A Dissertation Submitted to the Temple University Graduate Board
In Partial Fulfillment of the Requirements for the Degree
by
Examining Committee Members:
© Copyright 2020
by
Alexis M Weinnig All Rights Reserved
ii ABSTRACT
Human population growth and global industrial development are driving potentially irreversible anthropogenic impacts on the natural world, including altering global climate and ocean conditions and exposing oceanic environments to a wide range of pollutants. While there are numerous studies highlighting the variable effects of climate change and pollution on marine organisms independently, there are very few studies focusing on the potential interactive effects of these stressors. The deep-sea is under increasing threat from these anthropogenic stressors, especially cold-water coral (CWC) communities which contribute to nutrient and carbon cycling, as well as providing biogenic habitats, feeding grounds, and nurseries for many fishes and invertebrates. The primary goals of this dissertation are to assess the vulnerability of CWCs to independent and interacting anthropogenic stressors in their environment; including natural hydrocarbon seepage, hydrocarbon and dispersant concentrations released during an accidental oil spill (i.e. Deepwater Horizon), and the interacting effects of climate change-related factors and hydrocarbon/dispersant exposure. To address these goals, multiple stressor experiments were implemented to assess the effects of current and future conditions [(a) temp: 8°C and pH: 7.9; (b) temp: 8°C and pH: 7.6; (c) temp: 12°C and pH: 7.9; (d) temp: 12°C and pH:
7.6] and oil spill exposure (oil, dispersant, oil + dispersant combined) on coral health using the CWC Lophelia pertusa. Phenotypic response was assessed through observations of diagnostic characteristics that were combined into an average health rating at four points during exposure and recovery. Regardless of environmental condition, average health significantly declined during 24-hour exposure to dispersant alone and increased temperature resulted in a delay in recovery (72 hours) from dispersant exposure. The overall gene expression patterns varied by coral colony, but the dispersant exposure elicited the strongest response. Gene ontology (GO) enrichment analysis revealed that L. pertusa likely experienced varying stages of the cellular stress response (CSR) during exposure to iii oil, dispersant, and a decrease in pH. The most severe responses were associated with the dispersant exposure including GO terms related to apoptosis, the immune system, wound healing, and stress-related responses. However, the oil exposure induced an upregulation of metabolic pathways and energy transfer but a downregulation of cell growth and development, indicating that the coral nubbins could have been reallocating resources and reducing growth to maintain cellular homeostasis. The decrease in seawater pH elicited a similar response to oil through the enrichment of terms associated with a reduction in the cell cycle and development. Interestingly, the increase in temperature did not elicit a CSR that was detectable in the gene expression data. To further investigate the influence of hydrocarbon exposure on CWCs, comparisons of gene expression profiles were conducted using Callogorgia delta colonies that live in close proximity to active hydrocarbon seepage
(“seep”) areas with no current active seepage (“non-seep”) at two different sites in the Gulf of Mexico. There were fewer differentially expressed genes in the “seep” versus “non- seep” comparison (n=21) than the site comparison (n=118) but both analyses revealed GO terms indicating slight alterations in natural biological housekeeping processes, as opposed to a CSR. Our results indicate that distinct stages of the CSR are induced depending on the intensity of stress. This bolsters the idea that there is a stress response shared by all corals in response to a variety of stressors. These data provide evidence that CWCs can be more negatively impacted, both on the phenotypic and molecular levels, by exposure to chemical dispersants than to hydrocarbons alone. Gaining an understanding of how these communities respond, not only to independent stressors, but the combination of these stressors, provides vital information about how CWC communities will fair in current and future conditions.
iv
DEDICATION
To my late grandmother, Ellen Conwell Joslin, for showing me that women
belong at the forefront of Science, Math, and Technology.
v ACKNOWLEDGEMENTS
I cannot fully express the gratitude I have for my mentor and friend, Erik Cordes.
Without his unwavering support, advice, comradery, and compassion, I would not be where I am today. Erik has not only helped me grow as a scientist, he also emphasized the importance of being a mindful global citizen and a good person. In times when I let self-doubt cloud my view, Erik was the first one to lift me back up and help me recognize how far I had come. The opportunities that Erik provided have changed my career and my life, from trips around the world to trips to the bottom of the ocean. I know this is just the beginning of many scientific collaborations and a lifelong friendship. I look forward to our future adventures together.
I am also indebted to my dissertation committee for the encouragement they have provided over the years. Thank you to Robert Sanders and Rob Kulathinal for your mentorship and support, and to Cheryl Morrison who warmly welcomed me into the deep-sea coral community and who’s guidance and friendship I have been fortunate enough to treasure on land and at sea.
I am eternally thankful for my family. They have always supported my dreams and aspirations of becoming a marine scientist and I would not be the person I am today without the guidance, love, and honesty from both of my parents and my sister. My mother, Gail Joslin Weinnig, instilled in me from a young age to stand up for what I believe in and to forge my own path. My father, Michael Weinnig, taught me to always go above and beyond what is asked of you and to always keeps smiling. My sister,
Melissa J. Kampshmidt, has always encouraged me to pursue what I love and to not take flak from anybody. I also have to thank The Moutons for their constant support and
vi providing us with a welcoming home base in New Orleans when hurricanes tried to drive us out of the Gulf of Mexico - there is no place I would prefer to ride out a storm.
I feel so fortunate to have been able to call the Cordes Lab home throughout my doctoral degree. I have never worked in an environment that fostered so much laughter, collaboration, and friendship. I was lucky enough to overlap with amazing lab mates including Danielle DeLeo, Carlos Gomez, Steve Auscovitch, Jay Lunden, Amanda
Glazier, Abby Keller, Sam Georgian, April Stabbins, Melissa Betters, Ryan Gasbarro, and Emily Cowell. I am truly fortunate to have all of you in my life as fellow scientists and friends.
I am also incredibly appreciative to the undergraduate students that assisted in my research over the years. This work would not have been possible (and much less fun) without the diligent work of Adam Hallaj, Heejin Chung, Dan Deegan, Jessi Hart, and
Alex Barkman.
My Temple Biology friends have played a vital role in my success and happiness over the years. I am so thankful to have had Michele Repetto, Marianna Bonfim, Diana
Lopez, Sarah DeVaul Princiotta, Elizabeth Rielly, Marianne Gagnon, Nicole
Mazouchova, Janne Pfeiffernberger, and many others by my side throughout the many stages of this process. In addition, I have to thank the dedicated staff of the Biology
Department that always kept things running smoothly, including Toni Matthews, Tania
Stephens, Carla Woodson, and Sandya Verma.
I thank all of my friends, including Ryan Hulett, Shayle Matsuda, Sean Edgerton,
Noel Graham, and Alex McKinzie for their enduring love, support, and constructive criticism throughout the years – I would not be where I am today without you.
vii Thank you to the many programs and funding sources that made it possible for me to spend 199 days at sea during this journey, and even more so, thank you to the people that made those experiences so enjoyable and memorable. I am grateful to the ship crews aboard the D/V Ocean Inspector, M/V Ocean Intervention II, M/V Ocean Project, R/V
Falkor, R/V Atlantis, M/V Alucia, R/V Brooks McCall, NOAAS Ron Brown, NOAAS
Okeanos Explorer, E/V Nautilus and the vehicle crews of the ROV Global Explorer, ROV
Comanche, ROV SuBastian, HOV Alvin, HOV Nadir, ROV Jason, ROV Deep Discoverer, and ROV Hercules for their commitment to facilitating successful research expeditions and sample collections. I have been extremely fortunate to have worked alongside amazing scientists and people while at sea including Caitlin Adams, Amanda
Demopolous, Randi Rotjan, Sarah Seabrook, Sofia Ledin, Shannon Hoy, Kasey Cantwell,
Jenni McClain Counts, Luke McCartin, Sam Vohsen, and many others – I thank all of you for your hard work, comradery, and the many laughs along the way. I am extremely thankful for the funding that helped to support my dissertation research from the Gulf of
Mexico Research Initiative’s “Ecosystem Impacts of Oil and Gas Inputs to the Gulf”
(ECOGIG) program and Temple University for funding through teaching assistantships and the Doctoral Dissertation Completion Grant.
viii TABLE OF CONTENTS
ABSTRACT ...... iii
DEDICATION ...... v
ACKNOWLEDGEMENTS ...... vi
LIST OF TABLES ...... xiv
LIST OF FIGURES ...... xv
CHAPTER
1 INTRODUCTION ...... 1
1.1 Cold-Water Coral Ecosystems ...... 1
1.1.1 Cold-Water Corals of the Gulf of Mexico ...... 4
1.1.1.1 Lophelia pertusa ...... 4
1.1.1.2 Callogorgia delta ...... 5
1.2 Anthropogenic Stressors to Cold-Water Corals ...... 6
1.2.1 Climate Change and Ocean Acidification ...... 6
1.2.2 Oil and Gas Exploration and Extraction in Deep Water ...... 7
1.2.2.1 The Deepwater Horizon Oil Spill ...... 9
1.2.3 Multiple Stressors: Climate Change and Oil/Dispersant Exposure ...... 10
1.3 Cold-Water Coral Stress Responses ...... 11
1.4 Objectives ...... 13
2 COLD-WATER CORAL (Lophelia pertusa) RESPONSE TO MULTIPLE STRESSORS: HIGH TEMPERATURE AFFECTS RECOVERY ...... 15
ix 2.1 Abstract ...... 15
2.2 Introduction ...... 16
2.3 Methods ...... 22
2.3.1 Sample Collection and Colony Fragmentation ...... 22
2.3.2 Experimental Design ...... 24
2.3.3 Manipulation of Seawater Chemistry and Temperature ...... 28
2.3.4 Health Rating ...... 29
2.3.5 Data Analysis ...... 30
2.4 Results ...... 31
2.4.1 Aquaria Conditions and Carbonate Chemistry ...... 31
2.4.2 Exposure Effects on L. pertusa – Experiment 1 ...... 32
2.4.3 Exposure Effects on L. pertusa – Experiment 2 ...... 34
2.5 Discussion ...... 38
3 A DECREASE IN pH, INCREASE IN TEMPERATURE, AND POLLUTION EXPOSURE ELICIT DISTINCT CELLULAR STRESS RESPONSES IN A COLD- WATER CORAL (Lophelia pertusa) ...... 46
3.1 Abstract ...... 46
3.2 Introduction ...... 47
3.3 Methods ...... 52
3.3.1 Experimental Design and Sample Collection ...... 52
3.3.2 RNA Extractions, Sequencing, and Processing ...... 54
3.3.3 Analysis of Expression Patterns ...... 55
x 3.4 Results ...... 57
3.4.1 Overall Patterns of Differential Gene Expression ...... 57
3.4.2 Global Response to Pollution Exposure, Decrease in pH, and Increase in Temperature ...... 59
3.5 Discussion ...... 70
3.5.1 Variance in Colony Response ...... 71
3.5.2 Oil and Dispersant Exposure and a Decrease in Seawater pH Elicit Signs of a Cellular Stress Response ...... 71
3.5.2.1 Cellular Response to Oil Exposure ...... 72
3.5.2.2 Cellular Response to Dispersant Exposure ...... 74
3.5.2.3 Cellular Response to a Decrease in pH ...... 76
3.5.2.4 Cellular Response to an Increase in Temperature ...... 78
3.5.3 Cnidarian Environmental Stress Response Hypothesis ...... 79
3.5.4 Implications for DWH and Future Oil Spill Assessments ...... 81
4 PROXIMITY TO NATURAL HYDROCARBON SEEPAGE DOES NOT INFLUENCE THE BASELINE GENE EXPRESSION PROFILES OF A DEEP- SEA OCTOCORAL (Callogorgia delta) IN THE GULF OF MEXICO ...... 83
4.1 Abstract ...... 83
4.2 Introduction ...... 84
4.3 Methods ...... 87
4.3.1 Sample Selection and Collections ...... 87
4.3.2 RNA Isolation and Sequencing ...... 89
4.3.3 RNA Sequence Processing ...... 89
xi 4.3.4 Transcriptome Assembly ...... 90
4.3.5 Differential Gene Expression Analyses ...... 90
4.4 Results ...... 91
4.4.1 Callogorgia delta Reference Transcriptome ...... 91
4.4.2 Differential Gene Expression Analyses ...... 92
4.5 Discussion ...... 97
4.5.1 Shared Differentially Expressed Genes and Similarity in Expression Profiles .....98
4.5.2 Experimental Design Considerations and Complications ...... 99
4.5.3 Implications for DWH and Oil Spills in the Deep-Sea ...... 101
5 SUMMARY ...... 103
NOTES ...... 107
BIBLIOGRAPHY ...... 108
APPENDICES
A. MAGENTA MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 138
B. GREEN YELLOW MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 139
C. LIGHT GREEN MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 141
D. TURQUOISE MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 152
xii E. DARK GREEN MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 157
F. CYAN MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 161
G. DARK TURQUOISE MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 165
H. GREY MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 167
I. BLUE MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 168
J. BROWN MODULE: SIGNIFICANTLY ENRICHEDGENE ONTOLOGY TERMS ...... 175
K. PINK MODULE: SIGNIFICANTLY ENRICHED GENE ONTOLOGY TERMS ...... 192
L. SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN ‘SEEP’ AND ‘NON-SEEP’ SAMPLES OF C. delta ...... 195
M. SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN MC751 AND MC885 SAMPLES OF C. delta ...... 197
N. CORRESPONDING GENO ONTOLOGY (GO) TERMS FOR SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN ‘SEEP’ AND ‘NON- SEEP’ SAMPLES OF C. delta ...... 204
O. CORRESPONDING GENO ONTOLOGY (GO) TERMS FOR SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN MC751 AND MC885 SAMPLES OF C. delta ...... 207
xiii LIST OF TABLES
Table Page
2.1 Lophelia pertusa collection sites for experiments ...... 25
2.2 Summary of seawater chemistry parameters from each treatment in experiment 2 ...32
2.3 Experiment 1 two-way factorial ANOVA with health rating as the dependent variable showing the effect of timepoint and treatment ...... 34
2.4 Experiment 2 three-way factorial ANOVA for each timepoint with health ratings as the dependent variable showing the effect of pH, temperature, and treatment ...... 37
4.1 Callogorgia delta collection sites ...... 89
4.2 Callogorgia delta transcriptome assembly statistics ...... 91
4.3 Number of significantly differentially expressed genes of Callogorgia delta for the seep vs non-seep and MC885 vs MC751 site comparisons ...... 94
xiv LIST OF FIGURES
Figure Page
2.1 Overview map and a schematic of coral distribution with depth within the Gulf of Mexico ...... 20
2.2 Experimental set up and design ...... 23
2.3 Matrix for assessing the health rating of Lophelia pertusa ...... 30
2.4 Average health rating for Lophelia pertusa fragments over time throughout experiment 1 ...... 34
2.5 Average health rating for Lophelia pertusa fragments over time throughout experiment 2 ...... 36
3.1 Experimental design, sampling scheme, and RNA processing ...... 53
3.2 Variance and number of differentially expressed genes in response to colony, exposure, and climate (seawater temperature and pH) ...... 58
3.3 Heatmap of module-trait correlations ...... 60
3.4 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for turquoise and magenta ...... 62
3.5 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for light green and green yellow ...... 64
xv 3.6 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for blue and dark turquoise ...... 67
3.7 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for pink ...... 69
4.1 Callogorgia delta colonies growing in close proximity to active hydrocarbon seepage ...... 86
4.2 Principle coordinate analysis of the full expression profiles of Callogorgia delta samples ...... 93
4.3 Differential gene expression of Callogorgia delta in response to site (a) and proximity to seepage (b)...... 95
4.4 Venn-diagram specifying the number of unique and shared differentially expressed genes (padj < 0.05, log2FC = ±1) between the site comparison (MC751 vs MC885) and the seep proximity comparison (Seep vs Non-seep) ...... 96
xvi CHAPTER 1
INTRODUCTION
Humanity depends on healthy shallow and deep-water coral-based ecosystems for countless services, including their role in sustainable fisheries, the discovery of novel biomedical compounds, and as a host for ~25% of marine biodiversity (Moberg and Folke
1999; Buckner 2002; Knowlton et al. 2010; Henry and Roberts 2017). However, unlike any time in recent history, corals are facing rapid changes in ocean temperatures, pH, oxygen concentrations, and direct human disturbances, which put stress on their natural physiological processes and threatens the livelihood of the ecosystems they support
(Hughes et al. 2003; Hoegh-Guldberg et al. 2007; Levin et al. 2015). While the majority of research has focused on the impacts to shallow-water coral reefs, ~66% of coral species
(class Anthozoa; suborders Hexacorallia and Octocorallia) occur between 50-5,000 meters and corals across depths are vulnerable to anthropogenic impacts (Guinotte et al. 2006;
Cairns 2007). We must identify signs of non-lethal stress and advance our ability to monitor coral health, not only to document and understand impacts, but also to mitigate and reverse them.
1.1 Cold-Water Coral Ecosystems
The term “cold-water coral” (CWC) includes members of the Class Anthozoa
(Subclasses Hexacorallia and Octocorallia) and Class Hydrozoa (Family Stylasteridae) that inhabit areas of cooler seawater temperatures (4-12 °C), such as the deep sea or areas of cold-water intrusion like the fjords of Norway (Fosså et al. 2002). The Subclasses
1 Hexacorallia and Octocorallia contribute considerably to the vast diversity of CWCs seen throughout the world’s ocean (Cairns 2007; Roberts et al. 2009). Stony corals (Subclass
Hexacorallia; Order Scleractinia) produce calcium carbonate skeletons and can propagate to form complex reef frameworks under optimal conditions within the depths of 300-1000 meters. However, the vast majority of scleractinian CWCs are single-polyp solitary corals
(73.5%) and the remainder are colonial (26.5%) (Cairns 2007). Members of the Subclass
Octocorallia (octocorals) consist of soft corals and sea fan-like corals that generally do not secrete hard calcium carbonate skeletons but have a central axis and calcium carbonate sclerites distributed throughout the tissue (Bayer 1961; Cairns and Bayer 2009). Octocorals do not have the framework to build reefs but they are more broadly distributed than scleractinians and can form intricate octocoral gardens that act as the foundation for those ecosystems (Fabricius, K E Alderslade 2001; Cairns and Bayer 2009; Roberts et al. 2009;
Buhl-Mortensen et al. 2016).
Cold-water corals are an essential component of productive deep-sea ecosystem functionality worldwide. CWC communities contribute to nutrient and carbon cycling; provide heterogeneous biogenic habitats, feeding grounds, and nurseries for many fishes and invertebrates (Etnoyer and Warrenchuk 2007; Cordes et al. 2008; Oevelen et al. 2009;
Ross et al. 2010). They create a more complex and diverse localized setting with their calcium carbonate skeleton or proteinaceous axis which increases overall biodiversity in the region (Jensen and Frederiksen 1992; Mortensen and Fosså 2006; Cairns 2007; Cordes et al. 2008; Buhl-Mortensen et al. 2010). The increased habitat heterogeneity created by
CWCs forms niches that are occupied by fauna depending on their preference for various abiotic factors (i.e. food sources, current speed, protective covering) (Buhl-Mortensen et
2 al. 2010, 2016). For instance, many deep-sea octocorals host associated suspension feeding fauna that utilize the structure of the coral to become elevated off of the seafloor and up into the water column where there is increased current flow (Buhl-Mortensen and
Mortensen 2004). Also, the biodiversity of Lophelia pertusa reefs off of Norway has been characterized to show that the upper slopes of the reef consist of dense aggregates of live
L. pertusa colonies, as well as an array of octocorals, sponges, and actiniarians. Whereas the dead coral skeleton hosts the highest species richness by providing habitat for numerous small invertebrate species including hydrozoans, bivalves, sea urchins, and sponges
(Freiwald et al. 2004).
Benthic ecosystems supported by CWCs are distributed throughout the world’s ocean and can be found along continental slopes, canyon walls, and seamounts (Freiwald et al. 2004; Roberts et al. 2009). CWC distribution patterns are largely driven by abiotic factors including seawater temperature, aragonite saturation (Ωarag), depth, salinity, slope, oxygen, and the presence of hard substrate (Davies et al. 2008; Davies and Guinotte 2011;
Yesson et al. 2012; Sundahl et al. 2020). While humans have had knowledge of the existence of CWCs for centuries, there is still much to be learned about their habitat suitability and distribution patterns (Rogers 1999; Davies et al. 2008; Henry and Roberts
2017; Sundahl et al. 2020). With advances in technology and an influx of high resolution environmental data, habitat suitability models are increasingly able to accurately predict where CWC habitats might be located and are discovering that these ecosystems could be much more broadly distributed than previously thought (Henry and Roberts 2017;
Georgian et al. 2020; Sundahl et al. 2020; Wagner et al. 2020). While these CWC habitats
3 can be found worldwide, the focus of this study involves coral communities observed and collected from the Gulf of Mexico.
1.1.1 Cold-Water Corals of the Gulf of Mexico
The Gulf of Mexico (GoM) is the home to an array of benthic ecosystems, including active hydrocarbon seeps, brine pools, and shallow water, mesophotic, and cold-water coral ecosystems (Cordes et al. 2008, 2009; Quattrini et al. 2014). CWCs in the GoM and can be found along the continental shelf between 250 – 2500 m. These corals utilize authigenic carbonate outcrops that have remained from microbial activity at hydrocarbon seeps that are no longer active and along the Florida Escarpment, which is not made up of carbonates, down to 3,300 m (Schroeder 2002; Cordes et al. 2008; Quattrini et al. 2014). Both reef- building scleractinian and octocorals contribute to the diversity of corals found in deep water and two of the most common and widely distributed CWCs within the GoM are
Lophelia pertusa and the genus Callogorgia.
1.1.1.1 Lophelia pertusa
Lophelia pertusa is a cold-water scleractinian coral found most commonly between
300-600 meters depth and forms the largest biogenic habitat (deep-water reefs) in the GoM
(Schroeder 2002; Cordes et al. 2008; Roberts and Kohl 2018). The process that creates coral-reef frameworks in the deep-sea often occurs over millennia and one L. pertusa reef in the GoM (Roberts Reef; Viosca Knoll 906) has been dated to be ~300 ka using radiometric dating of two 16-m piston cores (Roberts et al. 2009; Roberts and Kohl 2018).
Characterization of the carbonate chemistry around L. pertusa reefs and thickets in the
4 GoM has indicated that these corals live in a range of pH between 7.85 – 7.95, an Ωarag range between ~1.2-1.7, and a density layer of 27 kg m-3, which is representative of North
Atlantic Central Water (NACW) (Lunden et al. 2013; Georgian et al. 2016). In addition, over 68 taxa have been documented living amongst L. pertusa thickets and the communities associated with L. pertusa are distinct from the communities supported by other foundation species (vestimentiferan tubeworms) (Cordes et al. 2008). While L. pertusa colonies are often found growing on authigenic carbonate outcrops and in close proximity to vestimentiferan tubeworms, evidence has shown that they do not derive nutrition from seep fluids but rely on marine snow from surface waters and small planktonic-derived organic matter (Cordes et al. 2008; Becker et al. 2009).
1.1.1.2 Callogorgia delta
The octocoral genus Callogorgia (Family Primnoidea) is represented by 24 species with a wide distribution and depth range (~40 – 2,000 m) (Cairns and Bayer 2002). Species of Callogorgia are commonly the host for invertebrates, such as ophiuroid brittle stars, and can act as nursery grounds for catsharks (Etnoyer and Warrenchuk 2007; Mosher and
Watling 2009; Cho and Shank 2010). There are records for three species (C. gacilis, C. americana, and C. delta) occurring in the upper bathyal region characterized by a specific temperature and bathymetric range in the GoM (Quattrini et al. 2013; Bayer et al. 2015).
Callogorgia delta is the deepest dwelling species of the three and is found primarily between ~400 - 900 m, often in large “meadows” of colonies in varying proximity to natural hydrocarbon seepage in the GoM (Quattrini et al. 2013, 2015). While most CWCs are likely to grow on the authigenic carbonate left behind after seepage subsides, ecological
5 niche modelling analysis determined that C. delta occurrences had significantly higher suitability indices at localities with seep activity than at inactive seeps (Quattrini et al.
2013). Due to C. delta’s significantly higher abundances and higher probabilities of occurring at active seep sites, this species may have mechanisms for coping with hydrocarbons or sulfides in the seawater released from the active seeps which allows them to occupy this niche.
1.2 Anthropogenic Stressors to Cold-Water Corals
Since the onset of the Industrial Revolution and the increase in greenhouse gas production, the human population is having a profound impact on the global climate. This has affected the oceans through increased seawater temperatures, lower pH, and a decrease in oxygen concentrations. In addition, the needs of a growing human population, including seafood and energy resources, are driving industries further offshore and into deeper waters. Both categories, universal human-driven environmental change and direct industrial disturbances, can have negative impacts on marine ecosystems; specifically, deep-sea benthic habitats where CWCs thrive (Roberts and Cairns 2014; Levin et al. 2015).
1.2.1 Climate Change and Ocean Acidification
As a result of climate and ocean change, marine environments are experiencing a combination of deoxygenation, ocean warming, and acidification (IPCC 2014). These changing conditions can drive alterations in biological and ecological systems and processes (Hoegh-Guldberg and Bruno 2010). Increasing ocean temperatures are linked to the greenhouse effect and rising global atmospheric temperatures. As seawater
6 temperatures rise, dissolved oxygen becomes less soluble and results in the expansion of oxygen minimum zones and deoxygenation (Keeling et al. 2010). In addition, the ocean acts as a sink for the surplus of CO2 in the atmosphere and approximately one fourth of the
CO2 emitted annually is absorbed by the ocean leading to ocean acidification (Doney et al.
2009; Hoegh-Guldberg and Bruno 2010; Mora et al. 2013; IPCC 2014; Hoegh-Guldberg et al. 2017). The coupling of ocean acidification, warming, and deoxygenation raises concern for the future of calcifying species, including CWCs.
Research on the impacts of climate change-related factors has primarily focused on shallow-water corals, but there are an increasing number of studies focusing on the response of CWCs (Philippart et al. 2011; Boyd et al. 2014, 2018; Hewitt et al. 2016;
Hoegh-Guldberg et al. 2017; Hughes et al. 2017; Sweetman et al. 2017). The overall understanding from these studies is that CWC perform best under a narrow temperature range and can maintain calcification at low pH levels, but that there is substantial individual- and population-level variability in CWC stress responses to these factors (Form and Riebesell 2012; Hennige et al. 2014, 2015, 2020; Lunden et al. 2014a; Movilla et al.
2014; Gori et al. 2016; Büscher et al. 2017; Kurman et al. 2017; Gómez et al. 2018).
1.2.2 Oil and Gas Exploration and Extraction in Deep Water
With a continued reliance on fossil fuels to power industrialization, there has been an increased effort over the last few decades to explore and exploit oil and gas resources in increasingly deeper waters across the globe. While the oil and gas industry has been depleting land-based and shallow-water resources, deep-water exploration and operational equipment and technology has greatly advanced over the last few decades, allowing for oil
7 and gas extraction from depths as great at 3,000 m to become routine (Kark et al. 2015).
The oil and gas process can be broken down into the three stages of i) exploration, ii) extraction/production, and iii) decommissioning. All three stages present distinct environmental and ecological threats to the surrounding pelagic and benthic ecosystems
(Cordes et al. 2016).
The exploration stage involves substantial reconnaissance through seismic surveys of the seafloor and underlying reservoirs, increased ship activity, and the installation of drilling rigs and equipment which can produce considerable sound and light pollution and disturb the surrounding biological communities and processes (Moore et al. 2000, 2012;
Longcore and Rich 2004; Southall et al. 2008; Nieukirk et al. 2012; Hawkins et al. 2014).
During the installation of infrastructure, the potential for direct disturbances to surrounding communities increases through the placement of anchors and pipelines, plus the disposal of drilling waste (Gray et al. 1990; Hall-Spencer et al. 2002; Ahmadun et al. 2009; Ulfsnes et al. 2013; Watling 2014; Edge et al. 2016). There is evidence that waste discharge, like drill cuttings, can negatively impact nearby CWC communities via the toxicity of the discharge or through smothering (Lepland and Buhl-Mortensen 2008; Purser and Thomsen
2012; Allers et al. 2013; Larsson et al. 2013). While the majority of the potential impacts of hydrocarbon extraction appear to be negative, the submerged infrastructure can provide habitat for benthic invertebrates, like CWCs and sponges, including Lophelia pertusa (Gass and Roberts 2006; Larcom et al. 2014). In addition to the potential impacts from routine oil and gas operations, there is the potential for accidental hydrocarbon release during a spill or wellhead blowout which can have an immediate impact. However, there is high variability in spacial and temporal impacts depending on the severity of the event. One
8 such event was the 2010 Macondo wellhead blowout in the Gulf of Mexico, known as the
Deepwater Horizon (DWH) oil spill.
1.2.2.1 The Deepwater Horizon Oil Spill
The Deepwater Horizon and the subsequent impacts are the most widely studied deep-water oil spill in human history (Joye et al. 2016; Passow and Overton 2021). The
DWH blowout released ~518 million liters of crude oil into the deep waters of the Gulf of
Mexico over a three-month period (Camilli et al. 2010; Crone and Tolstoy 2010; Cornwall
2015). Roughly half of the oil made it up to the surface, however, the rest of the hydrocarbons, in gaseous and aqueous states, remained in subsurface plume at ~1,100m and traveled with the currents ~50 km from the wellhead (Camilli et al. 2010). In response to this oil spill, approximately 7.5 million liters of chemical dispersants were applied at the surface and directly at the wellhead in an attempt to combat the effects. The surface oil and dispersants interacted with planktonic communities and detritus particles resulting in an oiled marine snow that sank down to the seafloor, which altered sediment-dwelling meiofauna communities and induced stress responses in oiled marine snow-covered mesophotic and cold-water corals (Passow et al. 2012; White et al. 2012; Montagna et al.
2013; Passow 2014; DeLeo et al. 2018).
A few recent studies have aimed to address the effects of oil and dispersants from the DWH on CWCs (Paramuriciea biscaya, Callogorgia delta, Leiopathes glaberrima, and Swiftia exserta) from the GoM (White et al. 2012; Fisher et al. 2014b; DeLeo et al.
2016, 2018; Frometa et al. 2017). Corals impacted by the DWH were found to be covered in brown flocculent material and exhibited signs of stress and mortality, including tissue
9 loss, enlarged sclerites, and excess mucus production (White et al. 2012). Such obvious physiological stress responses prompted experimental studies to analyze the individual effects of oil, chemical dispersants, and the combination of oil and dispersants. The consensus from these studies was that the dispersants and oil-dispersant mixtures were more toxic than oil exposures alone (DeLeo et al. 2016, 2018; Frometa et al. 2017).
1.2.3 Multiple Stressors: Climate Change and Oil/Dispersant Exposure
While there are numerous studies highlighting the variable effects of climate change and oil and chemical dispersant exposure on marine organisms independently, there are very few studies focusing on the cumulative effects of both climate change and oil/dispersant pollution. The effects of climate change on the waters of the GoM, including increased temperature, lower pH, and reduced dissolved oxygen concentrations, are a pervasive threat that will increasingly impact CWC communities in the future (Rabalais et al. 2002; Lunden et al. 2014a; Georgian et al. 2015). It is likely that these climate and ocean changes will intensify the more direct impacts of oil and dispersant exposure through increased metabolic demand. Increased and ongoing drilling efforts throughout the GoM put these communities at constant risk and that risk is likely to increase as ocean conditions are altered through ongoing climate change. Therefore, there is a pressing need to understand how these vital ecosystems will respond to both climate change and oil/dispersant exposures.
10 1.3 Cold-water Coral Stress Responses
Most previous studies on CWCs have focused on organismal-level physiological responses, with very few investigating the underlying molecular mechanisms (Form and
Riebesell 2012; Hennige et al. 2014; Lunden et al. 2014a; Gori et al. 2016; Gómez et al.
2018). However, in one cold-water coral, Desmophyllum dianthus, Carreiro-Silva et al.
(2014) observed an up regulation in genes of interest when exposed to decreased pH, but no significant differences in calcification and respiration rates (Carreiro-Silva et al. 2014).
This provides an example of why coupling phenotypic/physiological and molecular-level stress response data is so valuable; it is possible that sub-lethal, chronic effects may include shifts away from normal homeostasis at the molecular and cellular levels that are difficult to observe, yet they may indicate long-term pathophysiological effects. Therefore, considering both phenotypic/physiological responses in conjunction with molecular-level responses can enhance our understanding of how organisms respond to stressors in their environment.
When an organism is exposed to an environmental stressor the initial response includes the alteration of gene expression levels and the synthesis of proteins that drive homeostatic physiology. This is a part of a general stress response present across metazoans and in all cell types, known as the cellular stress response (CSR) (Kültz 2005, 2020; Aubin-Horth and Renn 2009; Simmons et al. 2009). The CSR is a coordinated system of transcription and translation events resulting in an increase of beneficial proteins that have the potential to reduce or reverse stress-induced damage, temporarily increase tolerance to this damage, or activate apoptosis of critically damaged cells (Welch 1987; Kültz 2005; Jiang et al.
2011). The dynamic nature of this response allows for a flexible but comprehensive
11 approach to handling stress-induced modifications which can manifest through the initiation and production of distinct CSR mechanisms depending on type, severity, or longevity of different stressors.
It has been suggested in studies of shallow-water anemones and corals that cnidarian stress could be detectable through a relatively consistent compliment of genes, which include CSR mechanisms, known as the cnidarian environmental stress response (ESR)
(Barshis et al. 2013; Aguilar et al. 2019; Cziesielski et al. 2019; Dixon et al. 2020). A recent metanalysis of gene expression data from the genus Acropora, a shallow-water scleractinian coral, found two distinct clusters of functional terms in response to various stressors. The first cluster (type A) included terms related to a late-stage CSR including the upregulation of oxidative stress response, immune response, protein folding and degradation, and cell death. Contrastingly, the second cluster (type B) involved less severe components of the CSR, like energy metabolism and growth (Dixon et al. 2020). Dixon et al. found that the difference between the two types of ESR were due to the intensity of the stress, with type A being induced under more intense and prolonged stressors.
While most studies that investigate transcriptome-level responses to stress have involved shallow-water coral species, there is evidence that genes involved in the CSR were differentially expressed by the deep-sea coral Paramuricea biscaya following exposure to oil and dispersants-containing flocculant material during the DWH oil spill
(DeLeo et al. 2018). The addition of more studies focused on molecular-level stress responses of CWCs will facilitate a more wholistic understanding of the general coral stress response that is shared across both shallow-water and cold-water corals.
12 1.4 Objectives
This body of work aims to address the impact of oil and chemical dispersant exposure on Lophelia pertusa, explore the interacting effects of increasing temperature, decreasing pH, and oil/dispersant exposure on L. pertusa, and investigate the influence of natural hydrocarbon exposure to Callogorgia delta found near active hydrocarbon seeps. I accomplished these objectives by first performing multiple stressor lab-based experiments exposing L. pertusa to oil and dispersant exposures, based on concentrations measured after DWH, under current and projected future seawater temperatures and pH (1: 7.9 and
8°C; 2: 7.6 and 8°C; 3: 7.9 and 12°C; 4: 7.6 and 12°C). Chapter Two involved the development of a methodology to assess and document stress-induced phenotypes in response to oil and dispersant exposures under the different pH and temperature combinations and track recovery for 96 hours after exposure. To investigate the underlying molecular pathways leading to the observed phenotypic stress responses observed in Chapter Two, Chapter Three consists of global gene expression analysis, including a Weighted Gene Correlation Network Analysis (WGCNA), to understand the cellular-level processes impacted by the individual stressors (oil, dispersant, pH and temperature) and to investigate any overlap in stress responses to the different stressors.
To gain insight into the influence of natural hydrocarbon seepage on CWCs, I performed differential gene expression analyses on C. delta colonies in varying proximity to active seep sites in Chapter Four. In the final chapter I summarize conclusions drawn from the previous three chapters and discuss how this study provides a comprehensive understanding that will aid in risk management, the development of effective response capabilities, and support continued advances in marine conservation and management
13 within the GoM and beyond through continuous monitoring of phenotypic and molecular- level stress responses.
14 CHAPTER 2
COLD-WATER CORAL (Lophelia pertusa) RESPONSE TO MULTIPLE STRESSORS: HIGH TEMPERATURE AFFECTS RECOVERY FROM SHORT-TERM POLLTION EXPOSURE
2.1 Abstract
There are numerous studies highlighting the impacts of direct and indirect stressors on marine organisms, and multi-stressor studies of their combined effects are an increasing focus of experimental work. Lophelia pertusa is a framework-forming cold-water coral that supports numerous ecosystem services in the deep ocean. These corals are threatened by increasing anthropogenic impacts to the deep-sea, such as global ocean change and hydrocarbon extraction. This study implemented two sets of experiments to assess the effects of future conditions (temperature: 8°C and 12°C, pH: 7.9 and 7.6) and hydrocarbon exposure (oil, dispersant, oil + dispersant combined) on coral health. Phenotypic response was assessed through three independent observations of diagnostic characteristics that were combined into an average health rating at four points during exposure and recovery. In both experiments, regardless of environmental condition, average health significantly declined during 24-hour exposure to dispersant alone but was not significantly altered in the other treatments. In the early recovery stage (24 hours), polyp health returned to the pre-exposure health state under ambient temperature in all treatments. However, increased temperature resulted in a delay in recovery (72 hours) from dispersant exposure. These experiments provide evidence that global ocean change can affect the resilience of corals to environmental stressors and that exposure to chemical dispersants may pose a greater threat than oil itself. 15 2.2 Introduction
The combination of human population growth and global industrial development is driving potentially irreversible anthropogenic impacts on the natural world. The current rate and scale of human development is unparalleled, and evidence has shown that human actions are altering the global climate and environment at startling rates (Root et al. 2003;
Philippart et al. 2011; IPCC 2014; Hewitt et al. 2016). Increased atmospheric CO2 concentrations contribute to both a decrease in global ocean pH (i.e. ocean acidification) as well as an increase in ocean temperatures (Feely et al. 2004; Doney et al. 2009; Queirós et al. 2015). Sea surface temperatures have increased on average by 0.6°C over the past
100 years and models suggest that the deep-sea is warming in some areas at a rate of 0.01-
0.1°C per decade (Solomon 2007; Purkey and Johnson 2013; IPCC 2014). The oceans’ surface and bathyal depths (200 – 4,000 m) are likely to experience increases in temperature of up to 4°C by 2100 (Mora et al. 2013; IPCC 2014; Sweetman et al. 2017).
Furthermore, the ocean’s continuous absorption of excess atmospheric CO2 results in a decrease in pH; the surface ocean pH is already 0.1 units lower than preindustrial times and may decline by another 0.3-0.4 units by 2100 (Brewer 1997; Caldeira and Wickett
2003; Orr et al. 2005).
In addition to a changing global climate, human activities continue to expose oceanic environments to a wide range of pollutants (Doney et al. 2009). Hydrocarbon pollution is one of the most common and widespread forms of marine pollution and can have long-standing effects on marine environments despite standing regulations and monitoring efforts (Moreno et al. 2013). Upwards of 1.3 million tons of oil is released annually into marine environments; however, roughly 47% of the oil released is from 16 natural seepage (Transportation Research Board and National Research Council 2003).
Additionally, as oil exploration/extraction moves farther offshore and into deeper waters, the risk of accidental release increases (Jernelv 2010; Muehlenbachs et al. 2013).
The Deepwater Horizon (DWH) blowout released at least 518 million liters of crude oil into the deep waters of the Gulf of Mexico (GoM) over a three-month period
(Camilli et al. 2010; Crone and Tolstoy 2010; Cornwall 2015). In response to this oil spill, approximately 7.5 million liters of oil dispersants were applied in an attempt to combat its effects. The purpose of chemical dispersant application is to increase the rate at which the oil is broken down by microorganisms by separating bulk oil into smaller droplets (Mu et al. 2014; Prince and Butler 2014). However, there is evidence that dispersants are not as effective at accelerating the biodegradation of hydrocarbons by the microbial community as originally assumed, bringing into question the purpose of their application after an event such as DWH (Kleindienst et al. 2015b).
One of the most extensive impacts of the DWH incident was on deep-sea ecosystems (Fisher et al. 2014b) (Figure 2.1a). Cold-water coral (CWC) communities are an essential component of healthy deep-sea ecosystems and function by contributing to nutrient and carbon cycling, and providing heterogeneous biogenic habitats, feeding grounds, and nurseries for many fishes and invertebrates (Etnoyer and Warrenchuk 2007;
Cordes et al. 2008; Oevelen et al. 2009; Ross et al. 2010). Within the GoM, CWC communities form a variety of habitats on hard substrata ranging from mesophotic coral assemblages, to upper slope Lophelia pertusa reefs, to deeper coral gardens (Fig. 1B).
Mesophotic habitats, generally found between 50 – 200 m, are comprised primarily of black corals and gorgonian octocorals, including Swifita exserta and Hypnogorgia pendula
17 (Etnoyer et al. 2016). While there are scleractinian coral species found at mesophotic depths, the larger L. pertusa reef structures are commonly found between 300 - 600 m in the GoM and form significant biogenic and structural habitat that supports a diverse community of organisms (Moore and Bullis 1960; Cordes et al. 2008). Lophelia pertusa larvae initially settle on hard substrate to form individual colonies that then continue to grow and expand both outward and upward, which over geological timescales (millennia and more) can eventually develop large reefs and CWC mounds (Roberts et al. 2006;
Wienberg and Titschack 2017). At greater depths ranging from 600 meters to over 2,000 m, coral gardens, mainly consisting of octocorals and black corals, attach and grow on authigenic carbonates near cold seeps or along the massive Florida Escarpment (Figure
2.1b) (Quattrini et al. 2014, 2016).
There is evidence that oil and dispersant were present in the water column and the surrounding sediments of all three CWC habitat types following DWH (Fisher et al. 2014b;
White et al. 2014; Silva et al. 2016). Shortly after the incident, observations of impacted coral colonies were documented and subsequently studied over time in both the mesophotic and deep coral gardens (White et al. 2012; Hsing et al. 2013; Fisher et al. 2014a; Silva et al. 2016) (Figure 2.1). While no direct observations of impact were made at the surveyed
L. pertusa reef sites, there is evidence of oil exposure and the potential that unobserved sub-lethal impacts occurred (Fisher et al. 2014b). Other corals impacted by DWH were found to be covered in brown flocculent material and exhibited signs of stress and mortality, including tissue loss, enlarged sclerites, and excess mucus production (White et al. 2012). Such obvious physiological stress responses prompted experimental studies to analyze the individual effects of oil, chemical dispersants, and the combination of oil and
18 dispersants on CWC species from GoM deep coral gardens (Paramuricea biscaya,
Callogorgia delta, Leiopathes glaberrima) (DeLeo et al. 2016) and mesophotic corals (S. exserta) (Frometa et al. 2017). The consensus from these studies was that the dispersants and oil and dispersant mixtures elicited stronger stress responses from the corals than oil exposures alone (DeLeo et al. 2016; Frometa et al. 2017).
