Griet Laenen

Finding the targets of a drug by integration of gene expression data with a protein interaction network

Polypharmacology, which focuses on designing drugs that bind efficiently to multiple targets, has emerged as a new strategic trend in todays drug discovery research. Many successful drugs achieve their effects via multi-target interactions. However, these targets are largely unknown for both marketed drugs and drugs in development. A better knowledge of a drugs mode of action could be of substantial value to future drug development, in particular for side effect prediction and drug repositioning. We propose a network-based computational method for drug target prediction, applicable on a genome-wide scale. Our approach relies on the analysis of gene expression following drug treatment in the context of a functional protein association network. By diffusing differential expression signals to neighboring or correlated nodes in the network, genes are prioritized as potential targets based on the transcriptional response of functionally related genes. Different diffusion strategies were evaluated on 235 publicly available gene expression datasets for treatment with bioactive molecules having a known target. AUC values of up to more than 90% demonstrate the effectiveness of our approach and indicate the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.

Methylome analysis for spina bifida shows SOX18 hypomethylation as a risk factor with evidence for a complex (epi)genetic interplay to affect neural tube development

BackgroundNeural tube defects (NTDs) are severe congenital malformations that arise from failure of neurulation during early embryonic development. The molecular basis underlying most human NTDs still remains largely unknown. Based on the hypothesis that folic acid prevents NTDs by stimulating methylation reactions, DNA methylation changes could play a role in NTDs. We performed a methylome analysis for patients with myelomeningocele (MMC). Using a candidate CpG analysis for HOX genes, a significant association between HOXB7 hypomethylation and MMC was found.MethodsIn the current study, we analyzed leukocyte methylome data of ten patients with MMC and six controls using Illumina Methylation Analyzer and WateRmelon R-packages and performed validation studies using larger MMC and control cohorts with Sequenom EpiTYPER.ResultsThe methylome analysis showed 75 CpGs in 45 genes that are significantly differentially methylated in MMC patients. CpG-specific methylation differences were next replicated for the top six candidate genes ABAT, CNTNAP1, SLC1A6, SNED1, SOX18, and TEPP but only for the SOX18 locus a significant overall hypomethylation was observed (P value = 0.0003). Chemically induced DNA demethylation in HEK cells resulted in SOX18 hypomethylation and increased expression. Injection of sox18 mRNA in zebrafish resulted in abnormal neural tube formation. Quantification of DNA methylation for the SOX18 locus was also determined for five families where parents had normal methylation values compared to significant lower values for both the MMC as their non-affected child. SOX18 methylation studies were performed for a MMC patient with a paternally inherited chromosomal deletion that includes BMP4. The patient showed extreme SOX18 hypomethylation similar to his healthy mother while his father had normal methylation values.ConclusionsThis is the first genome-wide methylation study in leukocytes for patients with NTDs. We report SOX18 as a novel MMC risk gene but our findings also suggest that SOX18 hypomethylation must interplay with environmental and (epi)genetic factors to cause NTDs. Further studies are needed that combine methylome data with next-generation sequencing approaches to unravel NTD etiology.

Galahad: a web server for drug effect analysis from gene expression

Galahad (https://galahad.esat.kuleuven.be) is a web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network. The web site is free and open to all and there is no login requirement.

ACE-inhibition induces a cardioprotective transcriptional response in the metabolic syndrome heart

Cardiovascular disease associated with metabolic syndrome has a high prevalence, but the mechanistic basis of metabolic cardiomyopathy remains poorly understood. We characterised the cardiac transcriptome in a murine metabolic syndrome (MetS) model (LDLR−/−; ob/ob, DKO) relative to the healthy, control heart (C57BL/6, WT) and the transcriptional changes induced by ACE-inhibition in those hearts. RNA-Seq, differential gene expression and transcription factor analysis identified 288 genes differentially expressed between DKO and WT hearts implicating 72 pathways. Hallmarks of metabolic cardiomyopathy were increased activity in integrin-linked kinase signalling, Rho signalling, dendritic cell maturation, production of nitric oxide and reactive oxygen species in macrophages, atherosclerosis, LXR-RXR signalling, cardiac hypertrophy, and acute phase response pathways. ACE-inhibition had a limited effect on gene expression in WT (55 genes, 23 pathways), and a prominent effect in DKO hearts (1143 genes, 104 pathways). In DKO hearts, ACE-I appears to counteract some of the MetS-specific pathways, while also activating cardioprotective mechanisms. We conclude that MetS and control murine hearts have unique transcriptional profiles and exhibit a partially specific transcriptional response to ACE-inhibition.

Highlights from the 11th ISCB Student Council Symposium 2015: Dublin, Ireland. 10 July 2015

Table of contentsA1 Highlights from the eleventh ISCB Student Council Symposium 2015Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana RahmanO1 Prioritizing a drug’s targets using both gene expression and structural similarityGriet Laenen, Sander Willems, Lieven Thorrez, Yves MoreauO2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organismsNagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael GromihaO3 Detection of Heterogeneity in Single Particle Tracking TrajectoriesPaddy J Slator, Nigel J BurroughsO4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecturePrzemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz PlewczynskiO5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classificationGiulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni FeliciO6 A Systems Biology Compendium for Leishmania donovaniBart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris LaukensO7 Unravelling signal coordination from large scale phosphorylation kinetic dataWesta Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

Highlights from the 11th ISCB Student Council Symposium 2015

Table of contentsA1 Highlights from the eleventh ISCB Student Council Symposium 2015Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana RahmanO1 Prioritizing a drug’s targets using both gene expression and structural similarityGriet Laenen, Sander Willems, Lieven Thorrez, Yves MoreauO2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organismsNagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael GromihaO3 Detection of Heterogeneity in Single Particle Tracking TrajectoriesPaddy J Slator, Nigel J BurroughsO4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecturePrzemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz PlewczynskiO5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classificationGiulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni FeliciO6 A Systems Biology Compendium for Leishmania donovaniBart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris LaukensO7 Unravelling signal coordination from large scale phosphorylation kinetic dataWesta Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

Applying Kernel Methods on Protein Complexes Detection Problem

During the last years, various methodologies have made possible the detection of large parts of the protein interaction network of various organisms. However, these networks are containing highly noisy data, degrading the quality of information they carry. Various weighting schemes have been applied in order to eliminate noise from interaction data and help bioinformaticians to extract valuable information such as the detection of protein complexes. In this contribution, we propose the addition of an extra step on these weighting schemes by using kernel methods to better assess the reliability of each pairwise interaction. Our experimental results prove that kernel methods clearly help the elimination of noise by producing improved results on the protein complexes detection problem.