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.

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.

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.

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.

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.

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.

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.