Bohannan, B. | bioRxiv Science

bioRxiv2019

Moving microbes: the dynamics of transient microbial residence on human skin

The human skin microbiome interacts intimately with human health, yet the drivers of skin microbiome composition and diversity are not well-understood. The composition of the skin microbiome has been characterized as both highly variable and relatively stable, depending on the time scale under consideration, and it is not clear what role contact with environmental sources of microbes plays in this variability. We experimentally mimicked human skin contact with two common environmental sources of microorganisms -- soils and plant leaves -- and characterized the dynamics of microbial acquisition and persistence on skin on very short time scales. Repeatable changes in skin community composition following encounters with environmental sources were observed, and these trajectories largely depend on donor community biomass distributions. Changes in composition persisted for at least 24 hours and through a soap and water wash following exposures to relatively high biomass soil communities. In contrast, exposures to lower biomass leaf communities were undetectable after a 24 hour period. Absolute abundances of bacterial taxa in source communities predicted transmission probabilities and residence times, independent of phylogenetic considerations. Our results suggest that variability in the composition of the skin microbiome can be driven by transient encounters with common environmental sources, and that these relatively transient effects can persist when the source is of sufficient biomass.\n\nImportanceHumans come into contact with environmental sources of microbes, such as soil or plants, constantly. Those microbial exposures have been linked to health through training and modulation of the immune system. While much is known about the human skin microbiome, the short term dynamics after a contact event, such as touching soil, have not been well characterized. In this study, we examine what happens after such a contact event, describing trends in microbial transmission to and persistence on the skin. Additionally, we use computational sampling model simulations to interrogate null expectations for these kinds of experiments. This work has broad implications for infection control strategies and therapeutic techniques that rely on modification of the microbiome, such as probiotics and faecal transplantation.

microbiology↗

bioRxiv2018

Use of RNA and DNA to Identify Mechanisms of Microbial Community Homogenization

Biotic homogenization is a commonly observed response following conversion of native ecosystems to agriculture, but our mechanistic understanding of this process is limited for microbial communities. In the case of rapid environmental changes, inference of homogenization mechanisms may be confounded by the fact that only a minority of taxa is active at any given point. RNA- and DNA-based community inference may help to distinguish the active fraction of a community from inactive taxa. Using these two community inference methods, we asked how soil prokaryotic communities respond to land use change following transition from rainforest to agriculture in the Congo Basin. Our results indicate that the magnitude of community homogenization is larger in the RNA-inferred community than the DNA-inferred perspective. We show that as the soil environment changes, the RNA-inferred community structure tracks environmental variation and loses spatial structure. The DNA-inferred community loses its association with environmental variability. Homogenization of the DNA-inferred community appears to instead be driven by the range expansion of a minority of taxa shared between the forest and conversion sites, which is also seen in the RNA-inferred community. Our results suggest that complementing DNA-based surveys with RNA can provide unique perspectives on community responses to environmental change.\n\nIMPORTANCETwo primary mechanisms by which community homogenization occurs are: 1) the loss of environmental heterogeneity driving community convergence, and 2) increased rates of biotic mixing, driven by exotic invasions or range expansions. Better identifying these mechanisms could help inform future mitigation strategies. Only a minority of soil taxa tends to be active at any time, which makes identifying these mechanisms difficult. To circumvent this problem, we measured prokaryotic community structure in two ways: RNA-based inference (which should enrich for active taxa), and DNA-based inference (which includes active and inactive taxa) along a gradient of land use change. Our results suggest that changes to soil heterogeneity impact the RNA-inferred community, while range expansions contribute to the homogenization of both DNA- and RNA-inferred communities. Thus, RNA-based community inference may be a more sensitive indicator of environmentally driven homogenization, and researchers interested in microbial responses to rapid environmental change should consider this method.

ecology↗