Printer Friendly

RNA-Seq data mining: downregulation of NeuroD6 serves as a possible biomarker for Alzheimer's disease brains.

1. Introduction

Alzheimer's disease (AD) is the most common cause of dementia worldwide affecting the elderly population, characterized by the hallmark pathology of amyloid-[beta] (A[beta]) deposition, neurofibrillary tangle (NFT) formation, and extensive neurodegeneration in the brain. The complex interaction between multiple genetic and environmental factors affecting various molecular pathways plays a keyrole in the pathogenesis of AD [1]. With regard to environmental factors, disturbed homeostasis of dietary metals, such as copper, aluminum, and iron, confers an increased risk of AD [2, 3]. With respect to genetic factors, genome-wide association studies (GWAS), composed of large cohorts of AD and controls, identified numerous common variants but with smaller risks associated with development of late-onset AD [4]. They include complement component receptor 1 (CR1), bridging integrator 1 (BIN1), clusterin (CLU), phosphatidylinositol binding clathrin assembly protein (PICALM), membrane-spanning 4-domains, subfamily A, member 4A/membrane-spanning 4-domains, subfamily A, member 6E (MS4A4/MS4A6E), CD2-associated protein (CD2AP), CD33 molecule (CD33), EPH receptor A1 (EPHA1), and ATP-binding cassette, subfamily A, member 7 (ABCA7) [4]. More recently, wholeexome sequencing (WES) studies discovered rare functional variants located in the genes encoding A[beta] precursor protein (APP), triggering receptor expressed on myeloid cells 2 (TREM2), and phospholipase D3 (PLD3), exhibiting a much greater contribution to protection or development of AD [5-7]. However, at present, the central molecular mechanism underlying neurodegeneration in AD remains largely unknown. Therefore, no curative therapies based on the molecular pathogenesis of AD are currently available.

The completion of the Human Genome Project in 2003 allows us to systematically study disease-associated profiles of the whole human genome. Particularly, microarray technologies enable us not only to identify disease-specific molecular signatures and biomarkers for diagnosis and prediction ofprognosis but also to characterize druggable targets for effective therapy. Actually, global transcriptome analysis of postmortem AD brains by microarray has identified a battery of genes aberrantly regulated in AD, whose role has not been previously predicted in its pathogenesis [8]. They include reduced expression of kinases/phosphatases, cytoskeletal proteins, synaptic proteins, and neurotransmitter receptors in NFT-bearing CA1 neurons [9], downregulation of neurotrophic factors and upregulation of proapoptotic molecules in the hippocampal CA1 region [10], disturbed sphingolipid metabolism in various brain regions during progression of AD [11], and overexpression of the AMPA receptor GluR2 subunit in synaptosomes of the prefrontal cortex [12]. However, previous studies failed to identify the set of definite biomarker genes, whose expression is consistently deregulated in AD brains across different studies [8]. The failure in reproducibility of the results is attributable to differences in study designs and samples, including the quality of RNA, disease stages, brain regions, cellular diversities, ethnicities, and microarray platforms [13].

Recently, the revolution of the next-generation sequencing (NGS) technology has made a great impact on the field of genome research. Whole RNA sequencing (RNA-Seq) serves as an innovative tool for the comprehensive transcriptome profiling on a genome scale in a high-throughput and quantitative manner [14,15]. RNA-Seq clarifies the unbiased expression of the complete set of transcripts at a single base resolution, including splice junctions and fusion genes, by providing digital gene expression levels with high reproducibility. RNA-Seq enables us to characterize the complex transcriptome, composed of mRNAs, noncoding RNAs, and small RNAs, theoretically at a single cell level, by aligning sequencing reads on reference genomes or assembling them de novo without references. For these reasons, RNA-Seq overcomes several drawbacks intrinsic to the microarray-based approach that is hampered by the difficulty in detection of novel transcripts and splice variants, the poor sensitivity of rare transcripts, and high backgrounds due to cross hybridization.

To identify biomarker genes relevant to the molecular pathogenesis of AD, we analyzed publicly available RNA-Seq datasets, composed of the comprehensive transcriptome of autopsied AD brains derived from two independent cohorts. First, we identified the core set of 522 genes deregulated in AD brains overlapping between both. Then, we verified the results of RNA-Seq by analyzing three independent microarray datasets of AD brains that are different in brain regions, ethnicities, and microarray platforms. Consequently, we found consistent downregulation of neuronal differentiation 6 (NeuroD6), a bHLH transcription factor involved in neuronal development and differentiation, serving as a possible biomarker for AD brains.

