Additional file 1 of PGRMC1 phosphorylation affects cell shape, motility, glycolysis, mitochondrial form and function, and tumor growth

Additional file 1:Figure S1. Detailed views of selected pathways identified by WebGestalt analyses. Related to Fig. 3. All panels are adapted from File S6. Heat map colors follow Fig. 3. (A) Principal component (PC) analysis of SWATH-MS proteomics results showing distribution of PC1 and PC2. PC1 corresponded to pathways associated with ribosomes and translation, while PC2 corresponded to pathways associated with mRNA splicing processing (see File S3). (B) Proteins associated with PI3K/AKT activity (WebGestalt Database: PC, DB_ID:1648, “Class I PI3K signaling events mediated by AKT”) are less abundant in TM cells. (C) F1/F0 ATPase subunits elevated in WT and TM cells. (D) Abundances of proteins associated with protein folding and microtubule function are altered by PGRMC1 phosphorylation status. Proteins detected in any of the following WebGestalt pathways or functions (1–4) or a manual search (5) are mapped against their expression profiles. 1) cellular component chaperonin-containing T-complex GO:0005832. 2) PC pathway Chaperonin-mediated protein folding DB_ID:710. 3) cellular component microtubule GO:0005874. 4) PC pathway Protein folding DB_ID:712. 5) Description from the list of 243 proteins (File S6) contains keywords “tubulin” or “microtubule” (manual search) (Adapted from File S6). 2-tailed t-test p-values for all sample comparisons are available in File S4. (E) Proteins associated with nuclear import/export that are elevated in DM cells. (F) Antigen processing and presentation enzymes are affected by PGRMC1 phosphorylation status. Manual additions to KEGG pathway ID:04612 “Antigen processing and presentation" (no yellow shading: from File S6 and File S5) are indicated with yellow highlighting. Figure S2. Highest and lowest differentially abundant proteins. Panels show the six most (+) and least (−) abundant proteins for each cell type that were significantly differentially abundant between cell types. Related to Fig. 3. Identical colored symbols depict the same protein in different cell types, where circles represent high abundance and triangles represent low abundance. E.g. Mitochondrial import receptor subunit TOM40 (O96008), CDP-diacylglycerol-inositol 3-phosphatidyltransferase (CDIPT, O14735) (phosphatidylinositol synthesis) and transcriptional coactivator PSIP1 (O75475) are more abundant in WT and TM. Aldehyde dehydrogenase 1A3 (P47895), APOC3 (P02656) and APOA1 (P02647) are less abundant in DM and TM, whereas the receptor tyrosine kinase ephrin type-A receptor 2 (EPHA2, P29317) is among the lowest abundance differential proteins in WT and TM. Protein abundances (measured ion intensities) for all proteins are available in File S2. Figure S3. Proteins associated with predicted Estrogen Receptor Related 1 (ERR1) transcription factor activity are more abundant in WT and TM cells. Related to Fig. 3 and File S6. Data are available via ProteomeXchange with identifier PXD014789. (A) Proteins quantified by SWATH-MS and predicted by WebGestalt Transcription Factor target enrichment analysis (WebGestalt Database: Transcription Target, Name: hsa_TGACCTY_V$ERR1_Q2, ID:DB_ID:2414) to be dependent upon ERR1 transcription (Adapted from File S6). Heat map colors follow Fig. 3. (B) Western blot of shRNA attenuation of ERR1 (top panel) in WT cells, compared to scramble shRNA control. Lower panel: beta actin loading control. (C) ERR1 attenuation by shRNA induces morphological changes to WT cells. (D) Ratio of rounded to other cells as scored for replicate images from each of the cell lines from B. p < 0.0005, 1 tailed t-test. (E) SWATH MS quantification of proteins significantly differentially abundant between cells expressing scramble shRNA or anti-ERR1 shRNA (according to the selection criteria of File S4). Fifty-seven proteins became more abundant after shRNA depletion of ERR1, and 19 proteins became less abundant. In this panel green depicts lower abundance and red depicts higher abundance. Brown depicts similar abundance. (F) Proteins present in (E) which are also present in the 243 protein list of File S6. Heat map colors follow Fig. 3. The left panel shows expression in the original result of File S6. The right panel shows expression in the presence of scramble shRNA or anti-ESSR1 shRNA. All proteins from Fig. S3A except Q9BPW8 were