Assessing Reef Health and Coral Diseases Through

The Pennsylvania State University The Graduate School Eberly College of Science

THE GOOD, THE BAD, AND THE UGLY:

ASSESSING REEF HEALTH AND CORAL DISEASES THROUGH

ASSOCIATED MICROBES AND HOST RESPONSE

A Dissertation in Biology by

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2014

The dissertation of Collin J. Closek was reviewed and approved* by the following:

Charles R. Fisher Professor of Biology Associate Dean, College of Science Chair of Committee

Mónica Medina Associate Professor of Biology Dissertation Advisor

Jennifer L. Macalady Associate Professor of Geosciences

Moriah L. Szpara Assistant Professor of Biochemistry and Molecular Biology

Douglas R. Cavener Professor of Biology Department Head of Biology

*Signatures are on file in the Graduate School

iii ABSTRACT

Coral reefs are one of the most productive and biologically diverse ecosystems; however, over the last 50 years the health of these ecosystems has drastically declined. Corals have symbiotic algae and other associated microbial organisms, which collectively contribute to and comprise the coral holobiont. As an integral part of the coral holobiont, bacteria are used as indicators of coral health. Yet, little is known regarding the bacterial diversity associated with coral reef environments, and even less has been published about the bacteria that are associated with isolated marine protected areas, such as the Papahānaumokuākea Marine National Monument (French Frigate Shoals, Northwestern Hawaiian Islands). To obtain a baseline, both sediment and water-associated bacteria were examined from select locations in the French Frigate Shoals. Other reef locations have also received legal protection, however many corals within protected areas still exhibit strong signs of disease (e.g. such as Puerto Morelos National Marine Park and the Virgin Islands National Park). Minimal research has been published on the bacterial diversity associated with diseased corals. To that end, we examined the bacterial communities associated with Orbicella faveolata exhibiting signs of Yellow Band Disease (YBD) within the Puerto Morelos National Marine Park, Mexico. Additionally, while studies have examined how changes in sea surface temperatures affect the coral-host at the gene-level, gaps remain regarding how corals respond to disease. We explored these gaps by examining the host transcriptomic response to YBD in Mexico and subsequently sampled an additional disease affecting O. faveolata, White Plague, as well as two diseases affecting Acropora palmata in the US Virgin Islands, White Band and White Pox. Of the four diseases examined, core responses across as well as within both species were detected. Differences in host response were also noted between each disease. Profiling the bacterial associations and host response allows us to gain a better understanding of the bacterial diversity associated with different health states of coral reef environments and how the host transcriptome responds to changes in health. These findings may be useful as baselines to diagnose the health state of other declining reef environments.

iv TABLE OF CONTENTS

LIST OF FIGURES ...... v

LIST OF TABLES ...... vi

ACKNOWLEDGEMENTS ...... vii

CHAPTER 1: GENERAL INTRODUCTION ...... 1

CHAPTER 2: CORAL-HOST AND MICROBIAL RESPONSE TO YELLOW BAND DISEASE ...... 20

CHAPTER 3: COMPARATIVE ANALYSIS OF CORAL TRANSCRIPTOMES ACROSS FOUR DISEASES ...... 50

CHAPTER 4: BACTERIAL CENSUS OF THE FRENCH FRIGATE SHOALS ...... 77

CHAPTER 5: PERSPECTIVES & CONCLUSIONS ...... 113

v LIST OF FIGURES

Figure 2.1: Sequence origins of the top-50 most abundant bacteria in HH and DD ... 29

Figure 2.2: Mean bacterial community of richest phyla across conditions ...... 31

Figure 2.3: Descriptive family richness stacked bar chart for the top-15 families in each condition ...... 33

Figure 2.4: Consistent gene expression plot across all health conditions ...... 36

Figure 2.5: Heatmap of the 15 most differentially expressed genes across all health conditions ...... 36

Figure 3.1: Photographs of the four diseases examined ...... 53

Figure 3.2: Venn diagram of all significant DEGs that resulted from A. palmata pairwise comparisons ...... 59

Figure 3.3: A) Venn diagram of all significant DEGs associated with WB, B) Venn diagram of all significant DEGs associated with WPx ...... 59

Figure 3.4: Venn diagram of all significant DEGs that resulted from O. faveolata pairwise comparisons ...... 63

Figure 3.5: A) Venn diagram of all significant DEGs associated with WPl, B) Venn diagram of all significant DEGs associated with YBD ...... 64

Figure 4.1: Map of the French Frigate Shoals and sampling locations ...... 84

Figure 4.2: French Frigate Shoals Principal Coordinatess Analysis ...... 87

Figure 4.3: Dendogram and relative abundance bar charts at the class level ...... 89

Figure 4.4: Venn diagram of the unique OTUs from water and sediment samples ..... 90

Figure 4.5: FFS species richness estimates ...... 91

Figure 4.6: Heatmap of NWHI vs. MCR water samples ...... 96

Figure 4.7: Heatmap of NWHI vs. LI water samples ...... 98

Figure 4.8: Dendogram of NWHI vs. LI samples ...... 99

Figure 4.9: Heatmap of NWHI vs. CRS sediment samples ...... 99

vi LIST OF TABLES

Table 2.1: Average number of bacterial taxa identified per condition out of the maximum number matched at each taxonomic level ...... 30

Table 2.2: Richness totals for each taxonomic level across all three health conditions ...... 30

Table 2.3: Associated Symbiodinium clades per sample ...... 34

Table 3.1: Annotated DEGs from Acropora palmata pairwise-comparisons with Log2 fold change values ...... 61

Table 3.2: Annotated DEGs from Orbicella faveolata pairwise-comparisons with Log2 fold change values ...... 65

Table 4.1: FFS Sample Metadata ...... 85

Table 4.2: FFS OTUs per sample sequenced ...... 91

Table 4.3A: Top 30 OTUs in seawater based on % of OUT make-up ...... 93

Table 4.3B: Top 30 OTUs in sediment based on % of OUT make-up ...... 94

Table 4.4: Significantly different OTUs (p<0.01) between seawater and sediment .... 95

vii ACKNOWLEDGEMENTS

I would like to first thank my advisor, Mónica Medina, who supported me and helped to make this research a reality. Thank you for encouraging me to pursue my interests and providing me with many amazing opportunities. I learned many things while apart of your lab and I am forever grateful. To my lab mates Mickey, Shini, Zer, Chris, Kevin, Erika, Bishoy, Aubrie, Manu, Michele, Kristen, Biz, Aki, Viri, and Ana Maria - without your support and contribution of your ideas, this research would have taken a different path. To the handful of undergraduate assistants Andres, Yuriana, Caroline, Mario, Jules, and Ashley – thank you for your many hours that aided this research, your generosity was infinite and I am glad we had the opportunity to work together. In addition, I would like to thank my committee members at Penn State, Chuck Fisher, Moriah Szpara, Jenn Macalady and at UC Merced, Sam Traina, Gary Andersen, Mike Beman, and Carolin Frank. Your constructive feedback and interest in these projects helped to fuel and strengthen this research. Both my time at UC Merced and at Penn State have shaped me as an individual and contributed immensely to my knowledge as well as my interests, for that I am truly grateful. My collaborators have also been instrumental in making these projects possible and contributed to my knowledge. Without their foresight, planning, hoop-jumping, and hospitality, the five field seasons required for this work would not have happened, the many countless samples would not have been collected, and the many wet lab procedures would not have been afforded. R. Iglesias-Prieto, P. Thomé, E. Jordán-Dahlgren, R. Rodríguez-Martínez, M.P. Francino, Y. Vallès, L. Amaral-Zettler, G. Andersen, T. DeSantis, E. Brodie, Y. Piceno, and M. Brandt thank you, it was truly a pleasure working with you. To the institutions that sponsored our collaborations and the multitude of people at Institute of Marine Sciences and Limnology of UNAM, Mexico, DOE - Joint Genome Institute, Lawrence Berkeley National Laboratory, Woods Hole Marine Biological Laboratory, Valencia Centre for Public Health Research, Smithsonian Tropical Research Institute in Panama, Caribbean Marine Biological Institute, and the University of the Virgin Islands, thank you for supporting this research.

To Kathryn McClintock and Carrie King, you are invaluable to the Biology and QSB Graduate Programs. Your willingness to assist, work miracles, and smile in the trenches never went unnoticed; YOU ROCK! Thank you. To the many faculty and staff at both Penn State and UC Merced your assistance, input, inspiration, and constructive discussions have contributed to this work. Your value in higher education is much appreciated; although you may not be named, I truly appreciate the efforts you have made to make these great institutions.

viii To my “graduate school support team”, Tracy, Michelle, Kristen, and Kandice, I have been blessed to make your acquaintances, but even more so to become your friend. Tacos and delicious dinners, pep talks and gripe sessions have all made this experience. Bishoy and Viri, I have never had two kinder roommates. Your support and generosity is astounding; I look forward to many years of collaboration and friendship. To my family and friends who have supported me throughout my life. Especially, my sister and brother who have been there for me always and to Trish and Roger for your endless love and support, thank you.

To My Love, Justin, I am glad we were on this journey together. I would do it all over again if it meant I got to do with you. You have been my rock and my energy since our fateful crossing.

Finally, I am indebted to my parents, Mom, Dad, Guy, and Kathy. Without their hard work, tireless efforts, and continuous support, none of this would have been possible.

To my parents. Thank you for always supporting me. I love you.

1 CHAPTER 1:

GENERAL INTRODUCTION

1.1 Coral biology Corals and the natural history of reefs Stony corals (scleractinians) are cnidarian animals that date back to the early Triassic period, approximately 240 million years ago (Medina et al., 2006; Romano and Palumbi, 1996). These animals are polyps that deposit a calcareous skeleton, which creates the structure known as coral reefs. Over millions of years, this process has helped create many of today’s mountain ranges and islands (Veron, 1995). Today, fewer than 1,000 species of corals exist (Cairns, 1999), but the 3-D structures they build are considered one of the most biologically diverse habitats on earth (Wilkinson, 2008). Often compared with rainforests, coral reefs have many associated small organisms that comprise this biodiversity and make them one of the most biologically productive marine ecosystems (Knowlton et al., 2010). The following subsections describe the life history of corals and the challenges todays’ reefs face.

The coral holobiont as a metaorganism The photic zone, which tropical corals inhabit, are nutrient-poor and yet corals thrive in these environments; an ecological phenomenon regarded as Darwin’s paradox (Darwin, 1842). Since the publication of Darwin’s The Structure and Distribution of Corals detailing his theory on the formation of coral reefs and atolls, corals have been discovered to engage in symbiosis with many other organisms, namely microorganisms. A community of organisms with a physical association to a host has been termed a holobiont (Margulis and Fester, 1991), and also a metaorganism (Bosch and McFall- Ngai, 2011). The coral holobiont (Rohwer et al., 2002) is comprised of the coral-host and a suite of microbes: viruses, bacteria, archaea, fungi, and protists (e.g. apicomplexans and Symbiodinium). While symbiosis may be mutualistic, it can also be parasitic or become parasitic (Douglas, 1998). Most studies have shown the coral holobiont is a dynamic,

2 symbiotic system that fluctuates, depending on environmental conditions and daily requirements, while maintaining a microbial net balance (Ainsworth et al., 2011; DeSalvo et al., 2008; Sorek et al., 2014). Perhaps these dynamic associations are less perplexing when we take into consideration the fact that these sedentary animals have evolved in microbial seas, where microbes have been abundant for millions of years (Flannery and Walter, 2012) and have made the planet habitable for all other organisms. Marine organisms are in constant contact with microbes of all sizes as marine environments are size continuums of aquatic microbes to macrobes (Azam and Malfatti, 2007). It has been proposed that no plant or animal is without their associated microbes (Rosenberg et al., 2007; Wahl et al., 2012), including corals. Beyond the reef structure, the coral animal provides microhabitats for microorganisms, from the surrounding water column or neighboring sediments, to colonize. These niches can be coarsely broken into three main areas: the calcium carbonate skeleton, the polyp tissue, and the surface muscus layer. The mucus layer, as well as other microhabitats, provides selectively suitable habitats that garner these microbial associations (Ritchie, 2006; Sweet et al., 2011).

Symbiodinium spp. associations The most studied microbial association in the coral holobiont is the symbiosis between coral and the dinoflagellate, Symbiodinium. Many tropical and sub-tropical corals harbor Symbiodinium spp., unicellular dinoflagellate algae, which harness light energy and transfer fixed-carbon photosynthates to the coral-host (Porter & Muscatine, 1977). This intercellular symbiosis was first noted in the 1930’s. Before that, corals were still classified as plants (Odum and Odum, 1955). Kawaguti and others noted that corals respired excess oxygen over carbon dioxide (Kawaguti, 1937; Younge, 1931). Later the system was further examined to assess algal components as well as energy exchanges between the coral host and Symbiodinium (Muscatine and Porter, 1977; Odum and Odum, 1955). It was determined that these endosymbionts occur within vacuoles, symbiosomes, inside the cytoplasm of gastro- and endodermal cells of corals (Trench, 1979). With this symbiosis the host’s metabolic waste is recycled by the Symbiodinium to produce up to

3 95% of a coral’s daily energy requirements in the form of photosynthates (i.e. glycerol, glucose, and amino acids) (Hoegh-Guldberg, 1999; Muscatine, 1990). These coral-algal associations determine the depth range that photosynthetic corals can successfully inhabit, limiting them to live within the photic zone. Algal symbiosis influences coral skeletal organic matrix deposition (Muscatine et al., 2005) allowing light-facilitated calcification to occur 2-3 times faster than under dark conditions (Gattuso et al., 1999). Paleontological evidence shows a presence of photosynthetic symbiosis back to the Triassic (240mya) (Muscatine et al., 2005). Coral development is also dependent on both algal-symbiosis establishment (Voolstra et al., 2009) and growth (Poland et al., 2013). Different Symbiodinium species produce different and specific metabolites (Klueter et al., in review) that are hypothesized to fulfill individual host-requirements and likely determine algal species associations in the host. While most coral-Symbiodinium studies have shown benefit to the host, some associations may be more opportunistic, possibly parasitic (Stat et al., 2008; LaJeunesse, unpublished). Among the coral holobiont interactions, this symbiosis has been extensively studied, however many basics of the symbiosis are unknown (e.g. whether the symbiosis is a true mutualism or can turn parasitic, the full nutrient cycle/exchange, and the determinant factor(s) for exclusivity in some coral-Symbiodinium interactions while others are more promiscuous).

Archaeal and bacterial associations In addition to coral-Symbiodinium symbiosis, corals can host a large diversity of associated archaeal and bacterial taxa, some of which are host-specific (Rohwer et al., 2001; Wegley et al., 2004). These findings have only been possible in recent years because of specific technological and biotechnological advancements. It is estimated that >99% of microbes are uncultivable, making assessments of bacterial and archael diversity immeasurable (Fuhrman and Campbell, 1998). Sequencing technologies allowed Carl Woese to distinguish the three domains of life (Archaea, Bacteria, and Eukarya) by sequencing the small-subunit ribosomal RNA gene (Winker and Woese, 1991; Woese and Fox, 1977). This advancement fueled sequencing as a methodological approach to

4 study natural microbial populations, which led Pace and colleagues (1986) to devise a radical technique: identify unknown organisms directly from the environment. By targeting conserved regions of the 16S rRNA gene with PCR, they were able to identify different microbial signatures from environmental DNA extracts. To visualize the 16S rRNA amplicons in a more cost-effective method, Muyzer and colleagues (1993) further optimized this approach by size separation through denaturing gradient gel electrophoresis (DGGE). These approaches were first employed to examine the coral-associated bacterial composition by Rohwer and colleagues (2001) and later reported 430 distinct ribotypes from three Caribbean corals (Rohwer et al., 2002). Associated archaea were found to be diverse as well (Wegley et al., 2004). In these studies, coral-associated bacteria were found to not only be highly diverse, but also to be coral species-specific, with minimal overlap (~5%) of taxa associated with the water column (Rohwer et al., 2002). This finding was later corroborated by Sunagawa and colleagues (2010) with four additional coral species. In both studies it was apparent that the diversity across species had not been fully captured, however the diversity captured by Sunagawa et al. (2010) was greater. These associations have also suggested potential ecological or physiological functional roles. As with other marine invertebrates (Hadfield, 2011), biofouling and developmental cues of corals are linked to bacterial presence and association (Apprill et al., 2009; Sharp et al., 2010; 2012). Nitrogen is one of the most potentially limited elements in the reef, and nitrogen fixation has been observed in both coral-associated archaea and bacteria (Beman et al., 2007; Lema et al., 2012; Lesser et al., 2004). Antibacterial properties observed in the mucus (Ritchie, 2006) are suspected to thwart other competing microbes that could be harmful to the holobiont balance. Associations also assist sulphur cycling, produce antimicrobial compounds, and inhibit cell-to-cell signaling (Krediet et al., 2013). Although not yet determined, these specific associations may also determine species distribution ranges, by fulfilling a specific niche.

Additional players in the holobiont Beyond those microbes mentioned, multiple apicomplexans have been noted in different

5 species of corals (Janouškovec et al., 2013; Toller et al., 2001). Although they have been shown to be present in all animal associations, they appear not to be harmful to their coral hosts (Kirk et al., 2013). Coral-fungal symbiont biodiversity estimates are limited, but show a wide array of species associations (Amend et al., 2012). Endolithic algae, macroalgal mats that inhabit the skeleton under the polyp body, also cooperate with the holobiont. When Symbiodinium are expelled, endolithics provide shade protection (Fine and Loya, 2002; Shashar and Stambler, 1992). Given the diversity associated with the holobiont, it is no surprise that the diversity of bacteriophages and viruses associated is also vast (Marhaver et al., 2008). Associated bacteriophages and viruses have been proposed as potential reservoirs for genes involved with sulfur degradation (Raina et al., 2010).

1.2 The status of coral reef ecosystems Shifting baselines: coral reefs in global decline Within the last 30-50 years, over half of coral reefs worldwide have been lost (Wilkinson, 2008). In the Caribbean alone, there has been an 80% decline in coral cover (Gardner et al., 2003). Human population growth and resource demands have contributed greatly to the worldwide degradation of marine ecosystems; the decline in coral reef health is one notable result of human development and other increasing anthropogenic pressures. Within the past ten years, coastal populations have increased 20%, to over 110 million people within the Caribbean region (Wilkinson, 2008), where the majority of sewage is historically untreated (Barnes, 1973; Rodríguez, 1981; Webber et al., 2003). Studies show that proximity to human-inhabited zones increase coral disease and degradation (Dinsdale et al., 2008; Hughes, 1994; Knowlton and Jackson, 2008; Sandin et al., 2008). Coral species, including Acropora palmata and Orbicella faveolata, previously Montastraea faveolata (Budd et al., 2012), have been listed as endangered by the International Union for Conservation of Nature (IUCN). It is widely accepted that coral bleaching and diseases are two of the main factors contributing to massive reef mortality (Aronson and Precht, 2001; Brown, 1997; Bruno et al., 2007; Harvell et al., 1999; 2002; Pandolfi et al., 2003; Sutherland et al., 2004). Bleaching occurs when the coral host dies

6 or loses its algal symbionts (Symbiodinium spp.) as a response to rising sea surface temperatures (SST), increased irradiance, and/or anthropogenic impacts (Brown, 1997; Bruno et al., 2003; Jones et al., 2004). With record-high SSTs, measured in the Caribbean during 2005 (Eakin et al., 2010; Liu et al., 2006) and then again globally in 2010 causing mass bleaching events (NOAA, 2011), climate change is no longer a silent threat. Warming water temperatures exacerbate bleaching events and increase the spread of coral disease (Cervino et al., 2004; Eakin et al., 2010). For decades much research has focused on corals and their symbiotic algae as indicators of health. However, research has also highlighted the coral’s diverse bacterial community as an additional and integral part of coral homeostasis maintenance (Knowlton and Rohwer, 2003). Coral-associated bacterial communities can also undergo changes in response to stress or disease (Pantos et al., 2003; Sato et al., 2009) and some bacteria under stressful conditions have been noted as agents of the disease (Cervino et al., 2004; Patterson et al., 2002). Bacteria have also been marked as bioindicators of aquatic environment health (Colwell, 1996; 2004; Harvell et al., 1999). Assessments of both water- and sediment- associated bacteria have led to greater understanding of the diversity and changes that occur to impacted marine environments (Dinsdale et al., 2008; Stewart et al., 2008). With predicted exponential population growth, anthropogenic burdens on corals, and the surrounding coastal marine ecosystems, will rise. As coral bleaching and disease events intensify, resulting in poor health and at times coral die-off, it becomes increasingly important to find biological indicators that will measure the health of the world’s remaining coral reefs. Up to this point, the majority of coral reef research has focused on coral bleaching and coral disease. Few studies have addressed the bacterial community associated with reef environments (healthy or degraded). Of the few “healthy” reef environments that are still remaining only a handful are geographically isolated or protected (e.g. the Northwestern Hawaiian Islands National Marine Monument). Impacts to reef ecosystems by local human populations have been measured by quantifying their associated bacterial communities compared to both neighboring and distant, increasingly impacted, locations (Dinsdale et al., 2008). These bacterial indicators underscored those more degraded and impacted reef environments. The use of these microbes as indicators

7 is a non-invasive and potentially increasingly necessary tool to assess the health of these precious, but declining environments.

Coral diseases Globally, approximately 18 coral diseases are currently described (Bourne et al., 2009). The Caribbean is the most disease impacted region, where successive disease outbreaks have led to entire community shifts from coral-dominanted community toward an algal- dominated ecosystem (Aronson and Precht, 2001; Weil et al., 2006). Along the densely populated coasts of the Caribbean Sea, Yellow Band Disease (YBD), also referred to as Yellow Blotch Disease, is increasingly more common and is decimating colonies of boulder star (Orbicella spp.) and brain corals, Colpophyllia natans (Cervino et al., 2004; Cervino et al., 2008; Foley et al., 2005). YBD epizootic events were first noted in the Caribbean Sea (Reeves, 1994), where Orbicella spp. serves as the dominant reef building species. In Bonaire, over a 7-year period, YBD reduced coral tissue coverage by 60% (Bruckner and Bruckner, 2006). In parallel with bleaching, warmer sea surface temperatures strengthen this persistent disease by initially killing the endosymbiotic Symbiodinium, which then subsequently kills the coral tissue, and ultimately kills the entire coral colony (Cervino et al., 2004). This sequence effectively turns productive reefs into macroalgal “wastelands” (Bruckner and Bruckner, 2006). Additionally, Bruno et al. (2003), has shown that influxes of nutrients exacerbate YBD and coral tissue loss in Orbicella spp. However, these research efforts still provide no understanding of which bacterial communities are associated with YBD or how this disease affects corals and their associated bacterial community. Only potential pathogenic agents have been proposed and partially tested (Cervino et al., 2004), but not confirmed under Koch’s postulate. YBD, or possibly a different disease with similar syndromes, has also been recently identified in Indo-Pacific reefs (Cervino et al., 2008). Three additional diseases have had a dramatic impact on reef building corals throughout the Caribbean. One disease, White Plague (WPl), has been noted to impact Orbicella spp. (Dustan, 1977) and two other diseases, White Band (WB) and White Pox (WPx) affect the branching Acropora spp. (Gladfelter, 1982; Holden, 1996). While the

8 ultimate impact of these diseases is much the same, the progression and the etiology of these diseases differ.

