Fatty acid and phospholipid biosynthetic ... - The FASEB Journal

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Fatty acid and phospholipid biosynthetic pathways are regulated throughout mammary epithelial cell differentiation and correlate to breast cancer survival M. Luisa Dória,*,1 Ana S. Ribeiro,*,1 Jun Wang,† Cândida Z. Cotrim,* Pedro Domingues,* Cecilia Williams,† M. Rosário Domingues,* and Luisa A. Helguero*,2 *Mass Spectrometry Centre, Organic Chemistry and Natural Products Research Unit, Department of Chemistry, Universidade de Aveiro, Campus de Santiago, Aveiro, Portugal; and †Department of Biology and Biochemistry, Center for Nuclear Receptors and Cell Signaling, University of Houston, Houston, Texas, USA This work combined gene and protein expression, gas chromatography-flame ionization detector, and hydrophilic interaction liquid chromatographytandem mass spectrometry to compare lipid metabolism changes in undifferentiated/proliferating vs. functionally differentiated mammary epithelial cells (MECs) and to study their correlation to breast cancer survival. Sixty-eight genes involved in lipid metabolism were changed in MEC differentiation. Differentiated cells showed induction of Elovl6 (2-fold), Scd1 (4-fold), and Fads2 (2-fold), which correlated with increased levels of C16:1 nⴚ7 and C18:1 nⴚ9 (1.5-fold), C20:3 nⴚ6 (2.5-fold), and C20:4 nⴚ6 (6-fold) fatty acids (FAs) and more phospholipids (PLs) containing these species. Further, increased expression (2- to 3-fold) of genes in phosphatidylethanolamine (PE) de novo biosynthesis resulted in a 20% PE increase. Proliferating/ undifferentiated cells showed higher C16:0 (1.7-fold) and C18:2 nⴚ6 (4.2-fold) levels and more PLs containing C16:0 FAs [PC(16:0/16:1), PG(16:0/18:2), PG(16: 0/18:1), and SM(16:0/18:0)]. Kaplan-Meier analysis of data from 3455 patients with breast cancer disclosed a positive correlation for 59% of genes expressed in differentiated MECs with better survival. PE biosynthesis and FA oxidation correlated with better prognosis in

ABSTRACT

Abbreviations: AA, arachidonic acid; ANOVA, analysis of variance; Cer, ceramide; CERK, ceramide kinase; CL cardiolipin; Comp, competent; Dif, functionally differentiated; EGF, epidermal growth factor receptor; CHKA, choline kinase; EMT, epithelial-mesenchymal transition; ER, estrogen receptor; ETNK1, ethanolamine kinase; FA, fatty acid; FBS, fetal bovine serum; GC-FID, gas chromatography-flame ionization detector; HILIC, hydrophilic interaction liquid chromatography; HR, hazard ratio; LC, long chain; LXR, liver X receptor; MDCK, MadinDarby canine kidney; MEC, mammary epithelial cell; MS, mass spectrometry; MS/MS, tandem mass spectrometry; MUFA, monounsaturated fatty acid; PAF, platelet- activating factor; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PL, phospholipid; PS, phosphatidylserine; PUFA, polyunsaturated fatty acid; qPCR, quantitative PCR; RXR, retinoid X receptor; SC-L, stem cell-like; SM, sphingomyelin; TAG, triacylglycerol; TLC, thin-layer chromatography 0892-6638/14/0028-4247 © FASEB

patients with breast cancer, including the basal-like subtype. Therefore, genes involved in mammary gland FA and PL metabolism and their resulting molecular species reflect the cellular proliferative ability and differentiation state and deserve further studies as potential markers of breast cancer progression.— Dória, M. L., Ribeiro, A. S., Wang, J., Cotrim, C. Z., Domingues, P., Williams, C., Domingues, M. R., Helguero, L. A. Fatty acid and phospholipid biosynthetic pathways are regulated throughout mammary epithelial cell differentiation and correlate to breast cancer survival. FASEB J. 28, 4247– 4264 (2014). www.fasebj.org Key Words: lipidomics 䡠 gene expression 䡠 mass spectrometry 䡠 Kennedy pathway 䡠 basal-like cancer The mammary gland undergoes active remodeling throughout female reproductive life and constitutes an excellent biological model to study molecular mechanisms and metabolic adaptations that regulate proliferation, invasion, differentiation, and apoptosis. It has long been acknowledged that full-term pregnancy at younger ages is associated with a reduction in breast cancer risk for the mother (1), possibly due to the depletion of the mammary stem and progenitor cell pools, which besides supporting mammary gland remodeling accumulate genetic and epigenetic alterations and may support neoplastic growth (2, 3). Tissue differentiation is associated with functional and structural alterations of the cellular membrane. Because phospholipids (PLs) make up 50% of the cell membrane mass, they can be a reliable measurement of cellular integrity and stem cell differentiation (4). 1

These authors contributed equally to this work. Correspondence: Department of Chemistry, Universidade de Aveiro, Campus de Santiago, 3810-193, Aveiro, Portugal. E-mail: [email protected] doi: 10.1096/fj.14-249672 This article includes supplemental data. Please visit http:// www.fasebj.org to obtain this information. 2

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Each cellular membrane has a unique set of proteins for its specialized function although the membrane’s biophysical properties are primarily determined by the lipid content and corresponding protein-lipid interactions. In terms of PL molecular characteristics, the acyl chain length directly correlates with the cell membrane rigidity and the thickness of the lipid bilayer. This is counterbalanced by the number of cis double bonds, which increase polarity and reduce PL rotation and movement. Accordingly, acyl chain length and the number and position of double bonds markedly influence fluidity, permeability, and stability of biological membranes (5). The membrane PL class has important biological consequences. For example, phosphatidylethanolamine (PE) and phosphatidylserine (PS) are cone-shaped PLs that favor membrane bilayer bending, whereas phosphatidylcholine (PC) has a conic shape and does not bend the lipid bilayer. Therefore, the ratio between conic- and cone-shaped PLs determines membrane curvature, cell morphology, and vesicle budding (6, 7). Sphingolipids influence the formation of membrane microdomains and are important regulators of membrane-initiated cell signaling (8, 9). Because the biophysical characteristics of cellular membranes depend on the extracellular environment and cellular function, the PL composition is intrinsic to each tissue. In fact, it has been shown that differentiation of intestinal epithelial cells is associated with changes in PL composition (10 –12) and that apical differentiation of the Madin-Darby canine kidney (MDCK) cell line is characterized by increased levels of saturated and hydroxylated long acyl chain-containing sphingolipids (13). Membrane PLs serve as storage for second messengers and inflammatory precursors released from cellular membranes by phospholipases. Thus, as development, growth, and aging proceed, the diversity of acyl chains required in PLs is supplied through dietary intake, de novo synthesis, and fatty acid (FA) metabolism and rearrangement (5). Altered lipid metabolism such as enhanced choline metabolism, FA synthesis, and increased lysophosphatidic acid levels is observed in many cancers, including breast cancer (14 –20). A large-scale lipid profiling study found higher C16-containing PCs in breast cancer than in normal breast tissues, the highest concentration being found in estrogen receptor (ER)-negative and grade 3 tumors, which suggests that these PLs are associated with cancer progression and poorer patient survival (21). A comparison of the PL profiles of mammary epithelial cells (MECs) with those of breast cancer cells showed that the latter have lower PE levels relative to total PL amounts. In addition, migratory cells were richer in alkylacyl PCs (22, 23). These results suggest that FA and PL metabolic pathways are altered in breast cancers and may contribute to the cellular morphology and metastatic potential. Therefore, to use lipid alterations for breast cancer theranostics, regulation of lipid metabolism in MEC proliferation and differentiation needs to be characterized. In this work, we used the HC11 cell line, a wellestablished model of MEC functional differentiation (24), to study FA and PL metabolism in actively proliferating/undifferentiated and in functionally differenti4248

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ated cells. After the identification of significant changes in gene expression and at the lipid molecular level, survival analysis with respect to lipid metabolism genes regulated as cells exited from the undifferentiated stage was performed with data from 3455 patients with breast cancer. In addition, cluster analysis was used to separate a group of patients based on the expression of lipid metabolism genes by their tumors and to identify the combined lipid gene expression pattern associated with good prognosis.

