CHARACTERIZING THE EFFECTS OF ANTHROPOGENIC DISTURBANCE ON DEEP-SEA CORALS OF THE GULF OF MEXICO

A Dissertation Submitted to the Temple University Graduate Board

In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY

by Danielle M. DeLeo December 2016

Examining Committee Members:

Erik E. Cordes, Advisory Chair, Biology Robert W. Sanders, Biology Rob J. Kulathinal, Biology Santiago Herrera, External Member, Lehigh University

i

© Copyright 2016

by

Danielle M. DeLeo All Rights Reserved

ii ABSTRACT

Cold-water corals are an important component of deep-sea ecosystems as they establish structurally complex habitats that support benthic biodiversity. These communities face imminent threats from increasing anthropogenic influences in the deep sea. Following the 2010 Deepwater Horizon blowout, several spill-impacted coral communities were discovered in the deep Gulf of Mexico, and subsequent mesophotic regions, although the exact source and extent of this impact is still under investigation, as is the recovery potential of these organisms. At a minimum, impacted octocorals were exposed to flocculant material containing oil and dispersant components, and were visibly stressed. Here the impacts of oil and dispersant exposure are assessed for the octocoral genus Paramuricea. A de novo reference assembly was created to perform gene expression analyses from high-throughput sequencing data. Robust assessments of these data for P. biscaya colonies revealed the underlying expression-level effects resulting from in situ floc exposure. Short-term toxicity studies, exposing the cold-water octocorals Paramuricea type B3 and Callogorgia delta to various fractions and concentrations of oil, dispersant and oil/dispersant mixtures, were also conducted to determine overall toxicity and tease apart the various components of the synergistic exposure effects. Finally, alterations in Paramuricea B3 gene expression profiles were inspected to characterize genome-wide changes induced by each treatment and putative genes under differential regulation. The experimental results provide evidence for a relatively high toxicity of chemical dispersants as compared to oil additions alone, elucidating the implications of applying oil dispersants to future oil spills. My findings

iii revealed signatures of cellular stress in floc-exposed corals associated with xenobiotic metabolism, immune and inflammatory responses as well as transcriptional suppression of vital cellular components like ribosomal proteins. The data also suggests poor recovery potential in our coral samples exposed to floc. In addition, promising biomarker candidates were identified from the differential expression data for use in future spill- impact monitoring.

iv

DEDICATION

To my husband for inspiring me to be curious and pursue our mutual love of science,

and my mother for her adoration and encouragement.

v ACKNOWLEDGMENTS

I first and foremost need to thank my advisor and mentor Erik Cordes for his support and guidance while pursuing my doctorate degree. I thank him for having unyielding confidence in me and my abilities as a scientist, and for the amazing travel opportunities I never expected to have- both near and far, and to the bottom of the ocean!

I am so grateful our paths crossed and I can only hope to follow in his footsteps as a successful marine scientist. I have confidence that our friendship and collaborations will not end here. Additional heartfelt thanks to my dissertation committee, Robert Sanders and Rob Kulathinal who have been great mentors over the years, and to Santiago Herrera whose recent guidance has been invaluable.

Equally important, I need to thank my husband and my best friend for over a decade, Nicholas, for venturing into a career of science alongside me and supporting my ambitions with sincere enthusiasm. His curiosity and appetite for knowledge inspires me every day. His fascination with science also emboldened me to pursue a career researching what I love- the ocean. Without his love and support, I would not have gotten through this chapter of my life and for that I am forever grateful.

I am thankful for my mother Donna, for her never-ending support and for instilling in me the strength to overcome any obstacle, the drive to follow my dreams and the gall to stand up for what I believe in. I am also thankful to my family, who each support me in their own way, including my grandmother Betti, my aunt Karan, Bill, and my beautiful niece Ariana- who inspires me to be a better person every day. Additional thanks to my in-laws, Ed and Sheila- who have supported me like one of their own for

vi over a decade now. And to Gabby, Mike and Eddy for showing me the meaning of true sibling camaraderie.

Words cannot describe my gratitude and adoration towards the graduate students in the Cordes lab whom I’ve had the pleasure of working alongside over the past few years. I am extremely thankful for Jay Lunden and Andrea Quattrini who helped train me when I first started graduate school and quickly became a much-needed support system.

Jay and Andrea were more than just cherished mentors; they became lifelong friends and valued colleagues. Additional thanks to Samuel Georgian for being a great friend and travel buddy, and for our port (mis)adventures.

I am forever thankful for my friends, Alexis Weinnig, Sarah DeVaul Princiotta and Elizabeth Reilly for their encouragement and for empowering me as a woman and as a scientist. And to the best friend I lost too early in life, Lee Ann, who loved life and learning and inspired me to follow suit. I am also thankful for the friends who have supported and encouraged me over the years in college and in graduate school including

Alanna Durkin, Carmela Romeo, Coady and Laura Knowles, Kim Reuter, Katherine

Papacostas, Carlos Gomez, Steven Auscavitch, Nichole Rigby, Craig Stanley Jr. and

Janne Pfeiffenberger.

Aspects of my dissertation research, as well as ongoing projects not presented here, would not have been possible without the assiduous efforts of several undergraduate students at Temple University, including Conall McNicholl, Melissa Kurman, Alexandria

Barkman, Samuel Nguyen, Kari Strouse, Jessica Hart and Lyanna Kessler.

Likewise, I am thankful for Charles Fisher, Amanda Demopoulos, Iliana Baums,

Fanny Girard, Veronica Radice, Helen White and Rachel Simister for sharing their

vii knowledge, resources and passion for science with me. I also need to thank several people for their laborious assistance at sea, including Dannise Ruiz-Ramos, Rich

Dannenberg and Danielle McKean.

Various facets of my graduate research relied on technical support from a number of people. I am grateful to the ship crews aboard the R/V Falkor, E/V Nautilus, R/V

Atlantis and vehicle crews of the ROV Global Explorer, ROV Hercules and the DSV

Alvin, for their assistance with sample collections. Additionally, I would like to thank the faculty and staff at Temple University who have supported me in many ways including

Richard Waring, Allen Nicholson, Toni Matthews, Derrick Love, Tania Stephens, Evelyn

Vleck, Daniel Spaeth and Sheryl Love.

I am extremely grateful for the funding that supported my dissertation research from the Gulf of Mexico Research Initiative’s “Ecosystem Impacts of Oil and Gas Inputs to the Gulf” (ECOGIG) program. The data fall under GRIIDC accession numbers R1 x

132.136.0004 and R1 x 132.136.0005. Funding was also provided through Temple

University in the form of teaching and research assistantships and travel grants to attend workshops and conferences. Additionally, the in situ impacted corals were collected during the AT18-3 cruise on the R/V Atlantis with the Alvin submersible and AUV

Sentry, with support to EEC from NSF RAPID grant OCE-1045079.

A final thanks to my Fox for brightening my life and lastly, to the city of

Philadelphia, my hometown and touchstone. Thank you for providing me with great food, libations, music and friends. Till we meet again.

viii TABLE OF CONTENTS

Page

ABSTRACT ...... iii

DEDICATION ...... v

ACKNOWLEDGMENTS ...... vi

LIST OF TABLES ...... xiv

LIST OF FIGURES ...... xv

LIST OF ILLUSTRATIONS ...... xvi

CHAPTER

1. INTRODUCTION ...... 1

1.1 The deep sea ...... 1

1.2 Coral fauna ...... 2

1.2.1 Cold-water corals ...... 5

1.2.2 Octocorals ...... 7

1.2.3 Genus Paramuricea ...... 8

1.3 Anthropogenic threats to cold-water corals ...... 9

1.3.1 Deep-water trawling and fisheries ...... 9

1.3.2 Climate change ...... 9

1.3.3 Global warming ...... 10

1.3.4 Oil and gas extraction in the deep sea ...... 11

1.4 The Deepwater Horizon disaster ...... 12

1.4.1 Deepwater Horizon impacts on cold-water corals ...... 13

ix 1.5 Cellular responses to environmental stress ...... 14

1.5.1 Chemical stressors ...... 14

1.5.2 RNA sequencing to investigate impacts ...... 16

1.6 Objectives ...... 17

2. GENE EXPRESSION PROFILING REVEALS DEEP-SEA CORAL RESPONSE TO THE DEEPWATER HORIZON OIL SPILL ...... 19

2.1 Abstract ...... 19

2.2 Introduction ...... 20

2.3 Methods ...... 23

2.3.1 Sample collections ...... 23

2.3.2 RNA isolation and sequencing ...... 25

2.3.3 Sequence processing and transcriptome assembly ...... 25

2.3.4 RNAseq differential and functional analysis ...... 29

2.4 Results and Discussion ...... 30

2.4.1 Paramuricea sp. reference transcriptome ...... 30

2.4.2 RNAseq Analysis ...... 34

2.4.3 Gene Ontology enrichment on over-expressed genes shared

among impacted corals ...... 49

2.4.4 Gene Ontology enrichment of genes distinctly over-

expressed in each impacted coral colony ...... 51

2.4.5 Evidence for xenobiotic biotransformation

in impacted corals ...... 53

2.4.6 Wound repair and tissue regeneration activated

in impacted corals ...... 55 x 2.4.7 Elevated apoptotic and proteolytic activity ...... 57

2.4.8 Evidence for immune-related responses ...... 58

2.4.9 Gene Ontology enrichment on under-expressed genes shared

among impacted corals ...... 61

2.4.10 Gene Ontology enrichment of genes distinctly under-

expressed in each impacted coral colony ...... 62

2.4.11 Suppression of vital processes ...... 63

2.4.12 Reduced ability to process toxins ...... 65

2.4.13 Inability to re-establish homeostasis ...... 66

3. RESPONSE OF DEEP-WATER CORALS TO OIL AND CHEMICAL DISPERSANT EXPOSURE ...... 69

3.1 Abstract ...... 69

3.2 Introduction ...... 70

3.3 Methods ...... 74

3.3.1 Sample collection and acclimatization ...... 74

3.3.2 Preparation of bulk-oil treatments ...... 75

3.3.3 Preparation of treatments using water-accommodated oil

fractions (WAF) only ...... 76

3.3.4 Fragmentation and exposure experiments ...... 78

3.3.5 Survival Analysis ...... 79

3.4 Results ...... 81

3.4.1 Oil exposure effects on Paramuricea type B3 ...... 81

3.4.2 Dispersant exposure effects on Paramuricea type B3 ...... 81

3.4.3 Oil/dispersant exposure effects on Paramuricea type B3 ...... 85

xi 3.4.4 Comparisons between treatments for Paramuricea type B3 ...... 85

3.4.5 Oil exposure effects on Callogorgia delta ...... 89

3.4.6 Dispersant exposure effects on Callogorgia delta ...... 89

3.4.7 Oil/dispersant exposure effects on Callogorgia delta ...... 90

3.4.8 Comparisons between treatments for C. delta ...... 90

3.4.9 Overall comparisons between treatments and concentrations ...... 91

3.4.10 Comparisons between species ...... 94

3.5 Discussion ...... 95

4. TRANSCRIPTOMIC RESPONSES OF A DEEP-SEA OCTOCORAL EXPERIMENTALLY EXPOSED TO CRUDE OIL AND DISPERSANT ...... 102

4.1 Abstract ...... 102

4.2 Introduction ...... 103

4.3 Methods ...... 105

4.3.1 Sub-lethal oil and dispersant exposures ...... 105

4.3.2 RNA extractions and sequencing ...... 106

4.3.3 RNAseq analysis & comparisons ...... 108

4.4 Results ...... 110

4.4.1 Sequencing and alignment to the reference assembly ...... 110

4.4.2 Paramuricea B3 bulk-exposures (individual comparisons) ...... 110

4.4.3 Paramuricea B3 bulk-exposures (treatment comparisons) ...... 114

4.4.4 Paramuricea B3 WAF-exposures (individual comparisons) ...... 119

4.4.5 Paramuricea B3 WAF-exposures (treatment comparisons) ...... 123

4.5 Discussion ...... 128

xii 5. Conclusions ...... 140

5.1 Summary of findings ...... 140

5.2 Protection and restoration of cold-water coral ecosystems ...... 142

5.3 Future research directions ...... 143

BIBLIOGRAPHY ...... 146

NOTES ...... 169

APPENDICES

PERMISSION FOR INCLUSION OF COPYRIGHTED MATERIAL ...... 170

xiii LIST OF TABLES

Table Page

2.1 Sequence similarity between the coral host transcriptome for Paramuricea spp and marine species ...... 28

2.2 Assembly statistics for the Paramuricea spp. de novo transcriptome reconstruction ...... 31

2.3 BLAST results for Paramuricea spp. reference assembly comparisons ...... 33

2.4 Number of differentially expressed genes ...... 36

2.5 Log2-fold changes (FC) for genes jointly over-expressed ...... 38

2.6 Log2-fold changes (FC) for genes jointly under-expressed ...... 41

2.7 Differentially expressed (DE) genes that possessed a gene ontological term with enrichment by category ...... 50

3.1 Pair-wise comparisons of K-M survival estimates ...... 86

3.2 K-M means for time-to-event estimates for C. delta and Paramuricea type B3 ...... 87

3.3 Log-rank tests of equality on survival distributions for the different levels of concentration ...... 92

3.4 Predictor variables in Cox regression analysis and calculated hazard ratios ...... 93

3.5 Log-rank tests on equality of survival distributions for both species ...... 94

4.1 Samples sequenced for further analysis of experimental exposure effects ...... 107

4.2 Annotations for genes over-expressed in Paramuricea type B3 samples by bulk-treatment ...... 115

4.3 Number of significant differentially expressed genes for the water-accommodated fraction (WAF) exposure series ...... 123

4.4 Annotations for the under-expressed genes for Paramuricea type B3 by WAF treatment ...... 124

4.5 Annotations for the over-expressed genes for Paramuricea type B3 samples by WAF treatment ...... 125

xiv LIST OF FIGURES Figure Page

2.1 Collections of impacted colonies of P. biscaya covered in brown flocculent material ...... 24

2.2 Histogram of transcript or contig lengths ...... 32

2.3 MA and Volcano plots ...... 35

2.4 Heat map for genes differentially expressed in both spill-impacted P. biscaya colonies ...... 37

3.1 Average health ratings over time for coral fragments exposed to various concentrations of bulk-mixtures ...... 83

3.2 Average health ratings over time for coral fragments exposed to various concentrations of water accommodated fractions ...... 84

3.3 Box-plots showing time-to-event estimates from the Kaplan-Meier survival analysis ...... 88

4.1 PCA plot showing global gene expression patterns in the bulk-exposures by colony genotype ...... 112

4.2 Sample and gene clustering heatmap of the 20 most variably expressed contigs induced by bulk-oil treatments ...... 113

4.3 PCA plot showing global gene expression patterns combined by bulk-exposure treatment ...... 117

4.4 Heatmap showing (Poisson) sample distances among treatments ...... 118

4.5 PCA plot showing global gene expression patterns in the water-accommodated fraction exposures by colony genotype ...... 120

4.6 Sample and gene clustering heatmap of the 20 most variably expressed genes in the WAF treatments ...... 122

4.7 PCA plot showing global gene expression patterns by WAF exposure treatment ...... 127

4.8 Heatmap showing (Poisson) sample distances among WAF treatments ...... 128

xv LIST OF ILLUSTRATIONS

Illustration Page

1.1 Basic structure of a coral polyp ...... 4

xvi CHAPTER 1

INTRODUCTION

1.1 The deep sea

The world’s oceans make up 95 percent of the habitable volume of the planet, most of which exists in the deep sea. The deep sea harbours distinctive habitats including sedimented basins and trenches (areas of subduction), abyssal plains, continental slopes and intersecting submarine canyons, as well as raised seamounts and mid-oceanic ridges.

However, over 90 percent of the deep-sea floor is thought to be comprised of extensive mud (silt / clay) plains lacking distinctive barriers (Glover and Smith, 2003).

The deep sea is broadly characterized by a low flux of organic energy, with the majority of fauna acquiring nutrition from surface detritus (Glover and Smith, 2003).

Correspondingly, these deep benthic communities typically have reduced biomass (< 1 percent) compared to shallow-water and terrestrial ecosystems (Demopoulos et al., 2003).

Reduced energy flow in conjunction with low temperatures (i.e., 4-12 °C) is thought to cause relatively low rates of respiration, growth and reproduction (Gage and Tyler,

1991). There are exceptions to this pattern including productive communities associated with hydrothermal vents and cold seeps that are supported by chemoautotrophic bacteria

(Van Dover, 2000), as well as certain assemblages focused in high flow / energy regions such as seamounts and whale falls (Glover and Smith, 2003). There is also evidence to suggest a large portion of undiscovered global biodiversity resides in the deep sea (Higgs and Attrill, 2015).

1 An important habitat-type found in the deep-sea are those supported by coral fauna. These animals function as important foundation species by altering the terrain of the sea floor and producing complex, heterogeneous habitat in areas otherwise characterized by muddy sediment, in turn promoting benthic biodiversity (Cordes et al.,

2008, Cordes et al., 2010). Coral within the deep sea benthos can be found as individual colonies, small to large patch reefs and massive carbonate mound structures or bioherms, extending hundreds of meters high and several kilometers wide (Roberts, 2006). Though they are globally distributed, they appear to be concentrated near areas with locally accelerated currents and / or enhanced food supply (Duineveld et al., 2004).

1.2 Coral fauna

Coral is a term broadly used to describe a large polyphyletic group of animals in the phylum Cnidaria that secrete continuous or discontinuous calcium carbonate or have a proteinaceous axis (Cairns, 2007). As basal metaozans, these organisms are diploblastic and biradial, with relatively primitive body plans. In general, this consists of a centrally located mouth surrounded by a ring of tentacles that extend down through a pharynx and into a gastrovascular cavity or coelenteron (Illustration 1.1). Sheets of tissue called mesenteries extend outward into the pharynx and connect to the undersurface of an oral disc, creating continuous compartments with the tentacles. These mesenteries essentially serve as the polyp core and are the source of digestive enzymes and gametes (Brusca et al., 1990).

There are seven distinct taxa of corals, five of which are also present in the deep sea: Scleractinia, Zoanthidea, Antipatharia, Octocorallia and Stylasteridae. Many coral

2 species are colonial and consist of numerous hollow polyps- cylindrical in shape, each with an oral disc located at the distal end (Roberts, 2006). Their tentacles also contain cnidocytes or stinging cells within nematocysts, which cnidarians use for predator defense and prey capture. The framework of many coral fauna consists of polyps supported by either a continuous external calcium carbonate (CaCO3) skeleton (the largest group being scleractinian corals) or small discontinuous CaCO3 sclerites embedded within the polyps and branches, coupled with an internal branch or axis made from CaCO3 and/or proteinaceous gorgonin (in the octocorals).

Corals, like all cnidarians, are relatively simple in composition with two germ layers, the outer ectoderm (epidermis) and inner endoderm (gastrodermis)- lining the coelenteron. The two layers are separated by gelatinous mesoglea, where gametes develop prior to being released into the coelenteron upon maturity. However they do have some specialized tissues including those involved in muscle contraction (Roberts, 2009).

Lacking advanced organs, gas exchange and waste remove occur via diffusion across the polyp’s surface. Despite being amongst the simplest of extant fauna, evidence suggests cnidarians are genetically complex, having retained various metazoan signaling pathways present in chordates but subsequently lost in higher order bilaterians (Technau et al.,

2005, Kusserow et al., 2005, Steele, 2005).

3 Gastrovacular Mesenterial Cavity or

Illustration 1.1- Basic structure of a coral polyp.

The tentacles of coral also house the photosynthetic algal symbionts characteristic of zooxanthellae corals (Rowan and Knowlton, 1995). Microbial symbionts also live within coral tissues, including those of azooxanthellate corals, which inhabit both shallow and deep, cold-water environments. Although the extent of this relationship is still uncertain, microbes are believed to have an important association with coral hosts (La

Rivière et al., 2016); when taken together they are commonly referred to as the coral holobiont (Thompson et al., 2014).

4 1.2.1 Cold-water corals

Approximately two-thirds of all known coral species (~5,100) are azooxanthellate and found at depths greater than 50 meters (m) (Cairns, 2007, Roberts, 2009). They can be found in cold waters reaching from the Artic to Antarctic and as deep as 6620 m

(Roberts, 2009). Deep-water corals are highly diverse and commonly reside at temperatures between 4-12 ºC and environmental salinities ranging from 34 – 37, with maximum abundance around 200 – 1000 m (Freiwald, 2003). Similar to their shallow- water counter-parts, cold-water corals can be habitat forming, creating complex, heterogeneous environments in areas otherwise characterized primarily by sediment and carbonate outcrops (Cordes et al., 2008). In addition to establishing the foundation of many benthic communities, cold-water corals function as shelter, feeding grounds, and nursery areas for many fish and invertebrate species. They also serve as useful paleoceanographic records (Wefer and Berger, 1991).

Although many cold-water coral species have distinct depth distributions, water temperature is believed to be the primary determinant of species distributions; optimal temperature ranges can vary by species (Roberts, 2009). The hydrostatic pressure associated with depth likely limits species distributions as well. Additional environmental controls limit cold-water coral distribution within their optimal temperature range including: availability of appropriate substratum (i.e., hard calcareous rock, skeletal or shell elements of other organisms or even soft substrata / sand), locally enhanced currents or routine water flow to transport nutrients and oxygen, and remove waste and suspended

5 sediments, as well as relatively constant salinity levels. Oxygen levels are also believed to structure cold-water coral distributions on a larger scale (Roberts, 2009).

As predominant suspension feeders, cold-water corals can derive nutrition from a range of food sources like zooplankton, particulate organic matter and detritus from surface primary production that makes its way to deep, benthic habitats and occasionally form aggregates referred to as marine snow (Silver et al., 1978). Layers of secreted coral mucous also function in heterotrophic feeding by trapping particulate matter from the water-column, in addition to the tasks of sediment cleansing and defense against various environmental stressors (Brown and Bythell, 2005). Although little is known about the reproductive ecology of deep-sea corals, there is evidence that growth and reproductive patterns are associated with seasonality in sufficient food supply (Andrews et al., 2002,

Brooke and Young, 2003, Cordes et al., 2001).

Deep-sea organisms are slow-growing and long-lived. Growth was classically measured both linearly through branch or skeletal extensions- commonly reported for scleractinians, and radially via thickening of the coral skeleton, often reported for octocorals (Roberts, 2009). Measurements are more recently supplemented with

(skeletal) isotope analyses- e.g., 210Pb and 14C (Adkins et al., 2004, Druffel et al., 1990).

Various species-specific growth rates have been published for cold-water corals though they are all on the order of micrometers to millimeters per year. Likewise, age estimates are species specific, with numerous species reported to be decades to centuries old, with some black coral predicted to be older than 4000 years (Roark et al., 2009). Regardless, their longevity and slow growth rates make them particularly vulnerable to anthropogenic disturbance (Emiliani et al., 1978, Druffel et al., 1990, Andrews et al., 2002).

6 Due to its remoteness, much of the deep sea has yet to be explored. Consequently, less is known regarding the basic biology and ecology of deep-water fauna and associates relative to their shallow-water counterparts (Gage and Tyler, 1991). As corals assist in building the foundation for benthic communities, damage to them can impact ecosystem biodiversity and functioning (Husebø et al., 2002, Freiwald, 2004). Further study of these communities is urgently required for conservation purposes, as these communities are increasingly subjected to anthropogenic disturbances linked to climate change, ocean acidification and resource extraction, including the catastrophic 2010 oil spill in the Gulf of Mexico (GoM), which led to mortality and damage to several species of octocorals

(White et al., 2012, Fisher et al., 2014b).

1.2.2 Octocorals

Proteinaceous octocorals (Octocorallia:Alcyonacea), named so for the eight-fold symmetry of their polyps, are one subgroup of corals with an internal axis for structural support and CaCO3 sclerites embedded within their polyps and branches. Over 65 percent of known octocoral families (n=45) are found in the cold waters (> 200 m) of the deep sea (Watling et al., 2011). Research on both shallow-water and deep-water species support low mortality and recruitment, and high longevity within Octocorallia (Andrews et al., 2002, Coma et al., 2004). Typically flabellate in shape, they can form dense fields

7 which serve as important habitat for numerous fish and invertebrate species (Roberts et al., 2006, De Clippele et al., 2015), and have more recently been recognized for their medicinal value (Correa et al., 2011).

1.2.3 Genus Paramuricea

The octocoral genus Paramuricea can be found globally from the subtidal zone to depths as great as 2600 m; colonies have patchy distributions associated with hard, elevated substrata (Yesson et al., 2012). Within the Gulf of Mexico, there is evidence to suggest segregation by depth within Paramuricea (Doughty et al., 2014). However, a recent study highlights the influence of water mass structure, and corresponding environmental variables, on shaping global species boundaries and the need for additional genetic data to improve species resolution (Radice et al., 2016). Radiocarbon dating (14C) on various Paramuricea spp. reveal colony ages ranging from approximately 70-100 years for relatively shallow species (~ 800 m; Sherwood and Edinger (2009)) to approximately 170-600 years old for deeper specimens (> 1000 m; Prouty et al. (2014)).

Several populations of Paramuricea spp. have recently endured serious impacts from anthropogenic disturbances (Cupido et al., 2008, White et al., 2012). Due to ambiguities in population connectivity, the recovery potential of populations impacted during the

Deepwater Horizon oil spill is uncertain but preliminarily hypothesized to take hundreds of years or more (Hsing et al., 2013, Fisher et al., 2014a, Fisher et al., 2014b).

8 1.3 Anthropogenic threats to cold-water corals

Technological advancements over the past century have expanded deep sea exploration capabilities, promoting research on cold-water ecosystems and fauna, while simultaneously enabling resource exploitation.

1.3.1 Deep-water trawling and fisheries

Bottom-trawling and other non-selective and destructive fishing techniques result in a wide range of negative biotic and abiotic effects on benthic ecosystems, disturbing and removing diverse organisms as bycatch and altering the complexity of the sea floor

(Roberts, 2006, Puig et al., 2012) in addition to adversely influencing deep-water fish populations. As cold-water corals support a range of commercially fished species, they are incidentally targeted (Hall–Spencer et al., 2002). These bottom-contact fisheries are currently believed to present the greatest impacts to deep-sea environments (Benn et al.,

2010).

1.3.2 Climate change

Environmental alterations brought about by anthropogenic-induced global climate change can also be stressful to cold-water coral ecosystems. Since the advent of the

Industrial Revolution and continued reliance upon fossil fuels, earth has entered into a new epoch labeled the Anthropocene (Steffen et al., 2007). In particular, ocean acidification poses an ongoing threat to these communities as atmospheric CO2 levels

9 continue to rise with approximately one-third of anthropogenic carbon recycled via oceanic absorption (Sabine et al., 2004). This dissolution of atmospheric CO2 causes a decrease in seawater pH, which has implications for the physiology (Georgian et al.,

2016) and skeletogenesis (energetically costly) of shallow and deep-water scleractinian corals (Langdon and Atkinson, 2005, Lunden et al., 2013) and potentially octocoral species.

1.3.3 Global warming

Accumulation of atmospheric CO2 has simultaneously caused average global temperatures to rise, with mean surface waters increasing by 0.7 °C over the past century

(Jones et al., 2007) and a predicted increase of 3 °C by 2100 (Cocco et al., 2013).

