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1.  A Genetic RNAi Screen for IP3/Ca2+ Coupled GPCRs in Drosophila Identifies the PdfR as a Regulator of Insect Flight 
PLoS Genetics  2013;9(10):e1003849.
Insect flight is regulated by various sensory inputs and neuromodulatory circuits which function in synchrony to control and fine-tune the final behavioral outcome. The cellular and molecular bases of flight neuromodulatory circuits are not well defined. In Drosophila melanogaster, it is known that neuronal IP3 receptor mediated Ca2+ signaling and store-operated Ca2+ entry (SOCE) are required for air-puff stimulated adult flight. However, G-protein coupled receptors (GPCRs) that activate intracellular Ca2+ signaling in the context of flight are unknown in Drosophila. We performed a genetic RNAi screen to identify GPCRs that regulate flight by activating the IP3 receptor. Among the 108 GPCRs screened, we discovered 5 IP3/Ca2+ linked GPCRs that are necessary for maintenance of air-puff stimulated flight. Analysis of their temporal requirement established that while some GPCRs are required only during flight circuit development, others are required both in pupal development as well as during adult flight. Interestingly, our study identified the Pigment Dispersing Factor Receptor (PdfR) as a regulator of flight circuit development and as a modulator of acute flight. From the analysis of PdfR expressing neurons relevant for flight and its well-defined roles in other behavioral paradigms, we propose that PdfR signaling functions systemically to integrate multiple sensory inputs and modulate downstream motor behavior.
Author Summary
A majority of behavioral patterns in flying insects depend upon their ability to modulate flight. In Drosophila melanogaster, mutations in the IP3 receptor gene lead to loss of voluntary flight in response to a natural stimulus like a gentle air-puff. From previous genetic and cellular studies it is known that the IP3R in Drosophila is activated by G-protein coupled receptors (GPCRs). However, GPCRs that act upstream of the IP3R in the context of flight are not known. Therefore, we performed a genetic RNAi screen to identify GPCRs which regulate flight. This screen was followed by a secondary suppressor screen that assessed the role of each identified GPCR in activating IP3/Ca2+ signaling. We found 5 such GPCRs. Our results demonstrate that these GPCRs are required during flight circuit development and during adult flight. One flight-regulating receptor identified was the Pigment Dispersing Factor Receptor (PdfR). This receptor is known to regulate behaviors such as circadian rhythms, geotaxis and reproduction. A spatio-temporal analysis of PdfR flight function indicates that it regulates both flight circuit development and acute flight through multiple neurons. We postulate that PdfR signaling could modulate and integrate multiple behavioral inputs in Drosophila and other flying insects.
PMCID: PMC3789835  PMID: 24098151
2.  Clustering phenotype populations by genome-wide RNAi and multiparametric imaging 
How to predict gene function from phenotypic cues is a longstanding question in biology.Using quantitative multiparametric imaging, RNAi-mediated cell phenotypes were measured on a genome-wide scale.On the basis of phenotypic ‘neighbourhoods', we identified previously uncharacterized human genes as mediators of the DNA damage response pathway and the maintenance of genomic integrity.The phenotypic map is provided as an online resource at for discovering further functional relationships for a broad spectrum of biological module
Genetic screens for phenotypic similarity have made key contributions for associating genes with biological processes. Aggregating genes by similarity of their loss-of-function phenotype has provided insights into signalling pathways that have a conserved function from Drosophila to human (Nusslein-Volhard and Wieschaus, 1980; Bier, 2005). Complex visual phenotypes, such as defects in pattern formation during development, greatly facilitated the classification of genes into pathways, and phenotypic similarities in many cases predicted molecular relationships. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cultured cells has become feasible in many organisms whose genome have been sequenced (Boutros and Ahringer, 2008). One of the current challenges is the computational categorization of visual phenotypes and the prediction of gene function and associated biological processes. With large parts of the genome still being in unchartered territory, deriving functional information from large-scale phenotype analysis promises to uncover novel gene–gene relationships and to generate functional maps to explore cellular processes.
