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Aquaculture is an extremely valuable and rapidly expanding sector worldwide, but concerns exist related to environmental sustainability. The sediment below aquaculture farms receives inputs of antimicrobials, metal-containing products, and organic matter from uneaten food and fecal material. These inputs impact the surrounding marine microbial communities in complex ways; however, functional diversity shifts related to taxonomic composition remain poorly understood. Here, we investigated the effect of pollution from marine fish farms on sediment bacterial communities. We compared the bacterial communities and functional bacterial diversity in surface sediments at salmon aquaculture and reference sites in Chiloé, southern Chile, using Roche 454 pyrosequencing of the 16S ribosomal RNA (rRNA) gene and the predictive metagenomics approach (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, PICRUSt). Bacterial diversity, measured as the inverse Simpson index, was significantly lower at aquaculture than at reference sites, while species richness, based on Chao’sestimator, was not significantly different. Nevertheless, community composition differed significantly between reference and aquaculture sites. We found that Gammaproteobacteria and several taxa involved in remediating metal contamination and known to have antimicrobial resistances were enriched at aquaculture sites. However, PICRUSt predicted functions indicated a degree of functional redundancy between sites, whereas taxonomic-functional relationships indicated differences in the functional traits of specific taxa at aquaculture sites. This study provides a first step in understanding the bacterial community structure and functional changes due to Chilean salmon aquaculture and has direct implications for using bacterial shifts as indicators of aquaculture perturbations. . . . . . Keywords Salmon aquaculture Bacterial communities Chile Pyrosequencing Organic loading Functional diversity Introduction and Agriculture Organization of the United Nations, FAO 2016). The world population is expected to reach ~ 9 billion Salmon aquaculture has rapidly expanded and Chile is the by 2050, with aquaculture activities playing a key role in its second largest producer of Salmo salar, after Norway (Food growth (Bostock et al. 2010; World Bank 2013). However, intensive salmon production systems require exogenous feed Electronic supplementary material The online version of this article inputs (Buschmann et al. 2008). Uneaten fish feed, fecal mat- (https://doi.org/10.1007/s13213-017-1317-8) contains supplementary ter, and excretory products accumulate in sediments below material, which is available to authorized users. fish cages (Carroll et al. 2003; Buschmann et al. 2006)and form a layer of soft black sediment (Holmer et al. 2008). * Katherine M. Hornick These sediments have lower pH (Hargrave et al. 1993), higher Katie.email@example.com concentrations of organic matter, and greater accumulation of nutrients, particularly phosphorus and nitrogen compounds Centro i-mar & Center for Biotechnology and Bioengineering (Karakassis et al. 1998), than reference areas. These organic (CeBiB), Universidad de Los Lagos, Camino Chinquihue km 6, inputs modify the physical and chemical properties of the 5480000 Puerto Montt, Chile 2 sediment and influence biogeochemical processes, which alter Horn Point Laboratory, University of Maryland Center for the structure of benthic microbial communities (McCaig et al. Environmental Science, 2020 Horns Point Rd., Cambridge, MD 21613, USA 64 Ann Microbiol (2018) 68:63–77 1999; Asami et al. 2005; Kawahara et al. 2009; Fodelianakis sediments below fish cages have demonstrated increases in et al. 2015). Organic inputs from Chilean salmon aquaculture bacterial abundances compared to reference sites (Mirto also shift stream ecosystems to a more heterotrophic state, et al. 2000; Vezzulli et al. 2002; Bissett et al. 2007; Castine which impairs ecosystem health (Kamjunke et al. 2017). et al. 2009), changes in bacterial community structure (Bissett Bacterial communities present in marine sediments provide et al. 2006, 2007, 2009; Garren et al. 2008), and functionality the important ecological role of nutrient cycling, mineraliza- (Christensen et al. 2000; Holmer et al. 2003; Bissett et al. tion, degradation, and diagenesis of organic matter (Deming 2009), indicating that identifying bacterial community chang- and Baross 1993; Gooday 2002; Vezzulli et al. 2002; es provides valuable insights regarding the ecosystem-wide Buschmann et al. 2008). These communities also play a vital response to aquaculture pollution and the potential biogeo- role in the transformation of pollutants (Benoit et al. 2003; chemical process modifications. Smith and Hollibaugh 1993); however, it remains unknown In soft-bottom communities, microbes provide important how inputs from salmon farming in Chile modify these bac- ecological services such as nutrient cycling and organic matter terial communities. The use of antimicrobials for preventing mineralization, so understanding the effect of pollution from and controlling pathogens in salmon aquaculture is common aquaculture is critical to understanding the ecosystem-wide in Chile, and has resulted in an increased antibiotic resistance response (Bissett et al. 2006; Castine et al. 2009). The re- of bacteria in the environment (Miranda and Zemelman sponse of bacterial communities to aquaculture inputs has 2002a, b; Buschmann et al. 2012). Fish farmers also use remained unexplored until recent years; however, advances metal-containing products and other pharmaceuticals to pre- in molecular techniques have enabled in-depth studies of the vent fouling, to feed and to treat fish in order to limit the response of benthic bacterial communities to organic deposi- spread of infections (Burridge et