RNAseq Analysis Of Hippocampal Microglia After Kainic Acid .
Bosco et al. Molecular Brain (2018) ARCHOpen AccessRNAseq analysis of hippocampal microgliaafter kainic acid-induced seizuresDale B. Bosco1, Jiaying Zheng1, Zhiyan Xu2, Jiyun Peng1, Ukpong B. Eyo1, Ke Tang3, Cheng Yan3, Jun Huang3,Lijie Feng4, Gongxiong Wu5, Jason R. Richardson6, Hui Wang2,7* and Long-Jun Wu1,8*AbstractMicroglia have been shown to be of critical importance to the progression of temporal lobe epilepsy. However, thebroad transcriptional changes that these cells undergo following seizure induction is not well understood. As such,we utilized RNAseq analysis upon microglia isolated from the hippocampus to determine expression pattern alterationsfollowing kainic acid induced seizure. We determined that microglia undergo dramatic changes to their expressionpatterns, particularly with regard to mitochondrial activity and metabolism. We also observed that microgliainitiate immunological activity, specifically increasing interferon beta responsiveness. Our results provide novelinsights into microglia transcriptional regulation following acute seizures and suggest potential therapeutic targetsspecifically in microglia for the treatment of seizures and epilepsy.IntroductionTemporal lobe epilepsy (TLE) represents the most common form of focal epileptic disorder. While severalpharmaceutical treatments are currently available tomitigate and reduce seizure occurrence, as many as onethird of patients display resistance to medication [1]. Assuch, an unmet need exists, requiring further investigation into the mechanisms underlying TLE. The rodentkainic acid (KA) epilepsy model can recapitulate manyof the physical features of TLE including behavioral seizures and neuropathological lesions [2]. Therefore, manyinvestigations have focused on how KA alters the activityand viability of neurons. However, comparatively littleattention has been paid to glial cells, including astrocytesand microglia, in epileptogenesis [3, 4].Comprising between 5 and 15% of total central nervous system (CNS) cells, microglia predominantly serveas the resident immune cell of the CNS. Recent evidencehas also revealed that microglia have a diverse set ofroles within the CNS, including directing neuronal maturation and supporting synaptic turnover [5, 6]. With regard to epilepsy, it was established relatively early that* Correspondence: huiwangph@ntu.edu.cn; wu.longjun@mayo.edu2Department of Pharmacology, School of Pharmacy, Nantong University, 19Qixiu Road, Nantong 226001, Jiangsu, China1Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, MN55905, USAFull list of author information is available at the end of the articlelarge numbers of reactive microglia can be found withinthe hippocampus of temporal lobe epilepsy patients[7, 8]. Our recent studies demonstrated that seizurescan acutely induce microglia-neuron interaction aswell as the changes in microglial landscape [9–12].Microgliosis and inflammatory cytokine release hasbeen observed within areas of neuronal damage implicating microglia in promotion of neuropathy [13].However, microglia may also have neuroprotectiveroles such as modulating excitotoxicity.Since microglia seem to be an important part of theepileptic response, we investigated how KA-induced seizures modulate microglial transcriptional activity andalters their phenotype. Specifically, we investigated hippocampal microglia since this brain region is one of themost affected by seizure [14]. To explore this, we performed RNAseq analysis, a powerful tool to determinewide scale phenotypic alterations, on isolated hippocampal microglia from mice that received KA. We reportthat KA-induced seizures resulted in significant transcriptional changes to microglia when compared tosham controls. Specifically, there are significant increasesin the expression of metabolic and mitochondrial pathways. Coincidently, we observed that immune relatedfactors were also being up-regulated, including severalchemokine factors such as chemokine ligand 5 (CCL5)and C-X-C motif chemokine 10 (CXCL10). We also 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 o/1.0/) applies to the data made available in this article, unless otherwise stated.
