Metabolic and functional consequences of the ...

Metabolic and functional consequences of the transcriptional repressor, Rev-erbα

By

Sean Patrick Gillis

B.S., University of Massachusetts, Amherst ,2014

Thesis

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the

Division of Biology of Medicine at Brown University

PROVIDENCE, RHODE ISLAND

May 2020

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This dissertation by Sean Gillis is accepted in its present form

by the Department of Molecular Biology, Cellular Biology, and Biochemistry as satisfying the

dissertation requirement for the degree of Doctor of Philosophy.

Date __________ _________________________________

Dr. Phyllis A. Dennery, Advisor

Recommended to the Graduate Council

Date __________ _________________________________

Dr. Philip Gruppuso, Reader

Date __________ _________________________________

Dr. Richard Freiman, Reader

Date __________ _________________________________

Dr. Louis Lapierre, Reader

Date __________ _________________________________

Dr. Shaon Sengupta, Reader

Approved by the Graduate Council

Date __________ _________________________________

Dr. Andrew Campbell, Dean of the Graduate School

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Sean Patrick Gillis

[email protected]

SUMMARY STATEMENT

Team-oriented scientist with a strong foundation in molecular biology, metabolism,

immunology, and industrial cell culture process development.

EDUCATION

Brown University

Expected May 2020

Doctor of Philosophy in Molecular Biology, Cellular Biology, and Biochemistry (MCB)

University of Massachusetts Amherst (UMass)

September 2010 to May 2014

Bachelor of Science in Biochemistry and Molecular Biology, GPA: 3.76

RESEARCH EXPERIENCE

Doctoral Student, Dennery Laboratory, Brown University September 2016 - Present

Thesis research focused on the role of the transcriptional repressor, Rev-erbα, on

metabolism and function

Main in vitro techniques utilized during graduate career included adherent cell culture,

qRT-PCR, western blotting, multicolor flow cytometry, lipid-based transfection for

purposes of RNAi/CRISPR-Cas9, immunofluorescent staining, and metabolic

measurement via the Seahorse bioanalyzer.

Participated in the husbandry, genotyping, and dissection of adult mice. Performed

multicolor immunohistochemistry, hematoxylin/eosin staining, differential immune cell

counts, and ELISA on lung tissue/ bronchoalveolar lavage

Research Associate, Baxter Healthcare, Milford, MA October 2014 August 2015

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- Conversion of attached cell culture process into a second-generation suspension

process for purpose of optimizing production of a recombinant hemophilia therapy

Regular duties included Bioreactor operator, activity assays, and AKTA protein

purification

Biologist I, Abbvie, Worcester, MA January –October 2014

- Conducted shake flask experiments to optimize basal for commercial cell lines

- Analyzed monoclonal antibody quantity and quality using affinity chromatography

- Responsibilities included media preparation, aseptic technique, and automated cell

counting,

- Gained experience working according to GLP, knowledge of GMP Practices

Undergraduate Researcher, Jung Turfgrass Pathology Lab, UMass

January -December 2013

Worked on developing protocol for bisulfite conversion of fungal DNA and PCR primer

design

Techniques used included sanger and bisulfite sequencing

Industrial Hygiene Chemistry Intern, Liberty Mutual Insurance, Hopkinton, MA

Summer 2012 and 2013

- Aldehyde, isocyanate, and polyaromatic hydrocarbon quantitation via high performance

liquid chromatography

- Analysis using inductively coupled plasma (ICPOES) to screen for harmful metals and

other elements

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PUBLICATIONS

Gillis SP and Dennery PA. Loss of the transcriptional repressor Rev-erbα upregulates

metabolism and proliferation. Journal of Biological Chemistry. In preparation.

Crane MJ, Xu Y, Henry WL Jr, Gillis SP, Albina JE, Jamieson AM. Pulmonary influenza A virus

infection leads to suppression of the innate immune response to dermal injury. PLoS

Pathogens.2018; 14(8): e1007212

HONORS/PRESENTATIONS

Society for Redox Biology and Medicine annual conference, Chicago, IL

November 2018

Poster presentation “Loss of the transcriptional repressor Rev-erbα results in increased

oxidative phosphorylation and reduced fatty acid utilization in mouse embryonic fibroblasts”

Brown MCB Graduate Program annual retreat, West Greenwich, RI

August 2018

Oral presentation “Metabolic dysregulation following loss of transcriptional repressor Rev-erbα”

Society for Redox Biology and Medicine annual conference, Baltimore, MD

December 2018

Poster presentation “Loss of the nuclear receptor Rev-erbα results in upregulation of glycolysis

in mouse embryonic fibroblasts exposed to hyperoxia

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Degree awarded cum laude, UMass, May 2014

Dean’s list honors, each semester, UMass, 2010-2014

Commonwealth Honors College member, UMass, 2010-2014

Dean, John and Abigail Adams scholarships, UMass, 2010-2014

OTHER SKILLS: Windows OS, Mac OS, Linux, GraphPad Prism, Adobe Photoshop, EndNote,

Microsoft Office Suite, Shell Scripting, Python Programming

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PREFACE/ACKNOWLGEMENTS

I would like to acknowledge Dr. Phyllis A. Dennery for her mentorship during my graduate career,

in addition to all members of the Dennery Laboratory. Particularly, I would like to thank Abigail

Peterson and Alejandro Scaffa for performing experiments as outlined in the methods of this

document and Dr. Jennifer Carr for her editorial comments. I would additionally like to

acknowledge Dr. Philip Gruppuso, Dr. Richard Freiman, and Dr. Louis Lapierre for their service

on my thesis committee. Additional thanks toward Dr. Shaon Sengupta for her service on my

committee as an outside reader of this dissertation. Finally, a debt of gratitude is owed to the

Brown Molecular Biology, Cellular Biology and Biochemistry Graduate Program with special

thanks to Dr. Mark Johnson, Dr. Judith Bender, and Raymond Windsor.

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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION .................................................................................................. 1

Rev-erbα and its mechanism of transcriptional repression ...................................................... 3

Rev-erbα and metabolic regulation .......................................................................................... 8

Clock-independent mechanisms of Rev-erbα metabolic regulation ........................................11

Rev-erbα and oxidative stress ................................................................................................12

Preface ..................................................................................................................................14

CHAPTER 2: REV-ERBα DISRUPTION UPREGULATES METABOLISM AND

PROLIFERATION .....................................................................................................................16

Abstract ..................................................................................................................................17

Introduction ............................................................................................................................17

Results ...................................................................................................................................18

The metabolic effects of Rev-erbα disruption .....................................................................18

The Functional Impact of Rev-erbα disruption ....................................................................33

Discussion..............................................................................................................................38

Conclusions ...........................................................................................................................43

Methods .................................................................................................................................43

Cell lines, culture, and synchronization ..............................................................................43

Determination of oxidative phosphorylation and glycolysis .................................................44

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Glycolysis Gene Array .......................................................................................................48

Quantitative PCR ...............................................................................................................48

Gene silencing and overexpression ...................................................................................49

Gene silencing and overexpression ...................................................................................49

Cell proliferation assays .....................................................................................................49

Growth curves ...................................................................................................................51

Immunoblotting ..................................................................................................................51

Migration Assays ...............................................................................................................51

Determination of Senescence ............................................................................................52

Statistical Analysis .............................................................................................................52

CHAPTER 3: REV-ERBα DISRUPTION INCREASES VULERNABILITY TO OXIDATIVE

STRESS ...................................................................................................................................53

Abstract ..................................................................................................................................54

Introduction ............................................................................................................................54

Results ...................................................................................................................................55

Discussion..............................................................................................................................59

Conclusions ...........................................................................................................................60

Methods .................................................................................................................................60

Cell Lines and Culture........................................................................................................60


Immunoblotting ..................................................................................................................60

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Cell viability assays and H2O2 treatment ............................................................................61

NADP+/NADPH ratio determination ...................................................................................61

Statistical Analysis .............................................................................................................61

CHAPTER 4: DISCUSSION ......................................................................................................62

Future Directions ....................................................................................................................62

Limitations in experimental design .........................................................................................64

The effect of SV40 immortalization .........................................................................................65

Summary ...............................................................................................................................66

BIBLIOGRAPHY .......................................................................................................................68

APPENDIX: ASSESSING PENTOSE PHOSPHATE PATHWAY EXPRESSION AND

LOCALIZATION IN VIVO ..........................................................................................................83

Abstract ..................................................................................................................................84

Introduction ............................................................................................................................84

Results ...................................................................................................................................84

Discussion..............................................................................................................................90

Conclusions ...........................................................................................................................90

Methods .................................................................................................................................91

Murine model and lung tissue preparation .........................................................................91


Immunohistochemistry (IHC) ..............................................................................................92

Immunoblotting ..................................................................................................................92

In vivo statistical analysis ...................................................................................................93

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LIST OF FIGURES/ILLUSTRATIONS

CHAPTER 1: INTRODUCTION .................................................................................................. 1

Illustration 1-1. Rev-erbα participates in transcription-translation factor feedback loops to

govern clock gene output ................................................................................................ 2

Illustration 1-2. Rev-erbα can bind RORE elements as two monomers or a Rev-DR2

element as a dimer .......................................................................................................... 6

CHAPTER 2: REV-ERBα DISRUPTION UPREGULATES METABOLISM AND PROLIFERATION

.................................................................................................................................................16

Figure 2-1. Verification of a Rev-erbα loss of function MEF model a) Rev-erbα gene

expression in WT and KO MEF cells ..............................................................................19

Figure 2-2. Rev-erbα disruption increases oxidative phosphorylation in MEFs ...............19

Figure 2-3. Loss of Rev-erbα increases glycolysis in MEFs ............................................21

Figure 2-4. Loss of Rev-erbα does not change mitochondrial numbers or mass .............22

Figure 2-5. Loss of Rev-erbα leads to upregulation in expression of enzymes in the

glycolytic and non-oxidative pentose phosphate pathways .............................................25

Illustration 2-1. Loss of Rev-erbα upregulates expression of enzymes in the non-oxidative

pentose phosphate pathway ...........................................................................................26

Illustration 2-2. Loss of Rev-erbα upregulates expression of enzymes in the glycolysis

pathway ..........................................................................................................................27

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Figure 2-6. Loss of Rev-erbα does not change expression of hexokinase, pyruvate

kinase, or aldolase c .......................................................................................................28

Figure 2-7. Stabilization of Rev-erbα reduces glycolysis .................................................30

Figure 2-8. Expression of TKT, RPIA, and pdhb1 are reduced following Rev-erbα

stabilization ....................................................................................................................30

Illustration 2-3. The promoter of RPIA contains an E-Box element .................................31

Figure 2-9. Ablation of arntl initially leads to a reduction in RPIA expression ..................32

Figure 2-10. Overexpression of arntl does not change metabolic enzyme expression ....33

Figure 2-11. Loss of Rev-erbα increases proliferation, growth, and vulnerability to

oxidative stress ...............................................................................................................33

Figure 2-12. Stabilization of Rev-erbα results in decreased proliferation and numbers ...34

Figure 2-13. Inhibition of glycolysis reduces proliferation in KO MEFs ............................35

Figure 2-14. Loss of Rev-erbα does not affect migration or baseline viability .................37

Figure 2-15. MEFs display lower levels of baseline senescence ....................................38

Illustration 2-4. The mitochondrial stress test approximates levels of oxidative

phosphorylation ..............................................................................................................45

Illustration 2-5. The glycolysis stress test uses extracellular acidification as an indirect

measurement of lactate production from glycolysis ........................................................46

Illustration 2-6. The glycolytic rate assay subtracts mitochondrial acidification for a more

specific index of glycolysis ..............................................................................................47

Illustration 2-7. Representative gating strategy ...............................................................50

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Figure 3-1. Rev-erbα disruption does not affect oxidative stress at baseline ...................57

Figure 3-2. Rev-erbα MEFs exhibit reduced viability when challenged with hydrogen

peroxide stress ...............................................................................................................58

Figure 3-3. Antioxidant enzyme expression following Rev-erbα disruption is not changed

when faced with an oxidative stress ...............................................................................59

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CHAPTER 1: INTRODUCTION

The transcriptional repressor and nuclear receptor Rev-erbα forms a key component of the core

circadian clock. The protein exerts its activity as a transcriptional repressor through direct binding

to retinoic acid response elements (ROREs) where it binds as two monomers or as a dimer to

recruit nuclear corepressor 1 and histone deacetylase 3 to create a restrictive chromatin

environment that prevents transcription. Through competition for a RORE in the promoter of the

protein BMAL1 with the transcriptional activator RORα, Rev-erbα forms a secondary transcription

factor feedback loop that can indirectly govern the expression of the large percentage of genes

that are known targets of the BMAL1/CLOCK heterodimer (1-3). Expression of “clock-controlled

genes” activated by BMAL1/CLOCK binding to E-Box elements govern a wide variety of

processes including the immune response, proliferation, the response to oxidative stress, and

metabolism (Illustration 1-1) (4,5). Outside of its normal role in the circadian clock, Rev-erb can

additionally bind to the promoters of genes in conjunction with tissue specific transcription factors

to repress transcription through mechanisms not based upon competition with RORα (6,7).

2

Illustration 1-1. Rev-erbα participates in transcription-translation factor feedback loops to govern clock gene output. Rev-erbα competes for binding of the BMAL1 promoter with RORα. BMAL1 forms a heterodimer with the protein CLOCK to bind an E-Box element in the promoters of Rev-erbα and RORα to initiate their transcription in response to its activation or repression (top). (bottom) The BMAL1/CLOCK heterodimer is known to initiate transcription of “clock-controlled genes” which make up a significant portion of the transcriptome.

The role that Rev-erbα may play in metabolic regulation has recently become of interest.

Characterization of its structure and mechanisms governing its post translational regulation have

led to the development of several techniques for modulating its activity to study its role in global

metabolism and individual enzymatic expression. Studies utilizing treatment with synthetic

agonists have demonstrated that activation of Rev-erbα can alter periodicity of expression of

metabolic gene oscillation in pathways such as gluconeogenesis and lipid metabolism. These

changes are coupled with observed increases in oxygen consumption and exercise performance

in a mouse model (8,9). These studies have been corroborated in studies in skeletal muscle where

levels of Rev-erbα have been shown to be directly correlated with levels of oxidative consumption

and a recent study in fibroblasts where stabilization of Rev-erbα resulted in increased oxidative

phosphorylation (10-12).

