Influence of host phylogeny and water physicochemistry on microbial assemblages of the fish skin microbiome

Abstract

The skin of fish contains a diverse microbiota that has symbiotic functions with the host, facilitating pathogen exclusion, immune system priming, and nutrient degradation. The composition of fish skin microbiomes varies across species and in response to a variety of stressors, however, there has been no systematic analysis across these studies to evaluate how these factors shape fish skin microbiomes. Here, we examined 1922 fish skin microbiomes from 36 studies that included 98 species and nine rearing conditions to investigate associations between fish skin microbiome, fish species, and water physiochemical factors. Proteobacteria, particularly the class Gammaproteobacteria, were present in all marine and freshwater fish skin microbiomes. Acinetobacter, Aeromonas, Ralstonia, Sphingomonas and Flavobacterium were the most abundant genera within freshwater fish skin microbiomes, and Alteromonas, Photobacterium, Pseudoalteromonas, Psychrobacter and Vibrio were the most abundant in saltwater fish. Our results show that different culturing (rearing) environments have a small but significant effect on the skin bacterial community compositions. Water temperature, pH, dissolved oxygen concentration, and salinity significantly correlated with differences in beta-diversity but not necessarily alpha-diversity. To improve study comparability on fish skin microbiomes, we provide recommendations for approaches to the analyses of sequencing data and improve study reproducibility.

Keywords: 16S; V4; aquaculture; meta-analysis; microbiota; phylosymbiosis; physicochemical factors.

Figure 1.

Distribution of studies analysed in the current study. 103 published studies containing relevant and original data on fish skin microbiomes were retrieved and filtered down to a final 35 unique BioProjects from 36 published manuscripts. From left to right, the number of studies that were removed for each quality assessment criteria. Some studies passed initial quality control steps that assessed for availability of 16S rRNA V4 fish skin microbiome data but were subsequently discarded downstream for the numbers and reasons shown.

Figure 2.

Relative abundance of fish skin microbiome bacterial community compositions at an ASV level, grouped by: (A) freshwater fish bacterial phylum level; (B) freshwater fish bacterial class level; (C) saltwater fish bacterial phylum level; and (D) saltwater fish bacterial class level.

Figure 3.

PCoA of a Weighted UniFrac dissimilarity matrix at the ASV level grouped and colour coded by cultivation system in which the fish were sampled or maintained.

Figure 4.

Alpha and beta diversity of freshwater fish skin microbiomes associated with features of water physicochemistry. Shannon (alpha) diversity is correlated using a linear regression model against associated physicochemical factors and correlated using Spearman’s correlation. Beta diversity is coloured according to associated physiochemical data using a principal coordinate analysis (PCoA) from a Weighted UniFrac dissimilarity matrix at an ASV level. (A) and (B) temperature, (C) and (D) conductivity, (E) and (F) pH, and (G) and (H) dO₂ concentration.

Figure 5.

Alpha and beta diversity of saltwater fish skin microbiomes associated with recorded physicochemical factors. Shannon (alpha) diversity is correlated using linear regression to associated physicochemical factors and correlated using Spearman’s correlation. Beta diversity is coloured according to associated physiochemical data using a principal coordinate analysis (PCoA) from a Weighted UniFrac dissimilarity matrix at an ASV level. (A) and (B) temperature. (C) and (D) conductivity, (E) and (F) pH, and (G) and (H) dO₂ concentration.