Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis
Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis
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DOI：
10.1186/ar1985
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发表时间：
2006-01-01
影响因子：
4.9
通讯作者：
Iwakura, Yoichiro
中科院分区：
文献类型：
--
作者：
Fujikado, Noriyuki;Saijo, Shinobu;Iwakura, Yoichiro
风湿性关节炎（RA）是一种自身免疫性疾病，影响全球约1%的人口。以前，我们发现人类T细胞白血病病毒I型转基因小鼠和白细胞介素1受体拮抗剂敲除小鼠产生类似于人类RA的自身免疫和关节特异性炎症。为了确定参与关节炎发病机制的基因，我们使用高密度寡核苷酸阵列分析了这些动物模型的基因表达谱。我们在其中一个动物模型中发现了1,467个基因，它们与正常对照小鼠的差异表达超过三倍。两种模型的基因表达谱相关性良好。我们提取了554个基因，其表达在两种模型中显着变化，假设在效应阶段的重要致病基因将在两种模型中发生变化。然后，这些通常改变的基因中的每一个都以1兆碱基对的规模映射到整个基因组中。我们发现这些基因的转录组图谱在染色体上分布并不均匀，而是形成簇。这些确定的基因簇包括主要组织相容性复合物I类和II类基因，补体基因和趋化因子基因，这是众所周知的参与RA的发病机制在效应阶段。这些基因簇的激活表明，抗原呈递和淋巴细胞趋化性对关节炎的发展是重要的。此外，通过搜索这样的簇，我们可以检测到边缘表达变化的基因。这些基因簇包括schlafen和跨膜四域亚家族A基因，其在关节炎中的功能尚未确定。因此，通过结合两种病因学不同的RA模型，我们成功地提取了在效应期关节炎发展中起作用的基因。此外，我们证明了通过转录组作图识别基因簇是一种有用的方法，可以在表达变化仅为边缘的基因中找到潜在的致病基因。
Rheumatoid arthritis ( RA) is an autoimmune disease affecting approximately 1% of the population worldwide. Previously, we showed that human T-cell leukemia virus type I-transgenic mice and interleukin-1 receptor antagonist-knockout mice develop autoimmunity and joint-specific inflammation that resembles human RA. To identify genes involved in the pathogenesis of arthritis, we analyzed the gene expression profiles of these animal models by using high-density oligonucleotide arrays. We found 1,467 genes that were differentially expressed from the normal control mice by greater than threefold in one of these animal models. The gene expression profiles of the two models correlated well. We extracted 554 genes whose expression significantly changed in both models, assuming that pathogenically important genes at the effector phase would change in both models. Then, each of these commonly changed genes was mapped into the whole genome in a scale of the 1-megabase pairs. We found that the transcriptome map of these genes did not distribute evenly on the chromosome but formed clusters. These identified gene clusters include the major histocompatibility complex class I and class II genes, complement genes, and chemokine genes, which are well known to be involved in the pathogenesis of RA at the effector phase. The activation of these gene clusters suggests that antigen presentation and lymphocyte chemotaxisis are important for the development of arthritis. Moreover, by searching for such clusters, we could detect genes with marginal expression changes. These gene clusters include schlafen and membrane-spanning four-domains subfamily A genes whose function in arthritis has not yet been determined. Thus, by combining two etiologically different RA models, we succeeded in efficiently extracting genes functioning in the development of arthritis at the effector phase. Furthermore, we demonstrated that identification of gene clusters by transcriptome mapping is a useful way to find potentially pathogenic genes among genes whose expression change is only marginal.