School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
The microbiota is the sum of bacteria, fungi, viruses, and parasites living in and on a given habitat or host. Though the terms are often used interchangeably, the microbiome refers to the genetic component of these microbes, and unless otherwise specified often refers to bacterial communities. All our experiences in health and disease are altered by either the microbiota or their metabolites, including but not limited to smell, taste, touch, energy metabolism, nutrient absorption and barrier and immune function. The shared microbiota between people is also influenced by their environment and strongly influenced by their pets.
Advances in high-throughput ("next generation") sequencing have allowed for culture-independent evaluation of these communities. The methods most commonly utilized to examine the microbiome include targeted amplicon sequencing, whole genome metagenomics and other downstream analyses: transcriptomics or proteomics. Statistical methods used in ecology studies are often employed, examining both intraindividual (alpha diversity) and interindividual (beta) diversity metrics and changes in community composition/structure.
In veterinary studies, the most commonly used approach is targeted amplicon sequencing. This methodology only allows for taxonomic identification and does not give information as to function or metabolites of these communities. It utilizes a gene universally conserved by a kingdom or large group of microbes. In bacteria, the 16S (subunit) ribosomal RNA gene is commonly selected, and for fungi the 18S and ITS (internal transcribed sequences) of the rRNA gene are used. The 16S rRNA gene has both highly conserved regions allowing for primer set design and hypervariable regions (V1 through V9) that enable one to distinguish bacterial taxa.
Most studies of the microbiome take a census of what constitutes a given community and how it changes with a variable. This can be a daunting task with the number of potential influences on the microbiome such as location (i.e., body site), age, breed, environment, time, diet, bathing and health status. Community structure is often assessed with a description of a relative proportion of a given taxon compared to other taxa.
The Skin Microbiome
Our understanding of the skin microbiome is limited. Microbiomic studies of the skin face a number of challenges. Samples are generally procured from surface swabs with low biomass. As such contaminants can have a dramatic effect on the study outcome necessitating careful collection and processing of samples along with numerous positive and negative controls. Regions of the 16S rRNA gene evaluated can also influence study outcome, making comparison across studies difficult. For example, the V4 region of the 16S rRNA gene has been demonstrated to underestimate the relative proportion of Propionibacterium acnes and Staphylococcus aureus in the skin microbiome compared to the V1-V3 region.
There are very few studies surveying the bacterial skin microbiome of dogs, and most of these characterize normal skin communities, with fewer examining disease states. Information on the feline microbiome is extremely sparse. Microbiome studies of human skin are slightly more abundant, and both differences and similarities with the companion animal microbiome have been elucidated. In people there are dry, moist and oily (sebaceous) microenvironments across the skin associated with differing microbial communities. In contrast, the microbiome of the haired skin of dogs is much more homogenous. Interindividual variability in both people and dogs is significant, influenced by the individual, environment and life-style. The most abundant phyla in healthy dogs include Proteobacteria, Firmicutes, Actinobacteria, and Bacteroides. In health, there is generally a higher level of microbial diversity compared to disease states. Cutaneous diseases most commonly studied in the human microbiome field include atopic dermatitis (AD), acne, psoriasis. With a similar prevalence, pathogenesis, therapeutic considerations and associated bacterial disease, atopic dermatitis (AD) in humans and dogs (cAD) has been a recent line of investigation into the microbiome.
Antimicrobials and the Skin Microbiome
The most common isolates associated with bacterial folliculitis in dogs are Staphylococcus spp. During flare states of cAD, commonly with bacterial folliculitis (superficial pyoderma), there is decreased microbial diversity, with S. pseudintermedius and S. schleiferi overrepresented. A similar progression has been documented in human AD with increased abundance of S. aureus and S. epidermitis. Following culture-directed systemic antimicrobial therapy for pyoderma with cAD, one study showed microbial diversity is restored, with decreased levels of Staphylococcus present. However, the recurrence of pyoderma in some dogs four to six weeks following treatment is accompanied by reduction in diversity.
A recent study documented that topical antibiotic treatment provided a longer lasting impact on the microbiota compared to relatively minor changes by topical antiseptics. Triple antibiotic ointment was shown to have a greater impact than mupirocin in overall microbiota disruption. The impact of commensal bacteria on maintaining health and barrier function of the skin is an important consideration, though we know relatively little about their role. Both coagulase negative and coagulase positive/variable Staphylococcus are common constituents of the skin microbiota. Host adapted coagulase negative commensals can alter the colonizing ability of coagulase positive Staphylococcus (S. aureus in humans and mice). This study showed that treatment with topical antibiotics can be disruptive to staphylococcal populations, with a greater effect on those with a higher baseline level of Staphylococcus. This disruption can promote exogenous association/potential colonization by the potential pathogen S. aureus.
There is still much to be learned from study of the skin microbiome, and the field is rapidly evolving. Baseline communities may be predictive in therapeutic responses, and competition amongst microbial communities can avail different therapeutic avenues. As processing and analytics progress, the diagnostic potential of auditing these communities is tremendous. In an era of antimicrobial resistance, consideration of the entire host's microbiota is crucial when treating a patient as disruption of the microbiota will be both local (e.g., skin) and systemic.
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