Aus dem Zentrum Physiologie und Pathophysiologie der Universität zu Köln Institut für Vegetative Physiologie Geschäftsführende Direktorin: Frau Universitätsprofessor Dr. med. G. Pfitzer

Transcriptional Regulation after Chronic Hypoxia

Exposure in Skeletal Muscle

Inaugural-Dissertation zur Erlangung der Doktorwürde der Hohen Medizinischen Fakultät der Universität zu Köln

Vorgelegt von Gabriel Willmann aus Furtwangen

promoviert am 30. Januar 2013

Gedruckt mit der Genehmigung der Medizinischen Fakultät der Universität zu Köln 2013

II Dekan: Universitätsprofessor Dr. med. Dr. h. c. Th. Krieg

1. Berichterstatterin: Frau Universitätsprofessor Dr. med. G. Pfitzer 2. Berichterstatter: Privatdozent Dr. med. J.-C. von Kleist-Retzow

Erklärung Ich erkläre hiermit, dass ich die vorliegende Dissertationsschrift ohne unzulässige Hilfe Dritter und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe; die aus fremden Quellen direkt oder indirekt übernommenen Gedanken sind als solche kenntlich gemacht.

Bei der Auswahl und Auswertung des Materials sowie bei der Herstellung des Manuskriptes habe ich Unterstützungsleistungen von folgenden Personen erhalten:

Prof. Dr. T. Khurana Dr. M. Budak Dr. S. Bogdanovich Olga Lozynska

Weitere Personen waren an der geistigen Herstellung der vorliegenden Arbeit nicht beteiligt. Insbesondere habe ich nicht die Hilfe einer Promotionsberaterin/ eines Promotionsberaters in Anspruch genommen. Dritte haben von mir weder unmittelbar noch mittelbar geldwerte Leistungen für Arbeiten erhalten, die im Zusammenhang mit dem Inhalt der vorgelegten Dissertationsschrift stehen.

Die Dissertationsschrift wurde von mir bisher weder im Inland noch im Ausland in gleicher oder ähnlicher Form einer anderen Prüfungsbehörde vorgelegt.

Köln, den 07.08.2012

Gabriel Willmann

III Nach entsprechender Anleitung der unten aufgeführten Personen, wurden mit Ausnahme des eigentlichen Microarray-Verfahrens mit bioinformatischer Auswertung alle Experimente von mir selbst ausgeführt.

Prof. Khurana hat als Laborleiter in der Abteilung „Physiology/ Pennsylvania Muscle Institute“ das Gesamtprojekt betreut. Dr. Budak und Olga Lozynska haben mich im Labor eingearbeitet.

Dr. Budak hat mir den Umgang mit RNA und der real-time PCR Validierung beigebracht, außerdem hat er mir die Analyse der Microarray Daten näher gebracht.

Dr. Bogdanovich hat mir den korrekten Umgang mit Mäusen und die Hypoxie Exposition, sowie die Technik der immunohistochemischen Färbungen beigebracht.

Olga Lozynska hat mich in die Technik der „Promoter-Klonierung“ sowie die Arbeit mit Zellkulturen eingearbeitet.

IV Table of contents

Table of contents

GLOSSARY ...... 3

Abbreviations ...... 3

Units ...... 5

1 INTRODUCTION ...... 7

1.1 Muscle tissue ...... 7 1.1.1 Skeletal muscle ...... 7 1.1.2 Known transcriptional changes in muscle atrophy and wasting ...... 9

1.2 Hypoxia ...... 10 1.2.1 Major molecular response to hypoxia via the hypoxia inducible factor (HIF) ...... 10 1.2.2 The role of HIF-1α in skeletal muscle ...... 11 1.2.3 Response of skeletal muscle to hypoxia ...... 12

1.3 Disease propensity - Duchenne Muscular Dystrophy ...... 13 1.3.1 Duchenne Muscular Dystrophy ...... 13 1.3.2 Molecular biology of DMD ...... 14 1.3.3 Pathogenesis ...... 15

1.4 Aim of the study and hypothesis ...... 16

2 MATERIALS & METHODS ...... 18

2.1 Materials ...... 18 2.1.1 Materials for hypoxia exposure ...... 18 2.1.2 Material for monitoring hematocrit levels ...... 18 2.1.3 Materials for molecular biology ...... 18 2.1.4 Materials for immunohistochemistry ...... 20 2.1.5 Materials for cell culture and luciferase assay ...... 21 2.1.6 List of primer pairs ...... 21

2.2 Methods ...... 22 2.2.1 Animals and hypoxia exposure ...... 22 2.2.2 Hematocrit level monitoring ...... 23 2.2.3 RNA isolation ...... 23 2.2.4 Affymetrix GeneChip expression profiling ...... 24 2.2.4.1 Linear amplification and cRNA labeling 24 2.2.4.2 Fragmentation and microarray hybridization 25 2.2.5 Quantitative RT-PCR ...... 26 2.2.6 Immunohistochemistry ...... 27 2.2.7 Cloning of HARP promoter ...... 28 2.2.8 Cell culture and luciferase assay ...... 28 2.2.9 Statistical analysis ...... 29

1 Table of contents

3 RESULTS ...... 30

3.1 Effects of chronic hypoxia on body and muscle weight as well as hematocrit ...... 30

3.2 Expression Profiling of hypoxic skeletal muscle ...... 31

3.3 Characterization of differentially expressed in hypoxic skeletal muscle ...... 34

3.4 Validation of expression profiling: structural & metabolic elements ...... 38

3.5 Characterization of HARP ...... 40

4 DISCUSSION ...... 43

4.1 Morphological and functional changes in hypoxic muscle ...... 43

4.2 Muscle adaptations of metabolic based pathways under chronic hypoxia ...... 44

4.3 Enhanced capacity for degradation ...... 46

4.4 Role of HARP downregulation in hypoxia ...... 48

4.5 Conclusion ...... 49

5 ABSTRACT ...... 50

5.1 Zusammenfassung ...... 50

5.2 Abstract ...... 51

6 REFERENCES ...... 52

7 APPENDIX ...... 61

7.1 Publications ...... 61

7.2 Supplementary data ...... 61

8 LEBENSLAUF ...... 71

2 Glossary

Glossary

Abbreviations

Ankrd10 repeat domain 10 Aqp4 aquaporin 4 ARNT aryl hydrocarbon receptor nuclear translocator ATP adenosin triphosphate Atp1b2 sodium/potassium-dependent ATPase subunit beta-2 bp Ca2+ calcium cDNA complementary deoxyribonucleic acid

CO2 carbon dioxide COPD chronic obstructive pulmonary disease

C2C12 mouse muscle cell line cRNA complementary ribonucleic acid DAVID Database for Annotation, Visualization and Integrated Discovery DAP -associated protein DB dystrobrevin DEPC diethyl pyrocarbonate DMD Duchenne muscular dystrophy DMEM Dulbecco's Modified Eagle Medium DNA deoxyribonucleic acid DGC/SGC /sarcoglycan complex EDL extensor digitorum longus e.g. exempli gratia ER enrichment score FBS fetal bovine serum FDR false discovery rate EPO erythropoietin Foxo forkhead transcription factor gastroc gastrocnemius muscle

3 Glossary

GAPDH glyceraldehyde-3-phosphate dehydrogenase GC guanine-cytosine GEO Expression Omnibus GLUT-1 glucose transporter-1 Grb14 growth factor receptor bound protein 14 HIF-1 hypoxia inducible factor-1 IGF-1 insulin like growth factor-1 luc luciferase mdx dystrophin deficient mouse MHC heavy chain MLC mysin light chain MM miss match mRNA messenger ribonucleic acid Mt1 metallothionein 1 MTC multiple tissue cDNA panel mTOR mammalian target of rapamycin MYH13 myosin heavy chain 13 MuRf-1 muscle ring finger protein 1 Mustn1 musculoskeletal, embryonic nuclear protein 1 NADH nicotinamide adenin dinucleotid dehydrogenase NADH-TR NADH-tetrazolium reductase NCBI national center for biotechnology information NMD neuromuscular disorders

N2 nitrogen

O2 oxygen P311/HARP high altitude-atrophy related protein PAS periodic acid Schiff PCR polymerase chain reaction PDK-1 pyruvate dehydrogenase 1 PM perfect match

PO2 oxygen pressure QF quadriceps femoris muscle qPCR quantitative polymerase chain reaction

4 Glossary

RBC red blood cell rpm revolutions per minute RNA ribonucleic acid RNase ribonuclease ROS reactive oxygen species RT room temperature RT-PCR reverse transcription-polymerase chain reaction Runx1 runt-related transcription factor 1 SAM significant microarray analysis SS sarcospan SYN syntrophin TA tibialis anterior muscle TM maximum temperature Vmax (maximum) velocity of contraction VEGF vascular endothelial growth factor VHL von Hippel Lindau WBC white blood cell

Units

˚C degree Celsius DA Daltons µ micro (10-6) g gram µg micro gram h hour hrs hours k kilo (103) kb kilobase kd kilodalton l liter µl micro liter M molar (mol/l)

5 Glossary

µM micro molar m milli (10-3) mb mega base pair min minute n nano (10-9) ng nano gram nm nano meter s second % percent

6 Introduction

1 Introduction

Physiological responses to hypoxia have been appreciated for many years and it is well established that healthy individuals exposed to high altitude hypoxia or a chronic hypoxic condition adopt protective mechanisms such as increased breathing, pulmonary vascular constriction or production of erythropoietin to enhance red blood cell mass 16,138. Tissues such as skeletal muscle also adapt to the changed hypoxic environment in order to meet the functional requirements. However, evidence exists that muscle tissue is not able to maintain its physiological aspects during prolonged, severe hypoxia exposure reacting with muscle wasting and atrophy 50,62,52,43. In addition, muscle tissue hypoxia and respiratory insufficiency is eminent in various diseases including neuromuscular disorders such as Duchenne Muscular Dystrophy (DMD) or chronic obstructive pulmonary disease (COPD) 32,56. In the following chapter I would like to emphasize basic skeletal muscle anatomy, introduce cellular hypoxic responses with emphasis on skeletal muscle tissue and give a brief overview of the disease propensity in regard to DMD as well as the hypothesis of our study.

1.1 Muscle tissue

1.1.1 Skeletal muscle

Traditionally, it has been thought that muscles achieve the type of functional diversity by 'specializing' in terms of expressing distinct functional properties that enable them to undertake their given tasks effectively and efficiently. Such adaptations are noted in various types of muscle. As a slow contracting muscle, soleus muscle is able to sustain large loads over prolonged periods of time in its role as a postural (anti-gravity) muscle. In contrast, small intrinsic hand muscles with their ability to provide fine and rapid movements do not provide muscle specialization to maintain large loads for long periods. Functional diversity of muscle groups has been studied and cataloged in terms of major differences of contractile (twitch) and metabolic (resistance to fatigue) properties 44. Thus, muscles are classified as slow (Type I) and fast (Type II) with various sub-classes within Type II such as IIa (fast oxidative), b (fast glycolytic) and c (intermediate) to accommodate finer physiological differences 33. Over the past few decades, various researchers have

7 Introduction

correlated the differences in physiological properties with the molecular makeup of different muscle types. Indeed, the composition of molecular components of the contractile apparatus, the sarcomere, in part determines physiological characteristics in muscle. The major components of the sarcomere in skeletal muscle are and myosin. While actin seems phylogenetically conserved across species, the myosin molecule in its different isoforms gives rise to a wide variability of the contractile properties such as isometric force, shortening velocity and power output of muscle cells. The myosin molecule in skeletal muscle is a hexameric polypeptide consisting of two myosin heavy chain (MHC) and four (MLC) subunits 33. Individual muscles are composed of a collection of different fiber types, which again are defined by their metabolic characteristics and unique MHC composition. The large range in contractile properties among fiber types enables the production of diverse mechanical outputs, from extremely rapid ballistic movements to slow sustained movements and anti-gravity support. Myosin isoform expression in fully differentiated adult muscle cells is highly plastic, exhibiting changes in response to a host of conditions including innervation and hormonal levels, exercise and disease. Thus, myosin isoform composition is crucial for the adaptation to specific motor requirements of the organism. Difference in expression of myosin heavy chain (MHC) isoforms is currently known to correlate well with physiological properties such as velocity of contraction (Vmax) and fatigue resistance activity for most muscle types 108. More recently, other components of , such as different isoforms of have also been shown to contribute to contractile properties such as [Ca2+] sensitivity and co-operativity of [Ca2+] activation 13. In addition to molecular properties, the compartmental organization of skeletal muscle also accounts for functional advantages 34. Skeletal muscle features a stereotypical pattern of so called motor units 44. These comprise of a α-motoneuron in the spinal cord propagating efferent signals from the central first motoneuron to the neuromuscular endplate. At this nerve terminal, action potentials cause acetylcholine (ACh) release into the subsynaptic space, where the neurotransmitter interacts with muscle surface receptors (ACh-receptors) and helps to transmit the signal through rapid changes of ionic conductance ultimately resulting in an all-or-none twitch contraction 53. The properties of motor neurons have a direct influence on the characteristic of skeletal muscle fiber composition 102. Typically, one α-motoneuron excites numerous (circa 1500 in M. gastrocnemius) myofibers at once (principle of divergence) and instead

8 Introduction

of a rapid all-or-non-twitch, these fibers undergo slow, graded contractions that increase muscle force only slowly and to a small degree. Most muscles with mixed fibers possess a majority of fast-twitch characteristics, which is in line with descriptions of similar fibers from Mm. extensor digitorum longus (EDL) of endurance-trained rats 77. In summary, anatomical and physiological properties of each type of skeletal muscle are constituted according to the required functional role enabling sophisticated and fine movements as well as fatigue resistance.

