Network and Pathway Analysis of Toxicogenomics Data

Abstract

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

“…Gene co-expression network analysis is a systems biology method used to describe the correlation patterns among genes across different experimental samples. It allows representing, investigating and understanding the complex molecular interactions within the exposed system [22,45]. The genes and their interactions are represented as a network (or graph) where the genes are the nodes of the network and their strength of similarity is represented as weighted edges between the nodes [46].…”

“…TGx shifts the focus from traditional end-point-driven analysis to a systems biology approach, allowing to better understand and predict the alterations in the molecular mechanisms leading to toxicity. In this context, different methods for the study of gene co-expression networks is of great interest to identify common patterns of expression among relevant genes [2][3][4][5][6]22].…”

Toxicogenomics and New Advances in Toxicology

“…Bu, veri işleme ve veri analizi için yeni kavramlar gerektirmiştir ve biyoinformatik yöntemlerin omik teknolojilerine uygulanmasını başlatmıştır. 2,25 Bu teknolojileri istatistiksel yönden değerlendirmek kolay değildir.…”

“…Stan-dartlarda eksikliklerin olması, gruplar arasındaki veri değişkenliğinin varlığı ve verilerin bağımsız olarak doğrulanması söz konusu olsa da omik çalışmalarından önemli sonuçlar elde edilmektedir. Bilim dünyasında kabul edilebilir standartların geliştirilmesi gerekli görülmektedir Metabolomik ve proteomik alanlarda benzer standardizasyon çalışmaları devam etmektedir 2,25.…”

NetCore: a network propagation approach using node coreness

“…NetCore's workflow consists of three main steps. As a first step, a PPI network was extracted from ConsensusPathDB ( 31 ). This PPI network was recently reported in an independent study as one of the top performing networks for identifying disease genes via network propagation ( 32 ).…”