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Current Protein & Peptide Science

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ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

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Review Article

Applications of iTRAQ and TMT Labeling Techniques to the Study of Neurodegenerative Diseases

Author(s): Kelu Li, Zichao Chen, Yonggang Zhang and Xinglong Yang*

Volume 21, Issue 12, 2020

Page: [1202 - 1217] Pages: 16

DOI: 10.2174/1389203721666201103085704

Price: $65

Abstract

Neurodegenerative diseases are caused by progressive lesions or loss of specific nerve cells, which endanger human health. However, the mechanism by which neurodegeneration manifests remains unclear. Proteomics can shed light on this question as well as help establish diagnostic standards and discover new drug targets. The power of proteomics for understanding neurodegenerative diseases has increased substantially with the application of iTRAQ and TMT labeling techniques. This review focuses on progress in these labeling techniques and their applications in neurodegeneration research.

Keywords: Neurodegenerative diseases, iTRAQ, TMT, proteomics, quantitative neuroproteomics, neuro-imaging.

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[1]
Skovronsky, D.M.; Lee, V.M.; Trojanowski, J.Q. Neurodegenerative diseases: new concepts of pathogenesis and their therapeutic implications. Annu. Rev. Pathol., 2006, 1, 151-170.
[http://dx.doi.org/10.1146/annurev.pathol.1.110304.100113] [PMID: 18039111]
[2]
Small, G.W.; Agdeppa, E.D.; Kepe, V.; Satyamurthy, N.; Huang, S.C.; Barrio, J.R. in vivo brain imaging of tangle burden in humans. J. Mol. Neurosci., 2002, 19(3), 323-327.
[http://dx.doi.org/10.1385/JMN:19:3:321] [PMID: 12540059]
[3]
Mosimann, U.P.; McKeith, I.G. Dementia with lewy bodies-diagnosis and treatment. Swiss Med. Wkly., 2003, 133(9-10), 131-142.
[PMID: 12707840]
[4]
Litvan, I.; MacIntyre, A.; Goetz, C.G.; Wenning, G.K.; Jellinger, K.; Verny, M.; Bartko, J.J.; Jankovic, J.; McKee, A.; Brandel, J.P.; Chaudhuri, K.R.; Lai, E.C.; D’Olhaberriague, L.; Pearce, R.K.; Agid, Y. Accuracy of the clinical diagnoses of Lewy body disease, Parkinson disease, and dementia with Lewy bodies: a clinicopathologic study. Arch. Neurol., 1998, 55(7), 969-978.
[http://dx.doi.org/10.1001/archneur.55.7.969] [PMID: 9678315]
[5]
Davis, A.A.; Leyns, C.E.G.; Holtzman, D.M. Intercellular Spread of Protein Aggregates in Neurodegenerative Disease. Annu. Rev. Cell Dev. Biol., 2018, 34, 545-568.
[http://dx.doi.org/10.1146/annurev-cellbio-100617-062636] [PMID: 30044648]
[6]
Taylor, J.P.; Hardy, J.; Fischbeck, K.H. Toxic proteins in neurodegenerative disease. Science, 2002, 296(5575), 1991-1995.
[http://dx.doi.org/10.1126/science.1067122] [PMID: 12065827]
[7]
Texier, Y.; Kinkl, N.; Boldt, K.; Ueffing, M. From quantitative protein complex analysis to disease mechanism. Vision Res., 2012, 75, 108-111.
[http://dx.doi.org/10.1016/j.visres.2012.08.016] [PMID: 23010258]
[8]
Gerszten, R.E.; Asnani, A.; Carr, S.A. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. Circ. Res., 2011, 109(4), 463-474.
[http://dx.doi.org/10.1161/CIRCRESAHA.110.225003] [PMID: 21817166]
[9]
Zhang, Y.; Zhu, Y.; He, F. An overview of human protein databases and their application to functional proteomics in health and disease. Sci. China Life Sci., 2011, 54(11), 988-998.
[http://dx.doi.org/10.1007/s11427-011-4247-x] [PMID: 22173304]
[10]
Shi, M.; Hwang, H.; Zhang, J. Quantitative characterization of glycoproteins in neurodegenerative disorders using iTRAQ. Methods Mol. Biol., 2013, 951, 279-296.
[http://dx.doi.org/10.1007/978-1-62703-146-2_19] [PMID: 23296538]
[11]
Andrade, E.C.; Krueger, D.D.; Nairn, A.C. Recent advances in neuroproteomics. Curr. Opin. Mol. Ther., 2007, 9(3), 270-281.
[PMID: 17608026]
[12]
Washburn, M.P.; Wolters, D.; Yates, J.R., III Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol., 2001, 19(3), 242-247.
[http://dx.doi.org/10.1038/85686] [PMID: 11231557]
[13]
Ross, P.L.; Huang, Y.N.; Marchese, J.N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; Purkayastha, S.; Juhasz, P.; Martin, S.; Bartlet-Jones, M.; He, F.; Jacobson, A.; Pappin, D.J. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics, 2004, 3(12), 1154-1169.
[http://dx.doi.org/10.1074/mcp.M400129-MCP200] [PMID: 15385600]
[14]
Dayon, L.; Sanchez, J.C. Relative protein quantification by MS/MS using the tandem mass tag technology. Methods Mol. Biol., 2012, 893, 115-127.
[http://dx.doi.org/10.1007/978-1-61779-885-6_9] [PMID: 22665298]
[15]
Gygi, S.P.; Rist, B.; Gerber, S.A.; Turecek, F.; Gelb, M.H.; Aebersold, R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol., 1999, 17(10), 994-999.
