SIRT3 Overexpression Inhibits Growth of Kidney Tumor Cells and

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SIRT3 Overexpression Inhibits Growth of Kidney Tumor Cells and Enhances Mitochondrial Biogenesis Huan Liu, Siying Li, Xiaohui Liu, Yuling Chen, and Haiteng Deng J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00260 • Publication Date (Web): 10 Aug 2018 Downloaded from http://pubs.acs.org on August 11, 2018

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Journal of Proteome Research

SIRT3 Overexpression Inhibits Growth of Kidney Tumor Cells and Enhances Mitochondrial Biogenesis Huan Liu1, Siying Li2, Xiaohui Liu1, Yuling Chen1, 3*, Haiteng Deng1* 1. MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China 2. University of California, Davis, CA95616 USA 3. Tsinghua University-Peking University Joint Center for Life Sciences, Beijing, China *

To whom correspondence should be addressed:

Yuling Chen, School of Life Sciences, Tsinghua University, Beijing, 100084, China, Tel: 8610-62797838; Fax: 8610-62797154; E_mail: [email protected]

Haiteng Deng, School of Life Sciences, Tsinghua University, Beijing, 100084, China, Tel: 8610-62790498; Fax: 8610-62797154; E_mail: [email protected]

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Abstract SIRT3 is a NAD+-dependent mitochondrial protein deacetylase implicated in the regulation of central metabolism and mitochondrial proteostasis. SIRT3 is downregulated in clear cell renal cell carcinoma (ccRCC), which is the most common form of renal cancer. Although ccRCC is characterized by a typical Warburg-like phenotype, mitochondrial dysfunction and elevated fat deposition, it is unknown whether SIRT3 plays a role in tumorigenesis and the development of this disease. In the present study, we found that SIRT3 overexpression and knockdown had opposing effects on the growth of ccRCC cells, decreasing and increasing the rate of cell proliferation, respectively. SIRT3 overexpression also increased mitochondrial mass in ccRCC cells. Unexpectedly, SIRT3 overexpression increased ROS levels, and sensitized cells to oxidative stress. Metabolomics and quantitative proteomics showed that SIRT3 overexpression alterd cellular metabolism and reversed the Warburg effect in ccRCC cells. Further studies demonstrated that SIRT3 promoted mitochondrial biogenesis by increasing both the expression and deacetylation of TFAM (transcription factor A, mitochondrial). Mutagenesis experiments revealed that acetylation of TFAM at K154 impaired TFAM interaction with mitochondrial DNA, thereby decreasing the activity of the protein and, consequently, mitochondrial biogenesis. Overall, our results suggest that SIRT3 regulates mitochondrial biogenesis and that its downregulation promotes a Warburg phenotype in ccRCC.

Keywords: SIRT3, clear cell renal cell carcinoma, mitochondrial biogenesis, proteomics, TFAM, deacetylation

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Introduction SIRT3 belongs to the sirtuin family, a conserved family of NAD+-dependent deacetylases, which regulate diverse cellular functions. SIRT3, SIRT4 and SIRT5 are the major deacetylases of mitochondria, but SIRT3 is the predominant regulator of mitochondria lysine acetylation.1-4 SIRT3 regulates the activity of key enzymes in several metabolic pathways, including respiratory chain reactions, ATP synthesis and the TCA cycle, with SIRT3-mediated deacetylation generally increasing the activity of these enzymes.3,

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SIRT3 can regulate ketone body production in mice through

deacetylation of 3-hydroxy-3-methylglutaryl CoA synthase 2 (HMGCS2) during fasting and calorie restriction.10 It also directly regulates redox homeostasis through deacetylation and activation of the manganese-dependent superoxide dismutase, SOD2.11-13 SIRT3 plays opposing roles in different cancers, functioning both as an oncogene and as a tumor suppressor. SIRT3 promotes survival and protects cells from cellular damage through modulation of p53 and other apoptotic factors in fibrosarcoma, cervical cancer and bladder cancer.14-17 SIRT3 expression is an independent predictor in esophageal cancer treatment, with higher expression correlating with worse prognosis.18 Conversely, SIRT3 can act as a tumor suppressor by suppressing ROS and destabilizing HIF-1α, and it is downregulated in colorectal carcinoma, osteosarcoma, leukemia and breast cancer.19-23 We have previously shown that SIRT3 is downregulated in cancer tissues from ccRCC patients, when compared with paired para-carcinoma tissues.24 ccRCC is the most common type of renal cancer and is the most lethal form of urinary cancer. It has

