Circadian Clock Regulation of the Glycogen Metabolism In

Home , Jay Dunlap

Circadian clock regulation of the

glycogen metabolism in Neurospora crassa

A Dissertation Submitted to the Faculty of

The Graduate School of the University of Cincinnati

In Partial Fulfillment of the

Requirements for the Degree

Doctor of Philosophy

Lily (Mokryun) Baek

Committee Chair: Christian I. Hong, Ph.D. Committee Members: Nelson Horseman, Ph.D. Sookkyung Lim, Ph.D. Sean Moore, M.S., M.D. Yana Zavros, Ph.D.

ABSTRACT

Circadian clocks generate rhythms in cellular functions, including metabolism, to align biological processes with the 24-hour environment. Industrialized modern society forces our work and activity to be outside of the conventional day, leading to adverse health effects, such as obesity and metabolic disorders. Therefore, identifying molecular pathways responsible for the development of diseases in the circadian-disturbed conditions is critical to find ways for alleviating the negative consequence of night shift work with keeping its societal demands.

Glucose homeostasis depends on extracellular signaling and allosteric control; however, the molecular mechanisms linking the circadian clock to glucose homeostasis remain largely unknown. Here we investigated the molecular links between the clock and glycogen metabolism, a conserved glucose homeostatic process, in a filamentous fungal model Neurospora crassa. We found that glycogen synthase (gsn) mRNA, glycogen phosphorylase (gpn) mRNA, and glycogen levels, accumulate with a daily rhythm controlled by the circadian clock. Furthermore, we identified that the core clock component, WCC directly binds to the gsn promoter, regulating periodic changes in GSN protein, phosphorylation and glycogen products. In addition, the WCC-controlled TFs, CSP-1 and VOS-1 cooperatively modulate the phase and amplitude of rhythmic expression of glycogen metabolic genes, and glycogen accumulation. Finally, the night preferred growth was disrupted in ∆gsn, ∆gpn, and clock mutant strains, demonstrating a potential physiological role for the clock in glycogen metabolism.

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TABLE OF CONTENTS PAGE

ABSTRACT ...... ii

TABLE OF CONTENTS………………………………………………………...iv

ACKNOWLEDGMENTS...... vii

LIST OF FIGURES...... viii

LIST OF TABLES ...... x

CHAPTER I BACKGROUND AND LITERATURE REVIEW………………1

1.1 Circadian rhythms..……………………………………………….…...... 1

1.1.1 The circadian clock in mammals ..…………………………………..5

1.1.1.1 Hierarchical entrainment of the circadian rhythm in mammals…5

1.1.1.2 Molecular structure of circadian clock in mammal………………7

1.2 Neurospora crassa and its circadian clock……………………………….9

1.2.1 Neurospora crassa and its life cycle…………………………………9

1.2.2 Neurospora crassa as a model organism for circadian studies…..…11

1.2.2.1 The molecular mechanisms of circadian rhythms in Neurospora crassa………………………………………………………..…13

1.2.2.2 Entrainment of the Neurospora crassa circadian clock………….14

1.3 Circadian rhythms and metabolism: How the clock regulates metabolism and vice versa………………………………..……………15

1.3.1. Metabolic dysfunctions in circadian disturbances………………..16

1.3.1.1 Shift work (non-genetic clock disturbance)...………………….16

1.3.1.2 Metabolic phenotypes in clock mutants…..…………………….17

1.3.2 Clock-controlled metabolism………………………...……….……19

1.3.2.1 Rhythms in genes encoding metabolic enzymes…………….…20

1.3.2.2 Non-transcriptional regulations of the clock in metabolism……22

1.3.2.3 Circadian rhythm in metabolic hormones………………………23

1.3.3 Metabolic entrainment of the circadian clock…………….….……..24

1.3.3.1 Feeding and food-entrainable oscillators……………………….24

1.3.3.2 Circadian clocks responding to metabolism……………… …...25

1.4 The circadian clock and glucose homeostasis ……………………………27

1.4.1 A brief review of glucose metabolism in mammals…………………27

1.4.2 The clock-controlled glucose metabolism in mammals…………….28

1.4.3 Glycogen metabolism……………………………………………….30

1.4.3.1 Glycogen synthesis…………………………………………….31

1.4.3.2 Glycogen degradation………………………………………….33

1.4.3.3 Glycogen metabolism in Neurospora crassa…………………..35

1.4.3.4 Clock-controlled glycogen metabolism………………………..38

PROBLEM STATEMENT……………………………………………………....40

CHAPTER II CIRCADIAN RHYTHMS IN GLYCOGEN METABOLISM IN NEUROSPORA CRASSA……………….……………………...41 OVERVIEW…………………………………………………………………..41 2.1 Introduction………………………………………………………………42

2.2 Materials and methods…………………………………………………...44

2.3 Results……………………………………………………………………49

2.4 Discussion……..…………………………………………………………51

Acknowledgement…………………………………………………………...63

CHAPTER III IDENTIFICATION OF TRANCRIPTION FACTORS CONNECTING THE CLOCK AND GLYCOGENMETABOLISM...64

OVERVIEW…………………………………………………………………...64 3.1 Introduction………………………………………………………………65

3.2 Materials and methods………...…………………………………………68

3.3 Results……………………………………………………………………73

3.4 Discussion………………………………………………………………..76

Acknowledgement…………………………………………………………...78

CHAPTER IV GLYCOGEN METABOLISM AND ITS CONSEQUENCE ON GROWTH AT NIGHT…………………..92

OVERVIEW………………………………………………………………..…92 4.1 Introduction………………………………………………………………93

4.2 Materials and methods…………...………………………………………95

4.3 Results……………………………………………………………………96

4.4 Discussion………………………………………………………………..98

CHAPTER V CONCLUSIONS/FUTURE DIRECTIONS…………………….103

Summary…………………………………….……………………………...103

Conclusions/Future directions…………….………………………………..104

Final thoughts………………………………………………………………113

Supplementary information………………………………………………...117

REFERENCES…………………………………………………………………122

ACKNOWLEDGMENTS

I would like to thank my PhD advisor, Dr. Christian Hong, for his continued support, encouragement and guidance. I would also like to thank my committee members,

Dr. Nelson Horseman, Dr. Sookkyung Lim, Dr. Sean Moore, and Dr. Yana Zavros for their constructive suggestions and support.

I am thankful to lab members, former and present, and special thanks to Dr. Toru

Matsu-ura and Kaoru Matsu-ura for their support, kindness, and friendship through my long years in HongLab. I would also like to thank all the collaborators and the personnel involved in my research project-Drs. Deborah Bell-Pedersen, Teresa

Lamb (Texas A&M, USA), Maria Celia Bertolini, Stela Virgilio (UNESP, Brazil).

Thanks also go to my colleagues and the department faculty and staff for making my time at UC a good experience.

Lastly, I dedicated this work to my family, without whom I would never be the person I am today, and thank for their unconditional love and endless support.

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LIST OF FIGURES ...... PAGE

Figure 1.1 Circadian oscillators are governed by a common mechanism ……….4

Figure 1.2 Race tube assay to detect circadian rhythm in Neurospora crassa ….12

Figure 1.3 Schematic representation of glycogen synthesis and degradation in Neurospora crassa ………………………………………………...... 37

Figure 2.1 Clock control of gsn and gpn mRNA levels, and rhythmic glycogen accumulation………………………………………………………....54

Figure 2.2 Glycogen accumulation and core clock gene expression in knockout strains lacking either gsn or gpn …………………………………….56

Figure 2.3 Circadian rhythm in GSN protein levels, and its phosphorylation…..58

Figure S2.1 Raw data of bioluminescence from gsn::luciferase and gpn::luciferase………………………………...... 61

Figure S2.2 Raw data of bioluminescence from frq::luciferase…………………62

Figure 3.1 CSP-1 regulates the rhythmic expression of gpn...... 79

Figure 3.2 VOS-1 binds rhythmically to the gsn promoter.……………………..81

Figure 3.3 VOS-1 influences rhythmic gsn, gpn, and glycogen levels………….83

Figure 3.4 Δcsp-1;Δvos-1 alters the phase and amplitude of rhythm in glycogen accumulation, not rhythmicity………………..…………...84

Figure 3.5 WCC directly binds to gsn promoter………………...………….……85

Figure 3.6 WCC controls gsn expression, promoting rhythmic glycogen Accumulation…………………………………………………………87

Figure S3.1 Raw data of bioluminescence from gsn::luciferase and gpn::luciferase……………...... 89

Figure 4.1 Schematic diagram of glycogen exhausted cultures for growth rate measurements……………………………………………………….100

Figure 4.2 Glycogen metabolism impacts rhythmic Neurospora crassa growth rates………………………………………………………...……….101

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Figure 5.1 Maintenance of phase is important for the rhythm of glycogen accumulation……………………………………………………….. 114

Figure 5.2 Schematic diagram of clock-controlled glycogen metabolism and its physiological consequence in Neurospora crassa………………116

LIST OF TABLES ...... PAGE

TABLE 2.1 Strains used in this study…………………………………...... 59

TABLE 2.2 Primers used in this study...…………………………………...... 60

TABLE 3.1 Strains used in this study…………………………………...... 90

TABLE 3.2 Primers used in this study...…………………………………...... 91

TABLE 4.1 Strains used in this study…………………………………...... 102

TABLE S5.1 Strains used in this study…………………………………...... 117

TABLE S5.2 Primers used in this study...…………………………………...... 117

CHAPTER I.

BACKGROUNDS AND LITERATURE REVIEW

1.1 Circadian rhythms

Circadian rhythm (Latin circa diem, meaning ‘about a day’) is an anticipatory internal time-keeping mechanism conserved in a wide range of living organisms from bacteria to human. It enables organisms to align temporal physiological processes with daily environmental changes, such as light-dark cycle, temperature, and food availability. The anticipation of environmental variations influences in biological processes to optimize physiological functions, such as metabolism, fitness, and even survival at the ideal time of a day. For example, the circadian clock in plants confers photosynthetic advantages such as enhanced chlorophyll contents, resulting in optimal photosynthetic carbon fixation at the certain time of a day (Dodd et al. 2005). In a model filamentous fungus, Neurospora crassa, a major peak in expression of metabolic genes occurs during the active phase (or daytime) through the circadian regulation, leading to more efficient storage of food- derived nutrients (Hurley et al. 2014). In animals, the circadian clock enables DNA repair mechanisms to occur during the late afternoon/early evening, following

DNA damage induced by solar irradiation during the daytime (Kang and Sancar

2009).

In human, alterations or disruptions of circadian rhythms are known to increase the risk of several diseases. However, molecular mechanisms of how circadian clock disturbances become a risk factor of diseases remain largely unknown (James et al. 2017). One of the prominent examples of clock disruptions is night shift work/activity. Under natural condition, most of the biological functions occur in a rhythmic manner, which is in synchrony with the circadian- controlled rhythms and behavioral (sleep/wake) cycle. Therefore, adverse effects may occur if the sleep/wake cycle is out of phase with rhythms controlled by internal clock mechanisms. In particular, circadian disruptions are potentially an essential part of the progression of several diseases, yet the underlying mechanisms remain largely unknown. Therefore, better knowledge of how perturbations of the internal clock system become risk factors for cancer or metabolic disorders including obesity, and diabetes mellitus will open new opportunities for mechanism-based therapeutics and preventions for diseases.

The first study of biological rhythms dates back to the 1700s by the work of the French scientist Jean-Jacques de Mairan, who observed daily movements of the plant leaves in Mimosa pudica (Mairan 1729). This cyclic opening/closing of the leaves continued even in the absence of sunlight, suggesting the existence of a self- sustained internal mechanism conferring cyclic behaviors. Today, this is known as the circadian clock. In the 1970s, researchers hypothesized that certain genes underlie the internal time-keeping mechanisms. This hypothesis was validated in studies using lower model organisms including fruit fly, Drosophila melanogaster

(Konopka and Benzer 1971), and filamentous fungus, Neurospora crass (Feldman

2 and Hoyle 1973). Subsequent studies accomplished identification and cloning of the core clock genes, which regulate autonomous circadian oscillations (McClung,

Fox, and Dunlap 1989; Dunlap 1999)

There are three defining characteristics of circadian rhythm; 1) free-running

(endogenous) oscillations, 2) entrainment, and 3) temperature compensation

(Pittendrigh, Bruce, and Kaus 1958).

Endogenous oscillator: Circadian rhythms persist even in the absence of external time cue. Each organism has a slightly different endogenous period (about 24 hours), defining circadian time that is derived by dividing endogenous period into

24 equal parts. For instance, the endogenous period of the N.crassa circadian clock is 22.5 hours. By convention, CT0 represents the onset of subjective day, and CT12 represents the onset of subjective night in diurnal organisms. Free-running rhythm is established by molecular clock mechanisms, consisting of positive and negative arms, though the individual clock genes are not always homologous among different organisms, they share similar mechanistic blueprint. The positive arm, clock transcription factors, induces the expression of negative clock elements that inhibit further activities of the positive arm. Subsequent inactivation and degradation of negative clock elements allow the positive elements to initiate a new cycle (Fig1.1).

Fig.1.1 Circadian oscillators are governed by a common mechanism All eukaryotic circadian systems use a clock mechanism involving oscillators that are composed of positive and negative elements, which form feedback loops (A). In these loops, the positive elements activate the expression of the clock genes, encoding negative elements that inhibit the activities of the positive elements. Phosphorylation of the negative elements leads to their eventual degradation, allowing the positive elements to restart the cycle. The core oscillator components are indicated for the model organisms including Drosophila melanogaster (B) mammals (C), and Neurospora crassa (D).

Entrainment: Circadian rhythms are synchronized to external time cues defined as zeitgeber (German meaning “time-giver”) including the light-dark cycle occurred by the Earth’s rotation. Therefore, a shift in the light-dark cycle (e.g., traveling across different time zones) enables the rhythms to align to the new phase.

Zeitgeber time (ZT) is determined by a period of the zeitgeber, such as

12h:12h/light:dark cycle. ZT0 indicates the lights on, or the onset of daytime while

ZT12 indicates the light off, or the onset of dark.

Temperature compensation: In general, most biochemical reaction rates speed up with increasing temperature. However, the period of circadian rhythms maintains relatively constant within a physiological range of ambient temperatures.

Temperature compensation is observed not only homeotherms but also poikilotherms such as insects, fungi and certain types of bacteria (Narasimamurthy and Virshup 2017).

1.1.1 The circadian clock in mammals

1.1.1.1 Hierarchical entrainment of the circadian rhythm in mammals

A mammalian clock in the suprachiasmatic nucleus (SCN) within the hypothalamus is regarded as the “master clock” coordinating peripheral circadian clocks by relaying light-triggered temporal information (Welsh et al. 1995; Schibler and Sassone-Corsi 2002). Observations of loss of rhythms in SCN-lesioned animal or their recovery by SCN transplantation experiments demonstrated that the oscillator in the SCN acts as the primary clock governing circadian rhythms in the whole body (Stephan and Zucker 1972; Silver et al. 1996).

The entrainment of SCN clock is initiated by photic information traveling from light-sensitive ganglion cells in the retina to the SCN through the retina hypothalamic tract (RHT) (Hirota and Fukada 2004). An opsin-based photopigment, melanopsin, is active within a network of intrinsically photosensitive retinal ganglion cells (ipRGCs), receiving inputs from the classical visual photoreceptors (rods and cones) via amacrine and cone bipolar cells (Hattar et al. 2002). Then, the melanopsin innervates the SCN with glutamatergic and pituitary adenylate cyclase‐activating peptide (PACAP) signals (Reppert and

Weaver 2002). Within the SCN, glutamatergic retinal terminals from melanopsin cells trigger a calcium-dependent signaling cascade, which activates the calcium/cyclic AMP response element (CRE)-binding protein (CREB) that induces genes including the core clock genes (Per1 and Per2) (Yan and Silver 2002;

Tischkau et al. 2003; Travnickova-Bendova et al. 2002). This light response is blunted when cellular activity in the SCN is high during the circadian day, whereas light exposure triggers the signaling cascade when the SCN is quiescent during the circadian night. Therefore, light induction of Per genes depends on the time of day.

Light exposures in the early night cause phase delays whereas in the late night advance the SCN clock (Tischkau et al. 2003; Akiyama et al. 1999).

Until the late 1990s, it was believed that SCN outputs or SCN-driven rhythmic behavior drive circadian rhythms in the periphery. However, by tracking the PER2 protein fused to a luciferase reporter (PER2::LUC), researchers have identified that the isolated cells exhibit rhythms that are achieved by the self- sustained circadian oscillator in an SCN-independent manner (Yoo et al. 2004).

After an observation that a serum shock is capable of synchronizing the rhythm in cultured mammalian cells, it became clear that a change in nutrients is a zeitgeber for the peripheral clocks (Balsalobre, Damiola, and Schibler 1998). Surprisingly, both rhythmic feeding and time-restricted feeding regimen are sufficient to drive the circadian expression of transcripts in the liver of clock-deficient mice (Reznick et al. 2013; Damiola et al. 2000; Stokkan et al. 2001). Therefore, if a diurnal organism encounters feeding during the resting/sleeping phase, the peripheral clocks could be desynchronized to the master SCN clock. However, the underlying mechanisms of how individual peripheral clocks are uncoupled from the central pacemaker through a response to metabolic changes are largely unknown.

1.1.1.2 Molecular structure of the circadian clock in mammals

In the early 1990s, with a discovery and cloning of the first mammalian circadian gene, Clock laid the groundwork for the elucidation of the mammalian clock (Vitaterna et al. 1994). Endogenous rhythm occurs through the interlocked feedback loop between transcription factor(s) and negative element(s) in all eukaryotes (Dunlap 1999). In mammals, two bHLH-PAS (basic helix-loop-helix

Per Arnt Sim) domain-containing transcription factors, BMAL1 (brain and muscle ARNT-like protein 1) and CLOCK (circadian locomotor output cycles kaput) form a protein complex during the subjective day. CLOCK:BMAL1 complexes bind to a specific DNA sequence motif (E-box, 5’-CACGTG-3’) on numerous genes including negative clock genes, Period (Per1, Per2, and Per3)

7 and Cryptochrome (Cry1 and Cry2) (Munoz, Brewer, and Baler 2006). In addition to CLOCK:BMAL1 complex, a transcription factor, NPAS2 (Neuronal PAS

Domain Protein 2) can functionally substitute for CLOCK, forming a complex with

BMAL1 within the SCN and certain brain region (Vitaterna et al. 1994; Bunger et al. 2000).

During the subjective afternoon, PER and CRY proteins reach maximum level and heterodimerize in the cytoplasm (St John et al. 2014; Zylka et al. 1998).

