RNA-Seq Data Mining: Downregulation of NeuroD6 Serves as a Possible Biomarker for Alzheimer’s Disease Brains
Abstract
Alzheimer’s disease (AD) is the most common cause of dementia worldwide with no curative therapies currently available. Previously, global transcriptome analysis of AD brains by microarray failed to identify the set of consistently deregulated genes for biomarker development of AD. Therefore, the molecular pathogenesis of AD remains largely unknown. Whole RNA sequencing (RNA-Seq) is an innovative technology for the comprehensive transcriptome profiling on a genome-wide scale that overcomes several drawbacks of the microarray-based approach. To identify biomarker genes for AD, we analyzed a RNA-Seq dataset composed of the comprehensive transcriptome of autopsized AD brains derived from two independent cohorts. We identified the core set of 522 genes deregulated in AD brains shared between both, compared with normal control subjects. They included downregulation of neuronal differentiation 6 (NeuroD6), a basic helix-loop-helix (bHLH) transcription factor involved in neuronal development, differentiation, and survival in AD brains of both cohorts. We verified the results of RNA-Seq by analyzing three microarray datasets of AD brains different in brain regions, ethnicities, and microarray platforms. Thus, both RNA-Seq and microarray data analysis indicated consistent downregulation of NeuroD6 in AD brains. These results suggested that downregulation of NeuroD6 serves as a possible biomarker for AD brains.
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Intersection of the microglia signature gene list with 46 publicly available data sets for differentially regulated genes in neurodegenerative and behavioral disorders revealed significant enrichment or depletion of microglia signature genes in 28 of the 46 data sets.
“We then intersected this gene list with 46 publicly available data sets for differentially regulated genes in neurodegenerative and behavioral disorders derived from microarray or RNA-seq analysis of intact tissue (39–53). Two-sided Fisher’s exact tests revealed significant enrichment or depletion of microglia signature genes in 28 of the 46 data sets (Table S3).”
From An environment-dependent transcriptional network specifies human microglia identity (2017) · cites this paper as [46]
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Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006) · Acta Neuropathologica · doi:10.1007/s00401-006-0127-z · Cited by 3,172 · see citing works
A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline
Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., Stefansson, H., Sulem, P., Gudbjartsson, D., Maloney, J., et al. (2012) · Nature · doi:10.1038/nature11283 · Cited by 1,738 · see citing works
Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease
Cruchaga, C., Karch, C. M., Jin, S. C., Benitez, B. A., Cai, Y., Guerreiro, R., Harari, O., Norton, J., Budde, J., Bertelsen, S., et al. (2014) · Nature · doi:10.1038/nature12825 · Cited by 461 · see citing works
U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer’s disease
Bai, B., Hales, C. M., Chen, P.-C., Gozal, Y., Dammer, E. B., Fritz, J. J., Wang, X., Xia, Q., Duong, D. M., Street, C., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310249110 · Cited by 335 · see citing works
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Research works printed but not linked: 30
- [1]Gatz M. Reynolds C. A. Fratiglioni L. Johansson B. Mortimer J. A. Berg S. Fiske A. Pedersen N. L. Role of genes and environments for explaining Alzheimer disease Archives of General Psychiatry 2006 63 2 168 174 10.1001/archpsyc.63.2.168 2-s2.0-32244435907 16461860 · DOI · Paper page
- [2]Walton J. R. Aluminum's involvement in the progression of alzheimer's disease Journal of Alzheimer's Disease 2013 35 1 7 43 10.3233/JAD-121909 2-s2.0-84876375186 23380995 · DOI
- [3]Squitti R. Siotto M. Polimanti R. Low-copper diet as a preventive strategy for Alzheimer's disease Neurobiology of Aging 2014 35 2 S40 S50 10.1016/j.neurobiolaging.2014.02.031 2-s2.0-84901740202 24913894 · DOI
- [4]Rosenthal S. L. Kamboth M. I. Late-onset Alzheimer’s disease genes and the potentially implicated pathways Current Genetic Medicine Reports 2014 2 85 101 24829845 PMC4013444
