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Transcriptomics in Alzheimer's Disease

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Transcriptomics in Alzheimer's Disease International Journal of Molecular Sciences Review Transcriptomics in Alzheimer’s Disease: Aspects and Challenges Eva Bagyinszky 1,2 , Vo Van Giau 1,2,* and SeongSoo A. An 2,* 1 Department of Industrial and Environmental Engineering, Graduate School of Environment, Gachon University, Seongnam 13120, Korea; [email protected] 2 Department of Bionano Technology, Gachon University, Seongnam 13120, Korea * Correspondence: [email protected] (V.V.G.); [email protected] (S.A.A.A.) Received: 10 April 2020; Accepted: 14 May 2020; Published: 15 May 2020 Abstract: Alzheimer’s disease (AD) is the most common cause of dementia. Although the heritability of AD is high, the knowledge of the disease-associated genes, their expression, and their disease-related pathways remain limited. Hence, finding the association between gene dysfunctions and pathological mechanisms, such as neuronal transports, APP processing, calcium homeostasis, and impairment in mitochondria, should be crucial. Emerging studies have revealed that changes in gene expression and gene regulation may have a strong impact on neurodegeneration. The mRNA–transcription factor interactions, non-coding RNAs, alternative splicing, or copy number variants could also play a role in disease onset. These facts suggest that understanding the impact of transcriptomes in AD may improve the disease diagnosis and also the therapies. In this review, we highlight recent transcriptome investigations in multifactorial AD, with emphasis on the insights emerging at their interface. Keywords: trancriptome; differently expressed genes; Alzheimer’s disease; neurodegeneration; noncoding RNA; alternative splicing; copy number variant; RNA array; RNA sequencing 1. Introduction Alzheimer’s disease is a complex disease since several genetic and epigenetic factors and gene–environmental interactions could be involved in disease onset. Neuropathological changes in the AD brain include progressive hippocampal and cortical atrophy, visible upon neuroimaging and macroscopic examination, suggesting intracellular neurofibrillary tangles (NFTs) of the hyperphosphorylated tau protein and extracellular depositions of amyloid-β (Aβ)1–42 peptide accompanied by neuronal and synapse loss and reactive gliosis [1]. Molecular genetic investigation of these pedigrees have resulted in the identification of the amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), and several risk factors were identified, which may impact AD onset. APP, PSEN1, and PSEN2 could be causative factors for AD with an earlier age of onset (early onset AD, EOAD, under 65 years). The majority of mutations in these genes could be associated with autosomal dominant inheritance. However, these mutations may be rare even among the early-onset AD (EOAD) patients. Several rare risk variants in Sortilin Related Receptor 1 (SORL1), triggering receptors expressed on myeloid cells (TREM2), andATP Binding Cassette Subfamily A Member 7 (ABCA7) may contribute to EOAD risk [2]. Apolipoprotein E (APOE) E4 allele has been identified as the main risk factor for late-onset AD (LOAD), but it may not define all disease cases [3]. APOE E4 explains approximately 25% of heritability in AD. In recent years, genome-wide association (GWAS), next-generation sequencing (NGS), and whole-genome/exome (WGS/WES) sequencing analyses have provided more insight into AD genetics. Several low penetrant common risk variants and rare mutations were also discovered, which could also impact the risk of AD or act as risk modifiers, such as clusterin (CLU), SORL1, ABCA7, Siglec-3 (CD33), Phospholipase D Family Member 3 (PLD3), Int. J. Mol. Sci. 2020, 21, 3517; doi:10.3390/ijms21103517 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2020, 21, 3517 2 of 20 Phosphatidylinositol Binding Clathrin Assembly Protein (PICALM), NME/NM23 Family Member 8 (NME8), TREM2, A-Kinase Anchoring Protein 9 (AKAP9), or A Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10)[4–8]. A combination of common variants (which minor allele frequencyInt. J. Mol. Sci. 2019 is, more 20, x FOR than PEER 5%) REVIEW may have a significant impact on AD onset. Variants2 of in21 these genes may notMember impact 8 (NME8), strongly TREM2, on the A-Kinase AD risk byAnchoring themselves, Protein but 9 their(AKAP9), combination or A Disintegrin could haveand a stronger correlationmetalloproteinase with disease domain-containing onset. Polygenic protein risk 10 scores, (ADAM10) based [4–8]. on commonA combination variants, of common may be useful in the estimationvariants (which of AD minor risk. allele These frequency genes is with more commonthan 5%) may risk have variants a significant could impact usually on AD be onset. involved in