Inactivation of the AMPK-GATA3-ECHS1 Pathway Induces Fatty Acid Synthesis That Promotes Clear Cell Renal Cell Carcinoma Growth

Author Manuscript Published OnlineFirst on November 5, 2019; DOI: 10.1158/0008-5472.CAN-19-1023 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Inactivation of the AMPK-GATA3-ECHS1 Pathway induces Fatty Acid

Synthesis that Promotes Clear Cell Renal Cell Carcinoma Growth

Running title: AMPK-GATA3-ECHS1 Pathway Inactivation Promotes ccRCC Growth

Yuan-Yuan Qu1,2,3†, Rui Zhao1,†, Hai-Liang Zhang1,3†, Qian Zhou1, Fu-Jiang Xu1,3,

Xuan Zhang2, Wen-Hao Xu1,3, Ning Shao1,3, Shu-Xian Zhou1,2, Bo Dai1,3, Yao Zhu1,3,

Guo-Hai Shi1,3, Yi-Jun Shen1,3, Yi-Ping Zhu1,3, Cheng-Tao Han1,3, Kun Chang1,3, Yan

Lin1,2,5, Wei-Dong Zang4, Wei Xu1,2,5, Ding-Wei Ye1,3,*, Shi-Min Zhao1,2,5,*, Jian-Yuan

Zhao1,2,5,*

1 Department of Urology, Fudan University Shanghai Cancer Center, the Obstetrics &

Gynecology Hospital of Fudan University, State Key Lab of Genetic Engineering and

School of Life Sciences,

2 Key Laboratory of Reproduction Regulation of NPFPC, Institutes of Biomedical

Sciences and Collaborative Innovation Center of Genetics & Development,

3 Department of Oncology, Shanghai Medical College,

Fudan University, Shanghai 200032, P.R. China

4 School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, P.R.

China

5 Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan

University, Chengdu 610041, P.R. China

Downloaded from cancerres.aacrjournals.org on September 27, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 5, 2019; DOI: 10.1158/0008-5472.CAN-19-1023 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

†These authors contributed equally to this work

* Correspondence should be addressed to Drs. Ding-Wei Ye ([email protected]),

Shi-Min Zhao ([email protected]) or Jian-Yuan Zhao ([email protected]).

Mailing address: Suite C321, Life Sciences Building, Fudan University, 2005 Songhu

Rd, Shanghai 200032, P.R. China.

Declaration of Interests

The authors have declared that no conflict of interest exists.

1 Abstract

2 The tumorigenic role and underlying mechanisms of lipid accumulation, commonly

3 observed in many cancers, remains insufficiently understood. In this study we

4 identified an AMP-activated protein kinase (AMPK)-GATA-binding protein 3

5 (GATA3)-enoyl-CoA hydratase short-chain 1 (ECHS1) pathway that induces lipid

6 accumulation and promotes cell proliferation in clear cell renal cell carcinoma

7 (ccRCC). Decreased expression of ECHS1, which is responsible for inactivation of

8 fatty acid oxidation (FAO) and activation of de novo fatty acid (FA) synthesis,

9 positively associated with ccRCC progression and predicted poor patient survival.

10 Mechanistically, ECHS1 downregulation induced FA and branched-chain amino acid

11 (BCAA) accumulation which inhibited AMPK-promoted expression of GATA3, a

12 transcriptional activator of ECHS1. BCAA accumulation induced activation of

13 mTORC1, de novo FA synthesis, and promoted cell proliferation. Furthermore, GATA3

14 expression phenocopied ECHS1 in predicting ccRCC progression and patient survival.

15 The AMPK-GATA3-ECHS1 pathway may offer new therapeutic approaches and

16 prognostic assessment for ccRCC in the clinic.

18 Statement of Significance:

19 Findings uncover molecular mechanisms underlying lipid accumulation in ccRCC

20 suggesting the AMPK-GATA3-ECHS1 pathway as a potential therapeutic target and

21 prognostic biomarker.

1 Introduction

2 Recently, dysregulated fatty acid (FA) metabolism has been observed in many types

3 of cancers, including renal cell carcinoma, breast cancer, prostate cancer, and lung

4 cancer (1-5). The relevance of FA metabolism to cancer-cell functioning, alongside

5 that of perturbed glucose metabolism—known as the Warburg effect—and altered

6 amino acid metabolism, which is represented by glutamine metabolism, is becoming

7 increasingly recognized.

8 Clear cell renal cell carcinoma (ccRCC), which accounts for approximately 80% of

9 diagnosed RCCs, exhibits intracellular lipid droplet accumulation and is closely

10 related to aberrant FA metabolism. In fact, obesity is a well-established independent

11 risk factor of ccRCC (6-9). Weight gain is also associated with increased ccRCC risk

12 (6), as are increased body mass index (7) and high dietary intake of saturated fat,

13 animal fat, or oleic acid (10). Excess lipids in cancer cells are stored in lipid droplets,

14 and high levels of lipid droplets are currently considered a hallmark of cancer

15 aggressiveness (11-14). The histological appearance of ccRCC cells, i.e., their clear

16 cytoplasm, is due to lipid accumulation (15), and suggests that metabolic

17 reprogramming may occur during ccRCC development. Furthermore, gene

18 expression profiling has revealed that the expression of FA synthesis genes, such as

19 acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN) (3,16), is significantly

20 increased in ccRCC, indicating enhanced de novo FA synthesis in ccRCC. In addition,

21 AMP-activated protein kinase (AMPK), the master sensor of cellular energy balance

22 (17,18), inhibits de novo FA synthesis and lipid accumulation through inhibitory 4

1 phosphorylation of ACC, thus maintaining cellular energy homeostasis and protecting

2 cells from metabolic stress (19). Loss of AMPK activity, which is frequently observed

3 in ccRCC (3,20), is correlated with enhanced de novo FA synthesis and lipid

4 accumulation (20).

5 Although evidence suggests a causative role of FA accumulation in ccRCC

6 occurrence and development, the underlying mechanisms remain unclear. Most

7 importantly, besides abnormal FA synthesis, the relevance of fatty acid oxidation (FAO)

8 in FA accumulation and cancer cell function is unknown. Aberrant activation of

9 mTORC1, which can be induced by intracellular elevation of branched-chain amino

10 acids (BCAAs), especially leucine, is frequently observed in ccRCC (21-23). Our

11 previous study revealed the existence of crosstalk between BCAA metabolism and

12 FAO via a reaction catalyzed by enoyl-CoA hydratase short-chain 1 (ECHS1) (24).

13 Therefore, dysregulated FAO can affect FA and protein synthesis simultaneously.

14 In this study, we found that ECHS1 expression is nearly absent in ccRCC tumors and

15 its absence predicted poor patient survival. In clinical samples, animal models, and

16 cultured cells, we validated that ECHS1 is regulated by the AMPK-GATA3 pathway,

17 and ECHS1 downregulation could inhibit AMPK via FA accumulation. Analysis of the

18 pathological mechanism revealed that loss of ECHS1 results in FAO block

19 BCAA-mediated mTORC1 activation and mTORC1 activation-induced de novo FA

20 synthesis, thus promoting cancer cell proliferation.

2 Materials and Methods

3 Reagents and Antibodies

4 AICAR (#A9978) and rapamycin (#553210) were purchased from Sigma-Aldrich (St.

5 Louis, MO, USA). Palmitic acid (1-13C, 99%, CAS#57677-53-9) and glucose (U-13C6,

6 CAS#110187-42-3) were purchased from Cambridge Isotope Laboratories. Primary

7 antibodies used in this study include β-actin (Cat No. A00702, Genscript, Piscataway,

8 NJ, USA), α-ECHS1 (Cat No. H00001892-D01P, Abnova, Taipei City, Taiwan),

9 α-GATA3 (Cat No. #5852, Cell Signaling Technology, Danvers, MA, USA), α-AMPKα

10 (Cat No. #2532, Cell Signaling Technology), α-pT172-AMPK (Cat No. #2535, Cell

11 Signaling Technology), α-4E-BP1 (Cat No. #9644, Cell Signaling Technology),

12 α-phospho-4E-BP1 (Thr37/46) (Cat No. #2855, Cell Signaling Technology), α-p70 S6

13 kinase (Cat No. #9202, Cell Signaling Technology), α-S6K1 (phospho T389+T412)

14 (Cat No. ab60948, Abcam, Cambridge, UK), α-SREBP1 (PA1-337, Thermo Fisher),

15 α-ACC (Cat No. ab45174, Abcam), α-FASN (Cat No. ab128870, Abcam), α-ATGL (Cat

16 No. #2138, Cell Signaling Technology), and α-LCAD (Cat No. ab82853, Abcam).

17 Among them, antibodies of SREBP1 (Supplementary Fig. 1A), ECHS1

18 (Supplementary Fig. 1B) and GATA3 (Supplementary Fig. 1C) were validated through

19 IHC in xenograft tumors grew from either wild-type or candidate gene knockout cells.

20 Cell culture

21 Human HEK293T (ATCC Number: CRL-11268), ACHN (ATCC Number: CRL-1611)

1 and 786-O cells (ATCC Number: CRL-1932) were purchased from Shanghai Cell

2 Bank. HEK293T and ACHN cells were cultured in high-glucose Dulbecco’s modified

3 Eagle’s medium (HyClone, South Logan, UT, USA) supplemented with 10% fetal

4 bovine serum (Invitrogen, Carlsbad, CA, USA), 100 units/ml penicillin (Invitrogen),

5 and 100 μg/ml streptomycin (Invitrogen). 786-O cells were maintained in RPMI 1640

6 medium (Invitrogen) containing 10% fetal bovine serum. The cells were incubated in 5%

7 CO2 at 37°C. Cell transfection was performed using polyethylenimine (linear, 25 KDa)

8 or Lipofectamine 2000 (Invitrogen). All cell lines were tested negative for mycoplasma

9 contamination and cell morphology is monitored throughout all processes according

10 to ATCC recommendations.