Bosco et al. Molecular Brain (2018) 11:34observed that microglia increased their responsivenessto interferon β, possibly through interferon regulatoryfactor 7 (Irf7). Thus, we show that KA-induced seizuressignificantly regulate the microglia transcriptome, providing novel directions for further investigation.ResultsKainic acid induced seizures significantly alters microglialgene expression profileTo begin our investigation, heterozygote CX3CR1GFP/ mice were treated with kainic acid (KA) via ICV injection to induce an acute seizure response [12]. Microgliain the mouse hippocampus show dramatic reactivity following KA-induced seizure strating at as early as 1 dayand peaks at 3 days after KA treatment [15]. We therefore focused on hippocampus microglia isolated viaFACS 3 days after KA-induced seizures. RNAseq libraries were constructed using the isolated cells and loadedonto an Illumina Hiseq platform. DEseq was used to determine differential gene expression. From the results,over 2300 differentially expressed genes were identified(Fig. 1a, Additional file 1: Table S1). Of these, we observed many of the suggested microglia specific genesincluding P2Y12, Tmem119, and Olfml3 [16]. Additionally, we detected only slight increases to myelin (e.g.,PLP), neuronal (e.g., Rbfox3, Map2), and astrocytes (e.g.,Page 2 of 13Gfap, Aldh1l1) markers within samples isolated fromKA treated mice, with only GFAP registering as significant. These factors were not detected within the controlsamples. Since it has been suggested that the phagocyticcapacity of microglia is substantially reduced followingKA-seizure [17] and that microglial could express GFAP[18] we believe that the genes alterations that weredeemed significant reflect microglia specific alterations.These results demonstrated the purity of microglia sorting. The overwhelming majority of differentiallyexpressed genes were up-regulated in the microgliasamples from KA treated mice with few genes beingdown-regulated when compared to sham controls(Fig. 1b-c). Table 1 lists the top 25 up-regulated andTable 2 the identified down-regulated genes. Table 3lists the top 25 genes found only in the KA-treatedanimals as determined by Padj values.We next determined whether KA-induced seizuresaffected microglial specific markers. Using the list determined by Hickman et al. [16], we found that sevenof the listed microglial markers were differentiallyexpressed (Fig. 2a, Additional file 2: Figure S1). Thesewere adenosine A3 receptor (Adora 3), crystallin beta A4(Cryba4), galactose-3-O-sulfotransferase 4 (Gal3st4), lipase member H (Liph), membrane-spanning 4-domains,subfamily A, member 6B (Ms4a6b), serine peptidaseFig. 1 Differentially expressed genes between the sham control and KA treated groups. a MA-plot of gene expression. All significant differentiallyexpressed genes (Padj 0.05) and locally weighted smoothing (LOESS) line are colored in red. b Heat map and hierarchical clustering was performedbased on all differentially expressed genes. Magenta indicates high relative expression, and cyan indicates low relative expression. c Volcano plot ofgene expression. All significant differentially expressed genes are colored in red and labeled by gene symbols
Bosco et al. Molecular Brain (2018) 11:34Page 3 of 13Table 1 Top 25 most up-regulated genesENSEMBLGene IDGene SymbolGene NameLog2 Fold ChangePadjENSMUSG0000001950522187Ubbubiquitin ng finger protein ic3ERGIC and golgi yosin, light polypeptide 6, alkali,smooth muscle and non- s19ribosomal protein bh5alkB homolog 5, RNA 20005Rpl9ribosomal protein G0000002595993691Klf7Kruppel-like factor 7 0655Sod1superoxide dismutase 1, tk11ipserine/threonine kinase 11 00031483244373Erlin2ER lipid raft associated guanylate-binding protein 9(Gbp9)10.274.58E-03ENSMUSG0000003485515945Cxcl 10chemokine (C-X-C motif) ligand 40Sp140 nuclear body Klhl5kelch-like spinster homolog eukaryotic translation initiation factor 4a2solute carrier family 4 (anion exchanger),member nKH and NYN domain 0379Sec13SEC13 homolog, nuclear pore and COPII coatcomplex 66Abcd1ATP-binding cassette, sub-family D (ALD),member f18rho/rac guanine nucleotide exchange factor(GEF) 22CD22 au2MAU2 sister chromatid cohesion factor(Mau2)10.014.58E-03Table 2 Down-regulated genesENSEMBLGene IDGene SymbolGene NameLog2 Fold ChangePadjENSMUSG0000000056211542Ccdc171adenosine A3 receptor(Adora3) 5.873.98E-02ENSMUSG0000009013722186Uba52ubiquitin A-52 residue ribosomal proteinfusion product 1(Uba52) l domain containing 171(Ccdc171) RNA 16–1(Mir16–1) 7.711.23E-02ENSMUSG0000000426313498Adora3atrophin 1(Atn1) rane and immunoglobulindomain containing 3(Tmigd3) 9.294.39E-02
