Medicine, Faculty of
Non UBC
Cellular and Physiological Sciences, Department of
DSpace
Acta Neuropathologica Communications. 2018 Dec 21;6(1):144
The Author(s).
Kajiwara, Yuji
Wang, Erming
Wang, Minghui
Sin, Wun Chey
Brennand, Kristen J.
Schadt, Eric
Naus, Christian C
Buxbaum, Joseph
Zhang, Bin
GJA1 (connexin43) has been predicted as the top key driver of an astrocyte enriched subnetwork associated with Alzheimer’s disease (AD). In this study, we comprehensively examined GJA1 expression across 29 transcriptomic and proteomic datasets from post-mortem AD and normal control brains. We demonstrated that GJA1 was strongly associated with AD amyloid and tau pathologies and cognitive functions. RNA sequencing analysis of Gja1−/− astrocytes validated that Gja1 regulated the subnetwork identified in AD, and many genes involved in Aβ metabolism. Astrocytes lacking Gja1 showed reduced Apoe protein levels as well as impaired Aβ phagocytosis. Consistent with this, wildtype neurons co-cultured with Gja1−/− astrocytes contained higher levels of Aβ species than those with wildtype astrocytes. Moreover, Gja1−/− astrocytes was more neuroprotective under Aβ stress. Our results underscore the importance of GJA1 in AD pathogenesis and its potential for further investigation as a promising pharmacological target in AD.
RESEARCH Open AccessGJA1 (connexin43) is a key regulator ofAlzheimer’s disease pathogenesisYuji Kajiwara1,7†, Erming Wang2,3†, Minghui Wang2,3, Wun Chey Sin4, Kristen J. Brennand1,2,5,6, Eric Schadt2,3,Christian C. Naus4, Joseph Buxbaum1,3,5,6 and Bin Zhang2,3*AbstractGJA1 (connexin43) has been predicted as the top key driver of an astrocyte enriched subnetwork associated withAlzheimer’s disease (AD). In this study, we comprehensively examined GJA1 expression across 29 transcriptomic andproteomic datasets from post-mortem AD and normal control brains. We demonstrated that GJA1 was stronglyassociated with AD amyloid and tau pathologies and cognitive functions. RNA sequencing analysis of Gja1−/− astrocytesvalidated that Gja1 regulated the subnetwork identified in AD, and many genes involved in Aβ metabolism. Astrocyteslacking Gja1 showed reduced Apoe protein levels as well as impaired Aβ phagocytosis. Consistent with this, wildtypeneurons co-cultured with Gja1−/− astrocytes contained higher levels of Aβ species than those with wildtype astrocytes.Moreover, Gja1−/− astrocytes was more neuroprotective under Aβ stress. Our results underscore the importance of GJA1in AD pathogenesis and its potential for further investigation as a promising pharmacological target in AD.Keywords: Alzheimer’s disease, GJA1, Cx43, connexin43, Gene networks, amyloidβ, AstrocyteIntroductionAlzheimer’s disease (AD) reflects multifactorial geneticand environmental perturbations that, in turn, causepleiotropic changes in molecular networks linking a hostof biological processes. By employing an integrative net-work biology approach to analyze a large-scale geneticand gene expression dataset in late onset Alzheimer’sdisease (LOAD), we previously conducted an unbiasedidentification and prioritization of gene networks associatedwith clinical and pathological progression of the disease[98]. In addition to a prominent driver of LOAD, an im-mune response subnetwork (module) governed by TYR-OBP, we identified other network modules within whichgene coexpression patterns were significantly changed inLOAD subjects compared to controls. In the current study,we focused on the identification and characterization ofcausal regulators of a module that shows a dramatic loss ofcoordination in AD and that includes APOE, a well-knownrisk factor of AD [18, 82]. This module was highly enrichedin astrocyte specific genes, with GJA1 predicted as its topregulator.GJA1, also known as connexin43 (Cx43), is a memberof the connexin family of proteins that is highly con-served among vertebrates. Members of the connexinfamily exist at the plasma membrane as hexameric com-plexes known as connexons, and function as connexinhemichannels allowing permeability to small moleculesand ions [28]. Two connexons at the apposed cell sur-face of adjacent cells form a trans-dimer called a gapjunction channel (GJC). Gap junctions are clusters ofgap junction channels, and mediate efficient and rapidbidirectional inter-cellular transmission and transport ofelectrical and chemical signals [55, 80]. Cx43 also func-tions as a unitary channel (hemichannel) to participatein paracrine communication [17], but this activity isoften associated with pathological conditions [21]. GJA1and GJB6 (connexin30) are predominantly expressed inmature astrocytes [27, 93, 99], forming astrocytic net-works facilitating propagation of calcium waves, potas-sium and glutamate buffering, and metabolic coupling[66, 70, 77, 78, 83, 89]. Astrocytic gap junctions are crit-ical for neuronal function, as evidenced by the profound* Correspondence: bin.zhang@mssm.edu; zhangb@hotmail.com†Yuji Kajiwara and Erming Wang contributed equally to this work.2Department of Genetics and Genomic Sciences, Icahn School of Medicine atMount Sinai, New York, NY 10029, USA3Mount Sinai Center for Transformative Disease Modeling, Icahn Institute ofGenomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai,New York, NY 10029, USAFull list of author information is available at the end of the article© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 https://doi.org/10.1186/s40478-018-0642-xneurological phenotypes in Gja1/Gjb6 double knockoutmice [56].Numerous autosomal dominant GJA1 mutations havebeen reported to cause oculodentodigital dysplasia (ODDD),which is characterized by craniofacial and limb dys-morphisms [42]. Patients with ODDD frequently mani-fest neurological symptoms including paraparesis, whitematter abnormalities, hearing and vision loss, and lowIQ [29, 54]. GJA1 mutations associated with ODDD aremostly loss-of-function, although a few gain-of-functioncases have also been identified, indicating that diversefunctional perturbations of GJA1 can lead to ODDD [42].Although mice deficient in astrocytic Gja1 are grosslynormal, they show reduced synaptic plasticity [30], in-creased propagation of synaptic depolarization and neur-onal inactivation (“spreading depression”) [84] andimpaired in rotarod performance [26]. Mice deficient inboth Gja1 and Gjb6 spontaneously develop pathologicalmyelin degeneration without overt neuronal atrophy[56]. Loss or reduced expression of Gja1 leads to en-larged infarct size upon induced-ischemia, and the lossof only the C-terminal tail of Cx43 appeared to mediatethe damaging effects [41, 44, 51, 64, 65, 85]. Thesestudies indicated that Gja1 plays a neuroprotectivefunction and restricts neuronal damage under oxidativeand metabolic stress.GJA1 has been previously investigated in the contextof Alzheimer’s disease. GJA1 (Cx43) immunoreactivitywas found to be enriched in astrocytes surrounding amyl-oid plaques in post-mortem AD brain [63] and a APP/PS1mouse model [59] and Cx43 hemichannel activity was in-creased in the APP/PS1 mouse model [96]. Cx43 defi-ciency or pharmacological blockade of connexins in APP/PS1 mice appeared to reduce dystrophic neurites, mito-chondrial oxidative stress, and cognitive impairment with-out altering amyloid pathology [73, 96, 97].Here we corroborated the evidence of GJA1 dysregulationin AD by analyzing GJA1 expression in a large number oftranscriptomic and proteomic datasets from pathologicallyand clinically characterized LOAD brain samples. Weshowed that a number of known AD risk factor genes weresignificantly correlated with Gja1 in multiple brain regionsin AD. We constructed and validated GJA1 regulated genenetworks in AD. We revealed that Gja1 regulated theexpression of more than half of the known AD risk factorgenes. We further demonstrated the effect of Gja1-defi-ciency on astrocyte function and the subsequent impact onco-cultured neurons.Materials and methodsData preprocessingThree clinical cohorts of post-mortem brain samplesfrom patients with Alzheimer’s disease (AD) symptomsof various severity, and normal controls were subject toRNA microarray and/or RNA-seq analysis to detectchanges in mRNA expression caused by AD pathology.As summarized in (64), RNA microarray assays wereperformed on RNAs extracted from prefrontal cortex(PFC), visual cortex (VC), and cerebellum (CB) of post-mortem brain cortex tissues in the Harvard Brain TissueBank (HBTRC) [98]. Details of the research such asneuropathological phenotypic traits of AD subjects andnormal controls, RNA microarray assays, data prepro-cessing and covariate adjustment were described in [98].Both RNA microarray and RNA-seq assays were con-ducted using RNAs collected from post-mortem brainsin the Mount Sinai Brain Bank (MSBB) cohort [31, 91].Experimental design and data analysis of RNA microar-rays on 19 distinct brain regions of post-mortem braintissues in the MSBB cohort were presented by Wang etal. [91]. Single-end RNA-seq assays were performed onRNAs extracted from four selected regions (BM10,frontal pole (FP); BM22, superior temporal gyrus (STG);BM36, parahippocampal gyrus (PHG); BM44, inferiorfrontal gyrus (IFG)) of post-mortem brain tissues in theMSBB cohort. The raw sequence reads were aligned tohuman genome hg19 with the star aligner (v2.3.0e) [22].Then the gene level expression was quantified by feature-Counts (v1.4.4) [49] based on Ensemble gene modelGRCh37.70. The gene level read counts data was normal-ized using the trimmed mean of M-values normalization(TMM) [76] method to adjust for sequencing library sizedifference. The normalized data was further adjusted forthe covariates postmortem interval (PMI), race, RNA in-tegrity number (RIN), gender, rate of exonic reads, andbatch using a linear mixed model [33], where batch wastreated as a random effect. The residuals from the regres-sion model were used for downstream analysis. Inaddition, proteomics assays were performed on proteinsextracted from the BM10 region of post-mortem brains inthe MSBB cohort, and raw counts summarized at thegene/protein level were provided by the Genomics Core atthe Icahn School of Medicine at Mount Sinai. These rawcounts were further corrected by covariates (PMI + AOD+ batch + gender) using a linear model described by Wanget al. [91], and the residuals after correction were used forfurther downstream analysis.The Religious Orders Study and Memory and AgingProject (ROSMAP) cohort consists of ROS and MAPstudies [7]. The detailed information regarding ROS-MAP study was described in previous studies [8, 9].Normalized RNA-seq expression data were downloadedfrom the AMP-AD Knowledge Portal at Synapse uponauthentication by the AMP-AD Consortium (doi:https://doi.org/10.7303/syn3388564). Genes with at least 1FPKM in at least 10% of the samples were selected andthen the data was corrected for confounding factors in-cluding batch, PMI, gender and RIN via a linear model.Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 2 of 20The residuals after correction were used for furtherdownstream analysis.Bayesian causal network analysisBayesian causal network was constructed by integratinggenome-wide gene expression, SNP genotype, and knowntranscription factor (TF)-target relationships. Briefly, wefirst computed expression quantitative trait loci (eQTLs)and then employed a formal statistical causal inferencetest (CIT) [62] to infer the causal probability between genepairs associated with the same eQTL. The causal relation-ships inferred were used, together with TF-target relation-ships from the ENCODE project, as structural priors forbuilding a causal gene regulatory network from the geneexpression data through a Monte Carlo Markov Chain(MCMC) simulation based procedure [100]. We followeda network averaging strategy in which 1000 networks weregenerated from the MCMC procedure starting with differ-ent random structure, and links that shared by more than30% of the networks were used to define a final consensusnetwork structure. To ensure the consensus network is adirected acyclic graph, an iterative de-loop procedure wasconducted, removing the most-weakly supported link ofall links involved in any loop. Following Zhang et al. [98],we performed Key Driver Analysis (KDA) on the consen-sus Bayesian network to identify key hub genes which reg-ulated many downstream nodes.RNA sequencing and data processingTotal RNAs were sequenced via the Illumina HiSeq 2500system with 100 nt paired-end read. Sequencing reads werealigned to mouse reference genome mm10 (GRCm38.75)STAR aligner [22] guided by UCSC gene model. Acceptedmapped reads were summarized to gene levels using thefeatureCounts [49] program. Raw count data were normal-ized by the voom function in the R limma package [75],and then differential expression was called by the moder-ated t-test implemented in limma. Differentially expressedgenes (DEGs) were defined to have at least 1.2-fold changein expression and BH-adjusted p < 0.05 for two compari-sons including Gja1−/− astrocytes versus wildtype astro-cytes and coculture of Gja1−/− astrocytes and neuronsversus co-culture of wildtype astrocytes and neurons. Theraw and processed data are available at doi: https://doi.org/10.7303/syn11711769.Construction of GJA1 centric co-expression networksWe constructed GJA1 centric consensus co-expressionnetworks from 8 cohort datasets from three cohorts in-cluding MSBB (4 brain regions), ROSMAP (1 brain re-gion) and HBTRC (3 brain regions). In each dataset, thegenes significantly correlated with GJA1 were identifiedbased on BH-corrected p value < 0.05. From those sig-nificant correlations, a directional voting method wasutilized to calculate the frequency of positive correlationas well as the frequency of negative correlations betweenGJA1 and each other gene. Gja1 centric networks werethen defined as a function of frequency threshold n (=1,2, …, 8}.Construction of GJA1 signaling mapStarting from GJA1 as the root, we searched through thekhaki module based Bayesian network for further threelayers of genetic neighborhood, and trimmed away thosesingle leaf nodes while keeping the remaining nodes.Then, we used the individual nodes or its first neighbor-hood in the remaining network to query the reactome(https://reactome.org/) and MSigDB database for geneontology enrichment. A GJA1 signal map was constructedby replacing each individual nodes in the network plotwith a relevant gene ontology entry acquired from thequery.Quantitative PCRQuantitative PCR was performed as previously described[38]. Briefly, total RNA was isolated using Qiagen RNeasykit, and 1–2 μg of total RNA was used to synthesize cDNAusing Ecodry (Clontech, 639,543, Mountain view, CA). Uni-versal probe library in combination with the primers listed(Additional file 1: Table S10) was used to perform qPCRwith KAPA PROBE FAST qPCR master mix (KAPABiosystems, KK4703, Wilmington, MA). Acquired Ctvalues were loaded onto qbase Plus software package(Biogazelle, Belgium) for data quality control andnormalization. Actb, Rpl13a, and Rplp0 were used asnormalizer genes.Cell culture and reagentsGeneration of Gja1−/− astrocytes were previously de-scribed [66]. WT and Gja1−/− primary astrocytes wereprepared by dissecting and dissociating forebrains ofP1–3 pups and culturing for 2 weeks in DMEM contain-ing 10% fetal bovine serum and penicillin/streptomycinin T75 flask. Contaminating microglia were reduced byvigorous shaking flasks at 500 rpm for 20min and re-placement of medium containing floating cells. MouseTNFα (14–8321-62) and IL-1β (14–8012-62) were pur-chased from eBiosciences and used at concentration of10 ng/ml. Carbenoxolone (C4790), lanthanum (575275),quinine (Q1125) were purchased from Sigma-Aldrich(St. Louis, MO, USA).Neuron astrocyte coculturesFor RNA-seq in astrocytes and neuron/astrocyte cocul-tures, cortical neuron cultures were prepared from E15.5wildtype C57BL6/J embryos as previously described [14].1 × 105 neurons were seeded in poly-D-lysine coated 6well plates with or without 1 × 105 wildtype or Gja1−/−Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 3 of 20astrocytes in neurobasal medium (Life Technologies,21,103–049, Grand Island, NY, USA) supplemented withB27 (Life Technologies, 17,504–044), and cultured for10 days. At div10, half of medium was replaced withneurobasal containing 20 μM Aβ 1–42 oligomer for fourdays, and total RNA was harvested using RNA-Bee(AMSBIO, Cambridge, MA, USA) following the manu-facturer’s instruction. Four replicate wells were used foreach condition.For MEA assays, E15.5 cortical neuron cultures wereprepared from wildtype C57BL6/J as above. MEA wellswere coated with poly-D-lysine and one day prior toplating 1 × 105 neurons, 5 × 104 wildtype or Gja1−/− as-trocytes were plated in neurobasal medium supplementedwith B27. At days in vitro (div) 16, neuron/astrocyte co-cultures were treated with 10 μM Aβ1–42 oligomers untildiv 20.For neuronal death and viability assays, 2 × 105 corticalneurons were plated onto PDL coated 12 well plate and1 × 105 wildtype or Gja1−/− astrocytes are plated inTranswells (Costar, 3460, Kennebunk, ME USA). Atdiv7, Transwells with astrocytes were inserted to neuronalcultures and incubated with the indicated Aβ1–42 for 4 or 7days. LDH assay was performed as in (Kajiwara, 2014)using LDH Cytotoxicity Detection Kit (Clontech, MountainView, CA, USA). MTTassay was performed as described in(Bruban 2015).Apoe and Aβ ELISAAstrocytes were seeded in 6 well plates at the density of100,000 cells per well in serum free DMEM medium for48 h with vehicle, 2 or 20 μM Aβ1–42 oligomers. MouseApoe secreted in conditioned medium was quantitatedusing mouse Apoe ELISA kit (Mybiosource, MBS705227,San Diego, CA, USA) following manufacture’s instruction.Astrocytes attached to the plate were used to prepare celllysates as described below. Mouse amyloidβ 40 and 42species were quantitatively determined using Human/RatAmyloid ELISA kits (294–64,701 and 294–64,501, WakoChemicals, Richmond, VA, USA).ImmunoblottingLysates were prepared from astrocytes underwent aboveprocedure in buffer containing phosphate buffered saline,protease inhibitor cocktail (Roche, Indianapolis, IN, USA),and 1x loading buffer. Lysates were resolved by denaturingSDS-PAGE gel and transferred to PVDF membrane. Themembranes were blotted with following primary antibody:Cx43 (1:500, 3512, Cell Signaling Technology); Apoe(1:1000, sc-6384, Santa Cruz Biotechnology); Actin(1:1000, A5060, Sigma-Aldrich), and specific signals weredetected with HRP-conjugated secondary antibodiesfollowed by SuperSignal West Dura (34,075, Thermo-Fisher Scientific).Multielectrode array recordingsRecordings of neuronal activity was performed on Axion768 channel system (The Maestro, Axion Biosystems,Atlanta, GA, USA) using 48-well plate, which records orstimulates neuronal activity from up to 16 electrodes perwell. Manufacture’s recommended settings were used fordetection of spikes, bursts and network bursts. Briefly,spikes were detected using Adaptive Threshold Crossingwith 6x Standard Deviation; bursts were defined as mini-mum of 5 spikes with 100ms of maximum inter-spikeinterval; and network bursts were defined as minimumof 10 spikes with 100 ms of maximum inter-spike inter-val and 20 ms of synchrony window. Spontaneous activ-ities were recorded at days in vitro (div) 10, 14, 18, and20 as follow. A MEA plate was set on heated stage at37C for at least 5 min prior to initiation of recordings,which lasted for 10 min. All the recordings and analysiswere performed on Axion Integrated Studio (Axion Bio-systems). Stimulation was performed div 14 and 20 usingthe same spike and burst settings as in spontaneous re-cordings. A stimulation given was comprised of 3 repeatedcycles of voltage-controlled (500mV/− 500mV), biphasic(500 μs each), and positive-first stimulus followed by 3msof floating duration [88], and was given to all electrodes.Stimulation was given 5 times with 5 s interval and stimu-lation data were analyzed using NeuralMetricTool (AxionBiosystems). Results are given as means of 5 stimulations.StatisticsAll of the statistical significance levels reported in thisstudy were corrected for multiple testing unless other-wise specified. Statistical analysis was performed usingeither R programming language or SPSS version 20 orhigher (IBM corporation, Somers, NY, USA). Amyloiduptake assays were analyzed by Student’s t-test. Spontan-eous neural recordings were analyzed by Man-Whitney,or Kruskal-Wallis non-parametric analysis.ResultsFigure 1 shows the workflow of the integrative networkanalysis and functional validation experiments performedin this study. We first examined the relationships betweenGJA1 mRNA expression and clinical and pathologicaltraits in 29 AD transcriptomic datasets. We also investi-gated the role of GJA1 in gene networks underlying AD.We then performed in vitro experiments to study the roleof GJA1 in regulating AD gene networks and AD relatedphenotypes using primary astrocytes purified and culturedfrom wildtype and astrocyte specific Gja1−/− mice. Gja1’starget gene signatures identified from the RNA-seq datafrom the in vitro experiments were then projected ontothe GJA1 centric networks to validate these networks’structures.Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 4 of 20GJA1 is a key regulator of an astrocyte specific genesubnetwork dysregulated in LOADSeveral studies have used co-expression network analysis tofind modules of co-regulated genes in AD [36, 60, 61]. Ourprevious study developed a novel network approach cap-able of integrating clinical and neuropathological data withlarge-scale genetic and gene expression [98]. This networkbiology approach led to a novel multiscale network modelof LOAD, which identified a number of coexpressed genemodules that were strongly associated with AD pathologicaltraits or underwent dramatic disruption of high-ordergene-gene interactions [98]. One such module, referred toas the khaki module in the original construction of this net-work, was of particular interest since it included APOE, thetop AD risk factor gene. Moreover, the average interactionstrength among its member genes in LOAD was reducedby 71% compared to that in normal control at a false dis-covery rate (FDR) < 2%, suggesting a huge loss of coordin-ation among this group of genes in AD. The khaki modulewas enriched for the genes in Gamma-aminobutyrate(GABA) biosynthesis and metabolism (24 fold enrichment(FE), Fisher’s exact test (FET) p = 0.046) and harbored 12(ALDOC, APOE, AQP4, ATP1A2, CSPG3, CST3, EDG1,EMX2, GJA1, PPAP2B, PRDX6 and SPARCL1) of 46 knownastrocyte marker genes, a 15-fold enrichment over whatwould be expected by chance (FET p = 6.55E-9). The mod-ule was also enriched for the expression of the commonvariants identified as genome-wide significant by AD gen-ome wide association studies (GWAS) (3-FE, FET p =1.92E-11). Bayesian causal network analysis showed thatGJA1 was the top driver of the module followed by FXYD1,STON2 and CST3 [98]. The key drivers of the correspond-ing causal network of the module were the nodes that hada large number of downstream nodes [90, 98]. These resultsindicate that GJA1 is a potential regulator of molecular net-works in AD. In the next subsection, we will investigate theassociation between Gja1 mRNA expression and AD. All ofthe statistical significance levels reported were corrected formultiple testing unless otherwise specified.GJA1 expression is associated with AD clinical andpathophysiological traitsTo gain insight into the role of GJA1 in cognitive functionsand AD pathogenesis, we first extensively investigated howGJA1 expression at the mRNA level was correlated withAD neuropathological traits in 29 gene expression datasetsfrom three AD cohort studies of aging and dementia thatincluded organ donation at death: the Mount Sinai/JJ PetersVA Medical Center Brain Bank (MSBB; Additional file 1:Table S1) [91], the Religious Orders Study and the RushMemory and Aging Project (ROSMAP) [8, 9] and in theHarvard Brain Tissue Resource Center Alzheimer’s Diseasestudy (HBTRC) [98]. We chose six different clinical andpathological criteria to evaluate the clinical relevance ofGJA1 on AD pathology and cognitive functions: the Mini-Mental State Examination (MMSE) score [25, 32], the sumof NFT density estimates for all cortical regions examined(NTrSum), Mean Plaque density (PLQ_Mn) for the es-timation of average plaque density, Braak stage scorefor quantitative assessment of neurofibrillary tanglesFig. 1 Overview of the integrative network analyses and validation experiments performed in the study. a. GJA1 mRNA expression changes andits correlations with clinical and pathological traits were systematically investigated in 29 datasets. GJA1-centric coexpression and regulatorynetworks. b. Workflow of in vitro functional validation study. Wildtype and Gja1−/− astrocytes with or without wildtype neurons were used toprepare RNA for sequencing and to perform various functional validations (c). d. GJA1’s gene signatures between wildtype and Gja1−/−astrocytes and between coculture of wildtype astrocytes and wildtype neurons and coculture of Gja1−/− astrocytes and wildtype neurons wereidentified from RNA-sequencing data from the experiments in b. e. GJA1’s gene signatures were used to validate the network structurespredicted from the transcriptomic datasets in human AD brains. Functional relevance of GJA1’s gene signatures was also investigatedKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 5 of 20[11], the Consortium to Establish a Registry for Alzhei-mer’s disease (CERAD: 1 for definite AD, 2 for probableAD, 3 possible AD, 4 for normal control) score forquantitative measure of neuritic plaques, and clinicaldementia rating score (CDR ranging between 0 and 5with 0 for normal control and 5 for severe dementia).In the microarray data in the ROSMAP cohort, GJA1expression was significantly correlated with CERADscore (r = − 0.15, p = 3.3E-3) and the MiniMental StateExamination (MMSE) score (r = − 0.14, p = 6.4E-3). Simi-lar results were observed in the ROSMAP RNA-seqdataset (Fig. 2a, and Additional file 1: Table S2), suggest-ing that the mRNA expression of GJA1 is associatedwith AD pathogenesis and dementia. The MSBB AD co-hort includes microarray and RNA-seq data from a bat-tery of distinct brain cortical regions and thus providesan excellent opportunity to investigate regional differ-ences in the correlation between GJA1 expression andAD neuropathological traits [91]. Among the 19 braincortex regions investigated in the MSBB AD microarraydata, GJA1 expression in six cortex regions includingBM10 (frontal pole), BM20 (inferior temporal gyrus),BM21 (middle temporal gyrus), BM32 (anterior cingu-late), BM36 (parahippocampal gyrus) and BM46 (dorso-lateral prefrontal cortex) was significantly correlatedwith at least three AD neuropathological traits (Fig. 2b,and Additional file 1: Table S2). Overall, GJA1 expres-sion in these six cortex regions displayed a significantpositive correlation with Braak stage score, PLQ_Mn,NTrSum and CDR. The MSBB AD RNA-seq data re-vealed a consistent pattern of correlation between GJA1expression and AD clinic traits across the cortical regionsstudied (Fig. 2c-e; Additional file 1: Table S2). Notably, inBM10, BM36 and BM44 cortex regions, the microarrayand RNA-seq data converged to show a consistent correl-ation between GJA1 expression and AD neuropathologicaltraits (Additional file 1: Table S2). Thus, in the MSBBcohort, the association between GJA1 expression andAD neuropathological traits was cortex-specific. At theprotein level, GJA1 in the brain cortex BM10 region wassignificantly correlated with CERAD (r = − 0.32, p =1.26E-07), PLQ_Mn (r = 0.37, p = 4.08E-10) (Fig. 2f) andCDR (r = 0.35, p = 5.69E-9) (Additional file 1: Table S2).Also the total soluble amyloidβ (Aβ) levels had a signifi-cant positive correlation with GJA1 protein levels in theBM10 region (r = 0.18, p = 0.0036).We showed previously in the HBTRC cohort thatGja1 had a significant correlation with Braak score (r =0.61 and 0.52 for dorsolateral prefrontal cortex and cere-bellum cortex regions, respectively) [98].We further checked how GJA1 mRNA expression wascorrelated with 30 known AD risk factor genes. As shownin Fig. 2g, a majority of those AD genes were significantlycorrelated to GJA1 in the five RNA-seq datasets.We also examined GJA1 differential expression be-tween various subgroups of AD severity with respect toeach individual AD neuropathological or functional cog-nitive trait using pairwise Student’s t-test. Consistentwith the correlation analysis, Gja1 expression increasedsignificantly as the disease deteriorated (Additional file 1:Table S3).Because APOE is one of the major AD risk factors, andage and sex are critical clinic covariables in AD neuropath-ology, we investigated whether age, sex and APOE geno-types had any impact on the association of GJA1 expressionwith AD clinical and pathological traits. We stratified thiscohort by age of death (AOD) (AOD> 85 versus AOD<85), sex (female versus male), and APOE genotypes (E23,E34 and E33). As demonstrated in Additional file 1:Table S4 and Additional file 2: Figure S1, GJA1 expres-sion is more significantly associated with clinical de-mentia rating (CDR) in the group with AOD > 85 thanthat with AOD < 85, in females than males, and in thegroup with APOE E33 than that with E34 or E23, acrossfour brain regions in the MSBB cohort, suggesting thatage, sex and APOE genotypes impact the association ofGJA1 expression with clinical and pathological traits.We further assessed if GJA1 mRNA expression wascorrelated with the variants of the known AD risk genesusing the RNA-seq data from the brain region BM36 inthe MSBB cohort. Among the 28 ADGWAS genes thathad identifiable variants in the present study, 18 had atleast one variant that possessed a significant correlationwith GJA1 (Additional file 2: Figure S2). For example,the transcript/isoform ENST00000532146 of the risk fac-tor CELF1 is significantly correlated with GJA1 expressionin BM10 and BM44, but not BM22 and BM36 whileENST00000534614 and ENST00000539254 are correlatedwith GJA1 expression only in BM36 and BM22, respect-ively (Additional file 2: Figure S2). These results suggestedthat variation in transcripts’ abundance might be an im-portant factor for determining the co-regulation betweenGJA1 and AD risk factors.In summary, both correlation and differential expres-sion analyses revealed that GJA1 was associated withamyloid and tau pathologies of AD as well as cognitivefunctions suggesting that GJA1 may play an importantrole in AD.Transcriptomic changes caused by Gja1 deficiency inmouse astrocytesTo validate the role for GJA1 in orchestrating the astro-cytic transcriptome, we purified and cultured primary as-trocytes from wildtype and astrocyte specific Gja1−/−mice, and identified differentially expressed genes (DEGs)by RNA-seq in Gja1−/− vs wildtype primary astrocyticcultures in the absence or presence of wildtype primarycortical neurons. Each group had four replicates. AllKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 6 of 20cultures were treated with 10 μM Aβ1–42 oligomer fromdiv 10 through 14, when total RNAs were harvested. Weidentified 2891 upregulated (termed AST(up)) and 2605downregulated (termed AST(dn)) DEGs upon the ablationof the Gja1 gene as compared to wildtype primary astro-cytes (Fig. 3a). We identified 573 upregulated genes(termed AST +NEU(up)) and 1391 downregulated genes(termed AST +NEU(dn)) in Gja1−/− vs. wildtype primaryastrocytes co-cultured with primary neurons (Fig. 3a).AST(up) shared 328 genes (11.3%) with AST +NEU(up)and 208 genes with AST +NEU(dn) while AST(dn) shared672 and 60 genes with AST-NEU(dn) and AST-NEU(up),respectively (Fig. 3a). These DEG signatures were enrichedfor a variety of biological pathways including translationalFig. 