3.1 Effect of UV and non-UV treated water on fish survival
For the V. harveyi infection experiments, fish mortality was recorded daily (Figure 1). Dead fish were removed from the experiment and subjected to bacteriological analysis to confirm mortality etiology. Figure 1A shows that S. aurata fish reared in non-UV treated water had significantly higher survival rates following V. harveyi infection (60% survival in summer and 20% in winter), compared to fish reared in UV treated water (no survivors in either season).
In our second round of experiments, we assessed S. iniae bacterial infection in the less susceptible S. aurata and more susceptible L. calcarifer fish (Figure 1B). For the S. iniae infection, the survival rate of L. calcarifer increased from 20% in UV treated water to 40% in non-UV treated water. The survival of S. aurata infected with S. iniae in UV treated water was 100% compared to 80% in non-UV treated water.
3.2 Commensal bacterial diversity at different spatial and temporal changes following pathogenic bacterial infection
Fish skin bacterial diversity estimates, including non-phylogenetic Chao1 species’ diversity, Shannon and Simpson diversity index, and Faith’s phylogenetic diversity (Figure 2) all showed a slightly higher diversity for non-UV treated water compared to UV treated water. Interestingly, during the infection (T1), we saw a remarkable decrease in fish skin flora diversity estimates of both V. harveyi and S. iniae pathogens and these diversities returned to their initial level at T2 and T3, corresponding to one week and one month post infection. Figure 2 presents diversity estimates at the different body sites (Abdomen, Gills and Lateral) during V. harveyi infection in the summer 2015 and winter 2016 experiments. At different time points, we noticed a higher similarity in those diversity estimates for both fish abdomen and lateral lines compared to gills which showed slightly higher estimates, however these differences showed to be insignificant when compared using Tukey’s test (Supplementary Figure S2).
To better illustrate these differences and to evaluate related patterns in the bacterial communities, PCoA ordination plots based on weighted unifrac distance matrix were generated for the different experiments. Figure 3A-D shows PCoA plots for S. aurata during V. harveyi infection (Figs. 3A & B) in summer 2015 and in winter 2016 respectively, while Figure 3C-D shows PCoA plots during S. iniae infection for S. aurata (Figure 3C) and L. calcarifer (Figure 3D).
Figure 3 and the pairwise statistical differences (Supplementary Table S5) show distinctly unique fish skin bacterial communities in fish from UV and non-UV tanks for S. aurata during both V. harveyi (Figure 3B) and S. iniae infection (Figure 3C) but not for L. calcifer (Figure 3D, Supplementary Table S5). Interestingly, S. aurata only showed a significant difference in community composition when comparing the water treatments at T0 but not at T1 for the V. harveyi infection. However, during the S. iniae infection, significant differences in bacterial communities were evident when comparing the UV and non-UV tanks at all time points. Note, there were no significant differences in the bacterial composition of gills at T0 when comparing UV and non-UV treatments for S. aurata during the V. harveyi infection (Supplementary Table S5).
When looking at core and unique microbial ASVs, only 21, 31 and 25% of all ASVs are shared between both UV and non-UV treatments for winter, 2016 V. harveyi infection and for S. iniae infection in S. aurata and L. calcifer respectively, these also constitute 93, 93 and 85% of weighted bacterial abundances, respectively (Supplementary Table S6). In contrast, the percent and weighted percentages of the shared and unique microbial communities were similar among the different time points and body sites. Notice, fish skin microbial communities in non-UV treatment had a higher percentage of unique ASVs compared to fish reared in UV treated tanks, this percentage declined during infection (Supplementary Table S6B) and gradually increased post infection. Interestingly, the number of shared ASVs has shown to positively correlate with disease severity and negatively correlate with survival rate (Figure 1).
