MICROBIOME VARIATION IN WILD VERSUS CAPTIVE EAGLE RAYS ( NARINARI)

A Dissertation Presented to The Academic Faculty

by

Mary McWhirt

In Partial Fulfillment of the Requirements for the Degree Master of Science in the School of Biological Sciences

Georgia Institute of Technology

August 2019

Copyright © Mary McWhirt 2019

MICROBIOME VARIATION IN WILD VERSUS CAPTIVE EAGLE RAYS (AETOBATUS NARINARI)

Approved by:

Dr. Frank Stewart, Advisor School of Biological Sciences Georgia Institute of Technology

Dr. Lisa Hoopes Director of Research, Conservation and Nutrition Georgia Aquarium

Dr. Brian Hammer School of Biological Sciences Georgia Institute of Technology

Date Approved: July 18, 2019

ACKNOWLEDGEMENTS

First, I would like to thank my advisor Dr. Frank Stewart for his guidance for allowing me the opportunity to learn a new set of skills. Your genuine enthusiasm for teaching and work creating academic programs such as CMDI and educational programs such as SWiMS are a constant reminder of the importance of education and community. I would also like to thank the members of the Stewart lab, in particular Zoe Pratte, for helping guide me along the way and their hands-on training during my time here at

Georgia Tech.

Thank you to the Georgia Aquarium and their commitment to conservation and research. Their partnership and support has allowed me to have a unique experience with in my work. Dr. Alistair Dove has been instrumental in the relationship between the

Stewart lab and the Georgia Aquarium and I would not have had this opportunity without him.

I would like to say a final thank you as well to the members of my thesis committee, Dr. Brian Hammer and Dr. Lisa Hoopes. In classes with Dr. Hammer, I gained a deeper understanding of microbial processes that has been beneficial throughout my program. Dr. Hoopes’ support from the Georgia Aquarium has been an integral part of this study, including sample collection and metadata. This work would not have been possible without your help.

Thank you to everyone who made my time at Georgia Tech possible.

iii TABLE OF CONTENTS

ACKNOWLEDGEMENTS iii

LIST OF TABLES v

LIST OF FIGURES vi

LIST OF ABBREVIATIONS vii

SUMMARY viii

CHAPTER 1. Introduction 1

CHAPTER 2. Materials and Methods 5 2.1 Sample collection and lab processing 5 2.2 Illumina data processing 7 2.3 Statistical analysis 7

CHAPTER 3. Results 10 3.1 Niche-site microbiomes structure by environment 11 3.1.1 Taxa in gill microbiomes 14 3.1.2 Taxa in skin microbiomes 15 3.1.3 Taxa in cloaca microbiomes 16 3.2 Captive ray microbiomes throughout a year 17 3.2.1 Effect of a monogenean trematode infection on microbiomes 17

CHAPTER 4. Discussion 20 4.1 Conclusions 24

APPENDIX A. Metadata for ray and water samples 26

REFERENCES 29

iv

LIST OF TABLES

Table 1- Number of samples used in statistical analysis per type and 10 environment.

Table 1- Permanova results of sample type and environment. 11

Table 3- Similarity percentages (SIMPER) beta diversity between 13 sample-habitat groups.

Table 4A- Metadata for all ray and water samples. 26

v LIST OF FIGURES

Figure 1- Non-metric MDS based on Bray-Curtis similarity of individually- 12 plotted duplicate spotted samples. All samples are shown together and then separate gill, skin, and cloaca NMDS plots are grouped by habitat.

Figure 2- Shannon Diversity Index and Chao1 as a representation of alpha 14 diversity between sample-habitat types.

Figure 3. Non-metric MDS based on Bray-Curtis similarity of samples from 19 the Ocean Voyager exhibit.

vi LIST OF SYMBOLS AND ABBREVIATIONS

rRNA Ribosomal ribonucleic acid

Na+ Sodium (+1) ion

Cl – Chloride (-1) ion

OTU Operational taxonomic unit

NT Near threatened

IUCN International Union for Conservation of Nature

IACUC Institutional Care and Use Committee

µm Micrometer

Cm Centimeter

Kg Kilogram

DNA Deoxyribonucleic acid

PCR Polymerase chain reaction

µl Microliter

BSA Bovine Serum Albumin s Seconds bp Base pairs

ASV Amplicon sequence variant

QIIME Quantitative Insights Into Microbial Ecology

SIMPROF Similarity profile routine

NMDS Nonmetric multidimensional scaling

vii SIMPER Similarity percentage

OV Ocean Voyager

viii SUMMARY

The microbial communities (microbiomes) associated with elasmobranchs are currently not well-understood. The (Aetobatus narinari) is a slow-maturing ray that is globally distributed in tropical and warm-temperate waters, and is listed as near-threatened by the IUCN Red List. To evaluate how the environment shapes the spotted eagle ray microbiome, we used 16S rRNA Illumina sequencing to compare the microbiomes of the dorsal skin, gill, and cloaca from a ray population sampled in Sarasota Bay, FL to those from a captive population in the

Ocean Voyager exhibit at Georgia Aquarium. Cloaca microbiomes of both populations had the lowest alpha diversity and highest beta diversity. The composition of the gill and skin microbiomes differed between captive and wild populations and are similar to, but distinct from, the water column communities while cloaca microbiomes are more divergent from that of the water. This pattern is consistent with that seen in teleost fishes and marine mammals. These results indicate a dual role for body niche and environmental conditions in shaping ray microbiomes and identify key taxa that may be important to the health of the rays.

ix CHAPTER 1. INTRODUCTION

Microbiomes of teleost fishes and marine mammals show a consistent pattern of having communities that are distinct within body-niche sites such as gills, skin, and gut.

