Intestinal microbiota shifts as a marker of thermal stress during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata)

doi:10.21203/rs.3.rs-4809319/v1

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Intestinal microbiota shifts as a marker of thermal stress during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata)

https://doi.org/10.21203/rs.3.rs-4809319/v1

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Climate change and its associated extreme events alter a number of physiological processes that also affect the homeostatic relationship of the host with their microbial communities. The aim of this study was to gain more insights on this issue, examining the effect of the record breaking-heat summer of 2022 on the gut microbiota of farmed gilthead sea bream (Sparus aurata), reared from May to August at the IATS research infrastructure (Spain´s Mediterranean coast). Fish were fed daily with four experimental diets, containing two different lipid levels (16% and 14%) with/without a commercial emulsifier (0.1%; Volamel Aqua, Nukamel). On August 9th, concurrently with the historical record of water temperature (30.49 ºC), fish were sampled for analysis of blood-stress markers and water/intestinal microbiota. Gut microbiota analysis clearly evidenced the increased abundance of bacteria of Spirochaetota phylum, mainly represented by the genus Brevinema. This microbiota shift was not driven by environmental colonization as this bacteria genus remained residual in water samples with the increase of temperature. Bayesian network and functional enrichment analyses suggested that the high abundance of Brevinema exploits and negatively enhances a condition of imbalance in intestinal homeostasis, which was almost completely reversed by the use of dietary emulsifiers in combination with low energized diets. This phenotype restoration occurred in concomitance with changes in circulating levels of cortisol and glucose. Altogether this highlights the potential use of Brevinema as a heat-stress biomarker, reinforcing the value of dietary intervention as a valuable solution to mitigate the negative impact of global warming on aquaculture production.

Biological sciences/Microbiology/Communities/Microbiome

Earth and environmental sciences/Environmental sciences/Environmental impact

Gut microbiota

Brevinema

heat stress

nutritional emulsifiers

dietary lipids.

Aquaculture is the fastest growing food production sector over recent decades with an average annual growth rate of 6.7% (FAO, 2022a). However, to guarantee the sustainability of the sector, the aquaculture industry needs to face widespread environmental challenges, including the adaptation of the production systems to climate change (Ahmed et al., 2019; FAO, 2022b). Global warming is in fact a main concern for the future of aquaculture, which is reinforced by the role of temperature as a master abiotic factor in ectotherm organisms (Islam et al., 2022; Reid et al., 2019; Volkoff & Rønnestad, 2020). Otherwise, while ocean warming poses a worldwide issue, the Mediterranean Sea is particularly vulnerable to temperature fluctuations because of its geographical location and semi-enclosed nature (Sakalli, 2017). Certainly, over the last 40 years, the rise of the Mediterranean surface temperature was more than three times higher than the registered increase in open oceans (IPCC, 2023; Pisano et al., 2020). Besides this, the occurrence of extremely warm episodes, defined as marine heat waves, becomes more and more frequent making the nearshore Mediterranean aquaculture highly vulnerable to climate change (Atalah et al., 2024; Dayan et al., 2023; Hamdeno & Alvera-Azcaráte, 2023).

At the organismal level, there is now increasing evidence that shifts in intestinal microbial communities’ merit special attention in a context of global warming, though precise and consistent information regarding the effect of heat-stress episodes on intestinal microbiota are still limited and sometimes contradictory, being associated most of the discrepancies to the duration and severity of the heat stress, culture system and developmental stage at which heat stress is induced (Diwan et al., 2023; Xavier et al., 2024; Zhou et al., 2023). In any case, gut microbiota actively participates in the regulation of numerous fish physiological processes, including growth, nutrient digestibility, disease resistance and stress response by means of a complex network of interactions within and among microbial populations (Brown et al., 2019; Butt & Volkoff, 2019; de Bruijn et al., 2017; Lopez Nadal et al., 2020; Ou et al., 2021). Thus, according to the hologenome theory of evolution, the holobiont (host-microbiome system) would act as a unit of evolutionary selection, facilitating the fast genomic changes of the microbiota and the adaptation of the holobiont to constantly changing environmental conditions (Simon et al., 2019). In this way, it must be noted that fish gut microbiota not only changes with sex and age (Piazzon et al., 2019; Zhao et al., 2020), but also with season and host genetics that ultimately shapes the microbial community to cope with changes in diet composition (Naya-Català et al., 2022; 2024; Piazzon et al., 2020) and other stressors including thermal stress. Indeed, feeding probiotics is considered a suitable approach to enhance productivity, health and welfare in pigs and broilers kept under heat stress conditions (Ringseis and Eder, 2022).

At the cellular level, heat stress is intertwined with oxidative stress, and different dietary modifications focused on feed additives with antioxidant activity have been considered in broiler chickens to buffer and/or prevent the excess production of reactive oxygen species (ROS) (Emami et al., 2021). Otherwise, the suppression of feed intake is a common compensatory feature across species to reduce the endogenous production of body heat that results from digestion and absorption of feed (Onagbesan et al., 2023). Such regulatory action is also accompanied by the increase of hepatic lipogenesis that enhances lipid disposition rates in poultry and other species in spite of the reduction of feed intake (Guo et al., 2021; Hao et al., 2016; Lan et al., 2021; Yin et al., 2021). Hence, sustained heat stress can compromise hepatic function by promoting hepatic steatosis, and the use of natural and synthetic emulsifiers have been considered as a nutritional therapy to alleviate the negative impact of heat stress in poultry (Yin et al., 2021). This action is supported, at least in part, by the pleotropic action of bile acids upon gut microbiota and the enterohepatic circulation system (Schubert et al., 2017; Sun et al., 2020). Likewise, there is now evidence in farmed barramundi (Lates calcarifer) that high fat diets reduced fish tolerance to extreme water temperatures (Gómez Isaza et al., 2019). Moreover, natural or synthetic emulsifiers improve growth and lipid digestion in a wide range of fish, including gilthead sea bream (Ruiz et al., 2023a; 2023b). However, it remains uncertain and poorly documented how we can monitor the extent to which dietary intervention can contribute to alleviate the negative impact of heat stress in gilthead sera bream. Thus, the aim of this study was to evaluate in a highly cultured Mediterranean fish the combined effect of dietary fat level and a commercial emulsifier (Volamel Aqua, Nukamel) on gut microbiota during the record-breaking summer of 2022 at the Spanish Mediterranean coast. To further understand the physiological significance of the achieved results, data on gut microbiota were used to model and unravel the causal relationships of bacterial populations using a Bayesian-Network (BN) approach (Soriano et al., 2023). The hypothesis of work is that the predictive modelling of changes in gut microbiota helps to better understand the impact of thermal stress in aquaculture productivity and sustainability, giving support to the design of effective remediation strategies to mitigate the negative impact of climate change on animal production.

Ethics Statement

All the procedures received approval from the Ethics and Animal Welfare Committee of the Institute of Aquaculture Torre de la Sal (IATS), the CSIC Ethics Committee (with the authorization number 1295/2022), and the Generalitat Valenciana (under the license number 2022-VSC-PEA-0230). These procedures were conducted at the registered aquaculture infrastructure facility of IATS (facility code ES120330001055), adhering strictly to the guidelines set forth in the European Animal Directive (2010/63/EU) and the Spanish legal framework (Royal Decree RD53/2013), for the protection of animals used in scientific experiments.

Animals

Juveniles of gilthead sea bream (Sparus aurata) were purchased in April 2022 (5.0 g mean body weight) from a commercial Mediterranean hatchery (Piscimar, Burriana, Spain), being acclimatized to the IATS-CSIC research infrastructure for five weeks under natural photoperiod and temperature conditions at the IATS latitude (40° 5′N; 0° 10′E). During all the trial, fish were maintained in an open flow-through system, ensuring oxygen content of water effluents above 85% saturation and unionized ammonia below 0.02 mg/L.

Diets

Four isoproteic plant-based diets with 6% fish meal and two different lipid levels (14%, 16%) with/without a commercial hydrophilic emulsifier (Volamel Aqua, Nukamel, Belgium) at 0.1% (1000 ppm) were formulated and produced by Research Diet Services (RDS, the Netherlands). The resulting diets were named as follows: high fat diet (HFD), high fat diet + emulsifier (HFD-EMS), low fat diet (LFD), and low fat diet + emulsifier (LFD-EMS) (Table 1).

