Ultraviolet Sunscreen on the Coral Reef: from Coral to Fish

ULTRAVIOLET SUNSCREEN ON THE CORAL REEF: FROM CORAL TO FISH

A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI`I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

ZOOLOGY

MAY 2012

By Frederique L. M. Kandel

Dissertation Committee:

Robert Kinzie III, Chairperson Kim Holland David Jameson Florence Thomas Robert Toonen

Keywords: MAA, Mycosporine‐like Amino Acid, Sunscreen, Ultraviolet Radiation, Fish, Coral DEDICATION

To my wonderfully loving, supportive and quirky family, especially: Irene, Aimee, Jeanne, Guy, Muriel, Olga, Georges, Christian, Annouck, Thaleia, George and the ones still to appear…

ACKNOWLEDGEMENTS

The work described in this manuscript has benefited from the contributions of many. Dr. Robert Kinzie III has offered his mentorship: precious advice, insights, support, and a great deal of his time from the inception to the end of this project. Dr. Florence Thomas, Dr. Kim Holland, Dr. Robert Toonen, Dr. David Jameson have provided very helpful comments, reviews and much appreciated encouragements. The field component of the project was made possible by the remarkable work ethic, good humor and friendship of Mindy Mizobe (dive buddy and research assistant). The laboratory portions of this work took place in several locations and much is owed to the expertise and logistical support from: Dr. Ruth Gates and her team, Hawaiì Institute of Marine Biology (HIMB)‐ Dr. Kenia Whitehead, University of Washington‐ the HIMB core facility team: Mindy Mizobe, Amy Agger, Chris Farrar‐ Dr. Bidigare’s lab at HIMB and UH Mānoa: Fenina Butler and Stephanie Christensen‐ Dr. David Jameson, UH Mānoa‐ Dr. Rappe’s lab, HIMB. Administrative and technical support was given by: the Zoology Department at UH Mānoa, HIMB, the UH foundation, the UH Mānoa Environmental Health & Safety Office, the University of Hawaiì Diving Safety Program. Staff members at HIMB have greatly assisted, especially the Residents and Security teams who gave many late nights and holidays boat rides to and from Coconut Island. Additionally, Marc Rice from Hawaiì Pacific Academy and Captain Chuck graciously offered boat time on the Big Island. Funding for this work was provided by: the National Science Foundation Pre‐ doctoral Fellowship program, Seagrant Hawaiì and the Living Ocean foundation. Many additional faculty members, colleagues and friends have provided inspiration, intellectual stimulation, help and/or moral support, amongst them: Bo Alexander, Dr. John Ambrose, Mitra Ahdieh, Dr. Charles and Mrs Birkland, Jessica Berryman, James Brown, Dr. Brian Bowen, Dr. Julie Brock, Dr. Pierre and Tania Dutrieux, Dr. Megan Donahue, Charlie Ebel, Elin and Fred Farrell, Helen Fujimoto, Dr. Ruth Gates,

Dr. John Godwin, Dr. Gordon Grau, Dr. Beth Hawkins, Dr. Tetsuya Hirano, Dr. Jo‐Ann Leong, Pricilla Oshiro, Dr. Ernie Reese, Dr. John Stimson, Dr. Andrew Taylor, Dr. Andre and Lucia Seale, Dr. Timothy Tricas, Dr. Jill Zamzow. Finally, my parents Jeanne Gautrot and Guy Kandel, as well as my sister and brother Muriel and George Kandel, and my partner George Losey all have helped in more ways than can be recounted here. To all, please, receive my heartfelt gratitude.

iii

ABSTRACT

Mycosporine‐like amino acids (MAAs) are UVR‐absorbing compounds ubiquitous in the aquatic environment. Metazoans appear to obtain them from their diet or from symbiotic microorganisms. The study of MAAs has been complicated by the lack of commercially available standards. Use of tandem mass spectrometry renders this problem tractable. My dissertation exploited the availability of previously published MAA fragmentation patterns to identify MAAs in the mucus of the corallivorous territorial butterflyfish, Chaetodon multicinctus. I next adapted both a powerful HPLC separation procedure and a tandem mass spectrometry technique to not only identify, but also quantify, MAAs in the absence of standards. With this method I examined MAAs through the trophic chain in C. multicinctus and its coral prey, Pocillopora meandrina, Porites compressa and Porites lobata. Specimens from three depths provided non‐overlapping levels of downwelling UVR irradiance to investigate if the four species studied modulated their MAA content with exposure level. I found that the MAAs present in Chaetodon multicinctus epithelial mucus are different from the MAAs in their diet: some, like mycosporine‐glycine, abundant in the coral diet, are not detectable in fish mucus and some, present in the epidermal mucus such as palythene and usujirene, are not found in the diet. These results are consistent with prior research on fish eyes, sea urchins, and pteropods. They suggest the possibility of selective uptake, translocation and transformation of MAAs by metazoan consumer organisms. The details of these processes are unknown. MAA concentrations in the three coral species decreased with increasing depth and the associated UVR reduction. This pattern was, however, not observed in the fish probably due to a ceiling effect. A model is proposed to illustrate this possibility. The corals also showed an interesting difference in MAAs diversity perhaps due to

iv differences in their Symbiodinium: Porites spp. known to have low diversity of symbionts also had a low diversity of MAAs compared to Pocillopora meandrina that harbors a greater diversity of zooxanthellae. My work also resulted in the addition of several MS2 patterns useful for identification. Further, the discovery of up to four new MAAs suggests the value of continuing to investigate these intriguing metabolites.

TABLE OF CONTENTS DEDICATION ...... i ACKNOWLEDGEMENTS ...... ii ABSTRACT ...... iv LIST OF TABLES ...... viii LIST OF FIGURES ...... ix CHAPTER 1: GENERAL INTRODUCTION ...... 1 CHAPTER 2: IDENTIFICATION OF SUNSCREEN COMPOUNDS IN FISH MUCUS...... 7 2.1 Introduction ...... 7 2.2 Methods ...... 11 2.2.1‐Field work, Study species and location ...... 11 2.2.2. Laboratory work ...... 11 2.2.2.a‐MAA extraction ...... 11 2.2.2.b‐MAA identification ...... 12 2.2.2.c‐Relative quantification ...... 13 2.3 Results ...... 14 2.3.1‐MAA identification ...... 14 2.3.2 Relative abundance ...... 16 2.4 Discussion ...... 17 CHAPTER 3: METHOD DEVELOPMENT ...... 20 3.1 Introduction ...... 20 3.2 Background ...... 21 3.3 Approach and Troubleshooting for HPLC Separation, Tandem Mass Spectrometry And Instrument Malfunctions ...... 23 3.4 Quantification: Empirical Testing Of An Extension Of Beer‐Lambert Law ...... 36 3.5 Conclusion ...... 43 CHAPTER 4. NATURAL SUNSCREEN COMPOUNDS IN THREE SPECIES OF CORAL AND THEIR FISH PREDATOR ...... 44 4.1 Introduction ...... 44 4.2 Methods ...... 47 4.2.1 Field work ...... 47 4.2.1.a Study species and location: ...... 47 4.2.1.b Sampling ...... 48 4.2.1.c Downwelling spectral irradiance measurements ...... 48 4.2.2 Laboratory work: Chemical identity of UVR blocking in fish mucus and their coral prey ...... 49 4.2.2.a MAA extraction ...... 49 4.2.2.b HPLC and tandem mass spectrometry ...... 50 4.3 RESULTS ...... 52 4.3.1 Measurements of relative downwelling UVR irradiance...... 52 4.3.2 Coral: Qualitative results for 3 species ...... 53 4.3.2.a Pocillopora meandrina ...... 53

4.3.2.b Porites lobata ...... 57 4.3.2.c Porites compressa ...... 57 4.3.3 Fish qualitative results ...... 59 4.3.4 Corals quantitative results...... 62 4.3.5 Fish quantitative results ...... 65 4.4 DISCUSSION ...... 66 4.4.1 Qualitative analysis ...... 66 4.4.2 Quantitative analysis...... 69 4.4.3 Conclusion ...... 72 CHAPTER 5: CONCLUSIONS ...... 73 LIST OF REFERENCES ...... 77

vii

LIST OF TABLES

Table 2.1. HPLC gradient protocol for MAA separation……………………………………… 13

Table 3.1. Gradient protocol for MAA separation as per Carreto et al. 2005….…… 23

Table 3.2. Problems and solutions…………………………………….………………………………. 30

Table 3.3. Gradient protocol for MAA separation with Kinetex columns…………… 31

Table 3.4. MAAs retention times ………………………………….….……………………….……… 32

Table 4.1. Gradient protocol for MAA separation with Kinetex columns …………… 51

Table 4.2. Order of elution and retention times of MAAs……………………………..... 51

Table 4.3. MAAs presence – absence summary………………………………………………… 61

viii

LIST OF FIGURES

Figure 2.1 A. Aminocyclohexenone molecular structure……………………………………. 8 Figure 2.1 B. Aminocyclohexenimine molecular structure……………………………….. 9 Figure 2.2. Spectrophotometric scan of an extract of C. multicinctus mucus…... 14 Figure 2.3. Fragmentation patterns for asterina and palythene……………………….. 15 Figure 2.4 A. Relative abundance of MAAs with depth: asterina and palythene 16 Figure 2.4 B. Relative abundance of MAAs with depth: all ions……………………….. 17 Figure 3.1. Picture of the pooling and boiling solvent ……………………………………… 24 Figure 3.2 A. Chromatograms before addition of an auxiliary pump ……………….. 28 Figure 3.2 B. Chromatograms after addition of an auxiliary pump …………………… 28 Figure 3.3 A. HPLC separation of a mixture of samples using formic acid instead of TFA ……………………………………………………………………………………… 29 Figure 3.3 B. Separation of the same mixture using TFA…………………………………… 29 Figure 3.4 A. UV absorbance and Mass chromatograms for an extract of Coral trout ocular lens Plectropomus leopardus…………….……………………… 33 Figure 3.4 B. Mass of each of the four MAAs……………………………………………………. 33 Figure 3.4 C. Wavelength maximum of each MAA ……………………………………. 34 Figure 3.4 D. MS2 fragmentation pattern for (a): palythine……………………………... 34 Figure 3.4 E. MS2 fragmentation pattern for (b): asterina ………………………………. 35 Figure 3.4 F. MS2 fragmentation pattern for (c) usujirene………………………………….. 35 Figure 3.4 G. MS2 fragmentation pattern for (d) palythene ……………………………….. 35 Figure 3.5. Palythine concentration. Comparing values computed from Equation 1 with values obtained from Equation 2 (Beer’s Law) ...………………..……………. 38 Figure 3.6. Palythine concentration. Comparing values computed from Equation 3 with values obtained from Equation 2 (Beer’s Law) …...…….…….……………… 39 Figure 3.7. Porphyra concentration. Comparing values computed from Equation 1 with values obtained from Equation 2 (Beer’s Law) …………………………….…. 40

Figure 3.8. Porphyra concentration. Comparing values computed from Equation 3 with values obtained from Equation 2 (Beer’s Law)……………………………. 40 Figure 3.9. Porphyra concentration vs. absorbance ………………………………………….. 41 Figure 4.1. Relative downwelling irradiance…...... 53 Figure 4.2. Typical Chromatogram of Pocillopora meandrina……………………………. 54 Figure 4.3 Chromatogram for the rare P. meandrina found in the deep territories……………………………………………………………….. 54 Figure 4.4. MS2 spectrum of mycosporine‐2 glycine……………………………………..…. 55 Figure 4.5. MS2 spectrum of mycosporine glycine……………………………………………. 56 Figure 4.6. MS2 spectrum of the putative palytine‐serine……………………………….… 56 Figure 4.7. MS2 spectrum of the putative mycosporine‐glycine‐NMA‐Thr……….. 57 Figure 4.8. Typical chromatogram for Porite lobata…………………………………………. 58 Figure 4.9. Typical chromatogram for Porites compressa ……………………………….. 58 Figure 4.10 A. Chaetodon multicinctus chromatograms: all MAAs…………………… 59 Figure 4.10 B. Chaetodon multicinctus chromatograms: selected MAAs…………. 60 Figure 4.11. MAAs quantitative analyses for Pocillopora meandrina………………..62‐63 Figure 4.12. MAAs quantification for Porites lobata………………………………………… 64 Figure 4.13. MAAs quantification for Porites compressa…………………………………. 64 Figure 4.14. MAAs quantification for Chaetodon multicinctus………………………….65‐66 Figure 4‐15. Model of MAA accumulation in fish mucus…………………………………… 71

CHAPTER 1: GENERAL INTRODUCTION

Naturally occurring solar radiation includes short‐wavelength, high‐energy Ultraviolet Radiation (UVR, wave lengths <250 ‐ 400nm). The shortest, most energetic part of this radiation (UVC <250 – 280nm) is absorbed by Earth's atmosphere, but longer wavelength radiation (UVB 280‐320nm, and UVA 320 – 400nm) does reach Earth's surface and has long been known to be responsible for an array of chemical and biological processes (Jones & Kok 1966, Moss & Smith 1981, Bühlmann et. al 1987, Lesser 2000, 2008, Karsten et al. 2009, Dahms & Lee 2010, Dahms et. al. 2011 ) some of which are known to affect ecological systems (Jokiel 1980, Worrest et al. 1981, Teramura 1983, Hobson & Hartley 1983, Wood 1987, Bothwell et al. 1994, Kinzie et al. 1998, Banaszak & Neale 2001, Wahl et al. 2004, Hansson & Hylander 2009, Nahon et al. 2011). Ultraviolet radiation has some beneficial roles in living organisms such as the UVB‐mediated conversion of vitamin D in our skin or UVA activation of photolyase, an enzyme that repairs DNA damage caused by UVB (Cockell & Knowland 1999). Moreover, UVA vision in various species of insects, fish and lizards is essential for processes such as foraging and mate recognition (McFarland & Loew 1994, Losey et al. 1999, Losey 2003, Cockell 2000, Knuttel & Fiedler 2001, Garcia & de Perera 2002, Smith, E J et al. 2002, White et al. 2003, Aviles et al. 2006, Leech & Johnsen 2006, Leech et al. 2009, Rick et al. 2012). However, most known UVR effects on living organisms are detrimental. UVR can cause DNA damage, corneal and epithelial inflammation and sunburn (Siebeck 1988, Martinez‐Levasseur 2011), as well as immunosuppression (Schwarz & Schwarz 2011). These effects are known to promote directly, or indirectly, certain types of cancer such as melanomas in humans and other mammals (Garibyan & Fisher, 2010). Ultraviolet radiation can also cause inhibition of photosynthetic primary production in plants (Jones & Kok 1966, Teramura et al. 1980, Teramura 1983, Gold & Caldwell 1983, Strid et al.

