Open Access

Identification and functional analysis of differentially expressed genes associated with cerebral ischemia/reperfusion injury through bioinformatics methods

  • Authors:
    • Xiaoli Shao
    • Wangxiao Bao
    • Xiaoqin Hong
    • Huihua Jiang
    • Zhi Yu
  • View Affiliations

  • Published online on: June 6, 2018     https://doi.org/10.3892/mmr.2018.9135
  • Pages: 1513-1523
  • Copyright: © Shao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Cerebral ischemia/reperfusion (I/R) injury results in detrimental complications. However, little is known about the underlying molecular mechanisms involved in the reperfusion stage. The aim of the present study was to identify a gene expression profile associated with cerebral ischemia/reperfusion injury. The GSE23160 dataset, which comprised data from sham control samples and post‑I/R injury brain tissues that were obtained using a middle cerebral artery occlusion (MCAO) model at 2, 8 and 24 h post‑reperfusion, was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in the MCAO samples compared with controls were screened using the GEO2R web tool. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs was performed using the online tool DAVID. Furthermore, a protein‑protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. In total, 32 DEGs at 2 h post‑reperfusion, 39 DEGs at 8 h post‑reperfusion and 91 DEGs at 24 h post‑reperfusion were identified, while 15 DEGs were common among all three groups. GO analysis revealed that the DEGs at all three time‑points were enriched in ‘chemotaxis’ and ‘inflammatory response’ terms, while KEGG pathway analysis demonstrated that DEGs were significantly enriched in the ‘chemokine signaling pathway’. Furthermore, following PPI network construction, Cxcl1 was identified as the only hub gene that was common among all three time‑points. In conclusion, the present study has demonstrated a global view of the potential molecular differences following cerebral I/R injury and may contribute to an improved understanding of the reperfusion stage, which may ultimately aid in the development of future clinical strategies.

Introduction

Globally, stroke is the second most frequent cause of mortality and the primary cause of serious long-term disability worldwide (1). Of all strokes, 87% are ischemic (2). Various mechanisms underlying ischemic stroke are driven by cell-cell interactions within brain, including excitotoxicity, calcium dysregulation, oxidative and nitrosative Stress, cortical spreading depolarizations, inflammation, necrosis, necroptosis and autophagy (3). In addition to a narrow therapeutic time window (4), ischemic stroke remains difficult to manage.

Although reperfusion has been proven to be beneficial for ischemic stroke (5), reperfusion may result in detrimental secondary damage, which is termed ischemia/reperfusion (I/R) injury. Early reperfusion of ischemic brain tissue has been associated with various negative consequences, including blood-brain barrier breakdown, which may result in cerebral edema and/or brain hemorrhage, neurovascular damage and neuronal death (6). Angiogenesis and vasculogenesis have also been detected following reperfusion (7). In addition, inflammation is induced by reperfusion injury and contributes negatively to long-term disease prognosis (8). The inflammatory response may result in subsequent oxidative injury, excitotoxicity and neuronal cell death (9). Chemokines, produced by resident microglial cells and other immune cells in the brain, contribute to the recruitment of circulating leukocytes and exaggerate the inflammatory response. Chemokines have been demonstrated to have both deleterious and beneficial roles in ischemia/reperfusion injury (10).

Microarray analysis has been previously employed to identify molecular variations in cerebral I/R injury (11,12). However, gene expression profiles at different reperfusion periods have not been investigated extensively. Therefore, the present study employed a microarray dataset from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) between control samples and cerebral I/R samples at 2, 8 and 24 h post-reperfusion, and subsequently analyzed the functions and interactions of these DEGs. The results of the current study may aid in improving the understanding of the molecular mechanisms underlying cerebral I/R injury.

Materials and methods

Microarray data

Microarray gene expression profiles from GSE23160 (12) were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), which is based on the platform of GPL6885 using Illumina MousRef-8 v2.0 Expression BeadChip (Illumina, Inc., San Diego, CA, USA). All of the samples were taken from male C57BL/6J mice (8–10 weeks). Following 2 h suture-induced middle cerebral artery occlusion (MCAO), the animals underwent reperfusion for 2, 8 or 24 h. Tissue extractions at 2, 8 and 24 h post-reperfusion and sham controls (n=4 per group) were included in this dataset.

