Commonly referred to as “red tide”, harmful algal blooms (HABs) formed by Karenia brevis occur frequently in the Gulf of Mexico (GOM). A bloom is defined as cell abundances >105 cells L-1. This thesis will focus primarily on Karenia brevis, formerly known as Gymnodinium breve, in the Gulf of Mexico. K. brevis is harmful because it produces brevetoxin, a ladder-frame polyether that acts as a potent neurotoxin in vertebrates. K. brevis commonly causes fish kills, respiratory irritation in humans, and Neurotoxic Shellfish Poisoning (NSP) if ingested. Blooms of K. brevis occur almost annually along the West Florida Shelf (WFS) in the late summer and early fall, when the coastal current is favorable for bloom initiation. Along the Texas-Louisiana shelf (TLS) however, blooms of K. brevis are infrequent and sporadic. While much is known of the blooms along the WFS due to their frequent presence, little is known of the mechanisms driving the blooms along the TLS due to their inconsistent presence. To understand the stochastic nature of HABs along the TLS, historical data of bloom occurrences from 1996 to present were compared with NOAA station PTAT2 wind, sea-level pressure, air and water temperature data and NCEP NARR-A sea-level pressure data. The difference in the monthly-mean along-shore component of the wind was statistically significant between bloom and non-bloom years in September (p<<0.001) and April (p=0.0015), with bloom years having a strong downcoast current. Monthly mean water temperature values yielded similar results between bloom and non-bloom years. Both March and September monthly-mean water temperature values were lower during non-bloom years with p-values of 0.01 and 0.048, respectively. These results suggest the possibly of forecasting for HABs along the TLS with currently measured, publicly available data.

<p>The capacity of marine organisms to adapt to natural and anthropogenic stressors is an integral component of ocean health. Harmful algal blooms (HABs), which are one of many growing threats in coastal marine ecosystems, represent a historically present natural stressor that has recently intensified and expanded in geographic distribution partially due to anthropogenic activities. In the Gulf of Mexico, HABs of <italic>Karenia brevis</italic> occur almost annually and produce neurotoxic brevetoxins that have been associated with large-scale mortality events of many marine species, including the common bottlenose dolphin (<italic>Tursiops truncatus</italic>). The factors resulting in large-scale dolphin mortality associated with HABs are not well understood, particularly in regards to the seemingly different impacts of HABs in geographically disjunct dolphin populations. My dissertation investigates a genetic basis for resistance to HABs in bottlenose dolphins in central-west Florida and the Florida Panhandle. I used both genome-wide and candidate gene approaches to analyze genetic variation in dolphins that died putatively due to brevetoxicosis and live dolphins from the same geographic areas that survived HAB events. Using restriction site-associated DNA sequencing, I identified genetic variation that suggested both a common genetic basis for resistance to HABs in bottlenose dolphins across the Gulf coast of Florida and regionally specific resistance. Many candidate genes involved in the immune, nervous, and detoxification systems were found in close genomic proximity to survival-associated polymorphisms throughout the bottlenose dolphin genome. I further investigated two groups of candidate genes, nine voltage-gated sodium channel genes selected because of their putative role in brevetoxin binding and four major histocompatibility complex (MHC) loci selected because of their genomic proximity to a polymorphism exhibiting a strong association with survival. I found little variation in the sodium channel genes and conclude that bottlenose dolphins have not evolved resistance to HABs via mutations in the toxin binding site. The immunologically relevant MHC loci were highly variable and exhibited patterns of genetic differentiation among geographic regions that differed from neutral loci; however, genetic variation at the MHC also could not fully explain variation in survival of bottlenose dolphins exposed to HABs. In my final chapter, I consider the advantages and drawbacks of the genome-wide approach in comparison to a candidate gene approach and, as laid out in my dissertation, I recommend using both complementary approaches in future investigations of adaptation in genome-enabled non-model organisms.</p> / Dissertation

Coral cover is in decline on a global scale, with increased mortality events being attributed to a number of global and regional stressors. While the impacts of global stressors (e.g. sea surface temperature rise, ocean acidification) are well documented, there is growing interest in identifying and understanding the impacts of regional stressors. The reason for this change in focus is that regional stressors can often work in combination, sometimes synergistically, with global stressors and that stressors on a regional scale tend to be more easily mitigated by management practices. One regional stressor that impacts a myriad of marine organisms in the southeastern United States is the annual red tide blooms produced by the dinoflagellate, Karenia brevis. Their impacts, along with the lipid soluble polyether neurotoxins they produce, termed brevetoxins, are well studied in economically important organisms, such as bivalves. However, little is known of their impacts on organisms that possess ecological importance, such as species of scleractinian coral. To address this gap in knowledge, a multifaceted study is discussed herein, which evaluated the effects of ecologically relevant concentrations of K. brevis and associated brevetoxins on different coral life history stages and coral species. The second chapter addresses the impacts of red tide on larval behavior, settlement and survival of the coral species Porites astreoides, as well as impacts of photochemical efficiency and oxidative stress within different coral species (P. astreoides larvae, P. astreoides adults, Acropora cervicornis, Cladocora arbuscula, and Phyllangia americana). The third chapter confers the use of broad-scale proteomic analysis to identify the cellular response of the non-model coral species, P. astreoides, following exposure to red tide. Coral larvae actively avoided both medium and high bloom conditions of K. brevis and brevetoxins, while percent larval settlement and survival were impacted following exposure to high bloom concentrations of K. brevis. Photochemical efficiency of in hospite Symbiodinium was reduced following exposure to both K. brevis and brevetoxin in P. astreoides larvae, as well as exposure to K. brevis in P. astreoides adults, while being unimpacted in A. cervicornis. Compared to controls, high bloom conditions resulted in an increase in biomarkers of lipid peroxidation in C. arbuscula. This was also seen in P. astreoides larvae at 24 hours; however, this difference was indistinguishable following 48 hours. Surprisingly, no other biomarker of oxidative stress analyzed were impacted. Broad-scale proteomic analysis of P. astreoides following exposure to red tide conditions revealed variable changes in proteome expression depending on if the corals were exposed to K. brevis or brevetoxins. Exposure to brevetoxins resulted in differential expression of proteins related to DNA organization, chromatin formation and transcription expression; while exposure to K. brevis resulted in differential expression of proteins related to redox homeostasis, protein folding, energy metabolism, and production of reactive oxygen species. The results of this study demonstrate the potential for annual red tide blooms to act as a regional stressor on coral species. They highlight the ability of red tide conditions to negatively impact coral at multiple life history stages and that the extent of these effects may be species specific. They also provide further incite of coral’s response to red tide exposure at the cellular level.

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.