1990), reduction of microbial motility (Sommaruga et al. 1996, Sommaruga & Buma 2000) and cell death induction (Godar 1999, Kulms et al. 1999). The long‐held belief that UVR attenuates too rapidly in the water column to affect aquatic organisms was challenged since the mid‐20th century (Jerlov 1957, 1976). Irradiance measurements in clear oceanic water have shown that 1% of the surface UVA at 375nm wavelength was still present at depths over 100 meters. The shorter wavelength UVB attenuates more rapidly, yet the 1% attenuation depth for wavelengths of 290nm was found to be as deep as 20 meters in the same waters (Smith & Baker 1981). The need to cope with UVR has led to the evolution of various protective mechanisms in marine organisms. Damage‐prevention strategy can be behavioral such as negative phototaxis observed in various species of ciliates, water fleas (Daphnia sp.) and copepods (Barcelo & Calkins 1979) or covering behavior in some species of urchins (Lenci et al. 1997, Storz & Paul 1998, Adam 2001, Verling 2002). Protection from UVR can also be conferred by the use of UVR blocking compounds. Melanization (sun tanning) has been observed in some cartilaginous fishes (Lowe & Goodman‐Lowe 1996) as well as Daphnia sp. (Hebert & Emery, 1990). Many cyanobacteria secrete scytonemin, a lipid‐soluble protective substance absorbing at 370nm (Garcia‐Pichel & Castenholz 1991). Additionally, a family of compounds called mycosporine‐like amino acids (MAAs) with absorbance maxima from 320nm to 360nm is found ubiquitously in marine organisms including cyanobacteria, dinoflagellates, copepods, symbiotic corals and even in fish eyes (reviewed in Carreto & Carrignan 2011). Recently, while studying UV vision in fish, Zamzow & Losey (2002) discovered that the mucus of the reef fish, spotted toby (Canthigaster jactator), absorbed UVR. Zamzow & Losey (ibid.) subsequently surveyed 137 species of reef fishes and found that the mucus of 90% of the species studied absorbed UVR. Additionally, the mucus absorbance correlated with depth of capture in three of species of fish studied. Furthermore, the mucus absorbance of Pacific Tidepool Sculpins (Cottidae: Teleostei),

decreased with higher latitude. Zamzow (2003a, b) hypothesized, but did not chemically verify, that the sunscreen properties of the mucus were due to MAAs. In addition to their strong UV absorbing properties, the sunscreen role of MAAs has been corroborated by various studies. Investigation of three MAAs has confirmed that the photo‐excitation of the MAA molecules generates no, or very low, fluorescence and the energy is dissipated harmlessly, mainly in form of heat (Conde et al. 2000, 2004, 2007). The concentration of MAAs decreases with depth and associated UVR exposure for several (but not all) species of corals and some macroalgae (Dunlap et al. 1986, Shick et al. 1995, Teai et al. 1997, 1998, Karsten et al. 1998a, Banaszak et al. 1998, Kuffner 1999, Lesser 2000, Corredor et al. 2000, Torregiani & Lesser 2007). The protective role of MAAs has been experimentally confirmed for several organisms. Mycosporine‐like amino acids defend the dinoflagellate Gyrodinium dorsum against UVB‐induced loss of motility and death (Klisch et al. 2001). Ultraviolet radiation causes a delay in the cleavage of the embryo of the sea urchin, Strongylocentrotus droebachiensis, but the delay is significantly reduced for embryos rich in MAAs (Adams & Shick 1996, 2001). Until recently, metazoans were not believed to be able to synthesize MAAs de novo because they lacked the necessary enzymatic pathways (Shikimate pathway, Shick et al. 1999, Shick & Dunlap 2002). Instead, symbiotic organisms such as scleractinian corals were thought to acquire MAAs from their dinoflagellate symbionts (Banaszak et al. 1995, b, 2000, 2006, Ishikura et al. 1997, Schick et al. 1999, Shick & Dunlap 2002, Shick 2004). Other consumer organisms such as echinoderms, krill and copepods appeared to acquire MAAs from their diet (Caroll & Shick 1996, Bandaranayake 1999, Newman et al. 2000, Whitehead et al. 2001). In fish, Mason et al. (1998) showed that when medaka, (Oryzias latipes) were given a diet either with or without MAAs, the levels of MAAs in their ocular tissues rose or fell, respectively. Additionally, Zamzow (2004) showed that if captive coral reef wrasses, Thalassoma duperrey, exposed to UVR were fed a man‐made diet either containing or devoid of Acanthophora spicifera, an algae rich in MAAs, the absorbance of the mucus increased for the fish given the A. spicifera but it decreased for the fish given a diet lacking the algae.

Traditionally, chemical analysis of MAAs has been achieved by separation of methanolic extracts using high precision liquid chromatography (HPLC). Identification and quantification is accomplished by comparing time of elution, absorbance maxima and peak area with those of MAA standards. This approach has several drawbacks: First, some MAAs are difficult to separate, and are similar enough in their absorption maxima, that they can be misidentified (Whitehead et al. 2001). More importantly, MAA standards are not available commercially and their fabrication is complex and costly (Carreto & Carignan 2011). The lack of readily‐available standards for many MAAs has impeded progress toward understanding the biology and biochemistry of these compounds (Whitehead & Hedges 2002). In view of this situation, Whitehead & Hedges (2003) obtained standards of seven common MAAs from the marine environment. They used tandem mass spectrometry (MS/MS or MS2) to fragment each MAA and record its fragmentation pattern (MS2spectrum). They presented the unique fragmentation patterns of these seven MAAs as a practical tool to identify them in the absence of standards. Others have since published the MS2spectra of several additional MAAs (Volkmann & Gorbushina 2006, Carignan et al. 2009). The purpose of my dissertation was first, to take advantage of the availability of this new method to identify UVR‐blocking compounds found in fish mucus. Second, I sought to follow each MAA through the trophic chain to determine if fishes sequester all, or only some, of the available blockers in their naturally occurring diet and whether fish can modulate sequestration according to the ambient UVR regime to which they are exposed. Additionally, we do not know if fishes also modify some of the MAAs consumed in order to use them as UVR blockers in the mucus. Such transformations are thought to occur in coral symbioses where phototrophically‐derived MAAs (called primary MAAs) are transformed into secondary MAAs by the animal host (Shick 2004, Banaszak et al. 2006). In chapter 2, I showed that the mucus of the butterflyfish, Chaetodon multicinctus, contained at least two main MAAs: asterina (absorption maximum 330nm)

and palythene (absorption maximum 360nm). I also examined whether the fish decreased the ratio of the longer versus the shorter wavelength MAAs as the UVR exposure changed in shallower water and found no evidence of a shift. This first project also emphasized the limitation of the initial method and called for methodological improvement that could add quantitative capability to MAA analysis. Chapter 3 describes the modification and adaptation of an efficient HPLC separation technique for use in conjunction with mass spectrometry. This led to a broadly‐applicable method that allows researchers to obtain both reliable identification and reasonably accurate quantification of MAAs in biological samples in the absence of standards. Chapter 4 provides the identities and concentrations of MAAs found in the reef habitat for an obligate corallivorous butterflyfish, Chaetodon multicinctus, and the three species of corals that form its diet: Porites compressa, Porites lobata, Pocillopora meandrina. Specimens were sampled from three different depths that provided non‐ overlapping levels of downwelling UVR irradiance levels. I found that P. meandrina, a species known to harbor various clades and subclades of zooxanthellae symbionts (Stat et al. 2009) presented a greater diversity of MAAs than either of the more symbiont‐specific Porites spp. Several of the major MAAs present in the coral did not appear at all in the fish mucus. Conversely, two long‐wavelength maximum MAAs, usujirene and palythene, not observed in the corals, were found in the fish mucus as well as traces of two possible unknown MAAs. Additionally, the concentration of the main MAAs detected in the corals decreased with depth and the concomitant reduction in UVR irradiance, a trend congruent with a sunscreen role for the compounds in coral. Surprisingly, the fish MAAs concentration did not show any significant trend with depth. As a side‐benefit, the MS2analysis made available the previously‐undescribed fragmentation patterns of several MAAs. Taken together, these findings lend some support to the theory that the MAA make‐up of symbiotic organisms is affected by the diversity of their symbiont

community. The results also strongly suggest a selective uptake of MAAs by Chaetodon multicinctus and the ability to not only translocate, but also to possibly transform the dietary MAAs. The mechanisms behind these processes remain unknown at this time. With the recent surprising discovery that MAA biosynthesis does not necessarily involve shikimate pathway intermediates, the story of these crucial and ubiquitous compounds in the marine world has just begun to be told.

CHAPTER 2: IDENTIFICATION OF SUNSCREEN COMPOUNDS IN FISH MUCUS. 2.1 Introduction

The epidermal mucus of bony fishes is thought to serve many functions (reviewed by Shephard 1994). These include, but are not limited to:  Reduction of drag to facilitate swimming by modifying boundary layers,  Lubrication to protect from abrasion and facilitate escape from predators,  Ion and water regulation and  Resistance to diseases by repelling various microorganisms as the mucus sheds or by harboring protective compounds. More recently while studying vision in fish, Zamzow & Losey (2002) discovered that the mucus of the coral reef fish, Canthigaster jactator, absorbed ultraviolet radiation (UVR). They went on to survey 137 species of coral reef fish and found that the mucus of 90% of these species absorbed UVR. Additionally, the mucus of two of the three species analyzed in detail showed a bathymetric effect. In these two species, the integrated absorbance of the mucus decreased, and the center of absorbance shifted towards longer wavelengths with increasing depth of capture. This follows the attenuation of UVR in the marine environment that is known to decrease with depth, with shorter wavelength attenuating more rapidly (Jerlov 1956, 1976). Further work on captive fishes showed that if the animals were protected from UVR by plastic screens, the UV absorbance of their mucus decreased relative to the absorbance immediately after capture (Zamzow 2004). Results presented by Zamzow & Losey (2002) provided evidence of a previously unnoticed function of fish epithelial mucus, sunscreen. They hypothesized, but did not verify, that the compounds responsible for the sunscreen properties were likely mycosporine‐like amino acids (MAAs). This is a family of over 25 compounds commonly found in cyanobacteria (Sinha & Häder 2008), dinoflagellates (Carreto et al. 1990, Lesser

1996, Banaszak et al. 2006), phytoplankton (Whitehead & Vernet 2000) and several of their invertebrate predators (Whitehead et al. 2001, Riemer et al. 2007, Hylander & Jephson 2010), red algae (Hoyer et al. 2002), coral (Shick et al. 1999, Yakovleva et al. 2004, Ferrier‐Pagés et al. 2007), echinoderm eggs and embryo (Adams & Shick 1996, 2001, Lesser 2010), and fish eggs and eyes (Mason 1998, Sinha et al. 2007, Carreto & Carrignan 2011). Mycosporine‐like amino acids are amino‐cyclohexenimine or amino‐ cyclohexenone rings linked to an amino‐acid or amino‐alcohol group (Bandaranayake 1998, Karentz 2001, Singh et al. 2008), their basic structure is presented in Figure 2.1. The idea that MAAs can serve as UVR filters comes from several lines of evidence. It was first inferred because of the environmentally relevant range of their absorption maxima, between 320 and 360 nm and their very high extinction coefficient (ε up to ~5000 M‐1 cm‐1) (reviews by Bandaranayake 1998, Karentz 2001, Whitehead & Hedges 2002). Additional substantiation came from in vitro studies of the MAAs shinorine, porphyra and palythine that have shown that after photo‐excitation, the energy is mainly dissipated in the form of heat (Conde et al 2000, 2004, 2007). O

OCH3

NH H O OH

Figure 2.1 A. Aminocyclohexenone molecular structure. R is methyl sulphonic acid or methanoic acid

N‐R1

OCH 3

NH R3 ‐O OH

Figure 2.1 B. Aminocyclohexenimine molecular structure. R1 can be various alcohols and/or carboxylic acids, R2 can be a hydrogen, methyl group or carboxylic acids, R3 can be a hydrogen or sulphonide hydroxide.

In the field, the concentration of MAAs decreases with depth and associated UVR exposure for several species of corals and some macroalgae (Dunlap et al. 1986, Shick et al. 1995, Teai et al. 1997, 1998, Karsten et al. 1998, Banaszak et al. 1998, Lesser 2000, Corredor et al. 2000, Torregiani & Lesser 2007). Other studies have also demonstrated experimentally a UVR protective role for MAAs. For instance UVR causes a delay in the cleavage of the embryo of the sea urchin Strongylocentrotus droebachiensis, but the delay is significantly reduced for embryos rich in MAAs (Adams & Shick 2001). Mycosporine‐like amino acids also protect the dinoflagellates Gyrodinium dorsum against UVR‐induced damage in the UVB range (280nm to 315nm wavelength) (Klisch et al. 2001). Taken together, these observations have firmly established MAAs as a major natural sunscreen substance in marine organisms. Traditionally these compounds are extracted from samples using methanol and then separated using high precision liquid chromatography (HPLC). The identity of each compound is determined by comparing the wavelength of maximum absorption and elution time of its chromatographic peak, with those of MAA standards. However, this method has several drawbacks. First, there are no commercially available standards for

MAAs (Whitehead & Hedges 2003, Carretto & Carignan 2011). Second, some MAAs are difficult to separate with HPLC and have the same or similar wavelength maxima, which can lead to identification errors. For instance HPLC indicated the presence of the MAA mycosporine‐glycine‐valine (MGV) in a pteropod and its predator, but a more detailed analysis revealed that it was not MGV but palythenic acid (Whitehead et al. 2001) Having obtained standards for seven common MAAs found in the marine environment, Whitehead & Hedges (2003) used electrospray ionization‐mass spectrometry (ESI‐MS) and tandem mass spectrometry (MS2) to characterize these MAAs. Details of their procedure are given in the methods section of this chapter. They published the specific fragmentation pattern (MS2spectrum) of each the following MAAs: asterina, palythene, palythenic acid, palythine, palythinol, porphyra, and shinorine. They proposed that their published characterizations would allow researchers to use tandem mass spectrometry for the identification of these MAAs without the need for standards. The main goal of the work presented in this chapter was to apply their method to establish whether the UVR absorbing compounds observed in the epidermal mucus of a reef fish were indeed MAAs and, if so, to identify them. Secondly, if the main function of the observed MAAs is UVR protection, one could expect a depth effect for fish whose territory covers a narrow depth range since UVR decreases with depth, with shorter wavelength attenuated first (Jerlov 1976). This chapter did not seek to provide an absolute MAA quantitation. However it allowed testing if the mucus absorbance shifted towards longer wavelengths with depth by examining change in the relative abundance of MAAs of shorter and longer wavelength maxima. This work sought to test two hypotheses: Hypothesis one: A methanolic extract of fish mucus will contain a mixture of MAAs. Hypothesis two: Assuming hypothesis 1 is confirmed, and the MAAs can be identified, then there will be a change in the MAA composition of the mucus of the fish

studied with depth, such that MAAs that absorb shorter wavelengths would be more prevalent in shallower water.

2.2 Methods

2.2.1‐Field work, Study species and location

Our model species is the territorial and obligate corallivorous butterflyfish, Chaetodon multicinctus. This study required a location where C. multicinctus was easily accessible and abundant. These criteria were met at our study site, Puakō on the Kona coast of Hawai`i Island. Mated pairs of this species live in temporally stable territories averaging 90m2 (from 75m2 to 207m2, Tricas 1986), and consequently spend their day within a limited depth range. We selected pairs of fish in three non‐overlapping bathymetric ranges 1.5m to 4.57m (5 to 15 feet), 7.62m to 10.67m (25 to 35 feet) and 15.24m to 18.29m (50 to 60 feet). Each pair was captured by scuba divers using a barrier and hand nets. Fish were placed in a bucket and slowly brought to a boat where their mucus was collected using a dulled scalpel blade as per Zamzow (2003a, b). The mucus samples were first placed immediately in scintillation vials, kept on dry ice, then freeze‐dried in a Labconco freeze drier and kept at ‐80°C until extraction. The fish were returned to their territory and released, unharmed, within less than an hour of capture.

