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Acetyl-CoA carboxylase 1 depletion suppresses de novo fatty acid synthesis and mitochondrial β-oxidation in castration-resistant prostate cancer cells

  • Author Footnotes
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    Shaoyou Liu
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    Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
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    # They contributed equally to this article.
    Jiarun Lai
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    # They contributed equally to this article.
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    Department of Urology, Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
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    # They contributed equally to this article.
    Yuanfa Feng
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    # They contributed equally to this article.
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    Department of Urology, Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
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    # They contributed equally to this article.
    Yangjia Zhuo
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    # They contributed equally to this article.
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Hui Zhang
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Yupeng Chen
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    Department of Urology, Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
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  • Jinchuang Li
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Xinyue Mei
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    State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, China
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  • Yanting Zeng
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    Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Jiaming Su
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    Department of Urology, Guangzhou Baiyun District Women And Children's Hospital, Guangzhou, 510410, Guangdong, China
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  • Yulin Deng
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    Department of Urology, Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, Guangdong, China
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  • Funeng Jiang
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Shengbang Yang
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Huijing Tan
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    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Chi Tin Hon
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    Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, 999078, China
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  • Sun Wei
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    The Central Laboratory, Affiliated Huadu Hospital, Southern Medical University, No. 48 Xinhua Road, Huadu District, Guangzhou, Guangdong 510800, People’s Republic of China
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  • Zhaodong Han
    Correspondence
    Correspondence to: Zhaodong Han, Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China.
    Affiliations
    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China
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  • Fen Wang
    Correspondence
    Correspondence to: Fen Wang, Center for Translational Cancer Research Institute of Biosciences and Technology Texas A&M University, Houston, TX, 77030, USA.
    Affiliations
    Center for Translational Cancer Research Institute of Biosciences and Technology Texas A&M University, Houston, TX, 77030, USA
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  • Weide Zhong
    Correspondence
    Correspondence to: Weide Zhong, Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
    Affiliations
    Guangdong Provincial Institute of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

    Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, China

    Department of Urology, Guangdong Key Laboratory of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 510230, Guangdong, China

    Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, 999078, China

    The Central Laboratory, Affiliated Huadu Hospital, Southern Medical University, No. 48 Xinhua Road, Huadu District, Guangzhou, Guangdong 510800, People’s Republic of China
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  • Author Footnotes
    # They contributed equally to this article.
Open AccessPublished:November 18, 2022DOI:https://doi.org/10.1016/j.jbc.2022.102720

      Abstract

      Cancer cells, including those of prostate cancer (PCa), often hijack intrinsic cell signaling to reprogram their metabolism. Part of this reprogramming includes the activation of de novo synthesis of fatty acids that not only serve as building blocks for membrane synthesis but also as energy sources for cell proliferation. However, how de novo fatty acid synthesis contributes to PCa progression is still poorly understood. Herein, by mining public datasets, we discovered that the expression of acetyl-CoA carboxylase alpha (ACACA), which encodes acetyl-CoA carboxylase 1 (ACC1), was highly expressed in human PCa. In addition, patients with high ACACA expression had a short disease-free survival time. We also reported that depletion of ACACA reduced de novo fatty acid synthesis and PI3K/AKT signaling in the human castration-resistant PCa (CRPC) cell lines DU145 and PC3. Furthermore, depletion of ACACA downregulates mitochondrial beta-oxidation, resulting in mitochondrial dysfunction, a reduction in ATP production, an imbalanced nicotinamide adenine dinucleotide phosphate (NADP+)/NADPhydrogen(H) ratio, increased reactive oxygen species (ROS), and therefore apoptosis. Reduced exogenous fatty acids by depleting lipid or lowering serum supplementation exacerbated both shRNA depletion and pharmacological inhibition of ACACA induced apoptosis in vitro. Collectively, our results suggest that inhibition of ectopic ACACA , together with suppression of exogenous fatty acid uptake, can be a novel strategy for treating currently incurable CRPC.

      Keyword

      Abbreviations:

      PCa (Prostate Cancer), CRPC (Castration-Resistant Prostate Cancer), mCRPC (Metastatic Castration-Resistant Prostate Cancer), ACC (Acetyl-CoA Carboxylase), ACACA (Acetyl-CoA Carboxylase-A), ATP (Adenosine Triphosphate), NADPH (Nicotinamide Adenine Dinucleotide Phosphate Hydrogen), ROS (Reactive Oxygen Species), shRNA (Short Hairpin RNA), TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), GSEA (Gene Set Enrichment Analysis), FDR (False Discovery Rate), LC/MS (Liquid Chromatography/Mass Spectrometry), OCR (Oxygen Consumption Rate), ACSL3 (Fatty Acid CoA Ligase Acsl3), SLC27A2 (Very Long-Chain Acyl-CoA Synthetase), ACADVL (Very Long-Chain Specific Acyl-CoA Dehydrogenase), CCLE (Cancer Cell Line Encyclopedia), FASN (Fatty Acid Synthase), ACLY (ATP Citrate Lyase), pACLY (Phospho-ATP-Citrate Lyase), ACSS2 (Acetyl-Coenzyme A Synthetase), FFA (Free Fatty Acid), CPT1 (Carnitine Palmitoyltransferase 1), LDLR (Low-Density Lipoprotein Receptor), CD36 (Cluster of Differentiation 36), SLC27A1 (Long-Chain Fatty Acid Transport Protein 1), FACS (Fluorescence-Activated Cell Sorting)

