×

Computational studies on Alzheimer’s disease associated pathways and regulatory patterns using microarray gene expression and network data: revealed association with aging and other diseases. (English) Zbl 1397.92352

Summary: Alzheimer’s disease (AD), which is one of the most common age-associated neurodegenerative disorders, affects millions of people worldwide. Due to its polygenic nature, AD is believed to be caused not by defects in single genes, but by variations in a large number of genes and their complex interactions, which ultimately contribute to the broad spectrum of disease phenotypes. Extraction of insights and knowledge from microarray and network data will lead to a better understanding of complex diseases. The present study aimed to identify genes with differential topology and their further association with other biological processes that regulate causative factors for AD, ageing (AG) and other diseases. Our analysis revealed a common sharing of important biological processes and putative candidate genes among AD and AG. Some significant novel genes and other variants for various biological processes have been reported as being associated with AD, AG, and other diseases, and these could be implicated in biochemical events leading to AD from AG through pathways, interactions, and associations. Novel information for network motifs such as BiFan, MIM (multiple input module), and SIM (single input module) and their close variants has also been discovered and this implicit information will help to improve research into AD and AG. Ten major classes for TFs (transcription factors) have been identified in our data, where hundreds of TFBS patterns are being found associated with AD, and other disease. Structural and physico-chemical properties analysis for these TFBS classes revealed association of biological processes involved with other severe human disease. Nucleosomes and linkers positional information could provide insights into key cellular processes. Unique miRNA (micro RNA) targets were identified as another regulatory process for AD. The association of novel genes and variants of existing genes have also been explored for their interaction and association with other diseases that are either directly or indirectly implicated through AG and AD.

MSC:

92C50 Medical applications (general)
92C40 Biochemistry, molecular biology
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Alon U., Introduction to Systems Biology: Design Principles of Biological Circuits, London, UK:Chapman and Hall, 2006.
[2] Aravind, L.; Anantharaman, V.; Balaji, S., The many faces of the helix-turn-helix domain: transcription regulation and beyond, FEMS Microbiol. Rev., 2, 231-262, (2005)
[3] Ashburner, M.; Ball, C. A.; Blake, J. A., Gene ontology: tool for the unification of biology. the gene ontology consortium, Nat. Genet., 25, 25-29, (2000)
[4] Atabakhsh, E.; Wang, J. H.; Wang, X., Ranbpm expression regulates transcriptional pathways involved in development and tumorigenesis, Am. J. Cancer Res., 2, 549-565, (2012)
[5] Berbenetz, N. M.; Nislow, C.; Brown, G. W., Diversity of eukaryotic DNA replication origins revealed by genome-wide analysis of chromatin structure, PLoS Genet., 6, (2010)
[6] Blalock, E.; Geddes, J.; Chen, K., Incipient alzheimer’s disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses, Proc. Natl. Acad. Sci. U. S. A., 101, 2173-2178, (2004)
[7] Boyle, A. P.; Song, L.; Lee, B. K., High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells, Genome Res., 21, 456-464, (2011)
[8] Carter, D. B.; Chou, K. C., A model for structure dependent binding of congo red to alzeheimer beta-amyloid fibrils, Neurobiol. Aging, 19, 37-40, (1998)
[9] Chen, W.; Lin, H.; Feng, P. M., Inuc-physchem: a sequence-based predictor for identifying nucleosomes via physicochemical properties, PLoS One, 7, e47843, (2012)
[10] Chen, X.; Xu, H.; Yuan, P., Integration of external signaling pathways with the core transcriptional network in embryonic stem cells, Cell, 133, 1106-1117, (2008)
[11] Chou, K. C., Review: structural bioinformatics and its impact to biomedical science, Curr. Med. Chem., 11, 2105-2134, (2004)
[12] Chou, K. C., Insights from modelling the tertiary structure of BACE2, J. Proteome Res., 3, 1069-1072, (2004)
[13] Chou, K. C., Modeling the tertiary structure of human cathepsin-E, Biochem. Biophys. Res. Commun., 331, 56-60, (2005)
[14] Chou, K. C.; Howe, W. J., Prediction of the tertiary structure of the beta-secretase zymogen, Biochem. Biophys. Res. Commun., 292, 702-708, (2002)
[15] Chou, K. C.; Tomasselli, A. G; Heinrikson, R. L., Prediction of the tertiary structure of a caspase-9/inhibitor complex, FEBS Lett., 470, 249-256, (2000)
[16] Cleveland, W. S.; Devlin, S. J., Locally weighted regression: an approach to regression analysis by local Fitting, J. Am. Stat. Assoc., 83, 596-610, (1988)
[17] Craft, S., Intranasal insulin therapy for alzheimer disease and amnestic mild cognitive impairment, Arch. Neurol., 69, 29-38, (2012)
[18] Croft, D.; O’Kelly, G.; Wu, G., Reactome: a database of reactions, pathways and biological processes, Nucleic Acids Res., 39, D691-D697, (2010)
[19] Deza, E.; Deza, M. M., Encyclopedia of distances, 94, (2009), Springer
[20] Duan, Z.; Li, F. Q.; Wechsler, J., A novel notch protein, N2N, targeted by neutrophil elastase and implicated in hereditary neutropenia, Mol. Cell. Biol., 24, 58-70, (2004)
[21] Duncan, D. T.; Prodduturi, N.; Zhang, B., Webgestalt2: an updated and expanded version of the web-based gene set analysis toolkit, BMC Bioinform., 11, P10, (2010)
[22] D.M. Dziuda, 2010. Basic analysis of gene expression microarray data, Data Mining for Genomics and Proteomics: analysis of Gene and Protein Expression Data, John Wiley and Sons, Hoboken, pp. 17-93.
