Longitudinal system-based analysis of transcriptional responses to type I interferons
Longitudinal system-based analysis of transcriptional responses to type I interferons
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DOI：
10.1152/physiolgenomics.00058.2009
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发表时间：
2009-08-01
影响因子：
4.6
通讯作者：
Baranzini, S. E.
中科院分区：
文献类型：
--
作者：
Pappas, D. J.;Coppola, G.;Baranzini, S. E.
Pappas DJ，Coppola G，Gabatto PA，Gao F，Geschwind DH，Oksenberg JR，Baranzini SE.基于纵向系统的I型干扰素转录反应分析。Physiol Genomics 38：362-371，2009。首次发表于2009年6月16日; doi：10.1152/physiolgenomics.00058.2009.- I型干扰素（IFN）是调节先天性和适应性免疫应答的多效性细胞因子。它们已被用于治疗自身免疫性疾病，癌症和病毒感染，并已被证明在细胞内引起差异反应，尽管共享单个受体。这种不同反应的分子基础仍然难以捉摸。为了确定差异I型IFN信号转导的潜在机制，我们使用全基因组微阵列来测量用IFN-α（2b）或IFN-β（1a）处理的人CD 4（+）T细胞内的纵向转录事件。我们确定了差异调节基因，分析他们的富集已知的启动子元件和途径，并构建了一个网络模块的基础上加权基因共表达网络分析（WGCNA）。WGCNA使用先进的统计方法来寻找相关基因的相互关联的模块。总的来说，CD 4（+）T细胞对IFN的不同反应与三个主要主题有关：迁移、抗原呈递和细胞毒性反应。对于迁移，WGCNA鉴定了前mRNA加工因子4同源物B和真核翻译起始因子4A 2的亚型特异性调节，其在细胞内的不同水平上起作用以影响趋化因子CCL 5的表达。WGCNA还鉴定了含有无菌α基序结构域的9样（SAMD 9 L）在IFN治疗的亚型独立效应中至关重要。与对照组相比，SAMD 9 L表达的RNA干扰增强了用IFN-β处理的活化T细胞的迁移表型。通过差异IFN治疗后的动态转录事件的分析，我们能够确定特定的签名，并发现新的基因，可能支持I型IFN反应。
Pappas DJ, Coppola G, Gabatto PA, Gao F, Geschwind DH, Oksenberg JR, Baranzini SE. Longitudinal system-based analysis of transcriptional responses to type I interferons. Physiol Genomics 38: 362-371, 2009. First published June 16, 2009; doi:10.1152/physiolgenomics.00058.2009.-Type I interferons (IFNs) are pleiotropic cytokines that modulate both innate and adaptive immune responses. They have been used to treat autoimmune disorders, cancers, and viral infection and have been demonstrated to elicit differential responses within cells, despite sharing a single receptor. The molecular basis for such differential responses has remained elusive. To identify the mechanisms underlying differential type I IFN signaling, we used whole genome microarrays to measure longitudinal transcriptional events within human CD4(+) T cells treated with IFN-alpha(2b) or IFN-beta(1a). We identified differentially regulated genes, analyzed them for the enrichment of known promoter elements and pathways, and constructed a network module based on weighted gene coexpression network analysis (WGCNA). WGCNA uses advanced statistical measures to find interconnected modules of correlated genes. Overall, differential responses to IFN in CD4(+) T cells related to three dominant themes: migration, antigen presentation, and the cytotoxic response. For migration, WGCNA identified subtypespecific regulation of pre-mRNA processing factor 4 homolog B and eukaryotic translation initiation factor 4A2, which work at various levels within the cell to affect the expression of the chemokine CCL5. WGCNA also identified sterile alpha-motif domain-containing 9-like (SAMD9L) as critical in subtype-independent effects of IFN treatment. RNA interference of SAMD9L expression enhanced the migratory phenotype of activated T cells treated with IFN-beta compared with controls. Through the analysis of the dynamic transcriptional events after differential IFN treatment, we were able to identify specific signatures and to uncover novel genes that may underpin the type I IFN response.