Changes in the skin microbiome during male maturation from 0 to 25 years of age

Abstract

Background

Skin microorganisms co‐develop with the human body and age influences the skin microenvironment and thus the skin bacterial community.

Objectives

To investigate the changes in the skin microbiota during male development.

Methods

High‐throughput 16S ribosomal RNA pyrosequencing was utilized to analyze the differences in bacterial composition of the skin in healthy males aged 0–25 years.

Results

There were significant differences in facial skin bacterial diversity (Shannon index) and richness (Chao index) among the 4 groups of subjects (p < 0.05). Streptococcus, Staphylococcus, Cutibacterium are dominant in males during growth, and regular changes occur with age after birth. Further analysis of skin bacteria between the 4 groups showed that the bacterial abundance of Cutibacterium acnes and Staphylococcus epidermidis tended to increase with age, and the bacterial abundance of Streptococcus, Rothia mucilaginosa, and Staphylococcus hominis tended to decrease with age.

Conclusions

There are some changes in cheek skin bacterial diversity during male development, and there is a relationship between skin bacterial changes and skin development processes.

1. INTRODUCTION

The skin is the largest organ of the human body and in most direct contact with the environment, with an estimated surface area of up to 2 m². ¹ As the largest organ of the human body, the skin is colonized by a wide and diverse range of microorganisms such as bacteria and fungi that exhibit significant intra‐ and inter‐individual variability ² , ³ as well as topographic and temporal diversity. ⁴ , ⁵ The diversity and abundance of the cutaneous microbial flora varies between gender, age, seasons, ethnicity. ⁶ Skin parameters such as pH and temperature also play a role in the growth or inhibitory effect of microorganisms. Microorganisms normally interact with each other and with our immune system to promote skin health and homeostasis.

The acquisition of skin microbiota occurs in the early stages of birth. In utero, the skin is sterile, devoid of any microorganism, and is colonized a few minutes after birth by commensal microorganisms of the mother, depending on the childbirth method. ⁷ , ⁸ , ⁹ This colonization process in the neonatal stage is essential for the establishment of immune tolerance towards commensal microorganisms. ¹⁰ Colonization of the microbiota continues during growth until reaching an equilibrium state in adulthood. ⁶ For the establishment of this microbiota, the skin provides essential nutrients, such as amino acids from the hydrolysis of proteins, fatty acids from the stratum corneum, sweat, lipid hydrolysis or sebum and lactic acids from sweat. After birth, the skin bacterial community gradually adapts to the skin environment over time, using skin provides essential nutrientspresent on the skin surface to survive. ¹¹ Skin microorganisms co‐develop with the human body and age influences the skin microenvironment and thus the skin bacterial community. ¹² , ¹³ This study focuses on the characteristics of skin microbiome changes during skin maturation in healthy males to investigate the differences in the bacterial composition of the skin of men at different ages and changes in the dominant bacteria, and to provide a theoretical basis for the analysis of male facial microorganisms.

2. MATERIALS AND METHODS

2.1. Instruments and materials

Sodium chloride (NaCl) and Tween 20 were purchased from Sinopharm Chemical Reagent Co. Ltd., Beijing, China; lysozyme, Bailingwei Chemical Technology Co., Ltd., Beijing, China; the DNeasy Blood and Tissue kit, QIAGEN, Germany. Other materials used were disposable sterile cotton swabs (Jiangsu Changfeng Medical Industry Co. Ltd., Jiangsu, China), 0.5‐mm glass beads (Shanghai Baili Biotechnology Co. Ltd., Shanghai, China).

2.2. Subjects and inclusion criteria

A total of 48 healthy Chinese local male volunteers aged 0–25 years, with a minimum age of 4 months, were participated in this study. They were divided into four groups: 0–3 years old (14 cases), 7–10 years old (seven cases), 13–18 years old (seven cases), and 20–25 years old (20 cases). Participants were healthy volunteers with no history of chronic skin conditions or autoimmune diseases. The subjects had not received any topical antibiotic, hormone, oral antibiotics, or antibacterial drugs for a month prior to the study. The subject was not allowed to clean the face and apply any cosmetics 8 h before sampling. All adult participants and parents/guardians of minors were informed the purpose of the study, and written consent was obtained according to the principles of the Declaration of Helsinki.

