Coral-Associated Bacterial Community Dynamics in Healthy, Bleached

Coral-Associated Bacterial Community Dynamics in Healthy, Bleached,

and Disease States

Dissertation by

Ghaida Hadaidi

In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy

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

November, 2018 2

EXAMINATION COMMITTEE PAGE

The dissertation of Ghaida Hadaidi is approved by the examination committee.

Committee Chairperson: Prof. Christian R. Voolstra

Committee Members: Prof. Daniele Daffonchio

Prof. Pascal Saikaly

Prof. Rebecca Vega Thurber

Ghaida Hadaidi

ABSTRACT

Coral-associated Bacterial Community Dynamics in Health, Bleaching, and Disease state.

Ghaida Hadaidi

Coral reefs are the proverbial rainforests of the ocean, but these spectacular structures are under threat from globally rising sea surface temperatures and ocean acidification. The Red

Sea and the Persian/Arabian Gulf (PAG) display unusually high sea surface temperatures, and therefore, provide a model for studying environmental change. Corals are so-called holobionts consisting of the coral host, photosynthetic algae (Symbiodiniaceae), along with other microorganisms, such as bacteria, archaea, fungi, and viruses. While the importance of bacteria to coral holobiont functioning is acknowledged, little is known about changes in the microbial communities under natural environmental stressors in the Red Sea and the PAG.

Accordingly, I investigated microbial community and mucus differences in bleached, healthy, and diseased corals. Analysis of the composition of mucus-associated microbial communities of bleached and healthy Porites lobata colonies from the Red Sea and the PAG were stable, although some regional differences were present. In a distinct study investigating coral disease, a broad range of corals in the Red Sea were shown to be infected with black band disease (BBD). Investigating the microbial community associated with BBD revealed the presence of the three main indicators for BBD (cyanobacteria, sulfate-reducing bacteria

(SRB), and sulfide-oxidizing bacteria (SOB). Last, I investigated the chemical composition

(carbohydrates) of the surface mucus layer of a range of Red Sea corals. Given that coral mucus represents a first line of defense, I was interested to examine whether mucus carbohydrate composition would point to a role of adaptation to the extreme environment of the Red Sea. This analysis showed that mucus consists of conserved sugars that are globally conserved. In summary, this thesis characterizes the microbial communities associated with a range of coral species in different health states (bleached, healthy, and diseased). The

5 microbial community patterns I characterized support the notion that bacteria contribute to coral holobiont health and possibly adaptation to extreme environmental conditions in the

Red Sea and PAG.

ACKNOWLEDGMENT

I am grateful to my advisor Christian Voolstra for giving me this great opportunity to be part of his group, conducting my work in his lab, and providing the resources and the enormous support during my PhD.

In addition, I would like to thank my committee members D. Daffonchio, P. Saikaly, R. V.

Thurber, for serving as my committee members, and their valuable comments on this dissertation. In the begging of my PhD, lab support, and computer work was not be possible without the great help and support of L. Yum. Samples collection and filed work was done by several of people that I would like to acknowledge M. Ziegler, C. Roder, A. Shore-Maggio,

T. Jensen, and G. Aeby. Also, I would like to thank CMOR and the Marine Biology Lab at

NYUAD for their assistance and support in field operations. C. Michell’s for library preparation and N. Rädecker for helping in the qPCR, and C. Arif for her help in DGGE.

Further, I want to thank the Center of Complex Carbohydrate Research of the University of

Georgia in Athens, Georgia, USA for sample preparation and analysis for GC/MS and the

KAUST Sequencing Core lab for their support.

Finally, I would like to acknowledge with gratitude, the love and the support of my family and friends (N, Zahran, S. Nadeef, S. Saade), who stood with me during this period.

TABLE OF CONTENTS

ABSTRACT ...... 4

ACKNOWLEDGMENT ...... 6

TABLE OF CONTENTS ...... 7

LIST OF ABBREVIATIONS ...... 11

LIST OF TABLES ...... 12

LIST OF FIGURES ...... 13

1. Introduction ...... 14

1.1. Coral reef ecosystems...... 14

1.2. The coral holobiont ...... 14

1.3. Surface mucus layer (SML) ...... 15

1.4. Coral bleaching ...... 16

1.5. Coral disease ...... 17

References ...... 19

SYNTHESIS ...... 26

References ...... 29

Chapter 1 ...... 31

2. Stable mucus-associated microbial communities in bleached and healthy corals of

Porites lobata from the Arabian Seas ...... 31

2.1. Abstract ...... 32

2.2. Introduction ...... 32

2.3. Material and Methods ...... 35

2.3.1. Study sites and coral mucus collection ...... 35

2.3.2. DNA extraction ...... 36

2.3.3. Identification of algal symbionts from mucus of bleached and healthy

Porites colonies ...... 36

2.3.4. 16S rRNA gene sequencing ...... 37

2.3.5. Bacterial community analysis ...... 38

2.3.6. Relative abundance of nifH and 16S rRNA genes using qPCR ...... 39

2.4. Results ...... 39

2.4.1. Algal symbionts associated with Porites lobata ...... 40

2.4.2. Bacterial community composition of coral mucus and seawater ...... 40

2.4.3. Bacterial community differences of coral mucus across regions ...... 42

2.4.4. Core microbiome of coral mucus ...... 47

2.4.5. Taxonomy-based functional profiling of bacterial communities ...... 47

2.5. Discussion ...... 49

2.6. Acknowledgements ...... 53

References ...... 55

Supplementary Information...... 61

Chapter 2 ...... 65

3. Ecological and molecular characterization of a coral black band disease outbreak in the Red Sea during a bleaching event...... 65

3.1. Abstract ...... 66

3.2. Introduction ...... 67

3.3. Material and Methods ...... 70

3.3.1. Black band disease surveys ...... 70

3.3.2. Sample collection of black band disease microbial mats and 16S rRNA

gene sequencing ...... 73

3.3.3. Sequence data processing and bacterial community analysis ...... 74

3.4. Results ...... 75

3.4.1. Distribution and prevalence of black band disease on the Red Sea reefs 75

3.4.2. Bacterial community composition of black band disease microbial mats 76

3.4.3. Black band disease representative bacterial consortia ...... 79

3.5. Discussion ...... 85

3.5.1. Black band disease distribution and prevalence in the Red Sea in

comparison to other global sites ...... 85

3.5.2. BBD, climate change, and coral bleaching ...... 86

3.5.3. Bacterial community composition of BBD microbial mats from the

southern central Red Sea reflects global microbial patterns with local

characteristics ...... 87

3.6. Conclusions ...... 89

3.7. Acknowledgements ...... 89

References ...... 91

Chapter 3 ...... 100

4. First insight of the carbohydrate composition of mucus from scleractinian corals from the central Red Sea ...... 100

4.1. Abstract ...... 101

4.2. Introduction ...... 102

4.3. Materials and Methods ...... 104

4.3.1. Collection of coral mucus ...... 104

4.3.2. Coral mucus carbohydrate composition analysis...... 104

4.3.3. Data analysis ...... 105

4.4. Results & Discussion ...... 106

4.5. Acknowledgement ...... 109

References ...... 110

APPENDICES ...... 118

LIST OF ABBREVIATIONS

AMOVA Analysis of molecular variance ANOVA Analysis of variance BBD Black band disease bp Base pair DGGE Denaturing gradient gel electrophoresis DMSO Dimethyl sulfoxide DMSP Dimethylsulfoniopropionate DNA Deoxyribonucleic acid DOC Dissolve organic carbon FOC Frequency of disease occurrence GBR Great barrier reef GC/MS Gas chromatography/ mass spectrometry ITS2 Internal transcribed spacer2 MDa Megadalton OTU Operational taxonomic units PAG Persian/Arabian Gulf PCR Polymerase chain reaction PSU Practical Salinity Unit PWPS Porites white patch syndrome qPCR Quantitative Polymerase chain reaction RNA Ribonucleic acid ROS Reactive oxygen species rRNA Ribosomal ribonucleic acid RS Red Sea SML Surface mucus layer SOB Sulfide-oxidizing bacteria SRB Sulfate-reducing bacteria SST Sea surface temperatures VLP Virus-like particles WPD White plague disease WS White syndrome ΔCt Delta cycle threshold ΔΔCt Delta- delta cycle threshold

LIST OF TABLES

Table 2.1. Summary statistics of 16S rRNA gene sequencing of mucus-associated bacteria from bleached and healthy coral colonies of P. lobata from the Persian/Arabian Gulf (PAG) and the Red Sea (RS)………………………………………………………………………. 49

Table 2.2. Site-specific bacterial taxa associated with mucus samples of P. lobata from the Persian/Arabian Gulf or the Red Sea ranked by relative abundance (average number of sequence counts)………………………………………………………………………….... 51

Table 2.3. Core microbiome of mucus-associated bacteria from bleached and healthy colonies of P. lobata from the PAG and the RS………………………………………….… 54

Table 3.1. Survey of black band disease (BBD)-affected coral colonies at 22 reef sites in the central Red Sea………………………………………………………………………..……. 79

Table 3.2. Survey of black band disease (BBD)-affected coral genera at an outbreak site in the southern central Red Sea (Al-Lith fringing reef 1, Saudi Arabia)…………………………. 83

Table 3.3. Summary of sequencing statistics and alpha diversity measures of bacterial communities associated with black band disease lesions from coral colonies in the southern central Red Sea (Al-Lith fringing reef 1, Saudi Arabia)…………………………………… 85

Table 3.4. Summary of bacterial taxa (OTUs) associated with black band disease (BBD) in corals from the southern central Red Sea and comparison with similar taxa from around the world, based on BLAST results (accession number, identity) of the BBD consortium of sulfide- oxidizing bacteria (SOB), sulfate-reducing bacteria (SRB), cyanobacteria, Firmicutes, and Vibrios……………………………………………………………………………………… 88

Table 4.1. Carbohydrate composition of coral mucus from a range of coral species from the Red Sea (n = 3 for each species)………………………………………………………… 110

LIST OF FIGURES

Figure 2.1. Bacterial community composition of mucus from bleached and healthy coral colonies of P. lobata from the Persian/Arabian Gulf (PAG) and Red Sea (RS)………… 50

Figure 2.2. Taxonomy-based functional profiling of bacterial communities associated with mucus from P. lobata from the Persian/Arabian Gulf and Red Sea……………………. 57

Figure 3.1. Black band disease survey locations of 22 reef sites along the central Red Sea coast of Saudi Arabia……………………………………………………………………. 78

Figure 3.2. Bacterial community composition of black band disease microbial mats from four coral genera…………………………………………………………………………. 86

Figure 4.1. Heatmap based on carbohydrate composition and abundance of mucus from five coral species of the Red Sea………………………………………………………………. 112

1. Introduction

1.1. Coral reef ecosystems

Coral reefs are among the most complex and biodiverse ecosystems in the world (Bruno

2001; Connell 1978; Idjadi & Edmunds 2006; Moberg & Folke 1999; Odum H & Odum E

1955). They provide habitat and protection for thousands of species (Moberg & Folke 1999).

Also, coral reefs are crucial to human societies and industries through fisheries, coastal protection, building materials, biochemical compounds, and tourism (Done et al. 1996;

Kuehlmann 1988; Smith 1978; Spurgeon 1992). Coral reefs cover has been estimated to range from approximately 0.1–0.5% of the ocean floor (Smith 1978; Spalding & Grenfell

1997). Unfortunately, coral reefs are under severe decline (Bellwood et al. 2004; Brown

1987; Bryant et al. 1998; Edmunds et al. 2014; Gardner et al. 2003; Hughes et al. 2003;

Pandolfi et al. 2003; Richmond 1993; Tanner 2017; Wilkinson 1993). The global decline of coral reefs is related to local and global anthropogenic activities, which contribute to an overall increase of coral bleaching and coral diseases (Edmunds et al. 2014; Grigg 1991;

Hoegh-Guldberg 1999; Hoegh-Guldberg 2012; Hoegh-Guldberg et al. 2007; Wilkinson

2004).

1.2. The coral holobiont

Scleractinian corals (hard corals) provide the massive three-dimensional framework of reefs via the deposition of calcium carbonate (Chalker & Barnes 1990; Domart-Coulon et al. 2001;

Rosenberg et al. 2007). Corals are colonial animals, where the basic unit is the coral . A polyp is composed of two layers, the epidermis and gastrodermis, which are separated by a connective layer called mesoglea (Blackall L et al. 2015; Domart-Coulon et al. 2001;

Rosenberg et al. 2007; Veron 2000). The entire coral colony is covered by a surface mucus layer (SML) (Rosenberg et al. 2007). 15

Corals live in a mutualistic relationship with dinoflagellates of the family Symbiodiniaceae

(zooxanthellae) (LaJeunesse et al. 2018),along with other microorganisms such as bacteria, archaea, fungi, and viruses (Knowlton & Rohwer 2003a; Rosenberg et al. 2007). This consortium forms a metaorganism that is referred to as the coral holobiont (Knowlton &

Rohwer 2003a; Rosenberg et al. 2007).

The algal symbionts reside within the gastrodermal host tissue where they photosynthetically fix carbon, which is then translocated to the coral host providing it with the energy required for calcification (Rosenberg et al. 2007). Due to the large genetic diversity within the genus

Symbiodinium, containing nine clades (A- I) (Coffroth 2005), a recent taxonomic revision erected the family Symbiodiniaceae, in which many of these clades are now recognized as genera (LaJeunesse et al. 2018).

Considering coral-associated bacteria, corals provide at least three different microbial habitats: (1) the surface mucus layer (SML), (2) coral tissue and (3) skeleton, wherein each compartment has a distinct bacterial community (Pollock et al. 2018;Rosenberg et al. 2007;

Sweet et al. 2011). The coral-associated bacterial communities play a critical role in the coral holobiont, for example by providing nutrients via nitrogen and sulphur cycling (Ceh et al.

2013; Lema et al. 2012; Lesser et al. 2004; Rädecker et al. 2015b; Raina et al. 2009; Yang et al. 2013) or protection against pathogen invasion through the production of antimicrobial compounds (Ritchie 2006b; Teplitski & Ritchie 2009).

1.3. Surface mucus layer (SML)

Coral mucus is discharged continuously by all corals with varying quantities (Brown &

Bythell 2005b). The so-produced SML plays a critical role in coral health where it acts as a nutrient carrier (Wild et al. 2005), protection from pathogens/environmental stressors, and sediment cleansing (Brown & Bythell 2005b; Bythell & Wild 2011; Ritchie 2006b). In fact,

16 the protective function of mucus surfaces is universally present in all multicellular animals

(Bythell & Wild 2011).

Coral mucus contains a large portion of photosynthates obtained from the algal symbiont besides other compounds acquired from heterotrophic feeding, and it is produced in the ectodermal gland cells (Brown & Bythell 2005b; Coffroth 1990b; Crossland et al. 1980;

Trench 1970). Coral mucus is a complex mixture of polysaccharides, glycoproteins (mucins), and lipids (Brown & Bythell 2005b; Coffroth 1990b). Polysaccharidesin the mucus account for 20 to 30% (Bythell & Wild 2011; Crossland 1987). Mucin glycoproteins are responsible for the gel properties of the mucus (Bythell & Wild 2011).

The SML constitutes a dynamic system whose composition varies between coral species and over time (Brown & Bythell 2005b), due to the periodic release of mucus from the coral surface to the environment (Bythell & Wild 2011). This periodical cycle creates habitat dynamics for the microbiota (Glasl et al. 2016b; Nelson et al. 2013). It has been proposed that

Poritid corals provide an ideal model to study this dynamic system (Coffroth 1990b).

Although mucus is in direct contact with seawater, mucus-associated microbial communities are distinct from the surrounding seawater (Frias-Lopez et al. 2002; Rohwer et al. 2001). In fact, mucus associated microbes comprise an important factor influencing coral holobiont health (Glasl et al. 2016b; Hadaidi et al. 2017; Lee et al. 2016a; Rohwer & Kelley 2004).

1.4. Coral bleaching

Coral bleaching, the breakdown of the symbiosis between corals and their algal symbionts as a response to stress, is now the main driver contributing to global reef loss due to an increase of sea surface temperatures due to climate change (Hughes et al. 2007). Notably, other factors can also cause bleaching, such as cold temperatures, high UV, prolonged aerial exposure, high sedimentation, high nutrient, low salinity, and high xenobiotics (Glynn 1991;

Glynn 1996; Lema et al. 2012; Shnit-Orland & Kushmaro 2009). 17

The molecular mechanism behind coral bleaching, as described by Baird et al. (2009), is initiated by the production of reactive oxygen species (ROS) in response to environmental stressors. Subsequently, photoinhibition of the photosystem II (PSII) is induced due to the accumulation of ROS in the symbiont cells. ROS can be removed by antioxidant molecules or enzymes such as superoxide dismutase, catalase, ascorbic acid, carotenoids, fluorescent pigments, and mycosporine glycine and the presence of these scavenging mechanisms will prevent ROS accumulation. Although it’s widely accepted that coral bleaching is mainly related to the symbiotic breakdown between the coral animal host and their micro-algae,

Rosenberg et al. (2008) showed that infection of Oculina patagonica with Vibrio shiloi can cause bleaching (Ainsworth et al. 2008; Bourne et al. 2007; Bourne et al. 2009; Rosenberg et al. 2007). A study by Bourne et al. (2007), using colonies of Acropora millepora, found that microbial community shifts occur prior to visual signs of bleaching and return to their pre- bleaching community structure after corals recover from the bleaching event. Mouchka et al.

(2010b) have found that there are differences in microbial assemblage between healthy and bleached corals.

1.5. Coral disease

Coral diseases are a crucial contributor to the decline of coral reefs worldwide (Bourne et al.

2009; Harvell et al. 1999; Harvell et al. 2009; Hoegh-Guldberg 2012; McLeod et al. 2010;

Randall & van Woesik 2015). The accumulation of various environmental factors such as elevated temperatures, reduced water quality, overfishing, and anthropogenic activities is driving disease outbreaks (Ban et al. 2013; Cinner et al. 2016; Harborne et al. 2017).

