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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 relative abundance of the 15 most important operational taxonomic units (OTUs) for distinguishing between family members. OTUs are ranked by the Gini index as determined by using the Random Forest algorithm trained to distinguish between family members. Each line represents the range of relative abundances observed for each OTU across individuals. The solid dot represents the median relative abundance for that OTU in that individual.
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Figure 3: The relative abundance of the 15 most important operational taxonomic units (OTUs) for distinguishing between family members. OTUs are ranked by the Gini index as determined by using the Random Forest algorithm trained to distinguish between family members. Each line represents the range of relative abundances observed for each OTU across individuals. The solid dot represents the median relative abundance for that OTU in that individual.

Mentions: Having identified the family’s core microbiota, we next attempted to identify variations on that theme within the family. We used the Random Forest machine-learning algorithm to identify OTUs that would allow us to distinguish between family members and obtained an out-of-bag error rate of 3.6%; at most, one sample from each individual was misclassified. When we limited the features to the top 15 OTUs that had the highest Gini index (Figure 3), the error rate was 7.2%. The low classification error rate indicated that each individual had a unique microbiota. The most obvious distinguishing OTUs included one affiliated with Catenibacterium (OTU 28), which had a high relative abundance in the parents and was most abundant in the father. The overall similarity of the parents’ microbiota is striking, as they are unrelated and spent more than 20 years apart prior to meeting. In spite of this, they still had similar community membership, but distinct abundances of those OTUs. The infant and two-year-old, both still at least partially breastfed, had an OTU that was affiliated with the Bifidobacterium (OTU 6). Although it was not one of the 15 strongest features, an OTU affiliated with the family Enterobacteriaceae (OTU 8; Gini: 1.53; median relative abundance: 24.5%) was also disproportionately high in the infant. The variations between the OTUs that distinguished the weaned children was more subtle and indicated that the differences were not due to the incidence of specific OTUs but were instead defined by the specific relative abundances of multiple OTUs. Overall, the difference in the microbiota of each family member was largely due to differences in abundance, not membership. These data support the hypothesis that one’s microbiota becomes individualized from an early age.


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

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

The relative abundance of the 15 most important operational taxonomic units (OTUs) for distinguishing between family members. OTUs are ranked by the Gini index as determined by using the Random Forest algorithm trained to distinguish between family members. Each line represents the range of relative abundances observed for each OTU across individuals. The solid dot represents the median relative abundance for that OTU in that individual.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The relative abundance of the 15 most important operational taxonomic units (OTUs) for distinguishing between family members. OTUs are ranked by the Gini index as determined by using the Random Forest algorithm trained to distinguish between family members. Each line represents the range of relative abundances observed for each OTU across individuals. The solid dot represents the median relative abundance for that OTU in that individual.
Mentions: Having identified the family’s core microbiota, we next attempted to identify variations on that theme within the family. We used the Random Forest machine-learning algorithm to identify OTUs that would allow us to distinguish between family members and obtained an out-of-bag error rate of 3.6%; at most, one sample from each individual was misclassified. When we limited the features to the top 15 OTUs that had the highest Gini index (Figure 3), the error rate was 7.2%. The low classification error rate indicated that each individual had a unique microbiota. The most obvious distinguishing OTUs included one affiliated with Catenibacterium (OTU 28), which had a high relative abundance in the parents and was most abundant in the father. The overall similarity of the parents’ microbiota is striking, as they are unrelated and spent more than 20 years apart prior to meeting. In spite of this, they still had similar community membership, but distinct abundances of those OTUs. The infant and two-year-old, both still at least partially breastfed, had an OTU that was affiliated with the Bifidobacterium (OTU 6). Although it was not one of the 15 strongest features, an OTU affiliated with the family Enterobacteriaceae (OTU 8; Gini: 1.53; median relative abundance: 24.5%) was also disproportionately high in the infant. The variations between the OTUs that distinguished the weaned children was more subtle and indicated that the differences were not due to the incidence of specific OTUs but were instead defined by the specific relative abundances of multiple OTUs. Overall, the difference in the microbiota of each family member was largely due to differences in abundance, not membership. These data support the hypothesis that one’s microbiota becomes individualized from an early age.

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.