Open Access

Gene microarray analysis of expression profiles in liver ischemia and reperfusion

  • Authors:
    • Xiaoyang Zheng
    • Huaqiang Zhou
    • Zeting Qiu
    • Shaowei Gao
    • Zhongxing Wang
    • Liangcan Xiao
  • View Affiliations

  • Published online on: July 13, 2017     https://doi.org/10.3892/mmr.2017.6966
  • Pages: 3299-3307
  • Copyright: © Zheng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Liver ischemia and reperfusion (I/R) injury is of primary concern in cases of liver disease worldwide and is associated with hemorrhagic shock, resection and transplantation. Numerous studies have previously been conducted to investigate the underlying mechanisms of liver I/R injury, however these have not yet been fully elucidated. To determine the difference between ischemia and reperfusion in signaling pathways and the relative pathological mechanisms, the present study downloaded microarray data GSE10657 from the Gene Expression Omnibus database. A total of two data groups from 1‑year‑old mice were selected for further analysis: i) A total of 90 min ischemia; ii) 90 min ischemia followed by 1 h of reperfusion, n=3 for each group. The Limma package was first used to identify the differentially expressed genes (DEGs). DEGs were subsequently uploaded to the Database for Annotation Visualization and Integrated Discovery online tool for Functional enrichment analysis. A protein‑protein interaction (PPI) network was then constructed via STRING version 10.0 and analyzed using Cytoscape software. A total of 114 DEGs were identified, including 21 down and 93 upregulated genes. These DEGs were primarily enriched in malaria and influenza A, in addition to the tumor necrosis factor and mitogen activated protein kinase signaling pathways. Hub genes identified in the PPI network were C‑X‑C motif chemokine ligand (CXCL) 1, C‑C motif chemokine ligand (CCL) 2, interleukin 6, Jun proto‑oncogene, activator protein (AP)‑1 transcription factor subunit, FOS proto‑oncogene, AP‑1 transcription factor subunit and dual specificity phosphatase 1. CXCL1 and CCL2 may exhibit important roles in liver I/R injury, with involvement in the immune and inflammatory responses and the chemokine‑mediated signaling pathway, particularly at the reperfusion stage. However, further experiments to elucidate the specific roles of these mediators are required in the future.

Introduction

Liver ischemia/reperfusion (I/R) injury is caused by blood deprivation and subsequent reperfusion. It caused the release of biological mediators contributing to liver dysfunction eventually (1). Although Liver IR injury is a main complication of hemorrhagic shock, resection and transplantation, its mechanisms haven't been described adequately (2). The pathophysiology of liver I/R injury may include ATP depletion, caused by decrease in oxidative phosphorylation, ROS (reactive oxygen species) creation, cytokines and chemokines production by kupffer cells, neutrophil accumulation, nitric oxide, apoptosis and necrosis (3). For example, liver I/R can induce Kupffer cell activation releasing TNF α. The increasing serum TNF α levels resulted in not only liver injury but also remote organ insult (4). Effects on hepatic secretory function and microsomal drug metabolizing systems varied in duration of ischemia or reperfusion. These may be related to lipid peroxidation rise (5). A lot of research suggested that liver I/R injury was age-dependent, which may be associated with neutrophil recruitment and function or NF-kB activation (6,7). The age-related mechanism of NF-κB activation in liver I/R injury could be related to recruitment of phosphorylated and ubiquitinylated NF-κB-inhibitoryprotein, IκBα, to the proteasome. This biological process can be stopped by expression decline of proteasome subunit, non-ATPase 4 (PSMD4) (8). Many methods and drugs had been applied to ameliorate liver I/R injury (911). Blood supply restoration was a primary step to treat ischemia damage in clinical work. But reperfusion itself may exacerbate organ injury induced by ischemia alone. Many therapeutic strategies should be considered when applied to reduce tissue injury (12). Nowadays, pathways, pivotal genes or cellular functions about liver ischemia and reperfusion, have not been demonstrated clearly. In order to explore more theoretical information about I/R injury precaution and treatment, we tried to compare different molecular mechanisms between liver ischemia followed by reperfusion and ischemia alone.