However, these factors may vary by region and time (Weil et al. 2006). Consequently, It is important to understand disease mechanisms and what measures can be taken to prevent coral disease distribution. Coral diseases are characterized via field surveys, pathological signs, morphological assessment, and at the immunological and microbial level (Bourne et al. 2009;

Rosenberg et al. 2007). The first coral disease was described in the 1970s, and since that time the description and discovery of coral diseases have been increased gradually (Antonius

1973; Garrett & Ducklow 1975; Green & Bruckner 2000; Miller & Richardson 2014; Porter et al. 2011; Sutherland et al. 2004; Weil 2004). Thus far, around 18 coral diseases have been identified (Bourne et al. 2009; Rosenberg et al. 2007). Although for some diseases, we have identified the disease-causing pathogen, other diseases are thought to be of polymicrobial origin, where non-beneficial bacteria invade the weakened coral host to produce a disease- like phenotype (Lesser et al. 2007).

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SYNTHESIS

The complexity of the coral holobiont makes understanding its response to coral bleaching and disease challenging. Many questions arise regarding host-microbiota-environment interactions, in order to preserve the future of coral reef ecosystem. On a global scale, sea surface temperatures (SSTs) are increasing due to climate change, resulting in large-scale coarl bleaching events (Hughes et al. 2003; Monroe et al. 2018).

This dissertation investigated changes that occur in coral-associated microbial communities during healthy, bleached, and disease states, to infer possible correlations between changes in the microbial community structure and coral health. To do this, mucus of bleached and healthy Porites lobata colonies from the Red Sea and the Persian/Arabian Gulf (PAG) were collected to investigate bacterial community and Symbiodiniaceae composition under natural environmental conditions, using 16S rRNA and ITS2 gene amplicon sequencing, respectively. Furthermore, disease baseline data in the Red Sea is sparse. To provide a baseline for coral disease studies in the Red Sea, I determined the broad distribution and prevalence of BBD, a globally present coral disease by conducting disease surveys and characterizing the microbial community of BBD microbial mats from the corals Coelastrea sp., Dipsastraea sp., Goniastrea sp., and Platygra sp. using high-throughput sequencing. In addition to that, I analyzed the carbohydrate composition of mucus from a range of Red Sea corals (Acropora pharaonis, Galaxea fascicularis, Pocillopora verrucosa, Porites lobata, and

Stylophora pistillata) to assess whether its composition may be related to adaptation to environmental extremes.

In the first chapter, the composition of mucus-associated microbial communities of bleached and healthy Porites lobata colonies from the Red Sea and PAG were characterized. I found a similar pattern between the microbial composition (i.e., bacteria and the algal symbionts) between bleached and healthy corals, but regional differences exist (Hadaidi et al. 2017) that 27 suggest that coral associated with the SML retains its protective function, even under conditions of coral bleaching.

In the second chapter I assessed prevalence and microbial community composition of corals with BBD (Antonius 1981). Since baseline data on BBD in the Red Sea is lacking, this chapter reports on the broad distributions and prevalence of BBD across the Red Sea coast, and further, characterized the bacterial community of BBD microbial mats duringa bleaching event in the southern central Red Sea. The prevalence of BBD in this data is low (0.04%) and similar to the prevalence of this disease elsewhere in the world (Hadaidi et al. 2018;

Sutherland et al. 2004), suggesting that BBD is a chronic threat in the Red Sea (Hadaidi et al.

2018). I could confirm that multiple coral species can be affected by BBD and with various level of severity (Bruckner et al. 1997; Dinsdale 2002; Green & Bruckner 2000; Peters 1993).

Interestingly, different susceptibility to BBD among coral taxa have been found globally, and the most vulnerable taxa are regionally different (Aeby et al. 2015b; Bruckner & Bruckner

1997; Hadaidi et al. 2018; Page & Willis 2006; Porter et al. 2001a). In fact, my collected data suggest that the genus Dipsastraea is an important BBD host in the Red Sea (Hadaidi et al.

2018). I could further confirm the presence of the three main key players in BBD, which suggests that the BBD consortium is not restricted to a certain coral species or region

(Barneah et al. 2007; Cooney et al. 2002; Dinsdale 2002; Frias-Lopez et al. 2003).

Importantly, coral disease in the Red Sea might become more prevalent under ocean warming, hence careful monitoring is needed to assess whether disease levels increase.

In the third chapter, I assessed the carbohydrate composition of coral mucus. Notably, the

SML represents the first line of defense against invading pathogens or mechanical insult.

This work analyzed the carbohydrate composition of several coral species from the central

Red Sea (Acropora pharaonis, Galaxea fascicularis, Pocillopora verrucosa, Porites lobata, and Stylophora pistillata). Nine main sugars have been detected to be the main mucus

28 components of the coral species evaluated (arabinose, fucose, galactose, glucose, mannose, xylose, and N-Acetyl Glucosamine) that were previously identified in corals from other regions of the world. In conclusion, coral mucus consists of a set of globally present sugars, which suggest that these sugars serve a universal function and are not related to the distinct environmental differences present in the Red Sea.

The three chapters in this dissertation provide further evidence that coral microbial communities contribute to the health state of coral holobionts. The stable composition of mucus-associated bacteria act as a conserved suit of protection for bleached and healthy corals. BBD-associated microbial consortia are globally similar and disease prevalence needs to be carefully monitored in the Red Sea. Moreover, the carbohydrate composition of coral mucus and its associated microbiota play a critical role in the structure and dynamics of this protective layer. This thesis is a strong argument that coral-associated microbial communities play an important role in coral functioning, although functional studies are needed to further confirm that here-derived findings. 29

References

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Frias-Lopez J, Bonheyo GT, Jin Q, and Fouke BW. 2003. Cyanobacteria associated with coral black band disease in Caribbean and Indo-Pacific reefs. Applied and Environmental Microbiology 69:2409-2413.

Green EP, and Bruckner AW. 2000. The significance of coral disease epizootiology for coral reef conservation. Biological Conservation 96:347-361. http://dx.doi.org/10.1016/S0006-3207(00)00073-2

Hadaidi G, Ziegler M, Shore-Maggio A, Jensen T, Aeby G, and Voolstra CR. 2018. Ecological and molecular characterization of a coral black band disease outbreak in the Red Sea during a bleaching event. PeerJ 6:e5169. 10.7717/peerj.5169

Monroe AA, Ziegler M, Roik A, Röthig T, Hardenstine RS, Emms MA, Jensen T, Voolstra CR, and Berumen ML. 2018. In situ observations of coral bleaching in the central Saudi Arabian Red Sea during the 2015/2016 global coral bleaching event. PLoS One 13:e0195814. 10.1371/journal.pone.0195814

Page C, and Willis B. 2006. Distribution, host range and large-scale spatial variability in black band disease prevalence on the Great Barrier Reef, Australia. Diseases of Aquatic Organisms 69:41-51.

Peters EC. 1993. Diseases of other invertebrate phyla: Porifera, cnidaria, ctenophora, annelida, echinodermata. Pathobiology of marine and estuarine organisms:393-449.

Porter JW, Dustan P, Jaap WC, Patterson KL, Kosmynin V, Meier OW, Patterson ME, and Parsons M. 2001. Patterns of spread of coral disease in the Florida Keys. In: Porter JW, ed. The Ecology and Etiology of Newly Emerging Marine Diseases. Dordrecht: Springer Netherlands, 1-24.

Sutherland KP, Porter JW, and Torres C. 2004. Disease and immunity in Caribbean and Indo- Pacific zooxanthellate corals. Marine Ecology Progress Series 266:273-302.

Chapter 1

2. Stable mucus-associated microbial communities in bleached and healthy corals of

Porites lobata from the Arabian Seas

Ghaida Hadaidi1, Till Röthig1, Lauren K. Yum1, Maren Ziegler1, Chatchanit Arif1, Cornelia

Roder1, John Burt2, Christian R. Voolstra1,*

1Red Sea Research Center, Division of Biological and Environmental Science and Engineering

(BESE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia

2Center for Genomics and Systems Biology, New York University Abu Dhabi, PO Box

129188, Abu Dhabi, United Arab Emirates

* Corresponding author

Author contributions

CRV and CR conceived the study and designed experiments; GH, CA, LKY performed experiments; GH, TR, LKY, MZ, CRV analyzed the data; CRV, GH, TR, MZ wrote the manuscript. JB and CRV contributed material/reagents/tools.

This manuscript was published in Scientific Reports (31/03/2017).

Hadaidi, G., Röthig, T., Yum, L. K., Ziegler, M., Arif, C., Roder, C., Voolstra, C. R. (2017).

Stable mucus-associated bacterial communities in bleached and healthy corals of Porites lobata from the Arabian Seas. Scientific Reports, 7, 45362. doi:10.1038/srep45362

2.1. Abstract

Coral reefs are increasingly subject to coral bleaching manifested by the loss of endosymbiotic algae from coral host tissue. Besides algae, corals associate with bacteria. In particular, bacteria residing in the surface mucus layer (SML) are thought to mediate coral health, but their role in coral bleaching is unknown. We collected mucus from bleached and healthy Porites lobata colonies from reefs in the Persian/Arabian Gulf (PAG) and the Red

Sea (RS) to investigate the SML microbiome using 16S rRNA gene amplicon sequencing.

We found that microbial community composition was notably similar in bleached and healthy corals, and the most abundant bacterial taxa were identical. At the same time, microbial communities from the PAG and RS differed in diversity and richness that aligned with predicted differences in sulfur- and nitrogen-cycling processes encoded by less abundant bacterial associates. Based on our data, we argue that bleached coral benefit from the uniform composition and distribution of SML bacteria that resemble their healthy coral counterparts and presumably provide a conserved suite of protective functions. Conversely, site-specific differences highlight flexibility of the microbiome that may underlie adjustment to local environmental conditions and contribute to the widespread success of Porites lobata.

2.2. Introduction

Corals live in an endosymbiotic relationship with photosynthetic algae of the genus

Symbiodinium, along with other microorganisms, such as bacteria, archaea, fungi, and viruses

(Knowlton & Rohwer 2003b; Rosenberg et al. 2007). This consortium constitutes a metaorganism commonly referred to as the coral holobiont (Rohwer et al. 2002). While the algal symbionts provide a large part of the coral host’s metabolic requirement (Muscatine et al. 1984), the role of coral-associated bacteria, although suggested to be functionally important, is less well understood (Bourne et al. 2016; Mouchka et al. 2010). Studies have shown that the bacterial community provides nutrients, confers protection from pathogen invasion through

33 antimicrobial production, and is indicative of coral health states (Koh 1997b; Ritchie 2006;

Roder et al. 2014 a; Roder et al. 2014b; Roder et al. 2015; Rypien et al. 2010; Wegley et al.

2007; Ziegler et al. 2016b).

Importantly, the coral host provides a range of habitats such as the coral tissue, skeleton, and surface mucus layer (SML) that harbor distinct and diverse bacteria, but only few studies characterized the differences in community composition and function (Bourne & Munn 2005;

Koren & Rosenberg 2006; Li et al. 2014; Sweet et al. 2011). In particular, the SML provides a protective barrier and constitutes a highly selected microbial environment critical for coral health (Brown & Bythell 2005a; Glasl et al. 2016; Ritchie 2006). Coral mucus is a complex mixture of carbohydrates, lipids, and proteins, and plays a fundamental role in structural support, heterotrophic feeding, sediment cleansing, defense against environmental stressors, besides a suite of other functions (Glasl et al. 2016; Shnit-Orland & Kushmaro 2009; Stabili et al. 2015). Coral mucus is produced in the ectodermal mucus gland cells and originates from photosynthates and compounds derived from heterotrophic feeding (Coffroth 1990; Marshall

& Wright 1993). In many cases, the SML is a transparent coat that changes over days to mucus sheets. Aged mucus sheets are detached from the coral surface into the water column, after which new fluidic mucus is produced at the coral surface leading to a new cycle (Coffroth

1990; Glasl et al. 2016). Detached coral mucus sheets are shown to play an important role in retaining and recycling nutrients and metabolites in coral reef ecosystems (Wild et al. 2004).

Anthropogenic impacts in the form of local (e.g., overfishing and nutrient enrichment) and global (e.g., ocean warming and ocean acidification) stressors have resulted in a substantial loss of coral cover over the last decades, manifested by an increase in coral bleaching and coral disease (Hoegh-Guldberg et al. 2007; Hughes et al. 2007; Maynard et al. 2015). Coral

34 bleaching describes the physical whitening of the coral colony due to loss of its pigmented algal symbionts and can be induced by different stressors, of which climate changed-induced global warming is the most threatening (Carpenter et al. 2008). Yet, the molecular mechanisms behind coral bleaching are still not completely understood. Previous studies have shown that bacteria can cause coral bleaching and demonstrated that coral bleaching is related to the presence of the bacterium Vibrio shilonii in the annual bleaching of the Mediterranean coral

Oculina patagonica during warm summer months (Kushmaro et al. 1996), but not all studies found Vibrio shilonii in bleached corals (Ainsworth et al. 2007). More generally, studies have found differences in microbial assemblages between bleached and healthy corals indicating that bacteria respond to coral bleaching, although the precise role of bacteria in coral health and coral bleaching is not well understood (Bourne et al. 2008; Li et al. 2014; Rosenberg et al.

2007).

Given the worldwide increase in coral bleaching and the projected increase in frequency of global mass bleaching events (van Hooidonk et al. 2013), it is crucial to better understand the contribution of bacteria to coral bleaching. In particular, analysis of bacteria associated with the SML might provide further insight, given that SML provides a first protective barrier of the coral host against invading microbes. However, only few studies have analyzed coral mucus-associated microbial communities in coral bleaching. In addition, the effect of coral bleaching on bacterial communities of corals from different locations is virtually unknown. In particular, studies analyzing microbial communities of corals from the PAG and RS might be highly informative in this regard, as both regions display high water temperatures, are considered potentially stressful environments for coral, and may provide a model for studying the effects of global environmental change, i.e. ‘future oceans’ (Burt et al. 2011; Roik et al.

2016a; Roik et al. 2016b).

In this study, we analyzed microbial communities of the SML from bleached and healthy coral colonies of Porites lobata that were collected in the PAG and the central RS using 16S rRNA gene amplicon sequencing. Our aim was to document microbial community composition and potential microbial shifts between bleached and healthy corals to further understand the role of

SML-associated bacteria in coral bleaching. To our knowledge, this is the first comparison of mucus-associated bacterial communities of bleached and healthy corals from the PAG and RS.

2.3. Material and Methods

2.3.1. Study sites and coral mucus collection

Coral mucus was collected from bleached and healthy Porites lobata colonies from two reefs in the PAG, Saadiyat (24°35'56.4"N 54°25'17.4"E; samples: PAG1 - PAG10) and Ras Ghanada

(24°50'53.2"N 54°41'25.1"E; samples: PAG21 - PAG30), and from three reefs in the central

RS, Shib Nazar (22°20'27.4"N 38°51'07.6"E; samples: RS1 - RS10), Al-Fahal (22°15'06.0"N

38°57'23.2"E; RS11 - RS20), and Inner Fsar (22°13'58.4"N 39°01'45.6"E; samples: RS21 -

RS30), in September and October 2012, respectively. For each reef, mucus from 5 bleached and 5 healthy coral colonies were collected at 6 to 8 m depth, comprising a total of 20 samples from the PAG and 30 samples from the RS. Following (Monroe et al. 2016), corals were considered bleached if at least 20 % of the colony surface had lost coloration. Mucus samples were collected using sterile syringes by sucking up mucus from the coral surface and by irritating the surface with the syringe tip while concomitantly collecting the released mucus.

Syringes were placed in sterile Whirl-Paks. Upon return to the boat, syringes were placed upside-down in order for the heavier mucus to settle. Water on top of mucus was discarded and remaining mucus was ejected into cryotubes and frozen in liquid nitrogen. Samples were stored at -80 ˚C. In addition to the mucus samples, 1 L of seawater from each reef was collected at 1 to 2 m depth with a cubitainer. Cubitainers were transported on ice and 500 ml of collected

36 water samples were subsequently filtered on 0.22 µm Milipore Durapore filters (Millipore,

Billerica, MA, USA). Filters were snap-frozen in liquid nitrogen and stored at -80 ˚C.

2.3.2. DNA extraction

100 µL of mucus were used for DNA extraction using the Qiagen AllPrep DNA/RNA Mini kit

(Qiagen, Hilden, Germany) according to the manufacturer’s protocol. For extraction of DNA from seawater filters, half of each filter was cut into small stripes with sterile razorblades and transferred into 2 ml test tubes. After adding 400 µL Qiagen RLT buffer, the samples were incubated on a rotating wheel for 20 minutes. Subsequent extraction steps were performed according to the manufacturer’s protocol. DNA concentrations were quantified on a NanoDrop

2000C spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

2.3.3. Identification of algal symbionts from mucus of bleached and healthy

Porites colonies

To determine algal symbionts, the ITS2 rDNA region was amplified with primers ITSintfor2 and ITS2CLAMP following (LaJeunesse & Trench 2000) with the following modifications: during touch-down PCR amplification the annealing temperature was decreased by 0.5 ˚C every cycle for 20 cycles, followed by 27 cycles at a final annealing temperature of 52 ˚C.

Symbiodinium types were determined by DGGE profiling and subsequent sequencing of prominent bands. Prominent bands were excised from the DGGE gel and re-amplified as described in (LaJeunesse 2002). PCR products were then purified with Illustra ExoStar enzyme mix (SelectScience, Bath, UK), and samples were sequenced bidirectionally at the KAUST

BioScience Core Laboratory (Thuwal, Saudi Arabia). Sequences were quality trimmed in

CodonCode Aligner (CodonCode Corporation, Centerville, MA). Forward and reverse sequences were assembled into contigs and aligned using ClustalW. Each contig was

BLASTed against a local reference database of Symbiodinium ITS2 sequences (Arif et al. 2014) and against ITS2 sequences of type C15 variants recently described from Porites spp. in the

Red Sea (Ziegler et al. 2015).