Identification of DEGs

GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/), an R-based web application (13), was employed to analyze DEGs between MCAO samples and sham samples. P<0.05 and |logFC|≥1.2 were set as the threshold criteria to identify genes that were differentially expressed in MCAO models. Subsequently, the DEGs at 2, 8 and 24 h post-reperfusion were screened for subsequent analyses. A Venn diagram was produced to indicate the intersection among DEGs in the various MCAO groups using FunRich software (version 2.1.1; www.funrich.org) (14).

Functional enrichment analysis of DEGs

To identify the biological processes, cellular components, molecular functions and biological pathways that the DEGs were significantly enriched in, Gene Ontology (GO) enrichment (15) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (16) were performed using the online tool Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/). P<0.05 was considered to indicate a significantly enriched term or pathway, which was calculated using a hypergeometric test. Heat map illustration of DEGs was performed with heat map illustrator software (version 1.0.3.7; http://hemi.biocuckoo.org) (17).

Construction of the PPI network

To further investigate the underlying molecular mechanisms of cerebral I/R injury, protein-protein interaction (PPI) networks for the DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://www.string-db.org/) (18). A combined score of >0.4 was selected to construct the PPI networks. The obtained PPI networks at 24 h post-reperfusion were subsequently visualized using Cytoscape software (version 3.5.1) (19). Finally, the topological properties of the networks at 2, 8 and 24 h post-reperfusion were analyzed and the degree of each node was calculated; genes with a degree >10 were defined as hub genes.

Results

Identification of DEGs

As demonstrated in Fig. 1, 32 DEGs at 2 h post-reperfusion, 39 DEGs at 8 h post-reperfusion and 91 DEGs at 24 h post-reperfusion were identified in the MCAO samples compared with the controls. Among them, 15 DEGs were common to all three injury samples, including C-C motif chemokine ligand (CCL)7, suppressor of cytokine signaling 3, CCL4, activating transcription factor 3, lipocalin 2, hemoglobin α adult chain 1, gap junction protein β2, CD14 antigen, CCL3, heat shock protein 1A, S100 calcium-binding protein A8 (calgranulin A), C-X-C motif chemokine ligand 1 (CXCL1), epithelial membrane protein 1, tissue inhibitor of metalloproteinase 1 and zinc finger protein 36, all of which were upregulated in the MCAO samples (Table I). Heat map and PPI network analysis at 2 and 8 h post-reperfusion are not presented due to the small number of identified DEGs.

Table I.

DEGs between cerebral I/R and sham control samples that were common among 2, 8 and 24 h post-reperfusion time-points.

Table I.

DEGs between cerebral I/R and sham control samples that were common among 2, 8 and 24 h post-reperfusion time-points.

Post-reperfusion time-point

2 h8 h24 h
Gene symbolGene nameP-valueLog FCP-valueLog FCP-valueLog FC
ATF3Activating transcription factor 35.01×10-62.0269643.56×10-51.6921717.49×10-31.591308
CCL3C-C motif chemokine ligand 35.13×10-103.2592397.05×10-63.2916524.81×10-42.300334
CCL4C-C motif chemokine ligand 41.32×10-104.4299541.29×10-64.472455.83×10-53.679271
CCL7C-C motif chemokine ligand 72.18×10-51.2201927.63×10-61.4269734.00×10-31.707457
CD14CD14 antigen1.38×10-71.7572825.92×10-72.5785732.56×10-32.353608
CXCL1C-X-C motif chemokine ligand 13.29×10-61.8066434.81×10-82.8787351.43×10-32.596958
EMP1Epithelial membrane protein 11.68×10-61.6886576.78×10-71.3836691.89×10-32.161470
GJB2Gap junction protein β23.16×10-81.9995691.15×10-71.7171019.13×10-51.822626
HBA-A1Hemoglobin α, adult chain 15.38×10-72.4360232.15×10-83.7577534.48×10-43.807134
HSPA1AHeat shock protein 1A1.69×10-63.0547883.80×10-52.5454661.59×10-21.299110
LCN2Lipocalin 24.06×10-91.3122454.47×10-153.2502331.72×10-53.408973
S100A8S100 calcium-binding protein A8 (calgranulin A)2.29×10-61.2408851.66×10-61.6559621.90×10-33.780381
SOCS3Suppressor of cytokine signaling 33.54×10-82.0097711.74×10-82.0049985.48×10-42.361302
TIMP1Tissue inhibitor of metalloproteinase 11.02×10-82.3761903.61×10-183.4031901.52×10-53.874533
ZFP36Zinc finger protein 362.01×10-51.5954286.26×10-71.4046821.17×10-21.330419
Bioinformatics analyses of DEGs

