Evolution of the Pgc-1 Protein Family in the Control of Oxidative Metabolism in Vertebrates

EVOLUTION OF THE PGC-1 PROTEIN FAMILY IN THE CONTROL OF OXIDATIVE METABOLISM IN VERTEBRATES

Christophe Marie Renaud Le Moine

A thesis submitted to the Department of Biology

In conformity with the requirements for

the degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

(July, 2008)

Mitochondrial biogenesis requires an intricate transcriptional coordination between the nuclear and mitochondrial genomes to establish the structural and functional components of the organelle. This coordination is paramount in vertebrate muscles where oxidative capacity must be adjusted to meet varying energy demands. I investigated the regulatory circuits controlling mitochondrial content in vertebrate muscle in the context of development, adaptation to nutritional status and temperature, and in an evolutionary perspective.

Initial experiments focused on the role of transcriptional regulators in the metabolic changes in the myocardium of aging rat. I hypothesized that the changes in oxidative capacity associated with aging would be primarily driven by the peroxisome proliferator activated-receptors (PPARs), the nuclear respiratory factors (NRFs) and their common coactivator PPAR coactivator-1 (PGC-1 . However, the reduction in oxidative capacity in the heart of old rats was independent of these regulatory axes and occurred partially through post-transcriptional processes.

The next series of experiments investigated the transcriptional networks regulating the metabolic remodelling in goldfish subjected to dietary and temperature stress. As a potent regulator of mitochondrial proliferation in mammals, I hypothesized that PGC-1 assumed a similar role in lower vertebrates. Similar to their mammalian homologues, PPAR and NRF-1 assumed their respective roles in regulating lipid metabolism and mitochondrial proliferation in goldfish. In contrast, PGC-1 was only a

ii good predictor of the PPAR axis, while PGC-1 was a better indicator of the NRF-1 and mitochondrial gene expression axis.

This apparent divergence of the PGC-1 homologues in vertebrates inspired the subsequent study, in which I investigated the evolutionary history of the PGC-1 family in vertebrates. Specifically, I sought to assess if PGC-1 functional divergence had a structural and evolutionary basis. PGC-1 phylogeny revealed asymmetric rates of evolution across the different domains of the protein. The domains essential to PGC-1 coactivating activity as well as PPARs interaction motifs were relatively well preserved in all lineages. In contrast, the NRF-1 interacting domain experienced accelerated rates of evolution in fish lineages compared to tetrapods. In addition, fish PGC-1 exhibited consequent insertions in this domain that could have important repercussions on its ability to bind NRF-1 and regulate mitochondrial gene expression.

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Co-Authorship

Chapters 1-4 are coauthored with my supervisor Dr. Chris Moyes, who contributed to experimental design, analysis, writing and provided the funding, but thankfully no benchwork. Excerpts from Chapter 1 (sections 1.2.3-1.2.4) were excerted from a published review paper (Moyes and Le Moine 2005). Chapter 2 (Le Moine et al. 2006) is a large collaborative study in collaboration with: O. Mathieu-Costello from University of

California San Diego coordinated animal care and tissue harvesting, Dr. G. McClelland

(currently at McMaster University) participated in the analysis of lipid oxidation enzymes activities and mRNA, and C. Lyons participated in mitochondrial enzymes activities and mRNA analyses). Chapter 3 (Le Moine et al. 2008) is coauthored with C. Genge who collaborated on experimental design and preliminary RNA analyses. When published,

Chapter 4 will be coauthored with Dr. S. Lougheed from Queen’s University who provided guidance in the evolutionary analyses.

Moyes CD, and Le Moine CMR. Control of muscle bioenergetic gene expression: implications for allometric scaling relationships of glycolytic and oxidative enzymes. J. Exp. Biol. 2005. 208: 1601-10.

Le Moine CMR, McClelland GB, Lyons CN, Mathieu-Costello O, and Moyes CD. Control of mitochondrial gene expression in the aging rat myocardium. Biochem. Cell Biol. 2006. 84: 191-8.

Le Moine CMR, Genge CE, and Moyes CD. Role of the PGC-1 family in the metabolic adaptation of goldfish to diet and temperature. J. Exp. Biol. 2008. 211: 1448- 55.

Acknowledgements

I would like to start by thanking Chris Moyes for his supervision, support, friendship over my time in his lab, and for even providing me with shelter over the past few months. The experience in the lab wouldn’t have been the same if it weren’t for all current and previous Moyesians who made this experience truly unique. Denise Michaud and Mel Fortner provided invaluable technical expertise and friendship. I also particularly thank the current members of the lab -Rhe, Alexander George, Skippy and Xtine- for their help in the lab and for enduring my rollercoasting mood and crazy tunes over the past few years! Big thanks to all my friends who passed through the department that made my time in Kingston not only studious but fun too, especially Justino, Sweets,

Krautchy, Islamilne, James and many more who I hope won’t be offended if by chance they read these pages.

Finally, I wouldn’t be there if it weren’t for the support and encouragements from my wife Laurence, and of course my son, Ben who provided additional motivation to be done on time in these last few months.

Table of Contents Abstract ...... ii Co-Authorship ...... iv Acknowledgements ...... v Table of Contents ...... vi List of Figures ...... ix List of Tables ...... x List of abbreviations ...... xi Chapter 1 . Introduction and General literature review...... 1 1.1 Overview ...... 1 1.2 Mitochondria and oxidative metabolism ...... 2 1.2.1 Mitochondrial structure ...... 4 1.2.2 Mitochondrial function ...... 7 1.2.3 Mitochondrial biogenesis ...... 8 1.2.4 DNA binding transcriptional regulators ...... 10 1.3 The PGC-1 family of coactivators ...... 13 1.3.1 PGC-1 Structure ...... 14 1.3.2 Regulation of PGC-1 expression and transcriptional activity ...... 16 1.3.3 Physiological roles of the PGC-1 family ...... 19 1.4 Evolution of transcriptional regulation ...... 22 1.4.1 Evolution of cis-elements ...... 22 1.4.2 Evolution of transcription factors ...... 24 1.5 Thesis overview ...... 26 Chapter 2 : Control of mitochondrial gene expression in the aging rat myocardium ...... 29 2.1 Abstract ...... 29 2.2 Introduction ...... 30 2.3 Materials and Methods ...... 32 2.3.1 Animals and tissue collection ...... 32 2.3.2 Enzyme assays ...... 33 2.3.3 Nucleic acids ...... 34 2.3.4 Statistical analysis ...... 35 2.4 Results and Discussion ...... 35 vi

2.4.1 Mitochondrial enzymes ...... 37 2.4.2 The PPAR axis ...... 40 2.4.3 Other transcriptional regulators ...... 41 2.5 Acknowledgements ...... 42 Chapter 3 . Role of the PGC-1 family in the metabolic adaptation of goldfish to diet and temperature ...... 48 3.1 Abstract ...... 48 3.2 Introduction ...... 49 3.3 Methods ...... 52 3.3.1 Animals and treatments ...... 52 3.3.2 Enzyme Analysis ...... 52 3.3.3 RNA Analysis ...... 53 3.3.4 Statistical analysis ...... 54 3.4 Results ...... 54 3.4.1 Mitochondrial enzyme activities ...... 54 3.4.2 Temperature acclimation and mRNA ...... 55 3.4.3 Dietary treatments and mRNA ...... 57 3.4.4 Relative abundance of PGC-1 family members ...... 58 3.4.5 Global patterns in transcriptional regulators ...... 59 3.5 Discussion ...... 60 3.5.1 Mitochondrial enzymes activities ...... 60 3.5.2 Regulation of expression of genes encoding mitochondrial enzymes ...... 61 3.5.3 Lipid homeostasis gene expression ...... 64 3.5.4 The PGC-1 family ...... 65 3.6 Acknowledgements ...... 66 Chapter 4 . Evolution of the PGC-1 family in vertebrates ...... 73 4.1 Abstract ...... 73 4.2 Introduction: ...... 74 4.3 Methods ...... 77 4.3.1 PGC-1 sequences: ...... 77 4.3.2 Phylogenetic analyses: ...... 78 4.4 Results ...... 79 vii

4.4.1 PGC-1 gene in vertebrates ...... 79 4.4.2 Protein homologies ...... 80 4.4.3 Phylogenetic divergence ...... 82 4.5 Discussion: ...... 84 4.5.1 Evolution of the PGC-1 family: ...... 85 4.5.2 PGC-1 evolution ...... 86 4.6 Summary ...... 91 Chapter 5. General Discussion ...... 101 Literature cited ...... 107 Appendix 1. PGC-1 amplified sequences and specific primers...... 130

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List of Figures Figure 1.1. Structure and similarities of the PGC-1 family members...... 27 Figure 1.2 Model of transcriptional regulation by PGC-1 ...... 28 Figure 2.1. Catalytic activities and mRNA levels of mitochondrial enzymes in the cardiac muscle of adult and very old rats...... 46 Figure 2.2. Transcript levels for transcriptional regulators of bioenergetic genes in the cardiac muscle of adult and very old rats...... 47 Figure 3.1. Mitochondrial enzyme activities in tissues of goldfish exposed to three temperatures (4, 20, 35ºC) and three diets (fasted, control and high fat diet)...... 68 Figure 3.2. Gene expression in goldfish exposed to three acclimation temperatures (4, 20, 35ºC)...... 69 Figure 3.3. Gene expression in goldfish exposed to three diets (fasted, control and high fat diet)...... 70 Figure 3.4. PGC-1 mRNA expressed as a proportion of PGC-1 transcript levels ...... 71 Figure 3.5. Putative regulators of metabolic gene expression in goldfish tissue...... 72 Figure 4.1. Phylogeny of the PGC-1 family...... 95 Figure 4.2. Synteny and structure of the PGC-1 gene in vertebrates...... 96 Figure 4.3. Structure and conservation of the PGC-1 protein in vertebrates...... 97 Figure 4.4. PGC-1 phylogeny in vertebrates...... 98 Figure 4.5. Phylogeny of the PGC-1 functional domains in vertebrates...... 99 Figure 4.6. Comparison of the difference of the mean number of substitution between the ray- finned fishes and the tetrapod lineage...... 100

List of Tables Table 2.1. List of primers and target rat sequences used for the RT-PCR amplification of the probes used in northern blotting analysis...... 44 Table 2.2. Morphological and molecular characteristics of adult and very old rats...... 45 Table 3.1. Real time PCR primers...... 67 Table 4.1. PGC-1 sequence information of representative vertebrate species used in this study...... 92 Table 4.2. Protein information for the PGC-1 homologues used in the phylogenetic reconstruction of the PGC-1 family...... 93 Table 4.3. Covalent modifications of PGC-1 and their conservation in vertebrates...... 94

List of abbreviations

a.a., amino acid

ACON, aconitase

AD, activation domain

AMP, adenosine monophosphate

AMPK, AMP kinase

ATP, adenosine triphosphate

BAT, brown adipose tissue

COX, cytochrome c oxidase

CS, citrate synthase

CPT, carnitine palmitoyltransferase

CREB, cyclic AMP response element binding protein

DBD, DNA binding domain

Drp, dynamin-related protein

EDTA, ethylenediaminetetraacetic acid

EF-1α, elongation factor -1α

ER, estrogen receptor

ERR, estrogen-related receptor

ETC, electron transport chain

ETS, E26 transformation-specific

F344BNF1, F1 hybrid F344 X Brown Norway

FADH2, reduced flavin adenine dinucleotide

FAO, fatty acid oxidation

FAS, fatty acid synthase

FOXO1, forkhead box O1

GCN5, general control of amino-acid synthesis 5

GTC, guanidium thiocyanate

GTF, general transcriptional machinery

HCF, host cell factor

HEPES, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HOAD, -hydroxyacylCoA dehydrogenase

HNF4 Hepatocyte nuclear factor 4

HOAD, -hydroxyacylCoA dehydrogenase

IDH, isocitrate dehydrogenase

IRS, insulin-response sequence

KO, knockout

LCAD, long-chain acyl-CoA dehydrogenase

MCAD, medium-chain acyl-CoA dehydrogenase

MAP kinase, mitogen activated protein kinase

MCMC, Metropolis coupled Markov chains

MEF, myocyte enhancing factor

MFN, mitofusin mtDNA, mitochondrial DNA

xii mtTFB, mitochondrial transcription factor B

NADH, reduced nicotinamide adenine dinucleotide

NHR, nuclear hormone receptor

NRF-1 (or -2), nuclear respiratory factor -1 (or -2)

OXPHOS, oxidative phosphorylation

PGC-1α, PPAR gamma coactivator-1α

PKB, protein kinase B

Pol II, RNA polymerase 2

POLRMT, polymerase (RNA) mitochondrial (DNA directed)

PPAR, peroxisome proliferator-activated receptor

PRC, PGC-1 related coactivator

PRMT1, protein arginine methyltransferase 1

RIP140, Receptor Interacting Protein of 140 kDa

RBD, RNA binding domain

RE, response elements

ROS, reactive oxygen species

RRM, RNA recognition motif

RT-PCR, reverse transcriptase polymerase chain reaction

RxR, retinoid acid receptor

SEM, standard error of the mean

Ser, serine

Sirt1, silent mating type information regulation 2 homolog 1

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SDS, sodium dodecyl sulfate

Sp1, specificity protein 1

SR, serine-arginine

SREBP1c, Sterol Regulatory Element Binding Protein 1c

TCA, tricarboxylic acid cycle

TF, transcription factor

TFAM, mitochondrial transcription factor A

Thr, threonine

TRAP, thyroid hormone receptor-associated protein

UCP1, uncoupling protein 1

VDAC, voltage-dependent anion channels

YY1, yin yang 1

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Chapter 1. Introduction and General literature review

1.1 Overview

Energy in the form of ATP is an absolute requirement for virtually all life processes. At the cellular level, these energetic needs are primarily met by glycolysis and oxidative phosphorylation (OXPHOS). ATP production rates can differ between tissues as a result of numerous quantitative and qualitative mechanisms, depending on the nature of the comparison. In a given cell, changes in ATP production are achieved by altering the flux of existing enzymes through mass action effects and regulation of enzymatic activity (allosteric modulation and covalent modifications). When facing long term changes in energy requirements, cells can also adjust the levels of enzymes through synthesis and degradation. These same mechanisms may also contribute to differences evident when comparing tissues (e.g., liver and muscle), though there is also a potential role for enzyme isoform differences. Furthermore, evolutionary variations between species can generate differences in energy metabolism through genetic mutations. Such mutations in the genes for enzymes might affect enzyme structure or function, or the nature of transcriptional regulation of the gene (e.g., basal or inducible expression).

Alternately, enzyme levels might be affected by mutations in the coding or regulatory regions of the transcription factors that affect the basal expression or responsiveness of genes of energy metabolism. Whether considering the variation in energy metabolism seen between cells over time, between tissues of an individual, or between homologous tissues of species, a recurring theme is the role of transcriptional regulation in the

determination of enzyme levels. The pathways of energy production are complex, and naturally changes in the levels of the enzymes of pathways must be coordinated. Thus, an emerging theme in this area is the mechanisms by which these genes are coordinated to achieve the appropriate levels of expression. In my thesis, I focus on the origins of variation in the mitochondrial content of vertebrate muscles, specifically the regulatory pathways that control metabolic gene expression. I investigated these transcriptional networks in the context of developmental variation in mammalian muscle (Chapter 2), adaptive response to metabolic stressors in fish tissues (Chapter 3) and the evolutionary trajectories of these regulators in vertebrate history (Chapter 4).

1.2 Mitochondria and oxidative metabolism

Under aerobic conditions, mitochondrial respiration produces most of the energy required to support cellular processes. Although, mitochondria are primarily energy producers, they also play important roles in cell death, biosynthesis, thermogenesis, redox balance and in the production of reactive oxygen species (ROS) (see Dulloo et al. 2004,

Fernie et al. 2004, Gulbins et al. 2003, Mozo et al. 2005, Turrens 2003). Accordingly, mitochondrial capacity must be adjusted to meet the fluctuations in energy demands of a cell or tissue. Muscles in vertebrates present an interesting system to study differences in mitochondrial content as wide variation in oxidative capacity can be seen between muscle types (e.g., oxidative vs. glycolytic fibers), within an individual (i.e., physiological remodeling), and across species (e.g., active vs. sedentary species).

Vertebrate muscle is generally classified according to its contractile characteristics. Mammals are generally thought to produce four main muscle fiber types, distinguished on the basis of the myosin heavy chain isoforms: Type I, IIa, IIb, and IIx/d.

Type I (or slow oxidative) is the most oxidative, typically possessing the smallest fiber diameter and greatest mitochondrial content. At the other extreme is the Type IIb (fast glycolytic) fiber with relatively large diameter and low mitochondrial content.

Mammalian muscles are relatively heterogeneous in fiber composition and possess a mosaic of fiber types. The proportion of each fiber type dictates the overall phenotype of the muscle (i.e., more or less oxidative). In contrast, fish have more homogeneous muscle types. Much of the muscle mass of the animal consists of glycolytic fibers, typically about 60% of the fish total body mass (see Sanger and Stoiber 2001). The red muscle is highly oxidative with mitochondrial contents three- to ten-times higher than white muscles, and accounts for about 10% of the muscle mass (e.g., Johnston and Moon

1981). In some species a pink muscle exists at the interface of the red and white muscles and presents a mixture of both fiber types (see Sanger and Stoiber 2001). The difference in the organization of muscles in fish relative to mammals has been used to advantage when assessing the influence of fiber type on muscle contractile properties.

The vertebrate muscle phenotype is relatively plastic. It changes throughout development and responds to changes in the internal and external environment. Though typically studied from the perspective of the contractile machinery, muscle remodeling includes changes in energy metabolism and mitochondrial content. Adaptive remodeling

of muscle energy metabolism of mammals is perhaps best studied in the context of development (i.e., from myogenesis to aging), activity levels (e.g., exercise training and detraining), and neuromuscular pathologies (reviewed in Biressi et al. 2007, Boveris and

Navarro 2008, Chabi et al. 2005, Conley et al. 2007, Hood et al. 2006). In fish, most studies of muscle remodeling of energy metabolism have focused on early growth

(Johnston 2006, Ochi and Westerfield 2007), environmental factors (primarily temperature) and endurance training (Antilla et al. 2008, Farrell et al. 1991, Guderley

2004, McClelland et al. 2006). Regardless of the natural or experimental paradigm, vertebrate muscles share a need to regulate their mitochondrial content, according to their energetic needs. Given the relative conservation of transcriptional regulation, it is likely that there is considerable overlap between the pathways that govern mitochondrial remodeling in these diverse models and paradigms. Thus, my thesis explores for both unifying themes and important distinctions among tissues, physiological states and species.

1.2.1 Mitochondrial structure

The organelle possesses two membranes: a simple outer membrane and a more elaborate, convoluted inner membrane. The composition of the outer membrane is similar to the plasma membrane, whereas the inner membrane is more specialized, an arrangement that is thought to reflect the endosymbiotic origin of mitochondria

(Falkenberg et al. 2007, Gray et al. 1999, Lang et al. 1999). The region between the two membranes is the inter-membrane space, and the innermost compartment is the matrix.

Each compartment is specialized for specific functions, many of which contribute to

OXPHOS.

The outer membrane is relatively permeable to small molecules and metabolites, primarily because of the presence of porins and voltage-dependent anion channels

(VDAC; Colombini 2004). The channels permit the movement of molecules smaller than about 10,000 Da. The inner membrane is highly folded, creating invaginations (cristae) in the matrix that maximize the surface area for the respiratory apparatus. This membrane is selectively permeable through specific channels, allowing electrochemical gradients to be maintained between the matrix and the inter-membrane space (Antonenko et al. 1991,

Bernardi 1999, Beavis 1992, Garlid 1996, O’Rourke 2007). In several locations, the two membranes are in close proximity, creating regions known as contact points, which play important roles in the protein translocation and nucleotide transport systems

(Hackenbrock 1966, Perkins et al. 1997, Schatz and Dobberstein 1996). The inner membrane is rich in cardiolipin, an acidic and hydrophobic phospholipid restricted to mitochondria, which promotes membrane stability and fluidity (Krebs et al. 1979). In addition to a structural role, cardiolipin is required for maximal activities of most complexes of the electron transport chain (ETC) and is thought to play an integral role in localization of cytochrome c in the inner membrane (Eble et al. 1990, Fry and Green

1981, Nicholls 1974, Rytomaa et al. 1992, Sedlak et al. 2006, Yue et al. 1991). The majority of the OXPHOS machinery is embedded in the inner membrane (ETC), or soluble in the mitochondrial matrix (Krebs cycle and -oxidation of fatty acids).

The classical view of the organelles as single and identical free-floating entities has been replaced by a more complex model of ultrastructural assemblies with distinct capacities (Bakeeva et al. 1978, Kirkwood et al. 1986, Palmer et al. 1977). For example, skeletal and cardiac muscle mitochondria can be either embedded between myofibrils

(intermyofibrilar), or located in close proximity of the sarcolemma (subsarcollemal)

(Palmer et al. 1977). In mammals, these mitochondria exhibit distinct biochemical capacities and can adapt differently to muscle development, activity, and oxygen availability (Koyes et al. 2005, Krieger et al. 1980, Menshikova et al. 2006, Riva et al.

2005, Van Ekeren et al. 1992). There does not appear to be such a difference between intermyofibrillar and subsarcolemmal mitochondria of fish (Battersby and Moyes 1998a).

In most tissues, mitochondria are organized in highly dynamic reticulum, which can be modulated in response to diverse regulators. Mitofusin proteins (MFN-1, 2) mediate fusion of the reticulum, and Dynamin-related proteins (Drp) regulate its fission

(Labrousse et al. 1999, Santel and Fuller 2001, Smirnova et al. 1998). The investigation of the regulatory pathways governing these adaptations is an active area of research, as these ultrastructural changes seem to have an impact on bioenergetic efficiency in diverse metabolic programs (Drew and Leuuwenburgh 2004, Hood 2001, Kirkwood et al. 1987).

However, my thesis is primarily focused on the regulation of mitochondrial levels rather than their structural qualities.

Mitochondria, as well as chloroplasts, are unique among extranuclear organelles by the fact that they possess a circular double-stranded DNA. Mitochondrial genomes are

small (approximately 16.5kb in vertebrates) and highly compact. Mitochondrial DNA

(mtDNA) of metazoans encodes for only 13 OXPHOS proteins. Interspersed among these protein coding genes are 22 tRNAs and 2 ribosomal RNAs, which collectively allow autonomous translation of the mitochondrial mRNA within the mitochondrion. The remaining structural and functional mitochondrial proteins are transcribed from nuclear genes. This includes proteins involved in regulation of mtDNA replication and transcription, such as TFAM and mtTFB. The separation of the mitochondrial genes into two genomes creates a situation where an intricate cross-talk between the two genomes must be established for proper assembly and function of the organelle (reviewed in

Garesse and Vallejo 2001, Goffart and Wiesner 2003).

1.2.2 Mitochondrial function

The primary role of the mitochondria in energy production is accomplished through a series of biochemical reactions occurring across the inner membrane. Reducing equivalents, primarily FADH2 and NADH, are produced in the Krebs cycle and other redox reactions involved in the metabolism of pyruvate, amino acids, and fatty acids.

These reducing equivalents are oxidized by Complex I and II of the ETC, which transfer the electrons to ubiquinone, a mobile electron carrier. As well, other redox enzymes, such as alpha glycerophosphate dehydrogenase and fatty acyl CoA dehydrogenase, act like

Complex II and donate electrons directly to ubiquinone. Reduced ubiquinone transfers its electrons sequentially to Complex III, cytochrome C, Complex IV (cytochrome C oxidase or COX) and finally oxygen. In parallel to the electron transfer reactions,

Complex I, III and IV pump protons across the inner membrane creating a proton gradient or proton motive force. Through F1-Fo-ATP synthase (Complex V), protons re- enter the matrix and drive ATP synthesis coupling the oxidative and phosphorylation reactions. The coupling of these processes –oxidation by the ETC and phosphorylation by

Complex V- is completely dependent on the low inner membrane permeability to protons. Therefore the leakage of protons via specific and unspecific mechanisms leads to a reduction of ATP synthesis, futile cycling of protons and accelerated heat production

(Brand 2005, Brookes 2005, Kadenbach 2003).

In most cases, the relative levels of the OXPHOS Complexes are maintained in close stoichiometry (Hatefi 1985, Schägger and Pfeiffer 2001). It also appears likely that the individual Complexes are arranged into large supercomplexes, which facilitates efficient electron transfer (Dudnika et al. 2005, Schägger and Pfeiffer 2000). Thus, when a cell must change its levels of mitochondria, it faces the challenge of coordinating the expression of the various genes that encode all the subunits of each complex, as well as ensuring the relative levels of the multisubunit Complexes remain in proportion to each other.

1.2.3 Mitochondrial biogenesis

Enzymes, like other proteins, are synthesized by the sequential processes of transcription (mRNA synthesis, processing and nuclear export), translation (initiation, elongation, termination) and post-translational modification (targeting, folding, and assembly). Though most researchers focus on the role of the synthetic steps, levels of

mitochondria and mitochondrial enzymes can also be influenced by degradative processes (e.g., proteolysis and organelle autophagy; Bota and Davies 2001, Luzikov

1999). Many mitochondrial enzymes are composed of subunits that are encoded by both genomes. Thus, synthesis of OXPHOS enzymes, such as COX, has the additional complication of coordinating genomic regulation in two compartments: the nucleocytoplasmic and mitochondrial. Most of the proteins needed to make a mitochondrion are encoded by the nuclear genome. Even the proteins required for mtDNA replication, transcription, and translation are nuclear encoded. Central to the control of mtDNA expression are the nuclear-encoded polymerase (POLRMT) and transcription factors (TF), such as TFAM and mtTFB (Cannino et al. 2007, Scarpulla

2008). Thus, although mtDNA encodes essential subunits of OXPHOS complexes, the bulk of the control of mitochondrial biogenesis is nuclear in origin.

Most recent studies assessing control of metabolic gene expression focus on factors that act via effects on transcriptional regulation of nuclear genes. The challenge in this field of molecular genetics is unraveling networks that involve extrinsic regulators

(endocrine, paracrine, autocrine, mechanical signals), signal transduction pathways

(receptors, protein kinases, protein phosphatases), transcriptional regulators (DNA- binding proteins, coactivators, histone modifying enzymes) and target genes (enhancer elements, repressor elements). Additional processes play important roles in the determination of mitochondrial enzyme levels: post-transcriptional, translational and post-translational events, including protein and RNA degradation (see Moyes and Hood

2003). However, in this era of high throughput mRNA measurements, there is a temptation to focus on the role of transcriptional regulation in determining the patterns seen in enzyme levels. This predilection leads to a focus on the role of specific TFs, and most of my thesis has been focusing on the transcriptional networks regulating mitochondrial adaptations in vertebrates.

