scholarly journals Personalised models for human – gut microbiota interaction

2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar
Keyword(s):  
Gut Microbiota ◽  
Bacterial Strains ◽  
Human Gut ◽  
Individual Genome ◽  
Human Gut Microbiota ◽  
Coloured Petri Net ◽  
Metabolic Models ◽  
Simulative Model Checking ◽  
The Individual ◽  
Genome Scale

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper. Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie. Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.

scholarly journals Personalised models for human – gut microbiota interaction

2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar
Keyword(s):  
Gut Microbiota ◽  
Bacterial Strains ◽  
Human Gut ◽  
Individual Genome ◽  
Human Gut Microbiota ◽  
Coloured Petri Net ◽  
Metabolic Models ◽  
Simulative Model Checking ◽  
The Individual ◽  
Genome Scale

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper. Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie. Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.

scholarly journals Personalised models for human – gut microbiota interaction

2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar
Keyword(s):  
Gut Microbiota ◽  
Bacterial Strains ◽  
Human Gut ◽  
Individual Genome ◽  
Human Gut Microbiota ◽  
Coloured Petri Net ◽  
Metabolic Models ◽  
Simulative Model Checking ◽  
The Individual ◽  
Genome Scale

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper. Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie. Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.

scholarly journals An extended reconstruction of human gut microbiota metabolism for personalized nutrition

2020 ◽  
Author(s):  
Telmo Blasco ◽  
Sergio Pérez-Burillo ◽  
Francesco Balzerani ◽  
Alberto Lerma-Aguilera ◽  
Daniel Hinojosa-Nogueira ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Metabolic Networks ◽  
Degradation Pathways ◽  
Personalized Nutrition ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Lack Of Information ◽  
Constraint Based Modeling ◽  
Genome Scale ◽  
Complex Question

ABSTRACTUnderstanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex question. However, when applied to nutritional questions, a major issue in existing reconstructions is the lack of information about degradation pathways of relevant nutrients in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of the human gut microbiota metabolism for personalized nutrition. AGREDA includes the degradation pathways of 231 nutrients present in the human diet and allows us to more comprehensively simulate the interplay between food and gut microbiota. We show that AGREDA is more accurate than existing reconstructions in predicting output metabolites of the gut microbiota. Finally, using AGREDA, we established relevant metabolic differences among clinical subgroups of Spanish children: lean, obese, allergic to foods and celiac.

The effect of diet on the fluctuations of human gut microbiota

MedPharmRes ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 22-24
Author(s):  
Binh Nguyen
Keyword(s):  
Individual Differences ◽  
Gut Microbiota ◽  
Microbiota Composition ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
The Individual ◽  
Gut Microbiota Composition

It was previously thought that the establishment of the gut microbiota was completed within the first two years of life, and this community maintains fairly stable throughout the adult lifetime thereafter. However, recent evidence shows that the gut microbiota composition is constantly changing in the gut environment and is heavily influenced by diet. The individual differences responding to diets would root on the fluctuations of gut microbiota if dietary fluctuations affect the composition of gut microbiota so significantly.

scholarly journals Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

2016 ◽  
Vol 35 (1) ◽  
pp. 81-89 ◽  
Author(s):  
Stefanía Magnúsdóttir ◽  
Almut Heinken ◽  
Laura Kutt ◽  
Dmitry A Ravcheev ◽  
Eugen Bauer ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Genome Scale

scholarly journals Variation and transmission of the human gut microbiota across multiple familial generations

2021 ◽  
Author(s):  
Mireia Valles-Colomer ◽  
Rodrigo Bacigalupe ◽  
Sara Vieira-Silva ◽  
Shinya Suzuki ◽  
Youssef Darzi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Gut Microbiota ◽  
Community Composition ◽  
Distribution Patterns ◽  
Genotype Distribution ◽  
Bacterial Strains ◽  
Human Gut ◽  
Transmission Rates ◽  
Functional Potential ◽  
Human Gut Microbiota

AbstractAlthough the composition and functional potential of the human gut microbiota evolve over the lifespan, kinship has been identified as a key covariate of microbial community diversification. However, to date, sharing of microbiota features within families has mostly been assessed between parents and their direct offspring. Here we investigate the potential transmission and persistence of familial microbiome patterns and microbial genotypes in a family cohort (n = 102) spanning 3 to 5 generations over the same female bloodline. We observe microbiome community composition associated with kinship, with seven low abundant genera displaying familial distribution patterns. While kinship and current cohabitation emerge as closely entangled variables, our explorative analyses of microbial genotype distribution and transmission estimates point at the latter as a key covariate of strain dissemination. Highest potential transmission rates are estimated between sisters and mother–daughter pairs, decreasing with increasing daughter’s age and being higher among cohabiting pairs than those living apart. Although rare, we detect potential transmission events spanning three and four generations, primarily involving species of the genera Alistipes and Bacteroides. Overall, while our analyses confirm the existence of family-bound microbiome community profiles, transmission or co-acquisition of bacterial strains appears to be strongly linked to cohabitation.

