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Global quantification of mammalian gene expression control.
Nature. 2011 May 19; 473(7347):337-42
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02 Jun 2011
New Finding
DOI: 10.3410/f.10925958.11855056
This paper describes a quantitative measurement of global mRNA and protein dynamics in mammalian cells.
Specifically, the authors used metabolic pulse labeling techniques to determine the levels and half-lives of all the detectable mRNAs and proteins in mouse NIH-3T3 cells. Then statistical analysis...Continue reading
Specifically, the authors used metabolic pulse labeling techniques to determine the levels and half-lives of all the detectable mRNAs and proteins in mouse NIH-3T3 cells. Then statistical analysis...Continue reading
This paper describes a quantitative measurement of global mRNA and protein dynamics in mammalian cells.
Specifically, the authors used metabolic pulse labeling techniques to determine the levels and half-lives of all the detectable mRNAs and proteins in mouse NIH-3T3 cells. Then statistical analysis was used to explore the correlations among the levels and stabilities of mRNAs and proteins. They observed that mRNA level and protein level have a better correlation than previously thought. It is also interesting to find that genes with similar mRNA and protein stability patterns usually are somehow functionally related.
Specifically, the authors used metabolic pulse labeling techniques to determine the levels and half-lives of all the detectable mRNAs and proteins in mouse NIH-3T3 cells. Then statistical analysis was used to explore the correlations among the levels and stabilities of mRNAs and proteins. They observed that mRNA level and protein level have a better correlation than previously thought. It is also interesting to find that genes with similar mRNA and protein stability patterns usually are somehow functionally related.
Disclosures
None declared
Add Comment
Coller J and Hu W: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 02 Jun 2011; DOI: 10.3410/f.10925958.11855056. F1000Prime.com/10925958#eval11855056
06 Jun 2011
New Finding, Technical Advance
DOI: 10.3410/f.10925958.11965054
The authors use metabolic pulse labelling of newly synthesized messenger (m)RNAs (using 4sU) and proteins (using heavy isotope-containing amino acids) in NIH3T3 mouse fibroblasts, followed by mRNA sequencing and mass spectrometry, to quantify absolute mRNA and protein abundance and stability in parallel....Continue reading
The authors use metabolic pulse labelling of newly synthesized messenger (m)RNAs (using 4sU) and proteins (using heavy isotope-containing amino acids) in NIH3T3 mouse fibroblasts, followed by mRNA sequencing and mass spectrometry, to quantify absolute mRNA and protein abundance and stability in parallel. The novelty of this study derives mainly from the analyses of mRNA and protein having been performed in the same experimental preparation, leading to several interesting insights and estimates.
The average protein stability was found to be about five times greater than that for mRNA (mean half-life of 46 hours for protein versus 9 hours for mRNA), and protein half-lives spanned a much larger dynamic range than those of mRNAs. Interestingly, no correlation was observed (R2=0.02) between protein and mRNA half-life. Calculations of mRNA and protein copy number revealed that, on average, proteins were 900 times more abundant than their corresponding mRNAs. The average transcription rate was found to be about two mRNA molecules per hour, and the median translation rate was found to be about 40 proteins per mRNA per hour. This study found a higher correlation (R2=0.41) between mRNA and protein levels than has previously been reported. While mRNA abundance was found to account for only 40% of the variance in protein abundance, the translation rate was found to be the dominant factor controlling protein levels, with mRNA and protein degradation exerting a relatively modest influence.
The average protein stability was found to be about five times greater than that for mRNA (mean half-life of 46 hours for protein versus 9 hours for mRNA), and protein half-lives spanned a much larger dynamic range than those of mRNAs. Interestingly, no correlation was observed (R2=0.02) between protein and mRNA half-life. Calculations of mRNA and protein copy number revealed that, on average, proteins were 900 times more abundant than their corresponding mRNAs. The average transcription rate was found to be about two mRNA molecules per hour, and the median translation rate was found to be about 40 proteins per mRNA per hour. This study found a higher correlation (R2=0.41) between mRNA and protein levels than has previously been reported. While mRNA abundance was found to account for only 40% of the variance in protein abundance, the translation rate was found to be the dominant factor controlling protein levels, with mRNA and protein degradation exerting a relatively modest influence.
Disclosures
None declared
Nairn A and Wiseman S: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 06 Jun 2011; DOI: 10.3410/f.10925958.11965054. F1000Prime.com/10925958#eval11965054
30 Jun 2011
New Finding, Technical Advance
DOI: 10.3410/f.10925958.12716054
This global study is interesting because it reveals that translation efficiencies play a major role in determining the cellular amounts of proteins, therefore highlighting the importance of post-transcriptional regulation in the control of gene expression.