Experimental setup and sample collection

After the acclimation period, randomly selected fish of 12.48 ± 0.25 g body weight were distributed in twelve 500 L tanks (3 tanks/diet; 45 fish/tank). Fish were fed with the experimental diets by hand once a day at a fixed time (12 a.m.) until visual satiety over the course of the trial (11 weeks from May to August). During the first half of the trial (46 days), emulsifier supplementation supported a 10% improvement of feed conversion ratio (FCR) with the increase of water temperature from 20ºC to 25ºC. Such improvement was masked during the second half of the trial with the achievement of the historical record of water temperature (30.49ºC, August 9th, 2022) at our latitude (Supplementary Fig. 1). At this time, water samples were taken for analyses of microbiota, and 12 fish per diet (4 fish per tank) were anaesthetized with 0.1 g/L of tricaine-methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO, United States) for blood and intestinal mucus sampling after a 48 hours fasting period. Samples from fish with the same genetic background but remaining at water temperature below 25ºC and fed with a commercial standard diet (Biomar, Palencia, Spain; EFICO 3053) were used as reference fish (REF) of microbiota composition analyses.

Blood was taken from caudal vessels using heparinized syringes, and fish were rapidly sacrificed by cervical section. Plasma was obtained by centrifugation at 3,000 × g for 20 min at 4°C, followed by storage of plasma aliquots at − 80°C for later analysis. A portion of the anterior intestine (~ 2 cm) was opened and washed with sterile Hank’s balanced salt solution before the scraping of intestinal mucus with the blunt edge of a sterile scalpel to collect the adherent intestinal microbiota. Mucus samples were then transferred to sterile Eppendorf tubes and maintained in ice until DNA extraction, immediately performed after sampling. To characterize water associated bacterial communities, 1 L of seawater was sampled from each of the experimental tanks using sterile glass bottles. Bacteria were then collected using a manifold filtration system with mixed cellulose esters filters with a pore size of 0.22 µm. Filters were transferred to individual sterile petri dishes and stored at -80ºC until DNA extraction.

Bacterial DNA extraction

DNA from adherent intestinal mucus microbiota (200 µL) was extracted using the High Pure PCR Template Preparation Kit (Roche) following the manufacturer’s instructions, including a previous lysis step with lysozyme (Sigma) at a concentration of 250 µg/mL for 15 min at 37ºC (Piazzon et al., 2019). DNA from water-associated bacterial communities was extracted using the purification kit DNeasy PowerSoil Pro (Qiagen). Filters were cut in small pieces and submitted to a mechanical lysis using the ceramic beads tubes provided in the kit, using FastPrep 24 homogenizer (MP Biomedicals) at 6 m/s for 30 s. Subsequent steps of the extraction were performed following the manufacturer’s instructions. DNA concentration and quality in both cases were checked using NanoDrop 2000c (Thermo Fisher Scientific) and agarose gel electrophoresis (1% w/v Tris-EDTA buffer). All DNA extracted samples were stored at -20ºC until sequencing.

Illumina Miseq sequencing of anterior intestine mucus

The V3-V4 hypervariable region of the 16S rRNA gene (341–805 nt) from adherent bacteria of intestine was sequenced with the Illumina MiSeq platform (2 x 300 paired-end run) at the Genomics Unit from the Madrid Science Park Foundation (FPCM, Spain) as described elsewhere (Piazzon et al., 2019). Raw sequenced data obtained was lodged in the Sequence Read Archive (SRA) under the Bioproject accession number PRJNA1137871 (BioSample accession numbers: SAMN42644020-67). Raw forward and reverse paired-reads were merged using VSEARCH v2.15.1 (Rognes et al., 2016), and then pre-processed using Prinseq v0.20.4 (Schmieder & Edwards, 2011). Sequences with > 5% of N bases were discarded, and terminal N bases were trimmed in both ends of all sequences. Sequences with a length < 300 bp, with Phred quality score < 20 in both sequence ends or with an average Phred quality score < 25 were excluded. Individual sequences identified as ASVs were aligned using Minimap2 v2.17-r941 (Li, 2021) with SILVA v138.1 (Yilmaz et al., 2014) as reference database.

ONT MinION sequencing of water microbiome

The V1-V9 region of the 16S rRNA gene (27-1492 nt) from bacteria of water samples was sequenced using the Oxoford Nanopore MinION device, and the sequencing kit 16S Barcoding 1–24 (SQK-16S024). An input of 50 ng of DNA were used for the PCR amplification, using the LongAmp Hot Start Taq 2× Master Mix (NEB, M0533S) and the primers provided in the kit (F: 5′ - ATCGCCTACCGTGAC - barcode - AGAGTTTGATCMTGGCTCAG − 3′ and R: 5′ - ATCGCCTACCGTGAC - barcode - CGGTTACCTTGTTACGACTT − 3′). Cycling conditions were 95°C for 1 min, followed by 30 cycles of 95°C for 20 s, 52°C for 30 s, and 65°C for 2 min with a final extension step of 65°C for 5 min (Toxqui-Rodriguez et al., 2023). After a clean-up step using AMPure XP Beads (Beckman Coulter), amplicons were quantified using PicoGreen dye (Thermo Fisher Scientific). Samples were pooled in 100 fmol pools and loaded in MinION devices using R9.4.1 Flow Cells (FLO-MIN106D). Raw sequenced data obtained was lodged in the Sequence Read Archive (SRA) under the Bioproject accession number PRJNA1137871 (BioSample accession numbers: SAMN42644068-79). Raw data were demultiplexed using MinKNOW v23.07.8 and basecalled using Guppy v7.0.8. The resulting FASTQ files were pre-processed using Porechop v0.2.4 (github.com/rrwick/Porechop) for barcode trimming, Nanofilt v2.8.0 (De Coster et al., 2018) for sequence length filtering, and Yacrd v0.6.2 (Marijon et al., 2020) for chimera detection and removal. Sequences identified as amplicon sequence variants (ASVs) were taxonomical assigned using Minimap2 v2.17-r941 (Li, 2021) with SILVA v138.1 (Yilmaz et al., 2014) as reference database.

Blood stress markers

Plasma glucose levels were assessed utilizing the Invitrogen™ Glucose Colorimetric Detection Kit (EIAGLUC; Invitrogen). Plasma cortisol levels were determined using an enzyme Immunoassay Kit (Ref. K003-H1W; Arbor Assays) according to the manufacturer’s instructions.

Data analyses

Blood biochemical data were analysed by one-way ANOVA using SigmaPlot v14 (Systat Software Inc, United States). Normality of the data was verified by Shapiro-Wilk test, and Dunn’s post-test was used for multiple comparisons among groups. Rarefaction curves, species richness estimates, and alpha diversity indices were obtained using phyloseq package for R (McMurdie & Holmes, 2013). Statistical differences in species richness and alpha diversity indices were determined by Kruskal-Wallis test using Dunn’s post-test, with a significance threshold of P < 0.05. Beta diversity among groups was determined by permutational multivariate analysis of variance (PERMANOVA), using the non-parametric method adonis from the R package Vegan (Oksanen et al., 2015) with 10,000 random permutations. Differences in bacterial relative abundances at phylum level were tested using two-way ANOVA. To study detailed microbiota differences among groups, partial least-squares discriminant analysis (PLS-DA) was performed using EZinfo v3.0 (Umetrics Umeå, Sweeden). Hotelling’s T² statistic was calculated and points above 95% confidence limit were considered outliers and excluded from the model. The quality of the PLS-DA model was evaluated by the parameters R2Y (cum) and Q2 (cum), which indicate the model fit and prediction ability, respectively. The contribution to group separation of the different bacterial genus was determined by the variable importance in projection (VIP) value. VIP score > 1 was considered the threshold level to determine discriminant variables in the PLS-DA model (Kieffer et al., 2016; Li et al., 2012).

