title: Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast.
abstract: 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.
authors.affiliation: Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Gábor
authors.initials: G
authors.lastname: Csárdi
authors.affiliation: Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Alexander
authors.initials: A
authors.lastname: Franks
authors.affiliation: Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: David S
authors.initials: DS
authors.lastname: Choi
authors.affiliation: Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America,; The Broad Institute of Harvard & MIT, Cambridge, Massachusetts, United States of America.
authors.forename: Edoardo M
authors.initials: EM
authors.lastname: Airoldi
authors.affiliation: Dept. of Biochemistry & Molecular Biology, University of Chicago, Chicago, Illinois, United States of America,; Dept. of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.
authors.forename: D Allan
authors.initials: DA
authors.lastname: Drummond
mesh_terms: D015966:Gene Expression Regulation, Fungal; D008957:Models, Genetic; D012323:RNA Processing, Post-Transcriptional; D012333:RNA, Messenger / Q000235:genetics* / Q000378:metabolism; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D014158:Transcription, Genetic
chemical_list: D012333:RNA, Messenger; D029701:Saccharomyces cerevisiae Proteins