MATERIALS AND METHODS Reagents Cell culture medium, gentamicin, and fetal bovine serum (FBS) were from PAA (Pasching, Austria). Insulin, epidermal growth factor (EGF), dexamethasone, o-prolactin, Oil Red O, Harris hematoxylin, protease inhibitor cocktail, and antirabbit secondary antibody coupled to horseradish peroxidase were from Sigma-Aldrich (St. Louis, MO, USA). Silica gel plates were from Merck (Darmstadt, Germany). FA and PL standards were purchased form Avanti Polar Lipids (Alabaster, AL, USA). TRIzol, the RNeasy Kit, the SuperScript III Kit, and Fast SYBR Green Master Mix were from Life Technologies (Grand Island, NY, USA). QIAzol and DNase I were from Qiagen (Valencia, CA, USA). BCA reagent, molecular weight markers, acrylamide, and bisacrylamide were from Bio-Rad Laboratories (Hercules, CA, USA). Polyvinylidene fluoride membrane was from Millipore (Billerica, MA, USA). ECL Plus was from Amersham (Little Chalfont, UK). The primary rabbit polyclonal antibodies used were the following: alkylglycerone phosphate synthase (Alkyl-DHAP synthase, GTX101396), choline kinase ␣ (GTX102994), and ethanolamine kinase 1 (GTX105887) were from GeneTex (Irvine, CA, USA); dihydroxyacetone phosphate acyltransferase (NBP1-30508), ceramide kinase (NB100-2911), and ␣-tubulin (NB100-690) were from Novus Biologicals (Cambridge, UK). Filipin was from Abcam (Cambridge, UK). The ABC kit was from Vector Laboratories (Southfield, MI, USA). Cell culture HC11 MECs were routinely grown in complete medium (RPMI 1640, 10% FBS, l-glutamine, 5 ␮g/ml insulin, 10 ng/ml EGF, and 50 ␮g/ml gentamicin) and proliferating/undifferentiated cells were obtained in these growth conditions [stem cell-like (SC-L) stage]. When cells reached confluence, EGF was depleted from the medium (RPMI 1640, 2% FBS, 5 ␮g/ml insulin, and 50 ␮g/ml gentamicin), and competent cells were obtained after 48 h [competent (Comp) stage]. Functional differentiation of Comp cells was induced by treatment with 100 nM dexamethasone and 1 ␮g/ml prolactin in EGF-depleted medium for 72 h [functionally differentiated (Dif) stage]. At 24 h before cells were collected, the medium was changed to serum-free medium plus the same additives. Animals Female BALB/C mice were fed ad libitum and were kept under a 12-h light-dark cycle. Mammary glands from 2-mo-old virgin, 10-d pregnant, and 6-d lactation mice were excised and fixed in 10% formalin for immunohistochemical analysis. The tissues used for this study were obtained by us in 2008 at

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DORIA ET AL.

the Karolinska Institutet (Stockholm, Sweden), in accordance with the accepted standards of humane animal care in agreement with the Public Health Service Policy and the Swedish National Board for Laboratory Animals and authorized by the South Stockholm Ethical Committee. Lipidomic analysis Lipid extraction, PL class separation, and quantification and identification of PL molecular species were performed as done by us previously (22, 25). In brief, total lipids were extracted with the method of Bligh and Dyer (26), and PL classes were separated by thin-layer chromatography (TLC) using silica gel plates with a concentrating zone of 2.5 ⫻ 20 cm. Different PL classes were identified with the use of standards run side by side in the plate. PL amounts in the total lipid extract (prior TLC separation) and in each spot from the TLC were measured with a phosphorus assay, according to a modification (23) of the method of Bartlett (27). Four independent differentiation experiments were analyzed in triplicate. For each differentiation experiment, no difference in the total PL amount per milligram of protein or per number of cells was observed. PL molecular species were identified by hydrophilic interaction liquid chromatography (HILIC)-mass spectrometry (MS), performed with an high-performance liquid chromatography system (Alliance 2690; Waters, Milford, MA, USA) coupled to an electrospray linear ion trap mass spectrometer (Thermo Finnigan, San Jose, CA, USA) using the same conditions as in Doria et al. (22). HILIC-MS was performed with internal standards [PC(14:0/14:0), PE(14:0/14:0), PS(14:0/14:0), PG(14:0/14:0), and PI(16:0/16:0)] to confirm and quantify the ion variations observed in the spectra. PL molecular species were identified carried out by interpretation of the tandem mass spectrometry (MS/MS) spectra as shown by us previously (23). Results were quantified either relative to the standards as in Ivanova et al. (28) or relative to the total amount of species identified in each class with similar results. Four independent differentiation experiments were analyzed in triplicate.

FAs were analyzed by gas chromatography-flame ionization detector (GC-FID) using a modification of the method of Aued-Pimentel et al. (29). Lipid extracts corresponding to 20 ␮g of PL were dried and dissolved in 1 ml of heptadecanoic acid (C17:0) internal standard solution (0.75 mg/ml in n-hexane), mixed with 0.2 ml of 2 M KOH-methanol, and vortexed for 1 min before addition of 2 ml of NaCl-saturated H2O. The mixture was centrifuged at 2000 rpm for 5 min. The upper phase was recovered, dried, and solubilized in 20 ␮l of hexane. The gas chromatograph injection port was programmed at 250°C and the detector at 270°C. The oven temperature was programmed in 3 ramps (50°C for 3 min, a 25°C/min increase to 180°C for 6 min, and a 40°C/min increase to 260°C for 3 min), performed for 19 min in total. Hydrogen was the carrier gas (flow rate, 1.7 ml/min). A DB-1 column (30 m, internal diameter of 0.250 mm, and 0.10-␮m film thickness; Agilent Technologies, Santa Clara, CA, USA) was used. Peaks corresponding to each identified FA were integrated and related to the sum area of all FAs identified. Four independent differentiation experiments were analyzed in duplicate. In all lipidomic analysis, statistical differences were evaluated with 1way analysis of variance (ANOVA) and Dunnett’s posttest and were considered significant at a value of P ⬍0.05. Microarray and bioinformatic analysis The HC11 differentiation gene expression has been reported previously (30), and corresponding raw data and detailed protocols for the microarray analysis are available from the ArrayExpress data repository (E-MEXP-969). For the pathway analysis, genes significantly up- or down-regulated were analyzed with overrepresentation/enrichment analyses using Pathway Studio (Elsevier, Rockville, MD, USA) using the software’s Gene Ontology gene sets. P values were calculated with Fisher’s exact test. In the survival analysis, mRNA levels for genes of interest were extracted from publically available microarray data of 3455 patients with breast cancer and related to survival using the Kaplan-Meier plotter online analysis tool (http://kmplot.com; ref. 31). Relapse-free sur-

TABLE 1. Enrichment of biological processes related to lipid metabolism during HC11 MEC differentiation Term

Lipid metabolic process Cellular lipid metabolic process Lipid biosynthetic process Fatty acid oxidation Fatty-acyl-Coenzyme A binding Lipid transport Lipid binding Response to lipid Phospholipid scrambling Lipid storage Glycolipid catabolic process Lipid particle Phospholipid biosynthetic process Phospholipid binding Sphingolipid catabolic process Long-chain fatty acid-coenzyme A ligase activity Regulation of lipid kinase activity Negative regulation of lipid storage Phospholipid homeostasis Sphingolipid metabolic process Phospholipid transport

Data source

Genes

P

Biological_process Biological_process Biological_process Ariadne ontology Molecular_function Biological_process Molecular_function Biological_process Biological_process Biological_process Biological_process Cellular_component Biological_process Molecular_function Biological_process Molecular_function Biological_process Biological_process Biological_process Biological_process Biological_process

50 26 21 11 6 13 24 5 2 4 2 5 8 10 2 3 2 3 2 6 4

3.15E-09 3.69E-08 5.19E-06 0.0027 0.003 0.006 0.007 0.01 0.016 0.024 0.026 0.027 0.030 0.030 0.038 0.044 0.051 0.061 0.082 0.087 0.11

Overrepresentation/enrichment analysis of differentially expressed genes during HC11 differentiation shows that several lipid metabolic pathways are involved.

