Modeling of interspecies electron transfer in anaerobic microbial communities

Quéméner, Elie Desmond-Le; Moscoviz, Roman; Bernet, Nicolas; Marcus, Andrew K.

doi:10.1016/j.copbio.2020.12.019

Cited by 26 publications

(5 citation statements)

References 46 publications

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“…therein, Dukovski et al (2021) ]. New models are exploring the potential of bioenergetics to study how microbial metabolic diversity and spatial heterogeneity of resources interact and shape community dynamics and resource niches in the complex soil environment ( Araujo Granda et al, 2016 ; Jayathilake et al, 2017 ; Borer et al, 2019 ; Gogulancea et al, 2019 ; Li et al, 2019 ; Ben Said et al, 2020 ; Calabrese et al, 2020 ; Dal Co et al, 2020 ; Desmond-Le Quéméner et al, 2021 ). Bioenergetics can thus be a useful complement to traditional biogeochemical models describing only the dynamics of C and nutrients.…”

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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“…AS (Leslie Grady Jr. et al., 1986 ) and AD (Batstone et al., 2002 ) systems have been modelled in the framework of community‐wide ODE systems (see Figure 1 ), representing microbial species as functional guilds, that is, as a population of microbes responsible for a metabolic pathway. AD models have been used in various contexts such as ruminal (Muñoz‐Tamayo et al., 2016 ), environmental (Desmond‐Le Quéméner et al., 2021 ) or gut microbiota (Kettle et al., 2018 ; Muñoz‐Tamayo et al., 2010 ). Since considerable efforts have been made to sequence microbes present in AD and AS at a global scale (Dueholm et al., 2022 ), one could readily envision enhancing parts of the AD and AS with metabolic models.…”

Section: Towards Genomics‐informed Phenomenological Models Of Microbi...mentioning

confidence: 99%

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Cerk,

Ugalde‐Salas,

Nedjad

et al. 2024

Microbial Biotechnology

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Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome‐scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta‐)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third‐generation sequencing, and we discuss the opportunities of long‐read sequencing, strain‐level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

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“…The intracellular electron transfer from donor to anode can be described by the Nernst-Monod equation, while the EET can be described by the Butler-Volmer equation. In addition, the electron transfer and energy loss in extracellular matrix can be characterized by the metal-like conduction equation [19].…”

Section: Quantitative Electron Transfer Kinetics Of Eamsmentioning

confidence: 99%

Conductive proteins‐based extracellular electron transfer of electroactive microorganisms

Zhang,

You,

Liu

et al. 2023

Quant. Biol.

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Electroactive microorganisms (EAMs) could utilize extracellular electron transfer (EET) pathways to exchange electrons and energy with their external surroundings. Conductive cytochrome proteins and nanowires play crucial roles in controlling electron transfer rate from cytosol to extracellular electrode. Many previous studies elucidated how the c‐type cytochrome proteins and conductive nanowires are synthesized, assembled, and engineered to manipulate the EET rate, and quantified the kinetic processes of electron generation and EET. Here, we firstly overview the electron transfer pathways of EAMs and quantify the kinetic parameters that dictating intracellular electron production and EET. Secondly, we systematically review the structure, conductivity mechanisms, and engineering strategies to manipulate conductive cytochromes and nanowire in EAMs. Lastly, we outlook potential directions for future research in cytochromes and conductive nanowires for enhanced electron transfer. This article reviews the quantitative kinetics of intracellular electron production and EET, and the contribution of engineered c‐type cytochromes and conductive nanowire in enhancing the EET rate, which lay the foundation for enhancing electron transfer capacity of EAMs.

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Modeling of interspecies electron transfer in anaerobic microbial communities

Cited by 26 publications

References 46 publications

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

Interacting Bioenergetic and Stoichiometric Controls on Microbial Growth

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Conductive proteins‐based extracellular electron transfer of electroactive microorganisms

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