Abstract
Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. This aging-related disease occurs due to the degenerative loss of tissue or cellular functions in the brain and due to genetic and epigenetic effects. This study was conducted on an RNA-seq dataset of PD collected from BA9 tissues to get insights to PD. A few RNA-seq based transcriptomics studies on PD are available. However, most of these studies are limited to differential expression analysis, i.e., individual gene-based analysis that ignores interactions and associations among genes to establish the association with the disease. Here, we initially identify differentially expressed genes and then construct a co-expression network on detected genes to identify modules. Module preservation analysis is carried out to find the non-preserved modules. We identify a non-preserved module with 73 (70 are annotated) genes. Differential connectivity analysis, topological analysis, and functional enrichment analysis are performed to find the initial set of interesting genes. Our finding is that 42 (60%) genes are significantly enriched in pathways, biological processes, or molecular functions, and they are topologically interesting. Among these genes, 19 can be linked to the PD based on evidence from literature. They are considered as biomarkers for PD. From the remaining 23 genes, 11 are expressed in brain region. Therefore, these genes may be further explored to understand their roles in PD and can be considered as potential biomarkers.
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Aharon-Peretz J, Rosenbaum H, Gershoni-Baruch R (2004) Mutations in the glucocerebrosidase gene and Parkinson’s disease in Ashkenazi Jews. N Engl J Med 351(19):1972–1977
Al-Aamri A, Taha K, Al-Hammadi Y, Maalouf M, Homouz D (2019) Analyzing a co-occurrence gene-interaction network to identify disease-gene association. BMC Bioinform 20(1):70
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genom Biol 11(10):R106
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25
Auer PL, Doerge RW et al (2011) A two-stage Poisson model for testing RNA-seq data. Stat Appl Genet Mol Biol 10(1):1–26
Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101
Barbiero JK, Santiago R, Tonin FS, Boschen S, da Silva LM, de Paula Werner MF, da Cunha C, Lima MM, Vital MA (2014) Ppar-\(\alpha\) agonist fenofibrate protects against the damaging effects of mptp in a rat model of parkinson’s disease. Prog Neuro Psychopharm Biolog Psychiatry 53:35–44
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol): 289–300
Borrageiro G, Haylett W, Seedat S, Kuivaniemi H, Bardien S (2018) A review of genome-wide transcriptomics studies in Parkinson’s disease. Eur J Neurosci 47(1):1–16
Bowen RL, Atwood CS (2004) Living and dying for sex. Gerontology 50(5):265–290
Braak H, Del Tredici K, Rüb U, De Vos RA, Steur ENJ, Braak E (2003) Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24(2):197–211
Chatterjee A, Singh KK (2001) Uracil-DNA glycosylase-deficient yeast exhibit a mitochondrial mutator phenotype. Nucleic Acids Res 29(24):4935–4940
Chaturvedi RK, Beal MF (2008) PPAR: a therapeutic target in Parkinson’s disease. J Neurochem 106(2):506–518
Chen SY, Feng Z, Yi X (2017) A general introduction to adjustment for multiple comparisons. J Thora Dis 9(6):1725
Cheng D, Jenner AM, Shui G, Cheong WF, Mitchell TW, Nealon JR, Kim WS, McCann H, Wenk MR, Halliday GM et al (2011) Lipid pathway alterations in Parkinson’s disease primary visual cortex. PLoS One 6(2):e17299
Chowdhury HA, Bhattacharyya DK et al (2020a) (Differential) Co-expression analysis of gene expression: a survey of best practices. IEEE/ACM TCBB 17(4):1154–1173