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TABLE 2. Lipid metabolism associated genes significantly changed throughout HC11 functional differentiation Ma Symbol

Fatty acid metabolism Acaa2 Acate2 Acsl1 Acsl5 Faah Fatty acid biosyntheses Elovl6 Fads2 Fads3 Scd1 Scd2 Fatty acid oxidation Acadm Acadvl Acox1 Acox2 Cpt1a Glycerol metabolism Agpat4 Aldh2 Dgka Dgkz Lpin1 Pnpla2 Steroid metabolism Srebf1 Ldlr Sc5d Phospholipid metabolism and biosynthesis Cds1 Chka Chdh Chpt1 Etnk1 Pcyt2 Gpd1l Gpd2 Phospholipid degradation Plcd1 Pld3 Lipl2 Pla2g4a Pla2g7 Sphingolipid metabolism Asah1 Cerk Gm2a Siat9 Lipid transport Abcd3 Acbd5

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Gene name

SC-L vs. COMP

SC-L vs. DIF

COMP vs. DIF

3-Ketoacyl-coenzyme A thiolase, mitochondrial Acyl-coenzyme A thioesterase 2, mitochondrial Acyl-coenzyme A synthetase long-chain family member 1 Predicted: similar to Acsl5 protein (LOC433256) Fatty acid amide hydrolase

0.9 ⫺0.7 0.3 ⫺1.0 0.7

0.7 ⫺0.7 0.6 ⫺0.4 0.9

⫺0.1 0.2 0.8 0.3 0.1

0.8

0.3

0.7

0.4 ⫺1.0 0.6 1.6

1.1 ⫺1.0 0.8 2.1

1.1 ⫺0.2 0.4 0.2

1.4 1.0

1.7 1.1

0.3 ⫺0.2

0.8 1.2 1.6

0.8 0.5 1.6

⫺0.2 ⫺0.9 0.3

⫺0.1 1.4 1.4 ⫺0.1 1.2 0.7

0.7 0.8 1.0 ⫺0.5 2.7 1.0

1.0 ⫺0.8 ⫺0.3 ⫺0.8 1.1 1.2

1.3 0.2 0.1

2.4 1.2 0.9

1.4 0.9 0.4

0.7 0.3 1.2 0.6 0.1 0.7 1.1 ⫺0.4

1.1 1.3 1.6 1.5 0.4 0.9 1.7 ⫺0.8

0.4 1.2 0.5 0.9 0.4 0.0 1.2 ⫺0.2

1.0 1.5 1.3 0.1

0.6 0.9 0.8 ⫺1.0

⫺0.3 ⫺0.2 0.0 ⫺0.1

0.4

⫺0.7

⫺1.0

1.2 0.7 1.8 ⫺0.9

1.0 0.8 2.4 ⫺0.5

0.2 0.1 1.1 0.4

ELOVL family member 6, elongation of long chain fatty acids Fatty acid desaturase 2 Fatty acid desaturase 3 Stearoyl-coenzyme A desaturase 1 Stearoyl-coenzyme A desaturase 2 Acetyl-coenzyme A dehydrogenase, medium chain Very long-chain specific acyl-coenzyme A dehydrogenase, mitochondrial Acyl-coenzyme A oxidase 1, palmitoyl acyl-coenzyme A oxidase 2, branched chain Carnitine palmitoyltransferase 1a, liver 1-Acylglycerol-3-phosphate O-acyltransferase 1 Aldehyde dehydrogenase 2, mitochondrial Diacylglycerol kinase, ␣ Diacylglycerol kinase, ␨ Lipin 1, transcript variant 1 Patatin-like phospholipase domain containing 2 Sterol regulatory element binding factor 1 Low density lipoprotein receptor Lathosterol oxidase

CDP-diacylglycerol synthase 1 Choline kinase ␣ Choline dehydrogenase Choline phosphotransferase 1 Ethanolamine kinase 1 Phosphate cytidylyltransferase 2, ethanolamine Glycerol-3-phosphate dehydrogenase 1-like Glycerol phosphate dehydrogenase 2, mitochondrial Phospholipase C, ␦1 Phospholipase D3 Lipase-like, abhydrolase domain containing 2 Phospholipase A2, group IVA (cytosolic, calcium-dependent) Phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma) Acid ceramidase Ceramide kinase GM2 ganglioside activator protein Sialyltransferase 9 ATP-binding cassette subfamily D member 3 Acyl-coenzyme A binding domain containing 5

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1.5 1.0

1.4 0.0 1.0 0.3 (continued on next page)

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TABLE 2. (continued) Ma Gene name

SC-L vs. COMP

SC-L vs. DIF

COMP vs. DIF

ATP-binding cassette, subfamily G (WHITE), member 1 ATP-binding cassette subfamily G member 3 Apolipoprotein D Diazepam binding inhibitor Oxysterol-binding protein-related protein 3 Oxysterol binding protein-like 6 Oxysterol binding protein-like 9 Phosphatidylinositol transfer protein Phospholipid scramblase 1 Phospholipid scramblase 3 Phospholipid transfer protein Sterol carrier protein 2, liver StAR-related lipid transfer (START) domain containing 5 START domain containing 10

0.9 0.6 0.2 2.2 0.5 0.0 1.4 0.5 ⫺1.0 0.3 1.4 0.9 0.7 0.4

0.7 0.9 0.0 2.9 ⫺0.9 1.3 1.2 1.0 ⫺0.1 ⫺0.7 0.3 0.9 0.4 0.8

1.2 0.6 1.3 0.5 ⫺0.8 NA 0.4 0.4 0.1 0.0 ⫺1.6 0.0 ⫺0.2 0.6

Leukotriene C4 synthase Prostaglandin-endoperoxide synthase 2 Prostaglandin D2 synthase (brain)

⫺0.9 ⫺2.2 0.2

0.1 ⫺3.8 1.3

0.7 ⫺0.7 N/A

1.7

1.0

0.3

1.5

0.4

⫺0.5

1.6

1.9

1.0

0.9 1.2 1.1 1.0 0.5

1.6 1.3 1.4 NA 0.8

0.8 0.5 0.5 0.1 ⫺0.1

Symbol

Abcg1 Abcg3 Apod Dbi Osbpl3 Osbpl6 Osbpl9 Pitpn Plscr1 Plscr3 Pltp Scp2 Stard5 Stard10 Icosanoid metabolism Ltc4s Ptgs2 Ptgds Signal transduction Pik3r3 Pik3r2 Pik3r1 Pip5k2c Pten Anxa1 Gpr120 Edg5

Phosphatidylinositol 3 kinase, regulatory subunit, polypeptide 3 (p55) Phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 2 (p85␤) Phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 1 (p85␣) Phosphatidylinositol-4-phosphate 5-kinase, type II, ␥ Phosphatase and tensin homolog Annexin A1 ␻⫺3 fatty acid receptor 1 Endothelial differentiation, sphingolipid G-proteincoupled receptor, 5

M (2 log of fold change): a value of 2 equals a 4-fold up-regulation or a 400% increase; a value of ⫺2 equals a 4-fold down-regulation or a decrease by 75%. A negative M value indicates down-regulation of the transcript; a positive value indicates up-regulation. An italic M value indicates a differentially expressed gene as defined by the empirical Bayes moderated t test. NA, not available.

