ABSTRACT
The Warburg effect is one of most-well studied metabolic phenomenon in cancer cells. For the most part, these studies have focused on enhanced rates of glycolysis observed in various models. The presumption has been that mitochondrial metabolism is suppressed. However, recent studies indicate that the extent of mitochondrial metabolism is far more heterogeneous in tumors than originally presumed. One tumor type with suppression of mitochondrial metabolism is renal cell carcinoma (RCC). Prior studies indicate that suppressed TCA cycle enzyme mRNA expression is associated with aggressive RCC. Yet, the mechanisms that regulate the TCA cycle in RCC remain uncharacterized. Here, we demonstrate that loss of TCA cycle enzyme expression is retained in RCC metastatic tissues. Moreover, proteomic analysis demonstrates that reduced TCA cycle enzyme expression is far more pronounced in RCC relative to other tumor types. Loss of TCA cycle enzyme expression is correlated with reduced expression of the transcription factor peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) which is also lost in RCC tissues. PGC-1α re-expression in RCC cells restores the expression of TCA cycle enzymes in vitro and in vivo and leads to enhanced glucose carbon incorporation into TCA cycle intermediates. Mechanistically, TGF-β signaling, in concert with histone deacetylase 7 (HDAC7), suppresses TCA cycle enzyme expression. In turn, pharmacologic inhibition of TGF-β restores expression of TCA cycle enzyme expression and suppresses tumor growth in an orthotopic model of RCC. Taken together, our findings reveal a novel role for the TGF-β /HDAC7 axis in global suppression of TCA cycle enzymes in RCC and provide novel insight into the molecular basis of altered mitochondrial metabolism in this malignancy.
INTRODUCTION
Clear cell renal cell carcinoma (ccRCC) is the most common histologic subtype of kidney cancer. Approximately 30% to 40% of patients with ccRCC present with metastases at initial diagnosis [1, 2]. Individuals with organ-confined RCC tumors are considered to have an excellent prognosis with treatment. In contrast, patients with advanced disease have poor survival rates. Thus, a better understanding of the factors leading to tumor progression in RCC and the development of novel therapeutic strategies are of potential significance. ccRCC is known to have striking metabolic features. The most-well characterized is increased expression of glycolytic genes that results from loss/mutation of VHL, a common tumor initiating event. Loss of the E3 ubiquitin ligase activity of VHL results in stabilization of the hypoxia inducible factors (HIFs) and subsequent upregulation of hypoxia-responsive genes [3–5]. Glycolytic enzymes are known HIF transcriptional target in cancer [6, 7].
While enhanced glycolysis is a shared feature of many caner types, the expression of enzymes involved in mitochondrial metabolism is more heterogeneous. Notably, ccRCC is among the tumor types with the most prominent decrease in the mRNA expression of TCA cycle enzymes [8]. In agreement, we previously noted reduced expression of fumarate hydratase (FH) protein in ccRCC [9]. Moreover, a recent stable isotope labelling study in ccRCC patients demonstrated reduced incorporation of glucose-derived carbons into TCA cycle metabolites[10]. These data are among the most compelling to demonstrate reduced TCA cycle metabolism in kidney cancer. The decreased mRNA expression of TCA cycle enzymes in kidney tumor likely has biological relevance. A major conclusion of the TCGA (The Cancer Genome Atlas) analysis of kidney cancer was a metabolic shift in aggressive tumors marked by the down-regulation of genes encoding enzymes of the TCA cycle including FH (fumarate hydratase), ACO2 (aconitase), SUCLG1 (succinate-CoA ligase, α subunit), OGDH (oxoglutarate dehydrogenase)[11]. Despite these findings, the molecular basis by which the TCA cycle is altered in ccRCC remains poorly understood. As a result, the implications of this alteration as it pertains to RCC tumor biology remains unknown.
