A Genetic and Molecular Investigation of the Circadian and Circatidal Clocks in the Horseshoe Crab, Limulus Polyphemus

AN ABSTRACT OF THE THESIS OF A GENETIC AND MOLECULAR INVESTIGATION OF THE CIRCADIAN AND CIRCATIDAL CLOCKS IN THE HORSESHOE CRAB, LIMULUS POLYPHEMUS

Stephen David Simpson for the degree of Master of Science in Biology presented on the 28th of August 2015

Abstract Approved:

______Christopher C. Chabot

Biological rhythms are cyclical behavioral and/or physiological changes that are driven by endogenous biological clocks. The most common type of rhythm, known as a circadian rhythm, oscillates with a period of about 24 hours. The endogenous clock that modulates circadian rhythms is composed of a set of “core” and accessory circadian genes. The core genes in animals are clock, cycle, period, nonphotoreceptive-cryptochrome2 and timeless and appear to be self-regulated by transcription-translation negative feedback loops thus allowing for synchronization and anticipation of the daily light and dark changes. Organisms that live in or visit the intertidal areas are also exposed to another type of environmental rhythm, which corresponds to the rise and fall of oceanic tides. Tidal rhythms are well documented in several intertidal animals, yet, unlike circadian rhythms, the molecular basis of the tidal clock system is largely unknown. One intertidal organism, the Atlantic horseshoe crab, Limulus polyphemus, is an important keystone species and has robust circadian and circatidal rhythms, which makes it a potential model system for the study of these rhythms and the mechanisms of the clocks that drive them. The aims of this thesis are: (1) to create Limulus genomic and transcriptomic datasets and (2) to investigate the presence of core clock gene homologs in the brain of Limulus as well as examine their potential modulation by time of day and time of tide. In these studies, the central nervous system of Limulus was used to develop a draft genome and transcriptome of this species. Four adult horseshoe crabs were selected and total RNA was extracted from their central nervous systems during the day and night and during high and low tides. Illumina library preparation, sequencing, subsequent assembly, and RNA-seq investigations indicated the presence of virtually all of the core clock gene homologs and accessory genes although with there was no clear rhythmic cycling of the transcription of any circadian core or accessory gene. Interestingly, total transcript expression showed more high versus low tidal phase differences than light versus dark photoperiodic differences. Additionally, the majority of the genes that seemed to be regulated according to these conditions did not have homologs in public bioinformatic databases, suggesting that these rhythmic genes are novel and currently unique to Limulus. Using data from the genome and transcriptome as a guide for primer design, QPCR was used to further explore the expression of the putative core circadian genes clock, npcryptochome2, cycle1 and timeless in the protocerebrum over conditions of photoperiod and tidal phase. To accomplish this aim, horseshoe crabs were placed into tide-simulating tanks and were subjected to light/dark and tidal cycles. QPCR was performed on the mRNA extracts from the central nervous system to quantify the expression of the circadian clock genes clock, cycle1, timeless and npcryptochrome2 over photoperiodic and tidal conditions. While core circadian transcripts were found to be expressed in all investigated sections of the central nervous system, there were no statistically significant rhythmic cycling of those genes within the protocerebrum with respect to either photoperiod or tidal phase. Overall, both the RNA-seq and QPCR results suggest a relatively constitutive expression of core circadian transcripts within the Limulus central nervous system. These results, when taken together, suggest at least five possible explanations; (1) These genes may not be involved with the circadian or tidal clocks; (2) Rhythmic mRNA expression of these genes may not be necessary for these clocks; (3) The circadian and tidal clocks share some genes and the mRNA of those shared genes oscillates out of phase with each other, masking true rhythmic expression; (4) Non-clock cells in the tissues examined are also expressing these genes at a high enough level to “dilute” the rhythms of mRNA expression; (5) Finally, there may be a heavy reliance of Limulus clocks on post-transcriptional and post-translational modifications. In order to explore some of these potential explanations, future studies should aim to systematically investigate putative circadian genes in Limulus. This will help to determine whether or not these genes are involved with one or more of these clocks and whether or not their transcription is required for measurable rhythms.

A GENETIC AND MOLECULAR INVESTIGATION OF THE CIRCADIAN AND CIRCATIDAL CLOCKS IN THE HORSESHOE CRAB, LIMULUS POLYPHEMUS

STEPHEN DAVID SIMPSON

A THESIS

Submitted to

Plymouth State University

In partial fulfillment of the requirements for the degree of

Master of Science in Biology

Defended August 28th 2015 Degree Conferred May 2017

This thesis has been examined and approved.

______Thesis Director, Christopher Chabot, Ph.D. Professor of Biology Department of Biological Sciences

______Heather Doherty, Ph.D. Assistant Professor of Molecular Genetics Department of Biological Sciences

______Mike Son, Ph.D. Assistant Professor of Microbiology Department of Biological Sciences

______Susan Swope, Ph.D. Professor of Chemistry Department of Atmospheric Science & Chemistry

______Date

ACKNOWLEDGEMENTS

Many thanks to the students of the Chabot Lab for working the most ludicrous work hours (consecutive 3AM dissections come to mind) and helping me to collect all the data needed for these studies. A special thanks to Rebecca Anderson, Kevin Chesmore, CJ Freeman, and Kyle Kenyon for putting up with me on all those late nights. Thank you also to the fine members of the Thomas Lab at UNH for their guidance in all things bioinformatics. Thank you to Heather Doherty, Mike Son, and Susan Swope, who guided me through all aspects of these studies and reinforced the intricacies and nuances of proper lab technique and experimental design. Thank you to my wonderful wife, Lauren Simpson, and my family for putting up with all my frustration while I worked my way through these projects. Finally, thank you to my advisor Christopher Chabot and his family, without whom I would never have been able to start or complete my work.

TABLE OF CONTENTS ACKNOWLEDGEMENTS ...... i TABLE OF CONTENTS ...... ii LIST OF FIGURES ...... iii LIST OF TABLES ...... iv CHAPTER 1: GENERAL INTRODUCTION ...... 1 1. Biological Rhythms ...... 1 2. Biological Clocks ...... 3 3. The Animal Circadian Clock ...... 5 4. Circatidal Rhythms ...... 8 5. Molecular Work on Circatidal Rhythms ...... 9 6. Importance of the Horseshoe Crab, Limulus polyphemus ...... 10 7. Circadian Studies in Limulus ...... 11 8. Tidal and Circatidal Studies in Limulus ...... 12 9. Specific Aims of the Studies ...... 13 CHAPTER 2: THE DRAFT GENOME AND TRANSCRIPTOME OF THE ATLANTIC HORSESHOE CRAB, LIMULUS POLYPHEMUS ...... 16 1. Introduction ...... 17 2. Methods and Materials ...... 18 3. Results ...... 20 4. Discussion ...... 23 CHAPTER 3: DAILY AND TIDAL EXPRESSION ANALYSIS OF LIMULUS CLOCK, CYCLE, TIMELESS AND NPCRYPTOCHROME MRNA ...... 47 1. Introduction ...... 47 2. Methods and Materials ...... 48 3. Results ...... 51 4. Discussion ...... 52 CHAPTER 4: GENERAL DISCUSSION ...... 60 LITERATURE CITED ...... 66

iii

LIST OF FIGURES

FIGURE 1.1 Drosophila melanogaster circadian molecules and some of their interactions ...... 14 FIGURE 1.2 Mus musculus circadian molecules and some of their interactions ...... 15 FIGURE 2.1 Gene map of the Limulus polyphemus mitochondrial genome ...... 27 FIGURE 2.2 Diagram of two nuclear mitochondrial (NUMT) sequences identified in the Limulus genome...... 28 FIGURE 2.3 Transcriptome assembly mapped to the genome assembly...... 30 FIGURE 2.4 Volcano plot of day versus night ...... 31 FIGURE 2.5 Volcano plot of high versus low tide ...... 33 FIGURE 2.6 Number of statistically significant transcripts in both the day/night and high tide/low tide volcano plots ...... 35 FIGURE 2.7 Biological Process BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide ...... 36 FIGURE 2.8 Molecular Function BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide...... 38 FIGURE 2.9 Cellular Component BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide...... 40 FIGURE 3.1 Diagram of Limulus central nervous system ...... 55 FIGURE 3.2 Diagram of Tidal Tanks ...... 56 FIGURE 3.3 Relative mRNA expression of the putative Limulus clock genes ...... 57 FIGURE 3.4 A lack of effects of photoperiod and tidal phase on the expression .... 58

LIST OF TABLES

TABLE 2.1 Limulus genome and transcriptome assembly statistics...... 42 TABLE 2.2 Comparison of Limulus mtDNA ...... 43 TABLE 2.3 Top four contigs of day/night and high/low tide conditions from RNA- Seq volcano plot analysis ...... 44 TABLE 2.4 Reads Per Kilobase Per Million Mapped Reads (RPKM) for common circadian and control genes...... 46

CHAPTER 1 GENERAL INTRODUCTION

1. Biological Rhythms

1.1. General Overview of Rhythms Biological rhythms are behavioral, physical or physiological responses to periodically occurring events that take place within a natural environment (Aschoff, 1981a). These rhythms repeat with a predictable frequency, and because they are nearly always driven by endogenous biological clocks, they can be entrained to environmental stimuli (Kleitman, 1949). By anticipating periodic environmental changes, these rhythms and the clocks that underlie them allow an organism to predict food availability, avoid predation and to even avoid desiccation (Nelson and Vance, 1979; Mistlberger, 1994; Chapman and Underwood, 1996). While these rhythms vary considerably, there are three general types: circadian (daily, periods of ~24h), ultradian (<24h) and infradian (>24h). Examples of these rhythms include the 24h daily behavior patterns of the Siberian hamster, which normally maintains a strict nocturnal pattern of activity (Refinetti, 2006), the tidal (~12.4h) locomotor activity patterns of the horseshoe crab (Chabot et al., 2004), and the annual migratory behavior patterns found in birds (Gwinner, 1996). Of the three types, circadian rhythms are by far the most common and most extensively studied. These rhythms have been described in many dozens of organisms in all five kingdoms of life (Aschoff, 1981a).

1.2. Importance of Circadian Rhythms Circadian rhythms are important for many living organisms because they allow for anticipatory behavioral and/or physiological responses to the daily photoperiod (Sharma, 2003). Plants, for instance, have numerous endogenous biological rhythms that are important for normal function. Many plants, including the model organism Arabidopsis thaliana, have rhythmic circadian movement of their leaves and cotyledon (Engelmann et al., 1992). This daily movement allows plants to maximize the surface area on their leaves that is exposed to sunlight and therefore maximize food production (Gamon, 1989). Other plant circadian rhythms include the production of CO2, the rate of cell elongation, and developmental hypocotyl elongation (Goh, 1983; Dowson-Day and Millar, 1999; Harmer et al., 2000). Animals, too, exhibit a variety of circadian rhythms that are also important to their daily function (Panda et al., 2002). The horseshoe crab, Limulus polyphemus, for example, has been shown to have nearly a million-fold increase in eye sensitivity at night (Barlow et al., 1980). Presumably, their increased perception allows for them to better locate mates in either day or night conditions (Barlow, 2001). Other animals, like the Siberian hamster, Photopus sungorus, and the common house mouse, Mus musculus, exhibit clear patterns of nocturnal behavior, which persist in constant conditions with a circadian frequency (DeCoursey, 1986; Refinetti, 2006). In humans, body temperature, metabolism and heart rate all decrease during the night and slowly increase before

2 dawn (Refinetti and Menaker, 1992; Scheer et al., 1999; Hastings et al., 2007). The decrease in metabolism during periods of rest allows for a conservation of energy and therefore increased fitness (Berger and Phillips, 1995). These examples underline the benefits of circadian rhythms and help to explain their fundamental importance and evolutionary conservation throughout kingdoms.

1.3. The Importance of Circadian Rhythms in Humans

1.3.1. Mental Health Appropriately synchronized human circadian rhythms are important for mental health. Seasonal affective disorder (SAD), induced in a segment of the human population by the reduced photoperiod in the winter and fall, is associated with disruptions to human circadian rhythms, which can manifest as depression and irregular sleep cycles (Rosenthal and Wehr, 1987; Anderson et al., 1994). Irregular circadian sleep-wake cycles have also been associated with bipolar disorder (BD) and are a recommended criterion for diagnosis (Jones et al., 2005; Plante and Winkelman, 2008). In order to help mitigate the challenges of disrupted circadian rhythms, therapies such as exposure to an artificial stimuli (light), and the adjustment of an individual’s sleep cycles (i.e. their rhythms) through the use of medicinal sleeping aids (e.g. melatonin) may be helpful as they both have been shown to cause a reduction in overall emotional dysfunction in individuals who suffer from SAD and BD, respectively (Harvey, 2008; Gordijn and Meesters, 2012).

1.3.2. Physical Health Physical health can also be negatively affected by abnormal circadian rhythms (Barion and Zee 2007). Frequent travelers often experience a temporary desynchronization of the circadian rhythm, commonly known as jet lag (Eastman et al., 2005). Jet lag has been shown to cause sleep abnormalities, irritability, headaches, sensitivity to light, and gastrointestinal discomfort (Sack et al., 2007) and these symptoms gradually dissipate after several days of adjustment to a new photoperiod. Alternatively, gradual sleep phase adjustments can be made prior to travel in order to preemptively counteract the negative effects of jet lag. Another behavior that significantly affects an individual’s daily rhythms, is “shift work”. Over one-third of the population in the United States works alternative shifts (Alterman et al., 2013) and this has been linked to several health problems including cardiovascular disease, diabetes, and an increase in certain types of cancer (Schernhammer et al., 2003; Puttonen et al., 2010; Pan et al., 2011). Lesser ailments caused by shift work (i.e. sleepiness and wakefulness) can be treated with timed light exposures, doses of melatonin and/or the use of stimulants/hypnotics (Sack et al., 2007). To mitigate the problems associated with shift work, some studies have suggested the use of constantly rotating shift schedules (continuous shift changes from morning to noon to night), a reduction in the amount of successive night shifts and flexible work schedules with consecutive days off (Knauth, 1996). This, in theory, should allow

3 sufficient recovery time for most individuals while identifying workers that are more sensitive to circadian disruptions that can then be removed from shift work to protect their health (Costa, 1998).

1.3.3. Blind People Individuals that are completely blind often have difficulty with the synchronization of their circadian rhythms. With the total absence of circadian cues (light), the rhythms of these blind individuals “free-run”, which causes the periodicity of their rhythms to gradually drift away from that of a normal 24-hour day (Lewy and Newsome, 1983). This can induce similar effects as described above for non-blind individuals suffering from jet lag (Nakagawa et al., 1992). Since re-synchronization by light in completely blind people is not possible, pharmaceutical approaches such as regular doses of melatonin (Sack et al., 2000) and tasimelteon (Lockley et al., 2015) have been used to synchronize blind individual’s circadian rhythms to coincide with the rest of society.