1.2.4 DNA binding transcriptional regulators

Mitochondrial biogenesis requires coordinated transcription of nuclear and mitochondrial genes. Coordination of gene expression is achieved through a small number of transcriptional regulators that regulate suites of genes: nuclear respiratory factor 1 (NRF-1), NRF-2, the peroxisome proliferator activated-receptors (PPARs) and the PPARγ coactivator-1 (PGC-1) family (Garesse and Vallejo 2001, Kelly and Scarpulla

2004, Scarpulla, 2002).

NRF-1 is a homodimeric DNA-binding protein that was first identified as an activator of the rat somatic cytochrome c gene (Evans and Scarpulla 1989). It has since been shown to regulate multiple genes involved in OXPHOS and mitochondrial proliferation (see Scarpulla 2008). NRF-1 upregulates the expression of nuclear subunits of all five Complexes of the ETC (Chau et al. 1992, Evans and Scarpulla 1990, Scarpulla

2008), and is a potent inducer of mtDNA transcription via interactions on the promoters of proteins that control transcription of mtDNA, namely TFAM and mtTFB (Gleyzer et al. 2005, Virbasius and Scarpulla 1994). It also plays a role in regulating genes involved in biosynthesis of heme, an essential cofactor of the components of the ETC (Aizencang

et al. 2000, Braidotti et al. 1994). It also controls genes that regulate the mitochondrial protein import and assembly systems (Blesa et al. 2007).

NRF-2, though unrelated in structure, is a TF that plays a similar role to NRF-1 in the coordination of genes encoding mitochondrial proteins. It is a member of the ETS family of transcription regulators, and functions as a heterotetramer composed of two

DNA-binding subunits (α) and either two β subunits or two γ subunits (Gugneja et al.

1995, Vibrasius et al. 1993). It was identified as an activator of rat COXIV but has since been shown to regulate multiple genes associated with mitochondrial biogenesis and oxidative metabolism (Vibrasius et al. 1993). NRF-2 can activate the expression of all nuclear-encoded COX subunits (Ongwijitwat et al. 2006, Ongwijitwat and Wong-Riley

2005), as well as several NRF-1 responsive genes involved in the regulation of mitochondrial transcription and protein translocation (Blesa et al. 2007, Virbasius and

Scarpulla 1994).

Like many metabolic genes, mitochondrial genes are regulated by members of the large nuclear hormone receptor (NHR) gene family. These TFs form homodimers or heterodimers and exert complex effects on target genes. With respect to mitochondrial genes, the most important NHRs are the PPARs. This NHR subfamily is composed of three members: α, β/δ and γ (for reviews, see Berger and Moller 2002, Gilde and Van

Bilsen 2003). The PPARs heterodimerize with a retinoid acid receptor (RxR). In the presence of agonists, such as polyunsaturated fatty acids, the heterodimer binds a PPAR response element in the promoter sequence of target genes to activate transcription.

PPARα and β/δ are the most prevalent isoforms in striated muscle, where they regulate genes related to lipid metabolism (Berger and Moller 2002, Nedergaard et al. 2005, Van

Bilsen et al. 2002). Similarly, estrogen receptors (ERs) homodimers can also regulate mitochondrial proliferation and function (see Chen et al. 2005, Gronemeyer et al. 2004).

Structurally related to ERs, the estrogen-related receptors (ERR) have recently emerged to have a central role in metabolic homeostasis as well (Scarpulla 2008). Unlike PPARs and ERs, these receptors are not known to have any natural ligands and therefore are classified as orphan receptors (Benoit et al. 2006). Another peculiarity of these receptors is their ability to bind DNA as monomers, homodimers or heterodimers (Tremblay and

Giguere 2007). ERR , in particular, appears to play an important role in the regulation of fatty acid oxidation reminiscent of that of the PPARs (Sladek et al. 1997, Vega and Kelly

1997). In addition, it promotes mitochondrial proliferation via direct and indirect regulatory pathways that remain to be fully characterized (Huss et al. 2002, Mootha et al.

2004, Schreiber et al. 2004).

Although many genes related to oxidative metabolism and mitochondrial metabolism share elements that bind NRFs and NHRs, no single TF binds all of the required genes. It wasn’t until a few years ago where the true “master regulator” of mitochondrial biogenesis was discovered (Puigserver et al. 1998). First identified as a coactivator of PPAR , PGC-1 was identified as the protein that could trigger mitochondrial biogenesis in brown adipose tissues (BAT) of mammals (Puigserver et al.

1998). When I began my thesis, there was relatively little information about the relative

importance of PGC-1 and its homologues in metabolic programs in mammals, and it wasn’t until recently that these regulators started to be investigated in other vertebrates

(McClelland et al. 2006, Ueda et al. 2005).

1.3 The PGC-1 family of coactivators

Over the past decade, our understanding of the molecular mechanisms controlling transcriptional activity has expanded exponentially. The traditional model, where activated TFs bind regulatory sequences to induce gene expression, was revealed to be much more complex with the discovery of a class of proteins acting as coregulators of gene expression (Chen et al. 1995, Horlein et al. 1995, McKenna et al. 1999, Onate et al.

1995). Initially described as a small family (Ptashne and Gann 1990), the list of characterized coregulators and their associated effects on gene expression is continuously expanding (see Lonard and O’Malley 2007). This class of regulators does not usually possess endogenous DNA binding capacities; they regulate gene expression through their different motifs that allow interactions with TF, the general transcriptional machinery and associated proteins (Lee et al. 2001, Lonard and O’Malley 2007, McKenna et al. 1999).

In the following sections, I review the structure and functions of the PGC-1 family of coactivators, around which most of my thesis work has been centered. PGC-1 , the founder and by far most studied member of the family, was initially discovered for its potent thermogenic effect in brown adipose tissues (Puigserver et al. 1998). Subsequent database searches revealed two structurally similar proteins, PGC-1 and PGC-1 related coactivator (PRC) (Andersson and Scarpulla 2001, Kressler et al. 2002, Lin et al. 2002a).

1.3.1 PGC-1 Structure

Each of the PGC-1 family members harbors a similar modular organization, relating to different functional domains across the protein (Fig 1.1A). The amino terminus of the paralogues contains a relatively well conserved transactivation domain

(Fig 1.1B; Huss et al. 2002, Sadana and Park 2007). When bound to activated TF, this activation domain interacts with primary coactivators such as SRC-1 and CBP/p300, involved in covalent remodeling of histones (Wallberg et al. 2003). When recruited, these proteins acetylate histones, relaxing the chromatin structure, thereby allowing the docking of the general transcriptional machinery and RNA polymerase 2 (Pol II) and subsequent transcription initiation (Leo and Chen 2000, Marmorstein 2001). The carboxyl portion of the proteins harbor an RNA binding motif highly conserved in all family members (Fig 1.1B). In PRC and PGC-1 this domain is immediately downstream of serine and arginine repeats, a combination characteristic of splicing factors and associated proteins, a function that has been associated with PGC-1

(Monsalve et al. 2000, Samoo et al. 1995). The C-terminus has also been reported to bind the TRAP/mediator complex, a potent activator of transcription through interactions with the general transcriptional machinery (Belakavadi 2006, Wallberg et al. 2003). While these structures are essential for the coactivating capabilities of the paralogues, these functions can only be carried upon binding to active TF, which relieves the inhibitory function of the central portion of the protein (Fig 1.1A; Puigserver et al. 1999).

Several independent binding motifs interspersed throughout the protein allow interactions with specific TFs (Fig 1.1A). The most conserved of these motifs across the

PGC-1 family members, are the leucine-rich motifs (LXXLL) termed nuclear receptor boxes (Fig 1.1). These LXXLL motifs are present in a variety of coactivators, and interact with hydrophobic residues of the ligand binding domain of NHRs (Darimont et al. 1998, Savkur and Burris 2004). Through these interaction motifs, PGC-1 interacts with multiple family members of the NHR superfamily including the PPARs, ERs, ERRs and retinoid receptors (Huss et al. 2002, Puigserver et al. 1998, Schreiber et al. 2003,

Vega et al. 2000, Wang et al. 2003).

As mentioned, the structure-function relationships are best studied for PGC-1 , but we are learning more about the other members of the family. Despite the structural similarities between the family members, only some of these interactions have been confirmed for PGC-1 (Hentschke et al. 2002, Kamei et al.2003, Kressler et al. 2002,

Rodriguez-Calvo et al. 2006), while to date, PRC has not been conclusively found to interact with NHRs through these motifs (Vercauteren et al. 2008). However, there appears to be some selectivity in the recruitment of NHR by PGC-1 paralogues. For example, PGC-1 selectively activates the ER isoform in a promoter dependent manner, contrasting with the pleiotropic effects of PGC-1 on both ER and ER responsive genes (Kressler et al. 2002). The mechanisms for this selectivity are poorly understood, but could be driven by variations in the spatial arrangement and proximal environment of the nuclear receptor boxes (Ko et al. 2002, Savkur and Burris 2004). Another conserved

feature of the PGC-1 paralogues is the presence of a tetrapeptide (DHDY) that allows interaction with Host Cell Factor (HCF) in the median portion of the protein (Fig 1.1)

(Lin et al. 2002a, Vercauteren et al. 2008). Interaction with HCF increases PGC-1 and

PGC1 transcriptional activity (Lin et al.2002a), and mediates the effects of PGC-1 and

PRC on NRF-2 activity (Vercauteren et al. 2008). In addition, both PRC and PGC-1 interact with NRF-1, ERR and cyclic AMP response element binding protein (CREB) via two independent regions (Vercauteren et al. 2006, Vercauteren et al. 2008). In PRC, the NRF-1 and CREB overlapping binding domains have been fine mapped but do not show significant similarity with the corresponding regions of other family members

(Vercauteren et al. 2006). Additional PGC-1 binding partners have been identified including the myocyte enhancing factor 2 C (MEF2c), the yin yang 1 (YY1) and the forkhead box O1 (FOXO1), but despite similarities in these loosely defined binding regions, these interactions have not been directly extended to the other family members

(Cunningham et al. 2007, Lin et al. 2003, McGee and Hargreaves 2004, Michael et al.

2001, Puigserver et al. 2003).

1.3.2 Regulation of PGC-1 expression and transcriptional activity

Our current understanding of the mechanisms regulating the PGC-1 family expression and activity is primarily based on extensive studies of the transcriptional and post-translational regulation of PGC-1 . In general, most studies find that PGC-1 activity is regulated through changes in levels (i.e. transcriptional regulation of the PGC-

1 gene). However, in recent years, the role of post-translational modification of PGC-

1 activity became increasingly important (Baek and Rosenfeld 2004, Hermanson et al.

2002, Lonard and O’Malley 2007, Rodgers et al. 2008).

PGC-1 expression is highly inducible in a variety of metabolic programs (see below; Finck and Kelly 2006, Scarpulla et al. 2008). The analysis of PGC-1 proximal promoter revealed the presence of several consensus binding sites with prominent roles in energy sensing. For example, a cAMP response element (CRE) has a prominent role in

PGC-1 induction via CREB in liver, BAT and muscles (Cao et al. 2004, Daitoku et al.

2003, Handschin et al. 2003, Herzig et al. 2001, Schaeffer et al. 2004). Another regulatory pathway involves an insulin-dependent response when the forkhead transcription factor FKHR binds insulin-response sequences (IRS) on PGC-1 promoter

(Daitoku et al. 2003). In addition, PGC-1 can coactivate MEF2c and its effect on its own expression creating a powerful autoregulatory loop (Czubryt et al. 2003, Handschin et al. 2003).

Like most coregulators, PGC-1 activity is also strongly post-translationally regulated. These modifications influence PGC-1 localization, stability and how it interacts with other proteins, affecting the overall effect on gene expression. In the current model of regulation, acetylated PGC-1 is in an inactive state and localized to nuclear speckles in association with transcriptional repressor complexes such as RIP140

(Lerin et al. 2006). Upon deacetylation by Sirt1 on several lysine residues across the protein (Fig 1.2), the coactivator becomes active and relocates to bind activated TF on target promoters (Gerhart-Hines et al. 2007, Rodgers et al. 2005). When bound to TF, 17

PGC-1 can recruit histone acetylase complexes (e.g., TRAP, CBP/p300) that relax the chromatin structure allowing transcription initiation (Wallberg et al. 2003). To terminate transcription, the GCN5 complex would reacetylate PGC-1 , promoting relocalization of the inactive coactivator to repressive subnuclear foci (Lerin et al. 2006). Further modulation of the PGC-1 activity through covalent modifications has been described

(Fig1.1A; Finck and Kelly 2006, Rodgers et al. 2008). Phosphorylation of three residues

(Thr262, Ser 265, Thr 298) by p38 MAP kinase prevents the interactions of PGC-1 with the p160 corepressor, and results in a more stable and active coactivator (Fan et al. 2004,

Puigserver et al. 2001). Similarily, AMP kinase phosphorylates Thr177 and Ser538 residues and activates PGC-1 function (Jager et al. 2007). In contrast, the protein kinase

Akt/PKB phophorylates PGC-1 on Ser570 and reduces the stability and activity of the coactivator (Li et al. 2007). Another class of regulator, the protein arginine methyltransferase 1 (PRMT1), activates PGC-1 through methylation of three arginine residues of the COOH-terminus (Teyssier et al. 2005). However, these regulatory pathways have been primarily investigated in vitro or in relatively different physiological contexts (i.e. insulin response in liver, skeletal muscle response to exercise) making it challenging to estimate the contribution of each pathway in a given phenotypic adaptation. Nonetheless, in Chapter 4, I identify which of these regulatory sites are conserved across broad vertebrate taxa.

1.3.3 Physiological roles of the PGC-1 family

PGC-1 , the founding member of the family, has garnered most of the attention in terms of metabolic adaptations. Initially described as an activator of the thermogenic response in BAT, its role in mammalian tissues has been extended to a variety of metabolic programs (Feige and Auwerx 2007, Finck and Kelly 2002, Puigserver et al.1998, Rodgers et al. 2008). Unlike most coactivators, PGC-1 expression is tissue specific, and enriched in highly oxidative tissues such as muscles, liver and BAT

(Puigserver et al. 1998). In mammalian muscle, PGC-1 emerged as a master regulator of oxidative capacity, controlling mitochondrial proliferation and lipid metabolism via activation of the NRF-1 and PPARs pathways (Vega et al. 2000, Wang et al. 2003, Wu et al. 1999). This pivotal role has been described in a variety of phenotypic adaptation including development (Buroker et al. 2008, Lin et al. 2002a), exercise (Baar et al. 2002,

Watson et al. 2007, Wright et al. 2007) and dietary manipulations (Nakazato and Song

2008, Sparks et al. 2005). In liver, PGC-1 is highly responsive to nutritional status and promotes gluconeogenesis and fatty acid oxidation through interaction with

FOXO1/HNF4 and ERR /PPARs pathways respectively (Hanniman et al. 2006,

Puigserver et al. 2003, Yoon et al. 2001). Consequently, tissue-specific and whole animals PGC-1 knockouts present compromised hepatic metabolism (Burgess et al.

2006, Koo et al. 2004, Leone et al. 2005, Lin et al. 2004). PGC-1 expression drives the thermogenic program in BAT through activation of PPAR and of the mitochondrial uncoupling protein 1 (UCP1), and regulates lipid homeostasis and mitochondrial

proliferation in both white adipose tissue and BAT (Gleyzer et al. 2005, Mazzucotelli et al. 2007, Tiraby et al. 2003, Uldry et al. 2006). Although gain/loss of function experiments provided invaluable insights in PGC-1 effects in other tissues such as brain and pancreas, the exact physiological function of the coactivator in these tissues remains uncertain (Lin et al. 2004, Tritos et al. 2003, Yoon et al. 2003).

Upon its discovery, PGC-1 presented extensive structural and functional similarities with PGC-1 (Kressler et al. 2002, Lin et al. 2002a). As is its homologue,

PGC-1 is expressed predominantly in tissues with high mitochondrial content, and activates regulatory pathways (e.g., PPARs, ERRs) promoting mitochondrial biogenesis and fatty acid oxidation (Kamei et al. 2003, Kressler et al. 2002, Lin et al. 2002a, Lin et al. 2003, St-Pierre et al. 2003). Much like PGC-1 , ectopic expression of PGC-1 drives the formation of more oxidative muscle fiber types via activation of MEFs, though it is restricted to Type IIx/d fibers (Arany et al. 2007, Mortensen et al. 2006). In BAT, the isoforms have a complementary role in mitochondrial proliferation associated with the thermogenic response (Uldry et al. 2006). In contrast, PGC-1 does not play a role in hepatic gluconeogenesis or skeletal muscle adaptation to exercise (Lin et al. 2003,

Mortensen et al. 2007, Russell et al. 2005), and has the unique capacity to interact with

SREBP1c to coordinate lipogenesis (Lin et al. 2005). Further distinctions between the function of both coactivators came from comparisons of animals in which either PGC-1 or PGC-1 is knocked out. The respective KO lines lead to similar mitochondrial and

ETC dysfunctions that were not compensated by the functional PGC-1 isoform. In

addition, the animals presented very distinct behavioural and metabolic adaptation to physiological stressors, reinforcing the complementarity rather than overlapping functional nature of PGC-1 and PGC-1 (Arany et al. 2005, Burgess et al. 2006, Leick et al. 2008, Leliott et al. 2006, Leone et al. 2005, Lin et al. 2004, St-Pierre et al. 2006,

Sonoda et al. 2007).

Of the three family members, PRC is probably the least investigated in the context of metabolic adaptations. In mammalian tissues, PRC is ubiquituously expressed at relatively low levels, and seems to be mostly responsive to proliferative signals

(Andersson and Scarpulla 2001, Gleyzer et al. 2005). In immortalized myoblast and preadipocyte cells, PRC expression is enriched during cell proliferation, but fails to respond upon differentiation and the associated increase in oxidative capacity (Andersson and Scarpulla 2001, Gleyzer et al. 2005, Kraft et al. 2006). In thyroid oncocytomas, PRC and NRF-1 seem to drive the exaggerated mitochondrial proliferation associated with these tumors (Savagner et al. 2003). PRC is also immediately induced after acute bouts of exercise in human muscles, but the functional significance of this increase is still unknown (Mortensen et al. 2007, Russell et al. 2005). Overall, the expression pattern, inducibility and stability of PRC suggest that it functions as an immediate early gene dependent on the proliferative status of cells (Vercauteren et al. 2006).

Collectively, the three coactivators have a profound effect on metabolic homeostasis in mammals, however their functional relevance in other lineages has been largely neglected. Furthermore, the similarities in structure and conservation of some

functional features of the PGC-1 family members suggest a common ancestry with independent evolutionary trajectories of the coactivators over the course of evolution. In my thesis, I investigated these aspects of the evolution of the PGC-1 family in a functional (Chapter 3) and phylogenetic (Chapter 4) perspective.

1.4 Evolution of transcriptional regulation

Virtually all biological processes and adaptations are the result of interactions between an organism’s regulatory factors and its gene repertoire. Consequently, the evolution of these regulatory networks is thought to play a crucial role in biological structural and functional diversity (Babu et al. 2004, Carroll 2005, Tautz 2000).

Regulatory networks are composed of two principal components, cis-elements composed of enhancer and regulatory DNA sequences, and trans-regulatory elements such as TF and associated proteins that collectively bind these cis-elements and regulate transcriptional activity. Considering the complexity of any regulatory network and the associated cascade of events, evolutionary forces driving changes of any of their components could have profound effects on an organism phenotype.

1.4.1 Evolution of cis-elements

Regulatory sequences are an integral part of gene regulatory networks, as the presence of enhancers and TF specific binding sites allow for transcription to occur. It has been argued that evolution of cis-regulatory elements is crucial to development of biological novelties (Carroll 2005, Rodríguez-Trelles 2003, Wray 2007). As these sequences are not coding functional proteins they are thought to be under different

evolutionary constraints and generally evolve faster than coding regions of DNA

(Ludwig et al. 2000, Wray et al. 2003). While mutations that occur on the coding region of a gene may have pleiotropic deleterious effects on the protein encoded, changes in the regulatory region would only affect some aspects of its expression (Carroll 2005). Gene duplication, a major source of biological novelty, often results in the neofunctionalization or subfunctionalization of one or both duplicate genes, creating temporal or spatial specialization of the resulting paralogues (Sémon and Wolfe 2007, Zhang 2003).

Mechanistically, this could be accomplished through modulation of the regulatory sequences driving the expression of the duplicates to maintain gene dosage, resulting in divergent responsiveness of the duplicates to different factors (Force et al. 1999, Lynch and Force 2000). Recent evidences of subfunctionalization by differential expression in vertebrate genes strongly support this mode of evolutionary change (Escriva et al. 2003,

Kleinjan et al. 2008). TF recognition motifs are usually small (4-10 nucleotides) and present some degeneracy, therefore the appearance or multiplication of regulatory sites is likely to happen by random mutation (Ludwig 2000, Scemama et al. 2002). When comparing mammalian genomes, a small proportion of regulatory regions is relatively conserved while the majority presents a high degree of plasticity (Dermitzakis and Clark

2002). Although compensatory changes may take place and maintain the overall functionality of the cis-regulatory region, there seems to be lineage-specific gain and loss of TF binding sites that could generate new functions or expression patterns (Dermitzakis and Clark 2002, Shashikant et al. 1998). However, despite the ever expanding availability

of genomic information, the degenerative nature of regulatory sites makes it difficult to evaluate their contribution to evolutionary change and warrants further direct empirical testing (Carroll 2005, Hoekstra and Coyne 2007).

1.4.2 Evolution of transcription factors

The initiation of transcription requires coordinated interactions between protein complexes (i.e., TF, coregulators) and regulatory sequences. In contrast to the “evo-devo” theory which attributes the majority of evolutionary changes to non-coding regulatory elements, an increasing body of evidence substantiates a prominent role for structural modification of TF as a major source of new biological functions and adaptations (see

Hoekstra and Coyne 2007, Hsia and McGinnis 2003). As any proteins, TF are subjected to evolutionary forces shaping their structure and function, however their modular architecture provides ground for selection to act only on a portion of their functions, without deleteriously affecting others (Hsia and McGinnis 2003). TF activity on a target promoter is primarily driven by two separate domains, a DNA-binding domain (DBD) which recognizes and binds specific nucleotide sequences, and a ligand- or cofactor- binding domain that dictates transcriptional activation. Modulating either of these domains can have profound effects on downstream gene expression as can be seen in the

NHR superfamily. The superfamily is thought to have evolved from a few orphan receptors (i.e. no cognate ligands) and have diversified during metazoan evolution notably through successive duplication events (Bertrand et al. 2004, Escriva et al. 2003).

Most NHR bind as hetero- or homodimers to two half sites DNA sequences arranged in

palindromes or direct repeats. Most receptors, such as ERs recognize a TGACCT half- site, however other family members including the glucocorticoid receptors have evolved to preferentially recognize TGTTCT regulatory elements (Beato 1989, Laudet et al.

1992). Extensive mutation analysis revealed that the mutation of two residues in the DBD was sufficient to drive the specificity to one or the other response element (Zilliacus et al.

1994). Similarily, the ligand-specificity of the PPARs, ERs and RARs subtypes has been narrowed down to a few amino-acid changes in the ligand binding pocket of the nuclear receptors (Bhat et al. 2004, Klaholz et al. 2000, Nettles et al. 2004, Sun et al. 2003, Xu et al. 2001). Therefore, relatively minor changes in the TF structure can have a major impact on their specificity and activity. Additional evidences of modular evolution come from the use of mutant animals to characterize the function of TFs. In these experiments where a TF is knocked-out or silenced, the mutant phenotype can often be rescued with an orthologous protein from evolutionary distant species. However, in general this rescue is only partial, arguing for both divergence and conservation of functions of the orthologous genes (Haun et al. 1998, Hsia and McGinnis 2003, Lutz et al. 1996). As discussed in earlier sections, other factors such as coregulators are powerful modulators of transcriptional activities. Although, they have not been studied in an evolutionary perspective the modular structure of coactivators reminiscent of TFs, suggest they could evolve in a much similar manner and create evolutionary opportunities to modulate regulatory functions as discussed in Chapter 4.

1.5 Thesis overview

The objective of this thesis is to examine the origins of variation in mitochondrial content in vertebrate muscles. In Chapter 2, I examine the bioenergetics changes associated with aging in the rat cardiac muscle. I primarily focused on the role of transcriptional regulators (PGC-1 , PPARs and NRFs) in determining the complex changes in mRNA levels and activities of mitochondrial enzymes. My main interests lie in the evolution of these coactivators, and thus in Chapter 3 I examined if the regulatory relationships shown in mammals also held for lower vertebrates. Using standard metabolic stressors in goldfish (temperature and diet), I investigated the role of the PGC-

1 family of coactivators and their associated binding partners in modulating mitochondrial capacities in muscles (red, white and cardiac) and liver. These analyses revealed potential functional divergences of PGC-1 paralogues in fish versus their mammalian homologues. Thus, in Chapter 4 I examined the evolutionary trajectory of the

PGC-1 family in vertebrates, concentrating on lineage-specific divergences of PGC-1 and their potential functional repercussions on mitochondrial regulatory pathways.

Collectively, these studies provide a better understanding of the regulatory and evolutionary determinants of mitochondrial capacities in vertebrates.

Figure 1.1. Structure and similarities of the PGC-1 family members. A) Functional domains of PGC-1 . Positions for covalent modifications via methylation (M), acetylation (A) and phosphorylation (P) are indicated. B) Structure similarities of the PGC-1 family members. The termini are the most conserved features of the paralogues (expressed in % amino acid similarity). The relative position of binding motifs similar to PGC-1 is represented on the respective homologues. (Adapted from Kressler et al. 2002, Rodgers et al. 2008, Scarpulla 2008)

Figure 1.2 Model of transcriptional regulation by PGC-1 . When activated, transcription factors (TF) bound to response elements (RE) recruit PGC- 1 . PGC-1 mediates the interactions with histone remodeling proteins (p300), mediator complexes (TRAP) and the general transcriptional machinery (RNA pol II, general transcription factors or GTF) thereby facilitating initiation of transcription. PGC-1 activity can be activated by phosphorylation (p38 MAPK, AMPK), deacetylation (Sirt1) or methylation (PRMT1), whereas corepressors (p160), acetylation (GCN5) or phosphorylation can inhibit its transcriptional activity (adapted from Rodgers et al. 2008, Finck and Kelly 2006)

Chapter 2: Control of mitochondrial gene expression in the aging rat myocardium

LeMoine CMR, McClelland GB, Lyons CN, Mathieu-Costello O, and Moyes CD. Biochem. Cell Biol. 2006. 84: 191-8.

