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Published in: Molecular Neurodegeneration 1/2018

Open Access 01-12-2018 | Research article

Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Authors: Erik C. B. Johnson, Eric B. Dammer, Duc M. Duong, Luming Yin, Madhav Thambisetty, Juan C. Troncoso, James J. Lah, Allan I. Levey, Nicholas T. Seyfried

Published in: Molecular Neurodegeneration | Issue 1/2018

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Abstract

Background

The complicated cellular and biochemical changes that occur in brain during Alzheimer’s disease are poorly understood. In a previous study we used an unbiased label-free quantitative mass spectrometry-based proteomic approach to analyze these changes at a systems level in post-mortem cortical tissue from patients with Alzheimer’s disease (AD), asymptomatic Alzheimer’s disease (AsymAD), and controls. We found modules of co-expressed proteins that correlated with AD phenotypes, some of which were enriched in proteins identified as risk factors for AD by genetic studies.

Methods

The amount of information that can be obtained from such systems-level proteomic analyses is critically dependent upon the number of proteins that can be quantified across a cohort. We report here a new proteomic systems-level analysis of AD brain based on 6,533 proteins measured across AD, AsymAD, and controls using an analysis pipeline consisting of isobaric tandem mass tag (TMT) mass spectrometry and offline prefractionation.

Results

Our new TMT pipeline allowed us to more than double the depth of brain proteome coverage. This increased depth of coverage greatly expanded the brain protein network to reveal new protein modules that correlated with disease and were unrelated to those identified in our previous network. Differential protein abundance analysis identified 350 proteins that had altered levels between AsymAD and AD not caused by changes in specific cell type abundance, potentially reflecting biochemical changes that are associated with cognitive decline in AD. RNA binding proteins emerged as a class of proteins altered between AsymAD and AD, and were enriched in network modules that correlated with AD pathology. We developed a proteogenomic approach to investigate RNA splicing events that may be altered by RNA binding protein changes in AD. The increased proteome depth afforded by our TMT pipeline allowed us to identify and quantify a large number of alternatively spliced protein isoforms in brain, including AD risk factors such as BIN1, PICALM, PTK2B, and FERMT2. Many of the new AD protein network modules were enriched in alternatively spliced proteins and correlated with molecular markers of AD pathology and cognition.

