Abstract
Purpose of Review
Neurodegenerative diseases are a heterogeneous group of complex conditions that lead to progressive degeneration or death of neurons. The economical and societal costs of these diseases are increasing rapidly, so there is an urgent need to find new solutions to remedy the situation. Understanding these diseases on a molecular pathway level could lead to development of better biomarkers and treatments, but the massive amounts of data involved makes this a challenge.
Recent Findings
Bioinformatics approaches can be used to manage and analyze data from the current high-throughput research technologies and provide means for novel discoveries in the field of neurodegenerative diseases.
Summary
This review introduces different bioinformatics approaches used to identify and study molecular targets and pathways, using examples from recent neurodegenerative diseases research.
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Uhlen M, Hallstro MBM, Lindskog C, Mardinoglu A, Ponten F, Nielsen J. Transcriptomics resources of human tissues and organs. Mol Syst Biol. 2016;12:862.
GTEx Consortium. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.
• Ardlie KG, Deluca DS, Segre AV, Sullivan TJ, Young TR, Gelfand ET, et al. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(80):648–60. Major overview of genotype-tissue specific expression in human tissues.
Lin S, Lin Y, Nery JR, Urich MA, Breschi A, Davis CA, et al. Comparison of the transcriptional landscapes between human and mouse tissues. Proc Natl Acad Sci. 2014;111:17224–9.
Kuhn A, Thu D, Waldvogel HJ, Faull RLM, Luthi-Carter R. Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods Nature Research. 2011;8:945–7.
Miller JA, Woltjer RL, Goodenbour JM, Horvath S, Geschwind DH, Drachman D, et al. Genes and pathways underlying regional and cell type changes in Alzheimer’s disease. Genome Med BioMed Central. 2013;5:48.
Watt AD, Perez KA, Rembach AR, Masters CL, Villemagne VL, Barnham KJ. Variability in blood-based amyloid-beta assays: the need for consensus on pre-analytical processing. J Alzheimers Dis. 2012;30:323–36.
Thambisetty M, Lovestone S. Blood-based biomarkers of Alzheimer’s disease: challenging but feasible. Biomark Med. 2010;4:65–79.
Guo JL, Lee VMY. Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat Med Nature Research. 2014;20:130–8.
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(80):1341–5.
Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW, Chen H, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(80):1331–6.
Van Rheenen W, Shatunov A, Dekker AM, McLaughlin RL, Diekstra FP, Pulit SL, et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. Nature Research; 2016;
Ferrari R, Hernandez DG, Nalls MA, Rohrer JD, Ramasamy A, Kwok JBJ, et al. Frontotemporal dementia and its subtypes: a genome-wide association study. Lancet Neurol. 2014;13:686–99.
• Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45:1452–8. Latest Alzheimer's disease genome-wide association study, identifying strongest AD related genetic variants.
• Nalls MA, Pankratz N, Lill CM, Do CB, Hernandez DG, Saad M, et al. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat. Genet. Nature Research. 2014;46:989–93. Latest Parkinson's diseases genome-wide association study, identifying strongest PD related genetic variants.
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. Nature Research; 2016;
Giri M, Lü Y, Zhang M. Genes associated with Alzheimer’s disease: an overview and current status. Clin Interv Aging Dove Press. 2016;11:665.
Pihlstrøm L, Morset KR, Grimstad E, Vitelli V, Toft M. A cumulative genetic risk score predicts progression in Parkinson’s disease. Mov Disord. 2016;31:487–90.
Mormino EC, Sperling RA, Holmes AJ, Buckner RL, De Jager PL, Smoller JW, et al. Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology Lippincott Williams & Wilkins. 2016;87:481–8.
Lacour A, Espinosa A, Louwersheimer E, Heilmann S, Hernández I, Wolfsgruber S, et al. Genome-wide significant risk factors for Alzheimer’s disease: role in progression to dementia due to Alzheimer's disease among subjects with mild cognitive impairment. Mol. Psychiatry. 2016;
Marden JR, Mayeda ER, Walter S, Vivot A, Tchetgen Tchetgen EJ, Kawachi I, et al. Using an Alzheimer disease polygenic risk score to predict memory decline in Black and White Americans over 14 years of follow-up. Alzheimer Dis. Assoc. Disord. 30:195–202.
Martiskainen H, Helisalmi S, Viswanathan J, Kurki M, Hall A, Herukka S-K, et al. Effects of Alzheimer’s disease-associated risk loci on cerebrospinal fluid biomarkers and disease progression: a polygenic risk score approach. J Alzheimers Dis. 2015;43:565–73.
