Title PGRMC 1 phosphorylation status and cell plasticity 1 : glucose metabolism , mitochondria , and mouse xenograft tumorigenesis

Authors Bashar M. Thejer,1,2,3 Partho P. Adhikary,1,2,4 Amandeep Kaur,5,6 Sarah L. Teakel,1 Ashleigh Van Oosterum,7 Ishith Seth,1 Marina Pajic,8,9 Kate M. Hannan,10,11 Megan Pavy,11 Perlita Poh,11 Jalal A. Jazayeri,1 Thiri Zaw,12 Dana Pascovici,12 Marina Ludescher,13 Michael Pawlak,14 Juan C. Cassano,15 Lynne Turnbull,16 Mitra Jazayeri,17 Alexander C. James,18,19,20 Craig P. Coorey,18,21 Tara L. Roberts,18,19,20,21 Simon J. Kinder,22 Ross D. Hannan,9,10,23,24,25 Ellis Patrick,26 Mark P. Molloy,12,27 Elizabeth J. New,5 Tanja N. Fehm,13 Hans Neubauer,13 Ewa M. Goldys,28,29 Leslie A. Weston,30,31 and Michael A. Cahill.1,10,*

Insig/SCAP/SREBP, modification of the first sterol lanosterol from yeast to mammals, and in its conferral of steroid responsiveness (reviewed by [14]). Association of PGRMC1 with low density lipoprotein receptor (LDLR) and Sigma-2 receptor (S2R) TMEM97 mediates an accelerated route for LDLR endocytosis [15], and in an S2R-ligand sensitive complex with Sigma-1 receptor it associates with Dopamine D1 receptor [16] (PGRMC1 is erroneously referred to as S2R in this paper [15,17,18]). PGRMC1 also associates with and modulates mitochondrial ferrochelatase, the final enzyme in the heme synthetic pathway [19], as well as interacting with the kinetochore microtubules of both meiotic and mitotic spindles and active aurora kinase at metaphase centromeres [20][21][22][23].
This present study was prompted by our previous discovery that PGRMC1 exhibited differential phosphorylation status between estrogen receptor positive and negative breast cancers. PGRMC1 was induced in the hypoxic zone of ductal carcinoma in situ breast lesions [44] at precisely the time and place that cells require a switch to glycolytic metabolism known as the Warburg effect, which resembles a reversion to stem-cell-like metabolism [56]. PGRMC1 was implicated in mediating the placental P4-dependent shift from aerobic towards anaerobic glucose metabolism in gestational diabetes [57]. We predicted the involvement of PGRMC1 with the onset of Warburg metabolism [44].
Furthermore, a PGRMC1 S57A/S181A double mutant (DM, Figure 1A) enabled the survival of otherwise lethal hydrogen peroxide treatment [44]. In the interim, we have proposed that PGRMC1 phosphorylation and other post-translational modifications regulate multiple alternative PGRMC1-dependent functions. We hypothesized that PGRMC1 is a signal hub protein with wide ranging effects on cancer and general cell biology [3,5,6,14]. Consistent with our overarching hypothesis, Sabbir [55] recently reported that PGRMC1 induced a P4-dependent metabolic change resembling the Warburg effect in HEK293 cells, which was associated with changes in PGRMC1 stability, post-translational modifications, and subcellular locations. Hampton et al. also demonstrated association of PGRMC1 with the insulin receptor in A549 cells, where PGRMC1 attenuation lowered glucose uptake [13].
We previously observed that MIA PaCa-2 pancreatic cancer (MP) cells exhibited marked morphological and metabolic changes when a S57A/S181A double mutant (DM) protein was expressed [58]. MP cells are widely used to study pancreatic cancer cell biology [59][60][61][62]. They exist in culture as a mixed adherent population of elongated "fibroblast-shaped" morphology, a minority population of rounded morphology with bleb-like protrusions, and some multicellular clumps, as well as some rounded suspension cells. MP cells have undergone epithelial-mesenchymal transition [63], and can further undergo mesenchymal-amoeboid transition (MAT), which is dependent upon the activity of Rho Kinase (ROCK) and causes a morphological change from "elongated" mesenchymal cells to rounded amoeboid cells [64]. Here, we examined the effects of altered PGRMC1 phosphorylation status on MP cells to gain insights into PGRMC1-dependent signaling, and its role in subcutaneous mouse xenograft tumorigenesis which requires Y180. This work is accompanied by two companion papers. In the first [65] we describe differences in metabolism associated with epigenetic genomic CpG methylation levels associated with PGRMC1 phosphorylation status. We also show that inability to phosphorylate the Y180 motif reduces the fidelity of genomic replication. In a second companion paper [7] we show that the combination of Y180 and Y139, which are both tyrosine phosphorylated [5], appeared in animal evolution at common ancestor of Cnidaria and Bilateria, preceding the evolution of bilateral body symmetry and mechanisms associated with embryological animal tissue differentiation. In combination, all three papers argue that the acquisition of PGRMC1 Y180 phosphorylation played a foundational role in animal evolution that explains why PGRMC1 can affect cell differentiation and plasticity in human disease.

