Abstract
A robust method was developed and optimized for enrichment and quantitative analysis of posttranslational modifications (PTMs) in serum/plasma samples by combining immunoaffinity purification and LC-MS/MS without depletion of abundant proteins. The method was used to survey serum samples of patients with acute myeloid leukemia (AML), breast cancer (BC), and nonsmall cell lung cancer (NSCLC). Peptides were identified from serum samples containing phosphorylation, acetylation, lysine methylation, and arginine methylation. Of the PTMs identified, lysine acetylation (AcK) and arginine mono-methylation (Rme) were more prevalent than other PTMs. Label-free quantitative analysis of AcK and Rme peptides was performed for sera from AML, BC, and NSCLC patients. Several AcK and Rme sites showed distinct abundance distribution patterns across the three cancer types. The identification and quantification of posttranslationally modified peptides in serum samples reported here can be used for patient profiling and biomarker discovery research.
Highlights
Combine isotopic labeling, offline fractionation, and LCMS/MS analysis
We have developed a robust workflow combining posttranslational modifications (PTMs) motif antibody enrichment and LC-MS/MS analysis for profiling different PTMs
The workflow has proven successful to analyze both plasma and serum samples for many commonly studied PTMs, including phosphorylation, acetylation, arginine methylation, and lysine methylation, with lysine acetylation and arginine mono-methylation yielding the highest number of unique peptide identifications
Summary
Kme (mono, di, tri) enrichment workflow from serum/plasma without the need for depletion of the abundant proteins. Among the PTMs surveyed, lysine acetylation (AcK) and arginine mono-methylation (Rme) were identified as the more prevalent PTMs in cancer patients’ sera. These PTMs were profiled in sera from patients with acute myelogenous leukemia (AML), breast cancer (BC), and nonsmall cell lung cancer (NSCLC). Clustering of the quantitative data for the AcK and Rme enrichments revealed patterns of modification specific to cancer type as well as patient pathology. Together, these data demonstrate the utility of PTM profiling of human serum samples for disease characterization and the potential for biomarker discovery
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