[http://dx.doi.org/10.1038/13690] [PMID: 10504701]
[16]
Ong, S.E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D.B.; Steen, H.; Pandey, A.; Mann, M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics, 2002, 1(5), 376-386.
[http://dx.doi.org/10.1074/mcp.M200025-MCP200] [PMID: 12118079]
[17]
Wang, F.; Cheng, K.; Wei, X.; Qin, H.; Chen, R.; Liu, J.; Zou, H. A six-plex proteome quantification strategy reveals the dynamics of protein turnover. Sci. Rep., 2013, 3, 1827.
[http://dx.doi.org/10.1038/srep01827] [PMID: 23661174]
[18]
Hedl, T.J.; San Gil, R.; Cheng, F.; Rayner, S.L.; Davidson, J.M.; De Luca, A.; Villalva, M.D.; Ecroyd, H.; Walker, A.K.; Lee, A. Proteomics Approaches for Biomarker and Drug Target Discovery in ALS and FTD. Front. Neurosci., 2019, 13, 548.
[http://dx.doi.org/10.3389/fnins.2019.00548] [PMID: 31244593]
[19]
Pütz, S.M.; Boehm, A.M.; Stiewe, T.; Sickmann, A. iTRAQ analysis of a cell culture model for malignant transformation, including comparison with 2D-PAGE and SILAC. J. Proteome Res., 2012, 11(4), 2140-2153.
[http://dx.doi.org/10.1021/pr200881c] [PMID: 22313033]
[20]
Li, Z.; Adams, R.M.; Chourey, K.; Hurst, G.B.; Hettich, R.L.; Pan, C. Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J. Proteome Res., 2012, 11(3), 1582-1590.
[http://dx.doi.org/10.1021/pr200748h] [PMID: 22188275]
[21]
Sullivan, P.M.; Zhou, X.; Robins, A.M.; Paushter, D.H.; Kim, D.; Smolka, M.B.; Hu, F. The ALS/FTLD associated protein C9orf72 associates with SMCR8 and WDR41 to regulate the autophagy-lysosome pathway. Acta Neuropathol. Commun., 2016, 4(1), 51.
[http://dx.doi.org/10.1186/s40478-016-0324-5] [PMID: 27193190]
[22]
Russell, C.L.; Mitra, V.; Hansson, K.; Blennow, K.; Gobom, J.; Zetterberg, H.; Hiltunen, M.; Ward, M.; Pike, I. Comprehensive Quantitative Profiling of Tau and Phosphorylated Tau Peptides in Cerebrospinal Fluid by Mass Spectrometry Provides New Biomarker Candidates. J. Alzheimers Dis., 2017, 55(1), 303-313.
[http://dx.doi.org/10.3233/JAD-160633] [PMID: 27636850]
[23]
Plubell, D.L.; Wilmarth, P.A.; Zhao, Y.; Fenton, A.M.; Minnier, J.; Reddy, A.P.; Klimek, J.; Yang, X.; David, L.L.; Pamir, N. Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue. Mol. Cell. Proteomics, 2017, 16(5), 873-890.
[http://dx.doi.org/10.1074/mcp.M116.065524] [PMID: 28325852]
[24]
Craft, G.E.; Chen, A.; Nairn, A.C. Recent advances in quantitative neuroproteomics. Methods, 2013, 61(3), 186-218.
[http://dx.doi.org/10.1016/j.ymeth.2013.04.008] [PMID: 23623823]
[25]
Casey, T.M.; Khan, J.M.; Bringans, S.D.; Koudelka, T.; Takle, P.S.; Downs, R.A.; Livk, A.; Syme, R.A.; Tan, K.C.; Lipscombe, R.J. Analysis of Reproducibility of Proteome Coverage and Quantitation Using Isobaric Mass Tags (iTRAQ and TMT). J. Proteome Res., 2017, 16(2), 384-392.
[http://dx.doi.org/10.1021/acs.jproteome.5b01154] [PMID: 28152591]
[26]
Pichler, P.; Köcher, T.; Holzmann, J.; Mazanek, M.; Taus, T.; Ammerer, G.; Mechtler, K. Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal. Chem., 2010, 82(15), 6549-6558.
[http://dx.doi.org/10.1021/ac100890k] [PMID: 20593797]
[27]
Pottiez, G.; Wiederin, J.; Fox, H.S.; Ciborowski, P. Comparison of 4-plex to 8-plex iTRAQ quantitative measurements of proteins in human plasma samples. J. Proteome Res., 2012, 11(7), 3774-3781.
[http://dx.doi.org/10.1021/pr300414z] [PMID: 22594965]
[28]
Eckert, A.; Schulz, K.L.; Rhein, V.; Götz, J. Convergence of amyloid-beta and tau pathologies on mitochondria in vivo. Mol. Neurobiol., 2010, 41(2-3), 107-114.
[http://dx.doi.org/10.1007/s12035-010-8109-5] [PMID: 20217279]
[29]
Xu, J. Patassini, S.; Rustogi, N.; Riba-Garcia, I.; Hale, B.D.; Phillips, A.M.; Waldvogel, H.; Haines, R.; Bradbury, Phil.; Stevens, Adam.; Faull, R.L.M.; Dowsey, A.W.; Cooper, G.J.S.; Unwin, R.D. Regional protein expression in human Alzheimer’s brain correlates with disease severity. Commun. Biol., 2019, 2(1)
[30]
Manavalan, A.; Mishra, M.; Feng, L.; Sze, S.K.; Akatsu, H.; Heese, K. Brain site-specific proteome changes in aging-related dementia. Exp. Mol. Med., 2013, 45, e39.
