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

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 variatio...

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Published inPLoS 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
LanguageEnglish
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)
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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.
AbstractList   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.
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.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.
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. Cells respond to their environment by making proteins using transcription and translation of mRNA. 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 and contain missing values. Here we show that when methods that account for noise are used to analyze much of the same data, mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels as commonly assumed, but rise much more rapidly. Regulation of translation achieves amplification of, rather than competition with, transcriptional signals. Our results suggest that for this set of conditions, mRNA sets protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.
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.
Audience Academic
Author Csárdi, Gábor
Franks, Alexander
Drummond, D. Allan
Airoldi, Edoardo M.
Choi, David S.
AuthorAffiliation 3 Dept. of Biochemistry & Molecular Biology, University of Chicago, Chicago, Illinois, United States of America
2 The Broad Institute of Harvard & MIT, Cambridge, Massachusetts, United States of America
Stanford University School of Medicine, UNITED STATES
4 Dept. of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
1 Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
AuthorAffiliation_xml – name: 3 Dept. of Biochemistry & Molecular Biology, University of Chicago, Chicago, Illinois, United States of America
– name: 1 Dept. of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
– name: 4 Dept. of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
– name: Stanford University School of Medicine, UNITED STATES
– name: 2 The Broad Institute of Harvard & MIT, Cambridge, Massachusetts, United States of America
Author_xml – sequence: 1
  givenname: Gábor
  surname: Csárdi
  fullname: Csárdi, Gábor
– sequence: 2
  givenname: Alexander
  surname: Franks
  fullname: Franks, Alexander
– sequence: 3
  givenname: David S.
  surname: Choi
  fullname: Choi, David S.
– sequence: 4
  givenname: Edoardo M.
  surname: Airoldi
  fullname: Airoldi, Edoardo M.
– sequence: 5
  givenname: D. Allan
  surname: Drummond
  fullname: Drummond, D. Allan
BackLink https://www.ncbi.nlm.nih.gov/pubmed/25950722$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2015 Public Library of Science
2015 Csárdi et al 2015 Csárdi et al
2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Csárdi G, Franks A, Choi DS, Airoldi EM, Drummond DA (2015) Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast. PLoS Genet 11(5): e1005206. doi:10.1371/journal.pgen.1005206
Copyright_xml – notice: COPYRIGHT 2015 Public Library of Science
– notice: 2015 Csárdi et al 2015 Csárdi et al
– notice: 2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Csárdi G, Franks A, Choi DS, Airoldi EM, Drummond DA (2015) Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast. PLoS Genet 11(5): e1005206. doi:10.1371/journal.pgen.1005206
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These authors contributed equally to this work.
The authors have declared that no competing interests exist.
Conceived and designed the experiments: DAD EMA. Performed the experiments: GC AF DSC DAD. Analyzed the data: GC AF DSC DAD. Contributed reagents/materials/analysis tools: GC AF DSC EMA DAD. Wrote the paper: DAD GC AF EMA.
OpenAccessLink http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pgen.1005206
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Snippet Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations...
  Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations...
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StartPage e1005206
SubjectTerms 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)
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Title Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast
URI https://www.ncbi.nlm.nih.gov/pubmed/25950722
https://www.proquest.com/docview/1680188555
https://pubmed.ncbi.nlm.nih.gov/PMC4423881
https://doaj.org/article/44623f390e4845648aaf06b2d44805b2
http://dx.doi.org/10.1371/journal.pgen.1005206
Volume 11
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