Dataset Information

Segregation of age-related skin microbiome characteristics by functionality.

ABSTRACT: Although physiological changes are the most evident indicators of skin aging by alteration of the skin's structure and function, we question whether skin aging is also affected by the structure and assembly process of the skin microbiome. We analysed the skin microbiomes of 73 healthy Chinese women in two age groups (25-35 years old and 56-63 years old) using 16S rRNA gene amplicon sequencing; the overall microbiome structure was significantly different between the two age groups. An analysis using ecological theory to evaluate the process of microbial community assembly processes revealed that the microbiomes of the older group were formed under a greater influence of the niche-based process, with the network of microbes being more collapsed than that of the younger group. Inferred metagenomic functional pathways associated with replication and repair were relatively more predominant in the younger group whereas, among the various metabolism-related pathways, those associated with biodegradation were more predominant in the older group. Interestingly, we found two segregated sub-typing patterns in the younger group which were also observed in the skin microbiomes of young Chinese women living in four other cities in China. The results of our study highlights candidate microbes and functional pathways that are important for future research into preventing skin aging and which could lead to a comprehensive understanding of age-related skin microbiome characteristics.

SUBMITTER: Kim HJ

PROVIDER: S-EPMC6856112 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Segregation of age-related skin microbiome characteristics by functionality.

Kim Hye-Jin HJ Kim Jin Ju JJ Myeong Nu Ri NR Kim Taeyune T Kim DooA D An Susun S Kim Hanbyul H Park Taehun T Jang Sue Im SI Yeon Jae Ho JH Kwack Ilyoung I Sul Woo Jun WJ

Scientific reports 20191114 1

Although physiological changes are the most evident indicators of skin aging by alteration of the skin's structure and function, we question whether skin aging is also affected by the structure and assembly process of the skin microbiome. We analysed the skin microbiomes of 73 healthy Chinese women in two age groups (25-35 years old and 56-63 years old) using 16S rRNA gene amplicon sequencing; the overall microbiome structure was significantly different between the two age groups. An analysis us ...[more]

PMID: 31727980

Similar Datasets

Project description:BackgroundHuman gut microbial functions are often associated with various diseases and host physiologies. Aging, a less explored factor, is also suspected to affect or be affected by microbiome alterations. By combining functional feature selection with supervised classification, we aim to facilitate identification of age-related functional characteristics in metagenomes from several human gut microbiome studies (MetaHIT, MicroAge, MicroObes, Kurokawa et al.'s and Gill et al.'s dataset).ResultsWe apply two feature selection methods, term frequency-inverse document frequency (TF-iDF) and minimum-redundancy maximum-relevancy (mRMR), to identify functional signatures that differentiate metagenomes by age. After features are reduced, we use a support vector machine (SVM) to predict host age of new metagenomes. Functional features are from protein families (Pfams), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, KEGG ontologies and the Gene Ontology (GO) database. Initial investigations demonstrate that ordination of the functional principal components shows great overlap between different age groups. However, when feature selection is applied, mRMR tightens the ordination cluster for each age group, and TF-iDF offers better linear separation. Both TF-iDF and mRMR were used in conjunction with a SVM classifier and achieved areas under receiver operating characteristic curves (AUCs) 10 to 15% above chance to classify individuals above/below mid-ages (about 38 to 43 years old) using Pfams. Better performance around mid-ages is also observed when using other functional categories and age-balanced dataset. We also identified some age-related Pfams that improved age discrimination at age 65 with another feature selection method called LEfSe, on an age-balanced dataset. The selected functional characteristics identify a broad range of age-relevant metabolisms, such as reduced vitamin B12 synthesis, reduced activity of reductases, increased DNA damage, occurrences of stress responses and immune system compromise, and upregulated glycosyltransferases in the aging population.ConclusionsFeature selection can yield biologically meaningful results when used in conjunction with classification, and makes age classification of new human gut metagenomes feasible. While we demonstrate the promise of this approach, the data-dependent prediction performance could be further improved. We hypothesize that while the Qin et al. dataset is the most comprehensive to date, even deeper sampling is needed to better characterize and predict the microbiomes' functional content.

Project description:BackgroundAccumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.ResultsAs a part of the 2018 CAMDA challenge, we functionally profiled the available ~?400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo.ConclusionsWe demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~?400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

Project description:BackgroundHost-specific microbiomes play an important role in individual health and ecology; in marine mammals, epidermal microbiomes may be a protective barrier between the host and its aqueous environment. Understanding these epidermal-associated microbial communities, and their ecological- or health-driven variability, is the first step toward developing health indices for rapid assessment of individual or population health. In Cook Inlet, Alaska, an endangered population of beluga whales (Delphinapterus leucas) numbers fewer than 300 animals and continues to decline, despite more than a decade of conservation effort. Characterizing the epidermal microbiome of this species could provide insight into the ecology and health of this endangered population and allow the development of minimally invasive health indicators based on tissue samples.ResultsWe sequenced the hypervariable IV region of bacterial and archaeal SSU rRNA genes from epidermal tissue samples collected from endangered Cook Inlet beluga whales (n = 33) and the nearest neighboring population in Bristol Bay (n = 39) between 2012 and 2018. We examined the sequences using amplicon sequence variant (ASV)-based analyses, and no ASVs were associated with all individuals, indicating a greater degree of epidermal microbiome variability among beluga whales than in previously studied cetacean species and suggesting the absence of a species-specific core microbiome. Epidermal microbiome composition differed significantly between populations and across sampling years. Comparing the microbiomes of Bristol Bay individuals of known health status revealed 11 ASVs associated with potential pathogens that differed in abundance between healthy individuals and those with skin lesions or dermatitis. Molting and non-molting individuals also differed significantly in microbial diversity and the abundance of potential pathogen-associated ASVs, indicating the importance of molting in maintaining skin health.ConclusionsWe provide novel insights into the dynamics of Alaskan beluga whale epidermal microbial communities. A core epidermal microbiome was not identified across all animals. We characterize microbial dynamics related to population, sampling year and health state including level of skin molting. The results of this study provide a basis for future work to understand the role of the skin microbiome in beluga whale health and to develop health indices for management of the endangered Cook Inlet beluga whales, and cetaceans more broadly.

Dataset Information

Segregation of age-related skin microbiome characteristics by functionality.

Publications

Segregation of age-related skin microbiome characteristics by functionality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets