The effects of repeated anandamide treatment on orexin, weight loss, and physical activity in zebrafish (Danio rerio) during caloric restriction Moeller, Eric S. Master’s Thesis Bloomsburg University of Pennsylvania Thesis Committee: Dr. Candice Klingerman Dr. William Schwindinger Dr. Jennifer Venditti Moeller 2 Abstract: Cannabis is widely used as a therapeutic to increase appetite in chemotherapy patients and to reduce anxiety in patients with stress disorders. While Cannabis use increases appetite and reduces anxiety, chronic use is paradoxically linked to lower rates of obesity. The effects of acute exposure to endocannabinoids have been studied, but the effects of repeated exposure has not. As endocannabinoids play a pivotal role in food consumption, overstimulation of the endocannabinoid system over time may cause a tolerance to cannabinoids, leading to lower basal endocannabinoid signaling and food consumption. Endocannabinoids regulate many neuronal pathways including reward, sleep, and hunger. The endocannabinoid 2-arachidonoylethanolamine (Anandamide or AEA) modulates physical activity during periods of moderate-intensity exercise and is implicated in the experience of a ‘runners high.’ Understanding how this system responds to repeated stimulation could provide insights into the link between endocannabinoids, weight loss, and physical activity. This study investigated how the repeated treatment with anandamide impacts physical activity and weight loss in zebrafish (Danio rerio) during a 21-day caloric restriction. Anandamide treatment significantly increased physical activity, but this increase gradually reduced over the course of the experiment. Caloric restriction alone showed reduced physical activity, as measured by the average velocity over a 10-minute observation period (0.15 cm/sec) compared to the negative control group (0.173 cm/sec), however the group treated with both AEA and caloric restriction had the highest average movement (0.200 cm/sec). Caloric restriction was verified via qPCR for orexin, a neuropeptide involved in regulating hunger. Groups subjected to caloric restriction had a greater expression of Moeller 3 orexin RNA compared to the non-calorically restricted groups. It was hypothesized that anandamide treatment would increase orexin signaling when food is limited, as an increase in physical activity would cause an increase in hunger. Interestingly, there were no differences in orexin expression due to anandamide treatment, and no change in cannabinoid receptor (CB1) expression was found. The results of this study indicate that anandamide treatment stimulates physical activity during periods of caloric restriction, however it does not stimulate orexin signaling. The groups treated with anandamide both showed weight loss similar to caloric restriction alone, indicating that repeated treatment of anandamide increases weight loss. These reductions in weight were likely independent of stress, as there were no differences in the rates of thigmotaxis, a common marker of stress in zebrafish, between the treatments during the course of the experiment. Moeller 5 Acknowledgements: I would like to thank my committee for their support throughout the completion of this research. They were generous with their time, quick to respond to my requests, and always willing to help when I needed it. I owe a special thanks to my Thesis Advisor, Dr. Candice Klingerman, who was always optimistic and provided positive support when I needed guidance. Each of my committee members supported me in their own way and were generous to share their knowledge and expertise. I would also like to thank Dr. Thomas Klinger, the Graduate Program Coordinator, for his support. He was the person that everyone knew to contact if they needed anything, and his support was the glue that held all of the graduate students together. I am honored to have had the opportunity to work as a Graduate Assistant under his guidance. His kind and gentle demeanor paired with his expertise and leadership resulted in one of the most pleasant and effective learning experiences that I have had. I would also like to thank all of the other professors that taught and guided me throughout the completion of my thesis. All of them were willing to help and contributed to my project, whether I was inspired by material in their class or through discussions about my research and methodology. I also thank Bloomsburg University for providing the opportunity for me to conduct this research and for providing the opportunity to work as a Graduate Assistant. I would like to thank the Husky Research Corporation and the Commonwealth of Pennsylvania University Biologists for their generous grant support for my project. Moeller 6 Table of Contents: Page(s): Content: 1 Title Page 2–3 Abstract 4 Approval Page 5 Acknowledgements 6 Table of Contents 7 - 21 Introduction 22 – 31 Methods 31 – 33 Results 33 – 51 Discussion 51 – 72 Literature Cited 72 – 77 Appendix I - IACUC Approval Form 77 - 87 Appendix II – Figures 87 – 122 Appendix III – Python Code Moeller 7 Introduction: Cannabinoids are known to increase appetite and are used medicinally to increase hunger in patients who have decreased appetite during chemotherapy (RazmovskiNaumovski et al., 2022). If there is a physiological adaptation to cannabinoid-induced appetite increases, there may be a compensatory mechanism that causes reduced appetite when cannabinoids are not present, similarly to how other drugs cause decreased sensitivity to normal signaling. Aside from this, endocannabinoids have wide-ranging effects beyond the main psychoactive effects of cannabis and are common throughout many endocrine and neuronal pathways and regulatory systems (Oltrabella et al., 2017). Endocannabinoids themselves may have widely differing functions compared to cannabis (Hoyer et al., 2010), as even different compounds of cannabis itself can have different effects (such as the wide differences between the psychoactive compound THC and the non-psychoactive compound CBD, which are the main cannabinoids found in cannabis). Understanding how endogenous cannabinoids such as anandamide and 2-arachidonoylglycerol affect physiological functioning when given as a treatment over time could help in the development of therapeutics and allow for the discovery of interactions between endocannabinoids and the pathways that they are involved in. The goal of this experiment was to investigate how repeated treatment with anandamide, one of the two main endocannabinoids, interacts with physical activity and weight loss during caloric restriction. As anandamide is expressed highly in the hypothalamus, the main site of appetite regulation (Timper and Brüning, 2017), and cannabinoids are known to increase hunger (Kirkham, 2009), we hypothesized that Moeller 8 anandamide treatment would increase hunger. To investigate this, we evaluated how anandamide affected physical activity and weight loss in zebrafish (Danio rerio). If anandamide caused an increase in hunger, it would likely stimulate the orexin system, which has a major function in feeding behavior and the motivation to seek out food in the fasted state (Sakurai, 2008). We predicted that if there was no extra food available, this increase in physical activity would lead to increased weight loss over time. We also predicted if anandamide increased hunger, it would increase the transcription of orexin. This was measured using quantitative PCR from cDNA created from RNA isolated from the brains of studied zebrafish. Orexin The orexin system consists of two neuropeptides, orexin-A and orexin-B. These two neuropeptides are derived from a common precursor, pre-pro-orexin, which is highly phylogenetically conserved (Sakurai, 2006). In humans, orexin-A and orexin-B are the ligands of two G protein-coupled receptors, the orexin 1 and orexin 2 receptors (OX-1R and OX-2R). Neurons that release orexin are termed orexinergic neurons, and the cell bodies of these neurons are located solely in the hypothalamus (Ebrahim et al., 2002). While the orexinergic neurons are located only in this area, they project their axons to many regions of the brain in both humans and zebrafish. In zebrafish, orexinergic neurons send projections from the lateral hypothalamus (LH) to the dorsal and ventral telencephalon, the ventromedial hypothalamus, the ventral, lateral, and caudal hypothalamic nuclei, the caudal zone of the periventricular gray zone of optic tectum, the periventricular nucleus of the posterior tuberculum, the lateral nuclei in the dorsal tectum Moeller 9 and tegmentum, and the locus coeruleus (Kaslin et al., 2004; Imperatore et al., 2018). Orexin-A and orexin-B are stored in neurosecretory vesicles located at the synaptic terminal of orexinergic neurons and are released in a Ca2+-dependent manner. Orexin signaling is neuroexcitatory, with binding of orexin to OX-1R or OX-2R on the postsynaptic neuron causing depolarization. Unlike humans, only the OX-2R has been identified in zebrafish (Wong et al., 2011). The presence of only a single orexin receptor makes zebrafish a good model organism for the study of orexin function, as zebrafish studies do not have to control for differences in signaling between the two different receptors that respond to the same ligands. Orexin signaling regulates many physiological functions, such as physical activity, sleep/wake cycle, energy homeostasis, and feeding behavior (Prober et al., 2006; Panula, 2010; Elbaz et al., 2017; Tsujino and Sakurai, 2013). Orexinergic neurons in the lateral hypothalamus receive inputs from orexigenic neuropeptide Y/agouti-related protein (NPY/AgRP) neurons and anorexigenic pro-opiomelanocortin/cocaine-andamphetamine related transcript (POMC/CART) neurons in the ARC. Signaling from NPY/AgRP neurons increases orexin signaling, while signaling from the POMC/CART neurons decreases orexin signaling. Orexinergic neurons also receive CB1-expressing glutamatergic inputs, so activation of the cannabinoid system also regulates orexinergic signaling in the hypothalamus. As one of the main functions of orexin is to increase physical activity during fasted states, we were interested in discovering if anandamide treatment increases orexin signaling to modulate physical activity during caloric restriction. Moeller 10 Endocannabinoids: The endocannabinoid system is an important retrograde signaling system that regulates a wide variety of physiological processes in the body (Alger, 2013). Due to its high degree of phylogenetic conservation and the numerous processes it is involved in, this system is a crucial part of normal functioning. Having been discovered in the 1990’s, the research on endocannabinoids is only recently gaining widespread interest. Many textbooks do not mention this crucial pathway, even though it has large implications for human health. As the number of individuals that use cannabis and cannabis-derived products increases and legalization of cannabis becomes more widespread, it is important to study the physiological consequences of cannabis use. Research regarding this topic will allow for healthcare professionals and researchers to understand not only normal neuronal signaling, but also how cannabis affects physiology. This knowledge could be used therapeutically, as cannabis is already used medicinally to treat several conditions such as nausea, pain, Tourette syndrome, epilepsy, anxiety disorders, and more. As research into this system continues, it will be possible to create more selective drugs that can treat these illnesses without the psychoactive effects and potential for abuse that comes with cannabis use. The endocannabinoid system is named after Cannabis, a drug derived from the plant Cannabis sativa, which, when ingested, exerts a psychotropic effect through its main psychoactive constituent, Δ9-tetrahydrocannabinol (THC). This plant has been used medicinally for thousands of years, and recently the FDA approved drugs Marinol (THC) and Epidolex (CBD) have been used clinically to stimulate appetite and relieve pain, nausea, seizures, and anxiety. While widely used recreationally, it also is used Moeller 11 medicinally to reduce seizures associated with epilepsy and to reduce the discomfort and nausea caused by cancer chemotherapy (Alger, 2002). There are two main endocannabinoid signaling molecules, Narachidonoylethanolamide (anandamide, or AEA) and 2-arachidonoylglyceraldehyde (2AG). These molecules bind to two endogenous cannabinoid receptors on presynaptic neurons, either the cannabinoid type 1 (CB1) receptor or the cannabinoid type 2 (CB2) receptor. The cannabinoid receptors are Gαi/o-coupled and have seven characteristic alpha helical transmembrane domains connected by three extracellular and three intracellular loops. The alpha helices form a binding pocket for agonist binding, which activates the attached heterotrimeric G-protein (Hua et al., 2017). G-protein coupled receptors (GPCRs) are transmembrane proteins with multiple membrane spanning domains. They are coupled to a specific G-protein, which consists of three protein domains, designated as α, β, and γ. The α and γ subunits are membrane bound, while the β subunit is bound to the γ subunit. While inactive, the three subunits are bound to one another and the α subunit is bound to a GDP molecule within a GDPspecific binding site (Rosenbaum, Rasmussen, and Kobilka, 2009). In the canonical model of GPCR signaling, G-proteins are activated when a specific signaling molecule binds to an associated GPCR. Signaling molecule binding induces a conformational change that causes coupling with the inactive G-protein. Binding of the G-protein causes the α subunit to release the bound GDP. The G-protein will then bind a GTP molecule, which activates the G-protein. The α and βγ subunits then dissociate from both each other and the GPCR and travel to activate secondary messengers (Digby, Sethi, and Lambert, 2008). There are multiple α and βγ subunit types Moeller 12 which associate with G-proteins of a specific family. In the case of the CB1 receptor, the Gαi subunit inhibits adenylyl cyclase, while the Gβγo subunit functions to activate phospholipase C-β (Howlett, Blume, and Dalton, 2010). Endocannabinoids are located throughout the central and peripheral nervous systems, including the brain, eyes, skin, immune system, reproductive organs, digestive system, and connective tissues (Ayakannu et al., 2013). The endocannabinoid system is highly conserved throughout evolutionary taxa, indicating that this system is crucial for normal functioning. The main endocannabinoid receptor in the brain is the CB1 receptor, although it can also be found diffusely in the peripheral nervous system (Mackie, 2005). The CB2 receptor is mainly associated with the immune system, but it has also been found in low concentrations in the brain and other tissues (Zou and Kumar, 2018). Anandamide is synthesized from arachidonic acid and ethanolamine precursors, while 2-AG is synthesized from arachidonic acid and glycerol. These molecules are synthesized on-demand from the postsynaptic neuron and bind to CB1 or CB2 receptors on the presynaptic neuron. Endocannabinoid binding causes a suppression of neurotransmitter release from the presynaptic neuron by decreasing presynaptic calcium entry (Kreitzer and Regehr, 2001). Synthesis of endocannabinoids is triggered by a rise in postsynaptic calcium entry during postsynaptic depolarization, as voltage-gated Ca+2 channels open and allow for calcium to flood into the postsynaptic neuron (Alger and Kim, 2011). Endocannabinoid synthesis enzymes are membrane-bound, as many of the precursors for both anandamide and 2-AG are hydrophobic and thus are located in the nonpolar region of the cell membrane. The endocannabinoids are synthesized by these membrane-bound enzymes and reside within the phospholipid bilayer, where they can Moeller 13 diffuse laterally along the membrane (Tian et al., 2005). The endocannabinoids are released from the postsynaptic cells via vesicles, with the endocannabinoids located within the vesicle membrane. Binding of these vesicles to the presynaptic neuron allows the endocannabinoids to diffuse laterally to the CB1 or CB2 receptors, where they bind to activate signaling (Nakamura et al., 2019). Endocannabinoids are quickly degraded in the presynaptic terminal. Anandamide is degraded by the enzyme fatty acid amide hydrolase (FAAH), while 2-AG is degraded by monoacylglycerol lipase (MGL) (Cajanus et al., 2016; Ueda et al., 2011). The rapid synthesis and degradation of endocannabinoids allows for rapid, transient, and precise regulation of synaptic transmission. Endocannabinoid Synthesis and Release Endocannabinoids are synthesized and released on-demand via a postsynaptic increase in intracellular Ca+2 and activation of Gq/11-coupled receptors. Voltage gated Ca+2 channels open during membrane depolarization in the postsynaptic neuron, which activates a series of enzymes attached to the interior of the cell membrane (Zou and Kumar, 2018). For 2-AG synthesis, phospholipase C hydrolyzes phosphatidylinositol 4,5bisphosphate (PIP2) precursors from the phospholipid bilayer to form inositol triphosphate (IP3) and diacylglycerol (DAG). DAG is then converted into 2-AG by diacylglycerol lipase (DAGL) and can then be secreted to the presynaptic terminal or degraded at the postsynaptic terminal (Zou and Kumar, 2018). Moeller 14 Anandamide is created from phosphatidylcholine and phosphatidylethanolamine precursors by N-acyl phosphatidylethanolamine-specific phospholipase D (NAPE phospholipase D). 