EBV相关淋巴瘤动物模型的差异表达基因筛选

发布时间：2018-04-08 13:16

本文选题：EBV　切入点：淋巴细胞　出处：《南华大学》2012年硕士论文

【摘要】：目的：检测分析Scid小鼠体内EBV相关淋巴瘤和正常人淋巴细胞两者宿主细胞的差异表达基因，筛选EBV诱发淋巴瘤的候选关键基因，探讨EBV相关淋巴瘤发生的可能分子机制。方法：采集健康献血员新鲜血液样本，分离出人淋巴细胞，分别保存正常淋巴细胞，复制Scid小鼠体内EBV相关淋巴瘤模型。采用4×44K的Agilent人类全基因组表达谱芯片进行检测，分析Scid小鼠体内EBV相关淋巴瘤和正常人淋巴细胞的差异表达基因，筛选出fold change≥2的基因为表达显著上调基因，Fold change≤0.5为表达显著下调基因。运用GO分类、KEGG代谢通路、Biocarta和Reactom调控通路及DAVID在线软件对差异基因进行功能聚类及通路分析，并结合STRING、Cytscape分析差异基因的相互作用，预测差异基因的生物学功能。结果：1、病理学诊断Scid小鼠体内诱发肿瘤为弥漫大B细胞淋巴瘤，且PCR证实此淋巴瘤为人源性，成功复制体内EBV相关淋巴瘤实验模型。6例样本标准化数据均采用LIMMA、BRB-Random Variance Model、SAM软件筛选差异表达基因，以PDR0.001共筛选出差异表达探针3928个。将未映射到HUGO的Probe剔除，再选取fold-change2倍变化视为表达差异显著，共筛选出202个差异表达基因，其中上调基因44个，下调基因158个，系统构建了6例同源EBV相关淋巴瘤（T）和正常淋巴细胞（N）的差异基因表达谱。 2．差异表达基因的生物信息学分析 Gene Ontology分类的BP分析显示，共30个上调基因参与27个BP分类，，其中与细胞周期和有丝分裂相关的“cell cycle”,“cell cycle phase”,“nucleardivision”,“mitosis”,“M phase of mitotic cell cycle”,“cell cycle process”,“organellefission”,“M phase”和“mitotic cell cycle”等10个GO BP分类的EASE score最低，均2.6E-09。上调基因CDC6, KIFC1, OIP5, NCAPG, KIF15, BUB1, CDCA2,AURKA, CEP55, PBK参与多个与细胞周期相关的生物过程。126个下调基因参与116个BP分类，其中“inflammatory response”,“response to wounding”,“immune response”,“defense response”,“taxis”,“chemotaxis”,“regulation ofsecretion”,“behavior”,“anti-apoptosis”和“protein kinase cascade”的EASE Score得分最低，均1.6E-04。下调基因CSF2, CCL2, CXCL5, CXCL2, TLR2, FCN1,LILRA5, IL1RAP, IL1B, THBS1, CFD, PTX3, FCGR3A, IL1A, IL1RN等主要与细胞损伤、炎症反应、免疫应答等生物学过程相关。 25个上调基因参与13个MF分类，其中主要涉及核酸结合活性，如“ATPbinding”,“adenyl ribonucleotide binding”,“adenyl nucleotide binding”,“purinenucleoside binding”和“nucleoside binding”的EASE Score得分最低，均0.006。参与多个分子功能的基因有CDC6, KIFC1, KIF15, BUB1, AURKA,PBK,TOP2A, GSG2, RAD51。123个下调基因参与12个MF分类，其中主要涉及分子信号与细胞因子受体结合，如“carbohydrate binding”,“cytokine binding”,“polysaccharide binding”,“protein binding”和“glycosaminoglycan binding”,“cytokine activity”“,interleukin-1receptor binding”的EASE Score的得分最低，均0.001。参与多种分子功能的基因有SELP, TNFAIP6, CCL2, C6ORF25, TLR2,PTX3, THBS1, NLRP3, IL1RN, IL1B, IL1A。利用“KEGG Pathway”,“BioCarta”和“Reactome”进行Pathway分析显示，当EASE Score0.05时，上调基因中不参与“KEGG-pathway”，2个基因参与1条“BioCarta-pathway”，5个基因参与1条“Reactome-pathway”；下调基因中64个基因参与7条“KEGG-pathway”，30个基因参与1条“BioCarta-pathway”，33个基因参与2条“Reactome-pathway”。将上调和下调差异表达基因涉及的生物学功能进行“Functional AnnotationClustering”分析，结果显示上调基因涉及的生物学功能分类聚成15个集合，其中富集分数最高的功能集合主要涉及细胞周期和细胞分裂；下调基因涉及的生物学功能分类聚成63个集合，富集分数最高的功能集合主要涉及细胞趋向性、细胞成分及蛋白、多糖结合活性。利用STRING、Cytoscape、PATHWAY及GO分类等生物信息学软件综合分析202个差异表达基因（上调44个，下调158个），对此202个基因进行生物学功能分析及预测，上调基因CDC6, KIFC1, OIP5, NCAPG, KIF15, BUB1, CDCA2,AURKA, CEP55, PBK等主要参与细胞周期、有丝分裂等生物学过程；上调基因TNFRSF13B, TNFRSF17, CXCL9，下调基因CSF2, CCL2, FOS, EGF, IL1A, IL1B,DUSP6等则与肿瘤相关信号通路密切相关，如Inflammation，Angiogenesis，MAPKsignal pathway，Adherens junction，NOD-like receptor signaling pathway等；下调基因CSF2, CCL2, CXCL5, CXCL2, TLR2, FCN1, LILRA5, IL1RAP, IL1B, THBS1,CFD、PTX3, FCGR3A, IL1A, IL1RN等主要与细胞损伤、炎症反应、免疫应答等生物学过程相关，如“response to wounding”,“inflammatory response”,“immuneresponse”。本实验结合上述多种生物信息学分析结果显示，15个差异表达基因在蛋白网络中处于中心位置，包括EGF, IL1B, PBK, CSF2, TLR2, DUSP6, HDC,CD68, TREM1, IL1A, CCL2, SPI1, PLAU, TGFB1和FOS，其中EGF, IL1B, PBK,CSF2, TLR2这5个基因在度数和介度中的排名均靠前，我们推测EGF, IL1B, PBK,CSF2和TLR2可能是导致EBV相关淋巴瘤发生的关键分子。 3.综合信号通路、基因生物学分类、基因间相互作用分析及已有文献报道，提示EBV可能上调宿主细胞PBK基因促进细胞增殖，下调宿主细胞EGF基因发挥抗凋亡作用，下调宿主细胞IL1β, CSF2, TLR2基因等降低细胞免疫能力，从而导致EBV相关淋巴瘤的发生。结论： 1、系统构建了体内EBV相关淋巴瘤和正常人淋巴细胞的差异基因表达谱，发现淋巴瘤细胞和正常淋巴细胞的基因表达模式存在明显差异。 2、生物信息学分析筛选出202个差异表达基因，包括44个上调基因和158个下调基因，表明EBV相关淋巴瘤的发生是一个多基因参与，多通路涉及、病毒基因与宿主基因相互作用的过程。 3、推测EGF, IL1β, CSF2, PBK和TLR2可能是导致EBV相关淋巴瘤发生的关键分子。