Then their repressive activity is determined by posttranslational modifications as well as nuclear-cytoplasmic translocation (Lee et al. 2001). These regulatory processes are mainly regulated by casein kinases CKIδ and CKIε (Takano, Isojima, and Nagai 2004; Keesler et al. 2000). Therefore, mutations in CKIδ/ε or CK phosphorylation site on PER could change the intrinsic period of the clock in the mammalian clock (Toh et al. 2001; Lowrey and Takahashi 2000; Yamada and

Forger 2010). Cytoplasmic PER protein and phospho-PER become degraded by the proteasome machinery, while nuclear PER:CRY complex inhibits the further transcriptional activity of CLOCK:BMAL1 (Yagita et al. 2002). Progressive degradation of the PER:CRY complex occurs at the end of the subjective night.

This cycle is reinitiated with the CLOCK:BMAL binding to E-box (Takahashi et al. 2008).

Two clock-controlled nuclear receptors, ROR (RAR-related orphan receptors) and REV-ERB α/β competitively regulate the expression of Bmal1, whose promoter region contains two conserved ROR-response elements (ROREs), known as the RORs/REV-ERBs binding site (Sato et al. 2004; Preitner et al. 2002;

Guillaumond et al. 2005). The presence of cooperative, interlocked feedback loops provides robustness against noise and environmental perturbations to maintain accurate circadian period, amplitude and phase in circadian transcriptional outputs, determining optimal time of local physiology (Liu et al. 2008).

1.2 Neurospora crassa and its circadian clock

1.2.1 Neurospora crassa and its life cycle

Since the historical report dating back to the 18th century, a filamentous fungus, Neurospora crassa has been an important model in genetics and fungal physiology as well as circadian rhythms. In 1941, Neurospora crassa was used to test the “one gene and one enzyme” hypothesis (Beadle and Tatum 1941) by Beadle and Tatum, who were awarded the Nobel Prize in 1958. Subsequent efforts led to the completion of genome sequencing (Galagan et al. 2003) and the establishment of the single-deletion knockout library providing more than two-thirds of knockouts of annotated genes (Colot et al. 2006). Given comprehensive genetic information and experimental resources, use of Neurospora crassa is attracted to study in classical genetics, molecular biology, morphogenesis as well as circadian rhythms.

Neurospora crassa undergoes various developmental stages, including vegetative growth and formation of different spores; conidia (asexual) and ascospore (sexual) (Raju 1980; Springer 1993; Springer and Yanofsky 1989).

Vegetative growth of N. crassa is occurred by elongation of multinucleated branched hyphae, which further form mycelium structure. A hypha is a tubular and branched structure which is divided into cell-like units known as septa. The total

9 mass of hyphae is defined as a mycelium. The portion of the mycelium that anchors the mold and absorbs nutrient is called the vegetative mycelium; the portion that produces asexual spores is termed as the aerial mycelium. Under optimal conditions, vegetative growth changes to an asexual development called the conidia formation.

This switch in growth behavior enables Neurospora crassa to disperse to a different location, which occurs about every ~24h. This has been recognized as a first circadian output of Neurospora crassa by Sargent, Briggs, and Woodward in 1966

(Sargent, Briggs, and Woodward 1966).

There exist two types of conidia; microconidia and macroconidia.

Microconidia are uninucleate structure, can function either as asexual reproduction or male gametes. Macroconidia are multinucleated, and its production from aerial hyphae happens in a daily manner (Maheshwari 1999). Pigmentation of macroconidia has been a reason of that Neurospora crassa was recognized as a

“red-bread mold” in 1843 in bakeries of Paris. Macroconidia are more prominent and abundant compared to microconidia. A conidium initiates polar growth by sending out a germ tube which then becomes the first hyphae.

As a heterothallic species, conidia act as paternal fertilizing elements. The nitrogen-limited condition induces sexual development. Mating occurs between two different mating types, A or a, by one developing a fruiting body called perithecia as the “female” strain (Glass, Grotelueschen, and Metzenberg 1990;

Metzenberg and Glass 1990). After contacting two opposing mating types, the female structure undergoes the fertilization followed by meiosis and production of sexual spores, ascospores, which are shoot away from the perithecia into the air as

10 units of dispersal. The germination of ascospore begins asexual developments including the formation of aerial hyphae, undergoing sporulation (Maheshwari

1991).

1.2.2 Neurospora crassa as a model organism for circadian studies

An observable circadian rhythm in conidial formation makes Neurospora crassa as a useful model system for circadian clock research (Sargent, Briggs, and

Woodward 1966). Use of special glass tubes called race tube enables researchers to easily monitor this circadian phenotype (Kramer 2007) (Fig.1.2). Briefly,

Neurospora crassa is inoculated at one end of ~300 mm long glass tube containing solid agar growth media. Then Neurospora crassa is grown in constant light (LL) for 12-24h before the shift to constant dark (DD), which sets the clock to the onset of the subjective night [CT12]. Then, the growth front is marked every 24h for about seven days). Neurospora clock mutants show altered period of conidiation rhythms ranging from 16.5 to 29h or arrhythmicity (Feldman and Hoyle 1973).

Later studies have identified that mutations in frq gene (i.e., point mutation) alter the period of the conidiation rhythm, while strains carrying frq-null mutations, either a truncated protein or deletion, do not show the rhythmic conidiation

(McClung, Fox, and Dunlap 1989). With the clock gene in Drosophila, this pioneering identification of clock gene in Neurospora crassa led to begin a similar mutagenesis screening in animals. The subsequent works characterized Neurospora clock proteins encoded by frq, wc-1 and wc-2 led to the establishment of a canonical clock model, FRQ-WC-based oscillator (FWO).

Fig.1.2 Race tube assay to detect circadian rhythm in Neurospora crassa A culture is inoculated at one end of a 30 cm long glass tube, termed to “race tube,” containing agar medium. After growth in the light overnight, the tube is transferred to constant darkness, which sets the clock to the dusk (CT12). The race tube was marked at the time of transfer and then marked every 24 hours. The period of the rhythm between peaks in conidiation is calculated after ~7days in DD.

1.2.2.1 The molecular mechanisms of circadian rhythms in Neurospora crassa

In the subjective morning, two positive clock elements, WC-1(White Collar complex-1) and WC-2 (White collar complex-2) proteins form a heterodimer protein complex called WCC (White Collar Complex) through their PAS (Per-

Arnt-Sim) domains (Ballario et al. 1996; Denault, Loros, and Dunlap 2001; Cheng et al. 2002). The WCC binds to the consensus DNA sequence called the clock box

(c-box) in the promoter of frq as well as at least 10% of the genome that peak during the subjective morning (Smith et al. 2010; Chen et al. 2009; Froehlich et al. 2002). frq mRNA accumulates in the morning and FRQ protein levels peaking 4-6 hours later. FRQ proteins progressively undergo phosphorylation, leading to its ubiquitous degradation, determining the stability of FRQ. Therefore, proper posttranslational regulations are critical for sustained circadian oscillations and the maintenance of circadian period length. FRQ protein forms a complex called to

FFC with its partner protein FRH (FRQ-RNA-Helicase), determining the proper

FRQ cellular localization (Cheng et al. 2005; Shi et al. 2010). FFC then interacts with WCC and promotes inactivation of WCC (He and Liu 2005; Schafmeier et al.

2005; Schafmeier et al. 2008; Hong et al. 2008), resulting in a decrease in the level of frq transcription. FRQ protein is further phosphorylated by protein kinases including casein kinases, CK1-a, and CKII (Baker et al. 2009). Hyper phosphorylated FRQ undergoes subsequent degradation through the ubiquitin- proteasome pathway, resulting in the re-initiation of a next cycle (Di Lorenzo et al.

2003; Larrondo et al. 2015).

1.2.2.2 Entrainment of the Neurospora crassa circadian clock

The circadian oscillators directly participate in the light entrainment through the WC-1 known as a blue light photoreceptor (Froehlich et al. 2002; Cheng et al.

2003). The WC-1 protein contains the LOV (Light, Oxygen or Voltage) domain, which is a conserved chromophore-binding domain in from fungi to plant. WC-1

LOV domain binds to the FAD (Flavin adenine dinucleotide) chromophore, triggering conformational changes of WC-1, inducing the expression of target genes as a transcription factor (Linden 2002). Therefore, the loss of WC-1 leads to impaired light-dependent clock functions including the induction of frq expression.

Chromatin Immunoprecipitation-Sequencing (ChIP-Seq) data have identified

WCC target genes (Hurley et al. 2014; Crosthwaite, Dunlap, and Loros 1997; Smith et al. 2010). There are at least ~200 genes with regions of their promoters bound by the WCC after a short light treatment (Smith et al. 2010). Most of the light-induced genes display rhythmic mRNA levels with a peak in the morning, matching with

WCC-active phase. However, all rhythmic genes are not morning specific. Later study revealed that WCC-controlled transcription factors also determine global rhythmicity in gene expression. For example, approximately 800 rhythmic genes are evening specific rhythm that is induced by CSP-1 (conidial separation-1), a direct target of WCC. As a transcriptional repressor, activation of CSP-1 generates the night-specific induction of its target genes, including wc-1 (Sancar et al. 2011).

This additional loop stabilizes the robustness of circadian clock against external glucose levels via glucose-sensitive csp-1, indicating that the existence of the nutrient-dependent clock entrainment (Sancar, Sancar, and Brunner 2012). Sancar

14 et al.,2012 and our previous work have demonstrated that the balancing act between

WC-1 and CSP-1 maintaining circadian rhythms against glucose perturbations, known as glucose compensation. Therefore, clock mutants carrying ∆csp-1 or wc-

1 overexpression show the loss of glucose compensation (Sancar, Sancar, and

Brunner 2012; Dovzhenok et al. 2015).

1.3 Circadian rhythms and metabolism: How the circadian clock regulates metabolism and vice versa

Industrialized modern society forces our work and activity to be outside of the conventional day, leading to health problems. Therefore, understanding how the circadian mechanism affects metabolic homeostasis is critical to prevent metabolic disorders associated with circadian disruption by inversed sleep/feeding behaviors

(e.g. night shift work).

In natural condition, rhythmic food intake coincides with cyclic gene expression of rate limiting metabolic enzymes, which are controlled by the circadian clock at the cellular level. Conversely, aberrant nutrient signaling feeds back to the core clock, exaggerating clock disruption-induced metabolic dysregulation. This section will review emerging evidence showing that circadian disruptions exert deleterious consequences on health and bi-directional relationship between the molecular clock and processes that regulate metabolism .

1.3.1 Metabolic dysfunctions in circadian disturbances

Today, an irrefutable population appears to undergo circadian clock disturbances caused by jet-lag, shift work, and sleep disruption. The behaviorally disturbed clock is likely to increase the development of metabolic abnormalities, such as obesity and diabetes (Froy 2010; Eckel-Mahan and Sassone-Corsi 2013).

The use of animal models offers an important opportunity to identify mechanisms by which dysregulation in the circadian system can lead to metabolic dysfunction in response to perturbations such as genetic, and behavioral disturbance (Kennaway et al. 2007; Rudic et al. 2004; Turek et al. 2005). Following sections cover recent epidemiological observation and animal studies involving circadian disruption which is associated with the development of metabolic disease.

1.3.1.1 Shift work (non-genetic clock disturbance)

It has been long observed that reduced sleep duration/quality is linked to impaired glucose tolerance homeostasis, increased body mass index, and altered level of metabolic hormones (Knutson and Van Cauter 2008; Nilsson et al. 2004;

Spiegel et al. 2009). Studies have further revealed that working the night shift has deleterious effects on health and resulted in an increased risk of obesity, cardiovascular disease, and metabolic dysfunctions accompanied with increased food intake, a preference for carbohydrate-rich foods, and alterations in the circulating glucose parameters (Al-Naimi et al. 2004; Canuto, Garcez, and Olinto

2013; Lin, Hsiao, and Chen 2009; Brum et al. 2015; Di Lorenzo et al. 2003).

Together, these studies highlight the adverse health effects of circadian disturbed by inverse sleep schedule in addition to sleep quality.

Nowadays, to better understanding of the metabolic consequence of desynchrony between the internal clock and environmental cycle, animal studies have been conducted with experiments in which subjects were forced to move/wake during their sleep phase. Mice subjected to be active during the sleep phase gained more weight, impaired metabolism as shown in shift workers (Fonken et al. 2010).

In addition, the loss of circulating glucose/triglycerides rhythm in rodents that were forced to move during their sleep phase with no alteration in clock proteins (PER1 and PER2) in the SCN (Salgado-Delgado et al. 2008), suggested that uncoupling the periphery clock from the SCN clock is responsible for the development of metabolic dysfunctions. A few human trials with a protocol controlling light/dark cycles showed that the circadian misalignment has adverse metabolic effects such as reduced glucose tolerance with same meals (Morris et al. 2015; Scheer et al.

2009). In summary, we hypothesize that one can find ways to alleviate the negative consequence of shift work with meeting its societal demands by identifying mechanisms responsible for the development of metabolic dysfunctions in the circadian disturbance,.

1.3.1.2 Metabolic phenotypes in clock mutants

To better understand how the circadian clock is linked with metabolism, numerous clock mutant mice have been developed and tested. Unlike WT animals,

Bmal1 knockout show hypoglycemia in response to insulin and display abnormal

17 metabolic phenotypes (Rudic et al. 2004; Kennaway et al. 2013). ClockΔ19 (a deficiency in the 19th exon) mice show obesity accompanied with increased serum triacylglycerol, and low liver triglyceride on a high-fat diet (Oishi et al. 2006).

Clock knockout mice show arrhythmicity in the liver while the rhythm in the brain remains, suggesting the tissue-specific role of CLOCK protein. This might be due to the NPAS2 protein can compensate for the loss of Clock in the brain, but not in the liver (Landgraf et al. 2016). Per2 knockout mice show altered lipid metabolism and reduced body weight accompanied with a reduction of the fat/lean ratio

(Grimaldi et al. 2010). The loss of Rev-Erb α/β also altered lipid metabolism, including high plasma LDL (low-density lipoprotein), altered hepatic/plasma triglyceride levels, low bile acid accumulation, increased circulating glucose, and reduced free fatty acid. Conversely, an administration of REV-ERB agonists in mice induces body weight loss, decreased lipogenesis, and elevated energy expenditure (Solt et al. 2012; Le Martelot et al. 2009).

Circadian regulations in metabolic processes require the coordinated actions from different tissue-specific regulations as well as the SCN clock-driven synchronizations. Therefore, tissue-specific elimination of clock gene is also required to elucidate tissue-specific function of clock genes and how each peripheral clock contributes to metabolic homeostasis system. A study using a liver-specific deletion of Bmal1 (L‐Bmal1‐/‐) has highlighted the physiological importance of the liver clock in metabolic homeostasis (Lamia, Storch, and Weitz

2008). The L‐Bmal1‐/‐ mice show defective rhythms in the expression of core clock genes as well as liver‐dominant metabolic genes, leading to aberrant glucose

18 homeostasis including fasting-induced hypoglycemia (Lamia, Storch, and Weitz

2008). In contrast, mice with pancreatic β cell-specific Bmal1 deletion show hyperglycemia and impaired glucose tolerance with normal body weights and compositions (Marcheva et al. 2010). Bmal1-overexpressing adipocytes have demonstrated that the lipid synthesis activity in modified adipocytes is much higher, allowing the cells to accumulate lipid deposits (Shimba et al. 2005).

As noted above, both environmental/behavioral-induced circadian disruption and genetic aberrations in clock machinery can lead to metabolic dysfunction.

Importantly, local circadian clocks regulate distinct function for tissue-specific metabolism. This may cause inconsistency of metabolic phenotypes (i.e., weight gain, blood glucose levels) among clock mutants. Therefore, both tissue-specific and non-genetic perturbationss such as altered sleep/feeding behaviors should be considered to investigate roles of circadian rhythms in regulating metabolic homeostasis.

1.3.2 Clock-controlled metabolism

In mammals, metabolites representing numerous metabolic pathways appear to be under circadian control, with multiple phases. In the mouse liver, carbohydrate, lipid, and nucleotide metabolites peak during the rest period while amino acid and xenobiotic metabolites peak at night (Eckel-Mahan et al. 2012). A big fluctuation of rhythm in metabolism is primarily generated by signals from the environment such as light giving rise to circadian rhythms, food ingestion, and hormone signals.

Recently, advanced experimental technics in genomics allowed us to discover that circadian rhythms in metabolism are mostly due to the circadian gene expression of metabolic genes. In addition, the circadian clock regulates the activity of the enzymes involved in metabolic pathways, where the level of a substrate or an intermediate or end-product is produced. This is achieved by broader regulatory mechanisms such as phosphorylation /dephosphorylation or overall cellular energy status reflected in the ratio of ATP/AMP. Because of complexity, a comprehensive description of that goes on regulating metabolism is beyond the scope of this thesis.

Thus, in this section, I will focus on the cellular processes that confer rhythmicity in metabolic enzymes with partial attention for some of the metabolic hormones displaying circadian rhythm.

1.3.2.1 Rhythms in genes encoding metabolic enzymes

Transcriptome studies comparing wild-type and clock-deficient mice have identified that the core clock regulates the rhythmic activity of circadian output genes, which account for overt rhythms in local physiology. The knowledge has rapidly expanded from what was known about those present in the nucleus via circadian transcription factors. A major portion of cycling transcripts is involved in metabolism, including hormone receptors, transporters, metabolic enzymes and its regulators (Zhang et al. 2014; Hughes et al. 2009; Menet and Hardin 2014). A phase of many cycling genes is in accordance with their subsequent protein synthesis, post-translational modification, and biochemical functions.

A pronounced portion of hepatic genes is under the circadian control through primarily CLOCK:BMAL1-mediated transcriptional regulation (Rey et al. 2011).

Cyclic hepatic enzymes constitute circadian rhythms in liver physiology, including glucose metabolism, bile acid/cholesterol metabolism, amino acid regulation as well as drug metabolism (Zhang, Yeager, and Klaassen 2009; Reinke and Asher

2016). Discovery of the role of REV-ERB α/β in both clock loops and metabolism have expanded our knowledge of clock-controlled metabolism. For example, loss of Rev‐erb α alters the rhythm of metabolic enzymes and regulators such as SREBP

(Sterol Regulatory Element‐Binding Protein), targeting genes involved in lipid homeostasis (Le Martelot et al. 2009; Duez and Staels 2008; Duez et al. 2008).

Conversely, in mice treated with synthetic REV-ERB agonists enhanced circadian expression of metabolic genes in the liver, skeletal muscle, and adipose tissue (Solt et al. 2012).

Interestingly, some clock-controlled transcriptional activators provide an additional contribution to the core clock by feeding back to individual clock components. For example, PGC-1α (peroxisome proliferator-activated receptor gamma coactivator 1-alpha) regulates the expression of Bmal1 and Rev-erb-α as well as genes involved in gluconeogenesis under starvation in the mouse liver (Liu et al. 2007). Because of its effect on the core clock, in Pgc-1α null mice, circadian clock gene expression is altered in addition to abnormal rhythms in activity, metabolic rate, and reduced fasting glucose level (Liu et al. 2007). Therefore, PGC-

1α has emerged as a critical component connecting the clock and metabolism by integrating the clock and metabolism.