1.3.4. Chronotherapy Timing is also important to consider when it comes to treatments for a variety of conditions and diseases. The time of day when treatment is given has been shown to significantly affect efficacy as well as side-effects in several diseases (Lévi and Schibler, 2007). This new field is known as chronotherapy (the science of the delivery of drug agents with respect to time of day) and has been used to more effectively treat rheumatoid arthritis, hypertension, asthma, and several types of cancer (White, 1996; Smolensky et al., 1999; Lévi, 2006; Westhovens, 2010). The underlying principle that directs chronotherapy is the fact that many cells exhibit differential gene expression by time of day and the protein products, in turn, temporally affect the metabolism of cells and tissues. Thus, these metabolic processes are targeted by time of day to allow for the greatest effect of a drug and/or therapy and the fewest side effects (Fu and Lee, 2003). Identifying and understanding biological rhythms and their underlying molecular clocks is important not only to human health but also to the basic understanding of many other species and is one of the reasons why these systems are so extensively studied.

2. Biological Clocks

2.1. Discovery of Endogenous Clocks While it has long been recognized that organisms have daily rhythms, the idea that these rhythms are controlled by endogenous clocks has gained scientific credence only over the past several decades. The first proposal of an endogenous timing system came from Erwin Bunning in the 1930s (Bunning, 1936). However, empirical evidence was not provided until the idea was tested in the late 1950s by Nanda and Hamner (Nanda and Hamner, 1958). In 1976, a series of papers published by Colin Pittendrigh and Serge Daan formally modeled these clock systems (Pittendrigh and

Daan, 1976; Daan and Pittendrigh, 1976) and described three general components: (1) an input that is synchronized by environmental stimuli, (2) an endogenous clock, and (3) an output in the form of a measurable rhythm. In the case of the circadian clock, the primary input is light and the output that is often measured is locomotor activity. More recent studies, however, show clock systems to be more complex, often with several inputs, a multitude of interconnected endogenous clocks, and many outputs (Silver and Rainbow, 2013).

2.2. Characteristics of Endogenous Clock Endogenous clocks all share three characteristics: they are temperature compensated, they allow for anticipation of environmental stimuli, and the rhythms they control persist in constant conditions. Temperature compensation allows the periods of biological rhythms to remain relatively constant across a wide range of temperatures (Bodenstein et al., 2012). This feature is obviously most important in exothermic organisms where external temperatures could render a non-temperature compensated clock useless (Van Someren, 2003). This has been demonstrated in the pineal organ of the lizard, Anolis carolinensis, which releases melatonin with a stable circadian period over a tested degree range of 22-37°C (Menaker and Wisner, 1983). Additionally, the circadian output of cultured rat fibroblasts and retinal cells in the golden hamster, Mesocricetus auratus, will maintain normal circadian periodicity within a range of 33-42°C (Tosini and Menaker, 1998; Izumo et al., 2003; Tsuchiya et al., 2003). Another aspect of endogenous clocks, anticipation, is the preparation of a physiological or behavioral process to a periodic external event (stimuli). Feeding anticipatory activity is one of the more well-documented examples and is seen in such diverse animals as rats and goldfish (Mistlberger and Rusak, 1987; Vera et al., 2007) where these animals become measurably active before scheduled feedings (Honma et al., 1983). Lastly, the fact that these activities persist in constant conditions demonstrates that an endogenous controller drives them. Interestingly, while in constant conditions, rhythms persist but at periods slightly different from 24h, depending on the species (Aschoff, 1981b). This feature likely facilitates synchronization to changing photoperiods throughout the year (Helm and Lincoln, 2017).

2.3. Entrainment of Endogenous Clocks

2.3.1. Zeitgebers Another feature of endogenous clocks is their ability to be entrained by external stimuli, known as “zeitgebers” (German for “time-giver”). As mentioned above, in the absence of effective zeitgebers, biological rhythms ”drift” with respect to the 24h cycle at periods different than 24h (Redman et al., 1983). While light is by far the most important entraining agent for circadian rhythms, endogenous clocks can be entrained by a variety of zeitgebers. Circadian entrainment by food availability, for example, has been shown in many species including the European rabbit (Cuniculus

5 oryctolagus), the house sparrow (Passer domesticus), and the goldfish (Carassius auratus) (Jilge et al., 2000; Hau and Gwinner, 1992; Sanchez-Vazquez et al., 1997). Other animals, like the Siberian hamster, have been shown to be able to entrain to induced activity (exercise) via the restricted availability of running wheels (Reebs and Mrosovsky, 1989). Additionally, relatively small fluctuations in temperature (2-3°C) can entrain behavioral rhythms in the fruit fly, chick, and zebrafish (Wheeler et al., 1993; Barrett and Takahashi, 1995; Lahiri et al., 2005).

2.3.2. Range of Entrainment In natural environments animals are almost always exposed to 24h periods of light/dark cycles. In the lab, the capacity of animals to entrain to more exotic light:dark cycles can be assessed. Humans, for example, can entrain to periods that range from 23.5 to 24.7-hour periods (Scheer et al., 2007) while mice exhibit a much larger range of 20 to 28-hours (Pittendrigh and Daan, 1976). The ability of endogenous clocks to entrain to a variety of periods is dependent on the genes that control these clocks (described more below). Animals that show shortened or lengthened periods that are caused by mutations to the clock (Golombek and Rosenstein, 2010), exhibit predictable changes in their ranges of entrainment.

3. The Animal Circadian Clock

3.1. Location of the Clock Biological clocks have been shown in many cases to be composed of groups of pacemaker cells which have been localized to specific areas of the brain in some groups of animals (Helfrich-Förster, 2004). In some rodents (hamsters and mice), the clock appears to be composed of approximately 20,000 pacemaker cells that are located within the suprachiasmatic nucleus (SCN) at the base of the brain, directly above the optic chiasm (Stephan and Zucker, 1972; Hastings and Maywood, 2000). The clock of the fruit fly (Drosophila melanogaster), however, has only approximately 150 pacemaker cells, which are distributed into four loose clusters in the brain (Rosato et al., 2006). Many other organisms have similar groupings of pacemaker cells located within their central brain and eyes (Helfrich-Förster, 2004). The ground crickets, Dianemobius nigrofasciatus and Allonemobius allardi, for instance, have several clusters within the cephalic ganglia portion of their central nervous system (Shao et al., 2006). Additionally, clock cells can be found within the eyes of the California sea hare, Aplysia californica, the house sparrow, Passer domesticus, and the homing pigeon, Columbia livia (Jacklet, 1969; Gaston and Menaker, 1968; Foa and Menaker, 1988). Within the cells are a complex series of molecular feedback loops and cascades, which form the basis of the molecular clock by allowing an organism to maintain endogenous biological rhythms that are synchronized to the external environment (Dunlap, 1999).

3.2. The Molecular Mechanism of the Drosophila melanogaster Clock Molecular analysis of the circadian clock began with the identification of three Drosophila melanogaster period mutants in the early 1970s (Konopka and Benzer, 1971). These mutations were found to shorten the period of the circadian rhythm (perS), lengthen it (perL) or cause it to become arrhythmic (per0). Molecular discoveries continued through the 1980s and 1990s with the elucidation of the Drosophila period gene sequence and the identification of the Drosophila timeless gene (Reddy et al., 1984; Sehgal et al., 1994). Our current understanding is that the cycling of the gene products of four “core” genes (clock, cycle, timeless and period) is controlled by two negative feedback loops that regulate transcription of these genes (Figure 1.1). In the first feedback loop, the protein products of clock and cycle (CLOCK and CYCLE, respectively) dimerize and bind to the enhancer box (E-box) sequence upstream of period and timeless, thereby enabling transcription of these genes (Takahashi, 1993; Glossop et al., 1999; Bae et al., 2000). Period and timeless mRNA are then translated within the cytoplasm and, as the abundance of the resultant proteins rise, they dimerize and are phosphorylated by the protein kinase products of several “accessory” clock genes (Edery et al., 1994b). Phosphorylation of PERIOD (PER) by one of these accessory genes, casein kinase 2 (CK2), along with dimerization with TIMELESS (TIM) promotes nuclear entry of PER and TIM (Figure 1.1). After dimerization and phosphorylation, PER-TIM complexes enter the nucleus and inhibit the transcription of the genes per and tim through the competitive removal of CLK and CYC from the E-box (Edery et al., 1994b). Another accessory gene, the kinase DOUBLETIME (DBT), inhibits PER nuclear entry through targeted degradation by phosphorylation effectively slowing down the clock. Thus, the accessory proteins CK2 and DBT act as temporal regulators of PER and TIM translocation into the nucleus and their subsequent negative feedback (through inhibition) on per and tim transcription (Nawathean and Rosbash, 2004; Cyran et al., 2005). A second negative feedback loop regulates CLK transcription (Figure 1.1). This loop is composed of two additional accessory genes, vrille (vri) and par domain protein 1 epsilon (pdp1ɛ). Vri and pdp1ɛ transcription are both activated by a CLK- CYC heterodimer that binds to the promoter region of both genes. Vri and pdp1ɛ are translated at rates 180 degrees out of phase with each other, which allows the VRI protein to accumulate first and repress clk transcription. As PDP1ɛ levels build in abundance, clk transcription is promoted, which reactivates transcription of per, tim, vri and pdp1ɛ (Figure 1.1), forming a negative feedback loop. Entrainment to light cycles is effected through activation of another protein, photoreceptive CRYPTOCHROME (pCRY), a blue light photoreceptor, which promotes degradation of TIM, thus synchronizing the molecular clock to photoperiods (Akten et al., 2003; Ozturk et al., 2011). These intracellular molecular feedback loops in turn control downstream physiological and behavioral changes in the fruit fly through the release of at least two molecules (Cyran et al., 2003). One of these, Pigment Dispersing Factor (PDF) is

7 thought to be one of the circadian outputs of Drosophila clock cells, as mutations to this neuropeptide cause disruptions to their circadian rhythms (Helfrich-Förster et al., 2000). PDF also acts to coordinate the molecular feedback loops between opposing circadian clocks within the lateral ventral neurons (Duvall and Taghert, 2012). Neuropeptide F (NPF) has also been shown to be important to the locomotor output of the Drosophila circadian clock by modulating their increase in activity in anticipation of dawn (He et al., 2013). Additionally, the mammalian homolog for NPF (NPY) has been shown to be a circadian output that regulates hundreds of gene mRNAs within the liver of the house mouse, Mus musculus (Erion et al., 2016).

3.3. The Molecular Mechanisms of the Mus musculus Clock The clock system of other model organisms like that of Mus musculus, the house mouse, share many similarities with the molecular clock of Drosophila (Figure 1.2). In general, the "core" circadian genes are the same (period, timeless, clock and cycle; Reppert and Weaver, 2000). However, the mouse has three period gene paralogs (per1, per2 and per3) and two cryptochrome genes so the specific mechanisms of the negative feedback loops that “beat out time” are different (Zylka et al., 1998; Van Der Horst et al., 1999). Like the Drosophila system, the mouse "master clock" cells (in mammals, located in the SCN) produce a homolog of CYC (BMAL1), which forms a heterodimer with CLK (Bunger et al., 2000; Maywood et al., 2003). Unlike the Drosophila system, the BMAL1/CLK heterodimer promotes transcription of CRY and PER1, 2 and 3. PER2 forms a heterodimer with CRY and enters the nucleus where they dissociate and inhibit the BMAL1/CLK heterodimer and simultaneously promote transcription of BMAL1 (Shearman et al., 2000). Likewise, CRY forms a heterotrimer with PER1 and PER3, which helps to inhibit the BMAL1/CLK complex. Outside of the nucleus and within the cytoplasm, CK1ɛ, a homolog of Drosophila DBT, phosphorylates PER variants to target them for degradation, much like the targeted degradation of PER in Drosophila (Lowrey et al., 2000). A secondary loop, similar to the Drosophila VRI/ PDP1ɛ/CLK loop, is that of REV-ERBα (whose transcription is promoted by the BMAL1/CLK heterodimer), which inhibits BMAL1 and CLK transcription thereby helping to regulate rhythmicity (Figure 1.2) (Preitner et al., 2002). However, unlike in Drosophila, CLK expression levels are constitutively expressed in M. musculus clock cells (Kondratov et al., 2003). Furthermore, the timeless gene and protein seem to play an insignificant role in the M. musculus model with no expression in the SCN and no known cycling of its mRNA or protein products while in constant conditions (Gotter et al., 2000; Hardin, 2004).

3.4. Circadian Molecular Variation in Non-Model Species Additional differences in the molecular gears of the circadian clock are seen in other animals. For example, while the monarch butterfly shares many of the same mechanisms as the Drosophila circadian clock (Froy et al., 2003), rather than forming a heterodimer between PER and TIM to regulate CLK and CYC, the butterfly

8 circadian system forms a heterotrimer between PER, TIM and a second cryptochrome protein, CRY2 (Zhu et al., 2005). Further species-specific differences are seen in the African clawed frog, Xenopus laevis, which has three unique cryptochrome paralogs in its retinal clock, the mRNAs of which rhythmically oscillate with different daily peaks (Zhu and Green, 2001). Like Drosophila, clock mRNA does not seem to cycle by time of day in the Xenopus retinal photoreceptors (Zhu et al., 2000). Other variations occur in the honeybee, Apis mellifera, which lacks a timeless ortholog, and instead has a mammalian-like cryptochrome. Additionally, the silk moth, Antheraea pernyi, appears to have a system devoid of temporal translocation of PER into the nucleus, instead most likely relying on a post-transcriptional mechanism involving per antisense RNA oscillations (Sauman and Reppert, 1996; Rubin et al., 2006). Overall, while the set of genes controlling the circadian clock is fairly consistent across animal species, there is a wide variety in the ways that they are regulated.

4. Circatidal Rhythms

4.1. Characteristics Circatidal rhythms are another type of endogenous biological rhythm that occurs in synchronization with the cyclical ebb and flow of the oceanic tides. These rhythms are found exclusively in those organisms that inhabit or visit the intertidal zone. These rhythms occur with an approximate period of 12.4 hours and control a variety of behavioral and physiological changes (Abello and Naylor, 1997; Saigusa, 2002; Stillman and Barnwell, 2004). As with circadian rhythms, the ability of intertidal organisms to entrain and anticipate the tidal cycle is likely advantageous not only for foraging and desiccation avoidance, but also for migration, predator avoidance, and conservation of energy (Zann, 1973; Marsh and Branch, 1979; Zeng and Naylor, 1996). As with any true endogenous biological rhythm, these rhythms exhibit temperature compensation (Zhang et al., 2013) and persist in laboratory conditions (Satoh et al., 2008). For example, the mangrove cricket, Apteronemobius asahinai, forages for food during low tide in the field. When placed in constant conditions in the lab they exhibit a locomotor activity periodicity of approximately 12.6 hours (Satoh et al., 2008). Additionally, while the shore crab, Carcinus maenas, is more active at high tide and less active at low tide (Lynch and Rochette, 2007), several studies have shown that this species too has an endogenous timing system that controls its circatidal rhythms (Reid and Naylor, 1990; Warman and Naylor, 1995; Abello et al., 1997). Although the circatidal rhythms of various organisms are well documented, the nature of the clocks that control these rhythms is somewhat controversial.

