Predicting pathway membership via domain signatures.

MOTIVATION: Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database. RESULTS: We present a classification model, which for a specific gene of interest can predict the mapping to a KEGG pathway, based on its domain signature. The classifier makes explicit use of the hierarchical organization of pathways in the KEGG database. Furthermore, we take into account that a specific gene can be mapped to different pathways at the same time. The classification method produces a scoring of all possible mapping positions of the gene in the KEGG hierarchy. Evaluations of our model, which is a combination of a SVM and ranking perceptron approach, show a high prediction performance. Moreover, for signaling pathways we reveal that it is even possible to forecast accurately the membership to individual pathway components. AVAILABILITY: The R package gene2pathway is a supplement to this article.

Estimating large-scale signaling networks through nested effect models with intervention effects from microarray data.

MOTIVATION: Targeted interventions using RNA interference in combination with the measurement of secondary effects with DNA microarrays can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades based on the nested structure of effects. RESULTS: We extend previous work by Markowetz et al., who proposed a statistical framework to score different network hypotheses. Our extensions go in several directions: we show how prior assumptions on the network structure can be incorporated into the scoring scheme by defining appropriate prior distributions on the network structure as well as on hyperparameters. An approach called module networks is introduced to scale up the original approach, which is limited to around 5 genes, to infer large-scale networks of more than 30 genes. Instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the P-value profile, resulting from differential gene expression calculation, to quantify effects. Extensive simulations on artificial data and application of our module network approach to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells show that our approach gives sensible results. Using a bootstrapping and a jackknife approach, this reconstruction is found to be statistically stable. AVAILABILITY: The proposed method is available within the Bioconductor R-package nem.

Large scale statistical inference of signaling pathways from RNAi and microarray data.

BACKGROUND: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. RESULTS: In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. CONCLUSION: Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.

Identification of cellular targets for the human papillomavirus E6 and E7 oncogenes by RNA interference and transcriptome analyses.

Specific types of human papillomaviruses (HPVs) cause cervical cancer, the second most common tumor in women worldwide. Both cellular transformation and the maintenance of the oncogenic phenotype of HPV-positive tumor cells are linked to the expression of the viral E6 and E7 oncogenes. To identify downstream cellular target genes for the viral oncogenes, we silenced endogenous E6 and E7 expression in HPV-positive HeLa cells by RNA interference (RNAi). Subsequently, we assessed changes of the cellular transcriptome by genome-wide microarray analysis. We identified 648 genes, which were either downregulated (360 genes) or upregulated (288 genes), upon inhibition of E6/E7 expression. A large fraction of these genes is involved in tumor-relevant processes, such as apoptosis control, cell cycle regulation, or spindle formation. Others may represent novel cellular targets for the HPV oncogenes, such as a large group of C-MYC-associated genes involved in RNA processing and splicing. Comparison with published microarray data revealed a substantial concordance between the genes repressed by RNAi-mediated E6/E7 silencing in HeLa cells and genes reported to be upregulated in HPV-positive cervical cancer biopsies.