- [6]Guerreiro R. Wojtas A. Bras J. Carrasquillo M. Rogaeva E. Majounie E. Cruchaga C. Sassi C. Kauwe J. S. K. Younkin S. Hazrati L. Collinge J. Pocock J. Lashley T. Williams J. Lambert J.-C. Amouyel P. Goate A. Rademakers R. Morgan K. Powell J. George-Hyslop P. S. Singleton A. Hardy J. TREM2 variants in Alzheimer's disease The New England Journal of Medicine 2013 368 2 117 127 10.1056/NEJMoa1211851 2-s2.0-84872057940 23150934 PMC3631573 · DOI · Paper page
- [8]Cooper-Knock J. Kirby J. Ferraiuolo L. Heath P. R. Rattray M. Shaw P. J. Gene expression profiling in human neurodegenerative disease Nature Reviews Neurology 2012 8 9 518 530 10.1038/nrneurol.2012.156 2-s2.0-84866062627 22890216 · DOI
- [9]Ginsberg S. D. Hemby S. E. Lee V. M. Eberwine J. H. Trojanowski J. Q. Expression profile of transcripts in Alzheimer’s disease tangle-bearing CA1 neurons Annals of Neurology 2000 48 1 77 87 10894219
- [10]Colangelo V. Schurr J. Ball M. J. Pelaez R. P. Bazan N. G. Lukiw W. J. Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling Journal of Neuroscience Research 2002 70 3 462 473 10.1002/jnr.10351 2-s2.0-0037010289 12391607 · DOI · Paper page
- [11]Katsel P. Li C. Haroutunian V. Gene expression alterations in the sphingolipid metabolism pathways during progression of dementia and Alzheimer's disease: a shift toward ceramide accumulation at the earliest recognizable stages of Alzheimer's disease? Neurochemical Research 2007 32 4-5 845 856 10.1007/s11064-007-9297-x 2-s2.0-33947577504 17342407 · DOI
- [12]Williams C. Shai R. M. Wu Y. Hsu Y.-H. Sitzer T. Spann B. McCleary C. Mo Y. Miller C. A. Transcriptome analysis of synaptoneurosomes identifies neuroplasticity genes overexpressed in incipient Alzheimer's disease PLoS ONE 2009 4 3 e4936 2-s2.0-63349107477 10.1371/journal.pone.0004936 PMC2654156 19295912 · DOI · Paper page
- [13]Chen J. J. Hsueh H.-M. Delongchamp R. R. Lin C.-J. Tsai C.-A. Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data BMC Bioinformatics 2007 8, article 412 10.1186/1471-2105-8-412 2-s2.0-38349088960 PMC2204045 17961233 · DOI
- [14]Wang Z. Gerstein M. Snyder M. RNA-Seq: a revolutionary tool for transcriptomics Nature Reviews Genetics 2009 10 1 57 63 10.1038/nrg2484 2-s2.0-57749195712 PMC2949280 19015660 · DOI · Paper page
- [15]Sutherland G. T. Janitz M. Kril J. J. Understanding the pathogenesis of Alzheimer's disease: will RNA-Seq realize the promise of transcriptomics? Journal of Neurochemistry 2011 116 6 937 946 10.1111/j.1471-4159.2010.07157.x 2-s2.0-79952446517 21175619 · DOI
- [18]Blalock E. M. Geddes J. W. Chen K. C. Porter N. M. Markesbery W. R. Landfield P. W. Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses Proceedings of the National Academy of Sciences of the United States of America 2004 101 7 2173 2178 2-s2.0-1242341923 10.1073/pnas.0308512100 14769913 PMC357071 · DOI · Paper page
- [19]Liang W. S. Reiman E. M. Valla J. Dunckley T. Beach T. G. Grover A. Niedzielko T. L. Schneider L. E. Mastroeni D. Caselli R. Kukull W. Morris J. C. Hulette C. M. Schmechel D. Rogers J. Stephan D. A. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons Proceedings of the National Academy of Sciences of the United States of America 2008 105 11 4441 4446 2-s2.0-41949086089 10.1073/pnas.0709259105 18332434 PMC2393743 · DOI · Paper page
- [20]Hokama M. Oka S. Leon J. Altered expression of diabetes-related genes in Alzheimer’s disease brains: the Hisayama study Cerebral Cortex 2014 24 9 2476 2488 10.1093/cercor/bht101 23595620 PMC4128707 · DOI
- [21]Huang D. W. Sherman B. T. Lempicki R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nature Protocols 2009 4 1 44 57 10.1038/nprot.2008.211 2-s2.0-61449172037 19131956 · DOI · Paper page
- [22]Kanehisa M. Goto S. Sato Y. Kawashima M. Furumichi M. Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG Nucleic Acids Research 2014 42 1 D199 D205 10.1093/nar/gkt1076 2-s2.0-84891760956 24214961 PMC3965122 · DOI
- [23]Schwab M. H. Druffel-Augustin S. Gass P. Neuronal basic helix-loop-helix proteins (NEX, neuroD, NDRF): spatiotemporal expression and targeted disruption of the NEX gene in transgenic mice The Journal of Neuroscience 1998 18 4 1408 1418 2-s2.0-0032519872 9454850 PMC6792741
