Microbiome dynamics in a large artificial seawater aquarium

49 Pages·2017·1.99 MB·English

AEM Accepted Manuscript Posted Online 9 March 2018
Appl. Environ. Microbiol. doi:10.1128/AEM.00179-18
Copyright © 2018 Patin et al.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
1 Microbiome dynamics in a large artificial seawater aquarium
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3 Nastassia V. Patin1#, Zoe A. Pratte1, Matthew Regensburger2, Eric Hall2, Kailen Gilde2,
4 Alistair D.M. Dove2, Frank J. Stewart1
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6 1. School of Biological Sciences, Georgia Institute of Technology, Atlanta GA USA lo
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8 2. Georgia Aquarium, Atlanta GA USA 30313 o
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10 # Address correspondence to Nastassia V. Patin, [email protected] //
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12 Running title: The Ocean Voyager microbiome m
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15 ABSTRACT (232 words, 250 max):
16 Artificial habitats for animals have high commercial and societal value. Microbial
17 communities (microbiomes) in such habitats may play ecological roles similar to those in
18 nature. However, this hypothesis remains largely untested. Georgia Aquarium’s Ocean
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19 Voyager (OV) exhibit is a closed-system aquatic habitat that mimics the oligotrophic
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20 open ocean and houses thousands of large marine animals, including fish, sea turtles, n
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21 and whale sharks. We present a 14-month time series characterizing the OV water
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22 column microbiome. The microbiome differed in composition and stability from that of r
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23 natural marine environments with similar chemical features. Composition shifted h
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24 dramatically over the span of two weeks and was characterized by bloom events :/
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25 featuring members of two heterotrophic bacterial lineages with cosmopolitan m
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26 distributions in the oceans. The relative abundances of these lineages were inversely m
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27 correlated, suggesting an overlap in ecological niche. Transcript mapping to rg
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28 metagenome-assembled genomes (MAGs) of these taxa identified unique n

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29 characteristics, including the presence and activity of genes for the synthesis and n
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30 degradation of cyanophycin, an amino acid polymer linked to environmental stress and y

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31 found frequently in cyanobacteria but rarely in heterotrophic bacteria. The dominant ,
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32 MAGs also contained and transcribed plasmid-associated sequences, suggesting a role 9

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33 for conjugation in adaptation to the OV environment. These findings indicate a highly
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34 dynamic microbiome, despite stability of water column physical and chemical s
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35 parameters. Characterizing how such fluctuations affect microbial function may inform
36 our understanding of animal health in closed aquaculture systems.
37
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38 IMPORTANCE (147 words, 150 max):
39 Public aquariums play important societal roles, for example by promoting science
40 education and helping conserve biodiversity. The health of aquarium animals depends
41 on interactions with the surrounding microbiome. However, the extent to which
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42 aquariums recreate a stable and natural microbial ecosystem is uncertain. This study
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43 describes the taxonomic composition of the water column microbiome over 14 months n
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44 in a large indoor aquatic habitat, the Ocean Voyager exhibit at Georgia Aquarium.
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45 Despite stable water column conditions, the exhibit experienced blooms in which a r
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46 single bacterial strain increased to over 65% of the community. Genome analysis h
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47 indicated that the OV’s dominant strains share unique adaptations, notably genes for :/
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48 storage polymers associated with environmental stress. These results, interpreted m
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49 alongside data from natural ocean systems and another artificial seawater aquarium, m
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50 suggest a highly dynamic aquarium microbiome and raise questions of how microbiome rg
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51 stability may affect the ecological health of the habitat. n

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52 INTRODUCTION
53 Microbial communities (“microbiomes”) perform critical functions that maintain
54 ecosystem health and stability (1–3). In the surface ocean, microbes drive the cycling
55 of carbon, nitrogen, and other elements like sulfur, as well as the atmospheric exchange
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56 of these elements (4). Water column microbes also affect the microbiomes of fishes
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57 and other higher organisms, both as beneficial symbionts and agents of disease (1,5). n
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58 Saltwater aquarium systems are designed to mimic natural marine environments as
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59 closely as possible, including by housing resident microbes that process nitrogenous r
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60 waste and prevent pathogenic infections. Hobbyists and aquaculture professionals h
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61 have long recognized that an “unhealthy” aquarium microbiome can lead to dangerous :/
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62 levels of ammonia or outbreaks of fish diseases (6), to the extent that there exists a m
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63 thriving market for bacterial additives (“probiotics”) meant to prevent these conditions m
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64 (7–9). However, the long-term dynamics of large aquarium microbiomes and their rg
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65 degree of resemblance to those of the natural environment are largely unknown. n

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66 Furthermore, while the health of aquarium megafauna like whales and sharks likely n
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67 relies on a stable and balanced microbiome (10), animal-microbe interactions in closed y

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68 aquatic systems are poorly studied. Aquaria, particularly those that do not experience ,
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69 water changes, therefore provide a unique opportunity to study microbiome diversity, 9