It is well-documented that each of the events...Continue reading
It is well-documented that each of the events...Continue reading
This global study is interesting because it reveals that translation efficiencies play a major role in determining the cellular amounts of proteins, therefore highlighting the importance of post-transcriptional regulation in the control of gene expression.
It is well-documented that each of the events involved in gene expression such as transcription, translation and degradation of either messenger RNAs (mRNAs) or proteins are subjected to regulation. However, a global picture describing how these different processes contribute to the definition of a cell’s proteome was still missing. In this article, Schwanhäusser et al. present large-scale measurements of mRNA and protein levels, as well as an evaluation of their turnover in a population of asynchronously growing NIH 3T3 cells. This was accomplished by simultaneous metabolic pulse labeling of newly synthesized transcripts and proteins prior to their extensive quantification using mRNA sequencing and mass spectrometry, respectively. This strategy allowed the generation of a mathematical model which, in turn, led to the prediction of transcription and translation rate constants for more than 5000 genes. The authors performed several statistical analyses on their results and were able to draw general conclusions concerning average copy numbers, synthesis rates and stabilities of mRNAs and proteins. Nevertheless, one of the most interesting concepts that emerges from this comprehensive study is that although there is a significant correlation between protein and mRNA abundance, the variations of protein levels observed in cells are more importantly explained by the diversity of translation rates. Besides, the enormous dataset accompanying this work constitutes by itself a valuable resource for future studies.
It is well-documented that each of the events involved in gene expression such as transcription, translation and degradation of either messenger RNAs (mRNAs) or proteins are subjected to regulation. However, a global picture describing how these different processes contribute to the definition of a cell’s proteome was still missing. In this article, Schwanhäusser et al. present large-scale measurements of mRNA and protein levels, as well as an evaluation of their turnover in a population of asynchronously growing NIH 3T3 cells. This was accomplished by simultaneous metabolic pulse labeling of newly synthesized transcripts and proteins prior to their extensive quantification using mRNA sequencing and mass spectrometry, respectively. This strategy allowed the generation of a mathematical model which, in turn, led to the prediction of transcription and translation rate constants for more than 5000 genes. The authors performed several statistical analyses on their results and were able to draw general conclusions concerning average copy numbers, synthesis rates and stabilities of mRNAs and proteins. Nevertheless, one of the most interesting concepts that emerges from this comprehensive study is that although there is a significant correlation between protein and mRNA abundance, the variations of protein levels observed in cells are more importantly explained by the diversity of translation rates. Besides, the enormous dataset accompanying this work constitutes by itself a valuable resource for future studies.
Disclosures
None declared
DesGroseillers L and Boulay K: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 30 Jun 2011; DOI: 10.3410/f.10925958.12716054. F1000Prime.com/10925958#eval12716054
12 Jul 2011
Confirmation, New Finding
DOI: 10.3410/f.10925958.12978054
I very much agree with the other evaluators (Karine Boulay and Luc DesGroseillers, Shari Wiseman and Angus Nairn, and Wenqian Hu and Jeff Coller) that this is an important and unique...Continue reading
I very much agree with the other evaluators (Karine Boulay and Luc DesGroseillers, Shari Wiseman and Angus Nairn, and Wenqian Hu and Jeff Coller) that this is an important and unique contribution to the quantitation of multiple aspects of gene regulation, from mRNAs to proteins.
Disclosures
None declared
Bahler J: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 12 Jul 2011; DOI: 10.3410/f.10925958.12978054. F1000Prime.com/10925958#eval12978054
22 Jul 2011
Technical Advance
DOI: 10.3410/f.10925958.13094054
This analysis of the kinetics of turnover of over 5000 protein and mRNA species will be of interest to all bioscientists who use cells in culture.
Unsynchronised, exponentially growing mouse NIH-3T3 cells were isotopically tagged using amino acids (Stable isotope labeling with amino acids in cell SILAC)...Continue reading
Unsynchronised, exponentially growing mouse NIH-3T3 cells were isotopically tagged using amino acids (Stable isotope labeling with amino acids in cell SILAC)...Continue reading
This analysis of the kinetics of turnover of over 5000 protein and mRNA species will be of interest to all bioscientists who use cells in culture.
Unsynchronised, exponentially growing mouse NIH-3T3 cells were isotopically tagged using amino acids (Stable isotope labeling with amino acids in cell culture; SILAC) at three time points, then tryptic peptides analysed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). mRNA was pulse-labelled for 2h with 4-thiouridine in order to distinguish pre-existing from newly synthesised species.
Protein is on average 5 times more stable than mRNA. The median protein half-life of 46h, compared with 9h for mRNA, agrees well with data from many laboratories that use RNA knockdown. As many as 180 copies can be translated per hour, with a median rate of about 40 proteins/h, and 100-fold dynamic range. Transcription rates are generally slower, with a median of 2/h, but can be up to 500/h. Protein lifespans have a larger range than mRNA, from a few to several hundred hours.
