SalmoSim: the development of a three-compartment in vitro simulator of the Atlantic Salmon GI tract and associated microbial communities
Background
Atlantic salmon are a fish species of major economic importance. Innovative strategies are being sought to improve salmon feeds and feed additives to enhance fish performance, welfare, and the environmental sustainability of the aquaculture industry. There is still a lack of knowledge surrounding the importance and functionality of the salmon gut microbiome in fish nutrition. In vitro gut model systems might prove a valuable tool to study the effect of feed, and additives, on the host’s microbial communities. Several in vitro gut models targeted at monogastric vertebrates are now in operation. Here, we report the development of an Atlantic salmon gut model, SalmoSim, to simulate three gut compartments (stomach, pyloric caecum and midgut) and associated microbial communities.
Results
The gut model was established in a series of linked bioreactors seeded with biological material derived from farmed adult marine phase salmon. In biological triplicate, the response of the in vitro system to two distinct dietary formulations (fish meal and fish meal free) was compared to a parallel in vivo trial over forty days. Metabarcoding based 16S rDNA sequencing, qPCR, ammoniacal nitrogen and volatile fatty acid measurements were undertaken to survey the microbial community dynamics and function. SalmoSim microbiomes were indistinguishable (p=0.230) from their founding inocula at 20 days and the most abundant genera (e.g. Psycrobacter, Staphylococcus, Pseudomonas) proliferated within SalmoSim. Real salmon and SalmoSim responded similarly to the introduction of novel feed, with majority of the taxa (96% Salmon, 97% SalmoSim) unaffected, while a subset of taxa (e.g. a small fraction of Psychrobacter) were differentially affected across both systems. Consistent with a low impact of the novel feed on microbial fermentative activity, volatile fatty acids profiles were not significantly different in SalmoSim pre- and post-feed switch.
Conclusion
This study represents an important step in the development of an in vitro gut system as a tool for the improvement of salmon nutrition and welfare. This system aims to be utilised as a pre-screening tool for new feed ingredients and additives, as well as being used to study antimicrobial resistance and transfer, and fundamental ecological processes that underpin microbiome dynamics and assembly.
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This is a list of supplementary files associated with this preprint. Click to download.
Fish meal and Fish meal free diets composition. Table summarises Fish meal and Fish meal free diets composition in percentage of the feed.
16S rRNA gene-targeted group-specific primers. Table lists primer sets that already published and validated in the literature. All primers were used on mouse faeces samples apart from Alphaproteobacterial specific primers that were used on marine biofilm samples.
First round PCR primers used for the first round of NGS library preparation.
Second round PCR primers used for the first round of NGS library preparation.
OTUs prevalence analysis by sub-setting full dataset into multiple core OTUs. The table summarises the number OTUs within each subset dataset (subset by the % of samples that share OTUs) and percentage of the total number of OTUs within the full dataset (100%). This table also shows the number of reads and the percentage of total reads (100%) within each of the subset datasets. Note: in the 60% subset three samples were lost as they did not retain any under that criteria OTUs: Id-val1-PC1, Id-Val2-MG4 and Id-Val2-PC1.
Bacterial group responses to feed change within different gut compartments in real salmon and SalmoSim based on qPCR data. The table summarises the Estimated Marginal Means output for each mixed-effect linear model run with different qPCR measured relative abundance values identifying the difference between real salmon and SalmoSim response to feed change (Fish meal to Fish meal free diet) within different gut compartments (S – stomach, PC – pyloric caeca, and MG – mid gut). P>0.05 values identify no change in the bacterial group, p<0.05 identifies decrease (Est is negative), and p<0.05 identifies increase (Est is positive) in the relative abundance of target group after the feed change. Bold values identify similarities between SalmoSim and real salmon samples. The SalmoSim values used for this test involves stable SalmoSim time points: days 16, 18 and 20 (Fish meal diet – once bacterial communities adapted to the SalmoSim environment), and days 36, 38 and 40 (Fish meal free diet – once bacterial communities adapted to feed change).
Physiochemical conditions measured within different real Atlantic salmon gut compartments. 1A-1C measured physicochemical conditions within real salmon (n=3) gut compartments: pH (1A), temperature (°C, 1B), dissolved oxygen (mg/L, 1C)
Specificity of the primers that target Lactobacillus and Mycoplasma genus. The results in figure summarise bacterial genus targeted by Lactobacillus (Figure 1 A, B, C) and Mycoplasma (Figure 1 D, E, F) specific primer set. It shows that of all genus captured by Lactobacillus primer pair 98% were Lactobacillus in fish 1, 78% in fish 2, and 65% in fish 3. While of all genus captured by Mycoplasma primer pair 95% were Mycoplasma in fish 1, 79% in fish 2, and 56% in fish 3.