[23] Eden, E.; Lipson, D.; Yogev, S.; Yakhini, Z., Discovering motifs in ranked lists of DNA sequences, PLoS Comput. Biol., 3, e39, (2007)
[24] Eden, E.; Navon, R.; Steinfeld, I.; Lipson, D.; Yakhini, Z., Gorilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists, BMC Bioinform., 10, 48, (2009)
[25] Gotz, J.; Ittner, L. M.; Lim, Y. A., Common features between diabetes mellitus and alzheimer’s disease, Cell. Mol. Life Sci., 66, 1321-1325, (2009)
[26] Gu, R. X.; Gu, H.; Xie, Z. Y.; Wang, J. F.; Arias, H. R., Possible drug candidates for alzheimer’s disease deduced from studying their binding interactions with alpha 7 nicotinic acetylcholine receptor, Med. Chem., 5, 250-262, (2009)
[27] Hardy, J.; Selkoe, D. J., The amyloid hypothesis of alzheimer’s disease: progress and problems on the road to therapeutics, Science, 297, 353-356, (2002)
[28] Hinck, L., The versatile roles of axon guidance, Cues Tissue Morphog. Dev. Cell, 7, 783-793, (2004)
[29] Ho, S. S.J.; Fulton, L. D.; Arenillas, D. J.; Kwon, A. T.; Wasserman, W. W., Opossum: integrated tools for analysis of regulatory motif over-representation, Nucleic Acids Res., 35, W245-W252, (2007)
[30] Hommet, C.; Mondon, K.; Constans, T., Review of cerebral microangiopathy and alzheimer’s disease: relation between white matter hyperintensities and microbleeds, Dementia Geriatric Cognitive Dis., 32, 367-378, (2011)
[31] Hooghe, B.; Broos, S.; van, R. F.; De, B. P., A flexible integrative approach based on random forest improves prediction of transcription factor binding sites, Nucleic Acids Res., 40, e106, (2012)
[32] Hotelling, H., Analysis of a complex of statistical variables into principle components, J. Educ. Psychol., 24, 417-441, (1933)
[33] Huang Y., Zhou X., Miao B., et al., 2009. An image based system biology approach for Alzheimer’s disease pathway analysis. IEEE NIH Life Sci. Syst. Appl. Workshop. pp. 128-32.
[34] Johnson, S. C., Hierarchical clustering schemes, Psychometrika, 2, 241-254, (1967)
[35] Kadota, M.; Sato, B; Duncan, Identification of novel gene amplifications in breast cancer and coexistence of gene amplification with an activating mutation of PIK3CA, Cancer Res., 69, 7357-7365, (2009)
[36] Kalir, S.; Alon, U., Using a quantitative blueprint to reprogram the dynamics of the flagella gene network, Cell, 117, 713-720, (2004)
[37] Kandasamy, K.; Mohan, S. S.; Raju, R.; Keerthikumar, S., Netpath: a public resource of curated signal transduction pathways, Genome Biol., 11, R3, (2010)
[38] Kanehisa, M.; Goto, S., KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28, 27-30, (2000)
[39] Kann, M. G., Protein interactions and disease: computational approaches to uncover the etiology of diseases, Briefings Bioinform., 8, 333-346, (2007)
[40] Kashtan N., Itzkovitz S., Milo R., et al., 2005. Network motif detection tool Mfinder tool guide, Technical report, Departments of Molecular Cell Biology and Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot.