To further the understanding of the screened DEGs and determine their potential roles following I/R injury, GO functional and KEGG pathway enrichment analyses were performed.

A total of 51 GO enriched terms for biological processes at 2, 8 and 24 h post-reperfusion were obtained. The 10 most enriched GO terms according to the P-value for 2, 8 and 24 h post-reperfusion groups are presented in Table II. Furthermore, ‘chemotaxis’ (GO:0006935), ‘inflammatory response’ (GO:0006954), ‘immune response’ (GO:0006955), ‘G-protein coupled receptor signaling pathway’ (GO:0007186), ‘response to toxic substance’ (GO:0009636), ‘neutrophil chemotaxis’ (GO:0030593), ‘positive regulation of tumor necrosis factor production’ (GO:0032760), ‘positive regulation of GTPase activity’ (GO:0043547), ‘lymphocyte chemotaxis’ (GO:0048247), ‘positive regulation of inflammatory response’ (GO:0050729), ‘cell chemotaxis’ (GO:0060326), ‘chemokine-mediated signaling pathway’ (GO:0070098), ‘positive regulation of ERK1 and ERK2 cascade’ (GO:0070374), ‘cellular response to interferon-gamma’ (GO:0071346), ‘cellular response to interleukin-1’ (GO:0071347), ‘monocyte chemotaxis’ (GO:0002548) and ‘cellular response to tumor necrosis factor’ (GO:0071356) were significantly enriched at all three post-reperfusion time-points (2, 8 and 24 h).

Table II.

Top 10 enriched GO biological process terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

Table II.

Top 10 enriched GO biological process terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

A, Top 10 enriched GO biological process terms for DEGs at 2 h post-reperfusion

GO IDGO termCount%P-valueGenes
GO:0030593Neutrophil chemotaxis515.635.65×10-6CXCL1, CCL3, S100A8, CCL4, CCL7
GO:0032570Response to progesterone412.501.80×10-5FOS, OXT, FOSB, GJB2
GO:0006954Inflammatory response721.882.24×10-5CXCL1, CCL3, S100A8, CCL4, CCL7, CD14, IL1A
GO:0045944Positive regulation of transcription from RNA polymerase II promoter1031.252.99×10-5FOS, CCL3, EGR2, ATF3, EGR4, FOSB, NPAS4, JUNB, IL1A, CYR61
GO:0071356Cellular response to tumor necrosis factor515.633.59×10-5LCN2, ZFP36, CCL3, CCL4, CCL7
GO:2000503Positive regulation of natural killer cell chemotaxis39.384.25×10-5CCL3, CCL4, CCL7
GO:0006935Chemotaxis515.634.73×10-5CCL3, S100A8, CCL4, CCL7, CYR61
GO:0051591Response to cAMP412.508.99×10-5FOS, OXT, FOSB, JUNB
GO:0070098Chemokine-mediated signaling pathway412.501.13×10-4CXCL1, CCL3, CCL4, CCL7
GO:0050729Positive regulation of inflammatory response412.501.69×10-4CCL3, S100A8, CCL4, CCL7

