Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as f...

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Autores principales:	Bas Teusink, Anne Wiersma, Leo Jacobs, Richard A Notebaart, Eddy J Smid
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/32372cd2a8f74c60a0f5bf8dabbcbaaa
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spelling	oai:doaj.org-article:32372cd2a8f74c60a0f5bf8dabbcbaaa2021-11-25T05:42:21ZUnderstanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.1553-734X1553-735810.1371/journal.pcbi.1000410https://doaj.org/article/32372cd2a8f74c60a0f5bf8dabbcbaaa2009-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19521528/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.Bas TeusinkAnne WiersmaLeo JacobsRichard A NotebaartEddy J SmidPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 6, p e1000410 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Bas Teusink Anne Wiersma Leo Jacobs Richard A Notebaart Eddy J Smid Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
description	In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy.
format	article
author	Bas Teusink Anne Wiersma Leo Jacobs Richard A Notebaart Eddy J Smid
author_facet	Bas Teusink Anne Wiersma Leo Jacobs Richard A Notebaart Eddy J Smid
author_sort	Bas Teusink
title	Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
title_short	Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
title_full	Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
title_fullStr	Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
title_full_unstemmed	Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.
title_sort	understanding the adaptive growth strategy of lactobacillus plantarum by in silico optimisation.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/32372cd2a8f74c60a0f5bf8dabbcbaaa
work_keys_str_mv	AT basteusink understandingtheadaptivegrowthstrategyoflactobacillusplantarumbyinsilicooptimisation AT annewiersma understandingtheadaptivegrowthstrategyoflactobacillusplantarumbyinsilicooptimisation AT leojacobs understandingtheadaptivegrowthstrategyoflactobacillusplantarumbyinsilicooptimisation AT richardanotebaart understandingtheadaptivegrowthstrategyoflactobacillusplantarumbyinsilicooptimisation AT eddyjsmid understandingtheadaptivegrowthstrategyoflactobacillusplantarumbyinsilicooptimisation
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Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.

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