1.1.2. Known transcriptional changes in muscle atrophy and wasting

A general loss of muscle mass is a characteristic response to a wide range of systemic diseases such as or cachexia, whereas disuse or denervation leads to atrophy in only specific muscles 70. In X-linked neuromuscular diseases such as DMD, muscle atrophy or wasting secondarily caused by respiratory insufficiency may even contribute to disease progression by additional muscle damage. Most types of muscle atrophy share an overall increase of protein degradation and suppressed rates of protein synthesis 128, and are characterized by activating a common set of transcriptional changes 71. The accelerated proteolysis appears mainly to be due to an activation of the ubiquitin - proteasome pathway 54. A number of experimental models of human diseases such as diabetes 105, cancer, cachexia 144,75, acidosis 82, COPD 56 or disuse atrophy 125 have all shown the involvement of ubiquitin in the proteolytic system. A reduction in activity of the insulin like growth factor-1 (IGF-1) and PI3K / Akt, a major growth related signaling pathway, leads to the induction of ubiquitin ligases atrogin-1 and MuRF1 10,41, which are again regulated by the FoxO family of transcription factors 109. Akt normally blocks the activation of atrogins, but activates downstream branches such as the mTOR pathway 47 which controls protein synthesis. Thus, together with FoxO it mediates the crosstalk between protein breakdown and synthesis 120. Furthermore, multiple studies have shown that other proteolytic pathways such as the autophagy / lysosome system mediated protein breakdown are recruited in various types of muscle atrophy and contribute to the overall atrophy program with protein breakdown 29,84,137,142. While there is general agreement that various disease states causing muscle atrophy share a common transcriptional program, it remains a conundrum whether sustained chronic hypoxia, in contrast to the adaptive response to acute hypoxia, has a detrimental effect on

9 Introduction

skeletal muscle. It also remains unknown if the effects of chronic hypoxia or respiratory insufficiency on skeletal muscle leading to muscle wasting share the same or a hypoxia specific transcriptional program as in other disease states, as the exact change in differential gene regulation of muscle exposed to chronic hypoxia is only partially understood.

1.2 Hypoxia

Oxygen is essential for all organisms and a change in oxygen concentration either in the form of insufficient oxygen content or excess of oxygen presents a fundamental physiological stimulus. Thus, maintenance of oxygen homeostasis is critical for all nucleated cells in the human body, which are able to sense oxygen and respond to oxygen deficiency accordingly. Tissue hypoxia may result from many conditions including environmental (high altitude exposure) or pathological conditions (e.g. DMD, COPD, anemia, chronic heart failure or tumor growth) and can be present as acute or chronic manifestation. However, acute and chronic hypoxic responses vary greatly depending on tissue type. While the adaptive responses to acute hypoxia in skeletal muscle are understood, the mechanism of muscle wasting during chronic hypoxia remains poorly understood. Interestingly, one unique aspect of skeletal muscle hypoxia is that its oxygen balance may be even further compromised by additional exercise or that exercise itself may result in tissue hypoxia under normoxic conditions.

1.2.1 Major molecular response to hypoxia via the hypoxia inducible factor (HIF)

The key protein involved in response to hypoxia is the transcription factor hypoxia- inducible factor-1 (HIF-1) 112 136. HIF-1, consisting of an oxygen regulated alpha and beta subunit, is ubiquitously expressed in all mammalian tissues including skeletal muscle 140. A second isoform HIF-2α as well as a third isoform Hif-3α exists, but is not uniformly expressed in all tissue types and does not universally respond to hypoxia 115. HIF-1α acts as a global regulator of oxygen homeostasis mediating both oxygen delivery as well as adaptation to hypoxic states by modifying expression of HIF-1 downstream targets 114. Under non-hypoxic conditions HIF-1α is rapidly hydroxylated by prolyl hydroxylase domain (PHDs) 58,85,15,35,81. It interacts with the von Hippel-Lindau (VHL) protein

10 Introduction

which targets HIF-1α for proteasomal degradation as a subunit of an ubiquitin–protein 24,127. Interestingly, the VHL protein functions both in adaptive hypoxic processes as well as in VHL defective tumorgenesis 58. Under hypoxic conditions HIF-1α hydroxylation reactions are inhibited and HIF-1α accumulates rapidly via dimerization with HIF-1ß also called aryl hydrocarbon receptor nuclear translocator (ARNT) with a half-life of only about 5 min 124,4,31. By binding to its hypoxia response elements, HIF-1α as a master transcriptional activator, is able to induce more than 100 genes, including vascular endothelial growth factor (VEGF), endothelin 1, erythropoietin (EPO), pyruvate dehydrogenase kinase 1 (PDK1) and glucose transporter 1 (GLUT-1) as well as various growth factors such as tumor necrosis factor α, epidermal growth factor or fibroblast growth factor 138,63. Thus, differential regulation of these genes by HIF-1α is aimed at promoting cell survival, angiogenesis, vascular remodeling, erythropoiesis and mediating a switch from oxidative to glycolytic metabolism under hypoxic conditions. All target genes of HIF-1α are regulated with the primary goal to adapt to the changed environment, and to compensate and adjust to the lack of oxygen in order to meet the physiological demand according to the respective tissue type 138,116,86,80.

1.2.2 The role of HIF-1α in skeletal muscle

Although HIF-1α is recognized as a master regulator of cellular responses to hypoxia its role in the adaptation process during hypoxia in skeletal muscle is still state of current debate. In skeletal muscle the HIF-1α protein is highly expressed even under normoxia and increases even after very acute systemic hypoxia 3,104,122. However, within the first hours of hypoxia, HIF-1α mRNA is again down modulated and expression levels decline towards normoxic levels thereafter 21. This has been shown in brain, liver and kidney, but not in skeletal muscle. However, a similar, time dependant regulation pattern in skeletal muscle is hypothesized. Evidence that the HIF-1α protein in human skeletal muscle is only diminutively altered when exposed to high altitude (acute and chronic exposure) exists 133. In contrast, angiogenesis and metabolic adaptations via VEGF and several glycolytic are well known to be mediated by HIF-1α hypoxia and have been shown to be differentially regulated in skeletal muscle hypoxia playing an important part in the adaptive response. Therefore, the key role of HIF-1α on adaptive mechanisms in hypoxic

11 Introduction

skeletal muscle tissue with the induction of several downstream targets is well documented, whereas its role during sustained chronic hypoxia is still debated.

1.2.3 Response of skeletal muscle to hypoxia

Potential adaptive responses of human skeletal muscle to hypoxia have first been described by Reynafarje in 1962 106. Today, there is ample evidence that adaptive responses to counteract acute hypoxic states exist, but that long term exposure to sustained hypoxia, e.g. during an expedition to the Himalayas, leads to general loss of body weight 11, followed by reduction of lean body mass and also muscle wasting 51,61. It seems, that the decrease in body weight and muscle mass during chronic high altitude or hypoxia exposure cannot solely be attributed to malabsorption, changes in nutrient preferences or eating habits 59. In fact it has been demonstrated that even an increased dietary protein intake in rats exposed to high altitude remained ineffective in reducing the depression of muscle tissue growth 9. Morphologically, chronic hypoxia exposure does not alter the capillary number per muscle fiber 76,83. However, later studies have described a decrease in fiber size resulting in more capillaries per area, but it is speculated that this decrease in fiber size is caused by down- regulation of protein synthesis during hypoxia 51,78,56. On the other hand exposure to hypoxia leads to a series of molecular adaptations in skeletal muscle. Both in vivo and in vitro studies have shown, that the transcriptional activator HIF-1α, which up-regulates glycolytic enzymes under hypoxia to increase anaerobic ATP production by converting glucose to lactate in order to compensate for reduced mitochondrial oxidative phosphorylation, plays a crucial role in regulating the shift from aerobic to anaerobic ATP production 18,52,135,42,111. However, this switch to cannot maintain the levels of total cellular ATP normally produced by oxidative phosphorylation. In addition, the activation of the anaerobic pathway by hypoxia increases the oxidative stress because of mitochondrial generation of reactive oxygen species (ROS) 20,45. For regulation of mitochondrial volume and decreased mitochondrial density, several genes have been described. A key role for attenuating ROS production has been described for cytochrome c oxidase and peroxisome proliferator-activated receptor y co-activator-1ß and α 141. In addition mitochondrial autophagy processes are believed to enable sufficient oxygen supply and thus reduce and counteract against ROS production 45. Furthermore it has recently been shown that transactivation of pyruvate dehydrogenase kinase 1 (PDK-1)

12 Introduction

by HIF-1α plays a crucial role in inhibiting acylCoa production by blocking the entry of pyruvate into the Krebs cycle and thus preserving cell and tissue integrity from ROS mediated damage under hypoxia 66,99. To maintain the energy homeostasis in skeletal muscle under hypoxia a greater shift to anaerobic glycolysis is apparent. In fact, several genes encoding glycolytic enzymes have been found to have HIF-1α responsive elements 113. Additional mechanisms mediated by HIF-1α for improving oxygen delivery to the muscle cell under hypoxia include VEGF and EPO 36. All these molecular changes in gene regulation in skeletal muscle during hypoxia are aimed to adapt to the changed environment. However, these adaptive mechanisms may be regulated differently during severe chronic hypoxia as noted in skeletal muscle biopsies taken from climbers returning from above 8000m, which may be of clinical importance in diseases with chronic hypoxia such as DMD 43,52.

1.3 Disease propensity – Duchenne Muscular Dystrophy

In the following chapter I would like to give a short overview over Duchenne Muscular Dystrophy, the most common X-linked neuromuscular disease that predominantly affects skeletal muscle leading to respiratory insufficiency followed by eminent hypoxemia.

1.3.1 Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy is the most common X-linked neuromuscular disease, affecting boys with a frequency of 1 in 3500 33. Clinical features are: a congenital origin, the manifestation of the disease in later childhood, respiratory insufficiency with disease progression and continuous muscle wasting. It is clinically noted that especially the lower trunk muscles are weak. This manifests itself in the way patients try to raise from the floor, using their hands to push themselves up (Figure 1). Male Figure 1. Gowers‘ sign patients affected by DMD are usually diagnosed at When raising from the floor, DMD patients typically use their hands to push themselves up. age 4-6 years. At this stage the disorder is

13 Introduction

manifested by enlarged calves, clumsiness when walking and soon thereafter the affected patient may have problems to stand up from the floor, climbing stairs or running. As the disorder progressively affects skeletal muscles, the ability to walk is lost at age 7-12 years 68. Bound to a wheelchair they rarely survive the third decade of their life and most DMD patients die due to respiratory or cardiac failure because of progressive damage to the diaphragm or cardiac myopathy 32.

1.3.2 Molecular biology of DMD

Duchenne Muscular Dystrophy is caused by deletions in the dystrophin gene located on Xp21. The translated full-length protein is 427 kD and has been shown to be a member of

Figure 2. Synaptic overview

Connection between costameric actin and the sarcolemma / extra-cellular matrix at the synapse (left) and extra-synaptic (right). Courtesy of TOB Krag, PhD, Department of Neurology, Rigshospitalet, Denmark.

14 Introduction

the β- / α- family of proteins due to its high number of spectrin repeats 48. The full-length gene product is predominantly expressed in skeletal muscle and cardiac muscle; there are several tissue-specific promoters of dystrophin and three independently regulated promoters exist for the full-length dystrophin 49. In addition to the full-length gene product, there are several isoforms expressed in various tissues resulting from alternative splicing of the 3’-end 65. The full-length dystrophin consists of a number of functional domains 2. In muscle, it is expressed throughout the sarcolemma and provides a link between actin and the sarcolemma through binding to the dystrophin-associated proteins (DAP) consisting of the dystroglycan / sarcoglycan complex (DGC/SGC), sarcospan (SS), dystrobrevin (DB) and syntrophins (SYN) as demonstrated in Figure 2 14. Defects in the DGC/SGC have been identified to cause the disease.

1.3.3 Pathogenesis

As mentioned above, the exact mechanism of the pathogenesis is not fully understood 26. It is believed that the lack of dystrophin causes membrane disruptions or increased membrane permeability as the membrane is unable to resist mechanical stress 101. Electron microscopic studies have shown membrane disruptions, so called delta lesions, in dystrophin deficient muscle 89. These are thought to lead to an influx of extra-cellular [Ca2+] and secondarily to a potential [Ca2+] leakage from the sarcoplasmatic reticulum. High concentrations of intracellular [Ca2+] again might lead to activation of proteolytic enzymes such as calpains, resulting in large scale proteolytic activity and possibly even more calcium influx due to modifications of the calcium leak channels 131,27. These events finally lead to necrosis, phagocytosis and ultimately fibrosis, which determine the long term fate of the muscle. In advanced stages of the disease, respiratory insufficiency due to severe muscle dysfunction is believed to be the most common cause of death in DMD patients. Even though pharmacological therapeutic strategies for DMD exist (often with adverse side effects) and recent research investigates potential novel therapeutic strategies, it seems critical to reveal the exact molecular mechanism of muscle wasting caused by sustained, chronic hypoxia to further improve the clinical treatment of DMD 92,19,87,23. This led to our study hypothesis investigating the direct, detrimental effect of chronic hypoxia on muscle tissue on a molecular level.

15 Introduction

1.4 Aim of the study and hypothesis

It is generally accepted that respiratory insufficiency followed by hypoxemia plays an important part in the morbidity and mortality of neuromuscular disorders (NMDs) such as DMD 103. But hypoxemia is also a feature of many other diseases including pulmonary hypertension, COPD, cancer, sepsis or diabetes mellitus affecting various oxygen dependant tissues Figure 3. Hypothesis of hypoxia mediated muscle damage 46,39,67,56. Since there is Hypothesis of hypoxia mediated muscle damage predicting acceleration of disease progression in neuromuscular disorders by setting up a vicious cycle where ample evidence that long ventilatory/respiratory insufficiency leads to hypoxia induced muscle damage followed by muscle wasting and dysfunction, which in turn worsens the term exposure to chronic respiratory insufficiency. hypoxia leads to general loss of body and muscle weight 11,61, we started to think of a hypothesis, where chronic hypoxia itself leads to muscle damage, followed by muscle wasting and dysfunction, predicting an acceleration of disease progression in DMD by setting up a vicious cycle (Figure 3). We hypothesize that the pre - existing respiratory insufficiency in DMD patients due to muscle dysfunction caused by the genetic deletion in the dystrophin gene leads to chronic hypoxemia and hypoxic muscle damage, which in turn worsens the respiratory insufficiency. Relatively little is known about the potential damaging effects of hypoxia on skeletal muscle on a molecular level. Unravelling this conundrum of the exact molecular makeup of skeletal muscle exposed to chronic hypoxia may prove to be of great clinical importance, since the molecular mechanisms may potentially be translated into therapeutic strategies for e.g. DMD patients with respiratory insufficiency. The primary goal of this study was to identify the molecular properties of wildtype control vs. hypoxic skeletal muscles. Therefore profiling of control and hypoxic skeletal

16 Introduction

muscle samples using Affymetrix® Mouse 430 ver. 2.0 GeneChips (ca. 45,000 mouse probe sets) was performed. Independent verification of expression changes was undertaken at RNA, protein and level using a variety of cellular and molecular biological methods.