2.3. Microbial specimen collection

All subjects were asked to wash their faces the night before and to collect skin bacteria the next morning. It was requested to take at least an 8‐h interval between face washing and collection. Samples were obtained by the swab method using sterile wetting solution (0.9% NaCl and 0.1% Tween 20) on the facial cheek skin of subjects. The 4 × 2‐cm² areas of the skin surface were rubbed with a sterile swab at least 40 times. To minimize cross‐contamination, fresh sterile gloves were used for each sample. The samples were immediately stored at −80°C for subsequent DNA extraction.

2.4. DNA extraction and illumina MiSeq sequencing

In this study, total bacterial DNA was extracted using the DNeasy Blood & Tissue Kit from QIAGEN. Refer to the kit instructions for the specific steps. The V1‐V2 region of the 16S rRNA gene was amplified with primers 27F (5´‐AGAGTTTGATCCTGGCTCAG‐3´) and 338R (5´‐TGCTGCCTCCCGTAGGAGT‐3´) using a thermocycler (GeneAmp 9700, ABI). The PCR reaction system and Illumina MiSeq sequencing refer to our previously published literature. ¹⁴

2.5. Processing of sequencing data

Operational taxonomic units (OTUs) were clustered with 97% similarity cut‐off using UPARSE (Version 7.1 http://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16S rRNA gene sequence was analysed by RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the SILVA 16S rRNA database using a confidence threshold of 70%.

2.6. Statistics analysis

The distance algorithm of Principal coordinate analysis (PCoA) is Bray‐Curtis. To compare differences between groups, the nonparametric Wilcoxon rank‐sum test was used to compare microbial diversity between the two groups. The linear discriminant analysis (LDA) effective size (LEfSe) algorithm was used to identify significantly different species between groups (LDA scores > 3.5k). The data were analyzed on the free online platform of Majorbio I‐Sanger Cloud Platform (www.i‐sanger.com).

3. RESULTS

3.1. Bacterial OTU clusters

The raw data obtained from high‐throughput sequencing were sorted and filtered to obtain valid sequences for subsequent analysis. 2.29 million valid sequences were obtained from 48 samples, with an average sequence length of about 322 bp (Table S1). At 97% similarity level, the samples were clustered and annotated, and a total of 3531 OTUs were obtained. 32 phylum, 1011 genus and 2081 species were annotated with taxonomic information based on bacterial OTUs. Facial microbial OTUs intersect in males of different ages during maturation (Figure 1). The results indicate that there are differences in bacterial composition between age groups on the OTU level. In the whole age range, the OTU level showed a trend of increasing and then decreasing and leveling off, where the samples in the age group of 7–10 years had 2213 OTUs, which was the highest in the OTU level in the four groups. There were 650 OTUs in the four groups, and 836 OTUs were unique to the 7–10 years group samples, which was the highest among the four groups.

FIGURE 1.

Venn diagram on operational taxonomic unit (OTU) level; red: 0–3 group; blue: 7–10 group; green: 13–18 group; yellow: 20–25 group.

3.2. Comparison of bacterial diversity

The bacterial alpha and beta diversity the three groups were summarized in Figure 2. We analyzed alpha diversity in the four skin microbiomes based on the estimated (Chao index, Figure 2A) richness values, and the Shannon diversity values (Shannon index, Figure 2B). The two metrics analyses gave similar results, revealing that there was significant difference in skin microbial diversity and richness among the four groups (p‐value < 0.05). The diversity and richness had the same trend of increasing from 0–3 years group to 7–10 years group and decreasing trend from 7–10 years group to 20–25 years group. 7–10 years group was the maximum value of diversity and richness, the most significant difference between 7–10 years and 20–25 years sample group.

FIGURE 2.

Diversity comparisons. (A) Histogram of Chao values. OUT. (B) Histogram of Shannon values. OUT. (C) Principal Coordinates Analysis (PCoA) of weighted UniFrac distances at different age groups. PCoA plots with intersample distances represented by two principal coordinates (PC1 and PC2), with closely positioned samples being more similar in composition. The difference test method is the nonparametric Wilcoxon rank‐sum test (*p‐value < 0.05; **p‐value < 0.01; ***p‐value < 0.001).

The overall structural similarity and variation between the microbiomes from four skin groups were then examined using the Principal Coordinates Analysis (PCoA) (Figure 2C). The sample group distance increases with increasing age. The distance between the samples in the 0–3 years group and the 7–10 years group was the smallest, indicating a small difference in bacterial diversity. The 13–18 years group were more dispersed, with large differences between different samples in the same group. Samples in the 0–3 years group and the 20–25 years group could be completely separated indicating the largest differences in the composition and abundance of skin bacteria in these two groups.