Bosco et al. Molecular Brain (2018) 11:34Page 4 of 13Table 3 Top 25 differentially expressed genes only observed in KA treated groupENSEMBLGene IDGene SymbolGene NamePadjENSMUSG0000006951617105Lyz2lysozyme 26.62E-04ENSMUSG0000006093819941Rpl26ribosomal protein L261.15E-03ENSMUSG0000000260226362AxlAXL receptor tyrosine l protein S4, X-linked2.25E-03ENSMUSG0000004931320660Sorl 1sortilin-related receptor, LDLR class A pl34ribosomal protein mal protein L34, pseudogene d gene 47052.25E-03ENSMUSG0000006352413806Eno1enolase 1, alpha 1 J21 Rik2RIKEN cDNA 9,930,111 J21 gene N cDNA A930011G23 gene2.25E-03ENSMUSG0000009073357294Rps27ribosomal protein S272.25E-03ENSMUSG0000007341812268C4bcomplement component 4B2.83E-03ENSMUSG0000000179412336Capns1calpain, small subunit 12.94E-03ENSMUSG0000000351872349Dusp3dual specificity phosphatase 32.94E-03ENSMUSG0000000556621849Trim28tripartite motif-containing 282.94E-03ENSMUSG0000000968718301Fxyd5FXYD domain-containing ion transport regulator 52.94E-03ENSMUSG0000002241520972Syngr1synaptogyrin 12.94E-03ENSMUSG0000002247711429Aco2aconitase 2, -spanning 4-domains, subfamily A, member 6D2.94E-03ENSMUSG0000002549854123Irf7interferon regulatory factor 72.94E-03ENSMUSG0000002622220684Sp100nuclear antigen Sp1002.94E-03ENSMUSG0000002643054354Rassf5Ras association (RalGDS/AF-6) domain family member 52.94E-03ENSMUSG0000003485473822Mfsd12major facilitator superfamily domain containing 122.94E-03inhibitor Kunitz type 1 (Spint1), and toll-like receptor 12(Tlr12). Since KA treatment has also been shown toinduce inflammatory responses [15], we also investigated our list of differentially expressed genes for potential inflammatory markers. Indeed, we found anumber of inflammatory factors are increased withinmicroglia isolated from KA treated mice, includingC-C motif chemokine ligand 5 (Ccl5), Ccl7, andC-X-C motif chemokine ligand 10 (Cxcl10) (Fig. 2b,Additional file 2: Figure S2). We determined that expression of several inflammatory and immunologicalresponse receptors are also increased (Fig. 2c). Thesereceptors included C-C motif chemokine receptor 2(Ccr2), C-X-C motif chemokine receptor 4 (Cxcr4),and Tlr1. Finally, a significant number cluster of differentiation (CD) markers were significantly increased(Fig. 2d). The majority of identified CD markers arerelated to immunological responses including CD40,CD69, and CD80 [19, 20]. These results suggest thatmicroglia are undergoing immunological activation inresponse to KA-induced seizures.Gene ontology analysis indicates significant increases tometabolic processesOur next step was to identify if any unifying features existedwithin our differential expression data set. As such, we utilized clusterProfiler to perform gene ontology (GO) analysis[21]. We investigated our data set using the three majorclassifications, cellular component, biological process, andmolecular function (Additional file 3: Table S2, Additionalfile 4: Table S3 and Additional file 5: Table S4). To furthervisualize our results, identified GO terms were inputinto REViGO [22]. This web-based application allows forlong lists of GO terms to be summarized and grouped basedon semantic similarities. REViGO analysis was run using theassociated Padj for each identified GO term, with mediumallowed similarity (0.7), and SimRel similarity measurement.TreeMaps were then generated for each ontology classification. Each box represents GO terms that are then groupedand colored based on keyword similarities. Box size indicates each terms level of significance as determined by inputPadj values. Added labels highlight overarching groupingterms. As Fig. 3a illustrates there are significant alterations