2.2.2. Laboratory work

2.2.2.a‐MAA extraction

Extraction of fish mucus samples took place directly in the scintillation vials by adding 0.25ml of 100% aqueous HPLC‐grade methanol to the vial. After sonicating the samples on ice for 5 minutes (Fisher Scientific FS60H) the vials were placed in the dark at ‐10C for 6 hours. The obtained methanolic extracts were then transferred to a 1ml centrifuge tube and centrifuged at 5000g for 5 minutes at room temperature, and the supernatant was collected and placed in 250μl autosampler vials.

2.2.2.b‐MAA identification

Electrospray ionization‐mass spectrometry (ESI‐MS) has been a valuable tool in identification of proteins and polypeptides (Chait & Kent 1992, Aebersold & Goodlett 2001). Basically, the sample is dissolved in a solvent and passed through a nozzle to produce a plume of tiny highly‐charged droplets that are injected into a mass spectrometer chamber. For each molecule “M” of interest (here an MAA) the mass spectrometer gives the ratio between the mass (m) of the ionized (protonated) molecule [M+H]+ by its charge (z) (here for all MAAs z is +1ev). This m/z ratio corresponds to the “molecular mass plus one”. In effect, the mass spectrometer provides the molecular mass of any molecule of interest. Then with tandem mass spectrometry (MS2) an electromagnetic field is applied to isolate the protonated molecule (based on its m/z ratio). Collision energy, in presence of a collision gas (helium here), fragments the selected molecule. The resulting fragmentation pattern (MS2 spectrum) is the typical “fingerprint” of the initial protonated MAA (also called parent‐ ion). The MS2 spectrum is unique for each MAA except for the cis‐trans isomers, palythene and usujirene, that have the same fragmentation pattern and the E/Z forms of palythenic acid (Carreto et al. 2005). Whitehead & Hedges (2003) provided the MS2 spectra of seven common marine MAAs using two different types of mass spectrometers: a classical triple quadrupole and a more recent ion trap model. Dr. Whitehead generously agreed to share her experience and to do an initial analysis of some samples at the mass spectrometry facility of the University of Washington using their triple quadrupole. Although separation is not absolutely critical for identification in a mass spectrometer, when analyzing complex natural samples as studied here, it is advantageous and customary to use an HPLC system to attempt at least a partial separation of the compounds of interest. Dr. Whitehead suggested a gradient separation using a narrow‐bore (150 x 21mm), reverse‐phase HPLC column with a eluent mixture consisting of a solution A, water:formic acid (99:1) and B,

methanol:formic acid (99:1) running at 250 µl min‐1. The HPLC gradient protocol is given in Table 2.1. Table 2.1. HPLC gradient Protocol for MAA separation.

Time, min Flow, ul/min A, % B, % 0 250 98 2 1 250 98 2 5 250 75 25 7 250 60 40 12 250 45 55 14 250 40 60

For the mass spectrometry the instrumentation and method are identical to the description found in Whitehead & Hedge (2003). The instrumentation at the University of Washington included a Shimadzu HPLC system (Kyoto, Japan) with a dual wavelength detector, interfaced to a Micromass Quattro II (QHQ) tandem mass spectrometer (Micromass Ltd., Manchester, UK) controlled by MassLynx software 4.0. The UV wavelengths monitored by the HPLC were 310 and 330nm.

2.2.2.c‐Relative quantification

The MassLynx software was used to obtain the mass chromatograms and calculate the peak area for each ionized MAA. But, without standards, the area does not give a direct estimate of the concentration present in each sample since MAAs differ in their ionization coefficient. This could lead to an overestimation of MAAs that are highly ionizable relative to those MAAs that are not. However, a measurement of the relative peak area of the ionized MAAs in each sample is enough to examine whether the relative contribution of each MAA changed with depth. To do so the peak area of each of the observed MAA mass chromatograms was divided by the sum of the peak areas of all the MAAs and multiplied by 100.

2.3 Results

2.3.1‐MAA identification

Fish mucus methanolic extracts clearly showed an absorbance in the MAA range (Figure 2.2) and the MS chromatogram revealed the presence of an ion of m/z 289 and an ion of m/z 285. The corresponding MS2 spectra of the protonated molecule 289 and 285 are given in Figure 2.3.

Spectrophotometric reading of C. multicinctus extract

0.3

0.25

0.2

0.15

0.1 Absorbance (OD) 0.05

0 250 270 290 310 330 350 370 390 wavelength in nm

Figure 2.2. Spectrophotometric scan of an extract of C. multicinctus mucus.

The observed MS2 spectra corresponded to the fragmentation pattern of asterina and palythene published in Whitehead & Hedges (2003). All samples examined contained both of these MAAs. Upon detailed examination of the mass chromatograms, an additional ion of m/z 303 seemed to be present at a level slightly above background. The presence of this ion in the mass chromatogram also corresponded to a slight elevation in absorbance at 330nm as observed in the UVR chromatogram. This could indicate the presence of minute amounts of palythinol, an MAA of m/z 303 and wavelength maximum 330nm. We also observed a protonated ion of m/z 189. This is

not the mass of an MAA but could be deoxygadusol, a precursor of MAAs of molecular mass 188 amu (Karentz 2001, Shick & Dunlap 2002). Deoxygadusol has a wavelength maximum in the UVA range at 268nm and if present would not be detected on the two UV channels monitored (310nm and 330nm).

Fragmentation pattern of parent ion (m/z 289), asterina.

186

100

197

% 199 abundance 137 230 167 155 243 289 274

0 m/z Relative 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 m/z

Fragmentation pattern of parent ion (m/z 285), palythene.

197 100

241 138 % 224 149 abundance

270 285 205

0 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 m/z Relative

Figure 2.3. Fragmentation patterns for asterina and palythene. ESI‐MS/MS spectra (MS2 spectra) of the protonated parent molecules 289 and 285. They match the spectra of the MAAs asterina and palythene as described by Whitehead & Hedges (2003).

2.3.2 Relative abundance

We first calculated the relative peak area of the two main MAAs present, asterina and palythene, to evaluate a possible variation in their relative abundance. A change in their relative abundance with depth would result in a spectral shifting of the mucus since they have very different absorption maxima: 330nm for asterina and 360nm for palythene. Surprisingly, I did not find any difference in their ratio with depth (Figure 2.4 A). I then took into account the amount of the two other ions assuming they were indeed palythinol and gadusol. Again the relative abundance remained unchanged with depth (Figure 2.4 B).

Relative abundance profile of MAAs vs Depth

100 90 80 70 60 50 40 30 20 10 0 Relative abundance (%) Asterina% Asterina% Asterina% Palythene% Palythene% Palythene% deep Intermediate shallow

Figure 2.4 A. Relative abundance of MAAs with depth: asterina and palythene. The peak area of the mass chromatograms for asterina and palythene. The observed MAA mass chromatogram was divided by the sum of the peak areas of all the MAAs and multiplied by 100. This allowed observing if the relative contribution of each MAA changed with depth. The graphs indicate the mean plus or minus one standard deviation.

Relative abundance profile of MAAs vs Depth

90 80 70 60 50 40 30 20 10 0 Relative abundance (%) Asterina% Gadusol% Asterina% Gadusol% Asterina% Gadusol% Palythinol% Palythinol% Palythinol% Palythene% Palythene% Palythene% deep Intermediate shallow

Figure 2.4 B. Relative abundance of MAAs with depth: all ions. As per Figure 2.4 A except, In addition to asterina and palythene we included the contribution of the two other ions assuming they were palythinol and gadusol. 2.4 Discussion

The result of this first MS2 study confirmed Zamzow & Losey’s (2002) hypothesis that there were MAAs in fish mucus. This analysis was, at the time, the first positive identification of these MAAs. Others have since then corroborated this finding on other fish species using standards (Eckes 2008). Our results however raised further questions and issues. I did not observe the expected change in the ratio of longer versus shorter wavelength maximum MAAs with depth. Zamzow’s (2003a) work on the absorbance of the mucus of three fish species Thalassoma duperrey, Canthigaster jactator and Chaetodon multicinctus showed a statistically significant shift of the center of absorbance towards longer wavelength with depth of capture for T. duperrey. A similar trend for C. multicinctus was not seen in the original multivariate analysis but became significant when considering standard length and depth separately (Zamzow 2003a). Fish mucus is a complex matrix of glycoproteins (mucins) (Asakawa 1970, Fletcher et al. 1976, Shepherd 1994). It is possible that, if present, a spectral shift of the mucus could be due to compounds other than MAAs, and consequently would not be detected in the

present study. However, this is unlikely because the UVR absorption spectra of the mucus extract examined here and the spectra of the mucus in the Zamzow study are remarkably similar. Additionally, Shick & Dunlap (2002) argued that attempts to correlate underwater irradiance spectra with the presence of MAAs of specific absorbance maxima have been unconvincing. In contrast, rather than a qualitative shift with depth, a total quantitative change in MAAs with higher concentration in shallow water is a more common observation (Dunlap et al. 1986, Shick et al. 1995, Teai et al. 1997, 1998, Karsten et al. 1998a, Banaszak et al., 1998, Lesser 2000, Corredor et al. 2000, Torregiani & Lesser 2007). In the absence of standards, it is possible to obtain an accurate quantification of each MAA using the area of the corresponding UVR peak obtained at its absorption maximum. This would require an excellent separation. In the work presented in this first study the MAAs did not separate adequately. In order to explore a change of MAA concentration in fish mucus with depth further work was needed to combine mass spectrometry techniques used here and an effective HPLC separation method as done in chapters 3 and 4. After demonstrating the presence of MAAs in the fish’s mucus the question of dietary origin was considered. At the time of this study, it was believed that animals were not able to synthesize MAAs de‐novo (Favre‐Bonvin et al. 1976, 1987, Bandaranayake 1998, Shick & Dunlap 2002, Reimer et al. 2007). This belief was based on two lines of evidence. First, the precursors of MAA synthesis, gadusol and deoxy‐ gadusol, were thought to be produced from 3‐dehydroquinate (DHQ). DHQ is a derivative of the Shikimate pathway and metazoans were believed to lack this pathway (Favre‐Bonvin et al. 1976, 1987, Hirata et al. 1979, Cockell & Knowland 1999, Shick & Dunlap 2002). Second, several studies demonstrated that animals without MAAs producing algal or bacterial symbionts acquired MAAs from their diet and translocated them to various tissues. For instance Carefoot et al. (1998) showed that the amount and type of MAAs in the tissues of the sea hare Aplysia dactylomela were affected by their diet. Adams & Shick (1996) also showed a relationship between dietary MAAs and

ovarian MAAs in the urchin Strongylocentrotus droebachiensis. In fish, Mason et al. (1998) showed that, if given a diet with or without MAAs, levels of MAAs in the ocular tissues would rise or fall, respectively. Additionally, Zamzow’s (2004) experimental work showed that, for the coral reef wrasse Thalassoma duperrey, both UV exposure and a diet rich in MAAs were necessary to maintain or increase a high level of mucus absorbance. In conclusion, the results of this study raised both ecological and methodological questions. I then set out to answer the latter first, so as to be better able to address the former. To study the dietary and the bathymetric effect on the absolute concentration of MAAs I needed to obtain standards or develop a suitable method for quantification without standards. Dr. Michael Lesser kindly provided standards of palythine and porphyra. However, these two MAAs were not observed in C. multicinctus in this work although they might be present in trace levels. Consequently, the next step after this study was to develop a method that would allow both identification and quantification of the MAAs without recourse to standards. I set out to adapt an efficient separation technique for use on the LC‐MS system currently available at the Hawai`i Institute of Marine Biology. Such a method would be very useful as it should render the study of MAAs more generally accessible.

CHAPTER 3: METHOD DEVELOPMENT 3.1 Introduction

Mycosporine‐like amino acids (MAAs) are a family of over 25 compounds common in marine organisms that have been shown to act as very powerful sunscreens with absorption maxima between 319nm and 360nm, along with high extinction coefficients ‐up to ~50,000 M‐1 cm‐1‐ (Dunlap & Shick 1998, Karentz 2001, Singh et al. 2008, Rastogi et al. 2010, Carreto & Carignan 2011). The methods commonly used to study them involve HPLC separation of methanolic extracts, with identification and quantification based on comparing retention times, wavelength of absorption maxima and chromatographic peaks with those of standards. However, such analyses are difficult because commercially available standards are lacking (Whitehead et al. 2001, Whitehead & Hedges 2003, Hylander & Jephson 2010). Also, some MAAs are very difficult to separate with classical HPLC protocols (Carreto et al. 2005) which leads to misidentification (Whitehead et al. 2001, Carreto & Carignan 2011). Whitehead & Hedges (2003) proposed that electrospray ionization tandem‐mass spectrometry (ESI‐ MS2) is a suitable method to identify these MAAs in the absence of standards. With this technique each MAA is ionized (protonated), isolated, and fragmented and the specific fragmentation pattern or MS2 spectrum is used as a “fingerprint” to identify the original ion. Whitehead & Hedges (2003) have published the MS2 spectra of seven MAAs common in the marine environment. In my previous work, described in chapter 2 of this dissertation, I used Whitehead and Hedges (2003) methods in collaboration with Dr. Whitehead to determine the identity of the ultraviolet absorbing compounds in fish mucus. The results presented in chapter 2 have underscored the need to move beyond a strictly qualitative study toward a reliable quantitative analysis of MAAs in the fish and their natural food source.

The purpose of the work described here is to provide a dependable method of obtaining both qualitative and quantitative analysis of MAAs in biological samples, in the absence of standards. This method uses a combination of HPLC and tandem mass spectrometry. The chapter is organized in 3 parts: BACKGROUND. CURRENT APPROACH AND TROUBLESHOOTING FOR HPLC SEPARATION, TANDEM MASS SPECTROMETRY AND POTENTIAL INSTRUMENTAL PROBLEMS. QUANTIFICATION: EMPIRICAL TESTING OF AN EXTENSION OF BEER LAMBERT LAW.

