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RNAi has become a very versatile tool to silence gene expression in a variety of organisms, in particular when classical genetic methods are missing. However, the application of this method in functional studies has raised new challenges in order to design RNAi reagents that minimize false positives and false negatives. Because the performance of reagents cannot be validated on a genome-wide scale, improved computational methods are required that consider experimentally derived quality measures. In this chapter, we describe computational methods for the design of RNAi reagents for invertebrate model organisms and human disease vectors, such as Anopheles. We describe procedures for designing short and long double-stranded RNAs for single genes, and evaluate their predicted specificity and efficiency. Using a bioinformatics pipeline we also describe how to design a genome-wide RNAi library for Anopheles gambiae.

RNA interference (RNAi) represents a powerful method to systematically study loss-of-function phenotypes on a large scale with a wide variety of biological assays, constituting a rich source for the assignment of gene function. The GenomeRNAi database (http://www.genomernai.org) makes available RNAi phenotype data extracted from the literature for human and Drosophila. It also provides RNAi reagent information, along with an assessment as to their efficiency and specificity. This manuscript describes an update of the database previously featured in the NAR Database Issue. The new version has undergone a complete re-design of the user interface, providing an intuitive, flexible framework for additional functionalities. Screen information and gene-reagent-phenotype associations are now available for download. The integration with other resources has been improved by allowing in-links via GenomeRNAi screen IDs, or external gene or reagent identifiers. A distributed annotation system (DAS) server enables the visualization of the phenotypes and reagents in the context of a genome browser. We have added a page listing 'frequent hitters', i.e. genes that show a phenotype in many screens, which might guide on-going RNAi studies. Structured annotation guidelines have been established to facilitate consistent curation, and a submission template for direct submission by data producers is available for download.

Background: Systematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively., Results: We present a comprehensive approach to the analysis of univariate phenotype measurements, such as cell growth. The method is based on a quantitative model and is demonstrated on two example Drosophila cell culture data sets. We discuss adjustments for technical variability, data quality assessment, model parameter fitting and fit diagnostics, choice of scale, and assessment of statistical significance., Conclusions: As a result, we obtain quantitative genetic interactions and interaction networks reflecting known biological relationships between target genes. The reliable extraction of presence, absence, and strength of interactions provides insights into molecular mechanisms.

The analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. Studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. Here we report a method to identify genetic interactions in tissue culture cells through RNAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. Computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of Ras-MAPK signaling. Large-scale genetic interaction mapping by RNAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks.

The design of RNA interference (RNAi) reagents is an essential step for performing loss-of-function studies in many experimental systems. The availability of sequenced and annotated genomes greatly facilitates RNAi experiments in an increasing number of organisms that were previously not genetically tractable. The E-RNAi web-service, accessible at http://www.e-rnai.org/, provides a computational resource for the optimized design and evaluation of RNAi reagents. The 2010 update of E-RNAi now covers 12 genomes, including Drosophila, Caenorhabditis elegans, human, emerging model organisms such as Schmidtea mediterranea and Acyrthosiphon pisum, as well as the medically relevant vectors Anopheles gambiae and Aedes aegypti. The web service calculates RNAi reagents based on the input of target sequences, sequence identifiers or by visual selection of target regions through a genome browser interface. It identifies optimized RNAi target-sites by ranking sequences according to their predicted specificity, efficiency and complexity. E-RNAi also facilitates the design of secondary RNAi reagents for validation experiments, evaluation of pooled siRNA reagents and batch design. Results are presented online, as a downloadable HTML report and as tab-delimited files.

Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

Transcription factors of the RUNX and GATA families play key roles in the control of cell fate choice and differentiation, notably in the hematopoietic system. During Drosophila hematopoiesis, the RUNX factor Lozenge and the GATA factor Serpent cooperate to induce crystal cell differentiation. We used Serpent/Lozenge-activated transcription as a paradigm to identify modulators of GATA/RUNX activity by a genome-wide RNA interference screen in cultured Drosophila blood cells. Among the 129 factors identified, several belong to the Mediator complex. Mediator is organized in three modules plus a regulatory "CDK8 module," composed of Med12, Med13, CycC, and Cdk8, which has long been thought to behave as a single functional entity. Interestingly, our data demonstrate that Med12 and Med13 but not CycC or Cdk8 are essential for Serpent/Lozenge-induced transactivation in cell culture. Furthermore, our in vivo analysis of crystal cell development show that, while the four CDK8 module subunits control the emergence and the proliferation of this lineage, only Med12 and Med13 regulate its differentiation. We thus propose that Med12/Med13 acts as a coactivator for Serpent/Lozenge during crystal cell differentiation independently of CycC/Cdk8. More generally, we suggest that the set of conserved factors identified herein may regulate GATA/RUNX activity in mammals.