In this study, we developed an automated approach using RNAi-mediated cell phenotypes, multiparametric imaging and computational modelling to obtain functional information on previously uncharacterized genes. To generate broad, computer-readable phenotypic signatures, we measured the effect of RNAi-mediated knockdowns on changes of cell morphology in human cells on a genome-wide scale. First, the several million cells were stained for nuclear and cytoskeletal markers and then imaged using automated microscopy. On the basis of fluorescent markers, we established an automated image analysis to classify individual cells (Figure 1A). After cell segmentation for determining nuclei and cell boundaries (Figure 1C), we computed 51 cell descriptors that quantified intensities, shape characteristics and texture (Figure 1F). Individual cells were categorized into 1 of 10 classes, which included cells showing protrusion/elongation, cells in metaphase, large cells, condensed cells, cells with lamellipodia and cellular debris (Figure 1D and E). Each siRNA knockdown was summarized by a phenotypic profile and differences between RNAi knockdowns were quantified by the similarity between phenotypic profiles. We termed the vector of scores a phenoprint (Figure 3C) and defined the phenotypic distance between a pair of perturbations as the distance between their corresponding phenoprints.
To visualize the distribution of all phenoprints, we plotted them in a genome-wide map as a two-dimensional representation of the phenotypic similarity relationships (Figure 3A). The complete data set and an interactive version of the phenotypic map are available at The map identified phenotypic ‘neighbourhoods', which are characterized by cells with lamellipodia (WNK3, ANXA4), cells with prominent actin fibres (ODF2, SOD3), abundance of large cells (CA14), many elongated cells (SH2B2, ELMO2), decrease in cell number (TPX2, COPB1, COPA), increase in number of cells in metaphase (BLR1, CIB2) and combinations of phenotypes such as presence of large cells with protrusions and bright nuclei (PTPRZ1, RRM1; Figure 3B).
To test whether phenotypic similarity might serve as a predictor of gene function, we focused our further analysis on two clusters that contained genes associated with the DNA damage response (DDR) and genomic integrity (Figure 3A and C). The first phenotypic cluster included proteins with kinetochore-associated functions such as NUF2 (Figure 3B) and SGOL1. It also contained the centrosomal protein CEP164 that has been described as an important mediator of the DNA damage-activated signalling cascade (Sivasubramaniam et al, 2008) and the largely uncharacterized genes DONSON and SON. A second phenotypically distinct cluster included previously described components of the DDR pathway such as RRM1 (Figure 3A–C), CLSPN, PRIM2 and SETD8. Furthermore, this cluster contained the poorly characterized genes CADM1 and CD3EAP.
Cells activate a signalling cascade in response to DNA damage induced by exogenous and endogenous factors. Central are the kinases ATM and ATR as they serve as sensors of DNA damage and activators of further downstream kinases (Harper and Elledge, 2007; Cimprich and Cortez, 2008). To investigate whether DONSON, SON, CADM1 and CD3EAP, which were found in phenotypic ‘neighbourhoods' to known DDR components, have a role in the DNA damage signalling pathway, we tested the effect of their depletion on the DDR on γ irradiation. As indicated by reduced CHEK1 phosphorylation, siRNA knock down of DONSON, SON, CD3EAP or CADM1 resulted in impaired DDR signalling on γ irradiation. Furthermore, knock down of DONSON or SON reduced phosphorylation of downstream effectors such as NBS1, CHEK1 and the histone variant H2AX on UVC irradiation. DONSON depletion also impaired recruitment of RPA2 onto chromatin and SON knockdown reduced RPA2 phosphorylation indicating that DONSON and SON presumably act downstream of the activation of ATM. In agreement to their phenotypic profile, these results suggest that DONSON, SON, CADM1 and CD3EAP are important mediators of the DDR. Further experiments demonstrated that they are also required for the maintenance of genomic integrity.
In summary, we show that genes with similar phenotypic profiles tend to share similar functions. The power of our computational and experimental approach is demonstrated by the identification of novel signalling regulators whose phenotypic profiles were found in proximity to known biological modules. Therefore, we believe that such phenotypic maps can serve as a resource for functional discovery and characterization of unknown genes. Furthermore, such approaches are also applicable for other perturbation reagents, such as small molecules in drug discovery and development. One could also envision combined maps that contain both siRNAs and small molecules to predict target–small molecule relationships and potential side effects.
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.
PMCID: PMC2913390  PMID: 20531400
DNA damage response signalling; massively parallel phenotyping; phenotype networks; RNAi screening
3.  Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis 
BMC Bioinformatics  2014;15:192.