1.3.2.2 Non-transcriptional regulations of the clock in metabolism

At the cellular level, circadian regulation in the metabolic system is not limited in the nucleus but also accomplished by post-transcriptional and posttranslational regulations (O'Neill et al. 2011), providing additional layers of controls that result in rhythmic abundance of metabolic enzymes encoded by genes expressed constantly. A large-scale circadian phosphoproteome study revealed that a large number of hepatic proteins (~25%) undergoes rhythmic phosphorylation, enabling to stimulate certain metabolic phases during a day, such as glycolysis, glycogen synthesis, and lipid biosynthesis (Robles, Humphrey, and Mann 2017).

Moreover, a study revealed that some mitochondrial proteins involved in metabolic pathways are influenced by clock-driven acetylation, which is altered in Clock−/− mice (Masri et al. 2013).

Despite rare frequency, non-clock functions of clock proteins in metabolic regulation have been observed in certain tissues. The PER2 protein binds to other proteins such as PPARs (peroxisome proliferator-activated receptor) that control a crucial part of liver metabolism including lipid storage, lipogenesis, and hepatic fatty acid oxidation (Grimaldi et al. 2010), or genomic regions encoding metabolic genes (Zani et al. 2013). Moreover, the CRY1 protein appears to interact with G- protein subunit, Gsα, reduces cAMP production by preventing coupling of G protein-coupled receptors to adenylyl cyclase (Zhang et al. 2010).

In summary, rhythms in metabolic enzymes are constituted by a complex network integrating numerous circadian regulations in a tissue-specific manner. Of

22 note, tissues meet their metabolic demands by using biochemical reactions that are temporally or spatially distinct from other tissues via metabolic regulators (i.e., nuclear receptors) expressed in some tissues over others. Therefore, studies identifying distinct metabolic regulators interacting with the circadian clock require further attention.

1.3.2.3 Circadian rhythm in metabolic hormones

The activity of metabolic enzymes maximizes catalytic function with the presence of metabolic hormones that show daily rhythm, which is likely integrated with enteric signals following feeding/fasting cycle. Previous studies showed circadian rhythms of metabolic hormones including insulin, ghrelin, and leptin. As a hallmark of type 2 diabetes development, insulin level is altered in clock-deficient mice (Marcheva et al. 2010; Zhao et al. 2012). In addition, night shift workers showed a rise in insulin secretion with a decrease in insulin sensitivity, displaying a prediabetic condition (Tucker et al. 2012). Ghrelin, known as a hunger hormone, is secreted by the oxyntic cells of the stomach before feeding time according to their own circadian clock (LeSauter et al. 2009). The main action of ghrelin is appetite stimulation. Therefore, the activity of the stomach ghrelin-secreting cells is synchronized by feeding through a clock-driven mechanism (Yannielli et al.

2007). Shift workers have shown disrupted ghrelin cycle, possibly explaining the previously observed tendency of overeating in this population (Schiavo-Cardozo et al. 2013). Lastly, some research findings revealed that adipose-secreted hormone

23 leptin is secreted in a circadian manner, with a peak during the night to early morning. Leptin is secreted by the white adipose tissue after the hepatic glucose level increases, and acts at the appetite centers in the hypothalamus (Gautron and

Elmquist 2011). It conveys signals of satiety for preventing overfeeding. Therefore, leptin levels correlate with increased adiposity in both human and mice. Both clock mutation and chronic jet lag are sufficient to disrupt the circadian rhythm of

C/EBPα-mediated leptin transcription in mice adipose tissue (Kettner et al. 2015).

Taken together, some nutrient-sensitive hormones were shown to have daily rhythms and important connection between metabolism and clock by environmental stimuli, feeding cycles. Of note, although the mechanisms remain largely unknown, metabolic hormones modulate the hypothalamic clock, providing metabolic information from food-responsive peripheral clocks to the central pacemaker within the SCN. Thus, this feedback loop adds another layer to the convoluted network between the circadian clock and metabolism.

1.3.3 Metabolic entrainment of the circadian clock

1.3.3.1 Feeding and food-entrainable oscillators

A non-photic zeitgeber, food entrains circadian rhythms in peripheral tissues.

The SCN clock appears to be much less affected by changes in feeding patterns, while peripheral tissues appear to be dependent on the feeding cycle. Therefore, the timing of food consumption entrains circadian oscillations in peripheral tissues independent of SCN, thereby uncoupling the phase of peripheral clocks from that

24 of the SCN (Damiola et al. 2000; Stokkan et al. 2001). However, studies showed that time-restricted feeding can alter locomotor activity, in particular, before mealtime. Mice fed during light phase exhibited the altered behavioral rhythm termed to “food anticipatory activity (FAA),” with an increased activity preceding the presentation of food. Along with FAA, almost all peripheral clocks, such as liver, altered their phases to the feeding schedule. Interestingly, entrainment to food can also occur in rodents with SCN lesions or disrupted clock genes (Storch and Weitz 2009), indicating that biological processes governing FAA appear to be distinct from the master clock. This raised expectations that the food-entrainable oscillators exist elsewhere. One candidate, the ghrelin hormone seems to participate in stimulating the appetitive during feeding restriction, and ghrelin receptor knockout animals show a reduction in FAA (LeSauter et al. 2009).

However, underlying neuronal mechanisms of how the induction of FAA is distinct from SCN clock in the brain remains unknown. In addition to the timing of feeding, both caloric restriction and prolonged fasting can induce phase advances in rodents

(Challet 2010). Therefore, identifying the precise stimuli and pathways involved in

FAA and understanding the effect of these nutrient-sensing pathways on the SCN remain important avenues for further study.

1.3.3.2 Circadian clocks responding to metabolism

Identification of candidates that transmit metabolic changes to the circadian clock has been emerged in circadian biology research. For example, the sirtuin family of proteins is recognized as a node connecting circadian rhythm and

25 metabolism in peripheral tissues (Haigis and Sinclair 2010). The nuclear sirtuin protein, SIRT1 is the oxidized form of Nicotinamide adenine dinucleotide (NAD+)- dependent histone deacetylase, allowing the histones to wrap the DNA more tightly.

The activity of SIRT1 is elevated when NAD+ is high during the fasting. In contrast,

SIRT1 is reduced after feeding, which promotes the conversion of NAD+ to NADH.

Cyclic NAD+ pronounces rhythmic activity of SIRT1 that binds CLOCK-BMAL1, promoting the deacetylation and degradation of PER2. An important property of

SIRT1 is the interaction with clock machinery in addition to targeting metabolic regulators (Belden and Dunlap 2008). SIRT1-mediated deacetylation of PGC-1α activates some proteins promoting gluconeogenic gene transcription and the inhibition of glycolytic gene transcription in the liver (Rodgers et al. 2005).

Like NAD+/NADH ratio, adenosine monophosphate (AMP) concentration is also important to provide the cellular energy status to the core clock machinery.

When cellular energy is at the low state, AMP levels are increased. Then, liver kinase B1 (LKB1) phosphorylates AMP-activated protein kinase (AMPK) that phosphorylates CRY1, leading to CRY1 degradation by ubiquitination (Lamia et al. 2009).

In summary, the ubiquitous functions of clock molecules interacting with metabolic regulators is a strategy to enhance (i.e., CLOCK:BMAL1 activity) proper phase alignment between the internal clock system and feeding behaviors. Future studies are necessary to provide molecular explanations for the growing number of metabolic disorders associated with circadian disruption, and vice versa.

1.4 The circadian clock and glucose homeostasis

1.4.1 A brief review of glucose metabolism in mammals

Dietary carbohydrates (sugars) provide a major portion of the daily energy requirement and produce variations in blood glucose levels. To prevent hyperglycemic/hypoglycemic excursions, which are deleterious for the peripheral tissues (i.e., brain), plasma glucose must be tightly controlled via glucose homeostatic processes. After a meal, circulating sugars (i.e., glucose) are rapidly captured, metabolized, or stored in tissues. In contrast, during the fasting, blood glucose is yielded by the released glucose from glycogen breakdown or gluconeogenesis (Wasserman 2009).

After feeding, glucose is taken up in the liver and other tissues. The excess glucose is stored initially as glycogen and is later converted into triacylglycerols via lipogenesis. In the liver, a glucose transporter, GLUT2 initiates further process dealing with high blood glucose concentrations by taking up free glucose decreasing its gradient between blood vessels and liver cells (Thorens 2015). Then, the captured glucose molecules undergo glycolysis or glycogen synthesis.

Glycolysis is critical to generating energy in most of living cells. In eukaryotes, the catabolism of glucose into pyruvate is conserved as a major pathway in generating

ATP. In the liver, key rate-limiting enzymes for glycolytic pathway include GK

(glucokinase), PFK-1 (phosphofructokinase-1), and L-PK (liver-type pyruvate kinase). In particular, L-PK is critical in the control of glycolysis in the liver. L-

PK is inhibited by PKA following a glucagon-mediated increase in intracellular

27 cAMP during fasting and is activated by insulin-mediated dephosphorylation under feeding conditions.

During the fasting, the stored form of glucose is broken down to buffer low blood glucose for other tissues, such as the brain. To prevent hypoglycemia, the pancreas releases glucagon that increases the cAMP concentration in the liver via the activation of adenylate cyclase, leading to the activation of PKA. This cascade of kinase action induces glucose release from the stored glycogen via glycogenolysis during an overnight fasting period. During prolonged fasting, de novo glucose synthesis or gluconeogenesis is responsible for the generation of glucose. Major non-carbohydrate precursors for gluconeogenesis are lactate and glycerol, which are released from skeletal muscle and the adipose tissues, respectively (Han et al. 2016).

1.4.2 The clock-controlled glucose metabolism in mammals

Blood glucose is a representative parameter of the circadian control of energy metabolism (Rudic et al. 2004). During activity and feeding, the blood sugar content is primarily determined by nutrient intake. Conversely, endogenous hepatic glucose production takes place in the maintenance of glucose levels during rest and fasting within a relatively narrow range. Global epidemic type 2 diabetes (T2D) incidence is associated with unhealthy lifestyles such as circadian disturbance (i.e., night-shift work, prolonged jet-lag), raising the importance of circadian clock in development of diabetes (Kalsbeek, la Fleur, and Fliers 2014). This correlation is proven by a human study showing that decreased postprandial glucose tolerance

28 was observed in participants that were subject to experience the clock disturbance induced by a circadian misalignment protocol which 12hr inversion of the behavioral and environmental cycles (Morris et al. 2016).

Loss of daily variations in blood glucose and impaired glucose homeostasis have been elucidated in diverse circadian clock mice mutants. In wild-type mice, plasma glucose levels displayed circadian variation with peaking at the subjective day (CT4/CT28) whereas the rhythm was disrupted in the absence of Bmal1, which shows arrhythmic phenotype in DD (Rudic et al. 2004). Clock mutants also displayed the impaired gluconeogenesis, which yields the circulating glucose when the hypoglycemic situation (Doi, Oishi, and Ishida 2010). Also, both Bmal1 knockout and Rev-erb-α knockdown resulted in defective glucose-stimulated insulin secretion (GSIS) in pancreatic beta cells (Lee et al. 2011; Vieira, Merino, and Quesada 2015). Per2 mutant (Per2Brdm1 mice harboring a deletion of exon 10 of the Per2 gene) show hyperinsulinemia with altered insulin sensitivity and hypoglycemia (Yang et al. 2009). Cry1/Cry2 double knockout mice exhibit elevated blood glucose in response to feeding after an overnight fast and impaired glucose tolerance (Tanida et al. 2007). Interestingly, the overexpression of hepatic

Cry1 reduce blood glucose concentrations and appear to improve insulin sensitivity in insulin-resistant mice (db/db), suggesting that the timed administration of CRY activators might be considered as therapeutic agents for type 2 diabetes (Zhang et al. 2010).

The circadian clock regulates critical genes that are involved in glucose metabolism. Along with BMAL1:CLOCK, REV-ERBα directly regulates the

29 expression of multiple gluconeogenic enzymes in the liver. For example, glucose molecules transport across between liver cell and blood via GLUT2, which is under circadian control with a peak in the morning and trough in the evening. In addition, key regulatory enzymes in gluconeogenesis, including G6Pase (glucose 6- phosphatase) and PEPCK (phosphoenolpyruvate carboxykinase) are under the clock control. Moreover, clock proteins, CRYs physically interact with some metabolic regulators such as the cAMP-mediated phosphorylation of cAMP response element-binding protein (CREB), regulating hepatic gluconeogenesis during fasting (Zhang et al. 2010).

1.4.3 Glycogen metabolism

Glycogen metabolism is an evolutionarily conserved glucose homeostatic processes in most organisms including certain bacteria, fungi, and mammals

(Roach et al. 2012; Adeva-Andany et al. 2016). In the vertebrates, liver and muscle predominantly store glycogen, which is a branched polymer of glucose residues

(Wasserman 2009). Skeletal muscle glycogen is mainly used for fueling itself during the muscle contraction, while liver glycogen is utilized for blood glucose homeostasis. Storage of glucose as glycogen form has some advantages; having a minor effect on the osmotic pressure and providing readily available energy when the body is fasted or even in the absence of oxygen (i.e., anaerobic exercise)

(Hearris et al. 2018).

Liver glycogen contents increase during the postprandial period and decrease between meals while muscle glycogen contents are relatively constant during the short fasting. Hence, hepatic glycogen undergoes daily fluctuations as glycogen synthesis and degradation occur during the activity/feeding and resting/starvation periods, respectively (van de Werve and Jeanrenaud 1987). In humans, approximately 30% of absorbed carbohydrates is stored as skeletal muscle glycogen and approximately 20% as liver during the postprandial period in which blood glucose depending upon timely storage of dietary glucose as glycogen

(Chryssanthopoulos et al. 2004; Ivy 2004). Defective glycogen metabolism may cause glycogen storage diseases as well as abnormal circulating glucose level.

Patients with deficient hepatic glycogen metabolic enzymes are unable to store glycogen, leading to postprandial hyperglycemia and the development of liver dysfunctions such as liver cirrhosis. In addition, glycogen metabolism is likely associated with diabetes. Impaired glycogen storage and excessive postprandial glucose level have been observed in type 2 diabetes, emphasizing the role of glycogen regulation in the pathogenesis of diabetes mellitus. However, details in the development of impaired glycogen storage and its implications in patients with type 2 diabetes remain unclear (Ozen 2007; Ashcroft et al. 2017).

1.4.3.1 Glycogen synthesis

Glycogen synthesis occurs when excessive circulating glucose enters toward glycogen storing cells via glucose transporters. In most animal tissues, glucose crosses the plasma membrane through glucose transporters via facilitated transport.

Glucose uptake into hepatocytes is performed by GLUT2 (Glucose transporter2), which facilitate glucose entry following the concentration gradient between blood and tissue. Mutations in the GLUT2 cause the impaired utilization of glucose including hyperglycemia in the postprandial period. On the other hand, skeletal muscle glucose uptake is carried out by GLUT4, being translocated to the plasma membrane from transport vesicles upon the presence of insulin or muscle contraction.

Glucose entered in a cell is rapidly phosphorylated to Glucose-6-phosphate

(G-6-P) by glucokinase in the liver or hexokinase in other tissues. Depending on the condition, G-6-P can be converted to either pyruvate for glycolysis or G-1-P

(Glucose-1-phosphate) for glycogen synthesis by phosphoglucomutase. If directed towards glycogen synthesis, UDPG-pyrophosphorylase converts G-1-P to UDP-G

(Uridine diphosphate-Glucose), which is added to the nonreducing end of pre- existing glycogen chain. Glycogen synthase catalyzes transferring the glucosyl unit of UDP-G to the hydroxyl group at a C4 (fourth carbon atom) terminus of pre- existing glycogen chain by forming an α-1,4-glycosidic linkage. However, the action of glycogen synthase requires a priming step which is catalyzed by glycogenin, forming a short glycogen chain (8-12 glucose units). Once a glycogen primer chain is synthesized, further extension of glycogen is taken over by glycogen synthase. Another glycogen elongating enzyme, branching enzyme, is required to form the α-1,6 linkages that make glycogen as a branched polymer. Branching has some advantages, increasing the water solubility of glycogen and creating more terminal ends, which are directly targeted by glycogen synthase or glycogen

32 phosphorylase. Thus, glycogen branching increases the rate of glycogen metabolism.

Activation of GS is mediated by both allosteric regulator (G-6-P) and reversible phosphorylation. Glycogen synthase is phosphorylated at multiple sites by kinases including PKA (protein kinase A), GSK3 (glycogen synthase kinase-3) and AMPK (AMP-dependent protein kinase) (Roach et al. 2012). Phosphorylation converts the active form of the glycogen synthase into an inactive form, leading to decreased catalytic rate. After feeding, increased an anabolic hormone, insulin, activates Akt (known as protein kinase B) in the cell, which in turn phosphorylates and inactivates GSK-3, thus resulting in the activation of glycogen synthase. Insulin signaling is also critical in the activation of PP1 (Protein phosphatase1), which functions to dephosphorylate and activate glycogen synthase. In addition, PP1 inhibits glycogenolysis via the dephosphorylation/inactivation of glycogen phosphorylase. Conversely, under fasting conditions, dephosphorylated GSK-3 phosphorylates the glycogen synthase, leading to the inhibition of glycogen synthesis.

1.4.3.2 Glycogen degradation

The release of G-1-P from glycogen is catalyzed by the coordinated action of two enzymes, glycogen phosphorylase and glycogen debranching enzymes.

Glycogen phosphorylase catalyzes only the cleavage of glycogen chain from the non-reducing end by breaking the α-1,4-glycosidic bond. Therefore, complete glycogen degradation requires glycogen debranching enzymes that break the α-1,6-

33 glycosidic bond at the branch points (Nakayama, Yamamoto, and Tabata 2001).

Unlike a hydrolytic cleavage yielding free glucose, which is then phosphorylated for entering glycolysis, the cleavage of glycogen is energetically efficient because the released glucose is already phosphorylated form. In particular, G-6-P is not able to diffuse out of the skeletal muscle cell. This is due to lack of glucose 6- phosphatase, which converts G-6-P to free glucose yielding blood glucose levels in liver(Bouche et al. 2004). These differences are due to the function that the muscle uses glucose for itself, whereas the liver maintains systemic glucose homeostasis

(Bouche et al. 2004).

The activity of glycogen phosphorylase is regulated by reversible phosphorylation and allosteric effectors, such as G-6-P and UDP-G which are products of glycogen phosphorylase (Agius 2015). In fasting, the presence of a catabolic hormone, glucagon, induces the intracellular cyclic AMP (cAMP) levels, leading to the activation of PKA that phosphorylates glycogen phosphorylase kinase (PhK). In turn, the activated PhK phosphorylates glycogen phosphorylase, promoting the glycogen breakdown (Thompson and Carlson 2017). On the other hand, after a meal, the release of insulin and its signaling cascade indirectly activates PP1, dephosphorylating glycogen phosphorylase, inhibiting glycogen breakdown.

