RNAi reagent design

Figure 1 from the Escobedo et al. micropublication

Micropublication relevant to TRiP fly stocks

April 9, 2019

Users of TRiP RNAi and sgRNA fly stocks take note: the Weake lab at Purdue University brought to our attention that some TRiP fly stocks carry a mutant allele of seveneless. Jonathan Zirin worked with Spencer Escobedo and Vikki Weake, as well as with folks at the Bloomington Drosophila Stock Center, to quickly identify the source, sequence the mutant allele, and pubilsh a micropublication so we can get the details to the community. Bottom line, as stated in the micropublication, "The presence of the sev[21]  mutation will not generally affect the use of these stocks, as the X...

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Screenshot of online tools

Navigating our online tools -- orthologs, literature mining, qPCR primers, and so much more!

February 14, 2019

We have been taking a critical look at how we organize our online tools on the Online Tools Overview page. And more generally, we have been thinking about new ways to spread the word about the many resources in our suite of online tools. One way that we at the DRSC like to think about these tools is how they fit into the start-to-finish order of events in a screen or other experimental project. Various tools help define lists of genes to be studied, help identify reagents for the study,...

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Stephanie E Mohr, Kirstin Rudd, Yanhui Hu, Wei R Song, Quentin Gilly, Michael Buckner, Benjamin E Housden, Colleen Kelley, Jonathan Zirin, Rong Tao, Gabriel Amador, Katarzyna Sierzputowska, Aram Comjean, and Norbert Perrimon. 12/9/2017. “Zinc Detoxification: A Functional Genomics and Transcriptomics Analysis in Drosophila melanogaster Cultured Cells.” G3 (Bethesda).Abstract
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.
Chen X and Xu L. 2016. “Genome-Wide RNAi Screening to Dissect the TGF-β Signal Transduction Pathway.” Methods in Molecular Biology. Publisher's VersionAbstract

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.

Yanhui Hu, Aram Comjean, Charles Roesel, Arunachalam Vinayagam, Ian Flockhart, Jonathan Zirin, Lizabeth Perkins, Norbert Perrimon, and Stephanie E Mohr. 10/11/2016. “FlyRNAi.org—the database of the Drosophila RNAi screening center and transgenic RNAi project: 2017 update.” Nucleic Acids Research. Publisher's VersionAbstract

The FlyRNAi database of the Drosophila RNAi Screening Center (DRSC) and Transgenic RNAi Project (TRiP) at Harvard Medical School and associated DRSC/TRiP Functional Genomics Resources website (http://fgr.hms.harvard.edu) serve as a reagent production tracking system, screen data repository, and portal to the community. Through this portal, we make available protocols, online tools, and other resources useful to researchers at all stages of high-throughput functional genomics screening, from assay design and reagent identification to data analysis and interpretation. In this update, we describe recent changes and additions to our website, database and suite of online tools. Recent changes reflect a shift in our focus from a single technology (RNAi) and model species (Drosophila) to the application of additional technologies (e.g. CRISPR) and support of integrated, cross-species approaches to uncovering gene function using functional genomics and other approaches.

Benjamin E Housden, Matthias Muhar, Matthew Gemberling, Charles A Gersbach, Didier YR Stainier, Geraldine Seydoux, Stephanie E Mohr, Johannes Zuber, and Norbert Perrimon. 10/31/2016. “Loss-of-function genetic tools for animal models: cross-species and cross-platform differences.” Nat Rev Genet. Publisher's VersionAbstract

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.

Publication describes TRiP resources

Publication describes TRiP resources

July 8, 2016

Liz Perkins and colleagues have published a paper describing the Drosophila Transgenic RNAi Project (TRiP) at Harvard Medical School. The article, published in the November 1, 2015 issue of Geneticsdetails the TRiP production pipeline, reagents generated, state of the collection, and validation efforts.

This is a great introduction to the many in vivo RNAi resources the DRSC/TRiP-FGR provides to the scientific community.


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Ian T Flockhart, Matthew Booker, Yanhui Hu, Benjamin McElvany, Quentin Gilly, Bernard Mathey-Prevot, Norbert Perrimon, and Stephanie E Mohr. 2012. “FlyRNAi.org--the database of the Drosophila RNAi screening center: 2012 update.” Nucleic Acids Res, 40, Database issue, Pp. D715-9.Abstract

FlyRNAi (http://www.flyrnai.org), the database and website of the Drosophila RNAi Screening Center (DRSC) at Harvard Medical School, serves a dual role, tracking both production of reagents for RNA interference (RNAi) screening in Drosophila cells and RNAi screen results. The database and website is used as a platform for community availability of protocols, tools, and other resources useful to researchers planning, conducting, analyzing or interpreting the results of Drosophila RNAi screens. Based on our own experience and user feedback, we have made several changes. Specifically, we have restructured the database to accommodate new types of reagents; added information about new RNAi libraries and other reagents; updated the user interface and website; and added new tools of use to the Drosophila community and others. Overall, the result is a more useful, flexible and comprehensive website and database.

Stephanie E Mohr and Norbert Perrimon. 2012. “RNAi screening: new approaches, understandings, and organisms.” Wiley Interdiscip Rev RNA, 3, 2, Pp. 145-58.Abstract

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.

Susan Armknecht, Michael Boutros, Amy Kiger, Kent Nybakken, Bernard Mathey-Prevot, and Norbert Perrimon. 2005. “High-throughput RNA interference screens in Drosophila tissue culture cells.” Methods Enzymol, 392, Pp. 55-73.Abstract

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.

Yanhui Hu, Charles Roesel, Ian Flockhart, Lizabeth Perkins, Norbert Perrimon, and Stephanie E Mohr. 2013. “UP-TORR: online tool for accurate and Up-to-Date annotation of RNAi Reagents.” Genetics, 195, 1, Pp. 37-45.Abstract

RNA interference (RNAi) is a widely adopted tool for loss-of-function studies but RNAi results only have biological relevance if the reagents are appropriately mapped to genes. Several groups have designed and generated RNAi reagent libraries for studies in cells or in vivo for Drosophila and other species. At first glance, matching RNAi reagents to genes appears to be a simple problem, as each reagent is typically designed to target a single gene. In practice, however, the reagent-gene relationship is complex. Although the sequences of oligonucleotides used to generate most types of RNAi reagents are static, the reference genome and gene annotations are regularly updated. Thus, at the time a researcher chooses an RNAi reagent or analyzes RNAi data, the most current interpretation of the RNAi reagent-gene relationship, as well as related information regarding specificity (e.g., predicted off-target effects), can be different from the original interpretation. Here, we describe a set of strategies and an accompanying online tool, UP-TORR (for Updated Targets of RNAi Reagents; www.flyrnai.org/up-torr), useful for accurate and up-to-date annotation of cell-based and in vivo RNAi reagents. Importantly, UP-TORR automatically synchronizes with gene annotations daily, retrieving the most current information available, and for Drosophila, also synchronizes with the major reagent collections. Thus, UP-TORR allows users to choose the most appropriate RNAi reagents at the onset of a study, as well as to perform the most appropriate analyses of results of RNAi-based studies.

Nadire Ramadan, Ian Flockhart, Matthew Booker, Norbert Perrimon, and Bernard Mathey-Prevot. 2007. “Design and implementation of high-throughput RNAi screens in cultured Drosophila cells.” Nat Protoc, 2, 9, Pp. 2245-64.Abstract

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.