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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes
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Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes

机译：功能性免疫组学：IgG自身抗体库的微阵列分析可预测小鼠对诱发糖尿病的未来反应

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摘要

One's present repertoire of antibodies encodes the history of one's past immunological experience. Can the present autoantibody repertoire be consulted to predict resistance or susceptibility to the future development of an autoimmune disease? Here, we developed an antigen microarray chip and used bioinformatic analysis to study a model of type 1 diabetes developing in nonobese diabetic male mice in which the disease was accelerated and synchronized by exposing the mice to cyclophosphamide at 4 weeks of age. We obtained sera from 19 individual mice, treated the mice to induce cyclophosphamide-accelerated diabetes (CAD), and found, as expected, that 9 mice became severely diabetic, whereas 10 mice permanently resisted diabetes. We again obtained serum from each mouse after CAD induction. We then analyzed, by using rank-order and superparamagnetic clustering, the patterns of antibodies in individual mice to 266 different antigens spotted on the chip. A selected panel of 27 different antigens (10% of the array) revealed a pattern of IgG antibody reactivity in the pre-CAD sera that discriminated between the mice resistant or susceptible to CAD with 100% sensitivity and 82% specificity (P = 0.017). Surprisingly, the set of IgG antibodies that was informative before CAD induction did not separate the resistant and susceptible groups after the onset of CAD; new antigens became critical for post-CAD repertoire discrimination. Thus, at least for a model disease, present antibody repertoires can predict future disease, predictive and diagnostic repertoires can differ, and decisive information about immune system behavior can be mined by bioinformatic technology. Repertoires matter.
机译：一个人目前的抗体库编码一个人过去的免疫学经历的历史。是否可以参考当前的自身抗体库来预测自身免疫疾病未来发展的耐药性或易感性？在这里，我们开发了一种抗原微阵列芯片，并使用生物信息学分析来研究在非肥胖糖尿病雄性小鼠中发展的1型糖尿病模型，该模型通过将小鼠在4周龄下暴露于环磷酰胺来加速和同步化疾病。我们从19只小鼠中获得了血清，对这些小鼠进行了诱导以诱导环磷酰胺加速糖尿病（CAD），并发现，正如预期的那样，有9只小鼠患有严重的糖尿病，而10只小鼠则永久抵抗糖尿病。在CAD诱导后，我们再次从每只小鼠获得血清。然后，我们通过使用秩序和超顺磁聚类分析了单个小鼠中针对芯片上266种不同抗原的抗体模式。一组选定的27种不同抗原（占阵列的10％）显示出CAD前血清中IgG抗体反应性的模式，该模式以100％的敏感性和82％的特异性区分了对CAD耐药或易感的小鼠（P = 0.017） 。出乎意料的是，在CAD诱导之前提供丰富信息的IgG抗体组并没有在CAD发作后分离出耐药和易感基团。新的抗原对于CAD后库的鉴别变得至关重要。因此，至少对于模型疾病而言，当前的抗体库可以预测未来的疾病，预测库和诊断库可以不同，并且可以通过生物信息技术挖掘有关免疫系统行为的决定性信息。曲目很重要。

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