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      On the Dependency of Cellular Protein Levels on mRNA Abundance.

      1 , 2 , 3
      Cell

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          Abstract

          The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

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          Author and article information

          Journal
          Cell
          Cell
          1097-4172
          0092-8674
          Apr 21 2016
          : 165
          : 3
          Affiliations
          [1 ] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
          [2 ] Cellular Networks and Systems Biology, University of Cologne, CECAD, Joseph-Stelzmann-Strasse 26, Cologne 50931, Germany. Electronic address: andreas.beyer@uni-koeln.de.
          [3 ] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland; Faculty of Science, University of Zurich, 8057 Zurich, Switzerland. Electronic address: aebersold@imsb.biol.ethz.ch.
          Article
          S0092-8674(16)30270-7
          10.1016/j.cell.2016.03.014
          27104977
          3d5fa960-a0a6-4060-86c3-6febae053fe7
          Copyright © 2016 Elsevier Inc. All rights reserved.
          History

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