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Bioinformatics Analysis of Transcription Profiling of Sepsis

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Bioinformatics Analysis of Transcription Profiling of Sepsis EJI0010.1177/1721727X15590946European Journal of InflammationWu et al. 590946research-article2015 Original article European Journal of Inflammation 2015, Vol. 13(2) 82 –90 Bioinformatics analysis of transcription © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav profiling of sepsis DOI: 10.1177/1721727X15590946 eji.sagepub.com Yanfeng Wu, Peng Xia and Changjun Zheng Abstract Sepsis is a fatal whole-body inflammatory response that complicates a serious infection. To elucidate the molecular mechanism of sepsis, transcription profile data of GSE12624 which included a total of 70 samples (34 sepsis samples and 36 non-sepsis samples) was downloaded. The t test based on Bayes method in limma package was used to identify differentially expressed genes (DEGs) between sepsis and non-sepsis samples (criterion: P value <0.05). Gene Ontology (GO) enrichment analysis was conducted to investigate the biological processes involved DEGs. Protein-protein interaction (PPI) network and sub-network analysis were conducted to investigate the interactions between DEGs. A total of 894 DEGs, including 479 downregulated DEGs and 415 upregulated DEGs, were identified in sepsis samples comparing with non-sepsis samples. GO enrichment analysis showed that DEGs mainly involved in cellular metabolic process, primary metabolic process, and response to organic cyclic compound. In the PPI network, four genes of CDC2, GTF2F2, PCNA, and SMAD4 with degrees more than 10 were identified. Subsequently, four sub-networks, in which genes of PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45 had relative high degrees were identified from the PPI network. After the discussion referring to previous studies, we suggested that CDC2, GTF2F2, PCNA, SMAD4 PSMA3, PTBP1, and GADD45 might be used as new therapeutic targets for sepsis. However, experiments should be further performed to prove the practical utility of these candidates. Keywords differentially expressed genes, functional enrichment analysis, protein-protein interaction network, sepsis Date received: 9 February 2015; accepted: 18 May 2015 Introduction Sepsis is a fatal whole-body inflammatory response shock patients is the highest mortality compared that complicates a serious infection, most com- with other early stages.3 The Surviving Sepsis monly with bacteria, fungi, viruses, and parasites in Campaign recently issued guidance for the clinician respiratory, genitourinary, would/soft tissue, and caring for patients with severe sepsis or septic central nervous system.1 The spectrum of sepsis shock.4 The guidelines are mainly organized into syndromes includes systemic inflammatory two ‘bundles’ of care: an initial management bun- response syndrome (SIRS), sepsis, severe sepsis, dle to be accomplished within 6 h of the patient’s and septic shock.2 Sepsis and severe sepsis are now presentation; and a management bundle to be major causes of high mortality among critically ill patients in non-coronary intensive care units (ICU). In the USA, the incidence of severe sepsis is The Department of Respiratory Medicine, the Second Hospital of Jilin approximately 300 cases per 100,000 population. University, Changchun 130041, PR China Notably, about 50% of patients suffers sepsis out- Corresponding author: side the ICU and 25% of patients developing into Changjun Zheng, Department of Spine Surgery, The Second Hospital of Jilin University, Ziqiang Street 218, Changchun, Jilin Province, 130041, severe sepsis will die during their hospitalization. PR China. Besides, the approximately 50% death rate of septic Email: [email protected] Wu et al. 83 accomplished in the ICU.5 Notably, patients treated Materials and methods with ‘bundles’ of illustrated improved guideline compliance showed lower hospital mortality.6,7 mRNA expression profile data However, searching for effective new therapies and The mRNA expression profile dataset GSE12624,21 reliable predictors depending on underlying bio- which is based on the platform GPL4204, was logic features of sepsis is still the emphasis of obtained from the Gene Expression Omnibus today’s studies.8 (GEO, http://www.ncbi.nlm.nih.gov/geo/) of At the early stage, sepsis was considered as a National Center of Biotechnology Information result of uncontrolled inflammatory response and (NCBI). The dataset includes a total of 70 subdata several clinical trials attempted to find effective from traumatized patients in intensive care units therapies through blocking inflammatory cascades, (ICU), including 36 non-sepsis samples and 34 such as tumor necrosis factor (TNF)-α antagonist,9 sepsis samples. anti-endotoxin,10 and steroids.11 However, immu- nosuppression is now suggested as a key