Chowdhury HA, Bhattacharyya DK et al (2020b) Differential expression analysis of RNA-seq reads: overview, taxonomy, and tools. IEEE/ACM TCBB 17(2):566–586
Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT (2017) Reproducible RNA-seq analysis using recount2. Nat Biotechnol 35(4):319
Costa-Silva J, Domingues D, Lopes FM (2017) RNA-Seq differential expression analysis: an extended review and a software tool. PloS One 12(12):e0190152
De Lau LM, Breteler MM (2006) Epidemiology of Parkinson’s disease. Lancet Neurol 5(6):525–535
Di Fonzo A, Chien H, Socal M, Giraudo S, Tassorelli C, Iliceto G, Fabbrini G, Marconi R, Fincati E, Abbruzzese G et al (2007) ATP13A2 missense mutations in juvenile Parkinsonism and young onset Parkinson disease. Neurology 68(19):1557–1562
Dick FD, De Palma G, Ahmadi A, Osborne A, Scott NW, Prescott GJ, Bennett J, Semple S, Dick S, Mozzoni P et al (2007) Gene-environment interactions in parkinsonism and Parkinson’s disease: the geoparkinson study. Occup Environ Med 64(10):673–680
Dumitriu A, Latourelle JC, Hadzi TC, Pankratz N, Garza D, Miller JP, Vance JM, Foroud T, Beach TG, Myers RH (2012) Gene expression profiles in parkinson disease prefrontal cortex implicate foxo1 and genes under its transcriptional regulation. PLoS Genet 8(6):e1002794
Dumitriu A, Golji J, Labadorf AT, Gao B, Beach TG, Myers RH, Longo KA, Latourelle JC (2015) Integrative analyses of proteomics and rna transcriptomics implicate mitochondrial processes, protein folding pathways and gwas loci in parkinson disease. BMC Med Geno 9(1):5
Eder T, Grebien F, Rattei T (2016) NVT: a fast and simple tool for the assessment of RNA-seq normalization strategies. Bioinformatics 32(23):3682–3684
Farin FM, Janssen P, Quigley S, Abbott D, Hassett C, Smith-Weller T, Franklin GM, Swanson PD, Longstreth W Jr, Omiecinski CJ et al (2001) Genetic polymorphisms of microsomal and soluble epoxide hydrolase and the risk of Parkinson’s disease. Pharmacogenet Genom 11(8):703–708
Filloux C, Cédric M, Romain P et al (2014) An integrative method to normalize RNA-Seq data. BMC Bioinform 15(1):188
Filteau M, Pavey SA, St-Cyr J et al (2013) Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish. Mol Biol Evolut 30(6):1384–1396
Finotello F, Di Camillo B (2015) Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Brief Funct Genom 14(2):130–142
Fischer D, Hauk TG, Müller A, Thanos S (2008) Crystallins of the \(\beta\)/\(\gamma\)-superfamily mimic the effects of lens injury and promote axon regeneration. Mol Cell Neurosci 37(3):471–479
Geisler S, Holmström KM, Skujat D, Fiesel FC, Rothfuss OC, Kahle PJ, Springer W (2010) PINK1/Parkin-mediated mitophagy is dependent on VDAC1 and p62/SQSTM1. Nat Cell Biol 12(2):119
Gerard M, Deleersnijder A, Daniëls V, Schreurs S, Munck S, Reumers V, Pottel H, Engelborghs Y, Van den Haute C, Taymans JM et al (2010) Inhibition of fk506 binding proteins reduces \(\alpha\)-synuclein aggregation and parkinson’s disease-like pathology. J Neurosci 30(7):2454–2463
Hadzi TC, Hendricks AE, Latourelle JC, Lunetta KL, Cupples LA, Gillis T, Mysore JS, Gusella JF, MacDonald ME, Myers RH et al (2012) Assessment of cortical and striatal involvement in 523 Huntington disease brains. Neurology 79(16):1708–1715
Halliday G, Del Tredici K, Braak H (2006) Critical appraisal of brain pathology staging related to presymptomatic and symptomatic cases of sporadic Parkinson’s disease. In: Parkinson’s Disease and Related Disorders, Springer, pp 99–103
Harvey B, Mark A, Chou J, Chen G, Hoffer B, Wang Y (2004) Neurotrophic effects of bone morphogenetic protein-7 in a rat model of Parkinson’s disease. Brain Res 1022(1–2):88–95
Hawkes C, Shephard B, Daniel S (1999) Is Parkinson’s disease a primary olfactory disorder? QJM Int J Med 92(8):473–480
