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Candidate biomarkers for type 1 diabetes In type 1 diabetes mellitus, the body’s immune system turns against insulinproducing beta cells in the islets of the pancreas. The destruction of beta cells results in chronic hyperglycemia that, if left untreated, may lead to organ damage, coma, and even death. Although the precise etiological basis of type 1 diabetes is uncertain, genetic as well as environmental factors appear to play roles in the development of the autoimmune disease. In contrast to type 2 diabetes, for which a number of risk factors such as obesity and a sedentary lifestyle have been identified, type 1 diabetes usually cannot be forecast before the onset of clinical symptoms. To discover protein biomarkers that might be predictive, Thomas O. Metz and co-workers at the Pacific Northwest National Laboratory (PNNL) and the U.S. Centers for Disease Control and Prevention performed a comparative proteomics analysis of blood from recently diagnosed diabetic patients and controls and reported the results in JPR (2008, 7, 698−707). According to Metz, “We’re looking for biomarkers that could be used to determine who is at risk for developing type 1 diabetes down the road.” This foreknowledge could aid in studies of the pathogenesis and prevention of type 1 diabetes. In particular, close family members of type 1 diabetes patients would benefit from reliable screening procedures. Currently, the best method for the prediction of at-risk individuals is to measure the levels of three autoantibodies against islet cell antigens. However, the specificity-adjusted sensitivities for the detection of two of these autoantibodies vary considerably among laboratories. Therefore, Metz and co-workers conducted a proteomics study to identify novel protein biomarkers that predict type 1 diabetes with higher sensitivity and specificity. The researchers used LC/MS/MS and LC/FTICR MS analyses to compare the plasma proteomes of 10 healthy controls with 10 patients that were recently
diagnosed with type 1 diabetes. Then, Metz and co-workers applied the accurate mass and time (AMT) tag strategy developed by Richard Smith at PNNL to quantitate proteins in the samples. In the first stage of this approach, the researchers combined individual con trol and patient samples to create two sample pools and depleted the six most Control
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Protein differences. An example of a heat map that shows differences in the peptide abundances of nine proteins between control and type 1 diabetic plasma.
abundant plasma proteins. Next, the pooled samples were digested with trypsin and fractionated by strong-cation exchange (SCX) chromatography. After performing LC/MS/MS on the fractionated samples and identifying the detected peptides, the researchers built an AMT tag database that served as a reference for the subsequent LC/ FTICR MS analyses. Metz says, “Once the lower-throughput LC/MS/MS analyses were complete and an AMT tag database was established, we switched to the higherthroughput, quantitative LC/FTICR MS analyses.” For label-free quantitation of peptides in the 20 individual samples, LC/FTICR MS was performed in triplicate on digested and depleted samples. Because of the larger dynamic range and higher sensitivity of FTICR mass spectrometers, SCX fractionation was
482 Journal of Proteome Research • Vol. 7, No. 2, 2008
not required. When the peptides were quantitated and identified by comparison to the AMT tag database, the following candidate biomarkers were found to differ in abundance between patients and controls: zinc-α-2-glycoprotein 1 (ZAG), clusterin, corticosteroid-binding globulin, lumican, and serotransferrin. On the basis of the known functions of these proteins, the researchers hy pothesized reasons for their altered expression levels in diabetic individuals. For example, ZAG, which was consistently up-regulated in patient samples versus controls, is a lipid-mobilizing factor. Insulin allows muscle and liver tissue to take up glucose. Because untreated individuals with type 1 diabetes lack insulin, their bodies use fats for energy. This switch might explain the increased expression of ZAG. Furthermore, lumican, a proteoglycan component of the extracellular matrix, was up-regulated in diabetic patients. Previous studies showed that lumican is overexpressed in the kidneys of diabetic patients with nephropathy, and this high expression level might represent an acute response to hyperglycemia. Metz says, “The functions of these candidate biomarkers are consistent with metabolic events that occur in diabetes.” However, only time will tell whether these candidate biomarkers are actually predictive or diagnostic of type 1 diabetes. The researchers are conducting follow-up studies with larger numbers of samples to confirm and extend the results of this study. “We’re applying some fairly cutting-edge technology to a very old problem,” adds Metz. “Diabetes has been recognized and studied since the time of the ancient Greeks, but so far, there are no good predictive biomarkers besides the three autoantibodies.” Nonetheless, the advanced proteomics techniques used by Metz and co-workers illustrate that methods to identify diabetes biomarkers have come a long way since the days when ancient Greeks tested for diabetes by tasting a patient’s urine to see if it was sweet. —Laura Cassiday
© 2008 American Chemical Society
