Proteomic analysis of bacterial communities associated with atopic dermatitis

Proteomic signatures of inflammatory skin diseases: a focus on atopic dermatitis

Introduction: Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by cutaneous and systemic inflammation and barrier abnormalities. Over the past few decades, proteomic studies have been increasingly applied to AD research to compliment transcriptomic evaluations. Proteomic analyses helped identify new biomarkers of AD, allowing investigation of both the cutaneous AD profile and the systemic inflammation associated with the disease.Areas covered: This review discusses key studies that utilized various proteomic technologies to analyze AD skin and/or blood, which facilitated discovery of biomarkers related to pathogenesis, disease severity, systemic inflammation, and therapeutic response. Moreover, this review summarizes proteomic studies that helped define various AD endotypes/phenotypes. A literature search was conducted by querying Scopus, Google Scholar, PubMed/Medline, and Clinicaltrials.gov up to January 2021.Expert opinion: Use of proteomics in AD has allowed for identification of novel AD-related protein biomarkers. This approach continues to evolve and is becoming increasingly common for the study of AD, in conjunction with other -omics platforms, as proteomics shifts to quicker and more sensitive methods for detection of potential protein biomarkers. Although many biomarkers have been identified thus far, future larger studies are necessary to further correlate these markers with clinical parameters.

Correlation Between Facial Skin Microbiota and Skin Barriers in a Chinese Female Population with Sensitive Skin

Background and Aim

The association of microbiota changes with sensitive skin remains controversial until now. Although a strong correlation is detected between skin microbiota distribution and biophysical parameters, there is little knowledge on the link between sensitive skin and skin microbiota in Chinese women. This study aimed to unravel the correlation between facial skin microbiota distribution and skin barriers in Chinese women with sensitive skin.

Materials and Methods

In total, 34 volunteers were enrolled, including 24 subjects with sensitive skin (SS group) and 10 subjects with non-sensitive skin (NS group). The cuticle moisture content, transepidermal water loss (TEWL), and facial skin sebum secretion were measured, and the facial skin surface morphology was evaluated. Sensitive skin samples were collected from the facial (SS-F group) and chest skin of subjects in the SS group (SS-C group), while non-sensitive skin samples were collected from the facial skin of subjects in the NS group (NS-F group). All skin samples were subjected to 16S rRNA sequencing.

Results

16S rRNA sequencing detected Actinobacteria, Firmicutes, and Proteobacteria as the three most common microbiota phyla and Propionibacterium, Paracoccus, and Corynebacterium as the three most common microbiota genera, and there were no significant differences in the relative frequency of Actinobacteria, Firmicutes, or Proteobacteria, or Propionibacterium, Paracoccus, or Corynebacterium among the SS-F, SS-C, and NS-F groups (P>0.05). We detected no significant difference in the diversity of bacterial communities among the SS-F, SS-C, and NS-F groups; however, the Shannon’s diversity index was significantly higher in the NS-F group than in the SS-C group. In addition, Spearman correlation analysis showed a correlation between the microbiota genera and skin physiological parameters (P<0.05).

Conclusion

This study preliminarily unravels the skin microbiota of sensitive skin using a high-throughput tool, and there are no microbiota genera with strong associations with skin physiological parameters.