We recently reported an integrative analysis on RCC tumor progression which included normal kidney, primary tumors as well as metastatic tissues [12]. This analysis demonstrated that loss of mRNA expression of PPARGC1A, which encodes for the transcription factor PGC-1α, as among the most suppressed genes. PGC-1α was originally identified as a transcriptional coactivator involved in mitochondrial function and thermogenesis in brown fat [13]. PPARGC1A is known to be expressed in metabolically active tissues such as the kidney. Recently, we identified a novel role for PGC-1α in suppressing both the expression of collagen genes and tumor progression in an orthotopic model of RCC[14]. Prior studies have demonstrated that the transcriptional regulation of collagen gene expression is mediated by transforming growth factor beta (TGF-β) [15]. Although TGF-β has been implicated in invasive behaviors in many cancers (including RCC) via its promotion of the epithelial to mesenchymal transition (EMT), its role in mitochondrial TCA cycle metabolism is undefined. These data led us to consider the interrelationship between TGF-β and PGC-1α in the context of RCC and the relevance of this axis to RCC metabolism.
Here, we demonstrate that global repression of TCA cycle enzymes is a unique feature of RCC which is also found in RCC metastatic deposits. Mechanistically, TGF-β and histone deacetylase 7 (HDAC7) cooperate to repress PGC-1α and TCA cycle enzyme expression. Moreover, pharmacologic inhibition of TGF-β can restore TCA cycle enzyme expression in vivo. Overall, our findings provide novel insight into the epigenetic basis of altered mitochondrial metabolism in RCC. Moreover, they are among the first data to demonstrate that mitochondrial aspects of classic Warburg metabolism are pharmacologically targetable in RCC.
RESULTS
The expression of TCA cycle enzymes is lost in ccRCC
Prior TCGA analyses demonstrated reduced mRNA expression of genes encoding TCA cycle enzymes. Apart from our previous study on fumarate hydratase (FH) [9], these data have not been validated at the protein level. We first examined primary ccRCC specimens and patient-matched adjacent normal kidney and found reduced protein levels of aconitase 2 (ACO2) and the alpha subunit of succinate-CoA ligase (SUCLG1) in RCC (Fig. 1A). In addition, we observed the relative expression of TCA cycle enzymes in a panel of RCC cell lines. RCC cell lines had decreased protein levels of ACO2 and SUCLG1 relative to RPTEC renal proximal tubule epithelial cells (Fig. 1B). We analyzed publicly available proteomics data released from TCGA (CPTAC) using the UALCAN analysis portal [16]. These data validated our findings of reduced protein expression of TCA cycle enzymes in ccRCC relative to normal kidney. (Fig. 1C). Analysis of proteomics data from other tumor types demonstrated that reduced expression of TCA cycle enzymes is far more pronounced in ccRCC relative to other tumor types (Fig.1C and S1). Whereas breast and colon cancers demonstrated slightly reduced expression, no changes were noted in ovarian and uterine cancers. VHL alterations are the most common tumor initiating event in ccRCC. An established sequelae of VHL loss is stabilization of hypoxia inducible factors (HIFs) which has been linked with alterations in mitochondrial metabolism [17]. However, we noted that both VHL null (RCC4 and RCC10) as well as VHL WT lines (CAKI-1 and RXF-393) had reduced expression of TCA cycle enzymes (Fig. 1B). We therefore assessed the relative mRNA expression of TCA cycle enzymes by VHL status in ccRCC in the TCGA data set (Fig. 1D). Consistent with our findings in cell lines, we found that both VHL WT and mutant tumors demonstrated reduced mRNA expression of TCA cycle enzymes relative to normal kidney (Fig. 1D). We next considered whether these changes were maintained in RCC metastatic tissues as TCGA only analyzed primary tumors. We recently reported the gene expression landscape of ccRCC progression which encompassed transcriptomic analysis of normal kidney (n = 9), primary RCC (n = 9), and metastatic RCC tissue deposits (n = 26) [12]. Notably, we find that the mRNA expression of TCA cycle enzymes is reduced in metastatic tissues indicating that the pronounced shift in TCA cycle metabolism is retained with tumor progression (Fig. 1E). As these samples were not patient-matched, we examined the expression of TCA cycle enzymes in a separate cohort which included patient-matched samples of normal kidney, primary tumor, and metastatic tissue. This analysis confirmed that the reduced mRNA expression of TCA cycle enzymes (i.e. ACO2, OGDH, SUCLG1, and MDH2) in primary tumor was retained in metastatic tissues (Fig. 1F).
