1. Introduction
Cancers, including breast cancer, remain a major challenge for healthcare. The complexity and adaptability of this disease continues to evade the development of effective and safe therapies. In the era of personalized medicine, the high throughput analysis of cancer metabolism under different challenges,
i.e. metabolomics, is expected to provide significant novel information and tools for the analysis of drug resistance, which remains one of the major clinical setbacks in cancer treatment [
1,
2]. Breast cancer is a heterogeneous disease with different subtypes presenting distinct cellular and molecular characteristics. The presence or absence of a number of hormone receptors in breast cancer subtypes is an important indicator used for the optimization of therapeutic strategies [
3]. Hormone receptors defining breast cancer subtypes are estrogen receptor alpha (ERα), progesterone receptor (PR) and the human epidermal growth factor receptor 2 HER2/neu (HER2 or ERBB2). These receptors may be present individually or in various combinations, which may provide information into the aggressiveness of the tumor and determine the therapeutic strategy [
4,
5,
6,
7]. ERα plays a crucial role in the development of hormone-dependent breast cancer and is present in more than 70% of breast tumors.
ERα, once activated with estradiol or other agonists, acts both directly as a transcription factor and indirectly by the modulation of other pathways involved in chromosome replication, cell cycle regulation, cell survival, and growth factor signaling [
8,
9]. The activation of the ERα pathway by estradiol increases cell proliferation and induces many genes directly involved in metabolism, such as glycolytic and lipogenic enzymes. Similarly, HER2 expression is also associated with enhanced lipogenesis. The transcription factor activity of ERα regulates the expression of metabolic enzymes that are providers of building blocks for cellular growth [
10,
11]. One of these ERα targets is stearoyl-CoA desaturase-1 (SCD1) [
12]. SCD1 is the principal supplier of monounsaturated fatty acids that are necessary for optimal membrane fluidity and membrane biogenesis and has emerged as a potential therapeutic target for lung, prostate, and breast cancer [
12,
13,
14]. Estradiol activation of ERα also leads to increased expression of carbonic anhydrase XII [
15,
16,
17]. Carbonic anhydrases (CA) are a family of 10 isoenzymes with different enzymatic properties and various subcellular localizations [
18]. CA are metaloenzymes that form bicarbonate from a reversible hydration of CO
2, thereby regulating the microenvironment acidity and tumor malignant phenotype [
19]. In addition, CA modulates tumor microenvironment acidity by supporting lactate flux in cancer cells [
20], thus the inhibition of CA isozymes is a promising anti-cancer therapy [
20,
21].
Ferulic acid (FA, 4-hydroxy-3-methoxy cinnamic acid) is an active compound derived from
Angelica sinensis, known to have several biological activities, including antioxidant, anti-inflammatory, anti-cancer, and anti-apoptotic properties [
22]. Moreover, FA can activate ERα in a manner that has been shown to be comparable to that of estradiol, stimulating MCF7 mammary carcinoma cell proliferation and inducing increased expression of both HER2 and ERα genes [
23]. Interestingly, FA also has significant antioxidant effects, and can inhibit several metalo-enzymes, including CA [
16,
17].
In this study, the impact of estradiol and FA, both ERα activators, on breast cancer cell metabolism was investigated with and without CA inhibition using the pan-CA inhibitor acetozolamide. The effects on metabolism were measured in the ER positive MCF7 breast carcinoma cell line using a previously-developed NMR-based metabolomics method [
24,
25,
26] for both quantitative and qualitative analyses of hydrophilic and lipophilic cellular extracts.
2. Results and Discussion
The present study investigates metabolic differences between the ERα +/low HER2 mammary carcinoma cell line MCF7 and MCF7 cells stably transfected with HER2 (MCF7HER2) (ERα +/high HER2) (
Figure 1), with the immortalized MCF10A normal mammary epithelial line used as an estrogen insensitive control [
12]. Metabolism in these cells was compared in untreated controls and following incubation with 17β-ED or FA, in the presence or absence of the CA inhibitor acetazolamide. The effect of these compounds on the three cell lines was investigated through the analysis of changes in cellular metabolic profiles using NMR
1H analysis with methods previously developed and used by our group [
24,
25,
26]. Previously indicated relations between the tested molecules and some of the major proteins responsible for cancer progression and metabolism are schematically presented (
Scheme 1) and were explored through metabolomics analysis in this work.
