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Complementary PTM Profiling of Drug Response in Human Gastric Carcinoma by Immunoaffinity and IMAC Methods with Total Proteome Analysis. | LitMetric

AI Article Synopsis

  • The study focuses on the importance of proteomic analyses to better understand cellular signaling and disease biology, particularly in identifying biomarkers.
  • Researchers utilized the gastric carcinoma cell line MKN-45 treated with specific inhibitors to explore the effectiveness of different strategies in both total protein and post-translational modification (PTM) analysis.
  • Findings revealed minimal changes in total protein levels but numerous phosphorylation alterations; the combination of enrichment methods and robust data analysis created a framework for comprehensive proteomic studies aimed at discovering potential therapeutic targets.

Article Abstract

Gaining insight into normal cellular signaling and disease biology is a critical goal of proteomic analyses. The ability to perform these studies successfully to extract the maximum value and discovery of biologically relevant candidate biomarkers is therefore of primary importance. Many successful studies in the past have focused on total proteome analysis (changes at the protein level) combined with phosphorylation analysis by metal affinity enrichment (changes at the PTM level). Here, we use the gastric carcinoma cell line MKN-45 treated with the c-Met inhibitor SU11274 and PKC inhibitor staurosporine to investigate the most efficient and most comprehensive strategies for both total protein and PTM analysis. Under the conditions used, total protein analysis yielded few changes in response to either compound, while analysis of phosphorylation identified thousands of sites that changed differentially between the two treatments. Both metal affinity and antibody-based enrichments were used to assess phosphopeptide changes, and the data generated by the two methods was largely complementary (non-overlapping). Label-free quantitation of peptide peak abundances was used to accurately determine fold-changes between control and treated samples. Protein interaction network analysis allowed the data to be placed in a biologically relevant context, and follow-up validation of selected findings confirmed the accuracy of the proteomic data. Together, this study provides a framework for start-to-finish proteomic analysis of any experimental system under investigation to maximize the value of the proteomic study and yield the best chance for uncovering actionable target candidates.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217380PMC
http://dx.doi.org/10.3390/proteomes3030160DOI Listing

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