2. Materials and Methods

2.1. RNA-Seq Datasets of AD Brains. To identify a comprehensive set of differentially expressed genes (DEGs) in the brains of AD patients compared with normal control (NC) subjects, we investigated FASTQ-formatted files of RNA-Seq datasets retrieved from the DDBJ Sequence Read Archive (DRA) (https://trace.ddbj.nig.ac.jp/DRASearch) under the accession number of SRA060572. It consisted of 15 separate samples derived from two independent cohorts, studied by the researchers in Emory University, Atlanta, here abbreviated as EMU, and by those in the University of Kentucky, Lexington, abbreviated as UKY [16]. The EMU dataset contains transcriptome of the frontal cortex isolated from three male and two female AD patients with age = 71.0 [+ or -] 8.2 years and postmortem interval (PMI) = 13.8 [+ or -] 7.2 hours and two male and two female NC subjects with age = 60.8 [+ or -] 3.3 years and PMI = 9.0 [+ or -] 2.6 hours. The UKY dataset contains transcriptome of the frontal cortex isolated from three female AD patients with age = 81.7 [+ or -] 3.2 years and PMI = 2.2 [+ or -] 0.4 hours and three female NC subjects with age = 85.0 [+ or -] 1.0 years and PMI = 2.8 [+ or -] 0.6 hours. The information on the Braak stage of AD pathology [17] is not available for any cases. In these experiments, total RNA was purified by oligo (dT) beads and converted to cDNA for PCR amplification using SMARTer PCR cDNA Synthesis Kit (Clontech). Then, PCR products were fragmented and processed for DNA library preparation via PCR amplification using NEBNext DNA Library Prep Master Mix Set for Illumina (New England BioLabs). The final DNA library products with size ~200bp were prepared for paired-end sequencing on HiSeq 2000 (Illumina).

After removing poly-A tails and low quality reads from the original data, we mapped short read data on the human genome reference sequence hg19 by using TopHat2.0.9 (http://ccb.jhu.edu/software/tophat/index.shtml). The expression levels were transformed into fragments per kilobase of exon per million mapped fragments (FPKM). We identified DEGs that satisfy the significance expressed as q-value representing FDR-adjusted P value < 0.05 by using Cufflinks2.1.1 (http://cufflinks.cbcb.umd.edu).

2.2. Microarray Datasets of AD Brains. To verify the results of RNA-Seq data analysis, we investigated three distinct microarray datasets of AD brains retrieved from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/ geo/) under accession numbers of GSE1297, GSE5281, and GSE11829. The GSE1297 dataset contains transcriptome of postmortem hippocampal CA1 tissues studied on a Human Genome U133A Array containing 22,215 transcripts (Affymetrix), and the data were normalized by the Microarray Analysis Suite 5.0 (MAS5) algorithm [18]. The samples were collected by the researchers in UKY. They were prepared from 31 age-matched individuals, composed of nine NC subjects (age = 85.3 [+ or -] 8.0 years; male = 7, female = 2), seven patients with incipient AD (age = 91.9 [+ or -] 6.2 years; male = 2, female = 5), eight with moderate AD (age = 83.4 [+ or -] 3.2 years; male = 2, female = 6), and seven with severe AD (age = 84.0 [+ or -] 10.6 years; male = 2, female = 5). The clinical stage of AD was defined by the Mini-Mental State Examination (MMSE) score as follows: the control (the score > 25), incipient (20-26), moderate (14-19), and severe (<14) AD. The information on the Braak stage of AD pathology is not available for any cases.

The GSE5281 dataset, alternatively named steph-affy-human 433773, contains transcriptome of laser microdissection (LCM)-captured layer III neurons derived from various brain regions studied on a Human Genome U133 Plus 2.0 Array containing 47,400 transcripts (Affymetrix), and the data were normalized by MAS5 [19]. The samples were collected by the researchers in AD Centers of Arizona, Duke University, and Washington University We studied the gene expression profile of cortical neurons in the superior frontal gyrus, which were isolated from 11 age-matched NC subjects (age = 79.3 [+ or -] 10.2 years; male = 7, female = 4) and 23 AD patients (age = 79.2 [+ or -] 7.5 years; male = 13, female = 10). The information on the Braak stage of AD pathology is not available for any cases.

The GSE36980 dataset contains transcriptome of postmortem brain tissues isolated from frontal and temporal cortices and the hippocampus studied on a Human Gene 1.0 ST Array containing 28,869 genes (Affymetrix), and the data were normalized by the robust multiarray average (RMA) algorithm [20]. The samples were collected by the researchers in Kyushu University, Japan, for the Hisayama study. We studied the gene expression profile of the hippocampus, derived from ten non-AD controls (age = 77.0 [+ or -] 9.0 years; male = 5, female = 5) and seven AD patients (age = 92.9 [+ or -] 6.1 years; male = 3, female = 4). The information on the Braak stage of AD pathology is not available for any cases.

To evaluate the statistically significant difference in gene expression levels between AD and NC or non-AD groups, we performed a two-tailed Welch i-test by using TTEST function of Excel. In some experiments, we performed receiver operating characteristic (ROC) analysis by using SPSS version 19 (IBM).

2.3. Molecular Network Analysis. We imported Entrez Gene IDs of DEGs into the Functional Annotation tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 [21]. DAVID extracts gene ontology (GO) terms enriched in the set of imported genes and identifies relevant pathways constructed by Kyoto Encyclopedia of Genes and Genomes (KEGG). The results are followed by statistical evaluation with the modified Fisher exact test corrected by multiple comparison tests. We considered P value < 0.05 after Bonferroni's correction as significant. KEGG is a publicly accessible knowledgebase that contains 337,524 manuallycurated pathways that cover a wide range of metabolic, genetic, environmental, and cellular processes and human diseases [22].