Establishment of cell lines
To investigate the role of PGRMC1 phosphorylation in MP cells, we stably transfected MP cells with the hemagglutinin (HA) epitope-tagged PGRMC1-HA plasmids including the wild-type (WT) sequence [66], the previously published S57A/S181A DM [44], and a novel S57A/Y180F/S181A triple mutant (TM), which also mutated the tyrosine phosphate acceptor of the SH2 target motif centered on Y180 [1,5,6] ( Figure 1A). Three independent stable cell lines were selected from each group ( Figure 1B-E). In stably transfected cells both 32 kDa 3xHA-tagged exogenous and a 24 kDa endogenous species were detected by an anti-PGRMC1 antibody, whereas only the 24 kDa species was present in MP cells ( Figure 1B). Both species were present at approximately equimolar ratios ( Figure 1C), and an anti-HA antibody detected only the 32 kDa species ( Figure   1D). We reasoned that any differences consistently observed between these biological triplicates should be due to the PGRMC1-HA mutations, rather than to undefined clonal artifacts. Subsequent experiments were performed using respective cell line triplicates 1-3 per PGRMC1-HA plasmid. Because of the dramatic effects observed, MP cells are included in our experiments as a literature reference point. MP differ from WT cells by not having undergone hygromycin selection, and by lack of overexpression of PGRMC1-HA. Therefore we cannot ascribe differences between MP and WT cells to PGRMC1-HA expression. The effects of the DM and TM PGRMC1 mutations are assessed relative to WT control levels.

PGRMC1 phosphorylation status alters MIA PaCa-2 cellular morphology
Like MP cells, freshly seeded WT cells exhibited predominantly elongated cell morphology with some rounded cells. DM and TM cells exhibited primarily rounded morphology ( Figure 1E), which was reminiscent of the reported mesenchymal-amoeboid transition (MAT) of MIA PacCa-2 cells [64]. After 72 hours of culture the proportion of round cells in DM and TM cultures was reduced, but still elevated relative to WT or MP (not shown). Transient transfections with the DM and TM plasmids (but not WT) led to similar increased levels cell rounding across the entire populations of cells by 24 hours after transfection (data not shown), indicating that the phosphorylation status of exogenous PGRMC1-HA affects cell morphology.

PGRMC1-dependent altered morphology requires Rho Kinase
The ROCK pathway is required for amoeboid phenotype and migration and its inhibition reverses MAT in MP cells [64,67]. ROCK inhibitor (ROCKI) reversed the rounded phenotype to elongated for DM and TM ( Figure 1F), supporting the hypothesis that morphological transition involves altered actin organization. It remains unclear from this result whether this process is related to true MAT.

PGRMC1 phosphorylation affects cell motility and invasion
To further investigate cell plasticity imposed by PGRMC1-HA phosphorylation mutants, we examined cellular motility via a scratch assay [68]. MP cells exhibited the lowest migration into the void of scratched areas, while TM and WT cells exhibited relatively elevated rates and modes of migration. DM cell migration was substantially greater than other cell lines (Figure 2A

