siRNA off-target effects in genome-wide screens identify signaling pathway members

A high-throughput genome-wide RNAi screen identifies modifiers of survival motor neuron protein

Spinal muscular atrophy (SMA) is a debilitating neurological disorder marked by degeneration of spinal motor neurons and muscle atrophy. SMA results from mutations in survival motor neuron 1 (SMN1), leading to deficiency of survival motor neuron (SMN) protein. Current therapies increase SMN protein and improve patient survival but have variable improvements in motor function, making it necessary to identify complementary strategies to further improve disease outcomes. Here, we perform a genome-wide RNAi screen using a luciferase-based activity reporter and identify genes involved in regulating SMN gene expression, RNA processing, and protein stability. We show that reduced expression of Transcription Export complex components increases SMN levels through the regulation of nuclear/cytoplasmic RNA transport. We also show that the E3 ligase, Neurl2, works cooperatively with Mib1 to ubiquitinate and promote SMN degradation. Together, our screen uncovers pathways through which SMN expression is regulated, potentially revealing additional strategies to treat SMA.

Treatments for spinal muscular atrophy aim to increase survival motor neuron (SMN) protein. Using a genome-wide RNAi screen, McCormack et al. identify modifiers of SMN expression, including genes that are involved in transcription regulation, RNA processing, and protein stability.

Seed-mediated RNA interference of androgen signaling and survival networks induces cell death in prostate cancer cells

Resistance to anti-androgen therapy in prostate cancer (PCa) is often driven by genetic and epigenetic aberrations in the androgen receptor (AR) and coregulators that maintain androgen signaling activity. We show that specific small RNAs downregulate expression of multiple essential and androgen receptor-coregulatory genes, leading to potent androgen signaling inhibition and PCa cell death. Expression of different short hairpin/small interfering RNAs (sh-/siRNAs) designed to target TMEFF2 preferentially reduce viability of PCa but not benign cells, and growth of murine xenografts. Surprisingly, this effect is independent of TMEFF2 expression. Transcriptomic and sh/siRNA seed sequence studies indicate that expression of these toxic shRNAs lead to downregulation of androgen receptor-coregulatory and essential genes through mRNA 3′ UTR sequence complementarity to the seed sequence of the toxic shRNAs. These findings reveal a form of the “death induced by survival gene elimination” mechanism in PCa cells that mainly targets AR signaling, and that we have termed androgen network death induced by survival gene elimination (AN-DISE). Our data suggest that AN-DISE may be a novel therapeutic strategy for PCa.

siRNA Specificity: RNAi Mechanisms and Strategies to Reduce Off-Target Effects

Short interfering RNAs (siRNAs) are processed from long double-stranded RNA (dsRNA), and a guide strand is selected and incorporated into the RNA-induced silencing complex (RISC). Within RISC, a member of the Argonaute protein family directly binds the guide strand and the siRNA guides RISC to fully complementary sites on-target RNAs, which are then sequence-specifically cleaved by the Argonaute protein-a process commonly referred to as RNA interference (RNAi). In animals, endogenous microRNAs (miRNAs) function similarly but do not lead to direct cleavage of the target RNA but to translational inhibition followed by exonucleolytic decay. This is due to only partial complementarity between the miRNA and the target RNA. SiRNAs, however, can function as miRNAs, and partial complementarity can lead to miRNA-like off-target effects in RNAi applications. Since siRNAs are widely used not only for screening but also for therapeutics as well as crop protection purposes, such miRNA-like off-target effects need to be minimized. Strategies such as RNA modifications or pooling of siRNAs have been developed and are used to reduce off-target effects.

Cancer biology functional genomics: From small RNAs to big dreams

The year 2021 marks the 20th anniversary of the first publications reporting the discovery of the gene silencing mechanism, RNA interference (RNAi) in mammalian cells. Along with the many studies that delineated the proteins and substrates that form the RNAi pathway, this finding changed our understanding of the posttranscriptional regulation of mammalian gene expression. Furthermore, the development of methods that exploited the RNAi pathway began the technological revolution that eventually enabled the interrogation of mammalian gene function-from a single gene to the whole genome-in only a few days. The needs of the cancer research community have driven much of this progress. In this perspective, we highlight milestones in the development and application of RNAi-based methods to study carcinogenesis. We discuss how RNAi-based functional genetic analysis of exemplar tumor suppressors and oncogenes furthered our understanding of cancer initiation and progression and explore how such studies formed the basis of genome-wide scale efforts to identify cancer or cancer-type specific vulnerabilities, including studies conducted in vivo. Furthermore, we examine how RNAi technologies have revealed new cancer-relevant molecular targets and the implications for cancer of the first RNAi-based drugs. Finally, we discuss the future of functional genetic analysis, highlighting the increasing availability of complementary approaches to analyze cancer gene function.

Hacking the Cancer Genome: Profiling Therapeutically Actionable Long Non-coding RNAs Using CRISPR-Cas9 Screening

Long non-coding RNAs (lncRNAs) represent a huge reservoir of potential cancer targets. Such "onco-lncRNAs" have resisted traditional RNAi methods, but CRISPR-Cas9 genome editing now promises functional screens at high throughput and low cost. The unique biology of lncRNAs demands screening strategies distinct from protein-coding genes. The first such screens have identified hundreds of onco-lncRNAs promoting cell proliferation and drug resistance. Ongoing developments will further improve screen performance and translational relevance. This Review aims to highlight the potential of CRISPR screening technology for discovering new onco-lncRNAs, and to guide molecular oncologists wishing to apply it to their cancer of interest.

