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.

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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.

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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

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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.

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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

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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).

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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

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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:

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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.

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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.

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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.

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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.

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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

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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-

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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.

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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

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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

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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).

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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,

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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

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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):

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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

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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.

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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:

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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

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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

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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