To study the intestinal bacterial interactions within the microbiota populations, a stochastic model, based on the construction of a comprehensive Bayesian Network (BN), was applied. For this purpose, the bacterial relative abundances at genera level of the four different groups (HFD; HFD-EMS; LFD; LFD-EMS) were merged and considered as input dataset for microbiota, while the lipid level and the presence/absence of the emulsifier in the diet were used as discrete experimental variables (Emulsifier & Lipid level). This model allowed the identification of the causal relationships between the set of variables and microbial taxa, defining a network hierarchy, by which the probabilistic dependence of one node to another is defined through a parent-child relationship. The build of the BN was performed using the on-line SAMBA tool (Structure-learning of aquaculture microbiomes using a bayesian approach) already described (Soriano, et al., 2023). The SAMBA software used in this study was an updated version that makes feasible to identify clusters of nodes (bacteria) densely connected to each other, using the Leiden community detection method (Traag et al., 2019). The resulting clusters were then enriched with clusterProfiler 4.0 (Wu et al., 2021), using the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways functional annotation protocol. The significance of the enrichment was calculated using a hypergeometric test, evaluating the differences between the number of OTUs within each cluster and the total number of OTUs (background, data not shown) involved in the pathways. Statistically significant values were then adjusted for multiple testing, using the Benjamini-Hochberg method to control the false discovery rate (FDR).

Richness and diversity of gut microbiota

Illumina sequencing produced a total of 6,679,185 high quality assigned reads for 48 gut samples with an average of 139,150 reads per sample. These reads were assigned to a total of 24,584 ASVs and a high percentage of them were classified up to genus level (85.7%), and more than 95% to the level of family (95.7%), order (97.9%), class (99.5%) and phylum (99.9%). Rarefaction analysis showed curves next to saturation (horizontal asymptote) (Supplementary Fig. 2) and considering the number of assigned sequences an appropriate coverage of the bacterial community was achieved. Figure 1 shows the richness (Chao1 and ACE) and diversity (Shannon and Simpson) indexes for the four experimental groups. Considering the Chao1 index, LFD-EMS fish showed a higher richness in comparison to fish fed non-supplemented diets regardless of fat level (HFD and LFD). The same fish showed a significant increase in microbiota richness in comparison to HFD when using the ACE estimator. In contrast, no differences in diversity indexes were found for any of the possible comparisons among groups.

Gut microbiota composition and discriminant analysis

Gut microbiota composition at phylum level is depicted in Fig. 2. The microbiota of fish with the same genetic background and not exposed to extreme summer temperatures (REF) was characterized by a predominance of the Proteobacteria phylum (> 70%). However, Spirochaetota phylum appeared in high abundance in fish sampled during extreme heat episodes, becoming the most abundant phylum in HFD fish. Notably, a significant decrease in the relative abundance of this phylum was observed with both emulsifier supplementation (P = 0.005) and reduction of dietary fat level (P = 0.011). Conversely, Proteobacteria abundances increased significantly with lipid reduction (P = 0.011) and emulsifier addition (P = 0.005), gradually restoring the dominance of this phylum in the adherent microbiota. Additionally, the abundance of the Fusobacteria phylum increased significantly (P = 0.033) in the experimental fish fed with emulsifier supplemented diets (HFD-EMS, LFD-EMS).

To further investigate differences in bacterial composition, permutational multivariate analysis of variance was performed showing statistically significant differences among the four experimental groups (PERMANOVA, F = 1.109, R² = 0.102, P = 0.047). In order to study in more detail these differences, two PLS-DA models were constructed and statistically validated, driving the separation of each experimental variable, emulsifier supplementation (Fig. 3a) and lipid level (Fig. 3b). The model based on emulsifier addition (R2Y = 99%, Q2 = 64%) achieved a distinct separation between the EMS and non-EMS groups, with the first two principal components accounting for over 93% of the total explained variance (Supplementary Fig. 3a). The model based on dietary lipid level (R2Y = 98%, Q2 = 56%) also facilitated a clear separation, although slightly less pronounced than that observed with the emulsifier variable, with approximately 86% of the total explained variance after two components (Supplementary Fig. 3b). Both models were constructed using four components, as determined by cross-validation to avoid overfitting of the model. Validation of the models by random permutations can be found in Supplementary Fig. 3c-d.

To determine the specific groups of bacteria driving the separation between experimental groups at a high level of confidence, only bacterial groups with a VIP value ≥ 1 and a relative abundance higher than 0.5% were selected. This selection resulted in 11 and 12 taxa considered responsible for the separation based on emulsifier and lipid level variables, respectively. Figure 4 shows the list of the 19 unique abundant taxa at genus level that drove the separation between groups in both models, seven specific of variable emulsifier model (Pseudomonas, Thauera, Cetobacterium, Bacillus, Staphylococcus, Micrococcus and Streptomyces) and eight from dietary lipid level (Devosia, Paracoccus, Ralstonia, Clostridium, Brachybacterium, Cutibacterium and Blastococcus genera, and Rhizobiaceae family). Additionally, four taxa were significantly discriminant in both models and, thus, their relative abundances were affected by both dietary variables (Brevinema, Vibrio and Photobacterium, and Beijerinckiaceae). Among them, Brevinema genus appeared as the most abundant bacterial taxa associated with heat-stressed fish in this study. Indeed, this unique genus represents almost the total contribution to Spirocheatota phylum (> 99%), being the main responsible for the shifts observed at phylum-level with a wide range of abundance that varied from 55,6% in HFD fish to 2.26% in LFD-EMS fish with the addition of emulsifier and the reduction of dietary lipid level.

Water microbiota composition

Nanopore sequencing of 12 water samples produced a total of 2,186,454 reads (~ 180,000 reads per sample) with a N50 length of 1.56 kb (Supplementary Fig. 4a). Rarefaction curves for all the samples were approaching horizontal asymptote, suggesting adequate sequencing depth (Supplementary Fig. 4b). Comparison of richness (Chao1 and ACE) and alpha diversity (Shannon and Simpson) estimators did not show significant differences between groups (Fig. 5a-d). Microbial composition at phylum level in all the groups was characterized by the dominance of Proteobacteria (37.5–47.8%), Bacteroidota (16.2–26.5%) and Cyanobacteria (14.5–20.1%) phyla (Fig. 5e). However, no statistical differences (Kruskal-Wallis test) were found in these three phyla or in the remaining phyla above 1% abundance. In the same line, no differences were found in lower taxonomic levels regarding beta diversity analysis. Focusing on the highly abundant Brevinema genus of gut samples, sequences of this taxa were detected in water samples but only at residual levels (0.018–0.072%).

Bayesian Network and functional profiles of gut microbiota

To build the BN, a SAMBA filter was applied to the raw data to get rid of the bacterial taxa when normalized counts were close to zero. The initial model was composed by a total of 98 nodes, which cover 94.24% of the total average abundances of the microbial population (data not shown). After filtering by Leiden hierarchical clustering, we identified up to eight clusters directly connected with the two experimental variables (Emulsifier & Lipid level) (Fig. 6). This interconnected network was composed by 65 nodes (89.2% abundance), including 17 out of 19 taxa that drive the separation between experimental groups by PLS-DA. With the objective of comprehending the functional profile of these modelled bacterial associations, an inferred metagenome pathway analysis was performed, and intriguingly two out of eight clusters (Cluster 1, 3) exhibited a significant functional enrichment (Fig. 7). Cluster 1, with a significant enrichment in the pathways related to Vibrio cholerae infection and Biosynthesis of various other secondary metabolites, was specially represented by Thauera and Vibrio with a higher abundance (> 0.5%) and discriminant value in the PLS-DA. Cluster 3 is globally more influenced by the variable Emulsifier, resulting in the enrichment of four additional metabolic routes (six in total): Biosynthesis of type II polyketide products, Bacterial invasion of epithelial cells, Isoflavonoid biosynthesis and Staurosporine biosynthesis. This functional enrichement was commanded by five abundant and discriminant bacteria (Streptomyces, Blastococcus, Clostridium, Pseudomonas and Brevinema). Both Pseudomonas and Brevinema emerged as the two most important nodes as they play a pivotal role as parent and, thereby, controlling the abundance of other bacteria. In addition to that, Brevinema was not only highly connected within the cluster, but also with nodes belonging to other clusters via Rothia (Cluster 7) and Beijerinckiaceae (Cluster 5).

