2. Materials and methods
2.4. Total RNA isolation and microarray analysis
Differences in gene expression in subject groups of rBM-MSCs or microglia were analysed using the Illumina system (Illumina, USA) in conjunction with Sentrix Rat-Ref-12-v1 Expression Bead Chips including gene-specific oligonucleotides (~22,000 genes, Illumina, USA). Briefly, Total RNA was isolated with TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to standard procedures. And the RNA quality and integrity were checked using Agilent Bioanalyzer Nano chip. c RNA amplification and labeling with biotin were performed using Illumina total prep RNA amplification kit (ambion, Inc., austin, TX) with 250 ng of total RNA as input material. cRNA yields were quantified with Agilent Bioanalyzer and 1.5 μg of cRNA was hybridized to Expression Bead Chip. cRNA was hybridized to arrays for 16 h at 58°C, washed and stained with streptavidin-Cy3 according to the manufacturer's protocol. Then the bead chips were centrifuged to dry and scanned on the Illumina
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Bead Array Reader confocal scanner. Differences in data distribution were analysed using Bead Studio software (Illumina, USA). Probe signals were quantile normalised, and those with a p-value of less than 0.05 were selected for further analysis.
2.5. Transcriptomic analysis using Ingenuity Pathway Analysis
Gene ontology and biological pathways and functions were determined using the web-based bioinformatics software Ingenuity Pathway Analysis (IPA; Ingenuity Systems, USA). A ±1.2 fold-change in expression levels was used as a cut-off to produce data sets of genes with a significantly changed expression. When a stringent
±1.5 cut-off was applied, the groups (experimental and control) cannot be assessed and the biological function of the gene network cannot be predicted. This is mainly because of the low abundance of changed genes (Figure 3,4, and 5). Genes related to same molecular functions were grouped and depicted as a network with indicate direct and indirect relationships as previous reported (31, 32).
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Figure 3. Transcriptomic network analysis in four different culture conditions of rBM-MSCs (>1.5 fold-change). Gene network related to cell migration was constructed algorithmically using Ingenuity Pathway Analysis. Transcriptome network of (a) rBM-MSCs cocultured with microglia, (b) LPS-treated rBM-MSCs, and (c) rBM-MSCs cocultured with LPS-stimulated microglia compared to control (rBM-MSCs only), respectively were shown. Red and green areas indicate up- and down-regulated genes, respectively. Differentially expressed genes were obtained from microarray data.
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Figure 4. Gene network related to cell migration in rBM-MSCs cocultured with LPS-stimulated microglia compared to rBM-MSCs cocultured with microglia (>1.5 fold-change). Gene network related to cell migration was constructed algorithmically using Ingenuity Pathway Analysis. Red and green areas indicate up- and down-regulated genes, respectively. Differentially expressed genes were obtained from microarray data.
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Figure 5. Gene network related to inflammatory response in LPS-stimulated microglia cocultured with rBM-MSCs compared to microglia only (>1.5 fold-change). Gene network related to inflammatory response was constructed algorithmically using Ingenuity Pathway Analysis. Red and green areas indicate up- and down-regulated genes, respectively. Differentially expressed genes were obtained from microarray data.
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2.6. Quantitative real-time PCR (qPCR)
The expression levels of genes were quantified with qPCR using SsoAdvanced Universal SYBR Green Supermix real-time PCR kit (Bio-Rad, USA) and cDNA and gene-specific primer pairs (Tables 1 and 2) on a Rotor-Gene Q system (Qiagen, USA). Reaction conditions were as follows: 95°C for 5 min, followed by 50 cycles of 95°C for 5 s and 60°C for 30 s. The threshold/quantification cycle (Ct/Cq) value was determined as the point where the detected fluorescence was statistically higher than the background levels. PCR products were analysed using melting curves constructed with Rotor-Gene 1.7 software (Qiagen, USA). PCR reactions were prepared independently in triplicates. Relative quantification of target gene expression was calculated using the 2−ΔΔCt method.