The transcriptional landscape of ccRCC reveals positive correlation between transcripts encoding TCA cycle enzymes and PPARGC1A
We next wanted to gain insight into the mechanism that drive the suppression of TCA cycle enzyme in RCC. We recently reported that PPARGC1A expression is progressively silenced with RCC tumor progression [14]. PPARGC1A encodes for the transcription factor peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α). Restoration of PGC-1α suppresses in vivo tumor progression in an orthotopic model of RCC [14]. PGC-1α is known to have a role in mitochondrial metabolism. We first performed correlation analysis of genes positively correlated with PPARGC1A in metastatic RCC tissues. KEGG analysis demonstrated that metabolic pathways including TCA cycle enzymes were among the most enriched pathways (Fig. 2A). Several genes of the TCA cycle enzyme demonstrated a statistically significant positive correlation with PPARGC1A including ACO2, SUCLG1, SDHA (succinate dehydrogenase A), SDHB (succinate dehydrogenase B), SUCLA2 (succinate-CoA ligase ADP-forming subunit beta), and DLAT (dihydrolipoamide S-acetyltransferase) (Fig. 2B and 2C). Furthermore, a positive correlation between PPARGC1A and several TCA cycle enzyme genes was also observed upon analysis of TCGA data (Fig. 2D and 2E).
PPARGC1A re-expression restores the expression of TCA cycle enzymes and mitochondrial function
The correlation studies led us to consider the role of PGC-1α loss on the expression of TCA cycle enzymes. Consistent with the decreased mRNA expression of PPARGC1A, RCC cell lines demonstrated a significant decrease in mRNA expression of TCA cycle enzymes including ACO2, OGDH, and SUCLG1 relative to normal kidney (Fig. 3A). PGC-1α expression was restored in CAKI-1 and RXF393 RCC cells. PGC-1α restoration led to increased protein expression of ACO2 and SUCLG1 in RCC cells (Fig. 3B). Consistent with the protein data, PGC-1α re-expression significantly increased the mRNA expression of TCA cycle enzymes in both RCC cells (Fig. 3C). Similar findings were found in RCC4 cells transduced with adenovirus containing cDNA for PGC-1α (Fig. S2A and S2B). Knockdown of PPARGC1A in RCC4 cells, which have a low but detectable level of PGC-1α [14], led to reduced TCA cycle enzyme expression (Fig. 3D). Moreover, these findings were relevant in vivo as restoration of PGC-1α in SN12PM6-1 RCC cells led to increased mRNA and protein levels of TCA cycle enzymes in orthotopic xenografts (Fig. 3E and 3F).
We next examined the effect of PGC-1α on mitochondrial DNA content. The amount of both mitochondrial DNA D-Loop structure and MT-CO2 gene, encoding for mtDNA-encoded cytochrome C oxidase II (MT-CO2), was significantly increased in PGC-1α expressing RCC cells (Fig. 3G). We next assessed the effects of PGC-1α on TCA cycle activity with LC-MS analysis using uniformly labeled [U-13C6] glucose. First, we noted that PGC-1α increased total unlabeled pools of multiple TCA cycle metabolites including citrate, cis-aconitate, fumarate, and malate (Fig. 3H). Furthermore, PGC-1α led to increased labeling of TCA cycle metabolites indicating the enhanced contribution of glucose-derived carbons to the TCA cycle (Fig. 3I). Collectively, these data indicate that PGC-1α restoration in RCC cells can promote the expression of TCA cycle enzymes as well as increase mitochondrial DNA content and enzyme activity.