In teleost fishes these body site microbiomes, including those in contact with the external environment (e.g., skin, gills; 1), are influenced by host-specific factors including life- stage and diet, and gut microbiomes have been shown to become more complex after reef settlement (2). Marine mammals, including dolphins (order Cetacea) and sea lions (order

Carnivora), also harbor microbiomes that vary by body site and host species (3).

Knowledge of the impact of changes in environment or of captivity on host microbiomes could also lead to increased understanding of the functional role of niche-specific bacterial communities. A comparison of fecal samples from wild and captive Australian sea lions (Neophoca cinereal) found differences in bacterial communities between sampling sites, which could be attributed to colony dynamics, behavior, and foraging sites (4). In another marine mammal, the humpback whale (Megaptera novaeangliae) skin-associated microbiota appears to relate to geographic area and metabolic state, including a Psychrobacter that may be an influence of humpback whale seasonal migrations (5). With a migratory life-cycle, Atlantic salmon (Salmo salar) undergo an increase in phylogenetic diversity in the skin bacterial community during the transition from freshwater to seawater (6). These microbiome variations occurring during environment transitions suggest that microbiomes composition will differ between wild and captive marine . However, the extent to which environmental variation shapes composition might be expected to vary depending on the body site of the

1 microbiome, with more external sites (e.g., skin) affected to a greater extent by environmental drivers compared to internal sites (e.g., gut). To explore the relative roles of environment versus body site niche in shaping microbiome composition, this study compares the gill, skin, and cloaca microbiomes from a wild and captive population of a marine elasmobranch.

As most studies of fish microbiomes have focused on teleost fishes, relatively little is known of microbiome structure and function in elasmobranchs, the subclass of

Chondrichthyes (cartilaginous fish) that includes sharks, rays, and skates. This is a critical shortcoming given the wide distribution of elasmobranchs and their ecological relevance as top predators in marine food webs. In teleost fish, the microbiomes of diverse body sites have been shown to play roles in host defense against pathogens, chemical waste processing, and acquisition of essential nutrients from food (7, 8). The microbiomes of elasmobranchs, are hypothesized to play similarly important roles, which likely vary depending on body site. For example, the gills of elasmobranchs, as in teleosts, aid in ion (Na+ and Cl-) absorption, osmoregulation, acid-base regulation, and waste excretion (9, 10). It is possible that the unique physical and chemical conditions of the gills select for the presence of a specific community of microbes that aid in these functions. The mucus layer of the skin is also important to elasmobranch health. The detection of antibiotic activity in skin mucus from two ray species, the cownose ray

(Rhinoptera bonasus) and the Atlantic stingray (Dasyatis sabina), suggests the possibility that the protective anti-pathogen role of skin mucus may be facilitated by resident microbes (11), potentially acting in concert with host-derived immune components such as lysozymes, lectins, and proteases (12). However, a full understanding of the functional

2 significance of elasmobranch microbiomes requires knowledge of factors shaping the microbiome composition.

The processes shaping the microbiomes of elasmobranchs remain relatively uncharacterized, although some work has begun in this area. A study comparing the gut microbiomes of bony fish and 3 shark species found that the shark species shared core microbial operational taxonomic units (OTUs) but varied drastically in the number of observed OTUs (alpha diversity; 13). A study of shark and ray microbiomes indicated that the skin microbiomes of the common thresher shark (Alopias vulpinus) were broadly consistent in taxonomic composition across individuals but distinct from the water column (14). This skin community showed an increase in the proportional abundance of gene pathways for disease defense, chemotaxis, iron acquisition, and membrane transport. These results together suggested that the microbial community is unique to the skin niche, shaped minimally by host species identity, and encodes pathways that may interact with host cells or impact host skin functions.

To further understand the drivers of microbiome composition, our study analyzes a unique sample set including microbiomes of both wild and captive populations of the spotted eagle ray (Aetobatus narinari). Aetobatus narinari is a reef-associated marine ray with a widespread distribution in tropical and warm-temperate waters (15). The small litter size, schooling behavior, and inshore habitats of A. narinari make it especially vulnerable to fishing impacts, and the IUCN Red List has listed the spotted eagle ray as near threatened (NT) with a decreasing population trend (16). An increased understanding of factors shaping A. narinari health, potentially including its microbiome, may have implications for conservation policy as well as management of captive

3 populations. We therefore leveraged a collaboration involving Georgia Aquarium, Mote

Marine Laboratory, and Georgia Tech to assess how A. narinari microbiome composition varies between two distinct environments (wild versus captivity) and among body sites.

Specifically, we analyzed samples from the gill, dorsal skin, and cloaca of rays from a wild population in Sarasota Bay, FL and a captive population in the Ocean Voyager exhibit at Georgia Aquarium. We also characterized microbiomes of individual rays in the captive population over a 12-month time period to determine microbiome stability over time and to assess the effect of an anti-parasite anthelmintic treatment on the microbiomes over time.

