Experiments and simulations on short chain fatty acid production in a colonic bacterial community

Understanding how production of specific metabolites by gut microbes is modulated by interactions with surrounding species and by environmental nutrient availability is an important open challenge in microbiome research. As part of this endeavor, we explore interactions between F. prausnitzii, a major butyrate producer, and B. thetaiotaomicron, an acetate producer, under three different in vitro media conditions in monoculture and coculture. In silico Genome-scale dynamic flux balance analysis (dFBA) models of metabolism in the system using COMETS (Computation of Microbial Ecosystems in Time and Space) are also tested for explanatory, predictive and inferential power. Experimental findings indicate enhancement of butyrate production in coculture relative to F. prausnitzii monoculture but defy a simple model of monotonic increases in butyrate production as a function of acetate availability in the medium. Simulations recapitulate biomass production curves for monocultures and accurately predict the growth curve of coculture total biomass, using parameters learned from monocultures, suggesting that the model captures some aspects of how the two bacteria interact. However, a comparison of data and simulations for environmental acetate and butyrate changes suggest that the organisms adopt one of many possible metabolic strategies equivalent in terms of growth efficiency. Furthermore, the model seems not to capture subsequent shifts in metabolic activities observed experimentally under low-nutrient regimes. Some discrepancies can be explained by the multiplicity of possible fermentative states for F. prausnitzii. In general, these results provide valuable guidelines for design of future experiments aimed at better determining the mechanisms leading to enhanced butyrate in this ecosystem.https://www.biorxiv.org/content/10.1101/444760v1https://www.biorxiv.org/content/10.1101/444760v1Othe

Biotechnology of health-promoting bacteria

Over the last decade, there has been an increasing scientific and public interest in bacteria that may positively contribute to human gut health and well-being. This interest is reflected by the ever-increasing number of developed functional food products containing health-promoting bacteria and reaching the market place as well as by the growing revenue and profits of notably bacterial supplements worldwide. Traditionally, the origin of probiotic-marketed bacteria was limited to a rather small number of bacterial species that mostly belong to lactic acid bacteria and bifidobacteria. Intensifying research efforts on the human gut microbiome offered novel insights into the role of human gut microbiota in health and disease, while also providing a deep and increasingly comprehensive understanding of the bacterial communities present in this complex ecosystem and their interactions with the gut-liver-brain axis. This resulted in rational and systematic approaches to select novel health promoting bacteria or to engineer existing bacteria with enhanced probiotic properties. In parallel, the field of gut microbiomics developed into a fertile framework for the identification, isolation and characterization of a phylogenetically diverse array of health-promoting bacterial species, also called next-generation therapeutic bacteria. The present review will address these developments with specific attention for the selection and improvement of a selected number of health-promoting bacterial species and strains that are extensively studied or hold promise for future food or pharma product development.Peer reviewe

Quantifying diet-induced metabolic changes of the human gut microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome-scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community and Systems-level Interactive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in in vitro. Focusing on metabolic interactions between the diet, gut microbiota and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals, and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention

Modelling the dynamics of Salmonella infection in the gut at the bacterial and host levels

International audienc

Probiotics, gut microbiota and their influence on host health and disease

The gastrointestinal tract (GIT) of mammals hosts a high and diverse number of different microorganisms, known as intestinal microbiota. Many probiotics were originally isolated from the GIT, and they were defined by the FAO/WHO as live microorganisms which when administered in adequate amounts confer a health benefit on the host. Probiotics exert their beneficial effects on the host through four main mechanisms: interference with potential pathogens, improvement of barrier function, immunomodulation and production of neurotransmitters, and their host targets vary from the resident microbiota to cellular components of the gut-brain axis. However, in spite of the wide array of beneficial mechanisms deployed by probiotic bacteria, relatively few effects have been supported by clinical data. In this regard, different probiotic strains have been effective in Antibiotic-Associated Diarrhea or Inflammatory Bowel Disease for instance. The aim of this review was to compile the molecular mechanisms underlying the beneficial effects of probiotics, mainly through their interaction with the intestinal microbiota and with the intestinal mucosa. The specific benefits discuss in this paper include among others those elicited directly through dietary modulation of the human gut microbiota.This article is protected by copyright. All rights reservedResearch in our lab is funded by Grants AGL2013-44039R and AGL2013-44761-P from the Spanish “Plan Estatal de I+D+I.” Part of the authors is also partially funded by the [15VI013] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). B. S. was recipient of a Ramón y Cajal postdoctoral contract from the Spanish Ministry of Economy and Competitiveness