3.2 Background

Mass spectrometry and tandem mass spectrometry have proven to be very useful tools to positively identify MAAs in biological samples in the absence of standards (Whitehead & Hedges 2003, Carignan et al. 2009, Carreto 2011). Another advantage of mass spectrometry is that even if some MAAs are not well separated by HPLC it is still possible to isolate the peak obtained for each ionized MAA on a mass chromatogram, provided that the MAAs have different masses. However, since the ionization coefficient of MAAs varies greatly among MAAs and experimental conditions, one cannot simply use the mass chromatograms for quantification without standards (Whitehead & Hedges 2002) as this would risk a gross over‐estimation of the abundance of easily ionizable MAAs like asterina compared with the less ionizable ones like mycosporine‐glycine. Furthermore, the biological relevancy of the compounds in my studies and in many others is associated with the absorption properties of MAAs in the UVR range. Consequently, it would be valuable to separate the various MAAs present in a sample and to observe, on a UVR absorption chromatogram, the contribution of each one, individually to the absorbance of the mixture. An ideal method for MAA analysis would have the following characteristics:

1‐The liquid chromatography should provide a good separation of all MAAs in one pass through the HPLC system 2‐The mobile phase fed to the mass spectrometer should allow sufficient electrospray ionization of the MAAs to provide a good sensitivity of the mass spectrometer signal 3‐The MS2 spectra for all MAAs present should be obtained in one pass. If these conditions were met, the identification could be made based on the mass and MS2spectra of each compound as well as its absorption maximum and established order of elution. Furthermore, MAA quantification in the absence of standards can then be made using an extension of the Beer‐Lambert law (Banaszak & Trench 1995, Whitehead & Hedges 2003, Tartarotti et al. 2001, Torres et al. 2007.) The law allows determination of the concentration of each MAA based on its published extinction coefficient and the area of the chromatographic peak obtained at its wavelength of maximum absorption. This method requires an HPLC system equipped with a UVR capable PDA (photo diode array) since it can capture UVR absorbance data on the entire range of wavelengths of any MAA present instead of just one wavelength per channel with only two or three channels. For MAAs whose extinction coefficient has not yet been published, the coefficient of a structurally similar MAA has been used as a good approximation (Whitehead & Hedges 2002, Carreto et al. 2005). To achieve the required criteria for the ideal method, I first examined the literature to compare MAA separation techniques. Detailed reviews of most methods are given in Carreto et al. (2005) and Carreto & Carignan (2011). In brief, the traditional HPLC schemes included isocratic elution using a C8 or a C18 column. These methods successfully separated some MAAs but as more compounds were discovered, the techniques in use proved unable to separate all of them. In particular, if a sample contained a mixture of strongly acidic and weakly acidic MAAs, full separation required elution with two different mobile phases. This led to the development of various gradient elution methods seeking to improve on the isocratic schemes. Carreto et al. (2005) published an original method using a gradient elution and two C18 columns in

series; this resulted in a good separation of a complex mixture of the 24 most common marine MAAs in about 45 minutes. I set out to apply and, where needed, to modify their HPLC method for use in conjunction with HIMB’s mass spectrometer.

3.3 Approach and Troubleshooting for HPLC Separation, Tandem Mass Spectrometry And Instrument Malfunctions

The Carreto et al. (2005) HPLC separation method uses a C18 column [Alltec Alltima (5 μm, 4.6mm i.d., 150mm length)] followed by a CapCell Pak UG [Shisheido (5μm, 4.6mm i.d., 250mm length)] protected with a guard column cartridge [Alltima, Alltech (4.6mm i.d.,20mm length)] (Carreto et al. 2005). The mobile phase is composed of solvent A: water and trifluoroacetic acid (TFA) 0.2% buffered with ammonia to pH=3.15; and solvent B: (80:10:10) (water TFA 0.2% with ammonia to a pH=2.2: methanol: acetonitrile). The gradient they used is given in table 3.1. Table 3.1. Gradient protocol for MAA separation as per Carreto et al. 2005. In practice when running several samples we found necessary to add 6 minutes of 100% eluent A at the end of the run to re‐equilibrate the columns before the next run. This led to a total run time of 56min per sample.

Time Percent Percent Flow rate (min) eluent A eluent B (ul/min) 0 100 0 1000 2 100 0 1000 15 80 20 1000 30 50 50 1000 50 50 50 1000

I first intended to try the method “as is” in spite of the fact that formic acid is usually preferred to TFA for mass spectrometry analysis since TFA can sometimes suppress electrospray ionization (ESI) and reduce sensitivity. As expected, this HPLC protocol provided an excellent separation of various samples containing mixtures of

MAAs (extracts of the coral Pocillopora meandrina mixed with palythine, and porphyra standards). However I encountered difficulties with the downstream mass spectrometry. The signal‐to‐noise ratio was very low due to an excessively high noise level even when running only solvent. Additionally, my highly aqueous solvent appeared to pool inside the front chamber of the ESI probe (Figure 3.1).

Figure 3.1. Picture of the pooling and boiling solvent in the mass spectrometer front chamber.

After a sample or two, the accumulated solvent would start boiling and the system had to be stopped to empty and clean the front chamber. Increasing the nitrogen auxiliary gas flow did not resolve this issue. Our MS system is specified to withstand solvent flow rates of up to 2ml min‐1, twice the rate I employed. With my highly aqueous solvent, the specification was not met, so I decided to reduce the flow rate going into the mass spectrometer. 24

The simplest way to reduce the flow to avoid the pooling issue would be to split the flow from the HPLC with a T junction. I installed such junction and empirically adjusted the length of line going to the MS and the line to the waste bottle until only 20% of the flow reached the mass spectrometer (equivalent to a 0.2ml min‐1 flow rate). This reduced the pooling problem although it did not completely eliminate it. This modification has the advantage of being initially inexpensive and simple to set up, but it presents some shortcomings. The flow rate reduction lowers the signal intensity to some extent, and the separation of close peaks can also be rendered less sharp due to a non‐avoidable addition of small dead volumes in the T junction. Another and more serious issue is the creation of a large volume of waste since 80% of the solvent goes directly to waste from the HPLC. This last issue was critical as my work took place during a world shortage of acetonitrile due to a drastically lowered production from the primary source, China. The shortage reached such crisis levels that chemists started to research and publish new versions of separation methods using alternate solvents (dos Santos Pereira et al. 2009, Fritz et al. 2009, Sandman 2009, Desai et al. 2011). In this context, I addressed my flow rate problem by adapting the Carreto method to smaller diameter columns (2.1mm instead of 4.6mm) of the same type. Such adaptation has the advantage of keeping the gradient identical except for a reduction of the flow rate according to the following formula: Flow Rate for new Column (ml/min) = [Diameter of new column (mm)/ Diameter of old column (mm)]2 * Flow Rate for old Column (ml min‐1). I obtained a flow rate of 190μl min‐1 that significantly reduced solvent use and the associated waste production. The separation with this new system was identical to that obtained in the original Carreto method. However, the signal‐to‐noise ratio in the mass spectrometer remained unacceptably low. The excessively high background level masked some of the compounds of interest as shown in the total ion concentration (TIC) chromatograms (Figure 3.2 A). Various washes of the HPLC and MS systems, as well as a change in the

brand of solvents as advised by the Thermo technical support team did not solve this problem. Further, while the solvent pooling was greatly reduced, it still occurred, though not as rapidly. I theorized that the spatial configuration of the front chamber was the origin of the problem. The auxiliary gas flow (N2), although necessary for desolvation, and the spray from the ion source itself, created a Venturi effect that prevented the condensate from the spray from evacuating through the exhaust opening. This possibly increased background noise levels as some exhaust would re‐circulate in the chamber. At this point a Thermo engineer contacted me to report that another client working on compounds with a comparable mass range had experienced similar background issues. As I had hypothesized, the problem was thought to be linked to a flaw in the design of the exhausts of the front chamber of the MS that created a back flow of gas and vaporized solvent after it had contacted the plastic exhaust tubing and fitting. The configuration of newer models did successfully address the situation with changes in exhaust position and diameter, but I did not have access to a newer instrument. However, according to the Thermo engineer the other client had considerably reduced the issue by using a small accessory pump to draw out the exhaust gas to reduce backflow. I installed a pump and was able to reduce the background to a level acceptable for my purpose (Figure 3.2.B). I then tested whether the use of formic acid instead of trifluoroacetic acid (TFA) in the mobile phase would further improve the signal‐to‐noise ratio. This proved not to be the case. The background ion levels generated with this modification were higher (Figure 3.3 A and B). Furthermore the separation was not as satisfactory. Palythine and shinorine overlapped and there were some elution orders changed compared to Carreto et al. (2005). Subsequently, Carignan et al. (2009) published a work that used formic acid in the mobile phase with its corresponding elution order that I could have used as a new reference. However I continued using TFA due to the higher quality of the MS signal in our instrument.

At this point the method was adequate to meet my purposes as it allowed proper identification as well as an adequate separation of all MAAs observed in my typical samples. The only drawback was a lengthy run time: 50 minutes for separation plus an additional 6 minutes to re‐equilibrate the column to 100% solvent A. Each MS run consisted of a series of pairs of scanning events: a simple MS scan giving the mass of any eluting ionized MAA (Molar mass +1 also noted m/z) followed by a MS2 scan giving the fragmentation pattern of the most intense ion found in the MS scan taken from a list of common marine MAAs. The list I created contains molar mass + 1= m/z =245, 246, 273, 275, 285, 289, 303, 305, 329, 333, 345, 347. This type of MS2 scan is called data‐dependent scanning. It is particularly useful when the mixture of MAAs in a sample is unknown, as the instrument will isolate, for each scan, the most abundant MAA and give its MS2 spectrum. Data‐dependent scanning avoids having to run the sample twice: one time to find out the mass of the MAAs present and their elution time and a second time to obtain the MS2 spectrum of the mass observed at this elution time. While the list I selected for data‐dependant scanning worked very well for my purposes, it can be modified to suit the needs of other users as new MAAs with masses not yet determined are being discovered. Satisfied with this method, I started to work on the quantitative aspect of the study. However, it became clear that the quantification was not reproducible as replicates would show over 100% variation in chromatographic peak area due to malfunctions in the HPLC system. A summary of most issues experienced, the instruments affected, solutions and outcomes is given in Table 3.2 as it could be a useful troubleshooting guideline for other users.

RT: 0.00 - 50.00 PDA response at

100000 280‐400nm

uAU 50000 0

100 MS response

Total ion

concentration 50

Relative Abundance 0 10 20 30 40 50 Time (min)

Figure 3.2 A. Chromatograms before addition of an auxiliary pump, the MS signal is drowned by the noise. The first chromatogram shows the UVR absorption from 280nm to 400nm. The second chromatogram is the mass chromatogram showing the relative abundance of all ions detected by the mass spectrometer (total ion concentration:TIC).

RT: 0.00 - 50.00 PDA response at 150000 280‐400nm

uAU 100000 50000 0 100 MS response Total ion 50 concentration

Relative Abundance 0 10 20 30 40 50 Time (min)

Figure 3.2 B. Chromatograms after addition of an auxiliary pump, the MS signal to noise ratio is getting better.

RT: 0.00 - 56.00 PDA response: at 400000 280‐400nm

uAU 200000

0 100 :

MS response

50 Total ion concentration 0 0 10 20 30 40 50

Relative Abundance Time (min)

Figure 3.3 A. HPLC separation of a mixture of samples using formic acid instead of TFA for the mobile phase. The separation is not as good and the signal to noise ratio is not improved.

RT: 0.00 - 40.14 PDA response at 280‐400nm 200000

uAU 100000 0 100 MS response 50 Total ion concentration

0 0 10 20 30 40 Relative Abundance Time (min)

Figure 3.3 B. Separation of the same mixture using TFA: this gave the best separation and signal to noise ratio.

Table 3.2. Problems and solutions found with the HIMB HPLC‐MS system.

Symptoms Issues Solution Outcome Loss of refrigeration in Refrigeration unit needs to -Refrigeration unit -Solved autosampler tray be replaced (a common replaced issue after a few years) Recurrent computer freeze Thermo suggested to -Thermo technician -The hard drive during operation of the LC-MS upgrade the Xcalibur upgraded to Xcalibur failed after upgrade software. 5.0 during the yearly and the computer preventative had to be replaced maintenance PDA unresponsive Incompatibility with the -Replacing an electronic -Solved upgrade of the software component and new firmware.

Large pulsations visible on After weeks of testing we -Rebuilding of the -Solved the absorption chromatogram found out the pump was pump. (pressure variation of the partially blocked (a HPLC pump). preventative maintenance engineer stacked a replacement washer over the old one leading to a partial plugging of the pump.) Lack of reproducibility of An improper part (wrong -Replacement of the -Solved quantitation. Malfunction of model tubing) had been needle assembly the autosampler. placed in the injection system by a preventative maintenance engineer. Double peaks on the This happened after the -Switch columns and -Did not resolve chromatograms while running upgrade but because it also test. the autosampler in no waste coincided with the -Make new batches of -Did not resolve. mode (smallest waste of installation of a new set of solution and test. sample per injection) columns we first looked for -Use “partial” or “full” -This is the ideal dead volume in the column loop mode for sample solution if the or a pH problem in the injection. sample amount is solution. Ultimately we found large enough but out the upgrade had led to not applicable with an increase in the amount of our small fish non-buffered wash solution sample volume. mixed with the injected -Empirically change the -Adequate result sample in no wash mode. setting on length of ------needle assembly tubing ------to reduce volume of ------wash solution front. -Place buffered solution -Best solution for in wash bottle (not small samples recommended for long requiring the “no period of time as the waste” mode. acid could corrode the syringe plunger and introduce contaminant.) Failure of the Nitrogen An electronic component -Replacement of -Solved generator failed. electronic component Error message upon starting Malfunctioning MS -Replacement of -Solved the Mass spectrometer. motherboard motherboard

After implementing these solutions the instruments were all finally working up to specification, my method was able to provide reliable information. However, one last hurdle led to another modification. My columns were accidentally damaged. Because the order for replacement columns was delayed, a Phenomenex representative offered us a no‐risk trial of a new type of column: Kinetex C18, end‐capped, 2μm particle size (Phenomenex). They claimed that these columns were able to shorten the separation time of most applications. This would require adapting my method but in view of the anticipated long wait for my regular back‐ordered columns I experimented with these columns. I tested the separation using extracts of fresh Pocillopora meandrina since it contained most of the MAAs encountered in my study. A single Kinetex column did not achieve the proper separation; however two columns in series gave satisfactory results. The final setup was: Kinetex C18 150 x 2.0mm followed by a Kinetex C18 100 x 2.0mm; protected by the corresponding Krudcatcher (all these are Phenomenex products). After modifying the pH of solvent A and the gradient, I was able to reproduce the same order of elution as the one published in Carreto et al. (2005). This new separation method had the advantage of being faster since each run took less than 25 minutes instead of 50 minutes. The modified gradient is presented in Table 3.3 and the resulting elution times are given in table 3.4.

Table 3.3. Gradient protocol for MAAs separation with Kinetex columns ‐Solvent A (water, trifluoroacetic acid (TFA) 2%, ammonia to pH=3.00 (instead of pH:3.15 in Carreto et al. 2005) ‐Solvent B (80:10:10) (water TFA.2% ammonia pH2.2:methanol: acetonitrile)

Time (min) Percent eluent A Percent eluent B Flow rate (ul/min) 0 100 0 190 1.25 100 0 190 9.38 80 20 190 18.75 50 50 190 25 50 50 190

Table 3.4. MAAs retention times. The order of elution is consistent with Carreto et al (2005).

Elution Substance Retention order time(min) 1 Shinorine 3.5 2 Mycosporine-2-glycine 4 3 Palytine-Serine 4.18 4 Palytine 4.5 5 Asterina 5.3 6 Porphyra-334 6.2 7 Mycosporine-Glycine 6.5 8 Palythine threonine 8.3 9 Palythinol 8.6 10 Mycosporine-methylamine-threonine 11.5 11 Usujirene 18.1 13 Palythene 18.8

The MAA identity could be confirmed using the MS2 spectra, the mass, the wavelength maximum and the order of elution. Figures 3.4 A to 3.4 G show examples of the data gathered to identify the main MAAs found in an extract from the lens of the Coral Trout, Plectropomus leopardus, from Australia donated by Maxi Eckes (Australian Institute of Marine Science). This extract had been already been titrated against MAA standards at AIMS and therefore was deemed to be a useful secondary standard containing palythine, asterina and palythene. In addition to these three MAAs, my method also identified usujirene, an MAA that had not been differentiated from palythene in the original AIMS titration.

RT: 0.00 - 38.00

b PDA response at

290‐400nm 100000

uAU d 50000 a c

0 100 MS response. b Total Ion Concentration 50 d a c

Relative Abundance 0 0 10 20 30 Time (min)

Figure 3.4 A. UV absorbance and mass chromatograms.‐ Data gathered by the LCMS to identify the four main MAAs: a,b,c,d found in Plectropomus leopardus lens extracts using the Kinetex columns separation method.