2-AG has a tonic concentration approximately 1000 times higher than anandamide in the brain, so it is thought that 2-AG is the main ligand for cannabinoid receptors in the brain and is responsible for normal endocannabinoid control over associated signaling pathways (Zou and Kumar, 2018). Due to its lower concentrations, anandamide is likely produced for rapid modulation of neuronal signaling under periods of stress, while 2-AG has a more tonic role in regulating neuronal pathways. The newly formed endocannabinoids are located in the cell membrane, where they can travel to the presynaptic terminal in a manner that is not well understood but thought to occur either by simple diffusion or by vesicular transport. The exact mechanism of endocannabinoid transport across the synapse has yet to be fully characterized, although several possible models exist such as vesicular transport, passive diffusion, and endocytosis (Fowler, 2012; Chicca et al., 2012). As endocannabinoids are lipophilic, once they are transported to the presynaptic neuron they incorporate into the plasma membranes and travel laterally into the binding pockets of the endocannabinoid receptors on the presynaptic cell (Dainese et al., 2010). Endocannabinoid Binding and Signaling Endocannabinoids bind to a small hydrophobic pocket in the CB1 receptor formed by helices 3, 5, and 6 (Tian et al., 2005). This binding then activates the coupled Gαi/o-protein that acts to inhibit the activity of adenylyl cyclase (AC). AC functions to Moeller 15 create cyclic AMP (cAMP) by dephosphorylating ATP, so its inhibition decreases the concentration of cAMP. cAMP functions as a secondary messenger molecule by binding to regulatory subunits on another secondary messenger, protein kinase A (PKA). Binding of cAMP to regulatory subunits on PKA activates catalytic subunits, which disassociate from the regulatory subunits and go on to activate either transcriptional changes or enzymatic changes within the cell by phosphorylating associated proteins (Ishikawa and Homcy, 1997). The reduction in cAMP caused by CB1 receptor activation inhibits this downstream signaling, which in turn acts to decrease presynaptic calcium influx (Kano, 2014). The inhibition of presynaptic calcium influx inhibits the release of neurotransmitters, causing a transient suppression of neuron signaling (Kreitzer and Regehr, 2001). The CB1 receptor is located in regions of the brain controlling hormone secretion (mostly concentrated in the hypothalamus) and CB1 receptor signaling in these regions has been shown to inhibit the synthesis and secretion of many hormones, possibly by either reducing cAMP signaling or by decreasing the hormone stimulating neurotransmitters in the hypothalamus. The role of endocannabinoids in the regulation of hormones could lead to a suppression of endocrine function during chronic THC use, although studies on changes in basal hormone levels in humans have been inconclusive (Hillard, 2015). Recent studies have shown that CB1 receptors are also located within the membranes of calcium containing endolysosomes within the cytoplasm. Endolysosomes are acid- and degradation enzyme-filled vesicles contained within the cell, and they usually function to degrade dysfunctional or downregulated proteins. Endosomes can also Moeller 16 be used to store transmembrane proteins, so that if more are needed on the cell membrane, the cell already has them available, and they can simply be moved to the membrane via exocytosis rather than being newly synthesized by transcriptional upregulation. Internal CB1 receptors are not used as a reserve however, as intracellular membrane bound CB1 receptors do not migrate to the cell surface (Grimsey et al., 2010). As cannabinoids are hydrophobic, they can diffuse through the cell membrane to reach the interior of the cell and activate internal receptors. Anandamide has been shown to bind to and stimulate intracellular CB1 receptors, releasing intracellular calcium stores either from endolysosomes via signaling with the secondary messenger nicotinic acidadenine dinucleotide phosphate (NAADP), or from the endoplasmic reticulum (ER) by activating phospholipase C-β. Active phospholipase C-β catalyzes the cleavage of PIP2 to form diacylglycerol and IP3. IP3 activates IP3-gated Ca+2-release channels located in the ER membrane, leading to an increase in intracellular calcium (Brailoiu et al., 2011). This is surprising, as the main function of membrane bound CB1 receptors is to decrease the concentration of intracellular Ca+2 in the presynaptic neuron (Mackie, 2006). This suggests that CB1 receptor signaling could either increase the concentration of Ca+2 or decrease the concentration of Ca+2 depending on if endocannabinoids bind to internal or external receptors, respectively. Further complicating the role of endocannabinoid signaling, CB1 receptors are also located intracellularly on mitochondria within neurons, where they can influence mitochondrial function through a number of possible methods (Nunn, Guy, and Bell, 2012). Binding of endocannabinoids to mitochondrial CB1 receptors could act to reduce the release of neurotransmitters by limiting the neuron’s production of ATP, as the Moeller 17 release of neurotransmitters is energetically costly (Bénard et al., 2012). The differences in the effect of CB1 receptors based on their location within the neuron demonstrates the complexity of endocannabinoid signaling and explains how its role in the regulation of neuronal signaling is not yet well characterized. Internal CB1 receptors have not been studied in as much depth as membrane bound CB1 receptors, but they could play a crucial role in producing the physiological effects of endocannabinoid signaling. It is interesting to note that activation of 2-AG synthesis also forms IP3 during the formation of diacylglycerol by phospholipase C-β. This allows for even a weak depolarization of the postsynaptic neuron to activate 2-AG synthesis, as IP3 signals to IP3-gated Ca+2-release channels in the ER of postsynaptic neuron to stimulate the release of more Ca+2, strengthening the depolarization signal. This means that even during weak postsynaptic depolarization, 2-AG synthesis can be stimulated via a positive feedback mechanism (Hashimotodani et al., 2007). Another interesting note is that the variety of physiological effects caused by endocannabinoid signaling differs from normal diversification of signaling. In most signaling pathways, differences in the physiological effects of signaling pathways occurs through receptor diversification, where single signaling molecules can have different effects depending on the type of receptor they bind to. With endocannabinoid signaling, both anandamide and 2-AG bind to CB1 receptors yet have different effects. This indicates that the variety of functions are not due to a difference in which receptor the ligand binds to but is due to a difference in which ligand binds to the receptor. As the two ligands are largely present in the same locations, the two endocannabinoids may compete with the same receptors, or bind to different receptors that can then have varying effects Moeller 18 (Hudson, Hebert, and Kelley, 2009). Possibly, 2-AG mediates normal physiological tone, as it is expressed at a higher normal concentration than anandamide but has less effect, while anandamide is synthesized under situations where neuronal signaling needs to be altered quickly (Luchicchi and Pistis, 2012). Endocannabinoid Degradation Endocannabinoid signaling is ceased upon hydrolysis of anandamide or 2-AG in the presynaptic neuron (Piomelli, 2003). The duration and strength of endocannabinoid signaling results from the amount of endocannabinoids synthesized by the postsynaptic neuron and the rate at which they are degraded by the presynaptic neuron. 2-AG is degraded into arachidonic acid and glycerol by MGL at the axon terminal of the presynaptic cell, while anandamide is degraded into arachidonic acid and ethanolamine by FAAH. The arachidonic acid, glycerol, and ethanolamine products can then either be further modified for use in other enzymatic pathways or recycled to form new endocannabinoids (Ahn, McKinney, and Cravatt, 2008). Endocannabinoids, Physical Activity, and Weight Loss The concentration of endocannabinoids in circulating blood and fat tissues increases with adiposity and when animals are fed high-fat diets. In one human study, AEA and 2-AG concentrations were measured in both abdominal and gluteal fat depots before and after weight loss in obese subjects. 2-AG was found to decrease after weight loss in both deposits, however AEA did not decrease (Bennetzen et al., 2011). Similarly, Moeller 19 aerobic exercise training was correlated to a reduction in circulating AEA levels, which was associated with increased weight loss and improved mood (Belitardo et al., 2019). AEA serum concentrations increase during physical activity, where it is thought to reduce pain associated with high-intensity aerobic exercise and induce euphoria by reducing inhibitory signaling to reward centers. As AEA signaling is mainly associated with the negative modulation of both emotional and physical stress, this could be an adaptive mechanism to reward physical activity even though the experience may be physically painful (Gamelin et al., 2016). While AEA secretion is high during aerobic exercise, circulating AEA is lower in subjects that regularly exercise and maintain a healthy body weight (Thompson et al., 2017). Endocannabinoids signal to increase hunger in the hypothalamus, where ghrelin interacts with the endocannabinoid system to increase hunger and are downregulated by leptin (Kola at al., 2008). Stimulation of the endocannabinoid system increases ghrelin release from the GI tract, increases fatty acid synthesis and storage in the liver, decreases insulin-stimulated glucose uptake in skeletal muscle, and promotes fat storage in adipose tissue (Isoldi and Arrone, 2008). These results indicate that the function of the endocannabinoid system with regards to exercise is to reduce physical activity, increase food intake, and stimulate energy storage. As increased endocannabinoid stimulation is associated with increased appetite, increased adiposity, and decreased physical activity, it is surprising that large national studies have found a decrease in the prevalence of obesity in individuals that regularly use cannabis. Two national studies investigating the link between obesity and cannabis use found that participants reporting no cannabis use had a 22.0% and 25.3% incidence of Moeller 20 obesity, while participants reporting cannabis use of at least 3 times per week had a 14.3% and 17.2% incidence of obesity (Le Strat and Le Foll, 2011) This appears contradictory, as increased endocannabinoid levels are correlated with obesity, while stimulation of this system via cannabis use decreases obesity. One important point to consider is that normal endocannabinoid signaling is very different from hyperstimulation of receptors during drug use. Tonically increased endocannabinoid signaling is correlated with increased hunger and decreased physical activity, however transient hyperstimulation of this signaling pathway from cannabis use downregulates CB1 receptors due to drug tolerance (D’Souza et al., 2016). If CB1 receptors are downregulated, signaling will be decreased and will only be elevated during cannabis use. This could explain the paradox described above, as the downregulation of CB1 receptors over time would cause a decrease in endocannabinoid signaling. Putting this together, when regular cannabis users ingest cannabis, they have increased appetite and decreased physical activity. However, when they are not affected by cannabis, they will have decreased appetite and increased physical activity. This would be true even though endocannabinoid levels may be high, since the CB1 receptors are downregulated. This could lead to a form of intermittent fasting, where regular cannabis users are not hungry before they use cannabis, and only have increased appetites when they use cannabis (Clark et al., 2018). If this model is correct, then any substance that hyperactivates CB1 receptors should result in CB1 receptor downregulation. Cannabis use is correlated with decreased physical activity, however increased AEA is correlated with increased physical activity and reward signaling during exercise (Raichlen et al., 2012). If hyperstimulation of CB1 Moeller 21 receptors with AEA causes both receptor downregulation and increases physical activity without the psychoactive effects of marijuana, it could potentially be a useful therapy for weight loss. Hypothesis: Due to the positive interaction of AEA with hunger and physical activity, treatment with anandamide will stimulate hunger and increase physical activity in zebrafish. If food is limited, this increase in physical activity will result in weight loss and increased transcription of orexin. As the zebrafish gain tolerance to the AEA treatment, there will be a reduction in physical activity, the rate of weight loss, and CB1 receptor expression. Specific Aims: • To determine the effect of repeated AEA treatment on body weight during caloric restriction (CR) • To determine the effect of repeated AEA treatment on physical activity during caloric restriction • To determine the effect of repeated AEA treatment on orexin and CB1 receptor expression during caloric restriction Methods: Moeller 22 Animal subjects: Zebrafish are an excellent model organism to study energy balance and metabolism, as they control energy balance and physical activity regulation through the same mechanisms as humans. Zebrafish are easy to house, as they are social, small, and can be group housed. As they live in water, each individual requires less space than rodent models. They are also easy to treat, as they can simply be immersed in a solution containing a treatment rather than having to be injected or administered drugs via food and water. The experiments described herein were conducted in accordance with an approved Bloomsburg University IACUC protocol (protocol # 159). Zebrafish husbandry: Adult zebrafish were introduced to the housing system in the freshwater fish room (Hartline Science Center; room B55), group housed (4 tanks, 10 fish/tank), and allowed to acclimate to the facility for 5 days (Figure 1). Fish were kept on a 14:10 day/night light cycle and water temperature was be maintained at 26°C ± 1.0°C using aquarium heaters. An air pump was used to oxygenate the water in the system. Water was filtered through a reverse osmosis/deionized (RO/DI) filtration system (Spectrapure) and delivered automatically to each aquarium from a holding tank. Initially, water quality was measured daily using an API Freshwater Master Test Kit. After water quality became stable, pH, ammonia, nitrate, and nitrite concentrations Moeller 23 were tested weekly. Water pH was maintained between 7-8, alkalinity between 50-150 mg/L CaCO3, salinity between 0.5-2.0 g/L, dO2 at 2 mg/L, CO2 below 20 mg/L, and nitrogenous waste at less than 0.02 mg/L (Lawrence, 2011). Adult fish were fed commercially available zebrafish food (Adult zebrafish diet; Pentair Aquatic Ecosystems) once per day. To determine the amount of food given to each group, the zebrafish were weighted and fed 3% total body weight (Dametto et al., 2018). Once per day after testing, the non-CR groups were fed 3% of total body weight while the 50% calorically restricted groups were fed 1.5% of total body weight. Determining body weight: The total weight of each group was collected before the experiment and once per day preceding AEA treatment. Weights were determined by transferring all fish from a group into a container with a pre-weighed amount of water on a scale. The change in total weight of the pre-weighed container after the addition of the fish was recorded After weighing, the water and fish were poured into a net, the net was briefly blotted on a paper towel to remove excess water, and the fish were transferred to the AEA administration container. The groups were weighed to determine small changes in total body weight that would be difficult to obtain using individual fish, and the individuals could not be tracked over the course of the experiment to determine individual differences in weight loss. In hindsight, measuring individuals would have been more appropriate as this would have allowed for statistics to be calculated even with the lack of repeated measures. Moeller 24 AEA administration: Prior to measuring physical activity, zebrafish were immersed into a container for 30 minutes containing either water (negative controls) or 10µM AEA and water. Piccinneti et al. found a maximal increase in brain AEA concentration after 30 minutes of treatment with this dose, with no additional increase in brain AEA concentration between 30 minutes and 120 minutes of immersion (Piccinetti et al., 2010). After immersion, the zebrafish were transferred to the physical activity testing chamber to measure physical activity. Recording physical activity: To test physical activity, the fish were removed from their enclosures and transferred to a testing chamber. Each group was videotaped for 10 minutes once per day using a camera positioned above the testing chamber. Then, idTracker, a program that identifies individuals and tracks their movement over time, was utilized to determine physical activity from the recordings (Pérez-Escudero et al., 2014). Moving fish to a new enclosure induces stress, so each group was allowed to acclimate to their new environment for 10 minutes before recording. After testing, the fish were returned to their normal enclosures and were fed. Weighing and video recording was conducted approximately 24h after feeding to minimize differences in activity due to recent feeding. Moeller 25 Measuring physical activity using idTracker: Videos of the fish were transferred from the video camera to a computer. idTracker identifies areas of darkness over a white background, so the testing chamber cannot have reflections, glare, or shadows. To increase the contrast between the testing chamber and the individuals, the videos were color-corrected using Adobe Premiere Pro. By adjusting the contrast, shadows, white levels, black levels, and highlights, the fish can be isolated on a white background to increase the accuracy of the tracking. After color correction, the video files were exported to a folder. To identify individuals over the course of the experiment, all of the videos were analyzed at once sequentially. This was achieved by naming each video in the group as “groupnameX” where ‘groupname’ corresponds to the treatment and ‘X’ corresponds to the day of treatment (For example, the video for day 5 of the No AEA CR group was named ‘NoAEACR5’). When videos end with a number, idTracker loads them in order and analyzes them as if they are one video, creating a single ‘Trajectories.txt’ file containing all the tracking information for each individual over the course of the 21 days. By knowing the number of frames in each video, a python script was written that splits the single ‘Trajectories.txt’ file into separate trajectories files for each day based on the number of frames. In the idTracker user interface, the number of individuals was set to 10, the minimum size was adjusted until all fish were highlighted, and the intensity was adjusted to highlight fish while excluding shadows that may be present. The ‘background comp’ function was used to identify the testing chamber. Moeller 26 Python Analysis: To analyze the data in the split ‘trajectories’ files, a python script was created. The ‘trajectories’ files were loaded, each X and Y position for each individual were added to lists, and the distance formula was used to determine the distance between each frame. Frames without tracking data are shown as NaN values, so for accuracy, the distance between frames was only calculated if two subsequent frames had both X and Y values. The total distance travelled was divided by the number of non-NaN value frames to calculate the average distance / frame. While the testing setup was the same for each video, the exact position of the testing chamber in the video was slightly different between videos. By measuring the dimensions of the testing chamber and setting the video resolution and framerate to a standard 480p x 480p at 29.95fps, we standardized the speed data to cm/second. To identify the range of X and Y coordinates of the testing chamber, we recorded the maximum and minimum X and Y positions of all fish to determine the dimensions of the chamber, assuming that at least one of the fish will move to the far top, bottom, right, and left of the testing chamber. The fish were inclined to try to jump out of the chamber into the barrier walls, which caused some tracking data to be found outside of the actual dimensions of the recording chamber. To account for this, the 100 largest and smallest X and Y values were removed from the list during size calculations. The results of this adjustment were verified by comparing the calculated dimensions to the actual pixel dimensions in a dot plot of the data where the walls of the chamber appeared to be. Moeller 27 This method allowed us to identify the area of the chamber in pixels for each video to determine how much of the 480p x 480p video contained the testing chamber. By knowing the area of the chamber in both pixels and in centimeters along with a standard framerate, the units were able to be converted from pixels/frame to cm/second. This method standardized the data in each video to account for differences in setup. When zebrafish are under stressful conditions, they exhibit a behavior called thigmotaxis, where they will stay close to the sides of their enclosure and spend less time in the middle of the enclosure during stressful periods. To measure stress, the amount of time spent near the sides of the chamber compared to the middle of the chamber was determined. The boundary between what was considered the sides and what was considered the middle of the chamber was determined by examining dot plots of individuals that spent most of their time near the sides of the chamber. By examining the ratio of the distance spent at the sides to the total distance of the chamber, it was determined that individuals which were thigmotactic were found in the outer 20% of the total area of the chamber. The number of frames spent within these areas was compared to the number of frames spent in the middle 80% of the chamber as a calculation for the percentage of time spent thigmotactic for each individual. The outer 80% of the chamber corresponds to 36% of the total area, so individuals were counted as thigmotactic if they spent more than 50% of their time at the