4 CHAPTER 2. MATERIALS AND METHODS

2.1 Sample collection and lab processing

Samples were collected in collaboration with Georgia Aquarium and Mote Marine

Laboratory. Sample collection was approved by the IACUC ethics committee at Georgia

Institute of Technology, under protocol A100161. Spotted eagle rays in the Ocean

Voyager exhibit were acquired from the wild within between 2009 and 2015 from

Sarasota Bay and the Florida Keys. Wild rays were found in the same geographic region.

Samples were taken from the gill, dorsal skin, and cloaca of each animal with one sterile swab per body site and then stored in RNAlater at -20 °C until lab processing.

Collection of the wild ray samples was from April to May 2018 and included 13 individual animals with one gill, one skin, and one cloaca sample from each ray. During wild sample collection, rays are circled with a net and brought on board the ship in a water bath. Water samples from the wild environment were collected in 3 locations.

Captive rays were sampled from February 2018 to March 2019 and also included 13 individual animals. However, some of the captive individuals were sampled more than once during that time period. Samples of the Ocean Voyager (OV) water column microbiome were obtained by filtration onto 0.2 µm Sterivex filters (as described in previous work (17)) as part of 3-year time series monitoring program involving biweekly collections; the water column samples analyzed here correspond to those collected nearest in time (within 2 weeks) to each animal sampling event. Metadata for the rays

(Table 4A) was provided by Mote Marine Laboratory (wild) and the Georgia Aquarium

(captive) and included environment, date of sampling, sex, disc width (cm), and weight

5 (kg) for all rays. Captive ray metadata also included diet, medication or treatments within

3 months of sampling, and blood chemistry notes.

The samples were later processed by transferring the swabs directly into

Powerbead tubes from the Qiagen DNeasy PowerSoil DNA extraction kits and extracted according to the manufacturer’s instructions. Each DNA extraction event included a blank to check for contamination from the kit solutions; each blank was carried through the subsequent PCR and sequencing process. PCR amplification of the V3-V4 regions of the 16S rRNA gene used primers 515F and 806R, modified to include sample-specific barcodes and Illumina sequencing adaptors (18). Each sample was amplified in duplicate with unique primer barcodes, including the blanks from the DNA extraction and the negatives from each amplification. The reaction mixtures were composed of 2 µl of template DNA, 0.3 µl of both the forward and reverse primers, 0.3 µl of bovine serum albumin (BSA) (20 mg/ml; New England BioLabs Inc.), 12.5 µl of iTaq Universal SYBR

Green Supermix (Bio-Rad Laboratories Inc.) and 9.6 µl of PCR-grade water.

Amplification was performed using denaturation at 94℃ for 3 minutes, followed by 35 cycles of denaturation at 94℃ (60s), primer annealing at 60℃ (120s), and primer extension at 72℃ (90s) and then a final extension at 72℃ for 10 minutes. Amplicon products were verified using gel electrophoresis and quantified fluorometrically using the

Qubit (Life Technologies). All blank extractions and negative controls were included in the sequencing pool. The amplicon products were pooled at equimolar concentrations and purified with Diffinity RapidTip2 PCR purification tips (Diffinity Genomics, NY).

Amplicons were sequenced on an Illumina MiSeq machine across 4 different runs, using a V2 500-cycle kit (250 X 250 bp) with 5% PhiX to increase read diversity. Duplicates

6 that failed to meet processing standards, as described below, from the first 2 Illumina sequencing runs were amplified in triplicate with unique primer barcodes and sequenced again. The first sequencing run contained all of the wild ray samples and a few captive ray samples, while runs 2-4 contained an even combination of re-amplified wild ray samples and captive ray samples.

2.2 Illumina data processing

A total of 319 individually-barcoded samples were sequenced. These sequences were trimmed using Trimmomatic with a minimum length of 50 bp and a Phred score of >25 (19). Forward-paired reads were then imported into QIIME2 v2018.11 (20) and analyzed using the Deblur method for identification of error-free sequences representing individual amplicon sequence variants (ASVs) (21). was assigned to ASVs using the SILVA database (silva-128-99-515-806-nb-classifier) (22) and the sequences classified as Chloroplast, Mitochondria, or Mollicutes were removed from resulting feature table. Mollicutes were a large proportion of ASVs found in the DNA extraction blank from the first Illumina sequencing run, indicating that their presence was due to kit contamination. The contaminated kit was not used again in lab processing and all ASVs identified as Mollicutes were removed from data before statistical analysis.

2.3 Statistical analysis

Data were analyzed for patterns related to body-niche site, environment (wild vs. captive), sex, date of sampling, disc width, weight, and medications (captive only). Alpha diversity analyses of Chao1-estimated richness and Shannon Diversity were done using the q2-diversity plugin in QIIME2 and beta diversity analyses were done using Primer-e

7, both with a rarefied sequencing depth of 800 sequences per sample. Another QIIME2

7 q2-diversity plugin analysis included Random Forest Analysis. After rarefying, samples were square-root transformed and analyzed for similarity in duplicate profiles using the

CLUSTER graph (23). CLUSTER graph uses the similarity profile routine (SIMPROF), which tests for the presence of groups in unstructured sets of samples and then creates a tree that shows the similarity percentage relationship among samples. Any duplicate samples that did not show a minimum cutoff of 75% relatedness were removed from analysis. Some of these sample were re-amplified in triplicate and sequenced on a later

Illumina run and analyzed again for CLUSTER similarity. If passing in triplicate, only the 2 closest-related amplicons were used for further statistical analysis. The 135 amplicon products passing quality control were then analyzed using the Basic

Multivariate Analysis with Bray-Curtis similarity in Primer-e 7 (Primer-E Ltd.), including nonmetric multidimensional scaling (NMDS) and Similarity Percentage analysis (SIMPER). SIMPER analysis indicated the average dissimilarity of microbial communities between sample group and the average contribution of each ASV to the dissimilarity of the group, with a 70% cutoff for low contributions to dissimilarity.

