Environmental Sensitivity of Mitochondrial Gene Expression in Fish

ENVIRONMENTAL SENSITIVITY OF MITOCHONDRIAL GENE EXPRESSION IN FISH

Katharina Bremer

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

(October, 2013)

Maintaining energy organismal homeostasis under changing physiological and

environmental conditions is vital, and requires constant adjustments of the energy metabolism.

Central to meeting energy demands is the regulation of mitochondrial oxidative capacity. When

demands increase, animals can increase mitochondrial content/enzymes, known as mitochondrial

biogenesis. Central to mammalian mitochondrial biogenesis is the transcriptional master

regulator PPARγ (peroxisome proliferator-activated receptor γ) coactivator-1α (PGC-1α), and

the network of DNA-binding proteins it coactivates (e.g. nuclear respiratory factor 1 and 2

[NRF-1, NRF-2], estrogen-related receptor α [ERRα], thyroid receptor α [TRα-1], retinoid X

receptor α [RXRα]). However, the mechanisms by which mitochondrial content in lower

vertebrates such as fish is controlled are less studied.

In my study I investigate underlying mechanisms of the phenomenon that many fish species

alter mitochondrial enzyme activities, such as cytochrome c oxidase (COX) in response to low

temperatures. In particular, I investigated (i) if the phenomenon of mitochondrial biogenesis

during cold-acclimation is related to fish phylogeny, (ii) what role PGC-1α and other

transcription factors play in mitochondrial biogenesis in fish, and (iii) if mRNA decay rates are

important in the transcriptional control of a multimeric protein like COX.

This study shows that mitochondrial biogenesis does not follow a phylogenetic pattern: while distantly related species displayed the same response to low temperatures, closely related species showed opposite responses. In species exhibiting mitochondrial biogenesis, little evidence was found for PGC-1α as a master regulator, whereas NRF-1 is supported to be an important regulator in mitochondrial biogenesis in fish. Further, there was little support for other transcription factors (NRF-2, ERRα, TRα-1, RXRα) to be part of the regulatory network. ii

Lastly, results on the post-transcriptional control mechanism of mRNA decay indicate that this mechanism is important in the regulation of COX under mitochondrial biogenesis: it accounts for up to 30% of the change in subunit transcript levels.

In summary, there is no simple temperature-dependent mitochondrial response ubiquitous in fish. Further, the pathways controlling mitochondrial content in fish differ from mammals in the important master regulator PGC-1α, however, NRF-1 is important in regulating cold-induced mitochondrial biogenesis in fish. Lastly, COX subunit mRNA decay rates seem to have a part in controlling COX amounts during mitochondrial biogenesis.

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Co-Authorship

This thesis is structured according to the manuscript format as outlined in the guidelines provided by The School of Graduate Studies and Research. All data chapters (2-4) are co- authored by Dr. Christopher D. Moyes, whose contributions include intellectual support in

project design, project development, financial support for lab and field work, and manuscript design and editing. The second data chapter was co-authored by Christopher T. Monk, an undergraduate student who collected a subset of the data for his undergraduate thesis project, and

Dr. Brendon J. Gurd who provided laboratory resources and manuscript editing.

Publications arising from and included in this thesis:

Bremer K & Moyes CD (2011). Origins of variation in muscle cytochrome c oxidase activity

within and between fish species. The Journal of Experimental Biology 214, 1888-1895.

Bremer K, Monk CT, Gurd BJ & Moyes CD (2012). Transcriptional regulation of temperature-

induced remodeling of muscle bioenergetics in goldfish. American Journal of

Physiology: Regulatory, Integrative and Comparative Physiology 303, R150-R158.

Unpublished manuscripts included in this thesis:

Bremer K & Moyes CD (2013). mRNA degradation: an underestimated factor on steady-state

transcript levels of cytochrome c oxidase subunits?

Publications arising from, but not included in this thesis:

Duggan AT, Kocha KM, Monk CT, Bremer K & Moyes CD (2011). Coordination of cytochrome

c oxidase gene expression in the remodelling of skeletal muscle. The Journal of

Experimental Biology 214, 1880-1887.

Davies R, Mathers KE, Hume AD, Bremer K, Wang Y & Moyes CD (2012). Hybridization in

sunfish influences the muscle metabolic phenotype. Physiological and Biochemical

Zoology 85, 321-331.

Acknowledgements

First and foremost I would like to thank my supervisor Chris Moyes. Chris took me on as a PhD student in his lab without any previous encounters in person or on the phone. So it was a little surprise when we first met and the start of an adventurous time, not only in his lab, but also here in Canada. After the first year of quite some cultural acclimatization I got to know Chris much better, and despite the many times of being lost in translation both, literally and culturally, he knew how to ask for the best I could do.

Sometimes it was very challenging but everything always had an amazing turn-out, if it was getting a manuscript done, presenting at a conference, or going to get fish once a week over the course of a whole winter….

His intellectual support and unique way of showing excitement encouraged me to trust myself and find my way. Now, I am ready to take all I have learned about lab work, science, and myself and move on to new challenges. So, all that is left for me to say to Chris is thank you, you are a great Doktorvater.

Besides, Chris, I am thankful for the time, advice, and effort, all my committee members, William

Bendena, Don Maurice, and Bruce Tufts, invested and gave me during the course of my thesis.

Further, I would like to thank Tika Kocha who introduced me to the Moyes’ lab specific customs and patiently answering my never-ending questions. Also, she was a great lab mate with whom it was fun discussing scientific questions and troubleshooting in the lab.

I thank Mel Fortner for being such a great real-timing and western blotting sister and, thinking of western blotting, I will not forget the numerous times Brittany Edgett gave me valuable advice.

For my first data chapter, field sampling at QUBS would not have happened without the support of Frank Phelan and Floyd Connor. They always had great tips on where to find the fish.

For helping catch them, I thank many people and friends for their patience, sitting with me in a

vi boat or freezing with me stumbling through the marshes in summer and winter time and going real ice fishing: Sarah Wyness, Tika Kocha, my supervisor Chris, Paul Debes, and Bruce Tufts.

Tice Post and Ron Kerr from the loading dock: thanks a lot for your immense assistance regarding all technical animal care facility problems, and for always having a smile and good chats for me.

Emotional support and fun times were ensured by all the past and present Moyes’ lab members. When I arrived in Canada and joined the Moyes lab, especially Rhiannon was a good friend to me introducing me to the city and Queen’s.

Finally, but not forgotten, I would like to thank my family at home and here in Canada, Paul.

Mama und Papa, seid ich klein bin habt Ihr mir immer vertraut, die richtgen Entscheidungen zu treffen. Als es darum ging nach Kanada zu gehen, brauchte ich kein schlechtes Gewissen zu haben, dass ich Euch verlasse und egoistisch handel, Ihr habt mir immer das Gefühl gegeben, hinter mir zu stehen und mich bei Allem unterstützt was mir wichtig ist. Ohne Eure

Unterstützung, hätte ich mir diesen Schritt, in Kanada meine Doktorarbeit zu schreiben, nicht so einfach zugetraut. Danke! Auch dafür, dass Ihr die letzten fünf Jahre immer für mich da ward.

Wenn auch nur über skype, so hat es in manchen Situationen sehr geholfen.

Paul, ohne das Wissen, dass Du da bist und Deine ständige Unterstützung in der gesamten

Zeit, die wir hier in Kanada waren hätte ich wohl so manche Zeit nicht so einfach überstanden.

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Table of Contents

Abstract ...... ii Co-Authorship ...... iv Acknowledgements ...... vi List of Figures ...... xi List of Tables ...... xii List of Abbreviations ...... xiii Chapter 1 : General introduction ...... 1 1.1 Mitochondria ...... 2 1.1.1 A brief history ...... 2 1.1.2 The powerhouse of the cell ...... 3 1.1.3 Cytochrome c oxidase - structure and function ...... 5 1.2 The regulation of aerobic energy metabolism ...... 8 1.2.1 The control of aerobic energy metabolism by cytochrome c oxidase ...... 10 1.3 Control of mitochondrial biogenesis ...... 13 1.3.1 Transcriptional control of COX genes ...... 13 1.3.2 Control of PGC-1α levels and activity ...... 17 1.3.3 What determines (COX subunit) transcript levels? ...... 19 1.4 Cytochrome c oxidase across the animal kingdom and why we are interested in fish ...... 25 1.5 Questions ...... 27 Chapter 2 : Origins of variation in muscle cytochrome c oxidase activity within and between fish species ...... 30 2.1 Abstract ...... 30 2.2 Introduction ...... 31 2.3 Materials and methods ...... 33 2.3.1 Fish ...... 33 2.3.2 Acclimation experiment ...... 35 2.3.3 Cytochrome c oxidase activity ...... 36 2.3.4 RNA extraction and reverse transcription ...... 37 2.3.5 Real-time PCR ...... 37 2.3.6 Statistics ...... 40 viii

2.4 Results ...... 40 2.4.1 COX activity ...... 40 2.4.2 Transcription factors and coactivator mRNA levels ...... 42 2.5 Discussion ...... 44 2.5.1 Species-specific seasonal thermal response in enzyme activity ...... 44 2.5.2 Acclimation versus acclimatization ...... 46 2.5.3 Control of mitochondrial oxidative capacity ...... 48 2.6 Acknowledgements ...... 52 Chapter 3 : Transcriptional regulation of temperature-induced remodeling of muscle bioenergetics in goldfish ...... 53 3.1 Abstract ...... 53 3.2 Introduction ...... 54 3.3 Materials and methods ...... 57 3.3.1 Fish and experimental setup ...... 57 3.3.2 COX activities ...... 58 3.3.3 RNA analyses ...... 59 3.3.4 Protein levels ...... 61 3.3.5 Statistics ...... 64 3.4 Results ...... 64 3.4.1 Yield of RNA and nuclear protein ...... 64 3.4.2 COX activity and transcript patterns ...... 65 3.4.3 Protein levels ...... 70 3.5 Discussion ...... 71 3.5.1 Transcriptional regulators ...... 72 3.5.2 DNA-binding proteins ...... 73 3.5.2.1 NRFs ...... 73 3.5.2.2 ERRα, TRα-1, PPARs, and RXRα ...... 74 3.5.3 Significance of changes in RNA and nuclear protein per gram tissue ...... 79 3.5.4 Perspectives and significance ...... 80 3.6 Acknowledgments ...... 81 Chapter 4 : mRNA degradation: an underestimated factor on steady-state transcript levels of cytochrome c oxidase subunits? ...... 82 ix

4.1 Abstract ...... 82 4.2 Introduction ...... 83 4.3 Materials and methods ...... 86 4.3.1 Fish and experimental setup ...... 86 4.3.2 Cytochrome c oxidase activities ...... 87 4.3.3 Decay assay and RNA extraction ...... 88 4.3.4 Real-time PCR ...... 89 4.3.5 Data analysis ...... 91 4.4 Results ...... 92 4.4.1 Steady-state enzyme activities and transcript levels ...... 93 4.4.2 Thermal sensitivity of mRNA decay rates ...... 95 4.5 Discussion ...... 105 4.5.1 COX activities and the uncoordinated stoichiometry of COX subunit mRNAs ...... 105 4.5.2 Can COX mRNA decay rates explain their steady-state pattern? ...... 108 4.6 Conclusion ...... 113 4.7 Acknowledgments ...... 113 Chapter 5 : General discussion ...... 114 References ...... 121

List of Figures

Figure 1.1. Schematic of the eukaryotic electron transport chain (ETC)...... 5 Figure 1.2. Schematic overview of the origin of COX subunits and the pathways resulting in a functional enzyme complex...... 14 Figure 1.3. Schematic overview of key pathways involved in mitochondrial biogenesis in mammals with PGC-1α as the master regulator...... 15 Figure 1.4. Classification of select fish groups...... 26 Figure 2.1. Daily mean water temperatures at a depth of 3.3m in Lake Opinicon...... 34 Figure 2.2. Water temperatures and COX activities at specific sampling points in central mudminnow. . 36 Figure 2.3. Relative COX activities in white muscle of fish collected in summer and winter...... 41 Figure 2.4. Relative COX activities and mRNA levels of transcription factors in white muscle of fish collected in summer and winter...... 43 Figure 3.1. Size estimation of target proteins...... 61 Figure 3.2. Relative transcript levels of transcriptional regulators and DNA-binding proteins after thermal acclimation...... 66 Figure 3.3. Relative nuclear protein levels of transcription factors after thermal acclimation...... 68 Figure 3.4. Relative COX activities, transcript levels of transcription factors, and target protein levels after thermal acclimation...... 69 Figure 4.1. Steady-state COX activities and transcript levels of COX subunits in white muscle after thermal acclimation...... 94 Figure 4.2. Average decay rates measured at 32°C and 4°C in 32°C and 4°C acclimated fish...... 97 Figure 4.3. Relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 32°C)...... 99 Figure 4.4. Average decay rates measured at 20°C and 4°C in 20°C and 4°C acclimated fish...... 103 Figure 4.5. Relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 20°C)...... 104 Figure 4.6. Steady-state transcript levels of COX subunits and fractions ascribed to transcription and decay differences between warm and cold acclimated goldfish...... 112

List of Tables

Table 2.1. Morphometric data for all seasonally acclimatized fish species and temperature acclimated central mudminnow...... 35 Table 2.2. Forward and reverse real-time PCR primer sequences for target genes...... 39 Table 2.3. Trends in COX activities and transcription factor mRNA levels in target species relative to summer/warm values...... 52 Table 3.1. List of target gene-specific forward and reverse real-time primers and their specific annealing temperatures...... 60 Table 3.2. U- and p-values of Mann-Whitney U test between medians of acclimation groups for histone H3 levels for the various target proteins...... 71 Table 4.1. Gene-specific forward and reverse real-time primers and their specific annealing temperatures...... 90 Table 4.2. Test results of unpaired t-tests for differences between steady-state mRNA ratios and COX activities...... 95 Table 4.3. Results of a mixed model on decay rates for two acclimation experiments...... 98 Table 4.4. Results of the t-test for the ratio of means of independent samples for two acclimation experiments...... 100 Table 4.5. Results of independent t-tests testing for differences in thermal sensitivity of decay rates between genes...... 100 Table 4.6. Results of paired t-tests between two assay temperatures (20°C and 4°C) for each acclimation group (20°C and 4°C)...... 102

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List of Abbreviations

ADP adenosine diphosphate AKAP A-kinase anchoring proteins AMP adenosine monophosphate AMPK AMP-activated protein kinase ATP adenosine triphosphate CAMK Ca2+/calmodulin-dependent protein kinase cAMP cyclic AMP CI confidence interval COX cytochrome c oxidase CREB cAMP response element-binding protein ERRα estrogen-related receptor α ETC electron transport chain FDR false discovery rate GCN5 amino-acid synthesis 5 HCF host cell factor 1 m7G 7-methylguanosine MEF2 myocyte enhancer factor 2 miRNA microRNA mtDNA mitochondrial DNA NGD no-go-mediated mRNA decay NMD nonsense-mediated mRNA decay NRF nuclear respiratory factor NSD nonstop-mediated mRNA decay OXPHOS oxidative phosphorylation p38 MAPK p38 mitogen-activated protein kinase PABP poly(A)-binding protein P-bodies processing bodies PGC-1α peroxisome proliferator-activated receptor ? coactivator 1a PKA protein kinase A

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PMF proton motive force PPAR peroxisome proliferator-activated receptor PRMT1 protein arginine N-methyltransferase 1 RIP140 receptor interacting protein 1 RNAi RNA interference ROS reactive oxygen species RXR retinoid X receptor SDS sodium dodecyl sulfate siRNA small interfering RNA SIRT1 NAD+-dependent deacetylase sirtuin-1

T2 3,5'-diiodothyronine

T3 3,5,3'-triiodothyronine TBP TATA-binding protein TRα thyroid hormone receptors α TSH thyroid-stimulating hormone

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Chapter 1: General introduction

For my thesis I studied the environmental sensitivity of mitochondrial gene expression in fish. Among the many abiotic factors exerting effects on fish, temperature and the response of fish to it has been under investigation for more than 100 years (see Sullivan, 1954). One reason for this considerable interest may be that, unlike mammals, fish are ectotherms (Greek ektós

“outside”, thermós “heat”). As such, they largely rely on external heat to control their body temperature (Davenport, 1992), which is a critical condition for all biological processes (Sizer,

1943). This key difference from mammals, which are endotherms (Greek endon “within”, thermós “heat”), necessitates major morphological, physiological, biochemical, and genetic modifications with changing temperatures. Cold exposure in particular requires that fish compensate for thermodynamically induced decreases in the rates of biological reactions, including energy production. Fish response to cold exposure includes mitochondrial biogenesis, an increase in mitochondrial content, expressed either enzymatically (catalytic units per g) or morphologically (e.g., volume density). My project explored the mechanisms that control this cold-induced mitochondrial biogenesis in fish, focusing on identifying elements that are broadly conserved in vertebrates and those that may be unique to fish.

In the following sections, I review the history of mitochondrial research, and discuss their role in aerobic energy production as well as the regulation of cytochrome c oxidase (COX), an enzymes involved in this process, considering transcriptional regulation of individual genes, post-transcriptional regulation of translation, and post-translational regulation of the enzyme.

Finally, I provide a brief overview of the phenomenon of mitochondrial biogenesis in animals

and why I think fish are an attractive study system for investigating this phenomenon.

1.1 Mitochondria

1.1.1 A brief history

The first structural description of mitochondria as cell organelles was published by Robert

Altmann (Altmann, 1890). He saw them as “elementary organisms” living in cells, calling them

“bioblasts”. Their modern name, mitochondrion, was established nine years later and derives from the Greek mitos for "thread" and khondrion for "little granule" in allusion to their peculiarity to form long chains (Benda, 1898). In the early 1900s, scientists began ascribing cell

respiration to certain particles within the mitochondria; however, they lacked without chemical

evidence (Kingsbury, 1912). In 1925, the cytochromes were discovered and the respiratory chain

was described (Keilin, 1925). Over the following 40 years, extensive research on mitochondria

continued to uncover their major role as the energy supply system of the cell (see Ernster &

Schatz, 1981). Today, they are also known to be involved in many cellular pathways, such as

signalling (e.g., reactive oxygen species, calcium), biosynthesis (e.g., heme, steroids), and

apoptosis.

Shortly after mitochondria were first described, their origin was examined and the

endosymbiotic theory was articulated (Mereschcowsky, 1905, 1910). Today, there is strong

support that mitochondria derive from a free-living α-proteobacteria, but whether they form a

sister group with Pelagibacteraceae (SAR11 clade) in the order of Rickettsiales (Thrash et al.,

2011), or whether they are most closely related to the oceanic mitochondria affiliated clade

(Brindefalk et al., 2011) is an ongoing debate (Gray, 2012). Furthermore, it is still under investigation whether endosymbiosis preceded or followed the acquisition of a nucleus and the origin of eukaryotes (Koonin, 2010).

Over time, symbiont-derived genes were transferred to the host genome, and the organelle

(Szklarczyk & Huynen, 2010) became semi-autonomous. However, the extent of gene transfer varies hugely across eukaryotes, ranging from 3 to 67 genes for mitochondrial-encoded proteins and from 0 to 27 genes for mitochondrial-encoded transfer RNA (see Adams, 2003). In vertebrates, the mitochondrial genome (mtDNA) only codes for 37 genes, 13 of which code for proteins, 22 for transfer RNA, and two for the rRNA subunits. Interestingly, all of the mtDNA- encoded proteins are essential subunits of the respiratory chain, which eventually puts the control of a cell’s energy production into a challenging bi-genomic collaboration.

1.1.2 The powerhouse of the cell

One of the main benefits of mitochondria is that oxidative phosphorylation (OXPHOS) produces 15 times more energy than glycolysis. The basis for the efficiency of OXPHOS is five enzyme complexes and two electron carriers associated with the inner mitochondrial membrane.

Complexes I through IV form the electron transport chain (ETC) and create a concentration gradient of protons across the membrane (Figure 1.1). Complex V, the ATP synthase, utilizes this gradient to produce ATP. The two electron carriers transfer electrons between some of the complexes. 3

NADH and FADH2, which are generated during the oxidation of fatty acids, carbohydrates,

and proteins, serve as electron donors for complex I (NADH-coenzyme Q oxidoreductase) and

complex II (succinate-Q oxidoreductase), respectively (Figure 1.1). From both complexes,

electrons are transferred on to ubiquinone, one of the two mobile electron carriers, and further to

complex III (cytochrome c reductase). Reduced complex III donates electrons to cytochrome c,

the second electron carrier. In the last step of the ETC, complex IV (COX) oxidizes cytochrome

c and reduces the final acceptor, oxygen, to form water.

During the process of electron transfer, complexes I, III, and IV translocate protons (H+) across the inner mitochondrial membrane, generating a proton motive force (Δpm, PMF) made up

of an electrical (Δψm) and chemical (ΔpH) gradient. The PMF is a driving force for a number of

processes, such as ion transport, and plays an important role in OXPHOS because it is harnessed by complex V, the F1FO-ATP synthase, to phosphorylate ADP, forming ATP. This chemiosmotic

coupling of phosphorylation and electron and hydrogen transfer was first hypothesized more than

50 years ago by Peter Mitchell (Mitchell, 1961) and was acknowledged with a Nobel Prize in

Chemistry in 1978.

Intermembrane space

Mitochondrial inner membrane Cyt c I Q III IV II

NADH + H+ NADH+

Succinate Fumarate Figure 1.1. Schematic of the eukaryotic electron transport chain (ETC). Blue arrows represent the flow of electrons across the four complexes (I-IV) and the mobile electron carrier ubiquinone and cytochrome c. Red arrows represent the translocation of protons (H+) from the mitochondrial matrix to the mitochondrial intermembrane space along the electron transport system and vice versa for the chemiosmosis at F1FO ATPase. This schematic is modification after and with permission of Katrinka M. Kocha.

1.1.3 Cytochrome c oxidase - structure and function

Among the four complexes of the ETC, complex IV, or COX, is an important site of control

of OXPHOS. The reasons leading to its crucial role in OXPHOS will be described in detail in the

following sections.

COX is a multimeric enzyme, but the total number of subunits varies greatly across the

phylogenetic tree, from two or three in some bacteria, to seven or eight in lower eukaryotes like

yeast, and up to 13 in mammals (see Kadenbach et al., 1983). Furthermore, while COX can exist

as a 13-subunit multimeric enzyme in mammals, it can also dimerize, forming a complex

comprising 26 subunits. Both forms efficiently transfer electrons and bind substrates (Suarezs et

al., 1984). However, heterodimerizing seems to be a necessary step for the proton pumping ability (Stanicová et al., 2007).

The two subunits of the bacterial heterodimeric heme aa3 oxidase are homologous to subunits 1 and 2 in all mitochondrial COX enzymes, forming the catalytic core of the enzyme complexes. As complex IV of the ETC, COX accepts the electrons from cytochrome c at its di- atomic copper center CuA located in subunit 2. Then they are transferred to cytochrome a in subunit COX1 and passed on to the bi-nuclear center of cytochrome a3 and CuB also located in subunit COX1. In this a3/CuB center the electrons are finally transferred to oxygen. In summary,

COX’s catalytic net reaction is as follows:

2+ + 3+ + 4 Fe -cytochrome c + 8 H (in) + O2 → 4 Fe -cytochrome c + 2 H2O + 4 H (out)

In most eukaryotes, COX subunits 1 - 3 are encoded in the mtDNA, while further subunits, gained with increasing organism complexity, are encoded in the nucleus (Capaldi, 1990). In vertebrates, all of the nuclear-encoded subunits (4, 5A, 5B, 6A, 6B, 6C, 7A, 7B, 7C, and 8) are thought to serve structural or regulatory roles. Their nomenclature, as presented here, is based on the molecular weight of each subunit, with the number of the subunit increasing with decreasing molecular weight. One widely accepted exception to this rule is that subunit 2 is smaller than subunit COX3 (see Cooper et al., 1991).

Of the 13 subunits, four (3, 5B, 6A, and 6B), are important for the structural integrity of the two monomers (Kadenbach et al., 2000). Subunit 5B has both structural and regulatory functions, while subunits 4, 5A, 6C, 7A, and 8 have regulatory functions. The function of subunits 7B and 7C has yet to be described.

The largest of all ten nuclear-encoded subunits, COX4, is important for the overall control of complex activity due to its two ATP binding sites (Napiwotzki et al., 1997; Acin-Perez et al.,

2011). Subunits 5A and 5B have opposing effects on the overall activity of the complex: a phosphorylated subunit 5B causes an inhibition of the complex’s activity by binding ATP

(Helling et al., 2012), whereas subunit 5A has a specific thyroid hormone binding site, which prevents this by ATP (Arnold et al., 1998). In contrast to the regulatory function of these subunits, Subunit 6C is proposed to host a second, low-affinity binding site for cytochrome c, which gives the complex its biphasic kinetics (Cooper, 1990). Subunit 7A is expressed in tissue- specific isoforms (Anthony et al., 1990), and so is subunit 8 (Kadenbach et al., 1990). In the latter case, expression is also species-specific (Linder et al., 1995). All regulatory control mechanisms will be discussed in more detail in the following sections.