Materials and methods

Microarray data

Gene expression profile dataset GSE10657 was obtained from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/), including 30 liver tissue samples (8). The annotation platform was GPL1261 [Mouse430_2] Affymetrix Mouse Genome 430 2.0 Array. A total of 30 liver tissue samples were collected for analysis of whole mouse genome microarrays. We selected the data of two groups (ischemia of 90 min and 90 min of ischemia followed by 1 h of reperfusion) from 1-year-old mice. Each group included 3 mice.

Data processing

The expression data were processed using the R package limma in Bioconductor (http://www.bioconductor.org/), including background correction, quantile normalization, log2 transformed and final probe summarization (13,14). We compared the gene expression of two groups of one-year old mice (ischemia of 90 min and 90 min of ischemia followed by 1 h of reperfusion). The criterion for differentially expressed genes (DEGs) are adjusted P-value < 0.05 and |log2fold-change (FC)|≥1.

Function annotation and KEGG pathway analysis

To explore the biological function of DEGs, we uploaded the target genes to the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david-d.ncifcrf.gov/). Gene Ontology (GO) annotation (15) associated with biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (16) pathway enrichment analysis were utilized to analyze the function and potential pathways of these DEGs. The P-value <0.05 and gene counts >2 were criteria of the both.

PPI network construction

We aimed to identify the possible interaction networks of DEGs by using STRING version 10.0, which covers over 2,000 organisms and provides direct (physical) and indirect (functional) associations (17). DEGs were put in STRING database to construct a PPI network. The confidence score for selection was ≥0.4. Cytoscape (http://www.cytoscape.org/) software was used to dispose the PPI network for visualization.

Results

Gene expression analysis

After comparing sample records from 1-year-old mice subjected to different conditions (90 min of ischemia followed by 1 h of reperfusion or ischemia of 90 min) (n=3 each group), 114 DEGs were selected to further analysis with the standard of|log2fold change (FC)|≥1 and adjusted P-values <0.05. (Table I and Fig. 1) Among the DEGs, 21 genes were downregulated, while another 93 were upregulated. Cyp4a14, Igsf6 and Cacna1 s were most notably changed of the 21 downregulated genes. Hspa1a, Il6, Hspa1b, Moxd1, Fos, S100a8, Atf3, S100a9, Thbs1 and Btg2 were the top ten increased of the 93 DEGs.

Table I.

Differentially expressed genes.

Table I.

Differentially expressed genes.

