1. Introduction<br />Autoimmune diseases are marked by abundant autoantibodies and by vigorously responding T cells targeted to selected self-antigens.<br />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.<br />
Nonobese diabetic - NOD<br />Cyclophosphamide-accelerated diabetes - CAD<br />1.1. Overall View of the Experiment<br />Male mice of the nonobese diabetic (NOD) strain spontaneously develop type 1 diabetes at a relatively low incidence and late age.<br />The onset of diabetes can be significantly accelerated and synchronized by exposing NOD mice to cyclophosphamide.<br />NOD mice (1 month)<br />NOD mice treated withcyclophosphamide.<br />Sample sera<br />Sample sera<br />Test the pre- and post-CAD sera from both susceptible and resistant mice.<br />Spotting 266 different antigens into the glass surface of a chip – Repertoire analysis on the IgG antibodies.<br />
2. Matherials and Methods<br />2.1. Mice<br />Male NOD mice were raised and maintained under pathogen-free conditions. The mice were 4 weeks old at the start of the experiments. Nineteen mice were studied individually.<br /><ul><li>2.2. CAD</li></ul>Diabetes onset was accelerated and synchronized as described by two injections of 200 mgkgcyclophosphamide, given at 4 weeks of age, and again, 1 week later.<br />The experimental protocol. (Quintana et al. 2004).<br />
2. Matherials and Methods<br />2.3. Diabetes<br />Blood glucose was measured weekly. A mouse was considered diabetic when its blood glucose concentration was 13 mM on two consecutive examinations.<br /><ul><li>2.4. Sera</li></ul>Serum samples were collected 1 day before the first injection of cyclophosphamideand 1 month after the second injection.<br /><ul><li>2.5. Antigen Microarray Chips</li></ul>The 266 antigens spotted on the microarray chips in these studies include proteins, synthetic peptides from the sequences of key proteins, nucleotides, and phospholipids.<br />
2. Matherials and Methods<br />2.6. Data Analysis<br />Superparamagnetic clustering (SPC) algorithm provides an inherent mechanism for identifying robust and stable clusters. As the measure of similarity between objects, it was used the Euclidean distance for both samples and antigens.<br />Clustering: Process of grouping a set of physical or abstract objects into classes of similar objects.<br />Hierarchical: Organize elements into a tree, leaves represent antigens and the length of the pathes between leaves represents the distances between antigens.<br />
2. Matherials and Methods<br />Determine subsets of the 266 antigens that would separate the sick and healthy mice:<br />Test one antigen at a time, replacing the reactivities (or ratios) with ranks according to their magnitude: 1 for the smallest, 2 for the second smallest, and so on.<br />To capture a collective effect of several antigens, it was selected the 27 antigens (10% of the 266 antigens in the study) with the highest ratio, and investigated how good they were collectively at separating sick from healthy mice.<br />Quintana et al. 2004<br />
3. Results and Discussion<br />The mice susceptible to future CAD induction are denoted by the filled rectangles at the top of the clustering box.<br />The mice resistant to future CAD induction are denoted by the empty rectangles.<br />Before cyclophosphamide, the CAD-susceptible mice manifested relatively elevated IgG reactivity to the top 19 antigens.<br />CAD-resistant mice manifested relatively elevated IgG reactivity to the remaining eight antigens.<br />Reactivity matrices of 27 antigens separate diabetic and healthy mice before CAD induction. (Quintana et al. 2004).<br />
3. Results and Discussion<br />Then it was used the 27 antigens effective in pre-CAD clustering to analyze the patterns of IgG antibodies developing in the diabetic and healthy mice post-CAD.<br />27 antigens failed to discriminate between the two groups of mice!<br />SPC of the serum samples post-CAD. (Quintana et al. 2004).<br />
3. Results and Discussion<br />Patterns of IgG antibodies expressed pre-CAD in male NOD mice can mark susceptibility or resistance to CAD induced later.<br />It was also found patterns of IgG antibodies characteristic of healthy or diabetic mice post-CAD, but these patterns required sets of antigens that differed from the informative pre-CAD set.<br />Prediction of future disease and diagnosis of present disease can depend on different data sets of information.<br />Idea that autoimmunity of certain specificities is not only compatible with health, but is essential for health.<br />
3. Results and Discussion<br />Three peptides of HSP70 (/p8,/p17,/p30) of the nineteen antigens targeted, are associated with T cell autoimmunity and so, human type 1 diabetes.<br />Glidian is an hormone associated with celiac disease, and celiac patients have been reported to have an increased incident of type 1 diabetes.<br />Possibility that LDL and HDL autoimmunity might actually be part of the collective of autoimmune reactions responsible for the primary development of type 1 diabetes.<br />?<br />?<br />Arise of new questions for further biological research.<br />
4. Proteomics – Mapping of cellular proteins<br />For a novel protein to be ‘validated’ as a drug target, it must be assigned a biological function and<br />plausibly linked to a disease. (Walter P. Blackstock, 1999)<br />
5. References<br />Walter P. Blackstock and Malcolm P. Weir (1999). Proteomics: quantative and physical mapping of cellular proteins. Trends Biotechnol. 17(3): 121-7.<br />G. Getz, et al (1999). Super-paramagnetic clustering of yeast genes expression profiles. Physica A: Statistical Mechanics and its Applications, v. 279, iss. 1-4, p. 457-464.<br />Francisco J. Quintana, et al (2004). Fuctional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes. PNAS. Vol. 101: 14615-14621. <br />
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