al. 2010). This has resulted tions from aquaculture. To our knowledge, studies providing in elevated copper (O’Brien et al. 2009) and zinc (Simpson in-depth analyses of microbial community composition and and Spadaro 2012; Simpson et al. 2013) concentrations in metabolic function of aquaculture-exposed environments in sediments. Because microbial communities respond rapidly Chile remain scarce and require attention to target more spe- to environmental variation, identifying changes in sediment cific and complex ecological studies. Microbes are generally microbial composition and function represents a useful indi- the first organisms to respond to chemical and physical chang- cator of aquaculture impacts on coastal environments. es in the environment and, due to their low trophic level, can However, the changes in sediment bacteria communities in be used as indicators of environmental change (Zak et al. and around aquaculture operations is complex and results 2011). Sediments under fish farms provide suitable tools to from the combination of heavy metals, antibiotics, and organic monitor the response of bacterial communities to aquaculture depositions. Therefore, scientific attention to understand envi- perturbations, because inputs and organic load are constantly ronmental complexities and to characterize impacts is critical. monitored and deposited, and the deposition site is known Most of our knowledge regarding the environmental im- (Fodelianakis et al. 2015). Furthermore, non-impacted sites pact of salmon cage culture has resulted from studies on shifts with similar physicochemical characteristics for comparison in benthic macrofaunal communities (Carroll et al. 2003; are easy to find (Bissett et al. 2007). However, the structure of Macleod and Forbes 2004; Tomassetti and Porrello 2005; sediment bacterial communities depends largely on the geo- Hall-Spencer et al. 2006). Many of these studies used video graphic region (Fodelianakis et al. 2015). In Chile specifically, surveillance and identified macrofauna and meiofauna to in- there are concerns regarding the impacts of fish farming on dividual species level within each sample, which requires previously pristine marine environments (Buschmann et al. large monetary, time, and skill investment to provide insight 2006), and it remains unknown how aquaculture inputs affect into impact (Castine et al. 2009). For the successful character- these sediment bacterial communities. ization of impacts, stable macrofauna distributions, low wave The aim of this study was to examine changes in sediment and current activity, and low water turbidity are necessary. bacterial communities associated with salmon aquaculture and However, macrofauna communities tend to be highly variable the potential links between functional inferences and commu- in space and time. In addition, strong tidal currents, high nity structure variation. We compared sediment bacterial com- depths (> 60 m) and turbidity associated with the salmon- munity compositions from reference and salmon aquaculture aquaculture region can inhibit the ability to monitor macro- sites in the coastal waters of Chiloé in southern Chile to gain fauna communities. Due to the rapid proliferation of salmon insight into the natural bacterial community composition, di- aquaculture in southern Chile, the improvement of monitoring versity, and metabolic function in southern Pacific coastal programs in relation to salmon cage culture is important. sediments and their response to organic loading and pollution Changes in bacteria community structure and abundance have resulting from salmon aquaculture. This study provides base- been used as a monitoring tool to investigate the impact of fish line information on bacterial composition modifications farms in Tanzania (Bissett et al. 2007) and in the tropics resulting from complex environmental modifications (Castine et al. 2009). Additionally, studies of subsurface Ann Microbiol (2018) 68:63–77 65 associated with salmon aquaculture; therefore, we discuss im- in southern Chile (Table 1), and we, thus, infer similar condi- portant future studies. tions at aquaculture sites. Reference sites were located ca. 2.5 km from salmon aquaculture sites (black symbols; Fig. 1). At each site, the diver retrieved sediment cores (15 cm Materials and methods inner diameter; N = 3). Immediately after retrieval, the surface sediments of each core (1–2 cm) were collected using a sterile Field site and sampling sampling device. Collected samples were placed in tagged plastic bags, stored in a cooler with gel packs, and brought Sediment samples were obtained in November 2012 in the to the laboratory (Centro i-mar in Puerto Montt) within 6 h, coastal waters of Chiloé, Chile (Traiguén, Quenac North, where they were frozen at − 80 °C until further analysis. Thus, we collected nine samples from reference sites and nine sam- and Quenac South; 42°32′01.3^ S, 073°23′52.8^ W) (Fig. 1). This region of Chiloé is characterized by strong tidal ples from aquaculture sites, for a total of 18 samples from both −1 currents (up to 18–20 cm s ), surface temperature ranges of sites. 