Dynamics of Protein Expression Reveals Primary Targets and Secondary Messengers of Estrogen Receptor Alpha Signaling in MCF-7 Breast Cancer Cells
Bibliographic record
Abstract
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 (SILAC) resulted in identification of 103 estrogen-regulated proteins, with only 40 of the corresponding genes having estrogen response elements. Selected reaction monitoring (SRM) assays were used to validate the differential expression of 19 proteins and measure the dynamics of their expression within 72 h 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). Because 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".