Blockade of TGF-β signaling rescues the expression of PPARGC1A and mitochondrial function in RCC
The profound role of PGC-1α on the expression of TCA cycle enzymes led us to consider the mechanism that promotes loss of PGC-1α expression in RCC. Prior studies indicated that loss of PGC-1α is HIF-dependent in RCC [18]. However, our analysis of TCGA data demonstrates that TCA cycle enzyme expression is reduced irrespective of VHL status indicating an alternate mechanism (Fig. 1D). We recently reported the increased mRNA expression of collagen (COL) family members is highly associated with RCC metastasis and that PGC-1α restoration suppresses the expression of COLs [14]. Prior studies have indicated a role for transforming growth factor beta (TGF-β) in promoting COL gene expression as part of the EMT program [15]. We therefore assessed TGF-β’s role in regulating the expression of PGC-1α. Consistent with TGF-β’s role in promoting COL expression, we find that pharmacologic inhibition of TGF-β suppresses the mRNA expression of several COL genes including COL1A1, COL5A1, COL5A2, and COL11A1 in CAKI-1 and RXF-393 cells (Fig. S3A and S3B). Consistent with the mRNA data, inhibition of TGF-β led to decreased protein expression of COL1A1 in RCC cells (Fig. S3C). Inhibition of TGF-β signaling utilizing multiple inhibitors (SB431542, LY2109761, and LY364947) resulted in an increase of PPARGC1A transcript levels in RCC cells (Fig. 4A and S3D). In addition, TGF-β inhibition led to a significant increase in mRNA expression of the TCA cycle enzymes in 769-P (Fig. 4B) and CAKI-1 cells (Fig. 4C). Accordingly, increased protein levels of ACO2 and SUCLG1 were observed in CAKI-1 cells treated with TGF-β inhibitors (Fig. 4D). In addition, HK2 renal epithelial cells treated with TGF-β led to reduced protein expression of TCA cycle enzymes (Fig. 4E). Based on these observations, we examined the impact of TGF-β inhibition on cellular bioenergetics. We measured the oxygen consumption rate (OCR) of RCC cells treated with either control (DMSO) or two pharmacological TGF-β inhibitors (Fig. 4F). Notably, both TGF-β inhibitors increased cellular bioenergetics in RCC cells compared to control cells (Fig. 4F). In particular, both inhibitors significantly increased basal respiration, ATP-linked respiration (assessed following oligomycin treatment), and maximal respiration (assessed following treatment with uncoupler FCCP) (Fig. 4G). SB431542 significantly increased non-mitochondrial respiration (following antimycin A treatment), whereas LY364947 had no effect (Fig. 4G). Collectively, these data demonstrate a role for TGF-β signaling in regulating both TCA cycle enzyme expression and cellular bioenergetics in RCC cells.
TGF-β inhibition reverses metabolic phenotypes of RCC in vivo
As noted previously, a major finding from the TCGA analysis of ccRCC was that loss of TCA cycle enzyme expression is associated with aggressive tumors. Given our in vitro findings, we next assessed if these findings are relevant in vivo to assess if mitochondrial aspects of Warburg metabolism could be reversed by pharmacologic means. CAKI-1 luciferase expressing RCC cells were injected orthotopically into the renal subcapsular region of SCID mice. RCC tumor bearing mice were then intraperitoneally treated with either 20% DMSO (control) or TGF-β inhibitor SB431542 (10 mg/kg in 20% DMSO). After 5 weeks of treatment, mice treated with SB431542 demonstrated significantly lower tumor burden as demonstrated by both bioluminescent imaging (Fig. 5A) and tumor weight (Fig. 5B). Analysis of tumor explants demonstrated increased expression of PPARCG1A mRNA and PGC-1α protein in mice treated with SB431542 (Fig. 5C and 5D). Moreover, TGF-β inhibitor treated tumors demonstrated increased expression of TCA cycle enzymes at both the mRNA and protein levels (Fig. 5E and 5F). Collectively, these data demonstrate the role of TGF-β in regulating the TCA cycle in RCC and that this can be targeted by pharmacologic means in vivo.