14–16 °C during spring, and a salinity of 30 ppt (Buschmann, unpublished data). From each of the three locations, sediment Laboratory and sequencing analysis was collected from a commercial-scale salmon farm (> 1000 tons of production) site and a reference site. Sediment samples DNA was extracted using a modified version of the hot were obtained as close as possible to pens (< 50 m) at a depth detergent/CTAB DNA extraction protocol (Zhou et al. of 30–45 m. The organic matter content was > 3.5%, indicat- 1996), using 5 g of sediment, 13.5 mL of extraction buffer, ing that the sediments were influenced by aquaculture (e.g., 100 μL Protease K, incubating with 1.5 mL of 20% SDS, then Carroll et al. 2003; Soto and Norambuena 2004). Chemical re-extracting with 4.5 mL of extraction buffer and 0.5 mL of changes in sediments due to aquaculture have been identified 20% SDS. Extracted DNA was verified and quantified by 1% AQN RQN AQS RQS RTR ATR Fig. 1 Locations of aquaculture and references sites of sampling in Chiloé, Chile. The aquaculture sites sampled were Quanac North (AQN), Quenac South (AQS), and Traiguén (ATR). Subsequent reference sites sampled were Quanac North (RQN), Quenac South (RQS), and Traiguén (RTR) 66 Ann Microbiol (2018) 68:63–77 Table 1 Water quality Variables Reference Aquaculture P-value measurements in the bottom waters and sediments near salmon Bottom waters farming on marine sediments in −1 southern Chile O (mg L ) 8.12 ± 0.75 7.5 ± 0.75 0.06 Delta redox (mV) 2.6 ± 64.3 − 109.8 ± 24,094.2 < 0.0001 Redox (mV) 279.4 ± 3144.9 221.6 ± 28,197.2 0.75 Sediment measurements −1 Nitrogen (mmol k ) 31.9 ± 14,138.1 124.1 ± 206,189 0.0001 −1 Phosphorus (mmol k ) 20.7 ± 1478 114.8 ± 393,529 < 0.00001 −1 Carbon (mmol k ) 192.2 ± 201.5 412.6 ± 557.9 0.001 Particulate organic matter (%) 2.09 ± 2.41 4.41 ± 14.20 0.017 The data presented are averages and variances from 29 active salmon farm sites with their respective reference sites (from Soto and Norambuena 2004) agarose gel electrophoresis with DNA marker (100–1000 pb, precalculated for genes in databases including the Kyoto 500 ng/mL Winkler). A second quantification and quality Encyclopedia of Genes and Genomes (KEGG) (Kanehisa check was performed by spectrophotometry using a Tecan and Goto 2000) and Clusters of Orthologous Groups of pro- Infinite® 200 PRO (Gene X-Press, Santiago, Chile). A teins (COG). In the present study, we used the KEGG database Labconco CentriVap® Vacuum Concentrator was used to and functional predictions were assigned up to KEGG dry DNA extracts. Orthology (KO) tier 3 for all genes. To simplify analysis, only Dried samples were sent to Macrogen, Ltd. (Seoul, Korea) tier 1 functions of Bmetabolism^, Bgenetic information for bacterial tag-encoded FLX-Titanium amplicon pyrose- processing^, Benvironmental information processing^,and quencing (TEFAP) on the 454 GS FLX System (Roche) using Bcellular processes^ were analyzed further, as the categories standard protocols (Sun et al. 2011). Bacterial 16S ribosomal of Borganismal systems^ and Bhuman disease^ were thought RNA (rRNA) gene amplicons were sequenced on 1/8th of a to be poorly relevant to environmental samples. The accuracy plate using Roche 454 sequencing technology with Titanium of the metagenome predictions was evaluated using weighted chemistry. nearest sequenced taxon index (weighted NSTI) scores The sequence processing was performed using the program (Langille et al. 2013). mothur v.1.31.2 (Schloss et al. 2009) with default command parameters, unless specified. Raw sequences were processed Data analysis and statistics by barcode, primers, length, and quality, and were denoised with the PyroNoise algorithm (Quince et al. 2009). The se- Differences of community richness were assessed using the quences were checked for chimeras using Perseus (Quince Chao estimator (Chao 1984). Community diversity was ex- et al. 2011) and UCHIME (Edgar et al. 2011)sequentially, amined using the inverse Simpson index (Simpson 1949), and were aligned to the Greengenes reference which takes species richness and species abundance into ac- (gg_13_8_99). The cluster command was used to assign se- count. To compare samples on an equal basis, all samples quences to operational taxonomic units (OTUs) using the were rarefied to even sampling depths prior to statistical nearest-neighbor algorithm. All subsequent OTU-based anal- analysis. yses were performed using a 97% sequence similarity cutoff. The metastats command in mothur was used to detect dif- Taxonomic analysis of representative OTUs was conducted in ferentially abundant OTUs from aquaculture and reference mothur using the Greengenes 16S rRNA gene database sites. The thetayc calculator (Yue and Clayton 2005) was used (DeSantis et al. 2006). Sequences are available via the NCBI to calculate the dendrogram describing the similarity between Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) the structures of reference and aquaculture communities based under accession number (PRJNA302218). on the OTUs in mothur. Non-metric multidimensional scaling We used the bioinformatics tool Phylogenetic Investigation (NMDS) was performed in mothur using the Bray–Curtis dis- of Communities by Reconstruction of Unobserved States tances between samples and the resulting ordination was vi- (PICRUSt) v.1.1.0 (Langille et al. 2013) togainfurther insight sualized using ggplot2 (Wickham 2009) in the statistical soft- into the putative metabolic functions of bacteria enriched at ware R 3.1.1 (R Core Team 2016). Bray–Cutis OTU-based aquaculture sites. This program uses marker genes, in this analysis of similarity (ANOSIM) (Clarke 1993)was per- case, 16S rRNA, to predict metagenome functional content. formed to test for significant differences between reference The metagenome gene functional content predictions are and aquaculture samples using 1000 Monte Carlo permutation Ann Microbiol (2018) 68:63–77 67 tests in the vegan R package (Oksanen et al. 2013). t-Tests community at the aquaculture sites (Table 3). The relative were used to determine the minimum significant difference in abundance of Bacilli decreased at all aquaculture sites. relative abundance species richness (Chao1) and diversity (in- Pairwise comparisons between respective reference and aqua- verse Simpson) of bacteria between reference and aquaculture culture sites revealed differences (Fig. 2). samples in R. We tested for statistical significance between Gammaproteobacteria was enriched at AQN and AQS, aquaculture and reference functional predictions using linear whereas it slightly decreased in ATR. The relative decrease discriminant analysis effect size (LEfSe) (Segata