSV1-TK) with ganciclovir (GCV) prodrug treatment is the most widely used approach for suicide gene therapy. This ‘suicide’ strategy allows direct reduction of tumors and clearance of donor cells should graft-versus-host disease (GvHD) arise after bone marrow transplantation. Given recent clinical outcomes, this suicide approach may also provide a key safety component for therapeutic gene transfer vectors that integrate. Although suicide gene therapy using HSV1-TK-encoding oncoretroviral vectors has been evaluated in the clinic, the success of this approach has been relatively modest. Reasons for this include: low gene transfer efficacy, reduced expression of the suicide gene, and insufficient conversion of substrate. Our goal is to overcome these limitations by using a novel lentiviral vector (LV) encoding an alternative kinase/prodrug combination. The rational for our innovative suicide gene therapy strategy is two-fold: 1) Lentiviral vectors can efficiently transduce not only dividing cells but also non-dividing cells. 2) Applying a faster viral enzyme like equine herpes virus type 4 thymidine kinase (EHV4-TK) could be advantageous as it has been shown to be kinetically superior to HSV1-TK at GCV phosphorylation. The aim of this study is to evaluate whether LV-mediated gene modification of target cells with EHV4-TK can lead to efficient killing following GCV treatment. We first constructed a LV expression system carrying the wild-type EHV4-TK cDNA with an IRES element followed by a truncated form of human CD19 (hCD19Δ). Human CD19 was chosen as a cell surface marker to allow functional titering of virus and for immuno-enrichment of transduced cells prior to infusion since it is not expressed in the T cell lineage. The truncated form lacks the intracellular domain and therefore does not signal. Use of an IRES element can abrogate some variegated expression seen with vectors having dual promoters. The LV was pseudotyped with VSV-g and concentrated by ultracentrifugation. After one infection, Jurkat cells (human T cell leukemia) showed a more than 80% functional and stable transduction efficiency (MOI = 10). Using hCD19Δ as a selective marker, transduced Jurkat cells were enriched to over 95% positive by immuno-affinity sorting. EHV4-TK-transduced Jurkat cells exhibited increased cell killing in response to GCV treatment (the apoptotic cell indexes with or without GCV were 69.4 ± 1.5 % and 18.8 ± 1.7 %, respectively; n=3). Highly efficient transduction (more than 60%) of primary human T cells was accomplished by a three time exposure to virus over 36 hours at MOI of 20. Next, we found that GCV efficiently killed transduced primary human T cells in a dose dependent manner. We are now comparing the efficiency of GCV conversion by HSV1-TK and EHV4-TK using LV-transduced cells that express the similar protein levels. We are also evaluating intracellular levels of GCV metabolites by HPLC. These results demonstrate that our novel suicide gene therapy strategy has significant potential for many clinical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.273
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2005
Admission routes1
Has abstractyes

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