Mitochondrial Regulation of Epigenetics and its Role in Human Diseases

I MITOCHONDRIAL REGULATION OF EPIGENETICS AND ITS ROLE IN HUMAN DISEASES Ricardo Jorge Bouça-Nova Coelho Dissertação de Mestrado em Oncologia 2013 II III Ricardo Jorge Bouça-Nova Coelho MITOCHONDRIAL REGULATION OF EPIGENETICS AND ITS ROLE IN HUMAN DISEASES Molecular Orientador Professor Doutor Valdemar de Jesus Conde Máximo Professor associado convidado Faculdade de Medicina - Universidade do Porto Investigador – Instituto de Patologia e Imunologia Molecular Dissertação de mestrado apresentada ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto em Oncologia – Especialização em Oncologia IV V Acknowledgments Devo o maior dos agradecimentos ao meu orientador, Prof. Doutor Valdemar Máximo, pela oportunidade de realizar a minha tese de mestrado integrada num dos seus projectos, por todo o apoio prestado, disponibilidade, dedicação e a confiança que sempre depositou nas minhas competências e pela oportunidade de explorar sempre mais um pouco as questões que foram surgindo durante a realização deste trabalho. Não posso também deixar de referir a transmissão de conhecimento, o entusiasmo e a boa disposição. Agradeço à Prof. Doutora Paula Soares por me ter aceite no grupo “Cancer Biology”, e por nos proporcionar as melhores condições para realizar um trabalho com qualidade e incentivandonos a ser sempre melhores. Um agradecimento ao Prof. Doutor Sobrinho Simões por ser o principal mentor do magnífico ambiente que caracteriza o IPATIMUP, sendo acima de tudo um exemplo para todos nós. Agradeço ao Prof. Doutor Jorge Lima pelo fornecimento dos “cibridos”, linhas sem as quais não poderia realizar este trabalho. Agradeço ainda ao Prof. Doutor Hugo Osório, pela transmissão de conhecimentos, na elaboração de parte das técnicas realizadas durante este trabalho. Não posso deixar de agradecer aos “jovens investigadores” e colegas de bancada que proporcionam o ambiente excepcional que se vive, no grupo “Cancer Biology”. A todos tenho que agradecer a disponibilidade, entreajuda e também amizade. Em especial agradeço à Joana Nunes, Rui Baptista e Ana Almeida por me terem ensinado parte das técnicas que foram a base deste trabalho e por estarem sempre disponíveis a esclarecer as dúvidas que foram surgindo no decorrer da elaboração deste trabalho. À Ana Dias e à Catarina Tavares, agradeço toda a disponibilidade e atenção. Ao Ricardo Celestino e ao João Vinagre agradeço por todos os conhecimentos transmitidos, companheirismo e boa disposição. Finalmente agradeço aos meus Pais, pela paciência e por sempre acreditarem em mim e me fazer acreditar. A todos um Muito Obrigado. VI VII Abbreviations 2-DTwo dimensional 2-HG2-hydroxyglutarate 5-hmC5-hydroxymethylcytosine 5-mC5-methylcytosine AceCS1/2Acetyl coenzyme A synthetase 1 or 2 ACLATP citrate lyase ACNAcetonitrile ACTBActin, cytoplasmic 1 AMLAcute myelogenous leukaemia AMPAdenosine monophosphate AMPKAdenosine monophosphate-activated protein kinase ANOVAAnalysis of variance ANXA2Annexin A2 ATPAdenosine triphosphate CAPZA1F-actin-capping protein subunit alpha-1 cDNAComplementary DNA CO 2 - Carbon dioxide CoACoenzyme A COX II - Cytochrome C oxidase subunit II CpGCytosine-phosphate-Guanine CRCaloric restriction Cyp DCyclophilin D D-LoopDisplacement loop DMEMDulbecco’s modified eagle medium DNADeoxyribonucleic acid DNMT1/3A/3B/3LDNA methyltransferases 1; 3A; 3B or 3L dNTPDeoxyribonuceotide triphosphate DTTDithiothreitol ECLEnhanced chemiluminescence EDTAEthylenediaminetetraacetic acid EEF2Elongation factor 2 EGFEpidermal Growth Factor VIII eIF3IEukaryotic translation initiation factor 3 subunit I eIF4AIEukaryotic Initiation factor 4A1 ENOIAlpha enolase ETCElectron transport chain FAD + /FADH 2 - Flavin Adenine Dinucleotide FBPFructose 2, 6 bisphosphate FBSFetal bovine serum FHFumarate hydratase GAPDHGlyceraldehyde 3 phosphate dehydrogenase GDHGlutamate dehydrogenase GDI2Isoform 2 of Rab GDP dissociation inhibitor beta GLSGlutaminase GLUT1Glucose transporter 1 GNMTGlycine N-methyltransferase H2B/3/4Histone 2B; 3 or 4 H 2 O 2 - hydrogen peroxide H3K9/14/18Histone H3 acetylated at lysines 9; 14 or 18 H4K5/8/12/16Histone H4 acetylated at lysines 5; 8; 12 or 16 HATHistone acetyltransferase HDACHistone deactylase Her-2Human Epidermal growth factor Receptor 2 HIF-1Hypoxia-inducible transcription factor 1 HIF-1αHypoxia-inducible transcription factor 1 α HKhexokinase HPRTHypoxanthine phosphoribosyltranferase 1 HSVHepes Simplex Virus IAAIodoacetamide IDH1/2Isocitrate dehydrogenase 1 or 2 IGF-1Isulin like Growth Factor 1 IGF-BP3IGF binding protein-3 JHDMJumonji-C-domain histone demethylases KLysine KATLysine acetyltransferase kbkilobases IX kDaKilodaltons KDACLysine deacetylase LDH-A/BLactate dehydrogenase A or B MALDIMatrix Assisted Laser Desorption Ionization MATMethionine adenosyltransferase MAT1A/2A/2BMethionine adenosyltransferase 1 alpha; 2 alpha or 2 beta MnSODManganese superoxide dismutase mPTPMitochondrial permeability transition pore mRNAMessenger RNA mtDNAMitochondrial DNA mTORMammalian target of rapamycin MWMolecular weight NA 3 VO 4 - Sodium orthovanadate NAD + /NADHNicotinamide adenine dinucleotide NADPHNicotinamide adenine dinucleotide phosphate NAMNicotinamide ND1NADH: ubiquinone oxidoreductase subunit 1 nDNAnuclear DNA NDUFS3NADH dehydrogenase (ubiquinone) Fe-S protein 3 NH 4 HCO 3 - Ammonium bicarbonate OAAOxaloacetate ºCDegrees centigrade OGTO GlcNAc transferase ONOvernight OXPHOSOxidative phosphorylation p53protein 53 PAICSPhosphoribosylaminoimidazole carboxylase PBSPhosphate buffered saline PCRPolymerase Chain Reaction PDKPyruvate Dehydrogenase Kinase PEPPhosphoenolpyruvate PFK6-phosphofructo-1-kinase PGC-1αPeroxisome proliferator-activated receptor gamma co-activator 1-alpha PI3KPhosphatidylinositol 3-kinase XVI 5.8 Sirt 3 and SOD-2 protein expression, regulation of activation ......................................... 96 Chapter 6 Conclusion and future perspectives .............................................................. 99 6.1 Conclusion .................................................................................................................... 101 6.2 Future perspectives ...................................................................................................... 102 Chapter 7 References ................................................................................................... 105 7.1References .................................................................................................................. 107 Chapter 8 Annexes ....................................................................................................... 119 8.1 Annexes ........................................................................................................................ 121 XVII Index of Figures Figure 1. An overview of Intermediary Metabolism ................................................................. 3 Figure 2. Signaling networks and their regulation of metabolism in proliferating cells ............. 8 Figure 3. Signalling and metabolic inputs into epigenetics ................................................... 11 Figure 4. Crosstalk between metabolism and epigenetics .................................................... 12 Figure 5. Schematic illustration of one-carbon metabolism................................................... 15 Figure 6. Role of acetyl-CoA in protein modification and growth........................................... 17 Figure 7. Schematic representation of the cybrid production method ................................... 22 Figure 8. Complex I Enzyme Activity Microplate Assay Kit (ab109721) (Abcam®) ............... 37 Figure 9. Illustration of CpGenome 5-hmC quantitation kit assay principle ........................... 39 Figure 10. Sequence analysis of the mtDNA ND1 gene ....................................................... 48 Figure 11. Sequence analysis of the mtDNA tRNA Leu (UUR) gene in the cybrid 3243Cy 9.7 ... 49 Figure 12. Sequence analysis of the mtDNA ND1 gene. ...................................................... 50 Figure 13. Sequence analysis of the mtDNA tRNA Leu (UUR) gene. ......................................... 50 Figure 14. Analysis of protein levels of COXII in all cell lines ................................................ 51 Figure 15. Mitochondrial DNA (mtDNA) haplogroup affiliation .............................................. 52 Figure 16. Analysis of complex I activity ............................................................................... 53 Figure 17. Western blot analysis for ND1 and NDUFS3 protein levels ................................. 54 Figure 18. Analysis of global ATP levels .............................................................................. 56 Figure 19. Analysis of DNA methylation status ..................................................................... 59 Figure 20. Real time PCR analysis for GNMT gene ............................................................. 60 Figure 21. Real time PCR analysis for MAT1A gene ............................................................ 61 Figure 22. Real time PCR analysis for MAT2A gene ............................................................ 63 Figure 23. Real time PCR analysis for MAT2B gene ............................................................ 64 Figure 24. Real time PCR analysis for DNMT1 gene. ........................................................... 66 Figure 25. Real time PCR analysis for DNMT3B gene ......................................................... 67 Figure 26. Analysis of global levels of 5-hydroxymethylcytosine .......................................... 68 Figure 27. Analysis of acetylated lysines .............................................................................. 69 Figure 28. Analysis of ACL protein expression ..................................................................... 70 Figure 29. Analysis of acetylated protein levels. A ............................................................... 71 Figure 30. Analysis of two dimensional gels stained with coomassie blue for the 143B and 143B Rho 0 cell lines. ...................................................................................................... 72 XVIII Figure 31. Identification of proteins in two dimensional gel stained with coomassie blue corresponding to the 143B sample................................................................................. 72 Figure 32. Analysis for the eIF4AI, GAPDH and ENOI proteins ............................................ 74 Figure 33. Immunoprecitation for Glyceraldehyde-3-phosphate dehydrogenase .................. 74 Figure 34. Analysis of Sirt3 protein expression..................................................................... 76 Figure 35. Analysis of SOD-2 and SOD-2K68 protein expression ........................................ 78 Figure 36. Genotype: phenotype correlations in human mitochondrial disease .................... 83 Figure 37. The role of the mitochondrial genome in energy generation ................................ 88 Figure 38. Acetylation is involved in multiple cellular functions ............................................. 93 Figure 39. Link between acetylation, tumorigenesis and the Warburg effect ........................ 96 Figure 40. A model on SIRT3-mediated SOD2 activation ..................................................... 97 XIX Index of Tables Table 1. Primers sequences, amplicon sizes and optimal annealing temperature used for real-time PCR ................................................................................................................ 31 Table 2. Primers used in the amplifications and sequencing of mtDNA genome ................... 33 Table 3. Primary antibodies and respective conditions in the procedure of western blot ....... 35 Table 4. Metabolite levels in the cell lines compared with 143B cell line ............................... 57 Table 5. List of proteins identified by mass spectrometry ...................................................... 73 XX XXI Resumo A mitocôndria desempenha um papel central na regulação do metabolismo celular. Como consequência, desempenha um papel importante numa grande variedade de condições patológicas, tais como: doenças neurodegenerativas, cancro, diabetes e envelhecimento. A actividade mitocondrial é em grande parte dependente da disponibilidade de nutrientes no meio ambiente e da sua conversão em energia útil. A conversão de macronutrientes, como por exemplo açúcares, lípidos e proteínas, em moléculas de elevado potencial energético tais como: adenosina trifosfato (ATP), acetil-coenzima A (acetil-CoA), S-adenosil-metionina (SAM) e nicotinamida adenina dinucleotídeo (NADH), ocorre através da mitocôndria e da glicólise. O ATP, a acetil-CoA, a SAM e a NAD + /NADH, por sua vez, são substratos de alta energia utilizados para reações de fosforilação de proteínas (incluindo histonas), acetilação/desacetilação de proteínas e metilação de proteínas e ADN, contribuindo assim para a regulação de várias vias celulares e para a remodelação epigenética da cromatina. Deste modo, a inter-relação entre o ADN mitocondrial e o metabolismo celular, tem um papel crucial na manutenção dos níveis de co-factores essenciais para o funcionamento de enzimas responsáveis pela regulação da epigenética celular, nomeadamente, regulação da metilação do ADN e acetilação de proteínas. As mutações no ADN mitocondrial, na sua maioria, induzem defeitos na fosforilação oxidativa. A disfunção mitocondrial e especialmente, a disfunção mitocondrial causada por mutações no ADN mitocondrial, têm sido implicadas numa grande variedade de patologias relacionadas com a idade. Na verdade, os defeitos em genes mitocondriais podem simular praticamente todos os sintomas associados com as doenças complexas comuns, confirmando a importância da bioenergética mitocondrial na saúde. O principal objectivo deste estudo foi revelar de que forma as mutações patogénicas no ADN mitocondrial e/ou variantes mitocondriais podem levar a mudanças epigenéticas, quer no padrão de metilação do ADN quer no padrão de acetilação de histonas e outras proteínas. Os nossos resultados demonstraram que a disfunção mitocondrial, causada por mutações no ADN mitocondrial, pela ausência de ADN mitocondrial, bem como pela presença de algumas variantes mitocondriais (ADN mitocondrial pertencente a diferentes haplogrupos), induz alterações no perfil metabólico da célula, na actividade do complexo I mitocondrial, na metilação do ADN e na acetilação de proteínas. Além disso, observou-se que a disfunção mitocondrial causada pela ausência de ADN mitocondrial, conduz ao reajustamento de vários processos celulares, através de alterações no grau de acetilação de várias proteínas, contribuindo para homeostasia celular e deste modo garantíndo a sobrevivência da célula. XXII Palavras chave: Mitocôndria; ADN mitocondrial; fosforilação oxidativa; metilação de ADN; acetilação de proteínas; XXIII Summary Mitochondria have a central role in energy uptake and energy production, and as consequence they play a key role in a wide variety of pathological conditions, such as cancer neurodegenerative diseases, diabetes and aging. The mitochondrial activity is largely dependent on the environmental availability of nutrients and their conversion to usable energy that takes place by the conversion, by mitochondrion and by glycolysis, of energy rich compounds, such as carbohydrates and fats, into adenosine triphosphate (ATP), acetylCoenzyme A (Acetyl-CoA), S-adenosylmethionine (SAM), and nicotinamide adenine dinucleotide (NADH). ATP, acetyl-CoA, SAM, and NAD + /NADH, in turn, are the high-energy substrates for protein (including histones) phosphorylation, acetylation and deacetylation reactions, and DNA and protein methylation, thus contributing to regulation of several cell pathways and epigenetic and chromatin remodelling. Thus, the interrelationship between mtDNA and cell metabolism have an essential role in the maintenance of the levels of essential co-factors for epigenetic mechanism such as DNA methylation and acetylation reactions. Mutations in the mitochondrial DNA (mtDNA), most of the time induce defects in oxidative phosphorylation (OXPHOS). Mitochondrial dysfunction, and especially mitochondrial dysfunction caused by mutations in mtDNA have been implicated in a wide range of age related pathologies. Indeed, mitochondrial gene defects can result in virtually all of the symptoms associated with the common complex diseases, confirming the importance of mitochondrial bioenergetics in health. The main goal of this study was to unveil how pathogenic mutations in mtDNA and/or mitochondrial variants may lead to epigenetic changes, namely alterations in the pattern of nuclear DNA methylation and/or acetylation of histones and other proteins. Our data, showed that mitochondrial dysfunction, caused either by mtDNA mutations or absence of mtDNA, as well as the presence of some mitochondrial variants (different mtDNA haplogroups) induce changes in cell metabolic profile, complex I activity, DNA methylation and protein acetylation. Furthermore, we observed that mitochondrial dysfunction caused by depletion of mtDNA, leads to the readjustment of several cellular processes through the acetylation of several proteins, allowing the cell to survive and maintain its homeostasis. Key words: Mitochondria; mtDNA; OXPHOS; DNA methylation; protein acetylation, XXIV 1 Chapter 1 Introduction 8 Figure 2. Signalling networks and their regulation of metabolism in proliferating cells. The figure show aspects of metabolism in proliferating cells including glycolysis; lactate production; TCA cycle; oxidative phosphorylation (OXPHOS); pentose phosphate pathway (PPP); glutaminolysis; and the biosynthesis of nucleotides, lipids, and amino acids. PI3K/Akt signalling downstream of the receptor tyrosine kinase (RTK) activation increases glucose uptake through the glucose transporter (GLUT1), and increases flux through glycolysis. mTOR promotes protein synthesis and mitochondrial metabolism Glucose can be processed through glycolysis for the production of ATP and pyruvate, pass through the PPP to generate ribose 5-phospate and NADPH, and also enter the mitochondrial-localized TCA cycle. P53 induces TP53-inducible regulator of glycolysis and apoptosis protein (TIGAR), and represses the glucose transporters GLUT1 and GLUT4. P53 enhaces the transcription of the gene for synthesis of cytochrome C oxidase 2 (SCO2) enhances mitochondrial respiration. Myc increases glutamine uptake and the conversion of glutamine into a mitochondrial carbon source by promoting the expression of the enzyme glutaminase (GLS); ACL indicates ATP citrate lyase; IDH, isocitrate dehydrogenase; PKM, pyruvate kinase isoforms 1 and 2; PFK, 6-phosphofructo-1-kinase; FBP, fructose 1,6bisphosphsate; α-KG, alpha ketoglutarate; NADPH, reduced nicotinamide adenine dinucleotide phosphate; PEP, phosphoenolpyruvate (Adapted from Gerhäuser, 2012). 