3.3. The composition of bacteria between between the different ages

The microbiomes of the different ages were dominated by five bacterial phylum (Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes and Cyanobacteria), with significant differences in Actinobacteria, Bacteroidetes and Cyanobacteria (p‐value < 0.01) (Figure 3A). Firmicutes accounted for the largest proportion of the 13–18 years group and the smallest proportion of the 20–25 years group. Actinobacteria showed an increasing trend during the maturing process, and Proteobacteria showed a comparable percentage of abundance in the first three groups, with a significant decrease in the 20–25 years group (p‐value < 0.01). As the age of the sample group decreased, the percentage of bacterial abundance in the Bacteroidetes gradually decreased (Figure 3D).

FIGURE 3.

Histogram of bacterial composition and differences between different age groups. (A) The relative abundance of community compositions for every group at the phylum level. (B) The relative abundance of community compositions for every group at the genus level. (C) The relative abundance of community compositions for every group at the species level. (D) Analysis of phylum differences across four groups. (E) Analysis of genus differences across four groups. (F) Analysis of species differences across four groups. Differences across four groups were revealed via the Kruskal–Wallis H test.

At the genus level, we analyzed the composition in the dominant genus (Table 1). The dominant genus of the samples differed among the age groups (Figure 3B,E), and the community composition was more homogeneous in the 20–25 years group and more abundant in the 7–10 years group. The dominant organism in the 0–3 and 7–10 years groups was Streptococcus, the dominant organism in the 13–18 years group was Staphylococcus, and the dominant organism in the 20–25 years group was Cutibacterium. Anoxybacillus was particularly prominent in the 13–18 years group, Neisseria was higher in the 7–10 and 13–18 years groups, and Pseudomonas was prominent in the 0–3 years group (p‐value < 0.01). In this part of the results, some regular variations were found. The relative abundance of Cutibacterium increased in steps with age (3.48% at 0–3 years group, 5.32% at 7–10 years group, 6.70% at 13–18 years group, and 50.80% at 20–25 years group). The relative abundance of Staphylococcus increased in steps with age (3.90% at 0–3 years group, 4.16% at 7–10 years group, 20.33% at 13–18 years group, and 22.29% at 20–25 years group). Relative abundance of Streptococcus decreases progressively with age (27.29% at 0–3 years group, 18.31% at 7–10 years group, 9.61% at 13–18 years group, and 1.49% at 20–25 years group). Relative abundance of Rothia decreases progressively with age (5.69% at 0–3 years group, 2.72% at 7–10 years group, 1.88% at 13–18 years group, and 0.14% at 20–25 years group).

TABLE 1.

Different genus of average relative abundance in all groups, showed only the top 5 genus in abundance.

Group	Genus name	Relative abundance (mean ± sd) (%)
0–3 years group	Streptococcus	27.29 ± 15.12
	Rothia	5.689 ± 5.655
	Pseudomonas	4.775 ± 8.884
	Staphylococcus	3.903 ± 6.647
	Cutibacterium	3.48 ± 6.387
7–10 years group	Streptococcus	18.31 ± 11.22
	Neisseria	6.358 ± 5.123
	Cutibacterium	5.322 ± 7.228
	Chloroplast	4.485 ± 4.892
	Micrococcus	4.479 ± 9.744
13–18 years group	Staphylococcus	20.33 ± 23.36
	Anoxybacillus	9.776 ± 8.544
	Streptococcus	9.609 ± 9.252
	Cutibacterium	8.701 ± 7.722
	Neisseria	6.172 ± 10.59
20–25 years group	Cutibacterium	50.8 ± 23.6
	Staphylococcus	22.29 ± 17.99
	Corynebacterium	5.024 ± 6.675
	Anoxybacillus	3.026 ± 10.7
	Pseudomonas	2.005 ± 1.999

The classifiable bacterial composition of the samples was analyzed at the species level, and the dominant species were: Cutibacterium acnes, Staphylococcus epidermidis PM221, Rothia mucilaginosa, and Staphylococcus hominis (Figure 3C). The relative abundance of C. acnes increased in steps with age (3.45% at 0–3 years group, 5.24% at 7–10 years group, 7.93% at 13–18 years group, and 48.88% at 20–25 years group). The relative abundance of S. epidermidis PM221 increased in steps with age (0.95% at 0–3 years group, 2.06% at 7–10 years group, 16.98% at 13–18 years group, and 20.40% at 20–25 years group). C. acnes (p‐value < 0.01), S. epidermidis PM221 (p‐value < 0.01) were significantly different between different age groups, and the bacterial percentage significantly increased with age. Rothia mucilaginosa (p‐value < 0.01) had significantly lower bacterial proportions with increasing age. The bacterial percentage of Staphylococcus hominis gradually decreased with increasing age, but there was no significant difference (p‐value > 0.05). Besides, Gemella haemolysans ATCC 10379 and Neisseria subflava also showed significant differences between the different groups (p‐value < 0.01) (Figure 3F).