In addition to pollution analyses, an increasing number of studies are focusing on
CWC responses to changing environmental conditions resulting from climate change including ocean warming, deoxygenation, and acidification. The overall understanding from these studies is that CWC can maintain calcification at low pH levels and are more strongly affected by increasing temperature and low dissolved oxygen concentrations, but that there is substantial individual- and population-level variability in CWC stress responses to these factors (Form and Riebesell 2012; Hennige et al. 2014; Naumann et al.
2014; Movilla et al. 2014; Georgian et al. 2016; Büscher et al. 2017; Kurman et al. 2017).
While there are numerous studies highlighting the variable effects of climate change and pollution on marine organisms independently, there are very few studies focusing on the potential interactive effects of both climate/ocean change and pollution. To date, the only studies that have attempted to address the interactive effects of oil pollution and climate change have involved coastal microbial and subsurface benthic-dwelling organisms
(Coelho et al. 2015, 2016). Within these studies, Coelho et al. found that the interactive effects of pH and oil pollution can negatively impact the composition and functionality of microbial communities in the sediment, with the potential to intensify the toxicity of oil in marine ecosystems and impair recovery after severe oil contamination events (Coelho et
19 al. 2015, 2016). However, these studies focused on estuarine and coastal benthic communities and did not incorporate dispersant exposure into the experimental design.
FIGURE 2.1 Overview map and a schematic of coral distribution with depth within the
Gulf of Mexico. a) Sites of confirmed and potential impact on coral communities in the
Gulf of Mexico following the Deepwater Horizon (DWH) oil spill (black square). Pink
20 circles indicate mesophotic coral sites impacted by DWH including Alabama Alps Reef
(AAR) and Roughtongue Reef (RTR). Green circles represent prominent Lophelia pertusa mounds where corals were collected for these experiments including Viasca Knoll 906 and
826 (VK906 and VK826). Yellow circles indicate impacted deep coral sites after DWH including Mississippi Canyon 118, 294, 297, 334, and 507 (MC118, MC294, MC287,
MC334, and MC507). Produced using ArcGIS 10, ESRI. b) A schematic of the cold-water coral communities of the Gulf of Mexico with depth. The depth profile corresponds to the black line in panel “a” drawn through AAR, VK906, and MC344. Examples of corals commonly found at each depth range/site include (from left to right), at mesophotic sites
(ARR): Swiftia exserta and Hypnogorgia pendula, Lophelia pertusa mounds (VK906), and at deep coral sites (MC344): Leiopathes glaberrima and Paramuricea biscaya. Note: corals not to scale, enlarged for effect.
The present study implemented a set of experiments to investigate the effects of a decrease in seawater pH, increase in seawater temperature, and exposure to oil and dispersants on the health of L. pertusa from the GoM (Figure 2.2). It is predicted that L. pertusa will exhibit a similar stress response compared to other CWC species when exposed to oil and dispersants in that the dispersants will have a more adverse effect than the oil alone. Additionally, as observed in past studies, it is expected that increased temperature will have a greater negative effect on the average health of the coral than lower pH (Lunden et al. 2014a). Also, it is predicted that the combination of increased temperature, decreased pH, and oil/dispersant exposure will produce a synergistic effect.
21 This new information will assist with the prediction of how ocean warming and acidification will affect the ability of L. pertusa to respond to future oil spills.
2.3 Methods
2.3.1 Sample Collection and Colony Fragmentation
Live Lophelia pertusa colonies were collected in the Gulf of Mexico in September
2016 and June-July 2017 aboard the DSV Ocean Inspector (Remotely Operated Vehicle
(ROV) Global Explorer), MSV Ocean Intervention II (ROV Global Explorer;
Oceaneering, Inc.), and DSV Ocean Project (ROV Comanche; Sub-Atlantic), respectively.
All corals were collected from Viosca Knoll leasing area designated by the U.S. Bureau of
Ocean Energy Management (BOEM). Corals were collected from two lease blocks within the Viosca Knoll region, 826 and 906 (VK826 and VK906) in a depth range of 392 – 483 m (Figure 2.1; Table 2.1). The Viosca Knoll sites contain prominent L. pertusa mounds with well documented environmental parameters (temperature, pH, salinity, total alkalinity, and aragonite saturation (War)) (Mienis et al. 2012; Lunden et al. 2013; Georgian et al. 2015; Kurman et al. 2017), which align with the parameters used for coral maintenance and experimental aquaria here. Collections from six distinct coral colonies were made in 2016 and another set of six collections were made in 2017. Individual colonies were collected at least 10 meters apart from conspecific colonies to increase the likelihood of sampling multiple distinct genotypes (Lunden et al. 2013). Each collection was contained and transported to the surface within a separate insulated “biobox” attached to the ROV. All live corals were kept separate in natural seawater in a temperature- controlled room while aboard the ship until transported in insulated containers from Port
Fourchon, Louisiana to Temple University, Philadelphia, Pennsylvania.
22
Figure 2.2 Experimental set up and design. Steps 1 and 2 were performed to prepare the oil, dispersant, and oil + dispersant mixtures before each exposure. Step 3 illustrates the 23 exposure experiments performed for experiments 1 and 2. Health ratings were taken for each coral nubbin at each time point (T0, T1, T2, T3). Experiment 1 consisted of high and low concentrations of oil and dispersant under ambient pH and temperature (7.9 and 8°C).
Experiment 2 consisted of four parts with oil and dispersant exposures run under different combinations of pH and temperature (1: 7.9 and 8°C; 2: 7.6 and 8°C; 3: 7.9 and 12°C; 4:
7.6 and 12°C). Artificial seawater (ASW) was the control for both experiments.
Once at Temple University, all corals were kept in 550-liter recirculating aquarium systems with artificial seawater (ASW) prepared using Instant OceanÒ or B-Ionic (EVS) and maintained at a temperature of ~8°C, salinity of 35 ppt, and a pH of ~7.9 (Lunden et al. 2014b). Corals were fed MarineSnow (Two Little Fishes) at least 3 days a week
(Hennige et al. 2014; Lunden et al. 2014a; b).
Prior to experimentation, the corals were fragmented into nubbins (3-6 polyps) and allowed to acclimate to aquaria conditions for at least two months. Nubbins from collections made in 2016 were used in the first experiment and nubbins from collections made in 2017 were used in the second experiment.
24
2.3.2 Experimental Design
Two independent sets of experiments were conducted in order to test the response of L. pertusa to multiple stressors. Experiment 1 was conducted to test low and high concentrations of oil and dispersants based on concentrations detected after the Deepwater
Horizon oil spill under ambient seawater conditions (pH: 7.9 and temp: 8°C) (White et al.
2012). The oil concentration detected in the brown flocculent material (floc) on the surface of live coral at the site MC294 (Figure 2.1a) measured 473.8 mg/L. The total amount of
Benzene, Tolulene, and Xylene (BTEX) would be 10.42 mg/L, which would account for the majority of the water-accommodated hydrocarbon fraction. The oil concentration detected in the sediments at MC294 was 9,579 mg/L with a BTEX of 210.74 mg/L. Based off these measurements the target high concentration of the water-accommodated fraction of oil tested was 200 mg/L and the target low concentration was 10 mg/L. Dispersant target concentrations were 1.5% of the high and low oil concentrations. The oil + dispersant mixture is the combination of the oil + dispersant concentrations. 25 For the target high oil concentration treatment (200 mg/L), 162 mL of surrogate oil was added to 1.34 L ASW and mixed at high speed for 48 hours to produce a stock solution at a target concentration of 2,000 mg/L. The target low oil concentration treatment (10 mg/L) was achieved by adding 54 mL of surrogate oil to 0.946 L ASW and mixed at high speed for 48 hours to produce a stock solution at the target concentration of 1,000 mg/L. A separatory funnel was used to retain only the water-accommodated fraction (WAF) and the insoluble layer was discarded. The high and low oil + dispersant mixtures were prepared using the same volumes of oil (162.345 mL and 54.11 mL), with 2.85 mL and 0.81 mL of
Corexit 9500A added (1.5% total oil) to 1.34 L and 0.946 L ASW, respectively, and mixed at high speed for 48 hours to produce a chemically-enhanced WAF (CEWAF). Again, a separatory funnel was used to separate the insoluble layer from the remaining CEWAF.
The dispersant stock solution was prepared by adding 2.85 mL of Corexit 9500A to 1.425
L of ASW with an initial concentration of 848 mg/L. The stock solutions were used to create the experimental treatments with targeted concentration of 200 mg/L (high oil concentration), 10 mg/L (low oil concentration), 153.86 mg/L (high dispersant concentration), 7.69 mg/L (low dispersant concentration), 200 mg/L + 1.5% (high oil + dispersant concentration), and 10 mg/L + 1.5% (low oil + dispersant concentration) by combining the stock solution with the appropriate volumes of ASW.
All solutions were transferred into acid-washed glassware aquaria prior to experimentation. There was an acknowledged and unavoidable loss of hydrocarbons and dispersants at the point of transfer from the stock solution container to the experimental aquaria. Therefore, the oil and dispersant concentrations are reported as targeted concentrations and referred to as “oil”, “dispersant” and “oil + dispersant” in the analyses.
26 For experiment 1, each aquarium (n = 4) had two nubbins from each collection (1-
6) for a total of twelve nubbins per aquarium and forty-eight nubbins in experiment 1.
Nubbins were acclimated to the conditions of the 56-liter recirculating experimental aquaria (pH: 7.9 and temp: 8°C) for 48 hours prior to oil and dispersant exposures. One nubbin from each collection was subjected to the treatment exposures (a) oil – high (200 mg/L), (b) oil – low (10 mg/L), (c) dispersant – high (153.86 mg/L), (d) dispersant – low
(7.69 mg/L), (e) oil + dispersant – high (200mg/L + 1.5%), (f) oil + dispersant – low
(10mg/L + 1.5%), and (g) control (ASW only) for 24 hours in individual acid-washed and autoclaved pint-size glass jars before being returned to the larger experimental aquaria for five days. Corals from each treatment type (oil, dispersant, oil + dispersant, control) were kept in separate aquaria to avoid any inadvertent mixing of treatment water. Each sample was photographed (Canon EOS 50D) and a health rating (See Figure 2.3 and Health
Ratings section below) was determined through directly assessing the physiological response by recording tentacle extension, filament state, mucus secretion, and tissue health at four time points (T0, T1, T2, T3).
Experiment 2 was made up of four parts and was conducted by exposing L. pertusa nubbins from collections 7-12 to concentrations of oil and dispersant treatments under two different pH (7.6 and 7.9) and temperature (8°C and 12°C) conditions. For all four parts of the experiment, each aquarium had one nubbin from each collection (7-12) for a total of six nubbins per aquaria, twenty-four nubbins per part, and ninety-six nubbins for the experiment. The four treatments include (a) surrogate-oil water-accommodated fraction
(200 mg/L), (b) Corexit 9500 dispersant (153.86 mg/L), (c) oil + dispersant (200mg/L +
1.5%), and (d) control (ASW only). No effect was seen in the target low concentration 27 treatments during experiment 1 so only the high concentrations were used in experiment 2.
Nubbins were acclimated to the experimental conditions for each part of the experiment;
1: ambient pH and temperature (pH: 7.9 and temp: 8°C), 2: low pH and ambient temperature (pH: 7.6 and temp: 8°C), 3: ambient pH and high temperature (pH: 7.9 and temp: 12°C), and 4: low pH and high temperature (pH: 7.6 and temp: 12°C) for 48 hours in 56-liter recirculating units. Then the nubbins were subjected to the treatment exposures
(a) oil, (b) dispersant, (c) oil + dispersant, and (d) control for 24 hours in an acid-washed
18-liter glass aquarium per treatment before being returned to the larger aquaria for five days (Figure 2.2). Each sample was photographed (Canon EOS 50D), a health rating recorded following the same methods as experiment 1, and a genetic sample taken for forthcoming gene expression analyses at the same time intervals as experiment 1 (T0, T1,
T2, T3).
2.3.3 Manipulation of Seawater Chemistry and Temperature
Throughout experiment 2, salinity, temperature, pH, and alkalinity were routinely monitored and recorded. Artificial seawater (B-ionic Seawater System, ESV Aquarium
Products Inc) was prepared during the experiments because it provides for accurate manipulation of the components of the carbonate system. The manipulation of seawater pH was achieved by bubbling pure CO2 gas into the treatment aquaria using an automated CO2 injection system (American Marine Inc, PINPOINT pH Monitor) as in Kurman et al.
(2017). Total alkalinity (TA) was measured three times during each experiment by acid- titration on an autotitrator (mettle-Toledo DL15, 0.1 mol L-1 HCl), with analysis of certified reference materials to confirm accuracy (Dickson Lab, batch #165; Dickson, Sabine, & 28 Christian, 2007; Kurman et al., 2017). CO2Calc software (Robbins et al. 2010) used pH and TA as input variables and the dissolution constants for boric acid and K1 and K2 from
Mehrbach et al. (1973) refit by Dickson & Millero (1987), KHSO4 from Dickson (1990), and total boron from (Lee et al. (2000) to calculate pCO2, HCO3-, CO32-, and War (Kurman et al. 2017). Salinity was measured throughout the experiments using a handheld refractometer (Vee Gee 43036), and temperature was recorded every 5 minutes using a temperature logger (onset HOBO PendantÒ).
2.3.4 Health Ratings
Each sample was photographed and monitored for signs of stress at the four time points during the experiment. The four main factors contributing to the health rating were tentacle extension, filament extension, mucus discharge, and tissue health (Figure 2.3).
Tentacle extension is a proxy for how active the polyps are, with a higher proportion of extended and active tentacles corresponding to healthier polyps. Corals have filaments that line the mesenteries inside each polyp, where digestion and gametogenesis take place
(Brusca, R.C., Brusca, G.J. 1990). When a polyp is under stress it extrudes these mesentery filaments outside the mouth of the polyp, therefore a greater number of filaments visible and protruding corresponds to a lower health rating. Another common stress response for a coral is to increase the amount of mucus discharge to the point where it can produce long mucus trails (Brown and Bythell 2005; Jatkar et al. 2010), which corresponds to the lowest health rating in this study. The final factor, tissue health, refers to the discoloration and detachment from the skeleton, indicating a lower health rating. Each factor was scored (0-
29 5) by three independent observers and then the average of all the factors determined the final health rating for each nubbin at each time point.
Figure 2.3 Matrix for assessing the health rating of Lophelia pertusa. Each factor
(tentacles, filaments, mucus discharge, tissue health) were scored from 0 - 5, with 0 indicating polyp mortality and 5 indicating signs of optimal health. An average of the four factors was taken for a coral health score for each nubbin at each time point.
2.3.5 Data Analysis
Experiment 1 health ratings were averaged for replicate coral nubbins in each treatment and plotted over time to investigate changes in average coral condition. For experiment 2, health ratings were averaged for replicate coral fragments in each
30 experimental condition pair (pH: 7.9 and temp: 8°C, pH: 7.6 and temp: 8°C, pH: 7.9 and temp: 12°C, pH: 7.6 and temp: 12°C) and treatment (oil, dispersant, oil + dispersant, and control) and plotted over time in R with the ggplot2 package to investigate changes in coral health state. A two-way factorial ANOVA was performed for experiment 1 with health as the continuous variable and treatment and time point as ordinal variables using each individual nubbin as a replicate within each time point. For experiment 2, a three-way factorial ANOVA was performed with health as the continuous variable, and pH, temperature, and treatment as ordinal variables, using each individual nubbin as a replicate within each time point. Tukey’s HSD test was used to test for pairwise differences. All statistical analyses were performed in JMP.
2.4 Results
2.4.1 Aquaria Conditions and Carbonate Chemistry
Experiment 1 was a pilot experiment to assess the effects of high and low concentrations of oil and dispersant, therefore salinity (35), temperature (8°C), and pH
(7.9) were maintained within a narrow range of the target values. Alkalinity was not directly measured during experiment 1 but all of the in-house artificial seawater has a target alkalinity of 2,300 µmol kg-1.During the course of experiment 2, the mean values of temperature, salinity, pH, and alkalinity were maintained within a narrow range of the target values of 8°C or 12°C, 35 ppt, 7.6 or 7.9, and 2,300 µmol kg-1 (Table 2.2). These parameters resulted in aragonite saturation (War) values of approximately 1.4, 0.84, 1.59, and 1.06 for the four experimental settings (pH: 7.9 and temp: 8°C, pH: 7.6 and temp: 8°C,
31 pH: 7.9 and temp: 12°C, pH: 7.6 and temp: 12°C), respectively (please see the
Manipulation of Seawater Chemistry and Temperature section in Methods).
2.4.2 Exposure Effects on L. pertusa - Experiment 1
No mortality was observed in any of the seven treatments (1: oil – high, 2: oil – low, 3: dispersant – high, 4: dispersant – low, 5: oil + dispersant – high, 6: oil + dispersant
– low, 7: control) throughout experiment 1. There was no effect of treatment alone on the average health rating across all time points (T0: pre-exposure, T1: 24 hours into exposure,
T2: 24 hours into recovery, T3: 96 hours into recovery) [ANOVA F (6,140) = 0.63, P =
0.7058]. However, there was a significant effect of time point [ANOVA F (3,140) = 18.118,
P <0.001] and a significant interaction between time point*treatment [ANOVA F (18,140) =
16.430, P <0.001] (Figure 2.4, Table 2.3). The post-hoc pairwise comparisons for time point show that the average health ratings at T0 and T3 were significantly different from
T1 [Tukey HSD, P = 0.0327, P = 0.0003]. The recovery time points (T2 and T3) were not significantly different from each other [Tukey HSD, P = 0.7404].
32 The significant interaction between treatment and time point revealed that the average health rating for the high dispersant concentration treatment at T1 was significantly different from all other treatment*time point combinations [Tukey HSD, P <0.001 for all
27 comparisons]. For the corals exposed to this treatment, the decrease in average health rating was due mostly to an increase in filament presence and mucus discharge while the nubbins were submerged. However, once the nubbins were returned to normal seawater
(T2) the polyps recovered close to the pre-exposure phenotype for the remainder of the experiment. In addition, the average health rating of the corals exposed to the high oil and dispersant treatment exhibited significant differences from the other treatments during the recovery time points (T2 and T3) [Tukey HSD, P <0.001 for all 37 of the 50 pairwise comparisons]. The corals in the high oil and dispersant concentration also displayed phenotypic changes 24 hours into the exposure (T1). Here, the decrease in average health was due mainly to a decline in tissue health, with portions of the coenenchymal tissue between the polyps detached from the skeleton. In contrast to the recovery observed in the high dispersant concentration treatment between T1 and T2, once the tissue detached from the skeleton it did not recover or reattach when it was returned to normal seawater and the average health rating remained lower than all other treatments for the remainder of the experiment.
33
Figure 2.4 Average health rating for Lophelia pertusa fragments over time throughout experiment 1. Fragments were exposed to high and low concentrations of Oil alone, dispersant (Disp) alone, and oil + dispersant (Oil + Disp) mixtures. Artificial seawater
(ASW) was used as the Control. Health rating scale 0-5. Bars show standard error.
2.4.3 Exposure Effects on L. pertusa - Experiment 2
In the initial phase of the experiment (T0), no mortality was observed in any of the ambient temperature treatments (pH: 7.9 and temp: 8°C, pH: 7.6 and temp: 8°C); however, 34 in both of the high temperature treatments (pH: 7.9 and temp: 12°C, pH: 7.6 and temp:
12°C) one nubbin died after exposure to the dispersant treatment. There was no difference among the average health ratings across the four treatments at T0 (pre-exposure) for any of the factors (pH, temperature, treatment) as well as their interactions, indicating that the corals were all at similar health states (except for the nubbin that died) after the acclimation period and prior to the exposures (Figure 2.5, Table 2.4).
During the 24-hour exposure period (T1) there was a significant effect of treatment
[ANOVA F (3,80) = 0, P = <0.001]. The post-hoc pairwise comparisons reveal that this effect was due to the decrease in the average health rating of the corals exposed to the dispersant treatment in relation to the other three treatments (oil, oil + dispersant, and control) under all pH and temperature combinations [Tukey HSD, P <0.001 for all 3 comparisons]. The decrease in health rating was due mostly to an increase in filament presence and mucus discharge while the nubbins were submerged in the dispersant treatment. There was no significant effect of the other factors (pH, temperature) or the interactions during the exposures.
Once the corals entered the recovery phase (T2) there was no longer an effect of treatment alone [ANOVA F (3,80) = 0.154, P = 0.459] but there was a significant effect of the interaction of temperature*treatment [ANOVA F (3,80) = 1.253, P = 0.021]. The temperature*treatment effect was driven by the interaction between the dispersant treatment and high temperature (12°C) [Tukey HSD, P <0.0061 for all 7 comparisons].
This suggests that the corals that were exposed to the dispersant treatment under high temperature experienced inhibited recovery within the first 24 hours, whereas the corals that were exposed to the dispersant treatment under ambient temperature (8°C) were able 35 to recover close to pre-exposure phenotypic conditions within 24 hours (T2). There was no effect of the other factors (pH, temperature, treatment) or the interactions during the initial recovery.
At the final time point (T3), 96 hours into the recovery period, there was no difference among the average health ratings for any of the factors (pH, temperature, treatment) or the interactions (Figure 2.5, Table 2.4). The average health rating of the corals that were exposed to the dispersant treatment at elevated temperature (12°C) was still lower than the other treatments at T3, however the temperature*treatment interaction was not significant due to high variability in the recovery response [ANOVA F (3,80) = 2.360, P =
0.078].
Figure 2.5 Average health rating for Lophelia pertusa fragments over time throughout experiment 2. Fragments were exposed to Oil alone, dispersant (Disp) alone, and oil + dispersant (Oil + Disp). mixtures under different combinations of pH (7.6 and 7.9) and
36 temperature (8 °C and 12 °C). The four combinations of pH and temperature are 1: 7.9 and
8°C; 2: 7.6 and 8°C; 3: 7.9 and 12°C; 4: 7.6 and 12°C. Artificial seawater (ASW) was used as the Control. Health rating scale 0-5. Bars show standard error.
TABLE 2.4 | Experiment 2 three-way factorial ANOVA for each timepoint with health rating as the dependent variable showing the effect of pH, temperature, and treatment
Time point 0: Pre-exposure Source of variation df SS F P pH 1 0.012 0.533 0.467 Temperature 1 6.83E-35 0.000 1.000 Treatment 3 0.038 0.583 0.628 pH*Temperature 1 0.001 0.029 0.864 pH*Treatment 3 0.095 1.442 0.237 Temperature*Treatment 3 0.023 0.356 0.785 pH*Temperature*Treatment 3 0.076 1.153 0.333
Error 80 1.758
Time point 1: 24 hours into exposure Source of variation df SS F P pH 1 0.012 0.175 0.676 Temperature 1 0.033 0.486 0.487 Treatment 3 6.481 0.000 <0.0001 pH*Temperature 1 0.032 0.486 0.492 pH*Treatment 3 0.121 0.603 0.615 Temperature*Treatment 3 0.199 0.992 0.401 pH*Temperature*Treatment 3 0.095 0.473 0.702
Error 80 5.354
Time point 2: 24 hours into recovery Source of variation df SS F P pH 1 0.064 0.522 0.472 Temperature 1 0.188 1.534 0.219 Treatment 3 0.319 0.872 0.459 pH*Temperature 1 0.053 0.431 0.513 pH*Treatment 3 0.095 0.259 0.855 37 Table 2.4. continued 3 1.253 3.416 0.021 Temperature*Treatment pH*Temperature*Treatment 3 0.049 0.133 0.940
Error 80 9.779
Time point 3: 96 hours into recovery Source of variation df SS F P pH 1 0.012 0.035 0.852 Temperature 1 0.255 0.765 0.385 Treatment 3 0.154 0.154 0.927 pH*Temperature 1 0.128 0.382 0.538 pH*Treatment 3 0.073 0.073 0.974 Temperature*Treatment 3 2.363 2.360 0.078 pH*Temperature*Treatment 3 0.3482 0.482 0.696
Error 80 26.700
2.5 Discussion
Lophelia pertusa exhibited a decline in health when exposed to high concentrations of dispersant compared to oil + dispersant mixtures or the oil-only treatment, regardless of pH and temperature conditions. In addition, coral fragments that experienced elevated seawater temperature showed slow recovery from dispersant exposure as compared to the fragments in ambient temperature seawater, which recovered their normal phenotype within 24 hours. These experimental data provide important insights into the potential toxicological impacts of oil and dispersant exposure under current and changing ocean conditions on an ecologically significant CWC species. While visible impacts to L. pertusa colonies were not observed after the DWH oil spill, there is evidence that some of the reefs were exposed to oil and possibly dispersants, and unobserved sub-lethal effects on these communities remains a possibility (Fisher et al. 2014a).
38 When considering the components and concentrations of the oil and dispersant mixtures, the hydrocarbon concentrations reported here likely overestimate the actual exposure concentrations given the complexity of crude oil, which is a composite of thousands of compounds with various hydrophilic and hydrophobic properties (Singer et al. 2000; Di Toro, D. M.; McGrath and Stubblefield 2007). Additionally, dispersants are composed of an assortment of polar and non-polar surfactants and solvents (Singer, MM;
George, S; Jacobson, S; Lee, I; Weetman, LL; Tjeerdema, RS; Sowby 1996). It is very likely that some portion of both the oil and dispersant adhered to the flask during mixing and in the experimental aquaria. Furthermore, there may have been loss of water- accommodated oil fractions from volatilization during aeration, coalescence, and/or microbial degradation (Couillard, C.M.; Lee, K.; Légaré, B.; King 2005). Therefore, it is difficult to resolve the exact concentrations of oil and dispersant that each coral polyp encountered at any given time during the exposures. However, if actual exposures were lower than target values, our results are conservative estimates of the effects of oil, dispersant, and oil-dispersant mixtures on L. pertusa. In addition, the oil and dispersant exposures under ambient seawater conditions were replicated with consistent health-rating results throughout the separate experimental trials.
One of the primary goals of this study was to better understand L. pertusa’s sensitivity to varying concentrations of oil and dispersant that CWCs might experience during an oil spill, based on measurements taken following the DWH spill (White et al.
2014). Our results show that corals that were exposed to the low concentrations of oil, dispersant, and the combination of the two during experiment 1 did not exhibit a decrease in overall health at any time point (Figure 2.4). However, it is important to recognize that
39 reproducing exact conditions experienced by coral colonies during an oil spill is challenging and involves multiple complex phases. Nevertheless, other experimental exposures of CWCs to oil and dispersants also found that as concentration increased, more adverse effects were observed (DeLeo et al. 2016).
Lophelia pertusa did not show signs of stress when only exposed to oil regardless of pH and temperature. In some cases, the coral polyps appeared more active (via tentacle extension) in the oil-only treatment relative to the controls (Figure 2.5; T1). The lack of stress response to oil might be attributed to L. pertusa’s common occurrence in the GoM around areas with low concentrations of natural hydrocarbons seeping from the seafloor and on hydrocarbon-derived authigenic carbonates (Cordes et al. 2008). While evidence suggests that seep-derived nutrition does not significantly contribute to L. pertusa’s energy uptake (Becker et al. 2009), it is possible that L. pertusa has developed a tolerance to the presence of hydrocarbon compounds in the surrounding water.
A more unexpected result was that the coral nubbins showed little response to the oil and dispersant mixtures during experiment 2. Generally, in previous oil and dispersant toxicity exposures the presence of dispersant, either alone or combined with oil, resulted in a negative health response (DeLeo et al. 2016; Frometa et al. 2017). This could be due to the differing concentrations or ratios of dispersant to oil used in other studies. Previous studies of oil and dispersant toxicity on CWCs (Paramuricea type B3, Callogorgia delta, and Leiopathes glaberrima) from the Gulf of Mexico tested chemically-enhanced water accommodated fractions (CEWAF) with a ratio of 10% of the amount of dispersant to oil
(DeLeo et al. 2016), in comparison to the 1.5% of dispersant to oil in the current study.
The target dispersant concentration (1.5%) was selected for this study based on the
40 estimation that approximately 500 million liters of oil and 7.5 million liters of dispersant were released during the DWH oil spill, or 1.5% volume of dispersant to total oil (White et al. 2012). The difference in ratio of oil to dispersant is likely a factor contributing to the various health responses of CWCs to oil and dispersant mixtures.
Within the treatments that induced stress responses, there was variability in recovery response between the nubbins. Some nubbins recovered close to pre-exposure condition while other nubbins died by the end of the experiment and many were in between these two extremes. While this could be because the nubbins were collected at distinct reef patches around Vioska Knoll it is likely that gene flow between these two sites is high given that L. pertusa populations in this area of the GoM are considered panmictic
(Morrison et al. 2011). However, previous genotyping efforts indicate that corals collected in this manner represent distinct ‘genets’ with potentially different physiological adaptations (Lunden et al. 2014a; Kurman et al. 2017). There is evidence for genotypic variation in coral stress response, both at the phenotypic and molecular level (Baums et al.
2013; Kurman et al. 2017; Griffiths et al. 2019). When exposed to low pH (7.6) for short
(2-week) or long (6-month) periods of time, some genets of L. pertusa were able to continue to calcify, while other genets experienced a net dissolution over the same timeframe (Kurman et al. 2017). When the same L. pertusa fragments were analyzed on the transcriptomic level, ‘collection’ (i.e. genet) had the greatest influence on the variance in gene expression patterns observed and each collection had unique up- and down- regulated transcripts, suggesting that these different collections could be utilizing different pathways to achieve the same goal of homeostasis under stress (Glazier et al. 2020).
Underlying genetic variation may predispose some individuals or populations to better
41 withstand various stressors; however, the ability to cope with one type of stress does not guarantee success when faced with other stressors or interactions between multiple stressors.
Coral health is complex and made up of myriad factors including not only the physiology of the coral itself, but also the microbial communities that live in the coral tissues and mucous. Zanveld et al. (2016) showed that the microbial community associated with shallow-water corals from the Great Barrier Reef was destabilized in response to local stressors and that response was intensified under warmer water temperatures. Following the DWH oil spill, the microbial community of the deep-water column of the GoM was significantly altered and remained in this altered state after the conclusion of the spill
(Kleindienst et al. 2015b; a). In addition, the microbial community of the floc found on
CWCs and in the surrounding sediments following the DWH showed evidence of microbial phylotypes with associated genes capable of oil degradation (Simister et al. 2016). While the microbial community and its influence on coral health has been more thoroughly characterized for shallow-water corals, it is likely equally significant for CWCs. There has been little research on the impacts of stressors on the microbiome of CWCs and it is likely that the microbiome of L. pertusa is altered by changes in temperature, pH, or the presence of oil and dispersant. Changes in the microbiome could lead to significant physiological consequences for the whole coral holobiont. We did not assess microbial community changes in this study, but this is an exciting area for further research.
This study also examined L. pertusa’s capacity to recover after short-term (24 hour) oil and dispersant exposures. Across variations in pH and temperature, the oil and oil + dispersant exposures did not cause a decrease in the average health rating during the
42 exposure (T1) and the average health rating remained consistent throughout the recovery phase (T2 and T3). In contrast, across all temperatures and pH values, the dispersant-only exposure caused a significant decrease in average health rating. Furthermore, the average health ratings of the corals that experienced the dispersant treatment in combination with the increased temperature were still significantly lower than the corals in the other treatments 24 hours into recovery (T2). However, the corals that were exposed to the dispersant treatment under ambient temperature were able to recover to the pre-exposure health state 24 hours into recovery (T2). This indicates that the initial and sustained stress to the coral nubbins from the increased temperature had an effect on their ability to cope with the additional stress brought on by the dispersant.
There is evidence in shallow-water coral ecosystems that heat stress, both in the short and long term, can impact a coral’s ability to cope with other forms of stress and/or disturbance (Zaneveld et al. 2016; Osborne et al. 2017; Wolff et al. 2018). Long-term stress from increased temperature can inhibit or even eliminate the ability of coral colonies or entire ecosystems to recover from disturbance (Osborne et al. 2017). In addition to potential long-term climate change-induced temperature increases, short-term seasonal increases in temperature can interact with localized stressors such as pollution and negatively impact coral health (Zaneveld et al. 2016).
Low pH (7.6) did not appear to have an effect on the overall average health of the corals over the short-term exposures of these experiments. Even though pH and alkalinity are important environmental parameters for coral health, it is possible that adverse effects were not observed because the coral response to low pH does not manifest itself through the factors measured by the average health rating. Coral response to temperature has been
43 documented to elicit macroscopic shifts in phenotype, with corals retracting their tentacles and showing visible signs of strain under thermal stress. This type of visible response has not been observed for changes in pH, even if the coral alters its physiology in an effort to mitigate the changes induced by low pH (Lunden et al. 2014a; Georgian et al. 2016).
Additional measurements of coral physiology, growth, and gene expression would be necessary to determine if there was an unobserved sub-lethal stress caused by ocean acidification.
Exposing L. pertusa to various combinations of oil and dispersants under different pH and temperature regimes allowed us to assess the potential impact of acute disturbances on a prominent CWC at current and projected future ocean conditions. As anticipated, the dispersant had a more adverse effect on coral health than oil. However, surprisingly the oil and dispersant mixtures did not elicit a strong stress response. While it was predicted that the combination of increased temperature, decreased pH, and oil/dispersant exposure would yield the lowest average health rating, this did not appear to be the case. The pH treatment had little impact on the visible health of the coral nubbins and it is likely that if there were adverse effects due to a decrease in pH they would be detectible at the cellular and molecular level (i.e. calcification fluid and gene expression), versus the macroscopic physiological changes assessed in this study. It was the combination of increased temperature and dispersant exposure that resulted in the lowest health ratings. While assessing the health rating through gross morphology is a reliable metric for observing coral stress response, there are undoubtedly other factors contributing to the overall health of the coral and its response to stress. These include factors such as tradeoffs between various physiological processes, altered patterns of gene expression, and changes in the
44 activity and composition of the microbial community. Future studies should focus on these responses, which require additional tools to reveal their more subtle effects. Regardless of the metrics used, it is apparent from this study that increased temperatures can affect the response of a coral to environmental pollution, and that the dispersants used in the DWH oil spill produced more significant effects on coral health than oil alone. These results should inform the types of responses considered in the future when faced with another accidental release of hydrocarbons in the deep ocean. Furthermore, surveys for impacts to coral colonies should occur during or immediately after exposure to environmental pollutants, and that metrics beyond the simple visual assessment of coral health be developed for future impact assessments.
45 CHAPTER 3
A DECREASE IN pH, INCREASE IN TEMPERATURE, AND POLLUTION
EXPOSURE ELICIT DISTINCT CELLULAR STRESS RESPONSES
IN A COLD-WATER CORAL (Lophelia pertusa)
3.1 Abstract
Lophelia pertusa is among the most abundant and wide-spread deep-sea corals and acts as the foundation for deep-sea ecosystems. These organisms are under increasing anthropogenic threat, including global ocean change and oil extraction. Here, we analyze the gene expression patterns from L. pertusa colonies exposed to oil and dispersant mixtures (control, oil, dispersant, and oil + dispersant) under current and future conditions
(temperature: 8°C and 12°C, pH: 7.9 and 7.6). The overall gene expression patterns varied by coral colony, but the dispersant exposure elicited the strongest response across treatments. A Weighted-Gene Correlation Network Analysis (WGCNA) identified networks of co-expressed genes in response to oil and dispersant exposures, a decrease in pH, and an increase in temperature. The WGCNA identified four modules (5,296 total genes) that were significantly up or down regulated in response to the oil and dispersant mixtures, seven modules (7,603 total genes) that were significantly up or down regulated in response to pH, and one module (1,008 genes) that was significantly up regulated in response to temperature. Gene ontology (GO) enrichment analysis of the modules revealed that L. pertusa likely experienced varying stages of the cellular stress response (CSR) during exposure to oil, dispersant, and a decrease in pH. The most severe responses were
46 associated with the dispersant exposure including GO terms related to apoptosis, the immune system, wound healing, and stress-related responses. However, the oil exposure induced an upregulation of metabolic pathways and energy transfer but a downregulation of cell growth and development. This indicates that the coral nubbins could have been reallocating resources and reducing energetically-demanding growth to maintain cellular homeostasis. The decrease in seawater pH elicited a similar response to oil through the enrichment of terms associated with a reduction in the cell cycle and development but an upregulation of endoplasmic reticulum stress. Our results indicate that distinct stages of the
CSR are induced depending on the intensity of stress and bolster the idea that there is an underlying stress response shared by all corals in response to a variety of stressors.
3.2 Introduction
Understanding the impacts of multiple stressors is essential as marine organisms are increasingly exposed to changes in their environment. These alterations can include factors related to climate change, human-induced physical disturbance, or exposure to pollutants.
The continued escalation of climate change has resulted in increasing seawater temperatures and subsequent decreases in pH and dissolved oxygen levels (Root et al.
2003; Crain et al. 2008; Keeling et al. 2010; Boyd et al. 2014; IPCC 2014; Hewitt et al.
2016). In addition, acute disturbance to marine habitats also pose a substantial threat to benthic organisms through actions such as bottom trawling, deep-sea mining, or the development, expansion, and spills associated with hydrocarbon extraction (Freiwald et al.
2004; Tillin et al. 2006; Jernelv 2010; Moreno et al. 2013). These physical and chemical changes are occurring simultaneously, and in many places, organisms are experiencing
47 multiple alterations to their environment at the same time. As climate change continues and the need for resource extraction drives industries into deeper, unexplored areas, the potential for organisms to experience one or multiple of these stressors increases. Mobile organisms can change locality, however, sessile organisms, which are often attached to the seafloor, must tolerate, acclimate, or die in response to these stressors.
Sessile organisms, such as corals and sponges, make up the foundation of ecosystems from shallow sun-lit coral reefs to deep-sea coral mounds and sponge gardens
(van Ofwegen et al. 2008; Roberts et al. 2009). Corals are particularly vulnerable to changes in temperature and changes in seawater pH, due to the calcification process that builds their calcium carbonate skeletons (Orr et al. 2005; Hoegh-Guldberg et al. 2007;
Pandolfi et al. 2011; Osborne et al. 2017). While there has been a substantial effort to understand how shallow-water corals will respond to changing environmental conditions
(Hughes et al. 2003, 2017), these efforts are still in their infancy in respect to deep-sea corals, even though these populations will likely experience similar changes to their environment (Levin et al. 2015; Thresher et al. 2015). Deep-sea corals are vulnerable to acute disturbances as well, such as the Deepwater Horizon (DWH) oil spill which resulted in the exposure to high concentrations of hydrocarbons and subsequent chemical dispersants for many deep-sea communities in the Gulf of Mexico (GoM) (Camilli et al.
2010; Crone and Tolstoy 2010; White et al. 2012; Cornwall 2015).
The DWH blowout was the first oil spill to occur in the deep ocean (~1,500m) and released >518 million liters of crude oil into the GoM and consequently, ~7.5 million liters of chemical dispersants in an attempt to mitigate the impact (Crone and Tolstoy 2010;
Camilli et al. 2012). There is well documented evidence that oil and dispersants from DWH
48 infiltrated ecosystems from the surface to the deep water of the GoM with some of the most extensive impacts being on deep-sea ecosystems, such as cold-water coral (CWC) communities (White et al. 2012; Fisher et al. 2014a). There were direct observations of disturbance at mesophotic and deep octocoral gardens, and while there were no direct observations of disturbance on the Lophelia pertusa reefs surveyed, there is potential that oil and dispersants were present in the surrounding water and sediment, indicating that these habitats were likely exposed to sub-lethal concentrations of oil, dispersants, or both
(Fisher et al. 2014b; White et al. 2014; Etnoyer et al. 2016; Silva et al. 2016). The full understanding of how this exposure impacted these communities may never be fully understood, but there is evidence that the presence of oil and/or dispersants elicits physiological and molecular-level stress responses from numerous species of mesophotic and deep-sea corals (White et al. 2012; DeLeo et al. 2016, 2018; Frometa et al. 2017;
Weinnig et al. 2020). A theme that has prevailed from these studies is that visual assessments of damage or stress of CWC communities after a deep-water oil spill are not sufficient to document the potential damage and underlying process that may be negatively impacted. Lab-based pollution exposure experiments have revealed that many of the phenotypic responses are visible but only from a close-up perspective (>6 inches from coral surface). Also, gene expression analyses are demonstrating that even with minimal visible phenotypic damage there is cellular-level damage occurring. Therefore, if the coral cannot remediate the cellular stress, portions or full colonies can undergo tissue necrosis and death.
Coral stress responses often manifest on the phenotypic level (i.e. bleaching, excess mucus production, tissue necrosis) but the mechanistic foundation of these stress responses is still under investigation for both shallow-water and deep-sea corals. These mechanisms
49 can be investigated through a combination of cellular biology and a variety of high- throughput molecular data including proteomics, metabolomics, and transcriptomics. With many studies investigating the coral transcriptomic response to numerous types of stressors
(pH, temperature, disease, pollution) there is increasing evidence that many of the transcriptome-level responses are part of a general stress response present across metazoans and in all cell types, known as the cellular stress response (CSR) (Kültz 2005;
Simmons et al. 2009; Dixon et al. 2020). The CSR is a complex system of corresponding transcription and translation events resulting in an increase of beneficial proteins that have the potential to reduce or reverse stress-induced damage, temporarily increase tolerance to this damage, or activate apoptosis of critically damaged cells (Welch 1987; Kültz 2005;
Jiang et al. 2011). The dynamic nature of this response allows for a flexible but comprehensive approach to managing stress-induced structural and functional modifications which can manifest through the activation and proliferation of distinct CSR mechanisms depending on type, severity, or longevity of different stressors. Most studies that investigate transcriptome-level responses to stress have involved shallow-water coral species, however, there is evidence that genes involved in the CSR were differentially expressed by the deep-sea coral Paramuricea biscaya following exposure to oil and dispersants-containing flocculant material during the DWH oil spill (DeLeo et al. 2018).
The addition of more studies focused on molecular-level stress responses of deep-sea corals will facilitate a more wholistic understanding of the general coral stress response that is shared across both zooxanthellate and azooxanthellate corals.
