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Cell Syst. 2017 Jun 28;4(6):636-644.e9. doi: 10.1016/j.cels.2017.05.001. Epub 2017 May 31.

Time-Resolved Proteomics Extends Ribosome Profiling-Based Measurements of Protein Synthesis Dynamics.

Author information

1
Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA; Departments of Mathematics and Biology, University of Pennsylvania, Philadelphia, PA 19104, USA.
2
Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94107, USA.
3
Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94107, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
4
Department of Urology, University of California, San Francisco, San Francisco, CA 94158, USA.
5
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
6
Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA; Departments of Mathematics and Biology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA. Electronic address: yss@berkeley.edu.
7
Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94107, USA. Electronic address: arun.wiita@ucsf.edu.

Abstract

Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-sequencing measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics.

KEYWORDS:

multiple myeloma; pSILAC; protein synthesis; proteomics; quantitative model; ribosome profiling; translational efficiency; translational regulation

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