1. Volume 2, Issue 3, Pages 159-171 (March 2016)
Deep Proteomics of Breast Cancer Cells Reveals that Metformin Rewires Signaling Networks Away from a Pro-growth State 
Francesca Sacco, Alessandra Silvestri, Daniela Posca, Stefano Pirrò, Pier Federico Gherardini, Luisa Castagnoli, Matthias Mann, Gianni Cesareni 
Cell Systems 
Volume 2, Issue 3, Pages (March 2016)
DOI: /j.cels
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2. Cell Systems 2016 2, 159-171DOI: (10.1016/j.cels.2016.02.005)
Copyright © 2016 Elsevier Inc. Terms and Conditions

3. Figure 1
Metformin Remodels the Breast Cancer Cell Proteome and Transcriptome
(A) Schematic representation of the experimental strategy to analyze the changes in transcriptome, proteome, and phosphoproteome profiles of MCF7 breast cancer cells after 24 hr of metformin treatment (10 mM). The coverage and overlap of the genes and gene products identified by the RNAseq and proteomic approaches are illustrated in the Venn diagram. Percentage and number of phosphorylation sites with protein expression levels quantified or not are shown in the pie chart.
(B) The correlation between the quantified proteome and transcriptome changes of each gene product after metformin treatment is shown as a density scatterplot.
(C) Protein expression and phosphorylation levels were compared and represented in the scatterplot. Proteins and phosphorylation sites were considered “regulated” by metformin according to the t test (FDR < 0.05). Comparison between biological replicates of MS-based phospho-proteomic experiments of metformin treated (Metf1-3) and untreated (NT1-3) cells. Each dot represents one protein.
(D) Metformin inhibits the mTOR pathway and hyperactivates AMPK. The metformin-induced change at the phosphorylation, protein, and transcript levels of mTOR direct and indirect substrates and AMPK direct substrates are shown as bar graph in a Log2 scale. Median and SD are shown.
Cell Systems 2016 2, DOI: ( /j.cels )
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4. Figure 2 Metformin Modulates Key Biological Processes
(A) Two-dimensional annotation enrichment analysis. Pathways modulated by metformin treatment at the proteome level in comparison with the transcriptome are plotted (Benjamin Hochberg FDR < 0.05). Each dot represents a specific KEGG pathway or GO-Biological Process (GO-BP) term. Groups of related pathways or GO-BP are labeled with the same color, as described in the inset. Position scores of the pathways at the transcriptome and proteome level are indicated in the x and y axes, respectively (Cox and Mann, 2012). Negative values indicate downregulation, whereas positive values upregulation.
(B) Schematic representation of the glycolysis pathway; for each enzyme, the corresponding metformin-induced change in mRNA and protein concentration is shown.
(C) The metformin-mediated changes in mRNA and protein concentration of enzymes controlling the oxidative phosphorylation are shown as a scatterplot. The protein complexes of the oxidative phosphorylation pathway are differentially color-coded.
(D) Kinase substrates motifs significantly (Benjamin Hochberg FDR < 0.02) overrepresented in hyper- and hypophosphorylated proteins after metformin treatment are represented in a bar graph. Position scores of the kinase substrates motif at the phosphoproteome level are indicated in the x axis, whereas the corresponding p values are shown in the y axis.
(E) GO-BP and Kegg pathways significantly (Benjamin Hochberg FDR < 0.05) overrepresented in inactivated and hyperactivated proteins after metformin treatment are represented as a bar graph. In the x axis, the enrichment factor of each category is plotted.
Cell Systems 2016 2, DOI: ( /j.cels )
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5. Figure 3
Metformin Decreases the SP1 Protein Concentration through the Hyperphosphorylation of Serine 59
(A) Schematic representation of the molecular mechanisms leading to the metformin-induced decrease of SP1 protein concentration. The metformin-induced changes in phosphorylation, mRNA, and protein levels in the three biological replicates are represented as squares differentially color-coded (blue, downregulation; green, upregulation).
