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Transcriptome sequencing of microglial cells stimulated with TLR3 and TLR4 ligands

Das, Amitabh, Chai, Jin Choul, Kim, Sun Hwa, Lee, Young Seek, Park, Kyoung Sun, Jung, Kyoung Hwa, Chai, Young Gyu·2015·BMC Genomics
DOI 10.1186/s12864-015-1728-5PMC PMC4497376
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Fig. 1 Schematic outline of experiments and data analysis
Fig. 1 Schematic outline of experiments and data analysisCC BY · PubMed Central
Fig. 2 Differential gene expression and downstream effect analysis of genes overexpressed in microglia upon TLR3 and TLR4 stimulation at two different time points. a Heat map representing RNA-seq gene expression of top 150 up-regulated inflammatory genes at 2 and 4 h after Poly (I:C) and LPS stimulation in BV-2 microglia cells compared with controls. P <0.001, fold change ≥1.5 log2 for the significant determination of DEGs. Each row shows the relative expression level for a single gene, and each column shows the expression level of a single sample. Yellow represents genes with increased expression, and green represents genes with decreased expression. b and c Gene Ontology analysis of functional annotations (biological process) associated with Poly (I:C) and LPS-inducible up-regulated genes in BV-2 microglia in comparison with the control
Fig. 2 Differential gene expression and downstream effect analysis of genes overexpressed in microglia upon TLR3 and TLR4 stimulation at two different time points. a Heat map representing RNA-seq gene expression of top 150 up-regulated inflammatory genes at 2 and 4 h after Poly (I:C) and LPS stimulation in BV-2 microglia cells compared with controls. P <0.001, fold change ≥1.5 log2 for the significant determination of DEGs. Each row shows the relative expression level for a single gene, and each column shows the expression level of a single sample. Yellow represents genes with increased expression, and green represents genes with decreased expression. b and c Gene Ontology analysis of functional annotations (biological process) associated with Poly (I:C) and LPS-inducible up-regulated genes in BV-2 microglia in comparison with the controlCC BY · PubMed Central
Fig. 3 Top IPA-based network involved in infectious diseases and canonical pathway analyses at 4 h after TLR3 and TLR4 stimulation. a, b Ingenuity® Bioinformatics pathway analysis of gene networks displaying interactions between infectious disease-related genes that were differentially expressed at 4 h after Poly (I:C) and LPS stimulation. Genes in white circles were not in our DEG dataset but were inserted by IPA because these genes are connected to this network. The activity of molecules highly connected to this network, namely, NF-κB2, STAT2, and IRF1 (hubs), was assessed using the IPA molecule activity predictor. c, d The most highly represented canonical pathways for differentially expressed genes in BV-2 microglial cells after Poly (I:C) and LPS stimulation
Fig. 3 Top IPA-based network involved in infectious diseases and canonical pathway analyses at 4 h after TLR3 and TLR4 stimulation. a, b Ingenuity® Bioinformatics pathway analysis of gene networks displaying interactions between infectious disease-related genes that were differentially expressed at 4 h after Poly (I:C) and LPS stimulation. Genes in white circles were not in our DEG dataset but were inserted by IPA because these genes are connected to this network. The activity of molecules highly connected to this network, namely, NF-κB2, STAT2, and IRF1 (hubs), was assessed using the IPA molecule activity predictor. c, d The most highly represented canonical pathways for differentially expressed genes in BV-2 microglial cells after Poly (I:C) and LPS stimulationCC BY · PubMed Central
Fig. 4 Inflammatory gene expression patterns in TLR3- and TLR4-stimulated BV-2 microglia cells. a Heat map representation depicting the expression of positive regulators of inflammatory genes (P <0.001; fold change ≥1.5 log2) at 2 and 4 h after Poly (I:C) and LPS stimulation compared with controls. b UCSC browser images representing normalized RNA-seq read densities after Poly (I:C) (left panel) and LPS (right panel) stimulation in BV-2 microglia cells compared with controls at the indicated times
