Optimized models for design of efficient miR30-based shRNAs

Matveeva, O. V.; Nazipova, N.N.; Ogurtsov, Aleksey Y.; Shabalina, Svetlana A.

doi:10.3389/fgene.2012.00163

Cited by 19 publications

(20 citation statements)

References 46 publications

(90 reference statements)

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“…The algorithm was able to achieve 80% specificity while maintaining 50% sensitivity. Notably, we were able to increase the specificity to 85% through the supplemental application of previously reported rules for shRNA selection (Figure 3E)(Fellmann et al, 2011; Matveeva et al, 2012). …”

Section: Resultsmentioning

confidence: 67%

“…We saw an overall Pearson correlation of 0.72 between experimentally derived potency measurements and computational predictions (Figure 2A). For comparison, DSIR achieves a correlation of 0.4 and a prior shRNA prediction algorithm trained on a subset of the sensor data used in this study achieves 0.56 (Matveeva et al, 2012; Vert et al, 2006). This indicates that shERWOOD achieves a roughly 180% increase in performance over currently existing siRNA prediction algorithms and a 126% increase in efficacy over existing shRNA specific prediction algorithms.…”

Section: Resultsmentioning

confidence: 99%

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A Computational Algorithm to Predict shRNA Potency

et al. 2014

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The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and have used this method to generate ~250,000 shRNA efficacy data-points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that target, specifically, the majority of each gene’s multiple transcripts. We have validated the performance of our shRNA designs using several orthogonal strategies and have constructed genome-wide collections of shRNAs for humans and mice based upon our approach.

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Section: Resultsmentioning

confidence: 67%

Section: Resultsmentioning

confidence: 99%

A Computational Algorithm to Predict shRNA Potency

et al. 2014

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“…47 This 22mer is the basis for the following shRNA design. For human CCR5 mRNA (GenBank: NM_000579.3), the result is TTTCCATACAGTCAGTATCAAT (22mer).…”

Section: Methodsmentioning

confidence: 99%

Optimization of Critical Hairpin Features Allows miRNA-based Gene Knockdown Upon Single-copy Transduction

Myburgh

Cherpin

Schlaepfer

et al. 2014

Molecular Therapy - Nucleic Acids

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Gene knockdown using micro RNA (miRNA)-based vector constructs is likely to become a prominent gene therapy approach. It was the aim of this study to improve the efficiency of gene knockdown through optimizing the structure of miRNA mimics. Knockdown of two target genes was analyzed: CCR5 and green fluorescent protein. We describe here a novel and optimized miRNA mimic design called mirGE comprising a lower stem length of 13 base pairs (bp), positioning of the targeting strand on the 5′ side of the miRNA, together with nucleotide mismatches in upper stem positions 1 and 12 placed on the passenger strand. Our mirGE proved superior to miR-30 in four aspects: yield of targeting strand incorporation into RNA-induced silencing complex (RISC); incorporation into RISC of correct targeting strand; precision of cleavage by Drosha; and ratio of targeting strand over passenger strand. A triple mirGE hairpin cassette targeting CCR5 was constructed. It allowed CCR5 knockdown with an efficiency of over 90% upon single-copy transduction. Importantly, single-copy expression of this construct rendered transduced target cells, including primary human macrophages, resistant to infection with a CCR5-tropic strain of HIV. Our results provide new insights for a better knockdown efficiency of constructs containing miRNA. Our results also provide the proof-of-principle that cells can be rendered HIV resistant through single-copy vector transduction, rendering this approach more compatible with clinical applications.

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“…Notably, we were able to increase the specificity to 85% through the supplemental application of previously reported rules for shRNA selection ( Fig. 3E) (Fellmann et al, 2011;Matveeva et al, 2012).…”

Section: Structure-guided Insights Expand the Shrna Prediction Spacementioning

confidence: 55%

“…When we consider only shRNAs with a 5' U, the correlation rises to 0.78, likely due to the greater number of data point available for training that algorithm. For comparison, DSIR achieves a correlation of 0.4 and a prior shRNA prediction algorithm trained on a subset of the sensor data used in this study achieves 0.56 (Matveeva, Nazipova, Ogurtsov, & Shabalina, 2012;Vert et al, 2006). This indicates that shERWOOD achieves a roughly 180% increase in performance over currently existing siRNA prediction algorithms and a 126% increase in efficacy over existing shRNA specific prediction algorithms.…”

Section: A Sensor-based Computational Algorithm To Predict Shrna Effimentioning

confidence: 76%

RNAi as a Routine Route Toward Breast Cancer Therapy

Hannon¹

2014

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)May 2014 (From -To) 1Sep2008-28Feb2014 REPORT TYPE Final DATES COVERED TITLE AND SUBTITLERNAi as a Routine Route Toward Breast Cancer Therapy 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-08-1-0572 5c. PROGRAM ELEMENT NUMBER AUTHOR(S)Gregory J. Hannon, PhD. 5d. PROJECT NUMBERemail: hannon@cshl.edu 5e. TASK NUMBER 5f. WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERCold Spring Harbor Laboratory One Bungtown Road Cold Spring Harbor, NY 11724 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)US Army Medical Research and Materiel Command Fort Detrick, MA 21702 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution unlimited SUPPLEMENTARY NOTES ABSTRACTDuring the first year of this innovator award, we made significant progress toward two of our aims. We constructed a third generation RNAi library and made that available to the breast cancer community. This resource has nearly 75,000 independent, sequence verified clones targeting ~18,000 human genes. A similar library for the mouse genome is nearing completion. We also scaled up our shRNA screening platform in preparation for lethality surveys of all suitable and available BC cell lines, including matched pairs of lines that have acquired resistance to herceptin in vitro. Relevant to our second aim, we have profiled microRNA from each of the identifiable epithelial cell types in the mouse mammary gland and are undertaking similar efforts in human. The goal is to develop microRNA sensor strategies that will permit visualization of each cell type in vivo and enable their isolation and manipulation in vitro. Finally, we showed that two microRNAs, let-7 and miR-93, can deplete tumor initiating cells from a number of basal breast cancer cell lines. SUBJECT TERMS INTRODUCTIONThe goal of this innovator award is to continue to develop and apply RNAi-based screening methods to discover new routes toward breast cancer therapy. The project has three sets of goals. First is to integrate genomic and geneti...

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Optimized models for design of efficient miR30-based shRNAs

Cited by 19 publications

References 46 publications

A Computational Algorithm to Predict shRNA Potency

A Computational Algorithm to Predict shRNA Potency

Optimization of Critical Hairpin Features Allows miRNA-based Gene Knockdown Upon Single-copy Transduction

RNAi as a Routine Route Toward Breast Cancer Therapy

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