We also imported Entrez Gene IDs of DEGs into the Core Analysis tool of Ingenuity Pathways Analysis (IPA) (Ingenuity Systems). IPA is a commercial knowledgebase that contains approximately 3,000,000 biological and chemical interactions with definite scientific evidence. By uploading the list of Gene IDs, the network-generation algorithm identifies focused genes integrated in global molecular pathways and networks. IPA calculates the score P value that reflects the statistical significance of association between the genes and the pathways and networks by Fisher's exact test. We considered P value < 0.05 by Fisher's exact test as significant.

3. Results

3.1. RNA-Seq Data Analysis of AD Brains. By RNA-Seq data analysis with the combination of TopHat and Cufflinks, we studied transcriptome of the frontal cortex of AD and NC derived from two distinct cohorts named EMU and UKY. We identified 587,301 and 766,998 consensus transcripts in total from datasets of EMU and UKY, respectively. Among them, we identified 1,226 DEGs for EMU and 2,625 DEGs for UKY that satisfy q-value (FDR-corrected P value) < 0.05 and fold change greater than 2.0 or smaller than 0.5, when compared between AD and NC groups. Then, we extracted the core set of 522 DEGs overlapping between both cohorts, composed of 470 downregulated and 52 upregulated genes in AD (see Supplementary Table 1 of the Supplementary Material available online at http://dx.doi.org/10.1155/2014/123165). Thus, downregulated genes greatly outnumbered upregulated ones in AD brains. Top 20 genes are listed in Table 1. Notably, the expression of neuronal differentiation 6 (NeuroD6), a brain-specific basic helix-loop-helix (bHLH) transcription factor [23], is greatly reduced at fold changes 0.095 for EMU and 0.159 for UKY in AD brains (q = 0.0023 for EMU and 0.0006 for UKY) (Table 1, italicized). Furthermore, lipid phosphate phosphatase-related protein type 4 (LPPR4; PRG1), a direct target gene of NeuroD6 [24], was also downregulated in AD brains of both cohorts (Supplementary Table 1). We identified totally 60 differentially spliced genes in the frontal cortex of AD, when the data derived from both cohorts were combined, although none of them were shared between both (Supplementary Table 2).

DAVID revealed that the set of 470 DEGs downregulated in the frontal cortex of AD are relevant to GO terms of "synaptic transmission" (GO:0007268; P = 2.545E - 17 corrected by Bonferroni) and "transmission of nerve impulse" (GO:0019226; P = 1.778E-15 corrected by Bonferroni). They are also relevant to the KEGG pathway named "neuroactive ligand-receptor interaction" (hsa04080; P = 0.0004 corrected by Bonferroni) (Figure 1). In contrast, the set of 52 genes upregulated in AD were not significantly associated with any GO terms or KEGG pathways. IPA showed that the core set of 522 DEGs have a significant relationship with top two different functional networks defined as "Cell-To-Cell Signaling and Interaction, Nervous System Development and Function, Neurological Disease" (P = 1.00E - 71) and "Hereditary Disorder, Neurological Disease, Psychological Disorders" (P = 1.00E - 71) (Figure 2). Taken together, these results suggest that a battery of the genes essential for neuronal interactions is coordinately downregulated in AD brains.

3.2. Microarray Data Analysis of AD Brains. To verify the results of RNA-Seq data analysis, we studied three distinct microarray datasets of AD brains numbered GSE1297, GSE5281, and GSE36980. We compared the core set of 522 DEGs of RNA-Seq with DEGs extracted from microarray datasets. First, we studied the GSE5281 dataset composed of transcriptome of LCM-captured cortical neurons in the superior frontal gyrus. We identified the set of 215 DEGs compared between AD and NC groups, including 210 downregulated and 5 upregulated genes in AD (Supplementary Table 3). Because downregulated genes greatly outnumbered upregulated classes, we thereafter focused on the downregulated set. Among them, we found that 15 genes correspond to the core set of 522 DEGs of RNA-Seq (Table 2). Notably, the expression of NeuroD6 was reduced at fold change = 0.238 in purified cortical neurons of the superior frontal gyrus in AD brains (P = 0.000066, Table 2, italicized). In contrast, the levels of expression of NeuroD1 were not significantly different between AD and NC brains (P = 0.530, not shown). ROC analysis indicated that the area under the ROC curve (AUC) is 0.893 for NeuroD6 and 0.474 for NeuroD1, and the levels of sensitivity and specificity for discrimination between AD and NC are acceptable for NeuroD6 (P = 2.494E - 04) but unacceptable for NeuroD1 (P = 0.811) (Supplementary Figure 1).

Next, we attempted to answer the question whether downregulation of NeuroD6 serves as a possible biomarker for diagnosis of AD by brain transcriptome profiling, regardless of differences in brain regions, microarray platforms, or ethnicities of samples. We analyzed two more datasets of transcriptome of postmortem hippocampal tissues isolated from Caucasian (GSE1297 on Human Genome U133A Array) or Japanese (GSE36980 on Human Gene 1.0 ST Array) AD patients. From the GSE1297 dataset, we identified the set of 131 DEGs downregulated in the hippocampal CA1 region at fold change of severe AD versus NC < 0.6 (Supplementary Table 4). We found that 25 genes of 131 DEGs correspond to the core set of 522 DEGs of RNA-Seq (Table 3). They also included NeuroD6 whose expression levels are reduced in Caucasian AD brains during progression of AD at fold change = 0.569 for the comparison between severe AD and NC (P = 0.0072, Table 3, italicized). From the GSE11829 dataset, we identified the set of 31 DEGs downregulated in the region-unrestricted hippocampus of Japanese AD patients, compared with non-AD controls (Supplementary Table 5). We found that 12 genes of 31 DEGs correspond to the core set of 522 DEGs of RNA-Seq (Table 4). Again, they included NeuroD6 whose expression levels are reduced in AD brains at fold change = 0.433 for the comparison between AD and non-AD (P = 0.0016, Table 4, italicized). Taken together, these observations suggest that downregulation of NeuroD6 serves as a fairly universal biomarker for diagnosis of AD by brain transcriptome profiling, regardless of differences in brain regions, microarrayplatforms, or ethnicities of samples.

