Overexpression and proliferation dependence of acyl‑CoA thioesterase 11 and 13 in lung adenocarcinoma

  • Authors:
    • Jen‑Yu Hung
    • Shyh‑Ren Chiang
    • Kuan‑Ting Liu
    • Ming‑Ju Tsai
    • Ming‑Shyan Huang
    • Jiunn‑Min Shieh
    • Meng‑Chi Yen
    • Ya‑Ling Hsu
  • View Affiliations

  • Published online on: July 18, 2017     https://doi.org/10.3892/ol.2017.6594
  • Pages: 3647-3656
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Abstract

The metabolites of fatty acyl‑Coenzyme A (CoA) and metabolic enzymes contribute to lipid biosynthesis, signal transduction, and gene transcription. Previous studies have indicated that elevated concentrations of specific free fatty acids in the plasma and overexpression of specific fatty acyl‑CoA metabolic enzymes are observed in patients with lung adenocarcinoma. However, there are >30 enzymes in this metabolic network and have been fully investigated. In the present study, the expression levels of enzymes in the acyl‑CoA synthetase (ACS) and acyl‑CoA thioesterase (ACOT) families were analyzed from six microarray expression datasets that were collected from Gene Expression Omnibus. Compared with adjacent non‑tumor lung tissue, lung adenocarcinoma tissue exhibited significantly higher ACOT11 and ACOT13 expression. Kaplan‑Meier plotter database analysis demonstrated that high levels of ACOT11 and ACOT13 were associated with a worse overall survival rate. The proliferation of the lung adenocarcinoma cell lines CL1‑0 and CL1‑5 was inhibited when ACOT11 and ACOT13 were downregulated by short hairpin RNA. Although ACOT11 and ACOT13 knockdown did not significantly affect the total amount of intracellular and medium‑free fatty acids, ACOT11 and ACOT13 knockdown-mediated growth inhibition was rescued by the addition of fatty acids. In conclusion, ACOT11 and ACOT13 were upregulated in clinical specimens of lung adenocarcinoma, which may contribute to increased cell proliferation through the increased availability of fatty acids. The metabolites of the two enzymes may be critical for development of lung adenocarcinoma.

Introduction

Metabolites are currently considered targets for cancer treatments, particularly amino acids and glucose (1). Fatty acyl-Coenzyme A (CoA) esters are essential components in lipid metabolism and are regulators of multiple cellular functions (2). Enzymes of the acyl-CoA synthetase (ACS) family, including the ACS long-chain, ACS medium-chain, ACS short-chain and ACS bubblegum families, ligate different lengths of fatty acid with CoA. Fatty acyl-CoA esters are hydrolyzed into free fatty acids and CoA by enzymes of the acyl-CoA thioesterase (ACOT) family (3,4). There are >15 ACOT enzymes and 20 ACS enzymes in humans, and these enzymes exhibit different tissue distribution, subcellular location and substrate specificity (3,4).

Lung cancer is a leading cause of cancer-associated mortality and is the second most commonly diagnosed cancer (5). The majority (~85–90%) of diagnosed cases of lung cancer are diagnosed as non-small cell lung cancer (NSCLC) and adenocarcinoma is the most common subtype of NSCLC (6). Compared with healthy individuals, patients with lung adenocarcinoma possess a significantly higher level of fatty acids (including arachidonic, palmitic, linoleic and oleic acid) in their plasma (7,8). In addition, overexpression of ACOT8 is associated with metastasis in lung adenocarcinoma (9). However, the cellular functions and the regulatory mechanisms of the majority of ACOT and ACS enzymes in lung adenocarcinoma remain unclear. In the present study, in order to systematically analyze the expression pattern of these enzymes, expression levels of ACOT and ACS enzymes were analyzed in clinical specimens of lung adenocarcinoma from six microarray datasets that were collected from an online database. In addition, the effect of these enzymes and their metabolic products on cell proliferation was measured in lung adenocarcinoma cell lines.

Materials and methods

Collection of microarray datasets of human lung adenocarcinoma specimens

Expression profiling microarray data of human lung adenocarcinoma clinical specimens, which was published between 2005 and 2015, was collected from the National Center for Biotechnology Information Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) (10). Microarray data from cell lines or small sample sizes (<10 samples) were excluded. Six microarray datasets including GSE2514 (11), GSE7670 (12), GSE10072 (13), GSE31210 (14), GSE32863 (15), and GSE43458 (16) were collected and the relative expression levels of fatty acyl-CoA metabolic enzymes between adjacent non-tumor lung tissue and lung adenocarcinoma were analyzed in these microarray datasets. The expression value was analyzed using the GEO2R interface (http://www.ncbi.nlm.nih.gov/geo/geo2r/).

