USP6 oncogene promotes Wnt signaling by deubiquitylating Frizzleds.

The Wnt signaling pathways play pivotal roles in carcinogenesis. Modulation of the cell-surface abundance of Wnt receptors is emerging as an important mechanism for regulating sensitivity to Wnt ligands. Endocytosis and degradation of the Wnt receptors Frizzled (Fzd) and lipoprotein-related protein 6 (LRP6) are regulated by the E3 ubiquitin ligases zinc and ring finger 3 (ZNRF3) and ring finger protein 43 (RNF43), which are disrupted in cancer. In a genome-wide small interfering RNA screen, we identified the deubiquitylase ubiquitin-specific protease 6 (USP6) as a potent activator of Wnt signaling. USP6 enhances Wnt signaling by deubiquitylating Fzds, thereby increasing their cell-surface abundance. Chromosomal translocations in nodular fasciitis result in USP6 overexpression, leading to transcriptional activation of the Wnt/ß-catenin pathway. Inhibition of Wnt signaling using Dickkopf-1 (DKK1) or a Porcupine (PORCN) inhibitor significantly decreased the growth of USP6-driven xenograft tumors, indicating that Wnt signaling is a key target of USP6 during tumorigenesis. Our study defines an additional route to ectopic Wnt pathway activation in human disease, and identifies a potential approach to modulate Wnt signaling for therapeutic benefit.

cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens.

MOTIVATION: Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens. RESULTS: Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test. CONTACT: xiaohua_zhang@merck.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hit selection with false discovery rate control in genome-scale RNAi screens.

RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median +/- kMAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.

Error rates and powers in genome-scale RNAi screens.

For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links.

Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens.

RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.

The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments.

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.

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

The progressive aggregation of Amyloid-ß (Aß) in the brain is a major trait of Alzheimer's Disease (AD). Aß is produced as a result of proteolytic processing of the ß-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 sAPPß, Aß40, and Aß42. 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 Aß40 production over Aß42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPß 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.

FAM129B is a novel regulator of Wnt/ß-catenin signal transduction in melanoma cells.

The inability of targeted BRAF inhibitors to produce long-lasting improvement in the clinical outcome of melanoma highlights a need to identify additional approaches to inhibit melanoma growth. Recent studies have shown that activation of the Wnt/ß-catenin pathway decreases tumor growth and cooperates with ERK/MAPK pathway inhibitors to promote apoptosis in melanoma. Therefore, the identification of Wnt/ß-catenin regulators may advance the development of new approaches to treat this disease. In order to move towards this goal we performed a large scale small-interfering RNA (siRNA) screen for regulators of ß-catenin activated reporter activity in human HT1080 fibrosarcoma cells. Integrating large scale siRNA screen data with phosphoproteomic data and bioinformatics enrichment identified a protein, FAM129B, as a potential regulator of Wnt/ß-catenin signaling.  Functionally, we demonstrated that siRNA-mediated knockdown of FAM129B in A375 and A2058 melanoma cell lines inhibits WNT3A-mediated activation of a ß-catenin-responsive luciferase reporter and inhibits expression of the endogenous Wnt/ß-catenin target gene, AXIN2. We also demonstrate that FAM129B knockdown inhibits apoptosis in melanoma cells treated with WNT3A. These experiments support a role for FAM129B in linking Wnt/ß-catenin signaling to apoptosis in melanoma.

Common seed analysis to identify off-target effects in siRNA screens.

Genome-scale small interfering RNA (siRNA) screens have become an increasingly popular approach to new target identification and pathway elucidation. However, the large data sets generated from siRNA screens have demonstrated high false-positive rates and the requirement for extensive experimental triage to distinguish true hits. A number of groups have independently reported the presence of siRNAs with identical seed sequences among their top screening hits. Based on these observations, we have developed a comprehensive technique for detecting and visualizing seed-based off-target effects in siRNA screening data. This is accomplished by analyzing the behavior of siRNAs that share identical seed sequences, which we refer to as common seed analysis (CSA). By applying these techniques to primary screening data of the Wnt pathway, we identify 158 distinct seed sequences that have a statistically significant effect on the assay. The promiscuous seed sequences identified in this manner can then be discounted in the analysis of follow-up experiments using single siRNAs. The ability to detect off-target effects when sufficient numbers of siRNAs share a common seed has significant implications for the design of siRNA screening experiments, data analysis, hit selection, and library design.

High-throughput transfection of differentiated primary neurons from rat forebrain.