Henderson-Smith A, Corneveaux JJ, De Both M, Cuyugan L, Liang WS, Huentelman M, Adler C, Driver-Dunckley E, Beach TG, Dunckley TL (2016) Next-generation profiling to identify the molecular etiology of Parkinson dementia. Neurol Genet 2(3):e75
Hossein-Nezhad A, Fatemi RP, Ahmad R, Peskind ER, Zabetian CP, Hu SC, Shi M, Wahlestedt C, Zhang J, Faghihi MA (2016) Transcriptomic profiling of extracellular RNAs present in cerebrospinal fluid identifies differentially expressed transcripts in Parkinson’s disease. J Parkinson’s Dis 6(1):109–117
Hu M, Cooper J, Beamish R, Jones E, Butterworth R, Catterall L, Ben-Shlomo Y (2011) How well do we recognise non-motor symptoms in a British Parkinson’s disease population? J Neurol 258(8):1513–1517
Huang DW, Sherman BT, Lempicki RA (2008) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocols 4(1):44–57
Hwang O (2013) Role of oxidative stress in Parkinson’s disease. Exp Neurobiol 22(1):11–17
Infante J, Prieto C, Sierra M, Sánchez-Juan P, González-Aramburu I, Sánchez-Quintana C, Berciano J, Combarros O, Sainz J (2015) Identification of candidate genes for Parkinson’s disease through blood transcriptome analysis in LRRK2-G2019S carriers, idiopathic cases, and controls. Neurobiol Aging 36(2):1105–1109
Infante J, Prieto C, Sierra M, Sánchez-Juan P, González-Aramburu I, Sánchez-Quintana C, Berciano J, Combarros O, Sainz J (2016) Comparative blood transcriptome analysis in idiopathic and LRRK2 G2019S-associated Parkinson’s disease. Neurobiol Aging 38:214-e1
Jiang H, Ren Y, Yuen EY, Zhong P, Ghaedi M, Hu Z, Azabdaftari G, Nakaso K, Yan Z, Feng J (2012) Parkin controls dopamine utilization in human midbrain dopaminergic neurons derived from induced pluripotent stem cells. Nat Commun 3:668
Kadonaga JT (2004) Regulation of rna polymerase ii transcription by sequence-specific dna binding factors. Cell 116(2):247–257
Kitada T, Asakawa S, Hattori N, Matsumine H, Yamamura Y, Minoshima S, Yokochi M, Mizuno Y, Shimizu N (1998) Mutations in the parkin gene cause autosomal recessive juvenile parkinsonism. Nature 392(6676):605
Kõks S, Overall RW, Ivask M, Soomets U, Guha M, Vasar E, Fernandes C, Schalkwyk LC (2013) Silencing of the WFS1 gene in HEK cells induces pathways related to neurodegeneration and mitochondrial damage. Physiol Genom 45(5):182–190
Kori M, Aydın B, Unal S, Arga KY, Kazan D (2016) Metabolic biomarkers and neurodegeneration: a pathway enrichment analysis of alzheimer’s disease, parkinson’s disease, and amyotrophic lateral sclerosis. Omics 20(11):645–661
Kurz A, Double KL, Lastres-Becker I, Tozzi A, Tantucci M, Bockhart V, Bonin M, García-Arencibia M, Nuber S, Schlaudraff F et al (2010) A53T-alpha-synuclein overexpression impairs dopamine signaling and striatal synaptic plasticity in old mice. PloS One 5(7):e11464
Kvam VM, Liu P, Si Y (2012) A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot 99(2):248–256
Labadorf A, Choi SH, Myers RH (2018) Evidence for a pan-neurodegenerative disease response in Huntington’s and Parkinson’s disease expression profiles. Front Mol Neurosci 10:430
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9(1):559
Langfelder P, Luo R et al (2011) Is my network module preserved and reproducible? PLoS Comput Biol 7(1):e1001057
Lesage S, Brice A (2009) Parkinson’s disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet 18(R1):R48–R59
Lesnick TG, Papapetropoulos S, Mash DC, Ffrench-Mullen J, Shehadeh L, De Andrade M, Henley JR, Rocca WA, Ahlskog JE, Maraganore DM (2007) A genomic pathway approach to a complex disease: axon guidance and parkinson disease. PLoS Genet 3(6):e98
Li B, Ruotti V, Stewart RM et al (2009) RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26(4):493–500
Li B, Zhang Y, Yu Y et al (2015) Quantitative assessment of gene expression network module-validation methods. Sci Rep 5:15258