Kaplan Meier (KM)-Plotter

The survival analysis in lung adenocarcinoma patients with different expression levels of ACOT11 and ACOT13 was performed using the KM-Plotter database (17). The prognostic value of each gene was analyzed by splitting patient samples into two groups according to median expression. After the subtype of lung cancer was restricted (‘Histology: adenocarcinoma’) and survival rate was analyzed through the ‘2015 version’ database and ‘excluded biased array’, 720 patients were analyzed.

Chemicals

Dimethyl sulfoxide (DMSO), puromycin, myristic acid, palmitic acid and stearic acid were purchased from Sigma-Aldrich (Merck KGaA, Darmstadt, Germany). Myristic acid, and palmitic acid and stearic acid were dissolved in DMSO.

Cell culture

Human lung adenocarcinoma cell lines CL1-0 and CL1-5 were provided by Dr Pan-Chyr Yang (Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan) and were cultured in RPMI-1640 supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (all Thermo Fisher Scientific, Inc., Waltham, MA, USA) in a humidified incubator at 37°C with 5% CO2 (18,19).

Short hairpin (sh)RNA and transfection

shRNA targeting ACOT11 (shACOT11; TRCN0000048912; targeting sequence: 5′-GTCTCCTCCTTGAAGATGCT-3′), ACOT13 (shACOT13-1; TRCN0000048954; targeting sequence: 5′-CGATATGAACATAACGTACAT-3′; shACOT13-2; targeting sequence: TRCN0000048956; targeting sequence: 5′-GAAGAGCATACCAATGCAATA-3′) and a negative control construct (luciferase shRNA, shLuc) were obtained from the National Core Facility for Manipulation of Gene Function by RNAi, miRNA, miRNA sponges, and CRISPR/Genomic Research Center, Academia Sinica (Taipei, Taiwan). CL1-0 and CL1-5 cells were transfected with each shRNA plasmid using Lipofectamine® 2000 reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. Transfected cells were selected and maintained in medium containing 2 µg/ml puromycin.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from cells using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to manufacturer's protocol. Complementary DNA (cDNA) was reverse transcribed from mRNA using the PrimeScript RT reagent kit (Clontech Laboratories, Inc., Mountainview, CA, USA) according to the manufacturer's protocol. PCR was performed using the following primers: ACOT13 forward, 5′-TCTGCTATGCACGGAAAGGG-3′ and reverse, 5′-TTTCCTGTGGCCTTGTTGGT-3′; ACOT11 forward, 5′- GCG ATC TGG AGA GCA GAG AC-3′ and reverse, 5′-GTGGCCACATTCTCCATCCA-3′; GAPDH forward, 5′-GAGTCAACGGATTTGGTCGT-3′ and reverse, 5′-TTGATTTTGGAGGGATCTCG-3′. PCR was performed on a StepOne Plus Real-Time PCT System (Applied Biosystems; Thermo Fisher Scientific, Inc.) using the Fast SYBR Green Master Mix (Applied Biosystems; Thermo Fisher Scientific, Inc.). The following settings were used: 1 cycle of 95°C for 20 sec, and 40 cycles of 95°C for 3 sec and 60°C for 30 sec. The mRNA expression levels were normalized to the expression level of GAPDH using the 2−∆∆Cq method (20).

Western blot analysis

Cells were lysed in radioimmunoprecipitation assay buffer (EMD Millipore, Billerica, MA, USA) and the total cell lysate was collected by centrifugation at 4°C, 12,000 × g for 15 min. Quantification of protein concentration was performed using a BCA protein assay kit (EMD Millipore). A total of 30 µg/lane protein was loaded, subjected to 10% SDS-PAGE and transferred to polyvinylidene difluoride membranes (EMD Millipore). Membranes were blocked with 5% dried skimmed milk in Tris-buffered saline with Tween-20 buffer and then incubated with the primary antibodies overnight at 4°C. Protein expression was detected using the following primary antibodies: Anti-ACOT11 (dilution, 1:3,000; cat. no. ab153835), anti-ACOT13 (dilution, 1:1,000; cat. no. ab166684; both Abcam, Cambridge, UK) and anti-GAPDH (dilution, 1:6,000; cat. no. MAB374; EMD Millipore). Each membrane was then incubated with secondary andibodies, including peroxidase conjugated goat anti-rabbit IgG (dilution, 1:5,000; cat. no. AP132P) and peroxidase conjugated goat anti-mouse IgG, (dilution, 1:5,000; cat. no. AP124P; both EMD Millipore) at room temperature for 1 h. Immunoreactive signals were detected with Immobilon horseradish peroxidase substrate enhanced chemiluminescence reagents (EMD Millipore) and analyzed with the Alpha Innotech FluorChem FC2 imaging system (ProteinSimple; Bio-Techne, Minneapolis, MN, USA).

