GENETIC AND NEURAL MECHANISMS REGULATING THE INTERACTION

BETWEEN SLEEP AND METABOLISM IN DROSOPHILA MELANOGASTER

by

Maria E. Yurgel

A Dissertation Submitted to the Faculty of

The Charles E. Schmidt College of Science

In Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

December 2018

Copyright 2018 by Maria Eduarda Yurgel

ii GENETIC AND NEURAL MECHANISMS REGULATING THE INTERACTION

BETWEEN SLEEP AND METABOLISM IN DROSOPHILAMELANOGASTER

by

Maria E. Yurgel

This dissertation was prepared under the direction of the candidate's dissertation advisor, Dr. Alex C. Keene, Department of Biological Sciences, and has been approved by the members of her supervisory committee. It was submitted to the faculty of the Charles E. Schmidt College of Science and was accepted in partial fulfillmentof the requirements for the degree of Doctor of Philosophy.

SUPERVISORY COMMITTEE:

Z!--�•r<. �� --- Alex C. Keene, Ph.D. Disserta · n Advis

,,. Tanja Godenschwege, Ph.p

Khaled Sobhan, Ph.D. Date� 7 Interim Dean, Graduate College

111 ACKNOWLEDGEMENTS

I would like to express my gratitude for all my committee members for their

guidance and support. First and foremost, I would like to thank my mentor, Dr. Alex

Keene, for believing in my potential and giving me the opportunity to pursue my PhD in

his laboratory. During these 5 years, I have been challenged to my limit and pushed to think, to be creative, and be productive. His passion and excitement for science fueled me to get out of my comfort zone, implement new techniques, and pursue questions that were not trivial. I am also greatly indebted to Dr. Tanja Godenschwege, for helping me both scientifically and personally through the highs and lows of my graduate education. Her extensive knowledge and thirst for new challenges made me appreciate research. I will be forever thankful for her always welcoming my questions, providing thoughtful advice, and above all, instilling a newfound confidence in myself. I also would like to thank Dr.

Greg Macleod for always providing experimental guidance and making me think outside the box. I am greatly indebted to Dr. Rod Murphey for creating an ideal environment for scientific pursuits and being extremely supportive of all graduate students. Finally, I have been lucky to have an informal advisor, Erik Duboue, who has always welcomed my questions and provided thoughtful experimental and career advice.

Moving from the University of Nevada, Reno to Florida Atlantic University was not an easy transition. I am eternally appreciative of my labmates and good friends,

Kazuma Murakami, James Jaggard, Melissa Slocumb, and Beatriz Robinson who made this journey enjoyable. Without their help and support I would not have succeeded.

iv The work presented in this dissertation would not have been possible without the

environment in the Keene lab. I wanted to thank all lab members for their patience,

willingness to help, and support through the many ups and downs of graduate life. In

particular, I wanted to express my deepest gratitude to John Tauber. His wit for research,

incredible talent, perseverance, and critical eye has challenged me to do better and be better. I am also thankful to Kreesha Shah, without her help my last year in lab would not have gone smoothly. Lastly, I am thankful to the post docs in the Keene Lab including

Pavel Masek, Masato Yoshizawa, Bethany Stahl, and Elizabeth Brown. In particular,

Elizabeth for being an incredible labmate, always willing to help and brainstorm with me.

There are innumerable graduate students and staff members of MC-19 that have been critical during my PhD. I would like to thank the members of the Macleod,

Godenschwege, Duboue, Stackman, and Murphey Labs. In particular, I want to thank

Jana Boerner for her collaborative work and patience to teach me microscopy. Finally, I would like to thank Silvana Jaramillo and Dr. Stacee Caplan for going above and beyond to help me with administrative issues throughout my PhD.

Above all, I would like to thank my family for their love and support. Their emphasis in education, culture, and knowledge is truly unprecedented, you are my inspiration. My mother and father have taught me from a young age to be persistent and

move through life with an ambitious mind. I will be forever thankful for their support in

every single decision in my life. My grandfather, Henrique, grandmothers, Maria Helena

and Adelia, and aunt, Maria Laura, for their unconditional love. Your love has grounded

me and given me strength and inspiration to reach my life goals. My sister, Martina, her

clever sense of humor, understanding and protective nature, has made life easier and

v more enjoyable. I am eternally grateful for her support. I would like to thank all my cousins for always giving me advice and supporting who I am, especially, Caio and

Marcela. Both of you have set a bar unreachably high both academically and personally and I am constantly looking up to both of you. Finally, I would like to thank my partner,

Paloma Amaral. Her love and support have given me the strength to carry on. I am honored to have you by my side. I am also very grateful for all the help she has given me in the lab, both experimentally and in editing my thesis.

Throughout my life I have been very lucky to have met incredible friends who have gone lengths to help me. I would like to thank my college friend, Kathryn Zingaro, for her loyalty and support throughout this endeavor. I am also greatly indebted to

Veronica Arinze, who showed me the fun side of life. Also, Ed and Gail Greenberg for taking the time to read my thesis and being supportive in every aspect of my personal and professional life. I would like to thank my mentor, Daniela Vilas Boas, for her invaluable guidance. Lastly, I thank my tennis coach, Mauro Brandao, for teaching me to be perseverant.

vi

ABSTRACT

Author: Maria Eduarda Yurgel

Title: GENETIC AND NEURAL MECHANISMS REGULATING THE INTERACTION BETWEEN SLEEP AND METABOLISM IN DROSOPHILA MELANOGASTER

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Alex C. Keene

Degree: Doctor of Philosophy

Year: 2018

Dysregulation of sleep and metabolism has enormous health consequences. Sleep loss is linked to increased appetite and insulin insensitivity, and epidemiological studies link chronic sleep deprivation to obesity-related disorders. Interactions between sleep and metabolism involve the integration of signalling from brain regions regulating sleep, feeding, and metabolism, as well as communication between the brain and peripheral organs. In this series of studies, using the fruit fly as a model organism, we investigated how feeding information is processed to regulate sleep, and how peripheral tissues regulate sleep through the modulation of energy stores.

In order to address these questions, we performed a large RNAi screen to identify novel genetic regulators of sleep and metabolism. We found that, the mRNA/DNA binding protein, Translin (trsn), is necessary for the acute modulation of sleep in accordance with feeding state. Flies mutant for trsn or selective knockdown of trsn in

Leucokinin (Lk) neurons abolishes starvation-induced sleep suppression. In addition,

vii genetic silencing of Lk neurons or a mutation in the Lk locus also disrupts the integration between sleep and metabolism, suggesting that Lk neurons are active during starvation.

We confirmed this hypothesis by measuring baseline activity during fed and starved states. We found that LHLK neurons, which have axonal projections to sleep and metabolic centers of the brain, are more active during starvation. These findings suggest that LHLK neurons are modulated in accordance with feeding state to regulate sleep.

Finally, to address how peripheral tissues regulate sleep, we performed an RNAi screen, selectively knocking down genes in the fat body. We found that knockdown of

Phosphoribosylformylglycinamidine synthase (Ade2), a highly conserved gene involved the biosynthesis of purines, regulates sleep and energy stores. Flies heterozygous for two

Ade2 mutations are short sleepers and this effect is partially rescued by restoring Ade2 to the fly fat body. These findings suggest Ade2 functions within the fat body to promote both sleep and energy storage, providing a functional link between these processes.

Together, the experimental evidence presented here provides an initial model for how the

peripheral tissues communicate to the brain to modulate sleep in accordance with

metabolic state.

viii

GENETIC AND NEURAL MECHANISMS REGULATING THE INTERACTION

BETWEEN SLEEP AND METABOLISM IN DROSOPHILA MELANOGASTER

LIST OF TABLES ...... xiii

LIST OF FIGURES ...... xiv

CHAPTER 1. LITERATURE REVIEW ...... 1

Introduction ...... 1

Why do we sleep? ...... 2

How sleep is regulated in mammals? ...... 3

Food intake and energy expenditure in mammals ...... 7

Mechanisms for sleep-metabolism interactions in mammals ...... 9

Fruit flies as a model system ...... 12

Sleep in Drosophila melanogaster ...... 13

Neurohormonal regulation of metabolism ...... 19

Dietary regulation of sleep ...... 21

Endocrine integration of sleep and metabolic function ...... 25

A role for the fat bodies in sleep regulation ...... 27

Nutrient sensors in the fruit fly brain ...... 29

Novel genetic regulators of sleep-metabolism interactions ...... 30

ix Conclusions ...... 32

CHAPTER 2. TRANSLIN IS NECESSARY FOR THE METABOLIC

REGULATION OF SLEEP ...... 33

Abstract ...... 33

Introduction ...... 34

Methods ...... 36

Drosophila maintenance and Fly Stocks ...... 36

Behavioral Analysis ...... 36

Pharmacological manipulation ...... 38

Paraquat treatment ...... 39

Caffeine treatment ...... 39

Protein, glycogen, and triglyceride measurements ...... 39

Proboscis Extension Reflex (PER) ...... 40

Blue dye assay ...... 41

Two-choice capillary feeding assay (CAFÉ) ...... 41

Quantitative RT-PCR ...... 42

Immunohistochemistry ...... 43

Statistical Analysis ...... 43

Results ...... 44

Discussion ...... 58

x CHAPTER 3. A SINGLE PAIR OF LEUCOKININ NEURONS ARE

MODULATED BY FEEDING STATE AND REGULATE SLEEP-METABOLISM

INTERACTION ...... 63

Abstract ...... 63

Introduction ...... 64

Methods ...... 66

Drosophila maintenance and Fly stocks ...... 66

Generation of GAL4 knock-in mutants ...... 66

Generation of UAS-Lk ...... 67

Behavioral analysis ...... 70

Immunohistochemistry ...... 70

Functional imaging of Lk neurons...... 71

Targeted multi-photon ablation of Lk neurons ...... 72

Statistical Analysis ...... 73

Results ...... 74

Discussion ...... 89

CHAPTER 4. ADE2 FUNCTIONS IN THE FAT BODY TO REGULATE SLEEP...... 97

Abstract ...... 97

Introduction ...... 97

Methods ...... 99

xi Fly Stocks ...... 99

Sleep analysis ...... 100

Protein, glucose, glycogen and triglyceride measurements ...... 100

Sleep deprivation ...... 101

Arousal Threshold ...... 101

Statistical Analysis ...... 101

Data Availability...... 102

Results ...... 102

Discussion ...... 116

CHAPTER 5. FINAL REMARKS ...... 124

Acknowledgements of contributions to dissertation ...... 127

REFERENCES ...... 129

xii

LIST OF TABLES

Table 1 Lines utilized for nutrient sensor RNAi screen ...... 68

Table 2 Lines utilized for fat body RNAi screen ...... 105

xiii LIST OF FIGURES

Figure 1 Diagram representing the neural circuitry involved in the regulation of

sleep and wake...... 5

Figure 2 Diagram representing circuitry regulating energy status...... 10

Figure 3 Neural regulation of sleep and arousal in Drosophila...... 17

Figure 4 A neural circuit connecting sleep centers in the fly brain...... 19

Figure 5 Functional conservation between organs and cell-types regulating

metabolic function...... 22

Figure 6 Starved flies suppress sleep...... 23

Figure 7 trsn is required for metabolic regulation of sleep...... 47

Figure 8 trsn is required for metabolic regulation of sleep...... 49

Figure 9 Characterization of sleep in trsn deficient flies...... 50

Figure 10 Characterization of sleep in trsn deficient flies...... 51

Figure 11 Starvation-induced feeding is normal in trsn mutant flies...... 54

Figure 12 Energy stores and free glucose are normal in trsn mutant flies...... 55

Figure 13 Spatial and temporal localization of trsn function...... 56

Figure 14 Spatial and temporal localization of trsn function...... 57

Figure 15 Adult-specific knock down of trsn disrupts sleep suppression...... 59

Figure 16 trsn functions in Leucokinin neurons to regulate sleep ...... 60

Figure 17 trsn functions in Leucokinin neurons to regulate sleep...... 61

Figure 18 Lk neurons are acutely required for starvation-induced sleep suppression. .... 62

xiv Figure 19 Leucokinin neuropeptide is required for metabolic regulation of sleep...... 75

Figure 20 . Leucokinin neuropeptide is required for metabolic regulation of sleep...... 76

Figure 21 Leucokinin neuropeptide is required for metabolic regulation of sleep...... 77

Figure 22 Leucokinin neuropeptide is required for metabolic regulation of sleep...... 78

Figure 23 Lateral horn leucokinin neurons are necessary for the metabolic regulation

of sleep...... 79

Figure 24 Lateral horn leucokinin neurons are necessary for the metabolic regulation

of sleep...... 81

Figure 25 Lateral horn leucokinin neurons are necessary for the metabolic regulation

of sleep...... 82

Figure 26 Lateral horn leucokinin neurons are necessary for the metabolic regulation

of sleep...... 85

Figure 27 Lateral horn leucokinin neurons have increased activity during starved

state...... 86

Figure 28 Lateral horn leucokinin neurons have increased activity during starved

state...... 88

Figure 29 AMPKα is a key nutrient sensor that function in LHLK...... 90

Figure 30 AMPKα is a key nutrient sensor that function in LHLK...... 91

Figure 31 Ade2 functions in the fat body to promote sleep...... 104

Figure 32 Ade2 function in the fat body to promote sleep...... 108

Figure 33 Ade2 function in the fat body to promote sleep...... 110

Figure 34 Ade2 expression in the fat body partially rescues sleep loss...... 112

Figure 35 Ade2 expression in the fat body partially rescues sleep loss...... 115

xv Figure 36 Ade2 expression in the fat body partially rescues sleep loss...... 117

Figure 37 Arousal threshold is normal in Ade2-RNAi and Ade2 mutants...... 118

Figure 38 Arousal threshold is normal in Ade2-RNAi and Ade2 mutants...... 119

Figure 39 Homeostatic recovery sleep is not altered in Ade2 mutants...... 120

Figure 40 Triglycerides and free glucose are altered in Ade2 knock down and

mutants...... 121

Figure 41 Current model for the regulation of starvation induced sleep suppression. .. 125

xvi CHAPTER 1. LITERATURE REVIEW

Introduction

It has long been observed that sleep deprivation leads to increased food intake.

Individuals that cannot sleep at night, often find themselves searching for highly caloric

food. Similarly, after ingesting a large meal, there is an increased propensity to fall

asleep. Though scientists may argue whether this nocturnal search for food is due to

anxiety or a true caloric need, it is undeniable that sleep and metabolism are tightly

interconnected.

During sleep, basal metabolic rate, which is defined as the amount of energy

(calories) that the body uses to maintain basic function, is significantly reduced 1.

Hormones necessary for glucose regulation are released during slow wave sleep, and

highlight the importance of adequate sleep for the normal functioning of daily metabolic

and hormonal processes 2. Disruption of either process affects each other in a

bidirectional manner. For example, epidemiological studies show that individuals that

sleep less than 6 hours per night have an increased body mass index 3. Sleep loss

correlates with increased appetite and insulin insensitivity, and short sleeping individuals

are more likely to develop obesity, metabolic syndrome, type II diabetes, and

cardiovascular diseases 3–5. Conversely, obese individuals often suffer from sleep apnea, which impairs breathing, obstructing the ability to reach deep stages of sleep 6,7.

Though studies from a diversity of phyla have identified conserved genetic mechanisms regulating sleep and metabolism interactions8–10, there remain fundamental

1 questions concerning the full mechanistic understanding of these complex processes. The overall goal of this dissertation is to dissect sleep-metabolism interactions through the identification of genetic, cellular, and neural pathways underlying these behaviors. In addition, this work will not only focus in understanding how the brain computes feeding information to regulate sleep, but in understanding how non-neural tissues, such as the adipose tissue, can regulate sleep by modulating nutrient stores.

Why do we sleep?

Sleep is undoubtedly a vital and complex process that occurs in many different

species 11,12. This biological process is characterized behaviorally by periods of

quiescence with stereotyped posture, state of reversibility, rebound after sleep

deprivation, and increased arousal threshold 13. In mammals and birds, wake and sleep

can further be distinguished through the measurement of brain electrical activity by

electroencephalograms (EEGs) 11.

While the answer for why we sleep seems obvious – to recover from a tiring day –

the biological function remains elusive. Several studies propose different hypothesis for

sleep function; these include, the energy allocation hypothesis, memory consolidation,

synaptic homeostasis, and clearance of toxins 14–18.

From an ecological perspective, sleep may protect against predation at times when

an animal may be vulnerable and promotes energy conservation 19. However, there are

many disadvantages of sleep. For example, during sleep, animals cannot forage for food,

enhance mating success, and thermoregulate. Therefore, sleep must be a fundamental

process for all organisms, otherwise, it would not have evolved.

2 During wakefulness, the electrical activity recorded in the brain is low-voltage,

high frequency, and fast 20,21. Conversely during sleep, EEG recordings show slow,

synchronized oscillations of neuronal activity. Sleep can be further divided into two

distinct phases: non-rapid eye movement (Non-REM) and rapid eye movement (REM) 22.

Within this division, there are different stages that can be characterized based on a combination of EEG, electro-oculogram (EOG), and electromyogram (EMG) recordings.

The transition between wake and sleep occurs during Non-REM sleep, stages 1– 4, and it

occurs within 30 min of the onset of asleep 23–25. These stages are marked by a change in

electrical activity, as well as decrease in muscle tone, temperature, and respiratory rate. In

the 5th stage, also known as REM, there is a total loss of muscle tone, the eyes are

constantly moving back and forth, and body temperature reaches its nadir. Interestingly,

heart rate and respiration are increased, and the brain electrical activity changes to that

similar of the waking state.

How sleep is regulated in mammals?

The search for brain structures regulating sleep was pioneered by Horace Magoun

and Giuseppe Moruzzi 26. They observed that electrical stimulation of the brain stem in

cats resulted in arousal, demonstrating how wakefulness is maintained. Following 60

years of research, different groups have further these studies by localizing sleep and

wake-promoting pathways to specific regions and determining the role of signaling in

mediating the shift between sleep and wakefulness 27.

The control between sleep and wake is mediated through the preoptic

hypothalamus, basal forebrain, lateral hypothalamus and brain stem (Fig. 1). The preoptic

area of the anterior hypothalamus (POA) has been implicated in sleep 28–30. Within this

3 region, GABAergic VLPO neurons are sleep active 31–33 and promote sleep through the

inhibition of wake promoting monoaminergic centers, which include the histaminergic tuberomammillary nucleus (TM), serotonergic dorsal and median raphe nuclei (DRN and

MRN), noradrenergic locus coeruleus (LC) , and the orexigenic lateral hypothalamus(LH) 34 (Fig. 1).

The lateral hypothalamic and posterior hypothalamic (PH) regions are known to

express the wake promoting neuropeptide hypocretin/orexin (hcrt) 35,36. However, within

this region, there is a subset of melanin-concentrating hormone cells (MCH) which are

sleep active 37,38. Thus, to promote wakefulness, hcrt neurons locally inhibit MCH

expressing while signaling to the monoaminergic nuclei to promote wakefulness. During

sleep, VLPO neurons within the POA, inhibit orexinergic neurons, which in turn, release

the inhibition on MCH neurons, promoting sleep 39.

While the POA/VLPO is a strong sleep-promoting region, the basal forebrain

(BF) is capable of both promoting sleep or wakefulness locally 28–30. The main cell types

in the BF are cholinergic, glutamatergic, and GABAergic. GABAergic somatostatin

expressing neurons in the BF, inhibit cholinergic, glutamatergic, and Parvalbumin-

expressing neurons, which are known to promote wakefulness 34. Together, the basic

circuit for sleep involves the POA/VLPO and BF inhibiting wake active regions such as

the LH and monoaminergic centers. Therefore, the transitions that occur between wake,

REM sleep and non-REM sleep depend on both local circuit as well as through a mutual

inhibitory interaction between these distinct neuronal populations described above (Fig.

1).

4 The transition between states, however, does not explain how sleep pressure is

built during the day or the timing of sleep. A hypothetical model for sleep regulation proposes that a homeostatic (S) and circadian process (C) must exist in order to unite sleep need and the period of sleep 40,41. Homeostatic sleep process refers to a period after

prolonged wakefulness where animal or individual sleeps for consolidated period of time.

One of the most well-studied molecules to have a function in building sleep pressure

during the day is adenosine. Studies found that adenosine accumulates during the day in

the BF and cortex and declines during sleep 42,43.

Figure 1 Diagram representing the neural circuitry involved in the regulation of sleep and wake.

Black oval circle denotes sleep promoting neurons and white circles denote wake promoting circuits. POA, preoptic area of the anterior hypothalamus; BF, basal forebrain; LH, lateral hypothalamus, PH, posterior hypothalamus; DRN, dorsal raphe nucleus; vPAG, ventral periaqueductal gray; LC, locus coeruleus; PB, parabrachial nucleus.

Adenosine is a secondary by-product of the breakdown of ATP and cAMP. There are 4 known adenosine receptors: A1R, A2aR, A2bR, and A3R 44. For sleep homeostasis,

A1R, expressed in the cortex, hippocampus, thalamus and cerebellum, and A2aR,

expressed in the striatum, have been more extensively studied 45,46. A1R is an inhibitory

Gi-coupled receptor, while A2A is an excitatory Gs-coupled receptor. Sleep is modified

5 by A1Rs through the inhibition of the cholinergic arousal system, inhibition of hcrt neurons in the LH, and wake promoting neurons in the BF 47–49. Conversely, A2aR

receptors promote sleep by exciting VLPO neurons 50. Thus, adenosine promotes sleep

through the excitation of sleep promoting neurons and inhibition of wake promoting

centers.

Timing of sleep is regulated through a circadian component. In all species the

period of sleep is determined by the rotation of the earth and time cues (zeitgebers, ZT),

that indicate day and night. The strongest ZT is sunlight. Interestingly, even in the

absence of light, the rest-activity rhythm persists with a periodicity of 24 hours per day 51.

The circadian modulation of sleep requires the suprachiasmatic nucleus (SCN) in the

hypothalamus. Lesioning the SCN affects the period of sleep but not the total amount 52,

indicating that sleep amount and timing of sleep are regulated by independent neural

populations 52.

One of the intrinsic properties that makes the SCN the principal circadian clock of

the brain is the presence of a cell autonomous transcription-based clockwork 53. The cell-

autonomous feature of the SCN was observed in cell cultures of the SCN, where

variations in electrical activity were recorded. As previously mentioned, in the absence of

light, the clock runs freely while maintaining 24hr periodicity. Thus, for the SCN to be

properly tuned with day/night cycles, sunlight must entrain the clock. This occurs through the detection of light by intrinsically photosensitive retinal ganglion cells located in the eye. These cells project to the SCN, synchronizing sleep to light/dark cycles 54,55.

Therefore, the combination of inputs from the retinal ganglion cells, signaling light from

6 the environment, in addition to the intrinsic molecular clocks driven by transcriptional and translational feedbacks, regulate the activity of the SCN 56,57.

The SCN projects to multiple regions to coordinate a diverse number of physiological functions to the light-dark cycle. One of the regions that is important for sleep is the dorsomedial hypothalamic nucleus (DMH) 58. The DMH projects to hcrt neurons, LC and VLPO, all involved in sleep and wakefulness, suggesting many potential pathways for how light-dark cycles can promote rest or activity 37,59. Taken together, sleep homeostasis is achieved through the sum of process S, which is sleep deficit, and process C, which is the increased arousability during the day 40,41. During a normal sleep- wake cycle, sleep pressure builds during the day and dissipates at night. Sleep deprivation leads to an increase of sleep deficit, process S, while process C persists, which is the activity cycle during the day and rest cycle during the night. During the second night the need for sleep will be at its peak, being almost impossible to remain awake.

Food intake and energy expenditure in mammals

Metabolism is defined as the balance between the anabolic and catabolic chemical reactions that occur in our body. The catabolic pathway functions to break down carbohydrates, protein, and fat ingested from diets, and transform it into energy (ATP).

The anabolic pathway is dependent on catabolism, it promotes synthesis of complex molecules from small molecules and it utilizes ATP. Even though these pathways occur in individual cells, cells associate into tissues that specialize in different aspects of metabolism. For example, the brain receives information from peripheral tissues and external inputs through sensory neurons, integrate information and release different hormones to tightly regulate metabolic process.

7 The regions in the brain responsible for the balance between hunger and satiety

were discovered through lesion experiments, intracranial drug delivery and electric brain

stimulation 60–63. Distinct nuclei of the hypothalamus, which included the LH,

paraventricular hypothalamus (PVH), ventral medial hypothalamus (VMH), DMH, and

arcuate nucleus (ARC), are responsible for maintaining energy homeostasis.

During starvation, α-pancreatic cells release glucagon when glucose levels are

low. In the liver, glucagon binds to glucagon receptors to promote gluconeogenesis and

glycogenolysis. Concomitantly, the hormone ghrelin, is released from the stomach to

promote food intake 61,64. The site of action of ghrelin is thought to be the VMH and

ARC. In the ARC, ghrelin modulates neuropeptide Y (NPY) and agouti related peptide

neurons (AgRP), which increases food intake 65–69. Interestingly, AgRP expressing

neurons also drive food seeking behaviors, since chemogenetic stimulation of AgRP

expressing neurons in the absence of food results in increased locomotor activity 70.