4.2. Underlying Clock System There are two major theories regarding the endogenous control of circatidal rhythms (Palmer, 1995; Palmer, 1997; Naylor, 1996; Naylor, 1997). The first is that there is a single circatidal clock with a period of about 12.4 hours, which is modulated

9 by the circadian clock (Naylor, 1997). The proposed mechanism for circadian/circatidal interaction is the promotion of locomotor activity by the circadian clock followed by locomotor inhibition by the circatidal clock via an unnamed inhibitory hormone. This theory has been used to explain the circadian peaks that are seen in some tidally rhythmic organisms such as the shore crab, Carcinus maenas (Naylor, 1996). However, these patterns in this species can also be explained by the presence of two separate clocks each running with a period of about 24.8 hours and 180 degrees out of phase with each other to generate the 12.4 hour circatidal rhythms (termed “circalunidian” clocks - Palmer and Williams, 1986; Palmer, 1995). In addition, the circalunidian hypothesis appears to better explain a number of other patterns of activity often seen in tidal organisms. For instance, observations such as “skipping” (in which an animal will spontaneously miss one of the two circatidal bouts of activity for several days) and splitting (in which one bout of activity will split into two) are best explained by the circalunidian clock hypothesis (Williams and Palmer, 1988). This theory also seems to best explain the behavioral activity patterns seen in several marine invertebrates including the grapsid crab, the sand hopper, and the horseshoe crab. These organisms frequently display skipping and splitting of their activity patterns while in constant laboratory conditions (Williams and Palmer, 1988; Rossano et al., 2009; Chabot et al., 2016), two phenomena that are very difficult to explain with the circadian-circatidal clock theory.

4.3. Multiple Clocks The temporal control of physiological and behavioral rhythms by multiple clocks appears to be quite common among animals. Other, non-tidal organisms have been shown to use multiple clocks to modulate daily rhythms. Drosophila, for example, have apparently evolved two 24h clock mechanisms to accomplish circadian timing. Within the Drosophila brain there are two groupings of circadian clock cells called either M cells (morning) or E cells (evening). These two groups of pacemaker cells work in conjunction through the neuropeptide PDF to produce separate bouts of activity per day, which constitutes their daily behavior (Duvall and Taghert, 2012). A similar system may have evolved in marine intertidal animals to produce two antiphase bouts of activity every 24.8h.

5. Molecular Work on Circatidal Rhythms

While much is known about many molecular mechanisms of circadian systems, very little is known about the circatidal system. In the mangrove cricket, Apteronemobius asahinai, RNA interference of the mRNA transcripts clock and period affects the circadian rhythm but not the circatidal rhythm (Takekata et al., 2012; Takekata et al., 2014b). These results suggest that the clocks controlling circatidal rhythms operate with at least some molecular components that are different than that of the circadian clock (or that clock and period may not be part of the clocks controlling tidal rhythms). However, there seems to be some similarity between the two clock systems. Inhibition of casein kinase 1 (CK1) isoforms, delta (δ) and epsilon

(ε) in Eurydice pulchra affected both the circadian and the circatidal rhythm (Zhang et al., 2013). As mentioned above, CK1ε is a part of the circadian clock in mammals in which it phosphorylates PER, targeting it for degradation (Lowrey et al., 2000). The disruption of both the circadian and tidal rhythms following CK1 pharmacological inhibition suggests that the circadian clock may share some molecular mechanisms with the tidal clocks. Indeed, one of the hypotheses of this thesis is that the two clocks systems controlling circadian and circatidal rhythms may share some of the same molecular mechanisms. The potential similarities between the clocks may have arisen from the process known as neofunctionalization, which is the duplication and functional mutation of various genes through positive Darwinian selection (Layeghifard et al., 2009) Thus, the molecular components of the circatidal clock system may have arisen from the circadian system whose genes were duplicated and re-purposed to coordinate with the tides instead of the solar day.

6. Importance of the Horseshoe Crab, Limulus polyphemus

6.1. Ecological Importance

6.1.1. Breeding and Foraging Behavior Limulus polyphemus, the Atlantic horseshoe crab, is a marine chelicerate that resides primarily along the eastern coastline of North America (Shuster, 1978). Groups of Limulus that live in regions that experience tidal changes often visit the intertidal zone for nesting. In general, female Limulus approach breeding beaches in the spring during high tides with an attached male. The females then dig a hole in the sediment along the high tide level of the beach and lay clutches of hundreds to thousands of eggs approximately 5-8cm deep (Penn and Brockmann, 1994). Unattached males often roam the intertidal zones during these times looking for nesting females in an attempt to fertilize eggs as a “satellite male” (Cohen and Brockmann, 1983). During the non-breeding season, when the waters are warm, both sexes spend a significant amount of time foraging for food in the sediment within the intertidal zone, thus bringing nutrients to the sediment surface (Kraeuter and Fegley, 1994; Lee, 2010). Because many other intertidal species rely on the eggs of Limulus and on the nutrients that they bring to the surface, they are considered a keystone species (Kraeuter and Fegley, 1994; Botton, 2009).

6.1.2. Limulus Eggs The availability of Limulus eggs is of crucial importance to migrating shore birds, especially the red knot, Calididris canutus rufa. This species migrates more than 9,000 miles from the tip of South America to the northern borders of Canada. In order to be able to complete this trip, they rely heavily on the availability of Limulus nests on Cape May, New Jersey to be able to regain enough fat reserves to complete migration (Karpanty et al., 2006). In the past, declines in red knot populations have been linked to over-harvested populations of horseshoe crabs in the Delaware Bay

11 area (Karpanty et al., 2006); however, since the 1990s, the Atlantic States Marine Fisheries Commission (ASMFC) has issued a number of state specific regulations and a call for regular assessments of Limulus populations. This has led to multispecies management plans and evidence of a rebound has been seen in Limulus and red knot populations within the Delaware Bay area (Smith et al., 2009; ASMFC, 2013).

6.2. Economic Importance Limulus also plays a large economic role in human commerce. For instance, horseshoe crabs are harvested both for their blood and their use as bait. At an estimated value of $15,000 per quart, extracts of amoebocyte blood are used as the most sensitive assay for the detection of bacterial endotoxins (Obayashi et al., 1985; Dolejš and Vaňousová, 2015). Animals are collected for bleeding and this process induces a mortality rate of at least 8% to 29% (Walls and Berkson, 2003; Hurton and Berkson, 2006; Leschen and Correia, 2010). Additionally, bleeding induces significant sub-lethal effects such as decrease in activity, suppression of tidal rhythms, and reductions of blood hemocyanin levels (Anderson et al., 2013). This reduction of hemocyanin may be of particular concern since this protein is the main blood oxygen carrier and is involved in immune function and general health (Sullivan et al., 1974). While attempts are underway to develop cheaper alternatives to LAL, extracting blood from the animals remains the most cost effective and sensitive way to assess contamination by bacterial toxins (Unger et al., 2014). In addition to their collection for blood, horseshoe crabs are used for eel and conch bait as they are easy to collect and highly effective (Manion et al., 2000). Overall, both the bait and LAL industries appear to have contributed significantly to population decreases and, because there is a continued strong demand for horseshoe crabs within these fisheries, concerns remain about the horseshoe crab population (Faurby et al., 2010).

7. Circadian Studies in Limulus

Limulus are also important for their use in the study of biological rhythms since they exhibit robust circadian and circatidal rhythms (Barlow, 1983; Chabot and Watson, 2010; Chabot et al., 2011; Battelle et al., 2013). Since the late 1970’s Limulus have been used as a model for circadian research. Electoretinograms (ERGs) from the lateral eyes show clear circadian rhythms when recorded across daily and constant conditions and have been used to investigate various aspects of the Limulus circadian system (Barlow et al., 1977a). Within the Limulus brain, evidence shows that a circadian clock, possibly located in the protocerebrum (Kass and Barlow, 1992), transmits efferent activity to the telson (tail), median ocelli (two photoreceptors located on the middle front of the animal), and the lateral eye, causing many structural changes in the photoreceptors, inducing much higher visual sensitivity at night (up to a million-fold - Barlow et al., 1980). Studies of the Limulus circadian system have covered many aspects of circadian physiology including light induced phase shifting

(Horne and Renninger, 1988), entrainment (Calman and Battelle, 1991), and studies attempting to identify the location of the primary clock (Battelle, 2002). Although Limulus circadian rhythms have been well studied, the molecular interactions of the genes and proteins that control them have yet to be identified. A number of putative circadian genes have been identified within the Limulus genome and transcriptome (Chesmore et al., 2016). Interestingly, the number and type of putative circadian genes is more similar to the Mus musculus circadian model than the Drosophila model. For instance, like M. musculus, Limulus have three period paralogs and two cryptochrome homologs. However, when nucleotide sequences of the period and cryptochrome genes were compared to both mammalian and invertebrate systems, putative Limulus circadian clock genes align more closely with invertebrates like Drosophila rather than with mammals like Mus musculus (Chesmore et al., 2016).

8. Tidal and Circatidal Studies of Limulus

8.1. Field Studies In the field, horseshoe crabs exhibit robust tidal behavioral rhythms. For instance, Limulus nesting and mating behavior occurs predominately during high tides in late spring/early summer (Shuster et al., 2003). The onset of this mating behavior seems to be modulated primarily by temperature and photoperiod (Cohen and Brockmann, 1983; Cheng et al., 2015). However, the synchronization of tidally rhythmic behavior seems to be primarily controlled by water level change. For example, when two groups of animals were placed in running wheels in natural conditions (a tidal estuary), and either anchored to the bottom (tidal cues in addition to all other natural cues) or tethered to a floating dock (no tidal cues in addition to all other natural cues), more animals anchored to the bottom were shown to exhibit tidal rhythms than animals attached to the floating dock, suggesting the water level change to be the dominate environmental stimulus for tidal rhythms (Chabot et al., 2010). Animals have also shown predictive tidal behavior in the field: animals generally move into intertidal zones approximately one hour before high tide and move back to deeper water about two hours after high tide (Barlow et al., 1986). Many of the factors that influence or control these tidal behavioral field observations were first observed and tested in the lab.

8.2. Lab Work When exposed to artificial conditions in the lab, adult Limulus synchronize to tidal cycles and their behavioral rhythms persist in constant conditions with circatidal periods (Chabot et al., 2004; Ehlinger and Tankersley, 2006; Chabot et al., 2008; Chabot and Watson, 2010). These results are also seen in larvae and juveniles (Ehlinger and Tankersley, 2006; Dubofsky et al., 2013). The persistence of these rhythms with periods of ~12.4h in constant conditions demonstrates the presence of an endogenous clock. In fact, when both eye sensitivity and locomotor activity are simultaneously monitored in adults during artificial tides and subsequent constant

13 conditions, animals demonstrate two rhythms that free-run with different periods: a circatidal rhythm of locomotion and a circadian rhythm of efferent activity to the lateral eye, strong evidence of the presence of two separate clock systems (Watson et al., 2008). In summary, the clear and robust circadian and circatidal rhythms make Limulus polyphemus an ideal species for studying the underlying mechanisms of the clocks controlling these rhythms.

9. Specific Aims of the Studies

While Limulus has strong circadian and circatidal rhythms, virtually nothing is known about the molecular mechanisms that control these rhythms. In addition, nothing is known about the effects of time of day or time of tides on transcriptional levels of the putative circadian genes or any other genes. The aims of the first study were (1) to create a draft genome and transcriptome; (2) to use those data to create a baseline for our circadian and circatidal gene expression work; and (3) to examine the relative expression of circadian genes over certain daily and tidal conditions. Interestingly, our findings suggest that, under the experimental conditions provided, none of the homologs for Drosophila core clock gene transcripts were found to cycle with respect to either time of day or tidal phase. However, both tidal and photoperiodic phase affected the transcription of large numbers of genes. Further, tidal phase had a greater impact on overall gene expression than photoperiodic phase. The second study aimed to focus on the protocerebrum, the suggested location of the circadian clock (Kass and Barlow, 1992), and to determine if a subset of putative “core” clock gene mRNA cycling occurs with respect to time of day or tide as mRNA transcript cycling is seen in almost every investigated endogenous clock system (Kojima et al., 2011). Drosophila, for instance, relies on the cyclical expression of period, clock and timeless mRNA for complete circadian timing (Hardin, 2004). Using the information from the draft genome and transcriptome, primers for clock, cycle1, cryptochrome2, and timeless were created to quantify these gene products in the protocerebrum using QPCR over tidal and daily periods. We found no statistically significant difference in transcript expression over daily or tidal conditions. These results are similar to the findings of our first study using RNA-seq, and together, suggest relatively constitutive expression of all of the canonical circadian genes. Overall, the data point to several possibilities that need to be tested in the future: (1) that the Limulus biological clocks do not rely on transcript expression levels but rather a series of post-transcriptional and/or translational mechanisms, (2) the investigated genes are not involved in the Limulus circadian or circatidal systems, (3) the protocerebrum is not the location of either clock system, (4) rhythmic transcript expression was masked by the expression of the two clock systems that were out of phase with one another, or (5) mRNA expression is “diluted” by surrounding non- clock cells that express these genes constitutively.

CHAPTER 1 FIGURES Figure 1.1: Drosophila melanogaster circadian molecules and some of their interactions

Figure 1.2: Mus musculus circadian molecules and some of their interactions.

THE DRAFT GENOME AND TRANSCRIPTOME OF THE ATLANTIC HORSESHOE CRAB, LIMULUS POLYPHEMUS

Stephen D. Simpson, Jordan S. Ramsdell, Winsor H. Watson III, and Christopher C. Chabot

International Journal of Genomics Volume 2017 (2017), Article ID 7636513, 14 pages

CHAPTER 2

THE DRAFT GENOME AND TRANSCRIPTOME OF THE ATLANTIC HORSESHOE CRAB, LIMULUS POLYPHEMUS

1. Introduction

The Atlantic horseshoe crab, Limulus polyphemus, is an important species for a variety of reasons. First, it is considered a keystone species within many estuaries and bays along the Atlantic coast of the United States. Moreover, in these habitats their foraging behavior releases trapped nutrients into their local environment (Jackson et al., 2005; Karpanty et al., 2006). Second, their eggs are a source of food for endemic and endangered long distance avian migrants. Third, they are used as bait for the eel and conch fisheries (Berkson and Shuster 1999). Fourth, their blood is the source of the most sensitive assay for gram-negative bacteria contamination of medical products (Bianchini et al., 1981; Novitsky, 1994; Ferrari & Targett, 2003). Finally, Limulus have long been used as model systems in neurobiological research. For example, lateral inhibition, one of the underlying principles of visual physiology, was first demonstrated in Limulus by Haldan Keffer Hartline, and as the result he won the Nobel Prize in Physiology or Medicine in 1967 (Hartline, et al., 1956). More recently, this species has been used as a model for the investigation biological rhythms, for instance, circadian rhythms of visual sensitivity (Barlow, 1983; Chabot et al., 2004; Watson et al., 2008). Circadian rhythms are produced by a combination of endogenous clocks and cues from the environment, (Dunlap, 1999; Reppert & Weaver, 2001). The identity of several core circadian genes that are responsible for producing these rhythms have been revealed in a number of invertebrates and vertebrates (Panda et al., 2002), and include period, clock, cryptochrome, cycle, timeless and other, accessory genes. Although these genes are labeled as circadian, due to their critical function within the circadian clock mechanism, they may also play a role in other types of biological rhythms including those that regulate seasonal activity (Nuesslein-Hildesheim et al., 2000). It has also been proposed, but not demonstrated, that they might be involved in shorter, (~12.5 hr) circatidal rhythms (Forward et al., 2003; Stillman and Barnwell, 2004; Watson et al., 2008). One of the overall goals of this study was to test this hypothesis in horseshoe crabs, which express both a circadian rhythm of lateral eye sensitivity (Barlow, 2001) and a circatidal rhythm of locomotion (Chabot et al., 2004; Chabot and Watson 2010). In this study we developed draft genomic and transcriptomic assemblies for Limulus polyphemus and then compared the genes expressed during high and low tides and during the day vs the night. Particular attention was paid to putative core and accessory circadian genes. We identified these, and then compared their expression, using RPKM values, across the different conditions (tides and Light:Dark(L:D)). Because no clear differences in the expression of putative circadian genes were apparent, we further examined some of the transcripts that did exhibit significant

18 day/night or high/low tide differences as a first step towards the identification of potential proteins involved in the temporal control of the behavior and physiology in this species.

