| The immune system is made up of a network of cells, tissues and organs of which the function is to protect our body from infections and illnesses. The diagnosis and treatment of complex diseases in human and the developing of effective molecular biomarker in animal breeding are the hot and difficult topic in biomedical research and animal diseases resistance breeding. However, the research has long been limited by biological experimental techniques and data analysis techniques. Fortunately, with the rapid development of high-throughput biotechnologies it becomes easier to obtain massive hith-throughput biological network datas, such as gene regulatory networks and protein interaction networks. However, the developing of new methord to analysis these complex data is the next challenge.This paper is mainly involved in the construction and evaluation of the immune gene network and the analysis of pig disease resistance transcriptome and whole-genome scaning datas by immune gene network analysis. The main results follow:1. The results of the collection of whole or almost-whole immune genome datasets and construction and evaluation of the immune gene network.In this study, data from six protein-protein interaction databases (BioGRID, InAct, HPRD, Innatedb, Reactome, MINT) and4gene pathway database (NCI/Nature pathway interaction, IMID, HumanCyc, Cancer cell map) are transformed into SIF format for large scare network integration. While taking advantage of the Functional Interaction Network established by Wu et al. in2010to evaluate and re-integrate the previous10gene network database, then set up a full gene interaction network contained16,219genes and984,215interactions.By collecting4Immunogene databases (Immport, IRIS, Immunome Database and Innatedb curated genes) and3literatures (Raymond et al., MAPK/NFkB Network, Calvano et al., Human immune genes and Waddell et al. Interferon regulated genes), we finally defined a collection of8806immune functional genes.Immune gene network was extract from the whole gene interaction network using immune functional related genes. By this way, we build an opened immune gene network which contains6816immune functional genes involved in15727genes and817419gene interactions and a closed immune gene network which contains6732immune function genes and375472immunogene interactions.By inspecting the coverage between the immune gene subsets network and the open immune gene network, we evaluated the integrity of immune gene networks. The results showed that in the case of small number of immune genes, the impact of the number of immune genes on the integrity of the gene network nodes is smaller and the richness of the edges in the network is bigger. In the case of a certain number of immune genes, more genes have less impact on the integrity of the immune network. The results further demonstrated that a collection of core immune gene subsets have far greater influence than other immune gene subsets to the integrity of the immune network. It was similar to the characteristic of Scare-free network.2. The results of immune gene networks analysis on gene expression profileA DE distant hybridization F2breeding group, which including334pigs was established for diseases resistance breeding. The peripheral blood cytokines levels and the proportion of T lymphocytes of every individual was determined before and after the stimulation of Poly (I:C). The whole bloods RNA of IFN-level extreme individuals were hybrid with Affymetrix Porcine Array. All individuals were conducted the genome scan by Illumina Porcine60K SNP chip array.In the first application,344down-regulated genes (370probes) and357up-regulated genes (450probes) were found respectively in IFN-a High group (23pairs).386down-regulated genes (426probes) and251up-regulated genes (306probes) were found in the INF-a Low group (24pairs). Five genes (ETV6, DMD, SMARCC1, AKAP9and SDHB) were found have differential expression probes in at least one group. GO analysis of differentially expressed genes shows that up-regulated genes are focused on defense response and regulation of apoptosis, while down-regulated genes are focused on lymphocyte activation and differentiation. The High group is more intense on defense response compared with Low group. There is probably an extra raised-regulation of kinase activity gene group in the Low group.Based on the gene pathway analysis of gene set enrichment analysis (GSEA), there is no significant differently regulated pathway in the two groups. By comparing the most obviously difference first ten pathways between high and low group, we found that seven out of ten up-regulated gene pathways are concentrated on interferon related pathways in the High group and Low group. While the down-regulated pathways show a big difference.3. The results of gene networks analysis on GWAS Loci further deductionIn the second application, we do the Genome-wide association studies (GWAS) on peripheral blood T lymphocyte subtypes counts (percentage) and CD4:CD8T cell ratio before and after the stimulation of Poly I:C using Genome Association and Prediction Integrated Tool(GAPIT) R package.We have identified an about8.4M region in pig chromosome5associated with both the CD4T cells counts and CD4:CD8T cell ratio. About130genes were found in this region after annotation to Sscrofa10.2. All the gene of GWAS Loci were used to extract the interaction from all gene interactions to built "causal gene network" of the CD4T cell counts and CD4:CD8T cell ratio, which the former including131genes and18022interactions and the later including126genes and16235interactions genes.The "causal gene network" as a true net was application to gene network analysis of gene profile of extreme individuals of both phenotypes. |
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