[Abstract]:Objective: to detect and analyze the differentially expressed genes of host cells of EBV related lymphoma and normal human lymphocytes in Scid mice, and to screen candidate key genes of EBV induced lymphoma, and to explore the possible molecular mechanism of EBV related lymphoma.
Methods: donors fresh blood samples isolated from human peripheral blood lymphocytes, preservation of normal lymphocytes respectively, EBV replication in Scid mice lymphoma model. Using 4 * 44K Agilent genome expression microarray detection, difference analysis of EBV in Scid mice lymphoma and normal human lymphocyte gene expression, screened fold change more than 2 genes were upregulated the expression of Fold gene, change gene expression was significantly reduced to less than 0.5. The use of GO classification, KEGG pathway, Reactom pathway and Biocarta and DAVID online software on different gene function analysis and pathway, and the combination of STRING, Cytscape gene interaction analysis, prediction of biological function difference genes.
Results: 1, the pathological diagnosis of Scid mice induced by tumor for diffuse large B cell lymphoma, PCR lymphoma and confirmed that this is the source of EBV in the experimental model of.6 related lymphoma samples standard data successfully copied by LIMMA, BRB-Random Variance Model, SAM software for screening differentially expressed genes were screened out by PDR0.001. 3928. The expression of the probe is not mapped to HUGO Probe removed, then select fold-change2 times change as expression differences were screened 202 differentially expressed genes, including 44 up-regulated genes and 158 down regulated genes, constructs 6 cases of homologous EBV related lymphoma (T) and normal lymphocytes (N) expression the spectrum of genes.
Bioinformatics analysis of 2. differentially expressed genes
Gene Ontology classification BP analysis showed that a total of 30 up-regulated genes in 27 BP classification, including cell cycle and mitosis related to the "cell cycle", "cell cycle phase", "nucleardivision", "mitosis", "M phase of mitotic cell cycle", "cell cycle," process "organellefission", "M phase" and "mitotic cell cycle" 10 GO BP classification EASE score minimum, 2.6E-09. KIFC1, OIP5 up-regulated genes CDC6, NCAPG, KIF15, BUB1, CDCA2, AURKA, CEP55, PBK participated in a number of cell cycle associated with the biological process of.126 downregulated genes involved in 116 a BP classification, including "inflammatory response", "response to wounding", "immune response", "defense response", "taxis", "chemotaxis", "regulation ofsecretion", "behavior", "anti-apoptosis" and "protein ki The lowest score of EASE Score was nase cascade ", all 1.6E-04. down regulated genes CSF2, CCL2, CXCL5, CXCL2, TLR2, FCN1, TLR2,", ",", ",", ",", ",", ",", "," and "were mainly related to biological processes such as cell injury, inflammatory reaction, immune response and so on.
The 25 up-regulated genes in 13 MF classification, which mainly relates to nucleic acid binding activity, such as "ATPbinding", "adenyl ribonucleotide binding", "adenyl nucleotide binding", "purinenucleoside binding" and "nucleoside binding" EASE Score 0.006. the lowest score, were involved in a number of molecular function genes CDC6, KIFC1, KIF15 BUB1, AURKA, PBK, TOP2A, GSG2, RAD51.123, downregulated genes involved in 12 MF classification, which mainly involves a combination of molecular signaling and cytokine receptors, such as "carbohydrate binding", "cytokine binding", "polysaccharide binding", "protein binding" and "glycosaminoglycan binding", "cytokine activity". Interleukin-1receptor binding "EASE Score 0.001. the lowest score, were involved in a variety of molecular function genes SELP, TNFAIP6, CCL2, C6ORF25, TLR2, PT X3, THBS1, NLRP3, IL1RN, IL1B, IL1A.
