Abstract
Parkinson’s disease is a gradually progressive neurodegenerative disorder characterized by a selective loss of dopaminergic neurons in the midbrain area called the substantia nigra pars compacta and cytoplasmic alpha-synuclein-rich inclusions termed Lewy bodies. The etiology and pathogenesis remain incompletely understood. The development of reliable biomarkers for the early and accurate diagnosis, including biochemical, genetic, clinical, and neuroimaging markers, is crucial for unraveling the pathogenic processes of the disease as well as patients’ progress surveillance. High-throughput technologies and system biology methodologies can support the identification of potent molecular fingerprints together with the establishment of dynamic network biomarkers. Emphasis is given on multi-omics datasets and dysregulated pathways associated with differentially expressed transcripts, modified protein motifs, and altered metabolic profiles. Although there is no therapy that terminates the neurodegenerative process and dopamine replacement strategy with L-DOPA represents the most effective treatment, numerous therapeutic protocols such as dopamine receptor agonists, MAO-B inhibitors, and cholinesterase inhibitors represent candidate treatments providing at the same time valuable network-based approaches to drug repositioning. Computational methodologies and bioinformatics platforms for visualization, clustering, and validating of molecular and clinical datasets provide important insights into diagnostic processing and therapeutic pipeline.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kalia L, Lang A (2015) Parkinson’s disease. Lancet 386:896–912
Geschwind DH, Konopka G (2009) Neuroscience in the era of functional genomics and systems biology. Nature 461:908–915
McDermott JE, Archuleta M, Stevens SL, Stenzel-Poore MP, Sanfilippo A (2011) Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks. Pac Symp Biocomput 2011:314–325
Galvan A, Devergnas A, Wichmann T (2015) Alterations in neuronal activity in basal ganglia-thalamocortical circuits in the parkinsonian state. Front Neuroanat 9:5
Miller D, O’Callaghan J (2015) Biomarkers of Parkinson’s disease: present and future. Metabolism 64:S40–S46
Ferrer I, Lopez-Gonzalez I, Carmona M, Dalfó E, Pujol A, Martínez A (2012) Neurochemistry and the non-motor aspects of PD. Neurobiol Dis 46:508–526
McKeith I (2004) Dementia with Lewy bodies. Dialogues Clin Neurosci 3:333–341
Wang Q, Zhang Y, Wang M, Song W-M, Shen Q, McKenzie A, Choi I, Zhou X, Pan P-Y, Yue Z (2019) The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease. Nat Commun 10:5234
Yang Q, She H, Gearing M, Colla E, Lee M, Shacka JJ, Mao Z (2009) Regulation of neuronal survival factor MEF2D by chaperone-mediated autophagy. Science 323:124–127
Musgrove RE, Helwig M, Bae EJ, Aboutalebi H, Lee SJ, Ulusoy A, Di Monte DA (2019) Oxidative stress in vagal neurons promotes parkinsonian pathology and intercellular α-synuclein transfer. J Clin Invest 130:3738–3753
Scherzer CR (2009) Chipping away at diagnostics for neurodegenerative diseases. Neurobiol Dis 35:148–156
Lottaz C, Toedling J, Spang R (2007) Annotation-based distance measures for patient subgroup discovery in clinical microarray studies. Bioinformatics 23:2256–2264
Diao H, Li X, Hu S, Liu Y (2012) Gene expression profiling combined with bioinformatics analysis identify biomarkers for Parkinson disease. PLoS One 7:e52319
Xu C, Chen J, Xu X, Zhang Y, Li J (2018) Potential therapeutic drugs for Parkinson’s disease based on data mining and bioinformatics analysis. Parkinsons Dis 2018:3464578