Measured value (qPCR, ammonia and protein concentrations) stability within different SalmoSim compartments fed on Fish meal and Fish meal free diets. The figure summarises the Estimated Marginal Means output for each mixed-effect linear model (Model 1) run with different values measured in different SalmoSim compartments (qPCR measurements, ammonia and protein concentrations) identifying the difference between different time points during the first (system fed on Fish meal diet) and last 20 days (system fed on Fish meal free diet) of validation experiment. A small p-value indicates that the two time points are statistically different, and p>0.05 indicates that two time points are not statistically different. The colour key illustrates the p-value: red end of spectrum denoting low p values (low correlation between time points) and dark green indicating high p values (no differences between timepoints).
Calculated alpha-diversity metrics within different gut compartments of real salmon and SalmoSim fed on Fish meal and Fish meal free diets. Figure visually represents different alpha diversity outputs within different gut compartments of real salmon in red and SalmoSim in yellow (stable time points: 16, 18 and 20 fed on Fish meal, and 36, 38 and 40 fed on Fish meal free diet) fed on Fish meal and Fish meal free diets. A visually represents effective richness (number of OTUs), B represents effective Shannon diversity. The lines above bar plots represent statistically significant differences after feed change. The stars flag the levels of significance: one star (*) for p-values between 0.05 and 0.01, two stars (**) for p-values between 0.01 and 0.001, and three stars (***) for p-values below 0.001.
In vivo phenotypic fish performance fed on two different feeds. Figure visually represents different phenotypic performance data of fish (n=32 per feed) fed on two different feed. A Atlantic salmon length in centimetres; B Atlantic salmon length in weight in kilograms; C Atlantic salmon percentage carcass yield; D Atlantic salmon gonad weight in grams; E Atlantic salmon gutted weight in kilograms; F Atlantic salmon liver weight in grams. Blue box plots represent data from salmon (n=32) fed on Fish meal free diet, and red represents Atlantic salmon fed on Fish meal diet (n=32).
VFA production within different SalmoSim compartments fed on different feeds. Figure represents 11 volatile fatty acid production within SalmoSim system fed on Fish meal and Fish meal free diets within different gut compartments. X axis represents the concentration of specific volatile fatty acid (mM) while the Y axis represents each gut compartment (stomach, pyloric caeca, midgut). Red colour denoted Fish meal and blue – Fish meal free diets. The lines above bar plots represent statistically significant differences between different feeds and gut compartments. The stars flag the levels of significance: one star (*) for p-values between 0.05 and 0.01, two stars (**) for p-values between 0.01 and 0.001, and three stars (***) for p-values below 0.001.
Posted 10 Feb, 2021
On 25 Jan, 2021
On 25 Jan, 2021
On 25 Jan, 2021
On 24 Jan, 2021
SalmoSim: the development of a three-compartment in vitro simulator of the Atlantic Salmon GI tract and associated microbial communities
Posted 10 Feb, 2021
On 25 Jan, 2021
On 25 Jan, 2021
On 25 Jan, 2021
On 24 Jan, 2021
Background
Atlantic salmon are a fish species of major economic importance. Innovative strategies are being sought to improve salmon feeds and feed additives to enhance fish performance, welfare, and the environmental sustainability of the aquaculture industry. There is still a lack of knowledge surrounding the importance and functionality of the salmon gut microbiome in fish nutrition. In vitro gut model systems might prove a valuable tool to study the effect of feed, and additives, on the host’s microbial communities. Several in vitro gut models targeted at monogastric vertebrates are now in operation. Here, we report the development of an Atlantic salmon gut model, SalmoSim, to simulate three gut compartments (stomach, pyloric caecum and midgut) and associated microbial communities.
Results
The gut model was established in a series of linked bioreactors seeded with biological material derived from farmed adult marine phase salmon. In biological triplicate, the response of the in vitro system to two distinct dietary formulations (fish meal and fish meal free) was compared to a parallel in vivo trial over forty days. Metabarcoding based 16S rDNA sequencing, qPCR, ammoniacal nitrogen and volatile fatty acid measurements were undertaken to survey the microbial community dynamics and function. SalmoSim microbiomes were indistinguishable (p=0.230) from their founding inocula at 20 days and the most abundant genera (e.g. Psycrobacter, Staphylococcus, Pseudomonas) proliferated within SalmoSim. Real salmon and SalmoSim responded similarly to the introduction of novel feed, with majority of the taxa (96% Salmon, 97% SalmoSim) unaffected, while a subset of taxa (e.g. a small fraction of Psychrobacter) were differentially affected across both systems. Consistent with a low impact of the novel feed on microbial fermentative activity, volatile fatty acids profiles were not significantly different in SalmoSim pre- and post-feed switch.
Conclusion
This study represents an important step in the development of an in vitro gut system as a tool for the improvement of salmon nutrition and welfare. This system aims to be utilised as a pre-screening tool for new feed ingredients and additives, as well as being used to study antimicrobial resistance and transfer, and fundamental ecological processes that underpin microbiome dynamics and assembly.
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