Thermodynamic modelling of synthetic communities predicts minimum free energy requirements for sulfate reduction and methanogenesis

Microbial communities are complex dynamical systems harbouring many species interacting together to implement higher-level functions. Among these higher-level functions, conversion of organic matter into simpler building blocks by microbial communities underpins biogeochemical cycles and animal and plant nutrition, and is exploited in biotechnology. A prerequisite to predicting the dynamics and stability of community-mediated metabolic conversions is the development and calibration of appropriate mathematical models. Here, we present a generic, extendable thermodynamic model for community dynamics and calibrate a key parameter of this thermodynamic model, the minimum energy requirement associated with growth-supporting metabolic pathways, using experimental population dynamics data from synthetic communities composed of a sulfate reducer and two methanogens. Our findings show that accounting for thermodynamics is necessary in capturing the experimental population dynamics of these synthetic communities that feature relevant species using low energy growth pathways. Furthermore, they provide the first estimates for minimum energy requirements of methanogenesis (in the range of −30 kJ mol−1) and elaborate on previous estimates of lactate fermentation by sulfate reducers (in the range of −30 to −17 kJ mol−1 depending on the culture conditions). The open-source nature of the developed model and demonstration of its use for estimating a key thermodynamic parameter should facilitate further thermodynamic modelling of microbial communities

Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network

Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements

Thioflavin T indicates mitochondrial membrane potential in mammalian cells

The fluorescent benzothiazole dye thioflavin T (ThT) is widely used as a marker for protein aggregates, most commonly in the context of neurodegenerative disease research and diagnosis. Recently, this same dye was shown to indicate membrane potential in bacteria due to its cationic nature. This finding prompted a question whether ThT fluorescence is linked to the membrane potential in mammalian cells, which would be important for appropriate utilization of ThT in research and diagnosis. Here, we show that ThT localizes into the mitochondria of HeLa cells in a membrane-potential-dependent manner. Specifically, ThT colocalized in cells with the mitochondrial membrane potential indicator tetramethylrhodamine methyl ester (TMRM) and gave similar temporal responses as TMRM to treatment with a protonophore, carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP). Additionally, we found that presence of ThT together with exposure to blue light (λ = 405 nm), but neither factor alone, caused depolarization of mitochondrial membrane potential. This additive effect of the concentration and blue light was recapitulated by a mathematical model implementing the potential-dependent distribution of ThT and its effect on mitochondrial membrane potential through photosensitization. These results show that ThT can act as a mitochondrial membrane potential indicator in mammalian cells, when used at low concentrations and with low blue light exposure. However, it causes dissipation of the mitochondrial membrane potential depending additively on its concentrations and blue light exposure. This conclusion motivates a re-evaluation of ThT’s use at micromolar range in live-cell analyses and indicates that this dye can enable future studies on the potential connections between mitochondrial membrane potential dynamics and protein aggregation

Comment les gradients d'énergie façonnent-ils la structure des communautés microbiennes? Etude de la théorie des états de transitions microbiens pour améliorer la dynamique des écosystèmes microbiens et application aux procédés de biotechnologie environnementale