      Introduction

      According to the statistics of 36 cancers in 185 countries, the incidence of prostate cancer (PCa) is 14.1%, and the mortality rate is 6.8%, which ranks as the second most common and fifth most deadly cancer in men [
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • Bray F.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ]. The latest released data estimate that nearly 270 thousand cases of PCa will be diagnosed yearly in the United States, ranking PCa the first male cancer incidence and the fifth leading cause of cancer death [
      • Siegel R.L.
      • Miller K.D.
      • Fuchs H.E.
      • Jemal A.
      Cancer statistics, 2022.
      ]. At the early stage, PCa is an indolent disease that grows slowly. However, PCa progresses quickly once it enters advanced and symptomized stages. Radical resection with androgen deprivation follow-up therapies is the first-line treatment for PCa. Most PCa eventually progress to metastatic androgen treatment-resistant disease called metastatic castration-resistant PCa (mCRPC) [
      • Cooperberg M.R.
      • Carroll P.R.
      Trends in Management for Patients With Localized Prostate Cancer, 1990-2013.
      ]. Although extensive progress has been made in PCa management [
      • Sandhu S.
      • Moore C.M.
      • Chiong E.
      • Beltran H.
      • Bristow R.G.
      • Williams S.G.
      Prostate cancer.
      ], there is still no cure for mCRPC. Therefore, it remains urgent to develop more effective strategies for the early diagnosis, treatment, and prevention of mCRPC.nd
      Cancer cells often hijack cell signaling to reprogram the metabolism to support their fast growth [
      • DeBerardinis R.J.
      • Chandel N.S.
      Fundamentals of cancer metabolism.
      ] [
      • Hanahan D.
      • Weinberg R.A.
      Hallmarks of cancer: the next generation.
      ]. Emerging evidence shows that abnormal fatty acid metabolism in cancer cells promotes cancer progression by providing crucial fatty acids needed for membrane synthesis, cell signaling, energy and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) production [
      • Currie E.
      • Schulze A.
      • Zechner R.
      • Walther T.C.
      • Farese Jr., R.V.
      Cellular fatty acid metabolism and cancer.
      ]. There are three main sources of fatty acids in cancer cells: (1) exogenous intake of fatty acids, (2) lipolysis, and (3) de novo fatty acid synthesis from acetyl-CoA [
      • Carracedo A.
      • Cantley L.C.
      • Pandolfi P.P.
      Cancer metabolism: fatty acid oxidation in the limelight.
      ]. Unlike most normal human cells, which obtain fatty acids through exogenous pathways, cancer cells depend on de novo synthesis [
      • Ookhtens M.
      • Kannan R.
      • Lyon I.
      • Baker N.
      Liver and adipose tissue contributions to newly formed fatty acids in an ascites tumor.
      ]. Therefore, hyperactivated de novo fatty acid synthesis has become a metabolic feature of many cancer types, including PCa, which is regarded as a promising target for cancer treatment [
      • Currie E.
      • Schulze A.
      • Zechner R.
      • Walther T.C.
      • Farese Jr., R.V.
      Cellular fatty acid metabolism and cancer.
      ] [
      • Rossi S.
      • Graner E.
      • Febbo P.
      • Weinstein L.
      • Bhattacharya N.
      • Onody T.
      • Bubley G.
      • Balk S.
      • Loda M.
      Fatty acid synthase expression defines distinct molecular signatures in prostate cancer.
      ].
      The rate-limiting enzyme in fatty acid de novo synthesis is acetyl-CoA carboxylase 1 (ACC1, encoded by acetyl-CoA carboxylase alpha (ACACA)), which catalyzes acetyl-CoA carboxylation to form malonyl-CoA. Both acetyl-CoA and malonyl-CoA are substrates for long-chain fatty acid synthesis [
      • Wakil S.J.
      • Abu-Elheiga L.A.
      Fatty acid metabolism: target for metabolic syndrome.
      ]. Therefore, targeting ACACA to inhibit de novo fatty acid synthesis has been tested in multiple types of cancer, including lung cancer cells, breast cancer, and hepatocellular carcinoma [
      • Svensson R.U.
      • Parker S.J.
      • Eichner L.J.
      • Kolar M.J.
      • Wallace M.
      • Brun S.N.
      • Lombardo P.S.
      • Van Nostrand J.L.
      • Hutchins A.
      • Vera L.
      • Gerken L.
      • Greenwood J.
      • Bhat S.
      • Harriman G.
      • Westlin W.F.
      • Harwood Jr., H.J.
      • Saghatelian A.
      • Kapeller R.
      • Metallo C.M.
      • Shaw R.J.
      Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models.
      ] [
      • Chajès V.
      • Cambot M.
      • Moreau K.
      • Lenoir G.M.
      • Joulin V.
      Acetyl-CoA carboxylase alpha is essential to breast cancer cell survival.
      ] [
      • Lally J.
      • Ghoshal S.
      • DePeralta D.K.
      • Moaven O.
      • Wei L.
      • Masia R.
      • Erstad D.J.
      • Fujiwara N.
      • Leong V.
      • Houde V.P.
      • Anagnostopoulos A.E.
      • Wang A.
      • Broadfield L.A.
      • Ford R.J.
      • Foster R.A.
      • Bates J.
      • Sun H.
      • Wang T.
      • Liu H.
      • Ray A.S.
      • Fuchs B.C.
      Inhibition of Acetyl-CoA Carboxylase by Phosphorylation or the Inhibitor ND-654 Suppresses Lipogenesis and Hepatocellular Carcinoma.
      ]. However, in other cases, inhibition of ACACA has been shown to promote cancer cell survival and metastasis [
      • Jeon S.M.
      • Chandel N.S.
      • Hay N.
      AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress.
      ] [
      • Rios Garcia M.
      • Steinbauer B.
      • Srivastava K.
      • Singhal M.
      • Mattijssen F.
      • Maida A.
      • Christian S.
      • Hess-Stumpp H.
      • Augustin H.G.
      • Müller-Decker K.
      • Nawroth P.P.
      • Herzig S.
      • Berriel Diaz M.
      Acetyl-CoA Carboxylase 1-Dependent Protein Acetylation Controls Breast Cancer Metastasis and Recurrence.
      ]. Interestingly, depletion of ACACA by siRNA in LNCaP cells decreases proliferation and increases apoptosis [
      • Brusselmans K.
      • De Schrijver E.
      • Verhoeven G.
      • Swinnen J.V.
      RNA interference-mediated silencing of the acetyl-CoA-carboxylase-alpha gene induces growth inhibition and apoptosis of prostate cancer cells.
      ] but not in benign human BPH-1 cells or normal human skin fibroblasts. Recently, we also discovered that the inhibition of ACACA in CRPC cells led to mitochondrial damage and suppressed cell proliferation both in vivo and in vitro [
      • Zhang H.
      • Liu S.
      • Cai Z.
      • Dong W.
      • Ye J.
      • Cai Z.
      • Han Z.
      • Liang Y.
      • Zhuo Y.
      • Luo Y.
      • Zhu X.
      • Deng Y.
      • Zhang Y.
      • Liu R.
      • Feng Y.
      • Lai J.
      • Zhou R.
      • Tan H.
      • Zhong W.
      Down-regulation of ACACA suppresses the malignant progression of Prostate Cancer through inhibiting mitochondrial potential.
      ]. However, it is still unclear how de novo synthesis of fatty acids contributes to CRPC cell metabolism and how it contributes to cancer cell progression in CRPC cells. Furthermore, how mCRPC cells escape ACACA depletion is also unknown. To address this unmet challenge, we depleted ACACA expression in the androgen-independent CRPC cell lines DU145 and PC3. We found that depletion of ACACA reduced de novo fatty acid synthesis and PI3K/AKT signaling, downregulated mitochondrial beta-oxidation, resulting in mitochondrial dysfunction, and induced apoptosis, which was exacerbated by reducing exogenous fatty acids. The results suggest that inhibition of ectopic ACACA, together with suppression of exogenous fatty acid uptake, can be a novel strategy for treating currently uncurable CRPC.

