Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

Abstract

Background:

The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets.

Objective:

The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis.

Methods:

We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs.

Results:

In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes.

Conclusion:

AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

Keywords

Alzheimer’s disease competitive endogenous RNA hub genes immune system

INTRODUCTION

Alzheimer’s disease (AD) is a common type of neurodegenerative disease with memory decline and disability. It is histopathologically characterized by the deposition of amyloid-β, neurofibrillary tangles, and loss of synapses [1 –4]; however, its etiology and pathogenesis have remained unclear for a century. Current treatments based on the amyloid cascade hypothesis have not been able to control AD progression. Therefore, it is essential to confirm the molecular basis of AD to design new pharmacological targets.

Numerous recent studies have reported that brain immune responses are closely associated with numerous aspects of AD pathogenesis. Immune genes including TREM2, CR1, CD33, and MS4A have been discovered through genome-wide association studies [5 –10]. For instance, TREM2 promotes AD pathogenesis by altering the inflammatory state of microglia [11]. Furthermore, the immune response is believed to be not only a downstream phenomenon but also the initiating factor in AD [12]. Immunosenescence caused by environmental, genetic, and other factors may result in downstream alterations in AD pathophysiology. Acute and chronic inflammatory processes play important roles in regulating APP expression or tau transmission [13 –15]. Activated microglia release an apoptosis-associated speck-like protein containing a CARD (ASC) protein, which acts as an inflammation-driven cross-seed for the amyloid-β pathology [16]. Furthermore, loss of NLRP3 inflammasome function reduces tau hyperphosphorylation and aggregation by regulating tau kinases and phosphatases [17].

Immune-related pathological phenomena result from the synergistic effects of interacting genes or RNAs [18 –20]. Therefore, an integrated analysis of protein-encoding genes and noncoding RNAs would help reveal the underlying immune mechanisms [21]. Long non-coding RNAs (lncRNAs) are involved in numerous biological phenomena including cell differentiation, immune response, and development [22], and serve as competitive endogenous RNAs (ceRNAs), competing with microRNAs (miRNAs) to bind to mRNAs, thus reducing miRNA-mediated repression of target genes [23]. However, it remains unclear whether ceRNA contributes to AD-related immune responses.

This study aimed to explore the hub genes and to establish a competitive regulatory network of lncRNA-miRNA-mRNA for AD-related immune phenomena. Postmortem hippocampal tissue of AD patients was used and hub genes and lncRNAs were screened for their involvement in immune phenomena.

METHODS

Data acquisition and annotation

The GSE48350 dataset (Affymetrix Human Genome U133 Plus 2.0 Array: Platform GLP570) was downloaded from NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) [24]. The dataset contained 253 brain tissue samples, from which 62 hippocampal samples were analyzed, including those from 19 AD patients and 43 normal controls (NC). Pathological analysis was performed for AD and NC subjects. The dataset provided information regarding the age and sex of subjects in the two groups (age: AD, 83.05±8.48 years; NC, 62.48±25.39 years; female sex: AD, 52.63%, NC, 46.51%). mRNAs and lncRNAs in the expression profiles were re-annotated in accordance with the HUGO Gene Nomenclature Committee (HGNC) database (http://www.genenames.org/) [25]. Screening was performed in accordance with the following criteria: 1) empty probes not matching any mRNA and lncRNA should be deleted; 2) probes matching both mRNA and lncRNA should be deleted; 3) for different probes matching the same mRNA or lncRNA, the average value should be considered the expression level of mRNA or lncRNA.

Differential expression analysis and acquisition of immune-related genes

Data analysis procedures are shown in Fig. 1. The robust multiarray averaging (RMA) method was used for background noise correlation and normalization. We used the R Limma package to screen differentially expressed RNAs (DERNAs) between AD and NC individual adjusted for age and sex. The screening thresholds were set at a false discovery rate (FDR) of <0.05 and |log₂FC|>0.5. Genes involved in immune-related biological processes (BP) or pathways were identified using AmiGO2 (http://amigo.geneontology.org/amigo) and KEGG (https://www.kegg.jp/) databases, using the search term ‘immune’. [26, 27]

Fig. 1

Schematic representation of the study protocol.

AmiGO2 is a web-based set of tools for searching and browsing Gene Ontology (GO) databases [26]. KEGG is a database resource for understanding high-level functions and utilities of biological systems [27]. The overlaps between DERNAs and these immune-related gene sets were considered to reflect AD-related immune genes.

Construction of the protein-protein interaction (PPI) network

We used the STRING database (http://string.embl.de/) to construct a PPI network [28]. STRING is a database of known and predicted protein-protein interactions. Interactions in STRING are derived from five main sources, including genomic context predictions, high-throughput lab experiments, (conserved) co-expression, automated text mining, and previous knowledge from databases. In this study, protein interaction pairs with a combined score of >0.9 were considered statistically significant and retained in the PPI network. The PPI network was visualized using Cytoscape v 3.6.0 software [29]. Furthermore, the top 20 hub genes were identified and ranked in accordance with their degree value, which, herein, refers to the number of nodes contacting other nodes in PPI networks, and the higher the degree value, the better the centrality of the node.

