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A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility.

ABSTRACT: Genetic interaction (GI) maps, comprising pairwise measures of how strongly the function of one gene depends on the presence of a second, have enabled the systematic exploration of gene function in microorganisms. Here, we present a two-stage strategy to construct high-density GI maps in mammalian cells. First, we use ultracomplex pooled shRNA libraries (25 shRNAs/gene) to identify high-confidence hit genes for a given phenotype and effective shRNAs. We then construct double-shRNA libraries from these to systematically measure GIs between hits. A GI map focused on ricin susceptibility broadly recapitulates known pathways and provides many unexpected insights. These include a noncanonical role for COPI, a previously uncharacterized protein complex affecting toxin clearance, a specialized role for the ribosomal protein RPS25, and functionally distinct mammalian TRAPP complexes. The ability to rapidly generate mammalian GI maps provides a potentially transformative tool for defining gene function and designing combination therapies based on synergistic pairs.

SUBMITTER: Bassik MC

PROVIDER: S-EPMC3652613 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility.

Bassik Michael C MC Kampmann Martin M Lebbink Robert Jan RJ Wang Shuyi S Hein Marco Y MY Poser Ina I Weibezahn Jimena J Horlbeck Max A MA Chen Siyuan S Mann Matthias M Hyman Anthony A AA Leproust Emily M EM McManus Michael T MT Weissman Jonathan S JS

Cell 20130208 4

Genetic interaction (GI) maps, comprising pairwise measures of how strongly the function of one gene depends on the presence of a second, have enabled the systematic exploration of gene function in microorganisms. Here, we present a two-stage strategy to construct high-density GI maps in mammalian cells. First, we use ultracomplex pooled shRNA libraries (25 shRNAs/gene) to identify high-confidence hit genes for a given phenotype and effective shRNAs. We then construct double-shRNA libraries from ...[more]

PMID: 23394947

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Project description:BACKGROUND:Common metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors. METHODS:We analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group. RESULTS:In contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing ?-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension. CONCLUSIONS:Our results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

Project description:All classes of RNA undergo processing. The two largest RNA processing complexes are those involved in rRNA biogenesis and mRNA splicing. Because of their complexity and essential roles, it has been difficult to dissect the functional interactions within these machines as well as to identify possible cross-talk with other steps in gene expression. Here we report the results of a high-density, quantitative genetic interaction map comprising 107,155 individual interactions involving 552 mutants, 166 of which are hypomorphic alleles of essential genes (This data is available at The Krogan Lab Interactome Database). Our data allow unique insights into the functional organization of the RNA processing machines. For example, we identify unexpected connections involving a component of the 19S proteasome, Sem1/Dss1. We show that Sem1 has genetic, physical and functional interactions with the mRNA export complex, Thp1-Sac3; it also interacts physically with a putative component of the COP9 signalosome, Csn12. We show using splicing-specific microarrays that Csn12 has an unanticipated role in mRNA splicing. Finally, we demonstrate the utility of this dataset in determining functions for uncharacterized ORFs. We show that deletion of the uncharacterized YNR004W causes a very similar splicing defect to that caused by deletion of TGS1, the enzyme that trimethylates the caps of snRNAs and snoRNAs. Keywords: E-MAP, RNA Processing, splicing, splicing-specific microarray, CSN12, COP9 signalosome Splicing-specific microarrays were used to assay the changes to splicing caused by deletion of several non-essential genes which had no previously characterized role in mRNA splicing but were suggested to be involved in this process by high-throughput genetic and/or protein-protein interaction data in Saccharomyces cerevisiae. The data includes samples collected from some mutants in log phase growth at 30 degrees C and from some mutants shifted to 16 degrees C, all competitively hybridized against wild type samples collected under the same conditions. .

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A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility.

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A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility.

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