1.4.3.3 Glycogen metabolism in Neurospora crassa

Like in higher eukaryotic organisms, fungi are capable of utilizing glycogen to cope with starvation during they go through diurnal changes. Fungal glycogen metabolizing enzymes and their functions are similar to mammals. The accumulation of glycogen is highly sensitive to environmental changes including carbon source availability and temperature in yeast (Roach et al. 2012; Wilson et al. 2010). Thus, fungal glycogen metabolism might be responsible for overall fitness and growth. Consistent with this idea, Neurospora crassa accumulates glycogen during the exponential phase of the vegetative growth while glycogen breakdown during the stationary phase accompanied with a peak of the expression of gsn (de Paula et al. 2002).

After glucose modification to UDP-glucose, glycogen synthesis is initiated by glycogenin (gnn), providing glycogen primers for further action of glycogen synthase (de Paula et al. 2005). In turn, the glycogen synthase (GSN, encoded by gsn) and branching enzyme (GBE, encoded by gbe) take over glycogen synthesis by forming α-1,4-glycosidic bonds and α-1,6-glycosidic bonds, respectively (de

Paula et al. 2002; Matsumoto, Nakajima, and Matsuda 1990). For GSN regulation,

PKA signaling cascades may play an important role via reversible phosphorylation that occurs at the c-terminus of GSN protein (de Paula et al. 2005; Freitas et al.

2010). However, there is no direct evidence supporting the PP1-mediated dephosphorylation of GSN in Neurospora crassa, though the role of PP1 as an opposite regulator in other eukaryotes. The only relevant study in yeast identified that the PP1 homolog, PP1A seems to dephosphorylate Gsy2p, which is a

35 predominant form of glycogen synthase (Castermans et al. 2012). Like other eukaryotes, the breakdown of glycogen is also accomplished by the coordinated actions between glycogen phosphorylase (GPN, encoded by gpn) and glycogen debranching enzyme (GDB, encoded by gbd). The activity of GPN is regulated by reversible phosphorylation and allosteric effectors (Wilson et al. 2010).

Changes in fungal glycogen levels are susceptible to environmental conditions. This property is mainly due to the expression of gsn whose promoter region contains multiple stress-sensitive DNA motifs, STRE (stress-responding elements, 5’-AGGGG-3’ (Ruis and Schuller 1995) and HSE (Heat shock element,

5’-nGAAn-3’) (Freitas and Bertolini 2004). The heat exposure down-regulates the expression of gsn, followed by reduced glycogen accumulation. In contrast, glycogen phosphorylase is activated under the heat shock condition, indicating reversible changes in the two opposing enzymes upon temperature challenge. In addition, Neurospora pH signaling pathways may regulate glycogen metabolism via PACC (pH-response transcription factor) that regulates both acid- and alkaline- expressed genes in response to ambient pH changes. Neurospora crassa cultured at alkaline condition (pH7.8) displayed down-regulated gsn followed by reduction of glycogen levels. In contrast, the paccKO strain showed up-regulated gsn in the same culture condition, suggesting a role of PACC in gsn regulation upon pH stress condition (Cupertino et al. 2012). Together, transcriptional regulations may account for regulation in glycogen synthesis or glycogen mobilization under different environmental conditions.

Fig. 1.3 Schematic representation of glycogen synthesis and degradation in Neurospora crassa. Glucose enters the cell and is converted to UDPG, the donor for glycogen synthesis. GNN, the initiator of glycogen synthesis, provides the nonreducing ends for glycogen elongation by the GSN. GPN enzyme catalyzes the breakdown of glycogen yielding G-1-P. GSN and GPN are controlled by reversible phosphorylation by the action of protein kinases and protein phosphatases.

1.4.3.4 Clock-controlled glycogen metabolism

In 1928, first cyclic changes in hepatic glycogen formation were observed in rabbits by Erik Forsgren (Forsgren 1928). The rhythm in glycogen was elucidated by later works showing that the glycogen levels peak at the end of the active phase and reach to lowest levels during the resting phase in other animals including mice and rats (Sollberger 1964). In 1976, Ishikawa and Shimazu observed the diurnal rhythms in the activity of glycogen synthase and phosphorylase, followed by the rhythm in glycogen formation in the liver of rats kept in LD 12h:12h with the feeding during the dark phase (Ishikawa and Shimazu 1976). An important finding is the rhythm in the activity of glycogen synthase is out of phase with that of glycogen phosphorylase, indicating that the rhythm in glycogen formation is accomplished by the feeding that triggers the activity of glycogen synthesis while the action of phosphorylase is inhibited. Another striking finding is the observation of the second peak of the glycogen synthase activity during the next daytime in rats fasted during the dark phase, indicating the existence of the unknown intrinsic pathways for the formation of cyclic glycogen. A recent study identified that the expression of hepatic glycogen synthase, Gys2, is regulated by the core clock transcription factor, CLOCK in mice. In wild-type, direct CLOCK binding to the region of gys2 determines the rhythmic expression of Gys2, followed by diurnal rhythm in glycogen accumulation (Doi, Oishi, and Ishida 2010). In contrast, Clock mutant mice displayed the reduced amplitude of rhythm in glycogen, indicating that the circadian clock plays an important role in hepatic glycogen metabolism.

However, the clock-controlled glycogen metabolism is not fully understood. For example, transcriptional regulations determining the rhythm in glycogen phosphorylase and subsequent effect of glycogen phosphorylase in the formation of rhythmic glycogen contents remain unknown.

In Neurospora crassa, identification of the molecular connection between glycogen metabolism and the circadian clock has not been initiated. A recent study analyzed the gsn and gpn promoter sequences suggested a poteintial regulation of glycogen metabolism by the Neurospora circadian clock. The promoter regions in both gsn and gpn have multiple DNA motifs that are recognized by several transcription factors that respond to environmental changes (Goncalves et al. 2011;

Cupertino et al. 2012). Interestingly, one of these transcription factors is known as

WCC target, which is the core clock transcription factor. allowed researchers to speculate on the existence of a molecular connection between circadian clocks and glycogen metabolism.

PROBLEM STATEMENT

An evolutionary advantage of the circadian clock optimizes metabolic efficiency through temporal separation of anabolic and catabolic reactions (such as gluconeogenesis and glycolysis), enabling organisms to anticipate daily environmental variations tied to the rising and setting of the sun. Today, clock disruptions (e.g., night shift work) appear to cause high prevalence of health problems including metabolic disorders in human. However, the underlying molecular mechanisms of how the circadian clock is coupled to metabolism, and how the development of metabolic dysfunctions in clock disturbed condition are largely unknown. This dissertation is a body of studies using a fungal model N. crassa to demonstrate the underlying mechanisms of clock-controlled glucose homeostatic process, glycogen metabolism, which is conserved from fungi to human. In this report, we demonstrate that a combinatorial circadian transcriptional regulation determines diurnal rhythms in glycogen metabolic genes, followed by rhythmic glycogen accumulations, peaking during the night. Furthermore, we show that proper glycogen metabolism is required for growth preference at night, suggesting a physiological role of the clock-controlled glycogen metabolism in

Neurospora crassa.

Chapter II

Circadian rhythms of glycogen metabolism in Neurospora crassa

Overview

The circadian clock regulates the expression of many genes involved in a wide range of biological functions such as metabolism. Here we identified the existence of circadian rhythm in glycogen metabolism including the rhythmic expression of genes encoding glycogen metabolizing enzymes, followed by rhythmic glycogen accumulation in wild-type Neurospora crassa grown in constant darkness, which reveals the endogenous circadian rhythms in the absence of external time cues. An arrhythmic mutant, ∆frq, abolishes all of rhythmic processes in glycogen metabolism, indicating periodic changes in glycogen metabolism is circadian clock-dependent. In addition, ∆frq exhibited constant low level of glycogen accumulation during the entire experiments. Together, these data establish a role of circadian clock in not only determination of rhythmicity but also overall function of glycogen metabolism in Neurospora crassa.

*This work has been submitted for publication as: Circadian clock regulation of the glycogen synthase (gsn) gene by the transcription factor WCC is critical for rhythmic glycogen metabolism in Neurospora crassa. PNAS, 2018, Mokryun Baek1*, Stela Virgilio2*,

Teresa Lamb3*, Oneida Ibarra3, Juvana Moreira Andrade2, Deborah Bell-Pedersen3+, Maria

Celia Bertolini2+, and Christian Hong1+#

2.1 Introduction

Internal circadian clocks allow organisms to coordinate internal biological processes with cyclic environments including food availability, and thus, provide the ability to anticipate daily energy richness/depletion. Time of day information serves to reset and synchronize the clock, leading to temporal regulation of the vast molecular rhythms, including transcription, translation, post-translational processes, as well as physiology and behavior. Particularly, clocks in metabolic tissues are sensitive to nutritional changes, allowing the integration of nutritional signals with the clock to maintain metabolic homeostasis. Consequently, a misalignment between the feeding cycle and the endogenous clock, or through circadian dysregulations, leads to a metabolic imbalance that promotes increased body weight, insulin resistance, and development of diabetes. Despite the importance of the clock in metabolic homeostasis, the molecular mechanisms connecting the clock and nutritional signals to metabolic homeostasis are not fully understood.

Previous studies have observed that the circadian clock aligns carbohydrate metabolic pathways to optimize plasma glucose homeostasis at certain time a day.

In the early active/feeding period, carbohydrate preference is increased with polysaccharides catabolizing enzymes, such as α-amylase that exhibits a rhythm with peaking during the middle of the resting phase in animals (Bellavia et al. 1990;

Piccione et al. 2008; Furukawa et al. 2005). The digested glucose molecules are absorbed into the small intestine accompanied with increased gene expression of intestinal glucose transporters at the beginning of the active period (Balakrishnan,

Tavakkolizadeh, and Rhoads 2012; Fatima et al. 2009). After the uptake into target

42 tissues, glucose molecules undergo multiple fates including storage such as hepatic glycogen that peaks at the end of the active phase, reflecting time-dependent differences in rates of glucose disposal in a day. Collectively, these findings illustrate the temporal coordination from the anticipation of glucose (carbohydrate) intake to the glucose disposal/release, optimizing glucose homeostasis against daily fluctuations of energy status.

The evolutionarily conserved glycogen metabolic pathways are composed of two opposite processes, the glycogen synthesis and glycogen breakdown. Glycogen concentration determined by relative activities of two opposing enzymes, glycogen synthase (GS) and glycogen phosphorylase (GP) which catalyzes the elongation of the glycogen chain and break down of glycogen, respectively (Wasserman 2009).

In eukaryotes, both enzymes are tightly controlled by allosteric factors, and by posttranslational regulations of both GS and GP. Each pathway becomes sequentially active or inactive in response to extracellular signals, such as insulin or glucagon hormone cascades in animals. Glucose 6-phosphate is an allosteric activator of both

GS and GP. Simultaneously, glucose 6-phosphate converts the GS into a better substrate for protein phosphatases, which lead to the covalent activation of glycogen synthase. Conversely, Glucose 6-phosphate inhibits the activity of GP

(Ercan-Fang et al. 2002). In addition, the reciprocal control between GS and GP is accomplished by reversible phosphorylation which activates glycogen synthase (by dephosphorylation of the GS) and simultaneously inactivates glycogen phosphorylase (by phosphorylation of the GP).

Based on homology, we initially focused on finding the existence of endogenous rhythm in glycogen in constant dark (DD), allowing the circadian clock to free runs in Neurospora crassa. After we found the circadian rhythm in glycogen accumulation, we characterized two opposing glycogen metabolic genes, gsn

(NCU6687) and gpn (NCU07027) that encode glycogen synthase and glycogen phosphorylase, respectively. Our data show that sequential rhythms in transcription, translation, and phosphorylation of GSN determine the diurnal accumulation of glycogen while rhythmic expression of gpn modulates amplitude and phase of glycogen abundance.

2.2 Material and Method

Strains

All strains used for this study are listed in Table 2.1. Wild-type strains 74-

OR23-IVA (FGSC 2489; mat A) and single gene deletion strains (Colot et al. 2006) were obtained from the Fungal Genetics Stock Center. To create luciferase reporter mutant, plasmids were constructed and transformed as previously described

(Ninomiya et al. 2004). 328-4 (ras-1bd) strain was transformed by introducing the plasmid targeting csr-1 locus, which encodes the cyclosporine A-binding protein.

This replacement of csr-1 gene leads Neurospora crassa to acquire cyclosporine A resistance (Bardiya and Shiu 2007). Each luciferase reporter strain was crossed with

∆frq strain (HygR). Cross progenies were selected by antibiotic resistance test

(hygromycin B (hph+), herbicide basta (bar+) and cyclosporine A (csr-1-), luciferase bioluminescence test, and ras-1bd phenotyping.

Culture condition

Conidia were prepared by growing in the minimal growth agar media

(1xVogel’s medium,) and collected with the autoclaved distilled water. The conidia suspension was inoculated to plates containing liquid culture media (1x Vogel’s medium, 0.5% arginine, and 50 ng/ml biotin) with 2% (wt/vol) glucose and incubated at 25 °C under the constant light (LL) for 36-48hr. Mycelial pad from plates were cut into 2-mm pieces and inoculated to 50ml of liquid culture media supplemented with 2% (v/v) of glucose. Prior to circadian time course experiment,

Neurospora crassa were grown in the LL for 24hr. This enables Neurospora crassa clock to set CT12 at the beginning of culture in the constant dark (DD) condition.

Each liquid culture was transferred to DD at the indicated time points and collected by filtering (0.22μm). The collected tissue was snap-frozen in liquid nitrogen and stored at -80°C until further use.

Bioluminescence assay

Conidia suspension was inoculated to race tube containing growth agar media (Vogel’s medium (pH 5.8), 0.1% glucose, 0.17% arginine, 50 ng/mL biotin,

1.5% (wt/vol) agar) supplemented with 12.5 μM Luciferin (Gold biotechnology,

Olivette, MO) . Race tube was kept at 25 °C in constant light (LL) overnight and

45 transferred to constant dark (DD) at 25°C. In vivo luciferase luminescence was collected every hour with a PIXIS CCD camera (Princeton Instruments) controlled by Winview/32 software (Roper Scientific, Sarasota, FL). The collected images were analyzed and plotted by ImageJ software and the customized Excel macro

(from Dr.Luis Larrondo’s lab), respectively.

RNA extraction and RT-PCR/northern blotting

Total RNA was collected from the frozen mycelia using Trizol (Molecular

Research Center, Cincinnati, OH) /chloroform/ethanol extraction, or as previously described (Lamb, Vickery, and Bell-Pedersen 2013). Northern blotting was performed as described (Lamb, Vickery, and Bell-Pedersen 2013). Target specific primers are listed in Table 2.2.

Protein extraction/western blot assay

Total protein was extracted from the ground mycelia by adding lysis buffer

(50 mM Tris-HCl, pH 8.0, 50 mM NaF, and 1 mM EDTA) supplemented with proteinase inhibitors (0.5 mM PMSF, and 1 μg/mL each of pepstatin A, leupeptin and aprotinin). V5-tagged protein was bound by primary mouse monoclonal anti-

V5 antibody (Invitrogen, Carlsbad, CA), recognized with a goat anti-mouse horseradish peroxide-conjugated secondary antibody (Bio-Rad Laboratories,

Hercules, CA). Immuno-reactivity was then visualized with Super Signal West Pico

Chemi-luminescence Detection (Thermo Scientific, Waltham, MA). To detect the

46 phosphorylated state of GSN, membranes were blocked with 5% non-fat milk in phosphate buffered saline (PBST) with 0.1% Tween-20. To verify phosphate- specific signal, protein extracts were treated with protein phosphatase (#P0753S,

New England Biolabs, Ipswich, MA) at the indicated time points. After exposure to X-ray film, images were scanned and analyzed by densitometry.

Glycogen quantification

The ground tissue was treated by lysis buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaF, and 1 mM EDTA) supplemented with proteinase inhibitors (0.5 mM

PMSF, 1 mM DTT, and 1 μg/mL each of pepstatin A, leupeptin and aprotinin).

Cellular extract was clarified by centrifugation (10,000 X g, for 10 min at 4ºC), and the supernatant was collected. This crude extract was treated by 20% TCA (final concentration) and undergone the centrifugation (5,000 X g, 10 min, 4ºC). The supernatant was precipitated with 95% cold ethanol, collected by centrifugation, washed twice with 66% ethanol and dried. The pellet was re-suspended in acetate solution (50mM sodium acetate, 5mM of CaCl2, pH 5.2) and digested with 10 mg/ml of α-amylase, and 30mg/ml of amyloglucosidase (Sigma Aldrich, St. Louis,

MO) at 37ºC for 16 h. Free glucose was measured using a glucose oxidase kit

(Sigma Aldrich, St. Louis, MO) and the glycogen content was normalized to total protein. Free glucose and total protein were quantified using a NanoDrop® ND-

1000 spectrophotometer at 505 nm and 280 nm, respectively. The glycogen content was calculated using a standard glycogen curve and the results were expressed in

µg of glycogen/mg total protein.

Data Analysis and Statistics.

For data comparison, statistical analysis was performed using two-tailed

Student’s t test. A P value <0.05 was considered statistically significant. Circadian time, CT, is derived by dividing the free-running period of a rhythm into 24 equal parts, whereby CT0 represents subjective dawn and CT12 represents subjective dusk. To determine circadian rhythmicity, data from Northern/Western blots were fit either to a sine wave or a linear line as previously described (Lamb et al. 2011;

Bennett et al. 2013). P values represent the probability that the sine wave best fits the data. To determine the circadian accumulation of glycogen, two independent analyses were performed. JTK_CYCLE was used with incorporating a period window of 20–26h and q value < 0.05 probability cutoff (Hughes, Hogenesch, and

Kornacker 2010). The second analysis was performed using the BioDare2 online tools (www.biodare.ed.ac.uk) using cubic detrended input and FFT-NLLS and

MFourFit algorithms allowing periods of 18-28h (Moore, Zielinski, and Millar

2014; Zielinski et al. 2014). This provided individual replicate analysis for testing statistical differences between different genotypes. Data from the real-time luciferase bioluminescence assays were detrended by subtracting the fitted polynomial from the raw data; and were analyzed by FFT (Fast Fourier transformation) and BioDare2 to determine the circadian rhythm as previously described (Matsu-Ura et al. 2016). .

