Temperature Modulation of Biological Clocks in a Reef-Building Coral

University of Calgary PRISM: University of Calgary's Digital Repository

Graduate Studies The Vault: Electronic Theses and Dissertations

2018-01-25 Temperature Modulation of Biological Clocks in a Reef-Building Coral

Wuitchik, Daniel Michael

Wuitchik, D. M. (2018) Temperature modulation of biological clocks in a reef-building coral (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/5465 http://hdl.handle.net/1880/106386 master thesis

University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY

Temperature Modulation of Biological Clocks in a Reef-Building Coral

Daniel Michael Wuitchik

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

JANUARY 2018

Abstract

Reef-building coral of the Great Barrier Reef demonstrate remarkably synchronized and precise timing of reproduction. They achieve this precision through sophisticated biological clock systems entrained by their local environment. This thesis explores how temperature and lunar light modulates the biological clocks of Acropora millepora by measuring transcriptomic changes with RNA-sequencing. Coral colonies were collected and maintained under artificial lunar light at the Heron Island Research Station under cool and warm temperature treatments corresponding to winter and summer averages at this locale. Individuals were sampled over a 24- hour period during a full lunar month to capture long-term daily profiles of gene expression. It was found that numerous biological clock genes were impacted by temperature and lunar light.

Furthermore, temperature and lunar phase altered the expression of interesting hormonal pathways involved in reproductive behaviours. These data will help elucidate the mechanisms underlying the precise timing of reproduction in reef-building coral and the effect that different temperatures have on this process.

ii Acknowledgements

I could not have asked for a better supervisor and mentor in Dr. Peter Vize. You have challenged me to learn an entirely new skill set while providing patient guidance. I would not have been able to do this without your mentorship and the freedom to learn my system and science at my own pace. I am truly grateful for my time in the lab, and I hope we collaborate in the years to come. To my committee members, thank you for helping me to see the bigger picture; your feedback has been pivotal in the design of this project.

The staff and facilities at Heron Island Research Center are top notch and without the quick and professional assistance from many people, my fieldwork would not have been possible. In addition, I would like to say thank you to the graduate students who shared my time on Heron as you helped me with fieldwork and the comradery made for some of the best experiences of my life. The department of biology at the University of Calgary has many unsung heroes that have helped me and my colleagues achieve all of our goals; your efforts were certainly noticed and greatly appreciated. There are many students in the Ecology and

Evolutionary Biology program who have been instrumental in sharing their ideas, enthusiasm, and contributed greatly to my understanding of varied subjects in ecology. I would like to thank

Jessica Hopson, Robert Morgante and Ramon Nagesan for your comradery and late-night coding sessions, which turned a difficult statistic course into an enjoyable experience. Thank you to

Kyle Wilson for great intellectual conversations, drinks, and letting me beat you at squash - your philosophy of science and academia is a model that we should all strive for.

This project would not have been possible without the contributions and resources provided by the Xenbase group and thoughtful assistance from Kamran Karimi, Vaneet Lotay

iii and Praneet Chaturvedi. Thank you for your aid in my bioinformatics and use of computational resources.

Lastly, my family has provided unwavering support throughout my career and pursuits of obscure interests. You have granted me the privilege to always remain curious. For this gift, I am truly thankful. Natalie, thank you for reminding me that all we need is a little movement in life.

Dad, I appreciate your late-career shift into the marine sciences and have appreciated your depth of knowledge in crayfish husbandry. Mom, you help me to see the colour in life and have demonstrated time and time again that art and science are not mutually exclusive.

iv Dedication

I have travelled far with my partner in crime and in science, Sara Smith. How cool is it that we can share a deep interest and career pursuing knowledge together? This journey has only been possible because you have pushed me to be better in everything I do. You are one of the few genuine people I have met, and your enthusiasm and fierce intelligence makes you a rock star in science. You are the best partner I could have ever dreamed of.

This thesis is dedicated to you. Always.

v Table of Contents

Abstract ...... ii Acknowledgements ...... iii Dedication ...... v Table of Contents ...... vi List of Tables ...... viii List of Figures and Illustrations ...... ix List of Symbols, Abbreviations and Nomenclature ...... xiii Epigraph ...... xiv

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Coral primer and reproduction ...... 1 1.2 Biological clocks ...... 4 1.2.1 Circadian clock ...... 6 1.2.2 Circalunar clock ...... 8 1.2.3 Circannual clock ...... 11 1.3 Study species: Acropora millepora ...... 12 1.4 Objectives and Purpose ...... 13

CHAPTER TWO: METHODS ...... 14 2.1 Site description and collection ...... 14 2.2 Experimental Design ...... 15 2.2.1 Aquaria ...... 15 2.2.2 Moonlight apparatus ...... 16 2.3 Experimental Design and Sampling ...... 18 2.4 RNA isolation ...... 19 2.4.1 RNA purification ...... 19 2.4.2 DNA contaminant removal ...... 20 2.5 Library Preparation and Sequencing ...... 21 2.6 Bioinformatics and Analyses ...... 21 2.6.1 Preprocessing and Quality Control ...... 21 2.6.2 Sequence alignment and read counts ...... 22 2.6.3 Normalization ...... 22 2.6.4 Data Distribution ...... 26 2.6.5 Principal Component Analysis and Outlier Removal ...... 28 2.6.6 Differential expression analyses ...... 30 2.6.7 Hierarchical Clustering ...... 33 2.6.8 Annotation ...... 33 2.6.9 Gene Set Enrichment Analyses ...... 33

CHAPTER THREE: RESULTS AND DISCUSSION ...... 35 3.1 Samples Sequenced ...... 35 3.2 Principal component analyses ...... 36 3.3 Differential Expression Analyses ...... 40 3.4 Hierachical Clustering ...... 43 3.4.1 Temperature x Phase x Hour analysis (Figure 3.10) ...... 43

vi 3.4.2 Temperature x Phase analysis (Figure 3.11) ...... 46 3.5 Temperature x Hour analysis (Figure 3.12) ...... 50 3.6 Gene Set Enrichment Analyses ...... 53 3.6.1 GSEA – Temperature Phase Hour interaction (Table 3.5) ...... 53 3.6.2 GSEA – Temperature Phase interaction (Table 3.6) ...... 57 3.6.3 GSEA – Temperature Hour interaction (Table 3.7) ...... 63 3.7 Venn Diagram of GSEA lists ...... 65 3.8 Rhythmic Process Genes ...... 66 3.8.1 Basic Leucine Zipper Domain (bzip) ...... 66 3.8.1.1 Bzip1 ...... 67 3.8.1.2 Bzip2 ...... 68 3.8.1.3 Bzip3 ...... 70 3.8.2 bzip discussion ...... 71 3.8.3 Cryptochrome ...... 72 3.8.3.1 Cry1.1 ...... 72 3.8.3.2 cry1.2 ...... 74 3.8.3.3 cry1.3 ...... 75 3.8.3.4 cry2 ...... 77 3.8.3.5 cry discussion ...... 78 3.8.4 Hairy and enhancer of split-related protein (helt) ...... 79 3.8.5 Circadian Locomotor Output Cycles Kaput (clock) ...... 81 3.8.6 Cycle ...... 83 3.8.7 clock interacting pacemaker (cipc) ...... 85 3.8.7.1 cipc ...... 85 3.8.7.2 cipc.1 ...... 87 3.8.7.3 Cipc discussion ...... 88

CHAPTER FOUR: CONCLUSION ...... 89 4.1 Summary ...... 89 4.2 Future Directions ...... 90

REFERENCES ...... 92

APPENDIX ...... 104

vii List of Tables

Table 2.1 Lunar intensity and sampling dates ...... 17

Table 2.2 Identification key for each sample with regards to treatment, colony, moon phase and sampling time ...... 18

Table 2.3 Full vs reduced models used in likelihood ratio tests in DESeq2 ...... 32

Table 3.1 Descriptive statistics of TapeStation RIN scores ...... 35

Table 3.2 Summary of samples sequenced belonging to each experimental factor ...... 35

Table 3.3 Descriptive statistics of mapped-reads to A. millepora transcriptome (Moya et al. 2012) ...... 36

Table 3.4 Number of Differentially Expressed Genes, based on FDR < 0.05 from likelihood ratio test ...... 41

Table 3.5 GSEA of TPH differentially expressed gene list with FDR q-value < 0.05 ...... 55

Table 3.6 GSEA of TP differentially expressed genes with FDR q-value < 0.05 ...... 58

Table 3.7 GSEA of TH differentially expressed genes with GO terms FDR < 0.05 ...... 64

viii List of Figures and Illustrations

Figure 1.1 Basic components needed for a molecular clock system in any organism from (Dunlap 1999)...... 6

Figure 1.2 Planula release in Pocillopora damicornis under various artificial moonlight regimens. Open circles represent full moon light intensity and filled circle are no moonlight. Figure from Jokiel et al. (1985)...... 10

Figure 1.3 Planula release of Pocillopora damicornis under two temperature treatments. Figure from (Crowder et al. 2014)...... 11

Figure 1.4 Underwater image of an Acropora millepora colony ...... 12

Figure 2.1 A) Map of heron island B) Aerial photo of Heron Island. © Photos reproduced with permission from fusion holidays Australia ...... 14

Figure 2.2 A) One of two identical experimental aquaria with labelled mini-colonies. B) Full Acropora millepora colony (Green individual)...... 15

Figure 2.3 Experimental aquaria with artificial moonlight suspended above (black boxes). ..17

Figure 2.4 Box-plot of the counts of raw reads per gene in each sample used in LRT analyses. Red boxes are from warm condition, blue from cool...... 23

Figure 2.5 Box-plot of the normalized counts of reads per gene in each sample used in LRT analyses. Red boxes are from warm condition, blue from cool...... 24

Figure 2.6 Box-plot of raw counts per gene in each sample used in Wald analyses. Red boxes are from first full moon, blue are from the second full moon...... 25

Figure 2.7 Box-plot of normalized counts per gene in each sample used in Wald analyses. Red boxes are from first full moon, blue are from the second full moon ...... 25

Figure 2.8 Histogram of the number of normalized counts assigned to each gene in the A. millepora transcriptome...... 27

Figure 2.9 Plot of the average counts per gene vs the variance of the same gene. The line represents the expected mean/variance relationship for a Poisson distribution...... 27

Figure 2.10 Plot of the normalized counts against dispersion estimates. Red line represents expected relationship between mean and dispersion of a negative binomial distribution...... 28

Figure 2.11 Principal component analyses, where coral colonies cluster together across the first and second principal component. A white individual (HW45) is seen in the green cluster, and a red individual (CR34) can be seen in the white colony cluster...... 29

ix Figure 2.12 Principal component analyses with outlier samples removed. Each sample is labeled by individual colony ...... 30

Figure 3.1 Principal component analysis of rlog transformed counts coloured by individuals ...... 37

Figure 3.2 Principal component analysis exploring temperature differences within colony clusters ...... 37

Figure 3.3 Principal component analysis exploring phase differences within colony clusters ...... 38

Figure 3.4 Principal component analysis exploring hour differences within colony clusters .38

Figure 3.5 Principal component analysis from samples used in FM vs FM. Data points are coloured as individual colonies ...... 39

Figure 3.6 Principal component analysis from samples used in FM vs FM. Data points are coloured by hour ...... 39

Figure 3.7 Principal component analysis from samples used in FM vs FM. Data points are coloured by moon phase ...... 40

Figure 3.8 Venn diagram of DEGs from each LRT analysis ...... 42

Figure 3.9 Venn diagram of first FM vs second FM Wald analysis, TPH LRT analysis, and rhythmic process genes ...... 43

Figure 3.10 Heatmap of top 100 annotated DEGs (FDR < 1.14 x 10-5) from TPH analyses ..45

Figure 3.11 Heatmap of top 100 (FDR < 1.44 x 10-6) annotated DEGs from TP analyses .....49

Figure 3.12 Heatmap of top 100 annotated genes (FDR < 5.78 x 10-10) from TH analysis ....52

Figure 3.13 Venn diagram of GSEA list of over-represented GO terms. Top left: TPH, Top Right: TP, Bottom: TH ...... 65

Figure 3.14 bzip1 (FDR = 1.17 x 10-14) hourly expression profile for warm and cool treatments and lunar phases ...... 67

Figure 3.15 bzip1 expression daily lunar expression pattern between first and second full moon treatments ...... 68

Figure 3.16 bzip2 (FDR = 1.11 x 10-10) hourly expression profile for warm and cool treatments and lunar phases ...... 69

Figure 3.17 bzip2 expression daily lunar expression pattern between first and second full moon treatments ...... 69

x Figure 3.18 bzip3 (FDR = 1.40 x 10-5) hourly expression profile for warm and cool treatments and lunar phases ...... 70

Figure 3.19 bzip3 expression daily lunar expression pattern between first and second full moon treatments ...... 71

Figure 3.20 Cry1.1 (FDR = 1.10 x 10-14) hourly expression profile for warm and cool treatments and lunar phases ...... 73

Figure 3.21 Cry1.1 expression daily lunar expression pattern between first and second full moon treatments ...... 73

Figure 3.22 Cry1.2 (FDR = 6.87 x 10-16) hourly expression profile for warm and cool treatments and lunar phases ...... 74

Figure 3.23 Cry1.2 expression daily lunar expression pattern between first and second full moon treatments ...... 75

Figure 3.24 Cry 1.3 (FDR = 1.38 x 10-11) hourly expression profile for warm and cool treatments and lunar phases ...... 76

Figure 3.25 Cry1.3 expression daily lunar expression pattern between first and second full moon treatments ...... 76

Figure 3.26 cry2 (FDR = 3.83 x 10-10) hourly expression profile for warm and cool treatments and lunar phases ...... 77

Figure 3.27 Cry2 hourly expression profile between first and second full moon treatments ..78

Figure 3.28 helt (FDR = 6.92 x 10-15) hourly expression profile for warm and cool treatments and lunar phases ...... 80

Figure 3.29 helt hourly expression profile between first and second full moon treatments ....80

Figure 3.30 clock (FDR = 4.28 x 10-20) hourly expression profile for warm and cool treatments and lunar phases ...... 82

Figure 3.31 clock hourly expression profile between first and second full moon treatments .82

Figure 3.32 cycle (FDR = 4.07 x 10-6) hourly expression profile for warm and cool treatments and lunar phases ...... 84

Figure 3.33 cycle expression daily lunar expression pattern between first and second full moon treatments ...... 84

Figure 3.34 cipc (FDR = 1.38 x 10-09) hourly expression profile for warm and cool treatments and lunar phases ...... 86

Figure 3.35 cipc hourly expression profile between first and second full moon treatments ...86

xi Figure 3.36 cipc.1 (FDR = 6.31 x 10-7) hourly expression profile for warm and cool treatments and lunar phases ...... 87

Figure 3.37 cipc.1 hourly expression profile between first and second full moon treatments 88

xii List of Symbols, Abbreviations and Nomenclature

3Q Third Quarter A. millepora Acropora millepora bp Base Pairs bzip Basic Leucine Zipper Domain cipc CLOCK interacting protein clock Circadian Locomotor Output Cycles Kaput cry Cryptochrome cyp Cytochrome P450 DEG Differentially Expressed Gene DNA Deoxyribonucleic acid EtOH Ethanol FDR False Discovery Rate FM Full Moon FQ First Quarter FSH Follicle Stimulating Hormone GBR Great Barrier Reef GO Gene Ontology hbp2 Heme binding protein - 2 LRT Likelihood Ratio Test Lux Lumen per square meter NM New Moon P. damicornis Pocillapora damicornis RIN RNA Integrity Number RNA Ribonucleic acid RNA-seq RNA sequencing RNAse Ribonuclease TGF-b Transforming growth factor beta signaling pathway TH Temperature x Hour analysis TP Temperature x Phase analysis TPH Temperature x Phase x Hour analysis UCDNA University of Calgary DNA services

xiii Epigraph

I’m such a profound believer that timing is everything; I would tattoo that on my arm.