2.1 Abstract

Aging induces complex changes in myocardium bioenergetic and contractile properties. Using F344BNF1 rats, we examined age-dependent changes in myocardial bioenergetic enzymes (catalytic activities and transcript levels) and mRNA levels of putative transcriptional regulators of bioenergetic genes. Very old rats (35 months) had showed a 22% increase in ventricular mass, with no changes in DNA/g or RNA/g. Age- dependent cardiac hypertrophy was accompanied by complex changes in mitochondrial enzymes. Enzymes of the Krebs cycle and electron transport system remained within

15% of the values measured in adult heart, with significant decreases in citrate synthase

(10%) and aconitase (15%). Transcripts for these enzymes were largely unaffected by aging, although mRNA levels of putative transcriptional regulators of these enzymes

(NRF-1 and NRF-2 subunit) increased by about 30-50%. In contrast, enzymes of fatty acid oxidation exhibited a more diverse pattern, with a 50% decrease in - hydroxyacylCoA dehydrogenase (HOAD), and no change in long chain acyl CoA dehydrogenase or carnitine palmitoyltransferase. Transcript levels for fatty acid oxidizing enzymes covaried with HOAD, which declined significantly by 30%. There were no significant changes in the relative transcript levels of regulators of genes for fatty acid oxidizing enzymes: peroxisome proliferator-activated receptor- (PPAR ), PPAR , or

PPAR coactivator-1 PGC-1 ). There were no changes in the mRNA levels of Sirt1, a histone-modifying enzyme that interacts with PGC-1 . Collectively these data suggest that aging cause complex changes in the enzymes of myocardial energy metabolism, triggered in part by NRF-independent pathways as well as post-transcriptional regulation.

2.2 Introduction

The adult mammalian heart relies on lipid oxidation to provide about 65% of its energy demands (Van der Vusse et al. 1992). The energy is produced when the mitochondrial -oxidation pathway metabolizes fatty acids derived from circulating lipids. As one of the most aerobic tissues, mammalian heart possesses a relatively high mitochondrial capacity and abundant enzymes associated with fatty acid transport and oxidation. Aging causes many changes in the energy metabolism of heart. The aging myocardium shifts its fuel preference away from fatty acid oxidation toward carbohydrate oxidation, which is a reversion to the neonatal state (Allard et al. 1994,

Sambandam et al. 2002). This change in fuel preference is typically paralleled by a reorganization of mitochondrial metabolism, including a shift in the gene expression and relative activities of fatty acid oxidizing enzymes (Hansford and Castro1982, Lee et al.

2002, Wanagat et al. 2002). This switch in fuel preference is thought to be maladaptive and contribute to the contractile dysfunction (Barger and Kelly 1999). The mitochondrial changes are only one element of the extensive myocardial remodelling with age. The ventricle structure changes due to hypertrophy of some cardiomyocytes, death of others and the resulting fibrosis that culminate in reduced ventricular performance (Besse et al.

1993, Kajstura et al. 1996, Swynghedaw et al. 1995). The age-dependent hypertrophy has parallels with the changes seen in cardiopathologies, including hypertensive hypertrophy

(Davila-Roman et al. 2002, de las Fuentes et al. 2003, Finck and Kelly 2002, Iemitsu et al. 2002, Leary et al. 2002, Lehman and Kelly 2002, Sack and Kelly 1998).

Expression profiling studies in aging myocardium reveal changes in mRNA levels for suites of genes associated with the stress response, cell death and signal transduction

(Bodyak et al. 2002). Studies exploring the regulatory basis of the bioenergetic remodelling of heart, reflected in mitochondrial losses and shifts in fuel preference, have focused on transcriptional regulators. One coactivator, PGC1 , is considered to be a master regulator of mitochondrial proliferation and function, acting via transcription factors that regulate networks of respiratory genes, including nuclear encoded mitochondrial enzymes and the regulators of mitochondrial DNA replication and transcription (Knutti and Kralli 2001, Puigserver and Spiegelman 2003, Wu et al. 1999).

PGC1 coactivates the nuclear receptors PPARs ( , / and ) (Barger and Kelly 2000,

Gilde and van Bilsen 2003) and the nuclear respiratory factor 1 and 2 (NRF-1 and 2)

(Puigserver and Spiegelman 2003, Wu et al. 1999). The PPARs have a prominent role in the regulation of most nuclear-encoded mitochondrial enzymes associated with fatty acid oxidation (Aoyama et al. 1998, Barger and Kelly 2000, Cheng et al. 2004, Djouadi et al.

1999, Gilde and van Bilsen 2003, Muoio et al. 2002), whereas the NRFs regulate the transcription of several mitochondrial proteins necessary to mitochondrial biogenesis and oxidative metabolism (Carter et al. 1992, Evans and Scarpulla 1989, Gugneja and

Scarpulla 1997, Lezza et al 2001, Martin et al, 1996, Mootha et al. 2004, Vallejo et al.

2000). PGC-1 can also exert effects that are independent of PPARs and NRFs. It is subject to post-translational regulation (Puigserver et al. 2001, Rodgers et al. 2005).

Recently, PGC-1 was also shown to interact with an NADH-dependent histone- remodelling enzyme, Sirt1 (Rodgers et al. 2005), affecting the expression of glycolytic genes. PPARs and NRFs are also regulated in ways that are independent of PGC-1 . For example, PPAR DNA binding activity is affected by regulatory ligands (Barger and Kelly

2000, Gilde and van Bilsen 2003) and NRFs are subject to covalent modification

(Gugneja and Scarpulla 1997, Martin et al. 1996, Vallejo et al. 2000).

Collectively, PGC1 and its network of regulators are thought to help maintain the stoichiometric relationships of mitochondrial enzymes, ensuring that they increase or decrease stoichiometrically. Much less is understood about the mechanisms by which the stoichiometric relationships among mitochondrial enzymes are changed, as is seen in the senescent and hypertrophic heart undergoing changes in fuel preference. In this study, we compared adult rats (12 months) with very old rats (36 months) to investigate the age- associated changes in the enzymatic and transcriptional machinery of mitochondrial metabolism of the rat cardiac muscle.

2.3 Materials and Methods

2.3.1 Animals and tissue collection

Adult male F1 hybrid F344 X Brown Norway (F344BNF1) rats (Harlan Sprague

Dawley Inc. Indianapolis, IN) were maintained, 2-3 in each cage, in a room maintained at

constant temperature in a 12hr/12hr light/dark cycle. The animals had ad libitum access to untreated tap water and standard chow (Harlan Teklad 8604). This hybrid strain of rat has a reduced incidence of pathologies associated with aging and therefore an increased longevity, which make it an interesting mammalian model to study the effect of aging

(Lipman et al. 1996). The present study was conducted in a pathogen-free facility accredited by the American Association of Accreditation of Laboratory Animal Care, in addition it was approved by the Animal Subjects Committee, University of San Diego,

California.

Cardiac muscles were collected from two age groups, adult (12 months) and very old (35-36 months) rats. The animals were weighed and then anaesthetized with 40-70 mg pentobarbital. The heart was excised, weighed and flash-frozen in liquid nitrogen, and stored in cryovials at -80 C. Subsequently, the tissue was powdered with a mortar and pestle in liquid nitrogen and stored at -80 C.

2.3.2 Enzyme assays

Powdered tissues were homogenized in a 1:20 dilution of extraction solution

(20mM Hepes, 1mM EDTA, 0.1% Triton X 100, pH 7.2) using a glass homogenizer. A

SpectraMAX Plus spectrophotometer (Molecular Devices Corp., Sunnyvale, CA) was used to measure specific activities of the different enzymes on 96 wells plates, using assays described by Moyes et al. 1997 unless otherwise noted: cytochrome c oxidase

(COX), isocitrate dehydrogenase (IDH), aconitase (ACON) (Gardner et al. 1994), citrate synthase (CS), hydroxyacyl-coA dehydrogenase (HOAD), carnitine

palmitoyltransferase (CPT) (McClelland et al. 2004) and long-chain acyl-CoA dehydrogenase (LCAD) (Nadal-Ginard et al. 2003).

2.3.3 Nucleic acids

DNA measurements were made on enzyme homogenates. Aliquots were digested overnight at 55 C using proteinase K (0.2 mg/ml) in 100 mM NaCl, 10 mM Tris-HCl pH

8.0, 25 mM EDTA, and 0.5 % SDS (pH 8.0). The DNA content of each digest was assayed against DNA standards (1 – 60 ng of purified genomic DNA) using Pico-Green

(Molecular Probes). The digested homogenates (2 l) and the DNA standards were loaded on 96-well black plate and incubated for 5 minutes with Pico-Green. Fluorescence was measured (excitation 480 nm, emission 520) with a Spectramax Gemini fluorometer

(Molecular Devices).

Frozen powdered tissues were weighed, diluted 10-40 times in guanidium thiocyanate and homogenized using a Polytron homogenizer. RNA was extracted using the acid-phenol chloroform procedure (see Moyes et al. 1997). A small volume of the aqueous phase was collected after the first phase separation for total RNA quantification.

Total RNA content was quantified using Ribo-Green (Molecular Probes) according to manufacturer’s instruction. The samples and standard (10 – 200 ng purified tRNA) were loaded on a 96-well plate and fluorescence measured (excitation 480 nm, emission 536 nm) on a Spectramax Gemini fluorometer. The remainder of the aqueous layer was further processed to purify RNA suitable for northern blot analysis. Further purification

was conducted to obtain mRNA from purified 250 g total RNA using a polyA+ purist kit

(Ambion).

Following denaturation, the purified RNA was separated in a 1% agarose- formaldehyde gel. The gels were blotted overnight onto a Duralon-UV membrane

(Stratagene) and UV crosslinked. Specific cDNA probes were generated by RT-PCR using rat cDNA and primers designed for each gene (Table 2.1). Hybridization and phophorimaging techniques were performed as described previously (see McClelland et al. 2004). On total RNA blots, signal intensities were corrected for loading differences by probing for 18S (total RNA blots). As an indicator of mitochondrial content that appeared unaffected by aging, we used citrate synthase mRNA as a reference gene for poly A+ blots analyses. It allowed us to see if changes in gene expression in putative transcriptional regulators paralleled the mitochondrial content in aging hearts.

2.3.4 Statistical analysis

Data are presented as mean values standard error of the means (SEM).

Significant difference between old and very old rats ( < 0.05) was established using two-tailed student t-tests.

2.4 Results and Discussion

Healthy, adult mammalian myocardium primarily relies on fatty acids as metabolic fuels. However, this fuel preference can change during development and pathology. For example, neonatal heart relies to a greater extent on carbohydrate oxidation. In many models, aging myocardium shifts back to the neonatal phenotype,

exhibiting reduced activities of fatty acid oxidizing enzymes (Allard et al. 1994,

Sambandam et al. 2002), but the extent of such changes appears to be a function of the strain and age of the animals studied (Lipman et al. 1996). Hypertension also leads to a shift in fuel selection, and since ventricular hypertrophy accompanies both hypertension

(Davila-Roman et al. 2002, de las Fuentes et al. 2003) and aging (Allard et al. 1994, Fink and Kelly 2002, Hansford and Castro 1982, Sambandam et al. 2002), it is conceivable that the shift away from fatty acid oxidation in aging may be a generalized response to ventricular hypertrophy seen in cardiopathologies. Most studies of bioenergetic remodeling of tissues focus on a series of transcriptional regulators that control mitochondrial content: NRF-1 and NRF-2, and the PPAR and PGC-1 gene families. In the present study, we examined the patterns of change in these transcriptional regulators associated with changes in the metabolic properties of the aging myocardium.

As in most studies, aging in these rats was accompanied by ventricular hypertrophy, with the relative ventricular mass (g ventricle per kg body mass) increasing by 20% (P = 0.0006, Table 2.2). The DNA content of the heart did not change when expressed per g ventricle and when changes in ventricular mass are taken into account, the heart of aged rats had 20% more DNA. Although recent studies suggest that cardiac muscle has some capacity for hyperplasia (Nadal-Ginard et al. 2003), it is likely that the increase in DNA content is attributable to a proliferation of cells other than cardiomyocytes, particularly fibroblasts. It is thought that the aging rat myocardium experiences a loss of myocytes, hypertrophic growth of the remaining myocytes, and

consequently to an overall reduction of the population of myonuclei in the muscle

(Kajstura et al. 1996, Swynghedaw et al. 1995).

2.4.1 Mitochondrial enzymes

Aging caused no more than modest decreases in the specific activities of Krebs cycles and electron transport enzymes (Fig 2.1A). Even where differences were significant (citrate synthase, aconitase) the reductions were only 10-15%. Thus, there was also a near preservation of the stoichiometric relationships between these mitochondrial enzymes. These modest effects of aging on mitochondrial enzymes activities are consistent with previous studies (e.g., refs Bodyak et al. 2002, Iemitsu et al. 2002). In contrast, the enzymes of fatty acid oxidation displayed greater variation. Notably, HOAD activity decreased by about 50% (p = 0.03), whereas CPT activities tended to increase, a pattern similar to that seen in previous studies (Hansford 1978, Hansford and Castro

1982), while LCAD activity was unaffected.

Next, we compared the expression profiles with the enzyme changes. The total

RNA/g levels did not change with aging (Table 2.2), which is consistent with previous studies (Besse et al. 1993). In the case of some enzymes with simple subunit structure, it is meaningful to compare the mRNA levels directly with enzymatic activities. Citrate synthase is a homodimer and the mRNA for the subunit was unaffected by aging (Fig

2.1B) whereas its activity declined significantly, but only by 10% (Fig 2.1A). We saw little change in COX activity (13 subunits), and there was no significant change in the

mRNA levels for one of its nuclear-encoded subunits (COX IV) or its substrate

(cytochrome c).

The mRNA levels for a collection of 5 genes involved in mitochondrial - oxidation changed in parallel, each decreasing by about 30% with age. Although only the changes in HOAD were statistically significant (p < 0.05), there was a clear similarity in the patterns. Furthermore, the mRNA levels for these other enzymes strongly correlated with HOAD (Fig 2.1C). In other words, individuals that had low HOAD mRNA levels also had low mRNA levels for the other enzymes of fatty acid oxidation. This strongly supports the concept of a network of genes regulated in parallel. A global downregulation of the FAO enzymes transcripts levels has been reported in induced cardiac hypertrophy and aging studies (Iemitsu et al. 2002, Sack et al. 1997).

Collectively, these data suggest that the genes for enzymes of fatty acid oxidation are regulated in parallel in adult and aging myocardium. However, it is noteworthy that the changes in mRNA levels did not always reflect the enzyme activities. For example,

CPT1 and CPT2 mRNA levels appeared to decline whereas CPT enzymatic activity did not change, thus the ratio of means of enzyme activities to mRNA levels increased by about 50%. In contrast to HOAD, where mRNA and enzyme decreased to a similar extent, LCAD enzyme activity remained normal when transcripts appeared to decline

(though this decrease was not statistically significant). These data suggest a potential role for post-transcriptional regulation of these gene products, as is seen in cardiac failure models (Lehman and Kelly 2002, Sack and Kelly 1998).

When we analyzed the mRNA levels of transcriptional regulators, several interesting patterns emerged. The genes encoding the basic elements of mitochondrial oxidative metabolism (Krebs cycle enzymes and the electron transport chain) are thought to be controlled by PGC-1 , acting through the nuclear respiratory factors. Since these enzymes did not change, we anticipated that the expression of NRF-1 and NRF-2 would also remain unchanged. Surprisingly, the mRNA levels for NRF-1 increased by 33% (p <

0.05) and NRF-2 by 24% (p=0.06) (Fig 2.2A). An increase in NRF-1 with aging has also been reported in skeletal muscle (Lezza et al. 2001). These observations raise two questions. The first question is why mRNA was elevated for these transcription factors.

The expression of the NRF-1 and NRF-2 genes are regulated in response to levels/ activity of PGC-1 (Mootha et al. 2004, Wu et al. 1999). In this study there was no corresponding increase in PGC-1 mRNA levels. In fact, the levels of the NRFs increased by 60-80% when expressed relative to their putative regulator, PGC-1 This does not in itself rule out a role of PGC-1 in controlling the genes for NRF-1 and NRF-

2 ; it is possible that PGC-1 transactivational capacity is stimulated despite a reduction in PGC-1 synthesis, which is suggested by the mRNA pattern. The PGC-1 protein may have been activated via post-transcriptional mechanisms (Puigserver et al. 2001,

Teyssier et al. 2005). An alternative possibility is that NRF-1 and NRF-2 genes were stimulated by other transcriptional regulators. A similar dysynchrony is seen in liver disease, where NRF-1 increases despite decreases in PGC-1 (Chen et al. 2005).

The second question raised in light of the elevated NRF-1 and NRF-2 levels is why there was no increase in the expression of genes thought to be regulated by these transcription factors. For example, neither cytochrome c and COXIV demonstrated an increase in mRNA levels, and each is regulated by NRF-1 and/or NRF-2 (Carter et al.

1992, Evans and Scarpulla 1989). If elevated NRF mRNA levels do in fact increase NRF synthesis, then it is possible that target genes escape the effects through post-translational inhibition of its DNA binding activity, as has been shown for both NRF-1 (Gugneja and

Scarpulla 1997) and NRF-2 (Martin et al. 1996, Vallejo et al. 2000).

2.4.2 The PPAR axis

The mRNA analyses suggest that the expression for five genes of fatty acid oxidation were regulated in parallel. Although only some of these genes have been studied in detail, most genes for enzymes of fatty acid oxidation are regulated through a

PPAR-PGC-1 pathway (Aoyama et al. 1998, Barger and Kelly 2000, Djouadi et al.

1999, Fink and Kelly 2002, Gilde and van Bilsen 2003, Lehman and Kelly 2002).

PPAR and are known to have overlapping roles in activating fatty acid oxidation as demonstrated through use of PPAR null mice and ligand-specific activation experiments (Cheng et al 2004, Muoio et al. 2002). However, it is unclear if a cross talk exists between the two nuclear receptors, and whether they regulate cardiac FAO independently. For example, a differential affinity for metabolic genes and a competition between the two transcriptional regulators could be a mechanism for the differential regulation of the FAO genes. In our study, PPAR and PGC-1 appeared to decline in

parallel, such that the ratio of the two was highly conserved within and between groups.

In contrast, PPAR / was relatively unaffected by aging (Fig 2.2B). Though the apparent declines in PPAR and PGC-1 were not significant, it is possible that we had insufficient power to show this minor decline was statistically significant. However, it is noteworthy that there was little correlation between the mRNA levels of HOAD and these transcriptional regulators (PGC-1 , PPAR , PPAR data not shown), unlike the situation with other fatty acid oxidizing enzymes (Fig 2.1C). While the regulation of fatty acid oxidation genes is normally focused on the PPAR-PGC-1 axis, other transcriptional regulators have been implicated in remodelling myocardial energetics. For example, part of the changes in MCAD expression during cardiac hypertrophy is attributed to changes in the interaction of the MCAD promoter with the orphan nuclear receptor chicken ovalbumin upstream promoter transcription factor I (COUP-TF) and Sp1 family (Sack et al. 1997).

2.4.3 Other transcriptional regulators

This work focused on the NRF and PPAR transcriptional regulators but recently, another transcriptional regulator has been shown to interact with PGC-1 to control metabolic gene expression. Sirt1 is the mammalian homologue of Sir2, a yeast histone deacetylase that has an important role in controlling gene expression during cellular aging

(Kaeberlein et al. 1999). Sirt1 has recently been shown to act through PGC-1 to control the expression of glycolytic genes (Rodgers et al. 2005). In this study, Sirt1 transcript levels were unchanged in response to aging.

These studies have focused on control of the bioenergetic remodelling with aging, but other aspects of myocardial function change with age. The changes in contractile protein are thought to be regulated by thyroid hormone and retinoic acid receptors

(Iemitsu et al. 2004, Long et al. 1999). These nuclear hormone receptors can act as binding partners with PPARs and NRF-1, and their potential role in control of mitochondrial gene expression is unclear.

In summary, we explored the relationships between mitochondrial enzymes and transcriptional regulators in aging rat myocardium. There was a general retention of levels and stoichiometries in enzymes of the Krebs cycle and electron transport. Whereas the mRNA patterns for Krebs cycle enzymes and electron transport proteins are consistent with transcriptional regulation, the levels of two putative transcriptional regulators (NRF-1 and NRF-2) increase with age. The fatty acid oxidizing enzymes showed more variable responses in terms of catalytic activity, yet the mRNA for these enzymes declined in parallel. Our results suggest that transcriptional regulation is important, but not the only route to control bioenergetic enzyme levels in the context of aging.

2.5 Acknowledgements

We thank Dr. Li Cui, Dr. Yan Ju, and Margarita Trejo-Morales for assistance with tissue harvesting. Funding for this research was provided by operating grants from the Natural

Sciences and Engineering Research Council of Canada (CDM) and the National Institutes

of Health (OMC), and salary awards from the Heart and Stroke Research Corporation of

Canada (GBM) and the American Federation of Aging Research (CNL).

Table 2.1. List of primers and target rat sequences used for the RT-PCR amplification of the probes used in northern blotting analysis.

Gene Forward primer Reverse primer (accession number) COX IV (RNCOX4) agcctaattggcaagmgagc gtcgtagtcccacttggc CPT1 (NM013200.1) gcaaactggaccgagaagag aagaaagcagcacgttcgat CPT2 (NM012930.1) agacgctcagcttcaacctc catmgctgcytctttggt CS (AF461496) gaaacatcrgttcttgatcc gtgtattccagatgtagtcwcgtaa Cyt c (NM012839) cgggacgtctccctaaga gctattaggtctgccctttc HOAD (NM130826.1) tggagtcaaaggggatgtggcagt agcagcaggcacacccaccatttt LCAD (BC062006.1) tggcattagcctctttttgg tggatgtgtgcgactgtttt MCAD (NM016986.1) atttggggaggatgacggag atgaaactccttggtgctcc NRF-1 (XM231566) gcatgctgaaactctcccta ctgcagtcccattcttcccca NRF-2 (XM344002.1) aatgtgtaagccaggccata tgagtgtggtgaggtctata PGC-1 (NM013196.1) cacaactcctcctcataaagc tcattactgaagtcgccatc PPAR (NM013196.1) gaagaacttcaayatgaacaaggt cagcatcccrtctttgttca PPAR (NM013141.1) acaacgctatccgctttgga tcgaacttgggctcaatgat Sirt1 (XM228146) ttggcaccgatcctcgaac cccagctccagtcagaactat

Table 2.2. Morphological and molecular characteristics of adult and very old rats.

Adult Very Old Body weight (g) 1 525.37 ± 35.69 518.73 ± 10.42 Cardiac muscle (mg) 1 1.27 ± 0.14 1.53 ± 0.045 * Cardiac specific Mass1 (mg/g body weight) 2.43 ± 0.10 2.96 ± 0.09 * DNA (mg/g tissue) 2 1.42 ± 0.10 1.41 ± 0.06 RNA (mg/g tissue) 2 1.42 ± 0.10 1.41 ± 0.06 Note: Values are represented as means ± SEM. * indicates statistical significance (P<0.05) between adult and very old individuals. 1 N = 13; 2 N = 5.

A) Enzymes activities

1.4

1.2

1.0 * * activities

e 0.8

t a

l 0.6 * Re 0.4

0.2

0 CS COX IDH Acon CPT LCAD HOAD

B) Transcripts

1.2

1.0

0.8 *

mRNA levels mRNA 0.6

t a

l 0.4 Re

0.2

0 CS COX IV Cyt c CPT1 CPT2 LCAD MCAD HOAD

C) Correlations

1 LCAD R2 = 0.79 MCAD R2 = 0.57 CPT1 R2 = 0.91 0.75 CPT2 R2 = 0.65

0.50

Relative mRNA levels mRNA Relative 0.25

0 0 0.25 0.50 0.75 1 Relative HOAD mRNA levels Figure 2.1. Catalytic activities and mRNA levels of mitochondrial enzymes in the cardiac muscle of adult and very old rats. (A) Specific activities (units/g tissue) of LCAD, HOAD, CPT, IDH, ACON, CS and COX in adults (open bars) and very old (gray bars) rats. Data are expressed as means + SEM, with the data scaled to be presented relative to the mean of the values for adult myocardium (N = 5). A value of 1 corresponds to 102 U CS, 98 U COX, 4 U IDH, 7.8 U Acon, 0.7 U CPT, 2.3 U LCD and 54 U HOAD. (B) The mRNA levels for selected enzymes of mitochondrial metabolism, based on northern blots of 20 g total RNA and expressed once corrected against 18S rRNA and normalized to the adult values (mean adult value arbitrarily set at 1.0). An asterisk (*) over bars from very old animals identifies a significant difference from adult (P < 0.05) compared with two-tailed t-test (N = 5 for each age group and gene except for CPT1 CPT2 and LCAD where N = 4 in very old animals). (C) Relationships between mRNA levels of HOAD versus that of other enzymes of fatty acid oxidation, including both adult and very old animals.

A) Transcription factors 1.6

l e

v 1.2

N R

m 0.8

e R 0.4

0 PGC-1a PPARa PPARd SIRT1 NRF1 NRF2 B) Transcription factors ratios 2.2 *

1.8

v e

l 1.4

N R

m 1.0

a l

e 0.6 R

0.2 0 PPARa PPARd SIRT1 NRF1 NRF2 PGC-1a PGC-1a PGC-1a PGC-1a PGC-1a Figure 2.2. Transcript levels for transcriptional regulators of bioenergetic genes in the cardiac muscle of adult and very old rats. (A) RNA levels of were determined from blots of 5 g polyA+ extracted from total RNA of heart tissue from N = 5 adults (open bars) and N = 5 very old (gray bars) hearts. The bars represent mean (± SEM) steady-state mRNA levels. As citrate synthase is an indicator of mitochondrial content and CS mRNA was not significantly affected by age (see Table 1), CS was used as a loading reference. Corrected data were expressed once normalized to the adult values (mean adult value arbitrarily set at 1.0). (B) The mRNA levels for each gene regulatory protein in A was expressed relative to PGC-1 (N = 5 in each age group). An asterisk (*) over bars from very old animals identifies a significant difference from adult (P < 0.05) compared with two-tailed t-test.

Chapter 3. Role of the PGC-1 family in the metabolic adaptation of goldfish to diet and temperature

LeMoine CMR, Genge CE, and Moyes CD. J. Exp. Biol. 2008. 211: 1448-55.

3.1 Abstract

In mammals, the peroxisome proliferator activator receptor coactivator-1 (PGC-

1) family members and their binding partners orchestrate remodeling in response to diverse challenges such as diet, temperature, and exercise. In this study, we exposed goldfish to three temperatures (4, 20 and 35°C) and to three dietary regimes (food deprivation, low fat and high fat) and examined the changes in mitochondrial enzyme activities and transcript levels for metabolic enzymes and their genetic regulators in red muscle, white muscle, heart and liver. When all tissues and conditions were pooled, there were significant correlations between the mRNA for the PGC-1 coactivators (both and

) and mitochondrial transcripts (citrate synthase), metabolic gene regulators including peroxisome proliferator activator receptors (PPAR , PPAR ), and nuclear respiratory factor-1 (NRF-1). PGC-1 was the better predictor of the NRF-1 axis, whereas PGC-1 was the better predictor of the PPAR axis (PPAR , PPAR , medium chain acyl CoA dehydrogenase). In contrast to these intertissue/developmental patterns, the response of individual tissues to physiological stressors displayed no correlations between mRNA for

PGC-1 family members and either the NRF-1 or PPAR axes. For example, in skeletal muscles, low temperature decreased PGC-1 transcript levels but increased mitochondrial enzyme activities (citrate synthase and cytochrome oxidase) and

transcripts for COX IV and NRF-1. These results suggest that in goldfish, as in mammals, there is a regulatory relationship between (i) NRF-1 and mitochondrial gene expression and (ii) PPARs and fatty acid oxidation gene expression. In contrast to mammals, there is a divergence in the roles of the coactivators, with PGC-1 linked to fatty acid oxidation through PPAR , and PGC-1 with a more prominent role in mediating NRF-1 dependent control of mitochondrial gene expression, as well as distinctions between their respective roles in development and physiological responsiveness.