2.3.4. 16S rRNA gene sequencing

DNA isolated from coral mucus and seawater were used for PCR amplification of a portion of the 16S rRNA gene. Five to 35 ng DNA from mucus samples and 1 to 3 ng DNA from seawater samples were used to amplify variable regions 5 and 6 of the 16S rRNA gene with the primers

784F [5’-

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGAGGATTAGATACCCTGGTA -3’] and 1061R [5’-

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCRRCACGAGCTGACGAC -3’]

(Andersson et al. 2008), which have been shown to amplify well with coral DNA (Bayer et al.

2013). Illumina MiSeq adaptor overhangs (underlined above; Illumina, San Diego, CA, USA) were used for subsequent library indexing. All PCRs were run in triplicates per sample using

Qiagen Multiplex PCR Kit (Qiagen) with 0.2 µM of each primer and a total reaction volume of 25 µL. Cycling conditions were as follows: 95 ˚C for 15 min, followed by 30 cycles of 95

˚C for 30 s, 55 ˚C for 90 s, 72 ˚C for 30 s, and a final extension cycle of 72 ˚C for 10 min.

Successful amplification was checked via 1 % agarose gel electrophoresis and sample triplicates were pooled in equimolar ratios. Pooled samples were cleaned with Agencourt

AMPpure XP magnetic beads system (Beckman Coulter, Brea, CA, USA) and subsequently underwent an indexing PCR to add Nextera XT indexing and sequencing adapters (Illumina) following the manufacturer’s protocol. PCR products were sequenced on the Illumina MiSeq platform at the KAUST BioScience Core Laboratory. Sequence data determined in this study are available under NCBI’s BioProject ID PRJNA352338, accessible at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA352338/.

2.3.5. Bacterial community analysis

Raw sequencing data were analyzed using mothur v.1.36.3 (Schloss et al. 2009). Sequence reads were split according to barcodes, assembled to contigs, and quality trimmed. Identical sequences were merged using the 'unique.seqs' command to save computation time, and the command 'count.seqs' was used to keep a count of the number of sequences over samples represented by the remaining representative sequence. Sequences that occurred only once across the entire dataset were removed. The remaining sequences were aligned against SILVA database release 119 (Pruesse et al. 2007) and pre-clustered (3-bp difference) (Huse et al.

2010). Chimeric sequences were removed using UCHIME (Edgar et al. 2011). Mitochondria,

Chloroplast, Archaea, Eukaryote, and unknown sequences were removed. Sequences were classified with the Greengenes database (McDonald et al. 2012) using a 60 % bootstrap cutoff. For subsequent OTU (Operational Taxonomic Unit)-based analyses, samples were subsampled to 2,827 sequences as determined by the sample with the lowest number of sequences; a 97 % similarity cut-off was then applied to obtain OTUs. Chao1 index, Simpson evenness, and Inverse Simpson Index were calculated as implemented in mothur. To assess differences between bacterial communities associated with mucus and seawater, analysis of molecular variance (AMOVA) was performed in mothur. AMOVA was further used to test for differences in bacterial communities between bleached and healthy colonies per region and for differences between regions. To determine OTUs that were associated with colonies of P. lobata from either the PAG or the RS, the statistical package IndicSpecies (Cáceres &

Legendre 2009) was used with a significance threshold of P ≤ 0.01. For determination of the core microbiome, all OTUs were considered that were presented in ≥ 75 % of all samples based on OTU abundance counts. Functional differences based on bacterial 16S community composition, were assessed with METAGENassist (Arndt et al. 2012). Input files were created in mothur using the ‘make.shared’ and ‘classify.otu’ commands based on all coral samples.

1,978 distinct OTUs were assigned, mapped, condensed into 500 functional taxa, and filtered based on interquantile range (Hackstadt & Hess 2009). After filtering, 375 functional taxa remained and were normalized over samples by sum and over taxa by Pareto scaling. These data were analyzed for ‘metabolism by phenotype’, and Euclidean distance measure (single clustering algorithm) was used to visualize the results in a heatmap.

2.3.6. Relative abundance of nifH and 16S rRNA genes using qPCR

To confirm the increased functional abundance of ‘Dinitrogen-fixing’ in coral colonies from the Red Sea (as inferred from METAGENassist), abundance of the nifH gene relative to abundance of the 16S rRNA gene was measured using quantitative PCR (qPCR). Reactions were run in triplicate per mucus sample on a 7900HT Fast Real-Time PCR System (Applied

Biosystems, USA) using a reaction volume of 20 µL containing 2 µL of DNA (approximately

1 ng), 10 µL of Platinum SYBR green qPCR Supermix-UDG (Invitrogen, USA), and 0.4 µL of ROX reference dye. For amplification of the nifH gene, the primers F2 and R6 (Gaby &

Buckley 2012) were used; for amplification of the 16S rRNA gene, primers 784F and 1061R were used (Andersson et al. 2008). Amplification reactions were performed with a primer concentration of 0.2 µM and with an initial polymerase activation step at 50 ˚C for 2 min and a denaturation step at 94 ˚C for 1 min followed by 50 cycles of 94 ˚C for 30 sec, 51 ˚C for 60 s, 72 ˚C for 60 s, and a final step of 72 ˚C for 3 min with a subsequent melting curve analysis.

Triplicate cycle threshold (Ct) values for each sample were averaged. Relative abundance differences based on differences in gene copy numbers were calculated using the equation ΔCt

= (CtnifH - Ct16S) for all mucus samples from the PAG and RS. Fold-change (FC) difference of

-ΔΔCt nifH between PAG and RS was calculated as FC = 2 with ΔΔCt = (ΔCtRS - ΔCtPAG).

Efficiency of qPCR was 82.12 % based on the formula E = 10(-1/slope) - 1 (Atallah et al. 2007) and the R2 of the standard curve was > 0.99.

2.4. Results

2.4.1. Algal symbionts associated with Porites lobata

To reveal potential correlations between bacterial communities and algal symbionts associated with mucus from P. lobata from the PAG and RS, we conducted ITS2 DGGE-fingerprinting and subsequent sequencing of prominent bands. Our analysis revealed that Porites from the

PAG were exclusively associated with Symbiodinium type C3 (Supplementary Table S2.1). In comparison, colonies from the RS were associated with Symbiodinium type C15 and C15 variants (C15h, C15n, C97), but we also found Symbiodinium types from clade D (D1, D1a,

D6) in a considerable number of coral colonies (41 %; Supplementary Table S2.1). In addition, one sample harbored Symbiodinium type A1. Overall, we did not detect differences in symbiont types between health states, but symbiont assemblages were different between the PAG and

RS and were more variable among corals from the RS. It should be noted though that due to difficulties in amplifying Symbiodinium DNA from some mucus samples (primarily bleached samples), ITS2 types could not be determined for all samples (i.e., in 36 % of all coral samples).

2.4.2. Bacterial community composition of coral mucus and seawater

We produced 55 16S rRNA gene libraries totaling 4,421,127 sequence reads from over 50

Porites mucus samples (five bleached and five healthy colonies from each of two reefs of the

PAG and three reefs of the RS) and five water samples (one water sample from each reef)

(Table 2.1). After quality filtering, 2,840,780 sequence reads with an average length of 292 bp were retained.

To assess overall differences in bacterial community composition, sequences were classified to the family level (Figure. 2.1). There was no apparent difference between bleached and healthy coral colonies from either the PAG or RS. Rather, mucus samples from the PAG and

RS appeared to be composed of the same bacterial families, but with varying degrees of abundance. For instance, corals from the PAG and RS were both dominated by bacteria from the family Pseudomonadaceae (~5 % to 47 %), Dermabacteraceae (~5 % to 18 %), and

Flavobacteraceae (~3 % to 19 %). The former two families were particularly abundant in samples from the RS, whereas the latter one in samples from the PAG. Water samples from the PAG and RS appeared highly similar, and were dominated by Flavobacteriaceae (~12 % to

16 %), Halomonadaceae (~10 % to 18 %), and Pelagibacteraceae (~9 % to 16 %), and markedly different from mucus samples (Figure. 2.1).

Table 2.1. Summary statistics of 16S rRNA gene sequencing of mucus-associated bacteria from bleached and healthy coral colonies of P. lobata from the Persian/Arabian Gulf (PAG) and the Red Sea (RS). Numbers are provided as means and standard deviation (SD); for corals n = 5, for water n = 1.

No. of No. of Inverse Simpson Region Condition Chao1* Seqs OTUs* Simpson* evenness* 37,634 171.2 PAG reef1 Bleached 218.4 (72.3) 17.6 (9.7) 0.10 (0.04) (23,659) (41.5) 62,224 202.8 PAG reef1 Healthy 280.2 (48.7) 24.3 (13.2) 0.12 (0.06) (43,410) (18.1) 51,272 188.8 PAG reef2 Bleached 259.2 (110.4) 21.4 (12.9) 0.11 (0.05) (28,950) (57.4) 47,882 176.2 PAG reef2 Healthy 224.2 (76.1) 17.9 (10.0) 0.10 (0.04) (32,597) (33.1) 25,079 141.2 RS reef1 Bleached 168.3 (48.0) 8.2 (3.5) 0.07 (0.01) (18,013) (34.4) 32,027 134.8 RS reef1 Healthy 162.1 (56.6) 10.5 (7.2) 0.07 (0.02) (19,659) (42.5) 22,450 139.6 RS reef2 Bleached 168.9 (57.2) 8.5 (3.3) 0.06 (0.01) (15,270) (39.2) 18,881 139.4 RS reef2 Healthy 165.6 (56.7) 9.1 (3.7) 0.07 (0.01) (12,127) (44.5) 11,302 127.8 RS reef3 Bleached 151.4 (24.8) 8.7 (3.3) 0.07 (0.02) (5,581) (24.9) 20,426 127.0 RS reef3 Healthy 176.7 (36.7) 8.2 (4.6) 0.06 (0.02) (10,672) (24.6) PAG reef1 Water 207,396 266 570 36.2 0.14 PAG reef2 Water 141,076 217 462 30.3 0.14 RS reef1 Water 271,394 208 373 25.2 0.12 RS reef2 Water 250,718 228 403 24.9 0.11 RS reef3 Water 324,309 184 395 25.8 0.14

*subsampled to 2,827 sequences; PAG reef1: Saadiyat; PAG reef2: Ras Ghanada; RS reef1: Shib Nazaar; RS reef2: Inner Fsar; RS reef3: Inner Fsar. Total number of OTUs: 2,225.

Figure 2.1. Bacterial community composition of mucus from bleached and healthy coral colonies of P. lobata from the Persian/Arabian Gulf (PAG) and Red Sea (RS). Depicted is a taxonomy stacked column plot on the phylogenetic level of family. Each color represents one of the 27 most abundant families. Remaining taxa are grouped under category ‘others’. Samples are ordered by site, reef, and health-state. Values displayed as means of n = 5 for corals and n = 1 for water samples, B: Bleached; H: Healthy, PAG reef1: Saadiyat; PAG reef 2: Ras Ghanada; RS reef1: Shib Nazar; RS reef 2: Al Fahal; RS reef3: Inner Fsar.

2.4.3. Bacterial community differences of coral mucus across regions

Besides the overall similarity of microbial community composition of mucus from P. lobata colonies, we were interested to assess fine-scale differences in microbial community composition across health states (i.e., bleached and healthy) and across regions (i.e., PAG and

RS). For this purpose, sequences were clustered to OTUs after subsampling to 2,827 reads

(Supplementary Dataset S2.1).

Estimates of Chao1 species richness as well as Simpson evenness and Inverse Simpson Indices

(diversity measures) were on average higher for bacterial communities from the PAG in comparison to the RS (all Pt-test ≤ 0.001) pointing to a more diverse and heterogeneous bacterial community in coral mucus from the PAG (Table 2.1, Supplementary Table S2.2). However, species richness and bacterial diversity were highest in seawater, and water samples from the

PAG and RS were significantly different from all coral mucus samples (PAMOVA< 0.001), but not from each other (PAMOVA ≥ 0.05). This indicates that differences in mucus-associated bacterial communities were not due to differences in the surrounding seawater, and water samples were excluded from further analyses.

Importantly, we did not find significant differences between bleached and healthy corals, irrespective of region (Supplementary Figure. S2.1, PAMOVA = 0.35 PAG; PAMOVA = 0.72 RS).

Following this, we assessed differences between all samples from the PAG and all samples from the RS and found significant differences between mucus-associated bacteria between regions (Supplementary Figure. S2.1, PAMOVA < 0.001). To further analyze this, we tested for bacterial taxa that are significantly associated with coral mucus from either region. We found

70 bacterial OTUs that were significantly associated with mucus samples from the PAG and

24 OTUs that were significantly associated with mucus samples from the RS (both P ≤ 0.01)

(Table 2.2). Notably, these regional indicator taxa were mostly relatively low abundant in the mucus samples (Table 2.2) in comparison to core microbiome members (see below, Table 2.3).

Table 2.2. Site-specific bacterial taxa associated with mucus samples of P. lobata from the Persian/Arabian Gulf or the Red Sea ranked by relative abundance (average number of sequence counts). Taxonomic classification of OTUs against Greengenes database (bootstrap value indicated if < 100).

Assoc. Relative OTU ID P value Taxonomy Value Abundance Persian/Arabian Gulf Otu0010 0.930 0.001 Candidatus Portiera sp. 42.38

Otu0024 0.993 0.001 Unknown species, family Flavobacteriaceae 23.04 Otu0026 0.996 0.001 Unknown species, family S25_1238 19.24 Otu0039 0.902 0.001 Polaribacter irgensii 13.11 Otu0046 0.939 0.001 Unknown species, family Cryomorphaceae 9.69 Otu0049 0.897 0.001 Flavobacterium sp. (94) 7.56 Otu0052 0.907 0.001 Unknown species, family Cryomorphaceae 7.29 Otu0053 0.919 0.001 Unknown species, class Gammaproteobacteria 7.29 Otu0054 1.000 0.001 Unknown species, family Flavobacteriaceae 7.02 Otu0057 1.000 0.001 Balneola sp. 6.71 Otu0059 0.893 0.001 Acholeplasma sp. 6.25 Otu0065 0.975 0.001 Unknown species, family Flavobacteriaceae 5.31 Otu0073 0.906 0.001 Unknown species, family C111 4.56 Otu0074 0.838 0.001 Unknown species, family Saprospiraceae 4.51 Otu0075 1.000 0.001 Unknown species, family Rhodospirillaceae 4.49 Otu0076 0.846 0.001 HTCC sp. 4.18 Otu0077 0.916 0.001 Fluviicola sp. 4.16 Otu0078 0.973 0.001 Unknown species, class Alphaproteobacteria 4.15 Otu0079 0.819 0.001 Photobacterium angustum (91) 4.04 Otu0082 0.877 0.001 Unknown species, family Flavobacteriaceae 3.85 Otu0083 0.924 0.001 Unknown species, class Alphaproteobacteria 3.6 Otu0087 0.943 0.001 Unknown species, family Cryomorphaceae 3.36 Otu0090 0.903 0.001 Unknown species, family Saprospiraceae 3.22 Otu0091 0.833 0.001 Unknown species, family Cryomorphaceae 3.15 Otu0092 0.671 0.001 Unknown species, kingdom Bacteria 3.04 Otu0096 0.894 0.001 Acholeplasma sp. 2.89 Otu0104 0.784 0.001 Candidatus_Portiera sp. 2.53 Otu0105 0.811 0.001 Unknown species, class Alphaproteobacteria 2.45 Otu0109 0.922 0.001 Unknown species, order Kiloniellales 2.24 Otu0112 0.956 0.001 Unknown species, family Rhodobacteraceae 2.00 Otu0126 0.500 0.01 Unknown species, kingdom Bacteria 1.42 Otu0128 0.644 0.002 Unknown species, order Rhizobiales 1.35 Otu0131 0.775 0.001 Unknown species, family Saprospiraceae 1.27 Otu0141 0.832 0.001 Unknown species, family OM60 1.16 Otu0144 0.839 0.001 Unknown species, family Cryomorphaceae 1.13 Otu0145 0.500 0.009 Wandonia sp. 1.13 Otu0148 0.742 0.001 Unknown species, family Microbacteriaceae 1.11 Otu0149 0.811 0.001 Unknown species, family OM27 1.05 Otu0150 0.775 0.001 Unknown species, family Methylophilaceae 1.05 Otu0152 0.773 0.001 Unknown species, order GMD14H09 1.04 Otu0163 0.707 0.001 Unknown species, family Flavobacteriaceae 0.93 Otu0165 0.548 0.002 Flammeovirga sp. 0.91 Otu0171 0.721 0.001 Unknown species, family Rhodospirillaceae 0.84 Otu0174 0.671 0.002 Unknown species, family Phyllobacteriaceae 0.8 Otu0176 0.626 0.001 Unknown species, family Saprospiraceae 0.78 Otu0196 0.632 0.001 Unknown species, order Spirobacillales 0.67 Otu0197 0.668 0.003 Turneriella sp. 0.67