100 m/z= 244.50-245.50 a

100 m/z= 288.50-289.50 b

100 m/z= 284.50-285.50

d c

0 0 10 20 30 Time (min)

Figure 3.4 B. Mass of each of the four MAAs. This is the ionized mass/charge noted m/z the charge is one so the ionized mass is [M+H]+ (molecular mass plus one.)

Wavelength maximum for Wavelength maximum for a b 320.00 330.00 200000 20000 100000 uAU uAU 0 0 300 400 300 400 wavelength (nm) wavelength (nm) Wavelength maximum for Wavelength maximum for c d 357.00 360.00

10000 50000 uAU uAU 0 0 300 400 300 400 wavelength (nm) wavelength (nm)

Figure 3.4 C. Wavelength maximum of each compound.

ms2 245.00 245 * 230 1000000

8000000

Intensity 6000000

4000000 186 209 2000000 197 0 100 150 200 250 m/z

Figure 3.4 D. MS2 fragmentation pattern for a. Corresponds to published MS2 for palythine (Whitehead & Hedge 2003)

ms2 289.00 289 * 120000000 100000000 274 80000000 60000000

Intensity 230 40000000 20000000 186 212 271 0 100 150 200 250 300 m/z

Figure 3.4 E. MS2 fragmentation pattern for b. Corresponds to published MS2 for asterina (Whitehead & Hedges 2003)

ms2 285.00 * 285 6000000 270 4000000 Intensity 2000000 226 197 241 0 100 150 200 250 m/z

Figure 3.4 F. MS2 fragmentation pattern for c. Corresponds to published MS2 for palythene (Whitehead & Hedges 2003)

ms2 285.00 285*

4000000

3000000 270

2000000 Intensity

1000000 226 197 241 0 100 150 200 250 m/z

Figure 3.4 G. MS2fragmentation pattern for d. Corresponds to published MS2 for palythene (Whitehead & Hedges 2003). Note that usujirene and palythene are geometric isomers and have the same fragmentation pattern and mass. To differentiate them it is necessary to separate them to compare their wavelength maximum 357nm and 360nm respectively and their order of elution: usujirene elutes before palythene with the Kinetex method as with the original Carreto et al. (2005) method.

The separation was sufficient to move on to quantification based on the HPLC chromatogram with this alternate HPLC method.

3.4 Quantification: Empirical Testing Of An Extension Of Beer‐Lambert Law

It has been suggested that it is possible to quantify each MAA in the absence of standards (Banaszak & Trench 1995, Whitehead & Hedges 2003, Tartarotti et al. 2001, Torres et al 2007.) This was done using the HPLC chromatogram peak area at the wavelength maximum using the published extinction coefficient and an extension of the Beer‐Lambert law. However, only one of these papers actually gave the formula used for quantification: Equation 1: (from Banaszak & Trench 1995)

C = (a * A * f) * (b* E)-1 Where:  a= peak area (mV s),  A=absorbance (non dimensional absorbance unit, AU),  f = flow rate (ml s‐l),  b= path length of flow cell (cm),  E =molar extinction coefficient (m mol‐1 cm‐1 Banaszak pers. com).  C= concentration in mmol l‐1 (Banaszak pers. com.) Because the published equation contained some typographic errors (Banaszak pers. com.) and a dimensional analysis revealed some inconsistencies in units, I decided to explore whether this model could be verified empirically, or else modified to provide a good quantitative estimate based on HPLC peaks. To do so I used the only two MAA primary standards at my disposal, palythine and porphyra. Typically in spectrophotometry the “main derivation of Beer‐Lambert law” also simply called “Beer’s law” is used to calculate concentration from the absorbance reading of a pure standard at its absorption maximum. The well‐known formula is shown below.

Equation 2:

C=A/(d*ε)  where C is the concentration of the standard given in mole/liter,  A is the absorption reading at the wavelength maximum (non‐ dimensional unit of AU: absorbance unit),  d is the path length of the spectrophotometer in cm,  ε is the extinction coefficient of the standard in M−1 cm−1 or L mol−1 cm−1. This linear relationship is very accurate with adequately diluted solutions, but breaks down at high concentrations as some solute particles in the light path are in the “shadow” of others. In practice, for MAAs as for many compounds, a solution with an absorbance lower than or equal to 1AU at the wavelength maximum (when measured with a 1cm path length spectrophotometer) will fall in the linear portion of the relationship (Whitehead pers. com.). I prepared a dilution series of the palythine standard and measured the absorbance at the palythine absorption maximum: λ=320nm (Badaranayake 1998 and ref. therein) in a Nanodrop 2000 spectrophotometer to calculate their concentrations using the Beer‐Lambert law. This provided a spectrophotometric‐based concentration for each sample in the series. Because the path length of the Nanodrop 2000 is 1mm, I started at a maximum absorbance of approximately 0.1AU (equivalent to 1AU for a 1cm path length). I then injected 10ul of each of these dilutions in the HPLC using the method previously described, and compared the concentration (in mol/L) of palythine calculated from the HPLC peak as per Equation 1 (Banaszak & Trench 1995) relative to the concentration obtained from the spectrophotometer data using Beer‐Lambert Law (Equation 2). My results did not give the expected correspondence between the two models (Figure 3.5). The percentage difference between the expected (spectrophotometer‐generated) and the observed value from the Banaszak formula (HPLC‐generated) varied between 4 and 88 %.

Palythine concentration calculated from HPLC data using Banaszak 1995 formula vs concentration calculated with Beers Law using the spectrophotometer 6.00E-05

5.00E-05

4.00E-05

3.00E-05

2.00E-05 y = 1.64x - 1E-10 2 1.00E-05 R = 0.98 Palythine concentration using HPLC (mol/L) Peaks using 0.00E+00 0 5E-06 1E-05 2E-05 2E-05 3E-05 3E-05 4E-05 4E-05 Palythine concentration using the spectrophotometer (mol/L)

Figure 3.5. Palythine concentration. Comparing values computed from Equation 1 with values obtained from Equation 2 (Beer’s Law). In the next four figures the dotted black line represents the expected values if there were perfect correspondence x=y between the statistics plotted.

I then reasoned that, for a given MAA peak, the HPLC chromatogram at its wavelength of maximum absorption represents basically a sum of successive spectrophotometer measurements (spectra) of the sample as it passes through the HPLC cell over time. This reasoning led to a modified equation below. Equation 3: C=a*f/(ε*d) Where C is the number of moles injected, given in mmoles (to obtain the concentration in mol L−1 it suffices to convert to moles and divide by the injection volume ); a is the calculated area under the absorbance peak in AU.s; f is flow rate in ml s−1 ε is the extinction coefficient in L mol−1 cm−1 and d is the path length of the HPLC cell in cm. (Note that our instrument possesses a 5cm flow cell instead of the standard 1cm cell.)

With this alternate equation, we found the obtained value was a multiple of the expected value. In effect use of Equation 3 resulted in a relationship where: HPLC‐generated value = 0.8 * Spectrophotometer‐generated value The minute Y‐intercept value (1E‐7) is rounded off to zero as, in practice; it represents an error smaller than the normal variability of the spectrophotometric reading.

Palythine concentration derived from HPLC peaks using Equation 3 vs amount calculated with Beers Law using the spectrophotometer 4.00E-05 3.50E-05 3.00E-05 2.50E-05 2.00E-05 1.50E-05 y = 0.8002x - 1E-07 R2 = 0.9974 1.00E-05 5.00E-06 Palythine concentration concentration Palythine HPLC(mol/L) peak from 0.00E+00 0.00E+00 5.00E-06 1.00E-05 1.50E-05 2.00E-05 2.50E-05 3.00E-05 3.50E-05 4.00E-05 Palythine concentration from spectrophotometer (mol/L)

Figure 3.6. Palythine concentration. Comparing values computed from Equation 3 with values obtained from Equation 2 (Beer’s Law)

To ascertain if these results held true for other MAAs, I used solutions of my second standard: porphyra measured at λ=334 nm (this MAAs’ absorption maximum). I obtained similar outcomes (Figure 3.7 and 3.8), an inconsistent relationship using the 1995 Banaszack & Trench formula (Equation 1) and a good correlation using my alternate formula (Equation 3) that led again to the simple linear equation of: HPLC‐generated value= 0.8 * Spectrophotometer‐generated value. Again, the Y‐intercept is rounded off to zero. The 20% “signal loss” of the HPLC‐generated value compared to the spectrophotometricaly‐derived reference, is assumed to be instrument‐related, possibly due to the data acquisition rate of the photo diode array, although I did not try to test this hypothesis. The 20% signal loss can be used as a correction factor to adjust the 39

HPLC derived amount to the 20% higher “true” spectrophotometer values. In the absence of more standards to verify this relationship for all MAAs, I accept that equation 3 accurately estimates amounts for all MAAs (after correcting for signal loss).

Porphyra concentration calculated from HPLC data using Banaszak formula vs amounts calculated from the spectrophotometer using Beers Law 3.00E-05

2.50E-05

2.00E-05

1.50E-05 y = 1.199x - 7E-06 R2 = 0.9626 1.00E-05 Porphyra concentration concentration Porphyra from HPLC (mol/L) peaks from 5.00E-06

0.00E+00 0.00E+00 5.00E-06 1.00E-05 1.50E-05 2.00E-05 2.50E-05 3.00E-05 Porphyra concentration using the spectrophotometer (mol/L)

Figure 3.7 Porphyra concentration. Comparing values computed from Equation 1 with values obtained from Equation 2 (Beer’s Law).

Porphyra concentration calculated from HPLC data using Equation 3 vs concentration calculated from spectrophotometer using Beers Law 3.00E-05

2.50E-05

2.00E-05

1.50E-05 y = 0.809x - 5E-07

Porphyra Porphyra 1.00E-05 R2 = 0.9984 5.00E-06 concentration from from concentration HPLC (mol/L) peaks 0.00E+00 0.00E+00 5.00E-06 1.00E-05 1.50E-05 2.00E-05 2.50E-05 3.00E-05 Porphyra concentration using the spectrophotometer (mol/L)

Figure 3.8 Porphyra concentration. Comparing values computed from Equation 3 with values obtained from Equation 2 (Beer’s Law).

To check the limits of this relationship with our system at HIMB I added data points with more concentrated solutions to observe when the linearity of the relationship broke down (Figure 3.9).

Porphyra concentration calculated with HPLC data vs absorbance (measured with a nanodrop)

7.00E-05

6.00E-05

5.00E-05

4.00E-05

3.00E-05

2.00E-05 peaks (mol/L) peaks

1.00E-05 calculated using HPLC Porphyra concentration 0.00E+00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Absorbance (AU)

Figure 3.9. Porphyra concentration vs. absorbance. The absorbance shown is measured on a 1mm path length nanodrop 2000. When using a more classical 1cm cell cuvette spectrophotometer the values of the x‐axis are multiplied by 10. Data presented in the previous graph. Added concentrations.

My original estimation of using solutions of absorbance less than or equal to 1AU in a 1cm path length spectrophotomer (0.1 for a 1mm path length) proved to be correct, and even a bit conservative. In practice this means that when analyzing an unknown sample it is safe to dilute it until it reaches a total maximum absorbance close to 1AU (or 0.1AU for the nanodrop). This will insure that each of the MAAs in the sample will be within an appropriate range for quantification using the corresponding HPLC peaks.

As an added precaution, in the absence of standards it is useful to use a mixture of samples to build a response curve. The sample mix should be serially diluted so as to bracket the absorbance level of all samples analyzed. Replicates of each dilution level are run through the HPLC and the graph of the response versus dilution level is examined for reproducibility and linearity. To sum up, using the Thermo deca XP plus I can now analyze, qualitatively and quantitatively, samples containing MAA mixtures in the absence of standards. Here I have proposed two equally valuable separation methods. I have found after running my samples (chapter 4) that while the Kinetex columns gave more rapid results, their lifetime appears shorter than the 5μm columns because they proved not very suited for a highly aqueous mobile phase. Users will have to decide if they are willing to sacrifice durability for speed. The methods presented here should be usable on the same instrument or easily adaptable to similar models of ion trap mass spectrometers. If a different HPLC instrument is utilized, it would be prudent to use, as I have done, one or two standards to verify or modify the correlation between the spectrophotometer‐generated quantitation using Beer‐Lambert law and the extension of the law to the HPLC chromatogram proposed here (Equation 3) for quantification. This method development did not address the topics of sample extraction and cleaning that have been described in detail by other authors (Carreto et al. 2005, Banaszak et al. 2006, Torregiani & Lesser 2007, etc.), or by my subsequent work (see chapter 4). It is important to note that to obtain the best separation possible, the dried extracts of the sample are re‐suspended in “solution A” just before injection, as recommended by Carreto et al. (2005). I also refer the readers to work on coral and fish to examine how MAA concentrations are normalized with soluble protein content or dry weight to compare values between samples. (Karentz et al. 1997, Torregiani & Lesser 2007, Hoyer 2002, Yakovleva & Baird 2005, Banaszak et al. 2006, Torres et al. 2007, and chapter 4 herein).

3.5 Conclusion

Our understanding of UVR protection in reef organisms is rendered even more critical in the context of the reduction of the ozone layer and as we enter a period of anthropogenic sudden climate change (Farman 1987, Gleason 1993, Smith et al. 1989, 1992). MAAs are important sunscreen compounds in coral reef organisms and likely critical for UVR protection (Dunlap & Shick 1998, Karentz 2001, Adams & Shick 1996, Badaranayake 1998, Dunlap et al. 1998, reviews by Rastogi et al. 2010 and Carreto & Carignan 2011). Until recently MAA research was restricted to the researchers who could produce or borrow the non‐commercially available standards they needed. My work should contribute to addressing these constraints. Much remains to be discovered and understood about mycosporine‐like amino acids. As our body of knowledge increases so should the momentum towards the commercial production of standards for all known MAAs. But in the meantime we now have tools to overcome this limitation.

