Tandem Mass Tag-Based Serum Proteome Profiling For Biomarker Discovery In Young Duchenne Muscular Dystrophy Boys
ACS OMEGA(2020)
摘要
Blood-accessible molecular biomarkers are becoming highly attractive tools to assess disease progression and response to therapies in Duchenne muscular dystrophy (DMD) especially in very young patients for whom other outcome measures remain subjective and challenging. In this study, we have standardized a highly specific and reproducible multiplexing mass spectrometry method using the tandem mass tag (TMT) strategy in combination with depletion of abundant proteins from serum and high-pH reversed-phase peptide fractionation. Differential proteome profiling of 4 year-old DMD boys (n = 9) and age-matched healthy controls (n = 9) identified 38 elevated and 50 decreased serum proteins (adjusted P < 0.05, FDR <0.05) in the DMD group relative to the healthy control group. As expected, we confirmed previously reported biomarkers but also identified novel biomarkers. These included novel muscle injury-associated biomarkers such as telethonin, smoothelin-like protein 1, cofilin-1, and plectin, additional muscle-specific enzymes such as UTP-glucose-l-phosphate uridylyltransferase, aspartate aminotransferase, pyruvate kinase PKM, lactotransferrin, tissue alpha-L-fucosidase, pantetheinase, and ficolin-1, and some pro-inflammatory and cell adhesion-associated biomarkers such as leukosialin, macrophage receptor MARCO, vitronectin, galectin-3-binding protein, and ProSAAS. The workflow including serum depletion, sample processing, and mass spectrometry analysis was found to be reproducible and stable over time with CV < 20%. Furthermore, the method was found to be superior in terms of specificity compared to other multiplexing affinity-based methods. These findings demonstrate the specificity and reliability of TMT-based mass spectrometry methods in detection and identification of serum biomarkers in presymptomatic young DMD patients.
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关键词
serum proteome profiling,biomarker discovery,muscular dystrophy,tag-based
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