When comparing the microbiome of different fish body sites (abdomen, gills and lateral line), Figures 3A and B do not show a clear separation. When testing the significant differences between fish body sites at different time points in UV and non-UV treatment (Supplementary Table S7), microbial communities do show significant differences but only at a few time points. Differences in microbial communities are evident when comparing the microbial communities of the lateral line to gills at T0 and T1 in the non-UV treatment in both summer and winter experiments, and once again when abdominal microbial communities were compared at T1 in the summer experiment (Supplementary Table S7A). The unweighted and weighted percentages of unique ASVs for different body sites at different time points for non-UV treatments (Supplementary Table S7B), clearly indicate the presence of different microbial communities when comparing gills and lateral line sites, reaching up to 50% unique ASVs at T2 for the gills site.
Figures 3A and B also show samples of microbial community variance at different time points. The most pronounced separation is indicated by axis1 and explains 66.7% of microbial variance in relation to different time points. These microbial variances are mainly seen at T1 (24 h post infection), while axis2 only explained 8.5% of the variation which corresponded to the changes accruing between UV and non-UV treatments during summer season in 2015 (Figure 3A). When this experiment was repeated in winter 2016 (Figure 3B), axis1 explained 80.7% of total bacterial variation at the different time points before, during and after infection. In both winter and summer experiments, before (T0) and after infection (T1), all body sites for both UV and non-UV treatments showed to be significantly different (Supplementary Table S8A). Interestingly, the microbial community on skin of surviving fish returned to its original composition two weeks post infection (comparing between T0 and T3, P-values > 0.05). When infecting both S. aurata (Figure 3C) and L. calcarifer fish species with S. iniae (Figure 3D), we attempted to monitor changes in the microbial communities at higher temporal resolution to understand their interactions and impacts on fish health. Therefore, additional sampling time points were added at 48 h (T1-2), 72 h (T1-3), 5 days (T1-5), 1 week (T2), and 2 weeks (T2-2) post infection. Differences in the microbial communities between the UV and non-UV treatments were observed (Figure 3C, Supplementary Table S5), in addition, the PCoA plot presented in Figure 3C also shows interesting temporal patterns. The microbial communities showed a gradual deviation from T0 downward along axis2 (explaining 19% of variance) for samples taken at T1 and T1-2 (24h and 48 h after infection, respectively), while at T1-3 (72 h after infection) the microbial communities began to return to the original composition, which was similar to T1 (Supplementary Table S8B). Interestingly, after five days (T1-5), one week (T2), two week (T2-2) microbial communities gradually moved to cluster with T3 (one month after infection) which was similar to the original microbial communities (P-value = 0.056 between T0 and T3). In the UV treatment, there were no significant differences at the different time points compared to T0 (Supplementary Table S8C), yet a significant difference was observed comparing different stages of infection (T1-2, T1-3, T1-5, T2, T2-2 and T3), quite similar to differences seen in the non-UV treatment.
During S. iniae infection of L. calcarifer, which is a highly susceptible fish species (see Figure 1B), T0 did not show a clear separation of the microbial communities or statistical differences between the non-UV and UV treatments (Supplementary Table S5). There were no clear differences at different time points neither before nor after infection in both non-UV and UV treatments (Supplementary Table S8D and S8E), except when comparing bacterial communities after 24 (T1) and 72 hours (T1-3) post infection. Interestingly, following V. harveyi (Figs. 3A and 3B) and S. iniae (Figs. 3C and 3D) infections, a major difference in the variance of the microbial communities was seen in the PCoA analysis, explaining 87.2 and 75.2% of variance in V. harveyi while for S. iniae infection, explained 44.2 and 46.2% of variance for axis 1 and 2 respectively.