A very important mechanism of OXPHOS regulation is the potential of subunits to switch between certain isoforms, as indicated above, changing the structure and/or function of complexes. So far in mammals, six COX subunits have been shown to possess isoforms: 4, 6A,

6B, 7A, 7C, and 8 (see Little et al., 2010; Pierron et al., 2012). While the isoforms of four of the subunits are expressed in a tissue-specific or developmental-specific manner, the expression of

COX4 isoforms seems to be regulated by physiological conditions. In the context of phylogeny, it is worth mentioning that a potential third whole genome duplication just before the divergence of teleost fish might have led to the formation of additional paralogs for COX5A and 5B. Despite the tissue-specific distribution of subunits 5A and 5B, a functional characterization has yet to be done (Little et al., 2010).

1.2 The regulation of aerobic energy metabolism

Efficient energy production through OXPHOS is crucial to the survival of all higher

organisms and thus tight regulation of the process is essential. OXPHOS flux is regulated at

many biochemical and cellular levels. Through mass action effects, OXPHOS can be affected by

substrates of the ETC (such as reducing equivalents (NADH, FADH2) or oxygen), as well as substrates and products of the F1FoATPase (phosphate, ADP, and ATP) (see Hüttemann et al.,

2008). Both the ETC and ATPase are regulated by the PMF, which can be considered a product

of the ETC and a substrate for the ATPase.

The earliest explanation for regulation of OXPHOS was Mitchell’s classical chemiosomotic

hypothesis (see section 1.1.2), which proposed the dependency of phosphorylation on electron

and hydrogen transfer, a relationship be called coupling (Mitchell, 1961). Mitchell proposed that

ATP synthesis is controlled by electrochemical potential, resulting in an inhibition of ATP

synthesis at high membrane potentials due to the inhibition of the proton pumps (complexes I,

III, and IV). Such membrane potentials have been shown to reach up to 160 - 220 mV (Hafner et

al., 1988) before inhibition occurs and result in multiple negative effects. One consequence is the

- formation of reactive oxygen species (ROS) like superoxide ions (O2 ) (Korshunov et al., 1997), which are known to increase deleterious mutations of DNA, including mitochondrial DNA.

Another less severe impact of high membrane potentials is the elevated loss of oxidative energy as heat due to the relationship of membrane potentials and innate proton leak (O’Shea et al.,

1984). Furthermore, membrane potentials >120 mV facilitate additional uncoupling by free fatty acids (Köhnke et al., 1993).

Superimposed on the classical concept of coupling is a Δp-independent mechanism of

respiratory control, based on allosteric ATP-mediated inhibition (Arnold & Kadenbach, 1997;

Kadenbach et al., 2010). The site of this control mechanism is complex IV (cytochrome c oxidase, COX) of the ETC. So far seven high-affinity binding sites for ATP or ADP and three sites specifically for ADP have been described for COX from bovine heart (Napiwotzki et al.,

1997). Specifically, either ATP or ADP can bind to the matrix domain of subunit 4 of COX

(COX4) (Arnold & Kadenbach, 1997). At high intramitochondrial ATP/ADP levels, ATP

binding has been shown to reduce the proton pumping efficiency by 50% (Napiwotzki et al.,

1997) and even inhibit the activity of the COX complex (Ferguson-Miller et al., 1976). One

important physiological short-term control mechanism that counteracts ATP-control makes use

of a metabolite of thyroid hormone (3,5'-diiodothyronine, T2). It binds to COX subunit 5A and

eliminates the allosteric inhibition of COX by ATP, resulting in increased respiration

independent of the cell’s energy state.

In a healthy, living cell both mechanisms of respiratory control operate to ensure the fine-

tuned control of energy synthesis. Under normal, relaxed conditions, respiratory control

underlies the feedback inhibition of COX by ATP, which goes along with membrane potentials

<140 mV and low ROS production. However, under stress, the ATP inhibition of COX is

switched off and ATP synthesis is solely controlled by Δp, allowing membrane potentials to

increase beyond 140 mV and thus increasing negative impacts such as ROS production.

In addition to ATP, other allosteric effectors such as ions (see Maréchal et al., 2013) and

hormones (Arnold et al., 1998) affect COX activity and in turn overall OXPHOS activity. An

important competitive inhibitor of oxygen binding at high ferrocytochrome c levels is nitric oxide. It binds to the bi-nuclear center a3/CuB in subunit 1 and prevents oxygen from binding instead (see Cooper, 2002).

The number of mechanisms affecting COX and hence fine-tuning overall aerobic energy production highlights COX’s role as an important regulator of OXPHOS. Therefore, a tight regulation of COX at the transcriptional, post-transcriptional, and post-translational levels is essential.

1.2.1 The control of aerobic energy metabolism by cytochrome c oxidase

COX’s role in the Δp-independent mechanism of respiratory control is strongly controlled by its post-translational modifications. In order to be susceptible to allosteric ATP-mediated inhibition, COX has to be phosphorylated (Bender & Kadenbach, 2000). Over the last 16 years,

18 reversible phospho-epitopes were identified among eight of the 14 COX subunits in mammals: COX1 (3 sites), COX2 (2 sites), COX4 (5 sites), COX5A (4 sites), COX5B (2 sites),

COX6A (1 site), and COX7C (1 site) (Helling et al., 2012). However, we currently lack good evidence for which of these phosphorylated residues is involved in the control of allosteric ATP- dependent inhibition, mostly due to methodological constrains. To date, Ser and/or Thr of subunit 1 (Helling et al., 2008) and Ser-2 of subunit 5B (Helling et al., 2012) appear to be involved in ATP-inhibition, whereas the phosphorylation of Ser-58 of isoform 1 of subunit 4

(COX4-1) is suggested to prevent allosteric inhibition by ATP (Acin-Perez et al., 2011).

The pathway involved in the phosphorylation of COX and the other complexes of the ETC has only recently been suggested to involve the cAMP-dependent protein kinase A (PKA), 10

although the association of PKA with mitochondria has been proposed since the 1970s (see

Valsecchi et al., 2013). In this pathway, PKA is activated by the secondary messenger cAMP and in turn phosphorylates subunits of complexes I and IV (Palmisano et al., 2007; Acin-Perez et al., 2009) of the ETC.

Originally, it was thought that the presence of cAMP in the mitochondria was due to cAMP diffusion from the cytosol; however, it has recently been shown that cAMP can be produced in mitochondria by an endogenous bicarbonate-sensitive adenylyl cyclase (Acin-Perez et al., 2009;

Di Benedetto et al., 2013). While it remains unclear how PKA enters mitochondria, its distribution within the organelle is very likely under the control of A-kinase anchoring proteins

(AKAPs), which target the regulatory subunit of PKAs and direct them to specific sites (see

Michel & Scott, 2002). AKAP splice variants have been identified with mitochondrial target signal sequences and have been found in close proximity to mitochondria, suggesting they are involved in the mitochondrial PKA shuttle (Valsecchi et al., 2013). Mitochondrial PKA is distributed throughout the mitochondria, from the outer and inner mitochondrial membrane to the intermembrane space and the matrix, and is thought to be involved in a variety of processes.

For example, PKA associated with the outer mitochondrial membrane is suggested to be involved in apoptosis, whereas PKA located in the matrix is suggested to be responsible for the phosphorylation of the OXPHOS complexes, including both activation and deactivation-related phosphorylations leaving the question, of how PKA distinguishes between them still open (see

Di Benedetto et al., 2013).

The phosphorylation mechanisms described above are in effect when the cell is in a non-

stressed, relaxed state, allowing for allosteric ATP-inhibition of COX. However, when a cell is

exposed to stress, phosphorylation is reversed. This reaction is suggested to be performed by a

Ca2+-induced mitochondrial protein phosphatase (Kadenbach et al., 2010). A constitutive

mitochondrial Ca2+ uptake, mediated by the endoplasmic reticulum-localized inositol trisphosphate receptor Ca2+ release channel, is an essential signal transduction mechanism for

optimal steady-state mitochondrial respiration (Cárdenas et al., 2010). Under stress, however,

elevated Ca2+ uptake may lead to the dephosphorylation of COX and result in elevated ATP

production controlled only by the first mechanism of respiratory control. To date eight

mitochondrial protein phosphatases belonging to the protein serine/threonine phosphatases and

protein tyrosine phosphatases have been reported (Pagliarini & Dixon, 2006), but the exact

mechanism behind their activation through Ca2+ is still under investigation.

In summary, the post-translational control of COX plays a major role in the regulation of

ATP synthesis. Reversible phosphorylation tightly controlled by mitochondrial Ca2+ levels is the

basis for switching allosteric ATP-inhibition on and off. Those modifications are fast responses

to physiological conditions impacting the immediate energy requirements of a cell.

1.3 Control of mitochondrial biogenesis

1.3.1 Transcriptional control of COX genes

Post-translational modifications can change an enzyme’s activity quickly and are an important feature of COX in the context of ATP synthesis. However, under certain physiological, developmental, or environmental conditions, long-term changes in the amount of respiratory enzymes are required. Due to spatial constraints, this might even happen in parallel with an increase in the number of mitochondria. The term “mitochondrial biogenesis” is generally used in the broadest sense, referring to increases in mitochondrial enzyme content and organelle content.

An increase in protein synthesis can be achieved in two ways: the induction of mRNA transcription followed by translation, and/or the re-entry of stalled mRNAs into the translational machinery. In the context of mitochondrial biogenesis, the first of these mechanisms and the underlying network of transcription factors has been under extensive investigation starting about

20 years ago (Evans & Scarpullas, 1989).

In the case of COX, transcriptional control becomes quite complex due to its bi-genomic nature. The transcription of the ten nuclear-encoded subunits, their transport to and import into the mitochondria and finally their assembly with the three mitochondrial subunits requires a high degree of coordination (Figure 1.2).

Figure 1.2. Schematic overview of the origin of COX subunits and the pathways resulting in a functional enzyme complex.

A major role in the transcriptional control of respiratory proteins has been assigned to the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), which is known as the

“master regulator” of mitochondrial biogenesis in mammals (Figure 1.3; Fernandez-Marcos &

Auwerx, 2011). As a coactivator, PGC-1α was first described in the context of binding peroxisome proliferator-activated receptor γ (PPARγ) in adaptive thermogenesis in mice

(Puigserver et al., 1998). Since then, the group of transcription factors targeted by PGC-1α has expanded, as has the list of genes under its control (Figure 1.3).

Figure 1.3. Schematic overview of key pathways involved in mitochondrial biogenesis in mammals with PGC-1α as the master regulator. Three examples for pathways triggering mitochondrial biogenesis are depicted. Each pathway leads to increases in signalling molecules, which in turn activate energy sensors. Those activate the transcription of respiratory genes through the activation of PGC-1α. The mRNAs of respiratory genes, such as shown COX subunits are imported into the mitochondrion. Finally, they assemble with the three mtDNA- encoded subunits to form the multimeric COX complex.

Most genes involved in mitochondrial function and biogenesis, such as OXPHOS, mtDNA

transcription and replication, protein import and assembly, and ion channels, respond to the

nuclear respiratory factor 1 (NRF-1) and/or NRF-2, also known as GA-binding protein

(Scarpulla, 2002). Though NRF-1 binds the promoters of target genes directly, NRF-2 regulates

these genes indirectly through the DNA-binding protein host cell factor 1 (HCF1) (Vogel &

Kristie, 2000). The two NRFs are equally important for the transcription of COX subunits 4, 5B,

6A, 6C, 7A, and 7C (Scarpulla, 2002). They also link the transcription of nuclear-encoded and

mitochondrial-encoded subunits by controlling the expression of mitochondrial transcription factor A, which is needed for mtDNA transcription (Fisher & Clayton, 1985).

Other metabolically relevant transcription factors that bind PGC-1α include the estrogen- related receptor α (ERRα), the thyroid hormone receptors α (TRα-1) and β (TRβ-1 and β-2), the retinoid X receptor (RXR), and the eponymous peroxisome proliferator-activated receptors

(PPAR) (Figure 1.3). The PPARs are a group with three members, PPARα, PPARβ/δ, and

PPARγ, that each dimerize with the retinoid-X-receptor (RXR) to exert transcriptional control of enzymes, transporters, and proteins involved in fatty acid oxidation. PPARs together with RXRs belong to the group of nuclear receptors that are activated through ligands such as fatty acids and

9-cis-retinoic acids, respectively. Once activated, they bind the peroxisome proliferator response elements in the promoter of the target gene and induce transcription. Following the same principles of activation, the TRs bind the hormone response elements either as monomers, homodimers, or heterodimers, with RXR as partner. In the absence of their ligand, thyroid hormone, they suppress the target gene’s transcription. Upon activation by thyroid hormones,

TRs induce transcription of genes involved in metabolism, growth, and development. The importance of one of the forms that thyroid hormones exists in, T2, on the control of ATP- production involving COX was described in section 1.1.2; the transcriptional effect of this hormone is mediated by 3,5,3'-triiodothyronine (T3) and its activation of TR. So far, it appears that COX subunits of both nuclear and mitochondrial origin are stimulated by the thyroid hormone/TR pathway (Wiesner, 1992). However, a strong tissue-specific pattern has been observed, with the liver as one of the most responsive tissues (Sheehan et al., 2004).

Furthermore, T3 has positive effects on the transcription of NRF-2α and the master regulator

PGC-1α (Rodrı́guez-Peña et al., 2002; Wulf et al., 2008).

The last major group of transcription factors regulating mitochondrial energy production is the estrogen-related receptor (ERR) family: ERRα, ERRβ, and ERRγ. ERRs are orphan receptors, meaning they have no known endogenous ligands. However, ERRα has been shown to interact with PGC-1α at its activation function 2 located in the ligand binding domain initiating transcriptional activation (Kallen et al., 2004). ERRs bind as monomers, homodimers, or heterodimers, such as ERRα/γ, to ERR response elements of target genes encoding proteins involved in lipid oxidation, OXPHOS, tricarboxylic acid cycle, mitochondrial import, mitochondrial dynamics, and oxidative stress defenses (Dufour et al., 2007; Giguère, 2008;

Villena & Kralli, 2008).

1.3.2 Control of PGC-1α levels and activity

The regulation of the comprehensive mitochondrial network of transcriptional control with

PGC-1α as a central factor depends greatly on the activation of coactivators. Multiple post- transcriptional modifications have an impact on the activity of PGC-1α, including phosphorylation, ubiquitination, methylation, acetylation, and SUMOylation (see Hock & Kralli,

2009). These post-translation modifications change PGC-1α’s activity or localization in response to cellular energetic and metabolic stress, such as exercise, cold, or caloric deprivation. Recent studies focus on the mechanisms that connect stressors to the effects on PGC-1α.

As a general cellular energy status sensor, AMP-activated protein kinase (AMPK) is regulated by AMP/ATP ratios. When energy is limiting, AMP levels rise, resulting in 17

conformational changes of AMPK, which alter its ability to be phosphorylated and activated by upstream kinases. AMPK has many targets one of which is PGC-1α (Jäger et al., 2007). By phosphorylating PGC-1α, AMPK causes the enhanced activity of this coactivator. At high energy levels, PGC-1α is heavily acetylated throughout its whole sequence by a protein called general control of amino-acid synthesis 5 (GCN5). However, under nutrient deprivation, an increase in the ratio of NAD+/NADH activates the NAD+-dependent deacetylase sirtuin-1

(SIRT1), which deacetylates PGC-1α, causing the co-activation of its target transcription factors

(Gerhart-Hines et al., 2007). The specific residues involved in this pathway are still unknown due to the sheer number of target residues (Fernandez-Marcos & Auwerx, 2011). A second, more recently described pathway activating SIRT1 through cAMP/PKA plays a role in a faster and more dynamic induction of fatty acid oxidation (Gerhart-Hines et al., 2011).

In addition to the SIRT1 and AMPK-based pathways, mitochondrial biogenesis is also influenced by cAMP/PKA pathway. Increased cAMP levels activate the cAMP-dependent protein kinase (PKA), which in turn activates p38 mitogen-activated protein kinase (p38 MAPK) followed by the phosphorylation of PGC-1α, and leading to an increase in the stability of the

PGC-1α protein (Puigserver et al., 2001). This PKA pathway is thought to be distinct from the

PKA pathway that impacts cAMP sensitivity to enzymatic regulation of OXPHOS discussed in section 1.2.1.

The fourth pathway activating PGC-1α is Ca2+-induced and occurs in muscle tissues under excessive exercise. Following the activation of the Ca2+/calmodulin-dependent protein kinase

(CAMK), PGC-1α is phosphorylated via p38 MAPK.

Superimposed on the post-translational regulation of PGC-1 activity is another level of control: PGC-1α transcriptional regulation. Many of the transcription factors and signalling

molecules involved in the network of mitochondrial biogenesis and the post-translational

modifications of PGC-1α are also involved in the transcriptional control of the PGC-1α gene. For

example, PKA-mediated activation of p38 MAPK leads to the phosphorylation of two

transcription factors, myocyte enhancer factor 2 (MEF2) and activating transcription factor 2,

which in turn bind the PGC-1α promoter and activate its transcription (Zhao et al., 1999;

Akimoto et al., 2005). Furthermore, PKA and CAMK phosphorylate the cAMP response element-binding protein (CREB), which directly activates the transcription of PGC-1α by binding its promoter (Herzig et al., 2001). Lastly, increases in Ca2+ activate the protein

phosphatase calcineurin A, which increases the ability of MEF2 to bind to the PGC-1α promoter

by phosphoepitope-specific dephosphorylation (Wu et al., 2001).

All the pathways described above clearly demonstrate the complexity of the network of

interactions and feedback loops involved in mitochondrial biogenesis (see Hock & Kralli, 2009;

Fernandez-Marcos & Auwerx, 2011).

1.3.3 What determines (COX subunit) transcript levels?

In addition to the transcriptional pathways that control COX gene mRNA synthesis, there

are a number of post-transcriptional events that determine transcript levels and rates of

translation. Multiple post-transcriptional processes regulate the stability and distribution of

mRNAs in the cell. In particular the stability of transcripts is an important factor to consider

when investigating their steady-state levels. Over the last 20 years the impact of mRNA decay 19

has gained greater prominence in studies of gene expression and is now considered a key site of

gene regulation (see Decker & Parker, 1994; Balagopal & Parker, 2009). This processes can alter

the relationship between mRNA synthesis and mRNA steady-state levels. While the former gives

insight into how genes are regulated, the latter helps explain why differences or changes in

protein synthesis may arise.

While still in the nucleus, pre-mRNA is processed in three major steps before becoming a

maturate mRNA: 5' capping, 3' polyadenylation, and RNA splicing. The 5'-capping is the

addition of 7-methylguanosine (m7G) to the 5' end of the pre-mRNA via (guanine-N7)- methyltransferase. Binding of the eukaryotic translation initiation factor 4E protein to the cap not

only initiates translation but also protects the transcript from degradation by 5′→3′

exoribonuclease. During the process of 3'-polyadenylation, ~250 adenine residues are added to

form a poly(A) tail, which is bound by multiple poly(A)-binding protein (PABP) to prevent

degradation by 3′→5′ exoribonuclease. Finally, RNA splicing involves the removal of introns

and splicing of exons from the pre-mRNA by spliceosomes to generate the mature mRNA.

Alternative splicing is an important process that can result in exon skipping, alternative 3′ or 5′

splice site selection, and intron retention generating different mature mRNAs that code for

different proteins (see Garneau et al., 2007; Nilsen & Graveley, 2010).

At any of those stages, including transcription, errors can occur that lead to defective

mRNAs. Surveillance mechanisms or so-called quality control mechanisms prevent aberrant

mRNA species from being translated. This is of particular importance because many diseases are

linked to faulty mRNAs (Frischmeyer & Dietz, 1999). The three mechanisms leading to

degradation of error-containing mRNAs are the nonsense-mediated mRNA decay pathway

(NMD), nonstop-mediated mRNA decay pathway (NSD), and the no-go-mediated mRNA decay

pathway (NGD).

The NMD is present in all eukaryotes and targets transcripts that contain premature

termination codons originating from mutations, frame-shifts, or inefficient processing. In the

NSD, mRNAs that lack a stop codon due to breakage or the absence of an in-frame stop codon

are targeted for decay. The last of the three surveillance mechanisms, the NGD, was only

discovered in 2006 (Doma & Parker, 2006). It occurs when the translation-elongation complex is

stalled, presumably due to defective mRNAs or ribosomes, or as a mechanism of post- transcriptional control.

Correctly transcribed and processed mRNAs leave the nucleus to enter transcription. mRNAs exist in three major states within a cell: they can be arranged in polysomes (Warner,

1963), stress granules, and processing bodies (P-bodies). The latter two are small (stress granules: 100 - 200 nm, P-bodies: 300 - 500 nm) cytosolic foci and play major roles in post- transcriptional processes like stalling the mRNA’s translation (stress granules), mRNA decay (P- bodies), and translational repression (P-bodies) (Balagopal & Parker, 2009). Additionally, P- bodies house the NMD described above and the post-transcriptional gene silencing also known

as RNA interference (RNAi). Both stress granules and P-bodies are formed dynamically and

mRNAs can shift between the three states (polysomes, stress granules, and P-bodies) depending on the cell’s physiological condition, resulting in an “mRNA cycle” (Eulalio et al., 2007;

Balagopal & Parker, 2009). When a transcript is no longer needed it is targeted for degradation and leaves the cycle.

Most mRNAs are degraded by “normal” decay mechanisms involving either endoribonucleases or exoribonucleases, specifically the 3′→5′ and 5′→3′ exoribonuclease. For both exoribonuclease-dependent decay processes, one of the two essential mRNA stability elements, the 5′ m7G cap or the 3′ poly(A) tail and their associated proteins, has to be removed.

The most common decay occurs through deadenylation and subsequent exonuclease activity.

Multiple eukaryotic deadenylases have been characterized so far; however, how and when deadenylation is initiated is still under investigation. Following deadenylation, the mRNA can be degraded from both ends. The 3′→5′ decay is performed by a multi-protein complex called exosome. Finally, the scavenger mRNA-decapping enzyme hydrolyses the remaining 5′-cap. For the 5′→3′ decay, the Lsm1-7 complex binds to the 3′ end instead of the exosome and initiates the

5′-decapping by the mRNA-decapping enzyme complex DCP1/DCP2. Once decapped, the mRNA is exposed to the 5′→3′ exoribonuclease Xrn1 and degraded. In contrast to exoribonuclease-mediated decay, mRNA decay by endoribonucleases is much more efficient and has also been described in the context of the NGD mentioned earlier (Gatfield & Izaurralde,

2004). It produces two mRNA fragments that can be targeted by both exoribonucleatic decay machineries described above.

All transcript decay processes involve the removal of at least one of the transcript’s stability elements. Besides the 5′ cap and 3′ poly(A) tail, AU-rich elements and their associated AU-rich binding proteins (AUBPs), Puf proteins (Goldstrohm et al., 2006), and stabilizing elements are

important factors facilitating mRNA decay in response to cellular signals and thus changing the

levels of gene expression.

Important regulatory signals of mRNA turnover are hormones. About 20 years ago, the

impact of estrogen on vitellogenin transcript levels in Xenopus liver was described to be more than 10,000 times. The researchers determined that this effect was due to a 30-fold increase in the stability of the mRNA (Brock & Shapiro, 1983). Though this hormone-gene combination is not important in energy metabolism, it raises the possibility of other hormone-mediated pathways that could act through mRNA stability to alter mitochondrial biogenesis. Recall that a key hormone in the regulation of energy metabolism is the thyroid hormone. It has been described in previous sections as affecting post-translational modifications of COX and the transcription of various COX subunits, but it also has post-translational effects on the mRNA stability of many genes. One target gene that would indirectly impact components of mitochondrial energy metabolism is the thyroid-stimulating hormone β (TSHβ). When T3 levels reach a certain threshold, TSHβ stability decreases and thus TSH protein levels decrease, leading to a drop in thyroid hormone secretion (Krane et al., 1991). Hence, the thyroid hormone can induce an increase of ATP production, and at the same time counteract this process by a negative feedback loop.