Pathway enrichment analysis

The Metascape is (http://metascape.org/) an online analytical tool used for gene annotation, functional enrichment, interactome analysis, and membership analysis [30]. In this study, Metascape was used for GO, KEGG, and reactome pathway analysis with an FDR of <0.01 as the cut-off value.

Construction of a ceRNA network

A ceRNA network reflects the associations among lncRNA, mRNA, and miRNA. Interaction pairs, including lncRNA-mRNA, lncRNA-miRNA, and miRNA-mRNA pairs, are necessary for constructing the network. Herein, we constructed a correlation matrix containing Pearson correlation coefficients of all lncRNA-mRNA pairs. AD-related miRNAs were identified from the Human MicroRNA Disease Database (HMDD; http://www.cuilab.cn/hmdd), which contains a number of miRNA-disease association entries from literature [31]. DELncRNA-miRNA interactions were predicted using DIANA-LncBase version 2 database (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php), which provided miRNA recognition elements (MREs) on lncRNAs supported by experimentally and in silico predicted evidence [32]. Finally, the starBase Version 2.0 database (http://starbase.sysu.edu.cn/), which records regulatory interaction networks among multiple classes of RNAs, was used to predict the target genes for AD-related miRNAs; thus, the miRNA-DEmRNA interaction pairs were ob-tained [33]. Overall, DElncRNA-miRNA-DEmRNA regulatory network was constructed and visualized using Cytoscape v 3.6.0 software.

RESULTS

Identification of AD immune-associated DEmRNAs and DElncRNAs

Eighteen DElncRNAs (nine significantly downregulated and nine significantly upregulated) and 1,334 DEmRNAs (889 significantly downregulated and 445 significantly upregulated) were identified with |log2FC|>0.5 and FDR < 0.05 (Fig. 2A, B and Supplementary Table 1). In total, 3,255 genes involved in immune-related phenomena were derived from the AmiGO2 and KEGG databases, respectively. Upon overlapping with DERNAs, 552 immune-related differentially expressed genes were identified (Fig. 2C).

Fig. 2

Analysis of differentially expressed RNAs (DERNAs). A) Volcano plot of DERNAs among Alzheimer’s disease (AD) patients and normal controls (NC); B) Hierarchical clustering heatmap of DERNAs; C) Venn diagrams of overlapping genes from DERNAs and immune-related genes derived from AmiGO2 and KEGG databases.

PPI network analysis

To delineate the interactions among the DEmRNA-coding proteins, we constructed a PPI network from the STRING database. In total, 365 nodes and 1,826 edges were included in the network; the nodes represented DEmRNAs, and the edges represented the interactions among DEmRNAs (Fig. 3). We determined the degree value for each mRNA in the PPI network and selected the top 20 mRNAs as hub genes (Fig. 4A).

Fig. 3

Protein-protein interaction networks of immune-related differentially expressed mRNAs (DEmRNAs) in Alzheimer’s disease. Nodes denote the protein-coding genes corresponding to DEmRNAs and edges denote the interactions among genes. The expression levels of DEmRNAs are indicated using colors. Red and blue circles indicate upregulated and downregulated DEmRNAs, respectively.

Fig. 4

Hub genes and pathway enrichment analysis based on the protein-protein interaction (PPI) network. A) The top 20 hub genes were identified by calculating the degree values in the PPI network. B) Pathway enrichment analysis of hub genes. C) The connection network for hub genes and its enriched pathway. D) PPI subnetwork constructed on the basis of the 20 hub genes. The size of the circle is associated with the degree value. The thicker the edge, the stronger the interaction between two hub genes.

Differentially expressed hub genes are listed in Table 1. Furthermore, we performed pathway analysis and constructed a subnetwork for the 20 hub genes (Fig. 4B-D), which revealed that these genes were enriched in “DAP12 signaling,” “adaptive immune system,” “neutrophil degranulation,” and “interferon gamma signaling” pathways.