- Drew Barrymore

xiv Chapter One: Introduction

1.1 Coral primer and reproduction

Reef-building coral are the foundation species for one of the most diverse ecosystems on the planet. Despite covering only 0.2% of the global seafloor, coral reefs are home to a 25% of all marine species (Chen et al. 2015). This rich diversity is surprising considering tropical coral reefs are found in nutrient-poor waters, which would be analogous to a terrestrial desert. Corals have achieved success in this unlikely location through a unique relationship with photosynthesizing dinoflagellates of the genus Symbiodinium (Thomas et al. 2014). This relationship allows for the necessary primary production on which the whole ecosystem is dependent. Corals provide refuge for Symbiodinium with stable conditions necessary for photosynthesis, and the coral host is then fed by the symbiont which satisfies most of the coral’s metabolic requirements (Venn et al. 2008). This relationship has made coral very successful, and their reproductive strategies have allowed coral to be widely distributed throughout the tropics

(Cowen & Sponaugle 2009).

Coral have an interesting array of reproductive strategies. Firstly, they can reproduce asexually. If a piece of a coral breaks off via fragmentation from the parent colony that fragment has the potential to settle on the benthos and form a new genetically identical colony (Wallace

1985). Coral also reproduce sexually; some species are hermaphroditic where they have both male and female gametes whereas others are gonochoristic and are exclusively male or female.

Additionally, there are two modes of reproduction in coral: brooding or broadcasting (Fadlallah

1983). Approximately 13% of coral are brooders whose strategy is to released sperm periodically into the water column, fertilization then occurs in a receptive polyp (Kerr et al. 2011). After fertilization, the egg develops and is released as a planula which is ready to settle immediately.

1 In contrast, 87% of coral have evolved a broadcast spawning strategy which relies on fertilization to occur directly in the water column (Kerr et al. 2011). It takes approximately a week for the coral planula to develop after fertilization before it is ready to settle on the benthos

(Hirose et al. 2008). Each strategy has corresponding costs and benefits. One major consequence driven by these different strategies translates into differing rates and distances of dispersal of coral planula where brooders disperse short distances and broadcasters can disperse planula much further. Another important difference between strategies is the frequency of spawning.

Brooders release planula on regular basis throughout the year, whereas broadcasting coral adhere to strict reproductive seasons and may only spawn once a year (Babcock et al. 1994).

Perhaps the most remarkable example of strict reproductive seasonality in the animal kingdom is demonstrated by broadcast spawning coral on the Great Barrier Reef (GBR). In the austral spring, most coral and many other invertebrates spawn over the course of a few days typically after a full moon (Babcock et al. 1986). There are many hypotheses as to ultimate cause of this behaviour and include: spawning when wind and current are the least (van Woesik 2010), appropriate salinity levels for larval development (Mendes & Woodley 2002), food availability

(Fadlallah 1983), swamping predators, solar insulation (Penland et al. 2004) and to avoid hybridization (Willis et al. 2006). It is unfortunately very difficult to test exactly which hypothesis is the ultimate cause driving this tightly synchronized reproduction (Mercier & Hamel

2010). Nevertheless, since so many broadcasting coral and invertebrates of the GBR share a similar spawning window it is safe to assume that the reproductive season offers conditions that are favourable for fertilization, dispersal and recruitment.

In addition to coinciding with favourable conditions, broadcasting coral are impacted by dispersal characteristics and the length of time at which gametes are viable. The seminal work by

2 Levitan et al. (2004) demonstrated that gametes from Montastraea spp are quickly diluted as they can disperse up to a kilometer away from the spawning coral in under two hours.

Additionally, Levitan et al. show that sperm are only viable to fertilize eggs for approximately 90 minutes after being released (2004). Therefore, to overcome limitations in gamete viability and dispersal coral must achieve highly synchronized reproduction to have any fitness potential at all.

Even the slightest deviation outside of a species spawning window can greatly lower the reproductive fitness of an individual.

In order to achieve synchronized spawning reef-building corals use environmental signals as proximate cues to control the exact timing of reproduction (Olive 1995). There could be many cues utilized by coral and are commonly thought to include changes in: solar insolation (Penland et al. 2004), wind (van Woesik 2010), moonlight (Sweeney et al. 2011), tides (Babcock et al.

1986), photoperiod (Babcock et al. 1994), sea temperature (Nozawa 2012) and time after sunset

(Vize et al. 2005). A leading theory is that seasonal changes in sea temperature initiates gametogenesis, after which the phase of the moon sets the date of reproduction while the time after sunset cues the precise moment of spawning (Babcock et al. 1986). While corals typically spawn at the same time on a particular reef, there is considerable variation within a species at different locations (Rosser 2013). For example, on the GBR coral that are near the mainland will spawn a month earlier than those located on off-shore reefs (Babcock et al. 1986). On the west coast of Australia with similar latitude, this difference is more extreme as oceanic A.millepora reproduce in the spring whereas coastal individuals spawn in autumn (Gilmour et al. 2016). The timing of reproduction is therefore complicated, and it is likely that many environmental factors are at play. These complex interactions between varied exogenous environmental cues most likely influence endogenous hormone and biological clock systems working in concert to

3 ultimately produce a precisely timed spawning event (Sorek & Levy 2014). Coral, therefore, make an excellent model for studying internal time keeping, as it is so critical to their overall fitness.

1.2 Biological clocks

Biological clocks generate rhythmic behaviours useful for an organism to adapt to their temporal niches. These rhythms vary in frequency and are often grouped into different categories. For example, ultradian rhythms are clocks shorter than a day whereas infradian rhythms occur at a frequency longer than a day. Examples of infradian rhythms would include migration of birds (Rensing & Ruoff 2002), growth rates and flowering of plants (Simpson &

Dean 2002) and gamete production in echinoderms (Mercier & Hamel 2009a). Examples of ultradian rhythms include the tidal emergence of marine midges (Kaiser et al. 2011), and the various components of sleep cycles in humans (Bass & Takahashi 2010). Chronobiology is the study of how organisms utilize oscillations in environmental signals and rhythmic cellular processes to anticipate changing environments.

This field mainly focuses on endogenous timing systems which fall into one of two categories: i) oscillatory clocks or ii) hourglass timers (Rensing et al. 2001). Oscillatory clocks utilize external inputs referred to as zeitgebers to entrain or calibrate an oscillating molecular pathway generating rhythmic behaviours that continue cycling even when the entraining signal is removed (Pittendrigh & Minis 1964). This phenomenon is known as free running, which unequivocally demonstrates that oscillating behaviours are generated within an organism and not a direct response to the environment. A classic example of this would the persistence of daily patterns of wheel running and sleeping behaviours of rodents even under constant darkness

4 conditions (Kornhauser et al. 1990). Hourglass timers, on the other hand, are unidirectional and lack oscillating mechanisms (Rensing et al. 2001). Consequently, hourglass timers require external signals in order to keep track of time, and do not show any form of entrainment. Brady et al. (2009) demonstrated that the precise hour of spawning in corals operates under an hourglass timer, as corals spawn in relation to artificially manipulated sunsets. The day of spawning, however, seems to be much more complicated and is probably regulated by an oscillatory clock (Jokiel et al. 1985). Coral likely utilize various timers both hour glass, and oscillatory for various aspects of reproduction which then leads to the precise timing of spawning.

Unfortunately, studying biological clocks in coral has been historically difficult.

Traditionally, chronobiologists have inferred clock functioning by measuring changes in the behaviour of an organism. Not only are behaviour observations difficult underwater, but the behaviours associated with reproduction in broadcasting coral only occur once a year. Therefore, an alternative to observing the behaviours generated by biological clocks is necessary for coral.

Luckily, how gene expression oscillates provides an excellent insight into the functioning of these clocks (Vize 2009; Fukushiro et al. 2011; Reitzel et al. 2013). Gene expression is modified by the environment, protein-protein, protein-DNA and hormone interactions (Reitzel et al. 2013) and a biological clock system operates through the actions of negative and positive feedback on the transcription of clock genes (Figure 1.1). A positive feedback element will increase the production of a particular clock protein through translation, these clock proteins eventually accumulate and once at sufficient levels act as a negative feedback to the whole system. In this way, transcription feedback loops generate rhythmic behaviours (reviewed by Dunlap 1999).

Figure 1.1 Basic components needed for a molecular clock system in any organism from (Dunlap 1999).

In order to study changes in gene expression, “next-generation” RNA-sequencing allows for quantitative analysis of the transcriptome of an organism at a fraction of the cost as what it would have been a decade ago (Wang et al. 2009). Even in non-model systems next-generation technologies have proven valuable in studying biological clocks (Kaniewska et al. 2015; Ruiz-

Jones & Palumbi 2015; Oldach et al. 2017). This technique not only grants access to studying clocks in coral it can inform of mechanistic elements that would be inaccessible by observation of behavioural changes.

1.2.1 Circadian clock

Circadian clocks are the most studied form of biological rhythm and have been characterized in plants (Simpson & Dean 2002), Drosophila (Bachleitner et al. 2007), polychaete worms (Zantke et al. 2013), birds (Binkley et al. 1971) mice (Panda et al. 2002), and of course coral (Vize 2009). The central core of the clock is widespread and ancient, and likely dates back to ancestors of protostomes and deuterostomes (reviewed by Dunlap 1999). The heart of the

6 circadian clock is controlled by a heterodimer of two proteins encoded by clock and cycle (bmal1 in mammals) (Shoguchi et al. 2013). When formed this clock:cycle dimer moves into the nucleus where it binds to specific E-box response elements that act as a promoter increasing the transcription of numerous other genes which ultimately controls various biological functions

(Gietl et al. 2012). In some organisms, the per gene plays a key role in this process, but these genes have not been identified in corals. Some of the clock genes include those that encode cryptochrome proteins (cry1 and cry2) (Wang et al. 2015), some of which are photo-responsive.

Furthermore, the accumulation of these proteins form a negative feedback loop which represses the activity of the clock:cycle dimer (Sancar 2004). This is an example of negative feedback which acts directly on the clock:cycle dimer, there is also a feed-forward loop where proteins directly modifying the gene expression of clock and cycle and this ultimately leads changes in the production of the clock:cycle dimer itself (Reitzel et al. 2013). Members of the bzip family are examples of transcription factors involved in this feed-forward loop (Gavriouchkina et al.

2010).

This system helps coral anticipate daily changes in its environment. For example, there are strong changes in food availability provided by photosynthesizing Symbiodinium (Thomas et al. 2014). This clock enables corals to alter their metabolic functions and to prepare for processing wastes that are input from Symbiodinium. Furthermore, circadian clocks help with the formation of an organic matrix that encourages calcification which is enhanced during the day

(Bertucci et al. 2015). This skeleton is very important as it is the foundation to how coral ultimately construct reefs. While there are many processes regulated by the circadian clock, how this impacts reproductive behaviours is not well known.

7 Non-photic elements can modulate and even entrain circadian clocks. For example, daily changes in temperature can entrain the phase of the circadian clock under constant light conditions (Dunlap et al. 2004). Temperature may be particularly influential on coral considering they are ectotherms and do not regulate their own body temperature. This said, circadian clocks are robust to temperature changes and a major defining feature of circadian clocks is that they are temperature compensated (Dunlap 1999). This means that the overall period of clock rhythmicity remains constant despite changing temperatures. Nevertheless, while the period of the clock is temperature compensated the amplitude and phase of the clock is not temperature independent.

To the best of our knowledge, no study has directly explored the relationship of temperature on internal clock systems in coral.

1.2.2 Circalunar clock

For an environmental signal to be useful in biological timekeeping, it must be robust and predictable. One such highly predictable signal comes from the light reflected off of the moon.

The overall intensity of light reflected corresponds to the moon phase as the moon orbits the earth every 29.5 days. At a full moon, light intensity reaches an apex, and just over a week later the intensity is halved as it reaches the third quarter moon phase. At new moon, starlight is the only light available to coral at night. As changes in the light input are the most common zeitgeber used by molecular clock pathways in order to synchronize behaviours (Bell-Pedersen et al. 2005) moonlight is likely responsible for entraining a circalunar clock. A connected yet alternative hypothesis is that the tides are the acting zeitgeber and not monthly cycles of moonlight. This is very unlikely, however, as when tidal influences are removed by placing coral in aquaria, coral maintains normal reproductive behaviours as long as there is access to

8 moonlight (Sweeney et al. 2011). Furthermore, artificial moonlight manipulations can influence the timing of coral reproduction and changes in intensity in relation to full moon is important

(Boch & Ananthasubramaniam 2011).

Circalunar clocks likely operate in a variety of marine invertebrates as spawning is highly correlated with a specific lunar phase (Mercier & Hamel 2010). Furthermore, there are clear examples of molecular and behavioural changes associated in: bristle worms (Zantke et al.

2013), rabbit fish (Takemura et al. 2006) and marine midges (Kaiser et al. 2011) to changing moon phases. Some of the best early evidence for an internal oscillatory lunar clock in corals is from a study by Jokiel et al. (1985) where it was demonstrated that a brooding coral Pocillopora damicornis releases planula in phase with the lunar cycle even when moonlight was removed or held constant (Figure 1.2). Much like rodents maintaining circadian behaviours under constant darkness, these results suggest that moonlight entrains behaviours just like those driven by a circadian clock, only on a longer cycle. Therefore, coral appear to have all the characteristics of a biological clock responding to lunar light.

How the circalunar clock operates in coral is understudied. It is possible that rather than an isolated clock system, the circalunar clock may simply be modifying parts of the circadian clock. Evidence for this include levels of cryptochrome 2 mRNA were found to be elevated at midnight during the full moon but not during the new moon (Levy et al. 2007). Furthermore, multiple studies have found significant differences in circadian clock gene expression levels over the course of a lunar cycle (Kaniewska et al. 2015; Brady et al. 2016; Oldach et al. 2017).

Temperature may modify the circalunar clock system in a similar manner to how it modifies circadian clocks in that it influences amplitude and phase of clock gene expression but not the period. Compelling evidence for the interactions of temperature on circalunar clocks is

9 seen by an experiment by Crowder et al. (2014). By experimentally manipulating the temperature, a phase shift in reproductive behaviours of P. damicornis was observed (Figure

1.3). Increasing the temperature caused planula to be released in this coral earlier in the lunar month and so it is possible that temperature modulates the circalunar clock in this system.

Figure 1.3 Planula release of Pocillopora damicornis under two temperature treatments. Figure from (Crowder et al. 2014).

1.2.3 Circannual clock

Circannual clocks are well characterized in plants and migratory behaviours in birds (Wood &

Loudon 2014) and changes in photoperiod and temperature are considered the most probable proximate mediators in the reproduction of echinoderms (Mercier & Hamel 2009b). Coral species live in tropical equatorial reefs where changes in photoperiod are minor. Some animals however still show circannual rhythms even without photic cues. For example, the emergence of ground squirrels and gonadogenesis are triggered by internal circannual timing despite living in constant darkness (Dunlap et al. 2004). So, while photoperiod is very important zeitgeber for many migratory species, coral inhabit equatorial reefs that do not have high yearly fluctuations in

11 photoperiod. Therefore, seasonal changes in temperature between winter and summer may influence gametogenesis and lead to reproductive seasonality.

1.3 Study species: Acropora millepora

Acropora millepora (Ehrenberg, 1834) is a reef-building hermaphroditic broadcast spawning coral. It reproduces in the austral spring, where it releases positively buoyant egg and sperm bundles. Once these bundles reach the surface, the packets break apart spilling both egg and sperm and allowing fertilization to occur in the water column. This species is widely distributed across the indo-pacific oceans and compared to other Acropora species it is relatively easy to identify (Cooper et al. 2011). Furthermore, A. millepora is essentially the model organism for broadcast spawning coral research and has a sequenced transcriptome that is pivotal for this research (Moya et al. 2012; Bay et al. 2013; Granados-Cifuentes et al. 2013; Bertucci et al.

2015; Matz et al. 2017).