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Table 1.: Quantitative real-time PCR primer sequences for genes encoding transcriptomic network related to cell migration
Gene Name Symbol NCBI Ref. seq Direction Primer sequence (5’-3’)
Rattus norvegicus
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Table 2: Quantitative real-time PCR primer sequences for genes encoding transcriptomic network related to inflammatory response
Gene Name Symbol NCBI Ref. seq Direction Primer sequence (5’-3’)
Rattus norvegicus
toll-like receptor 2 Tlr2 NM_198769.2
Forward GGA TCT TGA TGG
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2.7. Migration assay
Migratory activity of rBM-MSCs was determined using an 8 μm pore-size transwell system (Corning, USA). The upper side of the insert was coated with Matrigel (1: 10 dilution in 0.01M Tris (pH 8.0), 0.7% NaCl, Corning, USA) for 2 h at 37°C. The bottom chambers contained one of five different conditions for comparison: MEM with 10% FBS as positive control, MEM without FBS as negative control, MEM with 100 ng/mL LPS, 5 × 104 microglia with MEM, and LPS-stimulated 5 × 104 microglia with MEM. Inserts containing 2.5 × 104 rBM-MSCs were overlaid onto each conditioned well and incubated for 12 h. The inserts were gently washed with cold phosphate-buffered saline (PBS), and non-migrating cells remaining in the upper side of the inserts were removed with a cotton swab. The insert was fixed in Cytofix buffer (BD, San Jose, CA, USA) at 4°C for 30 min and stained with 10 μg/mL Hoechst 33342 at RT for 10 min. After washing twice with PBS, images were acquired using an Axiovert 200M fluorescence microscope (Zeiss, Jena, Germany). The excitation wavelength for Hoechst 33342 was 405 nm.
Migrating cells were counted in ten random 378.27 mm2 (710.52 μm× 532.38 μm) microscopic fields using ImageJ software.
2.8. Statistical analysis
Results were analysed using one-way analysis of variance (ANOVA) with Bonferroni’s multiple comparison test as a post hoc test and IBM-SPSS software (IBM, USA). I performed more than three independent experiments and carried out statistical analysis. Differences were considered significant for p values < 0 05.
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3. Results
3.1. Cellular movement-related transcriptomic changes in rat bone marrow- derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia
To evalutate the effect of LPS-stimulated microglia on rBM-MSCs, an in vitro coculture method was used (Figure 6(a)). For this study, rBM-MSCs were isolated from 8–12-week-old SD rats and primary microglia from the midbrain of 1-day-old rat pups. Groups of rBM-MSCs were seeded onto the bottom chamber and subjected to 4 different conditions (groups 1–4). Group 1 was the rBM-MSC-only control, group 2 was the coculture with microglia, group 3 was the LPS-treated rBM-MSCs, and group 4 was the coculture with LPS-stimulated microglia. There were no significant changes in cell density of rBM-MSCs (data not shown). In a microarray distribution analysis, gene expression was altered in groups 2, 3, and 4 compared to that of the control (Figure 7). Additionally, the most pronounced variation in the gene expression pattern was observed in group 4. Gene ontology analysis of the transcriptome from group 4 revealed that genes from several canonical pathways such as interferon signalling, death receptor signalling, hepatic fibrosis, and neuroinflammation showed variation in expression levels (Table 3). Expression of genes related to cellular functions, including death, survival, cell cycle, and cellular movement, was also highly altered in group 4 (Figure 8). I focused on the role of cell movement, because the homing of MSCs to the injury site is important for exerting anti-inflammatory effects (33). For detailed transcriptomic analysis of cellular movement, Genes related to cellular movement with altered expression were selected by IPA (Figure 6(b)). Heat map analysis showed clear differences in the gene expression pattern in group 4 compared to the control (Figure 6(c)).
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Figure 6. Cellular movement-related gene expression variation in rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia. (a) In vitro coculture experimental design. Four different conditions were used (groups 1–4): group 1, rBM-MSCs only (control); group 2, rBM-MSCs cocultured with microglia; group 3, LPS-treated rBM-MSCs; and group 4, rBM-MSCs cocultured with LPS-stimulated microglia. (b) Gene categorisation according to subgroups related to cellular movement. Genes involved with cellular movement were categorised in subgroups (chemotaxis, homing, migration, and infiltration) based on microarrays and Ingenuity Pathway Analysis. (c) Heat map of genes related to cell migration in the 4 different groups with altered expression levels.
Gene expression values are coloured from green (downregulated) to red (upregulated).
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Figure 7. Plotting of signal intensities in three different culture conditions of rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) compared to control (rBM-MSCs only). Correlation of gene expression variation in experimental groups. The X axes indicate the signal intensities of control and Y axes indicate (a) rBM-MSCs cocultured with microglia, (b) LPS-treated rBM-MSCs, and (c) rBM-MSCs cocultured with LPS-stimulated microglia, respectively. The black line indicates the criterion line which is standardized with control intensities.
