Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

被引:130
作者
Csardi, Gabor [1 ]
Franks, Alexander [1 ]
Choi, David S. [1 ]
Airoldi, Edoardo M. [1 ,2 ]
Drummond, D. Allan [3 ,4 ]
机构
[1] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA
[2] Broad Inst Harvard & MIT, Cambridge, MA USA
[3] Univ Chicago, Dept Biochem & Mol Biol, Chicago, IL 60637 USA
[4] Univ Chicago, Dept Human Genet, Chicago, IL 60637 USA
来源
PLOS GENETICS | 2015年 / 11卷 / 05期
基金
美国国家科学基金会;
关键词
GENE-EXPRESSION; SACCHAROMYCES-CEREVISIAE; PROFILING REVEALS; ABUNDANCE; QUANTIFICATION; TRANSCRIPTOME; TRANSLATION; SELECTION; SCALE; REGRESSION;
D O I
10.1371/journal.pgen.1005206
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear-properties of mRNA and protein measurements-which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
引用
收藏
页数:32
相关论文
共 85 条
[1]   Global signatures of protein and mRNA expression levels [J].
Abreu, Raquel de Sousa ;
Penalva, Luiz O. ;
Marcotte, Edward M. ;
Vogel, Christine .
MOLECULAR BIOSYSTEMS, 2009, 5 (12) :1512-1526
[2]   Estimating phenotypic correlations: correcting for bias due to intraindividual variability [J].
Adolph, S. C. ;
Hardin, J. S. .
FUNCTIONAL ECOLOGY, 2007, 21 (01) :178-184
[3]   Critical assessment of proteome-wide label-free absolute abundance estimation strategies [J].
Ahrne, Erik ;
Molzahn, Lars ;
Glatter, Timo ;
Schmidt, Alexander .
PROTEOMICS, 2013, 13 (17) :2567-2578
[4]  
Akashi H, 2003, GENETICS, V164, P1291
[5]   CORRECTING FOR RESTRICTION OF RANGE IN BOTH X AND Y WHEN THE UNRESTRICTED VARIANCES ARE UNKNOWN [J].
ALEXANDER, RA ;
HANGES, PJ ;
ALLIGER, GM .
APPLIED PSYCHOLOGICAL MEASUREMENT, 1985, 9 (03) :317-323
[6]  
[Anonymous], 2003, Bayesian Data Analysis
[7]  
[Anonymous], J AM STAT ASS
[8]   A disattenuated correlation estimate when variables are measured with error: Illustration estimating cross-platform correlations [J].
Archer, K. J. ;
Dumur, C. I. ;
Taylor, G. S. ;
Chaplin, M. D. ;
Guiseppi-Elie, A. ;
Buck, G. A. ;
Grant, G. ;
Ferreira-Gonzalez, A. ;
Garrett, C. T. .
STATISTICS IN MEDICINE, 2008, 27 (07) :1026-1039
[9]   Application of a correlation correction factor in a microarray cross-platform reproducibility study [J].
Archer, Kellie J. ;
Dumur, Catherine I. ;
Taylor, G. Scott ;
Chaplin, Michael D. ;
Guiseppi-Elie, Anthony ;
Grant, Geraldine ;
Ferreira-Gonzalez, Andrea ;
Garrett, Carleton T. .
BMC BIOINFORMATICS, 2007, 8
[10]  
Barnard J, 2000, STAT SINICA, V10, P1281