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.
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.
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.
PMCID: PMC4073188  PMID: 24934636
RNAi; Off-target effects; Data analysis; Seed region; miRNA; siRNA; shRNA; High-throughput screening
4.  GenomeRNAi: a database for cell-based and in vivo RNAi phenotypes, 2013 update 
Nucleic Acids Research  2012;41(Database issue):D1021-D1026.
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 ( 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.
PMCID: PMC3531141  PMID: 23193271
5.—the database of the Drosophila RNAi screening center: 2012 update 
Nucleic Acids Research  2011;40(Database issue):D715-D719.
FlyRNAi (, 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.
PMCID: PMC3245182  PMID: 22067456
6.  GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data 
PLoS ONE  2012;7(11):e49386.
High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.
The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.
With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at
PMCID: PMC3502531  PMID: 23185323
7.  A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila 
BMC Genomics  2009;10:220.
The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues.
By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level.
By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.
PMCID: PMC2697172  PMID: 19435510
8.  web cellHTS2: A web-application for the analysis of high-throughput screening data 
BMC Bioinformatics  2010;11:185.
The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.
The software guides the user through the configuration steps that are required for the analysis of single or multi-channel experiments. The web-application provides options for various standardization and normalization methods, annotation of data sets and a comprehensive HTML report of the screening data analysis, including a ranked hit list. Sessions can be saved and restored for later re-analysis. The web frontend for the cellHTS2 R/Bioconductor package interacts with it through an R-server implementation that enables highly parallel analysis of screening data sets. web cellHTS2 further provides a file import and configuration module for common file formats.
The implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at A standalone version as a virtual appliance and source code for platforms supporting Java 1.5.0 can be downloaded from the web cellHTS2 page. web cellHTS2 is freely distributed under GPL.
PMCID: PMC3098057  PMID: 20385013
9.  GenomeRNAi: a database for cell-based RNAi phenotypes 
Nucleic Acids Research  2006;35(Database issue):D492-D497.
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
PMCID: PMC1747177  PMID: 17135194
10.  FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets 
Nucleic Acids Research  2005;34(Database issue):D479-D483.
FLIGHT () is a new database designed to help researchers browse and cross-correlate data from large-scale RNAi studies. To date, the majority of these functional genomic screens have been carried out using Drosophila cell lines. These RNAi screens follow 100 years of classical Drosophila genetics, but have already revealed their potential by ascribing an impressive number of functions to known and novel genes. This has in turn given rise to a pressing need for tools to simplify the analysis of the large amount of phenotypic information generated. FLIGHT aims to do this by providing users with a gene-centric view of screen results and by making it possible to cluster phenotypic data to identify genes with related functions. Additionally, FLIGHT provides microarray expression data for many of the Drosophila cell lines commonly used in RNAi screens. This, together with information about cell lines, protocols and dsRNA primer sequences, is intended to help researchers design their own cell-based screens. Finally, although the current focus of FLIGHT is Drosophila, the database has been designed to facilitate the comparison of functional data across species and to help researchers working with other systems navigate their way through the fly genome.
PMCID: PMC1347401  PMID: 16381916
11.  Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file.  
Integrative Biology  2016;8(7):761-774.
We construct a pathway de novo for microenvironment-specific genetic regulators of acute lymphoblastic leukemia using RNAi screening, and mRNA data.
Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual ‘omics measurement. We leverage multiple ‘omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual ‘omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes – Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein – for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies – siRNA, shRNA, and CRISPR – generally, and will improve the utility of functional genomic studies.
PMCID: PMC5224708  PMID: 27315426
12.  Reliability analysis of the Ahringer Caenorhabditis elegans RNAi feeding library: a guide for genome-wide screens 
BMC Genomics  2011;12:170.
The Ahringer C. elegans RNAi feeding library prepared by cloning genomic DNA fragments has been widely used in genome-wide analysis of gene function. However, the library has not been thoroughly validated by direct sequencing, and there are potential errors, including: 1) mis-annotation (the clone with the retired gene name should be remapped to the actual target gene); 2) nonspecific PCR amplification; 3) cross-RNAi; 4) mis-operation such as sample loading error, etc.