Figure 1.4 Underwater image of an Acropora millepora colony

1.4 Objectives and Purpose

Circadian, and circalunar biological clocks may be key elements for accurate timing of reproduction in coral. Since coral show such tight synchronicity in timing, and the timing is critical to their overall fitness – coral makes an excellent system towards understanding the intricacies and functioning of biological clocks. How biological clocks work in coral remains understudied. The central purpose of this thesis is to characterize how lunar phase and temperature modulates biological clocks in coral over a lunar month by assessing daily gene expression profiles at a variety of time points.

13 Chapter Two: Methods

2.1 Site description and collection

Heron Island is located approximately 80 km offshore of Gladstone, Australia, and is one of the several islands that comprise the Capricorn Bunker group at the southern tip of the Great Barrier

Reef (Figure 2.1). This island was chosen for use of The Heron Island Research Station (HIRS).

HIRS is an ideal field site for its proximity to a healthy coral reef and access to the station's excellent aquaria and laboratory facilities.

Acropora millepora colonies (n=5) were collected on the northeast section of Heron Reef known as Libby’s Point (23°26'03.5"S 151°55'13.4"E). Colonies were retrieved via SCUBA diving between 5 m and 2 m depth on April 4th, 2016 (GBRMPA collections permit

G16/38344.1). Colonies of approximately 30 cm diameter were removed from the substrate using a hammer and chisel and transported in buckets with saltwater to HIRS aquaria.

Figure 2.1 A) Map of heron island B) Aerial photo of Heron Island. © Photos reproduced with permission from fusion holidays Australia (For permission letter, see Appendix)

14 2.2 Experimental Design

2.2.1 Aquaria

The experimental aquaria (Figure 2.3) were identical 66 cm x 176 cm x 40 cm outdoor tanks supplied continuously with unfiltered seawater. A shade cloth was placed over both tanks to reduce full daylight to 250-300 µmol s-1 m-2 (measured by a LI-COR LI-192 underwater quantum sensor). The shade cloth allowed for the daylight to match the light intensity of where the coral was collected on the reef crest. One tank was chilled to 21.5oC to mimic winter sea water temperatures with a Hailea HC-500a aquarium chiller. To avoid stress on the coral from sudden temperature shock, the corals were slowly acclimated from 27oC to 21.5oC by decreasing the temperature 2oC per day. The warm water condition was maintained at 27oC with three 300

W aquarium heaters to simulate a summer temperature. Water temperature was measured continuously with two HOBO Pendant Temperature/Light 64K Data Loggers.

Figure 2.2 A) One of two identical experimental aquaria with labelled mini-colonies. B) Full Acropora millepora colony (Green individual).

2.2.2 Moonlight apparatus

After sunset, a large tarp was placed over both aquaria to ensure no light pollution from surrounding buildings on the station would be perceived by the coral. To create artificial moonlight, a lunar light was constructed (Figure 2.2) to mimic the intensity and spectral qualities of moonlight. The lunar light apparatus consisted of a single 12 W 5000 k dimmable LED bulb enclosed in a box where two “1.2” (4 f stops) and one “0.6” (2 f stops) LEE neutral density filters were fitted over the lightbox opening. The neutral density filters were effective at dimming the brightness of the LED to match full moon levels (0.22 Lux, measure by Extech Easy View 33 light meter) without changing the spectra of the light. Both moonlights were connected to a dimmer switch that was manually adjusted to match the light intensities of different lunar phases.

Moonrise and moonset were matched (Table 2.1) with an automatic timer that was reset daily.

The tarp was removed immediately before sunrise every day.

Figure 2.3 Experimental aquaria with artificial moonlight suspended above (black boxes).

Table 2.1 Lunar intensity and sampling dates

Date Moonrise Moonset Moonrise % illuminated LUX illumination Phase

04-Apr-16 2:08 AM 3:20 PM - 17.10% 0.04 Acquired coral 22-Apr-16 - 5:50 AM 5:36 PM 100.00% 0.22 Full Moon 30-Apr-16 - 12:29 PM 11:55 PM 53.60% 0.12 Third Quarter 07-May-16 6:19 AM 5:47 PM 0.30% 0.00 New Moon 14-May-16 12:40 PM - 56.50% 0.12 First Quarter 21-May-16 - 5:28 AM 4:52 PM 99.70% 0.22 Full Moon

17 2.3 Experimental Design and Sampling

Each coral colony was broken into approximately 12 mini colonies that acted as clonal biological replicates. Each replicate was labelled, then equally divided into two experimental aquaria. Each of the 5 individual colonies were tagged with a specific coloured zip-tie (Table 2.2) so that each clonal biological replicate could be identified. The location of each biological replicate was randomly reassigned within each tank every 3 days to prevent confounding effects of differing flow or lighting. For each of the 5 individual colonies in both temperature treatments, a daily profile was constructed for lunar phase sampled.

Sampling dates occurred on quarterly moon phases, beginning with the first quarter on April

14th, 2016 and ending with the full moon on May 22nd, 2016 (Table 2.2). To create these profiles, a biological replicate was selected from each colony every 4 hours starting at 16:00 on every sampling day of the lunar phase (total of 360 samples). Samples consisted of a 1.0 g coral branch tip removed with coral cutters; a coral sample of this size is referred to as a ‘nubbin’ or fragment.

To not disturb the artificial moonlight spectrum, a red flashlight was used when sampling at night, as coral are unlikely to respond to red light (Wang et al. 2015).

Table 2.2 Identification key for each sample with regards to treatment, colony, moon phase and sampling time Treatment Individual Moon Phase Time "H" - warm "B" - blue "1" - First Quarter "1" – 16h00 "C" - cool "G" - green "2" - Full Moon "2" – 20h00 "R" - red "3" - Third Quarter "3" – 24h00 "W" - white "4" - New Moon "4" – 04h00 "Y" - yellow "5" - second First Quarter "5" – 08h00 “6” - second Full Moon "6" – 12h00

2.4 RNA isolation

The coral nubbin was homogenized using a mortar and pestle and mixed with 1.5 mL of Trizol per 1.0 g of coral tissue. Each homogenized sample was placed into a 1.7 mL sterile tube and incubated at room temperature for 5 minutes. 0.2 mL of chloroform was added per 1.0 mL of

Trizol, vigorously mixed then incubated for 3 minutes. The coral was spun in a micro-centrifuge at 14 000 g for 15 minutes. This separates the mixture into a lower red-phenol chloroform solution, a protein-rich interphase, and an upper aqueous phase with dissolved RNA. The top layer of aqueous phase was carefully removed by pipetting into a sterile 1.7 mL tube and the rest of the mixture was discarded.

As A. millepora, like other stony corals, secrets a thick mucus there is a high concentration of polysaccharides that carry over into the aqueous layer. Polysaccharides interfere with the isolation of high-quality RNA, therefore .25mL of a solution of 0.8 M sodium citrate and 1.2 M sodium chloride was added in proportion to 1mL of Trizol used to prevent polysaccharides from precipitating in subsequent steps. To precipitate RNA from solution, 0.25 mL of isopropanol per volume of Trizol used initially was added. Samples were then labelled (in accordance with sampling codes in Table 2.2) and frozen at -80oC. After completion of all sampling, the tubes were packed into a Styrofoam cooler with dry ice and transported back to

Canada for further processing. Samples were stored in a -80oC freezer upon arrival in Calgary.

2.4.1 RNA purification

19 Each sample was removed from -80oC and thawed on ice. They were then incubated for 10 minutes at room temperature and centrifuged at 14 000 g for 5 minutes. The supernatant was removed and the sample was washed with 1.0 mL of 75% EtOH. This ethanol washing step was repeated before the sample was dissolved into 50 µL of RNase-free water. Concentration and

RNA quality was then assessed with a NanoDrop 2000 spectrophotometer by calculating the

260/280 absorbance ratio. It was determined that many samples had DNA contaminants so an additional step to remove DNA was performed.

2.4.2 DNA contaminant removal

Approximately 10 µg of RNA from each sample was removed and placed into a sterile 1.5 mL tube. RNase-free water was added to bring the total volume to 170 µL. 30 µL of 1X DNase buffer and 2 units of DNase I enzyme was added to the mixture. Incubating at 37oC for 60 minutes assisted the rate of reaction of the DNase enzyme. The enzyme was then rendered inactive by heating the solution to 75oC for 10 minutes.

Contaminants were removed by adding 100 µL of phenol and 100 µL of chloroform to the mixture and spun at 14 000 g for 2 minutes. The aqueous layer was removed and placed in a new sterile tube. 1/10th volume of 3 M potassium acetate was added, and RNA was precipitated by adding 2.5 times volume of pure ethanol and incubating on ice for 10 minutes. The solution was centrifuged at 14 000 g for 10 minutes, the supernatant was removed, and the pellet was washed with 75% ethanol. The mixture was centrifuged again at 14 000 g and once the supernatant was removed, the pellet was left to dry for 10 minutes. The RNA pellet was then re- suspended in RNase-free water to bring the total concentration of RNA between 0.5-1 µg/µL and stored in a -80oC freezer.

2.5 Library Preparation and Sequencing

The concentrations of each sample were checked with a NanoDrop spectrophotometer and samples were adjusted to a concentration of 100 ng/µL by adding RNase-free water. The samples were distributed into 200 µL sterile strip tubes in a random order. The tubes were then sent to

UCDNA services to undergo an Agilent 4200 TapeStation assay to assess RNA quality and concentration. The TapeStation results yield an RNA quality metric called the RNA integrity number (RIN) for each sample, and only the top 224 samples (based on RIN scores) were selected for library preparation and sequencing. UCDNA services used an Illumina TruSeq stranded mRNA library protocol to generate single end reads of 75 base pairs (bp) on the

Illumina NextSeq500. UCDNA services reported that library preparations went well for all samples with exception of HB61, which generated a library approximately 40 bp shorter than the rest. For this reason, the sequencing results for HB61 were removed from analyses. Using the

Illumina NextSeq500, UCDNA loaded 16 samples per run for a total output of approximately

400 million reads (25 million reads/sample).

2.6 Bioinformatics and Analyses

2.6.1 Preprocessing and Quality Control

UCDNA services provided the sequencing results in fastq files. As each sample was split into four separate fastq files, a simple UNIX script was used to concatenate the multiple files into a single fastq file for each sample. The quality of raw sequences was assessed with FastQC

21 v0.11.5 (Andrews, 2010) and all samples passed quality thresholds. As the quality of reads were high, trimming and filtering of library adapters was unnecessary.

2.6.2 Sequence alignment and read counts

The sequences of each fastq file were aligned to the A. millepora reference transcriptome built by the Matz laboratory at the University of Texas at Austin (Moya et al. 2012) using Bowtie2 v2.3.3

(Langmead & Salzberg 2012). RSEM v1.3.0 (Li & Dewey 2011) was then used to calculate expression by counting the number of reads that are assigned to specific isogroups into a raw read count table. An isogroup is a 454 transcriptome assembler term for the collection of alternative splice variants of the same gene.

2.6.3 Normalization

The following analyses were all conducted using the R 3.4.0 statistical software (R Core Team,

2017) environment. The raw read count table from RSEM v1.3.0 (Li & Dewey 2011) was imported and a table of basic descriptive statistics was calculated for each sample. As many of the genes were not represented with any read counts, the count table was filtered so that any row

(gene) which contained all zero counts was removed.

To account for differences in library sizes the estimatedSizeFactors function was used in

DESeq2 v1.16.1 (Love et al. 2014). This function calculates the geometric mean for each gene across all samples, then the counts for each gene in each sample are divided by this mean. This creates a ratio, and the median of these ratios for each sample are the size factor. Finally, the size

22 factor for each sample is then multiplied by the raw read counts for each gene to adjust for differences in sequencing depth and library size between samples. From this, a normalized count table was generated to be used in all subsequent differential expression analyses. Normalization was checked visually by comparing horizontal boxplots of expression between raw (Figure 2.4 and Figure 2.6) and normalized (Figure 2.5 and Figure 2.7) read count tables.

Figure 2.4 Box-plot of the counts of raw reads per gene in each sample used in LRT analyses. Red boxes are from warm condition, blue from cool.

Figure 2.5 Box-plot of the normalized counts of reads per gene in each sample used in LRT analyses. Red boxes are from warm condition, blue from cool.

Figure 2.6 Box-plot of raw counts per gene in each sample used in Wald analyses. Red boxes are from first full moon, blue are from the second full moon.

Figure 2.7 Box-plot of normalized counts per gene in each sample used in Wald analyses. Red boxes are from first full moon, blue are from the second full moon

25 2.6.4 Data Distribution

The workflow of Gierlinksi et al. (2015) for assessing statistical models was adapted for the following section. For coded examples, please see Gierlinksi et al. (2015).

The distribution of read counts per gene was checked by generating a histogram of the number of genes with associated log transformed +1 read counts (Figure 2.8). The type of distribution is important for fitting appropriate models and choosing one of many available software packages used for differential expression analyses as each program has different underlying assumptions. To more accurately test if distribution belonged to a Poisson distribution, a variance/mean plot was created (Figure 2.9) where a linear relationship between mean and variance would be indicative of a Poisson distribution. The plot was visually inspected, and since these data deviated from expected relationships between mean and variance it was therefore suspected the count data would better fit a negative binomial distribution. To assess the fit of a negative binomial distribution, gene-wise dispersion was estimated with DESeq2 (Love et al. 2014) using the estimateDispersion function and the dispersion estimates for each gene were plotted against normalized counts (Figure 2.10). The fit was visually inspected, and since dispersion fit better to a negative binomial distribution than a Poisson distribution, DESeq2

(Love et al. 2014) was chosen for differential expression analyses as it fits the count data to negative binomial distribution.

Figure 2.8 Histogram of the number of normalized counts assigned to each gene in the A. millepora transcriptome.

Figure 2.9 Plot of the average counts per gene vs the variance of the same gene. The line represents the expected mean/variance relationship for a Poisson distribution.

Figure 2.10 Plot of the normalized counts against dispersion estimates. Red line represents expected relationship between mean and dispersion of a negative binomial distribution.

2.6.5 Principal Component Analysis and Outlier Removal

Expression variance often increases with the mean in RNA-seq experiments. Therefore, if a principal component analysis were to be conducted on normalized counts, the trends would be dominated by a few highly expressed genes. To compensate, a regularized-logarithm transformation (rlog) of the normalized counts was conducted with DESeq2 (Love et al. 2014). A principal component analysis was then plotted using the rlog counts to ascertain the clustering of sample-to-sample expression (Figure 2.11). The rlog transformation was only applied to visualize the ordinates of principal components and was not used in other analyses. Each variable

28 (temperature, phase, hour, individual) were identified to determine which variable summarized the variance between the first and second principal component. Different individuals clustered very tightly and is explored in detail in section 3.1. It was observed that two data points CR34 and HW45 were clustered in groups of different individuals. Due to the extremely consistent pattern these data, the two points were removed (Figure 2.12) from subsequent analyses.

individual B G 0

PC2 R W Y

-20

-20 0 20 40 60 PC1

individual B G 0

PC2 R W Y

-20

-20 0 20 40 PC1

Figure 2.12 Principal component analyses with outlier samples removed. Each sample is labeled by individual colony

2.6.6 Differential expression analyses

To determine which genes were differentially expressed as result of environmental and experimental interactions, various analyses were applied using DESeq2 (Love et al. 2014). First, various likelihood ratio tests (LRT) were used when comparing samples across four lunar phases

(FM, 3Q, NM, 1Q) (Table 2.3). LRT distinguishes deviances in the goodness of fit between a

30 full generalized linear model with a reduced model for the read counts assigned to every gene.

Therefore, the specific term or interaction removed from the full model reflects how that term or interaction contributes to the full model. The first LRT analysis, had the three-way interactions between temperature, phase and hour (TPH) removed from the full model. The second analysis had temperature and phase interaction (TP) removed, and the last analysis removed the temperature and hour interaction term (TH). The interactions between temperature, lunar phase, and time of day were of main interest for this study and were all tested. In addition, a Wald test for significance was used with DESeq2 to find pairwise differences between expressed genes in the first full moon and the second full moon (warm water samples only).

