Dead or alive: microbial viability treatment reveals both active and inactive bacterial constituents in the fish gut microbiota

Abstract

Aims: This study evaluated the microbial viability of fish gut microbiota in both digesta (faecal) and mucosal samples using a modified propidium monoazide (PMA) protocol, followed by 16S ribosomal RNA (rRNA) gene sequencing.

Methods and results: Digesta and gut mucosal samples from farmed yellowtail kingfish (Seriola lalandi) were collected and a modified PMA treatment was applied prior to DNA extraction to differentiate both active and nonviable microbial cells in the samples. All samples were then sequenced using a standard 16S rRNA approach. The digesta and mucosal samples contained significantly different bacterial communities, with a higher diversity observed in digesta samples. In addition, PMA treatment significantly reduced the microbial diversity and richness of digesta and mucosal samples and depleted bacterial constituents typically considered to be important within fish, such as Lactobacillales and Clostridales taxa.

Conclusions: These findings suggest that important bacterial members may not be active in the fish gut microbiota. In particular, several beneficial lactic acid bacteria (LAB) were identified as nonviable bacterial cells, potentially influencing the functional potential of the fish microbiota.

Significance and impacts of the study: Standardizing the methods for characterizing the fish microbiota are paramount in order to compare studies. In this study, we showed that both sample type and PMA treatment influence the bacterial communities found in the fish gut microbiota. Our findings also suggest that several microbes previously described in the fish gut may not be active constituents. As a result, these factors should be considered in future studies to better evaluate the active bacterial communities associated with the host.

Keywords: 16S rRNA; PMA; fish; gut; microbiota; viability; yellowtail kingfish.

Figure 1

Impact of sample type on the global bacterial communities associated with the fish gut. (a) PCoA plot representing unweighted Unifrac distances comparing the change in bacterial communities found in digesta and mucosal samples for all five fish replicates used in this study. (b) Boxplot presenting the median and IQR of the number of observed amplicon sequence variants (ASVs) identified in digesta and mucosal samples. The levels of significant difference are denoted by *P ≤ 0·05, **P ≤ 0·01 and ***P ≤ 0·001, following the Wilcoxon rank‐sum test.

Figure 2

Impact of sample type on the taxonomical composition of the fish gut microbiota. (a) Stacked barplot presenting the relative abundance (%) of the top 10 most abundant bacterial genus found in the gut microbiota of all five replicates, comparing digesta and mucosal samples. (b) Dotplot showing significantly differentially abundant amplicon sequence variants (ASVs) between digesta and mucosal samples, as identified using Deseq2.

Figure 3

Impact of propidium monoazide (PMA) treatment on the global bacterial communities associated with the fish digesta. (a) PCoA plot representing unweighted Unifrac distances comparing the change in bacterial communities found in PMA‐treated and control samples for all five digesta replicates used in this study. (b) Boxplot presenting the median and IQR of the number of observed amplicon sequence variants (ASVs) identified in PMA‐treated and control digesta samples. The levels of significant difference are denoted by *P ≤ 0·05, **P ≤ 0·01 and ***P ≤ 0·001, following the Wilcoxon rank‐sum test.

Figure 4

Impact of propidium monoazide (PMA) treatment on the taxonomical composition of the digesta‐associated microbiota. (a) Stacked barplot presenting the relative abundance (%) of the top 10 most abundant bacterial genus found in the digesta of all five fish replicates, comparing PMA‐treated and control samples. (b) Dotplot showing significantly differentially abundant amplicon sequence variants (ASVs) between PMA‐treated and control digesta samples, as identified using Deseq2.

Figure 5

Impact of propidium monoazide (PMA) treatment on the global bacterial communities associated with the fish gut mucosa. (a) PCoA plot representing unweighted Unifrac distances comparing the change in bacterial communities found in PMA‐treated and control samples for all five mucosal replicates used in this study. (b) Boxplot presenting the median and IQR of the number of observed amplicon sequence variants (ASVs) identified in PMA‐treated and control mucosal samples. The levels of significant difference are denoted by *P ≤ 0·05, **P ≤ 0·01 and ***P ≤ 0·001, following the Wilcoxon rank‐sum test.

Figure 6

Impact of propidium monoazide (PMA) treatment on the taxonomical composition of the gut mucosa associated microbiota. (a) Stacked barplot presenting the relative abundance (%) of the top 10 most abundant bacterial genus found in the mucosa of all five fish replicates, comparing PMA‐treated and control samples. (b) Dotplot showing significantly differentially abundant amplicon sequence variants (ASVs) between PMA‐treated and control mucosal samples, as identified using Deseq2.