Distinct Bacterial Composition Associated with Different Laboratory-Cultured Aiptasia Strains Across Two Thermal Conditions

Distinct Bacterial Composition Associated with Different Laboratory-cultured Aiptasia Strains Across Two Thermal Conditions

Thesis by Hanin Ibrahim Ahmed

In Partial Fulfillment of the Requirements For the Degree of Master of Science

King Abdullah University of Science and Technology Thuwal, Kingdom of Saudi Arabia

May, 2018

1 EXAMINATION COMMITTEE PAGE

The thesis of Hanin Ibrahim Ahmed is approved by the examination committee.

Committee Chairperson: Manuel Aranda Committee Members: Christian R Voolstra, Xosé Anxelu G. Morán

3 ABSTRACT Distinct bacterial composition associated with different laboratory-cultured Aiptasia strains across two thermal conditions Hanin Ibrahim Ahmed Coral reefs are crucial for the ecological sustainability of the oceans, yet, increasing sea surface temperature is threatening these ecosystems globally. Microbial communities associated with corals have become a recent research focus, as the associated microbiome may contribute to coral resilience to environmental stressors, e.g., heat stress. However, research in this area is hampered by the difficulty of working with corals. This study aims to use Aiptasia, a sea anemone, as a tractable laboratory model system to study the role of the coral microbiome. Analyses of the bacterial compositions associated with different Aiptasia strains across two temperatures (25 °C and 32 °C), based on 16S rRNA gene sequencing. This study aims also to identify a “core” microbiome associated with heat stress acclimation, as well as host-specific differences. In general, results showed that bacterial composition associated with Aiptasia strains differs significantly with temperature. Higher bacterial diversity and richness were observed when all Aiptasia strains were placed under heat stress. Moreover, results showed an increase in beta diversity and dispersion of bacterial communities in response to heat stress. These changes in the bacterial composition are in line with the recently described “Anna Karenina principle” for animal microbiomes, which suggests that the microbiomes of unhealthy individuals vary more than healthy and stable individuals. This study further shows that while temperature had the greatest effect on structuring the bacterial compositions, there were some variations better attributed to batch and host effects. This suggests that technical aspects have to be carefully addressed in the framework of microbiome studies. Members of a putative “core” microbiome associated with 32 °C Aiptasia have been identified as indicator species of heat stress (i.e., Francisella sp.,). Previous reports have shown that these indicator taxa are associated with saline environments and can tolerate high temperatures. Putative functional profiles based on taxonomic inference of associated bacterial taxa (i.e., enrichment and depletion of various metabolic processes) were also identified, implying functional differences of the microbiomes associated with Aiptasia strains in response to heat stress. Future studies

4 should more specifically examine how the microbiome influences the animal ability to respond to environmental changes.

5 ACKNOWLEDGEMENTS

First and foremost, I would like to offer my sincere gratitude to my supervisor Prof. Manuel

Aranda who has supported me throughout my research. I am very grateful for the continuous support of my thesis, for his patience, motivation and knowledge. I attribute the level of my master degree to his assistance and effort. His office was always open whenever

I had questions about my research or even any unrelated questions.

Beside my supervisor, I would like to express my gratitude to the members of examination committee; Prof. Christian Voolstra who gave useful advice that helped in addressing some of the results, and Prof. Xelu Moran for taking the time to examine the thesis.

Special thanks to Marcela Herrera for her guidance in all the time of the research and help in writing this thesis. She spent a lot of time to teach me and encourage me.

I am very thankful to Dr. Yi Jin Liew for his support, help and assistance. My thanks also go to all my fellow labmates, for their friendly smiles and a hello every time we met.

I also want to extend my gratitude to all my friends who had a good influence on my life. I also would like to thank my colleagues and the department faculty for making my time great at KAUST. The bioscience core lab has provided the support and equipment I have needed to produce and complete my research.

Finally, I want to send my deepest gratitude to my parents and to my sisters for providing me with continuous support and encouragement throughout my higher studies.

6 TABLE OF CONTENTS

EXAMINATION COMMITTEE PAGE ...... 2 ABSTRACT ...... 3 ACKNOWLEDGEMENTS ...... 6 TABLE OF CONTENTS...... 7 LIST OF ABBREVIATIONS ...... 8 LIST OF FIGURES ...... 9 LIST OF TABLES ...... 10 1. INTRODUCTION ...... 11 1.1 Coral reefs in a changing environment ...... 11 1.2 The coral holobiont ...... 11 1.3 Coral-associated microbial communities and environmental stressors ...... 15 1.4 Aiptasia sp. as model organism to study the coral holobiont ...... 16 2. PROJECT OBJECTIVES, RATIONALE AND SIGNIFICANCE ...... 19 3. MATERIALS AND METHODS ...... 20 3.1 Aiptasia Rearing ...... 20 3.2 DNA extraction ...... 20 3.3 16S rRNA gene amplification and sequencing ...... 21 3.4 Sequencing data processing ...... 21 3.5 Bacterial community analysis ...... 23 4. RESULTS ...... 25 4.1 Distinct bacterial microbiomes of Aiptasia and rearing water ...... 25 4.2 Temperature has the largest effect on the microbiome ...... 28 4.3 Aiptasia reared at higher temperatures display greater microbiome variability ...... 33 4.4 Aiptasia “core” microbiome responds to heat stress ...... 36 4.5 Host-specific microbiomes ...... 42 4.5.1. Red Sea Aiptasia microbiome ...... 42 5. DISCUSSION ...... 46 5.1 The microbiome of Aiptasia and rearing water ...... 46 5.2 Temperature has an effect on the microbiome compositions ...... 47 5.3 Treatment has the highest effect on structuring the bacterial community ...... 49 5.4 “Core” microbiome and indicator taxa ...... 50 5.5 Taxonomy-based functional profiling of bacterial communities change with temperature ..... 52 6. CONCLUSION ...... 54 7. REFERENCES ...... 55 8. APPENDICES ...... 64

7 LIST OF ABBREVIATIONS

AMOVA Analysis of Molecular Variance CaCO3 Calcium carbonate DMSP Dimethylsulfoniopropionate DMSO Dimethyl sulfoxide DSCs Dimethyl Sulfate Compounds OTU Operational taxonomic unit PCoA Principal Coordinate Analysis ppt Parts per thousand PSU Practical salinity unit ROS Reactive Oxygen Species RS Red Sea SSTs Sea surface temperatures UV Ultraviolet

8 LIST OF FIGURES

Figure 1. Interactions between the coral host and its symbiotic microbial groups ...... 13 Figure 2. Coral polyp anatomy and the colonization of Symbiodinium and microbial consortia within tissues ...... 15 Figure 3. Characterization of Aiptasia clonal lines (CC7 (male), F003 (female) and H2 (female) ...... 18 Figure 4. Individual rarefaction curves showing the OTU richness as a function of sequencing depth for the subsampled dataset (n = 15,456 sequences per sample)...... 22 Figure 5. Bacterial community composition of Aiptasia on the phylogenetic level of family (Greengenes database, bootstrap ≥ 60) ...... 26 Figure 6. Principal coordinate analysis based on Bray–Curtis dissimilarity of the bacterial communities ...... 27 Figure 7. Principal coordinate analysis based on Bray–Curtis dissimilarity of the bacterial communities...... 28 Figure 8. Cluster analysis of all samples based on pairwise beta-diversity values, which were estimated using Spearman rank correlation (ρ) and clustered using Ward’s method ... 29 Figure 9. Cluster analysis of each strain based beta-diversity showing that the temperature effect is higher than the batch effect...... 30 Figure 10. Correlation analyses based on beta diversity shows that the host genotype has higher effect than the symbiont type in structuring the microbiome ...... 32 Figure 11. Alpha diversity and richness measurements based on observed OTUs, Chao1, Inverse Simpson and Simpson’s evenness ...... 34 Figure 12. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity of OTU abundances reveals dispersion in bacterial composition of Aiptasia under heat stress 35 Figure 13. Heatmap displaying putative functional differences based on the bacterial communities of different Aiptasia groups across two temperatures...... 41

9 LIST OF TABLES

Table 1. Summary statistics (mean ± SD) of 16S rRNA gene sequencing of the bacterial communities associated with different laboratory-cultured strains of Aiptasia at 25 °C and 32 °C...... 33 Table 2. Aiptasia “core” bacterial OTUs. Only OTUs present in at least 75% of the anemone samples were selected ...... 38 Table 3. Indicator bacterial OTUs associated with Aiptasia across both temperatures ...... 40 Table 4. “Core” bacterial microbiome members of different Aiptasia strains regardless of temperature ...... 43 Table 5. “Core” bacterial microbiome members of Red Sea Aiptasia ...... 44

Table A1. The number of reads remains of each sample after the different steps that were used by mothur software to remove unwanted sequences ...... 64 Table A2. The abundance (shown in present) of phyla taxa and the Proteobacteria composition of Aiptasia (Greengenes database, bootstrap ≥ 60) ...... 67 Table A3. Excel file of "core" bacterial OTUs of all 25 °C and 32 °C Aiptasia groups, their annotation, the NCBI's accession number, and the references are shown ...... 67 Table A4. Excel file of "core" bacterial OTUs of Red Sea Aiptasia at 25 °C and 32 °C, their annotation, the NCBI's accession number, and the references are shown ...... 67

10 1. INTRODUCTION

1.1 Coral reefs in a changing environment Coral reefs are one of the most biologically diverse and productive ecosystems in the world [1]. Yet, they are severely threatened by anthropogenic activities such as coastal development, pollution and overfishing; and global changes like ocean warming and acidification [2]. These stressors have multiple, conflicting effects on reducing ecological functions and ecosystem resilience [3]. The extent to which different stressors may contribute to affecting these ecosystems, bleaching and mortality of corals, however, is still unknown and uncertain. Temperature is an essential factor in determining the distribution and diversity of marine organisms and ecosystems. Increasing sea surface temperatures (SSTs) due to global warming has caused the loss of almost 30% of coral reefs worldwide in the last 20-30 years [2, 4], and it has been predicted that more than 90% of world's coral reefs will die by 2050 [5]. This thermal stress causes corals to expel the microalgae that live in their tissues (i.e., a phenomenon known as coral bleaching), as this stops its normal functionality and no longer produces the required nutrients for the coral. In addition to high temperatures, several studies [6-11] have shown that coral bleaching can also be induced by other factors, including low salinity, increasing UV irradiation, bacterial infection, and eutrophication. Although coral bleaching is not always lethal, it is still the main reason for coral death [12]. Rising water temperatures also affect coral growth, reproduction and metabolism. Pollution and other human activities are further affecting coral reefs through ocean acidification, resulting from the uptake of high concentration of atmospheric carbon dioxide by the seawater [2, 13]. This excess of carbon dioxide reduces the water pH and decreases the carbonate ion concentration in the sea, which is important for corals and other marine organisms to produce and maintain their calcium carbonate skeletons.