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Table 3. Top 20 canonical pathways constructed algorithmically by Ingenuity Pathway Analysis in rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with lipopolysaccharide (LPS)-stimulated microglia compared to rBM-MSCs cocultured with microglia
Canonical Pathways
-log(p-value)
Number of genes
Interferon Signaling 13.7 20
Death Receptor Signaling 10.6 28
Hepatic Fibrosis / Hepatic Stellate Cell Activation 9.1 39
Neuroinflammation Signaling Pathway 8.71 53
iNOS Signaling 8.38 17
Activation of IRF by Cytosolic Pattern Recognition Receptors 8.21 20
TREM1 Signaling 7.51 21
Protein Ubiquitination Pathway 7.01 44
PPAR Signaling 6.87 23
Apoptosis Signaling 6.68 22
Role of JAK1, JAK2 and TYK2 in Interferon Signaling 6.65 11
Osteoarthritis Pathway 6.55 37
Granulocyte Adhesion and Diapedesis 6.47 33
TNFR2 Signaling 6.41 12
Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid
Arthritis 6.15 47
Colorectal Cancer Metastasis Signaling 6.09 49
IL-10 Signaling 6.03 18
Role of Pattern Recognition Receptors in Recognition of Bacteria and
Viruses 6 27
TNFR1 Signaling 5.96 15
Hepatic Cholestasis 5.61 29
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Figure 8. Top 20 list of function or diseases constructed algorithmically by Ingenuity Pathway Analysis (IPA) in rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia compared to rBM-MSCs cocultured with microglia
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3.2. Functional prediction of transcriptomic networks in rBM-MSCs cocultured with LPS-stimulated microglia
To acquire detailed information of the genes showing variation in expression, Cell movement-related gene expression networks of groups 2, 3, and 4 compared to the network of group 1 were generated using IPA (Figure 9). The most pronounced alternation in the gene expression networks were also observed in group 4, and the related genes were linked with direct relationships. Based on up- or downregulation and stream relationships, prediction analysis of the networks showed that cell migration is predicted to be activated in group 4 (Figure 10(a) and Table 4). Four genes with altered expression levels were identified as being highly related to the migratory activity of cells, which was confirmed using quantitative qPCR (Figure 10(b)). The expression levels of matrix metallopeptidases 3 and 9 (Mmp3 and Mmp9, resp.), vascular cell adhesion protein 1 (Vcam1), and intercellular adhesion molecule 1 (Icam1) were significantly higher in group 4 than in group 2.
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Figure 9. Transcriptomic network analysis in four different culture conditions of rat bone marrow-derived mesenchymal stem cells (rBM-MSCs). Gene network related to cell migration was constructed algorithmically using Ingenuity Pathway Analysis. Transcriptome network of (a) rBM-MSCs cocultured with microglia, (b) treated rBM-MSCs, and (c) rBM-MSCs cocultured with LPS-stimulated microglia compared to control (rBM-MSCs only), respectively were shown. Red and green areas indicate up- and down-regulated genes, respectively.
Differentially expressed genes were obtained from microarray data (>1.2 fold-change).
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Figure 10. Increase in migration of rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia. (a) Gene network related to cell migration was constructed, and cellular function was predicted algorithmically using Ingenuity Pathway Analysis. Red and green areas indicate up- and downregulated genes, respectively. Differentially expressed genes were obtained from microarray data (>1.2 fold-change). (b)Quantitative real-time PCR analysis of gene expression related to cell migration in rBM-MSCs cocultured with LPS-stimulated microglia compared to rBM-MSCs cocultured with microglia. Data represent the mean of three independent experiments. (mean ± SD) *p <0.05 versus rBM-MSCs cocultured with microglia.
