Evidence map›Paper›PMID 24997480›Full record

ArticleBiomaterials2014

MLV based viral-like-particles for delivery of toxic proteins and nuclear transcription factors.

Dai-Tze Wu, Monica J Roth

Open access · greenAbstract read
In one paragraph

Article in Biomaterials, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 21 citations in OpenAlex.

  1. Chemical reviews · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Dai-Tze WuDepartment of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, 675 Hoes Lane Rm 636, Piscataway, NJ, USA.
Monica J RothDepartment of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, 675 Hoes Lane Rm 636, Piscataway, NJ, USA. Electronic address: roth@rwjms.rutgers.edu.
Johnson University · US

Funding

THE ENVELOPE GENE PRODUCTS OF MURINE LEUKEMIA VIRUSR01CA049932 · NCI · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI ROTH, MONICA J. · 1990 to 2011
$3.0M
NCI NIH HHS R01 CA049932NCI NIH HHS R01CA49932
6 · The paper itself

Abstract

We have developed nanoparticles based on Murine Leukemia Virus virus-like-particles (VLPs) that efficiently deliver therapeutic bioactive proteins in their native state into target cells. Nuclear transcription factors and toxic proteins were incorporated into the VLPs from stable producer cells without transducing viral-encoded genetic material. Delivery of nuclear transcription factors required incorporation of nuclear export signals (NESs) into the vector backbone for the efficient formation of VLPs. In the presence of an appropriate targeting Env glycoprotein, transcription factors delivered and activated nuclear transcription in the target cells. Additionally, we show delivery of the bacterial toxin, MazF, which is an ACA-specific mRNA interferase resulted in the induction of cell death. The stable producer cells are protected from the toxin through co-expression of the anti-toxin MazE and continuously released MazF incorporating VLPs. This highly adaptable platform can be harnessed to alter and regulate cellular processes by bioactive protein delivery.

Indexed as

AnimalsBacterial ToxinsCell LineCell NucleusGenetic VectorsHEK293 CellsHumansLeukemia Virus, MurineMiceNuclear Export SignalsTranscriptional ActivationTranscription FactorsTransduction, GeneticVirionBacterial ToxinsNuclear Export SignalsTranscription FactorsBacterial toxinIntracellular deliveryProtein transductionTranscriptional factorsViral-like particles

Identifiers

PMID24997480
PMCPMC4139071
OpenAlexW2019092753

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.