4. Discussion

Previously, a number of microarray-based transcriptome studies of AD brains failed to identify the set of consistently deregulated genes across different studies [8]. RNASeq serves as an innovative technology for the comprehensive transcriptome profiling on a genome scale in a high-throughput and quantitative manner [14, 15]. To identify biomarker genes relevant to the molecular pathogenesis of AD, we first studied publicly available RNA-Seq datasets of AD brain transcriptome derived from two independent cohorts named EMU and UKY. We identified the core set of 522 DEGs consistently deregulated in AD brains of both cohorts. They include 470 downregulated and 52 upregulated genes in AD brains, relevant to synaptic transmission, neuroactive ligand-receptor interaction, nervous system development, and pathological processes of neuropsychiatric diseases by GO and pathway analysis. Then, we compared the results of RNA-Seq data analysis with those of three distinct microarray datasets of AD brains, which are different in brain regions, ethnicities, and microarray platforms. As a result, we identified consistent downregulation of NeuroD6 in AD brains throughout the datasets studied.

The NeuroD family of bHLH transcription factors, composed of three major members, such as NeuroD1 (BETA2), NeuroD2 (NDRF), and NeuroD6 (NEX1, MATH2, and ATOH2), acts as a differentiation factor for neural precursor cells in the developing central nervous system (CNS) [23]. Each member exhibits an overlapping but distinct spatiotemporal expression profile with partially redundant function in the formation of subpopulations of neurons. A previous study by in situ hybridization showed that NeuroD6 is expressed abundantly in mature adult neurons of the cerebral cortex, the hippocampus, and the cerebellum [25]. Although NeuroD6deficient mice exhibit no obvious defect in development, NeuroD1/NeuroD6 double knockout mice show arrest of terminal differentiation of granule cells in the hippocampus [26]. NeuroD6-expressing progenitor cells located in the subventricular zone have a capacity to differentiate into pyramidal glutamatergic neurons in upper cortical layers [27]. Both NeuroD2 and NeuroD6 regulate axonal fasciculation and proper formation of callosal fiber tracts [28]. NeuroD6 plays a key role in cell fate decision of subtypes of amacrine cells in the retina [29]. Constitutive expression of NeuroD6 triggers neuronal differentiation of PC12 cells, originated from a pheochromocytoma of the rat adrenal medulla, without requirement of nerve growth factor (NGF) [30]. NeuroD6 plays a decisive role in the switch from proapoptotic to antiapoptotic pathways during neuronal differentiation of PC12 cells [31]. Furthermore, NeuroD6 confers tolerance to oxidative stress by inducing antioxidant responses and by increasing the mitochondrial biomass [32]. Importantly, NeuroD6, by forming a coexpression network module with TBR1, FEZF2, FOXG1, SATB2, and EMX1, plays a key role in development of the human neocortex and hippocampus projection neurons that are severely degenerated in AD brains [33]. All of these observations suggest that NeuroDo acts as a key regulator of neuronal development, differentiation, and survival.

Previous studies identified LPPR4 and growth associated protein 43 (GAP43) as direct target genes for NeuroDo by binding assay to E-boxes located in target gene promoters [24, 34]. Importantly, we found that the core set of 522 DEGs of RNA-Seq include LPPR as one of the downregulated genes in AD brains of both cohorts, and the study also identified GAP43 as a downregulated gene in AD brains of the UKY cohort (not shown). Although the precise biological role of NeuroDo and its target genes in adult human brains remains unknown, the present observations suggest that downregulation of NeuroDo might be detrimental for neuronal survival under stressful conditions caused by extensive accumulation of extracellular and intracellular NFT in AD.

In conclusion, the present study using bioinformatics data mining approach suggested that downregulation of NeuroD6 serves as a possible biomarker for diagnosis of AD by brain transcriptome profiling. Since the sample sizes we studied are apparently small, these findings warrant further validation in larger cohorts of AD patients and adequate controls performed in a blinded manner.

http://dx.doi.org/ 10.1155/2014/123165

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The present study was supported by the JSPS KAKENHI (C22500322 and C25430054), the Dementia Drug Development Research Center (DRC) Project, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and the grant from the National Center for Geriatrics and Gerontology (NCGC26-20).

References

[1] M. Gatz, C. A. Reynolds, L. Fratiglioni et al., "Role of genes and environments for explaining Alzheimer disease," Archives of General Psychiatry, vol. 63, no. 2, pp. 168-174, 2006.

[2] J. R. Walton, "Aluminum's involvement in the progression of alzheimer's disease," Journal of Alzheimer's Disease, vol. 35, no. 1, pp. 7-43, 2013.