As mentioned at the beginning of this section, post-transcriptional modifications not only affect the amount of transcripts by degradation, but they can also regulate the timing of translation. The stalling of mRNA translation in stress granules, translational repression in P- bodies, and gene silencing, also known as RNA interference (RNAi), influence the coordination

of translation of mRNAs. The latter of the three mechanisms has received a great deal of

attention over the last 15 years. The mechanism of RNAi was first mentioned in 1984 as a genetic tool (Izant & Weintraub, 1984) and it took more than 10 years for the RNAi phenomenon

to be described as a regulatory mechanism in the nematode C. elegans (Fire et al., 1998) and in

plants (Hamilton & Baulcombe, 1999). The RNAi pathway is initiated either by microRNAs

(miRNA), or by small interfering RNAs (siRNA). Both are small (20 - 25 bp), non-coding RNA

molecules, but they differ in origin: miRNAs are encoded in the genome of the organism they

have an effect on (endogenous origin), whereas siRNAs are of exogenous origin and are taken up

by cells. Both are associated with proteins forming the RNA-induced silencing complex (RISC)

and therein bind complementary messenger RNA molecules in the cytosol. While the miRNAs

only pair imperfectly to their target mRNAs, siRNAs have almost perfect complementation to

the mRNA target sequences. These important differences lead to the distinct outcomes of the

RNAi process: miRNAs result in a hold in translation and the translocation of the mRNA to P-

bodies, while siRNAs lead to an increase in deadenylation and subsequent degradation. This

field of post-transcriptional control via miRNAs has lately been expanded to include

mitochondrial genes. Recent studies demonstrated that miRNAs are involved in the post-

transcriptional regulation of COX4 and ATP5G1 (a subunit of ATP synthase) mRNAs in rat

axons (Aschrafi et al., 2012) as well as the cytochrome c oxidase assembly protein (COX10)

mRNA in the human placenta (Colleoni et al., 2013). One study even described a nuclear

miRNA that is suggested to translocate into the mitochondria and target COX1 and COX2 in rat

hearts (Das et al., 2012).

1.4 Cytochrome c oxidase across the animal kingdom and why we are interested in fish

The ability to adjust energy production under changing physiological and environmental conditions is important to all organisms. Prokaryotes possess only two (or three in some cases)

COX subunits, but they can alter metabolism by switching between different respiratory chains and/or terminal oxidases. The evolution of additional (nuclear) COX subunits in eukaryotes enables them to adjust their COX activity in response to various environmental factors, including cellular oxygen levels. Many eukaryotic organisms can fine-tune COX specific activity and regulatory kinetics by altering subunit profiles, recruiting different isoforms of specific subunits.

Within vertebrates, an important regulatory mechanism is the ability to respond to energetic needs via the well described pathways of mitochondrial biogenesis. Best studied in mammals, mitochondrial content is altered in response to exercise (Holloszy, 1967), electrical stimulation

(Baar et al., 2002) and cold exposure (Puigserver et al., 1998; Wu et al., 1999). However, while in mammals cold exposure induces hypermetabolism, the opposite is true of fish: in fish, cold depresses metabolism, resulting in morphological, physiological, biochemical and genetic modifications (Shaklee et al., 1977; Johnston & Maitland, 1980; Egginton & Johnston, 1984;

Johnston & Wokoma, 1986; Orczewska et al., 2010). Mitochondrial proliferation, seen in many fish species during cold acclimation and winter acclimatization (Egginton & Sidell, 1989;

Guderley, 1990; Egginton et al., 2000), may be a compensatory mechanism to overcome the negative thermodynamic effects of cold on processes relying on enzymatic reactions (Figure 1.4;

Hazel & Prosser, 1974; Egginton & Sidell, 1989). 25

Protacanthopterygii Cyprinidae (carp and minnow)*† Cypriniformes Elopomorpha Catastomidae (sucker) Osteoglossomorpha Characiformes Ostariophysi Gymnoformes Clupeomorpha Siluriformes Ictaluridae (catfish) Salmoniformes*† Esocidae (pike)† Esociformes Gasterosteiodei (stickleback)* Umbridae (mudminnow) Stenopterygii Cyprinodontiformes (killifish) Ateleopodmorpha Tetraodontiformes Cyclosquamata Pleuronectiformes Scopelomorpha Dactylopteriformes Acanthomorpha Scorpaeniformes* Batrachoidiformes Neoteleostei Perciformes Zooarcidae (eelpout)* Acanthopterygii Euteleostei Lophiformes Gadiformes* Percopsiformes Tropical suborders Polymixiformes Percomorpha Lampridiformes Beryciformes Zeiformes Moronidae (white bass)* Stephanoberyciformes Percidae (perch,walley) Centrarchidae (sunfish)*†

Figure 1.4. Classification of select fish groups. Fish groups representing species that undergo mitochondrial compensatory modifications (*) and those that do not undergo mitochondrial modiﬁcaons (†) during cold-acclimation are indicated. Branch lengths are not scaled. Tree modified after Nelson (2006).

While many of the molecular mechanisms underlying mitochondrial biogenesis processes

are known in mammals, few studies have looked at the molecular side of regulatory pathways in

lower vertebrates such as fish. Studies on this topic are not only important to learn more about

these pathways in non-mammalian vertebrates, but they also give insight in to the evolution of

the pathway seen in mammals.

At the outset of my studies, it was argued that PGC-1α, the master regulator of

mitochondrial biogenesis in mammals, did not appear to exhibit the same key function in fish

(LeMoine et al., 2008). Interestingly, increases in mitochondrial enzyme activities and mRNA

levels during cold acclimation are complemented by decreases in PGC-1α mRNA in goldfish and 26

three-spined stickleback (LeMoine et al., 2008; Orczewska et al., 2010). It is currently unknown why PGC-1α mRNA declines under these conditions, but it appears that the protein in fish may not interact with NRF-1, an interaction that is critical for its role as a master regulator in mammals. The PGC-1α homologs of fish possess serine- and glutamine-rich insertions in the

NRF-1 binding domain of PGC-1α (LeMoine et al., 2010). These mutations might have caused a loss-of-function of this binding domain and thus interrupt PGC-1α’s important link to mitochondrial genes.

1.5 Questions

The peculiar cold-induced mitochondrial proliferation seen in skeletal muscle in fish is an intriguing tool to understand the regulatory control of mitochondrial biogenesis. When I first began this research, there were very few studies that explored the phylogenetic distribution of this response. Existing work suggested that a few taxa demonstrated mitochondrial biogenesis, while others, such as zebrafish (Danio rerio, Cyprinidae) or chain pickerel (Esox niger,

Esocidae) failed to respond in this manner (Figure 1.4).

Furthermore, while extensive work had been done to uncover the pathways of mitochondrial biogenesis in mammals, knowledge on fish was still scarce (Gracey et al., 2004; McClelland et al., 2006; LeMoine et al., 2008). Direct genetic manipulations, such as over-expression of NRF-

1, are not feasible in adult fish, and thus I used a multispecies survey as a tool to explore the correlations between transcription factors and mitochondrial enzymes. With this background, I began by asking the following questions in chapter 2 of my thesis: 27

(i) Does the phenomenon of mitochondrial biogenesis during cold-acclimation follow a

phylogenetic pattern in fish?

(ii) Can I verify the lack of importance of PGC-1α in fish, but see patterns similar to

mammals with respect to NRF-1 and NRF-2?

The answers to these questions suggested that goldfish (Carassius auratus) would be a

useful model to study the underlying regulatory pathways of mitochondrial biogenesis in fish.

Previous work has used transcript profiles to better understand why COX activities increase with

cold exposure (McClelland et al., 2006; LeMoine et al., 2008). In addition, only a few of PGC-

1α’s binding partners have been studied in the context of cold acclimation in fish. In chapter 3, I

conducted a comprehensive analysis of transcript and protein levels in goldfish acclimated to different temperatures to answer the following question:

(iii) Is there a stoichiometry between COX (both transcripts and enzyme) and critical

transcription factors (mRNA, total protein, and nuclear protein)?

I found that only NRF-1 paralleled COX activities at all levels (mRNA, protein, nuclear

protein), with other PGC-1 binding partners showing decreases at nuclear protein levels,

sometimes concurrently with increases in mRNA. In a series of parallel studies, it was noted that

transcript levels for COX genes did not change in parallel (Duggan et al., 2011), suggesting a

loss of the coordination that typifies mammalian studies focused on mitochondrial biogenesis.

One potential explanation is that gene expression (i.e. transcription) may be coordinated but that

post-transcriptional processes such as mRNA decay alter mRNA levels, leading to a loss of

stoichiometry. Thus, in my final chapter (Chapter 4) I assessed the pronounced importance of differential mRNA decay in the regulation of transcript levels by asking the following questions:

(iv) Do COX transcripts show different decay rates than housekeeping genes, implying that

changes in mRNA levels arose through mRNA decay rather than transcription?

(v) Does thermal acclimation affect the intrinsic rate of degradation for transcripts for

mitochondria genes?

Collectively, these studies help close gaps in the knowledge about cold-induced

mitochondrial biogenesis in fish. Furthermore, the integrative approach to understanding the

underlying mechanisms controlling mitochondrial biogenesis sheds light on the differences in

those pathways across taxa in a comparative way. This makes the studies not only of interest to

the field of fish physiology, but emphasizes their relevance to a variety of biological systems.

Chapter 2: Origins of variation in muscle cytochrome c oxidase activity within and between fish species

Bremer K & Moyes CD (2011). The Journal of Experimental Biology 214: 1888-1895.

2.1 Abstract

Mitochondrial content, central to aerobic metabolism, is thought to be controlled by a few transcriptional master regulators, including nuclear respiratory factor 1 (NRF-1), NRF-2 and peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α). Though well studied in mammals, the mechanisms by which these factors control mitochondrial content have been less studied in lower vertebrates. We evaluated the role of these transcriptional regulators in seasonal changes in white muscle cytochrome c oxidase (COX) activity in eight local fish species representing five families: Centrarchidae, Umbridae, Esocidae, Gasterosteidae and Cyprinidae.

Amongst centrarchids, COX activity was significantly higher in winter for pumpkinseed (2-fold) and black crappie (1.3-fold) but not bluegill or largemouth bass. In esociforms, winter COX activity was significantly higher in central mudminnow (3.5-fold) but not northern pike. COX activity was significantly higher in winter-acclimatized brook stickleback (2-fold) and northern redbelly dace (3-fold). Though mudminnow COX activity increased in winter, lab acclimation to winter temperatures did not alter COX activity, suggesting a role for non-thermal cues. When mRNA was measured for putative master regulators of mitochondria, there was little evidence for a uniform relationship between COX activity and any of NRF-1, NRF-2α or PGC-1α mRNA

levels Collectively, these studies argue against a simple temperature-dependent mitochondrial response ubiquitous in fish, and suggest that pathways, which control mitochondrial content in fish may differ in important ways from those of the better studied mammals.

2.2 Introduction

The ability to adjust mitochondrial oxidative capacity is critical to controlling energy production under changing physiological and environmental conditions. In mammals, mitochondrial content changes in response to factors such as exercise (Holloszy, 1967), electrical stimulation (Baar et al., 2002) and cold exposure (Puigserver et al., 1998; Wu et al., 1999). Fish respond in a similar manner to activity (e.g. Farrell et al., 1991), but they also induce mitochondrial proliferation in response to cold acclimation and winter acclimatization (Egginton

& Sidell, 1989; Guderley, 1990; Egginton et al., 2000). Unlike the situation in mammals, where cold exposure induces hypermetabolism, cold treatment of fish depresses metabolism, leading to a suite of morphological, physiological, biochemical and genetic modifications (Shaklee et al.,

1977; Johnston & Maitland, 1980; Egginton & Johnston, 1984; Johnston & Wokoma, 1986;

Orczewska et al., 2010). The underlying cause of cold-induced mitochondrial proliferation remains elusive but it is thought to reflect a degree of thermal compensation to overcome the negative thermodynamic effects on metabolism and metabolic enzymes (Hazel & Prosser, 1974;

Egginton & Sidell, 1989).

Regardless of the reason why fish remodel mitochondrial energy metabolism in the cold, the question of how such a change is achieved remains. Mitochondrial biogenesis is a complex 31

process requiring exquisite coordination of nuclear and mitochondrial genes. Each oxidative phosphorylation (OXPHOS) complex is composed of multiple subunits, many of which have paralogues. All mitochondrial OXPHOS complexes, except complex II, have subunits encoded by both nuclear genes and mitochondrial DNA (mtDNA). Transcriptional regulation plays a very important role in the control of mitochondrial biogenesis (Hock & Kralli, 2009). As described in recent reviews (Hock & Kralli, 2009; Scarpulla, 2011), central to this pathway are three transcription factors that have been identified as master regulators of mitochondrial biogenesis:

nuclear respiratory factor 1 (NRF-1), NRF-2 and peroxisome proliferator-activated receptor-γ

coactivator-1α (PGC-1α). The two NRFs are DNA-binding proteins that associate with elements

in the promoters of many genes that encode mitochondrial proteins. Once localized at the

promoter, they directly (NRF-1) or indirectly (NRF-2) bind PGC-1α, forming a complex that

recruits the general transcription machinery, activating transcription. The coactivator PGC-1α is

the most important member of the gene family including two homologues, PGC-1β and PGC-1-

related coactivator (PRC), which have overlapping roles with functional specialization (Hock &

Kralli, 2009; Scarpulla, 2011).

Each of these transcription factors that play a role in mammalian mitochondrial biogenesis is

also present in fish, but there is some uncertainty about their respective roles in lower

vertebrates. In a study on thermal acclimation of goldfish (Carassius auratus, Cyprinidae), cold

induced increases in COX activity, COX4 mRNA and NRF-1 mRNA, but PGC-1α mRNA was

decreased (LeMoine et al., 2008). Likewise, in three-spined stickleback (Gasterosteus aculeatus,

Gasterosteidae), cold acclimation induced increases in COX, COX4 mRNA and NRF-1 mRNA

but not PGC-1α mRNA (Orczewska et al., 2010). The lack of a uniform pattern of correlation

between PGC-1α and mitochondrial changes prompted the suggestion that the pathway of

regulation in fish may differ not just from that of mammals but also between species. The

diminished importance of PGC-1α may be due to mutations in the NRF-1 binding domain

(LeMoine et al., 2010) and it has been suggested that PGC-1β may be more important in the control of mitochondrial biogenesis in some fish (LeMoine et al., 2008).

In this study, our first goal was to investigate the patterns seen in the seasonal response of a broad range of ecologically diverse freshwater fish species. Secondly, we wanted to better understand the regulatory mechanisms that underlie the compensatory effects in mitochondrial

enzyme content in lower vertebrates compared with those in higher vertebrates.

2.3 Materials and methods

2.3.1 Fish

Eight local fish species were caught in Lake Opinicon and its nearby marshlands, about 50

km north of Kingston, ON, Canada. Lake Opinicon is a large (787 ha, or 7.87 km2), but relatively shallow (~50% is <5 m; maximum depth <11 m) and wind-exposed lake (Keast & Fox,

1992). Because of these physical conditions, it is classified as a polymictic lake (Agbeti & Smol,

1995), with water mixing in summer as well as spring and autumn, thereby reducing thermal variation regionally (Crowder et al., 1977; Keast & Fox, 1992). The four sunfish (Centrarchidae) representatives, pumpkinseed (Lepomis gibbosus, Linnaeus), bluegill (Lepomis macrochirus,

Rafinesque), black crappie (Pomoxis nigromaculatus, Lesueur) and largemouth bass

(Micropterus salmoides, Lacépède), as well as northern pike (Esox lucius, Linnaeus) were caught

by hook and line. Brook stickleback (Culaea inconstans, Kirtland), northern redbelly dace

(Chrosomus eos, Cope) and central mudminnow (Umbra limi, Kirtland) were caught in minnow traps in the marsh around the lake. No longer than 1 h after being caught, animals were

-1 -1 anaesthetized in buffered (0.8 g l NaHCO3) 0.4 g l tricaine methanesulphonate and killed by

cutting the spinal cord. White muscle samples were taken from the epaxial muscle below the

dorsal fin but above the lateral line and immediately frozen in liquid nitrogen. Fish were caught

in summer 2008 and 2009 and in winter/spring 2009/2010 (Figure 2.1).

Morphometric data including mass, length and condition factor of the fish are summarized in

Table 2.1. As these fish were sampled from their natural environment, experiencing the whole range of seasonally changing stimuli, it is not surprising that some of them showed significant differences in their condition factors between summer and winter.

28 24 20 16 12 8

Temperature [°C] [°C] Temperature 4 0

May July May July May March March March January January January SeptemberNovember SeptemberNovember 2008 2009 2010

Figure 2.1. Daily mean water temperatures at a depth of 3.3m in Lake Opinicon. Boxed areas indicate sampling periods (open, summer; filled, winter). 34

Table 2.1. Morphometric data for all seasonally acclimatized fish species and temperature acclimated central mudminnow. Masses (g), fork length (cm), and condition factor of seasonally acclimated species are given as averages for summer and winter fish (summer/winter). Mass [g] Fork length [cm] Condition factor* Seasonal acclimatization Brook stickleback 0.58±0.1/0.85±0.1† 3.91±0.1/4.33±0.1† 0.95±0.03/1.03±0.07 Northern redbelly dace 1.88±0.2/7.49±0.5† 5.73±0.2/8.61±0.2† 0.98±0.04/1.17±0.05† Pumpkinseed 171.34±5.2/111.21±8.4† 19.98±0.2/17.27±0.4† 2.15±0.02/2.14±0.05 Bluegill 123.07±3.8/87.77±6.5† 17.64±0.2/16.24±0.4† 2.24±0.05/2.03±0.04† Black crappie 193.52±34.2/180.46±20.1 22.48±1.2/21.83±0.7 1.57±0.06/1.66±0.06 Largemouth bass 392.08±95.4/629.60±80.0 30.29±3.7/33.85±1.5 1.39±0.11/1.53±0.06 Northern pike 594.84±83.4/690.88±91.3 47.38±2.3/47.69±2.8 0.54±0.01/0.62±0.02† Central mudminnow 5.15±0.8/3.69±0.8 7.68±0.4/6.72±0.6 1.07±0.03/1.02±0.04 Temperature acclimation (Central mudminnow) a 5.26±0.8a 8.10±0.3a 0.95±0.03a b 3.40±0.4a 7.15±0.2a 0.92±0.03a c 2.14±0.2b 6.47±0.2b 0.78±0.02a d 2.11±0.2b 6.66±0.2b 0.71±0.02b e 2.02±0.2b 6.41±0.3b 0.74±0.02b *After Ricker, 1975. †Significant differences between seasons. a - e represent sampling events during the experiment. For each parameter, data points sharing a superscript (a or b) are not significantly different from each other.

2.3.2 Acclimation experiment

Mudminnows, caught in the marsh area around Lake Opinicon during summer 2008, were held in a 723 l tank attached to a flowthrough system in the Animal Care facilities of Queen’s

University, Kingston. Water was supplied by the city system taken from Lake Ontario and filtered through a carbon filter prior to serving the fish tank. Fish were acclimated to the ambient water temperature of Lake Ontario for 4 weeks before the first sampling event. During the beginning of the experiment, water temperatures of Lake Ontario were the same as those in the

marshlands around Lake Opinicon, but decreased at a slightly lower rate than field water

temperatures throughout the experiment. Samples were taken every 27-42 days (Figure 2.2 A).

Acclimation temperatures were defined as the mean temperature over the 3 weeks prior to each

sampling event (Figure 2.2 A).

] 28 A) -1 2.0 B) 26 1.5 24 1.0 22 a 20 0.5 18 0.0 Relative COX [U g 16 b a b c d e c 14 12 10 e d Temperature [°C] Temperature 8 6 4 2 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 Time [d]

Figure 2.2. Water temperatures and COX activities at specific sampling points in central mudminnow. Results for relative COX activities (U g-1; +s.e.m) in white muscle are given for each sampling point, indicated by lower case letters (a-e).

2.3.3 Cytochrome c oxidase activity

For enzyme extraction, white muscle samples were powdered under liquid nitrogen. About

-1 50 mg was homogenized in 20 volumes of cold extraction buffer (25 mmol l K2HPO4, 1 mmol

l-1 EDTA, 0.6 mmol l-1 lauryl maltoside, pH 7.4) using a 7 ml Tenbroeck tissue grinder (Wheaton 36

Industries, Millville, NJ, USA). Homogenates were not centrifuged and were mixed prior to

enzyme measurements. Enzyme activity was determined spectrophotometrically (Molecular

Devices, Sunnyvale, CA, USA) at 550 nm and 25°C in 96-well plates (Corning, Corning, NY,

-1 -1 USA) using assay buffer (25 mmol l K2HPO4, 0.6 mmol l lauryl maltoside, pH 7.4) and

reduced cytochrome c (0.05 mmol l-1) as the substrate. Cytochrome c was reduced by ascorbic

-1 acid, then dialysed exhaustively in 25 mmol l K2HPO4 (pH 7.4) and frozen in aliquots. All samples were measured in triplicate.

2.3.4 RNA extraction and reverse transcription

RNA was isolated from powdered white muscle samples using a slight modification of the single-step method by guanidinium thiocyanate-phenol-chloroform extraction (Chomczynski &

Sacchi, 2006). The purified RNA pellet was dissolved in diethylpyrocarbonate-treated water and quantified at 260 nm prior to storage at -80°C. Additionally, absorption at 230, 270 and 280 nm was measured to test salt, phenol and protein contamination, respectively. Samples showed a

260:230 ratio of 1.86 ± 0.2, a 260:270 ratio of 1.43 ± 0.0, and a 260:280 ratio of 2.14 ± 0.1.

Reverse transcription of RNA and the removal of genomic DNA were carried out using the

QuantiTect Reverse Transcription Kit (Qiagen, Mississauga, ON, Canada) according to the manufacturer’s instructions.

2.3.5 Real-time PCR

All analyses were performed on an ABI 7500 Real Time PCR System (Foster City, CA,

USA) using the following protocol: 10 min at 95°C followed by 40 cycles of 15 s at 95°C, 15 s at

the annealing temperature (Table 2.2), 34 s at 72°C. The efficiency of each primer set for every

species was determined by real-time PCR with an appropriate dilution series of cDNA prior to

the sample analyses. Based upon these results, an appropriate cDNA concentration for each

reaction was chosen. Samples were then assayed in duplicate in a 25 μl total reaction volume

containing 5 μl cDNA [100 ng of NRF-1, NRF-2α, PGC-1α, TATA-binding protein (TBP) and

18S; 50 ng β-actin], 12.5 μl FastStart Universal SYBR Green Master mix (Roche Applied

Science, Penzberg, Bavaria, Germany), 3.5 μl double-distilled H2O and 2 μl each forward and

reverse primer (final concentration, 0.58 μmol l-1). Controls were run with water to ensure the

absence of contamination. Results were analysed according to the ∆Ct (cycle threshold) method

using TBP, β-actin and 18S ribosomal RNA as housekeeping genes with their calculated

geometric mean for each sample as standardized Ct (Pfaffl et al., 2004). Specific primers were designed to amplify single products of 91-175 bp in length (Table 2.2). Species-specific real- time PCR primers were constructed after larger amplicons were generated from consensus primers, cloned and sequenced.

Table 2.2. Forward and reverse real-time PCR primer sequences for target genes. Annealing Forward primer (5'-3') Reverse primer (5'-3') temperature (°C) NRF-1 PS,BG,LM,BC gcaccatctgactgcacagaata acctggatgagcgagactgtt 65 RD aaccgtagtgcagacgatca tggcaattctgacgcatctg 61 BS gggaaggagagctgcaagcc gacaccctctgtttctgctcctc 61 PI cgttgcggaccatcgttaagaact agtttgtgagggcacaaggtga 61 MM cgttgcggaccatcgttaagaact agcgatattctgtgcggtcagatg 61 NRF-2α PS,BG,LM,BC actctggagccatctggaactt aggctgatcaatggtgacggta 61,59 (BC) RD ttgaaggctaccgcaaagagca cataacccaaactgcccagtggat 61 BS acagattcagctctggcagttc tccggctggttgagtttgaa 61 PI ctctggagtcatctggagttgctt acctgtaggtcaatggtgactgtg 61 MM gaccagaagccacagtcacaat gctctgcttcagcaccttgatt 61 PGC-1α PS,BG,LM,BC ggacgtgaccaatgccagtga atagctgagttgggagtttgcgg 61,59 (LM) RD gacgtgaccaatgccagtgat atagctgagctgggagttagca 61 BS agtctccaaatgaccacaaggg gggttccagcaatctccaca 59 PI caatgccagtgaccagagctgt ttatagctgagctgggagtttgcg 61 MM cgtgaccaatgcaagtgaccaga gattgtagctgagctgggagttcg 61 TBP PS,BG,LM cacacatcaacagttcggcagcta aaccttggcacctgtgagtacaac 61,59 (LM) BC cctgcaaagttcctggacttca tcctggaaacagctctggttca 61 RD agtggtgcagaagttgggttttcc atgtgtaagcaccaggccctctaa 61 BS tcacggttagctgccaggaaatac ttcaagccgaatggggaacttcac 61 PI gagaatacgagagccaaggacaac tcctggaaacagctctggttca 61 MM tggtgtgtacaggagccaaaagtga agctgcccaccatgttctgaat 61 β- actin PS,BG,LM,BC,RD,BS tccaggctgtgctgtccctgta gtcaggatcttcatgaggtagtc 61,59 (LM,BC) PI, MM gacaggatgcagaaggagat acatctgctggaaggtggaca 61 18S* All species ggcggcgttattcccatgacc ggtggtgcccttccgtcaattc 61 *Data from Braun et al., 2009. PS, pumpkinseed; BG, bluegill; LM, largemouth bass; BC, black crappie; RD, northern redbelly dace; BS, brook stickleback; PI, northern pike; MM, central mudminnow. NRF, nuclear respiratory factor; PGC-1α, peroxisome proliferator-activated receptor-γ coactivator-1α; TBP, TATA-binding protein.