[http://dx.doi.org/10.1038/emm.2013.76] [PMID: 24008896]
[31]
Sepulveda-Falla, D.; Matschke, J.; Bernreuther, C.; Hagel, C.; Puig, B.; Villegas, A.; Garcia, G.; Zea, J.; Gomez-Mancilla, B.; Ferrer, I.; Lopera, F.; Glatzel, M. Deposition of hyperphosphorylated tau in cerebellum of PS1 E280A Alzheimer’s disease. Brain Pathol., 2011, 21(4), 452-463.
[http://dx.doi.org/10.1111/j.1750-3639.2010.00469.x] [PMID: 21159009]
[32]
Li, N.; Hu, P.; Xu, T.; Chen, H.; Chen, X.; Hu, J.; Yang, X.; Shi, L.; Luo, J.H.; Xu, J. iTRAQ-based Proteomic Analysis of APPSw,Ind Mice Provides Insights into the Early Changes in Alzheimer’s Disease. Curr. Alzheimer Res., 2017, 14(10), 1109-1122.
[http://dx.doi.org/10.2174/1567205014666170719165745] [PMID: 28730955]
[33]
Martin, B.; Brenneman, R.; Becker, K.G.; Gucek, M.; Cole, R.N.; Maudsley, S. iTRAQ analysis of complex proteome alterations in 3xTgAD Alzheimer’s mice: understanding the interface between physiology and disease. PLoS One, 2008, 3(7), e2750.
[http://dx.doi.org/10.1371/journal.pone.0002750] [PMID: 18648646]
[34]
Adav, S.S.; Park, J.E.; Sze, S.K. Quantitative profiling brain proteomes revealed mitochondrial dysfunction in Alzheimer’s disease. Mol. Brain, 2019, 12(1), 8.
[http://dx.doi.org/10.1186/s13041-019-0430-y] [PMID: 30691479]
[35]
Dey, K.K.; Wang, H.; Niu, M.; Bai, B.; Wang, X.; Li, Y.; Cho, J.H.; Tan, H.; Mishra, A.; High, A.A.; Chen, P.C.; Wu, Z.; Beach, T.G.; Peng, J. Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer’s disease. Clin. Proteomics, 2019, 16, 16.
[http://dx.doi.org/10.1186/s12014-019-9237-1] [PMID: 31019427]
[36]
Thygesen, C.; Metaxas, A.; Larsen, M.R.; Finsen, B.; Jürgen, G. Age-Dependent Changes in the Sarkosyl-Insoluble Proteome of APPSWE/PS1ΔE9 Transgenic Mice Implicate Dysfunctional Mitochondria in the Pathogenesis of Alzheimer’s Disease. J. Alzheimers Dis., 2018, 64(4), 1247-1259.
[http://dx.doi.org/10.3233/JAD-180197] [PMID: 29991132]
[37]
Rhein, V.; Song, X.; Wiesner, A.; Ittner, L.M.; Baysang, G.; Meier, F.; Ozmen, L.; Bluethmann, H.; Dröse, S.; Brandt, U.; Savaskan, E.; Czech, C.; Götz, J.; Eckert, A. Amyloid-beta and tau synergistically impair the oxidative phosphorylation system in triple transgenic Alzheimer’s disease mice. Proc. Natl. Acad. Sci. USA, 2009, 106(47), 20057-20062.
[http://dx.doi.org/10.1073/pnas.0905529106] [PMID: 19897719]
[38]
Bennett, S.; Grant, M.; Creese, A.J.; Mangialasche, F.; Cecchetti, R.; Cooper, H.J.; Mecocci, P.; Aldred, S. Plasma levels of complement 4a protein are increased in Alzheimer’s disease. Alzheimer Dis. Assoc. Disord., 2012, 26(4), 329-334.
[http://dx.doi.org/10.1097/WAD.0b013e318239dcbd] [PMID: 22052466]
[39]
Akiyama, H.; Barger, S.; Barnum, S.; Bradt, B.; Bauer, J.; Cole, G.M.; Cooper, N.R.; Eikelenboom, P.; Emmerling, M.; Fiebich, B.L.; Finch, C.E.; Frautschy, S.; Griffin, W.S.; Hampel, H.; Hull, M.; Landreth, G.; Lue, L.; Mrak, R.; Mackenzie, I.R.; McGeer, P.L.; O’Banion, M.K.; Pachter, J.; Pasinetti, G.; Plata-Salaman, C.; Rogers, J.; Rydel, R.; Shen, Y.; Streit, W.; Strohmeyer, R.; Tooyoma, I.; Van Muiswinkel, F.L.; Veerhuis, R.; Walker, D.; Webster, S.; Wegrzyniak, B.; Wenk, G.; Wyss-Coray, T. Inflammation and Alzheimer’s disease. Neurobiol. Aging, 2000, 21(3), 383-421.
[http://dx.doi.org/10.1016/S0197-4580(00)00124-X] [PMID: 10858586]
[40]
Johnson Erik, C.B.; Dammer Eric, B.; Duong Duc, M.; Yin, L. Thambisetty Madhav, Troncoso Juan C, Lah James J, Levey Allan I, Seyfried Nicholas T. Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease. Mol. Neurodegener., 2018, 13(1)
[41]
Jayasena, T.; Poljak, A.; Braidy, N.; Smythe, G.; Raftery, M.; Hill, M.; Brodaty, H.; Trollor, J.; Kochan, N.; Sachdev, P. Upregulation of glycolytic enzymes, mitochondrial dysfunction and increased cytotoxicity in glial cells treated with Alzheimer’s disease plasma. PLoS One, 2015, 10(3), e0116092.
[http://dx.doi.org/10.1371/journal.pone.0116092] [PMID: 25785936]
[42]
Minjarez, B.; Calderón-González, K.G.; Rustarazo, M.L.; Herrera-Aguirre, M.E.; Labra-Barrios, M.L.; Rincon-Limas, D.E.; Del Pino, M.M.; Mena, R.; Luna-Arias, J.P. Identification of proteins that are differentially expressed in brains with Alzheimer’s disease using iTRAQ labeling and tandem mass spectrometry. J. Proteomics, 2016, 139, 103-121.