[3] R. Squitti, M. Siotto, and R. Polimanti, "Low-copper diet as a preventive strategy for Alzheimer's disease," Neurobiology of Aging, vol. 35, no. 2, pp. S40-S50, 2014.

[4] S. L. Rosenthal and M. I. Kamboth, "Late-onset Alzheimer's disease genes and the potentially implicated pathways," Current Genetic Medicine Reports, vol. 2, pp. 85-101, 2014.

[5] T. Jonsson, J. K. Atwal, S. Steinberg et al., "A mutation in APP protects against Alzheimer's disease and age-related cognitive decline," Nature, vol. 487, no. 7409, pp. 96-99, 2012.

[6] R. Guerreiro, A. Wojtas, J. Bras et al., "TREM2 variants in Alzheimer's disease," The New England Journal of Medicine, vol. 368, no. 2, pp. 117-127, 2013.

[7] C. Cruchaga, C. M. Karch, S. C. Jin et al., "Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease," Nature, vol. 505, no. 7484, pp. 550-554, 2014.

[8] J. Cooper-Knock, J. Kirby, L. Ferraiuolo, P. R. Heath, M. Rattray, and P. J. Shaw, "Gene expression profiling in human neurodegenerative disease," Nature Reviews Neurology, vol. 8, no. 9, pp. 518-530, 2012.

[9] S. D. Ginsberg, S. E. Hemby, V. M. Lee, J. H. Eberwine, and J. Q. Trojanowski, "Expression profile of transcripts in Alzheimer's disease tangle-bearing CA1 neurons," Annals of Neurology, vol. 48, no. 1, pp. 77-87, 2000.

[10] V Colangelo, J. Schurr, M. J. Ball, R. P. Pelaez, N. G. Bazan, and W. J. Lukiw, "Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic

factor down-regulation and up-regulation of apoptotic and proinflammatory signaling," Journal of Neuroscience Research, vol. 70, no. 3, pp. 462-473, 2002.

[11] P. Katsel, C. Li, and V. Haroutunian, "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, vol. 32, no. 4-5, pp. 845-856, 2007.

[12] C. Williams, R. M. Shai, Y. Wu et al., "Transcriptome analysis of synaptoneurosomes identifies neuroplasticity genes overexpressed in incipient Alzheimer's disease," PLoS ONE, vol. 4, no. 3, Article ID e4936, 2009.

[13] J. J. Chen, H.-M. Hsueh, R. R. Delongchamp, C.-J. Lin, and C.-A. Tsai, "Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data," BMC Bioinformatics, vol. 8, article 412, 2007.

[14] Z. Wang, M. Gerstein, and M. Snyder, "RNA-Seq: a revolutionary tool for transcriptomics," Nature Reviews Genetics, vol. 10, no. 1, pp. 57-63, 2009.

[15] G. T. Sutherland, M. Janitz, and J. J. Kril, "Understanding the pathogenesis of Alzheimer's disease: will RNA-Seq realize the promise of transcriptomics?" Journal of Neurochemistry, vol. 116, no. 6, pp. 937-946, 2011.

[16] B. Bai, C. M. Hales, P.-C. Chen et al., "U1 small nuclear ribonucleoprotein complex and RNA splicing alterations in Alzheimer's disease," Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 41, pp. 16562-16567, 2013.

[17] H. Braak, I. Alafuzoff, T. Arzberger, H. Kretzschmar, and K. del Tredici, "Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry," Acta Neuropathologica, vol. 112, no. 4, pp. 389-404, 2006.

[18] E. M. Blalock, J. W. Geddes, K. C. Chen, N. M. Porter, W. R. Markesbery, and P. W. Landfield, "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, vol. 101, no. 7, pp. 2173-2178, 2004.

[19] W. S. Liang, E. M. Reiman, J. Valla et al., "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, vol. 105, no. 11, pp. 4441-4446, 2008.

[20] M. Hokama, S. Oka, J. Leon et al., "Altered expression of diabetes-related genes in Alzheimer's disease brains: the Hisayama study," Cerebral Cortex, vol. 24, no. 9, pp. 2476-2488, 2014.

[21] D. W. Huang, B. T. Sherman, and R. A. Lempicki, "Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources," Nature Protocols, vol. 4, no. 1, pp. 44-57, 2009.

[22] M. Kanehisa, S. Goto, Y. Sato, M. Kawashima, M. Furumichi, and M. Tanabe, "Data, information, knowledge and principle: back to metabolism in KEGG," Nucleic Acids Research, vol. 42, no. 1, pp. D199-D205, 2014.

[23] M. H. Schwab, S. Druffel-Augustin, P. Gass et al., "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, vol. 18, no. 4, pp. 1408-1418, 1998.

[24] M. Yamada, Y. Shida, K. Takahashi et al., "Prg1 is regulated by the basic helix-loop-helix transcription factor Math2," Journal of Neurochemistry, vol. 106, no. 6, pp. 2375-2384, 2008.

[25] A. Bartholoma and K.-A. Nave, "NEX-1: a novel brain-specific helix-loop-helix protein with autoregulation and sustained expression in mature cortical neurons," Mechanisms of Development, vol. 48, no. 3, pp. 217-228, 1994.

