Faculdade de Farmácia
The role of mitochondrial adaptations in the acquisition of the phenotype induced by LDL in breast cancer
Susana Cristina Cachapa Monteiro
Dissertação orientada pelo Professor Doutor Sérgio Dias e coorientada pela Professora Doutora Cecília Rodrigues.
Mestrado em Ciências Biofarmacêuticas
Universidade de Lisboa
Faculdade de Farmácia
The role of mitochondrial adaptations in the acquisition of the phenotype induced by LDL in breast cancer
Susana Cristina Cachapa Monteiro
Dissertação orientada pelo Professor Doutor Sérgio Dias e coorientada pela Professora Doutora Cecília Rodrigues.
Mestrado em Ciências Biofarmacêuticas
Abstract
Cancer cells can acquire biological capabilities that enable tumor growth and dissemination, including the reprograming of their metabolism in order to obtain the necessary energy and metabolic intermediates to sustain tumor proliferation. The energetic reprogramming in cancer was first proposed by Otto Warburg, who suggested an anomalous energetic metabolism in cancer cells. This metabolic adaptation is characterized by an increased dependence on mitochondrial respiration and fatty acid b-oxidation (FAO), which has been recently correlated with increased tumor progression and aggressiveness and is recognized as a critical metabolic pathway in triple negative breast cancer (TNBC) cells, an aggressive subtype of breast cancer which has poor prognosis due to its largely resistance to conventional chemotherapy. Exposure of triple negative breast cancer cells to lipid-enriched environments, namely LDL, leads to the acquisition of an aggressive phenotype, characterized by increased proliferative and migratory capacities of the cells. Currently, the metabolic pathways responsible for the acquisition of the LDL-induced phenotype in TNBC cells remain unknown.
The aim of this project is to characterize the metabolic program adopted by MDA-MB-231 TNBC cells exposed to LDL and determine its implication in the acquisition of the aggressive phenotype conferred by LDL, which is characterized by increased proliferative and migratory capacities. We investigated the expression of key regulators of mitochondrial biogenesis, glycolysis, oxidative phosphorylation (OXPHOS) and FAO to assess the deregulated metabolic pathways adopted by MDA-MB-231 cells and also investigated whether such modulations of metabolic pathways were conserved in other TNBC cell line, MDA-MB-436 and in the MCF-7 Luminal A breast cancer subtype. By inhibiting key enzymes of glycolysis, OXPHOS and FAO we have identified the metabolic pathways preferentially adopted by MDA-MB-231 cells to sustain their proliferation and migration capacities. We next investigated the impact of LDL exposure in the mitochondrial mass, mitochondrial membrane potential and mitochondrial dynamics of MDA-MB-231 cells and its implications in the aggressive phenotype induced by LDL.
In conclusion, we identified metabolic adaptations of MDA-MB-231 cells exposed to LDL. We observed that MDA-MB-231 cells can reprogram their metabolism in presence
of high amounts of extracellular lipids relying on glycolysis and OXPHOS to proliferate and migrate. The presence of exogenous LDL leads to adoption of a glycolytic phenotype related with PGC-1a modulation. Mitochondrial metabolism play a role in the aggressiveness of TNBC, characterized by increased mitochondria number and modulation of mitochondrial dynamics. We also identified PA as one of the components of the LDL molecule responsible for the modulation of the aggressive phenotype of TNBC.
Keywords: Cancer metabolism; Mitochondria; Triple negative breast cancer; Fatty acid
Resumo
Durante o desenvolvimento do tumor as células cancerígenas adquirem um conjunto de adaptações biológicas conhecidas como “the cancer hallmarks” que lhes permitem tornar-se malignas. Uma destas adaptações consiste na alteração do metabolismo energético, primeiramente observada por Otto Warburg. Em condições de oxigénio suficiente, as células normais metabolizam a glucose através da glicólise para sintetizar piruvato, que é transportado para a mitocôndria onde entra no ciclo dos ácidos tricarboxílicos para gerar ATP, pela chamada respiração aeróbia. Pelo contrário, as células cancerígenas, mesmo em condições de oxigénio suficiente para suportar a respiração aeróbia metabolizam o piruvato resultante da glicólise a lactato, através da fermentação, que acontece em células normais apenas em condições de insuficiência de oxigénio, uma vez que a quantidade de energia resultante deste processo é muito menor que a proveniente da oxidação do piruvato na mitocôndria. Warburg afirmou que seria uma deficiência ao nível das mitocôndrias a causa desta reprogramação metabólica levada a cabo pelas células cancerígenas, no entanto atualmente sabe-se que este fenómeno é devido a uma reprogramação metabólica para melhor satisfazer as necessidades do tumor a nível de intermediários metabólicos que suportam o seu rápido crescimento. Esta reprogramação do metabolismo baseia-se no aumento da utilização da respiração mitocondrial e da b-oxidação dos ácidos gordos, cuja importância e potencialidade como alvo terapêutico não estão ainda exploradas, devido à incidência do estudo do metabolismo tumoral ao nível da glicólise, glutaminólise e da síntese de ácidos gordos, embora tenha sido recentemente associada ao aumento da capacidade proliferativa do tumor e consequentemente à sua agressividade.
As mitocôndrias são responsáveis simultaneamente por funções vitais e pela morte da célula, tanto em condições normais, como em condições patológicas como o cancro. Estes organelos possuem uma dinâmica contínua ao nível do seu número e morfologia, que determina o balanço entre a produção da energia celular e a apoptose. Assim, a desregulação ao nível da mitocôndria está implicada na tumorigénese a na progressão tumoral e a sua regulação metabólica é fundamental. A dinâmica da mitocôndria varia continuamente dependendo da atividade metabólica da célula e de condições de stress a que está sujeita. Nestas condições e na ausência de nutrientes, as mitocôndrias tornam-se alongadas e estabelecem redes através de fenómenos de fusão, aumentando a capacidade
oxidativa. Em condições de baixa de concentração de oxigénio ou apoptose, fenómenos de fissão tornam as mitocôndrias mais fragmentadas. Apesar de serem conhecidos os processos que levam à alteração da morfologia da mitocôndria, e de se saber estarem alterados em vários tipos de cancro, a sua relação com a agressividade do tumor não está ainda estabelecida.
O cancro da mama é tipo mais comum de cancro em mulheres e exibe diferentes características biológicas e fenótipos clínicos. O subtipo mais agressivo de cancro da mama é o subtipo triplo negativo que está associado a um prognóstico clínico pouco favorável, devido à sua heterogeneidade e desconhecimento sobre as vias metabólicas mais favorecidas e ainda por não possuir à superfície das células os recetores que são atualmente os alvos da terapêutica convencional.
A obesidade é reconhecida como um fator de risco para o desenvolvimento de cancro da mama, embora a relação entre níveis elevados de lípidos no sangue e a incidência de cancro da mama não esteja ainda estabelecida. O nosso laboratório reportou recentemente que níveis elevados de colesterol (LDL) em doentes com cancro da mama estão associados a tumores maiores e mais metastáticos. Sabe-se também que as células cancerígenas têm uma necessidade acrescida de lípidos relativamente às células normais, pelo que aumentam o seu importe e síntese, e que a exposição de linhas do subtipo triplo negativo de cancro da mama a ambientes ricos em LDL resulta na aquisição de propriedades agressivas pelo tumor que levam à resistência à terapia. No entanto as alterações metabólicas requeridas em condições de elevada concentração de lípidos para este aumento de agressividade não estão ainda estabelecidas.
Com este trabalho, propusemo-nos a caracterizar o fenótipo metabólico de células de cancro da mama triplo negativo, MDA-MB-231, expostas a LDL e determinar a importância deste lípido para o fenótipo agressivo adotado por esta linha celular, caracterizado pelo aumento da sua capacidade proliferativa e migratória. Para determinar as vias metabólicas preferencialmente adotadas por estas células quando expostas a LDL fizemos uma análise à expressão génica de enzimas da glicólise, fosforilação oxidativa e b-oxidação dos ácidos gordos e inibimos enzimas-chave destas vias.