Table 1

Differential expression of the top 20 hub genes and key lncRNAs

ID	Symbol	Type	logFC	FDR	p
1559436_x_at	ARRB2	coding	0.687	2.372E-02	2.818E-03
201137_s_at	HLA-DPB1	coding	0.999	3.832E-02	4.827E-03
201199_s_at	PSMD1	coding	– 0.705	5.758E-04	6.975E-05
203560_at	GGH	coding	– 0.656	2.196E-02	2.660E-03
209906_at	C3AR1	coding	0.880	4.044E-02	5.091E-03
212249_at	PIK3R1	coding	0.800	5.389E-02	6.782E-03
212983_at	HRAS	coding	– 0.668	1.928E-03	2.890E-04
212999_x_at	HLA-DQB1	coding	0.747	3.635E-02	4.578E-03
214146_s_at	PPBP	coding	0.759	1.236E-02	1.554E-03
214230_at	CDC42	coding	– 1.761	6.792E-04	8.000E-07
214687_x_at	ALDOA	coding	– 0.617	3.922E-03	4.751E-04
214792_x_at	VAMP2	coding	– 0.786	4.598E-03	5.568E-04
214953_s_at	APP	coding	– 0.641	1.041E-02	1.259E-03
216033_s_at	FYN	coding	0.661	2.577E-03	3.122E-04
217456_x_at	HLA-E	coding	0.580	3.653E-02	4.412E-03
222230_s_at	ACTR10	coding	– 0.636	1.687E-02	4.432E-04
227184_at	PTAFR	coding	0.711	1.599E-02	2.013E-03
228113_at	RAB37	coding	– 0.745	2.284E-02	2.754E-03
232311_at	B2M	coding	0.751	7.847E-03	9.879E-04
235980_at	PIK3CA	coding	– 0.566	8.494E-03	1.005E-03
240578_at	LINC00836	lncRNA	0.838	2.043E-03	6.015E-06
1557617_at	DCTN1-AS1	lncRNA	–1.082	3.600E-03	1.785E-05

Construction of ceRNA network

To clarify the mechanism underlying immune-related phenomena in AD, we constructed a regulatory ceRNA network based on DElncRNA-miRNA-DEmRNA interactions. In total, 499 DElncRNA-DEmRNA interaction pairs were selected through a Pearson’s correlation coefficient of >0.6. Eighty-four DELncRNA-miRNA interaction pairs obtained from the DIANA-LncBase database with a miRNA target gene score (miTG-score) >0.8 were retained. Subsequently, we identified the target mRNAs for AD-related miRNAs, using the starBase database. After overlapping these targeted mRNAs and DEmRNAs, 719 miRNA-mRNA regulatory pairs were obtained. Together, a ceRNA network was generated, comprising 10 lncRNAs, 19 miRNAs, and 201 mRNAs (Fig. 5).

Fig. 5

Construction of the ceRNA network. The pink and blue nodes denote the upregulated and downregulated RNAs, respectively. Gray edges represent lncRNA-miRNA interactions, whereas pink edges represent other interactions between RNAs. LncRNAs, miRNAs, and mRNAs are denoted by squares, triangles, and circles, respectively.

Analysis of upregulation and downregulation ceRNA subnetworks

Based on lncRNA and mRNA expression levels, we generated upregulation and downregulation ceRNA subnetworks (Fig. 6A, B). The upregulation ceRNA network comprised 5 lncRNAs, 9 miRNAs, and 83 mRNAs; downregulation ceRNA network, 5 lncRNAs, 16 miRNAs, and 86 mRNAs. We determined a number of mRNAs linked with each lncRNA in the upregulation and downregulation ceRNA subnetworks, and the lncRNA linking most mRNAs was identified as the key lncRNA. Upregulated lncRNAs may have upregulated mRNAs in the upregulation subnetwork comprising five lncRNAs including LINC00836, AQP4-AS1, EMX2OS, LINC01094, and IGFBP7-AS1. Particularly, LINC00836 linked the most mRNAs, and was considered the key lncRNA. Interestingly, hub genes B2M, PIK3R1, and FYN in the PPI network were competitively regulated by LINC00836 through four miRNAs, including hsa-miR-106b, hsa-miR-17, hsa-miR-20a, and hsa-miR-93. Moreover, DCTN1-AS1 was identified as the key lncRNA in the downregulation subnetwork. Hub genes CDC42, ACTR10, and PIK3CA may have been downregulated by DCTN1-AS1. Furthermore, in order to clarify the potential biological functions of mRNA regulated by the key lncRNA, we selected all mRNAs linked to LINC00836 in the upregulated network and those linked to DCTN1-AS1 in the downregulated network for pathway enrichment analysis (Fig. 6C, D). Differential expression profiles of LINC00836 and DCTN1-AS1 are summarized in Table 1.

Fig. 6

Upregulation and downregulation subnetworks derived from ceRNA networks and pathway enrichment analysis for two key lncRNAs LINC00836 and DCTN1-AS1. A) Upregulation subnetworks. B) Downregulation subnetworks. The pink and blue nodes denote upregulated and downregulated RNAs, respectively. Gray edges represent lncRNA-miRNA interactions, whereas pink edges represent other interactions between RNAs. LncRNAs, miRNAs, and mRNAs are denoted by squares, triangles, and circles, respectively. C) Enriched pathways of mRNAs associated with LINC00836 in the upregulation ceRNA subnetwork. D) Enriched pathways of mRNAs associated with LINC00836 in the downregulation ceRNA subnetwork.

