| Metadata |
| datasetIdentifier | PASS00796 |
| datasetType | SRM |
| submitter | Andrei Drabovich <andrei.drabovich@utoronto.ca> |
| submitter_organization | University of Toronto |
| lab_head_full_name | Andrei Drabovich |
| lab_head_email | andrei.drabovich@utoronto.ca |
| lab_head_organization | Department of Laboratory Medicine and Pathobiology, University of Toronto |
| lab_head_country | Canada |
| datasetTag | E2-dynamics-SRM |
| datasetTitle | Dynamics of protein expression reveals primary targets and secondary messengers of estrogen receptor alpha signalling in MCF-7 breast cancer cells |
| publicReleaseDate | 2016-01-25 00:00:00 |
| finalizedDate | 2016-09-08 15:30:28 |
| summary | Estrogen receptor alpha (ERα)-mediated proliferation of breast cancer cells is facilitated through expression of multiple primary target genes, products of which induce a secondary response to stimulation. To differentiate between the primary and secondary target proteins of ERα signaling, we measured dynamics of protein expression induced by 17β-estradiol in MCF-7 breast cancer cells. Measurement of the global proteomic effects of estradiol by stable isotope labeling by amino acids in cell culture resulted in identification of 103 estrogen-regulated proteins, with only 40 of the corresponding genes having estrogen response elements. Selected reaction monitoring assays were used to validate the differential expression of 19 proteins and measure the dynamics of their expression within 72 hours after estradiol stimulation, and in the absence or presence of 4-hydroxytamoxifen, to confirm ERα-mediated signaling. Dynamics of protein expression unambiguously revealed early and delayed response proteins and well correlated with presence or absence of estrogen response elements in the corresponding genes.. Finally, we quantified dynamics of protein expression in a rarely studied network of transcription factors with a negative feedback loop (ERα-EGR3-NAB2). Since NAB2 protein is a repressor of EGR3-induced transcription, siRNA-mediated silencing of NAB2 resulted in the enhanced expression of the EGR3-induced protein ITGA2. To conclude, we provided a high-quality proteomic resource to supplement genomic and transcriptomic studies of ERα signaling. |
| contributors | Andrei P. Drabovich, Maria P. Pavlou, Christina Schiza and Eleftherios P. Diamandis |
| publication | Drabovich, A.P.; Pavlou, M.P.; Schiza, C.; Diamandis, E.P. Dynamics of protein expression reveals primary targets and secondary messengers of estrogen receptor alpha signaling in MCF-7 breast cancer cells. 2016, in revision. |
| growth | For SRM-based experiments, four biological replicates for each growth condition were used. Approximately 0.5×106 cells were seeded individually into 6-well tissue culture plates and left for one day for cell attachment. MCF-7 cells were transferred to a phenol red-free RPMI 1640 culture medium supplemented with 10% dextran-coated charcoal-treated FBS and grown for 24 hours. Following this, cells were stimulated with either 10 nM 17β-estradiol in 0.1% ethanol (final concentration) or 0.1% ethanol. Cells were grown as a monolayer for up to 72 hours after 17β-estradiol stimulation and then trypsinized and lysed. For ERα antagonist experiment, cells were treated with 10-10 to 10-6 M 4-hydroxytamoxifen (Sigma Aldrich) followed by 10 nM 17β-estradiol and grown for 36 hours. |
| treatment | |
| extraction | |
| separation | |
| digestion | Four biological replicates of approximately 2×105 cells (~30 μg of total protein) were reconstituted in 100 µL of 0.1 % RapiGest SF (Waters, Milford, MA), vortexed, sonicated three times for 30 s and centrifuged for 20 min at 16,000 g at 4°C to ensure absence of cell debris and completeness of lysis. Equimolar amounts of total protein derived from the non-stimulated “heavy” SILAC cells were added to serve as internal standards for the accurate relative quantification. Samples with 60 μg total protein were transferred to the 96-well plate, and proteins were denatured at 65°C using PCR thermocycler (MasterCycler 5332, Eppendorf, Germany), reduced with 10 mM dithiothreitol and alkylated with 20 mM iodoacetamide. Samples were then digested overnight at 37°C with sequencing grade modified trypsin (trypsin:total protein ratio 1:30; Promega, Madison WI, USA). RapiGest SF was cleaved with 1% trifluoroacetic acid and samples in the 