Community recovery dynamics in yellow perch microbiome after gradual and constant metallic perturbations

Abstract

Background: The eco-evolutionary processes ruling post-disturbance microbial assembly remain poorly studied, particularly in host-microbiome systems. The community recovery depends not only on the type, duration, intensity, and gradient of disturbance, but also on the initial community structure, phylogenetic composition, legacy, and habitat (soil, water, host). In this study, yellow perch (Perca flavescens) juveniles were exposed over 90 days to constant and gradual sublethal doses of cadmium chloride. Afterward, the exposure of aquaria tank system to cadmium was ceased for 60 days. The skin, gut and water tank microbiomes in control and treatment groups, were characterized before, during and after the cadmium exposure using 16s rDNA libraries and high throughput sequencing technology (Illumina, Miseq). Results: Our data exhibited long-term bioaccumulation of cadmium salts in the liver even after two months since ceasing the exposure. The gradient of cadmium disturbance had differential effects on the perch microbiota recovery, including increases in evenness, taxonomic composition shifts, as well as functional and phylogenetic divergence. The perch microbiome reached an alternative stable state in the skin and nearly complete recovery trajectories in the gut communities. The recovery of skin communities showed a significant proliferation of opportunistic fish pathogens (i.e., Flavobacterium). Our findings provide evidence that neutral processes were a much more significant contributor to microbial community turnover in control treatments than in those treated with cadmium, suggesting the role of selective processes in driving community recovery. Conclusions:The short-term metallic disturbance of fish development has important long-term implications for host health. The recovery of microbial communities after metallic exposure depends on the magnitude of exposure (constant, gradual), and the nature of the ecological niche (water, skin, and gut). The skin and gut microbiota of fish exposed to constant concentrations of cadmium (CC) were closer to the control negative than those exposed to the gradual concentrations (CV). Overall, our results show that the microbial assembly during the community recovery were both orchestrated by neutral and deterministic processes.

Unpicking the mysterious symbiosis ofMycoplasmain salmonids

“…Comparative genomics analyses between the Mycoplasma MAG and M. penetrans were undertaken and provide insight into the potential host-microbe interaction. Mycoplasmas have been widely reported within Salmo salar (23), and other teleosts (27). It is not uncommon to find that communities of gut microorganisms are dominated by mycoplasmas (53).…”

“…Nonetheless, demographic modelling of microbial communities suggest colonisation of salmonid guts by these organism as non-neutral, i.e. the rate at which these organisms colonise the gut, indicates a significant degree of specific adaptation to the host environment (26,27). In the current study, we aimed to explore the characteristics of Mycoplasma in salmonids, including cellular niche, taxonomic affiliations, genome structure and gene content.…”

The yellow perch (Perca flavescens) microbiome revealed resistance to colonisation mostly associated with neutralism driven by rare taxa under cadmium disturbance

“…Sequence analysis was performed with our bioinformatic pipeline as described previously [27,50]. The analysis of the alpha and beta diversity of metacommunities was proceeded using the Rhea package [51].…”

Microbiomes of clownfish and their symbiotic host anemone converge before their first physical contact

“…The distance matrix was then split to separate each condition (Control/Physical/Remote) in order to allow for the plotting and comparison of said conditions across all six time points (using ggplot2). GUniFrac distances were preferred over other distance metrics for two main reasons: rst, it is a measure of phylogenetic distance, which captures ecological changes since functional repertories are coupled with taxonomy in bacteria (reviewed in [28]). Second, GUniFrac distance uses both the phylogenetic divergence and an extra parameter α controlling the weight on abundant lineages in order to mitigate the in uence of highly abundant lineages over other lineages in UniFrac distance computing [29].…”