vival in all patients with breast cancer (n⫽3455) and those with the basal-like subtype (n⫽581) was observed toward the end point. The hazard ratio (HR) and log-rank test were calculated for the significance testing. In addition, survival cluster analysis was performed to analyze the effect of the lipid metabolism gene signature in samples from patients with breast cancer, as described by us previously (32). The expression profiles of these genes were used to group 258 clinical, primary breast cancer samples from a patient cohort from Uppsala, Sweden (33) by hierarchical clustering (Cluster and TreeView; Eisen Laboratory, Berkeley, CA, USA). Disease-free survival rates of the 2 patient groups were graphed using the Kaplan-Meier plot function in SigmaPlot (Systat Software, Inc., San Jose, CA, USA), and associations with pathological parameters (ER␣/progesterone receptor status, lymph node positivity, tumor size, and patient age) were calculated using Fisher’s exact test. Real-time quantitative PCR (qPCR) RNA was isolated using TRIzol, purified with an RNeasy Kit, and treated with DNase I, according to the manufacturer’s instructions. cDNA was synthesized using 1 ␮g of RNA with a SuperScript III kit. Then 10 ng of cDNA was amplified using Fast SYBR Green Master Mix in a 7500 Fast Real-Time PCR System (Life Technologies). Primers for cDNA were designed

to span introns to avoid possible amplification of genomic DNA. Primer sequences are provided in Supplemental Table S1. Melt-curve analysis was performed to ascertain the specific amplification. At least 2 independent experiments were analyzed in triplicate. Differential expression was determined using the ⌬⌬CT method. CT values were obtained from the linear phase of the logarithmic amplification using 7500 system software (version 2.0.1; Life Technologies). 18S rRNA, GAPDH, or ARHGDIA was used for normalization of gene expression. Student’s t test (2-tailed distribution and 2-sample unequal variance parameters) was used for statistical analysis of results. Immunoblotting Cells were washed with phosphate-buffered saline (PBS) and pelleted by centrifugation at 4°C for 2 min. The cell pellets underwent one freeze-thaw cycle and were resuspended in radioimmunoprecipitation assay buffer (pH 7.4) and protease inhibitor cocktail. The lysate was kept on ice for 20 min and was centrifuged at 14,000 g for 10 min at 4°C. Whole-cell extracts (40 ␮g of protein) were resolved by SDS-PAGE and transferred onto a polyvinylidene fluoride membrane. The membranes were blocked with 5% (w/v) milk protein dissolved in Tris-buffered saline and were incubated overnight at


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4°C with the indicated primary rabbit polyclonal antibodies. The luminescent signal was detected with ECL Plus.

Elongation and desaturation of FAs increase in functionally differentiated MECs and mimic changes in mammary gland secretory activation

Triacylglycerol (TAG) and cholesterol staining Cholesterol staining using filipin was performed according to the manufacturer’s suggestions, Oil Red O staining was performed as in Lamb et al. (34), and images were captured with the same settings in an inverted microscope (TiU; Nikon, Tokyo, Japan). Immunohistochemical analysis Paraffin-embedded mouse mammary glands were rehydrated in xylene followed by decreasing concentrations of ethanol to H2O. Antigen retrieval was performed in citrate buffer (pH 6.8) for 15 min at maximum microwave potency. Unspecific binding was blocked with 10% FBS in PBS. Incubation with the primary antibodies proceeded overnight at room temperature. The primary antibodies used were the same as those used for Western blots. The tissues were stained using an ABC kit as described previously by us (35) and counterstained with Harris hematoxylin.

RESULTS Lipid metabolism genes are overrepresented throughout MEC differentiation HC11 MECs can be grown under different conditions, which allows the generation of 3 stages of differentiation: undifferentiated cells that exhibit progenitor cell characteristics (36) and undergo epithelial-mesenchymal transition (EMT), proliferate, and share gene expression signatures with stem cells and basal-like breast cancer (SC-L stage; refs. 30, 32); Comp stage cells that are responsive to lactogenic stimuli, with low EMT and proliferation rates; and Dif stage cells that express ␤-casein. We have reanalyzed the data from our microarray study of HC11 cells in these 3 differentiation stages (30). Expression of genes in lipid metabolism was overrepresented (P⬍3⫻10⫺9; Table 1) with 68 regulated genes, of which 55 were up-regulated and 13 were down-regulated through differentiation (Tables 1 and 2). Gene Ontology analysis of the known functions of these genes suggested their primary involvement with lipid binding (24 genes), the lipid biosynthetic process (21 genes), lipid transport (13 genes), FA oxidation (11 genes), and the PL biosynthetic process (8 genes). Based on these observations, we analyzed whether the changes in gene expression were translated into modifications of the lipid profile throughout MEC differentiation, which to date remain unknown. Since HC11 cells in the SC-L stage share many similarities with gene expression signatures of breast cancers with poor prognosis and metastasizing properties, in the second part of the study, we correlated changes occurring as cells began to differentiate with survival of patients with breast cancer, in an attempt to understand whether breast cancer with higher expression of genes associated with an exit from the SC-L stage correlates with better prognosis. 4252

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Genes involved in FA metabolism were changed when HC11 cells were induced to differentiate (Table 2). Expression levels of Elovl6, Scd1, and Fads2 were confirmed by qPCR (Fig. 1A), and the effect on FA profiles was analyzed by GC-FID (Fig. 1B). Comp and Dif stages were characterized by up-regulation of Scd1 desaturase, which mainly catalyzes addition of monounsaturations to palmitic acid (C16:0) and stearic acid (C18:0), and Elovl6 elongase, which is active on saturated or on monounsaturated C16 and C18 acyl chains (Fig. 1C). In addition, Fads2, which is mostly active on long-chain (LC) polyunsaturated acids (PUFAs) was only up-regulated in the Dif stage. As a result, in Comp and Dif cells, C16:0, the essential FA linoleic acid (C18:2 n⫺6), and eicosapentaenoic acid (C20:5 n⫺3) levels were low, whereas palmitoleic acid (C16:1), oleic acid (C18:1), and arachidonic acid (AA; C20:4 n⫺6) levels were high compared with those in SC-L cells. The SC-L stage presented a characteristic profile with higher levels of C16:0, C18:2 n⫺6, and C20:5 n⫺3, as well as lower levels of C18:1, C16:1, and C20:4 n⫺6 (Fig. 1B). Notably, even though C16:0 decreased in the Comp and Dif stages, C18:0 levels did not increase, possibly due to its further desaturation by Scd1. The functional differentiation of HC11 cells in the Dif stage was further supported by high levels of C20:3 and C20:4 n⫺6, FAs normally found in breast milk (37). Mammary gland secretory activation is characterized by a switch from peroxisome proliferator activated receptor/retinoid X receptor (RXR)-induced ␤-oxidation during pregnancy to lipogenesis induced by liver X receptor (LXR)/RXR during lactation (38), resulting in synthesis of LC monounsaturated fatty acids (MUFAs), LC PUFAs, TAG, and secretion of TAG by the lactating mammary gland. Notably, 3 genes positively regulating FA ␤-oxidation (Cpt1a, Acadm, and Acadvl) were induced when cells became competent (on EGF withdrawal) and remained high in Dif cells (Fig. 2A and Table 2). In addition, LXR activation was evident, as detected by induction of its master regulator Srebf1 (Srebp1c) in the Comp stage and even more so in the Dif stage, along with induced expression of the LXR targets Abcg1, Ldlr, and Sc5d (Table 2). Moreover, up-regulation of Aldh2, Agpat4, Gpd1l, and Lpin1, which promote TAG synthesis, was also observed in the Dif stage (Fig. 2A and Table 2). These changes correlated with TAG accumulation (Fig. 2B). The microarray also disclosed down-regulation of genes involved in cholesterol binding and transport such as Osbpl6, Osbpl9, Apod, Scp2, and Stard5 in SC-L cells. Cholesterol was mostly found accumulated in discrete foci (Fig. 2C, arrows; left panel and top in middle panel) in SC-L cells, whereas in Comp and Dif cells cholesterol was found evenly distributed in the cellular membrane (Fig. 2C, arrows; right panel and bottom in middle panel). These results are in line with observations in other cell types in which an increase in LXR target Abcg1 redistributes cholesterol to the cell surface domains, making it accessible for high-density


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Figure 1. FA biosynthesis in HC11 cell differentiation. A) Confirmation of Elovl6, Scd1, and Fads2 mRNA levels by qPCR. *P ⬍ 0.05, ***P ⬍ 0.001 vs. SC-L; aP ⬍ 0.05, aaP ⬍ 0.01 vs. Comp. B) Acyl carbon chain length and saturation assessed by GC-FID. Data are means ⫾ sd from 4 independent experiments analyzed in duplicate.*P ⬍ 0.05 vs. SC-L; aP ⬍ 0.05 vs. Comp. C) Schematic representation of genes and products in FA biosynthesis regulated throughout HC11 differentiation. Gray box, high in Comp and Dif; gray box with black border, high in Comp and highest in Dif; white box, high in Dif; oval, high in SC-L; broken lines, similar regulation but only observed by MS/MS analysis of PLs (see Fig. 5); italic, not detected; SCD1, stearoyl-coenzyme A desaturase-1; SCD2, stearoyl-coenzyme A desaturase-2; FADS1, fatty acid desaturase 1; FADS2, fatty acid desaturase 2; ELOVL 6, elongation of very long chain fatty acid protein 6; ELOVL 1/3/5/7, elongation of very long chain fatty acid protein 1/3/5 or 7; ACOX1, acyl-coenzyme A oxidase, palmitoyl.

