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There are no drugs as effective as isotretinoin for acne. Deciphering the changes in the microbiome induced by isotretinoin in the pilosebaceous follicle of successfully treated patients can pave the way to identify novel therapeutic alternatives. We determined how the follicular microbiome changes with isotretinoin and identified which alterations correlate with a successful treatment response. Whole genome sequencing was done on casts from facial follicles of acne patients sampled before, during and after isotretinoin treatment. Alterations in the microbiome were assessed and correlated with treatment response at 20 weeks as defined as a 2-grade improvement in global assessment score. We investigated the α-diversity, β-diversity, relative abundance of individual taxa, Cutibacterium acnes strain composition and bacterial metabolic profiles with a computational approach. We found that increased β-diversity of the microbiome coincides with a successful treatment response to isotretinoin at 20 weeks. Isotretinoin selectively altered C. acnes strain diversity in SLST A and D clusters, with increased diversity in D1 strains correlating with a successful clinical response. Isotretinoin significantly decreased the prevalence of KEGG Ontology (KO) terms associated with four distinct metabolic pathways inferring that follicular microbes may have limited capacity for growth or survival following treatment. Importantly, these alterations in microbial composition or metabolic profiles were not observed in patients that failed to achieve a successful response at 20 weeks. Alternative approaches to recapitulate this shift in the balance of C. acnes strains and microbiome metabolic function within the follicle may be beneficial in the future treatment of acne., (© 2023 The Authors. Experimental Dermatology published by John Wiley & Sons Ltd.)

Background: The composition of the skin microbiome varies from infancy to adulthood and becomes most stable in adulthood. Adult acne patients harbour an 'acne microbiome' dominated by specific strains of Cutibacterium acnes. However, the precise timing of skin microbiome evolution, the development of the acne microbiome, and the shift to virulent C. acnes strain composition during puberty is unknown., Objectives: We performed a cross-sectional pilot study in a paediatric population to understand how and when the skin microbiome composition transitions during puberty and whether a distinct 'acne microbiome' emerges in paediatric subjects., Methods: Forty-eight volunteers including males and females, ages 7-17 years, with and without acne were enrolled and evaluated for pubertal development using the Tanner staging criteria. Sebum levels were measured, and skin microbiota were collected by sterile swab on the subject's forehead. DNA was sequenced by whole genome shotgun sequencing., Results: A significant shift in microbial diversity emerged between early (T1-T2) and late (T3-T5) stages of puberty, coinciding with increased sebum production on the face. The overall relative abundance of C. acnes in both normal and acne skin increased during puberty and individual C. acnes strains were uniquely affected by pubertal stage and the presence of acne. Further, an acne microbiome signature associated with unique C. acnes strain composition and metabolic activity emerges in late puberty in those with acne. This unique C. acnes strain composition is predicted to have increased porphyrin production, which may contribute to skin inflammation., Conclusions: Our data suggest that the stage of pubertal development influences skin microbiome composition. As children mature, a distinct acne microbiome composition emerges in those with acne. Understanding how both puberty and acne influence the microbiome may support novel therapeutic strategies to combat acne in the paediatric population., (© 2022 European Academy of Dermatology and Venereology.)

The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods' susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT., (© The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)