      Results

      (1) ACACA is highly expressed in PCa, and its expression level is associated with low disease-free survival

      First, we analyzed the public PCa dataset to mine the key abnormally expressed molecules related to the de novo fatty acid synthesis pathway in human PCa. In addition to the hallmark fatty acid metabolism gene set downloaded from the GSEA database (gsea-msigdb.org), we also identified 87 key genes that were most relevant to de novo fatty acid synthesis. Next, we downloaded multiple datasets containing PCa samples and noncancer prostate tissues from the GEO database, including the Taylor dataset (150 cases of cancer and 29 cases of noncancer), TCGA (492 cases of cancer and 52 cases of noncancer), and GSE70768 dataset (125 cases of cancer and 74 cases of noncancer). We identified 40, 23, and 41 significantly differentially expressed fatty acid metabolism-related genes from the above datasets, respectively. Among these genes, 14 genes overlapped. Among these 14 genes, the expression of 8 genes was increased in cancer tissues, and the expression of the other 6 was reduced (Fig-1 A-C). We then assessed how the expression levels of these genes were related to the clinical outcome of PCa. TCGA database profiling from GEPIA2 (http://gepia2.cancer-pku.cn/#index) showed that none of these 14 genes had an impact on the overall survival of PCa. However, the expression levels of four genes (ACACA, ACSL3, SLC27A2, ACADVL) were found to be significantly related to disease-free survival (DFS) time (Fig-1 D-G). Higher expression of ACSL3 and SLC27A2 was accompanied by higher DFS, while higher expression of ACACA and ACADVL was accompanied by lower DFS. Furthermore, analyses of the TCGA pancancer database (http://ualcan.path.uab.edu) revealed that the expression of ACACA in PCa was the highest among all cancer types (Fig-S1 A). Consistently, ACACA expression in PCa cells was the highest at both the mRNA and protein levels compared with all cell types in the Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle) (Fig-S1 B and C). Furthermore, analyses of the Grasso dataset (61 cases of cancer and 34 cases of noncancer) and Lapointe dataset (71 cases of cancer and 41 cases of noncancer) in the Oncomine database also revealed that ACACA was highly expressed in PCa (Fig-1 H-I). Consistent with the mRNA expression level, immunochemical staining of PCa and its adjacent noncancerous tissues demonstrated that PCa had a higher ACACA expression than adjacent noncancerous prostate tissue at the protein level (Fig- S1 D). As 27 cases mCRPC and 49 cases localized samples are including in the GSE35988 dataset, we further analysed and found that mCRPC had a high ACACA expression than the localized ones (Fig-1 J). Together, our findings suggest that ACACA is a potential target for suppressing de novo fatty acid synthesis in PCa.
      Figure thumbnail gr1
      Figure. 1High ACACA expression is associated with a shorter disease-free survival time. (A-C) Expression profiles of 14 key fatty acid metabolism genes in the indicated PCa databases (Taylor GSE70768 TCGA, respectively). (D-G) Disease-free survival time is significantly associated with ACACA, ACSL3, SLC27A2 and ACDVL expression levels in PCa patients. (H-I) Relative ACACA expression in the indicated dataset. (J) Representative images of immunohistochemistry staining of ACACA in the PCa tissue microarray. (K) Venn diagram model of the specific process in obtaining ACACA as the target gene. *P<0.05

      (2) Depletion of ACACA in CRPC cells reduces fatty acid content and suppresses β-oxidation in the mitochondria

      ACACA is the rate-limiting enzyme of de novo fatty acid synthesis and plays a crucial role in PCa initiation. To determine how ACACA contributes to the fatty acid metabolism of CRPC cells, short hairpin RNA (shRNA) was employed to deplete ACACA in DU145 and PC3 cells through stable transfection. Western blot analyses showed that the expression of ACACA was depleted at both the mRNA and protein levels (Fig-2 A and Fig-4 S-T). Consistently, the cellular fatty acid content was reduced in ACACA -depleted cells (Fig-2 B). Next, metabolomics was employed to profile the global change in ACACA-depleted cells. The abundance of 39 metabolites was found to be significantly changed in ACACA-depleted cells. We then analyzed the upregulated and downregulated metabolite profiles separately with a public metabolic bioinformatics analysis website (https://www.metaboanalyst.ca/). The results showed changes in 50 metabolic pathways. However, only the fatty acid metabolism pathway and the mitochondrial beta-oxidation of the long-chain fatty acid pathway were significantly downregulated (Fig-2 C-D). Further more, to discover the potential biological pathways related to ACACA in PCa, seven public datasets(Cambridge, CancerMap, GSE21019, GSE25136, GSE29079, GSE54460, GSE8218) were fitted into gene set enrichment analysis(GSEA). The result showed that pathways related to fatty acid metabolism and fatty acid beta-oxidation were statistical significance and pathway activated (Fig-2 E).
      Figure thumbnail gr2
      Figure. 2Depletion of ACACA in CRPC cells reduces fatty acid content and suppresses β-oxidation in the mitochondria. (A) Western blot analysis of ACACA expression in the indicated cell lines. (B) Quantification analysis of cellular free fatty acids in the indicated cell lines. (C-D) Metabolic pathway enrichment analysis of metabolites in ACACA knockdown cells. *P<0.05; NS: not statistically significant (P>0.05). (E) Bubble plot indicates the typical pathways related to ACACA in seven public datasets. Only the statistical significant pathway (adjusted p-value < 0.05) are showing.

