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Orphanet Journal of Rare Diseases
Published in: Orphanet Journal of Rare Diseases 1/2016

Open Access 01-12-2016 | Research

Common disease signatures from gene expression analysis in Huntington’s disease human blood and brain

Authors: Eleni Mina, Willeke van Roon-Mom, Kristina Hettne, Erik van Zwet, Jelle Goeman, Christian Neri, Peter A.C. ’t Hoen, Barend Mons, Marco Roos

Published in: Orphanet Journal of Rare Diseases | Issue 1/2016

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Abstract

Background

Huntington’s disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington’s disease signatures in a functional context.

Methods

Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: “biological process”, “cellular component”, “molecular function” and “disease or syndrome”.

Results

We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias.

Conclusions

Our results demonstrate that HD blood exhibits dysregulation that is similar to brain at a functional level, but not necessarily at the level of individual genes. We report two common signatures that can be used to monitor the pathology in brain of HD patients in a non-invasive manner. Our results are an exemplar of how signals in blood data can be used to represent brain disorders. Our methodology can be used to study disease specific signatures in diseases where heterogeneous tissues are involved in the pathology.
Appendix
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Literature
1.
MacDonald ME, Ambrose CM, Duyao MP, Myers RH, Lin C, Srinidhi L, Barnes G, Taylor SA, James M, Groot N, MacFarlane H, Jenkins B, Anderson MA, Wexler NS, Gusella JF, Bates GP, Baxendale S, Hummerich H, Kirby S, North M, Youngman S, Mott R, Zehetner G, Sedlacek Z, Poustka A, Frischauf AM, Lehrach H, Buckler AJ, Church D, Doucette-Stamm L, O’Donovan MC, Riba-Ramirez L, Shah M, Stanton VP, Strobel SA, Draths KM, Wales JL, Dervan P, Housman DE, Altherr M, Shiang R, Thompson L, Fielder T, Wasmuth JJ, Tagle D, Valdes J, Elmer L, Allard M, Castilla L, Swaroop M, Blanchard K, Collins FS, Snell R, Holloway T, Gillespie K, Datson N, Shaw D, Harper PS. A novel gene containing a trinucleotide repeat that is expanded and unstable on huntington’s disease chromosomes. Cell. 1993; 72(6):971–83.CrossRef
2.
Tabrizi SJ, Scahill RI, Owen G, Durr A, Leavitt BR, Roos RA, Borowsky B, Landwehrmeyer B, Frost C, Johnson H, Craufurd D, Reilmann R, Stout JC, Langbehn DR, TRACK-HD Investigators. Predictors of phenotypic progression and disease onset in premanifest and early-stage huntington’s disease in the TRACK-HD study: analysis of 36-month observational data. Lancet Neurol. 2013; 12(7):637–49.CrossRefPubMed
3.
van der Burg JMM, Björkqvist M, Brundin P. Beyond the brain: widespread pathology in huntington’s disease. Lancet Neurol. 2009; 8(8):765–74.CrossRefPubMed
4.
Sassone J, Colciago C, Cislaghi G, Silani V, Ciammola A. Huntington’s disease: the current state of research with peripheral tissues. Exp Neurol. 2009; 219(2):385–97.CrossRefPubMed
5.
Hodges A. Regional and cellular gene expression changes in human huntington’s disease brain. Hum Mol Genet. 2006; 15(6):965–77.CrossRefPubMed
6.
Tomita H, Vawter MP, Walsh DM, Evans SJ, Choudary PV, Li J, Overman KM, Atz ME, Myers RM, Jones EG, Watson SJ, Akil H, Bunney WE. Effect of agonal and postmortem factors on gene expression profile: Quality control in microarray analyses of postmortem human brain. Biol Psychiatry. 2004; 55(4):346–52.CrossRefPubMedPubMedCentral
7.
Runne H, Kuhn A, Wild EJ, Pratyaksha W, Kristiansen M, Isaacs JD, Régulier E, Delorenzi M, Tabrizi SJ, Luthi-Carter R. Analysis of potential transcriptomic biomarkers for huntington’s disease in peripheral blood. Proc Natl Acad Sci. 2007; 104(36):14424.CrossRefPubMedPubMedCentral
8.
Lovrecic L, Kastrin A, Kobal J, Pirtosek Z, Krainc D, Peterlin B. Gene expression changes in blood as a putative biomarker for huntington’s disease. Mov Disord. 2009; 24(15):2277–281.CrossRefPubMed