CHAPTER 4. NATURAL SUNSCREEN COMPOUNDS IN THREE SPECIES OF CORAL AND THEIR FISH PREDATOR

4.1 Introduction

Although ultraviolet radiation (UVR) has some beneficial roles in living organisms including humans, most UVR effects are detrimental (reviews by Cockell & Knowland 1999 and Gallagher & Lee 2006). The negative effects of UVR include DNA damage, cell death induction (Kulms et al. 1999), corneal and epithelial damage (sunburn) (Siebeck 1988, Martinez‐Levasseur et al. 2011), immunosupression (Schwarz & Schwarz 2011), inhibition of photosynthetic primary production in plants (Jones & Kok 1966, Teramura 1980 & 1983, Gold & Caldwell 1983, Wood 1987, Strid et al. 1990), and reduction of microbial motility (Sommaruga et al. 1996, Sommaruga & Buma 2000), to name a few. Some organisms exposed to UVR in their environment have developed blocking compounds to minimize UVR damage (Garcia‐Pichel & Castenholz 1993, Chalker et al. 1988, Banaszak et al. 1998, Cockell & Knowland 1999, Moisan & Mitchell 2001, Shick & Dunlap 2002, Banaszak & Lesser 2009, Hansson et al. 2007, Hansson & Hylander 2009, Rastogi et all. 2010). While it had been commonly believed that UVR does not penetrate water, in the early 20th century the presence of UVR deep into the sea was documented (Jerlov 1950). Harmful effects of UVR on benthic aquatic organisms have been studied since the 1980's (Jokiel 1980, Siebeck 1988, Wood 1987). Sessile benthic organisms in shallow water are constrained to remain at the same depth and might be expected to experience higher UVR exposure than nektonic and planktonic organisms, but evidence has also been found for negative impacts of UVR in the open ocean (Smith 1989, Smith et al. 1992, Karentz et al. 1991, Neale & Kieber 2000, Bancroft et al. 2007, Meador et al. 2009). Fishes from a wide range of habitats are impacted by UVR, and negative impacts can occur in several ways (Cullen et al. 1994, Ramos et al. 1994, Lowe & Goodman‐Lowe 1996, Freitag et al. 1998, Steeger et al. 1999, Hofer 2000, Kelly & Bothwell 2002). The DNA of larval Northern Anchovies and Pacific Mackerel (Hunter et al. 1979, Vetter et al. 44

1999) and cod (Lesser et al. 2001) is damaged by UVR. Juvenile fish can also be affected (Bell & Hoar 1950, Lowe & Goodman‐Lowe 1996, Kelly & Bothwell 2002, Kazerouni & Khodabandeh 2010). Adults of several species are impacted by UVR (Crowell 1930, Bullock & Coutts 1985, Kelly & Bothwell 2002): fish can get sunburned (Crowell 1930, Bullock 1982, McArdle & Bullock 1987, Lowe & Goodman Lowe 1996) and UVR can create photosensitivity to various substances. For both sunburn and photosensitivity the resulting skin ulcerations are an open path for infection that can lead to death (Bullock & Roberts 1981, Bullock & Coutts 1985, McArdle & Bullock 1987, Little & Fabacher 1994). Some cartilaginous fishes such as hammerhead shark use melanization (sun tanning) to guard themselves from sunburn (Lowe & Goodman Lowe 1996). In contrast, protection from UVR for bony fishes has been shown to be conferred by substances on the surface of the fish (Fabacher & Little 1995, Zamzow & Losey 2002, Zamzow 2003a). For many marine organisms UVR protection is due to the presence of a family of chemicals termed mycosporine‐like amino acids “MAAs” (Adams & Shick 1996, 2001, Dunlap & Shick 1998, Neale et al. 1998, Cockell & Knowland 1999, Karentz et al. 1991, 1997, Karentz 2001, Klisch et al. 2001, Suh et al. 2003, Ferroni et al. 2010, Rastogi et al. 2010). Until recently it was thought that the biochemical machinery involved in de novo syntheses of these compounds was derived from an enzymatic pathway that did not occur in animals, the shikimate pathway (Favre‐Bonvin et al. 1987, Shick et al. 1999, Shick & Dunlap 2002). In the case of symbiotic host animals such as sea anemones and corals, the animal is thought to obtain MAAs from their food and their algal endosymbionts, dinoflagelates in the genus Symbiodinium. The Symbiodinium‐produced MAAs are called primary MAAs. They can be translocated to the host tissues where some are transformed into other MAAs called secondary MAAs (Shick, 2004). However, this traditional view has been challenged by the recent discovery of some of the genes coding for enzymes of the shikimate pathway in the genome of the sea anemone Nematostella vectensis (Starcevic et al. 2008). More recently, Balskus & Walsh (2010) combined genetic and molecular techniques to show that de novo

synthesis of the MAA shinorine in Anabaena variabilis, a cyanobacteria, did not, in fact, involve the shikimate pathway, but instead the authors described an entirely different and new scheme for MAA synthesis. Their work has revolutionized our understanding of the biosynthetic pathway of MAAs. Nevertheless various studies support the idea that diet is the source of MAAs in some “non‐symbiotic” metazoans (Carroll & Shick 1996, Mason et al. 1998, Newman et al. 2000, Adams et al. 2001, Whitehead et al. 2001). In 2002, Zamzow & Losey discovered that many reef fish species could incorporate UVR blockers into the mucus that covers their bodies when exposed to UVR. Zamzow (2003a, 2004) then showed experimentally that fish exposed to UVR and given a synthetic food supplemented with Acanthophora spicifera, an alga rich in MAAs, increased the UVR absorbance of their mucus. In contrast, fish exposed to UVR but fed the synthetic diet without A. spicifera showed a reduction in UVR absorbance by the mucus. Zamzow & Losey (2002) further observed that the absorbance profiles of fish mucus are consistent with the profiles of various MAAs or mixtures of MAAs and related compounds, although they did not verify this chemically. I later followed up their work. In the absence of commercially available standards, I used the Whitehead & Hedges (2003) identification method using electrospray ionization and mass tandem spectrometry (ESI‐MS/MS) to confirm that some of the UVR blockers in reef fish mucus were indeed MAAs. (ASP photobiology conference 2006). Eckes et al. (2008) also substantiated this observation using High Precision Liquid Chromatography (HPLC) and MAA standards. While Zamzow (2003a, 2004) pointed to diet as the source of the UVR blockers in reef fish mucus, our preliminary findings led us to investigate the relationship between the MAAs in the diet, and those that eventually appear in the mucus. Do fishes sequester all or only some of the available MAAs in their diet? Can they modulate sequestration to match changes in the ambient UVR level? Furthermore, while there has been laboratory study of how the MAAs in a man‐made diet affect UVR protection in fish (Mason et al. 1998, Zamzow 2003a, 2004), there has been little direct study of MAA acquisition by fish from their normal diet in natural settings. These questions are

important both in terms of general understanding of evolutionary and biological processes in nature and in providing information on the way fish, which may be targets of culture or fisheries programs, protect themselves from damage due to UVR exposure. In the work presented here I sought to shed light on these points by identifying the MAAs found in both an obligate corallivorous fish species and the corals they eat in the natural reef setting. To establish if different levels of MAAs correlate with different levels of UVR exposure, I quantified the MAAs found in the fish and their coral prey as well as the percent of surface downwelling UVR irradiance in three different non‐ overlapping depth ranges. I tested four qualitative and two quantitative hypotheses. H0‐1 There will be no difference in the identities of the MAAs found among species of corals. H0‐2 There will be no difference with depth in the identities of the MAAs found for each coral species studied. H0‐3 There will be no difference with depth in the identities of the MAAs found in fish. H0‐4 The fish mucus will have the same suite of MAAs as the coral eaten. H0‐5 The corals will show no change in MAA concentration with depth. H0‐6 The fish mucus will show no change in MAA concentration with depth.

4.2 Methods

4.2.1 Field work

4.2.1.a Study species and location:

Chaetodon multicinctus, a territorial butterflyfish, is a well‐studied obligate corallivore. The combination of limited spatial distribution (the fish territory) and restricted food source, a few coral species, mainly Pocillopora meandrina, Porites compressa and Porites lobata (Tricas 1986, Hourigan 1987) offered a unique opportunity to sample the UVR blockers occurring both in the fish and in their scleractinian diet. This study

required a location where C. multicinctus was easily accessible and abundant at various depths. Puakō on the Kona coast of Hawai`i satisfied these requirements and was chosen as my study site.

4.2.1.b Sampling

During the summer of 2005, two divers observed and sampled pairs of C. multicinctus in each of three depth ranges: 1.5m to 4.57m ( 5 to 15 feet ), 7.62m to 10.67m ( 25 to 35 feet) and 15.24m to 18.29m (50 to 60 feet). We focused on one pair of fish at a time in their territory for two dives and placed small flagged weights on the coral colonies the fish fed from. During the following days (between 11am and 2pm) we captured both fish using a barrier and hand nets. Once the two fish were brought onto the boat, we used a dulled craft blade to collect epidermal mucus (Zamzow 2003a, b, 2004), that we placed in scintillation vials kept on dry ice. The fish were returned to their territory and released, unharmed within less than an hour of capture. On a subsequent dive to the territory we first verified that the fish were healthy, and then collected small samples of the marked corals and removed all the flagged weights. Coral samples consisted of the tip (~1cm to 2cm high) of a finger for Porites compressa and Pocillopora meandrina and a plug of the apical area of Porites lobata (~1.5cm2). We collected P. meandrina and P. compressa using bone shears and used a hammer and small chisel for the P. lobata plugs. In this study we sought to analyze the MAAs of the entire coral holobiont (coral host and its symbionts). From here‐on whenever the word coral is used it refers to the “holobiont” unless otherwise indicated. Once on shore the coral samples were also placed in borosilicate scintillation vials kept on dry ice. Both fish mucus and coral samples were then freeze‐dried in a Labconco freeze drier and kept at ‐80°C until extraction.

4.2.1.c Downwelling spectral irradiance measurements

To assess the relationship between the depth of the fish territory and UVR exposure, we measured relative downwelling irradiance with a USB 2000 spectrophotometer fitted 48

with a battery pack and a palm computer. We placed the entire setup in a custom made underwater housing (Will’s camera housing, Australia) and attached a UV transparent fiber with a cosine corrected sensor (Ocean Optics ref CC3 UV). With this instrument a diver took triplicate downwelling Ultraviolet irradiance measurements every 10 feet (~3m) from the water surface to the deepest part of the reef to establish percent irradiance relative to the irradiance just below the surface. The measurements were taken on a clear day in August around noon while the sun is at its zenith for the day. This would provide data during maximum UVR exposure and avoided variations due to cloud cover. The wave action was minimal for the area but the water surface was not perfectly flat as the locale is typically breezy.

4.2.2 Laboratory work: Chemical identity of UVR blocking in fish mucus and their coral prey

4.2.2.a MAA extraction

Extraction of coral and fish mucus samples took place directly in the scintillation vials by adding 100% aqueous HPLC‐grade methanol to the vial (0.25ml for mucus samples 6.0ml for coral samples). The samples were then sonicated on ice for 5 minutes (Fisher Scientific FS60H) and placed the vials in the dark at ‐14°C for 6 hours. The resulting methanolic extracts were then transferred to a centrifuge tube and centrifuged at 5000g for 5 minutes at room temperature. The supernatant was then collected and screened for UV absorbing characteristics with spectrophotometric readings (NanoDrop ND‐ 1000). A second (serial) extraction was applied to each of the coral samples and the second extract was pooled with the first (12ml total). This last step was eliminated for the fish samples since the second extraction did not yield any measurable UVR absorbance. The methanolic extracts were then filtered using C18 Sep Pak cartridges (Waters). Aliquots (100ul) of the filtered samples were placed in 200ul inserts contained in 2ml auto‐sampler vials and dried using a speed vac (Juan RC1022). Lastly I replaced the air in the vials with dry nitrogen gas and kept the vials at ‐80°C until LC‐MS analysis.

4.2.2.b HPLC and tandem mass spectrometry

I used HPLC followed by Mass Spectrometry to separate and identify MAAs. Carreto et al. (2005) devised a gradient chromatography method for MAAs providing better separation than the more commonly used isocratic schemes. Because the flow rate in their technique (~1ml min‐1) was too high for our mass spectrometer, I modified their method using smaller diameter columns, adjusting pH, solvents, gradient, and flow rate so as to keep the same order of elution. The columns used were: first a Kinetex C18 150 x 2.0mm followed by a Kinetex C18 100 x 2.0mm, (Phenomenex), protected by the corresponding Krudcatcher. The HPLC separations were performed with the two columns connected in series and thermostated at 30°C using a Thermo Surveyor MS pump and Thermo Surveyor PDA plus (photodiode array detector) scanning between 250 and 450nm. Eluent A was a pH 3.00 solution of aqueous trifluoroacetic acid 0.2% and ammonium hydroxide (instead of pH 3.15 in Carreto et al. 2005). Eluent B was a solution containing TFA 0.2% and ammonium hydroxide at pH2.20:methanol:acetonitrile (80:10:10,v:v:v) (adapted from Carreto et al. 2005). The autosampler automatically reconstituted the sample in 100ul of Eluent A just before injection. The modified gradient protocol is summarized in Table 4.1. The resulting retention times are given in Table 4.2. Table 4.1. Gradient protocol for MAA. Eluent A: water, trifluoroacetic acid (TFA) 2%, ammonium hydroxide to pH3.00. Eluent B (80:10:10) (water TFA.2% ammonium hydroxide pH2.2:methanol:acetonitrile).

Time (min) Percent eluent A Percent eluent B Flow rate (ul/min) 0 100 0 190 1.25 100 0 190 9.38 80 20 190 18.75 50 50 190 25 50 50 190

Table 4.2. MAAs retention times. The order of elution is consistent with Carreto et al 2005.

Elution Substance Retention order time(min) 1 Shinorine 3.5 2 Mycosporine‐2‐glycine 4 3 Palytine‐Serine 4.2 4 Palytine 4.5 5 Asterina 5.3 6 Porphyra‐334 6.2 7 Mycosporine‐Glycine 6.5 8 Unknown in damaged P. meandrina 6.9 9 Palythine threonine 8.3 10 Palythinol 8.6 11 Mycosporine‐methylamine‐threonine 11.5 12 Unknown a in C. multicinctus 14.4 13 Unknown b in C. multicinctus 15.9 14 Usujirene 18.1 15 Palythene 18.8 16 Unknown in P meandrina 23.5

I applied Whitehead & Hedges’ (2003) published method and spectra for the identification of MAAs using electrospray ionization and tandem mass spectrometry ESI‐ MS/MS with a Thermo Finnigan LCQ Deca XP Plus mass spectrometer system operated in the positive electrospray ionization (+ESI‐MS) mode (Whitehead & Hedges 2003). This positive‐ion mass spectral fragmentation permitted a precise identification of the most common MAAs even in the absence of standards since it allowed us to match the obtained MS2 spectra of the MAAs present to the specific published MS2 spectra. Using this method I gathered, for each observed MAA, the retention time, the absorbance maximum, the mass over charge (m/z) of each protonated molecule [M+H]+ (corresponding to the molecular mass of the MAA plus one), and the specific fragmentation pattern (MS2 spectrum). Injection volume was 5ul for the fish mucus extracts and 3ul for the coral extracts. During the HPLC analysis, spectra were continuously collected by the PDA from 250 – 550nm.

Except when indicated, each MAA was identified based on matching its wavelength maximum, mass, and MS2 spectrum with published data. Co‐elution times with pure standards of shinorine, palythine and porphyra graciously donated by Dr. Michael Lesser were also used as added confirmation. Following identification of individual MAAs, I obtained their concentrations using an extension of Beer‐ Lambert Law (modified from Banaszak & Trench 1995) and published molar extinction coefficients as described by Bandaranayake (1998). For all samples, MAA concentration was normalized to the soluble protein content of the filtered extract. I used Bio‐Rad Quick Start Bradford protein assay for the coral samples but due to the small volumes of the fish mucus extracts I utilized the more sensitive NanoOrange florescent assay (Invitrogen). Both assays were read on a Spectramax M2 spectrophotometer (Molecular Devices).

4.3 RESULTS

4.3.1 Measurements of relative downwelling UVR irradiance.

Figure 4.1 summarizes the percentage of downwelling irradiance found at various depths as compared to the levels found just under the surface of the water. As reported in previous work (Jerlov 1976) the relative irradiance decreased with depth with UVB diminishing faster than UVA. For clarity the graph only shows the percentage of downwelling irradiance for the upper and lower limits of each of the three depth‐ranges studied (deep, intermediate, shallow). Each fish territory is contained within one of the three non‐overlapping UVR exposure ranges. Additionally, the data shows that at the time of the measurements all territories including the deepest ones were exposed to some UVR, especially UVA.