B, Top 10 enriched GO biological process terms for DEGs at 8 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0030593Neutrophil chemotaxis1025.648.69×10-15CXCL1, CCL12, CCL3, S100A8, LGALS3, CCL9, CCL4, CCL7, FCGR3, CCL17
GO:0002548Monocyte chemotaxis820.511.19×10-12CCL12, CCL3, FLT1, LGALS3, CCL9, CCL4, CCL7, CCL17
GO:0070098Chemokine-mediated signaling pathway717.951.09×10-9CXCL1, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
GO:0071356Cellular response to tumor necrosis factor820.511.83×10-9LCN2, ZFP36, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
GO:0050729Positive regulation of inflammatory response717.952.52×10-9CCL12, CCL3, S100A8, CCL9, TLR2, CCL4, CCL7
GO:0006935Chemotaxis820.513.01×10-9CCL12, CCL3, FLT1, S100A8, CCL9, CCL4, CCL7, CCL17
GO:0071346Cellular response to interferon-gamma717.954.02×10-9CCL12, CCL3, CCL9, CCL4, GBP2, CCL7, CCL17
GO:0048247Lymphocyte chemotaxis615.385.34×10-9CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
GO:0071347Cellular response to interleukin-1717.951.09×10-8LCN2, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
GO:0006954Inflammatory response1025.641.76×10-8CXCL1, CCL12, CCL3, S100A8, CCL9, TLR2, CCL4, CCL7, CD14, CCL17

C, Top 10 enriched GO biological process terms for DEGs at 24 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0006954Inflammatory response2122.831.99×10-16CXCL1, CCL3, CCL2, S100A8, CCL21C, S100A9, CCL9, TLR2, CCL21A, PF4, FPR2, IDO1, CCL4, CCL7, CXCL10, SLC11A1, CYBA, CCL12, CHIL3, CD14, SPP1
GO:0006955Neutrophil chemotaxis1314.135.60×10-16CXCL1, CCL3, CCL2, S100A8, LGALS3, CCL21C, S100A9, CCL21A, CCL9, CCL4, CCL7, CCL12, SPP1
GO:0006956Chemokine-mediated signaling pathway1111.961.08×10-13CXCL1, CCL12, CCL3, CCL2, CCL21C, CCL9, CCL21A, PF4, CCL4, CCL7, CXCL10
GO:0006957Monocyte chemotaxis99.781.54×10-11CCL12, CCL3, CCL2, LGALS3, CCL21C, CCL9, CCL21A, CCL4, CCL7
GO:0006958Positive regulation of inflammatory response1010.871.92×10-11CCL12, CCL3, CCL2, S100A8, S100A9, CCL9, TGM2, TLR2, CCL4, CCL7
GO:0006959Cellular response to tumor necrosis factor1111.961.39×10-10LCN2, ZFP36, CCL12, CYBA, CCL3, CCL2, CCL21C, CCL9, CCL21A, CCL4, CCL7
GO:0006960Cellular response to interleukin-11010.871.76×10-10LCN2, CCL12, CCL3, CCL2, CCL21C, CCL9, SAA3, CCL21A, CCL4, CCL7
GO:0006961Lymphocyte chemotaxis88.701.96×10-10CCL12, CCL3, CCL2, CCL21C, CCL9, CCL21A, CCL4, CCL7
GO:0006962Chemotaxis1111.962.80×10-10CCL12, CCL3, CCL2, S100A8, S100A9, CCL9, PF4, FPR2, CCL4, CCL7, CXCL10
GO:0006963Cellular response to interferon-gamma99.781.33×10-9CCL12, CCL3, CCL2, CCL21C, CCL9, CCL21A, CCL4, GBP2, CCL7

[i] GO, Gene Ontology; DEGs, differentially expressed genes.

Additionally, DEGs were enriched in various GO cellular component terms; at 2 and 8 h post-reperfusion, DEGs were enriched in ‘extracellular region’ (GO:0005576), while ‘membrane’ (GO:0016020) was significantly enriched at both 8 and 24 h post-reperfusion (Table III). Furthermore, Table IV indicates that DEGs were significantly enriched in ‘cytokine activity’ (GO:0005125) and ‘chemokine activity’ (GO:0008009) GO molecular function terms at 2, 8 and 24 h post-reperfusion.

Table III.

GO cellular component terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

Table III.