While studies in skeletal muscle, hepatocytes, and fibroblasts have demonstrated that levels of

Rev-erbα can result in changes in expression of metabolic enzymes, changes in expression of

metabolic enzymes is often tissue specific (8,10,11,13). Additionally, while changes in levels of

oxidative phosphorylation following Rev-erbα stabilization have been shown to be attributable to

changes in mitochondrial biogenesis, whether changes in individual expression of metabolic

enzymes results in this phenotype remains a topic of debate (12).

The effect of Rev-erbα on enzymatic expression in gluconeogenesis and lipid metabolism is well

characterized, but little is known about the extent to which Rev-erbα regulates the output and

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expression of other metabolic pathways including glycolysis and the pentose phosphate pathway

(8,14-16). Research into the effect these pathways have on functions such as proliferation have

greatly informed the study of a number of diseases including cancer and metabolic disease.

Gaining a better understanding of how Rev-erbα modulates metabolism and function provides an

opportunity to connect the transcriptional regulation of the circadian machinery mechanistically to

changes in function. The translational promise of such basic insights is bolstered by a variety of

studies which demonstrate that simulated circadian disruption in models of shift work or jet lag

can alter global metabolism. For example, it is known that night shift work is statistically associated

with an increased risk of obesity and that social disturbance of normal sleep rhythms disrupts

glucose metabolism, increasing biomarkers of diabetes (17-20). The hypermetabolic phenotype

of many cancers has additionally led to inquiries into Rev-erbα activation as a means of tumor

suppression (14,21-24). The elucidation of undiscovered enzymatic targets of Rev-erbα in

metabolic pathways provides a unique opportunity to better understand how well characterized

methods of Rev-erbα activation or suppression may affect functional changes driving disease

progression. This will in turn provide insight into the utility of Rev-erbα as a therapeutic target and

an opportunity to assess the conservation of changes in enzymatic expression across tissue

types. The following chapter will introduce the discovery of Rev-erbα, the history of how its

mechanism of transcriptional repression was uncovered, and review of its effect on metabolism

and function.

Rev-erbα and its mechanism of transcriptional repression

The transcriptional repressor and nuclear receptor Rev-erbα was identified in 1990 when cDNA

that had homology with members of the thyroid/steroid hormone receptor superfamily was

detected in a library isolated from human fetal skeletal muscle. The transcript for Rev-erbα was

detected on chromosome 17 with a 269 base pair overlap with the gene encoding thyroid receptor-

α (c-erbAα). The message was detected in skeletal muscle by Northern blotting analysis, with its

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DNA sequence suggesting that the protein had the capacity for both DNA and ligand binding.

Analysis of DNA and protein sequences comparing the human Rev-erbα with the rat homolog of

the protein on the long arm of chromosome 10 demonstrated a large degree of conservation

across species, suggesting that the protein played an important role as a transcription factor (25).

The role of Rev-erbα as a transcription factor was confirmed in a subsequent study in which it

was determined that the protein could bind DNA. Using a polymerase chain reaction DNA binding

assay, it was found that Rev-erbα could bind a P box site characteristic of those bound by thyroid

and retinoid acid receptors with the traditional sequence AGGTCA that would allow for binding of

one of the first of the zinc fingers encoded by the DNA binding domain. These 6 base pair regions

were proceeded by a five-base pair AT rich sequence to form a newly characterized binding site

that was initially termed a “Rev-erb element.” Mutation of the sequence in DNA binding assays

abolished the ability of the protein to bind. Rev-erbα bound each of its target sequences in gene

promoters as a monomer. It was initially speculated that Rev-erbα acted as a transcriptional

activator (26).

This claim was later refuted by a study which demonstrated that Rev-erbα could bind its own

promoter as a homodimer in which each molecule bound a response element to mediate

transcriptional repression of its own encoding sequence. While individual binding of the Rev-erbα

promoter as a monomer was sufficient for transcriptional repression, this research determined

that homodimerization typically mediates transcriptional repression in HepG2 cells. It was

additionally determined that the C terminus of the protein, which encodes the DNA binding

domain, was required for binding (27). This discovery was then followed by research that identified

another nuclear receptor Rev-erbβ in a PCR screening of a cDNA library taken from rat brain.

The sequence encoding this gene shared a high degree of homology with Rev-erbα, with the final

protein sequence containing both an N terminal ligand binding domain and a C terminal DNA

binding domain. Expression was detected through in situ hybridization in the hippocampus,

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cerebellum, and dentate gyrus of the brain, demonstrating that the protein could be detected at

high levels in vivo (28). The first suggestion that Rev-erbα and Rev-erbβ mediate transcriptional

repression was determined when it was discovered that the “E region” of the protein, which

consists of alpha the ligand binding domain, as well as alpha helices 3 and 5, could bind nuclear

corepressor 1 (NCor1) which mediates an interaction with a “receptor interaction domain” that is

required for transcriptional repression (29). This finding was further substantiated by a study in

HepG2 cells which demonstrated that homodimeric binding of Rev-erbα to the promoter of the

transcriptional activator BMAL1 recruits NCor1 and subsequently histone deacetylase 3

(HDAC3). HDAC3 is known to alter local chromatin structure to remove acetylation that is required

for accessibility of the transcriptional machinery (see Illustration 1-2). Unlike previous studies, the

repression of BMAL1 was found to be dependent on homodimeric binding of Rev-erbα, with

mutation of a single response element abolishing transcription . Additionally, ablation of HDAC3

using small interfering RNAs (siRNAs) demonstrated that in the absence of HDAC3,

transcriptional repression of BMAL1 is attenuated (30). BMAL1 activates transcription of clock-

controlled genes as part of the diurnal cycling of the mammalian circadian rhythm through its

binding to E-Box elements in concert with the protein Circadian Locomotor Output Cycles Caput

(CLOCK) (5). The finding that Rev-erbα could repress the transcription of BMAL1 was significant,

cementing Rev-erbα as a secondary regulator of the expression of clock-controlled genes.

6

Illustration 1-2. Rev-erbα can bind RORE elements as two monomers or a Rev-DR2 element as a dimer (not shown). Using heme as a cofactor, Rev-erbα recruits nuclear corepressor 1 and histone deacetylase 3 to modify chromatin, induce heterochromatin formation, and repress transcription.

A subsequent study additionally determined that Rev-erbα could bind the promoter of neuronal

PAS protein 2 (NPAS2), a protein that is paralogous with CLOCK. This protein forms a

heterodimer with BMAL1 to mediate transcriptional activation. It is highly expressed in the

forebrain as well as the periphery of mammalian tissues. Through conserved recruitment of NCor1

and HDAC3 to response elements in the promoter of NPAS2, Rev-erbα mediates transcriptional

repression contributing in regulating circadian rhythmicity. This study also confirmed that Rev-

erbα could compete for response elements in promoters with the nuclear receptor RORα which

activates NPAS2 and BMAL1 transcription (31). It had been previously demonstrated in HepG2

cells that RORα could bind to the same response element as Rev-erbα, to activate transcription,

marking a turning point in the literature where this element became known in the literature as

“ROR response element (RORE)” rather ran a “Rev-erb element” (32). Chromatin

immunoprecipitation of the NPAS2 promoter confirmed that the competition for binding between

Rev-erbα and RORα mediates a secondary transcription-translation factor feedback loop. This

secondary regulation can modulate the primary circadian feedback loop consisting of BMAL1,

CLOCK, period, and cryptochrome genes which principally regulate clock-controlled gene

transcription (31) .

The transcriptional repression of Rev-erbα is modulated through its ligand binding domain of the

N terminus, which is able to find the coordination complex heme which consists of an iron ion

coordinated to a porphyrin organic ring. A study in drosophila demonstrated the nuclear hormone

receptor E75 was capable of binding heme as a cofactor, leading interest in whether this

7

mechanism was conserved in the instance of Rev-erbα. Heme was found to bind to the lipid

binding domain of Rev-erbα with a 1:1 stoichiometry. This leads to the subsequent recruitment of

Ncor1 and HDAC3 to repress transcription of BMAL1 and another known target of Rev-erbα,

Aminolevulinic acid synthase 2, which mediates the first step in heme biosynthesis. This

suggested that Rev-erbα could control heme biosynthesis as a means to regulate its own activity.

Depletion of intracellular heme led to increased transcription of Rev-erbα target genes and

decreased levels of Rev-erbα that coimmunoprecipitated with NCor1. Exogenous introduction of

heme produced decreased gene expression and increased the levels of Rev-erbα pulled down

by NCor1, confirming the specificity of the interaction (33). The importance of heme in regulating

the oscillation of Rev-erbα target genes would be substantiated by the finding that intracellular

heme levels oscillate in fibroblasts, contributing to a temporal pattern of Rev-erbα expression that

influences expression of BMAL1 and its downstream target Period2 (34).

The efficacy of Rev-erbα mediated transcriptional repression is not only influenced through levels

of intracellular heme, but through post translational modification as well. Post translational

modification had been previously shown to modulate the circadian transcription-translation factor

feedback loops in which Rev-erbα participates, with the proteasomal degradation of Period 1

being initiated by protein casein kinase Iέ (35) and inhibition of the glycogen synthase kinase 3β

(GSK3β) shaggy leading to an increase in the period of oscillation for core circadian genes

(36,37). A screening of the N terminus of Rev-erbα uncovered serine residues at positions 55 and

59 which appeared as potential GSK3β targets. Subsequent inhibition of GSK3β using lithium led

to loss of serine phosphorylation, ablation of Rev-erbα levels, a loss of NCor1/HDAC3 recruitment,

and an increase in BMAL1 expression. It was additionally established that construction of a

phosphomimetic mutant of Rev-erbα, in which serine 55 and 59 are swapped for negatively

changed aspartic acid, could rescue transcriptional repression and ablate expression of BMAL1.

8

The introduction of this phosphomimetic Rev-erbα mutant would provide a tool to study the effects

of Rev-erbα stabilization in future studies (9).

Rev-erbα and metabolic regulation

Amongst the well-established targets of the circadian clock are enzymes that mediate the central

metabolic pathway of glycolysis, gluconeogenesis, and oxidative phosphorylation. The discovery

of Rev-erbα as an important mediator of transcription-translation factor feedback loops as part of

the circadian clock has led to a wide deal of interest in the extent to which Rev-erbα can affect

metabolism through direct or indirect mechanisms. One of the first indications that Rev-erbα could

regulate transcription of metabolites was a study which demonstrated that expression of

apolipoprotein c3 (Apo-IIIc) could be repressed by direct binding to a RORE in its promoter. Apo-

IIIc is a glycoprotein that aids in the composition of triglyceride rich lipoproteins that impede the

hydrolysis of triglycerides important in the prevention of atherosclerosis. In Rev-erbα global knock

out mice, there were increased levels of serum Apo-IIIc accompanied by increased triglycerides.

This finding demonstrated the possible importance of Rev-erbα in metabolic regulation that could

have subsequent consequences for mammalian health (2).

In an effort to determine the scope of metabolic processes that may be regulated by Rev-erbα,

several studies have been conducted perturbing levels and activity of the protein in murine

models. Amongst the most notable of these studies was one in which a data set “cis acting targets

of trans acting factor on a genome wide scale” or cistrome was generated for both Rev-erbα and

Rev-erbβ to map their respective DNA binding locations on a genome wide scale. The study

determined that in the murine genome there is a high level of shared targets for genetic regulation

via Rev-erb isoforms and BMAL1, with metabolic processes being amongst the most represented

targets of genetic regulation. Amongst the shared targets of Rev-erbα and Rev-erbβ were targets

involved in lipid metabolism including, but not limited to ApoCIII, Sterol regulatory element-binding

proteins (SREBP), and cholesterol 7-alpha-hydroxylase (CYP7A1). Changes in the expression of

9

metabolic enzymes and transcription factors known to regulate metabolic processes correlated

with global changes in mouse physiology, with double knock out mice demonstrating higher levels

of serum triglycerides and glucose (38,39).

These findings were further substantiated by a subsequent study using mice treated with the

synthetic Rev-erb agonists SR9009 and SR9011. In the liver of mice treated with agonists,

expression of SREBP, which participates in sterol biosynthesis, and CYP7A1, which is the rate

limiting step in bile synthesis, were reduced (8). It had been previously demonstrated that Rev-

erbα indirectly controls the activation of SREBP through repression of the transmembrane protein

Insig2, which tethers SREBP to the endoplasmic reticulum in its inactivated form. The same study

demonstrated that Rev-erbα indirectly governs expression of CYP7A1 through Liver-X-receptor,

which activates CYP7A1 transcription (40). Additionally, mice treated with agonists demonstrated

a reduction in circulating triglycerides and glucose, suggesting that Rev-erbα activation is

inversely correlated with this phenotype. Reduction of lipid species in the blood also correlated

with an overall reduction in adipose tissue. Amongst other notable findings was that mice treated

with Rev-erbα agonists demonstrated increased energy expenditure as exhibited through an

increased level of oxygen consumption, which coupled with a reduction in overall weight suggest

that Rev-erbα may be a viable therapeutic target for the treatment of metabolic disorders (8).

While these findings suggest that activation of Rev-erbα can increase energy expenditure and

exercise performance, recent findings SR9009 and SR9011 can have effects on oxidative

phosphorylation and cell viability independent of Rev-erbα and Rev-erbβ demonstrate the need

for development of more specific agonists (41).

Findings that Rev-erbα could affect oxygen consumption in mice led to subsequent studies to

determine the effect of perturbation on oxidative phosphorylation. In the skeletal muscle of Rev-

erbα global knockout mice, there is reduced levels of mitochondrial content and oxidative function,

resulting in compromised performance on treadmill exercise (10). Conversely, in mice

10

overexpressing Rev-erbα or treated with SR9009 demonstrated increased oxygen consumption

and exercise capacity underlined by an increase in mitochondrial biogenesis (8,12). In the

skeletal muscle of Rev-erbα deficient mice, decreased biogenesis and increased mitophagy

demonstrated a direct correlation between Rev-erbα levels and mitochondrial numbers (10).

These findings were partially corroborated in a fibroblast model, where stabilization of Rev-erbα

led to an increase in oxidative phosphorylation that was correlated with an increase in

mitochondrial area and reduction in mitophagy (12). Additionally, in the skeletal muscle of mice

treated with SR9009, there is increased levels of oxidative phosphorylation driven by increased

mitochondrial biogenesis. This phenotype is underpinned by an increase in expression of the

master regulator of mitochondrial biogenesis Pparg coactivator 1 alpha (pgc-1α) and several

cytochrome oxidases in the electron transport chain (11). In this tissue type, pgc-1α activity is

mediated through the AMP-activated protein kinase, sirtuin 1 signaling pathway, which becomes

deactivated in the absence of Rev-erbα (10).

