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Serum Proteomes Distinguish Children Developing Type 1 Diabetes in a Cohort with HLA-Conferred Susceptibility

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Serum Proteomes Distinguish Children Developing Type 1 Diabetes in a Cohort with HLA-Conferred Susceptibility Diabetes Volume 64, June 2015 2265 Robert Moulder,1 Santosh D. Bhosale,1 Timo Erkkilä,2 Essi Laajala,1 Jussi Salmi,1 Elizabeth V. Nguyen,1 Henna Kallionpää,1 Juha Mykkänen,3,4 Mari Vähä-Mäkilä,3,4 Heikki Hyöty,5,6 Riitta Veijola,7 Jorma Ilonen,8,9 Tuula Simell,3,4 Jorma Toppari,3,4,10 Mikael Knip,11–14 David R. Goodlett,1,15 Harri Lähdesmäki,1,2 Olli Simell,3,4 and Riitta Lahesmaa1 Serum Proteomes Distinguish Children Developing Type 1 Diabetes in a Cohort With HLA-Conferred Susceptibility Diabetes 2015;64:2265–2278 | DOI: 10.2337/db14-0983 GENETICS/GENOMES/PROTEOMICS/METABOLOMICS We determined longitudinal serum proteomics profiles the serum proteome in healthy children and children from children with HLA-conferred diabetes susceptibil- progressing to type 1 diabetes, including new protein ity to identify changes that could be detected before candidates, the levels of which change before clinical seroconversion and positivity for disease-associated diagnosis. autoantibodies. Comparisons were made between chil- dren who seroconverted and progressed to type 1 diabetes (progressors) and those who remained auto- The measurement of islet cell autoantibodies is currently antibody negative, matched by age, sex, sample peri- the principle means of identifying an emerging threat of odicity, and risk group. The samples represented the developing type 1 diabetes (1). The risks associated with prediabetic period and ranged from the age of 3 months the appearance of islet antibodies have been evaluated in to 12 years. After immunoaffinity depletion of the most depth, and overall, the appearance of multiple biochemi- abundant serum proteins, isobaric tags for relative and fi absolute quantification were used for sample labeling. cally de ned autoantibodies correlates with progression Quantitative proteomic profiles were then measured to disease irrespective of family history, genetic risk for 13 case-control pairs by high-performance liquid group, or autoantibody combination (1). Nevertheless, it fi chromatography-tandem mass spectrometry (LC-MS/MS). still remains open whether nding even earlier indications Additionally, a label-free LC-MS/MS approach was used of future disease development is possible. Such markers to analyze depleted sera from six case-control pairs. could shed further light on disease etiology and poten- Importantly, differences in abundance of a set of tially be used in the evaluation of risks and preventive proteins were consistently detected before the appear- treatments. ance of autoantibodies in the progressors. Based on Proteomic analyses in the study of type 1 diabetes has top-scoring pairs analysis, classification of such pro- been previously reviewed (2) and applied in studies gressors was observed with a high success rate. Over- addressing differences in the sera of patients with diabe- all, the data provide a reference of temporal changes in tes and subjects without diabetes (3–5). Zhang et al. (5) 1Turku Centre for Biotechnology, University of Turku, Turku, Finland 12Research Program, Diabetes and Obesity, University of Helsinki, Helsinki, 2Department of Information and Computer Science, Aalto University School of Finland Science, Espoo, Finland 13Department of Pediatrics, Tampere University Hospital, Tampere, Finland 3Department of Pediatrics, University of Turku, Turku, Finland 14Folkhälsan Research Institute, Helsinki, Finland 4Department of Pediatrics, Turku University Hospital, Turku, Finland 15Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD 5 School of Medicine, University of Tampere, Tampere, Finland Corresponding author: Riitta Lahesmaa, riitta.lahesmaa@btk.fi. 6Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland Received 25 June 2014 and accepted 8 January 2015. 7University of Oulu and Oulu University Hospital, Department of Pediatrics, Oulu, Finland This article contains Supplementary Data online at http://diabetes 8Department of Clinical Microbiology, University of Eastern Finland, Kuopio, .diabetesjournals.org/lookup/suppl/doi:10.2337/db14-0983/-/DC1. Finland © 2015 by the American Diabetes Association. Readers may use this article as 9Immunogenetics Laboratory, University of Turku, Turku, Finland long as the work is properly cited, the use is educational and not for profit, and 10Departments of Physiology and Pediatrics, University of Turku, Turku, Finland the work is not altered. 