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ShRNA Sensor Analysis of 5' U converted shRNAs reveals 3' target bias exists in addition to 5' guide bias

ABSTRACT: shRNAs selected with the shERWOOD algorithm were converted to have a U at the 5' end of their guide. When endogenous 1U shRNAs were compared to artificial shRNA via the sensor algorithm, the endogenous shRNAs were found to be more efficacious. Purpose: Structural studies have hinted that the 5' end of shRNA guides is engulfed in the RISC complex. It has also been reported that shRNAs with a 5' U are more efficacious than those with other 5' caps. We wished to determine whether replacement of shRNA guide 5' nucleotides with a U, regardless of the corresponding target base, would increase their efficacy. Method: For each gene in the "druggable genome" 10 shRNAs were selected with the shERWOOD algorithm. In each case the score was assessed as if the guide had a 5' U. Sensor constructs were designed pairing 1U-guide shRNAs with their endogenous target. shRNAs were assessed for efficacy via the shRNA sensor assay (Fellmann et al. Mol Cell 2011). Results: shRNAs with artificial 5' Us were found to be less efficacious than those with an endogenous 5' U,

ORGANISM(S): synthetic construct

SUBMITTER: Simon Knott

PROVIDER: E-GEOD-62186 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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shRNA Sensor Analysis of 5' U converted shRNAs reveals 3' target bias exists in addition to 5' guide bias

Project description:shRNAs selected with the shERWOOD algorithm were converted to have a U at the 5' end of their guide. When endogenous 1U shRNAs were compared to artificial shRNA via the sensor algorithm, the endogenous shRNAs were found to be more efficacious.

2014-11-24 | GSE62186 | GEO

shRNA Sensor Analysis of DSIR Selected shRNAs

Project description:Purpose: Generate a large, high quality database of paired shRNA efficacy/sequence datapoints. Methods:Twelve shRNAs for each Refseq annotated human gene were selected based on the DSIR algorithm. Twelve batches of ~22K shRNAs (corresponding to 12 agilent chips) were then assessed for efficacy via the sensor method outlined in Fellmann et al, Mol Cell, 2011. Conclusions: Neighboring nucleotide combinations are best at predicting shRNA efficacy.

2014-11-24 | E-GEOD-62183 | biostudies-arrayexpress

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Project description:shRNA Sensor Analysis of DSIR Selected shRNAs

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Project description:shRNA Sensor Analysis of 5' U converted shRNAs reveals 3' target bias exists in addition to 5' guide bias

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shRNA off target analysis via RNAseq

Project description:shRNAs were assessed for off-target effects by comparing the gene expression profiles of cells that they had been infected into. shRNAs designed with the shERWOOD algorithm and house in the ultramir microRNA scafold were found to have very little off targeting. Purpose: A major detriment to RNAi is off-targeting. We wished to assess the level of off targeting of microRNA (ultramiR) housed shERWOOD shRNAs as compared to similar shRNAs in the TRC collection. Methods: 5 shRNAs targeting each of two genes were infected into the 4T1 cell line. For each gene one shRNA was selected from the TRC collection and one based on the shERWOOD algorithm. For each gene, the exrpession profiles of the corresponding shRNA infected cells were compared using RNAseq. Conclusions: Highly similar profiles were observed between shERWOOD selected shRNAs. TRC shRNAs produced profiles indicative of off-targeting.

2014-11-24 | E-GEOD-62184 | biostudies-arrayexpress

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Project description:The increasing emergence and re-emergence of RNA virus outbreaks underlines the urgent need to develop effective antivirals. RNA interference (RNAi) is a sequence-specific gene silencing mechanism that is triggered by small interference RNAs (siRNAs) or short hairpin RNAs (shRNAs), which exhibit significant promise for antiviral therapy. AGO2-dependent shRNA (agshRNA) generates a single-stranded guide RNA effector and presents significant advantages over traditional siRNA and shRNA. In this study, we applied a logistic regression algorithm to a previously published chemically siRNA efficacy dataset and built a machine learning-based algorithm with high predictive power. Using this algorithm, we designed siRNA sequences targeting diverse RNA viruses, including human enterovirus A71 (EV71), Zika virus (ZIKV), dengue virus 2 (DENV2), mouse hepatitis virus (MHV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and developed them into agshRNAs according to the rule of agshRNA design. These agshRNAs inhibited viral replication in infected cells and their efficiencies of antiviral effects displayed a consistent trend with the score ranking of siRNA sequences predicted by the algorithm. Using the agshRNA targeting EV71 as an example, we showed that the anti-EV71 effect of agshRNA was more potent compared with the corresponding siRNA and shRNA. Moreover, the antiviral effect of agshRNA is dependent on AGO2-processed guide RNA, which can load into the RISC. We also confirmed the antiviral effect of agshRNA in vivo. Together, this work develop a novel approach that combines machine learning-based algorithm and agshRNA design to custom design antiviral agshRNAs with high efficiency.

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2023-02-09 | PXD038048 | Pride

Small RNA sequencing of mouse muscle and heart samples after small hairpin RNA delivery

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