In addition to oxidative phosphorylation, there are a number of studies that demonstrate that Rev-

erbα can influence expression of genes involved in glucose metabolism. In hepatic cell lines

treated with SR9009, there is reduced expression of the rate limiting gluconeogenic enzyme

phosphoenolpyruvate carboxykinase 1 leading to a decrease in circulating glucose (14). This

finding correlated with several previous studies that demonstrated that treatment of hepatocytes

with heme decreased blood glucose, and the finding that double knockout Rev-erbα/Rev-erbβ

mice were hyperglycemic (38). While Rev-erbα levels demonstrate a clear correlation with levels

of gluconeogenesis and blood glucose levels, its role in regulation of glycolysis has proved less

clear. In the skeletal muscle of mice treated with Rev-erbα agonists there is an increase in the

expression of hexokinase-1 and pyruvate kinase which correlates with oxygen consumption and

exercise performance (8). However, in multiple studies in hepatocytes, changes in the expression

of rate limiting glycolytic enzymes was not detected (8). In fibroblasts expressing a stabilized

11

phosphomimetic Rev-erbα there was a specific upregulation of phosphofructokinase that was not

evident in other models (12). These findings demonstrate that across tissue types, Rev-erbα could

play an important role in glucose metabolism that is tissue specific.

Clock-independent mechanisms of Rev-erbα metabolic regulation

Mechanisms of Rev-erbα mediated regulation of metabolic genes are not limited to clock

conserved mechanisms, but non-clock, tissue specific mechanisms as well. Chromatin

Immunoprecipitation followed by gene ontology analysis of genes bound by Rev-erbα in the

mouse liver uncovered a mechanism through which Rev-erbα interacts with the liver lineage

transcription factor hepatocyte nuclear factor 6 (HNF6) to bind promoters. Rev-erbα interaction

with HNF6 allowed for increased binding of metabolic gene promoters following mutation of the

DNA binding domain, suggesting that regulation of the HNF6/Rev-erbα genes fall outside of

normal clock control. Genes involved in lipid metabolism were particularly enriched for Rev-erbα

and HNF6 binding, with several genes subsequently upregulated following global deletion of Rev-

erbα and Rev-erbβ in mice. Amongst these were the cell surface receptor CD36 which is involved

in fatty acid translocation and the lipid droplet protein Perilipin-2 which aids in fatty acid storage

(6). The importance of HNF6 in regulation of lipid metabolism genes in the mouse liver was further

reinforced by the finding that additional genes including lipoprotein lipase and fatty acid synthase

were upregulated following disruption of HNF6 in mice. Intriguingly, CD36 was additionally

upregulated following disruption of HNF6, verifying that this gene is regulated by a tissue specific

mechanism in the liver that is clock independent (42). Recent findings have indicated that Rev-

erbα can also interact with the glucocorticoid receptor in the liver to repress gene expression in

conjunction with HNF6. Glucocorticoid receptor is known to bind response elements in the

metabolic genes of the mouse liver following hormonal signaling of glucocorticoids from the

adrenal gland. Upon deletion of Rev-erbα in the mouse liver, a higher proportion of genes known

to be glucocorticoid receptor targets were upregulated following treatment with glucocorticoids.

12

Glucocorticoid receptor binding was found to be enriched in shared binding sites with Rev-erbα

and HNF6, with metabolic targets including genes involving lipid and carbohydrate metabolism.

Amongst these genes was glucokinase, the fourth hexokinase isoform, suggesting this

mechanism of transcriptional control may govern global glycolytic output (13)

Recent evidence has suggested that clock independent, tissue specific mechanisms of Rev-erbα

transcriptional regulation may extend beyond the liver. In Rev-erbα/Rev-erbβ double knock out

mouse embryonic fibroblasts, loss of Rev-erbs results in sensation of circadian cycling of genes

with binding sites for HNF1 and nuclear transcription factor 1 (NF-1) in their promoters, suggesting

that clock independent mechanisms of Rev-erbα mediated transcriptional control extend to

fibroblasts (43).

Rev-erbα and oxidative stress

Another area of research into the functional regulation exerted by Rev-erbα transcriptional

repression is its role in the response to oxidative stress. Oxygen is necessary to sustain eukaryotic

life due to its role as a terminal electron acceptor in the electron transport chain (ETC), where it

accepts electrons from water to enable the functionality of complexes 1-4. Despite its necessity,

excess oxygen can be toxic, leading to the formation of reactive oxygen species (ROS) in the

ETC. These species which include superoxide and hydroxide, can overwhelm cellular antioxidant

defenses by intracellular enzymes such as heme oxygenase I. Production of these compounds

can cause damage to all cellular organelles and attack the proteins, nucleic acids, and lipids that

form the necessary chemical building blocks for life. If the proper structure and function of these

compounds is disturbed, basic cellular functionalities including gene expression, protein folding,

and metabolism can be disrupted. If these processes are disturbed substantially, key decisions

regarding cell fate such as proliferation or whether to initiate programmed cell death can be made

(44). Oxidative stress can be impacted directly by metabolism, with the electron transport chain

being a major site of ROS generation (45). Additionally, the oxidative arm of the pentose

13

phosphate pathway is a major producer of the reducing equivalent Nicotinamide adenine

dinucleotide phosphate (NADPH) which is necessary for reduction of the antioxidant enzyme

glutathione, a key scavenger of reactive oxygen species (46). Thus, perturbation of metabolism

through Rev-erbα could have an added impact on the oxidative stress response.

One of the initial lines of evidence that Rev-erbα may be a regulator of the oxidative stress

response came from the finding that mice exposed to varying degrees of cigarette smoke display

a linear reduction in expression of Rev-erbα in the lungs, with whole smoke resulting in a more

drastic drop than smoke processed through a filter. Additionally, reduction in Rev-erbα expression

correlated with changes in expression of Period genes that form the central feedback loop of the

circadian clock (47). In the murine liver expression of the key antioxidant enzymes superoxide

dismutase 1, catalase, heme oxygenase 1, and cyclooxygenase 1 demonstrate a pattern of

circadian rhythmicity (48). Additionally, treatment of hepatocytes with the long chain fatty acid

palmitate, which is known to induce ROS production through c-Jun NH2-terminal kinase inhibition

of insulin signaling and beta-oxidation, reduced the amplitude of the cyclic expression of Rev-

erbα (49,50). This connection between oxidative stress and reduction in Rev-erbα expression was

further interrogated by a study in fibroblasts that demonstrated found a binding site for the

transcription factor nuclear ethryoid factor 2 (Nrf2), in the Rev-erbα promoter to activate

transcription (51). Nrf2 is sequestered in the cytosol by the protein Keap1, with oxidative stress

leading to ubiquitin mediated proteolysis of Keap1, followed by nuclear migration of Nrf2 for

activation of target gene transcription (52). The same study also demonstrated that nuclear factor

kappa B can bind a target element in the promoter of Rev-erbα to repress transcription, but this

transcriptional repression can be overcome with sufficient oxidative stress mediated activation of

Nrf2. These findings suggest that the balance between oxidative stress and the inflammatory

response may regulate levels of Rev-erbα (51).

14

While there is evidence that oxidative stress can drive transcription of Rev-erbα, whether or not

the protein serves a protective role in response to oxidative stress is contested. In a breast cancer

cell line Rev-erbα binds Poly [ADP-ribose] polymerase 1 (PARP1), a front-line responder to

oxidatively induced DNA damage that mediates repair through processes such as nucleotide

excision repair and non-homologous end joining. It had been previously demonstrated that

recruitment of Rev-erbα to sites of DNA damage is dependent on PARylation by PARP1 that is

mediated through Rev-erbα ligand binding domain, with binding of Rev-erbα to sites of damage

inhibiting non homologous end joining, homologous recombination, and clearance of H2AX DNA

repair foci (53). Binding of Rev-erbα to PARP1 in this model led to a inhibition of catalytic activity,

accumulation of DNA damage, and decreased viability when faced with hydrogen peroxide stress

(54). Conversely, while treatment with of glioblastoma and HepG2 cell lines with SR9009 resulted

in a reduction in cell viability, these phenotypes were not coupled with an increase in intracellular

reactive oxygen species (22).

In fibroblasts, stabilization of Rev-erbα is protective rather than destructive, contributing to the

controversy over the role of the protein plays in the antioxidant defense response. When faced

with hydrogen peroxide stress, there is increased protection. This phenotype is accompanied by

a decrease in intracellular ROS, a decrease in mitochondrial membrane potential, and an increase

in expression of the downstream Nrf2 targets catalase, superoxide dismutase, and heme

oxygenase (12). In totality, the tissue type and proliferative potential of each individual cell line

may dictate the correlation of Rev-erbα levels with the response to oxidative stress.

Preface

This body of literature provided the inspiration for our study in which we utilized a mouse

embryonic fibroblast loss of function model for Rev-erbα. We hypothesized that loss of Rev-erbα

mediated transcriptional repression would lead to upregulation of metabolic enzymatic targets,

resulting in changes in function. We found that perturbation of Rev-erbα led to specific changes

15

in expression of glycolytic and non-oxidative pentose phosphate pathway. Changes in enzymatic

expression were accompanied by changes in proliferation driven by glycolysis and increased

vulnerability to oxidative stress correlated with reduced availability of the reducing species

NADPH. These findings further elucidate the extent to which Rev-erbα can influence the

transcription of metabolic genes and determine changes in proliferation and the oxidative stress

response that may accompany these changes in expression.

16

CHAPTER 2: REV-ERBα DISRUPTION UPREGULATES METABOLISM AND

PROLIFERATION

By

Sean Patrick Gillis, Abigail Peterson, Alejandro Scaffa, and Phyllis A. Dennery

Acknowledgements: I would like to thank Abigail Peterson for helping with analysis of gene

array data and Alejandro Scaffa for performing senescence assays. Special thanks also to Dr.

Mitch Lazar for providing us mouse embryonic fibroblasts as a gift.

17

Abstract

Rev-erbα levels have been shown to govern expression of metabolic genes in several tissue types

(6,8,43,55). However, there remains a need for studies mechanistically linking changes in

expression of metabolic enzymes directly with changes in function. We hypothesized that loss of

Rev-erbα in MEFs would result in global changes in metabolism upregulation in the expression of

metabolic enzymes, resulting in changes in function. Rev-erbα disruption resulted in upregulation

of oxidative phosphorylation and glycolysis. These phenotypes were not accompanied by a

change in mitochondrial mass or numbers, but by upregulation in the expression of RPIA, TKT,

and hexokinase II. Upregulation of these genes was accompanied by an increase in proliferation,

with inhibition of hexokinase II resulting in a reduction in proliferation in KO cells. These findings

suggest that loss of Rev-erbα in fibroblasts results in increased glycolysis driven by hexokinase,

resulting in a hyperproliferative phenotype that is partially driven by glycolysis.

Introduction

Rev-erbα levels are known to affect expression of metabolic genes in skeletal muscle,

hepatocytes, and fibroblasts (6,11,12). Studies utilizing a mouse model following treatment with

Rev-erbα agonists in mice and in excised skeletal muscle have additionally demonstrated that

activation and stabilization of Rev-erb respectively, can increase oxidative metabolism (8,10).

However, to date the extent to which Rev-erbα levels directly impact global levels of glycolysis

and whether expression of rate limiting glycolytic enzymes is tissue specific or conserved is

unclear. Additionally, while datasets detailing transcriptional targets of Rev-erbα in liver, skeletal

muscle, and fibroblasts have been developed, there remains a need for mechanistic studies

linking changes in metabolism associated with Rev-erbα with changes in function (6,43,55).

This study utilized a mouse embryonic fibroblast loss of function model to examine the effect of

Rev-erbα disruption on global metabolism. In the absence of Rev-erbα, cells demonstrated an

increase in glycolysis, oxidative phosphorylation, and proliferation. These phenotypes were

18

accompanied by specific upregulation of gene expression in glycolysis and in the non-oxidative

pentose phosphate pathway (PPP). Upregulated expression of these genes resulting from

perturbation of Rev-erbα may provide a mechanistic basis for increased metabolism and

proliferation.

Results

The metabolic effects of Rev-erbα disruption

Levels of the transcriptional repressor Rev-erbα have been shown to repress gene expression in

the immune response, gluconeogenesis, and redox state (4,5,14). However, complete

mechanisms linking Rev-erbα transcriptional repression of metabolic genes to changes in function

have not been determined. To further determine the global metabolic consequences of Rev-erbα

disruption, an immortalized mouse embryonic fibroblast (MEF) cell line was derived from prenatal

day 13.5 mice Rev-erbα global wild type and knock out mice. Cell lines were immortalized using

simian vacuolating virus 40 (SV40) transformation. Rev-erbα KO MEFs to not express Rev-erbα

(Fig 2-1a) Additionally, no Rev-erbα protein was detected in KO MEFs while the protein was

readily detected in WT MEFs (Fig 2-1b).

19

Figure 2-1. Verification of a Rev-erbα loss of function MEF model a) Rev-erbα gene expression in WT and KO MEF cells. b) Western Blot analysis to measure Rev-erbα protein levels in WT and KO cells. The molecular weight of detected protein was between 60 and 80 kDa markers, with a molecular weight of 67 kDa as expected. Beta-actin was used as a loading control. Densitometry analysis of Rev-erbα levels in WT and KO MEFs. n = 3-4 independent experiments. * p < 0.05 by unpaired t-test. Error bars represented as mean ± SEM.

It has been previously demonstrated in a fibroblast model expressing a stabilized phosphomimic

form of Rev-erbα that increased Rev-erbα levels result in increased oxidative phosphorylation

and make fibroblasts more resistant to nutrient deprivation relative to WT (12). To determine

whether disruption of Rev-erbα would result changes in levels of oxidative phosphorylation, a

mitochondrial stress test was performed using the Seahorse Bioanalyzer. KO MEFs

demonstrated increased levels of oxidative phosphorylation relative to WT (Fig 2-2),

demonstrating that paradoxically, following any perturbation of Rev-erbα levels, there is an

increase in levels of oxidative phosphorylation (see methods for explanation of Seahorse assay

procedures).

Figure 2-2. Rev-erbα disruption increases oxidative phosphorylation in MEFs. (left) Oxygen consumption rate (OCR) trace from all biological and technical replicates of Seahorse mitochondrial stress tests. (right) Levels of basal respiration calculated in WT and KO MEFs. n = 3 independent experiments. * p < 0.05 by unpaired t-test. Data represented as mean ± SEM.