Subsequent elevation in deep sea temperature is predicted to be nearly 0.3 °C (Mora et al., 2013), leading to potential impacts on cold-water corals that have fairly narrow temperature niches (Davies and Guinotte, 2011). Likewise, ocean deoxygenation and the expansion of oxygen minimum zones are expected to accelerate as seawater temperatures rise and O2 becomes less soluble (Keeling et al., 2010). This too poses threats to cold- water corals, particularly those already residing in oxygen limited regions (Davies et al.,

2010, Lunden et al., 2013).

10 1.3.4 Oil and gas extraction in the deep sea

Expansion of oil and natural gas drilling in deep waters poses further risks to deep-water fauna, though drilling structures themselves can provide suitable colonization areas for several invertebrate species (Gass and Roberts, 2006, Larsson and Purser, 2011,

Larcom et al., 2014); the structures may also have positive influences on population connectivity (Atchison et al., 2008). Nevertheless, various extraction activities can adversely affect organisms including: physical damages caused by the placement of anchors and pipelines (Ulfsnes et al., 2013), toxic exposure to drilling byproducts

(Larsson et al., 2013), sedimentation and smothering, and exposure to chronic small-scale oil discharges (Larsson and Purser, 2011).

Within the U.S. economic exclusive zone (EEZ) alone, stretching 200 nautical miles from the coastline, approximately 1-3 minor oil spills are reported weekly (Cordes et al., 2016). Further, along the U.S. continental shelf more than 20 large oil spills greater than 160,000 liters were reported from 1971 to 2010, with 166 global reports between the years 1974-2008 (Anderson et al., 2012). These large-scale disturbances inflict impacts on a wide range of ecosystems. Exposure to crude oil and oil-dispersants applied during containment strategies are now known to have various effects on coral reproduction, health and survival (Cohen et al., 1977, Rinkevich and Loya, 1979, Loya and Rinkevich,

1980, Goodbody-Gringley et al., 2013, DeLeo et al., 2016). As resource extraction expands into deeper waters, renewed emphasis has been placed on large-scale oil spill impacts, particularly in the aftermath of the Deepwater Horizon oil spill which caused widespread economic, societal and ecological devastation.

11 1.4 The Deepwater Horizon disaster

The Deepwater Horizon (DWH) oil spill was one of the largest environmental disasters in history, releasing approximately 5 million barrels of crude oil at approximately 1500 m depth in the GoM over a three-month period (Crone and Tolstoy,

2010, Camilli et al., 2012). In addition, nearly 7 million liters of oil dispersants were applied during the ensuing cleanup efforts. Of this, approximately 3 million liters were applied at depth, for the first time in known history (Barron, 2012), without a comprehensive understanding of how this subsea application might alter the fate of oil and effect benthic ecosystems.

Recent findings have indicated that a large volume of this DWH oil was retained in deep-sea sediments (Brooks et al., 2015, Romero et al., 2015). This sunken oil may have originated from multiple sources including deep-water oil plumes (Camilli et al.,

2010), the sinking of oiled-particulate organic matter or “marine snow” (Passow et al.,

2012), and or byproducts of the surface-oil-burns (UAC, 2010, National Response Team,

2011). The long-term effects caused by large quantities of oil settling in the deep sea on the surrounding communities as well as exposure to components of the chemical dispersants applied are currently under investigation.

To date, studies assessing the in situ consequences of the DWH spill have found both sub-lethal and lethal impacts to various marine species, [fish (Whitehead et al.,

2012, Dubansky et al., 2013), mammals (Schwacke et al., 2014, Lane et al., 2015), birds

(Henkel et al., 2012) and invertebrates (White et al., 2012, McCall and Pennings, 2012)] and ecosystems, [shorelines (Michel et al., 2013) and marshes (Silliman et al., 2012, Lin and Mendelssohn, 2012), pelagic (Powers et al., 2013) and benthic communities (White

12 et al., 2012, Hsing et al., 2013, Montagna et al., 2013, Fisher et al., 2014a, Fisher et al.,

2014b)]. Shifts were also observed in microbial community structure (Hazen et al., 2010,

Bik et al., 2012) with evidence to suggest dispersant applications had inhibitory effects on the biodegrading abilities of marine microbes (Kleindienst et al., 2015).

1.4.1 Deepwater Horizon impacts on cold-water corals

Impacted deep-sea coral communities were discovered at a depth of approximately 1370 m, 11 km southwest of the Macondo well explosion (White et al.,

2012). Various species of octocorals were found within this lease block site, Mississippi

Canyon (MC) 294, and the most abundant of the impacted species was Paramuricea biscaya (Grasshoff, 1977). Many of the corals at the site were covered with brown flocculent material (hereafter referred to as floc), and exhibited characteristic signs of stress and mortality, including excess mucus production, sclerite enlargement and tissue loss. Further analysis of this floc revealed hydrocarbons from the Macondo well were indeed present (White et al., 2012) as were traces of the dispersant component Dioctyl

Sodium Sulfosuccinate or DOSS (White et al., 2014). Additional mesophotic and deep- water coral communities were subsequently found (Fisher et al., 2014b, Etnoyer et al.,

2015). The source of the floc is most likely oiled marine snow (Fisher et al., 2014b), however, the precise origin of this floc as well as the underlying effects leading to the observed physical damage and lethality at MC294 are still under investigation.

Organismal stress responses often manifest as a response to macromolecular cellular stress (Hauton, 2016). For this reason, investigating both phenotypic /

13 physiological reactions in conjunction with cellular responses can advance our understanding of how organisms respond to environmental disturbance.

1.5 Cellular responses to environmental stress

Responses to stress at the cellular level are conserved across kingdoms and often occur in two stages- early responses which attempt to counteract and repair damage, and apoptosis (cell death) and cell cycle maintenance (Kültz, 2005). Trailing the cellular stress response (CSR) is a state referred to as the cellular homeostatic response (CHR) which may vary by stressor, species and cell-type. The CHR continues until cellular conditions are again altered; it typically involves the homeostasis of ion balance and cell volume (Kültz, 2005) in addition to proteins and enzymes of various antioxidant pathways (Hauton, 2016). Both the CSR and CHR are energetically costly as is the maintenance of immune function (Folstad and Karter, 1992, Hauton, 2016) which may also be induced in response to environmental disturbance (Kültz, 2005). Therefore, the energetic constraints of an organism may limit its ability to respond to stress at the cellular level.

1.5.1 Chemical stressors

Chemical stressors can have a number of biological effects- contingent on chemical composition, which in turn influences uptake, down-stream processing and metabolism. Chemical pollutants can be ingested and absorbed across epithelia or diffuse across cell membranes. While some chemicals are toxic themselves, others may have

14 more toxic metabolic derivatives upon intracellular processing (Lewis and Santos, 2016).

Moreover, organisms have thresholds to their ability to detoxify and excrete noxious chemicals, likely correlated to toxicity level and overall energetic limitations. Once this ability is surpassed, toxic effects intensify and lead to adverse effects and visible damage.

Exposures to chemical pollutants have been shown to elicit a variety of cellular responses including oxidative stress, genotoxicity (i.e., DNA damage) and immunotoxicity

(Galloway and Handy, 2003, Lewis and Santos, 2016).

Cnidarians, like other metazoans, have highly conserved defensive pathways that can serve as potential biomarkers for stress (Goldstone, 2008, Reitzel et al., 2008, Venn et al., 2009, Shinzato et al., 2012, Elran et al., 2014). In particular, the “chemical defensome” consists of a network of evolutionarily conserved genes and proteins that are involved in various processes including the metabolism, biotransformation (i.e., chemical modification) and / or removal of xenobiotic toxins as well as protein homeostasis

(Goldstone et al., 2006, Goldstone, 2008). Defensome receptors react to toxic substances and damage, and include various ligand-activated transcription factors, cytochrome p450’s (CYPs) and other oxidases, conjugating enzymes, ATP-dependent efflux transporters and oxidative detoxification proteins (Goldstone et al., 2006). Upon detection, expulsion of foreign compounds is attempted. However, once in the cytoplasm, biotransformation occurs in an effort to inactivate or eliminate the compounds.

One biotransformation method is via oxidative modification by CYPs, which produce more polar / water-soluble compounds for excretion (Goldstone et al., 2006).

Exposure to polycyclic aromatic hydrocarbons (PAHs)- components of crude oil, was found to induce the upregulation of CYPs in various vertebrates and invertebrates and is

15 considered to be a useful biomarker for exposure (Aas et al., 2000, Bard, 2000, Carlson et al., 2004a, Solé et al., 2007, Whitehead et al., 2012).

1.5.2 RNA sequencing to investigate impacts

RNA sequencing (RNAseq) has become a powerful and affordable tool to measure genome-wide expression changes in response to environmental change, especially for species lacking extensive genomic information. Transcriptome analyses can detect variations in the abundance of mRNA’s functioning in both cellular processes and protein formation (Oleksiak et al., 2002, Aubin‐Horth and Renn, 2009, Crawford and Oleksiak, 2007, Ekblom and Galindo, 2011b, Whitehead et al., 2012). RNAseq can therefore be used on non-model organisms to study effects of environmental variation on gene expression. These analyses can then allow researchers to investigate underlying causes of phenotypic / physiological responses to environmental changes and disturbance.

Initial studies using genomic-scale approaches to investigate environmental stress have focused on the response of zooxanthellate, shallow-water corals (< 200 m) to thermal stress (Edge et al., 2005, Edge et al., 2008, Morgan et al., 2005) and bleaching

(DeSalvo et al., 2008, Voolstra et al., 2009). These stress studies support the involvement of oxidative stress and apoptosis (Richier et al., 2008, DeSalvo et al., 2008), while revealing additional transcripts encoding transcription factors, cell-signaling molecules and transport and extracellular matrix proteins (Desalvo et al., 2010).

16 Gene expression investigations specifically investigating DWH-oil exposure on the shallow-water killifish species, Fundulus grandis, similarly revealed differential expression related to immune responses, response to hypoxic conditions and CYPs.

Tumor necrosis factor (TNF) receptors, which function in immune signaling pathways, were also found to be up-regulated with a large number of 40S and 60S ribosomal proteins being down-regulated in response to the oil exposure (Garcia et al., 2012). Other studies using RNAseq to examine cellular effects of the DWH-oil spill have likewise demonstrated its utility in illuminating the overall extent of damage imposed on a variety of fauna (Dubansky et al., 2013, Rivers et al., 2013).

1.6 Objectives

Throughout the following chapters I attempt to elucidate the magnitude of impact imposed on cold-water corals by the Deepwater Horizion oil spill, through a combination of experimental toxicity tests and gene expression analyses. This work integrates both visible and cellular responses to two chemical pollutants, crude oil and the oil-dispersant

Corexit9500A®, to advance our understanding of how deep-sea corals respond to anthropogenic disturbances and more specifically to chemical stressors. Chapter Two explores the genome-wide expression changes of in situ spill-impacted P. biscaya colonies exposed to floc containing oil and dispersant. This chapter likewise describes the construction of a reference transcriptome assembly for Paramuricea which is utilized in later chapters. In Chapter Three, I investigate the toxicity and stress induced phenotypes resulting from experimental exposures to crude oil and dispersant using two abundant deep-water octocorals from the GoM: Paramuricea type B3 and Callogorgia delta.

17 Chapter Four then utilizes gene expression analyses to examine cellular-level impacts stemming from short-term experimental exposures on Paramuricea type B3 to oil, dispersant and oil / dispersant mixtures. I conclude by addressing the need for preemptive protections for deep-water ecosystems due to the unprecedented nature of deep sea restoration, and suggest important areas of continued research concerning anthropogenic impact monitoring and assessing the fate of cold-water corals in future ocean states.

18 CHAPTER 2

GENE EXPRESSION PROFILING REVEALS DEEP-SEA CORAL RESPONSE TO THE DEEPWATER HORIZON OIL SPILL

2.1 Abstract

Deep-sea coral communities are key components of the Gulf of Mexico ecosystem that was adversely affected by the Deepwater Horizon (DWH) oil spill. Coral colonies exposed to oil and dispersant exhibited mortality, damage and physiological signatures of stress. Understanding how corals respond to oil and dispersant exposure at the molecular level will elucidate the sub-lethal effects of the DWH disaster, and reveal broader patterns of coral stress responses. Gene expression profiles from RNAseq data were compared between corals at an impacted site and from a reference site. A total of

582 differentially expressed genes (≥ 4-fold) were shared among impacted Paramuricea biscaya colonies. Genes involved in the metabolism of xenobiotics, wound repair, tissue regeneration, and innate immunity were over-expressed in impacted corals. The majority of under-expressed genes included ribosomal proteins and genes vital to toxin processing, cellular maintenance and homeostasis, suggesting a diminished capacity to maintain crucial cellular mechanisms for coping with environmental stress. Our results provide evidence that deep-water corals exhibited genome-wide cellular stress responses to oil and dispersant exposure and will facilitate the development of diagnostic markers to be used in future oil spill response efforts.

19 2.2 Introduction

The deep oceans comprise the largest and most diverse ecosystem on the planet

(Snelgrove, 1999). Although the deep sea has historically been considered isolated from human disturbance, the increased reaches of human industrialization challenge this paradigm. Impacts of accelerated industrialization during the last century have been highlighted by the drastic decline in deep-water fisheries (Koslow et al., 2000, Devine et al., 2006) and the negative impacts on deep-sea corals from the Deepwater Horizon

(DWH) oil spill in the Gulf of Mexico (White et al., 2012, Fisher et al., 2014b).

Recent studies assessing the in situ consequences of the DWH spill have found both sub-lethal and lethal impacts in various marine species, [i.e., fish (Whitehead et al.,

2012, Dubansky et al., 2013), mammals (Lane et al., 2015), birds (Henkel et al., 2012), and invertebrates [(White et al., 2012, McCall and Pennings, 2012)] and ecosystems [i.e., coastal (Silliman et al., 2012, Michel et al., 2013) and benthic communities (White et al.,

2012, Montagna et al., 2013, Fisher et al., 2014a, White et al., 2014)]. In addition to shallow-water and water-column impacts, oil and dispersant entered deep waters via multiple pathways, impacting deep benthic communities. A persistent plume of oil microdroplets was present at approximately 1100 m depth (Reddy et al., 2012). In addition, deposition to the seafloor (Romero et al., 2015) occurred via sinking of oiled- particulate organic matter or “marine snow” (Passow et al., 2012) and/or as the byproducts of the surface-oil-burns (UAC, 2010, National Response Team, 2011). These pathways of oil and dispersant to the subsurface led to impacts on deep-sea and mesophotic coral habitats (White et al., 2012, Hsing et al., 2013, Etnoyer et al., 2015).

20 Cold-water coral communities impacted by the DWH disaster were first discovered at Mississippi Canyon (MC) 294, which is located 11 km southwest of the wellhead at a depth of approximately 1370 m. Several coral species were covered to varying degrees with a brown flocculent material (floc) that contained hydrocarbons from the Macondo well (White et al., 2012). The floc also contained the anionic surfactant, dioctyl sodium sulfosuccinate (DOSS), a major component of the chemical dispersants applied on surface slicks and at depth at the wellhead source (White et al., 2014). Since the initial discovery, three additional impacted deep-sea coral sites have been found at similar depths, 1550-1850 m (Fisher et al., 2014b) and three impacted, mesophotic sites were discovered in 60-90 m depths (Etnoyer et al., 2015).

The most numerous coral species impacted in the spill was the octocoral,

Paramuricea biscaya [Grasshoff 1977] (Deepwater Horizon Natural Resource Damage

Assessment, 2016). At the time of discovery, colonies exhibited signs of stress, including mucous production, tissue sloughing, and necrosis (White et al., 2012). Declines in health associated with hydroid colonization and continued tissue loss were subsequently discovered (Hsing et al., 2013). Because P. biscaya is long-lived (> 600 years) and slow- growing, 0.34 -14.20 µm yr-1 (Prouty et al., 2014), this species is particularly vulnerable to anthropogenic disturbances. Damage to these habitat-forming corals can in turn negatively affect biodiversity and ecosystem functioning (Husebø et al., 2002, Freiwald,

2004) because of their role in forming large, arboreal structures that provide shelter, feeding grounds and nursery areas for a diverse range of species including commercially important fisheries (De Clippele et al., 2015).

21 Understanding how corals respond to oil spills and dispersant application requires a more in depth understanding of their molecular and cellular responses to in situ exposure. As the frequency of human-induced disturbances increases in deeper waters due to resource extraction, pollution, bottom-trawling, and the ongoing threat of ocean acidification (Fosså et al., 2002, Thresher et al., 2011), further investigations into how corals respond to stress will improve our understanding of their resiliency to these challenges. Insight can be gained by examining the whole-organism physiological response to experimental oil and dispersant exposure, which has shown that the dispersant is at least as toxic as oil solutions at the same concentrations (DeLeo et al.,

2016).

Coral stress responses may be evident by examining changes in gene expression.

Through RNA sequencing, variations in the functioning of mRNA in both gene expression and protein structure can be detected (Ekblom and Galindo, 2011a, Whitehead et al., 2012). In this present study, we constructed a de novo transcriptome and compared gene expression patterns between P. biscaya colonies damaged during the DWH oil spill, with those found at a reference site. These expression patterns were also compared to other studies that explored the effects of environmental stressors on related cnidarians and other organisms exposed to oil.

Based on the phenotypic observations and documented physical damage to impacted P. biscaya at MC294 as well as the known contents of the floc found on the colonies, we hypothesize that damaged corals will display changes in relative expression indicative of general cellular stress responses and toxin processing. Elucidating the consequences of contaminant exposure on impacted cold-water corals at the molecular

22 level advances our understanding of stress response patterns and the damage imposed by the DWH incident. Further, this study will provide a basis for understanding the impacts of future events.

2.3 Methods

2.3.1 Sample collections

Spill-impacted Paramuricea biscaya colonies partially covered in floc containing macondo well oil and dioctyl sodium sulfosuccinate (DOSS), a component of the dispersants applied during cleanup efforts (White et al., 2012, White et al., 2014) were collected from site MC294 (November 2010; n=2) and preserved at depth in RNAlater using the remotely operated vehicle (ROV) Jason II (Figure 2.1). Paramuricea spp. colonies (n=4) without signs of visible impact were also collected and preserved at depth in RNAlater. However, visibly healthy “control” colonies of P. biscaya were not collected at the same site as all corals at MC294 were believed to have been exposed to hydrocarbons and dispersant to some degree (White et al., 2012). Control corals were of two genetic haplotypes B1 (P. biscaya) and B3 (Paramuricea type B3); these sister species exhibit no sequence differences in the MutS gene or their nuclear 28S gene but are polymorphic in their COI gene with 1-2 SNPs (Doughty et al., 2014). Two specimens were collected using the deep-submergence vehicle (DSV) Alvin (December 2010) from site MC344 (B1, depth ~1840 m), the closest known site to MC294 with the same

23 P. biscaya haplotypes (Doughty et al., 2014). Two additional colonies were collected using the ROV Schilling UHD (October 2011) at Atwater Valley (AT) 357 (B3, depth

~1040 m).

Figure 2.1- Collections of impacted colonies of P. biscaya covered in brown flocculent material, with traces of oil from the Macondo well and dispersant (DOSS). Samples were preserved at depth in RNAlater (2010).

The damaged well was releasing oil and gas from April until late July, and floc- exposed corals were sampled in November, 2010. If this were the primary pathway of exposure, initial responses of corals to the contaminants would have subsided long before the sampling point. However, the more likely pathway for the exposure of these corals and the generation of the flocculent material found covering them was via oil that rose to the surface and was transported to depth via oiled marine snow (Fisher et al., 2014b,

Passow, 2014). This process has a less certain timeline, but it remains unclear how much time had passed between the initial exposure the sampling date.

B1-type corals serve as controls for gene expression, preserved in situ (MC344), while B3 individuals represent variability within the closely related sister species

24 (AT357) that may have only recently diverged (Doughty et al., 2014). These individuals were included in the reference transcriptome assembly, but not in the pairwise comparisons of differential gene expression.

2.3.2 RNA isolation and sequencing

Total RNA was extracted using a Qiagen RNeasy Kit, with a duplicate control sample (type B3) also extracted to check for within colony variation among polyps.

Excess RNAlater was removed from tissues before homogenization. Three samples were extracted using a modified Trizol/Qiagen RNeasy protocol (Polato et al., 2010, Burge et al., 2013b), after low initial RNA yields. Concentrations were assessed using a

NanoDrop® ND-1000 and RNA quality was evaluated through gel electrophoresis via the presence of intact 28S and 18S ribosomal RNA bands. Assessment of RNA integrity and mRNA library construction were performed at the University of Wisconsin-Madison

Biotechnology Center using an Agilent 2100 BioAnalyzer and TruSeq RNA Library

Preparation Kit (v2). After adaptors were attached, all libraries were pooled and sequenced using an Illumina HiSeq2000 flow-cell producing 100 base pair (bp), paired- end reads.

2.3.3 Sequence processing and de novo transcriptome assembly

A custom bioinformatics pipeline was developed to retain high quality paired-end reads for each sample. Trimmomatic (Bolger et al., 2014) removed any remaining adapter

25 and barcode sequences attached during sequencing. Paired reads were then merged using

FLASH (Magoc and Salzberg, 2011) to produce longer reads, and passed to Jellyfish

(Marcais and Kingsford, 2012) for quality control and kmer read normalization. Counts from Jellyfish were then input into Quake (Kelley et al., 2010), which corrects for substitution-sequencing errors by adopting a kmer error-correction framework specifically intended for Illumina reads.

High quality, corrected reads from all samples (n=7) were combined to create a single consensus assembly for Paramuricea spp. using the RNA-seq assembly program

Trinity (Grabherr et al., 2011), following developer’s protocols (Haas et al., 2013) and custom scripts. Following assembly, sequences (>300 bp) were passed through a series of filters using the BLAST-like alignment tool, BLAT (Kent, 2002) in an effort to isolate coral and microbial sequences discretely and remove environmental contamination.

Similar to other studies (Shinzato et al., 2014, Pinzón and Kamel, 2015), contigs- representative of true mRNA transcripts, were primarily filtered with the transcriptome data of other marine species (Table 2.1) to characterize the coral host transcriptome.

More specifically these BLATs utilized transcriptome data from the shallow-water octocoral, Gorgonia ventalina (Burge et al., 2013a), other cnidarians and marine organisms, genomic data from the cnidarian Nematostella vectensis (Putnam et al., 2007)

(BLAT, <1e-5; Table 2.1) and the environmental nucleotide database (BLAT, <1e-40), an NCBI (National Center for Biotechnology Information) publically available database.

All sequencing data significantly (<1e-5) matching G. ventalina and / or other marine species that did not significantly hit the environmental database (<1e-40), was kept.

26 As a quality check for transcriptome assembly completeness, KEGG pathway mapping was performed using KAAS (KEGG Automatic Annotation Server) annotation tools (Moriya et al., 2007) in each of the conserved spliceosome, ribosome, and proteasome pathways to ensure the presence of a high percentage (> 90 percent) of major pathway components (M. Matz, pers. comm., similar to methods of Pinzón and Kamel

(2015)). To further assess the completeness of the final Paramuricea spp. transcriptome assembly, BLAT searches were performed (<1e-5) against related cnidarian assemblies,

Nematostella vectensis (Putnam et al., 2007) and Gorgonia ventalina (Burge et al.,

2013a). Similar to other studies (Polato et al., 2011, Burge et al., 2013b), OrthoDB v8

(Waterhouse et al., 2013) was used to acquire genomic orthologous sequences conserved in the cnidarian metazoans, Hydra magnipapillata and N. vectensis. Homologs were identified using conserved protein sequences from 257 orthologous groups using

TBLASTN (<1e-5).

27 Table 2.1- Sequence similarity between the coral host transcriptome for Paramuricea spp and marine species. The number of sequences for each species includes multiple matches against the Paramuricea spp. assembly. Significant sequence hits to Paramuricea spp.

-5 were keptSupplementary (e =1x10 Table). 1 . *denotesSequence similarity genomic between sequences.the coral host transcriptome for Paramuricea spp and marine species. The number of sequences for each species includes multiple matches against the Paramuricea spp. Significant sequences hits to Paramuricea spp. were kept (e =1x10-5). *denotes genomic sequences.

Classification Species Source Number of All contig Significant sequences matches Contig matches (e =10-05) Cnidaria Gorgonia ventalina Burge et al. 2013 90,230 254,709 178,068 Anthozoa Octocorallia

Cnidaria Acropora millepora Moya et al. 2012 52,963 39,352 26,166 Anthozoa Acropora palmata Polato et al. 2011 88,020 31,769 21,533 Hexacorallia Pocilliopora damicornis Traylor-Knowles et al. 2011 70,786 42,314 27,838 Scleractinia Porites asterodides Kenkel et al. 2013 30,740 8,975 6,361 Fungia scutaria Meyer lab (2013) 157,880 81,706 54,098 Montastraea cavernosa Meyer lab (2013) 222,244 130,482 87,719 Pseudodiploria stringosa Meyer lab (2013) 107,924 34,657 23,400 Seriatopora hystrix Meyer lab (2013) 214,882 47,555 32,514

Cnidaria Nematostella vectensis * Putnam et al. 2007 50,021 358,284 155,851 Anthozoa Anthopleura elegantissima Meyer lab (2013) 145,832 71,194 44,680 Hexacorallia Actinia

Cnidaria Hydra vulgaris Wenger & Galliot 2013 3,922 1,610 1,150 Medusozoa

Porifera Amphimedon queenslandica Degnan et al. 2008 13,397 16,087 6,634

Mollusca Crassostrea gigas Ensembl 7,658 28,916 12,648 Lottia gigantea Simakov et al. 2013 4,475 33,926 14,372

Echinodermata Strongylocentrotus Tu et al. 2012 29,072 32,815 12,736 pupuratus Hemichordata Saccoglossus kowalevskii JGI Genome portal (2013) 7,282 61,304 25,489

Chordata Branchiostoma floridae Putnam et al. 2008 398 48,345 17,996

28 2.3.4 RNAseq differential and functional analysis

Consensus sequences representative of mRNA transcripts (hereafter referred to as contigs), from each impacted P. biscaya sample (n=2) considered in this study as biological replicates of exposure, were compared to P. biscaya control samples (n=2).

Transcript and gene abundance estimates were obtained using Bowtie (Langmead et al.,

2009) and RSEM (Li and Dewey, 2011) based on the expected count or the number of reads mapping to a given gene identifier. FPKM-normalized (Fragments Per Kilobase of transcript per Million mapped reads) gene-level abundance estimates from RSEM were combined into a counts matrix and used as input for edgeR. Differentially expressed genes or DEGs were then identified using edgeR (Robinson et al., 2010) and expressed as log2-fold changes (FC) as data spanned several orders of magnitude. False discovery rate

(FDR) was controlled at 0.1 percent. Fold-changes were considered significant if greater than four-fold. Volcano plots, yielding a visual representation of the DEGs, were also generated via edgeR.

A heatmap was generated using the gplots package for R (Warnes and Schwartz,

2009) following the conversion (log2[x+1]) of the normalized expression data. The converted expression data was scaled to Z-Scores to make it comparable across exposure- type and genes. The gplots package was also used to construct a gene dendrogram with hierarchical clustering via the complete linkage method. Functional enrichment of discrete gene clusters was performed with DAVID using a medium classification stringency (Huang et al., 2008).