GO cellular component terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

A, Enriched GO cellular component terms for DEGs at 2 h post-reperfusion

GO IDGO termCount%P-valueGenes
GO:0005576Extracellular region1443.759.56×10-7CXCL1, CCL3, AVP, S100A8, PMCH, OXT, CCL4, CCL7, TIMP1, LCN2, NPTX2, IL1A, CD14, CYR61
GO:0005615Extracellular space1237.501.02×10-5LCN2, CXCL1, AVP, CCL3, S100A8, PMCH, OXT, CCL4, CCL7, CD14, IL1A, TIMP1

B, Enriched GO cellular component terms for DEGs at 8 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0005615Extracellular space1641.034.81×10-8CXCL1, CCL3, FLT1, S100A8, LGALS3, PMCH, CCL9, CCL4, CCL7, TIMP1, CCL17, LCN2, CCL12, SERPINA3N, DMKN, CD14
GO:0005576Extracellular region1435.901.39×10-5CXCL1, CCL3, S100A8, LGALS3, PMCH, CCL9, CCL4, CCL7, TIMP1, LCN2, CCL12, SERPINA3N, DMKN, CD14
GO:0009897External side of plasma membrane512.823.17×10-3LGALS3, OSMR, TLR2, CD14, FCGR3
GO:0016020Membrane2051.284.79×10-2GPR84, FLT1, S100A8, LGALS3, OSMR, FKBP5, MS4A6D, TLR2, SLC10A6, GJB2, FCGR3, HBA-A1, CH25H, PLIN4, HMOX1, ITGAD, SLC15A3, GBP2, EMP1, CD14

C, Enriched GO cellular component terms for DEGs at 24 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0009897External side of plasma membrane88.706.31×10-4FCGR2B, LGALS3, PDPN, CCL21C, FCGR4, TLR2, CD14, CXCL10
GO:0048237Rough endoplasmic reticulum lumen33.264.29×10-4LYZ2, LYZ1, CHIL3
GO:0009986Cell surface1010.872.33×10-3SLC11A1, THBD, FCGR2B, LGALS3, TNFRSF12A, IFITM3, FCGR4, TLR2, CD14, ANXA2
GO:0005886Plasma membrane3436.968.00×10-3GPR182, GPR84, S100A8, IFITM2, TNFRSF12A, IFITM3, VIM, S100A9, TLR2, CD52, FPR2, SLC11A1, P2RY6, DAB2, PLIN2, HMOX1, TGM2, STRA6, CLEC4D, ANGPT2, ACTB, PDPN, LILRB4A, GJB2, ANXA2, CYBA, THBD, FCGR2B, HSPB1, SCN4B, RGS9, EMP3, CD14, EMP1
GO:0016020Membrane4447.831.14×10-2GPR182, GPR84, GFAP, TSPO, S100A8, IFITM2, TNFRSF12A, IFITM3, S100A9, TLR2, CD52, FPR2, FXYD5, GLIPR2, SLC11A1, P2RY6, DAB2, PLIN2, HMOX1, CH25H, TGM1, TGM2, STRA6, CLEC4D, ACTB, LGALS3, PDPN, MS4A6D, LILRB4A, GJB2, ANXA2, HBA-A1, CYBA, RAB32, THBD, FCGR2B, SCN4B, RGS9, EMP3, SLC15A3, GBP2, CD14, EMP1, MVP

[i] GO, Gene Ontology; DEGs, differentially expressed genes.

Table IV.

Enriched GO molecular function terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

Table IV.

Enriched GO molecular function terms for DEGs between cerebral ischemia/reperfusion and sham control samples.

A, Enriched GO molecular function terms for DEGs at 2 h post-reperfusion

GO IDGO termCount%P-valueGenes
GO:0005125Cytokine activity618.752.48×10-5CXCL1, FOS, CCL3, CCL4, CD14, IL1A
GO:0008009Chemokine activity412.506.79×10-5FOS, CCL3, CCL4, CD14
GO:0000978RNA polymerase II core promoter proximal region sequence-specific DNA binding618.752.84×10-4FOS, EGR2, ATF3, FOSB, NPAS4, JUNB
GO:0001077Transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding515.639.82×10-4FOS, EGR2, FOSB, NPAS4, JUNB
GO:0003690Double-stranded DNA binding39.382.13×10-2FOS, FOSB, JUNB
GO:0003677DNA binding825.002.85×10-2ZFP36, FOS, EGR2, ATF3, EGR4, FOSB, NPAS4, JUNB