Para determinar a contribuição da mitocôndria para o fenótipo agressivo verificado investigámos a expressão génica de fatores de biogénese mitocondrial e a expressão das
proteínas responsáveis pela dinâmica mitocondrial. Verificámos ainda o número de mitocôndrias e a quantidade de DNA mitocondrial em células expostas a LDL em comparação com a condição controlo. Estudámos também a importância do fator de biogénese mitocondrial PGC-1a para a adoção preferencial da fosforilação oxidativa e da b-oxidação dos ácidos gordos na presença de colesterol através da verificação dos níveis de expressão génica de enzimas destas vias, e ainda a importância deste fator de biogénese mitocondrial para a capacidade de proliferação e migração das células de cancro da mama triplo negativo. Verificámos que a modulação PGC-1a está relacionada a adoção da glicólise por parte das células MDA-MB-231 para manter a sua atividade proliferativa.
Verificámos também a importância dos ácidos gordos presentes na molécula de LDL para o aumento da proliferação e migração das células de cancro da mama triplo negativo, através da exposição destas células a ácido palmítico, o ácido gordo saturado mais abundante na dieta que é simultaneamente um dos ácidos gordos com maior concentração no sangue. A exposição da linha celular MDA-MB-231 a ácido palmítico aumentou tanto a capacidade proliferativa como a capacidade migratória das células, o que sugere que este será um dos componentes da molécula de colesterol responsáveis pela agressividade do cancro da mama triplo negativo.
Comparámos a capacidade proliferativa e migratória das células MDA-MB-231 com outra linha de cancro da mama triplo negativo, MDA-MB-436 e com a linha celular MCF-7, do subtipo Luminal A, positiva para o recetor de estrogénio. Verificámos que a exposição a LDL é responsável por um aumento das capacidades proliferativa e migratória de ambas as linhas celulares, o que nos permite concluir que este não é um efeito isolado apenas numa linha celular de cancro da mama triplo negativo e inferir que a exposição a LDL poderá ser responsável pelo aumento de agressividade deste subtipo de cancro da mama. Também a capacidade proliferativa da linha celular MCF-7 aumentou na presença de LDL, o que permite inferir que a exposição a LDL contribui para a aquisição de um fenótipo agressivo tanto nos subtipos de cancro da mama responsivos a hormonas com nos subtipos que não possuem recetores hormonais. Ao contrário do verificado nas duas linhas de cancro da mama triplo negativo estudadas, a quantidade de DNA mitocondrial nas células da linha celular MCF-7 não aumentou com a exposição a LDL, o que pode ser indicativo da aquisição de vias metabólicas diferentes
para a agressividade dos dois subtipos de cancro da mama.
Com este trabalho verificámos a adoção preferencial de vias metabólicas em detrimento de outras por parte de células do subtipo triplo negativo do cancro da mama e abrimos novas perspetivas à importância da mitocôndria e da sua reprogramação a nível metabólico e da sua dinâmica para a agressividade deste subtipo agressivo de cancro da mama. São necessários mais estudos para determinar possíveis inibições metabólicas que possam em cooperação com as terapias convencionais contribuir para o tratamento de doentes com cancro da mama triplo negativo.
Palavras-chave: Metabolismo tumoral; Mitocôndria; Cancro da mama triplo negativo;
Aknowledgments
Firstly, I would like to thank to Professor Sérgio Dias for the opportunity of being part of Sérgio Dias Lab in Instituto de Medicina Molecular. Thanks for always believing me and in this project, for all of your commitment, all the suggestions, all the support and for allowing me to start my journey in science.
Thanks to Professor Cecília Rodrigues, for accepting my invitation to be my mentor, for the suggestions about my work and to my dissertation, and for providing me the antibodies required for the study of mitochondrial dynamics.
To Sandrina, who has been my supervisor, the closest person to me and my work, for teaching me how to work in science, all the techniques, how to develop my critical sense and how to overcome all the barriers in my experiments. For performing almost all the flow cytometry analysis of this dissertation, the PCR analysis of glycolysis, OXPHOS and FAO. I want to thank for the unmeasurable support, for all the suggestions that allowed me to grow both professionally and personally, I could not have asked for a better supervisor.
Thanks Ana and Neidy for all the suggestions to my work, for teaching me many techniques of cell culture, microscopy and so on. For being more than colleagues so many times and giving me personal advices. I would also like to thank to Beatriz who performed together with Sandrina the glycolysis, OXPHOS and FAO PCR analysis and TEM images. To Inês and Rita, for letting me participate in their work, it allowed me to grow. To all Sergio Dias Lab’s team, it has been a year full of experiences, I am grateful for your participation in this journey.
Thanks to Vanessa Morais and all Vanessa Morais Lab team for the insights in mitochondrial biology. Thanks to all the technicians of Instituto de Medicina Molecular, specially to Ana Nascimento and António Temudo, for supporting me on the bioimmaging facility and for all the advices.
To my closest friends and family, for all the patience and the support, for listening me to talk about my work, thanks for always being there for me.
Finally, to the persons who allowed me to do this journey, my parents, for the unconditional support, for always being there for me, for encouraging me to do my best during all my academic journey. There are no words to express how thankful I am for all your effort, all your dedication and all your patience.
I would like to thank my grandparents, who would be proud to see me reach this goal, wherever you are thanks for encouraging me and for always believing in me.
Table of contents 1. LIST OF FIGURES 11 2. LIST OF TABLES 12 3. LIST OF ABREVIATIONS 13 4. INTRODUCTION 16
4.1. DEREGULATED CELLULAR ENERGETICS IN CANCER 16
4.2. BREAST CANCER 18
4.3. BREAST CANCER METABOLISM 18
4.4. MITOCHONDRIAL METABOLISM IN CANCER 20
4.5. LIPID METABOLISM IN CANCER 23
5. AIMS 26
6. METHODS 28
6.1. CELL CULTURE 28
6.2. FLOW CYTOMETRY ANALYSIS 28
6.3. LIVE IMAGING MICROSCOPY 29
6.4. WESTERN BLOT. 29
6.5. WOUND HEALING ASSAY 29
6.6. PGC-1a KNOCKDOWN 30
6.7. QUANTITATIVE REAL-TIME PCR 30
6.8. STATISTICAL ANALYSIS 31
6.9. TRANSMISSION ELECTRON MICROSCOPY 31
7. RESULTS 35
7.1. METABOLIC ADAPTATIONS IN LDL-EXPOSED TNBC CELLS 35
7.1.1. ROLE OF GLYCOLYSIS 35
7.1.2. ROLE OF OXPHOS 37
7.1.3. ROLE OF FAO 39
7.2. EXPRESSION OF METABOLIC ENZYMES IN LDL-EXPOSED TNBC CELLS 41 7.3. ROLE OF MITOCHONDRIA IN LDL-EXPOSED TNBC CELLS 43 7.3.1. MITOCHONDRIA NUMBER AND MITOCHONDRIAL MEMBRANE POTENTIAL 43 7.3.2. MITOCHONDRIA DYNAMICS IN LDL-EXPOSED TNBC CELLS 45 7.4. ROLE OF PGC-1a IN LDL-EXPOSED TNBC CELLS 47
7.5. ROLE OF PA IN TNBC CELLS 50
7.6. ROLE OF LDL IN THE AGGRESSIVE PHENOTYPE OF OTHER BREAST CANCER CELL LINES 52
7.6.1. TNBCMDA-MB-436 CELL LINE 52
7.6.2. LUMINAL AMCF-7 CELL LINES 54
8. DISCUSSION 57
9. BIBLIOGRAPHY 67
1. List of figures
Figure 1. Schematic representation of glycolytic metabolism in cancer cells. ... 17 Figure 2. Schematic representation of FAO in cancer cells. ... 20 Figure 3. Schematic representation of mitochondrial dynamics. ... 22 Figure 4. Inhibition of glycolysis with 2-DG affects the proliferative and migratory capacities of MDA-MB-231 cells. ... 35 Figure 5. Inhibition of OXPHOS with Oligomycin impacts the proliferative and migratory capacity of MDA-MB-231 cells. ... 37 Figure 6. Inhibition of FAO with of Etomoxir impacts the migratory capacity of MDA-MB-231 cells and has no effect on proliferation. ... 39 Figure 7. LDL exposure impacts the expression of lipid receptors, enzymes from FAO, OXPHOS, glycolysis but produced no alteration in the expression of mitochondrial biogenesis factors. ... 41 Figure 8. LDL exposure increases the mitochondrial mass and mitochondrial membrane potential of MDA-MB-231 cells. ... 43 Figure 9. MDA-MB-231 cells have an increased expression of the mitochondrial fission protein Drp1 and no differences in expression of the Mfn1/2 and in the mitophagy p62 protein. ... 45 Figure 10. PGC-1a depletion affects MDA-MB-231 cells migration upon LDL exposure. ... 48 Figure 11. Palmitic acid exposition and FAO inhibition impacts the aggressive features of MDA-MB-231 cells. ... 50 Figure 12. Effect of LDL exposition in the metabolic features of MDA-MB-436 cells. 52 Figure 13. Effect of LDL exposition in the metabolic features of MCF-7 cells. ... 54 Supplementary Figure1. Inhibition of FAO with of Etomoxir impacts the migratory capacity of MDA-MB-231 cells and has no effect on proliferation………74