Fig. 4 Inflammatory gene expression patterns in TLR3- and TLR4-stimulated BV-2 microglia cells. a Heat map representation depicting the expression of positive regulators of inflammatory genes (P <0.001; fold change ≥1.5 log2) at 2 and 4 h after Poly (I:C) and LPS stimulation compared with controls. b UCSC browser images representing normalized RNA-seq read densities after Poly (I:C) (left panel) and LPS (right panel) stimulation in BV-2 microglia cells compared with controls at the indicated timesCC BY · PubMed Central
Fig. 5 Transcriptomic analysis of selected TF families in BV-2 microglial cells. a Heat map represents differential expression of NF-κB, STAT, KLF, and IRF TF families, as well as other TF genes, (P <0.001) at 2 and 4 h after Poly (I:C) and LPS stimulation in BV-2 microglial cells. b UCSC browser images representing normalized RNA-seq read densities for TF expression after Poly (I:C) (left panel) and LPS (right panel) stimulation in BV-2 microglia cells compared with controls. c Patterns of transcription factor motif enrichments within the promoters of the genes in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. d, e The activity of highly connected positive regulators of the inflammatory genes IRF1, IRF7, JUNB, NF-κBIA, STAT1, and CREPD led to the activation of this network, as assessed using the IPA molecule activity predictor in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. f, g Results of the GO term analysis using DAVID on genes that were regulated by NF-κBIA and STAT1 in Poly (I:C) and LPS response BV-2 microglial cells respectively. (H, I) The IL1A, CCL7 and CCL2 genes were significantly down-regulated in NF-κB inhibitor Bay 11-7082 (10 μM)-treated BV-2 microglial cells at 2 h and 4 h under inflammatory conditions (Poly (I:C) 5 μg/ml and LPS 10 ng/mL). Gene expression was normalized to GAPDH transcript levels. *P <0.05 and **P <0.001 compared with the control. The data represent three independent experiments
Fig. 5 Transcriptomic analysis of selected TF families in BV-2 microglial cells. a Heat map represents differential expression of NF-κB, STAT, KLF, and IRF TF families, as well as other TF genes, (P <0.001) at 2 and 4 h after Poly (I:C) and LPS stimulation in BV-2 microglial cells. b UCSC browser images representing normalized RNA-seq read densities for TF expression after Poly (I:C) (left panel) and LPS (right panel) stimulation in BV-2 microglia cells compared with controls. c Patterns of transcription factor motif enrichments within the promoters of the genes in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. d, e The activity of highly connected positive regulators of the inflammatory genes IRF1, IRF7, JUNB, NF-κBIA, STAT1, and CREPD led to the activation of this network, as assessed using the IPA molecule activity predictor in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. f, g Results of the GO term analysis using DAVID on genes that were regulated by NF-κBIA and STAT1 in Poly (I:C) and LPS response BV-2 microglial cells respectively. (H, I) The IL1A, CCL7 and CCL2 genes were significantly down-regulated in NF-κB inhibitor Bay 11-7082 (10 μM)-treated BV-2 microglial cells at 2 h and 4 h under inflammatory conditions (Poly (I:C) 5 μg/ml and LPS 10 ng/mL). Gene expression was normalized to GAPDH transcript levels. *P <0.05 and **P <0.001 compared with the control. The data represent three independent experimentsCC BY · PubMed Central
Fig. 6 Transcriptional and post-transcriptional regulatory effects on overall transcript output in BV-2 microglia cells. a, b Pie charts showing the three categories of transcriptional and post-transcriptional regulation of genes at 2 h (upper panel) and 4 h (lower panel) after Poly (I:C) and LPS stimulation in BV-2 microglia cells
Fig. 6 Transcriptional and post-transcriptional regulatory effects on overall transcript output in BV-2 microglia cells. a, b Pie charts showing the three categories of transcriptional and post-transcriptional regulation of genes at 2 h (upper panel) and 4 h (lower panel) after Poly (I:C) and LPS stimulation in BV-2 microglia cellsCC BY · PubMed Central
Fig. 7 RNA-seq analysis reveals that TLR3 and TLR4 induce epigenetic regulators and novel inflammatory related genes. a, c Heat map representation depicting the expression of epigenetic regulators families and novel inflammatory related genes (P <0.001; fold change ≥1.5 log2) at 2 and 4 h after Poly (I:C) and LPS stimulation compared with controls. b, d UCSC browser images representing normalized RNA-seq read densities of epigenetic regulators and novel inflammatory related genes after Poly (I:C) and LPS stimulation in BV-2 microglia cells compared with controls at the indicated times
