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

• Published in: PLoS genetics Vol. 11; no. 5; p. e1005206
• Main Authors: Csárdi, Gábor, Franks, Alexander, Choi, David S., Airoldi, Edoardo M., Drummond, D. Allan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2015; Public Library of Science (PLoS)
• Subjects: Competition; Datasets; Gene Expression Regulation, Fungal; Genes; Genetic aspects; Identification and classification; Messenger RNA; Methods; Models, Genetic; Noise; Physiological aspects; Proteins; Reproducibility of Results; RNA Processing, Post-Transcriptional; RNA, Messenger - genetics; RNA, Messenger - metabolism; Rodents; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Studies; Transcription (Genetics); Transcription, Genetic; Yeast; Yeasts (Fungi)
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1005206

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.