lipoprotein removal (39), and with early studies showing that a cholesterol increase is necessary for apical protein transport (40), a process that is most active during secretory activation. Therefore, HC11 cells recapitulate functional differentiation not only in terms of milk protein synthesis (24) but also with regard to activating lipogenesis toward LC MUFAs and LC PUFAs, TAG accumulation, and mobilization of cholesterol toward the cell membrane, similar to secretory activation in the mammary gland. PL biosynthesis and the cylindrical/conic-shaped PL ratio are regulated during differentiation The expression of 13 genes directly involved in PL metabolism was regulated throughout HC11 differentiation (Table 2 and Fig. 3A). Genes involved in PE and PC synthesis (Etnk1, Pcyt2, Chka, and Chpt1) were confirmed to be significantly up-regulated in the Comp and Dif stages using immunoblotting or qPCR (Fig. 3B). The effects due to changes in gene expression were confirmed by quantification of each PL class relative to the total PL content (Fig. 3C). PE was significantly increased in the Dif stage compared with that in the SC-L stage, whereas the same trend (although not significant) was observed in the Comp

stage. Because the microarray showed the highest Plcd1 in the Comp stage (Table 2), it remains to be established whether less PE in the Comp stage is related to its conversion into 1,2-diacylglycerol (1,2-DG). PC relative levels did not change significantly between the HC11 stages (Fig. 3C). However, because sphingomyelin (SM) was highest in the Dif stage, conversion of PC to SM by Sgms2 cannot be ruled out, even if the mRNA levels were not changed (Fig. 3B). This rendered a decrease in the cylindrical/conic-shaped PL ratio (PC/PE and PC/PS) when cells passed from a highly proliferative state to become functionally differentiated (Fig. 3D). Finally, cardiolipin (CL) levels were also highest in the Dif stage (Fig. 3C), possibly resulting from Gpdl1 and Cds1 up-regulation (Fig. 3A and Table 2). In the SC-L stage, Pla2g4a was up-regulated (Table 2). This enzyme decreases levels of AA-containing PLs in favor of free AA, therefore promoting inflammation. In addition, Dif cells showed the lowest levels of Pla2g7, which inactivates platelet-activating factor (PAF), thereby negatively regulating AA metabolism, inflammation, and apoptosis (41, 42). Therefore, as MECs differentiate, PL metabolism is changed to increase PE and CL synthesis and consequently increase the cylindrical/conic-shaped PL ratio, promoting membrane curvature and vesicle budding.


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Figure 2. TAG and cholesterol biosynthesis throughout HC11 cell differentiation. A) Schematic representation of glycerolipid metabolism regulation in HC11 cell differentiation. Gray box, high in Comp and Dif; gray box with black borders, high in Comp and highest in Dif; white box, high in Dif. Broken arrows indicate more than one step. ALDH2, aldehyde dehydrogenase 2 family (mitochondrial); G-3-P, glucose 3-phosphate; ACADM, acyl-CoA dehydrogenase, medium chain; ACADVL, acyl-CoA dehydrogenase, very long chain; AGPAT4, 1-acylglycerol-3-phosphate O-acyltransferase 1 (lysophosphatidic acid acyltransferase, ␦); CPT1A, carnitine palmitoyltransferase 1a, liver; DGK, diacylglycerol kinase; LPIN1, phosphatidate phosphatase; PNPLA2, patatin-like phospholipase domain containing 2; DgatA, diacylglycerol O-acyltransferase. [Adapted from Kyoto Encyclopedia of Genes and Genomes (KEGG).] B) Oil Red O staining shows TAG accumulation in Dif cells. C) Cholesterol staining with filipin I showing accumulation in discrete foci (arrows; left panel and top in middle panel) and membrane even distribution (arrows; right panel and bottom in middle panel). Experiment is representative of 2.

In addition, inflammatory mediators such as AA and PAF are reduced through inhibition of Pla2g4a and Pla2g7 expression. PE and PC together account for 60% of the cell membrane lipid mass (6). Consequently, it is expected that their biosynthesis is high in SC-L culture conditions in which HC11 cells proliferate. Therefore, we analyzed choline kinase (CHKA) and ethanolamine kinase (ETNK1) protein levels in mouse virgin mammary glands, which undergo low proliferation; in pregnancy, which is a highly proliferative phase; and in lactation (Fig. 4A). The CHKA and ETNK1 levels were low in virgin glands compared with those in pregnant 4254

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glands, in agreement with the need for PE and PC biosynthesis to support proliferation. Yet, the lactating glands showed the highest CHKA and ETNK1 levels, thus reinforcing the findings in HC11 MECs and suggesting that during mammary gland secretory activation PE biosynthesis is enhanced. Sphingolipid metabolism is regulated in MEC differentiation Changes in genes that target sphingolipid metabolism were observed (Table 2). Asah1, which metabolizes ceramide (Cer) to sphingosine, and Cerk, which con-


DORIA ET AL.

Figure 3. Phospholipid biosynthesis in HC11 cell differentiation. A) Schematic representation of phospholipid biosynthetic pathways regulated in HC11 cell differentiation microarray. Gray box, high in Comp and Dif; gray box with black borders, high in Comp and highest in Dif; white box, high in Dif; bold, high in Comp; italic: phospholipids not detected by TLC; Etn, ethanolamine; CHKA, choline kinase; ETNK1, ethanolamine kinase 1; PCYT2, phosphate cytidylyltransferase 2, ethanolamine; PCYT1A/B, phosphate cytidylyltransferase 1, choline ␣/␤ isoform; CEPT1, transcript variant 2, choline/ethanolaminephosphotransferase 1; CHPT1, choline phosphotransferase 1; PEMT, phosphatidylethanolamine N-methyltransferase; PTDSS1, phosphatidylserine synthase 1; PTDSS2, phosphatidylserine synthase 2; SMGS2, phosphatidylcholine:ceramide cholinephosphotransferase 2; PLCD1, phospholipase 1; PLCD3, phospholipase D3; LPIN1, phosphatidate phosphatase; DGK, diacylglycerol kinase; GPD1L, glycerol-3-phosphate dehydrogenase 1-like; CDS1, CDP-diacylglycerol synthase 1; PIPs, phosphatidylinositol phosphate species. [Adapted from Kyoto Encyclopedia of Genes and Genomes (KEGG).] B) Levels of mRNA (Pcyt2 and Chpt1) and proteins (ETNK1 and CHKA) involved in de novo PC and PE biosynthesis. C) PL classes separated by TLC. Relative levels of each class were calculated as a measure of phosphorus in each spot/total phosphorus in the lipid extract (%). X, unknown. D) Ratio between PL species which are known to be in high proportion in the plasma membrane and can be interconverted. Data are means ⫾ sem from 4 independent experiments carried out in duplicate. *P ⬍ 0.05, **P ⬍ 0.01 vs. SC-L.

