Along signal paths: an empirical gene set approach exploiting pathway topology

Gene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis clipper, that tries to fill in this gap. clipper implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expression datasets. Our results demonstrate the efficacy of clipper in the identification of signal transduction paths totally coherent with the biological problem.     

Gene set analysis in the cloud

Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions. AVAILABILITY AND IMPLEMENTATION: YunBe is open-source and freely accessible within the Amazon Elastic MapReduce service at s3n://lrcv-crp-sante/app/yunbe.jar. Source code and user's guidelines can be downloaded from http://tinyurl.com/yunbedownload.     

Non-invasive topology analysis of membrane proteins in the secretory pathway

We present a novel method to experimentally visualize in vivo the topology of transmembrane proteins residing in the endoplasmic reticulum (ER) membrane or passing through the secretory pathway on their way to their final destination. This approach, so-called redox-based topology analysis (ReTA), is based on fusion of transmembrane proteins with redox-sensitive GFP (roGFP) and ratiometric imaging. The ratio images provide direct information on the orientation of roGFP relative to the membrane as the roGFP fluorescence alters with changes in the glutathione redox potential across the ER membrane. As proof of concept, we produced binary read-outs using oxidized roGFP inside the ER lumen and reduced roGFP on the cytosolic side of the membrane for both N- and C-terminal fusions of single and multi-spanning membrane proteins. Further, successive deletion of hydrophobic domains from the C-terminus of the K/HDEL receptor ERD2 resulted in alternating localization of roGFP and a topology model for At ERD2 with six transmembrane domains.     

Gene set enrichment analysis for multiple continuous phenotypes

Background

Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.

Result

We propose here an extension of the linear combination test (LCT) to its new version for multiple continuous phenotypes, incorporating correlations among gene expressions of functionally related gene sets, as well as correlations among multiple phenotypes. Further, we extend our new method to its nonlinear version, referred as nonlinear combination test (NLCT), to test potential nonlinear association of gene sets with multiple phenotypes. Simulation study and a real microarray example demonstrate the practical aspects of the proposed methods.

Conclusion

The proposed approaches are effective in controlling type I errors and powerful in testing associations between gene-sets and multiple continuous phenotypes. They are both computationally effective. Naively (univariately) analyzing a group of multiple correlated phenotypes could be dangerous. R-codes to perform LCT and NLCT for multiple continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-260) contains supplementary material, which is available to authorized users.     

Presenilin-1 maintains a nine-transmembrane topology throughout the secretory pathway

Presenilin-1 is a polytopic membrane protein that assembles with nicastrin, PEN-2, and APH-1 into an active gamma-secretase complex required for intramembrane proteolysis of type I transmembrane proteins. Although essential for a correct understanding of structure-function relationships, its exact topology remains an issue of strong controversy. We revisited presenilin-1 topology by inserting glycosylation consensus sequences in human PS1 and expressing the obtained mutants in a presenilin-1 and 2 knock-out background. Based on the glycosylation status of these variants we provide evidence that presenilin-1 traffics through the Golgi after a conformational change induced by complex assembly. Based on our glycosylation variants of presenilin-1 we hypothesize that complex assembly occurs during transport between the endoplasmic reticulum and the Golgi apparatus. Furthermore, our data indicate that presenilin-1 has a nine-transmembrane domain topology with the COOH terminus exposed to the lumen/extracellular surface. This topology is independently underscored by lysine mutagenesis, cell surface biotinylation, and cysteine derivation strategies and is compatible with the different physiological functions assigned to presenilin-1.     

DNA repair: exploiting the Fanconi anemia pathway as a potential therapeutic target

DNA repair is an active cellular process to respond to constant DNA damage caused by metabolic processes and environmental factors. Since the outcome of DNA damage is generally adverse and long term effects may contribute to oncogenesis, cells have developed a variety of DNA repair mechanisms, which operate depending on the type of DNA damage inflicted. At least 15 Fanconi anemia (FA) proteins interact in a common pathway involved in homologous recombination. Inherited homozygous mutations in any of these FA genes cause a rare disease, Fanconi anemia, characterized by congenital abnormalities, progressive bone-marrow failure and cancer susceptibility. Heterozygous germline FA mutations predispose to various types of cancer. In addition, somatic FA mutations have been identified in diverse cancer types. Evidence exists that cells deficient in the FA pathway become dependent on alternative pathways for survival. Additional inhibition of such alternative pathways is thus expected to result in cell death, creating a relationship of synthetic lethality. Identifying these relationships can reveal yet unknown mechanisms of DNA repair and new targets for therapy.     