sides compared to in the middle of the tank. Experiment 1 – Physical Activity and Weight Loss Moeller 28 The goal of Experiment 1 was to determine if repeated AEA treatment influences physical activity and weight loss in zebrafish under a caloric restriction. There were two treatment groups and two control groups for this study, each with 10 zebrafish. The first control group was not treated with AEA and was fed 3% of body weight in food / day (No AEA, no CR). The second control group was not treated with AEA but was fed a 1.5% of body weight in food / day (No AEA, CR). The first treatment group was treated with 10µM AEA and fed 3% of body weight in food / day (AEA, no CR). The second treatment group was treated with 10µM AEA and fed 1.5% of body weight in food / day (AEA, CR). The experiment lasted for 21 days, and during each day of this experiment the fish were transferred to the weighing chamber and total group body weight was recorded. After each group of a treatment was weighed, they were transferred to the treatment chamber and immersed in either tank water (no AEA groups) or 10µM AEA (AEA groups) for 30 minutes. Following immersion, the fish were transferred to the physical activity testing chamber, allowed to acclimate for 10 minutes, then were videotaped for 10 minutes. Following physical activity testing, the fish were transferred back to their respective home tank and fed. Experiment 2 – Brain orexin and CB1 receptor mRNA quantification Zebrafish Brain Collection: After data collection on day 21 of the physical activity experiment, zebrafish were euthanized via immersion in an ice bath and brains were extracted on an inverted weigh- Moeller 29 boat on ice to prevent RNA degradation. The extracted brains were immediately put into tubes containing 1 mL Trizol and flash frozen in liquid nitrogen. The brains were stored at -80°C until needed. RNA Isolation and Purification: The zebrafish brains and Trizol were thawed on ice, transferred to a 1.5 mL RNase and DNase free centrifuge tube, and homogenized using a pestle. Following homogenization, 200 µL chloroform was added and the tubes were incubated on ice for 3 minutes before being centrifuged at 12,000xg for 15 min at 4°C. The upper aqueous phase was transferred via pipette to a new 1.5 mL centrifuge tube, while the lower and interphase regions were stored at -80°C. Next, 5 µg of RNase-free glycogen and 500 µL of 2-propanol was added to the upper phase, briefly mixed by hand, incubated on ice for 10 minutes, then centrifuged at 12,000xg for 10 minutes at 4°C. The supernatant was discarded and the pellet was resuspended in 1 mL of 75% ethanol. The tube was mixed by vortex and centrifuged for 5 min at 7,500xg at 4°C. The supernatant was carefully removed, and the pellet was allowed to air dry in the hood by evaporation. Once dry, the pellet was resuspended in 20 µL of DEPC-treated water and absorbance was measured using a NanoDrop. RNA not used for rt-PCR was stored at -80°C. Reverse-transcriptase PCR of Total RNA: Rt-PCR of the RNA samples was conducted using a ProtoScript II First Strand cDNA Synthesis Kit from New England Biolabs using the Oligo-d(T)23 VN primers. Moeller 30 The RNA samples and oligonucleotides were denatured for 5 min at 65°C. The 5x ProtoScript Reaction Mix and 10x ProtoScript II Enzyme Mix were added. The cDNA synthesis reaction was incubated at 42°C for 1 hour followed by heating the reaction to 85°C for 5 minutes to denature the enzymes. The cDNA was then stored at -20°C until quantification by qPCR. qPCR of Orexin and CB1 Receptor Primers for the CB1 receptor, orexin, and ß-actin were obtained from Integrated DNA Technologies. The qPCR reactions were conducted using 10 µL PerfeCTa SYBR Green SuperMix (Quantabio), 2 µL of 10 µM forward and reverse primer mix, 6 µL DEPC-treated water, and 2 µL of sample. qPCR was conducted using the CFX96 Touch Real-Time PCR Detection System and Bio-Rad CFX Manager (Bio-Rad). The products were initially denatured to 95°C to remove the SYBR inhibiting agent, then cycled 40 times with the following conditions: 94°C for 10 seconds, 56°C for 10 seconds, 72°C for 30 seconds, plate read. Single PCR product generation was confirmed using a melt curve, which was generated by raising the temperature from 65°C to 95°C at 0.5°C increments every 5 seconds and reading the plate at each increment. Each experimental sample was paired with a ß-actin control, and each reaction contained five 10-fold serial dilutions for each primer set consisting of pooled sample DNA. A no-template negative control was used to control for any sources of DNA contamination. For data analysis, the Cq value of the treatment primers for each of the Moeller 31 individuals was compared to it’s corresponding ß-actin control using the following formula: 2^(Treatment primer Cq – ß-actin primer Cq). Results: Experiment 1: Group Weight Change: As the weight was recorded for each group rather than each individual, no statistics can be conducted comparing the groups. The negative control group (No AEA, No CR) did not show a decrease over the course of the experiment and showed a slight increase in bodyweight toward the end of the experiment. The treatment groups all showed similar rates of weight loss, with the AEA, CR group having the lowest weight at the end of the experiment. Physical Activity Repeated measures analysis showed a significant effect of AEA on average zebrafish movement (F1,38 = 10.08; p = 0.00297), with average movement being greater for fish treated with AEA compared to control. There was a significant interaction between AEA and day, where the increase in movement caused by AEA gradually decreased over time to overlap with control (F20,760 = 11.08; P < 2e-16) (Figure 6). There was a significant interaction between CR and day (F20,720 = 4.086; p < 0.001), with movement decreasing compared to controls over the course of the Moeller 32 experiment. There was no significant interaction between AEA and CR (F1,36 = 3.296; p = 0.078) Fisher’s LSD post-hoc analysis of the four treatment groups showed a significant difference in average movement between each of the four treatment groups, with the No AEA, CR group having the lowest average movement (0.150 cm/sec), the No AEA, No CR group having the second lowest average movement (0.173 cm/sec), the AEA, No CR group having the second highest average movement (0.188 cm/sec), and the AEA, CR group having the highest average movement (0.200 cm/sec). Thigmotaxis: There were no differences between the groups in the number of individuals displaying thigmotaxis (F1,82 = 1.768; p = 0.187). Experiment 2: CB1 qPCR Results The CB1 qPCR data was screened for significant outliers using the Grubbs test, and one outlier was detected and removed, as the G score was greater than 2.5 (G = 4.91, U = 0.14, P < 2.6e-12). The data was checked for normality using a Shapiro-Wilk test and was found to be non-normal. Therefore, a Kruskal-Wallis rank sum test was chosen for analysis of the data. There were no significant differences in CB1 expression between the four groups (Figure 10). Moeller 33 Orexin qPCR Results Two significant outliers were identified using the Grubbs test and were removed from the dataset (G > 2.5, p < 0.05). The data were analyzed using a Shapiro-Wilk test for normality and found to meet the assumptions for a one-way ANOVA. There was significantly increased orexin expression in the CR groups compared the non-CR groups, but there were no differences in orexin expression between AEA treated and non-AEA treated groups. Discussion: Endocannabinoids in Physical Activity and Obesity: Regular physical exercise is a crucial part of maintaining health throughout a person’s lifespan, reducing the risk of numerous age-related diseases and increasing overall quality of life (Ruegsegger and Booth, 2018). With the steadily growing obesity epidemic, proper exercise and nutrition has become a powerful tool to prevent diseases rather than treating them (James, Rigby, and Leach, 2003). Most interventions attempt to curb obesity by prescribing diets, lifestyle changes, weight monitoring, and education (Reilly, 2006). The World Health Organization classifies obesity as a disease and considers the increasing rate of obesity to be an epidemic (James, 2008). Understanding the function of the endocannabinoid system in the regulation of weight loss and physical activity is important in the fight against increasing body weight, Moeller 34 as this system plays a major role in the development of metabolic disorders and obesity (Clark et al., 2018). Plasma endocannabinoid concentration is increased from high fat diets and correlates to several disorders including obesity, glucose intolerance, increased lipid storage, and reduced metabolism (Banni and Di Marzo, 2010). Luckily, chronic exercise programs have been found to reduce high-fat diet induced AEA and 2-AG levels and are a useful tool in the arsenal of a trained dietician (Gamelin et al. 2016). The western diet has a staggeringly high omega-6 / omega-3 fatty acid ratio (about 15:1), mainly due to a high consumption of vegetable oils (Simopoulos, 2002). Omega-6 fatty acids are precursors for both 2-AG and AEA, and a high ratio of omega6/omega-3 (estimated hunter-gatherer diets ranged from 1:1 to 3:1) increases the risk of developing inflammatory diseases, cardiovascular diseases, Alzheimer’s disease, and arthritis (Patterson et al., 2011; Clark et al., 2018). This increased intake of endocannabinoid precursors leads to a dietary dysregulation of the endocannabinoid system and implicates high basal endocannabinoid levels as a potent factor for obesity (Bennetzen et al., 2011). Endocannabinoids act to increase energy storage and uptake by stimulating hunger, palatability, and reward pathways involved with feeding (Clark et al., 2018). If the endocannabinoid system is chronically stimulated, the normal functions of this pathway are over-induced and could lead to the development of maladaptive eating behaviors such as chronic overeating, a loss of controlled eating behavior, and the development of food addiction (D’Addario et al., 2014). Understanding how the endocannabinoid system naturally regulates feeding and hunger is, therefore, vitally important in the quest to understand the mechanisms underlying the development of Moeller 35 obesity, the behaviors that cause it, and for the development of treatments to combat the obesity epidemic. If the overstimulation of the endocannabinoid system correlates with increased obesity, it would logically follow that the overstimulation of the endocannabinoid system via the use of Cannabis would also correlate with increased obesity. This is not the case however, as two independent national studies have shown that cannabis use is associated with a decreased risk of obesity. In these studies, it was found that the rates of obesity were 22% and 25.3%, respectively, for individuals reporting no cannabis use, and 14.3% and 17.2%, respectively, for individuals reporting regular cannabis use of at least three times per week (Le Strat and Le Foll, 2011). This seems at odds with the prevailing strategy for treating obesity through the endocannabinoid system, which has been to develop antagonists for the CB1 receptor such as the drug rimonabant. Human trials of rimonabant were unsuccessful. Although the drug appeared safe for human consumption and induced weight loss, the studies were discontinued due to a high incidence of adverse side effects such as a 26% likelihood of developing a sleeping disorder, anxiety, or psychomotor agitation and a 9% change of developing symptoms of depression (Isoldi and Aronne, 2008). Our results support the hypothesis that cannabinoid agonists, rather than antagonists, can be used to induce weight loss. While the individual weights of each fish in our experiment were not measured, group weight was recorded once per day during the experiment. The negative control group did not show a change in weight until day 14, where the group weight increased slightly from the starting weight. This suggests that feeding the group 3% of body weight in food once per day was slightly above the Moeller 36 maintenance amount needed to keep their bodyweight stable, but the decrease in body weight of the CR groups shows that feeding these groups 1.5% of body weight in food once per day was sufficient to induce CR. Each of our treatment groups lost a similar amount of weight over time, with the AEA, CR group showing the largest loss of body weight. Interestingly, the group treated with AEA alone lost weight compared to the negative control group and lost a similar amount of weight compared to the No AEA, CR group. This indicates that AEA was able to induce weight loss without the presence of CR and/or that AEA alone increases physical activity to the extent that 3% of body weight in food per day is not sufficient to maintain bodyweight. These results support the finding that exogenous cannabinoids do not cause obesity and may even protect from it (Le Strat and Le Foll, 2011). Our finding that AEA increased both physical activity and weight loss suggests that the weight loss was due to an increase in calories used for locomotion rather than a decrease in feeding, as each of the groups were fed the same amount. The orexin system is highly conserved throughout evolutionary taxa, with the OX-2R being found in older lineages and the OX-1R being found in mammalian species (Wong et al., 2011). This means that the orexin system is similar between humans and zebrafish, however zebrafish only have the OX-2R (Yokogawa et al., 2007). This makes orexin easier to study in zebrafish compared to mammalian models, as differences in functional characteristics between the OX-1R and OX-2R do not need to be accounted for in the experimental design. The main function of orexin is in the regulation of many physiological processes such as the sleep/wake cycle, energy homeostasis, and locomotion (Elbaz et al., 2013). Orexin cell bodies are only found in the lateral Moeller 37 hypothalamus but extend axonal projections to other parts of the brain to communicate information from the hypothalamus to other targets for a coordinated response to stimuli such as hunger and fatigue (Sundvik and Panula, 2015). Increasing the concentration of orexin stimulates wakefulness and physical activity during active periods (Prober et al., 2006), especially during periods of low energy (Sakurai, 2008). This functions as a mechanism to increase physical activity during periods of hunger, driving the individual to find food (Novak et al., 2005). The CB1 receptor also affects food intake, with increased CB1 receptor stimulation promoting obesity and reduced CB1 receptor stimulation reducing hunger (Gary-Bobo et al., 2007). There is increasing evidence that the endocannabinoid system and orexin system are closely associated in both location and function. Studies have shown that orexin-A increases CB1 receptor signaling through stimulating the synthesis of 2-AG, and it uses this to control physiological functions (Flores et al., 2013; Morello et al., 2016). Additionally, CB1 receptor and OX-2R expression overlaps in areas related to the regulation of sensory, motor, autonomic, endocrine, learning, memory, and emotion signals (Imperatore et al., 2018). They are also co-expressed in the region of the brain analogous to the basal ganglia in mammals (Palotai et al., 2014). The orexin and endocannabinoid systems interact with food intake and energy balance together, so we hypothesized that AEA administration would alter orexin signaling as well (Lau et al., 2017). The successful induction of CR for our groups was determined by investigating the relative expression of orexin RNA between the groups via qPCR. Orexin expression was significantly higher in both CR groups compared to the non-CR groups. As orexin is Moeller 38 expressed during periods of fasting and CR (Ebrahim et al., 2002), the increase in orexin for the CR groups showed that these groups were experiencing CR while the non-CR groups were not. Other studies have shown that during periods of fasting, orexin expression is significantly higher during CR compared to non-CR controls (Novak et al., 2005). There were no significant differences in orexin expression between AEA treated and non-AEA treated groups, which was surprising. As the endocannabinoid system and the orexin system are tightly interconnected in both function and location (Imperatore et al., 2018), changes in one system were expected to cause changes in the other system. There were significant outliers in the dataset that were removed, suggesting that more samples and replicates should be used to confirm our findings. Perhaps the treatment with AEA was not conducted for long enough to see changes in expression, the qPCR did not have enough samples to differentiate between small changes, or AEA simply does not interact with orexin. As 2-AG is the main endocannabinoid associated with orexin function, AEA may not significantly change orexin signaling. 2-AG is the main endocannabinoid involved in synaptic plasticity and neuronal modulation, with basal concentrations of 2-AG being higher and less variable compared to AEA (Luchicchi and Pistis, 2012). AEA is secreted during periods of imbalance to bring signaling systems back into balance. As these two systems have different functions, systems that utilize one endocannabinoid may not respond to another (Luchicchi and Pistis, 2012). This has interesting implications for the development of endocannabinoid-related drug development, as ligands can bind to the same receptor yet have different functions in the Moeller 39 endocannabinoid system rather than responses being classically receptor-mediated (Kendall and Yudowski, 2017). As endocannabinoids play diverse roles in the modulation of feeding behavior, sleep/wake cycle, physical activity, motor activity, exercise-induced euphoria, food craving, mood, and pain (Tantimonaco et al., 2014), it is no surprise that an imbalance in this system can cause weight gain. The endocannabinoid system plays dual roles, with one rewarding and aiding physical activity during strenuous and long exercise and one increasing appetite and stimulating adipose tissue (Dietrich and McDaniel, 2004; Maccarrone et al., 2010). An acute increase in endocannabinoid signaling during exercise has several positive effects, such as bronchodilation, vasodilation, and exercise-induced euphoria (Dietrich and McDaniel, 2004). Conversely, a chronic increase in signaling outside of exercise has several negative effects, such as increased weight gain, decreased insulin sensitivity, and decreased skeletal muscle metabolism (Maccarrone et al., 2004). As the use of Cannabis causes an acute overstimulation of the endocannabinoid system rather than a chronic one, it may exert a positive influence on metabolism by mimicking exercise (Irons et al., 2014), stimulating metabolism while also decreasing the body’s responsiveness to normal endocannabinoid signaling by downregulating the CB1 receptor (Clark et al., 2018). This model would result in acute periods of endocannabinoid system overstimulation followed by chronic periods of under stimulation, like the pattern seen in physically active individuals with a normal weight who trigger an increase in endocannabinoid synthesis during exercise but have low basal signaling the rest of the time (Thompson et al., 2017). If this model is correct, other cannabinoids that have a Moeller 40 similar effect to THC could cause an increase in metabolism and weight loss by mimicking physical activity. As AEA plays a role in physical activity and exercise reward, we hypothesized that daily treatment with AEA would increase physical activity, weight loss, orexin, and metabolism while downregulating the CB1 receptor. In our study, both groups treated with AEA had significantly higher average movement compared to the No AEA groups, indicating that AEA acts to increase physical activity with or without CR. This increase in physical activity is expected, as other studies have found that AEA treatment of zebrafish embryos and adults increases movement when administered acutely (Akhtar et al., 