Metabolome Analyses Uncovered a Novel Inhibitory Effect of Acyclic Retinoid on Aberrant Lipogenesis in a Mouse Diethylnitrosamine-Induced Hepatic Tumorigenesis Model Free

Hepatocellular carcinoma is the most common type of liver cancer and a leading cause of cancer-related death worldwide (nearly 600,000 deaths annually; ref. 1). Hepatocellular carcinoma is recorded with the worst prognosis, according to a population-based cancer registry data in China, in which the age-standardized 5-year relative survival rates for males and females are 10.2% and 10.3%, respectively (2). The high lethality of hepatocellular carcinoma is partly due to its high recurrence rate based on the concept of “field cancerization” (3). Acyclic retinoid (ACR), a synthetic vitamin A–like compound, was originally developed from a view of nutritional supplementation to improve the vitamin A content in hepatocellular carcinoma patients (4). Clinical trials revealed that ACR could significantly inhibit the recurrence of hepatocellular carcinoma after the removal of primary tumors (4, 5). It is hypothesized that this recurrence-preventive effect is associated with clonal deletion (6) through targeted elimination of cancer stem/progenitor cells, such as oval-like cells (7, 8). However, the detailed mechanisms underlying the prevention of hepatic tumorigenesis by ACR still need further investigation.

Metabolic alterations of cancer cells such as aerobic glycolysis are essential to generate energy and nutrients required for cancer cell processes (9). It is also believed that de novo lipogenesis contributes to the synthesis of membranes and signaling molecules in proliferating cancer cells (10). For example, serum metabolic profiling study demonstrated that the content of arachidonic acid (AA), a polyunsaturated fatty acid present in the phospholipids of cell membranes, was significantly elevated in hepatocellular carcinoma patients compared with the healthy controls (∼91.8-fold; ref. 11). In addition, metabolic reprogramming has been revealed to be regulated by proto-oncogenes and tumor suppressor genes, suggesting its primary function in tumorigenesis (12–14). Although viral hepatitis is the major causate of hepatocellular carcinoma, there is a growing recognition of the importance of metabolic syndrome such as obesity for the development of hepatocellular carcinoma (15). Dietary or genetic obesity can induce alteration of gut microbial metabolites such as increasing the levels of deoxycholic acid (DHA), which leads to chronic liver injury and facilitates hepatocellular carcinoma development (16). Interestingly, antibiotic treatments inhibiting DHA synthesis can prevent obesity-induced hepatocellular carcinoma development, suggesting abnormal metabolism of cancer cells is a potential therapeutic target for the systemic treatment and prevention of hepatocellular carcinoma (16, 17).

Our group and others have observed that ACR may suppress cell growth or inhibit hepatitis C virus replication by altering energy production and lipid metabolism in hepatocellular carcinoma cell lines (18, 19). However, in vitro study in malignant cells provides limited information to understand the mechanism of tumorigenesis. We reported that ACR prevented diethylnitrosamine (DEN)-induced hepatic tumorigenesis in obese and diabetic C57BKSL/J- +Lepr^db/+Lepr^db mice (db/db mice; ref. 20). ACR treatment reduced the prevalence of DEN-initiated liver precancerous lesions classified as foci of cellular alteration from 70% to 10%. Here, we performed metabolomics approaches to explore the molecular basis of the preventive effect of ACR on hepatic tumorigenesis from a metabolic point of view. To this end, comprehensive cationic and lipophilic metabolic profiles of liver tissues obtained from mouse DEN–induced hepatic tumorigenesis model were detected using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) and liquid chromatography time-of-flight mass spectrometry (LC-TOFMS), respectively (21). Our analyses revealed that ACR may suppress the enhanced lipogenesis during DEN-induced hepatic tumorigenesis.

ACR (NIK-333) was supplied by Kowa Co. Ltd. All-trans retinoic acid (atRA), DEN, and AA were purchased from Sigma-Aldrich.

