| چکیده انگلیسی مقاله |
A holistic view to polarization of kupffer cells by lipopolysaccharide and IL-4 Shiva Moein1, Kobra Moradzadeh1, Niloofar Nikaeen2, Yousof Gheisari *1,3 Objective and background Kupffer cells, resident macrophages of the liver are originated from yolk-sac (Davies et al 2013). These cells with their perivascular position have prominent roles both in homeostasis and pathology of the liver through secretion of various cytokines and chemokine, initiation of immune response, inflammation and regenerative cascades (Nguyen-Lefebvre & Horuzsko 2015). Due to wide spectrum of phenotypes of these cells, studying effects of peripheral mediators on the polarization and function of these cells would be better understood through the holistic viewpoint of systems biology (Chuang et al 2010). Keywords Kupffer cells, Systems biology, Microarray data analysis, Gene ontology, protein-protein interaction network Methodology In this study the microarray dataset GSE86397 which is the expression profile of mouse-extracted kupffer cells under the treatment of lipopolysaccharide (LPS) or IL4 was downloaded from Gene Expression Omnibus (GEO) database. The quality of dataset was evaluated by principle component analysis which was drawn by ggplot2 package of R software (Walter et al 2015). The differentially expressed genes(DEG) were calculated by GEO2R with False Discovery Rate of < 0.05%. The related transcription factors to DEGs were extracted by ChIP-X enrichment analysis (ChEA) and regulatory kinases of these transcription factors were acquired through Kinase Enrichment Analysis (KEA) of Enricher database (Kuleshov et al 2016). In addition, using Clupedia plugin of Cytoscape software version 3.5.1 protein-protein interaction network of DEGs was depicted (Bindea et al 2013). At the next step the network of DEGs, CHEA and KEA were merged together and network analysis was done through network analyzer tool of Cytoscape. Finally, the related pathways to genes of this integrated network were determined by Reactome database (Fabregat et al 2017). Results Quality evaluation by PCA demonstrated segregation of all the three categories by PC2. 3130 and 1300 genes differentially expressed in LPS-induced and IL-4-induced kupffer cells related to no-treatment group respectively (FDR< 0.05). The analysis of network constructed from merging DEGs, CHEA and KEA demonstrated key nodes related to polarization of kupffer cells to classical M1 and alternative M2 microphages. The top ten nodes according to degree and betweenness centralities for both treatments are shown in table 1. Also Gene Set Enrichment Analysis(GSEA) for all genes by Reactome database showed pathways mainly related to immune system, metabolism, cell cycle, hemostasis and so on. The top 10 pathways of each network is shown in Figure1. Conclusion Analyzing microarray data of kupffer cells stimulated by IL4 or LPS not only demonstrated previously confirmed key genes and signaling pathways related to macrophages polarization, but also some genes with not-determined function in such cells. PPARγ as one of the hubs with most degree and betweenness is the primer of differentiation of macrophages to alternative M2 cells as we saw its significant down-regulation in LPS –induced cells and a prominent up-regulation in IL4 treatment group (Bouhlel et al 2007). Gata1, Klf4, Spi/Pu1 and Crem are also among genes which their roles are determined in macrophages (Han et al 2017, Tanaka et al 2000, Zakrzewska et al 2010). Among hub nodes, Pou5f1 gene in LPS-induced cells and Tcfap2c in IL4-induced cells seem to be good targets for future experiments. In conclusion, our top-down approach let us to have a more holistic view on signaling pathways and key genes related to kupffer cells polarization. Top 10 genes