11 Human samples

12 Clinical specimens were collected from ccRCC patients who underwent radical

13 nephrectomy at Fudan University Shanghai Cancer Center from January 2007 to

14 December 2012. Fresh ccRCC and patient-matched normal tissues frozen at -80°C

15 were subjected to western blotting, oil red O staining, and BCAA concentration

16 analyses. RNA was extracted from human ccRCC and patient-matched normal tissue

17 samples preserved in RNAlater (Qiagen, Hilden, Germany). Formalin-fixed,

18 paraffin-embedded tissue blocks containing ccRCC and normal tissues were used for

19 IHC.

20 Animals

21 All animal procedures were approved by the Animal Care Committee at Fudan

22 University. All mice were housed with a 12:12-h light: dark cycle at 25°C. Experiments 7

1 were carried out using 6- to 8-month-old littermates. Mice were randomized into each

2 of the groups. The heterozygous Echs1 KO mice were generated using the

3 CRISPR-Cas 9 system, and their genotype was confirmed by PCR. Kidneys were

4 removed, and whole-cell homogenates were generated with 0.5% NP-40 buffer for

5 western blotting and homogenated with 80% methanol for BCAA analyses.

6 Formalin-fixed, paraffin-embedded tissue blocks were created using kidney tissues

7 from wild-type and Echs1 KO mice, respectively, for IHC. For oil red O staining, kidney

8 tissues were placed in OCT compound (Tissue Tek 4583) in a peel-away mold and

9 frozen at -80°C for further analysis. Samples were processed blindly during the

10 experiments and outcome assessment.

11 RNA extraction and quantitative real-time PCR

12 Total RNA was extracted from human ccRCC and patient-matched normal tissue

13 samples preserved in RNAlater and then converted to cDNA using random hexamers,

14 oligo (dT) primers, and Moloney murine leukemia virus reverse transcriptase (TaKaRa,

15 Otsu, Japan). The ECHS1, GATA3, and AMPK mRNA levels were measured by

16 quantitative real-time PCR using the ABI Prism 7900 sequence detection system

17 (Applied Biosystems, Foster City, CA, USA), with actin as an internal reference gene.

18 Each reaction was performed in triplicate. The primers used were listed in

19 Supplementary Table 1.

20 RNA sequencing

21 Total RNA was extracted from human ccRCC and patient-matched normal tissues

22 with TRIzol/CHCl3 (Life Technologies, Carlsbad, CA, USA) according to the 8

1 manufacturer’s protocol. RNA quality was examined by gel electrophoresis, and only

2 paired RNA of high quality was used for RNA sequencing. RNA sequencing libraries

3 were prepared according to the manufacturer's instructions and then sequenced

4 with the Illumina HiSeq 2000 at Genergy Inc, Shanghai.

5 Western blot analyses

6 Cultured cells or cells extracted from human ccRCC and patient-matched normal

7 tissues were lysed with 0.5% NP-40 buffer containing 50 mM Tris-HCl (pH 7.5), 150

8 mM NaCl, 0.5% Nonidet P-40, and a mixture of protease inhibitors (Sigma-Aldrich).

9 After centrifugation at 12,000 rpm and 4°C for 15 min, the supernatant of the lysates

10 was collected for western blotting according to standard procedures. Detection was

11 performed by measuring chemiluminescence on a Typhoon FLA 9500 (GE Healthcare,

12 Little Chalfont, UK).

13 Immunohistochemistry (IHC)

14 Sections of ccRCC tissues, adjacent normal tissues, Echs1 wild-type mouse kidney

15 tissues, and Echs1 heterozygous KO mouse kidney tissues were obtained from the

16 formalin-fixed, paraffin-embedded tissue blocks. The detailed procedures of

17 immunostaining were performed as mentioned in previous studies (25,26). Sections

18 were stained using the respective antibodies and the Envision detection kit (Dako,

19 Glostrup, Denmark). The immunostaining was measured based on the quantity of

20 immunoreactive cells (quantity score) and the intensity of immune staining (intensity

21 score), as previously described (25).

1 Oil red O staining

2 For oil red O staining, fresh tissues were placed in OCT compound (Tissue Tek 4583)

3 in a peel-away mold and frozen at -80°C for further analysis. Oil red O staining was

4 performed as previously reported (27). Briefly, the slides were brought to room

5 temperature and washed in running water to remove the OCT compound. Slides were

6 placed in 50% isopropanol for 3 min and in 100% isopropanol for 3 min and stained

7 with 0.5% oil red O (O-0625, Sigma-Aldrich) in 100% isopropanol for 2 h. Slides were

8 then differentiated in 85% isopropanol for 3 min three times, washed with running

9 water, and stained with Mayer’s hematoxylin for 15 s followed by bluing in running

10 water for 10 min. Slides were mounted with Glycerol Jelly Mounting Medium

11 (Beyotime, Haimen, China) before analyzed.

12 Total FFA and BCAA analyses

13 Total FFA levels were determined using the Free Fatty Acid Quantification

14 Colorimetric/Fluorometric Kit (BioVision, Milpitas, CA, USA) according to the

15 manufacturer’s instructions. The levels of palmitic acid, stearic acid, and arachidic

16 acid were determined using the GC-FID/MS method described by An et al (28).

17 BCAA levels were measured using the Agilent 6890-5973 GC-MS system (Santa

18 Clara, CA, USA). In brief, cells or whole-cell homogenates of tissues were harvested

19 in prechilled 80% methanol, and 1 mM Ribitol was added to the lysates as an internal

20 standard. After 12 h of vacuum drying, the samples were derivatized consecutively

21 with 1% methoxyamine hydrochloride/pyridine (70°C for 1 h) and 20%

22 N-tert-butyldimethylsilyl-N-methyltrifluoro-acetamide/pyridine (37°C for 30 min), and

1 then assayed using the Agilent 6890-5973 GC-MS system.

2 GC-FID/MS analysis of palmitic acid, stearic acid, and arachidic acid

3 CcRCC and patient-matched normal tissues (~50 mg) were homogenized in 600 μL of

4 precooled methanol using a tissue lyser (TissueLyser II, Qiagen). Supernatants were

5 collected after 10 min of centrifugation (12,000 x g, 4°C). Twenty microliters of internal

6 standards in hexane (1 mg/mL methyl heptadecanoate, 0.5 mg/mL methyl tricosanate,

7 and 28 mg/mL butylated hydroxytoluene) was added to a Pyrex tube, followed by

8 addition of 100 μL of the above supernatant and 1 mL of methanol-hexane mixture

9 (4:1, v/v). The tubes were cooled in liquid nitrogen for 15 min. Then, 100 μL of

10 precooled acetyl chloride was added, and the mixture was flushed briefly with nitrogen

11 gas. Tubes were screw-capped and kept at room temperature in the dark for 24 h.

12 Then, the tubes were cooled in an ice bath for 10 min followed by gradual addition of

13 2.5 mL of 6% K2CO3 solution (with shaking) for neutralization. After the tubes were left

14 to stand for 30 min, 200 μL of hexane was added to extract methylated fatty acids.

15 The mixture was left to stand for 10 min, and the upper layer was transferred into a

16 glass sample vial. This extraction process was repeated twice, and the combined

17 supernatants were evaporated to dryness. The residues were dissolved in 100 μL of

18 hexane and subjected to GC-FID/MS analysis. For tissues, approximately 10 mg of

19 sample was homogenized in 500 μL of methanol using a TissueLyser at 20 Hz for 90 s.

20 One hundred microliters of homogenate mixture was transferred into a Pyrex tube for

21 methylation as described above.

22 Methylated fatty acids were measured on a Shimadzu GCMS-QP2010Plus

1 spectrometer (Shimadzu Scientific Instruments, USA) equipped with a mass

2 spectrometer with an electron impact (EI) ion source and a flame ionization detector

3 (FID). One microliter of sample was injected into an Agilent DB-225 capillary GC

4 column (10 m, 0.1 mm ID, 0.1 μm film thickness) equipped with a splitter (1:60).

5 Helium gas was used as the carrier and makeup gas. The injection port and detector

6 temperatures were set at 230°C. The column temperature was set at 55°C for 1 min,

7 increased to 205°C at a rate of 30°C/min, kept at 205°C for 3 min, and increased to

8 230°C (5°C/min). MS spectra were acquired with an EI voltage of 70 eV and a m/z

9 range of 45–450. Methylated fatty acids were identified by comparison with a

10 chromatogram from a mixture of 37 known standards and confirmed on the basis of

11 mass spectral data. Each fatty acid was quantified with FID data from its signal

12 integrals and internal standards.

13 Stable isotope-labeled metabolites detection

14 For the 13C labelling palmitic acid detection, 5 mM D-Glucose (U-13C6) and 20 mM

15 non-labeled glucose were used to treat cells for 12 h. The cells were washed twice

16 with PBS and harvested for GC-FID/MS detection of palmitate. Since fatty acid is

17 elongated by two carbons each reaction, we summed [M+2] and [M+4] palmitate as

18 total 13C-labelled palmitate. The levels of M+2 and M+4 palmitate were analyzed

19 according to m/z ratio. For the 13C labelling acetyl-CoA detection, 2 mM palmitic acid

20 (1-13C) were used to treat cells for 12 h. Acetyl-CoA and 13C-labelled acetyl-CoA

21 derived from 13C palmitate was detected using a LC-MS/MS method as previously

22 described (29). MS/MS parameters of acetyl-CoA were as follows: Precursor ion 12

1 [M+H]+ , Precursor ion(m/z) : 810 for acetyl-CoA and 811 for 13C-acetyl-CoA ;

2 Quantifier ion(m/z) : 303 for acetyl-CoA and 304 for 13C-acetyl-CoA ; Qualifier ion

3 (m/z) 428 ; Fragmentor voltage 120V ; Collision energy 30eV. Each measurement

4 was obtained at least in triplicate.