B, Enriched GO molecular function terms for DEGs at 8 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0008009Chemokine activity717.953.98×10-10CXCL1, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
GO:0005125Cytokine activity820.511.54×10-7CXCL1, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17, TIMP1
GO:0048020CCR chemokine receptor binding410.262.14×10-5CCL3, CCL9, CCL4, CCL17

C, Enriched GO molecular function terms for DEGs at 24 h post-reperfusion

GO IDGO termCount%P-valueGenes

GO:0008009Chemokine activity1111.961.69×10-14CXCL1, CCL12, CCL3, CCL2, CCL21C, CCL9, CCL21A, PF4, CCL4, CCL7, CXCL10
GO:0005125Cytokine activity1213.044.78×10-9CXCL1, CCL12, CCL3, CCL2, CCL9, PF4, CCL4, CCL7, SPP1, TIMP1, CXCL10, IL11
GO:0048020CCR chemokine receptor binding55.448.52×10-6CCL3, CCL21C, CCL9, CCL21A, CCL4
GO:0031727CCR2 chemokine receptor binding33.261.30×10-4CCL12, CCL2, CCL7
GO:0008201Heparin binding44.353.43×10-2CCL2, PF4, CCL7, CXCL10
GO:0020037Heme binding44.354.94×10-2HBA-A1, CYBA, HMOX1, IDO1

[i] GO, Gene Ontology; DEGs, differentially expressed genes.

Table V presents the KEGG pathways that were significantly enriched in DEGs. KEGG analysis indicated that ‘chemokine signaling pathway’, ‘cytokine-cytokine receptor interaction’ and ‘toll-like receptor signaling pathway’ were significantly enriched in DEGs at 2, 8 and 24 h post-reperfusion. Furthermore, as demonstrated in Fig. 2, a total of 11 chemokine signaling pathway-associated genes were overexpressed in 24 h post-reperfusion injury samples compared with the sham control samples.

Table V.

Enriched KEGG pathways for DEGs between cerebral ischemia/reperfusion and sham control samples.

Table V.

Enriched KEGG pathways for DEGs between cerebral ischemia/reperfusion and sham control samples.

A, Enriched KEGG pathways for DEGs at 2 h post-reperfusion

KEGG entryPathway nameCount%P-valueGenes
mmu05132Salmonella infection618.751.27×10-6CXCL1, FOS, CCL3, CCL4, CD14, IL1A
mmu04380Osteoclast differentiation515.632.68×10-4FOS, SOCS3, FOSB, JUNB, IL1A
mmu04620Toll-like receptor signaling pathway412.502.11×10-3FOS, CCL3, CCL4, CD14
mmu04062Chemokine signaling pathway412.501.34×10-2CXCL1, CCL3, CCL4, CCL7
mmu04060Cytokine-cytokine receptor interaction412.502.41×10-2CCL3, CCL4, CCL7, IL1A
mmu05166HTLV-I infection412.503.35×10-2ZFP36, FOS, EGR2, ATF3

B, Enriched KEGG pathways for DEGs at 8 h post-reperfusion

KEGG entryPathway nameCount%P-valueGenes

mmu04062Chemokine signaling pathway717.952.28×10-5CXCL1, CCL12, CCL3, CCL9, CCL4, CCL7, CCL17
mmu04060Cytokine-cytokine receptor interaction615.388.01×10-4CCL12, CCL3, FLT1, OSMR, CCL4, CCL7
mmu05132Salmonella infection410.261.72×10-3CXCL1, CCL3, CCL4, CD14
mmu04620Toll-like receptor signaling pathway410.263.60×10-3CCL3, TLR2, CCL4, CD14
mmu04145Phagosome410.261.61×10-2TLR2, TUBB6, CD14, FCGR3
mmu05142Chagas disease (American trypanosomiasis)37.694.02×10-2CCL12, CCL3, TLR2

