| [Background]Burn injury is a complex traumatic event with various local and systemic effects on several organ systems in addition to the skin. Major burn injury induces a pathophysiological response that has a marked inflammatory component. As a consequence, an uncontrolled inflammatory response is triggered, which involves excessive activation of various inflammatory cells and release of cytokines, such as TNF-a, IL-6, PGE2 and nitric oxide (NO), that subsequently contribute to the development of systemic inflammatory response (SIRS), immune dysfunction and multiple organ failure. Thus, clarification of the mechanisms responsible for burn injury is essential for the improvement of treatment modalities.Post burn Immune dysfunction was caused by wound pain stimulation,stress, hypoxia and ischemia-reperfusion injury and other factors activated coagulation, fibrinolysis, kinin and complement system, together with the patient’s own factors and subsequent along with a variety of therapeutic factors altered immune cells and immune molecules microenvironment, leading to immune dysfunction occurs. The complex mechanism of Immune dysfunction makes it difficult to control, and it was the main reasons for severe burns difficult to improve the cure rate in recent years.Prior work has focused on the role of individual mediators (e.g., TNF or IL-1) or processes such as apoptosis and cellular death in nosocomial infections and organ injury after trauma. Rather than using a reductionist approach, the genome-wide expression patterns of blood leukocytes in the immediate postinjury period to better understand the overall priorities and patterns of gene expression underlying not only the initial injury response, but also the development of complications and delayed clinical recovery.Time-course microarray experiments are successfully used to capture the temporal profiles for thousands of genes simultaneously to provide information on dynamic changes in gene expression. Gene expression profiling provides a means of developing potential new therapeutic options after thermal injury, in which data sets obtained from global transcriptional patterns facilitate the identification of new targets and options for prevention and intervention of postburn immune dysfunction. Therefore, current research on immune dysfunction postburn injury should be carried out in a global context.In 2010 Lars h. Evers, Dhaval Bhavsar and others have been put forward: another potential pathway of new therapeutic options after thermal injuries is gene expression profiling, which has inspired new hope for finding genes involved in complications resulting from burn injury. Therefore, genetic dissection of burn injury should be carried out in a global context. These new data sets obtained from global transcriptional profiling could be essential for the development of new targets and options for the prevention and intervention of burn wound infections.Previously, James and Wenzhong successively applied time-course microarray experiments, with the aim of performing genome-wide expression analysis for comparison of the circulating leukocyte transcriptome after severe trauma and burn injury in a mouse model and human patients. Based on the transcriptional data, the group proposed a new paradigm for immunological response to severe injury. These experiments challenge the current paradigm regarding how the adult human responds to severe injury. Severe injury, whether a result of blunt trauma or burn injury produces a genomic storm in which up to 80% of the leukocyte transcriptome is altered. The changes occur rapidly in trauma within 4-12 h and are prolonged for days and weeks. The findings are consistent with a genomic storm that is neither chaotic nor erratic, but rather highly coordinated and reproducible. This storm likely represents a common transcriptional response to severe stress in humans regardless of its origin, with far more similarities than differences. At the level of the leukocyte transcriptome, alterations in the expression of classical inflammatory and antiinflammatory as well as adaptive immunity genes occur simultaneously, not sequentially after severe injury. However, they mainly compared the changes between burns and trauma and did not analyze the dynamic changes in gene expression after burn injury.[Objectives]To clarify the molecular mechanism underlying leukocyte activity following burn injury, we utilized a systematic bioinformatics approach integrating expression profile data to screen differentially expressed genes (DEGs), key biological functions and related pathways. Additionally, we constructed a postburn injury protein-protein interaction (PPI) network to identify potential biomarkers for burn injury. The collective findings further enhance our understanding of the regulatory mechanisms underlying leukocyte response postburn injury and provide prospective targets for the development of novel therapeutic strategies.