DISCUSSION

This study focused on immune-related hub genes involved in AD pathogenesis. We identified 20 hub genes through PPI network analysis and constructed a ceRNA network to explore the regulatory mechanisms among lncRNA, miRNA, and mRNA interactions.

Since this was a retrospective study including all samples, it was not necessary to determine the sample size. On analyzing the differentially expressed genes between the AD and NC groups, we identified 18 DElncRNAs and 1334 DEmRNAs. On PPI network analysis for the DEmRNAs, the top 20 hub genes were identified from degree values. B2M was characterized as a hub gene, encoding β2-microglobulin, a serum protein related to the major histocompatibility complex (MHC) class I heavy chain on the surface of almost all nucleated cells [34, 35]. Previous studies detected elevated soluble B2M in cerebrospinal fluid and blood in patients with AIDS dementia complex, and AD [36, 38]. Vanni reported that B2M was significantly upregulated in the brain tissue in Creutzfeldt-Jacob disease patients and slightly upregulated in AD patients [39]. B2M upregulation in the AD brain was correlated with viral infection, thus potentially supporting the hypothesis that viral infection is associated with AD pathogenesis. FYN was another key gene identified herein, belonging to the Src tyrosine kinases family. Aβ oligomer can bind with the cellular prion protein receptor on the neuronal surface with high affinity, thus inducing FYN-mediated pathological cascades and leading to the deterioration of synaptic plasticity and long-term potentiation [40 –44]. Aβ activates immune and adhesion molecules on the microglial surface, resulting in FYN recruitment, activation of extracellular signal-related protein kinase (ERK) and mitogen-activated protein kinase (MAPK) pathways, and the subsequent elevation in cytokine and chemokine levels. These pathological changes lead to neurotrophic and synaptic loss [45 –47]. FYN-related inhibitors including Saracatinib (AZD0530) have been used for developing AD drugs [48]. An ongoing Phase IIa clinical trial on saracatinib for AD has reported that reductions in brain FYN levels in AD patients may be a promising therapy for AD. Furthermore, herein, both PIK3R1 and PIK3CA were identified as hub genes belonging to class IA PI3K. Zhang reported that Aβ stimulates PI3K to induce superoxide production and cause neurotoxicity [49]. PIK3R1 interferes with the insulin signaling pathway in the brain of AD patients [50]. Aberrant PI3K signaling can lead to immune deficiency and immune dysregulation; however, further studies are required to investigate whether PI3K mediates immune processes in AD.

CDC42 encodes a cell cycle protein and is activated in Aβ₄₂-treated hippocampal neurons [51]. CDC42 can modify the morphology of dendritic spines by regulating the polymerization state of actin [52, 53]. GWAS identified one SNP (rs1140317) in HLA-DQB1, which was significantly correlated with entorhinal cortical thickness, serving as an AD neuroimaging biomarker [54]. Complement C3a receptor 1 (C3AR1) is an allergic toxin released during complement activation. Deletion of C3AR1 in PS19 mice alleviates tau pathologies, neuroinflammation, synaptic defects, and neurodegeneration [55]. Pro-Platelet basic protein (PPBP) is primarily involved in the regulation of mitosis, cAMP intracellular aggregation, and DNA synthesis. PPBP is reportedly differentially expressed in the sera of AD patients compared with normal controls [56]. B-Arrestin-2 (ARRB2) primarily regulates G-protein-coupled receptors. Variations in ARRB2 significantly increase the AD risk among Han Chinese individuals [57]. Vesicle-associated membrane protein 2 (VAMP2) is a vesicular membrane protein contributing to neuronal signal transduction and hormone release. Aβ₄₂ disrupts the interaction between VAMP2 and synaptophysin, thus hindering angiogenesis and finally affecting baseline neural activity in the brain [58]. Besides the well-recognized pathogenic gene APP, the role of other hub genes in AD has not yet been reported.

On pathway enrichment analysis based on the top 20 hub genes, four hub genes, B2M, FYN, PIK3R1, and PIK3CA, contributed to the DAP12 signaling pathway. DAP12 is an adapter for TREM2 signal transduction. TREM2-DAP12 signaling stimulates certain downstream TREM2 pathways, including the phosphorylation of spleen tyrosine kinase (SYK) and glycogen synthase kinase 3β (GSK3β) [59, 60]. Furthermore, microglial activation by Aβ in vitro can enhance TREM2-DAP12 interaction [61]. Although numerous studies have emphasized the importance of the DAP12 pathway in AD, it is still an interesting question how these hub genes contribute to this pathway.

