1. Introduction
Worldwide, breast cancer (BC) is the most commonly diagnosed cancer type and represents the leading cause of cancer death among women. It is genetically and morphologically highly heterogeneous and subgrouping relying on pathological and clinical data can only partially reflect the clinical diversity of the disease.
Generally, breast cancer is associated with somatic mutations in breast cells acquired during a women’s lifetime. 5–10 percent of all cases are hereditary breast cancer, and are associated with distinct mutations on specific genes such us
BRCA1,
BRCA2,
PTEN and
CHEK2. BRCA1 mutation carriers seem to have specific pathological features and gene expression profile [
1,
2].
BRCA1 gene has been extensively studied, more than 1600 mutations have been described and the majority of them are frameshifts mutations resulting in the deletion or non-functional protein.
BRCA1 is implicated in DNA double-strand break repair, transcriptional regulation, cell cycle control, apoptosis and resistance to chemotherapy [
3].
BRCA1 is known to interact with several cellular players, including p53, c-Myc, AKT and HIF-1α [
4,
5].
The comprehension of the functional significance of hereditary mutations may improve breast cancer prevention and the assessment of promising strategies of treatment.
In
BRCA1-driven tumorigenesis, in contrast with the two-hit theory [
6], the haploinsufficiency status produces genomic instability in breast epithelial cells and acts as a push toward malignant transformation. Several evidences suggest that the inactivation of a single
BRCA1 allele induces a huge number of genetic alterations and the consequent acquisition of “permissive” status that leads to the homozygous loss of
BRCA1 [
7,
8,
9].
The Warburg effect represents a major metabolic hallmark of cancer cells. The aerobic glycolysis occurs in normal cells during hypoxia and in cancer cells all the time. Hypoxia inducible factor-1α (HIF-1α) is the key regulator of the hypoxia response. In presence of low oxygen in the cell, pyruvate is converted to lactate instead of acetyl CoA, HIF-1α does not undergo proteasomal degradation and translocates into the nucleus, where it regulates the expression of genes involved in glucose metabolism. During hypoxia, normal cells undergo apoptosis, while cancer cells adapt to the low oxygen environment and survive [
10,
11].
The role of
BRCA1 in cancer cell metabolism remains to be elucidated, although very recently it has been shown that
BRCA1 haploinsufficiency regulates the oxidative mitochondrial metabolism and constitutes an early and important “hit” that drives the tumorigenesis process [
12,
13].
Mitochondria are the powerhouse of all living cells; they have a key role in several pathways such as apoptosis, Ca2+ homeostasis, ROS signaling and mitophagy. They are also involved in carcinogenesis, cancer progression and metastasis, playing a role in altered energy metabolism of cancer cells.
In this work, in order to characterize the main role of BRCA1-induced metabolic reprogramming, we performed DIGE experiments coupled with LC_MS/MS analysis to profile the mitochondria proteome of breast cancer cells expressing or not BRCA1.
The object of our study was to shed light on potential contribution of BRCA1 to the metabolic features of cancer cells, including the so-called ‘‘Warburg effect’’.
2. Materials and Methods
2.1. Cells Culture
MCF-10, MCF-7 and HCC1937 cell lines were purchased from the American Type Culture Collection (Rockville, MD, USA). Human breast cancer cell line MCF7 was grown in Dulbecco’s modified Eagle’s medium (DMEM) (Sigma Aldrich, Saint Louis, MO, USA) supplemented with 10% (w/v) fetal bovine serum (FBS) (Sigma Aldrich), 100 mg/mL streptomycin and 100 U/mL penicillin (Sigma Aldrich). MCF10 mammary epithelial cell line was grown in MEGM Mammary Epithelial Cell Growth Medium (Lonza, Walkersville, MD, USA) supplemented with 20 ng/mL epidermal growth factor (Lonza), 0.5 µg/mL hydrocortisone (Lonza), 100 ng/mL cholera toxin (Sigma Aldrich) and 10 µg/mL insulin (Lonza). HCC1937 cell line was homozygous for the BRCA1 5382C mutation and was used as a model of hereditary breast cancer. HCC1937 cells (ATCC) were grown in RPMI medium (ATCC) supplemented with 20% (w/v) fetal bovine serum (FBS) (Sigma Aldrich), 100 mg/mL streptomycin and 100 U/mL penicillin (Sigma Aldrich).
All cell lines were cultured at a constant temperature of 37 °C in a 5% carbon dioxide (CO2) humidified atmosphere.