[Methods]1 Description of data setsWe downloaded the GSE7404 microarray expression profile from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database through approved access. The microarray expression profile is based on the Affymetrix Mouse Genome 430 2.0 Array. We extracted the raw files on ermal injuiy(Mws www/ms,25%TBSA, 11 ickness) fbr further analysis. In total,32 samples were available, including 16 thermal injury samples and 16 controls. Samples were divided into four groups according to prior labels.2 Identification of differentially expressed genes (DEG)Statistical analyses were performed using open-source statistical software R version 3.01 (http://www.r-project.org). Gene expression profiling files were converted into expression measures. Background correction and quartile data normalization were performed with the robust multiarray average (RMA) algorithm to obtain expression profile data. Student’s t-test was used to identify differentially expressed genes (DEGs) between burn injury and sham-burn controls. P-values< 0.01 and fold-change≥2 were set as the cut-off levels to identify DEGs for further study.3 Functional enrichment analysisGene Ontology (GO) aims to obtain information on gene function by producing a controlled vocabulary applicable to all organisms. GO consists of three hierarchically structured vocabulary sets that describe gene products in terms of their associated biological processes, cellular components and molecular functions. GO function extracts biologically relevant terms from statistically significant GO terms for disease using DEGs as input. It efficiently applies the hierarchical relationships between GO terms and prevents dilution of potentially important biological concepts by reducing global and local redundancy. Significantly enriched GO terms are identified using hypergeometric test and finally selected with an adjusted P-value less than 0.01 calculated using the Benjamini-Hochberg FDR method.The KEGG (Kyoto Encyclopedia of Genes and Genomes) database is a collection of manually drawn pathway maps based on molecular interaction and reaction networks for metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, and human diseases. KEGG pathways were selected with adjusted P-values less than 0.05 calculated with the Expression Analysis Systemic Explorer (EASE) test implemented in the DAVID (Database for Annotation, Visualization, and Integrated Discovery) tool.4 Postburn protein-protein interaction (PPI) network constructionsProteins act in concert to exert specific functions usually suggests the development of disease. Following construction, the postburn PPI Network of DEGs was submitted to the Search Tool for the Retrieval of Interacting Genes (STRING) 9.1. The STRING database is dedicated to protein-protein interactions, including both direct (physical) and indirect (functional) associations. All associations in STRING are provided a probabilistic confidence score. In our analysis, only interactions with scores of at least 0.4 (Medium confidence) were retained. Interaction networks created from STRING were visualized using Cytoscape software. In the PPI network, proteins/genes are represented by nodes, and the interactions derived from experimental repositories and computational prediction methods between any two proteins/genes by edges. Hub proteins/genes in the network are the nodes with the highest number of interactions. MCL algorithm was applied to identify disease-related modules, and Cytoscape Network Analysis plugin applied to calculate the degrees of the nodes in the network. BinGO 2.44 functional analysis in Cytoscape software was employed to distinguish the modules with close relationships in the sub-network.5 Screening of biomarkers for immune system processGene Ontology (GO) functional enrichment were selected with adjusted P-values less than 0.01 calculated with the Expression Analysis Systemic Explorer (EASE) test implemented in the DAVID (Database for Annotation, Visualization, and Integrated Discovery) tool. The postburn PPI Network of DEGs was submitted to the Search Tool for the Retrieval of Interacting Genes (STRING) 9.1. Gene Ontology enrichment analysis of the selected function of the immune system-related genes mapped to the original process of protein at each time point of interaction to build a network of immune-related genes in protein interaction networks. MCL algorithm was applied to identify disease-related modules, and Cytoscape Network Analysis plugin applied to calculate the degrees of the nodes in the network. BinGO 2.44 functional analysis in Cytoscape software was employed to distinguish the modules with close relationships in the sub-network.6 Related protein expression levels detected for post burn injury in mice7-8 week male BALB/c mice, normal feeding for