Integrated analysis of facial microbiome and skin physio-optical properties unveils cutotype-dependent aging effects

Abstract

Background: Our facial skin hosts millions of microorganisms, primarily bacteria, crucial for skin health by maintaining the physical barrier, modulating immune response, and metabolizing bioactive materials. Aging significantly influences the composition and function of the facial microbiome, impacting skin immunity, hydration, and inflammation, highlighting potential avenues for interventions targeting aging-related facial microbes amidst changes in skin physiological properties.

Results: We conducted a multi-center and deep sequencing survey to investigate the intricate interplay of aging, skin physio-optical conditions, and facial microbiome. Leveraging a newly-generated dataset of 2737 species-level metagenome-assembled genomes (MAGs), our integrative analysis highlighted aging as the primary driver, influencing both facial microbiome composition and key skin characteristics, including moisture, sebum production, gloss, pH, elasticity, and sensitivity. Further mediation analysis revealed that skin characteristics significantly impacted the microbiome, mostly as a mediator of aging. Utilizing this dataset, we uncovered two consistent cutotypes across sampling cities and identified aging-related microbial MAGs. Additionally, a Facial Aging Index (FAI) was formulated based on the microbiome, uncovering the cutotype-dependent effects of unhealthy lifestyles on skin aging. Finally, we distinguished aging related microbial pathways influenced by lifestyles with cutotype-dependent effect.

Conclusions: Together, our findings emphasize aging's central role in facial microbiome dynamics, and support personalized skin microbiome interventions by targeting lifestyle, skin properties, and aging-related microbial factors. Video Abstract.

Keywords: Aging; Cutotype; Facial microbiome; Lifestyle; Metagenome-assembled genomes.

Fig. 1

Construction and analysis of the Facial Microbiome Genome Compendium (FMGC). A Overall workflow of this study including the multi-center sample and meta info collection, measurement of skin physio-optical properties using industrial-grade instruments such as Corneometer, pH meter, Glossymeter, Sebumeter, and Cutometer from Courage and Khazaka, Visia-CR from Canfield and Tivi700 from WheelsBridge, generation of a representative Facial Microbiome Genome Compendium (FMGC) consisting 2737 species level genomes and integrative analyses of the (meta)genomic features with the host meta-data and skin features. B–D Mapping rates comparison: Comparison of mapping rates of facial metagenome sequencing reads to MAGs from FMGC, SMGC, and UHSG. The facial metagenome data included B 498 sample from this study; C 506 sample from SMGC study and randomly selected 120 sample from UHSG study; and 822 sample including 3 sample sites from the iHSMGC study [5]. D Novelty assessment: Evaluation of FMGC MAGs’ novelty based on their overlap with reference microbial genomes in the SMGC and those used by GTDB-tk and metaPhlAn4. E Phylogenetic analysis of the non-redundant 3359 MAGs combined from the FMGC and SMGC genomes. The orange color strip for FMGC MAGs, grey for SMGC MAGs, and blue for UHSG MAGs, and light pink for novel MAGs in FMGC. The outer ring is colored by phyla that were annotated using GTDB-tk [33]. F Phylogenetic diversity (branch length of selected MAGs) expansion of the FMGC over the SMGC genomes at the phylum level. The top ten 10 phyla with the highest number of MAGs were selected. G Expansion of the FMGC MAGs over the SMGC genomes at the genus level. Top 10 genera with the largest number of MAGs were selected. Statistical significance indicated by: ns p > 0.05, p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, Wilcoxon rank sum test

Fig. 2

Two consistent cutotypes built with FMGC and the correlation between microbial community structure and aging and skin physio-optical properties. Cutotypes constructed using genus-level abundances derived from the A FMGC MAGs and B metaPhlan4. The PCoA plots display the cutotypes built with all samples together. The Sankey diagram illustrates the analysis of samples separated by region and the combined analysis of all samples, showcasing the variation in cutotypes. C The proportion of different cutotypes observed, when age, moisture, and sebum are evenly grouped according to their distributions. D The relative abundance of the driver of the cutotypes, when age, moisture, and sebum are evenly grouped according to their distributions. E The tile map showing the Pearson correlation between collected meta info and skin physio-optical properties. The width of curves is the explained variance of these factors to metagenome structure calculated with Envfit test, and colors for its p-value. F Mediation analysis examining relationship between age and microbiome structure, considering significant influencing factors such as sebum, moisture, and sensitivity. The arrow from the independent variable (age) to the mediating variable (sebum, moisture, and Tivi) illustrates the influence of the independent variable on the mediating variable. Likewise, the arrow from the mediating variable to the dependent variable (microbiome structure) demonstrates the impact of the mediating variable on the dependent variable. Finally, the arrow from the independent variable to the dependent variable indicates the overall effect of the independent variable on the dependent variable, encompassing both total effect and average direct effect. Average direct effects (ADE) refers to the direct effects of the independent variable on the dependent variable. It represents the effect of the independent variable on the dependent variable while controlling for the mediating variable(s). Total effect, on the other hand, refers to the overall effect (direct + indirect) of the independent variable on the dependent variable. p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, Wilcoxon rank sum test

Fig. 3

A microbiome-based facial age index (FAI) reveals effects of suboptimal lifestyle habits on skin aging. A Barplot showing the increase in Shannon diversity with advancing age. B Scatter plot displaying MAGs correlated with the calculated Facial Aging Index (FAI). Labeled dots represent MAGs with mean abundance higher than 1% or relatively higher absolute Spearman’s R (≥ 0.5). Framed dots indicate MAGs that also exhibit strong correlations with FAI. C Facial age index (FAI) built with marker microbes and its significantly positive correlation with chronical age, confirming the practicability of using facial skin microbes to access skin age. Noted that Wuhan samples were not included in the construction of FAI due to its limited age range and inclusion of acne samples. D Boxplot showing the FAI-residuals (with the effects of the chronical age removed) in participants with/without certain lifestyle habits and different cutotypes. E PCoA plot demonstrating the difference of overall microbiome structure among participants with/without certain lifestyle habits and different cutotypes. p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, Wilcoxon rank sum test

Fig. 4

Functional analysis reveals molecular pathways underlying skin aging and their associations with suboptimal lifestyle habits. A Boxplot showing the accumulated abundance of aging-related (Inflammation [58, 59], NAD consumption [–62, 71], and Oxidation/Glycosylation [–66]; red shadows) and anti-aging-related (Anti-Aging, Biosynthesis, and Anti-Oxidation; green shadows) pathways across different age groups. B Tile map plot displaying the effect size, as determined by the Wilcoxon rank sum test (filtered with adjusted one tailed p value < 0.1), between the youngest and oldest age groups, with or without make up habits and with or without stay-up-late habits. p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001, Wilcoxon rank sum test