9.
Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV, Krainc D. Genome-wide expression profiling of human blood reveals biomarkers for huntington’s disease. Proc Natl Acad Sci U S A. 2005; 102(31):11023–11028.CrossRefPubMedPubMedCentral
10.
Mastrokolias A, Ariyurek Y, Goeman JJ, van Duijn E, Roos RA, van der Mast RC, van Ommen GB, den Dunnen JT, ’t Hoen PA, van Roon-Mom WM. Huntington’s disease biomarker progression profile identified by transcriptome sequencing in peripheral blood. Eur J Hum Genet. 2015; 23:1349–56.CrossRefPubMedPubMedCentral
11.
Cai C, Langfelder P, Fuller TF, Oldham MC, Luo R, van den Berg LH, Ophoff RA, Horvath S. Is human blood a good surrogate for brain tissue in transcriptional studies?BMC Genomics. 2010; 11:589.CrossRefPubMedPubMedCentral
12.
Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3(1).
13.
14.
Liew CC, Ma J, Tang HC, Zheng R, Dempsey AA. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J Lab Clin Med. 2006; 147(3):126–32.CrossRefPubMed
15.
Langfelder P, Horvath S. WGCNA: an r package for weighted correlation network analysis. BMC Bioinforma. 2008; 9(1):559.CrossRef
16.
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4(1).
17.
Jelier R, Schuemie MJ, Roes PJ, van Mulligen EM, Kors JA. Literature-based concept profiles for gene annotation: The issue of weighting. Int J Med Inform. 2008; 77(5):354–62.CrossRefPubMed
18.
Jelier R, Schuemie MJ, Veldhoven A, Dorssers LC, Jenster G, Kors JA. Anni 2.0: a multipurpose text-mining tool for the life sciences. Genome Biol. 2008; 9(6):96.CrossRef
19.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139–40.CrossRefPubMed
20.
Ribeca P, Sammeth M. Pitfalls of correlation coefficients applied to gene expression data. 2011.
21.
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for r. Bioinformatics. 2008; 24(5):719–20.CrossRefPubMed
22.
Hettne KM, van Mulligen EM, Schuemie MJ, Schijvenaars BJ, Kors JA. Rewriting and suppressing UMLS terms for improved biomedical term identification. J Biomed Semant. 2010; 1(1):5.CrossRef
23.
Hettne KM, Stierum RH, Schuemie MJ, Hendriksen PJM, Schijvenaars BJA, Mulligen EMv, Kleinjans J, Kors JA. A dictionary to identify small molecules and drugs in free text. Bioinformatics (Oxford, England). 2009; 25(22):2983–991.CrossRef
24.
Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T. Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 2006; 34:729–32.CrossRef
25.
Wolstencroft K, Haines R, Fellows D, Williams A, Withers D, Owen S, Soiland-Reyes S, Dunlop I, Nenadic A, Fisher P, Bhagat J, Belhajjame K, Bacall F, Hardisty A, Nieva de la Hidalga A, Balcazar Vargas MP, Sufi S, Goble C. The taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Res. 2013; 41:557–61.CrossRef
26.
Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004; 32:267–70.CrossRef
27.
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005; 33:514–7.CrossRef
28.
Westfall PH, Young SS. Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment. A Wiley-Interscience publication: Wiley; 1993.
29.
30.
Jelier R, Goeman JJ, Hettne KM, Schuemie MJ, Dunnen JTd, Hoen PACt. Literature-aided interpretation of gene expression data with the weighted global test. Brief Bioinform. 2011; 12(5):518–29.CrossRefPubMed
31.
Björkqvist M, Wild EJ, Thiele J, Silvestroni A, Andre R, Lahiri N, Raibon E, Lee RV, Benn CL, Soulet D, Magnusson A, Woodman B, Landles C, Pouladi MA, Hayden MR, Khalili-Shirazi A, Lowdell MW, Brundin P, Bates GP, Leavitt BR, Möller T, Tabrizi SJ. A novel pathogenic pathway of immune activation detectable before clinical onset in huntington’s disease. J Exp Med. 2008; 205(8):1869–77.CrossRefPubMedPubMedCentral
32.
33.
Liu KY, Shyu YC, Barbaro BA, Lin YT, Chern Y, Thompson LM, James Shen CK, Marsh JL. Disruption of the nuclear membrane by perinuclear inclusions of mutant huntingtin causes cell-cycle re-entry and striatal cell death in mouse and cell models of huntington’s disease. Hum Mol Genet. 2015; 24(6):1602–16.CrossRefPubMed