PGRMC1 phosphorylation status affects proteins involved in many biological functions
To gain further insight into these effects, we performed a global proteomics analysis with the Sequential Window Acquisition of all Theoretical Mass Spectrometry (SWATH-MS) platform [69]. A total of 1330 proteins were reliably identified across the data set in at least one sample by tandem mass spectrometry (MS/MS) of at least 2 peptides using a combination of information dependent acquisition (IDA) and data independent SWATH-MS acquisition, using the Swissprot 2014_04 data base. Results are provided as Supplemental Information File 2. Approximately 50% of variation was explained by two principal components (PCs) in PC analysis, which corresponded approximately to "ribosomes and translation" (PC1, separated MP and DM from WT and TM) and "mRNA splicing and processing" (PC2, separated MP and WT from DM and TM) (Figure S1, Supplemental Information File 3). Of the identified proteins, 243 differed by 1.5 fold or more between any one or more comparisons with p<0.05 (t-test), and 235 of these withstood principal components analysis multiple sample correction. The Euclidian heat map clustering of those 243 proteins is provided in Figure 3, and in more detail as Supplemental Information File 6, revealing a suite of proteins which provided strong discrimination between the different PGRMC1-HA-induced cell conditions. Independent biological replicates of each of the cell types clustered tightly in clades that contain other replicates of the same cell type, with large distances between clades. We conclude that these differences consist primarily of specific PGRMC1-HA mutant-dependent effects.
Results from those six comparisons of protein abundance between the four sample types WT PGRMC1-HA protein induced the elevated abundance of many proteins involved in energy metabolism, including proteasomal components involved in protein degradation, and pathways for amino acid, carbohydrate, and fatty acid catabolism ( Figure 3). These proteins were annotated as both cytoplasmic and mitochondrial. Peroxisomal and lysosomal proteins were also upregulated in WT cells. A suite of proteins putatively involved in the recognition of mRNA by ribosomes, tRNA aminoacylation, the translation of proteins by ribosomes, and chaperone-mediated protein folding was generally less abundant in WT and TM than MP and DM cells (Figure 3, Supplemental Information File 6). Many of the changes in fatty acid and glucose metabolism enzymes resemble the effects of the insulin/glucagon system of metabolic regulation. Loss of PGRMC1 affects the SREBP-1/fatty acid homeostasis system [71], and PGRMC1 influences cell surface localization of insulin receptor and glucose transporters [13].
Chemical proteomics showed that PGRMC2 but not PGRMC1 promotes adipogenesis in 3T3-L1 preadipocytes following a gain of function interaction with a novel small molecule which displaced heme [72]. It will be interesting to examine whether that treatment mimics the effects of phosphorylation. (PGRMC2 possesses cognates to PGRMC1 Y180 and S181, as well has heme-chelating Y113 [6]. Mitochondrial proteins accounted for a large percentage of the proteins which were more abundant in WT than DM cells, and intriguingly many cytoplasmic proteins were more abundant proportionally to total protein in WT cells than DM ( Figure 3, Supplemental Information File 6). We also noted higher abundance components of ATP synthase in WT and TM cells ( Figure S2B), changes of proteins involved in chaperonin and microtubule function ( Figure S2C), and a group of proteins involved in major histocompatibility complex antigen processing and presentation, and proteolysis ( Figure S2E). The latter are reduced in DM cells and may be associated with their reduced invasiveness ( Figure 2C Consideration of the extreme (highest and lowest abundance) differential proteins for each cell type offers useful insight into the biology at play ( Figure S3). WT and TM cells exhibited overlap in the subset of most abundant proteins, which included PSIP1 transcriptional coactivator, TOM40 mitochondrial import channel, as well as CDIPT which catalyzes the biosynthesis of phosphatidylinositol (circles in Figure S3). One of the WT abundant proteins was K6PF phosphofructokinase (UniProt P08237), which catalyzes the rate limiting reaction and first committed step of glycolysis. The most abundant DM proteins included keratin 19, ubiquitin-associated protein 2-like, which is involved in stem cell maintenance [73], and methyl-CpG-binding protein 2, which was still more abundant in WT and TM cells, suggesting PGRMC1-mediated changes in genomic methylation (see accompanying paper [65]). The least abundant proteins shared a surprising mixed overlap between cell types (triangles in Figure S3). TM and WT cells shared low levels of Ephrin type-A receptor 2, a tyrosine kinase receptor, which was higher in DM (and MP) cells without being a top abundant protein in those cells. WT exhibited low levels of signal recognition particle 54 kDa protein, suggesting altered translation of endoplasmic reticulum proteins, and AL1A1 retinal dehydrogenase. This was notable because both DM and TM exhibited low levels of AL1A3 NAD-dependent aldehyde dehydrogenase involved in the formation of retinoic acid, suggesting alterations in retinoic acid metabolism by mutating S57/S181. DM and TM also shared low levels of ApoC3 and ApoA1 ( Figure S3), probably reflecting common lower lipoprotein synthesis by those cells. The striking feature of these overlapping results is that proteins in Figure   S3 represent only the half dozen absolutely lowest or highest abundance differential proteins expressed by each cell line. Taken together, our proteomics analysis revealed significant differences in the abundance of enzymes involved in a wide diversity of cell processes, many of which are directly implicated in cancer biology. The resemblance of WT and TM differential proteomics profiles suggests that the DM mutation activates signaling processes that are largely dependent upon Y180. (The sole difference between DM and TM proteins is the phosphate acceptor oxygen of Y180). Overall, this study indicated that PGRMC1 phosphorylation status exerts higher order effects in MIA PaCa-2 cells.
ERR1-regulated genes contribute to the PGRMC1-WT phenotype, but ERR1 activity is not affected by PGRMC1 Some mitochondrial proteins associated with energy metabolism were predicted by pathways enrichment analysis to be regulated by estrogen receptor related 1 (ERR1) transcription factor ( Figure S4A) in the comparisons of DM cells with both WT (adjP=0.004) and TM (adjP=0.04) (Supplemental Information File 4). Since ERR1 is a steroid receptor, we investigated the potential for a link between the biology of PGRMC1 and ERR1 by attenuating ERR1 levels in WT cells using shRNA. This changed cell morphology from predominantly elongated to rounded cells ( Figure S4B-D). SWATH-MS proteomics revealed that ERR1 indeed regulated genes differentially abundant between WT and DM cells observed in Figure S4A and Figure 3. However, PGRMC1 phosphorylation status affected the abundance of only a subset of ERR1-driven proteins.

PGRMC1 phosphorylation affects PI3K/Akt signaling
Strikingly, proteins associated with PI3K/Akt activity from Figure 3 and Supplemental Information File 6 were revealed by Supplemental Information File 5 to exhibit lower activity in TM cells ( Figure S2A) relative to MP (adjP=0.0063), WT (adjP=0.0001) and DM (adjP=0.0002) cells. We assayed the activity of substrates of the PI3K/Akt pathway by reverse phase protein array (RPPA) of two Akt substrates. Bad is phosphorylated by both PKA at S112 [74], and at S136 by Akt [75]. Whereas there was no significant difference between Bad S112 phosphorylation between DM and TM ( Figure 4A), phosphorylated S136 levels were lower in TM cells than in all other cells (p<0.001, Figure   4B). It was not possible to reliably quantify these levels relative to Bad itself since Bad signals were unreliably <3 times background levels (not shown). One of the best attested substrates of Akt is glycogen synthase kinase 3 beta (GSK3β), which is phosphorylated on S9 by Akt leading to inactivation of GSK3β [76]. Levels of phosphorylated GSK3β S9 were elevated in WT over MP cells, elevated once more by removing the inhibitory CK2 sites in the DM mutation, and reduced by the further mutation of Y180 in TM mutant cells ( Figure 4C-E). These results strengthen the model that the PI3K/Akt pathway that is activated in DM cells requires phosphorylated PGRMC1 Y180, and is therefore attenuated in TM cells.

PGRMC1 phosphorylation affects FAK activation and HSF levels
Pathways mapping (Supplemental Information File 5) suggested that transcription factor HSF1 activity could be involved in the difference between WT v. DM (adjP=0.006).
HSF1 has been linked with Focal Adhesion Kinase (FAK) activity [77], and FAK activity is dependent upon Rho/ROCK signaling which influences focal adhesion dynamics and tumor cell migration and invasion [78]. Reverse Phase Protein Array (RPPA) measurements showed that FAK1 tyrosine phosphorylation and increased HSF1 levels were all significantly elevated in WT and TM ( Figure 4F-H). Notably, this profile resembled the differential proteomics profile of Figure 3, rather than the ROCKdependent rounded morphology of Figure 1D.

Enhanced DM cell motility requires vinculin
Proteins involved with actin cytoskeleton were more abundant in DM cells ( Figure S5A), one of which was vinculin ( Figure S2A), an actin filament-binding protein associated with cell differentiation status, locomotion and PI3K/Akt, E-cadherin, and β-catenin-regulated Wnt signaling in colon carcinoma [79,80]. We attenuated vinculin levels in MP, WT, and DM cells via shRNA. MP and DM cells were obtained at first attempt, however, despite three attempts, WT cells could not be established. In the presence of scrambled shRNA control (shScr) DM cells exhibited elevated scratch assay motility relative to MP cells. However in anti-vinculin shRNA cells (shVNC), both MP and DM cell motility was reduced ( Figure S5). These results are consistent with the observed elevated levels of proteins involved in the actin cytoskeleton ( Figure S5A) contributing directly to the enhanced motility of DM cells ( Figure 1F-G). However, that hypothesis remains untested except for vinculin. PGRMC1 phosphorylation affects glucose uptake and lactate production: Figure 3 predicted altered glycolysis activity, which we investigated by glucose uptake and lactate production assays. Expression of all PGRMC1-HA proteins (WT, DM, and TM) led to significantly lower levels of both measures relative to MP, with DM cells exhibiting the lowest levels ( Figure 5A-B), confirming that PGRMC1 phosphorylation status regulates glucose uptake and lactate production, and consistent with recently reported regulation of Warburg metabolism by PGRMC1 [55], which we confirm is regulated by PGRMC1 phosphorylation status. Figure 3 also suggested that mitochondrial function may be affected by PGRMC1 phosphorylation status. Naphthalimide-flavin redox sensor 2 (NpFR2) is a fluorophore that is targeted to the mitochondrial matrix by a delocalized positive charge. Its fluorescence is elevated approximately 100-fold when oxidized and therefore provides a measure for the redox state of the inner mitochondrial matrix [81]. Cells treated with NpFR2 and assayed for fluorescence by flow cytometry revealed that the inner mitochondrial matrix of WT and TM cells was more oxidizing than MP and DM cells ( Figure 5C, D). This corresponded with the elevated expression of many nuclear-encoded mitochondrial proteins observed in Figure 3.

PGRMC1 phosphorylation affects mitochondrial function
To investigate this situation, we examined mitochondria using the fluorescent marker MitoTracker, whose affinity for mitochondria is affected by mitochondrial membrane potential (Δψm) [82]. Flow cytometry revealed the presence of two populations of MitoTracker-binding cells in each cell type: low and high MitoTracker-binding ( Figure   5E-G). These populations were approximately equal for MP, WT, and TM cells, however, DM cells exhibited overall lower relative fluorescence level in each population ( Figure   5E-F) and a higher proportion of cells with higher MitoTracker binding ( Figure 5E,G).
Notably, higher levels of mitochondrial proteins in WT and to some extent TM cells apparently did not correspond with higher Δψm caused by actively respiring mitochondria. Relative to MP cells, the maximal respiratory rate was reduced (between 2 and 3 fold in Figure 5H) by expression of DM or WT PGRMC1-HA, but not by TM cells ( Figure 5H). The relative profiles of basal ( Figure 5I) and maximal ( Figure 5J The single experiment including MP is shown. We conclude that the altered abundance of mitochondrial proteins due to PGRMC1 phosphorylation status detected in Figure 3 was accompanied by altered mitochondrial function. However, the relationship is not as simple as lower glucose uptake being associated with higher mitochondrial oxygen consumption, and may involve alterations in mitochondrial permeability to protons or other uncoupling mechanisms, for instance by altered cholesterol content [14]. The combination of higher glucose and mitochondrial oxygen consumption rates of MP and WT cells is reminiscent of Warburg aerobic glycolysis, with DM cells exhibiting aerobic mitochondrial respiration, and TM being intermediate, with elevate glucose consumption but high oxygen consumption.

PGRMC1 phosphorylation affects mitochondrial morphology and function
Possible differences in mitochondria due to altered protein expression or cell shape were explored by measuring mitochondrial content (area per cell), size (perimeter), and morphology, or form factor (FF). FF is a parameter derived from individual mitochondrial area and perimeter, where higher values correspond to a greater degree of filamentous than fragmented mitochondria [83,84]. Representative images of mitochondria are shown in Figure 6A. No significant differences in mitochondrial number were detected between cell types (not shown). Over the entire data set, elongated cells exhibited greater mitochondrial area, larger mitochondria, and greater average FF (avFF) (Kolmogorov-Smirnov p<0.0001; not shown). When analyzed according to PGRMC1 status (cell type), MP and WT cells were predominantly elongated, and DM and TM were predominantly rounded, as expected ( Figure 6A-B). We detected no significant differences in average mitochondrial area, perimeter or avFF between cell types for elongated cells, however rounded cell types exhibited significant differences between the cell types for area, perimeter, and avFF ( Figure 6B). All cells with avFF < 2.2 exhibited rounded cell shape, while all cells with avFF > 2.6 exhibited elongated shape ( Figure 6C). The observed avFF-associated transition from round to elongated cell shape was discrete for all cells except WT, occurring at avFF= 2.4 (MP), 2.2-2.6 (WT), 2.7 (DM) and 2.6 (TM). Notably, the single elongated TM cell also exhibited the highest avFF value for TM ( Figure 6C).
Holo-tomographic time-lapse videos [85] for each cell type with mitochondria visible are provided as Supplemental Information File 9. We conclude that PGRMC1 phosphorylation status probably influences mitochondrial content, size, and FF (degree of filamentation) by the same mechanisms that affect cell shape, consistent with the proposed influence of cytoskeleton on mitochondrial morphology and function [86].

PGRMC1 Y180 is required for subcutaneous mouse xenograft tumor growth
No significant differences in cell proliferation were observed for cell types in culture as measured by IncuCyte live cell imaging ( Figure 7A indicating that PGRMC1 Y180 was required for optimal tumor growth, demonstrating that the cellular responses to altered PGRMC1 phosphorylation indeed influence cancer biology. All WT, DM, TM and Y180F tumor tissue expressed the PGRMC1-HA proteins (not shown), and therefore arose from the injected cells. There were no obvious differences in histology between cell types based upon hematoxylin and eosin staining (not shown).