96-well plate were centrifuged at 290 g. Peptides were extracted with 10 μL OMIX C18 tips (Varian) using 12-channel pipettes and eluted with 10 μL 64.5% acetonitrile. Recovery from Omix C18 microextraction tips was ~80%, as previously estimated by SRM analysis of three BSA peptides. Heavy isotope-labeled peptide LSEPAELTDAVK* peptide was spiked into each digest and used as a quality control for C18 microextraction. Peptides were diluted to 130 μL with 0.1% formic acid in water. The following precautions were taken to minimize possible modifications of peptides, such as oxidation of methionines and deamidation of asparagines and glutamines, during storage and analysis: (i) supplementation of the protein digest with 0.4 M methionine, (ii) storage of tryptic peptides at -20°C until the use; and (iii) sealing of 96-well plates with silicone rubber mats and preservation of plates at 7°C during mass spectrometry analysis. |
| acquisition | Peptides were separated by 60-min C18 reversed-phase liquid chromatography (EASY-nLC, Proxeon, Odense, Denmark) and analyzed by a triple-quadrupole mass spectrometer (TSQ Vantage or TSQ Quantiva, Thermo Fisher Scientific Inc., San Jose, CA) using a nanoelectrospray ionization source, as previously described (13). Reproducibility of SRM signal was ensured by running a solution of 0.25 fmol/μL BSA every 9 runs. Carryover was estimated in the range 0.05-0.2%. Use of heavy-isotope labeled peptides as internal standards ensured stability and reproducibility of SRM analysis. Two technical replicates (40 μL each) and four biological replicates were analyzed for each biological condition. Blocks of four biological replicates were randomized within time-response and antagonist-response experiments. Proteins in the ERα-EGR3 network (Table S2) were quantified by a triple-quadrupole mass spectrometer TSQ Quantiva (Thermo Fisher Scientific Inc., San Jose, CA) with an identical chromatography setup. |
| informatics | Raw files recorded for each sample were analyzed using Pinpoint software, and areas of each “light” and “heavy” SRM transitions were extracted. Analysis of SRM data included normalization of all “light” endogenous peptides by spiked-in “heavy” peptides (to account for variability of mass spectrometry analysis), followed by re-normalization of “light” peptides of all proteins by a set of high-abundance house-keeping proteins (to account for variability of sample handling, cell lysis, and total protein analysis, and essentially, to normalize relative protein abundances to the equal number of cells in each biological condition).
To normalize SRM data, we followed a previously proposed intensity-based approach for pairs of “light” and “heavy” peptides (17), rather than a standard ratio-based approach used to analyze SILAC data. In the first step of data normalization, the mean area of two technical injections was calculated for each “light” and “heavy” SRM transitions. Since the equal amounts of a “heavy” cell lysate were spiked into each biological replicate, the median area of each “heavy” SRM transition was used in the second step to calculate a unique correlation coefficient for each biological replicate. In the third step, such coefficient was used to calculate the normalized area of “light” SRM transitions for each biological replicate. In the fourth step, areas of “light” SRM transitions of high-abundance house-keeping proteins (13 proteins and 24 transitions in the verification experiment and 7 proteins and 14 transitions in the ERα-EGR3-NAB2 network analysis) were used to calculate the median correlation coefficient for each biological replicate. Such coefficient was used to calculate the re-normalized areas of each “light” transition in each biological replicate. Intensities of two transitions of each peptide were summed to obtain the area of each “light” peptide in each biological replicate. We also assumed that the area of each unique peptide represented a proxy of the abundance of the corresponding protein. Finally, the mean abundance of each protein in four biological replicates and the corresponding standard deviations were calculated. Our normalization approach reduced the variability and facilitated an accurate analysis of relative protein abundances. |
| instruments | Thermo Scientific TSQ Vantage
Thermo Scientific TSQ Quantiva |
| species | Human |
| massModifications | static: C+57.021464 |