lipid metabolism,Variants inflammatoryin these genes may pathways, not impact orstrongly endocytosis. on the AD [ 9risk–11 by]. themselves, Although but an their emerging combination number of genes could have a stronger correlation with disease onset. Polygenic risk scores, based on common have been suggested to affect the risk of developing AD, their mechanistic insights and improved variants, may be useful in the estimation of AD risk. These genes with common risk variants could diseaseusually management be involved remain in lipid metabolism, limited due inflammatory to difficulties pathways, in determining or endocytosis. the [9–11]. functional Although consequences of genetican emerging associations. number Knowledgeof genes have ofbeen the suggested translational to affect impact the risk of of these developing findings AD, remained their limited. Studiesmechanistic are ongoing insights on and genetic improved variants disease in ADmanage riskment genes remain or candidates,limited due to and difficulties on the in physiological complexitydetermining of tissues. the functional Genome-wide consequences association of genetic studies associations. (GWAS), Knowledge next-generation of the translational sequencing (NGS), impact of these findings remained limited. Studies are ongoing on genetic variants in AD risk genes and whole-exomeor candidates,/ genomeand on the sequencing physiological (WES complexity/WGS) of studies tissues. on Genome-wide large AD cohorts association could studies provide a more detailed(GWAS), image next-generation on disease-associated sequencing factors (NGS), [7 an,8].d Findingwhole-exome/genome relationships sequencing between (WES/WGS) gene dysfunctions and differentstudies pathological on large AD mechanisms, cohorts could provide such as a neuronalmore detailed transports, image on disease-associated APP processing, factors calcium [7,8].homeostasis, and impairmentFinding relationships in mitochondria, between gene should dysfunctions be important and different [12,13 pathological]. mechanisms, such as neuronal transports, APP processing, calcium homeostasis, and impairment in mitochondria, should Emerging studies revealed that alternative gene expression regulation mechanisms, such as be important [12,13]. mRNA-transcriptionEmerging studies factor revealed interactions, that alternative non-coding gene expression RNAs, regulation alternative mechanisms, splicing, such or as copy number variants,mRNA-transcription could also impact factor interactions, neurodegeneration non-coding (FigureRNAs, alternative1)[ 14]. splicing, More or trends copy number are emerging on simultaneousvariants, could interrogation also impact of neurodegeneration transcriptome data(Figure to 1) study [14]. More the e fftrendsect of are newly emerging identified on genetic risk factorssimultaneous at the interrogation level of the of transcriptome transcriptome data [14 to]. study Hence, the thiseffect workof newly aims identified to describe genetic andrisk discuss the factors at the level of the transcriptome [14]. Hence, this work aims to describe and discuss the genetic geneticdiscovery discovery and and transcriptome transcriptome investigations investigations in multifactorial in multifactorial AD, with AD,emphasis with on emphasis the insights on the insights emergingemerging at their at interface.their interface. Keywords Keywords used used to searchto search the the review review topics topics were were transcriptomes, transcriptomes, Alzheimer’s disease,Alzheimer’s neurodegeneration, disease, neurodegeneration, differently expresseddifferently expressed genes, non-coding genes, non-co RNAs,ding RNAs, alternative alternative splicing, copy numbersplicing, variant copy (CNV), number gene variant expression (CNV), gene array, expression RNA array, sequencing, RNA sequ andencing, miRNA-based and miRNA-based methods. methods. Figure 1. Overview showing the workflow for transcriptomic studying in Alzheimer’s disease, from transcriptomic data generation to integration of regulatory information to assess gene regulatory networks. Int. J. Mol. Sci. 2020, 21, 3517 3 of 20 2. Differentially Expressed Genes (DEGs) in AD Differentially expressed genes (DEGs) are important targets in the discovery of biological pathways involved in different diseases, such as cancers or neurological diseases. The goal of DEG analysis is to find genes that could be up- or downregulated in disease, compared to unaffected controls. Over- or underexpression of different genes may result in alterations in metabolic, immune, and other pathways, leading to diseases [15,16]. DEGs could also impact the onset of neurodegenerative diseases, including Alzheimer’s disease. In addition, variations may be possible between the gene expression patterns of different brain areas [17]. It is important
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