Association of SENPs single-nucleotide polymorphism and breast cancer in Chinese population

SUMO-specific Cysteine Proteases (SENPs) have involvement in the initiation and progression of human cancers. In the present study, we evaluated the association of SENPs polymorphism with susceptibility as well as clinicopathologic features and patients' response of breast cancer (BC) in a Chinese population.We genotyped SENP1 (rs61918808), SENP2 (rs6762208), SENP7 (rs61697963) by sequencing in a case-control study including 210 BC patients and 225 healthy volunteers. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to assume the association strength.No significant association was found between polymorphism of the 3 SENPs and BC susceptibility. However, SENP1 rs61918808 (C>T) and SENP7 rs61697963 (A>C) was associated with HER-2 expression (P < .05). SENP2 rs6762208(C>A) was correlated with increasing risk of lymph node metastases (P < .05). Among the patients who received neoadjuvant chemotherapy, T allele and TT genotype of SENP1 rs61918808 were less likely to achieve pCR (P < .05).We first reported SENPs variants were not associated with BC risk in Chinese population, but presented specific effect on clinicopathological features of BC. Moreover, SENP1 rs61918808 may be a predictor for the clinical response in local advanced BC patients who received neoadjuvant chemotherapy.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens (https://depmap.org/R2-D2). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

Integrated analyses of multiple large-scale screenings can be complicated by batch effects and technical artefacts. McFarland et al. introduce DEMETER2, a hierarchical model coupled with model-based normalization, which allows the assessment of differential dependencies across genes and cell lines.

The application of gene silencing in proteomics: from laboratory to clinic

INTRODUCTION

Since the completion of genome sequencing, gene silencing technologies have emerged as powerful tools to study gene functions in various biological processes, both in vivo and in vitro. Moreover, they have also been proposed as therapeutic agents to inhibit selected genes in a variety of pathological conditions, such as cancer, neurodegenerative, and cardiovascular diseases. Area covered: This review summarizes the mechanisms of action and applications of genome editing tools, from RNA interference to clustered regularly interspaced short palindromic repeats-based systems, in research and in clinics. We describe their essential role in high-throughput genetic screens and, in particular, in functional proteomics studies, to identify diagnostic markers and therapeutic targets. Indeed, gene silencing and proteomics have been extensively integrated to study global proteome changes, posttranslational modifications, and protein-protein interactions. Expert commentary: Functional proteomics approaches that leverage gene silencing tools have been successfully applied to examine the role of several genes in various contexts, leading to a deeper knowledge of biological pathways and disease mechanisms. Recent developments of gene silencing tools have improved their performance, also in terms of off-targets effects reduction, paving the way for a wider therapeutic application of these systems.

Strategies for improving the specificity of siRNAs for enhanced therapeutic potential

INTRODUCTION

RNA interference has become a tool of choice in the development of drugs in various therapeutic areas of Post Transcriptional Gene Silencing (PTGS). The critical element in developing successful RNAi therapeutics lies in designing small interfering RNA (siRNA) using an efficient algorithm satisfying the designing criteria. Further, translation of siRNA from bench-side to bedside needs an efficient delivery system and/or chemical modification. Areas covered: This review emphasizes the importance of dicer, the criteria for efficient siRNA design, the currently available algorithms and strategies to overcome off-target effects, immune stimulatory effects and endosomal trap. Expert opinion: Specificity and stability are the primary concerns for siRNA therapeutics. The design criteria and algorithms should be chosen rationally to have a siRNA sequence that binds to the corresponding mRNA as it happens in the Watson and Crick base pairing. However, it must evade a few more hurdles (Endocytosis, Serum stability etc.) to be functional in the cytosol.

OSM potentiates preintravasation events, increases CTC counts, and promotes breast cancer metastasis to the lung

Background

Systemic and chronic inflammatory conditions in patients with breast cancer have been associated with reduced patient survival and increased breast cancer aggressiveness. This paper characterizes the role of an inflammatory cytokine, oncostatin M (OSM), in the preintravasation aspects of breast cancer metastasis.

Methods

OSM expression levels in human breast cancer tissue samples were assessed using tissue microarrays, and expression patterns based on clinical stage were assessed. To determine the in vivo role of OSM in breast cancer metastasis to the lung, we used three orthotopic breast cancer mouse models, including a syngeneic 4T1.2 mouse mammary cancer model, the MDA-MB-231 human breast cancer xenograft model, and an OSM-knockout (OSM-KO) mouse model. Progression of metastatic disease was tracked by magnetic resonance imaging and bioluminescence imaging. Endpoint analysis included circulating tumor cell (CTC) counts, lung metastatic burden analysis by qPCR, and ex vivo bioluminescence imaging.