      (3) Depletion of ACACA inhibits de novo fatty acid synthesis and PI3K/AKT signaling in CRPC cells

      To explore the role of ACACA in the cellular network, we profiled the cellular molecular interactions of ACACA with the String and Pathway Common database (https://string-db.org AND https://apps.pathwaycommons.org). The STRING database showed that 10 molecules had a close relationship with ACACA (Fig-3 A), which were all de novo fatty acid synthesis-related genes. FASN, the encoding gene of fatty acid synthase, which is a key enzyme for fatty acid synthesis, showed a coexpression relationship with ACACA in both databases. The relative coexpression coefficient score between ACACA and FASN was 0.686, which was the highest among the 10 genes (Fig-3 B-C). Next, above seven datasets and CIT dataset were used for GSEA analysis and the result showd that fatty acid synthease activity was statistical significance and pathway activated (Fig-3 D). After that, RNA-sequencing was employed to profile the global changes of ACACA-depletion cells. Bioinformatics analysis indicated that PI3K/AKT signaling pathway was significantly enriched(Fig-3 F) and the heatmap showed that gene related to fatty acid synthesis pathway and PI3K/AKT pathway were down-regulated (Fig-3 E). Western blot and immunofluorescence staining revealed that FASN expression was downregulated in ACACA-depleted cells (Fig-3 G, J). In addition, the levels of phosphorylated ACLY and ACSS2 were also downregulated in ACACA-depleted cells (Fig-3 G). ACLY and ACSS2 are both key enzymes of the de novo fatty acid synthesis pathway, which are responsible for catalyzing citrate and acetate to acetyl-CoA, respectively, providing the fundamental substrate for de novo fatty acid synthesis. As AMPK, PI3K/AKT, mTOR pathways are involved in modulating the fatty acid synthesis, we detected the protein expression level of their activation status. The resulted phosphorylated AKT were downregulated in both ACACA-depleted and ACACA inhibitor, 5-tetradecyloxy-2-furoic acid (TOFA) treated cells, while phosphorylated AMPK alpha was upregulated conversely. The expression of phosphorylated mTOR and total mTOR were not changed apparently (Fig-3 H, I). Furthermore, online data mining also revealed a positive correlation between ACACA and FASN, ACLY, ACSS2, AKT1, AKT2, AKT3 expression in the clinical sample databases (P<0.05) (Fig-S2). Together, the data suggest that depletion of ACACA effectively reduces the de novo fatty acid synthesis of CRPC cells via multiple mechanisms.
      Figure thumbnail gr3
      Figure. 3De novo synthesis of fatty acids and PI3K/AKT pathway is significantly inhibited in ACACA-depleted cells. (A-C) ACACA molecular interaction analysis with the Search Tool for the Retrievl of Interacting Genes/Proteins (STRING) and Pathway common database (https://string-db.org AND https://apps.pathwaycommons.org) (D) Fatty acid synthease activity was statistical significance in eight datasets. (E-F) Fatty acid synthesis pathway and PI3K/AKT pathway were down-regulated in the RNA-sequence of the ACACA-depletion cells. (G) Western blot analysis of FASN, ACLY, pACLY, and ACSS2 expression in the ACACA-depletion cells. (H-I) The protein expression level of AMPK alpha, PI3K/AKT, mTOR pathways in both ACACA-depleted and ACACA inhibitor (TOFA 10ug/ml, 72hous) treated cells. (J-K) Immunofluorescence staining of ACACA and FASN by confocal imaging in the indicated cell lines and quantitative results of relative fluorescence intensity with ImageJ (****P<0.0001).

      (4) Depletion of ACACA reduces mitochondrial β-oxidation in CRPC cells

      In addition to glucose and glutamine, fatty acids also serve as an energy source, which provides twice as much adenosine triphosphate (ATP) as carbohydrates through mitochondrial beta-oxidation[
      • Carracedo A.
      • Cantley L.C.
      • Pandolfi P.P.
      Cancer metabolism: fatty acid oxidation in the limelight.
      ]. Metabolomics characterization revealed that metabolites related to mitochondrial beta-oxidation in ACACA-depleted CRPC cells were significantly reduced. The Seahorse Mito Stress assay showed that the oxygen consumption rate (OCR) was decreased in ACACA-depleted cells (Fig-4 A), together with ATP production, maximal respiration, spare respiratory capacity, proton leak and basal respiration (Fig-4 B, C, D, E and F). However, the phenotypes above were almost rescued by the addition of exogenous palmitate. Consistently, similar situation was observed when ACACA was pharmacological inhibition by TOFA (Fig-4 G, H, I, J, K and L). The results suggest that depletion of ACACA reduces mitochondrial activity.Furthermore, MitoTracker staining showed that the mitochondrial potential was reduced in both ACACA-depleted and TOFA treated cells, which were also rescued by the addition of exogenous palmitate (Fig-4 M, N, O, P, Q and R).
      Figure thumbnail gr4
      Figure. 4Depletion of ACACA reduces mitochondrial β-oxidation in CRPC cells. (A-L) Depletion of ACACA reduces mitochondrial activity, and the phenotypes could be rescued by the addition of exogenous palmitate (100 uM, 72h). The same results are shown in TOFA treated cells. (M-R) MitoTracker staining showed that the mitochondrial potential was reduced in both ACACA-depleted and TOFA treated cells, rescued by the addition of exogenous palmitate. (S-T) The mRNA expression of ACACB was increased in ACACA-depleted cells. (U) The NADP+/NADPH ratio was increased in ACACA-depleted cells. (V) Western blotting showed that the expression of CPT1A and CPT1B was downregulated in ACACA-depleted cells. ( *P<0.05; NS: not statistically significant (P>0.05).
      Carnitine palmitoyltransferase 1 (CPT1) catalyzes the transfer of fatty acid-CoA conjugates to carnitine, an essential step for the mitochondrial uptake of fatty acids and the subsequent beta-oxidation in the mitochondria. Western blotting showed that the expression of CPT1A and CPT1B was downregulated in ACACA-depleted cells (Fig-4 V). Mitochondria membrane-localized ACACB allosterically inhibits CPT1 through the production of malonyl-CoA and negatively regulates fatty acid oxidation. The expression of ACACB was increased in ACACA-depleted cells, which synergistically downregulated CPT1 activity and suppressed mitochondrial beta oxidation (Fig-4 S and T). Fatty acid oxidation-derived NADPH is important for cancer cells to maintain NADP+/NADPH homeostasis. As expected, the NADP+/NADPH assay showed that the NADP+/NADPH ratio was increased in ACACA-depleted cells (Fig-4 U), which was likely caused by the reduction in mitochondrial beta oxidation of fatty acids.