17β-estradiol (ED) is an estrogen receptor activator that induces cell division in ER+ breast cancer cells. This effect is clearly confirmed in both MCF7 and MCF7HER2 cell lines where incubation with ED causes significant increases in cell proliferation in both MCF7 and MCF7HER2 cells (
Figure 2). Ferulic acid treatment also increased cell proliferation in both MCF7 and MCF7HER2 cells (
Figure 2), in agreement with previous studies [
23,
27]. Addition of the pan-CA inhibitor acetazolamide leads to a slight decrease in cell proliferation in both cell lines, in agreement with the previously observed anti-proliferative effect of CA inhibitors or CA knockdown [
20,
28]. In MCF10A cells, cellular proliferation was not affected by the treatments with ED, FA or acetazolamide (data not shown). The presence of CA in MCF7 cells has been previously established [
15,
20,
29] and although the role of CA is particularly pronounced in hypoxia [
20], CAs are expressed in cancer cell lines, even under normoxic conditions, where metabolism is enhanced due to the Warburg effect, with roles in pH regulation [
30] and lactate transport to the extracellular medium [
20]. Indeed removal of lactate from metabolically active cells is required to prevent intracellular acidification and a reduction in cell metabolism and proliferation [
20].
Metabolic profiles for hydrophilic and lipophilic extracts were measured for all three cell lines (MCF10A, MCF7 and MCF7HER2) under studied treatments, see spectra in
Figure 3.
Overall metabolic profile changes are best observed from the analysis of spectra. Principal component analysis (PCA) of the measured hydrophilic metabolites for the three cell lines incubated under different treatment conditions is shown in
Figure 4A. In the untreated control condition, MCF10A cells had a highly distinct profile from that of the other cell lines, with no significant difference in the major variances between MCF7 and MCF7HER2 cells. A similar overall result was observed following incubations with acetazolamide and ferulic acid. Treatment with 17β-estradiol created change in the metabolic profile between all three cell types with significantly reduced difference between MCF10A and MCF7 and MCF7HER2 cells. According to PCA shown in
Figure 4A, major PC1 variance between these three groups goes from over 70% in control group to under 50% in 17β-estradiol treated cells. At the same time PC2 component shows variance between MCF7 and MCF7HER2 cells only in 17β-estradiol treated cells. Analysis of treatments and controls for each cell line (
Figure 4B) show no significant effect on the overall profile in MCF10A cells for any of the tested treatments. In MCF7 and MCF7HER2 cells, 17β-estradiol once again had the most profound effect on PCA spectra when applied alone or in combination with acetazolamide resulting in significant changes from untreated controls. There was no measured effect on PCA of metabolic profiles following treatment of cells with ferulic acid or acetazolamide.
Further biological analysis of the effects of treatments can be performed using quantified metabolic data. Methods previously developed and utilized by our group [
24,
25,
26,
32] were used to quantify 38 metabolites previously listed as present in breast cancer cells using publically-available spectra, as shown in
Figure 5.
This relative comparison of the effects of different treatments on metabolite concentrations in three cell lines allows us to focus on changes caused by specific differences between cell lines.
Figure 6 and
Supplementary Table S1 present the average concentrations of 38 metabolites in MCF7 and MCF7HER2 cells in the six treatment conditions relative to the concentrations in MCF10A cells undergoing the same treatment. Concentration differences are apparent for all metabolites in all conditions between malignant cell lines (MCF7 and MCF7HER2) and the immortalized normal MCF10A cell line. Relative metabolic differences between the immortalized normal MCF10A cell line and malignant cell lines (MCF7 and MCF7HER2) are shown in
Supplementary Figure S1 for all metabolites, and major metabolic changes are determined using Statistical Analysis for Microarrays method (SAM) and are shown in
Supplementary Figure S2.
Several metabolites have lower concentrations in both MCF7 and MCF7HER2 cells in all treatments compared to MCF10A cells. These include a number of amino acids including some branched chain amino acids (leucine, valine, glutamate, glycine, methionine, serine, alanine, creatine, proline, asparagine, tryptophan), as well as betaine, glycerol-3-phosphate (G3P), myoinositol, GSSG. At the same time methylamine, malate, lactic acid cis-aconic acid, citric acid, glucose, adenosine, glycerophosphocholine (GCP), tyrosine, glutamine, phosphocholine (PC), choline, histidine, arginine and ethanol are consistently more concentrated in cancer cell lines. In general the two breast cancer cell lines responded similarly to treatments with the most apparent difference between the effects of treatments on MCF7 relative to MCF7HER2 cells being in concentration changes of serine. In order to put the observed differences into the context of cellular metabolism, we show relative metabolite concentration changes following individual treatments in MCF7 and MCF7HER2 cells relative to the equivalent treatment effect in MCF10A cells.
Relative concentrations for observed metabolites involved in glycolysis, Krebs cycle, and choline pathways as well as related amino acids are shown for different treatments in
Figure 7. Imbedded graphs show metabolite concentration changes following acetazolamide, 17β-estradiol and ferulic acid treatment in MCF10A, MCF7, and MCF7HER2 relative to untreated controls in the same cell lines.
