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.

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.

Single Seed Microbiota: Assembly and Transmission from Parent Plant to Seedling.

The seed acts as the primary inoculum source for the plant microbiota. Understanding the processes involved in its assembly and dynamics during germination and seedling emergence has the potential to allow for the improvement of crop establishment. Changes in the bacterial community structure were tracked in 1,000 individual seeds that were collected throughout seed developments of beans and radishes. Seeds were associated with a dominant bacterial taxon that represented more than 75% of all reads. The identity of this taxon was highly variable between the plants and within the seeds of the same plant. We identified selection as the main ecological process governing the succession of dominant taxa during seed filling and maturation. In a second step, we evaluated the seedling transmission of seed-borne taxa in 160 individual plants. While the initial bacterial abundance on seeds was not a good predictor of seedling transmission, the identities of the seed-borne taxa modified the phenotypes of seedlings. Overall, this work revealed that individual seeds are colonized by a few bacterial taxa of highly variable identity, which appears to be important for the early stages of plant development. IMPORTANCE Seeds are key components of plant fitness and are central to the sustainability of the agri-food system. Both the seed quality for food consumption and the seed vigor in agricultural settings can be influenced by the seed microbiota. Understanding the ecological processes involved in seed microbiota assembly will inform future practices for promoting the presence of important seed microorganisms for plant health and productivity. Our results highlighted that seeds were associated with one dominant bacterial taxon of variable taxonomic identity. This variety of dominant taxa was due to (i) spatial heterogeneity between and within plants and (ii) primary succession during seed development. According to neutral models, selection was the main driver of microbial community assembly for both plant species.

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.

Modelling the growth of Staphylococcus aureus on cooked broccoli under isothermal conditions.

In this study, we developed predictive models describing the growth of Staphylococcus aureus on cooked broccoli florets. A pool of 3.5 log CFU/g of five S. aureus strains were inoculated on 10 g broccoli portions. The samples were then stored at 10, 20, 30 and 37 °C, and colonies were enumerated at different time intervals. Baranyi and Roberts model was fitted to the data using a Bayesian Adaptive Markov Chain Monte Carlo for estimation of the growth parameters. S. aureus showed low growth at 10 °C on broccoli samples and at 20-37 °C interval, Baranyi and Roberts model fitted well to the experimental data (R2>0.97). Estimated growth parameters were correlated with the possibility of toxin production and indicate the potential presence of these biological hazards on contaminated broccoli after heat treatment. Additionally, linear regression was performed for growth rate as storage temperature function. This secondary model followed a linear tendency with R2=0.997 and was compared with two tertiary models (ComBase Predictor and Pathogen Modeling Program) and literature data, demonstrating similar growth rate values of both. These results can be helpful for food services and managers to establish food safety standards for S. aureus growth on cooked broccoli.

Physiologically Based Modeling of Food Digestion and Intestinal Microbiota: State of the Art and Future Challenges. An INFOGEST Review.

This review focuses on modeling methodologies of the gastrointestinal tract during digestion that have adopted a systems-view approach and, more particularly, on physiologically based compartmental models of food digestion and host-diet-microbiota interactions. This type of modeling appears very promising for integrating the complex stream of mechanisms that must be considered and retrieving a full picture of the digestion process from mouth to colon. We may expect these approaches to become more and more accurate in the future and to serve as a useful means of understanding the physicochemical processes occurring in the gastrointestinaltract, interpreting postprandial in vivo data, making relevant predictions, and designing healthier foods. This review intends to provide a scientific and historical background of this field of research, before discussing the future challenges and potential benefits of the establishment of such a model to study and predict food digestion and absorption in humans.

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.

Analysis and numerical simulation of an inverse problem for a structured cell population dynamics model.

In this work, we study a multiscale inverse problem associated with a multi-type model for age structured cell populations. In the single type case, the model is a McKendrick-VonFoerster like equation with a mitosis-dependent death rate and potential migration at birth. In the multi-type case, the migration term results in an unidirectional motion from one type to the next, so that the boundary condition at age 0 contains an additional extrinsic contribution from the previous type. We consider the inverse problem of retrieving microscopic information (the division rates and migration proportions) from the knowledge of macroscopic information (total number of cells per layer), given the initial condition. We first show the well-posedness of the inverse problem in the single type case using a Fredholm integral equation derived from the characteristic curves, and we use a constructive approach to obtain the lattice division rate, considering either a synchronized or non-synchronized initial condition. We take advantage of the unidirectional motion to decompose the whole model into nested submodels corresponding to self-renewal equations with an additional extrinstic contribution. We again derive a Fredholm integral equation for each submodel and deduce the well-posedness of the multi-type inverse problem. In each situation, we illustrate numerically our theoretical results.

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.

Signatures of ecological processes in microbial community time series.

BACKGROUND: Growth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection. RESULTS: We implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model. CONCLUSIONS: We present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.

Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota.

Whole Genome Shotgun (WGS) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems, both from the taxonomic and functional point of view. Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible. The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology. Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes. They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions. In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes. We formulate a model of these Combined Aggregated Functional Traits (CAFTs) accounting for a hierarchical structure of genes, which are associated on microbial genomes, further linked at the ecosystem level by complex co-occurrences or interactions. The model is completed with constraints specifically designed to exploit available genomic information, in order to favor biologically relevant CAFTs. The CAFTs structure, as well as their intensity in the ecosystem, is obtained by solving a constrained Non-negative Matrix Factorization (NMF) problem. We developed a multicriteria selection procedure for the number of CAFTs. We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota. We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential. This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data. Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems.