17 Materials & Methods

2 Materials & Methods

2.1 Materials

2.1.1 Materials for hypoxia exposure

Pegas 4000F gas mixer Columbus Instruments, USA

O2 tanks AirGas, USA

N2 tanks AirGas, USA Self made plastic hypoxia chamber

2.1.2 Materials for monitoring hematocrit levels

70% EtOH Sigma-Aldrich, USA Kimwipes Kimberly-Clark, USA Sterile lancets Caremax Inc, USA Micro-hematocrit capillariy tubes, heparinized Globe scientific, USA Crit-o-seal Cascade Healthcare, USA Clinical centrifuge fitted with hematocrit head Damon/IEL, USA Damon Micro-capillary reader Damon/IEL, USA

2.1.3 Materials for molecular biology

AB104-S and 602-S Balance Mettler Toledo, USA AB15 pH Meter Accumet, USA Centrifuge 5415 D Eppendorf, USA Speed Centrifuge Sorval Fisher Scientific, USA Force 7 micro-centrifuge Denver Instruments, USA PTC 200 Thermo-Cycler MJ Research, USA Stirrer Hot Plater PC420D Corning Life Sciences, USA Fotodyne 5-5333 Hood Fotodyne Inc., USA GeneQuant Pro RNA/DNA Calculator Amersham Biotech, UK Nanodrop ND-1000 spectrophotometer NanoDrop Technologies, USA

18 Materials & Methods

Agilent 2100 Bioanalyzer Agilent Technologies Inc., USA GeneChip® One-Cycle Target Labeling kit Affymetrix, USA G2500A GeneArray scanner Agilent Technologies Inc., USA Affymetrix® Mouse 430 ver. 2.0 GeneChip arrays Affymetrix, USA Orbit 1000 Shaker Labnet International, USA PTC-200 Peltier Thermal Cycler Applied Biosystems, USA Typhoon 8600 Fluorescence Imager Molecular Dynamics, USA Isotemp Incubator 655D Fisher Scientific, USA Incubator/Shaker 211 Labnet, USA UltraPure TM Agarose Invitrogen, USA Chloroform Molecular Biology Grade FisherBiotech, USA 1kb/100bp Ladder New England BioLabs, USA 2-Mercaptoethanol Sigma, USA DEPC-treated Water Ambion, USA Ethanol absolute molecular biology grade Sigma-Aldrich, USA 2-Propanol, 99.5+%, A. C. S. reagent Sigma-Aldrich, USA FastStart Taq DNA Polymerase Roche Diagnostics, Germany Gel Loading Solution Sigma, USA PCR Nucleotide Mix Roche Diagnostics, USA ABI 7900HT real time PCR system Applied Biosystems, USA Ready-LoadTM 100 bp DNA-Ladder Invitrogen, USA 384-Well Clear Optical Reaction Plate Applied Biosystems, USA Optical Adhesive Cover Starter Kit Applied Biosystems, USA RNeasy Mini and Micro Kit Qiagen Sciences, USA Quiaquick gel extraction kit Qiagen Sciences, USA Endo Free Plasmid Maxi and Mini kit Qiagen Sciences, USA Qiaprep Spin Miniprep kit Qiagen Sciences, USA PCR purification kit Qiagen Sciences, USA Multiple Tissue cDNA, MTC TM Clonetech, USA 2x TaqMan universal PCR Master Mix Applied Biosystems, USA Topo TA Cloning kit Invitrogen, USA One Shot Top10 cells Invitrogen, USA Prltk-vector Promega, USA

19 Materials & Methods

pgL3 vector Promega, USA SuperScript First-Strand Synthesis System Invitrogen, USA SYBR Green I nucleic acid gel stain Molecular Probes, USA SYBR GREEN PCR Master Mix Applied Biosystems, USA 2,2,2-Tribromoethanol, 97% Aldrich, USA Tri-Reagent Ambion, USA Sodium Hydroxide Molecular Biology Grade Fisher Scientific, USA Sodium Acetate, Trihydrate, Grade Fisher Scientific, USA Hydrochloric Acid Sigma, USA Isopentane (2-Methylbutane, 99.5%, HPLC grade) Sigma-Aldrich, USA UltraPureTM 10X TAE Buffer Invitrogen, USA S.O.C. Medium Invitrogen, USA Dimethyl sulfoxide (DMSO) Sigma-Aldrich, USA BD Difeco LB Broth Invitrogen, USA

2.1.4 Materials for immunohistochemistry

MagnaFire CCD camera Olympus, USA Olympus BX51 research microscope Olympus, USA Reichert-Jung 1800 cryostat Reichert-Jung, USA Superfrost Plus electrostatically charged slides Menzel-Gläser, Germany Alexa Fluor 488 Molecular Probes, USA Cy3 Jackson Immunolabs, USA Fetal Bovine Serum, triple 0.1 microm filtered Atlanta Biologicals, USA Glycerol ultraPureTM GibcoBRL, USA Methanol, absolute, acetone free Sigma, USA O.C.T. (Optimal Cutting Temperature) Compound Ted Pella, INC., USA Potassium Chloride, Enzyme Grade, crystal Fisher Scientific, USA Potassium Phosphate, monobasic, anhydrous SigmaAldrich, USA Sodium Chloride, Enzyme Grade FisherBiotech, USA Sodium Phosphate, dibasic, heptahydrate Sigma, USA Triton X-100 Sigma, USA Nikon microscope CCD camera Nikon, Tokyo, Japan

20 Materials & Methods

2.1.5 Materials for cell culture and luciferase assay

DMEM Gibco, USA 2% Horse serum SigmaAldrich, USA Trypson 0.25% Gibco, USA Fetal Bovine Serum (FBS) Hyclone, USA Calf Serum Hyclone, USA Geneticin, G-418 Gibco, USA Lipofectamine 2000 Invitrogen, USA Opti MEM Gibco, USA

3T3/C2C12 cells ATCC, USA Hood Sterile GuardIII Advance The Baker Company, USA

Jacketed Incubator Co2/H2O Thermo Scientific, USA 5/10/25ml sterile pipetts SigmaAldrich, USA Culture dishes 10/35mm SigmaAldrich, USA Dual Luciferase Reporter Assay E 1910 Promega, USA TD 20/20 Luminometer Turner Designs, USA

Invivo2 Ruskinn Technologies Ltd., UK

2.1.6 List of primer pairs

HARP (Gene ID: 27528): forward primer: 5’-CTC TGG CTT GAG TTT CTT GTG C-3’, reverse primer: 5’-CAA AAG CCT GCA TTT TCG G-3’ Aqp4 (Gene ID: 11829): forward primer: 5’-GCA TGA ATC CAG CTC GAT CTT T-3’, reverse primer: 5’-TTT GAG CTC CAC ATC AGG ACA G-3’

Atp1b2 (Gene ID 11932): forward primer: 5’-TAT GGT TAC AGC ACC GGG C-3’, reverse primer: 5’-CAG GGA ACA TGA CAA AGT GGC-3’

Grb14 (Gene ID 50915) forward primer: 5’-ACG GAA GCC CCA GTG CC-3’, reverse primer: 5’-TCC CGT ACC AAG AAA ACT CCA TC-3’

Mt1 (Gene ID 17748) forward primer: 5’-CAG CTT CAC CAG ATC TCG GAA-3’, reverse primer: 5’-CAT TTG GAG CAG CCC ACG-3’

21 Materials & Methods

Mustn1 (Gene ID 66175) forward primer: 5’-CCT GCC AGA GAG CTA CCA ACA-3’, reverse primer: 5’-GAT GTC CTG GTT CTT GGC CA-3’

Ankrd10 (Gene ID 102334) forward primer: 5’-GAT GAT GCC GAC AGA ATG CA-3’, reverse primer: 5’-TCT CCA TCC CAT TGA GCT GG-3’

Runx1 (Gene ID 12394) forward primer: 5’-GGT CGT GAG GAA TCC CAA AAT-3’, reverse primer: 5’-CTG GCC TGG GCT ACT GAG AA-3’

GAPDH (Gene ID: 14433): forward primer: 5’-AAG GGT GGA GCC AAA AGG GT-3’, reverse primer: 5’-CAT GGA CTG TGG TCA TGA GCC-3’

All primers were designed using Primer Express® (Applied Biosystems Inc., Foster City, CA, USA) and met the following criteria: GC content = 50-60%, Tm = 56 °C +/- 0.6°C, length = 20-25 nucleotides, no secondary structures predicted using MacVector 6.0 (Accelrys, San Diego, CA, USA) and were custom made by Invitrogen (Invitrogen, Carlsbad, CA, USA) and Idaho Technology Inc. (Idaho Technology Inc., Salt Lake City, UT, USA).

TaqMan Assays®

HARP: Mm00474046_m1, Applied Biosystems, USA Beta-actin: Mm4352933_E, Applied Biosystems, USA

2.2 Methods

2.2.1 Animals and hypoxia exposure

Adult normal (C57Bl/10) mice aged 10-12 weeks obtained from Jackson Laboratory (Jackson Laboratory, Bar Harbor, Maine, USA) were used in the experiments. At the beginning of the experiment mice were divided into two groups, with 16 controls (room condition) and 16 hypoxic (hypoxic condition) mice. For conditioning, the hypoxic group was gradually exposed to lower levels of hypoxia, in a specially designed and hermetically closed hypoxic chamber, using a special gas mixing system Pegas 4000 MF (Columbus

Instruments, Ohio, USA). Details are shown in supplementary data1. The oxygen (O2)

22 Materials & Methods

level was decreased from 21% to 8% according to our acclimatization protocol used on a previous high altitude climb to an altitude of approximately 6200m as noted in 90 supplementary data2 . The oxygen pressure (PO2) was calculated according to the reached altitudes by using the formula developed by JB West 139. Animal’s weight and food intake were monitored daily. Food and water were changed daily during the course of the experiment and available ad libitum. After 15 days animals were euthanized using CO2 and EDL, soleus, QF, TA and diaphragm muscles were dissected for expression profiling and validation at the mRNA level, functional and structural evaluation as well as physiological studies (data not shown as physiological studies were not performed by the author). All animal studies were carried out in accordance to the animal welfare Act of University of Pennsylvania.

2.2.2 Hematocrit level monitoring

Before and after the experiments the hematocrit was monitored by taking blood from the tail vein of control and hypoxic animals. Briefly, to determine the percent of formed cells in blood, of which 99% are red blood cells (RBCs), a fresh sample of tail vein blood was introduced into a capillary tube coated with heparin to prevent clotting (Damon, IEL, USA), the end sealed with a putty (Crit-o-seal, Cascade Healthcare, OR, USA), and the tube centrifuged to sediment the cells at lowest speed for 3min using a microcentrifuge (Damon, IEL, USA). The straw colored supernatant is the plasma, the RBCs sink to the bottom, and the white blood cells (WBCs) are seen as a thin buffy coat at the top of the RBC column. By determining the percent of the total amount of cells represented by the packed cells, the percent of RBCs in whole blood was determined using a micro-capillary hematocrit reader (Damon, IEL, USA) according to instructions on the chart which presents a standard protocol for monitoring hematocrit levels as described previously 134,6.

2.2.3 RNA isolation

Total RNA was isolated from each QF muscle by Tri-Reagent (Ambion, Austin, TX, USA) as described by the manufacturer. Isolated total RNA was cleaned up by RNeasy mini kit (Qiagen, Hilden, NRW, Germany) according to manufacturer’s instructions. Briefly, frozen muscle tissue was pulverized in liquid nitrogen using a mortar and pestle and

23 Materials & Methods

transferred into Tri-Reagent. The solution was centrifuged at 12,000 rpm for 10 min at 4°C and the supernatant was transferred to a fresh tube. RNA was purified by phenol/chloroform extraction, precipitated using isopropyl alcohol and finally re- suspended in diethyl pyrocarbonate (DEPC) treated water (Ambion, Austin, TX, USA). This RNA solution was re-purified using the RNeasy kit and eluted with 40 µl of DEPC- treated water. The purity and concentration of total RNA were determined by measurement of absorbance at 260 and 280nm using a Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). To satisfy our set purity criteria, we discarded all RNA samples that did not have a 260/280 ratio between 1.8 and 2.1. To satisfy our set criteria for integrity, we required that all RNA used in our experiments had single peaks for the 18S and 28S bands as determined by the Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA). Muscle tissue samples used in the study were obtained and processed with appropriate institutional approvals and in accordance with the animal welfare Act of University of Pennsylvania.

2.2.4 Affymetrix GeneChip expression profiling

Affymetrix® Mouse 430 ver. 2.0 GeneChip arrays (Affymetrix, Santa Clara, CA, USA) were screened using guidelines provided as previously described (http://www.affymetrix.com/support/technical/manual/expression_manual.affx) and optimized for expression profiling of skeletal muscle. 22,37,5

2.2.4.1 Linear amplification and cRNA labeling

Three microliter total RNA was used for each sample to obtain linearly amplified labeled cRNA by using the GeneChip® One-Cycle Target Labeling kit (Affymetrix, Santa Clara, CA, USA) as described by the manufacturer. Briefly, total RNA was used to generate double-stranded cDNA with the T7-oligo (dT) primer. This double-stranded cDNA was used in in vitro transcription and biotin labeling steps. Labeled cRNA yield and purity were determined by measuring the absorbance at 260 and 280nm. Criteria for excellent quality of cRNA 260/280 ratios were set between 1.9 and 2.1. Quality control of the labeled cRNA products were assessed by performing 1µg labeled cRNA on 2% agarose gels to see similar RNA smear type.

24 Materials & Methods

2.2.4.2 Fragmentation and microarray hybridization

Fifteen micrograms labeled cRNA of hypoxic and control QF muscle were fragmented and 10µg hybridized to Affymetrix® Mouse 430 ver. 2.0 GeneChip arrays (Affymetrix, Santa Clara, CA, USA) for 18–24hrs. Each microarray was washed and stained with streptavidin–phycoerythrin and scanned at a 6-µm resolution with Agilents model G2500A GeneArray scanner (Agilent Technologies Inc., Santa Clara, CA, USA). A visual quality control measurement was performed to ensure proper hybridization after each chip was scanned. Other quality control parameters such as scaling factors used to normalize the chips, average background, and noise were also evaluated. In addition, raw intensities for each probe set were stored in electronic formats by the GeneChip Operating System version 1.1 (GCOS1.1, Affymetrix, Santa Clara, CA, USA). Probe set expression summaries were calculated with the Microarray Suite version 5.0 (MAS5) algorithms. In brief, the intensity of one probe cell containing an oligonucleotide complementary to a specific sequence or perfect match (PM) was compared to the intensity of the adjacent probe cell which contains one mismatch (MM) in the center of the oligonucleotide. This analysis was repeated subsequently using multiple probe pairs for each gene. The expression level of each gene was calculated based on a comparison of hybridization, i.e. of the PM vs. MM signal. Differential expression of genes with consistent fold changes (≥ 2-fold) in QF was detected using previously described statistical methods that result in efficient detection of the most significant gene expression change22,37,5. Raw data is accessible by GSE9400 accession number in the Gene Expression Omnibus (GEO) public database at the national center for biotechnology institute (NCBI, http://www.ncbi.nlm.nih.gov/sites/entrez?db=geo). Data was normalized by GC-RMA algorithm in Genespring ver7.0 (Agilent Technologies Inc., Santa Clara, CA, USA) software and filtered to obtain at least one present call for each transcript in any condition at MAS5 output. Differentially expressed genes were identified by applying the two class unpaired data settings in Significant Microarray Analysis (SAM) ver. 2.21 software at the level of 0.1% false discovery rate (FDR). Differentially expressed genes were clustered by using the functional annotation-clustering tool of the Database for Annotation, Visualization and Integrated Discovery (DAVID 2010) (http://david.abcc.ncifcrf.gov). Functional Annotation Clustering reports groups of similar annotations, which makes the biology clearer and more focused to be read vs.

25 Materials & Methods

traditional chart reports. The grouping algorithm is based on the hypothesis that similar annotations should have similar gene members. The Functional Annotation Clustering integrates kappa statistics 25 to measure the degree of common genes between two annotations, and fuzzy heuristic clustering 7,8 to classify the groups of similar annotations according to kappa values. In this sense, the more common gene annotations share the higher chance, that they will be grouped together. The p-values associated with each annotation term inside each cluster have exactly the same meaning/values as p-values (Fisher Exact/ EASE Score) shown in the regular chart report for the same terms. The Group Enrichment Score (ER), the geometric mean (in -log scale) of member's p-values in a corresponding annotation cluster, is used to rank their biological significance. Thus, the top ranked annotation groups most likely have consistent lower p-values for their annotation members. The analysis parameters were: kappa similarity overlap of 6; 1 similarity threshold; initial and final group membership classification of 3; and 0.5 multiple linkage thresholds.

2.2.5 Quantitative RT-PCR

Briefly, total RNA were isolated and purified from each QF muscle or C2C12 cells by RNeasy micro kit (Qiagen, Hilden, Germany) as described. The purity and concentration of total RNA were determined as above gene chip samples. Subsequently, equal amounts of total RNA from each sample were then reverse transcribed into cDNA using oligo (dT) primers (Invitrogen, Carlsbad, CA, USA). These cDNA samples were amplified in an ABI 7900HT real time PCR system (Applied Biosystems Inc, Foster City, CA, USA) using gene specific primers (Idaho Technology Inc., Salt Lake City, UT, USA) noted above. PCR amplification was carried out in 20 µL of reaction mixture consisting of a 2x SYBR-PCR master mix (Applied Biosystems Inc, Foster City, CA, USA) and 0.5 µM of each gene specific primer pair. Amplification parameters consisted of 95˚C 10 min hot start DNA polymerase followed by 40 cycles of 95˚C for 10 s (denaturing) and 60˚C for 15 s (annealing/extension). SYBR Green I fluorescence intensity was measured at the end of each 60˚C cycle and during the 60-95˚C ramp of dissociation (i.e., immediately before the next cycle). Gene products were analyzed with the Sequence Detection Software (Applied Biosystems Inc, Foster City, CA, USA, version 2.2). The specificity of these products was confirmed with a single peak under the dissociation curve

26 Materials & Methods

analysis. We used the Delta Delta Ct method for computing gene expression levels as described 74 Relative quantification was presented as a fold change in gene expression between hypoxic and control conditions. A MTC TM (Multiple Tissue cDNA) cDNA panel (Clonetech Laboratories, Mountain View, CA, USA) was used to determine the expression levels of HARP in various adult tissues by semi-quantitative RT-PCR using FAM conjugated TaqMan probes (Applied Biosystems Inc, Foster City, CA, USA) specific for HARP (Mm00474046_m1 Applied Biosystems Inc, Foster City, CA, USA) and beta actin (4352933E, Applied Biosystems Inc, Foster City, CA, USA) which was used as endogenous control. 2ng of cDNA was amplified in 20µl of reaction mixtures containing 1pmole of forward and reverse primers each, 10 µl of 2x TaqMan universal PCR master mix, and 0.25 µM probes. The amplification was performed in a 7900HT Sequence Detection System (Applied Biosystems Inc, Foster City, CA, USA). Samples were run in triplicates and a serial dilution of linearized plasmid DNA was used to generate standard curves for detecting HARP and beta actin mRNA copy numbers.

2.2.6 Immunohistochemistry

In addition to the samples used for expression profiling, hypoxic and control muscles of EDL, soleus and diaphragm were used for independent biological validation by immunohistochemistry. All tissue sections were processed as previously described 37. Briefly, the samples were mounted on cardboard, rapidly frozen in liquid nitrogen chilled isopentane and stored at -80 °C. Serial cross-sections (5-10 µm) were cut using a Reichert- Jung cryostat 1800 (Reichert Microscope, Depew, NY, USA) and fixed in 100% cold methanol for 5 min before processing for immunocytochemistry. Sections were processed with hematoxylin and eosin (H&E) and anti-HARP (HARP, 1:250, Abbiotec, CA, USA) primary antibody. Control sections were processed as above, except that the primary antibody was omitted. No staining was observed in the control sections. Primary antibodies were detected using goat anti-rabbit Alexa Fluor 546 (1:500, Molecular Probes/Invitrogen, CA, USA) or Cy3 labeled (Jackson Immuno Research, West Grove, PA, USA) secondary antibodies. Pictures were taken using an Olympus BX51 microscope equipped with a Magnafire camera. PAS staining was performed according to manufactures instructions (Sigma-Aldrich, St. Louis, MO, USA). Briefly, sections of EDL and soleus muscle were

27 Materials & Methods

fixed in Formalin-Ethanol Fixative Solution, immersed in Periodic Acid Solution and Schiff’s reagent before counterstained in hematoxylin solution. Serial sections were pretreated with alpha amylase enzyme to control for specificity of the glycogen staining procedure.

2.2.7 Cloning of HARP promoter

The HARP promoter luciferase expression construct (HARP-luc) was generated cloning a mouse 1.3kb fragment of HARP (GenBank ID: EU447302.1) into the expression vector of pGL3 (Promega, Madison, WI, USA). Briefly, based on bioinformatics results, a 1.3kb HindIII/ HindIII PCR fragment of the HARP promoter region, was amplified using genomic mouse cDNA as template with (HARP-F: 5’-GCT CCC TGT TCA CTC TCT GCT TTC TAC ACC -3’, HARP-R: 5’-TGA TAC CCT GCT GTC CTG GCA ACG -3’) as a specific primer. This fragment was cloned into pGL3 vector using TOPO TA vector (PCR2.1® TOPO vector®, Invitrogen, Carlsbad, CA, USA) to obtain HARP-luc according to manufacturer’s instructions. The clone was sequenced to verify orientation and sequence.

2.2.8 Cell culture and luciferase assay

The C2C12 mouse muscle cell line was cultured as suggested by the provider (ATCC, Rockville, MD, USA) in DMEM culture media (Invitrogen, Carlsbad, CA, USA) supplemented with 10% FBS (Hyclone, South Logan, UT, USA) and 2mM L-glutamine (Invitrogen, Carlsbad, CA, USA). 20x103 cells were plated 16 hrs prior to transfection before 2µg of pGL3_HARP construct was co-transfected along with 5ng of Renilla luciferase (pRL-TK) using Lipofectamine2000 (Invitrogen, Carlsbad, CA, USA). The pRL-TK construct was used to control for transfection efficiency. Media was changed to differentiated media after 24 hrs post transfection (DMEM, supplemented with 2% Horse serum (Sigma-Aldrich, St. Louis, MO, USA). 72 hrs after changing the media cells were exposed to a hypoxic environment for 24 hrs containing

0.5% O2 using an Invivo2 200 hypoxia work station (Ruskinn Technologies Ltd., Leeds, West Yorkshire, UK). As a normoxic control, cells were kept under normoxic conditions at

CO2 5%. Post 24 hrs Dual Luciferase Assay (Promega, Madison, WI, USA) was performed

28 Materials & Methods

according to manufacturer instructions. All samples were analyzed using a TD -20/20 Luminometer (Turner Designs, Sunnyvale, CA, USA).

2.2.9 Statistical analysis

Two-way ANOVA (Fig.4A) and Student's t-test (all other figures) were used for determining statistical significance of the results. Level of significance was set at p - value < 0.05. The following convention is used for graphical representation: mean±SD; control

C57Bl/10 mice (blue); hypoxic C57Bl/10 mice (red).

29 Results

3 Results

3. 1 Effects of chronic hypoxia on body and muscle weight as well as hematocrit

In order to determine the effects of chronic hypoxia exposure in vivo, 16 mice were exposed for 15 days according to an acclimatization protocol as described in supplementary data2 from 21% to 8% of oxygen (O2). As shown in Figure 4A, body weights of the hypoxic group dropped significantly from day 3 compared with controls. During the hypoxia exposure at the end of the trial three animals of the hypoxic group died due to the effects of hypoxia (ncontrol =16, nhypoxic =13; p<0.0001; two-way ANOVA).

Figure 4. Effects of chronic hypoxia on body, muscle weight and hematocrit

(A) Comparison of individual body weights (left) as well as mean body weights (right) between control and hypoxic C57Bl/10 mice. Compared to control (blue), hypoxic mice (red) showed significantly decreased body weight within two weeks of exposure to 8% of hypoxia according to our acclimatization protocol. (B) Compared to control mice, there is a significant increase in hematocrit values in the hypoxic group after two weeks of exposure to hypoxia. Change of hematocrit was not significant at the beginning of the trial (C) Comparison of EDL weights in control and hypoxic C57Bl/10 mice. EDL weights were significantly decreased after 2 weeks of hypoxia compared to control EDL.

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As it is well known for the hematocrit levels to increase in response to hypoxia, we validated our hypoxic and control animals by measuring the hematocrit level on the first and last day of the experiment (Figure 4B). A significant increase in hematocrit was observed in hypoxic animals on day 15 compared to controls (49.69±4.80g vs.

63.00±1.60g; ncontrol=12, nhypoxic=8; p<0.0001; two-tailed unpaired student t- test). However, no significant change was observed on day 1, where hypoxic and control animals showed levels within the normal range for mice (48.92±2.64 vs. 48.53±2.71; 110 ncontrol=14, nhypoxic=15; p=0.70; two-tailed unpaired student t- test) . To test our hypothesis, that chronic hypoxia directly leads to muscle wasting, individual muscle weights for Extensor Digitorum Longus (EDL) were quantified by dissecting and weighing after euthanizing the animals. EDL, as illustrated in Figure 4C as well as other leg muscles such as tibialis anterior (TA), quadriceps femoris (QF) and gastrocnemius (see Table1) was significantly lighter in hypoxic than control mice. Exact numbers are shown in Table1.

Table1

Muscle weight of Tibialis anterior, quadriceps femoris and gastrocnemius

Control C57Bl/10 Hypoxic C57Bl/10 nmuscles= 8 nmuscles= 8 EDL EDL weight (mg) 13.29±2.04 10.81±1.65*

TA TA weight (mg) 46.11±3.19 34±2.8*

Quadriceps femoris QF weight (mg) 170.85±18.64 140.2±17.03*

Gastrocnemius Gastroc weight (mg) 177.31±12.56 123.7±10.44*

Results are presented as mean ± S.D.; statistical significance for EDL, TA, QF and gastroc (unpaired two- tailed student t-test) : *p<0.005

3.2 Expression profiling of hypoxic skeletal muscle

To define the expression profile of control versus hypoxic skeletal muscle, Affymetrix® Mouse 430 ver. 2.0 GeneChip arrays were screened with RNA extracted from control and hypoxic mouse quadriceps femoris (QF). Four independent RNA preparations were made

31 Results

for each control and hypoxic group and used for eight independent screening experiments yielding eight individual data sets (4 control and 4 hypoxic data sets). Unsupervised hierarchical clustering was performed for all eight individual data sets to determine the overall similarities within, and differences between, the control and hypoxic profiles. To demonstrate the overall similarity measurements of different genes within each condition (control and hypoxic) and differences between the conditions (control versus hypoxic), normalized raw data was condition-clustered (using control and hypoxic as conditions). According to the branch length of the control and hypoxic sub-trees as illustrated in Figure 5A, the four control samples were closely related to each other, as were the four hypoxic samples; importantly, the four control and four hypoxic samples were highly distinct from one another in a group-specific manner. The expression profiling data was further analyzed using standard bio-informatics and statistical approaches applying GeneSpring ver7.0 software. Based on a two-fold difference cut-off at an FDR of 0.1%, this method revealed 313 overall statistically significant transcripts in hypoxic versus control, of which 252 were found downregulated and 71 upregulated in hypoxia. The heatmap representation of Figure 5A shows all transcripts, that were differentially expressed in control and hypoxic gene chips as horizontally color-coded lines (each horizontal line represents one transcript). In Figure 5B all differentially expressed genes were visualized in a scatter graph, with green lines representing the two fold cutoff differences. Among all genes studied, HARP was noted for the most downregulated and Mustn1 for the most upregulated gene in hypoxia versus normoxia. These genes represent 0.56% (downregulated) and 0.16% (upregulated) of the total probe sets that were screened (45102). 22 transcripts emanated from the same genetic loci, thus reducing the actual number of genes being encoded by the transcripts to 291. Of the 252 genes that were found to be downregulated in hypoxic, 192 were well- characterized genes (representing 76% of all downregulated genes; 0.43% of all probe sets screened) and 60 encoded genes of unknown function (representing 24% of downregulated genes; 0.14% of all probe sets screened). In case of the 71 genes that were found to be upregulated in the hypoxic group, 56 were previously described genes (representing 79% of upregulated genes; 0.12% of all probe sets screened) while 15 encoded genes of unknown function (representing 21% of upregulated genes; 0.04% of all probe sets screened). The set of genes of unknown function are not discussed further in this study. Nevertheless, since this set of genes helps to accurately and comprehensively define the expression profile of skeletal muscle exposed to chronic hypoxia, this set is also part of a

32 Results

supplementary table (see supplementary data3) in the differentially regulated gene list. All primary data have also been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) database (accession number: GSE9400).

