microbial ecology of atlantic salmon, salmo salar, hatcheries: … · 93 costly die offs in the...

Microbial ecology of Atlantic salmon, Salmo salar, hatcheries: impacts of the built environment 1 on fish mucosal microbiota 2 3 4 5 Jeremiah J Minich1, Khattapan Jantawongsri2, Colin Johnston3, Kate Bowie3, John Bowman4, 6 Rob Knight5,6, *Barbara Nowak2, **Eric Allen1,5 7 8 1. Marine Biology Research Division, Scripps Institution of Oceanography 9 2. Institute for Marine and Antarctic studies, University of Tasmania 10 3. Tassal Operations Pty Ltd. Hobart, Tasmania 11 4. Tasmanian Institute of Agriculture, University of Tasmania 12 5. Center for Microbiome Innovation, University of California San Diego 13 6. Department of Pediatrics, University of California San Diego 14 15 *Corresponding author (study design and fish work): Barbara Nowak 16 **Corresponding author (microbiome): Eric Allen 17 18 ABSTRACT 19 Successful rearing of fish in hatcheries is critical for conservation, recreational fishing, and 20 commercial fishing through wild stock enhancements, and aquaculture production. Flow through 21 (FT) hatcheries require more water than Recirculating-Aquaculture-Systems (RAS) which 22 enable up to 99% of water to be recycled thus significantly reducing environmental impacts. 23 Here, we evaluated the biological and physical microbiome interactions of the built environment 24 of a hatchery from three Atl salmon hatcheries (RAS n=2, FT n=1). Six juvenile fish were 25 sampled from tanks in each of the hatcheries for a total of 60 fish across 10 tanks. Water and 26 tank side biofilm samples were collected from each of the tanks along with three salmon body 27 sites (gill, skin, and digesta) to assess mucosal microbiota using 16S rRNA sequencing. The 28 water and tank biofilm had more microbial richness than fish mucus while skin and digesta from 29 RAS fish had 2x the richness of FT fish. Body sites each had unique microbial communities 30 (P<0.001) and were influenced by the various hatchery systems (P<0.001) with RAS systems 31 more similar. Water and especially tank biofilm richness was positively correlated with skin and 32 digesta richness. Strikingly, the gill, skin and digesta communities were more similar to the 33 origin tank biofilm vs. all other experimental tanks suggesting that the tank biofilm has a direct 34 influence on fish-associated microbial communities. The results from this study provide 35 evidence for a link between the tank microbiome and the fish microbiome with the skin 36 microbiome as an important intermediate. 37 38 39 IMPORTANCE: 40 Atlantic salmon, Salmo salar, is the most farmed marine fish worldwide with an annual 41 production of 2,248 million metric tonnes in 2016. Salmon hatcheries are increasingly changing 42 from flow through towards RAS design to accommodate more control over production along with 43 improved environmental sustainability due to lower impacts on water consumption. To date, 44 microbiome studies on hatcheries have focused either on the fish mucosal microbiota or the 45 built environment microbiota, but have not combined the two to understand interactions. Our 46 study evaluates how water and tank biofilm microbiota influences fish microbiota across three 47 mucosal environments (gill, skin, and digesta). Results from this study highlight how the built 48 environment is a unique source of microbes to colonize fish mucus and furthermore how this 49 can influence the fish health. Further studies can use this knowledge to engineer built 50 environments to modulate fish microbiota for a beneficial phenotype. 51

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INTRODUCTION: 52 Aquaculture is the fastest growing agricultural industry now producing over 50 % of seafood by 53 volume globally (1). While freshwater systems currently outproduce marine systems (51.4 MT 54 vs. 28.7 MT; 2016), marine aquaculture has a tremendous potential to expand with estimates of 55 theoretical production of 15 billion tonnes (522 x increase) (2, 3). One of the primary challenges 56 to scaling aquaculture production is improving seed quality by increasing survival rates and 57 strengthening immune development of larvae and juveniles in the hatchery environment (4). 58 This becomes challenging particularly when there are an estimated 369 different species of fish 59 currently grown for commercial aquaculture with additional species in experimental production 60 (3). In terms of global aquaculture production, Atlantic salmon, Salmo salar, ranks first among 61 marine fish and is the 9th largest aquaculture fish species overall (3). Global growth in Atlantic 62 salmon production has primarily been driven by technological advancements in automated 63 feeding machines, reduced reliance on fishmeal based feeds, selective breeding to reduce 64 growout time to market from three years to one year (at sea) , and in the Northern hemisphere, 65 disease control through commercial adoption of vaccine development along with biological 66 control of parasite infection using cleaner fish (5). Note that neither Australia nor New Zealand 67 has issues with sea lice, but like the Northern hemisphere does have amoebic gill disease. 68 Improvements in hatchery technology is further reducing the environmental footprint of 69 aquaculture. Optimizing hatchery conditions is also important for mariculture, capture fisheries, 70 recreational fisheries, and conservation as many government programs rely on ocean 71 enhancement efforts to replenish wild populations. For example, in 2018 29 Alaska salmon 72 hatcheries used for ocean enhancement contributed to 34% of commercial harvest worth 453 73 million USD (6). Understanding the factors for which hatchery reared salmon exhibit altered 74 performance compared to wild salmon including faster growth rates, lower age to maturity, 75 higher overall survival, lowered lifetime reproductive success, and increased aggression 76 including competitiveness, may be important for improved ocean enhancement. (7–9). 77 78 Salmon are reared in two primary types of freshwater hatchery systems: flow through (FT) 79 which requires continuous new water and recirculating aquaculture systems (RAS) where up to 80 99% of water is recycled. Flow through (FT) hatcheries however, take in and release relatively 81 large volumes of water, usually from natural surface waters, and require water treatment and 82 settlement systems. The RAS systems have the potential to significantly reduce freshwater 83 requirements and thereby lower environmental impacts. One major concern for RAS systems 84 over traditional flow-through (FT) systems is a potential impact on fish health which is in part 85 thought to be due to microbial dysbiosis either to the fish or the environment (water). Atlantic 86 salmon hatcheries are primarily built near freshwater inputs such as streams or rivers whereby 87 water is filtered and either continually flowed through the tanks at approximately 300% daily or 88 used to replenish the RAS tanks at 2-7% daily. During the freshwater stage in both hatchery 89 systems, juvenile salmon, parr, are reared in circular tanks ranging from 3 m to 10 m in diameter 90 and 2-5 m in depth made from fiberglass, concrete, or other materials equipped with Oxygen 91 injectors (aerators). This period is crucial for salmon survival as disease outbreaks can cause 92 costly die offs in the system. Compared to flow through systems, enclosed RAS systems have 93 the benefit of requiring 93% less water from the environment and a 26-38% reduced 94 eutrophication on the environment (10, 11), but can also be more costly in energy use (24-40% 95 higher) (12). Because RAS systems are enclosed batch systems, biosecurity is theoretically 96 improved as conditions can be regulated and controlled much easier than in FT systems. In 97 addition, the feed conversion ratio (FCR) can be lower in a RAS system due to ability to control 98 all variables such as temperature and salinity (10). RAS systems may enable establishment of 99 stable, slow growing, bacterial communities in hatchery systems which can improve survival 100 rates in cod (13). Other studies however have suggested that water quality (higher recirculating 101 microbial loads, accumulation of metabolites, or accumulation of heavy metals from feeds) in 102