34.
Fonteh AN, Ormseth C, Chiang J, Cipolla M, Arakaki X, Harrington MG. Sphingolipid metabolism correlates with cerebrospinal fluid beta amyloid levels in alzheimer’s disease. PLoS ONE. 2015. 10(5).
35.
Di Pardo A, Maglione V, Alpaugh M, Horkey M, Atwal RS, Sassone J, Ciammola A, Steffan JS, Fouad K, Truant R, Sipione S. Ganglioside GM1 induces phosphorylation of mutant huntingtin and restores normal motor behavior in huntington disease mice. Proc Natl Acad Sci U S A. 2012; 109(9):3528–533.CrossRefPubMedPubMedCentral
36.
Li XJ, Li SH, Sharp AH, Nucifora FC, Schilling G, Lanahan A, Worley P, Snyder SH, Ross CA. A huntingtin-associated protein enriched in brain with implications for pathology. Nature. 1995; 378(6555):398–402. doi:10.​1038/​378398a0.CrossRefPubMed
37.
Kegel-Gleason KB. Huntingtin interactions with membrane phospholipids: Strategic targets for therapeutic intervention?J Huntington’s Dis. 2013; 2(3):239–50.
38.
Kegel KB, Sapp E, Alexander J, Valencia A, Reeves P, Li X, Masso N, Sobin L, Aronin N, DiFiglia M. Polyglutamine expansion in huntingtin alters its interaction with phospholipids. J Neurochem. 2009; 110(5):1585–1597.CrossRefPubMed
39.
40.
Oldham M, Langfelder P, Horvath S. Network methods for describing sample relationships in genomic datasets: application to huntington’s disease. BMC Syst Biol. 2012; 6(1):63.CrossRefPubMedPubMedCentral
41.
Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH. Functional organization of the transcriptome in human brain. Nat Neurosci. 2008; 11(11):1271–82.CrossRefPubMedPubMedCentral
42.
Sathasivam K, Neueder A, Gipson TA, Landles C, Benjamin AC, Bondulich MK, Smith DL, Faull RLM, Roos RAC, Howland D, Detloff PJ, Housman DE, Bates GP. Aberrant splicing of HTT generates the pathogenic exon 1 protein in huntington disease. Proc Natl Acad Sci U S A. 2013; 110(6):2366–370.CrossRefPubMedPubMedCentral
43.
Twelvetrees AE, Yuen EY, Arancibia-Carcamo IL, MacAskill AF, Rostaing P, Lumb MJ, Humbert S, Triller A, Saudou F, Yan Z, Kittler JT. Deslivery of GABAARs to synapses is mediated by HAP1-KIF5 and disrupted by mutant huntingtin. Neuron. 2010; 65(1):53–65.CrossRefPubMedPubMedCentral
44.
Butovsky O, Jedrychowski MP, Moore CS, Cialic R, Lanser AJ, Gabriely G, Koeglsperger T, Dake B, Wu PM, Doykan CE, Fanek Z, Liu L, Chen Z, Rothstein JD, Ransohoff RM, Gygi SP, Antel JP, Weiner HL. Identification of a unique TGF- β-dependent molecular and functional signature in microglia. Nat Neurosci. 2014; 17(1):131–143. doi:10.​1038/​nn.​3599. Accessed 27 May 2016.CrossRefPubMed
45.
46.
47.
48.
Forrest MP, Hill MJ, Quantock AJ, Martin-Rendon E, Blake DJ. The emerging roles of TCF4 in disease and development. Trends Mol Med. 2014; 20(6):322–31.CrossRefPubMed
49.
Suzuki Y, Yazawa I. Pathological accumulation of atrophin-1 in dentatorubralpallidoluysian atrophy. Int J Clin Exp Pathol. 2011; 4(4):378–84.PubMedPubMedCentral
50.
O’Hearn E, Holmes SE, Margolis RL. Spinocerebellar ataxia type 12. Handb Clin Neurol. 2012; 103:535–47.CrossRefPubMed
51.
Teive HAG, Munhoz RP, Arruda WO, Raskin S, Werneck LC, Ashizawa T. Spinocerebellar ataxia type 10 - a review. Parkinsonism Relat Disord. 2011; 17(9):655–61.CrossRefPubMed
52.
Evers MM, Toonen LJA, van Roon-Mom WMC. Ataxin-3 protein and RNA toxicity in spinocerebellar ataxia type 3: current insights and emerging therapeutic strategies. Mol Neurobiol. 2014; 49(3):1513–31.PubMed
53.
Menzies FM, Garcia-Arencibia M, Imarisio S, O’Sullivan NC, Ricketts T, Kent BA, Rao MV, Lam W, Green-Thompson ZW, Nixon RA, Saksida LM, Bussey TJ, O’Kane CJ, Rubinsztein DC. Calpain inhibition mediates autophagy-dependent protection against polyglutamine toxicity. Cell Death Differ. 2015; 22(3):433–44.CrossRefPubMed
54.
