Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling.

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

A PDMP model of the epithelial cell turn-over in the intestinal crypt including microbiota-derived regulations.

Human health and physiology is strongly influenced by interactions between human cells and intestinal microbiota in the gut. In mammals, the host-microbiota crosstalk is mainly mediated by regulations at the intestinal crypt level: the epithelial cell turnover in crypts is directly influenced by metabolites produced by the microbiota. Conversely, enterocytes maintain hypoxia in the gut, favorable to anaerobic bacteria which dominate the gut microbiota. We constructed an individual-based model of epithelial cells interacting with the microbiota-derived chemicals diffusing in the crypt lumen. This model is formalized as a piecewise deterministic Markov process (PDMP). It accounts for local interactions due to cell contact (among which are mechanical interactions), for cell proliferation, differentiation and extrusion which are regulated spatially or by chemicals concentrations. It also includes chemicals diffusing and reacting with cells. A deterministic approximated model is also introduced for a large population of small cells, expressed as a system of porous media type equations. Both models are extensively studied through numerical exploration. Their biological relevance is thoroughly assessed by recovering bio-markers of an healthy crypt, such as cell population distribution along the crypt or population turn-over rates. Simulation results from the deterministic model are compared to the PMDP model and we take advantage of its lower computational cost to perform a sensitivity analysis by Morris method. We finally use the crypt model to explore butyrate supplementation to enhance recovery after infections by enteric pathogens.

A mathematical model to investigate the key drivers of the biogeography of the colon microbiota.

The gut microbiota, mainly located in the colon, is engaged in a complex dialogue with the large intestinal epithelium through which important regulatory processes for the health and well-being of the host take place. Imbalances of the microbial populations, called dysbiosis, are related to several pathological status, emphasizing the importance of understanding the gut bacterial ecology. Among the ecological drivers of the microbiota, the spatial structure of the colon is of special interest: spatio-temporal mechanisms can lead to the constitution of spatial interactions among the bacterial populations and of environmental niches that impact the overall colonization of the colon. In the present study, we introduce a mathematical model of the colon microbiota in its fluid environment, based on the explicit coupling of a population dynamics model of microbial populations involved in fibre degradation with a fluid dynamics model of the luminal content. This modeling framework is used to study the main drivers of the spatial structure of the microbiota, specially focusing on the dietary fibre inflow, the epithelial motility, the microbial active swimming and viscosity gradients in the digestive track. We found 1) that the viscosity gradients allow the creation of favorable niches in the vicinity of the mucus layer; 2) that very low microbial active swimming in the radial direction is enough to promote bacterial growth, which sheds a new light on microbial motility in the colon and 3) that dietary fibres are the main driver of the spatial structure of the microbiota in the distal bowel whereas epithelial motility is preponderant for the colonization of the proximal colon; in the transverse colon, fibre levels and chemotaxis have the strongest impact on the distribution of the microbial communities.

The Contribution of Intestinal Gluconeogenesis to Glucose Homeostasis Is Low in 2-Day-Old Pigs.

Background: Active gluconeogenesis is essential to maintain blood glucose concentrations in neonatal piglets because of the high glucose requirements after birth. In several adult mammals, the liver, kidney, and possibly the gut may exhibit gluconeogenesis during fasting and insulinopenic conditions. During the postnatal period, the intestine expresses all of the gluconeogenic enzymes, suggesting the potential for gluconeogenesis. Galactose in milk is a potential gluconeogenic precursor for newborns.Objective: Our aim was to quantify the rate of intestinal glucose production from galactose in piglets compared with the overall rate of glucose production.Methods: A single bolus of [U-14C]-galactose was injected into 2-d-old piglets (females and males; mean ± SEM weight: 1.64 ± 0.07 kg) through a gastric catheter. Galactosemia, glycemia, and glucose turnover rate (assessed by monitoring d-[6-3H]-glucose) were monitored. Intestinal glucose production from [U-14C]-galactose was calculated from [U-14C]-glucose appearance in the blood and isotopic dilution. Galactose metabolism was also investigated in vitro in enterocytes isolated from 2-d-old piglets that were incubated with increasing concentrations of galactose.Results: In piglet enterocytes, galactose metabolism was active (mean ± SEM maximum rate of reaction: 2.26 ± 0.45 nmol · min-1 · 106 cells-1) and predominantly oriented toward lactate and pyruvate production (74.0% ± 14.5%) rather than glucose production (26.0% ± 14.5%). In conscious piglets, gastric galactose administration led to an increase in arterial galactosemia (from 0 to 1.0 ± 0.8 mmol/L) and glycemia (35% ± 12%). The initial increase in arterial glycemia after galactose administration was linked to an increase in glucose production rate (33% ± 15%) rather than to a decrease in glucose utilization rate (3% ± 6%). The contribution of intestinal glucose production from galactose was <10% of total glucose production in 2-d-old piglets.Conclusion: Our results indicate that there is a low contribution to glucose homeostasis from intestinal gluconeogenesis in 2-d-old piglets.