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RAS systems was detrimental for larvae survival and/or growth of common carp (14), sea bass 103 (15), and nile tilapia (16). For post-smolt Atlantic salmon reared in a RAS system, both salinity 104 (12, 22, and 32 ppt) and time (3, 4.5, 7 months) influenced microbial communities of the water 105 column, while the tank biofilm (which differed from water column) remained stable (17). Since 106 microbial communities are indicated as an important factor in RAS water quality, and thereby 107 fish health, it is important to understand how microbiomes of both the built environment along 108 with the fish mucus are influenced in RAS versus FT systems. 109 110 The importance of mucus (gill, skin, GI) microbiome (collection of microbial eukaryotes, 111 bacteria, archaea, and viruses) to animal health has been well documented and it is through 112 mechanisms such as competitive exclusion, production of antimicrobial compounds, and 113 microenvironment control to reduce pathogen growth and colonization (18). Mucosal 114 environments including the gill (19), skin (20), and gut (21) serve as important physical barriers 115 for disease and are important part of immune response. The skin and gut microbiomes of 116 Atlantic salmon are unique, differ by life stage (parr, smolt, adult), and differ depending upon 117 rearing environment (wild vs. hatchery). Furthermore, water has been shown to primarily 118 influence the skin community (22) which is further exemplified during migration from freshwater 119 to saltwater (23). Gut microbiomes of Atlantic salmon is primarily driven by the life stage rather 120 than environment (24, 25) in the wild which has been hypothesized to be due to changes in diet 121 along with increased consumption of water during the marine stage (26). The hatchery built 122 environment is a unique microbial habitat which has largely remained unexplored (27, 28). 123 Understanding the relationship between the built environment of the hatchery along with the 124 mucosal microbiome of the fish may be important for predicting fish health. 125 126 The purpose of this experiment was to evaluate how hatchery type (FT vs RAS) influences the 127 microbial community of fish mucus and subsequently fish health. This is the first study to 128 holistically evaluate the gill, skin, and gut microbiome of Atlantic salmon. We further combined 129 histological analyses of mucosal sites to connect microbial changes to mucus health. 130 131 132 METHODS 133 Six fish were randomly sampled from each of 10 tanks across three freshwater hatcheries. Fish 134 were collected and euthanized using the AQUI-S by husbandry technicians according to label 135 instructions. Biometrics including total length, mass, and condition factor were measured. Tank 136 conditions such as water temperature, salinity, and diameter were recorded and can be found in 137 the metadata file. 138 139 Histology: 140 Samples of gills, skin and intestine, fixed in 10% buffered formalin were trimmed, processed 141 using standard protocols for histology and embedded in paraffin. Sections of 4 µm were cut and 142 one section was used for each individual fish. The sections were stained with Alcian Blue/ 143 Periodic Acid – Schiff (AB/PAS) at pH 2.5 to quantify mucous cells in the gills per inter-lamellar 144 unit (ILU) under a bright field light microscope (Leica DM1000, Hamburg, Germany) (29, 30), to 145 count the number of mucous cells in the skin and the number of intestinal mucous cells, 146 normalized per area (31). For the gut morphometric measurements of the fold height, mucosa 147 thickness, fold width, muscularis thickness, fold height, mucosa thickness, fold width, and 148 muscularis thickness were done as previously described (32). Intestine sections stained with 149 haematoxylin and eosin (H&E) were analysed using Image-Pro Premier software. Ten intestinal 150 folds from one section from each region were included in the analysis. 151


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152 153 Microbiome processing: 154 For each of the ten tanks, six fish were collected individually using hand nets and 155 placed directly into a sterile sampling bucket and anesthetized using AQUI-S. 156 The mucosal microbiome was sampled as follows: gill by swabbing the second 157 gill arch on the left lateral side; skin by swabbing a 2 cm x 2 cm area posterior of 158 the operculum on the left lateral side under the dorsal fin; and digesta by 159 massaging the GI until a fecal pellet emerged. Swabs were then placed directly 160 into a 2 ml PowerSoil tube and frozen at -20 oC. In addition to biological samples, 161 two environmental samples were taken per tank including a 2 cm x 2 cm swab of 162 the inside of the tank just below the water line (biofilm) along with a 400 ul bulk 163 water sample from the tank. In total three body sites across 60 fish (180 164 samples) along with two environmental samples across ten tanks (20 samples) 165 were collected and processed for microbiome analysis. In addition, 21 technical 166 controls were included. 167 168 DNA extraction was performed at University of Tasmania Hobart using the 169 EarthMicrobiomeProject protocols (earthmicrobiome.org), specifically using the 170 ‘manual single-tube’ MoBio PowerSoil kit as to reduce well-to-well contamination 171 (33). A total of 21 positive controls of a microbial isolate (replicates of 10 fold 172 serial dilutions) were processed alongside the samples and then used to 173 determine sample success rate by calculating the sample exclusion criteria 174 based on read counts described in the Katharoseq method (27). Samples were 175 processed in triplicate 5 ul PCR reactions (34)[PMID: 30417111 ] using the 16S 176 v4 515/806 primers (35, 36) and then pooled at equal volume according to 177 Katharoseq (27). The final amplicon pool was processed using the Qiagen PCR 178 cleanup kit following EMP protocols and sequenced on a MiSeq 2x250 bp run 179 (37). Sequencing runs were processed in Qiita (38) using Qiime2 commands 180 (39). Samples were trimmed to 150 bp and then processed through the deblur 181 (40) pipeline which generates unique, single ASVs (amplicon sequence variants). 182 To determine which samples had been sequenced successfully, the Katharoseq 183 method (27), developed for low biomass sequencing, was applied. The cutoff 184 value for composition of a sample aligning to the target within the positive 185 controls was 90%. In this case, the cutoff value was 405 reads, but we rarified to 186 1000 reads to have higher depth of sequencing. Within Qiita, samples which did 187 not have histology metadata were excluded. 188 189 Statistical analysis: 190 Alpha diversity was calculated using richness (total observed unique ASVs) and 191 Faith’s Phylogenetic Diversity. Differences between body sites and 192 environmental variables was tested using non-parametric Kruskal-Wallis test with 193 the Benjamini Hochberg FDR 0.05 (41, 42). Correlations between richness of 194 environmental variables (tank and bioiflm) and salmon body site was calculated 195 using linear regression. For beta diversity we used weighted and unweighted 196 UniFrac (43, 44). Multivariate statistical testing of both continuous and 197 categorical variables was performed using ADONIS within Qiime(45). Pairwise 198 statistical comparisons of beta diversity measures were calculated using Mann-199 Whitney while multiple comparisons conducted using Kruskal-Wallis test. To 200 identify correlations between histological measures and specific microbes, a non-201 parametric, Spearman correlation was calculated for both the entire dataset 202