Results

Using tissue microarrays, we found that tumor cell OSM was expressed at the highest levels in ductal carcinoma in situ. This finding suggests that OSM may function during the earlier steps of breast cancer metastasis. In mice bearing MDA-MB-231-Luc2 xenograft tumors, peritumoral injection of recombinant human OSM not only increased metastases to the lung and decreased survival but also increased CTC numbers. To our knowledge, this is the first time that a gp130 family inflammatory cytokine has been shown to directly affect CTC numbers. Using a 4T1.2 syngeneic mouse model of breast cancer, we found that mice bearing 4T1.2-shOSM tumors with knocked down tumor expression of OSM had reduced CTCs, decreased lung metastatic burden, and increased survival compared with mice bearing control tumors. CTC numbers were further reduced in OSM-KO mice bearing the same tumors, demonstrating the importance of both paracrine- and autocrine-produced OSM in this process. In vitro studies further supported the hypothesis that OSM promotes preintravasation aspects of cancer metastasis, because OSM induced both 4T1.2 tumor cell detachment and migration.

Conclusions

Collectively, our findings suggest that OSM plays a crucial role in the early steps of metastatic breast cancer progression, resulting in increased CTCs and lung metastases as well as reduced survival. Therefore, early therapeutic inhibition of OSM in patients with breast cancer may prevent breast cancer metastasis.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0971-5) contains supplementary material, which is available to authorized users.

Evaluation and control of miRNA-like off-target repression for RNA interference

RNA interference (RNAi) has been widely adopted to repress specific gene expression and is easily achieved by designing small interfering RNAs (siRNAs) with perfect sequence complementarity to the intended target mRNAs. Although siRNAs direct Argonaute (Ago), a core component of the RNA-induced silencing complex (RISC), to recognize and silence target mRNAs, they also inevitably function as microRNAs (miRNAs) and suppress hundreds of off-targets. Such miRNA-like off-target repression is potentially detrimental, resulting in unwanted toxicity and phenotypes. Despite early recognition of the severity of miRNA-like off-target repression, this effect has often been overlooked because of difficulties in recognizing and avoiding off-targets. However, recent advances in genome-wide methods and knowledge of Ago-miRNA target interactions have set the stage for properly evaluating and controlling miRNA-like off-target repression. Here, we describe the intrinsic problems of miRNA-like off-target effects caused by canonical and noncanonical interactions. We particularly focus on various genome-wide approaches and chemical modifications for the evaluation and prevention of off-target repression to facilitate the use of RNAi with secured specificity.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Enhanced Functional Genomic Screening Identifies Novel Mediators of Dual Leucine Zipper Kinase-Dependent Injury Signaling in Neurons

Dual leucine zipper kinase (DLK) has been implicated in cell death signaling secondary to axonal damage in retinal ganglion cells (RGCs) and other neurons. To better understand the pathway through which DLK acts, we developed enhanced functional genomic screens in primary RGCs, including use of arrayed, whole-genome, small interfering RNA libraries. Explaining why DLK inhibition is only partially protective, we identify leucine zipper kinase (LZK) as cooperating with DLK to activate downstream signaling and cell death in RGCs, including in a mouse model of optic nerve injury, and show that the same pathway is active in human stem cell-derived RGCs. Moreover, we identify four transcription factors, JUN, activating transcription factor 2 (ATF2), myocyte-specific enhancer factor 2A (MEF2A), and SRY-Box 11 (SOX11), as being the major downstream mediators through which DLK/LZK activation leads to RGC cell death. Increased understanding of the DLK pathway has implications for understanding and treating neurodegenerative diseases.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Synthetic lethality and cancer

A synthetic lethal interaction occurs between two genes when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability. Key to exploiting synthetic lethality in cancer treatment are the identification and the mechanistic characterization of robust synthetic lethal genetic interactions. Advances in next-generation sequencing technologies are enabling the identification of hundreds of tumour-specific mutations and alterations in gene expression that could be targeted by a synthetic lethality approach. The translation of synthetic lethality to therapy will be assisted by the synthesis of genetic interaction data from model organisms, tumour genomes and human cell lines.

Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells

BACKGROUND

Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity.

METHODS

We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments.

RESULTS

Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment.

CONCLUSIONS

We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.

High-throughput validation of ceRNA regulatory networks

Background

MicroRNAs (miRNAs) play multiple roles in tumor biology. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive stoichiometric sequestration. Specifically, computational models predicted and experimental assays confirmed that miRNA activity is dependent on miRNA target abundance, and consequently, changes in the abundance of some miRNA targets lead to changes to the regulation and abundance of their other targets. The resulting indirect regulatory influence between miRNA targets resembles competition and has been dubbed competitive endogenous RNA (ceRNA). Recent studies have questioned the physiological relevance of ceRNA interactions, our ability to accurately predict these interactions, and the number of genes that are impacted by ceRNA interactions in specific cellular contexts.

Results

To address these concerns, we reverse engineered ceRNA networks (ceRNETs) in breast and prostate adenocarcinomas using context-specific TCGA profiles, and tested whether ceRNA interactions can predict the effects of RNAi-mediated gene silencing perturbations in PC3 and MCF7 cells._ENREF_22 Our results, based on tests of thousands of inferred ceRNA interactions that are predicted to alter hundreds of cancer genes in each of the two tumor contexts, confirmed statistically significant effects for half of the predicted targets.