20

In an effort to determine whether increased levels of oxidative phosphorylation would result in a

metabolic shift away from glycolysis, a seahorse glycolytic stress test was performed. KO MEFs

demonstrated increased levels of glycolysis at basal level.

While the glycolytic stress test suggests that there is increased glycolysis following Rev-erbα

disruption, this assay uses extracellular acidification of the extracellular medium as a proxy for

lactate and does not account for mitochondrial acidification. To control for background

mitochondrial acidification, seahorse glycolytic rate assays were performed (see methods). KO

MEFS exhibited increased levels of both basal glycolysis relative to WT (Fig 2-3, see methods for

explanation of assay procedure). Overall, results from seahorse assays demonstrate that

disruption of Rev-erbα in MEFs leads to increased glycolysis and oxidative phosphorylation.

21

Figure 2-3. Loss of Rev-erbα increases glycolysis in MEFs. (top) Extracellular acidification rate (ECAR) trace from for all biological and technical replicates of glycolysis stress tests. Levels of Basal glycolysis calculated in WT and KO cells. (bottom) Proton efflux rate (PER) measured from for all biological and technical replicates of Seahorse glycolytic rate assays. Levels of glycolysis normalized to WT. All Seahorse assay traces were normalized to 10,000 cells. * p < 0.05 WT vs KO by unpaired t-test. n = 3 biological replicates. Data represented as mean ± SEM.

In Rev-erbα SD MEFs, stabilized protein levels relative to WT resulted in an increased level of

oxidative phosphorylation that was associated with an increase in mitochondrial area and overall

biogenesis (12). Several studies in skeletal muscle cell lines have corroborated this finding, with

ectopic overexpression of Rev-erbα leading to increased oxidative phosphorylation (10,11). To

determine if the increase in oxidative phosphorylation in KO MEFs had a similar mitochondrial

22

underpinning, expression of the protein translocase of inner mitochondrial membrane protein 23

(Timm23) was measured (Fig 2-4). KO MEFs did not exhibit increased expression to WT. To

supplement this result, levels of translocase of outer mitochondrial membrane 20 (Tomm20) were

measured, with no difference exhibited in KO MEFs relative to WT (Fig 2-4) These results suggest

that while stabilization of Rev-erbα leads to changes in mitochondrial biogenesis that underlie

increased oxidative phosphorylation, this phenotype is most likely underpinned instead by

individual changes in enzymatic expression following loss of Rev-erbα transcriptional repression.

Figure 2-4. Loss of Rev-erbα does not change mitochondrial numbers or mass. (left) Expression of Timm23 in WT and KO MEFs ( n = 3 independent experiments). (right) Western blot for levels of the outer mitochondrial protein TOMM20 in WT and KO MEFs ( n = 9 independent experiments). Data represented as mean ± SEM.

The absence of changes in expression of Timm23 or levels of Tomm20 suggests it is possible

that loss of Rev-erbα mediated transcriptional repression results in increased glycolysis through

the upregulation of individual enzymes. To determine this, a glycolysis gene array was performed

to look at expression in the canonical glycolysis, gluconeogenesis, and pentose phosphate

pathways. Amongst the genes most upregulated in the absence of Rev-erbα were ribose-5-

phosphate isomerase and transketolase of the non-oxidative pentose phosphate pathway, which

is known to be primarily involved in the production of ribose-5-phosphate precursors for nucleotide

biosynthesis (Fig 2-5a, Illustration 2-1). It is however additionally possible than increase

23

expression of transketolase could lead to additional shuttling of the glycolytic intermediate

fructose-6-phosphate which may underlie increased glycolysis following Rev-erbα disruption

(46,56-59). Amongst the other genes that were upregulated were aldolase c, an isoform of the

sixth enzyme in the glycolysis pathway (60,61), and pyruvate dehydrogenase subunit b e1 which

encodes a subunit of the e1 enzyme in the three-enzyme pyruvate dehydrogenase complex

(62,63) (Fig 5a, Illustration 2-2). This enzyme demonstrated a four-fold increase on the array, but

this increase in expression was attenuated following completion of adequate biological qPCR

experiments that verified the increase in expression of each of these genes was roughly two fold

following loss of Rev-erbα. Expression of each of these genes remained consistent following

synchronization, which controls for circadian changes in nutrient availability (see methods) and in

unsynchronized cells, with exception of aldolase c which had its increase in expression attenuated

following medium change (Fig 2-5 b-d, Fig 2-6c).

25

Figure 2-5. Loss of Rev-erbα leads to upregulation in expression of enzymes in the glycolytic and non-oxidative pentose phosphate pathways. a) A table of preliminary targets upregulated in KO MEFs relative to WT in a glycolysis array (n = 1 biological sample per group). b) Expression of RPIA in unsynchronized (left panel) and synchronized MEFs (right panel). c-e) Expression of TKT, pdhb1, and hexokinase in unsynchronized (upper panels) and synchronized (lower panels). For qpcrs n = 3 independent experiments.*WT vs KO p < 0.05 by unpaired t-test. Data represented as mean ± SEM.

Key enzymes in the glycolysis pathway that were not included on the array included the rate

limiting enzymes hexokinase, phosphofructokinase, and pyruvate dehydrogenase. qPCR

experiments were performed to measure expression of these enzymes, with KO MEFs exhibiting

an increase in expression of hexokinase II. Expression of phosphofructokinase and both isoforms

of pyruvate kinase (pk m1/m2) were unchanged suggesting that increased glycolysis was

underpinned by regulation of hexokinase (Fig 2-5e, 2-6a-b). Pdhb1 expression was additionally

increased in synchronized cells, suggesting that increased shuttling of pyruvate from glycolysis

could result in the observed increase in oxidative phosphorylation (Fig 2-5d).

E

26

Illustration 2-1. Loss of Rev-erbα upregulates expression of enzymes in the non-oxidative pentose phosphate pathway. Outline of the pentose phosphate pathway with respective locations of RPIA and TKT with their fold changes in expression in Rev-erbα KO MEFs relative to WT. RPIA and TKT participate in the synthesis of ribose-5-phosphate, a key component of ribonucleotide precursors. The oxidative arm of the pentose phosphate pathway (right) is a key source of the cellular reducing equivalent NADPH which his necessary for reduction of glutathione and the antioxidant response.

27

Illustration 2-2. Loss of Rev-erbα upregulates expression of enzymes in the glycolysis pathway. (top) Outline of the key steps and metabolites in glycolysis with the corresponding locations of aldolc and pdhb1 enzymes with their respective fold changes in Rev-erbα KO cells relative to WT. (bottom) Location of the three rate limiting enzymes in glycolysis.

28

Figure 2-6. Loss of Rev-erbα does not change expression of hexokinase, pyruvate kinase, or aldolase c. a) Expression of phosphofructokinase in WT and KO MEFs ( n = 3 independent experiments). b) Expression of pyruvate kinase m1/m2 in WT and KO MEFs ( n = 3 independent experiments). c) Expression of aldolase c in unsynchronized and synchronized MEFs (n = 3-5 independent experiments). *WT vs KO p < 0.05 by unpaired t-test. Data represented as mean ± SEM.

29

While oxidative phosphorylation was paradoxically increased following disruption and stabilization

of Rev-erbα, little is known about the role of Rev-erbα in regulation of glycolysis. To test whether

the changes exhibited following Rev-erbα disruption were reversible, Rev-erbα SD cells, which

exhibit a stabilized phosphomimetic form of Rev-erbα that is resistant to proteasomal degradation,

were cultured along with WT. Rev-erbα SD cells exhibited a threefold increase in Rev-erbα protein

levels relative to WT, with a slower migration indicative of the phosphorylation of the protein, which

has been demonstrated in other instances to cause slower migration through sodium dodecyl

sulfate (64,65) (Fig 2-7a). Glycolytic rate assays using WT and SD cells subsequently

demonstrated that SD cells exhibit lower levels of glycolysis relative to WT (Fig 2-7b). SD cells

exhibited a high rate of non-glycolytic acidification following injection of 2’-deoxy-glucose that

resulted in lower levels of calculated glycolysis when subtracted from proton efflux during the

beginning of the Seahorse run (see methods for further explanation). Thus, glycolysis stress tests

confirmed while Rev-erbα disruption resulted in increased glycolysis, stabilization resulted in

reduced glycolysis.

30

Figure 2-7. Stabilization of Rev-erbα reduces glycolysis. a) Western Blot analysis to measure Rev-erbα protein levels in WT, KO, and SD cells. Beta-actin was used as a loading control. The molecular weight of Rev-erbα, detected in SD cells as slightly higher than that of the 67 kDa WT protein. Densitometry analysis of Rev-erbα levels in WT, KO, and SD MEFs (normalized to beta actin). b) Proton efflux rate trace for all biological and technical replicates of WT and SD MEFs normalized to cell number. Levels of basal glycolysis represented as ratios to WT. n = 3 independent experiments. # p < 0.05 WT vs KO by unpaired t-test. Data represented as mean ± SEM.

To test whether the inverse relationship between Rev-erbα levels and could be underpinned by

increased expression of enzymes detected in the KO background, gene expression was

measured in the SD background, with lower expression of RPIA, TKT, and pdhb1 measured

relative to WT (Fig 2-8), suggesting that this relationship extends to enzymatic expression.

Figure 2-8. Expression of TKT, RPIA, and pdhb1 are reduced following Rev-erbα stabilization. n = 3 independent experiments. # WT vs SD p < 0.05 by unpaired t-test. Data represented as mean ± SEM.

Rev-erbα is known to engage in transcriptional repression through direct binding to ROR response

elements (RORE) to recruit nuclear corepressor 1 and histone deacetylase 3. It is possible that

Rev-erbα may bind to the promoters of induvial enzymes. However, its means of transcriptional

repression is not relegated to direct binding (30). Rev-erbα can additionally compete with the

transcriptional activator RORα at a RORE in the promoter of BMAL1 (32). This protein forms a

31

heterodimer with the protein clock to bind to the promoters of clock-controlled genes at E-Box

elements to activate transcription. Amongst the genes which are thought to be regulated by the

BMAL1/CLOCK heterodimer, but have not been fully elucidated, are metabolic enzymes.

Promoters of genes were surveyed for RORE and E-Box elements, with E-Boxes found in the

promoter of RPIA. To test whether an indirect mechanism mediated through BMAL1 may

contribute to upregulation of metabolism enzymes following Rev-erbα disruption (Illustration 2-3),

arntl, the gene encoding BMAL1 was partially ablated in the KO background using pooled small

interfering RNAs, with partial ablation of arntl expression leading to a small, but significant

reduction in RPIA expression (Fig 2-9)

Illustration 2-3. The promoter of RPIA contains an E-Box element. The presence of an E-Box element in the RPIA promoter led to an inquiry into whether a loss Rev-erbα in KO MEFs could lead to increased expression of BMAL1, which could potentially bind E-Box elements in the RPIA promoter to drive its expression.

32

Figure 2-9. Ablation of arntl initially leads to a reduction in RPIA expression. a) Expression of arntl, the gene encoding BMAL1 in WT and KO MEFs. b) Expression of RPIA following siRNA mediated knockdown of arntl in the KO background. n = 3 independent experiments. *WT vs KO p < 0.05 by unpaired t-test. Data represented as mean ± SEM.

To determine whether a more robust perturbation of arntl expression may lead to an observed

increase in RPIA expression, plasmid mediated overexpression was utilized to robustly

overexpress arntl. Overexpression of arntl in the WT background did not lead to changes in

expression of RPIA, TKT, hexokinase, or pdhb1, suggesting that Rev-erbα regulates transcription

of these genes through another mechanism (Fig 2-10).

Arntl

33

Figure 2-10. Overexpression of arntl does not change metabolic enzyme expression a) Expression of arntl following plasmid mediated overexpression in the WT. b-e) Expression of RPIA, TKT, pdhb1, and hexokinase in the WT BMAL1 untransfected and overexpression backgrounds. n = 3 independent experiments for all tests. * p < 0.05 untransfected vs transfected by unpaired t-test. Data represented as mean ± SEM.

The Functional Impact of Rev-erbα disruption

The non-oxidative PPP is known to be important for the biosynthesis of the nucleotide precursor

ribose-5-phosphate, raising the possibility that increased expression of TKT and RPIA may be

necessary to facilitate increased proliferation (46,57). To test this premise, an EdU nucleoside

incorporation assay was performed, with KO MEFs exhibiting more proliferation relative to WT. A

growth curve using WT and KO MEFs also confirmed that increased proliferation manifests as

increased cell numbers in the KO background (Fig 2-11).

Figure 2-11. Loss of Rev-erbα increases proliferation, growth, and vulnerability to oxidative stress. (left) Proliferation assays performed with WT and KO MEFs, with EdU incorporation denoting the percentage of cells undergoing division. (right) A growth curve of WT and KO MEFs with counts over a 5-day span at daily intervals. n = 3 independent experiments * p < 0.05 WT vs KO by unpaired t-test. Data represented as mean ± SEM.

34

Proliferation and cell number were reduced in the SD background relative to WT, demonstrating

that these functional phenotypes inversely correlate with levels of Rev-erbα (Fig 2-12).

Figure 2-12. Stabilization of Rev-erbα results in decreased proliferation and numbers. a) Proliferation assays performed with WT and SD MEFs. b) A growth curve of WT and SD MEFs with counts over a 4-day span at daily intervals. n = 3 independent experiments * p < 0.05 WT vs SD by unpaired t-test. Data represented as mean ± SEM.

To determine whether upregulation of glycolysis or the non-oxidative PPP underpinned increased

proliferation, KO cells were treated with the transketolase inhibitor oxythiamine and 2-DG (66-68).

Glycolytic rate assays demonstrated that 2-DG, not oxythiamine reduced levels of glycolysis,

confirming that competitive inhibition of hexokinase reduces glycolysis in the KO background

(Fig2-13 a,b). Only 2-DG treatment reduced proliferation in the KO background, suggesting that

increased glycolysis may underlie this phenotype (Fig 2-13 c,d). However, additional experiments

in the WT background are required to compare the reduction in glycolysis proliferation following

inhibition of hexokinase.

35

Figure 2-13. Inhibition of glycolysis reduces proliferation in KO MEFs. a-b) Glycolytic rate assays of KO MEFs treated with varying concentrations of oxythiamine or 2’-deoxy-glucose for 24 hours. Glycolytic levels represented as a ratio to KO untreated. c-d) Proliferation assays performed with KO MEFs following treatment with the most effaceable concentrations of oxythiamine, 2’-deoxy-glucose, and RPIA siRNA. n = 3 independent experiments. Data represented as mean ± SEM.