2.3 Results

2.3.1 Circadian rhythm in glycogen accumulation and glycogen metabolic genes

To determine if glycogen levels are regulated by the circadian clock, wild type (WT) and arrhythmic clock mutant (∆frq) strains were cultured in constant darkness (DD), conditions in which the clock mechanism free-runs with an endogenous ~22.5h period. Robust circadian rhythms of glycogen abundance were observed in WT cells, with a peak at subjective night (DD32/CT22) (Fig.2.1A &

B). In contrast, glycogen levels were consistently low and arrhythmic in ∆frq cells, as compared to WT (Fig. 2.1A & B). Strains containing the gsn or gpn promoter fused to a codon-modified luciferase (luc) reporter show that both gsn::luc and gpn::luc oscillated with a period of 21.93h and 22.32h, respectively (Fig.2.1C &

D). Consistent with rhythmic bioluminescence data, both gsn and gpn mRNA levels oscillated in WT (Fig.2.1E & G), but not in ∆frq cells (Fig.2.1F & H), peaking in the subjective dawn (DD12/CT1 and DD36/CT2). These data demonstrate that gsn and gpn promoter activity, rather than mRNA turnover, is controlled by the circadian clock.

2.3.2. Glycogen oscillations is achieved by gsn rather than gpn

To determine if rhythmic glycogen abundance requires gsn or gpn, we assayed the clock and glycogen rhythms in ∆gsn and ∆gpn strains in DD. A frq promoter luciferase reporter transcriptional fusion construct (frq::luc) displayed

49 robust rhythmicity in both ∆gsn and ∆gpn cells with a period of 21.46h and 21.41, respectively (Fig. 2.2 A & B), ruling out the possibility that the loss of rhythms in

∆gsn and ∆gpn was the result of a defect in the core circadian clock mechanism

(Fig. 2.2 C & D). gpn mRNA rhythms were disrupted in ∆gsn cells, and gsn mRNA rhythms were abolished in ∆gpn cells, whereas the clock-controlled gene ccg-1 mRNA accumulated rhythmically in the mutant strains (Fig. 2.2C & D).

Furthermore, the overall levels of glycogen are low in ∆gpn cells at all times of the day compared to WT cells. The data may appear to show a low amplitude rhythm in glycogen levels in ∆gpn cells, but the rhythm does not meet statistical significance. (Figs. 2.2D & E). As expected, no glycogen was detected in ∆gsn cells lacking glycogen synthase (Fig. 2.2 F). Taken together, these data further supported the idea that circadian expression of gsn may be sufficient to drive rhythmic accumulation of glycogen. Therefore, we further focused on determining what controls rhythmic gsn expression, but also continued to examine factors contributing to gpn expression due to the role of GPN in the modulating the amplitude and phase of glycogen rhythms.

2.3.3. GSN and GSN phosphorylation show circadian rhythm

GSN is the rate-limiting enzyme for glycogen accumulation, suggesting that

GSN levels and/or activity might cycle in phase with the peak in glycogen accumulation. To begin to test this idea, we tagged GSN at the C-terminus with a

V5-epitope tag and measured GSN-V5 levels from cells harvested every 4 hrs in

DD over 2 days using anti-V5 antibody. Total GSN levels exhibited circadian

50 rhythms with a peak in the subjective night (~DD32) (Fig. 2.3A), similar to the peak in and glycogen levels (Fig. 2.1A), and consistent with the rise in gsn mRNA levels (Figs 2.1E). These suggested that rhythmic GSN drives the dawn peak in glycogen accumulation. However, the enzymatic activity of GSN is tightly controlled by phosphorylation, inactivating glycogen synthesis. Therefore, we tracked phosphorylation states of GSN at the different time in DD. Phosphorylation of GSN occurs when both GSN protein level and glycogen product peak (Fig. 2.3B).

Further support for clock-control of rhythmic gene expression being important for glycogen level rhythms is that while phosphorylated GSN accumulated rhythmically, the amount of phosphorylated GSN represented only a small fraction of total GSN (Fig. 2.3B). Thus, under these growth conditions, signaling mechanisms that regulate GSN activity likely have only a minor role, if any, in regulating rhythmic glycogen accumulation. Taken together, these data support that circadian control of gsn drives rhythmic accumulation of glycogen. Therefore, we next focused on determining what controls rhythms gsn expression.

2.4 Discussion

Many metabolic functions are under control of the clock to ensure that they are produced at the appropriate time of day, such as stimulating catabolism during the active phase to support increased energy demands (Green, Takahashi, and Bass

2008; Sancar et al. 2015; Hurley et al. 2014). The importance of clock control of metabolism is revealed by an increased incidence of metabolic disorders in mice and humans with a disrupted clock (Turek et al. 2005; Scheer et al. 2009). However,

51 the molecular mechanisms of circadian clock-controlled glucose homeostasis remain largely unknown. We utilized N. crassa as a model to uncover potentially conserved molecular mechanisms controlling rhythmic glycogen metabolism, a critical process in glucose homeostasis. We observed circadian oscillations of glycogen, gpn mRNA, gsn mRNA, and GSN protein levels. These data are consistent with previous animal studies demonstrating circadian rhythms of glucose metabolic parameters, including glycogen abundance, plasma glucose levels, and glucose tolerance (Lamia, Storch, and Weitz 2008).

We observed in-phase morning-specific mRNA levels of both gsn and gpn despite their opposing functions in glycogen metabolism. However, GSN and GPN function may not only depend on mRNA abundance, but also on their protein accumulation, enzymatic activities and localization. In animals and fungi, glycogen synthase and glycogen phosphorylase are regulated by allosterism and by reversible phosphorylation (Roach et al. 2012). Phosphorylated GSN becomes inactive, whereas phosphorylation is required for the activation of GPN. This results in a switch-like mechanism where one enzyme is active while the other one is inactive

(Roach et al. 2012). While we have not yet investigated the impact of the clock on

GPN phosphorylation, our data reveals the importance of transcription control of gsn and little, if any, role for phosphorylation of GSN in rhythmic glycogen accumulation. In addition, in yeast and skeletal muscle cells, GS and GP display differences in their cellular localization that is dependent on glycogen concentration, with GS entering the nucleus when glycogen is depleted and GP remaining cytoplasmic (Cid et al. 2005; Ferrer, Baque, and Guinovart 1997; Wilson et al.

2010). The nuclear localization of GS has been suggested to provide a warning signal that fuel levels are low, which then triggers transcription of genes necessary for increasing glycogen stores (Wilson et al. 2010). These data suggest the possibility that loss of rhythmic gsn expression in ∆gpn cells, as well as loss of gpn rhythmicity in ∆gsn cells (Fig. 2.2 C & D), may be due to changes in nuclear GSN- directed transcriptional control. As such, the coordinate regulation of gsn and gpn mRNA by the clock may provide a strategy to allow the organism to efficiently shift between glycogen synthesis and breakdown depending on the time of day to maximize energy production in the active phase, or in response to nutritional stress.

Fig 2.1: Clock control of gsn and gpn mRNA levels, and rhythmic glycogen accumulation. (A) Plot of glycogen levels from WT (black line, JTK_cycle q value<0.001) and ∆frq cells (gray line, JTK_cycle q value>0.05) (n>=4, ± SEM). Circadian time (CT) is calculated based on a free running period of 22.5h. (B) The average glycogen content from all 12 time points (Total), subjective day (DD12, 16, 20, 36, 40, and 44), and subjective night (DD8, 24, 28, 32, 48, and 52) in WT vs. ∆frq (n>=4± SEM, Student’s t test, *p<0.05, **p<0.01, *** p<0.001). (C-D) Representative trace of bioluminescence signals from gsn::luciferase (C) and gpn::luciferase (D) in WT (black line) and ∆frq (gray line). Bioluminescence data were detrended and analyzed by FFT (Fast-Fourier Transform) and BioDare (n3). Arbitrary units (a.u.) are shown. (E-H) gsn and gpn RNA levels from WT and ∆frq cells harvested at the indicated times in DD (solid black lines). Rhythmicity was determined using F tests of fit of the data to a sign wave for gsn and gpn in WT cells (dotted black lines, p<0.01). In ∆frq cells, rhythmicity was abolished as indicated by a better fit of the data to a line (dotted black lines). 28S rRNA was used as internal loading control.

Fig. 2-2: Glycogen accumulation and core clock gene expression in knockout strains lacking either gsn or gpn (A-B) Representative trace of frq::luc in ∆gsn (A), and ∆gpn (B). Bioluminescence data were analyzed by FFT and BioDare. Arbitrary units (a.u.) are shown. (C-D) Representative northern blots (left) of gsn, gpn, and clock-controlled gene ccg-1 mRNA isolated from ∆gsn or ∆gpn cells

56 harvested at the indicated times in DD. 28S rRNA was used as a loading control. The data for gsn in ∆gpn cells, and gpn in ∆gsn cells are plotted on the right (solid black lines, n4± SEM), with both having a better fit to a line (dotted lines). (E-F) Plot of glycogen levels from ∆gsn (E), and ∆gsn cells (F) (n2, ± SEM). Glycogen levels in ∆gpn and ∆gsn had a better fit of the data to a line (dotted gray lines).

Fig.2-3: Circadian rhythm in GSN protein levels, and its phosphorylation. (A) Representative western blot of GSN-V5 (A) from cells harvested at the indicated times in DD. Amido black staining of the membrane was used to normalize protein loading. The data are plotted on the bottom (n=3, ± SEM), and fit to a sign wave (dotted line) as described above (p<0.002). (B) Western blot of total protein isolated GSN-V5 cells harvested at the indicated times in DD and separated on a PhosTag gel to enable detection of phosphorylated GSN-V5 (P-GSN-V5). The samples were treated (+) or not (-) with lambda phosphatase (Ptase). The relative abundance of phosphorylated GSN-V5 to total GSN-V5 protein is plotted on the left (solid black line) (n=3, ±SEM). Rhythmicity was determined using F tests of fit of the data to a sign wave and is represented as a dotted black line (p<0.002).

Table. 2.1 Strains used in this study

Strain Genotype Purpose Glycogen FGSC2489 WT quantification, Northern blot Glycogen FGSC18932, ∆gsn::hph+ quantification, 18933 Northern blot Glycogen FGSC20154, ∆gpn::hph+ quantification, 20155 Northern blot Glycogen ∆frq ∆frq::bar+ quantification, Northern blot gsn::luc Pgsn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay gpn::luc Pgpn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay ∆frq, gpn::luc ∆frq::hph+, gsn::luciferase::bar::csr-1, LUC reporter assay ras-1bd ∆frq, gsn::luc ∆frq::hph+, gpn::luciferase::bar::csr-1, LUC reporter assay ras-1bd Pfrq::luciferase::his-3+, ras-1bd frq::luc(x661-4) LUC reporter assay gift from Dr.Luis Larrondo’s lab Glycogen ∆gsn::hph+, Pfrq::luciferase::his-3+, frq::luc ,∆gsn quantification, LUC ras-1bd reporter assay Glycogen ∆gpn::hph+, Pfrq::luciferase::his-3+, frq::luc ,∆gpn, quantification, ras-1bd LUC reporter assay GSN-V5 bar+::GSN::V5KI Western blot

Table. 2.2 Primers used in this study

primer Sequence purpose NCU0668 Northern 5'-CGCCGGCTCAGTAGACTTCTA-3' 7F blot NCU0668 Northern 5‘TGTAATACGACTCACTATAGGGAGTTCCTCCATGTAGCAGCCG-3' 7R blot NCU0702 Northern 5'-AGACTACTGGCTCGACTTCAACC-3' 7F blot NCU0702 Northern 5'-TGTAATACGACTCACTATAGGGAATCTTGGCCAGCTCCGTCA-3' 7R blot gsn::luc 5'CGGAATTATACGATTTAGGTGACTGCAGGCGAGATCGAGCAACCCA plasmid primer1 GAAAG-3' construct gsn::luc 5'GCCCTTCTTGATGTTCTTGGCGTCCTCCATTGTGGCTCAATATGAATG Plasmid primer2 GG-3' construct gsn::luc 5'GCGCCCGTCACCCATTCATATTGAGCCACAATGGAGGACGCCAAGAA Plasmid primer3 CATC-3' construct gsn::luc 5'TAGGTATTCTATAGTGTCGGATCCTCTAGGAGCTTGGACTTGCCGCCC Plasmid primer4 TTC-3' construct gpn::luc 5'CGGAATTATACGATTTAGGTGACTGCAGGGGTGGATGTAGGAATAT Plasmid primer1 G-3' construct gpn::luc 5'GCCCTTCTTGATGTTCTTGGCGTCCTCCATTGTAGGAGATGGATGAAT Plasmid primer2 GG-3' construct gpn::luc 5'GCAACCCCCCATTCATCCATCTCCTACAATGGAGGACGCCAAGAACA Plasmid primer3 TC-3' construct gpn::luc 5'TAGGTATTCTATAGTGTCGGATCCTCTAGGAGCTTGGACTTGCCGCCC Plasmid primer4 TTC-3' construct GSN-V5 Plasmid 5’-TGTCAAGTGGCACGAGGGC-3’ primer1 construct GSN-V5 5’CCTCCGCCTCCGCCTCCGCCGCCTCCGCCCCTGGTGCCGTTGAGTTGT Plasmid primer2 A-3’ construct GSN-V5 5’TGCTATACGAAGTTATGGATCCGAGCTCGCGCGAGCTCTGTTGCCTT Plasmid primer3 GA-3’ construct GSN-V5 Plasmid 5’-GGGAAGAGAGCTGGGTGTTGC-3’ primer4 construct GSN-V5 Plasmid 5’-GGCGGAGGCGGCGGAGGCGGAGGCGGAGG-3’ primer5 construct GSN-V5 Plasmid 5’-CGAGCTCGGATCCATAACTTCGTATAGCA-3’ primer6 construct GSN-V5 Plasmid 5’-AAAAAGCCTGAACTCACCGCGACG-3’ primer7 construct GSN-V5 Plasmid 5’-TCGCCTCGCTCCAGTCAATGACC-3’ primer8 construct

Supplementary figures

Supplementary Fig. 2.1 Raw data of bioluminescence from gsn::luciferase and gpn::luciferase Trace of bioluminescence signals from gsn::luciferase (A and B) and gpn::luciferase (C and D) in WT (left) and ∆frq (right) (n3, ± SEM). Arbitrary units (a.u.) are shown.

Supplementary Fig. 2.2 Raw data of bioluminescence from frq::luciferase Trace of bioluminescence signals from gsn::luciferase (A and B) and gpn::luciferase (C and D) in WT (left) and ∆frq (right) (n3, ± SEM). Arbitrary units (a.u.) are shown.

ACKNOWLEDMENTS

The authors are thankful to Dr. Stela Vigillo (Dr. Bertolini’s lab) for performing glycogen quantifications and northern blotting. Also, thank to Dr. Teresa Lamb for performing western blotting.

Chapter III

Identification of transcription factors linking the circadian clock to rhythmic glycogen metabolism

Overview

Observations presented in Chapter II enabled us to hypothesize that the circadian clok regulates glycogen metabolism via specific transcripiton factors.

Here, we demonstrated that WCC-controlled gsn drives the circadian accumulation of glycogen, while clock-controlled TFs, CSP-1 and VOS-1, cooperate with WCC to modulate the amplitude/phase of the glycogen oscillation by regulating gsn and/or gpn. Our data show a combinatorial TF network linking circadian clock and glycogen metabolism through the core clock TF WCC, along with other clock- controlled TFs, CSP-1 and VOS-1. Although the action of WCC is predominant to generate rhythmic expression of gsn, which determines rhythmicity of glycogen accumulation, additional transcriptional regulations from CSP-1 and VOS-1 are required to maintain correct phase/amplitude of rhythm in glycogen accumulation.

In addition to daily light/dark cycle, this complex transcriptional regulation might be due to Neurospora crassa’s strategy for incorporating environmental signals that may alter metabolic demands, because CSP-1 and VOS-1 respond to environmental changes, such as nutrition (glucose) and stress (pH), respectively.

3.1 Introduction

After observations of circadian-dependent oscillations in glycogen metabolism, we further focused on elucidating the molecular mechanisms by which the clock controls glycogen metabolism. In the FRQ/WCC oscillator, two PAS- domain containing GATA-type zinc finger transcription factors (TFs), White

Collar-1 (WC-1) and White Collar-2 (WC-2) dimerize to form the White Collar

Complex (WCC) (Ballario et al. 1996; Denault, Loros, and Dunlap 2001; Cheng et al. 2002). Two important tasks of the WC-1 protein is sensing a blue light as a photoreceptor; and inducing both light-responsive and clock-controlled genes as a transcription factor with its partner protein, WC-2. Upon light exposure, the WCC binds to light response elements (LREs) of target genes (Smith et al. 2010). On the other hand, WCC rhythmically binds to sequence motif called to “clock-box (c- box)” of targets in constant darkness. Transcriptional regulation at the core clock gene, frq promoter occurs through binding of the WCC to a pair of cis-acting sequences termed to clock-box (c-box) and proximal light-response element (pLRE)

(Froehlich et al. 2002), leading to its clock function in continual darkness in addition to light entrainment of the circadian clock.

Recent findings with the development of sequencing approaches (i.e., ChIP- seq and RNA-seq) allowed us to have insights into circadian controls in the expression of metabolic genes via hierarchical actions among clock TFs. ChIP-seq in cells given a short light pulse to activate the WCC revealed that WCC binding occurs at the promoters of ~24 TFs (Smith et al. 2010), which induce the global gene expression in Neurospora crassa. Therefore, a major peak in clock-dependent

65 expression occurs at the time of peak activity of WCC while first-tier TFs induce expression of genes that peak at a later phase with different levels of amplitude. In this way, a phase-specific TFs can potentially generate multiple peaks of gene expression phase in a day.

To verify if WCC induces the expression of gsn and/or gpn, we first explored

WC-2 ChIP-seq data, which identified light-induced WCC target genes. However, this data did not confirm the WCC binding in the promoter region of either gsn and gpn. This led us to identify indirect pathways connecting circadian clock and the expression of glycogen metabolic gene(s) through the clock-controlled TF(s), in which control cyclic gene expression in a combinatorial fashion with core clock

TFs. Based on previous studies suggesting potential cis-regulators binding to either gsn or gpn promoters, we found transcription factors that were previously described as participating in light and/or circadian clock signaling networks in addition to metabolism (Chen et al. 2009; Olmedo et al. 2010; Sancar et al. 2011; Smith et al.

2010; Wu et al. 2014).

Our first focused on CSP-1 (conidial separation-1), which is a well- characterized WCC-controlled TF. The closest homologues to CSP-1 are NRG1 and NRG2, which form a transcription factor complex regulating glucose metabolic genes in yeast (Berkey, Vyas, and Carlson 2004). As a transcriptional repressor,

CSP-1, controls the expression of ~800 genes, including wc-1 (Sancar et al. 2011).

Many genes that are direct targets of CSP-1 encode proteins involved in metabolism

(~200 genes) and the absence of csp-1 leads to aberrant expression of genes involved in lipid metabolism, resulting in the loss of circadian time-specific

66 membrane lipid synthesis (Sancar et al. 2011). Furthermore, CSP-1 differentially regulates the expression of wc-1 depending on glucose concentration. In the absence of csp-1, the ability to maintain period over a range of glucose concentrations, a process called nutritional compensation, is lost (Sancar, Sancar, and Brunner 2012; Dovzhenok et al. 2015). Because CSP-1 binds to the gpn promoter upon the short light exposure, we predicted CSP-1 might connect the circadian clock and glycogen metabolism by regulating the expression of gpn.