For every analysis, a Benjamini-Hochberg method for multiple testing was applied to adjust p-values (Benjamini & Hochberg 1995). Genes were filtered based on adjusted p-values less than 0.05 to generate a list of differentially expressed genes (DEGs).

Table 2.3 Full vs reduced models used in likelihood ratio tests in DESeq2

Likelihood Ratio Test Acronym Full Model Reduced Model Performed

~ individual + temperature + ~ individual + temperature + phase + hour + Temperature x Phase x Hour phase + hour + TPH temperature:phase + Interaction temperature:phase + temperature:hour + phase:hour temperature:hour + phase:hour + temperature:phase:hour

~ individual + temperature + ~ individual + temperature + Temperature x Hour phase + hour + phase + hour + TH Interaction temperature:phase + temperature:phase + temperature:hour + phase:hour phase:hour

~ individual + temperature + ~ individual + temperature + Temperature x Phase phase + hour + TP phase + hour + Interaction temperature:phase + temperature:hour + phase:hour temperature:hour + phase:hour

2.6.7 Hierarchical Clustering

The top 100 annotated DEGs for each analysis underwent a hierarchical agglomerative clustering based on Euclidian distances of expression patterns using heatmap2 as part of the gplots v3.0.1

(Warnes et al. 2016) package in R. The clustering process was visualized with heatmaps and a row dendrogram, which illustrates how similar one gene expression pattern is to another.

2.6.8 Annotation

The annotations of the A.millepora transcriptome were updated by performing a sequence similarity-based functional annotation using the BLASTx algorithm (Altschup et al. 1990).

Sequences were queried against the National Center for Biotechnology Information protein sequence database to retrieve any corresponding annotations from related species. Blast2GO v4.1.9 (Conesa et al. 2005) was used to find corresponding gene ontology (GO) terms, Enzyme

Codes, InterPro and KEGG annotations for each gene sequence.

2.6.9 Gene Set Enrichment Analyses

Gene Set Enrichment Analyses (GSEA) was conducted with Blast2GO v4.1.9 (Conesa et al.

2005). GSEAs are useful to highlight over-represented GO terms for each list of differentially expressed genes. Blast2GO calculates a statistic similar to the Kolmogorov-Smirnov statistic by generating enrichment scores (ES) for each GO term and uses a permutation test to produce a null distribution of ES. The ES for the differentially expressed genes are then compared to the null distribution, and it can be determined if a GO term is over-represented.

33 Normalized counts of genes associated with the top 10 GO terms from GSEA analyses were plotted on a heat-map using heatmap2 as part of gplots v3.0.1 (Warnes et al. 2016) R package. Genes tagged with the GO term “Rhythmic process” were also visualized individually with box-plots of normalized counts using ggplot2 v2.2.1 (Wickham, 2009).

34 Chapter Three: Results and Discussion

3.1 Samples Sequenced

Sample RNA quality was assessed using a TapeStation assay to provide an RIN quality score for each sample (Table 3.1). We had a limited budget that covered the costs of sequencing 224 samples. As this did not cover all of the samples gathered, cool temperature samples from the second FM were not sequenced. To maximize quality, samples were ranked based on RIN scores and the top 224 were selected for sequencing (for a summary of samples sequenced, see

Table 3.2). After mapping with Bowtie2 (Langmead & Salzberg 2012) to a reference transcriptome (Moya et al. 2012), an average of 19 million quality reads were mapped to each sample for a total of 4.2 billion mapped reads across all samples.

Table 3.1 Descriptive statistics of TapeStation RIN scores Mean Median Max Min Standard Deviation 5.77 5.9 7.7 1.5 0.95

Table 3.2 Summary of samples sequenced belonging to each experimental factor Temperature Individual Phase Hour Warm 124 Blue 44 FM 40 16h00 40 Cool 95 Green 44 3Q 53 20h00 39 Red 44 NM 52 24h00 37 White 42 FQ 50 04h00 36 Yellow 45 2nd FM 24 08h00 32 12h00 35

35 Table 3.3 Descriptive statistics of mapped-reads to A. millepora transcriptome (Moya et al. 2012) Mean Max Min 19187532 38303853 12717555

3.2 Principal component analyses

A principal component analysis (PCA) was conducted on the regularized-logarithm (rlog) transformed read counts to visualize sample-to-sample distances of overall expression. Samples grouped tightly into five clusters where PC1 was plotted against PC2; individual colonies were associated with each cluster and therefore was the biggest driver of overall expression differences (Figure 3.1).

To determine if additional clustering occured within each individual colony cluster, the same PCA was visualized differently by facetting to each individual cluster with ggplot2

(Wickham, 2009). There appeared to be no obvious trends of temperature (Figure 3.2), phase

(Figure 3.3) or hour (Figure 3.4) so overall the pattern of individuals driving main expression differences remain. This finding is not altogether surprising, as the experimental design involves repeated measures with genetic clones. Therefore, this analysis is picking up on the individual differences between genetically distinct individuals. However, this result is encouraging in that it confirms that we collected different biological replicates. This is important to distinguish, as it is would have been possible to collect genetically identical individuals if the colonies had grown from asexual fragments which could have moved in a storm or from other disturbances.

Figure 3.1 Principal component analysis of rlog transformed counts coloured by individuals

B G R −4 −28

32 −6 −29

−30 31 −8

−31 30 −10 temperature −17 −16 −15 −14 −13 −18 −16 −14 −12 −10 −8 Cold

PC2 W Y 13 Hot

12 −2

11 −3

10 −4 9

−5 −16 −14 −12 −10 −8 49 50 51 52 53 54 PC1

Figure 3.2 Principal component analysis exploring temperature differences within colony clusters

Figure 3.3 Principal component analysis exploring phase differences within colony clusters

Figure 3.4 Principal component analysis exploring hour differences within colony clusters

Figure 3.5 Principal component analysis from samples used in FM vs FM. Data points are coloured as individual colonies

Figure 3.6 Principal component analysis from samples used in FM vs FM. Data points are coloured by hour

Figure 3.7 Principal component analysis from samples used in FM vs FM. Data points are coloured by moon phase

3.3 Differential Expression Analyses

Differential expression was calculated for various likelihood ratio tests (LRT) with DESeq2

(Love et al. 2014) (Table 3.4). The LRT exploring genes differentially expressed by a three-way interaction between temperature, lunar phase, and time of day (TPH) uncovered 580 DEGs. This represents approximately 1.1% of the referenced transcriptome. The LRT analysis considering the interaction between temperature and hour (TH) during the day described 1562 DEGs or 3.1% of the transcriptome as differentially expressed. The final LRT analysis assessed interaction of temperature and moon phase (TP) and found 2067 DEGs; these genes represent 4.1% of the transcriptome.

40 Table 3.4 Number of Differentially Expressed Genes, based on FDR < 0.05 from likelihood ratio test

Number of Differentially Percent of transcriptome Interaction Expressed Genes (%)

Temperature x Hour x Phase 580 1.1

Temperature x Hour 1562 3.1

Temperature x Phase 2067 4.1

To determine if similar or unique genes are responding to these different experimental conditions, genes isolated from each LRT analysis were visualized with a Venn diagram (Figure

3.8). Overall 73 genes were shared between all LRT analyses, while only 20 genes were shared between TP and TPH. This is a small percentage of the TPH DEGs (12.5%) and an even smaller percentage of TP (3.5%) DEGs. 313 genes were differentially expressed in both TPH and TH analyses. This represents approximately half of TPH DEGS (54%) and 20% of TH DEGs, and this may suggest that time of day has a strong influence on the TPH analysis. TP had the most unique genes (1831) and the largest proportion (89%) that were unique. TP and TH shared 143 genes, which represents 6.9% of TP and 9.2% of TH.

The same process was repeated with the DEG list from the Wald analysis comparing the first and second full moon (FM vs FM). A Venn diagram was then generated with the FM vs FM and TPH analyses. As there were overlapping genes between these two analyses (83) the list of rhythmic genes that were differentially expressed in the TPH analysis was included to the Venn diagram in order to determine if these genes of interest were different between the FM phases

(Figure 3.9). Only one gene (cipc) was shared with these analyses, so in general rhytmic genes return to baseline between consecutive FM phases.

41 Careful examination of the genes that are unique to certain analyses and shared amongst others may build interesting questions worthy of further analyses. Unfortunately, these questions are simply outside of the scope of this thesis but the groundwork has been set for future considerations. These future considerations are discussed in detail in the concluding chapter.

Figure 3.8 Venn diagram of DEGs from each LRT analysis

Figure 3.9 Venn diagram of first FM vs second FM Wald analysis, TPH LRT analysis, and rhythmic process genes

3.4 Hierachical Clustering

3.4.1 Temperature x Phase x Hour analysis (Figure 3.10)

Individual colony expression counts were averaged within each sampling hour and lunar phase.

The mean counts of the top 100 annotated genes (FDR < 1.14 x 10-5) from the TPH analysis were then clustered based on their similarity in expression profiles and visualized in a heatmap

43 (Error! Reference source not found.). This list of genes undergoes noticeable changes across the lunar month, time of day, and temperature treatments. While not all of the annotations from

BLASTx are completely informative, there are some interesting genes uncovered from this analysis.

Firstly, two isoforms of heme binding protein 2 (hbp2) are clustered with bzip1 and a cry1 isoform, which are known circadian regulators (Fukushiro et al. 2011). They are up regulated at 08:00 and at 12:00, however it is difficult to get a full sense from the heatmap how temperature and phase interacts with this cluster. Furthermore, three isoforms of cry1 group together with clock, helt, a gamma-aminobutyric acid (GABA) receptor, and two heat shock genes (hsp70, hsp90) cluster together, all of which are known to play a role in mammalian circadian rhythms (Dardente et al. 2010). This result is supported by Bertucci et al. (2015) and is consistent with other studies in coral.

hsp70 – interacting protein has an interesting temperature and phase expression pattern as it is most upregulated in the warm temperature at 12:00 and 16:00 and appears to be more upregulated across FQ lunar phases in both temperature conditions across a range of time points.

Heat-shock proteins could be involved in an adaptive temperature signaling pathway used for temperature compensation, maintaining the period of rhythmic biological clocks (Franc et al.

2012). This is exemplified in an experiment manipulating the heat-shock pathway in Drosophila where it was shown to influence sleep behaviour (Kidd et al. 2015).

Overall, there are some interesting rhythmic genes that cluster together at various points in relation to lunar month, time of day, and temperature differences.

Figure 3.10 Heatmap of top 100 annotated DEGs (FDR < 1.14 x 10-5) from TPH analyses

45 3.4.2 Temperature x Phase analysis (Figure 3.11)

Counts for the top 100 annotated DEGs (FDR < 1.44 x 10-6) from the TP analysis were averaged within each lunar phase for individual colonies and hour. These averaged counts were clustered and visualized with a heatmap (Figure 3.11). There are clear differences across the lunar month and between cool and warm temperatures. However, the overall pattern is varied and there are no known rhythmic genes in the top 100 DEGs. Furthermore, it is impossible to ascertain if the expression patterns observed are part of a clock or simply responding to a changing environment.

While it is unknown if any of these responses are part of a clock, there are some interesting links to hormonal regulation changing with respect to lunar phase and temperature.

A DEG that is in the top 100 and regulates hormone biosynthesis is activin2. This gene is known to regulate follicle-stimulating hormone (FSH) biosynthesis (Nakamuram et al. 1990)

FSH is a gonadotropin hormone responsible for regulating reproductive processes by stimulating the activation of germ cells in many organisms (Findlay 1993). Activin2 is downregulated in the

FQ and NM moon phases, and is upregulated in 3Q and FM in the warm treatment. Conversely, expression levels in the cool treatment show a different pattern: activn2 is downregulated in the

FM and upregulated at 3Q and FQ. Tightly clustered with activin2 is gene that encodes a SMAD

(homologues of the Mad protein in Drosophilia and the Sma protein in C. elegans) binding protein. SMAD proteins are involved downstream of the activin/transforming growth factor beta signaling pathway (TGF-b) (Rosendahl et al. 2003) which has also been shown to directly interact with clock genes in humans (Gietl et al. 2012). In contrast, two E3 ubiquitin ligase genes show opposite expression patterns to activin2 and SMAD binding protein, as they are upregulated strongly in the warm FQ condition and downregulated in cool FQ. The TGF-b pathway is known to be repressed by E3 ubiquitin ligases (Lin et al. 2000), and these results

46 demonstrate multiple genes are involved in the regulation of hormones that influence gametogenesis and are modulated by temperature and moon phase.

Another potential link of temperature and moon phase controlling endocrine involvement with reproduction could be from the expression patterns of cytochrome (cyp) P450. The cypP450 family are highly diverse and have a range of functions (for a thorough review, see Nelson et al.

1996). One possible function of cypP450 is the production of aromatase (Barbaglio et al. 2007) which catalyzes the last step in the estrogen biosynthesis pathway. Estrogen is a key hormone involved in reproduction, and is produced in coral (Twan et al. 2006) however its role is not well understood. Rougée et al. (2015) measured cypP450 as a proxy for estrogen levels across a lunar month in a brooding coral (P. damicornis) and the authors found consistently high levels that were unchanging regardless of lunar phase. In contrast, the cypP450 detected in our results show an increase in expression in the 1Q phase in warm temperature treatments but not in the cool treatment. As p450s are a very diverse gene family and orthology is always unknown, these enzymes may have different roles.

Further evidence for differential expression of genes associated with gametogenesis is found in the expression pattern of apolipoprotein D that was upregulated in the cool treatment relative to the warm treatment, and was particularly down regulated in the warm FQ.

Apolipoproteins have been recognized in the development of oocytes in the starlet sea anemone

(Nomatostella vectenesis, Lotan et al. 2014) and it is possible that apolipoprotein D is a signal for oocyte development in A. millepora.

Another interesting finding in these results is the pattern of dopamine expression.

Dopamine directly inhibits coral spawning (Isomura et al. 2013) when applied exogenously. Our results show gene expression for dopamine beta-monoxygenase is upregulated in the cool FM

47 treatment and downregulated in the warm FM treatment. As A. millepora spawns after the full moon in warmer water, these results may suggest that dopamine is responding to temperature at a key lunar phase to repress spawning signals. The lack of dopamine expression in warm water under a FM phase may allow for some sort of cascading signal that helps time seasonal and monthly reproduction.

Figure 3.11 Heatmap of top 100 (FDR < 1.44 x 10-6) annotated DEGs from TP analyses

49 3.5 Temperature x Hour analysis (Figure 3.12)

Counts for the top 100 annotated DEGs (FDR < 5.78 x 10-10) from TH analysis were averaged by individuals and lunar phases within sampling hour. These averaged counts were clustered based on their similarity and visualized with a heatmap (Error! Reference source not found.). This visualization illustrates hourly differences in expression that also differ between warm and cool temperature treatments. There are two main patterns to these data.

The first half of the list is clustered with genes that are upregulated at 04:00 and 08:00 across both warm and cool treatments. At 12:00 however, these genes are more upregulated in the cool treatment when compared to the warm treatment. This suggests an interaction of both hour and temperature treatments. Of the first 10 genes, 6 are known to be involved in circadian rhythms (one isoform each of bzip3, bzip5, cry1 and two isoforms of cipc). This analysis is very robust at identifying genes associated with biological clocks and is consistent with other studies in coral (Reitzel et al. 2013; Brady et al. 2016; Oldach et al. 2017).

A cyp450 gene also follows this pattern and, as mentioned in the previous subsection, may be involved with hormone regulation. Furthermore, a delta 14-sterol reductase shows the same pattern as the cyp450 from this list. Delta 14-sterol reductase is involved with steroid hormone biosynthesis and metabolism (Clouse 2000) and was in the top 100 DEGs from TPH and TH analyses. While hormonal biosynthesis and regulation is not a dominant process like it was in the TP analysis, the TH analysis also hints at potential hormonal regulatory properties.