      (5) Reactive oxygen species (ROS) and apoptosis are increased in ACACA-depleted cells

      According to the RNA-sequencing profiling analysis, apoptosis pathway was significantly enriched with ACACA-depleted cells (Fig- 5 A). GSEA analysis with nine datasets showed that expression of ACACA statistically significant suppressed apoptosis pathway (Fig- 5 D). Further FACS analysis showed increased apoptosis in ACACA-depleted cells (Fig-5 E and F). Consistently, Western blot analysis also showed that depletion of ACACA increased the cleavage of caspase-3 and caspase-9 in CRPC cells (Fig-5 B and C). To determine whether depletion of ACACA induced oxidative stress in CRPC cells, CellROX Deep Red dye was used to assess cellular ROS. FACS analysis revealed that the cellular ROS level was increased in ACACA-depleted cells (Fig- 5 G and H). Consistently, confocal imaging also showed increased ROS staining in ACACA-depleted cells (Fig-5 I and J, L and K). Although mitochondria is the major site for the ROS production, the mitochondrial ROS production was decreased in the ACACA-depleted cells. The decreased mitochondrial ROS production was also rescued by the addition of exogenous palmitate, which had similar phenotype with the mitochondrial potential (Fig-S4).
      Figure thumbnail gr5
      Figure. 5Reactive oxygen species (ROS) and apoptosis are increased in ACACA-depleted cells. (A)Apoptosis pathway was enriched with ACACA-depleted cells according to the RNA-sequencing profiling analysis. (B-C) Western blot analysis showed that depletion of ACACA increased the cleavage of caspase-3 and caspase-9 in CRPC cells. (D) Expression of ACACA suppressed apoptosis pathway in nine public datasets. (E-F) FACS analysis of apoptosis and quantitative results. (G-H) FACS analysis of cellular ROS and quantitative analyses of relative fluorescence intensity. (I-L) Confocal imaging of cellular ROS and quantitative analyses of relative fluorescence intensity.

      (6) Elevated exogenous lipid uptake compensates for the loss of novel fatty acid synthesis in PCa cells

      LDLR, CD36, and SLC27A1 are the three major transporters for the uptake of exogenous fatty acids in cells. TCGA dataset analyses showed that the expression of LDLR, CD36, and SLC27A1 was significantly downregulated in PCa compared with normal prostate tissue (Fig-6 A). Quantitative RT-PCR analyses revealed that depletion of ACACA significantly increased the expression of LDLR, CD36, and SLC27A1 in PC3 cells. Consistently, depletion of ACACA also upregulated LDLR expression in DU145 cells, although CD36 and SLC27A1 expression was not significantly different (Fig-6 B and C). The data suggest that elevated uptake of exogenous lipid compensates for the loss of novel fatty acid synthesis due to the loss of ACACA. To test this hypothesis, the LDLR expression of ACACA depleted cells were successfully knockdowned by three LDLR-specific-siRNA, then FACS apoptosis analysis showed a increased apoptosis in the LDLR knockdowned ACACA-depleted cells (Fig-6 D). Furthermore, cells were cultured in medium containing a reduced concentration of fetal bovine serum (FBS) or delipidated FBS. FACS analysis showed that compared with those cultured in 10% FBS, ACACA-depleted cells cultured in 1% FBS and delipidated FBS had significantly increased apoptosis (Fig-6 E). In contrast, very little difference in apoptosis was observed in parental cells cultured in different serum concentrations. However, the apoptosis of the ACACA-depleted cells cultured in delipidated FBS was partially rescued by the addition of exogenous palmitate (Fig-6 F).
      Figure thumbnail gr6
      Figure. 6Elevated exogenous lipid uptake compensates for the loss of novel fatty acid synthesis in PCa cells. (A) Relative expression of LDLR, CD36, and SLC27A1 in the TCGA PCa dataset. (B-C) qPCR analysis of ACACA, LDLR, CD36, and SLC27A1 mRNA expression in the indicated cell lines. (D) FACS analysis of apoptosis in the LDLR knockdowned ACACA-depleted PCa cells.(E) FACS analysis of PCa cell apoptosis cultured in medium with different serum concentrations or delipidated FBS, and (F) the apoptosis were partially rescued by the addition of exogenous palmitate . *P<0.05

      (7) Blocking of exogenous lipid uptake exacerbated TOFA-induced apoptosis in CRPC cells

      TOFA, a potent competitive acetyl-CoA carboxylase inhibitor, which have been widely proven in inhibiting fatty acid synthesis. To verify the phenotype above, TOFA was employed as pharmacological inhibition of ACACA. PC3 or DU145 wlidtype cells were cultured in medium containing 10%FBS or 1%FBS or delipidated FBS, then co-treated with 0ug/ml, 5ug/ml or 10ug/ml TOFA and 0uM or 100uM palmitate for 72hours. Consistently, FACS analysis showed that compared with those cultured in 10% FBS, TOFA-treated cells cultured in 1% FBS and delipidated FBS had significantly increased apoptosis (Fig-7 B and C, Fig-S3 B and C). The cells cultured in 1%FBS or delipidated FBS medium with 10ug/ml TOFA treatment were almost dead (Fig-7 A, Fig-S3 A). In addition, western-blot analysis showed an increased cleaved-PPAR expression with the TOFA-treated cells, especially in 1%FBS and delipidated FBS groups. By contrast, phosphorylated AKT expression were down-regulated in those cells. Furthermore, confocal imaging showed an increased fluorescence intensity with the TOFA-treated cells, especially in 1%FBS and delipidated FBS groups (Fig-7 E).
      Figure thumbnail gr7
      Figure 7Blocking of exogenous lipid uptake exacerbated TOFA-induced apoptosis in CRPC cells. (A) Cells images of the CRPC cells cultured in 1%FBS or delipidated FBS medium with 10ug/ml TOFA treatment. (B-D) FACS analysis and WB showed the apoptosis in above cell culture conditions. And, (E-F) Confocal imaging of cellular ROS and quantitative analyses of relative fluorescence intensity. *P<0.05