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the ninth highest morbidity of all malignant cancers worldwide.25 ccRCC tumors characteristically exhibit a Warburg phenotype, mitochondrial dysfunction and elevated fat deposition.26 The reprogramming of metabolic pathways promotes cancer cell proliferation under conditions of nutrient deprivation and hypoxia, and facilitates evasion of host immune surveillance.27 Energy generated by glycolysis predominantly fuels ccRCC progression, with little energy supplied by mitochondrial respiration.28 As the major deacetylase of mitochondrial metabolic pathways, SIRT3 is important in cell metabolism and function. However, it remains unclear whether SIRT3 plays a role in the progression of ccRCC. In the present study, we established stable cell lines in which SIRT3 was either overexpressed or knocked down. We applied metabolomics and quantitative proteomics to investigate the effects of SIRT3 expression on cellular processes, mitochondrial function and metabolic reprogramming in ccRCC. We demonstrate that SIRT3 overexpression inhibits the growth of kidney tumor cells and enhances mitochondrial biogenesis.

Materials and Methods Cell culture All cell lines used in the present study were purchased from the Cell Bank of Type Culture Collection of Chinese Academy of Sciences (Shanghai, China). 293T cells were grown in DMEM (Wisent) media supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin at 37 °C in a humidified incubator with 5% CO2. 786-O cells were grown in RPMI 1640 media (Wisent, Canada) supplemented

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with 10% fetal bovine serum (Wisent) and 1% penicillin/streptomycin (Wisent) at 37 °C in a humidified incubator with 5% CO2. Plasmids, virus production and site-directed mutagenesis The stable cell line 786-O SIRT3 OE and 786-O plvx were prepared as previously described.29 Briefly, lentiviral expression vector pLVX-IRES-ZsGreen with reporter gene of GFP and the package vectors were obtained by courtesy of Dr. Jun Xu (Tongji University, Shanghai, China). The human SIRT3 cDNA was obtained from 293T cells. A Flag-tag sequence was added onto the C-terminus to create a recombinant human SIRT3 DNA. The recombinant human SIRT3 DNA was cloned into pLVX-IRES-ZsGreen1 to create the pLVX-SIRT3-IRES-ZsGreen1 vector. pLVX-SIRT3-IRES-ZsGreen1 or pLVX-IRES-ZsGreen1 was cotransfected into 293T cells with packing vectors when the cells reached 80-90% confluence. The cell culture was collected and concentrated with PEG6000. 786-O cells were transduced with lentiviral particles and polybrene. The GFP-positive cells were sorted and seeded into a single well of a 96-well plate by flow cytometer. The cells transduced with empty plvx-IRES-ZsGreen1 plasmids (786-O plvx) were used as control. PLL3.7 siRNA expression vector inserted with shRNA against SIRT3 was used to silence SIRT3 in 786-O cells. A stable cell line with SIRT3 knockdown was constructed. The cells transduced with non-targeting shRNA (NCi, TTCTCCGAACGTGTCACGT) were used as knockdown control cells. For the construction of TFAM overexpressed cell line, the human TFAM cDNA was obtained from the 786-O cell line, and a His-tag sequence was added onto the C-terminus to create a recombinant human TFAM DNA,