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using the Calour analysis tool (46). 203 204 205 RESULTS 206 A total of 60 fish were sampled from three unique hatcheries, one flow through (FT) and two 207 recirculating aquaculture systems (RAS). Within the hatcheries, a total of six fish were sampled 208 from each of ten unique tanks. To evaluate health status, fish were examined for 209 histopathological measurements within the gill, skin, and gastrointestinal tract. In addition, the 210 mucosal microbiome of three body sites (gill, skin, and digesta) was sampled across all 60 fish 211 along with environmental controls including the tank water and tank-associated biofilms. After 212 calculating sample cutoff measures and rarefying to 1000 reads, a total of 185 samples passed 213 QA/QC resulting in a total of 6,197 total unique ASVs (Supp Figure S1). Failures were not 214 associated with any particular hatchery system (success rate: 72/78 RAS 1, 56/60 RAS 2, 56/60 215 FT) or body site (success rate: 56/60 gill, 58/60 skin, 55/60 digesta). A total of 37 microbial 216 Phyla were represented in the dataset including one archaea (Euryarchaeota) and one 217 eukaryote (Apicomplexa) (Supp Figure S2). Digesta samples generally had higher levels of 218 Cyanobacteria, Firmicutes, Actinobacteria, and Fusobacteria whereas the skin and gill were 219 enriched with Bacteroidetes, Verrucomicrobia, and Acidobacteria. Across all body sites and built 220 environment samples, Proteobacteria was most dominant. 221 222 Statistical analyses of community composition revealed that body sites along with hatchery 223 system and further tank replicates were all significant drivers of community composition with 224 body site (P=0.001, R2 = 0.127 Unweighted Unifrac; P=0.001, R2 = 0.340 Weighted UniFrac) 225 being the strongest (Table 1). Furthermore, when stratifying for each body site (gill, skin, and 226 gut), microbial communities were significantly influenced by both hatchery location and across 227 individual tanks using both Unweighted and Weighted UniFrac (Table 1). 228 229 Microbial diversity differs according to sample type with water samples having the highest 230 richness (P=0.0015, KW 17.52) and phylogenetic diversity (P=0.0021, KW 16.79) (Figure 1a-b). 231 When comparing only fish mucus samples, the gill had less richness than the skin and digesta 232 (P=0.0056, KW 10.37) and lower phylogenetic diversity than the skin (P=0.0279, KW 7.16). 233 Microbial composition as assessed using Unweighted UniFrac distances, was primarily driven 234 by sample type followed by hatchery system with samples from the RAS generally being more 235 similar than the FT hatchery (Table 1, Figure 1c-d). In addition, water and biofilm samples were 236 highly distinguishable between the hatchery systems, particularly RAS vs. FT and clustered 237 more closely to gill and skin samples indicating that gill and skin microbiomes were more closely 238 related to the built environment. 239 240 We next assessed how facility type influenced the microbiome of both the fish body sites and 241 the built environment. Microbial richness of skin, digesta, tank biofilm and tank water was 242 generally higher in the RAS systems compared to FT (Figure 2a). When comparing only fish 243 body sites, both skin (P<0.0001, KW 21.16) and digesta (P=0.0058, KW 10.29) richness was 244 significantly different across hatcheries with RAS systems having approximately 2x more sOTUs 245 associated with skin and digesta compared to FT (Figure 2b). Post-hoc multiple comparison 246 tests demonstrated that for skin both RAS1 and RAS2 richness was greater than FT whereas 247 for digesta only RAS1 was higher than FT (Figure 2b). Compositionally, the microbial 248 communities were significantly different across hatcheries for all samples combined (Table 1). 249 When only analyzing microbial communities of specific body sites like gill, skin, and digesta, a 250 hatchery specific microbiome was still observed (Figure 2c-e). The hatchery specific microbiome 251 was also prevalent in the water column and tank biofilm (Figure 2f-g). On closer observation, 252 tank biofilm, tank water, and fish skin samples from the RAS systems were more similar 253