The bottom half of the heatmap shows a pattern of upregulation at 12:00 and 16:00 that is generally more pronounced in the warm treatment than the cool treatment. There are 11 known rhythmic genes following this pattern and include cry2, clock, clock2, bzip2, two isoforms of helt and 5 isoforms of cry1. These genes are discussed in detail in section 3.7. In addition to these

50 rhythmic genes, hsp70 and hsp90 are clustered with similar expression profiles where they are highly regulated in the warm treatment during peak daylight (12:00 and 16:00). As discussed earlier, it is possible that heat-shock proteins are aiding in the temperature compensation pathway of the rhythmic genes. Ultimately, this entire list is heavily represented by systems associated with biological clocks and show substantial rhythmicity between daytime and nighttime with some differences between temperature treatments.

Figure 3.12 Heatmap of top 100 annotated genes (FDR < 5.78 x 10-10) from TH analysis

3.6 Gene Set Enrichment Analyses

3.6.1 GSEA – Temperature Phase Hour interaction (Table 3.5)

A GSEA found 50 GO terms that were overrepresented (FDR q-value < 0.05) from the list of

DEGs in the TPH analysis (Table 3.5). Despite a long list, 5 major physiological functions are represented by these GO terms: i) photoreception and rhythmic processes, ii) stress response, iii) regulation of metabolism, iv) regulation of transcription, and v) regulation of biosynthesis. There are other associated GO terms, however the aforementioned 5 terms are linked to the regulation of rhythmic behaviours.

The top GO term from the entire list is “rhythmic process”. This clearly suggests that our analyses are robust at finding signals of biological clocks. Furthermore, DNA photolyase activity and deoxyribodipyrimidine photolyase activity are within the top 10 GO terms of this list and these terms are associated with photoreception and biological clocks (Wang et al. 2015).

Furthermore, it has been hypothesized that circadian clocks have evolved from DNA repair processes (Hardeland et al. 1995) and many DNA repair enzymes are flavoproteins.

Flavoproteins are highly similar to light-sensing cryptochrome proteins (Sancar 2004). The second GO term on this list is “DNA repair”, and this GO term may actually be picking up signals from biological clock systems and not exclusively a response to stress.

Of the top 10 GO terms in this list, 4 terms are associated with stress response, including

DNA repair, cellular response to DNA damage stimulus, response to stress, cellular response to stress. This is not surprising, as increased temperature is known to elicit a stress response from coral (Meyer et al. 2011; Polato et al. 2013; Wood & Fraser 2014) and much of the current literature is dedicated to understanding coral response to anthropogenic climate change (Hoegh-

53 Guldberg et al. 2008; Ross et al. 2011; Bellantuono et al. 2012; Barshis et al. 2013; Chen et al.

2015; Moya et al. 2015; Matz et al. 2017). Outside of the scope of this thesis, this dataset can provide a baseline of how coral respond to seasonal changes in temperatures, which can help inform future research contrast the response of coral to temperature anomalies.

As coral contain endosymbiont algae that photosynthesize during the day, there are diurnal differences in food availability to coral and should impact metabolism in general (Moya et al. 2012). There are multiple GO terms associated with various metabolic processes associated in this list, and furthermore circadian clocks influence various metabolic processes (Bass &

Takahashi 2010).

Regulation of gene expression is represented by multiple GO terms from this analysis, including transcription factor complex, regulation of transcription, regulation of RNA biosynthetic process, and regulation of gene expression. Many of the rhythmic genes (for example: bzip, cipc, clock, and helt) are transcription factors providing direct feedback to oscillating mechanisms and influencing various biological processes.

Lastly, there were many GO terms associated with “biosynthesis”. While there are many possibilities as to what molecules are being synthesized in response to seasonal changes in temperature, lunar phase, and time of day, these results are consistent with some of the pathways involved with hormone synthesis. This offers further evidence that hormones are responding to temperature, lunar phase and hourly differences, and this may have repercussion on the timing of reproduction.

Table 3.5 GSEA of TPH differentially expressed gene list with FDR q-value < 0.05

GO Name GO ID Size ES FDR q-value rhythmic process GO:0048511 17 -0.7343 0.0000 DNA repair GO:0006281 13 -0.7756 0.0000 nucleic acid metabolic process GO:0090304 53 -0.3874 0.0000 DNA photolyase activity GO:0003913 10 -0.8223 0.0000 cellular response to DNA damage stimulus GO:0006974 14 -0.7094 0.0000 response to stress GO:0006950 46 -0.3740 0.0000 cellular response to stress GO:0033554 35 -0.4156 0.0000 DNA metabolic process GO:0006259 17 -0.5575 0.0003 carbon-carbon lyase activity GO:0016830 13 -0.6143 0.0004 cell redox homeostasis GO:0045454 8 -0.7919 0.0004 deoxyribodipyrimidine photo-lyase activity GO:0003904 7 -0.7756 0.0013 endoplasmic reticulum GO:0005783 19 -0.4756 0.0021 lyase activity GO:0016829 18 -0.4784 0.0025 RNA metabolic process GO:0016070 36 -0.3457 0.0027 endomembrane system GO:0012505 21 -0.4382 0.0032 regulation of cellular metabolic process GO:0031323 30 -0.3659 0.0033 transcription factor complex GO:0005667 7 -0.7271 0.0033 regulation of nitrogen compound metabolic process GO:0051171 30 -0.3659 0.0034 regulation of metabolic process GO:0019222 35 -0.3335 0.0042 regulation of nucleobase-containing compound metabolic process GO:0019219 30 -0.3659 0.0042 regulation of RNA metabolic process GO:0051252 30 -0.3659 0.0044 regulation of primary metabolic process GO:0080090 30 -0.3659 0.0045

55 transcription, DNA-templated GO:0006351 29 -0.3522 0.0062 regulation of cellular macromolecule biosynthetic process GO:2000112 29 -0.3522 0.0063 regulation of transcription, DNA-templated GO:0006355 29 -0.3522 0.0064 regulation of RNA biosynthetic process GO:2001141 29 -0.3522 0.0064 heterocycle biosynthetic process GO:0018130 37 -0.3136 0.0064 regulation of nucleic acid-templated transcription GO:1903506 29 -0.3522 0.0066 RNA biosynthetic process GO:0032774 29 -0.3522 0.0066 nucleic acid-templated transcription GO:0097659 29 -0.3522 0.0067 organic cyclic compound biosynthetic process GO:1901362 37 -0.3136 0.0069 regulation of macromolecule biosynthetic process GO:0010556 29 -0.3522 0.0069 regulation of biosynthetic process GO:0009889 29 -0.3522 0.0069 regulation of cellular biosynthetic process GO:0031326 29 -0.3522 0.0077 regulation of macromolecule metabolic process GO:0060255 32 -0.3255 0.0097 regulation of gene expression GO:0010468 32 -0.3255 0.0103 oxidoreductase activity GO:0016491 50 -0.2618 0.0142 aromatic compound biosynthetic process GO:0019438 35 -0.3035 0.0149 protein disulfide isomerase activity GO:0003756 7 -0.6285 0.0167 intramolecular oxidoreductase activity, transposing S-S bonds GO:0016864 7 -0.6285 0.0171 positive regulation of biological process GO:0048518 3 -0.8811 0.0346 positive regulation of cellular process GO:0048522 3 -0.8811 0.0347 nucleobase-containing compound biosynthetic process GO:0034654 33 -0.2912 0.0348 amino-acid betaine transmembrane transporter activity GO:0015199 3 -0.8713 0.0353 quaternary ammonium group transmembrane transporter activity GO:0015651 3 -0.8713 0.0395 carnitine transmembrane transporter activity GO:0015226 3 -0.8713 0.0428 modified amino acid transmembrane transporter activity GO:0072349 3 -0.8713 0.0437 L-arabinose isomerase activity GO:0008733 4 -0.7577 0.0492 calcium ion binding GO:0005509 25 -0.3201 0.0497 organic cation transmembrane transporter activity GO:0015101 3 -0.8713 0.0497

3.6.2 GSEA – Temperature Phase interaction (Table 3.6)

A GSEA on the DEGs from TP analysis has the largest list (136) of GO terms that were over represented (Table 3.6); numerous regulatory processes dominate the top of this list. This includes the regulation of gene expression, metabolism, and biosynthetic processes. Further down the list are GO terms associated with cell growth and proliferation, as well as mitogen- activated protein kinases which not only influence transcription, but are involved in responding to various stimuli (Johnson & Lapadat 2002).

Five of the top 10 GO terms in this list are directly related to regulating biosynthetic processes in response to temperature and lunar phase. These processes could result in a vast array of products; however, it is possible that hormone production is a main driver of these results.

This would be consistent with activin2, SMAD proteins, E3 ubiquitin ligase, and dopamine genes expression regarding temperature and lunar phase. The GO terms in this analysis are therefore consistent with these processes.

Table 3.6 GSEA of TP differentially expressed genes with FDR q-value < 0.05

GO Name GO ID Size ES FDR q-value regulation of RNA metabolic process GO:0051252 92 -0.2492 0.0004 regulation of cellular macromolecule biosynthetic process GO:2000112 93 -0.2433 0.0004 regulation of metabolic process GO:0019222 118 -0.2238 0.0005 transcription factor complex GO:0005667 21 -0.4984 0.0005 oxidation-reduction process GO:0055114 57 -0.3083 0.0005 regulation of gene expression GO:0010468 108 -0.2254 0.0005 transcription factor activity, sequence-specific DNA binding GO:0003700 73 -0.2965 0.0006 regulation of nitrogen compound metabolic process GO:0051171 94 -0.2485 0.0006 regulation of biosynthetic process GO:0009889 93 -0.2433 0.0006 regulation of macromolecule biosynthetic process GO:0010556 93 -0.2433 0.0006 regulation of transcription, DNA-templated GO:0006355 91 -0.2439 0.0006 regulation of cellular biosynthetic process GO:0031326 93 -0.2433 0.0006 regulation of nucleic acid-templated transcription GO:1903506 91 -0.2439 0.0006 heterocycle biosynthetic process GO:0018130 116 -0.2273 0.0007 regulation of RNA biosynthetic process GO:2001141 91 -0.2439 0.0007 regulation of cellular metabolic process GO:0031323 102 -0.2458 0.0007 regulation of primary metabolic process GO:0080090 102 -0.2458 0.0007 nucleoside phosphate biosynthetic process GO:1901293 7 -0.8859 0.0007 regulation of macromolecule metabolic process GO:0060255 117 -0.2285 0.0008 aromatic compound biosynthetic process GO:0019438 116 -0.2273 0.0008 regulation of nucleobase-containing compound metabolic process GO:0019219 93 -0.2544 0.0008 organic cyclic compound biosynthetic process GO:1901362 122 -0.2283 0.0009 nucleotide biosynthetic process GO:0009165 7 -0.8859 0.0009 RNA biosynthetic process GO:0032774 93 -0.2321 0.0011

58 nucleic acid binding transcription factor activity GO:0001071 73 -0.2965 0.0011 nucleobase-containing compound biosynthetic process GO:0034654 104 -0.2552 0.0011 alpha-amino acid metabolic process GO:1901605 49 -0.3099 0.0011 deoxyribonucleotide biosynthetic process GO:0009263 5 -0.9090 0.0012 transcription, DNA-templated GO:0006351 92 -0.2379 0.0013 nucleic acid-templated transcription GO:0097659 92 -0.2379 0.0015 protein binding GO:0005515 183 -0.1699 0.0015 deoxyribonucleotide metabolic process GO:0009262 5 -0.9090 0.0019 cellular amino acid metabolic process GO:0006520 50 -0.2967 0.0026 pyrimidine-containing compound metabolic process GO:0072527 7 -0.7664 0.0027 pyrimidine nucleobase metabolic process GO:0006206 7 -0.7664 0.0028 small molecule metabolic process GO:0044281 97 -0.2167 0.0029 L-serine metabolic process GO:0006563 9 -0.6548 0.0044 cellular nitrogen compound biosynthetic process GO:0044271 144 -0.1769 0.0052 aspartate family amino acid metabolic process GO:0009066 17 -0.4792 0.0053 non-membrane spanning protein tyrosine kinase activity GO:0004715 8 -0.6912 0.0057 O-acyltransferase activity GO:0008374 5 -0.8322 0.0068 serine family amino acid biosynthetic process GO:0009070 7 -0.7176 0.0087 sequence-specific DNA binding GO:0043565 31 -0.3505 0.0097 single-organism metabolic process GO:0044710 166 -0.1534 0.0100 serine family amino acid metabolic process GO:0009069 21 -0.4162 0.0104 nucleobase metabolic process GO:0009112 8 -0.6593 0.0105 growth GO:0040007 5 -0.7903 0.0130 oxidoreductase activity, acting on the CH-NH group of donors, NAD or NADP as GO:0016646 6 -0.7185 0.0145 acceptor signal transducer, downstream of receptor, with protein tyrosine kinase activity GO:0004716 7 -0.6730 0.0183 signal transducer activity, downstream of receptor GO:0005057 7 -0.6730 0.0188 L-serine biosynthetic process GO:0006564 6 -0.7173 0.0240 transcription from RNA polymerase II promoter GO:0006366 22 -0.3799 0.0242 regulation of transcription from RNA polymerase II promoter GO:0006357 22 -0.3799 0.0245

59 DNA binding GO:0003677 103 -0.1823 0.0246 cell proliferation GO:0008283 7 -0.6476 0.0276 carnitine O-acyltransferase activity GO:0016406 4 -0.8319 0.0280 carboxylic acid metabolic process GO:0019752 70 -0.2123 0.0281 organic acid metabolic process GO:0006082 70 -0.2123 0.0282 actin filament-based process GO:0030029 4 -0.8141 0.0300 actin cytoskeleton organization GO:0030036 4 -0.8141 0.0303 carboxylic acid biosynthetic process GO:0046394 26 -0.3454 0.0305 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in linear GO:0016811 4 -0.8426 0.0306 amides organic acid biosynthetic process GO:0016053 26 -0.3454 0.0310 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds GO:0016810 6 -0.6762 0.0312 regulation of cell cycle GO:0051726 9 -0.5636 0.0314 oxoacid metabolic process GO:0043436 70 -0.2123 0.0315 pyrimidine nucleotide metabolic process GO:0006220 3 -0.9154 0.0317 pyrimidine nucleotide biosynthetic process GO:0006221 3 -0.9154 0.0322 glycine metabolic process GO:0006544 6 -0.6540 0.0324 oxidoreductase activity, acting on the CH-NH group of donors GO:0016645 9 -0.5521 0.0330 pyrimidine nucleoside monophosphate biosynthetic process GO:0009130 3 -0.9154 0.0331 single-organism organelle organization GO:1902589 16 -0.4362 0.0332 nuclear chromosome segregation GO:0098813 12 -0.4806 0.0333 calmodulin-dependent protein kinase activity GO:0004683 6 -0.6489 0.0333 pyrimidine-containing compound biosynthetic process GO:0072528 3 -0.9154 0.0333 cyclin-dependent protein serine/threonine kinase regulator activity GO:0016538 7 -0.6191 0.0334 ribonucleoside-diphosphate reductase activity GO:0061731 3 -0.9083 0.0336 oxidoreductase activity, acting on CH or CH2 groups, disulfide as acceptor GO:0016728 3 -0.9083 0.0342 RNA metabolic process GO:0016070 116 -0.1609 0.0342 nucleoside monophosphate biosynthetic process GO:0009124 3 -0.9154 0.0343 ribonucleoside-diphosphate reductase activity, thioredoxin disulfide as GO:0004748 3 -0.9083 0.0344 acceptor