Cell proliferation assay

For cell proliferation measurements, WST-1 (Clontech Laboratories, Inc.) was used. A total of 5×103 CL1-0 or 2.5×103 CL1-5 cells were seeded in 96-well plates. The proliferation rate was determined at a wavelength of 450 nm on a microplate spectrophotometer (PowerWave X340, BioTek Instruments, Inc., Winooski, VT, USA).

Free fatty acid quantification

A total of 1×106 CL1-0 cells were seeded into 10 cm dishes with 8 ml of culture medium. After 48 h, culture medium and cells were collected for free fatty acid quantification. Free fatty acids released into the culture medium and intracellular free fatty acid were quantified using a Free Fatty Acid Quantification Colorimetric/Fluorometric kit (BioVision, Inc., Milpitas, CA, USA) according to the manufacturer's protocol. The results were determined on a fluorescent microplate reader at excitation/emission=485/590 nm (FLX800 Microplate Fluorescence Reader; BioTek Instruments, Inc.).

Statistical analysis

All statistical analyses were performed using GraphPad Prism software (version 5.03; GraphPad Software, Inc., La Jolla, CA, USA). All error bars in the figures represent standard error of the mean. The differences between two independent groups were analyzed using a Student's t-test. A log-rank test was performed to assess differences in survival rate. P<0.05 was considered to indicate a statistically significant difference.

Results

High ACOT11 and ACOT13 expression is associated with lung adenocarcinoma and poor overall survival rate

To investigate whether the expression of ACSs and ACOTs was associated with lung adenocarcinoma, microarray datasets with lung adenocarcinoma specimens were collected from the GEO database. Following the exclusion of microarray data regarding cell line studies and small sample sizes (<10 samples), six microarrays that were performed by five different array platforms were selected for the current study (Fig. 1). A number of enzymes were significantly differentially expressed in lung adenocarcinoma tissue compared with non-tumor lung tissue (Tables I and II). ACOT11 and ACOT13 overexpression was observed in lung adenocarcinoma tissue across all selected microarray datasets (Table I). A KM-Plotter database was used to further investigate the association between the expression of ACOT11 and ACOT13 and clinical outcome. Poor overall survival rate of patients with lung adenocarcinoma was associated with high expression of ACOT11 and ACOT13 (P<0.001; Fig. 2). These results suggest that ACOT11 and ACOT13 serve roles in the development of lung adenocarcinoma.

Table I.

mRNA expression of ACOT enzymes in lung adenocarcinoma tissue compared with normal tissue, from the GEO database.

Table I.

mRNA expression of ACOT enzymes in lung adenocarcinoma tissue compared with normal tissue, from the GEO database.

VariableExpression ratio (normal/tumor)P-valueProbe
Adjacent non-tumor (n=19) vs. Lung adenocarcinoma (n=20);
GEO accession number: GSE2514; GPL8300 platform (11)
  ACOT1/ACOT21.1410.212036625_at
  ACOT70.8230.027937945_at
  ACOT80.8630.100336841_at
  ACOT110.8800.397132405_at
  ACOT130.715<0.000141058_g_at
Adjacent non-tumor (n=26) vs. Lung adenocarcinoma (n=26);
GEO accession number: GSE7670; GPL96 platform (12)
  ACOT1/ACOT21.0700.5266202982_s_at
  ACOT70.8610.1354208002_s_at
  ACOT80.8430.1336204212_at
  ACOT91.1200.8513221641_s_at
  ACOT110.5640.0002214763_at
  ACOT130.6780.0004204565_at
Non-tumor tissue (49) vs. Lung adenocarcinoma (58);
GEO accession number: GSE10072; GPL96 platform (13)
  ACOT1/ACOT21.0370.0002202982_s_at
  ACOT70.9910.4437208002_s_at
  ACOT81.0020.3531204212_at
  ACOT91.0100.1675221641_s_at
  ACOT110.9760.0001214763_at
  ACOT130.934<0.0001204565_at
Normal lung tissue (n=20) vs. Lung adenocarcinoma (n=226);
GEO accession number: GSE31210; GPL570 platform (14)
  ACOT21.0470.7030202982_s_at
  ACOT40.6990.0215229534_at
  ACOT61.1220.6088241949_at
  ACOT70.6930.7152208002_s_at
  ACOT80.6810.0338236514_at
  ACOT90.9810.7506221641_s_at
  ACOT110.2560.0017214763_at
  ACOT121.0070.9543238160_at
  ACOT130.549<0.0001204565_at
Adjacent non-tumor (n=58) vs. Lung adenocarcinoma (n=58);
GEO accession number: GSE32863; GPL6884 platform (15)
  ACOT11.0120.0049ILMN_2121389
  ACOT21.0070.0065ILMN_2188959
  ACOT40.9980.8718ILMN_1764321
  ACOT60.9990.7702ILMN_2156699
  ACOT70.9980.5223ILMN_1719178
  ACOT81.0040.5715ILMN_1679600
  ACOT90.9910.2383ILMN_2367070
  ACOT110.879<0.0001ILMN_1739594
  ACOT121.0050.0271ILMN_1785474
  ACOT130.9630.0010ILMN_2098743
Normal lung tissure (30) vs. Lung adenocarcinoma (80);
GEO accession number: GSE43458; GPL6244 platform (16)
  ACOT11.0170.07167975598
  ACOT21.0450.00027975602
  ACOT40.9820.07947975607
  ACOT60.9870.08107975613
  ACOT70.9600.00957912012
  ACOT81.0100.11378066598
  ACOT91.052<0.00018171802
  ACOT110.952<0.00017901613
  ACOT120.9950.42638112920
  ACOT130.9650.00398117219

