The Human Skin Microbiome Associates with the Outcome of and Is Influenced by Bacterial Infection

Abstract

The influence of the skin microbiota on host susceptibility to infectious agents is largely unexplored. The skin harbors diverse bacterial species that may promote or antagonize the growth of an invading pathogen. We developed a human infection model for Haemophilus ducreyi in which human volunteers are inoculated on the upper arm. After inoculation, papules form and either spontaneously resolve or progress to pustules. To examine the role of the skin microbiota in the outcome of H. ducreyi infection, we analyzed the microbiomes of four dose-matched pairs of "resolvers" and "pustule formers" whose inoculation sites were swabbed at multiple time points. Bacteria present on the skin were identified by amplification and pyrosequencing of 16S rRNA genes. Nonmetric multidimensional scaling (NMDS) using Bray-Curtis dissimilarity between the preinfection microbiomes of infected sites showed that sites from the same volunteer clustered together and that pustule formers segregated from resolvers (P = 0.001, permutational multivariate analysis of variance [PERMANOVA]), suggesting that the preinfection microbiomes were associated with outcome. NMDS using Bray-Curtis dissimilarity of the endpoint samples showed that the pustule sites clustered together and were significantly different than the resolved sites (P = 0.001, PERMANOVA), suggesting that the microbiomes at the endpoint differed between the two groups. In addition to H. ducreyi, pustule-forming sites had a greater abundance of Proteobacteria, Bacteroidetes, Micrococcus, Corynebacterium, Paracoccus, and Staphylococcus species, whereas resolved sites had higher levels of Actinobacteria and Propionibacterium species. These results suggest that at baseline, resolvers and pustule formers have distinct skin bacterial communities which change in response to infection and the resultant immune response.

Importance: Human skin is home to a diverse community of microorganisms, collectively known as the skin microbiome. Some resident bacteria are thought to protect the skin from infection by outcompeting pathogens for resources or by priming the immune system's response to invaders. However, the influence of the skin microbiome on the susceptibility to or protection from infection has not been prospectively evaluated in humans. We characterized the skin microbiome before, during, and after experimental inoculation of the arm with Haemophilus ducreyi in matched volunteers who subsequently resolved the infection or formed abscesses. Our results suggest that the preinfection microbiomes of pustule formers and resolvers have distinct community structures which change in response to the progression of H. ducreyi infection to abscess formation.

Copyright © 2015 van Rensburg et al.

Figures

FIG 1

Schematic of times for swab sampling chosen for processing prior to and following experimental inoculation with H. ducreyi.

FIG 2

Distribution of the four dominant phyla in resolvers (A) and pustule formers (B) preinfection. Pie charts represent the percent proportions of each phylum across all days from the corrected sequence data, with averages and standard deviations shown for Actinobacteria, Firmicutes, Proteobacteria, and Bacteroidetes.

FIG 3

Distribution of the 20 most abundant genera, other classified genera, and unclassified genera in resolvers (A) and pustule formers (B). All samples for which sequence data were obtained are shown; no sequences were obtained for the missing samples.

FIG 4

Nonmetric multidimensional scaling using Bray-Curtis dissimilarity of OTUs detected in preinfection samples from resolvers (R; n = 10) and pustule formers (P; n = 10) (P = 0.001, PERMANOVA). Samples that contained fewer than 450 sequence reads were excluded from the analysis. At preinfection, resolver sites and sites within volunteers clustered.

FIG 5

Nonmetric multidimensional scaling using Bray-Curtis dissimilarity of OTUs detected in endpoint samples from resolved sites (R; n = 11) and pustule sites (P; n = 9) (P = 0.001, PERMANOVA). At the endpoint, pustule sites and sites within volunteers clustered.