1.2 The coral holobiont All animals and plants are meta-organisms that live and interact together with a highly diverse and specific composition of microbes that provide functions linked to metabolism, immunity and adaptation to the environment, among others [14]. The functional unit of

11 coral reefs is the coral holobiont, a mutualistic partnership between the cnidarian host (i.e., the coral animal), endosymbiotic photosynthetic algae of the genus Symbiodinium and its associated microbial consortia (i.e., bacteria, archaea, fungi, viruses, and protists; Figure 1) [6, 15]. This complex association provides the foundation for one of the most diverse ecosystems on the planet. Yet, changes in this relationship can affect the health and functioning of the coral holobiont; breakdown of the host-dinoflagellate symbiosis and disequilibrium in the dynamics of the microbial community can lead to bleaching and death [6]. Hence, knowledge of each component and their respective interactions within the holobiont framework is essential to understand how corals respond to environmental changes such as ocean warming, ocean acidification, and eutrophication. The coral animal (i.e., colonies of many polyps) consists of three main layers (Figure 2): (1) the ectodermal and gastrodermal tissues, which are covered by a (2) mucus layer and sit on a (3) calcium carbonate (CaCO3) skeleton. Together, this structure provides different habitats for the associated microorganisms [16]. Symbiodinium dominates the first intracellular habitat (i.e., the gastrodermal layer, residing in its own habitat: specific membrane vesicles termed “symbiosomes”. These algal endosymbionts provide the coral with nutrients and energy by translocating photosynthetically fixed carbon to the host [17]. This is thought to be primarily glucose, which is used for vital functions such as reproduction, growth, and biocalcification [18, 19]. Moreover, a large amount of oxygen is produced due to photosynthesis and later used by the coral and its associated microorganisms for respiration [4, 6, 20]. Symbiodinium are also large producers of dimethyl sulfate compounds (DSCs), which function as osmolytes and antioxidants for both the algae and coral [21, 22]. Further, this nutrient source for the associated bacteria is also likely to play a role in structuring the microbiome that cycle carbon, nitrogen and sulfur within the coral holobiont [23, 24].

Figure 1. Interactions between the coral host and its symbiotic microbial groups. Numbers illustrate examples of possible roles and relationships between the coral and its symbionts: (1) photosynthate transferase, (2) nitrogen cycling, (3) main carbon source supplier and production of DMSP, (4) provides shelter and protection, it also has a role in the nutrient cycles, (5) gene transfer, (6) nutrient cycling such as S, C and N, and DMSP degradation, (7) shelter, protection and source of nutrients, and (8) antimicrobial activity, also has a role related to biomineralization, protection to skeletogenic cells and protection against UV. Letters indicate examples of possible relationships amongst the coral symbionts: (A) availability of C and S source through DMSP catabolism, (B) availability of N source, (C) transfer of beneficial genes, (D) availability of N source, (E) provides C and S source through DMSP production, and (F) nutrients exchange. Taken and modified from [25].

13 On the other hand, bacteria colonize mainly the mucus layer [26] but can also be in internal tissues including the gastrodermal cavity and skeleton; and a distinct bacterial assemblage can be found within each compartment [16, 27]. The mucus layer is a habitat for different beneficial bacteria, including nitrogen fixers and chitin decomposers [28-30]. Bacterial associations play a crucial role in the coral immunity by producing antibiotics and controlling infections that ultimately improve disease resistance [31-33]. Moreover, the endolithic microbial communities, which include cyanobacteria, supply almost 50% of the total host nitrogen needs, and have an active role in protecting the coral during bleaching events through photosynthetic activity. Diazotrophs (i.e., nitrogen-fixing bacteria) may also provide fixed nitrogen to Symbiodinium and to the coral animal itself [34]. In the early life stages of the coral, bacteria provide nitrogen to Symbiodinium [35] and likely to the coral larvae [36]. They also play an important role in larval recruitment and settlement [37]. These endolithic microbes also translocate nutrients from the skeleton to the coral tissue and keep the coral alive until re-colonization of Symbiodinium [33, 38-40]. Furthermore, bacteria also contribute to carbon and sulfur cycling [24, 41, 42], phosphorous fixation, metal homeostasis, organic remediation and production of secondary metabolites [43]. Groups of coral-associated bacteria such as Flavobacteriaceae [44], Pseudomonas and Oceanospirillales [45] can metabolize dimethylsulfoniopropionate (DMSP) and use its products for their metabolic processes. The catabolism of DMSP generates sulfur-based antimicrobial compounds that can inhibit the growth of coral pathogens like Vibrio coralliilyticus and V. owensii [46]. Bacteria and algae interactions can be affected by environmental stresses and in turn, affect the holobiont as a whole [6]. Thus, the manipulation of these key microbial groups may promote coral health through the regulation of critical symbiotic populations, antimicrobial productions and nutrient sources (Figure 2).

14 Figure 2. Coral polyp anatomy and the colonization of Symbiodinium and microbial consortia within tissues. (A) shows close up of the mucus layer, epidermis and upper gastrodermis, and (B) shows close up of the gastrovascular cavity. Taken from [6].

1.3 Coral-associated microbial communities and environmental stressors Recent research is focusing on understanding the interactions between the host, the microbiome and the environment [47-51]. It is known that variations in environmental parameters such as temperature, salinity and pH, and excessive nutrient loading can change a healthy stable coral microbiome to a disease unstable state [52, 53], but the mechanisms by which this transition occurs is still poorly understood. Corals have long generation times and so they might not be able to adapt to fast changes in the environment, ergo, the microbiome could potentially help them coping with new conditions on a short timescale [54]. This is the case of corals exposed to higher than normal sea surface temperatures [6, 55, 56], as it has already been shown that the microbiota changes during bleaching events [20, 48] and that some bacterial taxa are critical

15 for the host survival [57, 58]. For example, several studies have reported bacteria of the genus Endozoicomonas (order Oceanospirillales) as the most dominant taxa in different coral species; suggesting even a host-bacterial coevolution for some taxa [58-59]. Interestingly, these bacteria were found to be underrepresented during climate aberration [59, 60] Stressors in general have been observed to cause an increase in the abundance of some opportunistic and pathogenic taxa, particularly from the orders Vibrionales, Flavobacteriales, Rhodobacterales, Alteromonadales and Rhodospirillales just to mention a few [48, 61-63]. Yet, it remains unclear to which degree single strains of bacteria play a role in the tolerance or susceptibility to environmental stressors. Other studies [53, 67] have also reported shifts in the microbial composition of corals due to pollution, suggesting this as a potential early indicator of anthropogenic impacts on coral reefs. The same has been observed with overfishing [53, 64]. Changes in the coral-associated bacterial communities due to stressors can ultimately disrupt the holobiont's functioning by losing beneficial bacterial taxa or gaining pathogens likely to cause diseases [6, 25, 65]. Thus, knowledge on the microbial associations and its contribution to the host phenotype is necessary to better comprehend mechanisms of acclimatization and persistence of species to changing environments [53]

1.4 Aiptasia sp. as model organism to study the coral holobiont Corals are notoriously difficult to maintain in aquariums and studying them in situ is not always possible, which makes lab-based molecular and some ecological work challenging [66]. In contrast, the tropical sea anemone Aiptasia sp. has a fast growth rate, is able to produce large clonal populations under laboratory conditions, and the Aiptasia community has developed extensive genomic [67], transcriptomic [68, 69], proteomic [70] and eco- physiological resources [71-74]. Therefore, it has been adopted as a model system for coral research and has been used for a wide range of experiments during the last 30 years [66, 75, 76]. As such, these anemones have been beneficial in revealing the molecular and cellular, tissue-specific and organismal processes that underlie the cnidarian-dinoflagellate symbiosis. Like most of the reef building corals, Aiptasia is an anthozoan and lives in symbiosis with dinoflagellates of the genus Symbiodinium. However, unlike corals, Aiptasia

16 can also thrive in an aposymbiotic (i.e., symbiont-free) state for extended periods of time [75, 77] and be re-infected with different Symbiodinium types [78]. Furthermore, current research is also focusing on host-microbe interactions in this model system; particularly on bacteria but also on other poorly understood members of the coral holobiont like Archaea, viruses and diverse eukaryotic symbionts [65, 79, 80]. Understanding the interactions between coral pathogens and their hosts is, for example, of critical importance for the conservation of coral reefs as diseases are becoming increasingly more prevalent and frequent [81-83]. A good model system to study the virulence mechanisms of diseases and better understand the complex interplay between the coral host, its associated microbial community and invading pathogens is then needed, and Aiptasia has been shown to be a useful ‘surrogate’ host [84]. Two genetically distinct Aiptasia populations have been recently identified [85], a globally distributed lineage (H2) found in Hawaii, Japan, Mexico, the Mediterranean and Australia, and a local lineage (CC7) found in the South Atlantic coast of USA. Both lineages harbor different Symbiodinium species (Figure 3): H2 seems to occur exclusively in symbiosis with S. minutum (clade B) while CC7 can form stable associations with different Symbiodinium combinations (clade A and B). These symbiont specificities not only suggest coevolution in the Aiptasia-Symbiodinum relationship [85, 86] but also brings into question how host-specific the associations with other microbes are. Bacterial composition of Aiptasia has not been studied until recently [87-89]. Brown et al. (2016) examined wild populations from 10 different locations worldwide and revealed a high bacterial richness. Interestingly, the authors showed that geographic location and environmental factors, but not host specificity, determine the bacterial composition of this sea anemone. On the other hand, other studies [89, 90] have shown that the bacterial associations of lab-cultured H2 and CC7 Aiptasia strains (which belong to the global and local lineages respectively) are significantly different, suggesting a species-specific microbiota. Distinct microbiomes were also observed according to symbiotic states, suggesting some bacteria might be functionally important for the host-algal symbioses [88]. Moreover, changes in the bacterial composition after rearing animals in captivity have also been documented, highlighting the temporal dynamics of microbial communities [87]. Cultured isolates of Aiptasia-associated bacteria have recently been obtained [88]. This is promising, as it provides the foundation for future functional studies with the ultimate goal

17 of identifying bacteria that affect the holobiont phenotype in response to environmental changes such as increasing temperatures.

Figure 3. Characterization of Aiptasia clonal lines (CC7 (male), F003 (female) and H2 (female). (A) Maximum likelihood tree of ~2 kb concatenated SCAR (sequence characterized amplified region) genotyping markers showing Aiptasia two distinct genetic populations. Clonal lines are clustered according to their endogenous Symbiodinium spp. (B) Phenotype of Aiptasia from the CC7 lineage. Taken and modified from [86].