Conclusions

Further analysis of the AD brain proteome will continue to yield new insights into the biological basis of AD.
Appendix
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Literature
1.
go back to reference Prince M, Wimo A, Guerchet M, Ali G, Wu Y, Prina M. World Alzheimer report 2015: the global impact of dementia. In book world Alzheimer report 2015: the global impact of dementia (editor ed.^eds.). City: Alzheimer's Disease International; 2015. Prince M, Wimo A, Guerchet M, Ali G, Wu Y, Prina M. World Alzheimer report 2015: the global impact of dementia. In book world Alzheimer report 2015: the global impact of dementia (editor ed.^eds.). City: Alzheimer's Disease International; 2015.
2.
go back to reference Huan T, Zhang B, Wang Z, Joehanes R, Zhu J, Johnson AD, Ying S, Munson PJ, Raghavachari N, Wang R, et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33:1427–34.CrossRef Huan T, Zhang B, Wang Z, Joehanes R, Zhu J, Johnson AD, Ying S, Munson PJ, Raghavachari N, Wang R, et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33:1427–34.CrossRef
3.
go back to reference Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008;28:1410–20.CrossRef Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008;28:1410–20.CrossRef
4.
go back to reference 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;11:1271–82.CrossRef 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;11:1271–82.CrossRef
5.
go back to reference Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer's disease. Cell Syst. 2017;4:60–72 e64.CrossRef Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, et al. A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer's disease. Cell Syst. 2017;4:60–72 e64.CrossRef
6.
go back to reference O'Brien RJ, Resnick SM, Zonderman AB, Ferrucci L, Crain BJ, Pletnikova O, Rudow G, Iacono D, Riudavets MA, Driscoll I, et al. Neuropathologic studies of the Baltimore longitudinal study of aging (BLSA). J Alzheimers Dis. 2009;18:665–75.CrossRef O'Brien RJ, Resnick SM, Zonderman AB, Ferrucci L, Crain BJ, Pletnikova O, Rudow G, Iacono D, Riudavets MA, Driscoll I, et al. Neuropathologic studies of the Baltimore longitudinal study of aging (BLSA). J Alzheimers Dis. 2009;18:665–75.CrossRef
7.
go back to reference Gillet LC, Leitner A, Aebersold R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu Rev Anal Chem (Palo Alto, Calif). 2016;9:449–72.CrossRef Gillet LC, Leitner A, Aebersold R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu Rev Anal Chem (Palo Alto, Calif). 2016;9:449–72.CrossRef
8.
go back to reference Ting L, Rad R, Gygi SP, Haas W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods. 2011;8:937–40.CrossRef Ting L, Rad R, Gygi SP, Haas W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods. 2011;8:937–40.CrossRef
9.
go back to reference Rauniyar N, Yates JR 3rd. Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res. 2014;13:5293–309.CrossRef Rauniyar N, Yates JR 3rd. Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res. 2014;13:5293–309.CrossRef
10.
go back to reference Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT: Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease. Nature Scientific Data 2018. Ping L, Duong DM, Yin L, Gearing M, Lah JJ, Levey AI, Seyfried NT: Global quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease. Nature Scientific Data 2018.
11.
go back to reference Rangaraju S, Dammer EB, Raza SA, Gao T, Xiao H, Betarbet R, Duong DM, Webster JA, Hales CM, Lah JJ, et al. Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimer's disease-related proteins. Mol Neurodegener. 2018;13:34.CrossRef Rangaraju S, Dammer EB, Raza SA, Gao T, Xiao H, Betarbet R, Duong DM, Webster JA, Hales CM, Lah JJ, et al. Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimer's disease-related proteins. Mol Neurodegener. 2018;13:34.CrossRef
12.
go back to reference Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45:1452–8.CrossRef Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45:1452–8.CrossRef
13.