29 In accordance with Trinity’s annotation pipeline, the “Transcriptome Functional

Annotation and Analysis” program or Trinotate v2.0 (Grabherr et al., 2011) was used to functionally annotate DEGs, using the longest isoform per gene. BLAST (Altschul et al.,

1990) and Gene Ontology [GO] (Ashburner et al., 2000) output parameters were primarily used. Similar to other studies, i.e., Meyer et al. (2009), assigned gene names were based on the best blast hit with a gene name. For the scope of this study, analysis was restricted to DEGs shared between both spill-impacted colonies.

GOseq (Young et al., 2010) was then used to perform gene ontology enrichment analyses for over- and under-expressed gene sets 1) shared among impacted P. biscaya colonies and 2) sets unique to each impacted individual, similar to methods from Moya et al. (2012). As a large number (654 to 2,217) of significant DEGs (≥ 4-fold) were specific to each impacted coral colony, this analysis reduced the wide-range of effects caused by oil and dispersant-contaminated floc exposure into a list of affected cellular processes and functions. Furthermore, it simplified comparisons of the heterogeneous genome-wide response specific to each impacted coral colony.

2.4 Results and Discussion

2.4.1 Paramuricea sp. reference transcriptome

An average of 70.9 ± 8.16 SD million Illumina reads were generated in each sample. After the initial trimming process (Trimmomatic v0.22), approximately 97 percent of all paired-end reads were retained. An average of 61.9 percent (%) of those

30 paired-end reads were combined into longer sequences or contigs (via FLASH) and used in the de novo assembly.

A total of 260,617 contigs (representing mRNA transcripts) were assembled.

Approximately 30 percent (70,880 contigs) of these were retained as primarily host sequences after significant (<1e-5) matches were found between these contigs and other cnidarians and marine invertebrate assemblies (Table 2.1). Following the removal of an additional 2,322 contigs with significant matches to NCBI’s environmental database,

68,558 contigs remained (Table 2.2).

Table 2.2- Assembly statistics for the Paramuricea spp. de novo transcriptome reconstruction before filtering sequences against available (marine) transcriptomic data and after filtering.

Assembly statistic Pre-filtering Post-filtering Total genes 144,151 25,189 Total transcripts 260,617 68,558 GC content 38.85% 39.38% N50 1,451 2,474 Average contig length 967.76 1,758.54 Median contig length 570 1,363 Reconstruction size 252,214,357 120,562,037

The mean contig size was 1758.5 bases (Figure 2.2). The final assembly revealed an estimate of 25,189 genes with an N50 (i.e., weighted contig mean) of 2,474 bp (Table

2.2) and an approximate transcriptome reconstruction-size estimate of 120.6 Mb. The

31 average number of reads per contig was 2603.7 with an estimated transcriptome read coverage of approximately 4.12X.

The de novo transcriptome assembly shares a large portion of contiguous sequences or contigs (overlapping consensus sequences), with other cnidarian transcriptomes (67.7% match with Gorgonia ventalina and 43.3% match with

Nematostella vectensis, Table 2.3). The generated transcriptome also possesses a high percentage (99.6%, 256 of 257) of contigs with significant matches to conserved metazoan orthologs obtained from related cnidarians (OrthoDB), indicating a high quality assembly, comparable to methods and findings of Polato et al. (2011).

5000 4000 3000 Counts 2000 1000 0

0 5000 10000 15000 20000 25000 30000 Length of transcript

Figure 2.2- Histogram of transcript or contig lengths (bp) for the final Paramuricea spp. reference assembly.

32 Table 2.3- BLAST results for Paramuricea spp. reference assembly comparisons (top) of transcripts (n= 68,558) to the transcriptome (G. ventalina) and genome (N. vectensis) of related cnidarians and (bottom) matches to orthologous sequences (OrthoDB) conserved in the cnidarian metazoans, H. magnipapillata and N. vectensis. The number of unique sequence matches (unique hits) does not include sequences that hit multiple times.

BLAST Species Total Hits Pass cutoff (<1e-5) Hits Tossed Unique Hits G. ventalina 46,463 46,430 33 31,873 N. vectensis 29,711 24,474 5,237 534

Species Total Hits Pass cutoff (<1e-5) Hits Tossed Unique Hits N. vectensis 436,284 274,966 161,318 256 H. magnipapillata 244,242 138,045 106,197 256

The Paramuricea transcriptome provides a much-needed reference for cold-water corals and octocorals in general. As this genus is widespread and has been impacted by other anthropogenic disturbances (Cerrano et al., 2000, Coma et al., 2004), this transcriptome will be useful in future investigations of anthropogenic impacts affecting coral communities as resource extraction expands and evolves in the GoM and beyond.

This assembly will help to further evaluate oil spill impacts on coral ecosystems via comparisons to corals experimentally exposed to crude oil and dispersant fractions in addition to investigating the consequences of forecasted environmental conditions induced by global climate change.

33 2.4.2 RNAseq Analysis

A large suite of genes was differentially expressed in each impacted coral (Figure

2.3). In this study, we identified 1,236 differentially expressed genes or DEGs (≥ 4-fold) from P. biscaya impact sample 1: 669 over- and 567 under-expressed relative to the control group, and 2,799 DEGs from P. biscaya impact sample 2: 1,550 over- and 1,249 under-expressed (Table 2.4).

Among the two impacted samples (Figure 2.1), 582 DEGs were shared (with 236 over- and 346 under-expressed) and represent the primary focus of this chapter. The shared DEGs grouped into five main hierarchical clusters based on gene ontology (Figure

2.4). Significant gene ontology (GO) enrichment was found in the two largest clusters.

The most enriched terms for cluster a were the molecular functions (MF)

'ribonuclease/endonuclease activity' (p <0.001) and for cluster b the biological process

(BP)/ MF 'translation/ribosomal structure' (p<0.05). Approximately 63% of the over- expressed and 68% of the under-expressed shared-DEGs were annotated (Tables 2.5 and

2.6). The log2-fold change of these genes ranged from approximately 2.7-13.2.

34

Figure 2.3- MA and Volcano plots generated in edgeR displaying differentially expressed genes (dots) for impact sample 1 (top) and 2 (bottom) relative to control expression. MA plots (left) represent the log-ratio of fold-changes against the average abundance or count. The Volcano plots (right) show the statistical significance, based on FDR corrected p-values (log10[FDR]), relative to the magnitude of change – fold-change

(logFC). Emphasis was placed on genes with FDR £ 0.001 and FC ³ 4 (shown in red).

35 Table 2.4- Number of differentially expressed genes (≥ 4-fold) for each impacted P. biscaya individual, significantly over- or under-expressed (FDR=0.001) relative to ‘un- impacted’ P. biscaya. Various genes were found in both individuals (shared), of which only a portion of which have blast annotations (annotated).

Over- Under- Total Impact 1 669 567 1,236 Unique Impact 2 1550 1249 2,799 Total 236 346 582 Shared Annotated 90 244 334

36

Color Key

−1 0 1 Row Z−Score

a

b

Control Impact 1 Impact 2

Figure 2.4- Heat map for genes differentially expressed in both spill-impacted P. biscaya colonies relative to the expression in visibly healthy, control P. biscaya. Normalized gene expression values were scaled to Z-Scores for comparability. The constructed dendrogram (left) represents the hierarchical clustering of genes, yielding five main clusters. The most enriched GO terms for cluster a were the molecular functions (MF)

'ribonuclease/endonuclease activity' and the biological process (BP)/ MF

'translation/ribosomal structure' for cluster b.

37

Table 2.5- Log2-fold changes (FC) for genes jointly over-expressed for impact sample 1 and 2, average log2FC and their corresponding best-hit BLAST annotations. Genes are sorted based on descending average log2FC.

Log2FC Log2FC Average Gene ID Impact 1 Impact 2 Log2FC BlastX annotation c81012_g2 12.78 13.22 13.00 Deleted in malignant brain tumors 1 protein c92172_g1 13.54 12.27 12.90 Sentrin-specific protease 1 Transient receptor potential cation channel subfamily A c109447_g1 13.29 11.35 12.32 member 1 c92642_g3 11.37 12.98 12.18 TNF receptor-associated factor 6 c81012_g1 10.36 11.93 11.15 Deleted in malignant brain tumors 1 protein c104741_g1 10.68 11.45 11.06 Luciferin-binding protein c94370_g2 9.41 12.48 10.94 Neurexin-4 c102207_g2 9.27 10.92 10.09 Protocadherin Fat 1 c101774_g2 10.88 9.28 10.08 Amine sulfotransferase c112392_g2 9.51 10.61 10.06 Integrase/recombinase xerD homolog c110961_g4 8.67 10.81 9.74 Type I iodothyronine deiodinase c89909_g4 9.91 9.35 9.63 Serine/threonine-protein kinase/endoribonuclease IRE1 c97200_g3 9.85 9.37 9.61 Eukaryotic translation initiation factor 4 gamma 1 c92858_g1 9.13 9.37 9.25 Serine/threonine-protein kinase/endoribonuclease IRE1 c107005_g4 8.13 9.86 9.00 Serine/threonine-protein kinase/endoribonuclease IRE1 c92144_g2 8.56 8.83 8.70 Protein FAM111A c97200_g1 9.33 7.70 8.51 Eukaryotic translation initiation factor 4 gamma 3 c98145_g1 8.20 8.46 8.33 Sacsin c103455_g3 8.74 6.45 7.59 BTB/POZ domain-containing protein 2 c111620_g2 7.40 7.69 7.55 Epidermal growth factor-like protein 6 c100075_g1 9.39 5.41 7.40 Kelch-like protein 40 c103148_g2 8.89 5.38 7.14 Transposon Ty3-G Gag-Pol polyprotein c96321_g3 4.75 8.89 6.82 Contactin-associated protein like 5-3 c107392_g1 4.13 9.26 6.69 Tyrosinase c81405_g1 6.44 5.88 6.16 ATP-dependent DNA helicase RecQ c104180_g1 4.81 7.26 6.04 Synaptotagmin-17 c85756_g1 5.28 6.38 5.83 ATP-dependent DNA helicase Q-like 4A c85551_g2 5.65 5.84 5.74 Neurexin-4 c111127_g1 4.65 6.82 5.74 Fibropellin-1 c103580_g1 6.88 4.42 5.65 Probable G-protein coupled receptor 112 c82336_g1 4.61 6.54 5.57 P2X purinoceptor 7 c99957_g1 6.57 4.31 5.44 Neuropilin-1 c99999_g2 4.58 6.21 5.39 Gypsy retrotransposon integrase-like protein 1 c89873_g1 4.95 5.82 5.39 Tyrosinase c106598_g1 7.60 3.11 5.35 Protein FAM111A c107101_g2 4.50 5.87 5.18 Zinc finger MYM-type protein 2 c92004_g2 5.24 5.01 5.12 TNF receptor-associated factor 3 c110532_g2 4.89 5.16 5.02 Zona pellucida sperm-binding protein 2

38 Table 2.5 continued- RNA-directed DNA polymerase from mobile element c110271_g1 5.23 4.78 5.01 jockey c93159_g4 4.30 5.67 4.99 Sentrin-specific protease c104741_g3 5.29 4.56 4.92 Sticholysin-2 c110724_g3 4.18 5.60 4.89 STI1-like protein c101337_g2 4.53 5.11 4.82 ATP-dependent DNA helicase PIF1 c104839_g3 4.45 5.13 4.79 Uncharacterized protein C14orf28 homolog c107918_g1 4.16 5.36 4.76 Kielin/chordin-like protein c97858_g2 4.85 4.15 4.50 Excitatory amino acid transporter 1 c93689_g1 4.56 4.44 4.50 Hemicentin-1 c95755_g1 4.47 4.42 4.45 Cytochrome P450 1A1 Transient receptor potential cation channel subfamily A c107837_g2 3.20 5.61 4.41 member 1 c97309_g1 4.50 4.28 4.39 Contactin-4 Probable RNA-directed DNA polymerase from c105162_g2 5.27 3.33 4.30 transposon X-element c92813_g2 4.46 4.12 4.29 Usherin c99393_g1 3.13 5.43 4.28 Ephrin type-B receptor 3 c100234_g3 3.13 5.29 4.21 Epithelial discoidin domain-containing receptor 1 c110298_g1 3.33 5.08 4.21 Zinc finger protein 396 c103347_g2 3.73 4.68 4.21 Pro-Pol polyprotein c109804_g1 3.59 4.67 4.13 Protocadherin Fat 4 c100425_g1 3.33 4.85 4.09 Transcription factor SOX-8 c98465_g1 3.32 4.78 4.05 Peroxidasin c98122_g5 3.04 4.86 3.95 Transcription factor SOX-10 c98634_g3 4.42 3.47 3.95 ATP-dependent DNA helicase RecQ Probable RNA-directed DNA polymerase from c101928_g2 4.25 3.63 3.94 transposon BS c102655_g1 3.73 4.00 3.86 E3 ubiquitin-protein ligase CCNB1IP1 c104927_g1 4.16 3.56 3.86 N-acetylglucosaminyltransferase c101240_g2 3.49 4.20 3.85 Trace amine-associated receptor 7b c95371_g1 3.11 4.51 3.81 Short-chain collagen C4 c94411_g1 3.77 3.77 3.77 Homeobox protein EMX2 c98852_g1 3.78 3.64 3.71 Serine/threonine-protein kinase/endoribonuclease IRE1 Pancreatic secretory granule membrane major c107731_g1 4.07 3.29 3.68 glycoprotein GP2 c86576_g2 2.94 4.40 3.67 Kielin/chordin-like protein c108364_g2 2.97 4.30 3.63 Protein amnionless c88329_g1 2.92 4.28 3.60 Laccase-4 c101965_g1 3.40 3.70 3.55 Meprin A subunit beta Probable RNA-directed DNA polymerase from c104092_g1 3.60 3.48 3.54 transposon BS c96802_g1 3.22 3.80 3.51 Collagen alpha-1(XXI) chain c103701_g2 3.98 3.01 3.49 Putative zinc finger protein 724 Probable RNA-directed DNA polymerase from c108127_g1 3.68 3.25 3.46 transposon BS c105007_g1 3.94 2.87 3.41 Ubiquitin c100876_g3 3.92 2.89 3.40 Putative L-amino-acid oxidase YobN c103840_g1 3.20 3.56 3.38 G2/M phase-specific E3 ubiquitin-protein ligase

39 Table 2.5 continued- c105948_g1 3.05 3.69 3.37 ATP-dependent DNA helicase tlh1 c92470_g1 3.08 3.65 3.37 Transcription factor SOX-8 c95961_g2 3.59 3.10 3.34 Fibroblast growth factor 12 c94919_g2 3.58 3.08 3.33 Granulins c107197_g1 3.64 2.99 3.31 BTB/POZ domain-containing protein At4g01160 c110668_g2 3.02 3.46 3.24 Glycine receptor subunit alpha-1 c106079_g1 3.01 3.42 3.21 Potassium voltage-gated channel subfamily A-6 c98522_g1 3.13 3.12 3.12 Low density lipoprotein receptor adapter protein 1-B c105476_g1 3.10 3.04 3.07 Carboxypeptidase inhibitor SmCI c109305_g1 2.98 2.72 2.85 Tctex1 domain-containing protein 1-B

40

Table 2.6- Log2-fold changes (FC) for genes jointly under-expressed for impact sample 1 and 2, average log2FC and their corresponding best-hit BLAST annotations. Genes are sorted based on descending average log2FC.

Log2FC Log2FC Average Gene ID Impact 1 Impact 2 log2FC BlastX annotation c106525_g1 13.05 13.27 13.16 Neuroendocrine convertase 1 c89344_g1 12.25 12.47 12.36 Calreticulin c112734_g2 11.86 12.08 11.97 Transcription factor Dp-1 c60602_g1 9.68 13.80 11.74 Cytochrome c oxidase subunit 3 c24461_g1 11.57 11.79 11.68 60S ribosomal protein L4 c39128_g1 11.55 11.77 11.66 Lysosomal aspartic protease c1176_g1 11.50 11.72 11.61 40S ribosomal protein S3 c63369_g1 11.34 11.56 11.45 60S ribosomal protein L23a c86980_g1 11.25 11.47 11.36 Protein disulfide-isomerase A3 c14865_g1 11.20 11.42 11.31 60S ribosomal protein L17 c50661_g1 11.18 11.40 11.29 Polyadenylate-binding protein 1-A c104536_g1 11.14 11.36 11.25 Interferon-inducible GTPase 1 c61880_g1 11.06 11.29 11.18 40S ribosomal protein SA c52624_g1 10.96 11.18 11.07 ADP,ATP carrier protein 3 c79108_g1 10.91 11.13 11.02 40S ribosomal protein S3a c20434_g1 10.83 11.05 10.94 60S ribosomal protein L19 c82078_g1 10.69 10.91 10.80 Sodium- and chloride-dependent glycine transporter 2 c30733_g1 10.65 10.87 10.76 40S ribosomal protein S7 c68519_g1 10.63 10.85 10.74 Vigilin c6267_g1 10.60 10.82 10.71 40S ribosomal protein S17 c49950_g1 10.54 10.76 10.65 ATP synthase lipid-binding protein, mitochondrial c86839_g1 10.52 10.74 10.63 Urokinase-type plasminogen activator c75120_g1 10.50 10.72 10.61 Polyadenylate-binding protein, cytoplasmic and nuclear c23465_g1 10.47 10.69 10.58 40S ribosomal protein S10 c33475_g1 10.46 10.69 10.58 Arginine kinase c24521_g1 10.42 10.64 10.53 60S ribosomal protein L21 c37907_g1 10.41 10.63 10.52 60S ribosomal protein L44 c622_g1 10.41 10.63 10.52 60S ribosomal protein L31 c2869_g1 10.38 10.60 10.49 60S ribosomal protein L39 c77681_g1 10.32 10.54 10.43 60S ribosomal protein L22 c57024_g1 10.31 10.53 10.42 60S ribosomal protein L18a c64097_g2 10.28 10.50 10.39 Lipase 3 c78635_g3 10.27 10.49 10.38 Excitatory amino acid transporter 2 c78301_g1 10.23 10.45 10.34 Neuroendocrine convertase 1 c135270_g1 10.20 10.42 10.31 60S ribosomal protein L32 c118589_g1 10.15 10.37 10.26 40S ribosomal protein S30 c124620_g1 10.15 10.37 10.26 40S ribosomal protein S26 c68247_g1 10.14 10.36 10.25 Peptide methionine sulfoxide reductase c105682_g3 10.14 10.36 10.25 Protein ref(2)P 41 Table 2.6 continued- c126568_g1 10.12 10.35 10.23 60S ribosomal protein L34 c80020_g1 8.13 12.25 10.19 Upstream activation factor subunit spp27 c56424_g1 10.07 10.29 10.18 ATP-dependent RNA helicase DHX8 c33344_g1 10.03 10.26 10.15 40S ribosomal protein S25 c24376_g1 10.02 10.24 10.13 Ser/threonine-protein phosphatase 2B catalytic subunit 2 c40525_g1 10.00 10.22 10.11 60S ribosomal protein L38 c104536_g4 8.01 12.14 10.08 Interferon-inducible GTPase 1 c28453_g1 9.96 10.18 10.07 Betaine--homocysteine S-methyltransferase 1 Dolichyl-diphosphooligosaccharide--protein c66437_g1 9.90 10.12 10.01 glycosyltransferase subunit 1 c6231_g1 9.85 10.07 9.96 Probable phospholipid-transporting ATPase IIB c48196_g1 9.79 10.01 9.90 Sodium/potassium-transporting ATPase subunit alpha-1 c70468_g1 9.78 10.00 9.89 60S ribosomal protein L30 c41323_g1 9.78 10.00 9.89 60S acidic ribosomal protein P0 c55137_g1 9.70 9.92 9.81 Cathepsin L c17781_g1 9.66 9.88 9.77 Aspartate aminotransferase, cytoplasmic c69388_g1 9.66 9.88 9.77 Arginine kinase Cleavage and polyadenylation specificity factor subunit c24258_g1 9.65 9.87 9.76 3-I c42271_g1 9.61 9.83 9.72 Trehalase c287_g1 9.59 9.81 9.70 60S acidic ribosomal protein P1 c23167_g1 9.59 9.81 9.70 DNA replication licensing factor mcm4-B c65411_g1 9.55 9.78 9.66 Elongation factor 1-beta c75382_g1 9.54 9.76 9.65 Protein unc-119 homolog B c48661_g1 9.51 9.73 9.62 cAMP-dependent protein kinase type I regulatory subunit c29631_g1 9.51 9.73 9.62 Myosin heavy chain, muscle c138548_g1 9.50 9.72 9.61 Stonustoxin subunit alpha c55918_g1 9.47 9.69 9.58 Eukaryotic translation initiation factor 3 subunit E-A c41393_g1 9.47 9.69 9.58 S-adenosylmethionine synthase c97010_g1 8.13 11.00 9.57 Sterile alpha motif domain-containing protein 9-like c38181_g1 9.44 9.66 9.55 Alanine aminotransferase 2 c3396_g1 9.43 9.65 9.54 40S ribosomal protein S21 c23232_g1 9.41 9.63 9.52 Zinc transporter SLC39A7 c28642_g1 9.41 9.63 9.52 Iron/zinc purple acid phosphatase-like protein c53173_g1 9.40 9.62 9.51 40S ribosomal protein S29 c132448_g1 9.40 9.62 9.51 GTP-binding protein Rheb c11487_g1 9.38 9.60 9.49 Multidrug resistance protein 1 c113414_g1 9.37 9.59 9.48 T-complex protein 1 subunit zeta c24255_g1 9.36 9.58 9.47 Heat shock protein (40 kDa protein 4)/ DnaJ, B4 c55610_g1 7.39 11.52 9.45 60S ribosomal protein L5 c19556_g1 9.33 9.55 9.44 Calcineurin B homologous protein 1 c137878_g1 9.33 9.55 9.44 Translocon-associated protein subunit gamma c123852_g1 9.28 9.50 9.39 Transmembrane emp24 domain-containing protein eca c3171_g1 9.28 9.50 9.39 Guanine nucleotide-binding protein G(o) subunit alpha c38778_g1 9.27 9.49 9.38 Splicing factor 3B subunit 1 c47714_g1 9.27 9.49 9.38 RING finger protein 151 c10793_g1 9.26 9.48 9.37 Eukaryotic translation initiation factor 3 subunit C 42 Table 2.6 continued- c122098_g1 9.23 9.45 9.34 Nuclear protein 1 c24285_g1 9.22 9.44 9.33 rRNA 2'-O-methyltransferase fibrillarin c2693_g1 9.20 9.42 9.31 Delta-1-pyrroline-5-carboxylate synthase c25673_g1 9.20 9.42 9.31 Cdc42 homolog c78432_g1 9.16 9.38 9.27 S-phase kinase-associated protein 1 c87994_g2 7.20 11.32 9.26 Protein DD3-3 c56183_g2 9.13 9.35 9.24 cAMP-dependent protein kinase catalytic subunit c29580_g1 9.12 9.34 9.23 Putative cysteine proteinase CG12163 c62558_g1 9.12 9.34 9.23 Copper-transporting ATPase 2 c83990_g1 9.10 9.32 9.21 4-hydroxyphenylpyruvate dioxygenase c78635_g1 9.10 9.32 9.21 Excitatory amino acid transporter 2 c83505_g1 9.10 9.32 9.21 Legumain c23926_g1 9.09 9.31 9.20 Transketolase c43389_g1 9.06 9.28 9.17 V-type proton ATPase 16 kDa proteolipid subunit c13325_g1 9.04 9.26 9.15 Ras-related protein Rab-11B c115786_g1 9.03 9.25 9.14 DNA replication licensing factor mcm2 c27393_g1 9.01 9.23 9.12 Flotillin-1 c8889_g1 9.01 9.23 9.12 Tumor suppressor candidate 3 c123543_g1 9.00 9.22 9.11 Guanine nucleotide-binding protein subunit beta-1 c74057_g2 8.98 9.20 9.09 Elongation factor 2 c138040_g1 8.98 9.20 9.09 DnaJ homolog subfamily C member 7 c91843_g6 7.01 11.13 9.07 TNF receptor-associated factor 5 c131085_g1 8.93 9.15 9.04 ATP synthase subunit gamma, mitochondrial c115707_g1 8.93 9.15 9.04 Ras-related C3 botulinum toxin substrate 1 c26448_g1 8.90 9.12 9.01 Protein phosphatase PP2A 55 kDa regulatory subunit c39103_g1 8.90 9.12 9.01 AP-2 complex subunit mu-B c573_g1 8.88 9.10 8.99 Ras-related protein Rab-5C c1180_g1 8.88 9.10 8.99 14-3-3-like protein 2 c29723_g1 8.88 9.10 8.99 Glutamine synthetase cytosolic isozyme 1-1 c6415_g1 8.88 9.10 8.99 Thioredoxin domain-containing protein 5 c73134_g1 8.88 9.10 8.99 Ras-like GTP-binding protein Rho1 c71623_g1 6.91 11.04 8.98 40S ribosomal protein S20 c131280_g1 8.85 9.07 8.96 Dihydropteridine reductase c45169_g1 8.85 9.07 8.96 26S proteasome non-ATPase regulatory subunit 6 c31050_g1 8.83 9.05 8.94 Myosin regulatory light chain 2 c22209_g1 8.81 9.03 8.92 ER lumen protein-retaining receptor c116092_g1 8.78 9.00 8.89 AP-2 complex subunit alpha c62158_g1 8.78 9.00 8.89 Putative pterin-4-alpha-carbinolamine dehydratase c142314_g1 8.78 9.00 8.89 Sorting nexin-12 c23164_g1 8.76 8.98 8.87 Cleft lip and palate transmembrane protein 1 homolog 43 Table 2.6 continued- c48472_g1 8.76 8.98 8.87 Cytochrome b-c1 complex subunit Rieske, mitochondrial c56413_g1 8.76 8.98 8.87 Myosin light chain alkali c23152_g1 8.74 8.96 8.85 Protein transport protein Sec61 subunit gamma c124_g1 8.74 8.96 8.85 Transmembrane 9 superfamily member 3 c38953_g1 8.74 8.96 8.85 Probable ATP-dependent RNA helicase DDX4 c98716_g1 6.75 10.88 8.82 FAS-associated factor 2 c18932_g1 8.70 8.92 8.81 Tyrosine-protein kinase Src42A Eukaryotic peptide chain release factor GTP-binding c258_g1 8.70 8.92 8.81 subunit ERF3A c113762_g1 8.68 8.90 8.79 26S proteasome non-ATPase regulatory subunit 11 c132191_g1 8.68 8.90 8.79 Casein kinase II subunit alpha, chloroplastic c38150_g1 8.68 8.90 8.79 Polypeptide N-acetylgalactosaminyltransferase 5 c144810_g1 8.68 8.90 8.79 60S ribosomal protein L3 c118371_g1 8.68 8.90 8.79 Troponin C, isoform 1 c13298_g1 8.66 8.88 8.77 Dihydropyrimidinase c39007_g1 8.66 8.88 8.77 V-type proton ATPase subunit d 1 c38630_g1 8.66 8.88 8.77 NAD kinase c32386_g1 8.66 8.88 8.77 Clathrin heavy chain c29176_g1 8.64 8.86 8.75 Charged multivesicular body protein 1b c73753_g1 8.64 8.86 8.75 DEAD-box ATP-dependent RNA helicase 56 c88077_g1 8.64 8.86 8.75 Excitatory amino acid transporter 3 c145880_g1 8.64 8.86 8.75 60S ribosomal protein L5 c24041_g1 8.62 8.84 8.73 Arginine kinase c145384_g1 8.60 8.82 8.71 ATP-dependent RNA helicase DBP2 c44188_g1 8.60 8.82 8.71 Rho-related protein rac1A c137996_g1 8.60 8.82 8.71 Ubiquitin carboxyl-terminal hydrolase 4 Succinyl-CoA:3-ketoacid coenzyme A transferase 1, c57112_g1 8.58 8.80 8.69 mitochondrial c138009_g1 8.58 8.80 8.69 40S ribosomal protein S3a c44069_g2 8.56 8.78 8.67 Neurocalcin homolog c48431_g2 8.56 8.78 8.67 Nucleolar protein 56 c24447_g1 8.56 8.78 8.67 Golgi phosphoprotein 3 c18292_g1 8.53 8.76 8.64 NADPH--cytochrome P450 reductase c117658_g1 8.53 8.76 8.64 Calcineurin subunit B type 1 c22739_g1 8.51 8.73 8.62 Proteasomal ubiquitin receptor ADRM1 c53917_g1 8.49 8.71 8.60 ATP-dependent RNA helicase eIF4A c19365_g1 8.49 8.71 8.60 V-type proton ATPase subunit D c54628_g2 8.47 8.69 8.58 Duodenase-1

