Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induce diabetes
Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induce diabetes
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DOI：
10.1073/pnas.0404848101
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发表时间：
2004-10-05
影响因子：
11.1
通讯作者：
Cohen, IR
中科院分区：
文献类型：
--
作者：
Quintana, FJ;Hagedorn, PH;Cohen, IR
一个人现在的抗体库编码了他过去免疫经验的历史。目前的自身抗体库是否可以用来预测自身免疫性疾病的耐药或易感性？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非肥胖糖尿病雄性小鼠中发生的1型糖尿病模型，在该模型中，通过将小鼠在4周龄时暴露于环磷酰胺来加速和同步疾病。我们从19只小鼠中获得血清，处理小鼠以诱导环磷酰胺加速的糖尿病（CAD），并发现，正如预期的那样，9只小鼠变得严重糖尿病，而10只小鼠永久抵抗糖尿病。我们再次从CAD诱导后的每只小鼠获得血清。然后，我们分析，通过使用排名顺序和超顺磁性聚类，抗体在单个小鼠的模式，以266个不同的抗原点在芯片上。一组选定的27种不同抗原（阵列的10%）显示了CAD前血清中IgG抗体反应性的模式，该模式以100%的灵敏度和82%的特异性（P = 0.017）区分对CAD耐药或易感的小鼠。令人惊讶的是，在CAD诱导之前提供信息的IgG抗体集在CAD发作后没有分离耐药组和易感组;新抗原对于CAD后的库区分变得至关重要。因此，至少对于模型疾病，目前的抗体库可以预测未来的疾病，预测和诊断库可以不同，并且可以通过生物信息学技术挖掘关于免疫系统行为的决定性信息。收藏很重要。
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.