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Regulatory Mechanisms Underlying Sepsis Progression in Patients with Tumor Necrosis Factor‑Α Genetic Variations

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Regulatory Mechanisms Underlying Sepsis Progression in Patients with Tumor Necrosis Factor‑Α Genetic Variations EXPERIMENTAL AND THERAPEUTIC MEDICINE 12: 323-328, 2016 Regulatory mechanisms underlying sepsis progression in patients with tumor necrosis factor‑α genetic variations * * YANGZHOU LIU , NING HAN , QINCHUAN LI and ZENGCHUN LI Emergency Trauma Department, Shanghai East Hospital, Shanghai 200120, P.R. China Received November 4, 2014; Accepted November 18, 2015 DOI: 10.3892/etm.2016.3308 Abstract. The present study aimed to investigate the regula- and ubiquitination of the FUS protein. Furthermore, COPS2 tory mechanisms underlying sepsis progression in patients with and CUL3 may be novel targets of miR-15. tumor necrosis factor (TNF)-α genetic variations. The GSE5760 expression profile data, which was downloaded from the Gene Introduction Expression Omnibus database, contained 30 wild-type (WT) and 28 mutation (MUT) samples. Differentially expressed Multiple trauma, which is commonly associated with severe inju- genes (DEGs) between the two types of samples were identified ries and multiple organ failure, may lead to various complications, using the Student's t-test, and the corresponding microRNAs including sepsis and septic shock, which are major healthcare (miRNAs) were screened using WebGestalt software. An problems worldwide (1-3). There are 400,000-500,000 cases of integrated miRNA-DEG network was constructed using the sepsis in the United States annually (4). Antimicrobial therapy Cytoscape software, based on the interactions between the may be applied for the management of sepsis; however, the DEGs, as identified using the Search Tool for the Retrieval mortality rate associated with sepsis has increased, and was of Interacting Genes/Proteins database, and the correlation reported to be as high as 40% in 2003 (5). between miRNAs and their target genes. Furthermore, Gene Tumor necrosis factor (TNF)-α, a cytokine that is predomi- Ontology and pathway enrichment analyses were conducted nantly secreted by macrophages, has been shown to be involved for the DEGs using the Database for Annotation, Visualization in the regulation of numerous biological processes, including and Integrated Discovery and the KEGG Orthology Based cell proliferation, differentiation, apoptosis, lipid metabolism Annotation System, respectively. A total of 390 DEGS between and coagulation (6-8). However, the role of TNF-α in tumori- the WT and MUT samples, along with 11 -associated miRNAs, genesis remains unclear. This cytokine has been reported to were identified. The integrated miRNA‑DEG network consisted induce tumor necrosis and apoptosis, as well as to promote of 38 DEGs and 11 miRNAs. Within this network, COPS2 was tumor development (9). However, previous studies investigating found to be associated with transcriptional functions, while the role of TNF-α in clinical sepsis syndrome or septic shock FUS was found to be involved in mRNA metabolic processes. have reported conflicting results (10-12). TNF-α has been Other DEGs, including FBXW7 and CUL3, were enriched established as an effective marker in the diagnosis of neonatal in the ubiquitin-mediated proteolysis pathway. In addition, sepsis (13); however, the mechanisms underlying the regulatory miR-15 was predicted to target COPS2 and CUL3. The results role of TNF-α in the development of sepsis syndrome remain of the present study suggested that COPS2, FUS, FBXW7 and undefined. Genetic variations have previously been implicated CUL3 may be associated with sepsis in patients with TNF-α in the progression of numerous types of cancer (14,15). In genetic variations. In the progression of sepsis, FBXW7 and addition, the clinical outcomes of sepsis have been associated CUL3 may participate in the ubiquitin-mediated proteolysis with genetic polymorphisms in the genes encoding various pathway, whereas COPS2 may regulate the phosphorylation inflammatory cytokines (16). Menges et al (17) demonstrated that common variants of the TNF-α gene were associated with sepsis syndrome and mortality following severe injury. Reportedly, the common TNF-α gene variant carrying the TNF rs1800629 A allele is correlated with higher TNF-α Correspondence to: Dr Qinchuan Li or Dr Zengchun Li, serum concentrations and alteration of genes strongly associ- Emergency Trauma Department, Shanghai East Hospital, ated with proinflammatory and apoptosis (17). Furthermore, 150 Jimo Road, Shanghai 200120, P.R. China TNF rs1800629 A is closely associated with sepsis syndrome E-mail: [email protected] E-mail: [email protected] and mortality following multiple trauma (18). The present study re-analyzed the GSE5760 microarray *Contributed equally data deposited in the Gene Expression Omnibus (GEO) data- base by Menges et al (17), in order to detect genes that were Key words: tumor necrosis factor-α variation, sepsis, microarray differentially expressed between patients with and without data, ubiquitination, microRNA TNF-α genetic variations, and to identify their potential func- tions and pathways. Furthermore, the regulatory associations 324 LIU et al: REGULATORY MECHANISMS IN SEPSIS DEVELOPMENT between differentially expressed genes (DEGs) and microRNAs Integrated Discovery online software (https://david.ncifcrf. (miRNAs) were analyzed in order to elucidate the regulatory gov/) (25). P<0.05 was considered to indicate a significantly mechanisms underlying sepsis in patients with TNF-α genetic enriched GO term. In addition, pathway enrichment analyses variations, at the transcriptional and post transcriptional levels. were conducted using the KEGG Orthology Based Annotation System (KOBAS, version 2; http://kobas.cbi.pku.edu.cn/home. Materials and methods do), in order to identify the pathways in which the DEGs were involved. In addition, the statistical method of cumula- Microarray data. The GSE5760 gene expression profile data tive hypergeometric distribution was applied and P<0.05 was was downloaded from the GEO database (http://www.ncbi. considered to indicate a significantly enriched pathway. nlm.nih.gov/geo/). Based on the description provided by Menges et al (17), the profile data consisted of 30 wild‑type Results (WT) peripheral blood samples from 12 injured patients without the TNF-α rs1800629 A variant and 28 mutation Identification of DEGs between the WT and MUT samples, (MUT) peripheral blood samples from 10 injured patients and the associated miRNAs. Based on the preset criteria of carrying the TNF-α rs1800629 A variant. The technical FDR<0.05 and |log2FC|>0.05, a total of 390 genes were shown to replicate numbers for the WT samples were three replicates be differentially expressed between the WT and MUT samples, for 6 patients and two replicates for the other 6 patients. The including 238 genes that were upregulated and 152 genes that technical replicate numbers for the MUT samples were three were downregulated. Based on an established threshold for replicates for 8 patients and two replicates for 2 patients. miRNA searching, 11 miRNAs, including miR-141, miR-374, Menges et al (17) had used the GPL4204 platform (GE miR-204, miR-23, miR-182, miR-26, miR-15, miR-30, miR-34, Healthcare/Amersham Biosciences CodeLink UniSet Human I miR‑181 and miR‑130, were significantly associated with the Bioarray; GE Healthcare, Little Chalfont, UK). The annotation identified DEGs, and were selected for inclusion in the inte- information in the platform was also downloaded. grated miRNA-DEG regulatory network (Table I). Data preprocessing and identification of DEGs. The gene Construction of the integrated miRNA‑DEG regulatory expression value was calculated from raw microarray data of network. A total of 36 DEG interaction pairs were identified, the probe value. In the case that multiple probes corresponded according to their predicted protein-protein interactions. to one gene, the average value was calculated as the expres- Combined with the identified DEGs targeted by miRNAs, the sion level of this gene, whereas in the case that one probe integrated miRNA-DEG regulatory network was constructed. corresponded to multiple genes, the probe value was removed. This network comprised 49 nodes and 88 edges, involving Following transformation of the data by log2 and normaliza- 11 miRNAs and 38 DEGs, including 19 upregulated and tion using the median method (19), DEGs between the WT 19 downregulated genes (Fig. 1). and MUT samples were identified using the Limma package in R software (20) and Student's t-test. The Benjamini-Hochberg Biological function and pathway annotation of the DEGs. In procedure (21) was applied, in order to control the false order to investigate the functions of the DEGs associated with discovery rate (FDR). The threshold criteria for the DEGs TNF-α genetic variations, the identified DEGs were subjected were FDR<0.05 and |log2 fold change (FC)| of >0.05. to GO analysis. As presented in Table II, the DEGs were predominantly enriched in seven GO terms associated with Selection of miRNAs targeted to DEGs. Following identifica- transcriptional events, phosphorylation and RNA functions. tion of the DEGs, WebGestal software (version 2.0; Vanderbilt DEGs associated with transcriptional regulation were as follows: University, Nashville, TN, USA; http://bioinfo.vanderbilt. COPS2, THRB, SFPQ, SALL1, PNRC2, CTNND2, PIM1, SF1, edu/webgestalt/) (22) was used in order to identify miRNAs MAP3K10, TGFBR3, HOXA9 and WHSC1. DEGs associated that were associated with the DEGs. A threshold of adjusted with transcription were as follows: COPS2, PAPOLA, THRB, P<0.05 was used. SFPQ, SALL1, PNRC2, CTNND2, SF1, HOXA9 and WHSC1. In addition, DEGs associated
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