44 Table 2.6 continued- c124086_g1 8.42 8.64 8.53 Importin subunit alpha Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase c27432_g1 8.42 8.64 8.53 and dual-specificity protein phosphatase PTEN c52333_g1 8.42 8.64 8.53 S-formylglutathione hydrolase c14658_g1 8.42 8.64 8.53 Ubiquilin-1 c12872_g2 8.42 8.64 8.53 Endoplasmin homolog c117243_g1 8.40 8.62 8.51 Actin-related protein 3 c40523_g1 8.40 8.62 8.51 Radixin c17450_g1 8.40 8.62 8.51 Ubiquitin-conjugating enzyme E2 L3 c115732_g1 8.37 8.59 8.48 GPN-loop GTPase 1 c139228_g1 8.37 8.59 8.48 Aldehyde dehydrogenase family 16 member A1 c141130_g1 8.37 8.59 8.48 V-type proton ATPase subunit E c139604_g1 8.37 8.59 8.48 Protein sel-1 homolog 1 c25865_g1 8.35 8.57 8.46 Eukaryotic translation initiation factor 3 subunit B c82114_g1 8.35 8.57 8.46 Leucine-zipper-like transcriptional regulator 1 c75267_g1 8.35 8.57 8.46 Protein max c14813_g1 8.35 8.57 8.46 Ras-related C3 botulinum toxin substrate 1 c42191_g2 8.35 8.57 8.46 Phosphate carrier protein, mitochondrial c123745_g1 8.32 8.54 8.43 Malate dehydrogenase, mitochondrial Probable methylmalonate-semialdehyde dehydrogenase c115892_g1 8.32 8.54 8.43 [acylating], mitochondrial c25808_g1 8.30 8.52 8.41 ER membrane protein complex subunit 4 c11302_g1 8.30 8.52 8.41 Ubiquitin-conjugating enzyme E2 N c83798_g2 8.30 8.52 8.41 DnaJ homolog subfamily C member 5 c137820_g1 8.30 8.52 8.41 Ubiquitin-conjugating enzyme E2-24 kDa c113566_g1 8.27 8.49 8.38 Proteasome subunit alpha type-3 c76931_g1 8.27 8.49 8.38 26S proteasome non-ATPase regulatory subunit 1 c138807_g1 8.27 8.49 8.38 Prohibitin-2 c133244_g1 8.27 8.49 8.38 V-type proton ATPase subunit C c112632_g1 5.29 11.34 8.32 Retrovirus-related Pol polyprotein from transposon 297 c947_g1 6.19 10.32 8.25 T-complex protein 1 subunit alpha Mesencephalic astrocyte-derived neurotrophic factor c31024_g1 6.07 10.19 8.13 homolog Interferon-induced, double-stranded RNA-activated c66231_g1 6.32 9.62 7.97 protein kinase c97130_g3 10.27 5.49 7.88 Fibroleukin c87064_g1 9.41 6.13 7.77 Zinc finger protein 544 c92917_g2 4.93 10.58 7.76 NACHT, LRR and PYD domains-containing protein 3 c96905_g1 9.38 6.10 7.74 NACHT, LRR and PYD domains-containing protein 12

45 Table 2.6 continued- c97546_g2 7.18 8.30 7.74 Clavaminate synthase-like protein At3g21360 c52259_g1 5.07 10.14 7.61 ATP-dependent RNA helicase DBP2 c74878_g1 5.50 9.63 7.57 Protein hu-li tai shao c56924_g1 5.37 9.49 7.43 Cystathionine beta-synthase c74330_g1 4.85 9.93 7.39 Putative ATP-dependent RNA helicase me31b c116754_g1 4.40 10.04 7.22 Acetyl-CoA carboxylase c105852_g2 3.66 10.51 7.09 DNA endonuclease RBBP8 c86036_g3 4.99 9.12 7.06 Band 7 protein AGAP004871 c90433_g1 10.95 3.09 7.02 Kelch-like protein 8 c93846_g3 10.00 3.67 6.84 Tetratricopeptide repeat protein 28 c92823_g2 10.19 3.44 6.82 Gamma-aminobutyric acid receptor subunit delta c94538_g1 5.59 7.33 6.46 Steroid 17-alpha-hydroxylase/17,20 lyase c93846_g5 9.76 3.15 6.45 Tetratricopeptide repeat protein 28 c89012_g1 6.23 5.92 6.08 PiggyBac transposable element-derived protein 4 c96291_g2 3.49 8.54 6.01 Tetratricopeptide repeat protein 28 c98456_g2 8.08 3.66 5.87 Fibrinogen C domain-containing protein 1 c113241_g2 6.46 5.15 5.81 Contactin-associated protein-like 5 c101088_g1 3.60 7.60 5.60 Retrovirus-related Pol polyprotein from transposon 17.6 c77781_g1 6.38 4.31 5.35 Uncharacterized protein KIAA1958 c102078_g3 4.46 6.11 5.28 Wiskott-Aldrich syndrome protein c88093_g2 7.61 2.87 5.24 Clavaminate synthase-like protein At3g21360 Disintegrin and metalloproteinase domain-containing c84311_g1 5.06 5.39 5.22 protein 11 RNA-directed DNA polymerase from mobile element c105853_g1 4.08 6.07 5.08 jockey c95134_g1 3.76 6.17 4.96 Retrovirus-related Pol polyprotein from transposon 412 c111173_g1 4.08 5.55 4.81 Protein gooseberry c112948_g1 3.92 5.46 4.69 Transposon Ty3-G Gag-Pol polyprotein c111049_g1 4.73 4.59 4.66 Trace amine-associated receptor 1 c107802_g1 6.02 2.76 4.39 Kelch-like protein 40 c107064_g1 4.97 3.77 4.37 Lipase maturation factor 1 c92938_g3 2.98 5.67 4.32 Collagen alpha-1(XIV) chain c110664_g1 4.44 4.15 4.30 Probable G-protein coupled receptor 133 c90217_g2 4.58 3.92 4.25 Lysyl oxidase homolog 2 c111038_g1 3.73 4.70 4.22 Histone H3, embryonic Pleckstrin homology domain-containing family G c108890_g4 5.00 3.24 4.12 member 4B c95959_g2 4.55 3.26 3.90 Ribose-phosphate pyrophosphokinase 1

46 Table 2.6 continued- c112780_g1 3.00 4.58 3.79 Histidine decarboxylase Transient receptor potential cation channel subfamily M c103499_g1 4.35 3.15 3.75 member 6 c109601_g1 4.45 2.99 3.72 DNA damage response protein kinase DUN1 c103085_g1 3.22 4.04 3.63 Transposon Tf2-12 polyprotein c110631_g1 3.81 3.42 3.62 Retrovirus-related Pol polyprotein from transposon 17.6 c105406_g2 3.01 4.14 3.58 Fibroblast growth factor receptor 3 c109576_g2 4.29 2.75 3.52 Tectonic-2 c97407_g1 3.32 3.58 3.45 Uncharacterized protein C17orf59 c112078_g1 3.51 3.35 3.43 Mediator of RNA polymerase II transcription subunit 15 c89844_g2 3.32 3.53 3.43 Zinc finger protein 391 c113351_g2 3.41 3.02 3.21 Protocadherin Fat 4 c112856_g1 2.97 3.44 3.21 Retrovirus-related Pol polyprotein from transposon 297 c100277_g1 3.08 3.23 3.15 Polyamine oxidase c95244_g1 3.32 2.97 3.14 Cholesterol oxidase

47

Cnidarians, including corals, have highly conserved immune and defensive pathways that can serve as potential biomarkers for stress (Goldstone et al., 2006,

Goldstone, 2008, Reitzel et al., 2008, Venn et al., 2009, Shinzato et al., 2012, Elran et al.,

2014). In particular, the “chemical defensome” consists of a network of evolutionarily conserved genes and proteins that are involved in various processes including metabolism, biotransformation (i.e., chemical modification), and/or removal of xenobiotic toxins (i.e., polycyclic aromatic hydrocarbons (PAHs)- components of oil) as well as protein homeostasis (Reitzel et al., 2008, Tarrant et al., 2014). Both non-specific and stressor-specific responses to this environmental disturbance (floc) were found and are discussed in greater detail below.

Many aspects of cellular stress responses are not stressor-specific as they monitor macromolecular damage, however certain responses are an attempt to re-establish or maintain homeostasis, and may be stressor-specific. Although the differentially expressed genes are attributed to hydrocarbon and dispersant exposure in this study, it is possible that a portion of the DEGs detected were due to other differences between P. biscaya samples. Impacted corals were collected from MC294 and had visible floc attached to them at the time of collection. As all corals at MC294 were believed to have been exposed to hydrocarbons and dispersant to some degree, control P. biscaya colonies were collected from MC344 (30 km from MC294, at 1850 m depth), the closest known site with the same P. biscaya haplotypes (Doughty et al., 2014). At the time of collection, none of the observed P. biscaya colonies at MC344 exhibited any signs of exposure, although low levels of impact from the spill were documented in 2011 on a return visit to

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a different part of the site (Fisher et al., 2014b). Each impacted colony from MC294 had a suite of unique, differentially expressed genes in addition to the shared portion associated with cellular stress and toxin processing. For these reasons, we are confident that a majority of the differences observed here are associated with responses to floc and contaminant exposure, rather than differences in depth or location.

2.4.3 Gene Ontology enrichment of over-expressed genes shared among

impacted corals

GO enrichment analysis (GOseq) among over-expressed genes shared between the two impacted colonies (Table 2.7) revealed 77.7% of enriched terms were designated to biological processes, 12.9% to molecular functions, and 9.4% to cellular components.

Within biological processes, significant GO enrichment included: cell and neuron recognition, cell and biological adhesion, cell development and maturation, regulation of multi-organism process, unfolded-protein response and the apoptotic signaling pathway

(p <0.001). Elevated expression of gene transcripts for cell adhesion and apoptotic- and tumor suppression factors has also been observed in corals exposed to heat stress (Barshis et al., 2013).

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Table 2.7- Differentially expressed (DE) genes that possessed a gene ontological term with enrichment by category- unique and shared in each impacted sample, and broken down into significantly (≥4-fold, FDR=0.001) over- (up) or under- (down) expressed relative to the control group (top). Functional gene ontology (GO) enrichment analyses on the DE genes (bottom) revealed the number of enriched terms belonging to the following categories: biological process (BP), molecular function (MF) and cellular component (CC), for each impacted sample and shared between them.

Sample Total DE genes Impact 1 Impact 2 Shared Over-expressed 453 221 236 Under-expressed 1314 903 346 Enriched GO terms up MF 57 132 44 BP 195 540 265 CC 21 71 32 down MF 56 85 100 BP 112 224 318 CC 13 61 55

Among molecular functions, ATP-dependent helicase activity, monooxygenase and oxidoreductase activity (activity commonly linked to cytochrome p450), small ubiquitin-related modifier (SUMO)-protease activity, and collagen binding were among the significantly enriched GO terms (p <0.01). Alterations in DNA binding and protein modification also occur during apoptosis (Shearer et al., 2014). Enriched cellular components included septate junctions, extra-cellular matrices, and plasma membranes

(p<0.001). Thermally stressed corals have also exhibited changes in cytoskeletal and extracellular matrix proteins (Kenkel et al., 2013).

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2.4.4 Gene Ontology enrichment of genes distinctly over-expressed in each

impacted coral colony

Gene ontology enrichment analyses for over-expressed contigs unique to impact sample 1 revealed 273 enriched gene ontology terms: 71.4% corresponding to biological processes, 20.9% to molecular functions and 7.7% to cellular components (Table 2.7).

The most significantly enriched biological processes corresponds to DNA- recombination, integration and metabolic process, viral entry into host and related viral activity (p<0.0001). Although the coral holobiont contains viruses (plus bacteria, archaea and fungi) that are believed to play a role in homeostasis by controlling bacterial abundances, viruses are also associated with coral disease (Bourne et al., 2009).

Environmental stressors have been linked to increases in disease states as the corals become more susceptible due to damage with an increase in viral sequences found in corals exposed to thermal stress (Vega Thurber et al., 2008). Other significantly enriched processes include cellular aromatic and organic cyclic compound metabolic processes

(commonly seen in oil compound metabolism), SOS response (a DNA damage response) and ATP metabolic and catabolic processes (p<0.01).

Molecular functions significantly enriched include DNA polymerase activity, aspartic-type peptidase activity (proteolytic enzymes), assorted enzymatic (nuclease, transferase, hydrolase) and catalytic activity (p<0.0001) in addition to unfolded-protein binding (p<0.01). Enriched cellular components were predominantly the host cell-part and cytoplasm (p<0.001) and host-cell nucleus and organelle (p<0.01).

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Of the 181 enriched gene ontology terms for impact sample 2, 72.7% correspond to biological processes (Table 2.7). The most significantly enriched terms include: cell and biological adhesion, ECM organization and disassembly, multicellular organismal processes (metabolic and catabolic), collagen- organization, metabolic and catabolic processes, ossification and biomineral tissue development (p<0.0001). Additional enriched biological processes of interest include regulation of immune response, defense response, proteolysis (p<0.001), complement activation- lectin pathway, melanin biosynthetic process and cellular response to toxic substance (p<0.01). Lectin-like proteins (Zhang et al., 2012), melanin and the complement system are known to play important roles in invertebrate immunity (Palmer and Traylor-Knowles, 2012).

Enriched molecular functions (17.8% of total enriched) include ECM structural constituents, ion (cation, metal, calcium) and antigen binding, metallopeptidase activity,

DNA and collagen binding (p<0.0001), oxidoreductase-, scavenger receptor- and ion- gated channel- activity, and fibroblast growth factor receptor, chitin- and carbohydrate- binding (p<0.001). During wound repair, immune cells infiltrate the wound and phagocytize debris and foreign cells. Infiltrated cells then proliferate forming granulation tissue, which includes multiple cell types, collagen & basic ECM components (Palmer and Traylor-Knowles, 2012). Likewise, matrix metallopeptidase play a fundamental role in immune and inflammatory responses (Khokha et al., 2013) as they degrading ECM components and function in apoptosis and host defense (Traylor-Knowles et al., 2011).

The enriched cellular components were predominantly the extracellular region and matrix, collagen trimers, cell surface-, basement- and synaptic vesicle- membranes

(p<0.0001).

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2.4.5 Evidence for xenobiotic biotransformation in impacted corals

Genes involved in responses to organic cyclic compounds and the metabolism of xenobiotics were significantly over-expressed in impacted P. biscaya colonies (Table 2.5;

FDR ≤ 0.001, FC ≥ 4), including the cytochrome p450, CYP1A1 (hereafter, log2FC avg

± stdev = 4.45 ± 0.04). Receptors of the chemical ‘defensome’ in cnidarians react to toxic substances (Goldstone et al., 2006) which are biotransformed upon entering the cytoplasm in an effort to inactivate or eliminate the compounds. Oxidative modification by CYPs is one such biotransformation method where additional polar/water soluble compounds are produced for excretion. GO enrichment analyses yielded a significant enrichment in monoxygenase and oxidoreductase activity, two processes essential to intracellular biotransformation. N-acetyl transferases and sulfotransferases, involved in the second phase of xenobiotic oxidation (Gonzalez, 2005) were likewise over-expressed.

Although biotransformation typically results in detoxification, N-acetylation and sulfate conjugation may also produce toxic metabolites (Surh, 1998, Gamage et al., 2006).

CYPs are responsible for the biotransformation, detoxification and metabolism of most xenobiotics and are required for the efficient elimination of foreign chemicals from the body (Goldstone et al., 2006). These proteins have been used as a reliable biomarker for pollution exposure in aquatic invertebrates and vertebrates alike (Devaux et al., 1998,

Porte et al., 2001). While metabolic pathways that include CYP1A1 can form inert and soluble derivatives of xenobiotics, they can also lead to highly reactive electrophilic metabolites that can be toxic and induce cell transformations (Gonzalez, 2005). Evidence

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for elevated expression of CYPs and toxic biotransformation of oil has been found in marine invertebrates when exposed to DWH-source oil (Zhang et al., 2012) and Iranian crude oil (Han et al., 2014). In addition, exposure to polycyclic aromatic hydrocarbons

(PAHs) has induced elevated expression of CYPs in the Gulf killifish, Fundulis grandis

(Garcia et al., 2012). The significant elevations of CYP1A1 in both impacted corals was likely an attempt to biotransform intracellular toxins for downstream processing and restore cellular homeostasis.

Unlike studies examining invertebrate responses to water-accommodated fractions (WAF) of oil, the antioxidant enzymes glutathione S-transferase [GST] (Han et al., 2014), glutathione reductase (Solé et al., 2007, Han et al., 2014) and glutathione peroxidase (Solé et al., 2007), components of the ‘chemical defensome’ (Goldstone et al.,

2006), were not differentially expressed in impacted colonies in this present study.

Although antioxidant systems counterbalance potentially deleterious free radicals that can lead to oxidative stress, antioxidant production is under active regulation and corresponds to the dynamic needs of an organism (Lushchak, 2011), which may explain why they were not differentially expressed. There was also greater oxidative damage among invertebrates exposed to WAFs as compared to crude oil (Solé et al., 2007), suggesting differential effects based on oil composition. Lastly, GST activity in fish exposed to surfactants decreased in a time-dependent manner following initial increases (Uguz et al.,

2003), which could explain why these enzymes were not differentially regulated in floc- exposed corals at the time of sampling.

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Alternatively, P. biscaya impact samples did exhibit over-expression of several

ATP-dependent efflux transporters (fibropellin-1, 5.73± 1.53; P2X purinoceptor 7, 5.57±

1.36) which function as part of the cnidarian “defensome” alongside CYPs and other oxidases (Goldstone et al., 2006). These calcium-ion transporters aid in an organism’s response to organic cyclic compounds and inflammatory responses. Sulfotransferases, which perform similar enzymatic functions as GST (Luchmann et al., 2015) in downstream toxin processing were also highly over-expressed (10.1± 1.13), further supporting a xenobiotic stress response in the spill-impacted corals.

2.4.6 Wound repair and tissue regeneration activated in impacted corals

Floc-exposed cold-water corals at impact site MC294 exhibited varying levels of sloughing tissue and excess mucosal secretions (White et al., 2012). Numerous genes in the wound repair and tissue regeneration pathways were significantly over-expressed

(Table 2.5) in exposed colonies including a metallopeptidase (3.55± 0.21), peroxidasin

(4.05± 1.03), tyrosinase (5.39± 3.63), granulins (3.33± 0.35), epidermal growth factor

(EGF)-like proteins (7.54± 0.20) and fibropellin-1 (5.73± 1.53). Genes coding for collagens and fibronectin-like molecules were also significantly over-expressed and are essential for tissue regeneration (Bosch, 2007).

Among the genes significantly over-expressed in impacted P. biscaya was a membrane metallopeptidase Meprin A, which functions in inflammation and tissue remodeling by degrading extracellular matrix (ECM) components (Sterchi et al., 2008). 55

Mesogleal or ECM metalloproteinases govern wound repair through functioning as key regulatory enzymes for cell composition (Massova et al., 1998). High levels of misallocated meprins have been associated with cytotoxicity in certain tissues of vertebrates following acute stress (Sterchi et al., 2008). Peroxidasin (PXDN), another important mediator of ECM organization, phagocytosis and defensive pathways was likewise significantly over-expressed. Similar over-expression of PXDN was observed in

G. ventalina following pathogen exposure (Burge et al., 2013b) with constitutive over- expression found in thermotolerant hexacorals (Barshis et al., 2013).

Phagocytotic granular amoebocytes within the epithelial tissue of the octocoral relative, G. ventalina, have been found to activate pathways leading to melanin production in pathogen-infected epithelia (Mydlarz et al., 2008) and it is likely that similar activity occurs in compromised P. biscaya tissue. Tyrosinase, a copper-containing oxidase that functions in the formation of pigments such as melanins (Palmer and

Traylor-Knowles, 2012) was also significantly elevated among impacted corals.

Increased melanin synthesis was also found in compromised (Palmer et al., 2008), infected (Mydlarz et al., 2008) and thermally-stressed coral tissues (Mydlarz et al., 2009), further supporting the importance of wound repair mechanisms in the stress response of

P. biscaya, possibly as an attempt for partial colony survival following the initial exposure induced stress and polyp mortality.

Granulins, small proteins believed to have cytokine activity involved in inflammation, wound repair and tissue remodeling (Bateman, 1998) were significantly over-expressed as well. Cytokine activity was similarly enriched following DWH oil-

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WAF exposures on the mussel (invertebrate) M. galloprovincialis (Zhang et al., 2012).

Impacted P. biscaya also had significant elevations in genes coding for epidermal growth factor (EGF)-like proteins as well as EGF1 coding for fibropellin-1, an important component of the ECM. This further supports findings that environmental stressors induce changes in cytoskeletal and ECM proteins, a response also observed in shallow- water corals exposed to heat stress (Kenkel et al., 2013).

2.4.7 Elevated apoptotic and proteolytic activity

Significant elevations in (proteolytic) ubiquitin (3.41± 0.76), sacsin- a regulator chaperone of heat shock protein 70 (HSP70) machinery (8.33± 0.18) and a stress- inducible protein-like gene (4.89± 0.10), were found in floc-exposed P. biscaya. Prior genomic-scale approaches that investigated shallow-water coral heat-related stress responses (Edge et al., 2005, Morgan et al., 2005) and bleaching (DeSalvo et al., 2008,

Voolstra et al., 2009) have implicated the involvement of oxidative stress and apoptosis

(DeSalvo et al., 2008, Richier et al., 2008), including an elevated expression of HSPs

(Moya et al., 2012, Barshis et al., 2013, Moya et al., 2015)- although HSP70 was not differentially expressed in this study and is more commonly associated with thermal stress.

As molecular chaperones, HSPs function in the stabilization and repair of damaged proteins (and DNA), proteolysis of irreparable proteins and apoptosis of severely damaged cells (Kültz, 2005). Ubiquitin also plays a role in the nuclear factor-

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kappa-B (NFKB), inflammatory and apoptotic pathways (Chen, 2005). Although NFKB was not differentially expressed in the impacted P. biscaya colonies, altered expression is thought to be associated with an immediate response to stress along with additional transcription factors (Elran et al., 2014). Therefore, it is possible that this initial response subsided by the time the corals were collected, although it remains unclear how much time passed between the initial exposure and sampling.

2.4.8 Evidence for immune-related responses

Basal metazoans, including corals, have a basic, ancestral immune system from which other animals evolved their present immune functions (Goldstone, 2008, Reitzel et al., 2008, Venn et al., 2009, Shinzato et al., 2012). Invertebrates have innate immune defenses, with stress altering the expression of various immune components (Garcia et al., 2012). Immune-associated genes significantly over-expressed in spill-impacted P. biscaya included two tumor necrosis receptor-associated factors (TRAFs), TRAF3 (5.12±

0.16) and TRAF6 (12.2± 1.13), and a putative tumor suppressor gene, DMBT1 (13.0±

0.31).

TRAFs are components of the toll-like receptor pathway and function in stress response mechanisms associated with innate immunity, apoptosis, cell death and survival

(Arch et al., 1998, Bradley and Pober, 2001, Pratlong et al., 2015). TRAFs were also constitutively over-expressed in a hexacoral- Acropora hyacinthus (Barshis et al., 2013), and an octocoral Corallium rubrum (Pratlong et al., 2015), exposed to thermal stress.

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DMBT1 is thought to function in local inflammation and mucosal immune processes

(Kang and Reid, 2003) and increased expression in P. biscaya may have been correlated to the excess mucous secretions observed in impacted colonies (White et al., 2012).

Elevated expression of DMBT1 has likewise been found in vertebrates exposed to toxic herbicides (Genter et al., 2002). Our findings suggest that these immune-related genes are important components of cold-water coral stress responses and part of a conserved response to various acute environmental stressors, including toxin exposure.

The surface mucus layer of coral serves as the interface between their epithelium and the environment (Bythell and Wild, 2011). Coral mucus acts as a crude defense against environmental stressors and is known to play an important role in biological processes such as feeding, sediment cleansing and pathogen protection (Brown and

Bythell, 2005). It has shown potential to act as both a physiochemical barrier (Sutherland and Ritchie, 2004) as well as a growth medium for bacteria and potential pathogens in shallow-water coral species (Lipp et al., 2002). Therefore, the observed coral immune responses may have been elicited by oil degrading microbes and/or opportunistic pathogens present in the floc, overlaying the epithelial tissue of exposed P. biscaya.

Hydrocarbon exposure has similarly altered immune function in other organisms (Faisal and Huggett, 1993, Carlson et al., 2004b). Immune suppression was likewise observed for up to a year following coral bleaching events (Pinzón and Kamel, 2015).

It is possible that floc-exposed P. biscaya became susceptible to colonization by potentially harmful microbes as their immune status declined and tissue necrosis occurred. Past transcriptomic investigations into pathogen-induced stress on the closest

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sequenced relative to date, the shallow-water gorgonian G. ventalina (Burge et al.,

2013a), also suggests a key role of immune-related receptors (Bosch et al., 2009), inflammatory cascades (Mydlarz et al., 2009), anti-microbial compounds (Bosch et al.,

2009) and wound repair (i.e., matrix metalloproteinase and PXDN). Although several immune-associated genes differentially expressed in G. ventalina- Complement C3,

Tachylectin 5A, Protein G7c and Neuronal pentraxin-2, were not found, it is likely the stressor in question (pathogens) elicited a more direct / severe immune response.

Conversely, a gene coding for a cytolysin (sticholysin-2) was significantly over- expressed. Cytolysins have been found in the tentacles and tissues of other cnidarians

(Lanio et al., 2001, Martinez et al., 2001) and are thought to be associated with bacterial pathogenesis (Rosado et al., 2008) providing additional evidence for an immune-related response to the floc.

Our results support previous assertions that the epithelium of cnidarians serves a crucial function as the primary line of defense against invading pathogens and / or environmental change, as it conducts phagocytic activities and secretes mucous.

Secretions can act as a physiochemical barrier against invading environmental microbes as the mucous contains serine-protease inhibitors and anti-microbial peptides (Ocampo and Cadavid, 2015). However, once this barrier is compromised, infection can proceed and illict the distinct immune response revealed among impacted P. biscaya.