Figure 5. Expression profiling with unsupervised hierarchical clustering

(A) Four normoxic controls (C1-4) and four hypoxic samples (H1-4) on Affymetrix mouse 430 v2 GeneChips were overall clustered using the unsupervised hierarchical sample clustering method with pearson correlation similarity measurement in Genespring ver7.1 analysis software. Sample trees were shown based on this similarity on top of each GeneChip as vertical lines (bottom blue and red color codes of boxes were used for C and H samples). Under this overall similarity, 313 statistically significant transcripts were shown as horizontally color coded (see scale bar on top of the heat map figure) lines in each GeneChips as differentially expressed genes in hypoxic condition. (B) 313 differentially expressed genes were visualized in a scatter graph. Gene expressions were presented as normalized logarithmic expressions. Diagonal black lines represent the region of two fold differences. Most upregulated (Mustn1) and most downregulated (HARP) genes were indicated.

33 Results

3.3 Characterization of differentially expressed genes in hypoxic skeletal muscle

The known, differentially expressed genes were sorted into various functional annotation cluster groups (Table 2). Functional annotation clustering of genes was carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID 2.1) (http://david.abcc.ncifcrf.gov/). Affymetrix probe set ID numbers were used, compiled into a file, uploaded and analyzed at highest clustering stringency. The program searched at random 31 functional annotation sources and classified highly related genes into functional annotation groups. Only 21 clusters met our chosen criteria of an Enrichment score (ES) > 2.5 which indicates the highest stringency level and were therefore presented in this table. The ES of each gene cluster is defined as the minus log transformation on the geometric mean of p-values (modified Fisher exact test p-values) from enriched annotation terms associated with one or more of the gene group members. This clustering algorithm was not able to cluster 219 transcripts under the chosen highest stringency level. However, 21 clusters containing 72 different genes met the criteria of highest stringency with an ES > 2.5 and are described in Table 2. The top 3 groups comprised of clusters related to glycolysis and gluconeogenesis (ES=7.95), followed by pyruvate metabolism ES=6.85 and mitochondrial dysfunction (ES=6.42). These groups reached the highest ES in the functional annotation cluster of all differentially expressed genes. The following cluster included genes involved in calcium signaling in skeletal muscle with an ES of 6.18. In descending order the remaining 17 clusters were related to: PI3K/AKT signaling (ES=5.92), α-Adrenergic Signaling (ES=5.15), oxidative phosphorylation and ubiquinone biosynthesis (ES=4.90), GM-CSF signaling (ES=4.81), regulation of eIF4 and mTOR signaling (ES=4.64), galactose, fructose and mannose metabolism (ES=4.48), PPARα/RXRα activation (ES=4.25), citrate cycle (ES=4.20), insulin receptor signaling (ES=3,97), hypoxia signaling (ES=3,55), valine, leucine and ketone bodies (ES=2.94), protein ubiquitination pathway (ES=2.90), ILK signaling (ES=2.68), glycerophospholipid metabolism (ES=2.62), general apoptosis signaling (ES=2.59), FAK signaling (ES=2.56) and inositol metabolism (ES=2.51). The largest cluster was related to calcium signaling (9 out of 72 genes) followed by clusters related to glycolysis/gluconeogenesis, mitochondrial dysfunction and oxidative phosphorylation/ubiquinone biosynthesis (all 8 of 72 genes). The smallest cluster was related to general apoptosis including 2 downregulated genes calpain 3 and endonuclease G (2 of 72 genes).

34 Results

Table 2

Functional Cluster Annotation

Affymetrix ID Gene Symbol Description Fold Change

Cluster 1 Glycolysis and Gluconeogenesis (ES = 7,95)

1448119_at BPGM 2,3-bisphosphoglycerate mutase -11,0 1417951_at ENO3 enolase 3 (beta, muscle) -6,0 1419737_a_at LDHA lactate dehydrogenase A -5,4 1449137_at PDHA1 pyruvate dehydrogenase (lipoamide) alpha 1 -3,9 1416780_at PFKM phosphofructokinase, muscle -7,1 1418373_at PGAM2 phosphoglycerate mutase 2 (muscle) -3,4 1451149_at PGM1 phosphoglucomutase 1 -12,9 1435659_a_at TPI1 triosephosphate 1 -5,9

Cluster 2 Pyruvate Metabolism (ES = 6,85)

1416947_s_at ACAA1 acetyl-Coenzyme A acyltransferase 1 -4,4 1418073_at ACOT9 acyl-CoA thioesterase 9 3,4 1453173_at AKR1B10 aldo-keto reductase family 1, member B10 (aldose reductase) -7,3 1436070_at GLO1 glyoxalase I -3,1 1419737_a_at LDHA lactate dehydrogenase A -5,4 1416478_a_at MDH2 malate dehydrogenase 2, NAD (mitochondrial) -2,1 1449137_at PDHA1 pyruvate dehydrogenase (lipoamide) alpha 1 -3,9

Cluster 3 Mitochondrial Dysfunction (ES = 6,42)

1428323_at GPD2 glycerol-3-phosphate dehydrogenase 2 (mitochondrial) -7,3 1423711_at NDUFAF1 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex -2,2 1416834_x_at NDUFB2 NADH dehydrogenase (ubiquinone) 1 beta subcomplex -3,5 1448198_a_at NDUFB8 NADH dehydrogenase (ubiquinone) 1 beta subcomplex -2,3 1425143_a_at NDUFS1 NADH dehydrogenase (ubiquinone) Fe-S protein 1 -3,0 1418117_at NDUFS4 NADH dehydrogenase (ubiquinone) Fe-S protein 4 -11,5 1449137_at PDHA1 pyruvate dehydrogenase (lipoamide) alpha 1 -3,9 1428631_a_at UQCRC2 ubiquinol-cytochrome c reductase core protein II -2,3

Cluster 4 Calcium Signaling (ES = 6,18)

1423807_a_at CALM2 calmodulin 2 (, delta) -5,1 1417605_s_at CAMK1 calcium/calmodulin-dependent protein kinase I -3,4 1457311_at CAMK2A calcium/calmodulin-dependent protein kinase II alpha -10,5 1423942_a_at CAMK2G calcium/calmodulin-dependent protein kinase II gamma -3,6 1422598_at CASQ1 calsequestrin 1 (fast-twitch, skeletal muscle) -11,7 1460203_at ITPR1 inositol 1,4,5-triphosphate receptor, type 1 -12,4 1420820_at MYL9 myosin, light chain 9, regulatory 3,9 1427468_at PPP3CB protein phosphatase 3 (formerly 2B), catalytic subunit -3,6 1419738_a_at TPM2 2 (beta) -4,1

35 Results

Cluster 5 PI3K/AKT Signaling (ES = 5,92)

1423220_at EIF4E eukaryotic translation initiation factor 4E -3,1 1434976_x_at EIF4EBP1 eukaryotic translation initiation factor 4E binding protein 1 11,0 1421066_at JAK2 janus kinase 2 -2,7 1423605_a_at MDM2 oncoprotein Mdm2, p53-binding protein Mdm2 2,4 1415729_at PDPK1 3-phosphoinositide dependent protein kinase-1 2,3 1453127_at PPM1J protein phosphatase 2a -12,1 1437869_at PPP2R3A RIKEN cDNA 1500010M24 gene -4,6

Cluster 6 α-Adrenergic Signaling (ES = 5,15)

1449620_s_at ADCY9 adenylate cylase 9 -3,3 1434511_at PHKB phosphorylase kinase beta -4,2 1448602_at PYGM muscle glycogen phosphorylase -3,0 1423807_a_at CALM2 calmodulin 2 (phosphorylase kinase, delta) -5,1 1460203_at ITPR1 inositol 1,4,5-triphosphate receptor, type 1 -12,4 1425164_a_at PHKG1 phosphorylase kinase gamma -23,9

Cluster 7 Oxidative phosphorylation and ubiquinone biosynthesis (ES = 4,90)

1416567_s_at ATP5E ATP synthase, H+ transporting, mitochondrial F1 complex -2,3 1416278_a_at ATP5O ATP synthase, H+ transporting, mitochondrial F1 complex -2,1 1416834_x_at NDUFB2 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 2 -3,5 1448198_a_at NDUFB8 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 8 -2,3 1425143_a_at NDUFS1 NADH dehydrogenase (ubiquinone) Fe-S protein 1, 75kDa -3,0 1418117_at NDUFS4 NADH dehydrogenase (ubiquinone) Fe-S protein 4, 18kDa -11,5 1448292_at UQCR ubiquinol-cytochrome c reductase, 6.4kDa subunit -2,6 1428631_a_at UQCRC2 ubiquinol-cytochrome c reductase core protein II -2,3

Cluster 8 GM-CSF Signaling (ES = 4,81)

1457311_at Camk2a calcium/calmodulin-dependent protein kinase II alpha -10,5 1427468_at Ppp3cb protein phosphatase 3, catalytic subunit, beta isoform -3,5 1440878_at Runx1 runt related transcription factor 1 12,1 1421066_at Jak2 Janus kinase 2 -2,7 1423942_a_at Camk2g calcium/calmodulin -dependent protein kinase II gamma -3,6

Cluster 9 Regulation of eIF4 and mTOR Signaling (ES = 4,64)

1449940_a_at EIF2B4 eukaryotic translation initiation factor 2B, subunit 4 delta 2,1 1423220_at EIF4E eukaryotic translation initiation factor 4E -3,1 1434976_x_at EIF4EBP1 eukaryotic translation initiation factor 4E binding protein 1 11 1415729_at PDPK1 3-phosphoinositide dependent protein kinase-1 2,3 1453127_at PPM1J protein phosphatase 1J (PP2C domain containing) -12,1 1437869_at PPP2R3A protein phosphatase 2 (formerly 2A), regulatory subunit B -4,6

36 Results

Cluster 10 Galactose, Fructose and Mannose Metabolsim (ES = 4,48)

1453173_at AKR1B10 aldolase reductase B10 -7,3 1431063_at GANC glucosidase alpha, neutral -2,6 1451149_at PGM1 phosphoglucomutase 1 -12,9 1416780_at PFKM phosphofructokinase, muscle -7,1 1427213_at PFKFB1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 -13,1 1435659_a_at TPI1 triosephosphate isomerase 1 -5,9

Cluster 11 PPARα/RXRα Activation (ES = 4,25)

1426850_a_at MAP2K6 mitogen activated protein kinase kinase 6 -9,8 1449620_s_at ADCY9 adenylate cylase 9 -3,3 1448249_at GPD1 glycerol-3-phosphate dehydrogenase 1 (soluble) -23,0 1416947_s_at ACAA1 acetyl-Coenzyme A acyltransferase 1 -4,4 1428323_at GPD2 glycerol phosphate dehydrogenase 2, mitochondrial -7,3 1448460_at ACVR1 activin A receptor, type 1 -2,7 1421066_at Jak2 Janus kinase 2 -2,7

Cluster 12 Citrate Cycle (ES = 4,20)

1422577_at CS citrate synthase -3,9 1422500_at IDH3A 3 (NAD+) alpha -8,0 1416478_a_at MDH2 malate dehydrogenase 2, NAD (mitochondrial) -2,1

Cluster 13 Insulin Receptor Signaling (ES = 3,97)

1449940_a_at EIF2B4 eukaryotic translation initiation factor 2B, subunit 4 delta 2,1 1415729_at PDK1 3-phosphoinositide dependent protein kinase-1 2,3 1451063_at STXBP4 syntaxin binding protein 4 -21,1 1423220_at EIF4E eukaryotic translation initiation factor 4E -3,1 1434976_x_at EIF4EBP1 eukaryotic translation initiation factor 4E binding protein 1 11

Cluster 14 Hypoxia Signaling (ES = 3,55)

1423605_a_at MDM2 transformed mouse 3T3 cell double minute 2 2,4 1419737_a_at LDHA lactate dehydrogenase 1, A chain -5,4 1422712_a_at UBE2I ubiquitin-conjugating enzyme E2I 2,2

Cluster 15 Valine, Leucine and Ketone bodies (ES = 2,94)

1416947_s_at ACAA1 acetyl-Coenzyme A acyltransferase 1 -4,4 1451405_at PCCA propionyl-Coenzyme A carboxylase, alpha polypeptide -3,6 1428140_at OXCT1 3-oxoacid CoA -9,2

Cluster 16 Protein Ubiquitination Pathway (ES = 2,90)

1417056_at PSME1 proteasome (prosome, macropain) 28 subunit, alpha -4,3 1423605_a_at MDM2 transformed mouse 3T3 cell double minute 2 2,4 1422712_a_at UBE2I ubiquitin-conjugating enzyme E2I 2,2

37 Results

Cluster 17 ILK Signaling (ES = 2,68)

1426850_a_at MAP2K6 mitogen activated protein kinase kinase 6 -9,8 1420820_at MYL9 myosin light chain 9 3,9 1426642_at FN1 fibronectin 1 -4,6 1437869_at PPP2R3A protein phosphatase 2 (formerly 2A), regulatory subunit B -4,6 1418067_at CFL2 Cofilin-2 2,4 1453127_at PPM1J protein phosphatase 1J (PP2C domain containing) -12,1 1415729_at PDK1 3-phosphoinositide dependent protein kinase-1 2,3

Cluster 18 Glycerophospholipid Metabolism (ES = 2,62)

1448249_at GPD1 glycerol-3-phosphate dehydrogenase 1 (soluble) -23,0 1428323_at GPD2 glycerol phosphate dehydrogenase 2, mitochondrial -7,3 1452836_at LPIN2 lipin 2 4,2 1422744_at PHKA1 phosphorylase kinase alpha 1 -21,6

Cluster 19 General Apoptosis Signaling (ES = 2,59)

1421097_at ENDOG endonuclease G -2,6 1426043_a_at CAPN3 calpain 3 -12,4

Cluster 20 FAK Signaling (ES = 2,56)

1435694_at ARHGAP26 Rho GTPase activating protein 26 11,6 1415729_at PDK1 3-phosphoinositide dependent protein kinase-1 2,3 1426043_a_at CAPN3 calpain 3 -12,4

Cluster 21 Inositol Metabolism (ES = 2,51)

1449443_at DECR1 2,4-dienoyl CoA reductase 1, mitochondrial -9,5 1424048_a_at CYB5R1 cytochrome b5 reductase 1 -3,0 1435659_a_at TPI1 triosephosphate isomerase 1 -5,9 1451744_a_at PTGR2 prostaglandin reductase 2 -2,4

3.4 Validation of expression profiling: structural & metabolic elements

We used a number of independent methods to validate important functional components and predictions revealed from expression profiling of hypoxic skeletal muscle. Expression levels of 8 genes (HARP, Aqp4, Atp1b, Grb14, Mustn1, Mt1, Ankrd10, Runx1) randomly chosen out of the 20 most up and downregulated differential genes were independently

38 Results

verified at the mRNA level. RT-PCR results using SYBR green dye-labeled PCR products are shown in Figure 6A and fold changes of independent skeletal muscle samples of hypoxic QF revealed positive or negative expression patterns as in the microarray (Figure 6B).