5 NMR analysis of lipids

6 CcRCC and patient-matched normal tissues (~50 mg) were extracted in 1 mL of

7 precooled methanol using the TissueLyser II. Supernatants were collected after 10

8 min of centrifugation (12,000xg, 4°C). The extraction was repeated twice, and the

9 supernatants were combined and centrifuged (12,000xg, 4°C) for 10 min. The

10 supernatants were lyophilized after vacuum removal of methanol. The extracts were

11 then individually reconstituted in 600 μL of phosphate buffer (0.15 M, pH 7.43),

12 vortex-mixed, and centrifuged at 16,099×g, 4 °C, 10 min. The supernatants (550 μL)

13 were transferred into 5-mm NMR tubes for NMR analysis of lipids as previously

14 reported by An et al (28).

15 Gene silencing

16 For ECHS1, FASN, and LCAD silencing, stable shRNA KD cells were generated by

17 co-transfecting cells with pCMV-VSV-G, pCMV-Gag-Pol, and shRNA plasmids using

18 the calcium phosphate method. Transfected cells were cultured in Dulbecco’s

19 Modified Eagle’s Medium containing 10% FBS for 6 h. Twenty-four hours after

20 transfection, culture medium supernatant was collected and used for retrovirus

21 preparation to infect cells at 10% confluency in 90-mm-diameter dishes. Cells were

22 re-infected 48 h after the initial infection and selected using 5 μg/ml puromycin 13

1 (Amresco, Solon, OH, USA).

2 Synthetic oligos were used for siRNA-mediated silencing of ECHS1, GATA3, AMPK1,

3 PAX2, DMRT2, FOXI1, PBX1, and HOXB5, and scramble siRNA was used as the

4 control. Cells were transfected with siRNAs using Lipofectamine 2000 according to

5 the manufacturer’s protocol. KD efficiency was verified by qRT-PCR or western

6 blotting. The sequences within genes which shRNA targeted and DNA sequences of

7 siRNA were listed in Supplementary Table 1.

8 Gene knockout

9 GATA3-KO and ECHS1-KO cells were generated by the CRISPR-Cas9 genome

10 editing method, using the following guide sequences: GATA3:

11 5’-ACCACGTCCCGCCCTACTA-3’; ECHS1: 5’-CGCCAGGCGGGACAGCGAAC-3’.

12 Electrophoretic mobility shift assay (EMSA)

13 EMSA was conducted as previously described (30). Three pairs of

14 6-carboxy-fluorescein (FAM)-labeled double-stranded DNA probes containing a

15 putative GATA3-binding site were generated by annealing their respective

16 complementary oligonucleotides. The sequences of the oligonucleotides used were

17 listed in Supplementary Table 1.

18 Nuclear extract from HEK293T cells was prepared using the NE-PER Nuclear and

19 Cytoplasmic Extraction Kit (Pierce, Rockford, IL, USA) in accordance with the

20 manufacturer’s instructions. The FAM-labeled probe (1 pmol) and 20 μg of nuclear

21 extract were incubated in reaction buffer containing 5 mM MgCl2, 2 mM EDTA, 50

1 ng/μL poly (dI-dC), 2.5% glycerol, and 0.5 mg/ml bovine serum albumin for 20 min at

2 25°C. Omission of the nuclear extract served as a negative control. For the supershift

3 assay, 1 μL of GATA3 antibody (Santa Cruz Biotechnology, Dallas, TX, USA) was

4 added into the reaction mixture and incubated for 30 min prior to addition of the probe.

5 The samples were subjected to 10% non-denaturing polyacrylamide gel

6 electrophoresis and analyzed with the Typhoon FLA 9500 scanner.

7 Surface plasmon resonance

8 The binding kinetics and affinity of ECHS1 promoter probes (same probes as used in

9 EMSA assay) for GATA3 protein were analyzed by SPR using a Biacore T200

10 instrument (GE Healthcare). Purified soluble GATA3 protein (5 mg/mL) was covalently

11 immobilized on a CM5 sensor chip via amine coupling in 10 mM sodium acetate buffer

12 (pH 5.5). To determine the binding affinity of ECHS1 promoter probes for GATA3,

13 probes were diluted to a series of concentrations starting at 10 nM. SPR experiments

14 were run at a flow rate of 30 mL/min in PBS buffer, following the manufacturer’s

15 instructions.

16 Chromatin immunoprecipitation (ChIP) assay

17 ChIP assay was carried out using the EZ ChIP Kit (Upstate Biotechnology, Lake

18 Placid, NY, USA) as previously described (31). Briefly, ccRCC and adjacent normal

19 tissue samples were cross-linked by 1% (v/v) formaldehyde (Sigma-Aldrich) for 10

20 min at 37°C. DNA was then sonicated to generate 200- to 1000-bp DNA fragments.

21 The sheared chromatin was immunoprecipitated by incubation with GATA3 antibody

22 or normal rabbit IgG (Santa Cruz Biotechnology) overnight at 4°C. The DNA was 15

1 purified from the eluted solution and subjected to PCR amplification with forward

2 primer 5’-CTGGTCTCAAACTCCTGACGT-3’ and reverse primer 5’-

3 CCATTTGTGTACTTGCCCGGAT-3’ followed by agarose gel electrophoresis.

4 Plasmid constructs

5 Whole-length ECHS1 cDNA clones were purchased from OriGene (Rockville, MD,

6 USA). After confirming the sequence by Sanger sequencing, ECHS1 was cloned into

7 the pcDNA3.1-Flag vector (Invitrogen). The ECHS1 promoter reporter plasmid,

8 containing the 1221-bp ECHS1 core promoter region fragment, was synthesized by

9 GenScript Corporation (Nanjing, China) and cloned into the pGL3-basic vector

10 (Promega, Madison, WI, USA). To construct the GATA3 plasmid, the coding region of

11 GATA3 was amplified by PCR using XhoI- and EcoRI-tailed primers (Forward:

12 5’-ccctcgagATGGAGGTGACGGCGGACCAGCCGC-3’; Reverse: 5’-

13 cggaattcACCCATGGCGGTGACCATGCTGGAG-3’) from the cDNA obtained from

14 HEK293T cells. After digestion with XhoI and EcoRI, the amplified products were

15 cloned into the pcDNA3.1-Flag vector.

16 Dual-luciferase reporter assay

17 For the ECHS1 promoter luciferase reporter assay, HEK293T, 786-O, and ACHN cells

18 were seeded in a 24-well plate, respectively. The cells were transfected with 1 μg of

19 ECHS1 promoter reporter plasmid and 20 ng of pRL-TK vector (Promega); half of the

20 cells were additionally co-transfected with 50 ng of the pcDNA3.1-GATA3 expression

21 plasmid or empty pcDNA3.1 vector using Lipofectamine 2000. The transfection

22 efficiency was monitored using the Renilla luciferase pRL-TK vector as an internal 16

1 control. Two days after transfection, cell lysates were collected and subjected to

2 luciferase assay using the Dual-Luciferase Reporter Assay System (Promega)

3 according to the manufacturer’s protocol. Three independent transfection experiments

4 were conducted, and each luciferase assay was performed in triplicate. Normalized

5 data were calculated as the ratio of the firefly/Renilla luciferase activities.

6 Cell proliferation assay

7 Cell proliferation was assessed by the Cell Counting Kit-8 (Dojindo Laboratories,

8 Kumamoto, Japan). In brief, cells were seeded in a 96-well plate with 4×103 cells per

9 well and allowed to adhere. Cell Counting Kit-8 solution (10 μl) was added to each

10 well, and the cells were cultured in 5% CO2 at 37°C for 2 h. Cell proliferation was

11 determined by measuring the absorbance at 450 nm.

12 In vivo xenograft studies

13 Four- to six-week-old Balb/C nude mice were obtained (Shanghai SLAC Laboratory

14 Animal Co., Ltd, Shanghai, China) for in vivo xenografts. Control cells and cells stably

15 overexpressing ECHS1 from both 786-O and ACHN cell lines were subcutaneously

16 heterotransplanted into the left and right flank of each mouse. The mice were

17 maintained under conditions as specified. Tumor size was measured with a caliper

18 twice per week from the time of the formation of palpable tumors. Tumor volume was

19 calculated as the length × width2 × 0.52 (32). At the end of the experiment, following

20 euthanasia, tumors were excised, weighed, and imaged. All procedures were

21 performed with approval from the Animal Care Committee at Fudan University.

1 Statistics

2 Data are presented as the mean ± standard error of the mean (SEM), and

3 comparisons of groups are presented as the mean ± SEM and analyzed using the

4 Student’s t-test. The correlation between BCAA, lipid, or FA level and ECHS1

5 expression was quantified using Pearson’s correlation coefficient. PFS was defined

6 from the initiation of surgery to the date of disease progression or censoring at the

7 time of last follow-up. OS was defined as the time interval between the date of surgery

8 and the date of death or last follow-up, whichever occurred first. For survival analysis,

9 the relative mRNA expression of ECHS1 and GATA3 in ccRCC were measured using

10 the ratio of expression in ccRCC/matched normal tissues and were classified into 3

11 groups, respectively. “Low ECHS1 or GATA3 expression” denotes the ratio of ECHS1

12 or GATA3 mRNA expression in ccRCC/matched normal tissues of less than 1/3;

13 “middle ECHS1 or GATA3 expression” denotes the ratio of ECHS1 or GATA3 mRNA

14 expression in ccRCC/matched normal tissues of greater than 1/3 and less than 2/3;

15 “high ECHS1 or GATA3 expression” denotes the ratio of ECHS1 or GATA3 mRNA

16 expression in ccRCC/matched normal tissues of greater than 2/3. PFS and OS were

17 estimated using the Kaplan–Meier method, and the differences between the curves

18 were assessed by the log-rank test. All statistical analyses were performed using

19 SPSS software version 16.0 (SPSS Inc., Chicago, IL, USA). The p value was

20 two-tailed and considered statistically significant at < 0.05.