[i] GEO, Gene Expression Omnibus; ACOT, acyl-CoA thioesterase.

Table II.

mRNA expression of ACS enzymes in lung adenocarcinoma tissue compared with normal tissue, from the GEO database.

Table II.

mRNA expression of ACS enzymes in lung adenocarcinoma tissue compared with normal tissue, from the GEO database.

VariableExpression ratio (normal/tumor)P-valueProbe
Adjacent non-tumor (n=19) vs. Lung adenocarcinoma (n=20);
GEO accession number: GSE2514; GPL8300 platform (11)
  ACSBG10.8380.493732537_at
  ACSM10.8510.667834050_at
  ACSM2A1.1200.690237800_r_at
  ACSM30.7070.026033280_r_at
  ACSL11.1630.141840082_at
  ACSL30.9410.650633880_at
  ACSL41.1720.166338099_r_at
  ACSL61.0830.782531834_r_at
Adjacent non-tumor (n=26) vs. Lung adenocarcinoma (n=26);
GEO accession number: GSE7670; GPL96 platform (12)
  ACSBG10.9010.7335206466_at
  ACSBG20.8920.4825221716_s_at
  ACSF20.8610.3332218844_at
  ACSM10.6410.1782215432_at
  ACSM2A/ACSM2B0.6670.0532214069_at
  ACSM30.5870.0023210377_at
  ACSM51.4500.0357220061_at
  ACSS31.0980.3422219616_at
  ACSL11.1540.2702207275_s_at
  ACSL31.391<0.0001201660_at
  ACSL41.2400.0936202422_s_at
  ACSL50.8220.1353218322_s_at
  ACSL60.8520.0875216409_at
Non-tumor tissue (49) vs. Lung adenocarcinoma (58);
GEO accession number: GSE10072; GPL96 platform (13)
  ACSBG11.0250.0007206466_at
  ACSBG21.0160.0283221716_s_at
  ACSF20.9890.1715218844_at
  ACSM10.9980.8514215432_at
  ACSM2A/ACSM2B1.0030.6717214069_at
  ACSM31.0130.2957210377_at
  ACSM51.054<0.0001220061_at
  ACSS31.038<0.0001219616_at
  ACSL11.0410.0003207275_s_at
  ACSL31.052<0.0001201660_at
  ACSL41.073<0.0001202422_s_at
  ACSL51.0260.0613218322_s_at
  ACSL61.0000.9467216409_at
Normal lung tissue (n=20) vs. Lung adenocarcinoma (n=226);
GEO accession number: GSE31210; GPL570 platform (14)
  ACSBG12.0870.0085206465_at
  ACSBG21.0300.8264221716_s_at
  ACSF10.7120.0040218434_s_at
  ACSF20.6530.0005218844_at
  ACSM10.7160.2056215432_at
  ACSM2B1.0520.6555214069_at
  ACSM30.4220.0031205942_s_at
  ACSM51.0060.95421554514_at
  ACSS10.7670.0995234484_s_at
  ACSS20.8570.3373234312_s_at
  ACSS31.2200.1480219616_at
  ACSL11.1330.2698207275_at
  ACSL30.8260.0403201661_s_at
  ACSL41.701<0.00011557419_a_at
  ACSL60.8140.1151211207_s_at
Adjacent non-tumor (n=58) vs. Lung adenocarcinoma (n=58);
GEO accession number: GSE32863; GPL6884 platform (15)
  ACSBG11.0220.0072ILMN_2227011
  ACSBG21.011<0.0001ILMN_1730002
  ACSF10.953<0.0001ILMN_1698554
  ACSF20.9550.0007ILMN_1711928
  ACSM11.0000.9545ILMN_1661434
  ACSM2A1.0030.1501ILMN_1754517
  ACSM2B0.9990.6416ILMN_1765912
  ACSM30.960<0.0001ILMN_1662738
  ACSM40.9990.6885ILMN_1791923
  ACSM51.0040.0358ILMN_1801698
  ACSL11.068<0.0001ILMN_1684585
  ACSL30.9850.0964ILMN_2360605
  ACSL40.9990.6174ILMN_1691714
  ACSL50.9780.1879ILMN_2370882
Normal lung tissue (30) vs. Lung adenocarcinoma (80);
GEO accession number: GSE43458; GPL6244 platform (16)
  ACSBG10.9940.49657990683
  ACSBG20.9970.70008025011
  ACSF21.0210.14098008321
  ACSF30.9800.00437997863
  ACSM10.9910.21037999981
  ACSM2A/ACSM2B0.9710.00217993737
  ACSM30.9730.32887993756
  ACSM51.0080.35767993726
  ACSM61.0260.00037929497
  ACSS11.0210.03878065444
  ACSS21.0400.00098062041
  ACSS31.181<0.00017957386
  ACSL11.0470.00128103951
  ACSL31.0070.47608048733
  ACSL41.067<0.00018174474
  ACSL50.9700.05877930498
  ACSL60.9760.00218113938

