Identification of genes and pathways associated with multiple organ dysfunction syndrome by microarray analysis

Gu,Changwei; Qiao,Wanhai; Wang,Lina; Li ,Minmin; Song,Kang

doi:10.3892/mmr.2018.8973

Identification of genes and pathways associated with multiple organ dysfunction syndrome by microarray analysis

Authors:
- Changwei Gu
- Wanhai Qiao
- Lina Wang
- Minmin Li
- Kang Song
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Affiliations: Emergency Department, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, P.R. China
Published online on: May 4, 2018 https://doi.org/10.3892/mmr.2018.8973
Pages: 31-40
Copyright: © Gu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Multiple organ dysfunction syndrome (MODS) is characterized by the development of progressive physiological dysfunction of ≥2 organs or organ systems and is responsible for the majority of the morbidity and mortality among patients in intensive care units. The aim of the present study was to investigate the potential genes and pathways associated with MODS. The microarray dataset GSE60088 was downloaded from the Gene Expression Omnibus and used to identify differentially expressed genes (DEGs) between organ tissues (lung, liver and kidney) obtained from a murine model of MODS and healthy controls. The interactions between DEGs in lungs, liver and kidneys were revealed by Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Furthermore, protein‑protein interaction (PPI) data for DEGs were obtained from the Search Tool for the Retrieval of Interacting Genes and a PPI network was constructed. Additionally, DEGs that were common among the three organs were screened and transcription factors that regulated them were predicted using the iRegulon plugin. A total of 943, 267 and 227 DEGs were identified in lung, liver and kidney samples, respectively, between mice with MODS and healthy controls. In lung and liver samples, two pathways that were enriched with DEGs were identified and were common between lung and liver samples, including ‘cytokine‑cytokine receptor interaction’ and ‘Jnk‑STAT signaling pathway’, and examples of DEGs associated with these pathways include C‑X‑C motif chemokine ligand (Cxcl)1 and Cxcl10, and signal transducer and activator of transcription (Stat)1, respectively. Furthermore, two common pathways were identified in liver and kidney samples, which included ‘MAPK signaling pathway’ and ‘p53 signaling pathway’, and DEGs associated with these pathways included growth arrest and DNA damage‑inducible α. A total of 18 DEGs were common among lung, liver and kidney tissues, including CCAAT/enhancer binding protein β (Cebpb) and olfactomedin‑like 1 (Olfml1). Cebpb modulated various other DEGs, such as Cxcl1, and Olfml1 was regulated by Stat5A. These genes and pathways may serve roles in the progression of MODS and may be considered to be potential therapy targets for MODS.

