These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


PUBMED FOR HANDHELDS

Search MEDLINE/PubMed


  • Title: Reading patterns of proteome damage by glycation, oxidation and nitration: quantitation by stable isotopic dilution analysis LC-MS/MS.
    Author: Rabbani N, Thornalley PJ.
    Journal: Essays Biochem; 2020 Feb 17; 64(1):169-183. PubMed ID: 32065835.
    Abstract:
    Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides a high sensitivity, high specificity multiplexed method for concurrent detection of adducts formed by protein glycation, oxidation and nitration, also called AGEomics. Combined with stable isotopic dilution analysis, it provides for robust quantitation of protein glycation, oxidation and nitration adduct analytes. It is the reference method for such measurements. LC-MS/MS has been used to measure glycated, oxidized and nitrated amino acids - also called glycation, oxidation and nitration free adducts, with a concurrent quantitation of the amino acid metabolome in physiological fluids. Similar adduct residues in proteins may be quantitated with prior exhaustive enzymatic hydrolysis. It has also been applied to quantitation of other post-translation modifications, such as citrullination and formation of Nε-(γ-glutamyl)lysine crosslink by transglutaminases. Application to cellular and extracellular proteins gives estimates of the steady-state levels of protein modification by glycation, oxidation and nitration, and measurement of the accumulation of glycation, oxidation and nitration adducts in cell culture medium and urinary excretion gives an indication of flux of adduct formation. Measurement of glycation, oxidation and nitration free adducts in plasma and urine provides for estimates of renal clearance of free adducts. Diagnostic potential in clinical studies has been enhanced by the combination of estimates of multiple adducts in optimized diagnostic algorithms by machine learning. Recent applications have been in early-stage detection of metabolic, vascular and renal disease, and arthritis, metabolic control and risk of developing vascular complication in diabetes, and a blood test for autism.
    [Abstract] [Full Text] [Related] [New Search]