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which was then cloned into pLVX-IRES-mCherry plasmid. For the mutation assay, K154 was converted to glutamine as the mimic of acetylation by using KOD-Plus-Mutagenesis kit (TOYOBO, Osaka, Japan), and the mutated TFAM or wild-type TFAM was ligated to pLVX-IRES-mCherry vector. Lipofectamine 3000 (Life Technologies, Grand Island, NY) was used to perform transient transfection. All the primers used in this study was listed in Table S1. Cell proliferation assay with CCK-8 Cells were seeded in 96-well plates with 2000 cells/well. Cell proliferation rate was determined with the Cell Counting Kit-8 (CCK-8) (Dojindo, Kumamoto, Japan). CCK-8 reagents were added into wells after cells grew for 0, 12, 24, 36, 60, 72, 84 and 96 h respectively. The plates were incubated in cell incubator for 1.5 h. Absorbance at 450 nm was measured. Determination of mitochondrial mass The fluorescent probe MitoTracker Green FM (Cell Signaling Technology, Danvers, MA) was used to determine the mitochondrial mass of cells. The cells were washed twice with PBS and stained by 0.1 µmol/L MitoTraker with 0.1% BSA (w/v) at 37 °C for 30 min. Fluorescence was analyzed by BD FACSAria II Flow Cytometer (BD Biosciences, San Jose, CA). Detection of cellular reactive oxygen species (ROS) ROS levels in 786-O SIRT3 OE and 786-O plvx cells was detected using the CellROX® Deep Red Reagents (Invitrogen, Grand Island, NY) according to the manufacturer’s protocol. Briefly, cells in 6-well cell culture plated were added with CellROX® Deep Red Reagents of 5 µM and incubated at 37 °C for 30 min, followed 6


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by being removed the medium and washed three times with PBS. The fluorescence was measured with BD FACSAria II Flow Cytometer. Metabolomic analysis The cells were washed twice with ice-cold PBS and were extracted three times using 80% methanol. For targeted quantitative analysis, the TSQ Quantiva™ Triple Quadrupole Mass Spectrometer with positive/negative ion switching was used for targeted quantitation with selective reaction monitoring (SRM). The metabolite extracts were separated through a Synergi Hydro-RP column (2.5 µm, 2.0 mm×100 mm i.d., Phenomenex, Torrance, CA) interfaced with the mass spectrometer. Trace Finder was used to identify the peaks and extract the quantitative information of metabolites. Samples preparation of quantitative proteomics and mass spectrometry analysis For the quantitative proteomics analysis, samples were performed as previous study.30 Briefly, cells were washed twice with PBS, and lysed by lysis buffer containing 8 M Urea and protease inhibitor (BioTools, Jupiter, Florida) in PBS. Equal amount of proteins was reduced, alkylated and digested by trypsin, followed by being desalted and labeled by TMT reagents (Thermo, Pierce Biotechnology). The TMT labeled peptides were mixed and desalted using Sep-Pak C18 cartridges. The tryptic peptides were separated into 12 fracations with high-pH RPLC (XBridgeTM BEH300 C18 5µm, 300Å, 250 mm × 4.6 mm i.d., Waters；mobile phase A (2% acetonitrile, pH = 10.0) and B (98% acetonitrile, pH = 10.0)). For LC-MS/MS analysis, the peptides were separated by a C18 column (75 µm

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inner-diameter, 150 mm length, 5 µm, 300 Å) with a Thermo-Dionex Ultimate 3000 HPLC system, which was directly connected with a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer. A series of adjusted linear gradients according to the hydrophobicity of fractions with a flow rate of 300 nL/min was applied. The mass spectrometer was programmed to acquire in the data-dependent acquisition mode. The survey scan was from m/z 375 to 1550 with resolution of 120,000 at m/z 400. After one microscan, the top N most intense peaks with charge state 2 and above were dissociated by normalized collision energy of 35%. The isolation window was set at 0.7 Da width and the dynamic exclusion time was 60 s. The MS2 spectra were acquired with a resolution of 17,500, AGC target of 1e5 and maximum injection time (IT) of 60 ms. The generated MS/MS spectra were searched against the Uniprot Human database (January 10, 2015; 89105 sequences) using the SEQUEST searching engine in Proteome Discoverer 1.4 software. The search criteria were as follows: full tryptic specificity was required, one missed cleavage was allowed, carbamidomethylation on cysteine and TMT sixplex on lysine/peptide N-terminal were set as the fixed modifications, oxidation on methionine was set as the variable modification, precursor ion mass tolerances were set at 10 ppm for all MS acquired in an Orbitrap mass analyzer, and the fragment ion mass tolerance was set to 20 mmu for all MS2 spectra acquired. Peptide spectral matches (PSM) were validated using the Percolator provided by Proteome Discoverer software based on q-values at a 1% false discovery rate (FDR). Proteomic analysis was carried out in biological triplicates. Proteins were