detected in the shRNA experiment. Double headed arrows indicate proteins which differ in expression tendency between WT (left) and WT-scramble shRNA cells (right). This may be caused by the puromycin selection of both sh-scr and shERR1 cells but not the parental WT cells, however this requires further investigation. Proteins with Uniprot ID highlighted by asterisk (bold red) are those both originally predicted by WebGestalt to be associated with ERR1 transcription factor (A), and which exhibited significantly altered abundance after shRNA attenuation of ERR1 protein. Figure S4. The DM migratory phenotype is dependent upon Vinculin. Related to Fig. 3. (A) Proteins associated with actin-myosin contraction (WebGestalt Database: PC, DB_ID:144, “Smooth Muscle Contraction”) are more abundant in DM cells. Heat map colors follow Fig. 3. (B) Western blot of Vinculin (VCL) and actin levels in cells expressing scramble control (shScr) or anti Vinclulin (shVNC) shRNA. (C) Scratch assays as indicated were performed following the methods of Fig. 1e-f. (D) Boxplot showing results of 12 replicates of (C). ANOVA gave F = 78.7, p < 0.00001. The table shows post-hoc Dunnet’s T3 p-values. Figure S5. PGRMC1 phosphorylation status affects mitochondrial function. Related to Fig. 5. (A) Representative flow cytometry results of cells labeled with NpFR2. The percentage of the cell population to the right of the dashed reference line (interval labeled “B”, marked by the dotted line) is quantified for each measurement. White arrows indicate more oxidized NpFR2 fluorescence in WT and TM cells. (B) Boxplots of the percentage of cells exhibiting > 10 fluorescent intensity units to the right of the reference line in (A). n = 6 for each cell type, being 6 replicates of MP cells, or duplicate measurements of each of 3 independent lines 1–3 (n = 3 × 2 = 6) for WT, DM and TM cells. White arrows indicate the same differences as in (A). There was significant difference between the means (Kruskal-Wallis Test p < 0.0001). Independent sample median tests revealed that all medians were significantly different from one another (p < 0.001). (C) Representative flow cytometry results of cells labeled with MitoTracker. The respective panels depict fluorescence intensity (x axis) plotted against either side scatter (left/black plots) or cell number (right/red plots), with or without the addition of MitoTracker as indicated. MitoTracker affinity for mitochondria is increased with higher mitochondrial membrane potential (Δψm), such that respective left MitoTracker-treated populations/peaks represent low Δψm populations, and respective right MitoTracker-treated populations/peaks represent high ψm populations. Vertical dotted lines for Mitotracker-treated cells depict the MFI for MP cell low and high Δψm populations. The black arrow for DM cells with Mitotracker highlights reduced MFI of both the low and high Δψm DM cell populations. The white arrow highlights the reduced proportion of DM cells in the low Δψm peak. The same arrows are depicted for replicates in (D) and (E) respectively. (D) Median Fluorescent Intensity (MFI) for each of the cell populations observed from (C). n = 6 measurements per cell type, as per (B). Error bars depict standard deviation. The low and high Δψm populations correspond to the respective populations from (C). For low Δψm cells the means were significantly different between cell types (ANOVA, post-hoc Dunnet’s T3, p < 0.003). For high Δψm cells WT vs. TM (p < 0.05) and other comparisons (p < 0.004) were significantly different (Kruskall-Wallis). Tables D(L) and D(H) show pairwise comparison p-values for low Δψm (F(L)) and high Δψm (F(H)) cells. (E) The percentage of cells in each of the low and high Δψm populations from (C) and (D), from the same data. Standard deviation error bars for the respective high Δψm (upper error bar) and low Δψm (lower error bar) cell populations are given for each cell type. Color-coding and all other terminology follows (C). For low Δψm cells, Shapiro-Wilk’s test showed normal distributions, and Levene’s test revealed non-homogenous variance (p = 0.04). Table E shows p-values for ANOVA/post-hoc Dunnet’s T3 test pairwise comparisons. (F, G) Area under curve (AUC) plots for basal and FCCP curves from (Fig. 5c) showing one way ANOVA/post-hoc Bonferroni p-values between samples in the respective indicated regions.

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