1.3 Tools for measuring reef health Measuring reef health: changes in microbial communities and host response Recent availability of high-throughput tools has revolutionized the field of biology. These tools have allowed for sequencing of complete genomes, and continue to increase unprecedented output of biological data. With recent technologies, such as the PhyloChip (16S rDNA oligonucleotide arrays, developed by the Andersen lab at Lawrence Berkeley National Laboratory (LBL)) it is possible to reproducibly examine microbial diversity and abundance in environmental samples more efficiently than the traditional method of sequencing 16S clone libraries (Brodie et al., 2006; 2007). The PhyloChip tool has proven to be successful in distinguishing between microbial profiles of healthy coral and those exhibiting White Plague II symptoms (Sunagawa et al., 2009b). In the last few years, advances have been made in improving the PhyloChip to be a better tool for coral reef science by providing >4,000 coral-associated 16SrDNA sequences and demonstrating the applicability of PhyloChips in a case study (Sunagawa et al., 2009b). With these efforts, and the efforts of others, the G3 PhyloChip currently contains more than 60,000 operational taxonomic units (OTUs), a 6.8 fold identification increase from the previous generation chip. In addition, next generation sequencing platforms have made massive-parallel sequencing possible; by using labeled tags, many samples can be sequenced at one time. Sogin and colleagues (Sogin et al., 2006) demonstrated that by sequencing the hypervariable regions (e.g. V6), one could investigate the vast microbial taxonomic diversity in seawater. Changes in sequencing platforms, sequence length, and primer development have led to increased initiatives to explore the microbial diversity associated with various environments (e.g. Human Microbiome Project, Tara Oceans, Census of Marine Life, and Earth Microbiome Project). These methods have resulted in notable studies, which highlighted the unparalleled diversity associated with “environments” not previously thought to be distinct, e.g. the gut microbiome associated with obesity

9 (Turnbaugh et al., 2006), the microbiome associated with left and right hands (Fierer et al., 2008), the microbiome associated with built environments (Kembel et al., 2012). Application of these tools to reef environments allows for comparisons between water columns, reef organisms, and sediment. As well as afford comparisons between sites. Comparisons like these have been proven to show large differences in the diversity and abundance of bacterial taxa (Gaidos et al., 2011; Rohwer et al., 2002; Sunagawa et al., 2010). Additional array platforms, such as cDNA microarrays give us the ability to examine the effects of physiological stress or disease on organisms by examining transcriptomic data. Quantifying the expression of the messenger RNA (mRNA) in turn translates into the expression of a transcript or gene and allows one to assess the impact stresses induce on the organism (DeRisi et al., 1996; DeSalvo et al., 2012; Lashkari et al., 1997). Development of cDNA microarrays for A. palmata and O. faveolata have provided valuable insight into how reef building corals respond to, increasingly more common, stress events. G2 arrays contain over 11,000 features and have previously been used to address varying environmental stressors (DeSalvo et al., 2008; 2010; 2012) or gene expression in early life stages (Sunagawa et al., 2009a; Voolstra et al., 2009). The latest generation cDNA microarrays provide 9 times more detail than previous generations, which allow for identifying more genes that are differentially expressed in response to changes in health.

1.4 Thesis objectives and chapter summaries The advancements in biological technologies described earlier have made it possible to examine many samples while also examining multiple levels of a single complex system. This approach allows one to simultaneously examine a system at many scales, be it molecular, organismal, or ecosystem(s). A systems biological approach is necessary to disentangle the complexities and better understand biological interactions in a dynamic system, such as coral reefs. With rising impacts to these systems globally, it’s imperative to understand how the animal responds to disease, and how the coral-associated as well

10 as the reef-associated microbes reflect the health of this system. The following subsections address these three questions: 1. Does Yellow Band Disease affect the coral host transcriptome and its associated bacterial community structure? 2. What is the core host response and how do the transcriptomic profiles of Yellow Band, White Plague, White Band, and White Pox Disease differ? 3. What is the make-up of the associated water and sediment bacterial communities in the French Frigate Shoals?

Coral-host and microbial response to Yellow Band Disease The main objectives of the work described in Chapter 2 were to determine how both the host transcriptome and the bacterial community structure change under Yellow Band Disease. Three health states were examined in the coral O. faveolata by profiling the coral-host gene expression using cDNA microarrays, the associated Symbiodinium using RFLP analysis, and bacterial taxa with PhyloChips. All methods show similar trends in that the results, while consistent within condition, differed across conditions. These findings were surprising as the healthy appearing tissue on a diseased colony (HD) had a more similar host transcriptome profile to that of the diseased tissue, had an entirely different Symbiodinium profile, and a bacterial community reflecting a combination of both the diseased and healthy samples. This was the first study to explore multiple members of the coral holobiont in multiple health states (Closek et al., 2014).

Comparative analysis of coral transcriptomes across four diseases Chapter 3 describes the first study that compares coral transcriptomic responses to multiple diseases. This work compared two common diseases, White Band and White Pox, in the species A. palmata. In addition, common diseases in O. faveolata, White Plague and Yellow Band Disease, were compared to examine core responses and disease- specific responses of the host. The study demonstrated that the two coral species suffered similar impacts to their defense systems, but drastically differing responses between diseases. These results broaden our understanding of these four common Caribbean coral diseases and highlight the distinct affects these diseases have on their hosts.

11 Microbial census of the French Frigate Shoals Chapter 4 describes the results of the first microbial census of Papahānaumokuākea (the Northwestern Hawaiian Islands) Marine National Monument. The associated bacterial communities of water and sediment samples from the geographically isolated French Frigate Shoals (FFS) atoll were compared. Differences were noted between locations, but larger differences were observed when samples were compared to developed locations, such as Kiritimati and O’ahu. The results show that bacterial diversity in sediment is greater than in water and also isolated locations contain different, potentially less disturbed microbial communities, which could serve as benchmarks for reef health assessments.

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20 CHAPTER 2:

CORAL-HOST AND MICROBIAL RESPONSE TO YELLOW BAND DISEASE

Reprint of manuscript entitled: “Coral transcriptome and bacterial community profiles reveal distinct Yellow Band Disease states in Orbicella faveolata”, in press at The ISME Journal

Authors: Collin J. Closek1,2; Shinichi Sunagawa3; Michael K. DeSalvo4; Yvette M. Piceno5; Todd Z. DeSantis6; Eoin L. Brodie5; Michele X. Weber1,2; Christian R. Voolstra7; Gary L. Andersen5; Mónica Medina1,2.

Affiliations: 1. Biology Department, The Pennsylvania State University, University Park, PA, United States of America 2. School of Natural Sciences, University of California, Merced, CA, United States of America 3. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany 4. Phalanx Biotech Group, Inc., San Diego, CA, United States of America 5. Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America 6. Second Genome, Inc., South San Francisco, CA, United States of America 7. Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

21 2.1 Abstract Coral diseases impact reefs globally. Although we continue to describe diseases, little is known about the etiology or progression of even the most common cases. To examine a spectrum of coral health and determine factors of disease progression we examined Orbicella faveolata exhibiting signs of Yellow Band Disease (YBD), a widespread condition in the Caribbean. We used a novel combined approach to assess three members of the coral holobiont: the coral-host, associated Symbiodinium algae, and bacteria. We profiled three conditions: 1. healthy-appearing colonies (HH), 2. healthy-appearing tissue on diseased colonies (HD), and 3. diseased lesion (DD). Restriction fragment length polymorphism (RFLP) analysis revealed health state-specific diversity in Symbiodinium clade associations. 16S rRNA gene microarrays (PhyloChips) and O. faveolata cDNA microarrays revealed the bacterial community structure and host transcriptional response, respectively. A distinct bacterial community structure marked each health state. Diseased samples were associated with 2-3 times more bacterial diversity. HD samples had the highest bacterial richness, which included components associated with HH and DD, as well as additional unique families. The host transcriptome under YBD revealed a reduced cellular expression of defense- and metabolism-related processes, while the neighboring HD condition exhibited an intermediate expression profile. Although HD tissue appeared visibly healthy, the microbial communities and gene expression profiles were distinct. HD should be regarded as an additional (intermediate) state of disease, which is important for understanding the progression of YBD.

22 2.2 Introduction Corals engage in symbioses with a diverse array of microbes (Douglas, 1998; Wegley et al., 2007). Tropical corals thrive in nutrient-limited waters largely due to their symbioses (Muscatine and Porter, 1977). Recent analysis of this complex metaorganism (i.e. the coral holobiont) revealed great microbial diversity (Rohwer et al., 2002). One member of this holobiont, that many tropical and sub-tropical corals harbor, is a unicellular alga of the genus Symbiodinium, which harnesses light energy and transfers fixed carbon and other organic compounds to the coral-host (Trench, 1979). In addition, corals harbor a large diversity of bacteria, some of which appear to be host-specific (Roder et al., 2014; Rohwer et al., 2001; Sunagawa et al., 2010). Of the ones that have been studied, many are beneficial to the holobiont by promoting coral health, defense, and nitrogen fixation (Alagely et al., 2011; Chimetto et al., 2008; Lema et al., 2012; Lesser et al., 2004; Ritchie, 2006). These recent findings highlight the importance of deepening our understanding of host-microbe interactions in coral reef environments (Ainsworth et al., 2009; Mouchka et al., 2010), yet high throughput genomic analyses have revealed that most of the coral-associated bacteria are unclassified (Sunagawa et al., 2010). In the last 50 years, coral reef ecosystems have drastically declined (Wilkinson, 2008). Since the mid-1970‘s (Dustan, 1977) coral disease events have become pervasive throughout the world’s oceans (Bourne et al., 2009; Sutherland et al., 2004; Weil et al., 2006). Anthropogenic pressures such as increasing sea surface temperatures, coastal development, depletion of fisheries and high nutrient effluents, impact coral health and exacerbate disease outbreaks (Brandt and McManus, 2009; Bruno et al., 2003; Bruno et al., 2007; Cervino et al., 2004). Metagenomic approaches have revealed higher numbers of potentially pathogenic bacteria along gradients of anthropogenic activity (Vega Thurber et al., 2009). As human activities increase both locally and globally, reef systems continue to decline (Hughes et al., 2010; Pandolfi et al., 2003). In recent decades, the Caribbean Sea has become a disease hotspot (Dustan, 1977) and its shallow reefs are the most impacted worldwide (Bourne et al., 2009). Many coral diseases manifest themselves in necrosis of the coral tissue followed by macroalgal colonization of the bare skeleton (Knowlton and Jackson, 2008; Richardson, 1998).

23 Continuous episodes of disease can ultimately lead to a macroalgal-dominated reef with major consequences for biodiversity and the survival of scleractinian corals (Bruckner and Bruckner, 2006; Knowlton and Jackson, 2008). One common disease in the Caribbean is Yellow Band Disease (YBD), also known as “Yellow Blotch Disease”. YBD was first noted off the coast of the Florida Keys in 1994 (Reeves, 1994) and has since been identified throughout the region. YBD has been reported for the Orbicella annularis species complex, Montastraea cavernosa, and the Colpophyllia natans species complex (Bruckner and Bruckner, 2006). Orbicella spp. (formerly part of the genus Montastraea, but recently reclassified to the Orbicella genus (Budd et al., 2012)) are major reef-building species. Coral die-offs driven by YBD have detrimental, long-term consequences for Caribbean reefs (Gardner et al., 2003). YBD is characterized by a 1-5 cm wide, yellow to white, circular band that radiates outwards (Reeves, 1994). As it progresses, necrotic tissue sloughs off exposing the denuded skeleton. The band has been recorded to spread about 0.6 cm/month (Cervino et al., 2001), a relatively slow rate of progression compared to other coral diseases, e.g., White Plague Disease (WPD) typically progresses by 0.3-2 cm/day (Richardson, 1998). However, YBD lesions are often seen on multiple areas of a colony and can persist over multiple seasons until a large portion of or the entire colony is decimated. In some reefs, as high as 88% of Orbicella spp. colonies exhibited signs of YBD (Richards Dona et al., 2008). The gradient of yellow to white band color is a result of declining algal symbiont densities (Cervino et al., 2001). Cervino et al. (2004) identified four Vibrio spp. as the putative causative agents for YBD using traditional isolation and culturing techniques. Recently, YBD has been reported to affect other coral species in the Indo-Pacific, i.e. Diploastrea heliopora, Fungia spp. and Herpolitha spp. (Cervino et al., 2008). Given the rapid reef decline worldwide, there is a pressing need for novel approaches for coral disease diagnostics (Pollock et al., 2011). Both bacterial and Symbiodinium community composition have been assessed in some coral diseases (Cervino et al., 2004). However, the host physiological response has been evaluated mainly in the context of thermal stress (Barshis et al., 2013; DeSalvo et al., 2008) and never in conjunction with microbial community profiling. Host transcriptome profiling

24 can enhance our understanding of how corals are responding to disease outbreak. We combine these three approaches, i.e. bacterial taxonomic profiling, host transcriptome response, and Symbiodinium genotyping, to gain insight into the dynamics of YBD in the coral Orbicella faveolata. 16S rRNA gene microarrays, known as “PhyloChips”, provide a culture independent method to monitor relative abundance of a set of bacterial taxa under different conditions (Brodie et al., 2006), and have been extensively used in different biological systems (Mendes et al., 2011; Wu et al., 2010), including corals (Kellogg et al., 2012; Roder et al., 2014; Sunagawa et al., 2009b). Coral transcriptome profiling has been used to examine differential gene expression under multiple physiological and developmental conditions (Barshis et al., 2013; Bellantuono et al., 2012; DeSalvo et al., 2008; Kaniewska et al., 2012; Portune et al., 2010; Voolstra et al., 2009a; Voolstra et al., 2009b). Herein we extend the use coral cDNA microarrays to examine host response to disease. To our knowledge this is the first study to examine multiple facets of the holobiont in a range of health states, including healthy-appearing tissue neighboring the disease lesion. This approach aimed to allow a better understanding of the progression of YBD and to provide insights into part of the coral holobiont’s response. We show that beyond visual signs of a disease, the associated microbiome and coral-host transcriptome dramatically shift in Orbicella faveolata. The combination of molecular tools used here identified associated bacterial taxa and genes that distinguish samples, which appear phenotypically healthy, from healthy colonies. These results advance our understanding of YBD progression and could be used to design an early screen for detecting elevated transcription of key genes as well as presence of indicator taxa highlighted herein.

2.3 Materials & methods Sample collection and preparation We collected skeletal-tissue cores of Orbicella faveolata on September 2, 2008 along the coast of Puerto Morelos, Mexico. Samples were collected using hammer and corer (2 cm diameter) by SCUBA at an average depth of 6m at Los Jardines reef in Puerto Morelos National Marine Park (N 20.831230, W 86.874350). We collected a total of twelve

25 samples from eight colonies: one set of samples from four healthy colonies (HH), which exhibited no visually distinctive signs of stress or impacted health and two samples from four diseased colonies; one set of four samples from the advancing disease lesion (DD), yellowish tissue interface adjacent to the recently denuded skeleton; and one set of samples from neighboring tissue which had no apparent physical signs of disease (HD) 30-90cm from the lesion, on the same coral colonies. Using SCUBA we sampled each colony underwater and cores were placed into Whirl-Pak bags (Nasco, Fort Atkinson, WI). The samples were transferred to the boat, quickly rinsed with filtered seawater, wrapped in aluminum foil, and subsequently flash- frozen in a liquid nitrogen dry shipper. At UC Merced, excess skeleton was removed with mallet and sterile chisel. The residual upper tissue/skeleton (<0.5 cm) layer was ground into a homogenous powder using a sterilized mortar and pestle cooled to -78.5°C. The entire homogenization process took place on dry ice.

DNA Extraction and generation of 16S rDNA amplicons From each sample, we added 50 mg of frozen homogenized coral powder to a screw-cap tube. We performed DNA extractions as described in Sunagawa et al., 2010. We amplified 16S rRNA genes in 100 µl reactions using the extracted DNA (20 ng), 0.8 μM of the universal primers 27F (5'-AGAGTTTGATCCTGGCTCAG) and 1492R (5'- GGTTACCTTGTTACGACTT), Platinum Taq polymerase High Fidelity (0.4 µl), and PCR buffer (Invitrogen, Grand Island, NY). We ran gradient amplifications (annealing temperatures from 48 to 60°C) using 25 cycles. We purified pooled products for a given sample with the MinElute Kit (Qiagen, Valencia, CA), and hybridized them to third generation (G3) PhyloChips (Second Genome, South San Francisco, CA) as described in Hazen et al. (2010).

PhyloChip hybridization The 16S rRNA third generation (G3) PhyloChip was expanded to contain multiple oligonucleotide probes to identify across 59,222 operational taxonomic units (OTUs), representing a majority of the known diversity in Bacteria and Archaea. The PhyloChip

26 was designed to track population dynamics in the majority of known bacterial and archaeal taxa from publically available databases (Wu et al., 2010), which included 943 identified O. faveolata-associated sequences (Sunagawa et al., 2009b). We used a total of 230 ng of pooled gradient amplicon products from each sample for hybridization. One sample, HH4, did not yield sufficient PCR product for PhyloChip analysis. For the remaining samples, we followed published procedures for sample preparation for hybridization (Wu et al., 2010). PhyloChips were washed and stained following Affymetrix protocols. PhyloChips were scanned as previously described (Wu et al., 2010).

PhyloChip data analysis We generated CEL files from probe fluorescence intensities of the scanned microarrays using PhyloChip Analysis (PhyCA) parameters (Wu et al., 2010) through Stage 1 for taxa selection, allowing unclassified microbial taxa (operational taxonomic units – OTUs) to be included in the output files. Hybridization fluorescence intensities were scaled to the internal spike mix to allow comparison across samples. OTUs determined present in three or more samples in each condition were used for subsequent analyses. Presence/absence calling of each was based on positive hybridization of multiple probes that correspond to an OTU (average of 37 probes/OTU). The resulting fluorescence data were log2 transformed. We used the Bioconductor package 'limma' (Smyth, 2004) for further analysis in

R; log2-transformed values were compared using pair-wise comparisons. Results were filtered at a p-value≤0.05 and a log fold change (FC)≥2. The data were loaded into MeV v4.6.1 (Saeed et al., 2003) for visualization.

Symbiodinium 18S amplification and clade identification Symbiodinium 18S rRNA gene fragments were amplified using universal forward primer ss5 (5′-GGTTGATCCTGCCAGTAGTCATATGCTTG-3′), Symbiodinium-specific reverse primer ss3z (5-AGCACTGCGTCAGTCCGAATAATTCACCGG-3′) (Rowan and Powers, 1991), and HiFi Platinum Taq. The amplified fragments were digested with

27 TaqI restriction enzyme (Promega, Madison, WI), ran on a 1% agarose gel, and compared across Symbiodinium A-D clade standards following the protocol from Rowan and Knowlton (1995).

Host genotyping We assessed clonal similarities using the methods described in Davies et al. (2013). The eight coral colonies sampled proved to be genets. No clones were identified, as assessed by nine microsatellite markers: maMS8_CAA, Mfav5_CGA, Mfav7_CAT, Mfav3_ATG, Mfav4_TTTG, Mfav6_CA, Mfav9_CAAT, Mfav30_TTTTG, and Mfav8_CAA.

RNA extraction and amplification for coral cDNA arrays From each frozen homogenized coral sample described above, we isolated total RNA using QIAzol Lysis Reagent (Qiagen), two chloroform extractions, subsequent isopropanol precipitation and two successive 70% ethanol washes. RNA pellets were redissolved in 100 µl of nuclease-free water and quantified using a ND1000 NanoDrop. The samples were concentrated using 3M sodium acetate and ethanol precipitation, and further purified using the RNeasy MinElute Cleanup Kit (Qiagen). RNA quality was assessed with a 2100 Bioanalyzer (Agilent, Santa Clara, CA). With the modified steps outlined in DeSalvo et al. (2008; 2012), we amplified 1 µg of total RNA using the MessageAmp II aRNA kit (Ambion, Foster City, CA).

Composition of pooled reference sample and hybridization of cDNA A total of 3 µg of aRNA from each sample was reverse transcribed into cDNA and purified with the RNeasy MinElute Kit. Equal amounts of aRNA aliquots from each sample comprised the pooled reference. Reference and individual samples were labeled with Cy-3 and Cy-5 dyes, respectively. After fluorescent labeling, we purified samples with the RNeasy MinElute Kit to remove any excess dye. The second generation (G2) O. faveolata cDNA microarray slides, referred here on as cDNA microarrays, were designed and printed with 10,930 features at UC Merced’s Genome Core Facility (Aranda et al., 2011). We post-processed, hybridized,

28 and scanned the O. faveolata cDNA microarray slides as described in DeSalvo et al. (2012). Each of the twelve samples from the three health states (i.e. HH, HD, DD) were competitively hybridized against a pooled reference sample. After overnight hybridization, slides were immediately transferred to individual falcon tubes containing wash buffers (0.6xSSC and 0.01%SDS, followed by 0.06xSSC) to remove non-specific hybridization and unbound excess cDNAs. Slides were scanned using an Axon GenePix 4000B scanner (Molecular Devices, Sunnyvale, CA), and photomultiplier tube (PMT) gain settings were individually adjusted to balance and maximize the dynamic range for both channels. cDNA microarray data analysis Scanned TIFF images were analyzed with GenePix Pro 6.0, with the following steps: 1) find, align, and fill irregular features, and 2) flag features, which match the query: [Circularity] = 0 OR [Sum of Medians (635/532)] ≤ 400 OR [F532 % Sat.] > 5 OR [F635 % Sat.] > 5. GenePix result files were converted into the TIGR MultiExperiment Viewer file format using TIGR ExpressConverter (Version 2.1). Using TIGR MIDAS 2.22 (Saeed et al., 2003), data were LOWESS normalized, block- and slide standard deviation regularized, and in-slide duplicates were averaged. We filtered expression data to include only those features for which a minimum of three out of four samples per condition had fluorescence values. Resulting fluorescence data were log2 transformed. Using the Bioconductor package ‘limma’

(Smyth, 2004) for further analysis in R (R Core Team), the log2-transformed values were compared using pair-wise comparisons and adjusted p-values for multiple testing using a false discovery rate (FDR) <0.05 (Benjamini and Hochberg, 1995). Visualizations of the data were performed using MeV v4.6.1 (Saeed et al., 2003).

2.4 Results PhyloChips hybridizations

29 Of 59,222 OTUs complementary to PhyloChip probe sets, 4,102 hybridized and passed filter parameters (Hazen et al., 2010). Many were unclassified in the reference database and ~50% (2,068) lacked family level annotation.

More terrestrial-originated sequences in diseased samples A shift was observed towards increasing sequences of terrestrial origin in the diseased samples. The 50 most abundant sequences on the PhyloChip for HH and DD were markedly different. Bacterial sequences originally isolated from marine environments made up more than half of the most abundant in the HH sample (52%); a quarter (26%) were originally isolated from corals. In DD, only 22% were from marine environments, while 66% were sequences originally isolated from terrestrial environments. Only 2% of the hybridized spots in DD represented sequences originally isolated from corals (Figure 2.1).

Figure 2.1. The top-50 most abundant, unique sequences in HH (left) and DD (right) conditions, which contained sequences that were originally sampled from terrestrial (e.g. soil, fresh water, and mammal gut/feces) and marine environments (e.g. marine sediment, seawater, coral, marine animals). These delineations revealed an increase in terrestrial-associated sequences with DD samples.

Higher bacterial richness in diseased samples Overall, 70 classified bacterial phyla were represented. Most phyla were identified in diseased samples (Table 2.1). DD samples had almost double the amount of phyla (n=30) compared to healthy (n=16). HD had the most phyla associated (n=44). To measure

Unclassified(%) 84(25%) 179(53%) 204(61%) 249(74%)

DD[336] Classified(%) 252(75%) 157(47%) 132(39%) 87(26%)

) Colonies nidentified in DD (%) 30(43%) 68(25%) 47(26%) 44(23%)

HD(%)

Diseased

Unclassified(% 158(23%) 370(54%) 430(63%) 532(78%)

n identified in 44(63%) 118(43%) 78(44%) 70(37%)

52(22%)

HD[684] Classified(%) 526(77%) 314(46%) 254(37%) 1

) )

HealthyColonies nidentified in HH (%) 16(23%) 22(8% 16(9% 15(8%) Unclassified(%) 23(26%) 44(50%) 50(57%) 63(72%)

each taxonomic level across all three health conditions; classified richnessconditions; across three classified taxonomic each (left) all health level

The average number of bacterial taxa identified per condition out of the conditionthe of ofaveragetaxa numberper identified The out bacterial

Theaverage number bacterialof taxa identified per condition themaximumoutof HH[88] Classified(%) 65(74%) 44(50%) 38(43%) 25(28%) Table Table 2.1. matched at number taxonomic eachmaximum (unclassified excluded) level,

Table 1. 1. Table numbermatched eachat taxonomic level, unclassified taxa excluded. TaxonomicLevel (n) Phylum(70) Class(276) Order(179) Family(188) Richness totals totals Richnessfor

Richnesseachtotalsfor taxonomic level across all three health conditions; classified richness,vs. left,

Table Table 2.2. unclassified (right) vs. richness Table 2. Table unclassifiedrichness, right. TaxonomicLevel Phylum Class Order Family

31 relative richness each sample was compared to the grand total (Table 2.2). Relative richness revealed changes in the community structure of the 15 richest phyla (Figure 2.2). HH samples had the highest relative richness of Bacteroidetes, Spirochaetes, and Proteobacteria (Gammaproteobacteria). In DD, Firmicutes showed the greatest relative richness. Relative richness in HD was similar to DD, however Proteobacteria was richest and, of those, the majority was Alphaproteobacteria.