1.3 Mitochondrial genome and the role of mtDNA in the pathogenesis of mitochondrial dysfunction in cancer Mitochondria have a central role in energy uptake and energy production, and as consequence plays a key role in a wide variety of both pathological conditions, such as cancer neurodegenerative diseases, diabetes and aging and non-pathological (heat production, ROS generation, apoptotic and cellular differentiation) conditions. Several studies have focused their 9 attention on the influence of variability of mitochondrial DNA (mtDNA) on the abovementioned aspects (Wallace, 2010). The mtDNA represents less than 1% of the total cellular DNA. However, its mitochondria gene products are essential for normal cellular function. Mitochondrial DNA within a single cell generally has identical sequences which are described as homoplastic. Heteroplasmy or different mtDNA sequences within mitochondria of the same cell can occur in response to somatic mutations. (Chatterjee et al., 2006; Minocherhomji et al., 2012). The human mitochondrial genome is a 16.6kb circular DNA encoding for 13 proteins of the OXPHOS and the displacement loop (D-Loop), as well as 22 tRNAs and 2 rRNAs necessary for the translation of mitochondrial genes within mitochondrion. The D-Loop is a non-coding region within mtDNA contains cis-acting regulatory elements that are required for replication and transcription of mtDNA. All others mitochondrial proteins, including those involve in the replication and transcription of mtDNA are nuclear-encoded. Nuclear encoded proteins are imported by specialized complexes on the inner and outer mitochondrial membrane (Minocherhomji et al., 2012; Wallace and Fan, 2010). MtDNA variants define specific mtDNA haplotypes. Haplotypes with a common phylogenetic origin are categorized into haplogroups which display a continent-specific distribution. The mtDNA of the European population falls within nine different haplogroups (H, J, U, X, T, I, K, W and V) as identified by (Torroni et al., 1996) The mtDNA haplogroups were initially considered as being neutral and used only for phylogeny analysis and population studies. However, several studies revealed the importance of mtDNA variants in quality of aging and the susceptibility to late-onset pathologies, such as cancer, diabetes, cardiovascular and neurodegenerative disease (Fang et al., 2010; Santoro et al., 2006; Takasaki, 2009). It has been widely reported that mtDNA molecules belonging to different haplogroups may differ in degree of OXPHOS activity, and in turn, results in different percentages of oxygen consumption, ATP and mitochondrial ROS production or heat generation (Marcuello et al., 2009; Pello et al., 2008). Nevertheless there is also evidence that different mtDNA haplogroups may maintain similar efficiency in OXPHOS performance through the fine-tuning of ROS production and mitochondrial biogenesis. It has also been proposed that variants which alter OXPHOS coupling efficiency, thus less ATP and more heat production, are more frequent in cold areas (Elson et al., 2007). Depletion of mtDNA content as well as inherited mutations in mtDNA has been reported in a variety of diseases including cancer. Somatic mutations in the D-Loop region have been identified in several tumours. Since the D-Loop region contain regulatory elements involved in 10 mtDNA replication, it could affect the mtDNA copy number. Indeed tumour-specific changes in the mtDNA copy number have been reported in human cancer (Kulawiec et al., 2009; Tseng et al., 2006). Additionally, mtDNA dimers and mtDNA mutations have been found in patients with leukaemia, hepatocellular carcinoma, renal cell carcinoma, lung cancer, breast cancer, thyroid cancer, ovarian cancer, among others (Chandra and Singh, 2011; Máximo et al., 2002; Modica-Napolitano and Singh, 2004). Most pathogenic mtDNA mutations induces defects in OXPHOS. More recently, cells devoid of functional mitochondrial (Rho 0 cells) are been used in studies aiming to gain insight into the possible role of mitochondria in regulating or being associated with epigenetic alterations of the nuclear genome, either gene specific or genome wide (Minocherhomji et al., 2012). Additionally, several studies have been reporting that mitochondrial dysfunction induces changes in the expression of nuclear genes involved in metabolism, cell signalling, growth, differentiation and apoptosis (Delsite et al., 2002). 1.4 Metabolism and epigenetics A regulated cross-talk between metabolic pathways in the mitochondria and the epigenetic mechanism in the nucleus allows cellular adaptations to new environmental conditions. The term "epigenetics" originally defined by Waddington as "the causal interactions between genes and their products, which bring the phenotype into being" involves the understanding of chromatin structure and its impact on gene function (Waddington, 1942). Currently, epigenetic might be defined as modifications of the DNA or associated proteins, other than DNA sequence variation itself, that carry information content during cell division (Sharma et al., 2010). In addition to primary DNA sequence information, much of the information, regarding when and where to initiate transcription, is stored in covalent modifications of DNA and its associated proteins. The pattern of various modifications along the chromatin, such as DNA cytosine methylation and hydroxymethylation, acetylation, methylation, phosphorylation, ubiquitination, and SUMOylation of the lysine (K) and/or arginine (R) resides of histones are thought to determinate the genome accessibility to transcriptional machinery. An emerged concept is that information about a cell’s metabolic state is also integrated into the regulation of epigenetics and transcription (Figure 3) (Ward and Thompson, 2012). 11 Figure 3. Signalling and metabolic inputs into epigenetics Growth factors, hormones and cytokines activate the classic signalling pathways and downstream transcriptional factors which recruit chromatin-modifying enzymes to local chromatin. The nutrient levels and cell metabolism will affect levels of the metabolites which are required substrates of chromatin-modifying enzymes that use these metabolites to post-translationally modify both histones and DNA (Lu and Thompson, 2012). It is now appreciated that cells constantly adjust their metabolic state in response to extracellular signalling and/or nutrient availability (Vander Heiden et al., 2009). Most of the cellular signalling events are dictated by growth factors, cytokines or hormones, but metabolism still plays a significant role in transcription. This also has a potentially fusing logic, as most chromatin-modifying enzymes requires substrates or co-factors that are derived from various metabolic pathways, including glycolysis, fatty acid oxidation TCA cycle and OXPHOS (Teperino et al., 2010). The fluctuation of metabolite levels can modulate the activities of chromatin dynamics (Figure 4). As many complex diseases such as cancer and type II diabetes display abnormalities of cellular metabolism and epigenome, understanding the molecular connections between these processes may have therapeutic implications . 12 Figure 4. Crosstalk between metabolism and epigenetics. As glucose enters the glycolytic pathway, minor portion is branched to hexosamine biosynthetic pathway to produce GlcNAc which can be used as substrate for histone GlcNAcylation by GlcNAc transferases (OGT), oxidative phosphorylation (OXPHOS) determines the NAD + /NADH ratio which is important for the activities of Sirtuins. Several TCA cycle intermediates can be exported out of the mitochondria including citrate and alpha-ketoglutarate ( α - KG). Cytosolic citrate is converted to acetyl-CoA which is used as a donor for histoneacetyltransferases (HAT) – mediated histone acetylation. α -KG is used as co-factor for histone and DNA demethylation reactions by Jumonji-C domain containing histone demethylases (JHDM) and ten-eleven translocation protein (TET), respectively. The substrate for histone methyltransferases and DNA methyltransferases (DNMT) is S-adenosylmethionine (SAM), which is synthesized from essential amino acid methionine. The lower ATP/AMP ratio can activate AMP-activated protein kinase (AMPK), a kinase that phosphorylates histones (Adapted from Lu and Thompson, 2012). 1.5 Protein post-translational modifications and cell signal transduction Post-translational modification of proteins is a more efficient way to regulate cellular functions than gene transcription for animals and humans to adapt to different physiological and environmental conditions (Wang et al., 2013). In spite of the regulatory power garnered by protein kinase, other reversible protein PTMs are increasingly becoming appreciated as having important roles in cell signal transduction (Deribe et al., 2010). These modifications include protein acetylation, methylation, O-linked glcNAcylation, ADPribosylation, and many others; many of these modifications are conserved down to the level of prokaryotes (Figure 4) (Locasale and Cantley, 2011; Walsh et al., 2005) Current estimates from advances in mass spectrometry-based proteomics suggest that many of these modifications may be as prevalent in mammalian systems as the more studied phosphorylation. In contrast to reversible protein phosphorylation, each of these modifications 13 is carried out by a relatively small number of enzymes. The number of distinct acetyltransferases, methytransferases, GlcNAc transferases, ADP-ribosyltransferases, etc., is much smaller than the total number of unique protein kinases (Locasale and Cantley, 2011). Also, in contrast to the case of protein kinases, where physiological concentrations of the substrate ATP far exceed the Michaelis constant, the enzymes that carry out these modifications typically have Michaelis constants for their metabolic substrates that are close to the physiological concentrations. These substrates include acetyl-Coenzyme A (Acetyl-CoA), S-adenosylmethionine (SAM), N-acetylglucosamine, and adenosine diphosphate -ribose (ADP-ribose), and their levels are dynamically regulated by alterations in cellular metabolism. Changes in these concentrations can result from variations in metabolic flux occurring from countless mechanisms such as changes in nutrient uptake and/or mutations in metabolic enzymes. These changes can affect their concentrations by several orders of magnitude and in turn affect activity of their corresponding enzymes by shifting the substrate concentration to levels well above or below the Michaelis constant of the enzymes. Therefore, the diversity of signalling responses regulated through these modifications may have evolved from the ability of metabolic networks to adapt metabolism to achieve differing levels of substrate availability. These forms of signal transduction are more ancient than protein kinases and have evolved from more primitive versions of signal transduction observed in lower organisms (Locasale and Cantley, 2011). 1.6 DNA methylation - 5-methylcytosines (5-mC) and 5-hydroxymethylcytosine (5-hmC) Methylation of DNA at the carbon 5 of cytosine residues that precede guanines (referred to as CpG dinucleotides) is the most studied and best-known epigenetic modification. Alterations in the pattern of nuclear DNA methylation, such as hypomethylation of the repetitive elements associated with increased genomic instability, are frequently seen. The hypermethylation of specific GpG island in the promoter regions of several tumour-suppressor genes is commonly observed to be associated with transcriptional silencing of the gene (Sharma et al., 2010). DNA methylation is established by a family of proteins called DNA methyltransferases (DNMTs). Three active DNMTs have been described in mammals: DNMT1, DNMT3A and DNMT3B. DNMT1 exhibit great preference for hemimethylated DNA and, therefore, is responsible for maintaining methylation pattern after DNA replication. DNMT3A and DNMT3B are the de novo methyltransferases, because they can establish the DNA methylation pattern, although they show equal preference for unmethylated or hemimethylated DNA (Robertson, 2001). A third 14 homologue of this family, DNMT3L, lack the methyltransferase capacity but is known to assist the other members of the family in methylation reactions by interacting with their catalytic domains (Gowher et al., 2005). DNMTs transfer a methyl group from SAM to the substrate with the formation of the by-product S-adenosylhomocysteine (SAH). The altered redox state of cancer cells influence SAM levels through aberrant regulation of the methionine cycle. SAM is derived from the essential amino acid methionine through methionine adenosyltransferase (MAT) by combining methionine with ATP (Cai et al., 1996). SAM reactions included in onecarbon metabolism are coupled to polyamine synthesis and to the folate cycle . SAM is also a precursor of important metabolites like glutathione, through transsulfuration reactions (Huidobro et al., 2013). The nomenclature used to describe MAT enzymes can be confusing. There are three major multimeric MAT enzymes: MATI, MATII, and MATIII (Mato et al., 2002). MATI is a tetramer and MATIII is a dimer of the protein encoded by methionine adenosyltransferase I alpha ( MAT1A ), the “hepatic form” of the enzyme (Lu and Mato, 2005). In contrast, MATII is a dimer of the protein encoded by methionine adenosyltransferase II alpha ( MAT2A ) and is expressed in most extra hepatic tissues. A third gene, methionine adenosyltransferase II beta ( MAT2B ) , unrelated in amino acid sequence to MAT1A or MAT2A , encodes a regulatory subunit that physically associates with the MAT2A dimer, forming a heterotetramer (Martínez-Chantar et al., 2003). The enzyme glycine N-methyltransferase (GNMT) catalyses the SAM-dependent methylation of glycine to form sarcosine. Kerr suggested a role for GNMT in regulating the relative levels of SAM and SAH (Kerr, 1972). It is important to realize that SAM is the methyl donor for almost all cellular methylation reactions and therefore GNMT is likely to regulate cellular methylation capacity. In addition, GNMT has been proposed to link the de novo synthesis of methyl groups to the ratio of SAM to SAH, which in turn serves as a bridge between methionine and onecarbon metabolism (Figure 5) (Luka et al., 2009). 15 Figure 5. Schematic illustration of one-carbon metabolism. MAT indicates methionine adenosyltransferase; SAM, S-adenosylmethionine; SAH S-adenosyl-homocysteine; DNMT, DNA methyltransferases; R methyl group acceptors; MS methionine synthase; GNMT glycine N-methyltransferase (Adapted from Huidobro et al., 2013). A covalent methyl group is chemically stable. Therefore DNA methylation was considered as a relative static epigenetic mark. However during embryonic development there is extensive remodelling of the cellular methylome, suggesting the existence of enzymes that actively remove methylation marks. The ten-eleven translocation protein (TET) family membersTET1, TET2 and TET3 – are an α -KG and Fe (II)-dependent dioxygenase that hydroxylate the 5-mC of DNA to 5-hmC, utilizes α -KG, oxygen and Fe (II) as co-factors and releases succinate as by-product. Succinate and fumarate are inhibitors of TET enzymes. The function of 5-hmC is the topic of intense investigation and thought to serve as an intermediate for subsequent active (TET-dependent) or passive (replication-dependent) demethylation. From the metabolic perspective the demethylation mechanism by TET is interesting as it involves α -KG and succinate, key metabolites of the TCA cycle. Because of the dependency in key metabolites the rate of demethylation could be regulated at both levels of co-factor availability and feedback inhibition. 