背景:T辅助(Th)9细胞是独立于Th2细胞发育的Th细胞的新型亚群,其特征在于白细胞介素(IL)-9的分泌。研究表明Th9细胞参与各种疾病,如过敏性和肺部疾病(例如,哮喘,慢性阻塞性气道疾病,慢性鼻-鼻窦炎,鼻息肉,和肺发育不全),代谢性疾病(如,急性白血病,粒细胞白血病,乳腺癌,肺癌,黑色素瘤,胰腺癌),神经精神疾病(例如,阿尔茨海默病),自身免疫性疾病(例如,Graves病,克罗恩病,结肠炎,牛皮癣,系统性红斑狼疮,系统性硬皮病,类风湿性关节炎,多发性硬化症,炎症性肠病,特应性皮炎,湿疹),和传染病(例如,结核病,肝炎)。然而,关于它参与其他代谢的信息缺乏,神经精神病学,和传染病。
目的:本研究旨在鉴定Th2向Th9细胞转化过程中显著差异改变的基因,和它们的调节microRNAs(miRs)来自公开可用的小鼠模型的基因表达综合数据集,使用计算机分析来解开疾病过程中涉及的各种致病途径。
方法:使用从2个公开数据集(GSE99166和GSE123501)中鉴定的差异表达基因(DEGs),我们进行了功能富集和网络分析,以鉴定通路,蛋白质-蛋白质相互作用,miR-信使RNA关联,以及与Th2向Th9细胞转化相关的显著差异改变基因相关的疾病基因关联。
结果:我们提取了260个常见的下调,236共同上调,和来自数据集GSE99166和GSE123501的表达谱的634个常见DEGs。共差异表达的IL,细胞因子,受体,和转录因子(TFs)富集在7个关键的京都百科全书的基因和基因组途径和基因本体论。我们构建了蛋白质-蛋白质相互作用网络,并预测了参与Th2至Th9分化途径的顶级调控miRs。我们还确定了各种代谢,过敏和肺部,神经精神病学,自身免疫,和传染病以及Th2到Th9的分化可能起关键作用的癌。
结论:本研究确定了迄今为止尚未探索的Th9与疾病状态之间的可能关联。一些重要的IL,包括CCL1(趋化因子[C-C基序]配体1),CCL20(趋化因子[C-C基序]配体20),IL-13,IL-4,IL-12A,和IL-9;受体,包括IL-12RB1,IL-4RA(白介素9受体α),CD53(分化簇53),CD6(分化簇6),CD5(分化簇5),CD83(分化簇83),CD197(分化簇197),IL-1RL1(白细胞介素1受体样1),CD101(分化簇101),CD96(分化簇96),CD72(分化簇72),CD7(分化簇7),CD152(细胞毒性T淋巴细胞相关蛋白4),CD38(分化簇38),CX3CR1(趋化因子[C-X3-C基序]受体1),CTLA2A(细胞毒性T淋巴细胞相关蛋白2α),CTLA28和CD196(分化簇196);和TFs,包括FOXP3(叉头箱P3),IRF8(干扰素调节因子8),FOXP2(叉头箱P2),RORA(RAR相关孤儿受体α),AHR(芳烃受体),MAF(禽类肌膜膜纤维肉瘤癌基因同源物),SMAD6(SMAD家族成员6),JUN(Jun原癌基因),JAK2(Janus激酶2),EP300(E1A结合蛋白p300),ATF6(激活转录因子6),BTAF1(B-TFIIDTATA盒结合蛋白相关因子1),BAFT(碱性亮氨酸拉链转录因子),NOTCH1(神经源性位点缺口同源蛋白1),GATA3(GATA结合蛋白3),SATB1(富含AT的特殊序列结合蛋白1),BMP7(骨形态发生蛋白7),和PPARG(过氧化物酶体增殖物激活受体γ,能够在Th2向Th9细胞的转化中鉴定出显著的差异改变的基因。我们确定了一些可以针对DEG的常见miR。Th9在代谢性疾病中的作用研究的匮乏凸显了该领域的空白。我们的研究为探索Th9在各种代谢紊乱如糖尿病中的作用提供了理论基础。糖尿病肾病,高血压疾病,缺血性卒中,脂肪性肝炎,肝纤维化,肥胖,腺癌,胶质母细胞瘤和神经胶质瘤,胃恶性肿瘤,黑色素瘤,神经母细胞瘤,骨肉瘤,胰腺癌,前列腺癌,还有胃癌.