Conclusions

Our results suggest that the expression of a significant fraction of cancer genes may be regulated by ceRNA interactions in each of the two tumor contexts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3790-7) contains supplementary material, which is available to authorized users.

Novel SUMO-Protease SENP7S Regulates β-catenin Signaling and Mammary Epithelial Cell Transformation

SUMO post-translational modification of proteins or SUMOylation ensures normal cell function. Disruption of SUMO dynamics prompts various pathophysiological conditions, including cancer. The burden of deSUMOylating the large SUMO-proteome rests on 6 full-length mammalian SUMO-proteases or SENP. While multiple SENP isoforms exist, the function of these isoforms remains undefined. We now delineate the biological role of a novel SENP7 isoform SENP7S in mammary epithelial cells. SENP7S is the predominant SENP transcript in human mammary epithelia but is significantly reduced in precancerous ductal carcinoma in situ and all breast cancer subtypes. Like other SENP family members, SENP7S has SUMO isopeptidase activity but unlike full-length SENP7L, SENP7S is localized in the cytosol. In vivo, SUMOylated β-catenin and Axin1 are both SENP7S-substrates. With knockdown of SENP7S in mammary epithelial cells, Axin1-β-catenin interaction is lost and β-catenin escapes ubiquitylation-dependent proteasomal degradation. SUMOylated β-catenin accumulates at the chromatin and activates multiple oncogenes. Hence, non-tumorigenic MCF10-2A cells with reduced SENP7S exhibit greater cell proliferation and anchorage-dependent growth. SENP7S depletion directly potentiates tumorigenic properties of MCF10-2A cells with induction of anchorage-independent growth and self-renewal in 3D-spheroid conditions. Collectively, the results identify SENP7S as a novel mediator of β-catenin signaling and normal mammary epithelial cell physiology.

Phenotypic screening with primary neurons to identify drug targets for regeneration and degeneration

High-throughput, target-based screening techniques have been utilized extensively for drug discovery in the past several decades. However, the need for more predictive in vitro models of in vivo disease states has generated a shift in strategy towards phenotype-based screens. Phenotype based screens are particularly valuable in studying complex conditions such as CNS injury and degenerative disease, as many factors can contribute to a specific cellular response. In this review, we will discuss different screening frameworks and their relative utility in examining mechanisms of neurodegeneration and axon regrowth, particularly in cell-based in vitro disease models.

Triad of human cellular proteins, IRF2, FAM111A, and RFC3, restrict replication of orthopoxvirus SPI-1 host-range mutants

Viruses and their hosts can reach balanced states of evolution ensuring mutual survival, which makes it difficult to appreciate the underlying dynamics. To uncover hidden interactions, virus mutants that have lost defense genes may be used. Deletion of the gene that encodes serine protease inhibitor 1 (SPI-1) of rabbitpox virus and vaccinia virus, two closely related orthopoxviruses, prevents their efficient replication in human cells, whereas certain other mammalian cells remain fully permissive. Our high-throughput genome-wide siRNA screen identified host factors that prevent reproduction and spread of the mutant viruses in human cells. More than 20,000 genes were interrogated with individual siRNAs and those that prominently increased replication of the SPI-1 deletion mutant were subjected to a secondary screen. The top hits based on the combined data-replication factor C3 (RFC3), FAM111A, and interferon regulatory factor 2 (IRF2)-were confirmed by custom assays. The siRNAs to RFC1, RFC2, RFC4, and RFC5 mRNAs also enhanced spread of the mutant virus, strengthening the biological significance of the RFC complex as a host restriction factor for poxviruses. Whereas association with proliferating cell nuclear antigen and participation in processive genome replication are common features of FAM111A and RFC, IRF2 is a transcriptional regulator. Microarray analysis, quantitative RT-PCR, and immunoblotting revealed that IRF2 regulated the basal level expression of FAM111A, suggesting that the enhancing effect of depleting IRF2 on replication of the SPI-1 mutant was indirect. Thus, the viral SPI-1 protein and the host IRF2, FAM111A, and RFC complex likely form an interaction network that influences the ability of poxviruses to replicate in human cells.

Image-based siRNA screen to identify kinases regulating Weibel-Palade body size control using electroporation

High-content screening of kinase inhibitors is important in order to identify biogenesis and function mechanisms of subcellular organelles. Here, we present a human kinome siRNA high-content screen on primary human umbilical vein endothelial cells, that were transfected by electroporation. The data descriptor contains a confocal fluorescence, microscopic image dataset. We also describe an open source, automated image analysis workflow that can be reused to perform high-content analysis of other organelles. This dataset is suitable for analysis of morphological parameters that are linked to human umbilical vein endothelial cell (HUVEC) biology.

Knocking down the obstacles to functional genomics data sharing

This week, Scientific Data published a collection of eight papers that describe datasets from high-throughput functional genomics screens, primarily utilizing RNA interference (RNAi). The publications explore host-pathogen dependencies, innate immune response, disease pathways, and cell morphology and motility at the genome-level. All data, including raw images from the high content screens, are publically available in PubChem BioAssay, figshare, Harvard Dataverse or the Image Data Resource (IDR). Detailed data descriptors enable use of these data for analysis algorithm design, machine learning, data comparisons, as well as generating new scientific hypotheses.