A two layers monodomain model of cardiac electrophysiology of the atria.

Numerical simulations of the cardiac electrophysiology in the atria are often based on the standard bidomain or monodomain equations stated on a two-dimensional manifold. These simulations take advantage of the thinness of the atrial tissue, and their computational cost is reduced, as compared to three-dimensional simulations. However, these models do not take into account the heterogeneities located in the thickness of the tissue, like discontinuities of the fiber direction, although they can be a substrate for atrial arrhythmia (Hocini et al., Circulation 105(20):2442-2448, 2002; Ho et al., Cardiovasc Res 54(2):325-336, 2002; Nattel, Nature 415(6868):219-226, 2002). We investigate a two-dimensional model with two coupled, superimposed layers that allows to introduce three-dimensional heterogeneities, but retains a reasonable computational cost. We introduce the mathematical derivation of this model and error estimates with respect to the three-dimensional model. We give some numerical illustrations of its interest: we numerically show its convergence for vanishing thickness, introduce an optimization process of the coupling coefficient and assess its validity on physiologically relevant geometries. Our model would be an efficient tool to test the influence of three-dimensional fiber direction heterogeneities in reentries or atrial arrhythmia without using three-dimensional models.

A bilayer model of human atria: mathematical background, construction, and assessment.

AIMS: Atrial numerical modelling has generally represented the organ as either a surface or tissue with thickness. While surface models have significant computational advantages over tissue models, they cannot fully capture propagation patterns seen in vivo, such as dissociation of activity between endo- and epicardium. We introduce an intermediate representation, a bilayer model of the human atria, which is capable of recreating recorded activation patterns. METHODS AND RESULTS: We simultaneously solved two surface monodomain problems by formalizing an optimization method to set a coupling term between them. Two different asymptotically equivalent numerical implementations of the model are presented. We then built a geometrically and electrophysiologically detailed model of the human atria based on CT data, including two layers of fibre directions, major muscle bundles, and discrete atrial coupling. We adjusted parameters to recreate clinically measured activation times. Activation was compared with a monolayer model. Activation was fit to the physiological range measured over the entire atria. The crista terminalis and pectinate muscles were important for local right atrial activation, but did not significantly affect total activation time. Propagation in the bilayer model was similar to that of a monolayer, but with noticeable difference, due to three-dimensional propagation where fibre direction changed abruptly across the wall, resulting in a slight dissociation of activity. CONCLUSION: Atrial structure plays the dominant role in determining activation. A bilayer model is able to take into account transmural heterogeneities, while maintaining the low computational load associated with surface models.

Design of a proteolytic module for improved metabolic modeling of Bacteroides caccae.

The gut microbiota plays a crucial role in health and is significantly modulated by human diets. In addition to Western diets which are rich in proteins, high-protein diets are used for specific populations or indications, mainly weight loss. In this study, we investigated the effect of protein supplementation on Bacteroides caccae, a Gram-negative gut symbiont. The supplementation with whey proteins led to a significant increase in growth rate, final biomass, and short-chain fatty acids production. A comprehensive genomic analysis revealed that B. caccae possesses a set of 156 proteases with putative intracellular and extracellular localization and allowed to identify amino acid transporters and metabolic pathways. We developed a fully curated genome-scale metabolic model of B. caccae that incorporated its proteolytic activity and simulated its growth and production of fermentation-related metabolites in response to the different growth media. We validated the model by comparing the predicted phenotype to experimental data. The model accurately predicted B. caccae's growth and metabolite production (R2 = 0.92 for the training set and R2 = 0.89 for the validation set). We found that accounting for both ATP consumption related to proteolysis, and whey protein accessibility is necessary for accurate predictions of metabolites production. These results provide insights into B. caccae's adaptation to a high-protein diet and its ability to utilize proteins as a source of nutrition. The proposed model provides a useful tool for understanding the feeding mechanism of B. caccae in the gut microbiome.IMPORTANCEMicrobial proteolysis is understudied despite the availability of dietary proteins for the gut microbiota. Here, the proteolytic potential of the gut symbiont Bacteroides caccae was analyzed for the first time using pan-genomics. This sketches a well-equipped bacteria for protein breakdown, capable of producing 156 different proteases with a broad spectrum of cleavage targets. This functional potential was confirmed by the enhancement of growth and metabolic activities at high protein levels. Proteolysis was included in a B. caccae metabolic model which was fitted with the experiments and validated on external data. This model pinpoints the links between protein availability and short-chain fatty acids production, and the importance for B. caccae to gain access to glutamate and asparagine to promote growth. This integrated approach can be generalized to other symbionts and upscaled to complex microbiota to get insights into the ecological impact of proteins on the gut microbiota.