18 2. PROJECT OBJECTIVES, RATIONALE AND SIGNIFICANCE Sea surface temperatures have reached the highest limits ever since in the history of human civilization, driving irreversible effects in the world’s oceans [91, 92]. As a matter of fact, global coral bleaching events have been projected to be more recurrent and severe in the future [92]. It is estimated that the Great Barrier Reef, the world’s largest reef system, has lost 50% of its coral cover [93]; and that coral reefs worldwide might disappear by 2050 [92]. Therefore, studying the coral holobiont in a more comprehensive framework is crucial to better comprehend the susceptibility and resilience of corals to environmental stresses and diseases. The microbiome has been shown to have a role in the response of corals to heat stress [56], even suggesting microbial adaptation as a possible mechanism for thermal resilience. The aim of this study is to assess the changes of bacterial compositions associated with Aiptasia strains under heat stress through 16S rRNA gene sequencing and to determine potential “core” microbiome members associated to heat stress acclimation. Moreover, examining the bacterial compositions of Aiptasia strains, regardless of the temperature treatment, will allow assessing host-specific differences in bacterial communities. To do so, I will meet the objectives outlined below.

(1) Find bacterial indicator taxa of heat stress. To do this I will do 16S rRNA gene sequencing to examine the composition and structure of the bacterial community of Aiptasia that have been long-term acclimated to optimal culture (25 °C) and heat stress (32 °C) conditions. (2) Examine host-specific differences. I will characterize the bacterial diversity of different Aiptasia strains (H2, CC7, CC7 harboring Symbiodinium clade SSB01 − originally isolated from H2 – and RS). Comparing between (1) different symbionts within the same host line (CC7 vs. SSB01) and (2) different hosts with the same symbiont (H2 vs. SSB01), will allow me to disentangle their role in shaping the bacterial microbiome of Aiptasia. (3) Describe the Red Sea Aiptasia bacterial microbiome. The Red Sea is one of the warmest (up to 35°C in summer) and most saline (~41 PSU in Gulf of Aqaba) bodies of water in the planet, and it still warming faster than the global average [94]. Studying the bacterial community of Red Sea (RS) Aiptasia, which remains uncharacterized, could provide a model for understanding the relationship between bacteria and heat tolerance.

19 3. MATERIALS AND METHODS

3.1 Aiptasia Rearing Symbiotic Aiptasia of the clonal strains H2 [95] and CC7 [69], in addition to CC7 SSB01 (aposymbiotic CC7 animals infected with clade B Symbiodinium strain SSB01 [67]) and a Red Sea (RS) line (collected from the Saudi Arabian coast of the southern Red Sea, 20°08.272 N 40°13.303 E, in March 2015), were reared in the laboratory as described in [88, 89]. Animals were cultured in clear polycarbonate tanks (2 L capacity; Cambro Camwear, Huntington Beach, CA) filled with autoclaved natural seawater collected from the Red Sea (~39 PSU salinity and pH ~ 8). Tanks were kept in Intellus Control System Incubators (Model I-22LLVL, Percival Scientific, USA) incubators at 25 °C and 32 °C on a 12 h light: 12 h dark cycle under white-light (20-40 μmol photons m-2 s-1 of photosynthetically active radiation). Animals were fed with freshly hatched Artemia brine shrimp 3 times a week, and seawater in the tanks was changed 1-2 times per week. Food supply was interrupted 1 week prior to experiments in order to avoid potential Artemia contamination. All tanks were maintained under the same conditions described above with the exception of temperature.

3.2 DNA extraction Six anemones and two water samples were taken from two replicate tanks for each strain in both 25 °C and 32 °C treatments (24 anemone and 8 water samples for each treatment for a total of 64 samples) and processed according to Röthig and Costa et al. (2016) and Herrera et al. (2017). Briefly, 3 Aiptasia polyps of ~ 1.0 cm size were collected from each replicate tank and transferred into 1.5 mL microtubes, and the remaining water was carefully removed. DNA extractions were done using a spin column DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. Bacterial DNA from water was isolated by collecting 500 mL water from each tank in which the anemones were reared. Water was filtered through a 0.22 μm Durapore PVDF filter (Millipore, Billerica, MA, USA), and the filters were then cut in strips using a sterile razorblade, transferred into a 1.5 mL microtube and incubated in 400 μL AP1 buffer from DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) for 20 minutes on a rotating wheel. Further DNA extraction procedures followed

20 the manufacturer’s protocols. DNA concentrations of all samples were quantified on a NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

3.3 16S rRNA gene amplification and sequencing Regions V5 and V6 of the 16S rRNA gene were amplified using primers 784F [5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGAGGATT−AGATACCCTGGTA 3′] and 1061R [5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGA−CAGCRRCACGAGCTGACGAC 3′] [96] with Illumina (San Diego, CA, USA) adapter overhangs (underlined above). For each sample, PCRs were performed in triplicate using the Qiagen Multiplex PCR kit, with 25-50 ng DNA template from each anemone sample and ~ 3 ng DNA of the water samples and a final primer concentration of 0.6 μM in 15 μL final reaction volume. PCRs were performed as follows: an initial activation step of 15 min at 95 °C, 27 cycles each of 30 s at 95 °C, 90 s at 55 °C, and 30 s at 72 °C followed by a final extension step of 10 min at 72 °C. Triplicate PCRs for each sample were pooled, and subsequently cleaned using the Agencourt AMPure XP magnetic bead system (Beckman Coulter, Indianapolis, IN, USA) and indexed with Nextera XT barcoded sequencing adapters (Illumina, San Diego, CA, USA) and cleaned again following the manufacturer’s instructions. Indexed PCR products were then quantified with QuBit (dsDNA Broad Range Assay Kit, Thermo Fisher Scientific, Waltham, MA, USA) and pooled in equimolar ratios. Pooled samples were run in an 2% agarose gel electrophoresis to isolate and purify the final library (MinElute Gel Extraction Kit; Qiagen, Hilden, Germany) to then quality checked on the BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA) and sequenced at 6 pM with 12% phiX on the Illumina MiSeq, 2 × 300 bp paired-end v3 chemistry according to the manufacturer’s instructions.

3.4 Sequencing data processing Sequencing data set comprised 12,405,770 sequence reads. Reads were demultiplexed and adapters were removed with MiSeq Reporter (v.2.4.60.8; Illumina, San Diego, CA, USA). Data was analyzed using the software mothur [97] following the same procedures described in Röthig and Costa et al. (2016) and Herrera et al. (2017). Sequences were assembled into 12,405,770 contigs, trimmed and quality filtered (i.e., sequences with ambiguous bases, long homopolymers (>5) and insufficient length were removed). Singletons were removed

21 (3,887,923), and the remaining sequences were aligned against the SILVA database (release 119; [98]) and preclustered (2 bp difference; [99]). Chimeric sequences were removed (425,702) using VSEARCH [100] and sequences assigned to chloroplasts, mitochondria, archaea, eukaryotes, and other unwanted sequences were excluded (4,484; Table A1). Only sequences that were phylogenetically classified as bacteria (Greengenes database release gg_13_8_99; bootstrap = 60; [101]) were used for further analyses. From the resulting 4,977,746 sequences (average length of 294 bp) subsampling of 15,456 sequences per sample was done, which was sufficient to estimate for the majority of the bacterial diversity as shown by rarefaction analysis (Figure 4). Sequences were then clustered into Operational Taxonomic Units (OTUs) using a 97% similarity cutoff.

Figure 4. Individual rarefaction curves showing the OTU richness as a function of sequencing depth for the subsampled dataset (n = 15,456 sequences per sample). The curve becomes asymptotic as the OTU number saturates, indicating a sufficient coverage for each sample analyzed. OTUs were defined at 97% similarity level.

22 3.5 Bacterial community analysis Alpha-diversity indices, Principal Coordinate Analysis (PCoA) and Analysis of MOlecular VAriance (AMOVA; [102]) were performed as implemented in mothur. AMOVA was further used to test for differences in bacterial communities between temperatures and strains. A list of all defined OTUs and their distribution across samples was created using the commands make.shared, classify.OTU and get.OTUrep. Based on this we obtained a putative "core” bacterial microbiome (i.e., all OTUs that were present in 75% of the Aiptasia samples) for each treatment (25 °C and 32 °C) and strain (host-specific). Noted that the respective OTUs can also be present in water samples and be members of multiple microbiomes. Differences in the average abundance of shared OTUs between 25 °C and 32 °C “core” microbiomes were assessed using t-tests. Furthermore, the package IndicSpecies [103] executed in R [104] was used to identify OTUs that were significantly different between temperatures. This analysis was conducted on OTU counts for which abundance was more than 1% (i.e., OTUs below this threshold were removed) for each temperature tested following 999 permutations and a significance level (alpha) of 0.01. The representative sequence of each of these OTUs was then BLASTed against NCBI's GenBank nr database to identify previous occurrences of identical or similar bacterial sequences. In order to see differences between treatments beta-diversity distances were also computed for each of Aiptasia strain separately and visualized in PCoAs. As PCoAs show the distribution of data points along only two principal axes (i.e., they do not account for the overall variance present among all samples), multiple clustering analyses were performed on different subgroups of samples to assess the effect of (1) temperature, (2) strain (symbiont and host combination) and (3) batch on their respective bacterial compositions. For all clustering analysis, pairwise beta-diversity values were estimated using Spearman rank correlation (ρ), and clustered using Ward’s method. Distance calculation and plotting were performed with a custom Python script. METAGENASSIST (an automated taxonomic-to-phenotypic mapping tool) uses NCBI microbial taxonomy database containing phenotypic information for thousands of bacteria taxa that can be then used to predict probable functional profiles based on the 16S community composition [105]. Input files were created in mothur using the make.shared and classify.OTU commands. All 574 OTUs were averaged for each strain (i.e., 6 replicates

23 were merged) in each temperature and later assigned, mapped and condensed into 210 functional taxa, and later filtered based on interquartile range [106]. The remaining 200 functional taxa were normalized over sample by sum and over taxa by autoscaling. Data was further analyzed for "metabolism by phenotype" using the Spearman distance measure and Ward’s clustering method to visualize the results in a heatmap.

