Atopic dermatitis microbiomes stratify into ecologic dermotypes enabling microbial virulence and disease severity

Background Atopic dermatitis (AD) is a common skin disease affecting up to 20% of the global population, with significant clinical heterogeneity and limited information about molecular subtypes and actionable biomarkers. Although alterations in the skin microbiome have been described in subjects with AD during progression to flare state, the prognostic value of baseline microbiome configurations has not been explored. Objective Our aim was to identify microbial signatures on AD skin that are predictive of disease fate. Methods Nonlesional skin of patients with AD and healthy control subjects were sampled at 2 time points separated by at least 4 weeks. Using whole metagenome analysis of skin microbiomes of patients with AD and control subjects (n = 49 and 189 samples), we identified distinct microbiome configurations (dermotypes A and B). Blood was collected for immunophenotyping, and skin surface samples were analyzed for correlations with natural moisturizing factors and antimicrobial peptides. Results Dermotypes were robust and validated across 2 additional cohorts (63 individuals), with strong enrichment of subjects with AD in dermotype B. Dermotype B was characterized by reduced microbial richness, depletion of Cutibacterium acnes, Dermacoccus and Methylobacterium species, individual-specific outlier abundance of Staphylococcus species (eg, S epidermidis, S capitis, S aureus), and enrichment in metabolic pathways (eg, branched chain amino acids and arginine biosynthesis) and virulence genes (eg, β-toxin, δ-toxin) that defined a pathogenic ecology. Skin surface and circulating host biomarkers exhibited a distinct microbial-associated signature that was further reflected in more severe itching, frequent flares, and increased disease severity in patients harboring the dermotype B microbiome. Conclusion We report distinct clusters of microbial profiles that delineate the role of microbiome configurations in AD heterogeneity, highlight a mechanism for ongoing inflammation, and provide prognostic utility toward microbiome-based disease stratification. Atopic dermatitis (AD) is a common skin disease affecting up to 20% of the global population, with significant clinical heterogeneity and limited information about molecular subtypes and actionable biomarkers. Although alterations in the skin microbiome have been described in subjects with AD during progression to flare state, the prognostic value of baseline microbiome configurations has not been explored. Our aim was to identify microbial signatures on AD skin that are predictive of disease fate. Nonlesional skin of patients with AD and healthy control subjects were sampled at 2 time points separated by at least 4 weeks. Using whole metagenome analysis of skin microbiomes of patients with AD and control subjects (n = 49 and 189 samples), we identified distinct microbiome configurations (dermotypes A and B). Blood was collected for immunophenotyping, and skin surface samples were analyzed for correlations with natural moisturizing factors and antimicrobial peptides. Dermotypes were robust and validated across 2 additional cohorts (63 individuals), with strong enrichment of subjects with AD in dermotype B. Dermotype B was characterized by reduced microbial richness, depletion of Cutibacterium acnes, Dermacoccus and Methylobacterium species, individual-specific outlier abundance of Staphylococcus species (eg, S epidermidis, S capitis, S aureus), and enrichment in metabolic pathways (eg, branched chain amino acids and arginine biosynthesis) and virulence genes (eg, β-toxin, δ-toxin) that defined a pathogenic ecology. Skin surface and circulating host biomarkers exhibited a distinct microbial-associated signature that was further reflected in more severe itching, frequent flares, and increased disease severity in patients harboring the dermotype B microbiome. We report distinct clusters of microbial profiles that delineate the role of microbiome configurations in AD heterogeneity, highlight a mechanism for ongoing inflammation, and provide prognostic utility toward microbiome-based disease stratification.