Fish genetically selected for growth (GS) and reference (REF) fish were fed with CTRL (15% FM, 5–7% FO) or FUTURE (7.5% FM, 10% poultry meal, 2.2% poultry oil + 2.5% DHA-algae oil) diets during a 12-months production cycle. Samples from initial (t₀; November 2019), intermediate (t₁; July 2020) and final (t₂; November 2020) sampling points were used for Illumina 16S rRNA gene amplicon sequencing of the adherent microbiota of anterior intestine (AI). Samples from the same individuals (t₁) were also used for the gene expression profiling of AI by RNA-seq, and subsequent correlation analyses with microbiota abundances. Discriminant analyses indicated the gut bacterial succession along the production cycle with the proliferation of some valuable taxa for facing seasonality and different developmental stages. An effect of genetic background was evidenced along time, decreasing through the progression of the trial, namely the gut microbiota of GS fish was less influenced by changes in diet composition. At the same time, these fish showed wider transcriptomic landmarks in the AI to cope with these changes. Our results highlighted an enhanced intestinal sphingolipid and phospholipid metabolism, epithelial turnover and intestinal motility in GS fish, which would favour their improved performance despite the lack of association with changes in gut microbiota composition. Furthermore, in GS fish, correlation analyses supported the involvement of different taxa with the down-regulated expression of pro-inflammatory markers and the boosting of markers of extracellular remodelling and response to bacterium. Altogether, these findings support the combined action of the gut microbiome and host transcriptionally mediated effects to preserve and improve gut health and function in a scenario of different growth performance and potentiality.

Oxford Nanopore Technology (ONT) allows for the rapid profiling of aquaculture microbiomes. However, not all the experimental and downstream methodological possibilities have been benchmarked. Here, we aimed to offer novel insights into the use of different library preparation methods (standard-RAP and native barcoding-LIG), primers (V3–V4, V1–V3, and V1–V9), and basecalling models (fast-FAST, high-HAC, and super-accuracy-SUP) implemented in ONT to elucidate the microbiota associated with the aquatic environment and farmed fish, including faeces, skin, and intestinal mucus. Microbial DNA from water and faeces samples could be amplified regardless of the library–primer strategy, but only with LIG and V1–V3/V1–V9 primers in the case of skin and intestine mucus. Low taxonomic assignment levels were favoured by the use of full-length V1–V9 primers, though in silico hybridisation revealed a lower number of potential matching sequences in the SILVA database, especially evident with the increase in Actinobacteriota in real datasets. SUP execution allowed for a higher median Phred quality (24) than FAST (11) and HAC (17), but its execution time (6–8 h) was higher in comparison to the other models (0.6–7 h). Altogether, we optimised the use of ONT for water- and fish-related microbial analyses, validating, for the first time, the use of the LIG strategy. We consider that LIG–V1–V9-HAC is the optimal time/cost-effective option to amplify the microbial DNA from environmental samples. However, the use of V1–V3 could help to maximise the dataset microbiome diversity, representing an alternative when long amplicon sequences become compromised by microbial DNA quality and/or high host DNA loads interfere with the PCR amplification/sequencing procedures, especially in the case of gut mucus.

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

Fish were kept for six weeks at three different initial stocking densities and water O₂ concentrations (low-LD, 8.5 kg/m³ and 95–70% O₂ saturation; medium-MD, 17 kg/m³ and 55–75% O₂ saturation; high-HD, 25 kg/m³ and 60–45% O₂ saturation), with water temperature increasing from 19 °C to 26–27 °C. The improvement in growth performance with the decrease in stocking density was related to changes in skin and intestinal mucosal microbiomes. Changes in microbiome composition were higher in skin, with an increased abundance of Alteromonas and Massilia in HD fish. However, these bacteria genera were mutually exclusive, and Alteromonas abundance was related to a reactive behavior and systemic growth regulation via the liver Gh/Igf system, while Massilia was correlated to a proactive behavior and a growth regulatory transition towards muscle rather than liver. At the intestinal level, microbial abundance showed an opposite trend for two bacteria taxa, rendering in a low abundance of Reyranella and a high abundance of Prauserella in HD fish. This trend was correlated with up-regulated host gene expression, affecting the immune response, epithelial cell turnover, and abiotic stress response. Most of the observed responses are adaptive in nature, and they would serve to infer new welfare indicators for increased stress resilience.

Gilthead seabream (Sparus aurata) is a marine finfish of economic importance in aquaculture. Despite its adaptability to varying culture conditions, gilthead seabream culture can be affected by viral, bacterial or parasitic diseases. The main route of entry of pathogens is through mucosal surfaces. Teleost external and internal surfaces are covered by mucus, mainly comprised of highly glycosylated proteins called mucins. The mucin glycans regulate pathogen growth, adhesion, virulence and inter and intra species communication. Here, we characterized the gilthead seabream mucus glycosylation, compared it to previously described species and investigated associations with microbiota. 214 glycans were identified. The majority of the glycans were found at more than one epithelial surface, but 27, 22 and 89 O-glycan structures were unique to skin, gill and intestinal sample groups, respectively. Six O-glycan core types were observed. The majority of the seabream skin and gill O-glycans were neutral with unusual poly HexNAc motifs. In contrast, seabream intestinal O-glycans were highly acidic and not of the 'poly HexNAc' type observed in skin and gill. Furthermore, gilthead seabream gill mucosa had less oligomannose and more complex N-glycans compared to skin and intestine. The concentration and diversity of bacteria was similar in skin, gill and intestine, but the bacterial species differed between epithelia and co-varied with glycan epitopes. The presence of a complex mucus glycosylation with plenty of glycan epitopes for bacterial foraging, suggest that the skin mucosal defense in seabream includes an abundant resident microbiota. This large library of structures provides a platform for further studies, for example aiming to identifying glycans to use for diagnostic purposes, to study host-microbe interactions or disease intervention therapies.

In aquaculture systems, microbiomes of farmed fishes may contain thousands of bacterial taxa that establish complex networks of interactions among each other and among the host and the environment. Gut microbiomes in many fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to: i) infer information how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and ii) predict how the diversity and functional profile of that microbiome would change under other experimental variables. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of bayesian network scenarios created by SAMBA using data from: a) a microbial synthetic experiment; and b) the pan-microbiome of the gilthead sea bream ( Sparus aurata ) under different experimental feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems and can be used for modelling microbiome-host network relationships in any vertebrate organism, including humans, in any system and/or ecosystem.