The immune-related ceRNA regulatory network constructed herein comprises 10 lncRNAs. LY86-AS1 is reportedly negatively correlated with the Braak stages of AD [62], and the other nine lncRNAs have been reported herein for the first time. LINC00836 and DCTN1-AS1 were the key lncRNAs in the up- and downregulation subnetworks, respectively. Notwithstanding a clear functional annotation, these genes are highly and specifically expressed in the brain [63]. Enrichment analysis indicated that the mRNAs connected with the two key lncRNAs in ceRNA subnetwork were enriched in the “adaptive immune system” and “neutrophil degranulation” pathways. Adaptive immunity may be potentially involved in AD pathogenesis. The number of CD3 + T cells is greater in AD patients than in healthy controls, and a higher number of hippocampal CD3+ T cells is associated with tau-related pathologies [64, 65]. It remains unclear whether neutrophil degranulation is involved in AD pathogenesis. Among other pathways involving the key lncRNAs, LINC00836 may regulate the “interferon signal pathway,” while DCTN1-AS1 may regulate “MHC class II antigen presentation,” “Fc epsilon receptor (FCERI) signaling,” “fc-gamma receptor (FCGR)-dependent phagocytosis,” “platelet activation, signaling, and aggregation,” and other immune-related pathways independently, suggesting that LINC00836 and DCTN1-AS1 can regulate multiple immune-related response in AD. Another interesting finding is that the key lncRNAs potentially regulate the hub genes revealed in the PPI network. Upregulated LINC00836 may attenuate the inhibitory effect of hsa-miR-106b, hsa-miR-17, hsa-miR-20a, and hsa-miR-93 on B2M, PIK3R1, and FYN. In contrast, downregulated DCTN1-AS1 may enhance the inhibitory effects of hsa-miR-93, hsa-miR-15a, hsa-miR-16, hsa-miR-15b, and hsa-miR-92a on APP, CDC42, ACTR10, and PIK3CA. Together, the key lncRNAs can regulate immune-related phenomena through different aspects, potentially through their regulation of immune-related hub genes.

In conclusion, this study is the first to identify hub genes based on differentially expressed RNAs associated with immune function in AD. Twenty hub genes, including B2M, FYN, PIK3R1, and PIK3CA, were identified herein. Furthermore, the present results show that LINC00836 and DCTN1-AS1 are the key lncRNAs in the ceRNA network, suggesting the potential significance of lncRNAs in modulating AD-related immune phenomena. Although the potential function of these hub genes and lncRNAs warrant further in-depth investigation, the present study furthers the current understanding of AD-related immune processes and provides novel insights into the development of new immunotherapies for AD.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the Key Project of the National Natural Science Foundation of China (81530036); the National Key Scientific Instrument and Equipment Development Project (31627803); Mission Program of Beijing Municipal Administration of Hospitals (SML20150801); Beijing Scholars Program; Beijing Brain Initiative from Beijing Municipal Science & Technology Commission (Z161100000216137); Project for Outstanding Doctor with Combined Ability of Western and Chinese Medicine; and Beijing Municipal Commission of Health and Family Planning (PXM2019_026283_000003).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article:

References

Gouras

, Tsai

, Naslund

, Vincent

, Edgar

, Checler

, Greenfield

, Haroutunian

, Buxbaum

, Xu

, Greengard

, Relkin

(2000) Intraneuronal Abeta42 accumulation in human brain. Am J Pathol 156, 15–20.

Arriagada

, Growdon

, Hedley-Whyte

, Hyman

(1992) Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology 42, 631–639.

Scheff

, Neltner

, Nelson

(2014) Is synaptic loss a unique hallmark of Alzheimer’s disease. Biochem Pharmacol 88, 517–528.

Selkoe

(2002) Alzheimer’s disease is a synaptic failure. Science 298, 789–791.

Guerreiro

, Wojtas

, Bras

, Carrasquillo

, Rogaeva

, Majounie

, Cruchaga

, Sassi

, Kauwe

, Younkin

; Alzheimer Genetic Analysis Group (2013) TREM2 variants in Alzheimer’s disease. N Engl J Med 368, 117–127.

Jonsson

, Stefansson

, Steinberg

, Jonsdottir

, Jonsson

, Snaedal

, Bjornsson

, Huttenlocher

, Levey

, Lah

(2013) Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 368, 107–116.

Hollingworth

, Harold

, Sims

, Gerrish

, Lambert

, Carrasquillo

, Abraham

, Hamshere

, Pahwa

, Moskvina

; Alzheimer’s Disease Neuroimaging Initiative; CHARGE consortium; EADI1 consortium (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43, 429–435.

Lambert

, Heath

, Even

, Campion

, Sleegers

, Hiltunen

, Combarros

, Zelenika

, Bullido

, Tavernier

; European Alzheimer’s Disease Initiative Investigators (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 41, 1094–1099.