2.3.6 Statistics

All data are presented as means +s.e.m. for temperature comparison within one species and are plotted relative to summer values. The significance of differences between the two seasons for each species was tested by the non-parametric Mann-Whitney U-test for enzyme activity, relative mRNA levels of target genes and the morphometric data. The significance of differences between multiple groups in the acclimation experiment was tested by the Kruskal-Wallis test.

Results were tested at a significance level of 95%.

2.4 Results

2.4.1 COX activity

White muscle COX activity was not affected by season in three out of eight species: bluegill,

bass and pike. For simplicity, we term these species non-compensators. The remaining five

species (pumpkinseed, crappie, dace, stickleback and mudminnow) showed significant increases

(1.3- to 3.5-fold) in white muscle COX activity in winter (Figure 2.3). These species were

denoted as compensators.

There was no phylogenetic pattern that distinguished compensators and non-compensators.

Within centrarchids, pumpkinseed and crappie compensated, whereas bluegill and bass did not.

Within Esociformes, mudminnow compensated but pike did not show thermal compensation.

Mudminnows were held at seasonal temperatures from autumn through to winter. Mean

temperatures dropped by 14°C, from 19°C at the beginning to 5°C at the end of the experiment

(Figure 2.2 A). In contrast to acclimatized fish, the lab-acclimated fish showed no significant changes in COX activity (Figure 2.2 B).

4.0 summer/warm† * † ] ] 3.5 winter/cold -1 * 3.0 2.5 * * * 2.0 1.5 * 1.0

Relative COX [U g [U COX Relative 0.5 0.0 Cyprinidae Gasterosteidae Centrarchidae Esocidae Umbridae Esociformes

†

Bluegill Goldfish Pumpkinseed Black crappie Northern pike Brook stickleback Largemouth bass Central mudminnow Northern redbelly dace

Figure 2.3. Relative COX activities in white muscle of fish collected in summer and winter. Sample sizes for summer/winter were as follows: northern redbelly dace, 7/8; brook stickleback, 8/8; pumpkinseed, 8/7; bluegill, 8/7; black crappie, 13/10; largemouth bass, 7/10; northern pike, 8/8; and central mudminnow, 8/10. Results are expressed relative to summer/warm values and are plotted as means +s.e.m. Mean (+s.e.m.) enzyme activity (U g-1) for summer/winter was as follows: northern redbelly dace, 4.6 (0.3)/13.4 (1.0); brook stickleback, 2.6 (0.3)/5.2 (0.5); pumpkinseed, 1.4 (0.2)/2.5 (0.3); bluegill, 3.0 (1.0)/3.1 (0.4); black crappie, 4.5 (0.5)/5.8 (0.3); largemouth bass, 4.1 (0.8)/4.6 (0.4); northern pike, 1.4 (0.1)/2.1 (0.4); and central mudminnow, 4.0 (0.6)/13.5 (1.6). Significant differences (p ≤ 0.05) between seasons are indicated by an asterisk. †Goldﬁsh data for 4 and 20°C were obtained from previous studies (LeMoine et al., 2008).

2.4.2 Transcription factors and coactivator mRNA levels

The responses of each species in terms of transcription factor mRNA levels are depicted in

Figure 2.4. A previous study on goldfish acclimated to 5 and 20°C (LeMoine et al., 2008) found that cold acclimation was accompanied by an increase in COX activity and NRF-1 mRNA but a

decrease in PGC-1α mRNA (Figure 2.4 A). In extending this approach to eight other species,

there was no clear relationship between COX changes and the patterns seen in NRF-1, NRF-2α and PGC-1α mRNA levels. Five species showed thermal compensation in COX activity: dace, stickleback, pumpkinseed, crappie and mudminnow.

Among the compensators, NRF-1 mRNA increased in pumpkinseed (3-fold) and crappie (5- fold) by a factor that was much greater than the increase seen in COX activity. In contrast, no change was seen in NRF-1 mRNA in dace, stickleback or mudminnow (Figure 2.4), each of which showed a greater change in COX activity than did pumpkinseed and crappie. The level of mRNA for NRF-2α, a subunit of NRF2, did not change in any compensator, except for crappie, which showed a 2-fold increase in the cold (Figure 2.4).

As with goldfish, PGC-1α mRNA decreased significantly in three compensators: stickleback

(3-fold), pumpkinseed (10-fold) and mudminnow (2-fold). There was no significant change in

PGC-1α mRNA in crappie. In contrast to these species, dace PGC-1α mRNA increased significantly (7.5-fold) in winter fish (Figure 2.4).

4.0 10.0 4.0 A) summer/warm† Goldfish† B) Northern * C) Brook stickleback winter/cold† 8.0 redbelly dace 3.0 3.0 * * 6.0 * 2.0 2.0 4.0 * * * 1.0 1.0 n/a 2.0

0.0 0.0 0.0 6.0 D) Pumpkinseed 8.0 E) Black crappie 6.0 F) Central mudminnow 5.0 * 5.0 6.0 4.0 * 4.0 *

3.0 4.0 3.0 * 2.0 * * 2.0 Relative levels levels Relative 2.0 * * 1.0 1.0

0.0 0.0 0.0 6.0 12.0 14.0 G) * Bluegill H) * Largemouth I) Northern pike 5.0 10.0 bass 12.0

4.0 8.0 10.0 8.0 3.0 6.0 6.0 2.0 4.0 * 4.0 1.0 2.0 2.0

0.0 0.0 0.0 COX NRF-1 NRF-2α PGC-1α COX NRF-1 NRF-2α PGC-1α COX NRF-1 NRF-2α PGC-1α

Figure 2.4. Relative COX activities and mRNA levels of transcription factors in white muscle of fish collected in summer and winter. Sample sizes for summer/winter were as follows: northern redbelly dace, 7/7; brook stickleback, 5/7; pumpkinseed, 7/6; black crappie, 7/6; central mudminnow, 8/6; bluegill, 7/7; largemouth bass, 6/7; northern pike, 7/7. Results are expressed relative to summer/warm values and are plotted as means +s.e.m. Significant differences (p ≤ 0.05) between seasons are indicated by an asterisk. n.a., not available. †Goldﬁsh data for 4 and 20°C were obtained from previous studies (LeMoine et al., 2008).

As noted above, there were three species of fish that failed to show a seasonal response in

COX activity (Figure 2.3). In these species, NRF-1 mRNA did not change in pike, but significantly increased in bluegill (5-fold) and bass (9-fold). NRF-2α mRNA increased in bass

(3-fold) but did not change in bluegill or pike. PGC-1α mRNA did not change in any of these species between seasons (Figure 2.4).

2.5 Discussion

Low body temperatures lead to pleiotropic effects on metabolism and physiology, and long- term responses to temperature result in many physiological and morphological changes in ectotherms. A cold-induced increase in mitochondrial content, whether expressed in terms of morphometry (e.g. volume density, cristae density) or marker enzyme activity (e.g. COX, citrate synthase), probably corrects shortfalls in ATP production in the cold (Guderley, 2004). In mammals, the regulation of mitochondrial biogenesis is attributed to a set of master regulators of transcription that coordinate the expression of genes encoding mitochondrial proteins (Hock &

Kralli, 2009). However, a survey of the literature on fish models suggests that the process in fish may differ from that seen in mammals (LeMoine et al., 2008). In this study, we focused on the expression patterns seen in white muscle mitochondria of a variety of species found regionally, exploring the underlying control of these patterns.

2.5.1 Species-specific seasonal thermal response in enzyme activity

Cold-induced changes in mitochondrial markers, first shown in goldfish (Freed, 1965), have since been shown in many other ectotherms - crustaceans, amphibians, reptiles and annelids

(Bullock, 1955; Heinrich, 1977; Sommer & Pörtner, 2004). In contrast, some temperate species fail to change mitochondrial enzyme content in relation to temperature or season. The reasons for the different responses among studies and species are not clear, nor are the mechanisms by which the changes arise.

The variation in response among species does not appear to have a simple taxonomic basis.

As in a previous study on centrarchids (Tschantz et al., 2002), we saw increases in COX activity

in some species (pumpkinseed, 2-fold; crappie, 1.3-fold) but not others (bluegill, bass). We

found a striking difference in the response of two esociforms: northern pike and mudminnow.

The lack of response in pike is similar to the situation in chain pickerel (Esox niger), which show

a very modest response to cold acclimation (20% increase in COX activity) and no response to

winter acclimatization (Kleckner & Sidell, 1985). The closest relatives to Esociformes are the

Salmoniformes, which typically experience only a mild response to the cold, generally less than

a 75% increase in COX activity (Blier & Guderley, 1988; Guderley & Gawlicka, 1992; Guderley

et al., 1997; Battersby & Moyes, 1998; Egginton et al., 2000). Thus, the dramatic response of

mudminnows is atypical within this broader group.

Other taxonomic groups appear to have a more uniform response to the cold. Sticklebacks

(Gasterosteidae) all appear to show a pronounced increase in mitochondrial enzyme activities in

the cold: brook stickleback (Figure 2.3), three-spine stickleback (Vézina & Guderley, 1991;

Orczewska et al., 2010) and nine-spine stickleback (Guderley & Foley, 1990). Cyprinids (carp

and true minnows) also show increases in muscle mitochondrial enzyme activities: dace (Figure

2.3), goldfish (Freed, 1965; LeMoine et al., 2008), roach (Rutilus rutilus) and tench (Tinca tinca)

(Heap et al., 1985). The lack of a thermal response seen in tropical zebrafish (McClelland et al.,

2006; Duggan et al., 2011) is perhaps not surprising as the lower lethal temperature (7°C) for

this species (Lawrence, 2007) lies above the temperature at which thermal compensation is usually seen (< 5°C).

Collectively, these studies suggest that there are few phylogenetic patterns that could

indicate which species change their muscle mitochondrial enzymes with temperature.

Differences are seen in species that are closely related and even show similar locomotor patterns.

Both mudminnow and pike are ambush predators whereas both bluegill and pumpkinseed swim

in a labriform mode, yet these species pairs differ in their responses to season.

2.5.2 Acclimation versus acclimatization

One challenge in reconciling our results with those from previous studies is related to the

nature of the thermal challenge and confounding effects of other seasonal factors that accompany

the cold. Independent studies on the responses of rainbow trout (Oncorhynchus mykiss) to acclimatization and acclimation show a similar compensatory response in muscle oxidative capacity (Guderley & Gawlicka, 1992; Thibault et al., 1997; Battersby & Moyes, 1998; Egginton et al., 2000). However, there are studies where different responses in acclimation versus acclimatization have been found. For example, we found winter acclimatization to increase muscle mitochondrial enzyme activity in black crappie, whereas thermal acclimation had no effect in another study (Tschantz et al., 2002).

The disparity between acclimation and acclimatization is most compelling when the same researchers compared the two treatments. For example, chain pickerel (Kleckner & Sidell, 1985) and three-spine stickleback (Vézina & Guderley, 1991) have elevated mitochondrial enzyme activity after cold acclimation but the differences diminish when fish are winter acclimatized.

We found a marked difference in the response of mudminnow to lab acclimation versus field acclimatization. In lab fish, we saw no changes in muscle COX activity even after 35 days at 5°C 46

(Figure 2.2 B). However, mudminnows captured from the field in early April showed a 3-fold

increase in muscle COX activity relative to that of summer fish. Thus, it appears unlikely that the

seasonal effects we see in this species are due to simple effects of temperature.

Apart from temperature, the metabolic phenotype could be affected by many of the factors

that differ between seasons. Photoperiod can induce modifications in mitochondria in both

Atlantic cod (Pelletier et al., 1993) and rainbow trout (Martin et al., 2009). Oxygen is well

known to affect the glycolytic phenotype (Jørgensen & Mustafa, 1980; Lushchak et al., 1998;

Chippari-Gomes et al., 2005), though its effect on mitochondrial enzymes is still debated

(Hoppeler et al., 2003; Klimova & Chandel, 2008). Diet can have complex effects on

metabolism, though the effects are most commonly seen in liver (de la Higuera et al., 1999;

Hemre et al., 2002; Menoyo et al., 2004; LeMoine et al., 2008). Activity levels change during the season, and these may induce the equivalent of a training response. The significant decrease in the condition factor of the lab-acclimated fish (Table 2.1) did not have an impact on enzyme activity, nor was the significant increase in enzyme activity in the winter-acclimatized fish mirrored in the condition factors. Reproductive status may also affect muscle mitochondria, through hormone levels or locomotor behaviours, including migrations, mating, spawning activity and territory defence. In our study, stickleback, mudminnow and dace each showed an increase in muscle COX activity. These species begin spawning activity in spring (Abbott, 1870;

Peckham & Dineen, 1957; Reisman & Cade, 1967), which includes spawning migrations, nest building and guarding as well as active mating behaviour. A combination of factors triggers spawning in fish, primarily temperature and photoperiod (Lam, 1983). Changes in the latter

could play an important role in our observations of mudminnow and dace. Spawning itself induces mitochondrial modifications in rainbow trout (Kiesslingl et al., 1995).

In summary, this study and others demonstrate a wide range of patterns in the response of

muscle aerobic enzymes to thermal history. We have shown evidence that one source of

variation is a distinction between thermal acclimation and seasonal acclimatization. Nonetheless,

even closely related species with very similar acclimatization histories can have divergent

patterns of metabolic remodelling.

2.5.3 Control of mitochondrial oxidative capacity

Whether or not a species changes muscle mitochondrial content, the levels of mitochondria

are controlled by genetic mechanisms affecting the transcription of mitochondrial genes,

including those encoding proteins of mtDNA replication and transcription, and protein import

and assembly (Hock & Kralli, 2009). In mammals, the cascade from transcriptional master

regulators to mitochondrial gene expression and mitochondrial biogenesis is well understood

(Hock & Kralli, 2009; Scarpulla, 2011). The role of a master regulator of mitochondrial

biogenesis was first ascribed to NRF-1 (Evans & Scarpulla, 1990); however, more recent studies

support a greater role for coactivator PGC-1α (Scarpulla, 2011). Although a few studies have

assessed the potential role of these factors in adaptive remodelling in fish, the extent of the

parallel with mammals is not yet clear. In particular, there is little correlation between COX

activity and PGC-1α mRNA level in different muscles, dietary states, activity levels and

temperature treatments (McClelland et al., 2006; LeMoine et al., 2008; Orczewska et al., 2010).

This prompted the suggestion that the PGC-1α paralogue PGC-1β may be the major coactivator 48

for mitochondrial gene expression control in fish (LeMoine et al., 2008). Though the role for

PGC-1 paralogues may differ in fish versus mammals, these studies have found stronger support for a role for NRF-1 as master regulator.

One goal of our study was to broaden the species comparisons to better clarify the potential role of PGC-1α in controlling mitochondrial changes with temperature and/or season. Consistent with previous studies on goldfish (LeMoine et al., 2008) and stickleback (Orczewska et al.,

2010), we found in four species a cold-induced increase in COX activity but no increase in PGC-

1α mRNA level; three of these species (brook stickleback, pumpkinseed and mudminnow) showed a marked decrease in PGC-1α mRNA level. Only in dace was an increase in COX activity (3-fold) accompanied by an increase in PGC-1α mRNA level (7-fold), which is what is expected from mammalian models (Scarpulla, 2011). The large variation in PGC-1α mRNA between species could be related to its role in other cellular processes, including fatty acid oxidation (Vega et al., 2000) and muscle fibre specification (Lin et al., 2002b). Alternatively, changes in PGC-1α activity may be related to the coactivation of other DNA-binding proteins, including thyroid hormone receptors, peroxisome proliferator-activated receptors (PPARs), retinoic acid receptors and estrogen-related receptors (Puigserver & Spiegelman, 2003; Schreiber et al., 2003). Thus, even if PGC-1α plays a role in mitochondrial biogenesis in response to temperature, its regulation could be complicated by simultaneous changes in diet, fuel selection, activity level and reproduction (Dean, 1969; Miranda & Pugh, 1997; Suski & Ridgway, 2009).

Nonetheless, there is little evidence from fish studies to support a role of PGC-1α in determining changes in COX activity with temperature.

Mammalian studies have shown NRF-1 to be an important regulator of all nuclear-encoded

COX genes (Dhar et al., 2008). It acts by binding to NRF-1 promoter elements (Virbasius et al.,

1993), where it recruits PGC-1α (Scarpulla, 2011) The interplay between NRF-1 and PGC-1α is an important regulatory axis in mammalian muscle. Furthermore, transcriptional regulation of

NRF-1 appears central to the control of its activity (Hock & Kralli, 2009). Previous studies on fish have shown that increases in NRF-1 mRNA accompany increases in mitochondrial enzyme activities seen with diet, temperature and exercise (McClelland et al., 2006; LeMoine et al.,

2008; Orczewska et al., 2010). In this study, pumpkinseed and crappie both showed a significant increase in COX activity and NRF-1 mRNA level. However, the pattern was not consistent across species. Dace, stickleback and mudminnow each achieved an increase in COX activity without a corresponding increase in NRF-1 mRNA. Furthermore, two non-compensators

(bluegill and bass) showed significant increases in NRF-1 mRNA level with no change in COX activity. Despite this variation, NRF-1 appears to be the most likely candidate for a role as master regulator of mitochondrial biogenesis in fish. In mammals, post-transcriptional regulation of NRF-1 can play a role in determining its transcriptional activity (Hock & Kralli, 2009), so perhaps this level of regulation is more important in fish.

Like NRF-1, NRF-2 has an important role in the regulation of all nuclear-encoded COX genes in mammals (Ongwijitwat et al., 2006). Its regulation is more complex because the active transcription factor is a heterodimer of NRF-2α and either NRF-2β or NRF-2γ (Gugneja et al.,

1995). There is also clear evidence for post-transcriptional regulation of NRF-2 (Vallejo et al.,

2000), making measurement of mRNA less informative. Furthermore, apart from its role in the

control of mitochondrial genes, it regulates diverse cellular processes (Imaki et al., 2003;

Ristevski et al., 2004; Yang et al., 2007). The role of NRF-2 has been less studied in fish but

there is little evidence from our study of a role in the thermal remodelling of muscle. Only two

species showed seasonal changes in NRF-2α mRNA; its level increased in crappie, paralleling

changes in COX activity, and bass, which showed no change in COX activity. The present study

has shown that there is no phylogenetic pattern in the response of COX content in muscle to cold

acclimation and/or seasonal acclimatization (Table 2.3). Changes seen seasonally in free-living fish may be confounded by non-thermal cues, such as reproductive status.

Furthermore, the role of transcriptional master regulators of mitochondrial biogenesis appears to be more complex (Table 2.3) than is expected from mammalian studies. The transcriptional factors themselves may act in fundamentally different ways, and there is a potential role for post-transcriptional regulation.

Table 2.3. Trends in COX activities and transcription factor mRNA levels in target species relative to summer/warm values. Species COX NRF-1 NRF-2α PGC-1α GF ↑ ↑ ↔ ↓ RD ↑ ↔ n.a. ↑ PS ↑ ↑ ↔ ↓ BC ↑ ↑ ↑ ↔ BS ↑ ↔ ↔ ↓ MM ↑ ↔ ↔ ↓ BG ↔ ↑ ↔ ↔ LM ↔ ↑ ↑ ↔ PI ↔ ↔ ↔ ↔ Goldfish data obtained from previous studies LeMoine et al., 2008. GF, goldfish; RD, northern redbelly dace; PS, pumpkinseed; BC, black crappie; BS, brook stickleback; MM, central mudminnow; BG, bluegill; LM, largemouth bass; PI, northern pike. NRF, nuclear respiratory factor; PGC-1α, peroxisome proliferator-activated receptor-γ coactivator-1α.

2.6 Acknowledgements

This study was funded by a Discovery Grant (C.D.M.) of the Natural Sciences and

Engineering Research Council (NSERC) Canada. We would like to thank the staff of the

Queen’s University Biological Station for their support of these studies; in particular, Frank

Phelan and Bruce Tufts for their fishing expertise as well as students Sarah Wyness, Katrinka

Kocha, Trevor Snider, Christopher Monk, Paul Debes and Scott Taylor for their help in the field.

Chapter 3: Transcriptional regulation of temperature-induced

remodeling of muscle bioenergetics in goldfish

Bremer K, Monk CT, Gurd BJ & Moyes CD (2012). American Journal of Physiology: Regulatory,

Integrative and Comparative Physiology 303: R150-R158.

3.1 Abstract

Central to mammalian mitochondrial biogenesis is the transcriptional master regulator

peroxisome proliferator-activated receptor (PPAR)-γ coactivator-1α (PGC-1α), and a network of

DNA-binding proteins it coactivates. We explored the role of this pathway in muscle mitochondrial biogenesis in response to thermal acclimation in goldfish (Carassius auratus). We investigated the transcriptional response of PGC-1α, PGC-1β, and their antagonist the nuclear receptor interacting protein 1 (RIP140), as well as the mRNA and protein patterns of DNA- binding proteins that bind PGC-1, including nuclear respiratory factors (NRF) 1 and 2, retinoid X receptor α (RXRα), estrogen-related receptor α (ERRα), thyroid receptor α-1 (TRα-1), PPARα,

and PPARβ/δ, and the host cell factor 1 (HCF1), which links PGC-1 and NRF-2. Cold-

acclimated (4°C) fish had higher COX activities (4.5-fold) and COX4-1 mRNA levels (3.5-fold

per total RNA; 6.5-fold per gram tissue) than warm-acclimated (32°C) fish. The transcription

factor patterns were profoundly influenced by changes in RNA per gram tissue (2-fold higher in

cold fish) and nuclear protein content (2-fold higher in warm fish). In cold-acclimated fish,

mRNA per gram tissue was elevated for PGC-1β, RIP140, NRF-1, HCF1, NRF-2α, NRF-2β-2,

ERRα, PPAR β/δ, and RXRα, but other transcriptional regulators either did not change (PGC-1α,

PPARα) or even decreased (TRα-1). Nuclear protein levels in cold-acclimated fish were higher

only for NRF-1; other proteins were either unaffected (NRF-2α, ERRα) or decreased (NRF-

2β1/2, TRα, RXRα). Collectively, these data support the role for NRF-1 in regulating cold-

induced mitochondrial biogenesis in goldfish, with effects mediated by PGC-1β, rather than

PGC-1α.

3.2 Introduction

Animals regulate mitochondrial content to ensure that oxidative phosphorylation meets ATP

demands. When demands change as a result of physiological, developmental, or environmental

factors, tissues can alter mitochondrial content. In mammals, mitochondrial proliferation is

triggered by exercise, electrical stimulation, cold exposure, and dietary restrictions (Holloszy,

1967; Puigserver et al., 1998; Wu et al., 1999; Baar et al., 2002). The regulatory cascade that

changes mitochondrial content includes secondary messengers (e.g., cAMP and Ca2+) and bioenergetic intermediates (e.g., AMP and NAD+), which are in turn sensed by a suite of specific proteins considered energy sensors: Ca2+/calmodulin-dependent protein kinase IV (CaMKIV),

NAD+-dependent deacetylase sirtuin-1 (SIRT1), AMP-activated protein kinase (AMPK), and

p38 mitogen-activated protein kinase (p38 MAPK) (Hock & Kralli, 2009). These sensors

modulate the transcriptional regulators that affect the expression of genes encoding mitochondrial proteins. Central to this cascade is peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α), considered to be a master regulator of mitochondrial biogenesis as a 54

result of its ability to coactivate many of the transcription factors that regulate mitochondrial genes (Fernandez-Marcos & Auwerx, 2011). PGC-1α and its paralog PGC-1β localize to target genes by associating with DNA-binding proteins, interactions that can be antagonized by nuclear receptor interacting protein 1 (RIP140) (Christian et al., 2006; Hallberg et al., 2008).

Transcription factors that bind PGC-1α directly or indirectly include nuclear respiratory factors

(NRF) 1 and 2, host cell factor 1 (HCF1), retinoid X receptor α (RXRα), estrogen-related receptor α (ERRα), thyroid hormone receptor α-1 (TRα-1), and peroxisome proliferator-activated receptors (PPARs). In mammals, PGC-1α and PGC-1β have overlapping responsibilities, binding many of the same transcription factors (Lin et al., 2002a). Each of these factors has been implicated in the control of mitochondrial gene expression (Finck & Kelly, 2006; Hock & Kralli,

2009), and this PGC-1 network is responsible for modifying mitochondrial content in response to diverse physiological and developmental cues.