Bosco et al. Molecular Brain (2018) 11:34Page 5 of 13Fig. 2 Selected differential expressed genes. Expression results were investigated for genes relating to microglial specificity and inflammatory andimmunological regulation. a Microglial markers. b Secreted factors. c Related receptors. d CD markers. Values are expressed and mean standard error.**Padj 0.05. All gene listed in panel (c and d) had a Padj 0.05to intracellular factor expression, especially within themitochondria. Moreover, Biological process GO analysis showed that there seems to be significant alterations to microglial metabolism, with catabolismbeing at the forefront (Fig. 3b). It also identified thatmicroglia were activating viral defense mechanismsfollowing seizure. Finally, we observed that a numberof transferase activities were being undertaken following seizure (Fig. 3c).Kainic acid treatment may sensitize microglia tointerferon betaDelving deeper into the identified GO terms it wasobserved that a number of related terms were pertinent to type I interferons, specifically interferon β(IFN-β). Table 4 summarizes these identified GO terms.IFN-β is a type-I interferon that binds interferon-α/βreceptor (IFNAR) to regulate a multitude of signalingcascades particularly the JAK/STAT pathway [23].IFN-β has also been suggested to modulate microglialactivity in multiple sclerosis and pathological neovascularization [24, 25]. Since IFN-β signaling was wellrepresented within our GO analysis, we believe thatIFN-β is important to the microglial modulation thatoccurs following KA-induced seizures.Pathway analysis reveals both metabolic and immuneresponse processes are alteredFinally, we performed pathway analysis on the differential expression data set using the clusterProfiler enrichKEGG function (Fig. 4). Unsurprisingly, this analysiscorroborated our GO analysis results in that metabolismwas significantly enriched in our data set. We also identified several pathways relating to neurological diseases(i.e., Parkinson’s, Alzheimer’s, and Huntington’s disease)(Fig. 4). Using KEGGmapper we were able to further investigate which specific metabolic pathway were beingaffected. We found that Glycan, fatty acid and lipid, andnucleotide metabolism are all up-regulated within theKA treated samples. Moreover, we observed severalpathways involving glutamate utilization and isoprenoidbiosynthesis were also affected (Fig. 5a).While metabolism was by far the most significantly altered pathway term identified, several other pathways ofnote were identified, specifically those relating to neurodegenerative diseases (i.e., Parkinson’s, Huntington’s,Alzheimer’s) and viral response (i.e., Herpes simplex,Epstein-Barr, viral carcinogenesis). While it was consistent with our exploration avenue to observe pathways relating to neurodegenerative diseases, we observed viralresponses in both GO and pathway analysis. As such, wefurther explored the gene relationships underlying these
Bosco et al. Molecular Brain (2018) 11:34Fig. 3 (See legend on next page.)Page 6 of 13
Bosco et al. Molecular Brain (2018) 11:34Page 7 of 13(See figure on previous page.)Fig. 3 Functional classification of the differentially expressed genes. a Cellular component. b Biological process. c Molecular function. Visualizationof identified Gene Ontology terms was completed using REViGO [22]. Analysis was run using the Padj for each identified term, medium allowedsimilarity (0.7), and SimRel similarity measurement. Individual term size weight within each TreeMap was determined by associated Padj.identified pathway terms. Differential expressed genesidentified to be part of the indicated KEGG pathwayterms were analyzed with the GeneMANIA applicationfor Cytoscape V3.5.1 [26]. GeneMANIA utilizes bothpublished information and computational predictions