Primary neurons in culture are considered to be a highly relevant model in the study of neuronal development and activity. They can be cultivated and differentiated in vitro but are difficult to transfect using conventional methods. To address this problem, a capillary electroporation system called Cellaxess Elektra was developed for efficient and reproducible transfection of primary cortical and hippocampal neurons without significant impact on cell morphology and viability. The cells are transfected in any stage of differentiation and development, directly in cell culture plates. Genetic material is delivered in situ to as many as 384 samples at a time, which enables both high-throughput and high-quality screening for hard-to-transfect primary cells, meaning that gene function can be studied on a genome-wide scale in cells previously inaccessible to genetic manipulation.

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

We introduce a method for analyzing small interfering RNA (siRNA) genetic screens based entirely on off-target effects. Using a screen for members of the Wnt pathway, we demonstrate that this method identifies known pathway components, some of which are not present in the screening library. This technique can be applied to siRNA screen results retroactively to confirm positives and identify genes missed using conventional methods for on-target gene selection.

A 1,536-well ultra-high-throughput siRNA screen to identify regulators of the Wnt/beta-catenin pathway.

High-throughput siRNA screens are now widely used for identifying novel drug targets and mapping disease pathways. Despite their popularity, there remain challenges related to data variability, primarily due to measurement errors, biological variance, uneven transfection efficiency, the efficacy of siRNA sequences, or off-target effects, and consequent high false discovery rates. Data variability can be reduced if siRNA screens are performed in replicate. Running a large-scale siRNA screen in replicate is difficult, however, because of the technical challenges related to automating complicated steps of siRNA transfection, often with multiplexed assay readouts, and controlling environmental humidity during long incubation periods. Small-molecule screens have greatly benefited in the past decade from assay miniaturization to high-density plates such that 1,536-well nanoplate screenings are now a routine process, allowing fast, efficient, and affordable operations without compromising underlying biology or important assay characteristics. Here, we describe the development of a 1,536-well nanoplate siRNA transfection protocol that utilizes the instruments commonly found in small-molecule high throughput screening laboratories. This protocol was then successfully demonstrated in a triplicate large-scale siRNA screen for the identification of regulators of the Wnt/beta-catenin pathway.

The use of SSMD-based false discovery and false nondiscovery rates in genome-scale RNAi screens.

In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statistically controlling false discovery rate (FDR) and false nondiscovery rate (FNDR) appropriate for RNAi screens. In this article, the authors explore the utility of the SSMD-based method for hit selection in RNAi screens. As demonstrated in 2 genome-scale RNAi screens, the SSMD-based method addresses the unmet need of controlling for the proportion of siRNAs with a small effect among selected hits, as well as controlling for the proportion of siRNAs with a large effect among declared nonhits. Furthermore, the SSMD-based method results in reasonably low FDR and FNDR for selecting inhibition or activation hits. This method works effectively and should have a broad utility for hit selection in RNAi screens with replicates.

New regulators of Wnt/beta-catenin signaling revealed by integrative molecular screening.

The identification and characterization of previously unidentified signal transduction molecules has expanded our understanding of biological systems and facilitated the development of mechanism-based therapeutics. We present a highly validated small interfering RNA (siRNA) screen that functionally annotates the human genome for modulation of the Wnt/beta-catenin signal transduction pathway. Merging these functional data with an extensive Wnt/beta-catenin protein interaction network produces an integrated physical and functional map of the pathway. The power of this approach is illustrated by the positioning of siRNA screen hits into discrete physical complexes of proteins. Similarly, this approach allows one to filter discoveries made through protein-protein interaction screens for functional contribution to the phenotype of interest. Using this methodology, we characterized AGGF1 as a nuclear chromatin-associated protein that participates in beta-catenin-mediated transcription in human colon cancer cells.

High-throughput screening by RNA interference: control of two distinct types of variance.

The availability of genome-wide RNAi libraries has enabled researchers to rapidly assess the functions of thousands of genes; however the fact that these screens are run in living biological systems add complications above and beyond that normally seen in high-throughput screening (HTS). Specifically, error due to variance in both measurement and biology are large in such screens, leading to the conclusion that the majority of "hits" are expected to be false positives. Here, we outline basic guidelines for screen development that will help the researcher to control these forms of variance. By running a large number of positive and negative control genes, error of measurement can be accurately estimated and false negatives reduced. Likewise, by using a complex readout for the screen, which is not easily mimicked by other biological pathways and phenomena, false positives, can be minimized. By controlling variance in these ways, the researcher can maximize the utility of genome-wide RNAi screening.