et al. 2011). of Bacilli (28.9% to 3.1%), Gammaproteobacteria (10.7% to Spearman correlations relating inferred functional abundance 24.2%), and Alphaproteobacteria (36.8% to 8.2%) was com- from PICRUSt and taxonomic class abundances were per- pensated by the relative increase in Flavobacteriia (18.8% to formed for aquaculture and reference sites using R. All statis- 35.3%), Epsilonproteobacteria (0% to 11.1%), and tical analyses were evaluated at α =0.05. Actinobacteria (0.5% to 8.7%) at AQN (Fig. 2). The relative decrease of Bacilli (37% to 8.7%) and Alphaproteobacteria (23%% to 21.9%) was compensated by the relative increase Results in Gammaproteobacteria (18.2% to 48.7%), Planctomycetacia (0.5% to 1.2%), and Acidimicrobiia (0.6% to 1.2%) at AQS Bacterial community composition, richness, (Fig. 2). The differences between ATR and RTR were less severe and the only phyla that were slightly enriched at ATR and diversity were Deltaproteobacteria (1.5% to 1.7%) and Actinobacteria The final OTU dataset consisted of 43,267 reads, with a mean (0.4% to 0.8%). Within the class Gammaproteobacteria, of 7212 sequences per sample (Table 2). The number of ran- Psychrobacter was enriched at all aquaculture sites, domly subsampled sequences used for normalization from Shewanella was enriched at AQS, and Glaciecola was enriched at RTR (Fig. 2). Within Bacilli, Exiguobacterium each replicate sample was 1195, for a total of 21,510 se- quences, respectively. At 97% sequence similarity, the rare- decrea sed a t a quaculture sites a nd with in Alphaproteobacteria, Loktanella decreased at all aquaculture faction curves were relatively saturated, indicating that se- quencing effort captured a large proportion of the taxa present sites. Within Flavobacteriia, Olleya was enriched at AQN, AQS, and RTR (Fig. 2). in each sample (Fig. S1). The total number of OTUs observed in each site is shown in Table 2 and the number of OTUs per Bacterial communities were dissimilar between reference replicate is shown in Table S1. By using the most recent and aquaculture sites (ANOSIM, R =1.5, P =0.03). Greengenes database, we demonstrated that more than 97% Additionally, when the bacterial community composition of of tags could be unambiguously mapped at the genus level. the 18 sediment samples from reference and aquaculture sites The OTUs were classified into 47 bacterial classes and the was compared using an OTU-based approach, the results re- majority of these classes made up less than 1% of the bacterial vealed differences between sample types (Fig. 3, Fig. S2). The bacteria community structure of Quenac South aquaculture 2 community at each site. Gammaproteobacteria was the most abundant class at reference (29.1%) and aquaculture (51.3%) (AQS2) and Traiguén reference 3 (RT3) differed from all oth- er samples and sites. All reference samples clustered together, sites and represented a greater proportion of the bacterial Table 2 Observed number of a b c Library No. of sequences Normalization sequences, observed operational taxonomic units (OTUs), and OTUs coverage (%) Observed OTUs Inverse Simpson Chao1 coverage for 16S ribosomal RNA (rRNA) gene libraries of each site RQN 7923 93.6 133 8.9 275.4 RQS 6109 93.6 126 12.6 316.5 RTR 7923 92.3 146 6.1 321.2 AQN 7703 92.7 145 5.4 331.9 AQS 8329 93.4 124 5.3 301.5 ATR 5280 92.5 144 4.6 317.4 RQN, reference Quenac North; RQS, reference Quenac South; QTR, reference Traiguén; AQN, aquaculture Quenac North; AQS, aquaculture Quenac South; ATR, aquaculture Traiguén Total number of sequences obtained from three replicates in each sample Data were calculated at the 3% genetic distance level based on the same number of sequences (1195/sample) with mothur 68 Ann Microbiol (2018) 68:63–77 Table 3 Statistical test (metastats) results for class- and family-level To determine which bacterial classes may be contributing relative abundances between aquaculture and reference sites to differences in functional traits among sites, correlation anal- yses were performed using PICRUSt inferences relating bac- Reference Aquaculture P-value terial classes and genera (occurring at > 0.01% abundance) Class with second-tier functional classifications (Table S3). A great- Gammaproteobacteria 29.1 51.3 0.002* er number of correlations were observed between functional Bacilli 24.2 9.47 0.009* and taxonomic abundance in aquaculture sites. Overall, the Family majority of the correlations were negative at reference sites, Moraxellaceae 19.9 36.7 0.012* and about half were negative and half were positive at aqua- Shewanellaceae 0.019 5.36 0.06 culture sites. Positive taxonomic-functional correlations Pseudoalteromonadaceae 1.78 2.58 1 among the PICRUSt data, however, are likely a result of au- Halomonadaceae 0.24 3.25 0.24 tocorrelations, as functional traits were predicted from taxo- Exiguobacteraceae 8.06 3.76 0.37 nomic information (Staley et al. 2014). In aquaculture sites, Bacillaceae 6.06 1.33 0.12 the abundances of almost all second-tier functions were also Rhodobacteraceae 21.8 13 0.15 correlated with the abundances of at least one of the most abundant classes identified in either dataset (Table S2), most *Indicates significant values notably the Gammaproteobacteria and Flavobacteriia. At the genus level, there were the greatest number of correlations at with the aquaculture site Quenac South 3 (AQS3) present. aquaculture sites with the second-tier function of amino acid Similarly, Traiguén reference 1 and 2 (RT1, RT2) were the metabolism and, notably, Shewanella was correlated with a only reference sites present in the second cluster among aqua- number of second-tier functions (Table S3). In reference sites, culture sites (Fig. S2). NMDS cluster analysis revealed similar only one second-tier function, Bcarbohydrate metabolism^, results, with RT1 clustered among aquaculture samples and was c orr e lated w ith an a bunda nt class AQN3 