100% Other (No.16-70)

90% ABY1_OD1 Acidobacteria 80% Actinobacteria Bacteroidetes 70% Caldithrix_KSB1 60% Chlorobi Chloroflexi 50% Cyanobacteria

40% Firmicutes Gemmatimonadetes 30% Planctomycetes

20% Proteobacteria Spirochaetes 10% Verrucomicrobia WS3 0% HH HD DD

Figure 2.2. Mean community structure of the 15 richest phyla across all health conditions. Chlorobi and Verrucomicrobia were identified exclusively in the diseased samples. Bacteriodetes, Cyanobacteria, and Spirochaetes were richest in HH. Proteobacteria were richest in HD, dominantly Alphaproteobacteria. Firmicutes were richest in DD.

Differential family abundances Descriptive richness was measured by comparing the number of families associated with one sample to the total within each condition. For the top 15 families, composition varied

32 between health conditions (i.e. HH, HD, DD). In total, all health conditions comprised 28 bacterial families; 11 of those top families were found in all 3 conditions. Eleven families were most abundantly associated with only HD and DD (Figure 2.3). Sulfobacillus_FM was associated with both, HH and HD, but not DD. Vibrionaceae, Planococcaceae, and Aeromonadaceae were in the top 15 families in HH. Desulfovibrio_FM was most abundantly associated with HD and Halanaerobiaceae with DD. Additional significant classified families resulted from the limma pairwise comparisons (Supplemental Figure 1). Of these, Vibrionaceae was the most abundant family in HH samples. Based on log2 values, Planococcaceae, Enterobacteriales_Enterobacteriaceae, and Fusobacteriaceae were also abundant families in HH. Pirellula and BCf2-25, both Plantomycetes, were the two most abundant families associated with HD samples. Rhodoplanaceae, Ruminococcus, Azospirillaceae, and Phyllobacteriaceae, were differentially abundant in the HD condition. Peptostreptococcaceae was the most abundant family in DD (FC=3.01), but Halanaerobiaceae, Catabacter, and Ruminococcus were also prevalent.

Differential OTU abundances Limma pairwise analyses identified 124 significant OTUs at p<0.05 and FC≥2. When HH samples were compared to HD samples, 94 significant OTUs were identified. HD vs. DD comparison resulted in 44 significant OTUs. HH to DD comparisons identified 41 OTUs (Supplemental Figure 1). While some OTUs did overlap, a distinct bacterial community structure defined each of the health conditions. The most abundant OTUs in both the HH and HD samples were originally discovered in association with marine organisms, however the majority of these were unclassified. Across samples of all three health conditions the proportion of unclassified OTUs were similar at each taxonomic level.

Only HH (3) Only HD (1) Only DD (1) Only Across All (11) All Across Only HH &HD (1) Only Only HD &DD (11) Only Anaerolineae Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Cytophaga_FM Enterobacteriales_Enterobacteriaceae Peptostreptococcaceae Pseudomonadaceae Spirochaetaceae Symbiobacterium_FM Treponemaceae Acetivibrio_FM Anaerobrancaceae BA017_FM Catabacter_FM Desulfobacterium_FM Desulfotomaculum_FM Oleomonas_FM Pirellula_FM Polyangiaceae Saprospiraceae Spirosomaceae Sulfobacillus_FM Aeromonadaceae Planococcaceae Vibrionaceae Desulfovibrio_FM Halanaerobiaceae

33 Only HH (3) Only HD (1) Only DD (1) Only Across All (11) All Across Only HH &HD (1) Only Only HD &DD (11) Only Only HH (3) Only HD (1) Only DD (1) Only Across All (11) All Across Only HH &HD (1) Only Only HD &DD (11) Only large overlap large Anaerolineae Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Cytophaga_FM Enterobacteriales_Enterobacteriaceae Peptostreptococcaceae Pseudomonadaceae Spirochaetaceae Symbiobacterium_FM Treponemaceae Acetivibrio_FM Anaerobrancaceae BA017_FM Catabacter_FM Desulfobacterium_FM Desulfotomaculum_FM Oleomonas_FM Pirellula_FM Polyangiaceae Saprospiraceae Spirosomaceae Sulfobacillus_FM Aeromonadaceae Planococcaceae Vibrionaceae Desulfovibrio_FM Halanaerobiaceae amples, amples, while Anaerolineae Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Cytophaga_FM Enterobacteriales_Enterobacteriaceae Peptostreptococcaceae Pseudomonadaceae Spirochaetaceae Symbiobacterium_FM Treponemaceae Acetivibrio_FM Anaerobrancaceae BA017_FM Catabacter_FM Desulfobacterium_FM Desulfotomaculum_FM Oleomonas_FM Pirellula_FM Polyangiaceae Saprospiraceae Spirosomaceae Sulfobacillus_FM Aeromonadaceae Planococcaceae Vibrionaceae Desulfovibrio_FM Halanaerobiaceae was a HH. in Eleven The venn diagram and and venn Thediagram re in HDs in

he only top families families top re re highly abundantonly highly s was dominanat in HH dominanat HH wassamples.in

Vibrionaceae we Vibrionaceae

for the top condition. families eachT for in the 15 Acetivibrio_FM Aeromonadaceae Anaerobrancaceae Anaerolineae BA017_FM Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Catabacter_FM Cytophaga_FM Desulfobacterium_FM Desulfotomaculum_FM Desulfovibrio_FM Enterobacteriales_Enterobacteriaceae Halanaerobiaceae Oleomonas_FM Peptostreptococcaceae Pirellula_FM Planococcaceae Polyangiaceae Pseudomonadaceae Saprospiraceae Spirochaetaceae Spirosomaceae Sulfobacillus_FM Symbiobacterium_FM Treponemaceae Vibrionaceae chart Acetivibrio_FM Aeromonadaceae Anaerobrancaceae Anaerolineae BA017_FM Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Catabacter_FM Cytophaga_FM Desulfobacterium_FM Desulfotomaculum_FM Desulfovibrio_FM Enterobacteriales_Enterobacteriaceae Halanaerobiaceae Oleomonas_FM Peptostreptococcaceae Pirellula_FM Planococcaceae Polyangiaceae Pseudomonadaceae Saprospiraceae Spirochaetaceae Spirosomaceae Sulfobacillus_FM Symbiobacterium_FM Treponemaceae Vibrionaceae anococcaceae, anococcaceae, and

& DDDesulfovibrio_FM samples.& wa HD

in DDsamples. in

sociated with HD andwithSpirochaetaceae DD.sociated HD HD

0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% families as families top Descriptive stacked richnessDescriptive family bar re associatedwith HD re only

families such as Aeromonadaceae, families PlAeromonadaceae, as such HH Acetivibrio_FM Aeromonadaceae Anaerobrancaceae Anaerolineae BA017_FM Bradyrhizobium_FM Caedibacteraceae Cardiniaceae Catabacter_FM Cytophaga_FM Desulfobacterium_FM Desulfotomaculum_FM Desulfovibrio_FM Enterobacteriales_Enterobacteriaceae Halanaerobiaceae Oleomonas_FM Peptostreptococcaceae Pirellula_FM Planococcaceae Polyangiaceae Pseudomonadaceae Saprospiraceae Spirochaetaceae Spirosomaceae Sulfobacillus_FM Symbiobacterium_FM Treponemaceae Vibrionaceae

3. . igure igure 2 0% DD F the between table show we families richest Halanaerobiaceae was 90% 80% 70% 60% 50% 40% 30% 20% 10%

100%

0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 34 Distinct Symbiodinium populations & coral genotypes Restriction fragment length polymorphism (RFLP) signatures identified the majority taxa associated with each health condition. HH samples, two samples had clade A and two hosted clade C (Table 2.3). The HD samples were most diverse. All DD samples hosted clade A with one sample also harboring clade C. In addition, host genotyping confirmed that all coral colonies sampledTable were 3. Associated genets and Symbiodinium not clones. clades per sample furcate according to health condition. Clade C and A were Table 2.3. Associatedeach dominant Symbiodinium in two samples clades pe ofr HH. sample furcate according to health condition.Clade Clade C was C anddominant A were in HD.dominant Clade Ain two samples of HH. Clade C was dominantwas dominant in HD. in Clade the DD A samples.was dominant in DD samples.

Sample Clade 1˚ Clade 2˚ Clade 3˚ HH1 C HH2 A HH3 A HH4 C HD1 C HD2 C HD3 C D A HD4 C D B DD1 A C DD2 A DD3 A DD4 A

Distinct host-transcriptomic response After hybridization to the coral cDNA microarray, 6,620 genes passed the filtering criteria. At p <0.01 (n= 431), more than 80% of the genes exhibited increasing or decreasing expression trends, where HH and DD had opposite expression patterns, while HD consistently exhibited intermediate gene expression (Figure 2.4). The host cDNA array analysis (FDR = 5) resulted in 134 significantly expressed genes across all three pair-wise comparisons. HH vs. DD comparison resulted in 132 significantly differentially expressed genes (DEGs) with 83 up-regulated and 49 down-regulated genes in the DD samples (Supplemental Table 1). The five genes with the highest log fold change (FC) ranged from 2.82-3.74, but have unknown functions. Of the 132 DEGs, 63 were annotated

35 (Figure 2.5 for the condensed list and FC values). The annotated DEGs with the largest FC were Equinatoxin-5 (NCBI dbEST ID: CCHW6675) and Small cysteine-rich protein 1 (CCHW7708), which were down-regulated in DD (-2.70 and -2.64, respectively). Hemicentin-1 (CCHW6762) was the most up-regulated annotated gene in the diseased samples (FC = 2.57). Of the 132 significant genes, 117 were solely significant in the HH vs. DD comparison and not significant in the other comparisons. The HH vs. HD comparison revealed 16 significant DEGs. One (CCHW60626) was down-regulated and 15 were up-regulated in the HD samples. Only 2 of the up- regulated genes were annotated, Cyclic AMP-responsive element-binding protein 1 (CCHW1799) and Proteasome subunit alpha type-3 (CCHW9385). Two of the genes (CCHW12270 and CCHW6026) were found to be significant only in the HH vs. HD comparison. Only 1 unannotated gene (AOSF808) was differentially expressed between HD vs. DD. The DD condition was down-regulated (FC = -1.81) relative to HD.

2.5 Discussion Multiple stressors compromise coral health and leave them susceptible to disease. Although YBD was described in the Caribbean in the mid-90’s, the method of transmission remains elusive. How diseases affect the holobiont and its dynamic microbial assemblage, which may play a role in disease progression, is also still poorly known. Baseline descriptive data corresponding to different health states, exemplified in this study, are key to understanding the etiology of a disease.

Bacterial community shifts under different health states Coring provided reproducible results when assessing the coral-associated bacterial community structure as also observed by Kellogg et al. (2012). The community structure changed dramatically when comparing healthy (HH) to diseased (HD and DD) colonies. Differences were also observed between HD and DD. Our data are consistent with other studies that showed increased bacterial richness in impacted corals over healthy (Table 2.2) (Roder et al., 2014; Sunagawa et al., 2009b; Vega Thurber et al., 2009).

Figure 2.4. Differentially expressed genes show distinct expression differences across health conditions, with healthy-diseased often having an intermediate expression signal. Greater than 80% (n=438) of the 500 significant genes (p<0.01) show a trend of either continuous up (blue, n=221) or down (black, n=217) regulation of transcripts when comparing expression values across all three health conditions.

Figure 2.5. Heatmap of the 15 most up- as well as down-regulated DEGs across the three conditions determined by limma (FDR=5). Negative log FC represents down-regulated genes in DD, positive represent up-regulated in DD (left column) resulting from DDvs.HH comparison . NCBI dbEST ID and Annotations characterize the DEGs (right columns).

37 The highest bacterial richness was observed in the healthy-appearing tissue on the diseased colonies (HD). High bacterial diversity in these tissues likely reflects disequilibrium in the community structure and may represent a transitional community or competition after a disturbance (Costello et al., 2012). Opportunistic taxa may compete for available resources and take advantage of the impaired defense system. This ultimately may increase some members or new colonists, while decreasing other members and give rise to the increased taxa associated with disease (Roder et al., 2014; Sunagawa et al., 2009b; Vega Thurber et al., 2009). Richness may be further inflated because of factors such as proximity to the lesion, symbiont composition, and micro- environmental differences. The most abundant OTUs in this dataset were associated with sequences previously identified from coral, mammal guts, and sediments. OTUs frequently found in diseased colonies were often related to bacteria that have been associated with fecal or gut communities. This evidence supported the idea that a healthy microbial community may be destabilized by high nutrients (Bruno et al., 2003; Kimes et al., 2010) and terrestrial run-off. Fluctuations in community structure could exacerbate YBD. Vibrionaceae have been described as potential causative agents for YBD (Cervino et al., 2004). Cróquer and colleagues (2013) found Vibrio spp. to be dominant in diseased mucus. However, in this dataset and other YBD studies (Cunning et al., 2009), Vibrionaceae were noticeably more abundant (2-10x) in healthy samples (Supplemental Figure 1). Some Vibrios are commensal in healthy corals (Chimetto et al., 2008; Koenig et al., 2011; Krediet et al., 2013). This does not negate that it may be possible for Vibrios to become pathogenic as environmental conditions change or genomic modifications occur, such as activation, shuffling, or transfer of gene cassettes (Koenig et al., 2011). Firmicutes, such as Peptostreptococcaceae, Clostridiaceae, Thermoanaerobacteriaceae, Ruminococcus_FM, Halanaerobiaceae, and Clostridium_FM, were more abundant in diseased samples. Many of these are anaerobic families or facultative anaerobes, suggesting that under diseased conditions oxygen-limitation, may favor anaerobic bacteria. Peptostreptococcaceae was the most abundant family in DD samples and has been observed in other coral diseases, both Black Band (Sekar et al.,

38 2009) and White Plague (Sunagawa et al., 2009b). Sunagawa et al. (2009b) also observed abundant Clostridiaceae in White Plague samples. Firmicute_Clostridia along with Actinobacteria, both Gram-positive bacterial groups, dominated DD samples. Gram- negative bacteria were less represented in DD samples. The above families all contain known pathogens and many have been linked to sewage-related samples. HH samples were strongly associated with Proteobacteria_Gammaproteobacteria and Bacteroidetes, both gram-negative bacterial groups. Fusobacteria (Gram (-)), Acidobacteria (Gram (-)), Planctomycetes (Gram (-)), and Actinobacteria (Gram (+)) were each represented once. Pseudoalteramonas, which has been previously associated with YBD (Cervino et al., 2004) was not measured in high abundance. However, other Pseudomonadaceae were present at low abundance, in all health states. Abundances were even lower in diseased samples compared to HH and HD. Crenotrichaceae, which associates with sponges and has been sampled from seawater, was most abundant in the HD samples. These data supported the hypothesis that the bacterial community is shifting in regions adjacent to the lesion. Microbial taxa from the surrounding water column may colonize during these shifts, which suggests that the disease-associated community may change with season or geographic location.

Symbiodinium clade differences in various health states O. faveolata associates with Symbiodinium from clades A, B, C, and D (DeSalvo et al., 2010; Garren et al., 2006; Thornhill et al., 2010; 2013). Both A and C were present in HH. Clade C dominated the HD condition. Half of the HD samples also contained D with traces of either clade A and clade B. Finally, DD samples contained mostly clade A; however, one sample also hosted clade C. In 2004, Cervino and colleagues showed that intercellular Symbiodinium lysed before YBD-affected coral tissues displayed physical symptoms of the disease. The dominant clade A signature in these DD samples could be remnants of lysed cells. Alternatively, certain clade A genotypes have been present during or colonized coral polyps after stress events (DeSalvo et al., 2010; Rodríguez- Román et al., 2006; Stat et al., 2008).

39 Microbial interactions may be shaping Symbiodinium species composition or alternatively, Symbiodinium may affect the bacterial communities (Bourne et al., 2013). We recorded diverse Symbiodinium assemblages in the samples from the diseased colonies which may represent shifting communities. Our sample size and genotyping were too limited to provide conclusive evidence to support this hypothesis. However, our observations suggest that it would be worthwhile to further investigate how disease and microbial interactions impact coral-Symbiodinium assemblages.

Transcriptomic responses under disease & stress Host genets under healthy and diseased conditions showed distinct transcriptomes. Differences in gene expression reflect the differences between health states. However, in the HD condition, some DEGs responded similarly to DD and some were more similar to the HH response. These results illustrated that the HD condition is not always defined by intermediate expression. The clearly distinct DEG profiles for HH and DD showed that corals exhibit an extreme cellular response under YDB. For instance, respiration is reduced as mitochondrial ATP synthase-coupling factor 6 and other mitochondrial-associated genes appear to be suppressed once the disease manifests into a lesion (Supplemental Table 1). In addition, specific functional genes are differentially expressed between health conditions. Equinatoxin-5 (CCHW6675), a protein involved with pore-formation found in nematocysts of cnidarians (Anderluh et al., 1999), is down-regulated in DD. This protein has been proposed also as a precursor to the antimicrobial peptides of the magainin and dermaseptin families (Pungercar et al., 1997). The suppression of Equinatoxin-5 would leave the coral-host with reduced defense mechanisms. CCHW7708, small cysteine-rich protein 1 (SCRiP1), is also strongly suppressed in DD samples. SCRiP1 is part of the SCRiP gene family, originally uncovered in O. faveolata, and is the only SCRiP to have a β-defensin domain (Sunagawa et al., 2009a). The reduction of these two distinct antimicrobial peptides could impede the innate immune response that healthy corals would launch at the site of infection. In addition to suppressed defense mechanisms, Transcriptional corepressor tupA (CCHW2534) and

40 Histone H2A.v (AOSF622), which are involved in transcriptional repression and regulation, are strongly down-regulated in DD. Hemicentin-1 is highly up-regulated in diseased samples. Hemicentin-1 is part of the immunoglobin superfamily in human (Vogel and Hedgecock, 2001) and involved in immune response as well as stabilization of the germline syncytium in C. elegans (Vogel et al., 2006). Additional immune response proteins, such as TNF receptor-associated factor 3 (CCHW9787), are strongly up-regulated in diseased samples. This protein has been associated with hemorrhage and inflammatory response. Specifically, Cyclic AMP- responsive element-binding protein 1 (CREB1) has been associated with hypoxia- induced inflammatory processes (Comerford et al., 2003). PHD finger protein 21A (CCHW7679) inhibits transcription from RNA polymerase II promoter and the transcription factor ETV6 (CCHW7961) is a transcriptional repressor. Both were strongly up-regulated in DD. Strong mitochondrial suppression suggests that nutrients, which may have been provided by beneficial microbes are no longer available. Nor did the host invest in energy production. Although immune and inflammatory responses (also observed in coral bleaching studies) were identified, which have been observed in coral bleaching studies, we did not detect differential expression of reactive oxygen species (ROS) or heat shock proteins (HSPs) genes that appear in temperature stress studies (Barshis et al., 2013; DeSalvo et al., 2008; 2010; 2012).

HD as an intermediate health state These results showed that HD is an intermediate health state, which is distinct and potentially transitional. While HD appeared visually healthy, bacterial richness was >3- fold higher than in HH. This may be explained by the Intermediate Disturbance Hypothesis (Connell, 1978), where maximum species diversity is postulated to exist at intermediate regimes of disturbance. Although originally proposed for plant and animal diversity, our results suggest this may occur in microbial systems as well. Transcriptional changes indicate that the coral responds to disease, even in areas where the diseased colony does not exhibit signs of disease. A systemic response in colonies with YBD was

41 also observed by Mydlarz and colleagues (2009); higher lysozyme-like and antibacterial enzymatic activity was measured in both healthy appearing and diseased tissue. We measured a systemic response in diseased colonies by the change in DEGs. Some FC values were higher in HD than in DD, suggesting that certain unannotated genes play a larger role in the host transcriptome during this intermediate health state. Variance was higher for the HD sample set likely due to sampling a less defined area. Together, a distinct DEG profile, Symbiodinium population, and bacterial community structure defined HD as an intermediate health state in the progression of YBD.

Conclusions Coral colonies are complex microbial ecosystems that can now be examined experimentally through the lens of high throughput tools as those described herein. This study assessed the health state of coral colonies using two microarray technologies. Differences were distinguished in both the host transcriptome response and associated microbial communities. We used three conditions to examine a spectrum of health states and determine factors in the YBD progression. The HD condition was a distinct health state, where the associated bacterial community was mostly composed of the diversity associated with HH as well as DD, and host gene expression levels were also intermediate. This intermediate profile revealed that the entire colony responds to the disease, even though the HD tissue is visually indistinguishable from healthy tissue. The molecular signatures highlighted in this study provide a basis for future studies of YBD and other coral diseases to assess disease beyond phenotypic appearance. Future studies should use consistent collecting methods. In particular, samples from the HD condition have proven to be valuable. With comparative methods and further attention to intermediate heath states, we will learn more about coral health, disease dynamics, and subsequent changes in the coral holobiont.

2.6 Acknowledgements We would like to thank the Instituto de Ciencias del Mar y Limnología (ICML), Universidad Nacional Autónoma de México for providing facilities and collection

42 permits. Additionally we would like to thank those at the ICML, as well as Medina & Andersen Lab members who provided assistance in collecting, experimental, analytical methods. Especially, Adan Guillermo Jordán-Garza, Julia Schnetzer, & Erika Diaz- Almeyda for helping with sample collection. Nicholas Polato & Elizabeth Green for genotyping coral colonies. Lauren M. Tom for assistance with analyses. Justin Matthews for statistical input. Bishoy Hanna & Erika Diaz-Almeyda for additional draft comments. This study was supported by NSF awards IOS 0644438 and IOS 0926906 from NSF to MM.

Conflict of Interest Statement The authors claim no conflict of interest.

Supplementary information is available at http://www.nature.com/ismej/

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50 CHAPTER 3:

COMPARATIVE ANALYSIS OF CORAL TRANSCRIPTOMES ACROSS FOUR DISEASES

Manuscript entitled: “Comparative transcriptomics (Transcriptomic profile) of two Caribbean corals, Acropora palmata and Orbicella faveolata, across multiple diseases” in prep

Authors: Collin J. Closek1, Viridiana Avila Magaña1, Bishoy S. K. Kamel1, Erika M. Díaz- Almeyda1, Marilyn E. Brandt2, and Mónica Medina1

Affiliations: 1. Pennsylvania State University, University Park 2. University of the Virgin Islands

51 3.1 Abstract Only a few decades ago, Orbicella spp. and Acropora palmata were some of the most abundant corals in the shallow reefs of the Caribbean. These species are major reef building corals that contribute to the reef structure and provide many ecosystem services. Increasing environmental and anthropogenic stressors, such as high temperatures and nutrient effluents, have intensified coral disease outbreaks. Disease has ultimately contributed to significant die-offs of these important species throughout the region. Using a transcriptomic approach to better understand the physiological response of corals to disease, we investigated the following questions: (1) How do O. faveolata and A. palmata respond to more common diseases?, (2) Which genes exhibit enhanced expression under poor health conditions?, and (3) Do core responses and/or disease-specific responses exist? To examine these questions, we applied O. faveolata and A. palmata cDNA microarrays to four diseases, White Plague (WPl) and Yellow Band Disease (YBD) affecting O. faveolata as well as White Band (WB) and White Pox (WPx) affecting A. palmata, in the US Virgin Islands. Our results show dramatic core responses across diseases, as well as distinct disease-specific functional responses. Expression profile differences were noted across all diseases. Healthy and diseased samples show distinct profiles. Colonies exhibiting symptoms of YBD and WPl exhibited strong inflammatory responses as well as strong expression of genes involved in apoptotic responses. In A. palmata, WPx also induced a strong apoptotic response. All four diseases, WB, WPx, YBD, and WPl, exhibited increases in immune and defense responses as well as decreases in chemical defenses. In this study, a transcriptomic approach allowed us to distinguish between differences in the responses of hosts under varying diseases while also revealing common responses across diseases. This multi-species, multi-disease perspective allows for a simultaneous snapshot of host response and illustrates the complexity of disease dynamics in the reef.

