Integrating genome-scale metabolic models into the prediction of microbial kinetics in natural environments

Shapiro, Benjamin M.; Hoehler, Tori M.; Jin, Qusheng

doi:10.1016/j.gca.2018.08.047

Cited by 10 publications

(10 citation statements)

References 106 publications

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“…Traditional kinetic models (Monod equation) could not predict such behavior. Furthermore, bioenergetics-based models provide tools to link genome to population scale models [ Shapiro et al (2018) and Ref. therein, Dukovski et al (2021) ].…”

Section: Discussionmentioning

confidence: 99%

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

et al. 2022

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Microorganisms function as open systems that exchange matter and energy with their surrounding environment. Even though mass (carbon and nutrients) and energy exchanges are tightly linked, there is a lack of integrated approaches that combine these fluxes and explore how they jointly impact microbial growth. Such links are essential to predicting how the growth rate of microorganisms varies, especially when the stoichiometry of carbon- (C) and nitrogen (N)-uptake is not balanced. Here, we present a theoretical framework to quantify the microbial growth rate for conditions of C-, N-, and energy-(co-) limitations. We use this framework to show how the C:N ratio and the degree of reduction of the organic matter (OM), which is also the electron donor, availability of electron acceptors (EAs), and the different sources of N together control the microbial growth rate under C, nutrient, and energy-limited conditions. We show that the growth rate peaks at intermediate values of the degree of reduction of OM under oxic and C-limited conditions, but not under N-limited conditions. Under oxic conditions and with N-poor OM, the growth rate is higher when the inorganic N (NInorg)-source is ammonium compared to nitrate due to the additional energetic cost involved in nitrate reduction. Under anoxic conditions, when nitrate is both EA and NInorg-source, the growth rates of denitrifiers and microbes performing the dissimilatory nitrate reduction to ammonia (DNRA) are determined by both OM degree of reduction and nitrate-availability. Consistent with the data, DNRA is predicted to foster growth under extreme nitrate-limitation and with a reduced OM, whereas denitrifiers are favored as nitrate becomes more available and in the presence of oxidized OM. Furthermore, the growth rate is reduced when catabolism is coupled to low energy yielding EAs (e.g., sulfate) because of the low carbon use efficiency (CUE). However, the low CUE also decreases the nutrient demand for growth, thereby reducing N-limitation. We conclude that bioenergetics provides a useful conceptual framework for explaining growth rates under different metabolisms and multiple resource-limitations.

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Section: Discussionmentioning

confidence: 99%

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

et al. 2022

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“…We fixed the ATP flux of the maintenance metabolism at 109 ( 72 ), and calculated the fluxes through the pseudo-biomass reaction, and hence the specific growth rate, from the difference between the ATP production flux through ATP synthase and the consumption flux of the maintenance. The results gave a net specific growth rate ( 30 , 73 ).…”

Section: Methodsmentioning

confidence: 99%

“…We estimated the stoichiometric coefficients of the pseudo-biomass reaction by assuming that M. barkeri optimizes flux distribution through its metabolic network, including the metabolite fluxes from the methanogenesis pathway to biomass synthesis, in order to maximize growth rate. Accordingly, we analyzed the updated iMG746 genome-scale metabolic model of M. barkeri using FBA ( 39 , 72 ). FBA predicted steady-state flux distribution through metabolic networks from the objective of maximizing growth rates, under the stoichiometric constraints of metabolic reactions and within the permissible ranges of individual fluxes.…”

Section: Methodsmentioning

confidence: 99%

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

Jin

Shapiro

et al. 2022

Microbiol Spectr

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“…For acetoclastic methanogens, who seem to live on the edge of thermodynamic feasibility, it is important to integrate thermodynamic constraints based on metabolite levels. Recently, such a genome-scale metabolic modeling approach was developed to understand how microbes, among which acetoclastic methanogens cope with substrate concentrations that prevail in natural environments (Shapiro et al 2018). Such approaches should in the future be combined with additional cellular constraints based on either resource allocation (Basan 2018) or thermodynamics (Kümmel et al 2006) to become powerful predictors of growth phenotypes.…”

Section: Genome-scale Metabolic Modellingmentioning

confidence: 99%

Ecophysiology of Acetoclastic Methanogens

Stams

Teusink

Sousa

2019

Biogenesis of Hydrocarbons

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Acetate is the most important precursor for methane in the degradation of organic matter. Only two genera of methanogenic archaea, Methanosarcina and Methanothrix (former Methanosaeta), are able to grow with acetate as sole energy and carbon source. Phylogenetically, Methanosarcina and Methanothrix both belong to the Methanosarcinales. These two genera show besides morphological differences, interesting differences in physiology. Methanosarcina is a generalist that can grow on a variety of substrates, while Methanothrix

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Integrating genome-scale metabolic models into the prediction of microbial kinetics in natural environments

Cited by 10 publications

References 106 publications

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

Ecophysiology of Acetoclastic Methanogens

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