During the opportunistic sampling period of the Ocean Voyager spotted eagle rays, some animals were sampled on multiple dates. For further analysis of the captive rays across a 12-month time period, duplicates that passed the 75% cutoff were combined in QIIME2 after taxonomy assignment and after filtering to remove Chloroplast,

Mitochondria, and Mollicutes sequences, but before rarefying. Ocean Voyager water samples did not have duplicates and therefore were treated individually. The ray and water datasets were then rarefied to 1000 sequences and processed using the q2-diversity plugin in QIIME2 and the Basic Multivariate Analysis in Primer-e 7, with a square-root

8 transformation. Random forest analysis was done using the q2-sample-classifier in

QIIME2 to determine the features (ASVs) that are most predictive of the sample-habitat class of metadata. At once sampling time point each for animals D1 and S2 from the aquarium were sampled and then received a praziquantel bath for a monogenean infection and the effect of this was explored further.

9 CHAPTER 3. RESULTS

We analyzed 68 microbiome samples from wild spotted eagle rays sampled near

Sarasota, Florida, 54 samples from captive rays in the Ocean Voyager exhibit at Georgia

Aquarium, and water column samples from both environments (Table 1), along with physical data from the animals (Table 4A). Each ray sample was sequenced in duplicate and the resulting datasets rarefied to 800 sequence reads. This resulted in 86,450 reads and 2,567 unique ASVs. Clear differences in alpha and beta diversity were observed both among body-niche sites and between wild and captive animals, as discussed below. No differences in microbiome communities were evident when analyzed for sex, disc width, or weight of the rays. Sample groups were referred to as Sample-Wild or Sample-OV, where OV = Ocean Voyager (i.e., captive ray samples) and “Sample” is Gill, Skin, or

Cloaca (e.g., Cloaca-Wild or Skin-OV).

Table 1. Number of samples used in statistical analysis per type and environment.

Water Gills Skin Cloaca Total

Wild 6 26 22 20 74

Captive (OV) 7 18 16 20 61

10 3.1 Niche-site microbiomes structure by environment

The microbiomes of all three body sites (skin, gill, cloaca) differed in taxonomic

composition between wild versus captive spotted eagle rays (Figure 1). Differences in

body site and environment microbiomes were statistically significant (Table 2). However,

compared to gill and skin microbiomes, cloaca microbiomes exhibited higher levels of

inter-sample variability (dispersion) in both wild and captive datasets (Figure 1).

SIMPER analysis indicated that the highest dissimilarity in composition was between

Cloaca-Wild and Cloaca-OV groups and the lowest dissimilarity was between Gill-Wild

and Water-Wild (Table 3). The gill and skin samples from both environments (wild and

captive) also clustered with their respective water column samples (Figure 1). Shannon

diversity and Chao1 showed that both captive and wild cloaca groups have the lowest

alpha diversity (Figure 2). Captive rays indicated a general trend of having a lower alpha

diversity across all body sites, with a significant difference between Skin-Wild and Skin-

OV (p-value < 0.001).

Table 2. Permanova results of sample type and environment.

Source df SS MS Pseudo-F P(perm) Unique perms Sample-Type 3 79346 26449 14.082 0.001 997 Environment 1 38142 38142 20.308 0.001 999 Sample- Environment 3 28238 9412.7 5.0117 0.001 998 Res 136 2.55E+05 1878.2 Total 143 4.05E+05

11 Transform: Square root Resemblance: S17 Bray-Curtis similarity 2D Stress: 0.18 Sample-Habitat Gill-Wild Skin-Wild Cloaca-Wild Gill-OV Cloaca-OV Skin-OV Water-Wild Water-OV

Gills 2D Stress: 0.1 Skin 2D Stress: 0.17 Cloaca 2D Stress: 0.19

Figure 1. Non-metric MDS based on Bray-Curtis similarity of individually-plotted duplicate spotted eagle ray samples. All samples are shown together and then separate gill, skin, and cloaca NMDS plots are grouped by habitat.

12 Table 3. Similarity percentages (SIMPER) beta diversity between sample- habitat groups.

Groups Av. Dissimilarity (SIMPER)

Cloaca-Wild vs. Cloaca-OV 83.3

Water-Wild vs. Water-OV 75.3

Skin-Wild vs. Skin-OV 71.6

Skin-OV vs. Water-OV 70.6

Gill-OV vs. Skin-OV 66.2

Gill-Wild vs. Gill-OV 66

Gill-Wild vs. Skin-Wild 55.4

Gill-OV vs. Water-OV 54.4

Skin-Wild vs. Water-Wild 47.9

Gill-Wild vs. Water-Wild 46.4

13

Figure 1. Shannon Diversity Index and Chao1 as a representation of alpha diversity between sample-habitat types. Significance (p-value <0.001) is marked with a red star.

