Identified proteins

Table S1A: Proteins identified in partially purified MCF-7 (CTRL) cell nuclear extracts. Table S1B: Proteins identified in partially purified CTAP-ER_ expressing MCF-7 (Sample) cell nuclear extracts. Table S1C: Proteins specifically identified in partially purified CTAP-ER_ (Sample) vs MCF7 (CTRL) nuclear extracts

S1 Table -

Differentially expressed genes (DEGs) in HeyA8 (A) and ES-2 (B) OC cell lines with fold change and p-value. (XLSX)</p

Hierarchical representation of the 32 clusters generated by Splinecluster

<p><b>Copyright information:</b></p><p>Taken from "Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells"</p><p>http://www.biomedcentral.com/1471-2105/9/S2/S12</p><p>BMC Bioinformatics 2008;9(Suppl 2):S12-S12.</p><p>Published online 26 Mar 2008</p><p>PMCID:PMC2323661.</p><p></p

Comparison of ER beta interactors after either RNA or AGO2 depletion

Diffentially expressed proteins upon RNAse treatment and AGO2 silencing. In italic are displayed those proteins that do not fit the statistical significance parameters

S1 Raw image -

(TIF)</p

Molecular Mechanisms of Selective Estrogen Receptor Modulator Activity in Human Breast Cancer Cells: Identification of Novel Nuclear Cofactors of Antiestrogen–ERα Complexes by Interaction Proteomics

Estrogen receptor alpha (ERα) is a ligand-activated transcription factor that controls key cellular pathways <i>via</i> protein–protein interactions involving multiple components of transcriptional coregulator and signal transduction complexes. Natural and synthetic ERα ligands are classified as agonists (17β-estradiol/E₂), selective estrogen receptor modulators (SERMs: Tamoxifen/Tam and Raloxifene/Ral), and pure antagonists (ICI 182,780-Fulvestrant/ICI), according to the response they elicit in hormone-responsive cells. Crystallographic analyses reveal ligand-dependent ERα conformations, characterized by specific surface docking sites for functional protein–protein interactions, whose identification is needed to understand antiestrogen effects on estrogen target tissues, in particular breast cancer (BC). Tandem affinity purification (TAP) coupled to mass spectrometry was applied here to map nuclear ERα interactomes dependent upon different classes of ligands in hormone-responsive BC cells. Comparative analyses of agonist (E₂)- vs antagonist (Tam, Ral or ICI)-bound ERα interacting proteins reveal significant differences among ER ligands that relate with their biological activity, identifying novel functional partners of antiestrogen–ERα complexes in human BC cell nuclei. In particular, the E₂-dependent nuclear ERα interactome is different and more complex than those elicited by Tam, Ral, or ICI, which, in turn, are significantly divergent from each other, a result that provides clues to explain the pharmacological specificities of these compounds

Molecular Mechanisms of Selective Estrogen Receptor Modulator Activity in Human Breast Cancer Cells: Identification of Novel Nuclear Cofactors of Antiestrogen–ERα Complexes by Interaction Proteomics

Estrogen receptor alpha (ERα) is a ligand-activated transcription factor that controls key cellular pathways <i>via</i> protein–protein interactions involving multiple components of transcriptional coregulator and signal transduction complexes. Natural and synthetic ERα ligands are classified as agonists (17β-estradiol/E₂), selective estrogen receptor modulators (SERMs: Tamoxifen/Tam and Raloxifene/Ral), and pure antagonists (ICI 182,780-Fulvestrant/ICI), according to the response they elicit in hormone-responsive cells. Crystallographic analyses reveal ligand-dependent ERα conformations, characterized by specific surface docking sites for functional protein–protein interactions, whose identification is needed to understand antiestrogen effects on estrogen target tissues, in particular breast cancer (BC). Tandem affinity purification (TAP) coupled to mass spectrometry was applied here to map nuclear ERα interactomes dependent upon different classes of ligands in hormone-responsive BC cells. Comparative analyses of agonist (E₂)- vs antagonist (Tam, Ral or ICI)-bound ERα interacting proteins reveal significant differences among ER ligands that relate with their biological activity, identifying novel functional partners of antiestrogen–ERα complexes in human BC cell nuclei. In particular, the E₂-dependent nuclear ERα interactome is different and more complex than those elicited by Tam, Ral, or ICI, which, in turn, are significantly divergent from each other, a result that provides clues to explain the pharmacological specificities of these compounds

Estrogen responding genes per state.

<p>Among the entire gene set considered in the MCF-7 cell experiment, 1270 also responded in ZR-75.1 cells. These are referred to as common ‘estrogen-regulated genes’ (E2R genes) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088485#pone.0088485-Cicatiello1" target="_blank">[4]</a>. ‘Primary genes’ are their subgroup having a ER transcription factor binding site within 10 kb around the TSS. The figures show how E2R and primary genes are responding across the single-cell states of a six-state model. (<b>A</b>) Fraction of up-regulated and down-regulated E2R genes. (<b>B</b>) Fraction of first-responding E2R genes, i.e., of genes that respond for the first time in a given state. (<b>C</b>) and (<b>D</b>) show the analogous pattern of primary genes.</p

The number of single-cell states in the MCF-7 response to estrogen.

<p>(<b>A</b>) The mean squared error of the model fit to the microarray data decreases as function of the number of states: as expected, when the number of parameters increases, the quality of the fit improves. (<b>B</b>) The condition number is a measure of the similarity of the transcriptional profiles of the states. It increases as function of the number of states, , highlighting that over-fitting also increases with . A good balance between fit quality and over-fitting must be found. (<b>C</b>) The model posterior probability, derived by a Bayesian approach, has a peak at , which shows that a model with six states strikes a good balance between fit-to-data and model parsimony.</p

Marker genes in the MCF-7 system.

<p>In each state of a six-state model, genes are ranked by their state-expression fold change with respect to the first state. Here, only the top 50 are shown along with their ranking in the other states. For the top genes of state 2 also the rank assigned considering a maximum fold change criterion over the time course is shown for comparison (separated column). The state-based ranking criterion highlights marker genes which would otherwise pass unnoticed.</p