The present study is an extension of Chapter 2, in which phenotypic health ratings were used to assess the response of L. pertusa colonies under current and future conditions
50 (temperature: 8 °C and 12 °C, pH: 7.9 and 7.6) and hydrocarbon exposure (oil, dispersant, oil + dispersant combined) (Weinnig et al. 2020). Phenotypic health ratings showed that regardless of environmental condition, average health significantly declined during 24- hour exposure to dispersant alone but was not significantly altered in the other pollutant exposures. However, while there as not a significant effect, there were signs of phenotypic stress in the oil + dispersant mixture that were not present during the oil-alone exposure.
Temperature and pH did not show a significant impact on coral health at the 24-hour exposure timepoint, however, increased temperature resulted in a delay in recovery from dispersant exposure.
Here, we analyze the gene expression patterns from L. pertusa colonies after 24 hours of exposure to the oil, dispersant, and oil + dispersant mixtures under combinations of increased temperature and decreased pH (1: pH: 7.9 and temp: 8°C, 2: pH: 7.6 and temp:
8°C, 3: pH: 7.9 and temp: 12°C, 4: pH: 7.6 and temp: 12°C). It is predicted that a larger number of significant differentially expressed genes will be present during the dispersant exposures compared to the oil exposures and that the functional terms that are overrepresented in relation to dispersant exposure will be related to processes involved in stress response, immune response, and apoptosis. Also, it is hypothesized that exposures run under an increase in temperature will yield the most significant differentially expressed genes and that the functional terms that are over-represented in relation to an increase in temperature and a decrease in pH will be related to stress responses, apoptosis, pH homeostasis and calcification. This experimental design will allow us to identify gene expression patterns that contributed to the decrease in phenotypic health rating observed
51 when the corals were exposed to dispersant alone and investigate the molecular response of L. pertusa to an increase in temperature and a decrease in pH.
3.3 Methods
3.3.1 Experimental Design and Sample Collection
The full details of the live L. pertusa collections, maintenance, and experimental design are described in Chapter 2 (2.3 Methods). The coral fragments used in this study are from
“Experiment 2” which consisted of four parts and involved exposing L. pertusa nubbins to concentrations of oil and dispersant treatments under two pH (7.6 and 7.9) and temperature
(8 °C and 12 °C) conditions. For all four parts of the experiment, each aquarium had one nubbin from six distinct collections for a total of six nubbins per aquaria, twenty-four nubbins per part, and ninety-six nubbins for the experiment. Nubbins were acclimated to the experimental conditions for each part of the experiment; 1: ambient pH and temperature
(pH: 7.9 and temp: 8°C), 2: low pH and ambient temperature (pH: 7.6 and temp: 8°C), 3: ambient pH and high temperature (pH: 7.9 and temp: 12°C), and 4: low pH and high temperature (pH: 7.6 and temp: 12°C) for 48 hours in 56-liter recirculating units. The nubbins were transferred to the exposures (oil, dispersant, oil + dispersant, artificial seawater (ASW) control) for 24 hours and fragments of four of the six nubbins in each aquarium (sixty-four total samples) were flash frozen for RNA sequencing (Figure 3.1a).
52
Figure 3.1 Experimental design, sampling scheme, and RNA processing. a) Layout of experiment design. Coral nubbins were exposed to either a control (artificial seawater), oil, dispersant, or oil + dispersant treatment under four different pH and temperature combinations (7.9 and 8°C; 7.6 and 8°C; 7.9 and 12°C; 7.6 and 12°C). Four coral nubbins
53 were sampled from each tank. b) The RNA from each coral nubbin was extracted and sequenced on an Illumina HiSeq2500. c) The sequences were filtered for low quality and adaptors and then quantified against a Lophelia pertusa transcriptome.
3.3.2 RNA Extractions, Sequencing, and Processing
Total RNA was extracted from the coral fragments following a modified Trizol/Qiagen
RNeasy Kit extraction protocol (Polato et al. 2011; Burge et al. 2013) to minimize contamination and improve yield. Quality and concentration were evaluated in-house through gel electrophoresis and a NanoDrop® ND-1000. Total RNA was sent to Novogene
Corporation Inc., where an Aligent 2100 BioAnalyzer (Aligent Technologies) confirmed the RNA integrity and quantification, library preparation was performed (Eukaryotic mRNA nondirectional library-NEB) and high-throughput Illumina sequencing
(HiSeq2500, 150 base pair (bp), paired-end reads) was performed on all the samples yielding ~20M raw reads/sample (Figure 3.1b). The Novogene bioinformatics team removed the Illumina adaptor sequences and trimmed any low-quality reads prior to delivering the sequences. Quality of the reads was confirmed using FASTQC v0.11.5
(Andrews 2010). KRAKEN v1.0 (Wood and Salzberg 2014) was executed with default parameters and the standard database (includes all complete bacterial, archaeal and viral genomes available in NCBI’s RefSeq database) to identify and remove potential microbial contamination. The expression of transcripts from each sample were quantified using
Salmon (Patro et al. 2017) in quasi-mapping mode at an average mapping rate of 67.79 ±
2.96% using a L. pertusa transcriptome previously developed in-house (Figure 3.1c)
(Glazier et al 2020).
54 3.3.3 Analysis of Expression Patterns
All analyses were carried out in the R environment (v.3.5.2) (R Core Team 2019).
Differential gene expression analyses were conducted in DESeq2 (Love et al. 2014) to examine differential expression patterns relative to colony, exposure, and climate. The plotPCA function and the ggplot2 package (Wickham 2016) were used to visualize multivariate dispersion. Independent filtering to remove transcripts with low read counts was applied and genes with a false discovery rate (FDR) – adjusted p-value (Padj) < 0.05 and a log2-fold change (LFC) of > ± 1 were considered significantly differentially expressed. Venn diagrams were generated using the overlapper function in R to identify the number of unique and shared differentially expressed genes (DEGs) between the colonies, exposures, and climate conditions.
In order to determine which gene categories were influenced by the different pollutant exposures, increase in temperature, and decrease in pH, Weighted Gene Co-expression
Network Analyses (WGCNA; version 5.1) (Langfelder and Horvath 2008) were performed followed by functional enrichment analyses based on Gene Ontology (GO) classification
(Ashburner et al. 2000). Count data were log-transformed using DESeq2 (Love et al. 2014) and input into WGCNA where modules of co-expressed genes were identified by calculating pairwise Pearson correlations between each pair of genes to create signed networks based on gene expression direction (up or down regulated). A power adjacency function was used to transform the signed networks into a function of connectedness, using a soft threshold of 20, which best fulfilled the assumptions of a scale free network topology.
Next, network modules (groupings of genes) whose expression was highly positively correlated were identified. The minimum module size was 200 genes and modules were
55 merged if they shared <75% similar expression profiles. The Pearson correlations were used to determine associations between the network modules and exposure to pollutants
(ASW, oil, dispersant, and oil + dispersant), pH (7.9 and 7.6), and temperature (8°C and
12°C).
For modules of interest, functional enrichment analyses were conducted to identify which GO terms were over-represented compared to the genome-wide compliment of L. pertusa GO terms. For each GO term, the number of annotations assigned to genes within a module were compared to the number of annotations assigned to the rest of the data set to evaluate whether any ontologies were more highly represented within the module than expected by chance using the GO-MWU package (Fisher’s exact test, FDR <0.05) (Wright et al. 2015). GO-MWU analyses were run for biological process (BP), molecular function
(MF), and cellular component (CC). The output of significantly enriched GO terms (all GO terms had FDR <0.001) for each module from the BP GO-MWU analyses was used to create a list of BP GO terms used to construct a network of GO terms for each module, which were visualized in R using the metacoder package (Foster et al. 2017). Internal nodes were pruned from the networks for ease of visualization. R scripts for network construction, functional enrichment, and metacoder were modelled after tutorials for WGCNA from
Langfelder & Horvath, 2008 and scripts from Luter et al 2020 and Harder et al 2019
(Langfelder and Horvath 2008; Harder et al. 2019; Luter et al. 2020).
56 3.4 Results
3.4.1 Overall Patterns of Differential Gene Expression
In this study, significant differentially expressed genes (DEGs; Padj < 0.05 and LFC >
± 1) were identified in relation to colony, exposure, and climate. A PCA conducted with these DEGs showed samples aligned horizontally by colony (Figure 3.2a), with PC1 depicting 22% of the variation and vertically by exposure (Figure 3.2b), with PC2 depicting
17% of the variation. Climate did not contribute to the variance in expression patterns based on the PCA (Figure 3.2c).
In comparison to Colony 1, Colony 4 had the most unique DEGs (n=1,327), followed by Colony 3 (n = 789), and Colony 2 (n = 725). Colony 2 and 4 had the highest overlap in
DEGs (n = 441), followed by Colony 3 and 4 (n = 316), and Colony 2 and 3 (n = 175).
There were 313 DEGs that were shared across all colonies (Figure 3.2d).
In comparison to the control exposure (ASW), the most unique DEGs were associated with dispersant exposure (n = 866), followed by oil + dispersant exposure (n = 14) and exposure to oil alone (n = 12). There was not a high rate of overlap in DEGs in relation to exposure; the largest overlap was between dispersant and oil + dispersant exposures (n=6).
Dispersant alone and oil alone had 2 DEGs in common and the oil alone exposure and oil
+ dispersant exposure shared 1 DEG. There were only 2 DEGs shared across all exposures
(Figure 3.2e).
In comparisons to the control climate treatment (8°C & 7.9), a decrease in pH (8°C &
7.6) resulted in the highest number of unique DEGs (n = 450), followed by an increase in temperature (12°C & 7.9; n = 148), and a decrease in pH plus an increase in temperature
(12°C & 7.6) resulted in 146 unique DEGs. The decrease in pH (8°C & 7.6) and decrease
57 in pH plus an increase in temperature (12°C & 7.6) treatments had the highest number of shared DEGs (n = 48), followed by the overlap between the decrease in pH plus an increase in temperature (12°C & 7.6) and the increase in temperature (12°C & 7.9) treatments (n =
41), and the decrease in pH (8°C & 7.6) and the increase in temperature (12°C & 7.9) treatments shared the lowest number of DEGs (n = 23). All three climate treatments shared
47 DEGs, in comparison to the control climate treatment (8°C & 7.9) (Figure 3.2f).
Figure 3.2 Variance and number of differentially expressed genes in response to colony, exposure, and climate (seawater temperature and pH). a, b, c) PCA plots of the variance in
58 differentially expressed genes in relation to colony (a), pollution exposure (b), and climate
(c). d, e, f) Venn diagrams of the number of unique and shared differentially expressed genes (FDR<0.05; FC > ±1) based on colony (d), pollution exposure (e), and climate (f).
“Colony 1”, “Control”, and “8°C & 7.9” acted as the control in the respective comparisons.
3.4.2 Global Response to Pollution Exposure, Decrease in pH, and Increase in Temperature
WGCNA examined the differences in L. pertusa gene expression in response to the oil and dispersant exposures, a decrease in pH, and an increase in temperature. The 21,729 transcripts included in the analysis were assigned to 23 co-expression modules, ranging in size from 224- 2,526 genes (15 modules displayed in Figure 3.3). The modules were color coded and three modules (light green: 1,519 genes, green yellow: 771 genes, and magenta:
480 genes) were significantly down-regulated in response to ASW and oil but up-regulated in response to dispersant. The turquoise module consisted of 2,526 genes that were significantly up regulated in response to oil but down regulated in response to dispersant.
The magenta module was also upregulated in response to a decrease in pH (7.6), along with the dark green (351 genes), cyan (716 genes), and dark turquoise (1,078 genes) modules.
Three modules (blue: 2,442 genes, brown: 1,861 genes, and grey: 675 genes) were downregulated in response to a decrease in pH. Only the pink module (1,008 genes) was significantly upregulated under increased temperature (12°C). The WGCNA approach provided a method to identify genes responding to the different stressors directly, and to identify groups of genes, and subsequent gene ontology, that were responding in the same direction in response to the exposures.
59
Figure 3.3 Heatmap of module-trait correlations. Each row corresponds to a module and each column was defined as categorical traits and corresponds to either a temperature, pH, or pollution exposure. The number of genes corresponding to each module is given in parenthesis and the size of the circle corresponds to the number of genes. In the heatmap matrix, red indicates a positive correlation and blue indicates a negative correlation.
Correlations are based on Pearson’s R with only significant correlations depicted by numbers in the squares of the heatmap. 60 Functional enrichment analyses (GO-MWU) revealed GO terms that were overrepresented based on the genes housed in each module of interest (light green, green yellow, magenta, turquoise, blue, brown, grey, dark green, cyan, dark turquoise, and pink).
The GO terms categorized under the Biological Process (BP) are discussed in the following text because they are the most informative terms for these data and the Molecular Function
(MF) and Cellular Component (CC) terms can be found in the supplemental materials
(Appendix A - K).
Three modules (magenta, green yellow, and light green) consisted of genes that were significantly downregulated in the ASW control and oil exposure but upregulated in response to dispersant. The magenta module consisted of 480 genes associated with 13 overrepresented BP GO terms related to wound healing, negative regulation of development and growth, and calcium ion transport (Figure 3.4b, d, and Appendix A). The green yellow module consisted of 771 genes associated with 12 overrepresented BP GO terms, with the majority of the terms associated with the negative regulation of transcription in response to stress and two terms involved in response to extracellular stimulus (Figure 3.5b, d, and Appendix B). The light green module consisted of 1,519 genes associated with 201 overrepresented BP GO terms (Figure 3.5a, c, and Appendix C).
Many of these GO terms were associated with stress and defense responses such as wound healing, immune system and innate immune system responses, leukocyte processes, the
MAPK cascade, external stimulus, response to growth factor, and cell death. Additionally, processes related to development, especially of neural networks and larval/post-embryonic development were overrepresented.
61
Figure 3.4 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for turquoise and magenta. Module-specific heatmaps of gene expression of the respective modules: turquoise (a) and magenta (b). Columns represent individual samples, 62 which are organized by pollutant exposure (ASW, oil, oil + dispersant, dispersant) and each row represents a gene. The total number of genes is designated on the left of each heatmap.
Biological process gene ontology (GO) hierarchy networks constructed using the metacoder package in R with input data from analysis using the GO_MWU package in R for the respective modules: turquoise (c) and magenta (d). Branch and node colors indicate the biological process parent term to which distal nodes belong, with the central black node representing the biological process level of the GO hierarchy. Terms associated with numbered terminal nodes are provided in the list below the networks.
The turquoise module, containing 2,526 genes that were up regulated in response to oil but down regulated in response to dispersant, included 66 overrepresented BP GO terms
(Fig. 4.4a, c, and Appendix D). The majority of these terms were linked to metabolic process and involved pathways related to RNA processing and RNA catabolic processing, the respiratory electron transport chain that contributes towards ATP synthesis, the initiation of translation and transcription, and oxidative phosphorylation. In addition, terms were associated with NADH assembly and transfer of electrons during oxidative phosphorylation, ribosome biogenesis and assembly, and the direction of proteins towards the ER or a specific location in the ER and targeting of proteins to a membrane used during translation.
63
64 Figure 3.5 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for light green and green yellow. Module-specific heatmaps of gene expression of the respective modules: light green (a) and green yellow (b). Columns represent individual samples, which are organized by pollutant exposure (ASW, oil, oil + dispersant, dispersant) and each row represents a gene. The total number of genes is designated on the left of each heatmap. Biological process gene ontology (GO) hierarchy networks constructed using the metacoder package in R with input data from analysis using the GO_MWU package in R for the respective modules: light green (c) and green yellow (d). Branch and node colors indicate the biological process parent term to which distal nodes belong, with the central black node representing the biological process level of the GO hierarchy. Terms associated with numbered terminal nodes are provided in the list below the networks.
In addition to magenta, three other modules (dark green, cyan, dark turquoise) were significantly upregulated in response to a decrease in pH (7.6). The dark green module consisted of 351 genes associated with 47 overrepresented BP GO terms, with the majority of the terms associated with the cilium assembly and movement, including terms related to the cytoskeleton and microtubule movement (Appendix E). The cyan module consisted of
716 genes associated with 42 overrepresented BP GO terms related to ribosomal assembly and biogenesis, translation, and protein transport and localization (Appendix F). The dark turquoise module consisted of 1,078 genes that were associated with 25 overrepresented
BP GO terms (Figure 3.6b, d, Appendix G). The majority of the terms in the dark turquoise module were related to the regulation of translation and protein modification. There was also one term identified as response to endoplasmic reticulum stress.
65 Of the three modules (blue, brown, and grey) that were significantly downregulated in response to a decrease in pH (7.6), there were no GO terms that were identified to be overrepresented in the BP category from the 675 genes in the grey module (Apendix H).
The blue module consisted of 2,442 genes that correlated to 116 overrepresented BP GO terms involved in the cell cycle and the regulation of DNA synthesis, replication, and repair
(Figure 6a, c, and Appendix I). The brown module contained 1,861 genes that were associated with 245 genes related to cellular component organization, vesicle-mediated transport, development and morphogenesis, and the regulation of processes related to development and protein localization (Appendix J).
66
Figure 3.6 Heatmaps and biological process gene ontology (GO) networks of co-expressed genes for blue and dark turquoise. Module-specific heatmaps of gene expression of the respective modules: blue (a) and dark turquoise (b). Columns represent individual samples, which are organized by pH exposure (7.9 and 7.6) and each row represents a gene. The total number of genes is designated on the left of each heatmap. Biological process gene ontology (GO) hierarchy networks constructed using the metacoder package in R with
67 input data from analysis using the GO_MWU package in R for the respective modules: light green (c) and green yellow (d). Branch and node colors indicate the biological process parent term to which distal nodes belong, with the central black node representing the biological process level of the GO hierarchy. Terms associated with numbered terminal nodes are provided in the list below the networks.
Only the pink module exhibited a significant response to an increase in temperature.
The pink module consisted of 1,008 genes and 25 overrepresented BP GO terms that were upregulated in the 12°C treatments (Figure 3.7 and Appendix K). These GO terms are mostly related to various forms of development as well as a few terms that are related to the humoral immune response and bradykinin, a peptide that promotes inflammation.
68
Figure 3.7 Heatmap and biological process gene ontology (GO) network of co-expressed genes for pink. Module-specific heatmaps of gene expression of the pink module (a).
Columns represent individual samples, which are organized by temperature exposure (8 °C and 12 °C) and each row represents a gene. The total number of genes is designated on the
69 left of each heatmap. Biological process gene ontology (GO) hierarchy network constructed using the metacoder package in R with input data from analysis using the
GO_MWU package in R for the pink module (b). Branch and node colors indicate the biological process parent term to which distal nodes belong, with the central black node representing the biological process level of the GO hierarchy.
3.5 Discussion
Among all colonies and across both pH and temperature conditions, L. pertusa exhibited discrete gene expression patterns when exposed to oil versus dispersant exposures. In addition, there were distinct expression patterns observed as the effect of a decrease in seawater pH and an increase in seawater temperature. These molecular data provide key insights into the underlying mechanisms that could be driving the visible decrease in average health in response to dispersant and provide new insight about the stress responses to oil and pH that were not visible through the phenotypic data (Weinnig et al. 2020).While there has been a recent uptick in research pertaining to the coral holobiont transcriptomic response to stress or disturbance, much of that research has focused on shallow-water corals and the impacts of bleaching and heat stress. This study provides critical insight into the molecular response of a reef-building cold-water coral when exposed to changes in environmental conditions, as well as, oil and chemical dispersants, which are a constant threat with continued offshore drilling efforts.
70 3.5.1 Variance in Colony Response
Colony, pollutant exposure, and climate all contributed to the transcriptomic patterns observed during this experiment and the four colonies displayed variance in gene expression (Fig 3.1). There is increasing evidence that different genotypes or colonies of coral exhibit variation in the overall gene expression response to the same conditions or exposures (Madeira et al. 2012; Baums et al. 2013; Kelly and Hofmann 2013; Friedman et al. 2018; Glazier et al. 2020) and this is likely due to genetic variation between the different colonies. The colonies used in this study were collected at distinct reef patches around
Vioska Knoll (Table 2.1) and previous genotyping efforts throughout the region indicate that these patches represent distinct ‘genets’, even though this area of the GoM is considered panmictic (Morrison et al. 2011). While the individual colonies displayed variance in gene expression patterns, there is a clear signal of differentially expressed genes in response to a decrease in pH and pollutant exposure, regardless of colony.
3.5.2 Oil and Dispersant Exposure and a Decrease in Seawater pH Elicit Signs of a Cellular
Stress Response
When sessile organisms experience extreme or stressful environmental conditions, they often do not have the capacity to flee in order to seek a more suitable environment. In order to cope with these changes, the cellular stress response (CSR) is initiated, aimed at sustaining/restoring homeostasis and alleviated stress-induced damage to macromolecules
(i.e., nucleic acids, proteins, lipids) (Kültz 2005; Simmons et al. 2009). In conjunction with the CSR, an increase in the generation of reactive oxygen species (ROS) is a well- documented cellular-level stress response and it has been suggested that ROS could be a
71 “critical second messenger system” that carry signals about alterations of cellular redox potential to activate the CSR (Kultz, 2005; Jiang 2011). While all cells intrinsically have free radical searching mechanisms that reduce and repair oxidative stress, the excess of
ROS generated in response to stress must be neutralized by the antioxidant proteins so that they do not cause terminal damage. It appears from the functional enrichment analyses that
L. pertusa experienced distinct stages of the CSR and redox regulation in response to oil and dispersant exposures, as well as, a decrease in seawater pH.
3.5.2.1 Cellular Response to Oil Exposure
In response to the 24-hour oil exposure, functional enrichment analyses of the BP terms revealed that there was an upregulation of GO terms related to metabolism, energy transfer, and transcription and translation. In addition, there was a downregulation enriched for numerous pathways associated with cell growth and development (Figure 3.4c and 3.5c).
Shifts in transcription and translational processes involved in energy metabolism are among the early signals of the CSR. It has been previously reported that posttranscriptional mechanisms, including proteins related to RNA-processing (i.e. splicing), are regularly among the most overrepresented and abundant functional categories in other transcriptomic studies of the CSR (Kültz 2020). This trend is supported by this study, where numerous upregulated terms are linked to “RNA splicing” and “regulation of RNA splicing” in the turquoise module (Figure 3.4c). Upregulation of terms for “protein targeting to membrane”, “protein targeting to ER” (i.e. endoplasmic reticulum), and ribosome assembly and biogenesis are possible evidence for membrane and ER stress, which are known signifiers to the CSR. The overexpression of ribosomal proteins involved in
72 translation have been reported as a general stress response to pathogens in an octocoral
(Burge et al. 2013), clams (Gestal et al. 2007), abalone (Travers et al. 2010), urchins (Nair et al. 2005), and most similarly, an octocoral that was exposed to oil and dispersant containing floc during the DWH oil spill (DeLeo et al. 2018). The upregulation of many terms such as “oxidative phosphorylation”, “ATP synthesis coupled electron transport chain”, and “NADH dehydrogenase complex assembly” are further evidence that the corals exposed to oil were actively increasing their metabolic processes, likely to cope with stress on the cellular level. In addition to the BP terms, two terms from the Molecular Function
(MF) category of the turquoise module were related to “oxidoreductase activity, acting on
NAD(P)H” which is associated with the production of extracellular superoxide. Energy metabolism and control of cell growth are linked with increased demands for generating energy and reducing equivalents (NADH, NADPH) that are needed for cellular antioxidant systems (Kültz 2005).
Another method for conserving or diverting energy to address cellular-level stress responses is to slow down growth and development processes or undergo what is known as “growth arrest”. The downregulation of terms associated with “positive regulation of growth”, “positive regulation of cell development”, “anatomical structure development/morphogenesis” in the light green module (Figure 3.5c) in response to oil are evidence of this adaptive and integrated part of the CSR. The coupling of these processes, the upregulation of energy metabolism and the downregulation of growth and development, could result in the reallocation of NADPH/NADH and ATP needed for stress-related cell functions (Kültz 2005). While there is evidence that certain cornerstones of the CSR were activated in response to oil exposure, L. pertusa appears to be reallocating
73 resources in an attempt to mediate any downstream stress-induced damage, as opposed to removing terminally damaged cells through programed cell death (apoptosis).
3.5.2.2 Cellular Response to Dispersant Exposure
While the BP/MF GO terms discussed earlier from the turquoise module were downregulated in response to dispersant exposure (as opposed to upregulated in response to oil), there is evidence from the upregulation of BP GO terms in the light green, green yellow, and magenta modules (Figures 4.4d, 4.5c, and 4.5d) that the corals exposed to dispersant for 24 hours are also under the influence of the CSR. However, the responses to dispersant appear to be more severe and are likely leading to more permanent cellular damage, compared to the oil exposure response. For instance, terms that were upregulated in the light green module (Figure 3.5c) included “programed cell death” and “apoptotic process”. The induction of apoptosis is a stage of the CSR that is initiated when cells have passed a threshold of stress that they can no longer recover from (Kültz 2020). The downregulation of terms associated with energy metabolism from the turquoise module
(Figure 3.4c), in addition to GO terms upregulated in relation to “negative regulation of transcription from RNA polymerase II promoter in response to stress”, “negative regulation of nucleic acid-templated transcription”, and “negative regulation of RNA metabolic process” from the green yellow module (Figure 3.5d), are an indication that processes related to energy metabolism, translation, and transcription are being suppressed on the cellular level in response to dispersant. In addition, the upregulation of growth and developmental processes in the light green module (Figure 3.5c) could indicate that the
74 corals exposed to dispersant are past the stages of the CSR where growth arrest is beneficial to the overall mission of repairing or sustaining homeostasis through resource reallocation.
There were also numerous terms related to the mitogen-activated protein kinases
(MAPK) cascade that were enriched in the light green module, including “positive regulation of MAPK cascade”, “positive regulation of ERK1 and ERK2 cascade”, “signal transduction by protein phosphorylation”, “transmembrane receptor protein tyrosine kinase signaling pathway”, “peptidyl-tyrosine modification”, “positive regulation of GTPase activity”, and “Ras protein signal transduction”. The MAPK cascade is one way that cells communicate an extracellular signal down to the DNA in the nucleus and MAPK proteins regulate multiple transcription factors that lead to complex biological responses such as growth, differentiation, and death. This upregulation of terms is further evidence that there is not a suppression of complex processes during the coral exposure to dispersant.
However, there are also terms in the light green, green yellow, and magenta modules
(Figures 4.4d, 4.5c, and 4.5d) that indicate that the corals exposed to dispersant experienced an upregulation of the induction of the immune system, wound healing, and stress-related responses. In the magenta and light green modules (Figures 4.4c and 4.5c) GO terms were enriched for wound healing, including “wound healing, spreading of cells”, “epiboly involved in wound healing”, and “response to wound healing” in response to dispersant.
Dispersant-exposed corals exhibited varying levels of visible mucosal secretions and tissue detachment following 24-hour dispersant exposures (Weinnig et al. 2020). These wound healing processes could have been an attempt to mediate the stress responses observed on the phenotypic level. In the light green module (Figure 3.5c), there is an upregulation of terms enriched for “positive regulation of immune response”, “positive regulation for
75 innate immune response”, “positive regulation of lymphocyte/leukocyte activation”,
“positive regulation of defense response”, “immune effector process”, and “T cell activation” in response to dispersant and the combination of oil plus dispersant. Corals, like all basal metazoans, have an ancestral immune system that gave rise to the immune systems of more complex organisms (Goldstone 2008; Shinzato et al. 2011; Tarrant et al.
2018). Included in this ancestral immune system are innate immune functions that have altered gene expression patterns when exposed to stress (Burge et al. 2013; Toledo-
Hernández and Ruiz-Diaz 2014). Corals exposed to floc after the DWH oil spill also exhibited overexpression of pathways related to wound healing, tissue regeneration, and an activation of the innate immune response (DeLeo et al. 2018). These same terms related to immune function were downregulated in response to oil which indicated that the addition of dispersant to the mixture could result in this induction of cellular level immune responses.
3.5.2.3 Cellular Response to a Decrease in pH
A decrease in seawater pH (7.6 vs. 7.9) resulted in seven modules that were significantly up or down regulated, the most modules out of the different types of exposures
(pH, temperature, or pollutant). Of the four significantly upregulated modules (magenta, dark turquoise, cyan, and dark green), the magenta module was discussed previously for also being enriched for BP GO terms related to wound healing, the regulation of calcium ion transport, and a negative regulation of development and growth. While mucus excretions and tissue sloughing were not observed in the phenotypic response to pH, there could have been more cellular level damage that prompted the wound healing response
76 terms. The dark green, cyan, and dark turquoise modules (Figure 3.6d) involved the upregulation of BP GO terms enriched for cilium assembly and movement, ribosomal assembly and biogenesis, positive regulation of translation, post-translation protein modification, and a response to endoplasmic reticulum stress. Similar terms associated with translation and ribosome/ER processes were upregulated in response to oil (turquoise module) and in conjunction with the terms enriched for wound healing are indicative of signs of an early cellular stress response. Interestingly, “cilium movement” and “axonemal dynein complex assembly” were within the top ten GO BP terms significantly enriched for upregulated transcripts in the only other study to examine the transcriptomic response of
L. pertusa to a decrease in pH (Glazier et al. 2020).
The blue (Figure 3.6c) and brown modules were significantly downregulated in response to a decrease in pH and were enriched for BP GO terms associated with DNA synthesis, repair, and replication, as well as, regulation of the cell cycle, development, and morphogenesis. While it has been suggested that DNA repair enzymes are usually upregulated as a component of the CSR (Kültz 2005), the presence of terms related to
DNA repair, replication, and synthesis in conjunction with terms related to the cell cycle suggest that DNA damage and repair is linked to checkpoints within the cell cycle.
Ultimately, a reduction in the cell cycle can reduce overall growth and development. GO terms enriched for DNA repair, replication, and response to DNA damage stimulus were also downregulated in response to pH in a metanalysis of transcriptomic responses of the genus Acropora (Dixon et al. 2020) and a reduction in cell cycle progression was observed in Eunicea calyculata’s response to disease (Fuess et al. 2018). The downregulation of cell cycle terms, as well as, processes involved in development and morphogenesis could
77 suggest that the L. pertusa polyps exposed to a decrease in pH were undergoing a stage of
“growth arrest” in order to maintain cellular homeostasis.
3.5.2.4 Cellular Response to an Increase in Temperature
Only the pink module (Figure 3.7b), which contained terms enriched for developmental processes, regulation of the humoral immune response, and bradykinin catabolic process, was significantly upregulated with an increase in temperature (8°C vs
12°C). The upregulation of terms associated with the humoral immune response and the bradykinin catabolic process (a peptide that promotes inflammation) are slight indications of a stress response but due to the evidence from previous physiological studies on heat stress in CWC (Lunden et al. 2014a; Gori et al. 2016) and transcriptomic studies on shallow-water corals (Barshis et al. 2013; van de Water et al. 2015; Dixon et al. 2020), we had anticipated to observe indications of a more pronounced stress response. While there have been numerous studies that have investigated the transcriptional response of corals to heat stress, few have focused on the response of cold-water corals. The lack of response to temperature could be a result of a few different factors. First, the increased temperature treatment (12°C) is toward the upper limit of L. pertusa’s thermal tolerance but it did not induce mortality in previous studies so they may already be adapted to tolerate this temperature range (Lunden et al. 2014a). Also, the coral fragments were acclimated for 48 hours to the seawater conditions before the start of the experimental trials, so any initial cellular level responses to the increase in temperature would likely have passed. In order to elucidate the transcriptomic response of L. pertusa to an increase in seawater
78 temperature a study with a more comprehensive temperature design and sampling scheme should be conducted.
3.5.3 Cnidarian Environmental Stress Response Hypothesis
It has been proposed in studies on shallow-water anemones and corals that cnidarian stress might be detectable through a relatively consistent transcriptional response of compliments of genes, known as the cnidarian environmental stress response (ESR)
(Barshis et al. 2013; Aguilar et al. 2019; Cziesielski et al. 2019; Dixon et al. 2020). In a recent meta-analysis of Acropora corals, Dixon (2020) outlined that most RNASeq coral stress studies mentioned a downregulation of growth-related processes, and an upregulation of oxidative stress response, immune response, protein folding and degradation, and cell death (Meyer et al. 2011; DeSalvo et al. 2012; Kenkel et al. 2013,
2018; Maor-Landaw et al. 2014). In an effort to test the ESR hypothesis, Dixon (2020) performed differential gene expression and gene ontology enrichment analyses on numerous studies investigating the transcriptomic response in Acropora sp. to various stressors, including heat and cold stress, hyposalinity, immune challenge, low pH, and multiple stressors. While there was evidence of a cluster of functional terms (type A) related to the proposed ESR (upregulation of oxidative stress response, immune response, protein folding and degradation, and cell death), there was also evidence of a second cluster of genes (type B) that was oppositely regulated in type A. It was denoted that the difference in the type A and type B response was due to the intensity of the stress response, in that type B was observed under lower intensity stress and showed higher variability among the different studies.
79 Interestingly, there appears to be a similar pattern, with two distinct stress responses present between the oil exposures and the dispersant exposure. The dispersant exposure elicited a stronger stress response in comparison to the oil exposure which aligns with the phenotypic data, where there were few to no signs of stress in response to the oil exposure and more severe signs of stress in response to the dispersant exposure (Weinnig et al. 2020).
The upregulation of GO terms enriched for immune response and cell death in conjunction with the downregulation of functions related to metabolic process and transcription/translation align with observations made in other transcriptomic-based shallow-water coral stress responses (Dixon et al. 2020). Also, in line with the type A/B
ESR scenario proposed by Dixon (2020), the two distinct responses to oil and dispersant have consistently opposing expression patterns (turquoise vs. light green, green yellow, and magenta), which has been suggested to indicate that their regulation may be mechanically linked and that the negative relationship between these modules is an intrinsic property of the coral ESR.
The upregulation of GO terms enriched for processes related to wound healing, ribosomal assembly and biogenesis, and response to endoplasmic reticulum stress in conjunction with the downregulation of terms enriched for cell cycle regulation, development, and morphogenesis are indications that L. pertusa experienced a mild stress response during exposure to a pH of 7.6. Also, most of the modules that were significantly correlated with a decrease in pH were also significantly correlated in the same direction for one or more of the pollutant exposures (blue, brown, magenta, dark turquoise, and grey,
Figure 3.3), indicating that some of the same components of the coral CSR/ESR were influenced by both a decrease in pH and exposure to pollutants. However, an increase in
80 temperature did not provide substantial evidence for the onset of the CSR or ESR and no other exposures were significantly correlated with the pink module.
While most research to date has focused on zooxanthellate shallow-water corals, this study provides evidence that some of the same stress responses are present in an azooxanthellate scleractinian coral. This study also bolsters the idea that corals may have an underlying ESR that is triggered regardless of the type of stress through the addition of analysis of oil and dispersant exposure. The previous studies have focused on the environmental factors most significantly impacting shallow-water coral reef systems (heat, pH, hyposalinity, etc.) but this study indicates that the cold-water coral Lophelia pertusa elicits similar transcriptomic stress responses when exposed to varying intensities of pollution exposure and pH stress.
3.5.4 Implications for DWH and Future Oil Spill Assessments
Following the DWH oil spill, CWC communities were exposed to unprecedented concentrations of hydrocarbons in the surrounding seawater and to chemical dispersants, likely for the first time (White et al. 2012, 2014). While there was a substantial effort to assess the impact of the oil spill on the surrounding communities, the lack of previous research on the stress responses of CWCs lead to a knowledge gap about how to effectively evaluate this type of catastrophic event. Severe damage to select coral colonies was visually documented using high-definition cameras and transcriptomic analysis were conducted on a few impacted octocoral colonies but there remains to be a comprehensive way to rapidly evaluate the stress induced by a deep-water oil spill (White et al. 2012; Hsing et al. 2013;
Fisher et al. 2014a; b). However, the research that has stemmed from the DWH spill, in
81 conjunction with the sustained effort to understand and mitigate the effects of climate change on shallow-water corals, suggests that independent of the type of stress there is likely a general stress response happening on the cellular level that may not always manifest into visual phenotypic changes. In order to prepare for a future deep-water oil spill, we can use the decade of research following the DWH towards the development of an in situ monitoring protocol that includes visual assessments, samples for gene expression and proteomic profiles, and direct measurements of chemical cues that can signal stress, such as extracellular reactive oxygen species (Diaz et al. 2016; Grabb et al.
2019). It is also advisable to routinely monitor CWC health as ocean warming, acidification, and deoxygenation continue to progress. As observed in this study, pH stress can induce mild cellular stress responses which could impact these ecosystems with the predicted shoaling of the aragonite saturation horizon (Lunden et al. 2013). In addition, more research into the impacts of sequential and multiple stressors on CWCs will provide much needed data about how these ecosystems will fair when exposed to direct disturbance, like future oil spills, under potential future temperature, pH, and oxygen conditions. It is possible that if CWCs are already experiencing stress from one or more environmental conditions, the addition of stress from oil and dispersant exposure could push them past the point of mitigating stress on the cellular level to apoptosis and colony- wide death.
82 CHAPTER 4
PROXIMITY TO NATURAL HYDROCARBON SEEPAGE DOES NOT
INFLUENCE THE BASELINE GENE EXPRESSION PROFILES
OF A DEEP-SEA OCTOCORAL (Callogorgia delta)
IN THE GULF OF MEXICO
4.1 Abstract
The Gulf of Mexico (GoM) is populated by hundreds of active and inactive hydrocarbon seeps that support rich communities through fluids containing methane and/or hydrogen sulfide and the production of hard authigenic carbonate by the anerobic oxidation of methane performed by microorganisms. While some organisms rely on the fluids as a source of energy for survival, others such as cold-water corals (CWC) depend on the carbonate substrate left behind after the active seepage subsides in order to anchor themselves to the seafloor. Most CWCs only settle on authigenic carbonate outcrops after seepage subsides but one species of octocoral, Callogorgia delta, has been shown to live near areas of active seepage and on carbonates left behind at currently inactive seeps. While
C. delta appears to have an affinity for living around areas of active seepage, the effect of active seepage on CWCs is poorly understood. It is especially pertinent to understand this relationship in the wake of continued exploration and extraction of hydrocarbons from the deep waters of the GoM. In order to investigate the influence of natural hydrocarbon seepage on C. delta, we compared the in situ gene expression profiles of colonies found in areas of active hydrocarbon seepage (“seep”) and areas with no current active seepage
(“non-seep”) at two different sites in the GoM (MC751 and MC885). There were fewer
83 differentially expressed genes (DEGs; adjusted p-value <0.5, absolute log2-fold change
>1) in the “seep” versus “non-seep” comparison (n=21) than the site comparison (n=118) but both analyses revealed gene ontology terms indicating slight alterations in natural biological housekeeping processes, as opposed to any substantial impact at the cellular level from exposure to active seepage. Based on the gene expression profiles, proximity to hydrocarbon seepage did not have a detectable influence on overall Callogorgia delta health.
4.2 Introduction
The Gulf of Mexico (GoM) is home to hundreds of active hydrocarbon seeps where fluids rich in methane and/or hydrogen sulfide are steadily released from the seafloor and support endemic communities of organisms, including bathymodiolin mussels and vestimentiferan tubeworms (Fisher et al. 2007; Cordes et al. 2009). Much of these communities rely on methanotrophic bacterial symbionts for nutrition through the anaerobic oxidation of methane (Cordes et al. 2009). In addition to supporting these communities through methanotrophy, the microorganisms (microbes) at active seeps produce authigenic carbonate substrate as a biproduct of the anaerobic oxidation of methane (Wallmann et al. 1997; Thiel et al. 2001; Formolo et al. 2004). When seeps become inactive, meaning that there is no longer any fluid flow, the carbonate substrata remains and contributes significantly to the overall habitat heterogeneity within the deep benthic regions of the Gulf of Mexico (Fisher et al. 2007; Cordes et al. 2008, 2010).
It is common for cold-water coral communities to propagate in areas of past seepage but only after seepage has mostly subsided. Isotopic analysis have shown that the
84 scleractinian cold-water coral Lophelia pertusa does not incorporate nutrition from seep- derived primary production into its tissue (Cordes et al. 2008; Becker et al. 2009). Hard substrate, such as authigenic carbonate, is necessary for the successful recruitment of coral larvae and is often limited in the mostly soft-sedimented deep-sea which likely explains why coral assemblages are often found at these inactive seepage sites (Roberts et al. 2009).
However, the octocoral Callogorgia delta has been observed to live, and even thrive, near active hydrocarbon seepage (Figure 4.1) (Becker et al. 2009; Quattrini et al. 2013). Stable isotopic analyses of in situ tissue samples (d13C and d15N) have revealed that C. delta colonies that live in close proximity to an active seep site incorporate seep primary production into their tissues, an indication that this species might have an adaptation or symbiosis with microorganims that allow them to succeed in these areas (unpublished data,
Fisher and Cordes). Nevertheless, while C. delta has the capacity to live close to active seepage, colonies are also found in areas of no apparent seepage. The effect of active seepage on CWCs is poorly understood. Further insight into this relationship could reveal key aspects of CWC physiology, ecology, biogeography, and resilience to toxicity from natural hydrocarbon concentrations.
85
Figure 4.1 Callogorgia delta colonies growing in close proximity to visible active hydrocarbon seepage. The white/grey bacterial mats (Beggiatoa sp.) and live chemosynthetic tubeworms (Lamellibrachia sp) are indicative of an active cold-seep environment.
The Deepwater Horizon (DWH) oil spill exposed CWC communities in the GoM to elevated concentrations of hydrocarbons and chemical dispersants. Following the spill, numerous corals were found to be covered in hydrocarbon and chemical dispersant- containing floc and exhibiting phenotypic signs of stress (White et al. 2012, 2014; Fisher et al. 2014a). In the aftermath of DWH, several toxicity experiments were conducted to determine the effects of hydrocarbons and dispersants on CWC species, including
Paramuricea sp. B3, C. delta, Leiopathes glaberrima, Swiftia exerta, and L. pertusa
(DeLeo et al. 2016; Frometa et al. 2017; Weinnig et al. 2020). The conclusion from exposure studies was that dispersant exposure induced a more severe phenotypic stress response compared to the hydrocarbon exposure. Specifically, C. delta showed little to no effect of the hydrocarbons on the average health of the coral colonies (DeLeo et al. 2016).
C. delta may have reduced sensitivity to hydrocarbon exposure as a result of being adapted 86 to live in areas of active hydrocarbon seepage. The in situ impact to CWCs during the
DWH oil spill and subsequent experiments made it clear that we need to understand how hydrocarbon and dispersant exposures influence CWC communities, which includes understanding how natural seepage affects these corals.