DISCUSSION
In this paper we report new biology regulated by the phosphorylation status of PGRMC1.
Expression of the WT, DM, and TM PGRMC1-HA proteins from Figure 1A profoundly affected cell morphology and migratory behavior. The morphotypic change from WT to DM resembled MAT in MP cells, being sensitive to ROCKI ( Figure 1D). The DM and TM altered morphology is dependent upon activated ROCK, which leads to stiffening of cortical actomyosin. That process promotes cell migration via rounded bleb-associated mode through sufficiently large pores that does not depend upon extracellular proteolysis [87]. In human glioma cells over-expression of CD99 is implicated in MAT, resulting in rounded morphology, increased Rho activity, and enhanced migration [88]. These properties superficially resemble the phenotype of our DM cells ( Figure 1C-E), however DM cell migration involved pseudopodia and cell adhesion as evidenced in cell migration videos (Supplemental Information File 1). Very little else is known at the molecular level about the events that promote MAT and altered cell motility [89][90][91][92]. We provide here a global expression study of a possibly MAT-related process, and show that PGRMC1 phosphorylation status dramatically affects mitochondrial morphology and function. PGRMC1 influenced both cell shape and mitochondrial function and morphology ( Figure   6). The elongated cell morphology that predominated in MP and WT cells was associated with a higher index of filamentous rather than fragmented mitochondria. Cells with rounded morphology and more fragmented mitochondria predominated in DM and TM cells ( Figure 6). Such changes in mitochondrial function are driven by altered relative rates of mitochondrial fission and fusion, leading to mitochondrial fragmentation or elongated hypertubulation, respectively [93]. Fragmented mitochondria are associated with pathological conditions including cardiovascular and neuromuscular disorders, cancer, obesity, and the process of aging, associated largely with altered cell differentiation [93]. One of the proteins more abundant in WT and TM cells was Opa1 (O60313) (Supplemental Information File 6), a protein known specifically to regulate mitochondrial fission/fusion, and one that has been reported to interact directly with PGRMC1 [19]. PGRMC1 phosphorylation may alter mitochondrial morphology via Opa1.
The strongest driver of mitochondrial morphology appears to have been cell shape, or vice versa ( Figure 6). The cytoskeleton is thought to influence mitochondrial function and morphology [86], and proteomics pathways mapping suggested cytoskeletal changes. WT  Figure S2A).
DM cells also displayed elevated levels of proteins in the T-complex protein-1 ring complex (TRiC, also known as CCT) ( Figure S2D), which contributes to folding of proteins including actin, microtubules, cyclins B and E, Von Hippel-Lindau tumour suppressor, and p21Ras, and is known to influence deregulated growth control, apoptosis, and genomic instability [95,96], additionally contributing obligatory growth/survival functions in breast [97] and liver [98] cancers. This complex is highly likely to contribute to the altered cytoskeletal properties and rounded phenotype of DM cells. Strikingly in terms of the results of our present study, TCP1 (P17987, Figure S2D) expression is driven by oncogenic PI3K signaling in breast cancer [97], and we observe both PGRMC1dependent elevated PI3K/Akt activity and TRiC protein abundances in DM cells ( Figure   S2D).
In summary, many of the mitochondrial differences observed could be attributable to altered cytoskeletal properties. However, the differential mitochondrial functions of Figure 5 did not correspond well with cell shape, indicating that PGRMC1 also changes complex causative processes driven by more than mitochondrial morphology.
Mitochondrial cholesterol levels could also contribute to the effect. PGRMC1 is important in sterol metabolism, and mitochondrial cholesterol decreases the permeability of the inner membrane to protons, increasing the efficiency of electron transport chain yield [14]. Further work will be required to explain the mechanisms underlying the observed responses, which we are currently exploring.
Closer analysis of the results depicted in Figure 3 (as presented in Supplemental Information File 5 and Supplemental Information File 6) revealed that both the ATP synthase subunit beta of the F1 catalytic domain as well as the F0 proton pore domain were up-regulated in WT cells relative to DM cells ( Figure S2B). It is possible that the higher Δψm of DM cells is related to low levels of F0/F1 ATPase proton channel ( Figure   S2B), resulting in relatively inefficient proton gradient clearance. Once again, altered membrane cholesterol levels could also influence Δψm [14].  [22] confirmed a nucleolar localization for PGRMC1, where it was responsible for nuclear localization of nucleolin which they proposed was associated with stress response. The zebrafish knockout of PGRMC1 results in elevated levels of mPRα mRNA, but decreased levels of the corresponding protein [99], suggesting that PGRMC1 can indeed affect the translational efficiency of certain mRNAs by ribosomes, which is consistent with our pathways analysis results, and especially the principal components analysis of Figure S1 which predicts that ribosomes and translation contributed most to the differences between cells.
Pathways enrichment suggested that PI3K/Akt signaling was higher in DM and lower in TM cells, which suggested that PI3K/Akt signaling required Y180 for activity ( Figure 3, Figure S2B). PGRMC1 has long been recognized as a modulator of Akt activity (and downstream PI3K), with cell type-specific effects [13,41,44,45,[100][101][102]. Modulation of Akt activity was confirmed by assaying phosphorylation of Akt substrates ( Figure   4B,E-F). This predicted activation of signals by removal of the putative inhibitory CK2 consensus sites in the DM protein [5,44] was dependent upon Y180 because the triple mutant S57A/Y180F/S181A (which differs from DM by a single oxygen atom) exhibited a protein expression profile and metabolism that was more similar to WT than DM ( Figure 3). These changes were accompanied by altered glycolysis and mitochondrial function ( Figure 5), and reduced growth of subcutaneous tumors (Figure 7). In a methylomics study of these cells in an accompanying paper, the most significantly downregulated KEGG pathway in the TM/DM comparison was hsa04151 (PI3K-Akt signaling pathway) [65], strengthening the case for PI3K/Akt activation via PGRMC1 Y180.
Interestingly, PGRMC1 knockdown in hPSCs led to an increase in GSK3β inhibitory phosphorylation [51]. That merits examination of PGRMC1 phosphorylation status in that system, where PGRMC1 suppressed the p53 and Wnt pathways to maintain stem cell pluripotency. Similarly to our results, those authors concluded that "that PGRMC1 is able to suppress broad networks necessary for multi-lineage fate specification." Our hypothesis suggests that PGRMC1 Y180 phosphorylation and PI3K/Akt activity could be associated with elevated GSK-3β Ser9 phosphorylation and β-catenin signaling in some cancers [80].
Our initial hypothesis related to differential phosphorylation of PGRMC1, being potentially spatially and temporally associated with the onset of glucose-dependent metabolism (Warburg effect) [44]. While this manuscript was in preparation, Sabbir showed that PGRMC1 post-translational modification status in HEK293 cells changes in response to P4 treatment, which was accompanied by a PGRMC1-dependent increase in glycolysis [55]. Because the phospho-acceptor amino acid Y180 has been conserved in PGRMC1 proteins since early animal evolution [6,7], we believe it likely that PGRMC1 Y180-regulated modulation of metabolic and growth control that we have manipulated could represent a major newly identified foundational axis of animal cell biology whose perturbation is inconsistent with the maintenance of differentiated states acquired during the subsequent evolution of complex body plans.

Conclusion
We show that PGRMC1 phosphorylation status exerts higher order influence over a wide range of clinically important measures of cell biology. Although we can confidently deduce the existence of a PGRMC1 signal network, as yet we have identified neither immediate upstream PGRMC1 effectors nor downstream targets. PGRMC1-like proteins have possessed the cognate of phosphoacceptor Y180 since the appearance of early animals, and the increasing complexity of cellular adaptation to multicellular life during animal evolution has been associated with the acquisition of an increasingly sophisticated retinue of phosphorylation sites surrounding Y180, and elsewhere in the protein [6]. The stem cell-like zygote (most similar animal cells to the unicellular animal ancestor) expresses cross-phylum conserved genes involved in processes such as cell cycle, mitosis, and chromatin structure [103]. All of these processes can be influenced by PGRMC1 [3].
During animal development later embryological stages involve the induction of conserved germ layer-specific genes such as those for muscle [103]. This may be related to DM actin biology seen in our system, and suggests the hypothesis that CK2-like-site mediated negative regulation of PGRMC1 could be involved in these embryological processes, which merits further investigation. We propose that PGRMC1 phosphorylation status, especially of the motif surrounding Y180, dramatically regulates cellular identityconferring mechanisms with deep evolutionary history relating to bilateral animal origins, such as the stem cell-associated anaerobic/glycolytic metabolism of the Warburg effect [55,56]. As such, we predict that these processes could be involved in a wide range of human pathologies, and are particularly pertinent to cancer biology. Future studies should urgently explore the relationship between PGRMC1 signaling and diseases such as cancer, diabetes, Alzheimer's disease, and others [3]. The novel and highly pleiotropic nature of the PGRMC1 signaling system [3] means that it could potentially contain new avenues for pharmaceutical interventions. In an accompanying paper [65], we show that the cells characterized in this paper differ dramatically in genomic methylation and mutation rates.

