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Bioinformatics in the Identification of Novel Targets and Pathways in Neurodegenerative Diseases

  • Neurogenetics and Psychiatric Genetics (M Hiltunen and DR Marenda, Section Editors)
  • Published:
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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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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  2. GTEx Consortium. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    Article  PubMed Central  Google Scholar 

  3. • 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.

    Google Scholar 

  4. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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.

    Article  CAS  Google Scholar 

  6. 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.

    Article  CAS  Google Scholar 

  7. 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.

    CAS  PubMed  Google Scholar 

  8. Thambisetty M, Lovestone S. Blood-based biomarkers of Alzheimer’s disease: challenging but feasible. Biomark Med. 2010;4:65–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Guo JL, Lee VMY. Cell-to-cell transmission of pathogenic proteins in neurodegenerative diseases. Nat Med Nature Research. 2014;20:130–8.

    Article  CAS  Google Scholar 

  10. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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.

    CAS  PubMed  Google Scholar 

  12. 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;

  13. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  14. • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. • 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.

    Article  CAS  Google Scholar 

  16. 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;

  17. 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.

    Article  Google Scholar 

  18. 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.

    Article  PubMed  Google Scholar 

  19. 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.

    CAS  Google Scholar 

  20. 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;

  21. 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.

  22. 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.

    CAS  PubMed  Google Scholar 

  23. 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.

    Article  CAS  PubMed  Google Scholar 

  24. 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.

    Google Scholar 

  25. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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.

    PubMed  Google Scholar 

  27. 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.

    Article  CAS  PubMed  Google Scholar 

  28. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  31. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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.

    Article  CAS  PubMed  Google Scholar 

  34. • 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.

    Google Scholar 

  35. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 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.

    Article  CAS  PubMed  Google Scholar 

  39. 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.

  40. 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.

  41. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 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.

  43. Browning SR, Browning BL. Haplotype phasing: existing methods and new developments. Nat Rev Genet NIH Public Access. 2011;12:703–14.

    Article  CAS  Google Scholar 

  44. 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.

    CAS  Google Scholar 

  45. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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.

    Article  Google Scholar 

  47. 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.

  48. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 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.

    Article  CAS  Google Scholar 

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Correspondence to Jussi Paananen.

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Jussi Paananen declares that he has no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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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

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