Otu0206 0.742 0.001 Unknown species, order Flavobacteriales 0.62 Otu0207 0.697 0.001 Unknown species, class Alphaproteobacteria 0.62 Otu0209 0.671 0.001 Unknown species, family Flavobacteriaceae 0.6 Otu0211 0.689 0.001 Turneriella sp. 0.58 Otu0217 0.77 0.001 Unknown species, family Cryomorphaceae 0.56 Otu0240 0.592 0.001 Jannaschia sp. 0.47 Otu0242 0.548 0.005 Unknown species, family Saprospiraceae 0.45 Otu0244 0.500 0.008 Unknown species, family Cryomorphaceae 0.45 Otu0247 0.632 0.001 Unknown species, order Rhizobiales 0.44 Otu0250 0.588 0.007 Photobacterium damselae (84) 0.44 Otu0258 0.548 0.004 Unknown species, order GMD14H09 0.4 Otu0261 0.500 0.01 Unknown species, class Alphaproteobacteria 0.4 Otu0262 0.632 0.001 Unknown species, class Gammaproteobacteria 0.4 Otu0275 0.632 0.001 Unknown species, family Saprospiraceae 0.36 Otu0292 0.500 0.009 Unknown species, family Flavobacteriaceae 0.31 Otu0300 0.500 0.008 Saprospira sp. 0.29 Otu0302 0.500 0.008 Unknown species, phylum Bacteroidetes 0.29 Otu0304 0.632 0.001 Unknown species, class Gammaproteobacteria 0.29 Otu0325 0.548 0.005 Unknown species, family Saprospiraceae 0.27 Otu0328 0.500 0.008 Unknown species, phylum Bacteroidetes 0.25 Otu0334 0.500 0.009 Unknown species, class Pedosphaerae 0.25 Otu0347 0.592 0.003 Unknown species, kingdom Bacteria 0.24 Otu0372 0.500 0.008 Unknown species, class Gammaproteobacteria 0.22 Red Sea Otu0014 0.931 0.001 Unknown species, family OCS155 32.13 Otu0028 0.989 0.001 Candidatus Portiera sp. 16.67 Otu0034 0.966 0.001 SGSH944 sp. 14.49 Otu0042 0.874 0.001 Flavobacterium sp. 10.71 Otu0056 0.852 0.001 Candidatus_Portiera sp. 6.76 Otu0080 0.730 0.002 Unknown species, kingdom Bacteria 4.02 Otu0081 0.707 0.001 Unknown species, family Flavobacteriaceae 3.98 Otu0085 0.775 0.001 Candidatus Portiera sp. 3.49 Otu0086 0.775 0.001 Candidatus Portiera sp. 3.40 Otu0088 0.787 0.001 Unknown species, family Rhodobacteraceae 3.29 Otu0089 0.730 0.001 Unknown species, family Flavobacteriaceae 3.27 Otu0097 0.742 0.003 Unknown species, family Endozoicimonaceae 2.76 Otu0110 0.775 0.001 SGSH944 sp. 2.13 Otu0116 0.752 0.001 Methylobacterium mesophilicum (96) 1.89 Otu0125 0.683 0.001 Unknown species, class A712011 1.44 Otu0130 0.632 0.005 Unknown species, phylum Proteobacteria 1.31 Otu0134 0.614 0.007 Unknown species, family HTCC2089 1.24 Otu0140 0.658 0.003 Unknown species, class Alphaproteobacteria 1.16 Otu0142 0.632 0.002 SargSea-WGS sp. 1.15 Otu0143 0.753 0.001 Unknown species, family Pelagibacteraceae 1.13 Otu0159 0.577 0.009 Candidatus Portiera sp. 0.98 Otu0169 0.683 0.001 Unknown species, family Pelagibacteraceae 0.87

Otu0192 0.683 0.001 Unknown species, family Pelagibacteraceae 0.69 Otu0309 0.632 0.003 Zhihengliuella sp. (63) 0.29

Table 2.3. Core microbiome of mucus-associated bacteria from bleached and healthy colonies of P. lobata from the PAG and the RS. Core microbiome members are present at ≥ 75% in all coral mucus samples. Taxonomic classification of OTUs against Greengenes database (bootstrap value indicated if < 100). Relative abundance denotes average number of sequence counts over samples.

Relative OTU ID Taxonomy Relative Abundance Presence Otu0001 100 % Pseudomonas veronii 729.8 Otu0002 100 % Brachybacterium sp. 314.6 Otu0003 100 % Dietzia sp. 204.7 Otu0004 100 % Unknown species, family OCS155 87.3 Otu0005 100 % Unknown species, family Pelagibacteraceae 61.5 Otu0008 100 % Pseudomonas umsongensis (91) 53.1 Otu0011 100 % Caulobacter henricii 44.2 Otu0012 100 % Herbaspirillum sp. 43.3 Otu0018 100 % Brevibacterium aureum 30.8 Otu0020 100 % Pelomonas puraquae 27.5 Otu0029 100 % Sphingomonas echinoides 17.5 Otu0035 100 % Acinetobacter guillouiae (81) 14.8 Otu0036 100 % Propionibacterium acnes 14.7 Otu0016 98 % Unknown species, class Deltaproteobacteria 25.0 Otu0019 98 % Candidatus Portiera sp. 21.4 Otu0007 96 % Candidatus Portiera sp. 45.4 Otu0009 96 % Unknown species,family Rhodospirillaceae 38.6 Otu0021 96 % ZA3312c sp. 21.5 Otu0040 96 % Unknown species, family Pelagibacteraceae 10.6 Otu0006 94 % Unknown species, family Rhodobacteraceae 69.4 Otu0023 94 % Unknown species, class Alphaproteobacteria 19.5 Otu0015 92 % Unknown species, family Flammeovirgaceae 25.8 Otu0032 92 % Unknown species, family Pelagibacteraceae 12.9 Otu0037 92 % Phaeobacter sp. (99) 14.8 Otu0013 90 % Unknown species, family Flavobacteriaceae 31.5 Otu0017 90 % Unknown species, family Flavobacteriaceae 23.6 Otu0027 90 % Polaribacter irgensii (91) 16.4 Otu0051 90 % Unknown species, family Pelagibacteraceae 6.0 Otu0033 88 % Unknown species, order Rhizobiales 13.2 Otu0063 88 % Delftia sp. 6.4 Otu0010 86 % Candidatus_Portiera sp. 35.5 Otu0030 86 % Unknown species, family OM60 14.3 Otu0071 86 % Sphingobium yanoikuyae 5.1 Otu0050 84 % Unknown species, order MWH-UniP1 6.4 Otu0043 82 % Unknown species, family Piscirickettsiaceae 8.6 Otu0025 80 % Unknown species, family Flavobacteriaceae 21.9

Otu0041 80 % Unknown species, family Flavobacteriaceae 11.9 Otu0045 80 % Unknown species, family AEGEAN_112 8.4 Otu0068 80 % Staphylococcus epidermidis 5.8 Otu0022 78 % Polaribacter irgensii 22.8 Otu0044 78 % Unknown species, class Gammaproteobacteria 8.9 Otu0114 76 % Unknown species, order Actinomycetales 2.1 Otu0038 75 % Candidatus Portiera sp. 10.9 Otu0047 75 % Unknown species, family Pelagibacteraceae 7.4 Otu0048 75 % Unknown species, family Cryomorphaceae 7.3

2.4.4. Core microbiome of coral mucus

Despite the site-specific differences in microbial communities from the PAG and RS, we were interested in determining how many and which OTUs were consistently associated with coral mucus of Porites, as indicated by the bacterial community composition analysis

(Figure. 2.1). To do this, we determined all OTUs that were present in at least 75 % of all mucus samples and considered them members of the core microbiome. Following these criteria, we identified 45 OTUs to be consistently associated with mucus from P. lobata colonies, 13 of which were present in 100 % of mucus samples (Table 2.3). Importantly, 5 of these 13 constituted the most abundant bacterial taxa. This indicates that despite differences in the mucus-associated microbial communities of corals from the PAG and RS, the core microbiome consistently encompasses the most abundant bacteria shared between bleached and healthy coral mucus samples, irrespective of site. Notably, Pseudomonas veronii

(OTU0001), Brachybacterium sp. (OTU0002), and Dietzia sp. (OTU0003) that constituted the most abundant taxa are commonly associated with saline environments (Lo et al. 2013;

Röthig et al. 2016b; Zvyagintseva et al. 2001).

2.4.5. Taxonomy-based functional profiling of bacterial communities

To gain insight into putative functional differences associated with bacterial community differences, we applied taxonomy-based functional profiling (Figure. 2.2). The majority of samples grouped by region, although some samples from the PAG clustered with samples from the RS (Figure. 2.2). Within clustered samples from the PAG, the functions 'Sulfate

48 reducer’, ‘Ammonia oxidizer’, ‘Chitin degradation’, ‘Nitrite reducer’, ‘Xylan degrader’,

‘Sugar fermentor’, and ‘Dinitrogen-fixing’ were all less abundant in comparison to the RS where these processes were enriched. In contrast, ‘Nitrogen fixation’ and ‘Sulfide oxidizer’ were enriched in the PAG and either unchanged or depleted in the RS (Figure. 2.2). To confirm an enrichment of ‘Dinitrogen-fixing’ in the RS, we assessed the abundance of diazotroph communities via qPCR of the nifH gene and could confirm an on average higher abundance of diazotrophs in samples from the RS in comparison to the PAG (27 % increase;

Supplementary Figure. S2.2).

Persian/Arabian Gulf Red Sea -4 -2 0 2 4

RS22_B PAG2_B RS23_B PAG30_H RS24_B RS16_H PAG8_H PAG29_H RS18_H RS15_B RS11_B PAG4_B RS19_H RS30_H RS9_H RS6_H RS7_H RS3_B RS27_H PAG26_H PAG25_B PAG23_B RS17_H RS2_B RS13_B RS25_B RS4_B RS1_B RS14_B RS26_H RS29_H RS12_B RS21_B RS5_B RS20_H RS8_H RS28_H RS10_H PAG27_H PAG21_B PAG5_B PAG1_B PAG9_H PAG24_B PAG3_B PAG7_H PAG6_H PAG22_B PAG28_H PAG10_H er er er er z z z z ate r ader ader ader ader ader ifying r r r r r ading ading ading r r r r xidi xidi xidi xidi uty mentor adation o o o o r b r e f xy ite reducer ydrocarbon Denit ate reducer r o f Iron h ycin producer Sulfur reducer Sulfur Nit ydr Carbon fixation Sulfide Sul Xylan deg m Lignin deg Dehalogenation h Nitrogen fixation Dinitrogen-fixing Alkane deg Selenate reducer Sugars azine metabolism amicidin producer Ammonia r Chitin deg Biomass deg r Cellulose deg Sulfur metabolizing G At Cellobiose deg Strepto Propionate metabolism Naphthalene deg Chlorophenol deg Stores poly ades aromatic r

Deg

Figure 2.2. Taxonomy-based functional profiling of bacterial communities associated with mucus from P. lobata from the Persian/Arabian Gulf and Red Sea. Heatmap displaying putative changes in microbial community function. Changes are displayed on a relative scale with enrichment in red and depletion in blue.

2.5. Discussion

In this study, we compared bacterial community composition of the SML from bleached and healthy coral colonies of P. lobata from the PAG and RS in order to determine their structure, stability, and putative functional profiles. Our results show that bacterial community composition in the SML is highly similar between bleached and healthy coral. Importantly,

50 core microbiome members are comprised of abundant microbial associates, whereas site- specific differences exist for less abundant bacteria. Further, algal symbiont association concurred with bacterial community patterns, as we did not find differences between health states, but regional differences. For instance, we could confirm the presence of Symbiodinium type C3 in P. lobata from the PAG (Hume et al. 2015; Hume et al. 2016; Ziegler et al. in press), whereas P. lobata from the RS were associated with Symbiodinium types C15 (and variants thereof) as well as types from clade D (Ziegler et al. in press; Ziegler et al. 2015). However, we did not find apparent differences between bleached and healthy colonies. Hence, it remains to be seen whether a causal relationship between Symbiodinium and bacterial community patterns exists. Given that corals from both the PAG and RS are able to survive seasonal temperature maxima exceeding those form other regions, at least in part, due to harboring algal thermal tolerant symbionts that are commonly associated with high temperature environments

(Baker et al. 2004; Hume et al. 2015; Hume et al. 2016), it would be intriguing to find bacterial associates that co-occur with symbiont types. In a recent study with the coral model Aiptasia, microbial community patterns were distinct between symbiotic and aposymbiotic anemones, arguing for a connection between bacterial community composition and the cnidarian-algal symbiosis (Röthig et al. 2016a). At large, the presence of photosymbionts distinguishes the microbiomes of hosts from those without photosymbionts (Bourne et al. 2013), but microbiomes of juvenile corals hosting different Symbiodinium clades were indistinguishable

(Littman et al. 2009).

To our knowledge, this is the first study that compares mucus-associated bacteria from bleached and healthy Porites colonies in the PAG and RS. Porites spp. have high production rates of mucus that cover coral colonies in the form of mucus sheets that exhibit a distinct ageing cycle making it an ideal model system to study dynamics of the mucus-associated

51 microbiota (Coffroth 1990; Glasl et al. 2016). Commonly, a new fluid mucus layer is produced about every four weeks (Coffroth 1990; Glasl et al. 2016). This periodical release of mucus supposedly supports a stable microbiome and provides a protective barrier upon disturbance

(Glasl et al. 2016). This is supported by our data, as we find that mucus-associated bacteria in bleached Porites are similar to those in healthy Porites colonies. In contrast, Bourne et al.

(2008) showed a shift of bacteria associated with the tissue of bleached corals. This indicates that mucus-associated bacterial communities may be less dynamic than those that are tissue- associated. Accordingly, the stably associated bacteria in the SML may provide a protective function, even and especially when coral health is compromised as during coral bleaching. In this regard, Lee et al. (2016) showed that under heat stress the chemical composition of mucus in the coral Acropora muricata changed, which might either influence the associated bacterial community or be a consequence of it. Based on the presence of stable bacterial associates in our study, we infer that the mucus chemical composition did not change, although coral bleaching likely affected the availability of carbohydrates for mucus production (Coffroth

1990).

It is interesting to note that all mucus samples were dominated by few OTUs that were previously reported from saline environments, arguing that the high salinity of the PAG and

RS might indeed comprise a structural determinant of bacteria associated with corals in these regions. Further, the consistent presence of these taxa in all coral mucus samples irrespective of site or bleaching state implies that they play an important role in the coral holobiont. P. veronii has previously been found in fungiid corals experimentally exposed to high salinities

(49 PSU) (Röthig et al. 2016b). Dietzia sp. is found in the marine environment and in soil, human skin, and the intestinal tract of a carp, and plays a role in biodegradation, bioremediation, industrial fermentation, and carotenoid pigmentation (Gharibzahedi et al.

2014). The presence of Brachybacterium sp. has previously been reported in oil-contaminated coastal sand (Chou et al. 2007) and salt-fermented seafood (Park et al. 2011). In contrast, site- specific bacterial taxa displayed lower abundance on average. Nevertheless, these bacteria suggest that Porites spp. have the ability to harbor flexible, and presumably locally adjusted microbiomes, which might at least in part contribute to the resilience of this coral genus

(Hernandez-Agreda et al. 2016; Jessen et al. 2013).

Predictive bacterial functional profiling between the PAG and the RS revealed differences in the abundance of bacteria associated with sulfur and nitrogen cycling. Differences in sulfur cycling included a downregulation of ‘Sulfur oxidizer’ and ‘Sulfate reducer’ and an upregulation of ‘Sulfide oxidizer’ in samples from the PAG. Corals and especially their endosymbiotic algae are major producers of dimethylsulfoniopropionate (DMSP) (Raina et al.

2013). Its breakdown products, such as dimethylsulfoxid (DMSO), result mainly from microbial metabolism and play a significant role in the scavenging of harmful reactive oxygen species (ROS) (Sunda et al. 2002). Importantly, Symbiodinium produce elevated level of ROS during thermal stress, which may result in coral bleaching (Lesser 2006; Weis 2008).

Consequently, the high temperatures in the PAG likely triggers increased ROS production demanding increased availability of ROS scavengers such as DMSP and DMSO, which could explain the functional differences in sulfur cycling observed in SML-associated bacteria between the PAG and RS.

Differences in nitrogen cycling included increased abundance of ‘Dinitrogen-fixing’,

‘Ammonia oxidizer’, and ‘Nitrite reducer’ in the RS. Efficient nitrogen fixation and nitrogen recycling is essential for coral to thrive in nutrient-limited environments (Fiore et al. 2010).

The RS constitutes a highly oligotrophic environment where nitrogen is presumably not readily available for corals. Compared to the RS, nitrogen is not a limiting nutrient in the PAG

(Banerjee & Prasanna Kumar 2014), allowing for comparably high uptake for nitrogen sources

(increased abundance of ‘Nitrogen fixation’) (Grover et al. 2008) and lacking the need for efficient nitrogen recycling (decreased abundance of 'Ammonia oxidizer'). From our analyses, functional profiling supports that environmental conditions strongly influence bacterial nitrogen fixation in corals (Rädecker et al. 2015).

Taken together, in this study we found stable bacterial communities in the SML of bleached and healthy coral colonies of P. lobata from the PAG and the RS. This underscores the barrier function of coral mucus and we argue that bleached coral colonies benefit from the uniform composition and distribution of SML-associated bacteria that presumably provide protective functions. In line with this, we found several abundant and ubiquitous bacterial taxa that we identified as core microbiome members of coral mucus. Further, regional differences in the mucus microbiome between PAG and RS were represented by less abundant bacteria that could be associated with a shift in predicted microbial functional profiles. The specific regional bacterial taxa may thus contribute to the success of Porites colonies across a range of environmental conditions.

2.6. Acknowledgements

We would like to thank the Coastal and Marine Resources Core Lab (CMOR) in KAUST and the Marine Biology Lab at NYUAD for their assistance and support in field operations. We thank Nils Rädecker for assistance with qPCR experiments and analysis, and Craig Michell for support with amplicon sequencing. This publication is based upon work supported by the King

Abdullah University of Science and Technology (KAUST) Office of Sponsored Research

(OSR) under Award No. OSR-2015-CCF-1973

Additional information

Accession codes: Sequence data determined in this study are available under NCBI’s

BioProject ID PRJNA352338, accessible at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA352338/.

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Supplementary Information

Supplementary Table S2.1. Symbiodinium types associated with bleached and healthy Porites lobata from the Persian/Arabian Gulf (PAG) and the Red Sea (RS) using DGGE profiling of ITS2. Samples PAG1 – PAG10: Saadiyat reef; PAG21 – PAG30: Ras Ghanada reef; RS1 – RS10: Shib Nazaar reef; RS11 – RS20: Inner Fsar reef; RS21 – RS30: Inner Fsar reef. ND = not determined due to difficulties in amplifying Symbiodinium DNA from coral mucus samples.

