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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.

ORGANISM(S): synthetic construct

PROVIDER: GSE62186 | GEO | 2014/11/24

SECONDARY ACCESSION(S): PRJNA263391

REPOSITORIES: GEO

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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. 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,

2014-11-24 | E-GEOD-62186 | biostudies-arrayexpress

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

The loop position of shRNAs and pre-miRNAs is critical for the accuracy of Dicer processing in vivo

Project description:Short-hairpin RNA (shRNA)-induced RNAi is used for biological discovery and therapeutics. Dicer, whose normal role is to liberate endogenous miRNAs from their precursors, processes shRNAs into different biologically active siRNAs, affecting their efficacy and potential for off-targeting. We found that in cells, Dicer induced imprecise cleavage events around the expected sites based on the previously described 5'/3'-counting rules. These promiscuous non-canonical cleavages were abrogated when the cleavage site was positioned 2 nt from a bulge or loop. Interestingly, we observed that the ~1/3 of mammalian endogenous pre-miRNAs that contained such structures were more precisely processed by Dicer. Implementing a new "loop-counting rule", we designed potent anti-HCV shRNAs with substantially reduced off-target effects. Our results suggest that Dicer recognizes the loop/bulge structure in addition to the ends of shRNAs/pre-miRNAs for accurate processing. This has important implications for both miRNA processing and future design of shRNAs for RNAi-based genetic screens and therapies. Various shRNAs were expressed in Cell and processed by the RNase III enzyme Dicer. The profiles of the siRNA products were generated by deep sequencing with or without the Ago2-IP.

2012-12-25 | E-GEOD-41292 | biostudies-arrayexpress

The loop position of shRNAs and pre-miRNAs is critical for the accuracy of Dicer processing in vivo

2012-12-25 | GSE41292 | GEO

Sub-genomewide shRNAs constructed using an optimized selection algorithm and microRNA backbone provide stronger evidence for follow-up studies

Project description:Sub-genomewide shRNA libraries were constructed using the current RNAi consortium constructs as well as using the DSIR (siRNA algoirthm) and a novel shRNA specific algorithm (shERWOOD). All libraries were placed into mir30 expression vectors. The shERWOOD libraries were also placed in a vector harboring an optimized mir cassette (ultramir). Each library was screened using the pancreatic cell line A385. A concensus set of essential genes identified as the set for which two shRNAs depleted in each of the libries. For these genes, a great percentage of shERWOOD seletected shRNA depleted. In addition the placement of shERWOOD selected constructs into ultramir scaffoled increased the rate of shRNA depletion for essential genes further. Purpose: shRNA screens were carried out using various library construction strategies to identify the strategy that provides the best shRNA screening results. Method: Libraries were constructed using the TRC shRNA set as well as shRNAs identified using the DSIR and shERWOOD algorithms. shRNA libraries were cloned into mir30 expression vectors. shERWOOD shRNAs were also cloned into an expression vector harboring an optimized microRNA scaffold termed ultramir. Each resultant library was screened using the pancreatic cell line A385. Each library was analyzed separately to identify a set of genes where at least two shRNAs depleted. These gene sets were intersected to develop a set of essential genes. Results: The shERWOOD shRNA libraries provided the highest number depleting shRNAs for each essential gene. Further these shRNAs depleted to a greater extent than did the shRNAs from the other libraries. When shERWOOD libraries were placed into the ultramir cassette a greater number of shRNAs per essential gene depleted.

2014-11-24 | E-GEOD-62185 | biostudies-arrayexpress

shRNA Sensor Analysis of DSIR Selected shRNAs

Project description:Purpose: Generate a large, high quality database of paired shRNA efficacy/sequence datapoints.

2014-11-24 | GSE62183 | GEO

Transcriptome analysis of myeloma cells after IRF3 depletion by shRNAs

Project description:Whole transcriptome of IRF3-depleted myeloma cells was analysed using a high-throughput RNA-seq approach. IRF3 was depleted by 2 different shRNAs in MM1.S cells, and total RNA was isolated on day 4 post-lentiviral transduction. Poly A-tail enriched RNA was sequenced and the analysis of commonly regulated genes by both shRNAs from 2 independent experiments revealed that IRF3 regulates genes that are involved in cell cycle regulation.

2021-02-28 | GSE163482 | GEO

Sub-genomewide shRNAs constructed using an optimized selection algorithm and microRNA backbone provide stronger evidence for follow-up studies

2014-11-24 | GSE62185 | GEO

LNCaP prostate cancer cells stably transfected with shRNAs targeting ADRB2

Project description:Gene expression profiling of prostate adenocarcinoma LNCaP cells stably transfected with shRNAs targeting ADRB2 (shADRB2-2 and shADRB2-3) or control shRNA (shCtrl) incubated in 10% FCS.

2019-04-25 | GSE130204 | GEO

CD95L derived si- and shRNAs kill cancer cells through an RNAi mechanism by targeting survival genes [shL3.shR6.RNAseq.lg]

Project description:The death receptor CD95/Fas can be activated by immune cells to kill cancer cells. However, most siRNAs or shRNAs targeting either CD95 or CD95L induce DICE (Death Induced by CD95/CD95L Elimination), a form of cell death in which a combination of different cell death pathways are activated, that is selective for transformed cells, and that preferentially affects cancer stem cells. We now provide evidence that both CD95 and CD95L are part of a network of genes that contain sequences that when expressed as either siRNAs or shRNAs are toxic to cancer cells. They act through canonical RNAi by targeting the 3'UTRs of critical survival genes. We propose that these embedded toxic sequences are part of a conserved mechanism that regulates cell death, and we predict the existence of endogenous siRNAs, that when produced, induce cell death to regulate genome fidelity. Our data have implications for cancer therapy and the use of RNAi.

2017-09-16 | GSE101234 | GEO

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