52 3.2 Introduction Coral diseases have negatively impacted the Caribbean more than any other oceanic region (Bourne et al., 2009). White Band (WB) and White Pox (WPx) are two diseases readily noted affecting Acropora palmata. These diseases have only been described to affect Acropora in the Caribbean and have contributed to the regional decline in Acropora coral cover (Bruckner, 2009). As a result, both Caribbean species are listed as threatened under the United States Endangered Species Act and critically endangered under the IUCN Red List. White Band was first noted in 1977 off the coast of St. Croix, U.S. Virgin Islands (Gladfelter) and has since been observed across the Caribbean, causing near to complete mortality in some areas (Aronson and Precht, 2001). WB is most commonly described as loss of tissue followed by a thin white band of recently denuded skeleton, visually distinct from bleaching in that there is no tissue remaining (Figure 3.1). Two types of WB have been described, determined partly by the rate of progression. WB type I progresses at a rate of 1-21 mm/day and WB type II has been measured to progress up to 10 cm/day (Bruckner, 2009). Additionally, a zone of bleached tissue, 2-20cm in width, precedes the type-II lesion, however the lesion may advance to the bleached zone making it indistinguishable from type-I (Ritchie and Smith, 1998). In both types, the disease begins at the base of a colony and progresses towards the colony’s branches and tips, however type-II has also been noted to begin at the tips and move towards the base (Ritchie and Smith, 1998). Successional filamentous red algae colonize the recently barren skeleton and subsequently encrusting algae colonize (Gladfelter, 1982). The disease type can be difficult to confirm and therefore is often referred to as simply WB. About 20 years after WB was noticed in St. Croix, White Pox was documented in 1996 off of Key West, Florida (Holden, 1996). WPx is characterized by irregular white patches, devoid of tissue, typically on the top-side of a colony (Figure 3.1) (Patterson et al., 2002; Sutherland and Ritchie, 2004). This disease has only been observed on A. palmata and the signs of this disease are visually distinguished from predation by remaining intact skeleton. WPx progresses on the perimeter at an average rate of 2.5 cm2/day and the lesions can merge together to cause mortality to the entire colony

53 (Patterson et al., 2002; Sutherland and Ritchie, 2004). In the Virgin Islands, the decline of Acropora has primarily been attributed to WB, however recent studies suggest that WPx may be the primary factor hampering its recovery (Grober-Dunsmore et al. 2006). In addition, WPx may have been affecting corals in the Virgin Islands as far back 1970 (Rogers et al. 2005). Together WB and WPx have caused as much as 95% of the Acropora coral cover to decline in the Caribbean (Gignoux-Wolfsohn et al., 2013).

A. palmata with White Band O. faveolata with White Plague

A. palmata with White Pox O. faveolata with Yellow Band

Figure 3.1. Photographs taken during sampling of the four diseases examined: White Band on A. palmata (top, left), White Pox on A. palmata (bottom, left), White Plague on O. faveolata (top, right), and Yellow Band on O. faveolata (bottom, right).

Another dominant reef building coral in the Caribbean, Orbicella faveolata (formerly Montastraea faveolata Budd et al., 2012), commonly shows signs of White Plague and Yellow Band Disease. White Plague (WPl) was first described in Key Largo, Florida in 1975 (Dustan, 1977). Although WPl exhibits signs similar to the two other

54 white diseases described above, the edge of WP1 lesions are sharp with only apparently healthy tissue and bare skeleton, no necrotic tissue is evident (Figure 3.1) (Dustan, 1977). White Plague (WPl) affects many species. Three WPl types have been described based on rate of progression. Type I causes tissue rate loss of up to 3.1 mm/day (Dustan, 1977). Type II is more virulent progressing with a maximum tissue loss rate of 2 cm/day and a narrow (2-3mm) zone of bleached tissue at the lesion line (Richardson, 1998). Type III is the most aggressive affecting sides of large (>2m) colonies and tissue loss can exceed 10 cm/day (Richardson et al., 2001). Since progression rates are variable, types of WPl are difficult to detect. All WPl types can denude portions of a colony within a day, leaving only skeleton remaining. Therefor most studies have resorted to describing the disease as WPl type II or more simply WPl. Yellow Band Disease (YBD) was first noted in 1994 off of the Florida Keys (Reeves, 1994). A 1-5 cm wide band that is yellow to white in color characterizes YBD and progresses at a rate of 0.3-2 cm/month (Figure 3.1) (Bruckner and Bruckner, 2006). YBD progresses the slowest among the four diseases described here, but can affect multiple areas of a colony simultaneously, which can then coalesce and spread over the colony. The vacated skeletal area is then subsequently colonized by macroalgae (Cervino et al., 2001). YBD can be persistent, affecting colonies for years and ultimately causing mortality to an entire colony (Bruckner and Bruckner, 2006). All of these diseases have been shown to intensify with increased sea surface temperatures and bleaching events (Brandt and McManus, 2009; Kline and Vollmer, 2011; Mydlarz et al., 2009; Sutherland and Ritchie, 2004). Studies have also shown that increased nutrient levels exacerbate disease events (Bruno et al., 2003; Vega Thurber et al., 2013). Although these diseases often cause localized mortality to an area of the coral colony, they can persist and ultimately cause partial or total mortality to the whole colony. Most of the efforts to study these diseases have been focused on putative microbial pathogens and the progression of disease. WB has been shown to be highly transferable, likely by the snail Coralliophila abbreviata, and caused by bacterial pathogens (Kline and Vollmer, 2011). WPx has also been described as highly contagious to the nearest neighboring colonies and its causative

55 agent confirmed for regions in Florida to be the bacterium Serratia marcescens (Patterson et al., 2002; Sutherland et al., 2011), while the agent has shown mixed causal results in other locales (Lesser and Jarett, 2014). A putative infectious bacterial pathogen was suggested to cause WPl, but that has not been upheld by more recent investigations (Cardenas et al., 2012; Sunagawa et al., 2009). Recently, viral particles, similar to eukaryotic circular Rep-encoding single-stranded DNA viruses (SCSDVs) were noted in WPl samples and proposed as a probable cause of the disease (Soffer et al., 2013). In YBD, four Vibrio spp. were proposed as putative causative agents (Cervino et al., 2004). However, recent metagenomic analyses found low abundances of Vibrios associated with YBD samples suggesting there may be other microbes that contribute to the disease (Closek et al., 2014). Studies have examined coral-host responses to environmental changes, but few have examined the host response to disease. To examine these diseases, and assess how the coral-host responds to disease, we implemented cDNA microarrays to simultaneously measure differences in gene expression across thousands of genes (DeRisi et al., 1996; DeSalvo et al., 2012). cDNA microarrays have been successfully used in many organisms, including corals to examine differences in response to changes in both biotic and abiotic factors (e.g. Symbiodinium densities, salinity, light, temperature, and pH) (Barshis et al., 2013; Bellantuono et al., 2012; DeSalvo et al., 2008; Kaniewska et al., 2012). Transcriptomic studies have also inspected response in corals to disease, WB and YBD (Closek et al., 2014; Libro et al., 2013), highlighting the host as well as its symbionts’ physiological response to disease at the molecular level. Additionally, Closek et al. (2014) demonstrated the sensitivity of cDNA microarays allow for detection of disease prior to the appearance of physical signs of disease. This is the first study to examine coral-host response across two species and to multiple diseases. Our study indicates that not all diseases affect their host species equally. While there are similarities and core responses, coral transcriptomic responses differ across the four diseases. These findings further our understanding of the physiological impact these diseases have on A. palmata and O. faveolata.

56 3.3 Materials and methods Sample collection Skeletal-tissue cores from both visibly healthy as well as diseased coral colonies were sampled from August 27 - September 3, 2011 around St. Thomas and St. John, US Virgin Islands (USVI). Together, 24 A. palmata colonies were sampled, 12 were visibly healthy with no signs of disease, 6 with WPx, and 6 with WB. Additionally, 28 O. faveolata colonies were sampled, 14 were visibly healthy, 7 with WPl, and 7 with YBD. Samples collected from YBD colonies were collected from within the lesion, while samples from WPx, WB, and WPl were sampled near the tissue margin, as there is no tissue in the lesion area of these latter three. Visibly healthy colonies collected had no visible signs of stress or impacted health. Underwater using SCUBA, a hammer and corers, 1cm diameter cores were sampled from A. palmata and 2cm diameter cores were sampled from O. faveolata. The samples were then immediately placed into Whirl-Pak bags (Nasco, Fort Atkinson, WI), transferred to the boat, quickly rinsed in filtered seawater, wrapped in aluminum foil, and flash-frozen in a liquid nitrogen dry shipper.

Coral Powder Preparation & RNA Extraction Samples were transported back to UC Merced where the cores were processed. All preparation steps were completed on dry ice. Excess skeleton was removed using hammer and sterile, frozen chisel. The frozen, upper skeletal-tissue (<0.5cm) layer was subsequently ground into a homogenous powder using a frozen, sterilized mortar and pestle. From each sample, 0.05g of frozen homogenized coral powder was transferred to a sterile, RNase-free screw-cap tube on dry ice. We isolated total RNA using QIAzol Lysis Reagent (Qiagen, Valencia, CA), two chloroform extractions, subsequent isopropanol precipitation and two successive 70% ethanol washes. RNA pellets were redissolved in 100 µl of nuclease-free water and quantified using a ND1000 (NanoDrop, Wilmington, DE). Samples were further purified using the RNeasy MinElute Cleanup Kit (Qiagen). RNA quality was assessed with a 2100 Bioanalyzer (Agilent, Santa Clara, CA). With the modified steps outlined in

DeSalvo et al. (DeSalvo et al.; DeSalvo et al.), we amplified 0.5 µg of total RNA using the MessageAmp II aRNA kit (Ambion, Foster City, CA). In total, 36 colonies (18 colonies from each species; 6 samples from each of the three conditions affecting that species), were examined for host transcriptome expression using cDNA microarrays as they passed RNA quality assessments and filtering parameters listed below.

Composition of pooled reference sample and hybridization of cDNA A total of 3 µg of aRNA from each sample was reverse transcribed into cDNA and purified with the RNeasy MinElute Cleanup Kit. Equal amounts of aRNA aliquots comprised the pooled reference for each species. Reference and individual samples were labeled with Cy-3 and Cy-5 dyes, respectively. After fluorescent labeling, we purified samples with the RNeasy MinElute Cleanup Kit to remove any excess dye. The second generation (G2) A. palmata and O. faveolata cDNA microarray slides, referred here on as microarrays, were designed and printed with 13,566 and 10,941 features, respectively, at UC Merced’s Genome Core Facility (Aranda et al., 2011). We post-processed, hybridized, and scanned the microarrays as described in DeSalvo et al. (DeSalvo et al.). For both species, each of the 18 samples across the three health conditions (i.e. H, WB, WPx and H, WPl, YBD) were competitively hybridized against a pooled reference sample. After a 14 hr. hybridization, slides were immediately transferred to individual falcon tubes containing wash buffers (0.6xSSC and 0.01%SDS, followed by 0.06xSSC) to remove non-specific hybridization and unbound excess cDNAs. Slides were scanned using an Axon GenePix 4000B scanner (Molecular Devices, Sunnyvale, CA), and photomultiplier tube (PMT) gain settings were individually adjusted to balance and maximize the dynamic range for both channels. cDNA microarray data analysis Scanned TIFF images were analyzed with GenePix Pro 6.1 (Molecular Devices), with the following steps: 1) find, align, and fill irregular features, and 2) flag features, which match the query: [Circularity] = 0 OR [Sum of Medians (635/532)] ≤ 400 OR [F532 %

58 Sat.] > 5 OR [F635 % Sat.] > 5. GenePix result files were converted into the TIGR MultiExperiment Viewer file format using TIGR ExpressConverter (Version 2.1). Resulting mev and ann files were merged with a personal script. Using TIGR MIDAS 2.22 (Saeed et al., 2003), the raw data were LOWESS normalized, block- and slide standard deviation regularized, and in-slide duplicates were averaged.

Resulting fluorescence data were log2 transformed and filtered using personal script. Expression data were filtered to only include those features for which a minimum threshold of four out of six samples per condition had fluorescence values. We used a Rank Product analysis through the Bioconductor package ‘RankProd’ (Hong et al.) as well as ‘RankProdIt’ (Laing and Smith) for further analysis in R (R Core Team). Log2- transformed values were corrected for multiple testing using a false discovery rate (FDR) less than 0.05 (i.e. an adjusted p <0.05). Pair-wise comparisons were performed on a log2-scale using a fold. Additional visualizations of the data were performed using Venny (Oliveros, 2007) and MeV v4.8.1 (Saeed et al., 2003). Annotations were determined by Blast2GO with blastx against the SwissProt database in addition to manually curated annotations blasted against UniProtKB, HMMER3, as well as the O. faveolata and A. digitifera genome browsers.

3.4 Results Host-transcriptomic response Hybridizations to the corresponding species cDNA arrays resulted in 10,713 contigs on the A. palmata microarray and 9,354 contigs on the O. faveolata microarray that passed the filtering criteria. Filtering removed less than 25% of the features from both species experiments, as 78% of the transcripts spotted on the A. palmata microarray were retained and 85% were retained from the O. faveolata array across conditions. Correction for multiple comparisons (adj. p < 0.05) resulted in 147 significant differentially expressed genes (DEGs) for A. palmata across the three conditions and identified 241 significant DEGs within the three conditions compared with O. faveolata. Less than 50% of these DEGs were functionally annotated in both species, which will be the main focus

59 herein. The results from A. palmata experiment are presented first, followed by the results from O. faveolata experiment.

Differential disease response in A. palmata Of the 147 significantly different genes identified in the A. palmata experiment, 77 were overexpressed in WB when compared to the healthy (H) condition and all 31 significant DEGs were overexpressed in WPx when compared to H; 14 of those DEGs were overexpressed in both diseases. When compared to H, 46 DEGs were underexpressed in WB (Figure 3.2).

Figure 3.2. Venn Diagram compares the overlap between conditions of all significant DEGs that resulted from A. palmata pairwise comparisons between H/WPx and H/WB. Overexpressed DEGs in diseased (WPx>H & WB>H) as well as underexpressed DEGs (WPx

A. B.

Figure 3.3. A.Venn Diagram compares all significant DEGs associated with WB. Comparisons between H/WB and WB/WPx are presented with overexpressed DEGs (WB>H & WB>WPx) and and underexpressed DEGs (WBH & WBWPx).

60 The diseases examined in A. palmata were also compared to one another, which resulted in 10 DEGs overexpressed in WB above WPx, 8 of those overlapped with DEGs overexpressed in WB when compared to H (Figure 3.3A). Additionally, 18 DEGs were underexpressed in WB when compared with WPx, of which 10 were underexpressed in WB and 3 more were overexpressed in WPx in the comparisons to H (Figure 3.3B).

Differentially expressed genes in White Band WB samples had high expression of histone 3.3 (H3.3) compared to H and WPx (Table 3.1). H3.3 (CCHX15801) have been described to exacerbate apoptosis in human lungs contributing to the progression of chronic obstructive pulmonary disease (COPD) (Barrero et al., 2013), a disease that causes extensive lung inflammation and breakdown of lung tissue. Although H3.3 has not been noted as an antimicrobial peptide, many other histones have been described to have antimicrobial properties (Richards et al., 2001; Seo et al., 2013; Zasloff, 2002). Multiple proteins involved with extracellular and skeletal matrix, including two uncharacterized skeletal organic matrix proteins with the closest homology to Acropora millepora, were overexpressed in WB (e.g. fibropellin-1, bone morphogenetic protein 1, cartilage intermediate layer protein 1, and matrix metalloproteinases-25 & -16). CCHX13540, superoxide dismutase (SOD1), was underexpressed in WB. CCHX4450 (collagen alpha-1 chain-like) was low in WB and likely involved with cell wall structure along with other proteins involved in soft tissue structure (e.g. Collagen alpha-1(III) chain, Ankyrin-2, Paired box protein Pax-7). Many of the underexpressed genes in WB had the strongest alignment to proteins from photosynthetic organisms. These include CCHX4450, which strongly aligned to phosphoribosylamine--glycine ligase from coccolithophores (Read et al., 2013); CCHX9485, a nitrate transporter in Symbiodinium (Mayfield et al., 2013); and chloroplast soluble peridinin-chlorophyll a- binding protein precursor (CAWS1086), a light-harvesting protein initially isolated from Symbiodinium (Norris and Miller, 1994). CCHX8962 (purine or other phosphorylase family 1) was an additional underexpressed protein in WB, which appears to be of cyanobacterial origin and related to the broad nucleoside phosphorylase domain; its

Table 3.1. Annotated DEGs from Acropora palmata pairwise-comparisons with Log2 fold change values. (bold values = core response, significant in both H/WB & H/WPl) Table&3.1.&Acropora'palmata&pairwise/comparisons&with&Log2&fold&change&values.!(bold&Log2&value&=&core&response,&sig&in&both&H/WB&&&H/WPl)

Contig&ID Annotation UniProt&ID Log(2)&H/WB Log(2)&H/WPx Log(2)&WB/WPx AOKF900 Collagen!alpha21(III)!chain P02461 1.5 CAFB1445 Bone!morphogenetic!protein!1!homolog P98069 21.6 CAFC695 Leucine2rich!repeat2containing!protein!15 Q80X72 21.7 CAFF451 Cartilage!intermediate!layer!protein!1!(CILP21) Q66K08 21.5 CAOH1059 Superoxide!dismutase Q8HXQ3 22.3 CAOH570 Penicillin!acylase P12256 1.4 CAOH837 Citrate!lyase!subunit!beta2like!protein Q5I0K3 21.8 CAOI1971 UPF0740!protein!C1orf192!homolog Q3KPS4 21.5 CAOI725 Transient!receptor!potential!cation!channel!subfamily!A!member!1 Q6RI86 /1.9 /2.0 CAWS1086 Peridinin2chlorophyll!a2binding!protein!1 P80484 2.4 22.3 CAWS1414 Ankyrin22! Q01484 1.7 21.6 CAWS424 dinoflagellate!viral!nucleoprotein 1.7 22.1 CAWS941 Phosphoenolpyruvate!synthase! O83026 1.9 CCHX10566 Protein!Wnt28a P51028 21.7 CCHX10941 Protein!CBFA2T1 Q61909 21.7 CCHX11164 Neurexin21 Q63372 21.6 CCHX11203 Hemicentin21 Q96RW7 21.3 CCHX11406 MAM!and!LDL2receptor!class!A!domain2containing!protein!2 B3EWZ6 1.4 CCHX11579 Zinc!transporter!ZupT Q8ENQ1 1.5 CCHX11626 Dermatopontin!(Tyrosine2rich!acidic!matrix!protein) P83553 1.9 CCHX12475 Retinol!dehydrogenase!8! Q9N126 21.7 CCHX12601 Calcineurin!subunit!B!type!1 P63100 22.3 CCHX12647 97!kDa!heat!shock!protein Q06068 21.7 CCHX12724 UPAR_LY6!:!phospholipase!A2!inhibition /2.7 /3.6 CCHX13540 Superoxide!dismutase![Cu2Zn] O59924 1.8 CCHX13722 Stromelysin23 Q499S5 1.8 CCHX13897 Urea2proton!symporter!DUR3 F4KD71 1.6 CCHX14161 Matrix!metalloproteinase225!(MMP225) Q3U435 21.5 CCHX14431 small!cysteine2rich!protein!1a/3 22.1 2.0 CCHX14534 BRICHOS!:!hypothetical!protein!BRAFLDRAFT_98084 /2.1 /3.3 CCHX14588 Acidic!skeletal!organic!matrix!protein B3EWY7 1.5 CCHX15011 Collagen!alpha!chain B8V7R6 21.9 CCHX15247 Netrin!receptor!UNC5C O95185 1.6 CCHX15801 Histone!H3.3 P84245 22.1 CCHX16168 substance2k!receptor P47937 22.3 CCHX16831 Paired!box!protein!Pax27 P23759 1.3 CCHX17248 Nucleolar!protein!10 Q7T0Q5 21.9 CCHX1796 Uncharacterized!skeletal!organic!matrix!protein!1 B3EX00 21.5 CCHX1874 Coiled2coil!domain2containing!protein!135 Q0V9S9 22.1 CCHX2352 Olfactomedin2like!protein!2A Q8BHP7 21.8 CCHX2595 Dehydrogenase/reductase!SDR!family!member!7 Q9Y394 21.4 CCHX3746 Metalloproteinase!inhibitor!1 O02722 21.4 CCHX3751 Matrix!metalloproteinase216! P51512 21.4 CCHX4392 Argininosuccinate!lyase!2 Q981V0 2.3 22.6 CCHX4450 collagen!alpha21!chain2like 2.1 22.8 CCHX5194 Hemagglutinin/amebocyte!aggregation!factor!(18K2LAF) Q01528 /2.4 /1.6 CCHX5557 Fibropellin21 P10079 22.3 CCHX6643 Neurogenic!locus!notch!homolog!protein!1 A2RUV0 21.7 CCHX7131 Uncharacterized!skeletal!organic!matrix!protein!1 B3EX00 21.7 CCHX8167 Uromodulin! P07911 1.4 CCHX8962 purine!or!other!phosphorylase!family!1 2.1 CCHX9485 nitrate!transporter 2.1 21.9 CCHX9830 Nanos!homolog!1 E7FDB3 1.5

62 function however is unclear. Additionally, CCH4392, argininosuccinate lyase 2, found in Rhizobium, and a dinoflagellate viral nucleoprotein (CAWS424) were down in WB.

Differentially expressed genes in White Pox Although only 31 DEGs resulted in the WPx comparison against healthy A. palmata and only 10 of which were annotated, all were overexpressed (Table 3.1). SOD1 (CAOH1059) was only overexpressed in WPx. As part of the oxygen handing pathways, SODs convert reactive oxygen species (ROS) back into oxygen. When ROS is in high concentrations, SOD becomes overloaded and apoptosis results (Weis, 2008). Oxidative stress is evident in the WPx samples as SOD1 was expressed 2.3-fold more than in H. CCHX16168, substance-k receptor or neurokinin A, is strongly overexpressed in WPx samples. Neurokinin A, a tachykinin peptide in vertebrates with invertebrate isoforms, is involved in inflammatory response, mucus production, and a chemo attractant for infected tissues (Amatya et al., 2011; Douglas and Leeman, 2010; Loy et al., 2010; Schäffer and Gabriel, 2005). Additional proteins involved with signal transduction (e.g. Retinol dehydrogenase 8 and Nuerexin-1) were also high in WPx. Transcripts coding for calcineurin subunit B (CnB) type 1 (CCHX12601) were higher in WPx, but low in WB samples. CnB proteins protect against infection by inducing an inflammatory and activating innate immune cells (e.g. macrophages) in mice (Gaidos et al., 2011; Stat et al., 2012).