[http://dx.doi.org/10.1016/j.jprot.2016.03.022] [PMID: 27012543]
[43]
Skorobogatko, Y.V.; Deuso, J.; Adolf-Bryfogle, J.; Nowak, M.G.; Gong, Y.; Lippa, C.F.; Vosseller, K. Human Alzheimer’s disease synaptic O-GlcNAc site mapping and iTRAQ expression proteomics with ion trap mass spectrometry. Amino Acids, 2011, 40(3), 765-779.
[http://dx.doi.org/10.1007/s00726-010-0645-9] [PMID: 20563614]
[44]
Zhang, K.; Schrag, M.; Crofton, A.; Trivedi, R.; Vinters, H.; Kirsch, W. Targeted proteomics for quantification of histone acetylation in Alzheimer’s disease. Proteomics, 2012, 12(8), 1261-1268.
[http://dx.doi.org/10.1002/pmic.201200010] [PMID: 22577027]
[45]
Shen, L.; Liao, L.; Chen, C.; Guo, Y.; Song, D.; Wang, Y.; Chen, Y.; Zhang, K.; Ying, M.; Li, S.; Liu, Q.; Ni, J. Proteomics Analysis of Blood Serums from Alzheimer’s Disease Patients Using iTRAQ Labeling Technology. J. Alzheimers Dis., 2017, 56(1), 361-378.
[http://dx.doi.org/10.3233/JAD-160913] [PMID: 27911324]
[46]
Muenchhoff, J.; Poljak, A.; Song, F.; Raftery, M.; Brodaty, H.; Duncan, M.; McEvoy, M.; Attia, J.; Schofield, P.W.; Sachdev, P.S. Plasma protein profiling of mild cognitive impairment and Alzheimer’s disease across two independent cohorts. J. Alzheimers Dis., 2015, 43(4), 1355-1373.
[http://dx.doi.org/10.3233/JAD-141266] [PMID: 25159666]
[47]
Fei, S. Poljak Anne, Kochan Nicole A, Raftery Mark, Brodaty Henry, Smythe George A, Sachdev Perminder S. Plasma protein profiling of Mild Cognitive Impairment and Alzheimer’s disease using iTRAQ quantitative proteomics. Proteome Sci., 2014, 12, 5.
[http://dx.doi.org/10.1186/1477-5956-12-5]
[48]
Yao, F.; Hong, X.; Li, S.; Zhang, Y.; Zhao, Q.; Du, W.; Wang, Y.; Ni, J. Urine-Based Biomarkers for Alzheimer’s Disease Identified Through Coupling Computational and Experimental Methods. J. Alzheimers Dis., 2018, 65(2), 421-431.
[http://dx.doi.org/10.3233/JAD-180261] [PMID: 30040720]
[49]
Tokuoka, S.M.; Kita, Y.; Shimizu, T.; Oda, Y.; Yoshiya, O. Isobaric mass tagging and triple quadrupole mass spectrometry to determine lipid biomarker candidates for Alzheimer’s disease. PLoS One, 2019, 14(12), e0226073.
[http://dx.doi.org/10.1371/journal.pone.0226073] [PMID: 31821352]
[50]
Wang, H.; Wang, Y.; Hong, X.; Li, S.; Wang, Y. Quantitative Proteomics Reveals the Mechanism of Oxygen Treatment on Lenses of Alzheimer’s Disease Model Mice. J. Alzheimers Dis., 2016, 54(1), 275-286.
[http://dx.doi.org/10.3233/JAD-160263] [PMID: 27567828]
[51]
Wang, H.; Hong, X.; Wang, Y. Mitochondrial Repair Effects of Oxygen Treatment on Alzheimer’s Disease Model Mice Revealed by Quantitative Proteomics. J. Alzheimers Dis., 2017, 56(3), 875-883.
[http://dx.doi.org/10.3233/JAD-161010] [PMID: 28059791]
[52]
Dominiak, A.; Wilkaniec, A.; Wroczyński, P.; Adamczyk, A. Selenium in the Therapy of Neurological Diseases. Where is it Going? Curr. Neuropharmacol., 2016, 14(3), 282-299.
[http://dx.doi.org/10.2174/1570159X14666151223100011] [PMID: 26549649]
[53]
Kesse-Guyot, E.; Fezeu, L.; Jeandel, C.; Ferry, M.; Andreeva, V.; Amieva, H.; Hercberg, S.; Galan, P. French adults’ cognitive performance after daily supplementation with antioxidant vitamins and minerals at nutritional doses: a post hoc analysis of the Supplementation in Vitamins and Mineral Antioxidants (SU.VI.MAX) trial. Am. J. Clin. Nutr., 2011, 94(3), 892-899.
[http://dx.doi.org/10.3945/ajcn.110.007815] [PMID: 21775560]
[54]
Iqbal, J.; Zhang, K.; Jin, N.; Zhao, Y.; Liu, Q.; Ni, J.; Shen, L. Effect of Sodium Selenate on Hippocampal Proteome of 3×Tg-AD Mice-Exploring the Antioxidant Dogma of Selenium against Alzheimer’s Disease. ACS Chem. Neurosci., 2018, 9(7), 1637-1651.
[http://dx.doi.org/10.1021/acschemneuro.8b00034] [PMID: 29641182]
[55]
Zhang, J.; Sui, J.; Ching, C.B.; Chen, W.N. Protein profile in neuroblastoma cells incubated with S- and R-enantiomers of ibuprofen by iTRAQ-coupled 2-D LC-MS/MS analysis: possible action of induced proteins on Alzheimer’s disease. Proteomics, 2008, 8(8), 1595-1607.