3.1.1 Taxa in gill microbiomes

There were several key taxa in the gill microbiomes that contributed to the differences between the wild and captive microbial communities. The Gill-Wild and Gill-

OV groups had a 66% average dissimilarity (Table 3). One taxon that contributed to the dissimilarity between microbiomes was a Kordiimonas that had an 11.9% average abundance in the captive rays but a 0% average abundance in the wild. This Kordiimonas

14 was also found at 36.5% abundance in the captive water column samples and 0% abundance in the wild water samples. An Oceanospirillales found in 1.3% and 0% abundance in the gills of captive and wild rays, respectively, was also one of the important ASVs in determining the sample-habitat prediction in Random Forest Analysis.

Another key taxa in the gills was a member of the AEGEAN-169 marine group clade of

Rhodospirillales found at 3% abundance in the wild rays and 5.7% in the wild water but at <1% in the captive rays and 0% in the water. The Kordiimonas and Oceanospirillales were a higher proportion of both the Gill-OV and Water-OV microbiomes than the corresponding wild groups while the AEGEAN-169 marine group clade was higher in both the Gill-Wild and Water-Wild than captive samples, which likely contributed to the patterns seen in the NMDS clustering (Figure 1).

Despite the influence of the water column microbiomes of the environments on the wild and captive gills, there is some evidence of a core microbiome within this body site. Both communities were dominated by unidentified Gammaproteobacteria. Random

Forest Analysis indicated that one Gammaproteobacteria ASV that separated gill and water microbiomes shared a 95% identity with an uncultured bacterium clone found on a

Pacific mackerel (Scomber japonicus) (GenBank accession number JQ191779.1).

Another important community member identified by Random Forest was a

Psychrobacter that was not found in a significant proportion in either water column communities but 2.68% in the captive rays and 0.1% in the wild rays

3.1.2 Taxa in skin microbiomes

Skin microbiomes from wild and captive rays had a high average dissimilarity, at

71.6% (Table 3). Wild skin samples had a significantly higher Shannon diversity and

15 Chao1 index (Figure 2), suggesting that the wild skin had more species richness within samples. The wild microbiome communities had an average abundance of 8.8% from a

SAR11 clade and 4.7% from a Synechococcus while the captive rays had a 1.6% and

1.1% abundance, respectively. This SAR11 and Synechococcus were also found in 17% and 7.4% respective abundance in the wild water column and 0% for both in the captive water column

Both skin communities shared the same unidentified ASV with a 96.67% identity with an uncultured Helcococcus found in a skin sample of an Antarctic fur seal

(Arctocephalus gazelle) (GenBank accession number MH728102.1). Another important taxon from Random Forest Analysis was a Pseudoalteromonadaceae found at roughly the same abundance in the captive skin (1%) and captive water (1.2%), and at a higher abundance in the wild skin (1.4%) than in the wild water (0.3%), which suggests that this microbe is a stable member of the skin microbiomes. Although the skin microbiomes were influenced by the water column, they shared common community members that could suggest the presence of a core microbiome across environments.

3.1.3 Taxa in cloaca microbiomes

The cloaca communities had the highest dissimilarity (83.3%) between groups based on the SIMPER analysis (Table 3). Wild and captive cloaca environments both seemed to be highly variable and distinct from the water column (Figure 1). For the cloaca and water samples, Random Forest indicated that an unidentified Photobacterium

ASV was most informative for predicting sample types. Photobacterium was the dominant bacteria for both sets of spotted eagle rays, with an average abundance of 38% in the captive and 18.1% in the wild. It was also present at 0.1% in the wild water and

16 2.1% in the Ocean Voyager water, which indicated a concentration of this community member in the cloaca. Another important microbiome community member in the cloaca was a Vibrio at 10% in the wild rays, 17.6% in the captive, and <1% in both water column microbiomes. Both Photobacterium and Vibrio are members of the family

Vibrionaceae. However, about half of the wild cloaca samples did not have a detectable presence of either Vibrionaceae, while the samples that did were dominated at a combined average of 76%. Only one captive cloaca sample did not have a significant abundance of Vibrionaceae. The high beta diversity (Figure 1) and low alpha diversity

(Figure 2) could be explained by the dominant abundance of this family in some of the samples while completely lacking in others.

3.2 Captive ray microbiomes throughout a year

Spotted eagle rays from the Georgia Aquarium were sampled once yearly for annual exams and again if handled for other veterinary reasons. Dates sampled spanned a

12-month period and the number of samples from each individual animal varied (Table

4A). General patterns in clustering of body-niche sites were similar to those seen in the analysis of environmental effects (Figure 1, Figure 3). The gills and water clustered close to but distinct from one another while the skin shows some overlap with the gill and water but more variability. Cloaca microbial community composition was highly variable and separate from all other niche-sites (Figure 3).