one week, were randomly divided into four groups, sham,2h,6h,12h,24h five groups. Divided into normal control group and a group of 25% Ⅲ°TBSA burns. Western blotting analysis of whole blood leukocytes of mice free of the corresponding protein levels.7 Statistic analysisUsing R 3.01 and SPSS 19.0 statistical software for data analysis. Chip data processing and analysis applications were open source statistical software R 3.01. Get genetic differences among paired samples t-test; Gene Ontology learning, kegg pathway analysis and protein interaction network analysis using geometric algorithms and applications from ultra Benjamini-Hochberg method for data correction. Western blotting data using paired comparison between sample group paired sample t test, the comparison between groups using One Way ANOVA method.Inspection level:P< 0.05 was statistically significant.[Results]1 Statistical findingsStudent’s t-test was selected to identify DEGs between thermal injury and normal controls. A total of 2275 genes meeting criteria of FDR<0.05 and fold-change≥2 were classified as DEGs. Maximum alteration of gene expression occurred at one day post-injury, with upregulation of 658 genes and downregulation of 1167 genes. Fewer DEGs were detected with increasing time. HCA of 2725 probe sets with a coefficient of variation of 0.01 led to classification of DEGs into two main clusters. Cluster one was defined by upregulated DEGs and the larger cluster two by downregulated DEGs.2 Functional enrichment analysisGO functional enrichment analysis was conducted for upregulated and downregulated DEGs. For genes displaying altered expression patterns at 1 day postburn, GO functional enrichment analysis revealed that the majority of downregulated genes are involved in cellular, metabolic and immune system processes, and response to stimulus. Moreover, significantly upregulated genes at 1 day post-injury were associated with the following function terms: response to stimulus, metabolic process, immune system process, death, cellular process and biological regulation. Notably, immune system process and response to stimulus were captured by gene expression signatures throughout all time-points. Upregulated DEGs were enriched in following functional pathway groups:(1) immune system (Toll-like receptor, NOD-like receptor and T cell receptor signaling), (2) signal transduction (MAPK and Jak-STAT signaling), and (3) cell growth and death (p53 signaling). Downregulated DEGs were predominantly involved in Immune system-related pathways, including those for B cell receptor signaling, primary immunodeficiency, T-cell receptor signaling, antigen processing and presentation, Natural killer cell-mediated cytotoxicity, hematopoietic cell lineage and graft-versus-host disease. In addition to immune-related pathways, those related to amino acid metabolism were enriched.3 Protein-protein interaction (PPI) network constructionTo identify the proteins and biological modules that play crucial roles in the development of immune dysfunction postburn, we constructed the PPI network using DEGs at 1-day postburn injury. Our PPI network contained 5176 PPI pairs and 1211 nodes. Module analysis identified nine subnetworks with at least six members from the original network. In the subnetwork, Statl (down),App(up),Jun (up),Lck(down),Cdl9(down),Sash3(down),Chekl (up),No110 (up),Cdkl (up),Traf6 (up),Chmp4 (up) and Dtymk (down) were also located in the central position with higher degrees in the original network. Alterations in the genes/proteins in the central position of the network have more significant effects and may therefore be critical in the whole network.4 Screening of biomarkers for immune system processProtein-protein interaction network and module analysis suggested that some immune related genes, such as Lck, Ccr2, TLR2 and Myd88 could be of great value for further investigation. Finally, the expressions of LCK and MYD88 were confirmed by Western blotting.[Conclusion]1 The burn injury produces a series of pathophysio logical alterations at the leukocyte transcriptome level. These changes occur rapidly within less than 2 hours postburn injury and are prolonged for days, and even weeks.2 GO functional enrichment analysis for DEGs showed that genes mainly related to response to stimulus, metabolic, cellular and immune system processes, biological regulation and death are altered with disease progression. Immune system and metabolic processes, cellular stress response and apoptosis have been shown to be related to leukocyte postburn.3 PPI network analysis showed that 7 DEGs (Stat1, Jun, Lck, Cd19, Sash3, Chek1, Cd79a) are hub nodes.4 Protein-protein interaction network and module analysis suggested that some immune related genes, such as Lck, Ccr2, TLR2 and Myd88 could be of great value for further investigation. |
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