Dataset Information

GespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

ABSTRACT: Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-? signaling. gespeR is available as a Bioconductor R-package.

SUBMITTER: Schmich F

PROVIDER: S-EPMC4597449 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Publications

gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

Schmich Fabian F Szczurek Ewa E Kreibich Saskia S Dilling Sabrina S Andritschke Daniel D Casanova Alain A Low Shyan Huey SH Eicher Simone S Muntwiler Simone S Emmenlauer Mario M Rämö Pauli P Conde-Alvarez Raquel R von Mering Christian C Hardt Wolf-Dietrich WD Dehio Christoph C Beerenwinkel Niko N

Genome biology 20151007

Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducib ...[more]

PMID: 26445817

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Project description:Exogenous siRNAs are commonly used to regulate endogenous gene expression levels for gene function analysis, genotype-phenotype association studies and for gene therapy. Exogenous siRNAs can target mRNAs within the cytosol as well as nascent RNA transcripts within the nucleus, thus complicating siRNA targeting specificity. To highlight challenges in achieving siRNA target specificity, we targeted an overlapping gene set that we found associated with a familial form of multiple synostosis syndrome type 4 (SYSN4). In the affected family, we found that a previously unknown non-coding gene TOSPEAK/C8orf37AS1 was disrupted and the adjacent gene GDF6 was downregulated. Moreover, a conserved long-range enhancer for GDF6 was found located within TOSPEAK which in turn overlapped another gene which we named SMALLTALK/C8orf37. In fibroblast cell lines, SMALLTALK is transcribed at much higher levels in the opposite (convergent) direction to TOSPEAK. siRNA targeting of SMALLTALK resulted in post transcriptional gene silencing (PTGS/RNAi) of SMALLTALK that peaked at 72 h together with a rapid early increase in the level of both TOSPEAK and GDF6 that peaked and waned after 24 h. These findings indicated the following sequence of events: Firstly, the siRNA designed to target SMALLTALK mRNA for RNAi in the cytosol had also caused an early and transient transcriptional interference of SMALLTALK in the nucleus; Secondly, the resulting interference of SMALLTALK transcription increased the transcription of TOSPEAK; Thirdly, the increased transcription of TOSPEAK increased the transcription of GDF6. These findings have implications for the design and application of RNA and DNA targeting technologies including siRNA and CRISPR. For example, we used siRNA targeting of SMALLTALK to successfully restore GDF6 levels in the gene therapy of SYNS4 family fibroblasts in culture. To confidently apply gene targeting technologies, it is important to first determine the transcriptional interference effects of the targeting reagent and the targeted gene.

Dataset Information

GespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

Publications

gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens.

Similar Datasets

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