[http://dx.doi.org/10.1002/pmic.200700556] [PMID: 18351690]
[56]
Liu, T; Zhang, X. Z; Han, J. X; Nie, K. Using bioinformatics tools to explore cellular biological mechanisms of "Triple Energizer Acupuncture Method" in treating senile dementia 2019, 44(6), 424-6.
[57]
Yang, J.W.; Wang, X.R.; Zhang, M.; Xiao, L.Y.; Zhu, W.; Ji, C.S.; Liu, C.Z. Acupuncture as a multifunctional neuroprotective therapy ameliorates cognitive impairment in a rat model of vascular dementia: A quantitative iTRAQ proteomics study. CNS Neurosci. Ther., 2018, 24(12), 1264-1274.
[http://dx.doi.org/10.1111/cns.13063] [PMID: 30278105]
[58]
Iqbal, J.; Zhang, K.; Jin, N.; Zhao, Y.; Liu, X.; Liu, Q.; Ni, J.; Shen, L. Alzheimer’s Disease Is Responsible for Progressive Age-Dependent Differential Expression of Various Protein Cascades in Retina of Mice. ACS Chem. Neurosci., 2019, 10(5), 2418-2433.
[http://dx.doi.org/10.1021/acschemneuro.8b00710] [PMID: 30695639]
[59]
Rudrabhatla, P.; Grant, P.; Jaffe, H.; Strong, M.J.; Pant, H.C. Quantitative phosphoproteomic analysis of neuronal intermediate filament proteins (NF-M/H) in Alzheimer’s disease by iTRAQ. FASEB J., 2010, 24(11), 4396-4407.
[http://dx.doi.org/10.1096/fj.10-157859] [PMID: 20624930]
[60]
Gallart-Palau, X.; Lee, B.S.; Adav, S.S.; Qian, J.; Serra, A.; Park, J.E.; Lai, M.K.; Chen, C.P.; Kalaria, R.N.; Sze, S.K. Gender differences in white matter pathology and mitochondrial dysfunction in Alzheimer’s disease with cerebrovascular disease. Mol. Brain, 2016, 9, 27.
[http://dx.doi.org/10.1186/s13041-016-0205-7] [PMID: 26983404]
[61]
Wang, P.; Joberty, G.; Buist, A.; Vanoosthuyse, A.; Stancu, I.C.; Vasconcelos, B.; Pierrot, N.; Faelth-Savitski, M.; Kienlen-Campard, P.; Octave, J.N.; Bantscheff, M.; Drewes, G.; Moechars, D.; Dewachter, I. Tau interactome mapping based identification of Otub1 as Tau deubiquitinase involved in accumulation of pathological Tau forms in vitro and in vivo. Acta Neuropathol., 2017, 133(5), 731-749.
[http://dx.doi.org/10.1007/s00401-016-1663-9] [PMID: 28083634]
[62]
Ferreira, E.; Shaw, D.M.; Oddo, S. Identification of learning-induced changes in protein networks in the hippocampi of a mouse model of Alzheimer’s disease. Transl. Psychiatry, 2016, 6(7), e849.
[http://dx.doi.org/10.1038/tp.2016.114] [PMID: 27378549]
[63]
Abdi, F.; Quinn, J.F.; Jankovic, J.; McIntosh, M.; Leverenz, J.B.; Peskind, E.; Nixon, R.; Nutt, J.; Chung, K.; Zabetian, C.; Samii, A.; Lin, M.; Hattan, S.; Pan, C.; Wang, Y.; Jin, J.; Zhu, D.; Li, G.J.; Liu, Y.; Waichunas, D.; Montine, T.J.; Zhang, J. Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders. J. Alzheimers Dis., 2006, 9(3), 293-348.
[http://dx.doi.org/10.3233/JAD-2006-9309] [PMID: 16914840]
[64]
Sathe, G.; Na, C.H.; Renuse, S.; Madugundu, A.K.; Albert, M.; Moghekar, A.; Pandey, A. Quantitative Proteomic Profiling of Cerebrospinal Fluid to Identify Candidate Biomarkers for Alzheimer’s Disease. Proteomics Clin. Appl., 2019, 13(4), e1800105.
[http://dx.doi.org/10.1002/prca.201800105] [PMID: 30578620]
[65]
Russell, C.L.; Heslegrave, A.; Mitra, V.; Zetterberg, H.; Pocock, J.M.; Ward, M.A.; Pike, I. Combined tissue and fluid proteomics with Tandem Mass Tags to identify low-abundance protein biomarkers of disease in peripheral body fluid: An Alzheimer’s Disease case study. Rapid Commun. Mass Spectrom., 2017, 31(2), 153-159.
[http://dx.doi.org/10.1002/rcm.7777] [PMID: 27813239]
[66]
Schapira, A.H.; Jenner, P. Etiology and pathogenesis of Parkinson’s disease. Mov. Disord., 2011, 26(6), 1049-1055.
[http://dx.doi.org/10.1002/mds.23732] [PMID: 21626550]
[67]
Zhang, X.; Yin, X.; Yu, H.; Liu, X.; Yang, F.; Yao, J.; Jin, H.; Yang, P. Quantitative proteomic analysis of serum proteins in patients with Parkinson’s disease using an isobaric tag for relative and absolute quantification labeling, two-dimensional liquid chromatography, and tandem mass spectrometry. Analyst (Lond.), 2012, 137(2), 490-495.
[http://dx.doi.org/10.1039/C1AN15551B] [PMID: 22108571]
[68]
Witkowski, A.; Chan, G.K.L.; Boatz, J.C.; Li, N.J.; Inoue, A.P.; Wong, J.C.; van der Wel, P.C.A.; Cavigiolio, G. Methionine oxidized apolipoprotein A-I at the crossroads of HDL biogenesis and amyloid formation. FASEB J., 2018, 32(6), 3149-3165.