- [24]Yamada M. Shida Y. Takahashi K. Tanioka T. Nakano Y. Tobe T. Yamada M. Prg1 is regulated by the basic helix-loop-helix transcription factor Math2 Journal of Neurochemistry 2008 106 6 2375 2384 10.1111/j.1471-4159.2008.05579.x 2-s2.0-50849109446 18643870 · DOI
- [25]Bartholomä A. Nave K.-A. NEX-1: a novel brain-specific helix-loop-helix protein with autoregulation and sustained expression in mature cortical neurons Mechanisms of Development 1994 48 3 217 228 10.1016/0925-4773(94)90061-2 2-s2.0-0028116808 7545978 · DOI
- [26]Schwab M. H. Bartholomae A. Heimrich B. Feldmeyer D. Druffel-Augustin S. Goebbels S. Naya F. J. Zhao S. Frotscher M. Tsai M.-J. Nave K.-A. Neuronal basic helix-loop-helix proteins (NEX and BETA2/Neuro D) regulate terminal granule cell differentiation in the hippocampus Journal of Neuroscience 2000 20 10 3714 3724 2-s2.0-0034657665 10804213 PMC6772686
- [27]Wu S.-X. Goebbels S. Nakamura K. Kometani K. Minato N. Kaneko T. Nave K.-A. Tamamaki N. Pyramidal neurons of upper cortical layers generated by NEX-positive progenitor cells in the subventricular zone Proceedings of the National Academy of Sciences of the United States of America 2005 102 47 17172 17177 2-s2.0-28044442918 10.1073/pnas.0508560102 16284248 PMC1288007 · DOI · Paper page
- [28]Bormuth I. Yan K. Yonemasu T. Gummert M. Zhang M. Wichert S. Grishina O. Pieper A. Zhang W. Goebbels S. Tarabykin V. Nave K.-A. Schwab M. H. Neuronal basic helix-loop-helix proteins neurod2/6 regulate cortical commissure formation before midline interactions The Journal of Neuroscience 2013 33 2 641 651 10.1523/JNEUROSCI.0899-12.2013 2-s2.0-84872072593 23303943 PMC6704922 · DOI
- [29]Kay J. N. Voinescu P. E. Chu M. W. Sanes J. R. Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate Nature Neuroscience 2011 14 8 965 972 10.1038/nn.2859 2-s2.0-79960843532 21743471 PMC3144989 · DOI
- [30]Uittenbogaard M. Chiaramello A. Constitutive overexpression of the basic helix-loop-helix Nex1/MATH-2 transcription factor promotes neuronal differentiation of PC12 cells and neurite regeneration Journal of Neuroscience Research 2002 67 2 235 245 2-s2.0-0037080366 10.1002/jnr.10119 11782967 PMC2758487 · DOI
- [31]Uittenbogaard M. Chiaramello A. The basic helix-loop-helix transcription factor Nex-1/Math-2 promotes neuronal survival of PC12 cells by modulating the dynamic expression of anti-apoptotic and cell cycle regulators Journal of Neurochemistry 2005 92 3 585 596 10.1111/j.1471-4159.2004.02886.x 2-s2.0-13244284716 15659228 PMC1411982 · DOI
- [32]Uittenbogaard M. Baxter K. K. Chiaramello A. The neurogenic basic helix-loop-helix transcription factor NeuroD6 confers tolerance to oxidative stress by triggering an antioxidant response and sustaining the mitochondrial biomass ASN Neuro 2010 2 2 e00034 2-s2.0-84855723040 PMC2874871 20517466
- [33]Kang H. J. Kawasawa Y. I. Cheng F. Zhu Y. Xu X. Li M. Sousa A. M. M. Pletikos M. Meyer K. A. Sedmak G. Guennel T. Shin Y. Johnson M. B. Krsnik Ž. Mayer S. Fertuzinhos S. Umlauf S. Lisgo S. N. Vortmeyer A. Weinberger D. R. Mane S. Hyde T. M. Huttner A. Reimers M. Kleinman J. E. Šestan N. Spatio-temporal transcriptome of the human brain Nature 2011 478 7370 483 489 10.1038/nature10523 2-s2.0-80054993342 22031440 PMC3566780 · DOI · Paper page
- [34]Uittenbogaard M. Martinka D. L. Chiaramello A. The basic helix-loop-helix differentiation factor Nex1/MATH-2 functions as a key activator of the GAP-43 gene Journal of Neurochemistry 2003 84 4 678 688 10.1046/j.1471-4159.2003.01572.x 2-s2.0-0037312837 12562512 PMC1413589 · DOI
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- Authors
- Satoh, J.-I., Yamamoto, Y., Asahina, N., Kitano, S., & Kino, Y.
- Affiliations
- Department of Bioinformatics and Molecular Neuropathology, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan
- Venue
- Disease Markers
- Publisher
- Wiley
- Published
- 2014
- Volume
- 2014
- Pages
- 1-10
- ISSN
- 0278-0240
- 1875-8630
- PMC
- PMC4274867
- Cited by
- 46
- References
- 4
- Language
- en
- Funders
- Japan Society for the Promotion of Science (C22500322, C25430054, NCGC26-20)
- National Center for Geriatrics and Gerontology (C22500322, C25430054, NCGC26-20)
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