3.3 Bacterial community compositions
Figures 4-7 show the relative abundance of each bacterial phylum (different shades of the same color present different families of the same phyla) during V. harveyi and S. iniae infection experiments. Figure 4 illustrates the relative bacterial abundance during V. harveyi infection in the summer 2015 experiment, the bar graph shows three main bacterial phyla dominating the total bacterial abundance, Proteobacteria (blue, red and white), Firmicutes (pink) and Actinobacteria (yellow). At T0, before infection, Proteobacteria abundance was 63.9 ± 12.2%, followed by Firmicutes (14.8 ± 11.0%) and Actinobacteria (13.1 ± 8.1%). Following infection, these relative bacterial abundances changed at T1 (24 h after infection) and T2 (1 week after infection) and T3 (3 weeks after infection) yet they were still dominant and the final relative abundances at T3 were 56.6 ± 6.3% for Proteobacteria, 27.1 ± 19.0% for Firmicutes and 11.0 ± 4.9% for Actinobacteria. To better understand the changes in relative bacterial abundances and their effect on fish health before, during and after infection, we analyzed the relative bacterial abundances to pinpoint their significant changes at different sampling points using DeSeq (Supplementary Figure S3). DeSeq analysis showed seven ASVs to significantly differentiate at different time points. The most abundant ASV belongs to the Unclassified_Gammaproteobacteria Class ASV of Proteobacteria (Gray, Supplementary Figure S3) which mainly dominated T1 (24 h post infection) at a relative abundance of 24.1 ± 22.3%, followed by Delfita ASV genus at T0 (33.9 ± 15.3%). Interestingly, Unclassified_Gammaproteobacteria ASV was only abundant at T1, (24 h post infection). At one and three weeks post infection (T2 and T3), its relative abundance declined to less than 1% of the total bacterial abundance and was replaced with Delfita ASV, their relative abundances were 11.2 ± 8.2% and 24.2 ± 16.9% respectively (Figure 4).
When repeating the same experiment in winter 2016, we analyzed relative bacterial abundances in both UV and non-UV treated tanks (Figure 5). Figure 5 shows the similar three main bacterial phyla dominating the total bacterial abundance: Proteobacteria, Firmicutes and Actinobacteria. The relative abundances of these bacterial phyla proved to be different in the non-UV and UV treatment tanks. At T0, before infection, Proteobacteria abundance in non-UV vs. UV treated water was 57.3 ± 6.5% and 53.5 ± 9.1% respectively, followed by Actinobacteria (18.2 ± 9.6% and 15.6 ± 9.8%) and Firmicutes (15.1 ± 7.1% and 20.1 ± 9.3%). DeSeq analysis shows that the abundance of ten bacterial ASVs significantly differentiate in different UV treatments and time points Delfita and Bacillus genus of Proteobacteria and Firmicutes are among the most abundant ASVs (Supplementary Figure S4). Interestingly, Delftia (white) and Bacillus (yellow) showed a significantly different distribution following UV treatment at T0: the abundance of both Delftia and Bacillus decreased from 33.9 ± 15.3% to 4.9 ± 2.4%, and from 5.6 ± 3.6 to 0.2 ± 0.5% respectively. At T1, 24 h after infection with V. harveyi, the bacterial community was dominated by the Vibrio family (red), with a relative abundance of 85.0 ± 10.4% and 80.6 ± 13.7% for non-UV and UV treated water, respectively (Figure 4). Moreover, Delftia genus also showed a higher abundance in non-UV treated (2.9 ± 5.2%) compared to UV treated water (0.3 ± 0.3%), while Photobacterium (cyan, belonging to Vibrio family) showed a higher abundance in UV treated (1.9 ± 3.9%) compared to non-UV treated water (0.2 ± 0.8%) (Supplementary Figure S4). At T2 (one week post-infection) and T3 (three weeks post-infection), all fish from UV treated water perished, in non-UV treated tanks, the relative abundance of vibrio decreased to 58.9 ± 20.7% at T2 and to 6.6 ± 6.5% at T3 (Figure 4). On the other hand, Delftia increased from 6.9 ± 5.0% to 16.6 ± 8.7% at T2 and T3 respectively and the final relative abundances of the main bacterial phyla were 50.4 ± 10.9%, 35.1 ± 14.8% and 9.7 ± 2.9% for Proteobacteria, Actinobacteria and Firmicutes respectively, similar to the initial abundance at T0. Interestingly, the Delfita ASV was dominant at T0 for both summer (33.9 ± 15.3%, Figure 4), and winter experiment (29.5 ± 15.1%, Figure 5), whereas at T1, Delfita relative abundances significantly declined to 2.1 ± 3.9% and bacterial communities were dominated by Vibrio ASV abundance (53.5 ± 20.7%). However, a remarkable increase in the Unclassified_Gammaproteobacteria abundance at T1 was only observed in the summer experiments (24.1 ± 22.3%, Supplementary Figure S3).
