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Analyzing Compounds’ Mode of Action - A Use Case for New Approaches Utilizing Protein Interaction Networks and Prior Knowledge to Complement State-of-the-Art Gene Expression Analyses
Background: Scientists in pharmaceutical as well as academic research work together to solve the challenging puzzle from the basic causes of disease at the level of genes, proteins and cells up to a marketed new drug. Analyses of mode of action (MoA) of new chemical entities (NCEs) are a very important step in the development of new drugs. One distinguishes between effects induced by modulating the compounds’ actual target protein (on-target effects) and effects induced by additional, possibly unknown targets (off-target effects). Quite often knowledge about either of these effects is limited. Since MoA is mainly triggered by the interplay of proteins or signaling cascades, investigating the change and subsequent influence of the changed molecules in a protein interaction (PI) network is a promising initial step to further analyses. As more and more data from diverse sources becomes available, the integration of this knowledge is important for generating a deeper insight into biology. In addition, expression experiments based on disease tissue and/or compound treatment are frequently conducted to get insight into transcriptional changes that could explain compounds’ MoA. Status quo: MoA could be analyzed by investigating those parts of a PI network that show changes based on compound treatment. Mathematical or graph theoretical in silico methods to identify interesting parts of a network based on different criteria are widely used. Criteria range from detection of highly connected subgraphs to subgraphs maximizing weights assigned to parts of the network under investigation. These methods can be transferred to biology and can be used to, e. g. identify condition responsive subnetworks on various types of molecular networks. Present questions addressed mainly focus on the detection of subnetworks enriched in information from functional genomics, e.g. differentially expressed genes. They neglect the existence of distance regulatory functions on the post-transcriptional as well as post-translational level like miRNA interference or protein phosphorylation. Further, available methods usually detect relatively large modules. It is easily possible that more processes, i. e. the on- and several off-target effects, are covered by one larger module. Thus, the individual effects are difficult to detect and interpret. To be able to derive individual effects, it is necessary to reveal small modules that are related to the individual effects present in the biological system under investigation. Methods & Results: In this work, I made use of a gene expression data set investigating the inhibition of the TGF-beta signaling pathway by different compounds targeting TGF-betaR1. To gain a sound basis for follow-up analyses, different aspects of how to select the best suited normalization procedure for the underlying expression data are proposed in the first part of this thesis. To analyze compounds’ MoA, I propose a method that weights interactions between proteins based on different kinds of evidence. In this method, the relevance of the proteins is based on the biological relatedness to other possibly not deregulated protein coding genes. Thereby, analyses are expanded beyond transcriptional deregulation. To elucidate the biological relatedness, information on molecular function, biological processes and cellular compartment, information on transcription factor binding sites and literature-based confidence scores are integrated for weighting the edges between proteins. To transfer the network into the biological context of interest, expression experiments are used as anchoring points for the analyses. Further, I introduce modEx, a method to extract small modules out of a weighted protein interaction network. Modules extracted using modEx reflect the individual effects present in the biological system under investigation. For the expression data set used, the proposed edge scoring is shown to be superior to the widely accepted STRING scoring. Furthermore, modEx extracts modules that represent the underlying mechanism better than jActiveModule, a commonly used subgraph extraction method. These newly proposed approaches are applied to elucidate the MoA, i. e. the on- as well as off-target effects, of compounds. They are shown to grant a more focused view on the effects of compounds than current state-of-the-art methods applied for the analysis of gene expression data.
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