[http://dx.doi.org/10.1096/fj.201701127R] [PMID: 29401604]
[69]
Córsico, B.; Toledo, J.D.; Garda, H.A. Evidence for a central apolipoprotein A-I domain loosely bound to lipids in discoidal lipoproteins that is capable of penetrating the bilayer of phospholipid vesicles. J. Biol. Chem., 2001, 276(20), 16978-16985.
[http://dx.doi.org/10.1074/jbc.M011533200] [PMID: 11278925]
[70]
Ji, F.; Sreenivasmurthy, S.G.; Wei, J.; Shao, X.; Luan, H.; Zhu, L.; Song, J.; Liu, L.; Li, M.; Cai, Z. Study of BDE-47 induced Parkinson’s disease-like metabolic changes in C57BL/6 mice by integrated metabolomic, lipidomic and proteomic analysis. J. Hazard. Mater., 2019, 378, 120738.
[http://dx.doi.org/10.1016/j.jhazmat.2019.06.015] [PMID: 31203119]
[71]
Fuller, H.R.; Hurtado, M.L.; Wishart, T.M.; Gates, M.A. The rat striatum responds to nigro-striatal degeneration via the increased expression of proteins associated with growth and regeneration of neuronal circuitry. Proteome Sci., 2014, 12, 20.
[http://dx.doi.org/10.1186/1477-5956-12-20] [PMID: 24834013]
[72]
Lin, X.; Shi, M.; Masilamoni, J.G.; Dator, R.; Movius, J.; Aro, P.; Smith, Y.; Zhang, J. Proteomic profiling in MPTP monkey model for early Parkinson disease biomarker discovery. Biochim. Biophys. Acta, 2015, 1854(7), 779-787.
[http://dx.doi.org/10.1016/j.bbapap.2015.01.007] [PMID: 25617661]
[73]
Li, X.Z.; Zhang, S.N.; Wang, K.X.; Liu, S.M.; Lu, F. iTRAQ-based quantitative proteomics study on the neuroprotective effects of extract of Acanthopanax senticosus harm on SH-SY5Y cells overexpressing A53T mutant α-synuclein. Neurochem. Int., 2014, 72, 37-47.
[http://dx.doi.org/10.1016/j.neuint.2014.04.012] [PMID: 24795107]
[74]
Gupta, A.K.; Pokhriyal, R.; Khan, M.I.; Kumar, D.R.; Gupta, R.; Chadda, R.K.; Ramachandran, R.; Goyal, V.; Tripathi, M.; Hariprasad, G. Cerebrospinal Fluid Proteomics For Identification Of α2-Macroglobulin As A Potential Biomarker To Monitor Pharmacological Therapeutic Efficacy In Dopamine Dictated Disease States Of Parkinson’s Disease And Schizophrenia. Neuropsychiatr. Dis. Treat., 2019, 15, 2853-2867.
[http://dx.doi.org/10.2147/NDT.S214217] [PMID: 31632033]
[75]
Ping, L.; Duong, D.M.; Yin, L.; Gearing, M.; Lah, J.J.; Levey, A.I.; Seyfried, N.T. Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease. Sci. Data, 2018, 5, 180036.
[http://dx.doi.org/10.1038/sdata.2018.36] [PMID: 29533394]
[76]
Dong, M.X.; Feng, X.; Xu, X.M.; Hu, L.; Liu, Y.; Jia, S.Y.; Li, B.; Chen, W.; Wei, Y.D. Integrated Analysis Reveals Altered Lipid and Glucose Metabolism and Identifies NOTCH2 as a Biomarker for Parkinson’s Disease Related Depression. Front. Mol. Neurosci., 2018, 11, 257.
[http://dx.doi.org/10.3389/fnmol.2018.00257] [PMID: 30233306]
[77]
Lehnert, S.; Jesse, S.; Rist, W.; Steinacker, P.; Soininen, H.; Herukka, S.K.; Tumani, H.; Lenter, M.; Oeckl, P.; Ferger, B.; Hengerer, B.; Otto, M. iTRAQ and multiple reaction monitoring as proteomic tools for biomarker search in cerebrospinal fluid of patients with Parkinson’s disease dementia. Exp. Neurol., 2012, 234(2), 499-505.
[http://dx.doi.org/10.1016/j.expneurol.2012.01.024] [PMID: 22327139]
[78]
Li, K.W.; Ganz, A.B.; Smit, A.B. Proteomics of neurodegenerative diseases: analysis of human post-mortem brain. J. Neurochem., 2019, 151(4), 435-445.
[http://dx.doi.org/10.1111/jnc.14603] [PMID: 30289976]
[79]
Chen, Y.; Liu, X.H.; Wu, J.J.; Ren, H.M.; Wang, J.; Ding, Z.T.; Jiang, Y.P. Proteomic analysis of cerebrospinal fluid in amyotrophic lateral sclerosis. Exp. Ther. Med., 2016, 11(6), 2095-2106.
[http://dx.doi.org/10.3892/etm.2016.3210] [PMID: 27284291]
[80]
Tank, E.M.; Figueroa-Romero, C.; Hinder, L.M.; Bedi, K.; Archbold, H.C.; Li, X.; Weskamp, K.; Safren, N.; Paez-Colasante, X.; Pacut, C.; Thumma, S.; Paulsen, M.T.; Guo, K.; Hur, J.; Ljungman, M.; Feldman, E.L.; Barmada, S.J. Abnormal RNA stability in amyotrophic lateral sclerosis. Nat. Commun., 2018, 9(1), 2845.