[i] GEO, Gene Expression Omnibus; ACS, acyl-CoA synthetase.

ACOT11 and ACOT13 knockdown decreases cell proliferation but does not affect the level of free fatty acids

The CL1-0 and CL1-5 cell lines were established from a patient with lung adenocarcinoma and exhibit different invasive and metastatic properties (19). Similar protein levels of ACOT11 and ACOT13, and similar levels of intracellular and medium free fatty acid, were observed in the two cell lines (Fig. 3A and B). In order to determine the role of ACOT11 and ACOT13, shRNAs targeting ACOT11 (shACOT11) and ACOT13 (shACOT13-1 and shACOT13-2) were transfected into CL1-0 and CL1-5 cells. Cells treated with shACOT11 exhibited significantly decreased expression of ACOT11 (P<0.05; Fig. 4A). Similarly, cells treated with shACOT13-1/−2 exhibited significantly decreased expression of ACOT13 (P<0.01 and P<0.001, respectively; Fig. 4A). These changes were also exhibited at the protein level (Fig. 4B). Although decreasing expression of ACOT11 and ACOT13 did not result in a significant change in total free fatty acids (Fig. 4C), significantly decreased proliferation rates were observed in CL1-0 and CL1-5 cells treated with shACOT11 compared with the control (P<0.001 and P<0.01, respectively; Fig. 4D). In addition, CL1-0 cells treated with shACOT13-2, and CL1-5 cells treated with ahACOT13-1 exhibited significantly decreased proliferation rates compared with the control (P<0.001 and P<0.05, respectively; Fig. 4D). These results suggest that ACOT11 and ACOT13 are critical for proliferation in lung adenocarcinoma cell lines.

Free fatty acid supplements rescue the decreased proliferation rate induced by ACOT11 and ACOT13 knockdown

ACOT11 and ACOT13 are members of the Type II ACOT family (21). The substrates of ACOT11 and ACOT13 include medium (6–12 carbon) to long (13–21 carbon) chain acyl CoA (22). It was hypothesized that the observed decreased proliferation rates following ACOT11/13 knockdown were associated with insufficient availability of free fatty acids. Therefore ACOT11/13 knockdown CL1-0 cells were treated with a fatty acid mixture, including myristic (C14:0), palmitic (C16:0) and stearic acid (C18:0). The cell proliferation rate was significantly decreased in the control groups (medium and vehicle) following treatment with shACOT11/13-2 compared with the control luciferase shRNA group (P<0.05; Fig. 5); however, treatment with the fatty acid mixture (0.1–10 µM) restored shACOT11 and shACOT13-mediated growth inhibition. However, this effect was abolished following the addition of mixed free fatty acids at the highest dose (100 µM), which may indicate a negative feedback mechanism. The results suggest that the proliferation of lung adenocarcinoma cells is dependent on ACOT11 and ACOT13-mediated metabolic products.

Discussion

Dysregulation of metabolism is a hallmark of cancer development. Acyl-CoAs are involved in the biosynthesis of lipids, signal transduction and gene transcription (23). Previous studies have demonstrated that certain types of fatty acid are increased in the serum or plasma of patients with lung adenocarcinoma (7,8). Notably, the level of fatty acids (palmitic, stearic, oleic, linoleic, arachidonic and palmitoleic acid) decreased in the serum of patients with lung cancer compared with the healthy controls (24). This suggests that different types of lung cancer may possess unique metabolic networks. Although previous studies have demonstrated associations between a single enzyme and a specific tumor type, including the association between ACSL3, ACOT8 and liver cancer (25,26), very long-chain ACS-3 (ACSVL3) and lung cancer (27), and ACOT8 and metastatic lung adenocarcinoma (9), the associations between types of tumor and the enzyme network of acyl-CoA and acyl-CoA esters remain unclear. To systematically determine the potential roles of all ACS and ACOT enzymes in lung adenocarcinoma, the gene expression values from six different microarray datasets were analyzed using five different platforms. Several enzymes exhibited significantly different expression levels between normal and lung adenocarcinoma tissue. High expression of ACOT11 and ACOT13 was observed in tumors compared with normal tissue in each dataset. KM-Plotter analysis demonstrated that high ACOT11/13 expression was correlated with poor overall survival rate. Since the criteria was not restricted to metastatic lung adenocarcinoma, ACOT8 expression levels in tumor tissue were not significantly different compared with normal tissue in the present study. These results indicated that ACOT11 and ACOT13 enzymes are potential oncogenes in lung adenocarcinoma.