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.

Acid and rennet gels exhibit strong differences in the kinetics of milk protein digestion and amino acid bioavailability.

This study aimed at determining the kinetics of milk protein digestion and amino acid absorption after ingestion by six multi-canulated mini-pigs of two gelled dairy matrices having the same composition, similar rheological and structural properties, but differing by their mode of coagulation (acidification/renneting). Duodenal, mid-jejunal effluents and plasma samples were collected at different times during 7h after meal ingestion. Ingestion of the acid gel induced a peak of caseins and ß-lactoglobulin in duodenal effluents after 20min of digestion and a peak of amino acids in the plasma after 60min. The rennet gel induced lower levels of both proteins in the duodenum (with no defined peak) as well as much lower levels of amino acids in the plasma than the acid gel. Plasma ghrelin concentrations suggested a potentially more satiating effect of the rennet gel compared to the acid gel. This study clearly evidences that the gelation process can significantly impact on the nutritive value of dairy products.

The heat treatment and the gelation are strong determinants of the kinetics of milk proteins digestion and of the peripheral availability of amino acids.

This study aimed to determine the kinetics of milk protein digestion and amino acid absorption after ingestion of four dairy matrices by six minipigs: unheated or heated skim milk and corresponding rennet gels. Digestive contents and plasma samples were collected over a 7 h-period after meal ingestion. Gelation of milk slowed down the outflow of the meal from the stomach and the subsequent absorption of amino acids, and decreased their bioavailability in peripheral blood. The gelled rennet matrices also led to low levels of milk proteins at the duodenum. Caseins and ß-lactoglobulin, respectively, were sensitive and resistant to hydrolysis in the stomach with the unheated matrices, but showed similar digestion with the heated matrices, with a heat-induced susceptibility to hydrolysis for ß-lactoglobulin. These results suggest a significant influence of the meal microstructure (resulting from heat treatment) and macrostructure (resulting from gelation process) on the different steps of milk proteins digestion.

Kinetic modelling of lactate utilization and butyrate production by key human colonic bacterial species.

Butyrate is the preferred energy source for colonocytes and has an important role in gut health; in contrast, accumulation of high concentrations of lactate is detrimental to gut health. The major butyrate-producing bacterial species in the human colon belong to the Firmicutes. Eubacterium hallii and a new species, Anaerostipes coli SS2/1, members of clostridial cluster XIVa, are able to utilize lactate and acetate via the butyryl CoA : acetate CoA transferase route, the main metabolic pathway for butyrate synthesis in the human colon. Here we provide a mathematical model to analyse the production of butyrate by lactate-utilizing bacteria from the human colon. The model is an aggregated representation of the fermentation pathway. The parameters of the model were estimated using total least squares and maximum likelihood, based on in vitro experimental data with E. hallii L2-7 and A. coli SS2/1. The findings of the mathematical model adequately match those from the bacterial batch culture experiments. Such an in silico approach should provide insight into carbohydrate fermentation and short-chain fatty acid cross-feeding by dominant species of the human colonic microbiota.

Mathematical modelling of carbohydrate degradation by human colonic microbiota.

The human colon is an anaerobic ecosystem that remains largely unexplored as a result of its limited accessibility and its complexity. Mathematical models can play a central role for a better insight into its dynamics. In this context, this paper presents the development of a mathematical model of carbohydrate degradation. Our aim was to provide an in silico approach to contribute to a better understanding of the fermentation patterns in such an ecosystem. Our mathematical model is knowledge-based, derived by writing down mass-balance equations. It incorporates physiology of the intestine, metabolic reactions and transport phenomena. The model was used to study various nutritional scenarios and to assess the role of the mucus on the system behavior. Model simulations provided an adequate qualitative representation of the human colon. Our model is complementary to experimental studies on human colonic fermentation, which, of course, is not meant to replace. It may be helpful to gain insight on questions that are still difficult to elucidate by experimentation and suggest future experiments.

Using singular perturbations to reduce an epidemiological model: application to bovine viral diarrhoea virus within-herd spread.

Studying the spread of a pathogen in a managed metapopulation such as cattle herds in a geographical region often requires to take into account both the within- and between-herd transmission dynamics. This can lead to high-dimensional metapopulation systems resulting from the coupling of several within-herd transmission models. To tackle this problem, we aim in this paper at reducing the dimension of a tractable but realistic dynamical system reproducing the within-herd spread. The context chosen to illustrate our purpose is bovine viral diarrhoea virus (BVDV) transmission in a cattle herd structured in two age classes and several epidemiological states, including two infectious states (transiently and persistently infected). Different time scales, corresponding to the epidemiological and demographic processes, are identified which allow to build a reduced model. Singular perturbation technique is used to prove that, under some non-restrictive conditions on parameter values, the behaviour of the original system is quite accurately approximated by that of the reduced system. Simulations are also performed to corroborate the approximation quality. Our study illustrates the methodological interest of using singular perturbations to reduce model complexity. It also rigorously proves the biologically intuitive assumption that transiently infected individuals can be neglected in a homogeneous population, when capturing the global dynamics of BVDV spread.