Even though there are multiple brain nodes implicated in feeding behavior, the

connectivity between AgRP neurons and proopiomelanocortin (POMC) neurons also in

the ARC is of extreme importance to maintain homeostasis (Fig. 2) 71. During the fed

state, insulin is secreted from β-pancreatic cells and promotes absorption of glucose from the bloodstream to liver, adipose tissue and skeletal muscle. Secreted insulin binds to receptors in NPY/AgRP neurons leading to a decrease in activity while POMC neurons are directly activated via insulin signaling 63. POMC neurons also promote satiety

through the binding of leptin released from the adipose tissue 72–74.

Finally, the ARC which contain AgRP/NPY and POMC neurons project to multiple regions including the hcrt/MCH producing LH, which in response to starvation

8 and presence of ghrelin, induce depolarization of hcrt expressing neurons, stimulating food intake (Fig. 2; 75. Electrophysiological studies in slice preparation characterized the

functional connectivity between hcrt, NPY, and POMC neurons 76. Hypocretin/orexin

application suppresses spontaneous firing from POMC neurons, contributing to inhibition

of neurons responsible for satiety and promotion of feeding 77.

In summary, hormonal signals from peripheral tissues relay energy status to the

ARC of the hypothalamus. Hormones are sensed by receptors in this region which promote food intake and increase energy expenditure or decrease food intake. The discovery of circuits underlying feeding leveraged the understanding of how networks regulate energy homeostasis. Future studies will be pivotal in uncovering downstream nodes that regulate food intake and the hedonic aspects of feeding.

Mechanisms for sleep-metabolism interactions in mammals

Extensive evidence suggests that sleep disturbances affect metabolism. Cohort studies revealed that short sleep and poor quality of sleep are both associated with increased risk of diabetes 78.

9

Figure 2 Diagram representing circuitry regulating energy status.

The stomach secreted peptide, ghrelin, is secreted in response to fasting. Leptin and insulin, secreted from adipose and pancreatic cells, circulate in response to satiety. Energy homeostasis is achieved through the binding of these molecules in the hypothalamic melanocortin system. TRH, thyrotropin-releasing hormone; CRH, corticotropin-releasing hormone; OXY, oxytocin; Hcrt, hypocretin; MCH, melanin concentrating hormone; AGRP, agouti-related peptide; NPY, neuropeptide Y; POMC, pro-opiomelanocortin; α-MSH, α-melanocyte-stimulating hormone; PVH, paraventricular nucleus; LH, lateral hypothalamus; ARC, arcuate nucleus.

Similarly, individuals that are long sleepers (9 hours) are also at risk of developing diabetes 79. However, sleep duration is not the only factor influencing the risk of developing metabolic pathologies. Shift workers, who have eating and sleeping schedules reversed from their own internal circadian rhythms, show elevated glucose levels and insulin resistance 80. These examples illustrate a strong correlation between sleep and metabolism.

Studies in diverse phyla have identified conserved genetic mechanism regulating sleep and metabolism interactions8–10. In the mammalian system, the integration between

10 sleep and metabolism is regulated by sleep-active neurons located within VLPO 81.

VLPO neurons are central glucose sensing cells that contribute directly to sleep control in response to the organism’s internal metabolic status 81. In addition, sleep promoting MCH

neurons are also affected by nutritional state. MCH levels increase after fasting and

results in a reduction in energy expenditure 82. Finally, hcrt expressing neurons, are

directly inhibited by glucose, triglycerides, and leptin which inhibit wakefulness, thus,

promoting sleep. During starvation, ghrelin activates orexin expressing neurons which in

turn drives arousal and promotes food intake 39.

Interestingly, endocrine signals from peripheral tissues, such as the brown adipose

tissue (BAT), are also involved in both metabolism and sleep regulation 83. Brown fat

regulates energy expenditure and glucose disposal. This tissue is capable of converting

free fatty acids to heat via the action of uncoupling potein 1 (UCP1) in response to cold

and diet 84. Recent work has shown that BAT thermogenesis is required for sleep rebound

after sleep loss. Mice mutant for UCP-1 show impairments show impairments in the compensatory increase in sleep after deprivation 85, suggesting that BAT is necessary for

the homeostatic regulation of sleep. Sleep regulation from this tissue is thought to be

mediated through the secretion of leptin, tumor necrosis factor, and interleukin 1 and 6.

Candidate target regions in the brain include PVN, periaqueductal gray, parabrachial

nuclei, and raphe nuclei 86 . Additional studies have demonstrated that the brain also modulate BAT thermogenesis. Neural activation of hcrt/orexin projections increases BAT thermogenesis, raising the possibility that orexin neurons regulate thermogenesis during sleep and wakefulness 87,88.

11 Taken together, distinct tissues have been identified that contribute to modulate sleep and metabolic state, however, how they mechanistically synchronize sleep and metabolism has not been elucidated. The mammalian system provides a good framework to the understanding of how sleep and metabolism are integrated, however the complexity of the mammalian brain in addition to the difficulties to generate mutants, access deep brain tissues, and manipulate neuronal circuits imposes limitations to this model 89.

Fruit flies as a model system

The fruit fly, Drosophila melanogaster, is a well-established model to study many biological questions. Fruit flies not only possess a short life cycle but also display a robust repertoire of behaviors 90. Its genome contains approximately 14,000 genes which are conserved in mammals 91–94, thus supporting the notion that flies are an excellent organism to study the genetic basis of behavior.

Fruit flies also possess a genetic toolkit which allows for an exquisite level of control over genes, being able to knockdown and overexpress genes, and manipulate neurons in a spatial and temporal manner 95–100. One example is the bipartite GAL4/UAS system. This system was designed utilizing the transcriptional activator GAL4 from yeast, which can activate transcription by binding to the upstream activating sequence

(UAS), a specific sequence containing GAL4 binding sites 96,101. The sequence a gene of interest is cloned downstream of the UAS and genomic enhancers allow for the tissue- specific expression of the GAL4. This occurs when flies carrying the target (UAS) are crossed to flies expressing the GAL4 protein. The resulting progeny can the, be observed for behavioral alterations due to the manipulation of specific genes in the tissue of choice.

12 Fruit flies are also suitable to study the nervous system, with approximately

100,000 neurons and conserved circuit principles 95. It is possible to permanently reduce

the activity or silence neurons by the expression of potassium channels Kir 102, EKO 103 ,

DORK 104, and tetanus toxin which impair vesicle docking and release 105. In addition,

the expression of Shibiri, a temperature sensitive dynamin that plays an important role in

vesicle recycling in nerve terminals, grants the ability to transiently silence neurons at high temperatures 106,107. Conversely, chronic and acute activation of neurons can be

achieved through the expression of the bacterial depolarization-activated sodium channel,

NaChBac 108, or through the expression of the a temperature- and voltage gated cation

channel, TRPA1109, enabling the influx of ions at higher temperatures 109. More recently,

advancements in optogenetics, which allows for the manipulation of neurons in an intact

brain through the application of light, 110,111 in combination with the ability to record

neural activity through the use of fluorescent indicators 112, grant researchers the ability to

precisely and rigorously dissect neural circuits regulating behavior. Together, the wealth

of tools and the ability to use a combination of approaches to answer biological

questions, establishes Drosophila as an invaluable model organism.

Sleep in Drosophila melanogaster

Sleep can be characterized by physiological changes in brain activity or through

behaviors that accompany these changes 113. Flies, like mammals, display distinct

electrophysiological patterns that correlate with wake and rest 114,115. Additionally, flies

display all behavioral hallmarks of sleep including extended periods of behavioral

quiescence, rebound following deprivation, increased arousal threshold and species-

specific changes in posture 9,10. Multiple systems for behavioral analysis are available for

13 high-throughput detection and measurement of fly activity including infrared monitoring

and automated video tracking 116–119. Sleep in Drosophila is typically defined by periods

of behavioral quiescence lasting five minutes or longer. This characteristic associates with other behavioral hallmarks of sleep including enhanced arousal threshold and rebound following deprivation 9,10. Recent findings measuring local field potentials to

determine neural activity and arousal threshold as an indicator for reduced sensory

response suggest that flies enter a ‘deep sleep’ following approximately 15 minutes of

behavioral quiescence, raising the possibility that this is functionally analogous to slow

wave sleep in mammals 115. Similarities are also present at a molecular level, where the

sleep suppressing effects of the stimulants caffeine, cocaine, and modafinil are conserved

from flies to humans 10,120–122. Therefore, flies are an excellent genetic model for

investigating the regulation of sleep in mammalian systems.

A powerful genetic tool kit in Drosophila allows for the identification of genes

and neural circuits that regulate sleep 123. In flies and mammals, sleep-wake regulation

involves dynamic interactions between sleep and wake-promoting neural circuits 123,124.

Therefore, it is unlikely that sleep is controlled by a primary ‘sleep center’. Four neural

loci involved in sleep-wake regulation appear to be particularly important for sleep-wake

regulation: the sleep-promoting mushroom bodies, a dopamine modulation of dorsal fan-

shaped bodies (dFSB), the hypothalamus-like Insulin Producing Cells (IPCs), the

Ellipsoid Body (EB), as well as a sleep-suppressing role for the circadian pacemaker neurons (123,125; Fig. 3). The mushroom bodies are composed of ~5000 neurons that are

critical for sensory integration and olfactory memory 126–128. Ablation or genetic silencing

of the mushroom bodies disrupts sleep, while genetic activation of this structure induces

14 sleep, revealing that sleep is gated by mushroom body activity 129,130. The neurons

innervating the mushroom bodies are well characterized and include monoaminergic and

peptidergic modulatory neurons that are required for memory formation, and second-

order olfactory projection neurons that connect the mushroom bodies to the antennal lobe

131–133. While the sleep-regulating mushroom body-associated neurons are not well understood, the Dorsal Paired Medial (DPM) neurons innervate the mushroom bodies and are required for the formation of both memory and sleep 134–136. Silencing DPM neurons

results in fragmented sleep, raising the possibility that DPM-mushroom body

connectivity is important for sleep maintenance 136.The Drosophila central complex is a

neural center regulating locomotion and visual processing 137–139. Activation of neurons

forming the dFSB, a substructure within the central complex, robustly induces sleep 140.

The dFSB receives input from the protocerebral posteriolateral cluster 1 (PPL1) and

protocerebral posteriomedial 3 (PPM3) cluster of dopamine neurons that likely inhibit

dFSB activity to suppress sleep 141–143. The excitability of dFSB neurons changes in

accordance with sleep debt through a mechanism that is dependent on the Rho-GTPase activating protein cross-veinless, supporting the notion that this is a central regulator of sleep need 144. In addition to cross-veinless, the dFSB change in electrical excitability

from an ON to OFF state is mediated by two potassium conductance channels (Pimentel et al. 2016, Donlea et al 2017). Sleep debt accumulates throughout the day and switches

dFSB neurons ON through Shaker channels, which increase voltage gated A-type currents, promotes sleep. Sleep turns the dFSB OFF through Sandman, a two-pore potassium channel which decreases currents mediated by Shaker channels, thereby providing a biophysical mechanism for the regulation of sleep homeostasis.

15 The sleep-promoting effects of the dFSB are also critical for neuronal, behavioral

and structural plasticity. Wake-promoting PPL1 dopamine neurons that innervate the

dFSB are less active during early-life, resulting in enhanced sleep which is required for proper brain development 141. The dFSB is also central to interactions between sleep and

memory. The detrimental effects of early-life sleep deprivation on memory are rescued

by targeted blockade of dopamine signaling, and thermogenetic activation of dFSB

neurons facilitate the formation of long-term memory 140,145. Therefore, in addition to

homeostatic regulation of sleep, the dFSB appears to be critical for the integration of

sleep with developmental and experience-dependent modification of behavior.

The Ellipsoid Body (EB), a region known, among other functions, for generating special representations of the external world 138,146, is also involved in regulating sleep

drive 147. Acute thermogenetic activation of R2 neurons within the EB increased sleep

both during activation and also in the following day post neural activation, suggesting

that the R2 circuit is necessary for the homeostatic regulation of sleep. These neurons fire

at increased frequency during the day resulting in changes in synaptic strength,

suggesting that the EB generates sleep drive as a result of the plastic changes in the EB.

16

Figure 3 Neural regulation of sleep and arousal in Drosophila.

The fan-shaped bodies (FB), mushroom bodies (MBs) and Insulin Producing Cells (IPC) are sleep-promoting centers within the fly brain. A) Dopaminergic innervation to the dorsal FB inhibits activity and the sleep-promoting effects of this region. B) The sleep-promoting neurons that innervate the MBs are not well understood. C) The IPCs promote sleep through a mechanism that is dependent on the EGFR ligand rhomboid. The IPCs also receive excitatory input from octopamine neurons that suppresses sleep. D) The ventral Lateral Neurons (LNvs) regulate circadian function and arousal. These neurons receive inhibitory GABAergic input that functions to promote sleep.

Recent studies found that R2 cells of the EB promote sleep thorough the activation of

Exl2 neurons that innervate the dFSB 140, providing a mechanism for the generation of

sleep drive.

In addition to the model described above, recent work has demonstrated that

during wakefulness Helicon cells are responsive to visual inputs, receive inputs from the dFSB and have axonal projections to the R2 neurons of the EB, integrating the two main sleep/wake promoting regions of the fly brain 147,148. The neuropeptide AstA released by the dFSB during sleep, inhibits Helicon cells, decreasing responsiveness to visual stimuli.

17 During wakefulness, the dFSB is inactive, which lifts the inhibition of helicon cells, signaling to the EB to promote locomotor activity and signal sleep pressure to dFSB (Fig.

4).

There is also considerable overlap between the neural circuitry regulating sleep

and those that rregulate circadian rhythms. In Drosophila, the primary pacemaker

neurons are the ventral lateral neurons (LNvs), which express the neuropeptide pigment

dispersing factor (PDF) that promotes arousal and is essential for 24hr locomotor rhythms

when animals are placed in constant darkness 149,150. Sleep is enhanced in PDF signaling

mutants indicating a wake-promoting role the LNvs 149. The LNvs receive inhibitory

input through GABA-A receptor activity that enhances sleep duration 149,151. Numerous

cellular regulators of metabolism, including insulin signaling pathway components,

function in LNvs pacemaker cells to regulate 24hr rhythms indicating that these neurons

are involved in integrating metabolic cues with behavior 152.

The circadian regulation of sleep is also mediated by DN1p clock neurons 153,154.

Recent work has shown that DN1p neurons project to the anterior optic tubercle (AOTU),

a visual processing center of the fly brain, to promote consolidated sleep 154. The

tubercular-bulbar (TuBu) neurons within the AOTU relay information to the EB to

regulate sleep drive. A parallel circuit that functions to synchronize clock inputs with day

activity and night sleep involves DH44 -expressing neurons in the pars intercerebralis

(PI) 125,155,156.

Similar to TuBu neurons, Dh44 expressing neurons do not express component of

the molecular clock themselves but receive inputs from DN1p clock neurons. Genetic

ablation and activation of DH44+ neurons resulted in a strong disruption of rest:activity

18 rhythms suggesting that these neurons are modulators of locomotor rhythms 155. Finally, a pair of leucokinin-expressing neurons in the lateral horn of the fly brain, receive rhythmic activity from clock neurons to drive locomotor activity through the inhibition of sleep promoting dFSB 156. Taken together, these data suggest that there are multiple parallel pathways that act in concert to modulate rest and activity. Understanding how sleep- regulating neurons function within a dynamic network will be critical for understanding how sleep is modulated within the brain.

Figure 4 A neural circuit connecting sleep centers in the fly brain.

During wakefulness, the dFSB neurons (red) are inactive, which lifts the inhibition from Helicon cells (green), becoming responsive to visual inputs. Helicon cells have excitatory connections to the EB (blue) which generates sleep pressure and promotes locomotor activity. The sleep pressure is possibly, then, communicated to dFSB neurons either directly or indirectly. Conversely, activation of dFSB neurons during sleep inhibits Helicon cells by the release of AstA neuropeptide, increasing the threshold for visual stimuli and EB dependent-locomotor activity.

Neurohormonal regulation of metabolism

In mammals, the peptide hormones insulin and glucagon are critical for regulation of blood-glucose levels and energy availability 157,158. Drosophila possess functional orthologs of insulin and glucagon that appear to have conserved roles in the regulation of metabolic function 159 (Fig. 5). The glucagon ortholog adipokinetic hormone (AKH) is

19 expressed in peptidergic secretory cells of the corpora cardiaca (CC) 8,160,161. The CC

receives input from insulin producing cells (IPCs) and secretes AKH into the hemolymph

8,162. Ablation of the CC results in hypoglycemia, highlighting the importance of these

cells in glucose sensing and overall metabolic regulation 8. AKH binds to the

Adipokinetic Hormone Receptor (AKHR), a G-protein coupled receptor that is expressed in the brain, fat body, and possibly other tissues 8,163. The subsequent breakdown of

glycogen and lipids in muscles and fat body are then used for energy 164,165.

Manipulations that impair AKH signaling promote glycogen and triglyceride storage,

confirming a role for this pathway in controlling carbohydrate and fat metabolism 8,160,166.

AKH function is also implicated in a number of behaviors associated with hunger- induced motivation, including odor-conditioned feeding approach, feeding, and locomotor behavior 160,167. Ablation of AKH-producing cells reduces the hyperlocomotor and feeding responses to starvation and increases starvation resistance 160,163. Therefore,

AKH signaling potently regulates behavior in response to starvation, but it is unclear

whether these changes are due to altered energy stores or the acute effects of AKH

function.

The Drosophila genome encodes for 8 distinct insulin-like peptide (ilp) genes that

are structurally conserved with mammalian insulin and regulate metabolism, behavior,

and growth during development 162,168–171. These genes differ in expression, temporal

regulation, and function. Ilp2, ilp3 and ilp5 are expressed in 10-14 medial neurosecretory

cells that regulate many behaviors including sleep and feeding through the systematic

release of ILPs and direct innervation of the AKH-producing cells 172. In larvae, both ilp3

and ilp5 are transcriptionally downregulated in response to starvation, suggesting a role

20 in nutritional state-dependent regulation of behavior and metabolism 173. The 8 ILPs bind

to a single insulin-like receptor (dInR), which is proposed to express ubiquitously 174.

Unlike all other ilps, ilp6 is predominantly expressed in the liver and adipose tissue-like organ, the fat body, raising the possibility that insulin signaling in the fat body is auto-

regulated 175. Activating insulin signaling specifically in the fat body promotes fat storage similar to mammals 157,169. Therefore, ILPs and AKH have diverse functions in regulating metabolism and behavior.

Dietary regulation of sleep

Flies starved on a diet of agar alone become hyperactive and reduce sleep, but the specific dietary components required for normal sleep are not known (Keene et al., 2010;

McDonald & Keene, 2010; Fig. 6). In the wild, the Drosophila melanogaster diet consists primarily of complex sugars obtained by feeding primarily on rotting fruit 178.

The laboratory diet of Drosophila is significantly more complex, consisting of

carbohydrates, as well as protein and fat from yeast. Altering dietary components

robustly affects behavior, physiology, and longevity 179,180. A proper dietary balance of

sugar and yeast is important for the maintenance of homeostasis and fitness. For example,

raising dietary sugar concentration increases triglyceride levels, which can be suppressed

by simultaneously increasing the yeast concentration 181. The protein component of yeast

is required for proper growth and development in flies. Larvae fed a sugar diet are

severely undersized 168,182. Additionally, restricting caloric intake has been implicated in

increasing lifespan and reducing reproductive output of the animal, revealing diet-related

trade-offs between longevity and behavior 183–185. Interestingly, the lack of specific amino

acids is also responsible for the increased lifespan and decreased fecundity observed

21 under calorie restriction, while other nutrients do not contribute to these phenotypes 186.

However, it is not clear how amino acids, or caloric restriction affect sleep.

Figure 5 Functional conservation between organs and cell-types regulating metabolic function.

A) Overview of regions that communicate metabolic cues to the brain in mammals and flies (B). Regions are color-coded to match their proposed analogous structure. Fat body cells (yellow) secrete unpaired 2 (upd2) the functional ortholog of mammalian leptin. The fat body cells also secrete dILP6. The Insulin Producing Cells (blue, IPCs) appear to function similarly to mammalian pancreatic beta-cells and the hypothalamus. The fly corpora cardiaca (orange) secretes Adipkinetic Hormone (AKH), which is a functional ortholog of mammalian glucagon that is released from the pancreatic α-cells.

22

Figure 6 Starved flies suppress sleep.

The percentage sleep time is depicted over 24hrs of testing. Fed flies (orange) sleep more than flies starved on agar (blue). The behavior depicted is of flies tested under 12:12 light (L)/Dark (D) conditions.

A number of studies have examined the effects of different diets on fly sleep.

Flies fed a diet of 5% sucrose-alone have a similar sleep duration to flies fed normal food suggesting that dietary protein is not required for this behavior 177. An alternative study examining the contributions of dietary sugar and yeast to sleep architecture reported no difference in total sleep duration between flies fed a high or low calorie diet of sucrose and yeast 187. Interestingly, increasing the dietary sucrose concentration from 5% to 35% does not alter the total sucrose consumed, but suppresses sleep 188. Therefore, these studies seem to suggest that flies sleep normally when fed moderate concentrations of sucrose, but high concentrations of dietary sucrose suppress sleep through a mechanism that is independent of total caloric intake.

23 Gustatory and olfactory sensory inputs influence many behaviors including

locomotor activity and food-searching strategies 189,190. However, the effects of food on

sleep duration appear to be independent from these sensory inputs. Flies lacking the

sugar-sensing gustatory receptors Gr5a and Gr64a do not respond to sucrose and sleep

normally, suggesting that the effects of dietary sugar on sleep are independent from

sensory inputs 191,192. Supporting these findings, flies fed the non-caloric sweetener sucralose suppress sleep similarly to flies starved on agar 177. While dietary perception of

sugar does not appear to affect sleep duration, it may impact sleep quality. Feeding a

sugar-only diet to flies lacking sugar receptors results in fragmented sleep, suggesting

that sensory perception of sugar modulates sleep architecture, but not sleep duration 187.

Therefore, gustatory sensation of sugar may consolidate sleep bouts without affecting

total sleep duration.

Yeast represents the primary protein source for flies and its addition to the diet

suppresses sleep in male, while enhancing sleep in female flies 188. The nutritional value

of yeast is predominantly in the form of amino acids, and it remains to be determined

whether specific amino acids within dietary yeast modulate sleep. The amino acid

methionine appears to be critical for fecundity and lifespan, raising the possibility that

methionine may modulate sleep in aging animals 186. Furthering our understanding of

dietary contributions to sleep will require systematic approaches to measure both acute

and long-term effects of dietary components on sleep. Taken together, these studies indicate that a diet of sugar alone is sufficient for normal sleep duration and that consolidation of sleep bouts may be dependent on the sensory perception of sugar.

24 The differences observed among studies examining the effects of diet on sleep may be in part due to different components of ‘standard diets’ and protocols for food preparation

187. Recently, a holidic diet has been described to be sufficient for long-term survival in

Drosophila 185. This diet is composed of purified ingredients that include defined concentrations of amino acids, sucrose, and cholesterol, providing an opportunity to examine contributions of individual nutrients to sleep. Flies fed the holidic diet have similar sleep and activity to those maintained on a standard diet of sugar and yeast, but the effect of individual dietary components has not been tested 185. While it will be of great interest to determine the contributions of dietary components to sleep, it will remain difficult to account for changes in food consumption based on diet. Therefore, a confounding factor of these experiments will be the possibility of differences in calories consumed or temporal differences in meal consumption over the course of the sleep assay.

Endocrine integration of sleep and metabolic function

A number of genes have been shown to function within the IPCs to regulate sleep, locomotor activity, and circadian rhythms. Specific neurons within the IPCs express the ilp co-transmitters that regulate locomotor behavior 155. The Epidermal Growth Factor

Receptor (EGFR) ligand rhomboid-1 (rho) is predominantly expressed in the IPCs, and heat-shock induction of the EGFR ligands, rho and Star, induce sleep, supporting the notion that the IPCs acutely regulate sleep 193. Ablation of the IPCs increases sleep sensitivity to a reduced calorie diet, raising the possibility that these cells buffer against diet-induced alterations in sleep 194. In addition, the ortholog of the mammalian immediate early gene ARC is highly expressed in the IPCs. Mutants for Drosophila Arc1,

25 or flies with disrupted AKH function do not increase locomotor activity in response to

starvation, suggesting that the function of the IPCs and CC are required to integrate

activity and metabolic state 195. Therefore, the IPCs appear to be central integrators of

sleep and metabolic state and may function similarly to the mammalian hypothalamus to

integrate these processes.

The IPCs co-secrete the insulin-like peptides ilp2, Ilp3 and ilp5, along with other neuropeptide co-transmitters. Overexpression of ilp2, as well as ectopic activation of insulin signaling in the fat bodies or brain does not alter sleep, suggesting that insulin-like

signaling is not directly responsible for the IPC-dependent regulation of sleep 196.

Deletion of all three ilps expressed in the IPCs protects against age-related disruption in

sleep, suggesting the ilp release from IPCs regulates age or stress-dependent changes in

sleep 197. Future work examining the sleep phenotypes of ilp2, ilp3 and ilp5 flies, as well

as other neuropeptides that are expressed in the IPCs, may be informative in uncovering

the mechanism through which IPCs regulate sleep in response to aging and other

environmental factors.

