Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study aims to exploit bioinformatic tools in order to analyze microarray data of atherosclerotic aortic lesions of ApoE knockout mice, a model widely used in atherosclerosis research. In particular, a dynamic analysis was performed among young and aged animals, resulting in a list of 852 significantly altered genes. Pathway analysis indicated alterations in critical cellular processes related to cell communication and signal transduction, immune response, lipid transport, and metabolism. Cluster analysis partitioned the significantly differentiated genes in three major clusters of similar expression profile. Promoter analysis applied to functional related groups of the same cluster revealed shared putative cis-elements potentially contributing to a common regulatory mechanism. Finally, by reverse engineering the functional relevance of differentially expressed genes with specific cellular pathways, putative genes acting as hubs, were identified, linking functionally disparate cellular processes in the context of traditional molecular description. 1. Introduction Atherosclerosis is the leading pathological contributor to cardiovascular morbidity and mortality worldwide, characterized by the progressive accumulation of lipid and fibrous depositions in the vessel wall of medium-sized and large arteries. Although it has traditionally been viewed as simple deposition of lipids within the vessel wall, it is now assumed that atherosclerosis is a multifactorial disease that involves several genes and proteins, activated during its genesis, progress, and phenotypic manifestation. During atherogenesis, a complex endothelial activation and dysfunction induced by elevated and modified low-density lipoproteins and many other factors leads to a compensatory inflammatory response [1]. Current evidence supports a central role for inflammation, in all phases of the atherosclerotic process. Substantial biological data implicate inflammatory pathways in early atherogenesis, in the progression of lesions, and finally in the thrombotic complications of this disease [2]. Clinical investigations, population studies, and cell culture experiments have provided important clues to the pathogenesis of atherosclerosis. However, the use of animal models has had a crucial contribution in the research of the atherosclerotic course. Atherosclerosis will not be developed in laboratory mice under normal conditions. However, targeted deletion of the gene
References
[1]
G. Stoll and M. Bendszus, “Inflammation and atherosclerosis: novel insights into plaque formation and destabilization,” Stroke, vol. 37, no. 7, pp. 1923–1932, 2006.
[2]
G. K. Hansson, “Mechanisms of disease: inflammation, atherosclerosis, and coronary artery disease,” The New England Journal of Medicine, vol. 352, no. 16, pp. 1685–1695, 2005.
[3]
S. H. Zhang, R. L. Reddick, J. A. Piedrahita, and N. Maeda, “Spontaneous hypercholesterolemia and arterial lesions in mice lacking apolipoprotein E,” Science, vol. 258, no. 5081, pp. 468–471, 1992.
[4]
R. W. Mahley and S. C. Rall Jr., “Apolipoprotein E: far more than a lipid transport protein,” Annual Review of Genomics and Human Genetics, vol. 1, no. 2000, pp. 507–537, 2000.
[5]
Y. Song, M. J. Stampfer, and S. Liu, “Meta-analysis: apolipoprotein E genotypes and risk for coronary heart disease,” Annals of Internal Medicine, vol. 141, no. 2, pp. 137–147, 2004.
[6]
E. Newcomer and G. Lomow, Understanding SOA with Web Services, Addison-Wesley, New York, NY, USA, 2004.
[7]
R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, article R80, 2004.
[8]
A. Chatziioannou, P. Moulos, and F. N. Kolisis, “Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB,” BMC Bioinformatics, vol. 10, article 354, 2009.
[9]
M. M. Michal Blazejczyk and R. Nadon, “FlexArray: a statistical data analysis software for gene expression microarrays,” Génome Québec, Montreal, Canada, 2007, http://genomequebec.mcgill.ca/FlexArray.
[10]
G. Dennis Jr., B. T. Sherman, D. A. Hosack et al., “DAVID: database for annotation, visualization, and integrated discovery,” Genome Biology, vol. 4, no. 5, article P3, 2003.
[11]
D. A. Hosack, G. Dennis Jr., B. T. Sherman, H. C. Lane, and R. A. Lempicki, “Identifying biological themes within lists of genes with EASE,” Genome Biology, vol. 4, no. 10, article R70, 2003.
[12]
B. Zhang, S. Kirov, and J. Snoddy, “WebGestalt: an integrated system for exploring gene sets in various biological contexts,” Nucleic Acids Research, vol. 33, no. 2, pp. W741–W748, 2005.
[13]
Q. Zheng and X. J. Wang, “GOEAST: a web-based software toolkit for gene ontology enrichment analysis,” Nucleic Acids Research, vol. 36, pp. W358–W363, 2008.
[14]
A. A. Chatziioannou and P. Moulos, “Exploiting statistical methodologies and controlled vocabularies for prioritized functional analysis of genomic experiments: the StRAnGER web application,” Frontiers in Neuroscience, vol. 5, article 8.
