Troy Niamh M, Hollams Elysia M, Holt Patrick G, Bosco Anthony
Telethon Kids Institute, The University of Western Australia, Crawley, Australia.
Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia.
BMC Med Genomics. 2016 Feb 27;9:9. doi: 10.1186/s12920-016-0171-z.
Asthma is strongly associated with allergic sensitization, but the mechanisms that determine why only a subset of atopics develop asthma are not well understood. The aim of this study was to test the hypothesis that variations in allergen-driven CD4 T cell responses are associated with susceptibility to expression of asthma symptoms.
The study population consisted of house dust mite (HDM) sensitized atopics with current asthma (n = 22), HDM-sensitized atopics without current asthma (n = 26), and HDM-nonsensitized controls (n = 24). Peripheral blood mononuclear cells from these groups were cultured in the presence or absence of HDM extract for 24 h. CD4 T cells were then isolated by immunomagnetic separation, and gene expression patterns were profiled on microarrays.
Differential network analysis of HDM-induced CD4 T cell responses in sensitized atopics with or without asthma unveiled a cohort of asthma-associated genes that escaped detection by more conventional data analysis techniques. These asthma-associated genes were enriched for targets of STAT6 signaling, and they were nested within a larger coexpression module comprising 406 genes. Upstream regulator analysis suggested that this module was driven primarily by IL-2, IL-4, and TNF signaling; reconstruction of the wiring diagram of the module revealed a series of hub genes involved in inflammation (IL-1B, NFkB, STAT1, STAT3), apoptosis (BCL2, MYC), and regulatory T cells (IL-2Ra, FoxP3). Finally, we identified several negative regulators of asthmatic CD4 T cell responses to allergens (e.g. IL-10, type I interferons, microRNAs, drugs, metabolites), and these represent logical candidates for therapeutic intervention.
Differential network analysis of allergen-induced CD4 T cell responses can unmask covert disease-associated genes and pin point novel therapeutic targets.
哮喘与过敏致敏密切相关，但对于为何只有一部分特应性个体发展为哮喘的决定机制尚不清楚。本研究的目的是检验以下假设：变应原驱动的CD4 T细胞反应的差异与哮喘症状表达的易感性相关。
研究人群包括患有当前哮喘的屋尘螨（HDM）致敏特应性个体（n = 22）、无当前哮喘的HDM致敏特应性个体（n = 26）和HDM非致敏对照（n = 24）。将这些组的外周血单个核细胞在有或无HDM提取物的情况下培养24小时。然后通过免疫磁珠分离法分离CD4 T细胞，并在微阵列上分析基因表达模式。
对有或无哮喘的致敏特应性个体中HDM诱导的CD4 T细胞反应进行差异网络分析，揭示了一组哮喘相关基因，这些基因通过更传统的数据分析技术未被检测到。这些哮喘相关基因富含STAT6信号传导的靶标，并且它们嵌套在一个包含406个基因的更大的共表达模块中。上游调节因子分析表明，该模块主要由IL-2、IL-4和TNF信号传导驱动；该模块接线图的重建揭示了一系列参与炎症（IL-1B、NFkB、STATl、STAT3）、细胞凋亡（BCL2、MYC）和调节性T细胞（IL-2Ra、FoxP3）的枢纽基因。最后，我们鉴定了几种哮喘CD4 T细胞对变应原反应的负调节因子（例如IL-10、I型干扰素、微小RNA、药物、代谢产物），这些代表了治疗干预的合理候选物。
变应原诱导的CD4 T细胞反应的差异网络分析可以揭示隐蔽的疾病相关基因并确定新的治疗靶点。