Lambert

, Ibrahim-Verbaas

, Harold

, Naj

, Sims

, Bellenguez

, DeStafano

, Bis

, Beecham

, Grenier-Boley

; European Alzheimer’s Disease Initiative (EADI); Genetic and Environmental Risk in Alzheimer’s Disease; Alzheimer’s Disease Genetic Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology (2013) Metaanalysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45, 1452–1458.

10.

Jansen

, Savage

, Watanabe

, Bryois

, Williams

, Steinberg

, Sealock

, Karlsson

, Hägg

, Athanasiu

(2019) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature 51, 404–413.

11.

Ulrich

, Finn

, Wang

, Shen

, Mahan

, Jiang

, Stewart

, Piccio

, Colonna

, Holtzman

(2014) Altered microglial response to Aβ plaques in APPPS1-21 mice heterozygous for TREM2. Mol Neurodegener 9, 1–9.

12.

Talwar

, Kushwaha

, Gupta

, Agarwal

(2019) Systemic immune dyshomeostasis model and pathways in Alzheimer’s disease. Front Aging Neurosci 11, 290.

13.

Batista

CRA

, Gomes

, Candelario-Jalil

, Fiebich

, De Oliveira

ACP

(2019) Lipopolysaccharide-Induced neuroinflammation as a bridge to understand neurodegeneration. Int J Mol Sci 20, 2293.

14.

Brugg

, Dubreuil

, Huber

, Wollman

, Delhaye-Bouchaud

, Mariani

(1995) Inflammatory processes induce beta-amyloid precursor protein changes in mouse brain. Proc Natl Acad Sci U S A 92, 3032–3035.

15.

Liu

, Zhang

, Li

, Liu

, Wang

, Zhou

, Ye

, Wang

(2020) Peripheral inflammation promotes brain tau transmission via disrupting blood-brain barrier. Biosci Rep 40, BSR20193629.

16.

Venegas

, Kumar

, Franklin

, Dierkes

, Brinkschulte

, Tejera

, Vieira-Saecker

, Schwartz

, Santarelli

, Kummr

, Griep

, Gelpi

, Beilharz

, Riedel

, Golenbock

, Geyer

, Walter

, Latz

, Heneka

(2017) Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease. Nature 552, 355–361.

17.

Ising

, Venegas

, Zhang

, Scheiblich

, Schmidt

, Vieira-Saecker

, Schwartz

, Albasset

, McManus

, Tejera

, Griep

, Santarelli

, Brosseron

, Opitz

, Stunden

, Merten

, Kayed

, Golenbock

, Blum

, Latz

, Buée

, Heneka

(2019) NLRP3 inflammasome activation drives tau pathology. Nature 575, 669–673.

18.

Alexandrov

, Zhao

, Jones

, Bhattacharjee

, Lukiw

(2013) Expression of the phagocytosis-essential protein TREM2 is down-regulated by an aluminum-induced miRNA-34a in a murine microglial cell line. J Inorg Biochem 128, 267–269.

19.

Zhao

, Bhattacharjee

, Jones

, Dua

, Alexandrov

, Hill

, Lukiw

(2013) Regulation of TREM2 expression by an NF-κB-sensitive miRNA-34a. Neuroreport 24, 318–323.

20.

Gardner

, Wang

, Van Booven

, Whitehead

, Hamilton-Nelson

, Adams

, Starks

, Hofmann

, Vance

, Cuccaro

, Martin

, Byrd

, Haines

, Bush

, Beecham

, Pericak-Vance

, Griswold

(2019) RNA editing alterations in a multi-ethnic Alzheimer disease cohort converge on immune and endocytic molecular pathways. Hum Mol Genet 28, 3053–3061.

21.

Zhang

, Liu

, Wang

, Yu

, Dai

, Liu

, Luo

, Jiang

(2015) Integrated systems approach identifies risk regulatory pathways and key regulators in coronary artery disease. J Mol Med 93, 1381–1390.

22.

Mercer

, Dinger

, Mattick

(2009) Long non-coding RNAs: Insights into functions. Nat Rev Genet 10, 155–159.

23.

Salmena

, Poliseno

, Tay

, Kats

, Pandolfi

(2011) A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language. Cell 146, 353–358.

24.

Barrett

, Troup

, Wilhite

, Ledoux

, Rudnev

, Evangelista

, Kim

, Soboleva

, Tomashevsky

, Edgar

(2007) NCBI GEO: Mining tens of millions of expression profiles–database and tools update. Nucleic Acids Res 35, 760–765.

25.

Braschi

, Denny

, Gray

, Jones

, Seal

, Tweedie

, Yates

, Bruford

(2019) Genenames.org: The HGNC and VGNC resources in 2019. Nucleic Acids Res 47, 786–792.

26.

Gene Ontology Consortium (2010) The gene ontology in 2010: Extensions and refinements. Nucleic Acids Res 38, 331–335.

27.

Kanehisa

, Goto

(2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28, 27–30.

28.