24 4. RESULTS

4.1 Distinct bacterial microbiomes of Aiptasia and rearing water Taxonomic composition of the bacterial communities of different Aiptasia strains that have been long-term cultured in the laboratory under the same conditions but temperature (25 °C and 32 °C) was examined. At least, 22 phyla, 59 classes, 109 orders, and 219 families were recovered from Aiptasia. From these phyla, Proteobacteria (72.89% and 76.46%) and Bacteroidetes (21.88% and 20.43%) were the most dominant phyla across all strains at 25 °C and 32 °C, respectively (Table A2). Furthermore, within Proteobacteria, Gammaproteobacteria (55.21%) was the most abundant class in Aiptasia reared at 25 °C, followed by Alphaproteobacteria (32.68%) and Deltaproteobacteria (6.83%), compared to anemones at 32 °C, which showed significantly lower abundances of the aforementioned classes (21.52%, 12.74% and 2.66% respectively). Classification of sequences on the family level showed remarkable differences in the bacterial consortia associated with Aiptasia reared at 25 °C and 32 °C (Figure 5). Overall, Aiptasia at 32 °C showed higher number of taxa than and were mostly dominated by Alteromonadaceae (10.45%), Rhodobacteraceae (9.03%) and Flavobacteriaceae (2.89%%) but in lower abundances than 25 °C (24.63%, 12.72% and 11.40% respectively). The family Saprospiraceae was, however, almost twice as abundant (7.21%) at 32 °C compared to 25 °C (3.38%) (Figure 5). Although bacterial composition of the water appeared to be generally different from Aiptasia, significant differences were only observed for H2 at 25 °C (pAMOVA =

0.023) and RS at 32 °C (pAMOVA = 0.014) (Figure 6). On average, Flavobacteriaceae and Rhodobacteraceae together made up ~ 40% of the sequences of water at 25 °C, followed by Alteromonadaceae (9.90%). In contrast, water at 32 °C was mainly composed of Rhodobacteraceae (24.90%), Alteromonadaceae (16.00%) and Flavobacteriaceae (13.40%). Water samples were excluded from subsequent analyses in order to focus on differences between Aiptasia at 25 °C and 32 °C.

Figure 5. Bacterial community composition of Aiptasia on the phylogenetic level of family (Greengenes database, bootstrap ≥ 60). Each color represents one of the 20 most abundant families across all samples. Less abundant taxa (< 1%), comprising 205 distinct families, are grouped under 'others'. Pie charts display the average composition of anemones at 25 °C (left) and 32 °C (right). Sample labels indicate strain CC7, H2, RS and SSB01, batch (1 and 2) and replicate number (1, 2 and 3) respectively.

Figure 6. Principal coordinate analysis based on Bray–Curtis dissimilarity of the bacterial communities. The analysis shows that water samples are not significantly different from Aiptasia with the exception of H2 at 25 °C and RS at 32 °C. Each panel shows a strain: (A) CC7, (B) H2, (C) RS and (D) SSB01. Different shades of blue and red show the 25 °C and 32 °C treatments respectively. Batches are shown in square (batch 1) and triangle shapes (batch 2). Percentages on axes indicate the variation explained by the two coordinates.

27 4.2 Temperature has the largest effect on the microbiome Differences in bacterial compositions of Aiptasia at 25 °C and 32 °C were visualized in a Principal coordinate analysis (PCoA) plot. A clear separation of the bacterial composition of

Aiptasia reared at two different temperatures (Figure 7; pAMOVA < 0.001) was observed despite having considerable variation within and between each Aiptasia group (i.e., batch and strain effect, respectively) (Figures 5 and 6). Each Aiptasia group was significantly different at each temperature (pAMOVA CC7= 0.004, H2<0.001, RS= 0.003, SSB01=0.001)

Figure 7. Principal coordinate analysis based on Bray–Curtis dissimilarity of the bacterial communities. The analysis shows how the bacterial communities of Aiptasia cluster according to temperature treatment. Different shades of blue and red show the 25 °C and 32 °C treatments respectively. Ellipses represent 95% confidence intervals. Percentages on axes indicate the variation explained by the two coordinates.

28 Moreover, cluster analysis including all the samples showed three main clusters (Figure 8). The first cluster on the top consists mainly of the 32 °C samples with the only exceptions made by H2 at 25 °C and the absence of all RS. The second cluster below contains all the remaining 25 °C also with exception of the RS samples. RS individuals from both temperatures cluster together at the bottom, separated from all other strains indicating that the host strain genotype plays a role in structuring the microbiome compositions.

Figure 8. Cluster analysis of all samples based on pairwise beta-diversity values, which were estimated using Spearman rank correlation (ρ) and clustered using Ward’s method. Three clusters are shown: on the top mainly samples at 32 °C, then the samples at 25 °C and at the bottom RS separated from all other strains.

29 Additionally, cluster analyses were also done separately for each Aiptasia strain across temperatures in order to evaluate the batch effect vs. temperature effect (Figure 9). For every group, the same trend was observed, namely that samples from each Aiptasia strain clustered by temperature first and by batch second.

Figure 9. Cluster analysis of each strain based beta-diversity showing that the temperature effect is higher than the batch effect. (A) CC7 (B) H2 (C) RS and (D) SSB01, all at 25 °C and 32 °C.

30 At last, correlation analyses were also done in order to see how the host (genotype) or Symbiodinium type shape the microbiome. The first cluster compares different symbionts within the same host genotype (CC7 vs. SSB01; Figure 10a), showing that samples group together according to temperature regardless of the Symbiodinium type. Furthermore, the second cluster comparing different hosts with the same symbiont (H2 vs. SSB01; Figure 10b) revealed that host strain becomes the decisive clustering factor, meaning that the host genotype and not the Symbiodinium type has a stronger effect on microbiome structure. These results demonstrate that temperature has the largest effect on structuring the bacterial consortia associated with Aiptasia than other effects (i.e., batch, strain and Symbiodinium type).

Figure 10. Correlation analyses based on beta diversity shows that the host genotype has higher effect than the symbiont type in structuring the microbiome. (A) CC7 vs. SSB01 and (B) H2 vs SSB01.

32 4.3 Aiptasia reared at higher temperatures display greater microbiome variability

All strains were also significantly different between treatments (pAMOVA CC7 = 0.004, H2 < 0.001, RS = 0.003, SSB01 = 0.001). In general, OTU richness and diversity of Aiptasia groups at 32 °C were higher than 25 °C (Table 1, Figure 11). Number of observed OTUs was significantly different (p < 0.05) for H2 (p = 0.001) and RS (p = 0.01). Likewise, average Chao1 estimator of species richness was also higher for groups at 32 °C but only significantly different for H2 (p = 0.004) and RS (p = 0.004). Inverse Simpson and Simpson's evenness indices were also slightly higher for samples at 32 °C, though no significant differences where observed (p > 0.05). All p values were post Benjamini-Hochberg corrected.

Table 1. Summary statistics (mean ± SD) of 16S rRNA gene sequencing of the bacterial communities associated with different laboratory-cultured strains of Aiptasia at 25 °C and 32 °C. Observed Inverse Simpson’s Group Chao1* OTUs* Simpson* evenness* CC7 73.67 (11.94) 87.58 (16.37) 5.82 (4.07) 0.08 (0.06) H2 66.33 (10.61) 76.68 (9.73) 5.10 (1.86) 0.08 (0.03)

°C

25 RS 67.33 (9.83) 74.38 (10.55) 7.27 (1.53) 0.11 (0.03)

SSB01 73.50 (5.89) 79.50 (8.91) 9.51 (1.37) 0.13 (0.02)

CC7 89.67 (12.32) 105.31 (18.86) 7.77 (3.67) 0.09 (0.03) H2 95.33 (7.74) 105.06 (10.85) 11.63 (5.13) 0.12 (0.05)

°C

32 RS 84.67 (5.89) 102.92 (12.43) 9.69 (3.92) 0.11 (0.04) SSB01 80.33 (8.50) 89.77 (14.30) 11.39 (4.64) 0.15 (0.06)

*subsampled to 15,456 sequences.

In addition to increased richness and diversity of Aiptasia groups at 32 °C, results also showed an increase of beta diversity distances between replicate samples in all Aiptasia groups that were reared at 32 °C compared to 25 °C. Increased dispersion in PCoA plots of the bacterial consortia (Figure 12) was observed, highlighting the effect of heat stress on the variability of microbial communities and how individuals at 32 °C vary more in their bacterial composition than individuals at 25 °C. These findings are in line with the recently proposed “Anna Karenina principle” of animal microbiomes [107] and suggest that increasing the levels of the stressor (i.e., in this study, heat stress) results in a higher variability of the microbiome and increased beta diversity and that the microbiome structure cannot be fully restored even after long-term acclimation to increased temperature.

Figure 11. Alpha diversity and richness measurements based on observed OTUs, Chao1, Inverse Simpson and Simpson’s evenness. Different shades of blue and red show the 25 °C and 32 °C treatments respectively.

Figure 12. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity of OTU abundances reveals dispersion in bacterial composition of Aiptasia under heat stress. Different shades of blue and red show Aiptasia at 25 °C and 32 °C respectively. Each plot shows one Aiptasia group at both temperatures: (A) CC7, (B) H2, (C) RS, (D) SSB01. Percentages on axes indicate the variation explained by the two coordinates. All plots show greater beta diversity distances between anemones at 32 °C compared to those at 25 °C.

35 4.4 Aiptasia “core” microbiome responds to heat stress A total of 519 OTUs were identified for all anemone samples across both temperatures. From these, 302 and 394 were found in 25 °C and 32 °C, respectively. OTUs present in at least 75% of Aiptasia at each temperature were defined as members of “core” microbiome. At least 29 OTUs were associated with Aiptasia at 25 °C, from which only 4 were exclusively found in this temperature; whereas 36 total and 11 unique OTUs were determined for anemones reared at 32 °C. Between the two temperatures, 25 OTUs were shared (Table 2, Table A3). Only 7 OTUs out the 25 shared OTUs were significantly different (t-test, p < 0.05 post Benjamini-Hochberg correction) in their abundances (OTU001, OTU002, OTU003, OTU011, OTU014, OTU020 and OTU024). The genus Glaciecola sp. (family Alteromonadaceae), and taxa belonging to the family Rhodobacteraceae and order Rhizobiales were the most dominant OTUs at 32 °C, and significantly less abundant at 25 °C. Likewise, the first two, in addition to the family Flavobacteriaceae, were the most prominent taxa in 25 °C (and also compared to 32 °C). Furthermore, several OTUs were identified as candidate indicator taxa of heat stress (Table 3). Only 7 and 11 indicator OTUs were determined from all Aiptasia at 32 °C and 25 °C, respectively. The unstable microbial community (32 °C) encompassed unclassified Gammaproteobacteriaa and Alphaproteobacteria, family Oleiphilaceae, as well as the genera Francisella sp., Maricaulis sp. and Methylotenera sp. Previous reports (Table A3) show that these bacterial indicator taxa are associated with saline environments and can tolerate a wide range of temperatures, as well as being found in diseased tissues of marine organisms. Indicator OTUs characteristic of Aiptasia at 25 °C, on the other hand, included taxa belonging to the closely related orders Altermonodales and Pseudomonales, in addition to other unclassified Gammaproteobacteria, Alphaproteobacteria and Bacteroidetes. To further understand the implications of these differences in the composition of the bacterial communities across temperatures, taxonomy-based functional profiling was conducted so enrichment and depletion of different metabolic processes could be examined on a putative functional level (Figure 13). Overall, there were differences between temperatures. Functions such as “nitrogen fixation”, “nitrite reducer”, “chitin degradation”, “degradation of aromatic hydrocarbons” and “sulfate reducer” were consistently enriched in Aiptasia reared at 32 °C compared to 25 °C. Sulfur cycling processes (i.e., “sulfur reducer”,”sulfide oxidizer” and “sulfur oxidizer”), on the other hand, appeared to be

36 depleted under heat stress and vice versa enriched in 25 °C. Variations within each treatment were also observed and attributed to host effect (i.e., RS Aiptasia appears to be different compared to other strains in both temperatures, as well as SSB01 from 32 °C).