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

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.

Four functional profiles for fibre and mucin metabolism in the human gut microbiome.

BACKGROUND: With the emergence of metagenomic data, multiple links between the gut microbiome and the host health have been shown. Deciphering these complex interactions require evolved analysis methods focusing on the microbial ecosystem functions. Despite the fact that host or diet-derived fibres are the most abundant nutrients available in the gut, the presence of distinct functional traits regarding fibre and mucin hydrolysis, fermentation and hydrogenotrophic processes has never been investigated. RESULTS: After manually selecting 91 KEGG orthologies and 33 glycoside hydrolases further aggregated in 101 functional descriptors representative of fibre and mucin degradation pathways in the gut microbiome, we used nonnegative matrix factorization to mine metagenomic datasets. Four distinct metabolic profiles were further identified on a training set of 1153 samples, thoroughly validated on a large database of 2571 unseen samples from 5 external metagenomic cohorts and confirmed with metatranscriptomic data. Profiles 1 and 2 are the main contributors to the fibre-degradation-related metagenome: they present contrasted involvement in fibre degradation and sugar metabolism and are differentially linked to dysbiosis, metabolic disease and inflammation. Profile 1 takes over Profile 2 in healthy samples, and unbalance of these profiles characterize dysbiotic samples. Furthermore, high fibre diet favours a healthy balance between profiles 1 and profile 2. Profile 3 takes over profile 2 during Crohn's disease, inducing functional reorientations towards unusual metabolism such as fucose and H2S degradation or propionate, acetone and butanediol production. Profile 4 gathers under-represented functions, like methanogenesis. Two taxonomic makes up of the profiles were investigated, using either the covariation of 203 prevalent genomes or metagenomic species, both providing consistent results in line with their functional characteristics. This taxonomic characterization showed that profiles 1 and 2 were respectively mainly composed of bacteria from the phyla Bacteroidetes and Firmicutes while profile 3 is representative of Proteobacteria and profile 4 of methanogens. CONCLUSIONS: Integrating anaerobic microbiology knowledge with statistical learning can narrow down the metagenomic analysis to investigate functional profiles. Applying this approach to fibre degradation in the gut ended with 4 distinct functional profiles that can be easily monitored as markers of diet, dysbiosis, inflammation and disease. Video Abstract.

Migration of surface-associated microbial communities in spaceflight habitats.

Astronauts are spending longer periods locked up in ships or stations for scientific and exploration spatial missions. The International Space Station (ISS) has been inhabited continuously for more than 20 years and the duration of space stays by crews could lengthen with the objectives of human presence on the moon and Mars. If the environment of these space habitats is designed for the comfort of astronauts, it is also conducive to other forms of life such as embarked microorganisms. The latter, most often associated with surfaces in the form of biofilm, have been implicated in significant degradation of the functionality of pieces of equipment in space habitats. The most recent research suggests that microgravity could increase the persistence, resistance and virulence of pathogenic microorganisms detected in these communities, endangering the health of astronauts and potentially jeopardizing long-duration manned missions. In this review, we describe the mechanisms and dynamics of installation and propagation of these microbial communities associated with surfaces (spatial migration), as well as long-term processes of adaptation and evolution in these extreme environments (phenotypic and genetic migration), with special reference to human health. We also discuss the means of control envisaged to allow a lasting cohabitation between these vibrant microscopic passengers and the astronauts.

Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy.

Biofilms are spatially organized communities of microorganisms embedded in a self-produced organic matrix, conferring to the population emerging properties such as an increased tolerance to the action of antimicrobials. It was shown that some bacilli were able to swim in the exogenous matrix of pathogenic biofilms and to counterbalance these properties. Swimming bacteria can deliver antimicrobial agents in situ, or potentiate the activity of antimicrobial by creating a transient vascularization network in the matrix. Hence, characterizing swimmer trajectories in the biofilm matrix is of particular interest to understand and optimize this new biocontrol strategy in particular, but also more generally to decipher ecological drivers of population spatial structure in natural biofilms ecosystems. In this study, a new methodology is developed to analyze time-lapse confocal laser scanning images to describe and compare the swimming trajectories of bacilli swimmers populations and their adaptations to the biofilm structure. The method is based on the inference of a kinetic model of swimmer populations including mechanistic interactions with the host biofilm. After validation on synthetic data, the methodology is implemented on images of three different species of motile bacillus species swimming in a Staphylococcus aureus biofilm. The fitted model allows to stratify the swimmer populations by their swimming behavior and provides insights into the mechanisms deployed by the micro-swimmers to adapt their swimming traits to the biofilm matrix.

