Dataset Information

A random shRNA-encoding library for phenotypic selection and hit-optimization.

ABSTRACT: RNA interference (RNAi) is a mechanism for inhibiting gene expression through the action of small, non-coding RNAs. Most existing RNAi libraries target single genes through canonical pathways. Endogenous microRNAs (miRNAs), however, often target multiple genes and can act through non-canonical pathways, including pathways that activate gene expression. To interrogate all possible functions, we designed, synthesized, and validated the first shRNA-encoding library that is completely random at the nucleotide level. Screening in an IL3-dependent cell line, FL5.12, yielded shRNA-encoding sequences that double cell survival upon IL3 withdrawal. Using random mutagenesis and re-screening under more stringent IL3-starvation conditions, we hit-optimized one of the sequences; a specific nucleotide change and the creation of a mismatch between the two halves of the stem both contributed to the improved potency. Our library allows unbiased selection and optimization of shRNA-encoding sequences that confer phenotypes of interest, and could be used for the development of therapeutics and tools in many fields of biology.

SUBMITTER: Wang Y

PROVIDER: S-EPMC2525836 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Publications

A random shRNA-encoding library for phenotypic selection and hit-optimization.

Wang Yongping Y Wang Yun E YE Cotticelli M Grazia MG Wilson Robert B RB

PloS one 20080909 9

RNA interference (RNAi) is a mechanism for inhibiting gene expression through the action of small, non-coding RNAs. Most existing RNAi libraries target single genes through canonical pathways. Endogenous microRNAs (miRNAs), however, often target multiple genes and can act through non-canonical pathways, including pathways that activate gene expression. To interrogate all possible functions, we designed, synthesized, and validated the first shRNA-encoding library that is completely random at the ...[more]

PMID: 18779859

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Project description:BackgroundPraziquantel (PZQ) is the only widely available drug to treat schistosomiasis. Given the potential for drug resistance, it is prudent to search for novel therapeutics. Identification of anti-schistosomal chemicals has traditionally relied on phenotypic (whole organism) screening with adult worms in vitro and/or animal models of disease-tools that limit automation and throughput with modern microtiter plate-formatted compound libraries.MethodsA partially automated, three-component phenotypic screen workflow is presented that utilizes at its apex the schistosomular stage of the parasite adapted to a 96-well plate format with a throughput of 640 compounds per month. Hits that arise are subsequently screened in vitro against adult parasites and finally for efficacy in a murine model of disease. Two GO/NO GO criteria filters in the workflow prioritize hit compounds for tests in the animal disease model in accordance with a target drug profile that demands short-course oral therapy. The screen workflow was inaugurated with 2,160 chemically diverse natural and synthetic compounds, of which 821 are drugs already approved for human use. This affords a unique starting point to 'reposition' (re-profile) drugs as anti-schistosomals with potential savings in development timelines and costs.FindingsMultiple and dynamic phenotypes could be categorized for schistosomula and adults in vitro, and a diverse set of 'hit' drugs and chemistries were identified, including anti-schistosomals, anthelmintics, antibiotics, and neuromodulators. Of those hits prioritized for tests in the animal disease model, a number of leads were identified, one of which compares reasonably well with PZQ in significantly decreasing worm and egg burdens, and disease-associated pathology. Data arising from the three components of the screen are posted online as a community resource.ConclusionsTo accelerate the identification of novel anti-schistosomals, we have developed a partially automated screen workflow that interfaces schistosomula with microtiter plate-formatted compound libraries. The workflow has identified various compounds and drugs as hits in vitro and leads, with the prescribed oral efficacy, in vivo. Efforts to improve throughput, automation, and rigor of the screening workflow are ongoing.

Project description:Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a approximately 72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si_shRNA_selector/).

Dataset Information

A random shRNA-encoding library for phenotypic selection and hit-optimization.

Publications

A random shRNA-encoding library for phenotypic selection and hit-optimization.

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