HDAC7 acts as a corepressor for TGF-β mediated suppression of TCA cycle enzymes in RCC
We next investigated the mechanism by which TGF-β signaling repressed the expression of PPARGC1A and TCA cycle enzymes. TGF-β signaling regulates transcription by a complex network including SMAD proteins. SMAD complexes can inhibit transcription through interacting with transcriptional corepressors. Three SMAD corepressors have been identified including the homeodomain protein TG-interacting factors (TGIFs), Ski, and SnoN protein [19–21]. We therefore examined genes that are negatively correlated with these corepressors in the TCGA data set on ccRCC [22]. Intriguingly, this analysis identified that TGIF2 is inversely correlated with the expression of genes involved in the TCA cycle enzymes. In fact, the TCA cycle was the top-ranked gene set (Tab. S3A). Based on these findings, we next performed loss of function studies via siRNA mediated knockdown of TGIF2 in CAKI-1 cells. Knockdown of TGIF2 led to significantly increased mRNA levels of TCA cycle enzymes including ACO2, SUCLG1, OGDH, and SDHC (Fig. 6A). Correspondingly, the protein levels of SUCLG1 were upregulated by knockdown of TGIF2 (Fig. 6B). TGIF2 had been previously shown to be a transcriptional repressor by interacting with histone deacetylases (HDACs) including HDAC1 [21, 23]. We therefore assessed whether HDACs could contribute to silencing the expression of PPARGC1A and/or TCA cycle enzyme genes. We initially investigated the effects of the pan-HDAC inhibitor trichostatin A (TSA) on the expression of PPARGC1A in RCC cells. We found a significant increase in mRNA and protein expression of PGC-1α in RCC cells following treatment with TSA (Fig. 6C and 6D). Furthermore, TSA treatment increased the mRNA expression of TCA cycle enzymes in CAKI-1 cells (Fig. 6E). In concert, TSA treatment resulted in the increased protein expression of ACO2 and SUCLG1 in 769-P and CAKI-1 cells (Fig. 6F). We therefore assessed whether HDACs inversely correlated with TCA cycle enzymes (Tab. S3B). We found that HDAC1 and 7 are negatively correlated with the mRNA expression of TCA cycle enzymes in the TCGA data set. We thus knocked down both HDAC1 and HDAC7. We confirmed target gene knockdown via RT-qPCR (Fig. S4A). We found that HDAC1 knockdown significantly induced the mRNA expression of PPARGC1A in 769-P cells (Fig. 6G). HDAC7 knockdown had a modest effect on PPARGC1A mRNA. However, HDAC7 knockdown led to a prominent increase in TCA cycle enzyme mRNA levels of ACO2, SUCLG1, and SDHC (Fig. 6G). In agreement with the mRNA data, HDAC7 knockdown demonstrated increased ACO2 and SUCLG1 protein expression in RCC (Fig. 6H). Furthermore, we investigated potential SMAD binding sites within 1 kb of the SUCLG1 transcription start site using the Eukaryotic Promoter Database [24]. We thus assessed HDAC1/7 binding to this SMAD binding site. Whereas no significant enrichment of HDAC1 was found, marked enrichment of HDAC7 was observed at putative SMAD binding sites relative to IgG control (Fig. 6I and S4B). These findings led us to examine the expression of HDAC7 in ccRCC in TCGA data. We observed that HDAC7 expression was significantly increased in ccRCC relative to normal kidney at the mRNA and protein levels (Fig. 6J and 6K). The increased protein expression of HDAC7 was validated in ccRCC as compared with patient-matched normal kidney samples (Fig. 6L). Collectively, these data support a novel role for HDAC7 in the silencing of TCA cycle enzymes in ccRCC.