Liiv M, Cagalinec M, Hodurova Z, Vaarmann A, Mandel M, Zeb A, Kuum M, Hickey MA, Safiulina D, Choubey V et al (2015) Wolfram syndrome 1: from ER stress to impaired mitochondrial dynamics and neuronal development. SpringerPlus 4(1):P22
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550
Mason MJ, Fan G, Plath K et al (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genom 10(1):327
Mason RR, Mokhtar R, Matzaris M, Selathurai A, Kowalski GM, Mokbel N, Meikle PJ, Bruce CR, Watt MJ (2014) PLIN5 deletion remodels intracellular lipid composition and causes insulin resistance in muscle. Mol Metab 3(6):652–663
Medina IR, Lubovac-Pilav Z (2016) Gene co-expression network analysis for identifying modules and functionally enriched pathways in type 1 diabetes. PloS One 11(6):e0156006
Michel TM, Käsbauer L, Gsell W, Jecel J, Sheldrick AJ, Cortese M, Nickl-Jockschat T, Grünblatt E, Riederer P (2014) Aldehyde dehydrogenase 2 in sporadic Parkinson’s disease. Parkinsonism Relat Dis 20:S68–S72
Mitchell JC (1969) Social networks in urban situations: analyses of personal relationships in Central African towns. Manchester University Press, Manchester
Moran EP, Jx Ma (2015) Therapeutic effects of ppar\(\alpha\) on neuronal death and microvascular impairment. PPAR research 2015: https://doi.org/10.1155/2015/595426
Nam JH, Park ES, Won SY, Lee YA, Kim KI, Jeong JY, Baek JY, Cho EJ, Jin M, Chung YC et al (2015) TRPV1 on astrocytes rescues nigral dopamine neurons in Parkinson’s disease via CNTF. Brain 138(12):3610–3622
Nuber S, Petrasch-Parwez E, Arias-Carrión O, Koch L, Kohl Z, Schneider J, Calaminus C, Dermietzel R, Samarina A, Boy J et al (2011) Olfactory neuron-specific expression of A30P alpha-synuclein exacerbates dopamine deficiency and hyperactivity in a novel conditional model of early Parkinson’s disease stages. Neurobiol Dis 44(2):192–204
Nuytemans K, Theuns J, Cruts M, Van Broeckhoven C (2010) Genetic etiology of Parkinson disease associated with mutations in the SNCA, PARK2, PINK1, PARK7, and LRRK2 genes: a mutation update. Hum Mutat 31(7):763–780
O’Keeffe GW, Hegarty S, Sullivan A (2017) Targeting bone morphogenetic protein signalling in midbrain dopaminergic neurons as a therapeutic approach in Parkinson’s disease. Neuronal Signaling p NS20170027
Paisán-Ruız C, Jain S, Evans EW, Gilks WP, Simón J, Van Der Brug M, De Munain AL, Aparicio S, Gil AM, Khan N et al (2004) Cloning of the gene containing mutations that cause PARK8-linked Parkinson’s disease. Neuron 44(4):595–600
Park S, Choi SG, Yoo SM, Son JH, Jung YK (2014) Choline dehydrogenase interacts with SQSTM1/p62 to recruit LC3 and stimulate mitophagy. Autophagy 10(11):1906–1920
Planken A, Kurvits L, Reimann E, Kadastik-Eerme L, Kingo K, Kõks S, Taba P (2017) Looking beyond the brain to improve the pathogenic understanding of Parkinson’s disease: implications of whole transcriptome profiling of Patients’ skin. BMC Neurol 17(1):6
Riley BE, Gardai SJ, Emig-Agius D, Bessarabova M, Ivliev AE, Schüle B, Alexander J, Wallace W, Halliday GM, Langston JW et al (2014) Systems-based analyses of brain regions functionally impacted in Parkinson’s disease reveals underlying causal mechanisms. PloS One 9(8):e102909
Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140
Ruipérez V, Darios F, Davletov B (2010) Alpha-synuclein, lipids and Parkinson’s disease. Prog Lipid Res 49(4):420–428
Safran M, Dalah I, Alexander J, Rosen N, Iny Stein T, Shmoish M, Nativ N, Bahir I, Doniger T, Krug H, Sirota-Madi A, Olender T, Golan Y, Stelzer G, Harel A, Lancet D (2010) GeneCards Version 3: the human gene integrator. Database. https://doi.org/10.1093/database/baq020
