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.
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 rapid rise of CRISPR as a technology for genome engineering and related research applications has created a need for algorithms and associated online tools that facilitate design of on-target and effective guide RNAs (gRNAs). Here, we review the state-of-the-art in CRISPR gRNA design for research applications of the CRISPR-Cas9 system, including knockout, activation and inhibition. Notably, achieving good gRNA design is not solely dependent on innovations in CRISPR technology. Good design and design tools also rely on availability of high-quality genome sequence and gene annotations, as well as on availability of accumulated data regarding off-targets and effectiveness metrics. This article is protected by copyright. All rights reserved.
Yeast genetics and in vitro biochemical analysis have identified numerous genes involved in protein secretion. As compared with yeast, however, the metazoan secretory pathway is more complex and many mechanisms that regulate organization of the Golgi apparatus remain poorly characterized. We performed a genome-wide RNA-mediated interference screen in a Drosophila cell line to identify genes required for constitutive protein secretion. We then classified the genes on the basis of the effect of their depletion on organization of the Golgi membranes. Here we show that depletion of class A genes redistributes Golgi membranes into the endoplasmic reticulum, depletion of class B genes leads to Golgi fragmentation, depletion of class C genes leads to aggregation of Golgi membranes, and depletion of class D genes causes no obvious change. Of the 20 new gene products characterized so far, several localize to the Golgi membranes and the endoplasmic reticulum.
Genetic screens in the yeast Saccharomyces cerevisiae have identified many proteins involved in the secretory pathway, most of which have orthologues in higher eukaryotes. To investigate whether there are additional proteins that are required for secretion in metazoans but are absent from yeast, we used genome-wide RNA interference (RNAi) to look for genes required for secretion of recombinant luciferase from Drosophila S2 cells. This identified two novel components of the secretory pathway that are conserved from humans to plants. Gryzun is distantly related to, but distinct from, the Trs130 subunit of the TRAPP complex but is absent from S. cerevisiae. RNAi of human Gryzun (C4orf41) blocks Golgi exit. Kish is a small membrane protein with a previously uncharacterised orthologue in yeast. The screen also identified Drosophila orthologues of almost 60% of the yeast genes essential for secretion. Given this coverage, the small number of novel components suggests that contrary to previous indications the number of essential core components of the secretory pathway is not much greater in metazoans than in yeasts.
BACKGROUND: Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. RESULTS: We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). CONCLUSIONS: DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.