DISCUSSION
The molecular basis by which tumor cells remodel their metabolism remains an area of intense investigation. Current understanding of metabolic remodeling in renal cancers has mainly focused on the HIF transcription factors [25]. ccRCC is associated with inactivation of the VHL gene due to genetic or epigenetic alterations. VHL inactivation results in stabilization of HIFs and subsequent upregulation of hypoxia-responsive genes that are involved in metabolic reprogramming including glycolytic enzymes. Although the role of VHL and HIF pathway is well described in RCC tumor initiation, inactivation of VHL alone may not be sufficient to drive ccRCC tumorigenesis [26]. Furthermore, prior studies demonstrate that HIF-1α expression is often lost in ccRCC and that it actually has tumor suppressive effects in the context of kidney cancer [25].
Despite the emphasis on increased expression of glycolytic enzymes in tumor metabolism, several lines of evidence support that alterations in mitochondrial metabolism are observed in renal cancer. For instance, prior studies have established decreased mitochondrial respiratory chain proteins in RCC and that respiratory complex chain activity is inversely correlated with prognosis [27, 28]. Loss of PGC-1α has been linked to these phenotypes [18]. In addition, germline loss of function mutations of the TCA cycle enzyme genes including FH, SDHB, SDHC, and SDHD have been identified in renal cancer that appear to have an increased risk for more aggressive tumors [29–31]. Notably, one of the major conclusions of the TCGA data analysis of ccRCC was that loss of TCA cycle enzyme expression is associated with poorer patient outcomes [32]. However, the precise mechanisms by which this occurs has not been reported. At the same time, the global suppression the TCA cycle indicates an epigenetic mechanism.
PGC-1α has been implicated in promoting tumor growth in breast, pancreas, and melanoma [33–35]. Alternatively, PGC-1α appear to suppress metastasis in prostate cancer and in a subset of melanomas [36, 37]. This inconsistency could be due to tissue-specific metabolic pathways that maintain tumor growth. We recently reported a novel role for PGC-1α in the suppression of collagen expression. Notably, several collagens are highly expressed in metastatic RCC [14]. Given that TGF-β signaling is known to promote collagen gene expression, we investigated the impact of TGF-β in RCC metabolism and tumor progression. TGF-β is a multifunctional extracellular cytokine that regulates cell growth, differentiation, migration and adhesion [38, 39]. TGF-β can inhibit cell proliferation and is considered tumor suppressive in early stages of tumorigenesis. In contrast, TGF-β can promote tumor progression through promoting epithelial-mesenchymal transition (EMT) [15, 40]. TGF-β signaling has been linked with metabolic reprogramming in cancer cells. For instance, increased expression or activity of glycolytic enzymes during TGF-β induced EMT has been demonstrated in multiple cancer cells [41–43]. Although TGF-β signaling stimulates glycolytic phenotypes during EMT process, the effects of TGF-β on the TCA cycle in cancer remains largely unknown. Therefore, our findings add novel insight into metabolic remodeling in RCC tumors. These are the first data, to our knowledge, to demonstrate an inhibitory effect of TGF-β on the gene expression of TCA cycle enzymes. Prior studies have implicated the impact of TGF-β on reduced PPARGC1A transcripts levels in renal fibrosis [44]. However, the detailed molecular mechanism of TGF-β mediated transcriptional regulation of PPARGC1A was not fully elucidated nor were the effects on the TCA cycle reported.
TGF-β signaling mainly regulates gene expression through receptor-mediated phosphorylation of SMAD proteins. Activated SMAD proteins translocate into the nucleus where they bind to DNA in the regulatory region of target genes. The affinity of SMADs for DNA is weak. Thus, SMADs often cooperates with additional transcription factors in the regulation of TGF-β responsive genes [45]. Our data implicate a novel role for TGIF2 in regulating the TCA cycle. Compelling evidence of the relevance of this axis is demonstrated by our in vivo studies which demonstrate that inhibition of TGF-β leads to increased TCA cycle enzyme expression in renal tumors. Our studies are among the first to demonstrate that mitochondrial aspects of the Warburg effect can be pharmacologically reversed in vivo.