It is possible that IPCs, much like the pancreatic beta-cells, serve as tissue-type

autonomous nutrient sensors. There is also strong support for octopamine, the fly analog

of norepinephrine, targeting the IPCs to modulate sleep-wake behavior. Feeding flies

octopamine or genetically activating octopamine neurons potently promotes wakefulness

in Drosophila through the activation of PKA 198. Octopamine is expressed in ~100 cells

in the brain that innervate diverse brain regions including the mushroom bodies and IPCs

199,200. Expressing the Na+ bacterial channel NaChBac to hyperactivate distinct classes of

octopamine neurons demonstrates the role of the IPC-innervating anterior superior medial

26 protocerebrum (ASM) neurons in suppressing sleep 200,201. Selectively inhibiting PKA

activity in the IPCs through expression of a PKA regulatory subunit blocks the effects of

octopamine feeding on sleep, indicating that octopamine targets the IPCs 201. Disruption

of PKA function in other sleep promoting centers, including the mushroom bodies and

dorsal fan shaped bodies, does not block the wake-promoting effects of octopamine 201.

Physiological imaging with the genetically encoded cAMP sensor EPAC-FRET confirms

elevated cAMP levels in IPCs in response to octopamine, supporting the notion that these

neurons express a Gαs coupled octopamine receptor 201. In addition to sleep regulation, octopamine also targets the IPCs to regulate metabolism. While the wake-promoting effects are not dependent on ilp2 or ilp3, activating the octopamine-producing neurons

enhances triglyceride levels. This increase in triglycerides is partially blocked in ilp2, ilp3

double mutants suggesting that the regulation of fat metabolism by octopamine is

partially dependent on ilp2 and ilp3 196. It is likely that the sleep-suppressing octopamine

neurons that innervate the IPCs are distinct from those regulating energy stores. These

findings reveal that both the wake-promoting and metabolic effects of octopamine are

regulated by the IPCs, providing a link between insulin, sleep and metabolism.

A role for the fat bodies in sleep regulation

Adipose tissue senses overall nutrient levels in the animal and modulates behaviors through metabolic control of energy stores and secreted factors that regulate neural function 202,203. In Drosophila, the fat body is central to the control of energy

homeostasis and represents the primary site of glycogen and triglyceride storage, as well

as the main detoxification and immune organ of the fly (Hoshizaki, 2005). The

Drosophila fat body has been implicated in regulation of numerous behaviors including

27 courtship, feeding and egg-laying 204–207. Several sleep- regulating genes are expressed

preferentially in the fat body including Angiotensin-converting enzyme peptidase

(ACER). Genetic mutation or pharmalogical blockade of ACER have disrupted nightime

sleep, raising the possibility that the fat body functions to promote sleep 208. Fat body

function appears to be particularly important for regulating homeostatic sleep changes in

response to stressors including starvation and sleep-deprivation. Flies mutant for the

adipose triglyceride lipase brummer, a gene highly expressed in the fat body, have

elevated triglyceride stores and have an enhanced homeostatic response to sleep

deprivation 209,210. Conversely, flies mutant for lipid storage droplet 2 (lsd2) have

reduced triglyceride levels and do not display a homeostatic rebound in response to sleep

deprivation, revealing that triglyceride stores in the fat body enhance the homeostatic

response to sleep deprivation 210. Enhanced triglycerides do not appear to acutely regulate

sleep because flies mutant for lsd2 have normal sleep architecture 187. Therefore, the

homeostatic response to sleep deprivation and basal regulation of sleep are likely

regulated by distinct mechanisms.

Adipose tissue may modulate sleep through secretion of peptide hormones. In

mammals, leptin secreted from adipose tissue binds to hypothalamic receptors to

modulate sleep and feeding behavior 211. Sleep deprivation disrupts leptin function, and this likely contributes to the increased feeding and weight gain that accompany chronic sleep deprivation in mammals 5,212. Mice that lack the leptin receptor are obese and

hypersomnolent, fortifying the notion that leptin inhibits feeding and promotes sleep 213.

Recently, the Drosophia cytokine upaired 2 (upd2) was identified as an ortholog of

mammalian leptin 214. Secretion of upd2 from the fat bodies regulates insulin

28 accumulation and release from the IPCs 213. While the role for upd2 in sleep modulation

has not been studied, these findings suggest that upd2 may function through the IPCs to

regulate sleep in response to metabolic changes.

Nutrient sensors in the fruit fly brain

Metabolic changes are sensed by peripheral organs, such as the fat body, to

control nutritional homeostasis and feeding behavior. However, ingested nutrients are

also sensed directly by neurons in the brain providing a fast mechanism for regulating

energy balance.

A key region where direct nutrient sensing in the fly brain occurs is the PI. Within this region both IPCs and DH44 expressing neurons function as universal post-ingestive nutrient sensors. DH44 neurons express the gene diuretic neuropeptide 44, which is required for the selection of nutritive sugars over a nonnutritive sugar after a period of food deprivation 215. Ex-vivo calcium imaging experiments in DH44 neurons show a

robust response to the main circulating sugars, glucose, fructose, and trehalose,

suggesting that these neurons function as nutrient sensors. DH44 neurons target DH44

receptor expressing neurons in the brain, which stimulate proboscis extension reflex

response, thereby promoting food intake. Additionally, these neurons target a second

DH44 receptor which is expressed in the gut, to promote gut motility. Interestingly,

DH44 neurons are also directly activated by amino acids via a putative amino acid

transporter, CG13248, suggesting that DH44 neurons are permissive to many

macronutrients 216.

Similarly, the IPCs sense and respond directly to glucose from the circulatory

system 217,218 through glycolysis and activation of ATP-sensitive potassium channels. A

29 recent study in fruit fly larva, demonstrated that the essential amino acid leucine induces

insulin like peptide secretion through the L-type amino acid transporter, minidisc 219,

providing a faster mechanism for regulating food intake and use of nutrients after starvation.

Nutrient sensing neurons are not always permissive to many nutrients. Two to four neurons in the superior protocerebrum of the fruit fly brain express a narrowly tuned fructose receptor, Gr43a 220. High levels of fructose in the hemolymph are sensed directly through Gr43a receptors, regulating food intake in a state dependent manner. During the state of satiety, Gr43a neurons repress feeding while during hunger, it promotes feeding

220,221.

Taken together, regulation of nutritional status not only occur through the

paracrine action of hormones but directly via neural nutrient sensing mechanisms in the

brain. This allows for the identification of genes that function in nutrient sensing neurons

as well as the circuitry that integrate nutrient information to regulate behaviors.

Novel genetic regulators of sleep-metabolism interactions

A number of genes that are required for metabolic regulation of sleep have been identified in Drosophila. Drosophila foraging (for) encodes for a cGMP-dependent

Protein Kinase (PKG) that regulates feeding behaviors and context-dependent regulation of sleep. Naturally occurring polymorphisms in for result in diverse responses to foraging behaviors. forrover (forR) flies have higher levels of PKG activity than forSitter (forS) flies

and this polymophism results in a number of sleep-related behavioral differences. Flies

with the forS polymorphism do not suppress sleep in response to starvation suggesting

that modulation of PKG activity is critical for the integration of metabolism and sleep 177.

30 In addition to regulating metabolism and sleep, for appears to also regulate trade-offs

between resilience to sleep deprivation and starvation. Enhanced PKG activity increases

vulnerability to starvation-induced memory loss but protects against mechanically

induced sleep deprivation, suggesting that for is involved in a trade-off between sleep and memory loss 222. This effect is localized to the mushroom bodies, a region that is not

required for starvation-induced sleep suppression 177, raising the possibility that for acts

in different regions of the brain to regulate starvation-induced responses and sleep and

memory.

Both sleep and metabolism are influenced by the circadian system and disruption

of the transcriptional activators Clock or cycle increases sleep loss in response to

starvation 177. Interestingly, both wild-type and cycle mutant flies lack a compensatory

sleep rebound following starvation, suggesting that sleep loss through starvation involves

mechanisms that are distinct from mechanical or pharmacological sleep loss 210. The

increased sensitivity of Clock and cycle mutants to starvation-induced sleep suppression

is unlikely to be caused by reduced energy stores because adiposity is enhanced in cycle

mutants and selectively disrupting Clock in the fat bodies does not affect sleep

suppression during starvation 177,210. The effect of Clk on sleep regulation during

starvation appears to localize to the dorsally located populations of circadian neurons that

may receive inputs from the arousal promoting LNvs 177. These same dorsal clock

neurons have previously been suggested to propagate signals through the IPCs, raising

the possibility that Clock/cycle modulate insulin release 214. Therefore, a population of

circadian neurons regulates interactions between sleep and metabolism, possibly through interactions with IPC neurons.

31 Conclusions

Powerful genetic tools and behavioral assays are available for investigating the genes and neurons regulating interactions between sleep and metabolism in Drosophila.

The metabolic peptide hormones insulin and glucagon-like AKH are key regulators of sleep and locomotor activity. Forward genetic screens in the fly have led to the identification of many novel regulators of sleep and metabolism and it is likely that further study of these genes will advance our understanding of interactions between these processes. In addition, the role of the fat body in Drosophila needs to be further explored in order to define how sleep-regulating neurons are modulated in accordance with metabolic state. Examining interactions between sleep and metabolism provides the opportunity to explore how the brain communicates with peripheral metabolic organs to control physiology, metabolism and behavior.

32 CHAPTER 2. TRANSLIN IS NECESSARY FOR THE METABOLIC

REGULATION OF SLEEP

Abstract

Dysregulation of sleep or feeding has enormous health consequences. In humans,

acute sleep loss is associated with increased appetite and insulin insensitivity, while

chronically sleep-deprived individuals are more likely to develop obesity, metabolic

syndrome, type II diabetes, and cardiovascular disease. Conversely, metabolic state

potently modulates sleep and circadian behavior; yet, the molecular basis for sleep-

metabolism interactions remains poorly under-stood. Here, we describe the identification

of trsn, a highly conserved RNA/DNA binding protein, as essential for starvation-

induced sleep suppression. Strikingly, trsn does not appear to regulate energy stores, free

glucose levels, or feeding behavior suggesting the sleep phenotype of trsn mutant flies is

not a consequence of general metabolic dysfunction or blunted response to starvation.

While broadly expressed in all neurons, trsn is transcriptionally upregulated in the heads

of flies in response to starvation. Spatially restricted rescue or targeted knockdown

localizes trsn function to neurons that produce the tachykinin family neuropeptide

Leucokinin. Manipulation of neural activity in Lk neurons revealed these neurons to be

required for starvation-induced sleep suppression. Taken together, these findings

establish trsn as an essential integrator of sleep and metabolic state, with implications for

understanding the neural mechanism underlying sleep disruption in response to

environmental perturbation.

33 Introduction

In humans, sleep and feeding are tightly interconnected, and pathological

disturbances of either process are associated with metabolism-related disorders. Acute

sleep loss correlates with increased appetite and insulin insensitivity, while chronically

sleep-deprived individuals are more likely to develop obesity, metabolic syndrome, type

II diabetes, and cardiovascular disease 3,5,223. Conversely, in humans and rodents, internal

metabolic state potently modulates sleep and circadian behavior 224–226. Despite the

widespread evidence for interactions between sleep loss and metabolic dysfunction, little

is known about how these processes integrate within the brain.

Drosophila provides a powerful model system to investigate the integration of sleep

and metabolic state. Drosophila is amenable to genetic analysis, and most molecular

processes regulating sleep and metabolism are conserved from flies to mammals 113,227.

Further, flies display all the hallmarks of sleep including extended periods of behavioral

quiescence, rebound following deprivation, increased arousal threshold and changes in

electrophysiological readouts of brain wave activity 9,10 The value of Drosophila as a model

for sleep has been highlighted using forward and reverse genetic screens, which have identified novel genetic regulators of sleep including sleepless, Cyclin A, and the K+

channel shaker 228–230. While these findings have provided a framework to understand how

sleep is regulated under standard conditions, much less is known about how sleep is

modulated in response to acute environmental changes.

Changes in food availability present a common environmental challenge and

potently affect metabolism and sleeping behavior. Interactions between sleep and

metabolic state appear to be behaviorally conserved from flies to mammals. Both rodents

34 and insects suppress sleep in response to prolonged food deprivation, presumably to forage

for food, and sleep is disrupted in humans during prolonged fasting 177,210,224,226 . Sleep

suppression during starvation can occur independently of sensory inputs, or be modified

by taste neurons suggesting contributions from both internal metabolic processes and

sensory systems to regulate this change in behavior 177,187,231. A number of genes have been

implicated in the metabolic regulation of sleep including the circadian genes Clock and cycle, the adipose gene brummer lipase and the glucagon-like Adipokinetic Hormone

(AKH) 160,177,210. While these genes are modulators of sleep, they are also involved in

broader metabolic processes such as regulation of energy stores and feeding behavior, and

therefore, may represent more general regulators of physiological or behavioral

homeostasis 163,210,232.

To further understand the relationship between sleep and feeding state, we

combined a powerful behavioral assay with a high-throughput RNAi based screen, which

selectively disrupted genetic function in the central nervous system. We identify an

essential role for translin (trsn) in the metabolic regulation of sleep. Knockdown or genetic

mutation of trsn impairs starvation-induced sleep suppression. Structurally, trsn is evolutionarily conserved from flies to humans 233 and has been implicated in regulation of

RNA localization, endonuclease function, and monoamine synthesis 233–235. We localize

trsn-dependent modulation of sleep to Leucokinin (Lk) neurons, which have previously

been shown to regulate food intake, water homeostasis and locomotor behavior 236–238. We find that trsn functions in LK neurons to promote wakefulness during starvation. Therefore, these findings indicate trsn is a novel regulator of insulin transcription that serves as a selective integrator of sleep and metabolic state.

35 Methods

Drosophila maintenance and Fly Stocks

The trsn-RNAi lines are from the Vienna Drosophila Resource Center 99. The

RNAi lines have been renamed from original transformant identifiers as follows: trsn-

IR#1 (GD9963), trsn-IR#2 (GD9964) and trsn-IR#3 (108456). The trsnEP line is the

EPgy2 insertion trsnEY06981 and has previously been characterized 239–241. The trsnnull allele is an excision of the trsnEY06981 locus derived from mobilizing the EPgy2 insertion in the w1118 background that has been previously described240. This allele

removes the entire coding region of the gene and likely represents a null mutation. It has

previously been described as Δtrsn 240. The LK-GAL4 line is a promoter fusion of 3.6 kb

upstream of LK, cloned in the laboratory of YJK with a similar expression pattern to a

previously described line 236. The lines UAS-TNT and UAS-ShiTS1 have previously been described 105,107. The UAS-mCD8::GFP (32184;242) and UAS-GFP.nls (32184; 243)

transgenes have previously been described and were obtained from Bloomington. The

UAS-trsn transgene was generated by amplifying from GM27569 clone into a PhiC31

vector at the attP86Fb docking site on the 3rd chromosome by Zoltan Astolos (Aktogen,

Cambridge, UK). Three to five day old mated female flies were used for all experiments

in this study, except when noted.

Behavioral Analysis

The DAM system detects activity by monitoring infrared beam crossings for each

animal. These data were used to calculate sleep information by extracting immobility

bouts of 5 minutes using the Drosophila Sleep Counting Macro 244. For experiments

examining the effects of starvation on sleep, activity was recorded for one day on food,

36 prior to transferring flies into tubes containing 1% agar (Fisher Scientific) at ZT0 and

activity was monitored for an additional 24 hours. Change in sleep during starvation or

dietary manipulation was calculated as ((sleep duration (mins) experimental-sleep

duration (mins) baseline)/(sleep duration (mins) baseline))*100 as previously described177

.For experiments employing thermogenetic manipulation of LK neurons, only nighttime

sleep was analyzed because flies were unable to survive 24 hours of starvation at elevated

temperatures. Following 24 hours of acclimation, baseline sleep was measured on food at

22°C from ZT12-ZT24. On the following day at ZT8 flies were transferred to new tubes

containing either standard fly food (control) or 1% agar. The temperature was increased

to 31°C at ZT12 and activity was recorded through ZT24.

For tracking analysis, fly activity was recorded using a custom video acquisition system

245. Flies were anesthetized using cold-shock and loaded into standard 24-well tissue

culture plates (BD Biosciences 351147), with each well containing either 5% sucrose

dissolved in 1% agar (fed group) or 1% agar alone (starved group). The sucrose diet was

required as standard fly food is opaque and prevents efficient tracking. The plates were

placed in a chamber illuminated with white (6500K) LED lights (Environmental Lights

Inc. product no. dlrf3528-120-8-kit) on a 12:12 LD cycle, and with constant illumination

from 850-880nm infra-red (IR) lights (Environmental Lights Inc., product no. irrf850-

390). Video was recorded using an ICD-49 camera (Ikegami Tsushinki Co., Japan) fitted with an IR- transmitting lens (Computar Inc., Vari Focal H3Z4512 CS-IR 4.5-12.5 mm F

1.2 TV lens). An IR high-pass filter (Edmund Optics Worldwide, filter optcast IR 5x7 in. part no. 46,620) was placed between the camera and the lens to block visible light. Video was recorded at a resolution of 525 lines at 59.94 Hz, 2:1 interlace. Fly activity was

37 analyzed using Ethovision XT 9.0 video tracking software (Noldus Inc.). Sleep was

calculated by measuring bouts of inactivity >5 minutes using a previously described

Microsoft Excel macro 245.

For sleep deprivation experiments, flies were shaken in DAM2 monitors every 3-

4 minutes for 12 hours from ZT12 (onset of darkness) through ZT0 (onset of light) as

previously described 246. Stimulus was applied using a vortexer (Fisher Scientific,

MultiTube Vortexer) with a custom milled plate to hold DAM2 monitors and a repeat

cycle relay switch (Macromatic, TR63122). Sleep rebound was measured the following

day from ZT0-ZT12.

Pharmacological manipulation

For pharmacological manipulation of glucose and fatty acid utilization, flies were

loaded into tubes containing standard fly food. Following a 24 hour acclimation period,

flies were transferred at ZT0 into tubes containing standard fly food (control), food laced

with either 400mM 2-DG, 25µM etomoxir, or 400mM 2-DG and 25µM etomoxir and

sleep was measured for an additional 24 hours.

For gene-switch experiments, a 100mM stock solution of RU486 (Sigma, St.

Louis) was made in ethanol and stored in -20 °C. The stock solution was added to fly

food or 1% agar solution to a final concentration of 0.25mM RU486. Crosses were raised

at room temperature in normal fly food vials then transferred to individual DAM tubes

containing 0.25mM RU486; the flies were acclimated in the DAM monitor for 24 hrs. On

experimental day 1, sleep was recorded. On day 2, flies were flipped to DAM tubes

containing 1% agar and 0.25mM RU486; % sleep was recorded. RU486 effects during

38 experiment were calculated by comparing the amount of sleep during the baseline night

(without drug) with that during the treatment night.

Paraquat treatment

Paraquat dichloride (Sigma, St. Louis) was dissolved directly into 1% agar with

5% sucrose and poured into plates to obtain a concentration 1mM of Paraquat; DAM tubes were made similarly. Both w1118 controls and trsn mutant flies were raised at room temperature in normal fly food vials then transferred to individual DAM tubes containing

1mM Paraquat dichloride. The flies were acclimated in the DAM monitor for 24 hrs.

Sleep was measured for 5 days under standard light/dark cycles and percent sleep was monitored.

Caffeine treatment

Caffeine (Sigma, St. Louis) was dissolved in melted fly food and poured into plates to a concentration of 4mg/mL. Both w1118 and trsn mutant flies were raised at room temperature in normal fly food vials then transferred to individual DAM tubes containing standard food. The flies were acclimated in the DAM monitor for 24 hrs. On experimental day 1, sleep was recorded. On day 2, flies were flipped to DAM tubes containing 4mg/mL caffeine and percent sleep was recorded. Caffeine effects during each experiment were calculated by comparing the amount of sleep during the baseline night

(without drug) with that during the treatment night.

Protein, glycogen, and triglyceride measurements

Assays for quantifying triglyceride, glycogen and protein content of flies were performed as previously described 247. Two female flies aged 3-5 days were homogenized in Tris-HCl containing 140mM NaCl, pH 7.4, 0.1% Triton-X, 1X protease inhibitor 39 cocktail (Sigma Aldrich, P8340). Triglyceride concentration was measured using the

Stanbio Liquicolor Kit (Boerne, TX), and protein concentrations were measuring using a

BCA Protein Assay Kit (Pierce Scientific). Total glucose levels were determined using the Glucose Oxidase Reagent (Pointe Scientific) in samples previously treated with

8mg/mL amyloglucosidase in 0.2M Sodium Citrate buffer, pH 5.0 (Boston BioProducts).

Free glucose was measured in samples not treated with amyloglucosidase and then glycogen concentrations were determined by subtracting the free glucose from total glucose concentration. Both glycogen and triglyceride concentrations were standardized to the total protein content of each sample containing two flies.

Proboscis Extension Reflex (PER)

Three to five day old flies were collected and placed on fresh food for 24 hours,

then starved for the designated period of time in vials containing wet Kimwipe paper

(Kimberly-Clark Corporation). Flies were then anaesthetized under CO2, and their thorax

and wings were glued with nail polish (Electron Microscopy Science) to a microscopy

slide, leaving heads and legs unconstrained. Following 3-6 hours recovery in a

humidified chamber, the slide was mounted vertically under the dissecting microscope

(Leica, S6E) and PER was observed. PER induction was performed as described previously 248. Briefly, flies were satiated with water before and during experiments. Flies that did not water satiate within 5 minutes were excluded from the experiment. A 1 ml

syringe (Tuberculin, BD&C) with an attached pipette tip (TipOne) was used for tastant

presentation. Tastant was manually applied to tarsi for 2-3 seconds 3 times with 10

second inter-trial intervals, and the number of full proboscis extensions was recorded.

Tarsi were then washed with distilled water between applications of different tastants and

40 flies were allowed to drink water during the experiment ad libitum. Each fly was assayed

for response to multiple tastants. PER response was calculated as a percentage of

proboscis extensions to total number of tastant stimulations to tarsi.

Blue dye assay

Short-term food intake was measured as previously described 249. Briefly, flies

were starved for 24 or 48 hours on wet Kimwipes or maintained on standard fly food. At

ZT0 flies were then transferred to food vials containing 1% agar, 5% sucrose, and 2.5%

blue dye (FD&C Blue Dye No. 1). Following 30 minutes of feeding flies were flash

frozen on dry ice and individually homogenized in 400 µL PBS (pH 7.4, Ambion). Color

spectrophotometry was then used to measure absorbance at 655 nm in a 96-well plate

reader (Millipore, iMark). Baseline absorbance was determined by subtracting the

absorbance measured in non-dye fed flies from each experimental sample.

Two-choice capillary feeding assay (CAFÉ)

A modified volumetric drinking assay was used to test food consumption 250 as

previously described 246. Female flies were allowed to feed on a tube containing 100mM

sucrose or 5% yeast extract in water, while a second capillary tube provided access to

water alone (WPI, #1B150F-4 ID 1mm, OD 1.5mm, with filament). The capillary tubes

were inserted into an empty food vial at a 90° angle and vials were placed at a 45° angle.

The openings of the capillaries were aligned with the ceiling of the vial. Following 24

hours of fasting, 30-60 female flies were placed into a vial and food consumption was

measured. The volume consumed was calculated as the length of liquid missing from the

capillary multiplied by the cross-section of the inner diameter of the capillary. All measurements were adjusted for missing liquid due to evaporation using control capillary

41 tubes without flies. Consumption was measured every hour following the introduction of

flies into the assay. Taste compounds were mixed with Allura red food dye (FD&C red

#40) to a concentration of 3µl per 1ml dilution for better visibility in the capillary tube.

Following the conclusion of the assay flies were anaesthetized and the number of flies in

each vial was counted. Total consumption per fly was measured as volume consumed in

each capillary divided by number of live flies in the vial.

Quantitative RT-PCR

Flies were collected 5–7 days after eclosion. Ten or more flies were separated into

fed and starved groups and were flash frozen. Total RNA was extracted from fly heads

using the QIAGEN RNeasy Tissue Mini kit according to the manufacturer’s protocol.

RNA samples were reverse transcribed using iScript (Biorad), and the generated cDNA

was used for real-time PCR (Biorad CFX96, SsoAdvanced Universal SYBR Green

Supermix qPCR Mastermix Plus for SYBRGreen I) using 1.7 ng of cDNA template per

well and a primer concentra-tion of approximately 300 nM. The primers used were: trsn

(F-5’GCTCCGCCTTCTCCAGATACT3’ and R

5’CCGCCTCCAGGTAAATAACCA3’), actin 5C (F-

5’AGCGCGGTTACTCTTTCACCAC3’) and R-

5’GTGGCCATCTCCTGCTCAAAGT3’), and β-tubulin (F-

5’GCAGTTCACCGCTATGTTCA3’ and R-5’CGGACACCAGATCGTTCAT3’).

Triplicate measurements were conducted for each sample. Primers were purchased from

IDT technologies.

42 Immunohistochemistry

Fly brains were dissected in ice-cold PBS and fixed in 4% formaldehyde, PBS,

0.2% Triton-X 100 for 30 minutes. Brains were rinsed 3X with PBS, Triton-X for 10

minutes and incubated overnight at 4°C in 1:4 anti-ELAV, 1:20 NC82 (251 Iowa

Hybridoma Bank) and 1:1000 anti-TRSN 240. The brains were rinsed again in PBS-Triton

X, 3X for 10 minutes and placed in secondary antibodies (Goat anti-Mouse 555, and Goat

anti-rabbit 488; Life Technologies) for 90 minutes at room temperature. The brains were

mounted in Vectashield (VectorLabs) and imaged on a Leica SP8 confocal microscope.