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70 composition, and stability in a closed saltwater environment, as well as the links
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71 between the water column microbiome and animal health. Aquarium microbiomes, if s
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72 sampled over time alongside chemical parameters, also may be useful systems for
73 exploring factors driving microbial genome diversification and function.
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75 Literature on aquarium microbiomes, particularly in large systems, is sparse. The
76 Shedd Aquarium in Chicago recently initiated a long-term project to study the
77 microbiomes of their saltwater, freshwater, and animal-associated microbiomes (Shedd
78 Aquarium Microbiome Project;
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79 https://www.sheddaquarium.org/aquariummicrobiomeproject/), an effort that will
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80 certainly contribute to the existing body of knowledge and illuminate links between n
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81 habitat management, microbiome composition, and animal health. Indeed, a recent
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82 publication from Shedd showed that water column microbiome diversity and evenness r
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83 increased and stability decreased after a 90% water change in a 1,600 gallon saltwater h
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84 habitat (11). This study provided evidence for rapid turnover of microbial taxa following :/
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85 water changes in aquariums. However, in larger systems that use other methods to m
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86 maintain water quality, the water column microbiome’s stability and response to m
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87 perturbations may be very different. rg
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89 The Ocean Voyager (OV) exhibit at Georgia Aquarium is the largest indoor aquatic n
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90 habitat in the United States. It contains approximately 6.3 million gallons (23,814,000 y

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91 liters) of artificial seawater and houses a diverse array of teleost and cartilaginous fish, ,
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92 including whale sharks, manta rays, and other charismatic megafauna such as sea 9

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93 turtles. The water column is cycled through a series of treatments including foam
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94 fractionation, sand filtration, sulfur-based denitrification, and ozonation. In August 2016, s
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95 turbidity noticeably increased in the OV water column, suggesting a potential microbial
96 bloom. This event occurred in the early stages of a long-term sampling project and
97 provided an opportunity to study community dynamics leading up to and following the
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98 event, which did not adversely affect animal health but decreased visibility in the exhibit,
99 which is viewed by 2.4 million visitors every year. Because the OV is a closed system
100 that does not experience water changes and chemical parameters are maintained within
101 a narrow range, we hypothesized that the water column microbiome would be relatively
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102 stable over time, with the turbidity event as an exception.
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103 n
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104 We present a 14-month time series of OV microbiome data that suggests the opposite,
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105 highlighting surprising dynamics in community taxonomic composition. A comparison r
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106 with similar data sets from another artificial seawater aquarium (Shedd Aquarium) and h
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107 three geographically distinct surface water column samples supports the finding that :/
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108 artificial systems are more variable and diverse than natural marine systems. m
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109 Metagenomic and metatranscriptomic analysis was then performed on samples m
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110 collected from two time points characterized by very different communities and used to rg
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111 provide insight into key taxa associated with microbiome compositional shifts. These n

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112 analyses identified three nearly complete and transcriptionally active metagenome- n
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113 assembled genomes (MAGs) representing bacteria generally observed at low relative y

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114 abundances in the open ocean, but with dominant roles and potentially unique ,
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115 adaptations in the OV system. We discuss the potential determinants of OV microbiome 9

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116 dynamics and implications for understanding aquarium microbiome ecology and
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117 diversification. s
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119 METHODS
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121 Exhibit setup and management
122 Georgia Aquarium’s Ocean Voyager is an entirely closed system of 6.3 million gallons
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123 of artificial seawater (Atlanta tap water mixed with Instant Ocean Sea Salt (Instant
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124 Ocean Spectrum Products, Blacksburg, VA, USA)) designed to house primarily pelagic lo
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125 fauna of the open ocean. Exhibit water circulates through a filtration system employing e
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126 foam fractionators (protein skimmers), sand filters, ozone contact towers, countercurrent ro
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127 heat exchangers, sulfur-based denitrification (SDN) vessels, and a deaeration tower h
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128 (DAT). /
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129
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130 Water from the exhibit is brought to a sump via surface skimming and bottom return m
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131 lines (approximately a 2:1 ratio of surfacing skimming to bottom return). From the sump, g
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132 100% of the water is foam-fractionated through protein skimmers to remove small n

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133 organic compounds and then passed through sand filters to remove particulates and n
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134 support microbial nitrification of ammonia to nitrate. These filters have a gravel base y

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135 and .45-.55 mm silica filter sand designed to catch particulates such as fish waste or ,
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136 uneaten food. Following the sand filters, water is diverted via different side-stream 9