verts Cer to Cer-1-phosphate, were lowest in the SC-L stage (Fig. 4B and Table 2). The increase in ceramide kinase (CERK) protein levels was confirmed by immunoblotting (Fig. 4C). CERK expression was analyzed in mouse mammary glands throughout the reproductive cycle. Consistent with its highest expression in cells with a low proliferating rate (Comp cells), its staining was very strong in virgin glands, decreased in pregnancy, and increased in lactation (similar to the Dif stage; Fig. 4D). On the other hand, Siat9, which transforms glycosphingolipids into ganglioside GM3 and which activates phosphatidylinositol 3-kinase/Akt-dependent migration (43), was high in SC-L cells (Fig. 4B, E). Edg5 is a G protein-coupled receptor for sphingosine 1-phosphate, which inhibits Rac activation, migration, and metastasis (44). In agreement with SC-L cells undergoing EMT, Egd5 was down-regulated in this stage (Fig. 4B and Table 2). Therefore, regulation of sphingolipid metabolism in HC11 MECs reflects their proliferative and migratory potential. Length and saturation of PL acyl chains increase with MEC differentiation HILIC-MS/MS was used to identify PC, PE, PS, phosphatidylglycerol (PG), and phosphatidylinositol (PI) molecular profiles throughout HC11 cell differentia-

tion (Fig. 5A and Supplemental Fig. S1). Species found to be higher in SC-L than in Comp or Dif cells were PC(16:0/16:1), consisting of 19% total PC content, and PE(16:0/18:1), accounting for 12% of the total PE amount. In the SC-L stage, PLs with high levels of C16:0 acyl chains were also found in PG(16:0/18:2), PG(16: 0/18:1), and SM(16:0/18:0), all representing ⱖ18% total class composition. On the other hand, except for PI, PLs containing LC PUFAs increased in Dif cells, consistent with induction of Scd1, Elovl6, and Fads2 in this stage (Fig. 1) and in line with findings in MDCK apical polarization (13). PI(18:0/20:4) is the primary source for AA required for icosanoid biosynthesis in mammals (Human Metabolome Database HMDB09815 and ref. 45) and was decreased in the Dif stage. Alkylacyl PEs [such as PE(O-16:0/20:4) and PE(O18:1/20:4)] increased in Dif cells (Fig. 5). Formation of the ether bond in mammals is carried out by the peroxisomal enzymes alkylglycerone phosphate synthase and dihydroxyacetone phosphate acyltransferase. However, their gene expression was not significantly changed in the HC11 cell microarray, and the protein levels did not increase in Dif cells (not shown). Therefore, it is possible that the increase in alkylacyl species relates to an overall PE increase in the Dif stage or that the activity of the enzymes is regulated through posttranslational modifications.


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Figure 4. Regulation of PE and sphingolipid metabolism in MEC differentiation. A) Expression ETNK1 and CHKA throughout the mammary gland reproductive cycle. Mammary glands (2-mo-old, 10-d pregnant, and 6-d lactating) were stained for ETNK1 and CHKA and counterstained with hematoxylin and eosin. Experiment is representative of 2. Scale bars ⫽ 10 ␮m. B) Schematic representation of pathway genes regulated in the HC11 cell differentiation microarray and involved in sphingolipid metabolism. Gray box: high in Comp and Dif; gray box with black borders: high in Comp and highest in Dif; white box: high in Dif; oval: high in SC-L. SIAT9, sialyltransferase 9 (CMP-NeuAc:lactosylceramide ␣-2,3-sialyltransferase); ASAH1, acid ceramidase; SMPD1, sphingomyelin phosphodiesterase 1, acid lysosomal; S-1-P, sphingosine 1-phosphate; Cer-1-P, ceramide-1-phosphate; EDG5, endothelial differentiation, sphingolipid G-protein-coupled receptor, 5. [Adapted from Kyoto Encyclopedia of Genes and Genomes (KEGG).] C) CERK protein levels in HC11 cells. D) CERK immunostaining in mouse mammary glands. Scale bars ⫽ 10 ␮m. E) Siat9 mRNA levels by qPCR. Experiments are representative of 2. ***P ⬍ 0.001 vs. SC-L.

Correlation of lipid metabolism genes regulated in MEC differentiation with survival of patients with breast cancer We have previously shown that the gene expression profile of HC11 cells in SC-L stage clusters with that of poor-prognosis breast cancer (30). Therefore, we investigated whether the expression of the 68 lipid metabolism genes (55 up-regulated and 13 down-regulated; Table 2) regulated as cells stopped proliferating and began to differentiate correlated with survival of patients with breast cancer. For this purpose, we used 3 approaches: individual analysis of each gene using the online Kaplan-Meier plotter (31), multiple-gene and pathway-focused survival analysis (31), and gene clustering to expression data from 258 breast cancers (33) followed by survival analysis for the separated clusters and identification of the genes responsible for the separation. Individual Kaplan-Meier analysis of each gene showed that the level of expression significantly correlated with patient survival for 56 of 68 (82%) of these genes (Supplemental Table S2). Thus, lipid metabolism genes regulated during MEC differentiation are associated with survival of patients with breast cancer. For 40 of 68 (59%) genes a positive correlation between expression in MECs differentiation and patient survival was found (i.e., a high expression of genes up-regulated during MEC differentiation or low expression of genes down-regulated during MEC differentiation was indic4256

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ative of better prognosis in breast cancer). On the other hand, for 16 of 68 genes (24%), the correlation was negative, and for 12 of 68 genes (17%), there was either no significant correlation or survival data were unavailable. Therefore, the expression of the majority of genes up- or down-regulated in HC11 differentiation was positively correlated with breast cancer survival. In a second approach, we analyzed the combined implication of the up- and down-regulated genes in differentiation using multiple-gene survival analysis (Supplemental Fig. S2). The analysis showed that high average levels of the 55 genes that increased during HC11 differentiation were correlated with better survival, whereas levels of the 13 genes down-regulated during differentiation presented no significant correlation with patient survival. These results suggest that the sums of genes in lipid metabolism that increase during differentiation are associated with better survival. Next, we focused on 3 lipid metabolism pathways that, based on our analysis of the resulting lipid molecules, were significantly regulated when MECs reduced proliferation and differentiated. Genes involved in FA elongation and desaturation, FA ␤-oxidation and in de novo PE and PC synthesis through the Kennedy pathway were group analyzed in terms of effect on survival (Fig. 6). High expression of genes in FA elongation and desaturation as a whole positively correlated with patient survival. However, only genes up-regulated in HC11 differentiation showed a negative correlation (Fig. 6A).


DORIA ET AL.

Figure 5. Mass spectrometry analysis of PLs in HC11 cell differentiation. A) PL classes were separated and analyzed by HILIC-MS/MS. Heat map shows the changes in the Comp and Dif stages relative to those of SC-L cells. Most probable molecular species according to the MS/MS fragmentation pattern are indicated. Values to construct the heat map are the means ⫾ sd from 4 independent experiments. B) Relative changes in diacyl and alkylacyl PLs compared with those in total PLs in each class. Significant differences compared with the SC-L stage were analyzed by 1-way ANOVA and Dunnett’s posttest. *P ⬍ 0.05 vs. SC-L.

High expression of FA ␤-oxidation genes (those upregulated during HC11 differentiation as well as all genes in this pathway) were positively correlated with patient survival (Fig. 6B). Notably, even though high expression of Etnk1 was associated with lower survival (Supplemental Table S2), high combined expression of Kennedy pathway genes (those regulated during HC11 differentiation as well as all genes in this pathway) was correlated with better survival (Fig. 6C).

Finally, using an alternative approach to explore which of the regulated lipid metabolism genes contribute to better survival, we clustered 258 patients (33) based on their expression pattern of these 68 lipid metabolism genes and investigated differences in survival and which groups of genes were the dividing factors. This approach generated 2 separate clusters of patients. Survival analysis showed that patients in cluster 1 had better outcomes than patients in cluster 2 (Fig. 7A and Supplemental Table S3). The better survival in cluster 1 was narrowed to the up-regulation of 17 genes (group 1) and down-regulation of 14 genes (group 2; Table 3). Gene Ontology sets significantly represented by the group 1 genes were LC FA metabolism, LC FA transport, and prostaglandin biosynthesis. The better outcome also correlated with lower lymph node metastasis (Table 4). It is noteworthy to mention that these sets of data correspond to a cohort collected in Sweden, in which 213 cancers were ERpositive and in which 110 patients received either systemic adjuvant therapy as chemotherapy or endocrine therapy. Therefore, we corroborated the effects of these genes in a larger data set of 3455 patients using the Kaplan-Meier plotter (46). We found that high expression of group 1 genes or low expression of group 2 genes also correlated with better relapsefree survival in patients with ER-positive tumors, but not for the basal-like subtype specifically (Fig. 7B, C and Supplemental Table S4). Notably, Etnk1, which is part of Kennedy pathway, positively correlated with survival, but individually showed the opposite and was found in group 2. However, although in general it correlated with poor prognosis in all tumors, it was associated with better outcomes in basal-like tumors. In fact, all genes in the Kennedy pathway that were found to be down-regulated in SC-L cells were correlated with good prognosis for patients with basal-like tumors (Fig. 8). Similarly, for 12 of 14 genes downregulated in SC-L cells and found in group 2, a low level was associated with better overall survival only in ER-positive tumors, whereas for basal-like tumors, a high expression was superior (Fig. 7D). Collectively, these data suggest that lipid metabolism genes regulated during MEC differentiation, specifically those involved in FA oxidation and PL biosynthesis, are positively correlated with breast cancer survival and thereby may be valuable as biomarkers to predict outcome.