2.2. Full-Length Transfection of BRCA1 in HCC1937 Breast Cancer Cells
HCC1937/
wtBRCA1 cells were generated in our laboratory by full-length
BRCA1 cDNA transfection [
14,
15].
Parental HCC1937 were transfected with a pcDNA 3.1 plasmid containing the full-length BRCA1 gene. Cells between 30–40% confluency were incubated overnight with 2 mg of plasmid DNA, using the FuGENE 6 transfection reagent (Roche Molecular Biochemicals, Monza, Italy) according to the manufacturer’s instructions. Stable transfectants were selected using G418 (0.4 mg/mL) (Invitrogen Life Technologies, La Jolla, CA, USA). HCC1937/wtBRCA1 cells were amplified and used for further analysis
2.3. Whole Protein Extraction
Cells lines were washed with PBS and lysed with a buffer containing 15 mM Tris pH 7.5, 120 mM NaCl, 25 mM KCl, 0.5% Triton X-100, supplemented with protease and phosphatase inhibitor cocktail (Halt Protease Inhibitor Cocktail/Halt Phosphatase Inhibitor Cocktail, Thermo Fisher Scientific Inc., Bremen, Germany). Cell lysate was sonicated for 10 s and centrifuged at 15,000× g for 20 min. All the operations were performed at 4 °C. Supernatant was carefully removed and protein content was measured by the Bradford method (BioRad, Hercules, CA, USA); and the supernatants were stored at 80 °C.
2.4. Mitochondrial Fraction
Mitochondria were isolated according to previous literature [
16,
17,
18]. Briefly, normal and cancer cells were washed with ice cold PBS, collected by gentle scrapping in cold PBS and centrifugalized at 600×
g, at 4 °C for 10 min. Resulting pellet was suspended in 200 mM sucrose, 10 mM Tris–MOPS and 1 mM EDTA/Tris, pH 7.4 (STE buffer). Cells were homogenized by glass Potter homogenization and mitochondria were then isolated by serial centrifugations. Resulting supernatant was used as cytosolic fraction. The mitochondrial pellet was suspended in DIGE lysis buffer and protein content was assayed using the Bradford Protein Assay (Bio-Rad) according to the manufacturer’s instructions.
2.5. Isolation of Nuclear Fractions
Cells were collected with 1 mL of hypotonic lysis buffer (10 mM Tris-Cl pH 8.0, 1 mM KCl, 1.5 mM MgCl2, 1 mM DTT, supplemented with protease and phosphatase inhibitor cocktail) and incubated for 30 min on rotator at 4 °C. Cell lysate was centrifugated at 10,000× g for 10 min at 4 °C to isolate nuclei. Cytosol was supplemented with 1% Triton X-100. Both fractions were incubated on ice for 30 min and then centrifuged at 15,000× g for 20 min at 4 °C. Protein concentration was determined using the Bradford Protein Assay (Bio-Rad) according to the manufacturer’s instructions.
2.6. 2D-DIGE
Cell pellets were suspended in 7 M urea, 2 M tiourea, 4% chaps, 30 mM Tris pH 8.5, containing protease and Phosphatase inhibitor cocktail (Halt Protease Inhibitor Cocktail/Halt Phosphatase Inhibitor Cocktail, Thermo Fisher Scientific Inc., Bremen, Germany) and sonicated in an ultrasonic bath for 1 min. Proteins were quantified using the Bradford protein assay (Bio-Rad, Hercules, CA, USA), according to manufacturer’s instructions.
Protein extraction and solubilization were carried out in triplicate. Mitochondrial protein extraction was verified by western blot analysis.
2D-DIGE analysis was performed according to previously literature [
19]. Mitochondrial proteins from cancer cells were labeled using the CyDyes™DIGE Fluors (Cy2, Cy5 and Cy3, GE Healthcare, Buckinghamshire, UK) at the ratio of 1 μg protein: 400 pmol dye.
Three biological replicates were prepared for each cellular model in order to perform a statistical significant experiment. Cy3 and Cy5 were used to label the samples, and Cy2 was used to label the internal standard, a pool of samples created by mixing an aliquot of all biological samples used for the analysis.
Protein labelling was carried out incubating each proteins extract with the respective cyanine for 30 min in the dark, at the pH of 8.5 (
Table S1). Reactions were quenched adding 10 mM lysine, for 10 min in the dark.