Microbial communities play a key role in geochemical cycles and environmental bioprocesses. Despite their importance, the mechanisms involved in their structuration remain elusive and are poorly captured in current models. The modelling approach developed during this thesis stands as an alternative to the current empirical approaches. It relies on a novel theory of microbial growth (the MTS theory), which introduce a flux/force relationship between the microbial growth rate and the free energy gradients available in the biotope. The purpose of this thesis is to characterize the dynamic properties of the MTS model and to determine, through simulations, the part of the microbial communities’ spatio-temporal structuration that is intrinsically captured by the MTS theory and which does not pertain to parameters adjustment.Simulations firstly reveal that a characteristic of the MTS model is its ability to account for the simultaneous growth limitation by many resources of different kinds (electron acceptor/donor, but also nutrients), and to integrate them as stoichiometric limitations, giving rise to coherent populations dynamics.In a second stage, the MTS model has been used to predict the dynamics of microbial communities. Those studies revealed that the thermodynamics constraints on which the MTS kinetic theory is built intrinsically give rise to consistent ecological successions without the need to adjust specifically the parameters of each population. In the case of a simplified activated sludge ecosystem, after calibration using respirometric data, the model was able to reproduce ecosystem dynamics quantitatively with a reduced number of parameters compared to current Activated Sludge Models (ASM).In a third stage, a large database of experimental growth yield observations has been compiled from literature. The relationship between multiple physicochemical parameters characterizing the metabolisms (reduction degrees, catabolic energy...) and the growth yield has been investigated using statistical methods. This work confirms that microbial growth yields can be accurately predicted solely on thermodynamic properties of metabolic reactions. The growth yields predictor could be included in future developments of the MTS models.More generally, the work undertaken during this thesis evidenced that the MTS model proposes a formalization of the coupling between thermodynamic and dynamic variables of a microbial ecosystem. The simulated microbial populations and ecosystems display coherent dynamic behaviors. The model is able to account, by construction, for well-known ecological successions, without specific parameter adjustment. This model is peculiarly adapted to the prediction of the functional structure of communities in ecosystems dominated by selection by competition, rather than on species dispersion, diversification or genetic drift.Those results encourage the development of microbial ecosystems based on firmer theoretical grounds. Such models are necessary to the development of bioprocesses able to answer to the new technological and environmental challenges.Les communautés microbiennes jouent un rôle clef dans les cycles géochimiques et dans les bioprocédés environnementaux. Malgré leur importance, les mécanismes impliqués dans leur structuration restent méconnus et mal appréhendés par les modèles actuels. L’approche de modélisation développée au cours de cette thèse se présente comme une alternative aux approches empiriques actuelles. Elle repose sur une nouvelle théorie de la croissance microbienne (la théorie MTS), qui introduit une relation flux/force entre le taux de croissance microbien et les gradients d’énergie libre disponibles dans le biotope. L'objet de cette thèse est de déterminer par simulation les propriétés dynamiques des modèles MTS et dans quelle mesure la théorie est capable d'apporter une explication qualitative à la structuration spatio-temporelle des communautés microbiennes.Les simulations ont premièrement révélé que les modèles MTS sont capables de tenir compte de la limitation sur la croissance exercée simultanément par plusieurs ressources de type différent (accepteur/donneur d’électron, nutriment etc.), et de les intégrer sous la forme de limitations stoichiométriques, donnant lieu à des dynamiques de population cohérentes.Dans un deuxième temps, le modèle MTS a été utilisé pour prédire les dynamiques de communautés microbiennes simplifiées. Ce travail a révélé que les contraintes thermodynamiques sur lesquelles la théorie cinétique de MTS est construite donnent lieu à des successions écologiques cohérentes, sans qu’il n’y ai besoin d’ajuster spécifiquement les paramètres de chaque population. Dans le cas d’un écosystème de boues activées simplifié, après calibration sur des données respirométriques, le modèle MTS a été capable de reproduire quantitativement des dynamiques de l’écosystème, avec un nombre de paramètres moindre que celui de l’actuel Activated Sludge Model (ASM) faisant autorité dans le domaine.Dans un troisième temps, une grande base de données d’observations expérimentales de taux de croissance a été compilée depuis la littérature. La relation entre plusieurs paramètres physicochimiques caractéristiques des métabolismes (degrés de réduction, énergie catabolique …) et le rendement de croissance microbien a été étudiée en utilisant des méthodes statistiques. Ce travail confirme que le rendement de croissance microbien peut être bien prédit à partir des seules propriétés thermodynamiques des réactions métaboliques.Le travail entrepris durant cette thèse montre que le modèle MTS propose une formalisation du couplage entre des variables thermodynamiques et dynamiques d’un écosystème microbien. Les populations et écosystèmes microbiens simulés ont montré des dynamiques cohérentes. Le modèle est capable de rendre compte, par construction, de successions écologiques observées, sans nécessiter d’ajustement paramétrique spécifique. Ce modèle est particulièrement bien adapté pour prédire la structure fonctionnelle de communautés dans des écosystèmes dominés par la sélection sur la compétition, plutôt que sur la dispersion, la diversification ou la dérive génétique.Ces résultats encouragent le développement de modèles d’écosystèmes microbiens construits sur des bases théoriques plus solides. De tels modèles sont nécessaires au développement de bioprocédés plus aptes à répondre aux nouveaux défis technologiques et environnementaux