      Discussion

      Cancer cells frequently have an elevated capacity for de novo fatty acid synthesis to meet the needs of rapid cell proliferative tumors. PCa is no exception, which makes it susceptible to treatment to suppress fatty acid synthesis [
      • Menendez J.A.
      • Mehmi I.
      • Papadimitropoulou A.
      • Vander Steen T.
      • Cuyàs E.
      • Verdura S.
      • Espinoza I.
      • Vellon L.
      • Atlas E.
      • Lupu R.
      Fatty Acid Synthase Is a Key Enabler for Endocrine Resistance in Heregulin-Overexpressing Luminal B-Like Breast Cancer. Int.
      ]. In fact, many potential targets related to fatty acid synthesis have been explored thus far, including FASN, ACLY, and ACSS2 [
      • Röhrig F.
      • Schulze A.
      The multifaceted roles of fatty acid synthesis in cancer.
      ]. However, most efforts have been focused on FASN, although it has been reported that ACACA expression is elevated in breast cancer, liver cancer, lung cancer, and prostate cancer [
      • Svensson R.U.
      • Parker S.J.
      • Eichner L.J.
      • Kolar M.J.
      • Wallace M.
      • Brun S.N.
      • Lombardo P.S.
      • Van Nostrand J.L.
      • Hutchins A.
      • Vera L.
      • Gerken L.
      • Greenwood J.
      • Bhat S.
      • Harriman G.
      • Westlin W.F.
      • Harwood Jr., H.J.
      • Saghatelian A.
      • Kapeller R.
      • Metallo C.M.
      • Shaw R.J.
      Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models.
      ] [
      • Chajès V.
      • Cambot M.
      • Moreau K.
      • Lenoir G.M.
      • Joulin V.
      Acetyl-CoA carboxylase alpha is essential to breast cancer cell survival.
      ] [
      • Lally J.
      • Ghoshal S.
      • DePeralta D.K.
      • Moaven O.
      • Wei L.
      • Masia R.
      • Erstad D.J.
      • Fujiwara N.
      • Leong V.
      • Houde V.P.
      • Anagnostopoulos A.E.
      • Wang A.
      • Broadfield L.A.
      • Ford R.J.
      • Foster R.A.
      • Bates J.
      • Sun H.
      • Wang T.
      • Liu H.
      • Ray A.S.
      • Fuchs B.C.
      Inhibition of Acetyl-CoA Carboxylase by Phosphorylation or the Inhibitor ND-654 Suppresses Lipogenesis and Hepatocellular Carcinoma.
      ] [
      • Rios Garcia M.
      • Steinbauer B.
      • Srivastava K.
      • Singhal M.
      • Mattijssen F.
      • Maida A.
      • Christian S.
      • Hess-Stumpp H.
      • Augustin H.G.
      • Müller-Decker K.
      • Nawroth P.P.
      • Herzig S.
      • Berriel Diaz M.
      Acetyl-CoA Carboxylase 1-Dependent Protein Acetylation Controls Breast Cancer Metastasis and Recurrence.
      ]. Herein, we report that ACACA was highly expressed in PCa, especially in CRPC, and patients with higher ACACA expression had shorter disease-free survival times. Depletion of ACACA in CRPC cells inhibited de novo synthesis of fatty acids and PI3K/AKT pathway, suppressed proliferation and tumorigenesis, reduced mitochondrial beta oxidation, and increased ROS and apoptosis. We also reported that depletion of ACACA decreased the expression of FASN, ACLY, and ACSS2, the key enzymes for de novo fatty acid synthesis. Furthermore, we reported that reduced lipid or serum supplementation exacerbated apoptosis induced by ACACA depletion, suggesting that exogenous lipid uptake likely compensated for the loss of novel fatty acid synthesis in PCa cells. Therefore, suppression of lipid uptake is needed to improve the efficacy of blocking de novo synthesis therapy for PCa.
      In this study, we found that depletion of ACACA in CRPC cells inhibited mitochondrial function and reduced ATP production. Metabolomics analysis showed that the reduced metabolites in ACACA-depleted CRPC cells were enriched in the mitochondria beta-oxidation of the long-chain fatty acid synthesis pathways. Our data indicate that compensatory expression of ACACB located at the mitochondrial outer membrane is likely the causal factor of CPT1 inhibition in ACACA-depleted cells [
      • Currie E.
      • Schulze A.
      • Zechner R.
      • Walther T.C.
      • Farese Jr., R.V.
      Cellular fatty acid metabolism and cancer.
      ] [
      • Schlaepfer I.R.
      • Rider L.
      • Rodrigues L.U.
      • Gijón M.A.
      • Pac C.T.
      • Romero L.
      • Cimic A.
      • Sirintrapun S.J.
      • Glodé L.M.
      • Eckel R.H.
      • Cramer S.D.
      Lipid catabolism via CPT1 as a therapeutic target for prostate cancer.
      ] [
      • McGarry J.D.
      • Leatherman G.F.
      • Foster D.W.
      Carnitine palmitoyltransferase I. The site of inhibition of hepatic fatty acid oxidation by malonyl-CoA.
      ]. Consequently, decreased expression of the fatty acid transporter CPT1 will reduce the delivery of fatty acids to mitochondria for beta-oxidation, resulting in weakened mitochondrial function and increased energy stress in ACACA-depleted CRPC cells. Together with the imbalance of NADP+/NADPH, increased energy stress will induce elevation of the ROS level in the cells and lead to cell apoptosis.
      Our data revealed that diminishing ACACA-mediated de novo fatty acid synthesis induced cell damage and that depletion of ACACA increased the dependency on serum lipid supplementation in cell culture. The results suggest that the deficiency in de novo fatty acid synthesis in CRPC cells can be rescued by increasing the uptake of exogenous fatty acids, which is likely the mechanism by which cancer cells resist treatment to block de novo fatty acid synthesis [
      • Röhrig F.
      • Schulze A.
      The multifaceted roles of fatty acid synthesis in cancer.
      ]. Our results that depletion of ACACA led to increased expression of the lipid transporters exogenous LDLR, SLC27A1, and CD36 further support this notion. Therefore, lower serum lipid levels and inhibited lipid uptake can be an effective venue to increase the efficacy of ACACA-targeted therapy for PCa.
      In summary, ectopic expression of ACACA in PCa cells reduces the dependence on exogenous fatty acids, which are needed for fast membrane synthesis in PCa cells. Suppression of ACACA, together with blocking exogenous fatty acid uptake, will be a novel therapeutic strategy for currently uncurable CRPC (Fig 8.)
      Figure thumbnail gr8
      Figure 8Model illustrating that inhibition of de novo fatty acid synthesis in castration-resistant prostate cancer cells by depleting acetyl-CoA carboxylase 1 suppresses mitochondrial β-oxidation, leading to reduced proliferation and increased apoptosis, which is exacerbated by insufficient exogenous fatty acid uptake.

      Experimental Procedures

      Cell culture

      The human PCa cell lines DU145 and PC3 were purchased from the American Type Culture Collection (Manassas, VA, USA) and cultured in Dulbecco’s modified Eagle’s medium (Gibco, USA, C11995500CP) supplemented with 10% fetal bovine serum (Gibco, USA, 10270-106) or 10% Lipid depleted fetal bovine serum(VivaCell, C3840-0100), and 1% antibiotics (penicillin-streptomycin, Gibco, USA, 15140122). The cells were incubated in a 5% CO2 incubator at 37 °C. The cetyl-CoA carboxylase inhibitor TOFA (abcam, ab141578) and palmitate acid (Macklin, p815428) was utilized in the cell culture.
      The ACACA-specific shRNA and control vector were purchased from HYY Med Company (Guangzhou, China). The shRNA sequence was TACAAGGGATACAGGTATTTA. The plasmid contains sequences encoding puromycin resistance protein, green fluorescent protein (GFP), and shRNA. Two days after infection with the lentivirus, the infected cells were selected by growing in medium containing 2 mg/mL puromycin. Single clones of transfected cells were selected for verification and analyses. In addition, We seeded 1×10ˆ5 cells in six-well plates for fusion of 50–60% for 24 h; then cells were transfected with siRNAs mixed with siRNA-Mate (GenePharm, G04002) for 72h and transfection efficiency was verified via RT-qPCR. The sequences used for siRNA are listed in Supplementary Table 1.