2013), however research has not shown the effects of repeated AEA treatment on movement. The results of this experiment show that AEA treatment initially increased movement, but the difference gradually disappeared over the course of the experiment. This could be due to the formation of a tolerance to AEA; however, we did not find a decrease in CB1 receptor RNA expression that was expected to occur if the zebrafish were developing a tolerance. This may be due to ligand-induced endocytosis of the CB1 receptor (Leterrier et al., 2004), although more recent evidence suggests that the CB1 receptor is mainly degraded when internalized and intracellular pools are not used as a store for cell surface proteins (Grimsey et al., 2010). There appeared to be an interaction between AEA and CR, with a much broader variance in physical activity. Interestingly, this was an individual effect, with some individuals consistently moving more and some individuals consistently moving less throughout the course of the experiment. This variance was not seen in the AEA, No CR group, suggesting that individual differences in the effect of AEA are exacerbated by CR. Moeller 41 Normally, CR reduces movement over time as the organism moves less to maintain energy (Novak et al., 2005). This was seen in the No AEA, CR group that had a lower movement than the No AEA, No CR control group. Comparing groups, CR alone caused a reduction in physical activity and an increase in physical activity when paired with AEA. These individual differences may be due to the social behavior of zebrafish, which have a rudimentary social structure. As zebrafish are social animals, they have several methods for establishing social dominance and subordinance without risking harm (Oliveira, Silva, and Simoes, 2011). As the hierarchy is established, dominant fish tend to have enhanced activation of the swimming circuit, while the subordinate males have enhanced activation of the escape circuit (Miller at al., 2017). The swimming circuit maintains a normal sinusoidal body movement pattern to propel the fish through the water efficiently, while the escape circuit activates upon perception of a threat to quickly move the body away from the stimulus (Park et al, 2018). The decision between escape and swimming behavior is mediated by the endocannabinoid system, where 2-AG synthesis from fast motor neurons disengages slow swimming motor neurons and engages fast escape motor neurons to allow the escape system to override the swimming system (Song et al., 2015). It was also noted that endocannabinoid mediation of this pathway is dependent upon social experience, with prolonged 2-AG availability causing decreased movement in dominant fish and increased movement in subordinate fish (Orr et al., 2021). This finding could explain why some of the fish in our AEA, CR group had a much greater individual variance in movement compared to the other groups, although this same Moeller 42 variation was not seen in the AEA, No CR group. It is possible that CR exacerbates the effects of AEA on socially derived motor behavior and alters the probability of startling. Humans have different experiences when using cannabis, with some people reporting strong anxiolytic effects and others reporting paranoia and increases in anxiety (Crippa et al., 2009). The effects of THC on anxiety are known to be dosage-dependent, with lower doses generally reducing anxiety and higher doses increasing anxiety (Sharpe et al., 2020). However, there is no information on whether social experience changes the effect of THC on anxiety. Endocannabinoids are known to regulate stress by reducing glucocorticoid signaling at the hypothalamic-pituitary-adrenal axis, which can impact physical activity (Hill et al., 2010). Stress can impact movement, as activation of an organism’s fight-orflight response can cause variations in movement (Stewart and Kadri et al., 2010). In zebrafish, stress can be measured by investigating thigmotaxis, or side-clinging tendency (Stewart et al., 2012). Catching zebrafish with a net and transferring them to novel chambers could have induced stress, while endocannabinoid treatment may have impacted their response to that stress due to AEA’s anxiolytic properties (Ashton and Moore, 2011). As we had already gathered information about each group’s movement, we analyzed the physical activity data as an open field test to determine if the treatment process caused a difference in movement. In an open field test, zebrafish are placed into a different chamber and their movement is assessed for stress behaviors such as thigmotaxis, patterning, velocity of movement, freezing bouts, and time spent frozen (Stewart and Cachat et al., 2010). Moeller 43 To determine stress in our experiment, side-clinging tendency was analyzed for each group. The number of thigmotactic individuals per day was calculated and each of the groups were compared using a one-way ANOVA. There were no significant differences in the number of thigmotactic individuals between treatments, although there was an increase in the number of thigmotactic individuals in the AEA, CR group past day 17. These results indicate that there were no significant differences in stress between the groups, so differences in physical activity were not likely to be caused by an interaction between AEA and stress. From our data, daily treatment of zebrafish with 10µM AEA increased physical activity initially; however, this effect appeared to decline over time. We initially expected that AEA treatment would cause an upregulation of orexin due to AEA inducing greater physical activity, as the orexin system functions to increase wakefulness and feeding behavior (Tsujino and Sakurai, 2013). This was not observed, as there were no differences in orexin signaling due to AEA treatment. One study reported exogenous orexin stimulates the endocannabinoid system to reduce α-MSH signaling from POMC neurons, acting to drive hunger when orexin is high (Morello at al., 2016). This study found that the increase in hunger is mediated by 2-AG, which may be the reason why no difference was seen when the fish were treated with AEA in our study. Our data suggests that the caloric restriction was sufficient to increase hunger signaling, but that AEA treatment did not impact orexin signaling. CB1 agonist treatment has been shown to increase physical activity without affecting anxiety in mice and rat models (Pandolfo et al., 2007; Wiley, 2003). Other studies have shown that CB1 agonist treatment (especially with THC) causes a decrease Moeller 44 in movement (Smirnov and Kiyatkin, 2008), and still others have shown that Cannabis use does not decrease exercise performance in humans (Kramer et al., 2020). Our study showed that repeated AEA treatment increases physical activity in zebrafish, however differences between model organism, dose, treatment, and testing method seem to affect findings and, currently, the evidence is unclear. As AEA was sufficient to induce similar rates of weight loss compared to CR alone without causing these individual variances, cannabinoids may be a useful treatment for weight loss without CR, if, the number of calories consumed during treatment remains similar to the number of calories consumed to maintain body weight before the start of the treatment. Cannabis use alone has been shown to reduce the risk of obesity while in turn increasing average daily caloric intake (Smit and Crespo, 2001; Rodondi et al., 2006). This suggests that metabolism is increased either passively or through increased physical activity, with some studies finding that cannabis users had increased physical activity (Ong et al., 2021). It has also been suggested that cannabis use may function to substitute for physical activity physiologically, and physical activity has been identified as a powerful tool to reduce cravings in individuals attempting cannabis cessation (Irons at al., 2014). Endocannabinoids and Motor Activity: Cerebellum: The endocannabinoid system has been found to mediate long-term depression of Purkinje cells in the cerebellar cortex (Safo and Rehehr, 2005). The cerebellum controls Moeller 45 many essential aspects of movement, such as motor learning, control of smooth and accurate motions, associative learning, and balance (D’Angelo and Casali, 2012). Purkinje fiber long-term depression is a crucial part of motor learning, as this process is used to compare predicted movement outcomes with actual movement outcomes which allows for the acquisition and tuning of motor skills (Nguyen-Vu et al., 2013). CB1 receptors regulate both short-term and long-term plasticity in synapses between parallel fibers and Purkinje cells, contributing to the coordination and timing of movements (Su et a., 2013). Basal Ganglia: The basal ganglia are comprised of the striatum, globus pallidus, putamen, substantia nigra, and the pons. The proper functioning of dopaminergic neurons in these brain areas are crucial for the smooth control of movement, with dopamine dysfunctions resulting in movement disorders such as Parkinson’s disease, dystonia, chorea, and motor tics (Mello and Villares, 1997). Endocannabinoid receptors are highly concentrated in these brain areas, and affect motor activity through modulation of GABA, dopamine, and glutamate transmission (Fernández-Ruiz et al., 2002). It was found that cannabinoids have time- and dose-dependent effects on movement, with low and high doses of cannabinoids causing an initial increase in movement with a later decrease in movement (Sañudo-Peña, Tsou, and Walker, 1999). This supports the endocannabinoid system’s involvement with exercise, where activation of the endocannabinoid system would initially increase movement to promote exercise, then reduce movement following exercise to potentiate rest. This was also seen in our results, as treatment with AEA Moeller 46 caused an increase in physical activity. It was not determined if this increase in physical activity was followed by a decrease in physical activity, as the fish were only recorded for 10 minutes. As endocannabinoids regulate the activity of the basal ganglia, it has been used as a complementary and alternative medicine for Parkinson’s disease with 78% of patients self-reporting improvements in mood (56%), sleep (56%), motor symptoms (22%), and quality of life (22%) (Finseth et al., 2015). These results are also supported by the finding that medical cannabis improves motor and pain symptoms (Shohet et al., 2017). Endocannabinoids and Reward: Areas of motivation and reward such as the striatum contain an abundance of CB1 receptors that are colocalized with the dopamine D1 and D2 receptors (Kishida et al., 1980; Martin et al., 2008). While THC itself has rewarding properties, CB1 receptor stimulation also reinforces the effects of other drugs such as heroin, alcohol, and nicotine (Solinas, Goldberg, and Piomelli, 2008). It is not surprising to find the CB1 receptor highly expressed in brain areas involved in reward, as the endocannabinoid system plays a large role in mediating appetite and exercise reinforcement as discussed previously. These regions of the brain are stimulated naturally to reward behaviors such as eating, social interaction, sex, and exercise (Parsons and Hurd, 2015). As exercise is painful but healthy, a strong positive reinforcement mechanism is needed to overcome the negative reinforcement from the pain. The endocannabinoid system provides this mechanism, as during exercise it induces feelings of euphoria, reduces stress, activates reward pathways, Moeller 47 and reduces pain via induction of the common phenomenon of euphoria during moderate aerobic exercise known as the runner’s high (Fuss et al., 2015). It was originally thought that the runner’s high was caused by the activation of endorphins; however, both endorphins and endocannabinoids are released during exercise. Evidence now points to the endocannabinoid system being the main mediator of the runner’s high, as opioid blockage via the opioid receptor antagonist naltrexone did not decrease the development of a runner’s high in humans (Siebers et al., 2021). It was determined that cannabinoid receptors are crucial for the development of a runner’s high in mice, as anxiolysis and analgesia during running are dependent on the presence of functioning CB1 and CB2 receptors (Fuss et al., 2015). Endocannabinoids regulate signaling in many body systems, and function to maintain homeostasis by regulating neuronal and hormonal signaling. This system regulates many of the major signaling systems in animals including circuits involved in physical activity, feeding behavior, anxiety, hormone expression, immune system function, development, and more. Determining the effects of treating organisms with endocannabinoids must be done carefully, as the effects seen may be due to an interaction between many systems rather than from a direct effect. As studies continue to elucidate the effects of endocannabinoids, a clearer picture of normal endocannabinoid functioning will emerge. Our study showed that AEA increased average physical activity in zebrafish with and without CR. Treatment with both AEA and CR caused an interaction effect, where average physical activity increased after AEA alone but also resulted in a large variance in physical activity between individuals. AEA also caused increased rates of weight loss Moeller 48 compared to control, with or without CR. This may be due to an increase in metabolism due to increased physical activity, as food availability was restricted for all groups. The weight loss seen in the AEA, No CR group may be due to increased physical activity requiring them to consume more than 3% of food in body weight per day to maintain a stable body weight. AEA treatment also did not change side-clinging tendency, a test for anxiety in zebrafish. There were no differences in either CB1 receptor expression or orexin expression due to AEA, indicating that tolerance may be mediated through an alternative response to ligand-binding associated CB1 receptor degradation or that the subjects were not treated for a long enough time period to cause a downregulation in the CB1 receptor. Orexin expression changed in response to CR, indicating that the amount of food given to each group was enough to cause a significant difference in hypothalamic feeding circuits. The main endocannabinoid associated with the orexin system is 2-AG, so treatment with AEA may not have impacted the orexin system. Orexin expression is positively correlated with hunger, so it was expected that orexin expression would be increased in all groups that lost weight and had increased physical activity. This was not seen in our results, as the AEA, No CR group had increased physical activity and weight loss but did not have a significantly higher expression of orexin compared to the negative control. This indicates that treatment with AEA increased weight loss and physical activity without increasing hunger. The results of this study supported our hypothesis that repeated endocannabinoid activation via exogenous treatment with AEA causes increased physical activity and weight loss in zebrafish. While not conclusive, these results showed that stimulation of Moeller 49 the endocannabinoid system with AEA can cause weight loss and increased physical activity. Future Directions: This study focused on building a framework to test the effects of repeated anandamide treatment on zebrafish. From this study, we were able to develop methods to test physical activity, stress, changes in brain signaling, and weight change among populations of adult zebrafish. As the physical activity data in its raw form contains a large amount of potential information, the next step would be to develop programs to determine social behavior such as schooling behavior, dominance and subordinance, following behavior, etc. The programs that were developed as a part of this project would require major restructuring for use in other projects. They were developed as highly specialized tools to aid in the data collection for this project, so they require specific inputs and file organization to function. In the future, these programs should have flexibility implemented for use in other projects, along with directions on use and proper in-code comments to let other users understand how the code functions. This would be a substantial project, as both the front-end and back-end code requires a complete overhaul. Another area of interest would be in testing alternative CB1 and orexin primers to optimize the sensitivity of the qPCR experiment. We saw a wide variance in the CB1 results, so other procedures such as standard curves should be used, and the experiment should be repeated several times to determine the validity of the results. Other Moeller 50 endocannabinoids along with other agonists and antagonists should also be used to determine if the effects we found were due to AEA or could be replicated through the use of other agonists. Reproducing the experiment with THC would also be an interesting avenue for comparison, to see if there is indeed a decrease in movement with THC that is not seen with AEA. More molecular techniques could be implemented to determine the changes in the brain during this experiment. Western Blots and confocal microscopy investigating the pattern and abundance of orexin receptors and CB1 receptors would increase the validity of this data and would allow for the determination of whether qPCR is a valid test for CB1 receptor expression changes. As the samples for qPCR analysis from each of the fish have been saved, gender differences could be determined via PCR for genderspecific genes from each of the samples. This experiment was a foray into the question of whether AEA treatment positively correlates with weight loss and physical activity over time. There are many avenues to explore in the future and the techniques used here should be validated by replication and by cross-referencing the results from several similar tests. For example, to determine the effect of AEA on CB1 receptor expression it would be prudent to validate assays through multiple tests such as PCR, qPCR, Western Blotting, confocal microscopy, and immunohistochemistry. Another avenue to consider is whether the acute nature of AEA treatment used in this study impacted the results. Exercise increases AEA in the blood plasma acutely and is associated with weight loss (Thompson et al., 2017), high basal levels of endocannabinoids in the blood are associated with weight gain (Isoldi and Aronne, 2008), and antagonist treatment causes weight loss (Tantimonaco et al., Moeller 51 2014). It would be interesting to compare the results of this experiment to an experiment where the zebrafish were constantly housed in a low-dose solution of AEA, to see if the duration of treatment impacts the effects of endocannabinoids. Literature Cited: Ahn, K., McKinney, M. K., & Cravatt, B. F. (2008). Enzymatic pathways that regulate endocannabinoid signaling in the nervous system. Chemical Reviews, 108(5), 1687– 1707. https://doi.org/10.1021/cr0782067 Akhtar, M. T., Ali, S., Rashidi, H., Van Der Kooy, F., Verpoorte, R., & Richardson, M. K. (2013). Developmental effects of cannabinoids on zebrafish larvae. Zebrafish, 10(3), 283–293. https://doi.org/10.1089/zeb.2012.0785 Alger, B. E. (2002). Retrograde signaling in the regulation of synaptic transmission: Focus on endocannabinoids. In Progress in Neurobiology (Vol. 68, Issue 4, pp. 247– 286). https://doi.org/10.1016/S0301-0082(02)00080-1 Alger, B. E. (2013). Getting high on the endocannabinoid system. Cerebrum : The Dana Forum on Brain Science. 