C, Enriched KEGG pathways for DEGs at 24 h post-reperfusion

KEGG entryPathway nameCount%P-valueGenes

mmu04062Chemokine signaling pathway1111.962.50×10-7CXCL1, CCL12, CCL3, CCL2, CCL21C, CCL9, CCL21A, PF4, CCL4, CCL7, CXCL10
mmu04060Cytokine-cytokine receptor interaction1111.961.95×10-6CCL12, CCL3, CCL2, TNFRSF12A, CCL21C, CCL21A, PF4, CCL4, CCL7, CXCL10, IL11
mmu05323Rheumatoid arthritis66.521.43×10-4CCL12, CCL3, CCL2, TLR2, MMP3, IL11
mmu04620Toll-like receptor signaling pathway66.523.80×10-4CCL3, TLR2, CCL4, CD14, SPP1, CXCL10
mmu04668TNF signaling pathway66.525.41×10-4CXCL1, CCL12, CCL2, SOCS3, MMP3, CXCL10
mmu04145Phagosome77.616.73×10-4ACTB, CYBA, FCGR2B, FCGR4, TLR2, TUBB6, CD14
mmu05144Malaria44.353.20×10-3HBA-A1, CCL12, CCL2, TLR2
mmu05164Influenza A66.523.99×10-3ACTB, CCL12, CCL2, SOCS3, HSPA1A, CXCL10
mmu05142Chagas disease (American trypanosomiasis)44.352.58×10-2CCL12, CCL3, CCL2, TLR2

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

PPI network analysis

Genes with an interaction degree >10 in the PPI network analysis of DEGs at 2, 8 and 24 h post-reperfusion were defined as hub genes, which are listed in Table VI. CXCL1 was the only gene that was considered to be a hub gene at 2, 8 and 24 h post-reperfusion (Table VI). The constructed PPI network of 24 h post-reperfusion samples is presented in Fig. 3, which contains 67 nodes and 281 edges. Each node represents a DEG and each edge represents a PPI between two DEGs. At 24 h post-reperfusion, 23 genes served as hub genes, and of these hub genes, CCL2 exhibited the highest degree (Fig. 3).

Table VI.

Hub genes identified in PPI networks.

Table VI.

Hub genes identified in PPI networks.

A, Hub genes in the PPI network at 2 h post-reperfusion

GeneDegree
FOS16
CXCL111
ATF311

B, Hub genes in the PPI network at 8 h post-reperfusion

GeneDegree

TLR214
CXCL113
CD1412
CCL412
CCL312
CCL712
TIMP110

C, Hub genes in the PPI network at 24 h post-reperfusion

GeneDegree

CCL237
TLR228
CCL319
CD1419
CXCL1019
CXCL118
CCL717
CCL417
SLC11A116
TIMP116
MS4A6D14
LCN214
LILRB414
FPR214
SOCS313
SLC15A312
VIM12
FCGR2B12
CCL912
CCL1211
PF411
LGALS310
ACTB10

[i] PPI, protein-protein interaction.

Discussion

In the present study, 32 DEGs at 2 h, 39 DEGs at 8 h and 91 DEGs at 24 h post-reperfusion injury were identified between cerebral I/R and sham control samples. Previous studies have performed bioinformatics analysis to identify DEGs between MCAO models and controls (11,2023). However, to the best of our knowledge, the present study is the first to perform global gene expression profiling at three time-points following reperfusion, and the findings may lead to improvements in the understanding of the pathophysiological process of cerebral I/R injury. DEGs associated with inflammation have previously been associated with cerebral I/R injury (20,22), and the results of the present study were consistent with these previous reports, indicating a persistent inflammatory response in cerebral I/R injury.

