Dataset Information

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

ABSTRACT:

Objective

Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes.

Research design and methods

High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study in the U.S., Germany, Sweden, and Finland.

Results

Study site-specific patterns of gut colonization share characteristics across continents. Finland and Colorado have a significantly lower bacterial diversity, while Sweden and Washington state are dominated by Bifidobacterium in early life. Bacterial community diversity over time is significantly different by geographical location.

Conclusions

The microbiome of high-risk infants is associated with geographical location. Future studies aiming to identify the microbiome disease phenotype need to carefully consider the geographical origin of subjects.

SUBMITTER: Kemppainen KM

PROVIDER: S-EPMC4302256 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

Kemppainen Kaisa M KM Ardissone Alexandria N AN Davis-Richardson Austin G AG Fagen Jennie R JR Gano Kelsey A KA León-Novelo Luis G LG Vehik Kendra K Casella George G Simell Olli O Ziegler Anette G AG Rewers Marian J MJ Lernmark Åke Å Hagopian William W She Jin-Xiong JX Krischer Jeffrey P JP Akolkar Beena B Schatz Desmond A DA Atkinson Mark A MA Triplett Eric W EW

Diabetes care 20141217 2

<h4>Objective</h4>Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes.<h4>Research design and methods</h4>High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in t ...[more]

PMID: 25519450

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Project description:Teleost fish represent an invaluable repertoire of host species to study the factors shaping animal-associated microbiomes. Several studies have shown that the phylogenetic structure of the fish gut microbiome is driven by species-specific (e.g., host ancestry, genotype, or diet) and habitat-specific (e.g., hydrochemical parameters and bacterioplankton composition) factors. However, our understanding of other host-associated microbial niches, such as the skin mucus microbiome, remains limited. The goal of our study was to explore simultaneously the phylogenetic structure of the fish skin mucus and gut microbiome and compare the effect of species- and habitat-specific drivers on the structure of microbial communities in both tissues. We sampled 114 wild fish from 6 populations of 3 ecologically and phylogenetically contrasting Amazonian teleost species. Water samples were collected at each site, and 10 physicochemical parameters were characterized. The skin mucus, gut, and water microbial communities were characterized using a metabarcoding approach targeting the V3-V4 regions of the 16S rRNA. Our results showed a significant distinction between the phylogenetic profile and diversity of the microbiome from each microbial niche. Skin mucus and bacterioplankton communities were significantly closer in composition than gut and free-living communities. Species-specific factors mostly modulated gut bacterial communities, while the skin mucus microbiome was predominantly associated with environmental physicochemistry and bacterioplankton community structure. These results suggest that the variable skin mucus community is a relevant target for the development of microbial biomarkers of environmental status, while the more conserved gut microbiome is better suited to study long-term host-microbe interactions over evolutionary time scales.IMPORTANCE Whether host-associated microbiomes are mostly shaped by species-specific or environmental factors is still unresolved. In particular, it is unknown to what extent microbial communities from two different host tissues from the same host respond to these factors. Our study is one of the first to focus on the microbiome of teleost fish to shed a light on this topic as we investigate how the phylogenetic structure of microbial communities from two distinct fish tissues are shaped by species- and habitat-specific factors. Our study showed that in contrast to the teleost gut microbiome, skin mucus communities are highly environment dependent. This result has various implications: (i) the skin mucus microbiome should be used, rather than the gut, to investigate bacterial biomarkers of ecosystem perturbance in the wild, and (ii) the gut microbiome is better suited for studies of the drivers of phylosymbiosis, or the coevolution of fish and their symbionts.

Project description:BackgroundThe gut microbiome is important in modulating health in childhood. Metal exposures affect multiple health outcomes, but their ability to modify bacterial communities in children is poorly understood.ObjectivesWe assessed the associations of childhood and perinatal blood metal levels with childhood gut microbiome diversity, structure, species, gene family-inferred species, and potential pathway alterations.MethodsWe assessed the gut microbiome using 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing in stools collected from 6- to 7-year-old children participating in the GESTation and Environment (GESTE) cohort study. We assessed blood metal concentrations [cadmium (Cd), manganese (Mn), mercury (Hg), lead (Pb), selenium (Se)] at two time points, namely, perinatal exposures at delivery (N=70) and childhood exposures at the 6- to 7-y follow-up (N=68). We used multiple covariate-adjusted statistical models to determine microbiome associations with continuous blood metal levels, including linear regression (Shannon and Pielou alpha diversity indexes), permutational multivariate analysis of variance (adonis; beta diversity distance matrices), and multivariable association model (MaAsLin2; phylum, family, species, gene family-inferred species, and pathways).ResultsChildren's blood Mn and Se significantly associated with microbiome phylum [e.g., Verrucomicrobiota (coef=-0.305, q=0.031; coef=0.262, q=0.084, respectively)] and children's blood Mn significantly associated with family [e.g., Eggerthellaceae (coef=-0.228, q=0.052)]-level differences. Higher relative abundance of potential pathogens (e.g., Flavonifractor plautii), beneficial species (e.g., Bifidobacterium longum, Faecalibacterium prausnitzii), and both potentially pathogenic and beneficial species (e.g., Bacteriodes vulgatus, Eubacterium rectale) inferred from gene families were associated with higher childhood or perinatal blood Cd, Hg, and Pb (q<0.1). We found significant negative associations between childhood blood Pb and acetylene degradation pathway abundance (q<0.1). Finally, neither perinatal nor childhood metal concentrations were associated with children's gut microbial inter- and intrasubject diversity.DiscussionOur findings suggest both long- and short-term associations between metal exposure and the childhood gut microbiome, with stronger associations observed with more recent exposure. Future epidemiologic analyses may elucidate whether the observed changes in the microbiome relate to children's health. https://doi.org/10.1289/EHP9674.

Dataset Information

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

Objective

Research design and methods

Results

Conclusions

Publications

Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes.

Similar Datasets

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