ACOT11 is highly expressed in brown adipose tissue compared with other tissues (28). The ACOT11 structure comprises two ‘hotdog’ domains and a C-terminal lipid-binding steroidogenic acute regulatory transfer-related (START) domain (22). Although a previous study suggested that the START domain may be an important regulatory element for ACOT11 (29), the exact mechanisms underlying the regulation and function of ACOT11 remain unknown in lung adenocarcinoma and other tissues. ACOT11 knockout mice revealed increased energy consumption and resistance to high fat diet-induced obesity compared with control mice (30). In addition, loss of ACOT11 leads to resistance to obesity-induced inflammation and endoplasmic reticulum stress (30). A high fat diet significantly induced ACOT11 expression in mouse liver (3). These observations suggest that ACOT11 expression may serve as a risk factor for obesity. The results from the present study demonstrate that ACOT11 expression is associated with cell proliferation, suggesting that inhibition of ACOT11 may be a strategy to treat lung adenocarcinoma.

AOCT13 comprises a single ‘hotdog’ domain and is expressed in a number of tissues, including liver, heart, kidney and brown adipose tissue (3). In ACOT13 knockout mice, increasing concentrations of long-chain fatty acyl-CoA and decreasing concentrations of free fatty acids is detected in the liver (31). When mice are fed with a high-fat diet, ACOT13 knockout mice resist increases in glucose production in the liver (31). This observation suggests that ACOT13 serves a role in the regulation of lipid and glucose metabolism in the liver. Notably, ACOT13 interacts with phosphatidylcholine transfer protein (PC-TP), which possesses a START domain (32). Addition of recombinant PC-TP increases ACOT13 enzyme activity in vitro (32). The interaction affects the transcriptional activity of peroxisome proliferator-activated receptor alpha and hepatocyte nuclear factor 4 alpha in the liver (33). Since ACOT11 contains a START domain, these observations imply that ACOT13 may also interact with ACOT11. Since overexpression of ACOT11 and ACOT13 was observed in the present study, these interactions may regulate critical biological functions in lung adenocarcinoma.

The level of free fatty acids is significantly altered in brown adipose tissue and liver in ACOT11 and ACOT13 knockout mice, respectively (30,31). However, in the present study, ACOT11 and ACOT13 knockdown did not affect the level of total amount of free fatty acid in CL1-0 cells. There are two possible reasons for this. First, ACOT11 and ACOT13 may not be major lipid-metabolic enzymes in the lung adenocarcinoma cell. Decreasing levels of ACOT11 and ACOT13-hydrolyzed free fatty acids (medium to long-chain) may account for a small part of the total free fatty acid pool. Second, free fatty acids may be adequately supplied from the culture medium. Although ACOT11 and ACOT13 knockdown results in the reduction of certain types of free fatty acid, the effect may be diluted in the free fatty acid pool. Addition of free fatty acid mixture restored the growth inhibition. The results suggest that metabolic products of ACOT11 and ACOT13 (14 to 18 carbon) are important regulators for lung adenocarcinoma. The findings are summarized in Fig. 6.

In conclusion, the results from the present study reported the role of ACOT11 and ACOT13 in lung adenocarcinoma. High ACOT11 and ACOT13 expression was associated with lung adenocarcinoma and poor overall survival rate. Knockdown of ACOT11 and ACOT13 significantly decreased cell proliferation, an effect that could be rescued by supplementing cells with free fatty acids. To the best of our knowledge, this is the first report regarding the potential oncogenic properties of ACOT11 and ACOT13 in lung adenocarcinoma. ACOT11 and ACOT13 may represent targets for novel treatments for patients with lung adenocarcinoma.

Acknowledgments

The present study was supported by grants from the Ministry of Science and Technology of the Republic of China (grant nos. MOST 103-2320-B-037-006-MY3 and MOST 104-2314-B-037-053-MY4), the KMU-KMUH Co-Project of Key Research (grant no. KMU-DK 105002 from Kaohsiung Medical University) and the Chi-Mei Medical Center and Kaohsiung Medical University Research Foundation (grant no. HSU 104CM-KMU-01).