toidentify relationships between input genes. It will alsosuggest possible interaction partners not initially inputinto the query. Indeed, we demonstrate that the overwhelming majority of genes associated with the identifiedneurodegenerative pathways were related to mitochondrial function, specifically the electron transport chain(Fig. 5b). This is consistent with our GO analysis. Investigation of viral pathway term genes however revealed amore diverse set of groupings (Fig. 5c). These includegenes related to RNA polymerase complexes and histones.Both of which are consistent with the high levels oftranscriptional modulation observed. Additionally, several genes were associated with immunological regulation, such as complement C3, signal transducer andactivator of transcription 2 (Stat2), and antigen peptidetransporter 1 (Tap1).DiscussionThe majority of research into epilepsy has focused onneuronal hyperactivities and cell death. However, therole of glia, particularly microglia, in the pathogenesisTable 4 Type I interferon related GO termsGO Term IDTerm NamePadjGO:0032480negative regulation of type I interferonproduction0.0012GO:0032479regulation of type I interferon production0.0019GO:0034340response to type I interferon0.0046GO:0032606type I interferon production0.0055GO:0032648regulation of interferon-beta production0.0109GO:0032608interferon-beta production0.0166GO:0035456response to i
RESEARCH Open Access RNAseq analysis of hippocampal microglia after kainic acid-induced seizures Dale B. Bosco1, Jiaying Zheng1, Zhiyan Xu2, Jiyun Peng1, Ukpong B. Eyo1, Ke Tang3, Cheng Yan3, Jun Huang3, Lijie Feng4, Gongxiong Wu5, Jason R. Richardson6, Hui Wang2,7* and Long-Jun Wu1,8* Abstract Microglia have been shown to be of critical importance to the progression of temporal lobe epilepsy.
literature. Particular attention is given to utilizing M2 microglial polarization as a potential therapeutic option in treating diseases. Keywords: alternative activation, M2, microglia, neuroinflammation Background For the better part of a century, the function of microglia in the central
Microglia, the myeloid cells of the central nervous sys - tem (CNS), play important homeostatic roles in health and disease. In the healthy adult CNS, microglia exert modulatory and housekeeping functions [1-3] that span from synapse remodeling and maturation to secretion of neurotrophic factors [5] and regulation of the pool of 4,
The tools are used via the Linux command line, and will. cover important aspects of data QC and considerations to take when interpreting RNAseq results. Throughout this tutorial, there will be formatting cues to highlight various pieces of information. This is just background information. There are no tutorial-related tasks in these boxes.
analysis. Neurological scores, brain water content, and fluoro-Jade C (FJC) analysis were conducted. Correlation between TSG-6 and microglia polarization To examine the effect of TSG-6 on microglia polarization, 60 rats were randomly assigned into the following groups: sham, SAH, SAH vehicle, SAH rh-TSG-6 (5 μg), SAH scrambled siRNA group, and
microglia activation and proliferation. The natural flavonoid luteolin inhibits mTOR, mast cells and microglia and could have a significant benefit in ASD. Introduction Focal brain inflammation Increasing evidence indicates that brain in
lipopolysaccharide (LPS), and microglial activation was studied in brain areas of susceptibility for different neuro-degenerative diseases, such as the hippocampus, cortex, striatum, and midbrain. Our results show that under steady-state conditions, midbrain microglia present a more activated phenotype than their counterparts in other regions.
Merkel et al. 2015; Yushkevich et al. 2010) methods were used to segment hippocampal subfields. However, these studies need long imaging acquisition times and tedious, labor insensitive work by anatomically-trained tracers and thus, are not practical for the analysis of large-scale data-se
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