37 Table 2. Aiptasia “core” bacterial OTUs. Only OTUs present in at least 75% of the anemone samples were selected. Taxonomic classification of OTUs based on Greengenes (bootstrap values is shown if < 100). Presence percentage and mean abundances are shown for both temperatures. Common OTUs between treatments are marked in bold.

25 °C 32 °C OUT pres. Mean pres. Mean Taxonomy (bootstrap value) BLASTn closest hit (%) abundance (%) abundance

OTU001 100 4175.00 100 1945.88 Glaciecola sp. (60) Alteromonas mediterranea strain DE

OTU002 100 1940.46 100 1107.63 unclassified Rhodobacteraceae Aestuariivita atlantica strain

OTU003 100 1778.00 100 460.50 unclassified Flavobacteriaceae Kordia algicida strain NBRC 100336

OTU004 100 657.63 100 710.63 unclassified Hyphomonadaceae (55) Hellea balneolensis strain 26III/A02/215

OTU005 100 446.13 100 631.83 unclassified Saprospiraceae (86) Lewinella agarilytica strain SST-19

OTU006 100 292.04 100 842.75 unclassified Rhizobiales (80) Pelagibius litoralis strain CL-UU02

OTU007 100 314.17 100 393.92 unclassified Oleiphilaceae (67) Spongiibacter tropicus strain DSM 19543

OTU011 100 470.83 100 195.50 unclassified Gammaproteobacteria Methylophaga lonarensis strain MPL

OTU012 95.83 288.33 95.83 367.08 unclassified BME43 Jejudonia soesokkakensis strain SSK1-1

OTU013 95.83 515.79 95.83 286.54 Pelomonas puraquae (82) Pelomonas puraquae strain Ps10g

OTU014 83.33 599.21 83.33 21.25 unclassified Gammaproteobacteria Oceanococcus atlanticus strain 22II-S10r2

OTU015 87.5 89.63 87.5 483.00 unclassified Gammaproteobacteria Oceanococcus atlanticus strain 22II-S10r2

OTU016 100 133.92 100 404.04 Francisella sp. (80) Francisella halioticida strain Shimane-1

OTU017 91.67 301.96 83.33 181.33 unclassified Flavobacteriales (92) Luteibaculum oceani strain CC-AMWY-103B

OTU019 100 260.13 83.33 286.63 unclassified Vibrionaceae (75) Vibrio algivorus strain SA2

OTU020 79.17 27.38 100 543.38 unclassified Flammeovirgaceae (89) Ekhidna lutea strain BiosLi/39

OTU024 91.67 92.71 100 324.25 unclassified Alphaproteobacteria Maricaulis virginensis strain VC 5

OTU029 100 83.75 87.5 61.46 unclassified Phycisphaeraceae Algisphaera agarilytica strain 06SJR6-2 OTU030 91.67 275.50 87.5 17.54 Brevibacterium aureum (68) Brevibacterium antiquum strain VKM Ac-2118

38 OTU031 95.83 131.38 87.5 129.92 unclassified Sphingomonadaceae (57) Sphingopyxis marina strain FR1087 OTU033 79.17 152.00 75 50.88 unclassified BD73 Thalassospira mesophila strain MBE#74 OTU034 83.33 177.29 75 47.79 unclassified Alphaproteobacteria Pacificibacter maritimus strain KMM 9031 OTU048 79.17 26.67 87.5 35.83 unclassified CL50015 Rubinisphaera brasiliensis strain DSM 5305 OTU050 87.5 25.42 79.17 19.96 unclassified Deltaproteobacteria Thermoanaerobaculum aquaticum strain MP-01 OTU067 75 12.29 83.33 6.29 unclassified Hyphomicrobiaceae (87) Filomicrobium fusiforme strain DSM 5304

OTU010† 100 535.17 unclassified Spirobacillales Oligoflexus tunisiensis strain Shr3

OTU018† 83.33 114.21 unclassified Saprospiraceae Phaeodactylibacter xiamenensis strain KD52

OTU021† 79.17 306.71 unclassified BME43 Portibacter lacus strain YM8-076 OTU047† 87.5 42.71 Peredibacter tarrii (63) Peredibacter starrii strain A3.12

OTU008* 91.67 779.38 unclassified Spirobacillales Oligoflexus tunisiensis strain Shr3

OTU009* 79.17 750.21 unclassified Bacteria Caldithrix abyssi strain LF13

OTU022* 83.33 497.54 unclassified Deltaproteobacteria Gemmatimonas phototrophica strain AP64

OTU023* 100 440.13 Thalassobaculum salexigens (86) Thalassobaculum litoreum strain CL-GR58

OTU025* 75 374.38 unclassified Saprospiraceae (99) Lewinella nigricans strain ATCC 23147

OTU027* 91.67 332.21 unclassified Alphaproteobacteria Azospirillum zeae strain N7

OTU028* 83.33 258.17 Labrenzia (68) Labrenzia aggregata strain NBRC 16684 OTU038* 87.5 71.42 unclassified Polyangiaceae Minicystis rosea strain SBNa008 OTU039* 95.83 69.58 unclassified Alphaproteobacteria Lacibacterium aquatile strain LTC-2 OTU046* 75 44.79 unclassified Proteobacteria Thalassospira mesophila strain MBE#74 OTU054* 87.5 42.54 Plesiocystis sp. (89) Pseudenhygromyxa salsuginis strain SYR-2

†OTUs exclusively present in Aiptasia at 25 °C * OTUs only found in Aiptasia at 32 °C

39 Table 3. Indicator bacterial OTUs associated with Aiptasia across both temperatures. A denotes exclusivity (i.e., OTU is present in a specific group but not in any other). B stands for fidelity (i.e., OTU is present in all replicates of a group). A and B values range between 1 (strongest association) and 0. Stat refers to the association statistic, which indicates the strength of association for the respective OTU with the temperature group, and significance (p-value). Each OUT was classified according to Greengenes (bootstrap value is given if < 100). Asterisks denote p value significances: ***: p ≤ 0.001, **: p ≤ 0.01.

Indicator A B Stat p-value Taxonomy (bootstrap value) BLASTn closest hit OTU

OTU001 0.65 1.00 0.81 0.004** Glaciecola sp. (60) Alteromonas mediterranea strain DE OTU034 0.92 1.00 0.96 0.001*** unclassified Alphaproteobacteria Pacificibacter maritimus strain KMM 9031 OTU039 0.76 0.83 0.80 0.006** unclassified Alphaproteobacteria Lacibacterium aquatile strain LTC-2 OTU041 0.81 0.63 0.71 0.007** unclassified SC356 Echinicola shivajiensis strain AK12

OTU042 0.93 0.58 0.74 0.003** unclassified HTCC2089 Alcanivorax dieselolei strain B5

°C OTU043 0.99 0.54 0.73 0.002** unclassified Phycisphaerales Algisphaera agarilytica strain 06SJR6-2 25 OTU051 0.87 0.88 0.87 0.001*** unclassified Alteromonadales (90) Congregibacter litoralis strain KT71 OTU064 0.95 0.50 0.69 0.003** Pseudomonas sp. Pseudomonas luteola strain NBRC 103146 OTU068 1.00 0.50 0.71 0.002** unclassified Proteobacteria Desulfovibrio marrakechensis strain EMSSDQ4 OTU085 1.00 0.33 0.58 0.004** unclassified NS1112 Prolixibacter bellariivorans strain JCM 13498 OTU151 0.93 0.33 0.56 0.01** unclassified Proteobacteria Acanthopleuribacter pedis strain NBRC 101209

OTU007 0.68 1.00 0.83 0.01** unclassified Oleiphilaceae (67) Spongiibacter tropicus strain DSM 19543

OTU011 0.71 1.00 0.84 0.007** unclassified Gammaproteobacteria Methylophaga lonarensis strain MPL

OTU014 0.92 0.88 0.90 0.007** unclassified Gammaproteobacteria Oceanococcus atlanticus strain 22II-S10r2

°C

OTU016 0.81 1.00 0.90 0.001*** Francisella sp. (80) Francisella halioticida strain Shimane-1 32 OTU035 0.99 0.67 0.81 0.005** Maricaulis sp. (99) Maricaulis maris strain NBRC 102484 OTU058 1.00 0.42 0.64 0.002** Methylotenera mobilis Methylotenera versatilis strain 301 OTU101 1.00 0.46 0.68 0.001*** unclassified Alphaproteobacteria Dongia rigui strain 04SU4-P

Figure 13. Heatmap displaying putative functional differences based on the bacterial communities of different Aiptasia groups across two temperatures. Changes are displayed on a relative scale with enrichment in red and depletion in blue. The scale units show the change in the relative abundance of different bacteria that are present in the respective microbial communities. All OTUs were averaged (i.e., 6 replicates were merged) for each strain in each temperature.

41 4.5 Host-specific microbiomes To examine host-genotype specific differences we analyzed and compared the OTUs present in each group (regardless of temperature), 41 OTUs (i.e., present in at least 75% of the samples) were found to be associated with CC7, from which only 4 were exclusive to this group. Likewise, 42 (5 exclusives) with H2, 28 (3 exclusives) with RS and 35 (3 exclusives) with SSB01, respectively. Moreover, 21 OTUs were shared between all strains. Host-specific microbiomes can be identified by looking at OTUs that were unique for each strain (Table 4). From these “core” OTUs the most abundant one in CC7 was unclassified Alphaproteobacteria, whereas H2 was characterized by the presence of class BME43 and phylum Spirochaetes represented by Leptonema sp., and SSB01 by Rhodothermaceae. Yet, the abundances of the common OTUs differ in each strain, which explains the variations in their bacterial compositions.

