Adding Fuel to the Fire? The Skin Microbiome in Atopic Dermatitis.

Atopic dermatitis (AD) is a multifactorial, heterogeneous disease characterized by epidermal barrier dysfunction, immune system dysregulation, and skin microbiome alterations. Skin microbiome studies in AD have demonstrated that disease flares are associated with microbial shifts, particularly Staphylococcus aureus predominance. AD-associated S. aureus strains differ from those in healthy individuals across various genomic loci, including virulence factors, adhesion proteins, and proinflammatory molecules-which may contribute to complex microbiome barrier-immune system interactions in AD. Different microbially based treatments for AD have been explored, and their future therapeutic successes will depend on a deeper understanding of the potential microbial contributions to the disease.

Staphylococcal diversity in atopic dermatitis from an individual to a global scale.

Atopic dermatitis (AD) is a multifactorial, chronic relapsing disease associated with genetic and environmental factors. Among skin microbes, Staphylococcus aureus and Staphylococcus epidermidis are associated with AD, but how genetic variability and staphylococcal strains shape the disease remains unclear. We investigated the skin microbiome of an AD cohort (n = 54) as part of a prospective natural history study using shotgun metagenomic and whole genome sequencing, which we analyzed alongside publicly available data (n = 473). AD status and global geographical regions exhibited associations with strains and genomic loci of S. aureus and S. epidermidis. In addition, antibiotic prescribing patterns and within-household transmission between siblings shaped colonizing strains. Comparative genomics determined that S. aureus AD strains were enriched in virulence factors, whereas S. epidermidis AD strains varied in genes involved in interspecies interactions and metabolism. In both species, staphylococcal interspecies genetic transfer shaped gene content. These findings reflect the staphylococcal genomic diversity and dynamics associated with AD.

A genome catalog of the early-life human skin microbiome.

Metagenome-assembled genomes have greatly expanded the reference genomes for skin microbiome. However, the current reference genomes are largely based on samples from adults in North America and lack representation from infants and individuals from other continents. Here we used ultra-deep shotgun metagenomic sequencing to profile the skin microbiota of 215 infants at age 2-3 months and 12 months who were part of the VITALITY trial in Australia as well as 67 maternally-matched samples. Based on the infant samples, we present the Early-Life Skin Genomes (ELSG) catalog, comprising 9,194 bacterial genomes from 1,029 species, 206 fungal genomes from 13 species, and 39 eukaryotic viral sequences. This genome catalog substantially expands the diversity of species previously known to comprise human skin microbiome and improves the classification rate of sequenced data by 25%. The protein catalog derived from these genomes provides insights into the functional elements such as defense mechanisms that distinguish early-life skin microbiome. We also found evidence for vertical transmission at the microbial community, individual skin bacterial species and strain levels between mothers and infants. Overall, the ELSG catalog uncovers the skin microbiome of a previously underrepresented age group and population and provides a comprehensive view of human skin microbiome diversity, function, and transmission in early life.

Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions.

Human skin functions as a physical barrier to foreign pathogen invasion and houses numerous commensals. Shifts in the human skin microbiome have been associated with conditions ranging from acne to atopic dermatitis. Previous metagenomic investigations into the role of the skin microbiome in health or disease have found that much of the sequenced data do not match reference genomes, making it difficult to interpret metagenomic datasets. We combined bacterial cultivation and metagenomic sequencing to assemble the Skin Microbial Genome Collection (SMGC), which comprises 622 prokaryotic species derived from 7,535 metagenome-assembled genomes and 251 isolate genomes. The metagenomic datasets that we generated were combined with publicly available skin metagenomic datasets to identify members and functions of the human skin microbiome. The SMGC collection includes 174 newly identified bacterial species and 12 newly identified bacterial genera, including the abundant genus 'Candidatus Pellibacterium', which has been newly associated with the skin. The SMGC increases the characterized set of known skin bacteria by 26%. We validated the SMGC metagenome-assembled genomes by comparing them with sequenced isolates obtained from the same samples. We also recovered 12 eukaryotic species and assembled thousands of viral sequences, including newly identified clades of jumbo phages. The SMGC enables classification of a median of 85% of skin metagenomic sequences and provides a comprehensive view of skin microbiome diversity, derived primarily from samples obtained in North America.

Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data.

Recovering genomes from shotgun metagenomic sequence data allows detailed taxonomic and functional characterization of individual species or strains in a microbial community. Retrieving these metagenome-assembled genomes (MAGs) involves seven stages. First, low-quality bases, along with adapter and host sequences, are removed. Second, overlapping sequences are assembled to create longer contiguous fragments. Third, these fragments are clustered based on sequence composition and abundance. Fourth, these sequence clusters, or bins, undergo rounds of quality assessment and refinement to yield MAGs. The optional fifth stage is dereplication of MAGs to select representatives. Next, each MAG is taxonomically classified. The optional seventh stage is assessing the fraction of diversity that has been recovered. The output of this protocol is draft genomes, which can provide invaluable clues about uncultured organisms. This protocol takes ~1 week to run, depending on computational resources available, and requires prior experience with high-performance computing, shell script programming and Python.