Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ 85287.
Proc Natl Acad Sci U S A. 2013 Nov 12;110(46):18614-9. doi: 10.1073/pnas.1309390110. Epub 2013 Oct 28.
The development of new vaccines would be greatly facilitated by having effective methods to predict vaccine performance. Such methods could also be helpful in monitoring individual vaccine responses to existing vaccines. We have developed "immunosignaturing" as a simple, comprehensive, chip-based method to display the antibody diversity in an individual on peptide arrays. Here we examined whether this technology could be used to develop correlates for predicting vaccine effectiveness. By using a mouse influenza infection, we show that the immunosignaturing of a natural infection can be used to discriminate a protective from nonprotective vaccine. Further, we demonstrate that an immunosignature can determine which mice receiving the same vaccine will survive. Finally, we show that the peptides comprising the correlate signatures of protection can be used to identify possible epitopes in the influenza virus proteome that are correlates of protection.
如果有一种有效的方法可以预测疫苗的效果，那么新疫苗的开发将会得到极大的促进。这种方法也有助于监测个体对现有疫苗的疫苗反应。我们已经开发出“免疫签名”作为一种简单、全面、基于芯片的方法，用于在肽阵列上显示个体的抗体多样性。在这里，我们研究了这项技术是否可以用于开发预测疫苗效果的相关性。通过使用小鼠流感感染，我们表明自然感染的免疫签名可用于区分保护性和非保护性疫苗。此外，我们证明了免疫签名可以确定接受相同疫苗的哪些小鼠将存活。最后，我们表明构成保护相关签名的肽可用于鉴定流感病毒蛋白质组中与保护相关的可能表位。
