Glycation isotopic labeling with 13C-reducing sugars for quantitative analysis of glycated proteins in human plasma

Non-enzymatic glycation of proteins is a post-translational modification produced by a reaction between reducing sugars and amino groups located in lysine and arginine residues or in the N-terminal position. This modification plays a relevant role in medicine and food industry. In the clinical field, this undesired role is directly linked to blood glucose concentration and therefore to pathological conditions derived from hyperglycemia (>11 mm glucose) such as diabetes mellitus or renal failure. An approach for qualitative and quantitative analysis of glycated proteins is here proposed to achieve the three information levels for their complete characterization. These are: 1) identification of glycated proteins, 2) elucidation of sugar attachment sites, and 3) quantitative analysis to compare glycemic states. Qualitative analysis was carried out by tandem mass spectrometry after endoproteinase Glu-C digestion and boronate affinity chromatography for isolation of glycated peptides. For this purpose, two MS operational modes were used: higher energy collisional dissociation-MS2 and CID-MS3 by neutral loss scan monitoring of two selective neutral losses (162.05 and 84.04 Da for the glucose cleavage and an intermediate rearrangement of the glucose moiety). On the other hand, quantitative analysis was based on labeling of proteins with [(13)C(6)]glucose incubation to evaluate the native glycated proteins labeled with [(12)C(6)]glucose. As glycation is chemoselective, it is exclusively occurring in potential targets for in vivo modifications. This approach, named glycation isotopic labeling, enabled differentiation of glycated peptides labeled with both isotopic forms resulting from enzymatic digestion by mass spectrometry (6-Da mass shift/glycation site). The strategy was then applied to a reference plasma sample, revealing the detection of 50 glycated proteins and 161 sugar attachment positions with identification of preferential glycation sites for each protein. A predictive approach was also tested to detect potential glycation sites under high glucose concentration.