DISCUSSION In this work, we aimed to describe changes in lipid metabolism of MECs growing under conditions that inhibit epithelial differentiation and stimulate proliferation as well as when MECs are induced to functionally differentiate. We also intended to establish whether there is a correlation between lipid metabolism genes regulated as MECs exit the undifferentiated/proliferating stage and breast cancer survival. During lactation, the mammary gland induces several genes, including LXR target genes, which result in


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Figure 6. Group analysis of genes regulated in MEC differentiation and their correlation with breast cancer survival. Low gene expression (black) or high expression (gray) of groups of genes related to a pathway (listed at right) were correlated with patient survival, using publically available microarray and clinical data of 3455 patients with breast cancer (31). Genes up-regulated in HC11 cell differentiation are in gray; ACSL5 was down-regulated. Probability of relapse-free survival in all breast cancers, HR, and log-rank test were calculated for analysis of significance.

increased synthesis of LC MUFAs and LC PUFAs compared with that in the gland at pregnancy (38). The HC11 cell line has long been used to study lactogenic 4258

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hormone effects on gene and protein expression in MECs. Regarding lipid metabolism, it was only recently shown that a combination of the lactogenic hormones


DORIA ET AL.

Figure 7. Clustering of patients with breast cancer using the lipid gene expression profile and patient survival. A) Patients could be clustered into 2 groups based on correlation of their tumor’s gene expression with the HC11 expression profile. The visualized heatmap shows the 31 genes primarily responsible for the cluster division with a Kaplan-Meier plot (below) of disease recurrence for clusters 1 (red) and 2 (black) patients. Patients in cluster 1 showed significantly better survival (P⫽0.01). B) High expression of group 1 genes correlates with better survival also in ER-positive tumors (left panel) in a larger (3455) patient data set but not for basal-like tumors (right panel). C) Correspondingly, low expression of group 2 genes correlates with better overall survival for ER-positive tumors (left panel) but not for basal-like tumors (right panel).

insulin, cortisol, and prolactin induced Fads2 and Elovl6 expression that was consistent with C16:0 reduction and C16:1 increase compared with results for incubation with each hormone separately (47). Our results are in agreement with these studies and provide further evidence of consistency in reproducing functional differentiation of HC11 cells in vitro. Here, we further show that, similarly to the observations at the gene expression level in the whole mammary tissue during lactation (38), HC11 cells during differentiation induce

LXR activity and subsequent lipogenesis. Notably, in the Comp and Dif stages, genes involved in mitochondrial ␤-oxidation (i.e., Cpt1a, Acadvl, and Acadm) were up-regulated, which suggests that even though lipogenesis was induced by lactogenic hormone stimulation, a population of cells did not respond and ␤-oxidation was sustained. As cells underwent functional differentiation, a clear increase in length and desaturation of PL acyl chains, especially in the PE and SM classes, was observed, which


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TABLE 3. Genes regulated in MEC differentiation and clustering with prognosis signatures of 258 breast cancer cases Human symbol

Group 1: Up-regulated in cluster 1 and downregulated in cluster 2 ACOT9

Mouse symbol

Gene name

Acate2

Acyl-coenzyme A thioesterase 2, mitochondrial Acyl-coenzyme A synthetase long-chain family member 1 Predicted: similar to Acsl5 protein (LOC433256) 1-Acylglycerol-3-phosphate Oacyltransferase 1 Aldehyde dehydrogenase 2, mitochondrial Annexin A1 Diacylglycerol kinase, ␣ Diacylglycerol kinase, ␨ Oxysterol-binding protein-related protein 3 Oxysterol binding protein-like 9 Phospholipase A2, group IVA (cytosolic, calcium-dependent) Phospholipase A2, group VII (platelet-activating factor acetylhydrolase, plasma) Phospholipid scramblase 1 Phospholipid scramblase 3 Phospholipid transfer protein Prostaglandin D2 synthase (brain) Prostaglandin-endoperoxide synthase 2

ACSL1

Acsl1

ACSL5

Acsl5

AGPAT4

Agpat4

ALDH2

Aldh2

ANXA1 DGKA DGKZ OSBPL3

Anxa1 Dgka Dgkz Osbpl3

OSBPL9 PLA2G4A

Osbpl9 Pla2g4a

PLA2G7

Pla2g7

PLSCR1 PLSCR3 PLTP PTGDS PTGS2 Group 2: Down-regulated in cluster 1 and up-regulated in cluster 2 ABCG1

Plscr1 Plscr3 Pltp Ptgds Ptgs2 Abcg1

ACADVL

Acadvl

ACBD5

Acbd5

ACOX2

Acox2

CDS1 ELOVL6

Cds1 Elovl6

ETNK1 FAAH GPD1L

Etnk1 Faah Gpd1l

GPD2

Gpd2

OSBPL6 PIP5K2C

Osbpl6 Pip5k2c

SREBF1

Srebf1

STARD10

Stard10

ATP-binding cassette, subfamily G (WHITE), member 1 Very long-chain specific acylcoenzyme A dehydrogenase, mitochondrial Acyl-coenzyme A binding domain containing 5 Acyl-coenzyme A oxidase 2, branched chain CDP-diacylglycerol synthase 1 ELOVL family member 6, elongation of long chain fatty acids Ethanolamine kinase 1 Fatty acid amide hydrolase Glycerol-3-phosphate dehydrogenase 1-like Glycerol phosphate dehydrogenase 2, mitochondrial Oxysterol binding protein-like 6 Phosphatidylinositol-4-phosphate 5kinase, type II, ␥ Sterol regulatory element binding factor 1 START domain containing 10

Better survival

P

NS

0.07

Low

0.008

High

0.00001

Low

0.0021

High

0.00001

High High NS NS

0.05 0.02 0.12 0.1

High Low

0.01 0.0006

Low

0.00001

Low Low NS High High

0.00001 0.0007 0.3 0.00001 0.00001

High

0.002

High

0.00001

NA High

0.00001

High Low

0.003 0.00001

Low Low High

0.003 0.00001 0.0002

NS

0.3

NA Low

0.00001

High

0.00001

NA

Italic type indicates a negative correlation with expression in HC11 differentiation and better survival. NS, not significant; NA, not available.

is in line with findings in MDCK apical polarization (13) and consistent with induction of Scd1, Elovl6, and Fads2. Cholesterol redistribution throughout the cellular membrane was evident as HC11 cells differentiated (and acquired a more epithelial morphology), possibly intercalating between unsaturated acyl chains and making differentiated membranes thicker and stiffer as 4260

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suggested by Sampaio et al. (13). Further, a cholesterol gradient between cellular organelles toward the plasma membrane assists in protein sorting and plays a role in organizing the biosynthetic pathway (48), which may be important as cells differentiate. Chka and Etnk1 were up-regulated in mouse mammary gland terminal differentiation and in functionally


DORIA ET AL.