GAGE: generally applicable gene set enrichment for pathway analysis

Background  

Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.     

Gene structure, expression analysis, and membrane topology of RDH4

The murine retinol dehydrogenase RDH4 oxidizes several cis-isomers of retinol into their corresponding aldehydes. We have determined the structure of the murine gene, investigated the temporal and spatial expression of the enzyme, and analyzed the membrane topology of the enzyme. The gene has four translated exons, and several alternatively spliced exons in the 5'-untranslated region were identified. Immunohistochemical analysis showed expression of RDH4 in developing and adult mouse eye, particularly in the retinal pigment epithelium. In nonocular adult tissues, including liver, kidney, lung, and skin, RDH4 expression was widespread. The results suggest that RDH4 may have a dual and tissue-specific role in oxidation of 9-cis- and 11-cis-isomers of retinol into 9-cis-retinal and 11-cis-retinal, respectively. Furthermore, the lumenal orientation of the enzyme domain in the ER suggests that oxidation of both cis-isomers of retinol occurs in the ER.     

Gene set enrichment analysis: performance evaluation and usage guidelines

A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.     

Gene essentiality and the topology of protein interaction networks

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology.     

Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays

Background  

Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.     

Gene set enrichment analysis for non-monotone association and multiple experimental categories

Background  

Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed.     

Gene fusion analysis of membrane protein topology: a direct comparison of alkaline phosphatase and beta-lactamase fusions.

To compare two approaches to analyzing membrane protein topology, a number of alkaline phosphatase fusions to membrane proteins were converted to beta-lactamase fusions. While some alkaline phosphatase fusions near the N terminus of cytoplasmic loops of membrane proteins have anomalously high levels of activity, the equivalent beta-lactamase fusions do not. This disparity may reflect differences in the folding of beta-lactamase and alkaline phosphatase in the cytoplasm.     

Viral miRNAs exploiting the endosomal-exosomal pathway for intercellular cross-talk and immune evasion

The class of persistent gamma-herpesviruses has developed a variety of strategies that exploit host-cell regulatory pathways to ensure a long-lasting, well-balanced infection of their host. However when these pathways are deregulated, an otherwise harmless infection can lead to disease including cancer. We recently demonstrated that the human herpes virus 4 (HHV4) also known as Epstein-Barr virus (EBV), encodes for small regulatory non-coding microRNAs (miRNAs) that can be transferred from an infected cell to uninfected neighboring cells. Upon arrival these miRNAs are functional in the recipient cell, in that they are able to down regulate specific target genes. These secreted miRNAs are transported to recipient cells via small nano-sized vesicles (known as exosomes) that are of endosomal origin, formed as intraluminal vesicles (ILV) inside multivesicular bodies (MVB). One question that needs to be addressed is how viral miRNAs are sorted into these exosomes. Mature miRNAs, including those of viral origin, are loaded into RNA-induced silencing complexes (RISC) for gene silencing via blocking mRNA translation and/or initiating mRNA decay. Recent insights indicate that cytoplasmic RNA granules rich in RISC complexes are closely associated with endosomes. In fact, selective components of RISC, including GW182 and Argonaut proteins, miRNAs and mRNAs are present in exosomes. Thus miRNA function, mRNA stability and exosome-mediated intercellular communication converge at the level of endosomes. Since endosomes can be considered as key intracellular cross-roads that regulate communication of cells with their exterior, including neighboring cells, it is perhaps not surprising that viruses have found means to exploit this pathway to their benefit. Little is known however, how and if (micro) RNA species are specifically sorted into ILVs and what (micro)RNA-binding proteins are involved. Here we discuss recent developments relating to intracellular trafficking and function of miRNA-containing protein complexes that EBV may exploit for promoting or restricting miRNAs sorting into exosomes for intercellular regulatory functions. This article is part of a Special Issue entitled: MicroRNAs in viral gene regulation.     

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.