Jinho Yang1,2, Andrea McDowell1, Hochan Seo1, Sungwon Kim1, Taek Ki Min3, Young Koo Jee4, Youngwoo Choi5, Hae Sim Park5, Bok Yang Pyun6, Yoon Keun Kim7. 1. Institute of MD Healthcare Inc., Seoul, Korea. 2. Department of Health and Safety Convergence Science, Graduate School of Korea University, Seoul, Korea. 3. Department of Pediatrics, Soonchunhyang University College of Medicine, Seoul, Korea. 4. Department of Internal Medicine, Dankook University College of Medicine, Cheonan, Korea. 5. Department of Allergy and Clinical Immunology, Ajou University Medical Center, Suwon, Korea. 6. Department of Pediatrics, Soonchunhyang University College of Medicine, Seoul, Korea. bypyun@schmc.ac.kr. 7. Institute of MD Healthcare Inc., Seoul, Korea. ykkim@mdhc.kr.
Abstract
PURPOSE: Associations between a wide variety of diseases and the microbiome have been extensively verified. Recently, there has been a rising interest in the role the microbiome plays in atopic dermatitis (AD). Furthermore, metagenomic analysis of microbe-derived extracellular vesicles (EVs) has revealed the importance and relevance of microbial EVs in human health. METHODS: We compared the diversity and proportion of microbial EVs in the sera of 24 AD patients and 49 healthy controls, and developed a diagnostic model. After separating microbial EVs from serum, we specifically targeted the V3-V4 hypervariable regions of the 16S rDNA gene for amplification and subsequent sequencing. RESULTS: Alpha and beta diversity between controls and AD patients both differed, but only the difference in beta diversity was significant. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla in healthy controls and AD patients, accounting for over 85% of the total serum bacterial EVs. Also, Proteobacteria, Firmicutes, Actinobacteria, Verrucomicrobia, and Cyanobacteria relative abundances were significantly different between the AD and control groups. At the genus level, the proportions of Escherichia-Shigella, Acinetobacter, Pseudomonas, and Enterococcus were drastically altered between the AD and control groups. AD diagnostic models developed using biomarkers selected on the basis of linear discriminant analysis effect size from the class to genus levels all yielded area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of value 1.00. CONCLUSIONS: In summary, microbial EVs demonstrated the potential in their use as novel biomarkers for AD diagnosis. Therefore, future work should investigate larger case and control groups with cross-sectional or longitudinal clinical data to explore the utility and validity of serum microbiota EV-based AD diagnosis.
PURPOSE: Associations between a wide variety of diseases and the microbiome have been extensively verified. Recently, there has been a rising interest in the role the microbiome plays in atopic dermatitis (AD). Furthermore, metagenomic analysis of microbe-derived extracellular vesicles (EVs) has revealed the importance and relevance of microbial EVs in human health. METHODS: We compared the diversity and proportion of microbial EVs in the sera of 24 ADpatients and 49 healthy controls, and developed a diagnostic model. After separating microbial EVs from serum, we specifically targeted the V3-V4 hypervariable regions of the 16S rDNA gene for amplification and subsequent sequencing. RESULTS: Alpha and beta diversity between controls and ADpatients both differed, but only the difference in beta diversity was significant. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla in healthy controls and ADpatients, accounting for over 85% of the total serum bacterial EVs. Also, Proteobacteria, Firmicutes, Actinobacteria, Verrucomicrobia, and Cyanobacteria relative abundances were significantly different between the AD and control groups. At the genus level, the proportions of Escherichia-Shigella, Acinetobacter, Pseudomonas, and Enterococcus were drastically altered between the AD and control groups. AD diagnostic models developed using biomarkers selected on the basis of linear discriminant analysis effect size from the class to genus levels all yielded area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of value 1.00. CONCLUSIONS: In summary, microbial EVs demonstrated the potential in their use as novel biomarkers for AD diagnosis. Therefore, future work should investigate larger case and control groups with cross-sectional or longitudinal clinical data to explore the utility and validity of serum microbiota EV-based AD diagnosis.
Authors: Natasha A Winter; Peter G Gibson; Michael Fricker; Jodie L Simpson; Peter A Wark; Vanessa M McDonald Journal: Allergy Asthma Immunol Res Date: 2021-05 Impact factor: 5.764
Authors: Emily Jones; Régis Stentz; Andrea Telatin; George M Savva; Catherine Booth; David Baker; Steven Rudder; Stella C Knight; Alistair Noble; Simon R Carding Journal: Genes (Basel) Date: 2021-10-18 Impact factor: 4.141