GenelogFCP-value
Upregulated genes
  Hspa1a4.1898034954.40E-06
  Il63.4143772010.007214798
  Hspa1b2.8352494110.002570444
  Moxd12.7923336390.005880914
  Fos2.6356075070.008852822
  S100a82.4705879970.001612021
  Atf32.4292932740.048994575
  S100a92.3820889180.00198724
  Thbs12.3669268090.002259069
  Btg22.0240325190.036787526
  Ctla2a1.9875166686.94E-07
  Gem1.9573857103.11E-05
  Egr21.9459325050.004711597
  Ch25h1.9453862440.00116547
  Cyr611.8842839070.00418327
  Jun1.8349742400.02738022
  Dnajb11.8341596510.017880087
  Tnfaip61.7837883160.000235748
  Fgl21.7132669541.40E-05
  Rhob1.6498797830.01784261
  Junb1.5599277410.048521262
  Nfkbiz1.5023365620.02315746
  Apol11b1.4652326923.76E-05
  Pmaip11.4575365211.95E-06
  Snca1.4463935010.002008527
  G530011O06Rik1.4433440750.000226511
  Plscr11.4416076530.003825377
  Dusp11.4210571620.018558258
  Hspb11.4156096210.012319322
  Gm71731.4143390931.21E-07
  Cxcl11.4107536220.044905834
  Hbb-b21.3510339850.000672398
  Adamts11.3291966410.003830208
  Icam11.2909953800.004126491
  5730412P04Rik1.2839883379.54E-05
  Rasl11a1.2821423626.19E-07
  Maff1.2720684580.037153789
  2010002N04Rik1.2714993041.21E-07
  Rgs11.2511815520.003312274
  4833405L11Rik1.2475755744.51E-05
  Zfp361.2345248920.011557402
  Lcn21.2317785150.001751743
  Klf61.2184418040.012152268
  Chka1.2132802130.002802906
  Olfr15071.2112925550.000350502
  D530037H12Rik1.1977013366.69E-06
  H2-gs101.1965428230.00144295
  Fst1.1932491870.000621087
  Ell31.1820286933.54E-05
  P2ry101.1715255840.017352901
  2810404M03Rik1.1677876545.67E-05
  Ccl21.1633509470.002313274
  Hsd17b11.1581454560.000301852
  Il331.1429854720.000393507
  C765331.1422475224.89E-05
  Ppbp1.1376122310.011227964
  Id31.1325391370.03950213
  Ier31.1303412850.014790191
  1700016K19Rik1.1283881320.000105373
  D9Ertd596e1.1178178491.34E-05
  1200016E24Rik1.1062395550.040908811
  Sele1.1062225760.002874116
  Fam19a11.0972662214.82E-06
  Slfn41.0916630432.98E-05
  Snhg31.0909714440.002765684
  4833419O12Rik1.0874639821.21E-07
  Defa211.0817478210.000252684
  Gm103091.0816235123.19E-05
  Spin21.0813650886.39E-07
  3300002A11Rik1.0784938478.17E-06
  Pf41.0783442891.21E-07
  4930469G21Rik1.0744204960.000104134
  9530006C21Rik1.0671400750.003784627
  Procr1.0601120143.41E-05
  Cebpd1.0568924890.009736321
  Olfr3151.0549050051.28E-06
  Vpreb11.0497412656.09E-07
  Fabp51.0439465360.046394908
  Hbegf1.0419170090.002254356
  Akr1b71.0381177580.029888552
  1700010N08Rik1.0326038164.55E-05
  D9Wsu90e1.0319333170.013273235
  S100a61.0256781921.46E-05
  Arid5a1.0232147755.14E-05
  Srgap11.0206386871.71E-07
  Dusp51.0204380701.17E-05
  Gm140851.0182063520.000563399
  H3f3b1.0100597100.003098606
  Cytip1.0048732360.025437274
  B830004H01Rik1.0046834170.000757856
  Glipr11.0033618170.010390201
  Apol7b1.0024022979.71E-05
  Cpne91.0000767451.21E-07
Downregulated genes
  Cyp4a14−1.4916863230.046208079
  Igsf6−1.3574517111.21E-07
  Cacna1s−1.3392508770.000204248
  Gucy2c−1.3105506621.88E-07
  Emr4−1.2696681080.000244481
  C030010L15Rik−1.2357209653.00E-05
  1500015A07Rik−1.1374857282.58E-05
  AW125324−1.0974150177.03E-05
  BC023202−1.0961627841.21E-07
  Gm11818−1.0831289131.21E-07
  2810404F17Rik−1.0815647461.21E-07
  BC151093−1.0765813110.005019689
  1700011B04Rik−1.0443765740.000123774
  Otx2os1−1.0428386020.001185282
  Ttc26−1.0396361188.52E-07
  4933437I04Rik−1.0366932631.21E-07
  4921513H07Rik−1.0328711323.95E-06
  2010003K10Rik−1.0316839701.21E-07
  Gm9748−1.0269958611.92E-06
  Adam18−1.0242586971.21E-07
  9430082L08Rik−1.0063127511.21E-07

[i] FC, fold-change.

GO analysis and KEGG pathway

According to function annotation, the most significant biological processes included immune response (GO:0006955, P=1.37E-05), leukocyte migration involved in inflammatory response (GO:0002523, P=1.48E-05), inflammatory response (GO:0006954, P=5.96E-05), skeletal muscle cell differentiation (GO:0035914, P=1.08E-04), chemotaxis (GO:0006935, P=1.82E-04), response to lipopolysaccharide (GO:0032496, P=3.58E-04), positive regulation of transcription from RNA polymerase II promoter (GO:0045944, P=4.11E-04), and positive regulation of apoptotic process (GO:0043065, P=4.96E-04) (Table II and Fig. 2).

Table II.

GO biological process for DEGs (top 10).

Table II.

GO biological process for DEGs (top 10).