60 cellular macromolecule biosynthetic process GO:0034645 149 -0.1400 0.0345 MAP kinase kinase kinase activity GO:0004709 6 -0.6489 0.0345 deoxyribonucleoside monophosphate metabolic process GO:0009162 3 -0.9154 0.0346 pyrimidine nucleoside monophosphate metabolic process GO:0009129 3 -0.9154 0.0346 DNA-dependent protein kinase activity GO:0004677 6 -0.6489 0.0346 transmembrane receptor protein serine/threonine kinase activity GO:0004675 6 -0.6489 0.0346 cyclin-dependent protein kinase activity GO:0097472 11 -0.4897 0.0347 protein kinase C activity GO:0004697 6 -0.6489 0.0347 eukaryotic translation initiation factor 2alpha kinase activity GO:0004694 6 -0.6489 0.0347 calcium-dependent protein kinase activity GO:0010857 6 -0.6489 0.0348 macromolecule biosynthetic process GO:0009059 150 -0.1442 0.0348 methylenetetrahydrofolate reductase (NAD(P)H) activity GO:0004489 3 -0.9055 0.0348 ubiquitin homeostasis GO:0010992 45 -0.2489 0.0348 eukaryotic elongation factor-2 kinase regulator activity GO:0042556 6 -0.6489 0.0348 vitamin metabolic process GO:0006766 8 -0.5573 0.0348 organic hydroxy compound metabolic process GO:1901615 27 -0.3208 0.0349 phenol kinase activity GO:0018720 6 -0.6489 0.0349 ribosomal protein S6 kinase activity GO:0004711 6 -0.6489 0.0350 elongation factor-2 kinase activity GO:0004686 6 -0.6489 0.0350 cyclin-dependent protein serine/threonine kinase activity GO:0004693 11 -0.4897 0.0350 cGMP-dependent protein kinase activity GO:0004692 6 -0.6489 0.0350 eukaryotic elongation factor-2 kinase activator activity GO:0042557 6 -0.6489 0.0350 3-phosphoinositide-dependent protein kinase activity GO:0004676 6 -0.6489 0.0351 signal transducer, downstream of receptor, with serine/threonine kinase GO:0004702 6 -0.6489 0.0352 activity G-protein coupled receptor kinase activity GO:0004703 6 -0.6489 0.0352 oxidoreductase activity, acting on CH or CH2 groups GO:0016725 3 -0.9083 0.0352 MAP kinase activity GO:0004707 6 -0.6489 0.0352 NF-kappaB-inducing kinase activity GO:0004704 6 -0.6489 0.0352 cyclin-dependent protein kinase activating kinase regulator activity GO:0019914 6 -0.6489 0.0353

61 oxidoreductase activity, acting on the CH-OH group of donors, oxygen as GO:0016899 3 -0.9138 0.0353 acceptor kinase activator activity GO:0019209 6 -0.6489 0.0353 phosphorylase kinase activity GO:0004689 6 -0.6489 0.0354 MAP kinase kinase activity GO:0004708 6 -0.6489 0.0354 calcium-dependent protein serine/threonine kinase activity GO:0009931 6 -0.6489 0.0354 calcium-dependent protein kinase C activity GO:0004698 6 -0.6489 0.0354 phosphorylase kinase regulator activity GO:0008607 6 -0.6489 0.0355 deoxyribonucleoside monophosphate biosynthetic process GO:0009157 3 -0.9154 0.0355 AMP-activated protein kinase activity GO:0004679 6 -0.6489 0.0355 JUN kinase activity GO:0004705 6 -0.6489 0.0357 protein kinase activator activity GO:0030295 6 -0.6489 0.0357 cyclin-dependent protein kinase activating kinase activity GO:0019912 6 -0.6489 0.0357 deoxyribonucleoside diphosphate metabolic process GO:0009186 3 -0.9083 0.0357 cobinamide kinase activity GO:0008819 6 -0.6489 0.0359 water-soluble vitamin metabolic process GO:0006767 8 -0.5573 0.0359 SAP kinase activity GO:0016909 6 -0.6489 0.0360 phosphofructokinase activity GO:0008443 6 -0.6489 0.0365 ligand-gated cation channel activity GO:0099094 4 -0.7793 0.0383 JUN kinase kinase activity GO:0008545 6 -0.6489 0.0385 JUN kinase kinase kinase activity GO:0004706 6 -0.6489 0.0388 MAP kinase kinase kinase kinase activity GO:0008349 6 -0.6489 0.0432 sister chromatid segregation GO:0000819 11 -0.4804 0.0447 organophosphate biosynthetic process GO:0090407 24 -0.3285 0.0449 regulation of biological quality GO:0065008 78 -0.1852 0.0453 carbohydrate kinase activity GO:0019200 6 -0.6489 0.0458 chromosome segregation GO:0007059 14 -0.4091 0.0497

3.6.3 GSEA – Temperature Hour interaction (Table 3.7)

The GSEA of TH differentially expressed genes had the least (25) GO terms that were overrepresented out of all of the analyses (Table 3.7). Much like the TPH analyses, rhythmic processes are at the top of the list, and the TPH analyses is picking up signals of circadian rhythmic processes.

In this list 10 GO terms are directly related to signal transduction. This is consistent with other work in coral exploring diurnal changes in coral (Oldach et al. 2017). Calcium signaling has also been linked to the emergence of marine midges with relation to moon phase (Kaiser et al. 2016). It is therefore likely that various signal transduction pathways are being modified by biological clock systems and translating to reproductive behaviours.

Table 3.7 GSEA of TH differentially expressed genes with GO terms FDR < 0.05

GO Name GO ID Size ES FDR q-val rhythmic process GO:0048511 14 -0.8045 0.0000 circadian rhythm GO:0007623 14 -0.8045 0.0000 molecular transducer activity GO:0060089 19 -0.5403 0.0000 receptor activity GO:0004872 19 -0.5403 0.0000 cell redox homeostasis GO:0045454 6 -0.8101 0.0047 response to stress GO:0006950 24 -0.4034 0.0089 response to chemical GO:0042221 16 -0.4642 0.0211 transmembrane receptor activity GO:0099600 14 -0.4814 0.0261 transmembrane transporter activity GO:0022857 39 -0.3076 0.0264 cellular oxidant detoxification GO:0098869 4 -0.8460 0.0269 protein folding GO:0006457 18 -0.4241 0.0269 substrate-specific channel activity GO:0022838 11 -0.5455 0.0281 signal transducer activity GO:0004871 14 -0.4814 0.0282 intramolecular oxidoreductase activity, transposing S-S bonds GO:0016864 3 -0.9753 0.0284 signaling receptor activity GO:0038023 11 -0.5237 0.0285 channel activity GO:0015267 11 -0.5455 0.0287 cellular detoxification GO:1990748 4 -0.8460 0.0293 response to toxic substance GO:0009636 4 -0.8460 0.0298 calcium ion binding GO:0005509 19 -0.4257 0.0298 detoxification GO:0098754 4 -0.8460 0.0298 protein disulfide isomerase activity GO:0003756 3 -0.9753 0.0300 ion channel activity GO:0005216 11 -0.5455 0.0303 endoplasmic reticulum GO:0005783 17 -0.4504 0.0312 carbohydrate binding GO:0030246 4 -0.8627 0.0313 passive transmembrane transporter activity GO:0022803 11 -0.5455 0.0316

64 3.7 Venn Diagram of GSEA lists

The list of GO terms in each GSEA analyses is represented in a Venn diagram to illustrate uniqueness of GO terms represented in each analysis (Figure 3.13). Surprisingly, there were no

GO terms shared amongst all three analyses. Of the 50 significant GO terms from the TPH analysis, 24 were shared with TP and 7 with TH. No terms were shared between TH and TP analysis.

This Venn diagram demonstrates the importance of each analysis. While some processes were uncovered by two analyses, each individual LRT uncovered unique elements that may be important to both biological clocks and coral reproduction. Furthermore, there was not a single

GO term associated with all three, and so each analysis deserves considerations.

Figure 3.13 Venn diagram of GSEA list of over-represented GO terms. Top left: TPH, Top Right: TP, Bottom: TH

3.8 Rhythmic Process Genes

The central hypothesis of this thesis is that lunar phase and temperature modulate biological clocks in coral. The hypothesis is supported by the results presented in Table 3.5 Table 3.7 as well as Error! Reference source not found.Error! Reference source not found. where rhymic processes are differentially expressed across experimental treatments. Unfortunately, the previous figures do not display the nuance of these differences well but are useful for visualizing broader trends. To better understand these subtleties, the DEGs from the TPH analysis that were annoted by the GO term “rhythmic processes” were explored in detail and visualized on an individual gene basis in the subsequent subsections.

3.8.1 Basic Leucine Zipper Domain (bzip)

Proline and acidic amino acid-rich basic leucine zipper (bzip) belong to a family of transcription factors that are known be involved in circadian rhythms (Gachon et al. 2004). It is likely that bzip transcription factors act in a similar way in Drosophila where they directly regulate clock and cycle, which in turn acts as a feed-forward loop leading to their own repression (Reitzel et al.

2013). In a study exploring bzip family in zebrafish, it was found that these genes not only cycle in daily expression but immediately respond to light and then activate other circadian clock genes (Ben-moshe et al. 2010). This family has been well-characterized in coral for circadian changes in expression levels (Reitzel et al. 2013; Hemond & Vollmer 2015; Kaniewska et al.

2015; Ruiz-Jones & Palumbi 2015; Oldach et al. 2017). One bzip, thyrotroph embryonic factor

(tef), has been implicated with the reproductive behaviours in mice, as it influences pituitary gland development and the production of FSH (Moraes et al. 2012). It is therefore possible that

66 one of the following bzip genes are similar - or the same - as tef, and interact with the TGF-b pathway which may explain the differences discussed earlier.

3.8.1.1 Bzip1

The expression profiles of bzip1 are very similar between warm and cool treatments (Figure

3.14). Contradictory to some of the other circadian clock genes, bzip1 is up-regulated at night and down-regulated during the day. The expression levels decrease faster in the warm treatment than in the cool treatment, which is most noticeable at 12:00. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by

DESeq2 (q = 0.57, c2 = -1.09) (Figure 3.15).

Figure 3.14 bzip1 (FDR = 1.17 x 10-14) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.15 bzip1 expression daily lunar expression pattern between first and second full moon treatments

3.8.1.2 Bzip2

Expression of bzip2 is sinusoidal and most highly expressed in the morning at first light before being down-regulated at night (Figure 3.16). Expression levels start to decrease faster in the afternoon and overall expression is more down-regulated at night in the warm condition than cool. The FQ lunar phase in cool conditions decreases its expression at a slower rate in the early afternoon, especially in comparison to the NM. FM expression is higher than FQ at 04:00 in the warm treatment. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by DESeq2 (q = 0.13, c2 = 2.23) (Figure 3.17).

Figure 3.16 bzip2 (FDR = 1.11 x 10-10) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.17 bzip2 expression daily lunar expression pattern between first and second full moon treatments

69 3.8.1.3 Bzip3

The overall expression profile of bzip3 is sinusoidal, showing up-regulation in daylight and down-regulation at night (Figure 3.18). The expression profile appears to be phase-shifted in response to temperature treatments. The peak of expression in the cool condition is at 08:00 whereas expression peaks at 12:00 in the warm condition. In the warm condition, FM expression levels are higher at 04:00 and lower at midnight than the other lunar phases. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by DESeq2 (q = 0.42, c2 = 1.40) (Figure 3.19).

Figure 3.18 bzip3 (FDR = 1.40 x 10-5) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.19 bzip3 expression daily lunar expression pattern between first and second full moon treatments

3.8.2 bzip discussion

The three bzip genes presented here in detail all have a sinusoidal pattern of daily expression levels with a 24-hour period. While the period is the same, only bzip2 and bzip3 in the cool condition share the same phase with maximum expression occurring at 08:00 and minimum expression at 20:00 (Figure 3.16, Figure 3.18). bzip2 is unique in this group as it has highest expression levels at night. There are no consistent patterns with lunar phase across each bzip gene, and differences from lunar phase occur at different times in the day and at different temperatures. It is therefore difficult to assess how lunar phase influences this family of genes and what the possible biological consequences of these differences might be. While lunar phase

71 differences are inconsistent, temperature differences are consistent, as there is an amplitude shift across all three of these genes and shifts the phase of expression in bzip3 to peak 4 hours later.

These results are inconsistent with other studies (Kaniewska et al. 2015; Oldach et al.

2017) that found large lunar differences in day vs night expression levels in certain bzip genes.

Our work is consistent with night vs day expression levels in other studies, however lunar differences were not considered (Ruiz-Jones & Palumbi 2015).

3.8.3 Cryptochrome

Cryptochrome genes encode flavoproteins that are part of the photolyase superfamily which respond to certain wavelengths of light (Wang et al. 2015). cry1 encodes light-sensitive proteins that regulate the circadian clock (Yuan et al. 2017) whereas cry2 genes encode proteins that are light-independent and act as transcription repressors to clock systems (Wang et al. 2015). Both are core circadian clock genes, and the expression patterns in relation to temperature, time of day, and lunar phase of 4 isoforms of cry1 and 1 isoform of cry2 are explored in detail.

3.8.3.1 Cry1.1

Cry1.1 has a sinusoidal pattern of expression with maximum expression levels at 12:00 and minimum levels at 24:00 (Figure 3.20). Upon nightfall, expression levels decrease faster in the warm treatment than the cool treatment. In the warm treatment, NM and FQ lunar phases have a higher peak at 12:00. Furthermore, the NM phase has higher expression levels at 04:00 and lower expression levels at midnight than the other moon phases. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by

DESeq2 (q = 0.37, c2 = -1.50) (Figure 3.21).

Figure 3.20 Cry1.1 (FDR = 1.10 x 10-14) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.21 Cry1.1 expression daily lunar expression pattern between first and second full moon treatments

3.8.3.2 cry1.2 cry1.2 exhibits the general trend of being up-regulated during the day and down-regulated at night (Figure 3.22). In the warm treatment, expression levels peak earlier and are sustained throughout the day (with exception of NM) compared to the cool treatment. Furthermore, expression levels drop more rapidly in the warm treatment, especially at the onset of darkness. In the cool treatment, third quarter expression levels are more similar to warm patterns as they quickly peak in the morning. FQ expression in the cool condition is slower to peak and slower to descend. While it appears as though they may be a difference at 16:00 between the first FM and second FM, this gene was not shown to be statistically different in the Wald analysis performed by DESeq2 (q= .18, c2 = 2.04) (Figure 3.23).

Figure 3.22 Cry1.2 (FDR = 6.87 x 10-16) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.23 Cry1.2 expression daily lunar expression pattern between first and second full moon treatments

3.8.3.3 cry1.3 cry1.3 has a sinusoidal pattern of expression where it is more up-regulated in the day and downregulated at night (Figure 3.24). Overall there appears to be an amplitude shift between temperature conditions as peak expression levels (12:00) are greater in the warm than cool treatment. The FM treatment also appears to have a small amplitude shift as well, as expression levels are consistently less than the other phases in both treatments. While it appears that there are differences in expression at midnight between the first and second FM (Figure 3.24), this gene was not shown to be statistically different in the Wald analysis performed by DESeq2

(q=0.07, c2 = 2.58).

Figure 3.24 Cry 1.3 (FDR = 1.38 x 10-11) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.25 Cry1.3 expression daily lunar expression pattern between first and second full moon treatments

3.8.3.4 cry2 cry2 has a sinusoidal pattern of expression where it is up-regulated during the day and downregulated at night (Figure 3.26). There is an observeable amplitude shift between temperature treatments; there is a higher maximum level of expression in the warm treatment than in the cool treatment. There may also be a phase shift between treatments as expression levels peak earlier

(12:00) in the warm treatment. Levels of expression are higher in the 3Q than in the FQ lunar phases in the cool treatment at 04:00. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by DESeq2 (q = 0.38, c2 =

1.47) (Figure 3.27).

Figure 3.26 cry2 (FDR = 3.83 x 10-10) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.27 Cry2 hourly expression profile between first and second full moon treatments

3.8.3.5 cry discussion

The general expression patterns of the three isoforms of cry1 presented here (Figure 3.20, Figure

3.22 and Figure 3.24) are similar to each other and consistent in being upregulated during the day and down regulated at night (Levy et al. 2007; Oldach et al. 2017). The time between maximum and minimum expression remained constant (12 hours) despite temperature or lunar treatments.

This suggests that cryptochrome is temperature compensated. While the expression levels were higher in the FQ lunar phase than other moon phases at certain time points with cry1 isoforms, these differences are minor in comparison to the results from Brady et al. (2016) who found 5- fold higher levels of expression with cry1 at the FQ moon phase. The lunar differences in each cry1 isoform share no obvious trend as these differences occur at multiple time points and multiple phases at different temperatures. It was expected that, under FM nights, light reactive cry1 would respond to these levels, as in other studies (Levy et al. 2007; Kaniewska et al. 2015).