go back to reference Wingo TS, Duong DM, Zhou M, Dammer EB, Wu H, Cutler DJ, Lah JJ, Levey AI, Seyfried NT. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J Proteome Res. 2017;16:3336–47.CrossRef Wingo TS, Duong DM, Zhou M, Dammer EB, Wu H, Cutler DJ, Lah JJ, Levey AI, Seyfried NT. Integrating next-generation genomic sequencing and mass spectrometry to estimate allele-specific protein abundance in human brain. J Proteome Res. 2017;16:3336–47.CrossRef
14.
go back to reference McAlister GC, Nusinow DP, Jedrychowski MP, Wuhr M, Huttlin EL, Erickson BK, Rad R, Haas W, Gygi SP. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem. 2014;86:7150–8.CrossRef McAlister GC, Nusinow DP, Jedrychowski MP, Wuhr M, Huttlin EL, Erickson BK, Rad R, Haas W, Gygi SP. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem. 2014;86:7150–8.CrossRef
15.
go back to reference Kall L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007;4:923–5.CrossRef Kall L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007;4:923–5.CrossRef
16.
go back to reference Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.CrossRef Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.CrossRef
17.
go back to reference Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N, Kongi K, Cantuti L, Hanisch UK, Philips MA, et al. Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci. 2015;18:1819–31.CrossRef Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N, Kongi K, Cantuti L, Hanisch UK, Philips MA, et al. Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci. 2015;18:1819–31.CrossRef
18.
go back to reference Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/bioconductor package biomaRt. Nat Protoc. 2009;4:1184–91.CrossRef Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/bioconductor package biomaRt. Nat Protoc. 2009;4:1184–91.CrossRef
19.
go back to reference Zhong Y, Wan YW, Pang K, Chow LM, Liu Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics. 2013;14:89.CrossRef Zhong Y, Wan YW, Pang K, Chow LM, Liu Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics. 2013;14:89.CrossRef
20.
go back to reference Zambon AC, Gaj S, Ho I, Hanspers K, Vranizan K, Evelo CT, Conklin BR, Pico AR, Salomonis N. GO-elite: a flexible solution for pathway and ontology over-representation. Bioinformatics. 2012;28:2209–10.CrossRef Zambon AC, Gaj S, Ho I, Hanspers K, Vranizan K, Evelo CT, Conklin BR, Pico AR, Salomonis N. GO-elite: a flexible solution for pathway and ontology over-representation. Bioinformatics. 2012;28:2209–10.CrossRef
21.
go back to reference Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5:e13984.CrossRef Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5:e13984.CrossRef
22.
go back to reference de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.CrossRef de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.CrossRef
23.
go back to reference Wu TD, Reeder J, Lawrence M, Becker G, Brauer MJ. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. Methods Mol Biol. 2016;1418:283–334.CrossRef Wu TD, Reeder J, Lawrence M, Becker G, Brauer MJ. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. Methods Mol Biol. 2016;1418:283–334.CrossRef
24.
go back to reference Bai B, Hales CM, Chen PC, Gozal Y, Dammer EB, Fritz JJ, Wang X, Xia Q, Duong DM, Street C, et al. U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A. 2013;110:16562–7.CrossRef Bai B, Hales CM, Chen PC, Gozal Y, Dammer EB, Fritz JJ, Wang X, Xia Q, Duong DM, Street C, et al. U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease. Proc Natl Acad Sci U S A. 2013;110:16562–7.CrossRef
25.
go back to reference Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC, et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet. 2017;49:1373–84.CrossRef Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC, et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet. 2017;49:1373–84.CrossRef
26.
go back to reference Jack CR, Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ: Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 2010, 9:119–128.CrossRef Jack CR, Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ: Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 2010, 9:119–128.CrossRef