      Bioinformatic analysis

      The Cancer Genome Atlas (TCGA) and Taylor datasets were downloaded from https://xenabrowser.net/datapages/, the GSE70768 dataset was downloaded from the Gene Expression Omnibus (GEO) database, and the fatty acid metabolism gene set was downloaded from the website https://www.gsea-msigdb.org/gsea/index.jsp. The three datasets were analyzed for the differential expression of key genes related to fatty acid metabolism. The correlation coefficient between the target gene and each other gene in TCGA was first calculated by Pearson correlation analysis. Then, the obtained gene list was ordered via the decreasing levels of the correlation coefficient. The ordered gene list was further fitted in GSEA by the R package ‘Cluster Profilter’ using the annotation of ‘hallmark gene sets’. A false discovery rate (FDR)<0.25 and adjusted p value<0.01 were considered statistically significant.
      The profiling data of mRNAs and clinical characteristics for 495 PCa patients from the TCGA-PRAD project were downloaded and processed in the R package GDCRNAtools. The mRNA expression data in seven public datasets (Cambridge, CancerMap, GSE21019, GSE25136, GSE29079, GSE54460, GSE8218) were acquired from the Gene expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/).
      Gene set enrichment analysis: to discover the potential biological pathways related to ACACA in PCa, differentially expressed analysis was firstly performed between high- and low-expression subgroup based on the median expression levels of ACACA in each dataset. Then, the genes were ranked via the decreasing levels of Log2Foldchange. The ordered gene list was further fitted into gene set enrichment analysis (GSEA) by R package ‘clusterProfilter’. Adjusted p-value <0.05 and normalized enrichment score (NES) > 1 were considered as statistical significance and pathway activated. Adjusted p-value <0.05 and normalized enrichment score (NES) < -1 were considered as statistical significance and pathway suppressed.

      Histology

      Prostate tissues and xenografts were fixed, embedded, and sectioned as described [
      • Liu J.
      • Chen G.
      • Liu Z.
      • Liu S.
      • Cai Z.
      • You P.
      • Ke Y.
      • Lai L.
      • Huang Y.
      • Gao H.
      • Zhao L.
      • Pelicano H.
      • Huang P.
      • McKeehan W.L.
      • Wu C.L.
      • Wang C.
      • Zhong W.
      • Wang F.
      Aberrant FGFR Tyrosine Kinase Signaling Enhances the Warburg Effect by Reprogramming LDH Isoform Expression and Activity in Prostate Cancer.
      ]. The antigen epitopes were retrieved by microwave heating in citric acid antigen repair solution. Immunohistochemical staining was performed using an UltraSensitiveTM SP kit (MXB, #KIT-9710) according to the user manual. Tumor samples were stained with the following primary antibodies: ACC1 (CST, #3662). The slides were stained with 3,3’-diaminobenzidine (DAB) substrate (MXB, #DAB-0031), counterstained with hematoxylin and mounted in mounting medium.

      Free fatty acid quantitation assay

      Cells (1 × 106) were homogenized in 200 μl of 1% (w/v) Triton X-100-chloroform (RATIO) solution. After centrifugation at 13,000 × g for 10 minutes, the organic phases (lower phase) of the supernatant were collected and air-dried at 50 °C to remove chloroform. The samples were then vacuum-dried for 30 minutes and dissolved in 200 μl of Fatty Acid Assay Buffer by vortexing extensively for 5 minutes. The sample volumes were brought to 50 μl with Fatty Acid Assay Buffer. ACS Reagent and Master Reaction Mix were added to each sample according to the instructions of the Free Fatty Acid Quantitation Kit (Catalog Number MAK044, Sigma–Aldrich). The samples were incubated for 30 minutes at 37 °C in the dark. The absorbance at 570 nm (A570) was measured.

      Metabolite analysis

      The metabolite profiles of the cells were determined by liquid chromatography/mass spectrometry (LC/MS) [
      • Zhang H.
      • Liu S.
      • Cai Z.
      • Dong W.
      • Ye J.
      • Cai Z.
      • Han Z.
      • Liang Y.
      • Zhuo Y.
      • Luo Y.
      • Zhu X.
      • Deng Y.
      • Zhang Y.
      • Liu R.
      • Feng Y.
      • Lai J.
      • Zhou R.
      • Tan H.
      • Zhong W.
      Down-regulation of ACACA suppresses the malignant progression of Prostate Cancer through inhibiting mitochondrial potential.
      ]. Both downregulated and upregulated metabolites were analyzed on the Metabolite Set Enrichment analysis website for related profiling (https://dev.metaboanalyst.ca/MetaboAnalyst/upload/EnrichUploadView.xhtml).

      Quantitative real-time polymerase chain reaction (RT-PCR)

      Total RNA from tumor cells was extracted with the RNA extraction reagent NucleoZol (MN, #740404.200) according to the User’s manual. RNA quality and concentration were determined by spectrophotometer. RNA samples were reverse transcribed into cDNA using HiScript® III Reverse Transcriptase (Vazyme, # R323-01) according to the product guide. The cDNAs were mixed with 2 × volumes of ChamQ Universal SYBR qPCR Master Mix (Vazyme, #Q711) and analyzed with a 96-well real-time polymerase chain reaction (PCR) machine. Expression was normalized to that of GAPDH. The primer sequences used for quantitative (q)RT-PCR are listed in Supplementary Table 1.

      Western blot analysis

      Cell samples were lysed in radioimmunoprecipitation assay (RIPA) buffer (BoCai Biology, #R0127) in the presence of 100×phenylmethylsulfonyl fluoride (PMSF) (Beyotime, #ST506) protease inhibitor and 50×phosphatase inhibitor (BoCai Biology, #R0127). The gels for protein electrophoresis were prepared using the sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) Gel Preparation Kit 1 (Beyotime, #P0012A). The samples separated by SDS-polyacrylamide gel electrophoresis were electrotransferred to polyvinylidene fluoride (PVDF) membranes for 100 minutes at 300 mA. The membranes were blocked for 10 min using NcmBlot blocking buffer (NCM Biotech, #P30500) and incubated overnight at 4 °C with the primary antibody diluted in tris-buffered saline with Tween 20 (TBST) with bovine serum albumin (BSA) (5% w/v) (TBST-BSA). After washing with TBST 3 times, the membranes were incubated with the secondary antibody for 1 h at room temperature (RT). After washing 3 times with TBST to remove nonspecific binding, the membranes were incubated in chemiluminescent reagents (Millipore, #WBKLS0100). The images were captured with X-ray films, which were scanned with a densitometer for quantitation. The antibody information is listed in Supplementary Table 2.