1.7 Acetyl-CoA and metabolism The ancient energy precursor of metazoan metabolism is acetate. The main sources of cellular acetate are the mitochondrial processes of glucose-derived pyruvate oxidation, the oxidation of fatty acids and the amino acid catabolism (Wagner and Payne, 2011). These distinct metabolic pathways are all capable of yielding the activated form of cellular acetate, or acetyl- 16 CoA. How mitochondrial acetyl-CoA is used throughout the cell is heavily dependent on the cell type and the metabolic state of the organism. However, the primary function of acetyl-CoA throughout all cell types is to serve as a carbon donor in the TCA cycle. In general, during a state of nutrient excess, a proportion of the excess acetyl-CoA generated within the mitochondria is exported to the cytosol in the form of citrate, where it is reconverted into acetylCoA by ATP-citrate lyase (ACL) (Figure 6). In normal cells, it is possible that ACL becomes phosphorylated in response to growth factor dependent AKT activation, transiently stimulating acetyl-CoA production. However, in cancer cells, in which the PI3K/AKT pathway is constitutively active, ACL might become continuously active, allowing high levels of acetyl-CoA production even if the nutrient availability becomes limited (Biology and Mazurek, 2011). This would provide continuous acetyl-CoA and modulates metabolism in a growth promoting manner (for example by stimulating PKM2 degradation, which increases the availability of glycolytic intermediates for use in biosynthetic pathways) (Wellen and Thompson, 2012). Free cytoplasmic and mitochondrial acetate can also be converted into acetyl-CoA via an ATP-dependent mechanism by acetyl-CoA synthetase 1 (AceCS1) and 2 (AceCS2), respectively. The cytosolic acetyl-CoA is essential for the production of fatty acids and sterol, which are vitally important for the formation and maintenance of lipid membrane and protein acetylation, including histone acetylation in the nucleus (Wagner and Payne, 2011; Wallace and Fan, 2010; Wellen et al., 2009). The acetylCoA groups used to modify histones are predominantly produced by ACL (Figure 6). Loss of function of this enzyme reduces histone acetylation, with global consequences on gene expression (Wellen et al., 2009). In cancer cells aerobic glycolysis is elevated and this improved glucose catabolism results in an excess of the glycolytic end product-pyruvate, and although most of the pyruvate is converted into lactate and secreted out of the cells, part of the pyruvate enters in the mitochondria, where it is decarboxylated into acetyl-CoA by pyruvate dehydrogenase (PDH) (Zaidi et al., 2012). The mitochondrial acetyl-CoA combines with oxaloacetate (OAA) by the citrate synthase 2 generating citrate that enters the TCA cycle (Macdonald et al., 2011). In this step mitochondrial citrate can also be exported to the cytosol to be used as biosynthetic precursor for lipogenic pathways to support the highly demanding proliferating cells. The withdrawal of citrate exported to the cytoplasm may stop the TCA cycle unless additional pathways are engaged to supply OAA to keep the cycle going. Henceforth, a constant supply of OAA is crucial for cancer cells. Although many cancer cells use glutamine metabolism for anaplerotic supply of OAA, some cancer cells use a compensatory anaplerotic mechanism in 17 which pyruvate is carboxylated to form OAA in a reaction catalysed by pyruvate carboxylase (Cheng et al., 2011). Figure 6. Role of acetyl-CoA in protein modification and growth. Acetylation serve as indicator of nutrient sufficiency to the cell to signal that conditions are optimal for growth. Nutrient-sensitive acetylation of histones (and other transcriptional regulators) can regulate the expression of genes involved in cell growth. ACL indicates ATP citrate lyase; GF growth factor; GFR growth factor receptor; PEP phosphoenolpyruvate; F6P fructose 6 phosphate (Adapted from Wellen and Thompson, 2012). For a long time it was thought that glucose metabolism was the main source of citrate for the downstream pathways. However recent studies report that, in tumour cells with mitochondrial dysfunction or in proliferating cells under hypoxic conditions, reductive carboxylation of glutamine-derived α -KG is responsible for supplying citrate for de novo lipogenesis (Cheng et al., 2011). This pathway uses IDH1 and IDH2, cytosolic and mitochondria IDH isoforms, respectively (Zaidi et al., 2012). The acetylation process is regulated in a complex manner through several mechanisms partly including metabolism. The acetyl-CoA production itself probably represents an integration of nutrient and signalling cues. As mentioned above, citrate availability regulates the production acetyl-CoA by ACL, but ACL activity itself can be regulated by phosphorylation of ACL at Ser454, a target of both AKT and cAMP-dependent protein kinase (PKA), that increases its activity even at lower levels of citrate (Berwick et al., 2002; Sale et al., 2006; Wellen et al., 2009). 24 25 2.1 Hypothesis "Mitochondrial dysfunction" due to mutations in mtDNA genes and mitochondrial variants, mediate epigenetic cellular changes and may play key roles in phenotypic variation related to mitochondrial diseases and cancer. 2.2 Aim Having in mind the aforementioned observation and our hypothesis, we have developed a project with the following aim: To unveil how pathogenic mutations in mtDNA and/or mitochondrial variants may lead to epigenetic changes, namely alterations in the pattern of nuclear DNA methylation and/or acetylation of histones and other proteins. 26 27 Chapter 3 Materials and Methods 28 29 3.1 Cell lines and cell culture conditions The 143B and 143B Rho 0 are osteosarcoma-derived cell lines and were a kind gift from Dr. Keshav Singh (Roswell Park Cancer Institute, Buffalo USA). The 143B Rho 0 is derived from the 143B cell line, after depletion of mtDNA, hence they share the same nuclear background. The 143B Rho 0 cells were generated using herpes simplex virus (HSV), atransient expression of UL12.5, a protein encoded by the HSV UL12 gene with mitochondrial localization, leads to the degradation of mtDNA, through nuclease activity, leaving nuclear DNA intact. Mitochondrial mRNAs are also depleted as rapidly as the mtDNA, after HSV infection (Saffran et al., 2007). The cybrid cell lines, 3571Cy 1, 3571Cy 22, Cy 24, 3243Cy 9.7 and the CMPBR3 were previously established in our laboratory. Cybrid clones were constructed using three types of mitochondria donors: the cell line XTC.UC1, which harbours a mtDNA ND1 gene mutation (C insertion at 3571 position) and a mutation in the cytochrome b gene (missense substitution, E271K, 15557 G>A), resulting in three clones, two of them with a mtDNA ND1 gene mutation (3571Cy1 and 3571Cy 22) and a third clone with wild type mtDNA (Cy 24) (Bonora et al., 2006); platelets of a patient with the A3243T mutation in the mtDNA tRNA Leu (UUR) gene (3243Cy 9.7); and platelets from a healthy individual (wild type mtDNA) (CMPBR3). The 143B Rho 0 cell line was used, as mitochondria acceptor. All the cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM) high-glucose supplemented with 10% (v/v) inactivated and filtered fetal bovine serum (FBS), 1% (v/v) penicillin/streptomycin (PenStrep), 0.5% fungizone and 50µg/ml uridine. The cells were maintained at 37ºC, 5% CO 2 in a humidified incubator and cultured as a monolayer. The media and FBS, used in the experiments, were purchased from PAA as part GE Healthcare (UK); Trypsin-EDTA, PenStrep and fungizone were purchased from GIBCO, as part of Invitrogen Life Technologies (Carlsbad, California). 3.2 Nucleic acids extraction from cell lines 3.2.1 DNA extraction Plated cells (70-80% confluence), were washed in PBS 1 X (phosphate buffered saline), trypsinized (0.05% trypsin-EDTA), and pelleted (1200rpm, 5minutes). This procedure was performed using Invisorb Spin Tissue Mini Kit. The process was done according to the manufacturer’s instructions (Invisorb Spin Tissue Mini Kit, Invitek, Berlin, Germany) for DNA isolation from 10-10 6 eukaryotic cells/cell pellets. 30 DNA was quantified using NanoDrop ND-1000 Spectrophometer (Nanodrop Technologies, Inc., Delaware, USA) and DNase/RNase (GIBCO, Invitrogen, Carlsbad, CA, USA) free distilled water as blank. 3.2.2 RNA extraction For RNA extraction, 1ml of TRIZOL reagent (Life Technologies TM , California, USA), was added directly to the cell pellets previously washed twice in PBS 1 X buffer. The remaining protocol was performed according to manufacturer’s instructions (TRIZOL Reagent, Life Technologies TM ,California, USA). RNA concentrations were determined using the NanoDrop ND-1000 Spectrophometer (Nanodrop Technologies, Inc., Delaware, USA) and DNase/RNase (GIBCO, Invitrogen, Carlsbad, CA, USA) free distilled water as blank. 3.3 Polymerase Chain Reaction (PCR) Since the primers available for ND1 gene amplification, include the region where the mutation A3243T of the mtDNA tRNA Leu (UUR) gene is located, they were used to monitor both mutations. Additionally in order to confirm the sequences of the cell lines, PCR and DNA sequence were performed to confirm the absence of mutations in the 143B, CMPBR3, Cy 24 and 143B Rho 0 cell lines using the following conditions: PCR reactions were performed using ~100ng of genomic DNA extracted from cell lines, 0.1µM of each forward and reverse primers (Invitrogen, UK), 1x PCR Buffer (5x GoTaq Flexi Buffer, Promega, WI, USA), 1.5mM of Magnesium Chloride solution (Promega WI, USA) 40mM deoxyribonucleotide triphosphate (dNTPs) mix (Bioron GmbH, Germany) and 0.5 U of GoTAq DNA polymerase (Promega, WI, USA) and DNase/RNase distilled water (GIBCO, Invitrogen, Carlsbad, CA, USA) to fulfil the total reaction volume of 25µl. The sequences of the primer pair for ND1 and tRNA Leu (UUR) amplification, designed according to the Cambridge Reference Sequence, were: forward (5’-ACACCCACCCAAGAACAGGGTTT-3’) and reverse (5’- GTAGAATGATGGCTAGGGTACT-3’). PCR reactions were performed in BIO RAD MyCycel TM termal cycle (BIO RAD, CA, USA). PCR conditions were: 1 cycle of 5 minutes at 94ºC for initial denaturation, followed by 35 cycles of 30 seconds at 94ºC for denaturation, 30 seconds at 58 ºC for the appropriated annealing and extension of 30 seconds at 72ºC; the final extension was performed in 1 cycle of 5 minutes at 72ºC. All primer pairs used for Real-Time PCR with the exception of GNMT which was purchased to SABiosciences (Catalog Number PPH12533A) were design based in MAT1A; MAT2A; MAT2B; DNMT1 and DNMT3B sequence available on ENSEMBL human database. To rich the optimal 31 conditions, all primer pairs, were tested using a titration of template and primer, and a “Touchdown PCR” were performed (1 cycle of 10 minutes at 95ºC for initial denaturation, followed by 40 cycles of 1 minute at 58ºC - 62ºC for the annealing/extension). In Table 1, the primer sequences and the optimal annealing temperatures selected, after PCR optimization are describe. Table 1 . Primers sequences, amplicon sizes and optimal annealing temperature used for real-time PCR RReverse ; FForward 3.4 Agarose gel Electrophoresis In order to confirm de right amplification, after de PCR reaction, all products including a negative control to check for possible contaminations were separated by electrophoresis on a 2% w/v agarose (Lonza) gel in 1 X SGTB (prepared from stock solution 20x) (Grisp, Porto, Portugal). Once polymerized, the gel was place on an electrophoresis apparatus, previously filled with running buffer – 1 X SGTB. The samples were mixed with Gel Red (Biotium Inc,) plus loading buffer. One kb Plus DNA Ladder (Invitrogen, UK), was used as control for the fragment length that is being analysed. The fluorescent of the separated PCR products was visualized and photographed in the Quantity One – version 4.6.9 in the ChemiDoc TM XRS Imaging system (BIO RAD, CA, USA). Gene Primer sequence Amplicon size (bp) Annealing Temperature (ºC) MAT1A F: 5’-CAGCAATCCCCAGATATTGCCC-3’ 108 60 R: 5’-CTCTGTCTCGTCGGTAGCATAG-3’ MAT2A F: 5’-TGGCAGAACTACGCCGTAATGG-3’ 103 60 R:5’-ATGGGAAGCACAGCACCTCGAT-3’ MAT2B F: 5’-GGCTGTCCTGGAGAACAATCTAG-3’ 93 60 R:5’-ACAGTCACAGCACTTTCTTCGAGC-3’ DNMT1 F: 5’-CCAGGCAAACCACCATCACATCT-3’ 77 60 R: 5’-GGCTCTTTCAGACTCTTCCTGAG-3’ DNMT3B F: 5’-CAATCCTGGAGGCTATCCGCA-3’ 88 60 R: 5’-CTTAGCAGACTGGACACCTCC-3’ HPRT F: 5’- GCAGACTTTGCTTTCCTTGGTCAG-3’ 103 60 R: 5’- GTCTGGTTATATCCAACACTTCGTG-3’ 32 3.5 Mitochondrial DNA (mtDNA) characterisation and haplogroup affiliation 3.5.1 MtDNA Sequencing PCR products were subjected to a purifying treatment using 1U/µl exonuclease I and 0.05U/µl shrimp alkaline phosphatase (both from Fermantas Lisbon, Portugal) at 37ºC for 20 minutes, followed by heat inactivation for 15 minutes at 80ºC. For sequencing reaction of the PCR products, a mix reaction of 0.5µ of BigDye ® Terminator (Perking-Elmer, California, USA), 3.4µl of sequencing buffer (Perking-Elmer, California, USA), 0.3µl of primer (forward for ND1 analysis and reverse for tRNA Leu(UUR) analysis), 2 µl of purified PCR product and DNase/RNase-free distilled water (GIBCO, Invitrogen, Carlsbad, CA, USA) were added to a final volume of 10µl were placed in a PCR reaction tube. The sequencing reaction was performed in a BIO RAD MyCycler TM thermal cycle (BIO RAD, CA, USA) with the following conditions: an initial denaturation step at 94ºC for 10 seconds, followed by 35 cycles of 10 seconds at 94ºC, 30 seconds at 56 ºC for the appropriated annealing and elongation of 2 minutes at 60ºC; the final elongation was performed in 1 cycle of 10 minutes at 60ºC. In order to submit each sample to the ABI prism 3130XL Automatic sequencer Perkin-Elmer, Foster City, California, USA), the reaction product had to be purified and precipitated. The columns of Sephadex (Sephadex TM G-50 Fine, GE Healthcare Bioscience AB, Uppsala, Sweden) were prepared adding 750 µl of Sephadex (6.66g/100ml) to each eppendorf and further centrifuged at 3200 rpm for 4 minutes at 4ºC. The columns were transferred from the collecting tubes to a clean and previously identified tube, and the 10 µl of sequencing product was added to the centre of the column. A centrifugation was performed at 3200 rpm, for 4 minutes at 4ºC. At the final, 15µl of formamide (Applied Biosystem, Norwalk, USA) was added to each pellet in order to maintain the single stranded DNA. All the samples were then submitted to the ABI prism 3130XL Automatic sequencer (Perkin.Elmer, Foster City, California, USA), for further automated sequencing analysis. 3.5.2 Complete mtDNA sequencing and mtDNA haplogroup affiliation The complete mtDNA of the cell lines, 143B, CMPBR3, 3243Cy 9.7, and 3571Cy 1, was amplified in 32 overlapping fragment with primers and PCR conditions described in Table 2. The same primers were used to directly sequence both strands of the fragments using the Promegafmol ® DNA Cycle sequencing System and the Usb Thermo Sequence Radiolabelled Terminator Cycle Sequencing Kits. 33 Sequences were aligned and compared with the revised Cambridge Reference Sequence (rCRS; Andrews et al., 1999), by using the software BioEdit (Hall, 1999). The diversity observed in each cell line was automatically classified by using the software mtDNA-GeneSyn (Pereira et al., 2009). Haplotypes were also assigned to haplogroups by using the software Haplogrep (Kloss-Brandstatter et al., 2011), which also indicates the polymorphisms that are potentially private and the ones shared by ancestry. Table 2 . Primers used in the amplifications and sequencing of mtDNA genome Name CRS reference Sequence (5’- 3’) Fragment size (bp) Annealing (ºC) L16340 (16318–16340) AGCCATTTACCGTACATAGCACA 681 52 H408 (429–408) TGTTAAAAGTGCATACCGCCA L382 (362 – 382) CAAAGAACCCTAACACCAGCC 603 56 H945 (964 – 945) GGGAGGGGGTGATCTAAAAC L923 (902–923) GTCACACGATTAACCCAAGTCA 607 56 H1487 (1508–1487) GTATACTTGAGGAGGGTGACGG L1466 (1445 – 1466) GAGTGCTTAGTTGAACAGGGCC 629 58 H2053 (2073–2053) TTAGAGGGTTCTGTGGGCAAA L2025 (2004–2025) GCCTGGTGATAGCTGGTTGTCC 609 52 H2591 (2612–2591) GGAACAAGTGATTATGCTACCT L2559 (2538 – 2559) CACCGCCTGCCCAGTGACACAT 591 56 H3108 (3128–3108) TCGTACAGGGAGGAATTTGAA L3073 (3051–3073) AAAGTCCTACGTGATCTGAGTTC 640 52 H3670 (3690–3670) GGCGTAGTTTGAGTTTGATGC L3644 (3625–3644) GCCACCTCTAGCCTAGCCGT 623 58 H4227 (4247–4227) ATGCTGGAGATTGTAATGGGT L4210 (4189–4210) CCACTCACCCTAGCATTACTTA 625 55 H4792 (4813–4792) ACTCAGAAGTGAAAGGGGGCTA L4750 (4729–4750) CCAATACTACCAATCAATACTC 599 52 H5306 (5327–5306) GGTGATGGTGGCTATGATGGTG L5278 (5259–5278) TGGGCCATTATCGAAGAATT 593 58 H5832 (5851–5832 ) GACAGGGGTTAGGCCTCTTT L5781 (5762–5781) AGCCCCGGCAGGTTTGAAGC 626 58 H6367 (6387–6367) TGGCCCCTAAGATAGAGGAGA L6337 (6318 – 6337) CCTGGAGCCTCCGTAGACCT 601 58 H6989 (6918–6899) GCACTGCAGCAGATCATTTC L6869 (6850–6869) CCGGCGTCAAAGTATTTAGC 578 58 H7406 (7427–7406) GGGTTCTTCGAATGTGTGGTAG L7379 (7358 – 7379) AGAAGAACCCTCCATAAACCTG 580 56 H7918 (7937–7918) AGATTAGTCCGCCGTAGTCG L7882 (7861–7882) TCCCTCCCTTACCATCAAATCA 506 56 H8345 (8366–8345) TTTCACTGTAAAGAGGTGTTGG L8299 (8280–8299) ACCCCCTCTAGAGCCCACTG 603 56 H8861 (8882 – 8861) GAGCGAAAGCCTATAATCACTG L8799 (8779–8799) CTCGGACTCCTGCCTCACTCA 638 58 H9397 (9416–9397) GTGGCCTTGGTATGTGCTTT L9362 (9342 – 9362) GGCCTACTAACCAACACACTA 609 56 H9928 (9950 – 9928) AACCACATCTACAAAATGCCAGT L9886 (9865–9886) TCCGCCAACTAATATTTCACTT 617 56 H10462 (10481–10462) AATGAGGGGCATTTGGTAAA L10403 10383 – 10403) (AAAGGATTAGACTGAACCGAA 612 56 H10975 (10994 – 10975) CCATGATTGTGAGGGGTAGG L10949 (10930–10949) CTCCGACCCCCTAACAACCC 617 58 H11527 (11546–11527 CAAGGAAGGGGTAGGCTATG L11486 (11467 – 11486 AAAACTAGGCGGCTATGGTA 629 56 H12076 (12095–12076 GGAGAATGGGGGATAGGTGT 40 incubation time the wells were washed three times and 100µl of diluted HRP detection antibody (1:100) were added to each well and incubated for 30 minutes at 37ºC. For colorimetric detection, the wells were washed three times with assay buffer and 100µl of HRP detection reagent was added. The absorbance in each well was measure at 405 nm using the Synergy TM 4 Multi-Mode Microplate Reader – (BioTeck ® - Instruments Inc. Vermont USA). 