Brains were imaged in 2µm sections and are presented as the Z-stack projection through

the entire brain. For quantification of whole-brain TRSN levels, the entire brain was imaged in 2µm sections, merged into a single Z-stack as maximum fluorescence, and the total brain fluorescence was determined. For experiments examining co-localization, each channel was imaged separately, and the absence of bleed through was validated.

Statistical Analysis

Statistical analyses were performed using InStat software (GraphPad Software

5.0) or IBM SPSS 22.0 software (IBM). For analysis of sleep, we employed a one- or two-way ANOVA followed by a Tukey’s post hoc test. For PER experiments, each fly

was sampled three times with the same stimulus. The response was binary (PER yes/no),

and these three responses were pooled for values ranging from 0 to 3. The Kruskal-Wallis test (non-parametric ANOVA) was performed on the raw data from single flies, and

Dunn’s multiple comparisons test was used to compare different groups. For the capillary feeding assay, 30–60 flies were used per tube, and 4–20 tubes per group were tested. The

43 Wilcoxon signed rank test (non-parametric) with two-tailed p value was used to test significance on single groups.

Results

To address how sleep and metabolism are integrated, we sought to sought to identify integrators of these processes in the fruit fly, Drosophila melanogaster.

Knockdown of genes from randomly selected RNAi lines was achieved by expression of

UAS-RNAi under the control of the neuron-specific GAL4 driver, n-Synaptobrevin-

GAL4 (nSyb-GAL4) 99,252. Following 24 hr of baseline sleep measurements on food,

sleep was measured during 24-hr starvation on agar, and the change in sleep was

calculated as previously described 177. Starvation-induced sleep suppression was reduced in flies with neuron-specific knockdown of the RNA/DNA binding protein translin (trsn)

(Fig. 7A). To confirm the effect of trsn-RNAi on sleep, we tested two additional RNAi

transgenes. All three RNAi lines showed similar phenotypes; trsn knockdown flies slept

similarly to control flies on food, while sleep loss resulting from starvation was reduced

or absent (Fig. 7B–7E). Targeted knockdown of trsn in the fat body (yolk-GAL4) or

muscle (24b-GAL4), two tissues involved in energy storage, showed normal sleep

suppression in response to starvation (Fig. 9A), supporting the notion that trsn functions

primarily in neurons to regulate sleep.

In Drosophila, starvation induces hyperactivity in addition to sleep loss 160,177,195.

To determine whether trsn also regulates the hyperactivity response to starvation, we analyzed waking activity in fed and starved trsn knockdown flies. Neuronal knockdown of trsn had no effect on waking activity in fed flies but reduced starvation-induced hyperactivity (Fig. 9B). These findings are consistent with the notion that trsn does not

44 modulate sleep or activity in the fed state but is required for both sleep and locomotor

changes that result from starvation.

To validate that the sleep phenotype in trsn knockdown flies was not due to off-

target effects of RNAi, we measured sleep in flies with a mutation in the trsn locus. Both

male and female flies with a P element insertion in the trsn locus (trsnEP) or the excision

allele (trsnnull) are viable 240 and exhibit reduced sleep suppression during starvation (Fig.

8A-C, Fig. 9C ), phenocopying flies with neuron-specific RNAi knockdown. The waking activity of trsnnull flies phenocopies RNAi knockdown flies under fed conditions, while

starvation-induced hyperactivity is blunted or absent in trsn mutants (Fig. 9E).

A number of systems have been developed for high-resolution video tracking that may provide a more accurate measure of sleep compared to infrared-based monitoring systems 116–118,245. Tracking analysis revealed that w1118 control, but not trsnnull flies,

suppress sleep during starvation, confirming that the results obtained using infrared

tracking are not an artifact of the sleep acquisition system (Fig. 10A). Taken together, these findings indicate starvation-induced sleep suppression and locomotor activity are reduced in trsn mutant flies.

Starved flies utilize glucose and fatty acids to maintain metabolic homeostasis, and the availability of these energy sources may regulate sleep. To determine the energy

source required for normal sleep, we fed flies the glycolysis inhibitor 2-Deoxyglucose (2-

DG) 253 or the carnitine palmitoyltransferase antagonist, etomoxir, an inhibitor of fatty acid β-oxidation 254. Treatment with both of these drugs has been used extensively in mammals, and these inhibitors have similar effects on fly metabolism 231,255. Flies were

fed standard food laced with 400 mM 2-DG or 25 mM etomoxir and monitored for sleep

45 to determine whether the breakdown products of glucose or triglyceride stores (or both) contribute to reduced sleep during starvation. Flies fed 2-DG, but not etomoxir, significantly reduced sleep, suggesting that reduced glucose availability or the energy derived from its metabolism, rather than fatty acids, contribute to sleep suppression (Fig.

8D and data not shown).

When trsn mutant flies were subjected to the same protocol, no changes in sleep were observed with 2-DG feeding (Fig. 8D). The finding that trsn mutant flies are insensitive to sleep regulation in response to both acute food deprivation and pharmacological perturbation of energy utilization suggests trsn is critical for the integra- tion of sleep and metabolic state. It is possible that the reduced ability of trsn mutants to suppress sleep during starvation stems from a general inability to modulate sleep in response to environmental or pharmacological disruption.

46

Figure 7 trsn is required for metabolic regulation of sleep.

A-C. Sleep profile for hourly sleep averages over a 48 hour experiment. Flies are on food for day 1, then transferred to agar for day 2. Sleep does not differ between any of the groups for day 1. The trsn knockdown groups (nSyb>trsn; orange) sleep more than nSyb-GAL4/+ (black) and trsnIR/+ controls (grey) during day 2 (starved). D Control flies (nSyb-GAL4/+ and trsnIR/+) sleep significantly more on food (black) than when starved (blue, N≥36; P<0.001) while no signficant differences in sleep duration are observed in flies where nSyb-GAL4 drives expression of trsnIR#1 (N=45; P>0.98), trsnIR#2 (N=45; P>0.99), or trsnIR#3 (N=36; P>0.98). E. Quantifying the percentage change in sleep between fed (day 1) and starved (day 2) states reveals sigificantly greater sleep loss in nSyb-GAL4/+ controls (nSyb-Gal4/+ vs trsnIR#1/+, N≥38; P>0.95; nSyb- Gal4/+ vs trsnIR#2/+, N≥39; P>0.99; nSyb-Gal4/+ vs trsnIR#3/+, N≥37; P>0.99) compared to all three lines with neuronal expression of trsnIR#1(N≥38; P<0.01), trsnIR#2 (N≥39; P<0.001) and trsnIR#3 (N≥36; P<0.01).

To test this, sleep rebound was determined by mechanically shaking flies at 3–4 min intervals for 12 hr during the night (zeitgeber time [ZT]12–ZT24) and measuring sleep for 12 hr (ZT0–ZT12) the following day. Sleep-deprived trsn null flies showed a significant increase in daytime sleep that was not present in undisturbed controls (Fig.

10B). The sleep rebound in trsnnull flies was comparable to w1118 control flies, indicating 47 that trsn is dispensable for the homeostatic response to mechanical sleep deprivation

(Fig. 10B). In addition to mechanical deprivation, numerous pharmacological agents

including the stimulant caffeine and free-radical-inducing agent paraquat disrupt sleep in

flies 122,256. Both w1118 control and trsnnull flies significantly reduced sleep when fed food

laced with caffeine (Fig. 10C) or paraquat (Fig. 10D), supporting the notion that the loss

of starvation-induced sleep suppression in trsn mutant flies does not result from a

generalized inability to suppress sleep.

Flies with enhanced energy stores do not suppress sleep or increase activity in

response to starvation 160,255. Drosophila primarily stores energy as triglycerides and glycogen, and prolonged food-deprivation results in depletion of both stores. To test the possibility that trsn mutant flies do not suppress sleep when fasted due to increased energy stores, we measured triglyceride and glycogen levels using colorimetric assays standardized to total protein level 207,247. No differences in glycogen, triglyceride, or free

glucose levels were observed be-tween fed or 24 hr starved trsnnull flies and w1118 controls

(Fig. 12A-C), indicating that the loss of starvation-induced sleep suppression in trsn

mutant flies is not due to an increase in energy stores. Many metabolism-related genes

regulate both sleep and feeding 257, raising the possibility that trsn is generally required

for hunger-dependent behaviors. To determine whether trsn modulates reflexive food

acceptance response, we measured the proboscis extension reflex (PER) of flies starved

for 24 hr prior to behavioral testing (Fig. 11A)248,258.

48

Figure 8 trsn is required for metabolic regulation of sleep.

A. Sleep profile over 48 hours reveals that sleep in trsnEP and trsnnull does not differ from w1118 control flies on food. Both trsnEP and trsnnull mutant flies sleep more than control flies on agar. B. Sleep is significantly reduced in starved control flies (N≥54; P<0.001), while sleep differences are not significant in trsnEP (N=69; P>0.23) or trsnnull flies (N=58; P>0.98). C. Percentage sleep loss is also significantly reduced in trsnEP and trsnnull mutants compared to controls (N≥54; P<0.001). D) In control flies, sleep is significantly reduced in flies on agar (blue; N=44; P<0.001) or food laced with 2-deoxyglucose (2-DG; orange) (N≥64; P<0.001), compared to flies fed standard food (black). No differences are detected between flies fed standard food compared to agar or 2-DG in trsnnull mutants (N≥38; P>0.70). Bars for % change in sleep are mean ± SEM by one-way ANOVA. All other bars are mean ± SEM; P<0.01,**; P<0.001,*** by 2-way ANOVA.

49

Figure 9 Characterization of sleep in trsn deficient flies.

A. Sleep loss (%) in flies expressing trsnIR in the fat body (yolk-GAL4) or muscle (24b-GAL4). No significant differences are observed between trsn knockdown and control flies harboring GAL4 alone (N≥11; P>0.34 for both groups) B. Average waking activity in fed (black) and starved (blue) flies over 24 hrs. Waking activity in fed flies does not differ between any genotypes (N≥36; P<0.99). Under starved conditions, waking activity is increased in nSyb- GAL4/+ and trsnIR/+ control flies (N≥36; P<0.001), while no change in waking activity is detected in each of the three nSyb-GAL4>trsnIR knock down lines. C. In male flies, sleep is significantly reduced in starved w1118 controls (N=39; P<0.01), while sleep duration of trsnEP (N=45; P<0.76) and trsnnull flies (N=48; P>0.82) does not significantly differ on food and agar. D. Change in sleep (%) from fed to starved conditions in male flies show sleep loss is significantly greater in control flies compared to trsnEP and trsnnull flies (N≥39, P<0.001). E. Average waking activity in fed (black) and starved (blue) flies over 24 hours. Waking activity in fed flies does not differ between any genotypes (N≥54; P<0.66). Waking activity during starvation is increased in control (N=54; P<0.001) and trsnEP flies (N=69; P>0.66), while there is no effect of starvation on waking activity in trsnnull flies (N=68; P>0.99). Waking activity of starved trsnEP flies is reduced compared to controls.

50

Figure 10 Characterization of sleep in trsn deficient flies.

A. Video tracking analysis of sleep in fed and starved flies. In control flies, sleep is significantly reduced in fed control (black) compared to starved control (blue, N≥37; P<0.001), while no significant differences are observed in fed trsnEP or trsnnull mutant flies (N≥39; P>0.99). B. Daytime sleep from ZT0-ZT12 is significantly greater following mechanical sleep deprivation for 12 hours from ZT12-ZT24 (pink) compared to undisturbed flies (black) for w1118 control (N=32; P<0.001) and trsnnull genotypes (N=32; P<0.001). Total sleep does not differ between sleep-deprived control and trsnnull (N=32; P>0.43) flies or undisrupted control and trsnnull (N=32, P>0.23) flies from ZT0-ZT12. C. Sleep is reduced in control (N=32; P<0.05), trsnEP (N=32; P<0.01) and trsnnull (N=32; P<0.001) flies fed food containing caffeine (orange) compared to flies fed standard fly food (black). D. Sleep is reduced in control (N=32; P<0.01), trsnEP (N=32; P<0.01), and trsnnull (N=32; P<0.01) fed paraquat (orange) compared to flies fed standard fly food (black). All error bars are mean ± SEM. ; P<0.01,**; P<0.001,*** by 2-way ANOVA.

Total PER response did not differ between starved trsnnull and w1118 flies to sucrose concentrations of ranging from 1 to 1,000 mM (Fig. 11B), or 5% yeast extract (Fig.

11C), indicating that trsn is dispensable for reflexive feeding. To measure food consumption, we provided flies with 100 mM sucrose or 5% yeast extract in the capillary tube feeding (CAFE) assay (Fig. 11D) 250. Flies were starved for 24 hr prior to the start of the assay, and consumption was measured over 12 hr. No differences in total consumption of 100 mM sucrose or 5% yeast extract was detected between control and

51 trsnnull flies (Fig. 11E). To quantify feeding over a shorter timeframe, the blue dye assay

was used to determine the quantity of food consumed in fed and 24 hr starved flies over a

30 min period 249. No differences between control and trsnnull flies were detected in

overall consumption in the fed or starved state, indicating that trsn does not regulate

acute food consumption (Fig. 11F-G). Taken together, three independent feeding assays

indicate that trsn does not regulate feeding behavior during the starved state. In

Drosophila, trsn is expressed in the brain throughout development [30]. To determine

whether trsn is acutely regulated in response to sleep or feeding state, we measured trsn

transcript levels by qPCR in flies that were previously starved or sleep deprived. trsn was

expressed at low levels in the heads and bodies of fed flies and was specifically

upregulated in the head following 24 hr of starvation (Fig. 13A). No changes in trsn

transcript were detected after 12 hr of mechanical sleep deprivation, suggesting the

upregulation of trsn expression is not a generalized response to stress or environmental

perturbation (Fig. 13B). To confirm that TRSN protein is increased in response to

starvation, we performed immunohistochemistry on brains immunostained with anti-

TRSN. Quantification of whole-brain fluorescence confirmed that TRSN protein is

increased in response to starvation (Fig. 13C-D). In agreement with previous findings,

TRSN signal is below detection in trsnnull mutants and dramatically reduced in nSyb-

GAL4>trsn-IR flies, confirming the antibody specifically labels TRSN (data not shown and 240). Counterstaining with the neuronal marker embryonic lethal abnormal vision

(ELAV) revealed that TRSN and ELAV are expressed in all neurons during the fed and

starved states (Fig. 14A), suggesting the observed changes in protein levels are not due to altered protein localization. Together, these data suggest that at the RNA and protein

52 levels, trsn is increased in response to starvation. The finding that trsn is upregulated in

response to starvation raises the possibility that it functions acutely to modulate sleep.

RNAi targeted to trsn was acutely induced in 3-day-old animals using the GeneSwitch

system. Flies were fed food laced with 0.25 mM RU486, and sleep was measured on food

and agar. Adult specific pan-neuronal knockdown with all three RNAi lines under

regulation of elav-Switch impaired sleep suppression compared to genotype-matched

controls not fed RU486 or genetic controls lacking the trsnIR transgene (259,260; Fig. 14B

and Fig. 15). These findings, coupled with the upregulation of trsn in response to

starvation, provide evidence that trsn is required during adult-hood for the integration of sleep and metabolic state.

We next sought to identify neurons where trsn functions to modulate sleep.

Peptidergic neurons are critical regulators of many behaviors, including sleep and feeding

123,261,262; therefore, we screened GAL4 lines labeling defined populations of peptidergic

neurons or neurons previously shown to regulate sleep. We identified the Leucokinin

(Lk) neurons, where knockdown of trsn reduced sleep modulation in response to

starvation. Lk has been implicated in a host of fly behaviors including feeding and water

homeostasis, locomotion, and olfactory behavior 236,237. Driving membrane tethered

CD8::GFP with Lk-GAL4 labeled a single large neuron in the lateral horn and three pairs

of neurons in the subesophageal zone (236; Fig. 16A).

53

Figure 11 Starvation-induced feeding is normal in trsn mutant flies.

A. Diagram of the proboscis extension reflex (PER) assay. Tastant is supplied to the tarsi of a tethered female fly. B, C. No significant differences in PER are detected between control (black) and trsnnull mutants (grey) to increasing concentrations of sucrose (N≥10; 1mM, P>0.84; 10mM, P>0.21; 100mM and 1000mM P>0.95) (B) or 5% yeast extract (N=18; P>0.98) (C). D. Diagram of the Capillary Feeder Assay (CAFÉ) assay. E. No significant differences in sucrose (left bars, N=4; P>0.34) or yeast (right bars, N>4; P>0.18) were detected between control and trsnnull flies when presented with each tastant. F. Starved or fed flies are placed on food containing blue dye for 30 minutes and consumption is measured. G. Quantification of food intake reveals a significant increase in starved controls and trsnnull flies compared to fed flies from each genotype (N≥26; P<0.001). No differences were observed between genotypes in the fed (N≥29;P>0.99) or starved (N≥26;P>0.99) states All bars are mean ± SEM; P<0.001,*** by 2-way ANOVA.

54

Figure 12 Energy stores and free glucose are normal in trsn mutant flies.

A. Triglyceride levels did not differ between control (black) and trsnnull (grey) in the fed (N=20; P>0.92) or starved state (N=20; P>0.99). B. Glycogen levels did not differ between control (black) and trsnnull (grey) in the fed (N=16; P>0.67) or starved state (N=16; P<0.96). C. Free glucose did not differ between control (black) and trsnnull (grey) in the fed (N≥16; P>0.75) nor starved state (N≥13; P>0.81). All bars are mean ± SEM by two-way ANOVA.

Immunostaining brains of Lk-GAL4 flies driving nuclear GFP (UAS-GFP.nls)

revealed that the Lk-GAL4 neurons that are co labeled by TRSN antibody (Fig. 16B). In

addition, all three trsn-IR lines impaired starvation-induced sleep suppression when

expressed under the control of Lk-GAL4 (Fig. 17A), whereas restoration of trsn specifically in Lk-GAL4 neurons, or in all neurons with nSyb-GAL4, rescued starvation- induced sleep suppression to control levels (Fig. 17B). Therefore, trsn function in Lk neurons is essential for starvation-induced sleep loss.

55

Figure 13 Spatial and temporal localization of trsn function.

A. Expression of trsn is upregulated in the heads (N≥14; P<0.01) but not bodies of w1118 control flies (N≥14;P>0.99) following 24 hours of starvation. B. trsn transcript does not differ in heads between flies sleep-deprived for 12 hours from ZT12-ZT24 and undisturbed controls (N=3; P>0.17, NS). All bars are mean ± SEM; by t-test. C,D. Immunohistochemistry for whole-brain TRSN protein (B). Neuropils are labeled by NC82 for reference (magenta) and anti-TRSN (green) is observed throughout the brain. Whole-brain TRSN protein quantification of fluorescence intesity revealed TRSN is increased in starved flies compared to fed control (N≥6; P<0.002) by paired t-test.

To further examine the role of Lk neurons in sleep regulation, we blocked synaptic release from Lk neurons and measured sleep in fed and starved flies 140,236.

Chronic blockade of synaptic release in Lk neurons with tetanus toxin (TNT) impaired starvation-induced sleep suppression compared to control flies expressing an inactive form of TNT (UAS-IMP-TNT) or genetic controls harboring only a single transgene (105;

Fig. 17C and 18B).

56

Figure 14 Spatial and temporal localization of trsn function.

A. Immunostaining for anti-TRSN (green) and the neuronal marker anti-ELAV (red) reveals colocalization (yellow) between TRSN (green) and ELAV (red) protein levels in brains of fed (upper) and starved (lower) flies. Depicted is a representative section from the dorsomedial central brain, near the lateral horn region. Scale bar denotes 4μm. B. Percentage sleep loss in experimental flies treated with RU486 (orange bars) or controls without drug treatment (black bars). Sleep suppression is significantly reduced in elav-Switch>trsnIR#1 flies (N≥36; P>0.031), elav-Switch>trsnIR#2 (N≥68; P<0.011) and elav-Switch>trsnIR#3 (N≥34; P<0.041) flies fed RU486 compared to non-RU486-fed controls. There is no effect of RU486 feeding in flies harboring the elav-Switch transgene alone (N≥39; P>0.99). All other bars are mean ± SEM; P<0.05,*; P<0.01,**; by 2-way ANOVA

In fed conditions, silencing of Lk neurons increased sleep compared to controls

that approached significance, raising the possibility that these neurons are wake

promoting (Fig. 18B). To examine the effects of acutely silencing Lk-GAL4 neurons, the

dominant-negative form of the GTPase Shibire (ShiTS1) was expressed in Lk neurons, and sleep was measured in both fed and starved flies during the night period 107. Flies

expressing ShiTS1 in Lk-labeled neurons failed to suppress sleep at the non-permissive temperature of 31°C (Fig. 17D and Fig. 18 C-E). Control and experimental groups did not suppress sleep at 22°C due to the lower temperature and shortened duration of the assay (Fig. 18C). Therefore, Lk neurons are acutely required for modulation of sleep in response to starvation, supporting the notion that trsn function in Lk neurons is essential for the integration of sleep and metabolic state.

57 Discussion

Here, we have identified trsn as an essential regulator of sleep-metabolism interactions. While many genes have been identified as genetic regulators of sleep or metabolic state, multiple lines of evidence indicate that trsn functions as a unique integrator of these processes. trsn is not required for the homeostatic increase in sleep following mechanical deprivation or response to stimulants, suggesting trsn is not generally required for acute modulation of sleep. Further, trsn-deficient flies display normal feeding behavior, indicating that it is not required for modulation of behavior in response to food deprivation. Finally, energy stores in trsn mutant flies are normal, indicating that the starvation-induced sleep suppression phenotype is not due to increased nutrient storage. These results provide evidence that trsn is not required for the perception of starvation or the general induction of hunger-related behaviors but is required for the induction of wakefulness in the absence of food. While trsn is broadly expressed in the Drosophila nervous system, we localize the function of trsn in metabolic regulation of sleep to LK-expressing neurons. Targeted knockdown of trsn in Lk neurons disrupts metabolic control of sleep, while restoring trsn to Lk neurons rescues sleep regulation in trsn mutants.

In addition to regulating sleep, ablation of Lk neurons reduces meal number, while increasing consumption during individual feeding bouts, suggesting a role in feeding behavior 236. Lk is expressed in the subesophageal zone, the insect taste center, and in modulatory neurons within the lateral horn, raising the possibility that the sleep and feeding phenotypes associated with Lk mutations or manipulation of Lk neurons may localize to distinct brain regions 236. It is possible that the same populations of Lk neurons

58 regulate meal frequency and sleep or distinct neurons modulate each process.

Combinatorial genetic approaches to manipulate subsets of GAL4-labeled neurons in combination with recent advances in behavioral analysis of meal frequency may allow for the localization of LK neurons involved in each behavioral process 263–265.

Figure 15 Adult-specific knock down of trsn disrupts sleep suppression.

A. Sleep profiles depicting hourly sleep averages over a 48 hour experiment. Flies are placed on food for day 1, then transferred to agar for day 2. Flies harboring elav-Switch alone with RU486 treatment (orange) or elav-Switch alone without treatment (black). B-D. Experimental flies (elav-Switch>trsnIR; orange) fail to suppress sleep compared to genotype-matched controls without drug treatment (black).

59

Figure 16 trsn functions in Leucokinin neurons to regulate sleep

A. Whole-brain confocal reconstruction of Lk-GAL4>mCD8::GFP. GFP-expressing neurons (green) labeled the subeosphogeal zone and dorsal protocerebrum. The brain was counterstained with the neuropil marker nc82 (grey). Scale bar denotes 100μm.B. Immunostaining for anti- TRSN (red) in the brain of Lk-GAL4>UAS-GFP.nls reveals trsn is expressed in neurons labeled by Lk-GAL4. Depicted is a representative 2μm section from the lateral horn region. Scale bar denotes 10μm. The neuropil marker anti-nc82 (grey) is used as background.

In addition to its known role in the synthesis of non-coding RNA, TRSN

physically associates with Translin- Associated Protein X (TRAX) 266,267. TRSN and

TRAX are essential components for the RNA-induced silencing complex (RISC), suggesting a role in post-transcriptional gene silencing through the generation of small

RNAs. trsn knockout mice have diminished forebrain monoamine levels, indicating that

trsn may serve to regulate neurotransmitter synthesis 235. Further investigation of the

mechanistic relationship between trsn and neural regulation of sleep will provide a

framework to study the molecular properties and neural networks that are associated with

interactions between sleep and metabolic state.

60

Figure 17 trsn functions in Leucokinin neurons to regulate sleep.

A. Knock down of trsn in Lk-GAL4 neurons alone resulted in significantly reduced starvation- induced sleep loss in all three trsnIR lines compared to control flies harboring a UAS-trsnIR transgene alone (N≥52; P<.001) or Lk-GAL4 transgenes alone (N≥64; P<0.001). B. Expression of UAS-trsn under Lk-GAL4 control in the background of a trsnnull mutation restores starvation- induced sleep suppression compared to flies harboring either UAS-trsn (N=87; P<0.05); or the GAL4 lines alone (N=79; P<0.01). No significant differences were detected between Lk rescue and w1118 control flies (N≥38; P>0.10). C. Starvation-induced sleep suppression is abolished in flies expressing TNT in Lk-GAL4 neurons (N=39; P=0.96) compared to controls expressing inactive UAS-IMP-TNT (N=33, P<0.0001). Sleep duration on food does not differ significantly in Lk-GAL4>UAS-TNT (N≥34, P=0.50) compared to control flies, and flies expressing UAS- IMP-TNT (P=0.58). D. Flies were transferred to agar at ZT9 then sleep was measured at 31°C on food (black) or agar (blue) over the 12hr night (ZT12-ZT24). Genetic silencing of Lk-GAL4 abolished starvation-induced sleep suppression (N≥40, P=0.99) while control flies robustly suppressed sleep (N≥79, P<0.0001; UAS- ShiTS /+, N≥42, P<0.0001; Lk-GAL4/+, N≥51, P<0.0002). No differences were observed between genotypes in the fed state (UAS-ShiTS/+, P=0.84; Lk-GAL4/+, P= 0.76; Lk-GAL4>UAS-ShiTS , P=0.73; All columns are mean ± SEM; P<0.01,**; P<0.001,*** by 2-way ANOVA.

