Study subjects and measurement of skin physiological parameters
We analyzed skin microbiome and mycobiome from cheeks and foreheads of healthy younger (19–28 years old, Y-group) and older (60–63 years old, O-group) Korean women who were free from cutaneous disorders (Table 1 and Supplementary Table S1). All 61 subjects had been living in Seoul, Korea, for more than 3 years with normal skin conditions. We preferentially selected those who had sebum secretion greater than 30 arbitrary units and moisture greater than 50 arbitrary units in both groups. Among the measurements of moisture content, pH, sebum content, and transepidermal water loss (TEWL), only sebum and TEWL decreased significantly in the O-group compared to the Y-group in the cheeks (P = 2.25e−06, Wilcoxon rank-sum test; P = 0.019, Welch two-sample t test) and forehead (P = 1.33e−06, Wilcoxon rank-sum test; P = 0.003, Welch two-sample t test). Whereas no significant differences were found in the average values for moisture (cheeks: Y-group, 59.9; O-group, 56.6; forehead: Y-group, 61.1; O-group, 58.7) and pH (cheeks: Y-group, 6.0; O-group, 5.8; forehead: Y-group, 6.0; O-group, 5.6) between the two age groups.
Comparisons in cheek and forehead microbiome and mycobiome between the two age groups
We analyzed bacterial communities from 27 Y-group samples (cheeks, n = 13; forehead, n = 14) and 24 O-group samples (cheeks, n = 12; forehead, n = 12) and fungal communities from 28 Y-group samples (cheeks, n = 15; forehead, n = 13) and 32 O-group samples (cheeks, n = 16; forehead, n = 16), except for samples that were eliminated from the Illumina Mi-Seq sequencing due to low sequence reads (bacteria, < 10,000; fungi, < 1000 reads) (Supplementary Table S1). An average of 49,062 (Y-group) and 55,946 (O-group) merged sequences were obtained, and 5693 ASVs were generated and used for bacterial taxonomic assignments based on the SILVA database. An average of 12,478 (Y-group) and 11,055 (O-group) merged sequences were obtained, and 675 ASVs were generated and used for fungal taxonomic classification based on the UNITE database.
Principal coordinates analysis with Bray–Curtis distances showed a clear separation in bacterial communities between the Y-group and O-group of cheek and forehead skin microbiome (Fig. 1) (P = 0.011, P = 0.001, respectively; PERMENOVA). The fungal communities showed significant differences between the Y-group and O-group only in the cheeks (P = 0.001; PERMANOVA). The alpha-diversity of bacterial and fungal communities was significantly higher in the O-group than the Y-group on both the forehead and cheek (Fig. 1). Simpson’s diversity analysis, which considers both richness and evenness, was significantly higher in the O-group cheek community, indicating that the skin microbiome and mycobiome of older women consisted of certain dominant microbes.
Characterization of skin microbiome and mycobiome in cheeks and forehead of Y-group and O-group. Principal coordinates analysis was performed based on Bray–Curtis distance between the Y-group and O-group in cheeks and forehead microbiome and mycobiome. (a,b) In the cheeks, the bacterial (a) and fungal (b) communities were significantly different by age (Y-group, P = 0.011 and O-group, P = 0.001; PERMANOVA). (c,d) In the forehead, the bacterial (c) communities show significant differences (P = 0.001, PERMANOVA), but the fungal (d) communities did not. Alpha diversity of bacterial and fungal communities on (a,b) cheeks and (c,d) forehead of the Y-group and O-group (***P < 0.001, **P < 0.01, *P < 0.05, using Wilcoxon rank-sum test and Welch two-sample t test).
Taxonomic overview and signatures of aged related microbial communities in cheek and forehead
We identified 36 bacterial and 6 fungal phyla from 61 skin samples. The most abundant bacterial phyla, Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes, were present in the cheeks and foreheads of both the Y-group and O-group (Supplementary Fig. S1e,f).
We used the Linear discriminant analysis Effect Size (LEfSe) method to investigate the taxonomic biomarkers that contribute to age-related differences in the skin microbiome and mycobiome at the ASV level. In the cheek microbiome comparison, 39 bacterial ASVs were identified as preferentially abundant in one or the other age group: 8 ASVs in the Y-group and 31 ASVs in the O-group (Fig. 2a). Cutibacterium sp. (ASV2136 and ASV2107), Staphylococcus sp. (ASV3020), Lactobacillus iners (ASV3084), and Lactobacillus crispatus (ASV3075) were predominant in the Y-group. The 31 ASVs found in the O-group belonged to more diverse phyla (Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes, and Acidobacteria) than those in the Y-group. The fungus Malassezia restircta (ASV480) was more abundant on cheeks in the Y-group, whereas Trichobolus zukalii (ASV171), Lasioboolidium orbiculoides (ASV178), Mortierella polygonia (ASV600), Penicillium carneum (ASV147), and Debaryomyces prosopidis (ASV186) were abundant on cheeks in the O-group.
