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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P24. DOSE-TIME NETWORK IDENTIFICATION: A NEW METHOD FOR GENE REGULATORY NETWORK INFERENCE FROM GENE EXPRESSION DATA WITH MULTIPLE DOSES AND TIME POINTS Diana M Hendrickx 1* , Danyel G J Jennen 1 & Jos C S Kleinjans 1 . Department of Toxicogenomics, Maastricht University, The Netherlands 1 . *d.hendrickx@maastrichtuniversity.nl Toxicogenomics, the application of ‘omics’ technologies to toxicology, is a rapidly growing field due to the need for alternatives to animal experiments for toxicity testing of compounds. Identification of gene regulatory networks affected by compounds is important to gain more insight into the mode of action of a toxic compound. The response to a toxic compound is both time and dose dependent. Therefore, toxicogenomics data are often measured across several time points and doses. However, to our knowledge, there does not exist a method for gene regulatory network inference that takes into account both time and dose dependencies. Here we present Dose-Time Network Identification (DTNI), a novel gene regulatory network inference algorithm that takes into account both dose and time dependencies in the data. We show that DTNI can be used to infer gene regulatory networks affected by a group of compounds with the same mode of action. This is illustrated with gene expression (microarray) data from COX inhibitors, measured in human hepatocytes. INTRODUCTION Identifying and understanding gene regulatory networks (GRN) influenced by chemical compounds is one of the main challenges of systems toxicology. A GRN affected by one or more compounds evolves over time and with dose. The analysis of gene expression data measured at multiple time points and for multiple doses can provide more insight in the effects of compounds. Therefore, there is a need for mathematical approaches for GRN identification from this type of data. METHODS One of the mathematical approaches currently used for GRN inference is based on ordinary differential equations (ODE), where changes in gene expression over time are related to each other and to the external perturbation (i.e. the dose of the compound). Because gene expression data usually have less data points than variables (genes), ODE approaches are often combined with interpolation and/or dimension reduction techniques (PCA). A current method that combines ODE with both interpolation and dimension reduction techniques is Time Series Network Identification (TSNI) (Bansal et al., 2006). Here, we present Dose-Time Network Identification (DTNI), a method that extends TSNI by including ODE that describe changes in gene expression over dose in relation to each other and to time. We also adapted the original method so that it can include data from multiple perturbations (compounds). RESULTS & DISCUSSION By exploiting simulated data, we show that including ODE for expression changes over dose leads to improved GRN identification compared with including only ODE that describe changes over time. Furthermore, we show that DTNI performs better when including data from multiple perturbations (compounds) than when applying DTNI to data from a single perturbation. This suggests that the method is suitable to infer a GRN affected by compounds with the same mode of action. As an example, we infer the network affected by COX inhibitors from public microarray data of 6 COX inhibitors, measured in human hepatocytes, available from Open TG-Gates (http://toxico.nibio.go.jp/english/index.html) (Noriyuki et al., 2012). The interactions in the inferred network were compared to interactions from ConsensusPathDB, a database including interactions from 32 different sources (Kamburov et al., 2013). The inferred network was validated by leave-one out cross-validation (LOOCV). Six datasets were created from the original data by leaving out the data of one compound. The network constructed from the whole data set showed large overlap with the networks constructed from each of the LOOCV datasets. Edges in the network constructed from the whole data set, but not in the networks constructed from the LOOCV datasets were removed from the network. The remaining novel interactions, i.e. those that are not in ConsensusPathDB, have to be validated experimentally, e.g. by geneknockdown experiments. FIGURE 1. Workflow for identifying a gene regulatory network affected by a group of compounds with the same mode of action. REFERENCES Bansal M et al. Bioinformatics 22, 815-822 (2006). Noriyuki N et al. J Toxicol Sci 37,791-801 (2012). Kamburov A et al. Nucl Acids Res 41, D793-D800 (2013). 68
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Category: Poster 10th Benelux Bioinformatics Conference bbc 2015 P25. IDENTIFICATION OF NOVEL ALLOSTERIC DRUG TARGETS USING A “DUMMY” LIGAND APPROACH Susanne M.A. Hermans, Christopher Pfleger & Holger Gohlke * . Department of Mathematics and Natural Sciences, Institute for Pharmaceutical and Medicinal Chemistry, Heinrich- Heine-University, Düsseldorf, Germany. * gohlke@uni-duesseldorf.de Targeting allosteric sites is a promising strategy in drug discovery due to their regulatory role in almost all cellular processes. Currently, there is no standard method to identify novel pockets and to detect whether a pocket has a regulatory effect on the protein. Here, we present a new and efficient approach to probe information transfer through proteins in the context of dynamically dominated allostery that exploits “dummy” ligands as surrogates for allosteric modulators. INTRODUCTION Allosteric regulation is the coupling between separated sites in biomacromolecules such that an action at one site changes the function at a distant site. Allosteric drugs are popular, they often have less side effects then orthosteric drugs because the allosteric sites are less conserved. The identification of novel allosteric pockets is complicated by the large variation in allosteric regulation, ranging from rigid body motions to disorder/order transitions, with dynamically dominated allostery in between (Motlagh et al., 2014). Here we focus on dynamically dominated allostery with minimal or no conformational changes. Novel pockets do not have a known ligand, therefore we generate “dummy” ligands to function as surrogates for allosteric ligands. We have developed an efficient approach to probe information transfer through proteins using “dummy” ligands and detect if allosteric coupling is present between the novel pocket and the orthosteric site. METHODS In a preliminary study to test the general feasibility, the approach was applied to conformations extracted from a MD trajectory of the holo and apo structures of LFA1. The grid-based PocketAnalyzer program (Craig et al., 2011) is used to detect putative binding sites. “Dummy” ligands were generated for each detected pocket along the ensemble. Finally, the Constraint Network Analysis (CNA) software, which links biomacromolecular structure, (thermo-)stability, and function, is used to probe the allosteric response by monitoring altered stability characteristics of the protein due to the presence of the “dummy” ligand (Pfleger et al., 2013; Krüger et al., 2013; Pfleger, 2014). The results were compared to those of the holo structure with the bound allosteric ligand to validate the “dummy” ligand approach. RESULTS & DISCUSSION Remarkably, the usage of “dummy” ligands almost perfectly reproduced the results obtained from the known allosteric effector. Although it turned out that the intrinsic rigidity of the “dummy” ligands over-stabilizes the LFA1 structure, these results are already encouraging. Even for the LFA1 apo structures, where the allosteric pocket is partially closed, the results are in agreement with known allosteric effectors. Overall, the results obtained from the validation of the “dummy” ligand approach are encouraging. This suggests that our “dummy” ligand approach for the characterization of unexplored allosteric pockets is a promising step towards identifying novel drug targets. REFERENCES Craig, I.R. et al. J. Chem. Inf. Model. 51 2666–2679 (2011). Krüger, D. M. et al. Nucleic Acids Res. 41 340–348 (2013). Motlagh, H.N. et al. Nature 508 7496 331–339 (2014). Pfleger, C. et al. J. Chem. Inf. Model. 53 1007–1015 (2013). Pfleger, C. Doctoral Thesis, Heinrich Heine University, Düsseldorf, Germany (2014). 69
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