Another WCC-controlled TF candidate is VOS-1 (viability of spore, ncu05964), which is a homologue of VosA in Aspergillus nidulans. VosA protein regulates many genes involved in development, stress response and metabolism by binding at the certain DNA sequence (5’-CTGGCCAAGGC-3’) (Ahmed et al.

2013) which is presented in both gsn and gpn promoter region (Ni and Yu 2007;

Krijgsheld et al. 2013). Hence, we predicted that VOS-1 is another TF connecting the core clock and glycogen metabolism via transcriptional regulation.

Neither gsn or gpn were identified as direct WCC targets in genome-wide screens (Smith et al. 2010; Wu et al. 2014). However, I did not rule out the WCC binding to gsn because the existence of putative WCC binding sites in gsn promoter region (Fig.3.4A). It allowed us to explore the binding of WCC in gsn promoter by performing ChIP-qPCR with sequence-specific primers targeting WCC binding motifs in gsn promoter. Our data demonstrated that WCC directly binds to the promoter of gsn, determining the circadian rhythm of glycogen accumulation. Both

CSP-1 and VOS-1 regulates gpn and/or gsn, but they do not determine rhythmic

67 glycogen accumulation. Instead, they modulate amplitude and phase of rhythm in glycogen accumulation.

3.2 Material and methods

Strains

All strains used for this study are listed in Table 3.1. Mutant strains carrying luciferase reporter was crossed with single gene deletion mutants. Cross progenies were selected by antibiotic resistance test (hygromycin B (hph+), herbicide basta

(bar+) and cyclosporine A (csr-1-), luciferase test (bioluminator), and ras-1bd phenotyping. gsnOE strain was constructed as previously described (Lamb, Vickery, and Bell-Pedersen 2013).

Culture condition

Conidia were prepared by growing in the growth agar media (1xVogel’s medium,) and collected with the autoclaved distilled water. The conidia suspension was inoculated to plates containing liquid culture media (1x Vogel’s medium, 0.5% arginine, and 50 ng/ml biotin) with 2% (wt/vol) glucose and incubated at 25 °C under the constant light (LL) for 36-48hr. Mycelial pad from plates was cut into 2- mm pieces and inoculated to 50ml of liquid culture media supplemented with 2%

(v/v) of glucose. Prior to circadian time course experiment, Neurospora crassa were grown in the LL for 24hr. This enables Neurospora crassa clock to set CT12

68 at the beginning of culture in the constant dark (DD) condition. Each liquid culture was transferred to DD at the indicated time points and collected by filtering

(0.22μm). The collected tissue was snap-frozen in liquid nitrogen and stored at -

80°C until further use.

RNA extraction and RT-PCR

Total RNA was collected from the frozen mycelia collected at the indicated time points by using Trizol reagent (Molecular Research Center, Cincinnati,

OH)/chloroform/ethanol extraction method or as previously described (41). Total

RNA extracts were treated by DnaseA (Qiagen, Hilden, Germany), and quantified by Nanodrop (Invitrogen, Carlsbad, CA). 1µg of RNA was used for cDNA synthesis by using a kit (Promega, Madison, WI) and quantified by RT-PCR with

5X SYBR green mastermix (Applied Biosystems, Foster City, CA). Target specific primers are listed in Table 3.2.

ChIP-qPCR

ChIP-qPCR was accomplished as described previously, with slight modifications (55). Mycelia grown in liquid culture were cross-linked with 1% (v/v) formaldehyde and quenched by adding 125 mM glycine at the indicated time points.

The tissue was harvested and ground under liquid nitrogen. ChIP lysis buffer (50 mM Hepes, pH 7.4; 150 mM NaCl; 1 mM EDTA; 1% Triton TX-100; 0.1% SDS,

0.1% deoxycholate and proteinase inhibitors; 2 mg/ml of Leupeptin, Pestatin A and

1 mM of PMSF) was added to the ground tissue and sonicated. Protein was immunoprecipitated using primary antibody (α-WC-2 (15) or α-V5 (Invitrogen,

Carlsbad, CA) and Dynabeads® Protein A or G (Life Technologies, Carlsbad, CA).

MockIP without antibody was done in parallel as a negative control.

Immunoprecipitation complexes were washed with the following buffers sequentially; 1) ChIP lysis buffer 2) High salt ChIP lysis buffer (50 mM HEPES,

500 mM NaCl, 1 mM EDTA,1% Triton X-100, 0.1% deoxycholate), 3) LiCl wash buffer (250 mM LiCl, 0.5% NP40, 0.5% deoxycholate, 1 mM EDTA, 10 mM Tris-

HCl) and 4) 1x TE buffer (10 mM Tris-HCl and 1 mM EDTA). Protein-DNA complexes were eluted in elution buffer (0.1 M sodium bicarbonate and 1% SDS) and reverse-crosslinked by incubating at 65°C. After treatment of RNase and

Proteinase K (Qiagen, Hilden, Germany), DNA was isolated by phenol/chloroform extraction and analyzed by qPCR with sequence-specific primers listed in Table

3.2.

Bioluminescence assay

Conidia suspension was inoculated to race tube containing growth agar media (Vogel’s medium (pH 5.8), 0.1% glucose, 0.17% arginine, 50 ng/mL biotin,

1.5% (wt/vol) agar) supplemented with 12.5 μM Luciferin(Gold biotechnology,

Olivette, MO) . Race tube was kept at 25 °C in constant light (LL) overnight and transferred to constant dark (DD) at 25°C. In vivo luciferase luminescence was collected every hour with a PIXIS CCD camera (Princeton Instruments) controlled by Winview/32 software (Roper Scientific, Sarasota, FL). The collected images

70 were analyzed and plotted by ImageJ software and the customized Excel macro

(gifted by Dr.Luis Larrondo’s lab), respectively.

Glycogen quantification

The ground tissue was treated by lysis buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaF, and 1 mM EDTA) supplemented with proteinase inhibitors (0.5 mM

PMSF, 1 mM DTT, and 1 μg/mL each of Pepstatin A, Leupeptin and Aprotinin).

Cellular extract was clarified by centrifugation (10,000 X g, for 10 min at 4ºC), and the supernatant was collected. This crude extract was treated by 20% TCA (final concentration) and undergone the centrifugation (5,000 X g, 10 min, 4ºC). The supernatant was precipitated with 95% cold ethanol (5times of volume), collected by centrifugation, washed twice with 66% ethanol and dried. The pellet was re- suspended in acetate solution (50mM sodium acetate, 5mM of CaCl2, pH 5.2) and digested with 10 mg/ml of α-amylase, and 30mg/ml of amyloglucosidase (Sigma

Aldrich, St. Louis, MO) at 37ºC for 16 h. Free glucose was measured using a glucose oxidase kit (Sigma Aldrich, St. Louis, MO) and the glycogen content was normalized to total protein. Free glucose and total protein were quantified using a

NanoDrop® ND-1000 spectrophotometer at 505 nm and 280 nm, respectively. The glycogen content was calculated using a standard glycogen curve and the results were expressed in µg of glycogen/mg total protein.

Data Analysis and Statistics

For data comparison, statistical analysis was performed using two-tailed

Kornacker 2010). The second analysis was performed using the BioDare2 online tools (www.biodare.ed.ac.uk) using cubic detrended input and FFT-NLLS and

MFourFit algorithms allowing periods of 18-28h (Moore, Zielinski, and Millar

2014; Zielinski et al. 2014). This provided individual replicate analysis for testing statistical differences between different genotypes. Data from the real-time luciferase bioluminescence assays were detrended by subtracting the fitted polynomial from the raw data; and were analyzed by FFT (Fast Fourier transformation) to determine the circadian rhythm as previously described (Matsu-

Ura et al. 2016).

3.3 Results

3.3.1 CSP-1 regulates the expression of gpn

CSP-1 is a direct target of the WCC (Smith et al. 2010), binds to the promoter of gpn upon light exposure (Sancar et al. 2011), and was previously shown to be linked to glycogen metabolism (Goncalves et al. 2011). To determine if CSP-1 regulates rhythmic gpn, and possibly gsn mRNA levels, a gpn or gsn promoter::luciferase fusion was transformed into WT and ∆csp-1 cells. gpn::luc was arrhythmic in a ∆csp-1 strain (Fig. 3.1A). However, gsn::luc was still rhythmic in ∆csp-1 cells (Fig. 3.1B), although with a significantly reduced amplitude and shortened period (20h) compared to WT cells. In addition, overall glycogen levels were higher, but still rhythmic in the ∆csp-1 cells with an ~8 h phase advance (peak at DD24) (Fig. 3.1C & D), similar to the phase advance observed in gsn::luc reporter in ∆csp-1 cells (Fig. 3.1B). Although CSP-1 was described as a transcriptional repressor at target promoters (Sancar et al. 2011), low levels of gpn bioluminescence observed in ∆csp-1 cells compared to WT cells suggested that

CSP-1 activated gpn transcription. Activation of gpn transcription by CSP-1 was confirmed by observing an increase in gpn in strains that constitutively overexpress csp-1 from either the tubulin promoter or the quinic acid-inducible (qa-2) promoter

(Fig. 3.1E). Taken together, these data demonstrate that CSP-1 is required to activate rhythmic gpn transcription, and modulates, but is not necessary, for gsn mRNA or glycogen accumulation rhythms.

3.3.2. VOS-1 influences rhythmic gsn and gpn mRNA and glycogen levels.

Both gsn and gpn promoter regions contain a sequence that is similar to the consensus A. nidulans VosA DNA binding site (5’-CTGGCCAAGGC-3’)

(Fig.3.2A & B). Therefore, we examined if the circadian clock controls rhythms in the expression of vos-1 whose protein product might contribute to rhythms in gsn and/or gpn mRNA levels. Both vos-1::luc and VOS-1-V5 exhibited robust circadian oscillations, with a 22.5h period and peak in VOS-1-V5 during the subjective night (DD28/CT18), respectively (Fig.3.2C & D). Furthermore, VOS-1 binds rhythmically to the gsn and gpn promoters, also peaking in the subjective night (DD28/CT18) (Fig. 3.2E & F), consistent with the nighttime peak levels of

VOS-1 protein, and preceding the peak in gsn and gpn mRNA levels (Fig.2.1E &

F). However, circadian oscillations of gsn::luc and gpn::luc were still observed in the ∆vos-1 strain with a peak that matches the WT rhythm (Fig. 3.3A & B). These data indicate that low amplitude rhythms of gsn and gpn mRNA levels are sufficient for maintaining glycogen levels and rhythmicity (Fig. 3C & D). Thus, while VOS-

1 modulates the rhythmic expression of gsn and gpn, it is not required for glycogen rhythms (Fig. 3.3). Glycogen accumulation rhythms were also maintained in strains lacking both csp-1 and vos-1 (Fig. S3.1), with a peak similar to WT and the single

∆vos-1,but with higher overall levels of glycogen similar to the ∆csp-1. These data suggested an alternative possibility that the core circadian clock TF, WCC, directly regulates the expression of gsn and/or gpn.

3.3.3 WCC regulates rhythmic expression of gsn.

Based on the WCC-consensus binding site (Smith et al. 2010; Chen et al. 2009;

Froehlich et al. 2002), we identified four putative WCC binding sites within 2 kb upstream of the translation start site of gsn (Fig. 3.4A). ChIP assays confirmed light-induced recruitment of WC-2 to the binding sites present in the gsn promoter, but as expected, not to gpn, which lacks WCC binding sites (Fig. 3.4B).

Examination of WC-2 binding to the gsn promoter from cells grown in DD and harvested at different times of the day revealed that WC-2 is rhythmically recruited to the gsn promoter, with peak binding during the subjective day (DD14/CT3) (Fig.

3.4C). These data supported the idea that WCC directly regulates gsn rhythmic expression. We next examined if gsn mRNA and glycogen abundance rhythms were altered in ∆wc-1 cells. As expected for loss of a core clock component, glycogen rhythms and gsn::luc were abolished in ∆wc-1 cells (Fig. 3.5A,B & C).

These data indicated that the core clock component WCC directly drives rhythmic expression of gsn necessary for rhythmic glycogen accumulation.

To further assess the impact of transcriptional regulation of gsn on rhythmic glycogen accumulation, we constructed a strain that overexpressed gsn from the copper-sensitive tcu-1 promoter (Lamb, Vickery, and Bell-Pedersen 2013) which is inducible under the presence of copper chelator, BCS (Bathocuproinedisulfonic acid) (Fig.3.5D). Constitutive overexpression of gsn resulted in disruption of the circadian rhythm of glycogen accumulation and an ~3-fold increase in total glycogen levels compared to WT cells (Fig. 3.5 E & F). Taken together, these data indicated that WCC directly regulates the circadian expression of gsn, and

75 importantly, that rhythmic gsn mRNA levels are necessary for rhythmic glycogen accumulation.

3.4 Discussion

Recent studies using sequencing and parallel chromatin immunoprecipitation in genomic level demonstrated that the core clock components regulate a network of expression of genes involved in metabolism.

Furthermore, the tissue-specific auxiliary oscillators indicate the significance of integrations of spatial and temporal information into the whole body in response to various external changes.

For example, hepatic glycogen synthase (Gys2) is a direct target of the mammalian core clock protein CLOCK, and both GYS2 and glycogen abundance show dampened circadian oscillations in Clock mutant mice (12). The clock component and nuclear receptor REV-ERBα has been shown to play a key role in connecting the clock to metabolism in mammals (Duez and Staels 2008; Le

Martelot et al. 2009; Feng et al. 2011).

Similarly, in N. crassa, our data show that the WCC transduces the light information and drives the rhythmic expression of clock-controlled TFs as well as gsn. A clock-controlled TF,CSP-1, connects the circadian clock to metabolism by regulating approximately 200 genes involved in metabolic pathways, including gpn.

An important property of CSP-1 is sensing the extracellular glucose, suggesting

76 that CSP-1 is a central node connecting the crosstalk between the circadian clock and glucose metabolism in N.crassa.

Finally, our work characterized the role of VOS-1 which is thought to respond to stress condition and regulate genes involved in development as well as metabolism. Consistent with vos-1 being a direct WCC target, VOS-1 transduces environmental signals to downstream processes such as glycogen metabolism, through the circadian clock. gsn is known to be altered upon stress condition, such as heat or oxidative stress. This might be modulated by the action of VOS-1.

Interestingly, the complexity of this regulation, including possible redundancy of TF control of gsn and gpn mRNA expression, is reflected in the double Δcsp-1; Δvos-1 strain. We predicted that the double mutant would abolish glycogen rhythms with reduced glycogen levels. Instead, glycogen rhythms in

Δcsp-1; Δvos-1 strain were rhythmic, with identical glycogen levels to ∆csp-1 strain. These data suggest that unknown compensatory regulations for the loss of both CSP-1 and VOS-1, or insufficient glycogen breakdown under the conventional liquid culture containing glucose remain throughout test days.

Together, a combinatorial action of clock-related TFs (WCC, CSP-1, and

VOS-1) orchestrates the circadian regulation of glycogen metabolism. This complex regulation may be necessary for processing various inputs from the environment to adjust the timing of glycogen metabolism for maximum energy benefit. An integrative approach combining computational and experimental studies will allow us to better understand the multilayered regulations in glycogen metabolism with environmental perturbations.

Acknowledgement

The authors thank to collaborators (Drs. Stela Virgilio and Teresa Lamb) performing glycogen quantifications, northern blottings, and VOS-1 ChIP assays.

Fig. 3.1. CSP-1 regulates the rhythmic expression of gpn (A-B) Representative trace of bioluminescence signals from gsn::luciferase (A) and gpn::luciferase (B) in WT (black line) and ∆csp-1 (gray line). Data were de-trended and analyzed by FFT analysis. Arbituary units (a.u.) are shown. (C) Plot of glycogen levels from WT (black line) and ∆csp-1 cells (gray line) (n3, ± SEM). ∆csp-1 displays the robust rhythm of glycogen accumulation (JTK_cycle q value<0.001). (D) The average glycogen content from all 12 time points (Total), subjective day, and subjective night in WT vs. ∆csp-1 (n3± SEM, Student’s t test, *** p<0.001). For comparison, all data from wild-type were replotted.( (E) gpn mRNA levels are up- regulated in csp-1 overexression mutants. Pqa-2-csp-1, Ptubulin-csp-1 and WT cells were grown and harvested in constant light. Pqa-2-csp-1 cells were harvested at 0, 60, 120 and 240 min after quinic acid addition. Relative expression levels of csp-1 (black) and gpn (gray) were quantified by performing quantitative RT-PCR. actin was used for normalization. (n=3, ± SEM, Student’s t test; A significant difference is referred to as untreated Pqa-2-csp-1 or wild-type. *p<0.05, ***P <0.001).

Fig. 3.2: VOS-1 binds rhythmically to the gsn promoter. (A-B) Map of VOS-1 binding sites in the promoter region of gsn and gpn. The region amplified for ChIP- PCR is indicated as “PCR target,” and the primers are listed in Table 3.2. (C) Representative trace of bioluminescence signals from vos-1::luciferase in WT cells grown in DD for the indicated times. Arbitrary units (a.u.) are shown. (D) Representative western blot of VOS-1-V5 (top panel) from cells harvested at the indicated times in DD. The data are plotted below (n=3, ± SEM), and were fit to a sign wave (p<0.05). Amido black staining of the membrane was used to normalize protein loading. (E-F) ChIP-qPCR of VOS-1 binding to the promoter of both gsn

(E)and gpn (F) at the indicated time points in DD (n=2, ± SEM). Non-specific VOS- 1 binding on the 60S rRNA was used for normalization of the signal.

Fig. 3.3: VOS-1 influences rhythmic gsn, gpn, and glycogen levels (A-B) Representative trace of bioluminescence signal from gsn::luc (A) and gpn::luc (B) in WT (black line) and ∆vos-1 (gray line) cells (n4, ± SEM). Bioluminescence data were detrended and analyzed by both FFT and BioDare (Table 1). (C) Plot of glycogen levels from WT (black line; replotted from Fig. 2.1A), and ∆vos-1 cells (gray line) (n=5, ± SEM). ∆vos-1 displays rhythmic glycogen accumulation (dotted gray line; p<0.001), but with reduced amplitude and a phase advance compared to WT. (D) The average glycogen content from all 12 time points (Total), subjective day, and subjective night in WT vs. ∆vos-1 (n3± SEM, Student’s t test, ** p<0.01). For comparison, all data from wild-type were replotted.