Though all vertebrates possess a PGC-1α gene, its role appears to be different in fish

(LeMoine et al., 2010). In contrast to mammals, there is little evidence that PGC-1α participates in control of mitochondrial proliferation (LeMoine et al., 2008, 2010). Previous studies have shown that during cold acclimation, increases in mitochondrial enzyme activities and mRNA levels are typically accompanied by decreases in PGC-1α mRNA (LeMoine et al., 2008;

Orczewska et al., 2010; Bremer & Moyes, 2011). The paradoxical decreases may be related to loss-of-function mutations in the NRF-1 binding domain of PGC-1α, which has experienced an accelerated rate of evolution and multiple serine- and glutamine-rich insertions (LeMoine et al.,

2010). In contrast to the cold-induced decreases in PGC-1α mRNA, PGC-1β transcript levels

increase in cold-acclimated fish, suggesting that there might be a switch in the key regulator of mitochondrial biogenesis in fish (LeMoine et al., 2008).

In addition to the uncertainty about the role of the PGC-1 coactivators in fish, few of the binding partners have been studied in the context of cold acclimation. NRF-1 mRNA tends to increase under conditions where increases occur in cytochrome c oxidase (COX) activity and/or

COX subunit mRNA levels (LeMoine et al., 2008; Orczewska et al., 2010; Bremer & Moyes,

2011). However, it is not known whether in fish NRF-1 mRNA levels reflect changes in NRF-1 nuclear content. Likewise, apart from PPARs (McClelland, 2004) few studies have explored in fish the transcript or protein levels of other PGC-1 binding partners that have been shown to influence mammalian mitochondrial gene expression, including NRF-2, HCF1, RXRα, ERRα, and TRα-1.

In this study, we expand the analysis of muscle remodeling in fish to include the full breadth of transcription factors and regulatory proteins that intersect the PGC-1 axis. By using an integrative approach (enzyme activities, transcript levels, and protein levels), we identify what transcription factors play a dominant role in the control of muscle mitochondrial biogenesis in fish by taking changes in RNA and protein into account.

3.3 Materials and methods

3.3.1 Fish and experimental setup

Goldfish (Carassius auratus) were obtained from a wholesaler to the pet trade (Aleong’s

International, Mississauga, Canada) and kept in a 750-liter round (diameter: 132 cm; height: 75 cm), blue, plastic tank set up as a flow-through system in the animal care aquatic facility at

Queen’s University, Kingston, ON. The experiment was approved by the Queen’s University

Animal Care Committee (protocol no. 100143). The fish were fed commercial pellets (Wardley brand Premium Goldfish Medium) ad libitum and maintained at a 12-h:12-h light:dark photoperiod at 20°C for 6 wk before the experiment. Goldfish were then randomly separated into

750-liter flow-through tanks in two treatments of 13 individuals and maintained at the same feeding routine and photoperiod. The water temperatures were then changed at a rate of 1°C/day until the acclimation temperatures were reached (32 ± 2°C and 4 ± 1°C). For the warm treatment the final temperature was achieved by changing the cold (~13°C) and warm (~35°C) water inflow. For the cold treatment, a chiller (Frigid Units, Toledo, OH) was used along with sparse cold (~13°C) water inflow to maintain a flow-through system. After final temperatures were reached, an additional 33-day acclimation period passed before the fish were euthanized in a 2- liter solution of 0.4 g/l tricaine methane sulfonate (MS-222, Syndel Laboratories, Qualicum

Beach, Canada) and 0.8 g/l NaHCO3. Morphometric data, including masses and fork lengths of the fish, were taken before sampling to calculate Fulton condition factors (K = W/L3) where K is

Fulton’s condition factor, W is the weight of the fish in grams, and L is the length in centimeters

(here fork length) (Ricker, 1975). For body comparisons between acclimation groups, the 57

Bonferroni-corrected significance level p ≤ 0.0167 (0.05/3) was used since all three are

correlates of body metrics. Fish did not differ (U = 21, p = 0.028) in masses between the two acclimation groups (42.1 + 13.5 g for the warm treatment and 60.2 + 15.8 g for the cold treatment), whereas individuals from the warm treatment were slightly smaller (U = 18, p =

0.016) in fork length (11.4 + 1.0 cm) than those of the cold acclimated group (13.2 + 1.0 cm).

However, this had no significant impact (t = -1.76, p = 0.096) on the condition factors between

the warm (K = 0.0273 + 0.002) and cold acclimation group (K = 0.0247 + 0.002). White muscle

was then immediately dissected from the epaxial muscle below the dorsal fin, but above the lateral line, flash frozen in liquid nitrogen and stored at -80°C.

3.3.2 COX activities

For the COX extraction, white muscle samples were powdered under liquid nitrogen. Tissue

(40-70 mg) was homogenized in 20 vol of cold extraction buffer (25 mM K2HPO4, 1 mM EDTA,

0.6 mM lauryl maltoside, pH 7.4) using a 7-ml Tenbroeck tissue grinder (Wheaton Industries,

Millville, NJ). Homogenates were not centrifuged and were mixed by inverting before enzyme

measurements. The enzyme activity was determined spectrophotometrically (Molecular Devices,

Sunnyvale, CA) at 550 nm and 25°C on 96-well plates (Corning, Corning, NY) using an assay

buffer (25 mM K2HPO4, 0.6 mM lauryl maltoside, pH 7.4) and reduced cytochrome c (0.05 mM) as substrate and an extinction coefficient of 28.5 mM-1 cm-1. Cytochrome c was reduced by

adding the equivalent molar weight of ascorbic acid to oxidized cytochrome c; then, reduced

cytochrome c was dialyzed exhaustively in 25 mM K2HPO4 (pH 7.4) and frozen in aliquots at -

80°C. Reactions were run as follows: 2-5 μl of homogenate were added to the microplate 58

warmed to 25°C. Prewarmed assay mix (250 μl) was added to each well, and reactions were

followed for 2 min. Slopes were taken over a time period during which cytochrome c concentration declined by less than 10%, typically over the first 60-90 s. All 10 samples per acclimation group were measured in triplicate.

3.3.3 RNA analyses

RNA was isolated from a known amount of powdered white muscle using a slight modification of the single-step method by guanidinium thiocyanate-phenol-chloroform extraction (Chomczynski & Sacchi, 2006). The purified RNA pellet was dissolved in diethylpyrocarbonate (DEPC)-treated water and quantified spectrophotometrically at 260 nm

before storage at 80°C. The total RNA per gram tissue was estimated from the amount of white

muscle starting material and the RNA recovery. This enabled us to express the relative amounts of mRNA per gram tissue, permitting direct comparison between other parameters (e.g., COX activity/g wet tissue). Additionally, absorbance was measured at 270 and 280 nm to assess phenol and protein contaminations, respectively. Samples had an average 260:270 ratio of 1.42

(SD 0.03) and an average 260:280 ratio of 2.04 (SD 0.06). The removal of genomic DNA and reverse transcription of RNA were carried out by using the QuantiTect Reverse Transcription Kit

(Qiagen, Mississauga, Canada) according to the manufacturer’s instructions (using 1 μg

RNA/reaction).

Transcript levels were determined on an ABI 7500 Real Time PCR System (Foster City,

CA) using the following protocol: 1 cycle of 10 min at 95°C followed by 40 cycles of 15 s at

95°C, 15 s at the primer set specific annealing temperature (Table 3.1), and 34 s at 72°C as 59

elongation stage. The efficiency of each primer set was determined in real-time PCR with an appropriate dilution series of cDNA before the sample analyses.

Table 3.1. List of target gene-specific forward and reverse real-time primers and their specific annealing temperatures. Annealing Gene Forward primer (5'-3') Reverse primer (5'-3') temperature Source* (°C) COX4-1 agggatcctggctgcact tcaaaggtatgggggacatc 59 1 PGC-1α gacgtgaccaatgccagtgat atagctgagctgggagttagca 61 EU426842 PGC-1β ggatgctgacttccctgactttga tgtatggaggcaggggagtgat 63 EU426839 RIP140 acaacggtacctcccaggataaa gcctcaaaccttggataatctgc 67 JQ421026 NRF-1 aggccctgaggactatcgtt gctccagtgccaacctgtat 59 1 NRF-2α tggaggtgggtggcattca cctgatcctgactcgcaagtacat 67 JQ421022 NRF-2β-2 ggaacatctccattacatttagcagc gccgtcatcttcagcatgtctttg 67 JQ421023 ERRα accagatgtccgtgctgcagt cctcatccagcacgaagtcct 61 JQ421024 TRα-1 cacgtgtgtggaaaagatcg gggccagaagtgtgagatgt 59 AY973629 PPARα gagcccaagtttcagtttgc ggaagaggaactcgttgtcg 59 1 PPARβ tggagtccatgaggccatattgc tctcgctgaaaggtttgcgtagac 63 AY894894 RXRα cacccaatgatccagtcacgaaca agctcattccatcctgctcgtaga 63 AY197562 HCF1 cagtggtttgatgttggcatt tctggctgtagtcagggattgt 61 JQ421025 β-actin tccaggctgtgctgtccctgta gtcaggatcttcatgaggtagtc 59 2 EF1α caggtcatcatcctgaaccac aacgacggtcgatcttctc 59 AB056104 *1: LeMoine et al., 2008; 2: Duggan et al., 2011. See text for definitions of abbreviations.

Average primer set-specific efficiencies (Rasmussen, 2001) were E = 2.1 (SD 0.14).

Samples were assayed in duplicates in 25 μl reactions containing 12.5-100 ng cDNA (depending on the gene), 0.58 μM primers, and GoTaq PCR Master Mix (Promega, Madison, WI). No- template controls were run with water in place of cDNA to ensure the absence of contamination.

Results were analyzed according to the ∆Ct method using, β-actin and EF1α as housekeeping genes with their calculated geometric mean for each sample as standardized Ct (Pfaffl et al.,

2004). Gene-specific real-time PCR primers for all transcripts but COX4-1, NRF-1, PPARα, and

β-actin were either constructed after larger amplicons were generated from consensus primers based on multiple species or were based on published goldfish sequences. For primers that were constructed based on consensus sequences, amplicons were cloned and sequenced to ensure

targeting the correct gene. Sample sizes for all genes were between 7 and 10 for each acclimation group with subsets of samples being random.

3.3.4 Protein levels

Protein analyses were conducted using nuclear extracts, with five randomly chosen samples per acclimation group run in parallel for each target protein. Preliminary western blots were run with rodent samples to ensure that the antibodies (directed to mammalian epitopes) identified corresponding bands in both mammals and fish (Figure 3.1). Rodent samples were obtained from material archived from previous studies (Kocha et al., 2011).

A) B) C) rat beaver fish rat fish fish rat 100 kDa 75 kDa 75 kDa 75 kDa 63 kDa 63 kDa

50 kDa 48 kDa 48 kDa 35 kDa 35 kDa 37 kDa

D) E) F) rat beaver fish beaver fish rat beaver fish 75 kDa 75 kDa 48 kDa 63 kDa 63 kDa

48 kDa 48 kDa 35 kDa 35 kDa 35 kDa

Figure 3.1. Size estimation of target proteins. Bands of target proteins NRF-1 (A), NRF-2α (B), NRF-2β1/2 (C), ERRα (D), TRα (E), and RXRα (F) in goldfish, rat, and beaver. For each target protein a standard protein ladder allows for size estimation.

Nuclear protein fractions were extracted using NE-PER Nuclear and Cytoplasmic Extraction

Reagents (Thermo Fisher Scientific, Ottawa, Canada). We weighed the amount of starting material used for nuclear extraction. This in combination with the yield allowed us to estimate nuclear protein content per gram wet tissue.

Proteins in nuclear extracts were separated by sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE). Gels (10% acrylamide) were run at 160 V in a standard running buffer (25 mM Tris, 192 mM glycine, 0.1% SDS). Before the transfer, the gel was given 5 min to equilibrate in transfer buffer (25 mM Tris, 192 mM glycine, 20% methanol, pH 8.3). Then proteins were transferred to polyvinylidene fluoride Immobilon-P membranes (Millipore,

Billerica, MA) that were soaked in 100% methanol for 5 min before usage. The wet, electrical transfer was carried out at 4°C and constant 100 V for 1.25 h on a stir plate to ensure even heat distribution within the chamber. Thereafter, the transferred proteins were visualized using

Ponceau-S red and the gel was stained with Coomassie blue to verify efficient transfer. After the transfer, the membrane was blocked with 5% milk/TBST (20 mM Tris, 137 mM NaCl, 0.1%

Tween-20, pH 7.6) under steady agitation using an orbital shaker (~60 rpm) at 4°C overnight and probed with primary antibodies for 4 h at 4°C. The specific primary antibody dilutions were as follows: NRF-1 (Abcam, ab34682) 1:4,000; NRF-2α (Santa Cruz, sc-22810) 1:2,500; NRF-

2β1/2 (Santa Cruz, sc-28684X) 1:5,000; ERRα (Santa Cruz, sc-66882) 1:4,000; RXR (Santa

Cruz, sc-831) 1:4,000; TRα/β (Santa Cruz, sc-772) 1:4,000, and histone H3 (Abcam, ab1791)

1:1,000. So far, anti-PGC-1α, anti-PGC-1β, and anti-RIP140 antibodies targeted the proteins in mammalian species but not in goldfish.

The secondary antibody was the ImmunoPure goat anti-rabbit horseradish peroxidase

(HRP)-conjugated antibody (no. 31460; Thermo Fisher Scientific, Ottawa, Canada) used at a

1:10,000 dilution for 1 h at room temperature under steady agitation using an orbital shaker (~60

rpm). After each probing was completed, the membrane was washed six times for 5 min in

TBST. Target proteins were visualized via a chemiluminescent HRP substrate (Millipore)

according to the manufacturer’s protocol on the Cell Biosciences FluorChem HD2 system (Santa

Clara, CA).

Bands were analyzed using the band analysis tool of the AlphaView Software 3.2.2.0

(ProteinSimple, Santa Clara, CA). Target protein band intensities were corrected for the housekeeping protein histone H3 band intensities (probed for on the same blot as the respective target protein without stripping) and shown as relative values to the warm acclimation group.

The relative levels of mRNA and protein were each expressed relative to traditional denominators: mRNA per total RNA, corrected by mRNA for housekeeping genes, protein per nuclear protein, corrected by histone H3 levels. The estimates of yield for each individual enabled us to express each parameter per gram tissue. However, since the goal of the purification was to obtain pure product (RNA, protein) rather than maximal yield, we consider values for yield to be estimates. Nonetheless, the approach allows us to directly compare COX activities, mRNA and protein levels per gram wet tissue.

3.3.5 Statistics

All statistical analyses were conducted using STATISTICA (StatSoft, Tulsa, OK). First, heteroscedasticity between both acclimation groups as well as normal distribution for each acclimation group was tested for each parameter (morphometric data, enzyme activities, and mRNA levels) by using the Brown and Forsythe and Shapiro-Wilk’s test, respectively. Based on the results, for parameters with equal variances between acclimation groups and displaying normal distributions for both acclimation groups the t-test was used to test for differences between means of the acclimation groups. For parameters with either unequal variances between acclimation groups and/or nonnormal distributions of one or both of the acclimation groups, the

Mann-Whitney U test was used to test for differences between medians of the acclimation groups. For the comparison of protein levels between acclimation groups we used the Mann-

Whitney U test due to the small sample size (n = 5). The significance level for all analyses was set at α = 0.05. Results are plotted as arithmetic means +95% confidence intervals (95% CI) relative to the warm acclimation group.

3.4 Results

3.4.1 Yield of RNA and nuclear protein

Traditional analyses of RNA and protein express the values relative to the denominator that arises during purification: mRNA per total RNA, protein per total protein. In addition to expressing our RNA and protein data in the traditional units, we were able to estimate levels per

gram tissue by taking into account yields during purification. This facilitated direct comparison of the various parameters against a common axis (per gram tissue).

We observed changes in both RNA per gram wet tissue [683.5 (SD 214.9) in cold- acclimated, 341.6 (SD 100.6) in warm acclimated] and nuclear protein per gram wet tissue [34.3

(SD 4.1) in cold acclimated, 60.9 (SD 12.9) in warm acclimated]. In general the difference in yield means that values for RNA targets in cold-acclimated fish roughly doubled when expressed per gram wet tissue, and values of nuclear protein in cold-acclimated fish roughly halved when expressed per gram wet tissue.

The transcript levels expressed per total RNA and the nuclear protein levels corrected by histone H3 levels are shown in Figure 3.2 and Figure 3.3, respectively. Figure 3.4 displays the

COX activities, transcript, and protein levels per gram wet tissue.

3.4.2 COX activity and transcript patterns

Of the two acclimation temperature conditions of 32°C and 4°C, white muscle COX activity was

4.5-fold higher (U = 5, p = 0.001) in the cold-acclimated fish (Figure 3.4). Absolute COX activity levels for the warm and cold acclimation groups are 0.86 (SD 0.26) U g-1 fresh weight and 3.79 (SD 1.24) U g-1 fresh weight, respectively.

6 * warm acclimation (32°C) 5 * cold acclimation (4°C)

3 * * * 2

relative levels * * * 1

NRF-1 HCF1 ERRα TRα-1 RXRα COX4-1 PGC-1α PGC-1β RIP140 NRF-2α PPARα NRF-2β-2 PPARβ/δ Transcriptional DNA-binding proteins regulators

Figure 3.2. Relative transcript levels of transcriptional regulators and DNA-binding proteins after thermal acclimation. Results are displayed as relative values per total RNA (arithmetic mean +95% CI) of the cold-acclimated fish (closed bars) to the warm-acclimated fish (open bars). Sample sizes for the warm/cold acclimation group were as follows (p-values of statistical tests, t-test, or Mann-Whitney U test) between means/medians of acclimation groups are given in parentheses: COX4-1 (p = 0.001), NRF-1 (p = 0.023), PGC-1β (p = 0.002), NRF-2β-2 (p = 0.475), 10/10; PGC-1α (p = 0.009), HCF1 (p = 0.026), ERRα (p = 0.539), TRα-1 (p = 0.001), PPARα (p = 0.048), PPARβ/δ (p = 0.444), RXRα (p = 0.662), 7/7; NRF-2α (p = 0.027), 9/10; RIP140 (p = 0.143), 6/7. *Significant differences (p ≤ 0.05) between means/medians of the acclimation. See text for definitions of abbreviations.

The mRNA levels of subunit COX4-1 of this enzyme complex paralleled this trend, 3.5-fold higher in cold-acclimated fish (Figure 3.2, U = 8, p = 0.001). The COX4-1 mRNA levels per gram wet tissue were 6.5-fold higher in cold-acclimated fish (Figure 3.4, U = 0, p = 0.0002).

Among the two transcriptional coactivators, PGC-1α transcript levels were 75% lower in cold-acclimated fish (U = 4, p = 0.009), whereas the mRNA levels of PGC-1β were 3.5-fold higher (U = 10, p = 0.002). In the mRNA per gram wet tissue cold versus warm comparison,

PGC-1α did not differ (t = -1.81, p = 0.096), whereas PGC-1β was 8-fold higher in the cold- 66

acclimated fish (U = 0, p = 0.0002) (Figure 3.4). The transcript levels of the PGC-1α antagonist

RIP140 did not differ (Figure 3.2, t = 1.58, p = 0.143) between the two temperature states when

expressed per RNA but increased 3.5-fold (t = 2.92, p = 0.014) when expressed per gram wet tissue (Figure 3.4).

Of the DNA-binding proteins (Figure 3.2), cold-acclimated fish had higher mRNA levels of

NRF-1 (2-fold; U = 20, p = 0.023), HCF1 (2-fold; t = 2.53, p = 0.026), and NRF-2α (2-fold; U =

18, p = 0.027). For the remaining DNA-binding proteins (Figure 3.2), transcripts showed either no difference (NRF-2β-2, U = 16, p = 0.475; RXRα, t = 0.448, p = 0.662; ERRα, t = 0.63, p =

0.539, and PPARβ/δ, t = 0.79, p = 0.444) or were 50-60% lower (TRα-1, t = -4.28, p = 0.001, and

PPARα, U = 9, p = 0.048) in cold-acclimated fish.

relative nuclear protein levels 0 2 4 6 8 10 12 14 warm warm warm warm warm cold cold cold cold cold A) NRF-1 NRF-1 * Histone H3

B) NRF-2α NRF-2α Histone H3

C) NRF-2β1/2 NRF-2β1/2 Histone H3

D) ERRα ERRα Histone H3

E) TRα TRα Histone H3

F) RXRα RXRα * warm acclimation (32°C) Histone H3 cold acclimation (4°C)

Figure 3.3. Relative nuclear protein levels of transcription factors after thermal acclimation. Detailed view of target protein bands, NRF-1, NRF-2α, NRF-2β1/2, ERRα, TRα, RXRα, and associated histone H3 bands of the white muscle nuclear protein fractions of cold- and warm-acclimated fish (A–F). Relative abundances of target proteins in the nuclear fraction in white muscle corrected for histone H3. Results are displayed as relative values (arithmetic mean +95% CI) of the cold-acclimated fish (closed bars) to the warm-acclimated fish (open bars). p-values of the Mann-Whitney U test between medians of acclimation groups are NRF-1, p = 0.009; NRF-2α, p = 0.602; NRF-2β1/2, p = 0.076; ERRα, p = 0.251; TRα, p = 0.251; RXRα, p = 0.028. *Significant differences (p ≤ 0.05) between medians of the acclimation groups.

Taking into consideration the RNA yield, we were able to estimate transcript levels per gram tissue. Those transcription factors that were higher in cold-acclimated animal when expressed per RNA were even more elevated when expressed per gram tissue (Figure 3.4: NRF-1: 5-fold, U

= 4, p = 0.001; HCF1: 4-fold, U = 1, p = 0.003; and NRF-2α: 4-fold, U = 2, p = 0.0004). Each of the DNA-binding proteins that showed no response when expressed per RNA, became

significantly greater in the cold-acclimated fish when expressed per gram wet tissue (Figure 3.4:

NRF-2β-2: 3-fold, U = 5, p = 0.022; RXRα: 2.5-fold, U = 7, p = 0.025; ERRα: 2.5-fold, t = 2.27, p = 0.042, and PPARβ/δ: 2.5-fold, t = 4.45, p = 0.001. TRα-1 was still significantly lower in cold-acclimated fish when expressed as mRNA per gram wet tissue (Figure 3.4, t = -2.27, p =

0.043), but PPARα did not differ between acclimation groups when expressed relative to gram wet tissue (Figure 3.4, t = -0.36, p = 0.727).

13 *

12 warm acclimation (32°C) 11 cold acclimation (4°C) 10

9 * 8 * 7 *

6 * * * * 5 * 4 * * relative levels / g wet tissue 3 *

2 * 1 * * *

COX HCF1 TRα NRF-1 ERRα TRα-1 RXRα NRF-1 ERRα RXRα COX4-1 PGC-1α PGC-1β RIP140 NRF-2α PPARα NRF-2α NRF-2β-2 PPARβ/δ NRF-2β1/2 activity mRNA nuclear protein

Figure 3.4. Relative COX activities, transcript levels of transcription factors, and target protein levels after thermal acclimation. Results are displayed as relative values per gram wet weight (arithmetic mean +95% CI) of the cold- acclimated fish (closed bars) to the warm-acclimated fish (open bars). Sample sizes for the warm/cold acclimation group were the same as displayed in Figure 3.2, for COX it was 10/10. p-values of statistical tests (t-test or Mann-Whitney U test) between means/medians of acclimation groups are the following: COX activity, p = 0.001; transcript levels: COX4-1, p = 0.0002; PGC-1α, p = 0.096; PGC-1β, p = 0.0002; RIP140, p = 0.014; NRF-1, p = 0.001; HCF1, p = 0.003; NRF-2α, p = 0.0004; NRF-2β-2, p = 0.022; ERRα, p = 0.042; TRα-1, p = 0.043; PPARα, p = 0.727; PPARβ/δ, p = 0.001; RXRα, p = 0.025; for protein levels: NRF- 1, p = 0.009; NRF-2α, p = 0.117; NRF-2β1/2, p = 0.028; ERRα, p = 0.076; TRα, p = 0.009; RXRα, p = 0.009. See text for definitions of abbreviations. *Significant differences (p ≤ 0.05) between means/medians of the acclimation groups. 69

3.4.3 Protein levels

The analysis of the housekeeping protein histone H3 showed no significant difference

between the two acclimation groups for each of the target protein blots (Table 3.2).

Immunoblot analyses showed a 9.5-fold higher NRF-1 nuclear protein content in cold- acclimated fish (U = 0, p = 0.009) (Figure 3.3 A). We saw no significant difference in protein

levels of NRF-2, regardless of whether the antibody was directed to NRF-2α (Figure 3.3 B, U =

10, p = 0.602) or NRF-2β1/2 (Figure 3.3 C, U = 4, p = 0.076). No differences were detected in the levels of ERRα (Figure 3.3 D, U = 7, p = 0.251) or TRα (Figure 3.3 E, U = 7, p = 0.251), and

RXRα nuclear protein levels were lower in cold-acclimated fish (Figure 3.3 F, U = 2, p = 0.028).