TABLE 4. Clinicopathological properties between the 2 clusters defined by genes regulated in functionally differentiated HC11 cells Cluster

1 2 P

p53 mutation [n/N (%)]

LN⫹ [n/N (%)]

ER⫹ [n/N (%)]

PR⫹ [n/N (%)]

Size

Age (y)

39/102 (38) 35/156 (22) 0.0060815

26/101 (26) 58/148 (39) 0.0275501

78/102 (76) 139/151 (92) 0.0009101

67/102 (66) 123/156 (79) 0.0189839

21.11 ⫾ 14.79 23.04 ⫾ 10.55 0.2209148

59.19 ⫾ 14.25 63.60 ⫾ 13.33 0.0119827

LN, lymph node; ER, estrogen receptor; PR, progesterone receptor.

differentiated HC11 cells. Many other genes in de novo PE and PC synthesis were also up-regulated in Dif cells, and higher PE levels were confirmed. The PE/PC ratio increased as cells differentiated. Because PE is a key membrane fluidizing PL, which also increases curvature and vesicle budding (6), we speculate that it may be important for secretion in the lactating gland. Further, StarD10 transfers PC and PE between membranes (49) and was also up-regulated in Comp and Dif cells, indicative of PL reorganization in the cell membrane as cells entered the differentiation program. An increase in membrane curvature may also translate to the mitochondria, where CL is found in the internal membrane and PE is one of the most abundant PLs (50). During lactation, the mitochondria undergo morphological and functional changes, including increases in the number of inner membrane cristae (50) and in matrix density (51). We speculate that changes in the CL and PE levels may account for these changes. Further, PEs also underwent the most molecular rearrangements as cells differentiated. Therefore, major changes in PE class suggest that it is relevant in the epithelial differentiation process. Whether it is necessary for the differentiation to proceed or is an observation associated with cell differentiation remains to be established. FAs and PLs regulate cell signaling at different levels such as proliferation, invasion, apoptosis, and inflammation (19, 52–54). Therefore, it is relevant to compare changes found in lipid metabolites when cells slowed down proliferation and entered the differentiation program with findings in breast cancer. The SC-L stage (MECs stimulated with EGF) showed a higher proportion of C16:0/C16:1, C18:0/C18:1, and C18:2/ C20:4 FAs compared with more differentiated cells. The PL species PC(16:0/16:1) was found to be higher in the SC-L than in the Comp or Dif stage, was the second most abundant PC molecular species identified, and was also found among the most abundant overall. A recent study by Hilvo et al. (21) quantified PL molecular species in human breast cancer samples and correlated them to receptor status and grade, reporting that among other species, PC(16:0/16:1) was significantly higher in ER-negative vs. ER-positive and in grade 3 vs. grade 1 and 2 tumors. Further, its higher levels have also been reported in advanced stages of colorectal carcinoma (55). Notably, this species was found in lowest abundance in HC11 Dif cells, and in a previous study, we identified PC(16:0/16:1) at lower levels in nonmalignant MCF-10A cells than in T47-D or MDA-MB-231 breast cancer cells (22). Taken together, these results support PC(16:0/16:1) as a potential marker of disease progression. In addition, our previ-

ous studies identified low SM(18:1/24:1) and SM(18: 1/24:0) levels in metastatic cells compared with those in nonmalignant mammary cells (22, 23), and, conversely, we also found these to be low in SC-L HC11 cells compared with those in Dif cells. CHKA and ETNK1 staining was stronger in actively proliferating cells of the mammary gland during pregnancy than in virgin glands. High levels of choline phosphate and ethanolamine phosphate and of CHKA and ETNK1 have been related to proliferation and are found in many cancers including breast cancer (18). However, previous analysis of PE levels and their effect on the PE/PC ratio in breast cancer have disclosed conflicting results. For example, PE has been found to be significantly elevated in breast cancer compared with that in benign and noninvolved tissues (56), and increased PE and PC levels have been found in carcinogen-induced rat mammary tumors (57), whereas others showed that malignant tumors had lower PE/PC ratios than benign conditions (58). Our previous work comparing nonmalignant and mammary carcinoma cell lines also found decreased PE/PC ratios in the latter (22, 23). In our present study, survival analysis verified that high expression of the group of genes involved in de novo PE and PC biosynthesis correlates with better survival. Notably, Etnk1 expression analyzed independently from expression of other genes in this pathway did not. We hypothesize that in addition to PE biosynthesis, PE acyl chain length and saturation in relation to other PL classes present in the membrane are also determinant factors to be considered in these association studies. Furthermore, PE resulting from higher levels of ETNK1 activity can be metabolized to acetaldehyde by ethanolamine-phosphate phospholyase (Agxt2l1; ref. 59). Although this reaction has been reported to occur mainly in the liver, Agxt2l1 expression was detected in our microarray of HC11 cells (as not changed). Acetaldehyde is a toxic compound known to induce DNA damage and also inflammation in human breast cancer cells (60). Therefore, it is possible that in a context of deficient DNA repair mechanisms, such as that observed in many cancers, expression of Etnk1 with its product not being metabolized into PE but rather into acetaldehyde may explain negative correlation of Etnk1 expression with good prognosis. Survival analysis of individual genes or groups of functionally related genes that changed in HC11 cell differentiation showed in most cases positive correlation to survival of patients with breast cancer. Clustering of these genes with expression data for breast cancer from 258 patients separated the patients into 2 clusters with significantly different outcomes. Further-


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Figure 8. Correlation of genes regulated in MEC differentiation with breast cancer survival. Low gene expression (black) or high gene expression (gray) in de novo PE and PC biosynthesis was individually correlated with patient survival, using publically available microarray and clinical data for 3455 patients with breast cancer, of which 581 cancers were basal-like subtype (31). Probability of relapse-free survival in all breast cancers, HR, and log-rank test were calculated for analysis of significance.

more, we could identify 2 groups of genes that determined such differences in patient survival: one group that was up-regulated and one group that was downregulated in the good prognosis tumors. In the good prognosis patients (cluster 1), 9 of 17 up-regulated genes were also individually positively correlated with survival. On the other hand, the poor prognosis cluster 2 consisted of 12 of 14 genes that were up-regulated when cells reduced proliferation and entered differentiation. This result was unexpected because these genes were down-regulated in good prognosis cluster 1. The only ontology set significantly represented by cluster 2 4262

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genes was regulation of cholesterol metabolism. These results are not surprising because cholesterol has different functions depending on its localization: free or in lipid rafts. It can facilitate EGF receptor and Her2 interaction and ligand activation (61), induce breast cancer resistance protein ABCG2 efflux activity (62), prevent cell sensitization to receptor tyrosine kinase inhibitors (63), and also be converted into estrogen and progesterone to promote breast cancer (64). We hypothesize that the cluster analysis performed with this Swedish breast cancer cohort may be primarily representative of ER-positive tumors, because 213 of


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the 258 tumors were ER positive. This hypothesis was confirmed when a larger data set was used to validate the prognostic potential of this gene group, and we noted that in the basal-like breast cancer subtype, high expression of the group 2 genes was correlated with better survival. This finding is in line with our previous data for which gene expression of undifferentiated HC11 cells showed homology with that of basal-like breast cancer tumors (30, 32). In summary, a combination of genomic and lipidomic approaches allowed characterization of lipid metabolism changes in mammary epithelial cells undergoing proliferation and EMT and in epithelial and functional differentiation. Lipid metabolism is largely regulated by lifestyle factors and understanding its alterations in disease represents opportunities for prevention. Many of the genes and pathways identified in MECs positively correlated with breast cancer survival and deserve further study with the aim of developing novel theranostic targets. The authors thank Celeste Resende for technical help with mammary gland histological sectioning. This work was supported by federal funds through Programa Operacional Temático Factores de Competitividade (COMPETE) with coparticipation from the European Community Fund (FEDER) and national funds through Fundação para a Ciência e Tecnología (FCT) under the projects PTDC/SAUONC/112671/2009 and PTDC/SAU-ONC/118346/2010 (L.A.H.), Project Ciência 2008 (L.A.H.). The Mass Spectrometry Center, within the Organic Chemistry and Natural Products (QOPNA) research unit, is funded by the University of Aveiro, FCT, European Union, Quadro de Referência Estratégico Nacional (QREN), FEDER, and COMPETE projects PEst-C/QUI/UI0062/2011 and PEst-C/QUI/UI0062/2013 and RNEM. This work was also supported by grants from the Texas Emerging Technology Fund, under agreement 300-91958 and by faculty start-up funding from the University of Houston (C.W.). Author contributions: M.L.D. and A.S.R. carried out gas chromatography and MS/MS analysis; J.W. performed qPCR, survival and pathway analyses; C.Z.C. cultured cells and helped with lipid extraction; P.D. assisted with lipidomic analysis; C.W. directed and interpreted the qPCR, pathway, and survival analyses and helped to draft the manuscript; M.R.D. directed the lipidomic analysis and helped to draft the manuscript; and L.A.H. conceived the work, performed immunoblots, TAG and cholesterol staining, immunohistochemical analysis, and drafted the manuscript. The authors declare no conflicts of interest.

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Received for publication April 3, 2014. Accepted for publication June 9, 2014.

DORIA ET AL.