Tang H, Thomas PD, Abecasis GR, Altshuler D, Auton A, Brooks LD, et al. Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics Genetics. 2016;203:635–47.
Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain Oxford University Press. 2015;138:3673–84.
Keller MF, Saad M, Bras J, Bettella F, Nicolaou N, Simón-Sánchez J, et al. Using genome-wide complex trait analysis to quantify “missing heritability” in Parkinson’s disease. Hum Mol Genet. 2012;21:4996–5009.
Moskvina V, Harold D, Russo G, Vedernikov A, Sharma M, Saad M, et al. Analysis of genome-wide association studies of Alzheimer disease and of Parkinson disease to determine if these 2 diseases share a common genetic risk. JAMA Neurol. 2013;70:1268–76.
Holmans P, Moskvina V, Jones L, Sharma M, Vedernikov A, et al. International Parkinson’s disease genomics consortium, a pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson’s disease. Hum Mol Genet. 2013;22:1039–49.
Miller JA, Ding S-L, Sunkin SM, Smith KA, Ng L, Szafer A, et al. Transcriptional landscape of the prenatal human brain. Nature. 2014;508:199–206.
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391–9.
GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.
Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, et al. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014;17:1418–28.
Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, et al. A mesoscale connectome of the mouse brain. Nature. 2014;508:207–14.
Sunkin SM, Ng L, Lau C, Dolbeare T, Gilbert TL, Thompson CL, et al. Allen brain atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 2013;41:D996–1008.
• Langfelder P, Horvath S, Fisher R, Zhou X, Kao M, Wong W, et al. WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics. BioMed Central. 2008;9:559. One of the most widely used analysis software for studying genomic networks.
Zhang B, Gaiteri C, Bodea L-G, Wang Z, McElwee J, Podtelezhnikov AA, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell. 2013;153:707–20.
Shirasaki DI, Greiner ER, Al-Ramahi I, Gray M, Boontheung P, Geschwind DH, et al. Network organization of the huntingtin proteomic interactome in mammalian brain. Neuron. 2012;75:41–57.
Narayanan M, Huynh JL, Wang K, Yang X, Yoo S, McElwee J, et al. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Mol Syst Biol. 2014;10:743.
Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 2016;15:673–84.
Teunissen CE, Elias N, Koel-Simmelink MJA, Durieux-Lu S, Malekzadeh A, Pham T V, et al. Novel diagnostic cerebrospinal fluid biomarkers for pathologic subtypes of frontotemporal dementia identified by proteomics. Alzheimer’s Dement. (Amsterdam, Netherlands). Elsevier; 2016;2:86–94.
Trushina E, Dutta T, Persson X-MT, Mielke MM, Petersen RC, Selkoe D, et al. Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. Mufson E, editor. PLoS One. Public Library of Science; 2013;8:e63644.
Zhang S, Li Q, Liu J, Zhou XJ. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011;27:i401–9.
Di Guida R, Engel J, Allwood JW, Weber RJM, Jones MR, Sommer U, et al. Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics. 12:93.
Browning SR, Browning BL. Haplotype phasing: existing methods and new developments. Nat Rev Genet NIH Public Access. 2011;12:703–14.
Li J, Wang L, Jiang T, Wang J, Li X, Liu X, et al. eSNPO: an eQTL-based SNP ontology and SNP functional enrichment analysis platform. Sci Rep Nature Publishing Group. 2016;6:30595.
Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344(80):519–23.
Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y, et al. Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry NIH Public Access. 2010;67:739–48.
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science (80). 2006;313.
Lipponen A, Paananen J, Puhakka N, Pitkänen A. Analysis of post-traumatic brain injury gene expression signature reveals tubulins, Nfe2l2, Nfkb, Cd44, and S100a4 as treatment targets. Sci Rep. 2016;6:31570.
Chang K, Creighton CJ, Davis C, Donehower L, Drummond J, Wheeler D, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet Nature Research. 2013;45:1113–20.
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Jussi Paananen declares that he has no conflict of interest.
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This article is part of the Topical Collection on Neurogenetics and Psychiatric Genetics
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Paananen, J. Bioinformatics in the Identification of Novel Targets and Pathways in Neurodegenerative Diseases. Curr Genet Med Rep 5, 15–21 (2017). https://doi.org/10.1007/s40142-017-0115-8
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DOI: https://doi.org/10.1007/s40142-017-0115-8