21 Study approval

22 The procedures related to human tissues were carried out in accordance with the

1 ethical standards of Helsinki Declaration II and approved by the Institution Review

2 Board of Fudan University Shanghai Cancer Center. Written informed consent was

3 obtained from each patient before any study-specific investigation was performed.

1 Results

2 ECHS1 downregulation in ccRCC contributes to FAO inhibition and de novo

3 free FA synthesis

4 We compared lipid and free FA (FFA) levels in 24 pairs of ccRCC tumor and adjacent

5 non-cancer tissues. The levels of lipids (Fig. 1A and Supplementary Fig. 2) as well as

6 FFAs (Fig. 1B), including palmitic acid, stearic acid, arachidic acid, and total FFAs,

7 were higher in the tumor tissues than in the non-cancer tissues. mRNA levels of

8 adipose triglyceride lipase (ATGL) were downregulated by 55%, and those of FA

9 synthetic enzymes, such ACC and FASN, were upregulated by approximately 2.5-fold

10 in tumor tissues compared with non-tumor tissues (Fig. 1C). RNA sequencing

11 revealed that the mRNA levels of genes involved in FAO and lipid metabolism

12 pathways were significantly downregulated in the ccRCC tumors (Supplementary Fig.

13 3A). These results collectively suggested that decreased lipolysis inhibits FAO and

14 increases de novo FA synthesis in ccRCC.

15 Remarkably, in line with data in the Cancer Genome Atlas (TCGA) database

16 (Supplementary Fig. 3B), ECHS1, which is involved in both FAO and BCAA oxidation,

17 was downregulated by more than 80% in ccRCC tumor tissues compared with

18 non-tumor tissues as indicated by quantitative reverse transcription (qRT-)PCR of 367

19 pairs of ccRCC tissues (Fig. 1D), immunohistochemistry (IHC) of 12 pairs of ccRCC

20 tissues (Fig. 1E and Supplementary Fig. 3C), and western blotting of 40 pairs of

21 ccRCC tissues (Fig. 1F and Supplementary Fig. 4). Therefore, we investigated the

1 role of ECHS1 in regulating FA metabolism. Knockdown of ECHS1 in HEK293T and

2 786-O cells decreased 13C acetyl-CoA formation when these cells were cultured in

3 medium containing 13C-labeled palmitic acid (Fig. 1G), whereas overexpression of

4 ECHS1 increased the level of 13C acetyl-CoA in both HEK293T and 786-O cells (Fig.

5 1H).

6 Although homozygous deletion of Echs1 is embryonically lethal, heterogeneous

7 knockout (KO) of Echs1 using the CRISPR-Cas9 system in mice reduced Echs1

8 expression to approximately 50% of the level in wild-type mice (Fig. 1I), and led to the

9 accumulation of lipids (Fig. 1J and Supplementary Fig. 5A) and total FFAs (Fig. 1K) in

10 the kidneys of KO mice. Notably, ATGL was decreased and ACC and FASN increased

11 in the kidneys of Echs1-KO mice, as indicated by IHC (Fig. 1L–N and Supplementary

12 Fig. 5B–D) and western blotting (Fig. 1O). Echs1-knockdown (KD) mice phenocopied

13 the status of FA metabolism in ccRCC, suggesting that Echs1 downregulation may be

14 responsible for the reprogramming of FA metabolism in ccRCC.

15 GATA3, a transcription factor of ECHS1, is downregulated in ccRCC

16 The downregulation of ECHS1 mRNA in ccRCC prompted us to identify transcription

17 factors associated with ECHS1 expression. Using the online software TFBIND

18 (http://tfbind.hgc.jp/), we identified six consensus binding motifs in the promoter region

19 within the −2000 to +100 region of the ECHS1 gene that potentially bind

20 GATA-binding protein 3 (GATA3), doublesex- and mab-3-related transcription factor 2

21 (DMRT2), paired box 2 (PAX2), forkhead box I1 (FOXI1), PBX homeobox 1 (PBX1),

1 and homeobox D8 (HOXB5) (Supplementary Fig. 6A). Remarkably, in RNA

2 sequencing data, the expression of these transcription factors was significantly

3 decreased in ccRCC tumors compared with matched normal tissues (Supplementary

4 Fig. 6B). We next explored the potential regulatory effects of candidate transcription

5 factors on ECHS1 in VHL-mutant ccRCC derived 786-O cells, VHL-wild-type ccRCC

6 derived ACHN cells, and embryonic kidney derived HEK293T cells. In HEK293T,

7 786-O, and ACHN cells, which exhibited moderate ECHS1 expression

8 (Supplementary Fig. 7A), KD of GATA3, but not the other five transcription factors

9 (Supplementary Fig. 7B), led to decreased ECHS1 mRNA and protein expression (Fig.

10 2A, B). Moreover, GATA3 overexpression in HEK293T or 786-O cells enhanced

11 ECHS1 mRNA and protein expression (Fig. 2C, D), further indicating that GATA3 is a

12 transcription factor of ECHS1. Luciferase assay results confirmed that GATA3 could

13 activate ECHS1 transcription in HEK293T, 786-O, and ACHN cells (Fig. 2E). Among

14 the three potential GATA3-binding sites in the ECHS1 promoter (Supplementary Fig.

15 6), we validated that GATA3 could bind the −798/−787 and −537/−528 regions by

16 electrophoretic mobility shift assay (Fig. 2F) and surface plasmon resonance (SPR)

17 (Fig. 2G). These results confirmed that GATA3 transcriptionally activates ECHS1

18 expression.

19 AMPK regulates ECHS1 through GATA3

20 Considering that mRNA levels of GATA3 were downregulated in 367 ccRCC tumors

21 (Fig. 3A), and less ECHS1 promoter DNA could be immunoprecipitated with a GATA3

22 antibody from ccRCC tissue than from control tissue (Fig. 3B), we reasoned that 22

1 GATA3 is downregulated in ccRCC, as confirmed by IHC analysis (Fig. 3C and

2 Supplementary Fig. 8A) and western blotting (Fig. 1F and Supplementary Fig. 4).

3 Therefore, we evaluated the correlation between AMPK and ECHS1, which has been

4 previously reported to regulate GATA3 transcription (33) and whose level is

5 decreased in ccRCC (3), as confirmed in this study by IHC (Fig. 3D and

6 Supplementary Fig. 8B) and western blotting (Fig. 1F and Supplementary Fig. 4). KD

7 of the catalytic AMPK1 subunit led to decreased mRNA and protein levels of GATA3

8 and ECHS1 (Fig. 3E, F). Total AMPK activities in cells were indicated by the

9 phosphorylation levels of ACC and Raptor, two well-known AMPK substrates (Fig. 3E,

10 F). Conversely, activation of AMPK using AICAR increased GATA3 and ECHS1

11 mRNA and protein expression (Fig. 3G, H). KD of β-catenin, which mediates the

12 activating effect of AMPK on GATA3, reduced GATA3 and ECHS1 expression and

13 abrogated the activating effect of AICAR on GATA3 and ECHS1 (Fig. 3I). Notably,

14 GATA3 deletion in HEK293T cells reduced the mRNA (Fig. 3J) and protein (Fig. 3K)

15 levels of ECHS1 and rendered ECHS1 expression non-responsive to both AMPKα1

16 KD (Fig. 3J, K) and AICAR treatment (Fig. 3L, M), revealing that AMPK regulates

17 ECHS1 expression through GATA3.

18 AMPK-GATA3-ECHS1 pathway downregulation causes accumulation of FAs and

19 BCAAs

20 To verify the FAO and BCAA oxidation regulatory function of the AMPK-mediated

21 GATA3 and ECHS1 pathways (AMPK-GATA3-ECHS1 pathway), we examined each

22 of the components in this pathway on the level of FAs and BCAAs. KD of AMPK1 in 23

1 HEK293T and 786-O cells resulted in accumulation of FAs (Fig. 4A) and the BCAAs

2 leucine, isoleucine, and valine (Fig. 4B), and this effect could be reversed by

3 overexpression of either GATA3 or ECHS1 in these cells (Fig. 4A, B), confirming that

4 AMPK acts upstream of GATA3 and ECHS1 to regulate FA and BCAA levels.

5 Moreover, GATA3 KO in HEK293T cells led to accumulation of total FFAs (Fig. 4C)

6 and BCAAs (Fig. 4D); these increases were reversed by overexpression of ECHS1

7 (Fig. 4C, D), but not AMPK activation by AICAR (Fig. 4C, D), confirming that GATA3

8 functions downstream of AMPK and upstream of ECHS1 to regulate FA and BCAA

9 levels. Furthermore, ECHS1 KO in HEK293T and 786-O cells elevated the levels of

10 FAs (Fig. 4E) and BCAAs (Fig. 4F), and overexpression of GATA3 or activation of

11 AMPK by AICAR had negligible effects on ECHS1 expression, as well as FA and

12 BCAA levels (Fig. 4E, F), confirming that ECHS1 is under the control of AMPK and

13 GATA3 for regulation of FAs and BCAAs. To further substantiate this notion,

14 heterozygous Echs1 KO increased levels of FFAs (Fig. 1J and K) and BCAAs (Fig. 4G)

15 in mouse kidneys, and decreased AMPK, GATA3, and ECHS1 levels (Fig. 1F) were

16 accompanied with increased levels of FAs (Fig. 1B) and BCAAs (Fig. 4H) in ccRCC

17 tumors compared with those in adjacent normal tissues. This supported that

18 AMPK-GATA3-ECHS1 pathway downregulation results in FA and BCAA accumulation

19 in mice and in humans.

20 AMPK-GATA3-ECHS1 pathway downregulation activates mTORC1 and

21 feedback-inhibits AMPK

22 The levels of BCAAs, known potent activators of mTORC1 signaling, were negatively 24

1 correlated with ECHS1 mRNA levels in ccRCC (Fig. 5A, B), suggesting that

2 downregulation of the AMPK-GATA3-ECHS1 pathway activates mTORC1 signaling.