(B) The metformin-induced modulation at mRNA and protein level of SP1-controlled genes is represented as a bar graph.
(C) GO-BPs significantly (Benjamin Hochberg FDR < 0.02) enriched in downregulated SP1-controlled genes after metformin treatment are represented in a bar graph. In the x axis, the enrichment factor of each GO-BPs is plotted, whereas in the y axis the corresponding p values are shown.
Cell Systems 2016 2, DOI: ( /j.cels )
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6. Figure 4
Mapping the Metformin-Induced Changes of the Proteome and Phosphoproteome Profiles to Prior Literature Signaling Networks
(A) Global naive network of signaling information downloaded from the SIGNOR database.
(B) MCF7 breast-cancer-cell-specific signaling network where only relationships between proteins that were identified in our proteomic analysis are maintained.
(C) MCF7 breast-cancer-cell-specific signaling network where changes at the proteome and phosphoproteome level induced by metformin treatment are overlaid onto the cell-specific signaling network.
(D) Metformin perturbation network filtered according to the rules described in the Supplemental Experimental Procedures.
Cell Systems 2016 2, DOI: ( /j.cels )
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7. Figure 5
Metformin Rewires the Signaling Networks of MCF7 Breast Cancer Cells
(A) The effect of metformin on the phosphoproteome and proteome profiles of MCF7 cancer cells was mapped on a literature curated signaling network, as described in Figure 3. Node size is proportional to the protein expression change after metformin treatment. Green and blue nodes are respectively inactivated or activated following metformin treatment. Phosphorylation and de-phosphorylation reactions on specific residues are represented as edges between nodes. Phosphosites are colored according to their phosphorylation state after metformin treatment, as indicated in the legend.
(B) The metformin-induced changes at the proteome and transcriptome level of enzymes regulating the MAPK pathways are shown as a bar graph.
(C) Western blot analysis of MCF7 cells treated with 10 mM metformin.
(D) Western blot analysis of MCF7 cells co-treated with 10 mM metformin for the indicated times and with different concentration of TBB for 2.5 hr.
Cell Systems 2016 2, DOI: ( /j.cels )
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8. Figure 6
Metformin Rewires the mTOR Pathway and Modulates the Cell Response to Incubation with IGF
(A and B) Heat map of the phosphorylation levels of the significantly metformin-modulated mTOR (A) and AKT substrates (B).
(C) The experimental strategy applied to see how metformin-treated cells respond to IGF stimulation.
(D) Columns represent the signal intensity of the assayed signaling proteins (by Luminex technology, see Experimental Procedures), whereas rows represent different experimental conditions. Data were normalized to the relative basal activity in untreated cells; intensity values are color-coded in blue (maximum) and red (minimum), respectively.
(E) Western blot of MCF7 cells treated as explained above.
(F) Schematic representation of the relationships between the assayed signaling proteins. Nodes are color-coded (activated in blue and not affected in gray) and divided into two parts representing the fold activation level (reported next to the proteins and color coded by different shades of blue) after 15 min of IGF incubation in untreated (left) and metformin-treated (right) cells.
Cell Systems 2016 2, DOI: ( /j.cels )
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9. Figure 7
Metformin Inhibits the p70S6K-rpS6 Axis by Increasing the PP2A Holoenzyme Assembly
(A) Western blot of MCF7 and HTB-126 cells treated with 10 mM metformin for 24 hr or left untreated.
(B and C) Volcano plot of MS-quantified PPP2R5C interactors in MCF7 (B) and HTB-126 cells (C).
(D) MS-based quantification of pS497 of PPP2R5C in MCF7 and HTB-126 cells.
(E) Sequence alignment of the pS497 peptides of PPP2R5C and PPP2R5D.
(F) Model proposed for the metformin-mediated inhibition of the p70S6K-rpS6 axis.
Cell Systems 2016 2, DOI: ( /j.cels )
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