scholarly journals Defining Innate Immune Responses to the Human Gut Microbiota from Phylum to Strain

2021 ◽  
Author(s):  
Matthew P. Spindler ◽  
Sophia S. Siu ◽  
Ilaria Mogno ◽  
Zihua Li ◽  
Chao Yang ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Immune Responses ◽  
Pathogenic Bacteria ◽  
Innate Immune ◽  
Innate Immune Responses ◽  
Bacterial Strains ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Screening Platform

The functional potential of the gut microbiota remains largely uncharacterized. Efforts to understand how the immune system responds to commensal organisms have been hindered by the large number of strains that comprise the human gut microbiota. We develop a screening platform to measure innate immune responses towards 277 bacterial strains isolated from the human gut microbiota. We find that innate immune responses to gut derived bacteria are as strong as responses towards pathogenic bacteria, and vary from phylum to strain. Myeloid cells differentially rely upon TLR2 or TLR4 to sense particular taxa, an observation that predicts in vivo function. These innate immune responses can be modeled using combinations of up to 8 TLR agonists. Furthermore, the immunogenicity of strains is stable over time and following transplantation into new humans. Collectively, we demonstrate a powerful high-throughput approach to determine how commensal microorganisms shape innate immune phenotypes.

In vitro study of the interaction between human gut microbiota and herbal extracts using willow bark extract as an example

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
EM Pferschy-Wenzig ◽  
K Koskinen ◽  
C Moissl-Eichinger ◽  
R Bauer
Keyword(s):  
Gut Microbiota ◽  
In Vitro Study ◽  
Vitro Study ◽  
Bark Extract ◽  
Herbal Extracts ◽  
Human Gut ◽  
Human Gut Microbiota ◽  
Willow Bark

Metabolization of the herbal combination STW-5 by human gut microbiota in vitro

2017 ◽  
Author(s):  
EM Pferschy-Wenzig ◽  
A Roßmann ◽  
K Koskinen ◽  
H Abdel-Aziz ◽  
C Moissl-Eichinger ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Human Gut ◽  
Human Gut Microbiota

Substrate use prioritization by a coculture of five species of gut bacteria fed mixtures of arabinoxylan, xyloglucan, β-glucan, and pectin

2020 ◽  
Author(s):  
Y Liu ◽  
AL Heath ◽  
B Galland ◽  
N Rehrer ◽  
L Drummond ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Dietary Fiber ◽  
Plant Cell Wall ◽  
Bacterial Species ◽  
Short Chain ◽  
Emergent Properties ◽  
Human Gut ◽  
Fermentation Products ◽  
Plant Polysaccharides ◽  
Human Gut Microbiota

© 2020 American Society for Microbiology. Dietary fiber provides growth substrates for bacterial species that belong to the colonic microbiota of humans. The microbiota degrades and ferments substrates, producing characteristic short-chain fatty acid profiles. Dietary fiber contains plant cell wall-associated polysaccharides (hemicelluloses and pectins) that are chemically diverse in composition and structure. Thus, depending on plant sources, dietary fiber daily presents the microbiota with mixtures of plant polysaccharides of various types and complexity. We studied the extent and preferential order in which mixtures of plant polysaccharides (arabinoxylan, xyloglucan, β-glucan, and pectin) were utilized by a coculture of five bacterial species (Bacteroides ovatus, Bifidobacterium longum subspecies longum, Megasphaera elsdenii, Ruminococcus gnavus, and Veillonella parvula). These species are members of the human gut microbiota and have the biochemical capacity, collectively, to degrade and ferment the polysaccharides and produce short-chain fatty acids (SCFAs). B. ovatus utilized glycans in the order β-glucan, pectin, xyloglucan, and arabinoxylan, whereas B. longum subsp. longum utilization was in the order arabinoxylan, arabinan, pectin, and β-glucan. Propionate, as a proportion of total SCFAs, was augmented when polysaccharide mixtures contained galactan, resulting in greater succinate production by B. ovatus and conversion of succinate to propionate by V. parvula. Overall, we derived a synthetic ecological community that carries out SCFA production by the common pathways used by bacterial species for this purpose. Systems like this might be used to predict changes to the emergent properties of the gut ecosystem when diet is altered, with the aim of beneficially affecting human physiology. This study addresses the question as to how bacterial species, characteristic of the human gut microbiota, collectively utilize mixtures of plant polysaccharides such as are found in dietary fiber. Five bacterial species with the capacity to degrade polymers and/or produce acidic fermentation products detectable in human feces were used in the experiments. The bacteria showed preferential use of certain polysaccharides over others for growth, and this influenced their fermentation output qualitatively. These kinds of studies are essential in developing concepts of how the gut microbial community shares habitat resources, directly and indirectly, when presented with mixtures of polysaccharides that are found in human diets. The concepts are required in planning dietary interventions that might correct imbalances in the functioning of the human microbiota so as to support measures to reduce metabolic conditions such as obesity.

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