Assessment of microbial community interactions using different tools

Dissertação de mestrado em BioinformaticsMicrobial communities participate in many biological processes, directly affecting its surrounding environment. Thus, the study of a community’s behaviour and interactions among its members can be very useful in the biotechnology, environmental and human health fields. Nevertheless, decoding the metabolic exchanges between microorganisms and community dynamics remains a challenge. Computational modelling methods have gained interest as a way to unravel the interactions and behaviour. GSM models allow the prediction of an organism’s response to changes in genetic and environmental conditions. Thus, the extension of such method to a community level can help decode a community’s phenotype. In this work, different GSM models and current bioinformatics tools were used to model the metabolism of different microbial communities. The different tools’ performances were compared to assess which is currently the best method to perform an analysis on a community level. Distinct case studies regarding microbial communities for which its interactions were already known, were selected. To assess the tools’ performances, each tools output was compared to what was expected in theory. COBRA Toolbox's methods proved to be useful to build a community structure from individual GSM models, while pFBA and SteadyCom’s simulation methods can predict exchange between the organisms and the environment. Additionally, Dynamic Flux Balance Analysis (dFBA) approaches, such as DFBAlab and DyMMM, can successfully simulate metabolite and biomass variation over time. Nevertheless, these methods are more limited as they require specific organism information, which is not always available. Several GSM models are available for use. Nonetheless, their quality control has to gain attention as the simulations’ results are directly affected by the individual models accuracy to represent an organism’s metabolism. Thus, community model builders should carefully chose a GSM model, or combination of models before performing simulations.Comunidades microbianas participam em inúmeros processos biológicos, afetando diretamente o ambiente que as engloba. Assim, o estudo do comportamento de uma comunidade e interações entre os seus membros pode ser muito útil nas áreas da biotecnologia, ambiente e saúde. No entanto, descodificar as trocas entre microrganismos e a dinâmica de comunidades continua um desafio. Métodos de modelação computacional têm ganho interesse como forma de desvendar tais interações e comportamento de comunidades. Modelos metabólicos à escala genómica permitem prever a resposta de um certo organismo a mudanças genéticas e ambientais. Assim, a extensão de tal método ao nível de comunidade pode ajudar a prever o fenótipo de uma certa comunidade. No presente trabalho, diferentes modelos metabólicos à escala genómica e ferramentas bioinformáticas foram utilizados para modelar o metabolismo de diferentes comunidades microbianas, comparando o desempenho destas ferramentas para avaliar qual o melhor método para análise ao nível da comunidade. Casos de estudo distintos, relativos a comunidades para as quais se conhecem as interações, foram selecionados. Por fim, para aferir o desempenho das ferramentas, os respetivos resultados foram comparados ao teoricamente esperado. Os métodos da ferramenta COBRA Toolbox provaram ser úteis para construir a estrutura da comunidade, usando modelos metabólicos à escala genómica dos organismos individuais. Quanto a métodos de simulação, pFBA e SteadyCom são úteis para prever trocas entre os organismos e o ambiente que os envolve. Para além disso, abordagens dFBA, como DFBAlab e DyMMM, podem simular a variação da concentração de metabolitos e biomassa ao longo do tempo. No entanto, estes métodos apresentam limitações por requererem informação específica ao organismo, que nem sempre se encontra disponível. Vários modelos metabólicos à escala genómica estão disponibilizados. No entanto, o controlo na qualidade destes tem que ganhar atenção, visto que os resultados das simulações são diretamente afetados pela sua precisão na representação do metabolismo de um organismo e consequentemente, da comunidade. Assim, para construir um modelo de comunidades, é necessária uma seleção cuidadosa dos modelos individuais a usar, antes de serem feitas simulações

Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa

peer-reviewedBackground Faecalibacterium prausnitzii is a ubiquitous member of the human gut microbiome, constituting up to 15% of the total bacteria in the human gut. Substantial evidence connects decreased levels of F. prausnitzii with the onset and progression of certain forms of inflammatory bowel disease, which has been attributed to its anti-inflammatory potential. Two phylogroups of F. prausnitzii have been identified, with a decrease in phylogroup I being a more sensitive marker of intestinal inflammation. Much of the genomic and physiological data available to date was collected using phylogroup II strains. Little analysis of F. prausnitzii genomes has been performed so far and genetic differences between phylogroups I and II are poorly understood. Results In this study we sequenced 11 additional F. prausnitzii genomes and performed comparative genomics to investigate intraspecies diversity, functional gene complement and the mobilome of 31 high-quality draft and complete genomes. We reveal a very low level of average nucleotide identity among F. prausnitzii genomes and a high level of genome plasticity. Two genomogroups can be separated based on differences in functional gene complement, albeit that this division does not fully agree with separation based on conserved gene phylogeny, highlighting the importance of horizontal gene transfer in shaping F. prausnitzii genomes. The difference between the two genomogroups is mainly in the complement of genes associated with catabolism of carbohydrates (such as a predicted sialidase gene in genomogroup I) and amino acids, as well as defense mechanisms. Conclusions Based on the combination of ANI of genomic sequences, phylogenetic analysis of core proteomes and functional differences we propose to separate the species F. prausnitzii into two new species level taxa: F. prausnitzii sensu stricto (neotype strain A2–165T = DSM 17677T = JCM 31915T) and F. moorei sp. nov. (type strain ATCC 27768T = NCIMB 13872T).This research was conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2273, a Science Foundation Ireland’s Spokes Programme which is co-funded under the European Regional Development Fund under Grant Number SFI/14/SP APC/B3032, and a research grant from Janssen Biotech, Inc

In silico analysis of microbial communities through constraint-based metabolic modelling

Microbial communities are involved in many vital biological processes from elemental cycles to sustaining human health. The bacterial assemblages are remarkably under-studied as they are reluctant to grow in the laboratory conditions. Therefore, alternative omics-based approaches and computational modelling methods have been an active area of research to investigate microbial communities physiologically, ecologically and biochemically. In this thesis different microbial consortia involved in food production and also the human gut microbiota have been modelled and investigated. In the case of the human gut microbiota, the effects of malnutrition on the overall health of children from three different countries, namely, Malawi and Bangladesh, and Sweden have been studied. In each of the first two countries, a group of malnourished children going through food therapy as well as a healthy cohort were monitored to investigate the effect of food intervention on malnutrition, with their gut microbiota being the focal point. In this project, using metagenomics data we identified the dominant strains in each cohort, reconstructed genome-scale metabolic models (GEMs) for the most abundant ones and used our models to predict diet-microbe, microbe-microbe, and microbe- host interactions. Based on our results in this project, in addition to being less diverse, the gut microbiota of malnourished children showed a lower potency regarding the production of valuable metabolites. The second investigated microbial consortia were the ones used in fermented milk products. Based on the genome sequence and also experimental data for five selected strains, we reconstructed GEMs, curated the models and performed community modelling to predict their metabolic interactions. Using the simulation outcomes, we could predict a ratio for bacterial strains used in yogurt starter culture to maximise the production of acetaldehyde which is a key contributor to yogurt’s unique taste and aroma. GEMs are powerful tools to model an organism’s metabolic capabilities, and although numerous GEMs have been reconstructed, their quality control has not gained enough attention. Evaluation of a repository of semi-automatically reconstructed GEMs related to the human gut microbiota and another repository of manually curated ones was performed comparatively. Assessing these models from topological and functional aspects, it was shown that semi-automatically reconstructed models required extensive manual curation before they could be used for target-specific simulations. In constraint-based modelling, an objective function is usually optimised under particular environmental conditions, however, in case of the microbial communities, there is no distinct and relevant objective function. Therefore, an unbiased uniform randomised sampling algorithm was implemented for microbial communities. The samples acquired from the solution space were analysed statistically to see clustering patterns of the reactions and commensalistic relationships between the community members were identified. Overall, computational modelling paves the way towards gaining a mechanistic understanding of microbial communities and provides us with testable hypotheses and insight

Metabolic engineering of human gut microbiome: Recent developments and future perspectives

Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities