Reprogramming of glucose metabolism via PFKFB4 is critical in FGF16-driven invasion of breast cancer cells

The conversion of a benign epithelial tissue toward a fully malignant carcinoma entails extensive remodeling in tissue architecture, driven by both genetic and micro-environmental factors [1]. Epithelial–mesenchymal transition (EMT), an embryonic trans-differentiation program, contributes largely in cancer progression by converting primary tumor cells into invasive in nature [2]. During EMT, notable cellular changes include loss of cell adhesions, change in cell polarity, interactions between new plasma membrane receptors and remodeled extracellular matrix (ECM) components; all finally lead to cell migration and generation of a more plastic mesenchymal cells [2,3]. This change in cellular phenotypes essentially involves the remodeling of the ECM components including collagens, laminin, fibronectin, proteoglycans etc., through a series of qualitative and quantitative changes. This ECM remodelling thus appears to be a key step in facilitating tumor growth and spread by interfering with cell–cell adhesion, polarity, and by augmenting growth factor signaling [4].

In normal cells, multiple metabolic cycles are always operative which provide constant supply of energy and biomass. However, this metabolic program is altered in cancer cells to sustain their continuous growth/proliferation and to adapt to new microenvironment at distant location [5]. A high rate of glycolytic flux, even in the presence of functional mitochondria and oxygen, is a typical metabolic hallmark of tumors. Although this ‘aerobic glycolysis’ is actually less energy-efficient pathway than oxidative phosphorylation, this reprogrammed metabolism confers several growth advantages on cancer cells including cell death evasion, cell proliferation, angiogenesis, and metastasis [6,7]. Thus, the phenomenon of metabolic alterations appears to facilitate successful spread and growth of metastatic tumour at distant sites [5]. The molecular mechanism underlying this metabolic reprogramming of cancer cells is complex and unique for each cancer type. Pinpointing the altered metabolic step can lead to more effectively targeting the metabolism of cancer patient with the consequence of reduced cancer invasiveness.

Gradual acquisition of cell invasiveness can take place due to sequential genetic and epigenetic changes, triggering induction of EMT in response to micro-environmental cues, for example secreted growth factors [8]. The fibroblast growth factor (FGF) family, comprising of 22 ligands, exerts their functions through four, highly conserved, cell–surface tyrosine kinase receptors (FGFR1, FGFR2, FGFR3 or FGFR4) [9]. FGF/FGFR signaling has established roles in the control of multiple biological processes, such as endocrine homeostasis, wound repair, cellular proliferation, differentiation, and survival [10]. However, FGF/FGFR pathway has been demonstrated to have a significant relevance in tumor growth, metastasis, and resistance to anticancer therapies [11–14]. Aberrant FGF/FGFR activation may occur either in ligand-dependent or -independent manner in several cancer types, including breast cancer. Overexpression of FGF ligands cause excessive mitogenic signaling through FGFR, promoting cancer progression. Example includes the elevated expression of FGF2 [15] and its involvement in driving basal-like breast cancer through the autocrine activation of FGFR signaling [16]. FGF2 could also promote BC growth in hormone-independent manner, leading to endocrine therapy resistance [17]. Fillmore et al. has already demonstrated the potential involvement of paracrine FGF9/FGFR signaling in the estrogen-mediated expansion of a breast cancer stem-cell-like subpopulation in vitro [18]. FGF16, a member of FGF9 sub-family, is a 207-amino acid protein with a core region of 120 amino acids that acts through binding to heparin and cell surface FGF receptor (FGFR) [19]. Recently, FGF16 is implicated in the progression of hepatocellular [20], lung [21], and embryonic carcinoma [22] with unique mechanistic action for each cancer type. In continuation to these findings, we attempted to investigate the potential oncogenicity of FGF16 and the underlying mechanism in breast cancer progression.

In the present study, the oncogenic potential of FGF16 in breast cancer progression has been demonstrated, where the activation of EMT was found to be critical. Further, it was unraveled that a set of genes involved in ECM remodeling is regulated by FGF16. Detail studies uncovered a hitherto unidentified role of FGF16 in promoting aerobic glycolysis where a robust expression of PFKFB4 was particularly observed. Overall, our findings provide a basis for developing a new metabolism-targeted therapeutic intervention to reduce aggressiveness of breast cancer.

FGF signaling has been implicated in pathogenesis of many cancers [23]. To investigate the potential role of FGF16 in breast cancer development, we explored whether it can trigger EMT with immortalized human mammary epithelial cell-line MCF10A. In comparison with vehicle-treatment, cells treated with rhFGF16 exhibited morphological alteration, shifting from round cobblestone-like to fibroblast-like elongated shape (Figure 1A). At the molecular level, reduced expression (by ∼50%, P<0.05) of epithelial markers like desmoplakin (DSP) and E-cadherin (CDH1), and significant increase in mesenchymal markers like vimentin (VIM) and N-cadherin (CDH2) was observed in FGF16-treated cells (Figure 1B,C). Treatment of cells with FGFR-inhibitor, PD173074 (marked as PD), caused a marked reduction in mesenchymal markers and increase in epithelial genes (Figure 1B,C). The same trend was observed in MCF7 cell line (Supplementary Figure S1A). Similarly, combined treatment of FGF16 with its neutralizing antibody reversed the change in expression of EMT markers (Figure 1B,C), supporting a definite contribution of FGF16 in promoting EMT. This reduction in E-cadherin, concomitant with the increase in Vimentin, was reinforced at their protein levels by Western blot (Figure 1D) and confocal imaging (Figure 1E,F and Supplementary Figure S1B). In addition, the reorganization of actin cytoskeleton during morphological change in cells was quite evident from phalloidin staining (Supplementary Figure S1C). Here, pre-treatment of PD along with FGF16 exhibited partial morphological alterations. Further, morphometric analysis of nuclei in DAPI-stained cells revealed that FGF-treated cells had nuclei of significantly larger volume compared with those of control cells (Supplementary Figure 1D). This observation is in agreement with earlier report showing that the alteration in nuclear morphology and architecture are associated with transformation into cancer [24].

Growth factors, in general, confer their impact on any cellular events, majorly through the activation of signaling pathway(s). To verify this, the activation of P38, MAPK, JNK, and PI3K pathways were assessed and observed the MAPK pathway to be active by 40 ng/ml FGF16 at 1.5 h (Supplementary Figure S1 E,F and Figure 1G). Here, the working dose of PD and U0126 was also determined (Supplementary Figure S1G,H). Subsequent changes in the expression of CDH1 and CDH2 genes (Figure 1H) convinced the implication of MAPK pathway in FGF16-induced onset of EMT. Activation of EMT facilitates increased cell motility and invasiveness, key events in cancer metastasis [2,25]. At first, the wound healing assay was performed which exhibited a faster wound closure upon FGF16 treatment compared with vehicle-treated (control) MCF10A cells after 24 h (Figure 2A, compare panels ii and iii), while PD-treatment significantly (P<0.05) inhibited FGF16-mediated cell movement (Figure 2A, compare panels iii and iv). The results are quantitatively displayed in Figure 2B. This finding was verified in the MCF7 cell line (Supplementary Figure 1I-J). Similarly, matrigel invasion assay demonstrated that rhFGF16 stimulated invasion of MCF10A cells by ∼1.5 fold (Figure 2C,D, P<0.05), while PD-treatment remarkably prevented this invasion.

Since four isoforms make up FGFR, a natural question arises: which of the four isoforms is/are responsible for the early switch in FGF16-activated signaling? This question was addressed by estimating the relative binding affinities of the four isoforms to FGF16 using established in silico methods. The starting point was the crystal structure of FGFR1 bound to FGF9 (PDB ID: 5w59). Since FGF9 shares significant sequence homology with FGF16, homology models of FGF16 bound to all four isoforms were built using FGFR1 bound FGF9 as the template. Using the homology models as starting structures, molecular dynamics simulations were performed and the trajectories were analyzed using standard sampling techniques for estimating the relative binding free energies of the four isoforms with FGF16 (see Supplementary Methods, Supplementary Results and Supplementary Table S1) as: ΔΔG = 0.0, 2.2, 3.2 and 7.1 kcal/mol for FGFR1, FGFR2, FGFR3, and FGFR4, respectively. The trend in relative binding affinities was then compared with minor sequence alterations in the four isoforms at the binding interface with FGF16 (Supplementary Figure S2). Binding affinities matched with the sequence alterations, especially FGFR4, with the least predicted affinity showed the maximum variation, suggesting that the relative binding affinities follow the trend: FGFR1 > FGFR2 > FGFR3 >> FGFR4.

The clinical relevance of FGF16 expression was investigated. Q-PCR assay (n = 5 paired and 8 orphan; Figure 3A; P<0.005) and immuno-flourescence-based IHC analysis (n=6 Paired; Figure 3B) demonstrated elevated expression of FGF16 in human breast cancer compared to normal breast tissues. Then, we extended our investigation on xenograft mouse model. Here 4T1-FLAG or 4T1-FGF16 (both stably expressed) was injected subcutaneously into female Balb/c mice and tumors appeared. The size of the tumor, as induced by FGF16, was significantly bigger than that of empty vector transfected set (Figure 3C,D) supporting the contribution of FGF16 in the growth of primary tumor. Immunoflourescence-based IHC analysis demonstrated the reduction in E-cadherin (Figure 3E) and the increase in vimentin (Figure 3F) expression in FGF-induced tumor sections. Furthermore, a remarkable increase in the expression of laminin, a basement membrane protein involved in breast cancer invasion was also evidenced in tumor section (Figure 3G). In addition, prominent staining of Ki67 supported the proliferation of cancer cells in both sets (Figure 3H). Hematoxylin–Eosin staining of the respective tissue sections were performed (Supplementary Figure S3A). In summary, elevated expression in tumor samples and induction of cell invasiveness/migration supported the oncogenic potential of FGF16 in breast cancer.

To identify the functional signatures underlying FGF16-induced oncogenicity, whole cell transcript analysis was performed with MCF10A after exposure with FGF16 for 24 h. The treatment of 24 h was selected since the changes in EMT markers were evidenced at this time point (Figure 1). Notably, the expression of a large number of genes was found to be altered whereas several genes remained unchanged at this time point (Figure 4A). Within this list of genes, while applying fold change cutoff of 1.5 (log2 FC >0.58 or < −0.58) and P-value <0.05, 956 genes were found to be differentially altered; among them 691 genes were up- and 265 genes were down-regulated (Supplementary Table S2 and 3). Next, DAVID bioinformatic tool was applied and it was found that differentially expressed (DE) genes are involved in a variety of biological processes including angiogenesis, nucleosome assembly, telomere organization, protein metabolic process, ECM disassembly, cell adhesion etc. (Figure 4B). At the same time, pathway analysis by PANTHER Classification System revealed that these DE genes are associated with several cancer-associated signaling pathways including angiogenesis, RAS-pathway(P04393), EGFR signaling pathway (P00018), PDGF (P00047)-, FGF (P00021)-, VEGF(P00056)-, cadherin (P00012)- and integrin-signaling pathway (P00034) and glycolysis (P00024, Supplementary Figure S3B).