3 Confirming this hypothesis, increased phosphorylation of 4E-BP1 and S6K; readouts

4 of mTORC1 activation; increased expression of the mature form of sterol regulatory

5 element-binding protein (SREBP1), ACC, and FASN; and downregulated expression

6 of ATGL— all consequences of mTORC1 activation—were observed in ccRCC tumor

7 tissues in which AMPK, GATA3, and ECHS1 were downregulated according to IHC

8 (Fig. 5C–H and Supplementary Figs. 8A-D and 9A-D) and western blotting (Fig. 1F

9 and Supplementary Fig. 4). Based on western blot analysis of human samples, we

10 validated that expression levels of SREBP1, ACC, and FASN were correlated with

11 decreased AMPK, GATA3, and ECHS1 expression (Supplementary Fig. 10A-C).

12 Meanwhile, ATGL levels were correlated with increased AMPK, GATA3, and ECHS1

13 expression (Supplementary Fig. 10A-C). Heterozygous Echs1 KO in mice increased

14 the phosphorylation levels of S6K (Fig. 5I and Supplementary Fig. 11A) and 4E-BP1

15 (Fig. 5J and Supplementary Fig. 11B); increased SREBP1 (Fig. 5K and

16 Supplementary Fig. 11C), ACC (see Fig. 1M), and FASN (see Fig. 1N) expression;

17 and decreased ATGL expression (see Fig. 1L).

18 Furthermore, phosphorylation of 4E-BP1 and S6K and expression of SREBP1, ACC,

19 and FASN were activated, whereas ATGL expression was inactivated upon

20 AMPK1-subunit KD (Fig. 5L), GATA3 KD (Fig. 5M), and ECHS1 KD (Fig. 5N). In

21 contrast, 4E-BP1 and S6K phosphorylation and SREBP1, ACC, and FASN expression

22 were inactivated, whereas ATGL expression was activated upon AMPK activation by

1 AICAR (Fig. 5O), GATA3 overexpression (Fig. 5P), and ECHS1 overexpression (Fig.

2 5Q). Notably, mTORC1 inactivation induced by AMPK-GATA3-ECHS1 activation

3 could be rescued by supplemental BCAAs (Fig. 5O–Q, Supplementary Fig. 12A-C

4 showing BCAA levels in different treatments). Together with the fact that BCAA

5 deficiency blocked mTORC1 activation when AMPKα1, GATA3, and ECHS1 were

6 silenced (Fig. 5R–T), these results collectively confirmed that downregulation of the

7 AMPK-GATA3-ECHS1 pathway results in activation of mTORC1. Interestingly, as high

8 FA and BCAA levels inhibit AMPK activity (34-37), decreased ECHS1 feedback might

9 inhibit AMPK activities. This hypothesis was supported in Echs1 heterozygous KO

10 mice, which showed reduced AMPK phosphorylation (Fig. 5U and Supplementary Fig.

11 12D), as well as in ccRCC tissues, in which relative AMPK phosphorylation was

12 reduced (Fig. 1F). We also observed activated AMPK in ECHS1-overexpressing cells

13 (Fig. 5V) and inactivated AMPK in GATA3- or ECHS1-KD cells (Fig. 5M, N). These

14 results indicated that reduced ECHS1 feedback could inhibit AMPK activity and thus

15 form a vicious circle of FA accumulation.

16 ECHS1 inactivation promotes cancer cell proliferation through activating

17 mTORC1 and de novo FA synthesis

18 ECHS1 KD in ACHN and 786-O cells promoted their proliferation; however, this

19 proliferation-promoting effect was diminished by rapamycin treatment (Fig. 6A, B),

20 suggesting that mTORC1 activation is required. Moreover, rapamycin treatment

21 decreased the FFA level (Fig. 6C), but not lipid accumulation (Fig. 6D), in ECHS1-KD

22 cells, suggesting that lipid accumulation alone by ECHS1 downregulation did not 26

1 contribute to proliferation, and that mTORC1 activation and de novo FA synthesis

2 induced by ECHS1 downregulation may contribute to proliferation promotion.

3 Confirming this hypothesis, KD of LCAD, an FAO enzyme that led to accumulation of

4 lipids (Fig. 6E), but failed to activate mTORC1 (Fig. 6F) and failed to promote

5 proliferation of 786-O and ACHN cells (Fig. 6A, B). Moreover, the xenograft

6 growth-promoting ability of ECHS1 KD was abolished by rapamycin treatment of mice

7 bearing ACHN (Fig. 6G) or 786-O (Fig. 6H) cells.

8 Consistent with the observation that ECHS1 downregulation activates FASN and ACC

9 and thus, de novo FA synthesis, the formation of 13C palmitic acid from 13C glucose

10 derived 13C acetyl-CoA was enhanced by ECHS1 KD (Fig. 6I) and inhibited by ECHS1

11 overexpression (Fig. 6J) in HEK293T and 786-O cells. Notably, ECHS1 KD or

12 overexpression had negligible effects on palmitic acid formation (Fig. 6K, L) and

13 proliferation (Fig. 6M, N) in 786-O cells with FASN deletion. Moreover, FASN deletion,

14 which abrogated the effect on lipid accumulation in ECHS1-KD ACHN and 786-O cells

15 (Fig. 6O), rendered the growth of xenografted tumor cells non-responsive to ECHS1

16 KD (Fig. 6G, H). These results collectively showed that the ECHS1

17 downregulation-mediated promotion of de novo FA synthesis accounts for its

18 proliferation-promoting effects.

19 Decreased ECHS1 levels predict poor prognosis for patients with ccRCC

20 Finally, we determined the correlation between lipid accumulation and ccRCC

21 progression. In 367 ccRCC and paired adjacent normal tissues (Supplementary Table

1 2), FA levels were positively correlated with ccRCC stage (Fig. 7A) and grade (Fig.

2 7B). Moreover, higher tumor-to-normal lipid ratios were positively correlated with

3 ccRCC stage (Fig. 7C) and grade (Fig. 7D). These results suggested that FA levels as

4 well as relative lipid levels can predict ccRCC progression.

5 Relative GATA3 and ECHS1 mRNA levels in the same group of ccRCC samples were

6 inversely correlated with ccRCC stage (Fig. 7E) and grade (Fig. 7F). These results,

7 together with the finding that ccRCC ECHS1 mRNA levels were strongly negatively

8 correlated with the levels of lipids (Fig. 7G) and FAs (Fig. 7H), are in line with the

9 notion that GATA3 and ECHS1 inactivation-induced FA and lipid accumulation

10 predicts the progression of ccRCC.

11 Cox regression analyses were conducted to assess the prognostic value of ECHS1

12 and GATA3 expression and clinicopathological parameters for progression-free (PFS)

13 and overall (OS) survival (Supplementary Tables 3 and 4) for these ccRCC patients.

14 ECHS1 and GATA3 mRNA levels were independent prognostic factors for both PFS

15 (Fig. 7G, I) and OS (Fig. 7G, J), even after adjustment for known prognostic factors

16 such as N stage, M stage, and tumor grade. Survival analyses revealed that loss of

17 either ECHS1 or GATA3 was associated with inferior PFS and OS. PFS and OS

18 curves according to ECHS1 or GATA3 mRNA expression in ccRCC tissues were

19 distinctly tiered and statistically significant (p < 0.001) (Fig. 7G, I, J). Taken together,

20 these observations suggested that loss of ECHS1 expression contributes to FA

21 accumulation in the kidneys and ccRCC.

1 Discussion

2 Intracellular lipid droplet accumulation and activated FA synthesis are hallmarks of

3 ccRCC. Highly proliferative ccRCC cells show a strong lipid avidity, which is satisfied

4 by either increasing exogenous (or dietary) lipid and lipoprotein uptake or

5 overactivation of endogenous synthesis. Previous reports have suggested that FA

6 accumulation in ccRCC results from enhanced FA synthesis and that the rate-limiting

7 enzymes in the FA synthesis pathway, ACC and FASN, may represent the main cause

8 of FA accumulation and ccRCC pathological effects (3,38,39). Here, we report that

9 AMPK-GATA3-ECHS1 pathway inactivation induces FAO blockage, contributing to

10 ccRCC onset through accumulation of lipids and promotion of de novo FA synthesis.

11 Oxidized FAs constitute an important energy source for many cell types, including

12 proximal tubular epithelial cells, from which ccRCC is derived (40). Conversely, most

13 cancer cells, including ccRCC, produce energy from glucose through glycolysis rather

14 than from FA through β-oxidation and the tricarboxylic acid cycle. Our results revealed

15 that, besides de novo FA synthesis, the FAO pathway was shut down in ccRCC.

16 Isotopic tracing experiments confirmed that loss of ECHS1 blocked FAO and

17 decreased acetyl-CoA generation via FAO. This was in accordance with the

18 observation that cancer cells curtail the energy demand from FAs and provides an

19 explanation for the existing paradox in that increased FA levels do not lead to

20 enhanced energy export from this enhanced potential substrate in ccRCC

21 development.

1 Although enhanced FA synthesis and inhibition of FAO were observed simultaneously

2 in ccRCC tumors, we confirmed that FAO inhibition induced by ECHS1

3 downregulation plays a key role in FA metabolic reprogramming. Using isotopic

4 tracing, we found that downregulation of ECHS1 promoted de novo FA synthesis,

5 whereas overexpression of ECHS1 inhibited this. In cultured cells and Echs1

6 heterozygous KO mice, we confirmed that the alteration in de novo FA synthesis was

7 caused by activation of ACC and FASN. Our previous findings revealed that loss of

8 ECHS1 led to BCAA accumulation and consequently, activation of mTORC1, which

9 governs protein synthesis and is another signature of cancer (21-23). Furthermore,

10 we validated that ECHS1 silence induced mTORC1 activation subsequently

11 increased the expression of SREBP1 and its downstream targets, ACC and FASN,

12 and finally promoted de novo FA synthesis. In cultured cells and an animal xenograft

13 model, we found that FAO blockage alone did not provide the cells with proliferative

14 advantage; activated de novo FA synthesis was the key process to enhance cell

15 proliferation and tumor growth.