Fig. 7 RNA-seq analysis reveals that TLR3 and TLR4 induce epigenetic regulators and novel inflammatory related genes. a, c Heat map representation depicting the expression of epigenetic regulators families and novel inflammatory related genes (P <0.001; fold change ≥1.5 log2) at 2 and 4 h after Poly (I:C) and LPS stimulation compared with controls. b, d UCSC browser images representing normalized RNA-seq read densities of epigenetic regulators and novel inflammatory related genes after Poly (I:C) and LPS stimulation in BV-2 microglia cells compared with controls at the indicated timesCC BY · PubMed Central
Fig. 8 Confirmation of differentially expressed genes by quantitative reverse transcription-polymerase chain reaction. a, b IL-1ß, IL1A, TNF-α, PTGS2, CCL3, CCL4, CCL7, CXCL10, IRF1, IRF7, JUNB, NF-κBIA, CLEC4E, GPR84, SLC15A3 and KDM4A genes were significantly up-regulated in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. Gene expression was normalized to the GAPDH transcript levels. *P <0.05 and **P <0.001 compared with control. The data represent three independent experiments
Fig. 8 Confirmation of differentially expressed genes by quantitative reverse transcription-polymerase chain reaction. a, b IL-1ß, IL1A, TNF-α, PTGS2, CCL3, CCL4, CCL7, CXCL10, IRF1, IRF7, JUNB, NF-κBIA, CLEC4E, GPR84, SLC15A3 and KDM4A genes were significantly up-regulated in Poly (I:C)- and LPS-stimulated BV-2 microglia cells. Gene expression was normalized to the GAPDH transcript levels. *P <0.05 and **P <0.001 compared with control. The data represent three independent experimentsCC BY · PubMed Central
Fig. 9 Confirmation of differentially expressed genes and release of pro-inflammatory mediators in primary microglial cells. a and b IL-1ß, IL1A, TNF-α, PTGS2, CCL3, CCL4, CCL7, CXCL10, IRF1, IRF7, JUNB, NF-κBIA, CLEC4E, GPR84, SLC15A3 and KDM4A genes were significantly up-regulated in Poly (I:C)- and LPS treated primary microglia cells. Gene expression was normalized to the GAPDH transcript levels. c and d Primary microglial cell culture supernatants of Poly (I:C)- and LPS treated cells were subjected to ELISA to detect the levels of pro-inflammatory cytokines/chemokines. Therefore, primary microglial cells were treated with Poly (I:C)- and LPS for 2 h and 4 h, followed by quantification of DNMT3L, TNF-α, IL-1ß and CCL4 levels. Values are given in pg/ml. Means and standard deviations of the mean of three independent experiments are shown (*P <0.05, **P <0.001, compared with control). The data represent three independent experiments
Fig. 9 Confirmation of differentially expressed genes and release of pro-inflammatory mediators in primary microglial cells. a and b IL-1ß, IL1A, TNF-α, PTGS2, CCL3, CCL4, CCL7, CXCL10, IRF1, IRF7, JUNB, NF-κBIA, CLEC4E, GPR84, SLC15A3 and KDM4A genes were significantly up-regulated in Poly (I:C)- and LPS treated primary microglia cells. Gene expression was normalized to the GAPDH transcript levels. c and d Primary microglial cell culture supernatants of Poly (I:C)- and LPS treated cells were subjected to ELISA to detect the levels of pro-inflammatory cytokines/chemokines. Therefore, primary microglial cells were treated with Poly (I:C)- and LPS for 2 h and 4 h, followed by quantification of DNMT3L, TNF-α, IL-1ß and CCL4 levels. Values are given in pg/ml. Means and standard deviations of the mean of three independent experiments are shown (*P <0.05, **P <0.001, compared with control). The data represent three independent experimentsCC BY · PubMed Central

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Authors
Das, A., Chai, J. C., Kim, S. H., Lee, Y. S., Park, K. S., Jung, K. H., & Chai, Y. G.
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2015-07-10
Volume
16
Issue
1
ISSN
1471-2164
DOI
10.1186/s12864-015-1728-5
PMC
PMC4497376
Cited by
64
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0 (unspecified)

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Das, A., Chai, J. C., Kim, S. H., Lee, Y. S., Park, K. S., Jung, K. H., & Chai, Y. G. (2015). Transcriptome sequencing of microglial cells stimulated with TLR3 and TLR4 ligands. BMC Genomics. https://doi.org/10.1186/s12864-015-1728-5