61

Figure 18 Lk neurons are acutely required for starvation-induced sleep suppression.

A. Expression of UAS-trsn under control nSyb-GAL4 in the background of a trsnnull mutation restores starvation-induced sleep suppression compared to flies harboring either UAS-trsn (N≥37; P<0.05) or the GAL4 line alone (N≥82;P<0.01). No significant difference was detected between nSyb rescue and control flies (N≥37; P>0.66) .B. Sleep profile over 48 hours reveals that sleep in Lk-GAL4>UAS-TNT (red) flies is moderately increased compared to w1118 control flies (black) or flies expressing inactive IMP-TNT (grey) for day one on food. Sleep in Lk- GAL4>UAS-TNT is significantly greater for day two on agar compared to control and IMP- TNT-expressing flies. C. No significant differences for sleep duration on food (black) or agar (blue) were detected for any of the genotypes tested when flies were housed at 22°C (Fed vs Starved: control, N≥38, P=0.99; UAS- ShiTS/+; N≥87, P=0.83, Lk-GAL4/+, N=26, P=0.97, Lk- GAL4>UAS- ShiTS; N≥23, P=0.99). D. Sleep profile over 12 hours on food at 31°C reveals that sleep in Lk-GAL4>UAS-ShiTS (blue) flies does not differ from controls (black) or respective heterozygote controls (grey,brown). E. Sleep profile over 12 hours on agar at 31°C reveals that sleep suppression in Lk-GAL4>UAS-ShiTS (blue) sleep significantly more than w1118 controls (black) and heterozygote controls (grey,brown). All columns are mean ± SEM; P<0.01,**; P<0.001,*** by 2-way ANOVA.

62 CHAPTER 3. A SINGLE PAIR OF LEUCOKININ NEURONS ARE

MODULATED BY FEEDING STATE AND REGULATE SLEEP-METABOLISM

INTERACTION

Abstract

Dysregulation of sleep and feeding has widespread health consequences. Despite

extensive epidemiological evidence for interactions between sleep and metabolic

function, little is known about the neural or molecular basis underlying the integration of

these processes. Drosophila melanogaster potently suppress sleep in response to

starvation, and powerful genetic tools allow for mechanistic investigation of sleep-

metabolism interactions. We have previously identified neurons expressing the

neuropeptide leucokinin (Lk) as being required for starvation-mediated changes in sleep.

Here, we demonstrate an essential role for Lk neuropeptide in metabolic regulation of

sleep. Further, we find that the activity of Lk neurons is modulated by feeding state and

circulating nutrients, with reduced activity in response to glucose and increased activity

under starvation conditions. Both genetic silencing and laser-mediated microablation localize Lk-mediated sleep regulation to a single pair of Lk neurons within the lateral horn (LHLK) that project near primary sleep and metabolic centers of the brain. A targeted screen identified a critical role for AMP-activated protein kinase (AMPK) in starvation-modulated changes in sleep. Disruption of AMPK function in Lk neurons suppresses sleep and increases LHLK activity in fed flies, phenocopying the starvation state. Taken together, these findings localize feeding state-dependent regulation of sleep

63 to a single pair of neurons within the fruit fly brain and provide a system for investigating

the cellular basis of sleep-metabolism interactions.

Introduction

Dysregulation of sleep and feeding has widespread health consequences and

reciprocal interactions between these processes underlie a number of pathologies 3–5,268.

Sleep loss correlates with increased appetite and insulin insensitivity, while short-

sleeping individuals are more likely to develop obesity, metabolic syndrome, type II

diabetes, and cardiovascular disease 3,5,268. Although the neural basis for sleep regulation

has been studied in detail, little is known about how feeding state and changes in

metabolic function modulate sleep 14,257. Understanding how sleep and feeding states are

integrated may provide novel insights into the co-morbidity of disorders linked to sleep

and metabolic regulation.

Animals balance nutritional state and energy expenditure in order to achieve

metabolic homeostasis 269,270. In both flies and mammals, diet potently affects sleep

regulation, strengthening the idea that sleep and metabolic state interact 14,19,257.

Starvation leads to sleep loss, or disrupted sleep architecture, presumably to induce

foraging behavior, while high-calorie diets have complex effects on sleep depending on

macronutrient content 177,187,188,224. Behavioral and physiological responses to changes in

feeding state are modulated both by cell autonomous nutrient centers in the brain that

sense changes in circulating nutrients and through communication between brain and

peripheral tissues 263, yet the neural basis for the integration of sleep and feeding state remain poorly understood.

The fruit fly, Drosophila melanogaster, provides a powerful model for

64 investigating sleep regulation. Flies display all the behavioral hallmarks of sleep including extended periods of behavioral quiescence, rebound following deprivation, increased arousal threshold, and species-specific changes in posture 9,10. Many genetic mechanisms regulating sleep are conserved from flies to mammals. In addition, high throughput systems for sleep analysis in Drosophila have led to the identification of both novel and highly conserved sleep genes 11,271. Further, stimulants including caffeine, amphetamine, and cocaine have been shown to suppress sleep in flies 10,121,272. Thus, at the molecular, pharmacological, and behavioral levels flies provide a model for studying genetic regulation of mammalian sleep.

A number of genes and neurons that are required for the integration of sleep and feeding states have been identified including core-circadian clock genes, metabolic hormones, and sensory neurons 177,187,194,273. While many identified regulators of sleep- metabolism interactions broadly impact sleep and metabolic function 257, a mutation of the DNA/RNA Binding Protein, translin (trsn), disrupts starvation-induced sleep suppression without affecting sleep or metabolic regulation under fed conditions. While trsn is expressed throughout the nervous system, targeted knockdown in ~30 leucokinin

(Lk) neurons phenocopies trsn mutants raising the possibility that these neurons are required for the integration of sleep and metabolic state 274.

Here, we identify a single pair of Lk neurons in the lateral horn of the fly brain that are required for the integration of sleep and metabolic state. These neurons project to both sleep and metabolic control centers in the brain and are unique because they do not regulate sleep under fed conditions but are required for starvation-induced sleep suppression. Functional imaging reveals that LHLK neurons have reduced activity in

65 response to glucose application and increased activity under starved conditions. The

identification of single neurons that integrate sleep and metabolic state provide a model

for investigating the cellular mechanisms regulating the integration of sleep and

metabolic state.

Methods

Drosophila maintenance and Fly stocks

Flies were grown and maintained on standard food (Bloomington Recipe,

Genesee Scientific). Flies were kept in incubators (Powers Scientific; Dros52) at 25°C on

a 12:12 LD cycle with humidity set to 55-65%. The background control line used in this

study is w1118 fly strain, and all experimental flies were outcrossed 6-8 generation into

this background. The following fly strains were ordered from Bloomington Stock Center,

w1118(5905; 275), Lkc275(16324;236), elav-GAL4(8765; 276), Apterous-GAL4(3041;277),

UAS-TNT(28996; 105), UAS-impTNT (28840; 105), UAS-mCD8::GFP (32186; 278), UAS-

AMPKα (32108; 279), UAS-AMPKα-DN (32112; 280). The following lines were generated

in this study, Lk-/-(GAL4) and UAS-Lk. UAS-GCaMP-R and Lk-GAL4 were a kind gift from Greg Macleod and Young-Joon Kim, respectively. tsh-GAL80 281 was provided by

Julie Simpson. Drosophila lines used in the RNAi screen originate from the VDRC

library 99 and are described in (Table 1).

Generation of GAL4 knock-in mutants

Lk-/-(GAL4) was generated by Wellgenetics (Taipei City, Taiwan) using the CRISPR/Cas9

system to induced homology-dependent repair (HDR) using one guide RNA

(GATCTTTGCCATCTTCTCCAG). At gRNA target site a donor plasmid was inserted

containing a GAL4::VP16 and floxed 3xP3-RFP cassette 282. Following the ATG start

66 site bases 1 to 7 were replaced by the knock in cassette. All lines were generated in the

w1118 background 275. The insertion locus for both mutations was validated by genomic

PCR.

Generation of UAS-Lk

The full-length open reading frame of Leucokinin was amplified from the

Leucokinin-pOT2 plasmid (Drosophila Genomics Resource Center, #1378621) using

specific primers (Forward primer: GCCTTTGGCCGTCAAGTCTA and Reverse primer;

CTCCAAGTACCGCAGGTTCA) generated by Integrated DNA Technologies, IDT.

Amplified sequence was inserted into the pENTER vector (Invitrogen) via TOPO cloning

and subsequently recombined into pTW destination vector (DGRC, #1129) using

standard gateway cloning protocol as per manufacturer’s instructions (Invitrogen). The

plasmids were verified by sequencing (Genewiz LLC). Transgenic lines were established

via phiC31-mediated integration at the attp40 landing site 283 on the second chromosome

(BestGene Inc).

67

Table 1 Lines utilized for nutrient sensor RNAi screen Name CG Vienna Stock center ID FlyBase ID GD control V6000 cupcake CG8451 V48987(1) FBgn0031998 v3424 npfr1 CG1147 v9605 FBgn0037408

Gr43a CG1712 v39518 FBgn0041243

Glut 1 CG43946 v47179(2) FBgn0264574 v47178(3) v13326(1) Sut-1 CG5772 v9950 FBgn0028563

Tret-1 CG30035 v52360(3) FBgn0050035 v52361(2) v8126(1) TASK-6 CG9637 v9073 FBgn0038165

TASK-7 CG9361 v8565 FBgn0037690 dIRK CG44159 v28430(1) FBgn0039060 dIRK-2 CG4370 v4341 FBgn0039081 dIRK-3 CG10369 v3886 FBgn0032706 dSUR CG5772 v6750 FBgn0025710

68 Hex-C CG8094 v35337 FBgn0001187

AMPKα CG3051 v1827 FBgn0023169

PKA C2 CG12066 v30658(1) FBgn0000274 v30685(2) PKA R1 CG42341 v26328 FBgn0000275

PKA R2 CG15862 v39437(1) FBgn0000353 v39436(2) Prestin CG5485 v5341 FBgn0036770

MCT CG8028 v9163 FBgn0031010

Silnoon CG8271 v4607 FBgn0033657

69 Behavioral analysis

The Drosophila Activity Monitor System (DAMS) detects activity by monitoring infrared beam crossings for each animal 119. These data were used to calculate sleep information by extracting immobility bouts of 5 minutes using the Drosophila Counting

Macro 244,245. For experiments examining the effects of starvation on sleep, flies were kept on 12:12 LD cycle. Female flies were briefly anesthetized with CO2 and placed into plastic tubes containing standard food. All flies were given 24 hours to recover after being anesthetized. Activity was recorded for 24 hours on food, prior to transferring flies into tubes containing 1% agar diluted in dH2O (Fisher Scientific) at ZT0. Activity was monitored for an additional 24 hours on agar. For the screen, % change in sleep during starvation was calculated as the sleep duration on agar minus the sleep duration on food, divided by the sleep duration on food for each fly assayed multiplied by a hundred 177,284.

Immunohistochemistry

The brains of five to seven day old female flies were dissected between ZT4-ZT9 in ice-cold phosphate buffered saline (PBS) and fixed in 4% paraformaldehyde, PBS,

0.5% Triton-X for 30 minutes as previously described 285. Brains were then rinsed 3X with PBS, 0.5% Triton-X (PBST) for 10 minutes and overnight. In the following day, brains were incubated for 24 hours in primary antibody (1:1000 rabbit anti-Lk; 286 and mouse 1:20 nc82; Iowa Hybridoma Bank) diluted in PBST at 4°C. Brains were rinsed in

PBST, 3X for 10 minutes and placed in secondary antibody (1:400 donkey anti-rabbit

Alexa 555 and 1:200 donkey anti-mouse Alexa 64; Thermo Scientific), diluted in PBST for 90 minutes at room temperature. Finally, all samples were washed in PBST for a total of 120 minutes and mounted in Vectashield (VectorLabs). Samples were imaged in 2µm

70 sections with a Nikon A1R confocal microscope (Tokyo, Japan) using 20X or 60X oil

immersion objective. Images were then processed with NIS Elements 4.40 (Nikon).

Functional imaging of Lk neurons

Five to seven-day old female flies were collected and placed on vials containing

fresh food (Fed) or a wet KimWipe paper (Starved) for 24 hours. All experiments were

done between ZT4-ZT7 to account for rhythmic excitability of Lk neurons 156. For

imaging brain explants, previously established methods for calcium imaging were used

with modifications 221,287. Brains of fed or 24hr starved flies were dissected and placed in

glass wells (Pyrex) containing artificial hemolymph (140mM NaCl, 2mM KCl, 4.5mM

MgCl2, 1.5mM CaCl2, and 5mM HEPES-NaOH with pH=7) and allowed a 5-minute recovery period before being recorded. For 2-Deoxy-D-glucose (2-DG) experiments, fed brains were dissected and placed in 400mM 2-DG (Sigma Aldrich) in artificial haemolymph, 200mM glucose (Sigma Aldrich) in artificial hemolymph, or artificial hemolymph alone for a total of 70 minutes. Every 20 minutes solutions were exchanged.

Cover slips were treated with poly-L-lysine (Sigma Aldrich) to ensure that brains were in the same position during imaging and placed onto chamber (RC-21BBDW, Warner

Instruments). Fly brains were bathed in artificial hemolymph solution and imaged using a

20X air objective lens on an inverted confocal microscope (Nikon A1R on a Ti-E

Inverted Microscope). The pinhole was opened to 244.43µm to allow a thicker optical section to me monitored. UAS-GCaMP-R (GCaMP and mCherry) was expressed in Lk neurons and simultaneously excited with wavelengths of 488nm (FITC) and 561nm

(TRITC). Recording were taken for 120 seconds, capturing 1 frame/ 5 seconds with 512 x

512 resolution. For analysis, regions of interest (ROI) were drawn manually, capturing

71 the same area between experimental and control. The mean fluorescence intensity was subtracted from background mean fluorescence intensity for FITC and TRITC per frame.

Then, the ratio of GCaMP6.0 to mCherry was calculated and plotted as an average of the total time recorded per brain imaged.

In vivo imaging was performed using a previously described protocol with some modifications 288,289. Briefly, fed or 24 hr starved flies were anesthetized on ice and secured in 200µL pipette tip with head and proboscis accessible. The pipette tip was placed in a small chamber at an angle of 140°, allowing the dorsal and posterior surface of the brain to be imaged. A small hole was cut in the tin foil and fixed to the stage and fly head, leaving a window of cuticle exposed, then sealed using dental glue (Tetric

EvoFlow, Ivoclar Vivadent). The proboscis was extended and dental glue was used to secure it in place, ensuring the same position throughout the experiment.

A 21-gauge 1 1/4 needle (PrecisionGlide®, Becton Dickson) was used to cut a window in the fly cuticle. A drop of artificial hemolymph was placed on the cuticle and the connective tissue surrounding the brain was dissected. Flies were allowed to recover from procedure for 30-45 minutes in a humidified box. Mounted flies were placed under a confocal microscope (Nikon A1R on an Upright Microscope) and imaged using a 20X water-dipping objective lens. The pinhole was opened to 244 µm to allow a thicker optical section to be monitored. The settings and data analysis were performed as described above.

Targeted multi-photon ablation of Lk neurons

Female 3rd instar larvae expressing GFP in Lk neurons were selected and anesthetized in ethyl ether (Fisher Scientific, E134-1) for 2-5 minutes. Larva were placed

72 dorsal side towards a microscope slide and a cover slip was placed on the larvae. Ringer’s solution was applied onto the larvae below the coverslip. Larvae was imaged using a 25X water-dipping objective lens on Multi-photon microscope (Nikon A1R) containing a

Chameleon Vision II Ti:Sapphire tunable laser. Excitation laser light of 870 nm was used. Images were acquired at 1 frame per second with a resolution of 512 x 512 pixels.

For each neural ablation, a total of 4 frames were acquired. Two frames were captured prior to ablation for a duration of ~2 seconds, followed by ROI stimulation of 2-4

seconds, and 2 frames after ablation. Following ablations, larvae were placed in vials

containing food and allowed to grow. Sleep on food and on agar was measured 5-7 days

post-eclosion in the Drosophila Activity Monitor System. In order to verify which

neurons were ablated after behavioral assay, flies were anesthetized on ice and the central

nervous system (CNS) was dissected. Fly CNS was fixed in 4% paraformaldehyde, PBS,

0.5% Triton-X for 30 minutes. Following fixation, samples were imaged in 2µm sections

with a Nikon A1R confocal microscope (Tokyo, Japan) using 20X oil immersion

objective. Ablations that resulted in the formation of supernumerary neurons or deletions

of two different subpopulations of Lk neurons were removed from analysis.

Statistical Analysis

The experimental data are presented as means ± s.e.m. Unless otherwise noted, a

one-way or two-way analysis of variance (ANOVA) followed by Tukey’s pot-hoc test

was used for comparisons between two or more genotypes and one treatment and two or

more genotypes and two treatments. Unpaired t-test was used for comparisons between two genotypes. All statistical analysis were performed using InStat software (GraphPad

Software 6.0) with a 95% confidence limit (p < 0.05).

73 Results

Leucokinin neuropeptide has been implicated in regulation of feeding and

circadian activity, but a role in sleep has yet to be identified 156,236. We measured sleep in

fed and starved conditions in flies harboring two different mutations of the Lk locus.

Lkc275 is a hypomorphic allele containing a Piggy bac element upstream of the leucokinin

gene transcription start site (Fig. 19A) 236. We also used the CRISPR/Cas9 system to

generate a recombinant transgenic line (Lk-/-(GAL4)) with a GAL4 element inserted between

base 1 to 7 following the ATG start codon. Lk protein was not detected in the brains of

Lk-/-(GAL4) mutants and there was a reduction in expression in Lkc275 mutants, fortifying the

notion that these genetic modifications result in a robust reduction of Lk function (Fig.

19B-D). Sleep on food did not differ between Lkc275 and Lk-/-(GAL4) lines and w11118

controls, suggesting that Lk is not required for sleep regulation under standard feeding

conditions (Fig. 20A-B). Conversely, control w1118 flies robustly suppressed sleep under

starved conditions, while sleep in Lkc275 and Lk-/-(GAL4) flies did not differ between fed and

starved conditions, indicating that Lk is required for starvation-dependent modulation of

sleep (Fig. 20A-B). In agreement with previous findings, control flies display starvation

induced hyperactivity (Fig. 21A) 160,177. Starvation did not alter waking activity in Lkc275

and Lk-/-(GAL4) flies, indicating that Lk is required for both starvation induced changes in

sleep regulation and hyperactivity (Fig. 21A).

74

Figure 19 Leucokinin neuropeptide is required for metabolic regulation of sleep.

A. Diagram for the genomic organization of Lkc275 and Lk-/-(GAL4). Lkc275 contain a PiggyBac element inserted 929 base pairs 5’ to the transcription start site of the leucokinin gene (gold triangle). The dotted line corresponds to the cleavage site used for CRISPR/Cas9 Lk-/-(GAL4) mutant. Lk-/-(GAL4) contain a GAL4 element replacing base 1 to 7 upstream of the ATG site and a floxed 3xP3-RFP cassette (brackets, orange and light orange). B. C. and D. Immunohistochemistry using antibody against Leucokinin in Lkc275 mutants results in a reduction of protein levels (C) compared to w1118 control (B), while Lk-/-(GAL4) shows loss of Lk protein expression levels (D). nc82 denotes the neuropil marker (magenta). Scale bar = 100µm

75

Figure 20 . Leucokinin neuropeptide is required for metabolic regulation of sleep.

A. Sleep is significantly reduced in starved w1118 controls (n=140), while no significant differences are observed in Lkc275 (n=58, I=0.92) or Lk-/-(GAL4) (n≥47; p=0.48) mutant flies. Sleep on food does not differ between control and Lkc275(p=0.87) or Lk-/-(GAL4) (p=0.49). B. Sleep profile for hourly sleep averages over a 48 hour experiment. Flies are placed on food during day 1 (fed, grey), then transferred to agar during day 2 (starved, blue). Sleep does not differ between any of the groups during day 1. Lkc275 and Lk-/-(GAL4) have increased sleep on agar during day 2. C. Expression of UAS-Lk under control of elav-GAL4 in the Lkc275 mutant background (n=12) restores starvation-induced sleep suppression compared to flies harboring either UAS-Lk (n=23; p=0.97) or the GAL4 line (n=18, p=0.52) in the mutants background. Sleep duration on agar (starved) does not differ significantly between elav rescue and controls harboring one copy of mutation and one copy of the GAL4 (n=43, p=0.28) or UAS (n=25, p=0.54). D. Expression UAS-Lk under control of Lk-/-(GAL4) (n=30) restores starvation induced suppression compared to Lk-/-(GAL4) (n=15, p=0.99). Flies harboring one copy of the UAS-Lk alone suppress sleep in response to starvation (n=21). There were no significant differences during fed state between UAS-Lk alone and rescue (p=0.06), or Lk-/-(GAL4) (p=0.10). All columns are mean ± SEM by Two-way ANOVA with Tukey; **p<0.01; ***p<0.001.

76

Figure 21 Leucokinin neuropeptide is required for metabolic regulation of sleep.

A. Increase in waking activity during starvation is abolished in Lkc275 (n=58, p=0.99) and Lk-/- (GAL4) (n≥47, p=0.24) mutants compared to control flies (n=140). B. Average waking acitivity (beam breaks/waking minute) in fed and starved (blue) flies over 24 hours. Expression of UAS- Lk under control of elav-GAL4 in the Lkc275 mutant background (n=12) restores starvation- induced increase in waking activity compared to flies harboring either UAS-Lk (n=23, p=0.43 ) and GAL4 (n=18, p=0.99) in the mutants background. No significant differences were seen during starved state between flies harboring a copy of the mutation and a copy of the UAS (n=25, p=0.99) or GAL4(n=43, p=0.99) and rescue flies. C. Increase in waking acitivity following starvation is recued in flies expressing UAS-Lk under control of Lk-/-(GAL4) (n=30) compared to Lk-/-(GAL4) mutants(n=15, p=0.99). Flies harboring UAS-Lk alone increased waking activity following starvation (n=21). There were no significant differences during fed state between UAS-Lk alone and rescue (p=0.94), or Lk-/-(GAL4) (p=0.48). D. Sleep is significantly reduced in starved w1118 controls (n=140) and flies harboring one copy of Lkc275 (n≥66), while no significant differences are observed in flies harboring one copy of Lk-/-(GAL4) (n=70, p=0.65).

77

Figure 22 Leucokinin neuropeptide is required for metabolic regulation of sleep.

A. Sleep profile for hourly sleep averages over a 48 hour experiment. Flies are placed on food during day 1 (grey), then transferred to agar during day 2 (blue). Sleep does not differ between any of the groups during day 1. Lk-/-(GAL4) /+ (orange) have increased sleep on agar during day 2, while wild-type (dark grey) and Lkc275/+ (gold) suppress sleep. B. w1118 controls (n=140), flies harboring one copy of Lkc275 (n≥66) or Lk-/-(GAL4) alone (n≥70) show a significant increase in waking activity during starvation compared to fed state. C. One copy of the UAS-Lk transgene under control of one copy of Lk-/-(GAL4) (n= 92) restores starvation induced suppression compared to flies harboring one copy of Lk-/-(GAL4) (n=70, p=0.40). No significant differences were observed during starved state between heterozygous rescue flies and control flies harboring a copy of UAS-Lk alone (n=20, p=0.77). D. Whole-brain and ventral nerve cord confocal reconstruction of Lk-/-(GAL4) >CD8:GFP. GFP-expressing neurons (green) in the brain labeled one pair of neurons in the subeosphogeal zone, four pairs of neurons in the lateral horn, and three pairs of neurons in the medial protorecebrum with variable expression (ALK). In the VNC, Lk is expressed in ~10 pairs of neurons (abdominal Lk neurons; ABLKs). The brain and ventral nerve chord were counterstained with the neuropil marker nc82 (magenta). Scale bar = 100µm. All columns are mean ± SEM by Two-way ANOVA with Tukey; **p<0.01; ***p<0.001

78

Figure 23 Lateral horn leucokinin neurons are necessary for the metabolic regulation of sleep.

