Metabolic profiling of tumors, sera and skeletal muscles from an

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Metabolic profiling of tumors, sera and skeletal muscles from an orthotopic murine model of gastric cancer associated-cachexia Pengfei Cui, Caihua Huang, Jiaqi Guo, Qianwen Wang, Zhiqing Liu, Huiqin Zhuo, and Donghai Lin J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00088 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 21, 2019

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Journal of Proteome Research

Metabolic profiling of tumors, sera and skeletal muscles from an orthotopic murine model of gastric cancer associated-cachexia Pengfei Cui1, Caihua Huang2, Jiaqi Guo1, Qianwen Wang1, Zhiqing Liu1, Huiqin Zhuo3*, Donghai Lin1* Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China;

1

Department of Physical Education, Xiamen University of Technology, Xiamen, China;

2

Institute of Gastrointestinal Oncology, Medical College of Xiamen University, Xiamen University, Xiamen, China;

3

*Correspondence Authors: Donghai Lin, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China, E-mail: [email protected], Tel: 86-592-2186078;

Huiqin Zhuo, Institute of Gastrointestinal Oncology, Medical College of Xiamen University, Xiamen University, Xiamen 361005, China, E-mail: [email protected], Tel: 86-592-2292578.

Abstract Cachexia is a complex metabolic derangement syndrome that affects approximately 50-80% of cancer patients. So far few works have been reported to provide a global overview of gastric cancer cachexia (GCC)-related metabolic changes. We established a GCC murine model by orthotopicly implanting BGC823 cell line, and conducted NMR-based metabolomic analysis of gastric tissues, sera and gastrocnemius. The model with typical cachexia symptoms, confirmed by significant weight loss and muscle atrophy, showed distinctly distinguished metabolic profiles of tumors, sera and gastrocnemius from sham mice. We identified 20 differential metabolites in tumors, 13 in sera and 14 in gastrocnemius. Tumor extracts displayed increased pyruvate and lactate, and decreased hypoxanthine, inosine and inosinate, indicating significantly altered glucose and nucleic acid metabolisms. Cachectic mice exhibited up-regulated serum lactate and glycerol, and down-regulated glucose, which were closely related to hyperlipidemia and hypoglycemia. Furthermore, gastrocnemius transcriptomic and metabolomic data revealed that GCC induced perturbed pathways mainly concentrated on carbohydrate and amino acid metabolism. Specifically, cachectic gastrocnemius exhibited increased α-ketoglutarate and decreased glucose. In vitro study indicated that α-ketoglutarate could prompt myoblasts proliferation and reduce glucose deficiency-induced myotubes atrophy. Overall, this work provides a global metabolic overview to understand the metabolic alterations associated with GCC-induced muscle atrophy. Keywords: gastric cancer cachexia; an orthotopic murine model; metabolomic, transcriptomic; 1

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α-ketoglutarate; muscle atrophy.