On average there are ~900 protein copies per mRNA, and copy numbers per cell vary widely between different proteins, from a few dozen to a few million, prompting thoughts about the aptitude of specific cell lines for gene function studies. For example, the choice of A431 cells for the first studies of a receptor tyrosine kinase (RTK) (epidermal growth factor receptor[(EGFR]) was a happy one, given the presence of >1 million copies on the cell surface (NIH-3T3 cells have only about 3000), and contributed to Stanley Cohen {1} winning the Nobel Prize in 1986.
I enjoyed browsing through some of my own favourite genes. For instance, the recycling receptor Igf2r has a rather long half-life of 83h (26h for the mRNA). Collagen type 1 alpha1 has 70,000 copies while alpha2 has 30,000, roughly matching the expected 2:1 ratio and reflecting the matrix-producing function of fibroblasts. I expected actin (12 million copies), lamin, tubulin and histone levels to be high, but was surprised to note that galectin-1 (3.7 million) and macrophage migration inhibitory factor (1.5 million) are also very abundant. Galectin-1 also has a remarkably long protein half-life of 157h.
There are some clear messages here in terms of biological significance. mRNA level (equating to translation efficiency) explains about 40% of the variation in protein abundance. In contrast, protein degradation does not play a generally dominant role. Genes involved in constitutive processes such as translation and central metabolism have mRNA and protein species that are relatively stable, which is presumably explicable in terms of conservation of resources. Signalling and transcription factors have mRNA and protein that are both less stable. Genes with stable mRNA and 'unstable' protein are associated with secreted products which might have a short intracelluar lifespan. A comparison is made here with human breast cancer MCF7 cells, but it will be more interesting to compare the data with sets from more specialised lineages such as secretory epithelial or immune cells which store proteins over longer periods in preparation for functional challenges.
Unsynchronised, exponentially growing mouse NIH-3T3 cells were isotopically tagged using amino acids (Stable isotope labeling with amino acids in cell culture; SILAC) at three time points, then tryptic peptides analysed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). mRNA was pulse-labelled for 2h with 4-thiouridine in order to distinguish pre-existing from newly synthesised species.
Protein is on average 5 times more stable than mRNA. The median protein half-life of 46h, compared with 9h for mRNA, agrees well with data from many laboratories that use RNA knockdown. As many as 180 copies can be translated per hour, with a median rate of about 40 proteins/h, and 100-fold dynamic range. Transcription rates are generally slower, with a median of 2/h, but can be up to 500/h. Protein lifespans have a larger range than mRNA, from a few to several hundred hours.
On average there are ~900 protein copies per mRNA, and copy numbers per cell vary widely between different proteins, from a few dozen to a few million, prompting thoughts about the aptitude of specific cell lines for gene function studies. For example, the choice of A431 cells for the first studies of a receptor tyrosine kinase (RTK) (epidermal growth factor receptor[(EGFR]) was a happy one, given the presence of >1 million copies on the cell surface (NIH-3T3 cells have only about 3000), and contributed to Stanley Cohen {1} winning the Nobel Prize in 1986.
I enjoyed browsing through some of my own favourite genes. For instance, the recycling receptor Igf2r has a rather long half-life of 83h (26h for the mRNA). Collagen type 1 alpha1 has 70,000 copies while alpha2 has 30,000, roughly matching the expected 2:1 ratio and reflecting the matrix-producing function of fibroblasts. I expected actin (12 million copies), lamin, tubulin and histone levels to be high, but was surprised to note that galectin-1 (3.7 million) and macrophage migration inhibitory factor (1.5 million) are also very abundant. Galectin-1 also has a remarkably long protein half-life of 157h.
There are some clear messages here in terms of biological significance. mRNA level (equating to translation efficiency) explains about 40% of the variation in protein abundance. In contrast, protein degradation does not play a generally dominant role. Genes involved in constitutive processes such as translation and central metabolism have mRNA and protein species that are relatively stable, which is presumably explicable in terms of conservation of resources. Signalling and transcription factors have mRNA and protein that are both less stable. Genes with stable mRNA and 'unstable' protein are associated with secreted products which might have a short intracelluar lifespan. A comparison is made here with human breast cancer MCF7 cells, but it will be more interesting to compare the data with sets from more specialised lineages such as secretory epithelial or immune cells which store proteins over longer periods in preparation for functional challenges.
References
1.
Affinity labeling of the protein kinase associated with the epidermal growth factor receptor in membrane vesicles from A431 cells.