Figure 4.1. Relative downwelling irradiance. The shaded areas correspond to the three depth ranges of the study territories.

4.3.2 Coral: Qualitative results for 3 species

As reported in previous work (e.g., Banaszak et al. 2006), the MAA content of the coral samples varies by species.

4.3.2.a Pocillopora meandrina

Pocillopora meandrina presents the greatest array of MAAs (Figure 4.2): shinorine, palythine‐serine, asterina, porphyra, mycosporine glycine, palythine‐threonine, mycosporine‐methylamine‐threonine. It also contains traces of an unknown (peak 17) of mass and λmax consistent with those of MAAs (316amu and 328nm respectively). However, P. meandrina was uncommon in the deepest territories, and when it was found colonies were dead or looked unhealthy. Thus, we obtained only two samples of this species from 15.24m to 18.29m (50 to 60 feet). The MAAs concentration for these deep colonies was very low. Additionally they harbored a different MAA profile as well as an unknown UVR absorbing substance at elution time of 6.9 minutes of Mass+1=259 atomic mass unit (amu) and wavelength maximum λ=327nm not found in P. meandrina

living in intermediate and shallow territories (Figure 4.3). I did not pursue further analysis of this unknown compound.

300000

200000 5 uAU 100000 6 9 1 3 7 17 0 0 10 15 20 25 30 Time (min)

Figure 4.2. Typical Chromatogram of Pocillopora meandrina. Notes for all chromatograms in this paper: a‐ Each MAA is given a number corresponding to its order of elution as seen in the summary Table 4.3. b‐ The absorbance is expressed in the dimensionless unit μAU: micro absorbance unit. c‐ In order to show all MAAs the chromatograms shown are obtained for a wavelength range of 290nm‐400nm unless otherwise indicated.

8 30000

7 20000 2 uAU 10000 4

0 0 5 10 15 20 25 30 Time (min)

Figure 4.3. Chromatogram for the rare P. meandrina found in the deep territories. Mycosporine‐2 ‐glycine (peak 2) and the unknown (peak 8) were not observed at other depth.

Another trace compound only observed in P. meandrina at this depth is consistent with mycosporine‐2‐glycine in mass, wavelength maximum and order of elution. Since the MS2 spectrum has not yet been published for mycosporine‐2‐glycine, I first determined it using our samples. To further confirm the identity of this MAA I also obtained this MS2 spectrum using a previously standardized extract of Anthopleura elegantissima graciously provided by Maxi Eckes (AIMS) (Figure 4.4).

303 100 80

60 288 40 20 200 244 207 228 255 267

Relative Abundance % 0 150 200 250 30 m/z

Figure 4.4. MS2 spectrum of mycosporine‐2‐glycine. Molecular weight M=302amu (atomic mass unit) corresponding to a protonated mass of [M+H]+= m/z = 303.

Similarly, the MS2 spectrum of mycosporine‐glycine with our instrument (Figure 4.5) has not yet been published to our knowledge. Again I obtained it using not only our samples but also a previously standardized extract of the zoanthid, Palythoa tuberculosa, also provided by Maxi Eckes (AIMS). Mycosporine‐glycine did not appear to ionize as easily as the other MAAs observed, resulting in a small MS peak in the total ion concentration graph, but enough ions were generated to obtain an MS signal and obtain an MS2 spectrum (Figure 4.5). The MS2 spectra of palythine‐serine and mycosporine‐methylamine‐threonine have also not been published. Without standards, their identification was based on their

mass, wavelength maximum and order of elution consistent with Carreto et al. (2005). Figures 4.6 and 4.7 respectively show the MS2 spectra of these compounds.

200 100 80 60 40 198 180 210 228 20 146 152 168 136 186 214 246 Relative Intensity (%) 0 120 140 160 180 200 220 240 260 m/z

Figure 4.5. MS2 spectrum of mycosporine glycine. Molecular weight M=245amu (atomic mass unit) corresponding to a protonated mass of [M+H]+= m/z = 246.

275 100 80 60 40 216 245 260 20 172 207 229 Relative Intensity (%) 0 100 150 200 250 m/z

Figure 4.6. MS2 spectrum of the putative palytine serine. Molecular weight M=274amu corresponding to a protonated mass of [M+H]+= m/z = 275.

259 100 80 60

40 303 244 20 288 183 270 Relative Intensity (%) 0 150 200 250 300 m/z Figure 4.7. MS2 spectrum of the putative mycosporine‐glycine‐NMA‐Thr. Molecular weight M=302 corresponding to a protonated mass of [M+H]+= m/z = 303.

4.3.2.b Porites lobata

Porites lobata presents mainly asterina and mycosporine glycine, small amounts of palythine‐serine as well as traces of shinorine, and palythine (Figure 4.8). I also observed an undetermined UV‐absorbing substance of absorbance maximum ~271nm (too low to be an MAA) at elution time ~3.3 minutes. The substance did not appear in the Total Ion Concentration MS chromatogram so it might not ionize well or its mass range is below or above the target range. I did not attempt to further elucidate the origin of this peak.

4.3.2.c Porites compressa

The Porite compressa chromatogram (Figure 4.9) was very similar to its congener P. lobata, it contains mainly asterina, mycosporine‐glycine small amounts of palythine‐ serine, shinorine and palythine.

5 80000

60000

uAU 40000

7 20000 3 4 0 0 5 10 15 20 25 30 Time (min)

Figure 4.8. Typical chromatogram for Porite lobata.

5 250000 200000

150000

uAU 100000

50000 7 4 0 0 5 10 15 20 25 30 Time (min)

Figure 4.9. Typical chromatogram for Porites compressa.

4.3.3 Fish qualitative results

The LC‐MS.MS analysis revealed that the fish mucus extracts contain mainly asterina followed by usujirene and palythene (Figure 4.10 A and 4.10 B). Along with these three dominant MAAs we also identified traces of palythine, palythinol as well as two possible unknown compounds both of mass 284amu and absorption maxima (λmax) at wavelengths 330 and 333nm, respectively.

5 1000000 800000 600000 400000

200000 15 4 10 13 14 0 0 5 10 15 20 25 30 Time (min)

Figure 4.10 A. Chaetodon multicinctus chromatograms: all MAAs. This chromatogram uses the PDA in the range 290 to 400nm to show all MAAs.

15 120000 100000

80000 14 uAU 60000 40000 20000 0 0 5 10 15 20 25 30 Time (min)

Figure 4.10 B. Chaetodon multicinctus chromatograms: selected MAAs. This chromatogram uses the PDA at 360nm only to better show usujirene and palythene at ~18.2 and ~18.8 minutes.

A summary of the qualitative result for coral and fish is given in Table 4.3.

Table 4.3. MAAs presence – absence summary. A MAA is marked “X” if it contributes one of the major absorbance peaks of the UV chromatogram, it is marked “+“ if it contributes only a minor or trace peak. The cell is left blank if the MAA was not detected. The MAAs discovered only in the damaged P. meandrina found in the deeper territories are marked X*.

Elution Substance Retention λmax [M+H] + Pocilopora Porites Porites Chaetodon order time(min) (nm) meandrina lobata compressa multicinctus

1 Shinorine 3.5 333 333 X + + 2 Mycosporine‐ 4 331 303 X* 2‐glycine 3 Palythine‐ 4.18 321 275 X + + Serine 4 Palythine 4.5 320 245 + + + + 5 Asterina 5.3 330 289 X X X X 6 Porphyra‐334 6.2 334 347 X 7 Mycosporine‐ 6.5 310 246 X X X Glycine 8 Unknown in 6.8 327 259 X* damaged P. meandrina 9 Palythine‐ 8.3 321 289 X threonine 10 Palythinol 8.6 330 303 + + + 11 Mycosporine‐ 11.5 327 303 X methylamine‐ threonine 12 Unknown in C. 14.4 330 285 + multicinctus 13 Unknown in C. 15.86 333 285 + multicinctus 14 Usujirene 18.1 357 285 X 15 Palythene 18.8 360 285 X 16 Unknown in 23.5 328 317 + P. meandrina

4.3.4 Corals quantitative results.

The extracts from the three coral species studied showed a significant decrease in MAA concentration with depth. Figure 4.11 to 4.13.

Pm myc-NMA-thr nmol/mg prot vs Depth Levene test for equality of variance of the 50000 square root transformed data: Test statistic = 0.60, p‐value = 0.572 40000

30000 Kruskal‐Wallis on the square root 20000 transformed data. 10000 H = 8.59 DF = 2 P‐value: = 0.014

Pm myc-NMA-thr nmol/mg prot D I S DEPTH

Pm palythine-serine (nmol/mg prot) vs Depth 3000 Levene test for equality of variance of the 2500 square root transformed data: 2000 Test statistic = 2.58, p‐value = 0.137 1500 1000 Kruskal‐Wallis on the square root 500 transformed data. 0 H = 8.59 DF = 2 P = 0.014 D I S DEPTH Pm p-serine nanomoles/ mg prot nanomoles/ Pm p-serine Pm asterina in nmol/mg prot vs Depth Levene test for equality of variance of the square root transformed data: 2000 Test statistic = 1.01, p‐value = 0.407 1500

1000 Kruskal‐Wallis on the square root transformed data. 500 H = 8.59 DF = 2 P‐value = 0.014 0 Pm asterina nmoles/mg prot nmoles/mg Pm asterina D I S DEPTH

Figure 4.11. MAAs quantitative analyses for Pocillopora meandrina. All the tests reported for P. meandrina were performed on square root transformed data to comply with equality of variance assumption.

Pm palytine-threonine nmol/mg prot vs Depth 1200 Levene test for equality of variance of the 1000 square root transformed data: 800 Test statistic = 2.57, p‐value = 0.138 600 400 Kruskal‐Wallis on the square root 200 transformed data. 0 H = 7.12 DF = 2 P value: 0.028 D I S

Pm pal-threonine nmol/mgprot DEPTH

Pm shinorine nmol/mg prot vs Depth 800 Levene test for equality of variance of the 700 square root transformed data: 600 Test statistic = 0.93, p‐value = 0.432 500 400 300 Kruskal‐Wallis on the square root 200 transformed data. 100 0 H = 8.59 DF = 2 P value: 0.014 Pm shinorine nmol/mgprot D I S DEPTH

Pm porphyra nmol/mg prot vs Depth 700 Levene test for equality of variance of the 600 square root transformed data: 500 Test statistic = 0.43, p‐value = 0.668 400 300 Kruskal‐Wallis on the square root 200 100 transformed data: 0 H = 7.81 DF = 2 P = 0.020 Pm porphyra nmol/mg prot nmol/mg Pm porphyra D I S DEPTH

Pm myc-gly in nmol/mg vs Depth Levene test for equality of variance of the 600 square root transformed data: 500 Test statistic = 1.12, p‐value = 0.372 400 300 Kruskal‐Wallis on the square root 200 transformed data: H = 7.50 DF = 2 P value: 0.024 Pm myc-gly nmol/mg prot nmol/mg myc-gly Pm 100 D I S DEPTH

Figure 4.11 (Continued). MAAs quantitative analyses for Pocillopora meandrina. All the tests reported for P. meandrina were performed on square root transformed data to comply with equality of variance assumption. 63

Pl asterina in nmol/mg prot vs Depth 1600 Levene test for equality of variance of 1400 the Test statistic = 0.61, p‐value = 0.552 1200 1000 800 Kruskal‐Wallis 600 H = 14.58 DF = 2 P = 0.001 400 200 Pl asterina nmol/mg prot nmol/mg Pl asterina 0 D I S DEPTH

Pl myc-gly nmol/mg prot vs Depth 800 Levene test for equality of variance of 700 the log transformed data. 600 Test statistic = 2.63, p‐value = 0.094 500 400 Kruskal‐Wallis on the log transformed 300 data. 200 H = 11.01 DF = 2 P = 0.004 100 0 Pl myc-gly nanomol/mg prot nanomol/mg myc-gly Pl D I S DEPTH Figure 4.12. MAAs quantification for Porites lobata.

Pc asterina nmol/ mg prot vs Depth 5000 Levene test for equality of variance of the Test statistic = 0.49, p‐value = 0.626 4000

3000 Kruskal‐Wallis 2000 H = 9.54 DF = 2 P = 0.008 1000

Pc asterina nmol/mg prot nmol/mg Pc asterina 0 D I S DEPTH

Pc myc-glyc nmol/mg prot vs Depth 3000 Levene test for equality of variance of 2500 the Test statistic = 2.01, p‐value = 0.170 2000 1500 Kruskal‐Wallis 1000 H = 10.08 DF = 2 P = 0.006 500 0 Pc myc-glyc nmol/mg prot nmol/mg myc-glyc Pc D I S DEPTH Figure 4.13. MAAs quantification for Porites compressa.

4.3.5 Fish quantitative results

In contrast to the coral data, the quantification of each of the main MAAs in fish mucus extract, asterina, usujirene and palythene did not show any significant effect of depth (Figure 4.14). The λmax of palythene and usujirene and asterina are respectively 360nm, 357nm, 330nm. To test the idea that MAAs that absorb shorter wavelengths would be relatively more abundant in the mucus of fish in shallower waters, I also examined the ratio [(palythene + usujirene)/asterina] but this ratio also failed to show any pattern of change with depth (data not shown).

asterina in nmol/mg prot vs Depth 1400 Levene test for equality of variance of the

1200 Test statistic = 0.02, p‐value = 0.976

1000

800 Kruskal‐Wallis

600 H = 0.70 DF = 2 P = 0.703

400

asterina in nmol/mg prot nmol/mg in asterina 200 D I S DEPTH

palythene in nmol/mg prot vs Depth Levene test for equality of variance of the 18 Test statistic = 1.05, p‐value = 0.369 16 14 12 Kruskal‐Wallis 10 H = 2.19 DF = 2 P = 0.334 8 6 4 2

palythene in nmol/mg prot in nmol/mg palythene 0 D I S DEPTH

Figure 4.14. MAAs quantification for Chaetodon multicinctus.

usujirene nmoles/mg prot vs Depth Levene test for equality of variance of the 6 Test statistic = 1.25, p‐value = 0.308 5 4 Kruskal‐Wallis 3 H = 3.36 DF = 2 P = 0.186

1 usujirene nmol/mg prot 0 D I S DEPTH

Figure 4.14 (Continued). MAAs quantification for Chaetodon multicinctus. 4.4 DISCUSSION