Introduction Cachexia is a multifactorial metabolic syndrome that occurs in up to 80% of advanced cancer patients and contributes to approximately 30% of cancer mortality.1, 2 Cancer cachexia is defined as depletion of skeletal muscle mass, which cannot be fully reversed by conventional nutritional support and may lead to progressive functional impairment.3 Moreover, cancer cachexia induces anorexia, fatigue and depression, and also increases susceptibilities to infections and other complications. Furthermore, cachexia strongly influences the success of therapeutic treatments of cancer. As reported, up to 20% of cancer patients die directly from cachexia.4 So far, molecular mechanisms underlying cancer cachexia remain elusive. Expectedly, well understanding the molecular bases of cancer cachexia, could potentially attenuate the development of cachexia, and might thus prolong the survival of cancer patients.4 The pathophysiology of cachexia is characterized by a variable combination of anorexia, increased energy expenditure, and systemic inflammation, as well as impaired metabolism.5 Distinct disorders in carbohydrate, lipid, and protein metabolisms are closely related to cancer cachexia, which significantly contribute to the cachexia-associated clinical phenotype characterized by profound body weight loss and remarkable muscle wasting.6 The incidence of cancer cachexia varies with the tumor type, being 31% to 40% in sarcoma and breast cancers, and around 50% in lung, prostate and colon cancers, whereas 80-90% in pancreatic and gastrointestinal cancers.7 Particularly, gastric cancer patients have the highest incidence of cachexia, which might reduce greatly the therapeutic efficacy of gastric cancer. Until now, few experimental models of gastric cancer cachexia (GCC) have been established.8-10 Terawaki et al. previously screened 15 human gastric cancer cell lines and established two novel cell lines from MKN-45 cells: MKN45cl85 and 85As2, which could induce cachexia in mice by using subcutaneous implantation.9 Based on the two murine models, they further characterized the anorexia-related cachexia including enhanced plasma levels of cytokines, body composition changes and weight loss.10 It has been demonstrated that impaired metabolism acts as a major component in cancer cachexia, however, systematic metabolic signatures remain rarely been reported, especially in GCC. Recently, high throughput approaches (‘omics’) are extensively being employed to identify new biomarkers, pathways related to the pathophysiology of numerous diseases including cancer cachexia, and reveal targets for future therapies.11 Among these approaches, metabolomic analyses of the small-molecule metabolite profiles have been used to explore the surrogate serum biomarkers. For example, Torossian et al. found that blood hyperlipidemia and hypoglycemia are associated with cancer cachexia based on the subcutaneous colon-26 (C26) adenocarcinoma murine model.12,

13

Besides, studies have demonstrated that targeted the specific metabolic process could ameliorate cancer cachexia symptoms and reverse muscle atrophy.14,

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By using metabolomics integrated with

transcriptomics, Fukawa et al. revealed that pharmacological blockade of fatty acid oxidation could prevent cancer cachexia in a subcutaneous kidney murine model.14 Tseng et al. conducted comprehensive evaluations of the anti-cachectic activity of a novel histone deacetylase inhibitor in two murine models of cancer cachexia including C26 and Lewis lung carcinoma (LLC) models.15 Thus, it is conceivable that exploring the GCC-related systematic metabolic signatures through omics tools could 2


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potentially provide mechanistic understandings of GCC and effective therapies. In the present work, we established a murine model of GCC by orthotopicly implanting BGC823 cell line into BALB/c nude mice. The murine model was used to mimic clinical manifestations of GCC. Cachexia symptoms were clearly identified and confirmed based on the definition and criteria of cachexia.3 We then performed NMR-based metabolomic analysis of tumors, sera and gastrocnemius muscles derived from the murine model, aiming to reveal metabolic alterations, and identify differential metabolites and disturbed metabolic pathways, which were closely associated with GCC. We also integrated transcriptomic and metabolomic analysis to identify distinctly altered metabolic signatures in cachectic gastrocnemius. This work is of benefit to the systematic understanding of molecular mechanisms underlying GCC-induced skeletal muscle atrophy, and provides hints for the developments of new therapeutics for gastric cancer.

Materials and Methods Cell cultures The human gastric cell line BGC823 and murine C2C12 myoblasts were obtained from the China Center for Typical Culture Collection (CCTCC). BGC823 and C2C12 myoblasts were maintained in RPMI 1640 and high glucose DMEM (4 mM L-glutamine, 4500 mg/L glucose and no sodium pyruvate) supplemented with 100 units/ml penicillin, 100 μg/ml streptomycin and 10% fetal bovine serum (FBS, Hyclone) at 37°C in a humidified atmosphere of 5% CO2, respectively. C2C12 myoblasts were cultured in growth medium (high glucose DMEM supplemented with 10% FBS). At 85% confluence, myoblast differentiation was induced by incubation for 96 h in differentiation medium (high glucose DMEM supplemented with 2% horse serum) to form myotubes. In this study, the cell lines were used within 3-8 generations of culture. Animal experiments All animal experiments were performed according to protocols approved by Xiamen University Institutional Animal Care and Use Committee. Five to six-week-old male BALB/c nude mice were housed in Xiamen University Laboratory Animal Center and maintained in conditions of constant temperature and 12 h light/12 h dark cycles. Nude mice were randomized into groups with eight mice per group. The gastric carcinoma orthotopic model was conducted according to the previous method.16, 17