2 GJA1 mRNA/protein expression is correlated with AD pathogenesis, dementia and known AD risk factor genes. GJA1 mRNA expression iscorrelated with the MMSE score in the prefrontal cortex region in the ROSMAP cohort (a), the NTrSum score in the cortex region frontal pole(BM10) the MSBB cohort (b), the Braak score in the parahippocampal gyrus (BM36) in the MSBB cohort (c), and the CDR score in BM36 in theMSBB cohort (d), and CERAD in BM36 in the MSBB cohort (e). The GJA1 protein level is also correlated with the plaque mean density (PLQ_Mn)in the frontal pole (BM10) in the MSBB cohort (f). Inset was the correlation coefficient along its p value between individual clinical traits and GJA1mRNA/protein level. Severity of AD symptoms was classified based on each individual AD neuropathological and cognitive traits according to thecriteria established in the ROSMAP clinic codebook [91, 92]. g. Gja1 mRNA expression is correlated with a vast majority of the ADGWAS genes inthe MSBB (BM10, BM22, BM36 and BM44) and ROSMAP RNA-seq datasets. Colors represent Correlation coefficients: red, high; blue, low; yellow, inbetween. * and ** stand for significance at 5 and 0.5% level, respectively while ns indicates insignificant correlation and na stands for “notapplicable”. Shown in the heatmap are only the ADGWAS genes that passed RNA-seq data preprocessing, normalization and annotationKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 7 of 20processes, immune response, cell-cell communication,extracellular matrix, microtubule cytoskeleton, synaptictransmission, lipid biosynthesis, steroid biosynthesis, chol-esterol biosynthesis and cell-matrix adhesion (Fig. 3a-b,Additional file 1: Table S5).AST(up) was significantly enriched for the genes in anAβ network signature [16] (2.03 FE, FET p = 1.08 E-10),the AD genome-wide significant risk factor gene signa-ture (ADGWAS) (3.47 FE, FET p = 6.58E-6) (Fig. 2c)while AST +NEU(dn) was enriched for the genes in theAβ network signature (1.68 FE, FET p = 1.1E-3) (Fig. 3cand Additional file 1: Table S6). These results suggestedthat Gja1 is a critical regulator of the AD GWAS genesand may play an important role in Aβ metabolism.We further intersected the GJA1 KO DEG signatures withthe signatures from other inflammatory diseases to betterunderstand of the immune response component in GJA1regulated gene expression. We considered 2 well-establishedinflammatory gene signatures, an inflammatome signatureof 2461 genes from eleven rodent inflammatory diseasemodels [90] and the human macrophage and immune re-sponse enriched module of 2483 genes causally linked toobesity and diabetes [23]. These two inflammatory signa-tures share 758 genes [termed the core disease-relatedFig. 3 The impact of GJA1 deficiency on AD relevant gene ontologies and AD risk networks. a. Venn diagram for genes overlapping amongvarious differentially expressed genes (DEG) signatures. AST(dn) and AST(up) were down- and up- regulated DEG signatures in Gja1−/− vs.wildtype astrocyte cultures, while AST + NEU(dn) and AST + NEU(up) were down- and up- regulated DEG signatures in Gja1−/− vs. wildtypeastrocytes co-cultured with neurons. Top gene ontology for each individual DEGs were shown above each cluster. b. Heatmap of Geneenrichment significance (−log10(p)) for the DEG signatures. Top 10 enriched gene ontologies were shown for each individual DEGs. c. GJA1−/−signatures including AST(all), AST(up), AST(dn), AST + NEU(all), AST + NEU(up) and AST + NEU(dn) are enriched for three AD genetic gene setsincluding the one identified by the International Genomics of Alzheimer’s Project (IGAP) [1], the Abeta (Aβ) genetic network [16], the AD riskgene list (ADGWAS) [43]Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 8 of 20inflammatory gene (CDIG)]. About half of the CDIGs fallinto AST(up) (accounting for about 12.6% of AST(up), 6.57FE, FET p= 1.85E-208) and the intersection includes wellknown inflammatory markers (e.g., CD44, CD53, FCER1G,HCK, TYROBP and TREM2), inflammation complementcomponent members (C1QA, C1QB, and C1QC), CXC che-mokines (CXCL10, CXCL3 and CXCL6) and TNF Receptors(TNFRSF11B and TNFRSF13B). The CDIGs are also signifi-cantly enriched in AST +NEU(up) (accounting for about15% of AST +NEU(up), 7.84 FE, FET p= 1.06E-49). Theseresults suggest that inflammation is a critical component inGJA1-regulated gene expression.We then investigated expression changes of astrocyte-and neuron-specific genes in various gene signatures usingthe recently identified brain cell type specific signatures[57]. Astrocyte- and neuron-specific marker gene signa-tures are enriched in the down- and up-regulated gene sig-natures in the neuron and Gja1−/− astrocyte co-culturesversus the neuron and wildtype astrocyte co-cultures, re-spectively (Additional file 1: Table S9A). The co-culture sys-tems with and without Gja1−/− upregulated a significantportion of the neuron-specific marker genes when com-pared with the respective astrocyte alone models (with orwithout Gja1−/−) while down-regulating many astrocytemarker genes (Additional file 1: Table S9B). The gene listsfrom the abovementioned intersection analyses can befound in (Additional file 1: Tables S9C and S9D).Gja1 deficiency induced transcriptomic changes highlyoverlap the GJA1 centric gene networks in ADWe further examined the molecular mechanisms of GJA1in AD pathogenesis and cognitive function. We first ex-amined if these Gja1−/− DEG signatures were enriched inthe Khaki module where GJA1 resided based on our previ-ous study [98]. As shown in Additional file 1: Table S7,AST +NEU(dn), AST(dn) and AST(up) were all signifi-cantly enriched in the khaki module with FET p =3.74E-54 (9.0 FE), 3.64E-28 (4.4 FE), 1.28E-5 (2.1 FE),respectively. On the other hand, the overall signaturesAST(all) and AST +NEU(all) also significantly overlappedthe module with FET p = 3.03E-32 (3.2FE) and 1.9E-47(6.7 FE), respectively .To gain more insights into the signaling circuit of theGJA1 regulation in AD, we constructed the Bayesiancausal network for the khaki module and projected theGja1−/− DEG signatures onto this network in order todelineate the underlying causal relationship among themolecular constituents of this module. As shown inFig. 4a and Additional file 1: Table S7, 13 of the 19 pre-dicted key drivers except for GJA1 in the khaki modulewere regulated by GJA1 (4.94 FE, FET p = 4.70E-05).Strikingly, 11 out of the 13 GJA1 regulated key drivergenes were within the GJA1’s downstream network neigh-borhood (Additional file 2: Figure S3A). To better gaininsights into the functions of the Bayesian causal networkin Fig. 4a, we built up a GIA1 signaling pathway map. Asshown in Additional file 2: Figure S3B, GIA1 regulates adiversity of pathways such as regulation of gap junctionactivity, innate immune system, TGFβ signaling, DNArepair and lipid metabolism. These results suggestedthat Gja1 deficiency significantly impacted the khakimodule and thus strongly validated our previous pre-diction of GJA1 as a driver of this important AD relatedsubnetwork.To more comprehensively determine GJA1’s target genesin AD, we further systematically identified the genes corre-lated with GJA1 in a number of human AD cohorts. Genessignificantly correlated with GJA1 (Benjamini-Hochberg(BH)-corrected p-value < 0.05) were first identified in eachof eight datasets including four from the MSBB RNA-seqcohort, one from the ROSMAP RNA-seq data and threefrom the HBTRC cohort. Note that only the data from theAD subjects were used for the correlation analysis. Amajority voting method was utilized to calculate thefrequency of each correlation across the eight datasets(Additional file 1: Table S8). We defined consensusGJA1-centered correlation signature (CGCCS) as afunction of frequency threshold n, i.e., CGCCS(n) = {g |frequency(r(g, GJA1)) ≥ n}, where r(g, GJA1) representsa significant correlation between a gene g and GJA1 at FDR< 0.05, and n = 1, 2, …, 8. This process led to GJA1 centeredcorrelation networks, also denoted as CGCCS(n).Additional file 2: Figure S4 shows the enrichment ofthe GJA1 centered correlation networks for the previ-ously identified DEGs in AST and AST +NEU. CGCCS(4) was most significantly enriched for the AST(all) andAST +NEU(all) DEG signatures with FET p = 1.0E-330(3.2 FE) and 7.5E-311 (3.9 FE), respectively (Black lines,Additional file 2: Figure S4). CGCCS (6) is enriched forthe DEG signature in AST with FET p = 6.2E-309 (3.8FE). Figure 4b shows the network CGCCS (6) which in-cludes 201 up-regulated (red nodes, termed GJA1_centere-d_AST(up)) and 307 down-regulated (blue nodes, termedGJA1_centered_AST(dn)) genes upon the knockout ofGja1 in astrocytes. About 30% of the genes in each of thetwo GJA1_centered_signatures belonged to the khaki mod-ule (FET p = 1.52E-90, 32.37 FE). This GJA1-centeredcorrelation network included eight driver genes (AGT,BMPR1B, CST3, FXYD1, NTSR2, SLC15A2, SPON1, andSTON2) in the khaki module [98]. Similar results were de-rived for the AST +NEU(up) and AST +NEU(down) signa-tures. Therefore, GJA1 impacts the expression of many keynetwork drivers in the astrocytic subnetwork, suggesting itscentral role in this AD-related gene network. Furthermore,the analysis of the genes specific to the A1/A2 astrocytic ac-tivation in our DEG from Gja1−/− astrocytes [50] revealedthat most of pan-, A1-, and A2-specific genes were gener-ally upregulated (Additional file 2: Figure S5).Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 9 of 20Inflammatory cytokines downregulate expression of Gja1and the astrocytic subnetwork modulePreviously it has been shown that LOAD-relevant in-flammatory cytokines such as TNFα and IL-1β down-regulated expression of Gja1 in astrocytes [19, 74].Wildtype astrocytes were treated with TNFα, or IL-1β,or both for 7 days, confirming that Cx43 (Gja1 protein)was profoundly and synergistically reduced by both cyto-kines (Fig. 5a-b). Paradoxically, IL-1β significantly in-creased, but TNFα significantly decreased, Apoe proteinFig. 