Here we performed a reliability analysis on the Ahringer C. elegans RNAi feeding library, which contains 16,256 bacterial strains, using a bioinformatics approach. Results demonstrated that most (98.3%) of the bacterial strains in the library are reliable. However, we also found that 2,851 (17.54%) bacterial strains need to be re-annotated even they are reliable. Most of these bacterial strains are the clones having the retired gene names. Besides, 28 strains are grouped into unreliable category and 226 strains are marginal because of probably expressing unrelated double-stranded RNAs (dsRNAs). The accuracy of the prediction was further confirmed by direct sequencing analysis of 496 bacterial strains. Finally, a freely accessible database named CelRNAi ( was developed as a valuable complement resource for the feeding RNAi library by providing the predicted information on all bacterial strains. Moreover, submission of the direct sequencing result or any other annotations for the bacterial strains to the database are allowed and will be integrated into the CelRNAi database to improve the accuracy of the library. In addition, we provide five candidate primer sets for each of the unreliable and marginal bacterial strains for users to construct an alternative vector for their own RNAi studies.
Because of the potential unreliability of the Ahringer C. elegans RNAi feeding library, we strongly suggest the user examine the reliability information of the bacterial strains in the CelRNAi database before performing RNAi experiments, as well as the post-RNAi experiment analysis.
PMCID: PMC3087708  PMID: 21453524
13.  E-RNAi: a web application for the multi-species design of RNAi reagents—2010 update 
Nucleic Acids Research  2010;38(Web Server issue):W332-W339.
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, 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.
PMCID: PMC2896145  PMID: 20444868
14.  Identification of Drosophila Mitotic Genes by Combining Co-Expression Analysis and RNA Interference 
PLoS Genetics  2008;4(7):e1000126.
RNAi screens have, to date, identified many genes required for mitotic divisions of Drosophila tissue culture cells. However, the inventory of such genes remains incomplete. We have combined the powers of bioinformatics and RNAi technology to detect novel mitotic genes. We found that Drosophila genes involved in mitosis tend to be transcriptionally co-expressed. We thus constructed a co-expression–based list of 1,000 genes that are highly enriched in mitotic functions, and we performed RNAi for each of these genes. By limiting the number of genes to be examined, we were able to perform a very detailed phenotypic analysis of RNAi cells. We examined dsRNA-treated cells for possible abnormalities in both chromosome structure and spindle organization. This analysis allowed the identification of 142 mitotic genes, which were subdivided into 18 phenoclusters. Seventy of these genes have not previously been associated with mitotic defects; 30 of them are required for spindle assembly and/or chromosome segregation, and 40 are required to prevent spontaneous chromosome breakage. We note that the latter type of genes has never been detected in previous RNAi screens in any system. Finally, we found that RNAi against genes encoding kinetochore components or highly conserved splicing factors results in identical defects in chromosome segregation, highlighting an unanticipated role of splicing factors in centromere function. These findings indicate that our co-expression–based method for the detection of mitotic functions works remarkably well. We can foresee that elaboration of co-expression lists using genes in the same phenocluster will provide many candidate genes for small-scale RNAi screens aimed at completing the inventory of mitotic proteins.
Author Summary
Mitosis is the evolutionarily conserved process that enables a dividing cell to equally partition its genetic material between the two daughter cells. The fidelity of mitotic division is crucial for normal development of multicellular organisms and to prevent cancer or birth defects. Understanding the molecular mechanisms of mitosis requires the identification of genes involved in this process. Previous studies have shown that such genes can be readily identified by RNA interference (RNAi) in Drosophila tissue culture cells. Because the inventory of mitotic genes is still incomplete, we have undertaken an RNAi screen using a novel approach. We used a co-expression–based bioinformatic procedure to select a group of 1,000 genes enriched in mitotic functions from a dataset of 13,166 Drosophila genes. This group includes roughly half of the known mitotic genes, implying that it should contain half of all mitotic genes, including those that are currently unknown. We performed RNAi against each of the 1,000 genes in the group. By limiting the number of genes to be examined, we were able to perform a very detailed phenotypic analysis of RNAi cells. This analysis allowed the identification of 70 genes whose mitotic role was previously unknown; 30 are required for proper chromosome segregation and 40 are required to maintain chromosome integrity.