Figure 6. qPCR validation of microarray data

(A) Amplification plot of HARP gene expression is shown comparing control and hypoxic mice gastrocnemicus muscles in normalized delta reporter (ΔRn) vs. cycle graph. Blue colored amplification lines represent beta-actin expression, which is used for normalization of results. Green amplification bundle on the left indicate gene expression in normoxia, while the right bundle represents gene expression in hypoxia. (B) Comparison of qPCR validation analysis by microarray fold change. qPCR data represents mean of triplicate analysis using the forward and reverse primers (see experimental procedures) and SYBR green in the ABI 7900 HT sequence detection system. For both microarrays and qPCR data, positive fold change values denote higher expression in hypoxia. All presented qPCR data is p<0.05.

39 Results

Furthermore, metabolic features of the expression profile were also verified at the substrate level as presented in Figure 7. The overall downregulated expression pattern of many known genes involved in metabolic pathways such as glycolysis led to the prediction that the glycogen content was decreased in hypoxic skeletal muscle. In order to test this prediction, the glycogen content in hypoxic and control EDL and soleus was analyzed by PAS staining histochemically. Figure 7 shows PAS staining of control and hypoxic EDL and soleus with a lack of PAS staining in hypoxic tissue suggesting a significantly lower glycogen content in hypoxic EDL and soleus compared to control and supports the gene expression changes found in the microarray study in regard to metabolic changes seen in hypoxia.

Figure 7. Histochemical validation of metabolic changes.

Decreased glycogen content of EDL and SOL muscles in hypoxic compared to control tissues shown with Periodic Acid- Schiff (PAS) reaction.

3.5 Characterization of HARP

Relatively little is known about HARP, a 8 kDa protein also known as P311 and first found in neurons in late brain development 123. Being the most downregulated gene in our expression profile of hypoxic skeletal muscle and shown to be associated with smooth muscle differentiation, we wanted to characterize HARP in more depth 97,98. First a multiple tissue cDNA panel (MTC) was analyzed to see the expression level of HARP in skeletal muscle compared to various adult tissues as well as embryonic tissue of different ages. (Figure 8) The expression level of HARP in muscle tissue was set at 100% and showed the highest level of expression compared to other adult tissues, closely followed by brain (91%). Other expression levels of HARP in adult tissues measured were heart (15%),

40 Results

kidney (11%), lung (8%), liver (8%), testis (5%) and spleen (1%) compared to muscle tissue. In embryonic tissue, especially day 17 showed a very high expression level of

HARP with 118% normalized to muscle.

Figure 8: Multiple tissue cDNA panel of HARP

Tissue specific transcript levels of HARP. RT-PCR of HARP and control beta-actin expression levels was determined in various normal adult tissues and embryonic day 7 to day17. The graph shows arbitrary units of each PCR product given in %. All tissues were normalized to muscle tissue as 100%. Highest expression of whole tissues was found in skeletal muscle followed by brain and lowest in spleen. n=3

With HARP being highly expressed in normal skeletal muscle, but highly decreased hypoxic tissue at the mRNA level in our microarray study and in independent tissue samples, we further verified the decreased HARP expression pattern in hypoxia at the protein level in soleus histochemically as presented in Figure 9.

Figure 9: Histochemical analysis of HARP:

Validation of preferred enrichment of HARP in control vs. hypoxic soleus at the protein level by immunohistochemistry. To confirm microarray findings, cross sections of soleus were stained against HARP antibody. Right column shows more fluorescent labeling of HARP in control compared to hypoxia reflecting its differential expression pattern found in both conditions. Magnification 40x.

41 Results

We further tested whether hypoxia decreased promoter luciferase activity of HARP in differentiated C2C12 muscle cells. As shown in Figure 10A the HARP promoter-luciferase reporter construct (HARP luc) showed decreased activity by 30% in differentiated C2C12 cells exposed to 0.5% of oxygen for 24 hrs compared with controls. Control cells were normalized to 100%. To further validate the expression of HARP under hypoxia, qPCR of

C2C12 cells exposed to 0.5% of oxygen for 24 hrs was performed and revealed a decrease by 32% of normalized expression in hypoxic versus control (Figure 10B). This data is consistent with the downregulation of HARP in our expression profile and supports the hypothesis that HARP plays a pivotal role in the regulation of hypoxic skeletal muscle.

Figure 10: HARP luciferase assay and qPCR of hypoxic C2C12 cells.

(A) Hypoxia stimulated decrease of HARP promoter activity in differentiated muscle cells. The HARP promoter- luciferase reporter construct (HARP luc) was co-transfected with pRL-TK into C2C12 muscle cell line. Differentiated cells were exposed to 0.5% of oxygen for 24 hrs, HARP luc derived firefly luciferase activity was normalized to pRL-TK derived renilla luciferase activity (control) as 100% in normoxia. The graph represents an n=6 replicates. Blue bar shows HARP Luc activity in C2C12 cells or in control, red bar in hypoxia. HARP Luc is decreased to 70% in hypoxia compared to control. Results were statistically highly significant at p < 0.05. (B) Expression level of HARP luc construct in C2C12 cells after 24 hrs of exposure to 0.5% of hypoxia was also determined by qPCR. Harp luc decreased to 68% in hypoxia compared to control. Results were statistically highly significant at p<0.05.

42 Discussion

4 Discussion

This study provides important and novel insights concerning the direct effects of chronic hypoxia exposure on skeletal muscle tissue. In our study we used an approach with cDNA microarrays to systematically investigate the transcriptional changes leading to skeletal muscle wasting under chronic hypoxia. Although previous genomic analyses studying various causes of muscle atrophy / loss e.g. during cancer states or diabetes have described some of the changes seen in this study as well, a distinct pattern of transcriptional changes for muscle under hypoxia has now been suggested. Thus, hypoxia appears to elicit both positive and negative changes of gene expression and does not lead to a general suppression or activation of transcription. As discussed below, we have attempted to identify the molecular signature of chronic hypoxia in skeletal muscle. To further support our data, intensive independent validation of the microarray experiment using PCR and immunohistochemistry as well as physiological and anatomical studies were performed.

4.1 Morphological and functional changes in hypoxic muscle

It is well know that a progressive decrease in body and consequently muscle weight under chronic hypoxia occurs, but whether it is directly due to the effects of hypoxia or to changes in diet, lack of appetite, or the multiples stresses of the harsh hypoxic environment, remains obscure. There is ample evidence that hypoxia may be a sufficient trigger to elicit weight loss and decrease in food intake despite ample access to food 107,73. Although availability of palatable food was able to sustain body weight in humans exposed to moderate chronic hypoxia 60, body weight loss in our animal model was evident on day 3, despite the fact that food intake did not significantly drop until day 5 of exposure to hypoxia. This suggests that hypoxia may cause weight loss directly and independent of food intake. This anorexic effect of hypoxia has been previously observed in an animal study 93. Therefore, the overall decrease in body and muscle weight may be attributed to the effects of hypoxia, which may then lead to further chronic fasting. Changes in muscle functionality in mice were observed in EDL, a fast glycolytic muscle, and in the more oxygen dependent slow soleus muscle. In both muscle type absolute and specific forces were equally affected under hypoxia (data not shown as I did not perform these experiments). Interestingly, an increase of vascular elements and the count of (CNFs) in

43 Discussion

EDL revealed a higher number compared to soleus, which suggests that EDL may be able to regenerate better compared to soleus in hypoxia (data not shown as I did not perform these experiments myself). However, human and other animal studies have in the past revealed conflicting results 76,17,119,1,94. Nevertheless, it was no surprise that soleus muscle showed almost no decline in the fatigue test, but rather stayed at the same low force level. Capillary density was increased with a positive vascular index in EDL, but is likely to be caused by atrophy of muscle fibers under hypoxia, which is in agreement with observations in another study (data not shown as I did not perform these experiments myself) 78. Although muscle fiber cross-sectional area (CSA) did not decrease in either muscle type, a reduction has been reported in human studies, where subjects were exposed to 8 weeks of chronic hypoxia 51. Other reports are in line with this finding 83,50 However, morphologically both muscles looked distorted, with a smaller fiber size and increased fibrosis as well as focal damage, disrupted structure and integrity especially in hypoxic soleus pointing towards a debilitating process in muscle under hypoxia (data not shown as I did not perform these experiments myself).

4.2 Muscle adaptations of metabolic based pathways under chronic hypoxia

Under hypoxia the shift away from oxidative phosphorylation provides the muscle cell with an alternative way of producing ATP through breakdown of glucose to pyruvate and conversion to lactate by lactate dehydrogenase (LDH) 91,66. The findings presented here indicate that regulation of the energy metabolism is severely altered by chronic hypoxia exposure in muscle as suggested in Figure 11. It has been shown previously, that induction of pyruvate dehydrogenase kinase 1 (PDK1) by a HIF-1 dependent mechanism is responsible for inducing mitochondrial oxygen consumption changes and at the same time activates enzymes that are responsible for glycolysis 99,66,28. Moreover, it is important to mention that the decrease in oxidative phosphorylation occurs because the flow from pyruvate into the TCA cycle is reduced by increased activity of PDK1 and not only because of lack of oxygen on its own. Interestingly, PDK1 remained the only gene with an increase at the mRNA level in relation to the energy metabolism in our profile. This presents an interesting finding as PDk1 and HIF-1α proteins showed no change in human muscle biopsies after 7 – 9 days of exposure to moderate hypoxia (4500m) 133. All other genes in our expression profile related to mitochondrial oxygenation, glycolysis,

44 Discussion

gluconeogenesis and lactate production were decreased under hypoxia. This is in contrast to the adaptive response and suggests a metabolic crisis due to the effects of sustained chronic hypoxia, leading to the consumption of energy resources in the animals. Indeed, other studies have suggested an “active” Pasteur effect in which glycolytically derived lactate is actively expelled from the hypoxic cell 57,118. This is most likely due to the fact that exposure time and severity of hypoxia were different in our study.

Figure 11. Model for hypoxia mediated carbohydrate metabolism in skeletal muscle

In this schematic the differences in glucose utilization in normoxia (left panel) versus chronic hypoxia (right panel) are discussed. Our gene expression profile of the hypoxic tissue suggests a reduced glycogen metabolism (see also Figure 7) shown by downregulation of glycogen phosphorylase-m (Pygm), phosphorylase kinase alpha 1, (Phka1) and phosphorylase kinase gamma 1, (Phkg). Decreased levels of phosphofructokinase-m (Pfkm), phosphoglycerate mutase2 (Pgm2), 2, 3 bi-phosphoglycerate mutase (Bpgm) and triose phosphate isomerase (Tpi) suggest insufficient glucose catabolism towards pyruvate. Additionally, enzymes involved in the (TCA) such as citrate synthase (CS), isocitrate dehydrogenase3 alpha subunit (Idh3a) and malate dehydrogenase2 (Mdh2) as well as genes generating ATP via oxidative phosphorylation are downregulated. Induced pyruvate dehydrogenase kinase1 (Pdk1) inhibits pyruvate dehydrogenase E1 alpha1 (Pdha1) and blocks the conversion from pyruvate to acetyl-CoA, leading to a decreased activity of the TCA cycle. Glycerol-3-phosphate dehydrogenase1 (Gpd1) and glycerol-3-phosphate dehydrogenase2 (Gpd2) are downregulated and suggest limited gluconeogenesis in hypoxic condition. Overall the changes predict to end in low outputs of aerobic and anaerobic glucose metabolism in hypoxic skeletal muscle resulting in limited production of ATP and lactate. Red color indicates decreased, blue color increased expression and/or activity of enzymes in hypoxia relative to normoxia.

45 Discussion

A comparable effect has also been observed in patterns of gene expression in response to food deprivation 55. A comparison between significantly regulated genes of a fasting gene expression profile 71 and our transcriptome changes during chronic hypoxia showed some similarities, but also revealed many hypoxia specific genes (see supplementary data4). Moreover, metabolic changes at the level of the transcriptome and protein seem to vary and depend on specific conditions such as exposure time, eating habits, severity of hypoxia and age of animal 96,28 76.

4.3 Enhanced capacity for protein degradation

As described previously the transcriptional response of skeletal muscle after chronic hypoxia seems to affect protein synthesis and degradation 133,69. We propose that accelerated proteolysis underlying muscle loss is largely due to the activation of the ubiquitin-proteasome pathway in mouse skeletal muscle as shown in Figure 12. The ubiquitin-proteasome pathway has also been shown to be involved in muscle atrophy / loss caused by cachexia or cancer 130,12,64. Chronic hypoxia seems to enhance the capacity for protein degradation through a marked decrease of the insulin signaling inhibitor GRB 14. Interestingly, GRB 14 a known weight-loss responsive gene in skeletal muscle acts in the same regulatory network as AKT and IGF-1 100. A marked downregulation of GRB 14 may be a major cause for skeletal muscle loss during chronic hypoxia followed by further downstream action of the AKT / mTor, Foxo and ubiquitin-proteasome pathways. Although key regulatory atrogins such as atrogin1 109, which are activated under proteolysis were not altered in our hypoxia specific profile, mRNA levels of multiple 26S proteasome subunits were increased pointing towards an enhanced proteolysis. Interestingly, it has been previously demonstrated that activation of transcription factor Foxo3a not only leads to enhanced protein degradation in multiple forms of atrophy, but also further suppresses protein synthesis by activating translation inhibitor 4EBP 1 120. In line with these results, 4EBP 1 inhibitor was activated in our transcriptome analysis under hypoxia. Thus, hypoxia seems not only lead to decreased protein synthesis through AKT / mTOR, but additionally through Foxo signaling 121. However, Foxo transcription factors have also been found to promote the induction of cell survival pathways under oxidative 117 stress in cardiomyocytes .

