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Household Pet Ownership and the Microbial Diversity of the Human Gut Microbiota.

Ashley E Kates | Omar Jarrett | Joseph H Skarlupka | Ajay Sethi | Megan Duster | Lauren Watson | Garret Suen | Keith Poulsen | Nasia Safdar
Frontiers in cellular and infection microbiology | 2020

The human gut microbiome has a great deal of interpersonal variation due to both endogenous and exogenous factors, like household pet exposure. To examine the relationship between having a pet in the home and the composition and diversity of the adult gut microbiome, we conducted a case-control study nested in a larger, statewide study, the Survey of the Health of Wisconsin. Stool samples were collected from 332 participants from unique households and analyzed using 16S rRNA sequencing on the Illumina MiSeq. One hundred and seventy-eight participants had some type of pet in the home with dogs and cats being the most prevalent. We observed no difference in alpha and beta diversity between those with and without pets, though seven OTUs were significantly more abundant in those without pets compared to those with pets, and four were significantly more abundant in those with pets. When stratifying by age, seven of these remained significant. These results suggest that pet ownership is associated with differences in the human gut microbiota. Further research is needed to better characterize the effect of pet ownership on the human gut microbiome.

Pubmed ID: 32185142

Research resources used in this publication

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Associated grants

  • Agency: NLM NIH HHS, United States
    Id: T15 LM007359
  • Agency: NHLBI NIH HHS, United States
    Id: RC2 HL101468
  • Agency: NCRR NIH HHS, United States
    Id: KL2 RR025012

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This is a list of tools and resources that we have found mentioned in this publication.


phyloseq (tool)

RRID:SCR_013080

Software for handling and analysis of high-throughput microbiome census data.

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DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

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