Cells require some metals, such as zinc and manganese, but excess levels of these metals can be toxic. As a result, cells have evolved complex mechanisms for maintaining metal homeostasis and surviving metal intoxication. Here, we present the results of a large-scale functional genomic screen in Drosophila cultured cells for modifiers of zinc chloride toxicity, together with transcriptomics data for wildtype or genetically zinc-sensitized cells challenged with mild zinc chloride supplementation. Altogether, we identified 47 genes for which knockdown conferred sensitivity or resistance to toxic zinc or manganese chloride treatment, and more than 1800 putative zinc-responsive genes. Analysis of the 'omics data points to the relevance of ion transporters, glutathione-related factors, and conserved disease-associated genes in zinc detoxification. Specific genes identified in the zinc screen include orthologs of human disease-associated genes CTNS, PTPRN (also known as IA-2), and ATP13A2 (also known as PARK9). We show that knockdown of red dog mine (rdog; CG11897), a candidate zinc detoxification gene encoding an ABCC-type transporter family protein related to yeast cadmium factor (YCF1), confers sensitivity to zinc intoxication in cultured cells and that rdog is transcriptionally up-regulated in response to zinc stress. As there are many links between the biology of zinc and other metals and human health, the 'omics datasets presented here provide a resource that will allow researchers to explore metal biology in the context of diverse health-relevant processes.
Our understanding of the genetic mechanisms that underlie biological processes has relied extensively on loss-of-function (LOF) analyses. LOF methods target DNA, RNA or protein to reduce or to ablate gene function. By analysing the phenotypes that are caused by these perturbations the wild-type function of genes can be elucidated. Although all LOF methods reduce gene activity, the choice of approach (for example, mutagenesis, CRISPR-based gene editing, RNA interference, morpholinos or pharmacological inhibition) can have a major effect on phenotypic outcomes. Interpretation of the LOF phenotype must take into account the biological process that is targeted by each method. The practicality and efficiency of LOF methods also vary considerably between model systems. We describe parameters for choosing the optimal combination of method and system, and for interpreting phenotypes within the constraints of each method.
The transforming growth factor-β (TGF-β) family of cytokines figures prominently in regulation of embryonic development and adult tissue homeostasis from Drosophila to mammals. Genetic defects affecting TGF-β signaling underlie developmental disorders and diseases such as cancer in human. Therefore, delineating the molecular mechanism by which TGF-β regulates cell biology is critical for understanding normal biology and disease mechanisms. Forward genetic screens in model organisms and biochemical approaches in mammalian tissue culture were instrumental in initial characterization of the TGF-β signal transduction pathway. With complete sequence information of the genomes and the advent of RNA interference (RNAi) technology, genome-wide RNAi screening emerged as a powerful functional genomics approach to systematically delineate molecular components of signal transduction pathways. Here, we describe a protocol for image-based whole-genome RNAi screening aimed at identifying molecules required for TGF-β signaling into the nucleus. Using this protocol we examined >90 % of annotated Drosophila open reading frames (ORF) individually and successfully uncovered several novel factors serving critical roles in the TGF-β pathway. Thus cell-based high-throughput functional genomics can uncover new mechanistic insights on signaling pathways beyond what the classical genetics had revealed.
BACKGROUND: RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial. RESULTS: Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files. CONCLUSION: Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.
Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, "meta" information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.
Gene silencing through sequence-specific targeting of mRNAs by RNAi has enabled genome-wide functional screens in cultured cells and in vivo in model organisms. These screens have resulted in the identification of new cellular pathways and potential drug targets. Considerable progress has been made to improve the quality of RNAi screen data through the development of new experimental and bioinformatics approaches. The recent availability of genome-editing strategies, such as the CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system, when combined with RNAi, could lead to further improvements in screen data quality and follow-up experiments, thus promoting our understanding of gene function and gene regulatory networks.
Here, I discuss how RNAi screening can be used effectively to uncover gene function. Specifically, I discuss the types of high-throughput assays that can be done in Drosophila cells and in vivo, RNAi reagent design and available reagent collections, automated screen pipelines, analysis of screen results, and approaches to RNAi results verification.
The evaluation of specific endogenous transcript levels is important for understanding transcriptional regulation. More specifically, it is useful for independent confirmation of results obtained by the use of microarray analysis or RNA-seq and for evaluating RNA interference (RNAi)-mediated gene knockdown. Designing specific and effective primers for high-quality, moderate-throughput evaluation of transcript levels, i.e., quantitative, real-time PCR (qPCR), is nontrivial. To meet community needs, predefined qPCR primer pairs for mammalian genes have been designed and sequences made available, e.g., via PrimerBank. In this work, we adapted and refined the algorithms used for the mammalian PrimerBank to design 45,417 primer pairs for 13,860 Drosophila melanogaster genes, with three or more primer pairs per gene. We experimentally validated primer pairs for ~300 randomly selected genes expressed in early Drosophila embryos, using SYBR Green-based qPCR and sequence analysis of products derived from conventional PCR. All relevant information, including primer sequences, isoform specificity, spatial transcript targeting, and any available validation results and/or user feedback, is available from an online database (www.flyrnai.org/flyprimerbank). At FlyPrimerBank, researchers can retrieve primer information for fly genes either one gene at a time or in batch mode. Importantly, we included the overlap of each predicted amplified sequence with RNAi reagents from several public resources, making it possible for researchers to choose primers suitable for knockdown evaluation of RNAi reagents (i.e., to avoid amplification of the RNAi reagent itself). We demonstrate the utility of this resource for validation of RNAi reagents in vivo.