Sample name Condition Symbiodinium type Identity to ITS2 type sequence (%) PAG1 Bleached ND ND PAG2 Bleached ND ND PAG3 Bleached ND ND PAG4 Bleached ND ND PAG5 Bleached ND ND PAG6 Healthy C3 100 PAG7 Healthy C3 100 PAG8 Healthy C3 100 PAG9 Healthy C3 100 PAG10 Healthy C3 100 PAG21 Bleached ND ND PAG22 Bleached ND ND PAG23 Bleached ND ND PAG24 Bleached ND ND PAG25 Bleached ND ND PAG26 Healthy C3 100 PAG27 Healthy C3 100 PAG28 Healthy C3 100 PAG29 Healthy C3 100 PAG30 Healthy C3 100 RS1 Bleached ND ND RS2 Bleached A1 99 RS3 Bleached C15h 100 RS4 Bleached ND ND RS5 Bleached C97 100 RS6 Healthy D1 99 RS7 Healthy C15n 100 RS8 Healthy D1 100 RS9 Healthy ND ND RS10 Healthy D1a 100 RS11 Bleached D1 100 RS12 Bleached ND ND RS13 Bleached C15 100 RS14 Bleached ND ND RS15 Bleached C15 100

RS16 Healthy D1 100 RS17 Healthy C15n 100 RS18 Healthy ND ND RS19 Healthy ND ND RS20 Healthy C15n 100 RS21 Bleached D1 100 RS22 Bleached ND ND RS23 Bleached D1 100 RS24 Bleached C15n 100 RS25 Bleached C15/D1/D1a 100/100/100 RS26 Healthy C15n 100 RS27 Healthy C15n 100 RS28 Healthy D1/D6 100/100 RS29 Healthy C15n 100 RS30 Healthy C15n 99

Supplementary Table S2.2. Summary statistics of 16S rRNA gene sequencing of mucus-associated bacteria from bleached and healthy coral colonies of P. lobata from the Persian/Arabian Gulf (PAG) and the Red Sea (RS) (full dataset: 50 coral and 5 water samples).

Sample No. of No. of Inverse Simpson Condition Chao1* name Seqs OTUs* Simpson* evenness* PAG1 Bleached 42,715 178 199 22.1 0.12 PAG2 Bleached 12,468 122 148 4.7 0.04 PAG3 Bleached 67,164 209 298 25.1 0.12 PAG4 Bleached 14,702 135 157 9.7 0.07 PAG5 Bleached 51,122 212 291 26.2 0.12 PAG6 Healthy 93,539 226 331 39.2 0.17 PAG7 Healthy 109,719 215 307 37.6 0.18 PAG8 Healthy 25,004 182 215 12.9 0.07 PAG9 Healthy 9,404 189 243 19.9 0.11 PAG10 Healthy 73,454 202 306 12.1 0.06 PAG21 Bleached 48,911 176 236 23.0 0.13 PAG22 Bleached 101,940 209 312 34.9 0.17 PAG23 Bleached 35,099 140 165 7.8 0.06 PAG24 Bleached 36,141 278 423 32.6 0.12 PAG25 Bleached 34,270 141 160 8.5 0.06 PAG26 Healthy 22,493 147 164 10.4 0.07 PAG27 Healthy 48,223 198 244 25.4 0.13 PAG28 Healthy 101,028 223 349 31.6 0.14 PAG29 Healthy 47,732 163 180 12.5 0.08 PAG30 Healthy 19,933 150 183 9.7 0.06 RS1 Bleached 3,526 116 120 5.9 0.05 RS2 Bleached 17,421 147 199 8.3 0.06 RS3 Bleached 19,910 107 127 6.2 0.06

RS4 Bleached 33,414 141 163 6.3 0.05 RS5 Bleached 51,126 195 233 14.1 0.07 RS6 Healthy 13,414 124 144 7.8 0.06 RS7 Healthy 38,949 128 150 9.3 0.07 RS8 Healthy 18,131 89 105 4.9 0.06 RS9 Healthy 27,057 128 155 7.5 0.06 RS10 Healthy 62,584 205 257 23.1 0.11 RS11 Bleached 39,941 145 211 10.4 0.07 RS12 Bleached 16,671 143 161 6.5 0.05 RS13 Bleached 3,458 99 105 6.9 0.07 RS14 Bleached 15,935 111 125 5.3 0.05 RS15 Bleached 36,244 200 242 13.4 0.07 RS16 Healthy 38,669 145 168 9.7 0.07 RS17 Healthy 19,028 149 176 7.1 0.05 RS18 Healthy 9,036 157 200 12.7 0.08 RS19 Healthy 18,782 182 214 12.1 0.07 RS20 Healthy 8,892 64 70 3.9 0.06 RS21 Bleached 15,590 148 165 7.7 0.05 RS22 Bleached 10,802 86 115 4.2 0.05 RS23 Bleached 17,046 143 168 8.8 0.06 RS24 Bleached 10,244 137 172 9.3 0.07 RS25 Bleached 2,827 125 136 13.3 0.11 RS26 Healthy 11,681 97 127 4.9 0.05 RS27 Healthy 12,887 127 145 7.5 0.06 RS28 Healthy 30,899 160 213 16.1 0.10 RS29 Healthy 33,222 111 126 5.3 0.05 RS30 Healthy 13,440 140 172 7.1 0.05 PAGW1 Seawater 207,396 266 570 36.2 0.14 PAGW2 Seawater 141,076 217 462 30.3 0.14 RSW1 Seawater 271,394 208 373 25.2 0.12 RSW2 Seawater 250,718 228 403 24.9 0.11 RSW3 Seawater 324,309 184 395 25.8 0.14

*subsampled to 2,827 sequences; Samples PAG1 - PAG10: Saadiyat reef; PAG21 - PAG30: Ras Ghanada reef; RS1 - RS10: Shib Nazaar reef; RS11 - RS20: Inner Fsar reef; RS21 - RS30: Inner Fsar reef. Total number of OTUs: 2,225

Supplementary Figure S2.1. Bacterial community composition from coral mucus of healthy and bleached Porites lobata colonies from the Persian/Arabian Gulf (PAG) and the Red Sea (RS). Principal coordinate analysis based on Operational Taxonomic Unit (OTU) abundance (sequence counts) shows a partitioning of mucus microbiomes by region, but not by health state.

Supplementary Figure S2.2. Relative abundance of diazotrophs in mucus samples from P. lobata from the Persian/Arabian Gulf (PAG) and Red Sea (RS). Note that lower values indicate higher abundance; error bars = SE.

Chapter 2

3. Ecological and molecular characterization of a coral black band disease outbreak in

the Red Sea during a bleaching event

G. Hadaidi1; M. Ziegler1; A. Shore-Maggio2; T. Jensen1; G. Aeby3*, C.R. Voolstra1*

1Red Sea Research Center, Division of Biological and Environmental Science and

Engineering (BESE), King Abdullah University of Science and Technology (KAUST),

Thuwal, Saudi Arabia

2Institute of Marine and Environmental Technology (IMET), University of Maryland,

Baltimore County, Baltimore, Maryland, United States of America

3Hawai‘i Institute of Marine Biology, Kāne‘ohe, Hawaii, United States of America

* Corresponding author

Author contributions

Conceived and designed the study: C.R.V., G.A., M.Z.; conducted surveys/took samples and generated data: all authors; analyzed data and wrote the manuscript: G.H., M.Z., G.A.,

C.R.V.

This manuscript was published in PeerJ (12/07/2018).

Hadaidi, G., Ziegler, M., Shore-Maggio, A., Jensen, T., Aeby, G., & Voolstra, C. R. (2018).

Ecological and molecular characterization of a coral black band disease outbreak in the Red

Sea during a bleaching event. PeerJ, 6, e5169. doi:10.7717/peerj.5169

3.1. Abstract

Black Band Disease (BBD) is a widely distributed and destructive coral disease that has been studied on a global scale, but baseline data on coral diseases is missing from many areas of the Arabian Seas. Here we report on the broad distribution and prevalence of BBD in the Red

Sea in addition to documenting a bleaching-associated outbreak of BBD with subsequent microbial community characterization of BBD microbial mats at this reef site in the southern central Red Sea. Coral colonies with BBD were found at roughly a third of our 22 survey sites with an overall prevalence of 0.04 %. Nine coral genera were infected including

Astreopora, Coelastrea, Dipsastraea, Gardineroseris, Goniopora, Montipora, Pavona,

Platygyra, and Psammocora. For a southern central Red Sea outbreak site, overall prevalence was 40 times higher than baseline (1.7 %). Differential susceptibility to BBD was apparent among coral genera with Dipsastraea (prevalence 6.1 %) having more diseased colonies than was expected based on its abundance within transects. Analysis of the microbial community associated with the BBD mat showed that it is dominated by a consortium of cyanobacteria and heterotrophic bacteria. We detected the three main indicators for BBD (filamentous cyanobacteria, sulfate-reducing bacteria (SRB), and sulfide-oxidizing bacteria (SOB)), with high similarity to BBD-associated microbes found worldwide. More specifically, the microbial consortium of BBD-diseased coral colonies in the Red Sea consisted of

Oscillatoria sp. (cyanobacteria), Desulfovibrio sp. (SRB), and Arcobacter sp. (SOB). Given the similarity of associated bacteria worldwide, our data suggest that BBD represents a global coral disease with predictable etiology. Further, we provide a baseline assessment of BBD disease prevalence in the Red Sea, a still understudied region.

3.2. Introduction

The rise of coral disease outbreaks contributes to the decline of coral reefs globally (Cróquer

& Weil 2009; Harvell et al. 2009; Hoegh-Guldberg 2012; McLeod et al. 2010; Randall & van

Woesik 2015) and coral disease appears to be the most destructive factor on many reefs. For instances, the Caribbean has been named a “disease hot spot” due to the fast emergence, high prevalence, and virulence of coral diseases in this region (Rosenberg & Loya 2013). Coral disease outbreaks in the last decades in the Caribbean have resulted in significant losses in coral cover, diversity, and habitat (Aronson & Precht 2001; Bruckner 2002; Hughes 1994;

Precht et al. 2016; Weil 2002). Following the mass-bleaching event in 2005 in the US Virgin islands, coral disease outbreaks reduced coral cover by more than 50 % (Cróquer & Weil

2009; Miller et al. 2009).

Coral diseases were first reported in the Caribbean in the 1970s, including black band disease

(BBD), which is considered the most studied coral disease due to its widespread occurrence on reefs around the world (Bourne et al. 2011; Richardson 2004). Black band disease has been reported from reefs throughout the Caribbean, the Indo-pacific regions, the Red Sea, and the Great Barrier Reef (Al-Moghrabi 2001; Dinsdale 2002; Green & Bruckner 2000;

Kaczmarsky 2006; Lewis et al. 2017; Montano et al. 2012; Page & Willis 2006; Sutherland et al. 2004; Weil et al. 2012). BBD is the first described coral disease (Antonius 1973), affecting scleractinian and gorgonian corals (Green & Bruckner 2000; Sutherland et al. 2004;

Weil 2004). BBD prevalence generally is considered low (Dinsdale 2002; Edmunds 1991;

Weil 2002); however, this disease is a serious threat to coral reef ecosystems worldwide due to its persistence, leading to coral mortality in the long-term (Bruckner & Bruckner 1997;

Green & Bruckner 2000; Kaczmarsky 2006; Kuta & Richardson 1996; Page & Willis 2006;

Sutherland et al. 2004; Zvuloni et al. 2009). Susceptibility to BBD differs between coral taxa

68 and may result in long-term changes to coral community structure (Bruckner & Bruckner

1997; Page & Willis 2006). The abundance of BBD is affected by several environmental factors, including sea water temperature, water depth, solar irradiance, host population diversity, and anthropogenic nutrients (Al-Moghrabi 2001; Kaczmarsky 2006; Kuta &

Richardson 2002; Montano et al. 2013). Interestingly, seasonal temperatures influence BBD prevalence, with increased virulence during warmer summer months (Richardson & Kuta

2003; Rützler & Santavy 1983; Willis et al. 2004) as for example, in the Maldives, where sea surface temperatures above 28˚C promoted BBD infections (Montano et al. 2013).

BBD manifests as a dark band that migrates across the coral colony at a rate of > 1 cm/day

(Richardson 1998) leaving behind bare skeleton. The base of the BBD mat is anoxic and high in sulfide levels, causing damage and necrosis to coral tissue (Ainsworth et al. 2007; Carlton

& Richardson 1995; Richardson et al. 1997). The BBD mat is composed of a polymicrobial consortium, dominated by filamentous cyanobacteria, sulfate-reducing bacteria (SRB), including members of Desulfovibrio spp., sulfide-oxidizing bacteria (SOB) (Beggiatoa spp.), and other heterotrophic bacteria (Cooney et al. 2002; Miller & Richardson 2011; Sato et al.

2010). As a result of diel light changes, the microbial members of the BBD mat undergo vertical migrations, which causes the harmful microenvironment on top of the coral tissue

(Carlton & Richardson 1995; Miller & Richardson 2011; Richardson 1996). Oxygen depletion and high sulfide concentrations are produced by SRB, which is lethal to the coral tissues and considered the most important factor in BBD pathogenicity (Glas et al. 2012;

Richardson 1996; Richardson et al. 1997; Richardson et al. 2009). Although the functional composition of the BBD mat is conserved, the diversity of the microbial consortium in BBD differs according to geographic location and coral species (Cooney et al. 2002; Frias-Lopez et al. 2004; Sekar et al. 2006).

The occurrence of BBD in the Red Sea was first recorded by Antonius (1988) where the severity of BBD was measured from rare to moderate and mostly correllated with elevated temperatures and seawater pollution. However, baseline data on BBD prevalence in the Red

Sea is still lacking. To fill this gap, we conducted surveys to determine the distribution and prevalence of BBD across the central Red Sea reefs spanning 4 degrees of latitude. We also detected a bleaching-associated outbreak of BBD on a coral reef in the southern central Red

Sea and characterized the microbial community of BBD microbial mats from Coelastrea sp.,

Dipsastraea sp., Goniastrea sp., and Platygra sp. using high-throughput sequencing. We compared the microbial consortium to that reported from other regions of the world in order to identify biogeographic patterns in the main BBD consortium members.

3.3. Material and Methods

3.3.1. Black band disease surveys

Coral community structure and BBD prevalence was recorded at 22 sites spanning approx.

535 km along the coast of Saudi Arabia in the Red Sea (Fig. 3.1, Table 3.1). At least six reefs per region (Yanbu, Thuwal, Al-Lith) were surveyed with three additional reefs in Thuwal and one reef in Jeddah (80 km from Thuwal) that were surveyed as time permitted. The reefs sampled/assessed in this study do not fall under any legislative protection or special designation as a marine/environmental protected area. Under the auspices of KAUST (King

Abdullah University of Science and Technology), the Saudi Coastguard Authority issued sailing permits to the sites that include coral collection. At each site, divers counted coral colonies by genera along two replicate belt transects (25 m x 1 m). At the same time, point- intercept method was used to characterize the substrate at 25 cm intervals. All corals with

BBD lesions were identified along wider 25 x 6 m transects and photographed. Depending on depth and time limits, the length of transects were adjusted as necessary. Survey sites ranged in depth between 3 and 7.6 meters and all surveys were conducted from 19 October to 3

November 2015. The diver surveys were used to determine average percent coral cover, coral community composition, and colony densities. Underwater time constraints prevented counting all colonies within the larger 25 x 6 m belts surveyed for disease. Therefore, BBD prevalence was estimated by calculating the average colony density (by genus) within the 25 x 1 m transect and then extrapolating the colony counts to the wider 25 x 6 m disease survey area and using this as the denominator of prevalence calculations, i.e. (number of colonies with BBD lesions/total number of estimated colonies) * 100) (Aeby et al. 2015a). At the outbreak site, diseased coral colonies were so numerous that only 49 m2 of the transect could be surveyed. The frequency of disease occurrence (FOC) was calculated by dividing the number of sites having corals with BBD lesions by the total number of sites surveyed. A

71 localized BBD outbreak was discovered at one site and a chi-square goodness- of-fit test was used to examine differential distribution of the number of BBD versus healthy colonies among the coral genera affected by the disease. The chi-square test compares the observed vs. expected number of infected colonies based on the abundance of each coral genus in the field. Statistical analysis was performed using JMP statistical software (v. 10.0.2, SAS

Institute Inc., Buckinghamshire, UK).

Figure 3.1. Black band disease survey locations of 22 reef sites along the central Red Sea coast of Saudi Arabia. Survey points marked for Yanbu region (north), Thuwal (central), and Al-Lith (south). Sites with-out black band diseased coral colonies marked in black, sites with one or two diseased colonies in blue, and the site where a localized outbreak of BBD was observed is marked in pink.

Table 3.1. Survey of black band disease (BBD)-affected coral colonies at 22 reef sites in the central Red Sea.