A. Whole-brain and ventral nerve cord confocal reconstruction of Lk-GAL4>CD8:GFP. GFP- expressing neurons (green) in the brain labeled one pair of neurons in the subeosphogeal zone, one pair of neurons in the lateral horn, and three pairs of neurons in the medial protorecebrum with variable expression (ALK). In the VNC, Lk is expressed in ~11 pairs of neurons (abdominal Lk neurons; ABLKs). The brain and ventral nerve chord were counterstained with the neuropil marker nc82 (magenta). Scale bar = 100µm B. GFP expression in the ventral nerve chord (ABLK) of flies carrying Lk-GAL4>CD8::GFP is antagonized by tsh-GAL80 transgene. The brain and ventral nerve chord were counterstained with the neuropil marker nc82 (magenta). Scale bar = 100µm

To verify that the sleep phenotype was caused by loss of Lk, we restored Lk in the

background of each mutant and measured sleep. Pan-neuronal rescue (Lkc275;elav-

GAL4/UAS-Lk) restored starvation-induced sleep suppression (Fig. 20C) and starvation-

induced hyperactivity (Fig. 21 B), and these flies did not differ from heterozygous controls (Lkc275;elav-GAL4/+, UAS-Lk;Lkc275/+). Similarly, rescue in Lk neurons (Lk-/-

(GAL4);UAS-Lk), where the inserted GAL4 element faithfully drives expression in Lk

neurons (Fig. 22D), restored starvation-induced sleep suppression (Fig. 20D) and

starvation-induced hyperactivity (Fig. 21C) confirming that Lk is required for the

metabolic regulation of sleep. While starvation-induced sleep suppression was abolished in Lkc275 mutants, Lkc275 heterozygous flies suppressed sleep (Fig. 21D and Fig. 22A).

However, flies heterozygous for Lk-/-(GAL4) failed to suppress sleep in response to starvation (Fig. 21D and Fig. 22A) and displayed reduced starvation-induced

hyperactivity compared to control (Fig. 22B), raising the possibility that the phenotype is

79 dominant in CRISPR mutant flies. Expression of a rescue transgene in Lk neurons of flies

heterozygous for Lk (Lk-/-(GAL4)/+;UAS-Lk), restored starvation-induced sleep suppression

(Fig. 22C), confirming the specificity of phenotype in Lk-/-(GAL4) flies.

Leucokinin antibody labels the lateral horn Lk neurons (LHLK) and the

subeosophoageal ganglion Lk neurons (SELK), as well as a number of abdominal Lk

neurons (ABLK) neurons in the ventral nerve cord (Fig. 23A). To localize the population

of neurons that regulate starvation-induced sleep suppression, we restricted GAL4

expression primarily to the brain by expressing GAL80, a GAL4 repressor, in ventral

nerve-cord using teashirt-GAL80 (tsh-GAL80) 281. Expression of CD8::GFP (Lk-

GAL4>CD8:GFP;Lk-GAL80) revealed tsh-GAL80 blocks expression in all but two ventral nerve cord neurons, without affecting expression in the brain (Fig. 23B).

Silencing the remaining SELK, ALK, and LHLK neurons by expressing light-chain tetanus toxin (TNT) (tsh-GAL80;Lk-GAL4>UAS-TNT) abolished starvation induced sleep suppression, phenocopying the effects of silencing all Lk neurons (Lk-

GAL4>UAS-TNT) (Fig. 24A-B) 105. Further, no differences in sleep on food were

detected between groups, and there was no effect of expressing an inactive variant of

tetanus toxin light chain (impTNT) in Lk neurons, fortifying the notion that Lk neurons

are dispensable for sleep regulation on food. Taken together, these findings suggest Lk

neurons within the brain are required for sleep metabolism interactions.

80

Figure 24 Lateral horn leucokinin neurons are necessary for the metabolic regulation of sleep.

A. Blocking synaptic release in Lk neurons with TNT impairs starvation induced sleep suppression (n=29, p=0.77), while impTNT control supresses sleep (n≥27). No differences were observed between genotypes during fed state (p=0.06). B. Starvation induced sleep suppression is abolished in flies expressing TNT in tsh-GAL80;Lk-GAL4 (n=32, p=0.50), while controls expressing inactive impTNT suppress sleep (n=23). Sleep duration on food does not differ significantly between tsh-GAL80;Lk-GAL4>TNT and impTNT flies (p=0.60). C. TNT expression in Apterous expressing neurons abolishes starvation induced sleep suppression (n≥18, p=0.49) and show a significant increase in sleep during fed state, compared to impTNT that suppress sleep (n≥34). D. Expression of UAS-Lk under control of Apt-GAL4 in the Lkc275 mutant background (n=15) restores starvation-induced sleep suppression compared to flies harboring either UAS-Lk (n=16; p=0.99) or the GAL4 line (n=16, p=0.89) in the mutant background. Control flies harboring a copy of the mutation and one copy of the GAL4 (n≥16) or UAS (n=21) alone suppress sleep in response to starvation.

81

Figure 25 Lateral horn leucokinin neurons are necessary for the metabolic regulation of sleep.

A. Individual population of Lk neurons expressing GFP were ablated with a Two-photon microscope during 3rd instar larva. Post ablated flies and controls were transferred to vials containing food. Adult female flies were tested 5-7 days post-eclosion. Bilateral laser ablation of lateral horn leucokinin neurons (LHLK) impairs staration-induces sleep suppression (n=12, p=0.09), while ablation of a pair of anterior leucokinin neurons (ALK, n=8) and non-ablated controls (n= 14) suppress sleep in response to starvation. Student’s test, *p<0.05 B. Expression pattern of Lk during 3rd instar larval stage visualized with mCD8::GFP. The brain was counterstained with the neuropil marker nc82 (magenta). Scale bar = 100µm. C. Representative images of GFP-expressing Lk neurons post LHLK (right) and ALK ablation (middle), and treated but non-ablated controls (left). Red arrow indicate neurons that were ablated. Scale bar= 100µm. All columns are mean ± SEM by Two-way ANOVA with Tukey; ***p<0.001.

It has previously been reported that Apterous-GAL4 drives expression in the

LHLK neurons, as well as neurons in the optic lobe, antennal mechanosensory and motor centers (AMMC), and a small population of mushroom body neurons (Fig. 26A-B) 156,290.

Immunostaining with Lk antibody in Apterous-GAL4>UAS-CD8::GFP flies revealed co- localization exclusively localized to the LHLK neurons (Fig. 26A-B). To functionally

82 assess the role of LHLK neurons, we genetically silenced the LHLK neuron, as well as

other non-LK cells labeled by Apterous-GAL4. Silencing neurons labeled by Apterous-

GAL4 (Apt-GAL4>UAS-TNT) inhibited starvation-induced sleep suppression and

promoted sleep on food, while no effects were observed in flies expressing inactive

tetanus toxin (Apt-GAL4>UAS-impTNT) (Fig. 24C). To verify that the sleep phenotype

was due to blocking Lk release from LHLK neurons, we expressed Lk in neurons labeled

by Apt-GAL4 in the Lkc275 mutant background and measured sleep. Rescue in Apt-

expressing neurons (Lkc275;Apt-GAL4>Lkc275;UAS-Lk) restored starvation-induced sleep suppression to heterozygote control levels (Apt-GAL4;Lkc275/+ and UAS-Lk;Lkc275/+),

whereas flies harboring either GAL4 or UAS in the Lkc275 mutant background failed to

suppress sleep (Apt-GAL4;Lkc275 and UAS-Lk;Lkc275, Fig. 24D). These data support a

role for the LHLK neurons in starvation-induced sleep suppression, but do not rule out

the possibility that other neurons labeled by Apt-GAL4 also contribute to this phenotype.

To verify genetic silencing experiments, we sought to precisely ablate the LHLK neurons

and measure their role in starvation-induced sleep suppression. Multi-photon microscopy

has been used in diverse genetic models for targeted ablation of neuronal cell types 291–

293. All Lk neurons are present in third instar larvae and labeled by the Lk-GAL4,

providing the unique opportunity to independently ablate each subtype and measure the

effect on adult behavior in an intact animal (Fig. 25A). We selectively induced bilateral

ablations of LHLK or unilateral ablation of two control ALK neurons in immobilized 3rd

instar with a titanium sapphire multi-photon laser. The ablation of individual neurons

could be visualized in larvae as a disruption of GFP-labeled neuronal cell body (Fig.

25B). Following ablation larvae were transferred back into food vials and 5-7 day old

83 adult flies were tested for sleep under fed and starved conditions. After behavioral testing, brains were dissected and imaged to verify selective bilateral ablation of the

LHLK or unilateral ablation of two ALK neurons (Fig. 26C). Flies with ablated ALK neurons suppressed sleep during starvation similarly to controls (Fig. 25A). Conversely, bilateral ablation of the LHLK neurons abolished starvation-induced sleep suppression without affecting sleep on food, revealing an essential role for the LHLK neurons in the integration of sleep and metabolic state (Fig. 25C).

The finding that the LHLK neurons are required for starvation-induced sleep suppression raises the possibility that the activity of Lk neurons is modulated by nutritional state. We selectively expressed a GCaMP6.0-mCherry (UAS-GCaMP-R) fusion protein that allows for ratiometric detection of Ca2+ activity 294,295 in Lk neurons and measured the response to nutrients. The brains of fed controls or 24hr starved flies were imaged for GCaMP and mCherry signal ex vivo (Fig. 27A). Flies expressing the GCaMP-mCherry indicator in Lk neurons suppress sleep similarly to control flies harboring Lk-GAL4 alone, indicating that expression of the Ca2+ construct does not affect starvation-induced regulation of sleep (Fig. 28). The GCaMP/mCherry ratio was elevated in the LHLK of starved flies compared to fed controls, suggesting these neurons are more active during starvation

(Fig. 27B).

84

Figure 26 Lateral horn leucokinin neurons are necessary for the metabolic regulation of sleep.

A. Expression pattern of Apt-GAL4. GFP-expressing neurons (green) in the brain label LHLK neurons, optic, and mushroom body lobes. The brain was counterstained with the neuropil marker nc82 (magenta). Scale bar = 50µm. B. Immunostaining for anti-Leucokinin (red) in Apt- GAL4>mCD8::GFP (green) reveals LHLK localizes to neurons labelled by Apterous-GAL4 (orange). Depicted is a 14 µm section from the lateral horn region using a 60X oil immersion objective. Scale bar=10µm C. Diagram representative of targeted multi-photon ablation. 3rd instar larvae expressing UAS-mCD8::GFP in Lk neurons are placed drosally onto microscope slide. Two frames are captured prior to ablation (white arrow), followed by ROI stimulation, and 2 additional frames are captured after ablation, where GFP dispersion can be observed (yellow arrow). Following ablations, larvae are placed in vials containing food and allowed to grow. Sleep on food and on agar was measured 5-7 days post-eclosion in the Drosophila Activity Monitor System (DAMS). Lastly, immunohistochemistry is performed to verify ablated neurons. Scale bar=10µm.

85

Figure 27 Lateral horn leucokinin neurons have increased activity during starved state.

A. Diagram of ex-vivo Ca2+ imaging. (B and E) Average ratio of GCaMP6m/mCherry is increased in LHLK neurons during starved state compared to fed state ex-vivo (B, n≥8) and in- vivo (E, n≥19). B. No significant differences in GCaMP6m/mCherry were detected in SELK neurons during fed or starved state ex-vivo (n≥7, p= 0.95). D. Diagram of in-vivo Ca2+ imaging. F. Ex-vivo application of 2DG (400mM) laced with glucose (200mM) to fed fly brains (n=12) results in an increase of LHLK GCaMP6m/mCherry fluorescence levels compared to control (hemolymph-like solution alone, n=11) and glucose application (200mM, n=12). Glucose application alone showed a reduction in GCaMP6m/mCherry compared to haemlymph-like solution control. Pseudcolors were used to represent Ca2+ activity. Fluorescence intensity scale represents the ratio range of GCaMP6m/mCherry where max is a ratio=2 and min is ratio=0. Scale bar = 10µm. Error bars for Gcamp6m/mCherry ratio during fed vs starved state indicate SEM by unpaired t-test; ***p<0.001. All other error bars indicate SEM by One-way ANOVA with Tukey; ***p<0.001.

Conversely, no difference in the GCaMP/mCherry ratio between fed and starved

state was detected in the SELK neurons that localize to the subesophageal ganglion (Fig.

27C). To validate that the activity in Lk neurons is modulated by feeding state in an intact animal, we performed in vivo recordings in tethered flies (Fig. 27D). Briefly, a

86 portion of the head capsule was removed so that the LHLK neurons were accessible, and

the activity was recorded in flies that had been previously fed or starved for 24 hours

(Fig. 27E). In agreement with ex vivo findings, the GCaMP/mCherry ratio was elevated in the LHLK neurons of starved flies, fortifying the notion that Lk neurons are more active during starvation (Fig. 27E)

In mammals, the activity of sleep and wake promoting neurons are directly modulated by glucose and other circulating nutrients 75,81. It is possible that the activity of

Lk neurons is modulated in accordance with feeding state by sensory detection of

tastants, or indirectly result from detection of changes in circulating nutrients. To

differentiate between these possibilities, the brains of fed flies were removed and treated

with either glucose, or the competitive inhibitor of glycolysis, 2 deoxy-glucose (2DG)

287,296,297. Application of glucose reduced Ca2+ activity in Lk neurons compared to

controls treated with Drosophila hemolymph-like solution alone, suggesting these

neurons are sensitive to circulating glucose (Fig. 28F). Further, the combined application

of 2-DG and glucose increased Ca2+ activity to levels greater then hemolymph alone,

mimicking the starved state. Taken together, these findings indicate that the activity of Lk

neurons are modulated in accordance with nutrient availability and support the notion that

the LHLK neurons are more active during starvation, thereby suppressing sleep.

The localization of nutrient-dependent changes in activity to LHLK neurons raises the possibility that cell-autonomous nutrient sensors or signaling pathways function within Lk neurons to modulate sleep. To identify regulators of sleep that modulate activity of Lk neurons, we expressed RNAi targeted to 28 RNAi lines encoding putative nutrient sensors or signaling pathways using Lk-GAL4 and measured starvation-induced

87 changes in sleep (Fig. 29). RNAi knockdown of AMPKα Activated Protein Kinase in Lk

neurons alone abolished starvation-induced sleep suppression compared to GAL4

controls crossed to the isogenic host strain for the RNAi library (Fig. 30A-B) 99.

Figure 28 Lateral horn leucokinin neurons have increased activity during starved state.

A. Flies expression of UAS-GCaMP-R under Lk-GAL4 sleep significantly more on food (grey) than when starved (blue, n=24) similar to flies harboring UAS-GCaMP-R alone (n=32), Lk- GAL4 (n=31) alone, or w1118 control flies (n=32). No significant differences were detected on food between Lk-GAL4>UAS-GCaMP-R and w1118 control (p=0.45), UAS-GCaMP-R alone (p>0.99) or Lk-GAL4 alone (p=0.99). All columns are mean ± SEM by Two-way ANOVA with Tukey; ***p<0.001.

In addition, genetically restricting AMPK knockdown in flies harboring tsh-

GAL80 (tsh-GAL80;Lk-GAL4>AMPKα-RNAi) also impaired starvation-induced sleep suppression (Fig. 30C). Of particular interest is the finding that knockdown of AMPKα in Lk neurons (Lk-GAL4> AMPKα-RNAi) reduces sleep in fed flies rather than blocking the starvation induced sleep suppression, supporting the notion that inhibition of AMPK in Lk neurons alone is sufficient to induce changes in sleep that are indicative of the starved state. To validate findings obtained with RNAi, we expressed a dominant negative variant of AMPK (AMPKα-DN) in Lk neurons. Similar to the phenotypes

88 obtained with RNAi, flies expressing AMPKα-DN in all Lk neurons (Lk>AMPKα-DN) or primarily brain-restricted Lk neurons (tsh-GAL80;Lk-GAL4>AMPKα-DN) slept less than control flies expressing wild-type AMPK (AMPKα) (Fig. 30D), indicating that

AMPK functions in Lk neurons to promote sleep.

To determine whether inhibition of AMPK signaling changes the physiology of

Lk neurons to resemble a starved-like state, we genetically expressed AMPKα-RNAi under control of Lk-GAL4 and measured neuronal activity using UAS-GCaMP-R (Lk-

GAL4>UAS-AMPKα-RNAi; UAS-GCaMP-R). Genetic disruption of AMPKα increased

Ca2+ activity in LHLK neurons of fed flies compared to flies expressing UAS-GCaMP-R

alone (Fig. 30E). The increase phenocopies changes found in starved control flies,

suggesting loss of AMPK increases the activity of Lk neurons, thereby suppressing sleep

(Fig. 30F). Together these findings suggest AMPK is active within LHLK neurons

during the fed state, and reduced AMPK signaling during starvation increases LHLK

activity, thereby suppressing sleep.

Discussion

Our findings reveal that Lk neurons and Lk neuropeptide are selectively required

for the integration of sleep and metabolic state. Silencing of Lk neurons, or mutation of

the Lk locus does not affect sleep under fed conditions but abolishes starvation-induced

sleep suppression. Previous studies have identified a number of genes required for

starvation-induced changes in sleep or locomotor activity, yet many of these genes have

pleiotropic functions on behavior or metabolic function 160,177,210,298. For example, the

glucagon-like adipokinetic hormone (AKH) is responsible for energy mobilization, and

genetic disruption of AKH induces obesity and abolishes starvation-induced

89 hyperactivity 160,163,299. Similarly, the circadian transcription factors clock and cycle are required for starvation-dependent regulation of behavior and loss of function affects sleep both in fed and starved conditions 177. Conversely, neuropeptide F functions within a

subpopulation of circadian neurons and is selectively required for metabolic regulation of

sleep 273. Similarly, we find that Lk mutants and silencing of Lk neurons has little effect

on sleep under fed conditions but disrupts starvation-induced modulation of sleep.

Figure 29 AMPKα is a key nutrient sensor that functions in LHLK.

A.UAS-RNAi targeting nutrient sensors in Lk-GAL4 neurons. Control flies have Lk-GAL4 expressing UAS-GD60000 (+), isogenic host strain for the Vienna Drosophila Resource Center RNAi library. Quantifying the % change in sleep between day fed (day 1) and starved 2 (day 2) reveals a greater sleep suppression in the Lk-GAL4>UAS-GD control flies (n=56) compared to AMPKα-RNAi (n=9, p=0.04). Dashed lines indicate control mean ±2 SD.

90

Figure 30 AMPKα is a key nutrient sensor that function in LHLK.

A. Knock down of AMPKα in Lk neurons (Lk-GAL4, n=9, p>0.99) abolishes starvation induced sleep suppression, while control harboring a copy of Lk-GAL4 alone suppresses sleep (n=56). During fed state, sleep is significantly reduced in Lk-GAL4>UAS-AMPKα-RNAi compared to control. B. Expression of UAS-AMPKα-RNAi in brain Lk neurons (tsh-GAL80;Lk-GAL4) fail to suppress sleep in response to starvation (n≥70, p=0.06) compared to control flies that suppress sleep (n≥50). Sleep is significantly reduced in tsh-GAL80;Lk-GAL4 >UAS-AMPKα-RNAi compared to control. C. and D. Flies expressing AMPKα-DN in Lk neurons (C, Lk-GAL4;n=89) and brain Lk neurons (D, tsh-GAL80;Lk-GAL4;n≥52) significantly suppress sleep during starvation. During fed state sleep is significantly reduced in Lk-GAL4> UAS-AMPKα-DN and tsh-GAL80;Lk-GAL4>UAS-AMPKα-DN compared to controls expressing a wild-type copy of AMPKα in Lk neurons (n=73) or brain Lk neurons (n≥24). E. In-vivo Ca2+ imaging during fed state there is an increase in the ratio of GCaMP-R in LHLK neurons expressing UAS-AMPKα- RNAi;UAS-GCaMP-R (n=10) compared to GCaMP-R alone (n=8, p=0.01) F. During starved state no significant differences in LHLK Ca2+ activity are observed in Lk-GAL4>UAS-AMPKα- RNAi;UAS-GCaMP flies (n=8) compared to flies harboring GCaMP-R alone (n=8, p=0.44). Student’s test, *p<0.05.

These findings suggest that different neural mechanisms regulate sleep under basal conditions and in response to environmental perturbation.

91 The failure of Lk mutants to suppress sleep under starved conditions phenocopies

mutation of the RNA/DNA binding protein trsn. Loss of trsn does not impact feeding

behavior but impairs starvation-induced sleep suppression suggesting that trsn is not

generally required for hunger-induced behavior 284. While trsn is broadly expressed in the

fly nervous system 233,241, we previously found that selective knockdown of trsn in Lk neurons disrupted starvation induced sleep suppression 284. These findings raise the

possibility that trsn functions to regulate changes in physiology of Lk neurons to

modulate sleep under starved conditions. In mice, trsn is required for dendritic trafficking

of brain-derived neurotrophic factor (BDNF) mRNA and hippocampus-dependent

memory formation suggesting a critical role in synaptic function and plasticity 300,301.

Both sleep loss and starvation affect synaptic architecture and physiology 302–306, raising

the possibility that trsn is required for state-dependent modulation of synaptic function.

Translin also functions as a key component in the RNAi silencing complex (RISC)

234,307,308. In this context, trsn might post-transcriptionally regulate Lk translation, cellular

localization, or secretion. Therefore, it is possible that trsn may directly modulate Lk

function or may be indirectly required for the function of Lk neurons. Leucokinin is

expressed in 4 pairs of bona fide neurons in the brain and 11 pairs in the ventral nerve

cord, that regulate diverse behaviors and physiological processes 237,309,310. The brain and

ventral nerve cord Lk neurons all have distinct projection patterns suggesting unique

functions 237. The ABLK neurons in the ventral nerve cord, have axon terminations in the lateral heart nerves and abdominal ganglion 309 and have been implicated in response to

various stressors including starvation, desiccation, and ionic stress 310. The SELK

neurons, in the brain, connect the gustatory receptors to the subesophageal ganglia and

92 ventral nerve cord. Although a specific function has not been identified to SELK neurons,

silencing of all Lk neurons disrupts gustatory behavior and a mutation in the Lk locus

affects meal size 236,311, raising the possibility that these behaviors are regulated by SELK

neurons. Lastly, the LHLK neurons project to the superior lateral protocerebrum, medial

protocerebrum, and peduncle and axonal stalk of the mushroom bodies 237. Disruption of

Lk within these neurons attenuates circadian rhythms and this effect is localized to the

same LHLK neurons that modulate starvation- induced sleep suppression 156. It is

possible that the clock inputs that confer rhythmicity to Lk neurons are overridden by

feeding-fasting patterns.

The Drosophila genome encodes for a single Lk target, the leucokinin receptor

(Lkr), that is expressed in the lateral horn, the ventral nerve cord, and the sleep-

promoting fan-shaped body 156,236,312. The fan-shaped body, a subregion of the

Drosophila central complex, is a primary sleep-promoting region 140,144 raising the

possibility that Lk neurons signal to the fan-shaped body to promote sleep or wakefulness during starvation. Supporting this notion, functional analysis suggests Lk-Lkr connectivity is proposed to be inhibitory 156,313. Therefore, the increased activity within

LHLK neurons that we report during starvation may inhibit sleep-promoting fan-shaped

neurons, resulting in starvation-dependent sleep suppression. Functional imaging of the fan-shaped body in flies with ablated LHLK neurons will provide information about the downstream circuitry through which LHLK neurons suppress sleep under starved conditions.

In Drosophila, a number of circulating nutrients including fructose, trehalose, and glucose have been found to affect central brain physiology and behavior 287,314,315.

93 While nutrients may be detected by gustatory receptors expressed in the periphery to

regulate sleep 187,316, sugar receptors and transporters are also expressed in the brain 221.

The identification of LHLK neurons as being active under starvation conditions and

suppressed by glucose provide a system to investigate feeding-state dependent changes in

neural activity. A number of neurons in the fly brain are acutely regulated by feeding

state including the starvation-active Taotie neurons that inhibit insulin producing cells

(IPCs) of the pars intercerebralis to regulate insulin-like peptide release under nutrient deprivation conditions 218,219,317. Conversely the IPCs, which are cell autonomous nutrient

sensors themselves, are activated by glucose through the inhibition of KATP channels,

supporting the notion that ingested nutrients are directly sensed by neurons 318. Further,

the LHLK nutrient phenotype is similar to the neurons within the ellipsoid body labeled

by the sodium/glucose co-transporter SLC5A11 that are active during starvation and

promote feeding 297,319. SLC5A11 and its cognate neurons are required for a variety of

hunger-induced feeding behaviors, but the effect on sleep has not been identified 319. Our

screen found that knockdown of SLC5A11 in Lk neurons did not affect starvation-

induced sleep suppression suggesting alternative regulators of sleep. The identification of

LHLK neurons as starvation-active neurons provides a system for identification of

additional nutrient sensors that regulate sleep. The finding that the activity of LHLK

neurons are modulated by feeding state have parallels with sleep-regulating neurons in

mammals. In mice, both sleep and wake-promoting neurons sense changes in nutrient

availability, through direct detection of circulating glucose or hormonal cues 320,321. For

example, glucose inhibits KATP channels within the sleep-promoting VLPO neurons of

the hypothalamus, promoting slow wave sleep 81. Conversely, hypothalamic neurons

94 expressing the wake-promoting neuropeptide orexin/hypocretin (HCRT) are inhibited by

glucose and leptin while activated by the hunger hormone, ghrelin 75,322,323. Therefore, the

findings that LHLK neurons have increased activity during starvation which results in

wakefulness suggest that Lk neurons may play a role analogous to orexin/HCRT neurons

in mammals.

AMP-activated kinase functions as a cell-autonomous regulator of energy allocation and induces physiological changes associated with starvation 324,325. AMPK

consists of a heterotrimeric complex that is activated by AMP and modulates diverse

intercellular signaling pathways including mTOR, FoxO, and SIRT1326. Canonically,

AMPK is activated during starvation and increases neuronal activity, though this effect

varies by neuronal subtype 280,327 For example, in C. elegans, starvation-induced AMPK

activation leads to inhibition of neurons that modulate local search behavior in response

to food deprivation, while promoting activity in neurons that trigger dispersal behavior

328. Here, we use a dominant negative variant of AMPK with a mutation in the catalytic

domain of the alpha subunit to selectively disrupt AMPK function in Lk neurons 280.

