Skin is a complex ecosystem colonized by millions of microorganisms, including bacteria, fungi, and viruses. Skin microbiota is believed to exert critical functions in maintaining host skin health. Profiling the structure of skin microbial community is the first step to overview the ecosystem. However, the community composition is highly individualized and extremely complex. To explore the fundamental factors driving the complexity of the ecosystem, namely the selection pressures, we review the present studies on skin microbiome from the perspectives of ecology. This review summarizes the following: (1) the composition of substances/nutrients in the cutaneous ecological environment that are derived from the host and the environment, highlighting their proposed function on skin microbiota; (2) the features of dominant skin commensals to occupy ecological niches, through self-adaptation and microbe–microbe interactions; (3) how skin microbes, by their structures or bioactive molecules, reshape host skin phenotypes, including skin immunity, maintenance of skin physiology such as pH and hydration, ultraviolet (UV) protection, odor production, and wound healing. This review aims to re-examine the host–microbe interactions from the ecological perspectives and hopefully to give new inspiration to this field.

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Background: Despite the complexity, distinct ecological niches are believed to primarily drive the skin microbiome composition. Meanwhile, skin aging is a dynamic process with a spectrum of phenotypical changes, making it an attractive model for studying microbiome-phenotype interactions. Although a large number of studies confirmed the impact of chronological age in skin bacterial communities, the understanding of cross-kingdom microbiome variation with skin aging remains minimal. And this is not trivial because one’s skin condition or perceived age may deviate largely from their actual age as skin aging is a complex process combining chronological and extrinsic aging. Results: To this end, 822 facial microbial samples and skin phenotypes from the corresponding area were assessed in a Chinese cohort, the largest population size to date for skin shotgun metagenomic profiling. Our data revealed that among 14 measured variables, porphyrin and chronological age explained the most significant microbial variability. Consistent with previous studies based on 16S rRNA gene sequencing, we revealed increased biodiversity with aging and further specified age-associated species across kingdoms. While the abundance of most bacteria increased with age, two species, Cutibacterium acnes and Aeromicrobium choanae, declined. Microbiome undergoes active function selection from energy demands/growth to stress adaptation along aging. In addition, we characterized microbial changes in skin aging, asa combined consequence of both intrinsic and extrinsic reasons and reflecting the actual dynamic of niche conditions rather than chronological age. Using the multiple linear regression model, we predicted premature-aging/delayed-aging-related microbial species, mainly localizing to Moraxella osloensis and C. acnes. Furthermore, we validated the biological functions in vitro of some host-microbe interactions predicted by the microbiome-skin phenome association network. M. osloensis regulated collagen metabolism, extracellular matrix assembly and promoted cell senescence in human keratinocyte and fibroblast cells. Conclusions: We presume that application of both culture-independent and culture-dependent approaches can advance a good understanding of microbiome-phenotype interactions. Our study is of significance for designing interventions against aging-related skin conditions.