Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis

Background: 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. Results: 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. Conclusion: 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

A Bioinformatics Method Identifies Prominent Off-targeted Transcripts in RNAi Screens

Because off-target effects hamper interpretation and validation of RNAi screens, we developed a bioinformatics method, Genome-wide Enrichment of Seed Sequence matches (GESS), to identify candidate off-targeted transcripts from direct analysis of primary screening data. GESS identified a prominent off-targeted transcript in several screens, including MAD2 in a screen for components of the spindle assembly checkpoint. We demonstrate how incorporation of the results of GESS analysis can enhance the validation rate in RNAi screens

Pharmacologic Inhibition of the Anaphase-Promoting Complex Induces A Spindle Checkpoint-Dependent Mitotic Arrest in the Absence of Spindle Damage

SummaryMicrotubule inhibitors are important cancer drugs that induce mitotic arrest by activating the spindle assembly checkpoint (SAC), which, in turn, inhibits the ubiquitin ligase activity of the anaphase-promoting complex (APC). Here, we report a small molecule, tosyl-L-arginine methyl ester (TAME), which binds to the APC and prevents its activation by Cdc20 and Cdh1. A prodrug of TAME arrests cells in metaphase without perturbing the spindle, but nonetheless the arrest is dependent on the SAC. Metaphase arrest induced by a proteasome inhibitor is also SAC dependent, suggesting that APC-dependent proteolysis is required to inactivate the SAC. We propose that mutual antagonism between the APC and the SAC yields a positive feedback loop that amplifies the ability of TAME to induce mitotic arrest

Drug-induced eRF1 degradation promotes readthrough and reveals a new branch of ribosome quality control.

Suppression of premature termination codons (PTCs) by translational readthrough is a promising strategy to treat a wide variety of severe genetic diseases caused by nonsense mutations. Here, we present two potent readthrough promoters-NVS1.1 and NVS2.1-that restore substantial levels of functional full-length CFTR and IDUA proteins in disease models for cystic fibrosis and Hurler syndrome, respectively. In contrast to other readthrough promoters that affect stop codon decoding, the NVS compounds stimulate PTC suppression by triggering rapid proteasomal degradation of the translation termination factor eRF1. Our results show that this occurs by trapping eRF1 in the terminating ribosome, causing ribosome stalls and subsequent ribosome collisions, and activating a branch of the ribosome-associated quality control network, which involves the translational stress sensor GCN1 and the catalytic activity of the E3 ubiquitin ligases RNF14 and RNF25

A Time-Series Method for Automated Measurement of Changes in Mitotic and Interphase Duration from Time-Lapse Movies

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments.Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment.This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division

Explicit Modeling of siRNA-Dependent On- and Off-Target Repression Improves the Interpretation of Screening Results

RNAi is broadly used to map gene regulatory networks, but the identification of genes that are responsible for the observed phenotypes is challenging, as small interfering RNAs (siRNAs) simultaneously downregulate the intended on targets and many partially complementary off targets. Additionally, the scarcity of publicly available control datasets hinders the development and comparative evaluation of computational methods for analyzing the data. Here, we introduce PheLiM (https://github.com/andreariba/PheLiM), a method that uses predictions of siRNA on- and off-target downregulation to infer gene-specific contributions to phenotypes. To assess the performance of PheLiM, we carried out siRNA- and CRISPR/Cas9-based genome-wide screening of two well-characterized pathways, bone morphogenetic protein (BMP) and nuclear factor κB (NF-κB), and we reanalyzed publicly available siRNA screens. We demonstrate that PheLiM has the overall highest accuracy and most reproducible results compared to other available methods. PheLiM can accommodate various methods for predicting siRNA off targets and is broadly applicable to the identification of genes underlying complex phenotypes

Bile acid analogues are activators of pyrin inflammasome

the work was first published Jan 2019 in JBC - doi: 10.1074/jbc.RA118.005103. (PMID: 30647128) Since publication a represenative from the Lewis-Sigler Institute for Integrative Genomics reached out to inquire if we would publish the full CRISPR data from the paper including the gene-level scores and relevant significance thresholds for their CRISPR ORCS database (orcs.thebiogrid.org/). ORCS houses the most comprehensive collection of gene-level scores from published CRISPR screens and makes them freely available to the research community.. For more information about BioGRID ORCS, please consult our recent paper (PMID: 30476227)

A genome-wide homologous recombination screen identifies the RNA-binding protein RBMX as a component of the DNA-damage response

Repair of DNA double-strand breaks is critical to genomic stability and the prevention of developmental disorders and cancer. A central pathway for this repair is homologous recombination (HR). Most knowledge of HR is derived from work in prokaryotic and eukaryotic model organisms. We carried out a genome-wide siRNA-based screen in human cells. Among positive regulators of HR we identified networks of DNA-damage-response and pre-mRNA-processing proteins, and among negative regulators we identified a phosphatase network. Three candidate proteins localized to DNA lesions, including RBMX, a heterogeneous nuclear ribonucleoprotein that has a role in alternative splicing. RBMX accumulated at DNA lesions through multiple domains in a poly(ADP-ribose) polymerase 1-dependent manner and promoted HR by facilitating proper BRCA2 expression. Our screen also revealed that off-target depletion of RAD51 is a common source of RNAi false positives, raising a cautionary note for siRNA screens and RNAi-based studies of HR

Engineering digitizer circuits for chemical and genetic screens in human cells.

Cell-based transcriptional reporters are invaluable in high-throughput compound and CRISPR screens for identifying compounds or genes that can impact a pathway of interest. However, many transcriptional reporters have weak activities and transient responses. This can result in overlooking therapeutic targets and compounds that are difficult to detect, necessitating the resource-consuming process of running multiple screens at various timepoints. Here, we present RADAR, a digitizer circuit for amplifying reporter activity and retaining memory of pathway activation. Reporting on the AP-1 pathway, our circuit identifies compounds with known activity against PKC-related pathways and shows an enhanced dynamic range with improved sensitivity compared to a classical reporter in compound screens. In the first genome-wide pooled CRISPR screen for the AP-1 pathway, RADAR identifies canonical genes from the MAPK and PKC pathways, as well as non-canonical regulators. Thus, our scalable system highlights the benefit and versatility of using genetic circuits in large-scale cell-based screening

Benchmarking network algorithms for contextualizing genes of interest.

Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks