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Cited by 258Gut microbiota and healthOral microbiology and periodontitis researchProbiotics and Fermented Foods

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Aging progression of human gut microbiota

Open access
Xu, Congmin, Zhu, Huaiqiu, Qiu, Peng·2019·BMC Microbiology
DOI 10.1186/s12866-019-1616-2PMC PMC6819604
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Abstract

Background: Human gut microbiota are important for human health and have been regarded as a “forgotten organ”, whose variation is closely linked with various factors, such as host genetics, diet, pathological conditions and external environment. The diversity of human gut microbiota has been correlated with aging, which was characterized by different abundance of bacteria in various age groups. In the literature, most of the previous studies of age-related gut microbiota changes focused on individual species in the gut community with supervised methods. Here, we aimed to examine the underlying aging progression of the human gut microbial community from an unsupervised perspective. Results: We obtained raw 16S rRNA sequencing data of subjects ranging from newborns to centenarians from a previous study, and summarized the data into a relative abundance matrix of genera in all the samples. Without using the age information of samples, we applied an unsupervised algorithm to recapitulate the underlying aging progression of microbial community from hosts in different age groups and identify genera associated to this progression. Literature review of these identified genera indicated that for individuals with advanced ages, some beneficial genera are lost while some genera related with inflammation and cancer increase. Conclusions: The multivariate unsupervised analysis here revealed the existence of a continuous aging progression of human gut microbiota along with the host aging process. The identified genera associated to this aging process are meaningful for designing probiotics to maintain the gut microbiota to resemble a young age, which hopefully will lead to positive impact on human health, especially for individuals in advanced age groups.

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Crossref · 10.1186/s12866-019-1616-2

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Fig. 1 Sample overview using PCA. Using the relative abundance of 247 genera across all the 367 samples as input, we linearly transformed and visualized the data in a three-dimensional space. Each sample is represented by one dot, colored according to age. Samples from children younger than three (the dark blue dots) scattered most distantly, while older age groups were mixed together in the PCA space
Fig. 1 Sample overview using PCA. Using the relative abundance of 247 genera across all the 367 samples as input, we linearly transformed and visualized the data in a three-dimensional space. Each sample is represented by one dot, colored according to age. Samples from children younger than three (the dark blue dots) scattered most distantly, while older age groups were mixed together in the PCA spaceCC BY · PubMed Central
Fig. 2 SPD recovered aging progression with taxonomical composition of human gut microbiota. a Progression similarity matrix for all genera, with each element counting the number of progression orderings the two corresponding genera shared. b We manually picked the highlighted area from (a). These selected genera were consistent with a common set of putative progression orderings. c An overall minimal spanning tree of the 14 age groups based on the selected genera. Each node represents one age group
Fig. 2 SPD recovered aging progression with taxonomical composition of human gut microbiota. a Progression similarity matrix for all genera, with each element counting the number of progression orderings the two corresponding genera shared. b We manually picked the highlighted area from (a). These selected genera were consistent with a common set of putative progression orderings. c An overall minimal spanning tree of the 14 age groups based on the selected genera. Each node represents one age groupCC BY · PubMed Central
Fig. 3 Genera that first increased and then decreased during aging, especially sharply decreased in the 13th or 14th age groups, or both
Fig. 3 Genera that first increased and then decreased during aging, especially sharply decreased in the 13th or 14th age groups, or bothCC BY · PubMed Central
Fig. 4 Genera that exhibited general increasing patterns during aging
Fig. 4 Genera that exhibited general increasing patterns during agingCC BY · PubMed Central

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Research works printed but not linked: 2

  1. Padmanabhan R, Dubourg G, Lagier J-C, Couderc C, Michelle C, Raoult D, Fournier P-E. Genome sequence and description of corynebacterium ihumii sp. nov. Stand Genomic Sci. 2014; 9(3):1128. https://doi.org/10.4056/sigs.5149006. · DOI
  2. Anderson MJ. Permutation tests for univariate or multivariate analysis of variance and regression. Can J Fish Aquat Sci. 2001; 58(3):626–39. https://doi.org/10.1139/cjfas-58-3-626. · DOI

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Metadata

Authors
Xu, C., Zhu, H., & Qiu, P.
Affiliations
  • Georgia Institute of Technology (US)
  • Peking University (CN)
Corresponding author
Qiu, Peng
ORCID
Venue
BMC Microbiology
Publisher
Springer Science and Business Media LLC
Published
2019-10-28
Volume
19
Issue
1
Pages
236-236
ISSN
1471-2180 (ISSN-L)
DOI
10.1186/s12866-019-1616-2
PMC
PMC6819604
OpenAlex
W2982520321
Cited by
258
References
67
Citations by year
2026: 21 · 2025: 26 · 2024: 52 · 2023: 39 · 2022: 50 · 2021: 43 · 2020: 27
FWCI
9.92
Type
journal article
Language
en
Update notices
correction (2021-04-28)
Open access
Licence
Locations
  • BMC Microbiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (1552784)
  • Georgia Institute of Technology
  • China Scholarship Council
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
Keywords
gut microbiota, aging, age-related microbial changes, 16S rRNA gene sequencing, unsupervised clustering, microbial genera, probiotics, centenarians, inflammation, tumor-associated bacteria
MeSH terms
Gastrointestinal Microbiome, Unsupervised Machine Learning, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Bacteria, Child, Child, Preschool, DNA, Bacterial, DNA, Ribosomal, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Phylogeny, RNA, Ribosomal, 16S, Sequence Analysis, DNA, Young Adult

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