[26] M. H. Schwab, A. Bartholomae, B. Heimrich et al., "Neuronal basic helix-loop-helix proteins (NEX and BETA2/Neuro D) regulate terminal granule cell differentiation in the hippocampus," Journal of Neuroscience, vol. 20, no. 10, pp. 3714-3724, 2000.

[27] S.-X. Wu, S. Goebbels, K. Nakamura et al., "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, vol. 102, no. 47, pp. 17172-17177, 2005.

[28] I. Bormuth, K. Yan, T. Yonemasu et al., "Neuronal basic helix-loop-helix proteins neurod2/6 regulate cortical commissure formation before midline interactions," The Journal of Neuroscience, vol. 33, no. 2, pp. 641-651, 2013.

[29] J. N. Kay, P. E. Voinescu, M. W. Chu, and J. R. Sanes, "Neurod6 expression defines new retinal amacrine cell subtypes and regulates their fate," Nature Neuroscience, vol. 14, no. 8, pp. 965-972, 2011.

[30] M. Uittenbogaard and A. Chiaramello, "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, vol. 67, no. 2, pp. 235-245, 2002.

[31] M. Uittenbogaard and A. Chiaramello, "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, vol. 92, no. 3, pp. 585-596, 2005.

[32] M. Uittenbogaard, K. K. Baxter, and A. Chiaramello, "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, vol. 2, no. 2, Article ID e00034, 2010.

[33] H. J. Kang, Y. I. Kawasawa, F. Cheng et al., "Spatio-temporal transcriptome of the human brain," Nature, vol. 478, no. 7370, pp. 483-489, 2011.

[34] M. Uittenbogaard, D. L. Martinka, and A. Chiaramello, "The basic helix-loop-helix differentiation factor Nex1/MATH-2 functions as a key activator of the GAP-43 gene," Journal of Neurochemistry, vol. 84, no. 4, pp. 678-688, 2003.

Jun-ichi Satoh, Yoji Yamamoto, Naohiro Asahina, Shouta Kitano, and Yoshihiro Kino

Department of Bioinformatics and Molecular Neuropathology, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan

Correspondence should be addressed to Jun-ichi Satoh; satoj@my-pharm.ac.jp

Received 24 July 2014; Accepted 17 November 2014; Published 8 December 2014

Academic Editor: Lance A. Liotta

TABLE 1: Top 20 DEGs in the frontal cortex of AD overlapping between
two cohorts identified by RNA-Seq data analysis of SRA060752.

Entrez       Gene symbol   Gene name
Gene ID

6863         TACI          Tachykinin, precursor 1
5121         PCP4          Purkinje cell protein 4
793          CALB1         Calbindin 1, 28 kDa
54112        GPR88         G protein-coupled receptor 88
143162       FRMPD2        FERM and PDZ domain containing 2
6588         SLN           Sarcolipin
6750         SST           Somatostatin
63974        NeuroD6       Neurogenic differentiation 6
3358         HTR2C         5-Hydroxytryptamine (serotonin) receptor
                             2C
771          CA12          Carbonic anhydrase XII
845          CASQ2         Calsequestrin 2 (cardiac muscle)
23704        KCNE4         Potassium voltage-gated channel,
                             Isk-related family, member 4
6279         S100A8        S100 calcium binding protein A8
871          SERPINH1      Serpin peptidase inhibitor, dade H (heat
                             shock protein 47), member 1 (collagen
                             binding protein 1)
6275         S100A4        S100 calcium binding protein A4
26266        SLC13A4       Solute carrier family 13 (sodium/sulfate
                             symporters), member 4
3303         HSPA1A        Heat shock 70 kDa protein 1A
3304         HSPA1B        Heat shock 70 kDa protein IB
375061       FAM89A        Family with sequence similarity 89,
                             member A
100500849    MIR3916       MicroRNA 3916

Entrez       Chromosome locus                 Fold change
Gene ID                                   (AD versus NC: EMU)

6863         chr7: 97361270-97369784          0.028013577
5121         chr21: 41239346-41301322         0.060477403
793          chr8: 91070837-91095107          0.068756027
54112        chrl: 101002397-101008223        0.083735314
143162       chrlO: 49364156-49482941         0.086014876
6588         chrll: 107578100-107582787       0.091647542
6750         chr3:187386693-187388201         0.094938693
63974        chr7: 31377079-31380538          0.095418314
3358         chrX: 113818550-114144627        0.09625865

771          chrl5: 63615729-63674075         0.104187926
845          chrl: 116242519-116345204        3.491410523
23704        chr2: 223916861-223920355        4.436801472

6279         chrl: 153362507-153363664        5.018965399
871          chrll: 75273100-75283870         5.437863735

6275         chrl: 153516094-153518282        5.52659158
26266        chr7:135365246-135412933         6.580968881

3303         chr6: 31777395-31785719          6.599057552
3304         chr6: 31795511-31798031          6.599057552
375061       chrl: 231154703-231175995        8.186858725

100500849    chrl: 247342111-247374105        17.2291545

Entrez       q -value (EMU)        Fold change       q-value (UKY)
Gene ID                        (AD versus NC: UKY)