2. List of tables
Table 1. Amplified genes and primers from mitochondrial biogenesis factos and housekeeping genes used in qRT-PCR. ... 31 Table 2. Amplified genes and primers from lipid receptors and transporters, glycolysis, OXPHOS and FAO used in qRT-PCR. ... 33
3. List of abreviations
ACAD – Acetyl-CoA dehydrogenase
ACADM – Medium-chain acyl-CoA dehydrogenase ACADVL – Very long-chain Acyl-CoA dehydrogenase ACSL1 – Acyl-CoA synthetase long chain family member 1 AMP – Adenosine monophosphate
ATP – Adenosine triphosphate CCC – Circulating cancer cells CD-36 – Cluster of differentiation 36 CMTN – Cancro da mama triplo negativo CPT1 - carnitine palmitoyl transferase 1 enzyme ECAR – Extracellular acidification rate
ER – Estrogen rceptors
ERRa - Estrogen-related receptor alpha ETC – Electron transport chain
FAO – Fatty acid oxidation FAS – Fatty acis synthesis FASN – Fatty acid synthase FBS – Fetal Bovine Serum
FBSLPF – Fetal Bovine Serum lipoprotein free FDG - 18F-deoxyglucose
FDG-PET – 18F-deoxyglucose positron emission tomography
GSEA – Gene set enrichment analysis
HADHA – Hydroxyacyl-CoA dehydrogenase HER2 – Human epidermal growth factor receptor-2 HSP90 – Heat Shock Protein 90
LB – Loading Buffer
LDHA – Lactate dehydrogenase A
LDL – Human plasma low density lipoprotein LDL–R – LDL receptor
LSCs – Leukemia stem cells mtDNA – Mitochondrial DNA NRF1 – Nuclear respiratory factor 1
OCR – Oxygen Consumption Rate OXPHOS – Oxidative phosphorylation PA – Palmitic Acid
PCC – Primary tumor cells
PFK2 – 6-phosphofructo-2-kinase
PGC-1a - Peroxisome proliferator-activated receptor gamma coactivator 1-alpha PGC-1b - Peroxisome proliferator-activated receptor gamma coactivator 1-beta PHGDH – Phosphoglycerate dehydrogenase
PI3K – Phosphoinositide 3-kinase PKM2 – Pyruvate kinase isozyme M2
PPARa - Peroxisome proliferator-activated receptor alpha PR – Progesterone receptors
qRT-PCR – Quantitative Real Time Polymerase Chain Reaction SREBPs - Sterol regulatory elements binding proteins
TCA – Tricarboxilic acid
TFAM – Mitochondrial transcription factor A TNBC – Triple Negative Breast Cancer 2-DG – 2-deoxy-D-glucose
4. Introduction
4.1.Deregulated cellular energetics in cancer
Cancer represents a diverse group of diseases globally classified according to particular biological features, termed the hallmarks of cancer (Hanahan et al 2011). These features include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis and activating invasion and metastasis. In the past decade two more hallmarks were recognized: evading immune destruction and reprogramming energy metabolism (Hanahan et al., 2011). In fact, cancer metabolism has been recognized as a key feature in the initiation and progression of tumors and several metabolic adaptations that support tumor growth and proliferation have been described (Hanahan et al., 2013). Under aerobic conditions, normal cells metabolize glucose to pyruvate through glycolysis in the cytosol and thereafter to carbon dioxide through the TCA cycle in mitochondria, maximizing ATP production. Under anaerobic conditions, glycolysis is favored in normal cells and less lactate is produced. Otto Warburg suggested that mammalian proliferating cancer cells reprogram their energetic metabolism to convert the majority of glucose carbon to lactate by “aerobic glycolysis”, even in conditions of sufficient oxygen concentration to support mitochondrial respiration, or OXPHOS, suggesting that the alterations in the metabolism of cancer cells were caused by the malfunction of mitochondria – the named Warburg effect (Warburg et al., 1926) (Figure 1). The Warburg hypothesis has been validated by numerous studies and led to recent findings suggesting that metabolic reprogramming of cancer cells occurs as a consequence of mutations in oncogenes or tumor suppressors and modifications in growth factor signaling pathways to confer a metabolic advantage to cancer cells in the adverse tumor environment. The Warburg theory is still nowadays accepted and is the basis for tumor imaging by FDG- PET, a medical imaging technique used for cancer detection, based on the capacity of tissues to uptake the radiolabeled glucose analog FDG (Vander H. et al., 2009). It is accepted that increased glycolysis gives cancer cells survival and proliferative advantages and increased capacity to invade the tumor microenvironment (Han et al., 2013). Besides glucose, cancer cells can use other alternative energy sources (including glutamine, acetate and lactate) to obtain NADPH and intermediates needed for the Krebs cycle and for lipid synthesis (Anastasiou et al., 2012, Comerford et al., 2014).
Figure 1. Schematic representation of glycolytic metabolism in cancer cells.
Cancer cells metabolize glucose to phosphoenolpyruvate in the cytosol which is desphosphorylated by PKM2 to pyruvate, which in turn can be converted to lactate by LDHA or can enter in mitochondria and be oxidated through the TCA cycle to produce ATP. Glycolysis can be stimulated by PFK2 (encoded by PFKFB3 gene) which converts frutose-6-phosphate to frutose-2,6-bisphosphate which enters in the glycolytic flux. 3-phosphoglycerate resulting from glycolysis can be converted to 3-phosphohydroxypyruvate by PHGDH. Cancer cells can also rely on OXPHOS to produce ATP by several steps with the participation of mitochondrial complexes including NADH dehydrogenase (Complex I; encoded by NDUFB5 gene), Cytochrome c oxidase (Complex IV; encoded by Cox5b gene) and ATP synthase (Complex V; encoded by
ATP5g1 gene).
In contrast to the initial assumption that all tumors have a high glycolysis rate, recent observations revealed that tumors are indeed metabolically heterogeneous. Multiple studies have suggested that, despite showing enhanced glycolysis, cancer cells can produce significant amounts of ATP via mitochondrial respiration (LeBleu et al., 2014; Lu et al., 2014; Viale et al., 2015). For instance, the dependence of LSCs and pancreatic cancer stem cells on OXPHOS rather than glycolysis, contrarily to “Warburg’s hypothesis” was shown by targeting anti-apoptotic protein BCL-2, which is
overexpressed in these cells. BCL-2 inhibition in the chemotherapy-resistant LSCs, leads to a reduced OXPHOS and to a selective eradication of quiescent LSCs suggesting that LSCs strongly depends on OXPHOS to meet their energetic demands and sustain their survival (Lagadinou et al., 2013). Pancreatic cancer stem cells share several similarities with leukemia stem cells, with an up-regulation of genes related with mitochondrial function and strong dependence on mitochondrial respiration and a concomitant decreased dependence on glycolysis (Viale et al., 2014). Together, these results suggest the adoption of a common metabolic program by the most aggressive cancer cells across several tumor types.
4.2. Breast cancer
Breast cancer is the most common type of cancer in women. Clinical subtypes of breast cancer are defined according to the expression of ER, PR and HER2 (Mota et al., 2016). The majority of breast cancer are ER-positive (>60%), whereas about 20% are negative for ER, PR and HER2 (Harvey et al., 1999; Cleator et al., 2007). Luminal A is a subtype of breast cancer ER and PR positive and HER2 negative; Luminal B subtype is ER positive, negative or with a residual expression of PR and HER positive or negative; HER2 enriched subtype is only HER2 positive; Luminal/HER2 subtype is ER, PR and HER2 positive, and TNBC is ER, PR and HER2 negative (Matsumoto et al., 2016). TNBC is a therapy-resistant subtype of breast cancer which has a poor prognosis compared to the other breast cancer subtypes. Radiation therapy is the most useful strategy to target this cancer subtype and the traditional chemotherapeutic agents (5-flouoruracil and docetaxel) used to treat other breast cancer subtypes show little success in TNBC (Yagata et al., 2011). Therefore, the identification of additional molecular biomarkers to predict response to specific chemotherapy is required to further improve treatment strategies of TNBC.