27.
go back to reference Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, Bakardjian H, Benali H, Bertram L, Blennow K, et al. Preclinical Alzheimer's disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12:292–323.CrossRef Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, Bakardjian H, Benali H, Bertram L, Blennow K, et al. Preclinical Alzheimer's disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12:292–323.CrossRef
28.
go back to reference Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr, Kaye J, Montine TJ, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:280–92.CrossRef Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr, Kaye J, Montine TJ, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:280–92.CrossRef
29.
go back to reference Wang L, Oh WK, Zhu J. Disease-specific classification using deconvoluted whole blood gene expression. Sci Rep. 2016;6:32976.CrossRef Wang L, Oh WK, Zhu J. Disease-specific classification using deconvoluted whole blood gene expression. Sci Rep. 2016;6:32976.CrossRef
30.
go back to reference Allen M, Wang X, Burgess JD, Watzlawik J, Serie DJ, Younkin CS, Nguyen T, Malphrus KG, Lincoln S, Carrasquillo MM, et al. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases. Alzheimers Dement. 2018;14:352–66.CrossRef Allen M, Wang X, Burgess JD, Watzlawik J, Serie DJ, Younkin CS, Nguyen T, Malphrus KG, Lincoln S, Carrasquillo MM, et al. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases. Alzheimers Dement. 2018;14:352–66.CrossRef
31.
go back to reference De Strooper B, Karran E. The cellular phase of Alzheimer's disease. Cell. 2016;164:603–15.CrossRef De Strooper B, Karran E. The cellular phase of Alzheimer's disease. Cell. 2016;164:603–15.CrossRef
32.
go back to reference Hales CM, Dammer EB, Deng Q, Duong DM, Gearing M, Troncoso JC, Thambisetty M, Lah JJ, Shulman JM, Levey AI, Seyfried NT. Changes in the detergent-insoluble brain proteome linked to amyloid and tau in Alzheimer's disease progression. Proteomics. 2016;16:3042–53.CrossRef Hales CM, Dammer EB, Deng Q, Duong DM, Gearing M, Troncoso JC, Thambisetty M, Lah JJ, Shulman JM, Levey AI, Seyfried NT. Changes in the detergent-insoluble brain proteome linked to amyloid and tau in Alzheimer's disease progression. Proteomics. 2016;16:3042–53.CrossRef
33.
go back to reference Hales CM, Dammer EB, Diner I, Yi H, Seyfried NT, Gearing M, Glass JD, Montine TJ, Levey AI, Lah JJ. Aggregates of small nuclear ribonucleic acids (snRNAs) in Alzheimer's disease. Brain Pathol. 2014;24:344–51.CrossRef Hales CM, Dammer EB, Diner I, Yi H, Seyfried NT, Gearing M, Glass JD, Montine TJ, Levey AI, Lah JJ. Aggregates of small nuclear ribonucleic acids (snRNAs) in Alzheimer's disease. Brain Pathol. 2014;24:344–51.CrossRef
34.
go back to reference Hales CM, Seyfried NT, Dammer EB, Duong D, Yi H, Gearing M, Troncoso JC, Mufson EJ, Thambisetty M, Levey AI, Lah JJ. U1 small nuclear ribonucleoproteins (snRNPs) aggregate in Alzheimer's disease due to autosomal dominant genetic mutations and trisomy 21. Mol Neurodegener. 2014;9:15.CrossRef Hales CM, Seyfried NT, Dammer EB, Duong D, Yi H, Gearing M, Troncoso JC, Mufson EJ, Thambisetty M, Levey AI, Lah JJ. U1 small nuclear ribonucleoproteins (snRNPs) aggregate in Alzheimer's disease due to autosomal dominant genetic mutations and trisomy 21. Mol Neurodegener. 2014;9:15.CrossRef
35.
go back to reference Ash PE, Vanderweyde TE, Youmans KL, Apicco DJ, Wolozin B. Pathological stress granules in Alzheimer's disease. Brain Res. 2014;1584:52–8.CrossRef Ash PE, Vanderweyde TE, Youmans KL, Apicco DJ, Wolozin B. Pathological stress granules in Alzheimer's disease. Brain Res. 2014;1584:52–8.CrossRef
36.
go back to reference Maziuk BF, Apicco DJ, Cruz AL, Jiang L, Ash PEA, da Rocha EL, Zhang C, Yu WH, Leszyk J, Abisambra JF, et al. RNA binding proteins co-localize with small tau inclusions in tauopathy. Acta Neuropathol Commun. 2018;6:71.CrossRef Maziuk BF, Apicco DJ, Cruz AL, Jiang L, Ash PEA, da Rocha EL, Zhang C, Yu WH, Leszyk J, Abisambra JF, et al. RNA binding proteins co-localize with small tau inclusions in tauopathy. Acta Neuropathol Commun. 2018;6:71.CrossRef
