Screening Genes Related With Leukocyte Responses Early After Burn Injury:analysis Of Differentially Gene Expression Profiling Data In Mice

BackgroundBurn injury make the body produces many reaction, such as tissue necrosis, obvious stress, shock, infection and nutritional deficiencies. With treatment factors changed the body’s immune cells and immune molecules micro environment, so that the body’s immune function disorder. With the post burn immune dysfunction view put forward, it is thought to be cause severe post burn infection and multiple organ failure and death. Although the continuous development of burn treatment technology, severely burned patients did not significantly reduce mortality, post burn immune dysfunction is considered to be the main obstacle to improve the cure rate.Burning immune research has focused on the mechanism of immune cells (T cells, B cells, killer cells or mononuclear macrophage) and immune factors (such as TNF, IL-1 or IL-6, etc.), or the process of apoptosis, cell death, infection and organ damage. With the research of single gene and factor is difficult to explain the change of the whole cell or system, we need the whole genome level comprehensive knowledge of the disorder of immune function after burns. Gene chip technology and the emergence of bioinformatics technology provides possibility to understand the pathophysiological changes of the body after burn process from the transcription and DNA level.New technologies (gene chip and bioinformatics) and new research methods (functional genomics and proteomics) has been used in clinical practice in the field of biology. In addition, medical practice is given priority to with evidence-based medicine to study disease of prevention, diagnosis and treatment from the level of genes and proteins, and in order to development in the specificity of diagnosis, personalized therapy. Molecular biological information monitoring device (gene chip and mass spectrometer) will become the new requirements in the field of medical treatment. In addition, with the rapid development of bioinformatics, a large number of potential biomarkers can be used in disease diagnosis and treatment.In 2008, researchers successively found various changes of the immune system and inflammatory in the mouse after burn using bioinformatics and microarray analysis of immune cells genome. In 2010 Lars H. Evers and Dhaval Bhavsar have been put forward:Gene chip technology can provide new train of thought and therapeutic targets from the whole genome level of cells. With the research mostly starting from the experimental analysis of a cytokine gene expression, a large amount of data generated can’t be used reasonable. Therefore, current research on immune dysfunction should be further bioinformatics data mining and analysis.We had showed that the processes of immune system, stimulus response, cellular and biological regulation were altered with disease progression by bioinformatics analysis of the gene expression profiling data. So we will screen the genes related with leukocyte response in mice early after burn injury and enhance our understanding of the regulatory mechanisms by bioinformatics analysis of the gene expression profiling data.The experiments contained two parts as below:Part 1:Screening genes related stimulus response early after burn injuryObjectivesTo screen the genes related with leukocyte response in mice early after burn injury and enhance our understanding of the regulatory mechanisms by bioinformatics analysis of the gene expression profiling data.Methods1. Screening of datasets:we screened gene chip data from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/),the samples should meet the following conditions:1. total body surface area(TBSA)>20%, full thickness;2.the packet time less than 48 hours;3.the number of samples and the control of more than three. We extracted the GSE7404 data (Mouse musculus, circulating leukocyte, total body surface area (TBSA)>25%, full thickness) after the final chip quality assessment and classification. We selected from the group at one day post-burn to analyze the sample data, a total of 8 samples were available, including 4 samples of thermal injury and 4 controls. This microarray expression profile is based on the Affymetrix Mouse Genome 430 2.0 Array.2. Processing of data:we need to preprocess the data before analyzing the gene chip data. It includes data filtering、missing values filling、logarithmic transformation and standardization.3. Functional enrichment analysis:Gene 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. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database is a collection of manually drawn pathway maps based on molecular interaction and reaction networks for metabolism, and developed by the Japanese Kyoto University and University of Tokyo jointly. KEGG pathways were selected with adjusted p-values<0.01 calculated with the Expression Analysis Systemic Explorer test implemented in the DAVID tool (Database for Annotation, Visualization and Integrated Discovery) (http://david.abcc.ncifcrf.gov/).4. Protein-protein interaction (PPI) network constructions and analysis:The post-burn PPI Network of DEGs was submitted to the Search Tool for the Retrieval of Interacting Genes 9.1 (STRING9.1).The data were retained only interactions with scores of at least 0.4 (Medium confidence). The Markov Cluster (MCL) algorithm was applied to identify disease-related modules, and the Cytoscape Network Analysis plugin was used to calculate the degrees of nodes in the network. The STING database is a system of searching for interaction between known protein and predict protein, which includes the direct physical interaction and the correlation between protein and protein. As an open source network and visualization of data analysis software, it can construct visualization of molecular interaction networks to the molecular biology interaction network and high expressed gene data and other molecular state