3.2 Introduction

Mitochondrial oxidative phosphorylation (OXPHOS) produces most of the ATP required by cells under aerobic conditions. Mitochondrial content differs among tissues as a result of development and can be adjusted in response to physiological stressors

(Hood et al. 2006, Lyons et al. 2006, Moyes et al. 1997, Nelson 1990, Nicholls et al.

1986). Coordination of expression of suites of nuclear- and mitochondrial-encoded genes depends on both DNA-binding proteins and coactivators (for review see Goffart and

Wiesner 2006, Scarpulla 2006). Amongst the DNA-binding proteins, nuclear respiratory factors (NRFs) and peroxisome proliferator activated receptors (PPARs) are paramount.

NRF-1 regulates the expression of several subunits of the electron transport chain as well as the mitochondrial transcription factor TFAM (Choi et al. 2002, Virbasius and

Scarpulla 1994); PPARs form heterodimers that regulate genes involved in lipid metabolism (reviewed in Barger and Kelly 2000, Berger and Moller 2002, Van Bilsen et

al. 2002). These transcription factors interact with coactivators from the PGC-1 (PPAR gamma coactivator-1) family (Handschin and Spiegelman 2006, Monsalve et al. 2000,

Puigserver et al. 1999, Scarpulla 2006, Wallberg et al. 2003).

PGC- the first characterized PGC-1 family member, was identified as a stimulator of thermogenin expression in brown adipose tissue of mice (Puigserver et al.

1998). The other family members, PGC-1 and PRC (PGC-1 related coactivator), were subsequently discovered through database searches for PGC- homologues (Andersson and Scarpulla 2001, Lin et al. 2002a). In general, PGC-1 and PGC-1 have similar coactivation responsibilities, correlating with both developmental differences in mitochondrial content (Lehman et al. 2000, Mortensen et al. 2006, Uldry et al. 2006) and physiological adaptation to stressors such as muscular activity (Baar et al. 2002, Goto et al. 2000) and nutritional status (Lin et al. 2002b, Yoon et al. 2001). Although less is known about the function of PRC, this coactivator can stimulate mitochondrial biogenesis, as do its homologues, but it is constitutively and ubiquitously expressed

(Gleyzer et al. 2005, Vercauteren et al. 2006).

These transcriptional networks have been studied primarily in mammalian systems. Given their highly conserved structures, the DNA-binding proteins likely have similar roles throughout vertebrates, as has been shown for PPARs (Kondo et al. 2007,

Krey et al. 1993, Leaver et al. 2007). However, the role of the entire PGC-1 family in lower vertebrates has not been assessed. In this study, we explore the putative regulatory role of the coactivators and their binding partners in the metabolic adaptations of goldfish

induced by two common stressors: temperature and diet. Goldfish and other fish species increase mitochondrial content in response to cold acclimation through a suite of qualitative and quantitative biochemical responses (Guderley 1990, Johnston et al. 1990).

This induction of mitochondrial biogenesis is much like the mammalian response to cold exposure and exercise, which are known to be mediated by these transcription factors

(Hood et al. 2006, Liang and Ward 2006, Puigserver and Spiegelman 2003). Changes in diet also have a pronounced effect on the tissue-specific metabolic strategy and mitochondrial phenotypes in most vertebrate species (Blasco et al. 1992, Chanseaume

2007, Du et al. 2006, Ojano-Dirain et al. 2005). In fish, prolonged caloric restriction usually causes depletion of hepatic reserves and hypometabolism (Beamish 1964, Black and Love 1986, Blasco et al. 1992, Cui and Wang 2007, Rios et al. 2002). In contrast, lipid-rich diets increase fatty acid metabolism and storage, and depress de novo lipogenic pathways (Kolditz et al. 2008, Lin et al. 1977, Poston and McCartney 1974). In mammalian systems the PPARs and both PGC-1 and PGC-1 have been associated with the regulation of fatty acid oxidation whereas only PGC-1 seems to regulate lipogenic pathways (Kamei et al. 2003, Lee et al. 2006, Mazzucotelli et al. 2007,

Rodriguez-Cruz et al. 2006). Though these regulators have not been thouroughly studied in lower vertebrates, it is conceivable that they mediate similar metabolic adaptations.

The present study will allow us to assess the patterns of expression of these important regulators in the context of developmental variation and physiological responsiveness in an early vertebrate model.

3.3 Methods

3.3.1 Animals and treatments

Goldfish (Carassius auratus L.) were maintained in the lab at 20ºC in dechlorinated water in a flow-through tank and fed goldfish chow ad libitum (30% protein, 3% fat) (Hagen, Montreal, QC, Canada). Fish were divided in five treatments groups. One group of control fish was held at 20oC and fed goldfish chow ad libitum.

Two groups were subjected to thermal acclimation regimes (and fed goldfish chow ad libitum). Water temperature was changed by about 1oC every 2 days until the temperature reached the desired endpoint. One group was held at 4oC for six weeks and the other group was held at 35oC for three weeks. The last two groups were subjected to a dietary regime (and held at 20oC). For three weeks, one group was held without feeding while the other was fed a high fat diet (50% proteins, 20% fat) (Corey Aquafeed, Fredericton,

NB, Canada).

Fish were lethally anaesthetized with tricaine methane sulfonate (0.8 g/L sodium bicarbonate, 0.4 g/L MS-222), weighed and measured, then tissue samples were rapidly collected and flash frozen. Samples were powdered in liquid nitrogen and stored at –

80ºC. The computed condition factor (K) post treatment did not indicate any significant difference between groups (data not shown).

3.3.2 Enzyme Analysis

Approximately 50 mg of powdered tissue were homogenized in 20 vol of extraction buffer (20 mM Hepes, 1 mM EDTA, 0.1% Triton X-100, pH 7.4) with a

ground glass tissue homogenizer on ice. Specific enzyme activities were spectrophotometrically assayed in 96 well plates. Immediately following homogenization cytochrome c oxidase (COX) activity was assayed at 550 nm in a solution composed of

20 mM Tris (pH 8.0), 0.5% Tween 20 and 50 μM cytochrome C. Cytochrome c was reduced using ascorbic acid, then dialyzed exhaustively in 10 mM Tris (pH 8.0) and frozen in aliquots. Citrate synthase (CS) activity was measured at 412 nm in 20 mM Tris

(pH 8.0), 0.1 mM 5,5-dithio-bis(2-nitrobenzoic acid), 0.3 mM acetyl CoA and 0.5 mM oxaloacetate (omitted for control). Liver samples were assayed for CS immediately after

COX assay, whereas muscle homogenates were subjected to a single freeze-thaw cycle (-

80oC) prior to CS assay.

3.3.3 RNA Analysis

Total RNA was extracted from 30-50 mg of powdered tissue using the QIAGEN

RNeasy kit (Mississauga, ON, Canada) according to manufacturer’s instructions. RNA was reverse transcribed using (per μg RNA): 0.125 g oligo dT primers, 0.375 g random hexamers and 15 U of Promega AMV RT (Madison, WI, USA). Real time analysis was performed on an ABI 7500 RT PCR system (Foster City, CA, USA) using the following conditions: an initial denaturation for 10 min at 95ºC, followed by 40 cycles of 15 sec denaturation at 95ºC, 30 sec annealing at optimal primer temperature

(Table 3.1), and 36 sec extension at 72ºC. Each sample was assayed in duplicate in a 25

l reaction containing 2.5 l cDNA, 12.5 l SYBR green master mix (QIAGEN) and

0.58 M of each primer. Negative controls (no template or selected untranscribed RNA)

were run as well to ensure absence of contamination. Analysis was performed according to the Ct method using EF-1 as the housekeeping gene. Specific primers for each gene were designed to amplify a single product (using goldfish published sequences or consensus primers, see Table 3.1), as checked by regular PCR and dissociation curve analysis post real-time PCR.

3.3.4 Statistical analysis

For the treatment comparisons, all data are presented as mean values SEM, and expressed relative to the control treatment (fish held at 20oC and fed goldfish chow).

Significance between treatments was tested using a Kruskall-Wallis test followed by a

Mann-Whitney U test. Least squares linear regressions were established using the log- transformed gene expression data from all the treatments and all the tissues.

3.4 Results

3.4.1 Mitochondrial enzyme activities

Fish acclimated at colder temperatures exhibited increased oxidative capacity in skeletal muscles (Fig 3.1). In red muscle (Fig 3.1A), the activities of the Krebs cycle enzyme CS, and of the electron transport chain enzyme COX, were approximately 2 and

1.6-fold higher in goldfish acclimated at 4ºC vs. 35ºC. These changes were even more pronounced in white muscle of cold acclimated fish (Fig 3.1C), as CS and COX enzyme activity was respectively 2.3-fold and 3-fold higher than in the 35ºC group. The temperature acclimation had less impact on enzyme activity in liver (Fig 3.1F). There was no consistent pattern in CS activity (statistically highest at 20oC) whereas COX

activity increased at elevated temperature. Thus, skeletal muscles, but not liver, exhibited positive thermal compensation with both mitochondrial enzymes.

Diet had negligible effects on mitochondrial enzymes in most tissues, with results showing either non-significant patterns or minor but significant changes (Fig 3.1).

Compared to the normal diet counterparts, there was an approximate 50% decrease in CS activity in red muscle in response to the high fat diet (Fig 3.1B) and in liver in response to starvation (Fig 3.1F). COX activity was virtually invariant with dietary treatments (Fig

3.1B,D,F).

We measured both COX and CS for independent assessments of mitochondrial content. The stoichiometry between COX and CS was approximately 2 in both skeletal muscles and largely unaffected by stressors. In liver, there was relatively less CS, such that the activity ratio of COX:CS was close to 1 under all conditions, but with food deprivation, the decline in CS altered the stoichiometry (COX: CS about 2).

3.4.2 Temperature acclimation and mRNA

Skeletal muscles showed increases in mitochondrial enzyme activities in cold- acclimated fish, but the mRNA pattern was less uniform. For example, in red and white muscle, CS mRNA was unaffected by temperature (Fig 3.2A,B). Low temperature significantly increased COX IV mRNA (a nuclear encoded subunit) in both skeletal muscles, whereas COX I (a mitochondrial-encoded subunit) was significantly elevated only in red muscle; in white muscle, COX I was highest in the 35oC group (Fig 3.2A,B).

In heart, temperature had no effect on either CS or COX IV mRNA levels. In liver, CS

transcript level was inversely related to temperature (Fig 3.2D), but in comparison COX I and COX IV were relatively unaffected. Thus, temperature-induced changes in the mRNA of COX IV (but not COX I) were consistent with patterns of COX enzymatic activities, and there was little relationship between CS transcripts and enzymatic activities.

Temperature had no effect on the transcript level of medium chain acyl CoA dehydrogenase (MCAD) in either red muscle (Fig 3.2A) or heart (Fig 3.2C), but white muscle MCAD mRNA levels were directly related to temperature (Fig 3.2B). In liver,

MCAD expression was greatest at an intermediate temperature, 70% lower in the cold and 45% lower in the warm. The mRNA levels of the lipogenic enzyme fatty acid synthase (FAS) were only reliably detected in liver, where transcripts levels were depressed by 90% in the warm temperature group (Fig 3.2D).

Overall, NRF-1 mRNA levels were inversely related to temperature whereas

PPAR mRNA levels were directly related to temperature (Fig 3.2). PPAR mRNA expression was more complex, showing tissue specific patterns. PGC-1 expression was directly related to temperature in the three striated muscles (Fig 3.2A,B,C), but was unaffected in liver (Fig 3.2D). In contrast, PGC-1 mRNA was inversely related to temperature in red muscle and liver, but heart was unaffected. The same inverse trend occurred in white muscle but there was no significant temperature effect. With respect to the PGC-1 family members, changes in PGC-1 and PGC-1 mRNA were typically in opposite directions.

Considering the mRNA profiles in relation to temperature, with some exceptions there was a general relationship between (i) PGC-1 NRF-1 and mitochondrial genes, and (ii) PGC-1 and PPARs.

3.4.3 Dietary treatments and mRNA

As with the temperature treatments, dietary regimes revealed a lack of correlation between CS transcripts and enzyme activities. Unlike the temperature treatments, COX mRNA expression pattern also changed in tissues that displayed no change in enzymatic activity. CS mRNA levels increased in response to the high fat diet in red muscle 3-fold

(Fig 3.3A), whereas CS activities declined by 50% (Fig 3.1B). Likewise, CS mRNA in liver increased 6 fold (Fig 3. 3D) whereas enzymatic activity was unaffected (Fig 3.1F).

Food deprivation decreased the levels of mRNA for COX subunits in liver (COX I and

IV; Fig 3.3D) and red muscle (COX IV; Fig 3.3A), but increased COX I and COX IV transcripts levels in white muscle (Fig 3.3B) where a small but significant increase in activity was observed (Fig 3.1D). The mRNA levels for CS and COX IV were unaffected by any dietary regime in heart (Fig 3.3C).

The high fat diet reduced MCAD expression 40-80% in each tissue (Fig 3.3).

Food deprivation increased the MCAD mRNA level 2 fold in white muscle (Fig 3.3B) and decreased it 50% in liver (Fig 3.3D). While relatively unchanged in the livers of high fat fed fish, FAS mRNA levels were reduced by over 95% in the fasted animals (Fig

3.3D).

A high fat diet had no significant effect on NRF-1 mRNA levels in any tissue.

Likewise, the high fat diet had no effect on PPAR mRNA in most tissues, though liver demonstrated a modest increase in both PPAR and PPAR (Fig 3.3D). Caloric restriction had negligible effect on the expression of NRF-1 and PPAR in heart (Fig

3.3C), and white muscle (Fig 3.3B), but increased PPAR in skeletal muscles (Fig

3.3A,B), NRF-1 in red muscle (Fig 3.3A) and expression of all three in liver (Fig 3.3D).

The pattern of response of PGC-1 and PGC-1 mRNA to dietary manipulations differed among tissues. PGC-1 and PGC-1 showed parallel responses in the muscles.

In heart, neither dietary regime affected the mRNA for either coactivator (Fig 3.3C). In red muscle, high fat diet and food deprivation elevated both PGC-1 and PGC-

1 mRNA, though in fasted fish the 50% increase in PGC-1 mRNA was not significant

(Fig 3.3A). In white muscle, the mRNA levels for both coactivators tended to decrease with a high fat diet and to increase with food deprivation (Fig 3.3B). The dietary treatments did not affect the hepatic levels of PGC-1 , but the starved fish exhibited an approximate 5-fold increase in PGC-1 transcript levels (Fig 3.3D).

3.4.4 Relative abundance of PGC-1 family members

We were interested in the role of the transcriptional regulators and among them the PGC-1 family, so we looked at the relative importance of the family members in different tissues. We analyzed PRC in each tissue of control fish and found that it was ubiquitously expressed but at low levels (<5% of PGC-1 level in each tissue we examined (data not shown). Thus, the transcripts of PGC-1 and PGC-1 were the 58

predominant isoforms in each tissue, with their relative abundance presented in Fig 3.4.

In control fish, PGC-1 mRNA represented only about 10% of the total of the two coactivators in red and white muscle, about 25% in liver and 50% in heart. In heart, the relative proportion of the two coactivators was unaffected by any treatment. In the other tissues, PGC-1 transcript level was proportionally higher with decreasing temperature.

Fasting decreased the relative abundance of PGC-1 in liver about 50% but increased it about 5 fold in white muscle (Fig 3.4D).

3.4.5 Global patterns in transcriptional regulators

When all the tissues and treatments were combined, there were significant correlations between mRNA levels of the PGC-1 coactivators and their transcription factor binding partners (NRF-1, PPAR , PPAR ) and regulatory targets (CS, COX IV).

PGC-1 was the better predictor of mRNA levels for PPAR (Fig 3.5A versus 3.5B) and

PPAR (Fig 3.5C versus 3.5D). The coactivators had a weaker correlation with MCAD mRNA levels, and there was little difference in the regression coefficient with PGC-1 versus PGC-1 . MCAD mRNA was better correlated with PPAR (R2=0.48) and PPAR

(R2=0.46).

Of the two coactivators, PGC-1 mRNA was the better predictor of mRNA levels for NRF-1 (Fig 3.5H versus 3.5G), CS (Fig 3.5J versus 3.5I), and COX IV (Fig 3.5L versus 3.5K). There was also a strong relationship between CS and NRF-1 mRNA levels

(R2 = 0.54) (data not shown). As well, transcript levels for COX IV and COX I exhibited a strong correlation (R2 = 0.71) in skeletal muscle (data not shown). 59

3.5 Discussion

Animal tissues differ in their mitochondrial phenotype in terms of metabolic rate and the relative importance of mitochondrial metabolism in energy production and fuel selection. In addition to constitutive differences in mitochondrial content among tissues, each tissue can respond to metabolic stress to fine tune the bioenergetics of the cell.

Studies in mammals have identified several factors as central regulators of these adaptations (e.g., PGC-1s, NRFs, PPARs), but less is known about the role of these regulators in lower vertebrates. In the present study we subjected goldfish to diet and temperature challenges to investigate the metabolic response elicited, and the putative role of transcriptional regulators in this response.

3.5.1 Mitochondrial enzymes activities

In our comparisons between tissues and treatments, we assessed both COX and

CS to distinguish between enzyme specific changes and changes in mitochondrial content. The COX:CS stoichiometries (COX:CS about 2) were relatively unaffected by treatment and similar among skeletal fiber types (Fig 3.1). Liver showed a lower

COX:CS stoichiometry (close to 1), except with food deprivation, where CS decreased.

The relatively higher CS activities in liver, and the changes in response to food deprivation likely underscore its role in fatty acid synthesis rather than oxidative metabolism per se.

The dietary and thermal stressors caused different responses in individual tissues.

Diet exerted its main effects on liver (primarily on CS), whereas temperature affected

mainly the muscles. The increase in mitochondrial enzymes at low temperature is qualitatively similar to that seen in numerous other studies in fish (Battersby and Moyes

1998b, McClelland et al. 2006, Van der Thillart and Modderkolk 1978). Likewise, the absence of thermal compensation in liver is similar to findings previous studies

(Hardewig et al. 1999, Lucassen et al. 2006, Van der Thillart and Modderkolk 1978). Our goal was to use both intertissue differences and adaptive remodeling of tissues to generate a collection of conditions where the relationships between transcriptional regulators and their putative targets could be assessed.

3.5.2 Regulation of expression of genes encoding mitochondrial enzymes

Though cold temperature increased CS and COX enzyme activities to a similar extent in skeletal muscles, the transcript levels of each enzyme indicated differential modes of regulation.CS is a homodimer with no isoforms, making it easier to develop links between gene regulation and enzyme abundance. Though CS enzyme activities differed among tissues, and changed in individual tissues with stressors, there was very little relationship between CS enzyme activity and CS mRNA levels. This situation is usually interpreted as evidence of post-transcriptional determination of protein levels.

The lack of relationship is particularly clear in cold acclimation, where CS mRNA levels did not vary (Fig 3.2) despite positive thermal compensation of the enzyme (Fig 3.1), as well as diet, where CS mRNA increased without corresponding changes in enzyme activity. Post-transcriptional regulation of CS has been implicated in various contexts, including intertissue comparisons in trout (Leary et al. 1998), evolutionary and

developmental variation in scombrids (Dalziel et al. 2006), and thermal compensation in fish (Lucassen et al. 2006, McClelland et al. 2006). While transcriptional regulation did not account for enzymatic changes, it is still worthwhile to assess the nature of the regulation of CS gene expression. CS mRNA changed dramatically in two tissues, in both cases there was a corresponding change in PGC-1 mRNA: an increase in liver with low temperature (Fig 3.2D), and an increase in red muscle with a high fat diet (Fig 3.3A).

Furthermore, where intertissue differences were considered in combination with treatments (Fig 3.5), there was a very strong correlation between CS and PGC-1 mRNA

(R2= 0.75). Conversely, there was little relationship between mRNA levels of CS mRNA and PGC-1 with treatment. For example, increases in PGC-1 mRNA occurred in muscles at high temperature without changes in CS mRNA (Fig 3.2A-C). Though, when all tissues and treatments were pooled, there was a correlation between CS mRNA and

PGC-1 mRNA (Fig 3.5I), this relationship was driven by the liver data. If only muscles are considered, there was a weak correlation between the transcript levels of CS and

PGC-1 R2= 0.29) (data not shown) Despite its common use as a marker of mitochondrial content, relatively little is known about the regulation of the CS gene.

Promoter analysis in mammals suggests a role for Sp1 (Kraft et al. 2006). NRF-1 appears to potentiate Sp1 effects on respiratory gene expression (Seelan et al. 1996, Virbasius and

Scarpulla 1994). The levels of CS mRNA also correlated with NRF-1 mRNA (R2 =0.54), suggesting that PGC-1 and NRF-1 may each influence the regulation of CS expression.

In contrast to the situation with CS, the complex structure of COX with 13 subunits (10 nuclear genes and 3 mitochondrial genes) makes it more challenging to develop models of transcriptional regulation. Nonetheless, there was a strong relationship between COX enzyme activities and mRNA for COX IV (nuclear encoded) and, to a lesser extent, COX

I (mitochondrial encoded). The relationship was most clear in thermal remodeling of skeletal muscle, where COX IV transcripts paralleled increased COX enzyme activities in both red and white muscles. Other studies have found that changes in COX activity arise in response to transcriptional regulation (Hardewig et al. 1999). In addition, we found a strong correlation between COX I mRNA and COX IV mRNA in skeletal muscles with the various stresses (R2=0.71). In general, the various nuclear genes encoding COX subunits are regulated in a coordinated fashion (Evans and Scarpulla 1990, Ongwijitwat et al. 2006, Wiesner et al. 1992), and most studies in mammals suggest that several COX genes are regulated in a NRF-1/PGC-1 dependent manner (see Handschin and

Spiegelman 2006, Scarpulla 2006). In our study, NRF-1 mRNA was in general a good indicator of COX IV fluctuations in muscles; the relationship is clearest in thermal compensation of white muscle (Fig 3.2B). Surprisingly, there was no relationship between PGC-1 mRNA and the transcript levels of either NRF-1 or COX subunits; the lack of relationship is best illustrated in the context of thermal acclimation, where mitochondrial enzymes, mitochondrial transcripts, and NRF-1 mRNA all increased at low temperature while PGC-1 mRNA markedly decreased (Figs 3.1, 3.2). Though there is a modest correlation between NRF-1 and PGC-1 mRNA when the data are pooled (Fig

3.5G), this relationship is driven primarily by the liver data. There is no correlation between these parameters when only muscles are considered (R2= 0.03, data not shown).

There was a stronger relationship between mRNA of NRF-1 and PGC-1 in relation to the stressors in individual tissues (Figs 3.2 and 3.3), as well as the global relationship across tissues (Fig 3.5H).

These patterns of expression may not necessarily reflect direct interactions between the different factors and may involve additional regulators. However, collectively the data support a role for PGC-1 , but not PGC-1 in the control of mitochondrial gene expression in fish, and in the determination of intertissue differences and adaptive responses to physiological stressors.

3.5.3 Lipid homeostasis gene expression

In addition to changes in mitochondrial content, many physiological stressors lead to changes in fuel selection through effects on the mitochondrial enzymes involved in fatty acid oxidation. MCAD, an enzyme in the -oxidation of fatty acids, is an established target of PPAR-dependent regulation (Gulick et al. 1994). MCAD levels were only slightly affected by temperature, although a strong induction of MCAD expression was seen in white muscle in the warm acclimated group (Fig 3.2B). MCAD mRNA levels decreased in all tissues with a high fat diet (Fig 3.3). This would seem counterintuitive, given that an abundance of fat in the diet should lead to increased reliance on lipid.

However, it is conceivable that the reduction in MCAD reflects a response to fatty acid chain length profile rather than lipid content per se. Food deprivation caused a marked

reduction in FAS, consistent with a shift away from lipogenesis, as seen in other models

(Field et al. 2003, Kim et al., 2003, Li et al. 2006, Paulauskis and Sul 1988).

In most tissues, the changes in MCAD mRNA were paralleled by changes in mRNA of one or both PPARs. This is consistent with the situation in mammals (Cheng et al. 2004,

Tanaka et al. 2003, Vega et al. 2000). When reconciling changes in MCAD with the coactivators during physiological remodeling, there was a relatively weak relationship between MCAD and both PGC-1 and PGC-1 . In situations where MCAD mRNA level changed (e.g., liver with diet), the mRNA for PGC-1 and PGC-1 either failed to change, or changed in the opposite direction. Furthermore, when all tissues were pooled, there was a weak correlation between MCAD and both PGC-1 and PGC-1 . There was a stronger relationship between the coactivators and both PPAR and PPAR expression

(Fig 3.5), though in both cases the correlation was stronger with PGC-1 . Although these correlations could indicate shared sensitivity to common regulatory pathways, overall the results are similar to the scenario seen in mammals, where the role for PPARs is clear

(Vega et al. 2000, Wang et al. 2003).

3.5.4 The PGC-1 family

In mammals, PGC-1 and PGC-1 have overlapping roles, with similar capacities to induce mitochondrial biogenesis when overexpressed (Lehman et al. 2000,

St- Pierre et al. 2003, Wu et al. 1999). As well, both PGC-1 and PGC-1 correlate with the oxidative capacity of tissues (Esterbauer et al. 1999, Kressler et al 2002, Larrouy et al. 1999, Puigserver et al. 1998). Though PGC-1 garners most attention as a master

controller of mitochondrial content, and it typically occurs at higher levels than PGC-1

(Andersson and Scarpulla 2001, Kressler et al. 2002), the relative importance of PGC-1 has not been established. Null mutants for either PGC- or PGC-1 present distinct mitochondrial dysfunctions as well as tissue metabolic derangements that are not compensated by the respective functional family members (Lin et al. 2004, Leliott et al.

2006, Sonoda et al. 2007, Uldry et al. 2006). Though there may be redundancy in their capacities to regulate mitochondrial biogenesis as suggested in mammals (Uldry et al.

2006), there appears to be specific capabilities unique to PGC-1 In our study, we found that in each tissue PGC-1 mRNA was greater than PGC-1 (Fig 3.4). However, the differential expression of the PGC-1 and PGC-1 isoforms in the various stressors is further support for distinct roles for the two coactivators. While the DNA-binding proteins behaved much as expected from mammalian studies, there was an unexpected lack of coordination between mitochondrial gene expression and the putative “master controllers” of mitochondrial biogenesis.

3.6 Acknowledgements

We thank Rashpal Dhillon for his help with fish treatments. This study was funded through a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Table 3.1. Real time PCR primers. Gene specific primers were designed on goldfish or consensus sequences (see footnote for GENBANK accession numbers).