4 Network analysis on GJA1 genetic networks. a. BN (Bayesian Network) based on astrocyte (khiki) module. Gja1−/− DEG signatures wereprojected onto BN to detect overlapped gene signatures, which were classified into eight categories: up-regulated only in AST (in red uprighttriangle), down-regulated only in AST (in blue downside triangle), only up-regulated in AST + NEU (red square), only down-regulated in AST + NEU(blue square), up-regulated in both AST and AST + NEU (in pink diamond), up-regulated in AST but down-regulated in AST + NEU (in pinkhexagon), down-regulated in both AST and AST + NEU (in gray diamond), and down-regulated in AST but up-regulated in AST + NEU (in grayhexagon)). In enlarged label were genes that were validated experimentally. Gja1 was in black. In turquoise circle were genes non-overlapped.AST, Gja1−/− astrocyte culture while AST + NEU, Gja1−/− astrocyte culture in the presence of co-cultured neurons. b. GJA1 centered geneticnetworks inferred by projecting Gja1−/− DEG signatures onto Gja1 correlation concensus network (CGCCS(6)). Overlapped gene nodes weredenoted as in AKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 10 of 20levels (Fig. 5b). Following cytokine treatment, we testedGja1, Apoe and eight key network driver genes found tobe differentially expressed in Gja1−/− astrocytes by RNA-seq analysis, as a proxy to capture the network changes.qPCR analysis revealed that Gja1, Apoe and the otherastrocytic subnetwork drivers were similarly downregu-lated by these cytokines (Fig. 5c).Gja1 channel activity increases the expression of theastrocytic subnetwork moduleSince these cytokines inhibit GJC and potentiate hemi-channel activities [74], we asked whether inhibition ofGJC and hemichannel activities regulates Gja1 and otherastrocytic subnetwork drivers. Treatment of wildtype as-trocytes with carbenoxolone (CBX, inhibitor of GJC andhemichannel [2, 95]) or lanthanum (La3+, inhibitor ofhemichannel [2]) led to significant reduction of Gja1 andApoe protein levels (Fig. 6a-b). Interestingly, the CBXtreatment had broader effects on the reduction of thedriver genes (Fig. 6c), while La3+ had generally milder andmore selective effects, suggesting that GJC and hemichan-nel activities contribute to the regulation of distinct sets ofthe genes.We next tested whether Gja1 channel activation can alterthe astrocytic subnetwork. Gja1 hemichannel activity canbe increased by quinine [81]. Treatment of wildtype astro-cytes with quinine significantly upregulated Gja1 and Apoeprotein and mRNA levels, along with transcriptional upreg-ulation of a subset of the driver genes (Additional file 2:Figure S6). These data collectively supports Gja1, and spe-cifically its channel activity, as an important regulator ofastrocytic gene coexpression network including Apoe.Fig. 5 Inflammatory cytokines downregulates Gja1, Apoe, and other network genes. a. Wildtype primary mouse astrocytes were treated witheither IL-1β (10 ng/ml) or TNFα (10 ng/ml) or in combination for 7 days and levels of Cx43, Apoe expression were analyzed by immunoblot.Representative results from 4 (Gja1) and 3 (Apoe) independent experiments are shown. b. Protein levels of Cx43 (left) and Apoe (right) werequantitatively analyzed for these technical replicates after normalization to Actin. ANOVA followed by Bonferroni post-hoc tests are indicated byasterisks. * p < 0.05, ** p < 0.01, *** p < 0.001. c. Quantitative gene expression analysis in wildtype astrocytes with treatments similar to a) wasperformed to analyze the other key drivers of the GJA1-centered network. Results representative of two independent experiments are shown.Statistical analysis was performed as in B, and only the comparison between control and IL-1β/TNFα is shownKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 11 of 20Increased neuronal survival following Aβ treatment whenco-cultured with Gja1−/− astrocytesWe assessed the role for Gja1 in neuronal death and viabil-ity in a transwell co-culture system, in which corticalneurons were grown on the bottom of the plate, while astro-cytes were cultured on the transwell insert. This allowedphysical separation of the two cell types, while maintainingchemical continuity. Incubation of wildtype neurons andwildtype astrocytes with the indicated concentration of Aβ1–42 oligomers resulted in increased neuronal death [t (4) = −2.941, p = 0.030 at 2 μM; t (4) = − 5.857, p= 0.004 at 20 μM]and decreased viability [t (6) = − 5.395, p= 0.002 at 2 μM; t(6) = 4.671, p= 0.003 at 20 μM] at day 7 (but not at day 4(Fig. 7a)), in a dose dependent manner (Fig. 7b). Wildtypeneurons cultured with Gja1−/− astrocytes were significantlyresistant to death and loss of viability (Fig. 7b), consistentwith a previous study showing that Gja1−/− deficient astro-cytes conferred neuroprotection [69].Reduced Apoe secretion and synthesis in Gja1−/−astrocytesAPOE is a well-known risk gene for LOAD and primarilyproduced by human and mouse astrocytes as well asmicroglia and neurons [52]. In addition, APOE was in thecoexpression network module governed by GJA1. There-fore, we tested how Apoe levels were affected in the ab-sence of Gja1 in mouse primary astrocytes. QuantitativePCR revealed significantly reduced Apoe expression inGja1−/− astrocytes (t (6) = − 5.330, p = 0.002) (Fig. 8a).Consistently, we found lower Apoe protein levels in Gja1−/− astrocytes by immunoblot analysis of the lysates, withor without Aβ1–42 oligomer treatment (Fig. 8b). We ana-lyzed Apoe in the conditioned medium from these astro-cytes and found that secreted Apoe in the conditionedmedium was consistently reduced in Gja1−/− astrocytescompared to wildtype, with or without Aβ1–42 oligomertreatment (Fig. 8c). Two way ANOVA revealed that therewere significant main effects by genotype and treatment(F(1, 18)=121.618, p < 0.0005, F(2, 18)=9.364, p = 0.0016,respectively) but there was no interaction of genotype andtreatment (F(2, 18) = 0.305, p = 0.741].Neurons cocultured with Gja1−/− astrocytes producehigher levels of Aβ speciesSince our RNA-seq analysis revealed that Gja1 regulatedgenes were highly enriched in Aβ network, we thereforeFig. 6 GJA1 inhibitors downregulate Gja1 and Apoe, and other network genes. a. Wildtype primary mouse astrocytes were treated with 200 μMcarbenoxolone (CBX, gap junction inhibitor) or Lanthanum (La3+, hemichannel inhibitor) for 3 days and levels of Cx43, Apoe were analyzed byimmunoblot. b. Quantitative analysis of Cx43 and Apoe protein levels were normalized to Actin. ANOVA followed by Bonferroni post-hoc tests areindicated by asterisks. ** p < 0.01, *** p < 0.001. c. Quantitative gene expression analysis in wildtype astrocytes treated similar to A was performedto analyze GJA1 network drivers. Statistical analysis was performed as in b, and only the comparisons to control are shown. Results representativeof two independent experiments are shown for all experimentsKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 12 of 20analyzed Aβ production in neuron/astrocyte cocultures.Indeed, we observed higher levels of both Aβ40 and Aβ42species in the conditioned medium from neuronsco-cultured with Gja1−/− astrocytes at all time pointsexamined [Aβ40; t(4) = 17.684, p < 0.0005 (div6), t(4) =3.447, p = 0.026 (div13), t(4) = 11.112, p < 0.0005 (div16),t(4) = 11.112, p < 0.0005 (div20): Aβ42; t(4) = 8.422, p <0.0005 (div6), t(4) = 2.047, p = 0.110 (div13), t(4) =11.245, p < 0.0005 (div16), t(4) = 8.016, p < 0.001 (div20)](Fig. 8d-e). Because astrocytes have been shown to take upAβ, preferentially oligomers [67], we investigated the rolefor Gja1 in controlling Aβ clearance. When we testedwhether wildtype and Gja1−/− astrocytes were proficient intaking up exogenously applied fluorescently labeled Aβ1–42oligomers, we found that the number of Gja1−/− astrocyteswith cell-associated Aβ1–42 oligomers was significantlyreduced compared to wildtype astrocytes [t(20) = − 6.670,p < 0.0005] (Additional file 2: Figure S7).Reduced neuronal activity in response to Aβ treatmentwhen co-cultured with Gja1−/− astrocytesIn order to understand the impact of the network perturb-ation by loss of astrocytic Gja1−/− on neuronal functions,we co-cultured wildtype cortical neurons with either wild-type or Gja1−/− astrocytes on multielectrode arrays andcompared spontaneous and stimulated neuronal activitiesbefore and after Aβ1–42 oligomer treatment (Fig. 9a).Although few significant differences were detected prior toAβ treatment at 16 days in vitro (div), at 18 and/or 20 div(2 and 4 days after 10 μM Aβ1–42 treatment) most metricsof spontaneous activity (i.e. number of spikes, bursts, net-work bursts, and spikes per network burst) were signifi-cantly reduced in Gja1−/− astrocyte-cocultured neurons(Fig. 9b). The reduced spontaneous neuronal activity at div20 suggested that co-culture with Gja1−/− astrocytes madeneurons less responsive to Aβ-induced changes. Whilestimulated neuronal activity at div 14 was not affected byco-culture with Gja1−/− astrocytes (Fig. 10a), it was re-duced at 20 div (number of spikes, spikes per burst, meanfiring rate, and burst duration) (Fig. 10b-c).DiscussionFunctional interaction between neurons and astrocytesis an appealing research area that has expanded vigorouslyin recent years. The involvement of astrocytes in thepathogenesis of neurodegenerative diseases such as AD isjust beginning to be appreciated. Astrocyte-specific GJA1expression has been shown to be upregulated in the post-mortem AD brains [6, 34]. Consistent with but beyondthese studies, we predicted and validated not only upre-gualtion of GJA1 expression in AD but also its regulatoryrole in driving a large astrocyte-specific molecular networkunderlying AD. Indeed, our functional analysis using Gja1−/− astrocytes revealed that the Gja1 network contributedFig. 7 Gja1 contributes to neuronal death and reduces viability by Aβ1–42 treatment. Cortical neurons were plated onto 12 well plate andwildtype or Gja1−/− astrocytes grown on transwells were inserted to the neuron cultures. Indicated concentration of Aβ1–42 oligomers areapplied for 4 days (a) or 7 days (b) and neuronal death and viability was determined by LDH and MTT assays, respectively. Student’s t-tests wereused for all the statistical analysis. * p < 0.05, ** p < 0.01, *** p < 0.001. For all experiments, representative of at least two independent experimentsis shownKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 13 of 20to neuronal death and decreased their viability underAβ1–42 treatment and, which may explain synapse andneuron loss in the Alzheimer brains [20]. This is con-sistent with recent studies showing that GJA1 play crit-ical roles in AD-relevant phenotypes in AD mousemodels [40, 73, 96, 97]. On the other hand, a numberof other studies indicate that Gja1 isneuroprotective[41, 44, 51, 64, 65, 85]. Indeed, our data showed thatthe Gja1 network appeared to be involved in phagocytosisof Aβ, and maintenance of neuronal activities under Aβstress. Taken together with the previous findings of the in-crease of Cx43 protein and its channel activity specificallyin astrocytes surrounding plaques [59, 63, 96], we suggestthat the upregulation of GJA1 in AD brains may start as aneuroprotective response to amyloid plaques. It is inter-esting to note that previous clinical studies have consist-ently demonstrated an increased neuronal activity in thehippocampus and cortex in non-demented individuals athigh risk for AD including APOE4 carriers, minor cogni-tive impairment (MCI) or at presymptomatic stage of fa-milial Alzheimer’s disease [10, 72], and individuals withearly Alzheimer’s disease are prone to developing seizures[87]. Furthermore, the hippocampal hyperactivation is aviable therapeutic target for AD [4]. We speculate that therole of GJA1 in supporting neuronal activity in the ADbrain might explain partly the observation of elevatedbrain activity in the prodromal stage of AD.In the present study, we demonstrated GJA1 as a masterregulator, which regulates not an AD-related astrocyte-spe-cific gene subnetwork [98] but also an array of AD riskgenes including the most important AD gene APOE (Fig.4a-b). More broadly speaking, in in vitro cultured astrocytes,Gja1 deficiency caused significant change in expression of5496 genes involved a diverse of biological pathways andprocesses (Fig. 3b). Similarly, in in vitro astrocyte andneuron cocultures, thousands of genes were regulated byGja1 (−/−). Of note, more than half of the known AD riskfactor genes (Fig. 2g) and hundreds of inflammatome genesin chronic inflammation diseases [23] were the targets ofGJA1. Upregulation of GJA1 expression in AD brains of hu-man subjects and in vitro or in vivo perturbation of Gja1(e.g., Gja1 deficiency in primary cultured astrocytes in thepresent study) lead to a cascade of biological and patho-logical reactions and processes including subsequent alter-ation in cells’ biological functions and responses to theirenvironments, which are manifested as a complex and inter-twined GJA1 centered molecular network. Interestingly, ourdata pointed to the key role of Cx43 gap junction channelFig. 