Fig. 3.4 Δcsp-1;Δvos-1 alters the phase and amplitude of rhythm in glycogen accumulation, not rhythmicity. (A) Plot of glycogen levels from WT (black line) and Δcsp-1;Δvos-1 cells (gray line,) (n4, ± SEM). Δcsp-1;Δvos-1 displays the robust rhythm of glycogen accumulation (JTK_cycle q value<0.001). (B) The average glycogen content from all 12 time points (Total), subjective day, and subjective night in WT vs. Δcsp-1;Δvos-1 (n3± SEM, Student’s t test; **P<0.01,***P<0.001). For comparison, all data from wild-type were replotted.

Fig.3.5. The WCC directly binds to the promoter of gsn (A) Map of WCC binding sites in the promoter region of gsn. The region amplified for ChIP-PCR for WCC is indicated as “PCR target,” and the primers are listed in Table 3.2. (B) Plot of ChIP-qPCR data (% of input) for WC-2 binding (which complexes with WC-1 to form the WCC) to the indicated promoters from cells harvested in darkness with or without 15 or 30 min light treatment to induce WCC activity region (n3, ± SEM). WC-2 binding to the frq promoter served as a positive control. MockIP and ∆wc-2 cells served as negative controls. (C) Plot of ChIP-qPCR data (% of input) for WC-2 binding to the indicated promoters from cells harvested at the indicated times in DD. MockIP served as the negative control.

Fig.3.6 The WCC controls gsn expression and promotes rhythmic glycogen accumulation (A) Plot of glycogen levels from WT (black line; replotted from Fig. 1A), and ∆wc-1 cells (gray line) (n4, ± SEM). Glycogen levels in ∆wc-1 cells were better fit to a line. (B) The average glycogen content from all 12 time points (Total), subjective day (DD12, 16, 20, 36, 40, and 44), and subjective night (DD8, 24, 28, 32, 48, and 52) in WT vs. ∆wc-1 (n>=3± SEM, Student’s t test, **p<0.01). (C) Representative trace of bioluminescence signals from gsn::luciferase in WT (black line), and ∆wc-1 (gray line). Bioluminescence data were detrended and analyzed by FFT/BioDare. (D) Northern blot of gsn mRNA from WT and Ptcu-1-gsn cells treated with low (L; 25 µM Cu or BCS), medium (M; 100 µM Cu or BCS), high (H; 250 µM Cu or BCS) levels, or untreated (U), and harvested at DD24. rRNA served as a loading control. (E) Plot of glycogen accumulation from Ptcu-1-gsn cells (gray treated with 250 µM BCS over the indicated times in DD to constitutively overexpress gsn mRNA (gsnOE). (F) The average glycogen content from all 12 time points (Total), subjective day (DD12, 16, 20, 36, 40, and 44), and subjective night (DD8, 24, 28, 32, 48, and 52) in WT vs. gsnOE (n>=4± SEM, Student’s t test, *** p<0.001).

Supplementary figures

Supplementary Fig. 3.1 Raw data of bioluminescence in ∆vos-1 and ∆csp-1 Raw data of bioluminescence signals from gsn::luc (A and C) and gpn::luc (B and D) in ∆vos-1 (top panels) and ∆csp-1 (bottom panels) (n3, ± SEM).

Table. 3.1 Strains used in this study

Strain Genotype Purpose Glycogen FGSC2489 WT quantification ,ChIP-PCR Glycogen FGSC11348 ∆csp-1::hph+ quantification Glycogen FGSC13536 ∆vos-1::hph+ quantification ∆wc-1 Glycogen ∆wc-1::bar+ (DBP1224) quantification FGSC11124 ∆wc-2::hph+ ChIP-PCR VOS-1-V5 VOS-1-V5, gift from Dr. Jay Dunlap ChIP-PCR (DBP1107) vos-1::luc Pvos-1::luciferase::bar+, ras-1bd LUC reporter assay (DBP2363) Rhythmicity, DBP2623 bar+::Ptcu-1::gsn glycogen (gsnOE) concentration Ptub-csp-1 Ptub-csp-1::bar+::csr-1, ras-1bd ChIP-PCR Pqa-2-csp-1 Pqa-2csp-1::bar+::csr-1, ras-1bd ChIP-PCR gsn::luc Pgsn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay gpn::luc Pgpn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay ∆csp-1, ∆csp-1::hph+, LUC reporter assay gsn::luc Pgsn::luciferase::bar::csr-1, ras-1bd ∆csp- ∆csp-1::hph+, LUC reporter assay 1::gsn::luc Pgsn::luciferase::bar::csr-1, ras-1bd ∆wc-1::hph+ (FGSC11711), ∆wc-1, gsn::luc LUC reporter assay Pgsn::luciferase::bar::csr-1, ras-1bd ∆vos-1, ∆vos-1::hph+, LUC reporter assay gsn::luc Pgsn::luciferase::bar+::csr-1, ras-1bd ∆vos-1, ∆vos-1::hph+, LUC reporter assay gpn::luc gpn::luciferase::bar+::csr-1, ras-1bd

Table. 3.2 Primers used in this study

Primer Sequence Purpose gpnF 5'-AGTTCTACGGCCATGTCACC-3' qRT-PCR gpnR 5'-GCGACCGAGTTCTCGTAGTC-3' qRT-PCR actinF 5'-GTCCCCGTCATCATGGTATC -3' qRT-PCR actinR 5'-CTTCTCCATGTCGTCCCAGT-3' qRT-PCR csp-1F 5’-CACACTGTCCAGCTCCTCAA-3’ qRT-PCR csp-1R GATGGGAGTCATGGGAGAGA-3’ qRT-PCR NCU06687F_VOS1 5'-CCGTCTTTGGGCCAGCTTG-3' VOS-1 ChIP-PCR assay NCU06687R_VOS1 5'-GTCCTCCAGATCTGTGCAGTGC-3' VOS-1 ChIP-PCR assay NCU07027F_VOS-1 5'-CAGTCACGGTGCAGCATTCCA-3' VOS-1 ChIP-PCR assay 5'-CAACAACAGATATAGCTTGGGGAAC- NCU07027R_VOS-1 VOS-1 ChIP-PCR assay 3' 60S rL6_intF 5’-ATCGACTTGGCAAAAGGACCA-3’ VOS-1 ChIP-PCR assay 60S rL6_intR 5’-GTGAAAAAGCACACGCACACG-3’ VOS-1 ChIP-PCR assay Pgsn_F 5'-GATTCTGACTCTTCGGGTTG-3' WC-2 ChIP-PCR assay Pgsn_R 5'-CAACCTGAACCGAGTTTCC-3' WC-2 ChIP-PCR assay Pfrq_F 5'-GCAGAGGACCCTGAACTTTTC-3' WC-2 ChIP-PCR assay Pfrq_R 5'-TCTCTTGCTCACTTTCCCACAG-3' WC-2 ChIP-PCR assay

CHAPTER IV

GLYCOGEN METABOLISM AND ITS CONSEQUENCE ON GROWTH AT NIGHT

Overview

The discovery of the underlying mechanisms how the circadian clock determines the timing of glycogen accumulation led to us this question what is the functional role of temporal regulation of glycogen accumulation? I hypothesized that the accumulation of glycogen controlled by the circadian clock might influence in the linear growth of Neurospora crassa. In order to test this hypothesis, I measured the growth rate by modifying the conventional race tube assay following the liquid culture with different L/D duration. This condition allowed each

Neurospora crassa culture to have a different level of glycogen reserves, which are exhausted in race tubes without glucose supplement for next 24hr. Data indicated that the night-preferred growth of Neurospora crassa coincides with the peak of glycogen accumulation in this glycogen exhausted situation. This rhythmic growth event was abolished not only in the clock-null mutant (∆frq) but also glycogen metabolic mutants, ∆gsn or ∆gpn. Together, this chapter shows that the clock- controlled glycogen metabolism is required for other rhythmic biological processes, such as growth in Neurospora crassa. Importantly, rhythmic glycogen metabolism is required to maintain a circadian oscillation of Neurospora crassa growth rates, demonstrating a key role for rhythmic energy storage/usage in growth.

4.1 Introduction

Organisms have evolved to obtain an anticipatory internal time-keeping mechanism, circadian clock, to align biological processes with daily variations in environmental factors such as daily light-dark cycle caused by the earth’s rotation on its axis. This mechanism allows biological functions virtually maximize growth and survival.

An impact of clock-controlled metabolism to better fitness has been examplified by a plant model, Arabidopsis thaliana. In phototrophic organisms typically exhibit two distinct metabolic phases; synthesis of cellular precursors and energy storage that relies on harvesting photic energy in daytime, and a subsequent mobilization of storage compounds to ensure survival and growth at night.

Therefore, it is not surprising that length of day/night matching the internal period of the plant is vital to utilize the starch (Lu, Gehan, and Sharkey 2005).

Indeed, correct alignment between environmental cycle and internal rhythm is vital for better fitness and growth in the plant. For example, a study has shown that wild-type plant grown in 24-h T- (12h:12h/L:D) cycle grow better than in altered photoperiods (10h:10h/L:D or 14h:14h/L:D), indicating photoperiod matching with endogenous period confers growth advantages. This is likely due to inappropriate expression of clock molecules that involve in controlling the light- harvesting apparatus, chlorophyll contents. Together, plant clock may have a central role in controlling timed growth by adjusting carbon storage/utilization.

Consistent with this idea, plant mutant defective in starch synthesis or degradation show reduced growth at night (Wiese et al. 2007).

Further studies revealed that the circadian clock helps the plant not only be in preparation of photosynthetic processes at day, but also predict the time to determine the appropriate rate of starch breakdown. Clock mutant plant (cca1/lhy) show reduced starch accumulation (~20% less than WT) and faster rate of starch degradation rate (35% than WT), leading to earlier starch exhaustion (3-4hr) than

WT (Graf et al. 2010). Therefore, clock components are necessary for the normal circadian rhythm in the starch utilization, which is required optimal plant growth.

Together, diurnal growth patterns in Arabidopsis thaliana depend on the photoperiod allowing how much plant reserves carbon during the day, and proper circadian clock functions determining how much carbon reserves are supplied for growth during the night. However, the underlying molecular mechanisms whereby the clock paces starch utilization is largely unknown.

In N. crassa, asexual conidial developments occur during the night under the clock control. This prompted us to study if clock-controlled glycogen metabolism may coincide with growth preference at night in Neurospora crassa. We hypothesized that timed carbohydrate metabolism, particularly glycogen metabolism, is under the circadian clock, which is necessary for night-specific growth in Neurospora crassa. To investigate this hypothesis, we modified the conventional race tube assay. N. crassa possesses a robust and easily monitored circadian rhythm in conidiation, observed as a series of conidiation known to

“banding” followed by undifferentiated vegetative hyphae on a solid agar media

(Sargent, Briggs, and Woodward 1966). This simple analysis of clock-controlled

94 growth provides a great advantage to study of the Neurospora crassa clock and its physiological consequences.

In this study, we showed a promising result suggesting that the circadian clock determines the optimal timing for accumulation of glycogen which is required to a growth preference during the subjective night. To identify if proper glycogen metabolism is required for optimizing Neurospora crassa growth, we designed a

“glycogen exhausted condition” by combining glucose-contained liquid media culture allowing glycogen accumulation, and sequential growth on agar media forcing Neurospora to utilize the stored glycogen. Timing of liquid-agar media transition was selected by the data of glycogen accumulation data (peak at DD32 and trough at DD16/44). In wild-type, we confirmed the existence of night- preferred growth, which is abolished in clock mutant, ∆frq, in addition to glycogen metabolic deficient mutants, ∆gsn or ∆gpn.

4.2 Material and methods

Strains

Wild-type strains 74-OR23-IVA (FGSC 2489; mat A) and single gene deletion strains (Colot et al. 2006) were obtained from the Fungal Genetics Stock

Center (Manhattan, KS). The ras-1bd mutation was used in strains to measure growth rates on race tubes as described (Belden et al. 2007).

Growth rate measurement.

Conidial suspensions were inoculated and grown in 4ml of liquid culture media supplemented with 0.1% glucose at 25 °C under the constant light (LL).

Liquid cultures were then moved to DD and transferred to race tubes containing growth agar media (1X Vogel’s medium, 0.17% arginine, 50 ng/mL biotin, and 1.5%

(wt/vol) agar) at the indicated time points. Race tubes were kept in DD for 24hrs, and the growth front was marked. Races tubes were scanned and total growth length was measured by ImageJ software.

Data Analysis and Statistics

Data are presented as mean ± SEM. Statistical analysis was performed using

ANOVA test. P value <0.05 was considered statistically significant.

4.3 Results

Robust glycogen metabolism is required for circadian oscillations of growth rates in N. crassa.

We hypothesized that rhythmic glycogen accumulation would lead to a rhythmic growth rates, with faster linear growth from the peak (DD32) and slower linear growth from the trough (DD16 or 44) of glycogen abundance (Figure 2.1A).

To test this hypothesis, N. crassa cells were grown in liquid culture media containing 0.1% glucose in LL, and then shifted to DD at the appropriate time to

96 obtain DD16 (CT5), DD32 (CT22) and DD44 (CT11) timed cultures (Fig.4.1). This different L/D duration allowed each cell to accumulate different levels of glycogen which is determined by the circadian clock. The timed cultures were inoculated onto race tube media lacking glucose in DD, and growth was monitored for 24 hrs.

As expected, WT cells showed increased linear growth rates when cells were transferred to race tubes at subjective night (DD32/CT22, at the peak of glycogen abundance), and decreased linear growth rates when they were transferred during the subjective day (DD16/CT5) and late afternoon (DD44/CT11, at the trough of glycogen abundance). These data support previous studies suggesting that the circadian clock influences the rate of N. crassa growth in developing cultures

(Gooch, Freeman, and Lakin-Thomas 2004). As expected for an output of the clock, circadian changes of growth rates in the circadian clock mutant strain, ∆frq were abolished. Importantly, this subjective night-specific increase in growth rate was abolished in ∆gsn and ∆gpn cells (Fig. 4.2). Arrhythmic growth rates in ∆gpn cells were surprising given the continued rhythmic glycogen abundance with low amplitude (Fig. 2.2D) and may reflect an inability to utilize stored glycogen in the absence of GPN. Furthermore, the fast growth rate in ∆frq cells was unexpected given the low arrhythmic glycogen levels in the mutant (Fig. 2.1A). These data suggested the possibility that glycogen is broken down to glucose more efficiently in ∆frq cells, an idea that needs further investigation. In any case, the loss of circadian regulation, and the slow growth rate in the ∆gsn cells supports the idea that rhythmic accumulation of glycogen influences growth rates, and likely other aspects of cellular physiology, at specific times of the day.

4.4 Discussion

In mammals, glucose-derived glycogen from the liver supplies necessary blood glucose during fasting, providing the energy needed to carry out cellular and physiological functions. Circadian accumulation of glycogen in mammals (Doi,

Oishi, and Ishida 2010) and N. crassa (this study) support that temporal organization of energy storage and usage are important for biological functions. We hypothesized that one of those daily functions of N. crassa, growth, would be influenced by rhythmic glycogen levels and its metabolism. As predicted, we observed the faster growth of N. crassa when transferred from growth in liquid culture medium containing 0.1% glucose to solid race tube media lacking glucose during the peak of glycogen accumulation (Fig. 4.2). These data are consistent with previous studies suggesting that the clock influences the rate of N. crassa growth in developing cultures (Gooch, Freeman, and Lakin-Thomas 2004). In contrast, clock regulation of growth rate was abolished in both ∆gsn and ∆gpn cells, despite these cells having an intact circadian clock. Moreover, the overall growth rate correlated with the average abundance of glycogen in WT, ∆gsn, and ∆gpn cells

(Fig. 2.1A, 2.2C & D). Intriguingly, this correlation did not exist in ∆frq cells, which displayed faster growth rates compared to WT despite its slightly lower average glycogen content (Fig. 1B). We speculate that glycogen breakdown is somehow increased in ∆frq cells compared to WT, which would lead to increased glucose availability and faster growth. This may be through increased levels of

GPN protein and/or the debranching enzyme, or possibly through increased

98 mobilization of glycogen stores in the mutant. Future experiments will test these possibilities.

Fig.4.1 Schematic diagram of glycogen exhausted cultures for growth rate measurements. N. crassa cells were grown in liquid culture media (LCM) supplemented with 0.1% glucose in LL, and transferred to DD at the indicated time points. The cultures were then transferred to race tubes containing solid agar growth media without glucose at DD16, 32 or DD44. After 24 hr, total growth length was measured.

100

Fig.4.2 Glycogen metabolism impacts rhythmic Neurospora crassa growth rates. The average growth rate f for the indicated strains are plotted for each time point (n5, ± SEM, ANOVA, *P<0.05).

101

Table. 4.1 Strains used in this study

Strain Genotype Purpose 328-4 WT,ras-1bd Race tube assay ∆frq, Pgsn- frq10::hph+, Pgsn::luciferase::bar::csr-1, Race tube assay luc ras-1bd ∆gsn, X661- ∆gsn::hph+, Pfrq::luciferase::his-3+, ras- Race tube assay 4 1bd ∆gpn, X661- ∆gpn::hph+, Pfrq::luciferase::his-3+, ras- Race tube assay 4 1bd

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CHAPTER V CONCLUSIONS AND FUTURE WORKS

Summary

The research conducted in this study provides further insight into molecular oscillator linking other biological functions in Neurospora crassa by demonstrating the circadian regulation of glycogen metabolic pathways and its consequence on growth preference at night. These findings illustrate the significance of the ubiquity of the circadian clock, metabolism, and physiology in eukaryotes.

There were three primary outcomes of this research; we first demonstrated that the existence of circadian rhythms in glycogen metabolism is conserved mechanisms from fungi to human. Secondly, we further characterized the molecular details of the clock-controlled glycogen metabolism by identifying transcription factors linking the circadian clock and glycogen metabolic genes.

Lastly, we found a physiological consequence of clock-controlled glycogen metabolism by assessing the growth preference at night. In addition, I show a promising data from an initial study by using mutant strain carrying the night- expressed gsn (gsnANT), indicating the significance of maintaining the in-phase relationship between glycogen synthesis and breakdown for rhythmicity of glycogen accumulation.

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Conclusions and future works

Characteristics of circadian rhythms in glycogen metabolism

Although a big flow of today’s research is moving toward deciphering the circadian regulation in metabolism in clock study using higher organisms, very little work has focused on the molecular connection between the circadian clock and metabolism in Neurospora crassa. Our work presented in this dissertation show that the glycogen metabolism is highly conserved in eukaryotes. This work will highlight the clock-controlled metabolic output and the reasons why N.crassa is an excellent model to understand how the circadian clock is intertwined with other biological processes.

The circadian regulation of glycogen metabolism might be important for the anticipation of daily changes in energy richness/depletion. We first investigated if the intrinsic glycogen accumulation displays circadian rhythm. Cells with a functional clock showed rhythmic glycogen accumulation peaking during the subjective night compared to cells in a clock deficient strains which exhibited the constant low level of glycogen. Next, we explored if the expression and/or activity of glycogen metabolic enzymes determine the rhythmic glycogen accumulation.

