Dataset Information

Label-free Quantitative Analysis of Protein Expression Alterations in miR-26a-Knockout HeLa Cells using SWATH-MS Technology.

ABSTRACT: MicroRNAs (miRNAs) bind to the 3'-untranslated region of target mRNAs in a sequence-specific manner and subsequently repress gene translation. Human miR-26a has been studied extensively, but the target transcripts are far from complete. We first employed the CRISPR-Cas9 system to generate an miR-26a-knockout line in human cervical cancer HeLa cells. The miR26a-knockout line showed increased cell growth and altered proliferation. Proteomics technology of sequential window acquisition of all theoretical mass spectra (SWATH-MS) was utilized to compare the protein abundance between the wild-type and the knockout lines, with an attempt to identify transcripts whose translation was influenced by miR-26a. Functional classification of the proteins with significant changes revealed their function in stress response, proliferation, localization, development, signaling, etc. Several proteins in the cell cycle/proliferation signaling pathway were chosen to be validated by western blot and parallel reaction monitoring (PRM). The satisfactory consistency among the three approaches indicated the reliability of the SWATH-MS quantification. Among the computationally predicted targets, a subset of the targets was directly regulated by miR-26a, as demonstrated by luciferase assays and Western blotting. This study creates an inventory of miR-26a-targeted transcripts in HeLa cells and provides fundamental knowledge to further explore the functions of miR-26a in human cancer.

SUBMITTER: Shen H

PROVIDER: S-EPMC6362012 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Label-free Quantitative Analysis of Protein Expression Alterations in miR-26a-Knockout HeLa Cells using SWATH-MS Technology.

Shen Hexiao H Li Li L Teng Zhaowei Z Meng Tianqing T Kong Xiangbin X Hu Yan Y Zhu Yun Y Ma Lixin L

Scientific reports 20190204 1

MicroRNAs (miRNAs) bind to the 3'-untranslated region of target mRNAs in a sequence-specific manner and subsequently repress gene translation. Human miR-26a has been studied extensively, but the target transcripts are far from complete. We first employed the CRISPR-Cas9 system to generate an miR-26a-knockout line in human cervical cancer HeLa cells. The miR26a-knockout line showed increased cell growth and altered proliferation. Proteomics technology of sequential window acquisition of all theor ...[more]

PMID: 30718521

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Project description:BACKGROUND:SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification. METHODS:The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1. RESULTS:Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally. CONCLUSION:This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Dataset Information

Label-free Quantitative Analysis of Protein Expression Alterations in miR-26a-Knockout HeLa Cells using SWATH-MS Technology.

Publications

Label-free Quantitative Analysis of Protein Expression Alterations in miR-26a-Knockout HeLa Cells using SWATH-MS Technology.

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