Labeled extract were mixed and resuspended into Isoelectrofocusing (IEF) sample buffer containing 8 M urea, 4% CHAPS, 0.1 M DTT, 0.8% IPG buffer pH 3–10 NL. IEF was performed using non-linear precast IPG strip (pH 3–10 NL; 24-cm-long, GE Healthcare). The first dimension was carried out on GE Healthcare IPGphor unit, until a total of 70,000 Vh was reached. After the first dimension, IPG strips were equilibrated with SDS equilibration solution containing 10 mg/mL dithiothreitol, followed by a treatment with SDS equilibration solution containing 25 mg/mL iodoacetamide, after the IEF.
The Immobiline DryStrip gels were loaded and run on 10% acrylamide isocratic gels using the Ettan DALTsix Electrophoresis System ((GE Healthcare, USA)). Gels were run at 2 W per gel constant power at 20 °C until the bromophenol blue dye front had run off the bottom of the gels [
20].
After 2-DIGE, CyDye-labeled proteins were visualized using the Typhoon 9410 imager (GE Healthcare, San Francisco, CA, USA). Gels were scanned at a 100 μm resolution. Images were cropped using ImageQuant™ Version 5.0 (GE Healthcare, USA) to remove areas extraneous to the gel image.
Gel images were analyzed using the Software DeCyder V7.0 (GE Healthcare, USA). All sampled gel image pairs were processed by the DeCyder DIA (Differential In-gel Analysis) software module in order to co-detect and differentially quantitate protein spots in the images. Gel-to-gel matching of the standard spot maps from each gel, followed by statistical analysis of protein abundance change between samples, was done by use the DeCyder BVA (Biological Variation Analysis) software module. Unpaired t-test was used to compare protein levels in each group. A fold change of at least 1.5 and a two sided p-value < 0.05 was considered statistically significant.
Proteins differentially expressed were selected for mass spectrometry identification. To avoid spot mismatch, protein identification was directly performed on analytic gels.
Fifty-three spots of interest were manually excised from the gels, trypsin digested, and subjected to tandem mass spectrometry analysis.
2.7. Nanoscale LC-MS/MS Analysis
LC-MS/MS analysis was carried out using an Easy LC 1000 nanoscale liquid chromatography (nanoLC) system (Thermo Fisher Scientific, Odense, Denmark). The analytical nano LC column was a pulled fused silica capillary, 75 μm in-house packed to a length of 10 cm with 3 μm C18 silica particles from Dr. Maisch (Entringen, Germany).
The peptide mixtures were injected at 500 nL/min directly onto the analytical column. Peptides were eluted using a binary gradient. Mobile phase A was 0.1% formic acid, 2% acetonitrile, while mobile phase B was 0.1% formic acid, 80% acetonitrile. Gradient elution was done at 350 nL/min flow rate, and ramped from 0% B to 30% B in 15 min, and from 30% B to 100% B in additional 5 min; after 5 min at 100% B, the column was re-equilibrated at 0% B for 10 min before the subsequent injection. MS detection was done on a quadrupole-orbitrap mass spectrometer Q-Exactive (Thermo Fisher Scientific, Bremen, Germany) operating in positive ion mode, with nano electrospray (nESI) potential at 1800 V applied on the column front-end via a tee piece. Data-dependent acquisition was done using a top-5 method with resolution (FWHM), AGC target and maximum injection time (ms) for full MS and MS/MS of, respectively, 70,000/17,500, 1 × 10−6/5 × 10−5, 50/400. Mass window for precursor ion isolation was 2.0 m/z, while normalized collision energy was 30. Ion threshold for triggering MS/MS events was 2 × 10−4. Dynamic exclusion was 15 s.
Data analysis was performed using Proteome Discoverer 1.3 (Thermo Fisher Scientific, Bremen, Germany), using Sequest as search engine, and the HUMAN-refprot-isoforms.fasta as sequence database. Parameters applied for the analysis were the following: MS tolerance 15 ppm; MS/MS tolerance 0.02 Da; fixed modifications: carbamidomethylation of cysteine; variable modification: oxidation of methionine, phosphorylation of serine, threonine and tyrosine; enzyme trypsin; max. missed cleavages 2; taxonomy Human.
We accept only proteins identification performed with two successful peptide identifications (Xcorr >2.0 for doubly charged peptides, >2.5 for triply charged peptides, and >3.0 for peptides having a charge state > 3).