Notch signaling plays a fundamental role in cellular differentiation and has been linked to human diseases, including cancer. We report the use of comprehensive RNAi analyses to dissect Notch regulation and its connections to cellular pathways. A cell-based RNAi screen identified 900 candidate Notch regulators on a genome-wide scale. The subsequent use of a library of transgenic Drosophila expressing RNAi constructs enabled large-scale in vivo validation and confirmed 333 of 501 tested genes as Notch regulators. Mapping the phenotypic attributes of our data on an interaction network identified another 68 relevant genes and revealed several modules of unexpected Notch regulatory activity. In particular, we note an intriguing relationship to pyruvate metabolism, which may be relevant to cancer. Our study reveals a hitherto unappreciated diversity of tissue-specific modulators impinging on Notch and opens new avenues for studying Notch regulation and function in development and disease., (Copyright 2010 Elsevier Inc. All rights reserved.)

RNA interference (RNAi) screens have enabled the systematic analysis of many biological processes in cultured cells and whole organisms. The success of such screens and the interpretation of the data depend on the stringent design of RNAi libraries. We describe and validate NEXT-RNAi, a software for the automated design and evaluation of RNAi sequences on a genome-wide scale. NEXT-RNAi is implemented as open-source software and is accessible at http://www.nextrnai.org/.

The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments.

RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible at http://rnai.dkfz.de.

RNA interference (RNAi) has become a powerful genetic approach to systematically dissect gene function on a genome-wide scale. Owing to the penetrance and efficiency of RNAi in invertebrates, model organisms such as Drosophila melanogaster and Caenorhabditis elegans have contributed significantly to the identification of novel components of diverse biological pathways, ranging from early development to fat storage and aging. For the correct assessment of phenotypes, a key issue remains the stringent quality control of long double-stranded RNAs (dsRNA) to calculate potential off-target effects that may obscure the phenotypic data. We here describe a web-based tool to evaluate and design optimized dsRNA constructs. Moreover, the application also gives access to published predesigned dsRNAs. The E-RNAi web application is available at http://e-rnai.dkfz.de/.

Mesenchymal stem cells (MSCs) are promising candidates for cellular therapies ranging from tissue repair in regenerative medicine to immunomodulation in graft versus host disease after allogeneic transplantation or in autoimmune diseases. Nonetheless, progress has been hampered by their enormous phenotypic as well as functional heterogeneity and the lack of uniform standards and guidelines for quality control. In this study, we describe a method to perform cellular phenotyping by high-throughput RNA interference in primary human bone marrow MSCs. We have shown that despite heterogeneity of MSC populations, robust functional assays can be established that are suitable for high-throughput and high-content screening. We profiled primary human MSCs against human fibroblasts. Network analysis showed a kinome fingerprint that differs from human primary fibroblasts as well as fibroblast cell lines. In conclusion, this study shows that high-throughput screening in primary human MSCs can be reliably used for kinome fingerprinting. [ABSTRACT FROM AUTHOR]

The article talks about the gene-gene interactions to form a complex phenotype which can be used to assign genes for modifying the effects of mutation during disease & development. It also focuses on forming of a genetic interaction map for 21 phenotypic features of Drosophila cells by using RNA (ribose nucleic acid) interference & automated single-cell phenotypes. In addition it talks about reconstructing directional regulatory networks for interpretation of genetic alterations.

Amyloid-β peptide (Aβ) is believed to play a central role in the pathogenesis of Alzheimer's disease. In view of the side effects associated with inhibiting the secretases that produce Aβ, new molecular targets are required to provide alternative therapeutic options. We used RNA interference (RNAi) to systematically screen the Drosophila genome to identify genes that modulate Aβ production upon knock-down. RNAi of 41 genes in Drosophila cells significantly lowered Aβ without affecting general secretion or viability. After the γ-secretase complex components, the most potent effect was observed for platelet activating factor acetylhydrolase α (Paf-AHα), and, in mammalian cells, the effect was replicated for its ortholog PAFAH1B2. Knockdown of PAFAH1B2 strongly reduced Aβ secretion from human cells, and this effect was confirmed in primary cells derived from PAFAH1B2 knock-out mice. Reduced Aβ production was not attributable to altered β-amyloid precursor protein (APP) ectodomain shedding but was a result of an enhanced degradation of APP C-terminal fragments (CTFs) in the absence of PAFAH1B2 but not its close homolog PAFAH1B3. Enhanced degradation of APP CTFs was selective because no such effects were obtained for Notch or E-/N-cadherin. Thus, we have identified an important protein that can selectively modify Aβ generation via a novel mechanism, namely enhanced degradation of its immediate precursor. In view of the absence of a neurological phenotype in PAFAH1B2 knock-out mice, targeted downregulation of PAFAH1B2 may be a promising new strategy for lowering Aβ. [ABSTRACT FROM AUTHOR]