Blood stress markers

Plasma cortisol and glucose levels were altered by both dietary lipids and emulsifier supplementation (Fig. 8a-b). Thus, a statistically significant decrease of these two blood stress markers was found in HFD fish with the addition of the emulsifier, being the achieved values similar to those found in fish fed the LFD diet. The trend in LFD-EMS was to promote an additional decrease in plasma cortisol and glucose levels, though this effect was not statistically significant. Of note, the abundance of the Brevinema genus in gut samples mimicked the changing cortisol/glucose levels, making even more similar the effect of the emulsifier with the two tested dietary fat levels (Fig. 8c).

Exposure to high temperatures enhances basal metabolic activity and accelerates growth in farmed fish, but exceeding the upper thermal range tolerance triggers adverse physiological responses (Benítez-Dorta et al., 2017; Carney Almroth et al., 2015; Islam et al., 2022; Balbuena-Pecino et al., 2019), modifying the use and reallocation of metabolic fuels (Alfonso et al., 2021; Volkoff & Rønnestad, 2020). In this line, we focused herein on dietary energy level and nutritional emulsifiers as a possible solution to mitigate heat stress in farmed gilthead sea bream. In fact, heat stress largely affects lipid homeostasis in poultry but also in pigs, rodents and cows, increasing hepatic lipogenesis and body fatness as part of the adaptive metabolic features that limit heat production and ROS production, and thereby the risk of oxidative stress (Sanz Fernandez et al., 2015; Heng et al., 2019; Emami et al., 2021;Yasoob et al., 2022; Skibiel et al., 2024). There is also now evidence linking hepatic lipogenesis with changes in gut microbiota (Naya-Català et al., 2021a; Schoeler et al., 2023), and the present study provides new insights supporting the reshape of gut microbiota as a subrogate marker of heat stress in fish, and gilthead sea bream in particular. Certainly, it must be noted that the bulk of available literature describing the intestinal microbiota of gilthead sea bream agrees on a general pattern with a dominance of the phylum Proteobacteria, followed by Firmicutes, Actinobacteriota and Bacteroidota (Estruch et al., 2015; Firmino et al., 2021; Moroni et al., 2021; Naya-Català et al., 2021b; Piazzon et al., 2020). Accordingly, the same phylum-level distribution was found herein in fish not subjected to episodes of extreme temperatures (REF fish). However, major shifts occurred in the gut microbiota of fish exposed to extreme temperatures with the displacement of the typically dominant Proteobacteria by the Spirochaetota phylum, which is commonly associated to marine environments, though it generally represents a small fraction of the gut microbiota population (Naya-Català et al., 2022; Rimoldi et al., 2020).

The role of diet as a key factor in modulating the intestinal microbiota was also evidenced at phylum-level in the present study, where the different experimental diets lead the different regulation of the intestinal microbiota following the achievement of historical water temperatures at our latitude during the extreme hot summer 2022 (Fig. 2). The most notable changes occurred in the HFD group, where the Spirochaetota phylum accounted for over 50% of the total microbial abundance. However, this proportion was reversed with both the decrease of the dietary lipid level and the addition of the emulsifier, which would allow to lower the energy investment of fish in the digestion process and/or heat production with an excess of energy metabolic fuels, alleviating the negative effects of thermal stress as stated before in Nile tilapia (Oreochromis niloticus) (Wangkahart et al., 2022). Indeed, the independent addition of these two factors in the diet (HFD-EMS and LFD groups) shaped similar microbiota profiles, while the combination of both variables (LFD-EMS) shaped the lowest abundances of Spirochaetota phylum, suggesting an additive effect. This trend is also confirmed by the alpha diversity analysis, which revealed a significant lower biodiversity of the HFD group in comparison to LFD-EMS that also exhibited higher values of Chao1 and ACE indices and almost a complete reversion of the microbiota phyla towards the values achieved within the normal temperature range (Fig. 1). These findings agree with the observations made by Sánchez-Cueto et al. (2023) and Zhou et al. (2022), which also described a reduction in the bacterial biodiversity as a direct consequence of a thermal stress in greater amberjack’s (Seriola dumerili) and rainbow trout (Oncorhynchus mykiss), respectively. Although the effect of environmental temperature on the alpha diversity are not entirely consistent in the scientific literature, an overall reduction of the population complexity and variability could be associated to an increasing chance of developing dysbiosis due to a microbiota disequilibrium (Kriss et al., 2018; Sánchez-Cueto et al., 2023).

In the present study, the nutritionally mediated effects on the composition of gut microbiota populations were even exacerbated at lower taxonomic levels (Fig. 3). Indeed, both dietary lipid levels and the presence of emulsifier showed a clear discriminant role, which allowed to identify several abundant genera with a different dependence on the basis of their relationship with the experimental variables. According to this, we have established three main taxonomic groups (Fig. 4), which primarily reflect the presence the emulsifier in the case of Pseudomonas, Thauera and Cetobacterium cluster, while other bacterial taxa including Ralstonia, Clostriduium and Brachybacterium were apparently more responsive to the dietary lipid level. Finally, a third group was composed by highly abundant taxa (Brevinema, Vibrio, Photobacterium, and Beijerinckiaceae family), being their abundance shaped by both the emulsifier and the dietary lipid level. Comparing these gilthead sea bream results with previous studies in grass carp (Ctenopharyngodon Idella; Zhou et al., 2018), rainbow trout (Zhou et al., 2022) or yellowtail (Seriola lalandi; Soriano et al., 2018), it is difficult to establish a common pattern linking changes in gut microbiota composition with thermal stress, dietary lipid level or nutritional emulsifiers. The discrepancy on the achieved results also applies to intra-species comparisons, as evidenced by the recent study of Ruiz et al. (2023a), who reported in gilthead sea bream a high abundance of the genus Brevundimonas in concurrence with a significant reduction of Acinetobacter, Corynebacterium and Peptoniphilus with the use of bile salt supplemented diets. This apparent lack of uniformity can be attributed to a different fish strain, life background, developmental stage or culture rearing system among other factors, which makes difficult (if not impossible) the comparisons of gut microbiota results within and between farmed/wild fish species. In particular, in gilthead sea bream, this is supported by recent studies showing how the gut microbiota composition is modulated by age, sex, season, diet, rearing density and host genetics (Piazzon et al., 2019; 2020; Naya-Català 2022; Toxqui-Rodríguez et al., 2024). This, together with the great microbial diversity at low taxonomic levels, makes necessary the identification of bacterial biomarkers in close association with a given experimental condition. Therefore, in the absence of a single gold standard for fish intestinal microbiota, there is an urgent need to expand the list of potential microbiota biomarkers not only for their taxonomic identification, but above all for their functional role in interaction with the host (He et al., 2021).

At a closer look, it must be noted that Brevinema genus appeared in our experimental model as a highly abundant bacteria taxa that becomes highly influenced by the temperature and nutritional condition. In fact, Brevinema is a microaerophilic and gram-negative motile genus with an optimum growth temperature range between 30ºC and 34ºC that has been detected in the intestine of a number of fish species, including European sea bass (Dicentrarchus labrax; Alfonso et al., 2023), chinook salmon (Onchorhynchus tshawytscha; Steiner et al., 2022), rainbow trout (Brown et al., 2019), tilapia (Oreochromis spp.; Paimeeka et al., 2024), white cachama (Piaractus brachypomus; Castañeda-Monsalve et al., 2019), and gilthead sea bream (Huyben et al., 2020; Naya-Català et al., 2021a; Piazzon et al., 2019; Quero et al., 2023). Although a wide range of bacterial groups belonging to Spirochaetota phylum live in aquatic environments (Paster, 2010) the genus Brevinema, even in a gilthead sea bream context, has been found in association with host mucosas, remaining mostly absent in the surrounding water and sediments (Quero et al., 2023). This agrees with our results where the presence of Brevinema was only detected at a residual level in water samples, which suggests that its association with heat-stress episodes was not driven by the environmental colonization. In any case, gilthead sea bream studies, analysing the temporal succession of the intestinal microbiota, highlighted a pronounced increase of Brevinema with advancing age (Piazzon et al., 2019), and through the production cycle from residual levels in winter (< 0.001%) to 4% in the warm season (Naya-Català et al., 2022). Likewise, in other farmed fish such as chinook salmon, it has been described the gradual increase of Brevinema abundance in both faeces (transient microbiota) and mucosal samples (adherent microbiota) with the temperature rise from 8ºC to 20ºC in a recirculating aquaculture system (Steiner et al., 2022). Furthermore, also in gilthead sea bream, a previous study highlighted a strong positive association between the hepatic expression of key lipogenic scd1 gene and the presence in the intestine of Serratia, but also Brevinema (Naya-Català et al., 2021a).