4.3.Breast cancer metabolism
Fatty acids can be incorporated from the extracellular media, obtained from hydrolyzed triglycerides accumulated in lipid droplets or generated by de novo synthesis. FAS is an anabolic process required for membrane synthesis and for cell growth and proliferation, which are augmented in cancer cells. FAO, or β- oxidation, is used in energy-demanding tissues (heart and skeletal muscle) and in the liver under physiological conditions, with
an important role in the function of cancer cells, contributing towards to ATP production during tumor metabolic stress (LeBleu et al., 2009, Samudio et al., 2010). In these situations, CPT1 enzyme conjugates fatty acids with carnitine to translocate it to the mitochondria, where acylcarnitines undergo FAO (Carracedo et al., 2013) (Figure 2). Due to the focus on glycolysis, glutaminolysis and FAS, the importance of fatty acids metabolism for tumor cell biology and its potential as a putative therapeutic strategy are still unclear, but currently it is known that, as for glycolysis, FAO can support the anabolic demands of cancer cells and may be associated to tumor metastasis and therapy resistance (Carracedo et al., 2013). Importantly, recently several studies shed light in the importance of FAO for the progression and aggressiveness of TNBC (Camarda et al., 2016; Park et al., 2016).
The “metabolic switch” of cancer cells seems to be driven in part by oncogenic transformation and alterations in growth factor signaling pathways (Carracedo et al., 2013). The energy reprogramming of cancer cells to adopt FAO metabolism was found to be crucial for the activation of Src pathway in TNBC, by upregulating the proto-oncogene c-Src, involved in cell signaling and control of multiple biological functions. Src activation results in cancer formation and gives a metabolic advantage to cancer cells enabling cancer progression (Park et al., 2016). Besides c-Scr, MYC oncogene is up-regulated in several TNBC with a concomitant increase of FAO, but their role in TNBC remains unknown (Camarda et al., 2016). MYC overexpression is the necessary and unique condition to induce the FAO energetic dependence in the MYC-driven model of TNBC highlighting the importance of oncogene signaling pathways in metabolic adaptations of aggressive subtypes of breast cancer, particularly by impinging FAO dependence. Due to the increasing relevance of FAO in cancer cell metabolism, this pathway may represent an interesting therapeutic target, for instance by the targeting of key enzymes - such as the rate-limiting enzyme CPT1, which could be pharmacologically targeted by drugs already approved such as 3-KAT or Etomoxir (Carracedo et al. 2013). Indeed, the use of Etomoxir in the context of cancer metabolism has revealed new metabolic dependencies of cancer cells and it constitutes an attractive target to increase the effectiveness of cancer treatment in combination with conventional therapy. However, targeting single enzymes or even pathways in cancer cells is generally not enough for tumor elimination. Therefore, pharmacological inhibitors of FAO must be combined with chemotherapy or other targeting agents.
Figure 2. Schematic representation of FAO in cancer cells.
Fatty acids stored in lipid droplets enter in mitochondria through CPT1 enzyme and are metabolized through FAO. The first step of FAO is catalyzed by ACADs, which include ACADVL and ACADM to metabolize different groups of fatty acids characterized by the lenght of their carbon chain. Next, Acetyl-coA is metabolized in several steps to 3-hydroxyacyl-CoA and therefore to 3-ketoacyl-coA by HADHA. Acetyl-coA alternatively be synthetized from acetate through ACSL1 activity.
4.4. Mitochondrial metabolism in cancer
For some time now, several authors observed that mitochondria are continuously changing their shape, size and position (Lennon et al., 2014) in a dynamic process related with changes in the metabolic activity of the cell (Tobioka et al., 1956). Mitochondria can undergo dynamic processes changing their morphology and number, determining a balance between energy production and cell death programs by tightly controlled mechanisms that are often deregulated in several diseases such as in cancer. In cancer cells, mitochondrial dynamics is adapted to fulfill their bioenergetic and biosynthetic needs to sustain proliferation, migration, evasion to therapy and apoptosis. These alterations occur at the inner membrane structure and are related with nutrient availability or changes in demands in cellular energy, leading to alterations in the oxidative
phosphorylation capacity (Rossignol et al., 2004). Mitochondrial fission leads to a fragmentation of mitochondria and is important for maintaining the health status of the mitochondrial pool, allowing damaged mitochondria to be recycled or degraded via mitophagy or autophagy (Kubli et al., 2013), being an early step of the apoptotic cascade. This process is controlled via phosphorylation of Drp1which is recruited to the site of scission of the inner membrane (Lennon et al., 2014). The opposite event, mitochondrial fusion, is responsible for the formation of large extended mitochondria networks and is associated with the need of large energy demands or stress conditions allowing the cell to increase ATP production (Senft et al., 2016). Mitochondrial fusion is mediated by Mfn1 and Mfn2 proteins located in the outer mitochondrial membrane by a reorganization of the mitochondrial membranes via interaction of Mfn1 or Mfn2 on opposing mitochondria (Lennon et al., 2014) (Figure 3). Although the molecular processes related with fission and fusion events are well described, the link between the mitochondrial dynamics and specific mitochondrial functions such as energy production are still unclear. Mitochondrial biogenesis and the quality control of these organelles are up-regulated in cancer cells due to the central role of mitochondria in tumorigenesis and tumor progression. Mitochondrial biogenesis is controlled by nuclear genes to ensure the synthesis of enough organelles to meet an increased metabolic demand in cancer cells, while autophagy and mitophagy genes control the opposite event. Active mechanisms for mitochondrial quality control prevent the accumulation of defective mitochondria preventing pathogenic mitochondrial genome mutations in human cancers (Zong et al., 2016) and a balance between biogenesis and mitophagy regulate the mitochondrial mass within the cell, their function and quality, preserving the function of these organelles (Zong et al., 2016). Mitophagy is a specialized form of autophagy responsible for degradation and elimination of defective mitochondria (Randow et al., 2014). Damaged mitochondria carry membrane depolarization and a cascade of phosphorylation and ubiquitination of proteins as a signal for mitophagy and a reduced energy output is the signal to the energy sensor AMP kinase to trigger mitochondrial biogenesis. Apoptosis is controlled by the BCL-2 family, situated at the mitochondrial outer membrane, responsible for blocking the release of cytochrome c from the mitochondrial intermembrane space, in opposition to BAX and BAK pro-apoptotic proteins. The release of cytochrome c triggers the apoptosome, caspases and protease activation in the cytosol, which causes cell death (Czabotar et al., 2013). Most of the chemotherapeutic agents used in the clinics kill cancer cells activating the mitochondrial apoptosis pathway, therefore,
understanding the mitochondrial metabolism and the control of apoptosis by interfering with mitochondria homeostasis is crucial to improve the efficiency of TNBC treatment.
Figure 3. Schematic representation of mitochondrial dynamics.
Mitochondrial morphology is determined by cycles of fission and fusion events: increased fission, mediated by Mfn1 and Mfn2 results in fragmentation of mitochondria and is related with mitophagy; in other hand, fusion events is responsible for elongation and interconnection of mitochondria and is associated to an increase in oxidative phosphorylation capacity.
It was shown that invasive breast cancer cells enhance OXPHOS and mitochondrial biogenesis during tumor metastasis (LeBleu et al., 2014). Breast CCC up-regulate genes related to OXPHOS (Cox5b, Cox4i, ATPsynthase and CytC) and mitochondrial biogenesis (PGC-1α, PGC-1β, NRF1 and ERRα) in comparison to PCC and metastatic lung cells, suggesting a reversible up-regulation of certain genes during metastasis to increase mitochondrial biogenesis and respiration. PGC-1α, a master regulator of cellular signals regulating mitochondrial biogenesis, OXPHOS, fatty acid biosynthesis and degradation is one of the up-regulated genes and has been largely implicated in tumorigenesis (Villena et al., 2015, LeBleu et al., 2014, Torrano et al., 2016). Moreover, tumor cells ability to perform mitochondrial respiration is impaired when PGC-1α was inhibited, simultaneously with transcription of genes related with mitochondrial biogenesis and OXPHOS suggesting that PGC-1α is responsible for the increased
OXPHOS capacity by increasing both mitochondria biogenesis and the ATP production of the mitochondrial pool. These results suggest that PGC-1α mediates the metastatic process in breast tumors and the transition of PCC to CCC (LeBleu et al., 2014). PGC-1α knockdown and rotenone treatment have collectively been shown to led to a decrease in invasion and migratory capacities of cancer cells (Girnun et al., 2012) highlighting a metabolic vulnerability that can be exploited to increase the effectiveness of cancer treatment. However, it is important to elucidate how the metabolic programs are correlated with aggressive features of cancer cells, such as the enhanced proliferation and migration and reduced apoptosis.