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.

TRIM25 Enhances the Antiviral Action of Zinc-Finger Antiviral Protein (ZAP)

The host factor and interferon (IFN)-stimulated gene (ISG) product, zinc-finger antiviral protein (ZAP), inhibits a number of diverse viruses by usurping and intersecting with multiple cellular pathways. To elucidate its antiviral mechanism, we perform a loss-of-function genome-wide RNAi screen to identify cellular cofactors required for ZAP antiviral activity against the prototype alphavirus, Sindbis virus (SINV). In order to exclude off-target effects, we carry out stringent confirmatory assays to verify the top hits. Important ZAP-liaising partners identified include proteins involved in membrane ion permeability, type I IFN signaling, and post-translational protein modification. The factor contributing most to the antiviral function of ZAP is TRIM25, an E3 ubiquitin and ISG15 ligase. We demonstrate here that TRIM25 interacts with ZAP through the SPRY domain, and TRIM25 mutants lacking the RING or coiled coil domain fail to stimulate ZAP's antiviral activity, suggesting that both TRIM25 ligase activity and its ability to form oligomers are critical for its cofactor function. TRIM25 increases the modification of both the short and long ZAP isoforms by K48- and K63-linked polyubiquitin, although ubiquitination of ZAP does not directly affect its antiviral activity. However, TRIM25 is critical for ZAP's ability to inhibit translation of the incoming SINV genome. Taken together, these data uncover TRIM25 as a bona fide ZAP cofactor that leads to increased ZAP modification enhancing its translational inhibition activity.

Aurora B kinase is a potent and selective target in MYCN-driven neuroblastoma

Despite advances in multimodal treatment, neuroblastoma (NB) is often fatal for children with high-risk disease and many survivors need to cope with long-term side effects from high-dose chemotherapy and radiation. To identify new therapeutic targets, we performed an siRNA screen of the druggable genome combined with a small molecule screen of 465 compounds targeting 39 different mechanisms of actions in four NB cell lines. We identified 58 genes as targets, including AURKB, in at least one cell line. In the drug screen, aurora kinase inhibitors (nine molecules) and in particular the AURKB-selective compound, barasertib, were the most discriminatory with regard to sensitivity for MYCN-amplified cell lines. In an expanded panel of ten NB cell lines, those with MYCN-amplification and wild-type TP53 were the most sensitive to low nanomolar concentrations of barasertib. Inhibition of the AURKB kinase activity resulted in decreased phosphorylation of the known target, histone H3, and upregulation of TP53 in MYCN-amplified, TP53 wild-type cells. However, both wild-type and TP53 mutant MYCN-amplified cell lines arrested in G2/M phase upon AURKB inhibition. Additionally, barasertib induced endoreduplication and apoptosis. Treatment of MYCN-amplified/TP53 wild-type neuroblastoma xenografts resulted in profound growth inhibition and tumor regression. Therefore, aurora B kinase inhibition is highly effective in aggressive neuroblastoma and warrants further investigation in clinical trials.

High-Throughput Small Interfering RNA Screening Identifies Phosphatidylinositol 3-Kinase Class II Alpha as Important for Production of Human Cytomegalovirus Virions

High-throughput small interfering RNA (siRNA) screening is a useful methodology to identify cellular factors required for virus replication. Here we utilized a high-throughput siRNA screen based on detection of a viral antigen by microscopy to interrogate cellular protein kinases and phosphatases for their importance during human cytomegalovirus (HCMV) replication and identified the class II phosphatidylinositol 3-kinase class II alpha (PI3K-C2A) as being involved in HCMV replication. Confirming this observation, infected cells treated with either pooled or individual siRNAs targeting PI3K-C2A mRNA produced approximately 10-fold less infectious virus than the controls. Western blotting and quantitative PCR analysis of infected cells treated with siRNAs indicated that depletion of PI3K-C2A slightly reduced the accumulation of late but not immediate early or early viral antigens and had no appreciable effect on viral DNA synthesis. Analysis of siRNA-treated cells by electron microscopy and Western blotting indicated that PI3K-C2A was not required for the production of viral capsids but did lead to increased numbers of enveloped capsids in the cytoplasm that had undergone secondary envelopment and a reduction in the amount of viral particles exiting the cell. Therefore, PI3K-C2A is a factor important for HCMV replication and has a role in the production of HCMV virions.

IMPORTANCE

There is limited information about the cellular factors required for human cytomegalovirus (HCMV) replication. Therefore, to identify proteins involved in HCMV replication, we developed a methodology to conduct a high-throughput siRNA screen of HCMV-infected cells. From our screening data, we focused our studies on the top hit from our screen, the lipid kinase phosphatidylinositol 3-kinase class II alpha (PI3K-C2A), as its role in HCMV replication was unknown. Interestingly, we found that PI3K-C2A is important for the production of HCMV virions and is involved in virion production after secondary envelopment of viral capsids, the encapsidation of HCMV capsids by a lipid bilayer that occurs before virions exit the cell.

Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine

Background

Genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency.