2013:14. Alger, B. E., & Kim, J. (2011). Supply and demand for endocannabinoids. Trends in Neurosciences, 34(6), 304–315. https://doi.org/10.1016/j.tins.2011.03.003 Moeller 52 Ashton, C. H., & Moore, P. B. (2011). Endocannabinoid system dysfunction in mood and related disorders. Acta Psychiatrica Scandinavica, 124(4), 250–261. https://doi.org/10.1111/j.1600-0447.2011.01687.x Ayakannu, T., Taylor, A. H., Marczylo, T. H., Willets, J. M., & Konje, J. C. (2013). The endocannabinoid system and sex steroid hormone-dependent cancers. International Journal of Endocrinology, 2013. https://doi.org/10.1155/2013/259676 Banni, S., & di Marzo, V. (2010). Effect of dietary fat on endocannabinoids and related mediators: Consequences on energy homeostasis, inflammation and mood. Molecular Nutrition and Food Research, 54(1), 82–92. https://doi.org/10.1002/mnfr.200900516 Belitardo de Oliveira, A., de Mello, M. T., Tufik, S., & Peres, M. F. P. (2019). Weight loss and improved mood after aerobic exercise training are linked to lower plasma anandamide in healthy people. Physiology and Behavior, 201(December 2018), 191–197. https://doi.org/10.1016/j.physbeh.2018.12.018 Bénard, G., Massa, F., Puente, N., Lourenço, J., Bellocchio, L., Soria-Gómez, E., Matias, I., Delamarre, A., Metna-Laurent, M., Cannich, A., Hebert-Chatelain, E., Mulle, C., Ortega-Gutiérrez, S., Martín-Fontecha, M., Klugmann, M., Guggenhuber, S., Lutz, B., Gertsch, J., Chaouloff, F., … Marsicano, G. (2012). Mitochondrial CB 1 receptors regulate neuronal energy metabolism. Nature Neuroscience, 15(4), 558– 564. https://doi.org/10.1038/nn.3053 Moeller 53 Bennetzen, M. F., Wellner, N., Ahmed, S. S., Ahmed, S. M., Diep, T. A., Hansen, H. S., Richelsen, B., & Pedersen, S. B. (2011). Investigations of the human endocannabinoid system in two subcutaneous adipose tissue depots in lean subjects and in obese subjects before and after weight loss. International Journal of Obesity, 35(11), 1377–1384. https://doi.org/10.1038/ijo.2011.8 Blanco, A. M., Bertucci, J. I., Hatef, A., & Unniappan, S. (2020). Feeding and food availability modulate brain-derived neurotrophic factor, an orexigen with metabolic roles in zebrafish. Scientific Reports, 10(1), 1–18. https://doi.org/10.1038/s41598020-67535-z Brailoiu, G. C., Oprea, T. I., Zhao, P., Abood, M. E., & Brailoiu, E. (2011). Intracellular cannabinoid type 1 (CB1) receptors are activated by anandamide. Journal of Biological Chemistry, 286(33), 29166–29174. https://doi.org/10.1074/jbc.M110.217463 Cajanus, K., Holmström, E. J., Wessman, M., Anttila, V., Kaunisto, M. A., & Kalso, E. (2016). Effect of endocannabinoid degradation on pain. Pain, 157(2), 361–369. https://doi.org/10.1097/j.pain.0000000000000398 Chicca, A., Marazzi, J., Nicolussi, S., & Gertsch, J. (2012). Evidence for bidirectional endocannabinoid transport across cell membranes. Journal of Biological Chemistry, 287(41), 34660–34682. https://doi.org/10.1074/jbc.M112.373241 Clark, T. M., Jones, J. M., Hall, A. G., Tabner, S. A., & Kmiec, R. L. (2018). Theoretical Explanation for Reduced Body Mass Index and Obesity Rates in Cannabis Users . Moeller 54 Cannabis and Cannabinoid Research, 3(1), 259–271. https://doi.org/10.1089/can.2018.0045 Crippa, J. A., Zuardi, A. W., Martin-Santos, R., Bhattacharyya, S., Atakan, Z., McGuire, P., & Fusar-Poli, P. (2009). Cannabis and anxiety: a critical review of the evidence. Human Psychopharmacology, 24(7), 515–523. https://doi.org/10.1002/hup.1048 D’Addario, C., Micioni Di Bonaventura, M. V., Pucci, M., Romano, A., Gaetani, S., Ciccocioppo, R., Cifani, C., & Maccarrone, M. (2014). Endocannabinoid signaling and food addiction. Neuroscience and Biobehavioral Reviews, 47(1), 203–224. https://doi.org/10.1016/j.neubiorev.2014.08.008 D’Angelo, E., & Casali, S. (2012). Seeking a unified framework for cerebellar function and dysfunction: From circuit operations to cognition. Frontiers in Neural Circuits, 6(DEC), 1–23. https://doi.org/10.3389/fncir.2012.00116 Dainese, E., Oddi, S., & Maccarrone, M. (2010). Interaction of Endocannabinoid Receptors with Biological Membranes. Current Medicinal Chemistry, 17(14), 1487– 1499. https://doi.org/10.2174/092986710790980087 Dametto, F. S., Fior, D., Idalencio, R., Rosa, J. G. S., Fagundes, M., Marqueze, A., Barreto, R. E., Piato, A., & Barcellos, L. J. G. (2018). Feeding regimen modulates zebrafish behavior. PeerJ, 2018(8), 1–17. https://doi.org/10.7717/peerj.5343 Dietrich, A., & McDaniel, W. F. (2004). Endocannabinoids and exercise. British Journal of Sports Medicine, 38(5), 536–541. https://doi.org/10.1136/bjsm.2004.011718 Moeller 55 Digby, G. J., Sethi, P. R., & Lambert, N. A. (2008). Differential dissociation of G protein heterotrimers. Journal of Physiology, 586(14), 3325–3335. https://doi.org/10.1113/jphysiol.2008.153965 Ebrahim, I. O., Howard, R. S., Kopelman, M. D., Sharief, M. K., & Williams, A. J. (2002). The hypocretin/orexin system. Journal of the Royal Society of Medicine, 95(5), 227–230. https://doi.org/10.1258/jrsm.95.5.227 Elbaz, I., Foulkes, N. S., Gothilf, Y., & Appelbaum, L. (2013). Circadian clocks, rhythmic synaptic plasticity and the sleep-wake cycle in zebrafish. Frontiers in Neural Circuits, 7(JAN), 1–7. https://doi.org/10.3389/fncir.2013.00009 Elbaz, I., Levitas-Djerbi, T., & Appelbaum, L. (2017). The Hypocretin/Orexin Neuronal networks in zebrafish. In Current Topics in Behavioral Neurosciences (Vol. 33, pp. 75–92). Springer Verlag. https://doi.org/10.1007/7854_2016_59 Elbaz, I., Levitas-Djerbi, T., & Appelbaum, L. (2017). The Hypocretin/Orexin Neuronal networks in zebrafish. In Current Topics in Behavioral Neurosciences (Vol. 33, pp. 75–92). Springer Verlag. https://doi.org/10.1007/7854_2016_59 Fernández-Ruiz, J., Lastres-Becker, I., Cabranes, A., González, S., & Ramos, J. A. (2002). Endocannabinoids and basal ganglia functionality. Prostaglandins Leukotrienes and Essential Fatty Acids, 66(2–3), 257–267. https://doi.org/10.1054/plef.2001.0350 Moeller 56 Finseth, T. A., Hedeman, J. L., Brown, R. P., Johnson, K. I., Binder, M. S., & Kluger, B. M. (2015). Self-reported efficacy of cannabis and other complementary medicine modalities by Parkinson’s disease patients in Colorado. Evidence-Based Complementary and Alternative Medicine, 2015. https://doi.org/10.1155/2015/874849 Flores, Á., Maldonado, R., & Berrendero, F. (2013). Cannabinoid-hypocretin cross-talk in the central nervous system: What we know so far. Frontiers in Neuroscience, 7(7 DEC), 1–17. https://doi.org/10.3389/fnins.2013.00256 Fowler, C. J. (2012). Anandamide uptake explained? Trends in Pharmacological Sciences, 33(4), 181–185. https://doi.org/10.1016/j.tips.2012.01.001 Fuss, J., Steinle, J., Bindila, L., Auer, M. K., Kirchherr, H., Lutz, B., & Gass, P. (2015). A runner’s high depends on cannabinoid receptors in mice. Proceedings of the National Academy of Sciences of the United States of America, 112(42), 13105– 13108. https://doi.org/10.1073/pnas.1514996112 Gamelin, F. X., Aucouturier, J., Iannotti, F. A., Piscitelli, F., Mazzarella, E., Aveta, T., Leriche, M., Dupont, E., Cieniewski-Bernard, C., Montel, V., Bastide, B., Di Marzo, V., & Heyman, E. (2016). Effects of chronic exercise on the endocannabinoid system in Wistar rats with high-fat diet-induced obesity. Journal of Physiology and Biochemistry, 72(2), 183–199. https://doi.org/10.1007/s13105-016-0469-5 Gary-Bobo, M., Elachouri, G., Gallas, J. F., Janiak, P., Marini, P., Ravinet-Trillou, C., Chabbert, M., Cruccioli, N., Pfersdorff, C., Roque, C., Arnone, M., Croci, T., Moeller 57 Soubrié, P., Oury-Donat, F., Maffrand, J. P., Scatton, B., Lacheretz, F., Le Fur, G., Herbert, J. M., & Bensaid, M. (2007). Rimonabant reduces obesity-associated hepatic steatosis and features of metabolic syndrome in obese zucker fa/fa rats. Hepatology, 46(1), 122–129. https://doi.org/10.1002/hep.21641 Grimsey, N. L., Graham, E. S., Dragunow, M., & Glass, M. (2010). Cannabinoid Receptor 1 trafficking and the role of the intracellular pool: Implications for therapeutics. Biochemical Pharmacology, 80(7), 1050–1062. https://doi.org/10.1016/j.bcp.2010.06.007 Hashimotodani, Y., Takako, O., & Kano, M. (2007). Ca(2+)-assisted receptor-driven endocannabinoid release: mechanism relevant to associate presynaptic and postsynaptic activities. Current Opinion in Neurobiology, 17(3), 360–365. https://doi.org/doi: 10.1016/j.conb.2007.03.012 Hill, M. N., McLaughlin, R. J., Bingham, B., Shrestha, L., Lee, T. T. Y., Gray, J. M., Hillard, C. J., Gorzalka, B. B., & Viau, V. (2010). Endogenous cannabinoid signaling is essential for stress adaptation. Proceedings of the National Academy of Sciences of the United States of America, 107(20), 9406–9411. https://doi.org/10.1073/pnas.0914661107 Hillard, C. J. (2015). Endocannabinoids and the Endocrine System in Health and Disease. Handbook of Experimental Pharmacology, 231, 317–339. https://doi.org/doi:10.1007/978-3-319-20825-1_11 Moeller 58 Howlett, A., Blume, L., & Dalton, G. (2010). CB1 Cannabinoid Receptors and their Associated Proteins. Current Medicinal Chemistry, 17(14), 1382–1393. https://doi.org/10.2174/092986710790980023 Hoyer, D., Zorrilla, E. P., Cottone, P., Parylak, S., Morelli, M., Simola, N., Simola, N., Morelli, M., Lane, J. D., Morgan, M. M., Christie, M. J., Hillard, C. J., Budney, A. J., Hillard, C. J., Budney, A. J., Vandrey, R. G., Robbins, T., Nencini, P., Milella, M. S., … Green, R. (2010). Cannabinoids and Endocannabinoids. Encyclopedia of Psychopharmacology, 265–270. https://doi.org/10.1007/978-3-540-68706-1_42 Hua, T., Vemuri, K., Pu, M., Qu, L., Han, G. W., Wu, Y., Zhao, S., Shui, W., Li, S., Korde, A., Laprairie, R. B., Stahl, E. L., Ho, J.-H., Zvonok, N., Zhou, H., Kufareva, I., Wu, B., Zhao, Q., & Zhi-Jie Liu. (2017). Crystal Structure of the Human Cannabinoid Receptor CB1. Cell, 55(33), 9557–9561. https://doi.org/10.1016/j.cell.2016.10.004.Crystal Hudson, B. D., He, T. E., & Kelly, M. E. M. (2010). Ligand- and Heterodimer-Directed Signaling of the CB 1 Cannabinoid Receptor. 77(1), 1–9. https://doi.org/10.1124/mol.109.060251.Like Imperatore, R., D’Angelo, L., Safari, O., Motlagh, H. A., Piscitelli, F., de Girolamo, P., Cristino, L., Varricchio, E., di Marzo, V., & Paolucci, M. (2018). Overlapping Distribution of Orexin and Endocannabinoid Receptors and Their Functional Interaction in the Brain of Adult Zebrafish. Frontiers in Neuroanatomy, 12. Moeller 59 Irons, J. G., Babson, K. A., Bergeria, C. L., & Bonn-Miller, M. O. (2014). Physical activity and cannabis cessation. American Journal on Addictions, 23(5), 485–492. https://doi.org/10.1111/j.1521-0391.2014.12135.x Ishikawa, Y., & Homcy, C. J. (1997). The adenylyl cyclases as integrators of transmembrane signal transduction. Circulation Research, 80(3), 297–304. https://doi.org/10.1161/01.RES.80.3.297 Isoldi, K. K., & Aronne, L. J. (2008). The Challenge of Treating Obesity: The Endocannabinoid System as a Potential Target. Journal of the American Dietetic Association, 108(5), 823–831. https://doi.org/10.1016/j.jada.2008.02.019 James, P. T. (2004). Obesity: The worldwide epidemic. Clinics in Dermatology, 22(4 SPEC. ISS.), 276–280. https://doi.org/10.1016/j.clindermatol.2004.01.010 James, P. T., Rigby, N., & Leach, R. (2004). The obesity epidemic, metabolic syndrome and future prevention strategies. European Journal of Preventive Cardiology, 11(1), 3–8. https://doi.org/10.1097/01.hjr.0000114707.27531.48 Kano, M. (2014). Control of synaptic function by endocannabinoid-mediated retrograde signaling. Proceedings of the Japan Academy Series B: Physical and Biological Sciences, 90(7), 235–250. https://doi.org/10.2183/pjab.90.235 Kaslin, J., Nystedt, J. M., Östergård, M., Peitsaro, N., & Panula, P. (2004). The Orexin/Hypocretin System in Zebrafish Is Connected to the Aminergic and Moeller 60 Cholinergic Systems. Journal of Neuroscience, 24(11), 2678–2689. https://doi.org/10.1523/JNEUROSCI.4908-03.2004 Kendall, D. A., & Yudowski, G. A. (2017). Cannabinoid receptors in the central nervous system: Their signaling and roles in disease. Frontiers in Cellular Neuroscience, 10(January), 1–10. https://doi.org/10.3389/fncel.2016.00294 Kirkham, T. C. (2009). Cannabinoids and appetite: Food craving and food pleasure. In International Review of Psychiatry. https://doi.org/10.1080/09540260902782810 Kishida, T., Kito, S., Itoga, E., Yanaihara, N., Ogawa, N., & Saito, S. (1980). Immunohistochemical distribution of neuropeptides in the rat central nervous system. Acta Histochemica Et Cytochemica, 13(5), 463–485. https://doi.org/10.1267/ahc.13.463 Kola, B., Farkas, I., Christ-Crain, M., Wittmann, G., Lolli, F., Amin, F., Harvey-White, J., Liposits, Z., Kunos, G., Grossman, A. B., Fekete, C., & Korbonits, M. (2008). The orexigenic effect of ghrelin is mediated through central activation of the endogenous cannabinoid system. PLoS ONE, 3(3). https://doi.org/10.1371/journal.pone.0001797 Kramer, A., Sinclair, J., Sharpe, L., & Sarris, J. (2020). Chronic cannabis consumption and physical exercise performance in healthy adults: a systematic review. Journal of Cannabis Research, 2(1). https://doi.org/10.1186/s42238-020-00037-x Moeller 61 Kreitzer, A. C., & Regehr, W. G. (2001). Retrograde inhibition of presynaptic calcium influx by endogenous cannabinoids at excitatory synapses onto Purkinje cells. Neuron, 29(3), 717–727. https://doi.org/10.1016/S0896-6273(01)00246-X Lau, B. K., Cota, D., Cristino, L., & Borgland, S. L. (2017). Endocannabinoid modulation of homeostatic and non-homeostatic feeding circuits. Neuropharmacology, 124(Sp. Iss. SI), 38–51. Lawrence, C. (2011). Advances in Zebrafish Husbandry and Management. In W. H. Detrich, M. Westerfield, & L. I. Zon (Eds.), Methods in Cell Biology (Vol. 104, pp. 429–451). Academic Press Inc. https://doi.org/DOI 10.1016/B978-0-12-3748140.00023-9 Le Strat, Y., & Le Foll, B. (2011). Obesity and cannabis use: Results from 2 representative national surveys. American Journal of Epidemiology, 174(8), 929– 933. https://doi.org/10.1093/aje/kwr200 Leterrier, C., Bonnard, D., Carrel, D., Rossier, J., & Lenkei, Z. (2004). Constitutive endocytic cycle of the CB1 cannabinoid receptor. Journal of Biological Chemistry, 279(34), 36013–36021. https://doi.org/10.1074/jbc.M403990200 Luchicchi, A., & Pistis, M. (2012). Anandamide and 2-arachidonoylglycerol: Pharmacological properties, functional features, and emerging specificities of the two major endocannabinoids. Molecular Neurobiology, 46(2), 374–392. https://doi.org/10.1007/s12035-012-8299-0 Moeller 62 Luchicchi, A., & Pistis, M. (2012). Anandamide and 2-arachidonoylglycerol: Pharmacological properties, functional features, and emerging specificities of the two major endocannabinoids. Molecular Neurobiology, 46(2), 374–392. https://doi.org/10.1007/s12035-012-8299-0 Maccarrone, M., Gasperi, V., Catani, M. V., Diep, T. A., Dainese, E., Hansen, H. S., & Avigliano, L. (2010). The endocannabinoid system and its relevance for nutrition. Annual Review of Nutrition, 30, 423–440. https://doi.org/10.1146/annurev.nutr.012809.104701 Maccarrone, M., Gasperi, V., Fezza, F., Finazzi-Agrò, A., & Rossi, A. (2004). Differential regulation of fatty acid amide hydrolase promoter in human immune cells and neuronal cells by leptin and progesterone. European Journal of Biochemistry, 271(23–24), 4666–4676. https://doi.org/10.1111/j.14321033.2004.04427.x Mackie, K. (2005). Distribution of cannabinoid receptors in the central and peripheral nervous system. Handbook of Experimental Pharmacology, 168, 299–325. https://doi.org/10.1007/3-540-26573-2_10 Mackie, K. (2006). Mechanisms of CB1 receptor signaling: Endocannabinoid modulation of synaptic strength. International Journal of Obesity, 30, S19–S23. https://doi.org/10.1038/sj.ijo.0803273 Martín, A. B., Fernandez-Espejo, E., Ferrer, B., Gorriti, M. A., Bilbao, A., Navarro, M., Rodriguez De Fonseca, F., & Moratalla, R. (2008). Expression and function of CB1 Moeller 63 receptor in the rat striatum: Localization and effects on D1 and D2 dopamine receptor-mediated motor behaviors. Neuropsychopharmacology, 33(7), 1667–1679. https://doi.org/10.1038/sj.npp.1301558 Mello, L. E. A. M., & Villares, J. (1997). Neuroanatomy of the basal ganglia. Psychiatric Clinics of North America, 20(4), 691–704. https://doi.org/10.1016/S0193953X(05)70340-3 Miller, T. H., Clements, K., Ahn, S., Park, C., Ji, E. H., & Issa, F. A. (2017). Social status–dependent shift in neural circuit activation affects decision making. Journal of Neuroscience, 37(8), 2137–2148. https://doi.org/10.1523/JNEUROSCI.154816.2017 Morello, G., Imperatore, R., Palomba, L., Finelli, C., Labruna, G., Pasanisi, F., Sacchetti, L., Buono, L., Piscitelli, F., Orlando, P., Di Marzo, V., & Cristino, L. (2016). Orexin-A represses satiety-in Nakamura, Y., Dryanovski, D. I., Kimura, Y., Jackson, S. N., Woods, A. S., Yasui, Y., Tsai, S. Y., Patel, S., Covey, D. P., Su, T. P., & Lupica, C. R. (2019). Cocaineinduced endocannabinoid signaling mediated by sigma-1 receptors and extracellular vesicle secretion. ELife, 8, 1–33. https://doi.org/10.7554/eLife.47209 Nguyen-Vu, B., Kimpo, R., Rinaldi, J., Kohli, A., Zeng, H., Deisseroth, K., & Raymond, J. (2013). Cerebellar Purkinje cell activity drives motor learning. Nature Neuroscience, 16(12), 1734–1736. https://doi.org/10.1038/nn.3576 Moeller 64 Novak, C. M., Jiang, X., Wang, C., Teske, J. A., Kotz, C. M., & Levine, J. A. (2005). Caloric restriction and physical activity in zebrafish (Danio rerio). Neuroscience Letters, 383(1–2), 99–104. https://doi.org/10.1016/j.neulet.2005.03.048 Nunn, A., Guy, G., & Bell, J. D. (2012). Endocannabinoids in neuroendopsychology: Multiphasic control of mitochondrial function. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1607), 3342–3352. https://doi.org/10.1098/rstb.2011.0393 Oliveira, R. F., Silva, J. F., & Simões, J. M. (2011). Fighting zebrafish: Characterization of aggressive behavior and winner-loser effects. Zebrafish, 8(2), 73–81. https://doi.org/10.1089/zeb.2011.0690 Oltrabella, F., Melgoza, A., Nguyen, B., & Guo, S. (2017). Role of the Endocannabinoid System in Vertebrates: Emphasis on the Zebrafish Model. Development Growth and Differentiation, 59(4), 194–210. https://doi.org/10.1111/dgd.12351 Ong, L. Q., Bellettiere, J., Alvarado, C., Chavez, P., & Berardi, V. (2021). Cannabis use, sedentary behavior, and physical activity in a nationally representative sample of US adults. Harm Reduction Journal, 18(1), 1–10. https://doi.org/10.1186/s12954-02100496-2 Orr, S. A., Ahn, S., Park, C., Miller, T. H., Kassai, M., & Issa, F. A. (2021). Social Experience Regulates Endocannabinoids Modulation of Zebrafish Motor Behaviors. Frontiers in Behavioral Neuroscience, 15(May). https://doi.org/10.3389/fnbeh.2021.668589 Moeller 65 Palotai, M., Telegdy, G., & Jászberényi, M. (2014). Orexin A-induced anxiety-like behavior is mediated through GABA-ergic, α- And β-adrenergic neurotransmissions in mice. Peptides, 57, 129–134. https://doi.org/10.1016/j.peptides.2014.05.003 Pandolfo, P., Pamplona, F. A., Prediger, R. D. S., & Takahashi, R. N. (2007). Increased sensitivity of adolescent spontaneously hypertensive rats, an animal model of attention deficit hyperactivity disorder, to the locomotor stimulation induced by the cannabinoid receptor agonist WIN 55,212-2. European Journal of Pharmacology, 563(1–3), 141–148. https://doi.org/10.1016/j.ejphar.2007.02.013 Panula, P., Chen, Y. C., Priyadarshini, M., Kudo, H., Semenova, S., Sundvik, M., & Sallinen, V. (2010). The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. In Neurobiology of Disease (Vol. 40, Issue 1, pp. 46–57). Neurobiol Dis. https://doi.org/10.1016/j.nbd.2010.05.010 Park, C., Clements, K. N., Issa, F. A., & Ahn, S. (2018). Effects of social experience on the habituation rate of zebrafish startle escape response: Empirical and computational analyses. Frontiers in Neural Circuits, 12(February), 1–16. https://doi.org/10.3389/fncir.2018.00007 Parsons, L. H., & Hurd, Y. L. (2015). Endocannabinoid signalling in reward and addiction. In Nature Reviews Neuroscience. https://doi.org/10.1038/nrn4004 Moeller 66 Patterson, E., Wall, R., Fitzgerald, G. F., Ross, R. P., & Stanton, C. (2012). Health implications of high dietary omega-6 polyunsaturated fatty acids. Journal of Nutrition and Metabolism, 2012. https://doi.org/10.1155/2012/539426 Piccinetti, C. C., Migliarini, B., Petrosino, S., Di Marzo, V., & Carnevali, O. (2010). Anandamide and AM251, via water, modulate food intake at central and peripheral level in fish. General and Comparative Endocrinology, 166(2), 259–267. https://doi.org/10.1016/j.ygcen.2009.09.017 Piomelli, D. (2003). The molecular logic of endocannabinoid signalling. Nature Reviews Neuroscience, 4(11), 873–884. https://doi.org/10.1038/nrn1247 