Heat map for the bacterial and fungal ASVs on the (a) cheeks and (b) foreheads of Korean women that shows a significant difference between the two age groups from LEfSe analysis (LDA score > 2.5 for bacteria, LDA score > 3.3 for fungi on the cheeks and LDA score > 2.7 for bacteria, LDA score > 3.5 for fungi on the foreheads).
In the forehead microbiome comparison, 44 bacterial ASVs were identified as preferentially abundant in one or the other age group: 8 ASVs in the Y-group and 36 ASVs in the O-group (Fig. 2b). Cutibacterium sp. (ASV2136 and ASV2107), L. iners (ASV3084), L. crispatus (ASV3075), and Deinococcus sp. (ASV2904) were found in the Y-group. As was true for the cheek microbiome comparison, the 36 bacterial ASVs identified in the O-group forehead microbiome belonged to more diverse phyla than those in the Y-group. Four fungal ASVs were identified in forehead samples: Malassezia sympodialis (ASV500), Stagonosporopsis valerianellae (ASV61), and Mycosphaerella tassiana (ASV36) were abundant in the Y-group, and M. restricta (ASV489) was abundant in the O-group (Fig. 2b). These results showed the dominant microbes were different according to whether cheek or forehead and whether Y-group or O-group.
Identification of major physiological factors and associated microbes
We performed a regression analysis using ggpubr package in R to determine the relationship between the skin microbes and skin parameters, with significant differences between the Y-group and O-group (Fig. 3). Regression analysis of the top 15 bacterial and fungal genera and skin parameters revealed that Cutibacterium in the cheek and forehead increased significantly with increasing sebum. In addition, Cutibacterium in the cheek showed a significant increase with increasing TEWL. In the forehead, it was confirmed that Staphylococcus, a dominant skin bacterium, increased significantly with increasing TEWL (P = 0.007; t test). In the case of fungi, Mortierella and Neurospora were found to correlate with a sebum reduction on the cheek (P = 0.022 and P = 0.048, respectively; t test) significantly. In the forehead, Candida and Cladosporium decreased with increasing sebum (P = 0.031, and P = 0.027, respectively). Malassezia, the dominant fungus in the skin microbiome, was found to have a significant correlation with an increase in sebum on the forehead (P = 0.04; t test). These results inferred that skin physiology influences the skin microbiome and increases or decreases the abundance of specific skin microbes.
Regression plot for skin metadata and the bacterial and fungal genera with a significant correlation among the top 15 genera in (a,c) cheeks and (b,d) forehead. For measuring correlation coefficient and P value, Pearson correlation analysis was performed in R. The x-axis represents the value of metadata, and the y-axis corresponds to the relative abundance of the genus.
Functional profiles of cheek and forehead microbiomes
We performed Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) analysis to identify the potential functional differences in the microbial communities of the two age groups. From the cheek and forehead, respectively, 23 and 27 differentially abundant predictive metagenomic pathways, involved in the Kyoto Encyclopedia of Genes and Genomes (KEGG) categories of metabolism, environmental information processing, cellular processes, and genetic information processing, were identified (α = 0.05, LDA score > 3. 0) (Fig. 4). Pathways belonging to the metabolism category were dominant in each age group. In the cheek of the Y-group, pathways involved in energy metabolism by bacteria, such as glycolysis/gluconeogenesis, citrate cycle, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-alanine metabolism, and thiamine metabolism, were predominant, whereas in the cheek of the O-group, degradation-related pathways, such as fatty acid degradation, synthesis and degradation of ketone bodies, benzoate degradation, and chloroalkane and chloroalkene degradation, were predominant. In the forehead of the Y-group, glycolysis/gluconeogenesis, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-glutamine and d-glutamate metabolism, d-alanine metabolism, and thiamine metabolism pathway were significantly more abundant, whereas in the forehead of the O-group, fatty acid degradation, synthesis and degradation of ketone bodies, valine/leucine and isoleucine degradation, and limonene/pinene degradation pathway were significantly more abundant.