clustered among reference samples (Fig. 3). (Gammaproteobacteria), while the rare Verrucomicrobiae Bacterial diversity (inverse Simpson) differed significantly was the only other order with significant correlations. At the (P = 0.04) between aquaculture and reference sites, with genus level, Oceanibulbus was correlated with several higher diversity in reference (9.48 ± 6.25) than in aquaculture second-tier functions, but reference sites had less correlations (5.32 ± 2.07) sites (Table 2). The species richness (Chao1) overall. Interestingly, none of the functional trait abundances ranged from 275.4 to 331.9, with no statistical differences differed significantly between sites. (P > 0.05) between salmon aquaculture and reference sites, indicating that it is less sensitive than diversity to pressures caused by pollution from aquaculture. Discussion We found a significant difference in the bacterial community Functional characterization of bacterial communities structure between aquaculture and reference sites. Several au- thors have reported that bacterial community changes and The PICRUSt metagenome predictions had NSTI scores rang- respiration processes in sediments below fish farms reflect ing from 0.13 to 0.14, with an overall mean of 0.14 ± 0.003, impacts from nutrient enrichment (Christensen et al. 2000; which was lower than that reported for sediment bacterial Holmer et al. 2003; Bissett et al. 2007, 2009; Kawahara communities (0.17 ± 0.02; Langille et al. 2013). Lower et al. 2009). Nutrient enrichment from fish cages causes a NSTI values indicate that microbes in each sample are more significant increase in bacterioplankton abundance and het- closely related to sequenced genomes (Langille et al. 2013). erotrophic production (Sakami et al. 2003;Sarà 2007; Notably, PICRUSt showed little variation in the abundances Garren et al. 2008; Navarro et al. 2008; Nogales et al. 2011), of second- and third-tier KO functional gene annotations in as well as in the abundance of virus-like particles (Garren et al. aquaculture and reference sites (Table 4 and Table S3). The 2008). In addition, the bactericidal action of antibiotics can highest standard deviation observed within the tier 2 function- lead to changes in the composition of microbial communities al category was 0.08% of sequence reads in references sites by selectively inhibiting susceptible bacteria (Nogales et al. and 0.004% of sequence reads in aquaculture sites. When 2011). Heavy metals are highly persistent and several authors third-tier functional categories were compared, the maximum have demonstrated that heavy metal contamination signifi- standard deviation for a category was 0.04% of sequence cantly shapes bacterial community composition (Quero et al. reads in reference sites and 0.003% of sequence reads in aqua- 2015; Yao et al. 2017). Therefore, it is possible that the chem- culture sites, suggesting similarities in the distribution and ical changes in the aquaculture sediments (Table 1)were abundance of functional traits within treatments. reflected by the changes in bacterial groups identified in this Ann Microbiol (2018) 68:63–77 69 Fig. 2 The average relative abundances of bacterial classes in reference (RQN, RQS, RTR) and aquaculture (AQN, AQS, ATR) sites (a). Groups accounting for < 1% of all sequences in all sites are summarized in the group BOthers^. The relative ABY1 abundances of bacterial genera in Acidimicrobiia reference (RQN, RQS, RTR) and Acidobacteria−6 aquaculture (AQN, AQS, ATR) Actinobacteria sites (b). Groups accounting for Alphaproteobacteria AT−s2−57 < 1.5% of sequences at a site are Bacilli summarized in the group Clostridia BOthers^ Cytophagia Deltaproteobacteria Epsilonproteobacteria Flavobacteriia Gammaproteobacteria Others Phycisphaerae Planctomycetacia unclassified AQN AQS AT RQN RQS RT Site Arenibacter Bacillus Cobetia Exiguobacterium Gelidibacter Gillisia Glaciecola Krokinobacter Loktanella Lutimonas Maribacter Marinobacter Oceanibulbus Olleya Others Paenisporosarcina Phaeobacter Planococcus Planomicrobium Pseudoalteromonas Psychrobacter Salegentibacter Shewanella Ulvibacter unclassified Winogradskyella AQN AQS AT RQN RQS RT Site study; however, more research is necessary to test this hypoth- We found a significant decrease in bacterial biodiversity in esis, and to separate the importance of the different compo- aquaculture sediments compared to reference sediments. This nents that may affect these bacterial communities. conclusion was also supported by bacterial composition Relative abundance (%) Relative abundance (%) 70 Ann Microbiol (2018) 68:63–77 sites, which we assume results from differences in the combi- nations of antimicrobials, heavy metals, and organic deposi- tions that provide the sediment an increased amount of carbon, nitrogen, and phosphorus (Hargrave et al. 1997; Ruohonen et al. 1999; Storebakken et al. 2000). Unfortunately, we were unable to obtain data on the fecal microbiotas of salmon reared at the farms and recognize that this could explain pairwise differences between sites; therefore, future studies should in- corporate this into analyses. The number of OTUs was higher in aquaculture sites. Many studies have shown that impacted sites (from different sources of pollution) and reference sites are rich in bacterial OTUs (Fodelianakis et al. 2014, 2015; Sanz-Lázaro et al. 2015). In addition, some have demonstrated that impacted Fig. 3 Non-metric multidimensional scaling (NMDS) ordination of the community structures calculated with Bray–Curtis distances sites are richer in bacterial OTUs than reference sites (McCaig et al. 1999; Powell et al. 2003; Bissett et al. 2007; analysis, which indicated a significant difference in the com- Marcial Gomes et al. 2008), while others have found no dif- munity structure between sites. However, pairwise compari- ference between the two (Torsvik et al. 1996; Bissett et