3.4. KEGG functional abundance statistics

This statistic was first normalized to the OTU abundance table by PICRUSt (PICRUSt process stores Clusters of Orthologous Groups of proteins information and KEGG Orthology information corresponding to the greengene id), a step to remove the effect of the number of copies of 16S marker gene in the species genome; then by the greengene id corresponding to each OTU, we obtained OTU corresponding Clusters of Orthologous Groups (COG) family information and KO (KEGG Orthology) information; and the abundance of each COG and KO abundance were calculated. According to the information of KEGG database, the KO, Pathway and EC information can be obtained, and the abundance of each functional category can be calculated according to the OTU abundance.

There was no significant difference between the groups in the KEGG Pathway database including various metabolic pathways and synthetic pathways. EC analysis yielded that the different subgroups in this study exhibited differential abundance in enzyme expression (Figure 4). Among them, NADH:ubiquinone reductase (H(+)‐translocating) (1.6.5.3), Iron‐chelate‐transporting ATPase (3.6.3.34), and Protein‐N(pi)‐phosphohistidine–sugar phosphotransferase (2.7.1.69) showed significant differences in the different groups. They were both highest in the 20–25 age group. DNA‐directed DNA polymerase, DNA helicase and Histidine kinase do not differ between groups.

FIGURE 4.

The differentially predicted enzyme functional abundance in different age groups. Based on the information from the KEGG database, enzyme information can be obtained and enzyme functional abundance can be calculated based on operational taxonomic units (OTU) abundance. The Enzyme database stores information about enzymes, and each enzyme is uniquely identified by EC number, the EC number in the y‐coordinate of the figure can be found on the website for description (http://www.genome.jp/kegg/).

4. DISCUSSION

The skin changes as the body matures, and the microorganisms on the skin bring about changes that may result from a combination of intrinsic human factors and external environmental factors. In this paper, we chose infancy, childhood, adolescence and young adulthood to represent the different stages of human growth and maturation, and explored the compositional characteristics and species pattern changes of skin bacteria in healthy males during these stages of growth. These differences may be attributed to the continuous self‐regulation and self‐renewal of the skin microbiota in relation to the individual's skin environment. From 0 to 3 years old in the infant stage, from birth, the facial microbiome is in the stage of establishment over time, at this time the facial skin bacteria is still mainly Streptococcus accounted for the highest proportion. The age of 7–10 years is in the childhood stage, the results of Chao and Shannon indices and comparison of Beta diversity between samples showed that the highest species richness and diversity were found in the 7–10 years group, and the flora was more balanced, which should be related to the fact that skin bacteria are in a growth phase of constant communication with the environment at this age. The age group of 13–18 years is in the puberty stage when the bacterial abundance of Staphylococcus rapidly increases. Puberty is a transitional period, it is the beginning of sexual maturation and the skin microbiomes would undergo a large shift. Because sex hormones stimulate remodeling of the local sebaceous environment, microorganisms become dominated by lipophilic communities. At the age of 20 to 25 years old, when skin bacteria tend to mature, facial skin bacteria mainly consist of Cutibacterium, Staphylococcus, and Corynebacterium, and the species diversity becomes low.

The results of the high‐throughput analysis showed that the dominant bacteria in different age groups differed greatly and showed some interesting changes in the development process. As healthy males transition from infancy to adolescence, there is a decreasing trend in the abundance of Streptococcus (p‐value < 0.001) and Rothia (p‐value < 0.001) on the cheek skin. In contrast, more sexually mature individuals demonstrated increasingly predominant lipophilic Cutibacterium and Staphylococcus (p‐value < 0.001), which is consistent with previous observations comparing healthy children to those entering puberty. ¹⁵ In addition to these major dominant bacteria, some changes were observed in other genera. The genus Corynebacterium stabilized after a gradual increase in the bacterial percentage with age, but no significant difference was observed (p‐value > 0.001). Streptococcus (the first dominant bacteria in the 0–3 and 7–10 age groups), S. epidermidis (the first dominant bacteria in the 13–18 age group), and C. acnes (the first dominant bacteria in the 20–25 age group), as representatives of the dominant bacteria, will be highlighted from the following.

