Elsevier

Aquaculture

Volume 528, 15 November 2020, 735539
Aquaculture

Salinity and fish age affect the gut microbiota of farmed Chinook salmon (Oncorhynchus tshawytscha)

https://doi.org/10.1016/j.aquaculture.2020.735539Get rights and content

Highlights

  • Water salinity and fish age strongly impact farmed Chinook salmon gut microbiota composition and diversity.

  • A relatively low abundance of lactic acid bacteria was detected in farmed Chinook salmon regardless of salinity and fish age.

  • Fish age was more influential than water temperature and farming location on gut microbiota diversity within each ecosystem (freshwater and marine) of farmed chinook salmon.

Abstract

Detailed classification and characterisation of the gut microbial community and understanding of factors affecting the microbiota are essential to understand the relationship between Chinook salmon (Oncorhynchus tshawytscha) gut microbiota and fish health. Here we evaluated the multiple effects of biotic and abiotic factors on the gut microbial community composition of farmed Chinook salmon, based on high-throughput sequencing of 16S rRNA gene V1-V3 amplicons. Gastrointestinal microbial community composition was highly dynamic between freshwater and saltwater conditions but similar among individual fish. A high abundance of Proteobacteria and a relatively low abundance of lactic acid bacteria (LAB) were detected in farmed Chinook salmon regardless of salinity. Species richness and diversity were significantly higher in freshwater farmed salmon than in those farmed in the marine environment. Water temperature and farming location displayed relatively minor effects on gut microbiota, while fish age had significant effects on the beta diversity of gut microbiota in both freshwater and saltwater habitats. Our study provided a detailed description of the gut microbial community of farmed Chinook salmon during grow out and contributed to a greater understanding of the effects of fish age and water salinity on the gut microbiota modulation.

Graphical abstract

Water salinity and fish age were discovered to strongly impact the gut microbial community composition of farmed Chinook salmon (Oncorhynchus tshawytscha), data useful for feed development for sustainable aquaculture.

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Introduction

Enabled by the rapid development of next-generation sequencing (NGS) techniques over the past decade, studies of classification and characterisation of gut microbial communities have advanced from traditional culture-dependent and microscopy-based observations to culture-independent phylogenetic analyses. This progress has enabled a more qualitative and quantitative understanding of microbial community diversity allowing inference of microbial associated functional impacts and linkages in organisms reared in natural and engineered ecosystems, including commercial species (Ghanbari et al., 2015). Numerous fundamental studies on humans and other animals have interpreted the composition and functions of gut microbiota (Li et al., 2017; Petersen and Round, 2014; Stanley et al., 2016). Increasing attention has focused on exploring the diversity of fish gut microbiota for the longer-term purposes of developing sustainable aquaculture production systems (Mirghaed et al., 2018). At this stage, understanding is limited to developing a concept of the repertoire of microbiome species in aquaculture production systems (de Bruijn et al., 2017). The role of microbiomes in relation to fish feeding, digestion, energy homeostasis and growth have been extrapolated from data from mammalian systems (Volkoff and Butt, 2019) but there is still lacking information to link between microbiomes and farm performance and fish health. Microbiomes can vary among fish populations (He et al., 2018), therefore specific information is required especially in the context of farm environments and fish husbandry.

High-throughput 16S ribosomal RNA (16S rRNA) gene sequencing has been increasingly employed to describe a general microbial community composition due to the improvement in sequence lengths and a decrease in sequencing costs (Albertsen et al., 2015). The recent advancement of the 16S rRNA gene sequencing technologies and their application in the field of fish gastrointestinal microbiota profiling has demonstrated a largely different gut microbial community structure compared to that of humans and other homoeothermic animals (Brugman et al., 2018). In mammals, the predominant gut microbiota are anaerobes from the phyla Bacteroidetes and Firmicutes (Lucas López et al., 2017), while fish gut microbiota community is often dominated by facultative anaerobes from the phylum Proteobacteria (Villasante et al., 2019). Generally, the fish gut harbours a group of complex and dynamic microorganisms including fungi, yeast, viruses, protists, bacteria and archaea, however bacteria represent the most predominant fish gut microbiota (Rombout et al., 2011). Furthermore, a wide range of intrinsic and extrinsic factors have been demonstrated to influence the composition of microbiota in the fish gut, including salinity, temperature, geographical location, life stage, diet, farm management practices as well as antibiotic and probiotic utilisation (Egerton et al., 2018). Additionally, different methods of sample collection, phylogenetic profiling and bioinformatic analysis may also contribute to the interpretation of fish gut microbial diversity (Romero et al., 2014). This can hinder the comparison of results and leads to generalisation of types of microbial diversity present.