Comment les gradients d'énergie façonnent-ils la structure des communautés microbiennes? Etude de la théorie des états de transitions microbiens pour améliorer la dynamique des écosystèmes microbiens et application aux procédés de biotechnologie environnementale

Microbial communities play a key role in geochemical cycles and environmental bioprocesses. Despite their importance, the mechanisms involved in their structuration remain elusive and are poorly captured in current models. The modelling approach developed during this thesis stands as an alternative to the current empirical approaches. It relies on a novel theory of microbial growth (the MTS theory), which introduce a flux/force relationship between the microbial growth rate and the free energy gradients available in the biotope. The purpose of this thesis is to characterize the dynamic properties of the MTS model and to determine, through simulations, the part of the microbial communities’ spatio-temporal structuration that is intrinsically captured by the MTS theory and which does not pertain to parameters adjustment.Simulations firstly reveal that a characteristic of the MTS model is its ability to account for the simultaneous growth limitation by many resources of different kinds (electron acceptor/donor, but also nutrients), and to integrate them as stoichiometric limitations, giving rise to coherent populations dynamics.In a second stage, the MTS model has been used to predict the dynamics of microbial communities. Those studies revealed that the thermodynamics constraints on which the MTS kinetic theory is built intrinsically give rise to consistent ecological successions without the need to adjust specifically the parameters of each population. In the case of a simplified activated sludge ecosystem, after calibration using respirometric data, the model was able to reproduce ecosystem dynamics quantitatively with a reduced number of parameters compared to current Activated Sludge Models (ASM).In a third stage, a large database of experimental growth yield observations has been compiled from literature. The relationship between multiple physicochemical parameters characterizing the metabolisms (reduction degrees, catabolic energy...) and the growth yield has been investigated using statistical methods. This work confirms that microbial growth yields can be accurately predicted solely on thermodynamic properties of metabolic reactions. The growth yields predictor could be included in future developments of the MTS models.More generally, the work undertaken during this thesis evidenced that the MTS model proposes a formalization of the coupling between thermodynamic and dynamic variables of a microbial ecosystem. The simulated microbial populations and ecosystems display coherent dynamic behaviors. The model is able to account, by construction, for well-known ecological successions, without specific parameter adjustment. This model is peculiarly adapted to the prediction of the functional structure of communities in ecosystems dominated by selection by competition, rather than on species dispersion, diversification or genetic drift.Those results encourage the development of microbial ecosystems based on firmer theoretical grounds. Such models are necessary to the development of bioprocesses able to answer to the new technological and environmental challenges.Les communautés microbiennes jouent un rôle clef dans les cycles géochimiques et dans les bioprocédés environnementaux. Malgré leur importance, les mécanismes impliqués dans leur structuration restent méconnus et mal appréhendés par les modèles actuels. L’approche de modélisation développée au cours de cette thèse se présente comme une alternative aux approches empiriques actuelles. Elle repose sur une nouvelle théorie de la croissance microbienne (la théorie MTS), qui introduit une relation flux/force entre le taux de croissance microbien et les gradients d’énergie libre disponibles dans le biotope. L'objet de cette thèse est de déterminer par simulation les propriétés dynamiques des modèles MTS et dans quelle mesure la théorie est capable d'apporter une explication qualitative à la structuration spatio-temporelle des communautés microbiennes.Les simulations ont premièrement révélé que les modèles MTS sont capables de tenir compte de la limitation sur la croissance exercée simultanément par plusieurs ressources de type différent (accepteur/donneur d’électron, nutriment etc.), et de les intégrer sous la forme de limitations stoichiométriques, donnant lieu à des dynamiques de population cohérentes.Dans un deuxième temps, le modèle MTS a été utilisé pour prédire les dynamiques de communautés microbiennes simplifiées. Ce travail a révélé que les contraintes thermodynamiques sur lesquelles la théorie cinétique de MTS est construite donnent lieu à des successions écologiques cohérentes, sans qu’il n’y ai besoin d’ajuster spécifiquement les paramètres de chaque population. Dans le cas d’un écosystème de boues activées simplifié, après calibration sur des données respirométriques, le modèle MTS a été capable de reproduire quantitativement des dynamiques de l’écosystème, avec un nombre de paramètres moindre que celui de l’actuel Activated Sludge Model (ASM) faisant autorité dans le domaine.Dans un troisième temps, une grande base de données d’observations expérimentales de taux de croissance a été compilée depuis la littérature. La relation entre plusieurs paramètres physicochimiques caractéristiques des métabolismes (degrés de réduction, énergie catabolique …) et le rendement de croissance microbien a été étudiée en utilisant des méthodes statistiques. Ce travail confirme que le rendement de croissance microbien peut être bien prédit à partir des seules propriétés thermodynamiques des réactions métaboliques.Le travail entrepris durant cette thèse montre que le modèle MTS propose une formalisation du couplage entre des variables thermodynamiques et dynamiques d’un écosystème microbien. Les populations et écosystèmes microbiens simulés ont montré des dynamiques cohérentes. Le modèle est capable de rendre compte, par construction, de successions écologiques observées, sans nécessiter d’ajustement paramétrique spécifique. Ce modèle est particulièrement bien adapté pour prédire la structure fonctionnelle de communautés dans des écosystèmes dominés par la sélection sur la compétition, plutôt que sur la dispersion, la diversification ou la dérive génétique.Ces résultats encouragent le développement de modèles d’écosystèmes microbiens construits sur des bases théoriques plus solides. De tels modèles sont nécessaires au développement de bioprocédés plus aptes à répondre aux nouveaux défis technologiques et environnementaux