Due to an affinity for living near hydrocarbon seeps and the resilience observed during the experimental hydrocarbon exposure, investigating the transcriptional profiles of
C. delta colonies in close proximity and far away from natural hydrocarbon seeps may provide insight into habitat preference and ability to survive and recover in the event of future oil spills. This study aims to investigate the influence of natural hydrocarbon seepage on the in situ global gene expression patterns of C. delta colonies by comparing the profiles of colonies that inhabited areas of active hydrocarbon seepage (“seep”) verses areas with no apparent active seepage (“non-seep”). It is predicted that there will be more differentially expressed genes in the “seep” vs. non-seep” analysis than the comparison of the two sites. The gene expression profiles generated during this study represent the first in situ transcriptomic data for C. delta and the de novo reference transcriptome is a resource that will be available to the scientific community for future studies utilizing this species and closely related octocorals.
4.3 Methods
4.3.1 Sample Selection and Collections
Sample collections occurred aboard research cruises in the Gulf of Mexico during
2015 (EV Nautilus; ROV Hercules – Ocean Exploration Trust), 2016 (DSV Ocean
Inspector; ROV Global Explorer – Oceaneering, Inc.), and 2017 (MSV Ocean Intervention
87 II; ROV Global Explorer – Oceaneering, Inc.). The collection sites were Bureau of Ocean
Energy Management-designated lease blocks, Mississippi Canyon (MC) 751 (435-483 m) and MC885 (618-642 m), where Callogorgia delta colonies could be found living in close proximity to visible active seepage, as well as in areas of no visible active seepage (Table
4.1). Visible seepage was identified from ROV livestream high resolution video footage using one or more of the following signs; a) visible gas bubbles, b) patchy white and/or orange bacterial mats, genus Beggiatoa (Joye et al. 2004), c) presence of cold seep foundational species i.e., Bathymodiolus mussels and/or Lamellibrachia tubeworms
(Figure 4.1)(Fisher et al. 2007). To confirm the proximity to seepage, stable carbon (d13C) and nitrogen (d15N) isotope analyses were performed to determine to what extent the corals were incorporating seep primary production into their tissue. These analyses were performed in conjunction with the Fisher Lab at Penn State University. Numerous C. delta samples were collected from distinct coral colonies in “seep” areas and “non-seep” areas at each site and analyzed for stable isotopes. Samples were collected into separate insulated compartments on the ROV and brought to the surface where they were immediately preserved in RNAlater (Qiagen, Inc.) and frozen. The samples remained frozen during transportation back to the laboratory at Temple University (Philadelphia, PA.). Three samples from each proximity (seep and non-seep) at the two different sites (total n=12) were selected for RNASeq analysis based on the most relevant isotope values.
88 TABLE 4.1 | Callogorgia delta collection sites Site Lat. Long. Depth range (m) Years visited MC751 28.19374 -89.7993 434-451 2015, 2016, 2017 MC885 28.07132 -89.71145 434-449 2015, 2016
4.3.2 RNA Isolation and Sequencing
Total RNA was extracted following a modified Trizol/RNeasy Kit (Qiagen, Inc.) protocol to minimize contamination and improve yield from samples with limited tissue
(Polato et al. 2011; Burge et al. 2013). Extraction concentrations were measured with a
Nanodrop® ND-1000, RNA quality and potential contamination was evaluated with gel electrophoresis through the presence of intact 28S and 18S ribosomal RNA bands, and an
Agilent 2100 Bioanalyzer (Agilent Technologies) was used to check the RNA integrity and quantity. Total RNA was sent for mRNA library preparation and high-throughput Illumina sequencing (HiSeq4000; 150 base pair (bp), paired-end reads) at Novogene, USA.
4.3.3 RNA Sequence Processing
The quality of the reads was assessed using FASTQC v0.11.5 (Andrews 2010) and subsequently quality-trimmed and filtered using TRIMMOMATIC v0.36 (Bolger et al. 2014) in paired-end (PE) mode. Each read was scanned using a 5-base window and trimmed if the quality score (Phred) dropped below 30 (SLIDINGWINDOW:5:30). Leading and trailing bases were dropped if the quality dropped below a score of 3 (LEADING:3 TRAILING:3) and trimmed reads with resulting lengths shorter than 50 bases were excluded
(MINLEN:50). Sequencing adaptors were trimmed from sequences with CUTADAPT v1.15 89 (Martin et al. 2011) and random sequencing errors were corrected for with the k-mer-based method Rcorrector (Song and Florea 2015) and the k-mer counter JELLYFISH v2.2.6 (Marcais and Kingsford 2012). Microbial contamination was identified and removed using KRAKEN v1.0 (Wood and Salzberg 2014) and the standard database, which includes all complete bacterial, viral, and archaeal genomes available on NCBI’s RefSeq database.
4.3.4 Transcriptome Assembly
The filtered and trimmed paired-end sequences that were not classified as bacterial, microbial, archaeal, or viral by KRAKEN were used to generate a de novo reference assembly with Trinity v2.4.0 (Grabherr et al. 2011). The transcriptome assembly was assessed using
Transrate v1.0.3 (Smith-unna et al. 2016) and the Benchmarking Universal Single-Copy
Orthologs (BUSCO v.3.0.2) program (Simão et al. 2015; Waterhouse et al. 2018) using the hierarchical catalog of orthologs for metazoans OrthoDB v9 (http://www.orthologdb.org/).
Transcriptome annotations were performed using DAMMIT v1.0 using P-FamA, RFam,
OrthoDB, BUSCO, and uniref90 databases (Scott 2016).
4.3.5 Differential Gene Expression Analyses
To assess expression levels, the filtered and trimmed sequences were mapped and quantified against the de novo transcriptome using Salmon (Patro et al. 2017) in quasi- mapping mode. Significant differentially expressed genes (DEGs; adjusted p-value <0.5, absolute log2-fold change >1) in relation to site of collection (MC751 vs. MC885) and proximity to active seepage (Seep vs. Non-seep) were carried out using DESEq2 (Love et al. 2014) in RStudio v1.1.463. DESeq2 clusters DEGs using Pearson correlation distances
90 to exhibit similarities in expression patterns across samples. Gene IDs and subsequent gene ontology (GO) of DEGs were acquired through BLAST and Uniref databases
(https://blast.ncbi.nlm.nih.gov/Blast.cgi, https://www.uniprot.org/help/uniref).
4.4 Results
4.4.1 Callogorgia delta Reference Transcriptome
The de novo reference transcriptome had a total of 39,475 transcripts in the final assembly and the contigs ranged in size from 300 to 36,051 bp. The GC content was 0.43 and the n50 was 1,467. BUSCO categorized the transcriptome as 96.5% complete with
59.2% of the contigs as singletons and 37.5% as duplicates. The TransRate score was 0.19, compared to the optimal score of 0.20 (Table 4.2).
TABLE 4.2 | Callogorgia delta transcriptome assembly statistics
Number of transcripts 39,475
Shortest transcript 300
Longest transcript 36,051
GC content 0.43 n50 (bp) 1,467
TransRate Score 0.19
TransRate Optimal Score 0.2
91 4.4.2 Differential Gene Expression Analyses
Twelve samples of C. delta were collected near and far from cold seeps (n=3 replicates of each) at two sites and were subjected to transcriptome sequencing. The mapping rate from Salmon was 77.4±3.1% per sample, and 27,926 genes were recovered.
This was reduced to 15,420 genes after filtering and low abundance removal.
Principle component analysis failed to cluster host transcriptomes by site or proximity to seepage (Figure 4.2). Site had a higher influence on host transcriptomes where
113 genes were up-regulated (log2-Fold Change (FC); 1.3-5.5, mean=3.1) and 18 genes down-regulated (FC; 1.5-4.25, mean=2.7, Table 4.3, Figure 4.3a). In contrast, differential gene expression (DGE) between seeps was relatively low compared to the number of recovered genes. Only 11 genes were up-regulated (FC; 2.7-6.5, mean= 4.2) and 10 genes were down regulated (FC; 1.8-6.6, mean= 3.6) between seep vs non seep samples (Table
4.3, Figure 4.3b).
92
Figure 4.2 Principle coordinate analysis of the full expression profiles of Callogorgia delta samples. Each point represents a sample and the closer together the points, the more similar the expression profiles. Color indicate proximity to seepage, seep (red) or non-seep (blue).
Shape indicates site of collection, MC751 (circle) or MC885 (triangle).
93 TABLE 4.3 | Number of significant* differentially expressed genes of Callogorgia delta for the seep vs non-seep and MC885 vs MC751 comparisons. Overexpressed Under-expressed
Seep vs. Non-Seep Number of genes 11 10 Log2-fold change (FC) 2.7-6.5 1.8-6.6 Average Log2-FC 4.2 3.6 MC885 vs MC751
Number of genes 113 18 Log2-fold change (FC) 1.3-5.5 1.5-4.25 Average Log2-FC 3.1 2.7 *(padj < 0.05, log2FC = ±1)
Out of the 21 up and down regulated genes in response to seepage (Figure 4.3b), nine genes (42.8%) were ‘uncharacterized proteins’ and the remaining eleven genes were household genes (such as protein fosB-like, coiled-coil domain-containing protein 180- like, and integrase catalytic domain-containing protein; Appendix L). Out of the 121 differentially expressed genes in relation to site (Figure 4.3a), 48 genes (39.7%) were
‘uncharacterized proteins” (Appendix M). There were eight differentially expressed genes that were shared between the site comparison and the seep proximity comparison (Figure
4.4) and all shared genes were differentially expressed in the same direction. The shared terms included three uncharacterized proteins as well as “Tyrosince kinase receptor
Cad96Ca”, “coiled-coil domain-containing protein 180-like”, “Integrase catalytic domain- containing protein”, “zinc finger protein 862-like isoform X1”, and ‘trigger transposable
94 element-derived protein 6-like” – which was the most downregulated term in both the seep and site comparisons (FC – seep: -6.638 and site: -4.254; Appendix L, M).
95 Figure 4.3 Differential gene expression of Callogorgia delta in response to site (a) and proximity to seepage (b). Genes are hierarchically clustered based on Pearson’s correlations of expression across samples and differential gene expression was considered significant if adjusted p-values (FDR) < 0.05 and absolute log2-fold change (FC) was >1.
Rows are individual genes and columns are individual samples. (a) Heatmap showing 131 genes with significant differential expression relative to the site where the corals were collected, MC751 vs. MC885. (b) Heatmap showing 21 genes with significant expression relative to the proximity to active seepage, seep vs. non-seep.
Figure 4.4 Venn-diagram specifying the number of unique and shared differentially expressed genes (padj < 0.05, log2FC = ±1) between the site comparison (MC751 vs
MC885) and the seep proximity comparison (Seep vs Non-seep).
96 The GO terms associated with DEGs in the seep proximity comparison included terms from all three GO parent categories: biological processes (BP), cellular component
(CC), and molecular function (MF). GO terms associated with the under-expressed genes included integral component of membrane, nucleic acid binding, ATP binding, collagen- containing extracellular matrix, nitrogen compound metabolic process and the terms associated with the over-expressed genes included protein dimerization activity, DNA binding, regulation of transcription by RNA polymerase II, nucleic acid binding, and DNA integration (Appendix N).
The comparison between the two sites also yielded GO terms from all three GO parent categories (BP, CC, and MF). There were fewer genes (n=18) and subsequent GO terms that were under-expressed in relation to site including DNA binding, integral component of membrane, metal ion binding, ATP binding, intercellular protein transport, and actin cytoskeleton organization. In comparison, the 113 genes that were over-expressed yielded GO terms including G protein-coupled receptor activity, protein tyrosine phosphate activity, ATP binding, DNA replication, protein transport, protein dimerization activity, regulation of transcription by RNA polymerase II, MAP kinase tyrosine/serine/threonine phosphate activity, DNA catabolic process, ion channel activity, tumor necrosis factor receptor binding, autophagy, microtubule binding, zinc ion binding, and mitotic cytokinesis (Appendix O).
4.5 Discussion
Callogorgia delta is a deep-sea octocoral species that has an affinity for living around areas of active hydrocarbon seepage in the GoM (Quattrini et al. 2013), however,
97 there was not a noteworthy difference in the global gene expression patterns between C. delta colonies in relation to active seep proximity ( Figure 4.3b). The comparison between two different collection sites (MC751 and MC885) resulted in a larger number of differentially expressed genes in relation to site (Table 4.2, Figure 4.3a) but the subsequent
GO identifications indicated that these genes were related to typical housekeeping processes. There were eight differentially expressed genes (Figure 4.4) that were shared by both the seep and site comparisons which is an indication that distinct processes were not impacted by proximity to seep or location. These genes are likely involved in routine processes that the corals partake in regardless of seep proximity or locality. Based on the data provided in this study, it does not appear that C. delta employs altered regulation of a suite of specific genes within the coral transcriptome to augment tolerating hydrocarbon exposure in the GoM.
4.5.1 Shared Differentially Expressed Genes and Similarity in Expression Profiles
The shared genes in the two different comparisons (Figure 4.4) could be an indication that those genes are part of natural processes that are experiencing changes in gene expression intensity regardless of proximity to seepage or the site location. The four genes that had annotations were involved in GO processes including DNA binding and integration, transmembrane receptor protein tyrosine kinase activity, protein dimerization activity, and integral component of membrane. Many of these GO annotations and those of similar theme were present in the terms identified as differentially expressed in relation to both seep and site, including, nucleic acid binding, metal ion binding, ATP binding,
DNA binding and integration, integral component of membrane, and cytoplasm (Appendix
98 N and O). These terms indicate that these corals likely weren’t undergoing substantially different processes but maintaining homeostasis and normal biological processes.
The slight differences in gene expression profiles could also be explained, at least partially, by temporal cycles that the corals experience on a daily or monthly cycle.
Research on shallow-water corals has suggested the expression patterns of corals can change throughout the course of the day (Hemond and Vollmer 2015; Oldach et al. 2017;
Ruiz-jones and Palumbi 2017) and across tidal and lunar cycles (Brady et al. 2016; Oldach et al. 2017; Ruiz-jones and Palumbi 2017). Since the corals for this study were sampled across three different years and at different times of day, it is possible that natural rhythms and seasonality are an underlying factor influencing the slight differences in expression patterns. In addition, each sample was obtained from a distinct colony of C. delta and could represent different genotypes. There is evidence that genotype can have an influence in the variance of differentially expressed genes which could also be contributing to the slight difference in expression patterns observed here (Baums et al. 2013; Griffiths et al. 2019;
Glazier et al. 2020).
4.5.2 Experimental Design Considerations and Complications
Designing research projects around deep-sea species and habitats is always a challenge due to the considerable amount of effort to survey and collect samples from these depths. This study was designed and implemented after numerous hours of seafloor surveys with ROVs and manned submersibles (i.e. Deep Submergence Vehicle Alvin) equipped with high resolution video cameras which allowed for the visual identification of C. delta habitats in close proximity to areas of active hydrocarbon seepage (see 4.3.1 Sample
99 selection and collections). Even with a team of researchers with extensive knowledge of cold-seep habitats it became difficult to confidently designate areas as “seep” versus “non- seep”. The development of a way to measure seepage exposure or hydrocarbon concentrations in situ would undoubtably improve the experimental design of this project.
The stable isotope (d13C and d15N) data generated from C. delta tissue, which can indicate if an organism incorporates seep primary production into their tissues, was not always consistent between sites (unpublished data, Fisher and Cordes). Also, because it is not possible to determine stable isotope data in situ, the amount of seep primary production exposure that made it into the tissue of individual coral samples was not known until the samples were processed, often months after collection. Due to this constraint, as many samples of individual C. delta colonies were collected as possible to account for variability in stable isotope data and to ensure the sample size was achieved to perform a statistically sound transcriptomic analysis.
Another complication is the quality of RNA required for sequencing. It is notoriously difficult to obtain high quality molecular material from octocorals and this proved to be the case with the RNA extractions for this study. Even with these obstacles, three replicates of each sample type (“seep” and “non-seep”) were obtained at the two sites and the RNA material was successfully extracted and sequenced. Three replicates are required for the differential gene expression analysis models, but sample size has a substantial impact on the detectability of gene expression patterns (Love et al. 2014; Todd et al. 2016; Hembach et al. 2018). While we did not detect obvious alterations in gene expression patterns in relation to seep proximity or site, an increase in sample size may have detected differential expression patterns that were not well resolved in this study.
100 4.5.3 Tolerance for Hydrocarbon Exposure
There does not appear to be a meaningful difference in the gene expression profiles of C. delta colonies in “seep” versus “non-seep” areas. Additionally, C. delta colonies did not show phenotypic stress responses under experimental hydrocarbon exposures (DeLeo et al. 2016). The expression profiles present here and the phenotypic stress response data both indicate that C. delta has a natural tolerance for hydrocarbon exposure. This tolerance could be an adaptation acquired by C. delta from living in close proximity to low levels of hydrocarbon seepage for many generations. Also, there is recent evidence that some species of deep-sea octocorals could have symbiotic associations with chemoautotrophic microorganisms, such as bacteria of the SUP05 cluster (Vohsen et al. 2020).
The SUP05 cluster of gamma-proteobacteria include the sulfur-oxidizing symbionts found in both cold seep and hydrothermal vent fauna, such as Bathymodiolin mussels (Petersen et al. 2012). Vohsen et al (2020) found that fifteen SUP05 phylotypes made up a substantial amount of the coral microbiome (12-91%) in thirteen morphospecies of deep-sea corals sampled within the Gulf of Mexico, including C. delta. The authors determined that corals with a high relative abundance of SUP05 phylotypes occurred most frequently at sites with signs of active seepage. It is possible that the association with
SUP05 phylotypes allows the coral host to tolerate higher levels of hydrocarbon in the surrounding seawater through detoxification of the hydrogen sulfide by oxidizing it into elemental sulfur, before it has an impact on the coral. Alternatively, there is the potential that SUP05 phylotypes could be providing additional nutrition through chemoautotrophic primary production. However, the presence of the phylotypes does not demonstrate an unequivocal nutritional link because the stable isotope signature, indicative of
101 chemoautotrophic primary production may arise from the bacteria themselves.
Nevertheless, if there is an association with chemoautotrophic members of the coral microbiome, they do not appear to alter the global gene expression patterns of the C. delta host based on the analysis performed here.
102 CHAPTER 5
SUMMARY
The primary objective of this body of work was to assess the vulnerability of cold- water corals to independent and interacting human-induced stressors in their environment; including natural hydrocarbon seepage, hydrocarbon and dispersant concentrations released during an accidental oil spill, and the interacting effects of climate change-related factors and hydrocarbon/dispersant exposure. These objectives were accomplished by analyzing the phenotypic and molecular effects of oil and dispersant exposure on Lophelia pertusa under current and future ocean change scenarios and investigating the impact of natural hydrocarbon seepage exposure on gene expression profiles of Callogorgia delta colonies.
Lophelia pertusa is globally distributed and has been the focus of cold-water coral research worldwide; however, this work is the first to examine L. pertusa’s response to hydrocarbon and dispersant exposure and also address how these corals might respond to future oil spills under projected forthcoming ocean temperature and pH conditions. The experiments implemented in Chapter 2 were designed to investigate the effects of oil, dispersant, oil + dispersant, a decrease in pH, and an increase in temperature and the interactions between the different stressors. Coral nubbin health was assessed following a
24-hour exposure to oil and dispersant mixtures and tracked through a 96-hour recovery period. Chapter 2 focused on assessing overall coral health through gross morphology and the main findings were that the dispersant exposures had a more adverse effect on coral health than oil, however, the oil + dispersant mixtures did not elicit as strong of a phenotypic response. The combination of increased temperature and dispersant exposure
103 resulted in a delay in recovery from the dispersant exposure that was not observed during the control, oil, or oil + dispersant exposures. While assessing the health through gross morphology is a reliable metric for observing coral stress responses, there are additional ways to assess the overall health of the coral and its response to stress, including changes in gene expression patterns which is addressed in Chapter 3.
To get a deeper understanding of the cellular-level processes that were impacted during the 24-hour exposure to oil and dispersant mixtures under the different pH and temperature conditions, global gene expression analysis were performed in Chapter 3.
Dispersant exposure elicited the strongest response across treatments, but the overall gene expression patterns varied by coral colony. A Weighted-Gene Correlation Network
Analysis (WGCNA) classified networks of co-expressed genes in response to the different experimental variables (control, oil, dispersant, oil + dispersant, pH: 7.9, pH: 7.6, temperature: 8°C, and temperature: 12°C). Interestingly, even though there was not a significant phenotypic response to a decrease in pH, seven modules (7,603 total genes) were significantly up or down regulated in response to pH. Oil and dispersant mixtures resulted in four significantly up or down regulated modules (5,296 total genes) and just one module (1,008 genes) was significantly up regulated in response to temperature.
Subsequent gene ontology (GO) enrichment analysis of the modules revealed that processes involved in varying stages of the cellular stress response (CSR) were likely enacted in response to exposure to oil, dispersant, and a decrease in pH. The GO terms associated with exposure to dispersant indicated the most severe response, including wound healing, the immune system, stress related responses, and apoptosis. However, the
GO terms enriched during the oil exposure indicate that the coral nubbins could have been
104 trying to maintain cellular homeostasis through the upregulation of metabolic pathways coupled with a downregulation of cell growth and development. The decrease in seawater pH elicited a similar response to oil through the enrichment of terms associated with a reduction in the cell cycle and development but an upregulation of endoplasmic reticulum stress. Our results indicate that distinct stages of the CSR are prompted depending on the intensity of stress and bolster the idea that there is an underlying stress response shared by all corals in response to a variety of stressors.
Callogorgia delta has an affinity for growing near active natural hydrocarbon seeps in the Gulf of Mexico, unlike most cold-water corals that only colonize these sites after seepage has subsided. The effect of natural hydrocarbon seepage on CWCs is poorly understood and this provides an opportunity to investigate the influence of natural hydrocarbon seepage on C. delta global gene expression patterns in Chapter 4. The comparisons of in situ gene expression profiles of colonies found in areas of active hydrocarbon seepage (“seep”) and areas with no current active seepage (“non-seep”) at two different sites in the Gulf of Mexico found that there were fewer differentially expressed genes in the “seep” versus “non-seep” comparison (n=21) than the site comparison
(n=118). Interestingly, both comparisons yielded gene ontology that indicated minor variation in natural biological housekeeping processes, as opposed to stress responses. This dataset is complimentary to previous studies on the impacts of experimentally induced hydrocarbon exposure on C. delta and other CWCs.
By coupling phenotypic health assessments with transcriptomic analysis of the multiple stressor experiments, I was able to achieve a more wholistic view of the variation in coral stress responses to the different exposures. An interesting component to this was
105 that there were little to no phenotypic signs of stress linked to the decrease in seawater pH, however, the pH treatment was associated with the most modules (n = 7) with significant differentially expressed genes. The functional enrichment analysis of these modules revealed that the coral nubbins were likely experiencing a low-level cellular stress response, which had not manifested into visible phenotypic stress during the duration of the experiment. However, it is possible that if these low-level cellular stress responses are persistent in a long-term scenario, other cellular or physiological processes could be jeopardized. So, while it was the increase in temperature and dispersant combination that compromised the coral nubbin phenotypic-level recovery after exposure, the pH stress on the cellular level could also lead to unfavorable interactions with pollution exposure over time. In addition, two of the three chapters provide evidence that cold water corals can be more negatively impacted, both on the phenotypic and molecular levels, by exposure to chemical dispersants than to hydrocarbons alone. This is in line with many of the other toxicological studies conducted following the DWH oil spill that found chemical dispersants to be harmful to organismal health. All of this work can be used to advise best practices for oil spill cleanup and mitigation practices. It is becoming abundantly clear that the chemical dispersants used following the DWH are likely more harmful and persist longer in the environment than the hydrocarbons themselves.
106 NOTES
Chapter 2 was originally published in Scientific Reports in February 2020. Erik
Cordes, Carlos Gómez, and Adam Hallaj were co-authors on this study. Alexis Weinnig and Erik Cordes concieved the research and designed the experiments. Alexis Weinnig preformed the bulk of the experiments with help from Carlos Gómez and Adam Hallaj.
Alexis Weinnig analyzed the data with help from Carlos Gómez. Alexis Weinnig wrote the manuscript with help from Erik Cordes and all authors contirbuted edits towards the drafts prior to publicaiton.
Chapter 3 was completed with contributions from Santiago Herrera (Lehigh
Univeristy) and Erik Cordes. Alexis Weinnig performed the majority of the RNA extractions with help from Jessica Hart and Adam Hallaj. Alexis Weinnig designed and implimented the bioinformatics and figure generation with contributions from Sanitago
Herrera. Alexis Weinnig wrote the manuscript with help from Erik Cordes.
Chapter 4 is a component of a soon-to-be submitted manuscript as part of the broader ECOGIG II project. Sample collection was determined and perfomormed by Erik
Cordes, Illiana Baums (Pennsyvania State University), Charles Fisher (Pennsyvania State
University), Samuel Vohsen (Pennsyvania State University), Fanny Girard (Pennsyvania
State University), and Alexis Weinnig. RNA extractions and subsequent bioinformatics were performed by Alexis Weinnig. Amanda Glazier assisted in the C. delta de novo transcriptome assembly. Erik Cordes contributed thoughful insight into interpretation of the analysis. Alexis Weinnig wrote this chapter with help from Erik Cordes.
107 BIBLIOGRAPHY
Aguilar, C., J. B. Raina, S. Fôret, D. C. Hayward, B. Lapeyre, D. G. Bourne, and D. J.
Miller. 2019. Transcriptomic analysis reveals protein homeostasis breakdown in the
coral Acropora millepora during hypo-saline stress. BMC Genomics 20: 1–13.
doi:10.1186/s12864-019-5527-2
Ahmadun, F.-R., A. Pendashteh, L. C. Abdullah, D. R. A. Biak, S. S. Madaeni, and Z. Z.
Abidin. 2009. Review of technologies for oil and gas produced water treatment. J.
Hazard. Mater. 170: 530–551. doi:10.1016/j.jhazmat.2009.05.044
Allers, E., R. M. M. Abed, L. M. Wehrmann, T. Wang, A. I. Larsson, A. Purser, and D.
de Beer. 2013. Resistance of Lophelia pertusa to coverage by sediment and
petroleum drill cuttings. Mar. Pollut. Bull. 74: 132–140.
doi:10.1016/j.marpolbul.2013.07.016
Andrews, S. 2010. FastQC: A Quality Control Tool for High Throughput Sequence Data.
Ashburner, M., C. A. Ball, J. A. Blake, and others. 2000. Gene Ontology: tool for the
unification of biology. Nat. Genet. 25: 25–29. doi:10.1038/75556
Aubin-Horth, N., and S. C. P. Renn. 2009. Genomic reaction norms: Using integrative
biology to understand molecular mechanisms of phenotypic plasticity. Mol. Ecol.
18: 3763–3780. doi:10.1111/j.1365-294X.2009.04313.x
Barshis, D. J., J. T. Ladner, T. a Oliver, F. O. Seneca, N. Traylor-Knowles, and S. R.
Palumbi. 2013. Genomic basis for coral resilience to climate change. Proc. Natl.
Acad. Sci. U. S. A. 110: 1387–92. doi:10.1073/pnas.1210224110
Baums, I. B., M. K. Devlin-Durante, N. R. Polato, and others. 2013. Genotypic variation
influences reproductive success and thermal stress tolerance in the reef building
108 coral, Acropora palmata. Coral Reefs 32: 703–717. doi:10.1007/s00338-013-1012-6
Bayer, F. M. 1961. The Shallow-Water Octocorallia of the West Indian Region: A
Manual for Marine Biologists,.
Bayer, F. M., S. D. Cairns, R. T. S. Cordeiro, and C. D. Pérez. 2015. New records of the
genus Callogorgia (Anthozoa: Octocorallia) in the western Atlantic, including the
description of a new species. J. Mar. Biol. Assoc. United Kingdom 95: 905–911.
doi:10.1017/S0025315414001957
Becker, E. L., E. E. Cordes, S. A. Macko, and C. R. Fisher. 2009. Importance of seep
primary production to Lophelia pertusa and associated fauna in the Gulf of Mexico.
Deep. Res. Part I Oceanogr. Res. Pap. 56: 786–800. doi:10.1016/j.dsr.2008.12.006
Bolger, A. M., M. Lohse, and B. Usadel. 2014. Genome analysis Trimmomatic : a
flexible trimmer for Illumina sequence data. 30: 2114–2120.
doi:10.1093/bioinformatics/btu170
Boyd, P. W., S. Collins, S. Dupont, and others. 2018. Experimental strategies to assess
the biological ramifications of multiple drivers of global ocean change-A review.
Glob. Chang. Biol. 24: 2239–2261. doi:10.1111/gcb.14102
Boyd, P. W., S. T. Lennartz, D. M. Glover, and S. C. Doney. 2014. Biological
ramifications of climate-change-mediated oceanic multi-stressors. Nat. Clim. Chang.
5: 71–79. doi:10.1038/nclimate2441
Brady, A. K., B. L. Willis, L. D. Harder, and P. D. Vize. 2016. Lunar phase modulates
circadian gene expression cycles in the broadcast spawning coral Acropora
millepora. Biol. Bull. 230: 130–142. doi:10.1086/BBLv230n2p130
Brewer, P. G. 1997. Ocean chemistry of the fossil fuel CO2 signal: The haline signal of
109 “business as usual.” Geophys. Res. Lett. 24: 1367–1369. doi:10.1029/97GL01179
Brown, B. E., and J. C. Bythell. 2005. Perspectives on mucus secretion in reef corals.
296: 291–309.
Brusca, R.C., Brusca, G.J., H. 1990. Invertebrates, Sinauer Associates.
Buckner, A. W. 2002. Life-Saving Prod from Coral Reefs. Issues Sci. Technol. 18: 39–
44.
Buhl-Mortensen, L., and P. B. Mortensen. 2004. The distribution and diversity of species
associated with deep sea gorgonian corals off the Atlantic Canada, p. 849–879. In
Cold-Water Corals and Ecosystems. Springer-Verlag Berlin Heildelberg.
Buhl-Mortensen, L., A. Vanreusel, A. J. Gooday, and others. 2010. Biological structures
as a source of habitat heterogeneity and biodiversity on the deep ocean margins.
Mar. Ecol. 31: 21–50. doi:10.1111/j.1439-0485.2010.00359.x
Buhl-Mortensen, P., L. Buhl-Mortensen, and A. Purser. 2016. Trophic Ecology and
Habitat Provision in Cold-Water Coral Ecosystems Trophic Ecology and Habitat
Provision in Cold-Water Coral Ecosystems, In Marine Animal Forests. Springer
International Publishing.
Burge, C. A., M. E. Mouchka, C. D. Harvell, and S. Roberts. 2013. Immune response of
the caribbean sea fan, gorgonia ventalina, exposed to an aplanochytrium parasite as
revealed by transcriptome sequencing. Front. Physiol. 4 JUL: 1–9.
doi:10.3389/fphys.2013.00180
Büscher, J. V., A. U. Form, and U. Riebesell. 2017. Interactive Effects of Ocean
Acidification and Warming on Growth, Fitness and Survival of the Cold-Water
Coral Lophelia pertusa under Different Food Availabilities. Front. Mar. Sci. 4: 1–14.
110 doi:10.3389/fmars.2017.00101
Cairns, S. D. 2007. Deep-water corals: An overview with special reference to diversity
and distribution of deep-water scleractinian corals. Bull. Mar. Sci. 81: 311–322.
Cairns, S. D., and F. M. Bayer. 2002. Studies on western Atlantic Octocorallia
(Coelenterata: Anthozoa). Part 2: The genus Callogorgia Gray, 1858. Proc. Biol.
Soc. Washingt. 115: 840–867.
Cairns, S. D., and F. M. Bayer. 2009. A Generic Revision and Middle Missouri Plains
Phylogenetic Analysis Village Sites of the Primnoidae. Smithson. Contrib. to Zool.
629: 1–86. doi:10.5479/si.00810282.629
Caldeira, K., and M. E. Wickett. 2003. Anthropogenic carbon and ocean pH. Nature 425:
365–365. doi:10.1038/425365a
Camilli, R., D. Di Iorio, A. Bowen, and others. 2012. Acoustic measurement of the
Deepwater Horizon Macondo well flow rate. Proc. Natl. Acad. Sci. U. S. A. 109:
20235–20239. doi:10.1073/pnas.1100385108
Camilli, R., C. M. Reddy, D. R. Yoerger, and others. 2010. Tracking hydrocarbon plume
transport and biodegradation at deepwater horizon. Science (80-. ). 330: 201–204.
doi:10.1126/science.1195223
Carreiro-Silva, M., T. Cerqueira, a. Godinho, M. Caetano, R. S. Santos, and R.
Bettencourt. 2014. Molecular mechanisms underlying the physiological responses of
the cold-water coral Desmophyllum dianthus to ocean acidification. Coral Reefs 33:
465–476. doi:10.1007/s00338-014-1129-2
Cho, W., and T. M. Shank. 2010. Incongruent patterns of genetic connectivity among
four ophiuroid species with differing coral host specificity on North Atlantic
111 seamounts. Mar. Ecol. 31: 121–143. doi:10.1111/j.1439-0485.2010.00395.x
Coelho, F. J. R. C., D. F. R. Cleary, R. Costa, and others. 2016. Multitaxon activity
profiling reveals differential microbial response to reduced seawater pH and oil
pollution. Mol. Ecol. 25: 4645–4659. doi:10.1111/mec.13779
Coelho, F. J. R. C. R. C., D. F. R. R. Cleary, R. J. M. M. Rocha, and others. 2015.
Unraveling the interactive effects of climate change and oil contamination on
laboratory-simulated estuarine benthic communities. Glob. Chang. Biol. 21: 1871–
1886. doi:10.1111/gcb.12801
Cordes, E. E., D. C. Bergquist, and C. R. Fisher. 2009. Macro-ecology of gulf of Mexico
cold seeps. Ann. Rev. Mar. Sci. 1: 143–168.
doi:10.1146/annurev.marine.010908.163912
Cordes, E. E., M. R. Cunha, J. Galéron, and others. 2010. The influence of geological,
geochemical, and biogenic habitat heterogeneity on seep biodiversity. Mar. Ecol. 31:
51–65. doi:10.1111/j.1439-0485.2009.00334.x
Cordes, E. E., D. O. B. Jones, T. A. Schlacher, and others. 2016. Environmental impacts
of the deep-water oil and gas industry: A review to guide management strategies.
Front. Environ. Sci. 4. doi:10.3389/fenvs.2016.00058
Cordes, E. E., M. P. McGinley, E. L. Podowski, E. L. Becker, S. Lessard-Pilon, S. T.
Viada, and C. R. Fisher. 2008. Coral communities of the deep Gulf of Mexico.
Deep. Res. Part I Oceanogr. Res. Pap. 55: 777–787. doi:10.1016/j.dsr.2008.03.005
Cornwall, W. 2015. Deepwater Horizon: After the oil. Science (80-. ). 348: 22–29.
Couillard, C.M.; Lee, K.; Légaré, B.; King, T. L. 2005. Effect of dispersant on the
composition of the water-accommodated fraction of crude oil and its toxicity to
112 larval marine fish. Environ. Toxicol. Chem. 24: 1496–1504. doi:10.1897/04-267R.1
Crain, C. M., K. Kroeker, and B. S. Halpern. 2008. Interactive and cumulative effects of
multiple human stressors in marine systems. Ecol. Lett. 11: 1304–1315.
doi:10.1111/j.1461-0248.2008.01253.x
Crone, T. J., and M. Tolstoy. 2010. Magnitude of the 2010 Gulf of Mexico oil leak.
Science 330: 634. doi:10.1126/science.1195840
Cziesielski, M. J., S. Schmidt-Roach, and M. Aranda. 2019. The past, present, and future
of coral heat stress studies. Ecol. Evol. 9: 10055–10066. doi:10.1002/ece3.5576
Davies, A. J., and J. M. Guinotte. 2011. Global Habitat Suitability for Framework-
Forming Cold- Water Corals. PLoS One 6. doi:10.1371/journal.pone.0018483
Davies, A. J., M. Wisshak, J. C. Orr, and J. Murray Roberts. 2008. Predicting suitable
habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep. Res. Part I
Oceanogr. Res. Pap. 55: 1048–1062. doi:10.1016/j.dsr.2008.04.010
DeLeo, D. M., S. Herrera, S. D. Lengyel, A. M. M. Quattrini, R. J. Kulathinal, and E. E.
Cordes. 2018. Gene expression profiling reveals deep-sea coral response to the
Deepwater Horizon oil spill. Mol. Ecol. 27: 4066–4077. doi:10.1111/mec.14847
DeLeo, D. M., D. V. Ruiz-Ramos, I. B. Baums, and E. E. Cordes. 2016. Response of
deep-water corals to oil and chemical dispersant exposure. Deep. Res. Part II Top.
Stud. Oceanogr. 129: 1–11. doi:10.1016/j.dsr2.2015.02.028
DeSalvo, M. K., A. Estrada, S. Sunagawa, and M. Medina. 2012. Transcriptomic
responses to darkness stress point to common coral bleaching mechanisms. Coral
Reefs 31: 215–228. doi:10.1007/s00338-011-0833-4
Diaz, J. M., C. M. Hansel, A. Apprill, C. Brighi, T. Zhang, L. Weber, S. McNally, and L.
113 Xun. 2016. Species-specific control of external superoxide levels by the coral
holobiont during a natural bleaching event. Nat. Commun. 7: 1–10.
doi:10.1038/ncomms13801
Dickson, A. G.; Millero, F. J. 1987. A comparison of the equilibrium constants for the
dissociation of carbonic acid in seawater media. Deep Sea Res. Part A, Oceanogr.
Res. Pap. 34: 1733–1743. doi:10.1016/0198-0149(87)90021-5
Dickson, A. 1990. Standard potential of the reaction: AgCl(s) + 1/2 H2(g) = Ag(s) +
HCl(aq), and the standard acidity constant of the ion HSO4− in synthetic sea water
from 273.15 to 318.15 K. J. Chem. Thermodyn. 22: 113–127. doi:10.1016/0021-
9614(90)90074-z
Dickson, A. G., C. L. Sabine, and J. R. Christian. 2007. Guide to best Practices for Ocean
CO2 Measurement,.
Dixon, G. B., E. E. Abbott, and M. V. Matz. 2020. Meta‐analysis of the Coral
Environmental Stress Response: Acropora corals show opposing responses
depending on stress intensity. Mol. Ecol. 0–1. doi:10.1111/mec.15535
Doney, S. C., V. J. Fabry, R. A. Feely, and J. A. Kleypas. 2009. Ocean Acidification: The
Other CO 2Problem. Ann. Rev. Mar. Sci. 1: 169–192.
doi:10.1146/annurev.marine.010908.163834
Edge, K. J., E. L. Johnston, K. A. Dafforn, S. L. Simpson, T. Kutti, and R. J. Bannister.
2016. Sub-lethal effects of water-based drilling muds on the deep-water sponge
Geodia barretti *. Environ. Pollut. 212: 525–534. doi:10.1016/j.envpol.2016.02.047
Etnoyer, P. J., L. N. Wickes, M. Silva, J. D. Dubick, L. Balthis, E. Salgado, and I. R.
MacDonald. 2016. Decline in condition of gorgonian octocorals on mesophotic reefs
114 in the northern Gulf of Mexico: before and after the Deepwater Horizon oil spill.
Coral Reefs 35: 77–90. doi:10.1007/s00338-015-1363-2
Etnoyer, P., and J. Warrenchuk. 2007. A catshark nursery in a deep gorgonian field in the
Mississippi Canyon, Gulf of Mexico. Bull. Mar. Sci. 81: 553–559.
Fabricius, K E Alderslade, P. 2001. Soft corals and sea fans: a comprehensive guide to
the tropical shallow water genera of the central-west Pacific, the Indian Ocean and
the Red Sea., Australian Institute of Marine Science (AIMS).
Feely, R. A., C. L. Sabine, K. Lee, W. Berelson, J. Kleypas, V. J. Fabry, and F. J.
Millero. 2004. Impact of Anthropogenic CO 2 on the CaCO 3 System in the Oceans
Richard A. Feely ,. Science (80-. ). 305: 362–367. doi:10.1126/science.1097329
Fisher, C. R., A. W. J. J. Demopoulos, E. E. Cordes, I. B. Baums, H. K. White, and J. R.
Bourque. 2014a. Coral communities as indicators of ecosystem-level impacts of the
deepwater horizon spill. Bioscience 64: 796–807. doi:10.1093/biosci/biu129
Fisher, C. R., P.-Y. P.-Y. Hsing, C. L. Kaiser, and others. 2014b. Footprint of Deepwater
Horizon blowout impact to deep-water coral communities. Proc. Natl. Acad. Sci.
111: 11744–11749. doi:10.1073/pnas.1403492111
Fisher, C., H. Roberts, E. Cordes, and B. Bernard. 2007. Cold Seeps and associated
communities of the gulf of Mexico. Oceanography 20: 118–129.
doi:10.5670/oceanog.2007.12
Form, A. U., and U. Riebesell. 2012. Acclimation to ocean acidification during long-term
CO 2 exposure in the cold-water coral Lophelia pertusa. Glob. Chang. Biol. 18:
843–853. doi:10.1111/j.1365-2486.2011.02583.x
Formolo, M. J., T. W. Lyons, C. Zhang, C. Kelley, R. Sassen, J. Horita, and D. R. Cole.
115 2004. Quantifying carbon sources in the formation of authigenic carbonates at gas
hydrate sites in the Gulf of Mexico. 205: 253–264.
doi:10.1016/j.chemgeo.2003.12.021
Fosså, J. H., P. B. Mortensen, and D. M. Furevik. 2002. The deep-water coral Lophelia
pertusa in Norwegian waters : Distribution and fishery impacts. Hydrobiologia.
doi:10.1023/A
Foster, Z. S. L., T. J. Sharpton, and N. J. Grünwald. 2017. Metacoder: An R package for
visualization and manipulation of community taxonomic diversity data. PLoS
Comput. Biol. 13: 1–15. doi:10.1371/journal.pcbi.1005404
Freiwald, A., A. Grehan, J. H. Fosså, T. Koslow, and J. M. Roberts. 2004. Cold-Water
Corals: Out of Sight – No Longer Out of Mind,.
Friedman, L. E., T. D. Gilmore, and J. R. Finnerty. 2018. Intraspecific variation in
oxidative stress tolerance in a model cnidarian: Differences in peroxide sensitivity
between and within populations of Nematostella vectensis. PLoS One 13: 1–19.
doi:10.1371/journal.pone.0188265
Frometa, J., M. E. DeLorenzo, E. C. Pisarski, and P. J. Etnoyer. 2017. Toxicity of oil and
dispersant on the deep water gorgonian octocoral Swiftia exserta, with implications
for the effects of the Deepwater Horizon oil spill. Mar. Pollut. Bull. 122: 91–99.
doi:10.1016/j.marpolbul.2017.06.009
Fuess, L. E., W. T. Mann, L. R. Jinks, V. Brinkhuis, and L. D. Mydlarz. 2018.