References

1 

Tennant DA, Durán RV and Gottlieb E: Targeting metabolic transformation for cancer therapy. Nat Rev Cancer. 10:267–277. 2010. View Article : Google Scholar : PubMed/NCBI

2 

Faergeman NJ and Knudsen J: Role of long-chain fatty acyl-CoA esters in the regulation of metabolism and in cell signalling. Biochem J. 323:1–12. 1997. View Article : Google Scholar : PubMed/NCBI

3 

Ellis JM, Bowman CE and Wolfgang MJ: Metabolic and tissue-specific regulation of acyl-CoA metabolism. PLoS One. 10:e01165872015. View Article : Google Scholar : PubMed/NCBI

4 

Hunt MC, Tillander V and Alexson SE: Regulation of peroxisomal lipid metabolism: The role of acyl-CoA and coenzyme A metabolizing enzymes. Biochimie. 98:45–55. 2014. View Article : Google Scholar : PubMed/NCBI

5 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2015. CA Cancer J Clin. 65:5–29. 2015. View Article : Google Scholar : PubMed/NCBI

6 

World Health Organisation, . World Cancer Report 2014. Chapter 5.1. 2014.

7 

Wen T, Gao L, Wen Z, Wu C, Tan CS, Toh WZ and Ong CN: Exploratory investigation of plasma metabolomics in human lung adenocarcinoma. Mol Biosyst. 9:2370–2378. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Liu J, Mazzone PJ, Cata JP, Kurz A, Bauer M, Mascha EJ and Sessler DI: Serum free fatty acid biomarkers of lung cancer. Chest. 146:670–679. 2014. View Article : Google Scholar : PubMed/NCBI

9 

Jung WY, Kim YH, Ryu YJ, Kim BH, Shin BK, Kim A and Kim HK: Acyl-CoA thioesterase 8 is a specific protein related to nodal metastasis and prognosis of lung adenocarcinoma. Pathol Res Pract. 209:276–283. 2013. View Article : Google Scholar : PubMed/NCBI

10 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:(Database issue). D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI

11 

Stearman RS, Dwyer-Nield L, Zerbe L, Blaine SA, Chan Z, Bunn PA Jr, Johnson GL, Hirsch FR, Merrick DT, Franklin WA, et al: Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol. 167:1763–1775. 2005. View Article : Google Scholar : PubMed/NCBI

12 

Su LJ, Chang CW, Wu YC, Chen KC, Lin CJ, Liang SC, Lin CH, Whang-Peng J, Hsu SL, Chen CH and Huang CY: Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. Bmc Genomics. 8:1402007. View Article : Google Scholar : PubMed/NCBI

13 

Landi MT, Dracheva T, Rotunno M, Figueroa JD, Liu H, Dasgupta A, Mann FE, Fukuoka J, Hames M, Bergen AW, et al: Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS One. 3:e16512008. View Article : Google Scholar : PubMed/NCBI

14 

Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S, et al: Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 72:100–111. 2012. View Article : Google Scholar : PubMed/NCBI

15 

Selamat SA, Chung BS, Girard L, Zhang W, Zhang Y, Campan M, Siegmund KD, Koss MN, Hagen JA, Lam WL, et al: Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression. Genome Res. 22:1197–1211. 2012. View Article : Google Scholar : PubMed/NCBI

16 

Kabbout M, Garcia MM, Fujimoto J, Liu DD, Woods D, Chow CW, Mendoza G, Momin AA, James BP, Solis L, et al: ETS2 mediated tumor suppressive function and MET oncogene inhibition in human non-small cell lung cancer. Clin Cancer Res. 19:3383–3395. 2013. View Article : Google Scholar : PubMed/NCBI

17 

Győrffy B, Surowiak P, Budczies J and Lánczky A: Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic data in Non-Small-Cell Lung Cancer. PLoS One. 8:e822412013. View Article : Google Scholar : PubMed/NCBI

18 

Kao YR, Shih JY, Wen WC, Ko YP, Chen BM, Chan YL, Chu YW, Yang PC, Wu CW and Roffler SR: Tumor-associated antigen L6 and the invasion of human lung cancer cells. Clin Cancer Res. 9:2807–2816. 2003.PubMed/NCBI

19 

Chu YW, Yang PC, Yang SC, Shyu YC, Hendrix MJ, Wu R and Wu CW: Selection of invasive and metastatic subpopulations from a human lung adenocarcinoma cell line. Am J Respir Cell Mol Biol. 17:353–360. 1997. View Article : Google Scholar : PubMed/NCBI

20 

Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods. 25:402–408. 2001. View Article : Google Scholar : PubMed/NCBI

21 

Kirkby B, Roman N, Kobe B, Kellie S and Forwood JK: Functional and structural properties of mammalian acyl-coenzyme A thioesterases. Prog Lipid Res. 49:366–377. 2010. View Article : Google Scholar : PubMed/NCBI

22 

Cohen DE: New players on the metabolic stage: How do you like Them Acots? Adipocyte. 2:3–6. 2013. View Article : Google Scholar : PubMed/NCBI