Altogether, the above findings support the idea that Brevinema possesses a metabolic capacity that allows it to grow significantly in abundance due to thermal stress, exploiting and negatively enhancing a condition of imbalance in intestinal homeostasis. The construction of a Bayesian Network model by means of the SAMBA platform has in fact emphasized a multi-connected Brevinema that takes a leading role within its cluster, connecting and influencing some other bacteria such as Clostridium, Streptomyces, Blastococcus, Rothia and Beijerinckiaceae_family (Fig. 6–7). The sum of these connections depicts a potential dysbiotic risk that becomes evident by functional enrichment analysis, as it includes pathways correlated with Vibrio cholerae infections, bacterial invasion of epithelial cells and biosynthesis of biological active and toxic molecules such as staurosporines and polyketides. In this line, potential unfavourable conditions associated with a higher abundance of this genus have also been connected in the red hybrid tilapia with infectious diseases (Paimeeka et al., 2024). Likewise, in salmonids, the rise of Brevinema has been associated to the administration of chemotherapeutants (oxytetracycline) (Payne et al., 2022), also linked to an increased susceptibility to infectious disease and the up-regulated expression of immune relevant genes (Brown et al., 2019; Li et al., 2021). However, as already pointed out before, our dietary intervention was able to reverse, at least in part, the enterotype phenotype associated with heat episodes, resulting in a decreased Brevinema abundance in favour of other bacterial taxa belonging to Proteobacteria phylum, such as Vibrio, Photobacterium and family Beijerinckiaceae. Remarkably, the changing Brevinema abundance was also closely associated with changes in conventional blood-stress markers (e.g. cortisol, glucose), according to which the intestinal Brevinema mimicked the changing plasma cortisol and glucose levels, being achieved the lowest values with the combination or low dietary lipid levels and emulsifier supplementation in fish fed the LFD-EMS diet.

In summary, extreme heat episodes disrupted the homeostatic relationship between the gut microorganisms and the host, resulting in a disproportionate amount of Brevinema taxa in the intestine of farmed gilthead sea bream. Revisiting the current literature, the increased abundance of this opportunistic microorganism is becoming a generic marker of heat stress in farmed fish, and gilthead sea bream in particular. However, further research is needed to clarify and delve deeper into the mechanisms that constitute the basis of the Brevinema-host response against the global warming across species, developmental stages and rearing systems. In that sense, the construction of a BN model has contributed to disentangle the complex association of Brevinema with other bacteria taxa, making sense the functional enrichment analysis to the close association of Brevinema with the nutritionally mediated changes in conventional blood-stress markers. Altogether, these results open the door to monitor and delineate the best nutritional and environmental strategies to mitigate the negative impact of global warming in aquaculture production.

Funding

This work was supported by the TNA programme (PID18949) within H2020 AQUAEXCEL3.0 project (871108) to S.Cools and E. Croes for accessing to IATS-CSIC facilities. This publication reflects only the authors’ view, and the European Union cannot be held responsible for any use that may be made of the information contained herein. Additionally, this study forms part of the ThinkInAzul program and was supported by MCIN with funding from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat Valenciana (THINKINAZUL/2021/024). A presentation of this work by R. Domingo-Bretón was awarded with the Student Spotlight Award at Aquaculture Europe 2023 Conference.

Data Availability Statement

The datasets generated for this study can be found in the Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) under the Bioproject accession number PRJNA1137871 (BioSample accession numbers: SAMN42644020-79).

Competing interests

S. Cools and E. Croes were employed by the company Nukamel NV. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author contributions

Conceptualization: E. Croes, H. Boon, J. Pérez-Sánchez; Investigation: R. Domingo-Bretón, A. Belenguer, J.A. Calduch-Giner, P.G. Holhorea, F. Naya-Català, J. Pérez-Sánchez; Resources: S. Cools, E. Croes, H. Boon, J. Pérez-Sánchez; Writing – original draft preparation: R. Domingo-Bretón, F. Moroni, F. Naya-Català, J. Pérez-Sánchez; Writing – review and editing: all authors; Visualization: R. Domingo-Bretón, F. Moroni, P.G. Holhorea, F. Naya-Català, J. Pérez-Sánchez; Funding acquisition: J.A. Calduch-Giner, J. Pérez-Sánchez; All authors read and approved the final manuscript.