Ubiquitous disruption of AMPK in Drosophila induces hypersensitivity of the locomotor

response to starvation and reduces starvation resistance 280. Conversely, we find that

selectively disrupting AMPK function in Lk neurons promotes starvation-induced

hyperactivity and sleep loss during fed state, revealing neural circuit-specific function for

AMPK. Further, Ca2+ imaging reveals that knockdown of AMPK increases neural

activity, mimicking the starvation state. While our findings indicate that AMPK directly

modulates the activity of Lk neurons, it is also possible that AMPK modulates Lk

transcription or release. The findings that the activity of SELK neurons is not elevated

95 during starvation, raises the possibility of neuron-specific AMPK function. The identification of AMPK as a critical modulator of LHLK neuronal activity and state dependent changes of activity within LHLK neurons provides a system for identifying

novel nutrient-sensing and signaling mechanisms that modulate sleep. Further

investigation of feeding state dependent changes in Lk signaling, and the identification of

neuronal inputs and targets of LHLK neurons will provide mechanistic insight into how

animals integrate sleep and metabolic state.

96 CHAPTER 4. ADE2 FUNCTIONS IN THE FAT BODY TO REGULATE SLEEP

Abstract

Metabolic state is a potent modulator of sleep and circadian behavior and animals acutely modulate their sleep in accordance with internal energy stores and food availability.

Across phyla, hormones secreted from adipose tissue act in the brain to control neural physiology and behavior to modulate sleep and metabolic state. Growing evidence suggests the fat body is a critical regulator of complex behaviors, but little is known

about the genes that function within the fat body to regulate sleep. To identify molecular

factors functioning in non-neuronal tissues to regulate sleep, we performed an RNAi

screen selectively knocking down genes in the fat body. We found that knockdown of

Phosphoribosylformylglycinamidine synthase /Pfas (Ade2), a highly conserved gene

involved the biosynthesis of purines, sleep regulation and energy stores. Flies

heterozygous for multiple Ade2 mutations are also short sleepers and this effect is

partially rescued by restoring Ade2 to the Drosophila fat body. Targeted knockdown of

Ade2 in the fat body does not alter arousal threshold or the homeostatic response to sleep

deprivation, suggesting a specific role in modulating baseline sleep duration. Together,

these findings suggest Ade2 functions within the fat body to promote both sleep and

energy storage, providing a functional link between these processes.

Introduction

Animals balance nutritional state and energy expenditure in order to achieve

metabolic homeostasis 257,269. In the fruit fly, Drosophila melanogaster, feeding behavior

97 and metabolism are regulated by non-neuronal tissues including the muscle, the adipose-

like organ called the fat body, and the gastrointestinal tract 263,329. Similarly, in mammals,

endocrine hormones such as ghrelin, leptin, insulin, and glucagon are secreted from the

stomach, adipose tissue, and pancreas, to convey nutritional status to the brain regions

that regulate sleep and metabolism 330–332. Dysregulation of non-neuronal hormonal

signals leads to a number of metabolic diseases including obesity, diabetes, and insomnia

333. Therefore, mechanistic investigation of factors regulating brain-periphery

communication is critical to understanding disorders associated with sleep and

metabolism.

Adipose tissue senses overall nutrient levels in the animal and modulates hunger-

induced behaviors through controlling energy storage and secreting factors that act on the

nervous system to affect behavior 206,207,334. The insect fat body is central to the control of

energy homeostasis. It is the primary site of glycogen and triglyceride storage, and the

main detoxification organ in the fly, thereby exhibiting functions analogous to the

mammalian liver and adipose tissue 335. Genome-wide transcriptome analysis has identified many genes that are upregulated during starvation, including metabolic enzymes, cytochromes, metabolite transporters, kinases, and proteins involved in lipid metabolism 209. Even though the primary function of many of these genes in the

regulation of energy storage has been studied in detail, little is known about how they

may impact sleep and other behaviors.

Many of the genes and transmitters required for metabolic regulation of sleep and feeding

in mammals are conserved in Drosophila 124,227, and numerous conserved factors have

been identified that regulate sleep and metabolic function 257,333 . The GAL4/UAS system,

98 in combination with genome-wide RNAi libraries, allow for selectively decreasing gene

expression in the fly fat body and then measuring the effects on sleep 96,99. Growing

evidence suggests that the fat body regulates complex behaviors including sleep 204,336,337,

yet the molecular basis through which the fat body regulates sleep remains poorly

understood.

Here, we sought to identify sleep regulators in the fat body by selectively

decreasing the expression of genes that have been previously identified to be upregulated

in starved flies and then measuring their effects on sleep 209. We identified that

Phosphoribosylformylglycinamidine synthase (Ade2), a highly conserved gene involved

the biosynthesis of purines, is required for normal sleep in flies. Flies deficient for Ade2

are short-sleepers and have reduced triglyceride stores, suggesting that loss of Ade2 impairs energy storage and inhibits sleep. Disruption of Ade2 in the fat body does not

disrupt arousal threshold and homeostatic response to sleep deprivation, suggesting a

specific role in modulating baseline sleep duration. These findings provide a novel factor

that functions in the fat body to regulate sleep, and support growing evidence that a non-

neuronal metabolic tissue is critical for the proper regulation of sleep.

Methods

Fly Stocks

Flies were grown and maintained on standard food (Bloomington Recipe, Genesee

Scientific). Flies were kept in incubators (Powers Scientific; Dros52) at 25°C on a 12:12

LD cycle with humidity set to 55-65%. The background control line used in this study is w1118 fly strain, and all experimental flies were outcrossed 6-8 generations into this

background, unless already in this background. The following fly strains were ordered

99 from Bloomington Stock Center, w1118(5905;275), CG-GAL4 (7011;338), r4-GAL4(33832;

160), and hmlΔ3-GAL4 (30141; 339). Ade23-20, Ade21-6, and UAS-Ade2 were obtained from

D. Clark and have been previously characterized 340. Drosophila lines used in the RNAi

screen originate from the TRiP collection 96,98 and are described in (Table 2).

Sleep analysis

The Drosophila Activity Monitor System (DAMS) detects activity by monitoring

infrared beam crossings for each animal 119. These data were used to calculate sleep

information by extracting immobility bouts of 5 minutes using the Drosophila Counting

Macro 244,245. For experiments examining the effects of starvation on sleep, flies were kept on 12:12 LD cycle. 5-7 day old female flies were briefly anesthetized with CO2 and

placed into plastic tubes containing standard food. All flies were given 24 hours to

recover after being anesthetized. Activity was recorded for 24 hours on food (ZT0-ZT24).

Protein, glucose, glycogen and triglyceride measurements

Assays for quantifying triglyceride, glycogen, free glucose and protein content of

flies were performed as previously described 341. Two bodies from female flies aged 3-5

days were homogenized in buffer containing 50 mM Tris-HCl, pH 7.4, 140mM NaCl,

0.1% Triton-X, 1X protease inhibitor cocktail (Roche). Triglyceride concentration was

measured using the Infinity Triglyceride Reagent (ThermoFisher), and protein

concentrations were measuring using a BCA Protein Assay Kit (Pierce Scientific). Total

glucose levels were determined using the Glucose Oxidase Reagent (Pointe Scientific) in

samples previously treated with 8mg/mL amyloglucosidase (Sigma) in 0.2M Sodium

Citrate buffer, pH 5.0. Free glucose was measured in samples not treated with

amyloglucosidase and then glycogen concentrations were determined by subtracting the

100 free glucose from total glucose concentration. Free glucose, glycogen and triglyceride concentrations were standardized to the total protein content of each sample.

Sleep deprivation

Five-seven day old fruit flies were loaded into the DAM System and allowed to acclimate for 24 hours. Following acclimation, day sleep (ZT0-ZT12) was measured in undisturbed flies. Flies were then sleep deprived by mechanical stimulation every 2-3 minutes for 12 hours throughout the night time (ZT12-24). The mechanical stimulus was applied using a vortexer (Fisher Scientific, MultiTube Vortexer) and a repeat cycle relay switch (Macromatic, TR63122). Sleep rebound was measured the following day from

ZT0-ZT12.

Arousal Threshold

Arousal threshold was measured using the Drosophila Arousal Tracking system

(DART), as previously described 342. In brief, individual female flies were loaded in plastic tubes (Trikinectics, Waltham, MA) and placed on plastic trays containing vibrating motors. Arousal threshold was tested with sequentially increasing vibration intensities, from 0 to 1.2 g, in 0.3 g (200 ms) increments, with an inter-stimulus delay of

15 s, once per hour over 24 hours starting at ZT0. Flies were recorded continuously using a USB-webcam (Logitech) at 1 frame per second. The vibrational stimulus and video tracking parameters, and data analysis were performed using the DART interface developed in Matlab (MathWorks, Natick, MA).

Statistical Analysis

The experimental data are presented as means ± s.e.m. Unless otherwise noted a one-way (ANOVA) followed by Tukey’s post-hoc test was used for comparisons

101 between two or more genotypes and one treatment. Unpaired t-test was used for comparisons between two genotypes. For arousal threshold experiment, the non- parametric Mann Whitney U test was used to compare two genotypes. For two or more genotypes, a Kruskal-Wallis test followed by Dunn’s post hoc test was used. All statistical analyses were performed using InStat software (GraphPad Software 6.0) with a

95% confidence limit (p<0.05).

Data Availability

Strains and plasmids are available upon request. The authors affirm that all data

necessary for confirming the conclusions of the article are present within the article,

figures, and tables.

Results

To identify genes expressed in adipose tissue that regulate sleep, we performed an

RNAi screen by assaying the TRiP RNAi collection to selectively knock down genes in

the fat body 96,98. A total of 113 genes previously reported to be upregulated in whole flies during starvation 209 were selectively knocked down in the fat body using the GAL4

driver CG-GAL4 338, and female flies were then assayed for sleep (Fig. 31A) in

Drosophila Activity Monitors 119. Flies with RNAi targeted to the fat body were

compared to controls expressing RNAi targeted to luciferase (CG-GAL4>luc-RNAi).

Knockdown of Ade2 in the fat body (CG-GAL4>Ade2-RNAi) resulted in a loss of over

200 minutes of sleep, while a knockdown of the glucose transporter CG6484 (CG-

GAL4>CG6484-RNAi) and CG6767, a kinase involved in purine/pyrimidine metabolism,

(CG-GAL4>CG6767-RNAi) resulted in increased sleep (Table 2). We chose to focus analysis on the role of Ade2 in sleep regulation because of the robust sleep loss

102 phenotype identified in the screen. To verify these results, we retested the effects of Ade2

knockdown on sleep. In agreement with the screen, sleep was reduced in Ade2

knockdown flies (CG-GAL4>Ade2-RNAi) compared to control flies with CG-GAL4

driving RNAi targeted to luciferase (CG-GAL4>luc-RNAi) (Fig. 31B). Quantification of

sleep throughout the 24 hr testing period revealed that fat body specific knockdown of

Ade2 results in sleep loss with significant reductions during both the day and night

periods, suggesting Ade2 is required for both day and nighttime sleep (Fig. 31B-C).

The CG-GAL4 driver is expressed in both the hemocytes and fat body.338,343. To

identify the sleep-regulating cell-type, we knocked down Ade2 using an additional fat-

body GAL4 driver line, r4-GAL4, which exclusively labels the fat body (Lee and Park

2004). Targeting Ade2-RNAi with r4-GAL4 decreased both daytime and nighttime sleep

compared to control flies expressing luciferase (r4-GAL4>UAS-luc-RNAi), phenocopying knockdown with CG-GAL4 (Fig. 32A).

103

Figure 31 Ade2 functions in the fat body to promote sleep.

A. Histogram showing the distribution of sleep over 24 hours from fat body-specific knockdown of genes previously reported to be upregulated during starvation. Daily sleep is depicted as the difference between the mean of a group of ~80 viable lines tested. Black arrow indicates the Ade2-RNAi control line. B. Knock down of Ade2 in the fat body (CG-GAL4>UAS-Ade2- RNAi; n=84) decreases sleep during daytime (white; p<0.0001, t=6.241) and nighttime (black; p=0.0004, t=3.614) compared to control flies (CG-GAL4>UAS-luc-RNAi; n=115). Unpaired t- test. C. Sleep profile of hourly sleep for 24 hrs. White/black bars=lights on and off, respectively. ZT=Zeitgeber time. Sleep is reduced in flies expressing Ade2-RNAi in the fat body (pink) compared to control (grey). D. Sleep is reduced in Ade23-20/+ mutants (n=66) and Ade21-6/+ (n=90) during daytime (p<0.0001 for all groups) and nighttime (p=0.0002 and p=0.004) compared to w1118 control (n=110). One-way ANOVA, Light, F(2, 261)=11.20; Dark, F(2, 263)=45.91. E. Sleep profile of hourly sleep for Ade23-20/+ mutants (dark orange), Ade21-6/+ (light orange), and w1118 control (grey). All columns are mean ± SEM; *p<0.05; **p<0.01; **- *p<0.001.

104 Table 2 Lines utilized for fat body RNAi screen

BL Total Day Night Waking Total ABL

Stock Sleep Sleep sleep activity bout

#

FBgn0012034 41917 AcCOAs 1030.63 339.06 691.56 1.05 27.50 39.46

FBgn0000052 36686 ade2 722.50 67.50 655.00 0.88 17.67 42.63

FBgn0000078 57561 Amy-D 1025.42 351.67 673.75 0.92 29.83 36.56

FBgn0036449 25926 bmm 1057.33 392.00 665.33 1.16 30.82 36.44

FBgn0039241 53332 CG11098 979.06 284.06 695.00 0.66 22.88 46.56

FBgn0039649 34885 CG11198 1136.25 460.31 675.94 1.06 24.25 55.81

FBgn0034721 38214 CG11298 1212.50 503.75 708.75 1.07 23.50 52.07

FBgn0039299 57194 CG11854 1085.00 402.92 682.08 1.20 32.67 35.62

FBgn0039649 51476 CG11899 899.06 245.00 654.06 0.75 33.38 28.42

FBgn0039330 53379 CG11909 1085.63 419.38 666.25 0.78 29.19 41.18

FBgn0035228 40936 CG12091 1040.31 345.63 694.69 0.86 27.75 38.68

FBgn0027842 33635 CG12891 1060.00 397.50 662.50 0.97 30.20 41.02

FBgn0036419 58240 CG13482 1051.88 355.31 696.56 0.93 19.88 54.49

FBgn0034404 50676 CG15101 879.00 204.50 674.50 0.71 27.20 33.02

FBgn0025803 35341 CG17299 936.25 262.19 674.06 0.80 26.88 37.55

FBgn0035108 35188 CG18374 979.09 299.55 679.55 0.98 30.64 34.73

FBgn0034382 44510 CG18609 967.19 267.67 696.33 1.07 24.40 42.31

FBgn0029823 57739 CG3011 1095.00 415.00 680.00 0.76 28.78 41.68

FBgn0031645 43179 CG3036 830.00 168.75 661.25 0.86 23.25 37.89

FBgn0039361 31150 CG31092 991.25 325.31 665.94 1.51 32.81 37.64

FBgn0038463 35245 CG3534 866.25 233.13 633.13 0.93 33.43 28.72

FBgn0023507 53355 CG3835 1031.50 351.50 680.00 0.89 31.20 34.95

FBgn0034664 61330 CG4377 907.22 223.89 683.33 0.67 27.56 35.32

105 FBgn0032349 55629 CG4779 1084.06 385.94 698.13 1.23 22.88 50.50

FBgn0035950 60091 CG5288 1025.94 329.38 696.56 0.72 27.31 39.24

FBgn0039493 35486 CG5889 963.13 276.33 692.33 1.30 25.73 39.07

FBgn0029831 33932 CG5966 828.93 179.29 649.64 0.85 29.36 29.72

FBgn0034247 60372 CG6484 1200.00 477.00 707.00 1.09 17.40 75.96

FBgn0036030 60086 CG6767 1177.00 484.00 693.00 0.83 24.40 52.88

FBgn0033385 38305 CG8055 1043.24 367.65 675.59 0.85 27.41 39.16

FBgn0034003 57404 CG8094 963.13 300.94 662.19 0.75 32.25 30.98

FBgn0022073 36667 CG8846 1013.13 311.88 701.25 0.72 23.50 44.63

FBgn0031689 53892 Cyp28d1 1056.56 368.44 688.13 0.80 26.75 42.65

FBgn0015714 33887 Cyp6a17 975.50 311.00 664.50 1.10 32.80 32.07

FBgn0000473 64008 Cyp6a2 1011.79 335.71 676.07 0.96 25.07 43.28

FBgn0037249 27565 EIF-S10 869.69 180.31 689.38 0.68 23.30 37.87

FBgn0033465 56864 Etf-QO 887.14 281.43 605.71 0.90 32.71 30.25

FBgn0263773 63980 FOK 1040.45 353.64 686.82 0.73 28.91 39.11

FBgn0030013 31118 GIIIspla2 985.00 318.75 666.25 1.02 32.56 32.29

FBgn0030484 36717 GstT4 858.00 165.00 693.00 0.71 23.30 38.88

FBgn0001565 28991 hlc 925.83 238.08 673.33 0.90 21.92 44.75

FBgn0001208 29540 Hn 812.81 187.19 625.63 1.49 25.69 35.12

FBgn0264785 34717 hph 885.00 195.94 689.06 1.27 24.13 39.14

FBgn0034329 42599 IM1 1041.00 360.00 681.00 0.86 22.90 47.18

FBgn0025583 28788 IM2 875.31 237.50 637.81 1.63 25.06 36.49

FBgn0038465 57814 IRC 938.13 306.47 628.24 1.08 37.94 28.36

FBgn0001301 31251 Kelch 1 1042.50 347.50 695.00 0.73 26.29 42.49

FBgn0001301 55612 Kelch 2 1025.31 334.38 690.94 0.95 29.00 37.14

FBgn0034140 60400 Limostatin 1077.81 401.25 676.56 0.95 25.00 45.52

FBgn0017581 28357 LK6 1 1044.17 380.83 663.33 0.88 32.92 33.61

106 FBgn0017581 35352 LK6 2 905.94 196.25 709.69 0.65 22.56 42.63

FBgn0030608 32846 lsd2 1088.62 397.76 690.86 1.46 26.86 53.74

FBst0056039 56039 lsp1 996.00 311.50 684.50 0.82 27.20 38.87

FBst0031603 31603 luc 975.32 292.62 682.70 1.18 27.20 38.49

FBgn0033296 62252 MALA7 1039.06 396.56 642.50 1.03 31.25 38.96

FBgn0033297 55193 MALA8 1061.25 383.33 677.92 0.91 25.83 43.76

FBgn0032381 55346 MALB1 914.69 246.88 667.81 0.69 31.44 30.48

FBgn0032382 62253 MALB2 920.63 254.69 665.94 0.80 28.69 35.34

FBgn0029870 31157 Marf-RNAi 953.13 302.19 650.94 1.03 30.75 32.58

FBgn0027579 63587 mino 965.50 305.50 660.00 0.89 30.30 34.35

FBgn0010222 62268 Nmdmc 1063.13 366.88 696.25 0.84 26.38 41.24

FBgn0017558 28635 pdk 1064.29 374.64 689.64 1.03 23.86 46.59

FBgn0000489 27569 pkaC3 1093.75 392.81 700.94 0.93 27.19 43.02

FBgn0027601 55272 pudgy 1036.25 339.38 696.88 0.69 24.00 45.53

FBgn0016715 57766 Reg2 1094.58 407.92 686.67 1.05 22.50 53.33

FBgn0031971 43213 sirup 955.63 282.19 673.44 0.99 31.63 31.87

FBgn0024289 34556 Sodh1 913.75 242.50 671.25 1.26 27.31 36.86

FBgn0014031 51935 spat 922.00 281.50 640.50 0.93 31.80 29.83

FBgn0035147 44496 UDP 1020.00 328.75 691.25 0.94 28.75 38.73

FBgn0030904 33949 upd2 1 898.18 328.00 634.00 1.01 34.27 31.10

FBgn0030904 33988 upd2 2 860.63 203.13 657.50 0.96 21.81 44.19

107

Figure 32 Ade2 function in the fat body to promote sleep.

A. Knock down of Ade2 using the fat body specific driver, r4-GAL4, (r4-GAL4>UAS-Ade2- RNAi; n=45) results in a significant reduction in sleep during daytime (white; p=0.0021, t=3.19) and nighttime (black; p=0.003, t=3.04) compared to control flies (CG-GAL4>UAS-luc-RNAi; n=29). Unpaired t-test. B. No differences were observed in day and night sleep when knocking down Ade2-RNAi in hemocytes (hmlΔ3-GAL4, n=31) compared to control flies (n=36, p=0.2, t=1.28). Unpaired t-test C. Average bout length is significantly reduced during dayttime (light; p<0.0001, t=5.27) and nighttime (dark, p=0.022, t=2.30) in Ade2-RNAi (n=87) flies (grey) compared to control (CG-GAL4>UAS-luc-RNAi; black; n=115). Unpaired t-test. D. Knock down of Ade2 in the fat body (grey) reduced total sleep bout during dayttime (p<0.001, t=4.67) compared to control (black), while there is a significant increase during nighttime (p=0.005, t=2.83). Unpaired t-test.

Conversely, knocking down Ade2 in adult hemocytes using hmlΔ3-GAL4 did not

affect sleep (Fig. 32B, 339), supporting the notion that Ade2 functions in the fat body to promote sleep.

To confirm that the sleep loss phenotype observed with Ade2 knockdown was not due to off-target effects of RNAi, we assayed sleep in Ade2 mutant Drosophila. Two Ade2 mutants, Ade23-20 and Ade21-6, have been generated by P-element excision 340. For Ade21-

6, the deletion includes the transcription start site and part of the first coding exon

108 resulting in a null allele 340. While both alleles are homozygous lethal, heterozygous flies

are viable. Flies heterozygous for Ade23-20 or Ade21-6 sleep less than w1118 flies (the

background control strain) during the day and night, phenocopying results obtained with

RNAi knockdown in the fat body (Fig. 31D). Further, a sleep profile analysis shows sleep loss is reduced throughout the day and night confirming the sleep phenotype observed in RNAi knockdown flies (Fig. 31E).

Reduced sleep can be accounted for by a reduction in the total number of sleep bouts, shortened duration of individual sleep bouts, or a combination of both 344. RNAi

knockdown of Ade2 (CG-GAL4>UAS-Ade2-RNAi) resulted in reduced sleep bout length

compared to control flies (Fig. 32C), while total sleep bout number was reduced during

the day and increased during the night (Fig. 32D). Similarly, average sleep bout length

was significantly reduced in both Ade2 mutants (Ade23-20 and Ade21-6) compared to

controls, suggesting that both Ade2-RNAi and mutant flies present a less consolidated

sleep pattern (Fig. 33A). No difference in sleep bout number was detected during the

daytime in flies heterozygous for the Ade23-20 or Ade21-6 mutations compared to w1118 controls, while a significant increase in sleep bout number was detected for both heterozygous mutants during the night (Fig. 33B). Taken together, these experiments

suggest that the short sleeping phenotype of Ade2 deficient flies primarily derives from

the shortening of sleep bouts.

109

Figure 33 Ade2 function in the fat body to promote sleep.

3-20 1-6 A. Ade2 /+ (orange; n=61) and Ade2 /+ mutants (pale orange; n=85) reduced average bout length during daytime (p<0.0001 for all groups) and nighttime (p=0.0019 and p<0.0001) 1118 compared to w control (black; n=111). One-way ANOVA, Light, F(2, 265)=11.70; Dark, F(2, 3-20 254)=11.38. B. Total sleep bout during nighttime is significantly increased in Ade2 /+ (orange; 1-6 1118 p=0.0004) and Ade2 /+ mutants (pale orange; p=0.0074) compared to w control (black). One-way ANOVA, F(2, 265)=8.79. C. Waking activity does not differ between control flies (CG-GAL4>UAS-luc-RNAi; black) and Ade2 knock down in the fat body (grey) during daytime 1-6 (p=0.49, t=0.68) and nighttime (p=0.05, t=1.94). Unpaired t-test. D. Ade2 /+ mutants have 1118 increased waking activity compared to control flies (w ) during daytime (p<0.0001) and 3-20 nighttime (p=0.0018), while Ade2 /+ mutants have increased waking activity only during daytime (p=0.047). One-way ANOVA; Light, F(2, 265)=9.92; Dark, F(2,265)=6.03. All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

To determine whether the sleep phenotype might be explained by generalized

changes in locomotor activity, we analyzed waking activity (beam breaks/minute) in

Ade2 knockdown flies and flies heterozygous for each mutation. Waking activity did not

significantly differ between CG-GAL4>Ade2-RNAi flies compared to control flies, though CG-GAL4>Ade2-RNAi trended toward increased waking activity during the day

and the night (Fig. 33C). Waking activity was significantly increased in Ade21-6

heterozygous flies during daytime and nighttime compared to w1118 controls, while Ade23-

110 20/+ had a significant increase in daytime but not nighttime waking activity Fig. 33D).

Together, these findings suggest that disruption of Ade2 function induces hyperactivity, in addition to shortening sleep phenocopying starved flies.