al. sons demonstrated that bacterial composition varied between 2006; Zhang et al. 2008; Kawahara et al. 2009; Fodelianakis et al. 2015). The differences in these studies may be attributed Table 4 Percentages of predicted sequences assigned to second-tier to the differing dynamics of microbial communities in varying KEGG Orthology (KO) categories in the metagenomics dataset locations, differing geological and chemical characteristics (e.g., Bissett et al. 2006; Tamminen et al. 2011), or due to Function Aquaculture Reference the varying types or severity of the disturbance in each case. Metabolism 2.99 3.28 Amino acid metabolism 11.01 9.39 Effects of salmon farming on bacterial community Biosynthesis of other secondary metabolites 0.96 0.83 structure Carbohydrate metabolism 9.96 8.35 Energy metabolism 5.92 4.98 The NMDS showed a difference in the bacterial community at Enzyme families 1.83 1.54 aquaculture sites, a trend also noted in previous studies Glycan biosynthesis and metabolism 2.04 1.72 (Bissett et al. 2006). Although recent studies use the Lipid metabolism 4.09 5.24 UniFrac-based distance calculation (Lozupone et al. 2011) Nucleotide metabolism 3.3 3.7 for comparing microbial communities, we found that the Metabolism of cofactors and vitamins 4.46 4.05 Bray–Curtis index provided sharp contrasts on the differences in the microbial community composition of aquaculture sites Metabolism of other amino acids 1.83 1.92 Metabolism of terpenoids and polyketides 2.21 1.87 studied. One of the most notable differences among sites was the high abundance of Gammaproteobacteria at aquaculture Xenobiotics biodegradation and metabolism 3.37 3.77 sites. This result corresponds with previous research of fish Genetic information processing 2.46 2.42 farm sediments (Asami et al. 2005); however, those within Folding, sorting, and degradation 2.33 2.38 this class were related to potential sulfate-reducing bacteria Replication and repair 6.73 5.64 (SRB), whereas ours were not. PICRUSt inferences indicated Transcription 2.5 2.87 that Gammaproteobacteria could potentially be involved with Translation 4.52 3.86 several functions at aquaculture sites, but not reference sites. Cellular processes and signaling 3.71 3.11 Additionally, Gammaproteobacteria has been found to be the Environmental adaptation 0.13 0.44 most significant clade present in most marine sediments (Li Membrane transport 10.58 8.88 et al. 1999; Bowman and McCuaig 2003; Inagaki et al. 2003; Signaling molecules and interaction 0.16 0.57 Polymenakou et al. 2005), irrespective of pollution levels. In Cell communication 0.001 0.09 general, many of these studies on aquaculture-impacted sedi- Cell growth and death 0.54 0.99 ments (e.g., Bissett et al. 2006; Kawahara et al. 2009)have Cell motility 3.09 3.23 shown varying bacterial community composition among sites The blank lines separate tier 1 KO categories of organic enrichment, highlighting the need for more local- Functional categories for which no reads were assigned or omitted ized studies focusing on the various inputs from aquaculture. Predicted function only assigned at the first tier Microbes are at the bottom of the food chain; thus, changes in Ann Microbiol (2018) 68:63–77 71 their taxonomic structure and diversity would influence higher Tomova et al. (2015) found that bacteria from the genera trophic levels in coastal sediment communities. The enrich- Cobetia, Pseudoalteromonas, Psychrobacter,and ment of Gammaproteobacteria and the difference in specific Shewanella at Chilean aquaculture sites harbored multiple microbial diversities at the different aquaculture sites may antibiotic-resistant genes. These genera were enriched at aqua- result from biological traits to adapt, survive, and replenish culture sites, specifically, AQS, AQN, all aquaculture sites, under the predation pressure, inter- and intraspecific competi- and AQS, respectively. Pseudoalteromonas was also enriched tion resulting from temporal and spatial environmental chang- at RTR, which may indicate that these antimicrobials are af- es driven by salmon aquaculture. fecting distant sites; however, further research is necessary to The presence of metals at these sites is likely, as specific taxa test this hypothesis and measure antimicrobial concentrations involved in remediating metal contamination were enriched at in sediments at Chilean aquaculture and reference sites. aquaculture sites. Microbacterium, Psychrobacter,and One of the anomalies in our dataset was the low abundance Shewanella were significantly enriched at aquaculture sites and of Desulfobacterales at aquaculture sites (0.17% mean abun- this enrichment could be explained by several reasons. Several dance). Desulfobacterales are an order of strictly anaerobic Microbacterium strainsare tolerantto heavymetalssuchasnick- SRB and have been found in bacterial communities at aqua- el, cobalt, and cadmium (Brown et al. 2012;Iyer et al. 2017). culture sites (Bissett et al. 2006; Dowle et al. 2015). SRB play Psychrobacter strains can resist and accumulate several metals, a significant role in the mineralization of organic matter in specifically cadmium, lead, zinc, and copper (Abd-Elnaby et al. anaerobic environments and in the biogeochemical cycling 2016). Interestingly, Psychrobacter has been isolated from the of sulfur. Kawahara et al. (2009) reported a high abundance kidney of salmonids at several aquaculture sites in Scotland of SRB in sediment around torafugu (Takifugu rubripes)farm (McCarthy et al. 2013) and has previously been isolated from sediments (< 100 sequences per sample). Other researchers Chilean salmon farms (Roberts et al. 2014). Shewanella are an- have used quantitative polymerase chain reaction (PCR) ap- aerobic metal reducers and have been identified as a commonly proaches and have shown that organic enrichment associated occurring intestinal bacterium for salmon (Navarrete et al. 2009). with marine fish farms influences the abundance of SRB High concentrations of metals, specifically copper and zinc, have (Kawahara et al. 2008; Kondo et al. 2008, 2012). We did not been found under cages at fish farms treated with anti-fouling carry out chemical analysis and are unable conclude if the high paints (Simpson et al. 2013;Nikolaouetal. 