Functional immunomics: microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes

Abstract

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.

Fig. 1.

The experimental protocol. The numbers refer to the age (in weeks) of the mice. The black vertical lines at weeks 4 and 9 indicate serum sample collection. The gray vertical lines at weeks 4 and 5 indicate cyclophosphamide injection. The gray box at week 6 shows when the CAD-susceptible mice developed diabetes, and the gray box between weeks 11 and 13 shows the time of death of the untreated diabetic mice.

Fig. 2.

Reactivity matrices of 27 antigens separate diabetic and healthy mice before CAD induction. The rows are antigens and the columns are the mouse sera. Each antigen is identified by the number shown between the two reactivity matrices (see Table 1). (Left) Two-way SPC of the antigens and the serum samples pre-CAD. The length of a branch connecting to a cluster represents the stability of the cluster and filled boxes denote mice that later developed CAD. Open boxes denote mice that resisted CAD. (Right) SPC of the serum samples post-CAD. Filled boxes denote mice that developed CAD. Open boxes denote healthy mice that resisted CAD. The antigens used in the two images are the same and are presented in the same order.

Fig. 3.

Two-way SPC of 27 antigens that separate sick and healthy mice post-CAD. Filled boxes denote diabetic mice, and open boxes denote healthy mice.

Fig. 4.

Two-way SPC of 27 antigens that separate the sick and healthy mouse samples by using the pre- and post-CAD ratios. Filled boxes denote mice susceptible to CAD, and open boxes denote mice resistant to CAD.

Fig. 5.

Venn diagram showing antigens shared by the three lists of 27 antigens: list I, pre-CAD; list II, post-CAD; list III, sick and healthy mice by ratio. See Table 1.