Gene Forward primer (5’-3’) Reverse primer (5’-3’) PGC-1α1 atggcgtgggacaggtgta tgattggtcactgtaccacttgag PGC-1β2 tggggaagaggaggtctgc ccgtccaggctgtctgtg PRC3 cagttatgagcaggtggaca tctcgcctctatctgaatgg PPARα4 gagcccaagtttcagtttgc ggaagaggaactcgttgtcg PPARβ5 tggctttgtggatctcttcc gatctcgctgaaaggtttgc NRF-16 aggccctgaggactatcgtt gctccagtgccaacctgtat CS7 atggacttgatcgccaagc ccaccctcatggtcactgt COX IV8 agggatcctggctgcact tcaaaggtatgggggacatc COX I9 gtgcctgagccggaatagt tcagtttccgaatcctccaat MCAD10 caatggtcagaaaatgtggat ggcccatgtttaattccttt FAS11 ccaacacctcagtgcagttc ggaacgtcacaccttgctc EF1α12 atgcggtggaatcgacaa cagagagcaatgtcaatggtg

1XM678107;2XM678566;3XM001338200,CAAB01000022.1,CA371089;4AY198322, AM230808, AY590299; 5AY198322, AF342937, AJ416953, AY590301, AY356399;6 AF087671; 7BC045362, DQ059757, AY382596; 9AB111951,DQ656543, NC001606, AY996924; 10NM213010, NM016986, NM000016; 11XM682295, NM007988, NM004104, NM205155; 12AB056104, NM1312.

Red muscle Temperature Diet A) B) 4 oC Fasted 20 oC a Control 30 ab 35 oC High fat a a a b 20 a a a ab

b Enzyme activity Enzyme (U/g tissue) (U/g 10 b

0 White muscle C) a D)

6 b b b a 4 a b

a a Enzyme activity Enzyme (U/g tissue) (U/g 2 b b a

0 Liver E) F) b a a b a b 2 b a a b a

1 a

Enzyme activity Enzyme (U/g tissue) (U/g

0 CS COX CS COX

Figure 3.1. Mitochondrial enzyme activities in tissues of goldfish exposed to three temperatures (4, 20, 35ºC) and three diets (fasted, control and high fat diet). Values are expressed as means + SEM, and labelled with different letters to indicate statistical significance according to a Kruskall-Wallis test followed by a Mann-Whitney U test, with n=5-7 for each treatment.

Figure 3.2. Gene expression in goldfish exposed to three acclimation temperatures (4, 20, 35ºC). Values are expressed as means + SEM relative to controls (20ºC and control diet fish). Different letters indicate statistical significant differences between treatments according to a Kruskall-Wallis and Mann-Whitney U tests (n=7-17). 69

Figure 3.3. Gene expression in goldfish exposed to three diets (fasted, control and high fat diet). Values are expressed as means + SEM relative to controls (20ºC and control diet fish). Different letters indicate statistical significant differences between treatments according to a Kruskall-Wallis and Mann-Whitney U tests (n=7-17). 70

) b 0.2 A) Red muscle

b +PGC-1

a a ab 0.1

a a

/(PGC-1 b c

PGC-1 0

) B) White muscle b 0.6 ab

+PGC-1 0.4 b a b

0.2 /(PGC-1

b a a c

0 PGC-1

) C) Heart b a a 0.6 a a a

+PGC-1 b a 0.4

/(PGC-1 0.2 b

0 PGC-1

) D) Liver b 0.6 c bc

+PGC-1 0.4 a b b 0.2 /(PGC-1 a b a

0 PGC-1 Fasted Control High 4 oC 20 oC 35 oC fat

Figure 3.4. PGC-1 mRNA expressed as a proportion of PGC-1 transcript levels in red muscle (A), white muscle (B), heart (C) and liver (D) of goldfish acclimated at three acclimation temperatures (4, 20, 35ºC) and subjected to three dietary treatments (fasted, control and high fat diet). Bars represent means + SEM. Different letters indicate statistical significant differences between treatments according to a Kruskall-Wallis test followed by a Mann-Whitney U test (n=7-17).

Figure 3.5. Putative regulators of metabolic gene expression in goldfish tissue. Paired relative mRNA values for all tissues and all treatments were used; R2 values were determined by linear regression, all correlations were found significant (p<0.05) (n= 46- 53 per tissue).

Chapter 4. Evolution of the PGC-1 family in vertebrates

LeMoine CMR, Lougheed SC, and Moyes CD. In preparation for submission to J.Mol.Evol.

4.1 Abstract

In mammals, PGC-1 is a central regulator of oxidative metabolism through its interactions with NRF-1 and the PPARs. In lower vertebrates, however, PGC-1 seems to play a role in coordinating the PPAR axis, but does not appear to be involved in

NRF-1 regulation of mitochondrial content (see Chapter 3). To assess the structural basis of these divergent roles in mammals and fish, we investigated the evolutionary trajectory of PGC-1 homologues in representative vertebrate lineages. The phylogeny of the PGC-

1 paralogues suggested that the family diversified through repeated duplication events early in vertebrate evolution. Bayesian phylogenetic reconstruction of PGC-1 in representative vertebrate species revealed asymmetric evolutionary dynamics across the different functional domains of the protein. Specifically, PGC-1 exhibited a good conservation of the activation/PPAR interaction domain across vertebrates, whereas the

NRF-1 and MEF2c interaction domains experienced accelerated rates of evolution in

Actinopterygian (fish lineages) compared to Sarcopterygians (tetrapod lineages).

Furthermore, protein sequence analysis of these variable domains in lineages giving rise to modern teleosts revealed successive serine and glutamine residues insertions with important functional repercussions on PGC-1 control of mitochondrial proliferation in teleosts. Collectively, these results suggest a modular evolution of the PGC-1 protein in 73

vertebrates that could allow for lineage-specific divergences in the coactivating capabilities of this regulator.

4.2 Introduction:

The evolution of chordates shows an impressive diversification and specialization of structural and biochemical features. At the molecular level, these phenotypic adaptations are paralleled by the expansion and increased complexity of gene families in vertebrate history. While point mutations and subsequent selection generated some genetic variability in extant species, it was the large scale genomic duplication events that are thought to be mainly responsible for the diversity in vertebrate gene families (for review see Ohno 1999, Sémon and Wolfe 2007, Zhang 2003). At least two large scale duplication events (1R and 2R) are thought have occurred early in chordate evolution generating four paralogous genes in vertebrates for each protochordate/invertebrate orthologue (Ohno 1970, Ohno 1999, Panopoulou and Poustka 2005). Evidence from recently completed fish genomic assemblies support the earlier suggestions (Ohno 1970) that an additional round of duplication (3R) occurred early in the ray-finned fish lineage.

Thus, the one-four model for tetrapods transforms into the one-eight model for teleosts genes (Meyer and Van de Peer 2005, Taylor et al. 2003). Though gene-specific evolutionary trajectories forced many gene families to deviate from these predictions, multiple examples of the 1R/2R/3R theory are found throughout vertebrates (Hoegg et al.

2004, Meyer and Schartl 1999, Prohaska and Stadler 2004, Steinke et al. 2006).

Mechanistically, each duplication event provided organisms with two sets of genes presumably carrying redundant functions. Sustained selective pressure conserved one of the duplicates, while the other was relieved from such constraints and could rapidly evolve without deleterious consequences (Sémon and Wolfe 2007, Zhang 2003).

The rapid accumulation of mutations in the regulatory or coding sequence of a duplicate would allow a conservation of gene dosage and prevent aberrant phenotypes caused by the doubled expression of the ancestral gene product (see Conrad and Antonarakis 2007).

This often lead to the loss or pseudogenization of the rapidly evolving duplicate, as exemplified by the accumulation of nuclear hormone receptors (NHR) pseudogenes in vertebrate genomes (Zhang et al. 2008). In contrast, some duplicated genes may adopt a radically different evolutionary trajectory, and eventually undergo neofunctionalization or subfunctionalization of the ancestral protein (Sémon and Wolfe 2007, Zhang 2003).

The vertebrate retinoid acid receptors are good examples of such diversification, as family members assumed distinct expression patterns and ligand-binding abilities post- duplication (Escriva et al. 2006).

The PGC-1 gene family is a small family of transcriptional coactivators that plays a pivotal role in several aspects of metabolic regulation in mammals. In humans, there are three members: alpha, beta, and PGC-1 related protein (PRC). PGC-1 , the founding member of the family, was first characterized as an inducer of brown fat differentiation

(Puigserver et al. 1998). Since then, it has been linked to multiple metabolic programs in diverse mammalian systems (for review see Handschin and Spiegelman 2006, Puigserver

and Spiegelman 2003). PGC-1 and PGC-1 are relatively similar in structure and tissue distribution, but while they exhibit some functional redundancy in certain metabolic programs, distinctive capacities have been attributed to each coactivator (Handschin and

Spiegelman 2006, Lin et al. 2002a, Uldry et al. 2006). In comparison, PRC is much more divergent in structure and expression, and its functional responsibility in metabolic adaptations remains largely unexplored (see Andersson and Scarpulla 2001).

PGC-1 is the best studied, and arguably most important member of the family.

Its complex protein architecture allows it to interact with a plethora of regulators of gene expression, thereby conferring a major role in the coordination of the adaptation to developmental, physiological and environmental stressors. The protein has four main functional domains: the activation (AD), NRF-1 binding, MEF-2c binding, and RNA binding (RBD) domains (Figure 2C). Within the AD, canonical LXXLL motifs allow

PGC-1 to interact with members of the NHR superfamily (e.g., PPARs, ERRa, HNF4a), mediating their effects on metabolic homeostasis (Vega et al. 2000, Wu et al. 2002). In addition, PGC-1 possesses a PPAR -specific interaction domain that permits ligand- independent interactions with the NHR during brown fat differentiation (Puigserver et al.

1998). The ability of PGC-1 to exert effects on the muscle phenotype is via the nuclear respiratory factors (NRF-1 and 2) and MEF2c. The coactivator binds these transcription factors through independent motifs and mediates their respective roles on muscle metabolism (Lin et al. 2002b, Michael et al. 2001, Vercauteren et al. 2008, Wu et al.

1999). These features garnered PGC-1 the role of a master controller of oxidative

metabolism in mammals. However, my recent studies (Chapter 3) suggest that this role may not be conserved across vertebrate taxa. Specifically, in the context of metabolic remodeling in fish muscle in response to exercise, dietary and temperature stressors, mitochondrial adaptations seemed to occur in a PGC-1 independent manner, while other roles such as the regulation of lipid homeostasis appeared to be conserved (Chapter 3,

McClelland et al. 2006).

The modular structure of PGC-1 , with separate interaction sites involved in distinct metabolic programs, could allow for the different binding domains to follow independent evolutionary trajectories. Thus, while some functions of PGC-1 may be conserved throughout vertebrates, others could experience lineage–specific diversification. In the present study, we test these possibilities by evaluating the evolutionary history of PGC-1 in representative vertebrate species and the potential functional implications entailed.

4.3 Methods

4.3.1 PGC-1 sequences:

Protein sequences from the different family members were obtained from the

ENSEMBL and GENBANK databases (see Tables 4.1, 4.2). Sequences were also generated through reverse transcription PCR using muscle cDNA. Total RNA was extracted from flash frozen ground muscle tissues of targeted species (Table 4.1) using the RNeasy Extraction kit according to manufacturer’s instructions (QIAGEN,

Mississauga, ON, Canada). Total RNA was reverse transcribed and the PGC-1

orthologues amplified from each species using consensus primers and species specific primers (Appendix 1). The cycling and reactions conditions were optimized for each target using Pfx proofreading polymerase (Invitrogen, Burlington,ON, Canada ).

Amplicons were run on agarose gels, purified using Qiaquick gel extraction kit

(QIAGEN) and cloned with the QIAGEN cloning kit or TOPO TA kit (Invitrogen) following manufacturer’s instructions. Clones were sequenced at the Genome Quebec

Sequencing center (Montreal, QC, Canada) and at Queen’s University Genotyping

Facility (Kingston, ON, Canada).

4.3.2 Phylogenetic analyses:

PGC-1 sequences were aligned with ClustalW (Thompson et al. 1994), and through comparison of the nucleotide and translated proteins in MEGA 4.1 (Tamura et al.

2007), positions of gaps and misalignments between taxa were manually excluded from further analysis. To reconstruct the PGC-1 phylogeny, the protein sequences of the family members were aligned and a Neighbour-Joining tree constructed in ClustalW.

The PGC-1 sequences were partitioned in four functional domains relative to the nucleotide sequence of the rat PGC-1 homologue (NM_031347): the Activation

Domain (AD 93-593), NRF-1 domain (594-1200), MEF2c domain (1201-1683) and the

RNA binding domain (RBD 1684-2167) (Fig 4.3). Model test v. 3.7 (Poseda and

Crandall 1998) was implemented in conjunction to PAUP* (Swofford 2000) to select the best model of nucleotide evolution for each partition according to the Akaike Information

Criterion. The best model of nucleotide evolution for each partition and the whole

sequence was analysed using a Bayesian inference of phylogeny in MrBayes 3.1.2

(Huelsenbeck and Ronquist 2001, Ronquist and Huelsenbeck 2003). The Metropolis coupled MCMC used two incrementally heated Markov chains and was run for 1x106 generations, sampling every 100 generation. The first 250,000 trees were discarded as

“burn-in” to ensure the analysis reached stationarity for each partition. The data generated subsequently was used to establish the Bayesian posterior probabilities. Trace analyses were performed to ensure sufficient effective sample size in Tracer v.1.4

(Rambaut and Drummond 2007) and inferred tree topologies were retrieved in MEGA

4.1.

Pairwise comparisons within Sarcopterygians and Actinopterygians were estimated in MEGA4.1, according to the Kimura 2-parameters gamma distributed model of substitution for each domain. These data were permuted (n=103) and randomly assigned to each group to obtain a hypothesized frequency distribution of the mean difference of substitution between the two groups. The observed mean difference in substitutions between the two groups was compared to the hypothesized distribution and a p-value generated.

4.4 Results

4.4.1 PGC-1 gene in vertebrates

We retrieved PGC-1 homologues from representative vertebrate species through

RT-PCR and public database mining. The obtained sequences were translated and aligned with PGC-1 family members from NCBI and ENSEMBL databases. PGC-1

homologues from selected species (xenopus, rainbow trout and goldfish) were confirmed as mRNA products via real-time RT-PCR and northern blotting (Chapter 3, unpublished results). The phylogeny of the PGC-1 family was generated to confirm identity of the

PGC-1 sequences. As outgroups of the modern vertebrate PGC-1s, we included putative homologues from a tunicate (C.intestinalis) and two arthropod species (A. gambiae,

D.megalonaster). The amplified PGC-1 homologues all clustered with characterized

PGC-1 proteins, suggesting that the retrieved sequences were orthologous to the mammalian PGC-1 gene (Fig 4.1).

We used the ENSEMBL genome browser to analyze the genomic region surrounding the PGC-1 genes in representative species (Fig 4.2A). The synteny analysis revealed homologous organization in the model species examined (Fig 4.2A). In addition, the comparison of the predicted PGC-1 gene intron/exon organization presented a conserved organization in vertebrates (Fig 4.2B). The gene included 12 introns and 13 exons corresponding to a 798 to 975 amino acids polypeptide in tetrapods and fish respectively (Fig 4.2B). Collectively, these data suggested that the PGC-1 sequences obtained in the current study were true orthologues of the mammalian PGC-1 .

4.4.2 Protein homologies

The general architecture of the PGC-1 was relatively conserved across the vertebrate taxa sampled. The position of the specific amino acid (a.a.) residues discussed in the following sections refer to the location of the residues on the rat PGC-1 protein

(NP_112637). The amino terminus (AD) harbored the most homology of all the 80

functional domains of the protein. In particular, the two activation motifs, AD1 (a.a. 30-

40) and AD2 (a.a. 82-95), crucial to PGC-1 transcriptional activity (Sadana and Park

2007) were over 90% homologous across taxa. The three NHR boxes, other characteristic features of PGC-1 (Fig 4.3), were identical in all species investigated. In addition, the residues flanking these boxes as well as their spatial organization were highly conserved across species.

The central section of the protein was comparatively more heterogeneous across vertebrates. For our analysis we further divided it in the two major transcription factor interacting regions, the NRF-1 (a.a. 180-403) and MEF2c (a.a. 404-564) domains (Fig

4.3), as determined by previous studies (Michael et al. 2001, Vercauteren et al. 2008, Wu et al. 1999). Notably, in these two domains lineage-specific insertions occurred. In the

NRF-1 domain, consequent serine-rich insertions were shared by chondrosteans (12-13 a.a.), bowfin (22 a.a.) and teleosts (28-31 a.a.). Serine residues constituted 56-86% of the insertion. Also flanking the well-conserved tri-lysine residues in vertebrates (a.a. 187-

189), most teleosts exhibited a more variable (4-14 a.a.) glutamine-rich insertion. In that same domain, the interaction motifs for PPAR (a.a. 292-339) and Host Cell Factor

(HCF; a.a. 383-387) were overall reasonably conserved across species. The MEF2c domain was the least conserved partition of the protein, and presented an additional polyserine insertion (5-15 a.a.) specific to neopterygians. Although, as part of this domain, a region of unknown function (a.a. 448-471), presenting similarities with PGC-

1 , showed a high level of similarities across species.

On the carboxy terminus, the RNA recognition motif (a.a. 678-709) was 66% similar in the species investigated. Comparatively, the sequential serine-arginine repeats

(a.a. 565-598 and a.a. 620-632) were more divergent across taxa, but most species still exhibited a characteristically high proportion of these residues in this region, arguing for a relatively good conservation of these two motifs composing the RBD.

We also assessed the conservation of the numerous sites throughout the protein that are modified in ways that may regulate PGC-1 activity. All three p38 MAP kinase phosphorylation sites were present in all taxa (Table 4.3). In contrast, only one of the

AMP kinase targeted residue was conserved in most species, the other site being specific to tetrapods. Two methylation sites (Teyssier et al. 2005) were identical in all species, while a third site (Arg669) was absent of several tetrapod and fish species. The majority of lysine residues targeted for acetylation (Rodgers et al. 2005) were conserved in the species investigated. However, Lys320 was present only in Sarcopterygians (including tetrapods), and Lys441 was limited to amniotes (Table 4.3).

Thus, the majority of PGC-1 structure and functional sites were relatively preserved in vertebrate history. Though, in the ray-finned fishes, two major transcription factor binding domains (NRF-1 and MEF2c) presented unique and extensive amino acid insertions.

4.4.3 Phylogenetic divergence

The phylogeny of PGC-1 presented an interesting contrast between the major vertebrate lineages examined. The analysis of the whole sequence (Fig 4.4) revealed a

topology consistent with evolutionary relationships among vertebrates. However, when comparing the two main clades, the actinopterygians presented a higher degree of divergence as a group as well as among species (see variability among teleosts). To examine the basis for such radical difference, we analyzed the four main functional domains independently.

The Bayesian analysis of each partition resulted in an overall topology relatively consistent with the accepted classification, with a few exceptions (Fig 4.5). However, most exceptions, such as the position of the bowfin (NRF-1 domain) or of the lungfish

(RBD domain), have relatively low support from posterior probabilities (0.57 and 0.66 respectively) compared to the majority of the nodes (Fig 4.5B, D). As suggested by the protein alignment and architecture conservation, the AD was the most conserved functional domains of PGC-1 in the vertebrate species examined (Fig 4.5A). Analysis of the pairwise distances within the two major groups suggested no significant difference in the number of mutations accumulated in the actinopterygian and sarcopterygian lineages (Fig 4.6A). In contrast, the other domains presented dichotomous evolutionary trajectories (Fig 4.5B,C,D). For all three domains, the ray-finned fishes exhibited higher divergence than tetrapods (Fig 4.5, Fig 4.6B, C and D). In particular, fishes that experienced consequent insertions in the NRF-1 and MEF2c domains were the most divergent vertebrate species for these respective partitions (Fig 4.5B, C). Though the

RBD presented a similar topology, the magnitude of the difference between tetrapods and fishes was relatively more conservative.

In all three domains, the amphibians presented higher overall divergence than other sarcopterygian clades, a difference most evident for the MEF2c region (Fig 4.5C).

In fish, the domain-specific divergences were relatively heterogeneous across species.

Interestingly, in the two domains harboring extensive insertions in neopterygians (NRF-1 and MEF2c), the bowfin segregated closer to the teleost group than to the bichir and sturgeon. Conversely, across partitions, the primitive chondrosteans presented an overall divergence similar to tetrapods (Fig 4.5).

Overall, the generated phylogenies of PGC-1 collectively suggested asymmetric evolution across the different functional sites with radically different evolutionary trajectories of the NRF-1 and MEF2c domains in ray-finned fish.

4.5 Discussion:

The evolution of vertebrate transcriptional regulatory networks has been punctuated by the multiplication and structural diversification of the transcription factors.

Most studies in this area to date focus on the DNA binding transcription factors such as

HOX (reviewed in Hoegg and Meyer 2005, Hurley et al. 2005) and myogenic factors

(Atchley et al. 1994, Macqueen and Johnston 2008). In the evolution of the metabolic phenotype, the best studied gene family is the NHR superfamily (see Bury and Sturm

2007, Escriva et al. 2003). However, the recent emergence of coregulators as central mediators of complex physiological responses in mammals suggests a pivotal role of these factors in vertebrates’ evolution. In the current study, we evaluated the evolutionary

history of the PGC-1 family of coactivators and assess the divergence of PGC-1 in representative vertebrate taxa.

4.5.1 Evolution of the PGC-1 family:

The PGC-1 family likely evolved from an ancestral gene early in the metazoan history. A distant putative homologue was found in Drosophila with a role in regulating metabolic adaptation to nutritional status (Gershman et al. 2007). Additional database searches revealed the presence of potential homologues in mosquito and sea-squirt species. The putative ancestor harboured several features common to all three PGC-1 paralogues: a carboxyl terminus serine-arginine region in tandem with an RNA recognition motif, a putative HCF binding motif, and degenerate carboxyl leucine boxes that could mediate interactions with NHR (Gershman et al. 2007, Huang et al. 1998).

Based on the invertebrate sequences, it is likely that PGC-1 common ancestor lacked many of the functions of vertebrate PGC-1 paralogues. For example, PGC-1 from invertebrates lacks the N-terminal canonical LXXLL motifs and the PPAR binding site.

Though, this is not surprising considering that several of the NHR interacting with PGC-1 family members in mammals through these motifs (e.g., PPAR, ER) have no known homologues in invertebrates (Escriva et al. 2004, King-Jones and Thummel 2005).

The one-four model, often implicated in the diversification of gene families in vertebrates (Ohno 1999, Panopoulou and Poustka 2005), is consistent with the topology of the PGC-1 family. Our phylogenetic reconstruction of the PGC-1 family history suggested a first duplication event that gave rise to the PGC-1 / ancestor and PRC

ancestor. A second duplication could have produced the PGC-1 and paralogues while one of the PRC duplicate was lost. All three family members share common structural motifs and functions, however they exhibit distinct tissue distribution and responsiveness to metabolic signals (Andersson and Scarpulla 2001, Kressler et al. 2002, Lin et al.

2002a, Puigserver et al. 1998, Vercaueteren et al. 2006). These structural features collectively argue for a common ancestry of the family with subsequent neo- or subfunctionalization of the paralogues as seen in other vertebrate gene families (Lin et al.

2006, Woolfe and Elgar 2007).

4.5.2 PGC-1 evolution

We retrieved several PGC-1 homologues from representative vertebrate species.

Synteny and intron/exon arrangement analyses of selected sequences revealed that the retrieved sequences were orthologous to the mammalian PGC-1 (Fig 1A,B). It should be noted that despite extensive RT-PCR and database searches, we failed to find any evidence of a duplicate PGC-1 gene in any taxa investigated. The structural integrity of

PGC-1 was overall well conserved over the course of vertebrates’ history. Each of the four main functional domains presented a similar organization in all species, but followed independent evolutionary trajectories.

N- and C-termini

The amino terminus of the protein, carrying the activation domain, was the most preserved feature of the coactivator (Fig 4.5A, 4.6A). This region is critical for PGC-1 effects on gene expression; upon interaction with DNA bound proteins the coactivator

recruits histone remodelling proteins (SRC-1, CBP/p300) via this domain, facilitating the transcriptional activation of target genes (Puigserver et al. 1999, Puigserver et al. 2003).

Another highly conserved characteristic of this region is the presence and spacing of three leucine rich boxes, critical motifs for the interactions with members of the NHR superfamily (Oberkofler et al. 2003, Vega et al. 2000, Wu et al. 2002). The preservation of these NHR boxes would allow for similar capabilities to coactivate these transcription factors (e.g., PPARs) in all vertebrate taxa, as suggested by expression profiles in goldfish (Chapter 3).

The carboxyl terminus of the protein was comparatively more divergent in the ray-finned fish compared to tetrapods (Fig 4.5D, 4.6D). However, the RNA recognition motif was relatively well conserved across species, and while the SR domains presented some interspecific variability, all vertebrate PGC-1 paralogues exhibited a region rich in serine and arginine residues upstream of the RNA recognition motif which suggests a conservation of the RNA binding/processing function in vertebrates. In contrast, the high variability of the region flanking the RNA recognition motif in fish may prevent interactions with other transcription factors, such as NRF-1 and FOXO1 associated with this area (Puigserver et al. 2003, Vercauteren et al. 2008). Interestingly, when considering the PGC-1 family, the termini are the most conserved features of the proteins. Therefore, it seems that there is a strong selective pressure promoting the general integrity of the activation and RBD, essential to the coactivating capabilities of the paralogues.

NRF-1 and MEF2c domains

Within the central portion of PGC-1 , defining landmarks of the PGC-1 paralogues -the PPAR and HCF interaction domains- were also relatively well conserved across species, suggesting similar binding capacities. Considering the stasis of these features, the phylogenetic analysis of the functional domains presented an interesting paradox. First, the PGC-1 partitions exhibited asymmetric evolutionary dynamics (Fig 4.5). While the AD showed little variability within and between the sarcopterygian and actinopterygian clades, both the NRF-1 and MEF2c domains accumulated more mutations within each lineage (Fig 4.5A, B, C). Furthermore, this degree of divergence was significantly higher among ray-finned fishes (Fig 4.6B, C).

Concurrently, most fish species experienced several subsequent insertions within these regions (see Fig 4.3). An initial polyserine insertion in the NRF-1 binding domain likely occurred at the basis of the actinopterygian lineage, and was then repeatedly extended at the base of the neopterygian and teleost clades. Downstream of these residues, most teleost species also experienced a glutamine-rich insertion of variable length (see Fig

4.3). The heterogeneity of the NRF-1 domain along with the gradual expansion of the serine insertion suggests a relaxation of the functional constraints associated with this region in fish lineages. Indeed, the location of these residues may have important consequences on PGC-1 ability to interact with NRF-1. The NRF-1 domain has not been fine-mapped in PGC-1 , but in PRC the minimal NRF-1 interaction domain spans

34 a.a. containing tri-lysine residues in the central portion of the protein (Vercauteren et

al. 2006). In fish PGC-1 the polyserine insertion is directly located upstream of these conserved lysines, thus by homology within the putative NRF-1 binding domain.