When the immunoblot analyses were expressed per gram tissue, taking into account differences in nuclear protein yield, the same pattern emerged. Of the transcription factors examined, only NRF-1 increased significantly (Figure 3.4, U = 0, p = 0.009). Others were either lower in cold-acclimated fish or did not differ. Relative to warm fish, cold-acclimated fish had lower mass-specific levels of NRF-2β1/2 (U = 2, p = 0.028), TRα (Figure 3.4, U = 0, p = 0.009), and RXRα (Figure 3.4, U = 0, p = 0.009). There were no differences in the mass-specific levels of NRF-2α (U = 5, p = 0.117) or ERRα (U = 4, p = 0.076). In contrast, RXRα nuclear protein levels were 30% lower in cold-acclimated fish when expressed relative to histone H3 levels

(Figure 3.3 F, U = 2, p = 0.028) and 60% lower when expressed per gram wet tissue (Figure 3.4,

U = 0, p = 0.009).

Table 3.2. U- and p-values of Mann-Whitney U test between medians of acclimation groups for histone H3 levels for the various target proteins. Target protein U-value p-value NRF-1 4 0.076 NRF-2α 8 0.347 NRF-2β1/2 4 0.076 ERRα 7 0.251 TRα 7 0.251 RXRα 4 0.076

3.5 Discussion

Mitochondrial biogenesis is a very well-studied phenomenon in many fish species undergoing low temperature regimes: cold acclimation causes increases in mitochondrial content, expressed either as enzyme activities, inner membrane surface area, or mitochondrial volume density (Guderley, 2004). Though the underlying trigger for this change remains unclear, the more proximate mechanisms by which muscles induce mitochondrial biogenesis are becoming better understood (Scarpulla, 2011). Parallels between mechanisms of mitochondrial proliferation in fish and mammals are complicated by uncertainty about the role of PGC-1α, which is a master regulator of the process in mammals. In this study, we explored the patterns in the PGC-1α network of transcriptional regulators to explain why cold acclimation leads to increases in the activity of COX, an indicator of mitochondrial biogenesis. As in previous studies on fish (LeMoine et al., 2008; Bremer & Moyes, 2011; Duggan et al., 2011), mRNA for COX4-1 was elevated in cold-acclimated goldfish (Figure 3.2), consistent with a transcriptionally induced increase in COX activity (Figure 3.4).

3.5.1 Transcriptional regulators

PGC-1α is considered a master regulator of mitochondrial biogenesis in mammals due to its

ability to influence expression of suites of genes required for synthesis of mitochondrial proteins.

Recent studies have revealed the complexity of this pathway involving coactivators (PGC-1α and

PGC-1β) and corepressors (e.g., RIP140) (Hock & Kralli, 2009). In its activated form, PGC-1α regulates genes that encode proteins involved in oxidative phosphorylation and mitochondrial

DNA transcription and replication (Scarpulla, 2002). On the other hand, its paralog PGC-1β shows distinct functions but may be of equal importance for the control of mitochondria (St-

Pierre et al., 2003; Lelliott et al., 2006; Ishii et al., 2009; Frier et al., 2009). Recent findings revealed the induction of PGC-1β during skeletal muscle cell differentiation and its functional interaction with NRF-1 and ERRα under those conditions, leading to elevated mitochondrial respiration (Shao et al., 2010). As in previous studies (LeMoine et al., 2008; Bremer & Moyes,

2011) we find little evidence of a role for PGC-1α as a regulator of mitochondrial biogenesis in

fish. In contrast, PGC-1β transcript levels parallel COX4-1 transcript levels (Figure 3.2) and both

correlate with COX activity when each is expressed per gram wet tissue (Figure 3.4). This

pattern of marked increases of PGC-1β mRNA levels accompanying COX activity increases was

previously observed in other fish species (Bremer & Moyes, 2011) and studies (LeMoine et al.,

2008). Together, our results support a switch from PGC-1α to PGC-1β as the master regulator in

fish. PGC-1 activity is also influenced by the nuclear receptor corepressor RIP140. Like the

PGC-1s, RIP140 interacts with many DNA-binding proteins, but in contrast it recruits proteins

that inhibit transcription (Christian et al., 2006). However, some caution is warranted when

considering the control of PGC-1 and RIP140, given the extent to which these regulators are regulated post-translationally. PGC-1α and PGC-1β can be regulated by phosphorylation (p38

MAPK, AMPK), methylation (protein arginine N-methyltransferase 1, PRMT1), acetylation

(general control of amino-acid synthesis 5, GCN5), and deacetylation (SIRT1) (Puigserver et al.,

2001; Teyssier et al., 2005; Cantó & Auwerx, 2009; Kelly et al., 2009). PGC-1α and RIP140 are also reciprocally regulated by methylation (PRMT1) (Teyssier et al., 2005; Mostaqul Huq et al.,

2006). Nonetheless, it is difficult to argue the master regulator role for PGC-1α in mitochondrial proliferation when the PGC-1α transcript levels do not follow the pattern of COX activity at all.

In mammals, PGC-1α, PGC-1β, and RIP140 have been shown to interact with overlapping groups of DNA-binding proteins including NRF-1, NRF-2 (through HCF1), RXRα, ERRα, TRα-

1, and PPARs (Delerive et al., 2002; Scarpulla, 2011). It has not been shown if any or all of these interactions also occur in fish, and thus we found it prudent to analyse each of them.

3.5.2 DNA-binding proteins

3.5.2.1 NRFs

Early studies searching for mechanisms of coordination of mitochondrial biogenesis identified NRF-1 and NRF-2 elements in multiple genes encoding mitochondrial proteins

(Scarpulla, 2002). RNAi treatments targeting either NRF-1 (Dhar et al., 2008) or NRF-2

(Ongwijitwat et al., 2006) cause dramatic reductions in transcript levels for all nuclear-encoded

COX subunits, suggesting these DNA-binding proteins are central to the coordination of mitochondrial genes. Though there is no direct evidence that in fish NRF-1 or NRF-2α binds the

COX4-1 promoter, there is a striking parallelism between NRF-1 and COX: in cold-acclimated 73

fish, NRF-1 mRNA is 2-fold greater (Figure 3.2), its nuclear protein content 9.5-fold greater

(Figure 3.3 A), COX4-1 mRNA 3.5-fold greater (Figure 3.2), and COX activity 4.5-fold greater

(Figure 3.4). Adjusting all three parameters to the same denominator of gram wet tissue made

this parallelism even more pronounced, with 4.5-fold higher COX activity, 6.5-fold greater

COX4-1 mRNA, 4.5-fold higher NRF-1 mRNA, and a 5.5-fold greater NRF-1 nuclear protein content (Figure 3.4).

NRF-2, also known as GA-binding protein, functions as a heterotetramer of two NRF-

2α/NRF-2β heterodimers. PGC-1α and PGC-1β do not bind NRF-2β directly but use HCF1 as a linker protein (Lin et al., 2002a; Rosmarin et al., 2004). Initial analyses showed that cold acclimated fish had slightly higher levels of mRNA for HCF1 (50%) and NRF-2α (2-fold) and no change in NRF-2β transcript levels (Figure 3.2); no change was seen in NRF-2 nuclear protein levels (Figure 3.3 B and C). However, when expressing the mRNA and protein levels to the same denominator (per g wet tissue), the picture changes. The transcript levels of all three genes,

NRF-2a, NRF-2β-2, and HCF1, were significantly elevated with cold acclimation (Figure 3.4), suggesting a transcriptional activation. However, NRF-2 protein levels were unaffected by thermal acclimation, arguing against a role for NRF-2 in thermal remodeling (Figure 3.4). The incongruity between RNA and protein suggests a potential role for posttranscriptional control of the NRF-2 members (Sunesen et al., 2003; Kelly & Scarpulla, 2004).

3.5.2.2 ERRα, TRα-1, PPARs, and RXRα

PGC-1α is an important coactivator for nuclear receptors (NR). Typically, these DNA- binding proteins heterodimerize on elements of target genes where they are inactivated when 74

bound to corepressors and in the absence of their ligand but are activated upon binding of ligands

and thus initiate the transcription of the downstream target gene. In each case, ligands induce

conformational changes of the receptor and thus influence the efficacy of these DNA-binding

proteins. Orphan receptors, such as ERR, have no known natural ligand (Hummasti & Tontonoz,

2008).

Though each of these NRs has been implicated in studies associated with enhanced

mitochondrial biogenesis, we found no clear evidence that any of these factors plays a role in

fish thermal remodeling on the transcript and protein level. It is important to acknowledge the

caveat that changes in ligand levels may allow these receptors to play a role in regulation,

independent of changes in receptor levels. With this caveat in mind, our arguments use changes

in receptor RNA and protein to assess the potential role for these transcription factors in

mitochondrial biogenesis. In this regard, the molecular mechanisms that mediate change under

short-term challenges may differ from those involved in acute responses, where changes in

ligands are important.

ERRα is very well characterized for its diverse and extensive role in controlling

mitochondrial biogenesis (Dufour et al., 2007; Villena & Kralli, 2008). It is functional as a monomer or as a dimer. Though it does not require a natural ligand, synthetic ligands are able to influence its ability to bind coactivators and corepressors (Greschik et al., 2002). As with PGC-1,

ERR can be controlled by post-transcriptional mechanisms, including acetylation (Wilson et al.,

2010). ERR is able to bind either PGC-1α, activating transcription of energy metabolism-related

genes, or RIP140, repressing the same genes (Schreiber et al., 2004; Powelka et al., 2006). It

works with PGC-1α/β to regulate PGC-1α, NRF-1, NRF-2α, TRα, and PPARα as well as itself

(Vanacker et al., 1998; Schreiber et al., 2003; Villena & Kralli, 2008; Hock & Kralli, 2009).

Despite its role in mammals, we saw no clear indication of a role in temperature-induced

remodeling of fish muscle. There were no changes in either ERRα transcripts (Figure 3.2) or

nuclear protein (Figure 3.3 D) relative to RNA and histone H3 levels, respectively. However,

when plotted per gram wet tissue, there was significant increase in mRNA levels after cold

acclimation; however, this transcription response was not mirrored by a change in ERR nuclear

protein content (Figure 3.4).

Thyroid hormone receptors differ from ERRα in two ways: the natural ligands are well

known (3,5,3'-triiodothyronine, T3) and they form heterodimers with the retinoic acid receptor

(RXR) before the induction of the target gene transcription (Mangelsdorf et al., 1995). TRα-1, the dominant paralog in muscle (Blange et al., 1997; Viguerie & Langin, 2003), is a very important link between the nuclear and the mitochondrial genomes in the context of mitochondrial biogenesis. It regulates genes directly involved in mitochondrial metabolism

(Pillar & Seitz, 1997; Weitzel et al., 2003) but also the transcription of NRF-1 and NRF-2α

(Weitzel, 2001; Rodrı́guez-Peña et al., 2002). This pathway is known to be important in

mammals and other tetrapods (Seebacher, 2009), particularly in relation to cold exposure-

induced mitochondrial proliferation. Fish certainly use thyroid hormone to regulate homeostatic

properties, development, and adaptive remodeling (e.g., smoltification). The fish thyroid

hormone receptors show high sequence homology to other vertebrates (Essner et al., 1997; Liu et

al., 2000). However, there is little indication from our data that TRα-1 plays a role. Surprisingly,

we found that cold acclimation caused significant reductions by 60% and 30% in TRα-1

transcripts when expressed relative to RNA and gram wet tissue, respectively, without a

consequence to TRα nuclear protein levels (relative to histone H3) but with a significant 60% drop in protein levels per gram wet tissue. These overall patterns argue against a role for thyroid hormone in mediating cold-induced muscle mitochondrial biogenesis in fish. We did not assess the potential role of changes in levels of thyroid hormone, which could exert effects independent of receptor levels, but reductions in both RNA and protein levels argue against a role for this axis in cold-induced mitochondrial biogenesis.

Like TR, PPARs form heterodimers with RXR. They are ligand regulated, responding to diverse lipid intermediates (Ehrenborg & Krook, 2009). Also, PPARs are predominantly localized in the nucleus, whether active or inactive (Akiyama et al., 2002). PPARα is mainly involved in the control of the β-oxidation in liver, heart, muscle, and kidney (Abbott, 2009), whereas PPARβ/δ is present in many tissues but most abundant in brain, adipose tissue, skin and skeletal muscle (Ehrenborg & Krook, 2009). In muscle, PPARα and PPARβ/δ regulate the levels of enzymes in β-oxidation, thus influencing fuel preference of this tissue (Ehrenborg & Krook,

2009). In this study, we found no evidence of a role for PPARα in cold-induced mitochondrial biogenesis: its transcript levels decreased (relative to total RNA) (Figure 3.2) or did not change

(expressed per g tissue) (Figure 3.4). PPARβ/δ levels on the other hand remained unchanged or

even increased significantly after cold acclimation (Figure 3.2, Figure 3.4).The trends seen in the

transcript levels expressed per RNA are in agreement with results seen in zebrafish skeletal muscle (McClelland et al., 2006), whereas in the antarctic eelpout undergoing thermal

acclimation the inverse is seen in both PPARα and PPARβ/δ in liver tissue (Windisch et al.,

2011), and yet, the expression per gram wet tissue puts the context of control into a new perspective. The wide diversity of ligands of the PPARs turns their role in mitochondrial biogenesis into a complex network making it difficult to narrow it down under our tested conditions. Besides the nature of ligands, their quantity is also crucial in the activation of PPARs

(Kondo et al., 2007).

The NR studied here work by heterodimerizing with retinoid X receptors (RXRα, β, and γ), which are activated by vitamin A metabolites, such as 9-cis-retinoic acid (Mangelsdorf et al.,

1992). RXR activity is also regulated by ligand availability; the receptors remain as inactive tetramers in the absence of a ligand but dissociate when a ligand is available (Gampe Jr. et al.,

2000). Retinoic acid/RXR can play a role in muscle development (Le May et al., 2011), but its contribution to various signaling pathways due to its interaction with many NRs covers many tissues and even gave it the term of a master regulator (Ahuja et al., 2003). Although these receptors are obligate binding partners of PPARs and TRs, their role and regulation in the context of energy metabolism is less studied. For example, it is not known if RXR regulation contributes to control of mitochondrial proliferation through effects usually assigned to PPARs and TRs. In muscle, where RXRα is highly expressed (Mangelsdorf et al., 1992), we saw no change in its transcript levels expressed per RNA (Figure 3.2) but a significant increase in mRNA per gram wet tissue (Figure 3.4), whereas a significant decrease in the RXRα content in the nuclear protein fraction during cold acclimation was seen irrespective of the denominator

(Figure 3.3 F, Figure 3.4).

Overall, none of the NRs appear to change in ways that are consistent with a role in thermal

remodeling. Whether changes in ligands can override reductions or lack of changes in receptor

remains to be determined.

3.5.3 Significance of changes in RNA and nuclear protein per gram tissue

Most studies of RNA and protein rely on the conventional denominators incorporated into

the analyses (total RNA or total protein). We re-evaluated our results taking into consideration

the changes in the denominators for each of these indices. This permits us to estimate changes in

our targets of interest expressed per gram tissue, enabling a more robust comparison to enzyme

activity per gram tissue. Notwithstanding, a caveat about the accuracy of our RNA and protein

yield measurements, the differences in RNA and protein per gram tissue have profound influence

on several of our conclusions. The transformation strengthens some arguments (i.e., a role for

NRF-1) and points out some unexpected patterns. For example, it is commonly held that changes

in NR activity are driven by ligands. While this is likely the case for acute responses, the nature

of regulation under long-term acclimation/acclimatization is less certain. In this study, we were

surprised to find a marked disconnect between changes in transcript levels and nuclear protein

content. Furthermore, this incongruity became evident only after consideration of RNA per gram tissue and nuclear protein per gram tissue. With cold acclimation in particular, it is difficult to assess the relative importance of factors such as general stability of RNA and protein, which may contribute to changes in the respective denominators.

3.5.4 Perspectives and significance

The study of the thermal response of fish provides an interesting alternative perspective on the control of mitochondrial biogenesis. On one hand, fish respond in the same manner as mammals to physiological challenges such as exercise. However, the response to cold is distinct.

In mammals, cold leads to hypermetabolism in an effort to thermoregulate, whereas in fish, the reduced temperature imposes a reduction in metabolic rate. The differences in the structure of

PGC-1α probably also influence the genetic response of fish, though recent studies shed light on the extent of the parallelism with mammals. In this study, we investigated the participants in the

PGC-1α pathway, at least those most likely to be involved in the remodeling process. Though a role of NRF-1 appears certain, based on both the transcript as well as protein levels and supports the findings of previous studies, the complexity of ligand-dependent regulation of NR makes it difficult to rule out their roles in mitochondrial regulation. This is particularly important for those NRs that show constant mRNA or nuclear protein levels. Therefore, future studies on the regulatory network of mitochondrial biogenesis in fish should expand beyond the steady-state acclimation phase and be aware that transcription factors can have important roles in the early onset of mitochondrial remodeling during cold acclimation. Along with this approach more work on the protein level has to be done to further elucidate the regulatory network of mitochondrial biogenesis in fish compared with mammals.

3.6 Acknowledgments

We thank Christopher R. Mueller and his lab (Queen’s University) for providing the NRF-2 antibodies and advice on them. We also thank Katrinka M. Kocha, Melanie Fortner, and Brittany

Edgett (Queen’s University) for assistance and advice. This study was funded by a Discovery

Grant (CDM) from the Natural Sciences and Engineering Research Council (NSERC) Canada.

Chapter 4: mRNA degradation: an underestimated factor on

steady-state transcript levels of cytochrome c oxidase subunits?

Bremer K & Moyes CD (2013).

4.1 Abstract

Although steady-state mRNA levels are determined by both synthesis and degradation,

changes in mRNA levels are usually attributed to regulation of transcription. For cytochrome c

oxidase (COX), subunit transcript levels change non-stoichiometrically in response to cold acclimation in fish. The question remains whether those patterns are caused by differences in subunit transcription rates, decay rates, or both, making COX ideal to study the transcriptional control of multimeric proteins. In fish, cold acclimation typically leads to an increase in COX activity. I assessed decay rates of transcripts for COX subunits, including representatives with levels that decreased, increased in parallel with COX, or increased in excess of COX. Low temperature reduced the decay rate of all transcripts; however the thermal sensitivity was higher for COX genes than housekeeping genes. This has implications for studies that express changes in transcript relative to housekeeping controls. The lower decay rates for COX transcripts might explain 30-48% of the increase seen in these gene products with cold acclimation. I suggest that the reason for the exaggerated steady-state response of two subunits (COX6B-1 and COX7A-2) is due to decreased decay. However, decay rate differences could not explain the patterns seen with subunits that did not change in mRNA level with thermal acclimation (COX6A-2),

Interestingly, the decay patterns differed between two thermal acclimation studies, which may explain some of the heterogeneity seen in fish thermal acclimation studies. The differences in decay rates suggest that the apparent lack of stoichiomtery in mRNA levels may arise from post- transcriptional mechanism, leaving a role for “master regulators” in coordination genetic

responses for multi-subunit enzymes such as COX. Collectively, my results indicate that

temperature-induced differences in COX subunit mRNA levels in fish result from both

transcription and degradation. I suggest all subunits to collectively and coordinately be

controlled by transcription, and exaggerated responses of some subunits to be due to reduced

decay rates.

4.2 Introduction

Measurements of mRNA levels are a widely used approach to investigate gene expression in

a variety of experimental contexts, from mechanistic molecular genetics to ecological and

evolutionary frameworks (Gibson, 2002). Though steady-state transcript levels within a cell are

determined by rates of synthesis (transcription) and degradation, mRNA measurements are

usually inferred to arise from changes in synthesis.

Changes in transcription due to altered promoter activity may be attributed to modifications

in chromatin (histone, DNA) or binding of transcription factors and co-regulators, which

collectively affect the recruitment of the general transcriptional machinery and initiation of

transcription. Once the precursor mRNA (pre-mRNA) is made, it must be processed (i.e.,

splicing, polyadenylation, capping) and exported to the cytoplasm. There, many post- 83

transcriptional factors influence whether or not a specific mRNA enters translation. Such factors

are mRNA surveillance mechanisms (e.g., nonsense mediated mRNA decay, nonstop mediated

mRNA decay, and no-go decay), various decay pathways (i.e., exo- or endoribonucleases), and

stability controls (e.g. AU-rich elements, poly(A)-binding proteins) (see Garneau et al., 2007).

Apart from the factors that affect mRNA levels, there are other post-transcriptional controls

that alter the ability of specific transcripts to be translated. For example, in the pathway of RNA

interference (RNAi), some microRNAs (miRNA) function as gene silencers by binding target

mRNAs and preventing translation or initiating degradation (see Valencia-Sanchez et al., 2006).

The level of a specific mRNA may remain high but result in little translation because of the binding of a miRNA. Such mechanisms help explain the apparent loss of stoichiometry when

mRNA and protein levels change incongruently.

For many applications, the underlying cause of a change in transcript level is less important than the fact that the change has occurred. Conversely, transcript levels are often used to infer a change in gene regulation, and thus changes in transcript levels are also attributed to changes in

transcription. Even with single genes, it is difficult to quantitatively link the degree of gene

activation (i.e., mRNA synthesis) to mRNA accumulation, and thus these two parameters are often discussed in qualitative terms. However, when profiling transcripts of multimeric proteins and complex pathways, there is an underlying assumption that transcript levels should change in parallel to transcription.

During mitochondrial biogenesis, a network of genetic master regulators is thought to coordinate the transcription of genes encoding the five complexes (I-V) of the electron transport

chain (ETC). This effect has been described in mammals under exercise (Holloszy, 1967), electrical stimulation (Baar et al., 2002), and cold exposure (Puigserver et al., 1998; Wu et al.,

1999).

When mitochondrial biogenesis is induced in fish through cold acclimation, the mRNA levels of the various subunits of complex IV (cytochrome c oxidase, COX) of the ETC lack any stoichiometry (Duggan et al., 2011). Under conditions that cause a 2-fold increase in COX activities, mRNA levels for some subunits do not change at all (e.g. COX6B-2), while others change in parallel with COX activity (COX5B-2 and COX6A-2), and several show an exaggerated response (e.g. COX4-1 and COX7C). The potential influence of subunit-specific degradation rates on COX transcript profiles has not been well studied, and not in the context of thermal acclimation in an ectothermic animal. To date, there is some evidence for a role of mRNA degradation in the control of COX4 in mammals (Zhang & Wong-Riley, 2000), and some evidence of a role for miRNA in control of nuclear-encoded COX genes (miRNA-338;

Aschrafi et al., 2012), COX assembly (miRNA-210; Colleoni et al., 2013), and mitochondrial- encoded COX mRNA (miRNA-181c; Das et al., 2012).

In this study, I investigate (i) the stoichiometry of COX subunits’ transcript levels across multiple tissues, (ii) the factor of temperature on degradation, and (iii) how degradation might serve as a potential cause for steady-state transcript levels. Therefore, I use goldfish as a model organism and thermal acclimation to induce mitochondrial biogenesis, which is reflected in changes in COX activity allowing us to measure steady-state transcript levels of COX subunits and follow their degradation. The results of this study are not only elucidating the transcriptional

control of COX in fish but shed light on mRNA control in animals that are hugely governed by temperature.

4.3 Materials and methods

4.3.1 Fish and experimental setup

Goldfish (Carassius auratus) for both experiments were obtained from a wholesaler to the

pet trade (Aleong’s International, Mississauga, Canada) and kept in a 750-liter round (diameter:

132 cm; height: 75 cm), blue, plastic tank set up as a flow-through system in the animal care

aquatic facility at Queen’s University, Kingston, ON. The fish were fed commercial pellets

(Wardley brand Premium Goldfish Medium) ad libitum and maintained at a 12-h:12-h light:dark

photoperiod at ~20°C for 6 weeks before the experiment. The experiments were approved by the

Queen’s University Animal Care Committee. The details for one (32°C vs 4°C) of the two

experiments have been published previously (Bremer et al., 2012) where 10 fish were acclimated

to 32 ± 2°C and 10 fish were acclimated to 4 ± 1°C for 33 days. The 20°C vs 4°C experiment

was conducted as the 32°C vs 4°C experiment described previously (Bremer et al., 2012): 16 fish

were kept at the desired acclimation temperatures of 20°C and 4°C for 58 days. Of the 16 fish, 8

were exposed to decreasing water temperatures (1°C/day) until the acclimation temperature of

4°C was reached by using a chiller (Frigid Units, Toledo, OH, USA) along with sparse cold

(~13°C) water inflow to maintain a flow-through system. The exact acclimation temperatures for

the 58 day experiment were 22 ± 1°C and 4 ± 1°C. Hereafter, I will refer to each of the

experiments as “32vs4” and “20vs4”. At the final day of the experiment fish were euthanized in

a 2 l solution of 0.4 g/l tricaine methane sulphonate (MS-222, Syndel Laboratories, Qualicum

Beach, Canada) and 0.8 g/l NaHCO3. Morphometric data, including masses and fork lengths of

the fish, were taken prior to sampling to calculate Fulton condition factors (K = W/L3) (Ricker,

1975). For body comparisons between acclimation groups, the Bonferroni-corrected significance level p ≤ 0.0167 (0.05/3) was used, since all three are correlates of body metrics. The morphometric data for the 32vs4 experiment have been reported previously (Bremer et al.,

2012). For the 20vs4 experiment, mass and fork length of warm acclimated fish was not significantly different than those from the cold acclimated fish with 23.8 ± 3.5 g and 22.6 ± 3.0 g

(Mann-Whitney U test, U = 29, p = 0.753), and 9.7 ± 0.5 cm and 10.2 ± 0.7 cm (Mann-Whitney

U test, U = 19, p = 0.172), respectively. The condition of the 4°C acclimation group, however, was significantly lower (0.022 ± 0.002) than for the 20°C acclimation group (0.026±0.002)

(Mann-Whitney U test, U = 2, p = 0.002).