Results

Genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles.

Conclusions

The novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2807-y) contains supplementary material, which is available to authorized users.

Functional Genomic Screening Reveals Splicing of the EWS-FLI1 Fusion Transcript as a Vulnerability in Ewing Sarcoma

Ewing sarcoma cells depend on the EWS-FLI1 fusion transcription factor for cell survival. Using an assay of EWS-FLI1 activity and genome-wide RNAi screening, we have identified proteins required for the processing of the EWS-FLI1 pre-mRNA. We show that Ewing sarcoma cells harboring a genomic breakpoint that retains exon 8 of EWSR1 require the RNA-binding protein HNRNPH1 to express in-frame EWS-FLI1. We also demonstrate the sensitivity of EWS-FLI1 fusion transcripts to the loss of function of the U2 snRNP component, SF3B1. Disrupted splicing of the EWS-FLI1 transcript alters EWS-FLI1 protein expression and EWS-FLI1-driven expression. Our results show that the processing of the EWS-FLI1 fusion RNA is a potentially targetable vulnerability in Ewing sarcoma cells.

The stress phenotype makes cancer cells addicted to CDT2, a substrate receptor of the CRL4 ubiquitin ligase

CDT2/L2DTL/RAMP is one of the substrate receptors of the Cullin Ring Ubiquitin Ligase 4 that targets for ubiquitin mediated degradation a number of substrates, such as CDT1, p21 and CHK1, involved in the regulation of cell cycle and survival. Here we show that CDT2 depletion was alone able to induce the apoptotic death in 12/12 human cancer cell lines from different tissues, regardless of the mutation profile and CDT2 expression level. Cell death was associated to rereplication and to loss of CDT1 degradation. Conversely, CDT2 depletion did not affect non-transformed human cells, such as immortalized kidney, lung and breast cell lines, and primary cultures of endothelial cells and osteoblasts. The ectopic over-expression of an activated oncogene, such as the mutation-activated RAS or the amplified MET in non-transformed immortalized breast cell lines and primary human osteoblasts, respectively, made cells transformed in vitro, tumorigenic in vivo, and susceptible to CDT2 loss. The widespread effect of CDT2 depletion in different cancer cells suggests that CDT2 is not in a synthetic lethal interaction to a single specific pathway. CDT2 likely is a non-oncogene to which transformed cells become addicted because of their enhanced cellular stress, such as replicative stress and DNA damage.

gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens

Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-β signaling. gespeR is available as a Bioconductor R-package.

Deciphering Seed Sequence Based Off-Target Effects in a Large-Scale RNAi Reporter Screen for E-Cadherin Expression

Functional RNAi based screening is affected by large numbers of false positive and negative hits due to prevalent sequence based off-target effects. We performed a druggable genome targeting siRNA screen intended to identify novel regulators of E-cadherin (CDH1) expression, a known key player in epithelial mesenchymal transition (EMT). Analysis of primary screening results indicated a large number of false-positive hits. To address these crucial difficulties we developed an analysis method, SENSORS, which, similar to published methods, is a seed enrichment strategy for analyzing siRNA off-targets in RNAi screens. Using our approach, we were able to demonstrate that accounting for seed based off-target effects stratifies primary screening results and enables the discovery of additional screening hits. While traditional hit detection methods are prone to false positive results which are undetected, we were able to identify false positive hits robustly. Transcription factor MYBL1 was identified as a putative novel target required for CDH1 expression and verified experimentally. No siRNA pool targeting MYBL1 was present in the used siRNA library. Instead, MYBL1 was identified as a putative CDH1 regulating target solely based on the SENSORS off-target score, i.e. as a gene that is a cause for off-target effects down regulating E-cadherin expression.

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects

RNA interference and morphological profiling-the measurement of thousands of phenotypes from individual cells by microscopy and image analysis-are a potentially powerful combination. We show that morphological profiles of RNAi-induced knockdown using the Cell Painting assay are in fact highly sensitive and reproducible. However, we find that the magnitude and prevalence of off-target effects via the RNAi seed-based mechanism make morphological profiles of RNAi reagents targeting the same gene look no more similar than reagents targeting different genes. Pairs of RNAi reagents that share the same seed sequence produce image-based profiles that are much more similar to each other than profiles from pairs designed to target the same gene, a phenomenon previously observed in small-scale gene-expression profiling experiments. Various strategies have been used to enrich on-target versus off-target effects in the context of RNAi screening where a narrow set of phenotypes are measured, mostly based on comparing multiple sequences targeting the same gene; however, new approaches will be needed to make RNAi morphological profiling (that is, comparing multi-dimensional phenotypes) viable. We have shared our raw data and computational pipelines to facilitate research.

A Perspective on the Future of High-Throughput RNAi Screening: Will CRISPR Cut Out the Competition or Can RNAi Help Guide the Way?