Therefore, in fish these insertions could prevent interactions of PGC-1 with NRF-1, and consequently alter its capacity to mediate NRF-1 effects on mitochondrial capacity. This functional divergence could provide a mechanistic explanation for the absence of correlation between PGC-1 and the NRF-1 axis observed in goldfish tissues (Chapter

3). Similarly, in the MEF2c domain of Neopterygian, an additional serine insertion was paralleled by an increased divergence across species (Fig 4.5C). And apart from a relatively well conserved stretch (a.a. 448-471), this domain presented little homology between sarcopterygians and actinopterygians. In mammals, this region has not been assigned any other functions, and the exact residues allowing interactions with MEF2c have not been fully mapped yet (Michael et al. 2001). Interestingly, a polymorphism flanking the conserved region of that domain has been associated with decreased capacity to bind MEF2c in humans (Zhang et al. 2007). Therefore it is conceivable that the high variability of this domain reflects the low complexity of that region, and that despite this apparent divergence, PGC-1 and MEF2c assume an evolutionary conserved role in conjointly regulating muscle phenotype in vertebrates.

The phylogenies generated with each trees indicated that in general the fishes and the amphibians seem to be more divergent than other lineages. It is interesting to note that multiple species in these lineages are polyploids (Beçak and Kobashi 2004, Hellsten et al.

2007, Ohno 1999, Otto and Whitton 2000). It has been suggested, that such

polyploidization events demonstrate the genomic “plasticity” of these lineages characterized by much higher divergence rates than other vertebrates (see Robinson-

Rechavi and Laudet 2001, Venkatesh 2003, Wagner et al. 2004).

Regulatory sites

PGC-1 activity, as many regulators of gene expression, is strongly regulated post-translationally, consequently the protein harbours multiple residues targeted for covalent modification (Cao et al. 2004, Jäger et al. 2007, Rodgers et al. 2005, Teyssier et al. 2005). With the exception of a few sites, most of these regulatory sites were exceptionally conserved in all species (Table 4.3). However, the presence of a few lineage-specific sites may suggest interesting differences in the activity and adaptive role of the protein in these lineages. As an example, phosphorylation of PGC-1 by AMPK promotes mitochondrial biogenesis in murine muscle cells, and the mutation of both regulatory sites ablates PGC-1 coactivating activity on its own promoter (Jäger et al.

2007). Thus, the presence of both residues in tetrapods versus only one in all the other species, suggests potential differences in the PGC-1 responsiveness to AMPK in these species. This may in turn have important consequences on the role of the coactivator in metabolic programs, such as adaptation to exercise in muscle, modulated through AMPK.

Furthermore, it would explain the absence of a PGC-1 induction in response to endurance training in zebrafish (McClelland et al. 2006). Similarly, the absence of some residues targeted for deacetylation or methylation in several lineages (Table 4.3) could

indicate an overall lower basal activity of the coactivator in these species as suggested by mutation analyses in mammals (Rodgers et al. 2005).

4.6 Summary

Transcription factors play a major role in the development and specialization of adaptive traits in vertebrate species, but the modulation of their activities through coregulators provides finer adjustments of the tissue-specific response to a variety of signals. The present study suggests that the diversification and evolution of these coregulators such as the PGC-1 family could provide invaluable evolutionary opportunities to adaptively tailor regulatory networks and their effects in specific lineages. Specifically, the conservation of the activation domain, RBD and NHR boxes suggest that PGC-1 could assume a similar role in coactivating NHR across vertebrate taxa. In contrast, the potential disruption of the NRF-1 binding site in fish would reduce the function of PGC-1 as a mediator of mitochondrial biogenesis in these species.

Although gene expression studies in fish suggest such divergence in PGC-1 function

(McClelland et al. 2006, Chapter 3), direct molecular testing of these interactions are warranted to establish with certainty the nature of the coactivating capabilities of PGC-

1 in lower vertebrates.

Table 4.1. PGC-1 sequence information of representative vertebrate species used in this study.

Class/Subclass Taxon Accession number Chondrichthyes Elasmobranch Spiny dogfish (Squalus acanthias) pending Sarcopterygii Dipnoi African lungfish (Protopterus annectens) pending Amphibia Xenopus (Xenopus laevis) pending Leopard frog (Rana Pipiens) pending Reptilia Green Anole (Anolis carolinensis) pending Eastern painted turtle (Chrysemys picta) pending Aves Chicken* (Gallus gallus) NM001006457 Pigeon (Columbia livia domestica) pending Mammalia Platypus* (Ornithorhynchus anatinus) ENSOANT00000010665 Cow* (Bos Taurus) AY321517 Human* (Homo Sapiens) NM_013261 Rat* (Rattus norgevicus) NM_031347 Mouse* (Mus musculus) NM_008904 Actinopterygii Chondrostei Bichir (Polypterus bichir) pending Sturgeon (Acipenser transmontanus) pending Neopterygii Bowfin (Amia calva) pending Zebrafish (Danio rerio) pending Golden shiner (Notemigonus crysoleucas) pending Black ghost knifefish (Apteronotus albifrons) pending Goldfish (Carassius auratus) pending Rainbow trout (Oncorhynchus mykiss) pending Stickleback* (Gasterosteus aculeatus) ENSGACT00000025887 Medaka* (Oryzias latipes) ENSORLT00000006028 Swordfish (Xiphias gladius) pending Neopterygii Bowfin (Amia calva) pending Zebrafish (Danio rerio) pending Golden shiner (Notemigonus crysoleucas) pending Black ghost knifefish (Apteronotus albifrons) pending Goldfish (Carassius auratus) pending Rainbow trout (Oncorhynchus mykiss) pending Stickleback* (Gasterosteus aculeatus) ENSGACT00000025887 Medaka* (Oryzias latipes) ENSORLT00000006028 Swordfish (Xiphias gladius) pending

* indicates sequences retrieved from GENBANK and ENSEMBL databases.

Table 4.2. Protein information for the PGC-1 homologues used in the phylogenetic reconstruction of the PGC-1 family.

Species Protein accession number

PGC-1 putative ancestor Drosophila CG9809-RA (D. Megalonaster) Mosquito AAEL003768-PA (Aedes aegypti) Ciona ENSCINP00000016524 (Ciona intestinalis)

PRC PGC-1 Zebrafish ENSDARP00000073535 XP_683658.2 (Danio rerio) Green Puffer fish GSTENP00030607001 (Tetraodon nigroviridis) Stickleback

(Gasterosteus aculeatus) ENSGACG00000007308 Xenopus ENSXETP00000003593 ENSXETP00000027806 (Xenopus tropicalis) Chicken ENSGALP00000011997 ENSGALP00000001890 (Gallus gallus) Rat ENSRNOP00000023661 ENSRNOP00000025222 (Rattus norgevicus) Human ENSP00000278070 ENSP00000312649 (Homo Sapiens)

Table 4.3. Covalent modifications of PGC-1 and their conservation in vertebrates.

Covalent Domain Residue Presence of the site modification Lys77 Ac. Vertebrates AD Lys144 Ac. Vertebrates Thr177 P Tetrapods Lys183 Ac. Vertebrates Lys253 Ac. All species but dogfish Thr262 P Vertebrates Ser265 P Vertebrates NRF-1 Lys270 Ac Vertebrates Lys277 Ac. Vertebrates Lys320 Ac. Lungfish and Tetrapods Lys346 Ac. Vertebrates Thr298 P Vertebrates Lys412 Ac. Vertebrates Lys441 Ac. Amniotes MEF2c Lys450 Ac. All species but dogfish Ser538 P Vertebrates Arg665 M Vertebrates Arg667 M Vertebrates RBD Arg669 M Most vertebrates Lys757 Ac. Vertebrates Lys778 Ac. Vertebrates

aedes drosophilaCG9809 humanPRC ratPRC chickenPRC xenopusPRC PRC sticklebackPRC greenpufferPRC zebrafishPRC ciona zebrafishPGC1b xenopusPGC1b chickenPGC1b humanPGC1b PGC-1b ratPGC1b sturgeonPGC1a bichirPGC1a troutPGC1a medakaPGC1a sticklebackPGC1a swordfishPGC1a bowfinPGC1a zebrafishPGC1a shinerPGC1a goldfishPGC1a knifefishPGC1a dogfishPGC1a PGC-1a lungfishPGC1a ranaPGC1a xenopusPGC1a anolePGC1a turtlePGC1a pigeonPGC1a chickenPGC1a cowPGC1a humanPGC1a mousePGC1a ratPGC1a platypusPGC1a

Figure 4.1. Phylogeny of the PGC-1 family. Protein sequences from representative species were subjected to a Neighbour-Joining analysis. The linearilized tree was rooted with putative PGC-1 ancestors from two arthropod species, a fly (Drosphila megalonaster) and a mosquito (Aedes aegypti). The arrows indicate the position of the putative ancestral proteins from the arthropods and a primitive chordate (Ciona intestinalis). PGC-1 coding sequences (Table 4.1) were translated using MEGA4.1, other family members protein sequences were retrieved from ENSEMBL and GENBANK databases (Table 4.2).

Figure 4.2. Synteny and structure of the PGC-1 gene in vertebrates. A) Syntenic comparisons of the PGC-1 gene in representative species, the black boxes represent the genomic location of the genes. B) Deduced intronic (lines) and exonic (boxes) organization of the PGC-1 gene in vertebrates, adapted from the ENSEMBL prediction database for PGC-1 from human (ENSG00000109819), chicken (ENSGALG00000014398) and stickleback (ENSGACG00000019546).

Figure 4.3. Structure and conservation of the PGC-1 protein in vertebrates. The activation, NRF-1, MEF2c and RNA binding (RBD) domains, used to partition the PGC-1 sequences, are indicated on the rat protein sequence. Nuclear receptor boxes (in red) and Host Cell Factor (light blue) binding sites are represented on representative vertebrate species. The fish-specific serine and glutamine-rich insertions are indicated by an arrow and gold stripes. Regions of homologies between species are shown by colour coordinated vertical bands indicating the extent and relative position of these homologous regions.

Dogfish Bichir Sturgeon Bowfin Zebrafish Shiner Knifefish Goldfish Trout Swordfish Stickleback Medaka Lungfish Xenopus Rana Anole Turtle † Pigeon Chicken Platypus Cow Human Mouse Rat 0.1

Figure 4.4. PGC-1 phylogeny in vertebrates. The best model of evolution according to the Aikaike information criterion was selected for each nucleotide partition (see Figure 4.5), and the PGC-1 partitions were assembled and subjected to a Bayesian inference of phylogeny analysis. The tree topology is supported by 90% posterior probabilities except (†) 59%. The scale indicates the estimated number of substitution per site.

A) Activation domain B) NRF-1 domain

Dogfish Dogfish Bichir Medaka Sturgeon Stickleback Bowfin Swordfish Zebrafish Trout Goldfish Shiner Knifefish Knifefish Shiner Goldfish * Zebrafish Trout * Bowfin Medaka Bichir Stickleback Sturgeon * Swordfish Rat Lungfish § Mouse Xenopus § Human Rana Cow Platypus Platypus Pigeon * Chicken Chicken Pigeon † Turtle Turtle Anole Anole Cow Rana Human Xenopus Mouse Lungfish Rat

C) MEF2c domain D) RNA binding domain Dogfish Dogfish Bichir Lungfish Sturgeon * Sturgeon Bowfin Bichir Zebrafish Bowfin Shiner § Zebrafish † Knifefish Shiner Goldfish Knifefish Trout Goldfish Swordfish Trout Stickleback Stickleback Medaka § Medaka Turtle Swordfish Anole Platypus Xenopus Xenopus * Rana † Rana Pigeon Turtle Chicken Anole Platypus Pigeon Mouse Chicken Rat Human Human Cow Cow Mouse Lungfish * Rat 0.1

Figure 4.5. Phylogeny of the PGC-1 functional domains in vertebrates. The best model of evolution according to the Aikaike information criterion were selected for the activation (SYM+ ), NRF-1 (GTR+I+ ), MEF2c (TVM+I+ ) and RNA binding (GTR+I+ ) domains. Each separate partition was then subjected to a Bayesian analysis. The tree topologies are supported by 90% posterior probabilities except (*) 50%, (§) 70% and (†) 80%. The scale indicates the estimated number of substitution per site. 99

0.1

A) Activation Domain B) NRF-1 Domain 80 80 0.035 (p=0.28)

40 40 0.122

(p=0.001) Expected frequency Expected

0 0 -0.1 -0.05 0 0.05 0.1 -0.1 -0.05 0 0.05 0.1

C) MEF2c Domain D) RNA Binding Domain

80 80

0.366 (p=0.001) 0.115

40 40 (p=0.001) Expected frequency Expected

0 0 -0.2 -0.1 0 0.1 0.2 0.3 -0.1 -0.05 0 0.05 0.1 Mean difference of substitutions Mean difference of substitutions

Figure 4.6. Comparison of the difference of the mean number of substitution between the ray-finned fishes and the tetrapod lineage. The bars represent the expected frequency of the mean number of substitutions generated by 103 permutations with random assignment to each group of the Kimura 2-parameters pairwise differences within each clade. The white bars indicate higher number of accumulated in fish vs tetrapods, while grey bars indicate the opposite. The arrow indicates the observed mean difference between the two groups.

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Chapter 5. General Discussion

Mitochondrial biogenesis and maintenance requires the delicate coordination of a suite of genes from both the nuclear and mitochondrial genomes. The metabolic plasticity of vertebrate muscle presents an interesting model to study the regulatory networks coordinating changes in oxidative capacity as wide differences in mitochondrial content can arise as a result of developmental programs, adaptive remodeling and evolutionary history (Biressi et al. 2007, Boveris and Navarro 2008, Dalziel et al. 2004, Hood et al.

2006). I assessed the regulatory basis of these changes in the context of aging in mammalian muscle and metabolic remodeling in response to metabolic stressors in fish.

In addition, I investigated the evolutionary history of the PGC-1 family of transcriptional regulators in relation to their role in metabolic adaptations. Collectively, these studies contribute to our understanding of the evolution of the networks controlling mitochondrial variation in vertebrates.

In the first series of experiments of this thesis (Chapter 2), I investigated the metabolic phenotype in the aging cardiac muscle of rats. Aging is usually associated with changes in the contractile and metabolic properties of the myocardium (Barger and Kelly

1999). Specifically, the heart reverts to a neonatal state with an increased reliance on carbohydrate metabolism and a concurrent decrease in lipid oxidation (Allard et al. 1994,

Sambandam et al. 2002). I hypothesized that these changes were driven by a suite of transcriptional coordinators known to affect metabolic gene expression in mammals. The enzymatic and gene expression profiles of markers of mitochondrial oxidative capacity

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revealed complex changes associated with aging driven by both transcriptional and post- transcriptional mechanisms. Surprisingly when enzymatic mRNA levels decreased with age, their regulators, the PPARs and the NRFs, failed to parallel this decrease.

Furthermore, the expression of their common coactivator, PGC-1 , was largely unaffected by age. These results argued for additional mechanisms of regulation in the depressed oxidative capacity of the aging rat myocardium. The strain of rats used in these studies is well suited for these experiments; it does not experience several pathologies associated with age in other strains and therefore has an improved longevity (Lipman et al. 1996). Therefore, it would be interesting to compare different strains at different aging stages to see if these regulators (PPARs, PGC-1s, NRFs) could account for the differences in the pathologies associated with senescence in cardiac muscle.

Over the course of my studies, several other regulators (e.g., ERR , PGC-1 ) and regulatory mechanisms (i.e. post-translational modification of PGC-1 ) emerged as having a major impact on the control of oxidative capacity in mammals (see Goffart and

Wiesner 2003, Scarpulla 2008, Schreiber et al. 2004). Thus, it is conceivable that these regulatory pathways play a role in driving the complex metabolic remodeling observed in the senescent heart.

The second set of experiments in this thesis (Chapter 3) evaluated the role of transcriptional regulators in the response to temperature and dietary changes in goldfish.

Both of these stressors have profound impacts on the oxidative capacities of fish tissues

(Guderley 1990, Kolditz et al. 2008, Rios et al. 2002), however the regulatory basis of

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these changes has seldom been addressed. These studies were the first to investigate comprehensively the role of the PGC-1 family in the regulation of metabolic adaptation in a non-mammalian model. I found that fish DNA-binding proteins (NRF-1 and PPAR ) behaved much like their mammalian homologues in regulating mitochondrial proliferation and lipid metabolism. In contrast, PGC-1 seemed to play an evolutionary conserved role in the PPAR /lipid metabolism axis, but surprisingly did not correlate with NRF-1 expression and mitochondrial content in goldfish. Furthermore, PGC-1 was a better indicator of mitochondrial gene expression than its orthologue, apparently assuming an important role in the determination of mitochondrial content in goldfish.

Although these studies provided valuable information on the divergence of function of

PGC-1 family in vertebrates, several other functional aspects of these coactivators remained to be discovered in lower vertebrates. For example, PGC-1 expression was strongly induced in the liver of fasted animals; as in mammals PGC-1 plays a central role in hepatic energy homeostasis (Rodgers et al. 2008), it would be of interest to investigate the function of the coactivator in other metabolic pathways (i.e., gluconeogenesis) in fish species.

An interesting finding in these experiments was the correlation between PGC-1 and citrate synthase (CS) mRNA levels. CS is a mitochondrial enzyme that functions as a homodimer and presents no allosteric regulation, therefore CS mRNA is commonly used as an indicator of mitochondrial content (Annex et al. 1991, Remington 1992). However little is known about the regulation of the expression of this enzyme, but previous studies

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from our laboratory suggested an important role for the transcription factor Sp1 (Kraft et al. 2006). In the same studies, the differentiation of murine myoblasts into myotubes was accompanied by a parallel increase in both CS and PGC-1 mRNA while PGC-1 was undetectable (Kraft et al. 2006). Together, these two studies could suggest an evolutionary conserved role in PGC-1 in controlling CS expression; alternatively they may reflect shared sensitivity to common regulators that warrants further investigation.

The putative functional divergence of the PGC-1 family members observed in fish

(Chapter 3) set off the last studies of my thesis (Chapter 4). I hypothesized that this divergence in function was a reflection of independent evolutionary dynamics of the coactivators in different vertebrate lineages. The phylogeny of the PGC-1 homologues suggested that the family diversified early in vertebrate evolution. In addition, analysis of the functional domains of PGC-1 revealed differential evolutionary trajectories across the protein and among lineages. While most functional domains were relatively well conserved across vertebrates, the domains responsible for interactions with NRF-1 and

MEF2c presented more variability especially in the fish lineages. Furthermore, these fish species experienced several insertions in these interacting domains that could jeopardize the interactions between PGC-1 and these factors. Although direct molecular testing of these interactions should be undertaken, the expression profiles in goldfish (Chapter 3) and zebrafish (McClelland et al. 2006) support a dissociation of the PGC-1 and NRF-1 axes in fish.

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Another intriguing finding of these studies was the apparent conservation of the

PPAR binding domain of PGC-1 in all vertebrate species investigated. This domain is primarily responsible for mediating PGC-1 and PPAR -driven thermogenesis in the brown fat of mammals (Puigserver et al. 1998). Since non-shivering thermogenesis is unique to mammals, it would be interesting to evaluate if PGC-1 from non-mammalian species could drive this process in a mammalian system, thereby providing some insights in the evolution of the thermogenic program in vertebrates.

Considering the evolution of the PGC-1 family, the general topology argues for repeated duplication events early in vertebrates history (Chapter 4). Future studies could examine the evolution of this family in early vertebrates to better understand its evolutionary history. Specifically, it would be interesting to (1) narrow down exactly when this family diversified and (2) investigate the function and binding abilities of the

PGC-1 ancestors in early chordates.

Finally, these evolutionary investigations provided an interesting example of modular evolution, where the different functions of the coactivator evolved independently. While most of the work concerning the evolution of transcriptional regulation focuses on transcription factors and their regulatory elements (Carroll 2005,

Hoekstra and Coyne 2007), I believe that my studies provide ground for extending these studies to the evolution of coregulators of gene expression. Conceptually, the modular evolution of coregulators would allow for evolutionary forces to act on a portion of the functional capacities of these regulators, while unaffecting others. Therefore, the chances

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of generalized deleterious effects on gene expression could be reduced as only a portion of the role of the coregulator would be affected.

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Appendix 1. PGC-1 amplified sequences and specific primers.

The sequences for both terminal and internal primers are shaded (forward) or boxed (reverse).

>Pigeon catgtgcaaccaggactctgtatggagtgatctggagtgtgctgctctggttggcgaagaccagcctctttgcccggatctcccag aacttgacctctccgaattagatgtgaacgacctggatgcagacagctttctgggaggactcaagtggtacagcgaccagtcag agatcatctccaatcaatacagcaatgaacctgccaacatattcgagaagatagatgaagagagtgaggcaaacttgctagccgt tctcactgaaacactggacagcatccctgtggatgaggatgggttgccttcatttgatgcactgacagatggagatgtgaccaac gaaaatgccgctagcccttccccaatgcccgacggcacccctccaactcaggaggcagaagagccgtctctacttaagaagct cttgctggctccagccaacactcaactaagttacaatgaatgcagtggtctcagcacacaaaaccatgcaaacactaatcacagg atcagaacaagccctgtggttgttaagactgagaattcatggagcaataaggcgaagagcatttgtcaacagcaaaagcctcaa agacgtccctgctctgaacttctcaaatatctgactacaaatgatgaccctcctcagaccaaaccagcagagaacaggaacagc agcaaagagaaatgcacctccaaaaggaagcctcatctgcagactcagacaaatcatctgcaagccaaaccaacaagtttatca cttccattgacacccgagtcaccaaatgatcccaagggttccccatttgagaacaagactattgaacaaaccttaagtgtggaact ctctggaactgcaggcctaactccacctacgacccctcctcataaagccaaccaggataatccttttaggacttcacctaagccga agtcatcatgcaagactgttgtaccaccttccaaaaagccccgctatagtgagtcttctggttctcaaggaaataacccaaccaag aagggtccagaacagtctgagctgtatgtacagcttagcaagactacagaactgtccagtggacatgaggagagaaagacaaa acggcccagtttgcggctgtttggtgaccatgactactgtcaatctgtgaattcaaaatcggaaatacacattaaaatatcccagga acttcaggactccagacaactagaatgtaaggattcttcacctgggtggcagtgtcagatttgttcttctttagaacaagaccagtat ttcaagaaggagactttacagacaagtaagcagggatcccatggtaataacagaaaacagctccaagaccaggaaattcgggc tgaactgaataagcattttggtcaccccagccaagctgtttttgatgaagaagcagataagacaagagaactaagggacagtgat tacagtaatgaacaattctccaaactacctatgtttataaattcaggactagcaatggatggtctctttgatgacagtgaagatgaaa gtgataaactatgctacccttgggatgggacacaagcctattcattatttgatgtatcgccttcttgctcttcttttaactctccatgcag agattcagtgtctccacccaaatccttattttctcaaagatcccaaaggacactctctagatcaaggtcctttcctcaacgcaggtctt gttcccgttctccatattcccgatcgagatcaaggtcaccctgtagcagatcctcttcaagatcttgttgctattatgagtccagccac tgtagacaccgagcacacagaagttctccctcacatgcaagatcgcgatccagatcaccgtgcagtcgcagacccagatatgac agctatgaggaatatcagcatgaaaggctgaagagggaagaataccgcaaagagtatgaaaaacgggaatctgaaagggcca aacaaagggagagacagaggcagaaagcaattgaggaacgtcgtgtgatttatctgggtaaaatcagacctgacacaacccga acagaactgagggaccggttcgaagtttttggtgaaattgaggagtgcacagtaaatttgcaggatgatggaggcagctatggttt catcacctaccgctatacttgtgacgcctttgctgctcttgaaaatggatacactttacgcaggtcaaatgaacctgactttgagctgt acttttgtggacgcaagcaattttgcaagtctaactatgcagacctagattcaaactcagatgattttgatcctgcttccactaaaagc aagtacgactccatgaa

>Xenopus Laevis catgtgcaaccaggactctgtatggagtgacatagagtgtgctgctctggtcggtgaagaccagcctctgtgccccgatcttcctg aacttggtctctctgaacttgatgttaatgacttagatgccgacagcttcttgggtggattaaagtggtacagtgaccaatcagaaaa catttccaatcaatacagcagcgaatcatccaacatatttgagaagatagacgaggagaatgaagagaatttgctagcagttctta cagagacattggacagtctccctgtggatgaggatggattgccttcatttgatgcactgacagatggagatgtgaccaatgaacat gagcctagcctttcacctatgcctgacggcacccctccaattcaggaggcagaagagccgtcactacttaagaagctgttactgg

130

ctccagctaacgctcagctgatttacaatgagtgcgttggtttcactacgcagaaccatggcagctccagtcagaggatcagaac caattcttcggttattaagaccgagaatccatggagcagtaaaccgagggccatttgtcactcccaaaagccaccacggcgtcct tgctcagagctccttaagtacctgacttcaaatgacgaccctcctcagaccaaatcgacagagagcagaaacaacaggcttgac aaatgcagcagcaaaaagaagccgtacttacagccccagcaacattatcaagccaggtcaatgaatttgtcgctccccttgacgc ccgagtcaccaaatgaccccaagagttccccatttgagagcaagactattgaacgaggcttatgtatggagctctctggaactgc aggattaactccacctacaacacctcctcacaaagccaaccaagagaaccccttcaggacctctcctaagttgaaatcttgcaaat cttctgtgccacctgctaaaaaatcacgctacattgggtcttccagtatccaagttctatatccagctaagaaaggtccagagcaat cagaactttatgcacagctcagtaaagcgactgtggtaattggacaagaggagaaaaaatcaaagcgacctagtttacgacttttt ggagaccacgactactgtcagtttatgacttcaaaatctgaaagacatattagtttatcacaacaattacaggcctcaagacatcttg aatgtaaggatcctttgcctggcaaggaactgcaagtctgttcaaacactgaccaagagcaatgccagaaagacagcttaccagt cacaatgccaagttctcagaacagccataggaaaccgctccaggaccaggaaatacgagccgaactcaacaagcactttggg cacccaacgcaggcagtctacaacaatgaaactaaaataagtgaactggtggacagtaaatacagtgatgaacagttgtcaaga ctacctttgtttctaactgccgggttgggaatagatagtttgtttgatgacagcgaggatgaaaatgataaactgtgctatacttggg atgaaacacagtcatattcattatttgatgaatcaccgtctcgctctacttttaattctccaagtagatattcagtatccccaccagcatc cctgttttcacaaaggatatgctctagatcaagatctaggtccttttcacagttcagatcatcttctcgttccccatattctcgttcaaga tccagatctccatcaagcagatcatcatcagggtcttgctgctactctgagactgatcattgcagagaaaaaagttctccaatgtat gcacggtcaaggtcaccgcgtggtcgtaggcccagatatgacagctatgaggaataccaacatgaacggctgaagagggagg agtaccgcaaggagtataaaaagcgtgaatccgagagagcaaaacaaagagagagacaaaggcaaaaagccattgaagaac atagagtgattttcgtgggtaaaatgagatctagcatgagccgcacggagctgcgagcacggtttgaggtttttggagaaattgag gaatgcacagtaaatctgcgggacgatggcaattgttacggattcatcacctatcgctacacctgtgacgctttcgctgctcttgaa aatggatacacgctgcgcaggtcaaatgggccagactttgaggtttgcttttgtgaaagaaagcaggtgtgcaagtccaactatg cagacttagattcaaattcagatgactttgatcctgcttccaccgaaagcaagtacgactccatggattttgacagtttgctcaagga ggcgcagagaagcctgcgtaggtaa

