Global Analysis of Protein Folding Thermodynamics for Disease State Characterization

被引:27
作者
Adhikari, Jagat [1 ]
West, Graham M. [2 ]
Fitzgerald, Michael C. [1 ,3 ]
机构
[1] Duke Univ, Dept Biochem, Durham, NC 27708 USA
[2] Scripps Res Inst, Dept Mass Spectrometry & Prote, Jupiter, FL 33458 USA
[3] Duke Univ, Dept Chem, Durham, NC 27708 USA
基金
美国国家卫生研究院;
关键词
mass spectrometry; SILAC; proteomics; protein folding; chemical denaturation; breast cancer; MCF-7; MDA-MB-231; MCF-10A; PROTEOMICS APPROACH; GENE-FUNCTION; AMINO-ACIDS; MYOSIN-IIA; WILD-TYPE; CANCER; EXPRESSION; STABILITY; CLASSIFICATION; IDENTIFICATION;
D O I
10.1021/acs.jproteome.5b00057
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Current methods for the large-scale characterization of disease states generally rely on the analysis of gene and/or protein expression levels. These existing methods fail to detect proteins with disease-related functions and unaltered expression levels. Here we describe the large-scale use of thermodynamic measurements of protein folding and stability for the characterization of disease states. Using the Stable Isotope Labeling with Amino Acids in Cell Culture and Stability of Proteins from Rates of Oxidation (SILAC-SPROX) technique, we assayed similar to 800 proteins for protein folding and stability changes in three different cell culture models of breast cancer including the MCF-10A, MCF-7, and MDA-MB-231 cell lines. The thermodynamic stability profiles generated here created distinct molecular markers to differentiate the three cell lines, and a significant fraction (similar to 45%) of the differentially stabilized proteins did not have altered expression levels. Thus, the differential thermodynamic profiling strategy reported here created novel molecular signatures of breast cancer and provided additional insight into the molecular basis of the disease. Our results establish the utility of protein folding and stability measurements for the study of disease processes, and they suggest that such measurements may be useful for biomarker discovery in disease.
引用
收藏
页码:2287 / 2297
页数:11
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