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

Author(s):  
Mireia Valles-Colomer ◽  
Rodrigo Bacigalupe ◽  
Sara Vieira-Silva ◽  
Shinya Suzuki ◽  
Youssef Darzi ◽  
...  

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.

2021 ◽  
Vol 69 (10) ◽  
pp. 3209-3218
Author(s):  
Renbing Qin ◽  
Jin Wang ◽  
Chen Chao ◽  
Jinglin Yu ◽  
Les Copeland ◽  
...  

mBio ◽  
2020 ◽  
Vol 11 (5) ◽  
Author(s):  
Xiaoqian Yu ◽  
Thomas Gurry ◽  
Le Thanh Tu Nguyen ◽  
Hunter S. Richardson ◽  
Eric J. Alm

ABSTRACT Prebiotics confer benefits to human health, often by promoting the growth of gut bacteria that produce metabolites valuable to the human body, such as short-chain fatty acids (SCFAs). While prebiotic selection has strongly focused on maximizing the production of SCFAs, less attention has been paid to gases, a by-product of SCFA production that also has physiological effects on the human body. Here, we investigate how the content and volume of gas production by human gut microbiota are affected by the chemical composition of the prebiotic and the community composition of the microbiota. We first constructed a linear system model based on mass and electron balance and compared the theoretical product ranges of two prebiotics, inulin and pectin. Modeling shows that pectin is more restricted in product space, with less potential for H2 but more potential for CO2 production. An ex vivo experimental system showed pectin degradation produced significantly less H2 than inulin, but CO2 production fell outside the theoretical product range, suggesting fermentation of fecal debris. Microbial community composition also impacted results: methane production was dependent on the presence of Methanobacteria, while interindividual differences in H2 production during inulin degradation were driven by a Lachnospiraceae taxon. Overall, these results suggest that both the chemistry of the prebiotic and the composition of the microbiota are relevant to gas production. Metabolic processes that are relatively prevalent in the microbiome, such as H2 production, will depend more on substrate, while rare metabolisms such as methanogenesis depend more strongly on microbiome composition. IMPORTANCE Prebiotic fermentation in the gut often leads to the coproduction of short-chain fatty acids (SCFAs) and gases. While excess gas production can be a potential problem for those with functional gut disorders, gas production is rarely considered during prebiotic design. In this study, we combined the use of theoretical models and an ex vivo experimental platform to illustrate that both the chemical composition of the prebiotic and the community composition of the human gut microbiota can affect the volume and content of gas production during prebiotic fermentation. Specifically, more prevalent metabolic processes such as hydrogen production were strongly affected by the oxidation state of the probiotic, while rare metabolisms such as methane production were less affected by the chemical nature of the substrate and entirely dependent on the presence of Methanobacteria in the microbiota.


mBio ◽  
2017 ◽  
Vol 8 (6) ◽  
Author(s):  
Mia C. Theilmann ◽  
Yong Jun Goh ◽  
Kristian Fog Nielsen ◽  
Todd R. Klaenhammer ◽  
Rodolphe Barrangou ◽  
...  

