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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P12. PHENETIC: MULTI-OMICS DATA INTERPRETATION USING INTERACTION NETWORKS Dries De Maeyer 1,2,3* , Bram Weytjens 1,2,3 , Luc De Raedt 4 & Kathleen Marchal 2,3 . Centre for Microbial and Plant Genetics, KULeuven 1 ; Department for Information Sciences (INTEC, IMinds), UGent 2 ; Department for Plant Biotechnology and Bioinformatics, UGent 3 ; Department of Computer Science, KULeuven 4 . * dries.demaeyer@biw.kuleuven.be The omics revolution has introduced new challenges when studying interesting phenotypes. High throughput omics technologies such as next-generation sequencing and microarray technologies generate large amounts of data. Interpreting the resulting data from these experiments is not trivial due to the data’s size and the inherent noise of the underlying technologies. In addition to this, the “omics” technologies have led to an ever expanding biological knowledge which has to be taken into account when interpreting new experimental results. Interaction network in combination with subnetwork inference methods provide a solution to this problem by mining the current public interactomics knowledge using experimental omics data to better understand the molecular mechanisms driving the interesting phenotypes under study. INTRODUCTION Computational methods are becoming essential for analyzing large scale omics datasets in the light of current knowledge. By representing publicly available interactomics knowledge as interaction networks subnetwork inference methods can extract the actual molecular mechanisms that drive an interesting phenotype. The PheNetic framework is such a method that allows for mining interaction networks with multi-omics datasets. Using this framework different types of biological applications have been analyzed in the past such as KOtranscriptomics interpretation (De Maeyer, 2013), expression analysis (De Maeyer, 2015) and distinguishing driver from passenger mutation from eQTL experiments (De Maeyer). METHODS Interaction networks provide a flexible representation of public biological interactomics knowledge. These networks represent the physical interactions between genes and their corresponding gene products in the interactome of the organism under research (Cloots, 2011). The interaction network integrates different layers of homogeneous interactomics data, e.g. signalling, proteinprotein, (post)transcriptional and metabolic interactomics data, into a single heterogeneous network representation. The PheNetic framework uses interaction networks to find biologically valid paths which connect (in)activated genes selected from multi-omics data sets. These paths provide a biological explanation of how the genes from these data sets can trigger each other. Finding the best explanations or paths in the interaction network corresponds to finding that subnetwork that best explains the observed results and provides an insight into the molecular mechanisms that drive the interesting phenotype. Depending on the type of biological application and provided data different types of paths can be used to infer the subnetwork such as KOtranscriptomics interpretation (De Maeyer, 2013), expression analysis (De Maeyer, 2015) and interpreting eQTL experiments (De Maeyer). RESULTS & DISCUSSION In a first setup PheNetic was used to study the pathways and processes involved in acid resistance in Escherichia coli (De Maeyer, 2013). Using our framework we were able to determine the different molecular pathways that drive acid resistance and identify the regulators that underlie this phenotype. It was shown that subnetwork inference methods outperform naïve gene rankings in identifying the biological pathways associated with the phenotype under research based. In a second setup PheNetic was used to interpret expression data (De Maeyer, 2015) to extract from the interaction network those parts of the interaction network that show differences in expression. This method was provided as a web server that can be accessed at http://bioinformatics.intec.ugent.be/ phenetic and that allows for an intuitive and visual interpretation of the inferred subnetworks. In a third setup PheNetic was used to select driver mutations from passenger mutations in coupled genetictranscriptomics data sets from evolution experiments (De Maeyer). Evolved strains with the same phenotype are expected to have consistent changes in the same pathways. Therefore, finding the subnetwork that best connects the mutations to the differentially expressed genes over all strains is expected to identify the driver mutations over passenger mutations in combination with identifying the molecular mechanisms that induce the observed change in phenotype. This approach provides a systemic insight in both the biological processes and genetic background that induces phenotype. Based on the different approaches it can be concluded that PheNetic is a flexible framework for subnetwork selection that allows for solving a large variety of biological applications using multi-omics data sets. REFERENCES Cloots, L., & Marchal, K. (2011). Curr Opin Microbiol, 14(5), 599-607. De Maeyer, D., Renkens, J., Cloots, L., De Raedt, L., & Marchal, K. (2013). Mol Biosyst, 9(7), 1594-1603. De Maeyer, D., Weytjens, B., Renkens, J., De Raedt, L., & Marchal, K. (2015). Nucleic Acids Res, 43(W1), W244-250. De Maeyer, D., Weytjens, B., De Raedt, L., & Marchal, K. Molecular biology and evolution. Submitted 56