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considered to be differentially expressed with the ratio > 0.75 or < 1.3 while the p value was lower than 0.05. For the acetylated peptide search, TMT sixplex on lysine and acetylation on lysine were set as the variable modifications, and the other parameter were same with the quantitative proteomics search. The proteomics data were deposited onto the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009110. Western blotting Cells were lysed in RIPA lysis buffer supplemented with protease inhibitor cocktail and PMSF for 30 min on ice. The supernatant was collected after centrifugation at 14,000×g for 20 min at 4 °C. Protein concentration was measured using the BCA protein assay kit. Equal amount of proteins was separated by 12% 1D SDS-PAGE gel and transferred onto a PVDF transfer membrane with electro-blotting. After being blocked with 5% nonfat milk for 1 h at room temperature, the membrane was incubated overnight at 4 °C with primary antibody, and then incubated with anti-mouse secondary antibody labeled with HRP at room temperature for 1 h. The membrane was detected with ECL reagents (Engreen, China). β-actin was used as an internal control. Real-time Quantitative PCR (qPCR) Total RNA was isolated using Trizol according to the manufacturer’s instructions. cDNA was synthesized with the Reverse transcription kit. qPCR was performed by the Roche LightCycler® 480II Detection System with SYBR green incorporation and β-actin was used as an internal control. Relative expression was analyzed using the

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2-∆∆Ct method. For the measurement of mtDNA copy number, total DNA was isolated from cultured cells using TianAmp Genomic DNA kit (Tiangen, China). Primers were designed to amplify the 16S rRNA gene from the mtDNA genome and were normalized to the nuclear gene β2-microglobulin. The primers used were listed in Table S1. Immunoprecipitation assay Equal amounts of cell lysates from 786-O SIRT3 OE and 786-O plvx cells were incubated with anti-Flag antibody conjugated agarose resin at 4 °C for 2 hours. After being washed five times, bound proteins were boiled in SDS-PAGE running buffer. The supernatant was separated by 1D SDS-PAGE. Proteins were transferred onto PVDF

membranes,

and

analyzed

by

corresponding

antibody.

For

the

immunoprecipitation of TFAM complex, Ni-NTA resin was used to isolate TFAM complex from cells transfected with His-tagged TFAM. TFAM-LSP DNA binding assay Cells were harvested and lysed in 150 mM NaCl, 1% Triton X-100 and 50 mM Tris HCl, and the supernatant was incubated with a biotinylated LSP DNA probe consisting of a double strand DNA of light strand promoter (LSP). The DNA sequence was listed in Table S1. After incubation at 4 °C for 1 hour, streptavidin conjugated agarose was used to pull down the TFAM-DNA complex. Proteins from pull-down were detected by western blotting, and the relative protein abundance was analyzed by Image Lab 5.2.1 software. In vitro deacetylation assay

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293T cells were transiently transfected with His-tagged TFAM using polyethylenimine. 24 hours after transfection, the cells were treated with trichostatin A (TSA) and nicotinamide (NAM) for 10 h to hyperacetylate assay. Next, the cells were collected and lysed with lysis buffer. Ni-NTA resin was used to purify His-tagged TFAM. For in vitro deacetylation assay, the hyperacetylated purified TFAM protein was added with commercially available recombinant SIRT3 enzyme, and NAD+ was used as a cofactor. After incubation at 37 °C for 2 hours, the samples were separated by 12% SDS-PAGE gel, and the gel band containing TFAM was cut and performed in-gel digestion. The digested peptides were analyzed by mass spectrometry, and data was searched against TFAM sequence with acetylation on lysine as variable modification. The peak intensity of peptide containing acetylated K154 of TFAM was used to compare the acetylation level with or without SIRT3 incubation in vitro. Statistical method Statistical analysis was carried out by using GraphPad Prism 6.0 software. Student’s t test was used to determine the significant differences, and p values of 0.05 were considered to be significant.