37.
go back to reference Vanderweyde T, Yu H, Varnum M, Liu-Yesucevitz L, Citro A, Ikezu T, Duff K, Wolozin B. Contrasting pathology of the stress granule proteins TIA-1 and G3BP in tauopathies. J Neurosci. 2012;32:8270–83.CrossRef Vanderweyde T, Yu H, Varnum M, Liu-Yesucevitz L, Citro A, Ikezu T, Duff K, Wolozin B. Contrasting pathology of the stress granule proteins TIA-1 and G3BP in tauopathies. J Neurosci. 2012;32:8270–83.CrossRef
38.
go back to reference Hampel H, Blennow K, Shaw LM, Hoessler YC, Zetterberg H, Trojanowski JQ. Total and phosphorylated tau protein as biological markers of Alzheimer's disease. Exp Gerontol. 2010;45:30–40.CrossRef Hampel H, Blennow K, Shaw LM, Hoessler YC, Zetterberg H, Trojanowski JQ. Total and phosphorylated tau protein as biological markers of Alzheimer's disease. Exp Gerontol. 2010;45:30–40.CrossRef
39.
go back to reference Davison EJ, Pennington K, Hung CC, Peng J, Rafiq R, Ostareck-Lederer A, Ostareck DH, Ardley HC, Banks RE, Robinson PA. Proteomic analysis of increased Parkin expression and its interactants provides evidence for a role in modulation of mitochondrial function. Proteomics. 2009;9:4284–97.CrossRef Davison EJ, Pennington K, Hung CC, Peng J, Rafiq R, Ostareck-Lederer A, Ostareck DH, Ardley HC, Banks RE, Robinson PA. Proteomic analysis of increased Parkin expression and its interactants provides evidence for a role in modulation of mitochondrial function. Proteomics. 2009;9:4284–97.CrossRef
40.
go back to reference Hu A, Noble WS, Wolf-Yadlin A: Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 2016, 5. Hu A, Noble WS, Wolf-Yadlin A: Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 2016, 5.
41.
go back to reference Umoh ME, Dammer EB, Dai J, Duong DM, Lah JJ, Levey AI, Gearing M, Glass JD, Seyfried NT. A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol Med. 2018;10:48–62.CrossRef Umoh ME, Dammer EB, Dai J, Duong DM, Lah JJ, Levey AI, Gearing M, Glass JD, Seyfried NT. A proteomic network approach across the ALS-FTD disease spectrum resolves clinical phenotypes and genetic vulnerability in human brain. EMBO Mol Med. 2018;10:48–62.CrossRef
42.
go back to reference Bellucci A, Bugiani O, Ghetti B, Spillantini MG. Presence of reactive microglia and neuroinflammatory mediators in a case of frontotemporal dementia with P301S mutation. Neurodegener Dis. 2011;8:221–9.CrossRef Bellucci A, Bugiani O, Ghetti B, Spillantini MG. Presence of reactive microglia and neuroinflammatory mediators in a case of frontotemporal dementia with P301S mutation. Neurodegener Dis. 2011;8:221–9.CrossRef
43.
go back to reference Cherry JD, Tripodis Y, Alvarez VE, Huber B, Kiernan PT, Daneshvar DH, Mez J, Montenigro PH, Solomon TM, Alosco ML, et al. Microglial neuroinflammation contributes to tau accumulation in chronic traumatic encephalopathy. Acta Neuropathol Commun. 2016;4:112.CrossRef Cherry JD, Tripodis Y, Alvarez VE, Huber B, Kiernan PT, Daneshvar DH, Mez J, Montenigro PH, Solomon TM, Alosco ML, et al. Microglial neuroinflammation contributes to tau accumulation in chronic traumatic encephalopathy. Acta Neuropathol Commun. 2016;4:112.CrossRef
44.
go back to reference Maphis N, Xu G, Kokiko-Cochran ON, Jiang S, Cardona A, Ransohoff RM, Lamb BT, Bhaskar K. Reactive microglia drive tau pathology and contribute to the spreading of pathological tau in the brain. Brain. 2015;138:1738–55.CrossRef Maphis N, Xu G, Kokiko-Cochran ON, Jiang S, Cardona A, Ransohoff RM, Lamb BT, Bhaskar K. Reactive microglia drive tau pathology and contribute to the spreading of pathological tau in the brain. Brain. 2015;138:1738–55.CrossRef
45.
go back to reference Yoshiyama Y, Higuchi M, Zhang B, Huang SM, Iwata N, Saido TC, Maeda J, Suhara T, Trojanowski JQ, Lee VM. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron. 2007;53:337–51.CrossRef Yoshiyama Y, Higuchi M, Zhang B, Huang SM, Iwata N, Saido TC, Maeda J, Suhara T, Trojanowski JQ, Lee VM. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron. 2007;53:337–51.CrossRef
46.