ABSTRACT Therapeutically active glycosylated phytochemicals are ubiquitous in the human diet. The human gut microbiota (HGM) modulates the bioactivities of these compounds, which consequently affect host physiology and microbiota composition. Despite a significant impact on human health, the key players and the underpinning mechanisms of this interplay remain uncharacterized. Here, we demonstrate the growth of Lactobacillus acidophilus on mono- and diglucosyl dietary plant glycosides (PGs) possessing small aromatic aglycones. Transcriptional analysis revealed the upregulation of host interaction genes and identified two loci that encode phosphotransferase system (PTS) transporters and phospho-β-glucosidases, which mediate the uptake and deglucosylation of these compounds, respectively. Inactivating these transport and hydrolysis genes abolished or severely reduced growth on PG, establishing the specificity of the loci to distinct groups of PGs. Following intracellular deglucosylation, the aglycones of PGs are externalized, rendering them available for absorption by the host or for further modification by other microbiota taxa. The PG utilization loci are conserved in L. acidophilus and closely related lactobacilli, in correlation with versatile growth on these compounds. Growth on the tested PG appeared more common among human gut lactobacilli than among counterparts from other ecologic niches. The PGs that supported the growth of L. acidophilus were utilized poorly or not at all by other common HGM strains, underscoring the metabolic specialization of L. acidophilus. These findings highlight the role of human gut L. acidophilus and select lactobacilli in the bioconversion of glycoconjugated phytochemicals, which is likely to have an important impact on the HGM and human host. IMPORTANCE Thousands of therapeutically active plant-derived compounds are widely present in berries, fruits, nuts, and beverages like tea and wine. The bioactivity and bioavailability of these compounds, which are typically glycosylated, are altered by microbial bioconversions in the human gut. Remarkably, little is known about the bioconversion of PGs by the gut microbial community, despite the significance of this metabolic facet to human health. Our work provides the first molecular insights into the metabolic routes of diet relevant and therapeutically active PGs by Lactobacillus acidophilus and related human gut lactobacilli. This taxonomic group is adept at metabolizing the glucoside moieties of select PG and externalizes their aglycones. The study highlights an important role of lactobacilli in the bioconversion of dietary PG and presents a framework from which to derive molecular insights into their metabolism by members of the human gut microbiota. IMPORTANCE Thousands of therapeutically active plant-derived compounds are widely present in berries, fruits, nuts, and beverages like tea and wine. The bioactivity and bioavailability of these compounds, which are typically glycosylated, are altered by microbial bioconversions in the human gut. Remarkably, little is known about the bioconversion of PGs by the gut microbial community, despite the significance of this metabolic facet to human health. Our work provides the first molecular insights into the metabolic routes of diet relevant and therapeutically active PGs by Lactobacillus acidophilus and related human gut lactobacilli. This taxonomic group is adept at metabolizing the glucoside moieties of select PG and externalizes their aglycones. The study highlights an important role of lactobacilli in the bioconversion of dietary PG and presents a framework from which to derive molecular insights into their metabolism by members of the human gut microbiota.


2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar

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.


2020 ◽  
Vol 158 (6) ◽  
pp. S-479
Author(s):  
Sayar Abdulkhakov ◽  
Dilyara Safina ◽  
Maria Markelova ◽  
Tatyana Grigoryeva ◽  
Eugenia A. Boulygina ◽  
...  

2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar

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.


2017 ◽  
Author(s):  
David Gilbert ◽  
Monika Heiner ◽  
Leila Ghanbar

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.


2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Congmin Xu ◽  
Huaiqiu Zhu ◽  
Peng Qiu

Abstract Background Human gut microbiota are important for human health and have been regarded as a “forgotten organ”, whose variation is closely linked with various factors, such as host genetics, diet, pathological conditions and external environment. The diversity of human gut microbiota has been correlated with aging, which was characterized by different abundance of bacteria in various age groups. In the literature, most of the previous studies of age-related gut microbiota changes focused on individual species in the gut community with supervised methods. Here, we aimed to examine the underlying aging progression of the human gut microbial community from an unsupervised perspective. Results We obtained raw 16S rRNA sequencing data of subjects ranging from newborns to centenarians from a previous study, and summarized the data into a relative abundance matrix of genera in all the samples. Without using the age information of samples, we applied an unsupervised algorithm to recapitulate the underlying aging progression of microbial community from hosts in different age groups and identify genera associated to this progression. Literature review of these identified genera indicated that for individuals with advanced ages, some beneficial genera are lost while some genera related with inflammation and cancer increase. Conclusions The multivariate unsupervised analysis here revealed the existence of a continuous aging progression of human gut microbiota along with the host aging process. The identified genera associated to this aging process are meaningful for designing probiotics to maintain the gut microbiota to resemble a young age, which hopefully will lead to positive impact on human health, especially for individuals in advanced age groups.


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