3.14 Immunoprecipitation protocol 3.14.1 Preparing Cell Lysates The cells were plated until reach 70-80% of confluence. The culture medium was aspirated and the cells were washed three times with PBS1 X , and 350µl of 1 X Cell Lysis Buffer (20Mm Tris (pH7.5), 150 nM EDTA, 1% Triton x-100, 2.5mM Sodium pyrophosphate, 1 mM β - glycerophosphate, 1mM Na 3 VO 4 ,1µg/ml leupetin) (Cell Signaling Techonology ® ) was added to each plate (75 cm 2 ) and incubated on ice for 10 minutes. The cells were scraped off the plates and transferred to microcentrifuge tube followed by centrifugation for 10 minutes at 14,000 x g, 4ºC. The supernatant was transferred to a new tube and the lysates were stored at -20ºC. 3.14.2 Immunoprecipitation Total cell lysates were quantified using a DC TM Protein Assay (BIO-RAD Laboratories, Inc.). In order to perform a cell lysate pre-clearing 750µg of protein was added to 35 µl of Protein A agarose beads (50% bead slurry) (Cell Signaling Techonology ® ) and incubate at 4ºC for 1 hour. After, the protein samples were centrifuged at 4ºC for 10 minutes and the supernatant was collected into a new microcentrifuge tube. To perform a pull down of the acetylated lysines proteins the antibody against acetylated lysine (1:100) (polyclonal rabbit) (Cell Signaling Technologies, Inc.) was added to each sample, the same concentration of antibody was added in the negative control. As a negative control an antibody against TOM-20 (polyclonal rabbit) (Santa Cruz Biotechnology, Inc.) was used. The samples and the negative control, after the addition of the respective antibody, were incubated with gentle rocking overnight at 4ºC. After the incubation time, 35µl of Protein A beads (50 % beads slurry) was added and the samples incubated with gently rocking for 2 hours at 4ºC followed by a microcentrifuge for 30 seconds at 4ºC. The pellet was washed four times with 500µl of 1 X cell lysis buffer. The pellet was resuspended with 30µl of 3x SDS sample buffer followed by heating at 95ºC for 5 minutes and centrifuged for 1 minute at 14,000 x g. The samples, controls and total cell 41 lysates of each cell line were load on SDS-PAGE gel 40% bisacrylamide 29:1 (BIO-RAD Laboratories, Inc.) concentration of 12% (v/v). 3.15 Two-dimensional (2-D) gel electrophoresis 3.15.1 Protein Precipitation To perform the 2-D gels, the protein samples were quantified using a DC TM Protein Assay (BIORAD Laboratories, Inc.). For the 7cm strip and the 11cm strip, 150 µg and 300 µg of protein, respectively, was used. Protein precipitation was performed using the ProteoExtract ® Protein precipitation Kit (Calbiochem ® ). Protein samples analysis is often hampered by the presence of non-protein impurities such as buffer, salts and detergents that interfere with electrophoretic separations or enzymatic digestion. The ProteoExtract ® Protein precipitation Kit (Calbiochem ® ) provides a fast and efficient method for concentrating and cleaning up proteins. After the quantification of the protein samples the volume was adjusted to 200µl with RIPA buffer, and 800µl of cold precipitation agent (-20ºC) was added to each sample. The samples were incubated for 1 hour at -20ºC followed by centrifugation at RT at 10,000 x g to pellet the proteins. The supernatant was carefully aspirated without disturbing the pellet. The pellet was washed twice by adding 500µl of cold wash solution (-20ºC) and vortex briefly followed by centrifugation for 2 minutes at RT at 10,000 x g to pellet the proteins. To dry the pellet, the tubes were left open for 45 minutes on the lab bench. 3.15.2 Rehydration and sample application The protein samples was resuspended/rehydrated in 125µl, for 7cm strip, or 200µl, for 11cm strip, in IEF buffer [8M urea, 2% CHAPS and 50nM dithiothreitol (DTT)] to the IEF buffer was added Bio-Lyte ampholytes (BIO-RAD Laboratories, Inc.), and the samples were incubated for 1 hour at RT with gently agitation. After the incubation time the indicated volume of each sample (125µl for 7cm strip or 200µl for 11cm strip) was added as a line along the edge of each cannel in a clean dry disposable rehydration/equilibration tray. When all protein samples have been loaded into the rehydration/equilibration tray, the IPG strips (BIO-RAD Laboratories, Inc.) were placed with the gel facing down, onto the sample and incubated for 30 minutes at RT. After the 30 minutes, each of the IPG strips was overlaid with 2.5ml of mineral oil and the rehydration/equilibration tray was cover with the plastic lid and left overnight to rehydrate the IPG strips and load the protein samples. 42 Isoelectric focusing was performed in a Preotean IEF cell (BIO-RAD Laboratories, Inc.) under the following conditions: (i) 250 V, 15 minutes; (ii) voltage gradient up to 4000 V; and (iii) 4000 V until reach 14000 V-hour. The temperature was maintained at 20ºC and the current was kept at 50 microA per strip. At the end of this step the IPG strips were stored at -20ºC. Upon completion, strips were equilibrated in buffer containing 6M urea, 0.375 M Tris-HCl, pH 8.8, 2% SDS, 20% glycerol and 2% w/v DTT for 10 minutes, followed by a second equilibration using the same buffer containing 6M urea, 0.375 M Tris-HCl, pH 8.8, 2% SDS, 20% glycerol, 2.5% w/v iodoacetamide instead of DTT for another 10 minutes, this incubation was performed in the dark. After the incubation time, the IPG strip was placed into a SDS-PAGE gel containing an IPG well filled with melted overlay agarose. The electrophoresis was performed at 150-200V for 45 minutes. When the electrophoresis was concluded, the SDS-PAGE gel was washed with distilled water three times for 10 minutes. The protein samples were electrotransferred onto a Hybond ECL nitrocellulose (GE, Healthcare, UK) membrane for 2 hours at 365 mA or 120Volts followed by blocking the nitrocellulose membrane for 45 minutes in PBS-T 0.1% containing 5% (v/w) BSA (Sigma-Aldrich, Saint Louis, Missouri, USA) and the membrane was incubated with the antibody against acetylated lysines (1:2500) (Cell Signaling Technologies, Inc.) overnight at 4ºC. 3.15.3 Coloration with Coomassie Blue Coomassie dyes are a family of dyes commonly used to stain proteins in sodium dodecyl sulphate and blue native polyacrylamide gel electrophoresis. This treatment allows the visualization of protein spots. After completed electrophoresis, the gel was washed three times with distilled water for 10 minutes followed by the addiction of Coomassie Blue (Thermo Scientific) and incubated for 1214 hours at RT with gently rocking. 3.16 Mass Spectrometry Methods for Studying differentially acetylated proteins Differentially acetylated proteins (based on the differences in the intensity spots), between the two cell lines (143B and 143B Rho 0 ), were identified by Mass Spectrometry. For protein excision from the gel, was used a gel spot picker (OneTouch 2D gel spot picker, 1.5 mm diameter, Gel Company, USA), and the gel spots were transferred into a 0.25 mL microcentrifuge tube. 43 To each of the protein gel spots 200µl of a solution containing 50 % methanol in 50mM NH 4 HCO 3 (1:1 v/v) was added and mixed for 1min at the top speed of microcentrifuge tube mixer. The supernatant was discarded and this step was repeated once. For samples dehydration, 200µl of the dehydration solution at 50 % of Acetonitrile (ACN) in 50mM NH 4 HCO 3 (1:1 v/v) (recently prepared) was added and mixed for 5 min at the maximum speed of the microcentrifuge tube mixer and the supernatant was rejected followed by the addition of 200 μ L of ACN and incubate 30s. The protein gel spots samples were dried in a vacuum centrifuge concentrator until complete drying of the sample. (5–10 min). After, 100µl of the solution of 25mM DTT in 50mM NH 4 HCO 3 was added to each sample, and incubated for 20 minutes at 56ºC. After the incubation time 100 μ L of the alkylation solution freshly prepared 55mM IAA (iodoacetamide) in 50mM NH 4 HCO 3 was added, and incubated in the dark for 20 min at room temperature. The supernatant was discarded and each sample was washed with 200µl of ultrapure water by briefly vortex mixing, this step was preformed twice. In order to perform the second dehydration of the samples 200µl of the dehydration solution at 50 % of ACN in 50mM NH 4 HCO 3 (1:1 v/v) was added and mixed for 5 min at the maximum speed of the microcentrifuge tube mixer and the supernatant was rejected followed by the addition of 200 μ L of ACN and incubate 30 s. The protein gel spot samples were dried in a vacuum centrifuge concentrator until complete drying of the sample (5–10 min). To perform the tryptic digestion each gel sample was rehydrated with 20µl of 2ng/µl trypsin (Trypsin Gold, Mass Spectrometry GradePromega, WI, USA) in 0.01% surfactant (ProteaseMAX™ Surfactant,Trypsin Enhancer Promega, WI, USA), 50mM NH 4 HCO 3 for 10 minutes. After the 10 minutes, the samples were overlaid with 30µl of 0.01% surfactant in 50mM NH 4 HCO 3 and gently mix followed by incubation at 37ºC for 3hours. The content was pipetted and resuspended twice and transferred into a new tube, 20µl of 2.5% trifluoroacetic acid (TFA) was added to the gel pieces and mixed in a microcentrifuge tube shaker at the maximum speed for 15 minutes. The extract and the trypic digest were combined and the gel pieces were discarded. The combined solution was centrifuged for 10 minutes at 14.000 rpm and the supernatant was transferred into a new tube. In this step, if the trypic digest is not going to be analysed within a few hours, the samples can be stored at -20ºC. Using Ziptip C18 (EMD Millipore Corporation, Billerica, MA, USA), 10µl of wetting solution (100% ACN) was aspirated and dispensed to wash, this step was performed twice followed by 44 pipetting 10µl of equilibration solution (0.1% TFA in Mili-Q grade water) and dispensed, this step was performed twice. In order to bind the peptides, the samples was aspirated and dispensed in ten cycles (it is important to aspirate the digest into a maximum capacity of the tip) followed by aspiration of wash solution (0.1% TFA in Mili-Q grade water) into the tip and dispensed to waste. To perform the elution of the peptides with Matrix Assisted Laser Desorption Ionization (MALDI) matrix [1.2 µl of elution buffer (0.1% TFA/ 50% ACN) with NALDI matrix was previously pipetted], the samples was aspirated with the MADI matrix, and dispense in ten cycles and plated into a MALDI sample plate. 3.17 Statistical Analysis Whenever adequate, the results are presented as mean ± standard deviation. Statistical analysis was performed using One-Way ANOVA and posterior Bennett and Tukey tests, with p< 0.05 as the level of significance, in Graph Pad Prism software version 6. 45 Chapter 4 Results 46 47 4.1 Cybrid Cell Lines In order to evaluate the possible role of mitochondrial dysfunction, caused either by mutations/variants in mtDNA or mtDNA depletion, in the epigenetic landscape of the cell, we decided to take advantage of a suitable cell model. Thus, we choose a model, already established in our lab characterized by a genetic impairment of OXPHOS proteins; it should be noted that OXPHOS has both mtDNA and nDNA encoded proteins. To study specifically the functional effect of mtDNA mutations/variants, nuclear effects must be excluded and, so, cybrids cell lines were used. The cybrid cell lines displaying the mtDNA ND1 gene mutation (3571InsC), were already available in our lab. These cybrid cell lines were constructed using the cell line 143B Rho 0 , used as recipient cell line, whereas the cell line XTC.UC1 (harbouring the mtDNA ND1 gene mutation) was used as donor cell line. Three cybrid cell lines resulted from this construction, two of them had different percentage of heteroplasmy, for the mtDNA ND1 gene mutation (3571Cy 1 and 3571Cy 22), and one cybrid cell line with wt homoplasmy for the mtDNA ND1 gene (Cy 24). Taking into consideration that our purpose was to inactivate OXPHOS, we decided to take advantage of a known pathogenic mitochondrial mutation – an A-to-T transition at mtDNA nucleotide position 3243 in the tRNA Leu (UUR) gene – that will presumably affect all complexes with mtDNA encoded proteins (I, III, IV and V). This cybrid cell line was already established in our lab. To build the A3243T cybrid, platelets were extracted from a patient with mitochondrial myopathy due to a germ line A3243T mutation. Platelets were then fused 143B Rho 0 cell line. This cybrid was called 3243Cy 9.7. In addition, to the aforementioned cybrid cell lines used, we had already available a cybrid cell line resulting from the fusion of 143B Rho 0 cell line and platelets from a healthy individual - CMPBR3 cybrid cell line, and the parental cell line of the 143B Rho 0 – 143B. These two cell lines were used because we wanted to test if the mtDNA belonging to different haplogroups, with the same nuclear background (nDNA), have some influence the cellular epigenetic landscape. Additionally, we also used a cell line without mtDNA (143B Rho 0 ) in order to understand the putative role of mtDNA in the regulation of the cellular epigenetic landscape. The 143B Rho 0 cell line has already been used in other studies using cybrid cell lines, with the aim of studying the possible effect of mitochondrial dysfunction in various aspects of cellular physiology (Singh et al., 2005). 48 4.2 Evaluation of mtDNA mutation status in the cybrid cells 4.2.1 Mutations status of the cybrid cell lines (3571Cy 1 and 3571Cy 22) displaying a mtDNA ND1 gene mutation – (3571InsC) Figure 10. Sequence analysis of the mtDNA ND1 gene. A. Electropherogram of 3571Cy 1 showing the level of the C insertion at bp3571, according to the Cambridge Reference Sequence and, by an analysis of the electropherogram, presented approximately 60% of the mutation. B. An analysis of the electropherogram of 3571 Cy 22, presented approximately 80% of the mutation. C. Electropherogram of Cy 24 showing the normal sequence of the mtDNA ND1 gene. We confirmed the presence of the mtDNA ND1 gene mutation in the cybrid cell lines, 3571Cy 1 and 3571Cy 22. An analysis of the electropherograms of the 3571Cy 1 and 3571Cy 22 cybrid cell lines, presented approximately 60% and 80% of the mtDNA ND1 gene mutation, respectively. (Figure 10 A and B). Furthermore, we also confirmed the absence of mtDNA ND1 gene mutation in the Cy 24 cell line (Figure 10 C). 49 4.2.2 Mutation status of the cybrid cell line displaying a mtDNA tRNA Leu (UUR) gene mutation - A3243T cybrid Figure 11. Sequence analysis of the mtDNA tRNA Leu (UUR) gene in the cybrid 3243Cy 9.7. Electropherogram showing the level of A-to-T transition at mtDNA nucleotide position 3243, according to the Cambridge Reference Sequence (superimposed peaks suggest 60% mutated mtDNA). We confirmed the presence of the mtDNA tRNA Leu (UUR) gene mutation in the 3243Cy 9.7 cybrid cell line. An analysis of the electropherogram suggest the presence of approximately 60% of the mtDNA tRNA Leu (UUR) gene mutation, in the 3243Cy 9.7 cybrid cell line (Figure 11). 4.2.3 Sequencing of the 143B, 143B Rho 0 and CMPBR3 cell lines In order to be sure the mtDNA mutations under study were specific of the 3571Cy 1, 3571Cy 22 and 3243Cy 9.7, we confirmed its absence in CMPBR3, 143B and 143B Rho 0 . As seen in Figure 12, no other cell lines than 3571Cy 1 and 3571Cy 22 have the 3571InsC mutation, and only the 3243Cy 9.7 presents the A3243T mutation at the mtDNA tRNA Leu (UUR) gene (Figure 13). It should be stated that we could obtain a PCR product from 143B Rho 0 ; however, after sequencing, it displayed numerous point mutations which can be due to fragmentation of mtDNA or to amplification of nuclear pseudogenes of mtDNA (Parr et al., 2006). 56 Figure 18. Analysis of global ATP levels. A. Overview of the global ATP levels in all cell lines compared with 143B cell line. B. Analysis of the global ATP levels in cell lines harbouring mtDNA belonging to different haplogroups (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to143B cell line. C. Analysis of the global ATP levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. RQ indicates relative quantification. Data correspond to three biological experiments done in triplicates. Bars, standard deviation; columns, mean. Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. 