The use of "KEGG Pathway", "BioCarta" and "Reactome" by Pathway analysis showed that when EASE Score0.05, up-regulated genes do not participate in the "KEGG-pathway", 2 genes involved in the 1 "BioCarta-pathway", 5 genes involved in the 1 "Reactome-pathway"; down regulated genes in 64 genes involved in 7 "KEGG-pathway" 30, 1 "BioCarta-pathway" genes, 33 genes involved in the 2 "Reactome-pathway".
The up and down expression of genes involved in the biological function of "Functional AnnotationClustering" analysis, results showed that the biological function of classification of up-regulated genes involving poly 15 sets, of which the highest score of the set of functional enrichment mainly involved in cell cycle and cell division; down regulated genes involved in biological function classification of poly 63 sets, functional enrichment the highest score of the collection mainly involves cell tropism, cell composition and protein, polysaccharide binding activity.
The use of STRING, Cytoscape, PATHWAY and GO classification and bioinformatics analysis software 202 genes (44 up-regulated and 158 down regulated), this 202 gene analysis and prediction of biological function, increase KIFC1, OIP5, gene CDC6, NCAPG, KIF15, BUB1, CDCA2, AURKA, CEP55, PBK etc. mainly involved in cell cycle, mitosis and other biological processes; up-regulated genes TNFRSF13B, TNFRSF17, CXCL9, CCL2, down-regulation of CSF2, FOS, EGF, IL1A, IL1B, DUSP6 and tumor related signaling pathways are closely related, such as Inflammation, Angiogenesis, MAPKsignal pathway, Adherens junction, NOD-like receptor signaling pathway down regulated genes; CSF2, CCL2, CXCL5, CXCL2, TLR2, FCN1, LILRA5, IL1RAP, IL1B, THBS1, CFD, PTX3, FCGR3A, IL1A, IL1RN and cell injury, inflammatory response, immune response and other related biological processes, such as "R Esponse to wounding "," inflammatory response "," immuneresponse ". The combination of these kinds of bioinformatics analysis showed that the gene in the center, in the 15 differentially expressed protein networks including EGF, IL1B, PBK, CSF2, TLR2, DUSP6, HDC, CD68, TREM1, IL1A, CCL2, SPI1. PLAU, TGFB1 and FOS, EGF, IL1B, PBK, CSF2, TLR2 were on the 5 genes in the degree and betweenness in the top, we speculate that EGF, IL1B, PBK, CSF2 and TLR2 might be the key molecular EBV related lymphoma.
3. integrated signal pathway, gene classification, gene interaction analysis and has been reported, suggesting that EBV may increase the host cell PBK gene can promote cell proliferation and downregulation of EGF gene in the host cell apoptosis, down-regulation of host cell IL1 beta, CSF2, TLR2 gene to reduce cellular immunity, resulting in EBV related lymphoma happen.
Conclusion:
1, we constructed differential gene expression profiles of EBV related lymphoma and normal human lymphocytes in vivo, and found that there were significant differences in gene expression patterns between lymphoma cells and normal lymphocytes.
2, bioinformatics analysis screened 202 differentially expressed genes, including 44 up-regulated genes and 158 down regulated genes, indicating that the occurrence of EBV related lymphoma is a multi gene involvement, and multipath involves the interaction between viral genes and host genes.
3, it is presumed that EGF, IL1 beta, CSF2, PBK and TLR2 may be the key molecules that lead to the occurrence of EBV related lymphoma.

【学位授予单位】：南华大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R733.1;R-332

【参考文献】