The results of the pathogenic S. iniae infection experiment conducted in 2020 (Figure 6) show that bacterial communities of S. aurata fish skin at T0 (before infection) are slightly different compared to those at T0 in the previous experiments (summer 2015 and winter 2016, Figs. 4 and 5). The most abundant bacterial phyla in this experiment were Proteobacteria, Firmicutes and Bacteroidota with relative T0 abundances of 67.3 ± 4.3% and 66.1 ± 13.7 %, 11.7 ± 3.2% respectively for the non-UV treatment, and 2.4 ± 3.2%, and 4.2 ± 0.8% and 14.8 ± 12.3% respectively for the UV treated water. Actinobacteria, previously seen in the two V. harveyi experiments as one of the three main bacterial phyla, was less abundant (2.3%) during this experiment (Figure 6). Interestingly, at T1 (24 h post-infection) of the S. iniae infection (white, Figure 6), the relative abundance of S. iniae did not dominate fish skin lateral line (unlike fish infected with V. harveyi) and showed a relative abundance of 9.0 ± 8.6% and 5.9 ± 4.5% for non-UV and UV treatments, respectively. Yet, DeSeq analysis showed Unclassified_Vibrionaceae family ASV (brown, Supplementary Figure S5), and Unclassified_Gammaproteobacteria Class ASV (black, Supplementary Figure S5), to significantly differentiate at different time points and between UV and non-UV treatment. Unclassified_Vibrionaceae Family showed an increased abundance from 2.4 ± 1.6% at T0 to 18.3 ± 6.3% at T1 for non-UV treatment and from 13.7 ± 20.9% at T0 to 44.5 ± 27.6% at T1 for UV treated water. At T1-2 (72 h after infection), Unclassified_Vibrionaceae Family abundance further increased to reach 51.4 ± 7.4% for non-UV treated water. In addition, following the infection, Unclassified_Gammaproteobacteria (black) ASV showed an increased abundance at the recovery stage (T1-2, T1-3, T1-5 and T2), however its abundance was higher in the UV treatment compared to non-UV treated water (Supplementary Figure S5).
The S. iniae infection experiment was also conducted on L. calcarifer fish (Figure 7). At T0, before infection with S. iniae, the main bacteria phyla found on the lateral line of L. calcarifer were Proteobacteria, Firmcutes and Bacteroidota at relative abundances of 70.3 ± 3.6% and 72.8 ± 6.5% for non-UV treatment respectively, and 10.9 ± 2.2% and 8.2 ± 4%, 5.1 ± 2.3% and 7.3 ± 2.9% for UV treated water, respectively. At T1 (24 h post infection), there was a similar yet higher increase in S. iniae abundance in the skin (lateral line) of L. calcarifer compared to S. aurata. The relative abundances for S. iniae at T1, T1-2 and T1-3 for non-UV treated water were 10.0 ± 11.1%, 10.8 ± 9.8% and 27.5 ± 25.6% while for UV treated water they were 16.5 ± 12.4%, 27.6 ± 22.1% and 23.3 ± 19.7% respectively (Figure 7). DeSeq analysis showed the abundance of Unclassified_Rhodobacteraceae Family ASV to be significantly higher on fish skin before infection (T0), 24 h (T1) and 72 h (T1-2) post infection for non-UV treated water compared to UV treated water (Supplementary Figure S6).