4.5.1. Red Sea Aiptasia microbiome In total, 42 OTUs were associated with at least 75% of all Red Sea Aiptasia, from which 19 were present in both temperatures (Table 5). Only 5 OTUs were significantly different (t- test, p < 0.05 post Benjamini-Hochberg correction) in their abundances (OTU005, OTU007, OTU012, OTU024 and OTU032). Bacteria Lewinella agarilytica (Saprospiraceae), Spongiibacter tropicus (Oleiphilaceae), Jejudonia soesokkakensis (Flaviobacteraceae) and Criblamydia sequanensis (Criblamydiaceae) were significantly more abundant in 25 °C while increased Maricaulis virginensis (Hyphonomonadaceae) was observed at 32 °C. However, the majority of common OTUs between two temperatures were 100% present, including thermal bacteria belonging to Deltaproteobacteria (i.e., Thermoanaerobaculum aquaticum). Twelve “core” OTUs were only found at 25 °C, members belonging to the order Spirobacillales being the most abundant ones (Table A4). Further 11 OTUs were only found in the 32 °C samples, from which family Saprospiraceae dominated in abundance and presence (100%).

42 Table 4. “Core” bacterial microbiome members of different Aiptasia strains regardless of temperature. Only OTUs that are present in at least 75% of all anemone samples of each group but absent in other groups were selected. Taxonomic classification of OTUs based on Greengenes database (bootstrap value is given if < 100). Presence (%) and mean abundance are shown for each strain.

Mean OTU % Taxonomy (bootstrap value) BLASTn closest hit abundance

OTU009 75.00 27.67 unclassified Bacteria Caldithrix abyssi strain LF13

OTU044 83.33 18.42 Coccinistipes vermicola (79) Phaeocystidibacter luteus strain PG2S01

CC7 OTU053 91.67 70.17 unclassified Alphaproteobacteria Phenylobacterium composti strain 4T-6 OTU072 83.33 11.17 unclassified Phycisphaeraceae Phycisphaera mikurensis strain NBRC 102666

OTU021 100 445.83 unclassified BME43 Portibacter lacus strain YM8-076 OTU036 100 213.42 Leptonema sp. Leptonema illini strain 3055

OTU039 100 91.42 unclassified Alphaproteobacteria Lacibacterium aquatile strain LTC-2

H2 OTU070 75.00 12.42 Thalassospira xiamenensis Thalassospira indica strain PB8B OTU112 75.00 4.58 unclassified Bacteria Lentisphaera profundi strain SAORIC-696 OTU058 75.00 72.08 Methylotenera mobilis Methylotenera versatilis strain 301 OTU074 91.67 21.25 unclassified Methylobacteriaceae (54) Bradyrhizobium guangxiense strain CCBAU 53363

RS OTU095 75.00 10.00 unclassified Chitinophagaceae Sediminibacterium salmoneum strain NBRC 103935

OTU059 75.00 2.33 unclassified Bacteroidetes Aurantiacicella marina strain 2A-8 OTU062 75.00 28.83 unclassified Rhodothermaceae (98) Rubrivirga profundi strain SAORIC-476

SSB01 OTU101 83.33 12.00 unclassified Alphaproteobacteria Dongia rigui strain 04SU4-P

43 Table 5. “Core” bacterial microbiome members of Red Sea Aiptasia. Only OTUs present in at least 75% of all anemone samples were selected. Taxonomic classification of OTUs based on Greengenes (bootstrap value is shown if < 100). Presence percentage and mean abundances are shown for both temperatures. Common OTUs between treatments are marked in bold.

OTU RS (25 °C) RS (32 °C) taxonomy (bootstrap value) BLASTn closest hit pres. Mean pres. Mean (%) abundance (%) abundance

OTU001 100 1958.33 100 2010.33 Glaciecola sp. (60) Alteromonas mediterranea strain DE

OTU002 100 1783.50 100 1104.33 unclassified Rhodobacteraceae Aestuariivita atlantica strain

OTU003 100 4078.33 100 476.83 unclassified Flavobacteriaceae Kordia algicida strain NBRC 100336

OTU004 100 344.67 100 1221.50 unclassified Hyphomonadaceae (55) Hellea balneolensis strain 26III/A02/215

OTU005 100 573.17 100 246.33 unclassified Saprospiraceae (86) Lewinella agarilytica strain SST-19

OTU006 100 86.50 100 267.67 unclassified Rhizobiales (80) Pelagibius litoralis strain CL-UU02

OTU007 100 356.00 100 151.33 unclassified Oleiphilaceae (67) Spongiibacter tropicus strain DSM 19543

OTU011 100 778.17 100 332.00 unclassified Gammaproteobacteria Methylophaga lonarensis strain MPL

OTU012 100 108.33 83.33 17.00 unclassified BME43 Jejudonia soesokkakensis strain SSK1-1

OTU013 100 1186.17 100 430.17 Pelomonas Puraquae (82) Pelomonas puraquae strain Ps10g

OTU016 100 209.00 100 1048.50 Francisella sp. (80) Francisella halioticida strain Shimane-1

OTU020 83.33 47.67 100 353.83 unclassified Flammeovirgaceae (89) Ekhidna lutea strain BiosLi/39

OTU024 83.33 5.00 100 290.50 unclassified Alphaproteobacteria Maricaulis virginensis strain VC 5

OTU029 100 13.67 100 64.50 unclassified Phycisphaeraceae Algisphaera agarilytica strain 06SJR6-2 OTU030 100 97.50 100 16.83 Brevibacterium aureum (68) Brevibacterium antiquum strain VKM Ac-2118 OTU031 100 84.83 100 456.00 unclassified Sphingomonadaceae (57) Sphingopyxis marina strain FR1087 OTU032 83.33 54.83 100 10.00 unclassified Chlamydiales (97) Criblamydia sequanensis strain CRIB-18 OTU050 100 40.17 100 46.67 unclassified Deltaproteobacteria Thermoanaerobaculum aquaticum strain MP-01 OTU074 100 28.83 83.33 13.67 unclassified Methylobacteriaceae (54) Bradyrhizobium guangxiense strain CCBAU 53363

44 OTU010† 100 1026.33 unclassified Spirobacillales Oligoflexus tunisiensis strain Shr3

OTU014† 100 739.67 unclassified Gammaproteobacteria Oceanococcus atlanticus strain 22II-S10r2

OTU015† 100 109.33 unclassified Gammaproteobacteria Oceanococcus atlanticus strain 22II-S10r2

OTU017† 83.33 222.33 unclassified Flavobacteriales (92) Luteibaculum oceani strain CC-AMWY-103B

OTU019† 100 34.00 unclassified Vibrionaceae (75) Vibrio algivorus strain SA2

OTU021† 100 353.33 unclassified BME43 Portibacter lacus strain YM8-076 OTU033† 83.33 263.50 unclassified BD7-3 Thalassospira mesophila strain MBE#74 OTU046† 83.33 61.50 unclassified Proteobacteria Thalassospira mesophila strain MBE#74 OTU054† 100 24.00 unclassified Plesiocystis (89) Pseudenhygromyxa salsuginis strain SYR-2 OTU058† 100 71.50 Methylotenera mobilis Methylotenera versatilis strain 301 OTU080† 100 17.50 Streptococcus sp. (79) Streptococcus rubneri strain LMG 27207 OTU093† 83.33 10.67 Caulobacter vibrioides Caulobacter segnis strain ATCC 21756

OTU009* 83.33 1671.17 unclassified Bacteria Caldithrix abyssi strain LF13

OTU023* 100 377.00 Thalassobaculum salexigens (86) Thalassobaculum litoreum strain CL-GR58

OTU025* 100 1466.17 unclassified Saprospiraceae (99) Lewinella nigricans strain ATCC 23147

OTU027* 100 782.67 unclassified Alphaproteobacteria Azospirillum zeae strain N7 OTU035* 83.33 6.17 Maricaulis sp. (99) Maricaulis maris strain NBRC 102484 OTU039* 100 38.83 unclassified Alphaproteobacteria Lacibacterium aquatile strain LTC-2 OTU049* 83.33 102.50 unclassified Alphaproteobacteria Parvularcula oceani strain JLT2013 OTU067* 100 6.00 unclassified Hyphomicrobiaceae (87) Filomicrobium fusiforme strain DSM 5304 OTU071* 100 22.00 unclassified Phycisphaeraceae (97) Phycisphaera mikurensis strain NBRC 102666 OTU095* 83.33 12.67 unclassified Chitinophagaceae Sediminibacterium salmoneum strain NBRC 103935 OTU129* 100 8.50 Planctomyces sp. Planctopirus limnophila strain DSM 3776

† OTUs exclusively present in RS Aiptasia at 25 °C * OTUs only found in RS Aiptasia at 32 °C

45 5. DISCUSSION

5.1 The microbiome of Aiptasia and rearing water Microbial interactions have been recognized as an essential part of animals; they interact with their hosts in such a way that they can influence their fitness and survival. Understanding host-symbiont systems has become a field of study, especially in the ocean environments [14]. Studies of the basal metazoan Hydra have suggested the existence of a host-selected microbiome [108, 109], and it was not until recently that the microbial communities of Nematostella vectensis were described [110, 111], for example. A more complete knowledge of the microbial interactions within the coral holobiont is crucial to better comprehend the dynamics and resilience of these organisms. This can be achieved with the use of model organisms such as the sea anemone Aiptasia. Here, we describe the bacterial microbiomes associated with four different Aiptasia strains (CC7, H2, RS and SSB01) across two temperature treatments (25 °C and 32 °C). In general, the bacterial communities of all Aiptasia strains across temperatures were significantly different. Surprisingly, the bacterial community of the water collected from the same tanks in which Aiptasia were reared was not significantly different from the host samples. This was unexpected since previous studies on Aiptasia cultured under the same laboratory conditions [88, 89] showed a clear separation between both water and anemone samples. These differences are further highly supported by field-based studies that show significantly distinct microbial assemblages between host and water samples [47, 112-114]. However, sampling of the water column in field based studies can be biased as the large volumes, intrinsic properties (e.g., temperature, salinity and organic matter stratification, oxygen fluctuations, water circulation and mixing) and large water volumes are very different from the small volume (2 L capacity tanks) and stagnant conditions found used under laboratory conditions. Moreover, leakage of the microbiome to the water column can occur. Bacteria can produce and release spores into the surrounding water like it was suggested for some fungi growing on the epithelium of Hydra sp. [109], hence, explaining why the bacterial composition of water and biological samples can be similar under laboratory conditions. Further, it is important to note the lack of consistency in

46 laboratory-based studies as not all of them use water as a reference when analyzing the microbiome composition of the biological samples [115, 116]. Röthig et al. [88] and Herrera et al. [89] recently described the bacterial community of CC7 harboring Symbiodinium clade SSB01 and H2 Aiptasia also maintained under the same laboratory conditions. Their microbiome, however, were different from the one observed in this study (after approximately 1.5 years). Although they have a similar bacterial composition, the abundance of these taxa appears to have changed, while other taxa even disappeared. Both studies identified the families Alteromonadaceae, Flavobacteriaceae, Brevibacteraceae and Rhodobacteraceae in H2 but with different abundances, in which Alteromonadaceae increased with time while the other taxa decrease. Likewise, for SSB01 from this study and in Röthig et al. e.g., differential abundances of Flavobacteriaceae and Rhodobacteraceae (both increased with time), while Comamondaceae decrease, and the abundance of Alteromonadaceae remains the same. These findings highlight that even if Aiptasia harbors a “core” bacterial microbiome, its composition and can change over time. A similar effect was observed by a study from Brown et al. [87] which showed that microbial community structure of Aiptasia changed within a period of four months.