For more than a decade, RNA interference (RNAi) has brought about an entirely new approach to functional genomics screening. Enabling high-throughput loss-of-function (LOF) screens against the human genome, identifying new drug targets, and significantly advancing experimental biology, RNAi is a fast, flexible technology that is compatible with existing high-throughput systems and processes; however, the recent advent of clustered regularly interspaced palindromic repeats (CRISPR)-Cas, a powerful new precise genome-editing (PGE) technology, has opened up vast possibilities for functional genomics. CRISPR-Cas is novel in its simplicity: one piece of easily engineered guide RNA (gRNA) is used to target a gene sequence, and Cas9 expression is required in the cells. The targeted double-strand break introduced by the gRNA-Cas9 complex is highly effective at removing gene expression compared to RNAi. Together with the reduced cost and complexity of CRISPR-Cas, there is the realistic opportunity to use PGE to screen for phenotypic effects in a total gene knockout background. This review summarizes the exciting development of CRISPR-Cas as a high-throughput screening tool, comparing its future potential to that of well-established RNAi screening techniques, and highlighting future challenges and opportunities within these disciplines. We conclude that the two technologies actually complement rather than compete with each other, enabling greater understanding of the genome in relation to drug discovery.

High-throughput functional genomics using CRISPR-Cas9

Forward genetic screens are powerful tools for the discovery and functional annotation of genetic elements. Recently, the RNA-guided CRISPR (clustered regularly interspaced short palindromic repeat)-associated Cas9 nuclease has been combined with genome-scale guide RNA libraries for unbiased, phenotypic screening. In this Review, we describe recent advances using Cas9 for genome-scale screens, including knockout approaches that inactivate genomic loci and strategies that modulate transcriptional activity. We discuss practical aspects of screen design, provide comparisons with RNA interference (RNAi) screening, and outline future applications and challenges.

Pathway-based analysis of genome-wide siRNA screens reveals the regulatory landscape of APP processing

The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimer's Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A) recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the “regulatory landscape” of APP.

Functional Genomics in Pharmaceutical Drug Discovery

Targeted therapies in personalized medicine require the knowledge about the molecular changes within the patient that cause the disease. With the beginning of the new century, a plethora of new technologies became available to detect these changes and use this information as starting point for drug development. Next-generation genome sequencing and sophisticated genome-wide functional genomics' methods have led to a significant increase in the identification of novel drug target candidates and understanding of the relevance of these genomic and molecular changes for the diseases. As functional genomic tool for target identification, high-throughput gene silencing through RNA interference screening has become the established method. RNAi is discussed with its advantages and challenges in this chapter. Furthermore the potential of CRISPR/Cas9, a gene-editing method that has recently been adapted for use as functional screening tool, will be briefly reviewed.

Simultaneous analysis of large-scale RNAi screens for pathogen entry

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

Impact of RNA-guided technologies for target identification and deconvolution

For well over a decade, RNA interference (RNAi) has provided a powerful tool for investigators to query specific gene targets in an easily modulated loss-of-function setting, both in vitro and in vivo. Hundreds of publications have demonstrated the utility of RNAi in arrayed and pooled-based formats, in a wide variety of cell-based systems, including clonal, stem, transformed, and primary cells. Over the years, there have been significant improvements in the design of target-specific small-interfering RNA (siRNA) and short-hairpin RNA (shRNA), expression vectors, methods for mitigating off-target effects, and accurately interpreting screening results. Recent developments in RNAi technology include the Sensor assay, high-efficiency miR-E shRNAs, improved shRNA virus production with Pasha (DRGC8) knockdown, and assessment of RNAi off-target effects by using the C9-11 method. An exciting addition to the arsenal of RNA-mediated gene modulation is the clustered regularly interspaced short palindromic repeats/Cas9 (CRISPR/Cas) system for genomic editing, allowing for gene functional knockout rather than knockdown.

RNAi screening comes of age: improved techniques and complementary approaches

Gene silencing through sequence-specific targeting of mRNAs by RNAi has enabled genome-wide functional screens in cultured cells and in vivo in model organisms. These screens have resulted in the identification of new cellular pathways and potential drug targets. Considerable progress has been made to improve the quality of RNAi screen data through the development of new experimental and bioinformatics approaches. The recent availability of genome-editing strategies, such as the CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system, when combined with RNAi, could lead to further improvements in screen data quality and follow-up experiments, thus promoting our understanding of gene function and gene regulatory networks.

Computational detection and suppression of sequence-specific off-target phenotypes from whole genome RNAi screens

A challenge for large-scale siRNA loss-of-function studies is the biological pleiotropy resulting from multiple modes of action of siRNA reagents. A major confounding feature of these reagents is the microRNA-like translational quelling resulting from short regions of oligonucleotide complementarity to many different messenger RNAs. We developed a computational approach, deconvolution analysis of RNAi screening data, for automated quantitation of off-target effects in RNAi screening data sets. Substantial reduction of off-target rates was experimentally validated in five distinct biological screens across different genome-wide siRNA libraries. A public-access graphical-user-interface has been constructed to facilitate application of this algorithm.

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.

