Literature DB >> 19250064

Application of the SILAC (stable isotope labelling with amino acids in cell culture) technique in quantitative comparisons for tissue proteome expression.

Yuhuan Xu1, Shufang Liang, Guobo Shen, Xuejiao Xu, Qingping Liu, Zhizhong Xu, Fengming Gong, Minghai Tang, Yuquan Wei.   

Abstract

Stable isotope labelling has recently become a popular tool for the quantitative profiling of the proteome, especially the emergence and development of the SILAC (stable isotope labelling with amino acids in cell culture) technique. Here we have expanded the application of SILAC to comparison of the relative protein expression levels between two different states of tissues based on cultured cells with [2H]leucine labelling as an internal standard in mass spectra. The SILAC ratio of tissue proteins versus labelled cells was determined by the calculation of peak intensity of the pair of labelled and unlabelled peptide fragment ions from the mass spectra, and the relative expression level of proteins in two groups of tissues was estimated by calculating the ratio of their SILAC ratio. To validate our [2H]leucine-based differential proteome analysis for tissues, we successfully compared two known proteins, one up-regulated vimentin and one down-regulated enoyl-CoA hydratase in human renal cancerous tissues versus human normal kidney tissues, which was previously confirmed by other groups using conventional two-dimensional PAGE analysis. Furthermore, we identified a previously unknown down-regulated protein, COX4I1 (cytochrome c oxidase subunit 4 isoform 1), in renal carcinoma tissues by this [2H]leucine-based quantitative proteomics method, which was also validated by immunohistochemistry and Western-blot analysis. In conclusion, the application of the [2H]leucine-based quantitative technique can be effectively expanded to comparison of the expression levels for the tissue proteome at different states, which would help us to identify new candidate biomarkers for tumours.

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Year:  2009        PMID: 19250064     DOI: 10.1042/BA20090007

Source DB:  PubMed          Journal:  Biotechnol Appl Biochem        ISSN: 0885-4513            Impact factor:   2.431


  7 in total

1.  Downregulated expression of HSP27 in human low-grade glioma tissues discovered by a quantitative proteomic analysis.

Authors:  Guobo Shen; Shufang Liang; Zhizhong Xu; Liangxue Zhou; Shiyin Xiao; Xiangying Xia; Ronghui Li; Ying Liao; Chao You; Yuquan Wei
Journal:  Proteome Sci       Date:  2010-03-26       Impact factor: 2.480

2.  Secretome Analysis of Skeletal Myogenesis Using SILAC and Shotgun Proteomics.

Authors:  C Y X'avia Chan; John C McDermott; K W Michael Siu
Journal:  Int J Proteomics       Date:  2011-03-29

3.  SILAC-Based Mass Spectrometry Analysis Reveals That Epibrassinolide Induces Apoptosis via Activating Endoplasmic Reticulum Stress in Prostate Cancer Cells.

Authors:  Pinar Obakan; Carlos Barrero; Ajda Coker-Gurkan; Elif Damla Arisan; Salim Merali; Narcin Palavan-Unsal
Journal:  PLoS One       Date:  2015-09-09       Impact factor: 3.240

4.  PGRMC1 Is a Novel Potential Tumor Biomarker of Human Renal Cell Carcinoma Based on Quantitative Proteomic and Integrative Biological Assessments.

Authors:  Dan Zhang; Xiangying Xia; Xixi Wang; Peng Zhang; Weiliang Lu; Yamei Yu; Shi Deng; Hanshuo Yang; Hongxia Zhu; Ningzhi Xu; Shufang Liang
Journal:  PLoS One       Date:  2017-01-20       Impact factor: 3.240

5.  Downregulation of ATP1A1 promotes cancer development in renal cell carcinoma.

Authors:  Dan Zhang; Peng Zhang; Pengbo Yang; Yu He; Xixi Wang; Yanfang Yang; Hongxia Zhu; Ningzhi Xu; Shufang Liang
Journal:  Clin Proteomics       Date:  2017-05-04       Impact factor: 3.988

6.  SILAC-based quantitative MS approach for real-time recording protein-mediated cell-cell interactions.

Authors:  Xixi Wang; Yu He; Yang Ye; Xinyu Zhao; Shi Deng; Gu He; Hongxia Zhu; Ningzhi Xu; Shufang Liang
Journal:  Sci Rep       Date:  2018-05-31       Impact factor: 4.379

7.  SAE1 promotes human glioma progression through activating AKT SUMOylation-mediated signaling pathways.

Authors:  Yanfang Yang; Ziwei Liang; Zijing Xia; Xixi Wang; Yanni Ma; Zenghua Sheng; Qingjia Gu; Guobo Shen; Liangxue Zhou; Hongxia Zhu; Ningzhi Xu; Shufang Liang
Journal:  Cell Commun Signal       Date:  2019-07-25       Impact factor: 5.712

  7 in total

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