Several other experiments were performed to determine if Rev-erbα disruption resulted in

changes in other functional phenotypes. Amongst these was migration. Rev-erbα KO MEFs

exhibited increased glycolysis relative to WT. In cancer cell models, increased glycolysis

characteristic of the Warburg effect promotes a more invasive phenotype with enhanced migration

36

(69-71). To determine if increase glycolysis following Rev-erbα disruption would result in a similar

functional change, migration was interrogated using a scratch assay, with WT and KO MEFs

exhibiting no difference in ability to heal a wound. (Fig 2-14 a,b). Cell viability, as measured via

trypan blue exclusion, was also tested over a 4-day growth curve to determine if Rev-erbα

disruption imparted any improved survival at baseline. Trypan blue exclusion demonstrated no

change at baseline, demonstrating that changes in proliferation, not viability underlie increased

numbers in KO MEF (Fig 2-14c).

37

Figure 2-14. Loss of Rev-erbα does not affect migration or baseline viability. a) Representative images of WT and KO MEF migration assessed via scratch assay. Migration rate is denoted as the percentage of the corresponding field of the scratch closed 10 and 16 hours post wounding. b) Cell viability over 5-day growth curves in WT and KO MEFs. n = 3 independent experiments. Data represented as mean ± SEM

Finally, in an effort to determine whether SD cells demonstrate less proliferation and numbers

due to exit from the cell cycle, a flow cytometry senescence assay that detects cleavage of the

beta-galactosidase substrate C12-FDG was performed. Senescent cells are characterized as cells

that have halted replication following successive rounds of division, coupled with an enlarged

morphology and a specific immune secretory phenotype (72). Additionally, metabolomic profiling

of senescent human fibroblasts determined that there was an increase in glycolysis, the pentose

phosphate pathway, and pyruvate dehydrogenase transcripts compared to fibroblast in which

senescence was not induced (73). Disparities in quantities of senescent cells could influence the

percentage of WT, KO, and SD MEFs actively proliferating. An important biomarker of senescent

cells is activation of the enzyme β-galactosidase which resides in lysosomes and converts β-

galactosides to monosaccharides. Modern methodology allows for usage of carbon labeled β-

galactoside (C12-FDG) substrates, which when cleaved, result in a fluorescent signal that can be

detected using flow cytometry to quantify percentages of senescent cells (74,75). Previous

studies have demonstrated that treatment with Rev-erbα agonists can impair both proliferation in

cancer and non-cancerous cell lines, with treatment being lethal in cancer cell lines exhibiting

oncogene induced senescence (21). WT, KO, and SD cells exhibited minimal cleavage of C12FTG

(Fig 2-15, preliminary data). Although KO and SD cells exhibited statistically more senescence

compared to WT, additional biological replicates with an additional positive control is needed to

verify this result.

38

Figure 2-15. MEFs display lower levels of baseline senescence. % of WT, KO, and SD MEFs registering positive for hydrolyzed C12-FDG. *p < 0.05 WT vs KO, #p < 0.05 WT vs SD by unpaired t-test of three technical replicates n = 1 independent experiment (preliminary data)

Discussion

Rev-erbα, metabolism, and individual enzymatic expression

The transcriptional repressor Rev-erbα is known to influence expression of metabolic genes in

processes such as gluconeogenesis and lipid metabolism. However, the overall role that Rev-

erbα mediated transcriptional repression can have on metabolism and the resulting effect on

function remains unclear. My findings demonstrate that Rev-erbα levels influence overall levels

of oxidative phosphorylation accompanied by upregulation of genes in glycolysis and the pentose

phosphate pathway. In skeletal muscle tissue and cell lines, Rev-erbα levels are directly

correlated with levels of oxidative phosphorylation and exercise capacity (8,10,11). These findings

are corroborated by the finding that in fibroblasts, Rev-erbα stabilization increases oxidative

phosphorylation coupled with an increase in mitochondrial area (12). Paradoxically however,

* #

39

following Rev-erbα disruption there is an increase in oxidative phosphorylation. Metabolic

phenotypes between these tissues were not completely analogous however, with overexpression

of Rev-erbα leading to an increase in the expression of the master regulator of mitochondrial

biogenesis Pgc-1α in skeletal muscle, but not fibroblasts (8,12). While we hypothesized that

oxidative phosphorylation would be reduced, unlike in Rev-erbα SD cells these changes were not

accompanied by an increase in mitochondrial area or biogenesis, but through specific

upregulation of metabolic enzymes, the most notable in the context of oxidative phosphorylation

being pdhb1, which encodes a subunit of the first enzyme of the three enzyme pyruvate

dehydrogenase complex (62,63). Upregulation of this specific gene coupled with increased

expression of hexokinase may lead to an increase in flux from oxidative phosphorylation into

glycolysis in the form of pyruvate that drive increased oxidative phosphorylation. It is additionally

possible that an increase in the expression or tightness of coupling in the electron transport chain

might drive increased oxidative phosphorylation following Rev-erbα disruption. Following

stabilization of both Rev-erbα and Rev-erbβ in a skeletal muscle cell line there was increased

expression of cytochrome oxidase I (mt-Co1) and cytochrome oxidase II (mt-Co2) (11). Whether

levels of these proteins are increased following Rev-erbα disruption is unclear and could provide

further insight into the hypermetabolic phenotype of Rev-erbα KO MEFs.

Although it has been widely reported that Rev-erbα can influence the expression of genes in

gluconeogenesis and lipid metabolism, there has been little findings linking changes in the

expression of glycolytic enzymes with the overall output of the glycolysis pathway. While

increased expression of hexokinase I and pyruvate kinase were detected in the skeletal muscle

of mice treated with Rev-erbα agonists, these results were not conserved across tissue types,

with hepatocytes demonstrating no change in expression of rate limiting enzymes following

agonist treatment, and stabilization of Rev-erbα leading to an increase in phosphofructokinase

expression (8).

40

Rev-erbα levels exhibited an inverse relationship with levels of glycolysis that was specifically

accompanied by an increase in the expression of hexokinase II, but not phosphofructokinase as

measured in Rev-erbα SD fibroblasts (12). This increase in glycolysis could be attenuated through

treatment with the hexokinase inhibitor 2-DG. However, following inhibition of transketolase using

oxythiamine in the Rev-erbα KO background there was no change in glycolytic levels suggesting

that increased expression of hexokinase II, but not the non-oxidative pentose phosphate pathway

drives this phenotype. These findings mark the first evidence that glycolytic output can be

perturbed by disruption of Rev-erbα demonstrating that a fundamental cellular energy source can

fall under this form of transcriptional regulation.

Rev-erbα disruption resulted initially in upregulation of several enzymes in unsynchronized cells

including aldolase c, rpia, and pdhb1. A promoter survey of this genes determined that there was

an E-Box in the promoter of rpia. We found that expression of these genes was inversely

proportional to Rev-erbα levels in our mouse embryonic fibroblast model. Recent evidence in

colorectal cancer cells has demonstrated that BMAL1 can drive expression of aldolase c and

pdhb1 (76). To determine if RPIA expression is increased through BMAL/CLOCK binding in the

absence of Rev-erbα, ablation and overexpression of BMAL1 was performed in the Rev-erbα WT

background. Perturbation of Rev-erbα did not lead to significant changes in expression of RPIA

or other genes that demonstrated an inverse relationship in expression with Rev-erbα levels.

It may be possible that Rev-erbα exerts transcriptional control of metabolic genes through a

mechanism outside the traditional circadian clock. Recent evidence in a double knockout mouse

embryonic fibroblast model of Rev-erbα and Rev-erbβ found enrichment for HNF1 and NF-1

binding sites in the promoters of genes that halted circadian oscillation following loss the Rev-

erbs. Synchronized experiments in Rev-erbα WT and KO MEFs suggested that RPIA, TKT,

phdb1,and hexokinase II expression may be under circadian control, but these genes were not

found to be oscillating in double knockout fibroblasts (43). Future examination will be necessary

41

to determine the extent to which Rev-erbα mechanistically influences expression of these genes,

and whether this is mediated in a clock or non-clock manner.

Upregulated metabolism and proliferation following Rev-erbα disruption: A relationship to

cancer?

There is a growing body of evidence that Rev-erbα may be a therapeutic target in the treatment

of cancers of the breast, colon, and brain (21,23,77,78). Nr1d1, the gene encoding, Rev-erbα is

located within the Receptor tyrosine-protein kinase erbB-2 (ERRB2) amplicon of the long arm of

chromosome 17. Overexpression of this enzyme, which mediates phosphorylation of several

downstream kinases, including map kinase and protein kinase c, is a biomarker of breast cancer

and is correlated to poor prognosis (53). Cancer cells are known to increase glycolysis via the

phenomena known as the Warburg effect to drive uncontrolled proliferation (46,79,80). While

overexpression of the ERBB2 amplicon is known to be detrimental in breast cancer cells, there is

evidence that Rev-erbα may be a therapeutic target to delay cancer progression. This is clinically

evident in samples taken from triple negative breast cancer patients. These patient exhibit tumor

cells that lack expression of progesterone receptor, estrogen receptor, and have reduced

expression of ERBB2. This results in the most aggressive and proliferative form of breast cancer.

In immunohistochemistry experiments of tumor samples obtained from triple negative breast

cancer patients, increased expression of Rev-erbα was correlated with increased overall and

disease-free survival (23). Conversely, in studies of both cancerous and non-cancerous cell lines,

treatment with the Rev-erbα agonists SR9009 and GSK4112 can impede proliferation and delay

cell cycle progression (21,22,81). However, recent findings including a study that demonstrated

that SR9009 can have nonspecific effects on oxidative phosphorylation and glycolysis throw this

finding into question (41). Our findings indicate in fibroblasts that KO and SD cells exhibit

increased and decreased proliferation relative to WT, respectively. This finding correlates with the

42

trend exhibited with agonists, suggesting that loss of Rev-erbα may underlie hyperproliferative

phenotypes exhibited by cancer cells.

The phenotype of Rev-erbα KO fibroblasts resembles the Warburg effect not only through

increased proliferation, but through a specific increase in the expression of hexokinase II that is

necessary to facilitate proliferation. In several types of cancer, upregulation of hexokinase II is

known to facilitate increased production of metabolic intermediates for glycolysis, oxidative

phosphorylation, and nucleotide biosynthesis (82-84). The necessity of increased hexokinase

expression to the hyperproliferative phenotype of Rev-erbα was demonstrated through EdU

nucleoside incorporations assays. While hexokinase II is a key rate limiting enzyme that

contributes to the Warburg effect, phosphofructokinase and pyruvate kinase m2 additionally play

an important role in cancer progression (85-87). While expression of these enzymes has been

shown to be increased in skeletal muscle and SD fibroblasts, hexokinase II alone drives increased

proliferation in the absence of Rev-erbα (8,12). This finding further substantiates the body of

literature suggesting that loss of transcriptional repression may drive a hypermetabolic,

hyperproliferative phenotype that resembles oncogenesis.

While increased proliferation driven by glycolysis and increased expression of hexokinase II

results in a cancer like phenotype that is well represented in the literature, the absence of

attenuated proliferation following inhibition of transketolase or ablation of RPIA as hypothesized

runs contrary to a number of previous findings. The pentose phosphate pathway is known to be

the main source of nucleotide precursors for cell division, with RPIA directly catalyzing the

biosynthesis of ribose-5-phosphate (46). In an in vitro model of colon cancer, silencing of RPIA

resulted in a reduction of nucleotide incorporation and colony survival (88). The role of the non-

oxidative PPP is further corroborated in breast cancer cell lines, ablation of TKT led to a metabolic

flux away from nucleotide biosynthesis and towards glycolysis (56). While there was a clear

inverse relationship of Rev-erbα levels with expression of non-oxidative PPP enzymes, we were

43

unable to observe any changes in proliferation following PPP inhibition, reinforcing the idea that

the effect of enzymatic expression on proliferation may be cell specific.

Conclusions

In summary, Rev-erbα disruption results in a hypermetabolic state, with increased levels of

oxidative phosphorylation and glycolysis. Upregulated metabolism is correlated with an increase

in expression of genes in glycolysis and the pentose phosphate pathway. Inhibition of hexokinase

reduces levels of glycolysis and proliferation in the KO background, suggesting that increased

expression of this enzyme partially drives this functional phenotype.

Methods

The following methods were utilized to generate results, with novel methods described in detail

and standard techniques described briefly to provide technical specifics.

Cell lines, culture, and synchronization

A mouse embryonic fibroblast cell culture model was used as an in vitro means to test the effect

of Rev-erbα disruption of metabolism and function. These cells were a gift of Mitch Lazar at the

University of Pennsylvania. Fibroblasts were obtained from mice genotyped as Rev-erbα WT or

global knockout from prenatal 13.5 embryos. Fibroblasts were subsequently isolated using

standard protocols and immortalized using transfection of an SV40 promoter. Cells were cultured

at 37°C and 5% CO2 in DMEM supplemented with 10% fetal bovine serum,

penicillin/streptomycin, and glutamine.

Rev-erbα is a transcriptional repressor that forms a secondary transcription/translation factor

feedback loops that allow the cell to respond with a larger organism to environmental changes

such as light and to adjust processes like redox state and metabolism accordingly. While an in

vitro model is not regulated by zeitgebers such as light, fluctuations in nutrient availability in the

medium may lead to variation in the circadian phases of gene expression. To control for this,

44

MEFs are synchronized using serum starvation, followed by medium change. At a consistent time

of day, MEFs were plated and allowed to attach for 2 hours. The medium was then replaced by

serum free DMEM and allowed to grow for 24 hours. MEFs then were cultured for 48 hours in

serum containing medium prior to collection of RNA or protein (see Illustration 2-3).

Illustration 2-3. Synchronization of cells aligns circadian phase and controls for nutrient availability. MEFs were allowed to attach for 2 hours, followed by 48 hours in serum free medium, and 24 hours in medium with serum prior to collection.

Determination of oxidative phosphorylation and glycolysis

The Seahorse XF24 bioanalyzer (Agilent, Santa Clara, CA, USA) allows for measurement of

various metrics of metabolism via cells plated in a cartridge allowed to attach overnight. Cells are

then plated in the instrument with varying concentrations of drugs which allow for measurement

of oxygen consumption rate which is used as an index of oxidative phosphorylation and

acidification of the extracellular medium which is used as an index of glycolysis. The following will

describe the design of three assays used in the results section of this text: namely, the

mitochondrial stress test, glycolysis stress test, and the glycolytic rate assay.