2. Methods

2.1. Animals and Environmental Conditions For genomic sequencing, an individual horseshoe crab was wild-caught from Great Bay Estuary in Durham, NH (43°05'30"N and 70°51'55"W). Leg skeletal muscle tissue was removed and placed in liquid nitrogen for immediate DNA extraction (described below). For transcriptome sequencing, four animals were captured from Great Bay Estuary in Durham, NH and sacrificed at four different times: Day high tide (DHT, 0800), night high tide (NHT, 2030), evening low tide (ELT 1800) and during the day at low tide (DLT, 1530). DLT was collected while still active (during high tide), placed into a natural water flow-through tank located next to the bay with open exposure and sacrificed, while inactive (buried), at the time of low tide (1530). DHT and NHT were used to compare the expression of genes during the day versus the night, and DHT and DLT were used to compare expression during high and low tides. Tissues from ELT were sequenced and used to increase the overall depth of the combined transcriptome dataset. Animals were dissected and their entire central nervous system tissue (protocerebrum, subesophageal ganglia, ventral nerve cord and ganglia) was snap frozen on dry ice.

2.2. DNA Extraction 300 mg of frozen muscle tissue was pulverized using a sterile, autoclaved mortar and pestle. 19 mL of Qiagen G2 lysis buffer (Qiagen #1014636) spiked with 38µL of Qiagen RNase A stock solution (100 µg/µL, Qiagen #19101) was combined with the ground tissue in a sterile 50 mL conical tube. DNA was extracted per the Qiagen Genomic-Tip 500/G protocol (Qiagen genomic DNA Handbook, 08/2001). Extracted gDNA was stored at -80°C until library preparation.

2.3. RNA extraction for transcriptome sequencing Frozen whole CNS tissue was shipped on dry ice to University of Vermont Cancer Center DNA Analysis Facility (Burlington, VT) for extraction. Tissue was stored at -80°C until RNA extraction. Frozen tissue was homogenized in 1 mL of Ambion® TRIzol® Reagent (LifeTech, #15596-026) using a sterile, autoclaved mortar and pestle. RNA was extracted per the Ambion® TRIzol® protocol (LifeTech, MAN0001271) and purified using Qiagen RNeasy Mini Kit (Qiagen #74104) per manufacturers protocol (Qiagen RNeasy Mini Handbook, 06/2012).

2.4. Genome Illumina paired-end library construction and sequencing Extracted gDNA was sent to Vanderbilt Technologies and Advanced Genomics facility, Nashville, TN. The library was prepared using TruSeq DNA Sample Preparation Guide v2, catalog #FC-930-1021 (Illiumina, San Diego, CA, U.S.A). One µg of gDNA was sheared using a Covaris S2. Sheared ends were then repaired, adenylated, and the products ligated with Illumina adaptors. Ligated fragments were then size selected using Pippen Prep (Sage Science, Beverly, MA, U.S.A) and cleaned using Zymo Clean and Concentrator Kit (Zymo Research, Irvine, CA, U.S.A.). KAPA Hot Start (KAPA Biosystems, Wilmington, MA, U.S.A.) was then used the amplify libraries over a total of 14 PCR cycles. Clustering was performed using a cBot (Illumina) and paired-end sequencing was performed on a HiSeq 2000 (Illumina) over two lanes.

2.5. Transcriptome Illumina paired-end library construction and sequencing Extracted, purified RNA was quantified and analyzed using a Qubit 2.0 (Life Technology, Carlsbad CA) and an Agilent Bioanalyzer 2100 (Agilent Technology, Santa Clara CA). RNA libraries were prepared using Illumina TruSeq RNA Sample Prep LT version 2 (RS-122-2001/2002). 1 µg of total RNA was PolyA enriched using AMPure XP Magnetic Beads (Beckman Coulter #A63880). Complementary DNA libraries were created from the enriched RNA using Invitrogen Superscript II® per the manufacturers protocol. cDNA was fragmented, end repaired and adenylated followed by a ligation of Illumina adaptor indices. Illumina Reagents (Part #15012995) were used for PCR amplification. TruSeq libraries were quantified using Nanodrop, Qubit and qPCR (KAPA Biosciences kit #4824). Fragment size was determined using Agilent Bioanalyzer 2100 and clustering was performed on the Illumina cBOT. Sequencing was carried out at 12 pM on an Illumina HiSeq1000/1500.

2.6. Genome de novo assembly Illumina CASAVA pipeline was used to demultipex and filter Limulus genomic reads (reads with a Q<10 were removed). Reads were assembled in CLC Genomics Workbench (v 5.1.2.) using CLC Bio’s Proprietary CLC Assembly Cell 4.0 (CLC4) set to default parameters at the Hubbard Center for Genome Studies at the University of New Hampshire (Durham, NH).

2.7. Transcriptome de novo assembly Four unique conditions (DHT, NHT, ELT and DLT) were assembled separately in CLC Genomics Workbench (v 5.1.2.) using CLC Bio’s Proprietary CLC Assembly Cell 4.0 (CLC4) set to default parameters. Additionally, all four conditions were combined and again assembled using the same algorithm and parameters for use as a reference library.

2.8. Benchmarking Universal Single-Copy Orthologs (BUSCO) Analysis Genome and transcriptome completeness’s were assessed using BUSCO v1.1 using the eukaryotic linage for both and default parameters for the genome and transcriptome analyses, respectively.

2.9. mtDNA Analysis The previously published Limulus mitochondrial genome (NC_003057.1) was BLASTed against the genomic assembly and used to identify Limulus genomic contig 669. Contig 669 was then analyzed for coding regions and fully annotated using NC_003057.1 as a reference and visualized (Figure 1) using OrganellerGenomeDRAW (Lohse et al., 2013). Limulus mtDNA was then BLASTed against the Limulus genome assembly to look for nuclear mitochondrial (NUMT) sequences. To validate potential NUMT sequences, genomic contigs that contained homologous regions of mtDNA were extracted and compared for similarity.

2.10. Transcriptome Analysis Individual read sets from the four samples were mapped to the overall transcriptome assembly, with each contig given a unique identifying number. Reads per kilobase per million mapped reads (RPKMs) were used to determine relative fold change between day/night and high/low tides. Blast2GO (Conesa et al., 2005) was performed on each of the four RNA-seq mappings to look for expression variation in molecular groupings. Transcripts between conditions were normalized by identifying the condition with the highest number of sequences across all defined gene ontologies (GO) and dividing that condition's GO sequence numbers by the corresponding GOs of another condition marked for comparison. The average difference between GOs of the two individuals (to be compared) was then used as a normalization value for the actual sequence number of the individual with less sequences per GO.

2.11. Accession numbers Limulus genome and transcriptome sequence read data are located in the NCBI Sequence Read Archive under accession numbers [SRR4181534] and [SSR421559- SSR4215562], respectively. The Limulus mtDNA genome sequence is located on NCBI under the accession number JX983598.

3. Results

3.1. Limulus Genome Sequencing and Assembly Genomic sequences yielded a total of 620,743,244 one-hundred base pair (paired-end) reads (Table 2.1). The Limulus genome size is estimated to be 2.74 Gb based on biochemical analysis (Goldberg et al., 1975), and, based on that estimation, these sequences should result in an approximate 23X coverage of the genome (Table 2.1). De novo assembly of the genome resulted in an N50 of 4.7 kb with the largest contig being 68.9 kb. The total length of all contigs >=500bp was 1.4 Gb. To evaluate

21 the completeness of the genome assembly with respect to protein coding genes, BUSCO v1.1 genome assessment was used to look for evidence of 429 putative universal orthologs and yielded the following results: complete genes found (C) - 12.9%; duplicated genes found (D) - 1.6%; fragmented genes found (F) - 10.7%; genes missing (M) - 74.8%; number of genes used (N) - 429. Overall only 25.2% of the genes that were looked for were found within the genome (Simão et al., 2015).

3.2. Limulus mtDNA The overall length of the Limulus mitochondrial genome is 15,012 bp, which is longer than the other previously published Limulus mitochondrial genome at 14,985 bp (Lavrov et al., 2000). The majority of extra mtDNA appears to be in the non- coding origin of the mtDNA genome. However, there are a number of polymorphisms, insertions and deletions between the mitochondrial genome sequences, many of which occur in the coding regions (Table 2.2).

3.3. Limulus NUMT sequences Two NUMT sequences were identified as genomic contigs 269235 and 5819, which were 9,955 bp and 12,620 bp in length, respectively. These two nuclear contigs each had significant stretches of sequence that were clearly homologous to the extant mitochondrial genome. Specifically, Nuclear Contig 269235 contained a contiguous region of 2,008 bp that includes sequences homologous to the mitochondrial genes for nad2, methionyl-tRNA, lysidine synthase, glutamate-tRNA ligase, valine-tRNA ligase and 12s ribosomal RNA and contig 5819 includes 1,065 bp homologous to nad1. To confirm that the NUMT sequences were not simply errors in assembly, we compared the NUMTs and extant mitochondrial sequences. Neither of the NUMT sequences in genomic contigs 269235 or 5819 should have high levels of sequence identity expected if they were assembled from extant mitochondrial genome reads. Specifically NUMT sequences in genomic contig 269235 had an 84.4% average percent identity to mtDNA sequences and those in genomic contig 5819 had a 79.4% identity to the homologous mtDNA sequences (Figure 2.2).

3.4. Limulus Transcriptome Sequencing and Assembly The combined dataset had a total of 484,378,406 reads at a size of 100 bp and was de novo assembled in CLC to generate a reference assembly for analysis. The resulting transcriptomic assembly contained 92,348 contigs with a maximum contig size of 23.85 kb. Transcripts mapped back to the genome with 88.3% of the total transcripts, equating to 109.76 Mb, suggesting that the transcriptome assembly represents most of the exons present in our genome assembly (Figure 2.3). The completeness of the transcriptome was evaluated using BUSCO v1.1 transcriptome assessment to look for evidence of 429 putative universal orthologs, which yielded the following results: complete genes found (C) - 56.6%; duplicated genes found (D) - 10.7%; fragmented genes found (F) - 13.3%; genes missing (M) - 19.3%. Overall the transcriptome was found to have 80.7% of the total number of genes looked for in the

BUSCO analysis, more than three times that of the genome (Simão et al., 2015). Sequencing and assembly statistics obtained for the genomic and transcriptomic libraries are summarized in Table 2.1.

3.5. Effects of Photoperiod and Tidal Phase on Limulus Transcript Expression The primary purpose of the RNA-seq data was to generate a central nervous system transcriptome, while a secondary purpose was to generate a set of pilot data for potential differences with regards to photoperiod and tidal phase. Of the four conditions used for transcriptomic analysis (DHT, NHT, ELT and DLT), three were used for the following comparisons: DHT/NHT and NHT/DLT. Total read counts for each condition were 1.5X10^8 for DHT, 1.3X10^8 for NHT, 5.8X10^7 for ELT and 1.4X10^8 for DLT. To compare gene expression across the different treatments we mapped the reads from each dataset to the combined reference assembly using CLC Genomics Workbench’s (v5.1.2) RNA-Seq analysis toolkit. Volcano plot analysis (CLC v5.1.2) was used to analyze variation between night and day samples as well as high and low tide samples (Figs. 2.4 and 2.5). As expected, the majority of sequences for both conditions fell below the thresholds of statistical significance (below horizontal red line) and a minimum fold-change of 2 (in between vertical red lines). However, when the number of contigs that varied significantly were compared between the two treatments (Figure 2.6), there was a larger number of contigs with a minimum fold change of ≥2 that varied significantly in the high/low tide comparison than in the day/night comparison.

3.6. Expression of Gene Types with Respect to Photoperiod and Tidal Phase The majority of transcripts that were different in the photoperiod and tidal phase comparisons were novel (Colbourne et al., 2011). Therefore, only the top four identifiable genes with the lowest p-values and highest fold changes are shown for both comparisons in Table 2.3. Of the genes that could be identified when BLAST against NCBI, there were twice as many whose e-values were ≤1.0E-4 in conditions of tidal phase compared to conditions of photoperiod (Table 2.3). Identified genes with e-values <1.0E-22, which seem to be closely related to NCBI annotated homologs, were primarily developmental and regulatory in nature (Table 2.3). All transcript contigs from photoperiodic and tidal conditions were also annotated using the BLAST2GO pipeline, which uses Gene Ontology (GO) to define genes based on common functions and relationships. The GO terms “multi-organism process” (Figure 2.7), “molecular transducer activity”, “receptor activity”, “structural molecule activity” (Figure 2.8) and “translation regulator activity” (Figure 2.9), all varied greater than 40% when looking at the comparison between high and low tides. Only 1 gene ontology term, “structural molecule activity”, appeared to change more than 40% when looking at night vs day (Figure 2.8).

3.7. Targeted Analysis of Candidates Genes Involved in Circadian Pathways in Drosophila. Candidate genes homologous to Drosophila were selected as they are considered the core circadian genes from the model insect organism, Drosophila melanogaster. Cycle1 (KX014724) RPKM values had the largest absolute fold change with respect to tidal phase with a value of 4.3-fold (Table 2.4). Interestingly, cycle1 was also among the genes with the least amount of variance when looking at differences in photoperiod (1.1-fold). Cycle2 (KX014725), however, has approximately the same amount of change when looking at photoperiod or tidal phase (3.1-fold and 3.3-fold, respectively). Cryptochrome1, Cryptochrome2 (KX014723), Timeless (KX014719), Clock (KX014718) and Period variants A (KX014720), B (KX014721) and C (KX014722) all had absolute fold change values < 2.0 for both conditions of photoperiod and tidal phase. Relative RPKM values were also calculated for a number of circadian accessory genes (Price et al., 1998; Blau and Young, 1999; Benna et al., 2000; Lin et al., 2002) and potential control genes (Marten et al., 1994; Lupberger et al., 2002; Goossens et al., 2005; Etschmann et al., 2006), as shown in Table 2.4. The accessory genes timeout, double-time, vrille (KX014726) and casein kinase's Iα (KX014742), IIα (KX014727), IIβ (KX014741) and Iε (KX014743) show an absolute fold change of ≤1.1-fold in all but two cases: vrille, with respect to photoperiod (1.6-fold) and timeout with respect to tidal phase (1.9-fold). Additionally, 12 potential control genes were investigated: C-reactive protein (CRP), tubulin (TUB), ubiquitin (UBQ), succinate dehydrogenase complex, subunit A (SDHA), synaptotagmin (SYN) and seven variations of actin (ACT). All of the investigated control genes with the exception of the actin variants had an absolute fold change ≤ 1.2-fold for either condition. Notably, six of seven actin variations, a gene traditionally used for normalization in QPCR analysis, exhibit an absolute fold change ≥ 1.3-fold over tidal conditions and four of seven display a variation ≥ 1.1-fold over photoperiodic conditions. The neuropeptide, pigment-dispersing hormone (PDH), was also investigated as it has been described as a potential downstream regulator of circadian rhythms in arthropods (Pyza and Meinertzhagen, 1997; Renn et al., 1999). A PDH receptor-like homolog was identified in the Limulus transcriptome dataset and found to have an absolute fold change of 3.0-fold for conditions of tidal phase, whereas the absolute fold change for conditions of photoperiod was only 1.5-fold (Table 2.4).

