Aging progression of human gut microbiota

• Published in: BMC microbiology Vol. 19; no. 1; pp. 236 - 10
• Main Authors: Xu, Congmin, Zhu, Huaiqiu, Qiu, Peng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.10.2019; BioMed Central; BMC
• Subjects: 16S rRNA sequencing; Abundance; Adolescent; Adult; Age; Age Factors; Age groups; Aged; Aged, 80 and over; Aging; Aging (Biology); Algorithms; Artificial intelligence; Bacteria; Bacteria - classification; Bacteria - genetics; Biomedical engineering; Centenarians; Child; Child, Preschool; DNA, Bacterial - genetics; DNA, Ribosomal - genetics; Female; Gastrointestinal Microbiome; Gene expression; Genetics; Geriatrics; Gut microbiota; Health; Human gut microbiota; Humans; Infant; Infant, Newborn; Intestinal microflora; Literature reviews; Machine learning; Male; Microbiota; Microbiota (Symbiotic organisms); Microorganisms; Middle Aged; Neonates; Newborn infants; Oldest old people; Phylogeny; Probiotics; Relative abundance; RNA; RNA sequencing; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sample progression discovery; Sequence Analysis, DNA - methods; Taxonomy; Unsupervised Machine Learning; Variance analysis; Young Adult; United States > US; China; Georgia; Aging; Human gut microbiota; Sample progression discovery; 16S rRNA sequencing
• ISSN: 1471-2180; 1471-2180
• DOI: 10.1186/s12866-019-1616-2

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. 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. 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.