Here, we also report that TGF-β works in concert with HDAC7 to suppress the expression of TCA cycle enzymes in RCC. These are the first data to reveal that HDAC7 can suppress genes that are involved in mitochondrial metabolism. Although we found the increased mRNA expression of PPARGC1A in either HDAC1 or HDAC7 knockdown, no significant enrichment of these HDACs in the promoter region of PPARGC1A was found (data not shown). Thus, our findings support that HDAC7 mediates its effects directly at TCA cycle enzyme genes. These data indicate an alternate means by which mitochondrial metabolism could be activated in RCC. As supportive evidence, recent studies in glioma cells indicate that HDAC inhibitors can activate mitochondrial metabolism [46]. Future studies will focus on the role mitochondrial metabolism plays in tumor biology as well as response to therapies including immune checkpoint blockade.
In summary, our findings provide a novel insight into the molecular mechanisms driving metabolic reprogramming in renal cancer. Our studies demonstrate that TGF-β signaling represses PPARGC1A and TCA cycle enzymes and are the first to demonstrate a role for TGIF2/HDAC7 in the suppression of mitochondrial metabolism. Hence, these findings highlight an intriguing possibility that this axis could be targeted by epigenetic based therapies which are currently in use or in clinical trials.
MATERIALS AND METHODS
Cell culture
RCC cell lines (RCC10, CAKI-1, 769-P, and 786-0) were purchased from ATCC except for RCC4 (kindly provided by P. Ratcliffe, University of Oxford), and RXF-393 (NCI). All RCC cell lines were maintained as described previously [14]. HK2 renal epithelial cells were purchased from ATCC. Primary renal proximal tubule epithelial cells (RPTEC) were acquired from Lonza and grown in renal epithelial cell growth basal medium supplemented with 10% fetal bovine serum and penicillin streptomycin (100 U/mL) in 5% CO2 at 37°C. Cells were used within 10 passages of the initial stock and periodically screened for mycoplasma contamination.
TGF-β inhibitor treatment
RCC cells were grown in serum-free medium supplemented with 0.1% bovine serum albumin (Roche) and 2 mM glutamine for 24 h prior to treatment with either TGF-β (1 ng/mL, Millipore Calbiochem) or 10 uM TGF-β inhibitors (SB431542; Sigma-Aldrich, LY2109761 and LY364947; Selleckchem) for the indicated times.
siRNA transfection
RCC cells were transfected with 25 nM of a negative control siRNA or siRNA indicated target genes using Lipofectamine RNAiMAX reagent (Invitrogen) for 72 h.
Plasmid and virus infections
Human PPARGC1A cDNA was obtained from GeneCopoeia. Lentiviral shRNA constructs for PPARGC1A were purchased from Sigma. Lentiviral particles were generated by co-transfecting HEK293T cells with packaging plasmids using the calcium phosphate method. The detailed methods were described previously [14]. For adenoviral studies, RCC cells were transduced with either GFP or PPARGC1A (Vector Biolabs).
Cellular Bioenergetics
Cellular bioenergetics was determined using the Seahorse XFe96 Analyzer (Agilent Technologies). RCC cells were treated with DMSO or TGF-β inhibitors for 24 h prior to being plated in a Seahorse XF96 plate (25 × 103 cells per well; n=8/group). Cells rested overnight before measuring the oxygen consumption rate (OCR) the following day. Cells were then washed with extracellular flux media and allowed to equilibrate for 1 h prior to being exposed to the mitochondrial stress test.
Patient samples and gene expression profiling
Patient samples for gene expression profiling (normal=9, primary=9, metastasis n=26) were not patient-matched and are previously described [12]. A separate cohort of patient-matched samples (n=6/group) for the validation of gene expression profiling was obtained from UAB Hospital and has been described previously [12]. All studies were performed in accordance with the institutional IRB.