2.8. Western Blot Analysis
For 1D Western blot analysis, fifty µg of proteins sample was resolved by pre cast SDS-PAGE (Any kD™ and 4–20% Mini-PROTEAN Precast Protein Gels, Biorad) and electrotransferred to a nitrocellulose membrane with a Trans-blot turbo system (Biorad). Membranes were incubated using the following primary antibodies: HSP60 (1:1000, 4B9/89 Mouse, Thermo scientific, (Bremen, Germany); Cytc (1:1000, D18C7 Rabbit, Cell Signaling, Danvers, MA, USA); HIF-1α (1:1000, D5F3M Mouse, Cell Signaling, Danvers, MA, USA); H3 (1:1000, ,9715, Rabbit, Cell Signaling, Danvers, MA, USA); α-Tubulin (1:1000, 2144, Rabbit, Cell Signaling, Danvers, MA, USA); BRCA1 (1 μg/mL, clone D-20, Santa Cruz, Dallas, TX, USA ).
The detection of a primary antibody was done with anti-mouse horseradish peroxidase-conjugate secondary antibodies (Cell Signaling) for mouse primary antibody, antirabbit horseradish peroxidase-conjugate secondary antibodies (Cell Signaling) for Rabbit primary antibody. Blots were developed using the SuperSignal West Femto ECL substrate (Pierce, Thermo Fisher Scientific Inc., Bremen, Germany). Densitometric software (Alliance 2.7 1D fully automated software) was used to determine the percent distribution of blotted proteins after image acquisition by Alliance 2.7 (UVITEC, Eppendorf, Milan, Italy).
Data were analyzed and plotted using Excel spreadsheet (Microsoft, Redmond, WA, USA), and expressed as mean ± SEM (N), where SEM represents the standard error of the mean and N indicates the number of experimental repeats. Unpaired t-test was used to compare protein levels in each data set. A two sided p-value < 0.05 was considered statistically significant.
2.9. Pathway Analysis
Proteins differentially expressed were functionally correlated using the software Ingenuity Pathway analysis (Ingenuity Systems, version 42012434, Qiagen, Hilden, Germany,
www.ingenuity.com) (IPA). The software builds hypothetical protein interaction clusters based on a constantly updated Ingenuity Pathways Knowledge Base. Data sets containing proteins identifiers of molecules and corresponding expression values were uploaded into the application.
The system algorithmically generated a list of Networks based on their connectivity. Networks were “named” on the most common functional group(s) [
21,
22]. Each analysis was statistically evaluated by the Fischer exact test. This was used to calculate a
p-value defining the probability that each biological function and/or disease assigned to that network is due to a random event.
Molecular activity prediction (MAP) function was used to predict the upstream or downstream effects of deregulated protein on specific targets.
2.10. Immunofluorescence Microscopy Analysis
Cells (5 × 104/well) were plated onto glass cover slips in the wells of a 6-well culture dish. After the cultures reached 50% confluence, MitoTracker Green (Thermo Scientific) was added to cells and incubated for 1 h at 37 °C. Cells were washed 3× with pre-warmed PBS. Cells were fixed with 4% paraformaldehyde (Sigma-Aldrich) for 30 min. After permeabilization with 0.3% Triton X-100 (Sigma-Aldrich) in phosphate buffered saline (PBS) for 15 min, the cells were blocked with 10% FBS (Biowest, Nuaillé, France)) and 0.1% Triton X-100 in PBS for 1 h at room temperature and then incubated with anti HIF-1α (1:800, D5F3M, Cell Signaling) primary antibodies diluted in PBS containing 3% FBS. Goat anti-mouse Alexa-Fluor-647 (A-21235, Life Technologies) was used as the secondary antibodies at a concentration of 2 µg/mL in PBS containing 1% FBS for 45 min at room temperature. Nuclei were stained with DAPI (4′,6-diamidino-2-phenylindole). Slides were mounted with fluorescent mounting medium (Dako Cytomation) and images were acquired with DMi8 Leica microscope (Leica Microsystems Srl, Milan, Italy). The assays were repeated in three independent biological replicates.
To quantitatively determine subcellular localization of the expressed HIF-1α protein, the relative staining intensities in the nucleus and in the mitochondria were analyzed. Image analysis was performed using ImageJ software (Wayne Rasband, National Institute of Mental Health, Bethesda, MD, USA). In particular, the Coloc2 plugin was used to identify the localization of HIF-1α in the mitochondria (gray regions) and represented in the figure as Overlap Red: Green. Mitotracker and DAPI staining were used to define the mitochondrial and nuclear regions of interest (ROIs) drawn manually. After brightness and contrast adjustment, images were randomly selected from more than 25 cells per cell line. At least 5 ROIs containing non-specific staining were manually selected from the image as background which intensity value was subtracted from the image content. CTFC (corrected total cell fluorescence) was calculated, with the following formula, Integrated Density—(Area of selected cell * Mean fluorescence of background readings). The average ratio between the intensity of fluorescence in the nucleus and in the mitochondria was plotted.