Herenger Y, Stoetzel C, Schaefer E, Scheidecker S, Manière MC, Pelletier V, Alembik Y, Christmann D, Clavert JM, Terzic J, Fischbach M, De Saint Martin A, Dollfus H. Long term follow up of two independent patients with schinzel-giedion carrying SETBP1 mutations. Eur J Med Genet. 2015; 58(9):479–87.CrossRefPubMed
55.
Ko JM, Lim BC, Kim KJ, Hwang YS, Ryu HW, Lee JH, Kim JS, Chae JH. Distinct neurological features in a patient with schinzel-giedion syndrome caused by a recurrent SETBP1 mutation. Childs Nerv Syst ChNS Off J Int Soc Pediatr Neurosurg. 2013; 29(4):525–9.CrossRef
56.
Diguet E, Fernagut PO, Normand E, Centelles L, Mulle C, Tison F. Experimental basis for the putative role of GluR6/kainate glutamate receptor subunit in huntington’s disease natural history. Neurobiol Dis. 2004; 15(3):667–75.CrossRefPubMed
57.
Jarabek BR, Yasuda RP, Wolfe BB. Regulation of proteins affecting NMDA receptor-induced excitotoxicity in a huntington’s mouse model. Brain. 2004; 127:505–16.CrossRefPubMed
58.
Smith AK, Fang H, Whistler T, Unger ER, Rajeevan MS. Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome. Neuropsychobiology. 2011; 64(4):183–94.CrossRefPubMedPubMedCentral
59.
Hyndman KA, Boesen EI, Elmarakby AA, Brands MW, Huang P, Kohan DE, Pollock DM, Pollock JS. Renal collecting duct NOS1 maintains fluid-electrolyte homeostasis and blood pressure. Hypertension. 2013; 62(1):91–8.CrossRefPubMedPubMedCentral
60.
Lee KA, Hammerle LP, Andrews PS, Stokes MP, Mustelin T, Silva JC, Black RA, Doedens JR. Ubiquitin ligase substrate identification through quantitative proteomics at both the protein and peptide levels. J Biol Chem. 2011; 286(48):41530–1538.CrossRefPubMedPubMedCentral
61.
Kim W, Bennett EJ, Huttlin EL, Guo A, Li J, Possemato A, Sowa ME, Rad R, Rush J, Comb MJ, Harper JW, Gygi SP. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol Cell. 2011; 44(2):325–40.CrossRefPubMedPubMedCentral
62.
Niikura T, Hashimoto Y, Tajima H, Ishizaka M, Yamagishi Y, Kawasumi M, Nawa M, Terashita K, Aiso S, Nishimoto I. A tripartite motif protein TRIM11 binds and destabilizes humanin, a neuroprotective peptide against alzheimer’s disease-relevant insults. Eur J NeuroSci. 2003; 17(6):1150–1158.CrossRefPubMed
63.
Peng HM, Morishima Y, Jenkins GJ, Dunbar AY, Lau M, Patterson C, Pratt WB, Osawa Y. Ubiquitylation of neuronal nitric-oxide synthase by CHIP, a chaperone-dependent e3 ligase. J Biol Chem. 2004; 279(51):52970–2977.CrossRefPubMed
64.
Wagner SA, Beli P, Weinert BT, Nielsen ML, Cox J, Mann M, Choudhary C. A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol Cell Proteomics MCP. 2011; 10(10):111–013284.CrossRef
65.
Steffan JS, Agrawal N, Pallos J, Rockabrand E, Trotman LC, Slepko N, Illes K, Lukacsovich T, Zhu YZ, Cattaneo E, Pandolfi PP, Thompson LM, Marsh JL. SUMO modification of huntingtin and huntington’s disease pathology. Science (New York, N.Y.) 2004; 304(5667):100–4.CrossRef
66.
Kaltenbach LS, Romero E, Becklin RR, Chettier R, Bell R, Phansalkar A, Strand A, Torcassi C, Savage J, Hurlburt A, Cha GH, Ukani L, Chepanoske CL, Zhen Y, Sahasrabudhe S, Olson J, Kurschner C, Ellerby LM, Peltier JM, Botas J, Hughes RE. Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet. 2007; 3(5):82.CrossRef
67.
Harjes P, Wanker EE. The hunt for huntingtin function: interaction partners tell many different stories. Trends Biochem Sci. 2003; 28(8):425–33.CrossRefPubMed
68.
Carroll JB, Bates GP, Steffan J, Saft C, Tabrizi SJ. Treating the whole body in huntington’s disease. Lancet Neurol. 2015; 14(11):1135–1142.CrossRefPubMed
Metadata
Title
Common disease signatures from gene expression analysis in Huntington’s disease human blood and brain
Authors
Eleni Mina
Willeke van Roon-Mom
Kristina Hettne
Erik van Zwet
Jelle Goeman
Christian Neri
Peter A.C. ’t Hoen
Barend Mons
Marco Roos
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Orphanet Journal of Rare Diseases / Issue 1/2016
Electronic ISSN: 1750-1172
DOI
https://doi.org/10.1186/s13023-016-0475-2

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