Transcriptional analyses provide new insight into the late-stage immune response of
a diseased caribbean coral. R. Soc. Open Sci. 5. doi:10.1098/rsos.172062
Gass, S. E., and J. M. Roberts. 2006. The occurrence of the cold-water coral Lophelia
116 pertusa ( Scleractinia ) on oil and gas platforms in the North Sea : Colony growth ,
recruitment and environmental controls on distribution. Mar. Pollut. Bull. 52: 549–
559. doi:10.1016/j.marpolbul.2005.10.002
Georgian, S. E., D. DeLeo, A. Durkin, C. E. Gomez, M. Kurman, J. J. Lunden, and E. E.
Cordes. 2015. Oceanographic patterns and carbonate chemistry in the vicinity of
cold-water coral reefs in the Gulf of Mexico: Implications for resilience in a
changing ocean. Limnol. Oceanogr. 648–665. doi:10.1002/lno.10242
Georgian, S. E., S. Dupont, M. Kurman, and others. 2016. Biogeographic variability in
the physiological response of the cold-water coral Lophelia pertusa to ocean
acidification. Mar. Ecol. 37: 1345–1359. doi:10.1111/maec.12373
Georgian, S. E., K. Kramer, M. Saunders, W. Shedd, H. Roberts, C. Lewis, C. Fisher, and
E. Cordes. 2020. Habitat suitability modelling to predict the spatial distribution of
cold-water coral communities affected by the Deepwater Horizon oil spill. J.
Biogeogr. 1–12. doi:10.1111/jbi.13844
Gestal, C., M. Costa, A. Figueras, and B. Novoa. 2007. Analysis of differentially
expressed genes in response to bacterial stimulation in hemocytes of the carpet-shell
clam Ruditapes decussatus: Identification of new antimicrobial peptides. Gene 406:
134–143. doi:10.1016/j.gene.2007.07.030
Glazier, A., S. Herrera, A. Weinnig, M. Kurman, C. E. Gómez, and E. Cordes. 2020.
Regulation of ion transport and energy metabolism enables certain coral genotypes
to maintain calcification under experimental ocean acidification. Mol. Ecol. 1–17.
doi:10.1111/mec.15439
Goldstone, J. V. 2008. Environmental sensing and response genes in cnidaria: The
117 chemical defensome in the sea anemone Nematostella vectensis. Cell Biol. Toxicol.
24: 483–502. doi:10.1007/s10565-008-9107-5
Gómez, C. E., L. Wickes, D. Deegan, P. J. Etnoyer, and E. E. Cordes. 2018. Growth and
feeding of deep-sea coral Lophelia pertusa from the California margin under
simulated ocean acidification conditions. PeerJ. doi:10.7717/peerj.5671
Gori, A., C. Ferrier-Pagès, S. J. Hennige, F. Murray, C. Rottier, L. C. Wicks, and J. M.
Roberts. 2016. Physiological response of the cold-water coral Desmophyllum
dianthus to thermal stress and ocean acidification . PeerJ 4: e1606.
doi:10.7717/peerj.1606
Grabb, K. C., J. Kapit, S. D. Wankel, K. Manganini, A. Apprill, M. Armenteros, and C.
M. Hansel. 2019. Development of a Handheld Submersible Chemiluminescent
Sensor: Quantification of Superoxide at Coral Surfaces. Environ. Sci. Technol. 53:
13850–13858. doi:10.1021/acs.est.9b04022
Grabherr, M., B. Haas, M. Yassour, and others. 2011. Full-length transcriptome assembly
from RNA-Seq data without a reference genome. Nat. Biotechnol. 29: 644.
doi:http://dx.doi.org.libproxy.temple.edu/10.1038/nbt.1883
Gray, J. S., K. R. Clarke, R. M. Warwick, and G. Hobbs. 1990. Detection of initial effects
of pollution on marine benthos : an example from the Ekofisk and Eldfisk oilfields ,
North Sea. Mar. Ecol. Prog. Ser. 66: 285–299.
Griffiths, J. S., T. C. F. Pan, and M. W. Kelly. 2019. Differential responses to ocean
acidification between populations of Balanophyllia elegans corals from high and low
upwelling environments. Mol. Ecol. 28: 2715–2730. doi:10.1111/mec.15050
Guinotte, J. M., J. C. Orr, S. D. Cairns, A. Freiwald, L. Morgan, and R. George. 2006.
118 Will human-induced changes in seawater chemistry alter the distribution of deep-sea
scleractinian corals? Front. Ecol. Environ. 4: 141–146.
Hall-Spencer, J., V. Allain, and J. H. Fosså. 2002. Trawling damage to Northeast Atlantic
ancient coral reefs Trawling damage to Northeast Atlantic ancient coral reefs. Proc.
R. Soc. B Biol. Sci. 269: 507–511. doi:10.1098/rspb.2001.1910
Harder, A. M., J. R. Willoughby, W. R. Ardren, and M. R. Christie. 2019. Among-family
variation in survival and gene expression uncovers adaptive genetic variation in a
threatened fish. Mol. Ecol. 1035–1049. doi:10.1111/mec.15334
Hawkins, A. D., A. E. Pembroke, and A. N. Popper. 2014. Information gaps in
understanding the effects of noise on fishes and invertebrates. Rev Fish Biol Fish.
25: 39–64. doi:10.1007/s11160-014-9369-3
Hembach, K., C. Soneson, S. Tiberi, M. I. Love, R. Patro, and M. D. Robinson. 2018.
RNA sequencing data : hitchhiker ’ s guide to expression analysis.
Hemond, E. M., and S. V. Vollmer. 2015. Diurnal and nocturnal transcriptomic variation
in the Caribbean staghorn coral, Acropora cervicornis. Mol. Ecol. 24: 4460–4473.
doi:10.1111/mec.13320
Hennige, S. J., L. C. Wicks, N. A. Kamenos, D. C. E. E. Bakker, H. S. Findlay, C.
Dumousseaud, and J. M. Roberts. 2014. Short-term metabolic and growth responses
of the cold-water coral Lophelia pertusa to ocean acidification. Deep. Res. Part II
Top. Stud. Oceanogr. 99: 27–35. doi:10.1016/j.dsr2.2013.07.005
Hennige, S. J., L. C. Wicks, N. A. Kamenos, G. Perna, H. S. Findlay, and J. M. Roberts.
2015. Hidden impacts of ocean acidification to live and dead coral framework.
Hennige, S., U. Wolfram, L. Wickes, and others. 2020. Crumbling reefs and cold-water
119 coral habitat loss in a future ocean: evidence of ‘coralporosis’ as an indicator of
habitat integrity. Front. Mar. Sci. 7: 1–16. doi:10.3389/fmars.2020.00668
Henry, L.-A., and J. M. Roberts. 2017. Global Biodiveristy in Cold-Water Coral Reef
Ecosystems, p. 235–256. In Marine Animal Forests: The Ecology of Benthic
Biodiversity Hotspots.
Hewitt, J. E., J. I. Ellis, and S. F. Thrush. 2016. Multiple stressors, nonlinear effects and
the implications of climate change impacts on marine coastal ecosystems. Glob.
Chang. Biol. 22: 2665–2675. doi:10.1111/gcb.13176
Hoegh-Guldberg, O., and J. F. Bruno. 2010. The Impact of Climate Change on the
World’s Marine Ecosystems. Science (80-. ). 328: 1523–1529.
Hoegh-Guldberg, O., P. J. Mumby, A. J. Hooten, and others. 2007. Coral reefs under
rapid climate change and ocean acidification. Science 318: 1737–1742.
doi:10.1126/science.1152509
Hoegh-Guldberg, O., E. S. Poloczanska, W. Skirving, and S. Dove. 2017. Coral Reef
Ecosystems under Climate Change and Ocean Acidification. Front. Mar. Sci. 4.
doi:10.3389/fmars.2017.00158
Hsing, P.-Y., B. Fu, E. a. Larcom, and others. 2013. Evidence of lasting impact of the
Deepwater Horizon oil spill on a deep Gulf of Mexico coral community. Elem. Sci.
Anthr. 1: 000012. doi:10.12952/journal.elementa.000012
Hughes, T. P., A. H. Baird, D. R. Bellwood, and others. 2003. Climate change, human
impacts, and the resilience of coral reefs. Science (80-. ). 301: 929–933.
doi:10.1126/science.1085046
Hughes, T. P., M. L. Barnes, D. R. Bellwood, and others. 2017. Coral reefs in the
120 Anthropocene. Nature 546: 82–90. doi:https://doi.org/10.1038/nature22901
IPCC. 2014. Climate Change 2014 Synthesis Report Summary Chapter for Policymakers.
Jatkar, A. A., B. E. Brown, J. C. Bythell, R. Guppy, and N. J. Morris. 2010. Coral
Mucus : The Properties of Its Constituent Mucins. 883–888.
Jensen, A., and R. Frederiksen. 1992. The fauna associated with the bank-forming
deepwater coral Lophelia pertusa (Scleractinaria) on the Faroe shelf. Sarsia 77: 53–
69. doi:10.1080/00364827.1992.10413492
Jernelv, A. 2010. The threats from oil spills: Now, then, and in the future. Ambio 39:
353–366. doi:10.1007/s13280-010-0085-5
Jiang, F., Y. Zhang, and G. J. Dusting. 2011. NADPH oxidase-mediated redox signaling:
Roles in cellular stress response, stress tolerance, and tissue repair. Pharmacol. Rev.
63: 218–242. doi:10.1124/pr.110.002980
Joye, S. B., A. Boetius, B. N. Orcutt, J. P. Montoya, H. N. Schulz, M. J. Erickson, and S.
K. Lugo. 2004. The anaerobic oxidation of methane and sulfate reduction in
sediments from Gulf of Mexico cold seeps. Chem. Geol. 205: 219–238.
doi:10.1016/j.chemgeo.2003.12.019
Joye, S. B., A. Bracco, T. M. Özgökmen, and others. 2016. The Gulf of Mexico
ecosystem, six years after the Macondo oil well blowout. Deep. Res. Part II 1–16.
doi:10.1016/j.dsr2.2016.04.018
Kark, S., E. Brokovich, T. Mazor, and N. Levin. 2015. Emerging conservation challenges
and prospects in an era of offshore hydrocarbon exploration and exploitation.
Conserv. Biol. 29: 1573–1585. doi:10.1111/cobi.12562
Keeling, R. E., A. Körtzinger, and N. Gruber. 2010. Ocean deoxygenation in a warming
121 world. Ann. Rev. Mar. Sci. 2: 199–229. doi:10.1146/annurev.marine.010908.163855
Kelly, M. W., and G. E. Hofmann. 2013. Adaptation and the physiology of ocean
acidification. Funct. Ecol. 27: 980–990. doi:10.1111/j.1365-2435.2012.02061.x
Kenkel, C. D., E. Meyer, and M. V. Matz. 2013. Gene expression under chronic heat
stress in populations of the mustard hill coral (Porites astreoides) from different
thermal environments. Mol. Ecol. 22: 4322–4334. doi:10.1111/mec.12390
Kenkel, C. D., A. Moya, J. Strahl, C. Humphrey, and L. K. Bay. 2018. Functional
genomic analysis of corals from natural CO2-seeps reveals core molecular responses
involved in acclimatization to ocean acidification. Glob. Chang. Biol. 24: 158–171.
doi:10.1111/gcb.13833
Kleindienst, S., J. H. Paul, and S. B. Joye. 2015a. Using dispersants after oil spills:
Impacts on the composition and activity of microbial communities. Nat. Rev.
Microbiol. 13: 388–396. doi:10.1038/nrmicro3452
Kleindienst, S., M. Seidel, K. Ziervogel, and others. 2015b. Chemical dispersants can
suppress the activity of natural oil-degrading microorganisms. Proc. Natl. Acad. Sci.
112: 14900–14905. doi:10.1073/pnas.1507380112
Knowlton, N., R. E. Brainard, R. Fisher, M. Moews, L. Plaisance, and M. J. Caley. 2010.
Coral Reef Biodiversity, p. 65–78. In Life in the World’s Oceans. John Wiley &
Sons, Ltd.
Kültz, D. 2005. Molecular and evolutionary basis of the cellular stress response. Annu.
Rev. Physiol. 67: 225–257. doi:10.1146/annurev.physiol.67.040403.103635
Kültz, D. 2020. Evolution of cellular stress response mechanisms. J. Exp. Zool. Part A
Ecol. Integr. Physiol. 333: 359–378. doi:10.1002/jez.2347
122 Kurman, M. D., C. E. Gómez, S. E. Georgian, J. J. Lunden, and E. E. Cordes. 2017. Intra-
Specific Variation Reveals Potential for Adaptation to Ocean Acidification in a
Cold-Water Coral from the Gulf of Mexico. Front. Mar. Sci. 4: 1–14.
doi:10.3389/fmars.2017.00111
Langfelder, P., and S. Horvath. 2008. WGCNA: An R package for weighted correlation
network analysis. BMC Bioinformatics 9. doi:10.1186/1471-2105-9-559
Larcom, E. A., D. L. Mckean, J. M. Brooks, and C. R. Fisher. 2014. Growth rates ,
densities , and distribution of Lophelia pertusa on artificial structures in the Gulf of
Mexico. Deep. Res. Part I 85: 101–109. doi:10.1016/j.dsr.2013.12.005
Larsson, A. I., D. van Oevelen, A. Purser, and L. Thomsen. 2013. Tolerance to long-term
exposure of suspended benthic sediments and drill cuttings in the cold-water coral
Lophelia pertusa. Mar. Pollut. Bull. doi:10.1016/j.marpolbul.2013.02.033
Lee, K., F. J. Millero, H. Byrne, A. Feely, and R. Wanninkhof. 2000. Acid in 5 and. 27:
229–232.
Lepland, A., and P. Buhl-Mortensen. 2008. Barite and barium in sediments and coral
skeletons around the hydrocarbon exploration drilling site in the Træna Deep ,
Norwegian Sea. Env. Geol 56: 119–129. doi:10.1007/s00254-007-1145-4
Levin, L. A., N. L. Bris, and N. Le Bris. 2015. The deep ocean under climate change.
Science (80-. ). 350: 766–768. doi:10.1126/science.aad0126
Longcore, T., and C. Rich. 2004. Ecological light pollution. Front Ecol Eviron 2: 191–
198.
Love, M. I., W. Huber, and S. Anders. 2014. Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. 15: 550.
123 doi:10.1186/s13059-014-0550-8
Lunden, J. J., S. E. Georgian, and E. E. Cordes. 2013. Aragonite saturation states at cold-
water coral reefs structured by Lophelia pertusa in the northern Gulf of Mexico.
Limnol. Oceanogr. 58: 354–362. doi:10.4319/lo.2013.58.1.0354
Lunden, J. J., C. G. McNicholl, C. R. Sears, C. L. Morrison, and E. E. Cordes. 2014a.
Acute survivorship of the deep-sea coral Lophelia pertusa from the Gulf of Mexico
under acidification, warming, and deoxygenation. Front. Mar. Sci. 1: 74.
doi:10.3389/fmars.2014.00078
Lunden, J. J., J. M. Turner, C. G. McNicholl, C. K. Glynn, and E. E. Cordes. 2014b.
Design, development, and implementation of recirculating aquaria for maintenance
and experimentation of deep-sea corals and associated fauna. Limnol. Oceanogr.
Methods 12: 363–372. doi:10.4319/lom.2014.12.363
Luter, H. M., C. D. Kenkel, M. Terzin, T. Peirce, P. W. Laffy, K. Gibb, and N. S.
Webster. 2020. Gene correlation networks reveal the transcriptomic response to
elevated nitrogen in a photosynthetic sponge. Mol. Ecol. 1–11.
doi:10.1111/mec.15417
Madeira, D., L. Narciso, H. N. Cabral, M. S. Diniz, and C. Vinagre. 2012. Thermal
tolerance of the crab Pachygrapsus marmoratus: Intraspecific differences at a
physiological (CTMax) and molecular level (Hsp70). Cell Stress Chaperones 17:
707–716. doi:10.1007/s12192-012-0345-3
Maor-Landaw, K., S. Karako-Lampert, H. W. Ben-Asher, S. Goffredo, G. Falini, Z.
Dubinsky, and O. Levy. 2014. Gene expression profiles during short-term heat stress
in the red sea coral Stylophora pistillata. Glob. Chang. Biol. 20: 3026–3035.
124 doi:10.1111/gcb.12592
Marcais, G., and C. Kingsford. 2012. Jellyfish : A fast k-mer counter. 1–8.
Martin, A., M. J. Anderson, C. Thorn, S. K. Davy, and K. G. Ryan. 2011. Response of
sea-ice microbial communities to environmental disturbance: An in situ transplant
experiment in the Antarctic. Mar. Ecol. Prog. Ser. 424: 25–37.
doi:10.3354/meps08977
Mehrbach, C.; Culberson, C. H.; Hawley, J. E.; Pytkowicz, R. 1973. Measurement of the
Apparent Dissociation Constants of Carbonic Acid in Seawater at Atmospheric
Pressure. Limnol. Ocean. 18: 897–907. doi:10.4319/lo.1973.18.6.0897
Meyer, E., G. V. Aglyamova, and M. V. Matz. 2011. Profiling gene expression responses
of coral larvae (Acropora millepora) to elevated temperature and settlement inducers
using a novel RNA-Seq procedure. Mol. Ecol. 20: 3599–3616. doi:10.1111/j.1365-
294X.2011.05205.x
Mienis, F., G. Duineveld, A. Davies, S. Ross, H. Seim, J. Bane, and T. van Weering.
2012. The influence of near-bed hydrodynamic conditions on cold-water corals in
the Viosca Knoll area, Gulf of Mexico. Deep. Res. Part I-Oceanographic Res. Pap.
60: 32–45. doi:10.1016/j.dsr.2011.10.007
Moberg, F., and C. Folke. 1999. Ecological goods and services of coral reef ecosystems.
Ecol. Econ. 29: 215–233. doi:10.1016/S0921-8009(99)00009-9
Montagna, P. A., J. G. Baguley, C. Cooksey, and others. 2013. Deep-Sea Benthic
Footprint of the Deepwater Horizon Blowout. PLoS One 8.
doi:10.1371/journal.pone.0070540
Moore, D. R., and H. R. Bullis. 1960. A deep-water coral reef in the Gulf of Mexico.
125 Bull. Mar. Sci. Gulf Caribb. 10: 125–128.
Moore, M. V, S. M. Pierce, H. M. Walsh, S. K. Kvalvik, and J. D. Lim. 2000. Urban light
pollution alters the diel vertical migration of Daphnia. Verh. Internat. Verein.
Limnol. 27: 779–782. doi:10.1080/03680770.1998.11901341
Moore, S. E., R. R. Reeves, B. L. Southall, T. J. Ragen, R. S. Suydam, and C. W. Clark.
2012. A New Framework for Assessing the Effects of Anthropogenic Sound on
Marine Mammals in a Rapidly Changing Arctic. Bioscience 62: 289–295.
doi:10.1525/bio.2012.62.3.10
Mora, C., C. L. Wei, A. Rollo, and others. 2013. Biotic and Human Vulnerability to
Projected Changes in Ocean Biogeochemistry over the 21st Century. PLoS Biol. 11.
doi:10.1371/journal.pbio.1001682
Moreno, R., L. Jover, C. Diez, F. Sardà, and C. Sanpera. 2013. Ten Years after the
Prestige Oil Spill: Seabird Trophic Ecology as Indicator of Long-Term Effects on
the Coastal Marine Ecosystem. PLoS One 8: 1–10.
doi:10.1371/journal.pone.0077360
Morrison, C. L., S. W. Ross, M. S. Nizinski, S. Brooke, J. J??rnegren, R. G. Waller, R. L.
Johnson, and T. L. King. 2011. Genetic discontinuity among regional populations of
Lophelia pertusa in the North Atlantic Ocean. Conserv. Genet. 12: 713–729.
doi:10.1007/s10592-010-0178-5
Mortensen, P. B., and J. H. Fosså. 2006. Species diversity and spatial distribution of
invertebrates on deep–water Lophelia reefs in Norway. Proc. 10th Int. Coral Reef
Symp. 1849–1868. doi:10.1016/j.electacta.2014.08.113
Mosher, C. V, and L. Watling. 2009. Partners for life : a brittle star and its octocoral host.
126 Mar. Ecol. Prog. Ser. 397: 81–88. doi:10.3354/meps08113
Movilla, J., C. Orejas, E. Calvo, A. Gori, À. López-Sanz, J. Grinyó, C. Domínguez-
Carrió, and C. Pelejero. 2014. Differential response of two Mediterranean cold-
water coral species to ocean acidification. Coral Reefs 33: 675–686.
doi:10.1007/s00338-014-1159-9
Mu, J., F. Jin, X. Ma, Z. Lin, and J. Wang. 2014. Comparative effects of biological and
chemical dispersants on the bioavailability and toxicity of crude oil to early life
stages of marine medaka (Oryzias melastigma). Environ. Toxicol. Chem. 33: 2576–
2583. doi:10.1002/etc.2721
Muehlenbachs, L., M. A. Cohen, and T. Gerarden. 2013. The impact of water depth on
safety and environmental performance in offshore oil and gas production. Energy
Policy 55: 699–705. doi:10.1016/j.enpol.2012.12.074
Nair, S. V., H. Del Valle, P. S. Gross, D. P. Terwilliger, and L. C. Smith. 2005.
Macroarray analysis of coelomocyte gene expression in response to LPS in the sea
urchin. Identification of unexpected immune diversity in an invertebrate. Physiol.
Genomics 22: 33–47. doi:10.1152/physiolgenomics.00052.2005
Naumann, M. S., C. Orejas, and C. Ferrier-Pagès. 2014. Species-specific physiological
response by the cold-water corals Lophelia pertusa and Madrepora oculata to
variations within their natural temperature range. Deep. Res. Part II Top. Stud.
Oceanogr. 99: 36–41. doi:10.1016/j.dsr2.2013.05.025
Nieukirk, S. L., D. K. Mellinger, S. E. Moore, K. Klinck, R. P. Dziak, and J. Goslin.
2012. Sounds from airguns and fin whales recorded in the mid-Atlantic Ocean ,
1999 – 2009. J. Acoust. Soc. Am. 131: 1102–1112. doi:10.1121/1.3672648
127 Oevelen, D. Van, G. Duineveld, M. Lavaleye, F. Mienis, K. Soetaert, and C. H. R. Heip.
2009. The cold-water coral community as a hot spot for carbon cycling on
continental margins : A food-web analysis from Rockall Bank ( northeast Atlantic ).
54: 1829–1844. van Ofwegen, L., O. Breedy, and S. Cairns. 2008. Octocorallia – Octocorals Octocorallia
– Octocorals.
Oldach, M. J., M. Workentine, M. V. Matz, T. Y. Fan, and P. D. Vize. 2017.
Transcriptome dynamics over a lunar month in a broadcast spawning acroporid
coral. Mol. Ecol. 26: 2514–2526. doi:10.1111/mec.14043
Orr, J. C., V. J. Fabry, O. Aumont, and others. 2005. Anthropogenic ocean acidification
over the twenty-first century and its impact on calcifying organisms. Nature 437:
681–686. doi:10.1038/nature04095
Osborne, K., A. A. Thompson, A. J. Cheal, and others. 2017. Delayed coral recovery in a
warming ocean. Glob. Chang. Biol. 23: 3869–3881. doi:10.1111/gcb.13707
Pandolfi, J. M., S. R. Connolly, D. J. Marshall, and A. L. Cohen. 2011. Projecting coral
reef futures under global warming and ocean acidification. Science 333: 418–22.
doi:10.1126/science.1204794
Passow, U. 2014. Formation of rapidly-sinking, oil-associated marine snow. Deep Sea
Res. Part II Top. Stud. Oceanogr. 1–9. doi:10.1016/j.dsr2.2014.10.001
Passow, U., and E. B. Overton. 2021. The Complexity of Spills: The Fate of the
Deepwater Horizon Oil. Ann. Rev. Mar. Sci. 1–28.
Passow, U., K. Ziervogel, V. Asper, and A. Diercks. 2012. Marine snow formation in the
aftermath of the Deepwater Horizon oil spill in the Gulf of Mexico. Environ. Res.
128 Lett. 7. doi:10.1088/1748-9326/7/3/035301
Patro, R., G. Duggal, M. I. Love, R. A. Irizarry, and C. Kingsford. 2017. Salmon
provides fast and bias-aware quantification of transcript expression. Nat. Methods
14: 417–419. doi:10.1038/nmeth.4197
Petersen, J. M., C. Wentrup, C. Verna, K. Knittel, and N. Dubilier. 2012. Origins and
evolutionary flexibility of chemosynthetic symbionts from deep-sea animals. Biol.
Bull. 223: 123–137. doi:10.1086/BBLv223n1p123
Philippart, C. J. M., R. Anadón, R. Danovaro, and others. 2011. Impacts of climate
change on European marine ecosystems: Observations, expectations and indicators.
J. Exp. Mar. Bio. Ecol. 400: 52–69. doi:10.1016/j.jembe.2011.02.023
Polato, N. R., J. C. Vera, and I. B. Baums. 2011. Gene Discovery in the Threatened
Elkhorn Coral : 454 Sequencing of the Acropora palmata Transcriptome. 6.
doi:10.1371/journal.pone.0028634
Prince, R. C., and J. D. Butler. 2014. A protocol for assessing the effectiveness of oil spill
dispersants in stimulating the biodegradation of oil. Environ. Sci. Pollut. Res. 21:
9506–9510. doi:10.1007/s11356-013-2053-7
Purkey, S. G., and G. C. Johnson. 2013. Antarctic bottom water warming and freshening:
Contributions to sea level rise, ocean freshwater budgets, and global heat gain. J.
Clim. 26: 6105–6122. doi:10.1175/JCLI-D-12-00834.1
Purser, A., and L. Thomsen. 2012. Monitoring strategies for drill cutting discharge in the
vicinity of cold-water coral ecosystems. Mar. Pollut. Bull. 64: 2309–2316.
doi:10.1016/j.marpolbul.2012.08.003
Quattrini, A. M., I. B. Baums, T. M. Shank, C. L. Morrison, and E. E. Cordes. 2015.
129 Testing the depth-differentiation hypothesis in a deepwater octocoral. Proc. R. Soc.
B Biol. Sci. 282.
Quattrini, A. M., P. J. Etnoyer, C. Doughty, L. English, R. Falco, N. Remon, M.
Rittinghouse, and E. E. Cordes. 2014. A phylogenetic approach to octocoral
community structure in the deep Gulf of Mexico. Deep Sea Res. Part II Top. Stud.
Oceanogr. 99: 92–102. doi:10.1016/j.dsr2.2013.05.027
Quattrini, A. M., S. E. Georgian, L. Byrnes, A. Stevens, R. Falco, and E. E. Cordes.
2013. Niche divergence by deep-sea octocorals in the genus Callogorgia across the
continental slope of the Gulf of Mexico. Mol. Ecol. 22: 4123–4140.
doi:10.1111/mec.12370
Quattrini, A. M., C. E. Gómez, and E. E. Cordes. 2016. Environmental filtering and
neutral processes shape octocoral community assembly in the deep sea. Oecologia.
doi:10.1007/s00442-016-3765-4
Queirós, A. M., J. A. Fernandes, S. Faulwetter, and others. 2015. Scaling up experimental
ocean acidification and warming research: From individuals to the ecosystem. Glob.
Chang. Biol. 21: 130–143. doi:10.1111/gcb.12675
R Core Team. 2019. R: A language and environment for statistical computing.
Rabalais, N. N., R. E. Turner, and W. J. Wiseman. 2002. Gulf of Mexico Hypoxia,
A.K.A. “The Dead Zone.” Annu. Rev. Ecol. Syst. 33: 235–263.
doi:10.1146/annurev.ecolsys.33.010802.150513
Robbins, L. L., M. E. Hansen, J. A. Kleypas, and S. C. Meylan. 2010. CO2calc: A User-
Friendly Seawater Carbon Calculator for Windows, Mac OS X, and iOS (iPhone).
Roberts, H. H., and B. Kohl. 2018. Temperature Control of Cold-Water Coral (Lophelia)
130 Mound Growth by Climate-Cycle Forcing, Northeast Gulf of Mexico. Deep Sea
Res. Part I Oceanogr. Res. Pap. 1–17. doi:10.1016/j.dsr.2018.08.002
Roberts, J. M., and S. D. Cairns. 2014. Cold-water corals in a changing ocean. Curr.
Opin. Environ. Sustain. 7: 118–126. doi:10.1016/j.cosust.2014.01.004
Roberts, J. M., A. J. Wheeler, and A. Freiwald. 2006. Reefs of the deep: The biology and
geology of cold-water coral ecosystems. Science (80-. ). 312: 543–547.
doi:10.1126/science.1119861
Roberts, J. M., A. J. Wheeler, A. Freiwald, S. D. Cairns, and O. F. D. C. Habitats. 2009.
Cold-water Corals: The Biology and Geology of Deep-Sea Coral Habitats,.
Rogers, A. D. 1999. The Biology of Lophelia pertusa (LINNAEUS 1758) and Other
Deep-Water Reef-Forming Corals and Impacts from Human Activities. Int Rev
Hydrobiol 84: 315–406.
Root, T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rosenzweig, and J. A. Pounds.
2003. Fingerprints of global warming on wild animals and plants. Nature 421: 57–
60. doi:10.1038/nature01333
Ross, S. W., A. M. Quattrini, A. Y. Roa-Varón, and J. P. McClain. 2010. Species
composition and distributions of mesopelagic fishes over the slope of the north-
central Gulf of Mexico. Deep. Res. Part II Top. Stud. Oceanogr. 57: 1926–1956.
doi:10.1016/j.dsr2.2010.05.008
Ruiz-jones, L. J., and S. R. Palumbi. 2017. Tidal heat pulses on a reef trigger a fine-tuned
transcriptional response in corals to maintain homeostasis. Sci. Adv. 3: 1–10.
doi:10.1126/sciadv.1601298
Schroeder, W. W. 2002. Observations of Lophelia pertusa and the surficial geology at a
131 deep-water site in the northeastern Gulf of Mexico. Hydrobiologia 471: 29–33.
doi:10.1023/A:1016580632501
Scott, C. 2016. dammit: an open and accessible de novo transcriptome annotator.
Retrieved from https://github.com/camillescott/dammit.
Shinzato, C., E. Shoguchi, T. Kawashima, and others. 2011. Using the Acropora
digitifera genome to understand coral responses to environmental change. Nature
476: 320–323. doi:10.1038/nature10249
Silva, M., P. J. Etnoyer, and I. R. MacDonald. 2016. Coral injuries observed at
Mesophotic Reefs after the Deepwater Horizon oil discharge. Deep. Res. Part II
Top. Stud. Oceanogr. 129: 96–107. doi:10.1016/j.dsr2.2015.05.013
Simão, F. A., R. M. Waterhouse, P. Ioannidis, E. V. Kriventseva, and E. M. Zdobnov.
2015. BUSCO: Assessing genome assembly and annotation completeness with
single-copy orthologs. Bioinformatics 31: 3210–3212.
doi:10.1093/bioinformatics/btv351
Simister, R. L., E. W. Antzis, and H. K. White. 2016. Examining the diversity of
microbes in a deep-sea coral community impacted by the Deepwater Horizon oil
spill. Deep. Res. Part II Top. Stud. Oceanogr. 129: 157–166.
doi:10.1016/j.dsr2.2015.01.010
Simmons, S. O., C. Y. Fan, and R. Ramabhadran. 2009. Cellular stress response pathway
system as a sentinel ensemble in toxicological screening. Toxicol. Sci. 111: 202–
225. doi:10.1093/toxsci/kfp140
Singer, MM; George, S; Jacobson, S; Lee, I; Weetman, LL; Tjeerdema, RS; Sowby, M.
1996. Comparison of acute aquatic effects of the oil dispersant Corexit 9500 with
132 those of other Corexit series dispersants. Ecotoxicol. Environ. Saf. 35: 183–189.
doi:10.1006/eesa.1996.0098
Singer, M. M., D. Aurand, G. E. Bragin, J. R. Clark, G. M. Coelho, M. L. Sowby, and R.
S. Tjeerdema. 2000. Standardization of the preparation and quantitation of water-
accommodated fractions of petroleum for toxicity testing. Mar. Pollut. Bull. 40:
1007–1016. doi:10.1016/S0025-326X(00)00045-X
Smith-unna, R., C. Boursnell, R. Patro, J. M. Hibberd, and S. Kelly. 2016. TransRate
reference-free quality assessment of de novo transcriptome assemblies. Genome
Res. 1134–1144. doi:10.1101/gr.196469.115.Freely
Solomon, S. et al. 2007. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press
4.
Song, L., and L. Florea. 2015. Rcorrector: efficient and accurate error correction for
Illumina RNA-seq reads. Gigascience 4: 48. doi:10.1186/s13742-015-0089-y
Southall, B. L., A. E. Bowles, W. T. Ellison, and others. 2008. MARINE MAMMAL
NOISE- EXPOSURE CRITERIA : INITIAL SCIENTIFIC
RECOMMENDATIONS. Bioacoustics 17: 273–275.
doi:10.1080/09524622.2008.9753846
Sundahl, H., P. Buhl-mortensen, and L. Buhl-mortensen. 2020. Distribution and Suitable
Habitat of the Cold-Water Corals Lophelia pertusa , Paragorgia arborea , and
Primnoa resedaeformis on the Norwegian Continental Shelf. Front. Mar. Sci. 7: 1–
22. doi:10.3389/fmars.2020.00213
Sweetman, A. K., A. R. Thurber, C. R. Smith, and others. 2017. Major impacts of climate
133 change on deep-sea benthic ecosystems. Elem Sci Anth 5: 4.
doi:10.1525/elementa.203
Tarrant, A. M., S. L. Payton, A. M. Reitzel, D. T. Porter, and M. J. Jenny. 2018.
Ultraviolet radiation significantly enhances the molecular response to dispersant and
sweet crude oil exposure in Nematostella vectensis. Mar. Environ. Res. 134: 96–
108. doi:10.1016/j.marenvres.2018.01.002
Thiel, V., J. Peckmann, H. H. Richnow, U. Luth, J. Reitner, and W. Michaelis. 2001.
Molecular signals for anaerobic methane oxidation in Black Sea seep carbonates and
a microbial mat. Mar. Chem. 73: 97–112. doi:10.1016/S0304-4203(00)00099-2
Thresher, R. E., J. M. Guinotte, R. J. Matear, and A. J. Hobday. 2015. Options for
managing impacts of climate change on a deep-sea community. Nat. Clim. Chang. 5:
635–639. doi:10.1038/nclimate2611
Tillin, H., J. Hiddink, S. Jennings, and M. Kaiser. 2006. Chronic bottom trawling alters
the functional composition of benthic invertebrate communities on a sea-basin scale.
Mar. Ecol. Prog. Ser. 318: 31–45. doi:10.3354/meps318031
Todd, E. V., M. A. Black, and N. J. Gemmell. 2016. The power and promise of RNA-seq
in ecology and evolution. Mol. Ecol. 1224–1241. doi:10.1111/mec.13526
Toledo-Hernández, C., and C. P. Ruiz-Diaz. 2014. The immune responses of the coral.
Invertebr. Surviv. J. 11: 319–328.
Di Toro, D. M.; McGrath, J. A. y, and W. A. Stubblefield. 2007. Redicting the toxicity of
neat and weathered crude oil: toxic potential and the toxicity of saturated mixtures.
Environ. Toxicol. Chem. 26: 24–36.
Transportation Research Board and National Research Council. 2003. Understanding
134 Inputs, Fates and Effects in Detail, p. 63–159. In Oil in the Sea III: Inputs, Fates and
Effects. National Academies Press.
Travers, M. A., A. L. Meistertzheim, M. Cardinaud, C. S. Friedman, S. Huchette, D.
Moraga, and C. Paillard. 2010. Gene expression patterns of abalone, Haliotis
tuberculata, during successive infections by the pathogen Vibrio harveyi. J.
Invertebr. Pathol. 105: 289–297. doi:10.1016/j.jip.2010.08.001
Ulfsnes, A., J. K. Haugland, and R. Weltzien. 2013. Monitoring of drilling activities in
areas with presence of cold water corals.
Vohsen, S. A., H. R. Gruber-Vodicka, E. O. Osman, M. A. Saxton, S. B. Joye, N.
Dubilier, C. R. Fisher, and I. B. Baums. 2020. Deep-sea corals near cold seeps
associate with chemoautotrophic bacteria that are related to the symbionts of cold
seep and hydrothermal vent mussels. bioRxiv 1–37.
doi:https://doi.org/10.1101/2020.02.27.968453
Wagner, D., A. M. Friedlander, R. L. Pyle, C. M. Brooks, K. M. Gjerde, and T. A.
Wilhelm. 2020. Coral Reefs of the High Seas: Hidden Biodiversity Hotspots in Need
of Protection. Front. Mar. Sci. 7: 1–13. doi:10.3389/fmars.2020.567428
Wallmann, K., P. Linke, E. Suess, and others. 1997. Quantifying fluid flow, solute
mixing, and biogeochemical turnover at cold vents of the eastern Aleutian
subduction zone. Geochim. Cosmochim. Acta 61: 5209–5219. doi:10.1016/S0016-
7037(97)00306-2 van de Water, J. A. J. M., W. Leggat, D. G. Bourne, M. J. H. van Oppen, B. L. Willis,
and T. D. Ainsworth. 2015. Elevated seawater temperatures have a limited impact
on the coral immune response following physical damage. Hydrobiologia 759: 201–
135 214. doi:10.1007/s10750-015-2243-z
Waterhouse, R. M., M. Seppey, F. A. Simao, M. Manni, P. Ioannidis, G. Klioutchnikov,
E. V. Kriventseva, and E. M. Zdobnov. 2018. BUSCO applications from quality
assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35: 543–548.
doi:10.1093/molbev/msx319
Watling, L. 2014. Trawling exerts big impacts on small beasts. Proc. Natl. Acad. Sci.
111: 8704–8705. doi:10.1073/pnas.1407305111
Weinnig, A. M., C. E. Gómez, A. Hallaj, and E. E. Cordes. 2020. Cold-water coral
(Lophelia pertusa) response to multiple stressors : High temperature affects recovery
from short-term pollution exposure. 1–13. doi:10.1038/s41598-020-58556-9
Welch, W. J. 1987. The Mammalian Heat Shock (or Stress) Response: A Cellular
Defense Mechanism, p. 287–304. In M.Z. Atassi [ed.], Immunobiology of Proteins
and Peptides IV: T-Cell Recognition and Antigen Presentation. Springer US.
White, H. K., P.-Y. Hsing, W. Cho, and others. 2012. Impact of the Deepwater Horizon
oil spill on a deep-water coral community in the Gulf of Mexico. Proc. Natl. Acad.
Sci. 109: 20303–20308. doi:10.1073/pnas.1118029109
White, H. K., S. L. Lyons, S. J. Harrison, D. M. Findley, Y. Liu, and E. B. Kujawinski.
2014. Long-Term Persistence of Dispersants following the Deepwater Horizon Oil
Spill.
Wickham, H. 2016. ggplot2: Elegent Graphics for Data Analysis, Springer-Verlag.
Wienberg, C., and J. Titschack. 2017. Framework-Forming Scleractinian Cold-Water
Corals Through Space and Time: A Late Quaternary North Atlantic Perspective, p.
699–732. In S. Rossi, L. Bramanti, A. Gori, and O. Covadonga [eds.], Marine
136 Animal Forests: The Ecology of Benthic Biodiversity Hotspots. Springer
International Publishing.
Wolff, N. H., P. J. Mumby, M. Devlin, and K. R. N. Anthony. 2018. Vulnerability of the
Great Barrier Reef to climate change and local pressures. Glob. Chang. Biol. 24:
1978–1991. doi:10.1111/gcb.14043
Wood, D. E., and S. L. Salzberg. 2014. Kraken: ultrafast metagenomic sequence
classification using exact alignments. Genome Biol. 15: R46. doi:10.1186/gb-2014-
15-3-r46
Wright, R. M., G. V. Aglyamova, E. Meyer, and M. V. Matz. 2015. Gene expression
associated with white syndromes in a reef building coral, Acropora hyacinthus.
BMC Genomics 16: 1–12. doi:10.1186/s12864-015-1540-2
Yesson, C., M. L. Taylor, D. P. Tittensor, and others. 2012. Global habitat suitability of
cold-water octocorals. J. Biogeogr. 39: 1278–1292. doi:10.1111/j.1365-
2699.2011.02681.x
Zaneveld, J. R., D. E. Burkepile, A. A. Shantz, and others. 2016. Overfishing and nutrient
pollution interact with temperature to disrupt coral reefs down to microbial scales.
