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Record W2345341568 · doi:10.1038/mt.2016.91

Rapid and Efficient Stable Gene Transfer to Mesenchymal Stromal Cells Using a Modified Foamy Virus Vector

2016· article· en· W2345341568 on OpenAlexaff
Nathan P. Sweeney, Cathy Regan, Jiahui Liu, Antonio Galleu, Francesco Dazzi, Dirk Lindemann, C. Anthony Rupar, Myra O. McClure

Bibliographic record

VenueMolecular Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsWestern University
FundersMedical Research CouncilWellcome Trust
KeywordsMesenchymal stem cellTransduction (biophysics)Viral vectorBiologyGenetic enhancementTransgeneGene deliveryCell biologyStromal cellEx vivoRegenerative medicineVirologyCancer researchStem cellGeneIn vivoRecombinant DNABiotechnologyGenetics

Abstract

fetched live from OpenAlex

Mesenchymal stromal cells (MSCs) hold great promise for regenerative medicine. Stable ex vivo gene transfer to MSCs could improve the outcome and scope of MSC therapy, but current vectors require multiple rounds of transduction, involve genotoxic viral promoters and/or the addition of cytotoxic cationic polymers in order to achieve efficient transduction. We describe a self-inactivating foamy virus vector (FVV), incorporating the simian macaque foamy virus envelope and using physiological promoters, which efficiently transduces murine MSCs (mMSCs) in a single-round. High and sustained expression of the transgene, whether GFP or the lysosomal enzyme, arylsulphatase A (ARSA), was achieved. Defining MSC characteristics (surface marker expression and differentiation potential), as well as long-term engraftment and distribution in the murine brain following intracerebroventricular delivery, are unaffected by FVV transduction. Similarly, greater than 95% of human MSCs (hMSCs) were stably transduced using the same vector, facilitating human application. This work describes the best stable gene transfer vector available for mMSCs and hMSCs.

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)0.000
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 score0.052
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.047
GPT teacher head0.294
Teacher spread0.248 · 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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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