of IL4-induced based on degree Degree value Top 10 genes of IL4-induced based on betweenness betweenness value Pparg 648 Pparg 0.1085624 Cebpb 631 Cebpb 0.0886073 Sox9 517 Myc 0.0733763 Myc 483 Sox9 0.0506112 Gata2 459 Klf4 0.0454837 Tcfap2c 444 Tcfap2c 0.0425414 Gata1 438 Gata1 0.0415438 Gfi1b 406 Gata2 0.0404434 Klf4 401 Irf8 0.0370656 Spi1 384 Gfi1b 0.0311013 Table 1: Top ten genes of LPS-induced and IL4-induced kupffer cells based on degree and betweenness centralities Top 10 genes of LPS-induced based on degree Degree value Top 10 genes of LPS-induced based on betweenness betweenness value Cebpb 1305 Cebpb 0.09836588 Crem 1152 Crem 0.07168658 Myc 1125 Pparg 0.05830134 Pparg 1071 Myc 0.0468609 Spi1 894 Spi1 0.03804798 E2f1 871 Gata1 0.03251741 Gata1 862 E2f1 0.02990707 Klf4 829 Irf8 0.02979345 Pou5f1 781 Stat3 0.02569039 Tal1 740 Lmo2 0.02546172 a Figure1: Ten pathways with top combined score enriched for LPS-induced (a) and IL4-induced cells (b) by Reactome database. References: Bindea G, Galon J, Mlecnik B. 2013. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics (Oxford, England) 29: 661-3 Bouhlel MA, Derudas B, Rigamonti E, Dièvart R, Brozek J, et al. 2007. PPARγ Activation Primes Human Monocytes into Alternative M2 Macrophages with Anti-inflammatory Properties. Cell Metabolism 6: 137-43 Chuang H-Y, Hofree M, Ideker T. 2010. A Decade of Systems Biology. Annual Review of Cell and Developmental Biology 26: 721-44 Davies LC, Jenkins SJ, Allen JE, Taylor PR. 2013. Tissue-resident macrophages. Nature immunology 14: 986-95 Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, et al. 2017. Reactome pathway analysis: a high-performance in-memory approach. BMC bioinformatics 18: 142 Han YH, Kim HJ, Na H, Nam MW, Kim JY, et al. 2017. RORalpha Induces KLF4-Mediated M2 Polarization in the Liver Macrophages that Protect against Nonalcoholic Steatohepatitis. Cell Rep 20: 124-35 Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, et al. 2016. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research 44: W90-7 Nguyen-Lefebvre AT, Horuzsko A. 2015. Kupffer Cell Metabolism and Function. Journal of enzymology and metabolism 1: 101 Tanaka H, Matsumura I, Nakajima K, Daino H, Sonoyama J, et al. 2000. GATA-1 blocks IL-6-induced macrophage differentiation and apoptosis through the sustained expression of cyclin D1 and bcl-2 in a murine myeloid cell line M1. Blood 95: 1264-73 Walter W, Sanchez-Cabo F, Ricote M. 2015. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics (Oxford, England) 31: 2912-4 Zakrzewska A, Cui C, Stockhammer OW, Benard EL, Spaink HP, Meijer AH. 2010. Macrophage-specific gene functions in Spi1-directed innate immunity. Blood 116: e1-11 |
| نویسندگان مقاله |
شیوا معین | shiva moein
department of genetics and molecular biology, isfahan university of medical sciences, isfahan, iran
دانشگاه علوم پزشکی اصفهان، دانشکده پزشکی، گروه ژنتیک و بیولوژِ مولکولی
سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)
کبری مرادزاده | kobra moradzadeh
department of genetics and molecular biology, isfahan university of medical sciences, isfahan, iran
دانشگاه علوم پزشکی اصفهان، دانشکده پزشکی، گروه ژنتیک و بیولوژِ مولکولی
سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)
نیلوفر نیک آیین | niloofar nikaeen
department of electrical and computer engineering, isfahan university of technology, isfahan, iran
دانشگاه صنعتی اصفهان، دانشکده مهندسی برق و کامپیوتر
سازمان اصلی تایید شده: دانشگاه صنعتی اصفهان (Isfahan university of technology)
یوسف قیصری | yousof gheisari
department of genetics and molecular biology, isfahan university of medical sciences, isfahan, iran
دانشگاه علوم پزشکی اصفهان، دانشکده پزشکی، گروه ژنتیک و بیولوژِ مولکولی
سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)
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