The relative abundance of Streptococcus decreased in steps with age (27.29% at 0–3 years group, 18.31% at 7–10 years group, 9.61% at 13–18 years group, and 1.49% at 20–25 years group). Streptococcus is the predominant genus in infancy and early childhood. The results of this analysis were the same as those of Capone Kimberly A ⁸ for forehead sampling in infants from 0 to 12 months. Infant skin surface pH is higher than that of adults, ¹⁶ and Streptococcus are suitable for growth at a neutral pH, which may also be one of the reasons for the enrichment of Streptococcus in the facial skin of infants. Infant skin is not as thick as adult skin, the skin's immune system and barrier function are in the process of being established, and the skin's microflora is in the process of developing. The higher prevalence of atopic dermatitis in infancy and the fact that some studies have shown a decrease in the abundance of Streptococcus in the inflammatory lesions of patients and an increase in abundance after treatment also suggest that Streptococcus may have an impact on the recovery from atopic dermatitis, ¹⁷ and recent findings ¹⁸ have also shown that Streptococcus species improve skin structure and barrier function. Streptococcal secretions on human skin showed marked improvements on skin phenotypes such as elasticity, hydration, and desquamation. However, the specific mechanism of Streptococcus in maintaining healthy facial skin in infants and children has not been studied in depth. Streptococcus bacterial abundance decreases in adulthood, and Leung ¹⁹ showed that Streptococcus were more abundant in the 0–50 female population compared to males. In addition, Streptococcus have been associated with rosacea, ²⁰ However, whether there is a significant correlation between Streptococcus and rosacea needs to be further proven by scientific studies. Streptococcus is the dominant bacterium in infancy and becomes less abundant in adulthood, where it may play an important role in immature skin.

Staphylococcus is a resident bacterium found in high levels in the skin of healthy adults, but not in infancy. Staphylococcus showed rapid growth mainly between the ages of 7 to 10 years and 13 to 18 years, a period from childhood to adolescence when not only physical and mental development is rapid, but this study also shows that facial microorganisms are also in a period of rapid change at this stage. S. epidermidis, one of the skin resident bacteria, has been indicated in many studis to have an important role in maintaining the skin barrier. The abundant S. epidermidis contributes to skin barrier integrity. S. epidermidis secretes a sphingomyelinase that acquires essential nutrients for the bacteria and assists the host in producing ceramides, the main constituent of the epithelial barrier. ²¹ The phenol‐soluble regulatory proteins PSMγ and PSMδ of S. epidermidis can synergize with keratinocyte‐derived AMPs to kill skin‐conditioned pathogens such as Methicillin Resistant Staphylococcus aureus and Streptococcus pyogenes. ²² S. epidermidis lipoteichoic acid and some lipopeptides can suppress the inflammatory response to skin injury and accelerate wound healing, and their early skin colonization is essential for the development of immune cell subsets including effector T cells and MAIT cells, ²³ which may help the skin immune system to distinguish commensal from pathogenic bacteria. S. epidermidis plays many roles in antagonizing potential sources of infection, maintaining the skin barrier and participating in skin immune processes. S. epidermidis in this study showed a rapid increase between 7–10 and 13–18 years of age, and whether S. epidermidis plays a role in the development of the skin immune system and skin barrier establishment during this period is also something that needs to be further explored.