[41] Kohonen, T., Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59-69, (1982)
[42] Kohonen, T., Analysis of a simple self-organizing process, Biol. Cybern., 44, 135-140, (1982)
[43] Kong, W.; Mou, X.; Hu, X., Exploring matrix factorization techniques for significant genes identification of alzheimer’s disease microarray gene expression data, BMC Bioinform., 12, S7, (2011)
[44] Krauthammer, M.; Kaufmannd, C. A.; Gilliam, T. C.; Rzhetsky, A., Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in alzheimer’s disease, Proc. Natl. Acad. Sci. U. S. A., 101, 15148-15153, (2004)
[45] Kriegebaum, B. C.; Gutknecht, L.; Bartke, L., The expression of the transcription factor FEV in adult human brain and its association with affective disorders, J. Neural Trans., 117, 831-836, (2010)
[46] Kuo, W.; Jenssen, T.; Butte, A., Analysis of matched mrna measurements from two different microarray technologies, Bioinformatics, 18, 405-412, (2002)
[47] Lu, T.; Pan, Y.; Kao, S. Y., Gene regulation and DNA damage in the ageing human brain, Nature, 429, 883-891, (2004)
[48] Macian, F.; Lopez-Rodriguez, C.; Rao, A., Partners in transcription: NFAT and AP-1, Oncogene, 20, 2476-2489, (2001)
[49] Maes, O. C.; Xu, S.; Yu, B.; Chertkow, H. M., Transcriptional profiling of alzheimer blood mononuclear cells by microarray, Neurobiol. Aging, 28, 1795-1809, (2007)
[50] Miller, J. A.; Oldham, M. C.; Geschwind, D. H., A systems level analysis of transcriptional changes in alzheimer’s disease and normal aging, J. Neurosc., 28, 1410-1420, (2008)
[51] Nalbantoglu B., Tekir S.D., Ülgen K. Ö., 2012.Wnt signaling network in homo sapiens. In: Paula Bubulya (Ed.), Cell Metabolism - Cell Homeostasis Stress Response .
[52] Newman, J. C.; Weiner, A. M., L2L: a simple tool for discovering the hidden significance in microarray expression data, Genome Biol., 6, r81, (2005)
[53] Ottolenghi, C.; Uda, M.; Crisponi, L., Determination and stability of sex, BioEssays: News Rev. Mol. Cell. Dev. Biol., 29, 15-25, (2007)
[54] Panigrahi, P. P.; Singh, T. R., Computational analysis for functional and evolutionary aspects of BACE-1 and associated alzheimer’s releted proteins, IJCI Stud., 1, 322-332, (2012)
[55] Qiu, C., Preventing alzheimer’s disease by targeting vascular risk factors: hope and gap, J. Alzheimers Dis., 32, 721-731, (2012)
[56] Quackenbush, J., Microarray analysis and tumor classification, N Engl J. Med., 354, 2463-2472, (2006)
[57] Ray, M.; Zhang, W., Analysis of alzheimer’s disease severity across brain regions by topological analysis of gene co-expression networks, BMC Syst. Biol., 4, 136, (2010)
[58] Ray, M.; Ruan, J.; Zhang, W., Variations in the transcriptome of alzheimer’s disease reveal molecular networks involved in cardiovascular diseases, Genome Biol., 9, r148, (2008)
[59] Ronen, M.; Rosenberg, R.; Shraiman, B. I.; Alon, U., Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics, PNAS, 99, 10555-10560, (2002)
[60] Ruan J., Zhang W., 2006. Identification and evaluation of functional modules in gene co-expression networks. In: Proceedings of RECOMB Satellite Conferences on Systems Biology and Computational Proteomics, San Diego, CA, pp. 57-76.