46 Discussion

Figure 12. Model for hypoxia induced changes in skeletal muscle

Chronic hypoxia results in muscle atrophy, a decrease in muscle growth and protein synthesis as well as increased protein degradation. Due to hypoxia, HARP also known as P311, is highly downregulated, unable to block the response of TGFß1 hereby suggesting a reduction in muscle growth. Hypoxia also causes downregulation of Grb14 via the insulin receptor. As a result, the Akt / mTOR signaling pathway is not activated followed by a decreased protein synthesis due to increased inhibitory effect of EIF4EBP1 on EIF4E. In addition downregulation of the Akt / mTOR pathway induces muscle atrophy and protein degradation through activation of Foxo3a and Atrogin1. The atrophic effects under hypoxia caused by Atrogin1 are supported by an increase of the E2 ligases Ube2i and Roc1. These build a component of the SCF type ubiquitin-protein ligases (E3s), which activate the proteasome complex. Hypoxia causes an increase in cellular calcium concentration leading to activation of Calpain1, 2 and Caspase3, which contribute to increased muscle atrophy and protein degradation. A decrease in Calpastatin also increases the activity of Caspase3 and Calpain1 and 2. Compared to the ubiquitous calpains Calpain 3 is downregulated in hypoxia resulting in a loss of sarcomere integrity.

Since the ubiquitin-proteasome and autophagy-lysosome systems coordinately regulate muscle atrophy 79, it was no surprise to find several genes as part of these systems altered in response to hypoxia. A recent study documented autophagy activation during fasting and denervation in skeletal muscle 88. Evidence for the involvement of the autophagy- lysosome system is found with the upregulation of cathepsin-L 29. Furthermore it has been shown that excessive autophagy promotes severe muscle wasting resulting in myofibrillar disorganization, which may be reduced by high levels of Runx 1 137. Increased levels of Runx 1 in our hypoxia profile support this previous study and give insight into a possible

47 Discussion

mechanism of counteracting against myofibrillar disorganization under hypoxic condition. Hypoxia also seems to trigger another unique group of proteases termed caspases, which collectively degrade proteins 30. In our profile calpastatin, a substrate for caspase 3 and calpain, was downregulated, which may result in increased caspase 3 levels 40. Although caspase 3 was not differentially upregulated in our gene expression profile, we were able to detect it by cleaved immunohistochemistry in hypoxic skeletal muscle tissue (data not shown as I did not perform these experiments myself) suggesting a contribution to increased muscle damage caused by chronic hypoxia via dissociation of myofilaments.

4.4 Role of HARP downregulation in hypoxia

HARP also known as P311, a 8-kDa protein originally found in neurons and muscle 123, is characterized by the presence of a conserved PEST domain, which is generally targeted for degradation by the ubiquitin/proteasome system 132. It has been suggested that HARP is rapidly degraded by the ubiqitin/proteasome pathway and its expression seems to be dramatically decreased in pathways that regulate cellular growth 129. Although its functional role in skeletal muscle remains currently unknown, recent data suggests that HARP inhibits the expression of a major growth factor produced by myofibroblasts, TGF- beta1 and may promote muscle growth by increasing myoblast number 97. In addition P311 has been described to play a key role in hypertrophic scar formation 126. It has also been found to have a novel role in an alternative pathway of lipi-droplet accumulation, takes part in alveolar generation as well as being able to accelerate nerve regeneration in axotomized facial nerve 72,143,38. Previously, a decrease of HARP expression in muscle tissue has also been associated with multiple types of muscle atrophy 95,71, where protein breakdown exceeds the level of protein synthesis, resulting in net protein loss. Our gene expression profile is the first to report a downregulation of such great magnitude at the mRNA level in skeletal muscle tissue exposed to chronic hypoxia. Furthermore, we showed a marked decrease of a HARP promoter-luciferase construct by 30% in differentiated C2C12 muscle cells upon acute severe hypoxia, suggesting that the expression level of HARP is directly suppressed by hypoxia. Hence, we suggest that HARP may play a crucial role in the determination of muscle mass under hypoxia.

48 Discussion

4.5 Conclusion

In conclusion chronic hypoxia has a profound, detrimental effect on skeletal muscle structure and function, reflected by distinct changes in the pattern of gene expression at the level of the transcriptome. We believe that the approach of using expression profiling of genes, which are differentially expressed in skeletal muscle, helped to discover key pathways involved in hypoxia mediated muscle loss and is important for understanding the unique role of muscle function and wasting under chronic hypoxia. Indeed, three hundred thirteen (313) genes were differentially expressed in hypoxic skeletal muscle compared to normoxic muscle tissue. In addition a novel hypoxia related gene HARP was found to be differentially expressed in hypoxic muscle and independently validated. Key pathways involved in skeletal muscle dysfunction during chronic hypoxia were identified and while the present findings demonstrate the role of hypoxia in skeletal muscle pathophysiology, the identification of molecular mechanisms leading to muscle wasting under hypoxia suggest new approaches to possibly prevent this debilitating process. The study supported our hypothesis that hypoxia itself is detrimental to skeletal muscle and thus may contribute and worsen the already preexisting respiratory insufficiency in neuromuscular disorders such as in DMD patients due to muscle dysfunction. Therefore we propose that a state of chronic hypoxemia may lead to hypoxic muscle loss, which in turn worsens the respiratory insufficiency as part of a pathophysiological vicious cycle in various diseases such as DMD.

49 Abstract

5. Abstract

5.1 Zusammenfassung

Die respiratorische Insuffizienz spielt eine entscheidende Rolle in der Morbidität und Mortalität von neuromuskulären Erkrankungen, wie der Duchenne Muskeldystrophy (DMD). Tatsächlich geht man davon aus, dass letztendlich das Versagen der Atmung die häufigste Todesursache (60-80%) für Patienten mit DMD ist. Obwohl viel dafür getan wird, die Behandlung der respiratorischen Insuffizienz durch Beatmung für Patienten mit neuromuskulären Erkrankungen zu optimieren, sind die potentiellen Effekte der chronischen Hypoxie an der Skelettmuskulatur, dem primär betroffenen Gewebe der DMD, größtenteils noch unbekannt. Ziel dieser Studie war es zu untersuchen, ob Sauerstoffmangel die Skelettmuskulatur direkt schädigt. Dafür wurden C57Bl/10 Mäuse stufenweise einer normobaren, aber Sauerstoff armen Umgebung (bis zu 8% O2) für 2 Wochen ausgesetzt, um eine vergleichbare Hypoxämie wie bei DMD Patienten mit fortgeschrittener Ateminsuffizienz zu simulieren. Danach konnten mittels Genexpressionsanalyse des Transkriptomes von hypoxischer und normaler Muskulatur, 313 unterschiedlich regulierte Gene identifiziert werden. Diverse Gene involviert mit dem Abbau von Proteinen, der Atrophie des Muskelgewebes, dem Zelltod und des Muskelwachstums zeigten sich im Vergleich zu den Kontrollen unterschiedlich reguliert. Deutliche Änderungen im Expressionsmuster von bereits bekannten, Sauerstoff sensitiven Signalwegen wie dem Krebs-Zyklus, weisen auf eine hohe Sensitivität und Spezifität der Genveränderungen in Bezug auf unseren Hypoxie Versuch hin. Relevante Gene wurden mittels quantitativer Polymerasekettenreaktion an unabhängigen Proben validiert. Das Gen HARP, dessen verminderte Regulation unter Hypoxie bisher nicht bekannt war und eine potenziell bedeutende Rolle beim Muskelwachstum spielt, konnte identifiziert werden. Dieses Gen wurde zusätzlich in eine

Muskelzelllinie C2C12 eingebaut, um die Expression unter Hypoxie mittels Luciferasen Assay und quantitativer Polymerasekettenreaktion zu bestimmen. Zusammenfassend hat Hypoxie eine durchgreifende Auswirkung auf die Physiologie der Skelettmuskulatur, die durch die Änderung des Genexpressionsmusters unter Hypoxie deutlich wird. Die pathophysiologischen Auswirkungen in der Muskulatur weisen darauf hin, dass Hypoxie direkt zur Muskeldysfunktion beiträgt.

50 Abstract

5.2 Abstract

Respiratory impairment plays an important part in the morbidity and mortality of neuromuscular disorders (NMDs) such as Duchenne's muscular dystrophy (DMD). Indeed respiratory failure is believed to be the most common cause (c. 60-80%) of death in DMD patients. While efforts continue towards optimizing respiratory care for patients, relatively little is known about the potential direct effects of hypoxia on skeletal muscle, the primary tissue involved in DMD. To address whether hypoxia itself contributes to muscle patho-physiology, normal

C57Bl/10 mice were gradually exposed to a chronic hypoxic environment (down to 8% O2) for 2 weeks, in order to simulate levels of hypoxaemia reported in DMD patients with advanced respiratory insufficiency. The transcriptome was then analyzed by gene expression profiling of skeletal mouse muscle exposed to chronic hypoxia. We found 313 differentially regulated genes in hypoxic compared to control quadriceps femoris. Genes related to protein degradation, muscle atrophy, apoptosis and muscle growth were detected at high levels in hypoxic compared to control skeletal muscle suggesting that hypoxia may directly cause muscle loss and damage, thus contributing to muscle dysfunction via a hypoxia dependant transcriptional program. Expression patterns corresponding to known oxygen sensitive metabolic pathways such as the citric acid cycle with its mitochondrial content suggest that the profiling was sensitive and specific to hypoxia exposure. Major transcriptome-level changes were independently validated by quantitative, real-time polymerase chain reaction (qPCR) and studied anatomically (histology). Furthermore, a novel hypoxia responsive gene HARP was identified and found to be highly suppressed in hypoxia and seems to play a pivotal role in muscle growth. In addition HARP expression under hypoxia was verified in a muscle cell (C2C12) line using luciferase assay and qPCR. Taken together hypoxia was found to have a profound, detrimental effect on skeletal muscle function reflected by changes in the pattern of gene expression at the level of the trascriptome. These results demonstrate the role of hypoxia in skeletal muscle patho- physiology and suggest that hypoxia may directly contribute to skeletal muscle dysfunction in NMDs.

51 References

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60 Appendix

7 Appendix

7.1 Publications

Willmann G, Bogdanovich S, Budak MT , Baby SM, Wiesen MHJ, Lahiri S & Khurana TS. Role of hypoxia-mediated muscle damage in neuromuscular Disorders, XIth International Congress of Neuromuscular Diseases. Neuromuscular Disorders Vol. 16 Suppl. 1, 2006 G-P-3.13.

Mosqueira M*, Willmann G*, Zeiger U, Khurana TS. Expression profiling reveals novel hypoxic biomarkers in peripheral blood of adult mice exposed to chronic hypoxia. PLoS One. 2012;7(5):e37497. Epub 2012 May 22. *equal contribution

Part of the work was published.

7.2 Supplementary data

Supplementary data1. Hypoxia chamber setup

Self build, normobaric hypoxia chamber connected to Pegas 4000F gas mixer, Columbus Instruments, USA. Thermometer and Hygrometer were used to monitor room temperature.

61 Appendix

Supplementary data2. Hypoxia exposure was based on an acclimatization protocol used on a previous high altitude ascent.

Table S1, related to Figure 1; Protocol for chronic hypoxia exposure of C57Bl/10 mice

Simulated Study Day Po , mmHg O , % N , % 2 2 2 Altitude, m 1 160 21 79 0 2 118 15 85 1,830 3 100 13 87 3,050 4 91 12 88 3,660 5 83 11 90 4,270 6 76 10 90 4,880 7 69 9 91 5,490 8 63 8 92 6,200 9 76 10 90 4,880 10 69 9 91 5,490 11 69 9 91 5,490 12 69 9 91 5,490 13 63 8 92 6,200 14 63 8 92 6,200 15 63 8 92 6,200

62 Appendix

Supplementary data3. List of significant genes expressed in hypoxic skeletal muscle

Affymetrix ID Fold change Common Genbank 1427201_at 16,4 Mustn1 AJ277212 1422557_s_at 14,2 Mt1 NM_013602 1448199_at 12,2 Ankrd10 NM_133971 1440878_at 12,1 Runx1 BB795285 1435694_at 11,6 4933432P15Rik BB127065 1434976_x_at 11,0 Eif4ebp1 AV216412 1416065_a_at 10,8 Ankrd10 NM_133971 1451310_a_at 8,5 Ctsl J02583 1429787_x_at 8,4 D10Ertd749e AK008144 1419621_at 8,2 Ankrd2 NM_020033 1427131_s_at 7,3 1810012N18Rik AV234245 1425202_a_at 6,6 Ank3 BC021657 1425066_a_at 5,6 1110061O04Rik BC018294 1454890_at 5,6 Amot BG067039 1416061_at 5,5 Tbc1d15 BF577643 1425981_a_at 5,1 Rbl2 U47333 1425562_s_at 4,7 Trnt1 BM225164 1448736_a_at 4,7 Hprt NM_013556 1419281_a_at 4,7 Zfp259 BC021397 1441150_x_at 4,5 Mbd1 BB829165 1455952_at 4,5 Adprhl1 AI503324 1428389_s_at 4,4 2610318G08Rik AK012043 1424699_at 4,2 4921511K06Rik BC006583 1452836_at 4,2 Lpin2 AK021389 1420820_at 3,9 2900073G15Rik NM_026064 1424914_at 3,8 2310044G17Rik AK009800 1422660_at 3,7 Rbm3 AY052560 1418300_a_at 3,7 Mknk2 NM_021462 1417125_at 3,5 Ahcy NM_016661 1418073_at 3,4 Acate2 NM_019736 1448343_a_at 3,4 Nbr1 NM_008676 1452759_s_at 3,2 Ppfibp1 BI149999 1424683_at 3,2 1810015C04Rik BC019494 1438769_a_at 3,1 Thy28 BF719766 1449968_s_at 3,1 Acate2 NM_022816 1428539_at 3,0 2610207I05Rik AK011909 1434655_at 3,0 Foxk1 BB794583 1416015_s_at 2,9 Abce1 NM_015751 1424002_at 2,9 Pdcl3 BC005601 1416699_at 2,8 1110008F13Rik NM_026124 1449258_at 2,8 D11Wsu99e AV225714 1448399_at 2,6 Tax1bp1 NM_025816