Nat. Commun. 7: 1–12. doi:10.1038/ncomms11833
137 APPENDIX A
MAGENTA MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Regulation delta.rank pval level nseqs term name p.adj morphogenesis of an 0 2.01E-05 2 56 GO:0002011 0 epithelial sheet
regulation of protein 0 7.97E-06 2 24 GO:0032459 0 oligomerization
positive regulation of 0 4.44E-06 2 13 GO:0032461 0 protein oligomerization
regulation of protein 0 6.79E-06 3 14 GO:0032462 0 homooligomerization
regulation of ion 0 1.72E-05 5 355 GO:0043269 0 transport
GO:0044319; 0 1.66E-05 3 27 GO:0090504; epiboly 0 GO:0090505
negative regulation of 0 5.03E-06 4 250 GO:0045926 0 growth
negative regulation of 0 6.08E-06 2 159 GO:0048640 0 developmental growth
GO:0051093; negative regulation of 0 2.84E-07 2 693 0 GO:0045596 developmental process
regulation of calcium ion 0 2.24E-05 7 133 GO:0051924 0 transport
Cellular Component GO:0032420; 0 1.42E-05 2 54 stereocilium bundle 0 GO:0032421
Molecular Function
oxidoreductase activity, GO:0016646; 0 0.00012 4 22 acting on the CH-NH 0 GO:0016645 group of donors
138 APPENDIX B
GREEN YELLOW MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj
negative regulation of 0 3.73E-06 2 482 GO:0000122 transcription by RNA 0 polymerase II
GO:0031667; response to extracellular 0 2.13E-05 3 500 0 GO:0009991 stimulus
regulation of DNA-templated GO:0043618; 0 1.56E-06 2 58 transcription in response to 0 GO:0043620 stress
positive regulation of lipid 0 5.91E-05 4 104 GO:0045834 0 metabolic process
GO:0045892; GO:1903507; negative regulation of RNA 0 3.28E-05 2 734 0 GO:1902679; metabolic process GO:0051253
regulation of small molecule 0 4.76E-06 2 272 GO:0062012 0 metabolic process
negative regulation of transcription from RNA 0 1.64E-05 7 14 GO:0097201 0 polymerase II promoter in response to stress
Cellular Component
0 5.10E-05 4 4 GO:0042587 glycogen granule 0
Molecular Function
139 DNA-binding transcription GO:0001228; 0 5.13E-05 3 164 activator activity, RNA 0 GO:0001216 polymerase II-specific
GO:0003700; DNA-binding transcription 0 1.92E-05 2 474 0 GO:0000981 factor activity
G protein-coupled receptor 0 7.32E-05 4 230 GO:0004930 0 activity
calcium-transporting ATPase 0 6.76E-05 2 9 GO:0005388 0 activity
0.000408 GO:0019902; 0 3 136 phosphatase binding 0 23 GO:0019903 0.000351 0 4 13 GO:0070402 NADPH binding 0 31
140 APPENDIX C
LIGHT GREEN MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Regulation delta.rank pval level nseqs term name p.adj
GO:0000165; signal transduction by 0 3.76E-07 3 203 0 GO:0023014 protein phosphorylation
0 1.45E-07 2 325 GO:0001558 regulation of cell growth 0
GO:0001654; 0 1.96E-07 2 675 sensory organ development 0 GO:0007423
ameboidal-type cell 0 5.16E-06 4 271 GO:0001667 0 migration
0 1.40E-05 3 127 GO:0001709 cell fate determination 0.00096
GO:0001775; GO:0002252; 0 3.42E-08 2 848 immune response 0 GO:0045321; GO:0006955
GO:0002064; 0 1.02E-08 2 380 GO:0002065; epithelial cell development 0 GO:0002066
GO:0002165; GO:0007444; GO:0007560; GO:0048563; post-embryonic animal 0 1.35E-11 3 543 0 GO:0009886; organ development GO:0007552; GO:0048569; GO:0048707
GO:0002520; immune system 0 1.65E-05 2 453 GO:0048534; 0.00093 development GO:0030097
GO:0002521; 0 3.64E-07 4 240 leukocyte differentiation 0 GO:0046649
141 regulation of immune 0 7.43E-09 2 694 GO:0002682 0 system process
GO:0002694; 0 2.46E-07 2 222 GO:0050865; regulation of cell activation 0 GO:0051249
GO:0002696; positive regulation of cell 0 6.41E-06 3 129 GO:0050867; 0 activation GO:0051251 GO:0006897; 0 3.87E-06 3 460 import into cell 0 GO:0098657 GO:0006915; 0 2.22E-06 2 540 GO:0012501; cell death 0 GO:0008219
GO:0006935; GO:0042330; GO:0007409; 0 7.94E-07 2 641 taxis 0 GO:0007411; GO:0097485; GO:0061564
GO:0007167; enzyme linked receptor 0 2.44E-13 2 440 0 GO:0007169 protein signaling pathway
GO:0007264; small GTPase mediated 0 6.13E-09 5 170 0 GO:0007265 signal transduction
GO:0007281; 0 2.76E-06 2 723 GO:0007292; germ cell development 0 GO:0048477 GO:0007431; exocrine system 0 1.76E-06 2 128 GO:0007435; 0 development GO:0035272
GO:0007472; GO:0007476; GO:0035114; 0 6.39E-09 4 269 wing disc development 0 GO:0035120; GO:0035220; GO:0048737
imaginal disc-derived wing 0 6.39E-06 4 31 GO:0007474 0 vein specification
0 5.05E-06 4 107 GO:0008360 regulation of cell shape 0
142 GO:0009611; 0 5.07E-07 2 394 response to wounding 0 GO:0042060
post-embryonic 0 1.37E-08 2 658 GO:0009791 0 development endomembrane system 0 5.66E-07 3 346 GO:0010256 0 organization GO:0010562; GO:0045937; positive regulation of 0 4.34E-09 3 731 GO:0042327; protein modification 0 GO:0031401; process GO:0001934 GO:0010594; positive regulation of 0 5.85E-07 4 142 GO:0010595; 0 epithelial cell migration GO:0010634 GO:0010631; 0 1.62E-05 2 172 GO:0090130; tissue migration 0.00093 GO:0090132
regulation of epithelial cell 0 1.70E-08 3 169 GO:0010632 0 migration
GO:0010720; positive regulation of cell 0 4.33E-08 3 509 GO:0050769; 0 development GO:0051962
GO:0010721; negative regulation of 0 4.10E-06 3 342 GO:0050768; nervous system 0 GO:0051961 development
GO:0010769; regulation of cell 0 1.38E-09 2 386 0 GO:0022604 morphogenesis
GO:0010959; regulation of 0 1.68E-05 4 316 0.00092 GO:0034762 transmembrane transport
GO:0010975; regulation of cell 0 1.19E-11 4 558 GO:0120035; 0 projection organization GO:0031344
GO:0010976; positive regulation of cell 0 4.80E-07 2 410 GO:0031346; 0 projection organization GO:0045666
0 5.01E-07 2 83 GO:0014013 regulation of gliogenesis 0
positive regulation of 0 1.55E-05 4 44 GO:0014015 0.00094 gliogenesis 143 regulation of 0 1.80E-05 2 56 GO:0014066 phosphatidylinositol 3- 0.00091 kinase signaling positive regulation of 0 1.32E-05 2 28 GO:0014068 phosphatidylinositol 3- 0.00099 kinase signaling
0 1.64E-06 4 292 GO:0016050 vesicle organization 0
0 1.60E-06 2 199 GO:0016197 endosomal transport 0
GO:0016358; 0 1.73E-10 2 171 dendrite development 0 GO:0048813
GO:0018108; peptidyl-tyrosine 0 1.92E-05 7 108 0.00089 GO:0018212 modification
0 1.11E-07 2 77 GO:0019058 viral life cycle 0
regulation of anatomical 0 8.23E-09 4 704 GO:0022603 0 structure morphogenesis
GO:0030036; actin filament-based 0 6.11E-07 2 396 0 GO:0030029 process
GO:0030098; 0 1.33E-05 2 163 lymphocyte differentiation 0.00098 GO:0042110 positive regulation of cell 0 1.09E-06 5 167 GO:0030307 0 growth GO:0030334; GO:2000145; 0 5.81E-10 2 724 regulation of locomotion 0 GO:0040012; GO:0051270 GO:0030335; GO:2000147; positive regulation of 0 6.36E-06 6 406 0 GO:0040017; locomotion GO:0051272
epithelial cell 0 7.69E-09 2 532 GO:0030855 0 differentiation
GO:0031349; regulation of innate 0 6.29E-06 5 249 GO:0045089; 0 immune response GO:0045088
actin cytoskeleton 0 5.19E-06 5 57 GO:0031532 0 reorganization
144 regulation of response to 0 3.70E-07 2 517 GO:0032101 0 external stimulus
positive regulation of 0 1.09E-06 5 211 GO:0032103 response to external 0 stimulus
GO:0032535; regulation of cellular 0 7.62E-09 3 323 0 GO:0008361 component size
GO:0032990; GO:0000902; GO:0000904; GO:0048812; 0 1.69E-13 4 865 cell morphogenesis 0 GO:0120039; GO:0048858; GO:0031175; GO:0048667
GO:0035107; 0 1.32E-07 3 340 appendage development 0 GO:0048736
GO:0035239; 0 6.62E-09 3 888 GO:0002009; tube morphogenesis 0 GO:0060562
GO:0040008; 0 9.04E-09 3 679 regulation of growth 0 GO:0048638
GO:0043087; regulation of GTPase 0 7.85E-07 3 231 0 GO:0043547 activity
GO:0043207; GO:0009607; 0 1.36E-06 2 551 response to biotic stimulus 0 GO:0009617; GO:0051707
0 6.18E-07 2 355 GO:0043269 regulation of ion transport 0
GO:0043408; regulation of MAPK 0 1.23E-06 5 429 0 GO:0043410 cascade
GO:0043535; regulation of blood vessel 0 1.20E-06 2 63 0 GO:0043536 endothelial cell migration
regulation of multi- 0 1.39E-05 3 339 GO:0043900 0.00097 organism process positive regulation of 0 6.77E-06 2 158 GO:0043902 0 multi-organism process positive regulation of 0 1.88E-06 4 375 GO:0044089 cellular component 0 biogenesis 145 0 6.71E-06 2 226 GO:0045087 innate immune response 0
0 1.07E-07 2 346 GO:0045165 cell fate commitment 0
positive regulation of cell 0 4.30E-09 2 627 GO:0045597 0 differentiation
GO:0045664; 0 4.33E-12 2 671 regulation of neurogenesis 0 GO:0050767
regulation of glial cell 0 1.92E-06 2 48 GO:0045685 0 differentiation
GO:0045766; positive regulation of 0 6.36E-06 2 95 0 GO:1904018 vasculature development
GO:0045927; positive regulation of 0 1.94E-05 2 326 0.00088 GO:0048639 growth
regulation of small GTPase GO:0046578; 0 3.11E-07 3 180 mediated signal 0 GO:0051056 transduction
platelet-derived growth 0 6.53E-07 2 18 GO:0048008 factor receptor signaling 0 pathway
vascular endothelial 0 5.48E-06 2 46 GO:0048010 growth factor receptor 0 signaling pathway
ephrin receptor signaling 0 3.97E-06 2 56 GO:0048013 0 pathway
regulation of dendrite 0 1.99E-06 2 102 GO:0048814 0 morphogenesis
0 3.69E-06 3 183 GO:0050770 regulation of axonogenesis 0
regulation of dendrite 0 4.91E-06 5 163 GO:0050773 0 development
GO:0050776; regulation of immune 0 6.14E-07 4 506 GO:0002684; 0 response GO:0050778
GO:0050803; regulation of synapse 0 2.20E-06 2 263 0 GO:0050807 structure or activity
146 positive regulation of 0 2.99E-07 2 663 GO:0051050 0 transport
0 2.66E-06 2 321 GO:0051301 cell division 0
GO:0051345; regulation of hydrolase 0 1.03E-08 3 620 0 GO:0051336 activity
regulation of cytoskeleton 0 6.93E-08 5 342 GO:0051493 0 organization
regulation of cellular 0 6.01E-07 2 646 GO:0060341 0 localization
respiratory system 0 1.44E-05 2 331 GO:0060541 0.00095 development
regulation of vesicle- 0 3.02E-07 3 405 GO:0060627 0 mediated transport
regulation of ERK1 and 0 2.84E-06 2 124 GO:0070372 0 ERK2 cascade
positive regulation of 0 1.24E-05 3 79 GO:0070374 0.001 ERK1 and ERK2 cascade
GO:0070848; 0 3.06E-07 4 348 response to growth factor 0 GO:0071363
GO:0071417; cellular response to 0 1.33E-07 2 446 0 GO:1901699 nitrogen compound
cellular response to 0 1.55E-09 3 727 GO:0071495 0 endogenous stimulus anatomical structure 0 1.83E-05 3 158 GO:0071695 0.0009 maturation
regulation of anatomical 0 2.25E-07 2 511 GO:0090066 0 structure size
cellular response to 0 6.51E-06 2 627 GO:1901701 oxygen-containing 0 compound
147 regulation of organelle 0 6.97E-07 3 164 GO:1902115 0 assembly
positive regulation of 0 2.39E-08 3 570 GO:1902533 intracellular signal 0 transduction
0 9.38E-08 2 215 GO:1903706 regulation of hemopoiesis 0
regulation of cellular 0 2.45E-06 2 353 GO:1903827 0 protein localization
Cellular Component GO:0000139; 0 2.25E-05 2 509 GO:0044431; Golgi apparatus part 0 GO:0098791
0 3.34E-05 6 9 GO:0000815 ESCRT III complex 0
0 0.00058 4 290 GO:0005635 nuclear envelope 0
0 1.94E-12 2 623 GO:0005768 endosome 0
0 3.10E-07 2 253 GO:0005769 early endosome 0
0 9.60E-05 2 163 GO:0005770 late endosome 0
0 0.00048 3 162 GO:0005802 trans-Golgi network 0
0 0.00033 2 50 GO:0005902 microvillus 0
0 8.69E-06 2 251 GO:0005911 cell-cell junction 0
GO:0005938; 0 3.84E-07 2 390 cytoplasmic region 0 GO:0099568
148 GO:0010008; 0 1.11E-05 2 302 endosomal part 0 GO:0044440
GO:0012506; 0 4.29E-06 3 417 vesicle membrane 0 GO:0030659
GO:0014069; GO:0099572; 0 0.00043 2 153 neuron to neuron synapse 0 GO:0032279; GO:0098984
GO:0016324; 0 4.65E-06 3 384 apical part of cell 0 GO:0045177
0 2.49E-05 3 116 GO:0030027 lamellipodium 0
0 6.66E-06 1 431 GO:0030054 cell junction 0
0 0.00056 2 255 GO:0030133 transport vesicle 0
GO:0030141; 0 0.00069 4 614 secretory vesicle 0 GO:0099503
0 1.39E-08 2 59 GO:0030175 filopodium 0
0 1.80E-09 3 500 GO:0030424 axon 0
GO:0030425; 0 5.37E-09 3 478 dendrite 0 GO:0097447
GO:0030426; 0 9.81E-08 2 165 site of polarized growth 0 GO:0030427
secretory granule 0 1.39E-05 3 170 GO:0030667 0 membrane
platelet alpha granule 0 0.00029 4 12 GO:0031092 0 membrane
0 3.24E-07 2 261 GO:0031252 cell leading edge 0
149 0 6.77E-05 3 231 GO:0031253 cell projection membrane 0
0 4.64E-05 2 113 GO:0031256 leading edge membrane 0
0 0.00048 3 65 GO:0031902 late endosome membrane 0
0 0.00066 3 47 GO:0032587 ruffle membrane 0
GO:0033267; 0 6.77E-09 3 291 axon part 0 GO:0150034
0 0.00043 4 45 GO:0035579 specific granule membrane 0
0 0.00037 2 27 GO:0036452 ESCRT complex 0
0 0.00042 2 78 GO:0042581 specific granule 0
0 0.0001 3 90 GO:0043195 terminal bouton 0
GO:0043197; 0 1.32E-05 2 126 neuron spine 0 GO:0044309
GO:0043679; 0 0.00065 2 141 neuron projection terminus 0 GO:0044306
0 0.00013 2 46 GO:0044295 axonal growth cone 0
GO:0044297; 0 3.79E-08 2 592 cell body 0 GO:0043025
GO:0044456; 0 1.34E-12 1 579 synapse 0 GO:0045202
0 0.00022 2 74 GO:0045178 basal part of cell 0
perinuclear region of 0 6.33E-07 2 471 GO:0048471 0 cytoplasm
GO:0098562; cytoplasmic side of 0 0.00065 3 173 0 GO:0009898 membrane
150 0 1.49E-06 2 676 GO:0098590 plasma membrane region 0
0 5.06E-06 2 302 GO:0098793 presynapse 0
0 3.50E-07 2 254 GO:0098794 postsynapse 0
0 1.46E-07 3 127 GO:0098858 actin-based cell projection 0
Molecular Function
0 3.00E-05 4 166 GO:0003779 actin binding 0
GO:0005085; guanyl-nucleotide 0 3.96E-05 2 146 0 GO:0005088 exchange factor activity
GO:0005096; nucleoside-triphosphatase 0 8.14E-07 4 183 GO:0030695; 0 regulator activity GO:0060589
0 1.48E-05 3 276 GO:0008047 enzyme activator activity 0
GO:0019900; 0 1.23E-07 2 478 kinase binding 0 GO:0019901 protein domain specific 0 4.85E-10 3 503 GO:0019904 0 binding
0 3.67E-07 2 482 GO:0030234 enzyme regulator activity 0
GO:0030971; protein tyrosine kinase 0 6.27E-06 3 51 0 GO:1990782 binding
GO:0051020; 0 8.12E-09 4 371 GO:0017016; GTPase binding 0 GO:0031267 Lys63-specific 0 5.23E-05 2 13 GO:0061578 0 deubiquitinase activity
151 APPENDIX D
TURQUOISE MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj
ribosomal large subunit 0 1.01E-05 2 39 GO:0000027 0 assembly
ribosomal small subunit 0 1.49E-06 2 25 GO:0000028 0 assembly
GO:0000184; GO:0006612; GO:0006613; 0 2.95E-12 2 172 GO:0006614; protein targeting to membrane 0 GO:0045047; GO:0072599; GO:0070972
0 1.16E-05 2 47 GO:0000245 spliceosomal complex assembly 0
GO:0000375; GO:0008380; 0 1.27E-20 3 368 GO:0000377; mRNA processing 0 GO:0000398; GO:0006397
GO:0000956; 0 8.61E-14 2 211 GO:0006402; RNA catabolic process 0 GO:0006401
0 5.25E-19 2 167 GO:0002181 cytoplasmic translation 0
GO:0006119; GO:0022904; respiratory electron transport 0 1.18E-13 4 82 0 GO:0042773; chain GO:0042775
GO:0006120; NADH dehydrogenase complex 0 1.68E-11 5 45 GO:0010257; 0 assembly GO:0032981
152 GO:0006352; DNA-templated transcription, 0 7.80E-06 2 115 0 GO:0006367 initiation
GO:0006364; 0 1.83E-22 2 305 GO:0016072; ribosome biogenesis 0 GO:0042254
0 8.26E-08 2 24 GO:0006376 mRNA splice site selection 0
0 6.34E-34 6 696 GO:0006396 RNA processing 0
GO:0006412; GO:0043043; 0 8.42E-15 4 554 peptide metabolic process 0 GO:0006518; GO:0043604
0 9.53E-15 3 127 GO:0006413 translational initiation 0
0 9.86E-11 5 273 GO:0006605 protein targeting 0
0 1.54E-05 2 687 GO:0006886 intracellular protein transport 0
0 3.54E-07 4 354 GO:0007005 mitochondrion organization 0
0 1.63E-31 6 529 GO:0016071 mRNA metabolic process 0
ribonucleoprotein complex 0 1.97E-24 2 424 GO:0022613 0 biogenesis
0 5.54E-08 2 117 GO:0022900 electron transport chain 0
mitochondrial respiratory chain 0 1.80E-11 4 61 GO:0033108 0 complex assembly
GO:0034470; 0 4.63E-15 2 440 ncRNA metabolic process 0 GO:0034660
cellular protein-containing 0 6.54E-12 3 636 GO:0034622 0 complex assembly
153 GO:0034655; GO:0019439; organic cyclic compound 0 1.03E-06 2 439 GO:0044270; 0 catabolic process GO:0046700; GO:1901361
0 8.87E-11 2 71 GO:0042255 ribosome assembly 0
ribosomal large subunit 0 2.29E-12 3 81 GO:0042273 0 biogenesis ribosomal small subunit 0 1.57E-07 3 95 GO:0042274 0 biogenesis
GO:0043484; 0 1.78E-09 2 139 regulation of mRNA processing 0 GO:0050684
cellular amide metabolic 0 1.66E-11 2 654 GO:0043603 0 process mRNA cis splicing, via 0 1.35E-05 2 19 GO:0045292 0 spliceosome
GO:0048024; regulation of mRNA splicing, 0 1.26E-06 3 81 0 GO:0000381 via spliceosome
GO:0071826; ribonucleoprotein complex 0 1.56E-09 2 239 0 GO:0022618 subunit organization
establishment of protein 0 3.51E-07 3 370 GO:0072594 0 localization to organelle
establishment of protein 0 8.79E-10 2 185 GO:0090150 0 localization to membrane
RNA phosphodiester bond 0 1.19E-05 2 117 GO:0090501 0 hydrolysis
Cellular Component delta.rank pval level nseqs term name p.adj GO:0000123; 0 0.000133 4 78 GO:0031248; acetyltransferase complex 0 GO:1902493
0 0.000144 6 10 GO:0000243 commitment complex 0
GO:0000428; GO:0030880; 0 0.000209 2 122 RNA polymerase complex 0 GO:0016591; GO:0055029
154 0 8.25E-11 2 164 GO:0005681 spliceosomal complex 0
0 1.02E-06 2 69 GO:0005684 U2-type spliceosomal complex 0
0 4.82E-06 2 17 GO:0005685 U1 snRNP 0 0 3.11E-11 4 700 GO:0005730 nucleolus 0
small nucleolar 0 0.000172 2 26 GO:0005732 0 ribonucleoprotein complex
GO:0005740; GO:0005743; 0 1.55E-07 2 483 mitochondrial envelope 0 GO:0019866; GO:0031966
GO:0005746; GO:0070469; GO:0005747; 0 1.93E-13 2 49 respirasome 0 GO:0045271; GO:0030964; GO:0098803
0 7.08E-07 4 318 GO:0005759 mitochondrial matrix 0
GO:0005840; GO:0044391; 0 1.07E-15 3 346 cytosolic part 0 GO:0022626; GO:0044445
GO:0015934; 0 1.56E-13 2 129 large ribosomal subunit 0 GO:0022625
GO:0015935; 0 8.59E-11 2 93 small ribosomal subunit 0 GO:0022627 0 0.000118 3 244 GO:0016607 nuclear speck 0 GO:0030532; 0 9.71E-05 4 90 GO:0120114; Sm-like protein family complex 0 GO:0097525 0 2.61E-05 3 102 GO:0030684 preribosome 0
GO:0030867; rough endoplasmic reticulum 0 6.25E-05 5 42 GO:0098554; 0 membrane GO:0098556
GO:0031967; 0 1.24E-08 3 943 GO:0044429; organelle envelope 0 GO:0031975
0 6.66E-05 4 31 GO:0042788 polysomal ribosome 0
155 0 1.14E-07 4 725 GO:0044451 nucleoplasm part 0
0 1.35E-10 2 145 GO:0044455 mitochondrial membrane part 0
GO:0071004; 0 2.74E-06 2 25 prespliceosome 0 GO:0071010
0 0.00012 2 91 GO:0071013 catalytic step 2 spliceosome 0
0 2.47E-15 4 183 GO:0098798 mitochondrial protein complex 0
inner mitochondrial membrane 0 6.07E-17 5 82 GO:0098800 0 protein complex
0 6.34E-10 2 76 GO:1990204 oxidoreductase complex 0
0 1.02E-07 3 592 GO:1990234 transferase complex 0
Molecular Function delta.rank pval level nseqs term name p.adj
0 1.23E-07 2 227 GO:0003729 mRNA binding 0
structural constituent of 0 7.03E-26 2 208 GO:0003735 0 ribosome
GO:0003954; oxidoreductase activity, acting GO:0008137; 0 1.15E-09 3 37 on NAD(P)H, quinone or 0 GO:0050136; similar compound as acceptor GO:0016655
0 1.89E-14 1 420 GO:0005198 structural molecule activity 0
oxidoreductase activity, acting 0 2.68E-08 2 72 GO:0016651 0 on NAD(P)H 0 1.49E-06 2 55 GO:0019843 rRNA binding 0 ribonucleoprotein complex 0 1.57E-05 3 113 GO:0043021 0 binding
catalytic activity, acting on 0 6.68E-08 2 291 GO:0140098 0 RNA
156 APPENDIX E
DARK GREEN MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj GO:0000226; 0 4.32E-32 2 609 microtubule-based process 0 GO:0007017
GO:0001539; cilium or flagellum- 0 4.01E-11 3 23 0 GO:0060285 dependent cell motility
GO:0001578; microtubule bundle 0 2.86E-31 3 97 0 GO:0035082 formation
0 9.10E-41 2 74 GO:0003341 cilium movement 0
0 5.70E-13 2 30 GO:0003351 epithelial cilium movement 0
GO:0003352; regulation of cilium 0 2.28E-11 2 15 0 GO:0003356 movement
GO:0006165; 0 5.29E-05 2 42 nucleotide phosphorylation 0 GO:0046939 microtubule-based 0 2.09E-37 2 246 GO:0007018 0 movement GO:0007286; 0 5.07E-08 3 163 spermatid differentiation 0 GO:0048515
0 3.94E-06 3 16 GO:0007288 sperm axoneme assembly 0
GO:0007368; determination of bilateral 0 3.94E-08 2 184 GO:0009855; 0 symmetry GO:0009799
GO:0010970; cytoskeleton-dependent 0 2.53E-06 4 140 GO:0030705; 0 intracellular transport GO:0099111
nucleobase-containing 0 7.86E-05 4 15 GO:0015949 small molecule 0 interconversion
0 4.41E-06 2 8 GO:0018094 protein polyglycylation 0
157 0 2.55E-08 2 13 GO:0018095 protein polyglutamylation 0
peptidyl-glutamic acid 0 3.84E-12 6 26 GO:0018200 0 modification
GO:0030031; GO:0044782; 0 4.23E-49 2 382 cell projection assembly 0 GO:0060271; GO:0120031 GO:0030317; 0 3.66E-18 2 64 sperm motility 0 GO:0097722
GO:0032886; regulation of microtubule- 0 1.04E-05 2 188 0 GO:0070507 based process
intraciliary transport 0 4.75E-09 4 26 GO:0035735 involved in cilium 0 assembly
0 6.93E-09 2 18 GO:0036158 outer dynein arm assembly 0
0 1.64E-08 2 20 GO:0036159 inner dynein arm assembly 0
GO:0042073; microtubule-based protein 0 2.24E-09 5 46 GO:0098840; 0 transport GO:0099118
0 7.02E-06 2 30 GO:0044458 motile cilium assembly 0
epithelial cilium movement 0 2.96E-05 6 12 GO:0060287 involved in determination 0 of left/right asymmetry
cilium movement involved 0 2.55E-08 3 13 GO:0060294 0 in cell motility
regulation of microtubule- 0 1.39E-11 3 29 GO:0060632 0 based movement
axonemal dynein complex 0 1.24E-16 6 36 GO:0070286 0 assembly
0 7.53E-31 2 668 GO:0070925 organelle assembly 0
Cellular Component 0.000454 0 4 9 GO:0002177 manchette 0 07
158 GO:0005813; microtubule organizing 0 6.16E-09 2 485 0 GO:0005815 center
GO:0005814; microtubule organizing 0 5.06E-05 4 118 0 GO:0044450 center part
0 5.86E-15 3 26 GO:0005858 axonemal dynein complex 0
microtubule associated 0 3.25E-17 4 128 GO:0005875 0 complex GO:0005929; 0 3.91E-55 3 474 cilium 0 GO:0044441
GO:0005930; plasma membrane bounded 0 2.44E-42 2 136 GO:0032838; 0 cell projection cytoplasm GO:0097014
GO:0005938; 0 2.19E-25 4 390 cytoplasmic region 0 GO:0099568
0 6.40E-24 2 761 GO:0015630 microtubule cytoskeleton 0
0 1.50E-15 2 47 GO:0030286 dynein complex 0
GO:0030990; intraciliary transport 0 1.58E-09 2 21 0 GO:0030992 particle
0.000212 photoreceptor connecting 0 2 31 GO:0032391 0 47 cilium 0.000542 0 2 57 GO:0035869 ciliary transition zone 0 04 0 1.90E-11 4 116 GO:0036064 ciliary basal body 0
GO:0036126; 0 8.32E-20 5 88 9+2 motile cilium 0 GO:0097729
0 3.35E-07 2 17 GO:0036156 inner dynein arm 0
0 2.56E-09 2 9 GO:0036157 outer dynein arm 0 0 1.85E-21 2 41 GO:0044447 axoneme part 0 GO:0097223; 0 3.47E-29 2 188 sperm part 0 GO:0031514 0.001516 0 3 28 GO:0097225 sperm midpiece 0 55
0 6.90E-06 2 27 GO:0097228 sperm principal piece 0
0 5.76E-09 2 34 GO:0097542 ciliary tip 0 0 3.97E-05 2 36 GO:0097546 ciliary base 0 159 0 7.22E-05 4 123 GO:0097730 non-motile cilium 0
0.001188 GO:0097731; 0 5 66 9+0 non-motile cilium 0 32 GO:0097733
0 5.70E-05 2 5 GO:1990716 axonemal central apparatus 0
Molecular Function GO:0003774; 0 7.87E-06 2 82 motor activity 0 GO:0003777 nucleoside diphosphate 0 2.99E-09 2 12 GO:0004550 0 kinase activity GO:0008017; 0 4.73E-15 5 183 tubulin binding 0 GO:0015631
cytoskeletal protein 0 5.28E-09 3 494 GO:0008092 0 binding
GO:0008569; 0 3.87E-08 2 28 dynein light chain binding 0 GO:0045503
GO:0016776; nucleobase-containing 0 8.02E-06 3 39 0 GO:0019205 compound kinase activity
ligase activity, forming 0 9.34E-06 2 40 GO:0016879 0 carbon-nitrogen bonds
acid-amino acid ligase 0 2.18E-07 2 22 GO:0016881 0 activity
0 6.39E-06 2 22 GO:0043014 alpha-tubulin binding 0
dynein heavy chain 0 6.89E-08 3 10 GO:0045504 0 binding dynein intermediate chain 0 2.05E-07 3 35 GO:0045505 0 binding dynein light intermediate 0 2.93E-08 3 27 GO:0051959 0 chain binding
protein-glycine ligase 0 2.86E-06 3 9 GO:0070735 0 activity GO:0070739; protein-glutamic acid 0 1.60E-06 3 8 0 GO:0070740 ligase activity
160 APPENDIX F
CYAN MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj ribosomal large subunit 0 5.09E-08 3 39 GO:0000027 0 assembly
GO:0000184; GO:0006612; GO:0006613; protein targeting to 0 1.37E-19 2 172 GO:0006614; 0 membrane GO:0045047; GO:0072599; GO:0070972 GO:0000956; 0 3.60E-14 7 211 GO:0006402; RNA catabolic process 0 GO:0006401
0 2.69E-59 6 167 GO:0002181 cytoplasmic translation 0
GO:0006364; 0 2.30E-11 2 305 GO:0016072; ribosome biogenesis 0 GO:0042254
GO:0006412; GO:0043043; 0 8.14E-46 3 554 peptide metabolic process 0 GO:0006518; GO:0043604
0 1.28E-20 3 127 GO:0006413 translational initiation 0
0 3.39E-14 5 273 GO:0006605 protein targeting 0
0 3.51E-11 2 687 GO:0006886 intracellular protein transport 0
ribonucleoprotein complex 0 7.15E-09 2 424 GO:0022613 0 biogenesis protein localization to 0 4.19E-08 2 642 GO:0033365 0 organelle GO:0034655; GO:0019439; organic cyclic compound 0 1.17E-12 2 439 GO:0044270; 0 catabolic process GO:0046700; GO:1901361
161 0 4.07E-10 3 71 GO:0042255 ribosome assembly 0
ribosomal large subunit 0 3.03E-10 2 81 GO:0042273 0 biogenesis
ribosomal small subunit 0 1.32E-06 3 95 GO:0042274 0 biogenesis
cellular amide metabolic 0 9.51E-41 2 654 GO:0043603 0 process
cellular macromolecule 0 1.13E-11 2 689 GO:0044265 0 catabolic process
positive regulation of 0 6.98E-07 4 8 GO:0045901 0 translational elongation
establishment of protein 0 1.03E-11 4 370 GO:0072594 0 localization to organelle protein localization to 0 6.15E-10 2 346 GO:0072657 0 membrane
establishment of protein 0 1.12E-16 4 185 GO:0090150 0 localization to membrane
GO:0090662; ATP hydrolysis coupled 0 6.35E-07 5 50 GO:0099131; 0 transmembrane transport GO:0099132
maintenance of translational 0 2.39E-09 2 7 GO:1990145 0 fidelity
Cellular Component GO:0000322; 0 1.76E-07 5 23 GO:0000324; storage vacuole 0 GO:0000329
GO:0005618; external encapsulating 0 1.41E-14 3 35 0 GO:0030312 structure
GO:0005774; 0 2.28E-08 2 202 GO:0098852; vacuolar membrane 0 GO:0005765
GO:0005840; GO:0044391; 0 3.87E-48 2 346 cytosolic part 0 GO:0022626; GO:0044445 0 1.54E-15 2 75 GO:0005844 polysome 0
162 0 3.00E-08 2 19 GO:0009277 fungal-type cell wall 0
GO:0009506; 0 1.21E-07 3 10 plasmodesma 0 GO:0055044 GO:0009507; 0 6.93E-12 4 17 chloroplast 0 GO:0009536 GO:0015934; 0 1.12E-29 2 129 large ribosomal subunit 0 GO:0022625 GO:0015935; 0 5.41E-19 2 93 small ribosomal subunit 0 GO:0022627 proton-transporting two- 0 7.78E-06 3 36 GO:0016469 0 sector ATPase complex
0 4.97E-08 2 9 GO:0030445 yeast-form cell wall 0
0 2.60E-07 2 11 GO:0030446 hyphal cell wall 0
GO:0031430; 0 1.55E-05 4 51 A band 0 GO:0031672
0 1.22E-12 4 31 GO:0042788 polysomal ribosome 0
0 1.06E-05 2 136 GO:0043209 myelin sheath 0 GO:0062039; 0 1.08E-05 3 6 biofilm matrix 0 GO:0062040
Molecular Function
0 1.76E-06 4 227 GO:0003729 mRNA binding 0
structural constituent of 0 1.39E-53 2 208 GO:0003735 0 ribosome translation elongation factor 0 1.93E-07 3 24 GO:0003746 0 activity
0 1.40E-41 1 420 GO:0005198 structural molecule activity 0
proton transmembrane 0 1.33E-06 3 63 GO:0015078 0 transporter activity
GO:0016462; GO:0016818; 0 2.35E-05 5 616 pyrophosphatase activity 0 GO:0016817; GO:0017111 GO:0016887; 0 5.06E-05 3 394 ATPase activity 0 GO:0042623
163 GO:0019829; ATPase coupled ion GO:0042625; 0 6.03E-07 3 47 transmembrane transporter 0 GO:0022853; activity GO:0015662 0 2.94E-09 4 55 GO:0019843 rRNA binding 0 GO:0036442; proton-exporting ATPase 0 4.06E-10 12 24 GO:0044769; 0 activity GO:0046961
GO:0045182; 0 1.22E-05 1 109 GO:0090079; translation regulator activity 0 GO:0008135
proton-transporting ATP 0 3.97E-08 4 9 GO:0046933 synthase activity, rotational 0 mechanism
large ribosomal subunit 0 4.15E-05 2 8 GO:0070180 0 rRNA binding
164 APPENDIX G
DARK TURQUOISE MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj regulation of translational 0 4.47E-06 2 55 GO:0006446 0 initiation GO:0006986; GO:0035966; response to topologically 0 2.40E-07 3 128 0 GO:0034620; incorrect protein GO:0035967 GO:0010498; proteasomal protein catabolic 0 2.89E-17 2 281 0 GO:0043161 process
GO:0016567; protein modification by small 0 6.78E-12 8 619 GO:0032446; 0 protein conjugation or removal GO:0070647
GO:0016579; protein modification by small 0 8.20E-12 5 219 0 GO:0070646 protein removal
GO:0030433; 0 7.03E-08 3 81 ERAD pathway 0 GO:0036503
response to endoplasmic 0 8.12E-10 2 205 GO:0034976 0 reticulum stress
0 3.15E-06 2 17 GO:0043248 proteasome assembly 0
post-translational protein 0 5.73E-09 2 267 GO:0043687 0 modification
GO:0044257; GO:0030163; GO:0051603; 0 4.81E-19 2 498 protein catabolic process 0 GO:0006511; GO:0019941; GO:0043632
cellular macromolecule catabolic 0 9.10E-15 2 689 GO:0044265 0 process
165 positive regulation of 0 1.38E-05 2 20 GO:0060261 transcription initiation from RNA 0 polymerase II promoter
Cellular Component GO:0000502; 0 3.01E-24 4 78 GO:1905369; peptidase complex 0 GO:1905368 GO:0005838; 0 6.97E-17 2 25 proteasome regulatory particle 0 GO:0022624
0 4.67E-12 2 19 GO:0005839 proteasome core complex 0
proteasome regulatory particle, 0 2.20E-09 2 9 GO:0008541 0 lid subcomplex
proteasome core complex, alpha- 0 1.64E-06 2 8 GO:0019773 0 subunit complex
proteasome core complex, beta- 0 1.32E-07 2 9 GO:0019774 0 subunit complex
0 1.95E-05 2 7 GO:0031595 nuclear proteasome complex 0
GO:0031597; 0 1.32E-10 2 19 cytosolic proteasome complex 0 GO:0008540
GO:0101002; 0 2.66E-05 4 132 ficolin-1-rich granule 0 GO:1904813
Molecular Function translation initiation factor 0 7.40E-05 3 44 GO:0003743 0 activity
0 0.00016 2 210 GO:0004175 endopeptidase activity 0
GO:0032182; 0 1.30E-05 3 66 ubiquitin-like protein binding 0 GO:0043130
proteasome-activating ATPase 0 6.03E-06 2 6 GO:0036402 0 activity
166 APPENDIX H
GREY MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process NA
Cellular Component delta.rank pval level nseqs term name p.adj GO:0005618; 0 5.97E-05 3 35 external encapsulating structure 0 GO:0030312
0 7.08E-05 2 19 GO:0009277 fungal-type cell wall 0 0 0.00013 2 415 GO:0009986 cell surface 0 GO:0030430; 0 0.000142 2 9 host cell cytoplasm 0 GO:0033655
Molecular Function
0 0.000115 2 19 GO:0004629 phospholipase C activity 0
G protein-coupled receptor 0 1.54E-05 4 230 GO:0004930 0 activity phosphoric diester hydrolase 0 0.000114 2 56 GO:0008081 0 activity
G protein-coupled amine receptor 0 0.000635 5 29 GO:0008227 0 activity
GO:0038023; 0 0.00054 2 507 GO:0060089; molecular transducer activity 0 GO:0004888
167 APPENDIX I
BLUE MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj regulation of DNA 0 4.10E-11 3 72 GO:0000018 0 recombination
GO:0000070; 0 4.78E-12 3 173 GO:0000819; mitotic nuclear division 0 GO:0140014
GO:0000077; GO:0031570; 0 2.09E-10 3 112 DNA integrity checkpoint 0 GO:0044773; GO:0044774 GO:0000278; 0 1.88E-19 3 651 mitotic cell cycle 0 GO:1903047 GO:0000280; 0 4.53E-14 5 339 organelle fission 0 GO:0048285
GO:0000712; meiotic chromosome 0 1.93E-06 4 17 0 GO:0051307 separation
GO:0000723; 0 4.24E-16 2 94 telomere organization 0 GO:0032200
GO:0000724; double-strand break repair via 0 2.72E-12 7 95 0 GO:0000725 homologous recombination
GO:0000726; 0 1.97E-08 4 59 non-recombinational repair 0 GO:0006303
DNA double-strand break 0 4.27E-06 3 26 GO:0000729 0 processing
GO:0006259; 0 3.91E-33 2 781 DNA metabolic process 0 GO:0006974 GO:0006260; 0 7.77E-30 5 169 DNA replication 0 GO:0006261
0 7.20E-08 2 21 GO:0006270 DNA replication initiation 0
GO:0006271; 0 3.01E-12 4 24 DNA strand elongation 0 GO:0022616
168 0 5.35E-11 3 95 GO:0006275 regulation of DNA replication 0
0 4.86E-31 2 384 GO:0006281 DNA repair 0
0 3.32E-06 6 79 GO:0006282 regulation of DNA repair 0
GO:0006283; 0 1.23E-11 5 95 nucleotide-excision repair 0 GO:0006289 GO:0006296; DNA damage response, 0 4.37E-09 7 50 GO:0033683; 0 detection of DNA damage GO:0042769
nucleotide-excision repair, 0 1.74E-06 5 22 GO:0006297 0 DNA gap filling
GO:0006302; 0 5.55E-19 2 217 DNA recombination 0 GO:0006310
0 6.53E-12 6 696 GO:0006396 RNA processing 0
GO:0007052; 0 2.37E-06 6 132 spindle assembly 0 GO:0051225
0 3.26E-08 2 33 GO:0007062 sister chromatid cohesion 0
mitotic sister chromatid 0 1.93E-06 2 17 GO:0007064 0 cohesion GO:0007088; 0 1.03E-06 6 190 regulation of nuclear division 0 GO:0051783
GO:0007098; microtubule organizing center 0 4.83E-06 3 111 0 GO:0031023 organization
GO:0007131; reciprocal meiotic 0 7.34E-08 2 49 0 GO:0035825 recombination
female meiotic nuclear 0 9.10E-07 2 68 GO:0007143 0 division
GO:0007346; regulation of cell cycle 0 2.94E-07 2 694 0 GO:0010564 process
GO:0008156; negative regulation of DNA 0 4.13E-09 2 33 0 GO:2000104 replication
GO:0010948; negative regulation of cell 0 9.42E-10 2 277 0 GO:0045930 cycle process
0 7.20E-08 4 21 GO:0016233 telomere capping 0
169 DNA-dependent DNA GO:0031297; 0 2.53E-06 2 31 replication maintenance of 0 GO:0045005 fidelity
GO:0031572; mitotic G2/M transition 0 3.70E-06 2 41 GO:0007095; 0 checkpoint GO:0044818
telomere maintenance via 0 4.59E-07 4 18 GO:0032201 0 semi-conservative replication
regulation of telomere 0 5.13E-06 3 73 GO:0032204 0 maintenance GO:0032392; 0 2.99E-06 2 75 DNA geometric change 0 GO:0032508