GO IDGO TermCountP-value
GO:0006955Immune response91.37E-05
GO:0002523Leukocyte migration involved in inflammatory response41.48E-05
GO:0006954Inflammatory response95.96E-05
GO:0035914Skeletal muscle celldifferentiation51.08E-04
GO:0006935Chemotaxis61.82E-04
GO:0032496Response to lipopolysaccharide63.58E-04
GO:0045944Positive regulation oftranscription from RNApolymerase II promoter144.11E-04
GO:0043065Positive regulation of apoptoticprocess84.96E-04
GO:0006366Transcription from RNApolymerase II promoter78.04E-04
GO:0070098 Chemokine-mediatedsignaling pathway40.001213338

[i] Count, the number of DEGs involved in GO terms. DEGs, differentially expressed genes; GO, Gene Ontology

As for highly enriched pathways, TNF signaling pathway (P=1.57E-06), Malaria (P=5.41E-06), Influenza A (P=3.28E-05), and MAPK signaling pathway (P=3.72E-04) were detected (Table III).

Table III.

KEGG pathway analysis for DEGs (top 10).

Table III.

KEGG pathway analysis for DEGs (top 10).

KEGG IDKEGG TermCountP-value
mmu04668TNF signaling pathway81.57E-06
mmu05144Malaria65.41E-06
mmu05164Influenza A83.28E-05
mmu04010MAPK signaling pathway83.72E-04
mmu05166HTLV-I infection86.71E-04
mmu05143African trypanosomiasis48.72E-04
mmu05323Rheumatoid arthritis58.93E-04
mmu04915Estrogen signaling pathway50.001899238
mmu05134Legionellosis40.003588248
mmu05169Epstein-Barr virus infection60.005803772

[i] Count, the number of DEGs involved in KEGG terms. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Interaction network construction

All 114 DEGs were put in the String database. A PPI network included 94 nodes and 145 edges was constructed. We analyzed the network by Cytoscape. (Fig. 3) To get more useful information, PPI sub-networks were generated. Nodes with edges more than 6 were CCL2, JUN, CYR61, DUSP1, KLF6, BTG2, ZFP36, IL6, CXCL1, JUNB, NFKBIZ, MAFF, FOS, EGR2 and ATF3 (Fig. 4). Genes with interaction combined-score ≥0.9 were selected to form a PPI sub-network (Fig. 5). Hub proteins were FOS, CCL2, CXCL1, JUN, IL6 and DUSP1, all of which were upregulated.

Discussion

In the current study, 114 DEGs were recognized in the liver tissue from two groups of 1-year-old mice. The expression was significantly different between 90 min of ischemia and 90 min of ischemia followed by 1 h of reperfusion. Based on the pathway enrichment analysis, most DEGs enriched in immune response, leukocyte migration involved in inflammatory response, and inflammatory response, including genes like CXCL1, PLSCR1, IL6, CCL2, PROCR, PPBP, VPREB2, VPREB1, PF4, S100A8, S100A9, NFKBIZ, THBS1, and SELE. TNF signaling pathway and MAPK signaling pathway were recognized with highest count and low P-value. In PPI network, CXCL1, CCL2, IL6, JUN, FOS and DUSP1 were hub proteins.

In our results, the expression of CXCL1 and IL6 increased rapidly in 90 min of ischemia followed by 1 h of reperfusion, suggesting that reperfusion could induce severer damage or more organs dysfunction. CXCL1, also known as GRO-α, could be a therapeutic target with further research. For instance, depletion of CXCL1 can lessen angiogenesis activity and reduce tumor growth. AS a member of the CXC chemokine family, it involved in recruitment of leukocytes and their migration, and many other inflammatory conditions (18). Gomez-Rodriguez et al (19) discovered that the expression of CXCL1 can be regulated by MMP-10. The latter was necessary for tissue repair by inhibiting CXCL1. In vivo, pre-emptive hypoxia-regulated Haem oxygenase-1 (pHRE-HO-1) could reduce the level of IL6 and CXCL. It was helpful for tissue regeneration and thus alleviating critical limb ischemia injury (20). Ahuja et al (21) first proved that serum IL6 had an essential role in AKI-mediated lung neutrophil accumulation and lung injury by stimulating CXCL1 production in lung, which indicated that inhibition of CXCL1 may be a possible therapy of lung injury after AKI. Hepatic stellate cells (HSCs) had a significant effect on I/R- and endotoxin-induced acute hepatocyte injury. When suppressing the function of HSCs, the expression of TNF α, neutrophil chemoattractant CXCL1 and endothelin-A receptor were all decreased (22).