The advancement of modern computational tools, integrated bioinformatic analyses, semi-quantitative molecular and high throughput sequencing-based studies allow deeper taxonomic profiling of the whole gut microbial community. As the only salmonid species farmed on a significant scale in New Zealand (NZSFA, 2011), Chinook salmon (Oncorhynchus tshawytscha) has yet to be studied in terms of the whole gut microbial community composition in detail. In 2006, researchers used a culture-dependent method to investigate the reaction of gut microbiome from the hindgut of farmed juvenile Chinook salmon to exposure with the antibiotic erythromycin (Moffitt and Mobin, 2006). They described the resident heterotrophic aerobic microflora in the posterior intestine of Chinook salmon in a hatchery raceway environment. More recently, the gut microbial community composition of marine farmed post-smolt Chinook salmon in New Zealand was described using metabarcoding (Ciric et al., 2018). However, this study was performed during a summer heatwave when the salmon were fasting and presumably stressed. Hence, the aim of the current study was to conduct an in-depth classification of the gut microbiota of healthy smolt, post-smolt and adult Chinook salmon raised within freshwater (FW) and saltwater (SW) farms in New Zealand. The study provides a description of the core microbiome for O. tshawytscha, defines the bacterial taxa influential in identifying gut microbiomes of fish farmed at different salinities; and determines key factors that control microbiome composition. The full spectrum of the high-resolution phylogenetic gut microbial profiling will provide insights into the underlying mechanisms of complex functional relationships between the host, its associated microbiota and the external environment.

Section snippets

Salmon Management

Farmed Chinook salmon investigated in this project were reared at two FW farms located in the Tekapo canals (TC) and Ruataniwha hydro canals (RT), near Twizel, operated by Mount Cook Alpine Salmon Ltd. In addition, salmon were sourced from three SW farms located in 1) Titoki Bay (TB), Akaroa Harbour (Akaroa Salmon Ltd); 2) Otanerau Bay (OT), Marlborough Sound (New Zealand King Salmon Co. Ltd); and 3) smolt (BS) and grow-out (BG) sites located in Big Glory Bay, Stewart Island (Sanford Ltd) (Fig.

Characteristics of the High-throughput Sequences

MiSeq Illumina-based pair-ended amplicon 16S rRNA gene sequencing (V1-V3) of bacterial DNA resulted in a total of 2,370,341 quality-controlled reads and 1,581,292 effective reads after further quality filtration process, which resulted in an average of 11,702 ± 731 reads (mean ± SEM) per sample. This included removal of singleton OTUs, chimeric sequences and plant-derived sequences. Chloroplast sequences (made up 30% of the raw reads) and mitochondrial sequences (made up 19% of the raw reads)

Discussion

In the current study, the comparative analysis of faecal samples collected from Chinook salmon that vary by fish age, temperature range and geographical location from both FW and SW farms in New Zealand can provide a comprehensive picture of the gut microbial community of farmed Chinook salmon. Overall, the gut microbial communities detected in our study were consistent with those reported in other salmonids (Gajardo et al., 2016; Huyben et al., 2018; Zarkasi et al., 2016). We found the waster

Funding

This study was funded by the New Zealand Ministry of Business, Innovation and Employment (MBIE) Efficient Salmon research program [CAWX1606] and is a collaboration between the University of Tasmanian, Australia and the Cawthron Institute, New Zealand.

Sequence data

The nucleotide sequence data reported are available in the NCBI Sequence Read Archive (SRA) databases under the BioProject ID: PRJNA591374. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA591374.

Ethical Statement

All procedures performed in studies involving animals were in accordance with the ethical standards of the University of Tasmania Animal Ethics Committee (Ref. A0017883).

Author contributions

Conceptualization: JES, SPW, KS, BFN, JPB, CGC, RZ; Methodology: JES, SPW, KS, BFN, JPB, CGC, RZ; Data curation: RZ, JPB; Formal analysis and investigation: RZ, JPB; Resources: JES, SPW, KS, BFN, JPB, CGC; Writing - original draft preparation: RZ; Writing - review and editing: JES, SPW, KS, BFN, JPB, CGC; Funding acquisition: JES, SPW, KS; Resources: JES, SPW, KS, BFN, JPB, CGC; Project administration: JES, BFN, JPB, CGC; Supervision: BFN, JPB, CGC.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We appreciate the hard work of staff in the Cawthron Institute's field sampling group. We wish to thank the Ramaciotti Centre for Genomics for facilitating the MiSeq Illumina-based 16S rRNA sequencing. We would like to express our gratitude to all the scientists, technicians, and industry partners who contributed to this project.

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