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2.4.9 Gene Ontology enrichment of under-expressed genes shared among

impacted corals

A GO enrichment analysis among the shared under-expressed genes (Table 2.7) revealed 67.2% of enriched terms were biological processes, 21.2% were molecular functions and 11.6% were cellular components. Within biological processes, significant enrichments were found in the following categories: translation, actin filament organization, nucleoside and ribonucleoside metabolic and catabolic processes, protein secretion, organonitrogen metabolic process, and programmed cell death (p <0.001).

Within molecular functions, the most significantly enriched terms were related to the structural constituent of ribosomes, structural molecule activity and amino acid kinase activity (p<0.0001). Nucleotide-, ribonucleotide-, and guanosine diphosphate (GDP)- binding, kinase- and ATPase- activity were also significantly enriched (p<0.001).

Enriched cellular component include ribosome, ribonucleoprotein- and macromolecular- complex (p<0.0001) as well as the cytoplasmic component, fusome, cytoplasmic membrane-bound vesicle and lipid particle (p<0.001).

Protein and DNA processing and stability are associated with the eukaryotic cellular stress response (Feder and Hofmann, 1999, Pearce and Humphrey, 2001). When stress-induced protein or DNA damage occurs, the cellular stress response (CSR) is induced, elevating the expression of appropriate response and repair pathways.

Enrichment of this class of transcripts in our under-expressed genes may be due to the severity of the contaminant exposure on parts of the coral colonies, which at the time of

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sampling led to visible tissue damage rather than the continuation or mobilization of a defense response (Kültz, 2003).

2.4.10 Gene Ontology enrichment of genes distinctly under-expressed in each

impacted coral colony

Gene ontology enrichment analyses for under-expressed contigs unique to impact sample 1 revealed 181 enriched GO terms: 61.9% corresponding to biological processes,

30.9% to molecular functions and 7.2% to cellular components (Table 2.7). Of the biological processes the most significantly enriched terms (relative to the control group) correspond to: defense response to a protozoan, steroid biosynthetic process, response to interferon-beta (a cytokine with anti-inflammatory activity), regulation of autophagy

(p<0.001), response to gram-negative bacterium, innate immune response, cytokine- mediated signaling pathway, cytolysis and melatonin metabolism and biosynthesis

(p<0.01). Several of these categories are important to an organism’s response to stress, with reduced expression in immune-related responses and signaling potentially being indicative of the declining health status of the coral colony.

Enriched molecular functions include steroid monooxygenase and hydroxylase activity (p<0.001), lyse activity, oxidoreductase activity, chitin (part of the organic matrix

- important for biomineralization) and GDP binding (p<0.01). Enriched cellular components were predominantly the symbiont-containing vacuole membrane and nuclear membrane (p<0.01).

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Enrichment analyses for impact sample 2 revealed 370 enriched gene ontology terms: 60.5% corresponding to biological processes, 23.0% to molecular functions and

16.5% to cellular components (Table 2.7). The most significantly enriched of the under- expressed biological processes include: mitotic nuclear division, organelle fission, mitotic cell cycle process, regulation of- cell cycle, cyclin-dependent protein serine/threonine kinase activity and microtubule processes (p<0.0001) as well as mitotic spindle assembly checkpoint, regulation of G2/M transition, organelle organization and regulation of chromosome segregation and DNA replication (p<0.001). Although altered cell cycle and proliferation regulation play a role in apoptosis (Shearer et al., 2014), suppression of normal cell cycle regulation also occurred during acute stress responses of thermally stressed fish (Komoroske et al., 2015).

The most significantly enriched molecular functions include microtubule and tubulin binding (p≤0.0001), transferase and phosphatase activities, hydrolase activity, oxidoreductase activity and protein complex binding (p≤0.001). The enriched cellular components were predominantly the kinetochore, condensed chromosomal kinetochore

(p<0.0001) and spindle (p<0.001).

2.4.11 Suppression of vital processes

Approximately 13% of all annotated (32 out of 244) under-expressed genes

(Table 2.6) correspond to ribosomal proteins. Several 40S and 60S ribosomal subunits exhibited some of the highest observed log2-fold changes (11.2-13.2). Eukaryotic translation initiation factors (EIF3E), 9.58/8.46± 0.16, elongation factors, 9.66/ 9.09±

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0.16, and subunits of the 26S proteasome (PSMD), 8.71± 0.29, were also highly under- expressed. A range of 40S and 60S ribosomal proteins were likewise under-expressed in

F. grandis exposed to DWH oil (Garcia et al., 2012). Reduced expression of ribosomal proteins was also observed in invertebrates following thermal stress (Kenkel et al., 2013,

Pinzón and Kamel, 2015) and vertebrates during growth inhibition and apoptosis

(Goldstone and Lavin, 1993, Jiang et al., 1997).

The under-expression of ribosomal proteins and components of the EIF3E complex- required to initiate protein synthesis, which functions in nucleotide binding and protein metabolism (Chaudhuri et al., 1997), may be due to a reduction in overall protein synthesis as the corals struggled to endure aberrant environmental conditions. Depressed expression of PSMD subunits, required for the degradation of ubiquinated proteins, in addition to genes functioning in proteolysis (Table 2.6) further suggests impediments to translation and protein processing in impacted corals.

Ribosomal proteins were over-expressed in pathogen-exposed G. ventalina

(Burge et al., 2013b), although these were short-term (24 hr) incubations. In the marine abalone, Haliotis tuberculata, ribosomal proteins were under-expressed only in individuals near death (Travers et al., 2010). This response may elucidate the timing and the degree of impact the floc exposure inflicted on these cold-water corals, beyond the observable ‘stressed’ phenotype. Monitoring studies visualizing coral recovery post-spill at DWH impact sites (including MC294) found the degree of visible colony damage was correlated with lasting impacts and secondary colonization of coral skeleton by hydrozoans (Hsing et al., 2013). Therefore, the depressed expression of ribosomal

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proteins found here may indicate that the sampled corals, or at least some of the polyps within the colony segments directly examined here, had been exposed for an extended period of time and were near death.

2.4.12 Reduced ability to process toxins

Under-expression of a few key genes suggests that impacted P. biscaya colonies had a reduced ability to process toxins. An ATP-binding cassette gene known as ABCB1, or MDR1, was notably under-expressed (9.49± 0.16). This gene protects against cellular toxicity by excreting toxic compounds metabolized by CYPs (Bard, 2000, Borst and

Elferink, 2002). While over-expression of ATP-binding cassette genes serve as indicators for defense against cellular stress (Downs et al., 2006), suppressed expression has been linked to drug toxicity in vertebrates (Piquette-Miller et al., 1998, Dean et al., 2001) and in marine invertebrates exposed to high concentrations of anthropogenic biocides

(Kingtong et al., 2007). Under-expression of the chromatin-binding, nuclear protein 1

(NUPR1, 9.34± 0.16) was also observed. NUPRI is a widely expressed gene that is conserved across many taxa (Cano et al., 2011) with various functions including cell cycle regulation (Vasseur et al., 2002). Elevated expression in mammals is believed to promote anti-apoptotic activity (Su et al., 2001) and has been found in association with early responses to toxic substances (Vincent et al., 2012). Therefore, under-expression in impacted P. biscaya may be associated with elevated apoptotic activity, indicative of prolonged exposure and the overall state of cellular distress.

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2.4.13 Inability to re-establish homeostasis

General cellular stress responses utilize common mechanisms to react to macromolecular damage exceeding threshold levels, temporarily increasing tolerance limits to enable the stabilization of cellular homeostasis. When the effects of the stressor exceed the cells’ ability to maintain macromolecular integrity, apoptosis or programmed cell death is induced (Kültz, 2003, Kültz, 2005). Significant under-expression was likewise observed in a conserved stress-response gene from the DnaJ heat shock protein family (HSP40) – DNAJB4 (9.46± 0.16), which functions in apoptotic processes and ubiquination. A HSP90-like gene (8.53± 0.16) involved in protein folding, was likewise under-expressed, with differential expression of HSP90 also observed in heat-stressed corals (Shearer et al., 2014). Moreover, under-expression was observed in genes associated with apoptosis- cell cycle regulation (mitotic), and DNA-binding and modification (Tarrant et al., 2014). Similar to our findings, processes related to DNA synthesis and cellular organization (Venn et al., 2009), DNA binding, RNA processing, protein processing/degradation, signaling and structural components (Pinzón and Kamel,

2015), were under-expressed in cnidarians following environmental stress.

Maintaining intracellular calcium-ion (Ca2+) homeostasis is also crucial to cell functioning, with Ca2+ signaling participating in numerous cellular processes (DeSalvo et al., 2008). Genes functioning in Ca2+ binding and homeostasis were significantly under- expressed in floc-exposed P. biscaya including the C-type lectin calreticulin (12.4± 0.16) which also acts as an immune-related recognition receptor (Palmer and Traylor-Knowles,

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2012). Kenkel et al. (2013) found similar impediments to Ca2+ homeostasis in P. asteroides following thermal stress and a strong down-regulation of solute carriers.

Under-expression of solute carriers (SLC39A7 and SLC25A3, 9.51 and 8.45± 0.15) was also significant in impacted P. biscaya. SLC members function as bicarbonate transporters in corals and are believed to play an important role in biomineralization

(Zoccola et al., 2015). This provides further evidence of compromised health and basic organismal function following the hydrocarbon-and-dispersant floc exposure at MC294.

In conclusion, genome-wide signatures of distress were identified in the octocoral

P. biscaya following exposure to Deepwater Horizon oil and dispersant (White et al.

2012, 2014). The shared response encompasses evidence for toxin processing, altered immune responses, wound-repair mechanisms and apoptotic signaling pathways.

Impacted P. biscaya colonies exhibited expression patterns similar to other organisms exposed to DWH oil (i.e., F. grandis, Garcia et al. 2012), and invertebrates exposed to other environmental stressors like heat stress.

Evidence for xenobiotic metabolism and biotransformation via highly over- expressed CYP genes supports their use as a biomarker for future spill impact monitoring as drilling increases in deep waters. Like other environmental stress studies on cnidarians, the over-expression of TNF family members, specifically TRAFs in both impact samples, also make them likely candidates for use in future monitoring and conservation efforts.

Elevated stress proteins may have conferred a certain degree of resistance to cells less severely damaged upon contact with the floc and exposure to the crude oil/dispersant constituents. This may have enabled partial colony survival, which has been observed 67

with in situ impacted P. biscaya colonies at MC294 during subsequent monitoring (Hsing et al. 2013). However, the overall decline in health, stressed phenotypes, and suite of highly down-regulated ribosomal proteins also suggest that the colonies were unable to overcome detrimental effects incurred at the cellular level. Our findings suggest DWH- impacted P. biscaya exhibited a diminished capacity to modify gene expression for certain crucial cellular mechanisms key to coping with environmental stress, and it is apparent that many of the remaining colonies continue to decline in health and are unlikely to recover (Hsing et al. 2013).

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CHAPTER 3

RESPONSE OF DEEP-WATER CORALS TO OIL AND

CHEMICAL DISPERSANT EXPOSURE

3.1 Abstract

Cold-water corals serve as important foundation species by building complex habitat within deep-sea benthic communities. Little is known about the stress response of these foundation species yet they are increasingly exposed to anthropogenic disturbance as human industrial presence expands further into the deep sea. A recent prominent example is the Deepwater Horizon oil-spill disaster and ensuing clean-up efforts that employed chemical dispersants. This study examined the effects of bulk oil-water mixtures, water-accommodated oil fractions, the dispersant Corexit9500A®, and the combination of hydrocarbons and dispersants on two species of corals living near the spill site in the Gulf of Mexico between 500-1100 meter depths: Paramuricea type B3 and Callogorgia delta. Following short-term toxicological assays (0 – 96 hrs), both coral species examined showed more severe health declines in response to dispersant alone

(2.3 – 3.4 fold) and the oil-dispersant mixtures (1.1 – 4.4 fold) than in the oil-only treatments. Higher concentrations of dispersant alone and the oil-dispersant mixtures resulted in more severe health declines. C. delta exhibited somewhat less severe health declines in response to oil and oil/dispersant mixture treatments, likely related to its increased abundance near natural hydrocarbon seeps. These experiments provide direct evidence for the toxicity of both oil and dispersant on deep-water corals, which should be

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taken into consideration in the development of strategies for intervention in future oil spills.

3.2 Introduction

The Deepwater Horizon (DWH) oil spill was one of the largest environmental disasters in history, releasing approximately 5 million barrels of crude oil at depth in the

Gulf of Mexico (GoM) over a three-month period (Crone and Tolstoy, 2010, Camilli et al., 2012). In addition, nearly 7 million liters of oil dispersants were applied during the ensuing cleanup efforts. Dispersants are chemical emulsifiers that act to increase the rate of oil dispersion thereby increasing the amount of small oil droplets suspended in the water column, reducing oil slicks at the surface. Thus, dispersant applications affect the fate, transport and physical composition of oil. Of the 7 million liters of oil dispersants used, approximately 3 million liters were applied at depth for the first time in history

(Barron, 2012), without a comprehensive understanding of how this subsea application might alter the fate of oil and impact benthic ecosystems (National Research Council.,

2005)

Petroleum hydrocarbons released under high-pressure undergo a series of interconnected physical and chemical processes that affect their fate and transport in the deep sea (Kessler et al., 2011, Camilli et al., 2012, Reddy et al., 2012). Following the direct injection of disperant (Corexit 9527A and 9500A) to the Macondo well head at a depth of 1544 meters (Hazen et al., 2010), a large oil plume persisted for months centered at approximately 1100 meters (m) depth, without substantial biodegradation (Camilli et 70

al., 2010). Oil spewing from the wellhead encountered turbulent mixing and was emulsified as a result of its reduced buoyancy at depth and the application of dispersant

(Fodrie and Heck Jr, 2011). Measurements of water-column samples collected from this deep-water plume, defined by Camilli et al. (2010), indicated that a significant portion of water-soluble hydrocarbon components were retained in deep waters, with unknown portions of insoluble hydrocarbons drifting to the sea floor (Reddy et al., 2012). Despite some emulsification of oil throughout the water column, surface waters were still polluted with oil slicks (Fodrie and Heck Jr, 2011). At the surface, some components of the oil were then transformed into aggregations of marine snow (and floc) by coagulation with suspended particulates and planktonic organisms. Although this marine snow disappeared from the surface layers of the GoM within a month, it is likely that it sunk into the deep sea as the oil weathered (Passow et al., 2012).

Recent studies have found both lethal and sub-lethal effects of the DWH blowout on species inhabiting pelagic and coastal environments (Barron, 2012, Silliman et al.,

2012, Whitehead et al., 2012, Dubansky et al., 2013, Almeda et al., 2014). Prior studies have shown variable levels of crude oil toxicity on aquatic organisms with some fauna being more susceptible than others (Anderson et al., 1974, Bonsdorff et al., 1990, Coull and Chandler, 1992, Stark and Banks, 2003). Dispersant addition to the oil triggers a transient increase in hydrocarbon concentrations throughout the water-column (Pace et al., 1995), which can then lead to higher, more toxic exposures of dissolved and dispersed oil components upon contact with marine life.

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Spill-impacted deep-sea coral communities were first discovered at a depth of approximately 1370 m, 11 km southwest of the Macondo well explosion, at the lease block site Mississippi Canyon (MC) 294 (White et al., 2012). Various species of coral, primarily Paramuricea biscaya (Grasshoff, 1977), were found covered with brown flocculent material (floc), exhibiting characteristic signs of stress and mortality, including excess mucus production, sclerite enlargement, and tissue loss. Further analysis of this floc revealed hydrocarbons from the Macondo well were indeed present (White et al.,

2012). Whether the damage observed to the corals was induced by sinking oil-filled particulates, dissolved hydrocarbons, dispersants, or a combination of all of these sources is unknown. Subsequently, two additional sites were discovered to contain impacted deep-sea coral communities (Fisher et al., 2014b).

Deep-sea corals alter the terrain of the sea floor and produce complex, heterogeneous habitat, which promotes benthic biodiversity (Cordes et al., 2008, Cordes et al., 2010). In addition to reef-forming scleractinian corals, which generally occur at upper-slope depths (300-1000 m), octocorals form large, tree-like structures from the subtidal to over 3000 m depth. These corals colonize hard substrata, and can form dense fields (Roberts, 2006). By increasing the complexity of the seafloor, they provide shelter, feeding areas, and nursery grounds for many fish and invertebrates.

Because deep-sea corals build the foundation for these communities, damage to them can impact biodiversity and ecosystem function (Husebø et al., 2002, Freiwald,

2004). Their longevity and slow growth rates make them particularly vulnerable to anthropogenic disturbance (Grigg, 1974, Emiliani et al., 1978, Druffel et al., 1990,

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Druffel et al., 1995, Risk et al., 2002, Andrews et al., 2002, Roark et al., 2009). As crude oil reserves are abundant in the GoM, with 1.5 billion barrels of oil extracted from the sea floor each day (Minerals Management Service, 2009) it is now a critical time for further examination of deep-sea coral response to oil and dispersant exposure.

Here, the effects of oil, dispersant and oil-dispersant mixtures were tested experimentally on three species of deep-sea coral living near the DWH oil spill site in the

Gulf of Mexico, including Paramuricea type B3 and Callogorgia delta (Bayer et al.,

2015). P. biscaya was the most common of the corals impacted by the DWH oil spill

(White et al., 2012, Fisher et al., 2014b), and Paramuricea type B3 is the sister species to this coral (Doughty et al., 2014). Paramuricea type B3 was chosen because its shallower depth distribution (830-1090 m for Paramuricea type B3 vs. 1370-2600 m for P. biscaya with one individual collected at 850 m, Doughty et al. (2014)) results in higher survivorship shipboard, and to avoid further impact to the relatively small populations of

P. biscaya that have thus far been discovered. C. delta preferentially occupies habitats near natural oil seeps in the deep GoM (Quattrini et al., 2013), suggesting that the species may have evolved a tolerance for hydrocarbon exposure. Slow growth rates for deep, cold-water corals (Roark et al., 2009, Prouty et al., 2014) make them highly sensitive to natural and anthropogenic disturbances.

This study examined the effects of exposure to bulk oil-water mixtures, water- accommodated oil fractions (WAF), dispersants, and mixtures of hydrocarbons and dispersants using short-term toxicological assays (≤ 96hrs) that monitored phenotypic responses and survivorship. Specifically, we tested the hypotheses that oil/dispersant

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mixtures would be the most toxic to corals, and that C. delta would have a higher tolerance for hydrocarbons due to its affinity for natural seep habitats.

3.3. Methods

3.3.1 Sample collection and acclimatization

All samples were collected from two sites in the GoM. C. delta was collected from the Viosca Knoll 826 site at a depth of approximately 500 m (29°09.5’N,

88°01.0’W; Cordes et al., 2008; Davies et al., 2010). Paramuricea type B3 colonies were collected from a large population of corals at approximately 1050 m depth at Atwater

Valley (AT) 357 [27°58.6’N, 89°70.4’W; Doughty et al. (2014)]. At each site, corals were haphazardly collected with the remotely operated vehicles (ROV) Global Explorer

MK3 or Hercules.

Samples were taken on multiple dives, with 5-6 colonies of C. delta collected from VK826, and 5-6 colonies of Paramuricea type B3 gathered from AT357. Samples were collected several meters apart from conspecific colonies to reduce the likelihood of sampling clones. Corals were visually identified using live video stream from cameras attached to each ROV, before being collected with a manipulator arm and secured in an insulated “bio” box and or sealable collection quivers. When possible, branches of colonies were sampled to reduce impact.

At the surface, colonies were immediately transferred to containers with filtered seawater of the species-appropriate temperature and salinity (35 psu). C. delta was 74

maintained at approximately 8°C and later, Paramuricea type B3 at 5°C (the average in situ temperatures at depth) in a temperature-controlled room for the duration of the experiment. Temperature in holding vessels was continuously monitored using temperature probes (Hobo® Data Loggers). Corals were allowed to acclimate for 6-12 hours prior to experimentation.

3.3.2 Preparation of bulk-oil treatments

For the bulk-oil experiment three stock solutions were prepared: crude oil (MASS oil collected from the Macondo well during the spill), dispersant (Corexit 9500A), an oil/dispersant mixture, and artificial seawater controls. All solutions were made with sterile artificial seawater (ASW, Instant Ocean™) at 35 psu, the average in situ salinity for both sites. ASW allowed us to accurately maintain desired salinity and temperature for large volumes of water without the potential for introducing contaminants from the ship’s seawater system, and to avoid the unreliability of collecting buckets of seawater from over the side in variable sea states. We have used ASW to maintain other cold-water coral species alive in laboratory aquaria for extended periods of time without adverse effects (Lunden et al., 2014).

A stock bulk-oil solution was prepared at a concentration of 250 parts per million

(ppm) by adding 50 µL of MASS oil to 199.95 mL ASW. The solution was mixed at room temperature for a 24-hour period on an orbital shaker at approximately 500 rpm to achieve highest possible homogeneity. Oil dilutions were prepared from this stock solution. The subsequent oil concentrations were chosen in an attempt to determine the 75

threshold for lethal toxicity, following preliminary toxicity studies on a cold-water black coral Leiopathes glaberrima. Dispersant concentrations were the same as the oil concentrations so as to examine the relative toxicity of oil vs. dispersant. The oil/dispersant-mixture stock solution was prepared with an initial targeted concentration of 250 ppm each of crude oil and Corexit 9500A by adding 50 µL of each to 199.90 mL of ASW. The dispersant stock solution was prepared by adding 50 µL Corexit 9500A to

199.95 mL ASW to achieve an initial concentration of 250 ppm. Serial dilutions were prepared from each of the three stock solutions to produce three target concentrations: 25 ppm (High), 7.9 ppm (Medium) and 0.8 ppm (Low).

All solutions were placed into sterile 50 mL glass vials. These were then incubated at 5 or 8 °C, dependent on species, and mixed continuously at low speeds for

24 hours on an orbital shaker table to reduce separation and to encourage even oil distribution. Experiments were conducted between the 8-27 November 2012 onboard the

R/V Falkor.

3.3.3 Preparation of treatments using water-accommodated oil fractions

(WAF) only

For this experiment, stock solutions were prepared using only the water- accommodated oil fractions (WAF). For the WAF oil treatment, a higher oil volume (9.5 mL) of surrogate oil was added to 475 mL of ASW and mixed at high speeds (~350 rpm) in an attempt to produce a 1.2 mM WAF oil solution. The WAF was separated from the insoluble oil layer using a sterile separatory funnel, and used as a stock solution to 76

produce experimental treatments with targeted initial total hydrocarbon concentrations of

250 µM (High), 150 µM (Medium) and 50 µM (Low) WAF. Target concentrations were chosen to find lethal doses, as none of the previous bulk-oil (only) concentrations proved to be lethal. This was done using a standardized WAF protocol (S. Joye, pers. com) and based on the highest concentrations of oil detected during the spill (~300 µM, Joye et al.

(2011).

The oil/dispersant mixture treatment was prepared using the same oil volume, with 950 µL of Corexit 9500A added (one-tenth of the oil concentration) to produce a dispersant enhanced WAF (DE-WAF; oil/dispersant treatment), also mixed at high speeds (~350 rpm). As the dispersant concentrations in the bulk-oil exposures were not entirely lethal to C. delta in the short term and most of the observed health decline was seen towards the end of the exposures at the highest Corexit 9500A concentration, the range of dispersant concentrations was progressively increased from those used in the previous exposures to attempt to reveal the lethal concentration (LC50). The dispersant stock solution was made by adding 950 µL of Corexit 9500A to 475 mL of ASW, with an initial dispersant concentration of 848 mg/L (mixed at 200-300 rpm). All stock solutions were mixed at room temperature for 48-72 hours. Experimental solutions were then made from these two treatments with targeted initial oil concentrations of 250 µM (High), 150

µM (Medium) and 50 µM (Low) and targeted initial total dispersant concentrations of

176.7 mg/L (High), 106.0 mg/L (Medium) and 35.3 mg/L (Low).

All solutions were placed into sterile 50 mL acid-washed glass vials prior to experimentation. There was an anticipated and unavoidable loss of hydrocarbons and

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dispersant due to the adhesion of hydrophobic components to the dilution containers with each sequential transfer, as well as the chemical and coral-microbial alterations of hydrocarbons and dispersant components over the course of the treatments. Therefore, oil and dispersant concentrations are reported as conservative, initial targeted values only, and qualitatively designated as “High” “Medium” and “Low” in the analysis.

Experiments were conducted from 23 June 2013 to 3 July 2013 onboard the R/V

Nautilus.

3.3.4 Fragmentation and exposure experiments

For both bulk-oil and WAF experiments, four to six colonies of each species (n =

2) were fragmented into similar sized (approximately 3 cm to 6 cm tall), genetically identical replicates, or “nubbins” (n = 11) and placed into the oil, dispersant, oil/dispersant mixture and the control (ASW) treatments. Paramuricea type B3 had only three healthy colonies for the bulk-oil exposures. The number of polyps per nubbin varied for each species because of the wide range in polyp sizes and unique branching morphology. Samples were placed in 50 ml pyrex test tubes, mounted on a shaker table in a temperature controlled environment, and aerated every 24 hours by bubbling air into the tubes and gently inverting each sample.

Each sample was photographed together with a scale and monitored for signs of stress at four time points (24, 48, 72 and 96 hours) during the bioassay. Each experimental nubbin was assigned an overall health rating on a scale ranging from 0-5.

The percentage of live polyps and tissue-covered skeleton primarily contributed to this 78

rating: dead fragment (score of 0), <<50 percent (score of 1-2), ~50 percent (score of 3),

>>50 percent (score of 4-5), while the other stress responses further differentiated between scores. Ratings were further refined based on the following phenotypic stress responses: percentage of polyp retraction and or inflation, presence and persistence of mucus discharge, dead or darkened tissue, sloughing tissue and exposed skeleton. While polyp mortality, polyp retraction, mucus release, loose tissue, and exposed skeleton were observed in all three species, darkened tissue was specific to Paramuricea, with tissue whitening only observed in C. delta. Furthermore, C. delta displayed a distinctive polyp coiling, ultimately forming node-like structures that eventually disintegrated, leaving behind exposed skeleton. Samples and treatments were randomized in an attempt to reduce health-scoring bias.

3.3.5 Survival Analysis

Health rankings were averaged for replicate coral fragments in each experimental concentration and plotted over time to investigate health decline. This was done discretely for each round of experiments (bulk-oil or WAF), type of treatment (oil, dispersant and oil/dispersant) and species to determine the effect of concentration on fragment health over time. Health differences within the different treatments at the 96- hour end-point were tested using a non-parametric Kruskal-Wallis test, and if applicable

(p < 0.05), non-parametric post-hoc, pair-wise comparisons were performed using the

Wilcoxon method (using JMP® Pro 10.0.2).

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To investigate fragment survival over time, a Kaplan-Meier (K-M) “time to event” survival analysis was performed separately for each experimental series (IBM®

SPSS® Statistics v22, Kaplan and Meier (1958)). This test measures the fraction of fragments declining to a health status of 3 or below at each time point and generates a survival curve. To quantify differences amongst the survival curves for a given species and treatment, a Mantel-Cox Log-rank test was used to evaluate statistical significance (α

= 0.05); if significant, pair-wise comparisons were made, again using a Mantel-Cox Log- rank test.

An additional K-M analysis was performed to compare survival across species in each treatment. Only “event” occurrences contribute to survival estimates; the remaining data becomes censored in the analysis. For this reason the ASW control treatments, in which all fragments maintained health ratings > 3, were excluded from survival-estimate statistics during species comparisons. A similar percentage of censored cases were present in the oil, dispersant and oil-dispersant treatments for each species, and the pattern of censoring was similar.

