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Distinct metabolic features of seminoma and embryonal carcinoma revealed by combined transcriptome and metabolome analyses Aalia Batool, Su-Ren Chen, and Yi-Xun Liu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00007 • Publication Date (Web): 05 Mar 2019 Downloaded from http://pubs.acs.org on March 6, 2019
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Journal of Proteome Research
Distinct metabolic features of seminoma and embryonal carcinoma revealed by combined transcriptome and metabolome analyses Aalia Batool1, 2, Su-Ren Chen1 *, Yi-Xun Liu1
1State
Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese
Academy of Sciences, Beijing, 100101, China 2University
*For
of Chinese Academy of Sciences, Beijing, 100049, China
correspondence: Su-Ren Chen, PhD, 1 Beichen West Road, Chaoyang District, Beijing,
100101, China, E-mail:
[email protected] 1
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ABSTRACT Seminoma and embryonal carcinoma (EC), two typical types of testicular germ cell tumors (TGCTs), present significant differences in growth behavior, expression characteristics, differentiation potential, clinical features, therapy and prognosis. The purpose of this study was to compare the distinctive or preference metabolic pathways between seminoma and EC. The Cancer Genome Atlas revealed that many genes encoding metabolic enzymes could distinguish between seminoma and EC. Using well-characterized cell line models for seminoma (Tcam-2 cells) and EC (NT2 cells), we characterized their metabolite profiles using ultraperformance liquid chromatography coupled with Q-TOF mass spectrometry (UPLC/Q-TOF MS). In general, the integrated results from transcriptome and metabolite profiling revealed that seminoma and EC exhibited distinctive characteristics in the metabolisms of amino acids, glucose, fatty acids, sphingolipids, nucleotides, and drugs. Notably, attenuation of citric acid cycle/mitochondrial oxidative phosphorylation and sphingolipid biosynthesis, as well as increase of arachidonic acid metabolism and (very) long-chain fatty acids abundance occurred in seminoma as compared with EC. Our study suggests histologic subtype-dependent metabolic reprogramming in TGCTs and will lead to a better understanding of metabolic signatures and biology of TGCT subtypes. KEYWORDS: transcriptome; metabolome; integrated analysis; seminoma; embryonal carcinoma
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Journal of Proteome Research
INTRODUCTION Testicular germ cell tumors (TGCTs) are rare tumors in the general population but are the most commonly occurring malignancy among young males
1-3.
TGCTs develop from premalignant
intratubular germ cell neoplasia, also known as carcinoma in situ, that are believed to arise from failure of normal maturation of fetal germ cells from primordial germ cells into pre-spermatogonia 4. TGCTs are classified broadly into two major histologic groups: seminoma and non-seminoma germ cell tumors; non-seminoma can be further subdivided into embryonal carcinoma (EC), teratoma, choriocarcinoma, and yolk sac tumors 5. Tcam-2 and NT2 are typical tumor cell lines for seminoma and EC, respectively
6, 7.
Seminoma and EC present significant differences in expression
characteristics, clinical features, therapy and prognosis
2, 8, 9.
Transcriptome analysis and
immunostaining identification of seminoma and EC reveal a list of differentially expressed genes/proteins that could distinguish these two subtypes of TGCTs 10. Recent studies provide interesting evidences of a transformation between seminoma and EC. Transplantation of seminoma cell line Tcam-2 into the seminiferous tubules results in formation of seminoma, while transplantation into the flank or corpus striatum will trigger Tcam-2 cells to adopt an EC-like fate
11.
This in vivo model suggests that transition of seminoma to EC relies on
environmental signals. During this reprogramming, the environmental inhibition of BMP signaling seems to be the initial event, resulting in activation of NODAL signaling, upregulation of pluripotency factors (e.g., SOX2) and downregulation of seminoma markers (e.g., SOX17) 12, 13. The precise mechanism of cell fate determination and whether EC can transform into seminoma are still poorly understood. Can metabolism be involved in this transformation? Metabolism is one of the most complex cellular phenomena and there has been a great of interest in cancer metabolism. Metabolomics refers to the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism 14, 15. Metabolomics is recently introduced into cancer research and analyses have concerned a range of cancers including lung 16, colorectal 17, kidney 18, bladder 19, breast 20, gastric
21
and ovary 22; however, metabolomics
analysis of TGCTs is still lacking. Altered energy metabolism represents one of the hallmarks of cancer. Most cancer cells actively promote aerobic glycolysis (also termed as Warburg effect) that means higher glucose uptake 3
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followed by lactate fermentation, whereas a smaller fraction of glucose (or pyruvate) is employed for citric acid cycle and mitochondrial oxidative phosphorylation to sustain their metabolic requirements 23, 24.
Accordingly, several enzymes in the glycolytic pathway are emerging targets in anticancer
therapy
25, 26.
However, key molecular machinery that regulates the metabolic choice between
mitochondrial pyruvate oxidation and lactate fermentation is still elusive. Many cancers are also characterized by abnormally increased biosynthesis of fatty acids or lipids. Arachidonic acid is a polyunsaturated ω-6 fatty acid present in the phospholipids of cell membranes and produces a variety of biologically active metabolites (such as prostaglandins, leukotrienes, and lipoxins) through three different enzymatic pathways: the cyclooxygenase, lipoxygenase, and cytochrome P450 pathways
27, 28.
Arachidonic acid pathway metabolic enzymes and products, such
as phospholipase A2 and 20-HETE, have been considered as novel therapeutic targets in some cancers
29-31.
Sphingolipids are structural molecules of cell membrane phospholipids. Apart from
structural roles, sphingolipid metabolism has been considered as a critical mediator of cancer signaling and therapy
32-34.
The backbone of sphingolipids is the sphingosine molecule, formed by
the condensation of serine and palmitoyl CoA. Ceramide consists of a sphingosine long-chain base and an amide-linked fatty acyl chain that varies from 14 to 26 carbons in length 35. Will seminoma and EC present differences in above classical metabolic signatures of cancers? In the present study, we have merged the transcriptome and metabolomics datasets derived from seminoma and EC. Using this approach, we show here evidence of histologic subtype-dependent metabolic reprogramming in TGCTs, characterized by retarding citric acid cycle/mitochondrial oxidative phosphorylation and sphingolipid biosynthesis, as well as increasing arachidonic acid metabolism and (very) long-chain fatty acids abundance in seminoma as compared with EC. Thus, our work will lead to both a better understanding of TGCT biology and important advances related to the determination, characteristics, and transition of seminoma and EC.
MATERIALS AND METHODS Ethics statement All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of the Institute of Zoology, Chinese Academy of Sciences and in accordance with institutional and national guidelines. 4
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Cell culture The human seminoma cell line Tcam-2 and embryonal carcinoma cell line NTERA-2 cl.D1 [NT2/D1] were purchased from BeNa Culture Collection (Beijing, China) and American Type Culture Collection (ATCC, VA, USA), respectively. Cells were cultured in Dulbecco’s Modified Eagle’s Medium (Invitrogen, Beijing, China) containing 10% fetal bovine serum (Invitrogen), 1% penicillin/stretomycin (Invitrogen) at 37 °C with 5% CO2. Transcriptome analysis Clinical information and RNA sequencing data of TGCTs (69 seminoma and 31 EC) were extracted from TCGA (https://cancergenome.nih.gov/) following the instruction 9. Differentially expressed genes were compared between seminoma (n=69) and EC (n=31) according to the principle of |fold change|≥3 and p-value