4.5. Lipid metabolism in cancer
LDL is the circulating form of cholesterol, a multi-molecular aggregate composed by proteins and mostly cholesterol, among other lipids (Antalis et al., 2009). There is a vast epidemiological literature suggesting that obesity, and high cholesterol levels in particular, may result in greater tumor incidence and worse cancer patient outcomes (Maliniak et al., 2017; Mannell et al., 2017; Michels et al., 2007; Vona-Davis et al., 2007). Cancer cells have an increased requirement for cholesterol, and increase lipid synthesis and uptake to overcome this problem. In particular, our lab has recently described that elevated cholesterol levels in untreated breast cancer patients strongly correlates with bigger and more metastatic tumors (Rodrigues dos Santos et al., 2014b). Although this data suggests a strong association between lipid-enriched environments and tumor progression, the exact nature of the lipid metabolic alterations that fuel tumorigenesis is not well understood.
Importantly, lipid-enriched environments have been shown to favor tumor progression in experimental murine models of breast cancer, (Rodrigues dos Santos et al., 2014a; Nelson et al., 2013). In particular, our lab has shown that exposure of the TNBC cell lines MDA-MB-231 and HTB to LDL favors cell migration, proliferation and metastatic behavior in vitro and in vivo (Rodrigues dos Santos et al., 2014a). Mechanistically, exposure to LDL induces expression of genes related with cell survival and proliferation (akt, JNK and ERK pathways) and down-regulate genes associated with adhesion molecules (cadherin-related family member 3, CD226, Claudin7, Ocludin and integrinβ8) leading to an increase cell proliferation, migration, and loss-of-adhesion, which are characteristic of tumor progression (Rodrigues dos Santos et al., 2014a). Interestingly, GSEA of
microarray data from MDA- MB-231 breast cancer cells exposed to LDL for 48h, revealed increased expression levels of genes from the mitochondrial carnitine palmitoyl transferase system, inducing CPT1a (unpublished). Also, it has been reported that the SREBPs family, related with lipid homeostasis, is involved in the regulation of expression of genes related with fatty acids metabolism (Eberlé et al., 2004; Shimano et al., 2001) corroborating the results obtained in our lab (Teixeira, Nóbrega-Pereira et al., unpublished). Our preliminary results suggest that LDL-exposed MDA-MB-231 breast cancer cells have increased mtDNA content, less lactate production and higher ATP levels, suggesting an increase usage of the mitochondrial respiration of TNBC in lipid-enriched environments (unpublished). Together these results suggest that exposure of TNBC cells to LDL induce an increase capacity to proliferate, invade and migrate, simultaneously with an adoption of mitochondrial respiration as the main bioenergetic pathway.
5. Aims
There is evidence that TNBC cells can reprogram their metabolism to acquire an aggressive phenotype that fuels tumor progression (Camarda et al., 2016; Park et al., 2016) increasing its reliance in mitochondrial respiration and FAO (Viale et al., 2014; Carracedo et al., 2013). Mitochondrial dynamics as additionally been associated with increased proliferation, migration and therapy resistance of cancer cells (Senft et al., 2016). There are also evidences that tumor cells exposed to lipid-enriched environments leads to tumor progression in breast cancer (Rodrigues dos Santos et al., 2014a). However, despite this knowledge, the metabolic pathways - mitochondrial respiration, OXPHOS or/and FAO - responsible for the aggressive phenotype induced by LDL exposure in TNBC cells are not yet known.
In line with these observations, with the present project we propose to investigate the metabolic pathways adopted by TNBC cell line exposed to LDL and determine the role of this metabolic adaptations in the acquisition of proliferative and metastatic potential of these cells in order to ultimately determine the potential of its pharmacological targeting in increasing the effectiveness of breast cancer therapy.
Thus, the specific aims of this project are:
Aim 1. Characterization of the mitochondrial metabolism of MDA-MB-231 cells induced by hypercholesterolemia in vitro. We determined the metabolic pathways
altered with LDL exposure assessing the expression of key enzymes from glycolysis, OXPHOS and FAO.
Aim 2. Determination of the role of the mitochondrial metabolic alterations in the acquisition of the aggressive phenotype induced by LDL in TNBC cells. The selective
inhibition of glycolysis, OXPHOS and FAO allowed us to determine the role of each metabolic pathway in the proliferative and migratory capacities of MDA-MB-231 cells.
Aim 3. Characterization of the mitochondrial dynamics in MDA-MB-231 cells exposed to LDL. We investigated the alterations in mitochondrial number, index of
mitochondrial membrane potential and mitochondrial dynamics regulators in LDL-exposed cells.
6. Methods
6.1.Cell culture
MDA-MB-231, MDA-MB-436 and MCF-7 cells were cultured in DMEM (ThermoFisher Scientific) supplemented with 10% (v/v) heat-inactivated FBS (Gibco Invitrogen) and 1% Antibiotic-Antimycotic (Gibco Invitrogen), named complete DMEM and were incubated at 37ºC and 5% CO2 atmosphere.
For cell proliferation assays, MDA-MB-231, MDA-MB-436 and MCF-7 cell lines were seeded at a density of 105 cells/mL in round bottom 24-well plates in 500µL complete DMEM. After 30 hours of incubation the medium was replaced by DMEM 1% FBSLPF for 17h and 17h later the medium of the LDL-exposed group was replaced by fresh complete DMEM supplemented with LDL (Merck) at a final concentration of 100 ug/mL or PA at a final concentration of 0,4mM added alone or in combination with Etomoxir (Sigma-Aldrich) at a final concentration of 200µM, Oligomycin (Sigma-Aldrich) at a final concentration of 200nM and 2-DG (Calbiochem) at a final concentration of 2mM and the cells were incubated for 48h. The number of viable cells was determined by the Trypan Blue exclusion test by hemocytometer counts (at least 3 squares per well) in quadruplicates. Number of cells is represented as fold change relative to control.
For treatment of cells with PA, sodium palmitate (Sigma-Aldrich) was prepared by dissolving it in 1 ml of 0.1M NaOH and warming at 80 °C until clear. The solution was complexed with fatty acid-free BSA (Sigma-Aldrich) in a molar ratio fatty acid:BSA of 5:1; briefly, 0.325 g of BSA was dissolved in 8 ml 0.9% NaCl, and the mixture was warmed to 45 °C. The clear solution of palmitate was added drop-by-drop by pipette with agitation and the final solution was filtered at 0.45 µm.
6.2.Flow cytometry analysis
For Bodipy Lipid Probes (Molecular Probes) staining cells were harvested, centrifuged at 1000 rpm, washed and ressuspended in PBS. Bodipy Lipid Probes was added at a final concentration of 0,2µg/mL and incubated during 10 minutes at room temperature in the dark and then cells were washed in PBS. Flow cytometry analysis was performed in the BD LSRFortessa 2 cell analyzer or in the BD Accuri C6 cell analyzer.
6.3. Live Imaging Microscopy
MDA-MB-231 cells were seeded at a density of 2x105 in 35mm glass bottom dishes and after 24 hours transfected with 2µg Mito-YFP using FuGENE (ThermoFisher Scientific) and according to the manufacturer instructions. After 24h the medium was replaced by DMEM 1% FBSLPF and 24h latter MitoTracker Deep Red (Molecular Probes) was added to the cells at a final concentration of 50 nM in complete DMEM and incubated for 30 minutes. Live imaging was performed in a Zeiss Cell Observer Microscope with 63x oil objective and the images processed using Fiji software. The ratio of MitoTracker fluorescence vs. the mitochondrial dye fluorescence was used as an indicator of mitochondrial membrane potential.
6.4.Western blot.