Additionally, Cox regressions were performed to quantify the hazard (i.e. a decline in health) associated with a) treatment (Water, Oil, Dispersant/Oil and

Dispersant), b) concentration (High, Medium, Low, Zero), and c) species (C. delta or

Paramuricea type B3) for the two sets of experiments (bulk-oil and WAF). The “event” in the time-to-event analysis was reaching a health rating of 3 or below (3, 1, 2 or 0), as mortality was not observed in every treatment and concentration during the exposure.

The hazard ratios were calculated for each factor with respect to control treatment (a), the

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zero concentration (b) and C. delta (c), as we had hypothesized this to be the species most likely adapted to oil exposure. Cox regression was performed in IBM® SPSS® Statistics v22.

3.4 Results

3.4.1 Oil exposure effects on Paramuricea type B3

Complete fragment mortality was not observed for Paramuricea type B3 in the control, bulk-oil or oil-WAF treatments (Figure 3.1, A; Figure 3.2, A). In examining the effect of concentration on fragment condition at the end of the bulk-oil and WAF exposures, the Kruskal-Wallis test showed no significant differences among the 96-hour health ratings across all oil concentrations and controls (p > 0.05).

3.4.2 Dispersant exposure effects on Paramuricea type B3

Whole fragment mortality was observed in Paramuricea type B3 nubbins exposed to the High dispersant treatment (Figure 3.1, C). This decline in health originated in the dispersant mixture within 48-72 hours, with two of three colonies exhibiting complete fragment mortality at the end of the exposure period. The Kruskal-Wallis test revealed significant differences (p < 0.05) in health rankings for Paramuricea type B3 at the end of the exposure; pair-wise comparisons revealed significant differences between nubbins in the High dispersant relative to the control samples (p < 0.05).

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High coral fragment mortality was observed in the dispersant treatment across all concentrations tested in the WAF experiments. One of six Paramuricea type B3 replicates died in the Low dispersant solution, with complete mortality observed in four of six replicates in the Medium dispersant treatment by 96 hours. At High dispersant concentrations, four of six replicates were dead after only 48 hours, with complete mortality of all fragments after 96 hours (Figure 3.2, C). The Kruskal-Wallis test and pair-wise comparisons revealed significantly higher health ratings among the control

Paramuricea type B3 nubbins relative to all levels of dispersant (Low, Medium and

High; p < 0.005) as well as in the Low versus High dispersant concentrations (p < 0.005).

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(A) (B) C. delta bulk-oil

(C) (D) C. delta dispersant

(E) (F) C. delta oil/disp.

Time (hr) Concentration: Control Low Medium High

Figure 3.1- Average health ratings over time for coral fragments exposed to various concentrations of bulk-mixtures: oil (yellow), Corexit 9500A dispersant solutions (blue) and oil-dispersant (oil/disp.) combination mixtures (red). Concentration increases along a gradient from light to dark. Health rating scale 0-5. Bars show standard error. (Figure adapted from DeLeo et al. 2016, DOI: 10.1016/j.dsr2.2015.02.028).

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(A) Paramuricea B3 WAF-oil (B)(B) C. delta WAF-oil (C) 5 5 4 4 3 3 2 2 1 1 0 0 (C)(D) Paramuricea B3 dispersant (E)(D) C. delta dispersant (F) 5 5 4 4 3 3 2 2 1 1 0 0 verage health rating

A (G)(E) Paramuricea B3 oil/disp. (H)(F) C. delta oil/disp. (I) 5 5 4 4 3 3 2 2 1 1

0 24 48 72 96 0 24 48 72 96 Time (hr) Time (hr) Concentration:Concentration: Control Cont Lowr ol Medium Low High Medium High

Figure 3.2- Average health ratings over time for coral fragments exposed to various

concentrations of water accommodated fractions: oil (yellow), Corexit 9500A

dispersant solutions (blue) and water accommodated oil-dispersant (oil/disp.)

combination mixtures (red). Concentration increases along a gradient from light to

dark. Health rating scale 0-5. Bars show standard error. (Figure adapted from

DeLeo et al. 2016, DOI: 10.1016/j.dsr2.2015.02.028).

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3.4.3 Oil/dispersant exposure effects on Paramuricea type B3

Whole fragment mortality was observed in Paramuricea type B3 nubbins exposed to the High oil/dispersant treatment (Figure 3.1, E), with complete mortality in two of three fragments by 96 hours. There were significant health differences among concentrations (Kruskal-Wallis, p < 0.05), and subsequent pair-wise comparisons revealed significant differences between fragments in the High oil/dispersant relative to the control samples (p < 0.05).

During the WAF exposures, complete mortality was observed in the oil/dispersant mixture (DE-WAF), for one of six Paramuricea type B3 samples in both the Low and

High concentrations (Figure 3.2, E). The Kruskal-Wallis and post-hoc tests detected significant health differences in fragments exposed to all concentrations of the mixture relative to the controls (p < 0.05).

3.4.4 Comparisons between treatments for Paramuricea type B3

For comparisons made between treatments in the bulk-exposure series, the log- rank test revealed significant differences among the K-M survival estimates (χ = 7.62, df

= 2, p = 0.022); pairwise comparisons (Table 3.1) indicated these differences were between the oil and oil/dispersant treatments (p < 0.0167). The oil/dispersant treatment had the lowest mean estimated survival time of 87.6 hrs, compared to the overall mean estimate of 90.2 hrs (Table 3.2 [a] and Figure 3.3). In the WAF exposures there were also

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significant differences among time-to-event occurrences (χ = 57.3, df = 2, p < 0.001), and pair-wise comparisons affirmed significantly different estimates between all treatments.

The lowest time-to-event estimate was 82.5 hrs in dispersant compared to 96 hrs in oil and an overall average estimate of 90.8 hrs (Table 3.2 [b] and Figure 3.3).

Table 3.1- Pair-wise comparisons of K-M survival estimates in oil, dispersant and oil/dispersant treatments within the bulk-oil and oil-WAF exposure series, using a

Mantel-Cox log-rank analysis. Comparisons were done discretely for each coral species

C. delta and Paramuricea (type) B3 (Χ2= chi-square, α= 0.05). The event was a decline in health rating to 3 or below (bulk) or 1 and below (WAF). Bonferroni adjusted p-values for each within species comparison are p < 0.0167. (Adapted from DeLeo et al. 2016).

Log-rank Oil Dispersant Oil /Dispersant (Mantel-Cox) 2 p-val 2 p-val 2 p-val __ __ Bulk exposures Oil 1.766 0.184 0.284 0.594 __ C. delta Dispersant 1.766 0.184 __ 3.594 0.058 __ __ Oil/Dispersant 0.284 0.594 3.594 0.058 __ __ Paramuricea B3 Oil 3.958 0.047 10.634 0.001 __ __ Dispersant 3.958 0.047 2.401 0.121 __ __ Oil/Dispersant 10.634 0.001 2.401 0.121 __ __ WAFL. glaberri exposuresma OiOill 14.1270.152 0.0000.696 4.7887.364 0.0290.007 __ __ C. delta Dispersant 14.1270.152 0.0000.696 3.6516.919 00.009.056 __ __ Oil/Oil/Dispersant 7.3644.788 00.007.029 3.6516.919 0.0090.056 __ __ ParamuriceaWAF exposures B3 Oill 14.12746.594 0.000 4.7888.695 0.0290.003 __ __ C. delta DispersantDispersant 14.12746.594 0.000 25.7703.651 0.0000.056 Oil/Dispersant 8.695 0.003 25.770 0.000 __ __ Oil/Dispersant 4.788 0.029 3.651 0.056 __ __

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Table 3.2- K-M means for time-to-event estimates for C. delta and Paramuricea type B3, in bulk-oil (a) and WAF (b) exposures using a Mantel-Cox Log-rank analysis. The event was a decline in health rating to: a) 3 or below, b) 1 or below. (Table adapted from

DeLeo et al. 2016)

95% (a)Bulk exposure Confidence Interval Survival Std. Lower Upper Species Treatment Estimate Error Bound Bound

C. delta Bulk-oil 90.3 1.88 86.6 94.0 Dispersant 93.5 1.31 90.9 96.0 Oil/Dispersant 90.5 1.77 87.1 94.0 Overall 91.4 0.95 89.6 93.3 Paramuricea B3 Bulk-oil 91.9 2.25 87.5 96.3 Dispersant 91.0 2.04 87.0 95.0 Oil/Dispersant 87.6 2.50 82.7 92.5 Overall 90.2 1.27 87.7 92.7 Overall Overall 92.1 0.54 91.0 93.1

(b)WAF exposure 95% Confidence Interval Species Treatment Survival Std. Lower Upper Estimate Error Bound Bound

C. delta Oil WAF 93.5 1.52 90.5 96.5

Dispersant 89.1 2.05 85.1 93.1

Oil/Dispersant 92. 3 1.60 89.1 95. 4 Overall 91.6 0.99 89.7 93.6

Paramurciea B3 Oil WAF 96.0 0.00 96.0 96.0 Dispersant 82.5 2.45 77.7 87.3 Oil/Dispersant 94.5 0.88 92.8 96.2

Overall 90.8 0.98 88.9 92.7 Overall Overall 89.3 0.64 88.1 90.6

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C. delta

Treatment

Figure 3.3- Box-plots showing time-to-event estimates from the Kaplan-Meier

survival analysis for coral fragments in three different treatments: oil, dispersant

and oil/dispersant (Top row represents bulk-oil exposures and bottom row

represents oil WAF exposures). The event was a decline in health rating to 3 or

below (bulk) and 1 or below (WAF). Box ends represent standard error, line inside

the box represents the mean and whiskers represent 95% confidence intervals.

(Table adapted from DeLeo et al. 2016).

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3.4.5 Oil exposure effects on Callogorgia delta

There was no complete fragment mortality in the control or bulk-oil treatments

(Figure 3.1, B). However, one C. delta replicate in the Low oil-WAF died by the end of the exposure (Figure 3.2, B). The Kruskal-Wallis test showed no significant differences among the 96-hour health ratings across all concentrations of bulk and WAF oil (p>0.05).

3.4.6 Dispersant exposure effects on Callogorgia delta

C. delta showed a decline in health in the High dispersant (Figure 3.1, D), though complete fragment mortality was not observed during the 96-hour assay. The Kruskal-

Wallis test revealed significant differences (p < 0.05) in health rankings, with the High dispersant showing a significantly greater decline in health than the Medium and Low concentrations (p < 0.05).

During the WAF exposures, 75 percent of C. delta fragments died in the Low dispersant, 25% in the Medium and 75% in the High dispersant after 96 hours (Figure

3.2, D). Control fragment health was significantly higher relative to all concentrations of dispersant (p < 0.05).

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3.4.7 Oil/dispersant exposure effects on Callogorgia delta

Coral fragments also showed a decline in health within the High oil/dispersant treatment (Figure 3.1, F), but again complete fragment mortality was not observed.

Significant differences were detected between nubbins in the High oil/dispersant relative to the control samples (p < 0.05).

During the DE-WAF exposures, mortality was observed in one colony in the

Medium concentration and three of the four colonies in the High concentration (Figure

3.2, F). A Kruskal-Wallis test revealed significant health differences among treatments, with the Medium and High DE-WAF treatments significantly lower than the controls (p <

0.05), and the High DE-WAF also significantly lower than the Low treatment (p < 0.05).

3.4.8 Comparisons between treatments for C. delta

No significant differences were detected among K-M time-to-event estimates for

C. delta fragments in all treatments within the bulk-oil series (χ = 1.72, df = 2, p = 0.422), with an overall time-to-event (health rating of 3 or less) estimate of 91.4 hrs (Table 3.2

[a], Figure 3.3). However, significant differences were detected among treatment estimates in the WAF series (χ = 12.5, df = 2, p = 0.002); these differences were between the oil-only and dispersant-only treatments (Table 3.1). The lowest estimate was 89.1 hrs in the dispersant treatment relative to the 93.5 hrs in the oil, and an overall average time- to-event estimate of 91.6 hrs (Table 3.2 [b]). 90

3.4.9 Overall comparisons between treatments and concentrations

The Cox regression analysis for the bulk-oil series revealed significant differences

(χ = 57.8, df = 5, p < 0.001) among rates in health decline (to health-rating 3, ~50% survival) among treatments (control, oil, dispersant and oil/dispersant) and concentrations

(zero, low, medium and high). The High concentration significantly increased the hazard of reaching a health rating of 3 or below by 2.5-fold relative to control concentrations, but the Medium concentration did not significantly increase the hazard. Also, relative to controls, samples in the dispersant had an increased hazard risk of 2.3 fold, however the hazard increase in the bulk-oil and oil/dispersant mixture treatments were not significantly different from the control treatment (Table 3.3).

Similar regression analyses for the WAF exposures also revealed significant differences (χ = 176.470, df = 7, p < 0.001) among rates of health decline between treatments and concentrations. Relative to the controls, dispersant significantly increased the hazard of reaching a health rating of 3 or below by 3.4 fold, compared to 4.4 fold in the oil/dispersant treatment; being exposed to oil did not significantly increase the hazard.

In addition, the medium treatment concentrations significantly increased the hazard by

1.3-fold relative to the control concentration, whereas the high concentration increased it by 1.6 fold (Table 3.4).

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Table 3.3- Log-rank tests of equality on survival distributions for the different levels of

concentration in the bulk-oil and WAF exposure series. Significant p-values (p < 0.05)

in bold. Overall log-rank comparisons among concentrations Bulk-oil series Treatment Chi-Square df Sig. Control Log Rank (Mantel-Cox) 0 Bulk-oil Log Rank (Mantel-Cox) 0.548 2 0.760 Dispersant Log Rank (Mantel-Cox) 15.635 2 0.000 Oil/Dispersant Log Rank (Mantel-Cox) 21.793 2 0.000 WAF-oil series Control Log Rank (Mantel-Cox) 0 WAF-oil Log Rank (Mantel-Cox) 1.190 2 0.552 Dispersant Log Rank (Mantel-Cox) 33.246 2 0.000 Oil/Dispersant Log Rank (Mantel-Cox) 20.061 2 0.000

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Table 3.4- Predictor variables in Cox regression analysis and calculated hazard ratios of

the odds of reaching health-ratings of interest (≤ 3) for the Bulk-oil and WAF-oil

exposure series. Significant differences based on Wald test statistics; Low concentration

is not present because values are constant or linearly dependent. Hazard ratios were

calculated relative to the control water treatment, 0 mg/L concentration and C. delta

(species), respectively. (Table adapted from DeLeo et al. 2016).

Cox regression variables Bulk-oil Standard Variable Level Wald df p Hazard ratio error Treatment Treatment 14.908 3 0.002 Bulk-oil 0.011 1 0.918 0.963 0.355 Dispersant 7.067 1 0.008 2.322 0.317 Oil/disp. 0.123 1 0.725 1.133 0.367 Concentration Concentration 19.279 2 <0.001 Med conc. 0.272 1 0.602 0.833 0.350 High conc. 10.795 1 0.001 2.500 0.279 Species Species 0.745 2 0.689 Paramuricea B3 0.013 1 0.908 1.027 0.231 Cox regression variables WAF -oil Treatment Treatment 95.263 3 <0.001 WAF-oil 0.026 1 0.872 0.957 0.276 Dispersant 27.407 1 <0.001 3.404 0.234 Oil/disp. 41.745 1 <0.001 4.417 0.230 Concentration Concentration 10.977 2 0.004 Med conc. 3.912 1 0.048 1.342 0.149 High conc. 10.977 1 0.001 1.608 0.143 Species Species 15.701 2 <0.001 Paramuricea B3 1.807 1 0.179 0.817 0.137

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3.4.10 Comparisons between species

Significant differences between K-M survival estimates were detected within the dispersant treatment as well as the control treatment during species comparisons for the bulk-oil series (p < 0.05; Table 3.5). The lowest time-to-event estimate of 90.3 hrs was for Paramuricea type B3 compared to an overall time-to-event estimate of 94.4 hrs.

However, adding species into the Cox regression model did not improve fit, as species’ rate of decline comparisons were not significantly different.

Table 3.5- Log-rank tests on equality of survival distributions for both species in

the bulk-oil and WAF exposure series. Significant p-values (p < 0.05) in bold.

Overall log-rank comparisons among species Bulk-oil series Treatment Chi-Square df p-value

Control Log Rank (Mantel-Cox) 11.222 2 0.004 Bulk-oil Log Rank (Mantel-Cox) 2.496 2 0.287 Dispersant Log Rank (Mantel-Cox) 6.622 2 0.036 Oil/Dispersant Log Rank (Mantel-Cox) 5.709 2 0.058 WAF-oil series

Control Log Rank (Mantel-Cox) 60.353 2 <0.001 WAF-oil Log Rank (Mantel-Cox) 7.556 2 0.023 Dispersant Log Rank (Mantel-Cox) 2.584 2 0.275 Oil/Dispersant Log Rank (Mantel-Cox) 22.22 2 <0.001

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In the WAF exposures, these differences (p < 0.001) were also detected among species in the oil and oil/dispersant treatments. The lowest time-to-event estimate in oil was for Paramuricea type B3 at 68.8 hrs relative to 85.2 hrs for C. delta and an overall time-to-event estimate of 76.3 hrs. Paramuricea type B3 (64.3 hrs) had similarly low time-to-event estimates in the oil/dispersant treatment relative to 93.5 hrs for C. delta and an overall time-to-event estimate of 73.1 hrs. The Cox regression model also indicated that overall, Paramuricea type B3 were not significantly different from those of C. delta

(Table 3.4).

3.5 Discussion

All three deep-sea coral species examined showed more severe declines in health in response to dispersant alone and the oil-dispersant mixtures than the oil-only treatments. The experiments reported here are the first ever to investigate the effects of oil and dispersant exposure on live, cold-water corals collected from the deep sea.

Impacted corals have been observed at multiple sites in the deep GoM (Fisher et al.,

2014b), some covered with floc linked to oil from the Macondo well explosion (White et al., 2012). However, the unprecedented application of chemical dispersants in the deep- sea may have contributed to the observed pattern of impact. This exposure series provides crucial insight into the toxicological impacts of oil and dispersant release on three species of long-lived, habitat forming corals.

Regarding the components of the bulk-oil and WAF mixtures, hydrocarbon concentrations are likely an overestimate, given crude oil’s variable and complex 95

composition, containing thousands of compounds differing in hydrophobic and hydrophyllic tendencies (Clark and Brown, 1977, Singer et al., 2000, Di Toro et al.,

2007). Dispersants also contain a variety of polar and non-polar surfactants and solvents

(Singer et al., 1996). It is highly probable that there was adhesion of oil and dispersant constituents to the mixing flasks used during serial dilutions, as well as to experimental vials. Moreover, loss of water-accomodated oil fractions may have occurred through coalescence and surfacing throughout the exposure period (particularly in the bulk-oil exposure), volatilization during aeration, and/or biodegradation from the microbial communities associated with coral tissues (Couillard et al., 2005). Thus, it is difficult to determine the precise concentrations of oil and dispersant that each coral fragment may encounter at any given time during the course of the experiment but clearly actual exposures were lower than target values, making our results conservative estimates of the effects of oil, dispersant and oil/dispersant mixtures on deep-sea corals. Indeed, similar trends in health decline were observed within each treatment for all three species during four separate experimental trials.

The goal of this experiment was not to reproduce the exact conditions encountered by deep-water corals during the DWH spill, but rather to provide experimental evidence of their sensitivity to various concentrations of oil and dispersant.

Reproducing exact conditions encountered by deep-water corals during the DWH spill is challenging because oil, dispersant and seawater mixtures form complex multiphase systems; an organism may then be exposed to many components of the oil and dispersant in various forms (National Research Council, 1989, Langevin et al., 2004). It is also important to note that corals within the vicinity of the DWH may have been exposed to 96

these pollutants for longer than 96 hours. Long-term exposures may see additional effects but were not feasible due to the time limitations of experimenting at sea. There is also a low survival rate when transporting deep-sea corals back to laboratory aquaria; C. delta only survive for approximately 1-3 months, whereas we have had no success keeping

Paramuricea type B3 or P. biscaya alive over the long-term.

All three species of corals did surprisingly well in the oil treatments compared to the dispersant and oil/dispersant treatments (Figures 3.1 & 3.2). In some cases, the corals appeared healthier in both the bulk-oil and oil-WAF treatments relative to the controls

(e.g. C. delta, Figures 3.1 & 3.2). Although corals can be negatively impacted when covered by oil particulates or floc (White et al., 2012), it is also possible that corals are deriving some form of nutrition from hydrocarbon components, a process that is likely to be mediated by their associated microbial communities. Anecdotal evidence for this linkage comes from the finding of at least one species of octocoral (Callogorgia delta) with increased abundances around natural hydrocarbon seeps (Quattrini et al., 2013).

Previous studies of shallow-water octocorals also revealed non-selective hydrocarbon uptake of dispersed oil droplets into the gastrovascular cavity of the coral during water uptake (Cohen et al., 1977). Since additional food sources were not supplied during the exposure experiments, and most coral fragments within the oil treatments were frequently observed with a higher degree of polyp extension, similar uptake of dispersed oil components might have occurred.

Although our present study suggests MASS crude oil was not toxic over the range of concentrations tested in these experiments (Figures 3.1 & 3.2), the effect of oil

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exposure on corals may be dependent on life-history stage. Crude oil (from the Macondo well) exposures of scleratinian coral larvae induced mortality within 24 hours, while reducing settlement capabilities and post-settlement survival (Goodbody-Gringley et al.,

2013). This suggests an increased vulnerability for coral planulae larvae and juvenile stages, although there was an influence of larval size on exposure tolerance. Other studies have shown premature ejection of planula larvae after exposure to water-soluble fractions of Iranian crude oil (Loya and Rinkevich, 1979) and sub-lethal oil damage to the female reproductive systems of scleratinian corals (Rinkevich and Loya, 1979). Similar sub- lethal impacts may have been imposed on cold-water corals exposed to oil released from the DWH disaster, although these effects may not be manifested for a number of years.

Treatments containing dispersants in both exposure experiments were the most toxic to the corals and induced the highest degree of overall fragment mortality (Figures

3.1 & 3.2). As dispersants tend to increase the surface area of oil-water interactions, they may cause increased toxicological effects to marine organisms (Chandrasekar et al.,

2006, Goodbody-Gringley et al., 2013). However, in the WAF exposure series, dispersant-only solutions were more lethal than the oil/dispersant mixture treatments (as compared to the bulk-oil exposure series), though both treatments resulted in some mortality (Figures 3.1 & 3.2). Toxicity of dispersants is typically attributed to membrane disruption and impairment via surface-active compounds (Abel, 1974, National Research

Council, 1989). Exposure results in increased permeability of biological membranes, loss of total membrane function and/or osmoregulation (Partearroyo et al., 1990). Although

Corexit 9500A was created in an attempt to reduce the toxicity of its predecessors while increasing effectiveness for dispersing more vicous oils, studies have shown that 98

exposure effects are similar to older formulations, Corexit 9527 and 9554 (Singer et al.,

1991, Singer et al., 1995, Singer et al., 1996), which are now considered toxic to a variety of marine organisms.

The results from this toxicological assay suggest that dispersant addition during the ensuing cleanup efforts following the DWH spill may have caused more damage to cold-water corals than the initial release of crude oil into the deep sea. Dispersants were toxic at the higher concentrations tested here, and dispersed oil solutions proved to be more toxic than untreated oil solutions (Figure 3.1 & 3.2), as has been found in previous studies (Epstein et al., 2000, Mitchell and Holdway, 2000, Shafir et al., 2003,

Bhattacharyya et al., 2003, Milinkovitch et al., 2011, Rico-MartInez et al., 2013). The ability of different types of dispersants to emulsify petroleum hydrocarbon components into the water column as well as the relative toxicity of the dispersants and crude oil, contribute to the overall toxicity of each solution (Epstein et al., 2000). The dispersant and oil/dispersant treatments were lethal to all three species in this study, particularly in the WAF exposure series where dispersant concentrations were higher.

It has been observed in several toxicology studies that dispersant additon increases the total concentration of polycyclic aromatic hydrocarbon (PAH) components in surrounding water (Couillard et al., 2005, Hodson et al., 2007). Specifically, it increases the concentration of less water-soluble high-molecular-weight PAHs, some of which induce enzymatic activity (i.e. cytochrome P4501A) that can metabolize PAHs into toxic forms causing a variety of detrimental effects (Henry et al., 1997, Billiard et al.,

1999, Couillard et al., 2005). This could explain the more rapid decline in health for coral

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fragments exposed to the bulk-oil/dispersant and oil-WAF/dispersant mixtures, where it was likely that a larger proportion of crude oil compounds were made biologically available (Couillard et al., 2005, Schein et al., 2009). Larval exposure experiments on two species of shallow-water scleractinian corals, using BP Horizon source oil and

Corexit 9500A, showed a significant decrease in survival and settlement in dispersant solutions and oil-dispersant mixtures, with complete mortality after exposure to 50-100 ppm solutions of dispersant (Goodbody-Gringley et al., 2013). In larvae of hard and soft coral species exposed to dispersants and Egyptian crude oil, all dispersant treatments were more toxic than the oil-only treatments with the highest toxicity observed in oil- dispersed solutions, which also resulted in abnormal development and tissue degeneration

(Epstein et al., 2000).

Despite these results, it is unclear whether short-term exposures to oil and dispersant have long-term effects. Following brief (~24 hours) exposures to Arabian crude oil or dispersed-oil (with Corexit 9527), there were no significant long-term effects on the yearly in situ skeletal growth of shallow water, hermatypic corals in the genus

Diploria and Acropora (Dodge et al., 1984, LeGore et al., 1989). Though variability in growth rates during that year were not measured, similar experiments using a different scleractinian coral, Porites furcata, did reveal reduced growth in exposed fragments relative to controls (Birkeland et al., 1976). This indicates that although short exposure to oil and dispersant may not be lethal to these corals, additional sub-lethal impacts are possible, the extent of which need to be investigated further.

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Oil transport to benthic sediments likely occurred through a variety of pathways after the DWH spill, including direct particulate sinking and absorption into marine snow

(Passow et al., 2012). Exposure to oil-filled particulates may be more damaging to corals then the dissolved hydrocarbon components when additional stressors are present. As viscous particulates, such as flocs, settle onto benthic communities, the unavoidable exposure imposes many risks (Montagna et al., 2013) including the suffocation of sessile organisms. Floc was likely trapped in the mucous of corals (White et al., 2012) and may have also triggered the excretion of excess mucus in an attempt to remove the debris.

This is an energetically costly mechanism, which may lead to reduced health when coupled to additional environmental stressors (Crossland et al., 1980, Riegl and Branch,

1995).

In conclusion, exposure to relatively high concentrations of crude oil does not appear to be as lethal to these species of deep-sea corals as dispersant and mixtures of hydrocarbons and dispersant. However, it is possible that a longer exposure to sub-lethal oil concentrations may cause adverse effects that could not be observed in this short-term toxicological assay. Further examination into the relative effectiveness of different types of dispersants, coupled to examinations of their relative toxicity, is required. To improve future response efforts, alternative methods of oil cleanup are needed, and caution should be used when applying oil dispersants at depth, as it may induce further stress and damage to deep-sea ecosystems.