Our study also identified that CCL2 was upregulated in I/R group. It might indicate that reperfusion could aggravate inflammation reaction. Much research had tried to confirm the relationship between CCL2 and inflammation. For example, CCL2-CCR2 signaling could accelerate liver I/R injury, for the reason that CCL2 attracted inflammatory monocytes and CCR2-expressing neutrophil to move into liver from bone marrow (23). Heil et al (24) stated that CCL2, was related to the accumulation of macrophages in growing collateral vessels. In mouse femoral artery excision model, CCL2 and CCR2, played an important role in post-ischemic regenerative processes of skeletal muscle (25). CCL2/CCR2 dominated post-ischemic vessel growth (26). Zhang et al (27) found that in retinal vascular inflammation, the production of CCL2 required NAD (P) H oxidase activity.

The other three key genes in this study are JUN, FOS and DUSP1. Expression of FOS and DUSP1 were substantially elevated in stroke patients (28).

We analyzed the gene microarray data from a new point, the damage of reperfusion per se, while Huber et al (8) studied liver I/R injury emphasizing on the impact of age. There were some limitations of our study. Firstly, for the lack of preconditioning data, we can't continue to mine biological function under the circumstance of precondition or other more relations. Kapoor et al (29) proposed that liver ischemic preconditioning activated MAPK signaling pathway, permitting hepatocytes to sustain secondary damage. Oyaizu et al (30) suggested that in rat pulmonary ischemia-reperfusion models, Src PTK activation was the major reason for reperfusion-induced lung injury but not gene expression alteration. Secondly, GSE10657 only consisted of reperfusion data of one time point. We couldn't compare gene expression changes between different time points of reperfusion.

In conclusion, our study provides supplementary evidence for the hypothesis that Reperfusion itself creates injury during liver I/R. We identified 114 DEGs between Reperfusion following Ischemia and Ischemia alone. CXCL1, CCL2, IL6, JUN, FOS and DUSP1 were key genes in I/R injury. These genes may be the potential therapeutic target. However, more experimental researches are needed to verify.

Acknowledgements

This study was supported by The Natural Scientific Foundation of Guangdong Province (2016A030313255) and The Foundation of Sun Yat-sen University for Young Teachers (16ykpy36).

Glossary

Abbreviations

Abbreviations:

I/R

ischemia and reperfusion

DEGs

differentially expressed genes

DAVID

Database for Annotation Visualization and Integrated Discovery

PPI

Protein-protein interaction network

GEO

network Gene Expression Omnibus database

GO

Gene Ontology

BP

biological process

KEGG

Kyoto Encyclopedia of Genes and Genomes

References

1 

Li Y, Yang Y, Feng Y, Yan J, Fan C, Jiang S and Qu Y: A review of melatonin in hepatic ischemia/reperfusion injury and clinical liver disease. Ann Med. 46:503–511. 2014. View Article : Google Scholar : PubMed/NCBI

2 

Zhai Y, Petrowsky H, Hong JC, Busuttil RW and Kupiec-Weglinski JW: Ischaemia-reperfusion injury in liver transplantation-from bench to bedside. Nat Rev Gastroenterol Hepatol. 10:79–89. 2013. View Article : Google Scholar : PubMed/NCBI

3 

Papadopoulos D, Siempis T, Theodorakou E and Tsoulfas G: Hepatic ischemia and reperfusion injury and trauma: Current concepts. Arch Trauma Res. 2:63–70. 2013. View Article : Google Scholar : PubMed/NCBI

4 

Tanaka Y, Maher JM, Chen C and Klaassen CD: Hepatic ischemia-reperfusion induces renal heme oxygenase-1 via NF-E2-related factor 2 in rats and mice. Mol Pharmacol. 71:817–825. 2007. View Article : Google Scholar : PubMed/NCBI

5 

Lee SM, Park MJ, Cho TS and Clemens MG: Hepatic injury and lipid peroxidation during ischemia and reperfusion. Shock. 13:279–284. 2000. View Article : Google Scholar : PubMed/NCBI