      Immunofluorescence

      Cells were seeded in confocal plates and incubated at 37 °C for at least one night prior to analyses. The cells were fixed with 4% formaldehyde and then incubated with 0.5% Triton X-100. After incubation with the primary antibodies overnight at 4 °C, the cells were washed three times with phosphate-buffered saline (PBS) and then incubated with the secondary antibody (Alexa Fluor, Boster, Guangzhou, China) at room temperature for 1 hour. After washing, the cells were incubated with diamidino-2-phenylindole (DAPI) for 5 minutes. The images were taken by a confocal laser scanning microscope (LSM880, Leica, Germany). Fluorescence intensity was quantified by ImageJ software.

      Reactive Oxygen Species Detection

      Cells were seeded in six-well plates at least one night prior to the analyses. The cells were then incubated with dihydroethidium (DHE) (Beyotime Biotechnology, S0033) or MitoSOX™ Mitochondrial Superoxide Indicators (Invitrogen, M36008) at 37 °C in a CO2 incubator (avoiding light) for 30 minutes. The cells were then washed three times in serum-free cell culture medium. One well of each treatment was digested with trypsin for the preparation of a single-cell suspension to be analyzed with a flow cytometer. The other wells were then incubated with Hoechst 33342 for 30 min before confocal imaging. FlowJo and ImageJ software were employed to quantify the cell numbers and fluorescence intensity, respectively.

      MitoTracker assay

      Two sets of cells were seeded in six-well plates and cultured at 37 °C for at least one night in advance. MitoTracker Red (Life Technologies, M7512) was added to the cells. After incubation at 37 °C with CO2 (avoiding light) for 30 minutes, the cells were then incubated with Hoechst 33342 for 30 min before confocal imaging. One well for each sample was digested into a single-cell suspension for flow cytometry analyses. FlowJo and ImageJ software were employed to quantify the cell number and fluorescence intensity, respectively.

      Seahorse assay

      The Seahorse Fe 24 Extracellular Flux Bioanalyzer (Agilent) was used to measure the oxygen consumption rate (OCR) with the Mitostress kit according to the manufacturer’s protocol. Cells (30,000 per well) were seeded into specific culture plates in complete medium overnight. The culture medium was then changed to fresh Dulbecco’s modified Eagle’s medium (DMEM) containing 10 mM glucose, 2 mM L-glutamine, and 1 mM sodium pyruvate and incubated for 1 h. After incubation in a CO2-free incubator to guarantee precise measurements of extracellular pH. Oligomycin (Oligo, 1 mM), carbonyl cyanide 4-trifluoromethoxy-phenylhydrazone (FCCP) (1 mM), and rotenone/antimycin A (Rot/AA, 1 mM) were sequentially loaded into each well. The Seahorse assay was run in an XFe24 Extracellular Flux Analyzer (Agilent Technologies). The results were analyzed using Wave program 2.6.0 (SeahorseBioscience).

      Apoptosis assay

      A total of 1×106 cells were stained with an Annexin V-APC/7-AAD apoptosis kit (MultiSciences, China, #AP105) according to the user’s manual. Then, the stained apoptotic cells were detected by a flow cytometer. FlowJo software was employed to quantify the apoptosis rate.

      NADP/NADPH assay

      The NADP+/NADPH colorimetric detection kit was purchased from Beyotime, China, #S0179. We conducted experiments according to the manufacturer’s instructions. A total of 1×106 cells were seeded in a 6-cm dish and cultured for 24 hours. After removing the culture medium, 200 μl of NADP+/NADPH extract buffer was added to each well. The lysates were collected for centrifugation at 12,000 g and 4 °C for 10 min to remove the insoluble fractions. The supernatant was collected for the analyses. The NADPH standard and G6PDH working solutions were prepared according to the instruction manual. The supernatants (100 μl) were transferred to a centrifuge tube and heated in a water bath (60 °C) to decompose NADP+. Blank control, NADPH standard, and samples were loaded in a 96-well plate. The G6PDH working solution (100 μl ) was added to each well and mixed gently. The plates were incubated at 37 °C (protected from light) for 10 minutes. The developing solution (10 μl) was then added to each well. After incubation at 37 °C for 15 min (protected from light), the absorbance at 450 nm was measured. The NADP+/NADPH ratio was determined according to the instructions.

      RNA extraction and library construction

      TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's procedure was utilized to extract the RNA of cells and tissues. The RNA amount and purity of each sample was quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), poly(A)- or poly(A)+ RNA fragments were broken into small pieces by divalent cations at a high temperature. The final cDNA library was constructed from cDNA reverse-transcribed from the cleaved small RNA fragments. we performed the paired-end sequencing (PE150) on an illumina Novaseq™ 6000 (LC-Bio Technology CO., Ltd., Hangzhou, China) following the vendor's recommended protocol.

      Bioinformatics analysis of RNA-seq

      The sequence quality was also verified using fastp software (https://github.com/OpenGene/fastp). We used HISAT2 (https://ccb.jhu.edu/software/hisat2) to map reads to the reference genome of Homo sapiens GRCh38. The mapped reads of each sample were assembled using StringTie (https://ccb.jhu.edu/software/stringtie). Then, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome using gffcompare (https://github.com/gpertea/gffcompare/). StringTie was used to perform expression level for mRNAs by calculating FPKM (FPKM = [total_exon_fragments / mapped_reads(millions) × exon_length(kB)]). The differentially expressed mRNAs were selected with fold change > 2 or fold change < 0.5 and with parametric F-test comparing nested linear models (p value < 0.05) by R package edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html).

      Statistical analysis

      All experiments were performed at least three times. GraphPad software was used for statistical analysis. Data analyses were performed using the independent two-tailed t- test, and the error bars indicate the mean ± standard deviation. P < 0.05 was considered statistically significant.

      Data Availability

      All the data are available either in the main Manuscript or in the Supporting Document.

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      Conflict of interest

      The authors declare that they have no conflicts of interest with the contents of this article.

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