4.6 Global metabolic profile of the cell lines The global metabolic profile was achieved from an engaged service to the Metabolon ® Company. Changes in metabolic pathways, namely changes in the metabolite levels, were observed in mtDNA cells depleted (143B Rho 0 ) or bearing mutations/variants in mtDNA (143B, CMPBR3 3243Cy 9.7 and 3571Cy 1) (see Table 4). The depletion of mtDNA leads to a blockage of TCA cycle that can be seen by the alterations in the metabolite levels of this pathway. Furthermore, all cell lines presented alterations in the levels of some of the metabolites involved in PTMs and epigenetic landscape modifications, namely in the acetylation (acetyl-CoA) and methylation (methionine and s-adenosylhomocysteine) compared with the parental cell line (143B). The 143B Rho 0 , CMPBR3 and 3243Cy 9.7 cell lines showed alterations in the metabolite levels involved in the methylation reactions. These cell lines presented a statistically significant increase (p<0.05) in the levels of methionine and cysteine compared with the parental cell line (143B) (Table 4). Concerning the coenzyme A metabolism the 143B Rho 0 cell line showed a statistically significant increase (p<0.05) in the metabolites evaluated for this pathway, specifically the coenzyme A and acetyl-CoA compared with the 143B cell line. Additionally the cell lines bearing mtDNA mutations (3571Cy 1 and 3243Cy 9.7) also presented an increase levels in these metabolites compared with the parental cell line (143B). 57 Regarding the redox states, all cell lines evaluated showed an increase in the metabolites involved in the glutathione pathway, such as glutathione reductase (GSH), glutathione oxidase (GSSG) and cysteine-glutathione disulfide, compared with the 143B cell line (Table 4). This data, together with data on mitochondrial complex I activity, suggest that mtDNA mutations/variants induces changes in global cell metabolism. Table 4. Metabolite levels in the cell lines compared with 143B cell line. Red and green shaded cells indicate p ≤ 0.05 (red indicates that the mean values are significantly higher for that comparison; green values significantly lower). Light red and light green shaded cells indicate 0.05<p<0.10 (light red indicates that the mean values trend higher for that comparison; light green values trend lower). All data are normalized to Bradford protein assay values. 4.7 Analysis of DNA methylation status 4.7.1 Evaluation of 5-methylcytosine The global levels of 5-methylcytosine (5-mC) of all cell lines were evaluated using an easy and fast approach, a kit that allows the calculation of the percentage of global DNA methylation (5mC) in DNA isolated from all cell lines. 58 According to the results, either the presence of mtDNA mutations/variants as well as the depletion of mtDNA are able to cause alterations in the global DNA methylation status of the cell lines (Figure 19 A). In the haplogroup analysis (when we compare the global 5-mC levels between the cell lines with mtDNA belonging to different haplogroup) (Figure 19 B) and normalizing the data to haplogroup X (143B), the haplogroup J (CMPBR3) showed the highest DNA methylation level. The increase in the global 5-mC levels in the haplogroup J (CMPBR3) was statistically significant (p<0.01) comparing to the haplogroup X (143B). Additionally, the 143B Rho 0 cell line, showed the lowest DNA methylation level, being the global 5-mC levels statistically significant lower than those in the haplogroup X (143B) (p<0.001); J (CMPBR3) (p<0.001) and H (Cy 24) (p<0.01) (Figure 19 B). The cybrid lines harbouring the mutation in the mtDNA ND1 gene showed no differences in the percentage of 5-mC compared and normalized to the Cy 24 (wt mtDNA) cell line. In the other hand, the cybrid line with mtDNA tRNA Leu (UUR gene mutation ) (3243Cy 9.7) presented a statistically significant increase in the DNA methylation level, compared with the 3571Cy 1 (p<0.001), 3571Cy 22 (p<0.001) and the Cy 24 (p<0.001) cell lines (Figure 19 C). Data were normalized for Cy 24 cell line. 59 Figure 19. Analysis of DNA methylation status. A. Overview of global percentage of 5-mC in all cell lines compared with 143B cell line. B. Analysis of percentage of 5-mC in cell lines harbouring mtDNA belonging to different haplogroups (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ). C. Analysis of percentage of 5-mC in cell lines harbouring mtDNA belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7). 5-mC indicates 5-methylcytosine. Data correspond to three biological experiments done in triplicates. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (** p<0.01 and ***p<0.001). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. 4.7.2 Real time PCR for genes involved in the methylation cycle and DNA methyltransferases. 4.7.2.1 GNMT, MAT1A, MAT2A and MAT2B genes mRNA expression levels Trying to unveil the possible causes for the differences in the methylation pattern between the cell lines, we accomplished an evaluation of the mRNA levels, by real time PCR, for four of the genes involved in the methylation cycle (GNMT, MAT1A, MAT2A and MAT2B) as well as two DNA methyltransferases (DNMT1 and DNMT3B). Concerning GNMT gene expression, a statistically significant decrease were observed in the cell lines, harbouring mtDNA belonging to different haplogroups and/or different mtDNA mutations, compared with the parental cell line (143B) (Figure 20 A). When the mRNA levels were normalized for haplogroup X (143B), the haplogroups J (CMPBR3), H (Cy 24) and the 143B Rho 0 cell line showed a statistically significant decrease in the GNMT gene expression compared with the haplogroup X (143B) [X (143B) vs J (CMPBR3) p<0.001; X (143B) vs H (Cy 24) p<0.01 and X (143B) vs 143B Rho 0 p<0.01) (Figure 29 B). In the cell lines harbouring mutations in mtDNA, no statistically significant differences were observed concerning the cybrid cell lines displaying the mtDNA ND1 gene mutation, compared and normalized for Cy 24 (wt mtDNA) cell line. However the 3243Cy 9.7 cell line presented a 60 statistically significant increase (p<0.05) in the GNMT gene mRNA expression level compared with 3571Cy 1 cell line that presented the lowest GNMT gene expression (Figure 20 C). Figure 20. Real time PCR analysis for GNMT gene. A. Overview of GNMT gene mRNA levels in all the cell lines compared with 143B cell line. B. Analysis of GNMT gene mRNA levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. C. Analysis of GNMT gene mRNA levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. RQ indicates relative quantification. Data is normalized for HPRT gene expression and correspond to 3 experimental replicas. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05; ** p<0.01 and ***p<0.001). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. When the MAT1A mRNA levels were evaluated, an extremely low MAT1A gene expression level was observed in all cell lines. However, a statistically significant decrease in the mRNA levels of this gene was observed in all cell lines compared with the parental cell line (143B). (Figure 21 A). 61 Concerning the haplogroups analysis, the haplogroup J (CMPBR3), H (Cy 24) and the143B Rho 0 cell line presented a statistically significant decrease in the MAT1A gene mRNA level, when compared and normalized to haplogroup X (143B) [X (143B) vs J (CMPRB3) p<0.05; X (143B) vs H (Cy 24) p<0.01 and X (143B) vs 143B Rho 0 p<0.001] (Figure 21 B). When the MAT1A mRNA levels were normalized for the Cy 24 (wt mtDNA) cell line, all cell lines with mtDNA mutations showed a statistically significant reduction in the MAT1A gene expression, compared with Cy 24 cell line (Cy 24 vs 3571Cy 1 p<0.001; Cy 24 vs 3571Cy 22 p<0.001 and Cy 24 vs 3242Cy 9.7 p<0.001) (Figure 30 C). Figure 21. Real time PCR analysis for MAT1A gene. A. Overview of MAT1A gene mRNA levels in all cell lines compared with 143B cell line. B. Analysis of MAT1A gene mRNA levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. C. Analysis of MAT1A gene mRNA levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. RQ indicates relative quantification. Data is normalized for HPRT gene expression and correspond to 3 experimental replicas. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05; ** p<0.01 and ***p<0.001). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. 62 MAT2A mRNA levels were evaluated in all the cell lines and no statistically significant alterations in the MAT2A gene expression were observed compared with the parental cell line (143B) (Figure 22 A). However the same cell lines, 3571Cy 22 and 143B Rho 0 , that presented a statistically significant reduction in the MAT1A gene expression, presented an increased, although not statistically significant, in the MAT2A mRNA levels. The 3571Cy 22 and the 143B Rho 0 cell lines had the tendency to show an inverse relationship between the expression of the MAT1A and MAT2A genes (Figure 22 B and C). Concerning the haplogroup analysis, no statistically significant differences were also observed between the cell lines harbouring mtDNA belonging to different haplogroups (Figure 22 B). In the analysis of the cell lines with mutations in mtDNA, the 3571Cy 22 cell line showed a statistically significant higher MAT2A gene expression compared with the Cy 24 (p<0.05), 3571Cy 1 (p<0.01) and 3243Cy 9.7 (p<0.05) cell lines (Figure 22 C). 63 Figure 22. Real time PCR analysis for MAT2A gene. A. Overview of MAT2A gene mRNA levels in all cell lines compared with 143B cell line. B. Analysis of MAT2A gene mRNA levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. C. Analysis of MAT2A gene mRNA levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. RQ indicates relative quantification. Data is normalized for HPRT gene expression and correspond to 3 experimental replicas. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05 and ** p<0.01). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. Statistically significant variations in the MAT2B gene expression were observed between the cell lines harbouring mtDNA belonging to different haplogroups and/or different mtDNA mutations when compared with the 143B cell line (Figure 23 A). When the MAT2B gene mRNA levels were normalized for haplogroup X (143B), the haplogroup J (CMPBR3) presented the highest MAT2B gene expression and this difference was statistically significant from haplogroup X (143B) (p<0.05), and the haplogroup H (Cy24) (p<0.001). The haplogroup H (Cy 24) presented the lowest MAT2B gene expression level and this difference was statistically significant comparing with the haplogroup X (143B) (p<0.01) and with the 143B Rho 0 (p<0.001) cell line. No differences in MAT2B gene mRNA expression level were observed between the 143B and 143B Rho 0 cell lines (Figure 23 B). In the analysis of the cybrid lines with mtDNA mutations, all the cell lines showed a statistically significant alterations in the MAT2B gene mRNA levels compared to Cy 24 cell line. The 3571Cy 22 and 3243Cy 9.7 cell lines presented a statistically significant increase in the MAT2B gene expression when compared and normalized to Cy 24 cell line (Cy 24 vs 3571Cy 22 p<0.001; Cy 24 vs 3243Cy 9.7 p<0.001). The cell line displaying an mtDNA tRNA Leu (UUR) gene mutation (3243Cy 9.7) revealed the highest MAT2B gene mRNA expression level and this 64 difference was statistically significant comparing with the 3571Cy 1 (p<0.001) and 3571Cy 22 (p<0.001) cell lines. Additionally the 3571Cy 1 cell line showed the lowest MAT2B gene expression level , being this difference statistically significant comparing with 3571Cy 22 cell line (p<0.001) (Figure 23 C). Figure 23. Real time PCR analysis for MAT2B gene. A. Overview of MAT2B gene mRNA levels in all cell lines compared with 143B cell line. B. Analysis of MAT2B gene mRNA levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. C. Analysis of MAT2B gene mRNA levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. RQ indicates relative quantification. Data is normalized for HPRT gene expression and correspond to 3 experimental replicas. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05; ** p<0.01 and ***p<0.001). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. 65 4.7.2.2 DNA methyltransferases: DNMT1 and DNMT3B genes mRNA expression levels In order to verify if the presence of mtDNA mutations/variants as well as absence of mtDNA have some effect on the expression of DNA methyltransferases (DNMTs) we performed an evaluation of the mRNA levels of two DNMTs: DNMT1 “maintenance methyltransferase” and DNMT3B “de novo methyltransferase”. Concerning the DNMT1 mRNA expression levels, statistically significant alterations were observed, either in cell lines harbouring mtDNA belonging to different haplogroup and in cell lines harbouring different mtDNA mutations, compared with the parental cell line (143B) (Figure 24 A). When normalized for the mRNA levels of the haplogroup X (143B), the haplogroup J (CMPBR3) showed the lowest DNMT1 gene expression and presented a statistically significant differences from the haplogroup H (Cy 24) (p<0.05), X (143B) (p<0.001) and the 143B Rho 0 cell line (p<0.001). Additionally the haplogroup H (Cy 24) showed a statistically significant decrease (p<0.05) in the DNMT1 gene expression compared to haplogroup X (143B) (Figure 24 B). Regarding the cell lines harbouring mtDNA mutations, all of them showed a statistically significant reduction in the DNMT1 gene mRNA levels when compared and normalized to Cy 24 cell line [Cy 24 vs 3571Cy 1 (p<0.01); Cy 24 vs 3571Cy 22 (p<0.05) and Cy 24 vs 3243Cy 9.7 (p<0.01)] (Figure 24 C). 72 After the identification of the spots we performed a mass spectrometry analysis to identify each protein from behind each spot. It was possible to identify proteins in all samples with statistical confidence, except in sample 6. In the samples 3, 14, 15 it was possible to identify two proteins and in the sample 19 it was possible to identify three proteins in the same spot (see table 3). Figure 30. Analysis of two dimensional gels stained with coomassie blue for the 143B and 143B Rho 0 cell lines. A. Two dimensional analysis for the total cell lysates of the 143B cell line. B. Two dimensional analysis for the total cell lysates of the 143B Rho 0 cell line. A representative immunoblot of two experiments is shown. Figure 31. Identification of proteins in two dimensional gel stained with coomassie blue corresponding to the 143B sample. 140S ribosomal protein SA (RPSA); 2L-lactate dehydrogenase B (LDHB); 3Lupus La protein (SSB) and alpha enolase (ENOI); 4, 5, 6Transketolase (TKT); 7,8Pyruvate kinase (PKM); 9, 11Phosphoribosylaminoimidazole carboxylase (PAICS); 10Alpha-enolase (ENOI); 12Annexin A2 (ANXA2); 13-Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); 14X-ray repair complementing defective repair in Chinese hamster cells 6 (ku autoantigen 70kDa) and Heat shock protein 75 kDa (TRAP1); 15X-ray repair cross-complementing protein (XRCC) and Heat shock protein 75kDa (TRAP1); 16Isoform 2 of Rab GDP dissociation inhibitor beta (GDI2); 17Actin, cytoplasmic 1 (ACTB); 18Elongation factor 2 (EEF2); 19F-actin-capping protein subunit alpha1(CAPZA1), isoform 2 of Pyruvate dehydrogenase E1 (PDHB) and 60s acidic ribosomal protein (RPLP0); 20Eukaryotic translation initiation factor 3 subunit I (eIF3I); 21Eukaryotic initiation factor 4A-I(eIF4AI). 73 Table 5. List of proteins identified by mass spectrometry kDa indicates kilo Daltons In order to confirm that the differences observed are due to differences in acetylation and not in the protein levels, we performed a western blot analysis for some of the proteins identified in the mass spectrometry analysis. The protein levels of Glyceraldehyde-3-phosphate 74 dehydrogenase (GAPDH), alpha enolase (ENOI) and eukaryotic initiation factor 4A I (eIF4AI) were quantified (data not shown) and no differences were found in the protein levels between the 143B and 143B Rho 0 or all other cell line (Figure 32). Regarding the differences observed in the acetylation levels of the identified proteins, we decided to confirm some of these data with another technique and an immunoprecipitation for acetylated lysines was performed. After the immunoprecipitation of acetylated proteins, using the antibody against acetylated lysines, an immunoblot with an antibody against GAPDH protein, previous identified as the spot number 13, was performed in the two cell line. Indeed, we were able to confirm the increase level in acetylation of GAPDH protein presented in the 143B Rho 0 cell line (Figure 33). Figure 32. Analysis for the eIF4AI, GAPDH and ENOI proteins. Western blot analysis for the eukaryotic initiation factor 4AI (eIF4AI), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and alpha enolase (ENOI) and α -tubulin as an endogenous control in all cell lines. Figure 33. Immunoprecitation for Glyceraldehyde-3-phosphate dehydrogenase. IP indicates immunoprecipitates; Ctrl. Control; Lys.-Total cell lysates; The control was performed using an polyclonal antibody against TOM-20. 75 4.10 Sirt3 and SOD-2 protein expression, regulation of activation Since the levels of acetylated lysines can be regulated by the activity of protein deacetylases, we decided to study the expression levels of one of the cell deacetylases, and we decided to choose Sirt3, since a Sirt3 acts on mitochondrial proteins. With the purpose to verify if mtDNA mutations/variants as well as absence of mtDNA, affects the Sirt3 protein expression and if Sirt3 protein levels leads to changes in SOD-2 protein expression (SOD-2 is a Sirt3 target protein) and activity (SOD-2K68), whose deacetylation its related with activation, we evaluated by western blot, the protein levels of Sirt3, SOD-2 and SOD-2k68. No statistically significant differences in the Sirt3 protein expression levels, were observed between the cell lines compared with parental cell line (143B) (Figure 34 A and B). When normalized for Cy 24 (wt mtDNA) cell line, the cybrid lines with mtDNA ND1 gene mutation, showed no statistically significant differences in Sirt3 protein expression, by contrast the 3243Cy 9.7 cell line exhibited an increased in Sirt3 protein expression level, being this difference statistically significant when compared with 3571Cy 1 (p<0.05) and 3571Cy 22 (p<0.01) cell lines. 