3.2.1 Effect of a monogenean trematode infection on microbiomes

Two Ocean Voyager spotted eagle rays had samples taken from multiple time points across a 9-month time period. Individual D1 had gill and skin samples taken 3 times, from July 2018 to March 2019, and individual S2 had skin samples taken 2 times,

17 from June 2018 to March 2019. Metadata provided by the Georgia Aquarium indicated that both individuals had a monogenean trematode infection at one of the sampling times,

Nov18 for D1 and June18 for S2. Directly after sampling, the animals were treated with praziquantel baths, a common treatment for monogenean infections in elasmobranchs

(24-26). Water column microbiomes from these sampling events remained stable, dominated by a Kordiimonas and a Rhodobacteraceae, which indicated that changes in body-niche site microbiomes were not related to changes in the environment microbiota.

The NMDS analysis for animal D1 showed that the middle sampling point at the time of the praziquantel bath, Nov18, separated from the other two sampling events in the skin but not in the gills (Figure 3). Animal S2 only has two skin sampling time points but the

Mar19 skin sample clusters closely with the skin samples of the other rays and the un- treated time points of D1, while the June18 sample clusters more closely with the gills

(Figure 3).

18 Transform: Square root Resemblance: S17 Bray-Curtis similarity

2D Stress: 0.18 Sample Type Skin Gill Cloaca Water June18-S2 Nov18-D1

Mar19-D1

July18-D1 Nov18-D1

Mar19-S2

Mar19-D1

July18-D1

Figure 2. Non-metric MDS based on Bray-Curtis similarity of samples from the Ocean Voyager exhibit.

The highest proportion of the community members for D1 in both July18 and

Mar19 and for S2 in Mar19 skin samples were a Vibrio and unidentified bacteria. At the

Nov18 time point, the skin community of D1 was dominated by Vibrio (19.6%), a

Bacteroidia (35.4%), and a Fusobacterium (14.9%). The June18 skin sample of S2 had a

36.6% abundance of unidentified Gammaproteobacteria and 10% abundance of

Flavobacteriales. In the gills of D1, all 3 time points had Kordiimonas, Rhodobacteraceae and an ASV that shares a 97.5% identity with an alphaproteobacterium isolated from a microbial mat in the Great Barrier Reef (GenBank accession number GQ204813.1).

19 CHAPTER 4. DISCUSSION

This work shows that spotted eagle rays have niche-specific microbiomes that are influenced by, but distinct from, their environment. These patterns have been seen in reef fish and large marine mammals such as dolphins, sea lions, and humpback whales (1-5).

There has been some question about the relatedness of spotted eagle ray populations from different regions due to evidence of restricted genetic exchange among ocean basins and geographic differences in morphology (27, 28). However, both populations of rays in this study originated in the same geographic region, hopefully limiting the effect of genetic relatedness on the microbiome compositions. Therefore, differences between body-niche site microbiomes can be attributed to other factors that relate to environmental differences such as water column microbiota, diet, population density, or others.

Several prevalent taxa found in the gill microbiomes are related to their abundance in the water column from their environments. Captive spotted eagle rays have a higher abundance of Kordiimonas and Oceanospirillales compared to their wild counterparts. These taxa have both been shown to be stable and substantial members of the Ocean Voyager water column throughout a 14-month time-series (17). The

AEGEAN-169 marine group clade was found in higher proportions of the wild gills and water. This clade was shown to be stimulated during a toxic Akashiwo sanquinea bloom

(29, 30) and sampling dates of the wild rays were around the time of an increasing abundance of Karenia brevis along the southwest region of Florida (31). Stimulation of the AEGEAN-169 marine group clade during harmful algal blooms and its potential impact on the microbiomes of marine animals could have implications for conservation

20 under changing ocean conditions and warrants further study. The influence of the water column microbiome of the gills was anticipated due to the continuous movement of water over the gills and from previous work on other fish microbiomes (1, 32), however, there are several important taxa identified from Random Forest Analysis within the gill niche- site of the rays that are not a detectable proportion of the water microbiota.

One of these taxa is a Psychrobacter that, although relatively low in abundance in the rays, has been implicated in other studies as a consistent member of marine animal microbiomes (5, 33, 34). Previously associated with infections in fish, a large proportion of Psychrobacter-related sequences were found in the upper respiratory tract of healthy bottlenose dolphins, suggesting that members of this may be part of the normal flora (33). Tenacibaculum and Psychrobacter were established as part of the core skin microbiome of humpback whales from populations spanning 4 different environments

(5). The gills of the captive and wild spotted eagle rays also have Tenacibaculum as a microbial community member while the water columns do not. The presence of this microbe in mucosal layers of both humpback whales and spotted eagle rays suggests that these bacteria could be part of the normal flora of niche-site microbiomes in several marine animals. These two community members that are not found in the water column are evidence of a core microbiome within the gill body site of spotted eagle rays. Both gill microbiomes had a large proportion of bacteria that could not be identified past the class level, which could indicate the presence of more core microbes that are yet unknown. Other shared community members could potentially be related to processes of the gills such as urea transfer (35), gas exchange (10), mucosal immunity (36), and more.

Further research classifying these unique ASVs and metagenomes identifying

21 upregulated gene pathways could be beneficial to expand understanding of the gill- specific microbes and their functions.

An ASV found in the skin of both the captive and wild spotted eagle rays shares a similar sequence identity (96.7%) with a Helcococcus that has been identified as part of the skin microbiome of Antarctic fur seals (37). Helcococcus has also been found in the blowhole of a striped dolphin (Stenella coeruleoalba) (34) as well as in the Antarctic fur seals, indicating that this bacterium could be part of a normal flora for marine animals.