Prober, D. A., Rihel, J., Onah, A. A., Sung, R. J., & Schier, A. F. (2006). Hypocretin/orexin overexpression induces an insomnia-like phenotype in zebrafish. Journal of Neuroscience, 26(51), 13400–13410. https://doi.org/10.1523/JNEUROSCI.4332-06.2006 Raichlen, D. A., Foster, A. D., Gerdeman, G. L., Seillier, A., & Giuffrida, A. (2012). Wired to run: Exercise-induced endocannabinoid signaling in humans and cursorial mammals with implications for the “runner’s high.” Journal of Experimental Biology, 215(8), 1331–1336. https://doi.org/10.1242/jeb.063677 Razmovski-Naumovski, V., Luckett, T., Amgarth-Duff, I., & Agar, M. (2022). Efficacy of medicinal cannabis for appetite-related symptoms in people with cancer: a systemic review. Palliative Medicine. https://doi.org/doi:10.1177/02692163221083437 Moeller 67 Reilly, J. J. (2006). Tackling the obesity epidemic: New approaches. Archives of Disease in Childhood, 91(9), 724–726. https://doi.org/10.1136/adc.2006.098855 Rodondi, N., Pletcher, M. J., Liu, K., Hulley, S. B., & Sidney, S. (2006). Marijuana Use, Diet, Body Mass Index, and Cardiovascular Risk Factors (from the CARDIA Study). American Journal of Cardiology, 98(4), 478–484. https://doi.org/10.1016/j.amjcard.2006.03.024 Rosenbaum, D. M., Rasmussen, S. G. F., & Kobilka, B. K. (2009). The structure and function of G-protein-coupled receptors. Nature, 459(7245), 356–363. https://doi.org/10.1038/nature08144 Ruegsegger, G. N., & Booth, F. W. (2018). Health benefits of exercise. Cold Spring Harb Perspect Med., 8(7). https://doi.org/10.1101/cshperspect.a029694. Safo, P. K., & Regehr, W. G. (2005). Endocannabinoids control the induction of cerebellar LTD. Neuron, 48(4), 647–659. https://doi.org/10.1016/j.neuron.2005.09.020 Sakurai, T. (2008). Roles of Orexins and Orexin Receptors in Central Regulation of Feeding Behavior and Energy Homeostasis. CNS & Neurological Disorders - Drug Targets, 5(3), 313–325. https://doi.org/10.2174/187152706777452218 Sañudo-Peña, M. C., Tsou, K., & Walker, J. M. (1999). Motor actions of cannabinoids in the basal ganglia output nuclei. Life Sciences, 65(6–7), 703–713. https://doi.org/10.1016/S0024-3205(99)00293-3 Moeller 68 Sharpe, L., Sinclair, J., Kramer, A., De Manincor, M., & Sarris, J. (2020). Cannabis, a cause for anxiety? A critical appraisal of the anxiogenic and anxiolytic properties. Journal of Translational Medicine, 18(1), 1–21. https://doi.org/10.1186/s12967-02002518-2 Shohet, A., Khlebtovsky, A., Roizen, N., Roditi, Y., & Djaldetti, R. (2017). Effect of medical cannabis on thermal quantitative measurements of pain in patients with Parkinson’s disease. European Journal of Pain (United Kingdom), 21(3), 486–493. https://doi.org/10.1002/ejp.942 Siebers, M., Biedermann, S. V., Bindila, L., Lutz, B., & Fuss, J. (2021). Exercise-induced euphoria and anxiolysis do not depend on endogenous opioids in humans. Psychoneuroendocrinology, 126(January), 105173. https://doi.org/10.1016/j.psyneuen.2021.105173 Simopoulos, A. P. (2002). The importance of the ratio of omega-6/omega-3 essential fatty acids. Biomedicine and Pharmacotherapy, 56(8), 365–379. https://doi.org/10.1016/S0753-3322(02)00253-6 Smirnov, M. S., & Kiyatkin, E. A. (2008). Behavioral and temperature effects of delta 9tetrahydrocannabinol in human-relevant doses in rats. Brain Research, 1228, 145– 160. https://doi.org/10.1016/j.brainres.2008.06.069 Smit, E., & Crespo, C. J. (2001). Dietary intake and nutritional status of US adult marijuana users: results from the Third National Health and Nutrition Examination Survey. Public Health Nutrition, 4(3), 781–786. https://doi.org/10.1079/phn2000114 Moeller 69 Solinas, M., Goldberg, S. R., & Piomelli, D. (2008). The endocannabinoid system in brain reward processes. In British Journal of Pharmacology. https://doi.org/10.1038/bjp.2008.130 Song, J., Ampatzis, K., Ausborn, J., & El Manira, A. (2015). A Hardwired Circuit Supplemented with Endocannabinoids Encodes Behavioral Choice in Zebrafish. Current Biology, 25(20), 2610–2620. https://doi.org/10.1016/j.cub.2015.08.042 Stewart, A. M., Gaikwad, S., Kyzar, E., & Kalueff, A. V. (2012). Understanding spatiotemporal strategies of adult zebrafish exploration in the open field test. Brain Research, 1451, 44–52. https://doi.org/10.1016/j.brainres.2012.02.064 Stewart, A., Cachat, J., Wong, K., Gaikwad, S., Gilder, T., DiLeo, J., Chang, K., Utterback, E., & Kalueff, A. V. (2010). Homebase behavior of zebrafish in noveltybased paradigms. Behavioural Processes, 85(2), 198–203. https://doi.org/10.1016/j.beproc.2010.07.009 Stewart, A., Kadri, F., Dileo, J., Chung, K. M., Cachat, J., Goodspeed, J., Suciu, C., Roy, S., Gaikwad, S., Wong, K., Elegante, M., Elkhayat, S., Wu, N., Gilder, T., Tien, D., Grossman, L., Tan, J., Denmark, A., Bartels, B., … Kalueff, A. V. (2010). The Developing Utility of Zebrafish in Modeling Neurobehavioral Disorders. International Journal of Comparitive Psychology, 23, 104–120. Su, L. Da, Wang, D. J., Yang, D., Shen, Y., & Hu, Y. H. (2013). Retrograde cPLA2α/arachidonic acid/2-AG signaling is essential for cerebellar depolarization- Moeller 70 induced suppression of excitation and long-term potentiation. Cerebellum, 12(3), 297–299. https://doi.org/10.1007/s12311-012-0444-9 Sundvik, M., & Panula, P. (2015). Interactions of the orexin/hypocretin neurones and the histaminergic system. Acta Physiologica, 213(2), 321–333. https://doi.org/10.1111/apha.12432 Tantimonaco, M., Ceci, R., Sabatini, S., Catani, M. V., Rossi, A., Gasperi, V., & Maccarrone, M. (2014). Physical activity and the endocannabinoid system: An overview. Cellular and Molecular Life Sciences, 71(14), 2681–2698. https://doi.org/10.1007/s00018-014-1575-6 Thompson, Z., Argueta, D., Garland, T., & DiPatrizio, N. (2017). Circulating levels of endocannabinoids respond acutely to voluntary exercise, are altered in mice selectively bred for high voluntary wheel running, and differ between the sexes. Physiology and Behavior, 170, 141–150. https://doi.org/10.1016/J.PHYSBEH.2016.11.041 Tian, X., Guo, J., Yao, F., Yang, D. P., & Makriyannis, A. (2005). The conformation, location, and dynamic properties of the endocannabinoid ligand anandamide in a membrane bilayer. Journal of Biological Chemistry, 280(33), 29788–29795. https://doi.org/10.1074/jbc.M502925200 Timper, K., & Brüning, J. C. (2017). Hypothalamic circuits regulating appetite and energy homeostasis: Pathways to obesity. DMM Disease Models and Mechanisms, 10(6), 679–689. https://doi.org/10.1242/dmm.026609 Moeller 71 Tsujino, N., & Sakurai, T. (2013). Role of orexin in modulating arousal, feeding and motivation. In Frontiers in Behavioral Neuroscience (Vol. 7, Issue MAR). Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2013.00028 Ueda, N., Tsuboi, K., Uyama, T., & Ohnishi, T. (2011). Biosynthesis and degradation of the endocannabinoid 2-arachidonoylglycerol. BioFactors. https://doi.org/10.1002/biof.131 Wiley, J. (2003). Sex-dependent effects of Δ9-tetrahydrocannabinol on locomotor activity in mice. Neuroscience Letters, 352, 77–80. https://doi.org/10.1016/s03043940(03)01043-7 Wong, K. K. Y., Ng, S. Y. L., Lee, L. T. O., Ng, H. K. H., & Chow, B. K. C. (2011). Orexins and their receptors from fish to mammals: A comparative approach. General and Comparative Endocrinology, 171(2), 124–130. https://doi.org/10.1016/j.ygcen.2011.01.001 Yokogawa, T., Marin, W., Faraco, J., Pézeron, G., Appelbaum, L., Zhang, J., Rosa, F., Mourrain, P., & Mignot, E. (2007). Characterization of sleep in zebrafish and insomnia in hypocretin receptor mutants. PLoS Biology, 5(10), 2379–2397. https://doi.org/10.1371/journal.pbio.0050277 Zou, S., & Kumar, U. (2018). Cannabinoid receptors and the endocannabinoid system: Signaling and function in the central nervous system. International Journal of Molecular Sciences, 19(3). https://doi.org/10.3390/ijms19030833 Moeller 72 Appendix I – IACUC Approval Form Bloomsburg University Bloomsburg, Pennsylvania Animal Research Protocol Form Section A (must complete): Protocol # (Chair will assign) __159_______ Date: __8/23/21_________ Instructions: This form should be completed and six (6) copies sent to the Chairperson of the IACUC. The review will be completed within two (2) weeks. Protocols must by TYPED. Students must have the protocol co-signed by their faculty advisor. Projects involving experimentation or naturalistic observation require protocols. Name of Investigator(s): Candice M. Klingerman (PI), Eric Moeller (Graduate student) Department: Biological and Allied Health Sciences Title of Project: Behavioral effects of the Cannabinoid Agonist, Anandamide, in Zebrafish (Danio rerio) Semesters in which animals will be used (check all that apply and include year): Fall ____√____ Year _2021_____ Spring __√_____ Year _2022_____ Summer __√____ Year _2022_____ Species of Animals: Zebrafish (Danio rerio) Approximate number of animals being used: 60 Has this protocol been previously approved? No _____ Yes __√____ If yes, give protocol # _159____ and attach a copy of the approved protocol along with the letter of approval. If the present protocol is a replication of the previous one then it is not necessary to complete the rest of this form. Simply sign this form and submit it with a copy of the previously approved protocol and acceptance letter (six (6) copies of everything). Section B (fill out only if new protocol): Moeller 73 What type of hypnotics (i.e. sedatives, analgesics, anesthetics) will be used to eliminate pain sensation if surgical procedures will be performed? N/A If no hypnotics will be use to eliminate pain sensation in surgery, give complete rationale: N/A What euthanasia method will be used at the end of the experiment? Fish will be euthanized with tricaine methane sulfonate (TMS). Euthanasia will be performed by immersing the fish in 200 mg of TMS in 1 liter of water and sodium bicarbonate to buffer the solution to a pH of 6-7 until they are no longer moving or breathing. They will then be rapidly decapitated (Harper and Lawrence, 2011). Present a brief rationale for involving animals, and the appropriateness of the species and numbers to be used. It is important to use animals in vivo to study behavior, as in vitro or other methods are inappropriate. We will use very small numbers of fish (up to 60) in this study to minimize the number of fish used while achieving adequate numbers for statistical analysis. To the best of your knowledge, does this project duplicate an activity (e.g. research or classroom demonstration) that you or others have conducted: ____No_______. If yes, give scientific rationale for duplication. Present an abstract of your methods. Focus on procedures that may be painful or injurious to the animals (e.g. shock administration, surgery, food deprivation, restraint). If you feel that your procedure is controversial, then provide a copy of published research articles that have used your procedure. If a decision cannot be made the committee may ask for such documentation. Attach extra pages if necessary. Experiment. Behavioral effects of the Cannabinoid Agonist, Anandamide, in Zebrafish (Danio rerio) Summary Anandamide (ANA) is a fatty acid neurotransmitter that binds to the CB1 cannabinoid receptor in the central nervous system and the CB2 cannabinoid receptor in the periphery. The highest concentration of CB1 receptors in the brain can be found in the basal ganglia, particularly in output nuclei, and the cerebellum, implicating the endocannabinoid system affects motor behavior. When given intracerebroventricularly (ICV) ANA has been shown to increase physical activity, shown as increased locomotion when placed in an open field test (Sulcova et al., 1998). In addition, CB1 knockout mice are extremely sedentary, which suggests that the Moeller 74 cannabinoid system may function at least in part, to increase physical activity (Steiner et al., 1999). Cannabinoids appear to modulate motor behavior by altering the transmission of neurotransmitter systems in the basal ganglia. Cannabinoids also increase the firing rates of dopaminergic neurons in the ventral tegmentum, substantia nigra, and the medial forebrain bundle. Dopamine may also increase energy expenditure (Dietrich and McDaniel, 2004). As suggested by Dietrich and McDaniel (2004), activation of the endocannabinoid system may also participate in other adaptive responses to exercise. For example, ANA may facilitate blood flow during exercise by acting as a vasodilator. It also affects the respiratory system by acting as a bronchodilator. By adding ANA to a regime of a calorically-restricted diet and an exercise program, overweight individuals may experience an increase in physical activity and weight loss. In the current study, fish will be administered a low or high dose of ANA, while keeping their food intake constant, and physical activity will be measured. Hypothesis Anandamide will increase fish swimming behavior compared to fish treated with vehicle. Methods Fish will be brought into the freshwater fish room (Hartline Science Center; room B55), group-housed (~30 fish per 10 L aquaria (up to 100 fish in each is appropriate; Harper and Lawrence, 2011), and allowed to acclimate to the facility for at least 7 days. Afterwards, they will be separated into groups of 3-5 fish in 3 L holding tanks. (See Figure 1) 10 L 3L Figure 1. zebrafish aquaria Fish will be placed on a 12:12 day/night light cycle. Water temperature will be maintained at 28°C/82°F with aquarium heaters. An air pump will deliver oxygen to the water. Water will be filtered through a reverse osmosis/deionized (RO/DI) filtration system (Spectrapure) and delivered automatically to each aquaria from a holding tank. Water conditioner (Aqueon) and Instant Ocean Sea Salt (0.5-2.0 g/L) will be added. Moeller 75 Aquaria are specially-made to fit inside of a holding rack (Aquatic Habitats Benchtop System; Pentair Aquatic Ecosystems). See Figure 2a for a similar set up. A filter is located below the holding rack. The filter will contain material for biological, chemical, and mechanical filtration. A specialized tank system will also be utilized (Figure 2b) to make keeping track of fish weights and food intake more accurate. Figure 2a. original zebrafish aquaria setup Figure 2b. zebrafish aquaria setup with individual holding tanks Initially, water quality will be checked daily using an API Freshwater Master Test Kit. After water quality becomes stable, some parameters, like nitrogen, can be tested weekly. Oxygen will be tested weekly using an oxygen sensor. Water pH will be maintained between 7-8, alkalinity between 50-150 mg/L CaCO3, hardness at least 75 mg/L CaCO3, salinity between 0.5-2 g/L, dissolved oxygen at 2 mg/L, carbon dioxide below 20 mg/L, and nitrogenous waste less than 0.02 mg/L (Harper and Lawrence, 2011). Fish will be fed a diet containing Artemia (brine shrimp) and commercially available zebrafish food (Adult zebrafish diet; Pentair Aquatic Ecosystems). Experiment: Moeller 76 1. 2. 3. 4. Control, vehicle (n=15) Low dose anandamide, 10 uM (n=15) High dose anandamide, 100 uM (n=15) Ethanol (n=15) Experiment: Total number of animals: = 60 After acclimated to the laboratory, fish will be exposed to either 10 uM (low dose) or 100 uM (high dose) of ANA, vehicle, or 0.5% ethanol. Anandamide will be purchased as Arachidonoyl Ethanolamide from Sigma Aldrich (A0580) or Cayman Chemicals (90050). The vehicle will be a very small dose of ethanol which will be added to the treatment water at a similar amount as the ANA-treated fish. These doses of ANA have been adapted from Piccinetti et al., 2010. A treatment of 0.5% ethanol will be used as our positive control as many researchers report an increase in the physical activity of zebrafish acutely exposed at this dose (Echevarria et al., 2011; Gerlai et al., 2006; Gerlai et al., 2008). Fish will be exposed to the ANA, vehicle, or ethanol by placing them into a separate treatment tank for up to 1 hour. They will then be placed back into their normal aquaria and physical activity will be recorded using a video camera for an additional hour. Physical activity data will then be analyzed using the software idTracker, http://www.idtracker.es/. All fish will be euthanized after testing. At the time of euthanasia, blood may be collected for additional analysis. Additional Comments This experiment will be performed by a graduate student, Eric Moeller. He will work closely under my supervision for the duration of the project. As the principal investigator, my background includes extensive training in behavioral observation and surgical methods. I have more than 10 years of experience performing surgery in rodents and large mammals (e.g. brain cannulations and injections, ovariectomies, arterial and venous catheterizations). Specifically, I have performed acute and chronic procedures on swine, sheep, rats, mice, and hamsters. I have also performed minor surgeries on fish (ex. egg harvesting, swim bladder sx) and I am an avid aquarist that owns many tropical fish. I am a member of the Collaborative Institutional Training Initiative (CITI) (Lehigh University, Bloomsburg University) and recently completed the Responsible Care and Use of Laboratory Animals Training Program sponsored by the Department of Comparative Medicine at The Pennsylvania State University (Hershey, PA). I am also a member of the Society for the Study of Ingestive Behavior (SSIB) and the Society for Behavioral Neuroendocrinology (SBN). My research has been published in leading journals including the American Journal of Physiology, Respiratory Physiology and Neurobiology, Hormones and Moeller 77 Behavior, Frontiers in Systems and Translational Endocrinology, Behavioral Brain Research, and Dose-Response. References Dietrich, A., and McDaniel., W. 2004. Endocannabinoids and exercise. Br J Sports Med. 536-541. Echevarria et al., 2011. Alcohol-induced behavior change in zebrafish models. Rev. Neurosci. 22:85-93. Gerlai, R., Ahmad, F., and Prajapati, S. 2008. Differences in acute alcohol-induced behavioral responses among zebrafish populations. Alcohol Clin Exp Res. 32:17631773. Gerlai, R., Lee, V., and Blaser, R. 2006. Effects of acute and chronic ethanol exposure on the behavior of adult zebrafish (Danio rerio). Pharmacol Biochem Behavior. 85:752761. Piccinetti et al., 2010. Anandamide and AM251, via water, modulate food intake at central and peripheral level in fish. Gen Comp Endocrinol. 259-267. Steiner et al., 1999. Altered gene expression in striate projection neurons in CB1 cannabinoid receptor knockout mice. Proc Natl Acad Sci. 5786–90. Sulcova et al., 1998. Biphasic effects of anandamide. Pharmacol Biochem Behav. 