BACKGROUND: T helper (Th) 9 cells are a novel subset of Th cells that develop independently from Th2 cells and are characterized by the secretion of interleukin (IL)-9. Studies have suggested the involvement of Th9 cells in variable diseases such as allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, and pulmonary hypoplasia), metabolic diseases (eg, acute leukemia, myelocytic leukemia, breast cancer, lung cancer, melanoma, pancreatic cancer), neuropsychiatric disorders (eg, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus, systemic scleroderma, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, atopic dermatitis, eczema), and infectious diseases (eg, tuberculosis, hepatitis). However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases.
OBJECTIVE: This study aims to identify significant differentially altered genes in the conversion of Th2 to Th9 cells, and their regulating microRNAs (miRs) from publicly available Gene Expression Omnibus data sets of the mouse model using in silico analysis to unravel various pathogenic pathways involved in disease processes.
METHODS: Using differentially expressed genes (DEGs) identified from 2 publicly available data sets (GSE99166 and GSE123501) we performed functional enrichment and network analyses to identify pathways, protein-protein interactions, miR-messenger RNA associations, and disease-gene associations related to significant differentially altered genes implicated in the conversion of Th2 to Th9 cells.
RESULTS: We extracted 260 common downregulated, 236 common upregulated, and 634 common DEGs from the expression profiles of data sets GSE99166 and GSE123501. Codifferentially expressed ILs, cytokines, receptors, and transcription factors (TFs) were enriched in 7 crucial Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology. We constructed the protein-protein interaction network and predicted the top regulatory miRs involved in the Th2 to Th9 differentiation pathways. We also identified various metabolic, allergic and pulmonary, neuropsychiatric, autoimmune, and infectious diseases as well as carcinomas where the differentiation of Th2 to Th9 may play a crucial role.
CONCLUSIONS: This study identified hitherto unexplored possible associations between Th9 and disease states. Some important ILs, including CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9; receptors, including IL-12RB1, IL-4RA (interleukin 9 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte-associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196); and TFs, including FOXP3 (forkhead box P3), IRF8 (interferon regulatory factor 8), FOXP2 (forkhead box P2), RORA (RAR-related orphan receptor alpha), AHR (aryl-hydrocarbon receptor), MAF (avian musculoaponeurotic fibrosarcoma oncogene homolog), SMAD6 (SMAD family member 6), JUN (Jun proto-oncogene), JAK2 (Janus kinase 2), EP300 (E1A binding protein p300), ATF6 (activating transcription factor 6), BTAF1 (B-TFIID TATA-box binding protein associated factor 1), BAFT (basic leucine zipper transcription factor), NOTCH1 (neurogenic locus notch homolog protein 1), GATA3 (GATA binding protein 3), SATB1 (special AT-rich sequence binding protein 1), BMP7 (bone morphogenetic protein 7), and PPARG (peroxisome proliferator-activated receptor gamma, were able to identify significant differentially altered genes in the conversion of Th2 to Th9 cells. We identified some common miRs that could target the DEGs. The scarcity of studies on the role of Th9 in metabolic diseases highlights the lacunae in this field. Our study provides the rationale for exploring the role of Th9 in various metabolic disorders such as diabetes mellitus, diabetic nephropathy, hypertensive disease, ischemic stroke, steatohepatitis, liver fibrosis, obesity, adenocarcinoma, glioblastoma and glioma, malignant neoplasm of stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, prostate carcinoma, and stomach carcinoma.