PMCID: PMC2537813  PMID: 18797514
15.  GenomeRNAi: a database for cell-based RNAi phenotypes. 2009 update 
Nucleic Acids Research  2009;38(Database issue):D448-D452.
The GenomeRNAi database ( 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.
PMCID: PMC2808900  PMID: 19910367
16.  ScreenSifter: analysis and visualization of RNAi screening data 
BMC Bioinformatics  2013;14:290.
RNAi screening is a powerful method to study the genetics of intracellular processes in metazoans. Technically, the approach has been largely inspired by techniques and tools developed for compound screening, including those for data analysis. However, by contrast with compounds, RNAi inducing agents can be linked to a large body of gene-centric, publically available data. However, the currently available software applications to analyze RNAi screen data usually lack the ability to visualize associated gene information in an interactive fashion.
Here, we present ScreenSifter, an open-source desktop application developed to facilitate storing, statistical analysis and rapid and intuitive biological data mining of RNAi screening datasets. The interface facilitates meta-data acquisition and long-term safe-storage, while the graphical user interface helps the definition of a hit list and the visualization of biological modules among the hits, through Gene Ontology and protein-protein interaction analyses. The application also allows the visualization of screen-to-screen comparisons.
Our software package, ScreenSifter, can accelerate and facilitate screen data analysis and enable discovery by providing unique biological data visualization capabilities.
PMCID: PMC4014807  PMID: 24088301
RNAi screening; Data visualization; Database; Data analysis; Data mining
17.  A suite of MATLAB-based computational tools for automated analysis of COPAS Biosort data 
BioTechniques  2010;48(6):xxv-xxx.
Complex Object Parametric Analyzer and Sorter (COPAS) devices are large-object, fluorescence-capable flow cytometers used for high-throughput analysis of live model organisms, including Drosophila melanogaster, Caenorhabditis elegans, and zebrafish. The COPAS is especially useful in C. elegans high-throughput genome-wide RNA interference (RNAi) screens that utilize fluorescent reporters. However, analysis of data from such screens is relatively labor-intensive and time-consuming. Currently, there are no computational tools available to facilitate high-throughput analysis of COPAS data. We used MATLAB to develop algorithms (COPAquant, COPAmulti, and COPAcompare) to analyze different types of COPAS data. COPAquant reads single-sample files, filters and extracts values and value ratios for each file, and then returns a summary of the data. COPAmulti reads 96-well autosampling files generated with the ReFLX adapter, performs sample filtering, graphs features across both wells and plates, performs some common statistical measures for hit identification, and outputs results in graphical formats. COPAcompare performs a correlation analysis between replicate 96-well plates. For many parameters, thresholds may be defined through a simple graphical user interface (GUI), allowing our algorithms to meet a variety of screening applications. In a screen for regulators of stress-inducible GFP expression, COPAquant dramatically accelerated data analysis and allowed us to rapidly move from raw data to hit identification. Because the COPAS file structure is standardized and our MATLAB code is freely available, our algorithms should be extremely useful for analysis of COPAS data from multiple platforms and organisms. The MATLAB code is freely available at our web site (
PMCID: PMC2892394  PMID: 20569218
C. elegans; computational method; high-throughput screening; flow cytometry; Wormsorter
18.  Analysis of high-throughput RNAi screening data in identifying genes mediating sensitivity to chemotherapeutic drugs: statistical approaches and perspectives 
BMC Genomics  2012;13(Suppl 8):S3.
High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates.
In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation.
RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.
PMCID: PMC3535706  PMID: 23281588
19.  The Complex I Subunit NDUFA10 Selectively Rescues Drosophila pink1 Mutants through a Mechanism Independent of Mitophagy 
PLoS Genetics  2014;10(11):e1004815.