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137 processes. Countercurrent heat exchangers made of titanium plates maintain water
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138 temperature. Approximately 0.15% of the exhibit water is diverted to the SDN vessels, s
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139 which are designed to promote nitrate removal by microbial autotrophic denitrification by
140 sulfur-oxidizing bacteria. The SDN side-loop is comprised of two sets of four vessels,
141 each of which contains gravel, sulfur pellets, aragonite, and associated denitrifying
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142 microbes, primarily of the genera Thiobacillus and Sulfurimonas (unpublished data).
143 Two process loops provide the optimal hydraulic retention time of water in the SDN
144 system. The first loop is pumped and recirculated continuously through the SDN vessels
145 while the second loop titrates a small amount of water from the exhibit into the first loop
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146 and back out again to the exhibit. All of the water from this side stream, along with 25%
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147 of the original post-sand filter flow, also flows through an ozone contact tower, which n
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148 uses ozonation to kill microbes by oxidation and to raise water dissolved oxygenlevels,
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149 which were lowered by passage through the SDN system. Ozone dosing is regulated by r
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150 oxidation-reduction potential readings, with a target level of 675 mV. However, h
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151 ozonation intensity is increased intermittently to help regulate changes in water clarity. :/
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152 The remainder of the flow from the sand filters goes into the DAT, which also receives m
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153 100% of all side-stream processes. The DAT is filled with Brentwood media to create a m
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154 large surface area that promotes gas exchange, thereby allowing dissolved gasses like rg
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155 CO and O to reach equilibrium with the ambient environment. Water enters the DAT n
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156 through spray bars at the top and trickles down through the media. Water leaves the n
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157 DAT via gravity and a series of pipes that spread flow around the exhibit. y

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158 ,
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159 The system filter rate is close to 130,000 gallons (491,400 liters) per minute (GPM), 9

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160 which occurs through two “in series” filtration loops, each of which provide a filter rate of
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161 65,000 GPM. This process turns over the exhibit “tank only” volume of 4 million gallons s
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162 approximately once per hour. Chemical parameters within the exhibit are closely
163 monitored and maintained with minimal variation around target levels (Table S1). To
164 compensate for pH shifts outside the acceptable range, sodium carbonate or sodium
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165 bicarbonate is occasionally titrated into the OV water at the sump, before filtration. The
166 system is lit by overhead metal halide lighting and natural sunlight from a skylight over a
167 small portion of the southern end of the exhibit.
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169 Metadata collection
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170 Water column pH and salinity were measured at Georgia Aquarium using an Orion n
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171 VersaStar meter with a conductivity cell and ROSS Ultra pH/ATC Triode probe.
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172 Oxidation/reduction potential (ORP) was measured using a GF Signet 2757 inline r
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173 probe. Dissolved oxygen concentration was measured in situ using a HQ40d portable h
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174 multi-meter with luminescent probe (Hach Company, Loveland, Colorado, USA). Nitrate :/
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175 concentration was measured using a Dionex ICS 5000+ ion chromatography system m
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176 with a Dionex IonPac AS23 analytical column. Nitrite concentration was measured m
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177 using the USEPA Diazotization method on a DR6000 spectrophotometer (Hach). rg
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178 Alkalinity was determined through titration using 0.01 N HCl. All measurements and n

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179 samples were taken between 06:30 and 07:30. Samples were taken directly from the n
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180 habitat in a consistent location and analyzed within 5 hours. y

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181 ,
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182 Turbidity readings were taken every five minutes daily with a Filtertrak 660 sc Laser 9

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183 Nephelometer Sensor (Hach). Water for turbidity readings was pulled in from a pipe on
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184 the side of the exhibit, such that readings were taken from the middle of the water s
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185 column. Values for select dates were determined by the mean of all measurements on
186 that date. Samples were classified into three bloom condition categories based on
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187 Nephelometric Turbidity Units (NTUs), as follows: <30 NTUs: non-bloom, 30-35 NTUs:
188 transition, and > 35 NTUs: bloom (Table S2).
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190 Sampling scheme
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191 Samples for microbiome analysis were taken approximately every two weeks from May
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192 2016 until July 2017 (Table S2). At each time point, two liters of Ocean Voyager n
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193 surface water was filtered through each of four Sterivex cartridges (0.2 m pore size) e
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194 using a peristaltic pump. Cartridges were filled with nucleic acid stabilizing buffer ro
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195 (25 mM sodium citrate, 10 mM EDTA, 5.3 M ammonium sulfate, pH 5.2), sealed, and h
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196 placed on dry ice for transport back to Georgia Tech, where they were stored at -80˚C /
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197 until nucleic acid extraction.
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198 m
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199 DNA extraction g
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200 DNA was extracted from one of the four replicate Sterivex cartridges using a custom

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201 protocol as described in (12). Briefly, cells were lysed by flushing out nucleic acid u
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202 stabilizing buffer and replacing it with lysis buffer (50mM Tris-HCl, 40 mM EDTA, 0.73 M
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203 sucrose) and lysozyme (2 mg in 40 l of lysis buffer per cartridge), then incubating 2
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204 cartridges for 45 min at 37°C. Proteinase K was added and cartridges were resealed 9

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205 and incubated for 2 h at 55°C. The lysate was removed, and the DNA was extracted g
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206 once with phenol:chloroform:isoamyl alcohol (25:24:1) and once with s
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207 chloroform:isoamyl alcohol (24:1). Finally, DNA was concentrated by spin dialysis using
208 Ultra-4 (100 kDA, Amicon) centrifugal filters.
209
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