Ahmed N, Thompson S, Glaser M (2019) Global aquaculture productivity, environmental sustainability, and climate change adaptability. Environ Manage 63:159-172. https://doi.org/10.1007/s00267-018-1117-3
Alfonso S, Gesto M, Sadoul B (2021) Temperature increase and its effects on fish stress physiology in the context of global warming. J Fish Biol 98:1496-1508. https://doi.org/10.1111/jfb.14599
Alfonso S, Mente E, Fiocchi E, Manfrin A, Dimitroglou A, Papaharisis L, Barkas D, Toomey L, Boscarato M, Losasso C, Peruzzo A, Stefani A, Zupa W, Spedicato MT, Nengas I, Lembo G, Carbonara P (2023) Growth performance, gut microbiota composition, health and welfare of European sea bass (Dicentrarchus labrax) fed an environmentally and economically sustainable low marine protein diet in sea cages. Sci Rep 13:21269. https://doi.org/10.1038/s41598-023-48533-3
Atalah J, Ibañez S, Aixalà L, Barber X, Sánchez-Jerez P (2024) Marine heatwaves in the western Mediterranean: Considerations for coastal aquaculture adaptation. Aquaculture 588:740917. https://doi.org/10.1016/j.aquaculture.2024.740917
Balbuena-Pecino S, Riera-Heredia N, Vélez EJ, Gutiérrez J, Navarro I, Riera-Codina M, Capilla E (2019) Temperature affects musculoskeletal development and muscle lipid metabolism of gilthead sea bream (Sparus aurata). Front Endocrinol 10:173. https://doi.org/10.3389/fendo.2019.00173
Benítez-Dorta V, Caballero MJ, Betancor MB, Manchado M, Tort L, Torrecillas S, Zamorano MJ, Izquierdo M, Montero D (2017) Effects of thermal stress on the expression of glucocorticoid receptor complex linked genes in senegalese sole (Solea senegalensis): Acute and adaptive stress responses. Gen Comp Endocrinol 252:173-185. https://doi.org/10.1016/j.ygcen.2017.06.022
Brown RM, Wiens GD, Salinas I (2019) Analysis of the gut and gill microbiome of resistant and susceptible lines of rainbow trout (Oncorhynchus mykiss). Fish Shellfish Immunol 86:497-506. https://doi.org/10.1016/j.fsi.2018.11.079
Butt RL, Volkoff H (2019) Gut microbiota and energy homeostasis in fish. Front Endocrinol 10:9. https://doi.org/10.3389/fendo.2019.00009
Carney Almroth B, Asker N, Wassmur B, Rosengren M, Jutfelt F, Gräns A, Sundell K, Axelsson M, Sturve J (2015) Warmer water temperature results in oxidative damage in an Antarctic fish, the bald notothen. J Exp Mar Biol Ecol 468:130-137. https://doi.org/10.1016/j.jembe.2015.02.018
Castañeda-Monsalve VA, Junca H, García-Bonilla E, Montoya-Campuzano OI, Moreno-Herrera CX (2019) Characterization of the gastrointestinal bacterial microbiome of farmed juvenile and adult white Cachama (Piaractus brachypomus). Aquaculture 512:734325. https://doi.org/10.1016/j.aquaculture.2019.734325
Dayan H, McAdam R, Juza M, Masina S, Speich S (2023) Marine heat waves in the Mediterranean Sea: An assessment from the surface to the subsurface to meet national needs. Front Mar Sci 10:1045138. https://doi.org/10.3389/fmars.2023.1045138
de Bruijn I, Liu Y, Wiegertjes GF, Raaijmakers JM (2017) Exploring fish microbial communities to mitigate emerging diseases in aquaculture. FEMS Microbiol Ecol 94:fix161. https://doi.org/10.1093/femsec/fix161
De Coster W, D'Hert S, Schultz DT, Cruts M, Van Broeckhoven C (2018) NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34:2666-2669. https://doi.org/10.1093/bioinformatics/bty149
Diwan A, Harke SN, Panche A (2023) Impact of Climate Change on the Gut Microbiome of Fish and Shellfish. In: Diwan A, Harke SN, Panche A (eds) Microbiome of Finfish and Shellfish. Springer Nature Singapore, Singapore, pp. 255-294. https://doi.org/10.1007/978-981-99-0852-3_12
Emami NK, Jung U, Voy B, Dridi S (2021) Radical response: Effects of heat stress-induced oxidative stress on lipid metabolism in the avian liver. Antioxidants 10:35. https://doi.org/10.3390/antiox10010035
Estruch G, Collado MC, Peñaranda DS, Tomás Vidal A, Jover Cerdá M, Pérez Martínez G, Martinez-Llorens S (2015) Impact of fishmeal replacement in diets for gilthead sea bream (Sparus aurata) on the gastrointestinal microbiota determined by pyrosequencing the 16s rRNA gene. Plos One 10:e0136389. https://doi.org/10.1371/journal.pone.0136389
FAO. (2022a) The state of world fisheries and aquaculture: Towards blue transformation. 1-236. https://doi.org/10.4060/cc0461en
FAO (2022b) FAO strategy on climate change 2022–2031. 1-52. https://www.fao.org/documents/card/en?details=cc2274en
Firmino JP, Vallejos-Vidal E, Balebona MC, Ramayo-Caldas Y, Cerezo IM, Salomón R, Tort L, Estevez A, Moriñigo MÁ, Reyes-López FE, Gisbert E (2021) Diet, immunity, and microbiota interactions: an integrative analysis of the intestine transcriptional response and microbiota modulation in gilthead seabream (Sparus aurata) fed an essential oils-based functional diet. Front Immunol 12:625297. https://doi.org/10.3389/fimmu.2021.625297
Gomez Isaza DF, Cramp RL, Smullen R, Glencross BD, Franklin CE (2019) Coping with climatic extremes: Dietary fat content decreased the thermal resilience of barramundi (Lates calcarifer). Comp Biochem Physiol A Mol Integr Physiol 230:64-70. https://doi.org/10.1016/j.cbpa.2019.01.004
Guo Y, Balasubramanian B, Zhao Z, Liu W (2021) Heat stress alters serum lipid metabolism of chinese indigenous broiler chickens-a lipidomics study. Environ Sci Pollut Res 28:10707-10717. https://doi.org/10.1007/s11356-020-11348-0
Hao LY, Wang J, Sun P, Bu DP (2016) The effect of heat stress on the metabolism of dairy cows: Updates & review. Austin J Nutr Metab. 2016:1036.
Hamdeno M, Alvera-Azcaráte A (2023) Marine heatwaves characteristics in the Mediterranean Sea: Case study the 2019 heatwave events. Front Mar Sci 10:1093760. https://doi.org/10.3389/fmars.2023.1093760
He Y, Maltecca C, Tiezzi F (2021) Potential use of gut microbiota composition as a biomarker of heat stress in monogastric species: A review. Animals 11:1833. https://doi.org/10.3390/ani11061833
Heng J, Tian M, Zhang W, Chen F, Guan W, Zhang S (2019) Maternal heat stress regulates the early fat deposition partly through modification of m6A RNA methylation in neonatal piglets. Cell Stress Chaperones 24:635-645. https://doi.org/10.1007/s12192-019-01002-1
Huyben D, Rimoldi S, Ceccotti C, Montero D, Betancor M, Iannini F, Terova G (2020) Effect of dietary oil from Camelina sativa on the growth performance, fillet fatty acid profile and gut microbiome of gilthead Sea bream (Sparus aurata). PeerJ 8:e10430. https://doi.org/10.7717/peerj.10430
IPCC (2023) Climate Change 2021 – The physical science basis: working group I contribution to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781009157896
Islam MJ, Kunzmann A, Slater MJ (2022) Responses of aquaculture fish to climate change-induced extreme temperatures: A review. J World Aquac Soc 53:314-366. https://doi.org/10.1111/jwas.12853
Kieffer DA, Piccolo BD, Vaziri ND, Liu S, Lau WL, Khazaeli M, Nazertehrani S, Moore ME, Marco ML, Martin RJ, Adams SH (2016) Resistant starch alters gut microbiome and metabolomic profiles concurrent with amelioration of chronic kidney disease in rats. Am. J Physiol Renal Physiol 310:F857-F871. https://doi.org/10.1152/ajprenal.00513.2015
Kriss M, Hazleton KZ, Nusbacher NM, Martin CG, Lozupone CA (2018) Low diversity gut microbiota dysbiosis: Drivers, functional implications and recovery. Curr Opin Microbiol 44:34-40. https://doi.org/10.1016/j.mib.2018.07.003
Lan R, Wang Y, Wei L, Wu F, Yin F (2022) Heat stress exposure changed liver lipid metabolism and abdominal fat deposition in broilers. Ital J Anim Sci 21:1326-1333. https://doi.org/10.1080/1828051X.2022.2103461
Li H, Ma M, Luo S, Zhang R, Han P, Hu W (2012) Metabolic responses to ethanol in Saccharomyces cerevisiae using a gas chromatography tandem mass spectrometry-based metabolomics approach. Int J Biochem Cell Biol 44:1087-1096. https://doi.org/10.1016/j.biocel.2012.03.017
Li H (2021) New strategies to improve minimap2 alignment accuracy. Bioinformatics 37:4572-4574. https://doi.org/10.1093/bioinformatics/btab705
Li Y, Bruni L, Jaramillo-Torres A, Gajardo K, Kortner TM, Krogdahl Å (2021) Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon. Anim microbiome 3:8. https://doi.org/10.1186/s42523-020-00071-3
Lopez Nadal A, Ikeda-Ohtsubo W, Sipkema D, Peggs D, McGurk C, Forlenza M, Wiegertjes GF, Brugman S (2020) Feed, microbiota, and gut immunity: Using the zebrafish model to understand fish health. Front Immunol 11:114. https://doi.org/10.3389/fimmu.2020.00114
Marijon P, Chikhi R, Varre J (2020) Yacrd and fpa: upstream tools for long-read genome assembly. Bioinformatics 36:3894-3896. https://doi.org/10.1093/bioinformatics/btaa262
McMurdie PJ, Holmes S (2013) Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. Plos One 8:e61217. https://doi.org/10.1371/journal.pone.0061217
Moroni F, Naya-Català F, Piazzon MC, Rimoldi S, Calduch-Giner J, Giardini A, Martínez I, Brambilla F, Pérez-Sánchez J, Terova G (2021) The effects of nisin-producing Lactococcus lactis strain used as probiotic on gilthead sea bream (Sparus aurata) growth, gut microbiota, and transcriptional response. Front Mar Sci 8:659519. https://doi.org/10.3389/fmars.2021.659519
Naya-Català F, do Vale Pereira G, Piazzon MC, Fernandes AM, Calduch-Giner J, Sitjà-Bobadilla A, Conceição LEC, Pérez-Sánchez J (2021a) Cross-talk between intestinal microbiota and host gene expression in gilthead sea bream (Sparus aurata) juveniles: Insights in fish feeds for increased circularity and resource utilization. Front Physiol 12:748265. https://doi.org/10.3389/fphys.2021.748265
Naya-Català F, Wiggers GA, Piazzon MC, López-Martínez MI, Estensoro I, Calduch-Giner JA, Martínez-Cuesta MC, Requena T, Sitjà-Bobadilla A, Miguel M, Pérez-Sánchez J (2021b) Modulation of gilthead sea bream gut microbiota by a bioactive egg white hydrolysate: Interactions between bacteria and host lipid metabolism. Front Mar Sci 8:698484. https://doi.org/10.3389/fmars.2021.698484
Naya-Català F, Piazzon MC, Torrecillas S, Toxqui-Rodríguez S, Calduch-Giner J, Fontanillas R, Sitjà-Bobadilla A, Montero D, Pérez-Sánchez J (2022) Genetics and nutrition drive the gut microbiota succession and host-transcriptome interactions through the gilthead sea bream (Sparus aurata) production cycle. Biology, 11:1744. https://doi.org/10.3390/biology11121744
Naya-Català F, Torrecillas S, Piazzon MC, Sarih S, Calduch-Giner J, Fontanillas R, Hostins B, Sitjà-Bobadilla A, Acosta F, Pérez-Sánchez J, Montero D (2024) Can the genetic background modulate the effects of feed additives? Answers from gut microbiome and transcriptome interactions in farmed gilthead sea bream (Sparus aurata) fed with a mix of phytogenics, organic acids or probiotics. Aquaculture 586:740770. https://doi.org/10.1016/j.aquaculture.2024.740770
