Health Characteristics and Blood Bacterial Assemblages of Healthy Captive Red Drum: Implications for Aquaculture and Fish Health Management

Microbial community structure in a host-parasite system: the case of Prussian carp and its parasitic crustaceans

AIMS

The aim of the study was to investigate the skin microbiota of Prussian carp infested by ectoparasites from the genera Argulus and Lernaea.

METHODS AND RESULTS

Associated microbiota of skin of Prussian carp and ectoparasites were investigated by sequencing of the V3, V4 hypervariable regions of 16S rRNA using Illumina MiSeq sequencing platform.

CONCLUSIONS

According to the Spearman rank correlation test, the increasing load of ulcerations of the skin of Prussian carp was weakly negatively correlated with reduction in the abundance of the following taxa: Acrobacter, bacteria C39 (Rhodocyclaceae), Rheinheimera, Comamonadaceae, Helicobacteraceae and Vogesella. In this study, the microbiota of ectoparasites from the genera Lernaea and Argulus were characterized for the first time. The microbiota associated with L. cyprinacea was significantly different from microbial communities of intact skin mucosa of both infested and uninfested fish and skin ulcers (ADONIS, P ≤ 0·05). The microbiota associated with parasitic crustaceans L. cyprinacea were dominated by unclassified bacteria from Comamonadaceae, Aeromonadaceae families and Vogesella. The dominant microbiota of A. foliaceus were represented by Flavobacterium, Corynebacterium and unclassified Comamonadaceae.

SIGNIFICANCE AND IMPACT OF THE STUDY

Results from these studies indicate that ectoparasites have the potential to alter skin microbiota, which can play a possible role in the transmission of secondary bacterial infections in fish, caused by pathogenic bacteria.

Effects of aging on the skin and gill microbiota of farmed seabass and seabream

Background

Important changes in microbial composition related to sexual maturation have been already reported in the gut of several vertebrates including mammals, amphibians and fish. Such changes in fish are linked to reproduction and growth during developmental stages, diet transitions and critical life events. We used amplicon (16S rRNA) high-throughput sequencing to characterize the skin and gill bacterial microbiota of farmed seabass and seabream belonging to three different developmental age groups: early and late juveniles and mature adults. We also assessed the impact of the surrounding estuarine water microbiota in shaping the fish skin and gill microbiota.

Results

Microbial diversity, composition and predicted metabolic functions varied across fish maturity stages. Alpha-diversity in the seabass microbiota varied significantly between age groups and was higher in older fish. Conversely, in the seabream, no significant differences were found in alpha-diversity between age groups. Microbial structure varied significantly across age groups; moreover, high structural variation was also observed within groups. Different bacterial metabolic pathways were predicted to be enriched in the microbiota of both species. Finally, we found that the water microbiota was significantly distinct from the fish microbiota across all the studied age groups, although a high percentage of ASVs was shared with the skin and gill microbiotas.

Conclusions

We report important microbial differences in composition and potential functionality across different ages of farmed seabass and seabream. These differences may be related to somatic growth and the onset of sexual maturation. Importantly, some of the inferred metabolic pathways could enhance the fish coping mechanisms during stressful conditions. Our results provide new evidence suggesting that growth and sexual maturation have an important role in shaping the microbiota of the fish external mucosae and highlight the importance of considering different life stages in microbiota studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-020-00072-2.

Analysis of the fecal microbiota of fast- and slow-growing rainbow trout (Oncorhynchus mykiss)

BACKGROUND

Diverse microbial communities colonizing the intestine of fish contribute to their growth, digestion, nutrition, and immune function. We hypothesized that fecal samples representing the gut microbiota of rainbow trout could be associated with differential growth rates observed in fish breeding programs. If true, harnessing the functionality of this microbiota can improve the profitability of aquaculture. The first objective of this study was to test this hypothesis if gut microbiota is associated with fish growth rate (body weight). Four full-sibling families were stocked in the same tank and fed an identical diet. Two fast-growing and two slow-growing fish were selected from each family for 16S rRNA microbiota profiling. Microbiota diversity varies with different DNA extraction methods. The second objective of this study was to compare the effects of five commonly used DNA extraction methods on the microbiota profiling and to determine the most appropriate extraction method for this study. These methods were Promega-Maxwell, Phenol-chloroform, MO-BIO, Qiagen-Blood/Tissue, and Qiagen-Stool. Methods were compared according to DNA integrity, cost, feasibility and inter-sample variation based on non-metric multidimensional scaling ordination (nMDS) clusters.

RESULTS

Differences in DNA extraction methods resulted in significant variation in the identification of bacteria that compose the gut microbiota. Promega-Maxwell had the lowest inter-sample variation and was therefore used for the subsequent analyses. Beta diversity of the bacterial communities showed significant variation between breeding families but not between the fast- and slow-growing fish. However, an indicator analysis determined that cellulose, amylose degrading and amino acid fermenting bacteria (Clostridium, Leptotrichia, and Peptostreptococcus) are indicator taxa of the fast-growing fish. In contrary, pathogenic bacteria (Corynebacterium and Paeniclostridium) were identified as indicator taxa for the slow-growing fish.

CONCLUSION

DNA extraction methodology should be carefully considered for accurate profiling of the gut microbiota. Although the microbiota was not significantly different between the fast- and slow-growing fish groups, some bacterial taxa with functional implications were indicative of fish growth rate. Further studies are warranted to explore how bacteria are transmitted and potential usage of the indicator bacteria of fast-growing fish for development of probiotics that may improve fish health and growth.