16 Additionally, we demonstrated that decreased ECHS1 transcription resulted from

17 GATA3 downregulation, which in turn resulted from a decrease in AMPK levels. Upon

18 activation, AMPK inhibits anabolic pathways and promotes catabolism in response to

19 an increase in the AMP/ATP ratio by downregulating the activity of key enzymes of

20 intermediary metabolism (41-43). In its activated state, AMPK phosphorylates ACC

21 and inhibits its enzymatic activity, resulting in decreased FA synthesis (44). We found

22 that activated AMPK promoted FAO by increasing ECHS1 expression, further

1 indicating that AMPK could coordinate the synthesis and degradation of FAs and

2 maintain the intracellular FA balance. It has been previously shown that AMPK protein

3 levels are significantly decreased in ccRCC, and this contributes to a metabolic shift

4 toward increased FA synthesis (3). Our results further indicated that AMPK

5 inactivation may result in the loss of transcription of GATA3 and downstream ECHS1,

6 thus preventing FAO and enhancing de novo FA synthesis simultaneously by

7 transcriptionally activating ACC and FASN—finally leading to FA accumulation during

8 ccRCC development. Furthermore, high levels of BCAAs and saturated FAs were

9 reported to inhibit AMPK activity (34-37). Thus, AMPK inactivation-induced ECHS1

10 loss and FA/BCAA accumulation may lead to a positive feedback to downregulate

11 ECHS1.

12 In patients, ccRCC prognosis is currently determined based on anatomical and

13 histological factors. However, as ccRCC represents a metabolic disease, previously

14 identified molecular markers, including those from gene expression profiling and

15 sequencing results, are not recommended in routine practice. For example, the

16 relationship between body mass index and prognosis remains controversial; some

17 reports have suggested that increased body mass index indicates poor survival (38),

18 whereas others have associated this with improved survival (45). However, FASN

19 expression has consistently been reported to be associated with poor prognosis in

20 several tumor types, including RCC (46,47). Thus, developing reliable molecular

21 markers could improve the predictive accuracy of current prognostic systems for

22 ccRCC (48). We investigated the correlation between ECHS1 mRNA expression and

1 tumor progression and survival in a cohort of 367 patients with ccRCC. We

2 demonstrated that loss of ECHS1 in ccRCC tissues was correlated with tumor

3 progression and inferior PFS and OS. Furthermore, ECHS1 mRNA expression

4 constituted an independent prognostic factor for both PFS and OS, even after

5 adjustment for known prognostic factors. Our findings therefore indicate an integral

6 role of ECHS1 in the underlying biological mechanisms of tumorigenesis and in the

7 prognosis of patients with ccRCC.

8 In summary, our results showed that AMPK-GATA3-ECHS1 pathway inactivation

9 causes fatty acid metabolic reprogramming in ccRCC. ECHS1 showed good

10 prognostic ability and, together with in-vitro and in-vivo findings, indicated that

11 enhanced ECHS1 may slow down tumor cell proliferation. These insights may offer

12 new therapeutic approaches for the treatment and prognostic assessment of ccRCC

13 in the clinic.

1 Acknowledgements

2 This work was supported by Grants from the National Science Foundation of China

3 (Nos. 31330023 (W Xu), 81722021 (JY Zhao), 31671483 (W Xu), 81771627 (JY

4 Zhao), 31521003 (JY Zhao), 31821002 (SM Zhao), 91753207 (SM Zhao), 31930062

5 (SM Zhao), 81802525 (YY Qu), 81672544 (DW Ye), 81872099 (DW Ye), 31871432

6 (W Xu), Shanghai Rising-Star Program (No. 18QA1400300) (W Xu), National Key

7 R&D Program of China (Nos. 2018YFA0800300 (SM Zhao), 2018YFA0801300 (W

8 Xu), 2018YFC1004700 (W Xu)), Science and Technology Municipal Commission of

9 Shanghai, China (16JC1405301 (SM Zhao), 16JC1405302 (DW Ye), 18511108000

10 (DW Ye), 2018ZHYL0201 (DW Ye)), Shanghai Sailing Program (No. 17YF1402700)

11 (YY Qu), Shanghai Natural Science Foundation of China (No. 16ZR1406400) and

12 Shanghai "Rising Stars of Medical Talent" Youth Medical Talents–Specialist Program

13 (YY Qu).

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3 Index and Metastatic Renal Cell Carcinoma: Clinical and Biological

4 Correlations. Journal of clinical oncology : official journal of the American

5 Society of Clinical Oncology 2016

6 46. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in

7 cancer pathogenesis. Nature reviews Cancer 2007;7:763-77

8 47. Horiguchi A, Asano T, Asano T, Ito K, Sumitomo M, Hayakawa M. Fatty acid

9 synthase over expression is an indicator of tumor aggressiveness and poor

10 prognosis in renal cell carcinoma. The Journal of urology 2008;180:1137-40

11 48. Ljungberg B, Bensalah K, Canfield S, Dabestani S, Hofmann F, Hora M, et al.

12 EAU guidelines on renal cell carcinoma: 2014 update. European urology

13 2015;67:913-24

1 Figure Legends

2 Figure 1. ECHS1 downregulation in ccRCC contributes to FAO inhibition and de

3 novo FA synthesis.

4 (A) Oil Red O staining for lipids in ccRCC specimens and adjacent normal tissues

5 from patients (scale bars: 200 μm). Representative staining results (left) and a

6 summary of quantification analysis (N = 24 pairs, right graph) are shown. The

7 staining results for all samples are shown in Supplementary Figure 2.

8 (B) FFA levels in ccRCC tumors and normal tissues from patients.

9 (C) mRNA levels of FA metabolic enzymes in ccRCC tumors and normal renal tissues

10 as identified by RNA sequencing (N = 10).

11 (D) Real-time PCR analysis of ECHS1 mRNA levels in ccRCC tumors and normal

12 renal tissues (N = 367).

13 (E) IHC analysis of ECHS1 protein in ccRCC specimens and adjacent normal tissues

14 (scale bars: 200 μm). Representative staining results (top) and a summary of

15 quantification analysis (N = 12 pairs, lower graph) are shown. The results for all

16 samples are shown in Supplementary Figure 3.

17 (F) Western blot analysis of ccRCC tumors and adjacent normal renal tissues.

18 Representative results (left panel) and a summary of quantification analysis (right

19 panel) are shown. Results of patients #7 to #40 are shown in Supplementary

20 Figure 4. 1 Phosphorylation level of AMPK was normalized by AMPK protein level.

21 (G) Knockdown of ECHS1 in cells blocked 13C-labeled palmitic acid oxidation and

22 decreased 13C acetyl-CoA formation.

1 (H) Overexpression of ECHS1 in cells promoted the oxidation of 13C-labeled palmitic

2 acid.

3 (I) Schematic diagram of loss of heterozygosity of 10 bp located at Echs1 exon 2 of

4 C57BL/6 mice (upper graph). DNA sequencing confirmation and protein level

5 quantification are shown in the middle and lower panel, respectively.

6 (J–O) Oil Red O staining for lipids (J). Colorimetric quantification for FFAs (K). IHC for

7 ATGL (L), ACC (M), and FASN (N). Western blots for ECHS1, ATGL, ACC, and

8 FASN (O) in both Echs1+/- and wild-type mice. Representative staining results and

9 a summary of quantification analysis (N = 6 pairs) are shown in (J, L, M, and N).

10 The results for all 6 pairs of samples are shown in Supplementary Figure 5.

12 Figure 2. GATA3 regulates ECHS1 transcription.

13 (A) ECHS1 mRNA levels following GATA3 knockdown in cells.

14 (B) ECHS1 protein levels following GATA3 knockdown in cells. Quantification results

15 of western blots are shown in the lower panels.

16 (C) ECHS1 mRNA expression following GATA3 overexpression in cells.

17 (D) ECHS1 protein levels following GATA3 overexpression in cells. Quantification

18 results of western blots are shown in the lower panel.

19 (E) Effect of GATA3 on the transcriptional activity of the ECHS1 promoter in different

20 cell lines as determined by a dual-luciferase reporter assay.

21 (F) EMSA analysis was performed using FAM-labeled probe and nuclear extract from

22 HEK293T cells. Omission of the nuclear extract served as a negative control.

1 Supershift assay was performed with the addition of GATA3 antibody in the

2 reaction mixture.

3 (G) Surface plasmon resonance assay analysis of GATA3 binding with the ECHS1

4 promoter region.

6 Figure 3. AMPK regulates ECHS1 through GATA3.

7 (A) Real-time PCR analysis of GATA3 mRNA levels in ccRCC tumors and normal

8 renal tissues (N = 367).

9 (B) ChIP assay was performed in ccRCC specimens and matched normal renal

10 tissues with a GATA3 antibody and rabbit IgG as a control. The presence of the

11 GATA3-binding ECHS1 promoter was verified by PCR (left panel) and qPCR

12 (right panel).

13 (C, D) IHC for GATA3 (C) and AMPK (D) protein in ccRCC specimens and adjacent

14 normal tissues. Representative staining results (top) and a summary of

15 quantification analysis (N = 12 pairs, lower graph) are shown. The results for all

16 samples are shown in Supplementary Figure 8.