regulation of chromosome 0 2.81E-09 5 308 GO:0033044 0 organization
GO:0033045; regulation of chromosome 0 4.37E-08 3 117 0 GO:0051983 segregation
GO:0034470; 0 3.58E-11 2 440 ncRNA metabolic process 0 GO:0034660
GO:0034508; GO:0031055; 0 2.74E-07 2 25 centromere complex assembly 0 GO:0034080; GO:0061641
0 2.06E-06 4 43 GO:0036297 interstrand cross-link repair 0
error-prone translesion 0 1.52E-07 7 17 GO:0042276 0 synthesis
GO:0044772; 0 6.64E-11 4 201 cell cycle phase transition 0 GO:0044770
GO:0044786; 0 9.88E-11 2 47 cell cycle DNA replication 0 GO:0033260
GO:0045739; positive regulation of response 0 1.61E-07 2 67 0 GO:2001022 to DNA damage stimulus
negative regulation of cell 0 2.55E-08 5 378 GO:0045786 0 cycle
170 negative regulation of DNA 0 2.35E-10 2 40 GO:0045910 0 recombination
regulation of DNA metabolic 0 5.18E-15 2 322 GO:0051052 0 process
negative regulation of DNA 0 6.63E-16 4 110 GO:0051053 0 metabolic process
GO:0061982; 0 1.01E-07 3 143 meiosis I cell cycle process 0 GO:0007127
GO:0065004; protein-DNA complex subunit 0 4.02E-08 2 167 0 GO:0071824 organization
GO:0070192; chromosome organization 0 3.00E-13 4 113 0 GO:0045132 involved in meiotic cell cycle
GO:0071103; 0 3.23E-12 5 217 DNA conformation change 0 GO:0006323
0 7.68E-13 2 102 GO:0071897 DNA biosynthetic process 0
nucleic acid phosphodiester 0 5.78E-12 2 223 GO:0090305 0 bond hydrolysis
regulation of DNA-dependent 0 2.66E-12 2 53 GO:0090329 0 DNA replication
GO:0098813; 0 1.10E-15 3 228 chromosome segregation 0 GO:0007059 GO:0140053; 0 3.83E-08 2 84 mitochondrial gene expression 0 GO:0032543
GO:1901988; GO:1901991; 0 4.61E-09 7 194 cell cycle checkpoint 0 GO:0000075; GO:0007093
GO:1901990; regulation of cell cycle phase 0 3.88E-09 2 327 0 GO:1901987 transition
GO:1902749; regulation of cell cycle G2/M 0 2.09E-07 2 138 0 GO:0010389 phase transition
171 GO:1903046; 0 1.55E-11 2 291 GO:0051321; meiotic cell cycle 0 GO:0140013
regulation of response to DNA 0 3.82E-08 2 151 GO:2001020 0 damage stimulus
negative regulation of 0 3.30E-07 2 105 GO:2001251 0 chromosome organization
Cellular Component GO:0000228; 0 1.58E-20 4 465 GO:0044454; nuclear chromosome 0 GO:0000785 GO:0000313; 0 5.10E-06 2 61 organellar ribosome 0 GO:0005761 GO:0000428; GO:0030880; 0 1.32E-08 2 122 RNA polymerase complex 0 GO:0016591; GO:0055029
GO:0000775; chromosome, centromeric 0 1.93E-11 2 121 0 GO:0000776 region
GO:0000777; condensed chromosome, 0 6.11E-05 4 40 0 GO:0000779 centromeric region
GO:0000781; 0 5.23E-08 2 60 chromosome, telomeric region 0 GO:0000784
0 1.85E-18 2 131 GO:0000793 condensed chromosome 0
condensed nuclear 0 6.72E-10 2 70 GO:0000794 0 chromosome
0 1.75E-07 4 9 GO:0000796 condensin complex 0
0 9.48E-17 2 63 GO:0005657 replication fork 0
GO:0005663; DNA replication factor C 0 3.15E-05 2 6 0 GO:0031390 complex
GO:0005694; 0 1.91E-26 4 699 chromosome 0 GO:0044427 0 2.71E-05 3 700 GO:0005730 nucleolus 0
0 1.96E-07 3 318 GO:0005759 mitochondrial matrix 0
172 GO:0005777; 0 1.65E-06 5 128 peroxisome 0 GO:0042579 GO:0005813; 0 4.16E-05 4 485 microtubule organizing center 0 GO:0005815
GO:0005814; microtubule organizing center 0 9.29E-07 3 118 0 GO:0044450 part 0 4.22E-06 3 254 GO:0005819 spindle 0 0 1.10E-07 4 462 GO:0016604 nuclear body 0 GO:0030894; 0 2.36E-05 2 17 replisome 0 GO:0043601
GO:0031261; DNA replication preinitiation 0 3.15E-05 2 6 0 GO:0000811 complex
GO:0031967; 0 2.06E-06 2 943 GO:0044429; organelle envelope 0 GO:0031975
0 8.37E-06 2 42 GO:0032040 small-subunit processome 0
0 1.73E-05 2 83 GO:0032993 protein-DNA complex 0
0 7.50E-06 2 9 GO:0042555 MCM complex 0
0 4.69E-11 2 35 GO:0043596 nuclear replication fork 0
0 9.92E-14 3 725 GO:0044451 nucleoplasm part 0
transferase complex, 0 3.45E-08 3 195 GO:0061695 transferring phosphorus- 0 containing groups
0 7.60E-14 4 223 GO:0098687 chromosomal region 0
0 5.74E-05 3 183 GO:0098798 mitochondrial protein complex 0
0 1.06E-05 2 33 GO:1990391 DNA repair complex 0
Molecular Function 0 3.85E-06 2 50 GO:0003684 damaged DNA binding 0
0 7.87E-09 5 84 GO:0003697 single-stranded DNA binding 0
0 2.53E-10 2 141 GO:0004518 nuclease activity 0 173 GO:0004519; 0 2.61E-09 3 114 endonuclease activity 0 GO:0004540
GO:0008168; transferase activity, 0 9.75E-06 4 146 GO:0016741; 0 transferring one-carbon groups GO:0008757
GO:0008170; GO:0008276; 0 2.95E-05 5 79 N-methyltransferase activity 0 GO:0016278; GO:0016279
GO:0008408; 0 8.71E-05 2 59 GO:0004527; exonuclease activity 0 GO:0016796
0 2.49E-09 4 99 GO:0016779 nucleotidyltransferase activity 0
hydrolase activity, acting on 0 1.08E-06 3 478 GO:0016788 0 ester bonds
0 0.0001305 2 34 GO:0030515 snoRNA binding 0
GO:0034061; 0 1.70E-09 3 28 DNA polymerase activity 0 GO:0003887
0 8.42E-05 5 9 GO:0036310 annealing helicase activity 0
catalytic activity, acting on 0 8.66E-14 2 181 GO:0140097 0 DNA
catalytic activity, acting on 0 1.89E-07 2 291 GO:0140098 0 RNA
174 APPENDIX J
BROWN MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj GO:0001654; 0 1.71E-06 2 675 sensory organ development 0 GO:0007423 ameboidal-type cell 0 3.38E-08 4 271 GO:0001667 0 migration GO:0001775; GO:0002252; 0.0012 0 7.39E-06 2 848 immune response GO:0045321; 6582 GO:0006955 nematode larval 0.0010 0 2.25E-05 3 208 GO:0002119 development 989 GO:0002165; GO:0007444; GO:0007560; GO:0048563; post-embryonic animal 0 5.55E-11 3 543 0 GO:0009886; organ development GO:0007552; GO:0048569; GO:0048707 GO:0002253; activation of immune 0 1.20E-08 2 159 GO:0002757; 0 response GO:0002764
GO:0002429; immune response- 0 1.65E-07 4 97 GO:0002768; regulating cell surface 0 GO:0038093 receptor signaling pathway
GO:0002431; GO:0002433; Fc-gamma receptor 0.0017 0 7.45E-05 5 45 GO:0038094; signaling pathway 3913 GO:0038096
post-Golgi vesicle- 0.0011 0 1.76E-05 2 82 GO:0006892 mediated transport 3636 GO:0006897; 0 4.95E-14 5 460 import into cell 0 GO:0098657 receptor-mediated 0 4.44E-07 2 156 GO:0006898 0 endocytosis 0.0010 0 2.82E-05 5 164 GO:0006909 phagocytosis 6383 175 GO:0006935; GO:0042330; GO:0007409; 0 2.05E-09 2 641 taxis 0 GO:0007411; GO:0097485; GO:0061564
0 4.01E-07 4 140 GO:0007015 actin filament organization 0
GO:0007155; 0 2.60E-07 2 349 biological adhesion 0 GO:0022610 GO:0007160; 0 5.69E-07 2 97 cell-substrate adhesion 0 GO:0031589 establishment or 0 3.79E-06 2 276 GO:0007163 maintenance of cell 0 polarity
GO:0007167; enzyme linked receptor 0 7.17E-08 5 440 0 GO:0007169 protein signaling pathway
GO:0007264; small GTPase mediated 0 3.19E-07 2 170 0 GO:0007265 signal transduction
0 4.99E-08 2 654 GO:0007267 cell-cell signaling 0
GO:0007389; pattern specification 0.0011 0 1.51E-05 2 677 GO:0003002 process 4943
open tracheal system 0 1.32E-06 4 178 GO:0007424 0 development
GO:0007472; GO:0007476; GO:0035114; 0 1.18E-11 2 269 wing disc development 0 GO:0035120; GO:0035220; GO:0048737 post-embryonic 0 2.30E-10 2 658 GO:0009791 0 development 0.0010 0 2.82E-05 2 245 GO:0009798 axis specification 7527 endomembrane system 0 3.86E-08 2 346 GO:0010256 0 organization GO:0010631; 0 4.61E-07 2 172 GO:0090130; tissue migration 0 GO:0090132
positive regulation of 0.0033 0 8.93E-05 6 457 GO:0010638 organelle organization 8983
176 GO:0010720; positive regulation of cell 0 1.16E-08 2 509 GO:0050769; 0 development GO:0051962
GO:0010721; negative regulation of 0.0009 0 5.04E-05 2 342 GO:0050768; nervous system 2593 GO:0051961 development
GO:0010769; regulation of cell 0 5.14E-11 3 386 0 GO:0022604 morphogenesis
positive regulation of cell 0 1.03E-07 3 159 GO:0010770 morphogenesis involved in 0 differentiation
GO:0010975; regulation of cell 0 1.46E-13 3 558 GO:0120035; 0 projection organization GO:0031344
GO:0010976; positive regulation of cell 0 3.67E-09 7 410 GO:0031346; 0 projection organization GO:0045666
0.0033 0 9.08E-05 2 292 GO:0016050 vesicle organization 6134
GO:0016055; cell surface receptor 0.0010 0 2.99E-05 6 216 GO:1905114; signaling pathway involved 5263 GO:0198738 in cell-cell signaling
0.000101 GO:0016310; 0.0033 0 5 705 phosphorylation 1 GO:0006468 0579 mushroom body 0.0009 0 4.54E-05 2 57 GO:0016319 development 6154 GO:0016477; 0 2.26E-09 3 768 GO:0048870; localization of cell 0 GO:0051674
0 1.64E-08 3 126 GO:0016482 cytosolic transport 0
GO:0022407; positive regulation of cell 0.0017 0 6.36E-05 2 252 GO:0045785 adhesion 8571
regulation of anatomical 0 4.30E-16 2 704 GO:0022603 0 structure morphogenesis
0.000109 0.0040 0 4 162 GO:0022612 gland morphogenesis 1 6504
177 GO:0030036; actin filament-based 0 7.28E-12 3 396 0 GO:0030029 process
0 3.73E-08 3 233 GO:0030100 regulation of endocytosis 0
0.000127 0.0047 0 2 333 GO:0030155 regulation of cell adhesion 7 619 GO:0030334; GO:2000145; 0 2.35E-11 2 724 regulation of locomotion 0 GO:0040012; GO:0051270
GO:0030335; GO:2000147; positive regulation of 0 9.67E-10 3 406 0 GO:0040017; locomotion GO:0051272
GO:0030516; regulation of extent of cell 0.0012 0 6.85E-06 7 118 GO:0061387 growth 987
regulation of intracellular 0.0010 0 3.09E-05 2 294 GO:0032386 transport 4167
GO:0032535; regulation of cellular 0 3.92E-08 2 323 0 GO:0008361 component size
GO:0032880; GO:0070201; regulation of protein 0 3.53E-08 3 727 0 GO:0090087; localization GO:0051223
GO:0032956; regulation of actin 0.0012 0 7.79E-06 5 270 GO:0032970; filament-based process 1951 GO:1902903 GO:0032990; GO:0000902; GO:0000904; GO:0048812; 0 8.79E-12 2 865 cell morphogenesis 0 GO:0120039; GO:0048858; GO:0031175; GO:0048667 GO:0034329; 0.0017 0 8.24E-05 4 183 GO:0045216; cell junction organization 094 GO:0034330 adherens junction 0 1.07E-06 2 54 GO:0034332 0 organization 0.0009 0 4.68E-05 2 35 GO:0034333 adherens junction assembly 5238
178 substrate adhesion- 0 8.49E-08 4 33 GO:0034446 0 dependent cell spreading
GO:0035107; 0 1.00E-10 3 340 appendage development 0 GO:0048736 0.0010 0 3.46E-05 3 47 GO:0035112 genitalia morphogenesis 2041 GO:0035239; 0 1.83E-12 3 888 GO:0002009; tube morphogenesis 0 GO:0060562 GO:0036465; 0 3.64E-07 2 68 GO:0048488; synaptic vesicle recycling 0 GO:0140238 GO:0040008; 0 1.43E-08 2 679 regulation of growth 0 GO:0048638 GO:0042058; GO:1901184; regulation of ERBB 0 1.58E-06 6 82 0 GO:0042059; signaling pathway GO:1901185 GO:0043087; regulation of GTPase 0 1.63E-07 5 231 0 GO:0043547 activity GO:0043408; regulation of MAPK 0.0009 0 4.35E-05 2 429 GO:0043410 cascade 7087
positive regulation of cell 0 1.73E-09 2 627 GO:0045597 0 differentiation
GO:0045664; 0 9.42E-10 2 671 regulation of neurogenesis 0 GO:0050767
GO:0045773; positive regulation of 0 6.05E-08 3 98 0 GO:0050772 axonogenesis
0.000133 positive regulation of 0.0047 0 3 152 GO:0045807 4 endocytosis 2441
GO:0045927; positive regulation of 0 5.11E-09 2 326 0 GO:0048639 growth
regulation of small GTPase GO:0046578; 0 4.52E-06 2 180 mediated signal 0 GO:0051056 transduction
protein 0.0013 0 5.71E-06 2 138 GO:0046777 autophosphorylation 3333
0 4.46E-07 3 283 GO:0048193 Golgi vesicle transport 0
179 0.0017 0 7.33E-05 2 28 GO:0048311 mitochondrion distribution 5439
intracellular distribution of 0.0017 0 7.95E-05 3 17 GO:0048312 mitochondria 2414
GO:0048562; embryonic organ 0.0011 0 1.08E-05 2 370 GO:0048568 development 9048 0.0010 0 2.36E-05 2 630 GO:0048598 embryonic morphogenesis 8696 GO:0048754; morphogenesis of a 0.0009 0 5.68E-05 3 186 GO:0061138; branching structure 0909 GO:0001763 0.0011 0 1.09E-05 2 80 GO:0048806 genitalia development 7647
regulation of dendrite 0.0009 0 4.85E-05 2 102 GO:0048814 morphogenesis 3458
0 3.02E-06 7 183 GO:0050770 regulation of axonogenesis 0
regulation of dendrite 0 2.29E-07 4 163 GO:0050773 0 development
GO:0050803; regulation of synapse 0 6.75E-07 2 263 0 GO:0050807 structure or activity
GO:0050804; modulation of chemical 0.0010 0 3.54E-05 2 277 GO:0099177 synaptic transmission 101
0 2.09E-06 2 231 GO:0050808 synapse organization 0
positive regulation of 0.0009 0 5.89E-05 2 663 GO:0051050 transport 009
0.000126 regulation of DNA-binding 0 6 240 GO:0051090 0.0048 3 transcription factor activity
GO:0051093; negative regulation of 0 2.96E-07 4 693 0 GO:0045596 developmental process
negative regulation of 0 6.31E-07 5 535 GO:0051129 cellular component 0 organization GO:0051146; 0 3.12E-07 4 224 muscle cell differentiation 0 GO:0042692 GO:0051336; regulation of hydrolase 0.0009 0 4.83E-05 4 620 GO:0051345 activity 434
180 GO:0051963; regulation of synapse 0 1.00E-07 3 164 GO:0008582; 0 assembly GO:1904396 GO:0055001; 0 1.55E-06 2 121 muscle cell development 0 GO:0055002
GO:0060071; regulation of establishment 0 3.78E-05 2 61 0.001 GO:0090175 of planar polarity
regulation of cellular 0 1.80E-09 3 646 GO:0060341 0 localization
0.0010 0 3.17E-05 2 34 GO:0060438 trachea development 3093
respiratory system 0 4.41E-06 2 331 GO:0060541 0 development
regulation of vesicle- 0 5.15E-12 2 405 GO:0060627 0 mediated transport
0 4.89E-07 2 583 GO:0061024 membrane organization 0
muscle structure 0 2.04E-07 2 431 GO:0061061 0 development GO:0061458; reproductive system 0.0009 0 3.81E-05 3 402 GO:0007548; development 901 GO:0048608
GO:0061572; actin filament bundle 0.0013 0 6.00E-06 2 42 GO:0051017 organization 1579
GO:0070848; 0.0012 0 7.49E-06 2 348 response to growth factor GO:0071363 5
cellular response to 0.0012 0 7.33E-06 2 727 GO:0071495 endogenous stimulus 8205
GO:0072358; GO:0001568; cardiovascular system 0 6.77E-07 4 339 0 GO:0001944; development GO:0048514
GO:0072359; circulatory system 0 1.99E-08 3 613 0 GO:0007507 development
protein localization to 0 7.97E-11 6 112 GO:0072659 0 plasma membrane
181 regulation of anatomical 0 1.07E-07 2 511 GO:0090066 0 structure size
regulation of cellular 0.0012 0 1.01E-05 2 231 GO:0090287 response to growth factor 0482 stimulus negative regulation of GO:0090288; 0.0009 0 5.48E-05 5 124 cellular response to growth GO:0090101 1743 factor stimulus
supramolecular fiber 0 1.27E-09 2 273 GO:0097435 0 organization GO:0097581; lamellipodium 0.0012 0 7.70E-06 3 27 GO:0030032 organization 3457 0.0011 0 1.28E-05 2 170 GO:0098609 cell-cell adhesion 6279
GO:0099003; vesicle-mediated transport 0 2.22E-08 5 172 0 GO:0099504 in synapse
GO:0110053; regulation of actin filament 0.0009 0 4.03E-05 2 195 GO:0051495; organization 8039 GO:1902905
0.000115 GO:0120032; regulation of cell 0.0040 0 3 151 6 GO:0060491 projection assembly 3226
positive regulation of 0 8.55E-07 3 570 GO:1902533 intracellular signal 0 transduction
positive regulation of axon 0.0011 0 2.11E-05 2 33 GO:1902669 guidance 1111
GO:1903530; 0.0011 0 1.77E-05 2 502 regulation of secretion GO:0051046 236
0.000105 GO:1903649; regulation of cytoplasmic 0.0032 0 2 33 2 GO:1903651 transport 7869
regulation of cellular 0 8.66E-07 5 353 GO:1903827 0 protein localization
GO:1904948; midbrain dopaminergic 0.0033 0 9.96E-05 4 14 GO:1904953 neuron differentiation 3333
regulation of protein 0 3.80E-06 6 105 GO:1905475 0 localization to membrane
182 positive regulation of 0.0017 0 6.62E-05 2 68 GO:1905477 protein localization to 6991 membrane protein localization to cell 0 1.00E-09 3 163 GO:1990778 0 periphery
regulation of animal organ 0 1.34E-06 2 200 GO:2000027 0 morphogenesis
Cellular Component GO:0000139; 0 4.40E-12 3 509 GO:0044431; Golgi apparatus part 0 GO:0098791
GO:0000323; 0.000154 GO:0005773; 0 4 513 vacuole 0 9 GO:0005764; GO:0044437 0.007773 0.0340 0 2 28 GO:0000803 sex chromosome 8 9091 0 9.56E-05 2 87 GO:0001726 ruffle 0 GO:0005766; 0.008257 0.0355 0 5 122 GO:0035578; azurophil granule 4 5556 GO:0042582 0 3.63E-12 4 623 GO:0005768 endosome 0 0 3.75E-10 5 253 GO:0005769 early endosome 0 0.000537 0 2 163 GO:0005770 late endosome 0 6 GO:0005774; 0.000221 0 2 202 GO:0098852; vacuolar membrane 0 5 GO:0005765 0.000627 0 5 66 GO:0005776 autophagosome 0 8 0.010145 smooth endoplasmic 0 5 34 GO:0005790 0.0375 5 reticulum
endoplasmic reticulum- 0.013464 0.0441 0 4 85 GO:0005793 Golgi intermediate 9 1765 compartment GO:0005795; 0 1.37E-06 2 107 Golgi stack 0 GO:0031985 0.000522 0 2 24 GO:0005797 Golgi medial cisterna 0 1
0 2.11E-06 2 149 GO:0005798 Golgi-associated vesicle 0
183 0 8.34E-06 2 162 GO:0005802 trans-Golgi network 0
0.014753 0.0495 0 4 41 GO:0005884 actin filament 8 3271 GO:0005901; 0 7.59E-06 4 67 plasma membrane raft 0 GO:0044853 0 4.17E-06 2 251 GO:0005911 cell-cell junction 0 GO:0005912; 0 2.62E-07 2 174 anchoring junction 0 GO:0070161
0 4.00E-07 3 46 GO:0005913 cell-cell adherens junction 0
0.0194 0 0.004961 4 31 GO:0005923 bicellular tight junction 8052 GO:0005924; 0.0017 0 0.000814 3 132 GO:0030055; cell-substrate junction 8571 GO:0005925 0.000594 GO:0005938; 0 2 390 cytoplasmic region 0 7 GO:0099568 GO:0008021; 0 8.40E-05 6 155 exocytic vesicle 0 GO:0070382 GO:0010008; 0 5.63E-05 2 302 endosomal part 0 GO:0044440 GO:0012506; 0 9.27E-08 3 417 vesicle membrane 0 GO:0030659 0.003801 ER to Golgi transport 0.0166 0 2 30 GO:0012507 6 vesicle membrane 6667
GO:0014069; GO:0099572; 0 4.40E-06 2 153 neuron to neuron synapse 0 GO:0032279; GO:0098984 0.001256 0.0049 0 3 218 GO:0015629 actin cytoskeleton 2 1803 basolateral plasma 0.0234 0 0.005906 3 167 GO:0016323 membrane 5679 0.008746 GO:0016324; 0.0369 0 3 384 apical part of cell 9 GO:0045177 5652 0.001353 0.0046 0 2 5 GO:0016600 flotillin complex 3 875 0.003820 extrinsic component of 0.0164 0 2 127 GO:0019897 4 plasma membrane 3836
0.000922 extrinsic component of 0.0052 0 2 227 GO:0019898 9 membrane 6316
184 GO:0030016; 0.008708 GO:0043292; 0.0362 0 2 165 contractile fiber 1 GO:0030017; 6374 GO:0044449 GO:0030018; 0.0226 0 0.006022 4 79 I band GO:0031674 1905 0 1.20E-07 4 116 GO:0030027 lamellipodium 0 0 2.39E-07 1 431 GO:0030054 cell junction 0 0.000768 0.0018 0 2 25 GO:0030118 clathrin coat 3 1818 0.012887 GO:0030131; 0 2 16 clathrin vesicle coat 0.043 8 GO:0030125 0.014121 0.0443 0 3 12 GO:0030132 clathrin coat of coated pit 4 3962 0 1.32E-07 2 255 GO:0030133 transport vesicle 0 0 1.81E-06 2 201 GO:0030135 coated vesicle 0
0 9.03E-08 3 101 GO:0030136 clathrin-coated vesicle 0
0 8.00E-05 5 211 GO:0030139 endocytic vesicle 0
trans-Golgi network 0 0.000345 2 23 GO:0030140 0 transport vesicle
0.002238 GO:0030141; 0.0073 0 5 614 secretory vesicle 4 GO:0099503 5294 0.005958 0.0228 0 2 27 GO:0030315 T-tubule 4 9157 0 2.70E-12 2 500 GO:0030424 axon 0 GO:0030425; 0 3.48E-09 2 478 dendrite 0 GO:0097447 GO:0030426; 0 3.18E-05 3 165 site of polarized growth 0 GO:0030427 0.001181 Golgi-associated vesicle 0 2 59 GO:0030660 0.005 7 membrane 0.000243 0 4 97 GO:0030662 coated vesicle membrane 0 5 0.005831 clathrin-coated vesicle 0.0237 0 2 57 GO:0030665 6 membrane 5 secretory granule 0.0337 0 0.007826 3 170 GO:0030667 membrane 0787 0.002867 GO:0030669; clathrin-coated endocytic 0 5 29 0.01 5 GO:0045334 vesicle 0.001575 GO:0030904; 0.0061 0 2 8 retromer complex 6 GO:0030906 5385
185 0.001353 0.0046 0 2 5 GO:0031209 SCAR complex 3 875 extrinsic component of 0 7.24E-05 3 71 GO:0031234 cytoplasmic side of plasma 0 membrane 0 1.43E-07 2 261 GO:0031252 cell leading edge 0 0.013618 GO:0031254; 0.0432 0 2 8 cell trailing edge 1 GO:0001931 6923 0.012630 extrinsic component of 0.0434 0 2 30 GO:0031312 1 organelle membrane 3434 0.013070 GO:0031430; 0.0425 0 2 51 A band 2 GO:0031672 7426
0 2.01E-05 2 80 GO:0031594 neuromuscular junction 0
0 4.78E-06 4 79 GO:0031901 early endosome membrane 0
0.001695 0.0060 0 4 23 GO:0032580 Golgi cisterna membrane 1 6061 0.007053 0.0321 0 5 23 GO:0032839 dendrite cytoplasm 9 8391
endoplasmic reticulum- 0.009251 0.0378 0 4 49 GO:0033116 Golgi intermediate 3 9474 compartment membrane
GO:0033267; 0 1.69E-08 2 291 axon part 0 GO:0150034 0.000999 azurophil granule 0.0051 0 2 30 GO:0035577 3 membrane 7241 0.001009 0.0050 0 4 14 GO:0036062 presynaptic periactive zone 5 8475 0.0387 0 0.009149 2 82 GO:0042383 sarcolemma 0968 GO:0042641; 0.000418 GO:0001725; 0 3 36 actomyosin 0 8 GO:0097517; GO:0032432 0.000173 0 3 90 GO:0043195 terminal bouton 0 6 GO:0043256; 0 3.98E-05 3 5 GO:0043259; laminin-10 complex 0 GO:0043260 GO:0043679; 0 3.26E-06 2 141 neuron projection terminus 0 GO:0044306 0.005220 0.0215 0 2 46 GO:0044295 axonal growth cone 8 1899
186 GO:0044297; 0 1.54E-05 2 592 cell body 0 GO:0043025 0.011343 0.0397 0 4 125 GO:0044448 cell cortex part 4 9592 GO:0044456; 0 3.36E-17 1 579 synapse 0 GO:0045202 GO:0045121; 0.002303 0.0072 0 3 206 GO:0098857; membrane region 9 4638 GO:0098589 0.000606 0 4 97 GO:0045211 postsynaptic membrane 0 6 0.000215 0 2 86 GO:0045335 phagocytic vesicle 0 4 perinuclear region of 0 4.42E-06 4 471 GO:0048471 0 cytoplasm
0 2.33E-05 2 53 GO:0048786 presynaptic active zone 0
0.000399 0 5 116 GO:0055037 recycling endosome 0 8
0.003801 recycling endosome 0.0166 0 2 30 GO:0055038 6 membrane 6667
0.005958 0.0228 0 4 27 GO:0055120 striated muscle dense body 4 9157 0.0217 0 0.00519 2 22 GO:0061174 type I terminal bouton 9487 0.003918 0.0162 0 3 13 GO:0070822 Sin3-type complex 4 1622 0.004876 GO:0070852; 0.0171 0 2 56 cell body fiber 8 GO:0009925 0526 0.006100 0.0232 0 4 14 GO:0071437 invadopodium 1 5581
0.006100 endoplasmic reticulum 0.0232 0 4 14 GO:0071782 1 tubular network 5581
0.013618 ER membrane protein 0.0432 0 3 8 GO:0072546 1 complex 6923
0 7.80E-06 2 151 GO:0097060 synaptic membrane 0
0.010688 0.0381 0 2 341 GO:0098552 side of membrane 1 4433 0.000445 GO:0098562; cytoplasmic side of 0 2 173 0 1 GO:0009898 membrane
0 3.52E-09 2 676 GO:0098590 plasma membrane region 0
187 0 5.07E-10 2 302 GO:0098793 presynapse 0 0 2.91E-06 2 254 GO:0098794 postsynapse 0 0.004087 0 2 578 GO:0098796 membrane protein complex 0.016 7 plasma membrane protein 0 7.28E-05 2 216 GO:0098797 0 complex 0.013876 0.0428 0 4 127 GO:0098858 actin-based cell projection 2 5714 GO:0099512; 0.001262 0.0048 0 3 368 GO:0099081; supramolecular complex 7 3871 GO:0099080 neuron projection 0.0074 0 0.002041 2 37 GO:0120111 cytoplasm 6269 0.009226 periciliary membrane 0.0382 0 3 11 GO:1990075 1 compartment 9787
Molecular Function 0.003518 RNA polymerase II 0.0333 0 4 87 GO:0001085 9 transcription factor binding 3333 0.004970 transcription cofactor 0.0410 0 3 22 GO:0001221 7 binding 7143 0.002925 G protein-coupled receptor 0.0340 0 4 114 GO:0001664 6 binding 9091 phosphotyrosine residue 0 4.42E-06 3 22 GO:0001784 0 binding 0 3.18E-05 4 166 GO:0003779 actin binding 0
0.001285 GO:0004438; phosphatidylinositol-3- 0.0151 0 8 11 4 GO:0052744 phosphatase activity 5152
GO:0004672; 0 4.27E-06 2 461 GO:0016301; kinase activity 0 GO:0016773 protein serine/threonine 0 1.53E-06 3 223 GO:0004674 0 kinase activity GO:0004675; transmembrane receptor 0.003539 GO:0005024; 0.0326 0 5 6 protein serine/threonine 9 GO:0017002; 9231 kinase activity GO:0098821
non-membrane spanning 0.000128 0.0055 0 2 21 GO:0004715 protein tyrosine kinase 7 5556 activity
188 0.000327 phosphoprotein 0.0037 0 4 94 GO:0004721 3 phosphatase activity 037 0.000183 protein tyrosine 0.0052 0 5 38 GO:0004725 8 phosphatase activity 6316 non-membrane spanning 0.0142 0 0.001317 2 5 GO:0004726 protein tyrosine 8571 phosphatase activity
0.001612 GO:0005041; lipoprotein particle 0.0210 0 3 23 9 GO:0030228 receptor activity 5263 0.002048 GO:0005085; guanyl-nucleotide 0 4 146 0.02 7 GO:0005088 exchange factor activity
0.004934 Rho guanyl-nucleotide 0.0418 0 3 36 GO:0005089 2 exchange factor activity 1818
GO:0005096; 0.000320 nucleoside-triphosphatase 0.0041 0 4 183 GO:0030695; 1 regulator activity 6667 GO:0060589 0.000740 epidermal growth factor 0.0062 0 2 21 GO:0005154 7 receptor binding 5 GO:0005165; 0.001525 neurotrophin receptor 0.0189 0 5 8 GO:0005167; 3 binding 1892 GO:0005168 0.005374 0.0491 0 2 90 GO:0005178 integrin binding 9 2281 0.000205 0 4 6 GO:0005523 tropomyosin binding 0.005 5 GO:0005543; 0 2.83E-06 2 366 lipid binding 0 GO:0008289
0.000342 phosphatidylinositol-3,4,5- 0.0035 0 2 40 GO:0005547 4 trisphosphate binding 7143
cytoskeletal protein 0 4.70E-07 2 494 GO:0008092 0 binding 0.003840 0.0358 0 2 426 GO:0008134 transcription factor binding 8 4906 0.000326 0.0038 0 3 27 GO:0015026 coreceptor activity 8 4615
transferase activity, 0.000362 0.0034 0 3 574 GO:0016772 transferring phosphorus- 7 4828 containing groups
GO:0016791; phosphoric ester hydrolase 0 6.18E-05 2 216 0 GO:0042578 activity 0.002607 0.0357 0 7 85 GO:0017048 Rho GTPase binding 1 1429
189 0.003129 0.0355 0 2 54 GO:0017124 SH3 domain binding 6 5556
0 5.40E-05 3 126 GO:0017137 Rab GTPase binding 0
GO:0019900; 0 1.16E-05 4 478 kinase binding 0 GO:0019901 0.000262 GO:0019902; 0.0047 0 4 136 phosphatase binding 9 GO:0019903 619 protein domain specific 0 3.76E-06 3 503 GO:0019904 0 binding 0.002569 GO:0030674; 0.0341 0 2 113 molecular adaptor activity 9 GO:0060090 4634 phosphatidylinositol-3- 0 2.61E-05 2 45 GO:0032266 0 phosphate binding
0.000295 protein kinase A regulatory 0.0043 0 2 9 GO:0034237 6 subunit binding 4783
0.003992 0.0351 0 2 40 GO:0035254 glutamate receptor binding 7 8519 0.002925 GO:0035257; 0.0340 0 5 114 hormone receptor binding 6 GO:0051427 9091 0.003539 GO:0035615; 0.0326 0 2 6 endocytic adaptor activity 9 GO:0098748 9231 0.001911 0.0205 0 3 67 GO:0044325 ion channel binding 2 1282 0.0142 0 0.001317 2 5 GO:0045295 gamma-catenin binding 8571 0.000391 0.0033 0 2 36 GO:0045296 cadherin binding 3 3333
protein phosphorylated 0 8.88E-07 2 33 GO:0045309 0 amino acid binding
0.003387 protein heterodimerization 0.0340 0 2 300 GO:0046982 6 activity 4255 0.003539 0.0326 0 2 6 GO:0048185 activin binding 9 9231 cell adhesion molecule 0 1.40E-05 3 150 GO:0050839 0 binding
0 2.16E-05 2 76 GO:0051015 actin filament binding 0
0.000740 0.0062 0 3 21 GO:0051018 protein kinase A binding 7 5 GO:0051020; 0 3.42E-09 2 371 GO:0017016; GTPase binding 0 GO:0031267 190 0 4.75E-06 3 57 GO:0051219 phosphoprotein binding 0 0.000326 dynein light intermediate 0.0038 0 3 27 GO:0051959 8 chain binding 4615 GO:0052866; phosphatidylinositol 0.003160 GO:0034595; 0.0347 0 5 25 phosphate phosphatase 6 GO:0034593; 8261 activity GO:0106018
0.001429 GO:0061629; DNA-binding transcription 0.0166 0 4 233 9 GO:0140297 factor binding 6667
0.003539 armadillo repeat domain 0.0326 0 2 6 GO:0070016 9 binding 9231
coreceptor activity 0.000283 0.0045 0 2 12 GO:0071936 involved in Wnt signaling 6 4545 pathway
GO:1901981; phosphatidylinositol 0 6.13E-07 5 164 0 GO:0035091 binding
191 APPENDIX K
PINK MODULE: SIGNIFICANTLY ENRICHED
GENE ONTOLOGY TERMS
Biological Process delta.rank pval level nseqs term name p.adj 0.00038 0 2 7 GO:0001757 somite specification 0.0462 643 0.00036 regulation of humoral 0 3 59 GO:0002920 0.0455 833 immune response
GO:0003414; 0.00022 GO:0090171; growth plate cartilage 0 3 6 0.0444 46 GO:0003422; morphogenesis GO:0003429
chondrocyte development 0.00060 0 2 8 GO:0003433 involved in endochondral 0.045 787 bone morphogenesis
0.00039 glycosylceramide metabolic 0 3 16 GO:0006677 0.0429 058 process 0.00040 0 2 241 GO:0007517 muscle organ development 0.0375 734 0.00040 GO:0007586; 0 2 43 digestion 0.04 106 GO:0022600 0.00022 GO:0008228; detection of molecule of 0 3 6 0.0444 46 GO:0032490 bacterial origin
0.00022 0 4 6 GO:0010815 bradykinin catabolic process 0.0444 46 GO:0019229; 0.00056 0 2 103 GO:0045907; regulation of vasoconstriction 0.0368 286 GO:1903524 0.00029 0 3 15 GO:0043171 peptide catabolic process 0.04 824 5.08E- regulation of erythrocyte 0 4 44 GO:0045646 0 07 differentiation 0.00050 GO:0046528; 0 3 17 imaginal disc fusion 0.0333 166 GO:0046529 3.85E- 0 3 6 GO:0046844 chorion micropyle formation 0 06 0.00045 0 2 61 GO:0060021 roof of mouth development 0.0353 323
192 0.00060 GO:0060349; 0 4 64 bone morphogenesis 0.0429 916 GO:0060350
Cellular Component 2.04E- 0 2 9 GO:0005581 collagen trimer 0 05 1.67E- 0 2 541 GO:0005615 extracellular space 0 18 1.86E- 0 4 180 GO:0005788 endoplasmic reticulum lumen 0 05 3.66E- 0 2 415 GO:0009986 cell surface 0 05 5.98E- 0 2 199 GO:0031012 extracellular matrix 0 07 8.40E- transcription factor AP-1 0 2 5 GO:0035976 0.0167 05 complex 0.00016 0 2 6 GO:0098644 complex of collagen trimers 0.0143 548
Molecular Function
0.00044 GO:0000978; proximal promoter sequence- 0 8 189 0.0176 428 GO:0000987 specific DNA binding
GO:0001158; 1.48E- 0 2 49 GO:0035326; enhancer binding 0 05 GO:0000980
DNA-binding transcription 7.54E- 0 4 78 GO:0001227 repressor activity, RNA 0.025 05 polymerase II-specific
DNA-binding transcription 2.58E- GO:0001228; 0 4 164 activator activity, RNA 0.02 05 GO:0001216 polymerase II-specific
0.00012 GO:0001733; galactose 3-O- 0 4 5 0.02 028 GO:0050694 sulfotransferase activity
0.00065 GO:0003690; sequence-specific DNA 0 2 477 0.0211 226 GO:0043565 binding
0.00023 GO:0003700; DNA-binding transcription 0 2 474 0.0214 969 GO:0000981 factor activity
2.81E- 0 5 210 GO:0004175 endopeptidase activity 0.0143 05 0.00329 0 2 27 GO:0004177 aminopeptidase activity 0.0375 023
193 0.00063 GO:0004185; serine-type carboxypeptidase 0 2 8 0.0222 954 GO:0070008 activity
0.00115 cysteine-type endopeptidase 0 2 70 GO:0004197 0.0286 168 activity
GO:0004252; 1.78E- 0 2 95 GO:0008236; serine hydrolase activity 0 06 GO:0017171
0.00043 GO:0004553; hydrolase activity, acting on 0 2 78 0.0188 222 GO:0016798 glycosyl bonds
0.00018 extracellular matrix structural 0 2 37 GO:0005201 0.0154 741 constituent
intracellular calcium 0.00041 GO:0005229; 0 3 16 activated chloride channel 0.02 669 GO:0061778 activity
0.00013 0 2 35 GO:0008235 metalloexopeptidase activity 0.0167 6 9.57E- 0 5 124 GO:0008237 metallopeptidase activity 0 07 0.00013 0 5 66 GO:0008238 exopeptidase activity 0.0182 419 oxidoreductase activity, 0.00088 acting on paired donors, with 0 3 108 GO:0016705 0.025 991 incorporation or reduction of molecular oxygen
0.00314 platelet-derived growth factor 0 2 4 GO:0048407 0.0348 351 binding
2.80E- GO:0051213; 0 3 69 dioxygenase activity 0.0167 05 GO:0016706 0.00132 metalloaminopeptidase 0 2 10 GO:0070006 0.0273 404 activity 4.49E- GO:0070011; 0 2 366 peptidase activity 0 09 GO:0008233
GO:1990837; GO:0000976; 8.20E- GO:0044212; regulatory region nucleic acid 0 3 386 0.0222 05 GO:0001067; binding GO:0000977; GO:0001012
194 APPENDIX L
SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN ‘SEEP’
AND ‘NON-SEEP’ SAMPLES OF C. delta
Best log2 BLAST/UniRef GeneID baseMean lfcSE stat pvalue padj FoldChange match tigger UniRef90_ transposable 0.0000 UPI00077 466.210 -6.638 1.175 -5.651 1.6E-08 element-derived 84 AFDBE protein 6-like uncharacterized UniRef90_ 0.0439 protein UPI00106 21.974 -5.235 1.322 -3.959 7.5E-05 93 LOC114519401 CE96B uncharacterized protein XP_00216 0.0022 34.459 -4.464 0.928 -4.812 1.5E-06 LOC100210439 5361.1 42 Ê ZP domain- UniRef90_ 0.0020 containing UPI00106 72.582 -3.865 0.798 -4.844 1.3E-06 58 protein-like BBB54
Putative UniRef90_ 0.0001 exonuclease A0A2B4S 89.904 -3.852 0.704 -5.468 4.5E-08 59 R569 GH9 Tyrosine kinase UniRef90_ 0.0141 receptor A0A2B4RI 112.460 -3.052 0.704 -4.332 1.5E-05 34 Cad96Ca L2 protein N-
Under expressed genes terminal UniRef90_ 0.0089 glutamine UPI00106 235.671 -2.796 0.627 -4.457 8.3E-06 08 amidohydrolase- D458D like 2'-5'- UniRef90_ oligoadenylate 0.0439 UPI0010F 233.110 -2.183 0.550 -3.966 7.3E-05 synthase 1-like 93 CD1E6 isoform X1 uncharacterized UniRef90_ 0.0167 protein UPI000A2 238.347 -2.111 0.493 -4.284 1.8E-05 82 LOC110249269 A8292 Fibrinogen C sp|Q95LU3 domain- 0.0439 |FBCD1_M 74.628 -1.884 0.472 -3.994 6.5E-05 containing 93 ACFA protein 1 coiled-coil UniRef90_ domain- 0.0249 A0A3M6U 188.355 2.780 0.667 4.167 3.1E-05 containing 96 XQ3 protein 180-like zincgenes finger UniRef90_ 0.0023 protein 862-like UPI000719 79.043 2.963 0.620 4.779 1.8E-06 Over expressed 08 isoform X1 C087 195 uncharacterized UniRef90_ protein 0.0463 UPI00106 36.705 3.156 0.806 3.915 9.1E-05 LOC114532802 95 B6D7C isoform X1 uncharacterized UniRef90_ protein 0.0286 UPI001069 43.207 3.318 0.807 4.110 4.0E-05 LOC114532427 74 2E76 isoform X1 UniRef90_ 0.0425 uncharacterized UPI000265 55.972 3.579 0.893 4.010 6.1E-05 48 protein 486C uncharacterized UniRef90_ 0.0010 protein UPI00106 258.889 3.781 0.748 5.055 4.3E-07 07 LOC114538154 BD126 UniRef90_ 0.0463 UPI0010F 497.545 4.574 1.170 3.909 9.3E-05 95 protein fosB-like CC953 GRIP and coiled-coil UniRef90_ 0.0012 domain- UPI000948 37.787 4.763 0.960 4.961 7.0E-07 28 containing 25F1 protein 2-like Integrase catalytic UniRef90_ 0.0023 domain- A0A3P9N5 241.752 5.544 1.158 4.787 1.7E-06 08 containing W4 protein uncharacterized UniRef90_ 0.0000 protein UPI000E6 139.621 5.736 0.953 6.021 1.7E-09 12 PF11_0213-like B1588 uncharacterized UniRef90_ 0.0000 protein UPI001069 76.738 6.522 1.167 5.589 2.3E-08 96 LOC114530582 8C9A