>Green Anole catgtgcaaccaggactctgtatggagtgacatcgagtgtgctgccctggttggtgaagaccagcctctttgcccagatcttccag aacttgacctctctgagttagacgtgaatgacttggatgctgacagttttttgggtggactcaaatggtacagtgaccaatcagaaat aatttccaatcagtacgccaatgaatcatcaaacatcttcgagaagatagatgaagagaatgaagcaaacttgctagcagttctca cagagactttggacagtattcctgtggatgaagatggattgccttcatttgatgcactgacagatggagatgtgaccaacgaaaat gatgctagcccttcaccaatgcctgacggcacccctccagcgcaggaggcagaagagccgtctctacttaagaagctcttgttg gcaccagctaacatccagctaaattacaatgaatgcagtggcctcagcacgcacaaccatgcaaatgccaatcataggatcaga acaagccctgtggtggttaagactgagaattcatggagcaataagacgaagagcatttgtcaaccacaaaaaccacaaagacgt caatgctcggagcttctcaagtatctgaccaccaacgatgaccctccacagaccaaaccaacagagaacaggaacagcagca gagacaaatgcacctctaaaaagaaggtgctcttgcattctcagatgcatcatttgcaagccaaaccaatgagtttatcgcttccttt gacgcccgagtcaccaaatgatcccaagggttccccatttgagaacaagactattgaacaaactttaagtgtggaactctctgga actgcaggcctaactccgcctacaacccctcctcacaaagcgaaccaagacaacccttttaggacttcaccaaagccaaagtcg tcatgcaagtctgttatgccaccttccaaaaaaccccgctgcaaagagtcttccagctctcaaagacataacttaggtcggaaggg tccagagcagtcagagctgtatgcacagcttagtaagacgaccgtgctgtctgttggtcatgaggagaggaagacaaagcagc ctggtttgcggctgtttggtgaccatgactactgtcagtctgctaattcaaaaacaggaatacacatcaaccgatcccaggaacctc aggattccagacaagtagatttaaatggcaggttgcctgagcagtggtgtcatatttgttcttcttttgaacaacacaatcagtatgac aagagagagacttcacaggcaagcaagcaggatttgcaatgcaacaaccgcaaacagctccaagaccaagaaatccgggct gaactcaacaagcactttggtcaccttagccaagctgtttttgaggaagagatcaccaagatcagtgaactgagggtcaacagta

131

attctagtgatgaacaattctccaaactacctatgtttataaattctggactaacaatggatggtctttttgatgatagcgaagaagaa aatgacaaactatgttgctcgtgggatggaacacaggcctactcactgtttgacctgtcaccatcttgctcatcctttaattctccatg cagagattcagtgtctccacctaaatcccttctttctcaaagattccaaaggatacgctctcgatcacggtccttccctcagcacag gtcttgttcccattctccatattcccgatcaagatcaaggtcaccaggcagtagattctcctcaagatcttgttactattatgagtccag tcattgtagaccgcaggcatatagaagttctcccttgtattcaagatcacgatccagatcaccatatagccatagacccagatatga cagctatgaggaatatcaacatgaaaggctgaagagggaagaataccgcaaagaatatgaaaaacgggaatctgaaagggcc aaacaaagagaaagacagaggcagaaagcaattgaagagcagcgtgtaatttatgttggcaaaattgggcctgacacaacccg agcagaactaagggaccggtttgaagtttttggtgaaattgaggagtgctctgtgaatctacaggacaatggagataactatggttt catcacctaccgctacacttgtga

>Eastern Painted Turtle catgtgcaaccaggactctgtatggagtgatatagagtgtgctgctctggttggtgaagaccagcctctttgcccagatctcccag aacttgacctctctgaactagatgtgaatgacttggatgcagacggttttctgggtggactcaagtggtacagcgaccaatcagaa atcattaccaatcagtacagcaatgaatcatcaaatatattcgagaagatagatgaagagaatgaagcaaacttgctagcagttctc actgagacactggacagtatccctgtggatgaggatggattgccttcatttgatgcactgacagatggagatgtgaccaatgaaa atgatgctagcccttcaccaatgcccgacggcacccctccgactcaggaggcagaagagccgtctctacttaagaagctcttgct ggctccagccaacattcagctaaattacaatgaatgcagtggtctcagcacacaaaaccatgcaaacactaatcacaggatcag aacaagccctgtggttgttaagaccgagaattcatggagcaataaaccgaagagcatttgtcaacagcaaaagccacaaagac gtccctgctctgagcttctcaagtatctgactacaaatgatgaccctcctcagaccaaaccaacagagaccaggaacagcagca aagacaaatgcatctccaaaaagaagccccatctgcagtctcaggcacatcatttgcaagccaaaccaacaagtttatcacttcct ttgacacctgagtcaccaaatgatcccaagggttccccatttgagaacaagactattgaacaaaccttaagtgtggaactctctgg aactgcaggcctaactccacctacaacccctcctcacaaagccaatcaagataatcctttcaggacttcacccaagctgaagtcat catgcaagactattgtaccaccctcaaaaaagccccgctacagtgagtcttccagttctcaaggacataacacaatcaagaagag tccagaacagtctgagctgtatgcacagcttagcaagacaactgtactgtccagtggacatgaggagagaaagacaaaacggc ctagtttgcggctgtttggtgaccatgactactgtcaatctgtgaattcaaaaaccgaaatacacattaaaatatcccaggatcttca ggactccagacaacaagaatttaaagattctgcacctgggtggcagtgtcagatttgttcttctttagaacaagaccagtattacaa gagagagactttacaggcaagtaagcagagttcccactctagcagccgaaaacagctccaagaccaggatatccgggccgaa ctgaataagcactttggtcaccccagccaagctgtttttgatgaagaggcagataaggccagtgaactgagggacagtgattata gtaatgaacaattctccaaactacctatgtttataacttcaggactagctatggatggcctctttgatgacagtgaagatgaaagtga taaactatgctacccttgggatgggactgaagcctattcattatttgatgtatcgccttcttgctcttcttttaactctccatgcagagatt cagtgtctccacccaaatccttattttctcaaagatcccaaaggatacgctctagatcaatgtcctttcctcagcataggtcttgttccc gttctccatattcccgatcgagatcaaggtcgccatgtaacagatcctcttccagatcttgttactattatgagtccagccactgtaga catcgagcatacagaagttctcctttatatgcaagatcacgatccagatcaccgtatagttgtagacccagatatgacagctatgag gaatatcagcatgaaagactgaaaagggaagaataccgcaaagagtatgaaaaacgggaatctgaaagggccaaacaaagg gagaggcagagacaaaaagcaattgaagaacgtcgtgtgatgtatgtgggtaaaatcagagctgataccacccgaacagaact gagggaccggtttgaagtttttggtgaaattgaggagtgtacagtacatctgcgggatgatggagacagctatggtttcatcacct accgctacacttgtga

>Leopard Frog catgtgcaaccaggactctgtatggagtgacatcgagtgtgctgctctggttggtgaagaccagcccctttgcccagatcttccgg agctggatctttctgaacttgatgtgaacgatttggatgcggacagctttttgggtggattaaagtggtacagtgaccaatcagaaa acatttccaatcaatacagcaacgaatcatcaaacatatttgagaaaatagaggaggagaatgaagagaacctgctagcagttctt 132

acagaaacactggacagtctccctgtggacgaggatggattgccttcttttgacgcactgacagatggagatgtgaccagtgaac atgatccgagcctttcgtccctgcctgacggcacccctccaacacaggaggcagaagagccgtcactacttaagaagctcttact ggctccagcgaacactcagctgatttataatgaatgtgtcggactcagcgcacacaaccatgcaagccctaatcagaggatcag aacaagctctgcacctgttaagagcgagaactgttggagcaataaacccagaaacatttgtcaaccgcagaagccacagaggc gcccctgctcggagctccttaagtacctgactgcaaatgatgactctcctcagaccaaatcaacagagagcaggacgaataaca gatttgacaaatgcaccaaaaagaagccctgcttgcatcctctgccacattttcaagccaagtcaacgagtttgtcccttcctttgac acccgagtcaccaaatgaccccaagggttccccatttgagagcaagactattgaacgaaccttaagtgtggagctctctggaaca gcaggactaactccaccaacaacaccacctcacaaagccaaccaagataacccattcaggacttcttccaagccgaagtcttctt gcaagcctgttgtgcccccagctaaaaaatctcgttacattgagtcttccactattcaagttattaatgctaaaaaaggtccccaaca atctgaactatacgcacagctaagcaaaacctccgtggtcattggacaagaggagaaaaaagtgaagcgacctagtttacggct ttttggtgaccatgactactgtcagtttatgaattcaaaatcagaaatacgtataagcttaacacaagatctacaggcctcaagacaa cttgaatgtaagaatttgctgcctgggaaaaaattacaggtttgttcaactcaaaaacaagtggagagtctgaaagacagttttgag gtggcaatgccaagtcctcagaatggcaataggaagccactgcaagaccaagatatccgtgcagaactcaataagcactttggt gacccaacacaagcagtctatgaggaagagaacaaaaatggtgatgcaaggagcaatgattactgtgatgatcaattctctaga ctacctatgtttttaacctctggactgggtatggataatttgtttgatgaaagtgaagatgagaatgacaaactgtgctattcctggga tggaatacaatcctattcattatttgatgtatcaccttcctgctcaactttcaattctccaagcagatattccgtttccccaccaacatctc ttttttctcaaaggatatgttctcggtccagatctagatcttattctcaaaacagatcatgttctcagtctccatattcacgctcaagatc aagatctccatccagcagatcttcttcagggtcatgctgctgctatgaaactgatcattgcagacccaacagttctccagtctgtgc acgatcacggtcaagatcaccatacagtcgtaggcccagatacgacagttatgaggaataccaacacgagcggctgaagagg gaggagtaccgcaaggagtatgagaaacgtgaatcagagcgagccaaacaaagggagcggcaaagacaaaaagcagttga ggaacatagagttctctatgtcagtaagatgagaacaaacatgacccgtggtgaactgcaagcccgttttgaagtttttggtgaaat tgaagaatgtacagttaatctgcgggacgatggagactgttacggtttcatcacctaccgctacacttga

>Zebrafish atggcgtgggacaggtgtaatcaggattcggtgtggagagaactagagtgcgctgccttggtgggtgaagaccagcccctttgc cctgacctgcctgagcttgacctttctgagctggatgtcagcgacctcgacgcggatagctttctgggaggactcaagtggtaca gcgaccaatcagaaatcatttccagtcagtatggcaatgaagcatccaacctgtttgagaagatagatgaggaaaatgaggcca acttgctggcagtgctcacagaaaccctggacagtatcccagtggatgaagacgggttgccttcgttcgaagccctggcagatg gggacgtgaccaatgccagtgatcagagctgtccttctacccctgacggctcgccacgcaccccagagccagaggagccttcc ctgctgaagaagctcctcctggcccctgctaactcccagctcagctataatcaatacccaggtggcaaggcacagaaccatgca gccagcaaccaacggatcagaccagcacctgctgtagccaagacagaaaacccctggaacagcaaaccacgaggggcctgt cccaaccggtccatgagacgtccctgcactgagctgctcaagtacctcacctctagcgacgaggccttccagaccaaagccgg tgaagccaagagcacctggacaggttgcggcaaggacaggggaggtgcttgcatcttatcctgctcgtcttcttcctctccatcgt cctcgtccacctcctctttctcctccctgtcgtcctgctcgtcttccaccgcctccaaaaagaagacgtcctctgcctccccatcatc acagcagcagcagctggcagtgcaggcccagcgagccaaaccaaccatcttgccacttcctttgaccccagagtctccaaatg accacaagggatctccgtttgagaacaaaaccattgaacgcacactgagtgtggagatctgtggaaccccaggtctgacaccac ctaccacgcctcctcacaaagccagtcaagagaaccctttcaaagtatcactcaaaaacaagctgtcttcatgctctccctcggcc ctgacaagcaaaaggcccaggctgagtaatgggggctcttgccctcagccaaccagcggctctattcggaagggccttgtgga aaccctcagtcccagtcgcattctctttttggcagagcagactgagctctatgcccagctgagcaaggcatcctccactatgcccc aagggggcttggaggatcgtcggggcaagcggcccatgccccgtgtctttggcgatcatgactattgccaatctacaagcacaa aacgagacagcaccaccccagctgcagtggtacctgggccaacagagggccggcatgtggaatgtaaagacttgaacatgcc aacctccactactacgacatcatcgttgtcttccacccccccttcgtcttcctcactggccaggcagcttcaaggcctttccccaac

133

acctcaggaggcttgtccggacacatatgctcacgtgcagcaccacgactcaagctccaaaatgacaatggactgcagttctgg tggcaggaaacttcttcgggaccaggagatccgggacgagctcaacaagcactttggaaagcctcagcaagccttctatagcg gggtagtgggagagccgaggggcaaacagccaattgaggacagtgactctggggatgagtacccgggtctactcggcgact acatccacccaggtctgcctgatttcgaggacctggaggtaggccgggagcgcctgttctacttaggggaaggttctccactcg agctgctcctcgaagggtcaccctccagctccccttccagcagttccttttcatggtgctctgtctcgcctccttcctctcagctctcc ccacagcacctccgctggccacgctccatctcccgctcccgttctcgttcttcatctcaccacaggcgcagatccctctccaggtc tccctactcccgctccgggtctcccagcagccgttctccctcttggtctcctcgcaacatggacgaaagcactttcactcccagga ttcggagacccaggtatgacagctatgaggaataccagcacgagcgtctgaagcgagaggagtaccgacgcgactatgaaaa acgggaatgtgagagggccgagcagagggagagacaacggcaaaaagcaatagaggagaggcgagtggtgtatgtggga cgtcttcgcgccgacagcacacgcaccgagctcaaacgccgctttgaagtcttcggcgagattgaggagagcacagtcaacct gagacatgacggggataactttggctttatcacctaccgctacacttgtgatgctctcgctgcccttgagaatggacacactttgcg caggtcgaacgaacctcactttgagctctgccttggtggacaaaagcagtacagcaaatccaattacacagacttggattcccact ctgacgactttgatccagcctccactaaaagcaagtacgactccagga

>Rainbow Trout atggcgtgggacaggtgtaaccaggactcggtctggagagaattagagtgtgctgccttggttggcgaagaccaacccctctgt ccggacctccccgaactcgacctctcagagcttgatgtcagtgacctggatgcggacagcttcctgggcgggctcaagtggtac agtgaccaatcagagataatttccagtcaatatggcaacgaggcttccaacctctttgagatagatgaggaaaatgaggccaactt gctggcagtgcttacagaaacgctggacagcatcccagtggacgaggacggattgccttcgttcgaggccctggcagatggg gacgtgaccaatgccagcgaccagagctgtccctgcacccccgacggttcgccgcgcacccccgaaccagaggagccctcc ttgctgaagaagctccttctggcacccgcaaactcccagctcagctataatcaatacataggtgacaaggcacaaaaccatgcag ccagcgaccaccggatcagaccaccacctgctgttgtcaagacggagatctcctggaacagcaagccgagggggggctgtcc gcagcagagccgtctagtgaggcgcccgtgcaccgagctgctcaagtacctgaccgccaccgacgacatcctcctccagacc aaagccagcgacgccaagagcgcctgggggggtggtggcaaagacaagggcggcctgctgggcgcctcctcctccacctc ctcgtcgccgtcctcctcctccacctcttccttctcctccctctcctcctcctcctctatcgcctccaagaagaagtcgtcctcgtcgt ccgtcgtgtcacagcagcagcagcagcccaaaccaaccaccttgccacttcctttgaccccagagtctccaaatgaccacaagg gatcaccgtttgagagcaaatccattgaacgcacattgagtgtggagatctctggaaccccaggtctgacaccacctaccacgcc cccccacaaagccagtcaagagaaccctttcaaagcatcactcaaaaccaagttgtcttcatgctcctcgtctgccctggcgtgca agagggttcggctgagcgaggcgggcccctgcggccccccagccctaaccccgaccctaggtgggggtccctccaggaag ggccccgagcagactgaactgtatgcccagctgagcaaagcgtccactgccctccccaactcggtggtcaccgcaacgacag ggggttgccaggaggagccccgcggcttcagcgaccacgactattgtcagtcgccggcggccagcacaaaacgggacgctg acgctaatgtggctaacgcagcttatgctactgttaccatgacgactactatgacaacccctctgatgcccaatccggtcatggcg gaggacaggcatgtgaaatgtaaggactcagccatgccaccctcctccttttcttcttcctcctcctcttctacgtcttctcctacatct tcatcttctttggccaagcagctgcagcagagctcttaccctgtggctacggaggctcgggggcagggcgctcggggacagga ccggacccccaccacccagacaagagcacccccaccacagaccctagatggggaccagcattccaccagcaggaagcagc ccctgcgagaccaggagatccgggcggagctcaacaagcactttggccgtccccagcaagccatttacagccagggtgagaa ggggggggagttggtggtggaggaggggtggcagggaggaggggagaaggttgtcgccccgcaggcgtccgaggagaa ccctgctggggacgagtactactgcaagctccctggttctggttctactgggtacctgcacccgggtctcccgcccttccacgag gaacttgagctccaggaccgtggcgtggaggggcgctacctctacccctgggagggcacccccctggaaatgcttacgagtc ctgctcgccgtcctgctcgccgtccagtgctccccctcacgggtcgttcacttctccctttctcccccagacactttgtctggcccc gttcgggctccccctcctcccgttctcgctcccggtcccgcagttcctcctcccaccaccggaggcgctccctctccagctcacc

134

cgacggacgcccctcctccaggtctcgtcacaacatggactccagcacttaccgatccaggacccacaaaagccctcactcgc agtctcgatctcagtccagatcccccctcagccgcaggccaaggtacgacagctacgaagaataccaacacgagcgtctgaag cgggaggagtatcgccaggactaccagaagagggagtcccagagggccgagcagagggagaggcaaagtgaaaaagcaa tagaggagagacgggtggtgtacgtggggagactgaggtccgacagcacgcgaaccgagctaaagcggcgttttgaagtctt cggcgagatcgaagagtgtgccgtgaacctgagagatgacggggacaattttggtttcatcgcctaccgctacacttgtgacgc cttcgctgcccttgaaaacggacacaccttgcgccggtcaaacgagccccagttcgagctgtgcttcggtggacaaaagcagtt ctgcaaatcgaattatacagacttggattcccattctgatgactttgatccagcctccactaaaagcaagtacgactccatgga

>Golden Shiner atggcgtgggacaggtgtaatcaggactcggtgtggagagaactagagtgtgctgccttggttggtgaagaccagcccctttgc cctgacctgcctgagcttgacctctctgagctggatgtcagtgacctcgatgcagatacctttctgggaggactcaagtggtacag cgaccaatcagaaatcatttccagtcagtatggcaatgaagcatcaaacctgtttgagaagatagatgaggaaaatgaggccaac ttgctggcagtgctcacagaaaccctggacagtatcccagtggacgaagacggtttgccttcgttcgaggcactggcagatggg gacgtgaccaatgccagtgatcagagctgtccttccacccccgatggctcgccacgcaccccagagccagaggagccttccct gctgaagaagctcctcctggcgcctgctaactcccagctcagctataatcaatacccaggtggcaaggcacagaaccatgcag ccagcaaccaacggatcagaccaacacctgctgttgccaagacagaaaacccctggaacagcaaaacacgtggggcttgtcc caaccggtccatgagacgcccttgcactgagctgctcaagtacctcacctccagtgacgaggccttccagaccaaagccaggg aagccaagagcacctggacaggttgcagcaaggacaggggaggacctctgtgcacctcttcctgctcatcttcttcctctccatc gtcttcgtccacctcctctttctcgtccctgtcgtcctgctcctcttccaccgcctccaaaaagaagacgtcctctgcctccccatcat cacagcagcagcagcagcagcagcagctggcattgcaggcccagcgagccaaaccaaccatcttgccacttcctttgacccc agagtctccaaatgaccacaagggatctccgtttgagaacaaaaccattgaacgcacattgagtgtggagatctgtggaacccc gggtctgacaccacctaccacgcctcctcacaaagccagtcaagagaacactttcaaactatcactcaaaaacaagctttcttcat gctctcccttggccctgacaagcaaaaggcccaggctgagcaatgggggctcttgccctcagccaaccagcggctctattcgg aagggcccagagcagactgagctctatgcccagctgagcaaggcatccttcacaatgcccctagggggcttggaggagcgtc ggggcaagcggcccatgccccgcgtctttggcgatcacgactattgtcagtctacaagcacaaaacgagacagcaccaccaca gctactgcggtaactgggccaacggagggccggcatgtggaatgtaaagactcgaacatgccgacctcctctacttctacatcat cattgtcttccaccacccctttgtcttcctctctggccaggcagcttcagggcctttcccccactgctcaggaggcttgtctggacac agatgctcacggacaggaccacaagtccacttcaggctccgaaatttctgatgattgcagttctgctggcaggaaactactaagg gaccaagagatccgggaagagctcaacaagcactttggaaagcctcagcaagccttctatagcggggttatgggagagcaaa gggaaaaccggccgctagaggacagtgactctggggatgagtaccccggtcttctcagtgactacatgcacccggttctgcctg actttgaggaccctgaggttggccgggagcgcctgttctacttgggggaaggttctccactcgagctgctccccgaaggatcgc cctccagctccccttccagcagttcattctcatggagctctgtctcacctccttcaactcagctctccccacagcacctccgctggtc acgctccatctctcgcacccgtctctcatctcaccacagatacagatccctctccaggtctccatactcccactctgagtctcccga cagccgttctccttctaggtctcctcacaatatggacgatagcactattcttcccaggatttataaaagccctcggtcccagtctaatt ctatttttggtcggagacccaggtatgacagctacgaggaataccagcatgagcgtctgaagcaggaagagttccgacgtgact atgagaaacgggaatgtgagagagccgagcagagggagagacaacggcaaaaagcaatagaggagaggcgagtggtgtat gtgggacgtcttcgtgccaacagcacacgcacggagctcaaacgccgctttgaagtcttcggcgagattgaggagtgcacagt caacttgagacatgacggagacaactttggcttcatcacctaccgctacacttgtgatgcgctcgctgcccttgagaatggacaca ccttgcgcaggttgaatgagcctcattttgagctctgccttggtggacaaaagcagtacagtaaatcgaattacacagacttagatt cccattctgatgactttgatccagcctccactaaaagcaagtacgactccatgga

>Swordfish

135

atggcgtgggacaggtgtaaccaggactcggtgtggagagaattagagtgtgctgccttggttggtgaagaccagcccctctgc ccagacctccccgaacttgacctctcagagctggatgtcagtgacttagatgcagacagcttcctgggtggcctcaaatggtaca gtgaccaatcggagatcatttccactcagtatggcaatgaggcatccaatctttttgagaagatagatgaagaaaatgaggccaac ttgctggcagtgcttacagagaccctggacagcatcccggtggatgaggacggattgccttcgtttgaggccctggcagatggg gacgtgaccaatgccagtgaccggagctgtccctcctcccctgacggctcgccacgcaccccagagcctgaggagccttccct gctgaagaagctccttctggcaccggcaaactcccagctcagctataatcaatacacaggtggcaaggcacagaaccatgcag ccagcagcaaccaccggatcagaccaccacctgccgtcgtcaagacggagagcccctggaatggcaaagcaagagggggc tccagccaacaaaaccgccccgtgaggcggccttgcactgagctgctgaaatatctaacggccaccgatgacatcctgctccac accagagccagtgaagccaagagcacgtggggtggtgccagtggcagggacaagagtggcctgggtcttggcgcctcttcct cctcctcttcgccatcctcgtcatccacctcctcgttttcctccctctcctccacctcttcttcatcttcctccaccacctccaagaaga agtcagctgtgccatctcaacaacaacagcagcaacagcagcagcagcagcagcagcagccgcagcagcatcaccagcga gccaaaccaaccaccttgccacttcctttgaccccagagtctccaaatgaccacaagggatcaccgtttgagaacaaaaccattg aacacacattaagtgtggagattgctggaaccccaggtctgacaccacctaccacgcccccacacaaagccagtcaagagaat cctttcaaagcatcactcaaaaccaagttgtcttcatgttcctcctcagccttggcatgcaaaagagccaggctgagcgagttggg ccccggcgctctggccccggccccaggtgcctcaggcgggggccccaacaggaatggtcccgaacagactgagctctatgc ccagctgagcaaagcgtccaccgccctcccttactccgccacccaacacacagtggtgggcggcctggaggagcattgtatca ctagcaacaataagcgggcggcaccccgtagctacagtgaccatgattattgccaagcgtcagctagcactaagaaggatggc ggcagagccactgttaccatgagtacagcagcggaaagaccagtcacctcaggtgcccctgctgctcccatgcctactgcagg cacagtgggggacaggcatgtccaatgtaaggactcagccatgccaccatcctctttatcatcttcatcttcttgctctccatcatca gctccatctggtcctttggctaagcagcaaaattttgcatctgtggatggagaagcagtccgggtncaggggttagggaagcag acccttacacaaaccacccagatcccctcacaagaagccacaattgacagggaccaacaacacccttctgccaccagccggaa gctcctgtgcgaccaagatattagagcagagctcaacaagcactttggccatcccttacaagctctctatagtcagggtggtcag gagagagaaccagggagcaaaccgaacaaggttttagcacctcagtcccttgaggagggagcggatgactactactcccaga ggctgcctggctccagctacctgcatccagggttcctgcccttccacgatgagctagagctgggtgagggccgtgagagtcgct tcctctatccatgggagggcacccctctggacctactctttgactgccccccctgctctccctcctgttccccaccatccagctgct ccccttcgcgaggctctgtctccccaccttcttccctcctcctctcacctggcaggcctttctgctggaccagcagctggtcccgct cccgttctcgttcccactctggctcccgcagctcctcctcacactaccgaaggcggtccctctccagctcacctgacagacgccc ctcttcctggtctcgtcacaccacagaatcaagcacttttcgttcccggacactcaagagcccccaccctcagtctcgatctcctct cagccgcaggccaaggtatgacagctacgaggagtaccagcacgagaggctgaagagggaggagtaccgccgggactac gagaagcgggagtttgaaagggccgagcagagagagaggcaaaggcagaaggcaatagaggagagacgggtggtgtacg tggggcgactgaggtccaactgcacccggacagagttgaagcgccgctttgaagtctttggcgaaattgaagaatgtgcagtga acttgagggacgacggggacaattttggcttcatcacgtaccgctacacttgtgacgcctttgccgcccttgagaacggacacac cttacgccggtcaaacgagcctcagttcgagctgtgctttggtggacaaaagcagttctgcaaatcacattacacagacttggatt cccattcggacgactttgatccagcctccacaaaaagcaagtacgactccatgga