Region Site GPS

surveyed Montipora Dipsastraea Psammocora Gardineroseris Astreopora Pavona Platygyra Coelastrea* Goniopora* BBD no. of Total colonies no. of Total

BBD prevalence (%) BBD prevalence Marker 32 23.8664, 37.8913 0 726 0 Marker 35 23.8207, 37.9350 0 930 0 Abu Galaba 23.7891, 37.9393 0 848 0 Yanbu Fringing reef 1 24.1362, 37.9396 0 1,308 0 Marker 10 24.0189, 37.9666 0 1,749 0 Fringing reef 2 24.1452, 37.9149 0 1,830 0 Abu Madafi 22.0766, 38.7751 1 1 2,184 0.05 Al Fahal 22.1119, 38.8411 0 6,000 0 Al-Mashpah 22.0772, 38.7744 0 4,200 0 Inner Fsar 22.2358, 39.0304 1 1 6,852 0.01 Thuwal Shaab 22.2012, 38.9992 1 1 5,778 0.02 Shi’b Nazar 22.3409, 38.8521 0 3,294 0 Tahlah 22.2750, 39.0497 0 3,780 0 Qita al Kirsh 22.4257, 38.9957 0 5,748 0 Um Alkthal 22.1653, 38.9391 0 5,208 0 Jeddah La Plage 21.7092, 39.0832 1 1 474 0.21 Abu Lath 19.9554, 40.1543 0 5,556 0 South Reef 19.8985, 40.1514 1 1 3,720 0.03 Al-Lith 3 19.8608, 40.2282 0 4,320 0 Al-Lith Qita Al Kirsh 20.1407, 40.0931 1 1 6,588 0.02 Fringing reef 1 20.1732, 40.1613 1 15 2 1 1 1 1 1 23 1,281 1.72 Whaleshark reef 20.1230, 40.2118 1 1 2 1,716 0.12

3.3.2. Sample collection of black band disease microbial mats and 16S rRNA

gene sequencing

Microbial mats were collected from BBD infected coral genera (1 colony of Coelastrea sp., 2 colonies of Dipsastraea sp., 3 colonies of Goniastrea sp., and 1 colony of Platygra sp.) at the site of the observed BBD outbreak (Al-Lith fringing reef 1) in November 2015. Microbial mats were siphoned off the coral surface with Pasteur pipettes and transferred into ziplock bags under water. Sample replication was limited by obtainable coral species on this reef site and environmental conditions.

Samples were homogenized using bead-beating via TissuLyser II (Qiagen, Germany) twice for 30 sec at 30 Hz, 20 µl of the homogenate were boiled in sterile Milli-Q water at 99 ˚C for

5 min and subsequently 1 µl was directly used as PCR template. To amplify the variable region 4 of the 16S rRNA gene the following primers were used: 515F

[5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA

'3] and 806RB

[5'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACNVGGGTWTCTA

AT '3] (Apprill et al. 2015; Caporaso et al. 2012; Kozich et al. 2013). Primer sequences contained sequencing adaptor overhangs (underlined above; Illumina, San Diego, CA, USA).

Triplicate PCRs were performed for all samples with 0.2 µM of each primer in a total reaction volume of 25 µL using the Qiagen Multiplex PCR Kit. The following cycling conditions were used: 95 ˚C for 15 min, followed by 27 cycles of 94 ˚C for 45 s, 50 ˚C for 60 s, 72 ˚C for 90 s, and a final extension step of 72 ˚C for 10 min. Amplification was checked visually via 1 % agarose gel electrophoresis. Triplicate samples were pooled and cleaned with ExoProStar 1-Step (GE Healthcare, UK). An indexing PCR was performed on the cleaned samples to add Nextera XT indexing and sequencing adaptors (Illumina) following the manufacturer’s protocol and followed by sample normalization and library pooling. 16S

74 rRNA gene amplicon libraries were sequenced on the Illumina MiSeq platform using 2*300 bp overlapping paired-end reads with a 10 % phiX control at the KAUST Bioscience Core

Laboratory. Sequence data determined in this study are available under (NCBI Bioproject ID:

PRJNA436216).

3.3.3. Sequence data processing and bacterial community analysis

Processing of raw sequence data was conducted in mothur (version 1.36.1; (Schloss et al.

2009). Using the ‘make.contigs’ command, sequence reads were joined into contigs. Contigs longer than 310 bp and ambiguously called bases were excluded from the analysis.

Subsequently, sequences that occurred only once across the entire dataset (singletons) were removed. The number of distinct sequences were identified and counted, and the total number of sequences per sample was determined using the ‘count.seqs’ command.

The remaining sequences were aligned against SILVA database (release 119; (Pruesse et al.

2007). Sequences were pre-clustered allowing for up to a 2 nt difference between the sequences (Huse et al. 2010). Chimeras were removed using UCHIME as implemented in mothur (Edgar et al. 2011). Next, sequences were classified with Greengenes database

(release gg_13_8_99; bootstrap = 60; (McDonald et al. 2012), followed by the removal of chloroplast, mitochondria, Archaea, and eukaryote sequences. Further, we found three abundant bacterial families (Dermabacteraceae-Dietziaceae-Brevibacteriaceae) that were present in all disease samples and at high abundance in our negative control. The negative control was a sample containing water as a template for the PCR reaction. As these bacterial families are also known as kit/reagent/lab contaminants, they were excluded from the data set

(Salter et al. 2014). Some additional bacterial taxa that were found in high numbers in the negative control with low abundance in coral samples were excluded (Comamonadaceae-

Halomonadaceae-Staphylococcaceae). For further analyses, sequences were subsampled to

7,328 sequences per sample, which is the lowest number of sequences in a sample, and then

75 clustered into OTUs (Operational Taxonomic Units) at a 97% similarity cutoff. Reference sequences for each OTU were determined by the most abundant sequence (Supplemental

Data S1). Alpha diversity indices [i.e., Chao1(Chao 1984), Simpson evenness, and Inverse

Simpson Index (Simpson 1949) were calculated as implemented in mothur.

For detecting similarity of the three main microbial consortium members in BBD microbial mats to previously reported taxa from other studies, the representative sequences of the most abundant OTUs were BLASTed against the NCBI database (https://blast.ncbi.nlm.nih.gov) using a 98% similarity cutoff. Subsequently, our sequences were compared to matches of highly similar bacterial taxa by obtaining the respective coral species, their location, and colony health status. Furthermore, low abundant OTUs not previously reported from BBD, but with related properties to SRB, SOB, or Cyanobacteria were also BLASTed against the

NCBI database.

16S rRNA sequences of SOB and SRB were aligned and neighbor-joining trees were constructed based on Jukes-Cantor model with MAFFT (Katoh et al. 2017; Kuraku et al.

2013). All positions containing gaps and missing data were excluded and phylogenetic trees were visualized using Archaeopteryx.js.

3.4. Results

3.4.1. Distribution and prevalence of black band disease on the Red Sea reefs

We identified 30 coral genera within transects across 22 reef sites (Figure 3.1) with an average density of 16 coral colonies / m2 (SE ± 1.2) and an average coral cover of 43.8 % (SE

± 4.3). Colonies with BBD were found at 8 of 22 sites (Table 3.1, Figure 3.1). Over all study sites, nine coral genera were infected and include Astreopora, Coelastrea, Dipsastraea,

Gardineroseris, Goniopora, Montipora, Pavona, Platygyra, Psammocora. Approximately

74,090 colonies were examined for disease and overall BBD prevalence over all sites was low (0.04 %) because most sites had no signs of BBD. At the sites where BBD occurred, 7 of

76 the 8 sites had 1 to 2 colonies infected within survey areas (up to 300 m2) (avg. prevalence =

0.064 %) and one site had a localized BBD outbreak (Al-Lith fringing reef 1) where 21 infected colonies were found within 49 m2 of the transect (prevalence = 1.7 %) and an additional two colonies outside the transect (Table. 3.1). At this site, 18 coral genera were found within transects, but only nine coral genera exhibited signs of disease suggesting differential BBD susceptibility among coral genera (x2 = 45.67 df = 6, P < 0.001).

Dipsastraea appeared to be the most susceptible (prevalence = 6.1 %) with more diseased colonies than was expected based on its abundance within transects (Table 3.2). Dipsastraea represented 18.6 % of the coral colonies within transects but 68.2 % (15 of 22) of the BBD colonies.

Table 3.2. Survey of black band disease (BBD)-affected coral genera at an outbreak site in the southern central Red Sea (Al-Lith fringing reef 1, Saudi Arabia).

No. of coral No. of BBD % of coral Prevalence Coral Species colonies / survey cases / survey community % area (20m2) area (49m2) Astreopora 23 4.24 1 1.8 Coelastrea* 0 - 1 - Dipsastraea 101 18.63 15 6.1 Goniopora* 0 - 1 - Montipora 11 2.03 1 3.7 Pavona 5 0.92 1 8.2 Platygyra 28 5.17 1 1.5 Psammocora 28 5.17 2 2.9 Other coral genera 346 63.80 0 0 Total* 542 100 23 1.7 *Colonies of Coelastrea and Goniopora were only found outside the survey area and were not counted towards totals.

3.4.2. Bacterial community composition of black band disease microbial mats

Besides the ecological survey of BBD prevalence, we investigated the microbial consortium of the BBD mat of corals from the outbreak site in the southern central Red Sea that was also subject to a bleaching event (Al-Lith fringing reef 1). We assessed whether the same bacterial

77 players are associated with BBD in the Red Sea in comparison to other sites globally. Seven coral BBD microbial mat samples from the outbreak site included 1 colony of Coelastrea sp.,

2 colonies of Dipsastraea sp., 3 colonies of Goniastrea sp., and 1 colony of Platygra sp., which together yielded 555,093 raw 16S gene sequences with a mean length of 298 bp (Table 3.3).

After quality filtering and exclusion of chimeras and contaminant sequences, we retained

107,613 sequences for analysis of the BBD microbial mat microbiome. To assess bacterial community composition, sequences were classified to family level considering bacterial families that comprised > 1 % of the total sequence reads (Figure 3.2). The presence of cyanobacteria, SRB, and SOB was confirmed but at varying abundance. For instance,

Cyanobacteria such as Phormidiaceae ranged in proportion between 0 and 3.9 %, SRB such as

Desulfovibrionaceae between 0.9 and 33.8 %, and SOB such as Campylobacteraceae between

15 - 45 %. After subsampling to 7,328 sequences per sample, we found 351 distinct OTUs in the entire dataset (Supplemental Data S3.1). Species richness (Chao1) and bacterial diversity

(Inverse Simpson) were relatively similar between samples, ranging from 98 to 149 OTUs per sample (Table 3.3).

No. of No. of Inv. Simpson Sample Chao1* sequences OTUs* Simpson* evenness* Coelastrea 17,869 149 203 14.10 0.095 Dipsastraea 1 7,328 146 240 22.33 0.153 Dipsastraea 2 15,919 122 160 7.71 0.063 Goniastrea 1 16,201 120 152 8.18 0.068 Goniastrea 2 15,743 113 161 9.49 0.084 Goniastrea 3 14,466 136 159 11.57 0.085 Platygra 20,037 98 125 8.50 0.087

100%

75%

others f__Lachnospiraceae f__Flavobacteriaceae f__Phormidiaceae f__Pseudoalteromonadaceae o__Cytophagales f__Vibrionaceae c__Alphaproteobacteria 50% o__Vibrionales p__Bacteroidetes f__Oceanospirillaceae f__Rhodobacteraceae o__Clostridiales f__Alteromonadaceae Contribution to bacterial community (%) f__JTB215 f__Acidaminobacteraceae f__Desulfovibrionaceae f__Campylobacteraceae 25%

Platygyra

Coelastrea

Goniastrea 1 Goniastrea Goniastrea 3 Goniastrea Goniastrea 2 Goniastrea

Dipsastraea 1 Dipsastraea 2

Figure 3.2. Bacterial community composition of black band disease microbial mats from four coral genera. (one colony of Coelastrea, two colonies of Dipsastraea, three colonies of Goniastrea, and one colony of Platygyra) from an outbreak site in the southern central Red Sea (Al-Lith fringing reef 1, Saudi Arabia). Taxonomy stacked column plot on the phylogenetic level of family or to lowest resolved taxonomic level (f, family; o, order; p, phylum). Each color represents one of the 17 most abundant families. Remaining taxa are grouped under category `others'.

3.4.3. Black band disease representative bacterial consortia

We compared the sequences from representative bacterial BBD consortium members found in 4 coral genera in the southern central Red Sea to sequences obtained from other locations and coral taxa that were affected by BBD on a global scale. Coral disease microbial mat- associated OTUs that represent the three main bacterial consortium members in BBD were successfully identified in our samples:

Sulfide oxidizing bacteria (SOB): Beggiatoa sp., a common BBD- SOB member, was absent in our samples, despite microscopic white filaments in the disease lesions which suggested its presence. Another SOB-consortium member Arcobacter sp. was present in all

80 samples, which has been associated previously with BBD and with white plague disease

(WPD) (Sunagawa et al. 2009). The SOB-classified OTUs were the most abundant taxa in the dataset. Several OTUs were found to be associated with all coral genera (i.e., Coelastrea,

Dipsastraea, Goniastrea, and Platygra) with proportions of up to 22.6 % in all coral samples.

(OTU0001, 2, 4, 11, 16: all Arcobacter sp., OTU0010: Sulfurospirillum sp.). These SOB- associated OTUs were found to be similar to those found in different places around the world

(e.g., Philippines (Garren et al. 2009) and in the Caribbean including the Netherlands Antilles

(Klaus et al. 2011), US Virgin Islands (Cooney et al. 2002), and Puerto Rico (Sunagawa et al.

2009) (Table 3.4)), where they were associated with varying coral species (Figure 3.3A).

Sulfate-reducing bacteria (SRB): Two abundant OTUs were found to be associated with

BBD samples. These OTUs were annotated to Desulfovibrio sp. (OTU0005, OTU0006) with abundance ranges of 0.01 - 30.7 % in all coral samples. Similar SRB-OTUs were found in the

Caribbean (Sekar et al. 2008; Sunagawa et al. 2009) and Japan in different coral species (e.g.,

Montipora sp., Orbicella faveolata, and Siderastrea sidereal, Table 3.4). OTU0005

(Desulfovibrio dechloracetivorans) clustered together with SRB previously found in corals diseased with WPD (Sunagawa et al. 2009) and BBD (Sekar et al. 2006), while OTU0006

(Desulfovibrio marinisediminis) clustered away (Figure 3.3B), indicating that this is not a typical BBD consortium member.

Cyanobacteria: One cyanobacterium (OTU0023, Oscillatoria sp.) was found at proportions of up to 4% in our coral samples. Cyanobacteria of the same genus (99 % sequence similarity) have previously been found in BBD infected Pavona sp. in the GBR (Burger et al., 2016) and from other regions, e.g. the Caribbean (Casamatta et al. 2012), Hawaii (Aeby et al. 2015b), and Palau (Sussman et al. 2006) (Table 3.4).

Others: Although not belonging to the three main BBD bacterial consortium members,

Firmicutes have previously been reported in coral BBD (Barneah et al. 2007; Cooney et al.

2002; Klaus et al. 2011). Members were also found in our dataset at proportions of up to 24.8

% (OTU0003, 9, 13, 18). Furthermore, the Firmicutes-associated OTUs in our data were

similar to those found in Porites white patch syndrome (PWPS) (Séré et al. 2013) and WPD

(Roder et al. 2014a; Sunagawa et al. 2009) (Table 3.4).

We also retrieved sequences of Vibrio sp. (OTU0015, 29) from our dataset, at proportions of

up to 12.8 %. These OTU sequences also had a high similarity (98 - 99 %) to sequences from

BBD and WPD (Klaus et al. 2011; Sunagawa et al. 2009) (Table 3.4).

Table 3.4. Summary of bacterial taxa (OTUs) associated with black band disease (BBD) in corals from the southern central Red Sea and comparison with similar taxa from around the world, based on BLAST results (accession number, identity) of the BBD consortium of sulfide-oxidizing bacteria (SOB), sulfate-reducing bacteria (SRB), cyanobacteria, Firmicutes, and Vibrios.

Co Ident GenBank Health OTU Taxonomy Reference Host & location unt ity Acc No. state

SOB

Otu0001 913 Arcobacter sp. 99% EF089456 Barneah et al. BBD Favites and Dipsastraea, (2007) Red Sea KC527436 Roder et al. WPD Pavona duerdeni and (2014) Porites lutea, West Pacific HM768631 Klaus et al. BBD Faviidae, Meandrinidae, (2011) Gorgoniidae, Caribbean Otu0002 625 Arcobacter sp. 99% GU319311 Meron et al. Healthy Acropora eurystoma, Red (2011) Sea FJ203140 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean AB235414 Yasumoto-Hirose - non coral species et al. (2006) Otu0004 336 Arcobacter sp. 99% KT973145 Couradeau et al. - non coral species (2017)

JF344171 Acosta-González - non coral species et al. (2013)

FJ949362 Suárez-Suárez et - non coral species al. (2011) Otu0010 246 Sulfurospirillum 98% LC026456 Unpublished - non coral species sp. AF473976 Cooney et al. BBD Faviidae, Caribbean (2002) GU472074 Unpublished BBD -

Co Ident GenBank Health OTU Taxonomy Reference Host & location unt ity Acc No. state Otu0011 208 Arcobacter sp. 99% HM768558 Klaus et al. BBD Faviidae, Meandrinidae, and (2011) Gorgoniidae, Caribbean GQ413587 Garren, et al. - Porites cylindrica, West (2009) Pacific Otu0016 117 Arcobacter sp. 98% LC133150 Unpublished - non coral species

HE804002 Unpublished - non coral species

KF185679 Unpublished - non coral species

SRB

Otu0005 322 Desulfovibrio 98% AB470955 Unpublished Healthy Montipora sp., West Pacific dechloracetivoran s FJ202627 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean EF123510 Sekar et al. BBD Siderastrea siderea, (2008b) Caribbean Otu0006 294 Desulfovibrio 99% MF039931 Unpublished - non coral species marinisediminis KY771114 Unpublished - non coral species

KT373805 Unpublished - non coral species

Cyanoba cteria Otu0023 81 Oscillatoria sp. 99% KU579394 Buerger et al. BBD Pavona, Great Barrier Reef (2016) HM768593 Klaus et al. BBD Faviidae, Meandrinidae, (2011) Gorgoniidae,Caribbean GU472422 Unpublished BBD -

Firmicut es Otu0003 344 family JTB215 99% DQ647593 Unpublished - -

KC527313 Roder et al. WPD Pavona duerdeni and (2014) Porites lutea, Caribbean HM768569 Klaus et al. BBD Faviidae, Meandrinidae, (2011) Gorgoniidae, Caribbean Otu0013 199 Fusibacter sp. 99% GQ413281 Garren et al. - Porites cylindrica, West (2009) Pacific FJ202930 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean Otu0018 112 Fusibacter sp. 99% KF179748 Séré et al. (2013) PWPS Porites lutea, Western Indian Ocean GU472060 Unpublished BBD -