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1	Volman TJ, Hendriks T and Goris RJ: Zymosan-induced generalized inflammation: Experimental studies into mechanisms leading to multiple organ dysfunction syndrome. Shock. 23:291–297. 2005. View Article : Google Scholar : PubMed/NCBI
2	Henao FJ, Daes JE and Dennis RJ: Risk factors for multiorgan failure: A case-control study. J Trauma. 31:74–80. 1991. View Article : Google Scholar : PubMed/NCBI
3	Deitch EA: Multiple organ failure. Pathophysiology and potential future therapy. Ann Surg. 216:117–134. 1992. View Article : Google Scholar : PubMed/NCBI
4	Marshall JC: Inflammation, coagulopathy, and the pathogenesis of multiple organ dysfunction syndrome. Crit Care Med. 29 7 Suppl:S99–S106. 2001. View Article : Google Scholar : PubMed/NCBI
5	Wang H and Ma S: The cytokine storm and factors determining the sequence and severity of organ dysfunction in multiple organ dysfunction syndrome. Am J Emerg Med. 26:711–715. 2008. View Article : Google Scholar : PubMed/NCBI
6	Gustot T: Multiple organ failure in sepsis: Prognosis and role of systemic inflammatory response. Curr Opin Crit Care. 17:153–159. 2011. View Article : Google Scholar : PubMed/NCBI
7	Henninger DD, Panés J, Eppihimer M, Russell J, Gerritsen M, Anderson DC and Granger DN: Cytokine-induced VCAM-1 and ICAM-1 expression in different organs of the mouse. J Immunol. 158:1825–1832. 1997.PubMed/NCBI
8	Bratt J and Palmblad J: Cytokine-induced neutrophil-mediated injury of human endothelial cells. J Immunol. 159:912–918. 1997.PubMed/NCBI
9	Crouser ED: Mitochondrial dysfunction in septic shock and multiple organ dysfunction syndrome. Mitochondrion. 4:729–741. 2004. View Article : Google Scholar : PubMed/NCBI
10	Suzuki T, Takahashi T, Yamasaki A, Fujiwara T, Hirakawa M and Akagi R: Tissue-specific gene expression of heme oxygenase-1 (HO-1) and non-specific delta-aminolevulinate synthase (ALAS-N) in a rat model of septic multiple organ dysfunction syndrome. Biochem Pharmacol. 60:275–283. 2000. View Article : Google Scholar : PubMed/NCBI
11	Rockey DC, Bell PD and Hill JA: Fibrosis-a common pathway to organ injury and failure. N Engl J Med. 372:1138–1149. 2015. View Article : Google Scholar : PubMed/NCBI
12	Bunney WE, Bunney BG, Vawter MP, Tomita H, Li J, Evans SJ, Choudary PV, Myers RM, Jones EG, Watson SJ and Akil H: Microarray technology: A review of new strategies to discover candidate vulnerability genes in psychiatric disorders. Am J Psychiatry. 160:657–666. 2003. View Article : Google Scholar : PubMed/NCBI
13	Nylund L, Satokari R, Nikkilä J, Rajilić-Stojanović M, Kalliomäki M, Isolauri E, Salminen S and De Vos WM: Microarray analysis reveals marked intestinal microbiota aberrancy in infants having eczema compared to healthy children in at-risk for atopic disease. BMC Microbiol. 13:122013. View Article : Google Scholar : PubMed/NCBI
14	Maslove DM and Wong HR: Gene expression profiling in sepsis: Timing, tissue, and translational considerations. Trends Mol Med. 20:204–213. 2014. View Article : Google Scholar : PubMed/NCBI
15	Gutschner T, Hämmerle M, Eissmann M, Hsu J, Kim Y, Hung G, Revenko A, Arun G, Stentrup M, Gross M, et al: The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Res. 73:1180–1189. 2013. View Article : Google Scholar : PubMed/NCBI
16	Di Narzo AF, Tejpar S, Rossi S, Yan P, Popovici V, Wirapati P, Budinska E, Xie T, Estrella H, Pavlicek A, et al: Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data. J Natl Cancer Inst. 106:pii: dju247. 2014. View Article : Google Scholar : PubMed/NCBI
17	Gharib SA, Mar D, Bomsztyk K, Denisenko O, Dhanireddy S, Liles WC and Altemeier WA: System-wide mapping of activated circuitry in experimental systemic inflammatory response syndrome. Shock. 45:148–156. 2016. View Article : Google Scholar : PubMed/NCBI
18	Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
19	Carlson M, Falcon S, Pages H and Li N: org. Mm. eg. db: Genome wide annotation for Mouse. org. Mm. eg. db: Genome wide annotation for Mouse. R package version. 2012.
20	Carlson M: Mouse4302.db: Affymetrix Mouse genome 430 2.0 array annotation data (chip mouse4302). R package. version 3.1.3. 2016.
21	Smyth GK: Limma: linear models for microarray dataBioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R, Carey V, Huber W, Irizarry R and Dudoit S: Springer; New York, NY: pp. 397–420. 2005, View Article : Google Scholar
22	Glueck DH, Mandel J, Karimpour-Fard A, Hunter L and Muller KE: Exact calculations of average power for the Benjamini-Hochberg procedure. Int J Biostat. 4:Article 11. 2008. View Article : Google Scholar : PubMed/NCBI
23	Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, Lumley T, Maechler M, Magnusson A, Moeller S, et al: gplots: Various R programming tools for plotting data. R package. version 2. 2009.
24	Kolde R: Pretty heatmaps. 2015.https://cran.r-project.org/web/packages/pheatmap/pheatmap.pdfDecember 11–2015
25	Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI
26	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
27	Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 696:291–303. 2011. View Article : Google Scholar : PubMed/NCBI
28	Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Sanchez Naval M, Potier D, et al: iRegulon: From a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol. 10:e10037312014. View Article : Google Scholar : PubMed/NCBI
29	Booth V, Keizer DW, Kamphuis MB, Clark-Lewis I and Sykes BD: The CXCR3 binding chemokine IP-10/CXCL10: Structure and receptor interactions. Biochemistry. 41:10418–10425. 2002. View Article : Google Scholar : PubMed/NCBI
30	Haskill S, Peace A, Morris J, Sporn SA, Anisowicz A, Lee SW, Smith T, Martin G, Ralph P and Sager R: Identification of three related human GRO genes encoding cytokine functions. Proc Natl Acad Sci USA. 87:7732–7736. 1990. View Article : Google Scholar : PubMed/NCBI
31	Panzer U, Steinmetz OM, Paust HJ, Meyer-Schwesinger C, Peters A, Turner JE, Zahner G, Heymann F, Kurts C, Hopfer H, et al: Chemokine receptor CXCR3 mediates T cell recruitment and tissue injury in nephrotoxic nephritis in mice. J Am Soc Nephrol. 18:2071–2084. 2007. View Article : Google Scholar : PubMed/NCBI
32	Jastrow KM III, Gonzalez EA, McGuire MF, Suliburk JW, Kozar RA, Iyengar S, Motschall DA, McKinley BA, Moore FA and Mercer DW: Early cytokine production risk stratifies trauma patients for multiple organ failure. J Am Coll Surg. 209:320–331. 2009. View Article : Google Scholar : PubMed/NCBI
33	Robertson CM and Coopersmith CM: The systemic inflammatory response syndrome. Microbes Infect. 8:1382–1389. 2006. View Article : Google Scholar : PubMed/NCBI
34	Bhatia M and Hegde A: Treatment with antileukinate, a CXCR2 chemokine receptor antagonist, protects mice against acute pancreatitis and associated lung injury. Regul Pept. 138:40–48. 2007. View Article : Google Scholar : PubMed/NCBI
35	Quelle FW, Thierfelder W, Witthuhn BA, Tang B, Cohen S and Ihle JN: Phosphorylation and activation of the DNA binding activity of purified Stat1 by the Janus protein-tyrosine kinases and the epidermal growth factor receptor. J Biol Chem. 270:20775–20780. 1995. View Article : Google Scholar : PubMed/NCBI
36	Durbin JE, Hackenmiller R, Simon MC and Levy DE: Targeted disruption of the mouse Stat1 gene results in compromised innate immunity to viral disease. Cell. 84:443–450. 1996. View Article : Google Scholar : PubMed/NCBI
37	Oiva J, Mustonen H, Kylänpää ML, Kyhälä L, Alanärä T, Aittomäki S, Siitonen S, Kemppainen E, Puolakkainen P and Repo H: Patients with acute pancreatitis complicated by organ failure show highly aberrant monocyte signaling profiles assessed by phospho-specific flow cytometry. Crit Care Med. 38:1702–1708. 2010. View Article : Google Scholar : PubMed/NCBI
38	Boengler K, Hilfiker-Kleiner D, Drexler H, Heusch G and Schulz R: The myocardial JAK/STAT pathway: From protection to failure. Pharmacol Ther. 120:172–185. 2008. View Article : Google Scholar : PubMed/NCBI
39	Li Y, Zhao M, Yin H, Gao F, Wu X, Luo Y, Zhao S, Zhang X, Su Y, Hu N, et al: Overexpression of the growth arrest and DNA damage-induced 45alpha gene contributes to autoimmunity by promoting DNA demethylation in lupus T cells. Arthritis Rheum. 62:1438–1447. 2010. View Article : Google Scholar : PubMed/NCBI
40	Zhan Q: Gadd45a, a p53-and BRCA1-regulated stress protein, in cellular response to DNA damage. Mutat Res. 569:133–143. 2005. View Article : Google Scholar : PubMed/NCBI
41	Altemeier WA, Matute-Bello G, Gharib SA, Glenny RW, Martin TR and Liles WC: Modulation of lipopolysaccharide-induced gene transcription and promotion of lung injury by mechanical ventilation. J Immunol. 175:3369–3376. 2005. View Article : Google Scholar : PubMed/NCBI
42	Uhlig U, Haitsma JJ, Goldmann T, Poelma DL, Lachmann B and Uhlig S: Ventilation-induced activation of the mitogen-activated protein kinase pathway. Eur Respir J. 20:946–956. 2002. View Article : Google Scholar : PubMed/NCBI
43	Li LF, Yu L and Quinn DA: Ventilation-induced neutrophil infiltration depends on c-Jun N-terminal kinase. Am J Respir Crit Care Med. 169:518–524. 2004. View Article : Google Scholar : PubMed/NCBI
44	McGhan LJ and Jaroszewski DE: The role of toll-like receptor-4 in the development of multi-organ failure following traumatic haemorrhagic shock and resuscitation. Injury. 43:129–136. 2012. View Article : Google Scholar : PubMed/NCBI
45	Cooks T, Harris CC and Oren M: Caught in the cross fire: p53 in inflammation. Carcinogenesis. 35:1680–1690. 2014. View Article : Google Scholar : PubMed/NCBI
46	Wang W, Bergh A and Damber JE: Increased expression of CCAAT/enhancer-binding protein beta in proliferative inflammatory atrophy of the prostate: Relation with the expression of COX-2, the androgen receptor, and presence of focal chronic inflammation. Prostate. 67:1238–1246. 2007. View Article : Google Scholar : PubMed/NCBI
47	Bal RS and Anholt RR: Formation of the extracellular mucous matrix of olfactory neuroepithelium: Identification of partially glycosylated and nonglycosylated precursors of olfactomedin. Biochemistry. 32:1047–1053. 1993. View Article : Google Scholar : PubMed/NCBI
48	Takatori H, Nakajima H, Kagami S, Hirose K, Suto A, Suzuki K, Kubo M, Yoshimura A, Saito Y and Iwamoto I: Stat5a inhibits IL-12-induced Th1 cell differentiation through the induction of suppressor of cytokine signaling 3 expression. J Immunol. 174:4105–4112. 2005. View Article : Google Scholar : PubMed/NCBI
49	Takatori H, Nakajima H, Hirose K, Kagami S, Tamachi T, Suto A, Suzuki K, Saito Y and Iwamoto I: Indispensable role of Stat5a in Stat6-independent Th2 cell differentiation and allergic airway inflammation. J Immunol. 174:3734–3740. 2005. View Article : Google Scholar : PubMed/NCBI
50	Wei L, Laurence A and O'Shea JJ: New insights into the roles of Stat5a/b and Stat3 in T cell development and differentiation. Semin Cell Dev Biol. 19:394–400. 2008. View Article : Google Scholar : PubMed/NCBI