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The dynamics of a family's gut microbiota reveal variations on a theme.

Schloss PD, Iverson KD, Petrosino JF, Schloss SJ - Microbiome (2014)

Bottom Line: A combination of genetics, diet, environment, and life history are all thought to impact the development of the gut microbiome.Using 16S rRNA gene and metagenomic shotgun sequence data, it was possible to distinguish the family from a cohort of normal individuals living in the same geographic region and to differentiate each family member.This transition was associated with increased diversity, decreased stability, and the colonization of significant abundances of Bacteroidetes and Clostridiales.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Microbiology and Immunology, University of Michigan, 1520A Medical Science Research Building I, 1150 W. Medical Center Drive, Ann Arbor, MI 48109, USA.

ABSTRACT

Background: It is clear that the structure and function of the human microbiota has significant impact on maintenance of health and yet the factors that give rise to an adult microbiota are poorly understood. A combination of genetics, diet, environment, and life history are all thought to impact the development of the gut microbiome. Here we study a chronosequence of the gut microbiota found in eight individuals from a family consisting of two parents and six children ranging in age from two months to ten years old.

Results: Using 16S rRNA gene and metagenomic shotgun sequence data, it was possible to distinguish the family from a cohort of normal individuals living in the same geographic region and to differentiate each family member. Interestingly, there was a significant core membership to the family members' microbiota where the abundance of this core accounted for the differences between individuals. It was clear that the introduction of solids represents a significant transition in the development of a mature microbiota. This transition was associated with increased diversity, decreased stability, and the colonization of significant abundances of Bacteroidetes and Clostridiales. Although the children and mother shared essentially the identical diet and environment, the children's microbiotas were not significantly more similar to their mother than they were to their father.

Conclusions: This analysis underscores the complex interactions that give rise to a personalized microbiota and suggests the value of studying families as a surrogate for longitudinal studies.

No MeSH data available.


The inverse Simpson alpha diversity index of the metagenome at three time points for each individual in the family using operational protein families (OPFs) (A) and Kyoto Encyclopedia of Genes and Genomes orthology (KEGG KO) categories (B). The horizontal lines indicate the average diversity value for each individual.
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Figure 7: The inverse Simpson alpha diversity index of the metagenome at three time points for each individual in the family using operational protein families (OPFs) (A) and Kyoto Encyclopedia of Genes and Genomes orthology (KEGG KO) categories (B). The horizontal lines indicate the average diversity value for each individual.

Mentions: Finally, we assessed the genetic diversity of the family’s microbiota by performing shotgun metagenomic sequencing using samples collected from each individual at the beginning, middle, and end of the sampling period. Across all individuals, we observed a total of 4,499 KEGG categories and 675,908 OPFs. The inverse Simpson alpha diversity index calculated using OPFs (Figure 7A) followed a pattern that was similar to the diversity calculated using 16S rRNA gene sequence data (Figure 1A); no trends were observed when ORFs were assigned to KEGG categories (Figure 7B). When we assigned the ORFs to OPFs, 7.3% were shared across all family members, 36% were shared among the four weaned children, and 13% were shared between the two breastfed children. When we assigned ORFs to KEGG categories, 66% were shared across all family members, 77% were shared among the four weaned children, and 78% were shared between the two breastfed children. Whether we assigned the ORFs to clusters based on KEGG KOs or to OPFs, we were able to separate samples by individual, as there was a significant concordance between the taxonomic structure of the communities based on 16S rRNA gene sequences and the genetic structure of the communities based on both the KEGG KO and OPF data (ROTU-KEGG = 0.58, ROTU-OPF = 0.69; Figure 8). These results support the 16S rRNA gene-based analysis that the genetic composition is conserved between individuals, but that each individual has a unique microbiota.


The dynamics of a family's gut microbiota reveal variations on a theme.

Schloss PD, Iverson KD, Petrosino JF, Schloss SJ - Microbiome (2014)

The inverse Simpson alpha diversity index of the metagenome at three time points for each individual in the family using operational protein families (OPFs) (A) and Kyoto Encyclopedia of Genes and Genomes orthology (KEGG KO) categories (B). The horizontal lines indicate the average diversity value for each individual.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4109379&req=5

Figure 7: The inverse Simpson alpha diversity index of the metagenome at three time points for each individual in the family using operational protein families (OPFs) (A) and Kyoto Encyclopedia of Genes and Genomes orthology (KEGG KO) categories (B). The horizontal lines indicate the average diversity value for each individual.
Mentions: Finally, we assessed the genetic diversity of the family’s microbiota by performing shotgun metagenomic sequencing using samples collected from each individual at the beginning, middle, and end of the sampling period. Across all individuals, we observed a total of 4,499 KEGG categories and 675,908 OPFs. The inverse Simpson alpha diversity index calculated using OPFs (Figure 7A) followed a pattern that was similar to the diversity calculated using 16S rRNA gene sequence data (Figure 1A); no trends were observed when ORFs were assigned to KEGG categories (Figure 7B). When we assigned the ORFs to OPFs, 7.3% were shared across all family members, 36% were shared among the four weaned children, and 13% were shared between the two breastfed children. When we assigned ORFs to KEGG categories, 66% were shared across all family members, 77% were shared among the four weaned children, and 78% were shared between the two breastfed children. Whether we assigned the ORFs to clusters based on KEGG KOs or to OPFs, we were able to separate samples by individual, as there was a significant concordance between the taxonomic structure of the communities based on 16S rRNA gene sequences and the genetic structure of the communities based on both the KEGG KO and OPF data (ROTU-KEGG = 0.58, ROTU-OPF = 0.69; Figure 8). These results support the 16S rRNA gene-based analysis that the genetic composition is conserved between individuals, but that each individual has a unique microbiota.

Bottom Line: A combination of genetics, diet, environment, and life history are all thought to impact the development of the gut microbiome.Using 16S rRNA gene and metagenomic shotgun sequence data, it was possible to distinguish the family from a cohort of normal individuals living in the same geographic region and to differentiate each family member.This transition was associated with increased diversity, decreased stability, and the colonization of significant abundances of Bacteroidetes and Clostridiales.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Microbiology and Immunology, University of Michigan, 1520A Medical Science Research Building I, 1150 W. Medical Center Drive, Ann Arbor, MI 48109, USA.

ABSTRACT

Background: It is clear that the structure and function of the human microbiota has significant impact on maintenance of health and yet the factors that give rise to an adult microbiota are poorly understood. A combination of genetics, diet, environment, and life history are all thought to impact the development of the gut microbiome. Here we study a chronosequence of the gut microbiota found in eight individuals from a family consisting of two parents and six children ranging in age from two months to ten years old.

Results: Using 16S rRNA gene and metagenomic shotgun sequence data, it was possible to distinguish the family from a cohort of normal individuals living in the same geographic region and to differentiate each family member. Interestingly, there was a significant core membership to the family members' microbiota where the abundance of this core accounted for the differences between individuals. It was clear that the introduction of solids represents a significant transition in the development of a mature microbiota. This transition was associated with increased diversity, decreased stability, and the colonization of significant abundances of Bacteroidetes and Clostridiales. Although the children and mother shared essentially the identical diet and environment, the children's microbiotas were not significantly more similar to their mother than they were to their father.

Conclusions: This analysis underscores the complex interactions that give rise to a personalized microbiota and suggests the value of studying families as a surrogate for longitudinal studies.

No MeSH data available.