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Platforms for Analyzing Networks of Neurodegenerative and Psychiatric Diseases

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Handbook of Computational Neurodegeneration

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Abstract

Cellular functions are controlled by genetic networks, and possible mutations in genes produce aberrations in normal cellular activity, disrupting the fine-tuning of genetic networks and resulting in disease or disorder. As a result, carrying out a systematic analysis of how diseases alter the character of these networks is critical. The analyzation of neurodegenerative and psychiatric diseases networks and the relevant information about signaling molecules, genes, and proteins, and their interactions, enable us to extract and display information related to the diseases in a variety of ways.

In this chapter, we will investigate the most common pathways of the diseases, the genes involved, the binding proteins that would possibly inform about the pathological cascade of the Parkinson, ALS, and Alzheimer neurodegeneration and the underlying patterns of genetics among patients with Schizophrenia. Precision medicine’s development, in combination with the diversity of biological and biomedical data sources and their diverse character, make integrating and exploring the information they contain more difficult. Translational research platforms and tools have been developed and introduced as a possible option in light of these multifactorial issues. Researchers in order to investigate integrated data for hypothesis generation and validation, as well as data exploration, have used corresponding tools and platforms.

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References

  • Alzheimer’s Disease Data Initiative. https://www.alzheimersdata.org/

  • Balendra R, Isaacs A (2018) C9orf72-mediated ALS and FTD: multiple pathways to disease. Nat Rev Neurol 14(9):544–558

    Article  CAS  Google Scholar 

  • Banci L, Bertini I, Boca M, Girotto S, Martinelli M, Valentine J, Vieru M (2008) SOD1 and amyotrophic lateral sclerosis: mutations and oligomerization. PLoS ONE 3(2):e1677

    Article  Google Scholar 

  • Benatar M et al (2016) ALS biomarkers for therapy development: state of the field and future directions. Muscle Nerve 53(2):169–182

    Article  Google Scholar 

  • Bonafede R, Mariotti R (2017) ALS pathogenesis and therapeutic approaches: the role of mesenchymal stem cells and extracellular vesicles. Front Cell Neurosci 11

    Google Scholar 

  • Bradshaw W, Rehman S, Pham T, Thiyagarajan N, Lee R, Subramanian V, Acharya K (2017) Structural insights into human angiogenin variants implicated in Parkinson’s disease and Amyotrophic Lateral Sclerosis. Sci Rep 7(1)

    Google Scholar 

  • Cai L, Huang J (2018) Schizophrenia and risk of dementia: a meta-analysis study. Available via PubMed: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095111/. Accessed 20 Jan 2021

  • CellDesigner.: A modeling tool of biochemical network. http://www.celldesigner.org/

  • COPASI.: Biochemical System Simulator. http://copasi.org/

  • Deng Z, Lim J, Wang Q, Purtell K, Wu S, Palomo G, Tan H, Manfredi G, Zhao Y, Peng J, Hu B, Chen S, Yue Z (2019) ALS-FTLD-linked mutations of SQSTM1/p62 disrupt selective autophagy and NFE2L2/NRF2 anti-oxidative stress pathway. Autophagy 16(5):917–931

    Article  Google Scholar 

  • DisGeNET. https://www.disgenet.org/home/

  • Dong Y, Chen Y (2018) The role of ubiquitinated TDP-43 in amyotrophic lateral sclerosis. Neuroimmunol Neuroinflamm 5(2):5

    Article  Google Scholar 

  • Elsevier’s Pathway Studio. https://www.pathwaystudio.com/

  • Fasana E, Fossati M, Ruggiano A, Brambillasca S, Hoogenraad C, Navone F, Francolini M, Borgese N (2009) A VAPB mutant linked to amyotrophic lateral sclerosis generates a novel form of organized smooth endoplasmic reticulum. FASEB J 24(5):1419–1430

    Article  Google Scholar 

  • Fecto F (2011) SQSTM mutations in familial and sporadic amyotrophic lateral sclerosis. Arch Neurol 68(11):1440

    Article  Google Scholar 

  • Franz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD et al (2018) GeneMANIA update 2018. Nucleic Acids Res 46:W60–W64

    Article  CAS  Google Scholar 

  • Ghasemi M, Brown RH Jr (2018) Genetics of amyotrophic lateral sclerosis. Cold Spring Harbor Persp Med 8(5):a024125. https://doi.org/10.1101/cshperspect.a024125

  • Gene Ontology (GO). http://geneontology.org/

  • GeneMANIA. http://genemania.org/

  • Grasso M, Piscopo P, Confaloni A, Denti M (2014) Circulating miRNAs as biomarkers for neurodegenerative disorders. Molecules 19(5):6891–6910

    Article  Google Scholar 

  • Guo J, Yang X, Gao L, Zang D (2017) Evaluating the levels of CSF and serum factors in ALS. Brain Behav 7(3):e00637