17 (E, F) Knockdown of AMPKα1 led to decreased mRNA (E) and protein (F) levels of

18 GATA3 and ECHS1. Total AMPK activities in cells were indicated by the

19 phosphorylation levels of ACC and Raptor, two well-known AMPK substrates. 1

20 Phosphorylation level of ACC was normalized by ACC protein level. 2

21 Phosphorylation level of Raptor was normalized by Raptor protein level.

22 (G, H) Activation of AMPK using AICAR increased mRNA (G) and protein (H) levels of

1 both GATA3 and ECHS1. 1 Phosphorylation level of ACC was normalized by ACC

2 protein level. 2 Phosphorylation level of Raptor was normalized by Raptor protein

3 level.

4 (I) Knockdown of β-catenin reduced GATA3 and ECHS1 expression and abrogated

5 the activating effect of AICAR on GATA3 and ECHS1.

6 (J–M) GATA3 KO abrogated the regulatory effect of AMPK on ECHS1 at the mRNA (J)

7 and protein (K) levels and blocked the regulatory effect of AICAR on ECHS1 at

8 the mRNA (L) and protein (M) levels. 1 Phosphorylation level of ACC was

9 normalized by ACC protein level. 2 Phosphorylation level of Raptor was

10 normalized by Raptor protein level.

12 Figure 4. AMPK-GATA3-ECHS1 pathway downregulation leads to accumulation

13 of FAs and BCAAs.

14 (A, B) FA (A) and BCAA (B) levels in AMPKα1 knockdown cells and GATA3- or

15 ECHS1-overexpressing AMPKα1 knockdown cells. Data are shown as the

16 means ± SEM of three independent replicates. 1 Phosphorylation level of ACC

17 was normalized by ACC protein level.

18 (C, D) FA (C) and BCAA (D) levels in GATA3 KO cells and ECHS1-overexpressing or

19 AICAR-treated GATA3 KO cells. Data are shown as the means ± SEM of three

20 independent replicates. 1 Phosphorylation level of ACC was normalized by ACC

21 protein level.

22 (E, F) FA (E) and BCAA (F) levels in ECHS1 KO cells and GATA3-overexpressing or

1 AICAR-treated ECHS1 KO cells. Data are shown as the means ± SEM of three

2 independent replicates.

3 (G) BCAA concentrations in the kidney of Echs1+/− and wild-type mice (N = 6).

4 (H) BCAA concentrations in ccRCC tumors and matched normal tissues (N = 12).

6 Figure 5. AMPK-GATA3-ECHS1 pathway downregulation activates mTORC1,

7 and feedback inhibits AMPK.

8 (A, B) Ratio of ECHS1 mRNA expression between tumors and normal tissues was

9 negatively correlated with the ratio of each BCAA levels (A) and total BCAAs

10 levels (B) between tumors and normal tissues in patients with ccRCC.

11 (C–H) IHC analysis of p-4E-BP1 (C), p-S6K (D), SREBP1 (E), ACC (F), FASN (G),

12 and ATGL (H) proteins in ccRCC specimens and adjacent normal tissues (scale

13 bars: 200 μm). Representative staining results (top) and a summary of

14 quantification analysis (N = 12 pairs, lower graph) are shown. The results for all

15 samples are shown in Supplementary Figures 8 and 9.

16 (I–K) IHC analysis of p-S6K (I), p-4E-BP1 (J), and SREBP1 (K) in either ECHS1+/− or

17 wild-type mice. Representative staining results and a summary of quantification

18 analysis (N = 6 pairs) are shown. The results for all 6 pairs of samples are shown

19 in Supplementary Figure 11.

20 (L–N) Western blot analysis of endogenous ECHS1, p-T389-S6K, S6K,

21 p-T37/46-4E-BP1, 4E-BP1, SREBP1, ACC, FASN, and ATGL protein levels in

22 AMPKα1 knockdown cells (L), GATA3 knockdown cells (M), and ECHS1

1 knockdown cells (N).

2 (O-Q) Western blot analysis of endogenous ECHS1, p-T389-S6K, S6K,

3 p-T37/46-4E-BP1, 4E-BP1, SREBP1, ACC, FASN, and ATGL protein levels in

4 AICAR treated cells (O), GATA3 overexpressing cells (P) and ECHS1

5 overexpressing cells (Q) cultured with or without BCAAs.

6 (R-T) Western blot analysis of endogenous p-T389-S6K, S6K, p-T37/46-4E-BP1,

7 4E-BP1, SREBP1, ACC, FASN, and ATGL protein levels in AMPKα1 knockdown

8 cells (R), GATA3 knockout cells (S), and ECHS1 knockout cells (T) cultured with

9 or without BCAAs.

10 (U) IHC analysis of p-AMPK in ECHS1+/− and wild-type mice. Representative staining

11 results and a summary of quantification analysis (N = 6 pairs) are shown. The

12 results for all 6 pairs of samples are shown in Supplementary Figure 11.

13 (V) Western blot analysis of endogenous p-AMPK and AMPK in ECHS1

14 overexpressing cells.

16 Figure 6. ECHS1 inactivation promotes cancer cell proliferation through

17 activating mTORC1 and de novo FA synthesis.

18 (A, B) Growth curves of LCAD knockdown, ECHS1 knockdown, and

19 rapamycin-treated ECHS1 knockdown ACHN (A) and 786-O (B) cells.

20 (C, D) FFA (C) and lipid (D) levels in ECHS1 knockdown cells and rapamycin-treated

21 ECHS1 knockdown cells.

22 (E) Lipid levels in LCAD knockdown cells.

1 (F) Western blots for the pS6K and p4E-BP1 levels in LCAD knockdown cells.

2 (G, H) Tumor size between the control, ECHS1 knockdown, rapamycin-treated

3 ECHS1 knockdown, and ECHS1 and FASN double-knockdown 786-O cell (G)

4 and ACHN cell (H) xenografts in nude mice. Data are shown as the means ±

5 SEM for each group of mice (N = 10).

6 (I) Knockdown of ECHS1 promoted de novo FA synthesis from 13C acetyl-CoA.

7 (J) Overexpression of ECHS1 inhibited de novo FA synthesis from 13C acetyl-CoA.

8 (K, L) Knockdown of FASN abrogated the activation of de novo FA synthesis induced

9 by ECHS1 knockdown (K) and the inhibitory effect on de novo FA synthesis

10 induced by ECHS1 overexpression (L).

11 (M) Proliferation curves of control, ECHS1 knockdown, and ECHS1 and FASN

12 double-knockdown 786-O cells.

13 (N) Proliferation curves of control, ECHS1-overexpressing, and FASN

14 knockdown/ECHS1-overexpressing 786-O cells.

15 (O) Lipid levels in control, ECHS1 knockdown, and ECHS1 and FASN

16 double-knockdown cells.

18 Figure 7. Decreased ECHS1 levels predict poor prognosis of ccRCC.

19 (A) Correlation between FFA levels and tumor stage.

20 (B) Correlation between FFA levels and tumor grade.

21 (C) Correlation between lipid levels and tumor stage.

22 (D) Correlation between lipid levels and tumor grade.

1 (E) Ratio of ECHS1 (left graph) and GATA3 (right graph) mRNA expression in primary

2 ccRCC specimens/patient-matched normal tissues for all stages of ccRCC.

3 (F) Ratio of ECHS1 (left graph) and GATA3 (right graph) mRNA expression in primary

4 ccRCC specimens/patient-matched normal tissues for all grades of ccRCC.

5 (G, H) Correlations between ECHS1 expression and lipid levels (G) or FFA levels (H).

6 (I) Kaplan–Meier survival plots for PFS according to ECHS1 (top) and GATA3 (bottom)

7 mRNA expression in primary ccRCC specimens. “Low ECHS1 expression”

8 denotes a ratio of ECHS1 mRNA expression in primary ccRCC

9 specimens/patient-matched normal tissues of less than 1/3; “Middle ECHS1

10 expression” denotes a ratio greater than 1/3 and less than 2/3; and “High ECHS1

11 expression” denotes a ratio greater than 2/3.

12 (J) Kaplan–Meier survival plots for OS according to ECHS1 (top) and GATA3 (bottom)

13 mRNA expression in primary ccRCC specimens.

15 16

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f *** o R 0.0 0.2 0.4 0.6 3

c 1.2 3 3 3

c i

t Ratio of ECHS1 expression in a ccRCC/Normal 2 2 2

R 1.0 2 p-S6K IHC p-S6K 0.0 0.2 0.4 0.6 ACCIHC

1 1 1 SREBP1IHC

p-4E-BP1 IHC p-4E-BP1 1

Quantification of Quantification

Quantification of Quantification Quantification of Quantification

Ratio of ECHS1 expression in of Quantification ccRCC/Normal 0 0 0 0 Normal ccRCC Normal ccRCC Normal ccRCC Normal ccRCC

G H I J K

+ + Mouse kidney + Mouse kidney Mouse kidney T +/ +/ +/ N = 6 N = 6 N = 6 T 2.0 2.0 *** 2.0 *** *** Echs1 Echs1 Echs1 1.5 1.5 1.5 N N 1.0 1.0 1.0

- - - +/ +/ +/

p-S6K IHC p-S6K 0.5

0.5 0.5 SREBP1IHC p-4E-BP1 IHC p-4E-BP1 s1

s1 Quantification of Quantification

s1 Quantification of Quantification N = 12 N = 12 of Quantification ch ch 4 1.2 *** ch 0.0 0.0 0.0 E E *** E +/+ +/- 1.0 Echs1+/+ Echs1+/- Echs1+/+ Echs1+/- Echs1 Echs1 3 0.8 2 0.6

0.4 ATGL IHC ATGL FASN IHC FASN 1

Quantification of Quantification 0.2 Quantification of Quantification 0 0.0 Normal ccRCC Normal ccRCC

