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Record W2770636739 · doi:10.13034/jsst.v10i2.226

Characterization of the nitroreductase/metronidazole suicide gene system as a safeguard for cell based therapies

2017· article· en· W2770636739 on OpenAlexvenueno aff
Miranda Li

Bibliographic record

VenueJournal of Student Science and Technology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsSuicide geneThymidine kinaseGanciclovirCancer researchTransfectionEmbryonic stem cellCancer cellBiologyMedicineGeneGenetic enhancementCancerVirologyHerpes simplex virusVirusInternal medicineGeneticsHuman cytomegalovirus

Abstract

fetched live from OpenAlex

Cell-based therapies are promising treatment strategies for a variety of disorders ranging from cancer to spinal cord injuries. However, there is a risk of the transplanted cells becoming malignant. As a safeguard against this, suicide gene systems can be implemented so that transplanted cells can be eliminated if necessary by administering a pro-drug. Herpes simplex virus thymidine kinase (HSV-tk) paired with the pro-drug ganciclovir (GCV) is one of the most studied suicide gene systems. However, it can only kill cells that are actively dividing. Here we characterize another suicide gene system, nitroreductase (NTR) with its pro-drug metronidazole (MNZ), to investigate where in the cell cycle the killing occurs, hypothesizing that it could become an ideal candidate for eliminating transplanted cells irrespective of their proliferative status. Murine embryonic stem cells were transfected with vectors expressingeither HSV-tk or NTR and treated with the corresponding pro-drug. Confocal imaging and FUCCI (fluorescent ubiquitination-based cell cycle indicator) were used to identify where in the cell cycle the drug was active. MNZ was found to kill both dividing and non-dividing cells whereas GCV killed only the dividing cells. These resultssuggest that the NTR system may be a valuable addition or complement to HSV-tkLes thérapies cellulaires sont des stratégies promettantes en tant que traitements pour une variété de maladies. Celles-ci incluent le cancer et les traumatismes médullaires. Cependant, il y a un risque que les cellules implantées puissent devenir malignes. Afin de prévenir cela, des systèmes de gènes suicides peuvent être utilisés afin d’éliminer les cellules implantées si nécessaires par l’administration d’une prodrogue. La thymidine kinase, une enzymetrouvée chez les patients atteint du virus de l’herpès simplex (HSV tk), utilisée en conjonction avec la prodrogue ganciclovir (GCV), est un des systèmes de gènes suicides les plus étudiés. Cependant, il peut seulement tuer les cellules qui se divisent activement. Ici, nous caractérisons un autre système de gènes suicidaires, nitroréductase (NTR) avec sa prodrogue metronidazole (MNZ), afin d’étudier à quel point dans le cycle cellulaire la tuerie se déroule. L’hypothèse est que ce système pourrait être un candidat idéal afin d’éliminer les cellules transplantées, peu importe leur statut prolifératif. Des cellules de souche embryonnaires murines ont été transfectées avecdes vecteurs qui exprimaient soit HSV- tk ou NTR et traitées avec la prodrogue correspondante. La microscopie confocale et le système FUCCI (pour fluorescent ubiquitination-based cell cycle indicator) ont été utilisés afin d’identifier le point du cycle pendant lequel la drogue était active. Il a été trouvé que MNZ tuait les cellules qui sedivisaient et qui ne se divisaient pas, alors que GCV tuait uniquement les cellules qui se divisent. Ces résultats suggèrent que le système NTR pourrait être une addition ou un complément utile à HSV-tk.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.002
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.314
Teacher spread0.299 · 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 teacher head, 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
Published2017
Admission routes1
Has abstractyes

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