Global analysis of protein folding thermodynamics for disease state characterization.
Global analysis of protein folding thermodynamics for disease state characterization.
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DOI：
10.1021/acs.jproteome.5b00057
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发表时间：
2015-05-01
影响因子：
4.4
通讯作者：
Fitzgerald, Michael C.
中科院分区：
文献类型：
--
作者：
Adhikari, Jagat;West, Graham M.;Fitzgerald, Michael C.
关键词：
目前大规模表征疾病状态的方法通常依赖于基因和/或蛋白质表达水平的分析。这些现有的方法无法检测到具有疾病相关功能和表达水平不变的蛋白质。在这里，我们描述了大规模使用蛋白质折叠和稳定性的热力学测量来表征疾病状态。利用细胞培养氨基酸稳定同位素标记和蛋白质氧化率稳定性（SILAC-SPROX）技术，我们在三种不同的乳腺癌细胞培养模型（MCF-10A、MCF-7和MDA-MB-231）中检测了约800种蛋白质的折叠和稳定性变化。这里生成的热力学稳定性谱创建了不同的分子标记来区分三种细胞系，并且显著比例（~45%）的差异稳定蛋白没有改变表达水平。因此，本文报道的微分热力学分析策略创造了乳腺癌的新分子特征，并为该疾病的分子基础提供了额外的见解。我们的研究结果建立了蛋白质折叠和稳定性测量在疾病过程研究中的效用，并且它们表明这种测量可能对疾病中的生物标志物发现有用。
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 ~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 (~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.
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影响因子：
4.4
作者：
DeArmond, Patrick D.;Xu, Ying;Strickland, Erin C.;Daniels, Kyle G.;Fitzgerald, Michael C.
通讯作者：
Fitzgerald, Michael C.
DOI：
10.1158/1078-0432.ccr-13-0623
发表时间：
2013-07-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Dennison JB;Molina JR;Mitra S;González-Angulo AM;Balko JM;Kuba MG;Sanders ME;Pinto JA;Gómez HL;Arteaga CL;Brown RE;Mills GB
通讯作者：
Mills GB
影响因子：
2.9
作者：
Liemann, S;Glockshuber, R
通讯作者：
Glockshuber, R
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
8
作者：
Chang, Youngil;Schlebach, Jonathan P.;Park, Chiwook
通讯作者：
Park, Chiwook