All experiments were performed in accordance with protocols approved by the Institutional Committee of Animal Experiment of Gifu University and RIKEN. Four-week-old male db/db mice were obtained from Japan SLC, Inc. and housed under constant temperature (22°C ± 1°C), with free access to food and water, 12-hour light/dark cycles, and were fed laboratory pellets. Detailed experimental procedure has been described previously in ref. (20). Briefly, mice were divided into four groups (n = 6, respectively). Three groups were given tap water containing 40 ppm of DEN for the first two weeks and then fed with basal diet alone (DEN group) and basal diet containing 0.03% ACR (DEN-0.03ACR group) or 0.06% ACR (DEN-0.06ACR group) till the end of experiment. The method for DEN treatment used in this study has been proved to be sufficient to develop liver neoplasms in db/db mice (20, 22, 23). The fourth group fed with basal diet containing 0.06% ACR alone without DEN treatment (0.06ACR group) was used as the negative control for changes during DEN-initiated liver tumorigenesis (Fig. 1A). After sacrifice by CO₂ asphyxiation, liver tissues containing precancerous lesion but not cancer (approximately 60 mg/mouse) were isolated and stored at −80°C until further metabolic analyses. For histopathologic examination, 4-μm thick sections of formalin-fixed, paraffin-embedded livers were stained routinely with hematoxylin and eosin (H&E).

Frozen liver tissues were immersed into 50% acetonitrile solution containing internal standards in crushing tubes and centrifuged (2,300 × g, 4°C, 5 minutes) using a desk-model crusher BMS-M10N21 (BMS Co., Ltd). After that, top layer was centrifugally filtered (9,100 g, 4°C, 120 minutes) using an UltrafreeMC-PLHCC 5-kDa cut-off filter (Human Metabolome Technologies Inc). The filtrates were dried and resuspended in 50 μL of Milli-Q water for CE-TOFMS analysis using an Agilent CE-TOFMS System (Agilent Technologies) as previously described (21, 24).

Frozen liver tissues were immersed into acetonitrile solution with 1% formic acid containing internal standards in crushing tubes and centrifuged (1,500 rpm, 4°C, 120 seconds) for three times using a desk-model crusher BMS-M10N21 (BMS Co., Ltd) following another centrifugation (1,500 rpm, 4°C, 120 seconds) after adding 167 μL of Milli-Q water. After that, supernatant was collected by centrifugation (5,000 × g, 4°C, 5 minutes). Meanwhile, the flow-through was mixed with 500 μL of acetonitrile solution with 1% formic acid and 167 μL of Milli-Q water, and supernatant was collected again by centrifugation. The supernatants were then mixed and centrifugally filtered (9,100 × g, 4°C, 120 minutes) using Pall Nanosep 3K Omega filters (Pall Corporation). The filtrate was dried and resuspended in 100 μL of isopropanol/Milli-Q water solution (1:1) for LC-TOFMS analysis using an Agilent 1200 series RRLC system SL equipped with an Agilent LC/MSD TOF system (Agilent Technologies).

Raw data obtained were analyzed with KEIO MasterHands software version 2.13 as previously described (25). Quantification of the major metabolites was performed as described previously (18, 25).

Hepatocellular carcinoma cell line, JHH7 cells were kindly supplied by Dr. Matsuura (Jikei University School of Medicine, Tokyo, Japan) in June 2012 (26). The cells were maintained in DMEM (Wako Industries) containing 10% FBS (Mediatech), 100 U/mL penicillin/streptomycin, and 2 mmol/L l-glutamine (Mediatech) and grown at 37°C in a 5% CO₂ humidified incubator. No authentication for the cells was done by the authors.

Total RNA was isolated from JHH7 cells before (0 hour) and after treatment with 1 μmol/L atRA or 10 μmol/L ACR for 1 and 4 hours using a RNeasy Kit (Qiagen). The amount and purity of the isolated RNA were evaluated using a NanoDrop Spectrophotometer (NanoDrop Products). Then, oligonucleotide microarray experiment was performed using Affymetrix HG-U133 Plus 2.0 Array (Affymetrix). The arrays were scanned using a GenePix 4000B Microarray Scanner (Axon Instruments). Data normalization and analysis were performed with GeneSpring GX13.0 (Agilent Technologies) as previously described (27). All data are MIAME compliant, and the raw data were deposited in the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo; accession no. GSE71856).

JHH7 cells were treated with ethanol, 1 μmol/L atRA, or 10 μmol/L ACR for 4 hours, and then total RNA was isolated and quantified as described above. cDNA was synthesized using a PrimeScript RT Master Mix Kit (TaKaRa Bio). Oligonucleotide primers were designed using OligoPerfect Designer software (Invitrogen). The sequences of the primers (5′ to 3′) are as follows: GAPDH forward (CGACCACTTTGTCAAGCTCA) and reverse (AGGGGTCTACATGGCAACTG); CUOP transcription factor 2 (COUP-TFII, also known as NR2F2) forward (TGCCTGTGGTCTCTCTGATG) and reverse (ATATCCCGGATGAGGGTTTC). PCR reactions were performed on the Thermal Cycler Dice Real Time System (TP8000; TaKaRa Bio) with SsoAdvanced SYBR Green Supermix (Bio-Rad Laboratories).