Comparison of conventional and new energy-based models for modeling activated sludge dynamics

International audienc

Phagonaute: A web-based interface for phage synteny browsing and protein function prediction

International audienceDistant homology search tools are of great help to predict viral protein functions. However, due to the lack of profile databases dedicated to viruses, they can lack sensitivity. We constructed HMM profiles for more than 80,000 proteins from both phages and archaeal viruses, and performed all pairwise comparisons with HHsearch program. The whole resulting database can be explored through a user-friendly "Phagonaute" interface to help predict functions. Results are displayed together with their genetic context, to strengthen inferences based on remote homology. Beyond function prediction, this tool permits detections of co-occurrences, often indicative of proteins completing a task together, and observation of conserved patterns across large evolutionary distances. As a test, Herpes simplex virus I was added to Phagonaute, and 25% of its proteome matched to bacterial or archaeal viral protein counterparts. Phagonaute should therefore help virologists in their quest for protein functions and evolutionary relationships

Dynamics of co-substrate pools can constrain and regulate metabolic fluxes

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and electron carriers (e.g. ATP and NAD(P)H), but there are also other metabolites that act as cycled co-substrates in different parts of central metabolism. Here, we develop a mathematical framework to analyse the effect of co-substrate cycling on metabolic flux. In the cases of a single reaction and linear pathways, we find that co-substrate cycling imposes an additional flux limit on a reaction, distinct to the limit imposed by the kinetics of the primary enzyme catalysing that reaction. Using analytical methods, we show that this additional limit is a function of the total pool size and turnover rate of the cycled co-substrate. Expanding from this insight and using simulations, we show that regulation of these two parameters can allow regulation of flux dynamics in branched and coupled pathways. To support these theoretical insights, we analysed existing flux measurements and enzyme levels from the central carbon metabolism and identified several reactions that could be limited by the dynamics of co-substrate cycling. We discuss how the limitations imposed by co-substrate cycling provide experimentally testable hypotheses on specific metabolic phenotypes. We conclude that measuring and controlling co-substrate dynamics is crucial for understanding and engineering metabolic fluxes in cells