The mitochondrial stress test uses oxygen consumption rate via the instruments oxygen probe as

a means to assess levels of oxidative phosphorylation (Illustration 2-4). Prior to all seahorse

assays, a well of cells was sacrificed for counting. All technical replicates on seahorse assays are

normalized to a quantity of 10,000 cells, followed by the control condition set to a ratio of 1.

Experimental conditions are then normalized to control. The cells are also checked for uniform

confluence and that monolayers of adherent cells are not overly crowded. The assay utilizes

injections of three compounds, namely the ATP inhibitor oligomycin, which inhibits complex V of

45

the electron transport chain, Carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP)

which uncouples the electron transport chain, and a combination of rotenone/antimycin A which

inhibits complexes I and III of the electron transport chain to eliminate mitochondrial respiration.

The difference between the initial respiration exhibited at the beginning of the assay and the

remaining oxygen consumption and the remaining oxygen consumption following injection of

rotenone/antimycin a is defined as basal respiration, which is the main metric of oxidative

phosphorylation discussed in later results.

Illustration 2-4. The mitochondrial stress test approximates levels of oxidative phosphorylation. An example mitochondrial stress test trace with chemical injections at their respective cadence. Oxygen consumption is normalized to cell count and measured over 100 minutes. Basal respiration represents the difference between initial oxygen consumption and oxygen consumption remaining following electron transport chain inhibition using rotenone/antimycin a.

The glycolysis stress test functions in a similar manner, but rather than using oxygen consumption

rate this test measures the rate of extracellular acidification resulting from the production of lactate

as an index of glycolysis (Illustration 2-5). Over a 100-minute time course, a similar series of

injections of compounds into wells with plated cells occurs. Cells are plated in glucose free

46

medium, and an initial injection of glucose starts glycolysis. A second injection of oligomycin

inhibits ATP synthase to force a metabolic shift away from oxidative phosphorylation to glycolysis.

A final injection of the compound 2’-deoxy-glucose allows for inhibition of glycolysis to determine

levels of non-glycolytic acidification. Levels of non-glycolytic acidification are subtracted from

levels following glucose injection to determine glycolytic levels.

Illustration 2-5. The glycolysis stress test uses extracellular acidification as an indirect measurement of lactate production from glycolysis. An example glycolysis stress test trace with injections at the appropriate timings. Extracellular acidification is normalized to cell count and recorded over 100 minutes. Glycolysis is defined as the difference in extracellular acidification following injection of glucose and the hexokinase inhibitor 2’-deoxy-glucose.

47

While the glycolysis stress test provides an initial index of glycolysis, the assay does not control

for the amount of extracellular acidification that may be attributable to mitochondria. The

tricarboxylic acid (TCA) cycle produces carbon dioxide which may inflate assumed levels of

glycolysis in an assay that simply measures lactate (Illustration 2-6). Unlike the glycolysis stress

test, the glycolytic rate assay uses a simultaneous measurement of extracellular acidification and

oxygen consumption to determine a proton efflux rate (PER). Glucose is provided in the assay

medium rather than as an injection in the glycolysis stress test. In a shorter assay over a little

more than an hour, two compounds are injected into individual wells. The first injection is

rotenone/antimycin a which allows for calculation of the mitochondrial PER, followed by 2-DG to

determine PER not attributable to oxidative phosphorylation or glycolysis. Post 2-DG and

MitoPER are subtracted from total PER which is used as the final index of glycolysis.

Illustration 2-6. The glycolytic rate assay subtracts mitochondrial acidification for a more specific index of glycolysis. An example glycolytic rate assay trace with injections at the appropriate timings. Proton efflux rate (PER) is measured over a 100-minute course to determine glycolysis. The increase in PER following injection of the electron transport chain complex inhibitors rotenone/antimycin a allows for calculation of the amount of proton efflux attributable for glycolysis.

48

Glycolysis Gene Array

A Qiagen Glucose Metabolism RT2 Profiler PCR Array (Qiagen, Hilden, Germany) was used to

determine if expression of enzymes in glycolysis, gluconeogenesis, or the pentose phosphate

pathway was upregulated in the absence of Rev-erbα transcriptional repression. RNA was

isolated using a RNeasy prep kit that was also from Qiagen. Expression of microchip array targets

was measured using a GeneChip Instrument (Affymetrix, Santa Clara, CA, USA). Analysis of fold

changes of array targets was performed by our laboratory manager, Abigail Peterson.

Quantitative PCR

Quantitative PCR was used to measure relative expression of metabolic enzymes. Relative

expression of all mRNA targets was tested using the TaqMan gene expression system (Applied

Biosystems, Foster City, CA, USA). RNA was extracted following 24 hours of air exposure. Total

RNA was extracted using the TRIzol Plus RNA Purification System (Invitrogen, Carlsbad, CA,

USA) or a Qiagen RNeasy Kit (Qiagen, Hilden, Germany). A NanoDrop One® (Thermo Scientific,

Waltham, MA, USA) instrument was used to test the quantity and concentration of RNA. For arntl

silencing and overexpression, cell lysis was performed in plate with a Qiagen RNeasy kit

(Germantown, MD) used for downstream RNA processing according to manufacturer protocols.

cDNA was synthesized using the TaqMan Reverse Transcription System and run on an Applied

Biosystems 7300 Real-Time PCR instrument. TaqMan probes were as follows: nr1d1

(Mm00520711), ribose-5-phosphate isomerase (Mm00485790), transketolase (Mm00447559),

arntl (Mm00500226), pyruvate kinase muscle isoform (m1/m2) (Mm00834102), pyruvate kinase

liver/red blood cell isoform (Mm00443090), phosphofructokinase (Mm01309576), hexokinase I

(Mm00439344), hexokinase II (Mm00443385), aldolase c (Mm01298116), and pdhb

(Mm00499323). Samples were denatured at 95°C for 10 minutes, followed by 40 cycles of

denaturing at 95° for 15 seconds followed by extension at 60°C for 1 minute.

49

Gene silencing and overexpression

Lipid mediated siRNA transfection was used to knock down arntl expression. For anrtl silencing,

pooled arntl Qiagen flexitube siRNAs (catalog # 1027416) were transfected into KO MEFs using

Thermo Fisher RNAiMAX reagent with transfection performed per manufacturer instructions.

Thermo Fisher Ambion siRNA (catalog # AM16708) was used for ribose-5-phosphate isomerase

(RPIA) silencing. Arntl was overexpressed in WT MEFs using an Origene pCMV6 vector plasmid

(catalog # MR209553) using Thermo Fisher Lipofectamine 3000 reagent. RNA was harvested 24

hours post transfection.

Gene silencing and overexpression

Lipid mediated siRNA transfection was used to knock down arntl expression. For anrtl silencing,

pooled arntl Qiagen flexitube siRNAs (catalog # 1027416) were transfected into KO MEFs using

Thermo Fisher RNAiMAX reagent with transfection performed per manufacturer instructions.

Thermo Fisher Ambion siRNA (catalog # AM16708) was used for ribose-5-phosphate isomerase

(RPIA) silencing. Arntl was overexpressed in WT MEFs using an Origene pCMV6 vector plasmid

(catalog # MR209553) using Thermo Fisher Lipofectamine 3000 reagent. RNA was harvested 24

hours post transfection.

Cell proliferation assays

Nucleoside incorporation assays were performed as an index of proliferation. Cells were seeded

24 hours prior to assay start time. 5-ethynyl-2'-deoxyuridine (EdU) is a thymidine analog that

incorporated into cell during DNA synthesis in S phase of the cell cycle to quantify the proportion

of cell undergoing activate proliferation. Cells were then treated with 3 µM EdU provided by the

Click-iT EdU proliferation assay (Invitrogen, Carlsbad, CA, USA). Cells prepared according to

manufacturer’s protocol. Flow Cytometry was then performed using a BD FACS AriaIIIu at the

Brown Flow Cytometry core (Providence, RI, USA), with cells from the main population registering

as Alexaflour 488 positive deemed proliferative (Illustration 2-7). A minimum of 20,000 events

50

were recorded per sample. For non-oxidative PPP experiments, cells were treated with

oxythiamine or transfected with RPIA siRNA 24 hours prior to EdU treatment.

Illustration 2-7. Representative gating strategy. (top) The main population of MEF cells was selected followed by uniform selection of a FITC positive peak denoting cells which registered positive for nucleoside incorporation, which were deemed proliferating. (bottom) Voltages were set using a negative control for each experiment in which no nucleosides were added for incorporation.

51

Growth curves

28,000 MEFs were seeded in 6 cm2 plates for the WT, KO, and SD genotypes. Three plates of

MEFs were trypinisized and counted on a hemocytometer using Trypan Blue Reagent over a

period of four to five days, with live cell counts averaged and standard error of the mean

calculated. Cells used for growth assays were cultured for no more than 20 passages and no

more than two passages apart for each biological replicate.

Immunoblotting

Western blotting was used to measure relative protein levels. Total protein was isolated from Rev-

erbα WT, KO, and SD MEFs. Lysate was treated with Beta mercapaethanol, boiled, run on a 4-

12% Sodium Dodecyl Sulfate Polyacrylamide Gel (Invitrogen, Carlsbad, CA, USA), and

transferred to a PVDF membrane. Membranes were incubated with the following primary

antibodies and dilutions: Rev-erbα (#13418, Cell Signaling Technologies, Danvers, MA

monoclonal rabbit antibody, 1:1000), ToMM20 (#42406S Cell Signaling Technologies, Danvers,

MA, USA, 1:5000). Beta actin (#ab8227, abcam, Cambridge, MA, USA) and calnexin (#ADI-SPA-

860-F, Enzo, Farmingdale, NY, USA) were used as loading controls.

Migration Assays

Scratch assays were performed as an index of migration. MEF cells were seeded in 6 cm2 plates

and allowed to reach approximately 90% confluence. Cells were treated with 10 ug/mL mitomycin

c (Sigma Aldrich #50-07-7) for two hours to inhibit proliferation. A horizontal line was drawn on

each plate to ensure a uniform place of measurement after which a scratch was made in the

monolayer using a p200 pipette tip. 10x images of the scratch were obtained for a minimum of

two fields for triplicate plates of each genotype at 0, 10, and 16 hours post wounding. The MRI

Wound healing tool for ImageJ was used to quantify the area of the wound, with migration rate

defined as the difference between the original and closed areas of the wound computed as a

percentage.

52

Determination of Senescence

WT, KO, and SD MEFs were seeded in 6 well plates in triplicate. The C12FTG assay was

performed as previously described (75) by Alejandro Scaffa. Briefly, cells were treated with 100

nM balifomycin A1 for 1 hour, followed by incubation with 2 mM C12FTG for two hours. Cell

monolayers were subsequently washed twice with 1X phosphate buffered saline, trypisinized

using TripLE reagent (Thermo Fisher, Waltham, MA), and resuspended in 1x ice cold phosphate

buffered saline before being run on a BD FACS AriaIIIu at the Brown Flow Cytometry core

(Providence, RI, USA). Further analysis and quantification of senescent cell populations was

performed using FlowJo software (FlowJo LLC, Ashland, OR, USA).

Statistical Analysis

Graph Pad Prism 6 software was used for unpaired students t-tests for comparisons between

groups, with a p < 0.05 used as a threshold for significance. Experiments were repeated

biologically three times, with three technical triplicates for quantitative PCR and functional assays.

Seahorse assays contained up to nine technical replicates each. Error bars are reported as the

standard error of the mean.

53

CHAPTER 3: REV-ERBα DISRUPTION INCREASES VULERNABILITY TO OXIDATIVE

STRESS

By

Sean Patrick Gillis and Phyllis A. Dennery

Acknowledgements: Special thanks also to Dr. Mitch Lazar for providing us mouse embryonic

fibroblasts as a gift.

54

Abstract

Transcription of Rev-erbα can be driven by oxidative stress and stabilization of Rev-erbα in

fibroblasts is protective against oxidative stress (12,51). Metabolism can affect oxidative stress

and the antioxidant response through the electron transport chain and the pentose phosphate

pathway (45,46). We hypothesized that Rev-erbα disruption would result in decreased viability

and expression of antioxidant enzymes when faced with an oxidative stress. Rev-erbα KO MEFs

demonstrated no change in baseline expression of HO-1, MNSOD2, or levels of 5,5-dimethyl-1-

pyrroline N-oxide protein adducts. When faced with hydrogen peroxide stress, Rev-erbα KO

MEFs exhibit reduced viability relative to WT. This decrease in viability was not accompanied by

decreased expression of antioxidant enzymes, but rather an increase in the ratio of oxidized

NADP+ to NADPH. These findings suggest that perturbation of the pentose phosphate pathway

following Rev-erbα disruption reduces availability of NADPH to mount an antioxidant response,

rendering KO MEFs more vulnerable to oxidative stress.

Introduction

The functional effects of Rev-erbα disruption on MEFs may not be localized just to proliferation,

but to the oxidative stress response as well. In fibroblasts, The Rev-erbα promoter in is known to

be bound by Nrf2, a master transcriptional regulator of the oxidative stress response (51).

Following Rev-erbα stabilization, there is increased expression of catalase, manganese

superoxide dismutase 2, and heme oxygenase 1 (12). While Chapter 2 presented evidence that

increased expression of hexokinase II may mediate increase glycolysis and proliferation in Rev-

erbα KO MEFs, it is possible that the effect of perturbed metabolism may extend to the oxidative

stress response, particularly upregulation of enzymes in the non-oxidative pentose phosphate

pathway, which are downstream of the oxidative arm which is an important producer of the

reducing species NADPH which plays a key role in the antioxidant response (46). To interrogate

whether disruption of Rev-erbα would have the opposite effect of stabilization, expression of

55

antioxidant enzymes was measured under conditions of basal and oxidative stress, and the

viability of WT and KO MEFs when faced with hydrogen peroxide stress was measured.

Results

Previous research has established a role for Rev-erbα in protection against hydrogen peroxide

(H2O2) induced oxidative stress and it is known in fibroblasts that oxidative stress can drive

transcription of Rev-erbα through an element for nuclear erythroid factor 2 (Nrf2) (12,51). To date,

whether or not Rev-erbα serves a protective role against oxidative stress remains controversial.

At baseline, levels of the protein adducts produced by the free radical spin trap DMPO (89,90)

were unchanged (Fig 3-1a) . Additionally, gene expression of heme oxygenase 1 and manganese

superoxide dismutase 2, enzymes known to be upregulated through Nrf2 binding (12), were

unchanged in KO cells, suggesting no change in oxidative stress at baseline (Fig 3-1b,c). Protein

levels of MnSOD2 were additionally unchanged in KO MEFs (Fig 3-1d).