Cells were seeded in 96-well plates 24 hours before treatment with AA in the presence and absence of 10 μmol/L ACR in DMEM containing 5% FBS. After 48 hours, cell viability was determined using the Cell Counting Kit-8 (Dojindo Molecular Technologies) in a plate reader (ARVO MX, PerkinElmer Inc).

Cells were seeded in 10-cm dishes and allowed to grow to confluency. Cells were treated with 15 μmol/L ACR in FBS-free DMEM for 24 hours. The cell lysates were isolated and the contents of AA were measured using an ELISA kit (CEB098Ge, Cloud-Clone Corp.) according to the manufacturer's protocol.

MetaboAnalyst software (http://www.metaboanalyst.ca) was used to identify metabolites with significantly different levels across experimental conditions and apply the enrichment analysis of associated metabolic pathways (28). The Ingenuity Pathway Analysis (IPA) program (Ingenuity Systems) was used to identify networks and canonical pathways associated with differentially expressed genes following ACR treatment as previously described (27).

The statistical significance of differences between values was assessed using ANOVA with post hoc Tukey HSD test or two-tailed Student t test. Values of P < 0.05 were considered to indicate statistical significance. Unsupervised principal component analysis (PCA) was run in SIMCA-P+ (version 12.0, Umetrics). Heatmap visualization of the metabolic data was generated using PeakStat version 3.18 (Human Metabolome Technologies Inc). Metabolic map was visualized in the network context using Visualization and Analysis of Networks containing Experimental Data (29). Hierarchical clustering analysis of the microarray data was applied using GeneSpring GX13.0 (Agilent Technologies).

A total of 24 liver tissues (n = 6 per group) were obtained from mouse DEN–induced hepatic tumorigenesis model treated with or without ACR (Fig. 1A). Representative H&E staining images of the liver precancerous lesions and hepatocellular carcinoma observed in DEN group were presented in Fig. 1B. Peaks of 254 cationic metabolites (Supplementary Table S1) and 102 lipophilic metabolites (Supplementary Table S2) were detected using CE-TOFMS and LC-TOFMS, respectively. Metabolomics comparison using PCA analysis on CE-TOFMS data (Fig. 1C), LC-TOFMS data (Fig. 1D), and the combined data (Fig. 1E) revealed notable variations in the abundance of hepatic metabolites according differential ACR/DEN treatments. Notably, LC-TOFMS metabolomics data revealed the clearest distinction, suggesting an important role of lipid metabolism in the preventive effect of ACR on DEN-induced hepatic tumorigenesis. To provide a general view of the metabolic profiles, principle metabolic pathways of the detected metabolites were illustrated in Fig. 2 and Supplementary Fig. S1. Furthermore, a recent CE-TOFMS–based metabolomics study identified a novel biomarker pattern of ratio creatine/betaine of DEN-initiated hepatocellular carcinoma rat model, which could effectively predict the stage of human hepatocellular carcinoma (30). The ratio of creatine/betaine was calculated using the liver metabolomics data of our mouse DEN–initiated hepatocellular carcinoma model (Supplementary Fig. S2). Although no significance was observed, the ratio of creatine/betaine tended to increase in the DEN group.

Heatmap analysis of all detected metabolites by CE/LC-TOFMS also revealed strong and reproducible ACR/DEN–induced changes of liver metabolic profiles in mouse DEN–induced hepatic tumorigenesis model under differential experimental conditions (Fig. 3A). ANOVA analysis with post hoc Tukey HSD test identified a total of 61 metabolites were significantly changed across differential chemical treatments with the threshold P values less than 0.05 (Fig. 3B). The top five of the most significantly changed metabolites were 1H-imidazole-4-propionic acid, cis-4,7,10,13,16,19-docosahexaenoic acid, arachidonic acid, phenylalanine, and cis-8,11,14-eicosatrienoic acid (Fig. 3C). With more details, 88 metabolites were significantly changed in the DEN alone–treated mice by comparing with the control mice treated with 0.06% ACR alone without DEN treatment (DEN vs. 0.06ACR). Meanwhile, 68 and 79 metabolites were significantly differential in mice treated with DEN combined with 0.03% and 0.06% ACR, respectively, by comparing with the mice treated with DEN alone (DEN-0.03ACR vs. DEN and DEN-0.06 ACR vs. DEN, respectively). Moreover, 50 common metabolites were shared among all the comparisons, indicating that ACR may specifically target the metabolic pathways dysregulated in DEN-induced tumorigenesis (Fig. 3D).