Heat map for significantly different predicted functional pathways on (a) cheeks and (b) foreheads of Korean women by age based on LEfSe analysis (LDA score > 3.0).
The metabolism pathway for biotin, a water-soluble vitamin that is effective for skin health and essential for keratin production15, was more prevalent in the cheek and forehead of the Y-group. Interestingly, the metabolism pathway for lipoic acid, which is known to possess beneficial effects against skin aging and is used widely in cosmetic and dermatological products16,17, was significantly higher in the foreheads of the Y-group. We tracked the specific ASVs possessing these pathways, in both biotin metabolism and lipoic acid metabolism, Cutibacterium sp. (ASV2136 and ASV2130) and Staphylococcus sp. (ASV3008) were predicted to have the top three relative abundances in KOs. The relative abundances in biotin metabolism and lipoic acid metabolism of Cutibacterium sp. (ASV2136) were 24.9% and 26.1%, respectively. The relative abundances for each pathway for Staphylococcus sp. (ASV3008) were 10.2% and 18.7%, and for Cutibacterium sp. (ASV2130), they were 9.3% and 10.0%, respectively. We confirmed these two pathways in the genome of skin bacteria, C. acnes (Supplementary Fig. S2). These additional analyses support the reliability of the function in the skin environment of Cutibacterium. Interestingly, from the LEfSe result, Cutibacterium sp. (ASV2136) had a significantly higher abundance in the cheek and forehead microbiome of the Y-group. The pathway of biosynthesis of lipopolysaccharide, also known as bacterial endotoxins, showed higher abundance in the cheek and forehead microbiome of the O-group. The ASVs that contribute to inferring the LPS biosynthesis pathway were identified as Paraburkholderia sp. (ASV5030) and B. vesicularis (ASV4155). Also, pathways related to antibiotic biosynthesis (biosynthesis of vancomycin group antibiotics) and bacterial motility (bacterial chemotaxis and flagellar assembly; both belonging to the cellular processes category) were prominent in the cheek and forehead of the O-group. PICRUSt2 analysis implied that, regardless of skin site differences, the potential functions of the microbial community that compose the skin microbiome were similar according to age.
Network analysis on cheek and forehead microbiome and mycobiome
We performed SParse InversE Covariance estimation for Ecological Association Inference (SPIEC-EASI) analysis to evaluate the overall network of the skin microbes. The results of network density (D) on 81 cheek and 87 forehead ASVs, calculated using the ratio of the number of edges, showed higher network density in the skin microbiome of the Y-group (D = 0.015 and D = 0.001, in cheek and forehead, respectively) than the O-group (D = 0.007 and D = 0.007, respectively) (Fig. 5). To examine network correlation between bacteria and fungi, network density for Bacteria–Fungi (DBF) was calculated by the actual number of edges and a potential number of edges in a correlation ([bacterial nodes × fungal nodes]/2). We confirmed higher network density in the cheek of the Y-group (DBF = 0.008) than the O-group (DBF = 0) and edges of the major bacterial and fungal taxa, such as Staphylococcus sp. (ASV3008)—M. sympodialis (ASV500) and Roseomonas sp. (ASV4088)—M. restricta (ASV482), were observed in the cheek of the Y-group. In the forehead, edges of Methylobacterium sp. (ASV4314)—M. globosa (ASV454), Methylobacterium sp. (ASV4314)—Zygosaccharomyces rouxii (ASV208), and Venionella sp. (ASV3575)—M. sympodialis (ASV500) were observed in the Y-group, and edges of Cutibacterium sp. (ASV2107)—M. globosa (ASV461), Staphylococcus sp. (ASV3024)—M. arunalokei (ASV446), and Methylobacterium (ASV4314)—M. dermatis (ASV448) were observed in the O-group (DBF = 0.004). We found a network between bacteria and fungi with different kingdom levels in the skin microbiome, and especially, we confirmed that different genus or species level microbe was involved in the microbial network according to skin location and Y-, O-group.
Network analysis of the ASVs on (a) cheeks and (b) forehead of Korean women. Each node represents the ASV and the size of the node is based on relative abundance of each ASV. Color markings indicate the major taxa except for unidentified bacteria or fungi. Shapes represent the level of kingdom, Bacteria (bold) and Fungi (dotted line). The ASVs were selected for bacterial ASVs found in more than half of all samples on the cheeks and forehead, respectively, and for the fungal ASVs with a relative abundance of more than 0.1% in each of the cheeks and forehead samples. The D value is network density calculated using the ratio of the number of edges.
Source: Ecology - nature.com