23 

Hunt MC and Alexson SE: The role Acyl-CoA thioesterases play in mediating intracellular lipid metabolism. Prog Lipid Res. 41:99–130. 2002. View Article : Google Scholar : PubMed/NCBI

24 

Li Y, Song X, Zhao XJ, Zou LJ and Xu GW: Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 966:147–153. 2014. View Article : Google Scholar : PubMed/NCBI

25 

Chang YS, Tsai CT, Huangfu CA, Huang WY, Lei HY, Lin CF, Su IJ, Chang WT, Wu PH, Chen YT, et al: ACSL3 and GSK-3β are essential for lipid upregulation induced by endoplasmic reticulum stress in liver cells. J Cell Biochem. 112:881–893. 2011. View Article : Google Scholar : PubMed/NCBI

26 

Hung YH, Chan YS, Chang YS, Lee KT, Hsu HP, Yen MC, Chen WC, Wang CY and Lai MD: Fatty acid metabolic enzyme acyl-CoA thioesterase 8 promotes the development of hepatocellular carcinoma. Oncol Rep. 31:2797–2803. 2014.PubMed/NCBI

27 

Pei Z, Fraisl P, Shi X, Gabrielson E, Forss-Petter S, Berger J and Watkins PA: Very Long-Chain Acyl-CoA Synthetase 3: Overexpression and Growth Dependence in Lung Cancer. PLoS One. 8:e693922013. View Article : Google Scholar : PubMed/NCBI

28 

Adams SH, Chui C, Schilbach SL, Yu XX, Goddard AD, Grimaldi JC, Lee J, Dowd P, Colman S and Lewin DA: BFIT, a unique acyl-CoA thioesterase induced in thermogenic brown adipose tissue: Cloning, organization of the human gene and assessment of a potential link to obesity. Biochem J. 360:135–142. 2001. View Article : Google Scholar : PubMed/NCBI

29 

Thorsell AG, Lee WH, Persson C, Siponen MI, Nilsson M, Busam RD, Kotenyova T, Schüler H and Lehtiö L: Comparative structural analysis of lipid binding START domains. PLoS One. 6:e195212011. View Article : Google Scholar : PubMed/NCBI

30 

Zhang Y, Li Y, Niepel MW, Kawano Y, Han S, Liu S, Marsili A, Larsen PR, Lee CH and Cohen DE: Targeted deletion of thioesterase superfamily member 1 promotes energy expenditure and protects against obesity and insulin resistance. Proc Natl Acad Sci USA. 109:5417–5422. 2012. View Article : Google Scholar : PubMed/NCBI

31 

Kang HW, Niepel MW, Han S, Kawano Y and Cohen DE: Thioesterase superfamily member 2/acyl-CoA thioesterase 13 (Them2/Acot13) regulates hepatic lipid and glucose metabolism. FASEB J. 26:2209–2221. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Wei J, Kang HW and Cohen DE: Thioesterase superfamily member 2 (Them2)/acyl-CoA thioesterase 13 (Acot13): A homotetrameric hotdog fold thioesterase with selectivity for long-chain fatty acyl-CoAs. Biochem J. 421:311–322. 2009. View Article : Google Scholar : PubMed/NCBI

33 

Kang HW, Kanno K, Scapa EF and Cohen DE: Regulatory role for phosphatidylcholine transfer protein/StarD2 in the metabolic response to peroxisome proliferator activated receptor alpha (PPARalpha). Biochim Biophys Acta. 1801:496–502. 2010. View Article : Google Scholar : PubMed/NCBI

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September-2017
Volume 14 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
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Spandidos Publications style
Hung JY, Chiang SR, Liu KT, Tsai MJ, Huang MS, Shieh JM, Yen MC and Hsu YL: Overexpression and proliferation dependence of acyl‑CoA thioesterase 11 and 13 in lung adenocarcinoma. Oncol Lett 14: 3647-3656, 2017.
APA
Hung, J., Chiang, S., Liu, K., Tsai, M., Huang, M., Shieh, J. ... Hsu, Y. (2017). Overexpression and proliferation dependence of acyl‑CoA thioesterase 11 and 13 in lung adenocarcinoma. Oncology Letters, 14, 3647-3656. https://doi.org/10.3892/ol.2017.6594
MLA
Hung, J., Chiang, S., Liu, K., Tsai, M., Huang, M., Shieh, J., Yen, M., Hsu, Y."Overexpression and proliferation dependence of acyl‑CoA thioesterase 11 and 13 in lung adenocarcinoma". Oncology Letters 14.3 (2017): 3647-3656.
Chicago
Hung, J., Chiang, S., Liu, K., Tsai, M., Huang, M., Shieh, J., Yen, M., Hsu, Y."Overexpression and proliferation dependence of acyl‑CoA thioesterase 11 and 13 in lung adenocarcinoma". Oncology Letters 14, no. 3 (2017): 3647-3656. https://doi.org/10.3892/ol.2017.6594