Genes shared in A. palmata response to diseases Common genes between the WB and WPx when compared to H were all overexpressed (Table 3.1). CCHX12724 strongly aligned with phospholipase A2 inhibition (PLA2_inh) isozymes and the u-PAR/LY-6 domain. PLA2 has been classically associated with snake and insect venom (Fortes-Dias et al., 1994), but it has been more recently been found in cnidarians, including A. palmata and O. faveolata (Nevalainen et al., 2004; Nevalainen et al., 2013). PLA2 plays a vital role in downstream inflammatory responses (Leslie, 1997) and although present on both of our species arrays, CCHW7478 and CCHX1604, only the inhibitor (PLA2_inh) had significant differential expression.

63 Hemagglutinin/amebocyte aggregation factor-like (CCHX5194) is an innate immune response overexpressed in WB and WPx, which was initially isolated in the horseshoe crab Limulus polyphemus (Fujii et al., 1992) and found in invertebrates. This protein is part of the Dermatopontin family, which also includes Millepora cytotoxin-1, a proteinaceous toxin recently isolated from the nematocysts of fire coral, Millepora dichotoma (Iguchi et al., 2008).

Differential disease response in O. faveolata Of 241 significant DEGs identified in the O. faveolata pairwise comparisons, 72 DEGs were overexpressed in WPl and 63 DEGs were overexpressed in YBD when compared to H samples (Figure 3.4). Of those DEGs, 12 were overexpressed in both WPl and YBD. Additionally, in WPl and YBD 41 DEGs were significantly underexpressed when compared to H; only 15 of which were shared.

Figure 3.4. Venn Diagram compares the overlap between conditions of all significant DEGs that resulted from the O. faveolata pairwise comparisons between H/YBD and H/WPl. Overexpressed DEGs in diseased (YBD>H & WPl>H) and underexpressed DEGs (YBD

When disease conditions in O. faveolata were compared, 73 DEGs were overexpressed in WPl relative to YBD (Figure 3.5A). Of those, 41 genes were overexpressed in WPl when compared with H. Furthermore, of those 73 DEGs, 5 DEGs were overexpressed and 10 were underexpressed in YBD when compared with H. An additional 65 genes were overexpressed in YBD when compared with WPl, 31 of which

64 were also overexpressed in YBD and 5 were underexpressed in WPl when compared to H (Figure 3.5B).

A. B.

Figure 3.5. A. Venn Diagram compares all significant DEGs associated with WPl. Comparisons between H/WPl and WPl/YBD are presented with overexpressed DEGs (WPl>H & WPl>YBD) and underexpressed DEGs (WPlH & WPlYBD).

Differentially expressed genes in White Plague Of the significant DEGs identified when WPl was compared to H, the most overexpressed gene in WPl was CCHW5464, a lymphocyte antigen 6e-like protein (logFC = 5) (Table 3.2). Another (CCHW10491) is also overexpressed in WPl. This antigen-like protein is part of the u-PAR/LY-6 domain and a family of cell surface proteins in vertebrates (Classon and Coverdale, 1994), but has not been described in cnidarians. Additionally, many metal and calcium ion-binding cofactors were overexpressed in WPl, such as zinc: finger domain protein (CCHW12950), Betaine— homocysteine S-methyltransferase 1 (CCHW11546); iron: cytochrome p450 1a1 (CCHW5385); and calcium: pentraxin (CCHW9567), neuropilin-2 (CCHW10207), calumenin (CCHW4261), and CCHW3008. WPl also had increased expression of galaxin-2 (CCHW1988), which is involved with calcification of coral skeleton.

Table 3.2. Annotated DEGs from Orbicella faveolata pairwise-comparisons with Log2 65 foldTable&3.2.& changeOrbicella)faveolata values&pairwise/comparisons&with&Log2&fold&change&values.. (bold values = core response,!(bold&Log2&value&=&core&response,&sig&in&both&H/WPl&&&H/YBD significant in both H/WPl & H/YBD))

Contig&ID Annotation UniProt&ID Log(2)&H/WPl Log(2)&H/YBD Log2&WPl/YBD AOSF997 Peroxidasin3like!protein A1KZ92 /4.6 /1.5 3.2 CAGI2444 dynein!heavy!chain!axonemal3like 33.2 33.4 CCHW10045 Filamin3A! P21333 2.1 CCHW10129 CUB!and!peptidase!domain3containing!protein!1 B8V7S0 1.6 CCHW10207 Neuropilin32 O60462 31.8 2.3 CCHW1045 CUB!and!peptidase!domain3containing!protein!1 B8V7S0 1.7 2.6 CCHW10491 Lymphocyte!antigen!6!complex!locus!protein Q9Z1Q3 32.3 2.2 CCHW10590 Suppressor!of!cytokine!signaling!3 Q90X67 31.5 CCHW10610 Chymotrypsin3C Q3SYP2 1.6 CCHW10731 Trypsin!:!Shk P00767 2.8 2.5 CCHW10805 DBH3like!monooxygenase!protein!2 Q08CS6 /2.9 /1.6 1.3 CCHW11111 Lipase!ZK262.3 Q9XTR8 1.9 31.8 CCHW11307 Fibrinogen!beta!and!gamma!chains!:!Lectin_C Q8R1Q3 2.2 CCHW11441 50!kDa!hatching!enzyme P91953 1.7 2.4 CCHW11446 Peridinin3chlorophyll!a3binding!protein P51874 1.8 1.4 CCHW11537 Insoluble!matrix!shell!protein!1 P86982 31.0 CCHW11546 Betaine33homocysteine!S3methyltransferase!1 Q95332 31.5 CCHW11562 Acidic!mammalian!chitinase! Q9BZP6 2.1 CCHW11603 WSC!:!EGF_CA!chitinase 2.2 CCHW11939 WSC /3.6 /1.8 1.7 CCHW11954 Protein!DD333 Q58A42 1.5 CCHW12161 Sushi,!von!Willebrand!factor!type!A,!EGF!and!pentraxin!domain3containing!protein!1 Q4LDE5 2.0 1.6 CCHW12258 Structural!polyprotein!(p130) P27284 1.7 CCHW12950 Zinc!finger!CCCH!domain3containing!protein!14 Q8BJ05 33.7 2.9 CCHW13161 Caroteno3chlorophyll!a3c3binding!protein P55738 1.5 1.3 CCHW13177 Zinc!metalloproteinase!nas315! P55115 1.7 CCHW13479 Uncharacterized!skeletal!organic!matrix!protein!5 B8VIU6 1.7 CCHW13568 Pancreatic!secretory!granule!membrane!major!glycoprotein!GP2 P25291 31.7 1.9 CCHW13733 Leukotriene3B4!omega3hydroxylase!3 Q9EP75 31.7 CCHW15541 UPF0577!protein!KIAA13243like Q3UZV7 1.7 1.5 CCHW16256 Lymphotoxin3alpha!(Tumor!necrosis!factor!ligand!superfamily!member!1) Q06600 1.6 CCHW16303 Cubilin Q9TU53 31.6 CCHW16328 UDP3glucuronosyltransferase!3A2! Q8JZZ0 1.4 1.6 CCHW16409 sam3dependent!methyltransferase 32.7 1.7 CCHW16471 Insoluble!matrix!shell!protein!1!(IMSP1) P86982 31.3 CCHW16949 Basement!membrane3specific!heparan!sulfate!proteoglycan!core!protein!(HSPG) Q05793 32.2 32.0 CCHW17413 Brevican!core!protein Q28062 31.7 CCHW1811 Hydrogenase!maturation!factor!HoxX P31907 31.8 CCHW1988 Galaxin32 B8UU51 31.7 CCHW2121 Transmembrane!protein!144 Q8VEH0 2.4 2.4 CCHW2317 Variable!charge!X3linked!protein!1 Q9H320 31.7 CCHW2363 Angiotensin3converting!enzyme Q10751 1.9 CCHW2489 Failed!axon!connections!homolog D3ZAT9 31.9 CCHW2508 Cyclo(L3leucyl3L3leucyl)!synthase!(Cyclodileucine!synthase) Q65EX3 32.9 31.6 CCHW2692 fibroblast!growth!factor!receptor!homolog!23like 32.1 32.3 CCHW2713 Peroxiredoxin36 Q5ZJF4 31.3 1.3 CCHW2822 Ketol3acid!reductoisomerase Q3IE45 1.5 CCHW3008 Uncharacterized!calcium3binding!protein O81916 31.8 1.5 CCHW3020 Insoluble!matrix!shell!protein!1 P86982 2.0 CCHW3643 Integrin!alpha3X!(CD11!antigen3like!family!member!C) Q9QXH4 1.7 CCHW4261 Calumenin Q28BT4 31.8 CCHW5191 Ubiquinone/menaquinone!biosynthesis!C3methyltransferase!UbiE A7MTX1 32.3 1.7 CCHW5310 Tetratricopeptide!repeat!protein!28 Q96AY4 31.7 CCHW5385 cytochrome!p450!1a1 32.9 1.9 CCHW5464 lymphocyte!antigen!6e3like /5.0 /1.8 3.2 CCHW6092 ETS3related!transcription!factor!Elf33 P78545 31.6 CCHW6675 equinatoxin35 1.8 2.1 CCHW6741 Hydrogenase!maturation!factor!HoxX P31907 32.7 CCHW6901 High!affinity!nitrate!transporter!2.5 Q9LPV5 31.8 CCHW6973 Collagen!alpha31(III)!chain P12105 1.6 CCHW7002 lectin 2.5 1.7 CCHW7237 Collagenase!3 O18927 1.7 2.2 CCHW7521 leucine3rich!repeat3containing!protein!73 /2.1 /2.2 CCHW7560 Hydrogenase!maturation!factor!HoxX P31907 32.2 CCHW7706 Gamma3interferon3inducible!lysosomal!thiol!reductase A6QPN6 1.7 CCHW7708 small!cysteine3rich!protein!1 2.4 2.5 CCHW8141 Nitric!oxide!synthase,!inducible Q06518 31.6 CCHW8414 Nuclear!pore!complex!protein!Nup205 Q92621 31.4 CCHW8750 Neuropilin32! O60462 32.0 2.0 CCHW8752 hypothetical!protein!ATCVCan0610SP_205L 32.1 31.6 CCHW9383 Glucose363phosphate!isomerase Q3ZBD7 31.5 CCHW9567 von!Willebrand!factor!type!egf!and!pentraxin!domain3containing!protein!1 Q99J85 32.8 3.1 CCHW974 RING!finger!protein!32 Q4R5T4 /1.8 /2.4 66 CCHW5191 was overexpressed in WPl and had the best alignment to a methyltransferase from Vibrio campbellii. Underexpressed genes in WPl, included (CCHW7002) that strongly aligned with NnL, a novel lectin from the jellyfish, Nemopilema nomurai (Imamichi and Yokoyama, 2010). NnL has a fibrinogen-like domain and is predicted to agglutinate bacteria. Other lectins, such as mannan-binding lectins can mediate attachment and binding of both bacteria and viruses (Fraser et al., 1998). They are regarded as a first line of defense and necessary for innate immunity (Hamid et al., 2013; Worthley et al., 2005). CCHW11307, underexpressed in WPl, aligned to an additional fibrinogen-containing c-type lectin. The putative chitinase, CCHW11603, underexpressed in WPl lends to the idea that chitin regulation is reduced in WPl corals, which could lead to the increase in chitinivorous bacteria such as Vibrionaceae (Hunt et al., 2008). Gamma-interferon-inducible lysosomal thiol reductase (CCHW7706), which was underexpressed in WPl, plays a role in antigen processing as it is involved in protein lysosomal degradation. The reduced expression of zinc metalloproteinase nas-15, could reduce toxin production in WPl samples. Underexpression of lymphotoxin, CCHW16256, would potentially reduce the response to uncontrolled cell growth, including symbionts.

Differentially expressed genes in Yellow Band When YBD was compared to healthy O. faveolata samples, one of the most overexpressed genes in YBD was a CAGI2444, a gene from the dynein heavy chain domain-1 (Table 3.2). CCW2508 was overexpressed in YBD and aligned with hypothetical proteins from N. vectensis, but also with many hypothetical bacterial proteins, including one from Bacillus licheniformis cyclodileucine synthases, which produces cyclodileucine in bacteria (Bonnefond et al., 2011). Additionally, three hydrogenase maturation factors (CCHW6741, CCHW7560, CCHW1811) were overexpressed in YBD and align to sequences from the bacterium Bradyrhizobium diazoefficiens. YBD samples had high expression of the basement membrane-specific heparan sulfate proteoglycan core protein, hspg2 (CCHW16949). Hspg2 requires calcium binding and regulates endothelial growth. CCHW2692, fibroblast growth factor receptor

67 homolog 2-like (FGFR2), was overexpressed in YBD compared to both H and WPl. FGFR2 is involved in regeneration of tissue and has been noted in planarians as well as other invertebrates (Ogawa et al., 2002). CCHW8752, ATCVCan0610SP_205L, is a hypothetical protein that aligns to a Chlorella virus. This gene is overexpressed 2.1- logfold higher in YBD than H. This strong differential expression may suggest potential viral involvement in YBD. Overexpression of leukotriene-b4 omega-hydroxylase 3 (CCHW13733) in YBD, part of the heme group, suggests involvement in oxidoreductase, as hemes help degrade hydrogen peroxide that is harmful to cells (Hogle et al., 2014). Calcium binding was high in YBD via Cubilin (CCHW16303) along with other calcium- binding factors, which were also high in WPl. YBD samples had low expression of the transcripts coding for transmembrane proteins 144 and UPF0577 protein KIAA1324-like (CCHW2121 and CCHW15541). Underexpression of collagenase 3 (CCHW7237) in YBD, which binds both calcium and zinc, would likely reduce wound healing and degradation of extracellular matrix proteins. The reduced expression of 50 kDa hatching enzyme (CCHW11441), which is involved with hydrolysis of proteins, would retard protein recycling. YBD had reduced UDP- glucuronosyltransferase 3A2 as well, which would normally enhance toxin excretion. Caroteno-chlorophyll a-c-binding protein (CCHW13161) from dinoflagellates was underexpressed in YBD along with ketol-acid reductoisomerase (CHWW2822), which aligns to a Pseudoalteromonas haloplanktis strain.

Genes shared in O. faveolata response to diseases Cbg17660 (AOSF997) was overexpressed in both WPl and YBD (Table 3.2). Cbg17660 is a part of the animal heme peroxidase superfamily, which has oxygen-dependent microbicidal and immunological roles. Heme peroxidases have been isolated from corals and are part of a fusion protein that forms epoxide, an allene oxide (Koljak et al., 1997). The overexpression of zinc ion binding ring finger protein (CCHW974), DBH-like monooxygenase protein 2 (CCHW10805) which is involved with copper-binding and a peroxidasin-like protein (AOSF997) involved with calcium and heme are all metal binding factors that were shared across WPl and YBD. Leucine-rich repeat-containing

68 protein 73 (LRRC73), CCHW7521, was overexpressed in both WPl and YBD samples when compared to healthy O. faveolata. LRRs are found across all domains of life, and while broad in function, many have immune or innate immune functions. They have been identified in Nematostella vectensis and more recently a related family, nucleotide- binding domain and leucine-rich repeat containing (NLR) proteins, and have been described in the sponge Amphimedon queenslandica (Reitzel et al., 2008; Yuen et al., 2014). NLR have been found only in Metazoans and are involved in both defense from pathogenic microbes and in interactions with beneficial symbionts (Yuen et al., 2014). Additionally, two genes, CCHW10731 and CCHW7708, were underexpressed in both WPl and YBD compared to H samples. CCHW10731, Stichodactyla toxin (Shk), was first discovered in the Caribbean sea anemone Stichodactyla helianthus and was the most underexpressed gene in the WPl and YBD samples. CCHW7708, small cysteine- rich protein 1 (SCRiP1), which contains a β-defensin-like motif, was also underexpressed in O. faveolata diseased samples. Similarly, equinatoxin-5 (CHW6675), had reduced expression in WPl and YBD. This toxin was first found in the anemone, Actinia equina (Pungercar et al., 1997) and is a known pore-forming protein that functions as part of its defense repertoire. Peridinin-chlorophyll a-binding protein (CCHW11446) from Symbiodinium, was also low in both WPl and YBD when compared to H, likely a result of the Symbiodinium in these diseases.

3.5 Discussion Strong core and differential host response to disease This study is the first coral multi-disease transcriptomic comparison, which reveals the complexities and the core response corals express when challenged by various disease dynamics. Approximately one-third of the significant features had known functional annotations in both species, suggesting potentially novel functions and emphasizes the gap that exists in functional annotations of non-model species. Those that were annotated, or closely aligned with known functional groups, allowed us to determine both significant functional differences and similarities in A. palmata and O. faveolata. An apparent core response to disease was noted in each species, as a few captured DEGs were consistently

69 expressed in diseased samples when compared to corresponding healthy samples. These genes were some of the most differentially expressed genes in A. palmata when WB and WPx were compared to H, ranging from 1.6-3.6 log2FC (Table 3.1). In O. faveolata, there were more shared genes with log2FC as high as 4.9 (Table 3.2). Many of these “core responders” have implied immune-related functions, such as those associated with the u-PAR/LY-6 family. In all four diseases there were overexpressed members of u-PAR/LY-6 family, such as PLA2-inhibitor in WB & WPx as well as lymphocyte antigen 6e-like in WPl & YBD. Leucine-rich repeats were also overexpressed in YBD, WPl, and WB. An additional example of core response is SCRiP1. This protein family was first described in O. faveolata, in addition to other scleractinian corals, and has a variety of proposed functions including stress-response as well as development (Sunagawa et al., 2009). SCRiP1 is the only described SCRiP to have a β- defensin-like motif, which was strongly underexpressed in both WPl and YBD. However in A. palmata, SCRiP1a, which has no known function (Grasso et al., 2011; Sunagawa et al., 2009), but closely aligns with SCRiP3, was expressed 2-fold in WB samples when compared to both H and WPx. While the majority of the transcripts are coral in origin, some of the holobiont’s other members also make up a small portion of the features that were significantly different in this study. These differences provided a more complete picture of the dynamics of these complex systems.

A. palmata functional responses to disease compared to healthy In both WB and WPx, genes that involved inflammatory responses, calcineurin subunit B (CnB) type 1 (CCHX12601) and substance-k receptor or neurokinin A (CCHX16168) were underexpressed compared to H. PLA2_inh has been shown to be a part of the PAR/LY-6 family (Tirosh et al., 2013), which contains glycolipid-anchored membrane proteins as well as antigens (e.g. CD59) (Rooney and Morgan, 1992). Overexpressed PLA_inh, in both WB and WPx, likely causes the venom in their stinging cells to become less effective in causing pain to adversaries or prey and could be part of immune regulatory pathways. Additional immune-related responses, such as Hemagglutinin/amebocyte aggregation factor-like (CCHX5194) and Histone 3.3

70 (CCHX15801) were overexpressed in WB. The multiple proteins involved with extracellular and skeletal matrix overexpressed in WB corroborate with the findings of Libro and colleagues (2013), where DEGs involved with biomineralization and skeletal growth were overexpressed in WB samples. In WPx, oxidative stress was evident as superoxide dismutase (SOD) (CAOH1059) was 2.3-fold higher than in H, highlighting the involvement of various biological processes in the coral defense response. When compared with WB, WPx had higher expression of CAWS424, a dinoflagellate viral nucleoprotein. This novel protein, of algal-virus origin, functions as a substitute for histones in most dinoflagellates (Gornik et al., 2012). Differences, especially in likely Symbiodinium genes, were noted between the two diseases. All of the significant dinoflagellate-related genes noted (i.e. CAWS1086, CAWS424, CCHX4392, CCHX9485) were underexpressed in WB, while they were relatively overexpressed in WPx and H. This suggests there are significant differences in Symbiodinium involvement or activity in these disease samples, but more sampling would be required to verify true differences in Symbiodinium between the dynamics of these diseases.

O. faveolata functional responses to disease compared to healthy Results from the O. faveolata comparisons suggest more bacterial-related genes were expressed in YBD. Additional proteins, such as leucine-rich repeat-containing protein 73, LRRC73, (CCHW7521), which allows the host to recognize symbionts from pathogens, were overexpressed in both WPl and YBD. However, β-defensin-like SCRiP1, had reduced expression in both diseases, which mirrors findings in YBD from Closek and colleagues (2014). Chitinase (CCHW11603), regarded for fungal control, was also underexpressed in WPl. Additionally, NnL (CCHW7002) and c-type lectin (CCHW11307), which bind bacteria and viruses, were underexpressed in WPl. While ATCVCan0610SP_205L (CCHW8752), a gene of viral origin was overexpressed in both diseases, it was 2-fold higher in YBD. Suggesting that large DNA viruses, such as the Chlorella virus, may be involved in YBD, as was recently noted in WPl (Soffer et al., 2013).

71 Increased expression of heme peroxidase in YBD have also been observed in WB, octocoral wounding, and in soft corals when exposed to organic pollutants (Libro et al., 2013; Lõhelaid et al., 2014; Woo et al., 2014). Cytochrome p450 1a1 (CYP1A1), CCHW5385, was also significantly overexpressed in WPl. CYP1-like genes have been characterized in the blue mussel, Mytilus edulis and the CYP1A1 gene has involved in detoxification of environmental carcinogens (Zanette et al., 2013). These findings corroborate previous studies where increased diseased events occur in high nutrients and pollutants (Bruno et al., 2003; Dinsdale et al., 2008; Kelly et al., 2014).

Insight from multi-disease cross-species comparisons Our findings illustrate that all four diseases impact their host species differently. While certain transcripts are shared, and can therefore be considered disease or stress indicators, it is evident the diseases do not affect their host in the same ways. One common trend across all diseases was the impact to chemical defense strategies. Both WB and WPx in A. palmata had PLA2_inh strongly expressed, which would inhibit the production of effective venom, in this case toxins from the coral host, and render the coral more susceptible to predation. Similarly, in both WPl and YBD the Shk toxin was underexpressed, which would reduce defense effectiveness or perhaps impair the polyps’ defense mechanism, cnidocytes or stinging cells. Additionally, the coral holobiont is comprised of many microbial members, however the significant differences found between diseases and coral species highlight the imbalance of these microbial symbionts compared to healthy samples. These findings suggest more involvement of microbes, particularly viruses, than has been previously reported in these diseases. While perhaps an artifact of our comparisons, it is interesting to note that the expression profiles of significant genes in healthy A. palmata and WB are more similar than WPx. The same was also true in O. faveolata, where healthy was more similar to YBD than WPl. The speed at which WB and YBD cause mortality on the corals they affect are relatively slower than their counterparts, which may impact the magnitude of what is captured in transcriptomic studies and lead to this result. It is also important to note that the two species arrays have fixed features and not all features overlap between

72 arrays. Therefore omission of a gene in a species from this study is likely due to the limitation of the platform or more likely due to the lack of gene annotations for these species and should not be interpreted as insignificant for a disease.

Conclusions These results do not imply causation, however they do highlight the distinct differences in the conditions sampled. The host and its associated microbial communities are constantly contending with a suite of factors, and these results illustrate that the disruption to this symbiosis and the response we measured is not the same across species nor across diseases affecting the same species. As the first study to compare the host response to multiple coral diseases, it is evident that while gene expression profiles change, they do not change significantly. Only a small portion of the gene expression profiles examined changed reliably when compared across conditions. Across species as well as within species there appears to be a core response to disease. Both those core responses and the individual differences should be taken into account for future studies.

3.6 Acknowledgements We would like to thank the University of the Virgin Islands for providing research facilities, accommodations, and field assistance. Thank you to Anke Kleuter and Erick Archer for preparing the R scripts used in this study. This study was conducted under Virgin Islands Department of Planning and Natural Resources permit # STT-036-11. Funding for this project was provided in part by the National Geographic Young Explorer Grant 8839-10.