Harraz M, Dawson T, Dawson V (2011) MicroRNAs in Parkinson’s disease. J Chem Neuroanat 42:127–130
Chi J, Xie Q, Jia J, Sun J, Deng Y, Yi L (2018) Integrated analysis and identification of novel biomarkers in Parkinson’s disease. Front Aging Neurosci 10:178
Kim J, Inoue K, Ishii J, Vanti WB, Voronov SV, Murchison E, Hannon G, Abeliovich A (2007) A MicroRNA feedback circuit in midbrain dopamine neurons. Science 317:1220–1224
Wang G, van der Walt JM, Mayhew G, Li YJ, Züchner S, Scott WK, Martin ER, Vance JM (2008) Variation in the miRNA-433 binding site of FGF20 confers risk for Parkinson disease by overexpression of alpha-synuclein. Am J Hum Genet 82:283–289
Dong N, Zhang X, Liu Q (2017) Identification of therapeutic targets for Parkinson’s disease via bioinformatics analysis. Mol Med Rep 15:731–735
Krokidis M (2019) Identification of biomarkers associated with Parkinson’s disease by gene expression profiling studies and bioinformatics analysis. AIMS Neurosci 6:333–345
Szargel R, Shani V, Abd Elghani F, Mekies LN, Liani E, Rott R, Engelender S (2016) The PINK1, synphilin-1 and SIAH-1 complex constitutes a novel mitophagy pathway. Hum Mol Genet 25:3476–3490
Surmeier DJ, Guzman JN, Sanchez-Padilla J, Goldberg JA (2010) What causes the death of dopaminergic neurons in Parkinson’s disease? Prog Brain Res 183:59–77
Jha SK, Jha NK, Kar R, Ambasta RK, Kumar P (2015) p38 MAPK and PI3K/AKT signalling cascades in Parkinson’s disease. Int J Mol Cell Med 4:67–86
Venderova K, Park DS (2012) Programmed cell death in Parkinson’s disease. Cold Spring Harb Perspect Med 2:a009365
Cheng H-C, Ulane C, Burke R (2010) Clinical progression in Parkinson disease and the neurobiology of axons. Ann Neurol 67:715–772
Koeglsperger T, Palleis C, Hell F, Mehrkens JH, Bötzel K (2019) Deep brain stimulation programming for movement disorders: current concepts and evidence-based strategies. Front Neurol 10:410
Marsili L, Marconi R, Colosimo C (2017) Treatment strategies in early Parkinson’s disease. Int Rev Neurobiol 132:345–360
Magyar K, Stocchi F, Fossati C (2015) Rasagiline for the treatment of Parkinson’s disease: an update. Expert Opin Pharmacother 16:2231–2241
Grall-Bronnec M, Victorri-Vigneau C, Donnio Y, Leboucher J, Rousselet M, Thiabaud E, Zreika N, Derkinderen P, Challet-Bouju G (2018) Dopamine agonists and impulse control disorders: a complex association. Drug Saf 41:19–75
Benussi L, Binetti G, Ghidoni R (2017) Loss of neuroprotective factors in neurodegenerative dementias: the end or the starting point? Front Neurosci 11:672
Palfi S, Gurruchaga JM, Lepetit H, Howard K, Ralph GS, Mason S, Gouello G, Domenech P, Buttery PC, Hantraye P, Tuckwell NJ, Barker RA, Mitrophanous KA (2018) Long-term follow-up of a phase I/II study of ProSavin, a lentiviral vector gene therapy for Parkinson’s disease. Hum Gene Ther Clin Dev 29:148–155
Acknowledgments
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-05029).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Krokidis, M.G., Exarchos, T., Vlamos, P. (2021). Bioinformatics Approaches for Parkinson’s Disease in Clinical Practice: Data-Driven Biomarkers and Pharmacological Treatment. In: Vlamos, P. (eds) GeNeDis 2020. Advances in Experimental Medicine and Biology, vol 1338. Springer, Cham. https://doi.org/10.1007/978-3-030-78775-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-78775-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78774-5
Online ISBN: 978-3-030-78775-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)