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人类组织间和组织内mRNA与蛋白质浓度的全球变异性分析

Global variability analysis of mRNA and protein concentrations across and within human tissues.

作者信息

Wegler Christine, Ölander Magnus, Wiśniewski Jacek R, Lundquist Patrik, Zettl Katharina, Åsberg Anders, Hjelmesæth Jøran, Andersson Tommy B, Artursson Per

机构信息

Department of Pharmacy, Uppsala University, Uppsala SE-75123, Sweden.

DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden.

出版信息

NAR Genom Bioinform. 2019 Oct 30;2(1):lqz010. doi: 10.1093/nargab/lqz010. eCollection 2020 Mar.

Abstract

Genes and proteins show variable expression patterns throughout the human body. However, it is not clear whether relative differences in mRNA concentrations are retained on the protein level. Furthermore, inter-individual protein concentration variability within single tissue types has not been comprehensively explored. Here, we used the Gini index for in-depth concentration variability analysis of publicly available transcriptomics and proteomics data, and of an in-house proteomics dataset of human liver and jejunum from 38 donors. We found that the transfer of concentration variability from mRNA to protein is limited, that established 'reference genes' for data normalization vary markedly at the protein level, that protein concentrations cover a wide variability spectrum within single tissue types, and that concentration variability analysis can be a convenient starting point for identifying disease-associated proteins and novel biomarkers. Our results emphasize the importance of considering individual concentration levels, as opposed to population averages, for personalized systems biology analysis.

摘要

基因和蛋白质在整个人体中呈现出可变的表达模式。然而,尚不清楚mRNA浓度的相对差异在蛋白质水平上是否得以保留。此外,单一组织类型内个体间蛋白质浓度的变异性尚未得到全面探究。在此,我们使用基尼系数对公开可用的转录组学和蛋白质组学数据,以及来自38名供体的人类肝脏和空肠的内部蛋白质组学数据集进行深入的浓度变异性分析。我们发现,浓度变异性从mRNA到蛋白质的传递是有限的,用于数据标准化的既定“参考基因”在蛋白质水平上有显著差异,蛋白质浓度在单一组织类型内覆盖了广泛的变异性范围,并且浓度变异性分析可以作为识别疾病相关蛋白质和新型生物标志物的便捷起点。我们的结果强调了在个性化系统生物学分析中考虑个体浓度水平而非群体平均值的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79e0/7671341/3686c5aeb59c/lqz010fig1.jpg

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