8 Gja1 deficiency causes reduction of Apoe production and promotes Aβ production in neuron/astrocyte cocultures. a. Quantitative PCRanalysis of Apoe in wildtype (WT) and Gja1−/− astrocytes. b. Apoe, Cx43, and actin levels in wildtype and Gja1−/− astrocytes treated with theindicated concentration of Aβ1–42 oligomers for 48 h were determined by immunoblot analysis of cell lysates. c. Apoe secreted into theconditioned medium of astrocytes in B was quantitatively determined by specific ELISA in quadruplicate wells. d and e. Amyloidβ secreted to theconditioned medium of wildtype cortical neurons cocultured with either wildtype or Gja1−/− astrocytes was quantitatively determined by Aβ40and Aβ42 specific ELISAKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 14 of 20and hemichannel activities in regulating gene networks, andindicated that the network can be fine-tuned by pharmaco-logically modulating Cx43 channel activity (Fig. 6 and Add-itional file 2: Figures S5 and S8). Cx43 could be a usefultherapeutic target in diseases such as LOAD, ODDD, andother neuropsychiatric disorders [71]. In support for such ahypothesis, recent studies showed that Gja1-deficiency or aconnexin inhibitor (Boldine) treatment in APP/PS1 micehad beneficial effects [73, 96, 97], underscoring that Cx43 isa critical mediator of AD pathophysiology and an importanttherapeutic target for LOAD.One of the important novel discoveries in the presentstudy was the Gja1mediated production of ApoE in astro-cytes. APOE is mainly produced in astrocytes, but also inneurons and microglia when under stress [35]. APOE isthe major LOAD risk factor, critical to AD neuropathol-ogy and neurocognitions [15, 39]. We therefore focusedon characterizing Apoe in Gja1−/− astrocytes. Manipula-tion of Gja1 by genetic disruption, cytokines, CBX, andquinine in astrocytes as well as neuron/astrocyte/micro-glia culture system consistently resulted in the regulationof Apoe in the same direction as Gja1. Abnormal produc-tion of APOE, in particular, the risk-associated isoformAPOE4, caused either gain of toxic functions, whenover-expressed, such as neuronal toxicity, Aβ aggregationand tangle formation or loss of physiological functions,when under-expressed, such as Aβ clearance, synapticfunction and neurogenesis. Consistent with this notion,we observed drastic reduction in uptake and retention ofAβ oligomers by Gja1(−/−) astrocytes (Additional file 2:Figure S7 A-B), with concomitant increase in concentra-tion of both Aβ1–40 and Aβ1–42 species in in vitro cul-ture medium (Fig. 8d-e). Since APOE is known to beregulated by transcription factors such as LXR and RXRFig. 9 Gja1 deficiency reduces spontaneous neuronal activity in cortical neuron/astrocyte cocultures as determined by multielectrode array. a.Experimental scheme is indicated. One day prior to cortical neuron preparation and plating, wildtype or Gja1−/− astrocytes were plated onto 48-well MEA array plate in quadruplicate wells. Spontaneous activities of the cocultures were recorded at day 10, 14, 18 and 20 on Axion Maestro. Atday 16, cocultures were treated with 10 μM Aβ1–42 oligomers. b. Boxplots of the indicated metrics from the MEA recordings are shown. Eachcircles represent mean values of individual well, and the horizontal bars within the box indicates means of quadruplicates. Mann-Whitney U testswere used for statistical analysis. * p < 0.05Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 15 of 20[48], and taken together with downregulation of NR1H3(LXRA) expression in our RNA-seq data, we speculatethat NR1H3 regulation by GJA1 might have mediated theAPOE expression. Further investigation is required to dis-sect the mechanism underlying GJA1-mediated expres-sion of APOE and its biological significance, which wouldlead to the development of novel therapeutic strategies.A recent study identified the transcriptomic basis of thetwo distinct polarization states of A1 neurotoxic and A2neuroprotective astrocytes [50]. Interestingly, our RNA-seqanalysis revealed that most of pan-reactive, A1 and A2 spe-cific transcripts were significantly upregulated in Gja1−/−astrocytes (Additional file 2: Figure S5). This pattern of ex-pression is most similar to those induced by combination ofIL-1β and TNFα or C1q (Fig. 1a in [50]), and consistentwith this, our data showed that IL-1β and TNFα causeddownregulation of Cx43 and other key drivers, mimickingthe loss of Gja1. This pattern of expression may indicatethat Gja1 deficiency induces non-specific activation of eitherA1 or A2 polarized-astrocytes, or A1/A2 overlappingFig. 10 Gja1 deficiency reduces stimulated neuronal activity in cortical neuron/astrocyte cocultures as determined by multielectrode array. a andb. Cocultures were stimulated by 3 cycles of voltage-controlled biphasic stimulus from all electrodes within the wells at 14 days (a) and 20 days(b) in vitro. At least 5 times of repeated stimulations were recorded and analyzed. c. Two representative raster plots as recorded on day 20 areshown for wildtype neurons cocultured with wildtype astrocytes (upper panel) or with Gja1−/− astrocytes (lower panel). Y-axis represents 16electrodes and X-axis represents time after stimulation indicated by red arrowhead. Black horizontal scale bar indicates 100 milliseconds (ms).Each vertical bar represents a detected spikeKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 16 of 20polarization. Although these two possibilities need to beclarified by transcriptomic analysis at a single cell level, thesemixed transcriptomic signatures suggest how Gja1may con-tribute to opposing neuroprotective (Figs. 8, 9 and 10) andneurotoxic (Fig. 7) phenotypes, as demonstrated here as wellas in previous studies [41, 44, 51, 64, 65, 69, 85, 96, 97].Based upon the results from this study and the discus-sion above, we developed a working hypothesis of GJA1dysregulation in AD (Fig. 11), in which upregulation ofGJA1, a master regulator of astrocytic gene expression,concomitant with amyloid accumulation during Alzheimerpathogenesis ultimately drives AD clinical and pathologicaltraits as evidenced by highly significant correlation withAD progression and AD GWAS genes (Fig. 2a-g). Accord-ing to our model, GJA1 induction initially serves to supportneuronal functions by elevating gene expression signaturesfor the glial neurosupportive functions such as Abeta net-work (Fig. 3c), Apoe (Fig. 8a-c), vascular and neurodeve-lopment, extracellular matrix, and cytoskeletal and celladhesion (Fig. 3a-b) as well as suppressing inflammatoryresponse (Fig. 3b). These findings were supported by amyl-oid phagocytosis (Fig. 8d-e, Additional file 2: Figure S5)and multielectrode array (Figs. 9 and 10) assays showingthat the amyloid uptake, Apoe expression, and neuronalactivity are attenuated with Gja1−/− astrocytes. This isconsistent with several key clinical and pathological obser-vations in LOAD. For example, neuronal activity is foundto be enhanced at the early stage of Alzheimer’s diseasesubjects [4, 10, 72, 87] and evidence of cell cycle activationin AD neurons [3, 53, 58, 79, 86, 94], changes in extracellu-lar matrix [12, 46], and neuronal cytoskeletal components[5, 13, 45, 91] have been reported. However, we speculatethat chronic GJA1 gain of function could lead to detrimen-tal effects on neurons in a long run, as evidenced by ourobservation, in which prolonged neuron/astrocyte cocul-tures were better protected in the absence of Gja1 in astro-cytes (Fig. 7a-b) possibly due to excessive neuronal activityand glutamate-induced excitotoxicity [24, 37, 47, 68].In summary, this study elucidated the previouslyunrecognized role for GJA1 and its channel activity inregulating a key AD-related gene regulatory networkcomprised of APOE and other astrocyte-specific genes.We demonstrated that the disruption of this astrocyticgene coexpression network by knocking out Gja1 canFig. 11 A working hypothesis of GJA1 dysregulation in AD. Progressive upregulation of GJA1 in the brains during LOAD pathogenesis (inresponse to amyloid accumulation) serves to enhance the coordinated gene network function to orchestrate neuroprotective response byastrocytes including Aβ production and clearance (CST3, CLU, CTSB, ECE2, MEGF10, PSENEN and GSAP), APOE production (APOE, ABCA1, CAV1 andSPON1), and supporting neuronal activity (DLG4, NLGN3, GRINA and GRIN2D). However, the enhanced neuronal activity due to chronic andprolonged GJA1 upregulation triggers neuronal wear and eventual deathKajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 17 of 20contribute to LOAD-related pathology. This study repre-sents the first step towards a comprehensive functionalcharacterization of one of the most significantly dysregulatedmodules in our previous integrative network analysis ofLOAD brains. The strong validation of the predictive powerof our multiscale gene network approach paves a way to dis-sect the complex pathogenic mechanisms of LOAD.Additional filesAdditional file 1: Tables S1. Clinical and pathological traits, brainregions analyzed by transcriptome profiling, and sample classificationwith respect to the severity stage of each trait. Table S2. Correlationsbetween GJA1 expression and individual phenotypical traits. Table S3.Differential expression analysis. TableS4. Covariables' impacts on GJA1association with AD traits. Table S5. Gene ontology analysis. Table S6.Intersection of GJA1 gene signatures and AD gene signatures. Table S7.Intersection of GJA1 gene signatures and the khiki module. Table S8.GJA1_centered genetic network analysis. Table S9A. GJA1 DEGsignatures (AST+NEU) overlapped with brain cell type specific genesignatures. Table S9B. AST+NEU vs AST in Gja1 (-/-) or (+/+) DEGsignatures overlapped with brain cell type specific gene signatures. TableS9C. differentially expressed genes in neurons upon GJA1 KO or not inastrocyte and neuron coculture. Table S9D. Difference of change degreein AST+NEU_vs_AST_Gja1(-/-) vs AST+NEU_vs_AST_Gja1(+/+). Table S10.List of primer sets and Universal Probe Library (UPL) probe for qRT-PCR.