76 Figure 34. Analysis of Sirt3 protein expression. A. Western blot analysis for Sirt3 protein, and β -Actin protein as an endogenous control. B. Overview of Sirt3 protein expression levels in all cell lines normalized to 143B cell line. C. Quantification of Sirt3 protein expression levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. D. Quantification of Sirt3 protein expression levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. A representative immunoblot of three experiments is shown. Data are expressed as density of Sirt3 band per the density of the β -actin. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05; ** p<0.01). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. Concerning SOD-2 (a Sirt3 target) protein expression, although the 143B Rho 0 cell line showed a slight increase in Sirt3 protein expression level, a statistically significant increased (p<0.05) in SOD-2 protein expression and a statistically significant decrease (p<0.01) in SOD-2k68 protein level were observed, when compared with the parental cell line (143B) (Figure 35 C). Additionally, all cell lines showed an increase, not statistically significant, in SOD-2 protein expression compared with the 143B cell line (Figure 35 A and B). When normalized for Cy 24 (wt mtDNA) cell line, the cybrid lines with mtDNA mutations showed no statistically significant alterations in SOD-2 protein expression (Figure 35 D). 77 Concerning the activity of SOD-2 (SOD-2k68) all cell lines, with the exception of CMPBR3 and the 3243Cy 9.7 cell lines, exhibited a statistically significant decrease in the acetylation level of SOD-2k68 compared with parental cell line (143B) (Figure 20 A and B). Moreover, in the haplogroups analysis we observed a statistically significant decrease in the acetylation level of k68 at SOD-2 in the haplogroup H (Cy 24) (p<0.001) and 143B Rho 0 cell line (p<0.001) compared to the haplogroup X (143B) and J (CMPBR3) when normalized for the haplogroup X (143B) (Figure 34 C). In the analysis of cybrid lines harbouring mtDNA mutation, all cybrid cell lines showed a statistically significant increase in acetylation of k68 at SOD-2 when compared and normalized to Cy 24 cell line [Cy 24 vs 3571Cy 1 (p<0.001); Cy 24 vs 3571Cy 22 (p<0.05) and Cy 24 vs 3243Cy 9.7 (p<0.001)] (Figure 35D). Moreover, the 3243Cy 9.7 exhibited the highest acetylation levels of SOD-2k68, and exhibited a statistically significant difference when compared with the 3571Cy 1 (p<0.001) and the 3571Cy 22 (p<0.001) cell lines. The cell lines presenting mtDNA ND1 gene mutation revealed statistically significant differences between the level of acetylation of SOD-2k68, being the 3571Cy1 the cell line the one that exhibit a statistically significant increase in the acetylation of SOD-2k68 compared to the 3571Cy 22 (p<0.001) cell line (Figure 35 D). 78 Figure 35. Analysis of SOD-2 and SOD-2K68 protein expression. A. western blot analysis for proteins SOD-2, SOD-2K68 and β -Actin protein as an endogenous control. B. Overview of SOD-2 and SOD-2K68 protein expression levels in all cell lines normalized to 143B cell line. C. Quantification of SOD-2 and SOD-2K68 protein expression levels in cell lines harbouring mtDNA belonging to different haplogroups, (143Bhaplogroup X; CMPBR3haplogroup J; Cy 24-haplogroup H and 143B Rho 0 ), normalized to 143B cell line. D. Quantification of SOD-2 and SOD-3K68 protein expression levels in cell lines belonging to the same haplogroup without (Cy 24) and with mtDNA mutations (3571Cy 1, 3571Cy 22 and 3243Cy 9.7), normalized to Cy 24 cell line. A representative immunoblot of three experiments is shown. Data are expressed as density of SOD-2 or SOD-2k68 band per the density of the β -actin. Bars, standard deviation; columns, mean. The asterisks indicates the level of statistical significance (*p<0.05; ** p<0.01 and ***p<0.001). Data were subjected to one-way ANOVA and a posterior Bennett and Tukey test. 79 Chapter 5 Discussion 80 81 5.1 Mitochondrial energy metabolism and the epigenetics Many vital cellular parameters are controlled by mitochondria, namely, the regulation of energy production (ATP), β-oxidation of fatty acids, metabolism of amino acids and lipids, SAM production, modulation of oxidation-reduction (redox) states, regulation of the NAD + /NADH and FAD + /FADH 2 ratios, generation of ROS and contribution to cytosolic biosynthetic precursors such as cytosolic acetyl-CoA. Changes in these parameters can impinge on biosynthetic pathways, cellular signal transduction pathways, transcription factors and chromatin structure to shift the cell from a quiescent, differentiated state to an actively proliferating one (Desler and Marcker, 2011; Wallace, 2012; Wallace and Fan, 2010). The circular mtDNA is more susceptible to DNA damages in comparison to nDNA. Importantly, mtDNA molecules are not protected by histones, they are supported with only “rudimentary” DNA repair and are localized in close proximity to the ETC which continuous generates ROS. Thus, the mutation rate of mtDNA has been reported being up to 10-15 folds higher than that observed in nDNA in response to DNA damaging agents (Desler and Marcker, 2011). The mitochondrial activity is largely dependent on environmental availability of nutrients and their conversion to usable energy that takes place by the conversion, by mitochondrion and glycolysis, of energy rich compounds, such as carbohydrates and fats, into ATP, acetyl-CoA, SAM, and NADH. ATP, acetyl-CoA, SAM, and NAD + /NADH ratio, in turn, are the high-energy substrates for protein (including histones) phosphorylation, acetylation and deacetylation reactions, and DNA and protein methylation, thus contributing to regulation of several pathways and epigenetic process of chromatin remodelling (Wallace and Fan, 2010). Mutations in the mtDNA, most of the time induce defects in OXPHOS. Mitochondrial dysfunction, and especially mitochondrial dysfunction caused by mutations in mtDNA have been implicated in a wide range of age related pathologies, including cancer, diabetes and degenerative disorders (Figure 36A and B) (Tuppen et al., 2010). Indeed, mitochondrial gene defects can result in virtually all of the symptoms associated with the common complex diseases, confirming the importance of mitochondrial bioenergetics in health. In addition, it was reported that mtDNA variants, are able to regulate several intracellular functions and to influence global methylation levels and nuclear gene expression, having a role in human aging and longevity. (D’Aquila et al., 2013). Either way, OXPHOS efficiency and/or the activation of signalling pathways, regulates the substrate levels needed for PTMs and epigenetic regulation and thus the epigenetic landscape of the cell (DeBerardinis and Thompson, 2012; Lu and Thompson, 2012). 88 Figure 37. The role of the mitochondrial genome in energy generation. This highlights the importance of the mitochondrial genome in contributing polypeptide subunits to the five enzyme complexes that comprise the oxidative phosphorylation (OXPHOS) system within the inner mitochondrial membrane the site of ATP synthesis (Adapted from Taylor and Turnbull, 2005). 5.5 Global metabolic profile of the cell lines The mitochondrial function is fundamental to metabolic homeostasis. In addition to converting the nutrient flux, through the OXPHOS, into the energy molecule ATP, the mitochondria generate intermediates for biosynthesis and ROS that serve as a secondary messenger to mediate signal transduction and metabolism. Alterations of mitochondrial function have been observed in various metabolic disorders, including aging and cancer, among other (Cheng and Ristow, 2013). However, the mechanisms responsible for mitochondrial changes and the pathways leading to metabolic disorders remain to be defined. So far, our results showed that mtDNA variants and mtDNA mutations presented alterations in the OXPHOS complex I activity and ATP production. In fact in cancer cells, the metabolic shift may be due to defects in OXPHOS that force cancer cells towards glycolysis (Máximo et al., 2009). Conceptually, any alteration that disrupts the OXPHOS system will have a direct effect on the cell’s metabolism affecting the cell metabolic profile. The last 10 years have revealed a host of functions for metabolites and metabolic pathways that could not have been predicted from a conventional understanding of biochemistry. As a result it is no longer possible to view metabolism merely as a self-regulating network operating independent of other biological systems. Metabolism also affects cell signalling by providing substrates for PTMs that modulate protein trafficking, localization, and enzyme activity (DeBerardinis and Thompson, 2012). 89 In order to better understand the effect of mitochondrial variants and mitochondrial mutations in the metabolic alterations, we performed the global metabolic profile of the different cell lines in study. In fact, the cell lines analysed, presented changes in the levels of the metabolites involved in the PTMs, namely in acetylation (acetyl-CoA and CoA) in methylation (methionine and s-adenosylhomocysteine) compared with the parental cell line. Indeed, some studies have been reporting that mitochondrial diseases, as a results of mitochondrial dysfunction, can alter the function of several proteins via reversible or irreversible PTMs (Karve and Cheema, 2011; Ryan et al., 2012; Wu et al., 2010). Interestingly, the cell devoid of mtDNA (143B Rho 0 ) does not showed a decrease in the levels of NAD + , although the absence of OXPHOS to regulates the NAD + /NADH ratio. Rho 0 cells require the addition of pyruvate and other oxidants to growth media that are reduced by the plasma membrane electron transport contributing to maintain both growth and cytosolic NAD + availability (Larm et al., 1994). Actually, NAD + homeostasis, in Rho 0 cells is required for Sirtuins activation because these cells maintain Sirtuin levels as wild type cells (Broadley et al., 2011; Crane et al., 2013). Regarding the redox sates, all cell lines evaluated showed an increase in the metabolites involved in the glutathione pathways, compared with the parental cell line. Since, the reduced glutathione (GSH) protects against oxidative damage, but is transformed in the process to its oxidized form (GSSG) (Schafer and Buettner, 2001) it is likely that mitochondria dysfunction caused, either by mutations in mtDNA (ND1 and tRNA Leu (UUR) mutations) or absence of mtDNA (143B Rho 0 ), generate an increased amount of ROS and the GSH system in such instances is stressed to a higher degree than in normal mitochondrial function. 5.6 DNA Methylation status of the cell lines The importance of the interaction between mitochondrial and nuclear genomes has also emerged from studies on epigenetic changes and, more specifically, on DNA methylation of cytosines. DNA methylation functions as a switch that turns relevant genes on and off, a mechanism that is crucial in development, differentiation and homeostasis (Deaton and Bird, 2011). There is currently extensive ongoing research aiming the identification of specific changes in DNA methylation. Actually, DNA methylation has been confirmed as an important player in human disease, mediating genomic instability, silencing of tumour-suppressor genes and hyper-methylation of CpG island shores that may lead to the inception and progression of cancer (Baylin and Jones, 2011; Kriaucionis and Bird, 2003). 90 In fact, the efficiency of mitochondrial energy producing machinery and the mitochondrial metabolism can modulate the activity of MAT, the enzyme responsible for SAM synthesis from L-methionine and ATP (Pajares et al., 1992; Wallace, 2009; Wallace and Fan, 2010). Lastly, mitochondria contribute to epigenetics modification through the folate metabolism and the regulation of the metabolic switch between SAM and nucleotide synthesis through the one carbon Cycle. Besides, the dysfunction in mitochondrial activity may have direct effects on epigenetic markers and consequently, disrupt gene expression patterns and thus leading to changes in cellular functions. By the methylation analysis of the cell lines, we observed that mitochondrial variants and mitochondria dysfunction caused either by mutations in mtDNA or absence of mtDNA, presented alterations in the DNA methylation pattern, and also in the expression of genes involved in the one carbon metabolism. The cells devoid of mtDNA (143B Rho 0 ) presented a reduction in the methylation levels and a decrease in the expression of the GNMT and MAT1A genes, but do not showed alterations in the levels of 5-hmC. The depletion of mtDNA leads to a blockage of TCA cycle, leading to the accumulation of succinate. 5-Hydroxymethylcytosine is an oxidative product of 5-mC, catalysed by the TET family of enzymes and their activity can be inhibited by the succinate. The accumulation of succinate observed in the 143B Rho 0 , may leads to the inhibition of TET proteins, leading to the hydroxylation of 5-mC to 5-hmC by a TETindependent mechanism. However, oxidation of DNA has traditionally been considered a DNA damage event (Poulsen, 2005). Furthermore, oxidation of 5-mC appears to be a step in several active DNA demethylation pathways, which may be important for normal processes, as well as global hypomethylation during cancer development and progression (Kraus et al., 2012). In fact, the depletion of mtDNA in cancer cells with chemical or genetic treatments has been found to induce alterations in the DNA methylation pattern in 2-9% of the CpG islands surveyed. In most of the studies, with cells devoid of mtDNA, DNA hypomethylation is frequent finding. When mtDNA is reintroduced to the mtDNA-deficient cancer cell, 30% of the hypomethylated sitres become remethylated. This results indicated that there is a direct relationship between mitochondrial functional and epigenomic DNA methylation in cancer cells (Smiraglia et al., 2008), however, we do not know the exactly mitochondria signal that triggers epigenetic changes in the nucleus. Turk et al., reported that the presence of 8-hydroxyl-2-deoxyguanosine (8-OH-dG) in CpG nucleotide, the most studied DNA base lesion induced by oxidative stress in aging, diminishes the ability of DNA methyltransferase to methylate the adjacent cytosine (Wei et al., 2001). In addition, Valinluck et al. demonstrated that the oxidation of guanosine in 8-OH-dG and/or 5-mC in 5-hmC in a CpG dinucleotide significantly reduces the binding affinity 91 of methyl-CpG binding domain proteins (MBDs) to their recognition sequences, thus leading alterations in transcription (Turk et al., 1995). In addition, the intracellular redox state is also able to modulate the activity of MATs (Pajares et al., 1992; Valinluck et al., 2004). In other hand, we observed an increase in the 5-mC levels in the cell lines, harbouring the mtDNA tRNA Leu (UUR) gene mutation and in the haplogroup J (CMPBR3). The haplogroup J presented a decrease in the expression of GNMT, MAT1A and DNMT1, compared with other haplogroups, the only increase in the mRNA levels observed for the haplogroup J was the MAT2B gene. Bellizzi et al reported the increase in the methylation levels of haplogroup J compared with other haplogroups, but the reason for this increase in the level of 5-mC is not yet well understood (Bellizzi et al., 2012). Additionally, the haplogroup J have been extensively ascribed to low OXPHOS efficiency, given that the variants clustered in the J haplogroup mainly fall within mitochondrial complex I and complex III. Curiously, the cell line harbouring the mtDNA tRNA Leu (UUR) gene mutation, presented similar results for the expression on the evaluated genes, with exception of MAT1A gene, that was less expressed in the 3243Cy 9.7 cell line, compared with the haplogroup J (CMPBR3). It is possible that the mechanism that allows the haplogroup J to increase the methylation levels were the same presented in the 3243Cy 9.7 cell line. Indeed, we cannot establish a link between the changes in the pattern of DNA methylation and the expression of the evaluated genes, in spite of the two cell lines show an increase in the expression of the MAT2B gene. There are some possible explanations for the increase in the methylation levels observed in these cell lines, one of them is changes in the demethylation pathway. The 3243Cy 9.7 and the haplogroup J (CMPBR3) were the only cell lines that showed a reduction in the levels of 5hmC. For a better understanding of the reasons that leads to the increase in the DNA methylation levels of these two cell line, an evaluation of the activity of the enzymes involved in the one carbon metabolism should be performed and also an evaluation of the expression and activity of the TET enzymes. Alterations in the expression of genes involved in one carbon metabolism, were also observed in the cell lines displaying the mtDNA ND1 gene mutation (3571Cy 1 and 3571Cy 22), although these cell lines do not exhibited alterations in the 5-mC and 5-hmC levels. These observations reinforce the hypothesis that the alterations in the methylation pattern, due to alterations in mtDNA, may be related, not only with alterations in the gene expression, but also with other mechanisms. 