4. Discussion

Based on the fossil record, the Atlantic horseshoe crab, Limulus polyphemus is one of the most ancient of all living organisms (Dunlop, 2010) and therefore the genome and transcriptome presented here will likely both provide clues to evolutionary divergence and lead to the identification of novel genes (Warren et al., 2008). The draft sequences for the Limulus genome, transcriptome and the addition of a second mitochondrial sequence will also provide a dataset for mining biomarkers

24 and other molecular tools with which diversity and population studies can be conducted (Hauser et al., 2002). Thus, these data may also help to provide genetic markers that can be used to identify local sub-populations and lead to improved management of this important species, Finally, the sub-phylum chelicerata is under- represented when it comes to annotated genomes (currently there is only one other representative from the chelicerata sub-phylum which has had its genome sequenced (Ixodes scapularis; Hill and Wikel, 2005) and so adding a draft genome and transcriptome at 23X coverage and 38.5 Gb, respectively, will greatly increase the genetic reference for all Chelicerata. Limulus has at least two distinct endogenous clocks (Barlow, 1983; Watson et al., 2008). There is a circadian clock, which controls day/night sensitivity to light (Barlow et al., 1977b) and a clock system that controls circatidal rhythms of locomotor activity (Chabot et al., 2004). While several core and accessory genes are known to be part of the molecular structure of the circadian clock in model organisms such as Drosophila (Blau and Young, 1999; Benna et al., 2000; Lin et al., 2002), the molecular workings of the clock controlling circatidal rhythms are completely unknown (De la Iglesia and Hsu, 2010; Chabot et al., 2016). While investigations into the molecular mechanics of this clock in the intertidal mangrove cricket, Apteronemobius asahinai, have shown that the circadian genes period and clock are likely not involved in circatidal rhythms (Takekata et al., 2012; Takekata et al., 2014), our working hypothesis is that circatidal rhythms evolved from existing circadian mechanisms. Indeed, the findings that circatidal rhythms appear to be controlled by two clocks (“circalunidian clocks”; Palmer, 1989; Chabot et al 2016); each of which has an endogenous period (~24.8h) very close to circadian clocks (~24h), supports this idea. Homologs for all of the core circadian genes have been identified within Limulus (Table 2.4; Chesmore et al., 2016), two of which (cycle and timeless variants) seem to vary by time of day and tide (Table 2.4), supporting the idea that some of core circadian genes are somehow involved in modulating the circadian rhythm as well as the circatidal rhythm. Overall however, RNA-Seq analysis of the majority of core circadian transcript expression across conditions of photoperiod and tidal phase indicated only slight variation in expression (< 2-fold for nearly all) for those conditions. Additionally, QPCR data of the core circadian mRNA within the protocerebrum has shown no statistically significant variation of expression for clock, cycle1, or timeless (Simpson, unpublished results). Combined, these data suggest a relatively constitutive expression profile when looking at daily and tidal differences of most core circadian clock gene transcripts within the central nervous system as a whole. However, both variants of the core clock gene cycle seem to vary moderately (> 3-fold) with respect to tidal phase with only one variant (cycle2) varying moderately (> 3-fold) with respect to photoperiod. Additionally, investigations into downstream circadian regulators like the homolog of the neuropeptide PDH identified a transcript that is closely related to a PDH receptor, but shares a higher percent identity with the vertebrate receptor for corticotropin-releasing hormone (CRH). Changes in RPKM values for the transcript exhibited a much greater change (3-fold) from conditions of tidal phase rather than

25 from conditions of photoperiod (Table 2.4). Further BLAST investigations of PDH, CRH and precursors involved with their synthesis yielded no reads (Chesmore et al., 2016). With the majority of investigated circadian transcript expression exhibiting strong tendencies towards tidal phase rather than photoperiod, these findings may provide evidence of genes involved with the Limulus circatidal timing system. When the top 20 most significant transcript contigs were pulled out of the experimental groupings (high tide versus low tide and night vs day) and BLASTed against the NCBI database, only 20% came back with an identified hit (Table 2.3). Of the genes that were expressed differentially during conditions of photoperiod, only two had e-values < 1.0E-3. The exact function of these genes is not known, however, “DMX-like protein 2” seems to be involved in synaptic processes and “protein trapped in endotherm 1” may play a critical role in germ cell migration. The genes that exhibited the largest changes over conditions of tidal phase all had e-values < 1.0E-31. “Thyroid peroxidase-like” and “3 beta-hydroxysteriod dehydrogenase” seem to be involved in hormone production, “ALX homeobox protein 1” is involved with neural development in mice and “Ovostatin-like” is similar to a proteinase inhibitor found in chicken eggs (Table 2.3). The lack of definitive gene identification and the small percentage of transcripts that returned with BLAST evidence of homology suggests that the majority of transcripts found to respond to photoperiod and tidal phase in the Limulus transcriptome are novel and these findings support similar studies that found genes involved in ecological adaptation tend to be novel (Colbourne et al., 2011; Connor and Gracey, 2011). Overall trends in transcript expression indicate a higher degree of difference in transcription occurring during high vs low tide periods, than night vs day time periods. For example, there are nearly twice as many transcripts that are differentially expressed during high/low tide versus night/day (Figure 2.6). When comparing gene ontology term values (GO; Figures 2.7-2.9) there are five terms that vary with a minimum percent change ≥ 5%. When comparing these terms across conditions of night vs. day we see a minimum percent change of 5% and maximum percent change of 52%. However, when looking at those same terms over high and low tidal conditions there is a minimum percent change of 36% and a maximum percent change of 198% (Figures 2.7-2.9). Taken together the results suggest a transcription profile that is dominated by tides more than by photoperiod. Deeper sequencing and statistical power will perhaps lead to the teasing apart of subtle and potentially significant changes in transcript expression. The overall analysis of genome completeness using BUSCO was much less than that of the transcriptome (25.2% and 80.7% orthologs identified, respectively) suggesting recalcitrance by the genome and/or genome assembly to the BUSCO analysis (Simão et al., 2015). If the genome assembly were causing the low completeness score we would expect a mapping of the transcriptome assembly to the genome assembly to be poor. However, the transcriptome maps to the genome with 88.3% coverage (Figure 2.3). Additionally, when the genome assembly was compared to previously reported Limulus genome sizes, it was found to be about half of the expected size, indicating a large, a highly repetitive genome (Goldberg et al., 1975;

Nossa et al., 2014). Specifically, our assembly appeared to include highly frequent repetitive elements that represent a large factor of the genome. We hypothesize that it is those repeats in the genes (introns) that reduce the success of the findings universal orthologs. By contrast, the transcriptome cuts away these highly repetitive elements. Additionally, while not contributing to the low BUSCO score, other complexities with the genome structure, like the integration of mtDNA elements, may have facilitated increased collapse within the genome assembly. Nuclear incorporation of mtDNA (NUMT) sequences can cause problems when conducting mtDNA-based studies because they can be confused with the extant version of the mitochondrial genome. To identify potential NUMTs, the Limulus genome assembly was searched for sequences highly similar to the extant version of the mitochondrial genome using BLASTN, which yielded two contigs. In both contigs these putative NUMT sequences were flanked by non-mitochondrial sequences including hypothetical Zinc Finger MYM-type 1 coding sequences, which are not present in any known animal mtDNA genome. The presence of these Zinc Finger sequences suggested either that the mitrochondrial-like sequences were NUMTs or there was an error is the genome assembly causing the mtDNA sequences to be combined with the gDNA sequences. Results indicate that these sequences seem to be true NUMTs as the percent identity between the suspected NUMT sequences and the mtDNA sequences was less than 85% in either case (Figure 2.2). For each of these two putative NUMTs the nuclear and extant mitochondrial sequences are collinear, suggesting that these could have been derived from single translocation events and although there are two distinct mtDNA loci incorporated into two separate genomic contigs their similar levels of divergence from the extant versions may suggest a single common origin for both putative NUMTS. The draft genome and transcriptome will begin to provide a molecular guide for a host of environmental and experimental studies. This will allow for a deeper, more thorough understanding of the behavioral and environmental impacts and challenges these animals face. These datasets are an important addition to an ever- growing aggregation of genomic and transcriptomic databases.

CHAPTER 2 FIGURES Figure 2.1: Gene map of the Limulus polyphemus mitochondrial genome. Arrows indicate strand direction with the inner circle representing genes on the light strand while the outer circle represents genes on the heavy strand. ND1-6 represents nicotinamide adenine dinucleotide dehydrogenase (NADH) subunit variants. tRNA(“amino acid”) represents transfer RNA variants. COX1-3 represents the cytochrome c oxidase subunits. CYTB represents cytochrome b oxidase. ATP6 and ATP8 represent adenosine triphosphate variants 6 and 8. Rrn12 and rrn16 represent ribosomal subunits 12 and 16. Innermost dark grey circle represents distribution of GC content.

Figure 2.2: Diagram of two nuclear mitochondrial (NUMT) sequences identified in the Limulus genome. Limulus genomic contigs 269235 and 5819 show the length of each contig, the location of the NUMT sequences (yellow = NAD variants, blue = tRNAs, red = rRNA) and relative location of Limulus genomic sequences (grey = Zinc Finger MYM-type 1). The mitochondrial gene names are given along with the e- values and percent identities of the mitochondrial sequences to the NUMT sequences.

Figure 2.2Figure

Figure 2.3: Transcriptome assembly mapped to the genome assembly. A) Nucleotide Coverage: y-axis - total number of nucleotides that could be mapped for each transcript length bin (1000 nucleotides). B) Percent Mapped Bases: y-axis - total percentage of mapped bases in each bin. Black dashed line - the percentage of nucleotides in the transcriptome that could be mapped back to the genome.

A) 35

! 30 Unmapped Mapped 25

Nucleotide Coverage (Mb) Coverage Nucleotide 0

B) 100%

80%

60%

40%

20%

Percent Mapped Bases Percent 0%

Transcript Length (bps)

Figure 2.4: Volcano plot of day versus night showing the distribution of transcripts with respect to fold change (x-axis) and statistical significance (y-axis). Points above the horizontal red line indicate transcripts whose abundance is statistically significant

(p-value ≤ 0.05). Points to the left of -1 Log2 Fold Change and the right of +1 Log2 Fold Change indicate transcripts with at least a doubling in abundance relative to day and night. Large blue dots indicate top four contigs chosen for analysis in table 3.

re re 2.4 Figu

Figure 2.5: Volcano plot of high versus low tide showing the distribution of transcripts with respect to fold change and statistical significance.

re re 2.5 Figu

Figure 2.6: Number of statistically significant (p ≤ 0.05) transcripts (contigs) in both the day/night (red bars) and high tide/low tide (blue bars) volcano plots grouped by Log2 Fold Change. Results indicate a greater presence of statistically significant transcripts for tidal phase relative to photoperiod.

1000

800

High Tide vs. Low Tide 600 Day vs. Night

400

Number of Contigs Number 200

0 > 1 > 2 > 3 > 4 > 5 > 6 > 7 > 8 > 9 > 10

Absolute Log2 Fold Change !

Figure 2.7: Biological Process BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide. Data labels are shown in conjunction with their respective slice of the pie graph. Enlarged slices represent GO values with a percent change greater than 40%.

re re 2.7 825 585 Figu

Day/Low Tide Tide Day/Low Night/High Tide Tide Night/High

522

Day/High Tide Tide Day/High

Figure 2.8: Molecular Function BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide.

re re 2.8

Figu

367 719 249 Night/High Tide Tide Night/High Day/Low Tide Tide Day/Low 178 358

209

241 278 Tide Day/High 378

Figure 2.9: Cellular Component BLAST2GO values for RNA-Seq conditions: Day/High Tide, Night and Low Tide.

428 332

re 2.9 Figu Day/Low Tide Tide Day/Low

Night/High Tide Tide Night/High

300

Tide Day/High

CHAPTER 2 TABLES Table 2.1: Limulus genome and transcriptome assembly statistics.

Genome Transcriptome

Total number of reads 620,743,244 484,378,406 Number of reads matched 573,146,494 385,105,364 Number of reads not matched 47,596,750 99,273,042 Total size of assembly 1.48Gb 0.14Gb N75 2,053 1,012 N50 4,736 1,862 N25 9,523 3,497 Largest contig 68,988 23,854 Total number of contigs 744,122 93,348

Table 2.2: Comparison of Limulus mtDNA, JX983598.1 relative to NC_003057.1 using sites of polymorphic variance. Gene name is represented under locus. Size represents the length of the gene. Number of variable sites indicates the total number of nucleotides that differ between genomes. Number of substitutions is the combined number of transitions and transversions present at each locus. Number of frameshift mutations is the combined number of insertions and deletions present at each locus.

Number of Number of Number of Locus Size (bp) Frameshift Variable Sites Subtitutions Mutations ATP6 675 19 18 1 ATP8 156 4 4 − CYTB 1132 40 40 − COX1 1536 36 36 − COX2 685 18 18 − COX3 784 17 17 − NAD1 933 24 24 − NAD2 1017 31 31 − NAD3 345 13 13 − NAD4 1338 29 29 − NAD4L 300 12 12 − NAD5 1714 47 47 − NAD6 462 12 12 − rRNA 12S 799 12 12 − rRNA 16S 1298 26 22 4 Alanine 67 − − − Arginine 63 − − − Asparginine 65 1 1 − Aspartic Acid 67 1 1 − Cysteine 64 − − − Glutamic Acid 66 1 1 − Glutamine 66 2 2 − Glycine 64 − − − Histidine 69 − − − Isoleucine 67 1 1 − Leucine (CUN) 69 − − − Leucine (UUR) 66 1 1 − Lysine 70 − − − Methionine 70 − − − Phenylalanine 66 1 1 − Proline 67 1 1 − Serine (AGN) 73 − − − Serine (UCN) 73 44 35 9 Threonine 69 − − − Tryptophan 68 − − − Tyrosine 67 1 1 − Valine 69 − − −

Table 2.3: Top four contigs of day/night and high/low tide conditions from RNA-Seq volcano plot analysis. Contigs chosen were furthest away from the origin while still having an associated gene name in GenBank (Figures 4 and 5). Contig ID - unique identifier for the Limulus transcriptome dataset; Top Hit ID - accession ID for the top BLAST result for each contig; Predicted Gene/Protein - BLAST result gene or protein description. Top Hit E-Value - e-value for top BLAST result; P-Value - probability of statistical significance; Absolute Fold Change -difference in expression between tested conditions; Unique Gene Reads - total depth of reads found for a particular contig within a particular condition; RPKM - reads per kilobase per million mapped reads.