2014). These paints organic matter content in the sediments led to the formation of are used in Chilean salmon aquaculture to prevent biofouling on anaerobic sediment. Additionally, we do not understand tem- nets, which is critical to maintaining good water flow, ensuring poral bacterial community changes associated with salmon high dissolved oxygen concentrations, and maintaining fish aquaculture fallowing strategies, which complicates compre- health. Zinc is also a lesser component of paint formulation and hension of these results. Since 2010, new regulations in Chile is also a dietary additive in salmon feed (Maage et al. 2001). require area-specific fallowing periods, and this subject Interestingly, copper and zinc concentrations in sediments did requires further attention in order to understand how not change when they were monitored over a 12-month bacterial community modifications affect ecosystem fallowingperiod(Macleodetal. 2014), which suggests that, even functioning. Macleod et al. (2008) reported that the main eco- if we sampled during a recovery period, the potential presence of logical functions in affected benthic habitats were re- metals in the sediment is likely, thus affecting the bacterial com- established after 12 months under Australian salmon farms, munities. However, further research is necessary to characterize but there was no evidence regarding bacterial community potential microbial remediation of metal contamination at aqua- changes. Additionally, due to logistical constraints, samples culture sites and the various environmental and anthropogenic were stored on ice for several hours prior to freezing. The pressures at each aquaculture site. possibility that changes in bacterial community composition The presence of residual antimicrobials in aquaculture sed- occurred during this period cannot be excluded, and we rec- iments is likely, as several genera resistant to antibiotics were ommend that direct freezing in liquid nitrogen is used for enriched at these sites. Antimicrobials used in aquaculture are future studies. Thus, we suggest further collection, isolation, administered to fish mostly in food (Cabello 2006), which sequencing, and characterization, including detailed chemical results in increased antimicrobial concentrations in sediments analysis of sediments, due to the importance of this group to below cages (Armstrong et al. 2005), from where they can be sulfur cycling in sediments under fish farms. carried by currents to sediments at distant sites (Buschmann et al. 2012). These antimicrobials are the principal selective Functional changes resulting from salmon pressure for antibiotic resistance in sediment bacteria (e.g., aquaculture Dang et al. 2007; Tomova et al. 2015), and the impact of this process leads to changes in sediment bacteria diversity by Many of the predicted functional profile abundances were replacing susceptible communities of bacteria with resistant redundant between bacterial communities in reference and ones (e.g., Miranda and Zemelman 2002a; Kim et al. 2004). aquaculture sites. It is useful to supplement 16S rRNA 72 Ann Microbiol (2018) 68:63–77 analyses with metagenome studies, especially for broad sur- specific function at only aquaculture sites had a chance to be- veys with microbial ecology applications. Several studies come more abundant at these sites, in theory. However, more have demonstrated that this stable profile is due to the exis- research is necessary to test this hypothesis using environmental tence of functional gene redundancy among bacterial commu- data and considering time variation of these communities. nities (Fernandez et al. 2000; Schimel et al. 2007; Fierer et al. Marine sediments are affected by the interaction of geolog- 2012). Additionally, despite the quality of the functional pre- ical, hydrological, physicochemical, and biological factors, dictions by PICRUSt, they are largely dependent on the avail- and function as reservoirs of absorbed nutrients, pesticides, ability of annotated reference genomes. These results should toxic materials, and heavy metals (Köster and Meyer-Reil be interpreted cautiously, as PICRUSt may not be useful for 2001). The structure of bacterial communities is sensitive to high-resolution studies of functional ecology in diverse and changes in environmental conditions (Danovaro et al. 2000), especially perturbed ecosystems until their accuracy is better especially when subjected to nutrient input related to anthro- evaluated and/or databases are improved. A recent study pogenic activity (Hansen and Blackburn 1992), such as aqua- found differences in the relative abundance between culture. Aquaculture affects these communities in complex PICRUSt inferences and shotgun metagenomic data for every ways as a result of organic inputs, and through the use of second-tier functional trait (Staley et al. 2014). Although we antimicrobials, pesticides, and anti-fouling agents. obtained relevant information on the functions of specific Unfortunately, we did not measure environmental parameters groups through PICRUSt analysis, a shotgun metagenomic and simply provide a snapshot of these bacterial communities study would be valuable to allow for an accurate quantitative at one time as a first step in characterizing changes resulting assessment of the distribution of functional traits in this from aquaculture. The seasonal variability in marine bacterial ecosystem. communities (e.g., Fuhrman et al. 2006), their ability to re- Comparisons of taxonomic-functional