Whole-cell extracts were prepared using RIPA buffer (1% Triton X-100, 0.1 % SDS, 150 mM NaCl, 50 mM Tris-HCl pH 7.8, 5 mM EDTA, 0.5% NaDOC) containing proteinase inhibitors (Roche) and resolved using SDS-PAGE (10%, 12% Tris-HCl gels or 4-15% gradient gels (Mini-PROTEAN TGX, BioRad) under reducing conditions (in the presence of β-mercaptoethanol, BioRad) and Precision Plus Protein Standards (dual color, BioRad) was used. Proteins were subsequently blotted onto a nitrocellulose membrane (BioRad) following conventional protocols and hybridized using anti-human antibodies against β-Actin (1:15000; Sigma-Aldrich), Drp1 (1:200; Santa Cruz Biotechnology), Mfn1 (1:1000; Abcam), Mfn2(1:1000; Abcam), PGC-1a (Calbiochem) or p62 (1:4000; Abcam) and HRP-coupled secondary antibodies (1:6000; Promega) were used. The Pierce ECL (Thermo Scientific) detection system and Fuji Medical X-ray Films (FUJIFILM) or Chemidoc software were used to visualize the presence of the proteins in the nitrocellulose blots. Quantification of densitometric units was performed using the Scion Image software.
6.5.Wound healing assay
MDA-MB-231, MDA-MB-436 and MCF-7 cells were seeded at a density of 2 x 105 cells/mL in round bottom 24-well plates in 500µL complete DMEM. After 48 hours, when cells reach confluence, the medium of the LDL-exposed group was replaced by DMEM 1% FBSLPF for 24h. Two hundred microliters tips were used to make a scratch (“wound”) in the center of the well and after washing with PBS to eliminate detached
cells, the medium was replaced by fresh complete DMEM supplemented with Mitomycin C (Merck Milipore) at a final concentration of 0,5µM to block cell proliferation, LDL and drugs depending on the groups. Cells were observed under 4x objective on a Zeiss Primovert microscope coupled with the Zeiss AxioCam ERc 5s. Cells were allowed to migrate during 20h after each treatment, and two photos per well were taken at 0h and 20h and cell migration distance was determined by subtracting the wound length of 20h from 0h (ten measurements per well in triplicates) and expressed as percentage of wound closure.
6.6.PGC-1a knockdown
For PGC-1a knockdown, lentiviral shRNA construct targeting PGC-1α and a scramble
shRNA (hairpin sequence:
5’-CCGGCAACAAGATGAAGAGCACCAACTCGAGTTGGTGCTCTTCATCTTGTTG -3’) (Sigma; kindly provided by Dr. Arkaitz Carracedo) were used. For shRNA transduction, lentiviruses were produced in HEK293T cells two days after seeding at a density of 5x 106 cells/mL with co-vectors (pRev, pVSV-G and pRRE) using X-tremeGENE DNA Transfection Reagent (Roche). Lentivirus particles were collected 48h post-transfection and used to transduce MDA-MB-231 cells in the presence of polybrene at a final concentration of 1:1000. Transduced cells were selected for during 3 days before experiments through addition of puromycin (In vivoGen) at a final concentration of 1:20000 in the culture media.
6.7.Quantitative Real-Time PCR
For RNA analysis, total RNA from cells was extracted using TRIZOL (Life Technologies) and samples were reverse transcribed using random priming and Superscript Reverse Transcriptase (Life Technologies), according to the manufacturer’s instructions. qPCR was performed using DNA master SYBR Green I mix (Applied Biosystems) in an ABI PRISM 7700 thermocycler. Quantifications were made applying the ΔCt method (Ct of gene of interest-Ct of housekeeping gene) followed by 2^(-ΔΔCt) and primer sequences are described in Table 1 and Table 2. For mtDNA determination, total DNA was isolated from cells using phenol:chloroform:isoamyl alcohol (Sigma) and measured by assessing the levels of the human mitochondrial ND1 relative to nuclear β2-microglobulin gene. Quantifications were made applying the ΔCt method (Ct of nuclear DNA gene - Ct of mitochondrial DNA gene) followed by 2x2^(ΔCt) according to (Rooney et al., 2015).
Table 1. Amplified genes and primers from mitochondrial biogenesis factors and housekeeping genes used in qRT-PCR.
Gene name Primer Sequence
PGC1α hPGC1alfa-F hPGC1alfa-R CACCAGCCAACACTCAGCTA GTGTGAGGAGGGTCATCGTT PGC1β hPGC1beta-F hPGC1beta-R GGCAGGCCTCAGATCTAAAA TCATGGGAGCCTTCTTGTCT NRF1 hNRF1-F hNRF1-R CCATCTGGTGGCCTGAAG GTAGTGCCTGGGTCCATGA ERRα hERRalfa-F hERRalfa-R GGCGGCAGAAGTACAAGC ATTCACTGGGGCTGCTGT TFAM hTFAM-F hTFAM-R GAACAACTACCCATATTTAAAGCTCA GAATCAGGAAGTTCCCTCCA PPARα hPPARA-F hPPARA-R AGAGTGGGCTTTCCGTGTC GCCGCCTTCAGGTACAGTAG ND1 hmtND1-F hmtND1-R CCCTAAAACCCGCCACATCT GAGCGATGGTGAGAGCTAAGGT 18S h18S-F h18S-R GCCCTATCAACTTTCGATGGT CCGGAATCGAACCCTGATT β-2-microglobulin hß2-microglobulin F hß2-microglobulin R TCGCTCCGTGGCCTTAGCTGT CTTTGGAGTACGCTGGATAGCCTCC 6.8.Statistical analysis
All results are expressed as mean ± s.d., and analysed using unpaired two-tailed Student’s t-test. (* p<0.05, ** p<0.01, *** p<0.001)
6.9.Transmission Electron Microscopy
Cells were plated and treated as described for the cell proliferation assay. For processing and analysis for TEM in cells were fixed with a solution containing 2.5% glutaraldehyde (Electron Microscopy Sciences) plus 0.1% formaldehyde (Thermo Fisher) in 0.1 M cacodylate buffer (Sigma), pH7.3 for 1 h. After fixation, samples were washed and treated with 0.1% Millipore filtered cacodylate buffered (Sigma-Aldrich), post-fixed with 1% Millipore-filtered osmium tetroxide (Electron Microscopy Sciences) for 30 min, and stained en bloc with 1% Millipore-filtered uranyl acetate (Agar scientifics). Samples were dehydrated in increasing concentrations of ethanol, infiltrated and embedded in EMBed-812 medium (Electron Microscopy Sciences). Polymerization was performed at 60°C for 2 days, and ultrathin sections were cut in a Reichert supernova microtome, stained with
uranyl acetate and lead citrate (Sigma-Aldrich) and examined in an H-7650 transmission electron microscope (Hitachi) at an accelerating voltage of 100 kV. Digital images were obtained using a XR41M mid mount AMT digital camera (Advanced Microscopy Techniques Corp). For quantification, we examined 13-16 cells magnified at x 3000 per group and counted the number of mitochondria per cell visualized in the sectioned image.
Table 2. Amplified genes and primers from lipid receptors and transporters, glycolysis, OXPHOS and FAO used in qRT-PCR.