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Affymetrix ID Fold change Common Genbank 1427966_at 2,6 AW551849 1450963_at 2,6 4833420I20Rik AW412441 1426949_s_at 2,6 Tpr BM214109 1454817_at 2,5 6230425C22Rik BG066921 1419139_at 2,5 Gdf5 NM_008109 1420982_at 2,5 Rnpc2 NM_133242 1423605_a_at 2,4 Mdm2 AK004719 1423080_at 2,4 Tomm20 AK002902 1418067_at 2,4 Cfl2 AI323758 1415729_at 2,3 Pdpk1 BQ174223 1420540_a_at 2,2 Rit1 NM_009069 1434503_s_at 2,2 Lamp2 BB490768 1422712_a_at 2,2 Ube2i U31934 1449940_a_at 2,1 Eif2b4 NM_010122 1431012_a_at 2,1 Peci AK009478 1417770_s_at 2,0 Psmc6 AW208944 1425238_at 2,0 BC011413 1423334_at -2,0 1200007D18Rik AK003239 1423456_at -2,0 Bzw2 BM932775 1428698_at -2,0 2310004I03Rik BM939621 1451399_at -2,0 Brp17 BC008274 1429259_a_at -2,0 1810014B01Rik AW985991 1452055_at -2,0 Ctdsp1 BB770944 1416478_a_at -2,1 Mdh2 NM_008617 1419252_at -2,1 Eps15 BG067649 1455736_at -2,1 Mybpc2 AI326984 1416278_a_at -2,1 Atp5o NM_138597 1433658_x_at -2,1 Pcbp4 AV300794 1415727_at -2,1 Apoa1bp AV017766 1423371_at -2,2 Pole4 BF577544 1424168_a_at -2,2 Capzb U10406 1418896_a_at -2,2 Rpn2 NM_019642 1423711_at -2,2 Ndufaf1 BC018422 1416567_s_at -2,3 Atp5e NM_025983 1454711_at -2,3 Trio BB080177 1428631_a_at -2,3 Uqcrc2 BG075002 1428308_at -2,3 1110004D19Rik AK003410 1448198_a_at -2,3 Ndufb8 NM_026061 1423941_at -2,3 Camk2g BM227770 1449530_at -2,3 Trps1 NM_032000 1422797_at -2,3 Mapbpip NM_031248 1434214_at -2,4 0910001L09Rik BI525016 1451744_a_at -2,4 1810016I24Rik BC021466 1418584_at -2,4 Ccnh NM_023243

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Affymetrix ID Fold change Common Genbank 1421961_a_at -2,4 Dnajb5 AI664344 1455614_at -2,5 Nr1i3 AV225789 1431164_at -2,5 Rragd AK017818 1452346_at -2,5 1500032M01Rik AV032053 1431063_at -2,6 5830445O15Rik AA119261 1426719_at -2,6 Apbb2 BG067463 1421097_at -2,6 Endog NM_007931 1456707_at -2,6 BB320816 1448292_at -2,6 Uqcr NM_025650 1428864_at -2,6 5530400B01Rik AK017419 1452464_a_at -2,7 Metapl1 AJ414378 1428261_at -2,7 2310042L06Rik AK009756 1424078_s_at -2,7 Pex6 BC003424 1448460_at -2,7 Acvr1 NM_007394 1421066_at -2,7 Jak2 NM_008413 1452937_s_at -2,8 1810010N17Rik BQ031123 1436939_at -2,8 Cmya4 AV220213 1416483_at -2,8 Ttc3 BB833716 1417807_at -2,9 2700038N03Rik NM_027356 1425143_a_at -3,0 Ndufs1 BC006660 1451413_at -3,0 Cast AB026997 1448602_at -3,0 Pygm NM_011224 1424048_a_at -3,0 1500005G05Rik BC024618 1453207_at -3,0 Pcp2 BE865033 1449383_at -3,1 Adss NM_007421 1423220_at -3,1 Eif4e BB406487 1433628_at -3,1 BQ044689 1436070_at -3,1 BM933153 1450490_at -3,1 Kcna7 NM_010596 1457424_at -3,1 Eya1 BB760085 1448483_a_at -3,2 Ndufb2 NM_026612 1426981_at -3,2 Pace4 BI157485 1416793_at -3,2 Arl6ip2 NM_019717 1419109_at -3,2 Hrc NM_010473 1416625_at -3,2 Serping1 NM_009776 1442525_at -3,2 2610204L23Rik AW488042 1449620_s_at -3,3 D16Wsu65e AW125421 1428412_at -3,3 Smbp AK004283 1436570_at -3,3 BG143461 1416319_at -3,3 Adk NM_134079 1434348_at -3,3 Fez2 BM206792 1422645_at -3,3 Hfe AJ306425 1428259_at -3,3 2310075M15Rik AK010185 1429709_at -3,3 3110004O18Rik AI157548

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Affymetrix ID Fold change Common Genbank 1460645_at -3,3 Chordc1 NM_025844 1417605_s_at -3,4 Camk1 NM_133926 1415878_at -3,4 Rrm1 BB758819 1418373_at -3,4 Pgam2 NM_018870 1437077_at -3,4 AV300673 1417789_at -3,4 Ccl11 NM_011330 1435261_at -3,5 4732416N19Rik BQ174442 1460359_at -3,5 Armcx3 AK004598 1419352_at -3,5 0610007P06Rik BC003916 1456087_at -3,5 9430022M17Rik BB089547 1427468_at -3,5 Ppp3cb M81483 1452649_at -3,6 Rtn4 AK003859 1452671_s_at -3,6 Lman1 BG071597 1451405_at -3,6 Pcca AY046947 1423942_a_at -3,6 Camk2g BM227770 1418467_at -3,6 Smarcd3 NM_025891 1433672_at -3,6 4732479N06Rik AV119970 1418762_at -3,6 Daf1 NM_010016 1416589_at -3,6 Sparc NM_009242 1453578_at -3,7 Pter BM939099 1419367_at -3,7 Decr1 NM_026172 1435382_at -3,7 Ndn AW743020 1437259_at -3,7 Slc9a2 AV274006 1418888_a_at -3,8 Sepr NM_013759 1415732_at -3,8 Bat5 BG071718 1435383_x_at -3,8 Ndn AW743020 1449137_at -3,9 Pdha1 NM_008810 1431382_a_at -3,9 1700024K14Rik AK018895 1422577_at -3,9 Cs AB056479 1416752_at -3,9 Ldb3 NM_011918 1428411_at -4,0 1700020I14Rik AK004510 1424223_at -4,0 1700020C11Rik BC019205 1437165_a_at -4,0 Pcolce BB250811 1419738_a_at -4,1 Tpm2 AK003186 1437067_at -4,1 Phtf2 BM228625 1434511_at -4,2 Phkb BM233125 1460242_at -4,3 Daf1 NM_010016 1460424_at -4,3 1810008O21Rik BI411309 1435106_at -4,3 3732412D22Rik AV024662 1417056_at -4,3 Psme1 NM_011189 1428695_at -4,4 9130227C08Rik BB424872 1416947_s_at -4,4 Acaa1 NM_130864 1436915_x_at -4,4 Laptm4b AU024771 1436185_at -4,4 AI314180 BG076313

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Affymetrix ID Fold change Common Genbank 1425323_a_at -4,4 BC008155 BC008155 1453321_at -4,4 Fndc1 AK003938 1435115_at -4,5 Fndc5 AW556555 1453003_at -4,5 Sorl1 AK013519 1435893_at -4,5 Vldlr BB127955 1436161_at -4,6 Aprin BB442341 1437869_at -4,6 3222402P14Rik BF140684 1448487_at -4,6 Lrrfip1 NM_008515 1447277_s_at -4,6 Pcyox1 BB785407 1428769_at -4,6 1500010M24Rik AK005193 1452927_x_at -4,7 Tpi AW537828 1431033_x_at -4,7 Agl AA681807 1422437_at -4,7 Col5a2 AV229424 1427371_at -4,8 Abca8a BC026496 1417434_at -5,0 Gpd2 NM_010274 1435536_at -5,0 1700027M01Rik BE137091 1449059_a_at -5,0 Oxct1 NM_024188 1451322_at -5,0 2310016A09Rik BC024580 1423807_a_at -5,1 Calm2 BC021347 1417661_at -5,1 Rad52b NM_025654 1416148_at -5,1 Laptm4b BC019120 1433681_x_at -5,2 Capn3 AI323605 1428097_at -5,2 2510009E07Rik AK010940 1428745_a_at -5,3 2310003L22Rik AK009123 1421735_a_at -5,3 Siat8e NM_013666 1419737_a_at -5,4 Ldh1 NM_010699 1433745_at -5,4 Trio BB080177 1415918_a_at -5,5 Tpi NM_009415 1416666_at -5,5 Serpine2 NM_009255 1428113_at -5,7 4930403J22Rik BB278364 1435659_a_at -5,9 Tpi AA153477 1424041_s_at -5,9 C1s BC022123 1417951_at -6,0 Eno3 NM_007933 1416804_at -6,0 LOC114601 NM_053252 1441667_s_at -6,0 Smyd1 C78565 1422754_at -6,1 Tmod1 NM_021883 1449398_at -6,1 Rpl3l NM_025425 1421329_a_at -6,2 Smyd1 NM_009762 1451541_at -6,3 Bcs1l BC019781 1424918_at -6,3 2810453K03Rik BC020138 1449388_at -6,4 Thbs4 NM_011582 1438030_at -6,7 Rasgrp3 BB042252 1417653_at -6,8 Pva NM_013645 1437363_at -7,0 Homer1 BQ043238

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Affymetrix ID Fold change Common Genbank 1431199_at -7,0 0610031G08Rik BG271102 1460411_s_at -7,0 AW548124 BC022157 1427073_at -7,0 Lace1 AF397909 1416780_at -7,1 Pfkm NM_021514 1429144_at -7,1 2310032D16Rik AV291259 1460405_at -7,1 2810441C07Rik AV238183 1429083_at -7,1 Agl BB006088 1428323_at -7,3 Gpd2 BQ175968 1453173_at -7,3 2310005E10Rik AK019906 1439096_at -7,4 5730402C02Rik BB021589 1451262_a_at -7,5 Jtv1 BC026972 1455336_at -7,5 9030625G08Rik BG073155 1427183_at -7,5 Efemp1 BC023060 1423596_at -7,7 Nek6 BB528391 1426108_s_at -7,7 Cacnb1 AY094172 1424433_at -7,9 Mrsb BC021619 1422500_at -8,0 Idh3a NM_029573 1422501_s_at -8,0 Idh3a NM_029573 1418450_at -8,2 Islr NM_012043 1431335_a_at -8,3 Wfdc1 AK018575 1418474_at -8,5 1500005A01Rik NM_033146 1416713_at -8,5 2700055K07Rik NM_026481 1435605_at -8,6 Arp3b BB125424 1415864_at -8,6 Bpgm NM_007563 1422882_at -8,6 Sypl BE333485 1450203_at -8,7 Smyd1 NM_009762 1458087_at -8,9 BB135364 1418252_at -9,0 Padi2 NM_008812 1428140_at -9,2 Oxct1 AK010029 1425710_a_at -9,2 Homer1 AB019479 1423238_at -9,4 Itgb1bp2 AK003906 1449443_at -9,5 Decr1 NM_026172 1449969_at -9,7 Tmod4 NM_016712 1452106_at -9,7 Npnt AA223007 1428519_at -9,8 2610528E23Rik AK019979 1427168_a_at -9,8 Col14a1 AJ131395 1426850_a_at -9,8 Map2k6 BB261602 1423266_at -9,9 2810405K02Rik AI836168 1428444_at -10,0 Asb2 AK003566 1438639_x_at -10,0 4933417E01Rik BB045793 1457311_at -10,5 Camk2a AW490258 1429159_at -10,6 4631408O11Rik AK018605 1422671_s_at -10,9 Naalad2 NM_028279 1448119_at -11,0 Bpgm NM_007563

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Affymetrix ID Fold change Common Genbank 1421116_a_at -11,4 Rtn4 NM_024226 1441952_x_at -11,5 Lynx1 BB431070 1418117_at -11,5 Ndufs4 NM_010887 1457881_at -11,7 Osbpl6 BB197141 1422598_at -11,7 Casq1 NM_009813 1451488_at -11,8 1110028A07Rik AB054000 1437197_at -11,9 9430041O17Rik BB251748 1423608_at -11,9 Itm2a BI966443 1453127_at -12,1 Ppp2cz AK009235 1426043_a_at -12,4 Capn3 AF127766 1451149_at -12,9 Pgm2 BC008527 1427213_at -13,1 Pfkfb1 X98848 1417283_at -13,9 Lynx1 NM_011838 1422644_at -14,2 Sh3bgr NM_015825 1443575_at -14,6 2310040G24Rik BG795494 1429726_at -14,7 Slc16a9 AK004684 1422852_at -14,9 Cib2 NM_019686 1444504_at -15,8 1110001P11Rik AV012778 1416168_at -16,2 Serpinf1 NM_011340 1422315_x_at -16,3 Phkg NM_011079 1435567_at -17,6 Phka1 AI504378 1434008_at -17,8 Scn4b BE993937 1424511_at -17,8 Stk6 U80932 1439491_at -19,4 A230053A07Rik BB311440 1422743_at -20,2 Phka1 NM_008832 1450857_a_at -21,0 Col1a2 BF227507 1451063_at -21,1 Stxbp4 BB771462 1422744_at -21,6 Phka1 NM_008832 1449466_at -21,8 Tna NM_011606 1448249_at -23,0 Gpd1 BC019391 1425476_at -23,7 Col4a5 BM250666 1425164_a_at -23,9 Phkg J03293 1448484_at -25,8 Amd1 NM_009665 1427329_a_at -25,8 Igh-6 AI326478 1424362_at -26,6 D830019K17Rik BC016136 1430176_at -29,8 5430433E21Rik AK017385 1440084_at -30,8 AV380966 1449876_at -31,3 Prkg1 NM_011160 1449206_at -31,8 Mg29 NM_008596 1416835_s_at -34,4 Amd1 NM_009665 1435148_at -39,6 Atp1b2 BG261955 1417673_at -59,3 Grb14 NM_016719 1425382_a_at -78,8 Aqp4 U48399 1436736_x_at -122,9 HARP BB369191

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Supplementary data4. Comparison between a fasting microarray analysis and our hypoxic transcriptome changes

Lecker et al. found 72 genes differentially expressed in fasted mice. With an overlap of 25 genes many genes revealed to be different in our hypoxia profile.

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