Suppr超能文献

使用 SILAC-SPROX 对蛋白质中的乳腺癌相关构象变化进行大规模分析

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX.

机构信息

Department of Chemistry, Duke University , Durham, North Carolina 27708, United States.

出版信息

J Proteome Res. 2017 Sep 1;16(9):3277-3286. doi: 10.1021/acs.jproteome.7b00283. Epub 2017 Jul 27.

Abstract

Proteomic methods for disease state characterization and biomarker discovery have traditionally utilized quantitative mass spectrometry methods to identify proteins with altered expression levels in disease states. Here we report on the large-scale use of protein folding stability measurements to characterize different subtypes of breast cancer using the stable isotope labeling with amino acids in cell culture and stability of proteins from rates of oxidation (SILAC-SPROX) technique. Protein folding stability differences were studied in a comparison of two luminal breast cancer subtypes, luminal-A and -B (i.e., MCF-7 and BT-474 cells, respectively), and in a comparison of a luminal-A and basal subtype of the disease (i.e., MCF-7 and MDA-MB-468 cells, respectively). The 242 and 445 protein hits identified with altered stabilities in these comparative analyses included a large fraction with no significant expression level changes. This suggests thermodynamic stability measurements create a new avenue for protein biomarker discovery. A number of the identified protein hits are known from other biochemical studies to play a role in tumorigenesis and cancer progression. This not only substantiates the biological significance of the protein hits identified using the SILAC-SPROX approach, but it also helps elucidate the molecular basis for their disregulation and/or disfunction in cancer.

摘要

蛋白质组学方法用于疾病状态的特征描述和生物标志物的发现,传统上利用定量质谱方法来鉴定在疾病状态下表达水平改变的蛋白质。在这里,我们报告了大规模使用蛋白质折叠稳定性测量来使用稳定同位素标记与细胞培养中的氨基酸和蛋白质氧化率的稳定性(SILAC-SPROX)技术来表征不同类型的乳腺癌。通过比较两种腔乳腺癌亚型(即 MCF-7 和 BT-474 细胞)和比较腔 A 型和基底型疾病(即 MCF-7 和 MDA-MB-468 细胞),研究了蛋白质折叠稳定性差异。在这些对比分析中,确定了 242 个和 445 个具有改变稳定性的蛋白质命中,其中包括很大一部分没有显著表达水平变化的蛋白质。这表明热力学稳定性测量为蛋白质生物标志物的发现开辟了新途径。已从其他生化研究中确定的许多鉴定的蛋白质命中在肿瘤发生和癌症进展中发挥作用。这不仅证实了使用 SILAC-SPROX 方法鉴定的蛋白质命中的生物学意义,而且有助于阐明其在癌症中失调和/或功能障碍的分子基础。

相似文献

1
Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX.
J Proteome Res. 2017 Sep 1;16(9):3277-3286. doi: 10.1021/acs.jproteome.7b00283. Epub 2017 Jul 27.
2
Global analysis of protein folding thermodynamics for disease state characterization.
J Proteome Res. 2015 May 1;14(5):2287-97. doi: 10.1021/acs.jproteome.5b00057. Epub 2015 Apr 9.
3
Proteome-Wide Characterization of Phosphorylation-Induced Conformational Changes in Breast Cancer.
J Proteome Res. 2018 Mar 2;17(3):1129-1137. doi: 10.1021/acs.jproteome.7b00795. Epub 2018 Jan 30.
4
Discovery of Tamoxifen and N-Desmethyl Tamoxifen Protein Targets in MCF-7 Cells Using Large-Scale Protein Folding and Stability Measurements.
J Proteome Res. 2017 Nov 3;16(11):4073-4085. doi: 10.1021/acs.jproteome.7b00442. Epub 2017 Oct 11.
5
Discovery of Manassantin A Protein Targets Using Large-Scale Protein Folding and Stability Measurements.
J Proteome Res. 2016 Aug 5;15(8):2688-96. doi: 10.1021/acs.jproteome.6b00237. Epub 2016 Jul 8.
6
Characterization of the Saccharomyces cerevisiae ATP-Interactome using the iTRAQ-SPROX Technique.
J Am Soc Mass Spectrom. 2016 Feb;27(2):233-43. doi: 10.1007/s13361-015-1290-z. Epub 2015 Nov 3.
7
Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using Limited Proteolysis.
J Proteome Res. 2016 Dec 2;15(12):4666-4674. doi: 10.1021/acs.jproteome.6b00755. Epub 2016 Nov 17.
8
SILAC-pulse proteolysis: A mass spectrometry-based method for discovery and cross-validation in proteome-wide studies of ligand binding.
J Am Soc Mass Spectrom. 2014 Dec;25(12):2073-83. doi: 10.1007/s13361-014-0992-y. Epub 2014 Oct 15.