0.21 25.7 0.02 0.44 Low 13.37 45.69 28.16 46.05 Night RPKM RPKM Day 0.03 8.37 0.02 0.27 0.09 0.19 High 14.78 17.22 RPKM RPKM

5 2 6 703 493 740 3155 1353 (Low) Unique Unique Unique (Night) Gene Reads Reads Gene Reads Gene 1 1 8 11 50 427 801 631 (Day) (High)

Unique Unique Unique Gene Reads Reads Gene Reads Gene

Fold Fold Change Change Absolute Absolute Absolute 7.20E+01 8.37E+02 3.37E+02 5.87E+02 1.68E+02 3.27E+02 2.46E+02 3.90E+01

P-Value P-Value 1.66E-11 1.21E-11 1.90E-04 3.33E-07 4.29E-03 2.34E-04 1.10E-07 7.08E-05

Value Value 3.39E-23 4.13E-03 1.44E-02 1.14E-44 3.49E-32 0.00E+00 0.00E+00 0.00E+00 Top Hit E- Hit Top E- Hit Top

Table 2.3

Predicted Gene/Protein Predicted Gene/Protein Predicted

Protein trapped in endoderm-1-like in trapped Protein 2 protein DmX-like A1-like 8 member family dehydrogenase Aldehyde L23 protein 54S ribosomal Ovostatin-like peroxidase-like Thyroid 1-like type 5->4-isomerase dehydrogenase/Delta 3 beta-hydroxysteroid 1-like protein ALX homeobox

Top Hit ID Hit Top ID Hit Top XM_013938347 XM_013933266 XM_013932736 XM_013292029 XM_013925651 XM_013933409 XM_013936436 XM_013938589

ID ID Contig Contig Contig Day versus Night Tide versus High Low 23185 999 84677 22396 132 1065 9027 50915

Table 2.4: Reads Per Kilobase Per Million Mapped Reads (RPKM) for common circadian and control genes. The absolute fold change is shown for two conditions, day/night and high/low tide. Gene name - either homologous gene title identified through BLAST or designated name given based on percent identity to similar gene variants; Contig ID - contiguous sequence number given to a particular gene during assembly; RPKM values given for four individual animals (Begin Day High Tide, Day Low Tide, Day Between Tides and Begin Night High Tide); Absolute Fold Change Day/Night - the RPKM difference between Begin Day High Tide and Begin Night High Tide; Absolute Fold Change High/Low - difference between Begin Day High Tide and Day Low Tide.

RPKM Values Evening, Absolute Absolute Day High Day Low Tide Night High Gene Name Contig ID Low Tide Fold Change Fold Change Tide (0800) (1530) Tide (2030) (1800) Day/Night High/Low Circadian, Core Period A 12233 12.71 12.35 13.72 13.33 1.0 1.0 Period B 11892 6.87 4.49 5.92 6.26 1.1 1.5 Period C 13653 1.71 1.76 1.59 1.39 1.2 1.0 Clock 16515 10.65 10.97 9.84 11.28 1.1 1.0 Cryptochrome 1 3916 7.26 6.23 5.84 7.08 1.0 1.2 Cryptochrome 2 28249 4.92 6.18 6.12 6.34 1.3 1.3 Cycle 1 41238 1.84 0.43 1.20 1.75 1.1 4.3 Cycle 2 44233 0.40 0.12 0.19 0.13 3.1 3.3 Timeless 11686 5.28 2.85 3.43 4.09 1.3 1.9 Circadian, Accessory Timeout 19500 1.31 2.44 2.50 1.15 1.1 1.9 Double-time 12630 6.56 6.04 5.85 7.21 1.1 1.1 Vrille 35732 2.24 2.17 1.73 3.68 1.6 1.0 Casein Kinase Iα 2872 30.43 31.78 42.33 34.62 1.1 1.0 Casein Kinase IIα 8473 20.85 25.43 19.52 24.40 1.2 1.2 Casein Kinase IIβ 14285 16.29 16.82 15.39 17.24 1.1 1.0 Casein Kinase Iε 2873 17.76 20.04 14.61 18.69 1.1 1.1

Circadian, Regulatory PDHR/CRHR 25298 1.56 4.68 1.14 1.06 1.5 3.0

Control CRP 43896 0.36 0.38 0.36 0.36 1.0 1.1 Tubulin 11001 20.01 19.91 20.58 24.00 1.2 1.0 UBQ 243 407.00 408.00 335.00 407.00 1.0 1.0 SDHA 3186 124.00 139.00 142.00 129.00 1.0 1.1 SYN 23151 13.41 13.53 13.53 13.39 1.0 1.0 Actin 1 310 462.00 316.00 371.00 544.00 1.2 1.5 Actin 2 1424 224.00 137.00 162.00 184.00 1.2 1.6 Actin 3 1190 460.00 317.00 265.00 527.00 1.1 1.5 Actin 4 1173 366.00 346.00 379.00 381.00 1.0 1.1 Actin 5 334 1085.00 841.00 1169.00 1129.00 1.0 1.3 Actin 6 339 682.00 426.00 635.00 829.00 1.2 1.6 Actin 7 1232 1042.00 811.00 979.00 1046.00 1.0 1.3

CHAPTER 3

DAILY AND TIDAL EXPRESSION ANALYSIS OF LIMULUS CLOCK, CYCLE, TIMELESS AND NPCRYPTOCHROME mRNA

1. Introduction

The horseshoe crab, Limulus polyphemus, is important to human medicine, coastal marine environments, and the study of biological rhythms. Ecologically, horseshoe crabs are considered a keystone species both because of their foraging behavior, in which they overturn sediment and bring nutrients to the surface for a variety of marine organisms (Kraeuter and Fegley, 1994), and because their eggs serve as a crucial food source, particularly for migratory birds (Karpanty et al. 2006). Additionally, the blood of horseshoe crabs is harvested from hundreds of thousands of animals annually and is used to detect contaminants in medical supplies and products (Novitsky, 1984). This assay, which is derived from amoebocytes found in Limulus blood is known as the Limulus amoebocyte lysate (LAL) assay and is an industrial standard for pharmaceutical drug development and manufacturing. Lastly, this species has become a model species for the study of biological rhythms, in particular the circadian rhythm of eye sensitivity (Barlow, 1983). The Limulus lateral eye exhibits a robust circadian pattern of eye sensitivity, which is thought to be driven by a circadian clock within the brain (Barlow et al., 1977b; Kass and Barlow, 1992). While the molecular mechanisms for this clock are not known, they have been characterized in another invertebrate, the fruit fly, Drosophila melanogaster. In this species there are four “core” circadian genes including period, clock, cycle and timeless (Takahashi, 1993; Glossop et al., 1999; Bae et al., 2000). These genes and their protein products, along with several “accessory” genes, regulate their own transcription through negative feedback loops (Figure 1.1) (Akten et al., 2003; Nawathean and Robash, 2004; Cyran et al., 2005). Throughout the course of a day the abundance of several of these genes’ mRNA and protein products rise and fall creating a daily cyclical pattern of expression (Edery et al., 1994a) and driving locomotion (Nitabach and Taghert, 2008) via the release of the neurohormones neuropeptide factor F (NPF) and pigment dispersing factor (PDF) (Renn et al., 1999; Taghert and Shafer, 2006). In addition to circadian rhythms, Limulus also exhibit circatidal rhythms of locomotion, which have a shorter fundamental period (12.4h) (Chabot et al., 2007). During mating season, Limulus exhibit these rhythms in the field when they breed and deposit eggs in the sediment at high tides and when foraging in the intertidal zone from April to November (Rudloe, 1980; Lee, 2010). In the laboratory, these tidal locomotor rhythms persist in constant conditions indicating the presence of an endogenous clock system that controls this behavior (Chabot and Watson, 2010). However, the molecular mechanisms have not been identified for the clock controlling circatidal rhythms in Limulus or any other species (Chabot et al., 2016).

One purpose of this study was to investigate whether four of the core genes known to be part of the circadian clock in several arthropods (clock (clk), cycle1 (cyc1), npcryptochrome2 (npcry2), and timeless (tim)) may cycle in Limulus as they do in the Drosophila circadian system. A second purpose was to investigate whether these genes may have been duplicated and neofunctionalized, to become part of the Limulus clocks controlling circatidal rhythms. Similarities between homologs of these genes that have been recently identified in the Limulus genome and transcriptome and Drosophila suggest that they may play some role within Limulus endogenous clocks (Chesmore et al., 2016). A third purpose was to determine which parts of the horseshoe crab central nervous system (CNS) express mRNA of these putative circadian clock genes and to determine if any of these putative genes are modulated by photoperiod or tidal phase. Finally, we set out to identify control genes that may be constitutively expressed across varying photoperiodic and tidal conditions that could be used in this and future QPCR experiments. Our results suggest that mRNA for the putative circadian genes clk, cyc1, npcry2, and tim are present in all Limulus CNS tissues that were examined (protocerebrum, subesophageal ganglion, and ventral nerve cord) and that, interestingly, they do not vary significantly across conditions of photoperiod or tidal phase. In addition, the gene synaptotagmin was identified as a suitable “housekeeping control gene” for QPCR experimentation because it appears to be constitutively expressed during high and low tides as well as day and night in the CNS.

2. Methods

2.1. Animals and Environmental Conditions

2.1.1. Putative Clock Gene Expression in the CNS. Two male horseshoe crabs were used to initially determine the relative expression of putative core circadian clock mRNA in the three sections of the Limulus CNS (protocerebrum - PRO - including the cheliceral ganglion], subesophageal ganglion – SEG, and ventral nerve cord - VNC). Crabs were purchased from the Marine Biological Laboratory (Woods Hole, Cape Cod, MA) in early September and placed into artificial salt water holding tanks (Instant Ocean, Sea Salt Mix, SS15-10; 30 practical salinity units, psu) at 17±2°C. Animals were placed on a 12:12 light-dark cycle and allowed to acclimate for 7 days. Light intensity during the day averaged 4.2 µMol·m²·sec while light intensity during the night was 0 µMol·m²·sec. Animals were sacrificed at 0200 hours (mid-dark) and the entire CNS of each animal was excised and separated into three sections, the protocerebrum (PRO), the subesophageal ganglion (SEG), and the ventral nerve cord (VNC; Figure 3.1). Tissues were weighed and placed into the appropriate volume of ice cold Trizol® Reagent (1 mL/100 mg; Ambion, Cat. 15596-018). Samples were homogenized by hand for five minutes using 3 mL glass/Teflon homogenizers. The homogenate was aliquoted into several sterile 1.5 mL microcentrifuge tubes, capped, and stored at -80°C for later use in RNA extraction (described below).

2.1.2. The Effects of Time of Day and Tidal Phase. In order to investigate the effects of time of day and activity phase on mRNA levels, an additional 32 adult male (n=23) and female (n=9) horseshoe crabs were purchased from the Marine Biological Laboratory in mid-December and held in six 55-gallon tanks filled with artificial seawater (Instant Ocean, Sea Salt Mix, SS15-10; 30 psu). Pairs of tanks were equipped with a set of two pumps and an overflow tube that enabled water to be pumped back and forth between the tanks (Figure 3.2). Water pumped between tanks was gravity filtered through Bioball® (CoraLife, Franklin, Wisconsin) and fiber-pad filters. The water pumps were turned on and off every six hours using timers to simulate tidal water fluctuations. This created high tides at 0730 and 1930 hours that alternated with low tides at 0130 and 1330 hours in each tank (high tide = ~1 meter depth, low tide = ~0.2 meter depth). Animals were also exposed to a 12:12 light-dark cycle (lights on at 0730; lights off at 1930). Light intensity during the day averaged 4.2 µMol·m²·sec, while light intensity during the night was 0 µMol·m²·sec. Animals were maintained in these conditions for ten days prior to dissection. Animals were dissected under eight unique conditions (n=4): begin light (0800) high tide (BL-HT), begin light (0800) low tide (BL-LT), mid-light (1400) high tide (ML-HT), mid-light (1400) low tide (ML-LT), begin dark (2000) high tide (BD- HT), begin dark (2000) low tide (BD-LT), mid-dark (0200) high tide (MD-HT) and mid-dark (0200) low tide (MD-LT). Animals dissected during the dark phases were dissected under red light (λ<560nm). Since putative clock mRNA expression levels between tissue types was inconclusive, the PRO was used to investigate the effects of tidal and LD phases as it is the suggested location of the circadian clock (Kass and Barlow, 1992). Tissues were handled and processed as described above.

2.2. Tissue Dissection and RNA Extraction. The frozen samples were thawed on ice and centrifuged at 10,000 x g for 10 minutes at 4°C. The supernatant was removed and placed into new, sterile tubes. The supernatant was held at room temperature (~23°C) for 5 minutes, after which, 0.2 mL of chloroform was added per 1 mL of Trizol® (as described above). The tubes were then vortexed for approximately 15 seconds and incubated again at room temperature for 3 minutes. The samples were then centrifuged at 12,000 x g for 15 minutes at 4°C. The aqueous phase was removed and placed into a new sterile 1.5 mL microcentrifuge tube with 0.5 mL of isopropanol for every 1 mL of Trizol®. The samples were incubated at room temperature for 10 minutes and then centrifuged at 4°C for an additional 10 minutes at 12,000 x g. The supernatant was removed and discarded. The remaining pellet was washed with 1 mL of 75% ethanol per 1 mL of Trizol® and then vortexed (5 seconds) and centrifuged at 7,500 x g (5 minutes at 4°C). The wash was discarded and the tubes were left open, placed in a holding rack, inverted and suspended at an angle and allowed to dry for 20 minutes. The pellets were re- suspended in 100 µl of nuclease-free water (Cat. 129112, Qiagen, Venlo, Netherlands), incubated in a 55°C water bath for 15 minutes, and then stored at -80°C.

Samples were later treated with TURBO™ DNase (Cat. AM1907, Ambion, Carlsbad, California) per the manufacturer’s protocol. Briefly, 20 µl of DNase buffer and 2 µl of TURBO DNase was added to 100 µl of sample and gently shaken by hand. The samples were then incubated at 37°C for 30 minutes after which 20 µl of DNAase inactivation reagent was added and the samples vortexed. The tubes were incubated for 5 minutes, and the tubes were hand “flicked” every 1.5 minutes. The samples were then centrifuged at 10,000 x g for 1.5 minutes and the supernatant transferred to a new tube. Samples originating from the same tissue were thoroughly mixed and used for subsequent analysis. RNA concentrations were determined using a Qubit Fluorometer (model Q32866, Invitrogen, Carlsbad, California) per the manufacturer's recommendations. RNA integrity was confirmed using gel electrophoresis. cDNA libraries were created using iScript™ cDNA Synthesis Kit (Cat. 170-8891, BioRad, Hercules, California) in 40 µl reactions. Libraries were diluted to a final concentration of 7 ng/µl with Nuclease-free water.