pathogen- DEGs screening esis related with sepsis mortality.8 An apparent decrease in lipopolysaccharide (LPS)-stimulated The original files were downloaded using cytokine secretion of mediators and in immune Geoquery package22 in R language. The original effector cells were detected.12 Furthermore, several data at probe symbol level were first converted clinical trials have proved that immune-enhancing into expression values at gene symbol level. The therapies are related with positive effect.13,14 missing data were imputed using nearest neighbor Therefore, finding drugs to improve the immune averaging (KNN) method (k = 10) and normal- system may be new therapeutic methods. ized to a linear model using quantile normaliza- Hall et al. illustrated that granulocyte mac- tion of robust multichip averaging (RMA).23 The rophage colony stimulating factor (GM-CSF) had limma package24 in R language with t test based the function of inducing productions of neutrophils on Bayes methods25 was employed to identify and macrophages, which is beneficial for sepsis DEGs between traumatized patients with and patients.15 Another immunotherapeutic agent is without sepsis. P value <0.05 was used as the interleukin 7 which could support replenishment of threshold for DEGs inclusion. lymphocytes by promoting proliferation of T cells.16 Recently, Charalampos et al. retrieved 3370 studies Go enrichment analysis of DEGs from the entire Medline database and found 178 To investigate the DEGs at functional level, Gene different biomarkers have been evaluated. Among Ontology (GO)26 functional enrichment analysis the 178 biomarkers, 18 had been identified through was performed through the Gene set Analysis experimental study, 58 through both experimental Toolkit V2 platform27,28 and further adjust was per- and clinical studies, and 101 through clinical stud- formed using multiple testing correction based on ies, respectively.17 Notably, coagulation, comple- the Benjamini-Hochberg method.29 GO function ment, contact system activation, inflammation, and terms in three categories of molecular function apoptosis were all proved to be related with sepsis. (MF), cellular component (CC), and biological Despite the extensive researches, the pathology of process (BP) were investigated. In this study, sepsis still remains unclear. adjusted P value <0.05 was set as the cutoff crite- Identification of differentially expressed genes rion for functional categories. (DEGs) is a fundamental step for performing genome wide expression profiling,18 and analyses of function enrichment and protein-protein interac- PPI network construction and module detection tion (PPI) network inform people of how molecules PPI network analysis is an important method for or genes work.19,20 In this study, we first down- comprehensively understanding intracellular pro- loaded the expressional microarray data, and then cess. The IntAct,30 DIP,31 BIND,32 and HPRD33 identified the DEGs between sepsis and non-sepsis databases, containing known and predicted pro- samples. Besides, pathway analyses were per- tein-protein interactions, were used to construct the formed to demonstrate the function of these DEGs. PPI network. The identification of functional mod- Finally, a PPI network of DEGs was constructed ules (significantly differentially expressed sub- and module was detected. networks) within the PPI network was primarily 84 European Journal of Inflammation 13(2) performed using MCODE software34 for module Discussion analysis. Severe sepsis and septic shock are leading causes of death, covering approximately 30% to 50% of Results hospital-reported mortality.12 However, the treat- DEGs screening ment of sepsis is still unsolved. In this study, bioin- formatics analyses were performed to investigate T test based on Bayes method was used to identify the potential molecular mechanism of sepsis. the DEGs in mRNA expression profile data, with Consequently, 894 DEGs were identified between the criterion of P value <0.05. Accordingly, a total sepsis samples and non-sepsis samples. Functional of 894 DEGs were identified in sepsis samples enrichment analysis showed the DEGs were mainly comparing with non-sepsis samples. Among those participated in cellular metabolic process, primary 894 DEGs, 479 DEGs were downregulated (e.g. metabolic process, and response to organic cyclic PSMA9, PSMA10, PCNA, EIF3S4, and EIF3S12) compound. Subsequently, DEGs with relative high and 415 DEGs were upregulated (e.g. CDC2, degrees in the PPI network (e.g. CDC2, GTF2F2, CCNA2, GADD45A, PSMA3, PSMA6, PTBP1, PCNA, and SMAD4) and four sub-networks (e.g. RTL6, and GT2F2) in sepsis samples comparing PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and with those in non-sepsis samples, respectively. GADD45) were identified. Cell division cycle 2 homolog (CDC2) gene Enrichment analysis of DEGs encodes
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