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Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation

Abstract

We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about 5,600 (540 in E. coli) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.

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Figure 1: Absolute protein expression (APEX) profiling exploits the proportionality between the fractions of peptides expected and observed from a given protein.
Figure 2: APEX measurements are both reproducible and consistent with other abundance measurements.
Figure 3: APEX is a sensitive measure of differential expression.
Figure 4: mRNA abundance explains over 70% of variance in yeast protein abundance and about half of variance in E. coli protein abundance.
Figure 5: Yeast protein per mRNA ratios are correlated with amino acid frequencies and variance in molecular weight.

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Acknowledgements

We thank John Prince and Aleksey Nakorchevskiy for valuable discussion and help with computational analysis of peptide fragmentation. This work was supported by grants from the Welch (F-1515) and Packard Foundations, the National Science Foundation and National Institutes of Health. C.V. acknowledges support by the International Human Frontier of Science Program.

Author information

Authors and Affiliations

Authors

Contributions

E.M.M. designed the project; P.L., R.W., X.Y. conducted the experiments; P.L., C.V., R.W., E.M.M. analyzed the data and wrote the paper.

Corresponding author

Correspondence to Edward M Marcotte.

Supplementary information

Supplementary Data 1

Table of Oi values for all yeast proteins, protein abundances, and comparisons with other laboratories' data.

Supplementary Data 2

Table of Oi values for all E. coli proteins, protein abundances, and comparisons with other laboratories' data

Supplementary Data 3

Table of mouse T lymphoma nuclear protein analyses

Supplementary Data 4

Feature data for 4023 yeast peptides for training classifier (Weka .arff format)

Supplementary Data 5

Classifier for predicting peptides observed by mass spectrometry (Weka binary format)

Supplementary Notes

Supporting figures, controls, methods, and additional comparisons of APEX-based protein quantitation to mRNA expression measurements, codon bias, and protein features

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Lu, P., Vogel, C., Wang, R. et al. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25, 117–124 (2007). https://doi.org/10.1038/nbt1270

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