Anyone who has ever cleaned a bathroom probably faced biofilms, the dark, slimy deposits that lurk around taps and pipes. These structures are created by bacteria which abandon their solitary lifestyle to work together as a community, secreting various substances that allow the cells to organise themselves in 3D and to better resist external aggression. Unwanted biofilms can impair industrial operations or endanger health, for example when they form inside medical equipment or water supplies. Removing these structures usually involves massive application of substances which can cause long-term damage to the environment. Recently, researchers have observed that a range of small rod-shaped bacteria ­ or 'bacilli' ­ can penetrate a harmful biofilm and dig transient tunnels in its 3D structure. These 'swimmers' can enhance the penetration of anti-microbial agents, or could even be modified to deliver these molecules right inside the biofilm. However, little is known about how the various types of bacilli, which have very different shapes and propelling systems, can navigate the complex environment that is a biofilm. This knowledge would be essential for scientists to select which swimmers could be the best to harness for industrial and medical applications. To investigate this question, Ravel et al. established a way to track how three species of bacilli swim inside a biofilm compared to in a simple fluid. A mathematical model was created which integrated several swimming behaviors such as speed adaptation and direction changes in response to the structure and density of the biofilm. This modelling was then fitted on microscopy images of the different species navigating the two types of environments. Different motion patterns for the three bacilli emerged, each showing different degrees of adapting to moving inside a biofilm. One species, in particular, was able to run straight in and out of this environment because it could adapt its speed to the biofilm density as well as randomly change direction. The new method developed by Ravel et al. can be redeployed to systematically study swimmer candidates in different types of biofilms. This would allow scientists to examine how various swimming characteristics impact how bacteria-killing chemicals can penetrate the altered biofilms. In addition, as the mathematical model can predict trajectories, it could be used in computational studies to examine which species of bacilli would be best suited in industrial settings.

Exploring the Bacterial Impact on Cholesterol Cycle: A Numerical Study.

High blood cholesterol levels are often associated with cardiovascular diseases. Therapeutic strategies, targeting different functions involved in cholesterol transport or synthesis, were developed to control cholesterolemia in human. However, the gut microbiota is also involved in cholesterol regulation by direct biotransformation of luminal cholesterol or conversion of bile salts, opening the way to the design of new strategies to manage cholesterol level. In this report, we developed for the first time a whole-body human model of cholesterol metabolism including the gut microbiota in order to investigate the relative impact of host and microbial pathways. We first used an animal model to investigate the ingested cholesterol distribution in vivo. Then, using in vitro bacterial growth experiments and metabolite measurements, we modeled the population dynamics of bacterial strains in the presence of cholesterol or bile salts, together with their bioconversion function. Next, after correct rescaling to mimic the activity of a complex microbiota, we developed a whole body model of cholesterol metabolism integrating host and microbiota mechanisms. This global model was validated with the animal experiments. Finally, the model was numerically explored to give a further insight into the different flux involved in cholesterol turn-over. According to this model, bacterial pathways appear as an important driver of cholesterol regulation, reinforcing the need for development of novel "bacteria-based" strategies for cholesterol management.

Spatial Organization Plasticity as an Adaptive Driver of Surface Microbial Communities.

Biofilms are dynamic habitats which constantly evolve in response to environmental fluctuations and thereby constitute remarkable survival strategies for microorganisms. The modulation of biofilm functional properties is largely governed by the active remodeling of their three-dimensional structure and involves an arsenal of microbial self-produced components and interconnected mechanisms. The production of matrix components, the spatial reorganization of ecological interactions, the generation of physiological heterogeneity, the regulation of motility, the production of actives enzymes are for instance some of the processes enabling such spatial organization plasticity. In this contribution, we discussed the foundations of architectural plasticity as an adaptive driver of biofilms through the review of the different microbial strategies involved. Moreover, the possibility to harness such characteristics to sculpt biofilm structure as an attractive approach to control their functional properties, whether beneficial or deleterious, is also discussed.

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

A bilayer representation of the human atria.

Atrial fibrillation is the most commonly encountered clinical arrhythmia. Despite recent advances in treatment by catheter ablation, its origin is still incompletely understood and it may be difficult to treat. Computer modelling offers an attractive complement to experiment. Simulations of fibrillation, however, are computationally demanding since the phenomenon requires long periods of observation. Because the atria are thin walled structures, they are often modelled as surfaces. However, this may not always be appropriate as the crista terminalis and pectinate muscles are discrete fibrous structures lying on the endocardium and cannot be incorporated into the surface. In the left atrium, there are essentially two layers with an abrupt change in fibre orientation between them. We propose a double layer method, using shell elements to incorporate wall thickness, where fibre direction is independent in each layer and layers are electrically linked. Starting from human multi-detector CT (MDCT) images, we extracted surfaces for the atria and manually added a coronary sinus. Propagation of electrical activity was modelled with the monodomain equation. Results indicate that major features are retained while reducing computation cost considerably. Meshes based on the two layer approach will facilitate studies of AF.