We discovered that gsn and gpn are necessary for proper function of glycogen metabolism, and the transcriptional regulation of gsn is critical for rhythmic glycogen accumulation.

High throughput RNA-seq data show that most of the metabolic genes are highly enriched in the late subjective evening to the early subjective morning.

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Conversely, genes expressed in the circadian afternoon to evening are less involved in metabolic output but are enriched in activities favoring biosynthesis of cellular components and growth. Consistent with this data, we observed the expression of glycogen metabolic genes is rhythmic with a peak in the subjective morning. This temporal separation of biological functions is most likely synchronized to coordinate efficient regulation of available resources. Indeed, this is to take advantage of protection from environmental challenges including the nutrient condition which is known to feed back to the clock (Sancar, Sancar, and Brunner

2012; Dovzhenok et al. 2015). We also examined if impaired glycogen metabolism influences the core clock. Our tracking of the luciferase reporter fused to frq promoter showed that glycogen deficient mutants have no significant effects on the core clock.

Identification of TFs connecting the core clock and glycogen metabolism in

Neurospora crassa

To understand how the circadian clock conveys temporal information to influence the downstream rhythmic expression of both gsn and gpn, we searched for potential TFs including the core clock TF, WCC. Previously, WCC-ChIP-seq data did not confirm direct WCC-binding to glycogen metabolic genes. Therefore, our first model suggested that a clock-controlled transcriptional activator and a repressor would drive expression of gsn and gpn in opposite phase. Supporting evidence in favor of the first model was provided by work on CSP-1 which is known as a WCC-controlled transcriptional repressor. Binding to gpn upon short

105 light exposure was more convincing us to the model predicting that the CSP-1 suppresses the expression gpn to generate antiphasic expression between gsn and gpn.

Surprisingly, CSP-1 control of gpn expression did not determine rhythmic glycogen accumulation. We found that both promoter activity and the mRNA levels of gpn cycles over the course of the day with gsn. In addition, gpn is induced by

CSP-1, leading to rhythmic expression of gpn with a peak at the subjective day when the expression of gsn peaks. This simultaneous expression of two opposing genes, gsn, and gpn, is further modulated by VOS-1, which is another WCC- controlled TF that was shown to regulate late light-induced genes (Chen et al. 2009).

Furthermore, we discovered that ∆csp-1;∆vos-1 continued to show rhythmic glycogen accumulation. Therefore, we tested whether WCC directly regulates the expression of gsn, because the gsn promoter possesses four putative WCC binding sites. We confirmed that WCC binding increased in the light-treated samples as well as dark grown samples. The WCC binding to gsn drives sequential rhythms in

GSN protein modification, and accumulate glycogen synthesis with a peak in the middle of the circadian night. The gsn overexpression mutant, gsnOE supported this finding that rhythmic gsn promoter activity dominantly determines the rhythmicity of glycogen accumulation.

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Why does the rhythm in glycogen metabolic genes need the hierarchical actions of multiple TFs?

Previous ChIP-seq data demonstrated that the WCC directly controls the timing of expression of several TFs, which in turn, convey temporal information to downstream pathway to modulate amplitue or phase the number of ccgs involved in a broad range of biological processes including metabolism. Consistent with this idea, we discovered that circadian output pathways are more complex, possibly involving several TFs, which respond to different signals or cellular stress (e.g., light, nutrition, and temperature).

Importance of the circadian clock regulating the phase of glycogen metabolic genes

The in-phasic relationship between gsn and gpn expression raised the following question why are the two opposing genes (gsn and gpn) are expressed same time? Because of limited research in Neurospora crassa, it is still unclear whether GSN and GPN share protein kinases and/or phosphorylase as in mammals.

Therefore, identification of regulators under the clock control will unveil this unknown mechanism.

To further test the significance of phase control of glycogen metabolic genes,

I conducted an initial study by creating the gsnANT (Fig.5.1) carrying the expression of gsn controlled by the promoter of a hypothetical gene, ncu04931(Fig.5.1A).

Although the ncu04931 promoter activity is antiphasic to the endogenous gsn promoter (Fig.5.1B), we failed to see complete antiphasic expressed gsn from northern blotting using the gsnANT strain (Fig.5.1C). Consistent with loss of

107 rhythmicity in gsn, glycogen levels show constant low levels in DD. This may be due to antiphasic expression of gsn failed to maintain the rhythmic expression of gsn. Another possibility is that the ncu04931 promoter-driven expression of gsn constructs missed key cis-regulatory elements responsible for proper phase regulation of gsn. To test this possibility, future work must construct a mutant by targeting endogenous gsn locus, rather than external locus to avoid missing of any critical regulatory elements.

Physiological consequences of the clock-controlled glycogen metabolism

It has been shown that, while Neurospora crassa vegetative growth of hyphae continues to grow throughout the day and night, the overall growth rate is under the control of the clock (54). This output of the circadian clock may allow the cells to limit processes that can be negatively affected by light, such as DNA replication, to a time of day in which the cell is most protected. We discovered that clock-controlled glycogen metabolism is necessary for the faster growth during the subjective night in Neurospora crassa. Growth is one of the most energy demanding processes in the cell; therefore, based on these data, we predicted that the cell would store energy to support night-specific biological processes, such as

DNA repair or growth accompanied with increased glycolysis at night to produce

ATP.

Two future studies are required to elucidate the more precise role of the clock-controlled glycogen metabolism in the night-preferred growth. First, unlike

108 our prediction, Δfrq, which has constant low glycogen level in DD, show abnormally increased growth rate. This may be due to systemic changes in arrhythmicity because the circadian clock is an essential part of cell physiology that underlies many biological processes. Therefore, further study is required using mutant carrying a functional clock disconnected to only glycogen metabolism by mutating the binding motifs of WCC and/or other clock-TFs on either gsn or gpn.

Second, our data has not focused on the molecular details of how the clock connects to the glycogen breakdown, which in turn influence in the growth at night.

Therefore, it is essential to study if GPN protein and its phosphorylation show circadian rhythms. Moreover, identification of genes necessary for the biogenesis of cellular structural components for facilitating growth at night.

It should be noted that technical limitation of our liquid culture method may overlook the role of glycogen phosphorylase, which is known to increase its activity under the fasting status. Although the endogenous rhythm in glycogen accumulation, continuous glucose presented in media may reduce the activity of

GP, whose gene expression is clock-controlled. Therefore, testing with a higher organism with protocol controlling tightly fasting/feeding period will provide a better understanding of the role of GP, which may influence glycogen accumulation.

From Neurospora to Humans: what are the implications of the clock-controlled glycogen metabolism?

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Industrialized lifestyle leads to high food consumption, activity in the rest period, and the shortened sleep period, leading to high prevalence of obesity and metabolic dysfunctions including hyperinsulinemia, and hyperlipidemia. Therefore, it is important to investigate the relationship between food, metabolism, and the biological clock at the molecular level for preventing metabolic disorders, promoting well-being, and extending lifespan. The work presented in this thesis provides a foundation, for other animal studies. Consequently, the mechanistic understanding of circadian metabolic pathways in eukaryotes and their relevance to the clinical application will offer the opportunity to develop targeted interventions to alleviate the adverse effects of clocks disruptions in humans.

Recently, studies suggest that meal timing with distributions of macronutrients help prevent metabolic diseases (Sofer, Stark, and Madar 2015;

Wheeler et al. 2012; Solon-Biet et al. 2014). Consumption of an energy-rich meal in the evening led to significantly higher plasma glucose and insulin response compared to its consumption in the morning. In particular, higher postprandial plasma glucose in the evening than in the morning with identical meals, emphasizing a pivotal role of circadian rhythms in glucose homeostasis (Saad et al. 2012; Lindgren et al. 2009). A human trial assessed that the metabolic effect of carbohydrate-rich meals in the morning and fat-rich meals in the afternoon versus their inverse sequence of meals in the development of glucose tolerance

(Kessler et al. 2017). Data suggested the sequence following high-fat meals in the morning and high carbohydrate diet has adverse effects on the glycemic control in patients with impaired glucose tolerance. Therefore, the circadian

110 mechanistic-based nutritional approach will be a cornerstone to treat and/or prevent metabolic dysfunctions including diabetes.

Besides, studies in rodent models have shown that the timing of certain macronutrient consumption influences overall fitness, metabolic homeostasis, even inflammation (Hatori et al. 2012; Chaix et al. 2014). In ad libitum, mice consume high calories, leading to increased adiposity and decreased glucose tolerance with a high-fat diet, whose adverse effects are diminished when was restricted to the active phase of a day (Kohsaka et al. 2007). These observations suggested that consumption of specific macronutrients at certain time windows might be beneficial for metabolic homeostasis. Thus, the detection of the best time for carbohydrate (and other macronutrients) consumption in glycemic control could be a clinical interest.

The circadian clock is intertwined with metabolic processes for cell fitness

An important finding from this work is that the core clock optimizes the diurnal storage of glucose that provides energy required for the nocturnal growth, suggesting that the circadian clock influences overall cell fitness by controlling temporal regulation of metabolism. It has been well documented that the circadian clock controls night-specific cellular events, such as DNA repair in the face of the damaging effects of the UV-light during the daytime (Pittendrigh 1993). However, how the clock-controlled metabolism assists energy-demanding processes is largely unknown. Although it has not been frequently addressed, some animal studies show

111 a promising role of the clock-controlled metabolism for cell fitness/growth. For example, the circadian clock regulates time-of-day-dependent shifts in glycolysis versus oxidative phosphorylation within proliferating epithelial stem cells, minimizing DNA damage during S phase. This protective separation is presumably lost, leading to increased DNA damage in clock-deficient mice (Stringari et al.

2015). Indeed, cell-autonomous metabolic oscillations are observed in even cancer cells, U2OS osteosarcoma cells, show the circadian oscillation of intracellular glucose level (Altman et al. 2015). Of note, understanding the ubiquitous role of circadian clock in cancer cell requires more unbiased studies characterizing distinct clock or metabolism in different cell types because some cancers have altered metabolism that can feed back to the core clock (Warburg et al. 1965; Vander

Heiden et al. 2011). However, the precise role of altered cancer metabolism in the circadian clock has not been thoroughly addressed.

Some findings indicated that the malnutrition-induced obesity appears to increase cancer incidence and mortality (Anand et al. 2008). For example, high fat diet-induced adiposity will induce secretion of inflammatory proteins secreted by both adipocytes and macrophages, leading to further inflammation and tumorigenesis. Therefore, time-restricted fat consumption could be a nutritional strategy for reducing not only metabolic dysfunctions but also the inflammation- induced tumor progressions.

A systemic approach to mapping the circadian regulatory network

A full description of the circadian regulatory network would require identifying all the components, interactions and analyzing their behavior/phenotype

112 in different cells. However, this is likely impossible. Instead, an integrative approach combining mathematical/computational and experimental studies will be more feasible to describe a circadian network with a well-validated model. The direction of future work could be to map the clock-controlled metabolic network with perturbations. This will not only advance our understanding of clock- controlled network in N. crassa, but will help to uncover pathways that can be applied to higher eukaryotes. It will provide promising information to develop therapeutics that reduce the deleterious effects of the clock disruption, such as metabolic dysfunctions in circadian misalignments.

Final Thoughts

The data reported in this dissertation provide a greater understanding of the connection between the circadian clock and glycogen metabolism, and physiological output in the model organism, Neurospora crassa (Fig.5.3). We observed that coordinated metabolism and growth provides a physiological benefit in Neurospora crassa. As these pathways are conserved among eukaryotic organisms, we predict that the clock provides similar benefit in other organisms including mammals. Further studies identifying molecular mechanisms of the clock-controlled metabolism in the night-preferred cellular processes will provide valuable information concerning the human well-being or the design of therapeutic methods for better prevention/treatments for diseases with time-dependent and/or nutritional approaches.

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Figure 5.1 Maintenance of phase is important for the rhythm of glycogen accumulation. (A) Replotted from RNA-seq data (Sancar et al.,2015) comparing RNA-seq reads of gsn (black) and ncu04931(gray) (B) Representative trace of bioluminescence signals from gsn::luciferase (black) and ncu04931::luciferase (gray) in WT. Bioluminescence data were detrended and analyzed by FFT and BioDare (n3). Arbitrary units (a.u.) are shown. (C-D) Representative northern blot of gsn from WT (C) and gsnANT (D) cells harvested at the indicated times in DD. rRNA was used to normalize protein loading. (E) Plot of glycogen levels from

114 gsnANT (E) (n3, ± SEM). (F) The average glycogen content from all 12 time points (Total), subjective day (DD12, 16, 20, 36, 40, and 44), and subjective night (DD8, 24, 28, 32, 48, and 52) in WT vs. gsnANT (n>=3± SEM, Student’s t test, *** p<0.001).

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Fig.5.2 Schematic diagram of clock-controlled glycogen metabolism and its physiological consequence in Neurospora crassa

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Supplementary information

Table S5.1 Stains used in this study Strain Genotype Purpose Glycogen quantification, FGSC2489 WT Northern blot gsn::luc Pgsn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay ncu04931::luc Pgpn::luciferase::bar+::csr-1, ras-1bd LUC reporter assay ∆gsn::hph+, LUC reporter assay gsnANT Pncu04931::gsn::bar::csr-1, ras-1bd Northern blot

Table S5.2 Primers used in this study primer Sequence purpose NCU06687F 5'-CGCCGGCTCAGTAGACTTCTA-3' Northern blot 5‘TGTAATACGACTCACTATAGGGAGTTCCTCCA NCU06687R Northern blot TGTAGCAGCCG-3' ncu04931::luc 5’-CGGAATTATACGATTTAGGTGACTGCAGGCC Plasmid Primer1 CGCATGCGTGGACTACACT-3’ construct

5’- ncu04931::luc Plasmid CCCTTCTTGATGTTCTTGGCGTCCTCCATGATGG Primer2 construct CTCAGGGCTGTTTGATC-3' 5’- ncu04931::luc GCCAACAACAGATCAAACAGCCCTGAGCCATCA Plasmid Primer3 TGGAGGACGCCAAG construct AACATC-3' gsnANT 5’-CGGAATTATACGATTTAGGTGACTGCAGGTTTATAATCTGGAAGGTAPlasmid Primer1 AG-3' construct

5’GGGGAACACGCTGTGTGGTGTTGGAGGC gsnANT CTTATCATCATCATCCTTGTAATCCATGTTGT Plasmid Primer2 GTGTTTCTGATTACCG-3' construct

5’CGTTGGTATCGGTAATCAGAAACACACAA gsnANT CATGGATTACAAGGATGATGATGATAAGGC Plasmid Primer3 CTCCAACACCACACAGCG-3' construct

5’TAGGTATTCTATAGTGTCGGATCCTCTAG gsnANT Plasmid TTACTGGTCCTTGACAGCC-3' Primer4 construct

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Material and methods.

Strains

Wild-type strains 74-OR23-IVA (FGSC 2489; mat A) was obtained from the

Fungal Genetics Stock Center. To create gsnANT and luciferase reporter mutant, plasmids were constructed and transformed as previously described (Ninomiya et al. 2004). Primers used in these constructs are listed in Table S5.2. 328-4 (ras-1bd) strain was transformed by introducing the plasmid targeting csr-1 locus, which encodes the cyclosporine A-binding protein, leading to the cyclosporine A resistance (Bardiya and Shiu 2007).

Culture condition

Conidia were prepared by growing in the minimal growth agar media

(1xVogel’s medium,) and collected with the autoclaved distilled water. The conidia suspension was inoculated to plates containing liquid culture media (1x Vogel’s medium, 0.5% arginine, and 50 ng/ml biotin) with 2% (wt/vol) glucose and incubated at 25 °C under the constant light (LL) for 36-48hr. Mycelial pad from plates was cut into 2-mm pieces and inoculated to 50ml of liquid culture media supplemented with 2% (v/v) of glucose. Prior to circadian time course experiment,

Neurospora were grown in the LL for 24hr, enabling Neurospora clock to set CT12 at the beginning of culture in the constant dark (DD). Each liquid culture was transferred to DD at the indicated time points and collected by filtering (0.22μm).

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The collected tissue was snap-frozen in liquid nitrogen and stored at -80°C until further use.

Bioluminescence assay

Conidia suspension was inoculated to race tube containing growth agar media (Vogel’s medium (pH 5.8), 0.1% glucose, 0.17% arginine, 50 ng/mL biotin,

1.5% (wt/vol) agar) supplemented with 12.5 μM Luciferin (Gold biotechnology,

Olivette, MO). Race tube was kept at 25 °C in constant light (LL) overnight and transferred to constant dark (DD) at 25°C. In vivo luciferase luminescence was collected every hour with a PIXIS CCD camera (Princeton Instruments) controlled by Winview/32 software (Roper Scientific, Sarasota, FL). The collected images were analyzed and plotted by ImageJ software and the customized Excel macro

(gifted by Dr.Luis Larrondo’s lab), respectively.

RNA extraction and RT-PCR/northern blotting.

Total RNA was collected from the frozen mycelia using Trizol (Molecular

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Glycogen quantification

The ground tissue was treated by lysis buffer (50 mM Tris-HCl, pH 8.0, 50 mM NaF, and 1 mM EDTA) supplemented with proteinase inhibitors (0.5 mM

PMSF, 1 mM DTT, and 1 μg/mL each of pepstatin A, leupeptin and aprotinin).

MO) at 37ºC for 16 h. Free glucose was measured using a glucose oxidase kit

(Sigma Aldrich, St. Louis, MO) and the glycogen content was normalized to total protein. Free glucose and total protein were quantified using a NanoDrop® ND-

1000 spectrophotometer at 505 nm and 280 nm, respectively. The glycogen content was calculated using a standard glycogen curve and the results were expressed in

µg of glycogen/mg total protein.

Data Analysis and Statistics

For data comparison, statistical analysis was performed using two-tailed

Student’s t test. A P value <0.05 was considered statistically significant. Circadian time, CT, is derived by dividing the free-running period of a rhythm into 24 equal

120 parts, whereby CT0 represents subjective dawn and CT12 represents subjective dusk. To determine circadian rhythmicity, data from Northern/Western blots were fit either to a sine wave or a linear line as previously described (Lamb et al. 2011;

Kornacker 2010). The second analysis was performed using the BioDare2 online tools (www.biodare.ed.ac.uk) using cubic detrended input and FFT-NLLS and

MFourFit allowing periods of 18-28h (Moore, Zielinski, and Millar 2014; Zielinski et al. 2014). This provided individual replicate analysis for testing statistical differences between different genotypes. Data from the real-time luciferase bioluminescence assays were detrended by subtracting the fitted polynomial from the raw data, and were analyzed by FFT (Fast Fourier transformation) and

BioDare2 to determine the circadian rhythm as previously described (Matsu-Ura et al. 2016).

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