Top metabolic pathways associated with metabolites significantly changed between DEN and 0.06ACR groups (Fig. 4A), DEN-0.03ACR and DEN groups (Fig. 4B), and DEN-0.06ACR and DEN groups (Fig. 4C) were identified using functional enrichment analyses in MetaboAnalyst software. Of interest, the top 2 metabolic pathways “Protein biosynthesis” and “Alpha linolenic acid and linoleic acid metabolism” were common in all the comparisons. Quantification results of the metabolites significantly inhibited by ACR treatment involved in the “Protein biosynthesis” and “α-linolenic acid and linoleic acid metabolism” were summarized in Fig. 5A and B, respectively. In contrast, no significant changes of metabolites involved in glucose metabolism such as glucose, glucose-6-phosphate, and lactic acid were observed (Fig. 5C). Furthermore, we investigated the effect of ACR on AA-induced cell growth of human hepatocellular carcinoma cells JHH7. ACR inhibited the contents of AA in JHH7 cells (Supplementary Fig. S3) and significantly suppressed the cell growth of JHH7 induced by a low dose of AA (1 μmol/L; Supplementary Fig. S4).

Hierarchical clustering with Ward method of 44,907 genes detected by microarray analysis demonstrated diverse expression changes in hepatocellular carcinoma cells treated with ACR for 4 hours (Fig. 6A). A total of 973 differentially expressed genes in response to ACR by comparing with atRA for 4-hour treatments were identified with a fold change more than 2. Then, network analysis was performed on the altered gene expression profiles using IPA platform. The biologic functions of the top IPA-generated networks were summarized in Fig. 6B. The most highly populated networks were associated with the regulation of cell cycle and DNA replication, as ACR is well known to induce apoptosis and suppress cell proliferation in hepatocellular carcinoma cells (31). In addition, networks related to amino acid metabolism, protein synthesis, and lipid metabolism were also observed. For example, the biologic network entitled “Lipid Metabolism, Small Molecular Biochemistry, Vitamin and Mineral Metabolism” was highlighted in Fig. 6C. This network contains genes that play critical roles in controlling the development of tissues and organs such as the nuclear orphan receptor NR2F2. Then, the inhibitory effect of ACR on NR2F2 expressions was validated using real-time PCR (Fig. 6D).

Finally, to mine the connection between the metabolic and genetic actions of ACR observed in this study, a NR2F2-dependent regulatory network was generated by knowledge-based pathway analysis in IPA platform (Fig. 7). A potential mechanism is that NR2F2 may bind to retinoid X receptor, which is known as a molecular target of ACR (32), and regulate lipid metabolism including linoleic acid metabolism through the downstream of peroxisome proliferator-activated receptor (PPAR) signaling pathways (33).

There are raised efforts to target cancer metabolism as a potential anticancer strategy (17). However, these therapies have been partly elusive due to the poor understanding of metabolic phenotypes in the tissue-specific tumorigenic process. One major finding of our study was that alterations in lipid metabolism, but not glucose metabolism, were observed in DEN-induced hepatic tumorigenesis. ACR can significantly inhibit the DEN-induced acceleration of lipogenesis, suggesting lipogenic pathway as potential cancer targets (Fig. 4). Cancer metabolism has been long equated as aerobic glycolysis (known as "Warburg effect"; ref. 34), which is mainly based on the hypothesis of mitochondrial defects in cancer cells. However, this hypothesis is challenged by recent studies that most tumor mitochondria are not defective in their ability to carry out oxidative phosphorylation (12). Our previous in vitro study also raised questions regarding this issue that no significant difference in the content of lactic acid was observed between hepatocellular carcinoma cells, JHH7, and normal hepatic cells. In addition, the increased gene expression of pyruvate dehydrogenase kinase 4, which attenuates the flux of glycolytic carbon into mitochondrial oxidation, was found to be involved in the growth suppression action of ACR in JHH7 cells (18). As the JHH7 cells exhibited highly enhanced liver differentiation functions and stem cell–like features (35), it was possible that lipid metabolic reprogramming might be required in the initiation step of hepatic tumorigenesis. Consistently, increased biosynthesis of macromolecules, particularly lipids, has increasingly been recognized as an important component of cancer metabolic reprogramming (36). Limiting fatty acid synthesis can control cancer cell proliferation (10), whereas increased de novo fatty acid synthesis has been shown to correlate with breast cancer progression (37). This study further provided new evidences that lipid metabolic reprogramming might play a critical role in the development of hepatocellular carcinoma.