EU780347 Unpublished SWS Turbinaria mesenterina,

EF089469 Barneah et al. BBD Favites, Dipsastraea, Red (2007) Sea Otu0022 82 family 99% HM768582 Klaus et al. BBD Faviidae, Meandrinidae, Lachnospiraceae (2011) Gorgoniidae, Caribbean 98% AF473930 Cooney et al. BBD Faviidae, Caribbean (2002)

Co Ident GenBank Health OTU Taxonomy Reference Host & location unt ity Acc No. state

DQ647585 Unpublished - - Otu0027 62 Fusibacter sp. 99% JX391361 Unpublished - non coral species

HM768587 Klaus et al. BBD Faviidae, Meandrinidae, (2011) Gorgoniidae, Caribbean FJ202981 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean Otu0031 48 WH1-8 sp. 99% KF179804 Séré et al. (2013) PWPS Porites lutea, Western Indian Ocean KC527300 Roder et al. WPD Pavona duerdeni and (2014) Porites lutea, West Pacific FJ203165 Sunagawa et al. WPD Faviidae, Caribbean (2009) Otu0039 30 Defluviitalea 99% DQ647556 Unpublished - - saccharophila FJ202907 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean AF473925 Cooney et al. BBD Faviidae, Caribbean (2002) Vibrio sp. Otu0015 170 Vibrio sp. 99% KT974549 Couradeau et al. - non coral species (2017) 98% GU471972 Unpublished BBD -

98% MF461384 Unpublished - Eunicella labiata Otu0029 56 order Vibrionales 99% JQ727003 Witt et al. (2012) - non coral species, GBR

HM768601 Klaus et al. BBD Faviidae, Meandrinidae, (2011) Gorgoniidae, Caribbean FJ202558 Sunagawa et al. WPD Orbicella faveolata, (2009) Caribbean

Otu0010 Sulfospirillum sp., BBD, Red Sea A LC026456 Bacterium_enrichment_culture_clone_OB150, West Paciﬁc* 100 42 GU47207 Uncultured_bacterium_clone, BBD, unpublished 66 AF473976 Uncultured_epsilon_proteobacterium, Faviidae, Caribbean

Otu0001 Arcobacter sp., BBD, Red Sea

EF089456.2 Uncultured_bacterium_clone_BB2S16SI-13, BBD, Favites/Favia sp., Red Sea

HM768631.1 Uncultured_bacterium_clone_CD02013E10, BBD, Faviidae, Meandrinidae, Gorgoniidae, Caribbean 98 KC527436.1 Uncultured_bacterium_clone_Thai12_C12, WPD, Pavona duerdeni & Porites lutea, West Paciﬁc

FJ203140.1 Uncultured_bacterium_clone_SHFG542, WPD, Orbicella faveolata, Caribbean

AB235414.1 Epsilon_proteobacterium_PO-40, Paciﬁc*

GU319311.11 Uncultured_marine_bacterium_clone_A5M_UNP2_G11, healthy, Acropora eurystoma, Red Sea 96 Otu0002 Arcobacter sp., BBD, Red Sea

100 Otu0016 Arcobacter sp., BBD, Red Sea

63 HE804002.1 Uncultured_Campylobacterales_bacterium_isolate_2.1_clone_124b, unpublished* 28 96 KF185679.1 Uncultured_marine_bacterium_clone_J8-A10, unpublished* 74 LC133150.1 Uncultured_Arcobacter sp. isolate_RW4, unpublished*

Otu0004 Arcobacter sp., BBD, Red Sea 50 KT973145.1 Uncultured_prokaryote_clone_239833, Caribbean* 53 FJ949362.1 Uncultured_Campylobacteraceae_bacterium_clone_MS-A250, Mediterranean* 100 JF344171.1 Uncultured_epsilon_proteobacterium_clone_PET-047, North Atlantic*

Otu0011 Arcobacter sp., BBD, Red Sea

0.01 HM768558.1 Uncultured_bacterium_clone_CD02008D11, BBD, Faviidae, Meandrinidae, Gorgoniidae, Caribbean 99 GQ413587.1 Uncultured_bacterium_clone_2NT1c8_D01, healthy, Porites cylindrica, West Paciﬁc

B Otu0005 Desulfovibrio dechloracetivorans, BBD, Red Sea AB470955.1 Desulfovibrio sp. r02, Montipora sp., West Paciﬁc 100 FJ202627.1 Uncultured_bacterium_clone_SGUS668, WPD, Orbicella faveolata, Caribbean 100 EF123510.1 Uncultured_delta_proteobacterium_clone_STX_48f, BBD, Siderastrea siderea, Caribbean

MF039931.1 Uncultured_Desulfovibrio sp. clone_Otu112, unpublished*

92 KY771114.1 Uncultured_bacterium_clone_510396, unpublished* 74 Otu0006 Desulfovibrio marinisediminis, BBD, Red Sea 100 0.01 KT373805.1 Desulfovibrio oceani_strain_GSR_20, unpublished*

Figure 3.3. Overview and phylogenetic relationship of coral black band disease bacterial consortium members from the southern central Red Sea (Al-Lith, Saudi Arabia) and other regions. (A) Sulfide-oxidizing bacteria (SOB); (B) Sulfate-reducing bacteria (SRB). Phylogenetic trees were calculated using the neighbor-joining method, bootstrap values are indicated at the branches. The phylogenetic trees show NCBI accession numbers and sample name, health state of the coral species, host name, and region. Sequences from this study are in bold. The `*' indicates that the bacterial species were not found in coral species.

3.5. Discussion

In this study, we report on the distribution and prevalence of coral black band disease in the

Red Sea. Our surveys ranged from 19.9 to 24.1 degrees of latitude and confirm the continued presence of BBD in the central Red Sea. Molecular characterization of the bacterial community identified the three main bacterial members of the disease consortium across coral species at a BBD outbreak site in the southern central Red Sea.

3.5.1. Black band disease distribution and prevalence in the Red Sea in

comparison to other global sites

BBD is a global disease found in numerous regions, but its prevalence on coral reefs is generally low compared to other diseases such as white syndromes (WS) (Dinsdale 2002;

Edmunds 1991; Page & Willis 2006; Willis et al. 2004). The low prevalence recorded in this study is similar to levels reported elsewhere across the globe (Sutherland et al. 2004) with localized outbreaks of BBD also reported in the GBR (Sato et al. 2009), Hawaii (Aeby et al.

2015b), Jamaica (Bruckner & Bruckner 1997), Venezuela (Rodríguez & Cróquer 2008), and the Red Sea (Al-Moghrabi 2001). In the Red Sea, BBD was first discovered in the 1980s

(Antonius 1981) and our study confirms that BBD is a chronic threat to coral reefs in the Red

Sea with localized outbreaks continuing to occur.

BBD is not a selective disease; multiple species and various levels of severity can affect colonies within and between coral species and across reefs (Bruckner et al. 1997; Dinsdale

2002; Green & Bruckner 2000; Peters 1993). This was also observed in our study, where multiple species were infected, but with differences in prevalence among coral taxa. At the outbreak site, we found BBD prevalence to be highest in the genus Dipsastraea, which suggests that this genus may be an important host for BBD in the Red Sea. Our observations match previous reports and shows that this pattern is consistent through time (Antonius

1985). Interestingly, although differential susceptibility to BBD among coral taxa has been

86 found globally, the most vulnerable taxa differ by region. For example, in the Caribbean

Montastraea/Orbicella are commonly infected (Bruckner & Bruckner 1997; Porter et al.

2001b), Montipora in Hawaii (Aeby et al. 2015b), and Acropora on the GBR (Page & Willis

2006). It would be fruitful to examine the underlying defense mechanisms in the different coral taxa that lead to these differences in BBD occurrence.

3.5.2. BBD, climate change, and coral bleaching

The occurrence of BBD has been linked to elevated seawater temperatures (Boyett et al.

2007; Kuta & Richardson 2002; Muller & van Woesik 2011). The occurrence of a BBD outbreak during a bleaching event in the present study reflects previous reports from the

Caribbean, where the positive correlation between bleaching events and BBD incidence was proposed first (Brandt & McManus 2009; Cróquer & Weil 2009). For instance, in the Florida

Reef Tract, the prevalence of BBD increased from 0 to 6.7 % following bleaching events in

2014 and 2015 (Lewis et al. 2017). Also Cróquer & Weil (2009), found a significant linear correlation between coral bleaching and the prevalence of two other virulent diseases (yellow band disease and white plague) affecting Montastraea/Orbicella species. This further supports a strong relationship between bleaching events and the emergence of some coral diseases on a global scale. Understanding how climate change-related thermal anomalies and coral bleaching drive the emergence and virulence of coral diseases is essential for further research.

It has further been suggested that other anthropogenic activities, such as coastal pollution or ocean acidification, contribute to the increase of coral disease incidents (Jackson et al. 2001;

Muller et al. 2017; Rosenberg & Ben-Haim 2002). The surveyed outbreak area was adjacent to the outflow of a large aquaculture facility, which might have further aggravated the effects of the bleaching event due to increased nutrient availability (Roder et al. 2015; Ziegler et al.

2016). In comparison, other reefs in the Al-Lith area that were further away from the coast displayed similar levels of bleaching, but BBD prevalence stayed at baseline levels in these locations. This suggests that bleaching alone was not the only factor that could have contributed to the BBD outbreak. The synergistic effects of high temperatures and nutrient pollution find further support in the Caribbean where BBD prevalence increased in reef sites with direct sewage input compared to control sites (Sekar et al. 2008) and in the Bahamas, where BBD migration was faster in nutrient-enriched areas (Voss & Richardson 2006).

Further work is needed to directly examine the relationship between bleaching, nutrient stress, and BBD susceptibility.

3.5.3. Bacterial community composition of BBD microbial mats from the

southern central Red Sea reflects global microbial patterns with local

characteristics

Our results verify the presence of the three main consortium members in BBD microbial mats

(Cyanobacteria, SOB, SRB) from the southern central Red Sea. We identified Oscillatoria sp. as BBD-associated cyanobacterium, which is similar to the BBD-associated cyanobacteria in other regions of the world (Aeby et al. 2015b; Arotsker et al. 2015; Buerger et al. 2016;

Casamatta et al. 2012; Cooney et al. 2002; Frias-Lopez et al. 2003; Gantar et al. 2009; Glas et al. 2010; Meyer et al. 2016; Miller & Richardson 2011; Rasoulouniriana et al. 2009; Sato et al. 2010; Sussman et al. 2006). However, we retrieved only a low number of cyanobacterial sequences, although cyanobacterial filaments were visually abundant in the sampled microbial mats, which could possibly be related to primer amplification bias. In addition, members of the SOB and SRB functional groups (Arcobacter sp. and Desulfovibrio sp., respectively) from BBD microbial mats in the southern central Red Sea were similar to those found in other BBD-affected corals worldwide (Barneah et al. 2007; Cooney et al. 2002;

Klaus et al. 2011; Sekar et al. 2008). This confirms that BBD-associated bacteria are not restricted to a specific species or region (Barneah et al. 2007; Cooney et al. 2002; Dinsdale

2002; Frias-Lopez et al. 2003). Interestingly, we did observe white filaments within lesions that were morphologically similar to Beggiatoa, a sulfide-oxidizing bacterium associated with BBD in other regions (Cooney et al. 2002; Miller & Richardson 2011; Sato et al. 2010).

However, we found no sequences aligning with Beggiatoa in our study. This suggests that either the white filaments were not Beggiatoa or that the methods used were not adequate to extract and identify Beggiatoa. Aeby et al. (2015b) sequenced Beggiatoa from BBD lesions in Hawaii by first culturing the white filaments from lesions and then using universal bacterial primers 8F and 1513R for sequencing. However, they found that no DNA sequences were available for Beggiatoa found in BBD from other regions even though numerous studies using molecular techniques have been published. Further work is needed to clarify these discrepancies.

Beside the three main bacterial consortium members that dominate BBD microbial mats, we detected other bacterial families as part of the BBD consortium. Members of the Firmicutes were abundant in BBD microbial mats, which is consistent with other studies (Arotsker et al.

2016; Arotsker et al. 2009; Barneah et al. 2007; Cooney et al. 2002; Frias-Lopez et al. 2002;

Miller & Richardson 2011; Richardson 2004; Sekar et al. 2008). In addition, we detected the presence of Vibrio species. The pathogenicity of this genus has been documented previously in corals and other marine organisms (Ben-Haim et al. 2003; Harvell et al. 1999; Kushmaro et al. 1996) and more broadly Vibrios have been characterized as opportunistic taxa (Cervino et al. 2004; Rosenberg & Falkovitz 2004; Thompson et al. 2004; Ziegler et al. 2016). To date it is unknown whether this group plays a role in the etiology of BBD (Arotsker et al. 2009;

Barneah et al. 2007) (Meyer et al. 2016), or whether the high number of Vibrios could be

89 related to seasonal increases in the coral microbiome and coral bleaching (reviewed in

Rosenberg & Koren 2006; Tout et al. 2015).

3.6. Conclusions

Our study represents the first comprehensive assessment of Black Band Disease in the central

Red Sea. Elucidation of the bacteria associated with BBD microbial mats of corals at a southern reef site confirms that BBD represents a disease with predictable etiology where the three main bacterial players are globally distributed with regional differences. Notably, our reef survey data, in line with data from other regions, identify BBD as a widespread disease, but as one with low prevalence in comparison to other coral diseases. Additional surveys including other coral diseases as well as pathogen infection experiments with Red Sea corals could further increase our understanding of coral stress tolerance in this understudied coral reef region. Importantly, the prevalence of BBD might increase with ongoing ocean warming and thermal anomalies, as supported by the here-documented disease outbreak coinciding with a thermal anomaly and widespread coral bleaching. The collection of long-term monitoring disease data in the Arabian Seas is important in order to establish baselines, which can then assist in more accurate prediction of disease prevalence and potential impact of climate change on coral communities in this region.

3.7. Acknowledgements

We would like to thank the KAUST Coastal and Marine Resources Core Lab (CMOR) for their assistance and support in field operations and the KAUST Bioscience Core Lab (BCL) for sequencing. We wish to thank Craig Michell (KAUST) for sequence library preparation and Nikolaos Zarokanellos (KAUST) for help with Figure 2.1.

Data accessibility

Sequence data can be accessed through NCBI BioProject accession PRJNA436216 at NCBI

(https://www.ncbi.nlm.nih.gov/).

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100

Chapter 3

4. First insight of the carbohydrate composition of mucus from scleractinian corals

from the central Red Sea

G. Hadaidi1; H. M. Gegner1, M. Ziegler1,2, C. R. Voolstra1*

1Red Sea Research Center, Division of Biological and Environmental Science and Engineering

(BESE), King Abdullah University of Science and Technology (KAUST), Saudi Arabia

2Department of Animal Ecology and Systematics, Justus Liebig University Giessen,

Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany

* Corresponding author

Author contributions

Conceived and designed the experiments: MZ, CRV; generated & analyzed data: GH, HMG,

MZ, CRV; provided materials/tools/methods: MZ, HMG, CRV; wrote the manuscript: CRV,

MZ, GH.

This manuscript is submitted to Coral Reefs and its under review

101

4.1. Abstract

Corals mucus is continuously released by most corals and acts as an important protective barrier and as a substrate for host-associated microbial communities due to its complex composition of carbohydrates, lipids, and proteins. On a reef scale, coral mucus functions as a particle trap, thereby retaining nutrients and energy in the ecosystem. Given the distinct environmental conditions in the Red Sea, we sought to investigate the carbohydrate composition of mucus from the corals Acropora pharaonis, Galaxea fascicularis,

Pocillopora verrucosa, Porites lobata, and Stylophora pistillata from the central Red Sea.

Using Gas Chromatography/Mass Spectrometry (GC/MS), we detected Arabinose, Fucose,

Galactose, Glucose, Mannose, N-Acetyl Glucosamine (GlcNAc), Rhamnose, Ribose, Xylose as the main prevalent sugars. Although we detected significant differences between species with regard to coral mucus carbohydrate composition, it resembled mucus from corals elsewhere, and we could corroborate high abundance of arabinose in acroporid and poritid corals in line with previous studies. Taken together, carbohydrate composition of coral mucus seems to be a species-specific but conserved trait over geographical distances.

Characterization of additional compounds, e.g. lipids, are needed to provide a better understanding of the composition and putative roles of coral mucus for corals in the Red Sea.

102

4.2. Introduction

Coral mucus is a gel-forming layer that is constantly released to varying extents and dynamically regulated by all coral species (Brown & Bythell 2005; Bythell & Wild 2011).

When released and further dissolved in seawater, coral mucus is acting as a food source for bacteria due to its nutrient-rich components (Ducklow Hugh & Mitchell 1979; Moriarty et al.

1985; Vacelet & Thomassin 1991). In addition, it serves as an energy carrier and particle trap, and provides an important ecological role in the recycling of essential and limiting elements in the reef environment (Bythell & Wild 2011; Wild et al. 2004a).

Besides the importance of coral mucus in coral reef functioning, it also plays a fundamental role in the resilience of corals against environmental stressors (Brown & Bythell 2005;

Ritchie 2006), e.g. through protection from pathogens (Brown & Bythell 2005; Glasl et al.

2016; Shnit-Orland & Kushmaro 2009). Notably, coral mucus serves as a habitat for bacteria that release antibiotics and other metabolites, which affect coral physiology (Castillo et al.

2001; Koh 1997; Ritchie 2006; Rosenberg et al. 2007). As such, coral mucus might mediate functions related to coral health, immunity, and resilience (Glasl et al. 2016; Rosenberg et al.

2007). Accordingly, differences in the composition of mucus from different corals may contribute to differences in their ability to respond to environmental stressors (Hadaidi et al.

2017; Lee et al. 2016; Rohwer & Kelley 2004).