A miniaturized cell-based fluorescence resonance energy transfer assay for insulin-receptor activation.

This report describes the development, optimization, and implementation of a miniaturized cell-based assay for the identification of small-molecule insulin mimetics and potentiators. Cell-based assays are attractive formats for compound screening because they present the molecular targets in their cellular environment. A fluorescence resonance energy transfer (FRET) cell-based assay that measures the insulin-dependent colocalization of Akt2 fused with either cyan fluorescent protein or yellow fluorescent protein to the cellular membrane was developed. This ratiometric FRET assay was miniaturized into a robust, yet sensitive 3456-well nanoplate assay with Z' factors of approximately 0.6 despite a very small assay window (less than twofold full activation with insulin). The FRET assay was used for primary screening of a large compound collection for insulin-receptor agonists and potentiators. To prioritize compounds for further development, primary hits were tested in two additional assays, a biochemical time-resolved fluorescence resonance energy transfer assay to measure insulin-receptor phosphorylation and a translocation-based imaging assay. Results from the three assays were combined to yield 11 compounds as potential leads for the development of insulin mimetics or potentiators.

LRRTM3 promotes processing of amyloid-precursor protein by BACE1 and is a positional candidate gene for late-onset Alzheimer's disease.

Rare familial forms of Alzheimer's disease (AD) are thought to be caused by elevated proteolytic production of the Abeta42 peptide from the beta-amyloid-precursor protein (APP). Although the pathogenesis of the more common late-onset AD (LOAD) is not understood, BACE1, the protease that cleaves APP to generate the N terminus of Abeta42, is more active in patients with LOAD, suggesting that increased amyloid production processing might also contribute to the sporadic disease. Using high-throughput siRNA screening technology, we assessed 15,200 genes for their role in Abeta42 secretion and identified leucine-rich repeat transmembrane 3 (LRRTM3) as a neuronal gene that promotes APP processing by BACE1. siRNAs targeting LRRTM3 inhibit the secretion of Abeta40, Abeta42, and sAPPbeta, the N-terminal APP fragment produced by BACE1 cleavage, from cultured cells and primary neurons by up to 60%, whereas overexpression increases Abeta secretion. LRRTM3 is expressed nearly exclusively in the nervous system, including regions affected during AD, such as the dentate gyrus. Furthermore, LRRTM3 maps to a region of chromosome 10 linked to both LOAD and elevated plasma Abeta42, and is structurally similar to a family of neuronal receptors that includes the NOGO receptor, an inhibitor of neuronal regeneration and APP processing. Thus, LRRTM3 is a functional and positional candidate gene for AD, and, given its receptor-like structure and restricted expression, a potential therapeutic target.

Surprisingly minor influence of TRAV11 (Valpha14) polymorphism on NK T-receptor mCD1/alpha-galactosylceramide binding kinetics.

Defects in natural killer T (NK T) cell function and of interleukin-4 -production in SJL and NOD mice have been linked to susceptibility to autoimmune disease. As SJL and NOD mice both carry the T-cell receptor (TCR) alpha-chain locus "c" (Tcra(c)) haplotype, found in few other strains, we have attempted to determine the influence of Tcra polymorphism on NK T-cell recognition of ligand, selection, and immune responses. The majority of NK T cells use an "invariant" TRAV11J15 (previously called AV14J18 or Valpha14 Jalpha281) alpha- chain paired with either TRBV13-2, BV29, or BV1 to recognize ligands presented by mCD1 molecules, including the glycolipid alpha-galactosylceramide (alpha-GalCer). Sequencing of TRAV11 from the mouse strains B10.A (encoding the Tcra(b) haplotype), B10.A- Tcra(c), and NOD (Tcra(c)) shows that Tcra(c) has a single TRAV11 gene (TRAV11*01) and that Tcra(b) has a single expressed gene (TRAV11*02), plus a closely related pseudogene. There is no apparent difference in alpha-chain J-region usage or in the CDR3alpha sequence at the TRAV11-J15 junction between the haplotypes in TRAV11-bearing NK T cells. Using Biacore and tetramer-binding and decay assays, we have determined that the interaction between Tcra(c) TRAV11*01 NK T TCR and the mCD1/alpha-GalCer complex is slightly weaker than that of Tcra(b) (i.e., TRAV11*02) NK T TCR. These differences are minor compared with differences between agonist and antagonist ligands in other TCR systems, suggesting that it is unlikely that TCR polymorphism explains the defect in NK T cells in the autoimmune mouse strains.