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CHAPTER 4

TRANSCRIPTOMIC RESPONSES OF A DEEP-SEA OCTOCORAL

EXPERIMENTALLY EXPOSED TO CRUDE OIL AND DISPERSANT

4.1 Abstract

Deep-sea coral communities impacted in the aftermath of the Deepwater Horizon oil spill were discovered with oil and dispersant laden flocculent material covering their branches to varying degrees. Exposed species were visibly stressed and damaged seemingly in accordance with the degree of colony exposure. In addition to the floc, a growing body of evidence supporting both oil and dispersant transport and retention in deep waters makes other modes of chemical exposure feasible. The underlying cellular effects of oil and dispersant exposure, which ultimately manifests into discernable physical damage, is still unresolved for deep-water fauna. Gene expression profiles from corals exposed to experimental treatments of oil, dispersant and oil/dispersant mixtures were compared to controls for two different exposures series; the first using bulk, heterogeneous chemical mixtures and the second exclusively using the water- accommodated fractions of each treatment. Overall gene expression varied by coral genotype, but was most similar among fragments in control and oil-only treatments. The majority of differential gene expression was observed in the treatments containing dispersants. This includes important immune components and a cytochrome p450 involved in xenobiotic metabolism. Our results elucidate the effects of exposure to oil and dispersant alone and in combination, the utility of CYP450 as a biomarker for stress in deep-water fauna and the implications of dispersant exposure at the cellular-level.

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4.2 Introduction

Deep-sea ecosystems are increasingly threatened by resource extraction as oil and gas exploration expands into deeper waters. Despite catastrophic incidents like the

Deepwater Horizon (DWH) oil spill disaster which occurred in the Gulf of Mexico

(GoM) in 2010, drilling continues to occur in deep-waters (> 200 m) worldwide, with active ultra-deep (> 1000 m) drilling advancing in the GoM (Fisher et al., 2014b, Cordes et al., 2016). Drilling activities can be detrimental to deep-sea communities in various ways including physical disturbances by mooring anchors and pipelines (Ulfsnes et al.,

2013), exposure to toxic drill cuttings, muds and fluids (Gray et al., 1990, Larsson et al.,

2013), and accidental oil spills. In the United States alone from 1971 to 2010 there was an oil spill over 160,000 liters on average every 1.75 years (Anderson et al., 2012). Oil and gas extraction in deeper waters also increases the likelihood of accidental spills

(Muehlenbachs et al., 2013) like the DWH blowout which occurred at approximately

1500 m water depth.

The DWH blowout is one of the worst environmental disaster in U.S. history, and the largest accidental oil spill world-wide, releasing a record volume of oil into the deep waters of the GoM (McNutt et al., 2012). Approximately half of the released oil remained in subsurface oil plumes in deep (~1100 m) waters (Camilli et al., 2010), while a large quantity of the oil at the surface that was not burned during clean-up efforts, emulsified with plankton and detritus to form oiled marine snow (Passow et al., 2012). This oiled marine snow subsequently sank (Passow, 2014) and accumulated on the seafloor

(Chanton et al., 2014). Additionally, during active well discharge, an unprecedented

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volume of chemical dispersants (Corexit 9500A and 9527) was released both at the surface and at depth without prior toxicity testing on deep-sea fauna.

Impacted deep-sea coral communities initially found in the vicinity of Macondo well (~11 km) at lease block site MC294 (White et al., 2012), were exposed to a brown flocculent material containing Macondo well oil as well as dispersant constituents (White et al., 2014). Impacted coral species at this site, primarily the octocoral Paramuricea biscaya, exhibited various stress responses including abnormal sclerite development, excess mucous production and tissue necrosis as well as complete colony mortality

(White et al., 2012). Although the underlying cause of this damage is unknown it is speculated to have resulted at least partly from exposure to the oil and dispersant components within the floc (Chapter 2.4).

Initial toxicity tests using various fractions of crude oil, dispersant and oil/dispersant mixtures revealed that treatments containing dispersants were more toxic when presented at similar concentrations to oil, to several cold-water coral species including Paramuricea type B3 (DeLeo et al., 2016), a closely related sister species to P. biscaya (Doughty et al., 2014). Similar physiological responses were also observed in the experimental exposures including excess mucous release, tissue damage, necrosis and morality (DeLeo et al., 2016). However, no published studies to date have investigated the underlying effects of oil and dispersant exposure to deep-water corals beyond the visible response.

The advent of next-generation sequencing technology has further enabled researchers to study the effects of both natural environmental variation and anthropogenic 104

disturbances on non-model organisms (Oleksiak et al., 2002, Ekblom and Galindo,

2011a, Garcia et al., 2012). One such method- transcriptomics, utilizes RNA sequencing

(RNAseq) to measure variations in transcript abundance of putative genes, hereafter referred to as differential expression.

Transcriptomics can be utilized to detect changes in gene expression induced by exposure to oil and dispersant and elucidate the underlying causes of phenotypic change and mortality observed during experimental exposures (Chapter 3). In this study, gene expression patterns of Paramuricea type B3 exposed to bulk-oil and dispersant treatments (heterogeneous solutions, with dissolved and undissolved components), and treatments solely containing the dissolved, or water-accommodated fractions (WAF), were examined after 12 hrs. Expression profiles were compared to investigate the effect of treatment type- oil-only, dispersant-only and oil/dispersant mixtures, on Paramuricea type B3 and to illuminate the cellular impacts underlying the adverse effects found during the 96 hour toxicological assays (Chapter 3). Inferences were also made as to the effects of different chemical fractions (bulk vs. WAF) on coral gene expression as well as similarities to floc-exposed P. biscaya expression profiles (Chapter 2).

4.3 Methods

4.3.1 Sub-lethal oil and dispersant exposures

Coral colonies collected for experiments in the previous chapter (Chapter 3.3), were utilized for this work. Additional fragments from the Paramuricea type B3 colonies

(n=3) were placed in each treatment type: artificial sea-water (ASW; control), oil-only, 105

dispersant-only and an oil/dispersant mixture. Solutions were made following the protocols described in DeLeo et al. 2016 for both the bulk-exposure and WAF-exposure series (Chapter 3.3). One “sub-lethal” concentration (dilution) from each exposure series was chosen for sequencing across all treatments. The subsampled nubbins were exposed to the “High” concentration of oil and dispersant (~25 ppm) during the bulk-exposure series and the “Low” concentration of oil (~50 uM) and dispersant (~35.3 mg/L) during the WAF-exposure series. The concentrations were chosen (1) as there were visible phenotypic differences observed across treatments during the 96 hr exposures, (2) concentrations were deemed “sub-lethal” as severe stress responses and mortality was not induced across all treatment-types within the 96 hrs, and (3) to minimize the concentration differences between the bulk and WAF series. For further details on treatment protocols and concentrations see Chapter 3.3. Fragments were subsampled after

12 hours of exposure, prior to observations of visible damage, and fixed shipboard in

RNAlater to preserve changes in gene expression. Samples were than frozen and returned to the lab at Temple University for further processing.

4.3.2 RNA extractions & sequencing

RNA extractions were performed on the experimental nubbins sampled at 12 hrs, using a Qiagen RNeasy Kit (Table 4.1). RNA concentration was determined using a

NanoDrop® ND-1000. RNA integrity was evaluated with gel electrophoresis and an

Agilent Bioanalyzer at Fox Chase Cancer Center (FCCC, Philadelphia, PA). Library preparations, including a poly-A selection step to target primarily eukaryotic (coral host)

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RNAs, were performed on high quality (RIN values > 7) RNA samples (Illumina Truseq

RNA library kit) at the FCCC sequencing facility. Samples were multiplexed across rapid flow cells (which generate ~200-300M reads each) and sequenced using Illumina

Hiseq2500 technology to acquire ~100 bp single-end reads, at approximately 20-30 million reads per sample.

Exposure treatment Exposure Biological (concentration_series) time (hr) replicates High_bulk-oil 12 3 High_bulk-dispersant 12 3 High_bulk-oil & disp. 12 3 Seawater control_bulk 12 3 Low_WAF-oil 12 3 Low_WAF-dispersant 12 3 Low_WAF-oil & disp. 12 3 Seawater control_WAF 12 3

Table 4.1- Samples sequenced for further analysis of experimental exposure effects- oil, dispersant (Corexit 9500A), oil & dispersant (disp.) and (artificial) seawater controls, on the gene expression of Paramuricea type B3. The bulk-oil exposure series may contain undissolved oil and/or dispersant components, whereas undissolved portions were removed from water-accommodated-fraction (WAF) treatments. (Bulk “High”: oil and dispersant= ~25 ppm; WAF “Low”: oil= ~50 uM, dispersant= ~35.3 mg/L).

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4.3.3 RNAseq analysis & comparisons

Each sequenced sample was discretely mapped to the reference transcriptome assembly for Paramuricea sp. (DeLeo et al., submitted, Chapter 2.4) to acquire both raw and FPKM-normalized (Fragments Per Kilobase of transcript per Million mapped reads) abundance counts for all contigs or putative genes present in the assembly, hereafter referred to as the “individual comparisons”. This analysis was used to explore the heterogeneity of responses within treatments. Mapping, sequence counts and downstream analyses were done using the established pipeline and bioinformatics programs previously described in Chapter 2.3. Due to documented issues in detecting significant expression differences with low replicate number (Schurch et al., 2016), sequences from the biological replicates (coral colonies) were also pooled in each treatment to investigate the overall effect of treatment-type on gene expression, hereafter referred to as “treatment comparisons”. Raw and normalized abundance counts by treatment were subsequently obtained. Annotations were acquired for both comparisons based on where sequences mapped to the annotated Paramuricea reference assembly.

Global gene expression for both the “individual” and “treatment” comparisons was examined using methods described by Love et al. 2015 for the ‘RNAseq differential expression workflow’, rnaseqGene, using bioconductor packages in R (Huber et al.,

2015). Briefly, raw count matrices were first transformed into a DESeqDataSet using tools from the package DESeq2 (Love et al., 2014). As the variance in RNAseq raw counts tends to grow with the mean (Love et al., 2015), count matrices were then

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regularized-logarithm (rlog) transformed to stabilize the variance (homoscedasticity) in the count data across the mean (Love et al., 2014), for use in subsequent analyses.

The homoscedastic, rlog transformed matrices were than used to perform principal component analyses (PCA) to visualize the sample-to-sample (and treatment-to- treatment) distances. PCA’s were performed and visualized using the plotPCA function in R. Actual ‘sample distances’ were also calculated between treatments, using Poisson distance (Witten, 2011) with the “PoiClaClu” package (Witten, 2013). This was done to assess overall similarities among treatments. As this measure of dissimilarity accounts for the variance structure of count data, raw count matrices were used to calculate these distances (Witten, 2011). Distance matrices were plotted using the “pheatmap” (Kolde,

2015) and “RColorBrewer” (Neuwirth, 2014) packages.

A joint gene / sample clustering analysis was also performed for the “individual comparisons” of both exposure series (Bulk and WAF). A subset of the most variably expressed genes (n=20), with the highest variance across samples, was identified using abundance-count matrices and the “genefilter” package (Gentleman et al., 2016). As clustering is only relevant for genes carrying a signal, a small subset was chosen for comparisons- larger gene subsets did not change the overall clustering patterns. Heatmaps were made from these gene subsets based on relative rlog-transformed values; the amount of deviation within a sample relative to the gene’s average expression across all samples

(Love et al., 2015).

Differential expression was determined from comparisons between experimental treatments and the control treatment and calculated using FPKM-normalized abundance 109

count matrices (Li and Dewey, 2011) using the bioinformatics programs (Robinson et al.,

2010) and pipeline described in Chapter 2.3. Overlap among genes differentially expressed in the experimental treatments and those found for the spill-impacted octocoral

Paramuricea biscaya (Chapter 3.4), a closely related sister species (Doughty et al. 2014), was likewise assessed to elucidate commonalities among in situ and experimental oil and dispersant exposures; similar xenobiotic responses were expected. Such comparisons were also anticipated to reveal pollutant-specific (oil vs. dispersant) responses potentially present in the expression profiles of in situ exposed corals.

4.4 Results

4.4.1 Sequencing and alignment to the reference assembly

An average of 32 ± 3 SD million reads were obtained per Paramuricea type B3 fragment. Of those reads, approximately 99.9 percent passed quality checks and trimming prior to being aligned to the reference transcriptome for Paramuricea spp. On average, approximately 46.3 (± 11.1 SD) percent of the single-reads aligned to the reference, per sample, for the bulk-exposure series and 39.4 (± 8.70 SD) percent aligned for the WAF- exposure series.

4.4.2 Paramuricea B3 bulk-exposures (individual comparisons)

Differential gene expression analyses for all biological replicate fragments exposed to bulk-oil and dispersant treatments revealed only one differentially expressed 110

gene in the dispersant treatment (edgeR, FDR £ 0.05), likely due to the low statistical power resulting from small sample size. No annotation was acquired for this putative gene (contig, c14369_g1; log2FC 9.60) using Trinotate and subsequent standalone blasts to the G. ventalina transcriptome and N. vectensis genome (see Chapter 2.3 for more details). However, there was sequence overlap (e= 5 x 10-05) with another unannotated contiguous sequence from the Seriatopora hystrix transcriptome (Meyer, 2013)- a shallow-water scleractinian coral (Table 2.1). The normalized abundance counts for this gene were exceptionally high in two of the three replicates (colony B and C) in the dispersant as well as one bulk-oil replicate (colony C).

Principal components analysis using rlog normalized count data from all samples

(i.e., “individual comparisons”) revealed that overall global gene expression grouped by colony genotype (Figure 4.1). The two principal components explained 94 percent of the variability in the count data (PC1= 63 percent, PC2= 31 percent).

A gene/sample clustering heatmap shows the deviations in fragment expression for a given gene (rlog transformed), in this case the 20 genes with the most variance across samples, relative to the genes average expression (Figure 4.2). Similar trends are found with overall expression of this gene subset first grouping by colony genotype (A,

B, C), then more loosely (i.e., for colony A and B) shows similarities among the control and bulk-oil, and oil/dispersant and dispersant treatments. These highly variable genes encompass several unannotated contigs, uncharacterized or predicted proteins, a serine/threonine-protein kinase, an interferon-inducible GTPase, DNA polymerase, collagen trimer, A-kinase anchor protein and a complement C1q subcomponent.

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20 Colony A B C 0 Treatment control Dispersant PC2: 31% variance PC2: 31% OD -20 Oil

-20 0 20 40 PC1: 63% variance

Figure 4.1- PCA plot showing global gene expression patterns in the bulk-exposures by colony genotype (A, B and C) and treatment: control, oil-oily, dispersant-only and an oil/dispersant mixture. Raw count data was rlog transformed prior to performing the PCA analysis.

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Treatment Treatment 8 Colony control c99074_g1 6 Dispersant c107259_g2 OD 4 c93964_g1 Oil c94433_g1 2 c107259_g5 Colony c101125_g2 0 A c97820_g1 -2 B c14369_g1 C c97010_g1 -4 c98076_g8 c108149_g2 c90088_g3 c91179_g1 c95080_g1 c109019_g1 c108566_g4 c104316_g1 c96802_g1 c109962_g1 c101216_g2 20 21 23 19 73 75 85 74 17 16 22 18

Figure 4.2- Sample and gene clustering heatmap of the 20 most variably expressed contigs induced by bulk-oil treatments, across all colonies and biological replicates. The heatmap shows deviations in expression for a given gene relative to the genes average expression across all samples. Negative values (cool colors) indicate under-expression with positive values (warm colors) indicating over-expression. Variance was calculated from rlog transformed data, with darker colors representing higher variance.

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4.4.3 Paramuricea B3 bulk-exposures (treatment comparisons)

Analyses of combined expression data for coral nubbins exposed to the same bulk-oil and dispersant treatments (12 hrs), revealed 16 genes that were significantly differentially expressed (edgeR, FDR £ 0.05, minimum 4-fold-change). Log2-fold change (FC) ranged from 3.32 to 10.1. Blast annotations (via Trinotate) were obtained for

9 of the 16 differentially expressed genes (DEGs), some of which overlapped among treatments. All genes were over-expressed with respect to the control treatment. Six of the over-expressed genes were found in both the bulk-oil and dispersant-only treatments, although putative annotations were given to only five of those genes (Table 4.2). The remaining DEGs were found in the dispersant-only treatment. No significant differential expression was detected in the bulk-oil/dispersant (mixture) treatment after 12 hours of exposure, though elevated abundance counts were found for all DEGs found in the dispersant treatment.

The genes that were over-expressed in the dispersant-only and oil-only treatments

(Table 4.2) include various collagen trimers, important components of the extracellular matrix which function in cell adhesion (Lebbink et al., 2006). Transcription of an A- kinase anchor protein- an integral membrane component, was also significantly elevated.

Over-expression of a complement C1q subunit, involved in innate immune response and the activation of the complement immune pathway, was likewise found. C1q was also among the most variably expressed genes across samples (Figure 4.2).

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Among the genes differentially expressed in the dispersant-only treatment (Table

4.2) was cytochrome p450 (CYP) involved in the response to and metabolism of xenobiotics. CYP was also elevated in the oil/dispersant treatment, and less so in the bulk-oil treatment, relative to control expression. However, the differences were not significant following FDR p-value corrections. A tyrosinase involved in metal ion binding and melanin biosynthesis, was also over-expressed. Likewise, elevated expression of tyrosinase was observed in the bulk-oil/dispersant mixture but the difference was not significant. A peroxidase, with anti-microbial functions, involved in metal ion binding and oxidative stress responses, was also over-expressed. In addition, the enzyme isoaspartyl peptidase/L-asparaginase, involved in amino acid metabolism and proteolysis (Sallis and Burns, 1989, Pattnaik et al., 2000) was over-expressed.

Table 4.2- Annotations for genes over-expressed in Paramuricea type B3 samples by bulk-treatment (oil-only, disp.-only and oil/disp. mixture), relative to seawater controls sampled at the same 12 hour time-point; FDR £ 0.05, minimum 4-fold-change.

(Bulk exposures) Over-expression vs. Control (12hr)

Comparison (GENE) Tentative annotation Log2FC (± SD) Oil-only & Predicted protein 9.91 ± 0.28 Disp.-only (COL21A1) Collagen alpha-1(XXI) chain *† 5.58 ± 1.62 (COL6A1) Collagen alpha-1(VI) 5.04 ± 1.60 (COL4A1) Collagen alpha-1(IV) chain 4.64 ± 1.52 (AKAP1) A-kinase anchor protein 1‡ 4.62 ± 1.53 (C1QC) Complement C1q subcomponent subunit C 4.39 ± 1.52 Disp.-only (LPO) Lactoperoxidase ‡† 4.52 (CYP1A1) Cytochrome P450 1A1‡* 3.86 (ASRGL1) Isoaspartyl peptidase/L-asparaginase 4.21 (TYR) Tyrosinase ‡* 3.40 ‡ predicted protein found in Nematostella vectensis; * also observed in the in situ exposures on Paramuricea biscaya; †opposite observed in WAF. 115

Of the annotated DEGs, three were also found among those differentially expressed in the cold-water octocoral Paramuricea biscaya (DeLeo et al., submitted) - impacted in situ by the DWH spill. This includes the CYP450 and tyrosinase, see section

2.4. The third gene corresponds to a collagen trimer (Table 4.2).

The PCA analysis performed on the rlog normalized abundance counts grouped by treatment (i.e. “treatment comparisons”) revealed distinct expression patterns across all treatments (Figure 4.3). The first two principal components explained 90 percent of the variability in the count data (PC1= 58 percent, PC2= 32 percent). With respect to

PC1, which represents approximately 65 percent of the overall explained variance in

Figure 4.3, global gene expression is most similar between the control and bulk-oil treatments followed by the bulk-dispersant and oil/dispersant mixture treatments

A heatmap based on (Poisson) sample distances calculated between treatments shows similar trends (Figure 4.4). The largest sample distances were between the dispersant and oil treatments and the dispersant and control treatments.

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5.0

2.5

group control dispersant 0.0 OD oil PC2: 32% variance PC2: 32%

-2.5

-5.0 -4 0 4 PC1: 58% variance

Figure 4.3- PCA plot showing global gene expression patterns combined by bulk- exposure treatment: control, oil-only, dispersant-only and an oil/dispersant mixture. Raw count data was rlog transformed prior to performing the PCA analysis.

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14000

12000 dispersant 10000 8000 6000 4000 2000 control 0

OD

oil

dispersant control OD oil

Figure 4.4- Heatmap showing (Poisson) sample distances among treatments based on abundance counts combined by: control, (bulk) oil, dispersant and (bulk) oil/dispersant mixture (OD) treatments. Lighter colors represent larger sample distances. Raw count data was not transformed prior to calculating sample distances.

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4.4.4 Paramuricea B3 WAF-exposures (individual comparisons)

Differential expression analyses using discrete biological replicates, exposed to

WAF-oil and dispersant treatments (12 hrs), yielded only two DEGs within the oil/dispersant mixture treatment (edgeR, FDR < 0.1), likely due to limited statistical power. Both genes were under-expressed relative to the control treatment. One corresponds to the innate immune component- complement C3 (log2FC 3.08), while the other putative gene (contig c86712_g1; log2FC 2.17) was unannotated (BLAST) but shows sequence similarity to a DNA topoisomerase II (P87078.1) upon further BLAST queries to the Swiss-Prot database (Boutet et al., 2016). The highest overall expression of

C3 was found in the control fragments, with relatively high expression observed in one oil-only fragment and less so in one dispersant-only fragment (both colony c).

Similar to the bulk-exposures, the PCA using discrete (rlog normalized) count data from all samples exposed to WAFs (individual comparisons), showed global gene expression grouped by colony genotype (Figure 4.5). The two principal components explained 88 percent of the variability in the count data (PC1= 58 percent, PC2= 30 percent).

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30

20 Treatment control

10 dispersant OD oil 0 Colony a PC2: 30% variance PC2: 30% b -10 c

-20

-25 0 25 PC1: 58% variance

Figure 4.5- PCA plot showing global gene expression patterns in the water- accommodated fraction exposures by colony genotype (a, b and c) and treatments: control, oil-only, dispersant-only and an oil/dispersant mixture. Raw count data was rlog transformed prior to performing the PCA analysis.

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A gene/sample clustering heatmap showing deviations in sample expression for a given gene (rlog transformed), for the 20 most variably expressed genes, revealed a similar trend (Figure 4.6). Overall expression of this gene subset groups first by colony genotype (a, b, c), then by the control—oil and oil/dispersant—dispersant treatments.

This figure also shows a higher-level grouping among colonies b and c, reflecting the slightly higher overall similarity between these colonies. This gene subset was chosen to illustrate clustering patterns; larger gene subsets did not change the clustering arrangements. These highly variable genes encompass several unannotated contigs, uncharacterized or predicted proteins, a histone H1variant- protein B4, a core histone

H2A, the proteolytic enzyme- chymotrypsinogen, a metalloproteinase, a G2/mitotic- specific cyclin-B- essential for cell cycle control, and a delta-like protein 1- which may function in cell-cell communication.

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Treatment Treatment 8 Colony control c95726_g2 6 dispersant OD c99024_g1 4 oil c107259_g4 2 c107259_g5 Colony c109019_g1 0 a c58299_g1 b -2 c c16567_g1 c14369_g1 -4 c109506_g2 c100431_g2 c102347_g1 c95169_g3 c96856_g3 c102888_g2 c54704_g1 c108449_g1 c110301_g1 c93212_g1 c110065_g1 c109506_g1 46 45 43 53 51 50 44 42 49 52 47 48

Figure 4.6- Sample and gene clustering heatmap of the 20 most variably expressed genes in the WAF treatments, across biological replicates. The heatmap shows deviations in expression for a given gene relative to the genes average across samples. Negative values

(cool colors) indicate under-expression with positive values (warm colors) indicating over-expression. Variance was calculated from rlog transformed data, with darker colors representing higher variance.

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4.4.5 Paramuricea B3 WAF-exposures (treatment comparisons)

For Paramuircea type B3 fragments exposed to WAF treatments, there were 67 differentially expressed genes (DEGs) detected (edgeR, FDR ≤ 0.05, minimum 4-fold- change; Table 4.3); Log2-fold changes ranged from 2.83 to 9.40. Approximately 85 percent of the genes with significant differential expression were detected in treatments containing dispersant. Annotations were obtained for 31 putative DEGs using the blastx component of Trinotate. There were 20 unique gene annotations due to overlap among treatments, 13 of which were under-expressed in the treatments relative to the controls

(Table 4.4), with the remaining 7 over-expressed (Table 4.5).

Table 4.3- Number of significant differentially expressed genes for the water- accommodated fraction (WAF) exposure series (FDR ≤ 0.05, fold-change ≥ 4).

Expression in each treatment- oil, dispersant and oil/dispersant treatments is relative to the control group.

WAF treatment Under-expressed Over-expressed Oil 18 16 Dispersant 17 6 Oil/dispersant 9 1

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Table 4.4- Annotations for the under-expressed genes for Paramuricea type B3 by WAF treatment, after combining replicates (n=3). Expression in each treatment (oil-only, disp.- only and oil/disp. mixture) is relative to seawater controls sampled at 12 hrs.

(WAF exposures) Under-expression vs. Control (12hr)

Comparison (GENE) Tentative annotation Log2FC (± SD) All (NEK6) Serine/threonine-protein kinase Nek6 5.95 ± 1.55 treatments (H2B) Histone H2B 6.41 ± 2.66 (TSSK1B) Serine/threonine-protein kinase 1* 4.62 ± 0.37 (CK) Creatine kinase, flagellar* 4.48 ± 1.09 Oil-only & (KLHL17) Kelch-like protein 17 3.17 ± 0.02 Disp.-only Disp.-only & (REEP5) Receptor expression-enhancing protein 5 3.16 ± 0.03 Oil/disp. mixture (CKMT2) Creatine kinase S-type, mitochondrial 3.96 ± 0.58 Disp.-only (PKD1) Polycystin-1 3.62 (LPO) Lactoperoxidase ‡ 2.83 Oil/disp. (MATN3) Matrilin-3 3.05 mixture (AKAP1) A-kinase anchor protein 1, mitochondrial 3.04 (COL21A1) Collagen alpha-1(XXI) chain*‡ 3.31 (C3) Complement component 3 3.08 Oil-only n/a --- *Also a predicted protein found in Nematostella vectensis; ‡ Opposite exposure in bulk-oil exposure series (dispersant-only).

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Table 4.5- Annotations for the over-expressed genes for Paramuricea type B3 samples by WAF treatment (oil-only, disp.-only and oil/disp. mixture), relative to seawater controls sampled at the same time-point (12hr).

(WAF exposures) Over-expression vs. Control (12hr)

Comparison (GENE) Tentative annotation Log2FC Disp.-only (ORYB) Oryzain beta chain 3.68 (GNAQ) Guanine nucleotide-binding protein G(q) 5.63 subunit alpha* (CALR) Calreticulin* 4.98 (SLC25A3/ MPCP) Phosphate carrier protein, 8.32 mitochondrial* (UAF30) Upstream activation factor subunit spp27* 5.31 Oil/disp. (FGFR1) Fibroblast growth factor receptor homolog 1 3.03 mixture Oil-only (PHKB) Phosphorylase b kinase regulatory subunit 8.22 beta *Opposite expression pattern (under-expressed) found for in situ spill impacted P. biscaya colonies from MC294.

Four of the annotated genes were under-expressed across all WAF treatments: oil, dispersant and the oil/dispersant mixture (Table 4.4). This includes two serine/threonine- protein kinases involved in protein phosphorylation, a creatine kinase and the histone

H2B, a structural protein that helps organize eukaryotic DNA. All three of the putative kinases also matched predicted proteins (Trinotate, BlastP) from the Nematostella vectensis genome (Putnam et al., 2007).