L M N HEK293T 786-O HEK293T 786-O ACHN

HEK293T 786-O Scrambled siRNA －＋－－＋－－＋－ Scrambled siRNA －＋－－＋－ ECHS1 siRNA －－＋－－＋－－＋ Scrambled siRNA －＋－－＋－ GATA3 siRNA －－＋－－＋ AMPKα1 siRNA －－＋－－＋ ECHS1 ECHS1 ECHS1 p-S6K p-S6K p-S6K S6K S6K S6K p-4E-BP1 p-4E-BP1 p-4E-BP1 4E-BP1 4E-BP1 4E-BP1 SREBP1 SREBP1 SREBP1 ACC ACC ACC FASN FASN FASN ATGL ATGL ATGL p-AMPK p-AMPK Actin AMPK AMPK Actin Actin

O P Q HEK293T 786-O ACHN HEK293T 786-O ACHN HEK293T 786-O ACHN Vector ＋－＋－＋－＋－＋－＋－ Vector ＋－＋－＋－＋－＋－＋－ AICAR －＋－＋－＋－＋－＋－＋ ECHS1 －＋－＋－＋－＋－＋－＋ GATA3 －＋－＋－＋－＋－＋－＋ BCAAs －－＋＋－－＋＋－－＋＋ BCAAs －－＋＋－－＋＋－－＋＋ BCAAs －－＋＋－－＋＋－－＋＋ ECHS1 ECHS1 ECHS1 p-S6K p-S6K p-S6K S6K S6K S6K p-4E-BP1 p-4E-BP1 p-4E-BP1 4E-BP1 4E-BP1 4E-BP1 SREBP1 SREBP1 SREBP1 ACC ACC ACC FASN FASN FASN ATGL ATGL ATGL Actin Actin Actin

R S T U V 786-O 786-O 786-O AMPKα1 siRNA －＋－＋ GATA3 KO －＋－＋ ECHS1 KO －＋－＋ HEK293T 786-O ACHN

+ Mouse kidney BCAAs ＋＋－－ BCAAs ＋＋－－ +/ BCAAs ＋＋－－ 1.5 N = 6

s1 ECHS1-Flag －＋－＋－＋ p-S6K p-S6K *** p-S6K ch

E ECHS1 S6K 1.0 S6K S6K p-AMPK p-4E-BP1 p-4E-BP1 p-4E-BP1 - 0.5 +/

p-AMPKIHC AMPK

4E-BP1 of Quantification 4E-BP1 4E-BP1 SREBP1 0.0 Actin

SREBP1 Echs1 SREBP1 Echs1+/+ Echs1+/- ACC ACC ACC FASN FASN FASN ATGL ATGL ATGL Actin Actin Actin Downloaded from cancerres.aacrjournals.org on September 27, 2021. © 2019 American Association for Cancer Research. Figure 6

A B C D

ACHN 786-O Control shECHS1 Control shECHS1 Control shECHS1 Control shECHS1 shECHS1+rapamycin shECHS1+rapamycin 2.0 AuthorshLCAD ManuscriptshECHS1+rapamycin Published OnlineFirst2.0 onshLCAD NovembershECHS1 5, 2019;+rapamycin DOI: 10.1158/0008-5472.CAN-19-1023 Author manuscripts have been peer reviewed and accepted for publication but have** not yet been** edited. ns ns ** ** ** 3 ** 3 1.5 1.5

2 2

1.0 1.0

OD450 OD450

0.5 0.5 1 1 Relative lipids levels lipids Relative 0.0 0 0 0.0 ACHN 786-O ACHN 786-O Day 0 Day 1Day 2 Day 3 Day 4 Day 0 Day 1Day 2 Day 3 Day 4 acids fatty free total Relative

E F G H 786-O ACHN Control shLCAD ACHN 786-O 3 ** ** shScramble shScramble Scrambled siRNA ＋－＋－ shECHS1 shECHS1 2 LCAD siRNA －＋－＋ shECHS1+rapamycin shECHS1+rapamycin shECHS1+shFASN shECHS1+shFASN 1 p-S6K

S6K shScramble shECHS1+rapamycin shScramble shECHS1+rapamycin Relative lipids levels lipids Relative 0 shECHS1 shECHS1+shFASN shECHS1 shECHS1+shFASN

ACHN 786-O p-4E-BP1

) )

3 1000 3 800 4E-BP1 HEK293T 786-O 800 600 LCAD

600 **

Scrambled siRNA ＋－＋－ **

** ** ** 400 Actin ** LCAD siRNA －＋－＋ 400 200

LCAD 200 Tumor volume (mm volume Tumor Actin 0 (mm volume Tumor 0 9 12 15 18 21 9 12 15 18 21 Days after inoculation (days) Days after inoculation (days)

** ** ** ** I J 1.2 ** 1.0 ** 1.0 0.8 0.8 Control shECHS1 Vehicle ECHS1 0.6 1.5 0.6 2.5 0.4 ** ** ** ** 0.4

2.0 0.2 Tumor weight (g) weight Tumor 1.0 (g) weight Tumor 0.2

1.5 palmitic C

C palmitic C 0.0 0.0

13 13

1.0 0.5

acid levels acid acid levels acid 0.5

shECHS1 shECHS1 Relative

Relative shScramble 0.0 0.0 shScramble HEK293T 786-O HEK293T 786-O shECHS1+shFASN shECHS1+shFASN shECHS1+rapamycin shECHS1+rapamycin

K L M N

786-O 786-O 786-O 786-O Control shECHS1 Control shECHS1 Control ECHS1 Vehicle ECHS1 3.0 shECHS1+shFASN 2.0 ECHS1+shFASN 2.5 ** 1.5 2.5 2.0 ** ns 1.5 1.0 2.0

1.5 ns 0

C palmitic C

4 13 13 1.5 1.0 D 1.0

0.5 O

acid levels acid

acid levels acid OD450 0.5 1.0

0.5 Relative 0.0 Relative 0.0 0.5 Control shFASN Control shFASN 0.0 0.0 Day 0Day 1Day 2Day 3Day 4 786-O 786-O Day 0 Day 1Day 2 Day 3 Day 4

shECHS1 －＋－＋ ECHS1 －＋－＋

shFASN －－＋＋ shFASN －－＋＋

ECHS1 ECHS1 FASN FASN Actin Actin

* * ** 3 **

2 Control shECHS1 1 shECHS1+shFASN

Relative lipids levels lipids Relative 0 ACHN 786-O

A B

6 l

6 l l

l Stage I Grade 1 e

a ***

e a

*** *** v

Authorv Manuscript Published OnlineFirst on November 5, 2019; DOI: 10.1158/0008-5472.CAN-19-10235 *** m

5 e Grade 2

m *** Stage II ** ***

r l

e *** **

r l

** ns * o

ns o Author manuscripts have been peer reviewed and accepted for publications but have not yet been edited.** ** Grade 3 s ** ** Stage III 4 ns

4 N

A N

ns /

A ns *

/ ns

ns F Grade 4

Stage IV C F

C 3

3 f

o 2

c i

o 2

n 1

i n

1 R

i R 0 0 d d d s id id id As ci ci ci A ac ac ac F a a a l F tic ic ic tal itic ric dic ta i ar id o m ea hi To alm te ch T al St ac P S ra P Ar A

C E

A A a *** s 8

l 0.3 ***

N N

0.3 m

m r e ***

a **

r R

R *

o o

m **

** N

6 **

C C

N 0.2

ns A

d 0.2

R l

4 A

c n

f 0.1 n

0.1 i

2 o

N=5 N=8 N=6 N=5 s

l o

i N=252 N=37 N=39 t

i N=252 N=37 N=39 N=39

e R

a N=39

v a

0 e

R l

e 0.0 l

R 0.0 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV

D F

* l

ns l *

a A

s 8 l l ns

N 0.3 0.3 *

N m

** m

a r

r ns

6 N

d /

i 0.2 0.2

C C

p ns

i H

l 4

o n

n 0.1 0.1

i i

2 o

N=4 N=8 N=6 N=6

s a s N=24 N=151 N=159 N=33

N=24 N=151 N=159 N=33 o

v v

0 a

e R

l 0.0 l

R 0.0 Grade 1 Grade 2 Grade 3 Grade 4 Grade 1 Grade 2 Grade 3 Grade 4 Grade 1 Grade 2 Grade 3 Grade 4

G H

n i

8 7 Palmitic acid s

l y = 6.09-8.03x

l Stearic acid e a 6

v p = 0.0004

m 6

e Arachidic acid r

l 5

s Total FAs N

d 4 /

i 4

p C i y=4.07-4.78x p=0.002

l 3

f y=3.80-4.44x p=0.002

o R

2 c 2

o y=3.47-3.49x p=0.007

ccRCC/Normal

c i t 1

a y=2.91-2.59x p=0.003 Ratio of FAs level in Ratioof level FAs

R 0 0 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 Ratio of ECHS1 mRNA levels Ratio of ECHS1 mRNA levels in ccRCC/Normal in ccRCC/Normal

I J

100 100 Low ECHS1 expression p < 0.001 80 P < 0.001 Middle ECHS1 expression 80 Low ECHS1 expression High ECHS1 expression Middle ECHS1 expression 60 60 High ECHS1 expression 40 40

survival (%) survival 20 20

Progression-free Overall survival (%) survival Overall 0 0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time after surgery (months) Time after surgery (months)

100 Low GATA3 expression ) 100 Low GATA3 expression e

p = 0.029 % p = 0.032

(

r l

) 80 Middle GATA3 expression 80 Middle GATA3 expression

n (

High GATA3 expression i High GATA3 expression

o v

i 60

60 r

r l

r 40 40

o e

r 20 20

P O 0 0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Time after surgery (months) Time after surgery (months)

Inactivation of the AMPK-GATA3-ECHS1 Pathway induces Fatty Acid Synthesis that Promotes Clear Cell Renal Cell Carcinoma Growth

Yuan-Yuan Qu, Rui Zhao, Hai-Liang Zhang, et al.

Cancer Res Published OnlineFirst November 5, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-19-1023

Supplementary Access the most recent supplemental material at: Material http://cancerres.aacrjournals.org/content/suppl/2019/11/05/0008-5472.CAN-19-1023.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet Manuscript been edited.

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