Szklarczyk

, Morris

, Cook

, Kuhn

, Wyder

, Simonovic

, Santos

, Doncheva

, Roth

, Bork

, Jensen

, von Mering

(2017) The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45, 362–368.

29.

Shannon

, Markiel

, Ozier

, Baliga

, Wang

, Ramage

, Amin

, Schwikowski

, Ideker

(2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498–2504.

30.

Zhou

, Zhou

, Pache

, Chang

, Khodabakhshi

, Tanaseichuk

, Benner

, Chanda

(2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10, 1–10.

31.

Huang

, Shi

, Gao

, Cui

, Zhang

, Li

, Zhou

, Cui

(2019) HMDD v3.0: A database for experimentally supported human microRNA-disease associations. Nucleic Acids Res 47, 1013–1017.

32.

Paraskevopoulou

, Vlachos

, Karagkouni

, Georgakilas

, Kanellos

, Vergoulis

, Zagganas

, Tsanakas

, Floros

, Dalamagas

, Hatzigeorgiou

(2016) DIANA-LncBase v2: Indexing microRNA targets on non-coding transcripts. Nucleic Acids Res 44, 231–238.

33.

, Liu

, Zhou

, Qu

, Yang

(2014) starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42, 92–97.

34.

Cunningham

, Berggard

(1974) Structure, evolution and significance of beta2-microglobulin. Transplant Rev 21, 3–14.

35.

Mátrai

, Németh

, Miklós

, Szabó

, Masszi

(2009) Serum beta2-microglobulin measured by immunonephelometry: Expression patterns and reference intervals in healthy adults. Clin Chem Lab Med 47, 585–589.

36.

Brew

, Dunbar

, Pemberton

, Kaldor

(1996) Predictive markers of AIDS dementia complex: CD4 cell count and cerebrospinal fluid concentrations of β2-microglobulin and neopterin. J Infect Dis 174, 294–298.

37.

McArthur

, Nance-Sproson

, Griffin

, Hoover

, Selnes

, Miller

, Margolick

, Cohen

, Farzadegan

, Saah

(1992) The diagnostic utility of elevation in cerebrospinal fluid beta 2-microglobulin in HIV-1 dementia. Multicenter AIDS Cohort Study. Neurology 42, 1707–1712.

38.

Dominici

, Finazzi

, Polito

, Oldoni

, Bugari

, Montanelli

, Guaita

(2018) Comparison of β2-microglobulin serum level between Alzheimer’s patients, cognitive healthy and mild cognitive impaired individuals. Biomarkers 23, 603–608.

39.

Vanni

, Moda

, Zattoni

, Bistaffa

, Cecco

, Rossi

, Giaccone

, Tagliavini

, Haïk

, Deslys

, Zanusso

, Ironside

, Ferrer

, Kovacs

, Legname

(2017) Differential overexpression of SERPINA3 in human prion diseases. Sci Rep 7, 1–13.

40.

Laurén

, Gimbel

, Nygaard

, Gilbert

, Strittmatter

(2009) Cellular prion protein mediates impairment of synaptic plasticity by amyloid-beta oligomers. Nature 457, 1128–1132.

41.

Nygaard

, Strittmatter

(2009) Cellular prion protein mediates the toxicity of beta-amyloid oligomers: Implications for Alzheimer disease. Arch Neurol 66, 1325–1328.

42.

, Nygaard

, Heiss

, Kostylev

, Stagi

, Vortmeyer

, Wisniewski

, Gunther

, Strittmatter

(2012) Alzheimer amyloid-β oligomer bound to postsynaptic prion protein activates Fyn to impair neurons. Nat Neurosci 15, 1227.

43.

Bliss

, Lomo

(1973) Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol 232, 331–356.

44.

Malenka

, Nicoll

(1999) Long-term potentiation-a decade of progress. Science 285, 1870–1874.

45.

Jimenez

, Volpert

, Crawford

, Febbraio

, Silverstein

, Bouck

(2000) Signals leading to apoptosis-dependent inhibition of neovascularization by thrombospondin-1. Nat Med 6, 41–48.

46.

Moore

, Khoury

, Medeiros

, Terada

, Geula

, Luster

, Freeman

(2002) A cd36-initiated signaling cascade mediates inflammatory effects of β-amyloid. J Biol Chem 277, 47373–47379.

47.

Wyss-Coray

, McConlogue

, Kindy

, Schmidt

, Du

, Stern

(2001) Key signaling pathways regulate the biological activities and accumulation of amyloid-beta. Neurobiol Aging 22, 967–973.

48.

Nygaard

, Wagner

, Bowen

, Good

, MacAvoy

, Strittmatter

, Kaufman

, Rosenberg

, Sekine-Konno

, Varma

, Chen

, Koleske

, Reiman

, Strittmatter

, van Dyck

(2015) A phase Ib multiple ascending dose study of the safety, tolerability, and central nervous system availability of AZD0530 (saracatinib) in Alzheimer’s diseas. Alzheimers Res Ther 7, 35.