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Table 4. 67 genes related to cell migration in rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia compared to rBM-MSCs cocultured with microglia
No. Symbol Illumina Expr Fold Change Location
1 LYST ILMN_1367296 -1.84 Cytoplasm
2 FLNC ILMN_1360121 -1.64 Cytoplasm
3 MAP1B ILMN_1362692 -1.59 Cytoplasm
4 RND3 ILMN_1376851 -1.43 Cytoplasm
5 PELI1 ILMN_1357813 1.53 Cytoplasm
6 PYCARD ILMN_1372174 1.61 Cytoplasm
7 JAK2 ILMN_1361170 1.72 Cytoplasm
8 PDE4B ILMN_1353078 1.73 Cytoplasm
9 TNFAIP8 ILMN_1357231 1.81 Cytoplasm
10 NOD1 ILMN_1365499 1.93 Cytoplasm
11 SOD2 ILMN_1367263 2.01 Cytoplasm
12 CASP1 ILMN_1361826 2.07 Cytoplasm
13 USP25 ILMN_1363065 2.21 Cytoplasm
14 PTGES ILMN_1353392 2.24 Cytoplasm
15 SGK1 ILMN_1349269 2.35 Cytoplasm
16 MAP3K8 ILMN_1359072 2.46 Cytoplasm
17 NFKBIA ILMN_1356628 2.64 Cytoplasm
18 NLRP3 ILMN_1362786 2.83 Cytoplasm
19 SOCS1 ILMN_1371295 4.18 Cytoplasm
20 EIF2AK2 ILMN_1376267 5.19 Cytoplasm
21 Irgm1 ILMN_1376472 13.69 Cytoplasm
22 IFIT2 ILMN_1364134 32.84 Cytoplasm
23 BDNF ILMN_1360447 -1.45 Extracellular Space
24 COL3A1 ILMN_1354941 -1.28 Extracellular Space
25 HBEGF ILMN_1357783 -1.26 Extracellular Space
26 MMP9 ILMN_2039240 1.81 Extracellular Space
27 MMP3 ILMN_1361666 1.86 Extracellular Space
28 CSF3 ILMN_1349473 1.94 Extracellular Space
29 CCL5 ILMN_1355610 5.17 Extracellular Space
30 ATOH8 ILMN_1363251 -1.99 Nucleus
31 SOX11 ILMN_1359646 -1.49 Nucleus
32 SRF ILMN_1362760 -1.37 Nucleus
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No. Symbol Illumina Expr Fold Change Location
33 DNMT1 ILMN_1350922 -1.33 Nucleus
43 NFKBIZ ILMN_1362295 2.67 Nucleus
44 STAT1 ILMN_1372159 3.01 Nucleus
45 ATF3 ILMN_1363083 4.38 Nucleus
46 STAT2 ILMN_1376440 6.55 Nucleus
47 IRF7 ILMN_1352469 36.26 Nucleus
48 PDGFRB ILMN_1360414 -1.43 Plasma Membrane
49 BCAR1 ILMN_1364063 -1.22 Plasma Membrane
50 EGFR ILMN_1362571 1.24 Plasma Membrane
51 PDGFRA ILMN_1355008 1.25 Plasma Membrane
52 SEMA4C ILMN_1364995 1.27 Plasma Membrane
53 SDC4 ILMN_1352387 1.53 Plasma Membrane
54 NINJ1 ILMN_1354493 1.55 Plasma Membrane
55 IFITM3 ILMN_1352762 1.79 Plasma Membrane
56 CD40 ILMN_1355111 1.93 Plasma Membrane
57 HLA-G ILMN_1360773 2.06 Plasma Membrane
58 HAS2 ILMN_1370907 2.07 Plasma Membrane
59 ICAM1 ILMN_1354506 2.25 Plasma Membrane
60 LGALS3BP ILMN_1352246 2.30 Plasma Membrane
61 CD69 ILMN_1360103 2.34 Plasma Membrane
62 TLR3 ILMN_1355463 2.35 Plasma Membrane
63 RIPK2 ILMN_1352868 3.58 Plasma Membrane
64 CCRL2 ILMN_1362435 5.39 Plasma Membrane
65 HLA-A ILMN_1376270 5.41 Plasma Membrane
66 TLR2 ILMN_1353896 6.24 Plasma Membrane
67 VCAM1 ILMN_1364472 23.76 Plasma Membrane
Expr Fold Change: Experimental fold chang
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3.3. Increased migratory activity in rBM-MSCs cocultured with LPS-stimulated microglia
Based on the transcriptomic analysis and prediction, there were changes in migratory activity of rBM-MSC influenced by LPS-stimulated microglia. To test the prediction, FBS-containing media condition was used as positive control, and the number of migrating cells was significantly increased in rBM-MSCs when cocultured with LPS-stimulated microglia (Figures 11(a) and 11(b)).
