Core species and interactions prominent in fish-associated microbiome dynamics

Abstract

Background In aquatic ecosystems, the health and performance of fish depend greatly on the dynamics of microbial community structure in the background environment. Nonetheless, finding microbes with profound impacts on fish’s performance out of thousands of candidate species remains a major challenge. Methods We examined whether time-series analyses of microbial population dynamics could illuminate core components and structure of fish-associated microbiomes in the background (environmental) water. By targeting eel-aquaculture-tank microbiomes as model systems, we reconstructed the population dynamics of the 9605 bacterial and 303 archaeal species/strains across 128 days. Results Due to the remarkable increase/decrease of constituent microbial population densities, the taxonomic compositions of the microbiome changed drastically through time. We then found that some specific microbial taxa showed a positive relationship with eels’ activity levels even after excluding confounding effects of environmental parameters (pH and dissolved oxygen level) on population dynamics. In particular, a vitamin-B12-producing bacteria, Cetobacterium somerae, consistently showed strong positive associations with eels’ activity levels across the replicate time series of the five aquaculture tanks analyzed. Network theoretical and metabolic modeling analyses further suggested that the highlighted bacterium and some other closely-associated bacteria formed “core microbiomes” with potentially positive impacts on eels. Conclusions Overall, these results suggest that the integration of microbiology, ecological theory, and network science allows us to explore core species and interactions embedded within complex dynamics of fish-associated microbiomes.

Deterministic and stochastic processes generating alternative states of microbiomes

“…In medicine, for example, the structure of human gut microbiomes varies considerably among host individuals, exhibiting complex associations with host human health [ 20 , 22 ]. Furthermore, recent studies have suggested that fish-associated and plant-associated microbiomes can be classified into several community compositional clusters potentially differing in physiological impacts on host organisms [ 14 , 63 ]. To extend the discussion on functional divergence of microbiomes, the genomic information of the microbes constituting microbiomes need to be enriched with shotgun metagenomic analyses [ 64-66 ].…”

Basins of attraction of microbiome structure and soil ecosystem functions

“…Because our present data lacked the information of temporal changes in community structure, we are unable to discuss the frequency and pace of community structural transitions between basins of attraction. Monitoring of microbiome compositions (Faust et al, 2015; Hayashi et al, 2024; Yajima et al, 2023) is necessary for filling the gap between theoretical and empirical studies (Long et al, 2024).…”

“…Intriguingly, these alternative gut microbiomes ("enterotypes") differ in their associations with human disease such as type II diabetes and Crohn's disease (Costea et al, 2017). In addition to those studies on animal-associated microbiomes (Arumugam et al, 2011;Moeller et al, 2012;Yajima et al, 2023), studies on plantassociated microbiomes have started to reorganize our recognition of how multistability of phyllosphere/rhizosphere microbiome structure is associated with ecosystem-scale processes and functions (Toju et al, 2018(Toju et al, , 2016. Because hundreds or thousands of replicate community samples are available in such microbiome studies, it is now possible to discuss potential relationship between community structure and ecosystem functions based on statistical signs of the presence of multiple basins (and background attractors).…”

Soil prokaryotic and fungal biome structures associated with crop disease status across the Japan Archipelago

“…Based on the estimated concentrations of prokaryotic 16S rRNA and fungal ITS sequences in template DNA solutions ( 42 , 84 ), we calculated the ratio of prokaryotic DNA concentrations to fungal DNA concentrations in respective samples (prokaryote/fungus ratio) as follows:…”