[15]
Genomatix, http://www.genomatix.de.
[16]
Z. Xuan, F. Zhao, J. Wang, G. Chen, and M. Q. Zhang, “Genome-wide promoter extraction and analysis in human, mouse, and rat,” Genome Biology, vol. 6, no. 8, article R72, 2005.
[17]
A. Essaghir, F. Toffalini, L. Knoops, A. Kallin, J. van Helden, and J. B. Demoulin, “Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data,” Nucleic Acids Research, vol. 38, no. 11, article e120, 2010.
[18]
K. Cartharius, K. Frech, K. Grote et al., “MatInspector and beyond: promoter analysis based on transcription factor binding sites,” Bioinformatics, vol. 21, no. 13, pp. 2933–2942, 2005.
[19]
C. Pesquita, D. Faria, A. O. Falc？o, P. Lord, and F. M. Couto, “Semantic similarity in biomedical ontologies,” PLoS Computational Biology, vol. 5, no. 7, Article ID e1000443, 2009.
[20]
K. Moutselos, I. Maglogiannis, and A. Chatziioannou, “GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 12, pp. 3522–3527, 2011.
[21]
A. M. L. Liekens, J. de Knijf, W. Daelemans, B. Goethals, P. de Rijk, and J. Del-Favero, “Biograph: unsupervised biomedical knowledge discovery via automated hypothesis generation,” Genome Biology, vol. 12, no. 6, article R57, 2011.
[22]
R. Gr？bner, K. L？tzer, S. D？pping et al., “Lymphotoxin β receptor signaling promotes tertiary lymphoid organogenesis in the aorta adventitia of aged ApoE-/- mice,” Journal of Experimental Medicine, vol. 206, no. 1, pp. 233–248, 2009.
T. Oinn, M. Addis, J. Ferris et al., “Taverna: a tool for the composition and enactment of bioinformatics workflows,” Bioinformatics, vol. 20, no. 17, pp. 3045–3054, 2004.
[25]
I. Mierswa, M. Scholz, R. Klinkenberg, M. Wurst, and T. Euler, “YALE: rapid prototyping for complex data mining tasks,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940, August 2006.
[26]
C. A. Goble, J. Bhagat, S. Aleksejevs et al., “myExperiment: a repository and social network for the sharing of bioinformatics workflows,” Nucleic Acids Research, vol. 38, no. 2, pp. W677–W682, 2010.
[27]
J. Bhagat, F. Tanoh, E. Nzuobontane et al., “BioCatalogue: a universal catalogue of web services for the life sciences,” Nucleic Acids Research, vol. 38, no. 2, pp. W689–W694, 2010.
[28]
A. Hamik, Z. Lin, A. Kumar et al., “Kruppel-like factor 4 regulates endothelial inflammation,” The Journal of Biological Chemistry, vol. 282, no. 18, pp. 13769–13779, 2007.
[29]
Z. Cao, X. Sun, B. Icli, A. K. Wara, and M. W. Feinberg, “Role of Krüppel-like factors in leukocyte development, function, and disease,” Blood, vol. 116, no. 22, pp. 4404–4414, 2010.
[30]
J. K. Alder, R. W. Georgantas, R. L. Hildreth et al., “Kruppel-like factor 4 is essential for inflammatory monocyte differentiation in vivo,” Journal of Immunology, vol. 180, no. 8, pp. 5645–5652, 2008.
[31]
T. Yoshida, Q. Gan, and G. K. Owens, “Krüppel-like factor 4, Elk-1, and histone deacetylases cooperatively suppress smooth muscle cell differentiation markers in response to oxidized phospholipids,” American Journal of Physiology, vol. 295, no. 5, pp. C1175–C1182, 2008.
[32]
A. Nohara, J. Kobayashi, and H. Mabuchi, “Retinoid X receptor heterodimer variants and cardiovascular risk factors,” Journal of Atherosclerosis and Thrombosis, vol. 16, no. 4, pp. 303–318, 2009.
[33]
P. W. F. Hadoke, J. Iqbal, and B. R. Walker, “Therapeutic manipulation of glucocorticoid metabolism in cardiovascular disease,” British Journal of Pharmacology, vol. 156, no. 5, pp. 689–712, 2009.
[34]
S. Borrello, C. Nicolo, G. Delogu, F. Pandolfi, and F. Ria, “TLR2: a crossroads between infections and autoimmunity?” International Journal of Immunopathology and Pharmacology, vol. 24, no. 3, pp. 549–556.
[35]
C. J. Carter, “Convergence of genes implicated in Alzheimer's disease on the cerebral cholesterol shuttle: APP, cholesterol, lipoproteins, and atherosclerosis,” Neurochemistry International, vol. 50, no. 1, pp. 12–38, 2007.
