RNA-Seq data mining: downregulation of NeuroD6 serves as a possible biomarker for Alzheimer's disease brains.
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 . 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 . 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) . 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 . They include reduced expression of kinases/phosphatases, cytoskeletal proteins, synaptic proteins, and neurotransmitter receptors in NFT-bearing CA1 neurons , downregulation of neurotrophic factors and upregulation of proapoptotic molecules in the hippocampal CA1 region , disturbed sphingolipid metabolism in various brain regions during progression of AD , and overexpression of the AMPA receptor GluR2 subunit in synaptosomes of the prefrontal cortex . However, previous studies failed to identify the set of definite biomarker genes, whose expression is consistently deregulated in AD brains across different studies . 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 .
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 . 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  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 . 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 . 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 . 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 . 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 .
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.
3.1. 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 , 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 , 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.
Previously, a number of microarray-based transcriptome studies of AD brains failed to identify the set of consistently deregulated genes across different studies . 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) . 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 . 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 . NeuroD6-expressing progenitor cells located in the subventricular zone have a capacity to differentiate into pyramidal glutamatergic neurons in upper cortical layers . Both NeuroD2 and NeuroD6 regulate axonal fasciculation and proper formation of callosal fiber tracts . NeuroD6 plays a key role in cell fate decision of subtypes of amacrine cells in the retina . 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) . NeuroD6 plays a decisive role in the switch from proapoptotic to antiapoptotic pathways during neuronal differentiation of PC12 cells . Furthermore, NeuroD6 confers tolerance to oxidative stress by inducing antioxidant responses and by increasing the mitochondrial biomass . 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 . 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.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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).
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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; email@example.com
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.
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|Title Annotation:||Research Article|
|Author:||Satoh, Jun-ichi; Yamamoto, Yoji; Asahina, Naohiro; Kitano, Shouta; Kino, Yoshihiro|
|Date:||Jan 1, 2014|
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