Evidence map›Paper›PMID 35538082›Full record

ArticleScientific data2022

Comprehensive 16S rRNA and metagenomic data from the gut microbiome of aging and rejuvenation mouse models.

Jongoh Shin, Jung-Ran Noh, Donghui Choe, Namil Lee, Yoseb Song, Suhyung Cho, Eun-Jung Kang, Min-Jeong Go, Seok Kyun Ha, Jae-Hoon Kim and 5 more

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 3 institutions in 1 country.

Jongoh Shin *Department of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Jung-Ran Noh *Laboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Donghui ChoeDepartment of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Namil LeeDepartment of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Yoseb SongDepartment of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Suhyung ChoDepartment of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
Eun-Jung KangLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Min-Jeong GoLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Seok Kyun HaLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Jae-Hoon KimLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Yong-Hoon KimLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Kyoung-Shim KimLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Byoung-Chan KimImmunotherapy Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.
Chul-Ho LeeLaboratory Animal Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea. chullee@kribb.re.kr.
Byung-Kwan ChoDepartment of Biological Sciences and KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea. bcho@kaist.ac.kr.ORCID 0000-0003-4788-4184
Korea Research Institute of Bioscience and Biotechnology · KRKorea Advanced Institute of Science and Technology · KREuBiologics (South Korea) · KR

Funding

National Research Foundation of Korea (NRF) 2018M3A9H3024759National Research Foundation of Korea (NRF) 2019M3A9F3065867
6 · The paper itself

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.

Indexed as

AgingGastrointestinal MicrobiomeRNA, Ribosomal, 16SAnimalsDisease Models, AnimalMetagenomicsMiceRejuvenationRNA, Ribosomal, 16S

Identifiers

PMID35538082
PMCPMC9091251
OpenAlexW4229443229

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.