Additional File 12:

Figure S7. Anti-proliferative effects of curcumin, imatinib and curcumin+imatinib combination on CML cell viability. Curcumin and imatinib were tested for their anti-proliferative effects on K562 (a) and LAMA84 cells (b). The assays were performed by using curcumin and imatinib singly (using the reported doses) or in combination (20 μM curcumin held constant and imatinib at reported concentrations. In K562 cells combination compound treatments showed significant differences compared to single imatinib treatments for all doses tested (p < 0.001). In LAMA84 cells combination compound treatments showed significant differences compared to single imatinib treatments for lower doses tested (p < 0.001 at 0.1 and 0.2 μM), while no significant differences were observed between combination compound and imatinib at 0.5–5 μM because high cell death occurred. Combination Index (CI) analysis of growth inhibition in K562 (c) and LAMA84 cells (d) after 48 h incubation using curcumin (20 μM) and imatinib (different concentrations). Data from Fig. S6a and S6b were converted to Fraction Affected (FrAf) and plotted against Combination Index (CI). Results were as follows for imatinib concentration: ▲ = 0.1 μM; ♦ = 0.2 μM; ● = 0.5 μM; □ = 1 μM; ○ = 5 μM. Straight line on the graph designates a CI equal to 1. Combination Index interpretation was as follows: CI value of 1 indicates additivity; CI < 1 indicates synergism; and CI > 1 indicates antagonism. (PPTX 50 kb

Additional file 10: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1α axis

Figure S5. Representative western blots and corresponding densitograms showing that in K562 (a) and LAMA84 cells (b) curcumin decreased nuclear levels of HIF-1α. Ponceau S of nuclear extract was used as loading control. Intensities of proteins band (in Ponceau S the band used is indicated with arrow) were calculated from the peak area of densitogram by using Image J software. Ctrl: control cells. (PPTX 809 kb

Additional file 11: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1Îą axis

Figure S6. IPO7/miRNAs correlation. a Analysis performed by using microRNA target prediction software miRSearch V3.0 showed that IPO7 is a validated target of miR-22 and miR-9. b Analysis of predicted multiple targets performed by MicroRNA Target prediction (miRTar) tool ( http://mirtar.mbc.nctu.edu.tw/human/ ) revealed within the CurcuDown-Regulated dataset the presence of several of miR-22 targets beside IPO7. No target of miR-9 was found. (PPTX 179 kb

Additional file 3: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1Îą axis

Table S2. SWATH-MS Data. (XLSX 884 kb

Additional file 6: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1Îą axis

Table S3. DownReg Proteins_FunRichGOterms. (XLSX 50 kb

Additional file 7: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1Îą axis

Table S4. UpReg Proteins_FunRichGOterms. (XLSX 35 kb

Additional file 8: of SWATH-MS based quantitative proteomics analysis reveals that curcumin alters the metabolic enzyme profile of CML cells by affecting the activity of miR-22/IPO7/HIF-1Îą axis

Table S5. Regulated Proteins_ClueGO Results. (XLSX 22 kb

Validation of a Novel Shotgun Proteomic Workflow for the Discovery of Protein–Protein Interactions: Focus on ZNF521

The study of protein–protein interactions is increasingly relying on mass spectrometry (MS). The classical approach of separating immunoprecipitated proteins by SDS-PAGE followed by in-gel digestion is long and labor-intensive. Besides, it is difficult to integrate it with most quantitative MS-based workflows, except for stable isotopic labeling of amino acids in cell culture (SILAC). This work describes a fast, flexible and quantitative workflow for the discovery of novel protein–protein interactions. A cleavable cross-linker, dithiobis­[succinimidyl propionate] (DSP), is utilized to stabilize protein complexes before immunoprecipitation. Protein complex detachment from the antibody is achieved by limited proteolysis. Finally, protein quantitation is performed via ¹⁸O labeling. The workflow has been optimized concerning (i) DSP concentration and (ii) incubation times for limited proteolysis, using the stem cell-associated transcription cofactor ZNF521 as a model target. The interaction of ZNF521 with the core components of the nuclear remodelling and histone deacetylase (NuRD) complex, already reported in the literature, was confirmed. Additionally, interactions with newly discovered molecular partners of potentially relevant functional role, such as ZNF423, Spt16, Spt5, were discovered and validated by Western blotting

Additional file 2: of The phospholipase DDHD1 as a new target in colorectal cancer therapy

Figure S1. DDHD1 silencing. To evaluate DDHD1 silencing a. Real-time PCR and b. Western blot analysis were performed on SW480, HCT116, HS5 and HUVEC transfected for 48 or 72 h with scrambled siRNA or DDHD1 siRNA. (TIFF 6629 kb

Additional file 4: of The phospholipase DDHD1 as a new target in colorectal cancer therapy

Figure S2. Effects of DDHD1-expressing cells conditioned medium on DDHD1-silenced cell growth. Cell viability was measured by MTT assay on DDHD1-silenced SW480 cells in the presence of the conditioned medium (CM) of mock cells and DDHD1 overexpressing cells. (TIFF 3275 kb