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- Title
Comprehensive 16S rRNA and metagenomic data from the gut microbiome of aging and rejuvenation mouse models.
- Authors
Shin, Jongoh; Noh, Jung-Ran; Choe, Donghui; Lee, Namil; Song, Yoseb; Cho, Suhyung; Kang, Eun-Jung; Go, Min-Jeong; Ha, Seok Kyun; Kim, Jae-Hoon; Kim, Yong-Hoon; Kim, Kyoung-Shim; Kim, Byoung-Chan; Lee, Chul-Ho; Cho, Byung-Kwan
- Abstract
The gut microbiota is associated with the health and longevity of the host. A few methods, such as fecal microbiota transplantation and oral administration of probiotics, have been applied to alter the gut microbiome and promote healthy aging. The changes in host microbiomes still remain poorly understood. Here, we characterized both the changes in gut microbial communities and their functional potential derived from colon samples in mouse models during aging. We achieved this through four procedures including co-housing, serum injection, parabiosis, and oral administration of Akkermansia muciniphila as probiotics using bacterial 16 S rRNA sequencing and shotgun metagenomic sequencing. The dataset comprised 16 S rRNA sequencing (36,249,200 paired-end reads, 107 sequencing data) and metagenomic sequencing data (307,194,369 paired-end reads, 109 sequencing data), characterizing the taxonomy of bacterial communities and their functional potential during aging and rejuvenation. The generated data expand the resources of the gut microbiome related to aging and rejuvenation and provide a useful dataset for research on developing therapeutic strategies to achieve healthy active aging. Measurement(s) Microbiome Technology Type(s) Metagenome • Bacterial 16 S RNA Factor Type(s) Ageing • Co-housing • Parabiosis • Serum-injection • Akkermansia (AK) treatment Sample Characteristic - Organism Mus musculus Sample Characteristic - Environment animal cage Sample Characteristic - Location South Korea
- Subjects
GUT microbiome; LABORATORY mice; ACTIVE aging; FECAL microbiota transplantation; METAGENOMICS; BACTERIAL diversity; CENTENARIANS; MICROBIAL metabolites
- Publication
Scientific Data, 2022, Vol 9, Issue 1, p1
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-022-01308-3