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Stochasticity in Protein Levels Drives Colinearity of Gene Order in Metabolic Operons of Escherichia coli

Author

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  • Károly Kovács
  • Laurence D Hurst
  • Balázs Papp

Abstract

Gene order in some bacterial metabolic operons reflects ordering in the metabolic pathway. That this is true uniquely for operons expressed at low levels highlights the selective importance of fluctuations in protein levels.In bacterial genomes, gene order is not random. This is most evident when looking at operons, these often encoding enzymes involved in the same metabolic pathway or proteins from the same complex. Is gene order within operons nonrandom, however, and if so why? We examine this issue using metabolic operons as a case study. Using the metabolic network of Escherichia coli, we define the temporal order of reactions. We find a pronounced trend for genes to appear in operons in the same order as they are needed in metabolism (colinearity). This is paradoxical as, at steady state, enzymes abundance should be independent of order within the operon. We consider three extensions of the steady-state model that could potentially account for colinearity: (1) increased productivity associated with higher expression levels of the most 5′ genes, (2) a faster metabolic processing immediately after up-regulation, and (3) metabolic stalling owing to stochastic protein loss. We establish the validity of these hypotheses by employing deterministic and stochastic models of enzyme kinetics. The stochastic stalling hypothesis correctly and uniquely predicts that colinearity is more pronounced both for lowly expressed operons and for genes that are not physically adjacent. The alternative models fail to find any support. These results support the view that stochasticity is a pervasive problem to a cell and that gene order evolution can be driven by the selective consequences of fluctuations in protein levels.Author Summary: In bacteria, different enzymes from the same metabolic pathway are often encoded within one transcriptional unit, an operon. There is also, we show, a tendency for the enzymes that are needed earlier in the pathway to feature earlier in the operon, so-called colinearity. Why might this be? We test three ideas, one old and two new. The prior suggestion supposes that proteins of genes early in operons will be at a higher dose. Although some operons are like this, in general, we see no relationship of protein dose with colinearity. We also find no evidence that operons that frequently need up-regulation are any more likely to be colinear. A third model is, however, supported. If an operon is rarely expressed, then all the proteins for this part of metabolism can be lost by chance. Rebooting such metabolism is fastest if the operon is colinear. This model predicts, correctly, that colinearity should be more frequent in operons that are expressed at a low level. This result is important for at least two reasons. First, it supports the view that chance events (such as protein loss) within cells are important on a day-to-day basis. Second, it challenges the supposition that natural selection will be weakest on lowly expressed genes. Where chance events are concerned, natural selection can be strong on genes expressed at a low level.

Suggested Citation

  • Károly Kovács & Laurence D Hurst & Balázs Papp, 2009. "Stochasticity in Protein Levels Drives Colinearity of Gene Order in Metabolic Operons of Escherichia coli," PLOS Biology, Public Library of Science, vol. 7(5), pages 1-9, May.
  • Handle: RePEc:plo:pbio00:1000115
    DOI: 10.1371/journal.pbio.1000115
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    1. Markus W. Covert & Eric M. Knight & Jennifer L. Reed & Markus J. Herrgard & Bernhard O. Palsson, 2004. "Integrating high-throughput and computational data elucidates bacterial networks," Nature, Nature, vol. 429(6987), pages 92-96, May.
    2. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
    3. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    4. Long Cai & Nir Friedman & X. Sunney Xie, 2006. "Stochastic protein expression in individual cells at the single molecule level," Nature, Nature, vol. 440(7082), pages 358-362, March.
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