11Children’s Hospital, University of Helsinki and Helsinki University Central Hos- pital, Helsinki, Finland 2266 Serum Proteomes En Route to Type 1 Diabetes Diabetes Volume 64, June 2015 compared protein levels in plasma from patients with to report longitudinal proteomics profiles in children who type 1 diabetes and healthy subjects, observing significant develop type 1 diabetes as well as such profiles in healthy differences in the abundance of 24 proteins. Similarly, Zhi children. et al. (4) detected differences in the levels of 21 serum RESEARCH DESIGN AND METHODS proteins between patients with type 1 diabetes and healthy subjects; six of the proteins were validated by A schematic of the experimental design is illustrated in immunoassay. Fig. 1. Detailed description of the proteomics measure- Although in-depth comparisons of proteins in samples ments, samples comparisons, and availability of the raw from healthy subjects and patients with type 1 diabetes data are provided as supplementary information. fi have distinguished the diseased state, the identi cation of Subjects and Sample Collection changes preceding this aggressive autoimmune disease is All children studied were participants in the Finnish DIPP important for disease prediction and prevention. McGuire study (9), where children identified as at risk for type 1 et al. (6) used a proteomic approach to identify predictive diabetes based on HLA genotype were followed prospec- markers in the cord blood of children in whom type 1 tively from birth. Venous nonfasting blood samples were diabetes developed later. Although their measurements collected at each study visit; sera were separated and with surface-enhanced laser desorption/ionization mass spectrometry revealed different patterns, the discriminat- ing peaks were not identified. To establish the origin and changes associated with the development of type 1 diabetes, careful selection of appropriate study groups is essential, such as have been established by prospective sampling from at-risk individ- uals (7,8). The Finnish Type 1 Diabetes Prediction and Prevention (DIPP) project collected samples from Finnish children with HLA-defined predisposition to type 1 dia- betes (7,9), thus creating an extensive prospective sample collection from birth to diagnosis or otherwise healthy until 15 years of age. This resource has allowed investiga- tion of the longitudinal profiles of a wide range of factors in children who developed type 1 diabetes, using samples ranging from early infancy to diagnosis, as well as sample measurements from carefully matched control subjects (10–14). In the current study, we determined the longitudinal serum proteomics profiles of a group of children who aretype1diabetessusceptibleenrolledintheDIPP study.Themeasurementsweremadeinserafrom38 children comprising 19 type 1 diabetes case-control pairsmatchedbydateandlocationofbirth,sex,and HLA-conferred genetic risk. The samples selected for analysis represent the time course from autoantibody negativity to seroconversion to diagnosis. The analyses were made using two mass spectrometry–based quan- titative proteomics techniques. First, we used isobaric tags for relative and absolute quantification (iTRAQ) reagents, which have previously been extensively used in serum proteomics applications, including the devel- opment of robust analytical protocols and applied studies up to the scale of hundreds of subjects (15,16). Second, we used a label-free method, which — has also been applied in serum proteomics analyses Figure 1 Schematic presentation of the study design. Using a pro- spective longitudinal serum sample collection from children with an (17,18). The present results reveal a spectrum of HLA-conferred risk for type 1 diabetes. Samples were selected changesanddifferencesintheserumproteinprofiles based on clinical outcome and the titers of diabetes-associated between children progressing to type 1 diabetes and autoantibodies. The samples were prepared for proteomics analysis matched control subjects. Some of these changes by mass spectrometry. Comparisons were made between children who developed type 1 diabetes and age-, HLA risk–,andsex- were consistently detected before the appearance of matched control subjects. Two quantitative approaches were ap- autoantibodies. To our knowledge, this study is the first plied: first, iTRAQ reagents and second, a label-free approach. diabetes.diabetesjournals.org Moulder and Associates 2267 stored at 270°C within 3 h from collection. Serum islet LC-MS/MS Data Processing cell autoantibody (ICA) measurements were made as pre- The iTRAQ data were analyzed with ProteinPilot soft- viously described (19). For ICA-positive children, levels of ware using the Paragon identification algorithm (22) with a GAD antibody (GADA), tyrosine phosphatase-related pro- Human Swiss-Prot database (18 August 2011; 20,245 tein antibody (IA-2A), and insulin antibodies (IAA) were entries). The database searches were made in thorough also analyzed. mode, specifying 8plex iTRAQ quantification, trypsin The proteomics measurements were performed on sera digestion, and MMTS (S-methyl methanethiosulfonate) from
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