bioRxivMicrobiology
Spatiotemporal and ontogenetic variation, microbial selection, and predicted antifungal function in the skin-associated microbiome of a Rocky Mountain amphibian
Abstract
Host-associated microbiomes play important roles in host health and pathogen defense. In amphibians, the skin-associated microbiome serves as an innate immune defense with potential implications for disease management. Few studies have examined season-long temporal variation in the amphibian skin-associated microbiome, and the interactions between bacteria and fungi on amphibian skin remain poorly understood. We characterize season-long temporal variation in the skin-associated microbiome of the western tiger salamander (Ambystoma mavortium) for both bacteria and fungi between sites and across salamander life stages. 207 skin-associated microbiome samples were collected from salamanders at two Rocky Mountain lakes throughout the summer and fall of 2018, and 127 additional microbiome samples were collected from lake water and lake substrate. We used 16S and ITS next-generation sequencing data with Bayesian Dirichlet-multinomial regression to estimate the relative abundances of bacterial and fungal taxa, test for differential abundance, examine microbial selection, and derive alpha and beta diversity. The antifungal function of bacterial communities was predicted using stochastic character mapping and a database of antifungal bacterial isolates. We examined microbial absolute abundances using Bayesian negative binomial LASSO coupled with synthetic gene spike-ins. For both bacteria and fungi, we observed variation in community composition through time, between sites, and with salamander age and life stage. We found salamander skin to be selective for microbes, with many taxa disproportionately represented relative to the environment, and we observed selection for predicted antifungal bacteria. Ultimately, this ecological knowledge may assist in the conservation of amphibian species threatened by chytridiomycosis and other emerging diseases.
- organism: mouse
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: statistical model
- approach: Bayesian inference
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