92 Our data suggest that the mitochondrial dysfunctional can induce aberrant DNA methylation patterns, and in this way contrite to the aetiology of several diseases, such as cancer, aging and neurodegenerative disorders. 5.7 Protein lysine acetylation and ACL protein expression The importance of acetylation is strengthened by studies showing that deregulated acetylation is tightly linked to major diseases, such as cancer. In addition, it is well accepted that cancer cells shows a metabolic profile different from that of normal cells (Xu et al., 2013). The mitochondrial alterations in cancer cell leads to unique metabolic signatures. To coordinate the metabolic network, a general coordinator should be associated with all or most of the metabolic enzymes, and it should sense metabolic signals. Such general coordinator of metabolism is so far not identified (Xu et al., 2013). Acetylation has the potential to meet both criteria, and thus to be a general coordinator of metabolism. All KATs use acetylCoA as an acetyl group donor. Acetyl-CoA can be produced from glucose by the enzyme ACL, which generates acetyl-CoA from mitochondria-derived citrate. ACL dependent production of acetyl-CoA for lipogenesis is important for the proliferation of glycolytically converted tumour cells. Additionally, several studies, have been reporting that ACL is the major source of acetyl - CoA for histone acetylation (Wellen et al., 2009; Zaidi et al., 2012) . In fact, we observed differences between the cell lines in the pattern of acetylated lysines, which may be related with the differences in the mtDNA which assigned unique metabolic signatures, confirmed through the results from the global metabolic analysis. However, we do not observe differences in ACL protein expression levels between the cell lines. Indeed, the cell line devoid of mtDNA (143B Rho 0 ), does not presented a decrease in ACL protein expression, although it showed a general reduction in the protein acetylation levels. These results may be related with the fact that the depletion of mtDNA results in oxidative stress causing increase in the lipid peroxidation, and to keep on balance the synthesis and degradation of fatty acids, they must maintain the levels of acetyl-CoA through ACL expression regulation. Actually, Delsite et al, reported the increase in peroxidation of lipids in cells depleted of mtDNA (Delsite et al., 2002). Additionally, to support this hypothesis, the activity of ACL enzyme should be determined, as well as the levels and activity of other proteins responsible for the synthesis of acetyl-CoA, namely the AceCS1 and AceCS2. Through mass spectrometry analysis, we were able to identified nineteen proteins showing differences in acetylation levels between the two cell lines analysed (143B and 143B Rho 0 ). 93 We identified differences in acetylation levels in proteins involved in a number of cellular processes, including metabolism, protein synthesis, cellullar architecture and DNA repair. As mentioned above, protein acetylation can have numerous functional consequences including changes in: enzyme activation/inactivation; protein DNA interaction; protein stability; protein subcellular localization; and in specific functional complex formation (Figure 38). The 143B Rho 0 cell line has a unique metabolic signature due to the absence of mtDNA, leading to alterations in metabolism and other cellular processes. Several studies have been reporting that mtDNA depletion, results in altered expression of nuclear genes involved in signalling, cellular architecture, metabolism, cell growth and differentiation, and apoptosis (Delsite et al., 2002; Jahangir Tafrechi et al., 2005). Changes in the pattern of protein acetylation, due to changes in cell metabolism, signalling and other mechanisms, represent a more versatile and fast mechanism for cell adapted to various stimuli, than changes in protein expression. Figure 38. Acetylation is involved in multiple cellular functions (Xu and Zhao, 2011). From the analysis of the nineteen proteins identified, we were able to confirm the absence of changes in protein expression for three of them, eIF4AI, ENOI and GAPDH, between the 143B and 143B Rho 0 cell lines. This datum implies that the changes observed in the 2-D gels, were due to changes in the degree of protein acetylating and not changes in the expression of the core protein. 94 eIF4AI protein is an ATP-dependent helicase that are required for efficient ribosome binding, and delivery of the mRNA template, process involved in the protein synthesis. Protein synthesis is a complex, tightly regulated process in eukaryotic cells and its deregulation is a hallmark of many cancers. Translational control occurs primarily at the rate-limiting initiation step, where ribosomal subunits are recruited to template mRNAs through the concerted action of several eukaryotic initiation factors (eIFs). Protein synthesis, appears to play an important role in tumour initiation and progression, since its overexpression can cooperate with oncogenes to accelerate cell transformation in cell lines and in animal models, and its levels are elevated in many human cancers (Lee and Pelletier, 2012; Nasr et al., 2013). However, the exact role of eIF4I acetylation in not well understood. Nevertheless, we observed a decrease of eIF4I acetylation in 143B Rho 0 cell line, and this change in acetylation may represent an important role in the disruption of normal protein synthesis in consequence of mitochondrial dysfunction. ENOI is a key glycolytic enzyme in the cytoplasm of eukaryotic cells and is considered a multifunctional protein. ENOI catalyses the dehydration of 2-phosphoglycerate to phosphoenolpyruvate, in the last steps of the catabolic glycolytic pathway, and is also expressed on the surface of several cell types, where it acts as a plasminogen receptor, concentrating proteolytic plasmin activity on the cell surface (Pancholi, 2001). In addition, to this function as a glycolytic enzyme and plasminogen receptor, ENOI appears to have other cellular functions and subcellular localizations, distinct from its well-established function in glycolysis. Alterations in ENOI has been identified in diverse human pathologies, suggesting that it could be part of a group of universal sensors that response to multiple different stimuli (Díaz-Ramos et al., 2012). One of these stimuli may be the mitochondrial alterations that have been reported in a variety of human disorders. Thus, ENOI could be considered as a marker of pathological stress in a high number of diseases. Some of the more interesting and challenging issues regarding ENOI multifunction, that need to be addressed, are the role of ENOI as an inductor of intracellular signalling pathways, and the role of PTMs of ENOI and the implications on its subcellular distribution and function, and at this subject, the acetylation could provide an important clue to better understanding its multifunctional properties. In the past two decades, the glycolic enzyme GAPDH that was once considered “housekeeping” protein has been shown as being involved in many cellular processes in addition to the role it as in glycolysis, including: DNA repair, tRNA export, membrane fusion and transport, cytoskeletal dynamics and cell dead. Additionally, it has been reported that GAPDH is translocated to the nucleus, upon exposure to stressors, where participates in cell 95 death/dysfunction, and this translocation, to the nucleus, is mediated by acetylation at lysine 160 of GAPDH (Tristan et al., 2011). In fact, we observed an increase in the acetylation level of GAPDH in 143B Rho 0 cells. This cell line present a high oxidative stress due to the absence of mtDNA, and the increase in the acetylation levels of GAPDH could be a response to the cell to these high levels of stress. Furthermore, acetylation of the glycolytic/gluconeogenic enzyme GAPDH controls glucose flux in S.enterica, in which GAPDH acetylation favours glucose consumption through the induction of glycolysis, and GAPDH deacetylation, favours glucose production through gluconeogenesis. Despite a number of acetylated lysine residues in GAPDH were identified, their role in the control of glucose metabolism in mammalians is unknown. Understand if the acetylation of GAPDH also control glucose flux, may provide important clues to unveil some of the metabolic alterations reported in cancer in consequence of mitochondria dysfunction (Guan and Xiong, 2011). Regarding the role of GAPDH as a “housekeeping” protein, and taking in account our data, GAPDH protein expression represented a good "housekeeping", once no differences were observed in the protein expression between the cell lines with different alterations in mtDNA although, the same is not true regarding the levels of protein acetylation. Despite the large number acetylated proteins identified in mammalian cells, the number of KATs and KDACs is surprisingly small. The discrepancy between the number of acetylated proteins and the number of acetylation regulators suggests a highly interconnected acetylation regulation network and highlights the importance of the acetylation in cell physiology (Xu et al., 2013). Mitochondrial dysfunction, due to mutations in mtDNA can lead to changes in the levels of some co-factors, namely NAD + and acetyl-CoA of deacetylases, such as, Sirtuins, and thereby affecting protein acetylation. The acetylation is likely to be a coordinator of both cell signalling and cell metabolism, because of its versatile role in transcriptional and metabolic regulation (Figure 39). A better understanding of the role of acetylation, in metabolic coordination as well as the molecular understanding of the mechanisms by which it act, may provide new ways for the prevention and control of human tumours. 96 Figure 39. Link between acetylation, tumorigenesis and the Warburg effect. The interconnections between gene transcription, metabolism and metabolites are all coordinated by acetylation, deregulated coordination may alter both gene transcription and cell metabolism, two of the causal factors of the Warburg effect and tumorigenesis (Xu et al., 2013). 5.8 Sirt 3 and SOD-2 protein expression, regulation of activation At this point, we found that mitochondrial dysfunction, caused either by mtDNA mutations or absence of mtDNA, as well as the presence of some mitochondrial variants (different mtDNA haplogroups) induces changes in cell metabolic profile, complex I activity, DNA methylation and protein acetylation. To test if mitochondrial variants and mitochondrial dysfunction influence the cellular stress response by modulating Sirt3 protein expression, we evaluated the expression of the Sirt3 in our cell lines. Sirt3 is a sensor of the energetic and redox, dependent on NAD + , this analysis make sense. We did not found differences in the cells with mtDNA belonging to different haplogroups, as well as in 143B Rho 0 cell line, concerning Sirt3 protein expression. The difference found between the cell line displaying the mtDNA tRNA Leu (UUR) gene mutation (3243Cy 9.7) and the cell lines harbouring the mtDNA ND1 gene mutation (3571Cy 1 and 3571Cy 22), suggest that these mutations induces different cellular stress in the mitochondria. In fact, the 3243Cy 9.7 cell line presented a higher level of ROS production (data not shown) and it is known that the increase of ROS levels stimulates Sirt3 transcription (Chen et al., 2011). Considering the results observed in 143B Rho 0 cells, which are intrinsically exposed to very stressful conditions due to the absence of mtDNA, and consequently to mitochondrial dysfunctions, it may seem inconsistent that these cells keeps the Sirt3 protein levels. One 97 possible explanation, already proposed by D’Aquila et al, is that Rho 0 cells adopt a series of mechanisms to compensate their intrinsic oxidative stress status. Mitochondrial manganese superoxide dismutase (SOD-2) is an important antioxidant enzyme, and SOD-2 deficiency is associated with several human diseases (Miao and Clair, 2009). SOD2 is acetylated at Lys 68 and this acetylation decreases its activity. Sirt3 binds to SOD-2, deacetylating and activating it (Figure 40). 143B Rho 0 cell line was the only cell line, in study, that presented a significant increase in the SOD-2 protein expression level. This observation may be related with the oxidative stress induced SOD-2 expression, and is believed to be an important cellular defence mechanism against oxidative stress. The increase in SOD-2 protein expression levels and activity observed in 143B Rho 0 cell line, enforces the hypothesis that these cells have an intrinsic mechanism to deal with oxidative stress. In fact, several studies have been reporting the resistance of mtDNA-depleted cells to apoptosis induced by oxidative stress (Ferraresi et al., 2008; Park et al., 2004; Quinzii et al., 2010) . The cell lines displaying mutations in mtDNA presented a lower activity of SOD-2, these data may be explained by the absence of an intrinsic mechanism to handle with oxidative stress in these cells. Mitochondria dysfunction, due to mtDNA mutations may cause damage in SOD-2 protein and thus inhibiting their activity. What we observe in 143B Rho 0 cells may be a wellbalanced mechanism between the overproduction of ROS and the SOD2 activity. Figure 40. A model on SIRT3-mediated SOD2 activation (Adapted from Qiu et al., 2010). 104 105 Chapter 7 References 106 107 7.1References Ahn, B., Kim, H., Song, S., Lee, I.H., Liu, J., Vassilopoulos, A., Deng, C., and Finkel, T. (2008). A role for the mitochondrial deacetylase Sirt3 in regulating energy homeostasis. Proceedings of the National Academy of Sciences of the United States of America 105 , 14447–14452. Allfrey, G., Faulkner, R., and Mirsky, A.E. (1964). Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proceedings of the National Academy of Sciences 51 , 786–794. Andrews, R.M., Kubacka, I., Chinnery, P.F., Lightowlers, R.N., Howell, D.M., and Neil, T.& (1999). Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nature Genetics 23 , 3965. Baylin, S., and Jones, P. (2011). A decade of exploring the cancer epigenome—biological and translational implications. Nature Reviews Cancer 11 , 726–734. Baysal, B.E. (2007). A recurrent stop-codon mutation in succinate dehydrogenase subunit B gene in normal peripheral blood and childhood T-cell acute leukemia. PloS One 2 , e436. Baysal, B.E. (2008). Clinical and molecular progress in hereditary paraganglioma. Journal of Medical Genetics 45 , 689–694. Bellizzi., D., D’Aquila., P., Giordano, M., and Passarino, A.M.& G. (2012). Global DNA methylation levels are modulated by mitochondrial DNA variants. Epigenomics 4 , 17–27. De Benedictis, G., Rose, G., Carrieri, G., De Luca, M., Falcone, E., Passarino, G., Bonafe, M., Monti, D., Baggio, G., Bertolini, S., et al. (1999). Mitochondrial DNA inherited variants are associated with successful aging and longevity in humans. FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology 13 , 1532–1536. Bensaad, K., Tsuruta, A., Selak, M. a, Vidal, M.N.C., Nakano, K., Bartrons, R., Gottlieb, E., and Vousden, K.H. (2006). TIGAR, a p53-inducible regulator of glycolysis and apoptosis. Cell 126 , 107–120. Berger, S.L. (2007). The complex language of chromatin regulation during transcription. Nature 447 , 407–412. Berwick, D., Hers, I., and Heesom, K. (2002). The identification of ATP-citrate lyase as a protein kinase B (Akt) substrate in primary adipocytes. Journal of Biological Chemistry 277 , 33895–33900. Biology, C., and Mazurek, S. (2011). Pyruvate kinase type M2 : A key regulator of the metabolic budget system in tumor cells. The International Journal of Biochemistry & Cell Biology 43 , 969–980. Bitterman, K.J., Anderson, R.M., Cohen, H.Y., Latorre-Esteves, M., and Sinclair, D. a (2002). Inhibition of silencing and accelerated aging by nicotinamide, a putative negative regulator of yeast sir2 and human SIRT1. The Journal of Biological Chemistry 277 , 45099–45107. Bluemlein, K., Grüning, N., and Feichtinger, R. (2011). No evidence for a shift in pyruvate kinase PKM1 to PKM2 expression during tumorigenesis. Oncotarget 2 , 393–400. Böhm, V., Hieb, A.R., Andrews, A.J., Gansen, A., Rocker, A., Tóth, K., Luger, K., and Langowski, J. (2011). Nucleosome accessibility governed by the dimer/tetramer interface. Nucleic Acids Research 39 , 3093–3102. 108 Bonora, E., Porcelli, A.M., Gasparre, G., Biondi, A., Ghelli, A., Carelli, V., Baracca, A., Tallini, G., Martinuzzi, A., Lenaz, G., et al. (2006). Defective oxidative phosphorylation in thyroid oncocytic carcinoma is associated with pathogenic mitochondrial DNA mutations affecting complexes I and III. Cancer Research 66 , 6087–6096. Broadley, K., Larsen, L., Herst, P.M., Smith, R. a J., Berridge, M. V, and McConnell, M.J. (2011). The novel phloroglucinol PMT7 kills glycolytic cancer cells by blocking autophagy and sensitizing to nutrient stress. Journal of Cellular Biochemistry 112 , 1869–1879. Brown, K., Xie, S., Qiu, X., Mohrin, M., Shin, J., Liu, Y., Zhang, D., Scadden, D.T., and Chen, D. (2013). Report SIRT3 Reverses Aging-Associated Degeneration. Cell Reports 3 , 319–327. Bungard, D., Fuerth, B.J., Zeng, P.-Y., Faubert, B., Maas, N.L., Viollet, B., Carling, D., Thompson, C.B., Jones, R.G., and Berger, S.L. (2010). Signaling kinase AMPK activates stress-promoted transcription via histone H2B phosphorylation. Science 329 , 1201–1205. Burnichon, N., Brière, J.