4. Discussion
Recently, “mitochondrial medicine” has turned up as one of the main topics in a number of papers, some of which are focused on classical mitochondrial diseases, while many other deal with diseases in which mitochondrial dysfunction and/or mitochondrial stress signaling play important roles.
The increased interest of researchers in the biology of mitochondria and the discovery of their involvement in many types of diseases and ageing has been the driving force of mitochondrial proteomics. Mitochondrial proteomics aims to study changes in the levels of mitochondrial proteins as well as to characterize proteins that should have dual localization, and/or proteins that only in specific physiological/pathological conditions associate with mitochondria or are imported into mitochondria.
Here, 2D-DIGE was coupled with mass spectrometry, bioinformatics and molecular biology to elucidate the role of mitochondria in BRCA1-driven tumorigenesis. The study of the compartmentalization of proteins is essential for the complete understanding of their function and to elucidate the pathways in which they are involved.
As first result we propose the peculiar localization of HSP60 protein in response to
BRCA1 mutation status. In physiological conditions, HSP60 is a protein of the mitochondrial matrix that plays an important role in facilitating protein folding and in the maintenance of mitochondrial integrity. HSP60 associates with caspase-3 and it is responsible for the induction of its maturation and activation. The association of HSP60 with procaspase-3 and the 30-kDa caspase-3 occurs only in the cytosol [
33].
When HSP60 is released into cytosol, it plays a pro-apoptotic role; conversely, when HSP60 is accumulated into mitochondria, it plays a pro survival role. The accumulation of HSP60 into mitochondria, that we observe in our model, is consistent with a pro-survival role of HSP60 and may help to elucidate the pathways that lead to apoptosis suppression in BRCA1 mutated breast cancer cells.
As second result, we found that the expression of many factors associated with the metabolic rewiring of breast cancer cells is coherent with the switch versus the anaerobic glycolysis.
Mitochondrial energy production is the more efficient method for energy production. In cancer cells, when the level of oxygen is low, pyruvate is prevalently converted to lactate instead of acetyl CoA; hypoxia induces the expression of HIF-1α which, in turn, regulates the expression of several genes, some of which are involved in the glycolytic pathway [
34,
35,
36]. HIF-1α is able to block CoA metabolism in mitochondria as well as mitochondrial biogenesis and oxygen consumption. In
BRCA1 mutated cells line, the high levels of HIF-1α are coherent with the metabolic switch required for cellular adaptation to hypoxia [
37].
In highly proliferative cancer cells a de novo synthesis of fatty acids is observed. They are the major components of cell membrane and constitute the key substrates for energy production, lipid modification of proteins, and signaling molecule production. In several types of cancer, lipogenesis is increased by overproduction of fatty acid synthase (
FASN), the enzyme that converts acetyl CoA and malonyl CoA to fatty acids and acts as an oncogene by promoting cancer cell proliferation and growth [
38]. Coherently with this evidence, using the map function and our DIGE quantitative data, we predict the activation of fatty acid synthase in
BRCA1 mutated cell lines.
In
BRCA1 mutated cells, we also observed the up regulation of adenylate kinase A4 (AK4). The oncogenic potential of many cancers has been related with high levels of AK4. AK4 is an important regulator of cellular homeostasis through the regulation of cellular energy charge and AMPK activation. AK4 contributes to cancer process by maintaining the proper balance of adenosine nucleotides and by controlling the bioenergetic program switch of cancer cells [
39].
These evidences are in agreement with the switch toward aerobic glycolysis exhibited by BRCA1 mutated breast cancer cells.
Finally, the up regulation of two key enzymes, the glyceraldehyde-3-phosphate dehydrogenase and the fructose-bisphosphate aldolase A, reported as “possible mitochondrial”, correlate with the up regulation of glycolysis pathway supporting the Warburg’s hypothesis [
34].
Using subcellular fractionation analysis on purified mitochondria, we have found that in breast cancer cells the metabolic switch is speeded up in response to the presence of a peculiar mutation on BRCA1 gene.