(XLSX 234 kb)Additional file 2: Figure S1. Correlations between GIA1 and individualclinic traits stratified by AOD (age of death), gender and APOE genotypesin BM36 region. Figure S2. Correlation between Gja1 and individualtranscripts of ADGWAS genes. Figure S3. GJA1-centric Bayesian causalnetwork and GJA1 signaling pathway map. Figure S4. Enrichment ofGia1-/- gene signatures in GJA1 centric correlation networks. Figure S5.Regulation of A1/A2 astrocyte marker genes in Gja1-/- astrocytes. FigureS6. Quinine (GJA1 channel agonist) upregulates Gja1 and Apoe, andother network genes. Figure S7. A. Wildtype and Gja1-/- astrocytes weretreated with fluorescently labeled Aβ1-42 oligomers for 24 hours and thenumber of astrocytes in association with Aβ1-42 oligomers werequantitatively estimated by counting total cells (DAPI+) and Aβ positivecells. B. Representative images of phase contrast, Hilyte Fluor488-labeledAβ, and DAPI staining from wildtype (upper panels) and Gja1-/- (lowerpanels) astrocytes were shown. These images were taken by bright fieldmicroscope and the representatives of 2 independent experiments withsimilar results are shown. Figure S8. Complex regulation of Gja1 keydrivers in neuron/astrocyte/microglia cocultures by carbenoxolone andquinine. (DOCX 1.4 kb)AcknowledgmentsThis study was supported in parts by grants from the National Institutes ofHealth/National Institute on Aging (R01AG046170, RF1AG054014, RF1AG057440,R01AG057907). This study has been made possible through the support ofBrain Canada and the financial support of Health Canada, Michael SmithFoundation for Health Research, Genome BC and the Pacific Alzheimer’sResearch Foundation to CCN and WCS. CCN holds a Canada Research Chair.Availability of data and materialsThe RNA-seq and other Omics data from the MSBB cohort used in this studyare available at https://doi.org/10.7303/syn3159438, https://doi.org/10.7303/syn7392158, https://doi.org/10.7303/syn3157743, https://doi.org/10.7303/syn4645334, https://doi.org/10.7303/syn10901600 and https://doi.org/10.7303/syn5759470. The RNA-seq data from the Gja1 experiments are avail-able at https://doi.org/10.7303/syn11711769.Competing interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NewYork, NY 10029, USA. 2Department of Genetics and Genomic Sciences, IcahnSchool of Medicine at Mount Sinai, New York, NY 10029, USA. 3Mount SinaiCenter for Transformative Disease Modeling, Icahn Institute of Genomics andMultiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA. 4Department of Cellular and Physiological Sciences, University ofBritish Columbia, Vancouver, British Columbia V6T 1Z3, Canada. 5Departmentof Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA. 6Friedman Brain Institute, Icahn School of Medicine at MountSinai, New York, NY 10029, USA. 7Current address: Denali Therapeutics,, SouthSan Francisco,, CA 94080, USA.Received: 30 November 2018 Accepted: 1 December 2018References1. (2015) Convergent genetic and expression data implicate immunity inAlzheimer's disease. Alzheimers Dement 11:658–671. https://doi.org/10.1016/j.jalz.2014.05.17572. Anselmi F et al (2008) ATP release through connexin hemichannels and gapjunction transfer of second messengers propagate Ca2+ signals across theinner ear. Proc Natl Acad Sci U S A 105:18770–18775. https://doi.org/10.1073/pnas.08007931053. Arendt T, Rodel L, Gartner U, Holzer M (1996) Expression of the cyclin-dependentkinase inhibitor p16 in Alzheimer's disease. Neuroreport 7:3047–30494. Bakker A et al (2012) Reduction of hippocampal hyperactivity improvescognition in amnestic mild cognitive impairment. Neuron 74:467–474.https://doi.org/10.1016/j.neuron.2012.03.0235. Bamburg JR, Bloom GS (2009) Cytoskeletal pathologies of Alzheimer disease.Cell Motil Cytoskeleton 66:635–649. https://doi.org/10.1002/cm.203886. Begcevic I et al (2013) Semiquantitative proteomic analysis of humanhippocampal tissues from Alzheimer's disease and age-matched controlbrains. Clin Proteomics 10:5. https://doi.org/10.1186/1559-0275-10-57. Bennett DA (2006) Postmortem indices linking risk factors to cognition:results from the Religious Order Study and the Memory and Aging Project.Alzheimer Dis Assoc Disord 20:S63–S688. Bennett DA, Schneider JA, Arvanitakis Z, Wilson RS (2012) Overview andfindings from the religious orders study. Current Alzheimer Research 9:628–6459. Bennett DA et al (2005) The rush memory and aging project: study designand baseline characteristics of the study cohort. Neuroepidemiology 25:163–17510. Bookheimer SY et al (2000) Patterns of brain activation in people at risk forAlzheimer's disease. N Engl J Med 343:450–456. https://doi.org/10.1056/NEJM20000817343070111. Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-relatedchanges. Acta Neuropathol 82:239–25912. Brandan E, Inestrosa NC (1993) Extracellular matrix components and amyloid inneuritic plaques of Alzheimer's disease. Gen Pharmacol 24:1063–106813. Brion JP (1992) The pathology of the neuronal cytoskeleton in Alzheimer'sdisease. Biochim Biophys Acta 1160:134–14214. Bruban J et al (2015) Presenilin 1 is necessary for neuronal, but not glial,EGFR expression and neuroprotection via gamma-secretase-independenttranscriptional mechanisms. FASEB J 29:3702–3712. https://doi.org/10.1096/fj.15-27064515. Bu G (2009) Apolipoprotein E and its receptors in Alzheimer's disease:pathways, pathogenesis and therapy. Nat Rev Neurosci 10:333–344.https://doi.org/10.1038/nrn262016. Campion D, Pottier C, Nicolas G, Le Guennec K, Rovelet-Lecrux A (2016)Alzheimer disease: modeling an Abeta-centered biological network. MolPsychiatry 21:861–871. https://doi.org/10.1038/mp.2016.3817. Chever O, Lee CY, Rouach N (2014) Astroglial connexin43 hemichannelstune basal excitatory synaptic transmission. J Neurosci 34:11228–11232.https://doi.org/10.1523/JNEUROSCI.0015-14.201418. Corder EH et al (1993) Gene dose of apolipoprotein E type 4 alleleand the risk of Alzheimer's disease in late onset families. Science261:921–923Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 18 of 2019. De Vuyst E et al (2007) Connexin hemichannels and gap junction channelsare differentially influenced by lipopolysaccharide and basic fibroblast growthfactor. Mol Biol Cell 18:34–46. https://doi.org/10.1091/mbc.E06-03-018220. DeKosky ST, Scheff SW (1990) Synapse loss in frontal cortex biopsies inAlzheimer's disease: correlation with cognitive severity. Ann Neurol 27:457–464. https://doi.org/10.1002/ana.41027050221. D'Hondt C, Iyyathurai J, Himpens B, Leybaert L, Bultynck G (2014) Cx43-hemichannel function and regulation in physiology and pathophysiology:insights from the bovine corneal endothelial cell system and beyond. FrontPhysiol 5:348. https://doi.org/10.3389/fphys.2014.0034822. Dobin A et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics29:15–21. https://doi.org/10.1093/bioinformatics/bts63523. Emilsson V et al (2008) Genetics of gene expression and its effect ondisease. Nature 452:423–42824. Esposito Z et al (2013) Amyloid beta, glutamate, excitotoxicity inAlzheimer's disease: are we on the right track? CNS Neurosci Ther 19:549–555. https://doi.org/10.1111/cns.1209525. Folstein MF, Folstein SE, McHugh PR (1975) "Mini-mental state". A practicalmethod for grading the cognitive state of patients for the clinician. JPsychiatr Res 12:189–19826. Frisch C et al (2003) Mice with astrocyte-directed inactivation of connexin43exhibit increased exploratory behaviour, impaired motor capacities, andchanges in brain acetylcholine levels. Eur J Neurosci 18:2313–231827. Giaume C et al (1991) Gap junctions in cultured astrocytes: single-channel currents and characterization of channel-forming protein.Neuron 6:133–14328. Goodenough DA, Paul DL (2003) Beyond the gap: functions of unpairedconnexon channels. Nat Rev Mol Cell Biol 4:285–294. https://doi.org/10.1038/nrm107229. Gutmann DH, Zackai EH, McDonald-McGinn DM, Fischbeck KH, Kamholz J(1991) Oculodentodigital dysplasia syndrome associated with abnormalcerebral white matter. Am J Med Genet 41:18–20. https://doi.org/10.1002/ajmg.132041010630. Han Y et al (2014) Astrocyte-restricted disruption of connexin-43 impairsneuronal plasticity in mouse barrel cortex. Eur J Neurosci 39:35–45.https://doi.org/10.1111/ejn.1239431. Haroutunian V, Katsel P, Schmeidler J (2009) Transcriptional vulnerability ofbrain regions in Alzheimer's disease and dementia. Neurobiol Aging 30:561–573. https://doi.org/10.1016/j.neurobiolaging.2007.07.02132. Harrell LE, Marson D, Chatterjee A, Parrish JA (2000) The severe mini-mentalstate examination: a new neuropsychologic instrument for the bedsideassessment of severely impaired patients with Alzheimer disease. AlzheimerDis Assoc Disord 14:168–17533. Hoffman GE, Schadt EE (2016) variancePartition: interpreting drivers ofvariation in complex gene expression studies. BMC Bioinformatics 17:483.https://doi.org/10.1186/s12859-016-1323-z34. Hokama M et al (2014) Altered expression of diabetes-related genes inAlzheimer's disease brains: the Hisayama study. Cereb Cortex 24:2476–2488.https://doi.org/10.1093/cercor/bht10135. Huang Y, Mucke L (2012) Alzheimer mechanisms and therapeutic strategies.Cell 148:1204–1222. https://doi.org/10.1016/j.cell.2012.02.04036. Humphries CE et al (2015) Integrated whole transcriptome and DNAmethylation analysis identifies gene networks specific to late-onset Alzheimer'sdisease. J Alzheimers Dis 44:977–987. https://doi.org/10.3233/JAD-14198937. Hynd MR, Scott HL, Dodd PR (2004) Glutamate-mediated excitotoxicity andneurodegeneration in Alzheimer's disease. Neurochem Int 45:583–595.https://doi.org/10.1016/j.neuint.2004.03.00738. Kajiwara Y et al (2016) The human-specific CASP4 gene contributes toAlzheimer-related synaptic and behavioral deficits. Hum Mol Genet.https://doi.org/10.1093/hmg/ddw26539. Kim J, Basak JM, Holtzman DM (2009) The role of apolipoprotein E inAlzheimer's disease. Neuron 63:287–303. https://doi.org/10.1016/j.neuron.2009.06.02640. Koulakoff A, Mei X, Orellana JA, Saez JC, Giaume C (2012) Glial connexinexpression and function in the context of Alzheimer's disease. BiochimBiophys Acta 1818:2048–2057. https://doi.org/10.1016/j.bbamem.2011.10.00141. Kozoriz MG et al (2010) The connexin43 C-terminal region mediatesneuroprotection during stroke. J Neuropathol Exp Neurol 69:196–206.https://doi.org/10.1097/NEN.0b013e3181cd44df42. Laird DW (2014) Syndromic and non-syndromic disease-linked Cx43 mutations.FEBS Lett 588:1339–1348. https://doi.org/10.1016/j.febslet.2013.12.02243. Lambert JC et al (2013) Meta-analysis of 74,046 individuals identifies 11new susceptibility loci for Alzheimer's disease. Nat Genet 45:1452–1458.https://doi.org/10.1038/ng.280244. Le HT et al (2014) Gap junction intercellular communication mediated byconnexin43 in astrocytes is essential for their resistance to oxidative stress.J Biol Chem 289:1345–1354. https://doi.org/10.1074/jbc.M113.50839045. Lee VM (1995) Disruption of the cytoskeleton in Alzheimer's disease. CurrOpin Neurobiol 5:66346. Lepelletier FX, Mann DM, Robinson AC, Pinteaux E, Boutin H (2017) Earlychanges in extracellular matrix in Alzheimer's disease. Neuropathol ApplNeurobiol 43:167–182. https://doi.org/10.1111/nan.1229547. Lewerenz J, Maher P (2015) Chronic glutamate toxicity in neurodegenerativediseases-what is the evidence? Front Neurosci 9:469. https://doi.org/10.3389/fnins.2015.0046948. Liang Y et al (2004) A liver X receptor and retinoid X receptor heterodimermediates apolipoprotein E expression, secretion and cholesterolhomeostasis in astrocytes. J Neurochem 88:623–63449. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purposeprogram for assigning sequence reads to genomic features. Bioinformatics30:923–930. https://doi.org/10.1093/bioinformatics/btt65650. Liddelow SA et al (2017) Neurotoxic reactive astrocytes are induced byactivated microglia. Nature 541:481–487. https://doi.org/10.1038/nature2102951. Lin JH et al (2008) A central role of connexin 43 in hypoxic preconditioning.J Neurosci 28:681–695. https://doi.org/10.1523/JNEUROSCI.3827-07.200852. Liu CC, Liu CC, Kanekiyo T, Xu H, Bu G (2013) Apolipoprotein E andAlzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol 9:106–118.https://doi.org/10.1038/nrneurol.2012.26353. Liu WK, Williams RT, Hall FL, Dickson DW, Yen SH (1995) Detection of aCdc2-related kinase associated with Alzheimer paired helical filaments. Am JPathol 146:228–23854. Loddenkemper T, Grote K, Evers S, Oelerich M, Stogbauer F (2002)Neurological manifestations of the oculodentodigital dysplasia syndrome.J Neurol 249:584–595. https://doi.org/10.1007/s00415020006855. Lohman AW, Isakson BE (2014) Differentiating connexin hemichannels andpannexin channels in cellular ATP release. FEBS Lett 588:1379–1388.https://doi.org/10.1016/j.febslet.2014.02.00456. Lutz SE et al (2009) Deletion of astrocyte connexins 43 and 30 leads to adysmyelinating phenotype and hippocampal CA1 vacuolation. J Neurosci29:7743–7752. https://doi.org/10.1523/JNEUROSCI.0341-09.200957. McKenzie AT et al (2018) Brain cell type specific gene expression and co-expression network architectures. Sci Rep 8:8868. https://doi.org/10.1038/s41598-018-27293-558. McShea A, Harris PL, Webster KR, Wahl AF, Smith MA (1997) Abnormalexpression of the cell cycle regulators P16 and CDK4 in Alzheimer's disease.Am J Pathol 150:1933–193959. Mei X, Ezan P, Giaume C, Koulakoff A (2010) Astroglial connexinimmunoreactivity is specifically altered at beta-amyloid plaques in beta-amyloid precursor protein/presenilin1 mice. Neuroscience 171:92–105.https://doi.org/10.1016/j.neuroscience.2010.08.00160. Miller JA, Oldham MC, Geschwind DH (2008) A systems level analysis oftranscriptional changes in Alzheimer's disease and Normal aging. J Neurosci28:1410–142061. Miller JA, Woltjer RL, Goodenbour JM, Horvath S, Geschwind DH (2013)Genes and pathways underlying regional and cell type changes inAlzheimer's disease. Genome Medicine 5:4862. Millstein J, Zhang B, Zhu J, Schadt EE (2009) Disentangling molecularrelationships with a causal inference test. BMC Genet 10:23. https://doi.org/10.1186/1471-2156-10-2363. Nagy JI, Li W, Hertzberg EL, Marotta CA (1996) Elevated connexin43immunoreactivity at sites of amyloid plaques in Alzheimer's disease. BrainRes 717:173–17864. Nakase T, Fushiki S, Naus CC (2003) Astrocytic gap junctions composed ofconnexin 43 reduce apoptotic neuronal damage in cerebral ischemia.Stroke 34:1987–1993. https://doi.org/10.1161/01.STR.0000079814.72027.3465. Nakase T, Sohl G, Theis M, Willecke K, Naus CC (2004) Increased apoptosisand inflammation after focal brain ischemia in mice lacking connexin43 inastrocytes. Am J Pathol 164:2067–2075. https://doi.org/10.1016/S0002-9440(10)63765-066. Naus CC et al (1997) Altered gap junctional communication, intercellularsignaling, and growth in cultured astrocytes deficient in connexin43. JNeurosci Res 49:528–540Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 19 of 2067. Nielsen HM et al (2010) Astrocytic a beta 1-42 uptake is determined by abeta-aggregation state and the presence of amyloid-associated proteins.Glia 58:1235–1246. https://doi.org/10.1002/glia.2100468. Ong WY, Tanaka K, Dawe GS, Ittner LM, Farooqui AA (2013) Slowexcitotoxicity in Alzheimer's disease. J Alzheimers Dis 35:643–668.https://doi.org/10.3233/JAD-12199069. Orellana JA et al (2011) Amyloid beta-induced death in neurons involvesglial and neuronal hemichannels. J Neurosci 31:4962–4977. https://doi.org/10.1523/JNEUROSCI.6417-10.201170. Pannasch U et al (2011) Astroglial networks scale synaptic activity andplasticity. Proc Natl Acad Sci U S A 108:8467–8472. https://doi.org/10.1073/pnas.101665010871. Parikshak NN, Gandal MJ, Geschwind DH (2015) Systems biology and genenetworks in neurodevelopmental and neurodegenerative disorders. Nat RevGenet 16:441–458. https://doi.org/10.1038/nrg393472. Quiroz YT et al (2010) Hippocampal hyperactivation in presymptomaticfamilial Alzheimer's disease. Ann Neurol 68:865–875. https://doi.org/10.1002/ana.2210573. Ren R, Zhang L, Wang M (2018) Specific deletion connexin43 in astrocyteameliorates cognitive dysfunction in APP/PS1 mice. Life Sci 208:175–191.https://doi.org/10.1016/j.lfs.2018.07.03374. Retamal MA et al (2007) Cx43 hemichannels and gap junction channels inastrocytes are regulated oppositely by proinflammatory cytokines releasedfrom activated microglia. J Neurosci 27. https://doi.org/10.1523/JNEUROSCI.2042-07.200775. Ritchie, M. E. et al. limma powers differential expression analyses forRNA-sequencing and microarray studies. Nucleic Acids Res 43, e47,doi:https://doi.org/10.1093/nar/gkv007 (2015)76. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductorpackage for differential expression analysis of digital gene expression data.Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp61677. Rouach N, Koulakoff A, Abudara V, Willecke K, Giaume C (2008) Astroglialmetabolic networks sustain hippocampal synaptic transmission. Science 322:1551–1555. https://doi.org/10.1126/science.116402278. Scemes E, Giaume C (2006) Astrocyte calcium waves: what they are andwhat they do. Glia 54:716–725. https://doi.org/10.1002/glia.2037479. Smith TW, Lippa CF (1995) Ki-67 immunoreactivity in Alzheimer's disease andother neurodegenerative disorders. J Neuropathol Exp Neurol 54:297–30380. Sohl G, Maxeiner S, Willecke K (2005) Expression and functions of neuronalgap junctions. Nat Rev Neurosci 6:191–200. https://doi.org/10.1038/nrn162781. Stout CE, Costantin JL, Naus CC, Charles AC (2002) Intercellular calciumsignaling in astrocytes via ATP release through connexin hemichannels. 277:10482–10488. https://doi.org/10.1074/jbc.M10990220082. Strittmatter WJ et al (1993) Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familialAlzheimer disease. Proc Natl Acad Sci U S A 90:1977–198183. Tabernero, A., Giaume, C. & Medina, J. M. Endothelin-1 regulates glucoseutilization in cultured astrocytes by controlling intercellular communicationthrough gap junctions. Glia 16, 187–195, doi:https://doi.org/10.1002/(SICI)1098-1136(199603)16:3<187::AID-GLIA1>3.0.CO;2-# [pii]. https://doi.org/10.1002/(SICI)1098-1136(199603)16:3<187::AID-GLIA1>3.0.CO;2-# (1996)84. Theis M et al (2003) Accelerated hippocampal spreading depression andenhanced locomotory activity in mice with astrocyte-directed inactivationof connexin43. J Neurosci 23:766–77685. Theodoric N, Bechberger JF, Naus CC, Sin WC (2012) Role of gap junctionprotein connexin43 in astrogliosis induced by brain injury. PLoS One 7:e47311. https://doi.org/10.1371/journal.pone.004731186. Vincent I, Rosado M, Davies P (1996) Mitotic mechanisms in Alzheimer'sdisease? J Cell Biol 132:413–42587. Vossel KA et al (2013) Seizures and epileptiform activity in the early stagesof Alzheimer disease. JAMA Neurol 70:1158–1166. https://doi.org/10.1001/jamaneurol.2013.13688. Wagenaar DA, Pine J, Potter SM (2004) Effective parameters for stimulationof dissociated cultures using multi-electrode arrays. J Neurosci Methods 138:27–37. https://doi.org/10.1016/j.jneumeth.2004.03.00589. Wallraff A et al (2006) The impact of astrocytic gap junctional coupling onpotassium buffering in the hippocampus. J Neurosci 26:5438–5447.https://doi.org/10.1523/JNEUROSCI.0037-06.200690. Wang IM et al (2012) Systems analysis of eleven rodent disease modelsreveals an inflammatome signature and key drivers. Mol Syst Biol 8:594.https://doi.org/10.1038/msb.2012.2491. Wang M et al (2016) Integrative network analysis of nineteen brain regionsidentifies molecular signatures and networks underlying selective regionalvulnerability to Alzheimer's disease. Genome Med 8:104. https://doi.org/10.1186/s13073-016-0355-392. Wilson RS, Arnold SE, Schneider JA, Li Y, Bennett DA (2007) Chronic distress,age-related neuropathology, and late-life dementia. Psychosom Med 69:47–53. https://doi.org/10.1097/01.psy.0000250264.25017.2193. Yamamoto T, Ochalski A, Hertzberg EL, Nagy JI (1990) On the organizationof astrocytic gap junctions in rat brain as suggested by LM and EMimmunohistochemistry of connexin43 expression. J Comp Neurol 302:853–883. https://doi.org/10.1002/cne.90302041494. Yang Y, Mufson EJ, Herrup K (2003) Neuronal cell death is preceded by cellcycle events at all stages of Alzheimer's disease. J Neurosci 23:2557–256395. Ye ZC, Oberheim N, Kettenmann H, Ransom BR (2009) Pharmacological"cross-inhibition" of connexin hemichannels and swelling activated anionchannels. Glia 57:258–269. https://doi.org/10.1002/glia.2075496. Yi C et al (2016) Astroglial connexin43 contributes to neuronal suffering in amouse model of Alzheimer's disease. Cell Death Differ 23:1691–1701.https://doi.org/10.1038/cdd.2016.6397. Yi C et al (2017) Inhibition of glial hemichannels by boldine treatmentreduces neuronal suffering in a murine model of Alzheimer's disease. Glia65:1607–1625. https://doi.org/10.1002/glia.2318298. Zhang B et al (2013) Integrated systems approach identifies genetic nodesand networks in late-onset Alzheimer's disease. Cell 153:707–720.https://doi.org/10.1016/j.cell.2013.03.03099. Zhang Y et al (2016) Purification and characterization of progenitor andmature human astrocytes reveals transcriptional and functional differenceswith mouse. Neuron 89:37–53. https://doi.org/10.1016/j.neuron.2015.11.013100. Zhu J et al (2007) Increasing the power to detect causal associations bycombining genotypic and expression data in segregating populations. PLoSComput Biol 3:e69. https://doi.org/10.1371/journal.pcbi.0030069Kajiwara et al. Acta Neuropathologica Communications (2018) 6:144 Page 20 of 20
Article
eng
Reviewed
Vancouver : University of British Columbia Library
BioMed Central
Attribution 4.0 International (CC BY 4.0)
Faculty
Alzheimer’s disease
GJA1
Cx43
connexin43
Gene networks
amyloidβ
Astrocyte
GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis
Text