In this study, linoleic acid metabolism was identified as the mostly populated metabolic target of ACR (Fig. 3). Decreases in linoleic acid metabolites such as AA have been suggested as a potential mechanisms from mammary cancer prevention (38). An important issue is the difference between DEN model and human hepatocellular carcinoma. DEN is the most widely used chemical to induce hepatocellular carcinoma in mice, and DEN model is received as one of the best models to study the pathology of hepatocellular carcinoma, as it could mimic the inflammation–fibrosis process (39). The differences between the DEN model and human hepatocellular carcinoma observed by comparing their gene expression profiles have been reported (40). However, limited focus has been placed on their differences in metabolic profiles. A recent CE-TOFMS–based metabolomics study suggested that the metabolic profile of DEN model is effective to identify novel biomarkers for early diagnosis of human hepatocellular carcinoma (30). Indeed, inhibition of AA metabolic pathway has been recently reported to associate with the chemoprevention of high fat diet–enhanced colorectal carcinogenesis (41) and the apoptosis of hepatocellular carcinoma cells (42). Furthermore, significantly elevated content of AA was also reported in hepatocellular carcinoma patients compared with the healthy controls (11).

AA is known as a proinflammatory precursor playing etiologic roles in multiple cancers (43). It is possible that through inhibition of AA-regulated signaling pathways, ACR might prevent DEN-induced chronic liver inflammation, which finally contributes to hepatic tumorigenesis. This is reasonable as we previously showed enhanced inflammatory response and inhibited serum levels of TNFα and expression levels of TNFα, IL6, and IL1b mRNA in the liver of db/db mice treated with ACR compared with those treated with DEN alone (20). Furthermore, in a platelet-derived growth factor–overexpressed mouse model, genome-wide expression profile analysis revealed that the repressive effect of ACR on the development of hepatic fibrosis and tumors was related with inflammatory signaling pathways such as “MIF signaling” and “IL6 signaling” (44).

Further microarray analysis identified that inhibited NR2F2 expression in human hepatocellular carcinoma cells was associated with the metabolic effect of ACR through PPAR-dependent signaling pathways (Figs. 5 and 6). Deletion of NR2F2 led to embryonic lethality with defects in angiogenesis and heart development, suggesting an essential role of NR2F2 in embryogenesis and organization (45). It was known that aggressive tumor cells shared many characteristics with embryonic progenitors. The discovery of cancer stem cells further increased the interest in the interactions between cancer progression and embryologic development (46). Indeed, biologic function of NR2F2 has been proven in prostate carcinogenesis (47), suggesting NR2F2 as a potential drug target for preventing tumor progression.

In summary, to investigate the metabolic effect of ACR against DEN-induced hepatic tumorigenesis, comprehensive cationic and lipophilic metabolic analyses were performed using CE/LC-TOFMS. Significant preventive effect of ACR was observed on accelerated lipogenesis elicited by DEN, but not glucose metabolism. Pathway analysis suggested a crucial role of linoleic acid metabolism, such as the AA metabolic pathway, in the antitumorigenic action of ACR. Gene expression analysis identified NR2F2, a key transcription regulator of embryogenesis and tumorigenesis, was associated with the metabolic effect of ACR in human hepatocellular carcinoma cells. Our study provided potential therapeutic targets for the chemoprevention of hepatocellular carcinoma, as well as new insights into the mechanisms underlying hepatic tumorigenesis.

No potential conflicts of interest were disclosed.

Conception and design: X.-Y. Qin, S. Kojima

Development of methodology: X.-Y. Qin, S. Kojima

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): X.-Y. Qin, H. Tatsukawa, Y. Shirakami, M. Shimizu, H. Moriwaki

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): X.-Y. Qin, H. Moriwaki

Writing, review, and/or revision of the manuscript: X.-Y. Qin, K. Hitomi, N. Ishibashi, H. Moriwaki, S. Kojima

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): N. Ishibashi, H. Moriwaki, S. Kojima

Study supervision: H. Moriwaki, S. Kojima

This study was partly supported by the HMT Research Grant for Young Leaders in Metabolomics 2012 from Human Metabolome Technologies Inc. (to X.-Y. Qin), a Grant-in-Aid for Scientific Research on Innovative Areas “Chemical Biology of Natural Products” from the Ministry of Education, Culture, Sports, Science and Technology of Japan (to S. Kojima), and the Research on the Innovative Development and the Practical Application of New Drugs for Hepatitis B (H24-B Drug Discovery Hepatitis General 003) from the Ministry of Health, Labor and Welfare of Japan (to S. Kojima).

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