6 

Calkins CM, Bensard DD, Moore EE, McIntyre RC, Silliman CC, Biffl W, Harken AH, Partrick DA and Offner PJ: The injured child is resistant to multiple organ failure: A different inflammatory response? J Trauma. 53:1058–1063. 2002. View Article : Google Scholar : PubMed/NCBI

7 

Okaya T, Blanchard J, Schuster R, Kuboki S, Husted T, Caldwell CC, Zingarelli B, Wong H, Solomkin JS and Lentsch AB: Age-dependent responses to hepatic ischemia/reperfusion injury. Shock. 24:421–427. 2005. View Article : Google Scholar : PubMed/NCBI

8 

Huber N, Sakai N, Eismann T, Shin T, Kuboki S, Blanchard J, Schuster R, Edwards MJ, Wong HR and Lentsch AB: Age-related decrease in proteasome expression contributes to defective nuclear factor-kappaB activation during hepatic ischemia/reperfusion. Hepatology. 49:1718–1728. 2009. View Article : Google Scholar : PubMed/NCBI

9 

Jaeschke H and Woolbright BL: Current strategies to minimize hepatic ischemia-reperfusion injury by targeting reactive oxygen species. Transplant Rev (Orlando). 26:103–114. 2012. View Article : Google Scholar : PubMed/NCBI

10 

Abu-Amara M, Yang SY, Seifalian A, Davidson B and Fuller B: The nitric oxide pathway-evidence and mechanisms for protection against liver ischaemia reperfusion injury. Liver Int. 32:531–543. 2012. View Article : Google Scholar : PubMed/NCBI

11 

Zaouali MA, Boncompagni E, Reiter RJ, Bejaoui M, Freitas I, Pantazi E, Folch-Puy E, Abdennebi HB, Garcia-Gil FA and Roselló-Catafau J: AMPK involvement in endoplasmic reticulum stress and autophagy modulation after fatty liver graft preservation: A role for melatonin and trimetazidine cocktail. J Pineal Res. 55:65–78. 2013. View Article : Google Scholar : PubMed/NCBI

12 

Eltzschig HK and Collard CD: Vascular ischaemia and reperfusion injury. Br Med Bull. 70:71–86. 2004. View Article : Google Scholar : PubMed/NCBI

13 

Cai J, Perkumas KM, Qin X, Hauser MA, Stamer WD and Liu Y: Expression profiling of human schlemm's canal endothelial cells from eyes with and without glaucoma. Invest Ophthalmol Vis Sci. 56:6747–6753. 2015. View Article : Google Scholar : PubMed/NCBI

14 

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI

15 

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

16 

Kanehisa M, Goto S, Sato Y, Furumichi M and Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40:(Database issue). D109–D114. 2012. View Article : Google Scholar : PubMed/NCBI

17 

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43:(Database issue). D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI

18 

Bechara C, Chai H, Lin PH, Yao Q and Chen C: Growth related oncogene-alpha (GRO-alpha): Roles in atherosclerosis, angiogenesis and other inflammatory conditions. Med Sci Monit. 13:RA87–RA90. 2007.PubMed/NCBI

19 

Gomez-Rodriguez V, Orbe J, Martinez-Aguilar E, Rodriguez JA, Fernandez-Alonso L, Serneels J, Bobadilla M, Perez-Ruiz A, Collantes M, Mazzone M, et al: Functional MMP-10 is required for efficient tissue repair after experimental hind limb ischemia. FASEB J. 29:960–972. 2015. View Article : Google Scholar : PubMed/NCBI

20 

Jazwa A, Stepniewski J, Zamykal M, Jagodzinska J, Meloni M, Emanueli C, Jozkowicz A and Dulak J: Pre-emptive hypoxia-regulated HO-1 gene therapy improves post-ischaemic limb perfusion and tissue regeneration in mice. Cardiovasc Res. 97:115–124. 2013. View Article : Google Scholar : PubMed/NCBI

21 

Ahuja N, Andres-Hernando A, Altmann C, Bhargava R, Bacalja J, Webb RG, He Z, Edelstein CL and Faubel S: Circulating IL-6 mediates lung injury via CXCL1 production after acute kidney injury in mice. Am J Physiol Renal Physiol. 303:F864–F872. 2012. View Article : Google Scholar : PubMed/NCBI