[http://dx.doi.org/10.1038/s41467-018-05049-z] [PMID: 30030424]
[81]
Yin, S.; Lopez-Gonzalez, R.; Kunz, R.C.; Gangopadhyay, J.; Borufka, C.; Gygi, S.P.; Gao, F.B.; Reed, R. Evidence that C9ORF72 Dipeptide Repeat Proteins Associate with U2 snRNP to Cause Mis-splicing in ALS/FTD Patients. Cell Rep., 2017, 19(11), 2244-2256.
[http://dx.doi.org/10.1016/j.celrep.2017.05.056] [PMID: 28614712]
[82]
Sharma, A.; Varghese, A.M.; Vijaylakshmi, K.; Sumitha, R.; Prasanna, V.K.; Shruthi, S.; Chandrasekhar Sagar, B.K.; Datta, K.K.; Gowda, H.; Nalini, A.; Alladi, P.A.; Christopher, R.; Sathyaprabha, T.N.; Raju, T.R.; Srinivas Bharath, M.M. Cerebrospinal Fluid from Sporadic Amyotrophic Lateral Sclerosis Patients Induces Mitochondrial and Lysosomal Dysfunction. Neurochem. Res., 2016, 41(5), 965-984.
[http://dx.doi.org/10.1007/s11064-015-1779-7] [PMID: 26646005]
[83]
Zhang, J.; Huang, P.; Wu, C.; Liang, H.; Li, Y.; Zhu, L.; Lu, Y.; Tang, C.; Xu, R. Preliminary Observation about Alteration of Proteins and Their Potential Functions in Spinal Cord of SOD1 G93A Transgenic Mice. Int. J. Biol. Sci., 2018, 14(10), 1306-1320.
[http://dx.doi.org/10.7150/ijbs.26829] [PMID: 30123078]
[84]
Varghese, A.M.; Sharma, A.; Mishra, P.; Vijayalakshmi, K.; Harsha, H.C.; Sathyaprabha, T.N.; Bharath, S.M.; Nalini, A.; Alladi, P.A.; Raju, T.R. Chitotriosidase - a putative biomarker for sporadic amyotrophic lateral sclerosis. Clin. Proteomics, 2013, 10(1), 19.
[http://dx.doi.org/10.1186/1559-0275-10-19] [PMID: 24295388]
[85]
Su, D.; Zhang, Y.; Bi, F.; Xiao, B. [Proteomic analysis of the cerebrospinal fluid from patients with amyotrophic lateral sclerosis based on tandem mass spectrometry technique Nan Fang Yi Ke Da Xue Xue Bao, 2019, 39(4), 428-436.
[PMID: 31068286]
[86]
Leoni, E.; Bremang, M.; Mitra, V.; Zubiri, I.; Jung, S.; Lu, C.H.; Adiutori, R.; Lombardi, V.; Russell, C.; Koncarevic, S.; Ward, M.; Pike, I.; Malaspina, A. Combined Tissue-Fluid Proteomics to Unravel Phenotypic Variability in Amyotrophic Lateral Sclerosis. Sci. Rep., 2019, 9(1), 4478.
[http://dx.doi.org/10.1038/s41598-019-40632-4] [PMID: 30872628]
[87]
Zubiri, I.; Lombardi, V.; Bremang, M.; Mitra, V.; Nardo, G.; Adiutori, R.; Lu, C.H.; Leoni, E.; Yip, P.; Yildiz, O.; Ward, M.; Greensmith, L.; Bendotti, C.; Pike, I.; Malaspina, A. Tissue-enhanced plasma proteomic analysis for disease stratification in amyotrophic lateral sclerosis. Mol. Neurodegener., 2018, 13(1), 60.
[http://dx.doi.org/10.1186/s13024-018-0292-2] [PMID: 30404656]
[88]
Zhang, Y.; Burberry, A.; Wang, J.Y.; Sandoe, J.; Ghosh, S.; Udeshi, N.D.; Svinkina, T.; Mordes, D.A.; Mok, J.; Charlton, M.; Li, Q.Z.; Carr, S.A.; Eggan, K. The C9orf72-interacting protein Smcr8 is a negative regulator of autoimmunity and lysosomal exocytosis. Genes Dev., 2018, 32(13-14), 929-943.
[http://dx.doi.org/10.1101/gad.313932.118] [PMID: 29950492]
[89]
Correale, J.; Gaitán, M.I.; Ysrraelit, M.C.; Fiol, M.P. Progressive multiple sclerosis: from pathogenic mechanisms to treatment. Brain, 2017, 140(3), 527-546.
[PMID: 27794524]
[90]
Hasan, M.; Min, H.; Rahaman, K.A.; Muresan, A.R.; Kim, H.; Han, D.; Kwon, O.S. Quantitative Proteome Analysis of Brain Subregions and Spinal Cord from Experimental Autoimmune Encephalomyelitis Mice by TMT-Based Mass Spectrometry. Proteomics, 2019, 19(5), e1800355.
[http://dx.doi.org/10.1002/pmic.201800355] [PMID: 30724464]
[91]
Kroksveen, A.C.; Jaffe, J.D.; Aasebø, E.; Barsnes, H.; Bjørlykke, Y.; Franciotta, D.; Keshishian, H.; Myhr, K.M.; Opsahl, J.A.; van Pesch, V.; Teunissen, C.E.; Torkildsen, Ø.; Ulvik, R.J.; Vethe, H.; Carr, S.A.; Berven, F.S. Quantitative proteomics suggests decrease in the secretogranin-1 cerebrospinal fluid levels during the disease course of multiple sclerosis. Proteomics, 2015, 15(19), 3361-3369.