347352. I hereby certify that the information contained herein is true and correct to the best of my knowledge. _____________________________________________ Investigator(s) 8/23/21 Date _____________________________________________ Faculty Advisor (if applicable) 8/23/21 Date Effective Date: April 25, 2001 Moeller 78 Appendix II – Figures Figure 1: Zebrafish housing rack containing housing tanks. Moeller 79 A B Figure 2: Example of Dot Plot showing a thigmotactic individual. This graph shows that the individual was located near the sides for a majority of the recording time. The distance between points A and B were calculated and compared to the total distance of the chamber. By comparing several graphs between groups for each side of the chamber, it was determined that thigmotactic fish spent time in the upper and lower 10% of the total chamber distance Moeller 80 Forward Primer Reverse Primer CB1 GCACTGCGGAGTAAAGACCT GTGCTGGTTCCTATGGCAGT Orexin AGAAACGACTCTTCCGTCGC CGGCTTGATTCCGTGAGTTG ß-Actin TTCAAACGAACGACCAACCT TTCCGCATCCTGAGTCAATG Figure 3: Primers used for qPCR analysis of total brain RNA cDNA Figure 4: Total group body weight over 21 days between the four treatment groups Moeller 81 Figure 5: Change in total group body weight over 21 days between the four treatment groups Moeller 82 Figure 6: Difference in movement between AEA treated and AEA nontreated fish over the 21 days. Moeller 83 Figure 7: Difference in movement between CR and non-CR fish over the 21 days Moeller 84 Figure 8: Fisher’s LSD test on movement. Treatments with the same letter are not significantly different. Moeller 85 Figure 9: Number of individuals displaying thigmotaxis in each group over time Moeller 86 Figure 10: qPCR expression of CB1 receptor RNA compared to ß-actin RNA for each of the treatment groups Moeller 87 Figure 11: qPCR expression of orexin RNA compared to ß-actin RNA for each of the treatment groups Moeller 88 Appendix III – Python Code Trajectories Splitter: import easygui #Opening and creating text files trajectories_file = easygui.fileopenbox(msg='Trajectories File') #User Selects trajectories text file myfile = open(trajectories_file, 'r') #Opens trajectories file for reading #---------Creating The Split Files for Each Day-----------------day1 = open('Day_1_Trajectories.txt', 'w') day2 = open('Day_2_Trajectories.txt', 'w') day3 = open('Day_3_Trajectories.txt', 'w') day4 = open('Day_4_Trajectories.txt', 'w') day5 = open('Day_5_Trajectories.txt', 'w') day6 = open('Day_6_Trajectories.txt', 'w') day7 = open('Day_7_Trajectories.txt', 'w') day8 = open('Day_8_Trajectories.txt', 'w') day9 = open('Day_9_Trajectories.txt', 'w') day10 = open('Day_10_Trajectories.txt', 'w') day11 = open('Day_11_Trajectories.txt', 'w') day12 = open('Day_12_Trajectories.txt', 'w') day13 = open('Day_13_Trajectories.txt', 'w') day14 = open('Day_14_Trajectories.txt', 'w') day15 = open('Day_15_Trajectories.txt', 'w') day16 = open('Day_16_Trajectories.txt', 'w') day17 = open('Day_17_Trajectories.txt', 'w') day18 = open('Day_18_Trajectories.txt', 'w') day19 = open('Day_19_Trajectories.txt', 'w') day20 = open('Day_20_Trajectories.txt', 'w') day21 = open('Day_21_Trajectories.txt', 'w') #Creating empty sets and counters frame = [] frame_counter = 0 #Writing to files based on the number of frames in each video (frames found from Premiere Pro) for line in myfile: #Iterates through all of the lines in the combined trajectories file frame.append(line) #appends each line to the list 'frame' frame_counter += 1 #Counts +1 for each frame appended Moeller 89 if frame_counter <= 17970: #Writes to day1 while the frame_counter is less than the frames in video 1 day1.write(line) elif frame_counter <= 35962: #If above statement false, write to day 2 until frame 35962 day2.write(line) elif frame_counter <= 53954: day3.write(line) elif frame_counter <= 71768: day4.write(line) elif frame_counter <= 89750: day5.write(line) elif frame_counter <= 107742: day6.write(line) elif frame_counter <= 125734: day7.write(line) elif frame_counter <= 143726: day8.write(line) elif frame_counter <= 161718: day9.write(line) elif frame_counter <= 179532: day10.write(line) elif frame_counter <= 197524: day11.write(line) elif frame_counter <= 215516: day12.write(line) elif frame_counter <= 233508: day13.write(line) elif frame_counter <= 251500: day14.write(line) elif frame_counter <= 269492: day15.write(line) elif frame_counter <= 287484: day16.write(line) elif frame_counter <= 305298: day17.write(line) elif frame_counter <= 323290: day18.write(line) elif frame_counter <= 341104: day19.write(line) elif frame_counter <= 359096: day20.write(line) elif frame_counter <= 377088: day21.write(line) Moeller 90 #Closes all of the files myfile.close() day1.close() day2.close() day3.close() day4.close() day5.close() day6.close() day7.close() day8.close() day9.close() day10.close() day11.close() day12.close() day13.close() day14.close() day15.close() day16.close() day17.close() day18.close() day19.close() day20.close() day21.close() Zebrafish Data Analysis: #Imports import os import easygui import math #Bring in trajectories file and opening it for reading open_path = easygui.diropenbox(msg='Select Folder to Open') #Allows for the file to be selected save_path = easygui.diropenbox(msg='Select Folder to save to') for filename in os.listdir(open_path): print(filename) myfile = open(open_path + "\\" + filename, 'r') #-------------Empty lists for each individual-----------X1, X2, X3, X4, X5, X6, X7, X8, X9, X10 = [], [], [], [], [], [], [], [], [], [] Y1, Y2, Y3, Y4, Y5, Y6, Y7, Y8, Y9, Y10 = [], [], [], [], [], [], [], [], [], [] Moeller 91 #------------Reading the X and Y values into a list-------------for i in myfile: lines = myfile.readlines() for n in lines: #X coordinates for individual 1 xcoord1 = n.split('\t')[0] xfloat1 = float(xcoord1) X1.append(xfloat1) #Y coordinates for individual 1 ycoord1 = n.split('\t')[1] yfloat1 = float(ycoord1) Y1.append(yfloat1) #X coordinates for individual 2 xcoord2 = n.split('\t')[3] xfloat2 = float(xcoord2) X2.append(xfloat2) #Y coordinates for individual 2 ycoord2 = n.split('\t')[4] yfloat2 = float(ycoord2) Y2.append(yfloat2) # X coordinates for individual 3 xcoord3 = n.split('\t')[6] xfloat3 = float(xcoord3) X3.append(xfloat3) # Y coordinates for individual 3 ycoord3 = n.split('\t')[7] yfloat3 = float(ycoord3) Y3.append(yfloat3) # X coordinates for individual 4 xcoord4 = n.split('\t')[9] xfloat4 = float(xcoord4) X4.append(xfloat4) # Y coordinates for individual 4 ycoord4 = n.split('\t')[10] yfloat4 = float(ycoord4) Y4.append(yfloat4) # X coordinates for individual 5 xcoord5 = n.split('\t')[12] Moeller 92 xfloat5 = float(xcoord5) X5.append(xfloat5) # Y coordinates for individual 5 ycoord5 = n.split('\t')[13] yfloat5 = float(ycoord5) Y5.append(yfloat5) # X coordinates for individual 6 xcoord6 = n.split('\t')[15] xfloat6 = float(xcoord6) X6.append(xfloat6) # Y coordinates for individual 6 ycoord6 = n.split('\t')[16] yfloat6 = float(ycoord6) Y6.append(yfloat6) # X coordinates for individual 7 xcoord7 = n.split('\t')[18] xfloat7 = float(xcoord7) X7.append(xfloat7) # Y coordinates for individual 7 ycoord7 = n.split('\t')[19] yfloat7 = float(ycoord7) Y7.append(yfloat7) # X coordinates for individual 8 xcoord8 = n.split('\t')[21] xfloat8 = float(xcoord8) X8.append(xfloat8) # Y coordinates for individual 8 ycoord8 = n.split('\t')[22] yfloat8 = float(ycoord8) Y8.append(yfloat8) # X coordinates for individual 9 xcoord9 = n.split('\t')[24] xfloat9 = float(xcoord9) X9.append(xfloat9) # Y coordinates for individual 9 ycoord9 = n.split('\t')[25] yfloat9 = float(ycoord9) Moeller 93 Y9.append(yfloat9) # X coordinates for individual 10 xcoord10 = n.split('\t')[27] xfloat10 = float(xcoord10) X10.append(xfloat10) # Y coordinates for individual 10 ycoord10 = n.split('\t')[28] yfloat10 = float(ycoord10) Y10.append(yfloat10) #----------Calculating Distances Travelled-----------#Empty values for counts total_distance1 = 0 total_distance2 = 0 total_distance3 = 0 total_distance4 = 0 total_distance5 = 0 total_distance6 = 0 total_distance7 = 0 total_distance8 = 0 total_distance9 = 0 total_distance10 = 0 frames1 = 0 frames2 = 0 frames3 = 0 frames4 = 0 frames5 = 0 frames6 = 0 frames7 = 0 frames8 = 0 frames9 = 0 frames10 = 0 #Distance of Individual 1 t=0 n=1 for i in range(len(X1)-1): distance1 = math.sqrt(((X1[t]-X1[n])**2)+((Y1[t]-Y1[n])**2)) #Distance formula if math.isnan(distance1): #If the value of any coordinates in the formula is NaN, iterate one step t=t+1 n=n+1 Moeller 94 else: #If none of the values are NaN, count 1 in the frames and update total distance, then iterate one step frames1 = frames1 + 1 total_distance1 = total_distance1 + distance1 t=t+1 n=n+1 #Distance of Individual 2 t=0 n=1 for i in range(len(X2)-1): distance2 = math.sqrt(((X2[t]-X2[n])**2)+((Y2[t]-Y2[n])**2)) if math.isnan(distance2): t=t+1 n=n+1 else: frames2 = frames2 + 1 total_distance2 = total_distance2 + distance2 t=t+1 n=n+1 #Distance of Individual 3 t=0 n=1 for i in range(len(X3)-1): distance3 = math.sqrt(((X3[t]-X3[n])**2)+((Y3[t]-Y3[n])**2)) if math.isnan(distance3): t=t+1 n=n+1 else: frames3 = frames3 + 1 total_distance3 = total_distance3 + distance3 t=t+1 n=n+1 #Distance of Individual 4 t=0 n=1 for i in range(len(X4)-1): distance4 = math.sqrt(((X4[t]-X4[n])**2)+((Y4[t]-Y4[n])**2)) if math.isnan(distance4): t=t+1 n=n+1 else: Moeller 95 frames4 = frames4 + 1 total_distance4 = total_distance4 + distance4 t=t+1 n=n+1 #Distance of Individual 5 t=0 n=1 for i in range(len(X5)-1): distance5 = math.sqrt(((X5[t]-X5[n])**2)+((Y5[t]-Y5[n])**2)) if math.isnan(distance5): t=t+1 n=n+1 else: frames5 = frames5 + 1 total_distance5 = total_distance5 + distance5 t=t+1 n=n+1 #Distance of Individual 6 t=0 n=1 for i in range(len(X6)-1): distance6 = math.sqrt(((X6[t]-X6[n])**2)+((Y6[t]-Y6[n])**2)) if math.isnan(distance6): t=t+1 n=n+1 else: frames6 = frames6 + 1 total_distance6 = total_distance6 + distance6 t=t+1 n=n+1 #Distance of Individual 7 t=0 n=1 for i in range(len(X7)-1): distance7 = math.sqrt(((X7[t]-X7[n])**2)+((Y7[t]-Y7[n])**2)) if math.isnan(distance7): t=t+1 n=n+1 else: frames7 = frames7 + 1 Moeller 96 total_distance7 = total_distance7 + distance7 t=t+1 n=n+1 #Distance of Individual 8 t=0 n=1 for i in range(len(X8)-1): distance8 = math.sqrt(((X8[t]-X8[n])**2)+((Y8[t]-Y8[n])**2)) if math.isnan(distance8): t=t+1 n=n+1 else: frames8 = frames8 + 1 total_distance8 = total_distance8 + distance8 t=t+1 n=n+1 #Distance of Individual 9 t=0 n=1 for i in range(len(X9)-1): distance9 = math.sqrt(((X9[t]-X9[n])**2)+((Y9[t]-Y9[n])**2)) if math.isnan(distance9): t=t+1 n=n+1 else: frames9 = frames9 + 1 total_distance9 = total_distance9 + distance9 t=t+1 n=n+1 #Distance of Individual 10 t=0 n=1 for i in range(len(X10)-1): distance10 = math.sqrt(((X10[t]-X10[n])**2)+((Y10[t]-Y10[n])**2)) if math.isnan(distance10): t=t+1 n=n+1 else: frames10 = frames10 + 1 total_distance10 = total_distance10 + distance10 t=t+1 Moeller 97 n=n+1 #------Average Distance (pixels/frame)----------#Average for each individual avdist1 = total_distance1 / frames1 #Total distance divided by frames avdist2 = total_distance2 / frames2 avdist3 = total_distance3 / frames3 avdist4 = total_distance4 / frames4 avdist5 = total_distance5 / frames5 avdist6 = total_distance6 / frames6 avdist7 = total_distance7 / frames7 avdist8 = total_distance8 / frames8 avdist9 = total_distance9 / frames9 avdist10 = total_distance10 / frames10 #Total Average for group total_avdist = (avdist1 + avdist2 + avdist3 + avdist4 + avdist5 + avdist6 + avdist7 + avdist8 + avdist9 + avdist10) / 10 # ----------Zebrafish Thigmotaxis---------------------------------# Determines the Actual Location in regards to the tank # Determines the size of the video frame X1nonan = [item for item in X1 if not (math.isnan(item)) == True] Y1nonan = [item for item in Y1 if not (math.isnan(item)) == True] X2nonan = [item for item in X2 if not (math.isnan(item)) == True] Y2nonan = [item for item in Y2 if not (math.isnan(item)) == True] X3nonan = [item for item in X3 if not (math.isnan(item)) == True] Y3nonan = [item for item in Y3 if not (math.isnan(item)) == True] X4nonan = [item for item in X4 if not (math.isnan(item)) == True] Y4nonan = [item for item in Y4 if not (math.isnan(item)) == True] X5nonan = [item for item in X5 if not (math.isnan(item)) == True] Y5nonan = [item for item in Y5 if not (math.isnan(item)) == True] X6nonan = [item for item in X6 if not (math.isnan(item)) == True] Y6nonan = [item for item in Y6 if not (math.isnan(item)) == True] X7nonan = [item for item in X7 if not (math.isnan(item)) == True] Y7nonan = [item for item in Y7 if not (math.isnan(item)) == True] X8nonan = [item for item in X8 if not (math.isnan(item)) == True] Y8nonan = [item for item in Y8 if not (math.isnan(item)) == True] Moeller 98 X9nonan = [item for item in X9 if not (math.isnan(item)) == True] Y9nonan = [item for item in Y9 if not (math.isnan(item)) == True] X10nonan = [item for item in X10 if not (math.isnan(item)) == True] Y10nonan = [item for item in Y10 if not (math.isnan(item)) == True] # Removing points where the fish jumped out of the recording chamber # -----------Trimmed X Values--------X1sort = sorted(X1nonan) # Sorts the list X1trimmed = X1sort[100:-100] # Removes the first 100 and last 100 values X2sort = sorted(X2nonan) X2trimmed = X2sort[100:-100] X3sort = sorted(X3nonan) X3trimmed = X3sort[100:-100] X4sort = sorted(X4nonan) X4trimmed = X4sort[100:-100] X5sort = sorted(X5nonan) X5trimmed = X5sort[100:-100] X6sort = sorted(X6nonan) X6trimmed = X6sort[100:-100] X7sort = sorted(X7nonan) X7trimmed = X7sort[100:-100] X8sort = sorted(X8nonan) X8trimmed = X8sort[100:-100] X9sort = sorted(X9nonan) X9trimmed = X9sort[100:-100] X10sort = sorted(X10nonan) X10trimmed = X10sort[100:-100] # ----------Trimmed Y Values---------Y1sort = sorted(Y1nonan) # Sorts the list Y1trimmed = Y1sort[100:-100] # Removes the first 100 and last 100 values Y2sort = sorted(Y2nonan) Y2trimmed = Y2sort[100:-100] Y3sort = sorted(Y3nonan) Moeller 99 Y3trimmed = Y3sort[100:-100] Y4sort = sorted(Y4nonan) Y4trimmed = Y4sort[100:-100] Y5sort = sorted(Y5nonan) Y5trimmed = Y5sort[100:-100] Y6sort = sorted(Y6nonan) Y6trimmed = Y6sort[100:-100] Y7sort = sorted(Y7nonan) Y7trimmed = Y7sort[100:-100] Y8sort = sorted(Y8nonan) Y8trimmed = Y8sort[100:-100] Y9sort = sorted(Y9nonan) Y9trimmed = Y9sort[100:-100] Y10sort = sorted(Y10nonan) Y10trimmed = Y10sort[100:-100] total_minX = min(min(X1trimmed), min(X2trimmed), min(X3trimmed), min(X4trimmed), min(X5trimmed), min(X6trimmed), min(X7trimmed), min(X8trimmed), min(X9trimmed), min(X10trimmed)) total_maxX = max(max(X1trimmed), max(X2trimmed), max(X3trimmed), max(X4trimmed), max(X5trimmed), max(X6trimmed), max(X7trimmed), max(X8trimmed), max(X9trimmed), max(X10trimmed)) total_minY = min(min(Y1trimmed), min(Y2trimmed), min(Y3trimmed), min(Y4trimmed), min(Y5trimmed), min(Y6trimmed), min(Y7trimmed), min(Y8trimmed), min(Y9trimmed), min(Y10trimmed)) total_maxY = max(max(Y1trimmed), max(Y2trimmed), max(Y3trimmed), max(Y4trimmed), max(Y5trimmed), max(Y6trimmed), max(Y7trimmed), max(Y8trimmed), max(Y9trimmed), max(Y10trimmed)) #--------Calculating Dimensions of Testing Chamber (pixels)---------#Range of chamber (pixels) rangeX = total_maxX - total_minX rangeY = total_maxY - total_minY #Pixles in X Direction / Width Box (14.7 cm) = pixels/cm X area_pixel = rangeX * rangeY #Area of the chamber in pixels (calculated from ranges) Moeller 100 area_cm = 217.54 #Area of the chamber in cm (measured) pix_per_cm = area_pixel / area_cm #Calculating ratio of pixels per centimeter from the above calcs cm_per_frame = total_avdist / pix_per_cm #Determing the average distance moved per frame / centimeters activity = cm_per_frame * 29.95 #Finding the activity in cm / second (recorded at 29.95 fps) #Average Distance for Each Individual in cm / sec activity_ind1 = (avdist1 / pix_per_cm) * 29.95 #Total average distance moved per pixel converted to cm/sec activity_ind2 = (avdist2 / pix_per_cm) * 29.95 activity_ind3 = (avdist3 / pix_per_cm) * 29.95 activity_ind4 = (avdist4 / pix_per_cm) * 29.95 activity_ind5 = (avdist5 / pix_per_cm) * 29.95 activity_ind6 = (avdist6 / pix_per_cm) * 29.95 activity_ind7 = (avdist7 / pix_per_cm) * 29.95 activity_ind8 = (avdist8 / pix_per_cm) * 29.95 activity_ind9 = (avdist9 / pix_per_cm) * 29.95 activity_ind10 = (avdist10 / pix_per_cm) * 29.95 #Highest Activity highest_activity = max(activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10) #Lowest Activity lowest_activity = min(activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10) #Range of Activity activity_range = highest_activity - lowest_activity #----------Writing Analysis to a Text File------------------------#Writing to a text file with the data generated number = int(filename.split('_')[1]) number = number + 10 try: with open(save_path + "\\" + str(number) + os.path.splitext(filename)[0] + ' Raw Data.txt', 'w') as raw_datafile: raw_datafile.write( '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s' % Moeller 101 ( activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10 ) ) except FileNotFoundError: print("the directory does not exist") try: with open(save_path + "\\" + filename, 'w') as f: f.write("Data From: " + str(filename) + '\n') f.write('\n --------Total Distance Over Total Frames ---------') f.write('\nTotal Distance for Individual 1 is: ' + str(total_distance1) + ' over ' + str(frames1) + ' frames') f.write('\nTotal Distance for Individual 2 is: ' + str(total_distance2) + ' over ' + str(frames2) + ' frames') f.write('\nTotal Distance for Individual 3 is: ' + str(total_distance3) + ' over ' + str(frames3) + ' frames') f.write('\nTotal Distance for Individual 4 is: ' + str(total_distance4) + ' over ' + str(frames4) + ' frames') f.write('\nTotal Distance for Individual 5 is: ' + str(total_distance5) + ' over ' + str(frames5) + ' frames') f.write('\nTotal Distance for Individual 6 is: ' + str(total_distance6) + ' over ' + str(frames6) + ' frames') f.write('\nTotal Distance for Individual 7 is: ' + str(total_distance7) + ' over ' + str(frames7) + ' frames') f.write('\nTotal Distance for Individual 8 is: ' + str(total_distance8) + ' over ' + str(frames8) + ' frames') f.write('\nTotal Distance for Individual 9 is: ' + str(total_distance9) + ' over ' + str(frames9) + ' frames') f.write('\nTotal Distance for Individual 10 is: ' + str(total_distance10) + ' over ' + str(frames10) + ' frames') f.write("\n") f.write("\n --------Area Calculations and Conversions---------") f.write('\nminX = ' + str(total_minX)) f.write('\nminY = ' + str(total_minY)) f.write('\nmaxX = ' + str(total_maxX)) f.write('\nmaxY = ' + str(total_maxY)) f.write('\nArea (cm) = ' + str(area_cm)) f.write('\nArea (pix) = ' + str(area_pixel)) f.write('\nPix/cm = ' + str(pix_per_cm)) f.write('\ncm/frame = ' + str(cm_per_frame)) f.write('\n') f.write('\n-------------- Average Speed (cm/sec)----------') f.write('\n Avg. Speed Ind 1 = ' + str(activity_ind1)) f.write('\n Avg. Speed Ind 2 = ' + str(activity_ind2)) Moeller 102 f.write('\n Avg. Speed Ind 3 = ' + str(activity_ind3)) f.write('\n Avg. Speed Ind 4 = ' + str(activity_ind4)) f.write('\n Avg. Speed Ind 5 = ' + str(activity_ind5)) f.write('\n Avg. Speed Ind 6 = ' + str(activity_ind6)) f.write('\n Avg. Speed Ind 7 = ' + str(activity_ind7)) f.write('\n Avg. Speed Ind 8 = ' + str(activity_ind8)) f.write('\n Avg. Speed Ind 9 = ' + str(activity_ind9)) f.write('\n Avg. Speed Ind 10 = ' + str(activity_ind10)) f.write('\n') f.write('\nAverage Group Speed = ' + str(activity) + ' cm / sec') f.write('\nHighest Activity = ' + str(highest_activity)) f.write('\nLowest Activity = ' + str(lowest_activity)) f.write('\nRange of Activity = ' + str(activity_range)) f.write('\n\nTab Delineated Average Speed:\n' '%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s' % ( activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10 ) ) except FileNotFoundError: print("the directory does not exist") myfile.close() continue Zebrafish Movement Data Analysis for Graphs #Imports import os import easygui import math import glob #Bring in trajectories file and opening it for reading open_path = easygui.diropenbox(msg='Select Folder to Open') #Allows for the file to be selected save_path = easygui.diropenbox(msg='Select Folder to save to') for filename in os.listdir(open_path): print(filename) myfile = open(open_path + "\\" + filename, 'r') Moeller 103 # -------------Empty lists for each individual-----------X1, X2, X3, X4, X5, X6, X7, X8, X9, X10 = [], [], [], [], [], [], [], [], [], [] Y1, Y2, Y3, Y4, Y5, Y6, Y7, Y8, Y9, Y10 = [], [], [], [], [], [], [], [], [], [] #------------Reading the X and Y values into a list-------------for i in myfile: lines = myfile.readlines() for n in lines: #X coordinates for individual 1 xcoord1 = n.split('\t')[0] xfloat1 = float(xcoord1) X1.append(xfloat1) #Y coordinates for individual 1 ycoord1 = n.split('\t')[1] yfloat1 = float(ycoord1) Y1.append(yfloat1) #X coordinates for individual 2 xcoord2 = n.split('\t')[3] xfloat2 = float(xcoord2) X2.append(xfloat2) #Y coordinates for individual 2 ycoord2 = n.split('\t')[4] yfloat2 = float(ycoord2) Y2.append(yfloat2) # X coordinates for individual 3 xcoord3 = n.split('\t')[6] xfloat3 = float(xcoord3) X3.append(xfloat3) # Y coordinates for individual 3 ycoord3 = n.split('\t')[7] yfloat3 = float(ycoord3) Y3.append(yfloat3) # X coordinates for individual 4 xcoord4 = n.split('\t')[9] xfloat4 = float(xcoord4) X4.append(xfloat4) # Y coordinates for individual 4 ycoord4 = n.split('\t')[10] Moeller 104 yfloat4 = float(ycoord4) Y4.append(yfloat4) # X coordinates for individual 5 xcoord5 = n.split('\t')[12] xfloat5 = float(xcoord5) X5.append(xfloat5) # Y coordinates for individual 5 ycoord5 = n.split('\t')[13] yfloat5 = float(ycoord5) Y5.append(yfloat5) # X coordinates for