The relative abundance of C. acnes increased with age, the lowest abundance in childhood, a sharp increase in adolescence, and a peak in youth. The secretion of androgens in men, which can induce increased lipid secretion as well as hyperkeratosis of the follicular opening, promoting changes in the environment surrounding the lesion and providing a favorable environment for C. acnes colonization. ²⁴ C. acnes is currently recognized as the microorganism most associated with acne. Acne is a common chronic inflammatory follicular sebaceous gland disease affecting 85% of adolescents and young adults nationally and internationally. ²⁵ The high degree of C. acnes colonization of the faces of 20–25‐year‐old men in this study may be associated with excessive sebum secretion from the facial sebaceous glands of young men. Linuma ²⁶ experimentally hypothesized that C.acnes promotes sebum through upregulation of corticotropin‐releasing hormone expression by secretion ²⁷ and that a high percentage of the bacterial abundance of C. acnes, in turn, affects facial sebum levels in young men. Giacomoni ²⁸ mentioned that the physiological differences between males and females in the rate of sweating, sebum levels, and skin surface pH after exercise are higher in males than in females and that Cutibacterium is the dominant bacterium in the sebaceous gland site, settling in the sebaceous glands in moist areas, which may be one of the reasons why the genus Cutibacterium has a higher percentage of bacteria in males than in females in the 20–25 age group. Comparison of the microbial composition between revealed a higher diversity and abundance of skin bacteria in the 13–18 age group, whereas Cutibacterium had a very high abundance level in the 20–25 age group, so whether this is due to antagonism between flora caused by the high abundance of Cutibacterium and thus a decrease in skin bacterial diversity needs further study.

Differences in the abundance of enzyme levels by KEGG analysis revealed some enzymes that differed between groups. NADH: ubiquinone reductase (H(+)‐translocating) (1.6.5.3) is involved in NADH reduction and Iron‐chelate‐transporting ATPase (3.6.3.34) in ATP reaction, both of which are related to energy metabolism and are in high abundance in the 20–25 years group, which may be related to the exuberant energy metabolism in adulthood. DNA helicase (3.6.4.12) is in the highest abundance in the 20–25 years group, Fumarate reductase (quinol) (1.3.5.4) also showed a phased increase with age. This enzyme can reduce fumaric acid to succinic acid, which can inhibit passive and active skin allergic reactions. The decrease in skin allergic reactions may be related to the healthy maintenance of the skin as the microbial abundance tends to stabilize after skin maturation. 8‐oxo‐dGTP diphosphatase (3.6.1.55) was the only one of the top 20 enzymes that decreased in abundance with age. The results of the functional prediction analysis by aspect also suggest that differences in ]facial skin microbes in men of different ages may bring about differences in some metabolic pathways. KEGG analysis showed that the levels of energy metabolism‐related enzymes were different at different ages, and the two significantly different enzymes had the highest levels at the peak of energy metabolism between 20 and 25 years old. During the pubertal stage, human hormone secretion increases and sebaceous glands secrete oil relatively vigorously, and the abundance of lipophilic microorganisms increases. The microorganisms on the skin mature after puberty, creating a balanced situation to maintain skin health. The analysis in this study obtained a dynamic change in the bacterial abundance but the specific mechanisms between these major dominant bacteria and skin health during development need to be further investigated in depth.

Because sex hormones stimulate the remodeling of the local sebaceous gland environment, the mature microbes are dominated by lipophilic communities. The increase in Cutibacterium, Staphylococcus and decrease in Streptococcus may be signs of skin maturation. The sampling method used in this study was the cotton swab method, which mainly collected superficial skin microorganisms and had some drawbacks for the collection of deeper microorganisms such as pores. The small sample size in the transitional period groups (prepubertal: seven individuals, pubertal: seven individuals) is also a major limitation of this study. In addition, age and gender are two major factors that influence the composition of facial microbiota, and this study fixed the male gender to analyze the structure and changes of facial microorganisms in a healthy male population during development. We anticipate that further research will focus on specific mechanisms between skin bacteria and skin health, establishing the link between healthy skin and the skin microbiome.

5. CONCLUSION

During male development, significant changes in the microbial composition of the skin occur. Significant differences in skin microbial diversity and abundance existed between age groups, and the variability in bacterial community composition gradually increased with age. The analysis revealed that some bacteria at the genus level showed a regular trend during development, such as Cutibacterium and Staphylococcus showed a gradually increasing proportion during development, and Streptococcus and Rothia showed a gradually decreasing proportion during development. In adulthood, the skin is mainly occupied by Cutibacterium and Staphylococcus, but also by some other bacteria, such as Corynebacterium, Anoxybacillus. The specific role played by these emergence‐specific microorganisms during skin development and maturation is now little studied, and the influence of the skin microbiome on skin remains largely unknown and needs to be further explored.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supporting information

TABLE S1 Sample sequencing information of different age groups

Hu X, Tang M, Dong K, Zhou J, Wang D, Song L. Changes in the skin microbiome during male maturation from 0 to 25 years of age. Skin Res Technol. 2023;29:e13432. 10.1111/srt.13432

DATA AVAILABILITY STATEMENT

Research data are not shared.