Mutations in PINK1, a mitochondrially targeted serine/threonine kinase, cause autosomal recessive Parkinson's disease (PD). Substantial evidence indicates that PINK1 acts with another PD gene, parkin, to regulate mitochondrial morphology and mitophagy. However, loss of PINK1 also causes complex I (CI) deficiency, and has recently been suggested to regulate CI through phosphorylation of NDUFA10/ND42 subunit. To further explore the mechanisms by which PINK1 and Parkin influence mitochondrial integrity, we conducted a screen in Drosophila cells for genes that either phenocopy or suppress mitochondrial hyperfusion caused by pink1 RNAi. Among the genes recovered from this screen was ND42. In Drosophila pink1 mutants, transgenic overexpression of ND42 or its co-chaperone sicily was sufficient to restore CI activity and partially rescue several phenotypes including flight and climbing deficits and mitochondrial disruption in flight muscles. Here, the restoration of CI activity and partial rescue of locomotion does not appear to have a specific requirement for phosphorylation of ND42 at Ser-250. In contrast to pink1 mutants, overexpression of ND42 or sicily failed to rescue any Drosophila parkin mutant phenotypes. We also find that knockdown of the human homologue, NDUFA10, only minimally affecting CCCP-induced mitophagy, and overexpression of NDUFA10 fails to restore Parkin mitochondrial-translocation upon PINK1 loss. These results indicate that the in vivo rescue is due to restoring CI activity rather than promoting mitophagy. Our findings support the emerging view that PINK1 plays a role in regulating CI activity separate from its role with Parkin in mitophagy.
Author Summary
Two genes linked to heritable forms of the neurodegenerative movement disorder Parkinson's disease (PD), PINK1 and parkin, play important roles in mitochondrial homeostasis through mechanisms which include the degradation of dysfunctional mitochondria, termed mitophagy, and the maintenance of complex I (CI) activity. Here we report the findings of an RNAi based screen in Drosophila cells for genes that may regulate the PINK1-Parkin pathway which identified NDUFA10 (ND42 in Drosophila), a subunit of CI. Using a well-established cellular system and in vivo Drosophila genetics, we demonstrate that while NDUFA10/ND42 only plays a minimal role in mitophagy, restoration of CI activity through overexpression of either ND42 or its co-chaperone sicily is able to substantially rescue behavioral deficits in pink1 mutants but not parkin mutants. Moreover, while parkin overexpression is known to rescue pink1 mutants, it apparently achieves this without restoring CI activity. These results suggest that increasing CI activity or promoting mitophagy can be beneficial in pink1 mutants, and further highlights separable functions of PINK1 and Parkin.
PMCID: PMC4238976  PMID: 25412178
20.  Simultaneous analysis of large-scale RNAi screens for pathogen entry 
BMC Genomics  2014;15(1):1162.
Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.
We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.
Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-1162) contains supplementary material, which is available to authorized users.
PMCID: PMC4326433  PMID: 25534632
High-throughput high-content RNAi screening; Pathogen entry; Linear mixed model; Hit detection
21.  Synaptic Activity in Serotonergic Neurons Is Required for Air-Puff Stimulated Flight in Drosophila melanogaster 
PLoS ONE  2012;7(9):e46405.
Flight is an integral component of many complex behavioral patterns in insects. The giant fiber circuit has been well studied in several insects including Drosophila. However, components of the insect flight circuit that respond to an air-puff stimulus and comprise the flight central pattern generator are poorly defined. Aminergic neurons have been implicated in locust, moth and Drosophila flight. Here we have investigated the requirement of neuronal activity in serotonergic neurons, during development and in adults, on air-puff induced flight in Drosophila.
Methodology/Principal Findings
To target serotonergic neurons specifically, a Drosophila strain that contains regulatory regions from the TRH (Tryptophan Hydroxylase) gene linked to the yeast transcription factor GAL4 was used. By blocking synaptic transmission from serotonergic neurons with a tetanus toxin transgene or by hyperpolarisation with Kir2.1, close to 50% adults became flightless. Temporal expression of a temperature sensitive Dynamin mutant transgene (Shits) suggests that synaptic function in serotonergic neurons is required both during development and in adults. Depletion of IP3R in serotonergic neurons via RNAi did not affect flight. Interestingly, at all stages a partial requirement for synaptic activity in serotonergic neurons was observed. The status of serotonergic neurons was investigated in the central nervous system of larvae and adults expressing tetanus toxin. A small but significant reduction was observed in serotonergic cell number in adult second thoracic segments from flightless tetanus toxin expressing animals.
These studies show that loss of synaptic activity in serotonergic neurons causes a flight deficit. The temporal focus of the flight deficit is during pupal development and in adults. The cause of the flight deficit is likely to be loss of neurons and reduced synaptic function. Based on the partial phenotypes, serotonergic neurons appear to be modulatory, rather than an intrinsic part of the flight circuit.