Quantitative RT-PCR
Total RNA from human and mouse kidney samples was harvested using the RNeasy Mini Kit (Qiagen). Total RNA isolation from cultured cells was extracted using Trizol reagent (Ambion) according to the manufacturer’s instructions. cDNA was generated using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative RT-PCR was performed using the indicated Taqman primers in QuantStudio 6K Flex Real-Time PCR System (Applied Biosystem) (Table S1A). The mRNA expression of target gene was normalized to either ribosomal protein (RPLPO) or TATA binding protein (TBP). The normalized Ct value was quantified using the double delta Ct analysis.
ChIP-qPCR
The potential SMAD binding sites near the SUCLG1 transcriptional start site were identified using the Eukaryotic Promoter Database [24]. The ChIP experiment was conducted using the EZ-Magna ChIP Chromatin IP A/G kit (Millipore) according to the manufacturer’s instructions. After pull-down with either mouse IgG or HDAC1/7 antibody, input DNA and immunoprecipitated DNA were purified (Zymo Research). The target DNA enrichment was calculated based on the % input method. Primer sequences for ChIP assay are described in Table S1B.
Measurement of mitochondrial DNA content
DNA from RCC cells was extracted with the QIAamp DNA mini kit (Qiagene). Mitochondrial DNA content was analyzed by measuring the relative levels of mitochondrial DNA D-Loop and mitochondrial DNA-encoded MT-CO2 by qRT-PCR. Genomic DNA-encoded β-actin was used as a normalizer. The list of primers used for this study are presented in Table S2A.
Immunoblotting analysis
RCC cells were lysed with ice-cold RIPA buffer containing 1X protease inhibitor (Halt protease and phosphatase inhibitor cocktail, ThermoFisher). Human and mouse kidney samples were homogenized with microbeads (Bioexpress) containing SDS lysis buffer. Preparation of samples and Western blot analysis were described previously [12, 14]. Antibodies used in this study are described in Table S2B.
13C glucose incorporation analysis
CAKI-1 cells were grown to approximately 80% confluence. Cells were supplemented with fresh 5.5 mM [U-13C6] glucose (Cambridge Isotope Laboratories). After 24 hr incubation, metabolites were extracted using cold 80% HPLC graded methanol. The sample was centrifuged at 20,000g for 10 min and the supernatant was dried under vacuum. Pellets were reconstituted in solvent (water:methanol:acetonitrile, 2:1:1, v/v) and further analyzed by LC-MS as previously described [14, 47].
Orthotopic tumor challenge in vivo
Orthotopic tumor challenges were performed using 5-week old SCID male mice (Charles River Laboratories). Luciferase-expressing CAKI-1 cells (1.5×106) were mixed in a 1:1 ratio with Matrigel and injected under the left renal capsule. Bioluminescent imaging was performed after one week to assess for xenograft formation. For TGF-β inhibition in vivo, SB-431542 (Sigma-Aldrich) was dissolved with 20% DMSO and filtered through a 0.45 μm filter (Milipore). Mice received either 20% DMSO as a control group or diluted SB-431542 in 20% DMSO (10 mg/kg) three times per week for 5 weeks (n=3/group). Tumor progression was weekly evaluated by measuring the luciferase signal with IVIS Lumina III In Vivo System (PerkinElmer). All animal studies were conducted in accordance with the NIH guidelines and were approved by UAB Institutional Animal Care and Use Committee (IACUC).
ACKNOWLEDGMENTS
The research reported in this article was supported by Department of Veteran Affairs grant BX002930 and NIH/NCI grant R01CA20053. This work was also supported by the NIH (P30 CA013148).
Footnotes
↵* Currently at Rockerfeller University
We certify that there is no conflict of interest with any financial organization regarding the material described in this manuscript.