-J., Libé, R., Vescovo, L., Rivière, J., Tissier, F., Jouanno, E., Jeunemaitre, X., Bénit, P., Tzagoloff, A., et al. (2010). SDHA is a tumor suppressor gene causing paraganglioma. Human Molecular Genetics 19 , 3011–3020. Cai, J., Sun, W.M., Hwang, J.J., Stain, S.C., and Lu, S.C. (1996). Changes in S-adenosylmethionine synthetase in human liver cancer: molecular characterization and significance. Hepatology 24 , 1090– 1097. Calabrese, C., Iommarini, L., Kurelac, I., Calvaruso, M.A., Capristo, M., Lollini, P.-L., Nanni, P., Bergamini, C., Nicoletti, G., Giovanni, C. De, et al. (2013). Respiratory complex I is essential to induce a Warburg profile in mitochondria-defective tumor cells. Cancer & Metabolism 1 , 11. Cantó, C., Sauve, A. a, and Bai, P. (2013). Crosstalk between poly(ADP-ribose) polymerase and sirtuin enzymes. Molecular Aspects of Medicine. Carelli, V., Vergani, L., Bernazzi, B., Zampieron, C., Bucchi, L., Valentino, M.L., Rengo, C., Torroni, A., and Martinuzzi, A. (2002). Respiratory function in cybrid cell lines carrying European mtDNA haplogroups: implications for Leber’s hereditary optic neuropathy. Biochimica Biophysica Acta 1588 , 7– 14. Chandra, D., and Singh, K.K. (2011). Genetic insights into OXPHOS defect and its role in cancer. Biochimica et Biophysica Acta 1807 , 620–625. Chatterjee, a, Mambo, E., and Sidransky, D. (2006). Mitochondrial DNA mutations in human cancer. Oncogene 25 , 4663–4674. Chen, H., and Costa, M. (2009). Iron-and 2-oxoglutarate-dependent dioxygenases: an emerging group of molecular targets for nickel toxicity and carcinogenicity. Biometals 22 , 191–196. Chen, Y., Zhang, J., Lin, Y., Lei, Q., Guan, K.-L., Zhao, S., and Xiong, Y. (2011). Tumour suppressor SIRT3 deacetylates and activates manganese superoxide dismutase to scavenge ROS. EMBO Reports 12 , 534–541. Cheng, Z., and Ristow, M. (2013). Mitochondria and metabolic homeostasis. Antioxidants & Redox Signaling 19 , 240–242. 109 Cheng, T., Sudderth, J., Yang, C., Mullen, A.R., Jin, E.S., and Matés, J.M. (2011). Pyruvate carboxylase is required for glutamine-independent growth of tumor cells. Proceedings of the National Academy of Sciences 208 , 8674–8679. Christofk, H.R., Vander Heiden, M.G., Wu, N., Asara, J.M., and Cantley, L.C. (2008). Pyruvate kinase M2 is a phosphotyrosine-binding protein. Nature 452 , 181–186. Cotán, D., Cordero, M.D., Garrido-Maraver, J., Oropesa-Ávila, M., Rodríguez-Hernández, A., Gómez Izquierdo, L., De la Mata, M., De Miguel, M., Lorite, J.B., Infante, E.R., et al. (2011). Secondary coenzyme Q10 deficiency triggers mitochondria degradation by mitophagy in MELAS fibroblasts. FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology 25 , 2669– 2687. Crane, F.L., Navas, P., Low, H., Sun, I.L., and de Cabo, R. (2013). Sirtuin activation: a role for plasma membrane in the cell growth puzzle. The Journals of Gerontology. Biological Sciences 68 , 368–370. D’Aquila, P., Rose, G., Bellizzi, D., and Passarino, G. (2013). Epigenetics and aging. Maturitas 74 , 130– 136. Dang, C. V (1999). c-Myc Target Genes Involved in Cell Growth , Apoptosis , and Metabolism. Molecular and Cellular Biology 19 . Dang, L., White, D.W., Gross, S., Bennett, B.D., Bittinger, M. a, Driggers, E.M., Fantin, V.R., Jang, H.G., Jin, S., Keenan, M.C., et al. (2010). Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 465 , 966. Dang, W., Steffen, K.K., Perry, R., Dorsey, J. a, Johnson, F.B., Shilatifard, A., Kaeberlein, M., Kennedy, B.K., and Berger, S.L. (2009). Histone H4 lysine 16 acetylation regulates cellular lifespan. Nature 459 , 802–807. Deaton, A., and Bird, A. (2011). CpG islands and the regulation of transcription. Genes & Development 25 , 1010–1022. DeBerardinis, R.J., and Thompson, C.B. (2012). Cellular metabolism and disease: what do metabolic outliers teach us? Cell 148 , 1132–1144. DeBerardinis, R.J., Mancuso, A., Daikhin, E., Nissim, I., Yudkoff, M., Wehrli, S., and Thompson, C.B. (2007). Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proceedings of the National Academy of Sciences of the United States of America 104 , 19345–19350. Delsite, R., Kachhap, S., Anbazhagan, R., Gabrielson, E., and Singh, K.K. (2002). Nuclear genes involved in mitochondria-to-nucleus communication in breast cancer cells. Molecular Cancer 1 , 6. Deribe, Y.L., Pawson, T., and Dikic, I. (2010). Post-translational modifications in signal integration. Nature Structural & Molecular Biology 17 , 666–672. Desler, C., and Marcker, M. (2011). The importance of mitochondrial DNA in aging and cancer. Journal of Aging Research 2011 . Díaz-Ramos, A., Roig-Borrellas, A., García-Melero, A., and López-Alemany, R. (2012). α -Enolase, a multifunctional protein: its role on pathophysiological situations. Journal of Biomedicine & Biotechnology 2012 , 156795. 110 Dion, M.F., Altschuler, S.J., Wu, L.F., and Rando, O.J. (2005). Genomic characterization reveals a simple histone H4 acetylation code. Proceedings of the National Academy of Sciences 102 , 5501–5506. Domínguez-Garrido, E., Martínez-Redondo, D., Martín-Ruiz, C., Gómez-Durán, A., Ruiz-Pesini, E., Madero, P., Tamparillas, M., Montoya, J., von Zglinicki, T., Díez-Sánchez, C., et al. (2009). Association of mitochondrial haplogroup J and mtDNA oxidative damage in two different North Spain elderly populations. Biogerontology 10 , 435–442. Donohoe, D.R., Collins, L.B., Wali, A., Bigler, R., Sun, W., and Bultman, S.J. (2012). The Warburg effect dictates the mechanism of butyrate-mediated histone acetylation and cell proliferation. Molecular Cell 48 , 612–626. Elson, J.L., Turnbull, D.M., and Taylor, R.W. (2007). Testing the adaptive selection of human mtDNA haplogroups: an experimental bioenergetics approach. The Biochemical Journal 404 , e3–5. Fang, H., Shen, L., Chen, T., He, J., Ding, Z., Wei, J., Qu, J., Chen, G., Lu, J., and Bai, Y. (2010). Cancer type-specific modulation of mitochondrial haplogroups in breast, colorectal and thyroid cancer. BMC Cancer 10 , 421. Feng, Z. (2010). p53 regulation of the IGF-1/AKT/mTOR pathways and the endosomal compartment. Cold Spring Harbor Perspectives in Biology 2 , a001057. Ferraresi, R., Troiano, L., Pinti, M., Roat, E., Lugli, E., Quaglino, D., Taverna, D., Bellizzi, D., Passarino, G., and Cossarizza, A. (2008). Resistance of mtDNA-depleted cells to apoptosis. Cytometry. Part A : the Journal of the International Society for Analytical Cytology 73 , 528–537. Fontana, L., Partridge, L., and Longo, V.D. (2010). Extending healthy life span--from yeast to humans. Science (New York, N.Y.) 328 , 321–326. Gerhäuser, C. (2012). Cancer cell metabolism , epigenetics and the potential influence of dietary components. Biomedical Reseach 23 . Gibbons, J.J., Abraham, R.T., and Yu, K. (2009). Mammalian target of rapamycin: discovery of rapamycin reveals a signaling pathway important for normal and cancer cell growth. Seminars in Oncology 36 Suppl 3 , S3–S17. Gómez-Durán, A., Pacheu-Grau, D., López-Gallardo, E., Díez-Sánchez, C., Montoya, J., López-Pérez, M.J., and Ruiz-Pesini, E. (2010). Unmasking the causes of multifactorial disorders: OXPHOS differences between mitochondrial haplogroups. Human Molecular Genetics 19 , 3343–3353. Goodman, R.H., and Smolik, S. (2000). CBP / p300 in cell growth , transformation , and development. Genes & Development 14 , 1553–1577. Gowher, H., Liebert, K., Hermann, A., Xu, G., and Jeltsch, A. (2005). Mechanism of stimulation of catalytic activity of Dnmt3A and Dnmt3B DNA-(cytosine-C5)-methyltransferases by Dnmt3L. The Journal of Biological Chemistry 280 , 13341–13348. Guan, K.-L., and Xiong, Y. (2011). Regulation of intermediary metabolism by protein acetylation. Trends in Biochemical Sciences 36 , 108–116. Haigis, M., and Sinclair, D. (2010). Mammalian sirtuins: biological insights and disease relevance. Annual Review of Pathology 253–295. 111 Hall, T.A. (1999). BioEdit: a user-friendly biological sequence alignment editor and analysis program for windows 95/98/Nt. Nucleic Acids Symposium 41 , 95–98. Hanahan, D., and Weinberg, R. a (2011). Hallmarks of cancer: the next generation. Cell 144 , 646–674. Hardie, D.G. (2011). AMP-activated protein kinase: an energy sensor that regulates all aspects of cell function. Genes & Development 25 , 1895–1908. Vander Heiden, M.G., Cantley, L.C., and Thompson, C.B. (2009). Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324 , 1029–1033. Hinttala, R., Kervinen, M., Uusimaa, J., Maliniemi, P., and Finnilä, S. (2010). Mitochondrion Analysis of functional consequences of haplogroup J polymorphisms m . 4216T > C and m . 3866T > C in human MT-ND1 : Mutagenesis of homologous positions in Escherichia coli. Mitochondrion 10 , 358–361. Houtkooper, R.H., Pirinen, E., and Auwerx, J. (2012). Sirtuins as regulators of metabolism and healthspan. Nature Reviews Molecular Cell Biology 13 , 225–238. Huidobro, C., Fernandez, A.F., and Fraga, M.F. (2013). The role of genetics in the establishment and maintenance of the epigenome. Cellular and Molecular Life Sciences. 70 , 1543–1573. Jahangir Tafrechi, R.S., Svensson, P.J., Janssen, G.M.C., Szuhai, K., Maassen, J.A., and Raap, A.K. (2005). Distinct nuclear gene expression profiles in cells with mtDNA depletion and homoplasmic A3243G mutation. Mutation Research 578 , 43–52. Jenuwein, T., and Allis, C.D. (2001). Translating the histone code. Epigenetics 293 , 1074–1080. Jones, R.G., Plas, D.R., Kubek, S., Buzzai, M., Mu, J., Xu, Y., Birnbaum, M.J., and Thompson, C.B. (2005). AMP-activated protein kinase induces a p53-dependent metabolic checkpoint. Molecular Cell 18 , 283–293. Kanfi, Y., Naiman, S., Amir, G., Peshti, V., Zinman, G., Nahum, L., Bar-Joseph, Z., and Cohen, H.Y. (2012). The sirtuin SIRT6 regulates lifespan in male mice. Nature 483 , 218–221. Karve, T.M., and Cheema, A.K. (2011). Small changes huge impact: the role of protein posttranslational modifications in cellular homeostasis and disease. Journal of Amino Acids 2011 , 207691. Kayser, E.-B., Sedensky, M.M., and Morgan, P.G. (2004). The effects of complex I function and oxidative damage on lifespan and anesthetic sensitivity in Caenorhabditis elegans. Mechanisms of Ageing and Development 125 , 455–464. Kenney, M.C., Hertzog, D., Chak, G., Atilano, S.R., Khatibi, N., Soe, K., Nobe, A., Yang, E., Chwa, M., Zhu, F., et al. (2013). Mitochondrial DNA haplogroups confer differences in risk for age-related macular degeneration : a case control study. BMC Medical Genetics 14 , 1. Kerr, S.J. (1972). Competing Methyltransferase Systems Competing Methyltransferase Systems. The Journal of Biological Chemistry 247 , 4248–4252. King, M.P., and Attardi, G. (1989). Human cells lacking mtDNA: repopulation with exogenous mitochondria by complementation. Science 246 , 500–503. King, M.P., Koga, Y., Davidson, M., and Schon, E.A. (1992). Defects in mitochondrial protein synthesis and respiratory chain activity segregate with the tRNA(Leu(UUR)) mutation associated with 112 mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes. Molecular and Cellular Biology 12 , 480–490. Kloss-Brandstatter, A., Pacher, D., Schonherr, S., Weissensteiner, H., Binna, R., Specht, G., and Kronenberg, F. (2011). HaploGrep : A Fast and Reliable Algorithm for Automatic Classification of Mitochondrial DNA Haplogroups. Human Mutation 32 , 25–32. Koppenol, W.H., Bounds, P.L., and Dang, C. V (2011). Otto Warburg’s contributions to current concepts of cancer metabolism. Nature Reviews. Cancer 11 , 325–337. Kraus, T.F.J., Globisch, D., Wagner, M., Eigenbrod, S., Widmann, D., Münzel, M., Müller, M., Pfaffeneder, T., Hackner, B., Feiden, W., et al. (2012). Low values of 5-hydroxymethylcytosine (5hmC), the “sixth base,” are associated with anaplasia in human brain tumors. International Journal of Cancer 131 , 1577–1590. Krebs, H. a (1972). Some aspects of the regulation of fuel supply in omnivorous animals. Advances in Enzyme Regulation 10 , 397–420. Kriaucionis, S., and Bird, A. (2003). DNA methylation and Rett syndrome. Human Molecular Genetics 12 , R221–7. Kulawiec, M., Owens, K.M., and Singh, K.K. (2009). mtDNA G10398A variant in African-American women with breast cancer provides resistance to apoptosis and promotes metastasis in mice. Journal of Human Genetics 54 , 647–654. Kurdistani, S.K., Tavazoie, S., and Grunstein, M. (2004). Mapping global histone acetylation patterns to gene expression. Cell 117 , 721–733. Larm, J., Vaillant, F., Linnane, A., and Lawen, A. (1994). Up-regulation of the plasma membrane oxidoreductase as a prerequisite for the viability of human Namalwa rho 0 cells. Journal of Biological Chemistry 269 , 30097–30100. Lee, T., and Pelletier, J. (2012). Eukaryotic initiation factor 4F: a vulnerability of tumor cells. Future Medicinal Chemistry 4 , 19–31. Levine, A.J., and Puzio-Kuter, A.M. (2010). The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 330 , 1340–1344. Li, X., and Kazgan, N. (2011). Mammalian sirtuins and energy metabolism. International Journal of Biological Sciences 7 , 575–587. Lin, S.-J., Ford, E., Haigis, M., Liszt, G., and Guarente, L. (2004). Calorie restriction extends yeast life span by lowering the level of NADH. Genes & Development 18 , 12–16. Locasale, J.W., and Cantley, L.C. (2011). Metabolic flux and the regulation of mammalian cell growth. Cell Metabolism 14 , 443–451. Lu, C., and Thompson, C.B. (2012). Metabolic regulation of epigenetics. Cell Metabolism 16 , 9–17. Lu, S.C., and Mato, J.M. (2005). Role of methionine adenosyltransferase and S-adenosylmethionine in alcohol-associated liver cancer. Alcohol 35 , 227–234. 113 Luka, Z., Mudd, S.H., and Wagner, C. (2009). Glycine N-methyltransferase and regulation of Sadenosylmethionine levels. The Journal of Biological Chemistry 284 , 22507–22511. Macdonald, M.J., Longacre, M.J., Stoker, S.W., Kendrick, M., Brown, L.J., Hasan, N.M., Jitrapakdee, S., Fukao, T., Hanson, M.S., Fernandez, L.A., et al. (2011). Differences between Human and Rodent Pancreatic Islets. The Journal of Biological Chemistry 286 , 18383–18396. Marcuello, A., Martínez-Redondo, D., Dahmani, Y., Casajús, J. a, Ruiz-Pesini, E., Montoya, J., LópezPérez, M.J., and Díez-Sánchez, C. (2009). Human mitochondrial variants influence on oxygen consumption. Mitochondrion 9 , 27–30. Mardis, E.R., Ding, L., Dooling, D.J., Larson, D.E., McLellan, M.D., Chen, K., Koboldt, D.C., Fulton, R.S., Delehaunty, K.D., McGrath, S.D., et al. (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. The New England Journal of Medicine 361 , 1058–1066. Martínez-Chantar, M.L., García-Trevijano, E.R., Latasa, M.U., Martín-Duce, A., Fortes, P., Caballería, J., Avila, M. a, and Mato, J.M. (2003). Methionine adenosyltransferase II beta subunit gene expression provides a proliferative advantage in human hepatoma. Gastroenterology 124 , 940–948. Martinez-Pastor, B., Cosentino, C., and Mostoslavsky, R. (2013). A tale of metabolites: the cross-talk between chromatin and energy metabolism. Cancer Discovery 3 , 497–501. Martínez-Redondo, D., Marcuello, A., Casajús, J. a, Ara, I., Dahmani, Y., Montoya, J., Ruiz-Pesini, E., López-Pérez, M.J., and Díez-Sánchez, C. (2010). Human mitochondrial haplogroup H: the highest VO2max consumer--is it a paradox? Mitochondrion 10 , 102–107. Martinus, R.D., Linnane, A.W., and Nagley, P. (1993). Growth of rho 0 human Namalwa cells lacking oxidative phosphorylation can be sustained by redox compounds potassium ferricyanide or coenzyme Q10 putatively acting through the plasma membrane oxidase. Biochemistry and Molecular Biology International 31 , 997–1005. Mathupala, S.P., Ko, Y.H., and Pedersen, P.L. (2006). Hexokinase II : Cancer ’ s double-edged sword acting as both facilitator and gatekeeper of malignancy when bound to mitochondria. Oncogene 4777– 4786. Mato, J.M., Corrales, F.J., Lu, S.C., and Avila, M. a (2002). S-Adenosylmethionine: a control switch that regulates liver function. FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology 16 , 15–26. Matoba, S., Kang, J.-G., Patino, W.D., Wragg, A., Boehm, M., Gavrilova, O., Hurley, P.J., Bunz, F., and Hwang, P.M. (2006). P53 Regulates Mitochondrial Respiration. Science 312 , 1650–1653. Máximo, V., Soares, P., Lima, J., Cameselle-Teijeiro, J., and Sobrinho-Simões, M. (2002). Mitochondrial DNA somatic mutations (point mutations and large deletions) and mitochondrial DNA variants in human thyroid pathology: a study with emphasis on Hürthle cell tumors. The American Journal of Pathology 160 , 1857–1865. Máximo, V., Lima, J., Soares, P., and Sobrinho-Simões, M. (2009). Mitochondria and cancer. Virchows Archiv : an International Journal of Pathology 454 , 481–495. Metallo, C.M., and Heiden, M.G. Vander (2010). Metabolism strikes back : metabolic flux regulates cell signaling. Genes & Development 24 , 2717–2722. 120 121 8.1 Annexes While performing the work presented in this thesis, emerged the opportunity to participate in other works carried out in the group of Cancer Biology. Nelma Pértega-Gomes, José R. Vizcaíno, Susana Sousa, Ricardo Coelho , , Jjan Attig, Sarah Jurmeister, Elsa Oliveira, Céline Pinheiro, Carmen Jerónimo, Rui M. Henrique, Luísa Pereira , Alexandre Lobo da Cunha, Valdemar Maximo, Carlos Lopes and Fátima Baltazar. Metabolic switch in PCa progression (Submitted) Vinagre, J., Almeida, A., Populo, H., Batista, R., Lyra, J., Pinto, V., Coelho, R. , Celestino, R., Prazeres, H., Lima, L., Melo, M., Rocha, A., Preto, A., Castro, L., Pardal, F., Lopes, J.M., Santos, L.L., Reis, R., Sobrinho-Simões, M., Lima, J., Máximo, V., Soares, P., (2013). Frequency of TERT promoter mutations in human cancers. Nature Communications 4, 2185.