5.2 Temperature has an effect on the microbiome compositions

Our study shows that the bacterial communities of different Aiptasia strains are affected by temperature. Overall, anemones from the 32 °C treatment were more diverse and had increased abundances of specific bacterial taxa such as Rhizobiales, Rhodospirillales and Vibrionales when compared to 25 °C. These groups have been found to be consistently overrepresented in coral-associated microbial communities subjected to different types of stressors [53, 63, 117, 118]. Furthermore, corals’ microbial diversity has been shown to be higher with increasing seawater temperature [63, 119, 120], reduced pH [121], water pollution [50], and with diseases [41]. Accordingly, results showed an increase in richness (i.e., total number of observed OTUs) and alpha diversity (i.e., total number of species and their relative abundances) in the 32 °C Aiptasia bacterial communities. In context, disturbed corals’ ability to regulate or reject incoming bacteria from the environment may be reduced [53], thus harboring a larger number of taxa. However, this is not always the case, as no changes or even decrease in alpha diversity have also been documented [117, 118, 122].

47 Stress in human gut microbiome, for example, allows for opportunistic and pathogenic bacteria to dominate the microbial community, which in turn leads to a lower alpha diversity [123]. Similarly, an increase in beta diversity (i.e., turnover of species between samples) and dispersion of the bacterial communities in response to heat stress was also observed. Moreover, this has previously been described for corals [124] and other organisms [107, 124, 125]. Changes in the bacterial community composition resulting from the abovementioned perturbations are in line with the recently described “Anna Karenina principle” for animal microbiomes [107]. Emulating Tolstoy’s quote −“all happy families look alike; each unhappy family is unhappy in its own way” – this principle suggests that the microbiota of unhealthy individuals vary more than a healthy and stable one, so that an unstable microbiome is stochastic (i.e., random distribution) and the host is unable to regulate its community composition when disturbed. While the general principle has been confirmed in subsequent microbiome studies it has not been fully established if the observed increase in diversity in response to stress is a universal biological response or rather a bias introduced by sampling bias i.e., samples taken at different time might reflect different states of a dynamic microbiome restructuring process. Consequently, comparison of microbiomes from samples taken at different points during a dynamic restructuring process would suggest an increase of diversity. However, our study compared microbiomes of hosts subjected to a 2 years constant heat stress treatment. It can therefore be assumed that the host microbiomes reached a final stable state in all samples. The observed increase in beta diversity in our long-term heat stress samples therefore suggests that differences in sampling time is not the source of the increased dispersion and beta diversity in this study. Patterns consistent with this principle have not only been observed in corals, but also in higher organisms such as humans [107]. Hence, it is fair to say that Aiptasia falls within this pattern, as increased microbiome beta diversity was observed (i.e., greater distances between data points in the PCoA; Figure 11) for the anemones reared at high temperature.

48 5.3 Treatment has the highest effect on structuring the bacterial community

Coral bacterial microbiomes are complex, they are shaped through host-specific interactions (e.g., microbial recognition mechanisms and host immune responses) [126, 127], the interplay with other members of the community (e.g., viruses, fungi and Symbiodinium) [128], and geographical location and environmental settings [129, 130]. This study shows that despite a strong correlation between the microbiome composition and the host genotype, temperature appears to be the main factor structuring the microbial community in this experiment. Organisms are exposed to climate changes, which can directly affect the abundance and composition of their associated microbiomes. It is known that elevated sea temperatures cause shifts in the bacterial composition of corals, from mutualistic- to pathogen-dominated communities [31]. However, decreasing temperatures can also affect the microbiome, as it has been reported for clams, in which temperatures of 14°C and below promote the development of brown ring disease. Moreover, either high or low extreme temperatures can significantly increase disease-associated bacteria [131, 132]. It has been demonstrated that coral-associated bacterial communities have specific temperature tolerance thresholds and an increase of only 1°C can change the microbiome composition. However, below these thresholds, alterations in the microbial communities are not common [133], suggesting that coral microbiomes are more stable in the lower extreme of this temperature threshold. Results showed an increase in Alphaproteobacteria abundance and a decrease in Betaproteobacteria class in 32 °C samples. This is reminiscent of changes observed in diseased coral [134, 135] where both gamma and Betaproteobacteria classes were observed to decrease, while Alphaproteobacteria abundance increased, highlighting that increased temperature play a major role in shifting the coral-associated bacterial community towards more pathogenic taxa [136, 137]. Alteromonadaceae, Flammeovirgaceae and Rhodospirillaceae associated with corals have been shown to increase in their abundances at higher temperature [56]. Alteromonadaceae have further been shown to be associated with stressed or diseased corals and were identified as an indicator of aged coral mucus, but are also known to reside in healthy corals [58, 135]. As shown in this study, Alteromonadaceae were the most abundant taxa associated with Aiptasia samples at both temperatures. Moreover, Vibrionaceae are commonly to increase under heat stress [62, 63],

49 and results showed higher abundance of this taxa in 32 °C samples compared to 25 °C. It is known that bleached corals display different bacterial communities than healthy corals [138]. Thus, increased Vibrio taxa can be a sign of coral bleaching [48, 59], suggesting that some predictable changes in the associated bacterial compositions could inform which corals may bleach. It has been shown that the abundance of Vibrionales within the bacterial communities may also be altered by other factors such as Symbiodinium genotype [139].

5.4 “Core” microbiome and indicator taxa

The term “core” microbiome was first conceptualized with the aim of determining potentially critical microbes within the host-associated microbial communities. Coming from a human background [140, 141], a consistent group of functional microbial genes [141, 142] were defined to better understand the role of microbes in human health and the factors affecting their dynamics [140, 142, 143]. This concept has been, however, newly applied in the field of coral biology, and the criteria used to define the “core” microbiome have been inconsistent [144-146]. Thus, within this framework, technical aspects have to be considered when defining a “core” microbiome, especially when comparing across studies. Study design (e.g., number of samples, sequencing method and tools used to analyze the data), replication (e.g., number and quality of replicates), sampling effort (e.g., targeted region and sequencing depth) and analyses of membership (e.g., species occurrence frequency, persistence and connectivity) need to be considered in microbiome studies [145, 146]. Interestingly, this study revealed a variation within each Aiptasia group, even though all individuals were treated under the same conditions. Results pointed that this variation might stem from batch effects (i.e. tanks) sampled, and therefore this calls for attention when looking at the reproducibility of microbial community structure. Here, a putative “core” microbiome associated with Aiptasia was identified based on the presence of an OTU in at least 75% of all biological samples. First, each treatment (25 °C and 32 °C) was examined separately in order to gain an insight of “core” members that could be associated with heat stress, and then for each strain to examine host-specificity. However, these OTUs can also be present in the water, as this was not determined to be significantly different from Aiptasia. More “core” OTUs were found in the 32 °C Aiptasia samples, even though the majority were shared between both treatments. From these

50 “core” members, 11 taxa were consistently abundant in all Aiptasia groups, which may suggest functional importance to the stressed host. About half of the “core” members in both temperatures belonged to the class Gammaproteobacteria; from which the families Alteromonadaceae, Rhodobacteraceae and Flaviobacteraceae were the main contributing taxa. The first one, mainly represented by Glaciecola sp. (OTU001), was the most dominant taxa for both treatments, with increased abundance in 25 °C. This genus has already been reported in corals from hot environments (temperature range between 26 to 33°C) [56], and is particularly known for having genomic features related to cold adaptation [147] so that it has been linked to thermal tolerance [56]. Despite its low abundance, but significantly higher in 32 °C, Francisella sp. (OTU016) stands out as it has also been associated with diseased and/or stressed corals [148]. These are also common marine bacteria [149], found in endosymbiosis with ciliates [150] but also as intracellular pathogens of the Atlantic cod [151] and even humans [152]. It is not surprising then to find this is an indicator species for the 32 °C treatment. Increased water temperatures also promote the proliferation of Vibrio (Vibrionaceae) communities, a key player in coral bleaching [9, 61]. In the same manner, Flammeovirgaceae (OTU020) taxa increase in warm environments [56], as it was the case here (i.e., significantly more abundant in 32 °C). These, in addition to other families like Flavobacteriaceae (OTU003) and Rhodobacteraceae (OTU002), are also commonly associated with disease (e.g., white band disease) [153]. Interestingly, a very small number of “core” OTUs were exclusively present for each Aiptasia strain regardless of the temperature. Furthermore, neither the “core” microbiomes of H2 and SSB01, observed here, look to what has been previously described [88, 89]. Kordia sp. and Pseudomonas veronii were reported as the most dominant bacterial symbionts in H2 and SSB01; whereas in this study were Portibacter lacus (OTU021; BME43) and Rubrivirga profundi (OTU062; Rhodothermaceae), respectively. Moreover, Spirochaetes represented by Leptonema sp. (OTU036) were highly dominant in H2 (i.e., 100% presence and high abundances). They have been reported to dominate the bacterial microbiome associated with the red coral Corallium rubrum on a global scale [154]. Yet, the role of these bacteria is unclear in both diseased [155, 156] and healthy corals [155, 157]. Besides, several studies have found the association of Spirochaetes with the cold-water coral Lophelia pertusa [158] and the deep-sea bamboo coral Isidella tentaculum [159]. Of note is that the criteria used

51 here to define “core” microbiome is less conservative to what has been described in Röthig et al. [88] and Herrera et al. [89] (i.e., members should be present in at least 75%, instead of 100%, of all samples) and still they were missing from the “core” microbiomes recovered from this study. Moreover, Phenylobacterium composti (OTU053; Alphaproteobacteriaceae) was predominantly abundant in CC7 but not in other groups. This taxon, however, has only been found in cotton waste compost [160]. On the other hand, Deltaproteobacteria (OTU050), mainly represented by Thermoanaerobaculum aquaticum, was also found in different Aiptasia groups but especially in the Red Sea anemones (i.e., 100% of all samples across temperatures). This bacterium was originally isolated from hot springs, where they have an optimal growth at 60°C [144]. Yet, it has not been associated with corals or found in marine environments before. One could hypothesize that the presence of T. aquaticum might be related to thermal tolerance, but there is no evidence to support this.