78 It is therefore difficult to assess exactly how moonlight is interacting with cry1. However, temperature shows a robust amplitude shift where each isoform of cry1 increases expression in the warm treatment. This is a novel finding in the coral A. millepora.

cry2 patterns of expression are consistent with other experiments in coral (Levy et al.

2007; Oldach et al. 2017). The lunar expression differences are similar with some previous work

(Brady et al. 2016; Oldach et al. 2017) but inconsistent with the large differences seen by Levy et al. (2007). Temperature has a very interesting impact on cry2 expression, as there is a slight phase shift where peak expression is greater at 12:00 in the warm treatment and peaks at 16:00 in the cool treatment. Furthermore, there is a general amplitude shift where cry2 has greater maximum expression in the warm treatment.

3.8.4 Hairy and enhancer of split-related protein (helt) helt is involved in pituitary development and mediating the photoperiodic secretion of melatonin in mice (Akimoto et al. 2010). Furthermore, helt is known to be part of the Notch signaling pathway which plays a role in oocyte development in cnidarians (Käsbauer et al. 2007). The

Notch pathway has also shown to interact with E3 ubiquitin ligases so is likely regulating aspects of the TGF-b pathway described earlier. helt has shown diurnal expression differences in coral

(Ruiz-Jones & Palumbi 2015; Oldach et al. 2017).

In this experiment, helt shows a sinusoidal profile of expression where it is up-regulated during the day and down-regulated at night (Figure 3.28). Expression levels drop at a faster rate in the warm treatment starting at night (20:00) and have a lower minimum expression levels at

20:00 and 24:00. There were no differences between the first and second full moon with the

Wald analysis (q = 0.30, c2 = 1.66) (Figure 3.29). As levels of expression drop faster in the warm

79 condition, it is speculated that this gene may play a role in altered hormone synthesis and oocyte development from the TGF-b pathway at night time.

Figure 3.28 helt (FDR = 6.92 x 10-15) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.29 helt hourly expression profile between first and second full moon treatments

80 3.8.5 Circadian Locomotor Output Cycles Kaput (clock)

clock is at the very core of the circadian clock system (Reitzel et al. 2013) as it controls the transcription of most circadian clock genes. clock expression is shown to be entrained in coral larvae (Brady et al. 2011) and is commonly shown to have diurnal differences in expression

(Kaniewska et al. 2015; Ruiz-Jones & Palumbi 2015; Brady et al. 2016). clock expression follows a sinusoidal pattern where it reaches peak expression at noon and minimum expression at midnight (Figure 3.30). There is an amplitude shift between temperature treatments where the warm treatment has a slightly higher maximum expression at noon and has lower minimum expression levels at 20:00 and 24:00 compared with the cool treatment. There were no differences between the first and second full moon treatments with the Wald analysis (q = 0.39, c2 = 1.45) (Figure 3.31).

Figure 3.30 clock (FDR = 4.28 x 10-20) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.31 clock hourly expression profile between first and second full moon treatments

3.8.6 Cycle

Cycle is characterized by a sinusoidal pattern where it is the most up-regulated in the afternoon before being downregulated at night (Figure 3.32). The amplitude of this pattern is greater in the warm treatment than in the cool treatment. These differences are most extreme in the early morning and again in the early afternoon. While there are no obvious phase shifts, NM warm expression levels increase earlier in the morning and decrease faster in the afternoon compared to other lunar phases in the warm treatments. This gene was not shown to be statistically different between first and second full moon with the Wald analysis performed by DESeq2 (q = 0.11, c2 =

-2.30)(Figure 3.33).

Cycle is crucially important for circadian clocks as it encodes a protein that forms into the clock:cycle heterodimer which influences the transcription of a suite of different genes (Reitzel et al. 2013). These results differ from Brady et al. (2011) who found a four fold increase in cycle expression during the daytime in FM vs NM. In this expresiment there are no clear patterns of lunar phase across both temperature treatments.

Figure 3.32 cycle (FDR = 4.07 x 10-6) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.33 cycle expression daily lunar expression pattern between first and second full moon treatments

84 3.8.7 clock interacting pacemaker (cipc)

3.8.7.1 cipc clock-interacting pacemaker (cipc) is a negative regulator of the circadian clock (Yoshitane &

Fukada 2009) and has been involved in fish reproduction (Mekuchi et al. 2017). Little is known about the functionality of cipc in Cnidaria, however the sinusoidal pattern of expression of most circadian clock genes makes this gene worthy of further exploration (Figure 3.34 & Figure 3.36). cipc reaches maximum expression at 08:00 and minimum expression at 20:00. Expression levels are lower at 12:00 and 14:00 in the warm condition than in the cool condition. The Wald analysis between first and second FM found that there were no differences between the two lunar phases

(q = 0.38, c2 = 1.47) (Figure 3.35).

While other studies have shown higher elevations of expression levels of coral at noon vs midnight (Oldach et al. 2017), these results illustrate for the first time that expression levels begin to lessen during the day after they reach max expression at 08:00.

Figure 3.34 cipc (FDR = 1.38 x 10-09) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.35 cipc hourly expression profile between first and second full moon treatments

3.8.7.2 cipc.1 cipc.1 has a similar sinusoidal profile to that of cipc, where it reaches maximum expression at

08:00 and minimum levels at 20:00 (Figure 3.36). cipc.1 levels are lower at 12:00 in the warm treatment than in the cool treatment. FQ levels are higher at 16:00 and FM levels are lowest at

16:00 in the cool treatment. cipc.1 was the only circadian gene to be differentially expressed between first and second full moon treatments (q < 0.05, c2 = 4.24) (Figure 3.37). This difference likely occurs at 24:00. Since no other rhythmic genes showed differences between the first and second FM, it is difficult to assess the biological significance of this difference in cipc.1.

Figure 3.36 cipc.1 (FDR = 6.31 x 10-7) hourly expression profile for warm and cool treatments and lunar phases

Figure 3.37 cipc.1 hourly expression profile between first and second full moon treatments

3.8.7.3 Cipc discussion

While other studies have shown higher elevations of expression levels of cipc in coral at noon vs midnight (Oldach et al. 2017), these results illustrate for the first time that expression levels start to decrease during the day after they reach max expression at 08:00. This illustrates the importance of gathering a full daily profile rather than comparing noon to midnight, as many previous studies have (Levy et al. 2007; Ruiz-Jones & Palumbi 2015; Oldach et al. 2017).

Chapter Four: Conclusion

4.1 Summary

This experiment demonstrates that seasonal differences in temperature and lunar phase influence expression patterns of a wide variety of genes in A. millepora. Firstly, numerous clock genes were differentially expressed across temperature and lunar phase treatments. These clock genes shared a pattern of having an amplitude shift between temperature treatments with the warm treatment eliciting a greater range in expression levels. The period of these genes however, remained constant with temperature – this is consistent with other studies showing temperature compensation of clock genes (Kidd et al. 2015). While the period was the same, some genes had a subtle phase shift (for example bzip3), where peak expression occurred at a different time between the two temperature treatments. While the lunar phase did impact these clock genes, there were no consistent patterns across time of day and specific lunar phases. However, circadian clock genes appear to return to a baseline of expression levels from the first full moon treatment to the next full moon. This experiment did not identify the same lunar effects on circadian clock gene expression as previously reported in similar studies (Levy et al. 2007;

Kaniewska et al. 2015; Brady et al. 2016; Oldach et al. 2017), however, it has more sampling power and sequencing depth than this similar work. It is suggested that future research may explore lunar effects over multiple lunar months.

Lunar light and temperature however, had clear influences on signaling pathways involved in hormone biosynthesis. In particular, genes involved in the TGF-b and Notch signaling pathways were differentially expressed with lunar phase and temperature. These 89

pathways are known to be involved in the synthesis of reproductive hormones. Further evidence for reproductive processes being affected is through the expression of a gene known to encode dopamine. This is interesting result as dopamine has been shown to directly inhibit coral spawning (Isomura et al. 2013). I suggest that further research on how clocks interact with these pathways would be an interesting and novel field to explore.

Overrepresented GO terms from each list of DEGs corroborate the story of temperature and lunar light influencing biological clocks, hormone biosynthesis, and hormone regulation.

Rhythmic processes were the top GO term from GSEA analyses for both THP and TH. In the TP analyses, numerous regulatory GO terms were associated with this list, including GO terms that suggest hormone biosynthesis is being actively regulated.

To the best of our knowledge, this research represents the most in-depth RNA-seq experiment on coral to date. It establishes that temperature modulates many processes in coral including biological clocks. Furthermore, lunar light influences these clocks in surprising ways and may inform on biological processes behind seasonal hormone production. Since precise timekeeping is so important in the reproductive fitness of coral, the environmental modulation of this behaviour is a key component to the health of not just the species, but of the ecosystem as a whole.

4.2 Future Directions

Due to the high volume of data generated by this experiment, future research should focus on further exploration of these data. There are many more questions to ask, and the processes described in this thesis could be explored in further detail as there are likely other supporting

genes showing similar patterns. This thesis focused only on the top 100 DEGs of each analysis, and therefore future work carefully exploring the rest of the DEGs will provide novel and useful information. In addition to exploring these analyses more fully, additional analyses could be performed to address different aspects of these data.

A similar analysis on how Symbiodinium responds to changes in temperature and lunar light on daily profiles of gene expression could be utilized. This would be accomplished by mapping the sequenced reads from this experiment to a Symbiodinium reference transcriptome rather than the A. millepora transcriptome used in the present study. This would be exciting because Symbiodinium have their own biological clocks (Sorek et al. 2014) and they may be interacting with the coral host or responding to these environmental changes differently.

Current research in coral is biased towards the response of coral to anthropogenic climate change. This is particularly relevant as many populations of coral are threatened by climate change (Graham et al. 2015) and the Northern GBR has seen massive declines in coral populations as a response to higher temperature (Vercelloni et al. 2017). This dataset has extremely valuable information on how coral responds to normal temperature variations between the seasons on a molecular level and this can act as a baseline for future research, as well as informing studies of how contemporary climate change impacts coral.

References

Akimoto M, Nishimaki T, Arai Y (2010) Hes1 regulates formations of the hypophyseal pars

tuberalis and the hypothalamus. Cell Tissue Research, 340, 509–521.

Altschup SF, Gish W, Pennsylvania T, Park U (1990) Basic local alignment search tool. Journal

of Molecular Biology, 215, 403–410.

Babcock RC, Bull GD, Harrison PI et al. (1986) Synchronous spawnings of 105 scleractinian

corals species on the Great Barrier Reef. Marine Biology, 90, 379–394.

Babcock RC, Wills BL, Simpson CJ (1994) Mass spawning of corals on a high latitude coral

reef. Coral Reefs, 13, 161–169.

Bachleitner W, Kempinger L, Wülbeck C, Rieger D, Helfrich-Förster C (2007) Moonlight shifts

the endogenous clock of Drosophila melanogaster. Proceedings of the National Academy of

Sciences of the United States of America, 104, 3538–43.

Barbaglio A, Sugni M, Di Benedetto C et al. (2007) Gametogenesis correlated with steroid levels

during the gonadal cycle of the sea urchin Paracentrotus lividus (Echinodermata:

Echinoidea). Comparative biochemistry and physiology. Part A, Molecular & integrative

physiology, 147, 466–74.

Barshis DJ, Ladner JT, Oliver T a et al. (2013) Genomic basis for coral resilience to climate

change. Proceedings of the National Academy of Sciences of the United States of America,

110, 1387–92.

Bass J, Takahashi JS (2010) Circadian integration of metabolism and energetics. Science (New

York, N.Y.), 330, 1349–1354.

Bay LK, Guérécheau A, Andreakis N, Ulstrup KE, Matz M V. (2013) Gene expression

signatures of energetic acclimatisation in the reef building coral Acropora millepora. PLoS

ONE, 8, 1–10.

Bell-Pedersen D, Cassone V., Earnest D. et al. (2005) Circadian rhythms from multiple

oscillators: lessons from diverse organisms. Nature reviews. Genetics, 4, 121–130.

Bellantuono AJ, Granados-Cifuentes C, Miller DJ, Hoegh-Guldberg O, Rodriguez-Lanetty M

(2012) Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE,

7, 1–14.

Ben-moshe Z, Vatine G, Alon S et al. (2010) Multiple PAR and E4BP4 bZIP transcription

factors in zebrafish: diverse spatial and temporal expression patterns. Chronobiology

International, 27, 1509–1531.

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful

approach to multiple testing. Journal of the Royal Statistacal Society. Series B, 57, 289–

300.

Bertucci A, Forêt S, Ball E, Miller D (2015) Transcriptomic differences between day and night

in Acropora millepora provide new insights into metabolite exchange and light-enhanced

calcification in corals. Molecular Ecology.

Binkley S, Kluth E, Menaker M (1971) Pineal function in sparrows : circadian rhythms and body

temperature. Science, 174, 313–317.

Boch C, Ananthasubramaniam B (2011) Effects of light dynamics on coral spawning synchrony.

The Biological Bulletin, 161–173.

Brady AK, Hilton JD, Vize PD (2009) Coral spawn timing is a direct response to solar light

cycles and is not an entrained circadian response. Coral Reefs, 28, 677–680.

Brady AK, Snyder KA, Vize PD (2011) Circadian cycles of gene expression in the coral,

Acropora millepora. PloS one, 6, e25072.

Brady AK, Willis BL, Harder LD, Vize PD (2016) Lunar phase modulates circadian gene

expression cycles in the broadcast spawning coral Acropora millepora. Biological Bulletin,

230, 130–142.

Chen P, Chen C, Chu L, Mccarl B (2015) Evaluating the economic damage of climate change on

global coral reefs. Global Environmental Change, 30, 12–20.

Clouse SD (2000) Plant development : A role for sterols in embryogenesis. Current Biology, 10,

601–604.

Conesa A, Götz S, García-Gómez JM et al. (2005) Blast2GO: A universal tool for annotation,

visualization and analysis in functional genomics research. Bioinformatics, 21, 3674–3676.

Cooper TF, Berkelmans R, Ulstrup KE et al. (2011) Environmental factors controlling the

distribution of symbiodinium harboured by the coral Acropora millepora on the Great

Barrier Reef. PloS one, 6, 1–13.

Cowen RK, Sponaugle S (2009) Larval dispersal and marine population connectivity. Annual

review of Marine Science, 1, 443–466.

Crowder CM, Liang W-L, Weis VM, Fan T-Y (2014) Elevated temperature alters the lunar

timing of planulation in the brooding coral Pocillopora damicornis. PloS one, 9, e107906.

Dardente H, Wyse CA, Birnie MJ et al. (2010) A molecular switch for photoperiod

responsiveness in mammals. Current Biology, 20, 2193–2198.

Dunlap JC (1999) Molecular bases for circadian clocks. Cell, 96, 271–290.

Fadlallah YH (1983) Sexual reproduction, development and larval biology in scleractinian

corals. Coral Reefs, 2, 129–150.

Findlay JK (1993) An update on the roles of inhibin, activin, and follistatin as local regulators of

folliculogenesis. Biology of Reproduction, 48, 15–23.

Franc P, Despierre N, Siggia ED (2012) Adaptive temperature compensation in circadian

oscillations. PLoS Computational Biology, 8, 1–12.

Fukushiro M, Takeuchi T, Takeuchi Y et al. (2011) Lunar phase-dependent expression of

cryptochrome and a photoperiodic mechanism for lunar phase-recognition in a reef fish,

goldlined spinefoot. PLoS ONE, 6, e28643.

Gachon F, Fonjallaz P, Damiola F et al. (2004) The loss of circadian PAR bZip transcription

factors results in epilepsy. Genes & Development, 18, 1397–1412.

Gavriouchkina D, Fischer S, Ivacevic T et al. (2010) Thyrotroph embryonic factor regulates

light-induced transcription of repair genes in zebrafish embryonic cells. PLoS ONE, 5, 1–

10.