Next, we undertook an in-depth analysis of the altered transcription profile. Interestingly, from the total DE gene list, we observed that 41 genes related to extracellular matrix (ECM) organization were deregulated by FGF16 (Figure 4C). Moreover, the enrichment score plot from Gene Set Enrichment Analysis (GSEA) showed a differential distribution of ECM regulator genes (Figure 4D) in FGF16-stimulated cells. Taken together, the bioinformatic analysis of the altered transcription profile revealed a possible role of FGF16 in ECM regulation.

Considering ECM remodeling to be a key determinant of cancer progression, we were motivated to verify the change in a subset of ECM-related genes by Q-PCR assay in the cell-line. Hence, we picked up representative genes of collagen, integrin, laminin, keratin families, all being structural components of ECM. FGF16 stimulation caused up-regulation of KRT14, MCAM, LAMA5, LAMC2, and COL12A1 genes and down-regulation of ITGB6, ITGB2, ITGA6, COL4A2, and COL8A1 genes in MCF10A whereas treatment of receptor inhibitor (RI) with FGF16 somewhat reversed the changes in their expressions (Figure 4E). In continuation, immunofluorescence imaging confirmed the similar change in laminin and Integrin A6 in their protein levels (Supplementary Figure 3C,D) as well. On the contrary, the mRNA level of ITGA6 and ITGB6 were induced in the presence of FGF16 in MCF7 cell-line, which could be reverted with PD treatment (Supplementary Figure S3E). Altogether, the regulatory role of FGF16 over ECM related gene expression was established.

Change in the metabolic program is perhaps an emerging driving force to support detachment from ECM to initiate cell migration [5]. Based on our cues on pro-migratory role of FGF16, the change in metabolism was investigated in this regard. Interestingly, RNA-sequencing data revealed a group of transporters i.e. solute carrier (SLC) genes to be differentially altered (Supplementary Figure S4A) by FGF16. Among them, a prominent up-regulation in GLUT3 (SLC2A3) was indicated in FGF-treated cells. Q-PCR assay also validated the increase in GLUT3 by ∼2.5 fold (P<0.05) whereas pre-treatment with PD reverted the FGF-induced expression (Figure 5A). In contrast, such alteration was not evidenced in its isoform GLUT1 by FGF16 (Figure 5A). A similar trend was noticed in the MCF7 cell line as well (Supplementary Figure S4B). Through the up-regulation of GLUT3 expression, cells enhance glucose uptake and subsequently glycolysis. Hence, 2-NBD-glucose uptake assay was performed in MCF10A. The presence of FGF16 increased the glucose uptake (Figure 5B, P<0.05) that was compromised while GLUT3 was down-regulated by siRNA. Moreover, the combination of FGF16 and siGLUT3 showed the glucose uptake to be lower than FGF16 alone (Figure 5B). In parallel, the uptake was measured in presence of Apigenin, a flavonoid inhibitor of GLUT1 mediated glucose transport [26], to verify the assay performance. Apigenin alone was found to be able to lower the glucose uptake but jointly with FGF16 could retrieve the same extent of glucose uptake as was observed with FGF16 alone (Figure 5B). This finding could indicate the implication of GLUT3 over GLUT1 in FGF16-induced cellular glucose uptake. The knockdown efficiency of siRNA against GLUT3 was verified by QPCR assay (Supplementary Figure S4C).

Next, the influence of FGF16 on the expression of key glycolytic genes was assessed by Q-PCR assay. A noticeable increase was observed only for ENO1, GAPDH by FGF16 that was abrogated by PD pre-treatment (Figure 5C). However, the change in expression of HK1 and HK2 was merely remarkable (Figure 5C). Interestingly, between lactate dehydrogenase A and B (LDHA and LDHB), which govern the production of lactate, LDHA was also found to be elevated by ∼5 fold, not observed for LDHB, its other isoform (Figure 5C). The same trend was observed in MCF7 as well (Supplementary Figure S4D). Next, the alteration in metabolic profile of MCF10A was examined by 1D proton NMR-spectroscopy. FGF16 enhanced the production of endogenous intermediates of glycolysis (Figure 5D), namely glucose-6-phosphate (G6P), 2-phosphoglycerate (2PG) whereas pre-treatment with PD reversed this induction. Intriguingly, the intracellular level of lactate, the end-product of aerobic glycolysis, was found to be ∼2 fold higher despite a mere increase in pyruvate in FGF16-induced cells (Figure 5D). A prominent reversal in the level of all these intermediates was evident in the presence of PD (Figure 5D). Lactate, after being produced in cells, is mostly secreted in the medium. Hence the metabolic profile of extracellular portion was also examined. Partial least-square discriminant analysis (PLS-DA) of NMR spectral bins of extracellular metabolites demonstrated distinct separation among untreated control cells, cells treated with FGF16 alone or in combination with PD (Figure 5E). Concomitant with the intracellular level, FGF16 increased the secreted lactate concentration by 1.8-fold (P<0.05) whereas pre-treatment with PD decreased it by ∼80% (Figure 5F, P<0.05). This certainly indicates a FGF16-driven metabolic shift toward aerobic glycolysis concomitant with increase in cell number (Supplementary Figure S4E). Subsequently, we verified whether our earlier observation of induced wound closure by FGF16 is influenced by the increased cell proliferation. After finalizing the working dose of 5FU (Supplementary Figure S4F,G), wound healing assay demonstrated that the treatment of cells with 5-FU could merely affect the wound closure by FGF16 (Supplementary Figure S4H).

To establish a mechanistic link between FGF16 and glycolytic induction, the rate-limiting steps of this pathway were investigated. Since our RNA-sequencing data demonstrated a significant up-regulation of PFKFB4 expression by FGF16, we pinpointed on the members of PFK gene family to assess their potential role. Mammalian PFK-1 exists in three isoforms PFKP, PFKL and PFKM. Upon FGF16 over-expression, only PFKP showed a modest increase in its mRNA level, while the other two isoforms (PFKL and PFKM) were slightly decreased (Figure 6A). Two isozymes of PFK2, i.e. PFKFB3 and PFKFB4, showed elevated expression in many cancer types and are involved in altered glycolysis [27]. Interestingly, the expression of PFKFB4 was significantly enhanced by FGF16 (Figure 6A) whereas PD treatment abrogated the same. Here, the observation of PFKFB3 to be slightly decreased by FGF16 led us to hypothesize PFKFB4 to be a potential player in the control of FGF16-mediated glycolytic induction (Figure 6A). This finding was verified with MCF7 cell line (Supplementary Figure S5A). Western blot assay demonstrated a similar trend in their protein levels (Supplementary Figure S5B). Detail study uncovered that GLUT3 and PFKFB4 genes were turned on by the activated MAPK-signaling pathway as U0126-tretament reversed the FGF16-induced observation (Figure 6B). Subsequently, lactate assay was performed where an increase in extracellular lactate content was observed by FGF16 (Figure 6C), typical to our earlier finding (Figure 5F). Surprisingly, siRNA-mediated abrogation of PFKFB4 reduced this secretion by FGF16 by >2-fold (Figure 6C, P<0.05). However, down-regulation of PFKP reduced lactate production albeit maintained a moderately higher level in combination with FGF16 (Figure 6C, P<0.05).

Furthermore, we deciphered the influence of the FGF16-FGFR-PFKFB4 axis on facilitating the onset of ECM remodelling as well as EMT. For this, the changes in expression of two EMT regulators, ZEB1 and SNAIL, two mesenchymal markers, Vimentin (VIM) and N-Cadherin (CDH2), and one ECM degrading enzyme, Matrix Metalloproteinase-2 (MMP-2) were quantified at their mRNA level in both MDA-MB-231 (Figure 6D) and MCF10A (Supplementary Figure S5C). Noticeable up-regulation of all the five genes was observed by FGF16 (Figure 6D) that was compromised by PFKFB4-siRNA. In contrast, suppression of PFKP resulted in the up-regulation of all the five genes and maintained the very same trend in combination with FGF16 (Figure 6D) as observed with FGF16 alone. Similar trend in the change in expression of these selected genes was found in MCF10A (Supplementary Figure S5C) as well. This signifies that FGF16 and PFKFB4 share an equivalent mode of action to augment cell motility at molecular level. The knockdown efficiencies of shRNA and siRNA, against PFKP and PFKFB4, respectively, were verified by QPCR assay and Western Blot (Supplementary Figure S5D,E). This investigation was extended at the phenotypic level by matrigel invasion assay in MDA-MB-231 because of its aggressive nature. Here, FGF16-driven cell invasiveness was reduced by ∼50% (P<0.05) when PFKFB4 expression was abrogated by siRNA (Figure 6E). In contrast, shPFKP could not notably affect the cellular invasion alone or in combination with FGF16. Overall, our findings support the critical involvement of PFKFB4 isoform in FGF16-promoted metabolic shift followed by cell motility.

The major cause of fatality in breast cancer is its aggressiveness, achieved through increased motility/invasion of cells, promoted by growth factors and cytokines, secreted from tumor micro-environmental cells. Concurrently, reactivation of EMT in adult cells triggers localized invasion and hence is considered to be the first step in metastatic dissemination [28] and is correlated, to some extent, to poor patient survival [29–31]. This transient up-regulation of EMT-inducing transcription factors occurs as a response to the activation of intracellular signaling pathways by various growth factors including FGF [32]. Therefore, FGF signaling has been implicated in promoting the oncogenic progression [10]. Apart from developmental role in cardiomyocytes [33], FGF16 has been identified to be potentially involved in hepatocellular carcinoma [20] as well as in lung cancer [21]. Our investigation was initiated with the finding of elevated expression of FGF16 in human breast tumor samples. Detail study uncovered its involvement in the activation of MAPK signaling cascade leading to onset of EMT followed by cell invasion (Figures 1 and 2). In conjunction, key components of ECM were found to be altered (Figure 4) which may, in turn, support tumor growth and spread. Moreover, gradual growth in tumor size reinforced the oncogenic potential of FGF16. High expression of FGF8 has been implicated in promoting EMT and tumorigenesis in oral squamous carcinoma cells and in colorectal cancer [34,35]. Our finding further supports the emerging evidences demonstrating atypical expression of FGFs and aberrant FGF/FGFR signaling in cancer development.

Whole cell transcriptomic analysis unravelled that a group of genes of solute carrier (SLC) family is differentially regulated by FGF16, indicating a possibility of metabolic alteration, an oncogenic hallmark. The SLC superfamily encodes membrane transport proteins for exchanging molecules including glucose, amino acids, vitamins, nucleotides, inorganic ions, organic ions, neurotransmitters and drugs [36]. Altered expression of SLC genes has been demonstrated to affect various steps of tumorigenesis, including proliferation, apoptosis, invasion and metastasis, chemotherapy resistance [37]. Even SLCs have been considered to be a therapeutic target in the control of pancreatic and renal cell carcinoma [38,39]. Surprisingly, the elevation of mRNA level of GLUT3 (SLC2A3), a transporter with highest affinity for glucose, but not of its isoform GLUT1, was observed (Figure 5) and eventually the increase in glucose uptake by FGF16. High expression of GLUT3 has been demonstrated in brain cancer [40] and is responsible to resist certain chemotherapy drugs as well [41]. The essential role of GLUT3 in metabolic alteration to promote breast cancer brain metastasis is also established [42]. In addition, it is known that elevation of GLUT3 promotes EMT, invasion, metastasis and inflammation in triple negative breast cancer [43].

Concomitant with the elevation of glucose uptake, the glycolytic flux was found to be increased with the secretion of lactate (Figure 5). Whole cell metabolomics and expression profiling of glycolytic genes revealed that cells preferred glycolysis over TCA cycle under the influence of FGF16 (Figure 5). The intermediates of the glycolytic pathway are utilized in multiple biosynthetic processes and NADPH in sequestering reactive oxygen species to resist cell death [44]. Aerobic glycolysis is known to be one of the most prominent metabolic changes associated with cellular transformation into cancer. Lactate is not only a mere waste product of aerobic glycolysis; it is actually exploited through the onset of oncogenesis and cancer progression. Higher lactate production has been documented in pancreatic ductal adenocarcinoma [45], renal cell carcinoma [46], breast cancer [47], and lung cancer cells [48]. Lactate promotes invasion by tumor acidification, continuous proliferation by activating ‘metabolic symbiosis’, evasion of immune system and angiogenesis [49]. Further, lactate specifically assists EMT by reducing the pH of tumor microenvironment. It not only activates MMPs but also stimulates the expression of relevant transcription factors including VEGF, HIF-α, leading to cell migration [50].

In this report, PFKFB4 was unveiled to be specifically up-regulated by FGF16 (Figure 6). Between two PFK families, PFK-1 catalyzes the second rate-limiting step of glycolysis, converting fructose-6-phosphate into fructose-1, 6-bis phosphate. Three isoforms of mammalian PFK-1 exist. While PFKL and PFKM are enriched in liver and muscle, respectively, the third isoform, PFKP, is mainly expressed in platelet and also elevated in most human cancer cells [51,52]. PFKFB4 is also an emerging contributor in many cancer types including lung adenocarcinoma [53], breast cancer [54–57] and gastric cancer[58]. Apart from glycolysis, PFKFB4 governs cancer progression through various mechanisms-regulating transcription through phosphorylating SRCs [53], affecting autophagy [27] or inducing the production of hyaluran, a major constituent of ECM [56]. Mechanistically, PFK-1 is allosterically inhibited by several products of glucose metabolism including ATP, citrate, and H⁺ ions and is activated by fructose-2,6-bisphosphate (F26BP), a shunt product of glycolysis. F26BP has the ability to counteract the allosteric inhibition by ATP, thus stimulating PFK1, glucose uptake and flux through the entire glycolytic pathway as well [59]. The intracellular concentration of F26BP is controlled by a family of four bi-functional enzymes (PFKFB1–4), having both fructose-6-phosphate kinase and F26BP phosphatase activities [60]. The increased production of lactate by FGF16, a key highlight of our investigation, was mediated by PFKFB4 (Figure 6). Even FGF16-induced EMT/invasion of cells was found to be lowered while abrogating the expression of PFKFB4 (Figure 6). On contrary, these molecular changes remained unperturbed by PFKP knockdown (Figure 6). However, direct regulation between PFKFB4 and transcriptional machinery of EMT genes, if any, remained less ventured, thus providing a productive field for further investigation.

In conclusion, here we present a comprehensive mechanistic investigation on oncogenic potential of FGF16 in breast cancer through the initiation of EMT and invasion. The breast cancer cells were found to adopt the GLUT3–PFKFB4 axis under influence of FGF16 (Figure 7). Up-regulation of GLUT3 and PFKFB4 results in increased glucose fuelling in the cells coupled with elevated lactate production and secretion, indicating a metabolic shift towards glycolysis (Figure 7). This finding has the potential to open up a new avenue to design novel therapeutic agent(s) against FGF16–GLUT3–PFKFB4 axis that may reduce the invasiveness of breast cancer cells.

MCF10A was cultured in DMEM/F12 (Gibco) supplemented with 5% horse serum (Gibco), 20 ng/ml EGF (Peprotech, U.S.A.), 0.5 μg/ml hydrocortisone (Sigma-Aldrich, St. Louis, MO), 100 ng/ml cholera toxin (Sigma), 10 μg/ml insulin (Sigma), and 1% penicillin/streptomycin (Gibco).

MDA-MB-231 and 4T1 cell-lines were obtained from national cell repository at National Centre for Cell Sciences (NCCS), Department of Biotechnology, Government of India, Pune, India. The cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM; Gibco, Invitrogen, U.S.A.). MCF7 cells were cultured in RPMI-1640 (Gibco) medium. Both media were supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin/streptomycin at 37°C in 5% (v/v) CO₂.

Cells were starved for 16 h prior to the treatment with recombinant human FGF16 (rhFGF16; Peprotech) at 40 ng/ml for 24 h, unless otherwise mentioned. Treatment with PD173074 (PD; Cayman, U.S.A.), an inhibitor of FGF-receptor, was done at 20 ng/ml for 30 min prior to FGF16 treatment. 0.1% BSA in PBS was used as vehicle for growth factor treatment and DMSO was used as vehicle in ‘control’ cells for PD-treatment. The treatment with the inhibitors was as follows: 5-Fluorouracil (5-FU), a potent cell proliferation blocker, at 50 μM; 2-deoxy glucose, glycolytic inhibitor at 50 mM [61] and U0126, a highly selective inhibitor of MAPK/ERK kinase at 10 μM [62,63]. The control set of each inhibitor was given equal volume of vehicle.