Gene name Primer Sequence
CD36 h CD36 F h CD36 R GGTGTGGTGATGTTTGTTGC CAGGGCCTAGGATTTGTTGA LDL-R LDL-R F LDL-R R GATACCAAGGGCGTGAAGAG AAGCCATGAACAGGATCCAC SRBP1 SERP1Q F SERP1Q R TCCAGCGGGATCTGAAGCT TCCGGAACAGCCTGAAGAAG CPT1a h CPT1a-F h CPT1a-R ATGCGCTACTCCCTGAAAGTG GTGGCACGACTCATCTTGC CPT1b hCPT1B-F hCPT1B-R ATCATGGCGTGGATGATGT CCTCTCATGGTGAACAGCAA ACADVL hACADVL-F hACADVL-R ACGGGCGTACTGGGTGTT ATGGTGGAGGAGACCACTTG ACSL1 hACSL1-F hACSL1-R CCATGAGCTGTTCCGGTATTT CCGAAGCCCATAAGCGTGTT ACADM hACADM-F hACADM-R TGGATAACCAACGGAGGAAAAG CTGGGGTATCTGCTTCCACA HADHA hHADHA-F hHADHA-R CTGCCCAAAATGGTGGGTGT GGAGGTTTTAGTCCTGGTCCC COX5b hCox5b-F hCox5b-R GCTGCATCTGTGAAGAGGACAAC CAGCTTGTAATGGGTTCCACAGT ATP5g1 hATP5g1-F hATP5g1-R GCTGTTGTACCAGGGGTCTAA CTGGCGTGGGAAGTTGCTGT NDUFB5 hNDUFB5-F hNDUFB5-R CTTCCTCACTCGTGGCTTTC TTTCCCATGGTCTCCACTGT PKM2 h PKM2 F h PKM2 R CCACTTGCAATTATTTGAGGAA GTGAGCAGACCTGCCAGACT PFKFB3 h PFKFB3-F h PFKFB3-R ATTGCGGTTTTCGATGCCAC GCCACAACTGTAGGGTCGT LDHA h LDHA F h LDHA R ACCCAGTTTCCACCATGATT CCCAAAATGCAAGGAACACT PHGDH h PHGDH-F h PHGDH-R CTGCGGAAAGTGCTCATCAGT TGGCAGAGCGAACAATAAGGC
7. Results
7.1. Metabolic adaptations in LDL-exposed TNBC cells 7.1.1. Role of glycolysis
Figure 4. Inhibition of glycolysis with 2-DG affects the proliferative and migratory capacities of MDA-MB-231 cells.
a. Proliferative capacity by cell count ratio (Control, LDL, n=4) b. Migratory capacity
represented as percentage of wound closure at 20h by wound healing assay (Control, LDL, n=3) and c. Lipid droplets quantification using Bodipy staining by flow cytometry (Control, n=2; LDL, n=3; depicted as relative median fluorescent intensity, MFI) d. Representative images of wound closure at 0h and 20h by optical microscopy (4x objective) of Control and LDL-exposed MDA-MB-231 cells, untreated or treated with 2mM of 2-DG in the wound healing assay.
0 0,5 1 1,5 2 2,5 Untreated 2-DG Ce ll Co un t R at io Control LDL 0 10 20 30 40 50 60 Untreated 2-DG Wo un d cl os ur e (% m ig ra tio n di st an ce ) Control LDL 0 0,5 1 1,5 2 Untreated 2-DG Li pi d Dr opl et s ( fo ld M FI ) Control LDL *** ** *** ** ** *** a. b . c. 20h 0h *** * * ** Co nt ro l LD L Co nt ro l LD L 2-DG d .
To investigate the role of key energetic metabolic pathways in the acquisition of the aggressive phenotype induced by LDL exposure we inhibited selectively glycolysis, OXPHOS and FAO with specific drugs for enzymes of these metabolic pathways and analyzed their effects on cellular phenotypes.
To determine the role of glycolysis in the proliferative capacity of LDL-exposed MDA-MB-231 cells we treated cells with 2mM of the glucose analog 2-DG, which inhibits hexokinase2, a key enzyme of glycolysis and the predominant form of hexokinase in cancer cells (Peter et al., 2007). However, it did not abolish the increased proliferative capacity induced by LDL and did not affect the proliferative capacity of the cells under control conditions, but significantly decreased the cell number in LDL-exposed group in comparison to the untreated LDL-exposed group. (Figure 4 a.)
To assess if the migratory profile of MDA-MB-231 cell line was altered upon glycolysis inhibition, we performed a “scratch assay” protocol in the presence of 2-DG. The migratory capacity of these cells was indeed affected by glycolysis inhibition, with the suppression of the advantage given by LDL-exposure (Figure 4 b.), decreasing the percentage of wound closure of the group exposed to LDL to values similar to the control group. Treatment with 2-DG did not affect the wound closure capacity of cells in control conditions.
The increased lipid droplets content remains higher in the group exposed to LDL in comparison to the respective control, however, treatment with 2-DG significantly decreased the total amount of stored lipids of both control and LDL-exposed cells. Together these results suggest that in a LDL-enriched environment, glycolysis inhibition has an impact in both the migratory and proliferative capacities of MDA-MB-231 cells. The effect was nevertheless more accentuated in the migratory capacity where the increase migration/advantage of LDL-exposed cells was abolished.
7.1.2. Role of OXPHOS
a. Proliferative capacity by cell count ratio (Control, LDL, n=4) b. Migratory capacity
represented as percentage of wound closure at 20h by wound healing assay (Control, LDL, n=3) and c. Respective Lipid Droplets quantification using Bodipy staining by flow cytometry (Control, n=2; LDL, n=3 depicted as relative median fluorescent intensity, MFI) d. Representative images of wound closure at 0h and 20h by optical microscopy (4x objective) of Control and LDL-exposed MDA-MB-231 cells, untreated or treated with 200 nM of Oligomycin in the wound healing assay.
To investigate the role of OXPHOS in the metabolic and phenotypic features of MDA-MB-231 cell line exposed to LDL, we treated cells with 200nM of Oligomycin, which suppresses OXPHOS activity by inhibiting ATP synthase. Oligomycin treatment had no
0 0,5 1 1,5 2 2,5 Untreated Oligomycin Ce ll co un t ra tio Control LDL 0 0,5 1 1,5 2 Untreated Oligomycin Li pi d Dr op le ts (f ol d M FI ) Control LDL *** ** ** ** * b . LD L LD L Co nt ro l Co nt ro l Ol ig om yc i n * a. c. d . ** 20h 0h
Figure 5. Inhibition of OXPHOS with Oligomycin impacts the proliferative and migratory capacity of MDA-MB-231 cells.
0 10 20 30 40 Untreated Oligomycin Wo un d cl os ur e (% m ig ra tio n di st an ce ) Control LDL
effect on the proliferative potential of the LDL-exposed group but abolished the proliferative advantage induced by LDL in comparison to the Control group. (Figure 5
a.)
In terms of the migratory capacity of these cells, treatment with Oligomycin increased the migratory potential of both Control and LDL-exposed groups, maintaining the migratory advantage of the cells exposed to LDL in comparison to the Control group (Figure 5 b.).
In addition, the lipid droplets content of cells was increased in the group exposed to LDL but the lipid reserves of cells from the groups treated with Oligomycin were similar to the control group. (Figure 5 c.) These results suggest that OXPHOS inhibition does not have a negative impact on the proliferative and migratory capacities of TNBC cells exposed to LDL, and may in fact potentiate its effects.
7.1.3. Role of FAO
a. Proliferation assay by cell count ratio (Control, LDL, n=7 from two different
experiments) b. Lipid droplets quantification using Bodipy staining by flow cytometry (Control, LDL, n=6 from two different experiments; depicted as relative median fluorescent intensity, MFI) c. Migratory capacity represented as percentage of wound closure at 20h by wound healing assay (Control, LDL, n=3) and c. Lipid droplets quantification using Bodipy staining by flow cytometry (Control, LDL, n=4 depicted as relative median fluorescent intensity, MFI) of Control and LDL-exposed MDA-MB-231 cells, untreated or treated with 200mM of Etomoxir d. Representative images of wound closure at 0h and 24h by optical microscopy (4x objective).
0 10 20 30 40 50 60 Untreated Etomoxir Wo un d cl os ur e (% m ig ra tio n di st an ce ) Control LDL 0,0 0,5 1,0 1,5 2,0 Untreated Etomoxir Ce ll co un t ra tio Control LDL ** ** * *** ** a. b . c. 0h LD L LD L Co nt ro l Co nt ro l Et om ox ir d . 24h 0,0 0,5 1,0 1,5 2,0 Untreated Etomoxir Li pi d Dr opl et s ( fo ld, M FI ) Control LDL ** ***
Figure 6. Inhibition of FAO with of Etomoxir impacts the migratory capacity of MDA-MB-231 cells and has no effect on proliferation.
Treatment of cells with Etomoxir had no effect on the proliferative behavior of LDL-exposed MDA-MB-231 cells (Figure 6 a.) nor in the increased lipid droplets content of the cells upon LDL exposure.
The same treatment had an impact on the migratory capacity of these cells, indicated by the percentage of the wound closure, abolishing the migratory advantage given by LDL-exposure and drastically decreasing the wound closure in the presence of LDL (Figure 6
b.). Notably, the lipid droplets content of the cells remained unaltered (Figure 6 c.). Since
treatment with Etomoxir only affected the migratory capacity of MDA-MB-231, FAO may not be required for the proliferation of these cells.