Gierlinski M, Cole C, Schurch NJ et al. (2015) Gene expression statistical models for RNA-seq

data derived from a two-condition 48-replicate experiment. Bioinformatics, 31, 3625–3630.

Gietl A, Hock C, Birchler T, Fontana A (2012) Transforming growth factor-beta inhibits the

expression of clock genes. Annals of the New York Academy of Sciences, 1261, 79–87.

Gilmour J, Speed CW, Babcock R (2016) Coral reproduction in Western Australia. PeerJ, 4,

e2010.

Graham NAJ, Jennings S, Macneil MA, Mouillot D, Wilson SK (2015) Predicting climate-driven

regime shifts versus rebound potential in coral reefs. Nature, 518, 94–97.

Granados-Cifuentes C, Bellantuono AJ, Ridgway T, Hoegh-Guldberg O, Rodriguez-Lanetty M

(2013) High natural gene expression variation in the reef-building coral Acropora

millepora: potential for acclimative and adaptive plasticity. BMC genomics, 14, 228.

Hardeland R, Balzer I, Poeggeler B et al. (1995) On the primary functions of melatonin in

evolution : Mediation of photoperiodic signals in a unicell, photooxidation, and scavenging

of free radicals. Journal of Pineal Research, 18, 104–111.

Hemond EM, Vollmer S V. (2015) Diurnal and nocturnal transcriptomic variation in the

Caribbean staghorn coral, Acropora cervicornis. Molecular Ecology, 24, 4460–4473.

Hirose M, Yamamoto H, Nonaka M (2008) Metamorphosis and acquisition of symbiotic algae in

planula larvae and primary polyps of Acropora spp . Coral Reefs, 27, 247–254.

Hoegh-Guldberg O, Mumby PJ, Hooten AJ et al. (2008) Change and ocean acidification.

Science, 318, 1737–1742.

Isomura N, Yamauchi C, Takeuchi Y, Takemura A (2013) Does dopamine block the spawning

of the acroporid coral Acropora tenuis? Scientific Reports, 3, 10–13.

Johnson GL, Lapadat R (2002) Mitogen-activated protein kinase pathways mediated by ERK ,

JNK, and p38 protein kinases. Science, 298, 1911–1913.

Jokiel PL, Ito RY, Liu PM (1985) Night irradiance and synchronization of lunar release of

planula larvae in the reef coral Pocillopora damicornis. Marine Biology, 88, 167–174.

Kaiser TS, Neumann D, Heckel DG (2011) Timing the tides: genetic control of diurnal and lunar

emergence times is correlated in the marine midge Clunio marinus. BMC genetics, 12, 49.

Kaiser TS, Poehn B, Szkiba D et al. (2016) The genomic basis of circadian and circalunar timing

adaptations in a midge. Nature, 540, 69–73.

Kaniewska P, Alon S, Karako-Lampert S, Hoegh-Guldberg O, Levy O (2015) Signaling

cascades and the importance of moonlight in coral broadcast mass spawning. eLife, 4, 1–14.

Käsbauer T, Towb P, Alexandrova O et al. (2007) The Notch signaling pathway in the cnidarian

Hydra. Developmental Biology, 303, 376–390.

Kerr AM, Baird AH, Terry P (2011) Correlated evolution of sex and reproductive mode in corals

(Anthozoa : Scleractinia). Proceedings of the Royal Society B, 278, 75–81.

Kidd PB, Young MW, Siggia ED (2015) Temperature compensation and temperature sensation

in the circadian clock. Proceedings of the National Academy of Sciences, 112, E6284–

E6292.

Kornhauser JM, Nelson DE, Mayo KE, Takahashi JS (1990) Photic and circadian regulation of

c-fos gene expression in the hamster suprachiasmatic nucleus. Neuron, 5, 127–134.

Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nature Methods, 9,

357–359.

Levitan DR, Fukami H, Jara JA et al. (2004) Mechanisms of reproductive ioslation among

broadcast spawning corals of the Montastraea annularis species complex. Evolution, 58,

308–323.

Levy O, Appelbaum L, Leggat W et al. (2007) Light-responsive cryptochromes from a simple

multicellular animal, the coral Acropora millepora. Science, 318, 467–471.

Li B, Dewey CN (2011) RSEM : accurate transcript quantification from RNA-Seq data with or

without a reference genome. BMC Bioinformatics, 12, 323.

Lin X, Liang M, Feng X (2000) Smurf2 Is a ubiquitin E3 ligase mediating proteasome-dependent

degradation of Smad2 in tansforming growth factor-beta. The Journal of Biological

Chemistry, 275, 36818–36822.

Lotan T, Chalifa-caspi V, Ziv T et al. (2014) Evolutionary conservation of the mature oocyte

proteome. European Proteomics Association: Open Proteomics, 3, 27–36.

Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for

RNA-Seq data with DESeq2. Genome Biology, 1–21.

Matz M V, Treml EA, Aglyamova G V, Oppen MJH van, Bay LK (2017) Adaptive pathways of

coral populations on the Great Barrier Reef. bioRxiv, 114173.

Mekuchi M, Sakata K, Yamaguchi T, Koiso M, Kikuchi J (2017) Trans-omics approaches used

to characterise fish nutritional biorhythms in leopard coral grouper ( Plectropomus

leopardus ). Scientific Reports, 7, 1–7.

Mendes JM, Woodley JD (2002) Timing of reproduction in Montastraea annularis: Relationship

to environmental variables. Marine Ecology Progress Series, 227, 241–251.

Mercier A, Hamel JF (2009) Endogenous and exogenous control of gametogenesis and spawning

in echinoderms. In: Advances in marine biology, pp. 1–291.

Mercier A, Hamel JF (2010) Synchronized breeding events in sympatric marine invertebrates:

Role of behavior and fine temporal windows in maintaining reproductive isolation.

Behavioral Ecology and Sociobiology, 64, 1749–1765.

Meyer E, Aglyamova G V., Matz M V. (2011) Profiling gene expression responses of coral

larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel

RNA-Seq procedure. Molecular Ecology, 20, 3599–3616.

Moraes C De, Vaisman M, Conceic FL, Ortiga-carvalho M (2012) Pituitary development : a

complex, temporal regulated process dependent on specific transcriptional factors. Journal

of Endocrinology, 215, 239–245.

Moya A, Huisman L, Ball EE et al. (2012) Whole transcriptome analysis of the coral Acropora

millepora reveals complex responses to CO2-driven acidification during the initiation of

calcification. Molecular Ecology, 21, 2440–2454.

Moya A, Huisman L, Forêt S et al. (2015) Rapid acclimation of juvenile corals to CO2 -

mediated acidification by upregulation of heat shock protein and Bcl-2 genes. Molecular

Ecology, 24, 438–452.

Nakamuram T, Takio K, Eto Y et al. (1990) Activin-binding protein from rat ovary is follistatin.

Science, 247, 836–838.

Nelson D, Koymans L, Kamataki T et al. (1996) P450 superfamiliy: update on new sequences,

gene mapping, accession numbers and nomenclature. Pharmacogenetics, 6, 1–42.

Nozawa Y (2012) Annual variation in the timing of coral spawning in a high-latitude

environment: Influence of temperature. Biological Bulletin, 222, 192–202.

Oldach M., Workentine M, Matz M V, Fan T-Y, Vize PD (2017) Transcriptome dynamics over a

lunar month in a broadcast spawning acroporid coral. Molecular Ecology, 38, 42–49.

Olive PJW (1995) Annual breeding cycles in marine invertebrates and environmental

temperature: Probing the proximate and ultimate causes of reproductive synchrony. Journal

of Thermal Biology, 20, 79–90.

Panda S, Antoch MP, Miller BH et al. (2002) Coordinated transcription of key pathways in the

mouse by the circadian clock. Cell, 109, 307–320.

Penland L, Kloulechad J, Idip D, Van Woesik R (2004) Coral spawning in the western Pacific

Ocean is related to solar insolation: Evidence of multiple spawning events in Palau. Coral

Reefs, 23, 133–140.

Pittendrigh CS, Minis DH (1964) The entrainment of circadian oscillations by light and their role

as photoperiodic clocks. The American Naturalist, 98, 261–294.

Polato NR, Altman NS, Baums IB (2013) Variation in the transcriptional response of threatened

coral larvae to elevated temperatures. Molecular Ecology, 22, 1366–1382.

Reitzel AM, Tarrant AM, Levy O (2013) Circadian clocks in the cnidaria: Environmental

entrainment, molecular regulation, and organismal outputs. Integrative and Comparative

Biology, 53, 118–130.

Rensing L, Meyer-Grahle U, Ruoff P (2001) Biological timing and the clock metaphor:

oscillatory and hourglass mechanisms. Chronobiology International, 18, 329–369.

Rensing L, Ruoff P (2002) Temperature effect on entrainment, phase shifting, and amplitude of

circadian clocks and its molecular bases. Chronobiology International, 19, 807–864.

Rosendahl A, Speletas M, Leandersson K, Ivars F, Sideras P (2003) Transforming growth factor-

beta and activin smad signaling pathways are activated at distinct maturation stages of the

thymopoeisis. International Immunology, 15, 1401–1414.

Ross PM, Parker L, O’Connor WA, Bailey EA (2011) The impact of ocean cidification on

reproduction, early development and settlement of marine organisms. Water, 3, 1005–1030.

Rosser NL (2013) Biannual coral spawning decreases at higher latitudes on Western Australian

reefs. Coral Reefs, 32, 455–460.

Rougée LR a., Richmond RH, Collier AC (2015) Molecular reproductive characteristics of the

reef coral Pocillopora damicornis. Comparative Biochemistry and Physiology Part A:

Molecular & Integrative Physiology.

Ruiz-Jones LJ, Palumbi SR (2015) Transcriptome-wide changes in coral gene expression at noon

100

and midnight under field conditions. Biological Bulletin, 228, 227–241.

Sancar A (2004) Photlyase and cryptochrome blue-light photoreceptors. Advances in Protein

Chemistry, 69, 73–100.

Shoguchi E, Tanaka M, Shinzato C, Kawashima T, Satoh N (2013) A genome-wide survey of

photoreceptor and circadian genes in the coral, Acropora digitifera. Gene, 515, 426–431.

Simpson GG, Dean C (2002) Arabidopsis, the Rosetta stone of flowering time? Science (New

York, N.Y.), 296, 285–289.

Sorek M, Díaz-Almeyda EM, Medina M, Levy O (2014) Circadian clocks in symbiotic corals:

The duet between Symbiodinium algae and their coral host. Marine Genomics, 14, 47–57.

Sorek M, Levy O (2014) Coral spawning behavior and timing. In: Annual, Lunar, and Tidal

Clocks, pp. 81–97.

Sweeney AM, Boch C a, Johnsen S, Morse DE (2011) Twilight spectral dynamics and the coral

reef invertebrate spawning response. The Journal of Experimental Biology, 214, 770–777.

Takemura A, Ueda S, Hiyakawa N, Nikaido Y (2006) A direct influence of moonlight intensity

on changes in melatonin production by cultured pineal glands of the golden rabbitfish,

Siganus guttatus. Journal of Pineal Research, 40, 236–241.

Thomas L, Kendrick G a., Kennington WJ, Richards ZT, Stat M (2014) Exploring

Symbiodinium diversity and host specificity in Acropora corals from geographical extremes

of Western Australia with 454 amplicon pyrosequencing. Molecular Ecology, 23, 3113–

3126.

Twan W, Hwang J, Lee Y et al. (2006) Hormones and reproduction in scleractinian corals.

Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology,

101

144, 247–253.

Venn AA, Loram JE, Douglas AE (2008) Photosynthetic symbioses in animals. Journal of

Experimental Botany, 59, 1069–1080.

Vercelloni J, Mengersen K, Ruggeri F, Caley MJ (2017) Improved coral population estimation

reveals trends at multiple scales on Australia’s Great Barrier Reef. Ecosystems, 20, 1337–

1350.

Vize PD (2009) Transcriptome analysis of the circadian regulatory network in the coral

Acropora millepora. Biological Bulletin, 216, 131–137.

Vize PD, Embesi J a., Nickell M, Brown DP, Hagman DK (2005) Tight temporal consistency of

coral mass spawning at the Flower Garden Banks, Gulf of Mexico, from 1997-2003. Gulf of

Mexico Science, 23, 107–114.

Wallace CC (1985) Reproduction, recruitment and fragmentation in nine sympatric species of

the coral genus Acropora. Marine Biology, 88, 217–233.

Wang J, Du X, Pan W, Wang X, Wu W (2015) Photoactivation of the cryptochrome / photolyase

superfamily. Journal of Photochemistry & Photobiology, C: Photochemistry Reviews, 22,

84–102.

Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics.

Nature reviews. Genetics, 10, 57–63.

Willis BL, van Oppen MJH, Miller DJ, Vollmer S V., Ayre DJ (2006) The role of hybridization

in the evolution of reef corals. Annual Review of Ecology, Evolution, and Systematics, 37,

489–517. van Woesik R (2010) Calm before the spawn: global coral spawning patterns are explained by

102

regional wind fields. Proceedings of the Royal Society, 277, 715–722.

Wood JLA, Fraser DJ (2014) Similar plastic responses to elevated temperature among

differentially abundant brook trout populations. Ecology, 96, 1010–1019.

Wood S, Loudon A (2014) Clocks for all seasons: Unwinding the roles and mechanisms of

circadian and interval timers in the hypothalamus and pituitary. Journal of Endocrinology,

222.

Yoshitane H, Fukada Y (2009) CIPC-dependent phosphorylation of CLOCK and NPAS2 in

circadian clockwork. Sleep and Biological Rhythms, 7, 226–234.

Yuan Q, Metterville D, Briscoe AD, Reppert SM (2017) Insect cryptochromes : Gene duplication

and loss define diverse ways to construct insect circadian clocks. Molecular Biology and

Evolution, 24, 948–955.

Zantke J, Ishikawa-Fujiwara T, Arboleda E et al. (2013) Circadian and circalunar clock

interactions in a marine Annelid. Cell Reports, 5, 99–113.

103

Appendix

The following letter contains the e-mail permission to reprint copyright material as found on page 14:

Hi Daniel

Pleased to advise you are able to use the image as part of your thesis.

If it appears anywhere online then it must reference travelonline.com

Kind Regards,

Donna van Twest | Product Co-ordinator

Springwood Plaza Chambers, Suite 18, Level 1, 3-15 Dennis Rd Springwood Qld 4127 P. +61 7 3804 8450 D. +61 7 3804 8444 [email protected] | www.fusionholidays.com.au

From: Daniel Wuitchik [mailto:[email protected]] Sent: Thursday, 14 December 2017 12:47 PMTo: Reservations TravelOnlineCc: Product Fusion HolidaysSubject: Re: copyright map

Brilliant, thank you Marleen I greatly appreciate it.

Cheers, Daniel

From: Reservations TravelOnline Date: Wednesday, December 13, 2017 at 7:46 PMTo: Daniel Wuitchik Cc: Product Fusion Holidays Subject: RE: copyright map

Good Afternoon Daniel,

Thank you for your message.

I have sent this to my product team to check and will advise shortly.

Please do not hesitate to contact us should you have further questions/concerns!

Marleen Jones | Travel Consultant A. PO Box 1432 Springwood QLD Australia 4127 P. +61 7 3804 8411 F. +61 7 3208 3266 E. [email protected] W. www.travelonline.com

104

From: Daniel Wuitchik [mailto:[email protected]] Sent: Thursday, 14 December 2017 12:35 PMTo: Reservations TravelOnline Subject: copyright map

Hi there, I’m a masters student at the University of Calgary, and I was wondering if it would be possible to re-print a copyright photo in my thesis. The photo in question is a map of heron island, and can be found on your website at www.heronisland.net/location.html.

If this is not a possibility, no worries and I will not use your photo. It is an excellent map and would make a nice figure for my thesis.

Here is the photo for further clarification

Thank you for your time. Daniel Wuitchik

105