correlations between spond rapidly to environmental changes, the patterns they bacteria communities in aquaculture and reference sites suggest exhibit in distribution and abundance as a result of environ- less functional redundancy among specific members at each site. mental variables (e.g., Du et al. 2013), and the complexities of Several abundant taxa were correlated with functional traits in impacts due to aquaculture make it difficult to accurately as- aquaculture sites, notably Gammaproteobacteria, Flavobacteriia, sess changes in relation to environmental variables at a single and Alphaproteobacteria, but Synechococcophycideae also pre- time point. We also note the spatial and temporal variability in sented a high number of correlations that do not belong to one of environmental parameters at these locations and, therefore, the most abundant groups. In reference sites, the rare suggest that future studies incorporate multiple time points Verrucomicrobiae presented a high number of correlations. to characterize impacts both spatially and temporally. These data point to an important role of relatively rare groups in the community, by keeping important connections on a larger Bacterial communities as indicators of biotic integrity scale with other groups and displaying important functional traits. A previous study characterizing functional and taxonomic diver- The impacts of salmon cage farming on the surrounding mi- sity of marine sediments found that the organic carbon content of crobial environments has been studied using various ap- sediments may be important in structuring communities, more so proaches, including physicochemical changes to sediments than geography (Kimes et al. 2013). Another recent study sug- (Buschmann 2002;Sotoand Norambuena 2004), phytoplank- gested that the responses of functional traits to heavy metal con- ton and macrobenthos communities (Buschmann 2002; tamination depended more on environmental changes, while Buschmann et al. 1994, 2008;Sotoand Norambuena 2004; stochasticity played an important role in the formation and suc- Buschmann and Fortt 2005;Mulsow et al. 2006), and antibi- cession of phylogenetic composition for microbial communities otic resistance (Cabello et al. 2013). In Chile, research on the (Ren et al. 2016). Similarly, a previous study showed that sto- environmental effects of salmon aquaculture and the impact chastic processes played important roles in controlling the assem- on adjacent environments remain scarce, especially in relation bly and succession of the groundwater microbial community to production level (Buschmann et al. 2009). Although valu- (Zhou et al. 2014). In this study, we provide a snapshot of bac- able, the power of macrofaunal analyses is hindered because terial communities at aquaculture and reference sites. We specu- these organisms vary within and among different patches at lated that selection strength, mainly changes induced by aquacul- one time, especially in temperate seasonal environments ture, shaped and directed the functional shift pattern of sediment (Zajac et al. 2013). Therefore, such analysis would require bacterial communities, but their taxonomic composition had var- rigorous sampling to understand changes resulting from cul- ious shift patterns to achieve the same functional shift because ture cages (Fernandes et al. 2001). As microbial communities similar functional genes are widely distributed. For example, rapidly respond to environmental changes, using differences various taxa, including those enriched at aquaculture sites, were in benthic microbial community composition and function as correlated to several functional traits at aquaculture sites, but not indicators of environmental perturbations represents a power- at reference sites. So, each microbial population correlated with a ful monitoring tool. However, the benefits of this monitoring Ann Microbiol (2018) 68:63–77 73 tool are limited without a proper understanding of the specific areas. Bacteria may be better indices of biotic integrity than drivers of such shifts. the laborious task of identifying sediment microbes, especially Bacteria offer many advantages over other current or pro- with the advent of high-throughput sequencing, which enables posed bioindicator species, as they are highly ubiquitous, environmental bacterial diversity to be determined rapidly and highly abundant in all sediment types (Wang et al. 2012), cost-effectively. small, and respond rapidly to environmental changes (Zak Acknowledgements Katherine Hornick would like the acknowledge et al. 2011). Thus, a minimal amount of sediment is required Cristian Valenzuela and Dr. Daniel Varela for their assistance on specific for analysis. Current benthic macrofaunal techniques require protocols and methodological suggestions, as well as Dr. Edwin 500–1000 g of sediment. In this study, we used 5 g of sedi- Niklitschek for his help and direction with the statistical analyses during ment, but this could likely be reduced further. We acknowl- her stay in Chile. She would also like to thank Dr. Carlos Aranda for his help with the bioinformatics analysis. AHB acknowledges the support of edge that this study only encompassed a small dataset and is the Lenfest Ocean Program/Pew Charitable Trusts, FONDECYT nr. only representative of one type of condition, i.e., high water 1110845, and CONICYT Basal Program (FB-001). The help in the field flow. However, based on these preliminary findings and the by Adrián Villarroel and Juan Maulén is also specially acknowledged. advantages conferred by working on bacteria using high- throughput sequencing, a further spatio-temporal investiga- tion over large sample sets to explore their potential as References bioindicators is warranted. 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Annals of Microbiology – Springer Journals
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