Literature DB >> 24799698

Bet-hedging during bacterial diauxic shift.

Ana Solopova1, Jordi van Gestel2, Franz J Weissing3, Herwig Bachmann4, Bas Teusink5, Jan Kok6, Oscar P Kuipers7.   

Abstract

When bacteria grow in a medium with two sugars, they first use the preferred sugar and only then start metabolizing the second one. After the first exponential growth phase, a short lag phase of nongrowth is observed, a period called the diauxie lag phase. It is commonly seen as a phase in which the bacteria prepare themselves to use the second sugar. Here we reveal that, in contrast to the established concept of metabolic adaptation in the lag phase, two stable cell types with alternative metabolic strategies emerge and coexist in a culture of the bacterium Lactococcus lactis. Only one of them continues to grow. The fraction of each metabolic phenotype depends on the level of catabolite repression and the metabolic state-dependent induction of stringent response, as well as on epigenetic cues. Furthermore, we show that the production of alternative metabolic phenotypes potentially entails a bet-hedging strategy. This study sheds new light on phenotypic heterogeneity during various lag phases occurring in microbiology and biotechnology and adjusts the generally accepted explanation of enzymatic adaptation proposed by Monod and shared by scientists for more than half a century.

Entities:  

Keywords:  Gram-positive bacteria; metabolic fitness; phenotypic heterogeneity

Mesh:

Substances:

Year:  2014        PMID: 24799698      PMCID: PMC4034238          DOI: 10.1073/pnas.1320063111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  40 in total

1.  Metabolic behavior of Lactococcus lactis MG1363 in microaerobic continuous cultivation at a low dilution rate.

Authors:  N B Jensen; C R Melchiorsen; K V Jokumsen; J Villadsen
Journal:  Appl Environ Microbiol       Date:  2001-06       Impact factor: 4.792

2.  Bacterial persistence as a phenotypic switch.

Authors:  Nathalie Q Balaban; Jack Merrin; Remy Chait; Lukasz Kowalik; Stanislas Leibler
Journal:  Science       Date:  2004-08-12       Impact factor: 47.728

3.  Regulation of bacterial ppGpp and pppGpp.

Authors:  M Cashel
Journal:  Annu Rev Microbiol       Date:  1975       Impact factor: 15.500

Review 4.  Bistability, epigenetics, and bet-hedging in bacteria.

Authors:  Jan-Willem Veening; Wiep Klaas Smits; Oscar P Kuipers
Journal:  Annu Rev Microbiol       Date:  2008       Impact factor: 15.500

5.  (p)ppGpp controls bacterial persistence by stochastic induction of toxin-antitoxin activity.

Authors:  Etienne Maisonneuve; Manuela Castro-Camargo; Kenn Gerdes
Journal:  Cell       Date:  2013-08-29       Impact factor: 41.582

Review 6.  (p)ppGpp: still magical?

Authors:  Katarzyna Potrykus; Michael Cashel
Journal:  Annu Rev Microbiol       Date:  2008       Impact factor: 15.500

Review 7.  Nature, nurture, or chance: stochastic gene expression and its consequences.

Authors:  Arjun Raj; Alexander van Oudenaarden
Journal:  Cell       Date:  2008-10-17       Impact factor: 41.582

8.  Glucose-lactose diauxie in Escherichia coli.

Authors:  W F Loomis; B Magasanik
Journal:  J Bacteriol       Date:  1967-04       Impact factor: 3.490

9.  Metabolic control of persister formation in Escherichia coli.

Authors:  Stephanie M Amato; Mehmet A Orman; Mark P Brynildsen
Journal:  Mol Cell       Date:  2013-05-09       Impact factor: 17.970

10.  Functional analysis of a relA/spoT gene homolog from Streptococcus equisimilis.

Authors:  U Mechold; M Cashel; K Steiner; D Gentry; H Malke
Journal:  J Bacteriol       Date:  1996-03       Impact factor: 3.490

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  84 in total

1.  Design of a bistable switch to control cellular uptake.

Authors:  Diego A Oyarzún; Madalena Chaves
Journal:  J R Soc Interface       Date:  2015-12-06       Impact factor: 4.118

Review 2.  Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor.

Authors:  Heejoon Park; S Lee McGill; Adrienne D Arnold; Ross P Carlson
Journal:  Cell Mol Life Sci       Date:  2019-11-25       Impact factor: 9.261

Review 3.  A functional perspective on phenotypic heterogeneity in microorganisms.

Authors:  Martin Ackermann
Journal:  Nat Rev Microbiol       Date:  2015-07-06       Impact factor: 60.633

4.  Metabolic activity affects the response of single cells to a nutrient switch in structured populations.

Authors:  Alma Dal Co; Martin Ackermann; Simon van Vliet
Journal:  J R Soc Interface       Date:  2019-07-10       Impact factor: 4.118

5.  Wide lag time distributions break a trade-off between reproduction and survival in bacteria.

Authors:  Stefany Moreno-Gámez; Daniel J Kiviet; Clément Vulin; Susan Schlegel; Kim Schlegel; G Sander van Doorn; Martin Ackermann
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-15       Impact factor: 11.205

6.  (p)ppGpp, a Small Nucleotide Regulator, Directs the Metabolic Fate of Glucose in Vibrio cholerae.

Authors:  Young Taek Oh; Kang-Mu Lee; Wasimul Bari; David M Raskin; Sang Sun Yoon
Journal:  J Biol Chem       Date:  2015-04-16       Impact factor: 5.157

7.  Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies.

Authors:  Troy E Sandberg; Colton J Lloyd; Bernhard O Palsson; Adam M Feist
Journal:  Appl Environ Microbiol       Date:  2017-06-16       Impact factor: 4.792

Review 8.  Molecular and cellular bases of adaptation to a changing environment in microorganisms.

Authors:  Clara Bleuven; Christian R Landry
Journal:  Proc Biol Sci       Date:  2016-10-26       Impact factor: 5.349

Review 9.  Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution.

Authors:  Bram Van den Bergh; Toon Swings; Maarten Fauvart; Jan Michiels
Journal:  Microbiol Mol Biol Rev       Date:  2018-07-25       Impact factor: 11.056

10.  Rethinking the Hierarchy of Sugar Utilization in Bacteria.

Authors:  Chase L Beisel; Taliman Afroz
Journal:  J Bacteriol       Date:  2015-11-16       Impact factor: 3.490

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