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Figure 11. Migration assays with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) cocultured with LPS-stimulated microglia. (a) Images of migrated rBM-MSCs. Migratory activity of rBM-MSCs was examined under 5 different conditions: with foetal bovine serum (FBS) media (positive control), without FBS media (negative control), LPS-treated, cocultured with microglia, and cocultured with LPS-stimulated microglia. Migrating cells are colored cyan (10×
magnification). Representative migration images are shown for each condition. Scale bar: 100 µm. (b) Migrating cells were quantified by counting colored dots in the images. Data represent the mean of ten random 372.23 mm2 (710.52 µm × 532.38 µm) microscopic fields (mean ±SD). *p < 0.05 vs. negative control (media without FBS).
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3.4. Transcriptomic analysis in LPS-stimulated microglia cocultured with rBM-MSCs
The effects of rBM-MSCs on LPS-stimulated microglia were also determined via reversed conditions of the aforementioned coculture system in vitro. To that end, microglia were seeded on the bottom chamber as subject groups and subjected to 3 different conditions: control, LPS stimulation, and LPS stimulation with rBM-MSC coculture (Figure 12(a)). There were no significant changes in cell density of microglia (data not shown). The transcriptome of subject groups was analysed using microarrays and IPA. Comparison between LPS stimulation and LPS stimulation with rBM-MSC coculture showed changes in expression levels of genes related to cancer, organismal injury and abnormalities, and cell death (Figure 13). Gene ontology analysis of the transcriptome revealed altered expression levels of genes related to inflammatory response related canonical pathways such as triggering receptor expressed on myeloid cell 1 (TREM1) signalling, neuroinflammation, and rheumatoid arthritis (Table 4 and Figure 12(b)). Expression of genes related to TREM1 signalling and neuroinflammation was especially suppressed, as predicted (negative z-score). Focused gene expression analysis of the inflammatory response showed significantly altered levels in 64 genes between groups (Figure 12(c)).
Although the differences were more pronounced after LPS stimulation, remarkable changes were also observed between the presence and absence of rBM-MSCs.
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Figure 12. Inflammation-related gene expression variation in LPS-stimulated microglia cocultured with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs). (a) In vitro reverse coculture experimental design. Three different conditions were used (group 1–3): group 1, control (microglia only); group 2, LPS-stimulated microglia; and group 3, LPS-stimulation with rBM-MSC coculture. (b) Canonical pathway analysis was constructed algorithmically using Ingenuity Pathway Analysis based on microarray data. Bars indicate canonical pathways containing genes with significantly altered expression. Bar graph colours from blue (inhibition) to orange (activation) represent gene activity of the corresponding pathway according to z-score. (c) Heat map of genes related to inflammation with altered expression levels. Gene expression values are coloured from green (downregulated) to red (upregulated).
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Figure 13. Top 20 list of function or diseases constructed algorithmically by Ingenuity Pathway Analysis in LPS-stimulated microglia cocultured with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) compared to LPS-stimulated microglia.
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Table 5. Top 20 canonical pathways constructed algorithmically by Ingenuity Pathway Analysis in LPS-stimulated microglia cocultured with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) compared to LPS-stimulated microglia
Canonical Pathways -log(p-value) Number of genes
TREM1 Signaling 11.7 31
Neuroinflammation Signaling Pathway 10.4 73
Role of Macrophages, Fibroblasts and Endothelial Cells in
Rheumatoid Arthritis 9.65 70
Dendritic Cell Maturation 8.54 49
iNOS Signaling 7.17 19
IL-10 Signaling 7.04 24
Type I Diabetes Mellitus Signaling 6.93 31
Th1 Pathway 6.87 35
Role of PKR in Interferon Induction and Antiviral Response 6.83 18
Th1 and Th2 Activation Pathway 6.82 43
PI3K Signaling in B Lymphocytes 6.45 34
Altered T Cell and B Cell Signaling in Rheumatoid Arthritis 6.31 26
Colorectal Cancer Metastasis Signaling 5.97 51