6863           0.00232257          0.189297859        0.000638422
5121           0.00232257          0.263070248        0.000638422
793            0.00232257          0.229478357        0.000638422
54112          0.00232257          0.380695653        0.000638422
143162         0.00232257          0.344904773        0.00248927
6588           0.00398798          0.110806929        0.00396642
6750           0.00232257          0.042406817        0.000638422
63974          0.00232257          0.159354316        0.000638422
3358           0.00232257          0.211803744        0.000638422

771            0.00232257          0.453231488         0.0218249
845            0.00962874          5.322706439        0.000638422
23704          0.00232257          4.699574041        0.000638422

6279           0.00232257          2.142150734         0.0151369
871            0.00232257          2.129345418         0.0101566

6275           0.00232257          4.606560625        0.000638422
26266          0.00232257          2.996233377        0.000638422

3303           0.00232257           3.8880048         0.000638422
3304           0.00232257          2.366327899        0.000638422
375061         0.00232257          2.253222839        0.00248927

100500849      0.00762148          2.127294836        0.00287725

The core set of 522 DEGs in the frontal cortex of AD overlapping
between EMU and UKY satisfying q-value (FDR-corrected P value) <0.05
and fold change greater than 2.0 or smaller than 0.5 were extracted
by RNA-Seq data analysis of SRA060572. Top 10 downregulated and top
10 upregulated genes based on fold change in EMU are listed with
Entrez Gene ID, gene symbol, gene name, chromosomal locus, fold
change, and q-value. NeuroD6 is italicized. The complete list of 522
DEGs is shown in Supplementary Table 1.

TABLE 2: The set of 15 genes DEGs downregulated in cortical neurons
of the superior frontal gyrus of AD identified by microarray data
analysis of GSE5281 corresponding to RNA-Seq data analysis of
SRA060752.

Entrez    Gene symbol   Gene name
Gene ID

116       ADCYAP1       Adenylate cyclase activating polypeptide 1
                          (pituitary)
6750      SST           Somatostatin
10777     ARPP21        cAMP-regulated phosphoprotein, 21kDa
6511      SLC1A6        Solute carrier family 1 (high affinity
                          aspartate/glutamate transporter), member 6
728192    LINC00460     Long intergenic non-protein-coding RNA 460
891       CCNB1         Cyclin B1
523       ATP6V1A       ATPase, H+ transporting, lysosomal 70 kDa, V1
                          subunit A
7991      TUSC3         Tumor suppressor candidate 3
1741      DLG3          Discs, large homolog 3 (Drosophila)
63974     NeuroD6       Neurogenic differentiation 6
3382      ICA1          Islet cell autoantigen 1, 69 kDa
844       CASQ1         Calsequestrin 1 (fast-twitch, skeletal
                          muscle)
9577      BRE           Brain and reproductive organ-expressed
                          (TNFRSF1A modulator)
84900     RNFT2         Ring finger protein, transmembrane 2
9515      STXBP5L       Syntaxin binding protein 5-like

Entrez     Fold change        P value
Gene ID   (AD versus NC)

116        0.069636923     1.26039E - 07

6750       0.078547047     2.49899E - 05
10777       0.1474134      4.52823E - 06
6511       0.156759363     4.74541E - 05

728192     0.171656016     8.49002E - 07
891        0.182817517     1.34014E - 05
523        0.190442377     8.45909E - 05

7991       0.200399391     3.74354E - 05
1741       0.222930685     8.59311E - 06
63974      0.237572737     6.60728E - 05
3382       0.249762845     2.41638E - 05
844        0.271852315     3.03481E - 05

9577       0.306075086     9.92354E - 05

84900      0.306171729     4.52424E - 05
9515       0.360338998     8.58457E - 05

The set of 215 DEGs in LCM-captured frontal cortex neurons of AD
satisfying P value <0.0001 by two-tailed i-test and fold change
greater than 2 or smaller than 0.5 were extracted by microarray data
analysis of GSE5281. Among them, the set of 15 genes corresponding to
the core set of 522 DEGs identified by RNA-Seq data analysis of
SRA060572 are listed with Entrez Gene ID, gene symbol, gene name,
fold change, and P value. NeuroD6 is italicized. The complete set of
215 DEGs are shown in Supplementary Table 3.

TABLE 3: The set of 25 DEGs downregulated in the hippocampal CA1
region during progression of AD identified by microarray data
analysis of GSE1297 corresponding to RNA-Seq data analysis of
SRA060752.

Entrez    Gene symbol   Gene name
Gene ID

57172     CAMK1G        Calcium/calmodulin-dependent protein
                          kinase IG
7447      VSNL1         Visinin-like 1
10368     CACNG3        Calcium channel, voltage-dependent, gamma
                          subunit 3
55711     FAR2          Fatty acyl CoA reductase 2
10769     PLK2          Polo-like kinase 2
9331      B4GALT6       UDP-Gal: betaGlcNAc beta
                          1,4-galactosyltransferase, polypeptide 6
55312     RFK           Riboflavin kinase
5274      SERPINI1      Serpin peptidase inhibitor, clade I
                          (neuroserpin), member 1
9079      LDB2          LIM domain binding 2
1268      CNR1          Cannabinoid receptor 1 (brain)
5579      PRKCB         Protein kinase C, beta
63982     ANO3          Anoctamin 3
81831     NETO2         Neuropilin (NRP) and tolloid- (TLL-) like 2
440270    GOLGA8B       Golgin A8 family, member B
23236     PLCB1         Phospholipase C, beta 1
                         (phosphoinositide-specific)
27324     TOX3          TOX high mobility group box family member 3
6000      RGS7          Regulator of G-protein signaling 7
138046    RALYL         RALY RNA binding protein-like
5530      PPP3CA        Protein phosphatase 3, catalytic subunit,
                          alpha isozyme
1020      CDK5          Cyclin-dependent kinase 5
3751      KCND2         Potassium voltage-gated channel,
                          Shal-related subfamily, member 2
29114     TAGLN3        Transgelin 3
7534      YWHAZ         Tyrosine 3-monooxygenase/tryptophan
                          5-monooxygenase activation protein, zeta
                          polypeptide
63974     NeuroD6       Neurogenic differentiation 6
2744      GLS           Glutaminase