2.3. QPCR Setup and Run Parameters. Primers for all genes were designed using Primer-3 (MIT, v 0.4.0) with an amplicon size range of 120 to 140 base pairs (bp), and an optimum melt temperature of ~55°C (+/- 4°C). SYBR® Green Supermix (170-8880, Biorad) was used to quantify relative gene expression in conjunction with a Biorad CFX96 thermocycler. Reactions were set up with 10 µl of Supermix, 4 µl of cDNA (28 ng), 1 µl of each primer (10 µM) and 4 µl of nuclease-free water. Reactions were run using the following protocol: 95°C for 3 minutes followed by 40 cycles of 95°C for 15 seconds, 55°C for 30 seconds, and 72°C for 30 seconds. Sample pools and reagent kits whose no template control (NTC) or "no reverse transcriptase" (-RT) indicated amplification were not used for analysis.

2.4. QPCR Data Analysis. Reaction efficiencies for each sample (each with six replicates) were calculated using DART-PCR (Peirson et al., 2003) and only replicates with efficiencies of 90 percent (±5%) were used for analysis. Although differences in efficiencies of 5% can lead to a doubling of amplification product after only 26 PCR cycles, anomalies generated by efficiency variations were tested for and removed as outliers (Peirson et al., 2003). The theoretical starting fluorescence (R0), which is a metric that is proportional to the amount of starting template (i.e. quantity of mRNA for a given n gene), was calculated using the formula R0 = RCt (1 + E) where Ct is the threshold cycle, RCt is the fluorescence at the threshold cycle, E is the efficiency of the reaction and n is the cycle number.

2.5. QPCR Central Nervous System Tissue Selection.

Theoretical starting fluorescence values (R0) were calculated from technical replicates (N=6) with comparable efficiencies (90%±2.5). Technical replicates were

51 averaged and statistical outliers (determined as ±1.5 multiplied by the interquartile range, IQR) were removed. Means ±SEM were then used to compare relative expression.

2.6. QPCR Control Gene Selection.

Theoretical starting fluorescence values (R0) were calculated from triplicates of each of the six potential control genes (synaptotagmin, c-reactive protein, tubulin, TATA-binding protein, succinate dehyrogenase complex subunit A, and ubiquitin) across each of the eight experimental conditions. Average R0-values for each gene and condition were then compared to every other condition for that gene. For example, the average synaptotagmin R0-value for a single condition (ex. BL-HT) was divided by every other average synaptotagmin R0-value for every other condition to get eight “relative ratios” for synaptotagmin BL-HT (e.g. BL-HT/BL-LT, BL-HT/ML-HT, etc). This was repeated for each condition for synaptotagmin and every other gene. These “relative ratios” were then averaged for each gene and each condition. Relative average mRNA changes (± SEM) were used as a comparative metric to determine the gene with the least variance across all conditions.

2.7. QPCR Effects of Photoperiod and Tidal Phase on Putative Circadian Genes. Because synaptotagmin (syn) was found to vary the least across all conditions, it was used as a reference gene for normalization of expression of the genes of interest (clock (clk), cycle1 (cyc1), timeless (tim), and npcryptochrome2 (npcry2)). Thus, each gene of interest was run with a synaptotagmin control across all eight conditions (each condition with four experimental replicates) with six technical replicates for both the gene of interest and the control gene. For each plate (experimental replicate) synaptotagmin (syn) and gene of interest R0 values were averaged, the standard error was calculated, and statistical outliers (±1.5 IQR) were removed. The syn R0 values were then used to normalize the gene of interest R0 values so a comparison between plates could be made. Two-way Analysis of Variance (ANOVA) was used to investigate differences of tidal phase (high/low) as well as photoperiod (day/night).

3. Results

3.1. No Statistically Significant Differences in Putative Circadian Gene mRNA Expression in Various Regions of the Limulus Central Nervous System. Limulus putative npcryptochrome2 (npcry2), clock (clk) and cycle1 (cyc1) mRNA was expressed in the PRO, SEG and VNC. While expression seemed to be highest in the SEG (Figure 3.3), the difference in expression between the three tissues was not statistically significant for npcry2 (F(2,13) = 2.07, p = 0.17, clk (F(2,12) = 2.58, p = 0.12) or cyc1 (F(2,13) = 0.9, p = 0.43 ). The PRO, which included the cheliceral ganglia, was chosen for continued investigation due to its potential involvement with the circadian clock (Kass and Barlow 1992).

3.2. Synaptotagmin can be used as an Endogenous Control Gene for QPCR Analysis of the Effects of Photoperiod and Tidal Phase on mRNA Expression Synaptotagmin (syn), ubiquitin (ubq), c-reactive protein (crp), succinate dehydrogenase complex subunit A (sdha), tubulin (tub), and TATA-binding protein (tbp) were investigated and evaluated as potential control genes over conditions of photoperiod and tidal phase. Results indicate that synaptotagmin and ubiquitin had the lowest expression variance across all conditions including high/low tide as well as conditions of day/night with near identical relative expression values (1.1X ±0.14 and 1.1X ±0.13, respectively). However, because ubiquitin has been shown to be a post- transcriptional modulator of some circadian clocks (Lee et al., 2008), synaptotagmin was used for the normalization and comparison of our genes of interest across photoperiodic and tidal conditions.

3.3. No Statistically Significant Effects of Photoperiod or Tidal Phase on the Expression of Limulus Putative Circadian Gene mRNA. Neither time of day nor tidal phase had a statistically significant effect on the mRNA expression levels of cycle1, timeless, npcryptochrome2 or clock (Figure 3.4). Cyc1 had an overall relative change of 9.4% for conditions of tidal phase (high versus low tide; F(1,21) = 0.00, p = 0.95) and 110.7% for conditions of photoperiod (light versus dark; F(1,21) = 2.49, p = 0.13). The overall relative change for Tim expression was 45.8% for tidal phase (F(1,27) = 1.17, p = 0.29) and 16.1% for photoperiod (F(1,27) = 0.28, p = 0.59). Clk had an overall relative change of 33.3% for conditions of tidal phase (F(1,19) = 1.54, p = 0.23) and 16.8% for conditions of photoperiod (F(1,19) = 0.62, p = 0.44). The overall relative change for npcry2 had a calculated relative change of 0.9% for tidal conditions (F(1,23) = 0.11, p = 0.74) and 24.5% for photoperiodic conditions (F(1,23) = 2.29, p = 0.15).

4. Discussion

The lack of statistically significant daily rhythmic expression on Limulus putative circadian transcripts is atypical of most studied circadian systems. Cyclical mRNA expression of the circadian clock genes period, timeless, clock, and npcryptochrome is seen in insect circadian systems like the fruit fly, Drosophila melanogaster, with relative expression changes between 5 and 10-fold. Similar results are seen in the monarch butterfly, Danaus plexippus, though with lower relative expression changes (between 2 and 3-fold; Claridge-Chang et al., 2001; Zhu et al., 2008). Additionally, mammals such as the house mouse, Mus musculus, and the golden hamster, Mesocricetus auratus, display daily rhythmic patterns of expression for many circadian genes including per1, per2 and bmal (a cycle homologue; Ueda et al., 2002). Interestingly, most circadian systems that exhibit these cyclical patterns of expression also have a subset of circadian genes that are constitutively expressed (Rutila et al., 1998; Maywood et al., 2003). Thus, while clock, cycle1, npcryptochrome2, and timeless exhibit constitutive expression in Limulus, our

53 findings do not rule out the possibility that other putative circadian genes are cyclically expressed in this species. Constitutive expression of a subset of core circadian genes is not uncommon. The Drosophila circadian gene cycle, for example, has no apparent circadian or daily pattern of expression (Rutila et al., 1998). Likewise, the clock gene in the African clawed frog, Xenopus laevis, is constitutively expressed in the clock cells of their retinal photoreceptors (Zhu et al. 2000). Interestingly, Drosophila mutants that have been designed which generate per mRNA constitutively and maintain circadian rhythms of both the PERIOD protein product and locomotor activity (Cheng and Hardin, 1998). Another type of circadian clock mechanism is seen in cyanobacteria, which are able to maintain robust circadian periodicity without the rhythmic expression of any of their respective core clock genes’ mRNA (kaiA, B and C; Nakajima et al., 2005). Thus, it is possible that, in Limulus, the constitutive mRNA expression for the investigated putative clock genes may be indicative of a clock system that relies more heavily (or completely) on post-transcriptional mechanisms (PTMs) rather than the rhythmic abundance of mRNA transcripts. PTMs have been shown to be critical for both the circadian and tidal rhythms, even within organisms that seem to have circadian and tidal clocks mechanisms that differ. For instance, RNAi knockdowns of certain core circadian genes in the sea louse, Eurydice pulchra, will not affect the tidal rhythm, while disruptions to certain PTMs like phosphorylation by the kinase, CK1ɛ, will affect both the tidal and circadian rhythm (Zhang et al., 2013) suggesting that substrate phosphorylation is an important timing mechanism in clocks controlling both circadian and circatidal rhythms. If, however, the Limulus clock systems rely predominately on PTMs, this would be novel among animals since transcriptional-translational feedback loops have been identified as the clock mechanism in all animal species for which the mechanism is known (Tomioka and Matsumoto, 2010; Partch et al., 2014; Carre, 2016). It remains possible that some of the genes not included in this study, (period variants a, b and c and timeless) may cycle with regard to photoperiod or tidal phase and, as cited below, the temporal expression of these genes should be investigated. While constitutive mRNA expression of clock genes may be a normal part of some circadian clock mechanisms, the constitutive expression observed in our study could also be caused by the “masking” (or diluting) of the signals from a small set of clock cells that express these mRNAs rhythmically by much larger numbers of cells that express these genes constitutively. Putative circadian gene mRNA expression was observed in all tissues of the Limulus CNS (Figure 3.3). This could indicate that all or many of the cells of the CNS are transcribing these genes. If only a small number of clock cells are expressing putative clock genes rhythmically, then it’s possible that surrounding (non-clock) cells are diluting the rhythmic mRNA signal from those clock cells. However, the circadian clocks of Drosophila has been localized to only about 150 neurons per brain (Nitabach and Taghert, 2008) and, yet, studies that extract mRNA from their whole head containing well more than 100,000 neurons, which include per expressing non-clock cells, (Ewer et al., 1992; Chiang et al., 2011) still yield detectable and significant rhythms of clk, per, and tim mRNA. This suggests

54 that, at least in Drosophila, rhythmic circadian gene expression is not susceptible to dilution by surrounding cells (Hardin, 1994; Plautz et al., 1997). An apparent constitutive expression of these genes could also be observed if the two known endogenous clock systems masked each other’s signal. These clocks, the circadian clock and the circalunidian clocks, control the circadian rhythm of eye sensitivity and the circatidal rhythm of locomotion, respectively (Watson et al., 2008). First proposed in 1986 by Palmer and Williams, the circalunadian clock hypothesis states that there are two tidal clocks that run at periods of 24.8 hours and 180 degrees out of phase with each other (Palmer and Williams, 1986). These clocks effectively create bouts of activity every 12.4 hours, are able to synchronize to oceanic tides, and account for phenomena seen in the lab with tidal organisms such as spontaneous “skipping” and “splitting” of activity rhythms (Rossano et al., 2009 and Chabot et al., 2016). If, because of duplication and neofunctionalization, the same putative clock genes are part of the mechanisms of the circadian clock and tidal clocks and they are out of phase with each other (a situation that would be common for the control of tidal rhythms in areas with two tides/day), this could produce an apparent constitutive expression profile for putative clock gene mRNA. However, there is some evidence that these two clocks use different genes in at least two intertidal species. In the mangrove cricket, Apteronemobius asahinai, when clock and period mRNA signals are suppressed using RNAi knockdown, the circadian, but not the tidal rhythm, is affected (Takekata et al., 2012; Takekata et al., 2014b). Similar results are seen in the sea louse, Eurydice pulchra (Zhang et al., 2013). While these findings suggest different circadian and circalunidian clock mechanisms, it is also possible that the different clock systems rely on different components. Thus, clock and period may be crucial for the circadian clock but not the circalunidian clock in these species. The role and relative importance of the other core circadian genes to the circalunidian clock remains to be determined. In our experiment, we found transcripts of all of the putative circadian genes in question in all three parts of the central nervous system. Because the PRO contains the cheliceral ganglia, an area of the brain hypothesized to contain the circadian clock that modulates eye sensitivity (Kass and Barlow 1992), we chose to perform a more careful quantification of these transcripts on this part. However, the results were the same: constitutive expression of all four of the putative clock genes was seen in both LD and tidal cycles (Table 2.4; Simpson et al., 2017). However, it is still possible that some subsections of the CNS contain circadian or circatidally rhythmic clock gene mRNA levels expressed in clock cells that are diluted or masked by non-clock cells. Thus, overall our investigation leaves open a number of possible avenues through which to investigate the molecular mechanism and location of the circadian and circalunidian clocks: (1) The SEG and the VNC, both of which express putative Limulus circadian mRNA, should be more thoroughly investigated; (2) the mRNA expression profiles of other putative circadian genes should be measured; (3) new genes of interest that cycle with a circatidal or circadian frequency should be identified and investigated; and 4) groupings of clock oscillators should be systemically isolated and highly targeted expression analysis within those regions should be performed.

CHAPTER 3 FIGURES

Figure 3.1: Diagram of Limulus central nervous system showing divisions for the protocerebrum (PRO) including the cheliceral ganglion (black circles), subesophageal ganglion (SEG) and the ventral nerve cord (VNC).

Figure 3.2: Tidal tanks that allowed animals used in these experiments to be exposed to both LD and tidal cycles. To control water level, water pumps were controlled by timers such that alternating tides were created in adjacent tanks every six hours. (see methods for additional details).

Figure 3.3: Relative mRNA expression of the putative Limulus clock genes cryptochrome2 (cry2), clock (clk) and cycle1 (cyc1) in the protocerebrum (PRO), subesophageal ganglion (SEG) and ventral nerve cord (VNC). Values = means +/- SEM. 20

15 cry2 clk 10 cyc1

5 Relative mRNA Expression Expression mRNA Relative 0 PRO SEG VNC Tissue Type

Figure 3.4: A lack of effects of photoperiod and tidal phase on the expression of timeless, cycle1, cryptochrome2 and clock mRNA in the protocerebrum of Limulus (P>0.13). Black and white bar above the graph indicates conditions of light and dark. High tide conditions (water depth ~1 meter) - blue line, low tide conditions (water depth ~0.2 meter) - red line.