[61] Ruan, J.; Dean, A. K.; Zhang, W., A general co-expression network-based approach to gene expression analysis: comparison and applications, BMC Syst. Biol., 4, 8, (2010)
[62] Saeed, A. I.; Sharov, V.; White, J.; Li, J., TM4: a free, open-source system for microarray data management and analysis, Biotechniques, 34, 374-378, (2003)
[63] Said, M. R.; Begley, T. J.; Oppenheim, A. V., Global network analysis of phenotypic effects: protein networks and toxicity modulation in saccharomyces cerevisiae, PNAS, 101, 18006-18011, (2004)
[64] Sandelin, A.; Alkema, W.; Engstrom, P., JASPAR: an open-access database for eukaryotic transcription factor binding profiles, Nucleic Acids Res., 32, D91-D94, (2004)
[65] Schmalhofer, O.; Brabletz, S.; Brabletz, T., E-cadherin, β-catenin, and ZEB1 in malignant progression of cancer, Cancer Metastasis Rev., 28, 151-166, (2009)
[66] Schreiber, F.; Schwöbbermeyer, H., Mavisto: a tool for the exploration of network motifs, Bioinformatics, 21, 3572-3574, (2005)
[67] Sealfon, R.; Hibbs, M.; Huttenhower, C., GOLEM: an interactive graph-based gene-ontology navigation and analysis tool, BMC Bioinformatics, 7, 443, (2006)
[68] Sehgal, M.; Singh, T. R., Identification and analysis of biomarkers for repair proteins: a bioinformatic approach, J. Nat. Sci. Biol. Med., 2, 139-146, (2012)
[69] Shachar, R.; Unger, L.; Kupiec, M., A systems-level approach to mapping the telomere-length maintenance gene circuitry, Mol. Syst. Biol., 4, 172, (2008)
[70] Shen-Orr, S. S.; Milo, R.; Mangan, S.; Alon, U., Network motifs in the transcriptional regulation network of Escherichia coli, Nat. Genet., 31, 64-68, (2002)
[71] Sjoblom, T.; Jones, S.; Wood, L. D., The consensus coding sequences of human breast and colorectal cancers, Science, 314, 268-274, (2006)
[72] Sporns, O.; Honey, Ch. J., Small world inside big brains, PNAS, 103, 19219-19220, (2006)
[73] Stark, C.; Breitkreutz, B. J.; Reguly, T., Biogrid: a general repository for interaction datasets, Nucleic Acids Res., 34, D535-D539, (2006)
[74] Stekel, D.; Bioinformatics, Microarray, Analysis of differentially expressed genes, 110-138, (2003), Cambridge University Press New York
[75] Szklarczyk, D.; Franceschini, A.; Kuhn, M., The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored, Nucleic Acids Res., 39, D561-D568, (2011)
[76] Talbot, K.; Wang, H. Y.; Kazi, H., Demonstrated brain insulin resistance in alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline, J. Clin. Invest., 122, 1316-1338, (2012)
[77] Tanaka, Y.; Joshi, A.; Wilson, N. K., The transcriptional programme controlled by runx1 during early embryonic blood development, Dev. Biol., 366, 404-419, (2012)
[78] Tarawneh, R.; Holtzman, D. M., Biomarkers in translational research of alzheimer’s disease, Neuropharmacology, 59, 310-322, (2010)
[79] Tilgner, H.; Nikolaou, C.; Althammer, S., Nucleosome positioning as a determinant of exon recognition, Nat. Struct. Mol. Biol., 16, 996-1001, (2009)
[80] Tusher, V. G.; Tibshirani, R., Significance analysis of microarrays applied to the ionizing radiation response, Proc.Natl. Acad. Sci., 98, 5116-5121, (2001)
[81] Warren, J.; Strittmatter, M. D., Alzheimer’s disease: the new promise, J. Clin. Invest., 122, 1191, (2012)
[82] Wei, D. Q.; Sirois, S.; Du, Q. S.; Arias, H. R.; Chou, K. C., Theoretical studies of alzheimer’s disease drug candidate [(2,4-dimethoxy) benzylidene]-anabaseine dihydrochloride (GTS-21) and its derivatives, Biochem. Biophys. Res. Commun., 338, 1059-1064, (2005)
[83] Wernicke, S.; Rasche, F., FANMOD: a tool for fast network motif detection, Bioinformatics, 22, 1152-1153, (2006)
[84] Yamada, A.; Koyanagi, K. O.; Watanabe, H., In silico and in vivo identification of the intermediate filament vimentin that is downregulated downstream of brachyury during xenopus embryogenesis, Gene, 491, 232-236, (2012)
[85] Yang, Y. H.; Dudoit, S.; Luu, P., Normalization for cdna microarray data: a robust composite method addressing single and multiple slide systematic variation, Nucleic Acids Res., 30, e15, (2002)
[86] Yasuda, T.; Sugasawa, K.; Shimizu, Y., Nucleosomal structure of undamaged DNA regions suppresses the non-specific DNA binding of the XPC complex, DNA Repair (Amst), 4, 389-395, (2005)
[87] Zhang, B.; Kirov, S.; Snoddy, J., Webgestalt: an integrated system for exploring gene sets in various biological contexts, Nucleic Acids Res., 33, W741-W748, (2005)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.