Stochasticity in protein levels drives colinearity of gene order in metabolic operons of Escherichia coli

Károly Kovács, Laurence D. Hurst, B. Papp

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere1000115
JournalPLoS Biology
Volume7
Issue number5
DOIs
Publication statusPublished - May 2009

Fingerprint

Gene Order
operon
Operon
Escherichia coli
Genes
Proteins
genes
proteins
Metabolic Networks and Pathways
Enzymes
Bacterial Genomes
Bacterial Genes
enzyme kinetics
protein depletion
Enzyme kinetics
enzymes
Stochastic models
biochemical pathways
Metabolism
Up-Regulation

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)

Cite this

Stochasticity in protein levels drives colinearity of gene order in metabolic operons of Escherichia coli. / Kovács, Károly; Hurst, Laurence D.; Papp, B.

In: PLoS Biology, Vol. 7, No. 5, e1000115, 05.2009.

Research output: Contribution to journalArticle

@article{2f0410181e5f459ba292853f5823471d,
title = "Stochasticity in protein levels drives colinearity of gene order in metabolic operons of Escherichia coli",
abstract = "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 = "K{\'a}roly Kov{\'a}cs and Hurst, {Laurence D.} and B. Papp",
year = "2009",
month = "5",
doi = "10.1371/journal.pbio.1000115",
language = "English",
volume = "7",
journal = "PLoS Biology",
issn = "1544-9173",
publisher = "Public Library of Science",
number = "5",

}

TY - JOUR

T1 - Stochasticity in protein levels drives colinearity of gene order in metabolic operons of Escherichia coli

AU - Kovács, Károly

AU - Hurst, Laurence D.

AU - Papp, B.

PY - 2009/5

Y1 - 2009/5

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=66249146869&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=66249146869&partnerID=8YFLogxK

U2 - 10.1371/journal.pbio.1000115

DO - 10.1371/journal.pbio.1000115

M3 - Article

C2 - 19492041

AN - SCOPUS:66249146869

VL - 7

JO - PLoS Biology

JF - PLoS Biology

SN - 1544-9173

IS - 5

M1 - e1000115

ER -