MCF10A cells were starved for 16 h, treated with rhFGF16 for 48 h and the change in morphology was visualized and captured by Olympus 1X 83 inverted microscope.

For transient/stable transfection, cell-lines were seeded in 1 × 10⁵ cells/well in 6-well culture plate. After 24 h, 1 µg of construct was transfected using 2 µl Fugene-HD (Promega, Madison, U.S.A.) in Opti-MEM medium (Gibco) and kept for 5 h followed by replacement with fresh complete medium. The cells were incubated for 24 h before performing assays. For stable transfection, cells were selected in G418 (400 µg/ml). The shRNA mediated knockdown of PFKP expression was performed by transfection as described above.

The RNA interference was carried out by the siRNAs against PFKFB4 and non-targeting siRNA at 20 nM and GLUT3 at 10 nM using 10 µl INTERFERin reagent (Polyplus, USA) on cells seeded in 6-well culture plate. The cells were further incubated for 24 h before performing assays.

Total RNA was isolated from cells using RiboZol^™ RNA extraction reagent (Amresco) following the standard protocol. First-strand cDNA synthesis was done with RevertAid first strand cDNA synthesis kit (Thermo Fisher Scientific, U.S.A.) followed by Q-PCR assay by using Power SYBR green master mix (Invitrogen). The comparative C_T method (ΔΔC_T) was used to measure relative gene expression where the fold enrichment was calculated as: 1/2^ΔΔC_T. Here, ΔC_T is the C_T of the housekeeping gene subtracted from the C_T of target gene. ΔΔC_T is the ΔC_T of control sample subtracted from ΔC_T of treated sample. The fold change >1 means increased expression and <1 means decreased expression. The primer sequences are mentioned in Supplementary Table S4.

Following proper treatment/transfection, whole cell extracts were prepared from cell pellets using RIPA buffer (50 mM Tris-HCl pH 8, 150 mM NaCl, 1% NP40 and 1 mM EDTA) by incubating in ice for 20 min with Protease Inhibitor Cocktail (Roche) followed by sonication. Then, lysed cells were centrifuged at 10,000 rpm for 10 min and supernatants were taken for measuring concentration. The samples were electrophoresed on 7% or 10% or 12% SDS-PAGE and transferred on nitrocellulose membrane (Merck Millipore) followed by blocking with non-fat skimmed milk (3%) for 1 h and probed with specific primary antibodies for overnight at 4°C. For phospho-antibody, blocking was performed in BSA (5%). After incubating with HRP-tagged secondary antibody, blots were developed with Western ECL substrate (Bio Rad, Hercules, CA, U.S.A.). Primary antibodies used are enlisted in Supplementary Table S5.

Cells were seeded (8 × 10⁴ cells/well) on coverslips in 6-well plate and grown to 70% confluency. After required treatment/transfection, the cells were fixed with 4% paraformaldehyde for 15 min, permeabilized with 1% Triton X-100 for 10 min and blocked with 3% BSA for 1 h. Cells were then incubated with anti-VIM, anti-E-cadherin, anti-laminin and anti-ITGA6 antibodies for 1.5 h. Following washes with 1× PBS-T buffer for 5 min for three times, the cells were incubated with Alexafluor 488-conjugated secondary antibody for 1 h at RT. For Phalloidin staining, cells were incubated with Alexafluor 488-conjugated phalloidin for 1.5 h. The coverslips were then washed with PBS-T and mounted with Prolong Diamond Anti-fade Mountant with DAPI (P36966, Thermo Fisher Scientific). The images were captured with Leica TCA SP8 confocal microscope with Leica Application Suite (LAS). All the imaging experiments were done in duplicate.

Nuclei of DAPI-stained (n=30) cells, captured by confocal microscope from two independent experiments, were selected for each condition of experiment. Nuclei were marked to assess morphometric data i.e. area, aspect, area box, radius ratio and roundness. ImageJ (NHI, Bethesda, MD, U.S.A., http://rsbweb.nih.gov/ij/) with NMA plug-in (http://www.ufrgs.br/labsinal/nma/) was used. Nuclear Irregularity Index (NII) was calculated for each condition as described in [64] and fold change values with respect to control set was plotted with Graphpad Prism 5.01.

For RNA-seq analysis, total RNA was extracted from MCF10A cell line using RNAiso Plus (Takara Bio) following standard protocol. The sample was treated with DNase I and its quality was verified by Agilent Bioanalyzer® RNA 6000 Nano/Pico Chip. Samples with an RNA integrity number (RIN) > 7 were further processed. One microgram of total RNA was used for library preparation using a NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs). Sequencing was performed on NovoSeq 6000 at Sandor Speciality Diagnostics Pvt. Ltd. Raw Data Processing was performed by In-house perl scripts which removed low quality bases and adapter sequences. Cufflinks (Module: Cuffdiff) was used for differential expression analysis with parameters Log₂FC = 0.58 and P-value cut-off <= 0.05. Three biological replicates were used for each sample.

The biological and functional classification of differentially expressed genes was executed by using DAVID biological resources and PANTHER Classification System (http://pantherdb.org/). To get the idea of enrichment of gene sets in a specific biological process in an unbiased manner, Gene set enrichment analysis (GSEA) was performed with GSEA tool (Broad Institute) using default setting.

For extracellular metabolomics, the media from transfected/treated cells were mixed with ice-cold methanol: chloroform (1:1) solution, vortexed for 30 s and then subjected to cold centrifugation at 12000 rpm for 20 min at 4°C. Subsequently, the upper aqueous layer was collected, lyophilized and stored at −80°C till further analysis. For intracellular metabolic profiling, cells were washed with ice cold PBS and then immediately quenched with 50% chilled methanol, counted under microscope using haemocytometer and harvested. Cells were then lysed by sonication at amplitude 50 for 1 cycle of 30 sec pulse followed by 20 sec gap followed by addition of chilled chloroform. Then, the sample was vortexed for 5 min and the whole suspension was then centrifuged at 12000 rpm for 20 min at 4°C. The separated aqueous layer was collected and lyophilized properly. Dried samples were then stored at -80°C till use.

NMR spectra were acquired using a Bruker Avance 700 spectrometer operating at 700 MHz and at 298 K. Regular one-dimensional proton NMR spectra were obtained using the pulse program 1D NOESYPR1D with a relaxation delay of 2 s. Spectra were acquired with 512 scans and the obtained FID were processed using a line-broadening factor of 1 Hz and Fourier-transformed. Spectra were manually phased, baseline-corrected, and chemical-shift-referenced to TSP (δ = 0.00).

1D NMR spectra were subjected to multivariate data analysis using the program Metaboanalyst 5.0. The data were normalized and scaled using Autoscaling. Principal Component Analysis (PCA) was utilized for data overview and outlier detection. The data was modeled with the supervised method of Partial Least Squares - Discriminant Analysis (PLS-DA) using the same scaling and centering parameters.

The metabolites were identified and quantified using an untargeted profiling approach using Chenomx NMR Suite 8.3 Software (Chenomx, Inc., Edmonton, Alberta Canada). Six spectra from each experimental group were considered for quantification of extracellular metabolites and four spectra were used for intracellular metabolite quantification. The concentrations obtained from the spectra were plotted with GraphPad Prism (Version 5.01).

Glucose uptake was measured using a cell-based assay kit (#600470, Glucose uptake cell-based assay kit (Cayman Chemical, Ann Arbor, MI)). Briefly, cells were seeded (10000 cells/well) in a black, flat-bottomed 96-well microplate in triplicate. After 24 h, cells were treated with FGF16 alone or in combination with siGLUT3 or Apigenin, an inhibitor of glucose transport by GLUT1 (1:500) in glucose-free medium. Following 24-h incubation, cells were incubated with 100 μg/ml 2-NBDG (a fluorescently-labeled deoxy-glucose analog) for 30 min at 37°C in a humidified atmosphere of 5% CO_2. The cells were washed with PBS and the fluorescent signal was measured using Varioskan Flash Spectral Scanning Multimode Reader (Thermo Scientific). The experiment was done in three independent sets.

Lactate excreted by cell-line upon respective treatment/transfection in culture media was measured using a plate-based colorimetric assay kit (MET-5012, Cell Biolabs). Briefly, cells seeded in 96-well microplate were transfected with FGF16 alone or in combination with siPFKFB4 or shPFKP. Post-incubation of 24 h, exhausted media was collected and processed according to the manufacturer’s protocol. Lactate concentration was normalized with respect to cell number.

A total of 1 × 10⁵ cells/well were seeded in a 12-well plate. After 24 h, cells were treated with FGF16 alone or in combination with 2-DG or 5-FU. Next day, cells were counted before and after Trypan blue staining using haemocytometer. The experiment was performed in triplicate. Cell viability was calculated using the following formula: % viable cells = [1.00 − (Number of blue cells ÷ Number of total cells)] × 100

Female Balb/c mice (6-8 weeks old) were injected into mammary fat-pad with 1 × 10⁶ 4T1 cells (stably transfected with FLAG/FLAG-FGF16 constructs) suspended in 0.1 ml of PBS. After 10 days, primary tumors were visible and the tumor volume was monitored and measured (twice a week) for next 20 days followed by Avertin mediated anesthesia of mice and their sacrifice by cervical dislocation. The tumors were excised, measured and collected in formalin prior to prepare paraffin blocks. All animals were treated in accordance with the guidelines of the Committee on the Care and Use of Laboratory Animals of the Institute of Animal Resources and Institutional Animal Care and Use Committee (IACUC) and the ethical approval was obtained from central animal house and research facility, Bose Institute. For each group, 5 mice were used; among which tumor of three representative images were given. Entire animal experiment was done at animal house, Bose Institute. Tumor volume was calculated by the modified ellipsoidal formula: V = ½ (Length × Width²).

Immunohistochemical analysis was done using 5 µm thick paraffin-embedded sections of tumor xenograft. At first, the slides were de-waxed by heating at 70°C for 30 min followed by 5 min wash in xylene and rehydration with 100%, 90%, 70%, 50% ethanol up to pure distilled water. For heat-induced antigen retrieval, the sections were then kept in 10 mM citrate buffer (65°C) followed by wash with PBS and blocking in 3% FBS in 1× PBS for 1 h. Staining was done with anti-Laminin (1:400), anti-Ki67 (1:500), anti-E-cadherin (1:400) and anti-Vimentin(1:100) antibodies diluted in 1XPBS containing 1% FBS for 2h. After proper washing, the slides were incubated with the secondary antibody (Alexa Fluor-488 conjugated; 1:500) for 1.5 h followed by washing with PBS-T and staining with DAPI. The images were captured using Leica TCS SP8 microscope and LAS X software.Haematoxylin-Eosin staining of the sections was performed by following the standard protocol by professional pathologists.

Cells at 70% confluency were treated/transfected as mentioned above and prior to that, scratching was carried out with a 10 μl pipette tip (mentioned as t = 0 h at the figures). Cells were washed several times with PBS to remove the detached ones and supplied with new growth medium. Photographs of the scratches were taken at 0 h and 24 h with 10× objective lens of inverted microscope (Olympus 1X 83) equipped with in-built camera and software (CellSens). The images of 5FU-treated set were captured using 20× objective of the microscope equipped with camera LEICA DFC450C and in-built software (Leica Application Suite).The length of wound for all the sets was measured by ImageJ software. The experiment was performed in duplicate.