4.4.1 Qualitative analysis

The MAA profiles of the two Porites species are quite similar in that they both contain mainly mycosporine‐glycine and asterina as well as traces of shinorine and palythine‐ serine. In contrast, Pocillopora meandrina presents a greater array of MAAs in addition to mycosporine‐glycine and asterina. These MAAs are:  palythine‐threonine a recently indentified MAA first described in another pocilloporid (Carignan et al. 2009),  mycosporine‐methylamine‐threonine,  porphyra,  Traces of an unknown of mass 316amu and λmax 328nm. The greater similarity of MAAs in composition and relative abundance between species within the same genus of coral seems reasonable, especially since many Porites species appear to be indistinguishable using various genetic markers (Forsman et al. 2009). Further, the observation is rendered more significant in light of recent findings by Stat et al. (2009) who showed that Pocillopora meandrina can simultaneously harbor up to 3 different clades of zooxanthellae (A, C and D). In contrast, they found Porites lobata only contains clade C (Stat et al. 2009). Porites compressa has also been shown to harbor only clade C in other studies (LaJeunesse et al. 2004). Zooxanthellae have been

characterized using various techniques that possess different merits, limitations and levels of resolution (clades, subclades) (e.g., Apprill & Gates 2007, Thornhill et al. 2007, Stat et al. 2009). The findings of Stat et al. (2009) were obtained using both the ITS2 region of nuclear rDNA, and chloroplast 23SrDNA. In both cases they also observed that at the sub‐cladal level the symbionts in Pocillopora meandrina were more diverse than for Porites lobata. It has long been hypothesized that phylogenetic variation in symbiotic dinoflagellates could result in functional difference in the coral holobiont (Buddemeier & Fautin 1993). Here, the host species known to harbor a narrow range of symbiodinium (Porites lobata and Porites compressa) present an assemblage of MAAs less diverse than the species known to contain a greater variety of symbiodinium (Pocillopora meandrina). This is intriguing and calls for further studies. For fishes, previous studies have shown that UVR exposure and consumption of food rich in MAAs are required to produce UV‐absorbing compounds in the mucus (Zamzow 2004). Here, I found that the mucus of Chaetodon multicinctus is rich in asterina but the corals consumed, although rich in asterina, contained several other MAAs not found in the fish: mainly the primary MAAs, mycosporine‐glycine, shinorine and porphyra as well as the secondary MAAs, palythine‐serine, palythine‐threonine and mycosporine‐methylamine‐threonine. This could point to a selective sequestration of asterina by the fish in its mucus. An alternative explanation is that some of the MAAs found in the coral holobiont extracts might not be available to the fish if they are confined to zooxanthellae. Chaetodon multicinctus does not appear to digest the zooxanthellae from the consumed coral, since zooxanthellae are excreted in the fish feces (Dr. Timothy Tricas pers. obs.). However, the excretion of some zooxanthellae does not establish that none are digested. Furthermore, zooxanthellae leak most of their metabolites into the host tissues. In fact mycosporine‐glycine, has been identified in studies of isolated coral tissue (Banaszak et al. 2006). Consequently this alternate explanation is unlikely. Additionally, another study argues for the selective sequestration hypothesis. When Medaka fish (Oryzias latipes) was fed with Mastocarpus stellatus, an algae

containing shinorine and a small amount of asterina and palythine, the fish did accumulate asterina and palythine in their ocular structure but not shinorine (Mason et al 1998). The authors concluded that the animal demonstrated a selective absorption. More surprisingly in my study, palythene and its isomer usujirene were both found in C. multicinctus mucus extract but I did not detect them at all in the three corals eaten, Pocillopora meandrina, Porites compressa and Porites lobata. One could hypothesize that the fish obtain palythene and usujirene from other possible food sources, for instance I found that palythene and usujirene are available in the coral Montipora capitata, a species present in the C. multicinctus territories studied. However in the field we almost never observed the fish consuming this coral species, thus rendering such a scenario unlikely. Consequently the presence of usujirene and palythene in the fish mucus is consistent with the possibility that C. multicinctus may transform some of the MAAs consumed. Similarly, Whitehead et al. (2001), in their study of MAAs in phytoplankton, a phytoplanktivorous pteropod and its predator pteropod, found that the herbivorous pteropod harbored some MAAs not contained in the phytoplankton they were eating. They concluded that although they could not exclude the possibility that the herbivorous pteropods could have accumulated these MAAs if they fed on a different type of phytoplankton before sampling, a more plausible explanation was that bacteria in the guts of the pteropods transformed some of the ingested MAAs. According to our present understanding of MAA biosynthesis (Shick 2004, Carreto & Carignan 2005, Callone et al. 2006, Singh et al. 2008) usujirene and palythene are secondary MAAs that can be generated from porphyra which is synthesized from derivatives of mycosporine‐glycine. Mycosporine‐glycine and porphyra were clearly present in the consumed coral and may have been transformed by the fish into usujirene and palythene. At this point almost nothing is known about the mechanisms of translocation of MAAs from the digestive tract to mucus, let alone about mechanisms of transformation of consumed MAAs. While our results point towards the possibility of this conversion, further research is needed to understand the uptake, translocation and possible transformation of MAAs in fishes. We do not know if the fish

possess any enzymatic machinery able to transform consumed MAAs, or if instead, microorganisms such as bacteria present in the fish digestive system or even in its epidermal mucus are the origin of the transformation.

4.4.2 Quantitative analysis

The downwelling irradiance data confirm that our three depth ranges received non‐ overlapping levels of UVR irradiance that decreased with increasing depth. I had hypothesized that both the coral and the fish would show a decrease in the amount of MAAs with increasing depth. As expected, the normalized quantities of the main MAAs for each of the three coral species studied clearly decreased with depth/UVR exposure. The role of MAAs as a sunscreen compound is well recognized (Böhm et al., 1995, Bandaranayake 1998, Neale et al. 1998, Banaszack & Trench 2001, Torres et al. 2006, Shick & Dunlap 2002, Karentz 2001, Karsten et al. 2009, Riemer et al. 2007 and others), and the coral data presented here add to the body of evidence suggesting an important sun screening function of MAAs in corals. In contrast, the fish showed no trend of MAAs concentrations with depth (Figure 4.14). At first glance it seems reasonable to attribute this dissimilarity between the corals and the fish to possible differences in their coping mechanisms to UVR. A vagile organism like the fish might rely on various methods such as possible behavioral mechanisms added to chemical protection; in contrast the totally sessile corals do not have such options. Consequently, MAA concentrations in the coral would be more responsive to UVR exposure variation than for the fish. However, while we did not systematically attempt to describe and quantify behavior in this work, we did not detect behavioral differences between fish in the shallow versus deeper territories. Additionally, Zamzow’s (2003a, 2004) experimental work on Thalassoma duperrey did not find any correlation of any aspect of the fish behavior with UVR dose. Another possible source of difference between the coral and fish data might be methodological. I used the same LC‐MS method for both types of sample but due to the small amount of the fish samples I used a different protein assay for the normalization

of the fish data. One could argue that this might have affected the quantitative measures. However the coral showed the observed trend with depth even before normalization whereas the fish samples did not. More intriguingly, the absence of change in MAA concentration with depth in the fish (figure 4.14) or the lack of change in relative abundance between asterina and the two longer wavelength MAAs, palythene and usujirene, with depth (data not shown) does not corroborate the observations made using mucus absorbance spectra in this fish species at this location by Zamzow (2003a). Zamzow found that mucus absorbance clearly decreased with depth of capture and her results also indicated that the spectrum of mucus absorbance shifted towards shorter wavelengths at shallower depths. I cannot completely exclude the possibility that this apparent contradiction might, in part, be due to our different methodologies. I specifically measured the concentration of individual MAAs while Zamzow (2003a) measured the total absorbance of the entire mucus which contains other substances able to absorb UVR such as various proteins and glycoproteins (Asakawa 1970, Fletcher et al. 1976, Shephard 1994). However, another difference in between Zamzow (2003a) and this study might be of greater significance than methodological variation to explain our contradictory findings. The 23 fish in this study were caught during July and August, a time of maximum UVR incident irradiance, while the 9 fish examined in the Zamzow study were captured in March and April. Kuffner (1999) has shown that UVR irradiance on cloudless days in the winter in Hawai`i can be 55‐66% lower than in the summer. Zamzow (2003a) has found that the absorbance of the mucus can increase with UVR exposure. It is possible that, as exposure increases to very high levels, fish accumulate more MAAs until an upper limit is reached beyond which no more MAAs can be added to the mucus as shown in the model represented by Figure 4.15. This upper limit is suggested as “Emax” in Figure 4.15.

Accumulation

MAA

Emin Emax UVR exposure Level

Figure 4‐15. Model of MAA accumulation in fish mucus as a response to UVR exposure.

The downwelling irradiance measurements showed that, in August, significant UVR was present even in the deepest territories (Figure 4.10). If all fish captured in the summer had already received irradiance exposure levels at or above Emax then no difference would be detected between them. In contrast, the animals captured by Zamzow (2003a) could have experienced UVR exposure at or under Emax and would show an effect with depth. Both of our divergent findings would be consistent with such a model. A possible ceiling effect for MAAs accumulation in the mucus of fish has ecological implications as well as practical and economical relevance in the context of aquaculture as fish have long been known to sunburn in outdoor pens or clear shallow waters (Bullock & Coutts 1985, Berghahn et al. 1993, Ramos et al. 1994, Walter & Ward 1998, Beveridge 2004). We know that both exposure to UVR and diet affect MAA concentrations in fish (Zamzow 2004) but details of the mechanisms modulating MAA uptake and translocation in fish are unknown.

4.4.3 Conclusion

This work adds to the body of evidence pointing to an important role for MAAs as a natural sunscreen in the marine environment and their presence in, and possible transfer to, different levels of the trophic web. My results suggest a selective uptake of some MAAs by C. multicinctus and the ability to not only translocate but also to transform the consumed MAAs. Additionally, MAAs concentration in fish epidermal mucus may well show a ceiling effect. The mechanisms behind these processes are unknown and should be investigated. In conclusion, much remains to be understood about MAAs. In the past, progress in understanding these compounds has often been hampered by the lack of commercial standards used for identification. Today, technological advances and collaborations between multiple researchers are overcoming this obstacle by providing tools usable by all in the research community. The MS2 spectra presented here should prove useful for MAA identification by adding to the list previously produced by Whitehead (2003), Volkman & Gorbushina (2006) and Carignan et al. (2009).

CHAPTER 5: CONCLUSIONS

Early life on our planet had to contend with the challenge of ultraviolet radiation (UVR) levels much higher than those present today (Cockell 1998, 2000). As a result, living organisms developed various strategies to cope with UVR such as avoidance behaviors, DNA repair mechanisms and the use of UVR screening compounds. Mycosporine‐like amino acids (MAAs) are a family of UVR‐absorbing compounds encountered in many marine and fresh water taxa (Sinha et al. 2007) including:  cyanobacteria (Sinha & Häder 2008),  dinoflagellates (Carreto et al. 1990, Lesser 1996, Banaszak & Trench 2001, Banaszak et al. 2006),  poriferans (Bandaranayake 1996),  phytoplankton (Whitehead & Vernet 2000) and several of their invertebrate predators (Whitehead et al. 2001, Riemer et al. 2007, Hylander & Jephson 2010),  holothurians (Bandaranayake & Des Rocher 1999),  red algae (Hoyer et al. 2002),  corals and other cnidarians (Shick et al. 1999, Yakovleva et al. 2004, Ferrier‐ Pagés et al. 2007, Chapter 4 of this dissertation),  echinoderms (Adams & Shick 1996, 2001, Lesser 2010) and  fishes (Mason 1998, Sinha et al. 2007, Eckes et al. 2009, Chapters 2, 3 and 4 herein). The ubiquitous presence of this family of metabolites in aquatic organisms is an indication of their long evolutionary history and crucial importance. The study of the ecological and physiological functions of MAAs, in some cases, preceded their chemical characterization. For example, Shibata (1969), Jokiel & York (1982) and Kinzie (1993) examined the presence and levels of a UVR‐absorbing substance called S‐320, later shown to be MAAs, in some macroalgae and scleractininan corals (Dunlap & Chalker 1986, review by Shick & Dunlap 1993).

To date, multiple studies, including some of the work presented in this dissertation, are consistent with the role of MAAs in marine organisms as natural UVR protective compounds (e.g., Bandaranayake 1998, Karentz 2001, Conde et al 2000, 2004, 2007, Shick et al. 1995, Teai et al. 1997, 1998, Karsten et al. 1998, Banaszak et al., 1998, Lesser 2000, Corredor et al. 2000, Torregiani & Lesser 2007, Adams & Shick 1996, 2001, Klisch et al. 2001). Some MAAs have also proven to be powerful antioxidants (Dunlap & Yamamoto 1995, Nakayama et al. 1999, Suhn et al. 2003, Yakovleva et al. 2004). However the identification and quantification of MAAs has been complicated by the lack of commercially available standards, and because these standards are still time‐ consuming and expensive to make (Whitehead et al. 2001, Whitehead & Hedges 2003, Hylander & Jephson 2010, Carretto & Carignan 2011). In spite of such draw‐backs, this family of metabolites has generated a considerable body of research. A recent review of the chemical and ecological aspects of MAAs included no less than 289 references to research papers concerned with, or related to, their study since 1969 (Carreto and Carignan 2011). Today, as shown by this dissertation and the work of others, advances in HPLC separation methods and the use of LC‐ MS.MS techniques are addressing some of the challenges and improving accuracy in the identification and quantification of MAAs (Whitehead & Hedges 2002, 2003, Carreto et al. 2005, Volkmann & Gorbushina 2006, Chapter 3 herein). These technological advances should help us to gain a better understanding of MAAs, since much remains to be understood about them. Indeed, some MAAs are still unidentified, as shown by Chapter 4 of this work where a study focusing on no more than three coral and one fish species has uncovered up to four previously undescribed MAAs. Additionally, the long standing belief and early evidence about the requirement of the shikimate pathway for MAAs synthesis (Favre‐Bonvin et al. 1987, Shick et al. 1999, Shick & Dunlap 2002), has been recently challenged by the discovery that in some cyanobacteria some MAAs are synthesized through a completely different and novel pathway (Balskus and Walsh 2010).

Regardless of what pathway is used to synthesize MAAs, there is evidence that some metazoans such as fish cannot produce MAAs de novo. Instead, dietary MAAs are required for fish to harbor MAAs in their eyes or increase the absorbance of their epithelial mucus (Mason et al. 1998, Zamzow 2003a, 2004). Chapter 4 showed that for the corallivorous fish, Chaetodon multicinctus, the MAAs found in their epithelial mucus are different from the MAAs encountered in their diet in two ways: some MAAs like mycosporine‐glycine, abundant in the coral diet, are not detectable in the epidermal mucus of the fish, and some MAAs clearly present in the epidermal mucus such as palythene and usujirene are not detected in the diet. These results are consistent with the findings of other research on fish, sea urchins, and pteropods (Mason et al. 1998, Adams & Shick 2001, Whitehead et al. 2001). These studies, together with the findings presented here, suggest the possibility of selective uptake, translocation and transformation of MAAs by metazoan consumer organisms. Yet almost nothing is known about the mechanisms of these three processes and therefore they present an interesting avenue for future research. Results on the quantification of MAAs with depth, and the associated UVR exposure taken in the context of Zamzow’s (2003a) work on C. multicinctus, led to generation of a model that relates levels of UVR exposure to MAA accumulation in fish epidermal mucus (Chapter 4 herein). The model illustrates a possible ceiling effect for MAA accumulation. Such an ecologically‐relevant effect could be especially important in the context of global climate change. In conclusion, after over 50 years of scientific enquiry, much work is still needed to better understand the phylogeny, function, synthesis, uptake, translocation and transformation of MAAs. These fascinating metabolites are now being investigated with renewed vigor. The interest in MAAs has even reached the general public since the commercialization of MAAs. In the skin care industry the sunscreen and antioxidant properties of MAAs are being touted as able to prevent “the appearance of lines, wrinkles and other signs of photo‐aging” (Mibelle Biochemistry Group website, accessed on March 23rd 2012, http://www.mibellebiochemistry.com/products/active‐

protection/helioguard365.php). This characteristic, while attractive, is rather paradoxical since the study of MAAs has been shown to increase lines, wrinkles and the graying of the hair in, at least, the experience of the author.

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