Aguilar Ruiz, Jesús Salvador

Nepomuceno Chamorro, Isabel de los Ángeles

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idUS. Depósito de Investigación Universidad de Sevilla

Synergies of genes in Alzheimer’s diseaseIsabel A. Nepomuceno-Chamorro, Jesús S. Aguilar-RuizETSII, Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, SpainSchool of Engineering, Pablo de Olavide University, Seville, Spain{inepomuceno}@us.es{aguilar}@upo.es1 IntroductionNowadays, Alzheimer’s disease (AD) is the most common form of dementia af-fecting more than 25 million individuals worldwide. AD is a complex progressiveneurodegenerative disorder of the brain and the prevalence of Alzheimer’s dis-ease is expected to rise over the next decades [1]. Due to its poligenic nature,unconventional strategies for elucidating the genetic mechanisms and geneticrisk factors are necessary.The microarray technology has made possible the monitoring of hundredsof genes simultaneous. Most studies on analysis of microarray data are basedon the identification of differentially expressed genes on which further analysisis performed. There are several approaches to identify differentially expressedgenes as feature selection method. However, due to the AD’s polygenic nature,it is believed that AD is caused not by defect in a few genes, but rather byvariations in a large number of genes and their complex interplay. Therefore, asystems-level approach can provide new insights into the interplay of genes andit is an effective alternative for analyzing this complex disease.2 MethodA systems biology approach based on the inference of gene association networksfrom gene expression profiles is used. The methodology, named RegNet , isbased on model trees as a method to identify gene interaction networks and itfavours localized similarities over more global similarity, which is one of the ma-jor drawbacks of correlation-based methods. The methodology consists of threesteps. The first step involves building M5’ trees. M5’ is a model tree algorithm,an extension of regression tree algorithms, which builds several linear models,each one of them built in a leaf of the tree. The aim of the second step is toobtain a set of genes associated to other genes from their prediction ability bymeans of linear regression functions. Finally, the third step involves learning agraph model of gene-gene associations by assessing the significance of the setof hypothetical evidences. This method is reported in [2] and it is part of anetwork-based prognostic model reported in [3].IWBBIO 2013. Proceedings Granada, 18-20 March, 2013 512 Synergies of genes in Alzheimers disease3 Results and discussionIn this experiment we use the data set of [4]. It consists of 14 control and 19Alzheimer’s disease (AD) brain tissue samples. The 14 control samples are Braakstages 0-II with average age 80.1 years and 19 AD affected are Braak stages III-IV with average age 84.7 years (incipient AD). This dataset includes 35722 geneprobesets. We run our method on a subset of them which corresponds to 1663genes obtained from that dataset using a preprocessing step described in [5].Using David tool, we observed the association of genes with various disease:cardiovascular disease, alzheimer disease and type 2 diabetes. In an enrichmentanalysis using GO we detected several genes involved in immune, inflammatoryor defense response. In the study of Miller et al. [6], 558 transcripts which arecommon to AD and ageing were identified. We can assert that we found moreoverlapping set of genes between our results and Miller’s study than expectedby chance. In the study of Kong et al. [7], they identified 87 significant genes insevere AD representing immunity-related protein, metal-related protein, lipopro-tein, neuropeptide and ribosomal protein. We study the overlapping betweenthose significant genes in severe AD and our results obtained with incipient AD.In the input data set, an overlapping of 29 genes can be found between the listof 1663 and those 87 genes. Of these 29 genes, 12 genes were present in themajor subgraph (456 genes) of our analysis (greater than expected by chancep − value = 0.004). It is worth noting that in these 12 genes we found genesColrf115 and C20orf149 which are related with ribosomal protein, this genes aresignificant because impairments in protein synthesis occur in the earliest stagesof AD (see Ding et al., 2005). We also found IFITM2 inflammation-related gene,neuroinflammation is believed to be a culprit in AD pathogenesis (see Kong etal., 2009). Finally, we found CHGB, MTF1 and MT1M genes related to metalprotein. In the literature, we can observe that the level of metal ion metabolismis closely associated with AD (see Soderling et al., 2001).References1. Orevsivc, M., Lotjonen, J., Soininen, H.: Systems medicine and the inte- grationof bioinformatic tools for the diagnosis of Alzheimer’s disease. Genome Medicine,11,83 (2010)2. Nepomuceno-Chamorro, I., Aguilar-Ruiz, J., Riquelme-Santos, J.: Inferring generegression networks with model trees. BMC Bioinformatics, 11, 517 (2010)3. Nepomuceno, I., Azuaje, F. et al.: Prognostic transcriptional association networks:a new supervised approach based on regression trees. Bioinformatics, 27, 252–258(2011)4. Dunckley, T., et al.: Gene expression correlates of neurofibrillary tangles inAlzheimers disease. Neurobiol Aging, 27, 1359–1371 (2006)5. Ray, M., Ruan, J., Zhang, W.: Variations in the transcriptome of alzheimer’s diseasereveal molecular networks involved in cardiovascular diseases. Genome Biology, 9,R148 (2008)IWBBIO 2013. Proceedings Granada, 18-20 March, 2013 52Synergies of genes in Alzheimer’s disease 36. Miller, J., Oldham, M., Geschwind, D.: A Systems Level Analysis of TranscriptionalChanges in Alzheimer’s Disease and Normal Aging. Journal of Neuroscience, 28,1410–1420 (2008)7. Kong,W., Mou,X., Liu,Q., Chen,Z., Vanderburg,C.R., Rogers,J.T.,Huang,X.:Independent component analysis of Alzheimer’s DNA microarraygene expression data. Molecular Neurodegeneration, 4, 5, (2009)IWBBIO 2013. Proceedings Granada, 18-20 March, 2013 53

Synergies of genes in Alzheimer’s disease

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Synergies of genes in Alzheimer’s disease

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