JAK/Stat Signaling 5.88 24
Role of Pattern Recognition Receptors in Recognition of
Bacteria and Viruses 5.81 33
Crosstalk between Dendritic Cells and Natural Killer Cells 5.75 25
HMGB1 Signaling 5.64 32
Production of Nitric Oxide and Reactive Oxygen Species in
Macrophages 5.51 42
Death Receptor Signaling 5.46 25
Toll-like Receptor Signaling 5.46 22
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3.5. Functional prediction of transcriptomic networks and reduced inflammatory response in LPS-stimulated microglia cocultured with rBM-MSCs
To examine differences in transcriptomes between LPS-stimulated microglia with and without rBM-MSCs, gene expression networks related to inflammatory were generated using IPA in the corresponding groups (Figure 14 and Table 5). A total of 65 genes highly related to the inflammatory response were identified, and the genes were directly linked. Based on the differential expression (upregulation or downregulation), prediction analysis of networks showed that the inflammatory response is predicted to be inhibited by rBM-MSCs (Figure 15(a) and Table 6). Three genes with altered expression levels were highly related to inflammation, which was confirmed using qPCR (Figure 15(b)). LPS-induced upregulated levels of tumor necrosis factor (Tnf), C-C motif chemokine ligand 2 (Ccl2), and toll-like receptor 2 (Tlr2) genes were significantly decreased after coculture with rBM-MSCs. These predicted functional results were confirmed in experiments using cell cultures, where the number of activated cells induced by LPS stimulation showing swelled and round morphology was significantly decreased by rBM-MSCs (Figure 15(c)). Moreover, the levels of protein markers for microglial activation, CD40 and CD74, were also lower in the presence of rBM-MSC than after LPS stimulation without rBM-MSC, indicating that direct phenotypical activation of microglia was reduced by rBM-MSCs (Figure 16).
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Figure 14. Transcriptomic network analysis in two different culture conditions of LPS-stimulated microglia. Gene network related to inflammatory response was constructed algorithmically using Ingenuity Pathway Analysis. Transcriptome network of (a) LPS-stimulated microglia and (b) LPS-stimulated microglia cocultured with rBM-MSCs compared to control (microglia only), respectively were shown. Red and green areas indicate up- and down-regulated genes, respectively.
Differentially expressed genes were obtained from microarray data (>1.2 fold-change).
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Figure 15. Reduced inflammatory response in LPS-stimulated microglia cocultured with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs).
(a) Gene network related to inflammatory response was constructed, and cellular function was predicted algorithmically using Ingenuity Pathway Analysis. Red and green areas indicate up- and downregulated genes, respectively. Differentially expressed genes were obtained from microarray data (>1.2 fold-change). (b) Quantitative real-time PCR analysis of gene expression-related inflammation in LPS-stimulated microglia cocultured with rBM-MSCs compared to control (microglia only). (c) Activated microglia were counted in light microscopy images and quantified as the percentage of activated microglia/total cell number. Cells at the edge of the images were not counted. Scale bar: 20 μm. ∗p < 0.05 and ∗∗p < 0.01 versus control (microglia only).
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Table 6. 64 genes related to inflammatory response in LPS-stimulated microglia cocultured with rat bone marrow-derived mesenchymal stem cells (rBM-MSCs) compared to LPS-stimulated microglia
No. Symbol Illumina Expr Fold Change Location
1 AKT1 ILMN_1353102 -1.35 Cytoplasm
2 BIRC2 ILMN_1650704 1.47 Cytoplasm
3 BIRC3 ILMN_1365233 1.67 Cytoplasm
4 CASP1 ILMN_1361826 -1.27 Cytoplasm
5 CHUK ILMN_2039288 1.24 Cytoplasm
6 CYBB ILMN_1352424 -1.53 Cytoplasm
7 HMOX1 ILMN_1650285 -1.61 Cytoplasm
8 IRAK3 ILMN_1361037 -1.31 Cytoplasm
9 JAK1 ILMN_1354897 -1.38 Cytoplasm
10 JAK3 ILMN_1355371 1.22 Cytoplasm
11 MAPK14 ILMN_1352809 1.35 Cytoplasm
12 MAPK3 ILMN_1366612 -1.31 Cytoplasm
13 NCF2 ILMN_1365484 -1.71 Cytoplasm
14 NLRP3 ILMN_1362786 -1.66 Cytoplasm
15 NOS2 ILMN_1372107 1.38 Cytoplasm
16 PIK3CG ILMN_1351492 -1.59 Cytoplasm
17 PIK3R5 ILMN_1350193 -1.32 Cytoplasm
18 PLA2G4A ILMN_1360060 1.21 Cytoplasm
19 PLCG2 ILMN_1367941 -1.28 Cytoplasm
20 PPP3CA ILMN_1372414 1.29 Cytoplasm
20 PPP3CA ILMN_1372414 1.29 Cytoplasm