WebGestalt enrichment analyses
Gene Ontology (GO) and pathway enrichment analysis were conducted on differentially abundant proteins from Figure S2C using the WEB-based GEne SeT AnaLysis Toolkit

Mapping pathways to the expression heat map
The matrix of protein membership to pathway or functional group category is a resulting sparse matrix with 0/1 indicating that the respective protein is/is not present in the respective category. This matrix was clustered using the hclust implementation in the R Base Package (www.r-project.org/), using a binary distance and complete linkage, to reorder the columns (pathways in this case) according to the proportion of shared proteins. The resulting cladogram including overlapping features identified by all WebGestalt analyses appears to the right of pathways in Figure 3, and with complete accompanying protein and pathway identities in Supplemental Information File 5.

Principal Components Analysis on Proteomics results
Principal component analysis was used to examine the largest contributions to variation in the protein measurements. Wilcoxon rank-sum tests were used to identify the pathways that were positively or negatively associated with the principal component scores.

Sample Preparation for Western blots
Approximately 70% confluent cells in a T75 flask were washed twice with chilled PBS buffer and incubated with 500 µL radio immunoprecipitation assay buffer (RIPA buffer) Protein expression and activity levels were measured using a direct two-step sequential immunoassay and sensitive, quantitative fluorescence read-out.

Glucose uptake & Lactate production assay
Glucose uptake and lactate production assays were performed by using commercially available kits from Cayman chemical (#600470, #700510) following manufacturer's protocols. Glucose uptake was measured with a Fluostar Omega fluorescence microplate reader (BMG Labtech, Ortenberg, Germany) and lactate production was quantified with a Molecular Devices Spectra Max 190 microplate reader (Bio-Strategy P/L, Campbellfield, Vic., Australia).

NpFR2 redox assay
Intramitochondrial redox status was measured by naphthalimide flavin redox sensor 2 (NpFR2) [81]. Mia PaCa-2 and PGRMC1-HA-expressing stable cells (1x 10 6 ) were suspended in 2 mL complete media and seeded in six well plates and cultured for 24 hr at 37ºC and 5% CO2. Cells were washed with PBS, trypsinsed, harvested, and resuspended in 1 mL of fresh media containing 25μM NpFR2 in a 1.5 mL microcentrifuge tube, followed by incubation for 20 min at 37ºC. Cells were then centrifuged in a microcentrifuge at 180 x g, the pellet was resuspended once with 1 mL PBS followed by recentrifugation, and the washed pellet was again resuspended in 1 mL PBS. 500 μL cell suspension was loaded to a Gallios Flow Cytometer (Beckman Coulter) and fluorescence of 2x10 4 cells was detected using FL1 (green) channel.

Immunofluorescence microscopy
To detect the expression of exogenous HA tagged PGRMC1 in Figure 1E, cells were seeded on coverslips on a six well plate. The cells were washed with ice-cold PBS, mildly fixed with 3.7% formaldehyde for 5 minutes at 4ºC. The cells were then permeabilized with ice-cold 100% methanol for 10 minutes at -20ºC, followed by overnight incubation with anti-HA tag antibody (Sigma, H3663). The cells were washed extensively and incubated with FITC conjugated secondary antibody (Sigma, F8521) in dark for 1 hour at 4ºC. Cells were washed three times with PBS and counterstained with DAPI mounting solution. Images were captured using a Nikon Ti Eclipse Confocal microscope (Nikon Australia Pty Ltd).

Analysis of mitochondrial morphology
Mitochondria were quantified for cell shape (elongated/round), mitochondrial content (sum of mitochondrial area/cell), mitochondrial size (average perimeter/cell), and mitochondrial morphology or Formfactor (FF): a measure where higher values correspond to a greater level of filamentous mitochondria and lower values correspond to more highly fragmented mitochondria [84]. Formfactor (calculated as the P 2 /4πA) measures mitochondrial morphology based on the perimeter and area of shape. The calculation takes in to account not only changes in length, but also the degree of branching, making at an ideal form of measurement for the quantification of mitochondrial morphology.
To measure form factor, 1x10 5 cells were seeded onto Nunc 176740 four well plates with a 22x22mm #1.5 glass coverslip on the bottom. Cells were fixed and permeabilized as above, then incubated with Abcam mouse anti-mitochondrial IgG1 antibody (Abcam ab3298) and then with FITC-conjugated goat anti-mouse secondary antibody (Sigma-Aldrich F4018) and DAPI, followed phalloidin red staining and imaged with 3D-Structured Illumination Microscopy (SIM) on a DeltaVisionOMX Blaze microscope as described [111]. Images were processed using Fiji/ImageJ software [112], and Area and Perimeter values were extracted to calculate form factor. Cell morphology was scored as either 'round' or 'elongated' by JCC as part of the mitochondrial quantification process.