Results SIRT3 overexpression decreases cell growth and increases mitochondrial biogenesis To examine SIRT3 function in the progression of ccRCC, 786-O ccRCC cells were used to establish a stable cell line that constitutively overexpressed wild-type

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SIRT3. Cells were transduced with lentivirus encoding SIRT3 with a c-terminal flag-tag, and the resulting stable cell line was designated 786-O SIRT3 OE. Cells transduced with lentivirus encoding the empty plvx cassette were used as a control, and were designated as 786-O plvx. Two stable SIRT3 knockdown cell lines were also established using different, non-overlapping shRNA sequences against SIRT3, and were designated as 786-O SIRT3 KD1 and 786-O SIRT3 KD2. Cells stably expressing non-targeting shRNA (786-O NCi cells) were used as controls for all knockdown experiments. The expression of SIRT3 in these cells was confirmed by western blotting (Figure 1A and 1B). Proliferation rates of all cell lines were determined using the CCK-8 assay (Figure 1C and 1D). 786-O SIRT3 OE cells grew more slowly than control 786-O plvx cells. In contrast, 786-O SIRT3 KD1 and KD2 cells grew more quickly than control 786-O NCi cells. These results demonstrate that high SIRT3 expression inhibits cell growth, while low expression promotes cell growth in ccRCC. MitoTracker is a fluorescent dye that specifically labels mitochondria, enabling the visualization and quantification of these organelles within cells. As shown in Figure 1E, SIRT3 overexpression resulted in a significant increase in the overall fluorescence intensity of cells, suggesting an increase in mitochondrial mass. SIRT3 knockdown resulted in a significant decrease in mitochondrial mass (Figure 1F). The mtDNA copy number of 786-O SIRT3 OE cells and control cells was measured by qPCR, which also confirmed SIRT3 overexpression increased mitochondrial mass, shown in Figure S1. ROS are potent byproducts of electron transport chain activity

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and play a complex role in tumorigenesis. Cancer cells often exhibit elevated steady-state levels of ROS, making them susceptible to oxidative stress.31 We reasoned that an increase in the mitochondrial mass could increase the cellular ROS level in SIRT3-overexpressing cells. Indeed, the ROS level was higher in SIRT3-overexpressing cells than that in control cells, as detected using the CellROX® Deep Red Reagent (Figure 1G). Furthermore, SIRT3 overexpression also rendered cells more susceptible to oxidative stress (Figure 1H). SIRT3 Overexpression Reverses Warburg Metabolic Phenotypes and Alters Cellular Metabolism The fact that SIRT3 overexpression increased the number of mitochondria and the cellular ROS level suggests that SIRT3 plays a role in metabolic reprogramming in ccRCC cells. Using metabolomics, we quantified the relative abundance of metabolites in 786-O SIRT3 OE cells and control cells. The NAD level was significantly decreased in 786-O SIRT3 OE cells, when compared with plvx cells (Figure 2A). Fructose 6-phosphate, D-ribulose-5-phosphate/xylulose 5-phosphate and lactate levels were also decreased (Figure 2B, 2C and 2D), while cis-aconitic acid, iso-citrate/citrate

and

oxoglutaric

acid

levels

were

all

increased

in

SIRT3-overexpressing cells (Figure 2E, 2F and 2G), suggesting that SIRT3 overexpression reversed the Warburg effect. Moreover, the level of carnitine was lower in 786-O SIRT3 OE cells than that in controls, while acety-coA levels were higher (Figure 2H and 2I). Quantitative proteomics analysis of the effects of SIRT3 overexpression in

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ccRCC cells To identify changes in the proteome induced by SIRT3 overexpression, a quantitative proteomics analysis was performed on 786-O SIRT3 OE and plvx cells. A total of 7376 proteins were identified in three independent biological experiments. Of these, 764 proteins were upregulated (ratio >1.3 and p

SIRT3 Overexpression Inhibits Growth of Kidney Tumor Cells and

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