In the untreated control group (
Figure 6) the glycolysis pathway including lactate production is enhanced in the cancer cell lines compared to MCF10A cells. In addition, concentrations of Krebs cycle intermediates are also increased in MCF7 cells possibly resulting from increased utilization of glutamate (with its concentration decrease) or from other amino acids (e.g., branched amino acids) also showing reduced concentrations compared to MCF10A cells. Increases in Krebs cycle further leads to enhanced citrate production as well as possibly contributing additionally to lactate production. Higher concentrations of histidine in the cancer cell lines possibly indicates increased PPP pathway as previously noted [
33,
34,
35]. Average concentration change for lactic acid, succinic acid and histidine across different groups of samples are shown in in
Supplementary Figure S3. Additionally, choline concentrations as well as concentrations of phosphocholine (PC) and glycerophosphocholine (GPC) are increased in cancer cell lines, once again in agreement with previous results.
Addition of 17β-estradiol leads to a number of concentration changes between these three cell lines with differences in behavior when compared against normal MCF10A cells, as well as when comparing MCF7 and MCF7HER2 cells. MCF7 cells respond to estradiol by increasing the consumption of glucose, possibly increasing the flux through the PPP cycle with a noted relative increase in histidine concentration. The effect of estradiol on Krebs cycle is more significant in MCF7HER2 cells with higher increases in the concentration of all Krebs cycle intermediates except citrate, possibly due to the ER activation of fatty acid synthesis. The effect of estrogen receptor activation also leads to significant increases in relative concentrations of choline as well as phosphocholines and glycerophosphocholines and a decrease in betaine in MCF7 cells but not MCF7HER2 cells. Production of lactate increases in both MCF7 and MCF7HER2 cells.
Treatment with ferulic acid leads to lesser change in relative concentrations, however, major significant additional changes are the same as in 17β-estradiol treated cells. Once again lactate production is increased particularly in MCF7HER2 cells. Serine concentration is also increased in MCF7HER2 cells. Choline and phosphocholine are not significantly affected, however there is an increase in the relative concentration of glycerophosphocholine in MCF7HER2 cells. HER2 activated MCF7 cells can represent a hybrid between MCF7 and SKBR3 cells and treatment with hypothetic estrogen receptor activators can lead to activation of PLA
2 expression, altering ratio the between PC and GPC [
36].
Treatment with the CA inhibitor acetazolamide does not alter relative metabolite concentrations between the three cell lines significantly for the majority of metabolites. Major changes are in lysine and citrate levels with an increase in concentration in MCF7 cells relative to the other two cell lines, suggesting an effect of CA inhibition on fatty acid biosynthesis in this cell line. Concentrations of lactic acid increased the most in MCF7HER2 cells (
Supplementary Figure S3) possibly due to the reduced efflux out of the cell and this observation will need to be further investigated. Addition of acetazolamide to estradiol treatment leads to GPC concentration changes with a major decrease in MCF7 cells relative to cells treated only with estradiol as well as control cells. Choline and phosphocholine are directly related through the Kennedy pathway and their comparable relative concentrations are apparent in all treatment cases. Glycerophosphocholine can result from phosphatidylcholine in a PLA
2-driven reaction further producing choline and glycerol-3-phosphate. Combined treatment with acetazolamide and ferulic acid leads to major concentration increases in lactate in MCF7 cells possibly indicating reduced efflux out of cells. As there is a reduction in the relative concentration of serine in MCF7 cells it can also be hypothesized that glycolysis in these cells increasingly goes to the lactic acid shunt, which is corroborated by decreased relative concentrations of several Krebs cycle metabolites. Concentrations of choline as well as PC and GPC show relative increases in MCF7 cells in this case while choline, PC and GPC concentration changes were observed in ER activated cells.
Changes in lipid profiles are apparent from the analysis of NMR metabolic profiles of lipophilic metabolites.
Figure 8 shows PCA of spectra for the three cell lines comparing control and treated samples. The largest differences are in acetazolamide-treated cancer cells.
According to the trace analysis of PC1 (
Figure 8B), the most significant variances leading to the observed separation of control and acetazolamide-treated MCF7HER2 cells are in CH2 peak of lipids or cholesterols, CH2CH2COO and CHOCOR peak of lipids, and HC=CH peak of lipids and cholesterols. The observed differences suggest increases in lipid concentrations and a decrease in cholesterol concentrations following acetazolamide treatment.
To investigate this observation, we measured the expression of sterol-response element binding protein-1 (SREBP-1) as shown in
Figure 9 under different conditions. SREBP-1 is a transcription factor that shows higher activity in the induction of genes involved in fatty acid synthesis than those participating in cholesterol synthesis [
37,
38]. As expected, the activation of ERα with both agonists, 17β-estradiol and FA, enhances the expression of SREBP-1 in both MCF7 and MCF7HER2 cell lines compared to untreated controls. The pan-CA inhibitor acetazolamide alone induces SREBP-1 expression in MCF7 cells but not in MCF7HER2 cells. Further lipidomics analysis is warranted in order to explore in more detail the effect of these treatments on lipid metabolism.