Screening and functional analysis of the peroxiredoxin specifically expressed in Bursaphelenchus xylophilus--the causative agent of pine wilt disease

The pine wood nematode, Bursaphelenchus xylophilus, is the causal agent of pine wilt disease. Accurately differentiating B. xylophilus from other nematodes species, especially its related species B. mucronatus, is important for pine wood nematode detection. Thus, we attempted to identify a specific protein in the pine wood nematode using proteomics technology. Here, we compared the proteomes of B. xylophilus and B. mucronatus using Two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight (MALDI-TOF/TOF-MS) technologies. In total, 15 highly expressed proteins were identified in B. xylophilus compared with B. mucronatus. Subsequently, the specificity of the proteins identified was confirmed by PCR using the genomic DNA of other nematode species. Finally, a gene encoding a specific protein (Bx-Prx) was obtained. This gene was cloned and expressed in E. coli. The in situ hybridisation pattern of Bx-Prx showed that it was expressed strongly in the tail of B. xylophilus. RNAi was used to assess the function of Bx-Prx, the results indicated that the gene was associated with the reproduction and pathogenicity of B. xylophilus. This discovery provides fundamental information for identifying B. xylophilus via a molecular approach. Moreover, the purified recombinant protein has potential as a candidate diagnostic antigen of pine wilt disease, which may lead to a new immunological detection method for the pine wood nematode.

Specific inhibition of diverse pathogens in human cells by synthetic microRNA-like oligonucleotides inferred from RNAi screens

Systematic genetic perturbation screening in human cells remains technically challenging. Typically, large libraries of chemically synthesized siRNA oligonucleotides are used, each designed to degrade a specific cellular mRNA via the RNA interference (RNAi) mechanism. Here, we report on data from three genome-wide siRNA screens, conducted to uncover host factors required for infection of human cells by two bacterial and one viral pathogen. We find that the majority of phenotypic effects of siRNAs are unrelated to the intended "on-target" mechanism, defined by full complementarity of the 21-nt siRNA sequence to a target mRNA. Instead, phenotypes are largely dictated by "off-target" effects resulting from partial complementarity of siRNAs to multiple mRNAs via the "seed" region (i.e., nucleotides 2-8), reminiscent of the way specificity is determined for endogenous microRNAs. Quantitative analysis enabled the prediction of seeds that strongly and specifically block infection, independent of the intended on-target effect. This prediction was confirmed experimentally by designing oligos that do not have any on-target sequence match at all, yet can strongly reproduce the predicted phenotypes. Our results suggest that published RNAi screens have primarily, and unintentionally, screened the sequence space of microRNA seeds instead of the intended on-target space of protein-coding genes. This helps to explain why previously published RNAi screens have exhibited relatively little overlap. Our analysis suggests a possible way of identifying "seed reagents" for controlling phenotypes of interest and establishes a general strategy for extracting valuable untapped information from past and future RNAi screens.

High-content functional genomic screening to identify novel regulators of the PINK1-Parkin pathway

PINK1/PARK6 and Parkin/PARK2 are amongst the most commonly mutated genes associated with recessive forms of familial Parkinson's disease. Recent evidence indicates that the proteins they encode, PINK1 and Parkin, function in the same pathway to mediate the selective autophagic clearance of dysfunctional mitochondria. Upon mitochondrial damage, PINK1 is stabilized on the outer mitochondrial membrane where it phosphorylates ubiquitin, generating a signal for the recruitment and activation of Parkin. However, key mechanistic questions still exist regarding Parkin recruitment, including whether or not other factors are required for the PINK1 and Parkin pathway. We describe a method below using high-throughput RNA interference technology to interrogate the genome for novel components of the PINK1 and Parkin pathway.

High-content genome-wide RNAi screens identify regulators of parkin upstream of mitophagy

An increasing body of evidence points to mitochondrial dysfunction as a contributor to the molecular pathogenesis of neurodegenerative diseases such as Parkinson's disease. Recent studies of the Parkinson's disease associated genes PINK1 (ref. 2) and parkin (PARK2, ref. 3) indicate that they may act in a quality control pathway preventing the accumulation of dysfunctional mitochondria. Here we elucidate regulators that have an impact on parkin translocation to damaged mitochondria with genome-wide small interfering RNA (siRNA) screens coupled to high-content microscopy. Screening yielded gene candidates involved in diverse cellular processes that were subsequently validated in low-throughput assays. This led to characterization of TOMM7 as essential for stabilizing PINK1 on the outer mitochondrial membrane following mitochondrial damage. We also discovered that HSPA1L (HSP70 family member) and BAG4 have mutually opposing roles in the regulation of parkin translocation. The screens revealed that SIAH3, found to localize to mitochondria, inhibits PINK1 accumulation after mitochondrial insult, reducing parkin translocation. Overall, our screens provide a rich resource to understand mitochondrial quality control.

A CRISPR CASe for high-throughput silencing

Manipulation of gene expression on a genome-wide level is one of the most important systematic tools in the post-genome era. Such manipulations have largely been enabled by expression cloning approaches using sequence-verified cDNA libraries, large-scale RNA interference libraries (shRNA or siRNA) and zinc finger nuclease technologies. More recently, the CRISPR (clustered regularly interspaced short palindromic repeats) and CRISPR-associated (Cas)9-mediated gene editing technology has been described that holds great promise for future use of this technology in genomic manipulation. It was suggested that the CRISPR system has the potential to be used in high-throughput, large-scale loss of function screening. Here we discuss some of the challenges in engineering of CRISPR/Cas genomic libraries and some of the aspects that need to be addressed in order to use this technology on a high-throughput scale.