To verify that expression of Ade2 in the fat body is sufficient for normal sleep, we selectively restored Ade2 to the fat body in the background of Ade2 heterozygous flies and measured sleep. Heterozygous flies with Ade2 restored to the fat body (CG-

GAL4>UAS-Ade2; Ade23-20/+) slept more than Ade23-20/+ heterozygous mutants harboring the UAS-Ade2 transgene without the GAL4 (UAS-Ade2; Ade23-20/+) (Fig. 34A-

B). However, fat body expression did not fully rescue sleep, as rescue flies slept less than control flies harboring CG-GAL4 or UAS-Ade2 transgenes alone. Therefore, restoration of Ade2 to the fat body partially restores sleep to Ade23-20 mutant flies. Similarly, restoring Ade2 to the fat body of flies heterozygous for the Ade21-6 mutation (CG-

GAL4>UAS-Ade2; Ade21-6/+) partially restores sleep, with rescue flies sleeping significantly more than UAS-Ade2; Ade23-20/+ heterozygous flies, but less than flies harboring CG-GAL4 transgene alone (Fig. 34C-D). Expression of Ade2 in the fat body of flies heterozygous for Ade23-20 or Ade21-6 rescued both average sleep bout length during nighttime and sleep bout number during the day and nighttime to control levels (Fig.

35A-D). Similarly, the hyperactivity phenotype seen in Ade23-20 and Ade21-6 heterozygous mutant flies was restored in rescue flies, and these flies did not differ from heterozygous controls (Fig. 36A-B).

111

Figure 34 Ade2 expression in the fat body partially rescues sleep loss.

A. Fat body rescue of Ade23-20/+ (CG-GAL4>UAS-Ade2;Ade23-20/+; n=43) partially restores total sleep compared to CG-GAL4/+ (n=69, p<0.0001) control and UAS-Ade2/+ (n=31, p=0.014). Total sleep duration in rescue flies is increased compared to Ade23-20/+;UAS-Ade2 mutant control (n=30, p<0.0001). One-way ANOVA, F(3, 169)=37.76. B. Sleep profile for Ade23-20 rescue (gold) compared with Ade23-20/+;UAS-Ade2 (yellow), CG-GAL4/+ (grey), and UAS-Ade2/+ (black). White/black bars= lights on and off. ZT=Zeitgeber time. C. Total sleep is increased in flies expressing Ade2 in the fat body of Ade21-6 mutants (n=37) compared to Ade21-6/+;UAS-Ade2 controls (n=39, p<0.0001). Rescue flies are significantly different than control CG-GAL4/+ (grey, n=79, p=0.010). Ade21-6/+;UAS-Ade2 mutants have reduced sleep compared to UAS- Ade2/+ (n=31, p<0.0001) and CG-GAL4/+ (p<0.0001). One-way ANOVA, F(3, 182)= 41.94. D. Sleep for Ade21-6 rescue (dark green) compared with Ade21-6/+;UAS-Ade2 (light green), CG- GAL4/+ (grey), and UAS-Ade2/+ (black). E. Total sleep did not differ between CG-GAL4>UAS- Ade2(n=64) and w1118 (n=55, p>0.99), CG-GAL4/+(n=32, p=0.74), or UAS-Ade2/+ (n=31, p=0.51) controls. One-way ANOVA, F(3,178)=0.43. All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

112 To determine whether upregulation of Ade2 in the fat body is sufficient to promote sleep,

we overexpressed Ade2 in the fat body of wildtype flies (CG-GAL4>UAS-Ade2). Sleep in these flies did not differ from transgenic controls harboring CG-GAL4 or UAS-Ade2

alone (Fig. 34E). Taken together, these findings suggest Ade2 expression in the fat body

is necessary for normal sleep, but enhanced Ade2 expression is not sufficient to increase

sleep.

Sleep is associated with an elevated arousal threshold where animals are less

responsive to environmental stimuli 9,10,345. To determine whether Ade2 regulates arousal threshold, we measured sleep in the Drosophila ARousal Tracking system (DART, Fig.

37A) 115,342. Briefly, the system allows for automated video-tracking combined with controlled application of a vibration stimulus. The response of sleeping animals to the vibration is used to determine the arousal threshold (Fig. 37A). In agreement with infrared-based recordings, video-monitoring in the DART system confirmed reduced sleep in CG-GAL4>Ade2-RNAi flies with Ade2 knocked down in the fat body (Fig. 37B-

C). No differences in arousal threshold were detected between Ade2 knockdown and

control flies during the daytime or nighttime suggesting Ade2 affects sleep duration, but

not sleep-associated changes in arousal (Fig. 37D). Similarly, video monitoring in the

DART system confirmed sleep duration was reduced in Ade23-20 and Ade21-6

heterozygous flies, but no effect on arousal threshold was detected during the day or

night (Fig. 38A-C). Together, these results suggest that arousal threshold is not altered in

Ade2 deficient flies.

Growing evidence suggests that independent neural mechanisms regulate sleep

under undisturbed conditions and the homeostatic sleep rebound following deprivation

113 (Seidner et al. 2015; Liu et al. 2016). To determine if sleep homeostasis is intact in Ade2

deficient flies, we sleep deprived flies by mechanical shaking for 12-hours throughout the night, and measured sleep during the following day. The sleep deprivation protocol resulted in in significant sleep rebound in flies heterozygous for the Ade23-20 and Ade21-6

mutations, similar to controls (Fig. 38A-B). Together, these results suggest Ade2 is

dispensable for homeostatic sleep rebound.

Flies suppress sleep and increase waking activity in response to starvation and

when energy stores are depleted 160,177,274. Therefore, we reasoned that the loss of sleep in

Ade2 mutants may be due to reduced energy storage. To determine if energy stores are

dysregulated in Ade2 mutants, we measured whole-body triglycerides, glycogen, and free

glucose levels in fed Ade2 loss of function flies using colorimetric assays 341. Knockdown

of Ade2 selectively in the fat body (CG-GAL4>Ade2-RNAi) resulted in reduced

triglycerides and free glucose levels (Fig. 40A-C) without affecting glycogen levels.

Similarly, triglyceride and free glucose levels were reduced in Ade23-20 heterozygote

flies, while glycogen levels were unaffected (Fig. 40D-F). In Ade21-6 heterozygous flies,

only free glucose levels are reduced (Fig. 40E). Taken together, these findings suggest

Ade2 function in the fat body is required for the normal storage of triglycerides and free

glucose, supporting the notion that the reduced sleep may be caused metabolic changes

that place the fly in a starvation-like state.

114

Figure 35 Ade2 expression in the fat body partially rescues sleep loss.

3-20 3-20 A. Fat body rescue of Ade2 (CG-GAL4>Ade2 ;UAS-Ade2/+;pale orange; n=50) restores 3-20 3-20 average bout length during the night (dark) compared to Ade2 mutants (Ade2 ;UAS- Ade2/+;grey;n=34, p=0.008), but not during the day. Day average sleep bout length is different between rescue and CG-GAL4/+ (dark grey; n=83, p<0.0001) or UAS-Ade2/+ (black; n=31, 3-20 p=0.017). Night average sleep bout length is reduced in Ade2 ;UAS-Ade2/+ compared to control flies CG-GAL4/+ ( p<0.0001) or UAS-Ade2/+ (p= 0.036). One-way ANOVA, Light, F(3, 3-20 194)=17.76; Dark, F(3,184)=6.865. B. Ade2 rescues total sleep bout during the light (p=0.004) 3-20 and dark (p<0.0001) compared to Ade2 ;UAS-Ade2/+ mutants. Day total sleep bout differs 3-20 between Ade2 ;UAS-Ade2/+ mutants and control UAS-Ade2/+ and CG-GAL4/+ during light (p<0.0001) and dark (p<0.0001). One-way ANOVA, Light, F(3, 194)=12.08; Dark, 1-6 F(3,195)=9.68. C. During the night, Ade2 rescue (white, n=43) restores average sleep bout 1-6 1-6 length compared to Ade2 mutants (Ade2 ;UAS-Ade2/+;grey; n=39), but not during the day. Day sleep bout length is different between UAS-Ade2/+ (black, n=31, p=0.0066) and CG- 3-20 GAL4/+ (dark grey, n=78, p=0.0040) and Ade2 rescue. One-way ANOVA, Light, F(3, 1-6 187)=8.98; Dark, F(3,188)=5.412. D. Total sleep bout is restored in Ade2 rescue flies during 1-6 light (p=0.0003) and dark (p=0.003) compared to Ade2 ;UAS-Ade2/+ mutant controls. Day total sleep bout is significantly reduced between UAS-Ade2/+ and CG-GAL4/+ controls (p<0.0001) 1-6 1-6 compared to Ade2 mutant controls, while night sleep bout is increased in Ade2 mutant compared to controls (p=0.95). One-way ANOVA, Light, F(3, 187)=21.41; Dark, F(3,184)=4.50.

115 Discussion

To our knowledge, this study represents the first genetic screen for Drosophila

sleep regulators that specifically examine the role of non-neuronal tissue in sleep

regulation. The fat body is critical for regulating energy storage in Drosophila and has

been implicated in many behaviors including sleep regulation, courtship, circadian

rhythms, and feeding204,206,222,255,337. While the genetic examination of many behaviors,

including sleep, have predominantly focused on investigating the neural regulation of

behavior, the contribution of the fat body to behavioral regulation is less understood

206,207,347. A complete understanding of behavior will require systematic investigation of

the role of the fat body and other non-neuronal tissues in behavioral regulation.

The fat body-specific screen for sleep regulators described here identified Ade2

function within this tissue type as critical for normal sleep. These findings add to a

growing body of literature suggesting that the adipose tissue is a critical regulator of sleep

in both mammals and invertebrates 210,333,348. In inbred fly lines derived from wild caught

Drosophila, sleep duration positively associated with whole body triglyceride levels, and

Drosophila selected for starvation resistance have elevated fat body stores and prolonged

sleep 349–352. Similarly, flies mutant for the triglyceride lipase gene brummer have

elevated triglyceride stores and an enhanced homeostatic sleep response, while mutants

for the perilipin-like protein lipid storage droplet 2 (lsd2) have reduced triglyceride stores

and a lowered homeostatic sleep response 210. Together, these findings suggest

triglycerides, and perhaps energy stores more generally, are required for normal sleep in flies.

116

Figure 36 Ade2 expression in the fat body partially rescues sleep loss.

3-20 A. Fat body rescue of Ade2 restores waking activity during daytime(p<0.0001) and nightime 3-20 (p=0.044) compared to Ade2 ;UAS-Ade2/+ mutant controls. Waking activity is significantly 3-20 increased during light (p<0.0001) and dark (p<0.0001) in Ade2 mutant compared to UAS- Ade2/+ and CG-GAL4/+ controls. One-way ANOVA, Light, F(3, 194)=23.79; Dark, 1-6 F(3,196)=14.34. B. Waking activity is rescued in CG-GAL4>Ade2 ;UAS-Ade2/+ during 1-6 daytime (p<0.0001) and nightime (p<0.0001) compared to Ade2 ;UAS-Ade2/+ mutant controls. 1-6 Ade2 mutant control have increased waking activity during light (p<0.0001) and dark (p=0.02) compared to control flies. One-way ANOVA, Light, F(3, 187)=21.47; Dark, F(3,188)=13.23. All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

The role of adipose tissue in sleep regulation appears to be conserved across phyla. In mammals, leptin is secreted from adipocytes in response to nutritional state and acts on hypothalamic circuits in the brain to decrease feeding as well as increase energy expenditure 353,354. In addition, sleep is disrupted in leptin-deficient mice, suggesting a role for this adipose-derived hormone in regulating sleep 348. Supporting these findings, our RNAi screen found reduced sleep in flies with fat body knockdown of upd2, a proposed Drosophila ortholog of mammalian leptin 214.The identification of Ade2 as well as a number of additional candidate sleep regulators suggest a central role for the fat body in sleep regulation.

117

Figure 37 Arousal threshold is normal in Ade2-RNAi and Ade2 mutants.

A. The Drosophila Arousal Tracking (DART) software records fly movement while simultaneously controlling mechanical stimuli via a digital analog converter (DAC). Mechanical stimuli are delivered to three platforms, each housing twenty flies under the control of two motors. Mechanical stimuli of increased strength were used to assess arousal threshold (shown on the computer screen). Arousal thresholds were determined hourly, starting at ZT=0. B. Video- tracking analysis of sleep. Sleep during daytime (white, p<0.0001, t=6.11) and nighttime (black, p<0.0001, t=5.47) is significantly reduced in flies expressing Ade2-RNAi in the fat body (CG- GAL4>UAS-Ade2-RNAi; n=105) compared to control (CG-GAL4>UAS-luc-RNAi; n=114). Unpaired t-test. C. Sleep profile over a 24 period. White/black bars represent lights on and off. ZT denotes Zeitgeber time. Ade2 knock down flies (turquoise) sleep less than control (black). D. Arousal threshold during dayttime (p=0.06) and nighttime (p=0.07) does not differ between CG- GAL4>UAS-Ade2-RNAi (turquoise; n=79) and control (dark green;n=85). Mann-Whitney U; Light, 2783; Dark, 2807.

118

Figure 38 Arousal threshold is normal in Ade2-RNAi and Ade2 mutants.

A. Video-tracking analysis shows reduced sleep during daytime (p=0.045) and nighttime (p=0.034) in Ade23-20/+ (n=46) compared to w111 control flies (n=87). Ade21-6/+ mutant flies (n=80, p<0.0001) sleep significantly less than control flies during day and night (p<0.0001). One way-ANOVA, Light, F(2,210)=15.11; Dark, F(2,210)=17.94 B. Sleep profile representative of data in (E). w1118 control flies (black) slept more than Ade23-20/+ (blue) and Ade21-6/+ (grey) mutants. C. Arousal threshold does not differ between w1118 control (dark green n=71) and Ade23- 20/+ (blue, n=29) and Ade21-6/+ (light blue; n= 63) during light and dark. Kruskall Wallis; Light, 6.87; Dark, 3.02. All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

Ade2 encodes a phosphoribosylformylglycinamidine synthase that plays a critical role in purine synthesis in nearly all living organisms 355. Mutations in Ade2, or other components of the de novo purine biosynthesis pathway, have been implicated in the arrest of cell growth as well as reduced fertility and lifespan, suggesting broad biological functions of this gene 356,357. For example, flies heterozygous for Ade2 mutations develop necrosis as pupae, suggesting haploinsufficiency may result in physiological abnormalities 340. The possibility that targeted disruption of Ade2 in the fat body disrupts development of this organ, or its function in energy storage, is supported by our findings that whole-body triglycerides and/or free glucose levels are reduced in Ade2-deficient flies. In addition, it is possible that reduced levels of purines themselves contribute to the sleep phenotype. In both flies and mammals, adenosine promotes sleep, and the accumulation of adenosine during periods of wakefulness is associated with increased

119 sleep drive 358,359. Therefore, it is possible that a reduction in adenosine or other purinergic signaling contributes to the sleep loss phenotype.

Figure 39 Homeostatic recovery sleep is not altered in Ade2 mutants.

A. Sleep profile for hourly sleep for 36 hours. Flies are undisturbed on the first 12 hours (ZT0- ZT12), lights on (white bars). In the subsequent night (black bars, ZT12-ZT24), flies are mechanically sleep deprived (red), and rebound is measured in the next 12 hours during the light period (ZT0-ZT12). ZT denotes Zeitgeber time. B. w1118 control (n=78, p=0.0001), Ade23-20/+ (n=51, p=0.0002), and Ade21-6/+ mutants (n=50, p<0.0001) significantly rebound after sleep deprivation (purple) compared to undisturbed day (light purple). Two-way ANOVA, F(2, 352)=8.75. All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

Our findings suggest that overexpression of Ade2 in an otherwise wild type fly

does not promote sleep, suggesting Ade2 is essential for normal sleep, rather than

variation in expression levels of this gene regulating amounts of sleep. In addition, flies

with either mutation in the Ade2 gene have a partial restoration of sleep, which suggests

Ade2 may function in the brain or other tissue to regulate sleep. Ade2 is ubiquitously

expressed and it is possible that the haploinsufficiency may be caused by dysregulated

purinergic signaling or developmental abnormalities in additional brain regions. In flies,

many neural circuits have been found to regulate sleep including the central complex,

circadian neurons, and gustatory neurons 140,142,153,187. In addition, more recent work has

revealed a critical role for non-neuronal tissue, such as glia, in sleep regulation 360,361. 120 Targeted disruption of Ade2 in additional cell types may help reveal novel insights into

Ade2 function.

Figure 40 Triglycerides and free glucose are altered in Ade2 knock down and mutants.

A.-C. Triglyceride (A, p<0.001, t=0.56) and free glucose levels (B, p=0.007, t=0.25) are reduced in flies with Ade2 knock down in the fat body (CG-GAL4>UAS-Ade2-RNAi; n=13) compared to control flies (CG-GAL4>UAS-luc-RNAi; n=15), while there are no significant differences in glycogen levels (C, n=13, p=0.053, t=2.025). Unpaired t-test. D.-F. Triglyceride (D, n=14, p<0.0001) and free glucose levels (E, p<0.0001) are reduced in Ade23-20/+ mutants (n=14) compared to w1118 controls (n=14), while there are no significant differences in glycogen levels (F, p=0.75). Ade21-6 mutants (n=15) show a reduction in free glucose levels (E, p<0.0001) compared to w1118 controls. Triglyceride (D, p=0.47) and glycogen (F, p=0.09) stores do not differ between Ade21-6 and control flies. One-way ANOVA; TAG, F(2,40)=28.59; Free Glucose, F(2,40)=90.30; Glycogen, F(2,40)=2.45 . All columns are mean ± SEM; *p<0.05; **p<0.01; ***p<0.001.

In flies, starvation results in increased waking activity in addition to reduced sleep duration. We find that waking activity is significantly increased, or trends towards an increase, in Ade2-deficient flies, indicating that the mutant phenotype recapitulates the hyperactivity induced by starvation. Further, our analysis suggests that Ade2 is required for normal sleep under baseline conditions, while it is dispensable for sleep homeostasis.

121 Mechanically sleep depriving flies throughout the night results in a rebound the following day, a response that is unaffected in Ade2-deficient flies. These data support the notion that independent genetic mechanisms underlie regulation of sleep in undisturbed conditions and during sleep rebound. Along these lines, distinct neural circuits have been identified regulating baseline sleep and recovery sleep in Drosophila 362,363. Similarly, arousal threshold is unaffected in Ade2 mutant flies suggesting the quality of the sleep is not dysregulated, but rather the observed phenotype is specific to sleep. Together, these findings suggest Ade2 regulates baseline sleep duration, but may be dispensable for regulating sleep depth and homeostasis.

A growing body of evidence suggests that the levels of energy storage molecules are critical for normal sleep regulation. Starvation potently reduces whole-body triglycerides and glycogen in Drosophila and is associated with reduced sleep 364. In addition, sleep duration and triglycerides are enhanced in flies selected for starvation resistance 246 Therefore, it is possible that loss of Ade2 in the fat body induces a chronic starvation-like state, where animals suppress their sleep. Alternatively, the fat body may regulate sleep through a mechanism that is independent of signaling energy stores.

Taken together, the confirmed role of Ade2 and the identification of additional candidate genes that function within the fat body to either promote or inhibit sleep, supports a central role for the fat body in sleep regulation. The fat body may regulate sleep directly through controlling circulating nutrients that are sensed by the brain or by hormonal communication. In both flies and mammals, sleep-modulating neurons directly sense glucose, raising the possibility that circulating glucose levels regulate sleep 81,219.

The identification of genes that function within the fat body, combined with genetic

122 technology for in vivo imaging of sleep circuits, will allow for investigation of how non- neuronal gene regulation modulates sleep circuits within the brain. The identification of

Ade2, and other candidate regulators of sleep, provide a platform for investigating the role of periphery-brain communication in sleep regulation.

123 CHAPTER 5. FINAL REMARKS

Here we have investigated sleep-metabolism interaction in the fruit fly with the

goal to understand how the brain processes energy status to regulate sleep and how

peripheral tissues signal to the brain to regulate sleep. In Chapter 2, we have identified

one of the first genes involved in the regulation of sleep-metabolism interaction and localized the gene function to a specific population of neurons. We found that two mutations in the trsn locus and knock-down of trsn pan-neuronally results in flies that fail to suppress sleep during starvation. Additionally, we found that trsn is specific for the behavioral integration between sleep and metabolism since stress response, energy stores, and feeding behavior are unaltered in trsn mutant flies. Additionally, we found that trsn is transcriptionally upregulated in the heads of wild type flies in response to starvation.

Finally, targeted knockdown localized trsn function to Lk-expressing neurons. Silencing

Lk neurons results in flies that fail to suppress sleep in response to starvation, demonstrating a critical role for Lk neurons in the metabolic regulation of sleep.

In Chapter 3, we have investigated the function of Lk neuropeptide in the metabolic regulation of sleep and begun to explore the mechanisms by which a single pair of Lk neurons are modulated by nutrients and, in turn, regulate sleep. We found that

Lk neuropeptide is required for starvation induced sleep suppression. We also demonstrated through ex-vivo and in-vivo imaging that Lk neurons have increased activity in response to starvation and reduced activity in response to glucose. Through genetic silencing and laser-mediated microablation, we found that a pair of Lk neurons in

124 the lateral horn of the fly brain is necessary for sleep-metabolism interaction. Finally, we showed that disruptions in AMPK function suppress sleep during fed state, phenocopying starvation. Our current model proposes that during starvation, the lack of nutrient availability in circulation activates LHLK neurons. Within these neurons trsn is upregulated and AMPK downregulated. As a result of LHLK activation, Lk neuropeptide is secreted, promoting wakefulness during starvation (Fig. 41).

Figure 41 Proposed model for the regulation of starvation induced sleep suppression.

During starved state, the lack of nutrients increase activity in LHLK neurons. Translin is upregulated and AMPK is dowregulated. The ativity of LHLK neurons results in Lk neuropeptide secretion which targets unkown neurons expressing Lk receptor. During fed state, glucose reduces the activity of LHLK neurons, which increases AMPK activity. Translin is downregulated in these neurons during fed state.

Taken together, experiments performed in Chapters 2 and 3 established a function for trsn and Lk in the interaction between sleep and metabolism, revealing a model to understand the neural mechanisms which underlie sleep modifications in response to nutritional demands. Further investigation of potential transcript targets of trsn within

LHLK neurons would be necessary to provide a cellular mechanism for trsn-Lk interaction. In addition, to have a better understanding of the nutrient-sensing mechanism and circuitry underlying sleep-metabolism interaction, it would be interesting to

125 investigate neurotransmitter or neuropeptide inputs to LHLK and targets of these neurons.

In Chapter 4, we turned to the fat body to understand how sleep is modulated by metabolism. We performed a screen targeting genes upregulated in the fat body during starvation. We identified Phosphoribosylformylglycinamide synthase/Pfas (Ade2) as a critical regulator sleep. Targeted knockdown of Ade2 in the fat body decreases sleep both during the day and the night suggesting that Ade2 is necessary to promote sleep. We found that Ade2 disruption does not affect rebound after sleep deprivation and arousal threshold, suggesting a role in the regulation of baseline sleep duration. Interestingly, triglyceride and glucose levels are altered in both Ade2 mutants and in fat body-targeted

Ade2 knock down. These data suggest that the short sleeping phenotype could be due to a decrease in energy stores and glucose, mimicking a starvation-like state. However, we cannot discard the possibility that Ade2 regulates sleep through an independent mechanism of energy store regulation.

Together, the findings in Chapter 4 provide evidence for the fat body in sleep regulation. To better understand the communication between peripheral tissues and the brain in sleep regulation, it would be interesting to determine whether Ade2 modulates major processes that occur in the fat body, such as glucose and lipid metabolism.

Additionally, it would be valuable to investigate whether a product of purine synthesis targets sleep promoting regions of the fly brain or if neuropeptides secreted by the fat body are modulated by Ade2 function and, in turn, target sleep/wake neurons in the fruit fly.

126 Growing evidence shows that sleep dysregulation is increasingly common in our

society and it leads to a number of health issues, such as obesity, Type II diabetes, and

metabolic syndrome. With the pressing need to understand the mechanisms underlying

the relationship between these processes, the present study provides an entry point to

determining the genes and neural circuit regulating sleep-metabolism interaction.

Acknowledgements of contributions to dissertation

In Chapter 1, the literature presented has been partially published and reviewed the

current state of knowledge on sleep and metabolism in both mammals and insects257. In

Chapter 2, the work presented identified translin (trsn), the first gene regulating the

interaction between sleep and metabolism. The research shown has been published and it

involved many people284. Kazuma Murakami conducted behavioral experiments with

mutants and RNAi lines, as well as rescue and the screen identifying the neural

population where trsn functions to regulate sleep and metabolism. Bethany Stahl

performed the immunohistochemistry, Pavel Masek performed the feeding experiments,

Aradhana Mehta and Rebecca Heidker performed behavioral. Wesley Bollinger

conducted the video tracking experiments. Finally, Robert M. Gingras and Justin R.

DiAngelo aided with quantification of energy stores. In Chapter 3 of this dissertation, the

research project identified the mechanism and circuits underlying the metabolic

regulation of sleep. The work presented had the help of Priyanka Kakad and Tanja

Godenschwege to generate the UAS-Lk lines. Lastly, in Chapter 4, the published work demonstrated that the fruit fly adipose tissue has a role in modulating sleep. Kreesha

Shah and Elizabeth Brown helped with the Drosophila Arousal Tracking System and

127 sleep deprivation experiments. Ryan Bennick and Justin DiAngelo quantified energy stores in the mutants365.

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