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77 CHAPTER 4:

BACTERIAL CENSUS OF THE FRENCH FRIGATE SHOALS

Manuscript entitled: “A Bacterial Census of the French Frigate Shoals, Papahānaumokuākea (Northwestern Hawaiian Islands) Marine National Monument” in prep

Authors: Collin J. Closek1, Linda A. Amaral-Zettler2,3, and Mónica Medina1

Affiliations: 1. Pennsylvania State University, University Park, PA, USA 2. Marine Biological Laboratory, Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Woods Hole, MA, USA 3. Brown University, Department of Earth, Environment and Planetary Sciences, Providence, RI, USA

78 4.1 Abstract The French Frigate Shoals (FFS), the largest atoll in the Northwestern Hawaiian Islands (NWHI) located 902 km northwest of Honolulu, O’ahu, is arguably one of the healthiest and least disturbed coral reef environments on Earth. Only a few locations around the world are as protected or reflect what reef conditions were like 30-50 years ago. In 2006, the Census of Marine Life, lead by the Census for Coral Reef Ecosystems (CReefs) ocean realm project and the National Oceanic and Atmospheric Administration (NOAA), conducted a 20-day cruise to initiate the first multi-taxon census of the recently designated Papahānaumokuākea Marine National Monument in the NWHI. The census surveyed all three domains of life. Over 2,000 unique reef plants/animals were surveyed from the FFS atoll and, for the first time, both seawater and reef sediment samples were collected to assess bacterial diversity associated with this isolated reef environment. We examined these samples by high-throughput sequencing of the V6 hypervariable region of the 16S ribosomal RNA (rRNA) gene to acquire a bacterial baseline for health in NWHI. Across replicates in both seawater and sediment samples, the abundant bacterial community structure was less variable within sample types than between sample types. Alphaproteobacteria (e.g. SAR 11), Cyanobacteria (e.g. Prochlorococcus and Synechococcus), and Deferribacteres were most abundant in seawater. Gammaproteobacteria (e.g. Sinobacteraceae), Actinobacteria, and Deltaproteobacteria were most abundant in the sediment samples. We used operational taxonomic units (OTUs) as a measure of taxonomic richness. Only 3.75% of the OTUs overlapped between the two sample types. Sediment samples displayed greater richness than the water column. These results support the idea that sediment and water samples harbor different bacterial communities and that samples collected during this expedition are unique. Samples were also compared to samples from other central Pacific island locations, O’ahu, Moorea, and the Northern Line Islands. Large differences in bacterial community taxa were detected between FFS and developed islands, O’ahu, Kiritimati, and Tabuaeran. The bacterial community composition of FFS reflected that of Moorea and of undeveloped atolls, Kingman and Palmyra. The results presented herein constitute

79 a baseline for healthy reef-associated bacterial communities and underscores the value of comparative microbial assessments for environmental studies.

80 4.2 Introduction The Census of Marine Life was a scientific initiative to assess the diversity, abundance, and distribution of life in the world’s ocean. Lasting from 2000-2010, this 10-year, multinational survey is the most comprehensive effort to assess marine life to date, providing an unparalleled baseline for future marine studies (O'dor et al., 2010). The Census effort included 540 expeditions and involved more than 2,700 scientists globally. Results of this research, excluding microbes, confirmed approximately 250,000 previously formally described marine species, including 1,200 formally described species new to science, and discovered more than 5,000 species still to be formally described (Costello et al., 2010). As a part of this initiative, the Census of Coral Reef Ecosystems (CReefs) was organized in 2004 to conduct an unprecedented global census of coral reef ecosystems. The CReefs 5-year field project aimed to assess the biodiversity associated with tropical coral reefs through molecular analyses and standardized sampling methods (Knowlton et al., 2010). Such baseline information was gathered with the ultimate goal of assisting in determining the vulnerability of coral reefs to anthropogenic stressors (Knowlton et al., 2010). In 2006, CReefs and partner scientists conducted the first survey of the biodiversity in the French Frigate Shoals, Papahānaumokuākea (PIFSC, 2007). Papahānaumokuākea comprises the Northwestern Hawaiian Islands (NWHI), which are separated by 250 km of open-ocean from the main Hawaiian Islands. The NWHI extend for 2,000 km, encompassing 362,073 km2. On June 26, 2006 these islands were established as a protected area under legislation of the United States government with no commercial fishing allowed since June 2011. As a result the NWHI were designated as the Papahānaumokuākea Marine National Monument, the most isolated group of islands on earth, roughly 3,000 km from the nearest continent (Fautin et al., 2010). The monument is larger than all of the United States National Parks combined and arguably one of the largest protected marine areas. Largely uninhabited by human settlements, the NWHI are among the few remaining marine systems to have minimal human impact. CReefs selected the French Frigate Shoals (FFS), the largest atoll of the Hawaiian Islands, as the NWHI area to be surveyed. The FFS lie 1,330 km northwest of Honolulu; made up of 12 sparse sandbars and La Perouse in the center of the lagoon, a

81 37m high outcropping - the oldest volcanic rock in the Hawaiian Islands. The 32 km long, 29 km wide crescent-shaped, open atoll is made up of 0.25 km2 of land, while the total coral reef area covers more than 938 km2. Fautin and colleagues (2010) have reviewed previous surveys of the NWHI (Edmondson, 1933; Maragos and Gulko, 2002; Vroom et al., 2006). However, the CReefs expedition was the first census of the monument and the first multi-taxon census from all domains of life in the NWHI. The CReefs survey of the FFS was conducted from October 8-28, 2006 under the National Oceanic and Atmospheric Administration (NOAA) aboard the R/V Oscar Elton Sette. The team, comprised of 20 scientists from 12 institutions, used 14 different sampling methods across 13 habitats. This effort discovered over 100 species new to science, possibly more, still to be formally described (Knowlton et al., 2010). Over 4,100 samples were collected for molecular analysis, 2,100 unique morphospecies were noted during the cruise, and 40,000 photographs were taken to document these samplings (Knowlton et al., 2010). Surveys confirmed that the FFS has the highest coral diversity in the NWHI and the second highest number of fish species next to the Pearl and Hermes Atoll (Friedlander et al., 2009; Maragos and Gulko, 2002). The expedition was also the first implementation of the Autonomous Reef Monitoring Structures (ARMS) project (Plaisance et al., 2011). A total of 12 ARMS were deployed around the lagoon, fore reefs, and back reefs. Recovery of these ARMS in 2007 showed prototypes were successful in sampling invertebrates: mollusks (28%), ascidians (24%), crustaceans (19%), and bryozoans (11%) (Knowlton et al., 2010). Some additional notable findings from the CReefs 2006 expedition: 1) the first report of the hermit crab, Calcinus isabellae (Godwin and Baums, 2008), previously thought to only exist in French Polynesia; 2) two non-native solitary tunicates, Cnemidocarpa irene and Polycarpa aurita (Godwin and Baums, 2008); 3) six species of octopus, three new to science (Knowlton et al., 2010); 4) 66 species of stony coral, 27 likely endemic, 1 undescribed species only seen in NWHI (Friedlander et al., 2009). Further findings of the expedition can be found in the Final Cruise Report (PIFSC, 2007) and are reviewed in Friedlander et al. (2009). In addition to assessing the macro-organismal reef diversity, abundance, and

82 distribution, CReefs also examined the microbial community structures of the FFS. The vast bacterial and archaeal diversity associated with reefs have mostly been examined in the contexts of symbiosis and health states of corals and sponges (Beman et al., 2007; Hentschel et al., 2002; Ritchie, 2006; Rohwer et al., 2002; Sunagawa et al., 2010; Taylor et al., 2013). Less emphasis has been placed on the surrounding environment (i.e. seawater and sediment). Although the water column community in reef systems has received attention (Dinsdale et al., 2008; Garren et al., 2008), it is normally in contrast to corals or sponges and the minimal microbial overlap with the surrounding water, rather than compared to sediment (Polónia et al., 2014; Rohwer et al., 2002; Sunagawa et al., 2010). Bacterioplankton in Hawaiian surface waters have been described as relatively stable, without seasonal fluctuation (Giovannoni and Vergin, 2012; Shi et al., 2011). Heterotrophic microbes in carbonate reef skeletal structure and sediments are important in the remineralization of organic matter and crucial for nutrient recycling in oligotrophic tropical seas (Rasheed et al., 2002; Rasheed et al.; Schöttner et al., 2011; Wild et al., 2005). Sediments act as highly permeable structures that filter particulate and dissolved organic matter derived from living or dead biomass (Beer et al., 2005). As such, together, water and sediments can be examined for microbial community structures as proxies for health of the local environment (Halliday et al., 2014). In an effort to profile the microbial diversity of the NWHI, CReefs collected samples from both water column and sediment at various points and depths around the FFS. The goals of this study were to characterize the bacterial community structure associated with surface seawater and sediment from the FFS reef environments. Additionally, we aimed to compare how these community profiles contrasted with other central Pacific studies. Comparing the bacterial community associated with water column depths to the bacterial composition associated with sediments at various sampling points around the FFS demonstrates the distinctness of the sediment- and water-associated communities there. This project generated the first microbial community profiles associated with the FFS and the first bacterial abundance measurements of one the healthiest reef environments remaining on Earth today.

83 4.3 Materials and methods Seawater and Sediment Collections at Sampling Stations The expedition collected seawater and sediment samples from nine sites across the FFS. Seawater samples were collected (n=39) from various water column depths and 1.5L of water, on average, was filtered onboard through sterile syringes and 0.22 µm Sterivex filters (Millipore, Billerica, MA, USA). Puregene Lysis Buffer (Qiagen, Valencia, CA, USA) added to filters preserved DNA frozen at -20 °C until subsequent processing. Sediment samples were collected via SCUBA (n=27) in sterile Whirl-Pak bags (Nasco, Fort Atkinson, WI) from the surface sediment at the same locations as water samples. Of the sediment sample, 1 ml (1-10 g, depending on make-up) was placed into 15 ml sterile centrifuge tubes with 5ml of RNAlater (Ambion, Foster City, CA, USA) and frozen at - 20 °C for further processing. The samples selected for sequencing (n=8, 4 water and 4 sediment), were determined by site as well as depth. The samples came from four sites, two lagoon (LPR2 (23.8202 °N, 166.3294 °W) & LP (23.7696 °N, 166.2608 °W)) and two fore reef (FR1 (23.8634 °N, 166.1877 °W) & FR3 (23.6347 °N, 166.1858 °W)), that were considered sites of greatest interest from the CReefs census based on habitat diversity and macro-organismal diversity (Figure 4.1 & Table 4.1). No sampling took place in coral colonies or live rock, following permit guidelines.

DNA Extraction and 16S rRNA gene V6 Library Sequencing Once sampling was completed, DNA extractions were carried out at the Marine Biological Laboratory (MBL). Water samples were extracted using the Puregene Core Kit A (Qiagen) following previously described methods (Amaral-Zettler et al., 2009) and stored at -80 °C. DNA from sediment samples were extracted using the Ultra Clean Soil DNA Extraction kit (MO BIO, Carlsbad, CA, USA). Amplifications were optimized at UC Merced using 5 ng of DNA template, dNTP Mixture (Takara, Mountain View, CA,

USA), Platinum Hi-Fidelity Taq, buffer, and MgSO4 (Invitrogen, Grand Island, NY, USA). Cocktail primers, 967F+barcode and 1046R (Huber et al., 2007), were used to target the bacterial V6 hypervariable region of the 16S rRNA gene. Barcoded amplicons were generated at 94 °C for 2 min, 30 cycles of (94 °C for 30 s, 55 °C for 30 sec, 72 °C

84 for 1 min), 72 °C for 2 min, and held at 4 °C. Amplicons were purified with QIAquick PCR Purification kit and MinElute columns (Qiagen). Sequencing was performed on Genome Sequencer FLX (Roche, Basel, Switzerland) with the LR70 kit (Roche) at MBL.

Figure 4.1. Map of the French Frigate Shoals. The four locations where water and sediment were sampled are indicated by the yellow pin.

Sequence Processing and Bacterial Community Structure Analyses Resulting sequences were trimmed of adapter and primer sequences. Low-quality reads were removed as described previously (Huber et al., 2007; Huse et al., 2010). All taxonomic assignments and analyses were conducted through the Visualization and Analysis of Microbial Population Structures (VAMPS) database (Huse et al., 2014) unless stated otherwise. Taxonomic classifications were assigned via Global Alignment

85 VAMPS ID VAMPS BPC_HWI_Bv6_HWI_1B BPC_HWI_Bv6_HWI_3B BPC_HWI_Bv6_HWI_5B BPC_HWI_Bv6_HWI_7B BPC_HWI_Bv6_HWI_2B BPC_HWI_Bv6_HWI_4B BPC_HWI_Bv6_HWI_6B BPC_HWI_Bv6_HWI_8B CreefsID FFS_0210 FFS_0224 FFS_0230 FFS_0215 FFS_0210 FFS_0224 FFS_0230 FFS_0215 11.262 11.147 11.262 11.147 19.763 15.650 19.763 15.650 Lon_MinDec -166 -166 -166 -166 -166 -166 -166 -166 Lon_Deg 49.214 46.178 51.806 38.081 49.214 46.178 51.806 38.081 Lat_MinDec 23 23 23 23 23 23 23 23 Lat_Deg 7666 13270 15169 16867 18240 13463 18768 13056 No. of Seq of No. Fine Fine Clear Clear Clear Notes Turbid Coarse Coarse

5 -- 10 25 10 10 23 25 Depth (m) Depth Type Water Water Water Water Sediment Sediment Sediment Sediment NWHI sample NWHI metadata sample LP LP FR1 FR3 FR1 FR3 LPR2 LPR2 Location Sample metadata. Table Table 4.1. LP_S LP_W FR1_S FR3_S FR1_W FR3_W Sample LPR2_S LPR2_W Table 4.1.

86 and Sequence Taxonomy (GAST) (Huse et al., 2008; Sogin et al., 2006), using a reference database based on the SILVA database (Pruesse et al., 2007) and criteria of two-thirds or better full-length sequence consensus with RDP Classifier (Wang et al., 2007). Independent taxonomic clusters were determined using a combination of ESPRIT, Single-Linkage Preclustering (SLP), and Mothur with all available V6 sequences in VAMPS to assign Operational Taxonomic Units (OTUs) as described in (Huse et al., 2010). We used pair-wise alignment with average linkage to cluster OTU sequences at 97% sequence similarity. The class followed by a numerical sequence represents OTU identifiers. OTU richness estimates for rarefied samples were projected using iNext (Chao and Jost, 2012; Hsieh et al., 2013). Between-subjects t-test was used with a Welch approximation to determine significant OTUs (p < 0.01) using TIGR Multiple Experiment Viewer (MeV) 4.8.1 (Saeed et al., 2003). A principal coordinates analysis (PCoA) with a UniFrac distance matrix was conducted with QIIME 1.8 (Caporaso et al., 2010). All sequences conform to the Minimum Information about a MARKer gene Sequence standard (Yilmaz et al., 2011).

Comparative Analyses Across Pacific Studies Resulting sequences were compared to other publicly available VAMPS datasets from central Pacific Ocean locations. FFS seawater samples were compared to water samples from Moorea, French Polynesia, part of the Moorea Coral Reef (MCR) Long Term Ecological Research (LTER) Site (McCliment et al., 2011; Nelson et al., 2011). MCR samples were collected in January 2008 and May 2009 from four LTER stations Paopao Bay (17.4901°S, 149.8231°W), fore reef (17.4751°S, 149.8371°W), back reef (17.4771°S, 149.8201°W), fringing reef (17.4851°S, 149.8341°W) and 5 km offshore (17.4301°S, 149.8631°W). FFS sediment samples were compared to coral reef sediment (CRS), both RNA and DNA sequences, from course-grained carbonate sand collected November 7, 2008 from the interior flat of Checker Reef (21.4667 °N, -157.8 °W) in Kāne’ohe Bay of O’ahu, Hawai’i. MCR and CRS were sequenced using the same methods as with the FFS samples, listed above. FFS samples were compared to metagenomes from the Northern Line Islands (LI). Screened samples were downloaded from

87 http://metagenomics.anl.gov/linkin.cgi?project=40 and http://metagenomics.anl.gov/linkin.cgi?project=9220. Taxonomic assignments of LI trimmed sequences were classified via all available GAST bacterial assignments. All sample sets were normalized to percent (i.e. normalized sample count = number of reads within a taxonomy / total number of reads in a dataset). Sample sets were compared at the class and genus taxonomic levels. Community dendrograms were created with Tree in the vegan package (Team, 2013) using the Bray-Curtis distance metric and FigTree v1.4.2 http://tree.bio.ed.ac.uk/software/figtree/. Comparative frequency heatmaps were created using a selection filter of 1% to 100% taxon representation and the Bray-Curtis clustering method with vegdist in the vegan package. All representative V6 sequences can be accessed at http://vamps.mbl.edu/.

4.4 Results FFS Library Statistics In total, we obtained 116,499 sequences among the eight samples sequenced. Sequencing depth for the water samples ranged from 13,270-18,240, while sediment samples ranged from 7,666-18,768 sequences per sample. Principle coordinate analysis indicated distinct communities between the two sample types when water-associated and sediment- associated sequences were compared (Figure 4.2).

Figure 4.2. Principal Coordinatess Analysis (PCoA) by FFS sample type. The scatterplot of a 2D unweighted principal coordinate 1 (PC1) vs. principle coordinate 2 (PC2) visualizes the clustering of samples and the distinct difference between water (left) and sediment samples (right).

88 Taxonomy-based Community Structure When sorted by taxonomic counts at the Phylum level, samples grouped by sample type. The bacterial phyla Proteobacteria, Cyanobacteria, and Bacteroidetes were abundant across all samples. Additionally, water samples contained Deferribacteres dominantly, while sediment samples primarily contained Acidobacteria, Actinobacteria, Chloroflexi, Lentisphaerae, Nitrospirae, Planctomycetes, and Verrucomicrobia. Sediment exclusively contained Gemmatimonadetes and WS3. Roughly 10% of the sample fractions comprised of organelles containing chloroplast, which had taxonomic classification down to the Class level. Sediment Eukaryotes were composed of the classes Bacillariophyta (diatoms), Chlorophyta, Chlorarachniophyceae, and Streptophyta. Bacillariophyta, Cryptomonadaceae, and Chlorophyta made up the classified fraction of Eukarya in the water samples. Relative abundances of the abundant bacterial taxa (i.e. ≥1%) were assessed at the Class level in both water and sediment environments. Sediment samples associated with twice the numbered of classified taxa (n=16) than those proceeded from water (n=8) samples (Figure 4.3). Proteobacteria constituted more than half of the bacterial taxa in both environments. Water samples were dominated by Alphaproteobacteria (44%) and Gammaproteobacteria (15%). Sediment samples contained the highest concentration of Gammaproteobacteria (30%) in addition to Deltaproteobacteria (14%) and Alphaproteobacteria (9%). Cyanobacteria were more abundant in the water samples relative to sediment, comprising 25% and 5% respectively. Actinobacteria were consistently more abundant in the sediment samples relative to the water samples, while the opposite trend was true for Flavobacterium. Defferibacteres comprised 3% of water samples, but were not abundant across the sediment samples. Both Sphingobacteria and Planctomycetacia each made-up more than 5% of the sediment bacterial community, however they were not abundant taxa in the water samples. Additionally, Acidiobacteria, Acidiobacteria_Gp22, and Holophagae, all from the phylum Acidiobacteria, as well as Clostridia, Gemmatimonadetes, and Verrucomicrobiae were all exclusively abundant across sediment samples.

nt community nt ndogram (left) shows similarities between (left) similarities samples ndogrambetween shows up differs greatly between types. up samplebetween differs greatly - level Dendogram and de and The level Bar Dendogram Charts. - Class Curtis distance metric. The bar charts of relative abundance are grouped sample water relative charts of are by metric. bar Curtis abundancewhere The grouped type, distance - Figure 4.3. Figure using Bray rich sediments consiste is 4 (topless Both relativelyshow samples. types than sample charts) however abundant taxa,the structures make in community

90 OUT-based Community Structure The 116,499 sequences clustered into 9,875 total OTUs based on 3% distance criterion. Of those 1,807 were from water and 8,516 OTUs were sediment samples. Singletons made up more than 55% of the OTUs in water and sediment samples; 1,055 (58.4%) in water and 4,729 (55.5%) in sediment. However, these singletons comprised less than 5.5% of the total sequences obtained and were not included in the subsequent results. Water samples contained 460-818 OTUs (752 unique) and sediment samples contained 2,573-3,961 OTUs (3,787 unique). Only 164 (3.75%) of those unique OTUs overlapped between both environments (Figure 4.4), resulting in 4,375 non-overlapping OTUs.

Figure 4.4. Venn Diagram of the 752 and 3,787 unique OTUs associated with water and sediment samples, respectively; only 164 OTUs overlapped. Resulting in a combined 4,375 unique OTUs detected between water (W) and sediment (S) samples without singletons (wosg).

Observed OTU richness (S) values per sample were further examined (Table 4.2). Sediment samples were 4.5 times richer than water samples on average. While sequencing captured a water OTU mean richness of 700 and a sediment mean richness of 3,171, Chao1 estimated the water and sediment samples were half as rich as projected (Figure 4.5).

Table 4.2. Resulting OTUs per sample sequenced

Table 4.2. Resulting OTUs per sample sequenced. Sample Location Type Depth (m) No. of Seq OTU Richness (S) Chao1 LPR2_W LPR2 Water 10 13270 460 1031 LP_W LP Water 25 15169 731 2045 FR1_W FR1 Water 5 16867 792 1459 FR3_W FR3 Water 10 18240 818 1855 LPR2_S LPR2 Sediment 10 7666 2573 5715 LP_S LP Sediment -- 13463 2926 6007 FR1_S FR1 Sediment 23 18768 3961 8188 FR3_S FR3 Sediment 25 13056 3224 6496

4000

● ●

● 3000

● 2000 Richness 1000 ● ●

● 0

0 2000 4000 6000 8000 10000 12000 14000

Number of individuals

Figure 4.5. Species richness estimates of rarefied samples are plotted for the four FFS water samples (bottom plots: black, green, yellow, cyan) and sediment samples (top plots: blue, pink, grey, red). Extrapolated projections are represented by dotted lines.

92 The abundant OTU composition (i.e. ≥1%) comprised 65.9% of the water samples and 11.2% of the sediment samples (Table 4.3). Of the abundant water OTUs, 32.3% involved eight Alphaproteobacteria that included SAR11 as well as SAR11_Pelagibacter and SAR116 OTUs, which were among the 25 OTUs identified as significantly different from sediment (Table 4.4). Cyanobacteria, both Synechococcus and Prochlorococcus, made up the abundant proportion of the water OTUs (12.9% and 11.2%, respectively). Deferribacterales_SAR406, Burkholderiales_Alcaligenaceae, Oceanospirillales_SAR86, and Bdellovibrionales_Bdellovibrionaceae were also more abundant in water samples than sediment. In sediment, Gammaproteobacteria OTUs were the most abundant. Additionally, Acidobacteria_Gp22, Actinobacteria_Acidimicrobiales, Puniceicoccales_Puniceicocaceae_Coraliomargarita, Holophagae, Sphingobacteriales_Flammeovirgaceae_Reichchenbachiella, and Planctomycetales_Planctomycetaceae_Blastopirellula OTUs were more abundant in sediment.

Comparisons to Other Pacific Island Reef Systems The resulting sequences were also compared to other reef studies conducted in the central Pacific Ocean. Both the Moorea coral reef (MCR) (McCliment et al., 2011) and O’ahu coral reef sediment (CRS)(Gaidos et al., 2011) studies used the same V6 primers and sequencing methods as the FFS study. While the two Line Islands studies (LI & KLI) (Dinsdale et al., 2008; Kelly et al.) used metagenomic approaches. The FFS water samples had similar bacterial community composition to the MCR samples and clustered by similar reef environments (Figure 4.6). Comparisons with MCR showed Synechococcus were higher in the lagoon/bay water samples, and grouped with the FFS lagoon samples, LP_W and LPR2_W. Prochlorococcus was highest in the MCR shallow offshore and reef water samples, which correlated with the FFS fore reef samples, FR1_W and FR3_W.

