Impact of antibiotics and probiotics on aquatic and fish microbiome.
Microbial community associated with water : We characterized the rearing tank water BC at two taxonomic levels; the phylum and family. Tank BC diversity diverged among the treatments, with the top 10 most abundant families making up the majority of reads. Proteobacteria were the most common phylum among all treatments (control (70%), antibiotic (68%), probiotic (51%)). Bacteroidota (13 %), and Actinobacteriota (17%) were also common phylum in the control treatment water. Moreover, in the antibiotic treated water, Firmicutes (24%) and Bacteroidota (12%) were common phyla after Proteobacteria. On the other hand, in the probiotic treated water, Bacteroidota (12%) and Firmicutes (8%) were the common phyla after Proteobacteria (Supplementary Figure S1). At the family level, the most common aquatic associated bacterial taxa were members of Comamonadaceae, a family of the Betaproteobacteria accounting for 30%, 28%, and 35% bacterial taxa in control, antibiotic, and probiotic waters, respectively. Mycoplasmataceaewere found in all samples, but at relatively higher abundance in antibiotic challenge water compared to probiotic and control waters. Members of Oxalobacteraceae were also found in all sampled tanks but at higher abundance in the probiotic and control tanks relative to the antibiotic tanks. Other notable freshwater-associated bacterial taxa at the family level were Flavobacteriaceae ,Pseudomonadaceae , Sporichthyaceae andAeromonadaceae (Figure 2A).
To quantify treatment effects on the aquatic BCs, alpha and beta diversity indices for water samples were compared for the three treatment groups (antibiotic, probiotic, control). Alpha diversity analysis (Chao1, PD) showed no significant differences among the groups (Chao1: KW 5, P > 0.05; PD: KW 3, P> 0.05). However, our PCoA plot showed clear separation between the water samples based on treatments (Figure 2B). PERMANOVA results confirmed that the overall community structures were significantly different among the three groups (F-value 8.9; R-squared: 0.22; p-value < 0.001). Pairwise comparison also showed that the three groups are different from each other, but with the probiotic treatment group compared to antibiotic treatment group showing the highest dissimilarity (probiotic- control F: 2.17, P<0.001; probiotic- antibiotic F: 2.86, P< 0.001; control-antibiotic F: 2.77, P < 0.001). Moreover, the average dissimilarity within treatments was higher for the control tanks (73.2%) compared to our probiotic (65.4%) and antibiotic treatment tanks (61.8%).
Microbial community associated with gut: Firmicutes were the most common phylum for the control and probiotic group fish (46%, and 49%, respectively). On the other hand, members of Desulfobacterota were the most common bacteria in the antibiotic treated fish gut microbiomes (Supplementary Figure S2A). We also compared members of Firmicutes phylum among the treatments at the family level. Within the Firmicutes phylum, Mycoplasmataceae was the most common gut associated bacterial taxa across all treatments, in addition to other important taxa (Supplementary Figure S2B). For example, control and probiotic treated fish had Mycoplasmataceae (control (65%), probiotic (50%)), Streptococcaceae (control (30%), probiotic (28%)), andLactobacillaceae (control (2%), probiotic (17%)) present. However, in the antibiotic group, different families were present within Firmicutes phylum (Mycoplasmataceae (68%),Streptococcaceae (14%), and Leuconostocaceae (5%) (Supplementary Figure S4.2B). At the family level, the most common gut associated bacterial taxa across all treatment groups were members ofDesulfovibrionaceae (related to Desulfobacterota phylum) andMycoplasmataceae (Figure 3A). While Streptococcaceaehad high relative abundances in control group, samples inprobiotic groups had high relative abundances Lactobacillaceae.Moreover, members of Pseudomonadaceaehad high relative abundances in antibiotic group (Figure 3A). Unlike in the tank water microbiome,Mycoplasmataceae was higher in the control and probiotic groups compared to the antibiotic group. We also found two important fish associated pathogens, Enterovibrio and Photobacterium, in the fish gut microbiome, but at low abundance.
To identify the treatment and parental (dams and sires) effects on the gut microbial BC, alpha diversity indices for gut samples were compared. Alpha diversity analysis (Chao1, PD) for the gut microbiome BC showed no significant differences among the treatments (Chao1: KW 2.8, P> 0.05; PD: KW 3.2, P > 0.05), sires (Chao1: KW 6.9, P > 0.05; PD: KW 6.8, P> 0.05), and dams (Chao1: KW 5.3, P > 0.05; PD: KW 8.9, P > 0.05). Beta diversity variation was also explored using Bray Curtis distance matrices and a PCoA plot. The PCoA plot showed clear separation among the samples based on treatments (Figure 3B). PERMANOVA results confirmed that the overall BC structures were significantly different among the treatments (Table 1). Treatment alone had the highest influence on the gut microbial community (Pseudo-F:6.1, P value < 0.05). Pairwise comparisons also showed that the three treatment groups exhibit significant difference in beta-diversity, with the probiotic versus control treatment samples showing the highest dissimilarity (probiotic- control F: 3.01, P <0.001; probiotic- antibiotic F: 2.85, P < 0.001; control-antibiotic F: 1.52, P< 0.05). Moreover, the average within treatment group BC dissimilarity was higher for the control (82.2%) than the probiotic (77%) and antibiotic treatments (80.5%), indicating that the control group had higher diversity than the other two groups in the fish hindgut. Dams alone did not have significant effects. However, sires had marginal significant effect effects on BC structures (Table 1).
Association between gut and aquatic microbial community: We evaluated the relationship between the tank water microbiome BC and the fish gut microbiome BC. Chao1 and PD (diversity measures) showed significant differences in the species richness of the two sample types; overall, diversity was significantly higher in the water samples than gut samples (P <0.001, Mann-Whitney U test: 2191.5). The PCoA plot (Figure 4) showed clear separation between the gut and water samples. Moreover, PERMANOVA test also revealed that the clusters showed in PCoA plot were significantly different (Pseudo-F: 39.6, Pvalue< 0.05).