49.

Zhang

, Hu

, Qian

, Chen

, Zhou

, Wilson

, Miller

, Hong

(2011) . Microglial MAC1 receptor and PI3K are essential in mediating β-amyloid peptide-induced microglial activation and subsequent neurotoxicity. J Neuroinflamm 8, 3.

50.

Liolitsa

, Powell

, Lovestone

(2002) Genetic variability in the insulin signalling pathway may contribute to the risk of late onset Alzheimer’s disease. J Neurol Neurosurg Psychiatry 73, 261–266.

51.

Mendoza-Naranjo

, Gonzalez-Billault

, Maccioni

(2007) Abeta1-42 stimulates actin polymerization in hippocampal neurons through Rac1 and Cdc42 Rho GTPases. J Cell Sci 120, 279–288.

52.

Matus

(2000) Actin-based plasticity in dendritic spines. Science 290, 754–758.

53.

Penzes

, Vanleeuwen

J-EE

(2011) Impaired regulation of synaptic actin cytoskeleton in Alzheimer’s disease. Brain Res Rev 67, 184–192.

54.

Lee

, Han

, Kim

, Horgousluoglu

, Risacher

, Saykin

, Nho

(2018) Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer’s disease. AMIA Jt Summits Transl Sci Proc 2017, 124–131.

55.

Litvinchuk

, Wan

, Swartzlander

, Chen

, Cole

, Propson

, Wang

, Zhang

, Liu

, Zheng

(2018) Complement C3aR inactivation attenuates tau pathology and reverses an immune network deregulated in tauopathy models and Alzheimer’s disease. Neuron 100, 1337–1353.

56.

Shen

, Liao

, Chen

, Guo

, Song

, Wang

, Chen

, Zhang

, Ying

, Li

, Liu

, Ni

(2017) Proteomics analysis of blood serums from Alzheimer’s disease patients using iTRAQ labeling technology. J Alzheimers Dis 56, 361–378.

57.

Jiang

, Yu

, Wang

, Zhang

, Hu

, Tan

, Sun

, Tan

, Zhu

, Tan

(2014) The genetic variation of ARRB2 is associated with late-onset Alzheimer’s disease in Han Chinese. Curr Alzheimer Res 1, 408–412.

58.

Russell

, Semerdjieva

, Empson

, Austen

, Beesley

, Alifragis

(2012) Amyloid-β acts as a regulator of neurotransmitter release disrupting the interaction between synaptophysin and VAMP2. PLoS One 7, e43201.

59.

Xing

, Titus

, Humphrey

(2015) The TREM2-DAP12 signaling pathway in Nasu–Hakola disease: A molecular genetics perspective. Res Rep Biochem 5, 89.

60.

Peng

, Malhotra

, Torchia

, Kerr

, Coggeshall

, Humphrey

(2010) TREM2-and DAP12-dependent activation of PI3K requires DAP10 and is inhibited by SHIP1. Sci Signal 3, ra38.

61.

Zhao

, Wu

, Li

, Jiang

, Gui

, Liu

, Sun

, Zhu

, Piña-Crespo

, Zhang

, Chen

, Bu

, An

, Huang

, Xu

(2018) TREM2 is a receptor for β-amyloid that mediates microglial function. Neuron 97, 1023–1031.

62.

Cao

, Li

, Zhao

, Cui

, Hu

(2019) Identification of age-and gender-associated long noncoding RNAs in the human brain with Alzheimer’s disease. Neurobiol Aging 81, 116–126.

63.

Fagerberg

, Hallström

, Oksvold

, Kampf

, Djureinovic

, Odeberg

, Habuka

, ahmasebpoor

, Danielsson

, Edlund

, Asplund

, Sjöstedt

, Lundberg

, Szigyarto

, Skogs

, Takanen

, Berling

, Tegel

, Mulder

, Nilsson

, Schwenk

, Lindskog

, Danielsson

, Mardinoglu

, Sivertsson

, Feilitzen

, Forsberg

, Zwahlen

, Olsson

, Navani

, Huss

, Nielsen

, Ponten

, Uhlén

(2014) Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol Cell Proteomics 13, 397–406.

64.

Späni

, Suter

, Derungs

, Ferretti

, Welt

, Wirth

, Gericke

, Nitsch

, Kulic

(2015) Reduced β-amyloid pathology in an APP transgenic mouse model of Alzheimer’s disease lacking functional B and T cells. Acta Neuropathol Commun 3, 71.

65.

Merlini

, Kirabali

, Kulic

, Nitsch

, Ferretti

(2018) Extravascular CD3+T cells in brains of Alzheimer disease patients correlate with tau but not with amyloid pathology: An immunohistochemical study. Neurodegener Dis 18, 49–56.