93 7.7% 2.8% 2.8% 2.0% 1.7% 1.6% 1.3% 1.2% 1.0% 1.0% 1.0% 1.0% 1.0% 0.9% 0.8% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.6% 0.6% 0.6% 0.6% 0.5% 3.1% 2.0% 1.9% 1.9% 1.3% 1.0% 0.9% 0.8% 0.8% 0.7% 0.7% 0.6% 0.6% 0.6% 0.6% 0.6% 0.5% 0.5% 0.5% 0.5% 0.5% 0.4% 0.4% 0.4% 0.4% 0.4% 0.3% 0.3% 0.3% 0.3% 16.8% 11.9% 11.2% %&of&S&OTUs %&of&W&OTUs 83 65 52 43 38 933 898 713 650 559 551 549 539 537 472 466 398 399 414 378 375 375 369 373 345 313 308 305 76.5 69.5 67.5 60.5 44.5 43.5 40.5 Total 9287 6557 6202 4240 1586 1526 1092 S&Avg 233.5 232.5 112.5 94.75 88.75 73.25 72.75 65.25 61.25 57.25 53.75 39.25 35.75 371.25 246.75 159.75 118.25 102.25 4 1 3 1 2 1 2 1 1 1 2 1 0 3 0 0 2 0 0 1 0 2 34 15 12 13 32 10 26 15 992 938 933 832 474 481 492 619 356 332 306 293 291 281 290 263 267 246 242 236 215 208 178 175 172 162 157 152 143 Total 1491 S&Total 6 5 4 3 1 1 0 0 0 0 3 2 7 1 0 7 0 0 0 1 0 0 0 0 0 31 83 20 932 895 712 648 558 549 548 538 536 470 465 398 396 388 378 375 373 369 358 345 312 308 303 193 240 9253 6542 6190 4227 1554 1522 1082 W&Total W&Total up (singletons excluded)up (singletons - 99 97 78 77 233 178 162 137 134 987 934 930 639 473 450 409 379 355 332 306 293 291 278 270 261 260 245 242 229 215 208 178 174 172 162 157 152 143 99.5 94.5 89.5 1485 388.5 380.5 270.5 139.5 134.5 117.5 93.75 93.25 92.25 86.25 75.75 W&Avg S&Total 1635.5 1547.5 223.75 137.25 116.25 2313.25 1056.75 class class class class class class class class class class class Rank Rank genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus family family family family family family family family family family family family family family family family family family family family family family family family family family family family orderx orderx species on % of make on OTUof %

), ), based B ) and sediment and )( sediment A Taxonomy Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Synechococcus Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Prochlorococcus Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Alteromonas Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;SAR406 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Synechococcus Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Pseudoalteromonadaceae;Pseudoalteromonas Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Owenweeksia Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Taxonomy Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Iamiaceae;Iamia Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Nitrospinaceae;Nitrospinaceae;Entotheonella Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Pelagibius Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Cyanobacteria;Cyanobacteria;SubsectionII;SubgroupII;Pleurocapsa Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Chromatiaceae;Nitrosococcus Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Eudoraea Bacteria;Gemmatimonadetes;Gemmatimonadetes Bacteria;Proteobacteria;Deltaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Phyllobacteriaceae;Mesorhizobium Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Acidobacteria;Acidobacteria_Gp22;Unassigned;Unassigned;Gp22 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Top 30 OTUs in each sample type, ( in 30 Topseawater type, sample each OTUs

!Top!30!OTUs!in!each!sample!type,!seawater!(A)!and!sediment!(B),!based!on!%!of!OTU!make@up!(singletons!excluded).!! Cluster&ID Alphaproteobacteria_03_2 Cyanobacteria_03_3 Cyanobacteria_03_1 Alphaproteobacteria_03_1 Gammaproteobacteria_03_15 Alphaproteobacteria_03_32 Deferribacteres_03_8 Gammaproteobacteria_03_66 Alphaproteobacteria_03_14 Alphaproteobacteria_03_57 Alphaproteobacteria_03_12 Gammaproteobacteria_03_24 Cyanobacteria_03_5 Gammaproteobacteria_03_69 Alphaproteobacteria_03_30 Gammaproteobacteria_03_139 Alphaproteobacteria_03_22 Bacteroidetes_03_764 Bacteroidetes_03_22 Alphaproteobacteria_03_35 Gammaproteobacteria_03_8 Alphaproteobacteria_03_53 Gammaproteobacteria_03_25 Alphaproteobacteria_03_69 Bacteroidetes_03_95 Alphaproteobacteria_03_9 Alphaproteobacteria_03_17 Bacteroidetes_03_47 Alphaproteobacteria_03_60 Firmicutes_03_730 Cluster&ID Gammaproteobacteria_03_71 Gammaproteobacteria_03_10 Gammaproteobacteria_03_65 Gammaproteobacteria_03_16 Actinobacteria_03_36 Deltaproteobacteria_03_666 Actinobacteria_03_30 Actinobacteria_03_5 Gammaproteobacteria_03_75 Gammaproteobacteria_03_303 Gammaproteobacteria_03_195 Alphaproteobacteria_03_261 Gammaproteobacteria_03_551 Cyanobacteria_03_68 Gammaproteobacteria_03_166 Gammaproteobacteria_03_7 Gammaproteobacteria_03_156 Gammaproteobacteria_03_188 Gammaproteobacteria_03_229 Gammaproteobacteria_03_149 Gammaproteobacteria_03_36 Gammaproteobacteria_03_466 Bacteroidetes_03_29 Gemmatimonadetes_03_56 Deltaproteobacteria_03_136 Gammaproteobacteria_03_80 Alphaproteobacteria_03_1369 Alphaproteobacteria_03_689 Acidobacteria_03_58 Actinobacteria_03_456 Table Table 4.3. Table&4.3. A. B.

94 7.7% 2.8% 2.8% 2.0% 1.7% 1.6% 1.3% 1.2% 1.0% 1.0% 1.0% 1.0% 1.0% 0.9% 0.8% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.7% 0.6% 0.6% 0.6% 0.6% 0.5% 3.1% 2.0% 1.9% 1.9% 1.3% 1.0% 0.9% 0.8% 0.8% 0.7% 0.7% 0.6% 0.6% 0.6% 0.6% 0.6% 0.5% 0.5% 0.5% 0.5% 0.5% 0.4% 0.4% 0.4% 0.4% 0.4% 0.3% 0.3% 0.3% 0.3% 16.8% 11.9% 11.2% %&of&S&OTUs %&of&W&OTUs 83 65 52 43 38 933 898 713 650 559 551 549 539 537 472 466 398 399 414 378 375 375 369 373 345 313 308 305 76.5 69.5 67.5 60.5 44.5 43.5 40.5 Total 9287 6557 6202 4240 1586 1526 1092 S&Avg 233.5 232.5 112.5 94.75 88.75 73.25 72.75 65.25 61.25 57.25 53.75 39.25 35.75 371.25 246.75 159.75 118.25 102.25 4 1 3 1 2 1 2 1 1 1 2 1 0 3 0 0 2 0 0 1 0 2 34 15 12 13 32 10 26 15 992 938 933 832 474 481 492 619 356 332 306 293 291 281 290 263 267 246 242 236 215 208 178 175 172 162 157 152 143 Total 1491 S&Total 6 5 4 3 1 1 0 0 0 0 3 2 7 1 0 7 0 0 0 1 0 0 0 0 0 31 83 20 932 895 712 648 558 549 548 538 536 470 465 398 396 388 378 375 373 369 358 345 312 308 303 193 240 9253 6542 6190 4227 1554 1522 1082 W&Total W&Total 99 97 78 77 233 178 162 137 134 987 934 930 639 473 450 409 379 355 332 306 293 291 278 270 261 260 245 242 229 215 208 178 174 172 162 157 152 143 99.5 94.5 89.5 1485 388.5 380.5 270.5 139.5 134.5 117.5 93.75 93.25 92.25 86.25 75.75 W&Avg S&Total 1635.5 1547.5 223.75 137.25 116.25 2313.25 1056.75 class class class class class class class class class class class Rank Rank genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus genus family family family family family family family family family family family family family family family family family family family family family family family family family family family family orderx orderx species Taxonomy Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Synechococcus Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Prochlorococcus Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae;Alteromonas Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;SAR406 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Cyanobacteria;Cyanobacteria;SubsectionI;Unassigned;Synechococcus Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Pseudoalteromonadaceae;Pseudoalteromonas Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Cryomorphaceae;Owenweeksia Bacteria;Proteobacteria;Alphaproteobacteria;Rhodobacterales;Rhodobacteraceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Acetivibrio Taxonomy Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Iamiaceae;Iamia Bacteria;Proteobacteria;Deltaproteobacteria;Desulfobacterales;Nitrospinaceae;Nitrospinaceae;Entotheonella Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales;Acidimicrobiaceae Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Alteromonadaceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae;Pelagibius Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Cyanobacteria;Cyanobacteria;SubsectionII;SubgroupII;Pleurocapsa Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Chromatiaceae;Nitrosococcus Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Bacteroidetes;Flavobacteria;Flavobacteriales;Flavobacteriaceae;Eudoraea Bacteria;Gemmatimonadetes;Gemmatimonadetes Bacteria;Proteobacteria;Deltaproteobacteria Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Phyllobacteriaceae;Mesorhizobium Bacteria;Proteobacteria;Alphaproteobacteria;Rhodospirillales;Rhodospirillaceae Bacteria;Acidobacteria;Acidobacteria_Gp22;Unassigned;Unassigned;Gp22 Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales !Top!30!OTUs!in!each!sample!type,!seawater!(A)!and!sediment!(B),!based!on!%!of!OTU!make@up!(singletons!excluded).!! Cluster&ID Alphaproteobacteria_03_2 Cyanobacteria_03_3 Cyanobacteria_03_1 Alphaproteobacteria_03_1 Gammaproteobacteria_03_15 Alphaproteobacteria_03_32 Deferribacteres_03_8 Gammaproteobacteria_03_66 Alphaproteobacteria_03_14 Alphaproteobacteria_03_57 Alphaproteobacteria_03_12 Gammaproteobacteria_03_24 Cyanobacteria_03_5 Gammaproteobacteria_03_69 Alphaproteobacteria_03_30 Gammaproteobacteria_03_139 Alphaproteobacteria_03_22 Bacteroidetes_03_764 Bacteroidetes_03_22 Alphaproteobacteria_03_35 Gammaproteobacteria_03_8 Alphaproteobacteria_03_53 Gammaproteobacteria_03_25 Alphaproteobacteria_03_69 Bacteroidetes_03_95 Alphaproteobacteria_03_9 Alphaproteobacteria_03_17 Bacteroidetes_03_47 Alphaproteobacteria_03_60 Firmicutes_03_730 Cluster&ID Gammaproteobacteria_03_71 Gammaproteobacteria_03_10 Gammaproteobacteria_03_65 Gammaproteobacteria_03_16 Actinobacteria_03_36 Deltaproteobacteria_03_666 Actinobacteria_03_30 Actinobacteria_03_5 Gammaproteobacteria_03_75 Gammaproteobacteria_03_303 Gammaproteobacteria_03_195 Alphaproteobacteria_03_261 Gammaproteobacteria_03_551 Cyanobacteria_03_68 Gammaproteobacteria_03_166 Gammaproteobacteria_03_7 Gammaproteobacteria_03_156 Gammaproteobacteria_03_188 Gammaproteobacteria_03_229 Gammaproteobacteria_03_149 Gammaproteobacteria_03_36 Gammaproteobacteria_03_466 Bacteroidetes_03_29 Gemmatimonadetes_03_56 Deltaproteobacteria_03_136 Gammaproteobacteria_03_80 Alphaproteobacteria_03_1369 Alphaproteobacteria_03_689 Acidobacteria_03_58 Actinobacteria_03_456

Table&4.3. A. B.

6.04EP04 4.21EP03 3.45EP03 9.47EP03 9.17EP04 2.97EP04 3.56EP03 5.21EP03 4.28EP04 1.65EP03 2.26EP03 6.53EP03 8.36EP03 4.13EP03 2.90EP03 6.61EP03 1.93EP04 2.68EP03 8.47EP03 6.25EP04 6.06EP03 2.26EP03 5.99EP03 5.99EP03 5.99EP03 95 Adj&p&value 7.91 8.48 5.96 8.39 7.35 7.55 9.80 6.79 6.22 7.96 8.99 6.76 9.24 6.20 6.97 9.80 7.00 7.00 7.00 15.32 13.31 19.45 10.73 22.46 15.14 t&value 0 0 0 0 0 3.11 0.50 4.00 0.50 0.50 0.50 3.56 3.70 0.96 1.89 2.58 0.96 1.29 0.82 0.50 0.50 0.50 12.40 74.32 15.29 S&StdDev 3 1 1 1 1 1 9 5 17 8.5 3.5 5.5 1.25 1.25 1.25 1.25 67.5 13.5 9.75 8.25 2.75 2.75 2.75 232.5 11.75 S&mean

0 0 0 0 0 0 0 0 0 0 0 4.08 1.15 1.15 0.82 0.96 0.50 3.56 89.47 89.74 33.44 16.55 32.43 300.53 266.23 W&StdDev 8 6 5 5 1 1 1 1 1 1 1 1 1 1 1 16 162 137 4.25 1.25 380.5 270.5 223.75 2313.25 1056.75 W&mean 4 3 2 1 0 2 2 0 0 34 13 10 68 54 47 39 36 33 22 20 11 11 11 930 270 S&Total 3 0 0 0 0 0 1 0 0 0 0 0 64 32 24 20 17 20 895 648 548 9253 4227 1522 1082 W&Total 64 34 26 20 17 68 54 47 39 36 34 22 20 11 11 11 898 650 549 933 290 Total 9287 4240 1526 1092 0.0022 0.0029 0.0024 0.0056 0.0045 0.0029 0.0034 0.0072 0.0027 0.0084 0.0082 0.0065 0.0034 0.0038 0.0352 0.0169 0.0523 0.0363 0.0046 0.0179 0.0706 0.0495 0.0716 0.1154 0.0731 Avg_Gdist class class Rank genus genus genus genus genus genus genus genus genus genus genus family family family family family family family family family orderx orderx species Taxonomy Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;SAR406 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR116 Bacteria;Proteobacteria;Gammaproteobacteria;Oceanospirillales;SAR86 Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae Bacteria;Proteobacteria;Deltaproteobacteria;Bdellovibrionales;Bdellovibrionaceae Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Alphaproteobacteria;Rickettsiales;SAR11;Pelagibacter Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Proteobacteria;Gammaproteobacteria Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Blastopirellula Bacteria;Actinobacteria;Actinobacteria;Acidimicrobiales Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Ectothiorhodospiraceae;Thiohalospira;alkaliphila Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Bacteroidetes;Sphingobacteria;Sphingobacteriales;Flammeovirgaceae;Reichenbachiella Bacteria;Proteobacteria;Gammaproteobacteria;Xanthomonadales;Sinobacteraceae Bacteria;Planctomycetes;Planctomycetacia;Planctomycetales;Planctomycetaceae;Blastopirellula Bacteria;Acidobacteria;Holophagae Bacteria;Acidobacteria;Acidobacteria_Gp22;Unassigned;Unassigned;Gp22 Bacteria;Verrucomicrobia;Opitutae;Puniceicoccales;Puniceicoccaceae;Coraliomargarita Significantly different OTUs (p<0.001) between seawater (above the line) and sediment (below theOTUsand line) (below(p<0.001) (above sedimentline) Significantly different seawaterthe between !Significantly!different!OTUs!(p!

Table&4.4. OTU&ID Alphaproteobacteria_03_2 Alphaproteobacteria_03_1 Alphaproteobacteria_03_32 Deferribacteres_03_8 Alphaproteobacteria_03_14 Alphaproteobacteria_03_12 Gammaproteobacteria_03_69 Alphaproteobacteria_03_384 Betaproteobacteria_03_139 Deltaproteobacteria_03_173 Alphaproteobacteria_03_313 Alphaproteobacteria_03_911 Gammaproteobacteria_03_16 Gammaproteobacteria_03_7 Planctomycetes_03_342 Actinobacteria_03_131 Gammaproteobacteria_03_1334 Gammaproteobacteria_03_515 Gammaproteobacteria_03_140 Bacteroidetes_03_149 Gammaproteobacteria_03_3891 Planctomycetes_03_304 Acidobacteria_03_1510 Acidobacteria_03_3276 Verrucomicrobia_03_514 96

with

offshore

dance is is dance samples samples from y clusters and color y and clusters water

samples samples clustered . Sample abun.Sample ) ) water (blue = = deep (blueoffshoresurface water /lagoon, & reef /lagoon, yellow =

NWHI

ace water water ace(5m Heatmap Heatmap comparing by reef location rather than than rather by geographic location locales. reef

Figure 4.6. Figure MCR. NWHI and MCR b similarities are Sample indicated locations coded by bay pink (200m), = surf shallow the indicated heatmap by colors.

97 Further comparisons to the LI samples determined that the FFS water samples had a bacterial community more similar to Palmyra and Kingman, two uninhabited atolls in the Northern Line Islands. FFS water samples were less similar to the developed islands, Tabuaeran and Kiritimati, where populations are 2,500 and 5,000 people, respectively (Figure 4.7). The Kingman water sample from Dinsdale et al., 2008 was an outlier that grouped more closely with FFS sediment samples (Figure 4.8). The CRS samples from Kaneohe Bay, O’ahu had strong bacterial community composition distinctions that set the majority of the CRS samples apart from the FFS sediment (Figure 4.9). Deltaproteobacteria and two Gammaproteobacteria (Xanthomonadales_Sinobacteraceae and Gammproteobacteria_NA) were highly abundant across all FFS sediment samples, while much lower in the CRS samples. Additionally, Phyla such as OP11 and TM7 were abundant in some of the CRS samples, but not detected in FFS samples. Betaproteobacteria Burkholderiaceae_Ralstonia was abundant in the majority of the CRS samples, but absent from FFS sediment samples. Actinobacteria such as Propionibacteriaceae_Propionibacterium and Corynebacteriaceae_Corynebacterium as well as the Firmicutes Staphylococcus and Lactococcus were only abundant in the O’ahu CRS sediment samples.

4.5 Discussion Large differences in microbial community structure across reef environments These data represent the first microbial census of the FFS and serves as a bacterial baseline for healthy coral reefs. The census measured fairly consistent bacterial community structures within the water column and surface sediments, however sharp differences were confirmed in both richness and relative abundance of taxa between the two environments. Minimal OTU overlap (3.75%) between the two environments was observed and none of the abundant OTUs overlapped environments (Table 4.3). Although rarefied species richness estimated all samples to be roughly twice as rich as what our sequences captured (Table 4.2), comparisons to other central Pacific Ocean studies highlighted consistency with the bacterial communities associated with the surface water column and reef locations (Figure 4.6). The comparisons also identified

98 water

, 2014 and LI LI ,2014 Palmyra and et et al. bacterial richness bacterial associated with richness NWHI water samples clustered with KLI samples KLI NWHI clustered with water , 2008. Kritimati and Tabuaeran studies from ,2008.Kritimati and both et et al. Heatmap Heatmap comparing ale Figure 4.7. Figure NWHI samples and from LI. samples Palmyra from Kingman Kelly and from Dinsd the FFS were samples. distant from more

Figure 4.8. Dendogram depicts similarities between FFS water samples and undeveloped atolls, Kingman and Palmyra. LI_Kingman sample from Dinsdale et al., 2008 clustered more with FFS sediment samples than other LI or FFS samples.

Figure 4.9. Heatmap comparing sediment samples from NWHI and CRS. NWHI sediment samples were more distant from CRS and the community structure was less consistent across CRS samples. Sediment samples clustered by geographic locales more than by reef location.

100 differences between FFS from locations with higher anthropogenic impacts (Figures 4.7 & 4.9). These findings show the unique microbial niches both sediment and the surrounding water column afford.

FFS seawater bacterial community Bacterioplankton in Hawaiian surface waters have been described as relatively stable, without seasonal fluctuation (Giovannoni and Vergin, 2012; Shi et al., 2011). While we sequenced one time point from each location, OTU relative abundances did slightly differ between sites, however the most abundant OTUs in the FFS seawater samples were consistently composed of Alphaproteobacteria (SAR 11, SAR11_Pelagibacter, and SAR 116), Cyanobacteria (Synechococcus and Prochlorococcus), Gammaproteobacteria (Alteromonas and SAR86) and Deferribacteres_SAR406 (Table 4.3). SAR11 was the most abundant in FFS water samples and is the most abundant bacterial class in the open- ocean (Giovannoni et al., 1990). Ocean surface waters being dominated by SAR11 and Prochlorococcus, has been well documented (Brown et al., 2012; Giovannoni and Stingl, 2005; Rusch et al., 2007; Yooseph et al., 2010) and our findings are consistent with previous shallow seawater column surveys (Zinger et al., 2011). While, Prochlorococcus is more dominant than Synechococcus in open ocean systems, both are responsive to environmental differences (Cuvelier et al., 2010; Johnson et al., 2006; Ottesen et al., 2013; Rocap et al., 2002; Worden and Binder, 2003) and play a dominant role in ocean and coastal primary production, respectively (Liu et al., 1997; Partensky et al., 1999). FFS samples had similar bacterial community structures to the undeveloped LI atolls. Although the LI metagenomes were produced with a different sequencing platforms and DNA preparation methods, comparisons to 16S amplicon sequences have been done in a few studies and have demonstrated overlap between the two methods amongst abundant taxa (Poretsky et al., 2014; Steven et al., 2012). Taxonomic assignments may have been limited due to the differences in sequence classification programs used (Bazinet and Cummings, 2012) and the resolution obtained (Steven et al., 2012). However, comparisons to the LI samples were informative and bacterial compositions from both LI studies showed greater dissimilarities between FFS and

101 developed atolls. Minimal to no detectable Vibrio species were found the in FFS water samples, which mirrored findings from locations most distant from human populations in the LI and Philippines (Dinsdale et al., 2008; Garren et al., 2008). Dinsdale and colleagues (2008) took additional water samples on TCBS selective media to culture Vibrios spp. and found increasing concentrations with higher human populations, with the highest concentration at Kiritimati. Comparing FFS samples to the MCR LTER sample set provided stronger geographical context to the FFS water samples. Water samples from LPR2 and LP, the lagoon area of FFS, clustered with MCR water samples from the opening of Cooks Bay, Moorea (Figure 4.6). The bay/lagoon cluster had high abundance of Synechococcus and less abundant Prochlorococcus, while the opposite was true for the FFS reef cluster (i.e. FR1_W and FR3_W samples). This is inline with findings from the MCR study where Synechococcus was measured to be abundant in Cooks Bay, although Synechococcus was more abundant in the reef relative to offshore samples (Nelson et al., 2011). LPR2 and LP were the two sites with the highest diversity of macroorganisms during the 2006 census (PIFSC, 2007). LPR2_W was characterized as turbid and as such, particulate suspension may have contained taxa common in the silty conditions typical of a bay. LP, a rock outcropping in the middle of the lagoon, serves as habitat for many birds and other marine organisms, which likely increases the amount of nutrients and sedimentation in the area, making the condition more typical to that of a bay. Higher abundance of Prochloroccus suggests FR1 and 3 likely have strong inputs from the neighboring pelagic water, as the broad pattern of higher Synechococcus concentrations within reefs and higher Prochloroccus concentrations in adjacent pelagic waters has been well established (Charpy and Blanchot, 1998 & 1999). FR3_W from the southern edge of the atoll, close to Disappearing Island, clustered with other MCR fringing and back reef water samples. While water samples clustered mostly with reef and lagoon/bay environments, FR1_W clustered with shallow offshore samples, indicating that the FR1 site may have more influence from offshore currents or have less influences from the terra firma. FR1 is on the outer edge of the atoll, in a location

102 composed of mostly shallow fringing reef, underwater sandbars, and neighboring this area the depth is >100m with a steep gradient towards the ocean-side.