PMCID: PMC3459902  PMID: 23029511
22.  An integrative approach for a network based meta-analysis of viral RNAi screens 
Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization.
To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules.
We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.
Electronic supplementary material
The online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users.
PMCID: PMC4331137  PMID: 25691914
Network analysis; RNAi screening; Virus-host interactions
23.—the database of the Drosophila RNAi screening center and transgenic RNAi project: 2017 update 
Nucleic Acids Research  2016;45(Database issue):D672-D678.
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 ( 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.
PMCID: PMC5210654  PMID: 27924039
24.  False negative rates in Drosophila cell-based RNAi screens: a case study 
BMC Genomics  2011;12:50.
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.
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.
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.
PMCID: PMC3036618  PMID: 21251254
25.  RNAi screen of Salmonella invasion shows role of COPI in membrane targeting of cholesterol and Cdc42 
A genome wide RNAi screen identifies 72 host cell genes affecting S. Typhimurium entry, including actin regulators and COPI. This study implicates COPI-dependent cholesterol and sphingolipid localization as a common mechanism of infection by bacterial and viral pathogens.
Genome-scale RNAi screen identifies 72 host genes affecting S. Typhimurium host cell invasion.Step-specific follow-up assays assign the phenotypes to specific steps of the invasion process.COPI effects on host cell binding, ruffling and invasion were traced to a key role of COPI in membrane targeting of cholesterol, sphingolipids, Rac1 and Cdc42.This new role of COPI explains why COPI is required for host cell infection by numerous bacterial and viral pathogens.
Pathogens are not only a menace to public health, but they also provide excellent tools for probing host cell function. Thus, studying infection mechanisms has fueled progress in cell biology (Ridley et al, 1992; Welch et al, 1997). In the presented study, we have performed an RNAi screen to identify host cell genes required for Salmonella host cell invasion. This screen identified proteins known to contribute to Salmonella-induced actin rearrangements (e.g., Cdc42 and the Arp2/3 complex; reviewed in Schlumberger and Hardt, 2006) and vesicular traffic (e.g., Rab7) as well as unexpected hits, such as the COPI complex. COPI is a known organizer of Golgi-to-ER vesicle transport (Bethune et al, 2006; Beck et al, 2009). Here, we show that COPI is also involved in plasma membrane targeting of cholesterol, sphingolipids and the Rho GTPases Cdc42 and Rac1, essential host cell factors required for Salmonella invasion. This explains why COPI depletion inhibits infection by S. Typhimurium and illustrates how combining bacterial pathogenesis and systems approaches can promote cell biology.
Salmonella Typhimurium is a common food-borne pathogen and worldwide a major public health problem causing severe diarrhea. The pathogen uses the host's gut mucosa as a portal of entry and gut tissue invasion is a key event leading to the disease. This explains the intense interest from medicine and basic biology in the mechanism of Salmonella host cell invasion.
Tissue culture infection models have delineated a sequence of events leading host cell invasion (Figure 1; Schlumberger and Hardt, 2006): (i) pathogen binding to the host cell surface; (ii) activation of a syringe-like apparatus (‘Type III secretion system 1', T1) of the bacterium and injection of a bacterial toxin cocktail into the host cell. These toxins include SopE, a key virulence factor triggering invasion (Hardt et al, 1998), which was analyzed in our study; (iii) toxin-triggered membrane ruffling. To a significant extent, this is facilitated by SopE-triggered activation of Cdc42 and Rac1 and subsequent actin polymerization at the site of infection; (iv) engulfment of the pathogen within a vesicular compartment (SCV) and (v) maturation of the SCV, a process driven by a second Type III secretion system (T2), which is expressed by the pathogen upon bacterial entry (Figure 1). This sequence of events mediates Salmonella invasion into the gut epithelium and illustrates that this pathogen can be used for probing mechanisms of host cell actin control, membrane biogenesis, vesicle formation and vesicular trafficking.