20:00

14:00 14:00

8:00 8:00 Time of Day Time

Clock Cycle 1 Cycle 2:00 2:00

5 4 3 2 1 0 5 4 3 2 1 0

Figure 3.4Figure

Tide High Tide Low 20:00 20:00

14:00 2

8:00 8:00 Time of Day Time

2:00 2:00 Cryptochrome Timeless

5 4 3 2 1 0 5 4 3 2 1 0

Relative Fold Change Change Fold Relative Change Fold Relative

CHAPTER 4

GENERAL DISCUSSION

1. The First Limulus Transcriptome is Sequenced and Assembles More Completely than the Genome

With the completion of this work, the first Limulus polyphemus transcriptome has been sequenced and is now available for further investigation. This transcriptome represents the expression of many or most of the mRNAs within the Limulus central nervous system and will help to foster additional molecular studies including further investigations of the molecular mechanisms of circadian and circatidal rhythms. Additionally, these data will help to garner insights into the comparative evolution of this species and help towards the overall annotation of the genome. In particular, assembly of the transcriptome provides over ninety three thousand contiguous sequences and provides more putative protein sequences than that of the genome assembly. BUSCO (Benchmarking Universal Single-Copy Orthologs) completeness analysis scored the transcriptome as being 55.5% more complete than the genome. Of the 429 universal orthologs used in BUSCO analysis, only 25.2% were identified in the genome assembly while the transcriptome assembly identified 80.7% (Simão et al., 2015). Additionally, the transcriptome mapped back to the genome with 88.3% of the transcriptomic contigs present within the genome (Figure 2.3). Although the genome assembly had nearly twice the amount of reads than the transcriptome (6.2X10^8 for the genome and 4.8X10^8 for the transcriptome, Table 2.1), the genome assembly seems to be resistant to BUSCO analysis. However, the BUSCO pipeline was able to complete its operation suggesting that the poor quality was not due to an incomplete analysis but to other factors. Since BUSCO analysis was able to identify 80.7% of the universal orthologs within the transcriptome and the great majority of the transcriptome is present within the genome, the universal orthologs searched for by BUSCO are most likely within the genome but the genome assembly is likely too fragmented for BUSCO to identify the sequences. This is possibly due to the large size of the genome and its high polymorphic composition (Nossa et al., 2014; Richards and Murali, 2015). The current estimated genome size for Limulus is 2.7 Gb (Goldberg et al., 1975; Nossa et al., 2014), while the current genome assembly calculated a genome size of 1.5Gb. The Limulus genome shows evidence of multiple whole-genome duplication events and appears to contain a large number of highly repetitive sequences (Nossa et al., 2014). These characteristics may have facilitated the collapse of many repetitive genes and sequences into single sequences, causing the genome size to look artificially small while also indirectly inhibiting BUSCO analysis. Full annotation and structural analysis of the organization of the Limulus genome will help to clarify these findings.

2. Most Environmentally Responsive Genes are Novel

The majority of genes found in the Limulus transcriptome that respond to environmental elements (photoperiod and tidal phase) are novel, as they are not represented in the current genetic databases and are unique to Limulus. When transcripts with a >10-fold expression and a p-value <0.05 for conditions of photoperiod and tidal phase were extracted from the transcriptome assembly and BLASTed against the NCBI database, only a fifth came back with an identifiable hit (Table 2.3). The water flea, Daphnia pulex, has also been shown to have a suite of environmentally responsive genes that have no homologs in any currently available database, suggesting a heavy reliance of these organisms on the duplication and manipulation of various genes to suit their needs and respond to harsh environmental conditions (Colbourne et al., 2011). Even without directly identifying the transcripts we were able to look at general expression trends and group sequences into gene ontology terms to get an understanding of what the general expression profile of Limulus transcripts looks like with respect to photoperiod and tidal phase. This has given additional insight into the nature of their genetic response to environmental changes and will allow for the comparison of these transcript profiles to other tidal organisms.

3. Transcript Expression is Predominately Tidal

Overall transcript expression for the entire central nervous system between conditions of high/low tides and light/dark seems to indicate a higher pattern of differential expression when looking at high tide versus low tide, as opposed to light versus dark. There were nearly a third as many transcripts that had a difference in expression ≥2-fold when comparing tidal rather than daily differences (Figures 2.4- 2.6). Although the experiment was designed as a preliminary examination of mRNA structure and content and thus did not produce statistically significant differences, there are some interesting comparisons that can be made between some gene ontology groups. For instance, the expression of genes that are involved in structural molecule activity is three times greater when comparing differing tidal conditions as opposed to differing daily conditions (Figure 2.8). This trend holds true when looking at a number of different molecular functions and cellular components, including transducer activity, receptor activity, structural molecule activity (Figure 2.8), and genes involved in the extracellular region (Figure 2.9). However, it is possible that these transcript profiles are the result of individual variances of transcript expression rather than a difference due to daily or tidal conditions. Validation of these findings will require higher statistical power using more animals over more standardized conditions.

4. Putative Circadian Genes are Expressed in all Three Sections of the Central Nervous System

Putative circadian transcripts were expressed in the protocerebrum, subesophageal ganglion and ventral nerve cord of the Limulus central nervous system.

Putative circadian transcripts were also found among RNA-seq results from preparations of whole central nervous systems (Table 2.4). There was, however, some variability in transcript expression. The protocerebrum appeared to show the highest mRNA abundance of the circadian gene cycle1 (Figure 3.3) while the subesophageal ganglion appeared to show the most cryptochrome2 and clock mRNA (albeit with very large variance - Figure 3.3). If cells in the central nervous system other than pacemaker cells are expressing these transcripts, then it may become more difficult to elucidate both differential expression of various clock genes as well as the location of various biological clocks due to a smaller “signal to noise” ratio. Expression of these genes are observed in non- clock cells in other species. The fruit fly, Drosophila melanogaster has, in addition to the pacemaker cells, glial-like cells covering the surface of the medulla portion of their optic lobe that express the PERIOD protein and presumably period mRNA (Ewer et al., 1992). However, in this species as well as others, extracts from whole heads show significant rhythms of period and other clock transcripts even when the clock cells that are expressing those rhythms comprise less than 0.15% of the cells harvested (Hardin, 1994; Plautz et al., 1997). Immunohistochemical results are also able to differentiate between the well-defined clusters of pacemaker neurons and the more general and evenly spread PER expressing glial cells (Helfrich-Förster, 1995). In order to determine if these genes are being expressed by cells all throughout the Limulus central nervous system or if their expression is localized to a few clusters, it will be necessary to perform additional localization experiments using antibodies generated against Limulus proteins or in situ probes against clock mRNA. As long as the core proteins are the same as found in other animal species, this will help to identify the location of the circadian clock(s) within the Limulus central nervous system.

5. A Lack of Daily or Tidal Effects on Putative Circadian Gene mRNA Expression

The majority of putative core and accessory circadian clock gene mRNAs appear to be constitutively expressed in the Limulus central nervous system across daily and tidal conditions (Table 2.4). However, there were two transcripts that had absolute fold changes greater than 2.0. Cycle1, for instance, had an absolute fold change of 3.0 when comparing daily conditions while cycle2 had an absolute fold change of 3.1. Cycle2 also had an absolute fold change of 4.3 when comparing tidal conditions (Table 2.4) suggesting that the transcription of these gene variants warrants further examination. While several circadian systems have at least one core circadian gene whose mRNA is constitutively expressed (Dunlap, 1999), no known animal circadian system has fewer than two core circadian genes whose mRNA cycles with respect to time of day (Siepka et al., 2007; Tovin et al., 2012; Partch et al., 2014). In an attempt to address the potential for interference from surrounding non-clock cells expressing our genes on interest and the possibility of multiple clocks, we focused on the protocerebrum (PRO; Figure 3.1), the suggested location of the circadian clock (Kass and Barlow, 1992), and on a subset of putative circadian genes using QPCR.

When the expression of putative core clock genes clock, cycle1, timeless, and cryptochrome2 were investigated in the protocerebrum using QPCR, we again found no statistically significant daily cycling of those transcripts (Figure 3.4). However, timeless expression appeared to be down-regulated (2-fold) during high tide and up- regulated (2-fold) during low tide (Figure 3.4). Similar results are seen from head extracts (excluding the VNC) from the housefly, Musca domestica, for both cycle and cryptochrome; however, other circadian genes were found to cycle with a daily frequency (Codd et al., 2007). Thus, both of the techniques that we used (RNAseq and QPCR) indicate that these genes are constitutively expressed across conditions of photoperiod in the entire CNS and in the isolated protocerebrum. There are several alternate hypotheses for these findings: these transcripts are constitutive in clock cells and (1) the Limulus clock systems do not require mRNA cycling; (2) or these genes are not involved with one or more of the clock systems of Limulus; on the contrary, these transcripts are not constitutive in clock cells but only appear to be so because (3) there are multiple clocks that are out of phase with each other, thus masking rhythmic expression; (4) the expression of these genes by surrounding non-clock cells, mask the rhythmic signal. Lastly, (5) post-transcriptional mechanisms may be responsible for driving the clocks. While some of the above hypotheses are more difficult to test, there is some evidence that the clocks controlling circatidal and circadian rhythms have similar mechanisms. For example, the phosphorylating kinase, CK1ε, seems to be a molecular component of both the circadian and tidal clocks in the sea louse, Eurydice pulchra, as its inhibition affects both rhythms (Zhang et al., 2013). On the contrary, period and clock RNAi exposure disrupted only the circadian rhythm and not the circatidal rhythm in the intertidal mangrove cricket, Apteronemobius asahinai, (Takekata et al., 2012; Takekata et al., 2014b). Likewise, molecular disruptions of the circadian rhythm of Eurydice pulchra left their circatidal swimming behavior unchanged. Taken together, the results suggest that both that the circadian and tidal clocks may share some (but not all) of their molecular components and that mRNA cycling may not be necessary for the maintenance of circatidal rhythms in these species. If Limulus clocks do share some molecular components then this may contribute to a type of “masking” of rhythmic mRNA by other rhythms of the same mRNA out of phase with one another. For example, although they are homologs, this type of “masking” is seen in the expression of cryptochrome genes within the circadian clock of the African clawed frog, Xenopus laevis, which run at opposite 180 degree phases with one another and, thus, appear constitutive (Zhu et al., 2001). To determine whether or not there is this type of “masking” by multiple clocks or if rhythmic mRNA expression is necessary for these clocks, it is essential to determine if the investigated genes are involved with the circadian as well as the tidal clocks. Specifically, RNAi should be used to determine (1) if circadian gene mRNA is necessary for the tidal clock in Limulus and (2) if the inhibition of those transcripts affects the circadian clock. The possibility that post-transcriptional mechanisms contribute to Limulus clock systems is not unusual as many organisms also use post-transcriptional

64 modifications along with transcriptional regulation. For instance, retinal photoreceptors of Xenopus laevis express nocturnin (a deadenylase) with a circadian period. The rhythmic expression of this enzyme, which targets mRNA for degradation, suggests its regulatory involvement with the circadian clock (Baggs and Green, 2003). Additionally, the Chinese tussah silk moth, Antheraea pernyi, uses antisense inhibition to maintain PER protein oscillation rather than rely on PER nuclear translocation (Sauman and Reppert, 1996). Other types of post-transcriptional modifications such as rhythmic polyadenylation and RNA methylation have also been shown to be involved circadian regulation of rhythmic protein expression (Kojima et al., 2012; Fustin et al., 2013). Post-transcriptional events like those described above could have evolved to play a major role in Limulus clock systems due to the potential need of putative circadian genes to function with different timing and stimuli in more than one type of endogenous oscillator. However, there is no known animal clock system that relies primarily on post-transcriptional mechanisms in place of rhythmic transcript expression (Partch et al., 2014).

6. Potential Outputs of Limulus Clock Systems

The mRNAs of the neurotransmitter neuropeptide F (NPF) and its receptor (NPFR), both of which have orthologs in the Limulus transcriptome and are thought to be involved in the output from the circadian clock of Drosophila (Lee et al., 2006; Chesmore et al., 2016), were found to vary by 1.4 and 1.2-fold, respectively. Similar results were seen in Drosophila brains under light and dark conditions (NPF maximum change=1.75-fold and NPFR1 maximum change=1.5-fold; He et al., 2013). The presence of these transcripts and their apparent similarity to the fold changes seen in Drosophila mRNA suggests a potential for their involvement in one or more of the Limulus clocks. Another identified output form the Drosophila circadian system, pigment- dispersing hormone (PDH) (Renn et al., 1999), was not found among the Limulus CNS transcripts. However, a homolog for PDH receptor was identified. The detection of the receptor and not the neuropeptide may not be surprising since the receptor is large (~3,000bp) while the neuropeptide is very small (~200bp) and could be easily missed in a transcriptome assembly. Importantly, the receptor was found to vary as much as 2-fold with respect to time of day and tide in the current study. In Drosophila PDHR expression is required for the expression of circadian rhythms and the presence of this gene in Limulus again suggests that this receptor and its ligand may be an output from one or more of their clocks (Im and Taghert, 2010). In Drosophila PDH neuropeptide is also vital to the maintenance of circadian rhythms since the destruction of cells containing PDH also disrupts circadian rhythms (Hermann et al., 2012). Another receptor, the corticotropin releasing hormone (CRH) receptor, was identified with a 3.0-fold change in expression between conditions of tidal phase and a 1.5-fold change between conditions of photoperiod (Table 2.4). CRH is a neurotransmitter and stress hormone found primarily in vertebrates but also in

65 molluscan haemocytes (Ottaviani et al., 1998). When not released due to physical stressors, CRH oscillates with a circadian frequency, which is modulated by the suprachiasmatic nucleus (SCN) acting on the hypothalamo-pituitary-adrenal (HPA) axis in mammals (Chan and Debono, 2010). While thus far the evidence supporting the importance of these hormones and receptors is just suggestive, their importance should be further investigated to determine their involvement (if any) in the clock systems of Limulus.

7. Summary

The presence of core circadian clock gene homologs within the Limulus genome and CNS transcriptome suggests their potential involvement with one or more of the Limulus clocks. When the mRNA expression of circadian clock gene homologs was examined in the whole CNS using RNA-seq (all putative circadian genes), and in the protocerebrum using QPCR (clock, cycle1, timeless and cryptochrome2), the results showed no statistically significant cycling of those mRNAs with respect to time of day or tidal phase (Table 2.4 and Figure 3.4). These results differ from most published findings on animal circadian systems, although cyanobacteria have a circadian system devoid of daily rhythmic mRNA (Nakajima et al., 2005). If this is true, there are five possible explanations. (1) There is the possibility that two tidal oscillators and a circadian oscillator are confounding our ability to observe rhythmic mRNA expression; (2) or that non-clock cells are expressing these transcripts constitutively and thus “diluting” our measurements. On the other hand, (3) rhythmic expression of mRNA may not be necessary for these clocks in Limulus (4) and these clocks may be heavily dependent on PTMs. (5) Lastly, these genes may not be involved with these endogenous clocks in Limulus. Looking forward, it’s critical to further examine light:dark effects on mRNA and protein levels from all of the clock genes within the tissues described in this study. These results should help to determine if the Limulus circadian clock system is either like other arthropod systems in that some of the mRNAs cycle or if they exhibit constitutive expression and are truly unique. Similar experiments to look at proteins and mRNA from these circadian genes under different tidal conditions and in highly specific sub-sections of the central nervous system may also help to delineate, for the first time, the mechanisms and location of a clock system that drives tidal rhythms.

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