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An analysis and validation pipeline for large-scale RNAi-based screens.

ABSTRACT: Large-scale RNAi-based screens are a major technology, but require adequate prioritization and validation of candidate genes from the primary screen. In this work, we performed a large-scale pooled shRNA screen in mouse embryonic stem cells (ESCs) to discover genes associated with oxidative stress resistance and found several candidates. We then developed a bioinformatics pipeline to prioritize these candidates incorporating effect sizes, functional enrichment analysis, interaction networks and gene expression information. To validate candidates, we mixed normal cells with cells expressing the shRNA coupled to a fluorescent protein, which allows control cells to be used as an internal standard, and thus we could detect shRNAs with subtle effects. Although we did not identify genes associated with oxidative stress resistance, as a proof-of-concept of our pipeline we demonstrate a detrimental role of Edd1 silencing in ESC growth. Our methods may be useful for candidate gene prioritization of large-scale RNAi-based screens.

SUBMITTER: Plank M

PROVIDER: S-EPMC3546318 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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An analysis and validation pipeline for large-scale RNAi-based screens.

Plank Michael M Hu Guang G Silva A Sofia AS Wood Shona H SH Hesketh Emily E EE Janssens Georges G Macedo André A de Magalhães João Pedro JP Church George M GM

Scientific reports 20130116

Large-scale RNAi-based screens are a major technology, but require adequate prioritization and validation of candidate genes from the primary screen. In this work, we performed a large-scale pooled shRNA screen in mouse embryonic stem cells (ESCs) to discover genes associated with oxidative stress resistance and found several candidates. We then developed a bioinformatics pipeline to prioritize these candidates incorporating effect sizes, functional enrichment analysis, interaction networks and ...[more]

PMID: 23326633

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Project description:BACKGROUND AND PURPOSE:Prehospital scales have been developed to identify patients with acute cerebral ischemia (ACI) because of large vessel occlusion (LVO) for direct routing to Comprehensive Stroke Centers (CSCs), but few have been validated in the prehospital setting, and their impact on routing of patients with intracranial hemorrhage has not been delineated. The purpose of this study was to validate the Los Angeles Motor Scale (LAMS) for LVO and CSC-appropriate (LVO ACI and intracranial hemorrhage patients) recognition and compare the LAMS to other scales. METHODS:The performance of the LAMS, administered prehospital by paramedics to consecutive ambulance trial patients, was assessed in identifying (1) LVOs among all patients with ACI and (2) CSC-appropriate patients among all suspected strokes. Additionally, the LAMS administered postarrival was compared concurrently with 6 other scales proposed for paramedic use and the full National Institutes of Health Stroke Scale. RESULTS:Among 94 patients, age was 70 (±13) and 49% female. Final diagnoses were ACI in 76% (because of LVO in 48% and non-LVO in 28%), intracranial hemorrhage in 19%, and neurovascular mimic in 5%. The LAMS administered by paramedics in the field performed moderately well in identifying LVO among patients with ACI (C statistic, 0.79; accuracy, 0.72) and CSC-appropriate among all suspected stroke transports (C statistic, 0.80; accuracy, 0.72). When concurrently performed in the emergency department postarrival, the LAMS showed comparable or better accuracy versus the 7 comparator scales, for LVO among ACI (accuracies LAMS, 0.70; other scales, 0.62-0.68) and CSC-appropriate (accuracies LAMS, 0.73; other scales, 0.56-0.73). CONCLUSIONS:The LAMS performed in the field by paramedics identifies LVO and CSC-appropriate patients with good accuracy. The LAMS performs comparably or better than more extended prehospital scales and the full National Institutes of Health Stroke Scale.

Project description:BACKGROUND:Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. RESULTS:We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~?2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. CONCLUSIONS:Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

An analysis and validation pipeline for large-scale RNAi-based screens.

Publications

An analysis and validation pipeline for large-scale RNAi-based screens.

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