go back to reference Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ, Castellani RJ, Crain BJ, Davies P, Del Tredici K, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71:362–81.CrossRef Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ, Castellani RJ, Crain BJ, Davies P, Del Tredici K, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71:362–81.CrossRef
47.
go back to reference McKenzie AT, Moyon S, Wang M, Katsyv I, Song WM, Zhou X, Dammer EB, Duong DM, Aaker J, Zhao Y, et al. Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener. 2017;12:82.CrossRef McKenzie AT, Moyon S, Wang M, Katsyv I, Song WM, Zhou X, Dammer EB, Duong DM, Aaker J, Zhao Y, et al. Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener. 2017;12:82.CrossRef
48.
go back to reference Kato M, Han TW, Xie S, Shi K, Du X, Wu LC, Mirzaei H, Goldsmith EJ, Longgood J, Pei J, et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell. 2012;149:753–67.CrossRef Kato M, Han TW, Xie S, Shi K, Du X, Wu LC, Mirzaei H, Goldsmith EJ, Longgood J, Pei J, et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell. 2012;149:753–67.CrossRef
49.
go back to reference King OD, Gitler AD, Shorter J. The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 2012;1462:61–80.CrossRef King OD, Gitler AD, Shorter J. The tip of the iceberg: RNA-binding proteins with prion-like domains in neurodegenerative disease. Brain Res. 2012;1462:61–80.CrossRef
50.
go back to reference Yeo G, Holste D, Kreiman G, Burge CB. Variation in alternative splicing across human tissues. Genome Biol. 2004;5.CrossRef Yeo G, Holste D, Kreiman G, Burge CB. Variation in alternative splicing across human tissues. Genome Biol. 2004;5.CrossRef
51.
go back to reference Wang X, Codreanu SG, Wen B, Li K, Chambers MC, Liebler DC, Zhang B. Detection of proteome diversity resulted from alternative splicing is limited by trypsin cleavage specificity. Mol Cell Proteomics. 2018;17:422–30.CrossRef Wang X, Codreanu SG, Wen B, Li K, Chambers MC, Liebler DC, Zhang B. Detection of proteome diversity resulted from alternative splicing is limited by trypsin cleavage specificity. Mol Cell Proteomics. 2018;17:422–30.CrossRef
52.
go back to reference Raj T, Li Y, Wong G, Ramdhani S, Wang Y-c, Ng B, Wang M, Gupta I, Haroutunian V, Zhang B, et al: Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer's disease. bioRxiv 2017. Raj T, Li Y, Wong G, Ramdhani S, Wang Y-c, Ng B, Wang M, Gupta I, Haroutunian V, Zhang B, et al: Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer's disease. bioRxiv 2017.
53.
go back to reference Serrano-Pozo A, Qian J, Muzikansky A, Monsell SE, Montine TJ, Frosch MP, Betensky RA, Hyman BT. Thal amyloid stages do not significantly impact the correlation between Neuropathological change and cognition in the Alzheimer disease continuum. J Neuropathol Exp Neurol. 2016;75:516–26.CrossRef Serrano-Pozo A, Qian J, Muzikansky A, Monsell SE, Montine TJ, Frosch MP, Betensky RA, Hyman BT. Thal amyloid stages do not significantly impact the correlation between Neuropathological change and cognition in the Alzheimer disease continuum. J Neuropathol Exp Neurol. 2016;75:516–26.CrossRef
54.
go back to reference Bishof I, Dammer EB, Duong DM, Kundinger SR, Gearing M, Lah JJ, Levey AI, Seyfried NT. RNA-binding proteins with basic-acidic dipeptide (BAD) domains self-assemble and aggregate in Alzheimer's disease. J Biol Chem. 2018;293:11047–66.CrossRef Bishof I, Dammer EB, Duong DM, Kundinger SR, Gearing M, Lah JJ, Levey AI, Seyfried NT. RNA-binding proteins with basic-acidic dipeptide (BAD) domains self-assemble and aggregate in Alzheimer's disease. J Biol Chem. 2018;293:11047–66.CrossRef
Metadata
Title
Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease
Authors
Erik C. B. Johnson
Eric B. Dammer
Duc M. Duong
Luming Yin
Madhav Thambisetty
Juan C. Troncoso
James J. Lah
Allan I. Levey
Nicholas T. Seyfried
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Molecular Neurodegeneration / Issue 1/2018
Electronic ISSN: 1750-1326
DOI
https://doi.org/10.1186/s13024-018-0282-4

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