After each of the two experiments white muscle for the 32vs4 and the 20vs4 experiment were immediately dissected from the epaxial muscle below the dorsal fin, but above the lateral line, flash frozen in liquid nitrogen and stored at -80°C.

4.3.2 Cytochrome c oxidase activities

For the cytochrome c oxidase (COX) extraction, white muscle samples (n = 10 for each acclimation group of the 32vs4 experiment; n = 8 for each acclimation group for the 20vs4 experiment) were powdered under liquid nitrogen. The subsequent steps followed the protocol for COX activity as described previously (Bremer et al., 2012). All samples were measured in 87

triplicates. COX activities for the 32vs4 experiment presented in the present study have been published previously (Bremer et al., 2012).

4.3.3 Decay assay and RNA extraction

For each acclimation group, six samples were randomly chosen for the decay assay. Frozen white muscle tissue (350 - 400 mg) was homogenized in 15 ml of cold non-denaturing stability assay buffer (50 mM Tris-HCl, pH7.6, 150 mM NaCl, 1% Triton X 100) and divided into two

7.5 ml volumes. One half was then incubated at 4°C and the other at 20°C for the 20vs4 experiment, or 32°C for the 32vs4 experiment. Subsamples of 1 ml were then taken after 1, 2, 4,

8, 16, 20, and 30 min after the start of the experiment. Immediately after sampling, I proceeded to the RNA extraction according to the TRIzol® Reagent (Invitrogen Corporation, ON, Canada)

protocol with few modifications. The samples for all steady-state transcript levels were extracted

using a slight modification of the single-step method by guanidinium thiocyanate-phenol-

chloroform extraction (Chomczynski & Sacchi, 2006). The purified RNA pellet was dissolved in

nuclease-free water and quantified at 260 nm prior to storage at -80°C. Additionally, absorptions

at 230, 270 and 280 nm were measured to test salt, phenol and protein contaminations,

respectively. Purities for white muscle steady-state samples from the 32vs4 experiment were

reported in my previous publication (Bremer et al., 2012). White muscle steady-state samples

from the 20vs4 experiment had a 260:230 ratio of 1.38 ± 0.15, a 260:270 ratio of 1.38 ± 0.03,

and a 260:280 ratio of 1.96 ± 0.05. The ratios for the degradation samples for both experiments

were as follows: 260:230 of 1.74 ± 0.31, 260:270 of 1.33 ± 0.04, and 260:280 of 1.84 ± 0.1 for

the 32vs4 experiment, and 260:230 of 1.63 ± 0.28, 260:270 of 1.32 ± 0.14, and 260:280 of 1.83 ±

0.1 for the 20vs4 experiment.

Reverse transcription of RNA and the removal of genomic DNA were carried out by using

the QuantiTect Reverse Transcription Kit (Qiagen, Mississauga, ON, Canada) according to the

manufacturer’s instructions.

4.3.4 Real-time PCR

All real-time PCR analyses were performed on an ABI 7500 Real Time PCR System (Foster

City, CA, USA) using the following protocol: after 10 min at 95°C, 40 cycles of 15s at 95°C, 15s

at annealing temperature (Table 4.1), 34s at 72°C. The efficiency of each primer set for every

species was determined in real time PCR with an appropriate dilution series of cDNA prior to the

sample analyses. Based upon the result, an appropriate cDNA concentration for each reaction was chosen. Samples were then assayed in duplicates in 25 μl total reaction volume containing 5

μl cDNA (ng of cDNA per reaction differed between target genes) 12.5 μl FastStart Universal

SYBR Green Master (Roche Applied Science, Penzberg, Bavaria, Germany), 3.5 μl doubly-

distilled H2O and 2 μl each of forward and reverse primer (final concentration, 0.58 μM).

Controls were run with water to ensure the absence of contamination. Results for the steady state mRNA levels were analyzed according to the ∆Ct method using β-actin and EF-1α as housekeeping genes with their calculated geometric mean for each sample as standardized Ct

(Pfaffl et al., 2004). Specific primers were used to amplify single products of 81-201 bp length for the steady-state mRNA levels, and newly designed in the 3’ end of each gene for the mRNA

decay analysis (Table 4.1). Of all the steady-state transcript levels presented in the current study,

COX 4-1 levels of the 32vs4 experiment have been published previously (Bremer et al., 2012).

Table 4.1. Gene-specific forward and reverse real-time primers and their specific annealing temperatures. Annealing Gene Forward primer (5’–3’) Reverse primer (5’–3’) temperature Source* (°C) Steady state primers COX 1 cgcaggagcatcagtagacctaa tactgggagatggctggaggtt 55 1 COX 2 ccccagtccgtattttagtatccg ggaggcgatgaaggcagtttga 61 1 COX 3 atccggggttacagttacatgag ggagagcggtgaagtagaatcca 61 1 COX 4-1 agggatcctggctgcact tcaaaggtatgggggacatc 59 2 COX 4-2 aaacttgccttgtacaggcttacg aagaagaagatgccgcccaac 56 1 COX 5A-1 ggacaaatctggcccacac caatgccaagctcttcaggt 59 3 COX 5B-2 ttcccacagatgatgagcag ccgcagtgttgtcttcttca 61 3 COX 6A-2 ctggaagatcctgtccttcg aatgcggaggtgtggatatg 63 3 COX 6B-1 gaagatnaagaactacaggacggc ctcttgtagacyctctggtacca 59 3 COX 6B-2 atgccaaaaagctctggatg caggacagagggcagagaga 59 3 COX 6C atgtcacttgcaaagccagcaatgag tttcctgggstctgwtactgtgaacttgaa 59 3 COX 7A-2 gaaccgtatgaggagccaaa gggttcgtggactgtctgat 59 3 COX 7B gattgctggagccacattct atgcaatgcctgttgaagtg 59 3 COX 7C accaggaaagaacctgcc aatccactgccgaagaaca 50 3 β-actin tccaggctgtgctgtccctgta gtcaggatcttcatgaggtagtc 59 3 EF-1 caggtcatcatcctgaaccac aacgacggtcgatcttctc 59 4 mRNA decay primers COX 4-1 ggcagaggaagtatgtttatggag cactgttttcatagtcccatttggc 59 1 COX 5B-2 cctctgtgaggaggacaacact tcagtgtggaagctcatgatggac 59 1 COX 6A-2 cgcatccgaacaaagccat agtggtgaggtccctcgtaa 59 1 COX 6B-1 see steady-state primers COX 7A-2 see steady-state primers COX 7C see steady-state primers β-actin see steady-state primers EF-1α see steady-state primers * 1: this study; 2: LeMoine et al., 2008; 3: Duggan et al., 2011; 4: Bremer et al., 2012

4.3.5 Data analysis

All statistical analyses were performed using R (Version 2.14.2, R Development Core Team,

2012). For steady-state COX activities and mRNA levels of both acclimation experiments (32vs4 and 20vs4), ratios of values from cold acclimated fish over warm acclimated fish were calculated according to Fieller’s theorem (Fieller, 1954) using the R package mratios (Dilba et al., 2012).

The same method was used to calculate all ratios in the decay experiments. The advantage of using Fieller’s method for the calculation of ratios of two means is that it allows for unbounded variances to avoid arbitrarily large deviations from the expected confidence levels, which is a major problem in almost all other methods for ratio calculations (Franz, 2007). However, ratios with unbounded variances do not permit further statistical analyses. In those cases I only discussed the means.

Fieller ratios, their variances, and tests for their differences from 1, or in other words, whether the two groups were significantly different from each other, were performed using the function t.test.ratio implemented in the mratios package.

To test for differences between steady-state mRNA ratios and COX activities unpaired t- tests were used. As this involves multiple comparisons for both experiments I controlled for the false discovery rate (FDR) by adjusting p-values after Benjamini & Hochberg (1995).

For the decay rate calculation, relative Ct values were first ln-transformed. Decay rates (i.e.,

[transcripts] over time) were then estimated as the slope of the linear regression of the relative ln- transformed Ct values. For the decay rate analyses, the difference between two assay temperatures within each acclimation group was tested using paired sample t-tests. This accounts

for the non-independence for each sample (fish individual) measured at both assay temperatures.

To test for differences in thermal sensitivity of decay rates between genes unpaired t-tests were

used.

To account for non-independence in the decay rate data resulting from the experimental

design, a mixed model was used for the analyses of these data. Non-independence in data across

assay temperatures and genes due to repeated measurements on every sample was accounted for

by including the random effects factor “sample” in the model. Similarly, to account for non-

independence in repeated measurements for each sample at each assay temperature (every

sample was assayed at two temperatures) I including the random effects factor “sample by assay

temperature”. Non-independence of data among genes for each sample (every sample of an acclimation group was tested for multiple genes within each assay temperature) I included the

random effects factor “sample by gene”. Furthermore, I tested for heteroscedasticity among

genes by using likelihood ratio tests between models with homogenous variance for all genes and

models with heterogeneous variances for all genes. Fitting heterogeneous variances improved the

model for the 32vs4 comparison ( = 53.9, p < 0.001) and for the 20vs4 comparison ( =

12.2, p = 0.032). Significance of fixed model terms were tested by F-tests for which the

denominator degrees of freedom were approximated according to Kenward and Roger (1997).

4.4 Results

This study contrasts patterns seen in two thermal acclimation experiments: 32°C vs 4°C

(32vs4) and 20°C vs 4°C (20vs4). The goal is to assess if the differences seen in stoichiometries 92

between COX activity and transcript levels can be attributed to differences in mRNA degradation between gene products or between experiments.

4.4.1 Steady-state enzyme activities and transcript levels

The first question of this study was whether temperature-induced changes in COX activity correlated with changes in COX subunit mRNA. In the 32vs4 experiment, COX activity was 4.5- fold higher in the cold acclimated fish (t10 = 4.53, p = 0.001) (Figure 4.1 A). The mRNA for half of the 14 subunits paralleled COX activity (COX1, COX2, COX3, COX4-1, COX5B-2,

COX6B-2, and COX6C) (Figure 4.1 A, Table 4.2). Of the remaining seven subunits, four showed a thermal response greater than that shown by COX (COX5A-1: 9.5-fold, COX6B-1:

10.6-fold and COX7A-2: 15.6-fold, and COX7C: 9.2-fold). The ratios of three subunits were significantly lower than COX activity and not affected by temperature (COX4-2, COX6A-2, and

COX7B) (Figure 4.1 A, Table 4.2).

In the 20vs4 experiment, COX activity was not different between the two acclimation groups (t10 = 0.93, p = 0.374) (Figure 4.1 B). The reason for the discrepancy in the responses of

COX activities between the two experiments is due to both, the 20°C acclimated fish increasing in COX activity by 2-fold relative to the 32°C acclimated fish (20°C acclimated fish COX activity: 1.74 ± 0.45 U g-1; 32°C acclimated fish COX activity: 0.86 ± 0.26 U g-1) and the 4°C acclimated fish from the 20vs4 experiment showing a 2.5-times lower COX activity compared to the 4°C fish from the 32vs4 experiment (4°C acclimated fish from the 20vs4 experiment COX activity: 1.50 ± 0.21 U g-1; 4°C acclimated fish from the 32vs4 experiment: 3.79 ± 1.24 U g-1).

For this experiment, only a subset of COX subunits (COX4-1, COX5B-2, COX6A-2, and

COX7C) was investigated. All of the four subunits investigated paralleled COX activity, and as such did not display a significant temperature-response (Figure 4.1 B, Table 4.2). However,

before correcting for multiple comparisons using FDR, subunits COX4-1, COX5B-2, and

COX7C responded to temperature with 1.5 to 2-fold increases in the cold acclimated fish

compared to the warm (Table 4.2).

34 c 6 A) B) 20 5 18 16 c c c 4 [cold / warm] [cold 14 12 3 10 8 * 2 6 4 c 1 Fold change Fold 2 1 0 c -1 0 COX 1 2 3 4-1 4-2 5A-15B-2 6A-26B-1 6B-2 6C 7A-2 7B 7C COX 4-15B-2 6A-2 7C activity COX subunits mRNA activity COX subunits mRNA Figure 4.1. Steady-state COX activities and transcript levels of COX subunits in white muscle after thermal acclimation. Goldfish were acclimated to 32°C and 4°C (A) and 20°C and 4°C (B). Error bars represent the 95% CI. *Asterisks indicate significant difference (p ≤ 0.05) of COX activity ratios from 1. C indicates significant difference (FDR ≤ 5%) of mRNA ratios from COX activity.

Both experiments show that some COX mRNA levels change in parallel with COX activity.

Additionally, in the 32vs4 experiment four subunits (COX5A-1, COX6B-1, COX7A-2, and

COX7C) exhibit exaggerated temperature-responsiveness, whereas COX7C, one of those four highly temperature-responsive subunits, did not respond to temperature in the 20vs4 experiment when COX activity was not responsive. 94

Table 4.2. Test results of unpaired t-tests for differences between steady-state mRNA ratios and COX activities. Results are given for both acclimation experiments (32°C vs 4°C and 20°C vs 4°C). Degrees of freedom (d.f.), t-values, p-values, and q-values are given for each test.

Gene d.f. t-value p-value q-valueBH 32°C vs 4°C experiment COX1 15 0.84 0.412 0.536 COX2 15 1.78 0.096 0.178 COX3 15 0.28 0.783 0.848 COX4-1 18 1.11 0.280 0.404 COX4-2 15 3.86 0.002 0.013 COX5A-1 15 2.66 0.018 0.039 COX5B-2 15 0.02 0.985 0.985 COX6A-2 NA NA NA NA COX6B-1 15 3.51 0.003 0.013 COX6B-2 15 1.57 0.137 0.223 COX6C 15 0.49 0.629 0.743 COX7A-2 15 3.60 0.003 0.013 COX7B 15 2.72 0.016 0.039 COX7C 15 2.90 0.011 0.036 20°C vs 4°C experiment COX4-1 12 2.41 0.033 0.065 COX5B-2 12 2.18 0.049 0.065 COX6A-2 12 1.38 0.192 0.192 COX7C 12 2.73 0.018 0.065

4.4.2 Thermal sensitivity of mRNA decay rates

Based on the mRNA patterns seen in the 32vs4 experiment, I selected a group of COX subunits to investigate the impact of degradation on their steady-state transcript levels. I chose one subunit that did not respond to temperature (COX6A-2), two that appeared to change in parallel with COX activity (COX4-1 and COX5B-2), and three that responded to the low temperature in excess of the change seen in COX activity (COX6B-1, COX7A-2, and COX7C). I

included β-actin and elongation factor 1α (EF-1α), the two housekeeping genes used for

determining the steady-state levels of mRNA for the COX subunits.

When considering the impact of temperature on fish in vivo, there is the potential for mRNA decay kinetics to be affected by both holding temperature and thermal history. In other words, the decay rates seen in cold- and warm-acclimated fish in vivo would be affected by both thermodynamic effects on decay pathways and changes in the machinery that controls mRNA decay as part of acclimation-dependent remodelling. Experimentally, these effects can be assessed independently: (i) acclimation effects can be assessed by comparing the warm- and cold-acclimated fish at a common temperature, which I did at both 32°C and 4°C, and (ii) the effects of holding temperature can be assessed by comparing decay rates seen in warm acclimated fish assayed at 32°C to cold acclimated fish assayed at 4°C.

When investigating mRNA degradation rates, the change in total RNA should be taken into account as a potential decrease in total RNA would underestimate the decay rates for each target gene. In my experiments I found total RNA to decrease by up to 20% over the time course of the experiment. To correct for this change decay rates were individually adjusted to the decrease in total RNA in each sample.

In this experiment, decay rates were higher at the 32°C assay temperature than at the 4°C assay temperature for both acclimation groups. Further, this response was not different between the two acclimation groups or among genes (Figure 4.2, Table 4.3). Those findings suggest that the thermal history of the fish did not have an impact on the degradation machinery.

Assay temperature Assay temperature Assay temperature Assay temperature 4°C 32°C 4°C 32°C 4°C 32°C 4°C 32°C 0.03 A) B) C) D) 0.00

-0.03

-0.06

-0.09

-0.12 COX4-1 COX5B-2 COX6A-2 COX6B-1 -0.15 0.03 ecay rate

D E) F) G) H) 0.00

-0.03

-0.06

-0.09

-0.12 COX7A-2 COX7C β-actin EF-1α -0.15 Figure 4.2. Average decay rates measured at 32°C and 4°C in 32°C and 4°C acclimated fish. Decay rates of 32°C acclimated fish (closed circles) and 4°C acclimated fish (open circles) were measured for six COX subunits (A-F), and the two housekeeping genes (G and H). Error bars represent the approximate 95% CI. Horizontal line indicates a zero decay rate.

Table 4.3. Results of a mixed model on decay rates for two acclimation experiments. Acclimation temperatures, assay temperatures, and genes were taken as fixed effects, and fish, fish by acclimation temperature, and fish by assay temperature were taken as random effects for both acclimation experiments (32°C vs 4°C and 20°C vs 4°C). Degrees of freedom (d.f.), denominator degrees of freedom (d.d.f.), F-values, and p-values are given for each term tested. Term d.f. d.d.f. F p-value 32°C vs 4°C experiment Acclimation temperature 1 10.0 0.0 0.969 Assay temperature 1 10.0 30.5 <0.001 Gene 7 51.1 101.5 <0.001 Acclimation temperature : Assays temperature 1 10.0 0.1 0.747 Acclimation temperature : Gene 7 51.1 2.3 0.038 Assay temperature : Gene 7 28.6 41.0 <0.001 Acclimation temperature : Assay temperature : Gene 7 28.6 1.9 0.100 20°C vs 4°C experiment Acclimation temperature 1 10.0 23.1 <0.001 Assay temperature 1 9.9 19.2 0.001 Gene 5 35.5 17.8 <0.001 Acclimation temperature : Assays temperature 1 9.9 2.8 0.128 Acclimation temperature : Gene 5 35.6 0.5 0.738 Assay temperature : Gene 5 19.5 1.7 0.186 Acclimation temperature: Assay temperature : Gene 5 19.5 3.7 0.015

As mentioned above, for each gene, and irrespective of acclimation history, the decay rate was higher at 32°C than 4°C (Figure 4.2, Table 4.3). The magnitude of the difference was difficult to express, given that some of the rates at cold temperature were extremely low. I focused on the most biologically relevant comparison of fish assayed at their respective temperatures. The warm fish had a higher decay rate for β-actin (2.1-fold) and EF-1α (2.5- fold), corresponding to a Q10 of 1.3 and 1.4, respectively. The COX genes appeared more sensitive to temperature than the housekeeping genes, though assessing this statistically was problematic

(Figure 4.3, Table 4.4). COX5B-2 and COX6A-2 each showed the greatest thermal sensitivity, with 4.8-fold higher decay rates at 32°C (Q10 of 1.8). COX4-1 decay was 4.2-fold higher (Q10 of

1.7), COX7A-2 was 3.4-fold higher (Q10 of 1.5), and the lowest sensitivity was observed for subunit COX6B-1, with a 3.19-fold difference (Q10 of 1.5).

28 26 * 32˚C:32˚C 24 4˚C:4˚C 22 20 18 16 14 * 12 10 8 * Relative decay rates decay Relative 6 *† 4 *† * * 2 0

COX4-1 β-actin EF-1α COX5B-2COX6A-2COX6B-1COX7A-2 Figure 4.3. Relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 32°C). Error bars represent the 95% CI. *Asterisks indicate significant differences (p ≤ 0.05) of a ratio from 1. †Crosses indicate unbounded variances.

To test which genes differed in their responsiveness compared to the two housekeeping genes (β-actin and EF-1α), I could only analyse COX4-1, COX6A-2, and COX6B-1 due unbounded variances (infinite boundaries) in the remaining subunits. Of the three subunits, none significantly differed in their responsiveness from the housekeeping genes (Figure 4.3, Table

4.5). However, the overall trend was that the COX subunits were more temperature-responsive than the housekeeping genes: all subunits responded at least 1.2-times as strongly as the housekeeping genes, and two subunits (COX5B-2 and COX6A-2) were twice as temperature- responsive as the housekeeping genes. 99

Table 4.4. Results of the t-test for the ratio of means of independent samples for two acclimation experiments. The ratios tested are the relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 32°C). Degrees of freedom (d.f.), t-values, and p-values are given for each gene. Gene d.f. t-value p-value 32°C vs 4°C experiment COX4-1 6.9 4.4 0.003 COX5B-2 10.0 2.6 0.028 COX6A-2 7.7 5.3 <0.001 COX6B-1 8.0 4.1 0.003 COX7A-2 9.6 3.4 0.007 β-actin 9.7 4.0 0.003 EF-1α 9.7 5.5 <0.001 20°C vs 4°C experiment COX4-1 7.6 1.5 0.173 COX5B-2 8.4 1.0 0.332 COX6A-2 8.0 2.4 0.042 β-actin 10.0 0.1 0.889 EF-1α 8.3 0.4 0.685

Table 4.5. Results of independent t-tests testing for differences in thermal sensitivity of decay rates between genes. The ratios reflect the relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 32°C). Degrees of freedom (d.f.), t-values, and p-values are given for each ratio test. Unbounded ratios had to be omitted from this test. Gene d.f. t-value p-value 32°C vs 4°C experiment COX4-1 vs β-actin 10 1.8 0.099 COX4-1 vs EF-1a 10 1.5 0.160 COX6A-2 vs β-actin 10 1.8 0.105 COX6A-2 vs EF-1a 10 1.9 0.092 COX6B-1 vs β-actin 10 0.5 0.596 COX6B-1 vs EF-1a 10 0.6 0.596 20°C vs 4°C experiment COX4-1 vs β-actin 10 1.6 0.151 COX4-1 vs EF-1a 10 1.5 0.177

100

There were some fundamental differences in the mRNA decays patterns seen between the

two thermal acclimation experiments. In contrast to the 32vs4 experiment, acclimation

significantly altered the temperature-responsiveness of decay rates for each of the genes (Figure

4.4 A-F, Table 4.3). Mechanistically, it appears that the thermal history altered the degradation

machinery and statistically this required separate assessments of the assay temperatures with

respect to both acclimation temperatures. Interestingly, the decay rates measured at 4°C were

similar in both experiments. Though acclimation history did not affect decay rates in the 32vs4

experiment, in this study fish acclimated to 20°C had lower decay rates than those acclimated to

cold temperature.

For the 4°C acclimated fish, the decay rates were temperature-responsive in all genes, except

β-actin, with higher decay rates at 20°C assay temperature. The most pronounced response was

detected in subunit COX5B-2 (7.1-fold, Q10 of 3.4), followed by COX6A-2 (6.0-fold, Q10 of

3.1), COX4-1 (5.7-fold, Q10 of 3.0), and the two housekeeping genes EF-1α (1.9-fold, Q10 of 1.5) and β-actin (1.5-fold, Q10 of 1.3) (Figure 4.4 G, Table 4.6). However, in the 20°C acclimated

fish, none of the genes showed a significant response to assay temperature (Figure 4.4 G, Table

4.6).

101

Table 4.6. Results of paired t-tests between two assay temperatures (20°C and 4°C) for each acclimation group (20°C and 4°C). Degrees of freedom (d.f.), t-values, and p-values are given for each ratio test. Gene d.f. t-value p-value 20°C vs 4°C experiment COX4-1 4 3.0 0.039 COX5B-2 5 2.6 0.048 COX6A-2 4 6.4 0.003 4°C COX7C 4 4.4 0.012 β-actin 5 2.5 0.054 EF-1α 5 5.0 0.004 COX4-1 5 1.1 0.341 COX5B-2 5 0.5 0.619 COX6A-2 5 1.7 0.142 20°C COX7C 5 1.4 0.234 β-actin 5 2.5 0.053 EF-1α 5 2.3 0.072

102

Assay temperature Assay temperature 4°C 20°C 4°C 20°C 0.06

0.03 A) B)

0.00

-0.03

-0.06

-0.09

-0.12 -0.15 COX4-1 COX5B-2 -0.18 0.06

0.03 C) D)

0.00

-0.03

-0.06

-0.09 Decay rates -0.12 -0.15 COX6A-2 COX7C -0.18 0.06

0.03 E) F)

0.00

-0.03

-0.06

-0.09

-0.12 -0.15 β-actin EF-1α -0.18 38 * 34 G) 20˚C assay temperature 30 4˚C assay temperature 26 22 18 14 10 † Relative decay rates decay Relative * 6 *† † 2 † * -2

COX4-1 β-actin EF-1α COX5B-2 COX6A-2 Figure 4.4. Average decay rates measured at 20°C and 4°C in 20°C and 4°C acclimated fish. Decay rates of 20°C acclimated fish (closed circles) and 4°C acclimated fish (open circles) at each of the two assay temperatures (20°C and 4°C) for four COX subunits (A-D) and the two housekeeping genes (E and F). Panel G summarizes relative decay rates (20°C assay temperature over 4°C assay temperature) of 4°C (open circles) and 20°C acclimated fish (closed circles). Error bars represent the approximate 95% CI. *Asterisks indicate significant differences (p ≤ 0.05) of a ratio from 1. Horizontal line indicates a zero decay rate in panel A-F and a ratio of 1 in panel G. 103

In the biologically relevant context (i.e. rates measured at temperatures corresponding to acclimation temperature), COX6A-2 displayed a higher decay rate (3.2-fold, Q10 of 2.1) in the warm acclimated fish compared to the cold acclimated fish (Figure 4.5, Table 4.4). However,

COX6A-2 did not differ in its response to temperature from β-actin or EF-1α (Figure 4.5, Table

4.5).