Entrez        Fold change           P value
Gene ID   (severe AD versus NC)

57172          0.119791216        0.00015044

7447           0.176659556        0.003811085
10368          0.212906942        0.000645324

55711          0.230090829        0.00066547
10769          0.264329482        0.001588852
9331           0.284246871        0.005450676

55312          0.326351561        0.000250327
5274           0.337526915        0.006332563

9079           0.340999225        0.003326867
1268           0.344653589        0.008057005
5579           0.345004889        0.002186833
63982          0.372084936        0.008600399
81831          0.393977566        0.000965658
440270         0.422538873        0.004711934
23236          0.43589904         0.001873881

27324          0.450425735        0.002724595
6000           0.451971533        0.006045425
138046         0.453463038        0.001029166
5530           0.461727097        0.001385837

1020           0.464742579        0.00340998
3751           0.489787298        0.004105453

29114          0.536481218        0.00215522
7534           0.556167619        0.006101886

63974          0.568549476        0.007199422
2744           0.591660135        0.007897416

The set of 131 DEGs downregulated in the hippocampal CA1 region among
incipient, moderate, and severe AD and NC groups by one-way ANOVA
satisfying P value <0.01 and fold change of severe AD versus NC
smaller than 0.6 were extracted by microarray data analysis of
GSE1297 Among them, the set of 25 genes corresponding to the core set
of 522 DEGs identified by RNA-Seq data analysis of SRA060572 are
listed with Entrez Gene ID, gene symbol, gene name, fold change, and
P value. NeuroD6 is italicized. The complete list of 131 DEGs are
shown in Supplementary Table 4.

TABLE 4: The set of 12 DEGs downregulated in the hippocampus
of Japanese AD patients identified by microarray data analysis of
GSE36980 corresponding to RNA-Seq data analysis of SRA060752.

Entrez    Gene symbol   Gene name
Gene ID

63974     NeuroD6       Neurogenic differentiation 6
10368     CACNG3        Calcium channel, voltage-dependent, gamma
                          subunit 3
5176      SERPINF1      Serpin peptidase inhibitor, clade F (alpha-2
                          antiplasmin, pigment epithelium derived
                          factor), member 1
348980    HCN1          Hyperpolarization activated cyclic
                          nucleotide-gated potassium channel 1
5774      PTPN3         Protein tyrosine phosphatase, nonreceptor
                          type 3
8507      ENC1          Ectodermal-neural cortex (with BTB-like
                          domain)
266722    HS6ST3        Heparan sulfate 6-O-sulfotransferase 3
2903      GRIN2A        Glutamate receptor, ionotropic, N-methyl
                          D-aspartate 2A
51299     NRN1          Neuritin 1
125113    KRT222        Keratin 222 pseudogene
1428      CRYM          Crystallin, mu
221692    PHACTR1       Phosphatase and actin regulator 1

Entrez       Fold change         P value
Gene ID   (AD versus non-AD)

63974        0.433171474       0.001616741
10368        0.496685808       0.004499281

5176         0.522226034       0.00019849

348980       0.523810594       0.004508668

5774          0.54910964       0.001537752

8507         0.559652363       0.003288053

266722       0.560440342       0.001753288
2903         0.581203611       0.002390123

51299        0.590013553       0.001828335
125113       0.592296133       0.004941811
1428         0.594516424       0.002824762
221692       0.597310446       0.002464967

The set of 31 DEGs downregulated in the hippocampus of Japanese AD
patients satisfying P value <0.005 by two-tailed i-test and fold
change smaller than 0.6 were extracted by microarray data analysis of
GSE36980. Among them, the set of 12 genes corresponding to the core
set of 522 DEGs identified by RNA-Seq data analysis of SRA060572 are
listed with Entrez Gene ID, gene symbol, gene name, fold change, and
P value. NeuroD6 is italicized. The complete list of 31 DEGs are
shown in Supplementary Table 5.
COPYRIGHT 2014 Hindawi Limited
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2014 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Satoh, Jun-ichi; Yamamoto, Yoji; Asahina, Naohiro; Kitano, Shouta; Kino, Yoshihiro
Publication:Disease Markers
Article Type:Report
Date:Jan 1, 2014
Words:6205
Previous Article:Potential novel biomarkers for diabetic testicular damage in streptozotocin-induced diabetic rats: nerve growth factor beta and vascular endothelial...
Next Article:CC chemokine receptor 5: the interface of host immunity and cancer.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters