40
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Overflow metabolism in E. coli results from efficient proteome allocation

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Overflow metabolism refers to the seemingly wasteful strategy in which cells use fermentation instead of the more efficient respiration to generate energy, despite the availability of oxygen. Known as Warburg effect in the context of cancer growth, this phenomenon occurs ubiquitously for fast growing cells, including bacteria, fungi, and mammalian cells, but its origin has remained mysterious despite decades of research. Here we study metabolic overflow in E. coli and show that it is a global physiological response used to cope with changing proteomic demands of energy biogenesis and biomass synthesis under different growth conditions. A simple model of proteomic resource allocation can quantitatively account for all of the observed behaviors and accurately predict responses to novel perturbations. The key hypothesis of the model, that the proteome cost of energy biogenesis by respiration exceeds that by fermentation, is quantitatively confirmed by direct measurement of protein abundances via quantitative mass spectrometry.

          Related collections

          Most cited references50

          • Record: found
          • Abstract: found
          • Article: not found

          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Growth rate-dependent global effects on gene expression in bacteria.

            Bacterial gene expression depends not only on specific regulatory mechanisms, but also on bacterial growth, because important global parameters such as the abundance of RNA polymerases and ribosomes are all growth-rate dependent. Understanding of these global effects is necessary for a quantitative understanding of gene regulation and for the design of synthetic genetic circuits. We find that the observed growth-rate dependence of constitutive gene expression can be explained by a simple model using the measured growth-rate dependence of the relevant cellular parameters. More complex growth dependencies for genetic circuits involving activators, repressors, and feedback control were analyzed and verified experimentally with synthetic circuits. Additional results suggest a feedback mechanism mediated by general growth-dependent effects that does not require explicit gene regulation if the expressed protein affects cell growth. This mechanism can lead to growth bistability and promote the acquisition of important physiological functions such as antibiotic resistance and tolerance (persistence). Copyright 2009 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Shifts in growth strategies reflect tradeoffs in cellular economics

              The growth rate-dependent regulation of cell size, ribosomal content, and metabolic efficiency follows a common pattern in unicellular organisms: with increasing growth rates, cell size and ribosomal content increase and a shift to energetically inefficient metabolism takes place. The latter two phenomena are also observed in fast growing tumour cells and cell lines. These patterns suggest a fundamental principle of design. In biology such designs can often be understood as the result of the optimization of fitness. Here we show that in basic models of self-replicating systems these patterns are the consequence of maximizing the growth rate. Whereas most models of cellular growth consider a part of physiology, for instance only metabolism, the approach presented here integrates several subsystems to a complete self-replicating system. Such models can yield fundamentally different optimal strategies. In particular, it is shown how the shift in metabolic efficiency originates from a tradeoff between investments in enzyme synthesis and metabolic yields for alternative catabolic pathways. The models elucidate how the optimization of growth by natural selection shapes growth strategies.
                Bookmark

                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                6 October 2015
                3 December 2015
                03 June 2016
                : 528
                : 7580
                : 99-104
                Affiliations
                [1 ] Department of Physics, University at San Diego, La Jolla, of California California 92093-0374, USA.
                [2 ] Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland.
                [3 ] Section of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, California 92093 USA.
                [4 ] Department of Integrative Structural and Computational Biology, Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
                [5 ] Institute for Theoretical Studies, ETH Zürich, 8092 Zürich, Switzerland.
                Author notes
                []Correspondence and requests for materials should be addressed to T.H. ( hwa@ 123456ucsd.edu ).
                Article
                NIHMS727853
                10.1038/nature15765
                4843128
                26632588
                9d077797-06f7-4edb-8038-596cc8318f68

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Categories
                Article

                Uncategorized
                Uncategorized

                Comments

                Comment on this article