Holo-tomographic imaging
Holo-tomographic video imaging was performed on a NanoLive (Switzerland) 3D Cell Explorer fluo (AXT Pty Ltd, Warriewood, NSW) equipped with a NanoLive live cell incubator (AXT Pty Ltd). 1x10 4 cells were seeded into a FluoroDish cell culture dish 35mm, 23mm well (World Precision Instruments, FD35) and maintained in phenol red free DMEM medium (Sigma-Aldrich, D1145) supplemented with 10% fetal bovine calf serum (Sigma-Aldrich, F9423), 2 mM glutamine (Sigma-Aldrich, G7513) and 1% penicillin-streptomycin for 48 hours. Immediately prior to imaging the medium was removed and replaced with 400 μL of the same medium, followed by transfer to the live cell incubator chamber of the 3D Cell Explorer. Cells were incubated at 37°C, 5% CO2 and 100% humidity for the duration of the time-lapse. Three dimensional holotomographic images were captured every 20 seconds for the duration of the time-lapse using the Nanolive STEVE software. For Supplemental Information File 9 the center plane of each 96 slice stack was exported after capture using the built in STEVE export wizard as an .avi movie file. These files were exported at 5 frames per second (100x actual speed) to visualize cellular dynamics.

Subcutaneous mouse xenograft tumors
Cells were expanded in culture for a maximum of 2 weeks before injection. Cells were trypsinized, pelleted, washed with PBS and stored on ice. Cell count was determined using a hemocytometer and trypan blue. Two million cells were resuspended in 100 µL

Statistical Analyses
Unless specified otherwise, statistical analysis was performed using the SPSS package

Consent for publication
Not applicable.

Availability of data and materials
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (http://www.ebi.ac.uk/pride) with the dataset identifiers PXD014716 ( Figure 3) and PXD014789 ( Figure S4). Further data are provided in 9 supporting information files available from the journal web page.   Pathways significantly enriched at the adjP>0.001 level between all 6 comparisons of "red" and "blue" differential proteins (red = higher abundance, blue = lower abundance, white = equal abundance). Top left: the proteomic heat map of 243 significantly differential proteins. A color code for WebGestalt pathways is given at top right.

M.A.C. is scientific advisor to and minor shareholder of Cognition
Bottom: WebGestalt pathways mapping. This image is derived from Supplemental Information File 6, which contains all protein and WebGestalt pathway identities. (A) Glucose uptake by cell lines using the Cayman "Glycolysis" kit. The boxplots represent four technical replicates each for each of independent stably transfected sublines 1-3 of each condition WT, DM and TM (lines from Figure 1). i.e. n=4x3 = 12 per condition. For MP cells, 12 replicates of the MP cell line were performed. A Shapiro-Wilk test showed the data were not normally distributed. There was significant difference between the means (Kruskal-Wallis Test p<0.0001). Pairwise Two-Sample Kolmogorov-Smirnov Tests revealed that all means were significantly different from one another (each p<0.0002).
(B) Lactate secretion by cell lines. Details follow (A), except duplicates of each stable cell line were measured (n=3x2 = 6 per condition) using the Cayman "Glycolysis Cell-Based Assay" kit, which measures lactate secretion. One way ANOVA showed that the means were significantly different between cell types (F=167.65, p<0.000001). Levene's test revealed unequal variances (F=1.25, p=0.017). Inter cell-type comparison post-hoc Dunnet's T3 tests revealed that the means of all pairwise comparisons were significantly different from one another at the p<0.003 level, except the WT-TM comparison which was not significant (p=0.211).
(C) 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. (D) Boxplots of the percentage of cells exhibiting >10 fluorescent intensity units to the right of the reference line in (C). n=6 for each cell type, being 6 replicates of MP cells, or duplicate measurements of each of 3 independent lines 1-3 (n=3x2=6) for WT, DM and TM cells. White arrows indicate the same differences as in (C). 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).       Figure  S4A except Q9BPW8 were detected in the shRNA experiment. Double headed arrows indicate proteins which differ in expression tendency between WT (left) and WTscramble 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. (B) Features from A, viewed at the adjP<0.05 level for each red and blue comparison, and showing adjusted p-values (adjP) for each comparison where adjP<0.05 (or adjP<0.1 for two comparisons as indicated by paler coloring). Blue means that proteins associated with a given the feature were less abundant in that cell line, with red indicating higher abundance. Because separate WebGestalt analyses were performed for red and blue lists of proteins from Supplemental Information File 4, some features were significant for both red and blue. In that case the color and adjP for the most significant analysis is given, with the other cell being colored black.
Supplemental Information File 6. An excel file with heat map protein IDs and pathways for red vs. blue pathways adjP<0.001. Related to Figure 3. This file is derived from the results of Supplemental Information File 5, and is the source file for Figure 3. Proteins suggested by clustering by inferred models of evolution (CLIME) analysis to co-evolve with PGRMC1 with log likelihood ratio greater than 12 [14] are present in the list, marked yellow for mitochondrial localization (WebGestalt GO:0005739) or green for cytoplasmic (P00387).

Supplemental Information File 7.
Original WebGestalt results files for the analysis of Supplemental Information File 9. A zip archive containing time lapse Holotomographic video .avi files of cells. These images are based upon differences in refractive index [85], and are provided for the dynamic visualization of mitochondria. Prominent visible features include small white lipid droplets and cholesterol-rich mitochondria [14], as well as nuclear membrane and nucleoli. The previously described MIA PaCa-2 cell bleb-like protrusions [63] are apparent as highly dynamic rearrangements of the cytoplasmic membrane, which may contribute to intercellular communication.