196 APPENDIX M
SIGNIFICANT DIFFERENTIALLY EXPRESSED GENES BETWEEN MC751
AND MC885 SAMPLES OF C. delta
log2 Best BLAST/UniRef baseMea lfc pval GeneID FoldCh stat padj match n SE ue ange tigger transposable - UniRef90_UPI0007 1.1 0.00 0.027 element-derived protein 466.210 -4.254 3.6 7AFDBE 61 0 738 6-like 63 sphingomyelin - UniRef90_UPI0010 0.9 0.00 0.011 phosphodiesterase 2- 186.337 -3.840 3.9 6CBA8E 65 0 761 like 79 - UPF0711 protein UniRef90_UPI0010 1.0 0.00 0.043 136.317 -3.568 3.4 C18orf21 homolog 6A950F 25 1 048 79 - 0.8 0.00 0.011 Uncharacterized protein CapteP218257 106.272 -3.453 3.9 69 0 881 73 - zinc finger protein 239- UniRef90_UPI0010 0.9 0.00 0.029 614.961 -3.304 3.6 like 6BF2BF 10 0 461 31 - Tyrosine kinase receptor UniRef90_A0A2B4 0.7 0.00 0.001 112.460 -3.292 4.6 Cad96Ca RIL2 07 0 468 54 - centromere protein M- UniRef90_UPI0010 0.7 0.00 0.015 200.173 -2.888 3.8 like 69F539 45 0 871 76 - coatomer subunit beta- UniRef90_UPI0010 0.7 0.00 0.014 134.811 -2.860 3.8 like 699A39 34 0 973 96 ectonucleotide
Under expressed genes - pyrophosphatase/phosph UniRef90_UPI0010 0.7 0.00 0.038 449.480 -2.809 3.5 odiesterase family 694E5B 96 0 451 29 member 3-like - 0.7 0.00 0.033 Formin-like protein Drf_GBD 99.217 -2.779 3.5 75 0 655 84 - uncharacterized protein UniRef90_UPI0010 0.7 0.00 0.048 332.266 -2.728 3.4 LOC114528530 6988EA 96 1 230 29 RNA-binding protein - UniRef90_UPI0010 0.7 0.00 0.046 with serine-rich domain 791.025 -2.606 3.4 693982 57 1 889 1-B-like 41 - pre-mRNA-splicing UniRef90_UPI0010 0.5 0.00 0.016 454.850 -2.135 3.8 factor cwf2-like 6BB9E0 54 0 791 56 thyroid transcription - UniRef90_UPI000B 0.5 0.00 0.025 factor 1-associated 212.757 -2.028 3.6 AFD4DD 48 0 358 protein 26 homolog 99 197 - UniRef90_A0A2P8 0.4 0.00 0.002 Large subunit GTPase 1 1463.797 -2.019 4.4 ZBJ4 50 0 550 87 - GATOR complex UniRef90_UPI0010 0.4 0.00 0.049 225.489 -1.627 3.4 protein NPRL2-like 6D635C 77 1 946 12 coiled-coil domain- - UniRef90_UPI0010 0.4 0.00 0.041 containing protein 14- 2457.619 -1.606 3.4 6D71C2 59 0 430 like isoform X1 96 - nucleoporin NDC1-like UniRef90_UPI0010 0.4 0.00 0.045 1067.996 -1.595 3.4 isoform X1 69D8E7 61 1 360 57 RNA-directed DNA UniRef90_A0A2B4 0.3 3.4 0.00 0.041 polymerase from mobile 102.473 1.324 SP02 79 90 0 990 element jockey DELTA-alicitoxin- UniRef90_UPI0010 0.3 3.6 0.00 0.027 1511.287 1.443 Pse2b-like 6A5E53 95 56 0 878 uncharacterized protein UniRef90_UPI0010 0.4 3.5 0.00 0.037 1695.774 1.622 LOC114519108 6A3C8D 58 44 0 187 angiopoietin-1 receptor- UniRef90_UPI0010 0.4 3.7 0.00 0.022 129.307 1.629 like 6BB3E6 36 40 0 790 chromodomain- UniRef90_UPI0010 0.4 3.6 0.00 0.027 helicase-DNA-binding 1568.518 1.783 6CBAEB 87 63 0 738 protein 1-like Chromosome partition UniRef90_A0A517 50533.54 0.5 3.4 0.00 0.046 1.805 protein Smc P5A5 2 24 47 1 533 serine/threonine-protein UniRef90_UPI0010 0.4 4.1 0.00 0.006 kinase PAK 3-like 227.380 1.929
FC9D2C 65 50 0 750 isoform X2 UniRef90_UPI0010 0.5 3.6 0.00 0.028 syntenin-1-like 7139.770 1.976 69C153 42 47 0 191 cysteine protease UniRef90_UPI0010 0.5 3.5 0.00 0.037 2672.520 2.012 ATG4D-like 6D036F 68 43 0 187 expressed genes
1-acylglycerol-3- phosphate O- UniRef90_UPI0010 0.5 3.5 0.00 0.039 4161.967 2.019 Over acyltransferase 694C49 75 12 0 989 ABHD5-like uncharacterized protein UniRef90_UPI0010 0.5 3.6 0.00 0.027 2530.903 2.039 LOC114528583 6D4FB7 58 52 0 878 nose resistant to UniRef90_UPI0010 0.5 3.7 0.00 0.021 fluoxetine protein 6-like 1210.260 2.050 6A125E 44 65 0 457 isoform X1 uncharacterized protein UniRef90_UPI0010 0.5 3.6 0.00 0.027 172.163 2.080 LOC114975128 FC793C 68 61 0 740 uncharacterized protein UniRef90_UPI0010 0.6 3.6 0.00 0.029 106.317 2.218 LOC114526128 6D8FB1 11 30 0 470 cannabinoid receptor UniRef90_UPI0010 0.5 3.8 0.00 0.018 39.974 2.220 type 1A-like 6A113B 81 17 0 177 uncharacterized protein UniRef90_UPI0010 0.6 3.4 0.00 0.049 111.590 2.253 LOC114528616 69C83B 60 12 1 946 protein canopy homolog UniRef90_UPI0010 0.4 5.0 0.00 0.000 52.882 2.264 4-like 6CEB77 51 19 0 438
198 uncharacterized protein UniRef90_UPI0010 0.6 3.4 0.00 0.046 61.721 2.279 LOC114950278 FC83AB 61 48 1 496 zinc finger protein 862- UniRef90_UPI0007 0.6 3.7 0.00 0.024 79.043 2.315 like isoform X1 19C087 23 15 0 473 nuclear factor erythroid UniRef90_UPI0010 13728.20 0.6 3.5 0.00 0.033 2.324 2-related factor 3-like 6ABFE2 9 49 80 0 805 uncharacterized UniRef90_UPI0010 0.6 3.7 0.00 0.019 exonuclease C637.09- 4726.318 2.344 6B4D5E 18 90 0 572 like uncharacterized protein UniRef90_UPI0010 0.5 3.9 0.00 0.011 1866.118 2.353 LOC114528075 6CF130 90 88 0 489 0.4 4.9 0.00 0.000 Uncharacterized protein SMAR001314-PA 347.107 2.360 73 92 0 468 excitatory amino acid UniRef90_UPI0010 0.6 3.7 0.00 0.022 988.748 2.372 transporter 3-like 693D4A 35 37 0 990 uncharacterized protein UniRef90_UPI0010 0.6 3.9 0.00 0.014 34.736 2.394 LOC114538401 6A1110 12 11 0 540 UniRef90_A0A3Q3 0.6 3.6 0.00 0.031 Uncharacterized protein 3580.217 2.425 H183 72 09 0 533 NEDD4-binding protein UniRef90_UPI0010 0.7 3.4 0.00 0.041 3527.813 2.457 1-like 6D39E6 02 98 0 212 uncharacterized protein UniRef90_UPI0010 0.7 3.5 0.00 0.038 1098.380 2.475 LOC114518148 6C78B9 02 28 0 451 transcription factor AP- UniRef90_UPI0010 0.5 4.5 0.00 0.002 102.978 2.512 1-like 6DA64C 52 52 0 075 uncharacterized protein UniRef90_UPI0010 0.6 4.1 0.00 0.006 2274.909 2.570 LOC114531648 6BD8FA 19 52 0 750 uncharacterized protein UniRef90_UPI0010 0.6 3.9 0.00 0.014 187.727 2.587 LOC114531158 6BCF2D 61 14 0 499 RNA-directed DNA UniRef90_A0A2B4 0.5 4.5 0.00 0.002 polymerase from mobile 107.909 2.588 SX38 68 60 0 037 element jockey Zinc finger MYM-type UniRef90_A0A498 0.6 3.7 0.00 0.023 167.332 2.594 1-like protein N342 96 30 0 439 uncharacterized protein UniRef90_UPI0010 0.5 4.5 0.00 0.002 146.122 2.651 LOC114526378 6D3758 85 32 0 185 dual specificity protein UniRef90_A0A3M6 0.6 4.0 0.00 0.010 3124.489 2.663 phosphatase 10-like TS56 63 16 0 668 0.6 3.9 0.00 0.011 Uncharacterized protein tetur12g00070.1 5501.328 2.677 73 78 0 761 uncharacterized protein UniRef90_UPI0010 0.7 3.6 0.00 0.027 66.210 2.688 LOC114974566 FC7C25 35 60 0 740 uncharacterized protein UniRef90_UPI0010 0.7 3.8 0.00 0.019 888.038 2.704 LOC114526069 6B0251 12 00 0 217 deoxyribonuclease-1- UniRef90_UPI0010 0.5 4.8 0.00 0.000 1238.412 2.723 like 6B7B0F 63 40 0 799 transcription factor UniRef90_UPI0010 0.6 4.2 0.00 0.005 5332.238 2.730 MafK-like 6AFE26 42 51 0 004 uncharacterized protein UniRef90_UPI0010 0.7 3.6 0.00 0.025 164.385 2.751 LOC114971009 FCA1D4 46 89 0 940 uncharacterized protein UniRef90_UPI0010 0.6 4.1 0.00 0.007 2661.524 2.756 LOC114521387 6A9878 68 26 0 412
199 interferon-induced, double-stranded RNA- UniRef90_UPI0010 0.6 4.3 0.00 0.003 941.684 2.768 activated protein kinase- 6D21E8 34 65 0 428 like predicted protein 0.7 3.5 0.00 0.039 EDO42325 282.227 2.790 [Nematostella vectensis] 94 13 0 989 0.7 3.9 0.00 0.014 Bcl-2 Bcl-2 123.481 2.815 19 13 0 499 BGLTMP006037- 0.7 3.8 0.00 0.015 980.258 2.823 PA 28 77 0 871 transient receptor potential cation channel UniRef90_UPI0010 0.7 3.6 0.00 0.027 34.476 2.852 subfamily A member 1 9BB749 81 51 0 878 isoform X2 A disintegrin and metalloproteinase with UniRef90_UPI0010 0.7 3.7 0.00 0.024 97.394 2.854 thrombospondin motifs 6AB0FA 68 17 0 473 9-like transcription factor AP- UniRef90_UPI0010 0.6 4.4 0.00 0.002 6565.574 2.855 1-like isoform X1 6A8A30 48 06 0 957 0.8 3.5 0.00 0.033 Uncharacterized protein TriadP20369 26.280 2.916 15 76 0 944 TNF receptor-associated UniRef90_UPI0010 0.7 3.7 0.00 0.024 652.525 2.955 factor 3-like 6CD4BE 96 14 0 473 uncharacterized protein UniRef90_UPI0010 0.8 3.6 0.00 0.026 LOC114542249 isoform 44.698 2.955 69B08F 04 78 0 737 X1 monocarboxylate UniRef90_UPI0010 0.8 3.5 0.00 0.033 505.108 2.992 transporter 10-like 6B9DF9 36 79 0 805 armadillo repeat- UniRef90_UPI0010 0.7 4.2 0.00 0.005 containing protein 4-like 1169.049 3.002 6D7B40 13 08 0 852 isoform X1 interferon-induced helicase C domain- UniRef90_UPI0010 0.8 3.5 0.00 0.038 2806.505 3.005 containing protein 1-like 6CEB1E 50 34 0 156 isoform X2 uncharacterized protein UniRef90_UPI0009 0.7 3.8 0.00 0.016 22.366 3.024 LOC110060621 E376FE 85 51 0 995 uncharacterized protein UniRef90_UPI0010 0.7 4.1 0.00 0.006 29.883 3.025 LOC114518698 6AC1A0 27 63 0 605 galactosylceramide UniRef90_UPI0010 0.7 3.8 0.00 0.018 185.589 3.027 sulfotransferase-like 6D679F 92 21 0 121 0.7 4.2 0.00 0.005 Glycosidase I2H3D4_TETBL 375.547 3.075 29 20 0 611 UniRef90_A0A2G8 0.8 3.5 0.00 0.038 Uncharacterized protein 325.074 3.086 KZL8 74 30 0 451 autophagy-related UniRef90_UPI0010 0.5 5.4 0.00 0.000 653.146 3.087 protein 8-like 6B526D 71 05 0 103 uncharacterized protein UniRef90_UPI0007 0.7 4.3 0.00 0.003 22.078 3.117 LOC107341241 7A759C 22 19 0 949 cyclic AMP-dependent UniRef90_UPI0010 0.8 3.8 0.00 0.018 transcription factor 653.771 3.159 69C213 27 19 0 121 ATF-3-like 200 2'-5'-oligoadenylate UniRef90_UPI0010 0.7 4.0 0.00 0.011 7553.934 3.160 synthase 1A-like 69A59C 89 04 0 037 ras-like GTP-binding UniRef90_UPI0010 0.9 3.4 0.00 0.045 227.420 3.193 protein rhoA 698E91 23 61 1 313 uncharacterized protein UniRef90_UPI0010 0.8 3.6 0.00 0.027 242.608 3.231 LOC114539018 6ABD3E 85 52 0 878 echinoderm UniRef90_UPI0010 0.9 3.4 0.00 0.041 microtubule-associated 20.022 3.249 FC6F50 30 93 0 732 protein-like 6 Tripartite motif- UniRef90_A0A2B4 0.7 4.3 0.00 0.003 68.623 3.251 containing protein 59 RBP8 44 70 0 428 CARD_2 domain- 0.9 3.4 0.00 0.045 CARD_2 2510.821 3.268 containing protein 45 59 1 313 0.8 3.7 0.00 0.021 PAC:15706771 19.030 3.289 76 54 0 907 uncharacterized protein UniRef90_UPI0008 0.8 3.8 0.00 0.018 59.400 3.299 LOC108371307 11A722 63 24 0 121 volume-regulated anion UniRef90_UPI0007 0.7 4.1 0.00 0.005 channel subunit 3040.553 3.323 7A106A 91 99 0 953 LRRC8A-like predicted protein 0.6 5.0 0.00 0.000 EDO31703 871.857 3.323 [Nematostella vectensis] 62 16 0 438 UniRef90_UPI0005 0.9 3.5 0.00 0.033 hypothetical protein 28.739 3.335 EE86B3 30 87 0 645 serine incorporator 1- UniRef90_UPI0010 0.7 4.4 0.00 0.002 307.399 3.367 like 6D3C96 59 34 0 860 tigger transposable UniRef90_UPI0010 0.8 4.0 0.00 0.010 element-derived protein 896.202 3.399 FC9846 45 20 0 597 6-like uncharacterized protein UniRef90_UPI0010 0.8 4.2 0.00 0.005 LOC114532427 isoform 43.207 3.431 692E76 08 49 0 004 X1 sp|Q9NL38|MA66_ 0.7 4.5 0.00 0.001 N66 matrix protein 330.133 3.551 PINMA 75 81 0 918 uncharacterized protein UniRef90_UPI0010 1.0 3.5 0.00 0.038 39.768 3.554 LOC114949555 FCACAA 09 22 0 930 protein canopy homolog UniRef90_UPI0010 0.5 6.4 0.00 0.000 109.248 3.569 4-like 6CFA45 54 45 0 001 Putative nucleolar UniRef90_A0A2B4 0.7 4.7 0.00 0.001 944.818 3.591 protein C2C4.08 RTX7 58 39 0 139 uncharacterized protein UniRef90_UPI0010 0.9 3.8 0.00 0.017 141.904 3.608 LOC114519153 6B630A 38 45 0 276 uncharacterized protein UniRef90_UPI0010 0.8 4.5 0.00 0.002 114.988 3.651 LOC114534575 6AA33C 01 60 0 037 FecR domain- 0.7 4.8 0.00 0.000 UniRef90_C6B952 60.005 3.661 containing protein 48 93 0 677 Cold shock-induced 0.9 3.9 0.00 0.014 TIR_2 227.923 3.681 protein TIR2 43 01 0 878 TNFAIP3-interacting UniRef90_UPI0010 23472.12 0.9 4.0 0.00 0.009 3.711 protein 1-like 6C5A35 0 17 46 0 776 protein ABHD4-like UniRef90_UPI0010 0.9 3.8 0.00 0.015 5498.842 3.714 isoform X1 6A515A 55 88 0 303
201 uncharacterized protein UniRef90_UPI0010 1.0 3.4 0.00 0.049 430.175 3.735 LOC114530745 6A0DF6 93 18 1 430 uncharacterized protein UniRef90_UPI0007 0.9 4.0 0.00 0.008 108.676 3.741 LOC107342651 7B26F7 16 82 0 777 coiled-coil domain- UniRef90_A0A3M6 0.6 5.6 0.00 0.000 containing protein 180- 188.355 3.754 UXQ3 66 37 0 047 like SWIM-type domain- UniRef90_A0A3M6 1.0 3.4 0.00 0.048 containing protein 30.389 3.763 TBX3 96 33 1 142 (Fragment) dual specificity protein UniRef90_UPI0010 14194.55 0.9 4.0 0.00 0.011 3.823 phosphatase 4-like 69B44A 0 56 00 0 110 TNF receptor-associated UniRef90_UPI0010 0.8 4.2 0.00 0.004 431.939 3.841 factor 3-like isoform X1 6AA39C 98 77 0 646 UniRef90_A0A2B4 1.1 3.5 0.00 0.034 Uncharacterized protein 52.589 4.002 RCL3 21 69 0 738 Ephrin type-B receptor sp|P54753|EPHB3_ 1.0 3.6 0.00 0.025 52.160 4.051 3 HUMAN 96 97 0 398 uncharacterized protein UniRef90_UPI0010 0.8 5.1 0.00 0.000 LOC114532802 isoform 36.705 4.132 6B6D7C 01 61 0 234 X1 solute carrier organic anion transporter family UniRef90_UPI0010 0.9 4.3 0.00 0.003 446.826 4.166 member 4A1-like 6BA550 61 36 0 690 isoform X1 uncharacterized protein UniRef90_UPI0010 1.0 4.1 0.00 0.006 58.916 4.197 LOC114530174 6AC6E0 06 73 0 448 40S ribosomal protein UniRef90_A0A2B4 1.2 3.4 0.00 0.049 250.548 4.369 S14 S1W5 78 18 1 430 uncharacterized protein UniRef90_UPI0010 1.0 4.1 0.00 0.006 3160.339 4.387 LOC114536723 6ADEDD 46 93 0 037 Integrase catalytic UniRef90_A0A3P9 1.1 3.8 0.00 0.018 domain-containing 241.752 4.429 N5W4 59 22 0 121 protein uncharacterized protein UniRef90_UPI0010 0.8 5.2 0.00 0.000 232.067 4.443 LOC114529009 6C86DD 41 86 0 136 centrosome-associated UniRef90_UPI0011 1.0 4.4 0.00 0.002 protein CEP250-like 4623.694 4.555 764EFC 29 26 0 860 isoform X1 uncharacterized protein UniRef90_UPI0010 0.9 4.6 0.00 0.001 93.289 4.581 LOC114970358 FC838B 75 97 0 310 uncharacterized protein UniRef90_UPI0010 1.2 3.8 0.00 0.017 200.623 4.668 LOC114522980 6B7D56 15 44 0 276 uncharacterized protein UniRef90_UPI0009 1.3 3.5 0.00 0.040 16.185 4.702 LOC109485277 479712 42 03 0 855 Putative RNA-directed UniRef90_A0A2B4 0.9 4.7 0.00 0.001 DNA polymerase from 37.983 4.703 SUK8 85 75 0 011 transposon BS uncharacterized protein UniRef90_UPI0010 0.8 5.4 0.00 0.000 99.441 4.882 LOC114976141 FC6C9A 93 65 0 080 adhesion G protein- UniRef90_UPI0010 1.1 4.4 0.00 0.002 coupled receptor E3-like 1496.451 4.899 691525 09 17 0 860 isoform X1 202 predicted protein 1.3 3.6 0.00 0.027 EDO36049 11.795 5.043 [Nematostella vectensis] 74 71 0 190 UniRef90_A0A267 1.3 3.9 0.00 0.012 Amino acid transporter 368.254 5.198 GUV7 17 46 0 865 UniRef90_A0A1V0 1.2 4.4 0.00 0.002 Packaging ATPase 102.918 5.390 SDX0 06 70 0 683 uncharacterized protein UniRef90_UPI000E 0.9 5.8 0.00 0.000 139.621 5.544 PF11_0213-like 6B1588 53 19 0 023
203 APPENDIX N
CORRESPONDING GENO ONTOLOGY (GO) TERMS FOR SIGNIFICANT
DIFFERENTIALLY EXPRESSED GENES BETWEEN ‘SEEP’ AND ‘NON-
SEEP’ SAMPLES OF C. delta
Best GO BLAST/UniRef GeneID GO Annotation GO ID Category match tigger transposable UniRef90_UPI00077 nucleus; DNA GO:0003677; element-derived MF AFDBE binding GO:0005634 protein 6-like uncharacterized UniRef90_UPI00106 protein CE96B LOC114519401 uncharacterized protein XP_002165361.1 LOC100210439Ê ZP domain- integral UniRef90_UPI00106 containing CC component of GO:0016021 BBB54 protein-like membrane nucleic acid Putative UniRef90_A0A2B4S binding; GO:0003676; MF exonuclease R569 GH9 exonuclease GO:0004527 activity integral component of membrane; ATP GO:0004714; Tyrosine kinase UniRef90_A0A2B4R binding; BP,CC GO:0005524; receptor Cad96Ca IL2 transmembrane GO:0016021 receptor protein tyrosine kinase Under expressed genes activity nitrogen compound metabolic process; protein-N-terminal protein N-terminal asparagine GO:0006807; glutamine UniRef90_UPI00106 BP,MF amidohydrolase GO:0008418; amidohydrolase- D458D activity; protein- GO:0070773 like N-terminal glutamine amidohydrolase activity defense response 2'-5'- to virus; 2'-5'- GO:0001730; oligoadenylate UniRef90_UPI0010F BP,MF oligoadenylate GO:0003725; synthase 1-like CD1E6 synthetase GO:0051607 isoform X1 activity; double- 204 stranded RNA binding
uncharacterized UniRef90_UPI000A2 protein A8292 LOC110249269 collagen- containing extracellular GO:0005615; matrix; GO:0008061; Fibrinogen C extracellular sp|Q95LU3|FBCD1_ GO:0016020; domain-containing CC,MF space; integral MACFA GO:0016021; protein 1 component of GO:0046872; membrane; GO:0062023 membrane; chitin binding; metal ion binding coiled-coil UniRef90_A0A3M6U domain-containing XQ3 protein 180-like zinc finger protein protein UniRef90_UPI00071 862-like isoform MF dimerization GO:0046983 9C087 X1 activity uncharacterized protein UniRef90_UPI00106
LOC114532802 B6D7C isoform X1 uncharacterized protein UniRef90_UPI00106
LOC114532427 92E76 isoform X1 uncharacterized UniRef90_UPI00026
protein 5486C uncharacterized UniRef90_UPI00106 protein BD126 LOC114538154 nucleus; DNA Over expressed genes binding; DNA- binding GO:0003677; UniRef90_UPI0010F BP,CC, transcription factor GO:0003700; protein fosB-like CC953 MF activity; regulation GO:0005634; of transcription by GO:0006357 RNA polymerase II metal ion binding; peptidyl-prolyl GRIP and coiled- GO:0003755; cis-trans isomerase coil domain- UniRef90_UPI00094 GO:0006457; BP,MF activity; containing protein 825F1 GO:0046872; intracellular 2-like GO:0046907 transport; protein folding
205 integral Integrase catalytic component of GO:0003676; UniRef90_A0A3P9N BP,CC, domain-containing membrane; nucleic GO:0015074; 5W4 MF protein acid binding; GO:0016021 DNA integration uncharacterized UniRef90_UPI000E6 protein B1588 PF11_0213-like uncharacterized UniRef90_UPI00106 protein 98C9A LOC114530582
206 APPENDIX O
CORRESPONDING GENO ONTOLOGY (GO) TERMS FOR SIGNIFICANT
DIFFERENTIALLY EXPRESSED GENES BETWEEN MC751 AND MC885
SAMPLES OF C. delta
Best GO BLAST/UniRef GeneID GO Annotation GO IDs Category match tigger transposable UniRef90_UPI00077 nucleus; DNA GO:0003677; element-derived MF AFDBE binding GO:0005634 protein 6-like integral component of membrane; sphingomyelin sphingomyelin GO:0004767; UniRef90_UPI00106 BP,CC, phosphodiesterase phosphodiesterase GO:0006685; CBA8E MF 2-like activity; GO:0016021 sphingomyelin catabolic process UPF0711 protein UniRef90_UPI00106
C18orf21 homolog A950F Uncharacterized CapteP218257 protein metal ion binding; nucleic acid binding; GO:0003676; zinc finger protein UniRef90_UPI00106 BP,MF regulation of GO:0006355; 239-like BF2BF transcription, DNA- GO:0046872 templated integral component of membrane; ATP binding; GO:0004714; Tyrosine kinase UniRef90_A0A2B4R BP,CC transmembrane GO:0005524;
Under expressed genes receptor Cad96Ca IL2 receptor protein GO:0016021 tyrosine kinase activity centromere protein UniRef90_UPI00106
M-like 9F539 membrane coat; structural molecule GO:0005198; coatomer subunit UniRef90_UPI00106 BP,CC, activity; intracellular GO:0006886; beta-like 99A39 MF protein transport; GO:0016192; vesicle-mediated GO:0030117 transport ectonucleotide integral component GO:0003676; pyrophosphatase/ph of membrane; UniRef90_UPI00106 GO:0016021; osphodiesterase CC,MF hydrolase activity; 94E5B GO:0016787; family member 3- metal ion binding; GO:0046872 like nucleic acid binding
207 actin binding; Rho GO:0003779; GTPase binding; Formin-like protein Drf_GBD BP,MF GO:0017048; actin cytoskeleton GO:0030036 organization uncharacterized UniRef90_UPI00106 protein 988EA LOC114528530 RNA-binding protein with serine- UniRef90_UPI00106
rich domain 1-B- 93982 like pre-mRNA-splicing UniRef90_UPI00106
factor cwf2-like BB9E0 thyroid transcription UniRef90_UPI000B factor 1-associated AFD4DD protein 26 homolog
cytoplasm; nucleolus; GO:0005524; ATP binding; GO:0005525; ATPase activity; GO:0005730; Large subunit UniRef90_A0A2P8Z CC,MF GTP binding; GO:0005737; GTPase 1 BJ4 ribosomal large GO:0016887; subunit binding; GO:0043022; ribosome binding GO:0043023
GATOR complex UniRef90_UPI00106
protein NPRL2-like D635C nucleus; DNA coiled-coil domain- GO:0003677; UniRef90_UPI00106 BP,CC, binding; regulation of containing protein GO:0005634; D71C2 MF transcription, DNA- 14-like isoform X1 GO:0006355 templated nucleoporin NDC1- UniRef90_UPI00106
like isoform X1 9D8E7 integral component of membrane; G RNA-directed DNA protein-coupled GO:0003964; polymerase from UniRef90_A0A2B4S BP,CC, receptor activity; GO:0004930; mobile element P02 MF RNA-directed DNA GO:0007166; jockey polymerase activity; GO:0016021 cell surface receptor signaling pathway DELTA-alicitoxin- UniRef90_UPI00106
Pse2b-like A5E53 uncharacterized UniRef90_UPI00106 protein
Under expressed genes A3C8D LOC114519108 integral component angiopoietin-1 UniRef90_UPI00106 of membrane; protein GO:0004725; CC,MF receptor-like BB3E6 tyrosine phosphatase GO:0016021 activity
208 ATP binding; DNA GO:0003677; chromodomain- binding; DNA GO:0003678; helicase-DNA- UniRef90_UPI00106 helicase activity; BP,MF GO:0005524; binding protein 1- CBAEB chromatin GO:0006281; like remodeling; DNA GO:0006338 repair
chromosome; GO:0003677; cytoplasm; ATP GO:0005524; Chromosome binding; DNA GO:0005694; UniRef90_A0A517P BP,CC, partition protein binding; chromosome GO:0005737; 5A5 MF Smc condensation; DNA GO:0006260; replication; sister GO:0007062; chromatid cohesion GO:0030261
serine/threonine- UniRef90_UPI0010F protein kinase PAK C9D2C 3-like isoform X2 synapse; actin cytoskeleton GO:0007268; organization; UniRef90_UPI00106 GO:0030036; syntenin-1-like BP,CC chemical synaptic 9C153 GO:0035556; transmission; GO:0045202 intracellular signal transduction cytoplasm; cysteine- GO:0005737; cysteine protease UniRef90_UPI00106 BP,CC, type peptidase GO:0006914; ATG4D-like D036F MF activity; autophagy; GO:0008234; protein transport GO:0015031 1-acylglycerol-3- transferase activity, phosphate O- UniRef90_UPI00106 MF transferring acyl GO:0016746 acyltransferase 94C49 groups ABHD5-like uncharacterized UniRef90_UPI00106 protein D4FB7 LOC114528583 integral component of membrane; nose resistant to UniRef90_UPI00106 transferase activity, GO:0016021; fluoxetine protein 6- CC,MF A125E transferring acyl GO:0016747 like isoform X1 groups other than amino-acyl groups uncharacterized UniRef90_UPI0010F protein C793C LOC114975128 uncharacterized UniRef90_UPI00106 protein D8FB1 LOC114526128 integral component cannabinoid UniRef90_UPI00106 of membrane; GO:0004949; receptor type 1A- CC,MF A113B cannabinoid receptor GO:0016021 like activity 209 uncharacterized UniRef90_UPI00106 protein 9C83B LOC114528616 protein canopy UniRef90_UPI00106
homolog 4-like CEB77 uncharacterized UniRef90_UPI0010F protein C83AB LOC114950278 zinc finger protein UniRef90_UPI00071 protein dimerization MF GO:0046983 862-like isoform X1 9C087 activity nuclear factor UniRef90_UPI00106 erythroid 2-related ABFE2 factor 3-like uncharacterized UniRef90_UPI00106 exonuclease B4D5E C637.09-like uncharacterized UniRef90_UPI00106 protein CF130 LOC114528075 Uncharacterized SMAR001314-PA protein excitatory amino integral component UniRef90_UPI00106 GO:0015293; acid transporter 3- CC,MF of membrane; 93D4A GO:0016021 like symporter activity uncharacterized UniRef90_UPI00106 protein A1110 LOC114538401 Uncharacterized UniRef90_A0A3Q3
protein H183 NEDD4-binding UniRef90_UPI00106 MF RNA binding GO:0003723 protein 1-like D39E6 uncharacterized UniRef90_UPI00106 protein C78B9 LOC114518148
cytoplasm; nucleus; DNA-binding GO:0003700; transcription factor GO:0005634; transcription factor UniRef90_UPI00106 BP,CC, activity; sequence- GO:0005737; AP-1-like DA64C MF specific DNA GO:0006357; binding; regulation of GO:0043565 transcription by RNA polymerase II
uncharacterized UniRef90_UPI00106 protein BD8FA LOC114531648 uncharacterized UniRef90_UPI00106 protein BCF2D LOC114531158
210 RNA-directed DNA DNA binding; RNA- polymerase from UniRef90_A0A2B4S GO:0003677; MF directed DNA mobile element X38 GO:0003964 polymerase activity jockey integral component Zinc finger MYM- UniRef90_A0A498N GO:0016021; CC,MF of membrane; protein type 1-like protein 342 GO:0046983 dimerization activity uncharacterized UniRef90_UPI00106 protein D3758 LOC114526378 MAP kinase tyrosine/serine/threon dual specificity UniRef90_A0A3M6 ine phosphatase GO:0004725; protein phosphatase MF TS56 activity; protein GO:0017017 10-like tyrosine phosphatase activity Uncharacterized tetur12g00070.1 protein uncharacterized UniRef90_UPI0010F protein C7C25 LOC114974566 uncharacterized UniRef90_UPI00106 protein B0251 LOC114526069 endoplasmic reticulum; nucleus; deoxyribonuclease GO:0000737; activity; GO:0003677; deoxyribonuclease I GO:0004530; deoxyribonuclease- UniRef90_UPI00106 BP,CC, activity; DNA GO:0004536; 1-like B7B0F MF binding; DNA GO:0005634; catabolic process; GO:0005783; DNA catabolic GO:0006308 process, endonucleolytic host cell nucleus; GO:0003677; nucleus; DNA transcription factor UniRef90_UPI00106 GO:0003700; CC,MF binding; DNA- MafK-like AFE26 GO:0005634; binding transcription GO:0042025 factor activity uncharacterized UniRef90_UPI0010F protein CA1D4 LOC114971009 uncharacterized UniRef90_UPI00106 protein A9878 LOC114521387 interferon-induced, integral component GO:0004672; double-stranded UniRef90_UPI00106 of membrane; ATP CC,MF GO:0005524; RNA-activated D21E8 binding; protein GO:0016021 protein kinase-like kinase activity
211 predicted protein [Nematostella EDO42325 vectensis] Bcl-2 Bcl-2 BGLTMP006037-PA transient receptor potential cation integral component UniRef90_UPI00109 GO:0005216; channel subfamily CC,MF of membrane; ion BB749 GO:0016021 A member 1 channel activity isoform X2 A disintegrin and metalloendopeptidase metalloproteinase activity; zinc ion GO:0004222; UniRef90_UPI00106 with BP,MF binding; integrin- GO:0007229; AB0FA thrombospondin mediated signaling GO:0008270 motifs 9-like pathway transcription factor UniRef90_UPI00106 AP-1-like isoform A8A30 X1 Uncharacterized TriadP20369 protein
tumor necrosis factor receptor binding; zinc ion binding; innate immune response; negative regulation of GO:0001817; NF-kappaB GO:0005164; transcription factor GO:0008063; TNF receptor- UniRef90_UPI00106 activity; regulation of GO:0008270; associated factor 3- BP,MF CD4BE cytokine production; GO:0032088; like regulation of defense GO:0033209; response to virus; GO:0045087; Toll signaling GO:0050688 pathway; tumor necrosis factor- mediated signaling pathway
uncharacterized protein UniRef90_UPI00106
LOC114542249 9B08F isoform X1 integral component monocarboxylate UniRef90_UPI00106 of membrane; GO:0016021; CC,MF transporter 10-like B9DF9 transmembrane GO:0022857 transporter activity armadillo repeat- UniRef90_UPI00106 containing protein D7B40 4-like isoform X1
212 interferon-induced helicase C domain- UniRef90_UPI00106
containing protein CEB1E 1-like isoform X2 uncharacterized UniRef90_UPI0009E protein 376FE LOC110060621 uncharacterized UniRef90_UPI00106 protein AC1A0 LOC114518698
Golgi apparatus; integral component GO:0001733; of membrane; galactosylceramide UniRef90_UPI00106 BP,CC, GO:0005794; galactosylceramide sulfotransferase-like D679F MF GO:0009247; sulfotransferase GO:0016021 activity; glycolipid biosynthetic process
Glycosidase I2H3D4_TETBL Uncharacterized UniRef90_A0A2G8
protein KZL8 autophagosome GO:0000421; membrane; GO:0005874; autophagy-related UniRef90_UPI00106 cytoplasmic vesicle; BP,CC GO:0006914; protein 8-like B526D microtubule; GO:0015031; autophagy; protein GO:0031410 transport uncharacterized UniRef90_UPI00077 protein A759C LOC107341241 nucleus; DNA binding; DNA- cyclic AMP- GO:0003677; binding transcription dependent UniRef90_UPI00106 BP,CC, GO:0003700; factor activity; transcription factor 9C213 MF GO:0005634; regulation of ATF-3-like GO:0006357 transcription by RNA polymerase II
2'-5'-oligoadenylate synthetase activity; GO:0001730; 2'-5'-oligoadenylate UniRef90_UPI00106 BP,MF double-stranded RNA GO:0003725; synthase 1A-like 9A59C binding; defense GO:0051607 response to virus GTP binding; ras-like GTP- GTPase activity; GO:0003924; UniRef90_UPI00106 binding protein BP,MF small GTPase GO:0005525; 98E91 rhoA mediated signal GO:0007264 transduction uncharacterized UniRef90_UPI00106 protein ABD3E LOC114539018
213 cytoplasm; echinoderm microtubule; GO:0000226; microtubule- UniRef90_UPI0010F BP,CC, microtubule binding; GO:0005737; associated protein- C6F50 MF microtubule GO:0005874; like 6 cytoskeleton GO:0008017 organization
Tripartite motif- integral component UniRef90_A0A2B4R GO:0008270; containing protein CC,MF of membrane; zinc BP8 GO:0016021 59 ion binding mitochondrion; CARD_2 domain- GO:0005739; CARD_2 BP,CC defense response to containing protein GO:0051607 virus PAC:15706771 uncharacterized UniRef90_UPI00081 protein 1A722 LOC108371307 volume-regulated anion channel UniRef90_UPI00077 integral component CC GO:0016021 subunit LRRC8A- A106A of membrane like predicted protein [Nematostella EDO31703 vectensis] UniRef90_UPI0005E hypothetical protein E86B3 serine incorporator UniRef90_UPI00106 integral component CC GO:0016021 1-like D3C96 of membrane tigger transposable UniRef90_UPI0010F nucleus; DNA GO:0003677; element-derived MF C9846 binding GO:0005634 protein 6-like uncharacterized protein UniRef90_UPI00106
LOC114532427 92E76 isoform X1 extracellular region; GO:0004089; sp|Q9NL38|MA66_P carbonate N66 matrix protein CC,MF GO:0005576; INMA dehydratase activity; GO:0008270 zinc ion binding uncharacterized UniRef90_UPI0010F protein CACAA LOC114949555 protein canopy UniRef90_UPI00106
homolog 4-like CFA45 nucleic acid binding; Putative nucleolar UniRef90_A0A2B4R GO:0003676; MF oxidoreductase protein C2C4.08 TX7 GO:0016491 activity uncharacterized UniRef90_UPI00106 protein B630A LOC114519153 214 uncharacterized UniRef90_UPI00106 protein AA33C LOC114534575 nucleosome; DNA GO:0000786; FecR domain- BP,CC, UniRef90_C6B952 binding; nucleosome GO:0003677; containing protein MF assembly GO:0006334 Cold shock-induced integral component TIR_2 CC GO:0016021 protein TIR2 of membrane mitotic cytokinesis; TNFAIP3- UniRef90_UPI00106 regulation of GO:0000281; interacting protein BP C5A35 phosphatidylinositol GO:0014066 1-like 3-kinase signaling protein ABHD4-like UniRef90_UPI00106
isoform X1 A515A uncharacterized UniRef90_UPI00106 protein A0DF6 LOC114530745 uncharacterized UniRef90_UPI00077 protein B26F7 LOC107342651 coiled-coil domain- UniRef90_A0A3M6 containing protein UXQ3 180-like SWIM-type UniRef90_A0A3M6 domain-containing MF zinc ion binding GO:0008270 TBX3 protein (Fragment) MAP kinase dual specificity UniRef90_UPI00106 tyrosine/serine/threon protein phosphatase MF GO:0017017 9B44A ine phosphatase 4-like activity
tumor necrosis factor receptor binding; zinc ion binding; innate immune response; negative regulation of GO:0001817; NF-kappaB GO:0005164; transcription factor GO:0008063; TNF receptor- UniRef90_UPI00106 activity; regulation of GO:0008270; associated factor 3- BP, MF AA39C cytokine production; GO:0032088; like isoform X1 regulation of defense GO:0033209; response to virus; GO:0045087; Toll signaling GO:0050688 pathway; tumor necrosis factor- mediated signaling pathway
Uncharacterized UniRef90_A0A2B4R
protein CL3 215 Ephrin type-B sp|P54753|EPHB3_H
receptor 3 UMAN uncharacterized protein UniRef90_UPI00106
LOC114532802 B6D7C isoform X1 solute carrier organic anion UniRef90_UPI00106 transporter family BA550 member 4A1-like isoform X1 uncharacterized UniRef90_UPI00106 protein AC6E0 LOC114530174 ribosome; structural GO:0003735; 40S ribosomal UniRef90_A0A2B4S BP,CC, constituent of GO:0005840; protein S14 1W5 MF ribosome; translation GO:0006412 uncharacterized UniRef90_UPI00106 protein ADEDD LOC114536723 Integrase catalytic UniRef90_A0A3P9N nucleic acid binding; GO:0003676; domain-containing BP, MF 5W4 DNA integration GO:0015074 protein uncharacterized UniRef90_UPI00106 protein C86DD LOC114529009 centrosome- associated protein UniRef90_UPI00117
CEP250-like 64EFC isoform X1 uncharacterized UniRef90_UPI0010F protein C838B LOC114970358 uncharacterized UniRef90_UPI00106 protein B7D56 LOC114522980 uncharacterized UniRef90_UPI00094 protein 79712 LOC109485277 DNA binding; RNA- Putative RNA- directed DNA GO:0003677; directed DNA UniRef90_A0A2B4S polymerase activity; GO:0003964; BP, MF polymerase from UK8 transposase activity; GO:0004803; transposon BS transposition, DNA- GO:0006313 mediated uncharacterized UniRef90_UPI0010F protein C6C9A LOC114976141 adhesion G protein- UniRef90_UPI00106 coupled receptor 91525 E3-like isoform X1 216 predicted protein [Nematostella EDO36049 vectensis]
endoplasmic reticulum; integral component of GO:0005388; membrane; ATP GO:0005524; Amino acid UniRef90_A0A267G binding; calcium CC,MF GO:0005783; transporter UV7 transmembrane GO:0016021; transporter activity, GO:0046872 phosphorylative mechanism; metal ion binding
ATP binding; GO:0004519; UniRef90_A0A1V0S endonuclease Packaging ATPase MF GO:0005524; DX0 activity; metal ion GO:0046872 binding uncharacterized UniRef90_UPI000E6 protein PF11_0213- B1588 like
217