individual 6 xcoord6 = n.split('\t')[15] xfloat6 = float(xcoord6) X6.append(xfloat6) # Y coordinates for individual 6 ycoord6 = n.split('\t')[16] yfloat6 = float(ycoord6) Y6.append(yfloat6) # X coordinates for individual 7 xcoord7 = n.split('\t')[18] xfloat7 = float(xcoord7) X7.append(xfloat7) # Y coordinates for individual 7 ycoord7 = n.split('\t')[19] yfloat7 = float(ycoord7) Y7.append(yfloat7) # X coordinates for individual 8 xcoord8 = n.split('\t')[21] xfloat8 = float(xcoord8) X8.append(xfloat8) # Y coordinates for individual 8 ycoord8 = n.split('\t')[22] yfloat8 = float(ycoord8) Y8.append(yfloat8) # X coordinates for individual 9 xcoord9 = n.split('\t')[24] xfloat9 = float(xcoord9) Moeller 105 X9.append(xfloat9) # Y coordinates for individual 9 ycoord9 = n.split('\t')[25] yfloat9 = float(ycoord9) Y9.append(yfloat9) # X coordinates for individual 10 xcoord10 = n.split('\t')[27] xfloat10 = float(xcoord10) X10.append(xfloat10) # Y coordinates for individual 10 ycoord10 = n.split('\t')[28] yfloat10 = float(ycoord10) Y10.append(yfloat10) #----------Zebrafish Thigmotaxis---------------------------------#Determines the Actual Location in regards to the tank #Determines the size of the video frame X1nonan = [item for item in X1 if not(math.isnan(item)) == True] Y1nonan = [item for item in Y1 if not(math.isnan(item)) == True] X2nonan = [item for item in X2 if not(math.isnan(item)) == True] Y2nonan = [item for item in Y2 if not(math.isnan(item)) == True] X3nonan = [item for item in X3 if not(math.isnan(item)) == True] Y3nonan = [item for item in Y3 if not(math.isnan(item)) == True] X4nonan = [item for item in X4 if not(math.isnan(item)) == True] Y4nonan = [item for item in Y4 if not(math.isnan(item)) == True] X5nonan = [item for item in X5 if not(math.isnan(item)) == True] Y5nonan = [item for item in Y5 if not(math.isnan(item)) == True] X6nonan = [item for item in X6 if not(math.isnan(item)) == True] Y6nonan = [item for item in Y6 if not(math.isnan(item)) == True] X7nonan = [item for item in X7 if not(math.isnan(item)) == True] Y7nonan = [item for item in Y7 if not(math.isnan(item)) == True] X8nonan = [item for item in X8 if not(math.isnan(item)) == True] Y8nonan = [item for item in Y8 if not(math.isnan(item)) == True] X9nonan = [item for item in X9 if not(math.isnan(item)) == True] Y9nonan = [item for item in Y9 if not(math.isnan(item)) == True] Moeller 106 X10nonan = [item for item in X10 if not(math.isnan(item)) == True] Y10nonan = [item for item in Y10 if not(math.isnan(item)) == True] #Lists of each of the lists of X and Y values, will be easier to iterate through ind_X_list = [X1nonan, X2nonan, X3nonan, X4nonan, X5nonan, X6nonan, X7nonan, X8nonan, X9nonan, X10nonan] ind_Y_list = [Y1nonan, Y2nonan, Y3nonan, Y4nonan, Y5nonan, Y6nonan, Y7nonan, Y8nonan, Y9nonan, Y10nonan] minimum_list_length = min(len(X1nonan), len(X2nonan), len(X3nonan), len(X4nonan), len(X5nonan), len(X6nonan), len(X7nonan), len(X8nonan), len(X9nonan), len(X9nonan)) # ----------Writing Analysis to a Text File------------------------# Writing to a text file with the data generated try: with open(save_path + "\\" + os.path.splitext(filename)[0] + ' Movement Data.txt', 'w') as f: f.write('X1\tY1\tX2\tY2\tX3\tY3\tX4\tY4\tX5\tY5\tX6\tY6\tX7\tY7\tX8\tY8\tX9\tY9\ tX10\tY10\n') for i in range(minimum_list_length): try: f.write(str(X1nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y1nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X2nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y2nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X3nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y3nonan[i]) + '\t') Moeller 107 except IndexError: f.write('\t') try: f.write(str(X4nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y4nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X5nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y5nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X6nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y6nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X7nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y7nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X8nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y8nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X9nonan[i]) + '\t') except IndexError: f.write('\t') Moeller 108 try: f.write(str(Y9nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(X10nonan[i]) + '\t') except IndexError: f.write('\t') try: f.write(str(Y10nonan[i]) + '\t') except IndexError: f.write('\t') f.write('\n') continue except FileNotFoundError: print("the directory does not exist") myfile.close() continue Zebrafish Thigmotaxis #Imports import os import easygui import math #Bring in trajectories file and opening it for reading open_path = easygui.diropenbox(msg='Select Folder to Open') #Allows for the file to be selected save_path = easygui.diropenbox(msg='Select Folder to save to') thigfile = open(save_path + '\\' + 'Thigmotaxis_Results.txt', 'w') thigfile.write('Thigmotaxis Results: 1 = Thigmotatic, 0 = Non-Thigmotatic\n') thigfile.write('Day\tInd1\tInd2\tInd3\tInd4\tInd5\tInd6\tInd7\tInd8\tInd9\tInd10\tTotal\n ') for filename in os.listdir(open_path): day = 1 print(filename) myfile = open(open_path + "\\" + filename, 'r') #-------------Empty lists for each individual-----------X1, X2, X3, X4, X5, X6, X7, X8, X9, X10 = [], [], [], [], [], [], [], [], [], [] Y1, Y2, Y3, Y4, Y5, Y6, Y7, Y8, Y9, Y10 = [], [], [], [], [], [], [], [], [], [] Moeller 109 #------------Reading the X and Y values into a list-------------for i in myfile: lines = myfile.readlines() for n in lines: #X coordinates for individual 1 xcoord1 = n.split('\t')[0] xfloat1 = float(xcoord1) X1.append(xfloat1) #Y coordinates for individual 1 ycoord1 = n.split('\t')[1] yfloat1 = float(ycoord1) Y1.append(yfloat1) #X coordinates for individual 2 xcoord2 = n.split('\t')[3] xfloat2 = float(xcoord2) X2.append(xfloat2) #Y coordinates for individual 2 ycoord2 = n.split('\t')[4] yfloat2 = float(ycoord2) Y2.append(yfloat2) # X coordinates for individual 3 xcoord3 = n.split('\t')[6] xfloat3 = float(xcoord3) X3.append(xfloat3) # Y coordinates for individual 3 ycoord3 = n.split('\t')[7] yfloat3 = float(ycoord3) Y3.append(yfloat3) # X coordinates for individual 4 xcoord4 = n.split('\t')[9] xfloat4 = float(xcoord4) X4.append(xfloat4) # Y coordinates for individual 4 ycoord4 = n.split('\t')[10] yfloat4 = float(ycoord4) Y4.append(yfloat4) # X coordinates for individual 5 Moeller 110 xcoord5 = n.split('\t')[12] xfloat5 = float(xcoord5) X5.append(xfloat5) # Y coordinates for individual 5 ycoord5 = n.split('\t')[13] yfloat5 = float(ycoord5) Y5.append(yfloat5) # X coordinates for individual 6 xcoord6 = n.split('\t')[15] xfloat6 = float(xcoord6) X6.append(xfloat6) # Y coordinates for individual 6 ycoord6 = n.split('\t')[16] yfloat6 = float(ycoord6) Y6.append(yfloat6) # X coordinates for individual 7 xcoord7 = n.split('\t')[18] xfloat7 = float(xcoord7) X7.append(xfloat7) # Y coordinates for individual 7 ycoord7 = n.split('\t')[19] yfloat7 = float(ycoord7) Y7.append(yfloat7) # X coordinates for individual 8 xcoord8 = n.split('\t')[21] xfloat8 = float(xcoord8) X8.append(xfloat8) # Y coordinates for individual 8 ycoord8 = n.split('\t')[22] yfloat8 = float(ycoord8) Y8.append(yfloat8) # X coordinates for individual 9 xcoord9 = n.split('\t')[24] xfloat9 = float(xcoord9) X9.append(xfloat9) # Y coordinates for individual 9 ycoord9 = n.split('\t')[25] Moeller 111 yfloat9 = float(ycoord9) Y9.append(yfloat9) # X coordinates for individual 10 xcoord10 = n.split('\t')[27] xfloat10 = float(xcoord10) X10.append(xfloat10) # Y coordinates for individual 10 ycoord10 = n.split('\t')[28] yfloat10 = float(ycoord10) Y10.append(yfloat10) #----------Calculating Distances Travelled-----------#Empty values for counts total_distance1 = 0 total_distance2 = 0 total_distance3 = 0 total_distance4 = 0 total_distance5 = 0 total_distance6 = 0 total_distance7 = 0 total_distance8 = 0 total_distance9 = 0 total_distance10 = 0 frames1 = 0 frames2 = 0 frames3 = 0 frames4 = 0 frames5 = 0 frames6 = 0 frames7 = 0 frames8 = 0 frames9 = 0 frames10 = 0 #Distance of Individual 1 t=0 n=1 for i in range(len(X1)-1): distance1 = math.sqrt(((X1[t]-X1[n])**2)+((Y1[t]-Y1[n])**2)) #Distance formula if math.isnan(distance1): #If the value of any coordinates in the formula is NaN, iterate one step t=t+1 n=n+1 Moeller 112 else: #If none of the values are NaN, count 1 in the frames and update total distance, then iterate one step frames1 = frames1 + 1 total_distance1 = total_distance1 + distance1 t=t+1 n=n+1 #Distance of Individual 2 t=0 n=1 for i in range(len(X2)-1): distance2 = math.sqrt(((X2[t]-X2[n])**2)+((Y2[t]-Y2[n])**2)) if math.isnan(distance2): t=t+1 n=n+1 else: frames2 = frames2 + 1 total_distance2 = total_distance2 + distance2 t=t+1 n=n+1 #Distance of Individual 3 t=0 n=1 for i in range(len(X3)-1): distance3 = math.sqrt(((X3[t]-X3[n])**2)+((Y3[t]-Y3[n])**2)) if math.isnan(distance3): t=t+1 n=n+1 else: frames3 = frames3 + 1 total_distance3 = total_distance3 + distance3 t=t+1 n=n+1 #Distance of Individual 4 t=0 n=1 for i in range(len(X4)-1): distance4 = math.sqrt(((X4[t]-X4[n])**2)+((Y4[t]-Y4[n])**2)) if math.isnan(distance4): t=t+1 n=n+1 else: Moeller 113 frames4 = frames4 + 1 total_distance4 = total_distance4 + distance4 t=t+1 n=n+1 #Distance of Individual 5 t=0 n=1 for i in range(len(X5)-1): distance5 = math.sqrt(((X5[t]-X5[n])**2)+((Y5[t]-Y5[n])**2)) if math.isnan(distance5): t=t+1 n=n+1 else: frames5 = frames5 + 1 total_distance5 = total_distance5 + distance5 t=t+1 n=n+1 #Distance of Individual 6 t=0 n=1 for i in range(len(X6)-1): distance6 = math.sqrt(((X6[t]-X6[n])**2)+((Y6[t]-Y6[n])**2)) if math.isnan(distance6): t=t+1 n=n+1 else: frames6 = frames6 + 1 total_distance6 = total_distance6 + distance6 t=t+1 n=n+1 #Distance of Individual 7 t=0 n=1 for i in range(len(X7)-1): distance7 = math.sqrt(((X7[t]-X7[n])**2)+((Y7[t]-Y7[n])**2)) if math.isnan(distance7): t=t+1 n=n+1 else: frames7 = frames7 + 1 Moeller 114 total_distance7 = total_distance7 + distance7 t=t+1 n=n+1 #Distance of Individual 8 t=0 n=1 for i in range(len(X8)-1): distance8 = math.sqrt(((X8[t]-X8[n])**2)+((Y8[t]-Y8[n])**2)) if math.isnan(distance8): t=t+1 n=n+1 else: frames8 = frames8 + 1 total_distance8 = total_distance8 + distance8 t=t+1 n=n+1 #Distance of Individual 9 t=0 n=1 for i in range(len(X9)-1): distance9 = math.sqrt(((X9[t]-X9[n])**2)+((Y9[t]-Y9[n])**2)) if math.isnan(distance9): t=t+1 n=n+1 else: frames9 = frames9 + 1 total_distance9 = total_distance9 + distance9 t=t+1 n=n+1 #Distance of Individual 10 t=0 n=1 for i in range(len(X10)-1): distance10 = math.sqrt(((X10[t]-X10[n])**2)+((Y10[t]-Y10[n])**2)) if math.isnan(distance10): t=t+1 n=n+1 else: frames10 = frames10 + 1 total_distance10 = total_distance10 + distance10 t=t+1 Moeller 115 n=n+1 #------Average Distance (pixels/frame)----------#Average for each individual avdist1 = total_distance1 / frames1 #Total distance divided by frames avdist2 = total_distance2 / frames2 avdist3 = total_distance3 / frames3 avdist4 = total_distance4 / frames4 avdist5 = total_distance5 / frames5 avdist6 = total_distance6 / frames6 avdist7 = total_distance7 / frames7 avdist8 = total_distance8 / frames8 avdist9 = total_distance9 / frames9 avdist10 = total_distance10 / frames10 #Total Average for group total_avdist = (avdist1 + avdist2 + avdist3 + avdist4 + avdist5 + avdist6 + avdist7 + avdist8 + avdist9 + avdist10) / 10 #--------------Determine non-nan values and add them to a list # ----------Zebrafish Thigmotaxis---------------------------------# Determines the Actual Location in regards to the tank # Determines the size of the video frame X1nonan = [item for item in X1 if not (math.isnan(item)) == True] Y1nonan = [item for item in Y1 if not (math.isnan(item)) == True] X2nonan = [item for item in X2 if not (math.isnan(item)) == True] Y2nonan = [item for item in Y2 if not (math.isnan(item)) == True] X3nonan = [item for item in X3 if not (math.isnan(item)) == True] Y3nonan = [item for item in Y3 if not (math.isnan(item)) == True] X4nonan = [item for item in X4 if not (math.isnan(item)) == True] Y4nonan = [item for item in Y4 if not (math.isnan(item)) == True] X5nonan = [item for item in X5 if not (math.isnan(item)) == True] Y5nonan = [item for item in Y5 if not (math.isnan(item)) == True] X6nonan = [item for item in X6 if not (math.isnan(item)) == True] Y6nonan = [item for item in Y6 if not (math.isnan(item)) == True] X7nonan = [item for item in X7 if not (math.isnan(item)) == True] Y7nonan = [item for item in Y7 if not (math.isnan(item)) == True] X8nonan = [item for item in X8 if not (math.isnan(item)) == True] Y8nonan = [item for item in Y8 if not (math.isnan(item)) == True] Moeller 116 X9nonan = [item for item in X9 if not (math.isnan(item)) == True] Y9nonan = [item for item in Y9 if not (math.isnan(item)) == True] X10nonan = [item for item in X10 if not (math.isnan(item)) == True] Y10nonan = [item for item in Y10 if not (math.isnan(item)) == True] # Removing points where the fish jumped out of the recording chamber # -----------Trimmed X Values--------X1sort = sorted(X1nonan) # Sorts the list X1trimmed = X1sort[100:-100] # Removes the first 100 and last 100 values X2sort = sorted(X2nonan) X2trimmed = X2sort[100:-100] X3sort = sorted(X3nonan) X3trimmed = X3sort[100:-100] X4sort = sorted(X4nonan) X4trimmed = X4sort[100:-100] X5sort = sorted(X5nonan) X5trimmed = X5sort[100:-100] X6sort = sorted(X6nonan) X6trimmed = X6sort[100:-100] X7sort = sorted(X7nonan) X7trimmed = X7sort[100:-100] X8sort = sorted(X8nonan) X8trimmed = X8sort[100:-100] X9sort = sorted(X9nonan) X9trimmed = X9sort[100:-100] X10sort = sorted(X10nonan) X10trimmed = X10sort[100:-100] # ----------Trimmed Y Values---------Y1sort = sorted(Y1nonan) # Sorts the list Y1trimmed = Y1sort[100:-100] # Removes the first 100 and last 100 values Y2sort = sorted(Y2nonan) Y2trimmed = Y2sort[100:-100] Moeller 117 Y3sort = sorted(Y3nonan) Y3trimmed = Y3sort[100:-100] Y4sort = sorted(Y4nonan) Y4trimmed = Y4sort[100:-100] Y5sort = sorted(Y5nonan) Y5trimmed = Y5sort[100:-100] Y6sort = sorted(Y6nonan) Y6trimmed = Y6sort[100:-100] Y7sort = sorted(Y7nonan) Y7trimmed = Y7sort[100:-100] Y8sort = sorted(Y8nonan) Y8trimmed = Y8sort[100:-100] Y9sort = sorted(Y9nonan) Y9trimmed = Y9sort[100:-100] Y10sort = sorted(Y10nonan) Y10trimmed = Y10sort[100:-100] total_minX = min(min(X1trimmed), min(X2trimmed), min(X3trimmed), min(X4trimmed), min(X5trimmed), min(X6trimmed), min(X7trimmed), min(X8trimmed), min(X9trimmed), min(X10trimmed)) total_maxX = max(max(X1trimmed), max(X2trimmed), max(X3trimmed), max(X4trimmed), max(X5trimmed), max(X6trimmed), max(X7trimmed), max(X8trimmed), max(X9trimmed), max(X10trimmed)) total_minY = min(min(Y1trimmed), min(Y2trimmed), min(Y3trimmed), min(Y4trimmed), min(Y5trimmed), min(Y6trimmed), min(Y7trimmed), min(Y8trimmed), min(Y9trimmed), min(Y10trimmed)) total_maxY = max(max(Y1trimmed), max(Y2trimmed), max(Y3trimmed), max(Y4trimmed), max(Y5trimmed), max(Y6trimmed), max(Y7trimmed), max(Y8trimmed), max(Y9trimmed), max(Y10trimmed)) #--------Calculating Dimensions of Testing Chamber (pixels)---------#Range of chamber (pixels) rangeX = total_maxX - total_minX rangeY = total_maxY - total_minY #Pixles in X Direction / Width Box (14.7 cm) = pixels/cm X Moeller 118 area_pixel = rangeX * rangeY #Area of the chamber in pixels (calculated from ranges) area_cm = 217.54 #Area of the chamber in cm (measured) pix_per_cm = area_pixel / area_cm #Calculating ratio of pixels per centimeter from the above calcs cm_per_frame = total_avdist / pix_per_cm #Determing the average distance moved per frame / centimeters activity = cm_per_frame * 29.95 #Finding the activity in cm / second (recorded at 29.95 fps) #Average Distance for Each Individual in cm / sec activity_ind1 = (avdist1 / pix_per_cm) * 29.95 #Total average distance moved per pixel converted to cm/sec activity_ind2 = (avdist2 / pix_per_cm) * 29.95 activity_ind3 = (avdist3 / pix_per_cm) * 29.95 activity_ind4 = (avdist4 / pix_per_cm) * 29.95 activity_ind5 = (avdist5 / pix_per_cm) * 29.95 activity_ind6 = (avdist6 / pix_per_cm) * 29.95 activity_ind7 = (avdist7 / pix_per_cm) * 29.95 activity_ind8 = (avdist8 / pix_per_cm) * 29.95 activity_ind9 = (avdist9 / pix_per_cm) * 29.95 activity_ind10 = (avdist10 / pix_per_cm) * 29.95 #Highest Activity highest_activity = max(activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10) #Lowest Activity lowest_activity = min(activity_ind1, activity_ind2, activity_ind3, activity_ind4, activity_ind5, activity_ind6, activity_ind7, activity_ind8, activity_ind9, activity_ind10) #Range of Activity activity_range = highest_activity - lowest_activity #Zebrafish Thigmotaxis count1, count2, count3, count4, count5, count6, count7, count8, count9, count10 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 thig1, thig2, thig3, thig4, thig5, thig6, thig7, thig8, thig9, thig10 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 minX = 0.10 * rangeX maxX = 0.90 * rangeX minY = 0.10 * rangeY Moeller 119 maxY = 0.90 * rangeY #-----------Individual 1 Thigmotaxis Calculator---------count = 0 for i in X1nonan: if X1nonan[count] > minX and X1nonan[count] < maxX and Y1nonan[count] > minY and Y1nonan[count] < maxY: count1 += 1 count += 1 else: count += 1 if count1/count < 0.50: thig1 += 1 # -----------Individual 2 Thigmotaxis Calculator---------count = 0 for i in X2nonan: if X2nonan[count] > minX and X2nonan[count] < maxX and Y2nonan[count] > minY and Y2nonan[count] < maxY: count2 += 1 count += 1 else: count += 1 if count2/count < 0.50: thig2 += 1 # -----------Individual 3 Thigmotaxis Calculator---------count = 0 for i in X3nonan: #If Non-Thigmotatic count 1 if X3nonan[count] > minX and X3nonan[count] < maxX and Y3nonan[count] > minY and Y3nonan[count] < maxY: count3 += 1 count += 1 else: count += 1 #If in the middle less than 50% of the time, count as thigmotatic (1) else count as nonthigmotatic (0) if count3 / count < 0.50: thig3 += 1 # -----------Individual 4 Thigmotaxis Calculator---------count = 0 for i in X4nonan: Moeller 120 if X4nonan[count] > minX and X4nonan[count] < maxX and Y4nonan[count] > minY and Y4nonan[count] < maxY: count4 += 1 count += 1 else: count += 1 if count4 / count < 0.50: thig4 += 1 # -----------Individual 5 Thigmotaxis Calculator---------count = 0 for i in X5nonan: if X5nonan[count] > minX and X5nonan[count] < maxX and Y5nonan[count] > minY and Y5nonan[count] < maxY: count5 += 1 count += 1 else: count += 1 if count5 / count < 0.50: thig5 += 1 # -----------Individual 6 Thigmotaxis Calculator---------count = 0 for i in X6nonan: if X6nonan[count] > minX and X6nonan[count] < maxX and Y6nonan[count] > minY and Y6nonan[count] < maxY: count6 += 1 count += 1 else: count += 1 if count6 / count < 0.50: thig6 += 1 print(str(count6 / count)) # -----------Individual 7 Thigmotaxis Calculator---------count = 0 for i in X7nonan: if X7nonan[count] > minX and X7nonan[count] < maxX and Y7nonan[count] > minY and Y7nonan[count] < maxY: count7 += 1 count += 1 else: count += 1 if count7 / count < 0.50: thig7 += 1 Moeller 121 # -----------Individual 8 Thigmotaxis Calculator---------count = 0 for i in X8nonan: if X8nonan[count] > minX and X8nonan[count] < maxX and Y8nonan[count] > minY and Y8nonan[count] < maxY: count8 += 1 count += 1 else: count += 1 if count8 / count < 0.50: thig8 += 1 # -----------Individual 9 Thigmotaxis Calculator---------count = 0 for i in X9nonan: if X9nonan[count] > minX and X9nonan[count] < maxX and Y9nonan[count] > minY and Y9nonan[count] < maxY: count9 += 1 count += 1 else: count += 1 if count9 / count < 0.50: thig9 += 1 # -----------Individual 10 Thigmotaxis Calculator---------count = 0 for i in X10nonan: if X10nonan[count] > minX and X10nonan[count] < maxX and Y10nonan[count] > minY and Y10nonan[count] < maxY: count10 += 1 count += 1 else: count += 1 if count10 / count < 0.50: thig10 += 1 thigfile.write(str(filename.split('_')[1]) + '\t') thigfile.write('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t' % (thig1, thig2, thig3, thig4, thig5, thig6, thig7, thig8, thig9, thig10) ) thigfile.write(str(thig1 + thig2 + thig3 + thig4 + thig5 + thig6 + thig7 + thig8 + thig9 + thig10) + '\n') myfile.close() Moeller 122 continue