The thick mucus layer of ray skin may provide a unique habitat niche for this microbe, as

Helcoccocus spp. are typically facultatively anaerobic (38).

Cloaca microbiomes for both captive and wild rays had the lowest alpha diversity.

The most abundant members were a Photobacterium and a Vibrio, which are members of the family Vibrionaceae. Spotted eagle rays in the Ocean Voyager exhibit are fed hard- shell clam, blue crab, surf clam, shrimp, squid, whelk, and scallops. In the wild, they have been known to eat a variety of benthic species including crustaceans, molluscs, gastropods, hermit crabs, and teleost fish (8, 39). Although we do not have microbiome analyses from the ray food, the wide beta diversity dispersal (Figure 1) and low alpha diversity (Figure 2) of the cloaca samples suggests that the communities could be transient and influenced by their diet. The prevalence of Vibrionaceae is consistent with analyses of fish gut microbiomes, including an increased abundance in post-settlement of reef fish (2, 40). Photobacterium and Vibrio are present in both the wild and captive water column microbiomes but at much lower abundances, which suggests an enrichment of these bacteria in the cloaca niche-site.

22 Another interesting, although unanticipated, finding of this study is the cycling of the skin community after monogenean trematode infection of captive individuals D1 and

S2. If we assume, with a consistent diet and water column microbiome, that any microbiome samples at a time point without a disturbance like an infection or treatment can be considered “normal” or the base community for that individual animal, then the skin microbiome of D1 returned to normal after the infection. Although we do not have a skin sample from prior to the infection for individual S2, the skin microbiome of the untreated sample appears to be similar in composition to that of the other rays.

Monogeneans attach to the skin and gills of fish but infections in elasmobranchs do not typically reach high enough densities in the wild to cause harm to the animals (22).

However, in confined environments such as aquariums they can become a substantial problem.

Evidence from our study indicates that infection by monogenean trematodes results in a simplified community on the skin with existing taxa, potentially due increased immune functions that disrupt the community and allow those taxa to outcompete other weakened community members to take over a larger niche-space. However, these results appear to be temporary, as the skin microbiome had returned to “normal” within 4 months of the treatment for animal D1. The gill microbiome showed less disturbance in the community, although the infection was concentrated in the gills. Stability could imply that the microbial community members of the gill are more specialized for functions like those described above. Understanding of this effect is limited, as it was an unexpected finding of the larger body site microbiome study. Further work on the effect of monogenean infection on microbiomes of rays could potentially result in use of regular

23 microbiome samples being used to indicate the presence of an infection. These monogenean infections are common in aquaculture and aquariums and further work on the effect of the infection or the treatments on the healthy niche-site microbiomes could be beneficial for these industries.

4.1 Conclusions

Prior to this work, spotted eagle ray (Aetobatus narinari) body-niche site microbiomes had not been well-characterized. Elasmobranchs are an important part of the marine ecosystem and many species are threatened due to anthropogenic effects and low fecundity, therefore it is important to expand our understanding of these animals and of marine host-associated microbiomes. Microbial communities of the skin, gills, and cloaca are strongly dependent on the environment, exhibiting clear separation between wild and captive populations. However, some taxa are consistent throughout both the wild and captive ray populations, which suggests that these microbes could be playing a role in host functions. Although effects of sex, disc width, and weight were not seen in this study, it is possible that other factors not included such as diet, age, host genetics or population density also play a role in the microbiome communities of ray body sites.

While microbiomes vary across environments, they appear to be stable within an individual animal across multiple time points. Individual animals with an infection showed that the skin community was affected but returned to a normal composition within months. Overall, these results describe variation in body-site microbiomes of wild and captive spotted eagle rays due to changes in the environment but stability within individuals across time points, indicating that the microbial communities are an important part of host health.

24 APPENDIX A: METADATA FOR RAY AND WATER SAMPLES

Table 4A. Metadata for spotted eagle ray and water samples.