>Lungfish cctgaacttgacctttctgaacttgatgtgaatgaattaaatgcagacagctttctgtgtggattcaagtggtacggtgaccaaccag agatcatttccagtcaatattgcaatgaatcatcaagtctgtttgagaagatagatgacgagaatgaggccaacttgctggccgca cttacggagaccttggaaagcatccctgtagatgaggatggactaccttcctttgaagccttggcagaggggggtgtgcccacta tgaatgatcccagtcctccagtggtacctgatggtgcccctccaaccccagaggctgaagaaccgtctctacttaagaagcttttg ttggccccactgaatgcacaacttaattctaatgaatgcagaggacttgctgtacaaactcaagtaagcactaatcagaagctcag aatgaactctgcagttgtcaagatggaaaattcatggaacaataaagcaagaggcatttgtcagcaacagaagtcacagagacg

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accctgctcagaactcctgaaatatctgacagcatgtgacgatgacccttcccagacaaaactcacagacaacaggattagcaat aaagaaagatgtatttctaaaaagaaacctagtctgcagtctcaccagtcataccattctcaagcaaagccaacaatgctatcactt cctctgactccagattcaccaaatgatcccaagagttccccatttgaaaataagactatcgaacaaacattgagtgtggagctctca ggcactgcatgtctaactccacctactacacctccacacaaagcaagccaggataatcctttcaagacttctttgaagcctgttgag tcattcaagtcctcacaatcaccagttaaaaagcaacgctttagtgaccctttgacacctcaagggaattgtccactcaagaaggg cccagaacaaactgaactgtatgcacagctcagcaaaacttcagtattaatcaatggaccagaggagaggaaagggaagcgg cctagtttgcgactatatggtgaccatgactactgtcaagctgtgaatgcaaaggctgacatacaaattactgtgtcgcaggattca cagtacttcaggcagtcagtagggaaatataatgtttcccatgggcatcaacttcatcttcatttaccttctgttcaaacagacagatc tggaaaagaaacacagcagctgaacagacatcctgaccaactaacaaacaacaggaaacatctacaagaccaggaaatccgt gatgaactcaataggcactttggccacccaaaccaagctttttttgataaaagtatagctaaattcagtgagtcacaagacaatgat gccagtgctaacttttattctaaactgcctctgtgcataaatgcaggaatgcctgcaaatggtatctttgatgaaagtgaggatgatg gtgataaactcctttattcctgggatggcgagcaagcagatgtattatttgaagaatgtaattcatgctcaccttacagttctccccgg agaggatctgtctcaccacccaaatctttatttttgaaaagaacttgcagaggaagatctagatctcggtcattctccaggcacaga tcgtgttctcgctcatcatattctcactcaagatcaagatcaccacacagtagatcctcttcaagatcatgttactgtgactccgacaa tctcataagcagatccagtacaagccctgttcccactcacgttctagatccaggtcaccatacaggcacagaacaaggtatgaca gctatgaggaattatgacagctatgaggaatatcagcatgaaaggctgaaaagagaggaatatcgacgtgattatgagaaacga gaatttgaaagagcaaaacaaagggaaagacagaagcaaaaggcaattgaagagaggcgtgttatttacatcgataaacttaga tctggtataacaagaacagaactcaaacgtcgctttgaagtttttggtgaaatagaagagtgcactgtaaatctgagagatgatgg agacagttatggatttataacttaccgttatacctgtgatgcatttgctgctcttgagaatggatatacattgcgcaggtcaagcgaac ctcagtttgagatgtgctttggtggacgtaagcagttctgcaagtctgaatacacagacttagattgtaacttggatgactttgaccc agcttctaccaaaagcaagtacgactccatgga >Dogfish cctgaacttgacctttctgagctcgatgtgaatgacttggacacagaaaattttctgggtggactcaagtggtacaatgaccaatca gaaataatttccaatcagtatggcaatgaatcatctgatctctttgagaagatggatgaagaaaatgaggctaacttgttggctgcc ctaacggagacactggacagcatcccgatagatgaggatggactgccttcattcgatgcactaacagatggggatgtgacccat gtcaatgatcctaggcctccctcaacgcctgatggcactcctccgactccagaggctgaagagccatctctattgaagaagctctt gctggctccagctaattcacagtttatctacaatgaatgccatggaggcagcacgcacacccatgtgaccaataataacaggatta gaccaagccctgctgttgtcaagggggaggcaatttcaagttccaagtcaaggaatatttgccaacagcagaagtcacaggccc agcgccgcccatgcacagaattactgaaatatctgactgctagtgatgaccctcctcagtccaaaataaatgaccacaaaaatagt agtagagacaaatgcatctccaaaaggaagatacagctgcaatctcaacaatcacataacctacaagctagaccaacaactctgt ccctgcccttgacaccggaatcatcaaatgatcccaaaggttccccatttgagactaagaccattgaacgtacgctgagtgtgcaa ctctctggtactgctggtctgacaccaccaaccactcctccacataaagctaatcaagataatccgtttaagactgcagtcaaacac aggtcatcttgcaattcctcagtgcgcagtccaccaccaaagaaatcacgcttaagtgattctgtctctaactctcaactgagcagt cctataaagaagggtcctgaacaaactgaactgtatgcccagctcagtaagacttcagatttacccagtgcacttagcattaccca aagtgaagagaggaaggtgaaacggatgagcacaaggctctttggtgaccatgactactgtcagttcataaatgcaaaggcag aatcattaaataccaaggcgttctgcaaggcacaagaacccaaagacaataaactccaagatcacccttgcagagaacccttgtc tgggacgcacaagcaaaactttctttctgctaatgaagatcaaggatataagaaagaaagtcagcagctggatacgcagcctatc atctactctaataccaggatacaactgaaagaccaagagatccgggctgaactaaataagcactttggacatccaagtcaagcct tctatgatgaagtagaagataaggcaaggcatcagagagaaaaagacttttaccatgaatattattcaaaactgcctacttatctca gtgcagggatgtctctggatggcgtgtttgatgacagtgatgatgagactgaaaaatttctctatccatgggatggaacacccgat gggttattagaaagaccttgctctcgttctctgtccagttccccctgcagcgattccgtctattcaccaaagacttatgcaccttctca aagaatagtcagggcaaggtctagatctagttcctaccccagacgtagagcttattctcgctccccatattctcattccagatcaag

137

atctccatacagtagatcctcttctagatcacatcactactatgattcagggcgcagtagggtacaacacaggtctcgtggaagcc cttgctcatattcaaggtcacgctccaggtcaccctacagccgttataatagacccaggtatgacagctatgaggaatatgaaaat gagaggctgaagagggaggaatatcgaagagactatgagaaacgtgaatttgaaagagcaaagcaaagggagcgacaaag gcaaaaggcaattgaggaacgccgtgttatttatgtgggaaaagtcaggggtgacataacacgtacagaactgaagaggcgctt tgaagtttttggcgaaattgaagagtgcactgtccatttgcgtgaagatggtgacaactatggtttcatcacgtatcgttacacttgtg atgcatttgctgcccttgagaatggacacactttgcgaagatctaatgagcccccttttgaattgtgctttggtggccgtagacagtt ctacaagtctaactatgcagatctag

>Bichir tctgcgtggagtgaaatagagtgtgctgctctggttggtgaagaccagccactttgctccgatctccccgagctcgacctctctga actggatgtcagtgacctggatgcagacagcttcctgagtggactcaaatggtacagtgaccaatcagaaatcatttccagtcagt atggcaatgaatcttcaaatttgtttgagaagctagatgaggaaaatgaggccaatttgctggcagttcttactgaaaccctggaca atatcccggtggatgaagatggactgccttcattcgatgcgctggcagatggggacgtgaccagtgccagcaatcttagccgcc catcaacacccgatggctcctctccagtcccagaggcagaagagccgtctctattgaagaagcttctgctggcacctgccaactc gcagcttagttataatcaatacacaggtggcaatgcgcagactaatcctaggatgaggccagcccctgctgttgtcaaggctgaa agcatttggaacagcaaaccaagggtcacctgccagcagcagtcgaagccacagaggcgcccatgctctgagttactcaaata cctaacttctagcactgagcctacagagaccaaacccagggaactgaaaagcaacagcaaggacaaaaacggcacagtctac tctttggcctcttcttcctcctcctccacttcctcttccttcaaaaagaagtcacagctgccacctcagcagccaccgcatcaccagc cagccaaaccaaccaccttgcctcttcctttgaccccagagtctccaaatgaccacaaaggatctccatttgagagtaaaaccatt gaacgtacattgagtgttgagctttctggaacagcaggtctgacacctcccaccacccctcctcacaaagccaaccaggataatc ctttcaagccatcaataaaaaccaagttgccctcatgcaacatttcgtccctgccatgcaaaaagcctcattacaacagtaccaaca gttgttctttggtaacaaacccaattaagaagggtccagaacaaacagaactgtatgcccatctcagcaatgttccagcattaccc gttgggtcagaagagcgaaaggtacaacgatctaatacacgcttgtttggtgaccacgattattgccagtctttaaatataaaggg gcaagtttcccttaaccggctgcaggacagtcaagaaggcaggcatcaagaatgtaaagacttctctttatcgcctgtgaagcag gaatgtgcatttctaccaggccaaggccagcagttcaacaaagaaggagagcaacatgaaggtggttctttagagtgctgtcact caaatagtaggaagctacttaaagaccaagaaattcgtgaggaactaaataagcactttggtcacccccaacaagctgtctttagc aaggaggaggaaaagtttaatagtactcagacagaaactaactctggtgatgagtattgctgtaaactgtctgggtatgtccaacc tggactaccactggaaggagtgtttgatgatagtgaagatgagagtgacaagttcctgtatccctgggatgtgacgcattcagacc tgttatttgaagggtccccttcctgttcaccttcgagttgttccccatcccggagttcagtttcccctcccaaatcgcttttcttttcaga gcgtcttcaccgttccagatcaacatcgcagtctcgatccaggaccaggtcttaccatcgacactggtcctgctcgagatcacctt actccagatcaaggtcacgatccccctacagcaggtctttgtcacgcacccgctgtgacaacgactcaggtcataacagaccca gatcccacagaagtccacattcatattcaagatcacgatccaggtctccctataatcgaagaccaaggtatgatagttatgaagaat accagcatgagaggttaaagagagaagagtatcaacgagattacgagaagcgtgaatttgaaagagctaaacagagagaaag acaaaggcaaaaggcaattgaggaaaggcgtgttgtgtatgtgggtagactaaggtctgatataacccggacagagctgaagc gtcgctttgaagtttttggtgaaatcgaagagtgcactgtaaacatgagagatgatggagataatattggttttatcacctaccgctat acatgtgatgcctttgctgcccttgaaaatggacacagcttacgtcggctcaatgaacctcagtttgagttgtgctttgggggatgc aagcagttctgccaatcaaattacacagatttagattcccattcagatgattttgatcctgctgctactaaaagcaagtacgactccat gga

>Goldfish atggcgtgggacaggtgtaatcaagattcggtgtggagagaactagagtgtgctgccttggttggtgaagaccagcctctttgcc cggacctgcctgagcttgacctctctgagctggatgtcagtgacctcgatgcagatatttttctgggaggactcaagtggtacagc 138

gaccaatcagaaatcatttccagtcagtacggcaatgaaacatcgaacctgtttgagaagatagatgaggaaaatgaggccaact tgctggcagtgctcacagaaaccctggacattatcccagtggatgaagacgggttgccttcgttcgaggccctggcagatgggg acgtgaccaatgccagtgatcagagctgtccttccaccccagacggctcaccacgcaccccagagccagaggagccttccctg ctgaagaagctcctcctggcgcctgctaactcccagctcagctataatcaatacccaggtggcaaggcacagaaccatgcagcc agcaacctacggatcagaccaacacctgctgttgccaagacagaaaacccctggaacaccaaaccacgaggggcctgtccca accggtctgtgagacacccttgcactgagctgctcaagtacctcacctctagcgacgaggccttccagaccaaagccagggaa gccaagggcatctggacaggttgtggcaaggacaggggaggggcttgcacatcctcctgctcatcatcttcctctccttcatcctc gtctacctcctctttctcctccctgtcatcctgctcctcttccacctccaaaaagaagacaccctctgcctccccttcatcgcagcag cagcaggagcagcagctggcagtgcaggcccagcgagccaaaccaaccatcttgccacttcctttgaccccagagtctccaaa tgaccacaagggatctccgtttgagaacaaaaccactgaacgcacactgagtgtggagatctgtggaaccccaggtctgacacc acctaccacgcctcctcacaaagccagtcaagagaaccctttcaaagtatcactcaaaaacaagctgtcttcatgctctccctcgg ccctgacaagcaaaaggcccaggctgagcaatgggggctcttgccctcagccaaccagtggctctattcgaaagggcccaga gcagaccgagctatatgcccagttgagcaaggcatcctccacaatgcccctagggggcttggaagagcgtcggggcaagcgg cccatgccccggggcaagcggcccatgccccgcgtctttggcgatcacgactattgccagtctacaagcacaaaacgagaca gcaccactacagctgcagcattaacttggccagtggagggccggcatgtggaatgtaaagactcgaacatgccaacctcctctg cttctacttcatcgttgtcttccgcctccccttcgtcttcctttctggcccggcagcttcagggcgtttcccccaaagctcagggggct tgtccggacatggatgctcacggacaggaccacgactccacttcgggctccagaacgtcagtagatggcagctctgctggcag gaaactacttagggaccaggagatacgggaagagctcaacaagcactttggaaagcctccacaagccttctatagcaggatagt tggagaacagaggagcatccggcctttagaggacagagttggatacctcagtcttcttaatgattacatacacccgggtttgcctg actttgacgacctggaggtaggccgggagcacctgctctacttgggggaaggttcttcacttgagctgctcctcgaagggtcgcc ctccagctccccttccagcagttcattctcatggagctctgtctcgcctccatccactcagctctcaccacagcacctccgctggcc acgctctatctcccgctcctgttcctcatctcaccacagacgcagatccctctccaggtctccaaactcccgctccgagtcccctga caactgttctccctcttggtctcctcacaatatggacaatagcacttttattcccaggatttatagaagccctcaatcccaggctcattc tatttttggtcggagacccaggtatgacagctatgaagaataccagcatgtgcgtctgaagcgggaagagtacagacgtgacaat gagaagcgggaatgtgagcgggccgagcagagggagaaacaacagcaaaaagcaatagaggagaggcgagtggtgtatg tgggacgtcttcgtaccgacagcacacgcacagagctcaaacgccgctttgaagtcttcggcgaaattgaggagtgtacagtca acttgagacatgacggggataactttggcttcatcacctaccgctacacttgtgatgcgctcgctgcccttgagaatggacacacc ttgcgcaggtcgaatgagcctcactttgagctctgccttggtggacaaaagcagtacagtaaatcgaattacactgacttagattcc cattctgaggactttgatccagcctccactaaaagcaagtacgactccatg

>Black Ghost Knifefish tctgcgtggagtgaaatagagtgtgctgctttggttggggaagatcagcccctttgtcctgatttgcccgaactggacctttccgag ctggacgtcagtgacctggacgcggatagctttctgggcggactcaagtggtacagtgaccaatcagaaatcatttccagccagt atggcaacgagtcatccaacctgttcgagaagatagatgaggaaaatgaggccaacttgctggcagtgctcacagaaaccctg gacagtatcccagtggatgaagacgggttgccttcgttcgaggccctggcagatggggacgtgaccaatgccagtgatcagag ctgtccttccaccccagacggctcaccacgcaccccagagccagaggagccttccctgctgaagaagctcctcctggcgcctg ctaactcccagctcagctataatcaatacccaggtggcaaggcacagaaccatgcagccagcaacctacggatccgaccaaca cctgctgttgccaagacagaaaacccctggaacaccaaaccacgaggggcctgtcccaaccggtctgtgagacacccttgcac tgagctgctcaagtacctcacctctagcgacgaggccttccagaccaaagccagggaagccaagagcgcctggacaggttgt ggcaaggacaggggaggggcttgcacatcctcctgctcatcatcttcctctccttcatcctcgtctacctcctctttctcctccctgtc ttcctgctcctcttccacctccaaaaagaagacaccctctgcctccccatcatcgcagcagcagcaggagcagcagctggcagt gcaggcccagcgagccaaaccaaccatcttgccacttcctttgaccccagagtctccaaatgaccacaagggatctccgtttga

139

gaacaaaaccactgaacgcacactgagtgtggagatctgtggaaccccaggtctgacaccacctaccacgcctcctcacaaag ccagtcaagagaaccctttcaaagtatcactcaaaaacaagctgtcttcatgctctccctcggccctgacaagcaaaaggcccag gctgagcaatgggggctcttgccctcagccaaccagtggctctattcgaaagggcccagagcagaccgagctatatgcccagtt gagcaaggcatcctccacaatgcccctagggggcttggaagagcgtcggggcaagcggcccatgccccgcgtctttggcgat cacgactattgccagtctacaagcacaaaacgagacagcaccactacagctgcagcattaacttggccagtggagggccggca tgtggaatgtaaagactcgaacatgccaacctcctctgcttctacttcatcgttgtcttccgcctccccttcgtcttcctttctggcccg gcagcttcagggcgtttcccccaaagctcagggggcttgtccggacatggatgctcacggacaggaccacgactccacttcag gctccagaacgtcagtagatggcagctctgctggcaggaaactacttagggaccaggagattcgggaagagctcaacaagca ctttggaaagcctccacaagccttctatagcaggatagttggagaacagaggagcatccggcctttagaggacagagttggata cctcagtcttcttaatgattacatacacccgggtttgcctgactttgacgacctggaggtaggccgggagcacctgctctacttggg ggaaggttcttcacttgagctgctcctcgaagggtcgccctccagctccccttccagcagttcattctcatggagctctgtctcgcc tccatccactcagctctcaccacagcacctccgctggccacgctctatctcccgctcctgttcctcatctcaccacagacgcagat ccctctccaggtctccaaactcccgctccgagtcccctgacaactgttctccctcttggtctcctcacaatatggataatagcactttt attcccaggatttatagaagccctcaatcccaggctcattctatttttggtcggagacccaggtatgacagctatgaagaataccag catgtgcgtctgaagcgggaagagtacagacgtgacaatgagaagcgggaatgtgagagggccgagcagagggagaaaca acagcaaaaagcaatagaggagaggcgagtggtgtatgtgggacgtcttcgtaccgacagcacacgcacagagctcaaacgc cgctttgaagtcttcggcgaaattgaggagtgcacagtcaacttgagacatgacggggataactttggcttcatcacctaccgcta cacttgtgatgcgctcgctgcccttgagaatgggcacactttgcgcaggtcgaatgagcctcactttgagctctgccttggtggac aaaagcagtacagtaaatcgaattacactgacttagattcccattctgaggactttgatccagcctccactaaaagcaagtacgact ccagga

>Sturgeon atggcgtgggacattgtagtaaaccaggactctgcgtggagtgaaatagagtgtgccgctctggttggcgaagaccagccactc tgctcagacctcccagaactcgacctttctgaactagacgtcagtgacctggatgcagatagctttctgggtggactaaagtggta cagtgaccaatcagaaatcatttccaatcagtatggaaatgaatcatctaacttgtttgagatagatgaggaaaatgaggccaactt gctggcagttcttactgaaaccctggacagtatcccagtggatgaggacggattgccttcattcgaagcgctggcagatgggga cgtgaccaatgccagcaatcccagccgcccattgacacccaatggcgcccctccaaccccagaggcagaggagccatctctat tgaagaagctcttgcttgctccagctagctcccagttcagttataatcaatacacaggtggcaacgcacaaaatcatgcacctaata atcatagaatcagaccaacccttgctgttgtcaagacggaaaatgcttggaacagcaaggcgagggcaggctgccagcagca gcagctgcaggacaagcgggtgaggcgcccgtgctcagagctactcaagtacctgactgccagtgaagagcctgtacacacc aaacctagtgaacagaaaagaatcagcagtaaggacaaaagcagcgctttgtcgtcatcttcttcttcttcttcttcctcatcctcca ccaaaaagaagccgcagctgcagtctcagcagcagtcacatcaccagcaagccaaaccaaccaccttgccacttcctttgacc ccagactctccaaatgaccacaaaggatctccgtttgagaataaaaccattgaacgcgcattgagtgttgagctttctggaacagc aggtctgacacctcccaccactccacctcacaaagtcagccaagataatcctttcaagccttcaatgaagcccaagtcatcaccat gcagctcttcagcccccccatgcaaaatgccatgctacagtgattcctccacttgtcccctggcaatcagcccagtaaagaaggg tcctgaacaaacagaactgtacgcccagcttagcaaggcatccgcattgcctggggggcaggaagagcgaaaggggaaacg taccaccacgcgtttatttggtgatcatgactactgccagtccttaagcacaaaagggggtgtttcccttaactggtcacaggaccc gcaggatgacaggcatcaggaatgtagagactccccatcctctgctgtgcaacagcggagtcatttcttgcctgaacaggacca gcggaacagtagggaagggaggcaacaacaacagcagcaccatgaagggaagcaggttgacaacagccactcgaacagc aggaaaccgcttcgagaccaagaaatccgggcggaactcaacaagcacttgggtcacccaaacaagccatttacaatgagga ggtggaaaaggccgtcagccgccaggcagagagggactctggagatgagtattattgtaaactgcccgggtacatccgccca ggcctccccttggaagggatgttcgatgaccgtgaggacgagagtgacaagttcctgtacacctgggacgggacccattcgga 140

cctcttgtttgaaggttctctcttcctgctccccttccagctgctccccttcacggggctcagtttctccacccaaatccctcttctcctc acagcgttttcaccggcccaggtcgacttcccgctcctggtctaggtccaggtcccacccccggcgacggtcctacccccgatc tccgtactcccgttcaaaatcgaggtccccatacagcaggtcctcattacgatctcgacacgacaacgattcaagccacagtaga cccaggtcccacaaaagtccacattcacattcaagatcccgatctaggtccccctacaaccggaggcccaggtatgacagctac gaggaataccagcacgagagactaaagagggaggaatatagacaggattatgaaaaacgtgaatacgaaagagcgaaacag agagagagacagaggcaaaaagcaattgaggagcgccgtgtcgtgtatgtgggcaggctcaggtctgacataacccgcacag agccgaagcaccgctttgaagtttttggcgaaatcgaagagtgtacagtaaatctgagagatgacggggacaattttggtttcatc acctaccgctatacttgtgatgccttcgctgcccttgaaaatggacacaccttacgcaggtcaaatgaacctcagtttgagttgtgct tcgggggacgcaagcagttctgcaaatcaaattatacagacttagattcccattctgatgactttgacccagcttccactaaaagca agtacgactccatgga

>Bowfin tctgcgtggagtgaaatagagtgcgcggccctggtgggagcagaccagcccctgtgccccgacctccctgagctcgacctctc tgagctggacgtcagtgacctggacgccgatagctttctgggcggactcaaatggtacagcgaccaatcggagatcatttccaat cagtacggcagcgagtccgccaacctgttcgagaagatagatgaggaaaatgaggcgaacttgctggccgttctcaccgagac cctggacagcatcccggtggacgaggacggactgccttcgtttgaggcgctggccgatggagacgaggccagtgccagcga ccacagctgcccgtccacccctgatggctctccgcccacccccgaggccgaggagccttctctactgaagaagctcttgctggc tccggcgaactcccagctcagttataatcaatacacaggtggcaaggtgcaaaatcatgcaccaagtaatcacaggatcagacc gacgcctgctgttgtcaagacagaaaacccttcttggaatagcaagcagaggagtccctgtccgcagcagcagccgccgaagc cggcgaggcgtccgtgcgctgagctgctcaagtacctaacgggcagcgatgagacgctcccgaccaaagccagcgagccga aaagcagcagtggcgtcggcgggggcagaagtagcagcagtaaagacaaaactggcagcggcgcggcctgttcggcgtctt cctcctccaccttttcctcctcctcctccacctcctccacctctccggcctctaagaagaagccagagctgccgcctcatcaccag cgagccaaaccaactaccttgccactccccctgacccccgagtctccaaatgaccccaagggatctccatttgagaacaaagcc attgaacgcacactgagcgtggagctctcggggacagcaggtctgacaccacctaccacgcctcctcacaaagccagtcaaga gaaccctttcaaagtatcactcaaaaacaagctgtcttcatgctctccctcggccctgacaagcaaaaggcccaggctgagcaat gggggctcttgccctcagccaaccagtggctctattcgaaagggcccagagcagaccgagctatatgcccagttgagcaaggc atcctccacaatgcccctagggggcttggaagagcgtcggggcaagcgggcccattgcccccgcgtctttggcgatcacgact attgccagtctacaagcacaaaacgagacagcaccactacagctgcagcattaacttggccagtggagggccggcatgtggaa tgtaaagactcgaacatgccaacctcctcttcctcctccacggtgcagcgtcagagctgcgccagggccgagcaggacgtgtg gctgggcagggaggccgggcagcagcaggccaagaagcaggacagcggcggcccctcaaacggcaggaaacccctccg agaccaagagatccgggcggagctcaacaagcacttcgaaccccccaaacgggccatctacagcgagggcgagacggcgg acccccagccccgcgaggagagcgactccggtgacgagctctactgcccgctgcctggctacacgcacgccagcctgcccct cgacggggtcttcgacgacagcgaggacgagagcgagaagttcctgtacccctgggagggcacgcacctggacctgctgttc gaaggctccccatcctcctcgccctccagctgctccccttcgcgcggctccgtctcgccccccaagtccctgttctcctcccagc ggttctactggccccggtcagcgtcccgctcccgctcccactctcgcacggacgcccaccgccggagacggtcctactccagg tctccctactcccgctccaggtccaggtccccctacagccgctcctcctcacggtcccgtcacggcgcagactccagcggcagc cggcctcgcgctcacagaagcccccattcccactcccgatcgcactccggctcgcccttcagccgtcgcccaaggtacgacag ctacgaggagtaccagcatgagcggctcaagagggaggagtaccgccgggactacgagaagcgcgagttcgagcgagccg agcagagggagagacagcggcagaaggccattgaggagagacgcgtggtgtacgtggggagactgtgggccgacatgacc cgggcggagctgaagaggcgctttgaagtgttcggtgaaatcgaggagtgcgccgtcaatctgagagacgatggggacaattt cggcttcatcacgtaccgctacacatgtgacgcctgcgccgccttggagaacggacacaccttgcgccggtccaacgagccgc

141

agttcgagctgtgcttcggcggacgaaagcagttctgcaaatcaaattacacagacttggattcccattctgacgacttcgatccg gcctccaccaagagcaagtacgactccatgga

142