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

Teusink B, Wiersma A, Jacobs L, Notebaart RA, Smid EJ - PLoS Comput. Biol. (2009)

Bottom Line: Flux balance analysis in particular has been successful in predicting metabolic phenotypes.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.

View Article: PubMed Central - PubMed

Affiliation: Top Institute Food and Nutrition (WCFS), Wageningen, The Netherlands. bas.teusink@falw.vu.nl

ABSTRACT
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.

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Robustness analysis of in silico growth on glycerol as a function of the oxygen uptake rate and comparison with experimental data (calculated from the data presented inTable 1).(A) Impact of oxygen uptake on optimal lactate (green), acetate (red) and ethanol (black) fluxes. Dashed box indicate the oxygen consumption rate measured experimentally. Above and uptake rate of 13 mmol h−1 gDW−1 growth is no longer energy limited, resulting in variability in fluxes: the diverging lines indicate the maximum and minimum flux value at each oxygen uptake rate. (B) Impact of oxygen uptake on the growth rate. (C) Experimentally derived fluxes are included for comparison.
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pcbi-1000410-g003: Robustness analysis of in silico growth on glycerol as a function of the oxygen uptake rate and comparison with experimental data (calculated from the data presented inTable 1).(A) Impact of oxygen uptake on optimal lactate (green), acetate (red) and ethanol (black) fluxes. Dashed box indicate the oxygen consumption rate measured experimentally. Above and uptake rate of 13 mmol h−1 gDW−1 growth is no longer energy limited, resulting in variability in fluxes: the diverging lines indicate the maximum and minimum flux value at each oxygen uptake rate. (B) Impact of oxygen uptake on the growth rate. (C) Experimentally derived fluxes are included for comparison.

Mentions: Data presented are yield data averaged over 2–4 independent experiments during growth between an OD600 of 0.2 and 0.7. Yield data are in mmol gDW−1. Negative values indicate uptake of the compound. The last two columns indicate the constraints used for the robustness analysis of Figure 3. Other constraints can be found in Dataset S1.


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

Teusink B, Wiersma A, Jacobs L, Notebaart RA, Smid EJ - PLoS Comput. Biol. (2009)

Robustness analysis of in silico growth on glycerol as a function of the oxygen uptake rate and comparison with experimental data (calculated from the data presented inTable 1).(A) Impact of oxygen uptake on optimal lactate (green), acetate (red) and ethanol (black) fluxes. Dashed box indicate the oxygen consumption rate measured experimentally. Above and uptake rate of 13 mmol h−1 gDW−1 growth is no longer energy limited, resulting in variability in fluxes: the diverging lines indicate the maximum and minimum flux value at each oxygen uptake rate. (B) Impact of oxygen uptake on the growth rate. (C) Experimentally derived fluxes are included for comparison.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2690837&req=5

pcbi-1000410-g003: Robustness analysis of in silico growth on glycerol as a function of the oxygen uptake rate and comparison with experimental data (calculated from the data presented inTable 1).(A) Impact of oxygen uptake on optimal lactate (green), acetate (red) and ethanol (black) fluxes. Dashed box indicate the oxygen consumption rate measured experimentally. Above and uptake rate of 13 mmol h−1 gDW−1 growth is no longer energy limited, resulting in variability in fluxes: the diverging lines indicate the maximum and minimum flux value at each oxygen uptake rate. (B) Impact of oxygen uptake on the growth rate. (C) Experimentally derived fluxes are included for comparison.
Mentions: Data presented are yield data averaged over 2–4 independent experiments during growth between an OD600 of 0.2 and 0.7. Yield data are in mmol gDW−1. Negative values indicate uptake of the compound. The last two columns indicate the constraints used for the robustness analysis of Figure 3. Other constraints can be found in Dataset S1.

Bottom Line: Flux balance analysis in particular has been successful in predicting metabolic phenotypes.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.

View Article: PubMed Central - PubMed

Affiliation: Top Institute Food and Nutrition (WCFS), Wageningen, The Netherlands. bas.teusink@falw.vu.nl

ABSTRACT
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.

Show MeSH