Sample Date Disc Width Weight Sample ID Type Host Type Sampled Habitat Sex (cm) (kg) 180424T1-G Gill Eagle Ray 4/24/18 Wild Male 60-90 0-20 180424T1-S Skin Eagle Ray 4/24/18 Wild Male 60-90 0-20 180424T2-C Cloaca Eagle Ray 4/24/18 Wild Female 60-90 0-20 180424T2-G Gill Eagle Ray 4/24/18 Wild Female 60-90 0-20 180424T2-S Skin Eagle Ray 4/24/18 Wild Female 60-90 0-20 180424T3-C Cloaca Eagle Ray 4/24/18 Wild Female 150+ 60+ 180424T3-G Gill Eagle Ray 4/24/18 Wild Female 150+ 60+ 180424T3-S Skin Eagle Ray 4/24/18 Wild Female 150+ 60+ 180426T1-C Cloaca Eagle Ray 4/26/18 Wild Male 150+ 60+ 180426T1-G Gill Eagle Ray 4/26/18 Wild Male 150+ 60+ 180426T1-S Skin Eagle Ray 4/26/18 Wild Male 150+ 60+ 180426T2M-G Gill Eagle Ray 4/26/18 Wild Male 150+ 60+ 180426T2M-S Skin Eagle Ray 4/26/18 Wild Male 150+ 60+ 180426T2M-C Cloaca Eagle Ray 4/26/18 Wild Male 150+ 60+ 180427T1-C Cloaca Eagle Ray 4/27/18 Wild Female 90-120 20-40 180427T1-G Gill Eagle Ray 4/27/18 Wild Female 90-120 20-40 180427T1-S Skin Eagle Ray 4/27/18 Wild Female 90-120 20-40 180501TIF-C Cloaca Eagle Ray 5/1/18 Wild Female 60-90 0-20 180501TIF-G Gill Eagle Ray 5/1/18 Wild Female 60-90 0-20 180503T1-C Cloaca Eagle Ray 5/3/18 Wild Male 150+ 40-60 180503T1-G Gill Eagle Ray 5/3/18 Wild Male 150+ 40-60 180503T1-S Skin Eagle Ray 5/3/18 Wild Male 150+ 40-60 180503T2-G Gill Eagle Ray 5/3/18 Wild Female 90-120 0-20 180503T2-S Skin Eagle Ray 5/3/18 Wild Female 90-120 0-20 180503T3M-G Gill Eagle Ray 5/3/18 Wild Male 120-150 20-40 180503T3M-S Skin Eagle Ray 5/3/18 Wild Male 120-150 20-40 180503T3M-C Cloaca Eagle Ray 5/3/18 Wild Male 120-150 20-40 180503T4M-C Cloaca Eagle Ray 5/3/18 Wild Male 120-150 20-40 180503T4M-G Gill Eagle Ray 5/3/18 Wild Male 120-150 20-40 180504T2M-G Gill Eagle Ray 5/4/18 Wild Male 150+ 40-60 180504T2M-S Skin Eagle Ray 5/4/18 Wild Male 150+ 40-60 180504TIF-C Cloaca Eagle Ray 5/4/18 Wild Female 150+ 60+

25 Table 4A (continued)

180504TIF-G Gill Eagle Ray 5/4/18 Wild Female 150+ 60+ 180504TIF-S Skin Eagle Ray 5/4/18 Wild Female 150+ 60+ Longboard- Water Water Water 5/3/18 Wild Water Water Water Motedock- Water Water Water 5/4/18 Wild Water Water Water Redroof-Water Water Water 5/4/18 Wild Water Water Water OV022618 Water Water 2/26/18 OV Water Water Water OV022819 Water Water 2/28/19 OV Water Water Water OV031518 Water Water 3/15/18 OV Water Water Water OV062118 Water Water 6/21/18 OV Water Water Water OV070518 Water Water 7/5/18 OV Water Water Water OV110118 Water Water 11/1/19 OV Water Water Water OV112918 Water Water 11/29/18 OV Water Water Water SER-C1-G Gill Eagle Ray 2/28/18 OV Female 120-150 40-60 SER-C2-G Gill Eagle Ray 3/1/18 OV Male 120-150 20-40 SER-C3-C Cloaca Eagle Ray 3/1/18 OV Male 120-150 20-40 SER-C3-G Gill Eagle Ray 3/1/18 OV Male 120-150 20-40 SER-C3-S Skin Eagle Ray 3/1/18 OV Male 120-150 20-40 SER-S1-C Cloaca Eagle Ray 2/28/18 OV Female 120-150 40-60 SER-S1-G Gill Eagle Ray 2/28/18 OV Female 120-150 40-60 SER-S1-S Skin Eagle Ray 2/28/18 OV Female 120-150 40-60 SER-C3-C2 Cloaca Eagle Ray 11/12/18 OV Male 120-150 20-40 SER-D1-S Skin Eagle Ray 11/14/18 OV Male 120-150 20-40 SER-G1-C Cloaca Eagle Ray 11/13/18 OV Male 120-150 20-40 SER-K1-C Cloaca Eagle Ray 11/12/18 OV Male 120-150 20-40 SER-M1-C Cloaca Eagle Ray 11/15/18 OV Male 90-120 20-40 SER-M2-C Cloaca Eagle Ray 11/13/18 OV Male 120-150 20-40 SER-N1-G Gill Eagle Ray 11/15/18 OV Male 120-150 20-40 SER-O1-C Cloaca Eagle Ray 11/11/18 OV Female 120-150 40-60 SER-D1-G2 Gill Eagle Ray 11/14/18 OV Male 120-150 20-40 SER-S2-S Skin Eagle Ray 6/27/18 OV Female 150+ 40-60 SER-C2-G2 Gill Eagle Ray 7/1/18 OV Male 120-150 20-40 SER-C2-S Skin Eagle Ray 7/1/18 OV Male 120-150 20-40 SER-D1-C Cloaca Eagle Ray 7/1/18 OV Male 120-150 20-40 SER-D1-G Gill Eagle Ray 7/1/18 OV Male 120-150 20-40 SER-D1-S2 Skin Eagle Ray 7/1/18 OV Male 120-150 20-40 SER-D1-C2 Cloaca Eagle Ray 3/8/19 OV Male 120-150 20-40

26 Table 4A (continued) SER-D1-S3 Skin Eagle Ray 3/8/19 OV Male 120-150 20-40 SER-D1-G3 Gill Eagle Ray 3/8/19 OV Male 120-150 20-40 SER-S2-S2 Skin Eagle Ray 3/6/19 OV Female 150+ 40-60

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