pap_Quintana_2004 - Colloquium Functional immunomics:...

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Colloquium Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes Francisco J. Quintana*, Peter H. Hagedorn †‡ , Gad Elizur , Yifat Merbl*, Eytan Domany , and Irun R. Cohen* § Departments of *Immunology and Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel; and Plant Research Department, Risø National Laboratory, DK-4000 Roskilde, Denmark 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 devel- oped 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 ex- pected, 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 an- tibodies 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% speci±city ( P 5 0.017). Sur- prisingly, 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 in- formation about immune system behavior can be mined by bioin- formatic technology. Repertoires matter. A utoimmune diseases are marked by abundant autoantibod- ies and by vigorously responding T cells targeted to selected self-antigens (1). Immunology has tended to focus on such blatant reactivities (2) and has paid relatively less attention to the autoimmunity detectable to nonclassical self-antigens and to the low levels of global autoreactivity detected in healthy subjects (3–6). An important question is whether bioinformatic analysis of the global autoantibody repertoire can predict if a subject will resist or develop an autoimmune disease before the disease is actually induced by an environmental insult. Can the analysis of immune repertoires contribute to predictive medicine? The present study uses microarray technology and bioinformatic analysis to address that question in an animal model of type 1
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This note was uploaded on 02/08/2010 for the course ECEN 689-601 taught by Professor Staff during the Spring '10 term at Texas A&M.

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pap_Quintana_2004 - Colloquium Functional immunomics:...

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