5.5 Taxonomy-based functional profiling of bacterial communities change with temperature Nitrogen-cycling microbes play an essential role in stabilizing the coral-algae symbiosis and holobiont functioning [161]. Results of this study showed an enrichment in nitrogen cycling functions such as “nitrogen fixation” and “nitrite reducer” in the 32 °C treatment, which might indicate increased nitrogen availability under heat stress. Interestingly, Rhizobiales were present in all 32 °C samples and also in high abundances. Spirochaetes taxa were present more in 32 °C in some of Aiptasia group. In tropical corals, the role of these bacteria is still unclear [155, 156]. Yet, Spirochaetes taxa present in termite guts are the best and well-studied and have been found to contribute to nitrogen fixation [162]. Furthermore, several studies have reported that nitrogen fixation activity in corals depends on environmental conditions [163, 164]. Increased temperature and dissolved organic carbon (DOC) availability together also increase nitrogen fixation activity in corals [161, 164]. On the other hand, differences in sulfur cycling included only 4 processes; “sulfate reducer” were enriched in 32 °C samples, but “sulfide oxidizer”, “sulfure oxidizer”, “sulfur reducer” were depleted in 32 °C samples. Symbiodinium and several other beneficial microbes have a role in holobiont sulfur cycling. Coral-associated bacteria like Flavobacteriaceae [44] Halomonas sp. [165], Roseobacter sp., Pseudomonas sp. and Oceanospirillales [45, 166] can metabolize dimethylsulfoniopropionate (DMSP), utilizing its

52 products for their own metabolic processes. Results indicate, however, that sulfur cycling might be affected under heat stress. This could lead to a decrease in DMSP and corresponding changes in microbiome assembly and function. It has also been shown that DMSP and DMSO concentrations decline in corals under thermal stress, which in turn leads to increased Reactive Oxygen Species (ROS) in coral tissues highlighting that sulfur compounds play a role in the quenching of ROS in corals [167]. Further studies using metatranscriptomics to study microbiome functioning under heat stress have the potential to provide more insight and direct evaluation of the functional characteristics of the associated microbiome.

53 6. CONCLUSION

The coral holobiont is the functional unit of coral reefs. Understanding the interactions between host, symbionts and the associated microbiome will help to comprehend their role and function in coral resilience. Microbial relationship with the host have become a recent research focus, as the associated microbiome may contribute to coral resilience to environmental stressors. Yet, the functions of the coral-associated bacteria are not well known, which raises the necessity of a model organism to study coral holobiont. Aiptasia sp., a sea anemone is a tractable laboratory model system for coral biology [75]. In general, The work presented here showed that the microbiome of anemones differs significantly with temperature. Moreover, the temperature has an impact on the microbial compositions in which heat stress showed higher bacterial diversity and richness. In addition to that, results displayed an increase in beta diversity and dispersion of microbial communities in response to elevated temperature, which has been described by Anna Karenina principle in which the ability of the host to regulate the composition of bacterial communities reduced in response to stressors. Results also demonstrated that temperature has the greatest effect on structuring the associated bacteria than other effects (i.e., batch, strain and Symbiodinium genotype). Members of "core" microbiome have been identified as indicator species of heat stress (i.e., Francisella sp., Maricaulis sp. and Methylotenera sp.), which have been reported before as an indicator taxa associated with saline environments and can tolerate high temperatures. Based on the putative functional profile; processes such “nitrogen fixation” and “nitrite reducer” were enriched and “sulfur reducer”, ”sulfide oxidizer” and “sulfur oxidizer” were depleted in 32 °C samples. Yet, there are many questions need be answered to understand the host microbiome. Further studies based on genomics and transcriptomics approaches are required to comprehend host-microbes’ interactions and understand the roles and functions of Cnidarian microbiome in response to stress.

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63 8. APPENDICES Table A1. The number of reads remains of each sample after the different steps that were used by mothur software to remove unwanted sequences total no. of poor quality removal of post-alignment removal of removal of sequences filtering singletons rescreening chimeras “undesirable” sample (make.contigs) (screen.seqs) (split.abund) (screen.seqs) (remove.seqs) (remove.lineage) CC7_r1.1 144090 122974 94025 88660 87940 87816 CC7_r1.2 124199 105337 80452 72473 72224 72065 CC7_r1.3 216884 183432 136708 122623 122316 122006 CC7_r2.1 127157 108872 78783 68973 68428 68214 CC7_r2.2 127203 107287 74507 63168 62255 62024 CC7_r2.3 89617 75831 51644 48986 48570 48534 H2_r1.1 48770 40727 20710 19723 15480 15456 H2_r1.2 87147 73270 53704 50497 49684 49628

°C

H2_r1.3 84085 71117 46265 36072 30982 30752

25 H2_r2.1 151451 128995 96334 94570 93191 93187 H2_r2.2 85443 72192 52728 45568 44351 44212 H2_r2.3 90196 75973 47474 46275 38946 38935 H2_r2.2 101827 86148 62985 58577 58079 58043 H2_r2.3 70895 60094 42734 40445 39870 39862 RS_r1.1 47647 40628 29225 28220 27908 27906 RS_r1.2 150470 127216 91406 86830 86468 86403 RS_r1.3 36644 30746 22769 22043 21409 21407 RS_r2.1 57089 46983 30947 29954 29890 29885 SSB01_r1.1 178966 151795 105549 100292 98557 98462 SSB01_r1.2 236979 173041 95553 67680 60945 60219 SSB01_r1.3 101045 85675 58772 55285 54561 54485

64 SSB01_r2.1 107795 89561 58750 55631 54890 54866 SSB01_r2.2 133059 112546 75783 71172 66678 66567 SSB01_r2.3 78409 66006 45723 43826 43358 43295 H2O_CC7_1 210437 150268 62168 60858 49575 49556 H2O_CC7_2 299742 216819 118849 117430 98173 98173 H2O_H2_1 339463 247536 113165 111674 91121 91119 H2O_H2_2 142208 103357 51186 49788 40663 40663 H2O_RS_1 376881 285618 150399 147466 128501 128501 H2O_RS_2 389852 297027 157191 154589 138495 138491 H2O_SSB01_1 491413 373456 204037 200486 172505 172475 H2O_SSB01_2 432444 325888 164249 161478 137751 137732 CC7_r1.1 227843 169913 99799 96470 93744 93744 CC7_r1.2 244807 183478 94876 93359 82641 82641 CC7_r1.3 223466 169247 103021 97609 88045 88045 CC7_r2.1 240570 170142 86074 83777 66475 66475 CC7_r2.2 242260 177532 115134 110488 109187 109186 CC7_r2.3 234636 164706 95041 92144 84186 84185 H2_r1.1 °C 236785 170561 106641 102695 98359 98359

32 H2_r1.2 248909 187830 113584 110900 108127 108127 H2_r1.3 261539 200225 126150 120262 118376 118376 H2_r2.1 59714 50332 35815 34091 33596 33560 H2_r2.2 104612 88207 63165 61045 60027 60009 H2_r2.3 116910 99222 72371 67935 67262 67182 RS_r1.1 151783 127329 85593 83900 81959 81954 RS_r1.2 48164 40484 28477 27989 27842 27792

RS_r1.3 101614 85638 60747 59801 59558 59558 RS_r2.1 115150 95495 59414 52051 45664 45664

65 RS_r2.2 110055 81464 53470 49347 49005 49005 RS_r2.3 178966 151795 105549 100292 98557 98462 SSB01_r1.1 218021 157279 82458 65396 58233 57709 SSB01_r1.2 87436 73969 50146 47235 45350 45299 SSB01_r1.3 90890 76899 54145 52205 51773 51721 SSB01_r2.1 145416 121868 86915 79724 77487 77455 SSB01_r2.2 242955 180684 119468 104306 102733 102642 SSB01_r2.3 162231 136100 95059 85244 84184 84099 H2O_CC7_1 361066 263605 119734 118300 94875 94873 H2O_CC7_2 315710 236534 112409 111130 90989 90982 H2O_H2_1 274742 204070 107265 102906 87785 87687 H2O_H2_2 667641 498433 275028 257849 219689 219199 H2O_RS_1 371810 281211 146831 144795 124404 124403 H2O_RS_2 363386 267960 138168 136253 117506 117505 H2O_SSB01_1 245688 191646 110387 107913 100325 100309 H2O_SSB01_2 357940 268346 137832 132602 123137 123119

66 Table A2. The abundance (shown in percent) of phyla taxa and the Proteobacteria composition of Aiptasia (Greengenes database, bootstrap ≥ 60). Less abundant taxa (< 1%), comprising 18 phyla, are grouped under 'others'. Taxon 25 °C (%) 32 °C (%) Proteobacteria 72.89 76.47 Bacteroidetes 21.88 20.43

Actinobacteria 2.17 0.38

Phyla Planctomycetes 1.10 1.59 unclassified bacteria 0.68 6.14 others < 1% 1.96 1.13 Alphaproteobacteria 32.68 42.06

Betaproteobacteria 4.84 2.77 Deltaproteobacteria 6.83 11.69 Epsilonproteobacteria 0.00 0.00

composition Gammaproteobacteria 55.21 38.31 Proteobacteria Proteobacteria unclassified Proteobacteria 0.43 5.16

Table A3. Excel file of "core" bacterial OTUs of all 25 °C and 32 °C Aiptasia groups, their annotation, the NCBI's accession number, and the references are shown. Only OTUs present in at least 75% of the anemone samples were selected. Taxonomic classification of OTUs based on Greengenes (bootstrap values is shown if < 100). Presence percentage, mean abundances are shown for both temperatures.

Table A4. Excel file of "core" bacterial OTUs of Red Sea Aiptasia at 25 °C and 32 °C, their annotation, the NCBI's accession number, and the references are shown. Only OTUs present in at least 75% of the anemone samples were selected. Taxonomic classification of OTUs based on Greengenes (bootstrap values is shown if < 100). Presence percentage, mean abundances are shown for both temperatures.