The composition of coral mucus is derived from photosynthates produced by the algal symbionts and compounds acquired through heterotrophic feeding (Brown & Bythell 2005;

Coffroth 1990; Davies 1984). While coral mucus consists mainly of carbohydrates (Brown

& Bythell 2005b), it also contains complex mixtures of glycoproteins (mucins), polysaccharides, and lipids (Brown & Bythell 2005; Coffroth 1990). A number of studies have looked into the compositional differences of carbohydrates from coral mucus and found that although mucus production and composition varies across and within species, similar

103 sets of carbohydrates are found (Brown & Bythell 2005; Coffroth 1990; Meikle et al. 1988;

Vacelet & Thomassin 1991; Wild et al. 2010; Wild et al. 2005).

The Red Sea is a particularly saline and warm environment, harboring corals that are adapted to live in these extreme environments. To provide a first account of the carbohydrate composition of mucus from a range of Red Sea corals and how that might relate to the prevailing environment, we analyzed five coral species (Acropora pharaonis, Galaxea fascicularis, Pocillopora verrucosa, Porites lobata, and Stylophora pistillata) using Gas

Chromatography/Mass Spectrometry (GC/MS) and compared our findings with studies from other regions.

104

4.3. Materials and Methods

4.3.1. Collection of coral mucus

Coral mucus was collected from 5 coral species (3 replicates: Acropora pharaonis, Galaxea fascicularis, Pocillopora verrucosa, Porites lobata, and Stylophora pistillata) from the Red

Sea, Shaab reef (22°12'02.3"N 38°59'59.6"E). In August 2016, mucus samples were collected from the surface of the colonies at 4 - 4.5 m depth using sterile syringes by tapping the coral surface and collecting of mucus. On the boat, the syringes were stored inverted on ice for ca.

20 minutes until the mucus. Then, the supernatant seawater was ejected and the mucus was transferred into cryotubes and immediately frozen in liquid nitrogen. After snap-freezing, samples were stored at – 80˚C.

4.3.2. Coral mucus carbohydrate composition analysis

Samples were sent to the Center of Complex Carbohydrate Research of the University of

Georgia in Athens, Georgia, USA for sample preparation and analysis

(https://www.ccrc.uga.edu/services/index.php). 1 ml of coral mucus samples were purified and desalted using dialysis tubing with a molecular cutoff of 50 kDa to remove any smaller molecular weight contaminants such as excessive amounts of salt for glycosyl composition analyses. The cleaned-up sample material was then used in its entirety for composition analyses due to low yields of actual sample material after dialysis. In order to better compare the samples with each other the used sample material for each sample was assumed being

~100 µg. The sample was transferred and its container washed 3 times with same volume DI water and transferred as well. Samples were dialyzed for 96 hours and freeze dried subsequently, before used for composition analyses. Glycosyl composition analysis was performed by combined gas chromatography/mass spectrometry (GC/MS) of the per-O- trimethylsilyl (TMS) derivatives of the monosaccharide methyl glycosides produced from the sample by acidic methanolysis as described previously by Santander et al. (2013). Briefly, the

105 sample (~100 µg) with inositol (internal standard, 20 µg) was heated with methanolic HCl in a sealed screw-top glass test tube for 18 h at 80 °C. After cooling and removal of the solvent under a stream of nitrogen, the samples were treated with a mixture of methanol, pyridine, and acetic anhydride for 30 min. The solvents were evaporated, and the samples were derivatized with Tri-Sil® (Pierce) at 80 °C for 30 min. GC/MS analysis of the TMS methyl glycosides was performed on an Agilent 7890A GC interfaced to a 5975C MSD, using an

Supelco Equity-1 fused silica capillary column (30 m x 0.25 mm ID). Supplementary Figure

S3.1 shows the total ion chromatogram of sample E30 representative for all samples.

Supplementary Tables S4.1 – S4.5 provide the results of the composition analyses for all samples. The major carbohydrate residues detected in all samples were galactose, mannose and glucose in varying quantities followed by smaller amounts of Ara, Rib, Rha, Xyl and

GlcNAc. Furthermore, we detected C16:0 and C18:0 in almost every sample, two saturated fatty acids.

4.3.3. Data analysis

All analyses were conducted based on mole percent of total carbohydrate values (Tables S1.4

– S4.5). Distribution differences were assessed with MetaboAnalyst 4.0 (Chong et al. 2018).

Data were normalized by sum and auto scaled for heatmap generation, in order to visualize data and show clustering of mucus carbohydrates. Permutational multivariate analysis of variance (PERMANOVA) was used to test for significant differences between mucus carbohydrate profiles across all coral species and between pairwise species combinations using PRIMER v6 (Clarke & Gorley 2006). To test for differential abundance of distinct carbohydrates, data were checked for normality and homogeneity of variances with Shapiro-

Wilk test and Levene test, respectively. Since the data were not normally distributed, nonparametric Kruskal-Wallis tests were conducted for each carbohydrate using R (v3.3.3)

(RStudio Team 2015).

106

4.4. Results & Discussion

In this study, we analyzed the carbohydrate composition of mucus from five coral species

(Acropora pharaonis, Galaxea fascicularis, Pocillopora verrucosa, Porites lobata, and

Stylophora pistillata) of the central Red Sea (Figure S4.1, Table S4.1, Table S4.2, Table

S4.3, Table S4.4, Table S4.5). Overall, we identified nine sugars, although we cannot exclude the presence of other sugars due the removal of compounds < 50 kDa as part of the dialysis process (see Material & Methods). The sugars mannose, glucose, galactose, and xylose were present in all species (sorted by their relative abundance across samples). We also detected arabinose, fucose, the amino sugar N-acetyl glucosamine, rhamnose, and ribose, but only in some species (Table 4.1). A. pharaonis and P. lobata were the coral species with the most diverse set of carbohydrates in this analysis.

Table 4.1. Carbohydrate composition of coral mucus from a range of coral species from the Red Sea (n = 3 for each species). Values are provided as means and standard deviation (SD). Values are expressed as mole percent of total carbohydrate; n.d. – not detected. The total mol percentage may not add to exactly 100% due to rounding. Acropora Galaxea Pocillopora Stylophora Porites lobata pharaonis fascicularis verrucosa pistillata Carbohydrate Mean SD Mean SD Mean SD Mean SD Mean SD

Arabinose 38.53 22.51 n.d. n.d. 18.47 13.88 n.d. Fucose 1.27 1.21 5.57 4.82 n.d. 11.23 8.80 n.d. Galactose 4.10 3.58 25.07 11.20 4.90 46.97 15.54 1.80 Glucose 25.10 10.72 18.33 6.82 33.63 13.38 10.07 3.01 34.93 8.78 Mannose 22.30 12.30 35.13 15.47 45.47 7.80 7.27 3.76 49.00 1.45 N-Acetyl Glucosamine 1.83 1.59 1.57 1.78 n.d. 4.87 0.67 4.40 Rhamnose 1.20 n.d. n.d. 0.60 n.d. Ribose 2.10 n.d. n.d. 0.30 n.d. Xylose 5.77 6.19 14.33 7.25 19.27 9.27 0.83 0.58 14.00 8.70

We found significant differences between coral species with regard to their carbohydrate mucus composition (PPERMANOVA < 0.05). Subsequent pairwise comparisons showed that the following species pairs were significantly different from each other (all PPERMANOVA < 0.05):

107

A. pharaonis - S. pistillata, A. pharaonis - G. fascicularis, A. pharaonis - P. lobata, P. lobata

- G. fascicularis, P. lobata - S. pistillata, and P. lobata - P. verrucosa. Thus, A. pharaonis and P. lobata were the most distinct with regard to their mucus carbohydrate composition.

Species-specific differences were related to significantly different abundances in the following sugars arabinose, fucose, galactose, glucose, mannose, xylose, and N-Acetyl

Glucosamine (all PKruskal-Wallis < 0.05). Besides the presence of species-specific mucus carbohydrate compositions, such differences have been linked to factors such as depth, irradiance, aging, and temperature (Brown & Bythell 2005; Coffroth 1990; Crossland 1987;

Crossland et al. 1980; Daumas et al. 1981; Ducklow Hugh & Mitchell 1979; Lee et al. 2016;

Wild et al. 2004a; Wild et al. 2004b). Moreover, it has been shown that the composition of coral mucus can be affected by different Symbiodinium types (Littman et al. 2009).

Clustering of coral species by their carbohydrate composition illustrated that arabinose, rhamnose, and ribose were particularly abundant in the mucus of A. pharaonis and fucose, galactose, and N-acetyl Gucosamine were particularly abundant in P. lobata in comparison to the other coral species (Figure 4.1). In contrast, glucose, mannose, and xylose were more prevalent in G. fascicularis, P. verrucosa, and S. pistillata. Notably, the pocilloporid species

P. verrucosa and S. pistillata were most similar with regard to their mucus carbohydrate composition, suggesting that host phylogeny might contribute to mucus differences, possibly due to similarities/differences in their associated microbiome (Neave et al. 2016). Our findings corroborate a study by Wild et al. (2010) where arabinose was present in high concentrations in mucus released from acroporid corals. Further, Meikle et al. (1988) found that arabinose is present in high concentrations in Acropora formosa and suggested that it might be transferred from the algal symbiont to the coral host, since arabinose is not a common metabolite of animal cells. Interestingly, fucose and rhamnose have low bacterial

108 degradability and a function in coral mucus seems thus far unassigned (Amon et al. 2001;

Ogawa et al. 2001; Wild et al. 2010). It will be interesting to explore the function of mucus carbohydrates that cannot be accessed by mucus-associated bacteria.

Figure 4.1. Heatmap based on carbohydrate composition and abundance of mucus from five coral species of the Red Sea. Differences are displayed relative to the mean abundance for each carbohydrate over samples and species; blue denotes lower relative abundance, red denotes higher relative abundance.

At large, arabinose, fucose, galactose, glucose, mannose, N-acetyl glucosamine, and xylose seem common constituents of coral mucus, as they have been reported from coral species

(Acropora muricata, Fungia fungites, Paehyseris speciosa, Pocillopora sp., and Stylophora

sp.) across a large geographic range of regions (Aqaba, Jordan; Heron Island, Australia;

Kenting National Park, Taiwan) at varying abundances (Lee et al. 2016; Meikle et al. 1988;

109

Richards et al. 1983; Wild et al. 2010; Wild et al. 2005). This suggests that the main carbohydrates in coral mucus are conserved, regardless of geographic region. Nevertheless, mucus composition is plastic and fine-scale differences may contribute to the adaptation of corals to different environments. For instance, shifts in the concentration of some mucus components may occur under thermal stress that favor growth of certain bacteria and contribute to physiological difference of the coral holobiont (Lee et al. 2016). On the other hand, Hadaidi et al. (2017) showed that bacterial communities were stable in bleached and healthy corals of Porites lobata from the Red Sea and Persian/Arabian Gulf.

Taken together, in this study we found that coral mucus consists of a conserved set of carbohydrates with distinct species-specific profiles. As such, coral species in the Red Sea do not exhibit distinct mucus profiles with regard to the harsh environmental conditions of the

Red Sea. Further work is needed to elucidate the formation and dynamics of mucus from corals in this region to elucidate how such differences potentially contribute to coral physiology under adverse environmental conditions. Detailed investigations of the coral mucus composition are needed to better understand the regulation and dynamics of mucus formation.

4.5. Acknowledgement

This study was funded by the King Abdullah University of Science and Technology

(KAUST) under FCC/1/1973-22-01. This work was supported by the Chemical Sciences,

Geosciences and Biosciences Division, Office of Basic Energy Sciences, U.S. Department of

Energy grant (DE-SC0015662) to Parastoo Azadi" at the Complex Carbohydrate Research

Center.

110

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Supplementary information

Supplementary Figure S4.1. Glycosyl composition analysis – Total ion chromatogram (TIC) of the TMS-glycoside derivatives of sample E30 representative for all samples

Supplementary Table S4.1. Acropora pharaonis glycosyl composition results: Calculated mass and molar percentage of glycosyl residues detected by GC-MS.

E5 E14 E26

Glycosyl residue Mass Mol Mass Mol Mass Mol

(µg) %1 (µg) %1 (µg) %1

Arabinose (Ara) 5.2 44.3 7.1 57.6 0.5 13.7

Ribose(Rib) 0.2 2.1 n.d. - n.d. -

Rhamnose (Rha) 0.2 1.2 n.d. - n.d. -

Fucose (Fuc) 0.2 1.4 0.3 2.4 n.d. -

Xylose (Xyl) 0.3 2.6 0.2 1.8 0.5 12.9

Mannose (Man) 2.3 16.7 2 13.8 1.7 36.4

Galactose (Gal) 0.9 6.6 0.8 5.7 n.d. -

115

Glucose (Glc) 3.1 22.1 2.4 16.2 1.7 37

N-Acetyl Glucosamine (GlcNAc) 0.5 2.9 0.5 2.6 n.d. -

SUM 13 99.9 13.4 100.1 4.4 100

Total Carbohydrate % by weight 13 % 13.4 % 4.4 %

Supplementary Table S4.2. Stylophora pistillata glycosyl composition results: Calculated mass and molar percentage of glycosyl residues detected by GC-MS.

E2 E11 E37

Glycosyl residue Mass Mol Mass Mol Mass Mol

(µg) %1 (µg) %1 (µg) %1

Arabinose (Ara) n.d. - n.d. - n.d. -

Ribose(Rib) n.d. - n.d. - n.d. -

Rhamnose (Rha) n.d. - n.d. - n.d. -

Fucose (Fuc) n.d. - n.d. - n.d. -

Xylose (Xyl) 0.3 9.8 0.2 8.2 0.6 24

Mannose (Man) 1.9 50.4 1.2 47.5 1.5 49.1

Galactose (Gal) 0.1 1.8 n.d. - n.d. -

Glucose (Glc) 1.2 33.6 1.1 44.3 0.8 26.9

N-Acetyl Glucosamine (GlcNAc) 0.2 4.4 n.d. - n.d. -

SUM 3.7 100 2.4 100 3 100

Total Carbohydrate % by weight 3.7 % 2.4 % 3 %

1Values are expressed as mole percent of total carbohydrate; n.d. – not detected The total mol percentage may not add to exactly 100% due to rounding.

Supplementary Table S4.3. Galaxea fascicularis glycosyl composition results: Calculated mass and molar percentage of glycosyl residues detected by GC-MS.

E6 E33 E43

Glycosyl residue Mass Mol Mass Mol Mass Mol

(µg) %1 (µg) %1 (µg) %1

Arabinose (Ara) n.d. - n.d. - n.d. -

Ribose(Rib) n.d. - n.d. - n.d. -

Rhamnose (Rha) n.d. - n.d. - n.d. -

116

Fucose (Fuc) 0.3 8.3 n.d. - 0.8 8.4

Xylose (Xyl) 0.6 16.7 0.6 20.1 0.6 6.2

Mannose (Man) 1.5 31.5 1.7 52.1 2.4 21.8

Galactose (Gal) 1.2 25.2 0.5 13.8 4 36.2

Glucose (Glc) 0.7 14.8 0.5 14 2.9 26.2

N-Acetyl Glucosamine (GlcNAc) 0.2 3.5 n.d. - 0.2 1.2

SUM 4.5 100 3.2 100 10.9 100

Total Carbohydrate % by weight 4.5 % 3.2 % 10.5 %

Supplementary Table S4.4. Porites lobata glycosyl composition results: Calculated mass and molar percentage of glycosyl residues detected by GC-MS.

E7 E30 E41

Glycosyl residue Mass Mol Mass Mol Mass Mol

(µg) %1 (µg) %1 (µg) %1

Arabinose (Ara) 1.7 5.8 26.3 33.3 14.2 16.3

Ribose(Rib) 0.1 0.3 n.d. - n.d. -

Rhamnose (Rha) 0.2 0.6 n.d. - n.d. -

Fucose (Fuc) 1.2 3.7 18 20.9 8.7 9.1

Xylose (Xyl) 0.4 1.5 0.4 0.5 0.4 0.5

Mannose (Man) 4.1 11.6 4.6 4.8 5.6 5.4

Galactose (Gal) 20.3 58 27.7 29.2 56.1 53.7

Glucose (Glc) 4.6 13.2 6.8 7.2 10.3 9.8

N-Acetyl Glucosamine (GlcNAc) 2.3 5.3 4.7 4.1 6.6 5.2

SUM 34.9 100 88.5 100 101.8 100

Total Carbohydrate % by weight 34.9 % 88.5 % 97.9 %

1Values are expressed as mole percent of total carbohydrate; n.d. – not detected The total mol percentage may not add to exactly 100% due to rounding.

117

Supplementary Table S4.5. Pocillopora verrucosa glycosyl composition results: Calculated mass and molar percentage of glycosyl residues detected by GC-MS.

E9 E12 E44

Glycosyl residue Mass Mol Mass Mol Mass Mol

(µg) %1 (µg) %1 (µg) %1

Arabinose (Ara) n.d. - n.d. - n.d. -

Ribose(Rib) n.d. - n.d. - n.d. -

Rhamnose (Rha) n.d. - n.d. - n.d. -

Fucose (Fuc) n.d. - n.d. - n.d. -

Xylose (Xyl) 0.7 18.5 0.2 10.4 0.7 28.9

Mannose (Man) 2.3 50.7 0.7 36.5 1.5 49.2

Galactose (Gal) n.d. - 0.1 4.9 n.d. -

Glucose (Glc) 1.4 30.8 1 48.2 0.7 21.9

N-Acetyl Glucosamine (GlcNAc) n.d. - n.d. - n.d. -

SUM 4.3 100 2 100 2.8 100

Total Carbohydrate % by weight 4.3 % 1.9 % 2.7 %

1Values are expressed as mole percent of total carbohydrate; n.d. – not detected The total mol percentage may not add to exactly 100% due to rounding.

118

APPENDICES

Supplementary Dataset S2.1. OTU abundance over samples with annotation and reference

OTU sequence of bleached and healthy Porites lobata from the Arabian Seas.

Supplementary Dataset S3.1. OTU count table, taxonomic annotation, reference sequence for black band disease (BBD) lesion samples from coral colonies collected during a BBD outbreak in the southern central Red Sea.