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P13. THE ROLE OF HLA ALLELES UNDERLYING CYTOMEGALOVIRUS SUSCEPTIBILITY IN ALLOGENEIC TRANSPLANT POPULATIONS Nicolas De Neuter 1,2* , Benson Ogunjimi 3 , Anke Verlinden 4 , Kris Laukens 1,2 & Pieter Meysman 1,2 . Advanced Database Research and Modeling (ADReM), University of Antwerp 1 ; Biomedical informatics research center Antwerpen (biomina) 2 ; Centre for Health Economics Research and Modeling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp 3 ; Antwerp University Hospital 4 . * nicolas.deneuter@uantwerpen.be In this study, we aim to characterize those HLA alleles that increase or decrease the risk of cytomegalovirus infections following tissue or organ transplants. This HLA-dependent susceptibility will then be explained using state-of-the-art HLA peptide affinity methods to identify the underlying molecular reason. This insight can greatly aid prediction of those transplantation patients that are most at risk from cytomegalovirus infection. INTRODUCTION Patients suffering from disorders of the hematopoietic system or with chemo-, radio-, or immuno- sensitive malignancies such as leukemia often receive hematopoietic stem cell transplantation therapy (HSCT). The transplantation is preceded by a conditioning regimen that eradicates the recipient’s malignant cell population through intensive chemotherapy and irradiation, simultaneously ablating the recipient’s bone marrow. Self (autologous) or non-self (allogeneic) hematopoietic stem cells are then reintroduced into the recipient after which they are allowed to reestablish hematopoietic functions. HSCT is associated with high morbidity and mortality and requires careful monitoring of patients during the weeks following transplantation. Opportunistic cytomegalovirus (CMV) infections are one of the major causes of this high morbidity and mortality and can occur in up to 80% of HSCT patients, depending on the use of prophylactic treatment or pre-emptive therapy and the serological CMV status of donor and recipient. CMV disease can manifest itself as life-threatening pneumonia, gastrointestinal disease, retinitis, encephalitis or hepatitis. The relevance of HLA alleles in varicella zoster virus associated disease has recently been demonstrated by our group (Meysman et al., 2015) and similar insights might be gained in CMV related disease. Several studies have already shown a correlation between the incidence of CMV infection and the presence of certain human leukocyte antigens (HLA) alleles in the transplant recipient. However, the exact alleles identified in previous studies are very inconsistent, likely due to small sample sizes and type I multiple testing errors. METHODS Anonymized patient records on the HLA alleles, CMV infection and serological status of 1284 transplant recipients were collected from the Antwerp University Hospital (UZA). This data set was further extended with publicly available HLA data from transplant patient and the counts for the HLA alleles of each loci present were combined. A hypergeometric distribution was used to test HLA loci (A, B, C, DRB1, DQB1 and DPB1) for statistical over- or underrepresentation of their respective alleles. HLA alleles were tested for over- or underrepresentation in two test populations: recipients who were seropositive for CMV before transplantation and recipients who developed a CMV infection posttransplantation. In the later case, we also examined if donor seropositivity had an influence on the CMV infection status. The P value cutoff used is 0.05 and was adjusted with a Bonferroni correction for multiple testing, in this case the number of alleles tested per loci. Putative nonameric peptides were generated in silico from CMV protein sequences available in online protein sequence repositories such as the UniProt Knowledgebase. Three complementary methods were employed to predict the affinity of each putative nonameric peptide to the significantly enriched or depleted HLA alleles. The methods used were: NetCTLpan, the stabilized matrix method (SMM) and an in-house-developed approach called CRFMHC. Peptide-binding affinity results of each predictor were normalized against the affinity of a restricted panel of human proteins and used to compare results between predictors. Additionally, each CMV protein was assessed for depletion of high-affinity peptides using a hypergeometric distribution. RESULTS Preliminary results on a small portion of the UZA data reveals HLA alleles underlying either CMV seropositivity or CMV infection with a trend towards significance but do not reach the Bonferroni corrected threshold. We expect the additional data to increase the power of the analysis. REFERENCES Meysman,P. et al. (2015) Varicella-Zoster Virus-Derived Major Histocompatibility Complex Class I-Restricted Peptide Affinity Is a Determining Factor in the HLA Risk Profile for the Development of Postherpetic Neuralgia. J. Virol., 89, 962–969. 57
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