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compositionally than from the FT system (Figure 2, Supp Figure S3, Supp Figure S4), with 254 RAS1 also having unique communities apart from RAS2. 255 256 Next we directly evaluated the relationship between environmental microbiome of the tank water 257 and tank biofilm with the fish mucus. For each individual tank, microbial richness of the biofilm 258 (Figure 3a) and the tank water (Figure 3b) was compared to the richness of fish within that tank 259 for the three body sites: gill, skin, and digesta. Both skin and digesta was positively correlated 260 with tank biofilm (P=0.0001, R2 =0.2835; P=0.002, R2 =0.2042) and water richness (P=0.0014, 261 R2 =0.2336; P=0.0264, R2 =0.1296) indicating that tank biofilms have a slightly stronger impact 262 than tank water on fish mucus richness, with skin being the most impacted (Figure 3a-b). Since 263 hatchery environmental microbes seemed to influence fish mucus microbes and unique 264 microbial populations exist across hatcheries and within tank replicates within a hatchery, we 265 hypothesized that within a tank, fish mucus microbial composition should be more similar to the 266 biofilm and water of that tank as compared to tanks from other hatcheries. Here we report that 267 the gill, skin, and digesta of the fish is more similar to the tank biofilm of origin compared to 268 tanks from other hatcheries (Figure 3c) whereas for tank water this only is true for gill and skin 269 (Figure 3d). Both gill and skin are more similar to the tank biofilm and tank water than digesta 270 (Figure 3e). To understand how microbial communities differ across hatchery types, we 271 compared the beta diversity within sample types from the three hatcheries. Tank water, tank 272 biofilm, and skin communities are more similar between RAS hatcheries as compared to the FT 273 hatchery (Supp Figure S4a-b). Since the skin microbiome was the most influenced body site on 274 the fish, we calculated the differentially abundant sOTUs (n=65) between the RAS and FT 275 systems (Supp Figure S4c). Of the 65 differentially abundant skin sOTUs, 44 were present in 276 the water or tank biofilm communities, while 17 were only found on the skin (Supp Figure S4d). 277 Skin microbes that were associated with RAS systems included Saprospirales, Cytophagales, 278 Sphingobacteriales, Verrucromicrobia, and Methylophilales (Methylotenera sp), whereas the FT 279 was enriched in Pseudomonas, Pseudomonadales, and Enterobacteriales (Supp Figure 4e). 280 Additionally, Aeromonadales were highly enriched in the fecal detritus in the FT hatchery while 281 much of the FT associated microbes were not found in the detritus suggesting they are indeed 282 water or biofilm specific. 283 284 Upon establishing a direct relationship between the microbiome of the hatchery environment, 285 we next assessed how fish health is related to these changes. Broad mucosal histopathology 286 was performed on eight endpoint measures across the gill (Supp Figure S5), skin (Supp Figure 287 S6), and gastrointestinal tract (Supp Figure S7). In all but one measure, a heighted score was 288 demonstrated in RAS systems compared to FT for the fish sampled with RAS1 being slightly 289 higher than RAS1 (Figure 4). Furthermore, we tested if the microbiome of the fish was driven by 290 these histology scores and found that for Unweighted UniFrac measures, where rare taxa are 291 more heavily weighted in a phylogenetic context, the skin microbiome was significantly 292 associated with mucous cell numbers in the gill (Adonis: P=0.025) and skin communities 293 (Adonis: P=0.006 and P=0003 while the gut microbiome was also associated with mucous cell 294 numbers in the skin (Adonis: P=0.015) (Table 2). When analyzing weighted UniFrac, which 295 looks primarily at relative abundances of sOTUs in a phylogenetic context, gill and skin 296 microbial communities were associated with GI mucous cell numbers (Adonis: gill P=0.026, skin 297 P=0.014) while the gut microbiome was associated with mucous cell numbers in the skin 298 (Adonis: P=0.002) (Table 2). 299 300 Recirculating Aquaculture Systems utilize microbes to recycle and remove nitrogenous waste 301 products generated from uneaten feed, fish feces, and other organic wastes. We identified and 302 quantified the types and relative abundances of these various types of known microbes 303 (bacteria and archaea) in this system to understand if known RAS-associated microbes were 304


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playing a role in colonization within fish mucus or the environment (Figure 5a). The only known 305 RAS-associated ammonia-oxidizing bacteria (AOB) found in the system was the family 306 Nitrosomonadaceae which was present in all of the hatcheries and sample types (Figure 5b-e) 307 and perhaps slightly enriched in the tank biofilm community (Figure 5f). Nitrite-oxidizing bacteria 308 (NOB), primarily the family Nitrospiraceae and Nitrospira spp., were generally in higher relative 309 abundances in the RAS environmental components including the water and biofilm (Figure 5e-f) 310 along with the skin, digesta, and gill (Nitrospiraceae only) indicating a possible transfer event 311 (Figure 5b-d). Note for digesta samples, both NOB organisms were not detected in any of the 312 FT reared fish. For denitrifying autotrophs, Rhodobacter spp. and Hydrogenophaga spp. were 313 enriched across all hatcheries and sample types with Rhodobacter spp. being in slightly higher 314 abundances in some FT systems. For heterotrophic denitrifiers, Pseudomonas spp. were the 315 most dominant and specifically were approximately 20-100x higher in the FT water and tank 316 biofilms as compared to the RAS systems (Figure 5e-f). In addition to being enriched in the 317 environment, Pseudomonas spp. were also consistently higher in the gill, skin, and digesta of 318 fish reared in FT compared to RAS (Figure 5b-d). Lastly two methanogens were detected albeit 319 at very low frequencies and only in the tank biofilm (Methanocorpusculum sp.) and the digesta 320 (Methanosphaera sp.) from the RAS. 321 322 323 DISCUSSION 324 The mucosal environment is paramount for fish health as it is the first line of defense against 325 pathogen invasion. Specifically, a healthy mucosal environment protects against infection 326 through several endogenous mechanisms including mucus production, immune components 327 such as lysozymes, antimicrobial peptides, immunoglobulins, and exogenous mechanisms 328 through establishment of a healthy microbiome. In this study, we investigated the means by 329 which the mucosal environment of Atlantic salmon is influenced by the rearing environment. We 330 evaluated three unique hatcheries utilizing two rearing methodologies including Recirculating 331 Aquaculture Systems (RAS) and Flow-through (FT) systems. 332 333 In both the biosecure RAS and FT hatchery environments, Atl salmon have unique microbial 334 communities on their gill, skin, and digesta. These fish associated mucosal microbiomes along 335 with the tank and biofilm communities are further differentiated across hatchery systems by 336 comparing RAS vs. FT systems. RAS systems are known to harbor their own unique microbial 337 communities both in the biofilter but also within the hatchery system where fish are reared (47), 338 Previous studies however, have not looked at the built environment microbiomes simultaneously 339 with the fish mucosal microbiomes. For these hatchery systems, alpha diversity is higher in RAS 340 compared to FT hatcheries for the following sample types: skin, digesta, tank water, and tank 341 biofilm microbiomes. Fish skin and digesta richness is further positively associated with both 342 tank biofilm and tank water richness suggesting an influence of the environment microbiota on 343 fish associated microbiota, with the biofilm association being the strongest. Skin microbiomes 344 have been implicated as important for maintaining fish health, thus understanding any potential 345 negative implications or drivers of dysbiosis is important for fish welfare (48, 49). Tank biofilms 346 can be challenging to monitor and control. Further research should focus on how manipulating 347 tank surfaces through material science and engineering could be used to promote fish health. 348 349 Beta-diversity is significantly different across the three hatcheries when looking at individual 350 sample types: gill, skin, digesta, water, and biofilm. Fish mucosal sites were more 351 phylogenetically similar to both water and biofilms within their own tank as compared to tanks 352 from other hatcheries indicating a microenvironment effect. By performing histology of fish GI, 353 skin, and gill we confirmed that the fish mucosal microbiome is associated with fish health. 354


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355 RAS are becoming popular for growing salmon smolts offering many benefits including 356 minimized water use and waste generation along with improving survival rates of fish during 357 transfer to net pens (50–53). Waste water is purified by processing through one of two main 358 types of biofilters (fixed film or single sludge) which utilizes a variety of bacteria and archaea 359 (54, 55). The biofilters are primarily comprised of heterotrophs and chemoautotrophs that 360 transform and detoxify ammonia and nitrate species (56). The common ammonia-oxidizing 361 archaea and bacteria found in these systems include Nitrosopumilus (archaea), Nitrosomonas, 362 Nitrosococcus, and Nitrosospira (47). In our study, only ammonia-oxidizing bacteria within the 363 family Nitrosomonadaceae were present and were highest in the RAS tank and water systems 364 as well as RAS reared fish gill, skin, and digesta. Following ammonia oxidation, Nitrospira and 365 Nitrobacter are the primary bacteria responsible for nitrite oxidation in RAS biofilters (47). 366 Bacterial sOTUs from Nitrospira sp and unclassified sOTUs within the family Nitrospiraceae 367 were in higher relative abundance in the RAS hatcheries for tank water, tank biofilm, skin, 368 digesta, and moderately in the gill. In the final step of nitrogen recycling, denitrification is carried 369 out by both autotrophs and heterotrophs. The primary autotrophic bacteria associated with 370 denitrification in RAS systems include Thiomicrospira, Thiothrix, Rhodobacter, and 371 Hydrogenophaga (47). Both Rhodobacter and Hydrogenophaga were found in the hatcheries 372 although in similar relative abundances across the FT and RAS hatcheries both in the tank 373 environment and fish mucus. The primary Heterotrphic microbes associated with denitrification 374 in RAS systems includes Pseudomonas, Paracoccus, and Comamonas sp (47). All three were 375 abundant in the hatcheries with Pseudomonas being the highest of the three and generally 376 higher in the flow through hatchery compared to RAS. In conclusion, various RAS associated 377 microbes which are responsible for Nitrogen cycling, particularly Nitrification, in the biofilters 378 were present in our study and higher in the RAS built environment along with RAS reared fish 379 mucus suggesting that these microbes are not being solely sequestered in the biofilter but 380 instead also are circulated through the fish tanks and may be colonizing fish mucus. 381 382 When excess organic matter including fish feed and fish feces accumulates in a RAS tank, the 383 heterotrophs can quickly bloom and outcompete nitrifying microbes (47). This overgrowth and 384 imbalance may contribute negatively to flesh flavor, thus future studies are warranted to 385 understand which microbes and what metabolic pathways may play this role (57). While most 386 hatcheries are used for producing seed to then transfer to ocean growout cages, complete 387 salmon production cycles in land based RAS is becoming more common. Furthermore, both FT 388 and RAS systems may be colonized by various microbial inputs from the air, water, fish feed, 389 fish flesh, technicians, and biofilter type, thus understanding the contributions of each in a 390 system will be important both for future experimental designs and for fish health (58). 391 392 The built environment microbiome may originally be colonized by both animal excrement 393 including mucus along with environmental sources such as water. The sustained built 394 environment microbiome is both a result of the new animal host deposition of cellular material 395 but can also propagate based on host associated animal matter. Furthermore, the built 396 environment community can then influence the microbial communities of animal hosts residing 397 there. Understanding the extent by which the animal’s microbiome can be influenced by its 398 surroundings and then associated to a phenotype such as fish health or development will be 399 important for experimental design where microbiome readout is a standard measure. This is 400 commonly referred to as the ‘cage effect’ and has primarily been demonstrated in mouse 401 studies where animals which share the same cage have more similar fecal samples, likely due 402 to coprophagy (59, 60). Cage effect can explain up to 31% of variation in mouse feces 403 compared to only 19% resolved by host genetics (61). For this reason, our experimental design 404 included three separate tanks per treatment group (hatchery) along with multiple fish biological 405


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replicates per tank. To our knowledge, this is the first experiment to demonstrate a tank effect in 406 fish which is due to both water and tank biofilm formation and influences primarily the fish skin 407 and digesta. Since aquariums use a variety of material types to culture fish, it would be 408 important for future studies to evaluate how biofilm formation changes with respect to tank 409 material type (e.g. concrete, PVC, HDPE, fiberglass, etc). 410 411 Quantifying fish health can be a challenging and expensive endeavor which does not often 412 easily scale for large hatchery operations. Fish mucus contains various immune components 413 such as lysozymes, immunoglobins, lectins, crinotoxins, and antimicrobial peptides (62). Here 414 we used histology as a measure of mucosal health across the gastrointestinal tract, gill, and 415 skin. Elevated skin mucous cell numbers are generally reflective of healthy fish whereas 416 depleted mucosal cells may indicate a recent mucosal discharge due to stress or disease (63, 417 64). Mucosal cell numbers however, can also be influenced by the sampling location on the fish 418 body, sex, diet, and age or development stage, thus care must be taken when interpreting 419 results (31, 65, 66). In our study, skin and gill mucosal cell numbers were elevated in both RAS 420 systems compared to the FT suggesting that RAS fish may have been more healthy or less 421 stressed. Furthermore, these elevated skin mucous cell numbers positively correlated to 422 microbial richness and phylogenetic diversity on the skin and were associated with changes in 423 microbial composition. For the gastrointestinal tract, the fold height, mucosa thickness, 424 muscularis thickness, and mucous cell number were higher in fish reared in the RAS compared 425 to the FT hatchery. Overall, the RAS reared fish had a more complex GI tract compared to fish 426 reared in flow through systems. Vertebrate gut microbiomes are often driven by diet, habitat, 427 and age (67). Since diet and age was controlled for in this study, we hypothesize that 428 differences in microbial communities in the tank water column and biofilm are driving gut 429 microbiome differences by drinking or grazing. Microbiome is essential for development and 430 differentiation of mucous cells, for example gnobiotic model of zebrafish showed reduced 431 numbers of mucous cells in their intestine (Bates et al 2006). For each unique mucosal site, 432 distinct microbial communities were present and differentiated between the FT and RAS 433 systems. Differences in GI communities between RAS and open water systems may be 434 indicative of microbial exposure in the environment (68). Atlantic salmon reared in a RAS which 435 were infected by Aeromonas salmonicida, also had differentiated gut microbiomes as compared 436 to healthy fish (69). By demonstrating how the environmental microbiome is influenced by 437 hatchery design which in turn influences the fish mucosal microbiome and subsequent health, 438 our study demonstrates the utility of developing environmental and/or fish microbiome sampling 439 as a potential fish tank health indicator. Future studies should evaluate more tanks and include 440 metrics such as survival rate, growth rate, and body composition analysis to determine how the 441 environmental microbiome may drive fish performance in the hatchery setting. 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456


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Tables 642

643

644

645

646

647

648

649

650

651

652

653

Table 1. Multivariate statistical testing of drivers of microbial beta diversity

Unweighted UniFracmetadata column Variable_type method R2 P R2 P R2 P R2 P

Body site sample_type categorical adonis 0.127 0.001 Nan Nan Nan Nan Nan Nan

Hatchery system ylk_tank_system categorical adonis 0.062 0.001 0.124 0.001 0.172 0.001 0.091 0.001

Tank number ylk_sbt_tank_number categorical adonis 0.110 0.001 0.268 0.001 0.294 0.001 0.284 0.001

Weighted UniFrac

Body site sample_type categorical adonis 0.340 0.001 Nan Nan Nan Nan Nan Nan

Hatchery system ylk_tank_system categorical adonis 0.053 0.001 0.231 0.001 0.229 0.001 0.084 0.031

Tank number ylk_sbt_tank_number categorical adonis 0.119 0.002 0.423 0.001 0.388 0.001 0.433 0.001

Combined gill skin gut


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654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

Table 2. Multivariate statistical testing of effects of microbiome on fish health metrics

Unweighted UniFrac gill skin gut

Variable_type method R2 P R2 P R2 P

Fish length host_height continuous adonis x 0.726 x 0.106 0.569

Fish mass host_weight continuous adonis x 1.000 x 1.000 1.000

Fish K factor sal_k_factor continuous adonis x 1.000 x 1.000 1.000

Fish histology gut sal_histo_gi_fold_height continuous adonis x 1.000 x 1.000 1.000

Fish histology gut sal_histo_gi_mucosa_thickness continuous adonis x 1.000 x 1.000 1.000

Fish histology gut sal_histo_gi_fold_width continuous adonis x 1.000 x 1.000 1.000

Fish histology gut sal_histo_gi_muscularis_thickness continuous adonis x 1.000 x 1.000 1.000

Fish histology gut sal_histo_gi_goblet_cells continuous adonis x 0.148 x 0.073 0.666

Fish histology gill sal_histo_gill_mucous_cell_num_gill continuous adonis x 0.081 0.749 0.025 0.056

Fish histology skin sal_histo_skin_mucous_cells_permm2_skin continuous adonis x 0.424 0.905 0.003 0.915 0.015

Weighted UniFrac R2 P R2 P R2 P

Fish length host_height continuous adonis x 0.116 x 0.051 x 0.873

Fish mass host_weight continuous adonis x 1.000 x 1.000 x 1.000

Fish K factor sal_k_factor continuous adonis x 1.000 x 1.000 x 1.000

Fish histology gut sal_histo_gi_fold_height continuous adonis x 1.000 x 1.000 x 1.000

Fish histology gut sal_histo_gi_mucosa_thickness continuous adonis x 1.000 x 1.000 x 1.000

Fish histology gut sal_histo_gi_fold_width continuous adonis x 1.000 x 1.000 x 1.000

Fish histology gut sal_histo_gi_muscularis_thickness continuous adonis x 1.000 x 1.000 x 1.000

Fish histology gut sal_histo_gi_goblet_cells continuous adonis 0.936 0.026 0.946 0.014 x 0.294

Fish histology gill sal_histo_gill_mucous_cell_num_gill continuous adonis x 0.203 x 0.378 x 0.131

Fish histology skin sal_histo_skin_mucous_cells_permm2_skin continuous adonis x 0.360 x 0.154 0.970 0.002


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Figures 669

670

671 Figure 1. Microbial ecology (16S rRNA) of three tanks each from three hatchery systems 672 (water and tank biofilm) and Atlantic salmon gill mucus, skin mucus, and digesta. Alpha 673 diversity measures of a) total richness and b) Faiths Phylogenetic Diversity evaluated by 674 non-parametric Kruskal-Wallis test with Benjamini-Hochberg 0.05 FDR. Beta diversity 675 measures of unweighted UniFrac distances colored by c) sample type and by d) hatchery 676 system. 677 678

679

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d


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684

685

686 687 Figure 2. Inter-hatchery effects on microbial ecology of built environment and fish body sites. a) 688 Richness (total observed sOTUs) distributions per each tank across body sites, tank biofilm, and 689 water column from the three types of hatcheries RAS 1, RAS 2, and FT. b) Tank replicates are 690 combined per hatchery to enable multiple group statistical analysis of richness comparisons 691 (Kruskal-Wallis). Beta diversity distributions depicted through PCoA plots of Unweighted 692 UniFrac distances across tanks for each unique environment: c) salmon gill, d) salmon skin, e) 693 salmon digesta, f) tank water, and g) tank biofilm. 694 695

Water Tank biofilm

DigestaGill Skin

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f g

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digestagill tank side

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696

697 698 Figure 3. Relationship between built environment and fish mucosal microbiome. Correlation 699 between a) tank biofilm richness and fish mucus richness along with b) tank water richness and 700 fish mucus (linear regression). Beta diversity measures to test similarity (Unweighted UniFrac) 701 of fish mucus to c) tank biofilm and d) tank water. Pairwise comparisons of similarities within a 702 tank versus similarities to other tanks from across hatcheries with Mann-Whitney test. e) Overall 703 fish mucosal similarities compared to tank biofilm and water indicate gill and skin are more 704 similar to environment than digesta (Kruskal-Wallis) 705 706

707

0 100 200 300

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Atl

salm

on

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P = 0.002, R2 = 0.2042, m=0.4549

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P = ns

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skin vs

. tank biofilm

skin vs. ta

nk water

digesta vs. tank b

iofilm

digesta vs. ta

nk water

Within-tank comparisons

0 100 200 300

0

100

200

300

Tank water richness

Atl

salm

on

mu

co

sal sit

e r

ich

ness

(mean

, err

or)

digesta

gill

skin

P= ns

P= 0.0264, R2 =0.1296, m=0.4654

P= 0.0014, R2 = 0.2336, m=0.6321

Tank water

0.0

0.2

0.4

0.6

0.8

1.0

Un

weig

hte

d U

niF

rac d

ista

nce

tan

k w

ate

r vs

fis

h m

ucu

s (

med

ian

, IQ

R)

** **

ns

Mann-Whitney

within-ta

nk

across-hatcherie

s

within-ta

nk

across-hatcherie

s

within-ta

nk

across

-hatcherie

s

within-ta

nk

across-hatcherie

s

Figure 3.

a b

c d

e


https://doi.org/10.1101/828749

708 709 Figure 4. Histopathology analysis including gut morphometry and mucous cell counts from skin 710 and gill of the fish from flow through (FT) and two RAS hatcheries (RAS 1 and RAS 2 hatchery 711 systems. Hatchery systems were compared using non-parametric Kruskal-Wallis test. Skin 712 mucous cell counts shown as both per length of epidermis section and per surface area or the 713 epidermis. All fish sampled were clinically normal. 714

715

716

717

718

719

720

721

722

723

0

50

100

150

200

250

Hatchery Location

His

tolo

gy s

cale

(m

ean

, S

EM

)Kruskal-Wallis, multiple comparisons (Benjamini-Hochberd FDR 0.05)

GI_fold_height

GI_mucosa_thickness

GI_fold_width

GI_muscularis_thickness

GI_goblet_cells

mucous_cell_num_gill

mucous_cell_num_skin

mucous_cells_permm2_skin

**

FT (3

,5,7

)

RAS 1

(4,6,9

)

RAS 2

(12,

16,18)

**

ns**** ****

****

****

****

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,

16,18)

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,

16,1

8)

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,

16,1

8)

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,

16,1

8)

FT (3

,5,7)

RAS 1

(4,6

,9)

RAS 2

(12,

16,1

8)

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,16

,18)

FT (3

,5,7

)

RAS 1

(4,6

,9)

RAS 2

(12,

16,1

8)

Gastrointestinal tract Gill Skin


https://doi.org/10.1101/828749

724 Figure 5. Distribution of RAS associated microbes in Salmon hatcheries. a) RAS systems are 725 designed to recycle nitrogenous waste, primarily from uneaten feed and fish feces, using a 726 series of nitrification and denitrification steps through microbial filters. The primary microbes 727 involved in these processes and detected in the systems (AOB - ammonia oxidizing bacteria, 728 NOB - Nitrite oxidizing bacteria, and denitrification) are listed. The distribution of these RAS-729 associated microbes are listed as mean relative counts per 1000 according to each hatchery 730 type (FT = orange, RAS 1 = purple, RAS 2 = yellow) across each particular sample type 731 including (b) gill, (c) skin, (d) digesta, (e) tank water, and (f) tank biofilm. 732 733

734

735

736

737

738

739

f__N

itroso

monad

acea

e

f__N

itrosp

irac

eae

g__Nitr

ospira

g__Nitr

obacte

r

g__Rhodobac

ter

g__Hyd

rogen

ophaga

g__Par

acocc

us

g__Com

amonas

g__Pse

udomonas

g__Fusi

bacte

r

f__D

esulfo

vibri

onacea

e

g__M

ethan

osphae

ra

g__M

ethan

ocorp

usculu

m

0.1

1

10

100

1000

Co

un

ts p

er

1000 (

mean

, S

EM

)

f__N

itroso

monad

acea

e

f__N

itrosp

irac

eae

g__Nitr

ospira

g__Nitr

obacte

r

g__Rhodobac

ter

g__Hyd

rogen

ophaga

g__Par

acocc

us

g__Com

amonas

g__Pse

udomonas

g__Fusi

bacte

r

f__D

esulfo

vibri

onacea

e

g__M

ethan

osphae

ra

g__M

ethan

ocorp

usculu

m

0.1

1

10

100

1000

Co

un

ts p

er

1000 (

mean

, S

EM

)

f__N

itroso

monad

acea

e

f__N

itrosp

irac

eae

g__Nitr

ospira

g__Nitr

obacte

r

g__Rhodobac

ter

g__Hyd

rogen

ophaga

g__Par

acocc

us

g__Com

amonas

g__Pse

udomonas

g__Fusi

bacte

r

f__D

esulfo

vibri

onacea

e

g__M

ethan

osphae

ra

g__M

ethan

ocorp

usculu

m

0.1

1

10

100

1000

Co

un

ts p

er

1000 (

mean

, S

EM

)

f__N

itroso

monad

acea

e

f__N

itrosp

irac

eae

g__Nitr

ospira

g__Nitr

obacte

r

g__Rhodobac

ter

g__Hyd

rogen

ophaga

g__Par

acocc

us

g__Com

amonas

g__Pse

udomonas

g__Fusi

bacte

r

f__D

esulfo

vibri

onacea

e

g__M

ethan

osphae

ra

g__M

ethan

ocorp

usculu

m

0.1

1

10

100

1000

Co

un

ts p

er

1000 (

mean

, S

EM

)

f__N

itroso

monad

acea

e

f__N

itrosp

irac

eae

g__Nitr

ospira

g__Nitr

obacte

r

g__Rhodobac

ter

g__Hyd

rogen

ophaga

g__Par

acocc

us

g__Com

amonas

g__Pse

udomonas

g__Fusi

bacte

r

f__D

esulfo

vibri

onacea

e

g__M

ethan

osphae

ra

g__M

ethan

ocorp

usculu

m

0.1

1

10

100

1000

Co

un

ts p

er

1000 (

mean

, S

EM

)

RAS 1

RAS 2

FT

Gill Skin Digesta

Water Tank biofilm

RAS System

Uneaten feedFecal wastte

NH3 & NH4

Nitrification

NO2 NO3

Denitrification

N2

AOB NOBf__Nitrosomonadaceae f__Nitrospiraceae

g__Nitrospirag__Nitrobacter

NO2

Autotrophs Heterotrophsg__Rhodobacter g__Paracoccusg__Hydrogenophaga g__Comamonas

g__Pseudomonas

Sulphate reductiong__Fusibacterf__Desulfovibrionaceae

AOB NOB Denitrifiers

Auto Hetero

SulR Met AOB NOB Denitrifiers

Auto Hetero


Auto Hetero

SulR Met

AOB NOB Denitrifiers

Auto Hetero


Auto Hetero

SulR Met

a

b c d

e f

Methanogenesisg__Methanosphaerag__Methanocorpusculum

Figure 5


https://doi.org/10.1101/828749

740

741

Supplemental Figures 742 743 744

745 Supplemental Figure S1. Limit of detection calculation of positive control titrations. Application of 746 Katharoseq method results in a cutoff value of 405 reads indicating that positive control samples 747 which have 405 reads would then have 90% of those reads aligning to the target organism. 748 749


https://doi.org/10.1101/828749

750 Supplemental Figure S2. Microbial phyla (37) present in the study including bacteria, *archea, 751 and ** one microbial eukaryote. 752 753 754 755 756 757 758 759 760

dige

sta

gill sk

in

tank

biofilm

tank

wat

er

feca

l pellet

CyanobacteriaFirmicutes

TenericutesProteobacteria**Apicomplexa

Chloroplexi*Euryarchaeota

PlanctomycetesNitrospirae

BacteroidetesSpirochaetes

Actinobacteria

TM7BRC1GN02

Unknown bacteriaWPS-2

Armatimonadetes

AcidobacteriaElusimicrobia

OD1Verrucomicrobia

ChlorobiChlamydiae

GemmatimonadetesOP3SR1

FusobacteriaTM6

FibrobacteresThermiNKB19

ThermotogaeSynergistetes

OP11FBP

Lentisphaerae

FT RAS 1 RAS 2 FT RAS 1 RAS 2 FT RAS 1 RAS 2


https://doi.org/10.1101/828749

761

762 763 Supplemental Figure S3. Top 20% most abundant sOTUs (minimum 100 reads across sample 764 types) 765 766 767 768 769

sO

TU

s 1

-1259 (

min

100 r

eads, origin

al 6197)

digesta gill skintank

water

fecal-detrit

us

RAS 1 RAS 2 FT RAS 1 RAS 2 FT RAS 1 RAS 2 FT


https://doi.org/10.1101/828749

770 771 Supplemental Figure S4. Microbial differences between hatchery type. Beta-diversity 772 comparisons between all three hatcheries (FT, RAS 1, and RAS 2) for individual sample types 773 including tank water, tank biofilm, skin, digesta, and gill for a) Unweighted Unifrac and b) 774 Weighted UniFrac. c) Comparing skin microbiomes of fish from the RAS vs. the FT hatcheries, 775 65 sOTUs were differentially abundant. d) Source tracking of differentially abundant skin 776 microbes from water and tank biofilm communities demonstrate that the majority are founds in 777 built environment. e) Differentially abundant skin microbes colored by taxonomic order found 778 across the three hatcheries and abundances within the tank water and tank biofilm. 779 780 781 782 783 784 785 786 787 788 789 790

75Pft_

vs._

RAS_2

75Pft_

vs._

RAS_1

RAS_2

_vs.

_RAS_1

75Pft_

vs._

RAS_2

75Pft_

vs._

RAS_1

RAS_2

_vs.

_RAS_1

75Pft_

vs._

RAS_2

75Pft_

vs._

RAS_1

RAS_2

_vs.

_RAS_1

75Pft_

vs._

RAS_2

75Pft_

vs._

RAS_1

RAS_2

_vs.

_RAS_1

75Pft_

vs._

RAS_2

75Pft_

vs._

RAS_1

RAS_2

_vs.

_RAS_1

0.4

0.6

0.8

1.0

Un

we

igh

ted

Un

iFra

c

(Dis

tan

ces:

Med

ian

, IQ

R)

Tank

Water

Tank

Biofilm

Skin Digesta Gill

0.0

0.2

0.4

0.6

0.8

Rela

tive a

bu

nd

an

ce

RAS 1 RAS 2 FT

tank

biofilm

tank

water

RAS FT RAS FT

tank detritus

Atl salmon

skin mucous

75Pft_v

s._R

AS_2

75Pft_v

s._R

AS_1

RAS_2

_vs.

_RAS_1

75Pft_v

s._R

AS_2

75Pft_v

s._R

AS_1

RAS_2

_vs.

_RAS_1

75Pft_v

s._R

AS_2

75Pft_v

s._R

AS_1

RAS_2

_vs.

_RAS_1

75Pft_v

s._R

AS_2

75Pft_v

s._R

AS_1

RAS_2

_vs.

_RAS_1

75Pft_v

s._R

AS_2

75Pft_v

s._R

AS_1

RAS_2

_vs.

_RAS_1

0.0

0.1

0.2

0.3

0.4

0.5

Weig

hte

d N

orm

alize

d U

niF

rac

(Dis

tan

ces

: M

ed

ian

, IQ

R)

Tank

Water

Tank

BiofilmSkin Digesta Gill

Weighted UniFracUnweighted UniFrac ba

c d

k__Bacteria;p__Bacteroidetes;c__[Saprospirae];o__[Saprospirales];f__Saprospiraceae; (5)

FT RAS 1 RAS 2

Diff

ere

ntia

lly a

bundant

sO

TU

s 1

-65 (

RA

S v

s F

T)

Skin (RAS vs FT) Water - full

Biofilm - full

1

0.1

0.01

0.001

e

k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Procabacterialesk__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Alteromonadales (2)k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Pseudomonadaceae;g__Pseudomonas; (4)k__Bacteria;p__Acidobacteria;c__Sva0725;o__Sva0725;k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales (5)k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales (3)

k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales (4)

k__Bacteria;p__Gemmatimonadetes;c__Gemmatimonadetes;k__Bacteria;p__Nitrospirae;c__Nitrospira;o__Nitrospiralesk__Bacteria;p__Planctomycetes;c__Planctomycetia;o__Pirellulalesk__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacteralesk__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales (3)

k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales (4)

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales (3)

k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae (4)k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Nitrosomonadalesk__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Aeromonadales

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae (8)

k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadalesk__Bacteria;p__Verrucomicrobia (5)Unclassified (3)


https://doi.org/10.1101/828749

791 792

793 Supplemental Figure S5. Atlantic salmon’s gills stained with Alcian Blue/ Periodic Acid – Schiff 794 (AB/PAS) at pH 2.5 illustrating the mucous cells (arrow) in gill lamellae of a well-orientated gill 795 filament (*). The gill mucous cells were quantified in ten gills interlamellar unit (ILU) for each 796 sample under the light microscope. Scale bar 100 µm. 797 798


https://doi.org/10.1101/828749

799 Supplemental Figure S6. 800 Atlantic salmon’s skin section stained with Alcian Blue/ Periodic Acid–Schiff (AB/PAS) at pH 2.5 801 showing the mucous cells (M) containing mixed mucin, purple in colour, in the epidermis (Epi). 802 The skin mucous cells were counted in 5 random areas of each fish sample with the same 803 magnification (S=scale, Der=dermis, Mus=muscle). Scale bar 50 µm. 804 805


https://doi.org/10.1101/828749

806 Supplemental Figure S7. Atlantic salmon’s intestine section stained with haematoxylin and 807 eosin (H&E) demonstrating the general organization of the intestinal wall. Vertical and 808 horizontal lines show how the fold height (BA1), mucosa thickness (BA2), fold width (CT1), and 809 muscularis thickness (CT2) were measured in the morphometrical analyses using Image-Pro 810 Premier 9.1 software. The intestinal mucous cells (*) in the measured fold were counted under 811 the light microscope while capturing images for the gut morphometrical analyses. Scale bar 812 100 µm. 813 814


https://doi.org/10.1101/828749

microbial ecology of atlantic salmon, salmo salar, hatcheries: … · 93 costly die offs in the...

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