[http://dx.doi.org/10.1002/pmic.201400142] [PMID: 26152395]
[92]
Kroksveen, A.C.; Guldbrandsen, A.; Vaudel, M.; Lereim, R.R.; Barsnes, H.; Myhr, K.M.; Torkildsen, Ø.; Berven, F.S. In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis. J. Proteome Res., 2017, 16(1), 179-194.
[http://dx.doi.org/10.1021/acs.jproteome.6b00659] [PMID: 27728768]
[93]
A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell, 1993, 72(6), 971-983.
[http://dx.doi.org/10.1016/0092-8674(93)90585-E] [PMID: 8458085]
[94]
Ratovitski, T.; Chaerkady, R.; Kammers, K.; Stewart, J.C.; Zavala, A.; Pletnikova, O.; Troncoso, J.C.; Rudnicki, D.D.; Margolis, R.L.; Cole, R.N.; Ross, C.A. Quantitative Proteomic Analysis Reveals Similarities between Huntington’s Disease (HD) and Huntington’s Disease-Like 2 (HDL2) Human Brains. J. Proteome Res., 2016, 15(9), 3266-3283.
[http://dx.doi.org/10.1021/acs.jproteome.6b00448] [PMID: 27486686]
[95]
Datta, A.; Chai, Y.L.; Tan, J.M.; Lee, J.H.; Francis, P.T.; Chen, C.P.; Sze, S.K.; Lai, M.K.P. An iTRAQ-based proteomic analysis reveals dysregulation of neocortical synaptopodin in Lewy body dementias. Mol. Brain, 2017, 10(1), 36.
[http://dx.doi.org/10.1186/s13041-017-0316-9] [PMID: 28800743]
[96]
McKeith, I. Commentary: DLB and PDD: the same or different? Is there a debate? Int. Psychogeriatr., 2009, 21(2), 220-224.
[http://dx.doi.org/10.1017/S1041610208008624] [PMID: 19173763]
[97]
Manix, M.; Kalakoti, P.; Henry, M.; Thakur, J.; Menger, R.; Guthikonda, B.; Nanda, A. Creutzfeldt-Jakob disease: updated diagnostic criteria, treatment algorithm, and the utility of brain biopsy. Neurosurg. Focus, 2015, 39(5), E2.
[http://dx.doi.org/10.3171/2015.8.FOCUS15328 ] [PMID: 26646926]
[98]
Chen, C.; Xiao, D.; Zhou, W.; Shi, Q.; Zhang, H.F.; Zhang, J.; Tian, C.; Zhang, J.Z.; Dong, X.P. Global protein differential expression profiling of cerebrospinal fluid samples pooled from Chinese sporadic CJD and non-CJD patients. Mol. Neurobiol., 2014, 49(1), 290-302.
[http://dx.doi.org/10.1007/s12035-013-8519-2 ] [PMID: 23912784]
[99]
Chen, L.N.; Shi, Q.; Zhang, B.Y.; Zhang, X.M.; Wang, J.; Xiao, K.; Lv, Y.; Sun, J.; Yang, X.D.; Chen, C.; Zhou, W.; Han, J.; Dong, X.P. Proteomic Analyses for the Global S-Nitrosylated Proteins in the Brain Tissues of Different Human Prion Diseases. Mol. Neurobiol., 2016, 53(8), 5079-5096.
[http://dx.doi.org/10.1007/s12035-015-9440-7 ] [PMID: 26392294]
[100]
Wang, C.; Zhao, D.; Shah, S.Z.A.; Yang, W.; Li, C.; Yang, L. Proteome Analysis of Potential Synaptic Vesicle Cycle Biomarkers in the Cerebrospinal Fluid of Patients with Sporadic Creutzfeldt- Jakob Disease. Mol. Neurobiol., 2017, 54(7), 5177-5191.
[http://dx.doi.org/10.1007/s12035-016-0029-6] [PMID: 27562179]
[101]
Kroksveen, A.C.; Aasebø, E.; Vethe, H.; Van Pesch, V.; Franciotta, D.; Teunissen, C.E.; Ulvik, R.J.; Vedeler, C.; Myhr, K.M.; Barsnes, H.; Berven, F.S. Discovery and initial verification of differentially abundant proteins between multiple sclerosis patients and controls using iTRAQ and SID-SRM. J. Proteomics, 2013, 78, 312-325.
[http://dx.doi.org/10.1016/j.jprot.2012.09.037 ] [PMID: 23059536]
[102]
Ferreira, P.G.; Muñoz-Aguirre, M.; Reverter, F.; Sá Godinho, C.P.; Sousa, A.; Amadoz, A.; Sodaei, R.; Hidalgo, M.R.; Pervouchine, D.; Carbonell-Caballero, J.; Nurtdinov, R.; Breschi, A.; Amador, R.; Oliveira, P.; Çubuk, C.; Curado, J.; Aguet, F.; Oliveira, C.; Dopazo, J.; Sammeth, M.; Ardlie, K.G.; Guigó, R. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun., 2018, 9(1), 490.
[http://dx.doi.org/10.1038/s41467-017-02772-x ] [PMID: 29440659]
[103]
ElHajj, Z.; Cachot, A.; Müller, T.; Riederer, I.M.; Riederer, B.M. Effects of postmortem delays on protein composition and oxidation. Brain Res. Bull., 2016, 121, 98-104.
[http://dx.doi.org/10.1016/j.brainresbull.2016.01.005] [PMID: 26791740]
[104]
Kolla, V.; Jenö, P.; Moes, S.; Tercanli, S.; Lapaire, O.; Choolani, M.; Hahn, S. Quantitative proteomics analysis of maternal plasma in Down syndrome pregnancies using isobaric tagging reagent (iTRAQ). J. Biomed. Biotechnol., 2010, 2010, 952047.
[http://dx.doi.org/10.1155/2010/952047] [PMID: 19902006]

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