Sahu A, Chowdhury HA et al (2020) Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder. Brain Behav Immun Health 2:100023
Seale P, Conroe HM, Estall J, Kajimura S, Frontini A, Ishibashi J, Cohen P, Cinti S, Spiegelman BM (2011) PRDM16 determines the thermogenic program of subcutaneous white adipose tissue in mice. J Clin Investig 121(1):96–105
Shi GX, Cai W, Andres DA (2013) RIT subfamily small GTPases: regulators in neuronal differentiation and survival. Cell Signal 25(10):2060–2068
Singer GA, Lloyd AT, Huminiecki LB et al (2004) Clusters of co-expressed genes in mammalian genomes are conserved by natural selection. Mol Biol Evolut 22(3):767–775
Soldner F, Stelzer Y, Shivalila CS, Abraham BJ, Latourelle JC, Barrasa MI, Goldmann J, Myers RH, Young RA, Jaenisch R (2016) Parkinson-associated risk variant in distal enhancer of \(\alpha\)-synuclein modulates target gene expression. Nature 533(7601):95
Soreq L, Guffanti A, Salomonis N, Simchovitz A, Israel Z, Bergman H, Soreq H (2014) Long non-coding RNA and alternative splicing modulations in Parkinson’s leukocytes identified by RNA sequencing. PLoS Comput Biol 10(3):e1003517
Spillantini MG, Schmidt ML, Lee VMY, Trojanowski JQ, Jakes R, Goedert M (1997) \(\alpha\)-Synuclein in Lewy bodies. Nature 388(6645):839
Tesson BM, Breitling R, Jansen RC (2010) DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinform 11(1):497
Valente EM, Abou-Sleiman PM, Caputo V, Muqit MM, Harvey K, Gispert S, Ali Z, Del Turco D, Bentivoglio AR, Healy DG et al (2004) Hereditary early-onset Parkinson’s disease caused by mutations in PINK1. Science 304(5674):1158–1160
Van Duijn C, Dekker M, Bonifati V, Galjaard R, Houwing-Duistermaat J, Snijders P, Testers L, Breedveld G, Horstink M, Sandkuijl L et al (2001) PARK7, a novel locus for autosomal recessive early-onset Parkinsonism, on chromosome 1p36. Am J Hum Genet 69(3):629–634
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440
Weinert BT, Timiras PS (2003) Invited review: theories of aging. J Appl Physiol 95(4):1706–1716
Winklhofer KF, Haass C (2010) Mitochondrial dysfunction in Parkinson’s disease. Biochimica et Biophysica Acta (BBA)-Mol Basis Dis 1802(1):29–44
Woodard CM, Campos BA, Kuo SH, Nirenberg MJ, Nestor MW, Zimmer M, Mosharov EV, Sulzer D, Zhou H, Paull D et al (2014) iPSC-derived dopamine neurons reveal differences between monozygotic twins discordant for Parkinson’s disease. Cell Rep 9(4):1173–1182
Wu R, Xm Liu, Jg Sun, Chen H, Ma J, Dong M, Peng S, Jq Wang, Jq Ding, Li Dh et al (2017) DJ-1 maintains energy and glucose homeostasis by regulating the function of brown adipose tissue. Cell Dis 3:16054
Xia R, Mao ZH (2012) Progression of motor symptoms in Parkinson’s disease. Neurosci Bull 28(1):39–48
Zhao Z, Cao L, Reece EA (2017) Formation of neurodegenerative aggresome and death-inducing signaling complex in maternal diabetes-induced neural tube defects. Proc Nat Acad Sci 114(17):4489–4494
Zwiener I, Frisch B, Binder H (2014) Transforming RNA-Seq data to improve the performance of prognostic gene signatures. PloS One 9(1):e85150
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PB thanks financial support from the Ramalingaswamy Re-entry Fellowship from the Department of Biotechnology (DBT), Ministry of Science & Technology, Government of India. HAC acknowledges Ministry of Minority Affairs for financial assistance in terms of UGC-MANF fellowship.
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Chowdhury, H.A., Barah, P., Bhattacharyya, D.K. et al. Identification of potential Parkinson’s disease biomarkers using computational biology approaches. Netw Model Anal Health Inform Bioinforma 10, 10 (2021). https://doi.org/10.1007/s13721-020-00280-5
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DOI: https://doi.org/10.1007/s13721-020-00280-5