    Article  Google Scholar 

  • Healy D, Abou-Sleiman P, Gibson J, Ross O, Jain S, Gandhi S, Gosal D, Muqit M, Wood N, Lynch T (2004) PINK1 (PARK6) associated Parkinson disease in Ireland. Neurology 63(8):1486–1488

    Article  CAS  Google Scholar 

  • Hedrich K, Winkler S, Hagenah J, Kabakci K, Kasten M, Schwinger E, Volkmann J, Pramstaller P, Kostic V, Vieregge P, Klein C (2006) RecurrentLRRK2 (Park8) mutations in early-onset Parkinson’s disease. Mov Disord 21(9):1506–1510

    Article  Google Scholar 

  • Horwitz T, Lam K, Chen Y, Xia Y, Liu C (2018) A decade in psychiatric GWAS research. Mol Psychiatry 24(3):378–389

    Article  Google Scholar 

  • Informatics for Integrating Biology and the Bedside – i2b2. https://community.i2b2.org/wiki/pages/viewpage.action?pageId=342684

  • Johnson J, Mandrioli J, Benatar M, Abramzon Y, Van Deerlin V, Trojanowski J, Gibbs J, Brunetti M, Gronka S, Wuu J, Ding J, McCluskey L, Martinez-Lage M, Falcone D, Hernandez D, Arepalli S, Chong S, Schymick J, Rothstein J, Landi F, Wang Y, Calvo A, Mora G, Sabatelli M, Monsurrò M, Battistini S, Salvi F, Spataro R, Sola P, Borghero G, Galassi G, Scholz S, Taylor J, Restagno G, Chiò A, Traynor B (2010) Exome sequencing reveals VCP mutations as a cause of familial ALS. Neuron 68(5):857–864

    Article  CAS  Google Scholar 

  • KEGG DISEASE Database. https://www.genome.jp/kegg/disease/

  • KEGG NETWORK Database. https://www.genome.jp/network/nt06420+N01020

  • Koppers M, van Blitterswijk M, Vlam L, Rowicka P, van Vught P, Groen E, Spliet W, Engelen-Lee J, Schelhaas H, de Visser M, van der Kooi A, van der Pol W, Pasterkamp R, Veldink J, van den Berg L (2012) VCP mutations in familial and sporadic amyotrophic lateral sclerosis. Neurobiol Aging 33(4):837.e7–837.e13

    Article  CAS  Google Scholar 

  • Krüger R, Sharma M, Riess O, Gasser T, Van Broeckhoven C, Theuns J, Aasly J, Annesi G, Bentivoglio A, Brice A, Djarmati A, Elbaz A, Farrer M, Ferrarese C, Gibson J, Hadjigeorgiou G, Hattori N, Ioannidis J, Jasinska-Myga B, Klein C, Lambert J, Lesage S, Lin J, Lynch T, Mellick G, de Nigris F, Opala G, Prigione A, Quattrone A, Ross O, Satake W, Silburn P, Tan E, Toda T, Tomiyama H, Wirdefeldt K, Wszolek Z, Xiromerisiou G, Maraganore D (2011) A large-scale genetic association study to evaluate the contribution of Omi/HtrA2 (PARK13) to Parkinson’s disease. Neurobiol Aging 32(3):548.e9–548.e18

    Article  Google Scholar 

  • Lesage S, Brice A (2009) Parkinson’s disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet 18(R1):R48–R59

    Article  CAS  Google Scholar 

  • Lev N, Roncevich D, Ickowicz D, Melamed E, Offen D (2006) Role of DJ-1 in Parkinson’s disease. J Mol Neurosci 29(3):215–226

    Article  CAS  Google Scholar 

  • Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in biological networks. Bioinformatics 21:3448–3449

    Article  CAS  Google Scholar 

  • Martins M, Rosa A, Guedes L, Fonseca B, Gotovac K, Violante S, Mestre T, Coelho M, Rosa M, Martin E, Vance J, Outeiro T, Wang L, Borovecki F, Ferreira J, Oliveira S (2011) Convergence of miRNA expression profiling, α-synuclein interacton and GWAS in Parkinson’s disease. PLoS ONE 6(10):e25443

    Article  CAS  Google Scholar 

  • Miller D, O’Callaghan J (2015) Biomarkers of Parkinson’s disease: present and future. Metabolism 64(3):S40–S46

    Article  CAS  Google Scholar 

  • Ming F, Tan J, Qin L, Zhang H, Tang J, Tan X, Wang C (2020) The PARK2 mutation associated with Parkinson’s disease enhances the vulnerability of peripheral blood lymphocytes to paraquat. BioMed Res Int 2020:1–9

    Article  Google Scholar 

  • NeuroDNet. https://bioschool.iitd.ac.in/NeuroDNet/

  • Paré G, Leaver C, Bourget C (2018) Diffusion of the digital health self-tracking movement in Canada: results of a national survey. J Med Internet Res 20(5):e177. https://doi.org/10.2196/jmir.9388

  • Park J, Blair N, Sue C (2015) The role of ATP13A2 in Parkinson’s disease: clinical phenotypes and molecular mechanisms. Mov Disord 30(6):770–779

    Article  CAS  Google Scholar 

  • Parkinson N, Ince P, Smith M, Highley R, Skibinski G, Andersen P, Morrison K, Pall H, Hardiman O, Collinge J, Shaw P, Fisher E (2006) ALS phenotypes with mutations in CHMP2B (charged multivesicular body protein 2B). Neurology 67(6):1074–1077

    Article  CAS  Google Scholar 

  • Renaud L, Picher-Martel V, Codron P, Julien J (2019) Key role of UBQLN2 in pathogenesis of amyotrophic lateral sclerosis and frontotemporal dementia. Acta Neuropathologica Commun 7(1)

    Google Scholar 

  • Sandstedt P, Littorin S, Johansson S, Gottberg K, Ytterberg C, Kierkegaard M (2018) Disability and contextual factors in patients with amyotrophic lateral sclerosis – a three-year observational study. J Neuromuscul Dis 5(4):439–449

    Article  Google Scholar 

  • Siddiqui I, Pervaiz N, Abbasi A (2016) The Parkinson disease gene SNCA: evolutionary and structural insights with pathological implication. Sci Rep 6(1)

    Google Scholar 

  • STRING., Protein-Protein Interaction Networks – Functional Enrichment Analysis. https://string-db.org/

  • Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C (2017) The STRING database in 2017: quality controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45(1):D362–D368. https://doi.org/10.1093/nar/gkw937

    Google Scholar 

  • Tan L et al (2016) Toward precision medicine in neurological diseases. Ann Transl Med 4(6):104

    Article  Google Scholar 

  • The National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/

  • Toth R, Atkin J (2018) Dysfunction of optineurin in amyotrophic lateral sclerosis and glaucoma. Front Immunol 9

    Google Scholar 

  • tranSMART. https://wiki.transmartfoundation.org/display/transmartwiki/tranSMART+Project+wiki+Home

  • UniProt. https://www.uniprot.org/

  • van Rooij E, Purcell A, Levin A (2012) Developing microRNA therapeutics. Circ Res 110(3):496–507

    Article  Google Scholar 

  • Vingill S, Brockelt D, Lancelin C, Tatenhorst L, Dontcheva G, Preisinger C, Schwedhelm-Domeyer N, Joseph S, Mitkovski M, Goebbels S, Nave K, Schulz J, Marquardt T, Lingor P, Stegmüller J (2016) Loss of FBXO 7 (PARK 15) results in reduced proteasome activity and models a parkinsonism-like phenotype in mice. EMBO J 35(18):2008–2025

    Article  CAS  Google Scholar 

  • Walker E, Kestler L, Bollini A, Hochman K (2004) Schizophrenia: etiology and course. Annu Rev Psychol 55(1):401–430

    Article  Google Scholar 

  • Wang L, Guo J, Zhang W, Xu Q, Zuo X, Shi C, Luo L, Liu J, Hu L, Hu Y, Yan X, Tang B (2011) Follow-up study of variants of the GIGYF2 gene in Chinese patients with Parkinson’s disease. J Clin Neurosci 18(12):1699–1701

    Article  CAS  Google Scholar 

  • Yoshino H, Tomiyama H, Tachibana N, Ogaki K, Li Y, Funayama M, Hashimoto T, Takashima S, Hattori N (2010) Phenotypic spectrum of patients with PLA2G6 mutation and PARK14-linked parkinsonism. Neurology 75(15):1356–1361

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Informatics, Ionian University, Corfu, Greece

    Katerina Kadena & Evgenia Lazarou

Authors
  1. Katerina Kadena
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  2. Evgenia Lazarou
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Editors and Affiliations

  1. Department of Informatics, Ionian University Department of Informatics, Corfu, Greece

    Panayiotis Vlamos

  2. Wilfrid Laurier University, Brantford, Canada

    Ilias S. Kotsireas

  3. ResearchGate, ETH Zurich ResearchGate, Zurich, Zürich, Switzerland

    Ioannis Tarnanas

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Kadena, K., Lazarou, E. (2022). Platforms for Analyzing Networks of Neurodegenerative and Psychiatric Diseases. In: Vlamos, P., Kotsireas, I.S., Tarnanas, I. (eds) Handbook of Computational Neurodegeneration. Springer, Cham. https://doi.org/10.1007/978-3-319-75479-6_5-1

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  • DOI: https://doi.org/10.1007/978-3-319-75479-6_5-1

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  • Print ISBN: 978-3-319-75479-6

  • Online ISBN: 978-3-319-75479-6

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