A kelch-like protein involved in actin binding and protein ubiquination was under-expressed in both the WAF-oil and dispersant-only treatments. Additionally, a mitochondrial creatine kinase (and N. vectensis predicted protein), and an integral

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membrane component- the receptor expression-enhancing protein 5, were under- expressed in both the dispersant-only and oil/dispersant mixture treatments.

Within the dispersant treatment, a peroxidase and a glycoprotein, polycystin- an integral membrane protein involved in cell and matrix interactions, were also under- expressed. Though the peroxidase expression difference was significant only in the dispersant treatment, similar depression was observed in the WAF-oil/dispersant, and less so in the WAF-oil. Four additional differentially expressed genes were found in the oil/dispersant mixture treatment: matrilin-3 involved in extracellular matrix organization,

A-kinase anchor protein, a collagen trimer and complement C3- an important innate immune component also involved in inflammatory responses.

Of the seven annotations obtained for the over-expressed genes found in the WAF exposure series, five were unique to the dispersant-only treatment (Table 4.5), including an oryzain beta chain involved in proteolysis. Unexpectedly, the four remaining (over- expressed) DEGs were under-expressed in the in situ DWH-spill impacted octocoral P. biscaya (see section 2.4). This includes a signaling protein- guanine nucleotide-binding protein (G protein), the Ca2+ binding C-type lectin- calreticulin which functions in the unfolded protein response, a phosphate carrier protein- solute carrier family 25 member

(SLC25A3), and an upstream activation factor involved in transcription.

The PCA analysis performed on the (rlog normalized) abundance counts grouped by treatment also revealed distinct expression profiles across treatments (Figure 4.7).

The two principal components explained 87 percent of the variability in the count data

(PC1= 68 percent, PC2= 19 percent). Regarding PC1, which represents approximately 78

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percent of the total variance explained by Figure 4.7, global gene expression was most similar for the control and WAF-oil treatments followed by the dispersant and oil/dispersant mixture treatments. Similar observations were found for the bulk-exposure series.

3

group control 0 dispersant OD oil PC2: 19% variance PC2: 19% -3

-5 0 5 PC1: 68% variance

Figure 4.7- PCA plot showing global gene expression patterns by WAF exposure treatment: control, oil-only, dispersant-only and an oil/dispersant mixture. Raw count data was rlog transformed prior to performing the PCA analysis.

A heatmap based on (Poisson) sample distances calculated between treatments shows similar trends (Figure 4.8). The largest distances were between the oil/dispersant mixture—control and oil/dispersant mixture—WAF-oil treatments. Sample distances between the dispersant and both the control and oil treatments was also relatively high, with the smallest sample distance found among the control—WAF-oil treatments.

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12000

10000 control 8000

6000

4000

2000 oil 0

OD

dispersant

control oil OD dispersant

Figure 4.8- Heatmap showing (Poisson) sample distances among WAF treatments based on abundance counts combined by control, oil, dispersant and oil/dispersant mixture

(OD) treatments. Lighter colors represent larger sample distances.

4.5 Discussion

The gene expression analyses from this study elucidate the cellular stress responses of Paramuricea type B3 following exposure to different fractions of crude oil

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and dispersant (Corexit 9500A). Further, it helps clarify the cellular effects underlying the stressed phenotypes documented in Chapter 3.4.

Examining the genome-wide expression data discretely for biological replicates revealed a heterogeneous response to the treatments consistent with colony genotype. It is possible that certain genotypes are more resilient to stress than others. The genome-wide response of colony C was particularly divergent (Figure 4.2). It is possible that this is due to underlying genetic differences between the colonies although collections were made at the same geographic site, with coral colonies physically separated by several meters.

This suggests coral colonies may respond differently to environmental contaminants, at least upon initial exposure, possibly due to underlying genomic differences and or epigenetic modifications. Some variation in phenotypic responses and health ratings was also observed during both 96 hr toxicity exposures (DeLeo et al., 2016).

Alternatively, as oil, dispersant and seawater form complex multiphase mixtures

(National Research Council, 1989, Langevin et al., 2004), it is possible that the chemical constituents in contact with the fragments varied within each treatment. However, as global expression grouped tightly by genotype, as did the clustering of the most variably expressed genes, and fragments were assigned at random to treatment vials, this remains unlikely.

Detection of differentially expressed genes in the individual comparisons of both the bulk- and WAF- exposure series was exceedingly limited. Significant detection was likely limited due to the inflationary effect of small sample size and multiple testing

(Pawitan et al., 2005) on FDR significance corrections (Benjamini and Hochberg, 1995),

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which is the recommended statistical significance measure for gene expression data

(Storey and Tibshirani, 2003). However, collections of deep-sea fauna remain challenging and limited. Once collections are in hand, ex situ survival is also unpredictable.

As gene expression for each treated fragment was measured relative to its genetically identical control fragment (artificial seawater), it seems logical that only a small portion of genes expressed in the clonal fragments would change in response to the treatment conditions after only 12 hrs. Because these represent different parts of a single colony consisting of numerous clonal polyps, the baseline genetic background was identical. That means that any differences detected, even the limited few differences observed here, are significant and a direct result of the treatments. It is also possible that a more pronounced initial response occurred prior to sampling, during the early hours of exposure (< 12 hrs), as stress-related gene expression changes have been found for shallow-water corals within 3-6 hrs (Rodriguez-Lanetty et al., 2009, Meyer et al., 2011,

Shearer et al., 2014). Furthermore, it is equally possible that the initial response of

Paramuricea type B3 was delayed (>12hrs) as these corals reside in cold waters (~5 °C) and have reduced metabolic rates (Gillooly et al., 2001). Cellular stress responses and subsequent gene expression are dynamic and visible health decline in Paramuricea type

B3 fragments became evident only after 48-72 hrs of exposure for the concentrations sampled (DeLeo et al., 2016).

Despite the lack of statistical power in the individual comparisons, one putative gene (c14369_g1) was highly over-expressed in the bulk-dispersant treatment, although no putative annotation was acquired. Contig c14369_g1 was also the most variably

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expressed gene found among the expression data (Figure 4.2). This reveals the need for further sequence characterization as more sequencing data becomes available for octocorals and deep-water species in general.

Similar to the bulk-exposure series, only two putative genes were differentially expressed for the WAF- individual comparisons, albeit at a less stringent FDR cutoff.

Both genes were under-expressed in the oil/dispersant mixture. Although no BLAST annotation was obtained for the putative gene c86712_g1, it shows sequence similarities to a DNA topoisomerase II (Swiss-Prot) which functions in the breakage and rejoining of

DNA strands during replication. This type (II) of topoisomerase functions to separate daughter strands to prevent entanglement and supercoiling that can result in cell death

(Roca, 1995). If c86712_g1 is indeed a type II topoisomerase, under-expression could be indicative of subsequent cell death and apoptotic processes associated with cellular stress responses (Shearer et al., 2014) that could of been exacerbated upon further exposure.

Complement C3 was likewise under-expressed in the oil/dispersant treatment and is central to activating the complement system- one of the most important components of innate immunity (Li et al., 2011). The complement system has multiple functions and acts as the first line of immune defense; pathways subsequently lead to inflammatory reactions, opsonization (identifying and targeting foreign particles for destruction) and membrane lysis of invading pathogens (Lovoll et al., 2007). Expression was variable across colonies, and elevated more consistently in colony C. Under-expression of C3 in biological replicates exposed to the oil/dispersant treatment may be indicative of immune suppression as the fragments became increasingly stressed by the chemical constituents.

Complete mortality was observed for one replicate in the “Low” oil/dispersant treatment

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by 96 hrs. In addition, the WAF oil/dispersant treatment increased the hazard of reaching a health rating of 3 or below by 4.4-fold (Table 3.4) relative to 3.4-fold in the WAF- dispersant treatment. Immune suppression was likewise observed in P. biscaya upon prolonged exposure to oil and dispersant containing floc following the DWH spill.

The PCA analyses performed after combining the expression data by treatment revealed distinct responses in global gene expression across treatments with more similarities observed across the control and oil treatments (e.g., group 1) and the dispersant and oil/dispersant mixture treatments (e.g., group 2) for both exposure series.

In each case, global expression in group 1 and group 2 were separated by the principal component explaining the majority of the observed variance (PC1) in expression. This agrees with the phenotypic responses and health rating data generated during the 96 hr exposures described in Chapter 3.4. During those exposures, coral fragments of all biological replicates appeared healthiest in the control and oil treatments across concentrations, with no observed mortality. Conversely, phenotypic stress responses (i.e., mucus production, sloughing tissue, darkened and dead polyps), health decline and mortality were observed in both the oil/dispersant mixture and dispersant-only treatments.

Similarities in global gene expression patterns within each aforementioned

‘group’ was further corroborated via the sample distances calculated between treatment- type, with the largest dissimilarities (distances) found in the between group comparisons

(e.g., dispersant–control or oil/dispersant–oil). This indicates that the addition of dispersant, in various fractions, to the treatment-type altered gene expression and subsequent cellular processes in as early as 12 hrs. While the addition of crude oil to

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seawater also changed expression patterns, the alteration was relatively less severe during this short-term exposure.

These data suggest that short-term dispersant exposure, alone or in addition to crude oil, may elicit a cascade of altered expression patterns that could be detrimental to

Paramuricea type B3 among other coral species. Further testing is needed to understand whether changes induced by the short-term exposure (12 hrs) are adequate to invoke visible impacts at the organismal level or if a more prolonged exposure (24-96 hrs) is required to further impede vital cellular processes leading to the visible damage and mortality observed in Chapter 3 (DeLeo et al., 2016).

The differential gene expression analyses (by treatment) for the bulk-oil exposures revealed several over-expressed genes in the dispersant-only group that were also over-expressed by in situ spill-impacted P. biscaya (Chapter 2.4). Among these is cytochrome p450 (CYP), responsible for the biotransformation and metabolism of foreign chemicals (xenobiotics) for excretion from the cell, preventing toxicity. CYPs have been elevated in various organisms, aquatic and terrestrial, exposed to oil and other environmental pollutants (Zhang et al., 2012, Garcia et al., 2012).

Elevated expression of CYPs supports their use as biomarkers for pollutant exposure in the deep sea, even for exposures as short as 12 hrs. It also implies that the elevated CYP expression found in DWH impacted P. biscaya (Chapter 2.4) was exacerbated by the dispersant components present in the oiled floc (White et al., 2014).

Elevated CYP in all treatments relative to control expression likewise suggests that longer exposures would elicit further elevations in CYP expression.

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Another over-expressed gene in the bulk-dispersant treatment, tyrosinase, functions in melanin formation (Palmer and Traylor-Knowles, 2012). Melanin is a pigment believed to play a crucial role in invertebrate innate immunity (Cerenius and

Söderhäll, 2004). Tyrosinase was similarly elevated in impacted P. biscaya with elevated melanin production found in the tissues of other stressed corals (Palmer et al., 2008,

Mydlarz et al., 2008, Mydlarz et al., 2009), including pathogen infected tissue of the octocoral G. ventalina (Mydlarz and Harvell, 2007).

Granular amoebocytes, cellular immune components referred to as immunocytes, function in cnidarian wound repair and tissue regeneration and are believed to be the site of melanin synthesis (Mydlarz et al., 2008). Amoebocytes can be found in the mesoglea of octocorals (Mydlarz et al., 2008)- a gel matrix of collagenous connective tissue, linking the epithelium of the epidermis and the gastrodermis (Meszaros and Bigger, 1999,

Mullen et al., 2004, Ellner et al., 2007). Inflammatory responses are known to cause an influx of amoebocytes into affected coral branches leading to subsequent phagocytotic processes (Meszaros and Bigger, 1999), which characteristically elicit peroxidase activity

(Olano and Bigger, 2000). Significant elevations in putative peroxidase expression was also observed in Paramuricea type B3 fragments exposed to dispersants suggesting ongoing inflammatory reactions at the time of sampling. Elevated peroxidase activity was similarly observed in the octocoral Swiftia exserta within 2 hrs following tissue damage

(Olano and Bigger, 2000).

Inflammatory reactions are known to elicit cellular cascades that degrade the extracellular matrix (ECM); this includes the digestion of collagens by matrix metalloproteinases. Disruption of the ECM allows amoebocytes to infiltrate impacted

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tissues and prevent further spread of pathogens or foreign substances (Olano and Bigger,

2000), via processes like melanization- encapsulation in melanin (Cerenius and

Söderhäll, 2004). Following ECM degradation, collagen deposition occurs (Geraint and

Zumla, 1999). Collagens, in addition to macromolecules such as laminins and fibronectin-like molecules, are also essential to tissue regeneration (Bosch, 2007).

Elevated expression of several collagens (type 1) were found in dispersant-only and bulk- oil exposed corals, providing support for an immune/inflammatory-related response in experimentally exposed Paramuricea type B3 fragments. This implies that environmental stress induces changes in cytoskeletal and ECM components, a response also observed in shallow water hexacorals exposed to thermal stress (Barshis et al., 2013).

In addition to the genes associated with melanization and inflammation, elevated expression of another innate immune component, complement C1q, was found (in bulk- oil and dispersant-only treatments). C1q is the first subcomponent of the C1 complex corresponding to the classical pathway of complement activation. It plays a key role in recognizing immune complexes and initiating the complement pathway in addition to probable apoptotic body clearance (Kishore and Reid, 2000). Elevated expression of C1q suggests the bulk-oil and dispersant treatments evoked immune-related responses during the 12 hours of exposure that would subsequently lead to complement activation. This further advocates the crucial role innate immunity plays in coral responses to their environment and more specifically to xenobiotic exposure as brief as 12 hrs. Elevated immune responses were likewise observed in DWH spill-impacted P. biscaya (Chapter

2.4).

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There was a relatively high number of genes differentially expressed in the WAF exposures. It is important to note that in addition to solutions primarily containing water- accommodated chemical fractions, the concentrations of oil and dispersant were higher in this exposure series. As undissolved portions were removed prior to exposure, the composition of oil and dispersant constituents was expected to be different from the more heterogeneous mixtures used in the bulk-exposure series. For these reasons, it was assumed the WAF treatments would evoke a distinct response, though opposite gene expression patterns were not anticipated. However, the elevated peroxidase activity observed in the bulk-exposures, presumptively associated with immune-related phagocytosis, was significantly depressed in the WAF-dispersant and relatively low in all treatments.

Likewise, complement C3 was significantly under-expressed. This enzyme is vital to complement activation, an essential pathway of the innate immune system (Janeway et al., 1997); C3 also functions in inflammatory reactions. Though under-expression was only significant in the oil/dispersant mixture, expression was low in all treatments suggesting corals exposed to the WAFs had a reduced ability to respond appropriately to cellular stress and damage. This corresponds to the phenotypic effects observed during the longer (96 hr) exposures, where polyp stress (mucus), discoloration, mortality and tissue necrosis was observed (Chapter 3.4), indicating the fragments were not able to maintain cellular homeostasis. As inflammatory response components primarily localize around injured or infected tissue, it is also possible that the sampled tissue was not targeted for this response and that resources were being allocated to different areas of the nubbin.

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Depressed collagen expression in the WAF (oil/dispersant), coupled with under- expression of ECM structural constituents and membrane proteins, further suggests ECM and cytoskeletal components were altered following exposure to oil and dispersant. This was contrary to expression observed in the bulk-series and in situ spill impacted P. biscaya profiles (Chapter 2.3). It is possible that the expression changes observed in the bulk-treatments already occurred in fragments exposed to WAFs prior to sampling (i.e., <

12 hrs) due to higher concentrations and / or exposure to more biologically available oil and dispersant components (Couillard et al., 2005). These expression patterns were primarily observed in treatments containing dispersants, which tend to increase the surface area of oil-water interactions (Chandrasekar et al., 2006, Goodbody-Gringley et al., 2013) and may increase cellular stress or toxicity correlated with impediments to innate immune and inflammatory responses.

Across all WAF treatments, serine/threonine-protein kinases were significantly under-expressed, similar to observations in floc-exposed P. biscaya (Chapter 2.4).

Alterations in serine/threonine-protein kinase activity have previously been associated with apoptosis in environmentally stressed corals (Shearer et al., 2014). Comparable reductions in creatine kinase expression across treatments was also observed. Creatine is involved in the metabolism of amino acids and polyamine derivatives. As polyamines are essential to cell growth and maintenance they are believed to be crucial to cell survival.

In vertebrates it has been hypothesized that abnormal polyamine metabolism may be associated with certain pathologies (Moinard et al., 2005), with reduced creatine kinase activity, coupled with inflammatory response components, found in association with disease (Stucki et al., 1996). Inhibition of creatine kinase activity in oil and dispersant

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exposed corals may therefore be indicative of impediments to cellular homeostasis and irreversible cell damage.

There were several genes over-expressed in WAF treatments with opposite expression patterns in floc-exposed P. biscaya. This includes calreticulin, a lectin-like chaperone protein primarily found in the endoplasmic reticulum (ER). The high down- regulation in spill-impacted P. biscaya may be associated with the sustained floc exposure as opposed to the short-term WAF contact experienced by these Paramuricea type B3 nubbins. Calreticulin is known to participate in the synthesis of various molecules and regulates Ca2+ homeostasis via the modulation of Ca2+ storage and transport (Mery et al., 1996, Kishore and Reid, 2000). It has also been proposed that over-expression of calreticulin in vertebrate cells make them more susceptible to apoptosis (Prathyuman et al., 2010), which may be the case for WAF-exposed nubbins at the time of sampling if the exposures resulted in cellular toxicity and damage.

Moreover, evidence for elevated energy metabolism is seen through the over- expression of a mitochondrial, phosphate carrier protein (and solute carrier family member) across all treatments. In vertebrates, phosphate carrier proteins in the inner mitochondrial membrane play a crucial role in cellular energy metabolism by catalyzing the transport of inorganic phosphate into the mitochondrial matrix (Kramer, 1996).

Elevated expression may therefore signify the energetic cost associated with the cellular stress responses prompted by the oil and dispersant WAF exposures.

In conclusion, both the bulk- and WAF- oil and dispersant evoked distinct gene expression changes in Paramuricea type B3 after 12 hrs of exposure. A high percentage

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of the significant expression changes were observed in treatments containing dispersant.

The disparities observed between the two exposure series are likely due to the differences in oil and dispersant composition within the treatment solutions and higher relative concentrations in the WAF-exposure series. Regardless of these differences, genome- wide expression was heterogeneous among genotypes and most similar between the control and oil treatments and the dispersant and oil/dispersant treatments. This finding parallels the phenotypic observations made during relatively prolonged exposures to the same treatments (Chapter 3). Differential gene expression analyses comparing treatments to controls of the same genetic background also revealed the usefulness of CYP as a biomarker in future monitoring of oil spills in the deep sea and the important role innate immunity, inflammation and in particular melanin production, plays in the response of cold-water corals to environmental pollutants.

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CHAPTER 5

CONCLUSIONS

5.1 Summary of findings

Resource extraction continues to evolve and expand in the deep-sea, although little is known about how deep-water fauna respond to environmental change and disturbance. The main objective of my dissertation was to elucidate the magnitude of impact caused by the Deepwater Horizon (DWH) disaster on cold-water coral communities by assessing the overall toxicity and cellular effects imposed by oil and dispersant exposure.

In Chapter 2, I utilized next-generation sequencing technology to assemble and characterize a reference transcriptome for the octocoral genus Paramuricea for use in differential gene expression analyses on the DWH spill impacted octocoral P. biscaya.

These analyses revealed an intracellular response to xenobiotics, inflammatory responses linked to increased melanin production, the activation of additional innate immune components and wound repair mechanisms. Cold-water octocorals appear to share many rudimentary stress-induced responses with their shallow-water counterparts and cnidarian relatives. This study provides evidence for a common mode of response to a variety of environmental stressors that comparably alter an organism’s environment in an unfavorable manner. While it appears that there is increased expression related to non- specific immune responses and epithelial/wound repair associated mechanisms, there is also evidence that the floc exposure was toxic, leading to increased proteolysis activity, apoptosis and cell death. The gene expression data also suggests that while certain stress

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induced processes likely kept these particular P. biscaya colonies from undergoing complete mortality in the aftermath of the DWH disaster, colony health likely degraded overtime as the duration of the exposure increased. The overall decline in health, stressed phenotype and suite of highly down-regulated ribosomal proteins also suggest that the colony was struggling to overcome the detrimental effects incurred at the cellular level and were unlikely to recover. These data will prove useful towards biomarker development aimed at monitoring future oil and pollutant exposure and has yielded promising candidates including CYPs, melanin-associated tyrosinases and immune- related TRAFs.

In Chapter 3, I conducted a series of experimental exposures to look at the overall toxicity of oil and dispersant on the deep-water octocorals C. delta and Paramuricea type

B3- a close relative of P. biscaya. For both species, corals were healthiest in the control and oil-only treatments. C. delta exhibited a visibly delayed response to the oil and dispersant treatments, although insignificant, which may be correlated to their natural occurrence near hydrocarbon seeps. Taken together, these exposures revealed that the oil dispersants applied (Corexit 9500A) during DWH spill clean-up efforts were more toxic to these octocorals than oil exposures at similar concentrations. Due to the difficulty of collections and limited survival of deep-water fauna in aquaria, this study was also one of the few published ex situ exposure experiments on deep-water octocorals. This research further reveals the implications of using dispersants in deep waters and suggests alternative containment measures should be taken during future spill responses efforts.

In Chapter 4, I was able to synthesize the previous two chapters by performing

RNAseq analyses on Paramuricea type B3 colonies briefly exposed to oil and dispersant

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treatments. Global gene expression patterns revealed a heterogeneous response to exposure, with evidence to suggest that some genotypes may be more resilient to chemical stressors. The majority of differential gene expression was observed in treatments containing dispersants including potential biomarkers from Chapter 2, CYP and tyrosinase, and important components of the innate immune-complement pathway.

The results of this chapter also support the health-rating observations from Chapter 3, with overall expression profiles being the most similar between the control and oil treatments and the dispersant and oil/dispersant treatments. Exposure to dispersants alone or in conjunction with oil caused considerable changes to genome-wide expression patterns, and ensuing cellular processes, with respect to isolated oil exposures. The results also suggest that detrimental cellular effects resulting from dispersant exposure, which manifested into physical damage by 96 hrs in Chapter 3, can occur in as little as 12 hours of exposure.

5.2 Protection and restoration of cold-water coral ecosystems

To date, large scale deep-water restoration efforts are non-existent despite limited preliminary success of gorgonian transplants at approximately 85 m depth in the

Mediterranean (A. Gori, unpublished data). In addition to developing appropriate methodologies and technology capable of carrying out sizable restoration efforts in the deep sea (> 200 m), preemptive management strategies are imperative to protecting these habitats before restoration is necessary (Van Dover et al., 2014). Although the effects of global stressors like climate change will be near impossible to mitigate, limiting impacts from more localized anthropogenic activities like pollution, resource extraction and

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invasive fishing techniques, may prevent accelerated declines and synergistic impacts in these vulnerable communities. Likewise, establishing deep, marine protected areas and / or extending existing sanctuaries into deep waters will limit impacts to cold-water corals and associated fauna.

5.3 Future research directions

Despite advancements in deep sea technology and exploration, further research is needed regarding the basic biology and ecology of cold-water coral ecosystems. As progress is made to improve our baseline knowledge of these communities, more complex investigations and experimentation is essential to predicting the fate and persistence of cold-water corals in future oceans.

An initial consideration for future work should be towards biomarker development to assess anthropogenic disturbances, particularly those associated with ongoing oil and gas extraction in the deep sea. Results from previous chapters have already identified promising candidates, including a cytochrome p450 which is used as a biomarker in other systems (Aas et al., 2000, Devaux et al., 1998, Porte et al., 2001).

Biomarker advancement will allow minimally invasive diagnostics of affected communities and possibly reveal health status and prospective recovery. The ability to detect xenobiotic exposure and anthropogenic-induced cellular stress from small clippings of coral tissues will enhance effective oil spill monitoring strategies, utilizing numerous specimens, without causing further impediments to ecosystem dynamics.

Due to potential declines in cold-water coral communities affected by global climate change and anthropogenic activities, understanding the plasticity of physiological

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and cellular responses to environmental stress is imperative. Identifying specific species and larger taxonomic groupings that may be more susceptible and / or resilient to environmental stress could inform deep-water conservation efforts. Likewise, pinpointing particularly resilient coral species will aid future restoration work in the Gulf of Mexico as well as other impacted deep-water habitats. Investigating genomic or epigenetic factors

(i.e., DNA methylation) leading to increased resilience in the face of disturbance would likewise aid these efforts.

In general, marine systems are simultaneously subjected to multiple anthropogenic stressors in conjunction with natural abiotic and biotic fluctuations (Lewis and Santos, 2016). To fully understand how marine fauna are affected by environmental disturbance, it is important to understand the combined effect of both natural and anthropogenic change on an organism’s ability to respond and recover. The influence of multiple stressors interacting at both the individual and population level is poorly understood in marine ecosystems at present and even more so in the deep sea. Due to ongoing global change and the increasing threat of human disturbance in deep waters, understanding how stressors combine to affect foundation species, including cold-water corals, is crucial. For example, hypoxia naturally occurs in marine systems and is currently considered one of the fastest growing marine stressors, with an exponential increase in observed hypoxic zones in the past forty years (Diaz and Rosenberg, 2008).

Increased hypoxic regions are correlated to various anthropogenic inputs, including nutrients, PAHs and other chemical contaminants (Lewis and Santos, 2016). Synergistic toxic effects appear to be dependent on the suppression of the chemically-induced aryl- hydrocarbon (AhR) receptor pathway, although responses seem to vary by species

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(Fleming and Di Giulio, 2011, Matson et al., 2008). It is hypothesized that if chemical metabolites are more toxic than parental compounds, chemical toxicity may be suppressed in the presence of hypoxia due to AhR suppression and vice versa (Lewis and

Santos, 2016). As future oil spills will likely arise in regions also affected by changing environmental conditions, research investigating combined impacts of pollutant exposure in the face of thermal, acidified and deoxygenated conditions are relevant to the persistence of cold-water coral ecosystems.

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NOTES

Chapter 3 was published in Deep-Sea Research II in 2016 (Volume 129, pp 137-

147) with three co-authors: Dannise Ruiz-Ramos (Pennsylvania State University), Iliana

Baums (Pennsylvania State University) and Erik Cordes (Temple University). Dannise

Ruiz-Ramos and Iliana Baums performed similar exposures on another species of cold- water coral Leiopathes glaberrima, the results of which are not presented here. Iliana

Baums and Erik Cordes helped conceive research ideas, appropriate statistical analyses and contributed to editing the original manuscript. Chapter 2 was recently submitted to

Molecular Ecology and is currently under review with four co-authors: Stephen Lengyel

(Temple University), Andrea Quattrini (Temple University / Harvey Mudd College), Rob

Kulathinal (Temple University), and Erik Cordes (Temple University). Erik Cordes assisted with research design while Stephen Lengyel and Rob Kulathinal played a crucial role in helping establish a bioinformatics workflow for the RNAseq analyses- methods also used in Chapter 4. Andrea Quattrini and Erik Cordes also edited the original submitted manuscript. Throughout this dissertation work, co-authors were helpful in data collection, data analysis, and manuscript editing, while the final analyses, writing, and figures included here were conducted by me.

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