57

Figure 3-1. Rev-erbα disruption does not affect oxidative stress at baseline. a) Densitometry analysis of total 5,5-dimethyl-1-pyrroline N-oxide protein adducts normalized to beta actin levels in WT and KO MEFs. DMPO densitometry was defined as the sum of the four most prominent adduct species, defined as arrows on the western blot (n = 6 independent experiments) b) Expression of HMOX1 and SOD2 in WT and KO MEFs (n = 3-4 independent experiments) c) MnSOD2 levels in WT and KO MEFs, western blot and levels normalized to levels of a calnexin loading control (n = 8-9 independent experiments). Data represented as mean ± SEM.

While there was no change in basal levels of oxidative stress following Rev-verbα disruption, it

remained possible that Rev-erbα KO MEFs would demonstrate a different response to oxidative

stress. Treatment of KO MEFs with hydrogen peroxide resulted in a significant reduction in

viability relative to WT (Fig 3-2a). In SD cells, upregulation of HO-1, catalase, and MnSOD2 leads

to increased protection against hydrogen peroxide stress. It was thus hypothesized that Rev-erbα

disruption would result in decreased expression of these enzymes which may underlie an

increased vulnerability to oxidative stress. Expression of HO-1 was increased relative to WT, with

MnSOD2 expression unaffected by Rev-erbα disruption (Fig 3-3).

90 kDa

-

70

kDa -

30

kDa -

20

kDa -

MnSOD2

58

Figure 3-2. Rev-erbα MEFs exhibit reduced viability when challenged with hydrogen peroxide stress. a) Viability measured by trypan blue exclusion of WT and KO MEFs following 24-hour treatment with 500 µM H2O2 b) Ratio of NADP+/NADPH in WT and KO MEFs p < 0.05 by unpaired t-test compared with WT (WT H2O2 in panel A) Data represented as mean ± SEM.

While there was upregulation of the non-oxidative PPP following Rev-erbα, inhibition of this

pathway did not result in changes in proliferation or glycolysis. The non-oxidative PPP is

downstream of the oxidative PPP which is known to be important in the generation of NADPH

which is necessary for reduction of the key antioxidant molecule glutathione (46). KO MEFs

demonstrate a higher ratio of NADP+/NADPH, suggesting that perturbation of the non-oxidative

PPP following Rev-erbα disruption results may reduce the fitness of KO cells to mount an

antioxidant defense (Fig 3-2b).

B

59

Figure 3-3. Antioxidant enzyme expression following Rev-erbα disruption is not changed when faced with an oxidative stress. a-b) Expression of HMOX1 and SOD2 in WT and KO MEFs following 24 hours of 500 µM H2O2 (n = 3-4 independent experiments) *p < 0.05 WT vs KO by unpaired t-test. Data represented as mean ± SEM.

Discussion

Despite not affecting proliferation or metabolism, it is possible that increased expression of non-

oxidative PPP enzymes may underlie the reduced viability of Rev-erbα KO MEFs when faced with

hydrogen peroxide stress. Elucidating the role of these enzymes in the antioxidant response may

help to better define the role Rev-erbα plays in response to oxidative stress. Stabilization

increased viability of Rev-erbα SD cells following hydrogen peroxide stress, and disruption

reduced viability relative to WT as expected. However, unlike in the stabilized model, there was

not downregulation of MnSOD2 expression and levels, or HO-1 as expected. Rather expression

of MnSOD2 was unchanged with expression of HO-1 remaining elevated when faced with

oxidative insult (12). Oxidative stress is known to drive transcription of Rev-erbα in fibroblasts

(51), and a lack of correlation in expression antioxidant targets in the absence of Rev-erbα

suggests that the protein does not regulate expression of these transcripts. Thus, perturbation in

expression of the non-oxidative pentose phosphate pathway, which is downstream of the

oxidative arm of the pathway, the principle source of cellular NADPH, may underlie oxidative

vulnerability in KO MEFs (91). The increase in NADP+/NADPH ratio in KO MEFs suggests that

loss of Rev-erbα depletes the cellular supply of reduced NADPH. Whether the reduction in the

cellular pool of NADPH results in an increase in levels of oxidized glutathione, rendering the cell

more vulnerable to oxidative stress, remains to be determined.

It is possible that other mechanisms may result in an increase vulnerability to oxidative stress

following Rev-erbα disruption in mouse embryonic fibroblasts. In hepatocytes palmitate is known

to induce oxidative stress and alter the cyclic expression of Rev-erbα (49,50). Whether

60

supplemental of MEFs with this fatty acid would alter the response to oxidative stress is unknown.

Additionally, while Rev-erbα is known to delay the DNA damage response in breast cancer cells

(53), KO MEFs would be expected to demonstrate an increased fitness if loss of Rev-erbα

resulted in more expeditious DNA repair. However, lack of quantitation of DNA damage foci in KO

MEFs dictates that its known interaction with PARP1 could possibly be conserved in fibroblasts

and result in changes in viability.

Conclusions

Loss of Rev-erbα results in decreased viability when faced with hydrogen peroxide stress relative

to WT. This reduction in viability is not correlated with expression of enzymes that mediate the

antioxidant response, but by an increase in the ratio of oxidized NADP+ to reduced NADPH. This

suggests that perturbation of the non-oxidative pentose phosphate pathway reduces levels of

NADPH rendering KO MEFs less able to mount an antioxidant response.

Methods

The following methods were utilized to generate results, with novel methods described in detail

and standard techniques described briefly to provide technical specifics.

Cell Lines and Culture

For cell culture methods used in this chapter please see methods as indicated in Chapter 2.

Quantitative PCR

Quantitative PCR methods using the Taqman gene expression system and Applied Biosystems

7300 Real-Time PCR instrument are detailed in Chapter 2. Taqman probes specifically used for

generation of data in this chapter include Hmox1 (Mm00516006) and Sod2 (Mm01313000).

Immunoblotting

Immunoblotting methods outlined in this chapter are detailed in Chapter 2. Antibodies used in

this chapter include DMPO (gift of Ronald Mason, National Institute for Environmental Health

61

Sciences, Research Triangle Park, NC, USA), MnSOD2 (#06-984, Upstate® EMD Millipore,

Burlington, MA, USA , 1:1000). Beta actin (#ab8227, abcam, Cambridge, MA, USA) and calnexin

(#ADI-SPA-860-F, Enzo, Farmingdale, NY, USA) were used as loading controls.

Cell viability assays and H2O2 treatment

To test for a difference in baseline viability, 28,000 MEFs were seeded in 6 cm2 plates. Total cells

were collected for up to five days post seeding. Cells were mixed with trypan blue. Cells excluding

trypan blue were denoted as live with the percentage of live cells divided total used to calculate

viability. Cells treated with hydrogen peroxide received treatment for 24 hours at a 500 µM

concentration.

NADP+/NADPH ratio determination

Reagents from Abcam (catalog # ab65349) were used according to manufacturer protocols.

Three million WT and KO cells were utilized for each biological measurement. DNA was sheared

via syringing prior to NADP+ decomposition. Optical plates were read at 1 hour following addition

of assay developer. For individual total species and NADPH quantities, assay readings were

normalized to protein amounts quantified via BCA assay.

Statistical Analysis

For description of statistical methods outlined in this chapter, please see Chapter 2.

62

CHAPTER 4: DISCUSSION

Future Directions

Our research into the effect of Rev-erbα disruption in a MEF model inspires several lines of inquiry

that can be carried on into the future. Rev-erbα KO MEFs exhibit increased levels of oxidative

phosphorylation relative to WT, a paradoxical phenotype when compared with fibroblasts of a

stabilized model. Our hypothesis that increased levels of oxidative phosphorylation result from

upregulation in the absence of Rev-erbaα as opposed to mitochondrial biogenesis will require

further interrogation. Upregulation of the gene pdhb1 in the KO background, which provides a link

between glycolysis and oxidative phosphorylation as part of the pyruvate dehydrogenase

complex, suggests an initial approach (62,63). In the Rev-erbα KO background there is increased

glycolysis relative to WT when measured using glycolysis stress tests and glycolytic rate assays.

Whether this output results solely in the anaerobic production of lactate or aerobic shuttling of

pyruvate to the tricarboxylic acid cycle remains an open line of inquiry. Techniques such as carbon

labeling could provide an answer as to whether upregulated glycolysis in the KO background

contributes to increased oxidative phosphorylation. This technique involves the introduction of a

carbon-13 labeled substrate that is taken up by the cells, with downstream samples prepared for

mass spectrometry or nuclear magnetic resonance analysis of isotopic signatures to determine

the flux of metabolic pathways (92). Such an experiment could determine whether upregulated

glycolysis contributes to oxidative production via pyruvate flux to the tricarboxylic acid cycle.

In skeletal muscle following Rev-erbα disruption, genes encoding complexes of the electron

transport chain are upregulated following stabilization (11). Although we hypothesized initially that

disruption of Rev-erbα would result in lower levels of oxidative phosphorylation, this finding in a

63

different tissue type warrants investigation into electron transport chain targets that were not

included in our study. Additionally, while our study provides several measurements used to

assess mitochondrial biogenesis, future studies using electron microscopy to examine

mitochondrial morphology in the KO background would provide a more complete examination of

this phenotype (93).

In the Rev-erbα KO background there was upregulation of transketolase and ribose-5-phosphate

isomerase of the non-oxidative pentose phosphate pathway. While functional experiments

suggest that perturbation of these enzymes leads to changes in the response to oxidative stress

rather than proliferation or viability, the functional consequences of Rev-erbα mediated

transcriptional regulation of these targets warrant further interrogation. In KO MEFs, there is an

increased ratio of oxidized NADP+ to NADPH, suggesting a lower quantity of reduced glutathione.

To confirm or refute this hypothesis, measurement of the redox state of glutathione using establish

methods such as colorimetric assays will be crucial (94). Additionally, while inhibition of

transketolase and ablation of ribose-5-phosphate isomerase did not result in changes in

proliferation, whether upregulation of these enzymes results in increased nucleotide biosynthesis

is unknown. Measurement of oligonucleotides using carbon-13 labeling can determine whether

upregulation of the non-oxidative pentose phosphate pathway results in increased quantities of

the nucleotide precursor ribose-5-phosphate (92).

There is a recent body of literature examining whether activation of Rev-erbα may be effective in

halting the progression of cancer. Rev-erbα KO MEFs demonstrate a hypermetabolic and hyper

proliferative phenotype, with increased expression of hexokinase II possibly underlying increased

proliferation. It remains an open question as to whether Rev-erbα disruption increases cell cycle

progression and if the protein interacts with other oncogenic factors. In neuroblastoma cell lines,

the Myc family of transcription factors that have been recently shown to induce Rev-erbα, dampen

the oscillation of BMAL1 expression, and drive cancer progression (24). Interrogation as to

64

whether this interaction is conserved in other fibroblasts or it Rev-erbα interacts with other

oncogenic factors may elucidate further mechanisms driving a hyperproliferative phenotype in KO

MEFs.

Limitations in experimental design

While our findings in a mouse embryonic Rev-erbα loss of function model provide several lines of

direct inquiry, modern methodology provides avenues to systematically test the role of Rev-erbα

mediated transcriptional repression on metabolism. While our findings suggest that Rev-erb may

directly or indirectly regulate the expression of genes in glycolysis and the pentose phosphate

pathway, they do not determine if these genes are direct targets of Rev-erbα. A variety of large

datasets have been generated recently both to look at the targets of Rev-erbα and Rev-erbβ, the

tissues of Rev-erbα global WT and KO mice (6,39). Examination of these large Chromatin

Immunoprecipitation datasets will provide valuable insight into whether the effect of Rev-erbα

levels on expression of transketolase, ribose-5-phosphate isomerase, and hexokinase are

conserved across mammalian tissue types.

Additionally, several experiments described in chapters 2 and 3 of this work have procedural

design limitations that could limit the significance of experimental findings. While gene expression

of Timm23 and levels of Tomm20 were unchanged in KO MEFs relative to WT (Figure 4), electron

microscopy is needed to perform a more complete evaluation of mitochondrial morphology (93).

In knockout MEFs, increased glycolysis is correlated with an increase in hexokinase, but not

pyruvate kinase. Gene expression findings for pyruvate kinase are limited in that one set of RT-

PCR primers is used to determine combined expression levels for the m1 and m2 isoforms of

pyruvate kinase. This approach limits the ability to determine the individual expression of each

isoform and to assess whether in increase in one relative to the other underlies increased

glycolysis. Determination of pkm2 protein levels may be particularly important, as induction of this

enzyme plays an important role in the hyper glycolytic phenotype of cancer cells exhibited in the

65

Warburg effect (87,95). Additionally, while inhibition of hexokinase via 2’-deoxy-glucose

attenuated glycolysis and proliferation (Figure 13), inclusion of a WT control is needed for a more

complete comparison of the effect of Rev-erbα disruption of these phenotypes. Finally, while

levels of senescent cells are relatively low in WT, KO, and SD genotypes relative to the total

number of cells in each sample (Figure 15), a positive control composed of senescent fibroblasts

is needed to more completely evaluate these findings.

The effect of SV40 immortalization

While immortalized MEF cells served as an efficient model in which to study the effects of Rev-

erbα disruption on metabolism and function, it is possible that SV40 immortalization results in

phenotypes different than those of primary fibroblasts. The widely conserved result of viral

transformation of cell lines is the induction of mutations and translation of viral oncoproteins that

interfere with normal cellular growth and biosynthetic processes (96). In the context of SV40, the

most notable of these viral oncoproteins are the large and small T antigen. The large T antigen is

known to bind to the tumor suppression protein p53 allowing the immortalized MEF cells to

overcome cell cycle arrest (97). These finding confirm that transformed MEF cells will have greater

rates of proliferation compared with primary fibroblasts.

The effect of SV40 transformation extends not just to proliferation, but to metabolism as well. In

foreskin fibroblasts, serial transduction of DNA viruses including SV40 leads to a metabolic profile

indicative of an increased dependency on glycolysis relative to oxidative phosphorylation for

energy production (98). Mechanistically, small T antigen is known to bind protein phosphatase 2A

(PP2A) to inhibit its activity, resulting increased phosphorylation of downstream kinases that are

PP2A targets (99). Amongst these kinases is Protein kinase B, alternatively known as AKT, which

is activated following SV40 transformation (100). In hematopoietic leukocyte cell line, stimulation

of AKT has shown to increase rates of aerobic glycolysis following plasmid mediated

overexpression, while maintaining comparable levels of oxidative phosphorylation relative to

66

untreated controls (101) . Another enzyme that can be activated in a similar manner is AMP-

activated protein kinase. In immortalized human foreskin and embryonic kidney fibroblasts,

expression of the small T antigen protects cells from glucose deprivation (102).

