Diabetes mellitus is an incurable disease, so it is necessary to establish a model to screen biomarkers for early warning in order to minimize the likelihood of long-term complications. Current- ly, advanced glycation...Diabetes mellitus is an incurable disease, so it is necessary to establish a model to screen biomarkers for early warning in order to minimize the likelihood of long-term complications. Current- ly, advanced glycation end products （AGEs） are considered to be biomarkers of many diseases, such as diabetes and its complications. In this study, a model for further proteomics study was es- tablished to analyze the glycation of HSA with 18 O-labeling strategy. 30 peptides were randomly se- lected to optimize tryptic digestion and 18O-labeling condition by HPLC-ESI/TOF. The best tryptic di- gestion condition was： HSA： Trypsin = 50： 1, w/w for 20 h. The best t8 O-labeling condition was to di- lute urea to 1 M and adjust KH2 POa--K2 HPO4 buffer pH to 6.0 to give a final labeling efficiency of 98.5 ± 0.7%. The inter- and intra-day precisions and stability were satisfactory. This model was es- tablished and optimized for further quantitative proteomics study.展开更多
基金Supported by the National Key Technology R&D Program of China(2012YQ040140,2012CB91060)the National Natural Science Foundation of China(21205005)
文摘Diabetes mellitus is an incurable disease, so it is necessary to establish a model to screen biomarkers for early warning in order to minimize the likelihood of long-term complications. Current- ly, advanced glycation end products （AGEs） are considered to be biomarkers of many diseases, such as diabetes and its complications. In this study, a model for further proteomics study was es- tablished to analyze the glycation of HSA with 18 O-labeling strategy. 30 peptides were randomly se- lected to optimize tryptic digestion and 18O-labeling condition by HPLC-ESI/TOF. The best tryptic di- gestion condition was： HSA： Trypsin = 50： 1, w/w for 20 h. The best t8 O-labeling condition was to di- lute urea to 1 M and adjust KH2 POa--K2 HPO4 buffer pH to 6.0 to give a final labeling efficiency of 98.5 ± 0.7%. The inter- and intra-day precisions and stability were satisfactory. This model was es- tablished and optimized for further quantitative proteomics study.