14 20˚C:20˚C 12 4˚C:4˚C

4 † Relative decay rates decay Relative † * 2

COX4-1 β-actin EF-1α COX5B-2COX6A-2 Figure 4.5. Relative decay rates of cold- and warm-acclimated fish assayed at their respective holding temperatures (4°C or 20°C). Error bars represent the 95% CI. *Asterisks indicate significant differences (p ≤ 0.05) of a ratio from 1. †Crosses indicate unbounded variances.

104

4.5 Discussion

Low temperature triggers increases in mitochondrial enzymes and content (mitochondrial biogenesis) in many fish species (see Bullock, 1955; Somero, 2004). The mitochondrial enzyme cytochrome c oxidase (COX) is increased in activity in response to a decrease in temperature in many fish species. Here, I explore the potential role of post-transcriptional control of COX subunits in fish under thermal acclimation and how the process of degradation might impact steady-state transcripts levels.

4.5.1 COX activities and the uncoordinated stoichiometry of COX subunit mRNAs

Most fish respond to cold temperature by increasing muscle COX activities. However, there are several studies where no thermal compensation is seen (Hardewig et al., 1999; Bremer &

Moyes, 2011). In this study I compared two acclimation experiments, one where thermal compensation was seen (32vs4) and one where no change occurred (20vs4).

The high temperature-responsiveness of COX activity in white muscle seen in the 32vs4 experiment (4.5-fold) is in agreement with a multitude of previous studies that showed pronounced increases in mitochondrial enzyme activities in the cold (Freed, 1965; Caldwell,

1969; Heap et al., 1985; Vézina & Guderley, 1991; Orczewska et al., 2010). A common explanation for such a remodelling of muscle bioenergetics is to ensure sufficient energy production at low temperature. It is surprising that the mitochondrial proliferative response was not seen in a second (20vs4) experiment. It is unlikely that the difference between the two experiments was due to the upper temperature chosen because a previous study showed little

105

difference between fish acclimated to 20°C vs 35°C, and both thermal conditions yielded COX

activities that were significantly lower than those seen in fish acclimated to 4°C (LeMoine et al.,

2008). When comparing the two experiments, COX activities in the cold-acclimated fish where

lower in the 20vs4 experiment and the activities in the warm-acclimated fish were higher in the

20v4 experiment. While we cannot rule out some aspect of the uncertain physiological history of

the fish, it is noteworthy that the fish used for the 20vs4 experiment were 3-times smaller (22.6 ±

3.0 g and 10.2 ± 0.7 cm versus 60.2 ± 15.8 g and 13.2 ± 1.0 cm in the 32vs4 experiment). The lack of response in COX activity in these smaller fish may be related to the phenomenon of size-

related winter mortality in fish (Hurst, 2007). The 4°C fish of the 20vs4 experiment might have

had too little energy reserves to invest in mitochondrial remodelling leading to a lack in thermal

compensation. Despite the unexpected pattern in COX response, it creates an opportunity to

explore the determinants of COX synthesis.

As shown in previous studies (Duggan et al., 2011), thermal acclimation in the 32vs4

experiment led to changes in transcripts of COX subunits that did not universally parallel COX

activity nor each other. Where COX activity increased 4.5-fold, some subunits failed to increase

(COX4-2, COX6A-2, and COX7B), some changed in parallel with COX activity (COX1, COX2,

COX3, COX4-1, COX5B-2, COX6B-2, and COX6C), and others changed much more than did

COX activity (COX5A-1, COX6B-1, COX7A-2, and COX7C). These data raise a number of

questions and issues. First, in experiments where researchers measure mRNA levels of a single subunit, caution is advised to assume that the enzyme changes in parallel. Second, it is possible that some subunits are hyper-responsive to cold, and thus would be expected to increase even

106

when there is no change in COX activity. In the 20v4 experiment, three subunits (COX4-1,

COX5B-2, COX7C) increased about 2-fold in the cold acclimated fish, though the difference was not significant using the q-values.

When evaluating the levels of mRNA for a multimeric enzyme, one question may be whether to expect the mRNA for each subunit to exist at equivalent amounts and that the genes change in a coordinated manner during remodelling (Duborjal et al., 2002). Large differences in the molar concentration of nuclear-encoded COX subunits have been observed for fish before

(Little et al., 2010), and this may also be the reason for why changes seen in remodelling are not stoichiometric. One explanation may be that mRNAs can be translated with different efficiencies, meaning different steady-state mRNA levels may be needed to produce the sufficient protein for COX biosynthesis. Also, it may be unwise to make the assumption that the entire pathway from a gene to its final product as protein has evolved in ways that produce exactly enough transcript in any given circumstance. However, an important aspect in this story is whether the observed changes in mRNA levels are due to mRNA synthesis versus decay. In other words, these COX genes may have coordinated transcription, but non-stoichiometric patterns in steady-state levels arise through post-transcriptional processes.

I investigated mRNA degradation as a potential factor leading to the uncoordinated stoichiometry seen in steady-state transcript levels. My hypothesis was that the most temperature-responsive subunits display a stability that is more highly temperature dependent. I also hypothesize that the subunits that show no change in steady state level have a stability that parallels that of the housekeeping genes.

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4.5.2 Can COX mRNA decay rates explain their steady-state pattern?

Many researchers use mRNA steady-state levels to make arguments about patterns of

transcription, yet it is recognized that steady state levels are affected by mRNA degradation as well as mRNA synthesis. In this study, I investigated the decay rates of subunits that did not change with temperature (COX6A-2), subunits that responded to temperature in parallel with

COX activity (COX4-1 and COX5B-2), and subunits that could be described as highly

temperature-responsive (COX6B-1, COX7A-2, and COX7C), at least in the 32vs4 experiment.

Since transcript levels are often “corrected” using housekeeping genes, the decay rates for β- actin and EF-1α were also analyzed.

Though many enzymes and processes have been studied in relation to acclimation, this is the first that has looked at how mRNA decay rates may change with temperature. In the first experiment (32vs4), where COX activity changed, I saw no acclimation effect on the thermal response of decay rate for any of the subunits. This means all subunits responded to the two assay temperatures in the same way in both the warm and cold acclimated fish. This result suggests that the degradation machinery itself was not modified over the course of acclimation in a way that changes its turn-over at high or low temperatures. In contrast, the 20vs4 experiment, where COX activity unexpectedly did not change, marked acclimation effects were seen. Though mRNA decay rates in cold acclimated fish showed the expected response to assay temperature, similar to that seen in the 32vs4 experiment, decay rates in warm acclimated fish appear much less temperature sensitive. Thus, acclimation appeared to affect some aspects of the general mRNA decay pathway, such as the amount and/or efficiency of ribonucleases or poly(A)-binding

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protein (PABP), suggesting fairly stable degradation machineries in the 20°C fish over a relatively broad range of temperatures.

Apart from questions about acclimation effects, I also investigated whether thermal sensitivities of decay rates differ between genes. This helps resolve if the observed differences in steady-state mRNA stoichiometry are related to subunit specific decay rates.

Overall, it appeared that the decay rates for the COX subunits had a higher thermal sensitivity than the decay of the two housekeeping genes. Thus, if effects of temperature on transcription were the same, one would expect to see a greater effect on steady state mRNA levels for COX genes because of the RNA decay kinetics.

To put these differences in context, consider the influence on the mRNA levels of COX4-1 and the two housekeeping genes, β-actin and EF-1α, each assayed in fish at their respective holding temperatures. The COX4-1 decay rate was twice as temperature sensitive as the decay rates for β-actin or EF-1α. Such a pattern has obvious implications for studies comparing genes of interest to housekeeping genes. The origin of gene-specific differences in the stability and decay of mRNA species is not known, though many possibilities are obvious. Assuming that the housekeeping genes are “typical”, the question is why the influence of temperature on decay of

COX4-1, for example, is greater than housekeeping genes. It is possible that COX4-1 mRNA could have sequences-specific motifs that bind stabilizing proteins, which could account for lower decay rates. For example, COX4-1 may bind more of the stabilizing RNA binding proteins, such as poly(A)-binding protein (PABP) or AU-rich binding proteins (AUBPs) associated with the 3'-poly(A) tail or AU-rich elements, respectively (see Garneau et al., 2007).

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However, it is not known if such factors have a temperature sensitivity that could explain why

COX subunits differ from housekeeping genes. Similarly, the thermal sensitivity of

endoribonucleases is not known, which may be important in genes that differ in sequences in

ways that alter the vulnerability for endoribonuclease attack. The degradation through

endoribonucleases is an important factor in the control of transcripts that underlie extracellular

stimuli (Tourrière et al., 2002) and as such may play a role in the control of COX subunits. The possibilities for increased stability and therefore reduced decay rate have been mentioned above with an emphasis on the binding of stabilizing proteins to mRNA species. One example specific for COX subunit mRNAs is the cytochrome c oxidase L-form transcript-binding protein. It has been identified as a tissue and subunit-specific binding protein impacting the expression of COX subunits in bovine (Preiss & Lightowlers, 1993).

Though the nature of the data makes statistical support challenging, it is possible to estimate the effects of differential thermal sensitivity of decay rates on the mRNA patterns seen with thermal acclimation, partitioning the effects on mRNA synthesis vs decay. For housekeeping genes, the assumption is that mRNA levels do not change as a function of the treatment. In this study, the Ct values for the housekeeping genes supported this assumption. Given the decay rates for the housekeeping genes, ~48% for β-actin and ~40% for EF-1α, this would suggest that the housekeeping gene transcription rate also declined by 40-50% in response to temperature.

In contrast to housekeeping genes, the decay rates for COX genes were more sensitive to temperature. Thus, some of the increase seen in COX mRNA in the cold can be attributed to reduced decay rather than increased relative transcription. For the two subunits showing the

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exaggerated temperature-response, COX6B-1 and COX7A-2, COX6B-1 mRNA increased 10.6-

fold. Since the decay rate was ~33% reduced in the cold acclimated fish than the warm acclimated fish, I infer that a 3.3-fold difference in COX6B-1 mRNA could be attributed to reduced decay, and that there was a 7.3-fold increase due to transcription. Likewise for COX7A-

2, about 30% of the 15.6 fold increase in mRNA in the cold could be attributed to reduced decay,

suggesting an 11.1-fold increase due to transcription (Figure 4.6 A). For the subunits that change

with temperature in parallel with COX activity (COX4-1 and COX5B-2), decay accounted for up

to one fifth of the observed change: ~24% of the 6-fold change in COX4-1 and ~21% of the 4.4-

fold change in COX5B-2. Thus, for all four genes that showed an increase in mRNA level, a

reduced decay rate contributed to the effect.

For COX genes that do not change with temperature, the question is whether decay rates mask the effects of a putative increase in transcription, as would be expected if all COX genes were regulated in parallel. For the one subunit of the 32vs4 experiment (COX6A-2) and all subunits of the 20vs4 experiment, COX4-1, COX5B-2, and COX6A-2, the mRNA did not change with acclimation and the apparent decay rate paralleled the housekeeping genes, suggesting that peculiar decay rates do not explain why this gene shows an anomalous pattern

(Figure 4.6).

As an approximation, I suggest that the fraction of changes in mRNA that is not due to general decay is due to transcription. However, there are mechanisms by which mRNA can increase without changes in transcription. In some scenarios, mRNA can be stalled in translation and accumulate in so called stress granules or P-bodies, also known as RNA interference

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(Balagopal & Parker, 2009). Recently, multiple studies found some evidence of a role for miRNA in the control of nuclear-encoded COX genes (miRNA-338; Aschrafi et al., 2012), COX assembly (miRNA-210; Colleoni et al., 2013), and mitochondrial-encoded COX mRNA

(miRNA-181c; Das et al., 2012). This process of gene silencing would allow an mRNA species to accumulate, and re-enter translation when needed. Such a mechanism could help reconcile the differences in subunit stoichiometry, explaining the apparent lack of coordination of COX genes.

34 c 6 A) B) 20 5 18 16 c 4

[cold / warm] [cold 14 12 3 10 8 * 2 6 4 1 Fold change Fold 2 1 0 -1 0 COX 4-1 5B-2 6A-2 6B-1 7A-2 COX 4-1 5B-2 6A-2 activity COX subunits mRNA activity COX subunits mRNA

Figure 4.6. Steady-state transcript levels of COX subunits and fractions ascribed to transcription and decay differences between warm and cold acclimated goldfish. Steady-state cytochrome c oxidase (COX) activities and transcript levels of COX subunits in white muscle of goldfish acclimated to 32°C and 4°C (A) and goldfish acclimated to 20°C and 4°C (B). Error bars represent the 95% CI. A change in steady-state mRNA levels could arise from an increase in synthesis or degradation. The grey symbols show the level of mRNA that would have been expected had decay rates for the COX subunits not decreased to a greater extent in the cold than did decay rates for housekeeping genes. *Asterisks indicate significant difference (p ≤ 0.05) of mRNA and COX activity ratios from 1. C indicates significant difference (p ≤ 0.05) of mRNA ratios from COX activity. †Crosses indicate unbounded variances.

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4.6 Conclusion

My study adds an important aspect to the interpretation of steady-state transcript levels in multimeric proteins, and in particular to the regulation of COX subunits in the context of thermal acclimation in fish. The lack in stoichiometry seen in COX subunits can partially be explained by the differences in the subunit-specific decay rates. The impact of decay rates seems to correlate inversely with the thermal responsiveness of mRNA levels. This means, the more a subunit responds to low temperatures with increases in mRNA, the more of this increase is due to a decrease in this particular mRNA decay rate. Thus, taking into account different decay rates

among subunits tends to reduce the magnitude of deviations from stoichiometric changes in

thermal acclimation. In summary, caution is warranted when trying to describe gene expression

based on mRNA levels.

4.7 Acknowledgments

I thank Christopher T. Monk for his previous help with the 32vs4 acclimation experiment

and Katherine Reilly for her assistance during the mRNA decay experiments. This study was

funded by a Discovery Grant (CDM) from the Natural Sciences and Engineering Research

Council (NSERC) Canada.

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Chapter 5: General discussion

Maintaining energy homeostasis within an organism is vital and as such requires constant adjustments in energy metabolism. Mitochondria, the so called powerhouses of the cell, have a central role in the energy metabolism. When an organism faces unfavourable energetic conditions, adjustments in the mitochondrial energy production (oxidative metabolism) play a major role in balancing energy demand and supply. The increase in mitochondrial enzymes and mitochondrial content, termed mitochondrial biogenesis, is widely spread across taxa and results in the increase of energy production. The underlying regulatory mechanisms of mitochondrial biogenesis, however, have most extensively been studied in mammals. In mammals, the “master regulator” of mitochondrial biogenesis is the transcriptional coactivator PGC-1α (Fernandez-

Marcos & Auwerx, 2011). PGC-1α coactivates many transcription factors (e.g. NRF-1, NRF-2,

PPARs, TRα-1, and ERRα) that are involved in the expression of a variety of mitochondrial genes (Scarpulla, 2008, 2011).

In taxa other than mammals, the complex control of mitochondrial biogenesis is far less understood. Of the PGC-1α-activated transcription factors, NRF-1 and PPARs are the most studied factors in animals other than mammals, with fish comprising the biggest group

(McClelland et al., 2006; LeMoine et al., 2008; Orczewska et al., 2010; Windisch et al., 2011).

The importance of other transcription factors, such as NRF-2, TRα-1, and ERRα, in the control of mitochondrial biogenesis in fish remains to be evaluated.

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To date, PGC-1α homologues have been identified in invertebrates, such as gastropods

(Larade & Storey, 2006) and insects (Tiefenböck et al., 2010), and in chordates, such as tunicates

(see LeMoine et al., 2010) and vertebrates (fishes, birds, amphibians, reptiles, and mammals)

(see Lin et al., 2005; Seebacher et al., 2009). In vertebrates, a comparative sequence analysis revealed that PGC-1α is highly conserved in its activation interaction domain, yet exhibits major differences in its NRF-1 and MEF2 interaction domain between fish and tetrapod lineages

(LeMoine et al., 2010). The sequence divergence suggests activation pathways for PGC-1α are similar across vertebrates but that NRF-1 and MEF2c interactions with PGC-1α differ between fish and tetrapods.

Few studies have investigated the role of PGC-1α in mitochondrial biogenesis in fish and these came to disparate conclusions in the role of PGC-1α, though all are based on correlation analysis. Goldfish and zebrafish (both cyprinids), lack a response in PGC-1α mRNA

(McClelland et al., 2006; LeMoine et al., 2008), whereas three-spined stickleback seem to follow the mammalian PGC-1α-mediated mitochondrial biogenesis (Orczewska et al., 2010).

The goal of my thesis research was to elucidate the pathways of mitochondrial biogenesis in fish, assessing the extent to which they overlap with the better studied mammalian pathways. To do so, I employed an integrative approach, studying broad biological levels, from phylogeny to physiology, spanning enzymatic, translational, post-transcriptional, and transcriptional control mechanisms.

Given the scarcity of studies on fish, my first step was to expand the range of species studies, to explore models to identify any imprint of phylogenetic pattern on the response. Of the

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eight fish species I investigated (pumpkinseed, bluegill, black crappie, largemouth bass, northern

pike, central mudminnow, northern redbelly dace, brook stickleback), five increased COX

activity levels in response to natural winter acclimatization and laboratory cold-acclimation.

Also, cold-responses were not related to phylogeny; species within one family even displayed

opposing responses.

Regarding the underlying pathways controlling mitochondrial content in fish, my results indicate that they differ in important aspects from those in mammals. The PGC-1α gene failed to

respond in four out of five investigated species where mitochondrial proliferation was seen. In

contrast, NRF-1 resembled the mammalian response more closely. NRF-1 mRNA positively

responded during mitochondrial biogenesis in three (pumpkinseed, black crappie, and goldfish)

of six species. Further, large (up to 10-fold) increases in NRF-1 mRNA were observed in species

that did not display increased COX activity levels. Collectively, my results suggest that PGC-1α

is not involved in the control of mitochondrial biogenesis in fishes, whereas NRF-1 seems to

have impact in some of the species studied. However, there are a number of possible

explanations for the patterns I saw. One is that my approach relies on establishing correlations

between transcripts. This is a weakness because transcript levels do not necessarily reflect a

proteins function, due to post-transcriptional modifications like mRNA degradation, gene

silencing via miRNAs, post-translational modifications. All these steps of control in gene

expression may not only differ in a single species but may also differ between species explaining

some of the incongruent patterns seen. In later studies, I get around this by focusing on a single

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species and specifically address the extent to which protein and mRNA patterns parallel each other.

Despite the necessary caution when interpreting results based on transcript levels, one explanation for the differences between fish and mammals may be that PGC-1α family members play different roles in fish and mammals. In situations where PGC-1α mRNA does not parallel mitochondrial changes, PGC-1β does (LeMoine et al., 2008), suggesting that it may be the

“master regulator” of mitochondrial biogenesis in fish. In mammals, PGC-1α and PGC-1β exhibit overlapping and complementary responsibilities (Finck & Kelly, 2006; Hock & Kralli,

2009), binding many of the same transcription factors (Lin et al., 2002a). In my study on cold- acclimated goldfish, I found support for the potential switch in master regulators from PGC-1α to

PGC-1β. In my study, transcript levels of PGC-1α and PGC-1β even displayed opposing patterns, with a 75% drop in PGC-1α and an 8-fold increase in PGC-1β of cold relative to warm acclimated goldfish. With respect to the difficulty drawing conclusions on a proteins function based on its mRNA levels, my studies would have been greatly aided by an antibody for PGC-

1α. Unfortunately, even in mammals the antibodies are questionable. The divergence among fish and between fish and mammals further places limits on the extent to which commercial antibodies work. Perhaps in the future reliable antibodies will help uncover the response of PGC-

1α on its protein level. For other transcription factors I had the opportunity to investigate their thermal responses on transcript and protein levels. My analysis on goldfish further revealed that

NRF-1 protein levels paralleled NRF-1 mRNA levels, supporting a central role of NRF-1 in mitochondrial biogenesis in goldfish. None of the other PGC-1α binding partners (NRF-2,

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ERRα, RXRα, and TRα-1) changed in ways that suggest a role in cold-induced mitochondrial proliferation. NRF-2 showed a 4.5-fold increase in mRNA, suggesting that it may have a role in control in fish, but nuclear protein levels actually declined by 50%. For other factors such as

ERRα and RXRα, increased mRNA levels were accompanied by no change or a decrease in protein levels, respectively. TRα-1 did not respond to low temperatures in either mRNA or protein levels. As mentioned before, discrepancies in mRNA and protein levels can arise from transcriptional, post-transcriptional, translational processes. Therefore, caution in making assumptions that changes in mRNA of enzymes and regulators reflect a change with functional consequences is required. My approach to this controversy is to separate the elements into categories.

First, there is the question about the extent to which transcript levels reflect gene expression.

For a multimeric enzyme, such as COX, there is the presumption, perhaps itself naïve, that evolution of molecular genetics would lead to parallel genetic responses among all the genes needed for COX synthesis. This impression of gene coordination is reinforced by the discussion of master regulators as mechanisms for coordination. In mammalian experiments, changes in mitochondrial biogenesis tend to be accompanied by parallel changes in COX subunits. In contrast, fish seem to lack this exquisite coordination. Under conditions that lead to a change in

COX activity, there is little appearance of stoichiometric changes in COX subunit mRNA

(Duggan et al., 2011).

A second question is, whether post-transcriptional processes have the potential to create an impression of loss of stoichiometry in COX gene transcription. There is a growing awareness of

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the importance of post-transcriptional pathways, such as mRNA degradation that influence

mRNA levels. In other words, if an individual transcript is differentially targeted for degradation,

then it might change in abundances with a magnitude that does not reflect the change in the rate

of synthesis. In my thesis, I explored this in the context of the uncoordinated responses of COX subunits in relation to COX activity by comparing degradation rates among COX subunit transcripts that differ in their response to temperature. My results indicate that all subunits seem to be controlled by mRNA decay to a relatively high degree (up to 48%). However, adjusting steady-state mRNA levels for reduced decay rates indicated that increases in mRNA levels, to the point of paralleling COX activity, were caused by increases in transcription for all subunits.

Despite non-significance among decay rates of subunits, I suggest that the reason for the exaggerated steady-state response of two subunits (COX6B-1 and COX7A-2) is due to decreased decay.

A third question, and related to the previous two, is whether individual COX transcripts are subject to regulation that prevents mRNAs from being translated. Increasing the repression of translation by miRNA could prevent the extreme increase in e.g. COX6B-1, COX7A-2, and

COX7C mRNA from manifesting as more protein. On the other hand, releasing this repression would allow a transcript to be translated at greater rates even if its mRNA levels did not change.

For example, the lack in response in mRNA for COX6A-2 in cold acclimation could mean more was translated despite the lack of a genetic response. More recently, multiple studies found some evidence of a role for miRNA in the control of nuclear-encoded COX genes (miRNA-338;

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Aschrafi et al., 2012), COX assembly (miRNA-210; Colleoni et al., 2013), and mitochondrial- encoded COX mRNA (miRNA-181c; Das et al., 2012).

Collectively, my thesis contributed to the field of mitochondrial biogenesis in animals and filled some of the important gaps. From a phylogenetic perspective down to the post- transcriptional control of mitochondrial genes I elucidated parts of the pathway of mitochondrial biogenesis in fish. Yet, future research is necessary to further understand this complex interplay of control mechanisms on multiple levels. Additional research on PGC-1β as a potential master regulator in fish may shed important light on their control of mitochondrial biogenesis and on the discordance in control mechanisms between fish and mammals. The mechanism of mRNA degradation as part of post-transcriptional control, investigated in my thesis might bear the greatest impact. It seems that post-transcriptional control has only more recently gained attention in gene regulation and might be, besides other fields of research, of importance in understanding the underlying causes for mitochondrial biogenesis.

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