The metabolic effects of SV40 immortalization are not just limited to glycolysis, but the pentose

phosphate pathway as well. In SV40 transformed human fibroblasts, there is increased activity of

the enzyme transaldolase relative to untransformed controls. Transaldolase mediates the

transformation of glyceraldehyde 3-phosphate to fructose-6-phosphate in the non-oxidative

pentose phosphate pathway, affecting the supply of NADPH and ribonucleotides. Intriguingly,

increased transaldolase activity in SV40 transformed human fibroblasts was correlated with lower

levels of NADPH and catalase activity (103).

In totality, SV40 immortalization is known to affect glycolysis, the pentose phosphate pathway,

proliferation, and the oxidative stress response. These findings bring to light an important

limitation in interpreting results in an immortalized MEF model of Rev-erbα disruption, namely that

the effects of Rev-erbα disruption may be compounded or attenuated by SV40 transfection

relative to an untransformed fibroblast.

Summary

In conclusion, disruption of Rev-erbα results in increased levels of glycolysis that drives increased

proliferation in a phenomenon resembling the Warburg effect. Increases observed in oxidative

phosphorylation and glycolysis are most likely attributable to individual changes in enzymatic

expression in the absence of changes in mitochondrial mass. Increased expression of hexokinase

leads to an upregulation of glycolysis, but changes in non-oxidative PPP expression do not impact

this phenotype. Rather, increased expression of the non-oxidative PPP may result in a depletion

of the cellular pool of NADPH which leads to a reduced ability to combat oxidative stress. These

findings demonstrate that Rev-erbα can modulate oxidative phosphorylation, glycolysis,

proliferation, and the response to oxidative stress. These changes are driven through specific

67

changes in enzymatic expression of hexokinase II, the non-oxidative pentose phosphate, and the

pyruvate dehydrogenase complex. The findings that Rev-erbα can robustly modulate glycolysis

and proliferation in fibroblasts further inform the body of literature exploring the protein as a

therapeutic target for cancer and reinforce its role in response to oxidative stress. More

fundamentally, disruption of Rev-erbα in mouse embryonic fibroblasts has wide ranging effects

on metabolism and function which may inform future studies into the role of the protein in other

tissue types and potential therapeutic applications.

68

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APPENDIX: ASSESSING PENTOSE PHOSPHATE PATHWAY EXPRESSION AND

LOCALIZATION IN VIVO

By

Sean Patrick Gillis, Abigail Peterson, and Phyllis A. Dennery

Acknowledgements: Abigail Peterson for her work engaging in husbandry of mice and collection

of lung tissue.

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Abstract

The finding that enzymes of the non-oxidative pentose phosphate pathway led to an inquiry into

whether such changes could be recapitulated in the lung. RPIA and TKT expression were

unchanged globally in the lung following Rev-erbα disruption. However, transketolase exhibited

strong localization in the bronchi and novel localization in the nucleus of alveolar type II cells.

Globally, transketolase protein levels were reduced in the lungs of Rev-erbα KO mice relative to

WT. Reduction in transketolase protein levels was correlated with a reduction in levels of the

alveolar type I cell marker aquaporin I, suggesting reduction in transketolase levels may be

correlated with loss of this key functional cell type.

Introduction

The Dennery laboratory’s focus on the effect of hyperoxia on the lungs of premature infants,

coupled with a previous result that demonstrated that Rev-erbα levels are ablated in the murine

lung following exposure to hyperoxia (95% O2 / 5% CO2) for 72 hours compared with room air (21

% O2 / 5% CO2), led to the inquiry on the consequences of Rev-erbα disruption on the lung in vivo

(104). Expression of the non-oxidative pentose phosphate pathway in MEFs prompted an inquiry

into whether such changes could be recapitulated in RNA extracted from the entire murine lung

of adult mice.

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Results

Expression of both enzymes was lower overall in the lung than in MEFs, exhibiting a high degree

of biological variability between animals and no significant difference in expression between mice

genotyped as WT or KO (Fig Apx-1)

Figure Apx-1. Expression of non-oxidative PPP enzymes in unchanged in the murine lung. Expression of transketolase (left) and ribose-5-phosphate isomerase (right) exhibit in the murine lung exhibits a high degree of biological variability. n = 8 independent experiments. Error bars represented as mean ± SEM

The high degree of biological variability in terms of non-oxidative pentose phosphate pathway

expression in the lung demonstrated the need to determine if changes in expression of these

enzymes was more localized to expression in an individual pulmonary cell type. To accomplish

this, immunohistochemistry was performed using to look at colocalization of transketolase with

surfactant protein c which is produced by alveolar type ii cells which are necessary for surfactant

production to expand the surface area of the lung to facilitate gas exchange and for the

regeneration of alveolar type I cells which help form the proper compartmentalization of alveoli

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(105). Transketolase exhibited a novel, strong nuclear signal across genotypes that colocalized

with cytoplasmic surfactant protein c (Fig Apx-2)

Figure Apx-2. Lung immunohistochemistry for transketolase demonstrates novel nuclear localization Transketolase (red) demonstrates a strong nuclear signal (white arrow) in alveolar fields colocalizing with cytoplasmic signal from surfactant protein c.

Rev-erbα KO MEFs demonstrated increased proliferation and numbers relative to WT that

correlated with increased expression of non-oxidative pentose phosphate pathway genes. To see

if the frequency of transketolase expression in alveolar type II cells would be increased in the KO

lung relative to WT, quantification was performed. The majority of transketolase positive cells

were registered as alveolar type ii cells. When computed as a percentage of total positive cells in

alveolar fields, or frequency in alveolar type ii cells, there was no significant change in

transketolase frequency (Fig Apx-3)

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Figure Apx-3. Frequency of transketolase expression is unchanged in the murine lung. Frequency of transketolase positive cells in alveolar fields, quantified as percentage of TKT positive cells in alveolar II cells, double positive for transketolase and surfactant protein c, and as a total percentage of cells in alveolar fields. n = 5 independent experiments, 5 40x fields per experiment. Error bars represented as mean ± SEM

To assess whether a high degree of colocalization with transketolase would correlate with any

difference in alveolar type ii cell numbers, the quantity of cells that registered as surfactant protein

c positive was also quantified, with a no difference in quantity alveolar type ii cells in the KO lung

relative to the WT (Fig Apx-4). This result ran contrary to results exhibited in MEFs, where

disruption of Rev-erbα resulted in increased proliferation. This result suggests a level of cell

specificity in terms of functional effects of Rev-erbα disruption in the lung.

Figure Apx-4. Alveolar type II cell numbers are unchanged following Rev-erbα. Alveolar type II cell numbers are reduced following Rev-erbα disruption. (left) Representative IHC images of alveolar fields in WT and KO lungs. (right) percentage of alveolar type II cells in alveolar fields. p < 0.05 WT vs KO n = 5 independent experiments, 5 fields counted.

88

A striking observation of transketolase expression in the lung was that transketolase is highly

expressed in the bronchi which direct the flow of air from the trachea to the alveoli for gas

exchange via the capillaries (Fig Apx-5) No difference in the frequency of transketolase

expression in these structures was detected, but the overall fluorescence intensity of expression

remains to be evaluated.

Figure Apx-5. Transketolase is abundantly expressed in the bronchi. (left). Representative IHC images of tkt (red) and dapi (blue). (left) Percentage of bronchiolar cells registering as TKT+. n = 5 independent experiments, 5 40x fields counted per experiment. Error bars represented as mean ± SEM

To determine the overall correlation of TKT levels to quantities of important functional lung cell

types, western blotting was performed for TKT in total lung tissue (Fig Apx-6a). In contradiction

to IHC results, TKT levels were reduced in the overall lung with these changes correlating with a

reduction in levels of the alveolar type I cell marker aquaporin 1 (106), but not the myofibroblast

marker vimentin (107) (Fig Apx-6b). This result suggests that a reduction in transketolase, which

is functionally associated with nucleotide biosynthesis and proliferation is correlated with a

reduction in alveolar type I cell numbers, which may predispose the lung to injury when faced with

an oxidative insult.

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Figure Apx-6. Ablation of transketolase levels correlates with reduction of the type 1 cell marker aquaporin 1. a) densitometry analysis of transketolase levels in Rev-erbα WT and KO mice b) Representative levels of vimentin in the WT and KO lung c) Representative levels of aquaporin I in the WT and KO lung. n = 5 independent experiments. *p<0.05 WT vs KO. Error bars represented as mean ± SEM

Discussion

Questions remain as to the effects of Rev-erbα disruption in vivo on tissue types that have not

been as well characterized as the liver. The Dennery laboratory is particularly interested in

whether metabolic changes exhibited in fibroblasts can be recapitulated in the lung, where there

is little to no data regarding the effects of Rev-erbα perturbation. We conducted an initial inquiry

as to the prevalence of transketolase in the lung, finding that levels of transketolase were

downregulated in the knockout background globally, with transketolase exhibiting a strong, novel

nuclear signal in alveolar type II cells (see appendix). This finding runs contrary to its known

localization in the cytoplasm. It has been recently reported in a model of liver cancer that

transketolase can relocate to the nucleus to and interact with signal transducer and activator of

transcription 1 (STAT1) to inhibit genes involved in the synthesis of bile acids (108). Nuclear

translocation of transketolase was additionally shown to be a predictor of reduced disease-free

and overall survival in hepatocellular carcinoma patients, driving a hyperproliferative phenotype

with increased cell cycle progression (58). Whether this nuclear translocation of transketolase is

conserved in the lung or our mouse embryonic fibroblasts model remains a question to be

addressed.

Conclusions

RPIA and TKT expression in unchanged following Rev-erbα disruption in the mouse lung.

Transketolase exhibits a novel nuclear localization to the nuclei of alveolar type II cells, and its

protein levels are reduced globally in the KO lung relative to WT. Reduction in transketolase levels

91

is correlated with loss of the alveolar type I cell marker aquaporin I suggesting a correlation of the

non-oxidative PPP with lung alveolarization.

Methods

The following methods describe technical details for procedures used to generate data in the

appendix of this document which detail the in vivo effects of Rev-erbα disruption on the murine

lung.

Murine model and lung tissue preparation

C57BL/6 mice embryos that were WT or global Rev-erbα KO were obtained from the Jackson

Laboratory (Bar Harbor, ME, USA). DNA was obtained through digestion of samples taken from

mouse ears, amplified using Sybr Green Master Mix (Thermo Fisher, Waltham, MA, USA), run on

a 1% agarose gel for 15 minutes, then visualized using a Bio-Rad Gel Doc EZ documentation

system (Bio-Rad, Hercules, CA, USA). Mice were maintained in a uniform diurnal light/dark cycle

in the Brown Animal Care Facility (Providence, RI, USA). Adult mice are postnatal day 60 and

neonatal mice are postnatal day 3 at time of tissue harvest. Samples were harvested at Zeitgeber

time 3. Mice were euthanized using either ketamine injection or cervical dislocation. Lung tissue

was collected through snap freezing the left lobe of then lung and bronchoalveolar lavage through

1X phosphate buffered saline wash. For assessment of lung morphology, lungs were inflated with

1% agarose and embedded in paraffin at the Brown Molecular Pathology Core (Providence, RI,

USA). 5 µM lung tissue slices were sectioned using a Leica RM2265 automated microtome at

the Brown Pathology Core. Sections were cured on charged slides at 65°C

Quantitative PCR

Relative expression of RPIA and TKT was assessed in RNA isolated using the Trizol System as

previously described with primers identical to those used in MEFs.

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Immunohistochemistry (IHC)

Slides were deparaffinized in xylene followed by rehydration using a 100% v/v to 50% v/v alcohol

series. Antigen retrieval was performed through boiling in citrate unmasking solution (Cell

Signaling Technologies, Danvers, MA) , followed by cool down to room temperature. Sections

were permeabilized using PBS-T (PBS with 0.2% v/v tween), samples were then blocked using

PBS with 0.25% v/v milk. An additional mouse on mouse blocking solution was applied if primary

and secondary antibody species matched (MKB-2213, Vector Laboratories, Burlingame, CA,

USA). Primary antibody incubation consistent of antibody diluted in washing solution (PBS, 10%

v/v PBS-T, 0.1% Bovine serum albumin, and 0.5% v/v milk). Secondary antibody incubation was

then performed through antibody dilution in washing buffer, followed by overnight DAPI staining

using Prolong Diamond (Thermo Fisher, Waltham, MA , USA) reagent protected from light.

Primary antibodies were as follows transketolase (#sc-390179, Santa Cruz Biotechnology, Dallas,

TX, USA, 1:50), surfactant protein C (#ab90716, Abcam, Cambridge, MA, USA, 1:200), vimentin

(#ab92547, Abcam, Cambridge, MA, USA, 1:500), and hopx (sc-398703, Santa Cruz

Biotechnology, Dallas, TX, USA, 1:50). Secondary antibodies were as follows goat-anti-mouse

Alexa Flour 488 Igg (#ab150077, Abcam, Cambridge, MA, USA). Images were obtained on the

FITC, TRITC, and DAPI channels of a Zeiss Axiovert 200M Fluorescence Microscope in the

Leduc Bioimaging Facility of Brown University (Providence, RI, USA). Quantification was

performed using manual counting of nuclei positive for each signal using imageJ software, with

the numerator or denominator denoted as the nuclei positive for the specified target.

Immunoblotting

Western blotting was performed as detailed above. Primary antibodies and dilutions were as

follows aquaporin I (#ab168387, Abcam, Cambridge, MA, USA), aquaporin V (#ab784860).

Transketolase and surfactant protein C antibodies were the same as used in IHC experiments,

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with all primary antibody dilutions at 1:1000. Calnexin (#ADI-SPA-860-F, Enzo, Farmingdale, NY,

USA) was used as a loading control.

In vivo statistical analysis

Graph Pad Prism 6 software was used for unpaired students t-tests for comparisons between

groups, with a p < 0.05 used as a threshold for significance. Experiments were repeated

biologically five times, with three technical triplicates for quantitative PCR and five fields taken for

image quantification in IHC analysis. Error bars are reported as the standard error of the mean.

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