Epidermal Barrier Integrity is Associated with Both Skin Microbiome Diversity and Composition in Patients with Acne Vulgaris

Abstract

Background: Although the changed lipid environment of the pilosebaceous unit and the growth of lipophilic Cutibacterium acnes (C. acnes) during puberty has long been considered as the trigger of acne vulgaris, the involvement of the interaction between the epidermal barrier integrity and the skin microbiome in this disease has not been fully elucidated.

Objective: The aim of this study was to analyze the differences in the epidermal barrier and skin microbiota in patients with acne vulgaris and their correlation.

Methods: The skin microbial samples and epidermal barrier data from 74 acne patients and 19 healthy subjects were collected in this cross-sectional study. The microbial diversity was analyzed based on a high-throughput sequencing approach that targets the V3-V4 region of the bacteria 16S ribosomal RNA genes.

Results: Compared with healthy controls, acne patients had significantly increased transepidermal water loss (TEWL), pH levels, sebum, porphyrins, and red areas, and reduced skin microbiome diversity according to the goods coverage diversity index (p = 0.021), Shannon diversity index (p = 0.037), and Simpson diversity index (p = 0.023). Moreover, the diversity gradually decreased with the increase in acne grading. Based on the principal coordinate analysis (PCoA) analysis plot, the skin microbiota of acne patients and healthy controls could be divided into two different sets, which could not be used to separate acne patients with different disease severity. Finally, this study found that both TEWL and sebum were negatively associated with the Shannon and Simpson diversity index. Meanwhile, the taxa Enhydrobacter and Stenotrophomonas were positively associated with TEWL, stratum corneum hydration, respectively.

Conclusion: This study demonstrated that acne vulgaris exists in patients with both damaged epithelial barriers and associated microbiota dysbiosis; the findings will help improve the understanding of the disease and may contribute to the development of better treatment options.

Keywords: epidermal barrier; skin microbiome.

Figure 1

Comparison of different diversity indices between acne patient samples and healthy control samples, and between samples from patients with different severity of acne. Goods coverage, Shannon, and Simpson diversity index, representing community richness, were calculated for acne (AC), healthy control (HC), and the severity of acne, defined as S1 (score: 1–18), S2 (score: 19–30), and S3 (score: ≥31). (A) Goods coverage diversity index (p=0.021) between AC and HC. (B) Shannon diversity index (p=0.037) between AC and HC. (C) Simpson diversity index (p=0.023) between AC and HC. (D) Goods coverage diversity index (p=0.00068) among acne patients with different severity, Goods coverage diversity index (p=0.032) between S1 and S2, goods coverage diversity index (p=0.00041) between S1 and S3. (E) Shannon diversity index (p=0.094) among acne patients with different severity, Shannon diversity index (p=0.022) between S1 and S3. (F) Simpson diversity index (p=0.098) among acne patients with different severity, Simpson diversity index (p=0.03) between S1 and S3.

Figure 2

Comparison of the skin microbiota between acne patients and healthy controls. (A) Difference between and within groups of AC and HC were assessed by ANOSIM analysis. (B) Principal coordinate analysis (PCoA) plot with different relative abundances of OTUs between acne (AC) patients and healthy controls (HC). (C) The top 20 microbes with relative abundance in AC and HC at the genus level.

Figure 3

The PCoA plot with different relative abundances of OTUs among the mild, moderate, and severe acne patient groups. The severity of mild, moderate, severe acne was defined as S1 (score: 1–18), S2 (score: 19–30), and S3 (score: ≥31).

Figure 4

Correlation between bacteria and skin barrier parameters was assessed by Pearson’s correlation test and displayed as a heat map.