FFS surface sediment community Although reef microbial studies have focused more on seawater than sediment, globally marine sediments have been found to be associated with more bacterial diversity (Gaidos et al., 2011; Halliday et al., 2014; Lozupone and Knight, 2007; Schöttner et al., 2012). Likewise, the FFS sediment was more diverse than FFS water samples. Only 11.2% of the bacterial community structure was comprised of abundant OTUs (i.e. > 1%). Gammaproteobacteria (Sinobacteraceae and unclassified Gammaproteobacteria), Actinobacteria (Acidimicrobiales_Iamia), and Deltaproteobacteria_Entotheonella were abundant in the FFS sediments (Table 4.3). More than 80% of the bacterial community associated with sediments were “rare” (i.e. OTUs < 1%), however it has been suggested that the rare community is also inactive (Gaidos et al., 2011) or perhaps the less active portion of the bacterial community (Novitsky, 1987). LPR2 and LP had coarse sediments composed mostly of coarse sand grains and small bedrocks. The increased macro- diversity at those locations, including mollusks shells and Halimeda found in the coarse sediment, likely contributed higher levels of particulate organic matter (POM), dissolved organic matter (DOM), and N content as guano from the concentration of birds roosting on La Perouse may lend to the differences noted in the bacterial community structure from the fore reef samples. Fine sand grains and fine grains of bedrock characterized the fore reef sediments, FR1_S and FR3_S, which had higher abundances of Planctomycetaceae. The FFS sediment samples clustered together with some CRS samples from Kane’ohe Bay, O’ahu, however the CRS samples had large differences in taxa and FFS samples clustered more closely with FFS sediment (Figure 4.9). The most abundant taxa in FFS sediment were Gamma- and Deltaproteobacteria, which were less abundant or absent across the CRS samples. Although CRS community structure was less consistent, the FFS sediments lacked abundance of multiple taxa that all CRS samples had in high abundance (i.e. Firmicutes_Bacilli, Burkholderiaceae_Ralstonia, as well as

103 Actinobacteria, both Propionibacteriaceae_Propionibacterium and Corynebacteriaceae_Corynebacterium). None of these taxa were abundant in FFS samples. These taxa have been detected before in marine water and coral samples, albeit from populated zones, and they also have known pathogenic members (Chiou et al., 2010; Fera et al., 2007). Propionibacterium is a genus predominantly associated with human skin (Qian et al., 2012). These differences could be due to differences in active taxa and differences in local environments, but pollution events as well as additional anthropogenic influences to Kane’ohe Bay have been well documented (Hunter and Evans, 1995; Smith et al., 1981; Stimson and Conklin, 2008) and these historical impacts draw a clear distinction from FFS. The most abundant taxa associated with FFS sediment had similar abundances, but each sample also contained unique abundant taxa, further reinforcing the differences between CRS and FFS. LPR2_S had a higher abundance of Sphingobacteriales than all samples compared, while FR3_S had higher concentrations of Acidimicrobiaceae and Flammeovirgaceae. Although FR1_S and FR3_S both had higher Planctomycetaceae abundances, LP_S and FR1_S clustered together due to taxa abundance similarities in Iamiaceae iamia, Nitrospinaceae, and Cyanobacteria_Pleurocapsa. CRS sediment was characterized as calcareous sandy sediment, however similar sediment characterization in FR1 and FR3 did not parallel the CRS bacterial community makeup. Unlike MCR bay water samples, CRS samples, which were collected in a bay, did not have a similar community structure to FFS lagoon sediment samples. As determined by Kelly and colleagues (2014) the macro-diversity reflect the microbial composition, and the degraded reefs in Kane’ohu Bay likely lend to the large differences in bacterial communities between FFS and CRS samples.

FFS microbial diversity as a baseline for healthy reefs in a fluctuating environment This is the first census of the FFS microbial community and it provides a snapshot of the microbial diversity associated with the NWHI. Additionally, the community overlap with Kingman and Palmyra Atolls provides strong support for constituting the FFS as a baseline for healthy reefs. Sharp community differences between FFS samples and

104 developed islands, O’ahu, Kiritimati, and Tabuaeran, illustrate inconsistencies to the FFS bacterial community. Overall, the bacterial community structure across locations in the FFS had similar dominant taxa with minimal overlap between sediments and the water column above. The high bacterial richness measured in FFS is consistent with findings from other reef studies (Gaidos et al., 2011; McCliment et al., 2011; Rohwer et al., 2002; Sunagawa et al., 2010) and our results reflect the community diversity model observed previously of a few dominant taxa with many less abundant OTUs (Sogin et al., 2006). SAR11, Synechococcus, and Prochlorococcus dominated FFS seawater. The FFS sediment, while richer compared to the water bacterial community, was distinct from sediments in O’ahu. We did not observe sharp differences between the FFS sites examined. The differences we did observe among the abundant taxa may be short-lived due to physical turnover and varying environmental conditions (Boudreau et al., 2001; Schöttner et al., 2011). The resulting bacterial diversity is likely a result of inactive or dead cells as well as those active microbes fit for the environmental conditions (Gaidos et al., 2011) and associations with the high macro-diversity measured (Kelly et al., 2014; PIFSC, 2007). While locations did show differences in abundance, replicated sampling and concomitant environmental measurements, as was performed with the MCR and LI studies, would be required to provide confidence in location or depth differences observed. Comparing samples to the MCR and LI studies provided confidence in the FFS community structures observed. Placing this diversity in an environmental context would improve our understanding of the functional roles microbial communities play in these isolated reefs (Azam and Malfatti, 2007). Human influences and anthropogenic pollutants on these isolated atolls are undeniable (Hillgarth, 2010; Morishige et al., 2007), and although pristine reefs are unlikely, the FFS reefs are healthier by our metrics relative to other reefs examined. The consistency between the FFS community structure with MCR water samples, and the distinct differences noted from the O’ahu reef sediment samples, highlight these results as a benchmark for healthy reef environments. Grouping with the

105 healthiest atolls from the LI provides confidence that these samples from the FFS should be considered as a baseline for healthy reef water and sediments in future studies. Our data demonstrate the importance of examining microbial communities in marine environments as part of any biodiversity survey (Amaral-Zettler et al., 2010). Given their numerical dominance, microbial taxa may be better indicators of habitat health than higher taxa. Our results provide continued support for protection and conservation of the NWHI and the unique organisms it contains. The NWHI can be compared to both neighboring and more distant locations, representing a continuum of human activity. By comparing across marine environments we will be on the path to better understanding the bacterial diversity that exists in the FFS. This in turn will help us gain a more complete understanding of marine microbes and the value of the Papahānaumokuākea Marine National Monument.

4.6 Acknowledgements This work was supported by the MIRADA LTERS grant DEB-0717390 (LAZ) from the United States National Science Foundation (NSF). We would like to acknowledge the Josephine Bay Paul Center for Comparative Molecular Biology and Evolution W.M. Keck Sequencing Facility for sequencing support and Elizabeth McCliment for technical assistance as well as sequencing preparation. Additionally, we would like to thank the Census of Marine Life for initiating this effort and the many people who were a part of the research expedition and contributed to the collection of these samples, especially Emmanuel Soto for sampling the microbial samples.

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113 CHAPTER 5:

PERSPECTIVES & CONCLUSIONS

In the “era of genomics”, we have come to accept the quick advancements that have been made in biology. Our field is no longer compartmentalized, it is integrated and systems- focused; the norm for most areas of biology is the combination of computational, wet lab, and field-based studies. Some of the latest major advancements have been made in the areas of molecular biology and microbiology. Together these areas have exposed us to the microbial world in which we live. As a result, we no longer view organisms as single entities, but rather as metaorganisms in symbiosis with their microbes; the sum of their parts (Bosch and McFall-Ngai, 2011; McFall-Ngai et al., 2013). Corals too have many associated microorganisms and the reefs they build are teeming with life, life that is unfortunately becoming increasingly more rare. Fifteen years ago, much like the human microbiome, we were unaware of the symbiosis present in corals and its vitality to the maintenance of homeostasis. In fact, many parallels exist in research findings between the human microbiome and the coral holobiont. The human microbiome project (HMP), particularly the gut microbiome, could serve and has already served as a theoretical framework for future coral microbial studies, as the HMP studies have been expansive and across many institutions and countries, providing diversity and multiple vast data sets. Though the coral holobiont has many members, whose facets nor their roles are well described, high throughput tools, such as those described herein, are providing us insights into the health and biology of the coral holobiont. By understanding microbial interactions and the interplay between the coral animal and its symbionts, we can better understand and act upon threats impacting coral reefs worldwide. This dissertation represents an initial systems biological approach to understanding how the coral-host, as well as associated microbes, change in the face of disease and reflect the health state of the local environment.

114 5.1 Coral-host and microbial response to Yellow Band Disease From early studies (DeSalvo et al., 2008; Sunagawa et al., 2009) we expected to find differences between healthy and stressed, or diseased, colonies. Unexpectedly, we also saw large differences in the intermediate health state, HD. This was surprising as one would hypothesize that HD since it appears healthy, would also reflect the healthy condition. This was believed to be the case until we started to examine health conditions beyond the two categories of ‘diseased’ and ‘healthy’. As described in Chapter 2 differences were present across all conditions sampled, at all levels examined. This unanimous response across all measured holobiont members illustrates the extent to which the disease affects the holobiont. It is likely that HD is in fact a gradient and highly variable condition that could be characterized into many stages of disease progression. The increased phylogenetic resolution and number of genes examined on both array platforms improved profiling of the associated bacteria and the host transcriptome, respectively. The increased number of bacterial taxa associated with samples from diseased colonies implies an imbalance in the coral system and perhaps susceptibility to opportunists or pathogens. These results show that while visual signs of disease are a physical manifestation of disease, at a systems level, diseases impact well beyond the active visually apparent lesion and potentially across the entire colony. Other efforts to examine coral disease have seen differences between diseased and healthy colonies, but the intermediate health state sampled in this study (HD) highlights the need for future studies to consider new, novel, measurements of health within a colony. Additionally, the parallel examination of multiple holobiont members, as conducted here, is essential, as it provides essential context needed to effectively describe, and therefor understand, a complicated, non-model system. The systems approach, used here, while beneficial, requires one to collect a variety of measurements, examine them separately, and finally incorporate results together. This challenge adds valuable time to the process when asking these important questions to a system in peril. Fortunately, more diverse high-throughput measurements, beyond those incorporated here, may be able to help clarify what is negatively impacting these reefs and shed some light on the etiology of YBD and other coral diseases. This

115 holistic approach would be better executed with effectively coordinated efforts similar to those devised for the Census of Marine Life. Large initiatives such as the Census of Marine Life and the Human Microbiome Project have bettered our understanding of diverse microscopic worlds. At the same time, streamlining of high-throughput technologies has radically changed the field of biology. While high-throughput techniques have become status quo in recent years, many areas of biology and ecology still lack both the much-needed information and infrastructure to effectively manage and use data generated by these methodologically and computationally advanced techniques. While the case of large regional studies is en vogue, local studies should take advantage of continued environmental measurements and follow reef tracts over time to assess health states of the system longitudinally. Data and findings from time-series studies may be more valuable if consistent ecosystem assessments are conducted at designated sites, at regular intervals, mimicking approaches like those followed in locations such as the Harvard Forrest. Initial efforts for such monitoring systems are beginning to be developed (Duffy et al., 2013), however support and buy-in will be required to make them function as envisioned. Comparative health studies provide valuable information and samples collected can continue to be valued as they are compared to other datasets. Our study of YBD is now able to be compared to future studies and will serve as a valuable dataset due to the conditions collected and the multiple taxa surveyed.

5.2 Comparative analysis of coral transcriptomes across four diseases Building on findings from Chapter 2, Chapter 3 aimed to examine multiple diseases impacting Caribbean corals. These diseases affect two major reef building species, however, despite more than 20 years of research, most of the etiology is either unknown or putative, and unfortunately, the Caribbean reef health has not improved. In order to improve research efforts being made, it is helpful to understand the impacts these diseases have on their coral hosts. Our results show that the host response differs across these diseases and the impact to the hosts may help us better understand what is causing these diseases or, at minimum, exacerbating them. Here, we found all photosynthesis- related genes differentially underexpressed in WB, however not in the healthy, WPx,

116 WPl, or YBD samples. This suggests that diseases may be impacting Symbiodinium more than in other known diseases. Symbiodinium is known as the first target in YBD, where the cells lyse within host (Cervino et al., 2001). Perhaps similar or potentially more drastic responses occur intercellualrly with WB. Additionally, viruses have been suggested to be involved with WPl (Soffer et al., 2013). Our findings show an increase in a gene of Chlorella viral origin in the WPl samples, but an even greater expression of the same gene was seen in YBD. Although this is not evidence for causation, these results do show significant increase in the expression of this gene in diseased samples compared to healthy O. faveolata. Viral associations with coral have been known for less than 10 years (Marhaver et al., 2008), and their involvement in coral health and disease is an area deserving more exploration. Finally, across all of the diseased conditions in both species there was strong down-regulation of toxin genes; these diseases impeded these corals’ chemical defense mechanisms. This supports findings from Chapter 2, where in YBD chemical defenses were under-expressed and likely impacts the coral’s ability to forage or defend itself adequately (Closek et al., 2014). While these findings have helped us better understand how corals respond to disease, as well as the different effects of disease, one limitation to these kinds of studies is the general lack of annotated genes. Inference and homologies determined the annotations for all of the genes listed, and while informative, we have only a vague understanding, or working model, of the coral immune system (Weis, 2008). Findings from these and previous studies underscore that innate immunity plays a critical role in a coral’s response to stress and disease (Closek et al., 2014; Libro et al., 2013; Mydlarz et al., 2009), but the quest for understanding how innate immunity functions in corals is an area for further investigation. Additionally, while 13,000+ features offer many potential genes, only a small proportion of the genes were expressed differently across conditions. Platforms such as RNAseq may help reveal other important gene motifs. Other systems such as Aiptasia pallida may be simpler systems for use in follow-up studies, as they can be easily maintained and are a model system for Cnidaria, lending itself to more in-depth experimental studies. Fortunately, work on multiple coral genomes are currently underway. These system studies have been and will continue to be valuable resources for

117 transcriptomic studies like these. Nonetheless, comparative studies are necessary to get a better understanding on both the causes of these diseases and impacts these diseases are having on corals globally. Our studies are limited by the functional gene annotations available to non-model systems. Further development of model systems that can allow us to understand the roles genes of interest play in coral physiology will help to better inform our understanding of coral health and immunity.

5.3 Microbial census of the French Frigate Shoals Given the high diversity of microbes, particularly bacteria, harbored by corals in lawfully protected zones (e.g. the Papahānaumokuākea Marine National Monument), sampling sediment and water-associated bacteria has proven to be a valuable approach in determining the health of an ecosystem. The isolated islands and atolls of Papahānaumokuākea have minimal connections to human populations. While water samples had similar abundant taxa community structures to water samples collected in the more populated Moorea, sediment samples differed from sediment from O’ahu. Sediments from O’ahu had greater diversity as well as a different make-up for those taxa that were common, i.e. >1% of the community structure. These studies were all conducted through the same sequencing initiative and therefor used similar collecting and sequencing methods. Differences found may be due to other factors, but we cannot rule out the impact human populations have on their environment. Differences in community structure were noted between sampling locations at French Frigate Shoals (FFS), but those differences also corresponded to higher macrofauna measurements taken at those locations. On average the sites had consistent bacterial communities, which further support a determination of stable health. This study highlights the bacterial community associated with an environment rarely surveyed and that is geographically one of the most distant tropical reef systems from any continent. The FFS census was an important expedition, as it highlighted the many biological values extant in this island chain. By all measures taken (i.e. animal, plant, and microbial) during the Census of Marine Life expedition, the FFS was rated healthy, perhaps one of the healthiest reef systems. This large, federally protected zone, while

118 isolated can still be negatively impacted by distant and/or local disturbances. Continued research should be conducted to monitor the health of this environment and the impact of seasonal or human disturbances. While the survey collected samples from a single timepoint, it would be informative to know whether the microbial communities are stable seasonally or whether the Pacific Garbage Patch is changing the microbial communities before impacting the higher trophic levels. Additionally, while we can state that FFS water samples are on par with other healthy central Pacific reefs, a comparative study within the NWHI would be warranted and a longterm evaluation of the monument would add greater value to this unique ecosystem. This study contributes to our understanding of the importance a well-balanced ecosystem and the top-down/bottom-up roles macrobes and microbes play in sustaining the ecological function. Similar to the Line Islands studies (Dinsdale et al., 2008; Kelly et al., 2014), this study compared both local (i.e. O’ahu) and distant locations (i.e. Moorea & Northern Line Islands). Those locations provide a spectrum of human populations (populations from 5,000 to 1 million people) and anthropogenic impact from developed to undeveloped islands within the central Pacific compared to the FFS. We showed based on bacterial community structure that the FFS is one of the few remaining healthy reef systems. This is the first bacterial assessment of the FFS and the consistency of the associated microbial communities found can serve as baseline to be compared to other locations as well as to more impacted sites as a metric of health.

5.4 Conclusions The work described within has addressed three questions needing to be addressed to better understand how coral reefs, the coral animal, and it’s associated microorganisms respond to environmental changes. Over 380 samples were collected across five sampling trips in an effort to address the following questions: 1. Does Yellow Band Disease affect the coral host transcriptome and its associated bacterial community structure? 2. What is the core host response and how do the transcriptomic profiles of Yellow Band, White Plague, White Band, and White Pox Disease differ?

119 3. What is the make-up of the associated water and sediment bacterial communities in the French Frigate Shoals?

We found that Yellow Band Disease drastically affected both the host transcriptome and the associated bacterial community structure. Similarly in the other diseases examined, we observed significant core responses to the chemical defenses and innate immunity response of corals, while also observing distinct differences in the gene expression profile of each condition. Bacterial communities in sediment from the French Frigate Shoals were more diverse than water-associated communities, and differed from sediments in another more populated Hawaiian locale (i.e. O’ahu). These studies provided promising evidence that the tools used to address these questions can be used to assess the changes and therefor the summation of the coral’s or reef’s health. Rapid advances in sequencing and computing technologies are aiding our understanding of the health of coral reefs and in turn guide implementation of effective management strategies. The previous chapters are case examples of: 1) profiling the host transcriptome (Chapters 2 & 3), 2) phylotyping and comparing the bacterial community composition (Chapters 2 & 4) and 3) genotyping the coral host and Symbiodinium populations (Chapter 2) to assess the natural variation occurring in healthy and disturbed reef environments. Although determining the health of an environment is challenging, it is more difficult without standards or a baseline. As environments decline our baseline shifts (Knowlton and Jackson, 2008). If we have no earlier reference point of what the environment once was, it is difficult to know how the environment has changed. This is more beneficial when we have measurements to quantify the health of the environment. Without historical ecological measurements quantitative comparisons are difficult and less informative. While previous methods have allowed one to measure environmental change, modern molecular tools may allow us to measure impacts to environments before physical changes occur. Modern tools have increased our ability to measure more precisely and with higher throughput, allowing for more localities, metrics, and sample types to be collected. The research presented herein is a representation of these

120 advancements. Advancements that have allowed us to measure and note differences in many high-throughput methods to cumulatively determine baselines for coral reef health. As performance of these technologies increase, we will be able to profile all parameters needed within a few minutes, rather than weeks to months. Pocket sequencers and in-the-field sample extraction/analysis are not figments of our imagination or the future they are current realities. Unfortunately we lack increase in funding and support for long term projects, yet those will be the ones that we learn the most from. As impacted environments are studied we have a responsibility to use the resulting science to inform and advance our understanding of those ecosystems. With new technologies we are able to sample more and make more accurate characterizations of the health and stability of both the organisms and environments we study. I hope these baseline studies will continue to inform and serve future comparative studies. While limitations allowed for analysis of only a subset of the samples in this compilation, the results obtained are important in understanding coral health and the remaining samples are being examined for other research concentrations in the Medina Lab. For example, a community-sequencing proposal is currently being funded by the Joint Genome Institute to examine the bacterial, archaeal, and fungal taxa associated with the samples as a biogeographic survey. Additionally, proteomic studies of skeletons from Yellow Band Disease colonies are being examined and preliminary results show potential acidic conditions in the disease skeleton (B. Hanna, pers. comm.). As we continue to examine various aspects of these samples, the aim is to learn more about this complex metasystem as well as further our understanding of coral health, the good, the bad, and the ugly. We started with questions to understand how the health of coral is reflected by different biological measures, we now know that bacterial community structures and coral transcriptomes strongly reflect both the health of the coral holobiont and the surrounding reef environment. What will we find next?

121 5.5 References Bosch TCG, McFall-Ngai MJ (2011). Metaorganisms as the new frontier. Zoology 114: 185-190.

Dinsdale EA, Pantos O, Smriga S, Edwards R, Angly F, Wegley L et al (2008). Microbial ecology of four coral atolls in the Northern Line Islands. PLoS One 3.

Duffy JE, Amaral-Zettler LA, Fautin DG, Paulay G, Rynearson TA, Sosik HM et al (2013). Envisioning a Marine Biodiversity Observation Network. BioScience 63: 350- 361.

Knowlton N, Jackson JBC (2008). Shifting baselines, local impacts, and global change on coral reefs. PLoS Biology 6.

Libro S, Kaluziak ST, Vollmer SV (2013). RNA-seq Profiles of Immune Related Genes in the Staghorn Coral Acropora cervicornis Infected with White Band Disease. PLoS One 8: e81821.

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McFall-Ngai MJ, Hadfield MG, Bosch TCG, Carey HV, Domazet-Loso T, Douglas AE et al (2013). Animals in a bacterial world, a new imperative for the life sciences. Proceedings of the National Academy of Sciences 110: 3229-3236.

122 Mydlarz LD, Couch C, Weil E, Smith G, Harvell CD (2009). Immune defenses of healthy, bleached and diseased Montastraea faveolata during a natural bleaching event. Diseases of Aquatic Organisms 87: 67-78.

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VITA

COLLIN JOHN-ERIK CLOSEK

Education 2005 Bachelor of Science, Biology, University of Georgia, Athens, GA, USA. 2010 Doctoral Candidate, Quantitative & Systems Biology, University of California, Merced, CA, USA. 2014 Doctor of Philosophy, Biology, The Pennsylvania State University, University Park, PA, USA.

Publications Collin J. Closek, Shinichi Sunawaga, Michael K. DeSalvo, Todd Z. DeSantis, Yvette M. Piceno, Eoin L. Brodie, Michele X. Weber, Christian R. Voolstra, Gary L. Andersen and Mónica Medina. “Coral transcriptome and bacterial community reveal distinct Yellow Band Disease states in Orbicella faveolata”, The ISME Journal, 2014.

Janelle R. Thompson, Hanny E. Rivera, Collin J. Closek, Mónica Medina. “Bacteria in the coral holobiont: partners through evolution, development, and ecological interactions”, Frontiers Microbiology, in review.

Collin J. Closek, Linda A. Amaral-Zettler, and Mónica Medina. “A Bacterial Census of the French Frigate Shoals, Papahānaumokuākea (Northwestern Hawaiian Islands) Marine National Monument”, in prep.

Collin J. Closek, Viridiana Avila Magaña, Bishoy S. K. Kamel, Erika M. Díaz- Almeyda, Marilyn E. Brandt, and Mónica Medina. “Comparative transcriptomics across multiple diseases in two Caribbean corals, Acropora palmata and Orbicella faveolata”, in prep.

Professional Experience 2013 – 2014 Graduate Student Researcher, The Pennsylvania State University 2013 Teaching Fellow, Biology, The Pennsylvania State University 2010 – 2012 Teaching Fellow, Cell Biology, University of California, Merced 2008 – 2013 Graduate Student Researcher, University of California, Merced 2008 – 2010 Teaching Assistant, Mol. & Cell Bio, University of California, Merced 2007 – 2008 Research Assistant II, University of California, Merced

Awards 2013 The Pennsylvania State University Braddock Graduate Fellowship 2013 U.S. DOE Joint Genome Institute Community Sequencing Award 2012 Sigma Xi Grants-in-Research 2011 National Geographic Society Research and Exploration Grant 2010 – 2013 University of California, Merced Graduate Summer Fellowship 2010 UC-CUBA Graduate Student Research Grant 2010 American Museum of Natural History Lerner-Gray Grant