Evidence map›Paper›PMID 26627251›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

Enhancing potency of siRNA targeting fusion genes by optimization outside of target sequence.

Kseniya Gavrilov, Young-Eun Seo, Gregory T Tietjen, Jiajia Cui, Christopher J Cheng, W Mark Saltzman

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

9 citing papers in PubMed, 17 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Kseniya GavrilovDepartment of Cellular & Molecular Physiology, Yale University, New Haven, CT 06511; Department of Biomedical Engineering, Yale University, New Haven, CT 06511.
Young-Eun SeoDepartment of Biomedical Engineering, Yale University, New Haven, CT 06511.
Gregory T TietjenDepartment of Biomedical Engineering, Yale University, New Haven, CT 06511.
Jiajia CuiDepartment of Biomedical Engineering, Yale University, New Haven, CT 06511.
Christopher J ChengDepartment of Biomedical Engineering, Yale University, New Haven, CT 06511.
W Mark SaltzmanDepartment of Cellular & Molecular Physiology, Yale University, New Haven, CT 06511; Department of Biomedical Engineering, Yale University, New Haven, CT 06511 mark.saltzman@yale.edu.
Yale University · US

Funding

IMMUNOHEMATOLOGY/TRANSFUSION MEDICINE RESEARCH TRAININGT32HL007974 · NHLBI · YALE UNIVERSITY · PI JEANNE E HENDRICKSON, Diane S Krause · 2001 to 2026
$8.1M
Optimizing Therapeutic Revascularization by Endothelial Cell TransplantationR01HL085416 · NHLBI · YALE UNIVERSITY · PI POBER, JORDAN S, SALTZMAN, W. MARK · 2006 to 2019
$5.9M
Molecular and Cellular Transport in MucusR01EB000487 · NIBIB · YALE UNIVERSITY · PI IWASAKI, AKIKO, SALTZMAN, W. MARK · 2002 to 2019
$5.0M
Molecular and Cellular Transport in MucusR56EB000487 · NIBIB · YALE UNIVERSITY · PI SALTZMAN, W. MARK · 2008 to 2008
$402k
NCI NIH HHS 1F31CA17429801A1NHLBI NIH HHS HL085416NHLBI NIH HHS R01 HL085416NHLBI NIH HHS T32 HL007974NIBIB NIH HHS EB000487NIBIB NIH HHS R01 EB000487NIBIB NIH HHS R56 EB000487
6 · The paper itself

Abstract

Canonical siRNA design algorithms have become remarkably effective at predicting favorable binding regions within a target mRNA, but in some cases (e.g., a fusion junction site) region choice is restricted. In these instances, alternative approaches are necessary to obtain a highly potent silencing molecule. Here we focus on strategies for rational optimization of two siRNAs that target the junction sites of fusion oncogenes BCR-ABL and TMPRSS2-ERG. We demonstrate that modifying the termini of these siRNAs with a terminal G-U wobble pair or a carefully selected pair of terminal asymmetry-enhancing mismatches can result in an increase in potency at low doses. Importantly, we observed that improvements in silencing at the mRNA level do not necessarily translate to reductions in protein level and/or cell death. Decline in protein level is also heavily influenced by targeted protein half-life, and delivery vehicle toxicity can confound measures of cell death due to silencing. Therefore, for BCR-ABL, which has a long protein half-life that is difficult to overcome using siRNA, we also developed a nontoxic transfection vector: poly(lactic-coglycolic acid) nanoparticles that release siRNA over many days. We show that this system can achieve effective killing of leukemic cells. These findings provide insights into the implications of siRNA sequence for potency and suggest strategies for the design of more effective therapeutic siRNA molecules. Furthermore, this work points to the importance of integrating studies of siRNA design and delivery, while heeding and addressing potential limitations such as restricted targetable mRNA regions, long protein half-lives, and nonspecific toxicities.

Indexed as

Gene Expression Regulation, LeukemicApoptosisBase SequenceBinding SitesCell Line, TumorCell SurvivalDrug Delivery SystemsFusion Proteins, bcr-ablGene TargetingHEK293 CellsHumansK562 CellsLactic AcidLeukemia, Myelogenous, Chronic, BCR-ABL PositiveNanoparticlesOncogene Proteins, FusionBCR-ABL1 fusion protein, humanFusion Proteins, bcr-ablLactic AcidOncogene Proteins, FusionPolyglycolic AcidPolylactic Acid-Polyglycolic Acid CopolymerRNA, MessengerRNA, Small InterferingTMPRSS2-ERG fusion protein, humanBCR-Abldrug deliveryleukemiaRNA interferenceTMPRSS2-ERG

Identifiers

PMID26627251
PMCPMC4672813
OpenAlexW2174098010

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.