In the current study, enrichment analysis revealed that ‘chemotaxis’, ‘chemokine activity’ and ‘chemokine signaling pathway’ terms were significantly enriched for the obtained DEGs. Furthermore, members of the chemokine family were the most abundant among the upregulated genes that were common among 2, 8 and 24 h post-reperfusion time-points, including CCL3, CCL4, CCL7 and CXCL1. Chemokines have been reported to have complex and essential roles in I/R injury, which involves extensive leukocyte and neutrophil infiltration, subsequently exaggerating the ischemic area (24). Following ischemic stroke, chemokines are primarily produced by resident microglial cells in the brain and infiltrating immune cells, which leads to further leukocyte recruitment and activation (25). In the present study, CCL2 exhibited the highest degree in the PPI network at 24 h post-reperfusion, which is consistent with previous studies (26,27). Accordingly, CCL2 mRNA expression was initially increased at 6 h post-reperfusion, peaking 2 days later. Additionally, CCL3 was previously described to be upregulated post-I/R injury via the induction of monocyte accumulation in the ischemic brain (28,29), and the expression of CCL3 post-reperfusion has been reported to be time-dependent (30). CCL7, as a mast cell-derived product, has been reported to be involved in the recruitment of inflammatory cells into the ischemic sites (31), subsequently contributing to stroke pathology (32). CXCL1, identified as a hub gene in the PPI networks at 2, 8 and 24 h post-reperfusion in the present study, was reported to be increased in the cerebrospinal fluid of patients that have suffered from a stroke (33). However, both neurotoxic and neuroprotective effects have been demonstrated for chemokines in post-stroke inflammation (28).

It is established that inflammation is a major contributor to stroke pathophysiology, and the immune system has been implicated in all stages of the ischemic cascade, from the acute damaging events to the progression of tissue repair (34). Microglia cells, which are closely associated with inflammation, were reported to become rapidly activated following ischemia (35). Furthermore, various pro-inflammatory factors, including interleukin (IL)-1β, IL-6, tumor necrosis factor-α, reactive oxygen species, nitric oxide and prostaglandin E2, were reported to be produced by activated microglia and contribute to neuronal death in cerebral ischemia (36). In the present study, specific cytokines were not measured. Further studies are required to investigate the association between the chemokine family and pro-inflammatory cytokines, which further elucidate the pathophysiological process following cerebral I/R injury.

The results of the present study revealed that the toll-like receptor signaling pathway was significantly enriched at 2 h post-reperfusion, suggesting early transcriptional activation. TLR2, a vital factor in the inflammatory response and tissue damage, has been reported to be implicated in cerebral ischemic damage (37). Microglia cells produce cytokines and chemokines following the stimulation of TLR2 (38). Furthermore, leukocyte and microglial infiltration, and neuronal death, were reported to be attenuated by TLR2 inhibition (39), indicating a potential novel therapeutic strategy.

In conclusion, the current study identified a set of DEGs that were altered between cerebral I/R injury samples and sham control samples. The findings may provide novel insight into the potential mechanisms underlying the development of cerebral I/R injury. Our future studies will be aimed at unveiling the potential diagnostic and prognostic value of these hub genes, which may ultimately aid the translation of these targets into clinical practice.

Acknowledgements

Not applicable.

Funding

The present study was supported by grants from the Projects of Medical and Health Technology Program in Zhejiang Province (grant no. 201482575) and the Projects of Technology Development Program in Hangzhou City (grant no. 20140633B66).

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Authors' contributions

XS and WB were responsible for study design. XH, HJ and XS were responsible for data acquisition, analysis, and interpretation. WB and ZY drafted the manuscript. ZY interpreted the results. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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August-2018
Volume 18 Issue 2

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Spandidos Publications style
Shao X, Bao W, Hong X, Jiang H and Yu Z: Identification and functional analysis of differentially expressed genes associated with cerebral ischemia/reperfusion injury through bioinformatics methods. Mol Med Rep 18: 1513-1523, 2018
APA
Shao, X., Bao, W., Hong, X., Jiang, H., & Yu, Z. (2018). Identification and functional analysis of differentially expressed genes associated with cerebral ischemia/reperfusion injury through bioinformatics methods. Molecular Medicine Reports, 18, 1513-1523. https://doi.org/10.3892/mmr.2018.9135
MLA
Shao, X., Bao, W., Hong, X., Jiang, H., Yu, Z."Identification and functional analysis of differentially expressed genes associated with cerebral ischemia/reperfusion injury through bioinformatics methods". Molecular Medicine Reports 18.2 (2018): 1513-1523.
Chicago
Shao, X., Bao, W., Hong, X., Jiang, H., Yu, Z."Identification and functional analysis of differentially expressed genes associated with cerebral ischemia/reperfusion injury through bioinformatics methods". Molecular Medicine Reports 18, no. 2 (2018): 1513-1523. https://doi.org/10.3892/mmr.2018.9135