SopE is a key virulence factor of invasion and triggers the activation of Cdc42 and Rac1 and subsequent actin polymerization at the site of infection. We have employed a SopE-expressing S. Typhimurium strain and RNAi screening technology to identify host cell factors affecting invasion. First, we developed an automated fluorescence microscopy assay to quantify S. Typhimurium entry in a high-throughput format (Figure 1C). This assay was based on a GFP reporter expressed by the pathogen after invasion and maturation of the SCV. Using this assay, we screened a ‘druggable genome' siRNA library (6978 genes, 3 oligos each, 1 oligo per well) and identified 72 invasion hits. These included established regulators of the actin cytoskeleton (Cdc42, Arp2/3, Nap1; Schlumberger and Hardt, 2006), some of which have not been implicated so far in Salmonella entry (Pfn1, Cap1), as well as proteins not previously thought to influence infection (Atp1a1, Rbx1, COPI complex). Potentially, these hits could affect any step of the invasion process (Figure 1A).
In the second stage of the study, we have assigned each ‘invasion hit' to particular steps of the invasion process. For this purpose, we developed step-specific assays for Salmonella binding, injection, ruffling and membrane engulfment and re-screened the genes found as hits in the first screen (four siRNAs per gene). As expected, a significant number of ‘hits' affected binding to the host cell, others affected binding and ruffling (e.g., Pfn1, Itgβ5, Cap1), a few were specific for the ruffling step (e.g., Cdc42) and some affected SCV maturation, namely Rab7a, the trafficking protein Vps39 and the vacuolar proton pump Atp6ap2. Thus, our experimental strategy allowed mechanistic interpretation and linked novel hits to particular phenotypes, thus providing a basis for further studies (Figure 1).
COPI depletion impaired effector injection and ruffling. This was surprising, as the COPI complex was known to regulate retrogade Golgi-to-ER transport, but was not expected to affect pathogen interactions at the plasma membrane. Therefore, we have investigated the underlying mechanism. We have observed that COPI depletion entailed dramatic changes in the plasma membrane composition (Figure 6). Cholesterol and sphingolipids, which form domains (‘lipid rafts') in the plasma membrane, were depleted from the cell surface and redirected into a large vesicular compartment. The same was true for the Rho GTPases Rac1 and Cdc42. This strong decrease in the amount of cholesterol-enriched microdomains and Rho GTPases in the plasma membrane explained the observed defects in S. Typhimurium host cell invasion and assigned a novel role for COPI in controlling mammalian plasma membrane composition. It should be noted that other viral and bacterial pathogens do show a similar dependency on host cellular COPI and plasma membrane lipids. This includes notorious pathogens such as Staphylococcus aureus (Ramet et al, 2002; Potrich et al, 2009), Listeria monocytogenes (Seveau et al, 2004; Agaisse et al, 2005; Cheng et al, 2005; Gekara et al, 2005), Mycobacterium tuberculosis (Munoz et al, 2009), Chlamydia trachomatis (Elwell et al, 2008), influenza virus (Hao et al, 2008; Konig et al, 2010), hepatitis C virus (Tai et al, 2009; Popescu and Dubuisson, 2010) and the vesicular stomatitis virus (presented study) and suggests that COPI-mediated control of host cell plasma membrane composition might be of broad importance for pathogenesis. Future work will have to address whether this might offer starting points for developing anti-infective therapeutics with a very broad spectrum of activity.
The pathogen Salmonella Typhimurium is a common cause of diarrhea and invades the gut tissue by injecting a cocktail of virulence factors into epithelial cells, triggering actin rearrangements, membrane ruffling and pathogen entry. One of these factors is SopE, a G-nucleotide exchange factor for the host cellular Rho GTPases Rac1 and Cdc42. How SopE mediates cellular invasion is incompletely understood. Using genome-scale RNAi screening we identified 72 known and novel host cell proteins affecting SopE-mediated entry. Follow-up assays assigned these ‘hits' to particular steps of the invasion process; i.e., binding, effector injection, membrane ruffling, membrane closure and maturation of the Salmonella-containing vacuole. Depletion of the COPI complex revealed a unique effect on virulence factor injection and membrane ruffling. Both effects are attributable to mislocalization of cholesterol, sphingolipids, Rac1 and Cdc42 away from the plasma membrane into a large intracellular compartment. Equivalent results were obtained with the vesicular stomatitis virus. Therefore, COPI-facilitated maintenance of lipids may represent a novel, unifying mechanism essential for a wide range of pathogens, offering opportunities for designing new drugs.
PMCID: PMC3094068  PMID: 21407211
coatomer; HeLa; Salmonella; siRNA; systems biology

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