J Biol Chem 1982 Apr 25; 8(257):4019-22
PMID: 6279621
Disclosures
None declared
Aplin J: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 22 Jul 2011; DOI: 10.3410/f.10925958.13094054. F1000Prime.com/10925958#eval13094054
14 Sep 2011
New Finding
DOI: 10.3410/f.10925958.14542236
This paper represents an important contribution to the field because, for the first time, it accurately quantitates the relationship between different stages of gene expression on a genome-wide scale in a mammalian cell. Gene expression comprises several stages, including transcription, translation, and...Continue reading
This paper represents an important contribution to the field because, for the first time, it accurately quantitates the relationship between different stages of gene expression on a genome-wide scale in a mammalian cell. Gene expression comprises several stages, including transcription, translation, and degradation of messenger RNA and proteins. Each of these processes is tightly regulated and affects the net protein level in a cell. Using a combination of high-throughput proteomic and genomic techniques, the authors tackle the complex interplay between these processes.
The paper reports mRNA and protein levels and turnover measured in parallel for more than 5000 mouse genes. The authors used metabolic pulse labeling coupled with mass spectrometry and next-generation sequencing to obtain quantitative results. Based on these measurements the synthesis rates were predicted for mRNA and proteins on genome-scale. They report that mRNA and protein levels are fairly correlated in the cell, with mRNA levels explaining about 40% of variability in protein levels. At the same time, no correlation was observed between mRNA and protein half-lives. Instead, translation efficiency is found to be the best predictor of the protein abundance in a cell, highlighting the importance of the translational control. Furthermore, the genes with similar mRNA and protein stability shared functional association, indicating that they could have evolved under similar constraints. For example, many genes involved in transcription regulation and cell-cycle specific processes have unstable mRNAs and proteins, which is consistent with the requirement for rapid regulation. In addition to important findings reported in this paper, the released data sets constitute an important resource for further studies on inter-relation between different stages of gene expression.
The paper reports mRNA and protein levels and turnover measured in parallel for more than 5000 mouse genes. The authors used metabolic pulse labeling coupled with mass spectrometry and next-generation sequencing to obtain quantitative results. Based on these measurements the synthesis rates were predicted for mRNA and proteins on genome-scale. They report that mRNA and protein levels are fairly correlated in the cell, with mRNA levels explaining about 40% of variability in protein levels. At the same time, no correlation was observed between mRNA and protein half-lives. Instead, translation efficiency is found to be the best predictor of the protein abundance in a cell, highlighting the importance of the translational control. Furthermore, the genes with similar mRNA and protein stability shared functional association, indicating that they could have evolved under similar constraints. For example, many genes involved in transcription regulation and cell-cycle specific processes have unstable mRNAs and proteins, which is consistent with the requirement for rapid regulation. In addition to important findings reported in this paper, the released data sets constitute an important resource for further studies on inter-relation between different stages of gene expression.
Disclosures
None declared
Park P, Tolstorukov M and Ferrari F: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 14 Sep 2011; DOI: 10.3410/f.10925958.14542236. F1000Prime.com/10925958#eval14542236
16 Sep 2011
Sarah Teichmann
F1000 Genomics & Genetics
EMBL-European Bioinformatics Institute & Wellcome Trust Sanger Institute,
Cambridge, UK.
F1000 Genomics & Genetics
EMBL-European Bioinformatics Institute & Wellcome Trust Sanger Institute,
Cambridge, UK.
New Finding
DOI: 10.3410/f.10925958.14620057
This work presents datasets for absolute mRNA and protein numbers and turnover in mammalian cell lines on a much more comprehensive scale than any previous publication. The data are mainly based on high-throughput sequencing and mass spectrometry and allow analysis of the regulatory mechanisms controlling...Continue reading
This work presents datasets for absolute mRNA and protein numbers and turnover in mammalian cell lines on a much more comprehensive scale than any previous publication. The data are mainly based on high-throughput sequencing and mass spectrometry and allow analysis of the regulatory mechanisms controlling expression levels. The datasets will provide useful resources for further studies of this subject.
The authors confirmed previous findings of limited correlation between mRNA and protein abundances and showed that the variance in protein expression levels is mainly due to different translation rates. Interestingly, protein expression amplifies the input from transcription, as the average protein was much more abundant than the corresponding mRNA.
The authors confirmed previous findings of limited correlation between mRNA and protein abundances and showed that the variance in protein expression levels is mainly due to different translation rates. Interestingly, protein expression amplifies the input from transcription, as the average protein was much more abundant than the corresponding mRNA.
Disclosures
None declared
Teichmann S and Hebenstreit D: F1000Prime Recommendation of [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 16 Sep 2011; DOI: 10.3410/f.10925958.14620057. F1000Prime.com/10925958#eval14620057
F1000Prime Recommendations, Dissents and Comments for [Schwanhäusser B et al., Nature 2011, 473(7347):337-42]. In F1000Prime, 24 May 2013; F1000Prime.com/10925958
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Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.
DOI: 10.1038/nature10098
PMID: 21593866