RNA interference (RNAi) leads to sequence-specific knockdown of gene function. The approach can be used in large-scale screens to interrogate function in various model organisms and an increasing number of other species. Genome-scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High-throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high-content image data. The results of large-scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity, and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high-throughput approaches and specific to RNAi screens, false discovery due to off-target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches.
BACKGROUND: High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention. RESULTS: We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene. CONCLUSIONS: RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.
RNA interference (RNAi) is an effective tool for genome-scale, high-throughput analysis of gene function. In the past five years, a number of genome-scale RNAi high-throughput screens (HTSs) have been done in both Drosophila and mammalian cultured cells to study diverse biological processes, including signal transduction, cancer biology, and host cell responses to infection. Results from these screens have led to the identification of new components of these processes and, importantly, have also provided insights into the complexity of biological systems, forcing new and innovative approaches to understanding functional networks in cells. Here, we review the main findings that have emerged from RNAi HTS and discuss technical issues that remain to be improved, in particular the verification of RNAi results and validation of their biological relevance. Furthermore, we discuss the importance of multiplexed and integrated experimental data analysis pipelines to RNAi HTS.
RNA interference (RNAi) provides a powerful reverse genetics approach to analyze gene functions both in tissue culture and in vivo. Because of its widespread applicability and effectiveness it has become an essential part of the tool box kits of model organisms such as Caenorhabditis elegans, Drosophila, and the mouse. In addition, the use of RNAi in animals in which genetic tools are either poorly developed or nonexistent enables a myriad of fundamental questions to be asked. Here, we review the methods and applications of in vivo RNAi to characterize gene functions in model organisms and discuss their impact to the study of developmental as well as evolutionary questions. Further, we discuss the applications of RNAi technologies to crop improvement, pest control and RNAi therapeutics, thus providing an appreciation of the potential for phenomenal applications of RNAi to agriculture and medicine.
This protocol describes the various steps and considerations involved in planning and carrying out RNA interference (RNAi) genome-wide screens in cultured Drosophila cells. We focus largely on the procedures that have been modified as a result of our experience over the past 3 years and of our better understanding of the underlying technology. Specifically, our protocol offers a set of suggestions and considerations for screen optimization and a step-by-step description of the procedures successfully used at the Drosophila RNAi Screening Center for screen implementation, data collection and analysis to identify potential hits. In addition, this protocol briefly covers postscreen analysis approaches that are often needed to finalize the hit list. Depending on the scope of the screen and subsequent analysis and validation involved, the full protocol can take anywhere from 3 months to 2 years to complete.
To evaluate the specificity of long dsRNAs used in high-throughput RNA interference (RNAi) screens performed at the Drosophila RNAi Screening Center (DRSC), we performed a global analysis of their activity in 30 genome-wide screens completed at our facility. Notably, our analysis predicts that dsRNAs containing > or = 19-nucleotide perfect matches identified in silico to unintended targets may contribute to a significant false positive error rate arising from off-target effects. We confirmed experimentally that such sequences in dsRNAs lead to false positives and to efficient knockdown of a cross-hybridizing transcript, raising a cautionary note about interpreting results based on the use of a single dsRNA per gene. Although a full appreciation of all causes of false positive errors remains to be determined, we suggest simple guidelines to help ensure high-quality information from RNAi high-throughput screens.
RNA interference (RNAi) has become a powerful tool for genetic screening in Drosophila. At the Drosophila RNAi Screening Center (DRSC), we are using a library of over 21,000 double-stranded RNAs targeting known and predicted genes in Drosophila. This library is available for the use of visiting scientists wishing to perform full-genome RNAi screens. The data generated from these screens are collected in the DRSC database (http://flyRNAi.org/cgi-bin/RNAi_screens.pl) in a flexible format for the convenience of the scientist and for archiving data. The long-term goal of this database is to provide annotations for as many of the uncharacterized genes in Drosophila as possible. Data from published screens are available to the public through a highly configurable interface that allows detailed examination of the data and provides access to a number of other databases and bioinformatics tools.
RNA interference has re-energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Looking back on the lessons that have been learned from the first wave of technology developments and applications in this exciting field, we provide both a user's guide for newcomers to the field and a detailed examination of some more complex issues, particularly concerning optimization and quality control, for more advanced users. From a discussion of cell lines, screening paradigms, reagent types and read-out methodologies, we explore in particular the complexities of designing optimal controls and normalization strategies for these challenging but extremely powerful studies.
This chapter describes the method used to conduct high-throughput screening (HTs) by RNA interference in Drosophila tissue culture cells. It covers four main topics: (1) a brief description of the existing platforms to conduct RNAi-screens in cell-based assays; (2) a table of the Drosophila cell lines available for these screens and a brief mention of the need to establish other cell lines as well as cultures of primary cells; (3) a discussion of the considerations and protocols involved in establishing assays suitable for HTS in a 384-well format; and (A) a summary of the various ways of handling raw data from an ongoing screen, with special emphasis on how to apply normalization for experimental variation and statistical filters to sort out noise from signals.