0,0 0,5 1,0 1,5 2,0
PGC1α PGC1β NRF1 ERRα TFAM PPARα
Fo ld e xp re ss io n (n ro m al iz ed to 1 8S ) Control LDL
7.2. Expression of metabolic enzymes in LDL-exposed TNBC cells
a. Quantitative PCR analysis of the relative expression of a. Lipid receptors and FAO
genes (Control, LDL, n=4) b. OXPHOS and glycolysis genes (Control, LDL, n=4) and c. Mitochondrial biogenesis factors, normalized to18S gene expression (Control, LDL, n=4). 0,0 1,0 2,0 3,0 4,0 5,0
CD36 LDL-R SRBP1 CPT1a CPT1b ACADVL ACSL1 ACADM HADHA
Fo l e xp re ss io n (n ro m al iz ed to 1 8S ) Control LDL *** *** ** * *** * ** *** * * ** *** ** *** a. b . c. 0,0 0,5 1,0 1,5 2,0 2,5
COX5b ATP5g1 NDUFB5 PKM2 PFKFB3 LDHA PHGDH
Fo ld e xp re ss io n (n ro m al iz ed to 1 8S ) Control LDL
Figure 7. LDL exposure impacts the expression of lipid receptors, enzymes from
FAO, OXPHOS, glycolysis but produced no alteration in the expression of mitochondrial biogenesis factors.
To assess the metabolic pathways preferentially adopted by MDA-MB-231 cells upon LDL exposure we have analyzed the genetic expression of enzymes from FAO, OXPHOS, and glycolysis. Quantitative PCR analyses revealed that LDL exposure leads to a significant increase in the expression of enzymes from FAO: CPT1a and ACADVL genes (Figure 7 a.) and simultaneously several enzymes from OXPHOS: Cox5b, ATP5g1 and NDUFB5 genes and glycolysis: PKM2, PFKFB3, LDHA and PHGDH genes (Figure
7 b.) suggesting that LDL induced several metabolic adaptations in MDA-MB-231 cells.
We have also assessed expression mitochondrial biogenesis factors and proteins, which remains unaltered, except for PPARa gene (Figure 7 c.).
0 10 20 30 40 50 60 Nu m be r o f m ito ch on dr ia p er ce ll w ith in e va lu at ed se ct io n control LDL 0,0 1,0 2,0 3,0 Mi to Tr ac ke rDe ep Re d/ Mi to -YF P control LDL
7.3. Role of mitochondria in LDL-exposed TNBC cells
7.3.1. Mitochondria number and mitochondrial membrane potential
a. Quantification of the number of mitochondria per cell in the imaged section (Control,
n=18 cells, LDL n=15 cells) b. Representative cell sections by TEM (1500x, ‘M’ identify mitochondria and ‘LD’ identify Lipid Droplets), Control (upper panel) and LDL-exposed (lower panel); scale bar = 2µM c. Relative mtDNA content based on the mitochondrial ND1 gene relative to the nuclear b-2-microglobulin gene in LDL-exposed group relative to control group (Control, n³11 cells, LDL n³11 cells) d. Ratio of MitoTracker Deep Red fluorescence and Mito-YFP fluorescence used as an index of mitochondrial membrane potential (Control, n=21 cells, LDL, n=18 cells) e. Representative images of MitoTracker Deep Red transmembrane potential-dependent staining of mitochondrial mass (left), Mito-YFP staining of mitochondria (center) and Mito-YFP staining of mitochondria(right) from Control and LDL-exposed groups; scale bar = 10µM.
* 0 0,5 1 1,5 mt DN A co nt en t (N D1 g en e) Control LDL * ***
MitoTracker Mito-YFP MitoTracker/MitoYFP
a. b . c. d . e. Co nt ro l LD L M M M M LD 10µM
Figure 8. LDL exposure increases the mitochondrial mass and mitochondrial
To investigate the effect of LDL-exposure on the number of mitochondria, we counted the total of mitochondria in TEM imaged sections (Figure 8 b.). MDA-MB-231 cells exposed to LDL have significantly more mitochondria than cells from the control group (Figure 8 a.). mtDNA content of both groups of cells as evaluated by qPCR analyses of the human mitochondrial ND1 gene relative to the nuclear β-2-microglobulin gene in DNA samples, revealed increased levels in LDL-exposed MDA-MB-231 cells comparing to control (untreated) cells (Figure 8 c.).
Next, we investigated whether LDL exposure affected the mitochondrial membrane potential, which is indicative of mitochondrial function, performing a staining with the mitochondrial marker Mito-YFP and the fluorescent dye MitoTracker Deep Red which stains mitochondria depending on their membrane potential (Figure 8 e.) The ratio between MitoTracker Deep Red and Mito-YFP was used as an index of mitochondrial membrane potential. As shown in (Figure 8 d.), LDL exposure increased the mitochondrial membrane potential.
0 0,5 1 1,5 De ns ito me tr ic u ni ts co rr ect ed b y act in (f ol d ) Mfn1 Control LDL
7.3.2. Mitochondria dynamics in LDL-exposed TNBC cells
a. Western blot analysis of Mfn1 expression levels using anti-Mfn1 antibody (upper
panel), analyzed by measuring band density (lower panel), actin was used as an internal control (Control n=4, LDL n=3) b. Western blot analysis of Mfn2 expression levels using anti-Mfn2 antibody (upper panel), analyzed by measuring band density (lower panel), (actin was used as an internal control (Control and LDL, n=8) c. Western blot analysis of Drp1 expression levels using anti-Drp1 antibody (upper panel), analyzed by measuring band density (lower panel), b-actin was used as an internal control; Control and LDL, n=8) d. Western blot analysis of Drp1 expression levels using anti-Drp1 antibody (upper panel), analyzed by measuring band density (lower panel), actin was used as an internal control (Control and LDL, n=4).
Control LDL Mfn2 b-Actin a. b . Mfn1 Control LDL c. d . Drp1 b-Actin Control LDL P62 b-Actin Control LDL 0,0 0,5 1,0 1,5 Mfn2 De ns ito me tr ic u ni ts co rr ect ed b y act in (f ol d) Control LDL 0 0,5 1 1,5 2 De ns ito me tr ic u ni ts co rr ect ed b y act in (f ol d) p62 Control LDL 0 0,5 1 1,5 De ns ito me tr ic u ni ts co rr ect ed b y AC TI N (f ol d) Drp1 Control LDL * b-Actin
Figure 9. MDA-MB-231 cells have an increased expression of the mitochondrial fission protein Drp1 and no differences in expression of the Mfn1/2 and in the mitophagy p62 protein.
We further investigated whether LDL induces alterations in the mitochondrial dynamics of MDA-MB-231 cells. We focused on the mitochondrial fusion proteins Mfn1 and Mfn2 to assess mitochondrial fusion. Mfn1 and Mfn2 expression remained unaltered in the group exposed to LDL relative to the Control group (Figure 9 a. and Figure 9 b.). However, the levels of the Drp1 protein, involved in mitochondrial fission are increased upon LDL-exposure (Figure 9 c.). We also assessed if LDL exposition had an effect on cellular autophagy by investigating the protein levels of the autophagic protein p62, which was not affected by LDL exposition (Figure 9 d.).
0,0 1,0 2,0 3,0 SCshRNa PGC-1⍺ shRNA Ce ll Co un t R at io Control LDL 0 10 20 30 40 50 60 SC shRNA PGC-1⍺ shRNA Wo un d cl os ur e (% m ig ra tio n di st an ce ) Control LDL
7.4. Role of PGC-1a in LDL-exposed TNBC cells
0,0 2,0 4,0 6,0 8,0 10,0
PGC1alfa CPT1a COX5b ATP5g1 NDUFB5
Fo ld e xp re ss io n MDA-MB-231 shSCR control MDA-MB-231 shSCR LDL MDA-MB-231 sh1166 control MDA-MB-231 sh1166 LDL 0,0 0,5 1,0 1,5 2,0 SCshRNA PGC-1⍺ shRNA mt DN A co nt en t (N D1 g en e) Control LDL 0,0 0,5 1,0 1,5 De ns io me tr ic u ni ts co rr ect ed b y act in (f ol d) PGC-1a Control LDL * *** ** * * * a. b . c. * *** * d . *** e. PGC-1a b-Actin Control LDL *** ** ** * SC shRNA Control SC shRNA LDL PGC-1a shRNA Control PGC-1a shRNA LDL