22 

Stewart RK, Dangi A, Huang C, Murase N, Kimura S, Stolz DB, Wilson GC, Lentsch AB and Gandhi CR: A novel mouse model of depletion of stellate cells clarifies their role in ischemia/reperfusion- and endotoxin-induced acute liver injury. J Hepatol. 60:298–305. 2014. View Article : Google Scholar : PubMed/NCBI

23 

Zhang J, Xu P, Song P, Wang H, Zhang Y, Hu Q, Wang G, Zhang S, Yu Q, Billiar TR, et al: CCL2-CCR2 signaling promotes hepatic ischemia/reperfusion injury. J Surg Res. 202:352–362. 2016. View Article : Google Scholar : PubMed/NCBI

24 

Heil M, Ziegelhoeffer T, Wagner S, Fernández B, Helisch A, Martin S, Tribulova S, Kuziel WA, Bachmann G and Schaper W: Collateral artery growth (arteriogenesis) after experimental arterial occlusion is impaired in mice lacking CC-chemokine receptor-2. Circ Res. 94:671–677. 2004. View Article : Google Scholar : PubMed/NCBI

25 

Contreras-Shannon V, Ochoa O, Reyes-Reyna SM, Sun D, Michalek JE, Kuziel WA, McManus LM and Shireman PK: Fat accumulation with altered inflammation and regeneration in skeletal muscle of CCR2-/−mice following ischemic injury. Am J Physiol Cell Physiol. 292:C953–C967. 2007. View Article : Google Scholar : PubMed/NCBI

26 

Cochain C, Rodero MP, Vilar J, Récalde A, Richart AL, Loinard C, Zouggari Y, Guérin C, Duriez M, Combadière B, et al: Regulation of monocyte subset systemic levels by distinct chemokine receptors controls post-ischaemic neovascularization. Cardiovasc Res. 88:186–195. 2010. View Article : Google Scholar : PubMed/NCBI

27 

Zhang W, Rojas M, Lilly B, Tsai NT, Lemtalsi T, Liou GI, Caldwell RW and Caldwell RB: NAD(P)H oxidase-dependent regulation of CCL2 production during retinal inflammation. Invest Ophthalmol Vis Sci. 50:3033–3040. 2009. View Article : Google Scholar : PubMed/NCBI

28 

Adamski MG, Li Y, Wagner E, Yu H, Seales-Bailey C, Soper SA, Murphy M and Baird AE: Expression profile based gene clusters for ischemic stroke detection. Genomics. 104:163–169. 2014. View Article : Google Scholar : PubMed/NCBI

29 

Kapoor S, Berishvili E, Bandi S and Gupta S: Ischemic preconditioning affects long-term cell fate through DNA damage-related molecular signaling and altered proliferation. Am J Pathol. 184:2779–2790. 2014. View Article : Google Scholar : PubMed/NCBI

30 

Oyaizu T, Fung SY, Shiozaki A, Guan Z, Zhang Q, dos Santos CC, Han B, Mura M, Keshavjee S and Liu M: Src tyrosine kinase inhibition prevents pulmonary ischemia-reperfusion-induced acute lung injury. Intensive Care Med. 38:894–905. 2012. View Article : Google Scholar : PubMed/NCBI

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September-2017
Volume 16 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Zheng X, Zhou H, Qiu Z, Gao S, Wang Z and Xiao L: Gene microarray analysis of expression profiles in liver ischemia and reperfusion. Mol Med Rep 16: 3299-3307, 2017
APA
Zheng, X., Zhou, H., Qiu, Z., Gao, S., Wang, Z., & Xiao, L. (2017). Gene microarray analysis of expression profiles in liver ischemia and reperfusion. Molecular Medicine Reports, 16, 3299-3307. https://doi.org/10.3892/mmr.2017.6966
MLA
Zheng, X., Zhou, H., Qiu, Z., Gao, S., Wang, Z., Xiao, L."Gene microarray analysis of expression profiles in liver ischemia and reperfusion". Molecular Medicine Reports 16.3 (2017): 3299-3307.
Chicago
Zheng, X., Zhou, H., Qiu, Z., Gao, S., Wang, Z., Xiao, L."Gene microarray analysis of expression profiles in liver ischemia and reperfusion". Molecular Medicine Reports 16, no. 3 (2017): 3299-3307. https://doi.org/10.3892/mmr.2017.6966