Corning® BioCoat™ Matrigel® Invasion Chamber (Catalog No. 354481) was used to assess in vitro invasion by FGF16. Briefly, 1 × 10⁵ cells were added in serum-free medium in the upper chamber and allowed to invade in presence of either 10% FBS or rhFGF16 alone or in combination with PD173074 in the lower chamber.

To check the effect of PFKFB4 and PFKP, cells were first transfected with respective siRNA/shRNA and next day were trypsinized, counted and seeded in upper chamber to allow the cells to invade in presence of 10% FBS. After incubating for 22 h at 37°C in 5% CO₂, the invaded cells on the lower surface were fixed in methanol, stained with crystal violet and counted under microscope (Leica Microsystems). The images were captured with 20× objective of the microscope equipped with camera LEICA DFC450C and in-built software (Leica Application Suite). The experiment was performed in duplicate.

Primary malignant breast tumor and adjacent normal tissue specimens were collected from affected individuals with informed written consent. The study was approved by the Institutional Ethics Committee of Saroj Gupta Cancer Centre and Research Institute, under regulation of the Govt. of India (Registration No. ECR/250/Inst/WB/2013/RR-20). All tissue samples were stored in −80°C till further experiments. Whole RNA was extracted from five paired and eight orphan samples following TRIzol method followed by cDNA synthesis and Q-PCR analysis. Relative change in gene expression was normalized with 18SrRNA level. IHC was performed following the above-mentioned protocol with anti-FGF16 antibody (1: 50). All clinical and histo-pathological information were collected simultaneously. Collection/processing of tissue samples and follow-up experiments were done in SGCC&RI, Kolkata. The clinicopathological features of each of the clinical samples are mentioned in Supplementary Table S6.

All data were calculated and analyzed using Microsoft excel/GraphPad Prism (Version 5.01) and expressed as mean ± SD of independent replicates of each experiment where ± SD was represented by error bars. Differences between treatments and controls were compared using one-way analysis of variance (ANOVA), followed by the post-hoc multiple comparisons—Tukey’s Honest Significant Test (HSD) whenever applicable (>2 experimental conditions), or using a Student’s t-test, otherwise (=2 experimental conditions). P<0.05 was considered as significant.

All primary data and constructs are freely available upon request to the corresponding author. The RNA-seq data will be deposited to the public repository during the process of revision.

The authors declare that there are no competing interests associated with the manuscript.

The study was funded by research grants provided to M.B. from DST-INSPIRE faculty scheme of the Department of Science and Technology (DST), Government of India. M.B. acknowledges research grant (File No. 76(Sanc.)-BT/P/Budget/RD-20/2017) sanctioned by Science and Technology and Bio-technology department, Government of West Bengal. S.K. acknowledges Indian Council of Medical Research (ICMR), Government of India for providing financial assistance in the form of fellowship and the contingent grant. N.M. is recipient of fellowship from Council of Scientific and Industrial Research (CSIR), Government of India. S.N. is supported by Early Career Award, Science and Engineering Research Board (SERB); Department of Science and Technology (DST), Government of India (File No.ECR/2015/000206); Department of Science and Technology and Biotechnology (DSTBT); Government of West Bengal [grant number FST/P/S&T/9G-21/2016], awarded to S.N. S.R. is supported by University Grants Commission-Junior Research Fellowship.

Swarnali Kar: Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review & editing. Nilanjana Maji: Formal analysis, Investigation, Methodology. Kamalika Sen: Data curation, Software, Investigation, Methodology. Stuti Roy: Investigation, Methodology. Atanu Maity: Investigation, Visualization, Methodology. Shubhra Ghosh Dastidar: Software, Supervision, Investigation, Visualization, Methodology, Writing—review & editing. Somsubhra Nath: Resources, Supervision, Writing—review & editing. Gautam Basu: Resources, Data curation, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review & editing. Moitri Basu: Conceptualization, Resources, Data curation, Supervision, Writing—original draft, Project administration, Writing—review & editing.

We acknowledge Dr. Mayurika Lahiri, Associate Professor, IISER-Pune for providing MCF7 and MCF10A cell lines. We thank Prof. Siddhartha Roy and his group members at Bose Institute for giving access to their laboratory facilities to perform experiments. We thank Dr. Kuladip Jana, Bose Institute for the assistance in performing in vivo experiment and Prof. Jayanta Mukhopadhyay, Bose Institute for his support in this project.

2-NBDG

2-NBD glucose

1D NMR

One-dimensional nuclear magnetic resonance

ATP

Adenosine triphosphate

BC

breast cancer

BSA

Bovine Serum Albumin

D₂O

Deuterium oxide

DAPI

4′,6-diamidino-2-phenylindole

DMSO

Dimethylsulfoxide

ECM

extracellular matrix

EDTA

Ethylenediaminetetraacetic acid

EMT

epithelial–mesenchymal transition

FGF

fibroblast growth factor

GAPDH

glyceraldehyde-3 phosphate dehydrogenase

h

hour

MMP

matrix metalloproteinase

NaCl

sodium chloride

PBS

Phosphate buffered saline

PBST

Phosphate Buffered Saline with Tween 20

PMSF

phenyl methyl sulfonyl fluoride

RI

receptor inhibitor

SD

Standard deviation

shRNA

Short hairpin ribonucleic acid

siRNA

Small interfering ribonucleic acid

TCA

Tricarboxylic Acid

TSP

Trimethylsilylpropanoic acid