引用本文的文献

2
3
Analysis of Brain Protein Stability Changes in a Mouse Model of Alzheimer's Disease.
J Proteome Res. 2024 Oct 4;23(10):4443-4456. doi: 10.1021/acs.jproteome.4c00406. Epub 2024 Sep 18.
4
Large-scale characterization of drug mechanism of action using proteome-wide thermal shift assays.
bioRxiv. 2024 Aug 14:2024.01.26.577428. doi: 10.1101/2024.01.26.577428.
5
Protein Folding Stability Profiling of Colorectal Cancer Chemoresistance Identifies Functionally Relevant Biomarkers.
J Proteome Res. 2023 Jun 2;22(6):1923-1935. doi: 10.1021/acs.jproteome.3c00045. Epub 2023 May 1.
6
Comparative Analysis of Protein Folding Stability-Based Profiling Methods for Characterization of Biological Phenotypes.
J Am Soc Mass Spectrom. 2023 Mar 1;34(3):383-393. doi: 10.1021/jasms.2c00248. Epub 2023 Feb 20.
7
Hidden information on protein function in censuses of proteome foldedness.
Nat Commun. 2022 Apr 14;13(1):1992. doi: 10.1038/s41467-022-29661-2.
8
Analysis of Brain Protein Stability Changes in Mouse Models of Normal Aging and α-Synucleinopathy Reveals Age- and Disease-Related Differences.
J Proteome Res. 2021 Nov 5;20(11):5156-5168. doi: 10.1021/acs.jproteome.1c00653. Epub 2021 Oct 4.
9
Comparative Analysis of Mass-Spectrometry-Based Proteomic Methods for Protein Target Discovery Using a One-Pot Approach.
J Am Soc Mass Spectrom. 2020 Feb 5;31(2):217-226. doi: 10.1021/jasms.9b00041. Epub 2019 Nov 22.
10
Network-based method for drug target discovery at the isoform level.
Sci Rep. 2019 Sep 25;9(1):13868. doi: 10.1038/s41598-019-50224-x.

本文引用的文献

1
Discovery of Age-Related Protein Folding Stability Differences in the Mouse Brain Proteome.
J Proteome Res. 2016 Dec 2;15(12):4731-4741. doi: 10.1021/acs.jproteome.6b00927. Epub 2016 Nov 17.
2
Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using Limited Proteolysis.
J Proteome Res. 2016 Dec 2;15(12):4666-4674. doi: 10.1021/acs.jproteome.6b00755. Epub 2016 Nov 17.
4
Urinary proteome alterations in HER2 enriched breast cancer revealed by multipronged quantitative proteomics.
Proteomics. 2016 Sep;16(17):2403-18. doi: 10.1002/pmic.201600015. Epub 2016 Aug 5.
6
Proteomic Analysis of Urine to Identify Breast Cancer Biomarker Candidates Using a Label-Free LC-MS/MS Approach.
PLoS One. 2015 Nov 6;10(11):e0141876. doi: 10.1371/journal.pone.0141876. eCollection 2015.
7
Prediction of Recurrence and Survival for Triple-Negative Breast Cancer (TNBC) by a Protein Signature in Tissue Samples.
Mol Cell Proteomics. 2015 Nov;14(11):2936-46. doi: 10.1074/mcp.M115.048967. Epub 2015 Jul 24.
9
The proteomic landscape of triple-negative breast cancer.
Cell Rep. 2015 Apr 28;11(4):630-44. doi: 10.1016/j.celrep.2015.03.050. Epub 2015 Apr 16.
10
Global analysis of protein folding thermodynamics for disease state characterization.
J Proteome Res. 2015 May 1;14(5):2287-97. doi: 10.1021/acs.jproteome.5b00057. Epub 2015 Apr 9.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验