information. Also, it can analysis the relevance of interaction between large-scale protein and protein, protein and DNA.5. Statistical analysis methods:Statistical analyses were performed using open-source statistical software R version 3.01. The gene expression profile data were recalculated and normalized using the Robust Multi-array Average(RMA) algorithm. The Student’s t-test and the fold-change (FC) method were used to select DEGs between burn injury and sham burn controls. All genes with P-values<0.01 and |logFC|>2 were set as the cutoff values to identify DEGs for further analysis. Genetic differences were obtained by paired samples t-test; Gene Ontology (the gene ontology), KEGG pathway analysis and protein interaction network analyses using super geometric algorithms and Benjamini-Hochberg method for data correction.Results1. The results of gene chip analysis:Of the 259 leukocyte response-related DEGs screened at 1 day post-burn,118 were up-regulated and 141 were down-regulated. KEGG pathway enrichment analysis showed the first 10 enrichment pathway with threshold requirements significantly. For the immune system (including Toll like receptor signaling pathway; B cell receptor signaling pathway; T cell receptor signaling pathway); In the process of cell growth and cell death pathway (p53 pathway; apoptosis; signal transduction pathway);pathway in the process of environmental information. The most significant gene enrichment is the Toll like receptor pathway.2. Stimulus response related Protein-protein interaction (PPI) network analysis: Stimulus response related Protein-protein interaction (PPI) network formed by 226 nodes and 1232 sides. We found that Jun (Stress=67), Statl (Stress=67), Bcl2 (Stress=56), Stat3 (Stress=54), Tlr2 (Stress=47), Myd88 (Stress=45), Jak2 (Stress=41), Lck (Stress=40), Hck (Stress=38), Ptpn6 (Stress=38) genes located in the center with higher degrees in the network. In order to filter out the important node gene that stimulates the reaction process, we selected intensity coefficient is larger than 4 and the interaction than 6 for the next step analysis through the MCL analysis method of the original protein interaction network. We found that seven sub networks were selected. Finally through the Protein-protein interaction network analysis showed that Lck (down), STAT1 (down), MyD88 (up),STAT3 (up) and Jun (up) genes not only with maximum interaction in the original network, but also with the center position in the MCL network module.Conclusion1. The differentially expressed genes associated with the immune system, cell growth and death, turn signal pathways, and the most significant of them is the Toll-like receptors signaling pathways.2. Lck, Stat1, Myd88, Stat3 and Jun might be critical players in the development of leukocyte response in mice early after burn injury.Part 2:Verifying the differentially expressed genes related with leukocyte responses early after burn injury in mice by Real-time PCRObjectivesTo verify the differentially expressed genes identified in mice by experiments on animals.MethodsThe establishment of scald model and animals grouping:Fifty adult BALB/c mice (by the experimental Animal Center of Southern Medical University) were selected. The model of third-degree burn wound was designed on the hairless area of 4cm×4cm with 97℃ hot water for 15 seconds after anesthetized with 1% pentobarbital sodium (50mg/kg). The mice were randomly divided into five groups, sham,2h,6h,12h and 24h. We used in Real-time qPCR test to analysis the expression of Lck, Statl, Myd88, Stat3 and Jun in whole blood leukocytes of mice.Results1. Using MM-GAPDH as internal reference, the relative expression values of LCK in whole blood leukocytes of mice on experiment group at 2h,6h,12h and 24h are 0.817±0.108,0.644±0.186,0.369±0.120 and 0.536±0.131.Compared with the control group, the difference was statistically significant (P< 0.05). According to the data, the relative expression values of LCK at a low level at 12h, and decreased within 24 hours after burn.2. Using MM-GAPDH as internal reference, the relative expression values of Stat-1 in whole blood leukocytes of mice on experiment group at 2h,6h,12h and 24h are 0.780±0.127,0.527±0.136,0.338±0.157 and 0.219±0.110. Compared with the control group, the difference was statistically significant (P<0.05). According to the data, the relative expression values of Stat-1 decreased continuing within 24 hours after burn.3. Using MM-GAPDH as internal reference, the relative expression values of Myd88 in whole blood leukocytes of mice on experiment group at 2h,6h,12h and 24h are 1.498±0.114,1.399±0.082,1.433±0.160 and 1.434±0.200. Compared with the control group, the difference was statistically significant (P<0.05). According to the data, the relative expression values of Myd88 significantly increased at 2h, and higher expressed within 24 hours after burn.4. Using MM-GAPDH as internal reference, the relative expression values of Stat-3 in whole blood leukocytes of mice on experiment group at 2h,6h,12h and 24h are 1.424±0.107,1.529±0.094,1.396±0.080 and 1.680±0.129. Compared with the control group, the difference was statistically significant (P<0.05). According to the data, the relative expression values of Stat-3 significantly increased at 12h, and higher expressed within 24 hours after burn.5. Using MM-GAPDH as internal reference, the relative expression values of Jun in whole blood leukocytes of mice on experiment group at 2h,6h,12h and 24h are 1.197±0.061,1.535±0.129,1.748±0.079 and 1.571±0.120. Compared with the control group, the difference was statistically significant (P<0.05). According to the data, the relative expression values of Jun significantly higher expressed at 24h, and increased continuing in 24 hours after burn.ConclusionLck, Stat1, Myd88, Stat3 and Jun mRNA expression in consistent with the results of gene chip data analysis, our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several potential biomarkers for burn treatment.