The intersection of microbiome and host at the skin interface: genomic- and metagenomic-based insights

Abstract

The past two decades have been marked by a surge in research to understand the microbial communities that live in association with the human body, in part stimulated by affordable, high-throughput DNA sequencing technology. In the context of the skin, this Perspective focuses on the current state of genomic- and metagenomic-based host-microbe research and future challenges and opportunities to move the field forward. These include elucidating nonbacterial components of the skin microbiome (i.e., viruses); systematic studies to address common perturbations to the skin microbiome (e.g., antimicrobial drugs, topical cosmetic/hygienic products); improved approaches for identifying potential microbial triggers for skin diseases, including species- and strain-level resolution; and improved, clinically relevant models for studying the functional and mechanistic roles of the skin microbiome. In the next 20 years, we can realistically expect that our knowledge of the skin microbiome will inform the clinical management and treatment of skin disorders through diagnostic tests to stratify patient subsets and predict best treatment modality and outcomes and through treatment strategies such as targeted manipulation or reconstitution of microbial communities.

Figure 1.

Topographical representation of the dominant types of bacteria present in the skin microbiome, based on 16S rRNA surveys. Shown are the three most common/abundant genera: Staphylococcus, Corynebacterium, and Propionibacterium. Data were gathered from Gao et al. (2007), Costello et al. (2009), Grice and Segre (2011), and The Human Microbiome Project Consortium (2012).

Figure 2.

Phylogenetic tree of 35 major bacterial phyla. Highlighted in red are those phyla from which 85% of phages have been isolated and sequenced. Data from Holmfeldt et al. (2013).

Figure 3.

Example of longitudinal profiling of an acute traumatic injury (open fracture), overlaid with clinical metadata to identify potential microbiomic features associated with intervention (surgery) and outcomes. The y-axis represents longitudinal time points sampled. Swab samples were collected from the open fracture (prior to closure) or the surgical wound (after closure). (Top) Alpha diversity metrics; (middle) bacterial load; and (bottom) relative abundance of the top five most abundant bacterial taxa (EA Grice, unpubl.).