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O'Hara B, Simpson G, Solymos P, Stevens H, Wagner H (2015) Vegan: Community Ecology Package. R Package Version 2.2-1 2:1-2
Onagbesan OM, Uyanga VA, Oso O, Tona K, Oke OE (2023) Alleviating heat stress effects in poultry: updates on methods and mechanisms of actions. Front Vet Sci 10:1255520. https://doi.org/10.3389/fvets.2023.1255520
Ou W, Yu G, Zhang Y, Mai K (2021) Recent progress in the understanding of the gut microbiota of marine fishes. Mar Life Sci Techno 3:434-448. https://doi.org/10.1007/s42995-021-00094-y
Paimeeka S, Tangsongcharoen C, Lertwanakarn T, Setthawong P, Bunkhean A, Tangwattanachuleeporn M, Surachetpong W (2024) Tilapia lake virus infection disrupts the gut microbiota of red hybrid tilapia (Oreochromis spp.). Aquaculture 586:740752. https://doi.org/10.1016/j.aquaculture.2024.740752
Paster BJ (2010) Phylum XV. Spirochaetes. In: Krieg NR, Staley JT, Brown DR, Hedlund BP, Paster BJ, Ward NL, Ludwig W, Whitman WB (eds) Bergey’s Manual of Systematic Bacteriology. Springer New York, New York, pp. 471-566. https://doi.org/10.1007/978-0-387-68572-4_4
Payne CJ, Turnbull JF, MacKenzie S, Crumlish M (2022) The effect of oxytetracycline treatment on the gut microbiome community dynamics in rainbow trout (Oncorhynchus mykiss) over time. Aquaculture 560:738559. https://doi.org/10.1016/j.aquaculture.2022.738559
Piazzon MC, Naya-Català F, Perera E, Palenzuela O, Sitjà-Bobadilla A, Pérez-Sánchez J (2020) Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream. Microbiome 8:168. https://doi.org/10.1186/s40168-020-00922-w
Piazzon MC, Naya-Català F, Simó-Mirabet P, Picard-Sánchez A, Roig FJ, Calduch-Giner J, Sitjà-Bobadilla A, Pérez-Sánchez J (2019) Sex, age, and bacteria: how the intestinal microbiota is modulated in a protandrous hermaphrodite fish. Front Microbiol 10:2512. https://doi.org/10.3389/fmicb.2019.02512
Pisano A, Marullo S, Artale V, Falcini F, Yang C, Leonelli FE, Santoleri R, Buongiorno Nardelli B (2020) New evidence of Mediterranean climate change and variability from sea surface temperature observations. Remote Sens 12:132. https://doi.org/10.3390/rs12010132
Quero GM, Piredda R, Basili M, Maricchiolo G, Mirto S, Manini E, Seyfarth AM, Candela M, Luna GM (2023) Host-associated and environmental microbiomes in an open-sea mediterranean gilthead sea bream fish farm. Microb Ecol 86:1319-1330. https://doi.org/10.1007/s00248-022-02120-7
Reid GK, Gurney-Smith H, Marcogliese DJ, Knowler D, Benfey T, Garber AF, Forster I, Chopin T, Brewer-Dalton K, Moccia RD, Flaherty M, Smith CT, De Silva S (2019) Climate change and aquaculture; considering biological response and resources. Aquac Environ Interact 11:569-602.
Rimoldi S, Gini E, Koch JFA, Iannini F, Brambilla F, Terova G (2020) Effects of hydrolyzed fish protein and autolyzed yeast as substitutes of fishmeal in the gilthead sea bream (Sparus aurata) diet, on fish intestinal microbiome. BMC Vet Res 16:118. https://doi.org/10.1186/s12917-020-02335-1
Ringseis R, Eder K (2022) Heat stress in pigs and broilers: role of gut dysbiosis in the impairment of the gut-liver axis and restoration of these effects by probiotics, prebiotics and synbiotics. J Anim Sci Biotechnol 13:126. https://doi.org/10.1186/s40104-022-00783-3
Rognes T, Flouri T, Nichols B, Quince C, Mahe F (2016) VSEARCH: A versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584
Ruiz A, Andree KB, Furones D, Holhorea PG, Calduch-Giner J, Viñas M, Pérez-Sánchez J, Gisbert E (2023a) Modulation of gut microbiota and intestinal immune response in gilthead seabream (Sparus aurata) by dietary bile salt supplementation. Front Microbiol 14:1123716. https://doi.org/10.3389/fmicb.2023.1123716
Ruiz A, Andree KB, Sanahuja I, Holhorea PG, Calduch-Giner JÀ, Morais S, Pastor JJ, Pérez-Sánchez J, Gisbert E (2023b) Bile salt dietary supplementation promotes growth and reduces body adiposity in gilthead seabream (Sparus aurata). Aquaculture 566:739203. https://doi.org/10.1016/j.aquaculture.2022.739203
Sakalli A (2017) Sea surface temperature change in the mediterranean sea under climate change: A linear model for simulation of the sea surface temperature up to 2100. Appl Ecol Environ Res 15:707-716. https://doi.org/10.15666/aeer/1501_707716
Sánchez-Cueto P, Stavrakidis-Zachou O, Clos-Garcia M, Bosch M, Papandroulakis N, Lladó S (2023) Mediterranean Sea heatwaves jeopardize greater amberjack’s (Seriola dumerili) aquaculture productivity through impacts on the fish microbiota. ISME Commun 3:36. https://doi.org/10.1038/s43705-023-00243-7
Sanz Fernandez MV, Johnson JS, Abuajamieh M, Stoakes SK, Seibert JT, Cox L, Kahl S, Elsasser TH, Ross, JW, Clay Isom S, Rhoads RP, Baumgard LH. (2015) Effects of heat stress on carbohydrate and lipid metabolism in growing pigs. Physiol Rep 3:e12315. https://doi.org/10.14814/phy2.12315
Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863-864. https://doi.org/10.1093/bioinformatics/btr026
Schoeler M, Ellero-Simatos S, Birkner T, Mayneris-Perxachs J, Olsson L, Brolin H, Loeber U, Kraft JD, Polizzi A, Martí-Navas M, Puig J, Moschetta A, Montagner A, Gourdy P, Heymes C, Guillou H, Tremaroli V, Fernández-Real JM, Forslund SK, Burcelin R, Caesar R (2023) The interplay between dietary fatty acids and gut microbiota influences host metabolism and hepatic steatosis. Nat Commun 14:5329. https://doi.org/10.1038/s41467-023-41074-3
Schubert K, Olde Damink SWM, von Bergen M, Schaap FG (2017) Interactions between bile salts, gut microbiota, and hepatic innate immunity. Immunol Rev 279:23-35. https://doi.org/10.1111/imr.12579
Simon J, Marchesi JR, Mougel C, Selosse M (2019) Host-microbiota interactions: From holobiont theory to analysis. Microbiome 7:5. https://doi.org/10.1186/s40168-019-0619-4
Skibiel AL (2024) Hepatic mitochondrial bioenergetics and metabolism across lactation and in response to heat stress in dairy cows. JDS Commun 5:247-252. https://doi.org/10.3168/jdsc.2023-0432
Soriano B, Hafez AI, Naya-Català F, Moroni F, Moldovan RA, Toxqui-Rodríguez S, Piazzon MC, Arnau V, Llorens C, Pérez-Sánchez J (2023) SAMBA: Structure-learning of aquaculture microbiomes using a bayesian approach. Genes 14:1650. https://doi.org/10.3390/genes14081650
Soriano EL, Ramírez DT, Araujo DR, Gómez-Gil B, Castro LI, Sánchez CG (2018) Effect of temperature and dietary lipid proportion on gut microbiota in yellowtail kingfish (Seriola lalandi) juveniles. Aquaculture 497:269-277. https://doi.org/10.1016/j.aquaculture.2018.07.065
Steiner K, Laroche O, Walker SP, Symonds JE (2022) Effects of water temperature on the gut microbiome and physiology of Chinook salmon (Oncorhynchus tshawytscha) reared in a freshwater recirculating system. Aquaculture 560:738529. https://doi.org/10.1016/j.aquaculture.2022.738529
Sun R, Xu C, Feng B, Gao X, Liu Z Critical roles of bile acids in regulating intestinal mucosal immune responses. Therap Adv Gastroenterol 14:17562848211018098. https://doi.org/10.1177/17562848211018098
Toxqui-Rodríguez S, Holhorea PG, Naya-Català F, Calduch-Giner J, Sitjà-Bobadilla A, Piazzon C, Pérez-Sánchez J (2024) Differential reshaping of skin and intestinal microbiota by stocking density and oxygen availability in farmed gilthead sea bream (Sparus aurata): A behavioral and network-based integrative approach. Microorganisms 12:1360. https://doi.org/10.3390/microorganisms12071360
Toxqui-Rodriguez S, Naya-Catala F, Sitja-Bobadilla A, Piazzon MC, Perez-Sanchez J (2023) Fish microbiomics: Strengths and limitations of MinION sequencing of gilthead sea bream (Sparus aurata) intestinal microbiota. Aquaculture 569:739388. https://doi.org/10.1016/j.aquaculture.2023.739388
Traag VA, Waltman L, van Eck NJ (2019) From Louvain to Leiden: Guaranteeing well-connected communities. Sci Rep 9:5233. https://doi.org/10.1038/s41598-019-41695-z
Volkoff H, Rønnestad I (2020) Effects of temperature on feeding and digestive processes in fish. Temperature 7:307-320. https://doi.org/10.1080/23328940.2020.1765950
Wangkahart E, Bruneel B, Wisetsri T, Nontasan S, Martin SAM, Chantiratikul A (2022) Interactive effects of dietary lipid and nutritional emulsifier supplementation on growth, chemical composition, immune response and lipid metabolism of juvenile Nile tilapia (Oreochromis niloticus). Aquaculture 546:737341. https://doi.org/10.1016/j.aquaculture.2021.737341
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G (2021) clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation 2:100141. https://doi.org/10.1016/j.xinn.2021.100141
Xavier R, Severino R, Silva SM (2024) Signatures of dysbiosis in fish microbiomes in the context of aquaculture. Rev Aquac 16:706-731. https://doi.org/10.1111/raq.12862
Yasoob TB, Khalid AR, Zhang Z, Zhu X, Hang S (2022) Liver transcriptome of rabbits supplemented with oral Moringa oleifera leaf powder under heat stress is associated with modulation of lipid metabolism and up-regulation of genes for thermo-tolerance, antioxidation, and immunity. Nutr Res 99:25-39. https://doi.org/10.1016/j.nutres.2021.09.006
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, Schweer T, Peplies J, Ludwig W, Gloeckner FO (2014) The SILVA and "All-species Living Tree Project (LTP)" taxonomic frameworks. Nucleic Acids Res 42:D643-D648. https://doi.org/10.1093/nar/gkt1209
Yin C, Tang S, Liu L, Cao A, Xie J, Zhang H (2021) Effects of bile acids on growth performance and lipid metabolism during chronic heat stress in broiler chickens. Animals 11:630. https://doi.org/10.3390/ani11030630
Zhao R, Symonds JE, Walker SP, Steiner K, Carter CG, Bowman JP, Nowak BF (2020) Salinity and fish age affect the gut microbiota of farmed Chinook salmon (Oncorhynchus tshawytscha). Aquaculture 528:735539. https://doi.org/10.1016/j.aquaculture.2020.735539
Zhou C, Gao P, Wang J (2023) Comprehensive analysis of microbiome, metabolome, and transcriptome revealed the mechanisms of intestinal injury in rainbow trout under heat stress. Int. J Mol Sci 24:8569. https://doi.org/10.3390/ijms24108569
Zhou C, Yang S, Ka W, Gao P, Li Y, Long R, Wang J (2022) Association of gut microbiota with metabolism in rainbow trout under acute heat stress. Front Microbiol 13:846336. https://doi.org/10.3389/fmicb.2022.846336
Zhou JS, Chen HJ, Ji H, Shi XC, Li XX, Chen LQ, Du ZY, Yu HB (2018) Effect of dietary bile acids on growth, body composition, lipid metabolism and microbiota in grass carp (Ctenopharyngodon idella). Aquacult Nutr 24:802-813. https://doi.org/10.1111/anu.12609

Table 1

Formulation and proximate composition of experimental diets used in the feeding trial.
Ingredients (%)	HFD	HFD-EMS	LFD	LFD-EMS
Sunflower meal Vital Wheat Gluten Wheat flour Soybean meal 48 Soy protein concentrate Corn gluten Poultry meat meal Rapeseed oil Haemoglobin meal Fish meal 999 LT Fish oil Lecithin Vitamin Mineral Premix¹ Lysine Limestone Monocalcium phosphate	14.84 10.90 10.50 10.00 8.25 8.00 8.00 6.61 7.00 6.00 6.00 1.00 1.00 1.00 0.78 0.12	14.84 10.90 10.50 10.00 8.25 8.00 8.00 6.61 7.00 6.00 6.00 1.00 1.00 1.00 0.68 0.12	14.33 10.78 12.97 10.00 8.25 8.00 8.00 6.54 7.00 6.00 4.00 1.00 1.00 1.00 1.00 0.12	14.33 10.78 12.97 10.00 8.25 8.00 8.00 6.54 7.00 6.00 4.00 1.00 1.00 1.00 0.90 0.12
Volamel Aqua		0.100		0.100
Proximate composition (%)
Protein	47.0	47.0	47.0	47.0
Fat	16.0	16.0	14.0	14.0
Ash	6.4	6.4	6.4	6.4
Fiber	2.6	2.6	2.6	2.6
Moisture	7.0	7.0	7.0	7.0
¹Premix (IU or mg / kg diet): Ascorbic acid phosphate, 160 mg; Biotin, 0.8 mg; Cholecalciferol, 2,000 IU; Choline, 800 mg; Cobalt carbonate, 0.40 mg; Copper sulphate, 6 mg; Cyanocobalamin, 0.12 mg; Folic acid, 4 mg; Inositol, 240 mg; Iodate, 4 mg; Iron Sulphate, 80 mg; Menadione, 12 mg; Magnesium, 500 mg; Manganese sulphate, 24 mg; Niacin, 144 mg; Pantothenic acid, 40 mg; Pyridoxine, 20 mg; Retinol 10,000 IU; Riboflavin, 16 mg; Selenium selenite, 0.24 mg; Thiamine, 16 mg; Tocopherol, 240 mg; Zinc sulphate, 100 mg.

Competing interest reported. S. Cools and E. Croes were employed by the company Nukamel NV. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supp.Fig.1.tif
Figure 1. Profile of mean seawater temperatures at the IATS latitude during the entire experiment.
Supp.Fig.2.tif
Figure 2. Rarefaction curves obtained from the sequencing data of the different microbiota intestinal samples included in this study.
Supp.Fig.3.tif
Figure 3. Graphical representation of the contribution of each component to variance explained (R2Y) and predicted (Q2) in PLS-DA models driving the separation of groups based on emulsifier addition (a) and lipid level (b). Validation of both models by random permutations (c-d).
Supp.Fig.4.tif
Figure 4. Length distribution of full 16S rRNA gene sequenced reads from water samples of this experiment (a). Rarefaction curves obtained from the sequencing data of water samples (b).

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Intestinal microbiota shifts as a marker of thermal stress during extreme heat summer episodes in farmed gilthead sea bream (Sparus aurata)

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Abstract

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Introduction

Material and Methods

Ethics Statement

Animals

Diets

Experimental setup and sample collection

Bacterial DNA extraction

Illumina Miseq sequencing of anterior intestine mucus

ONT MinION sequencing of water microbiome

Blood stress markers

Data analyses

Results

Richness and diversity of gut microbiota

Gut microbiota composition and discriminant analysis

Water microbiota composition

Bayesian Network and functional profiles of gut microbiota

Blood stress markers

Discussion

Declarations

References

Table

Additional Declarations

Supplementary Files

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