Briefly, left paramedian abdomen of the mice, under anesthesia with pentobarbital sodium (30

mg/kg) was opened, and the stomach was carefully exposed. Then, 50 μl of a single cell suspension containing 2×106 cells was injected into the subserosa of the greater curvature of stomach slowly and gently with no apparent injury using a microsyringe. Equal volume of medium was injected into the same position of sham operation group. A visible bulge subserosal translucent vesicle was formed. The stomach was carefully returned to its position in the body and the incision was closed in 2 layers using 4-0 surgical sutures. All steps were carried out aseptically. Body weight and food intake were recorded every 2-3 days. Based on the criteria of cancer cachexia,3, 18 the mice were sacrificed when they lost 15% of their body weight. Blood samples were obtained from the retro-orbital plexus, and sera were prepared. Thereafter, gastric tissues and gastrocnemius muscles were rapidly dissected, weighted and snap-frozen with liquid nitrogen. The sera and tissues were stored at -80 °C prior to analyses. In addition, the gastric carcinoma subcutaneous model was also established. On day 0, mice in the 3



model group received a 200 μl sub-cutaneous injection containing 2×106 tumor cells in the right flank. Mice in the control group received the same volume of PBS. Body weights and tumor volumes of the mice were measured every 2-3 days. When the tumor volumes reached 1.5 cm3 (about day 26), mice were sacrificed. Tumor volume was assessed using the previous method.13 Western blot Gastrocnemius muscles and cells were lysed in RIPA lysis buffer with the protease-inhibitor cocktail (Roche). Lysates were loaded to SDS-PAGE and transferred to PVDF membranes (GE Healthcare) for immunoblotting analysis. The following primary antibodies were used: MuRF1 (ab172479; Abcam, 1:1,000), Fbx32/Atrogin1 (ab168372; Abcam, 1:1,000), myod1 (ab64159; Abcam, 1:1,000) and GAPDH (10494; Proteintech, 1:2,000). HRP-conjugated secondary antibodies were purchased from Multi Sciences. Finally, blots were visualized by enhanced chemiluminescence reagents (Amersham Biosciences). GAPDH was used as an internal control. NMR sample preparation Aqueous metabolites were extracted from gastric tissues and gastrocnemius muscles of BGC823 mice (termed as BGC823 gastric tissues and BGC823 gastrocnemius) and sham mice (controls) for NMR analyses according to the protocol described previously.19 Generally, tissues were extracted with prechilled methanol, chloroform and water (4:4:2.85) to generate a two-phase extract. Only the upper phase was lyophilized and dissolved in 550 μL of deuterated phosphate buffer (50 mM, pH 7.4) containing 0.1 mM TSP. D2O acted as a field frequency lock. After centrifugation (12000 g at 4°C for 10 min) to remove the precipitates, the supernatants (500 μL) were transferred into 5-mm NMR tubes for NMR measurement.19 Serum samples (300 μL) of BGC823 mice (termed as BGC823 sera) and sham mice were thawed on the ice, and mixed with 210 μL of deuterated phosphate buffer (50 mM, pH 7.4). After centrifugation (12000 g at 4°C for 10 min), the supernatants (500 μL) were transferred into 5-mm NMR tubes for NMR measurement. NMR measurements All NMR spectra were acquired at 298 K on a Bruker Avance III 850 MHz spectrometer (Bruker BioSpin, Germany) equipped with a TCI cryoprobe. For tissue extracts, 1D 1H spectra were obtained with the pulse sequence NOESYGPPR1D [RD–G1-90°–t–90°–τm–G2-90°–ACQ]. Relaxation delay (RD) was 2 s, short delay (t) was 4 μs, and the mixing time (τm) was 10 ms. A spectra width of 17 KHz was used with an acquisition time (ACQ) of 1.88 s, and a total of 32 transients for gastrocnemius and 64 transients for gastric tissues were collected into 64K data points. For serum samples, 1D

1H

CPMG spectra were acquired using the pulse sequence

[RD-90°-(τ-180°-τ)n-ACQ] with water suppression. RD was 2 s, spin-echo delay (τ) was 300 μs. A spectra width of 17 KHz was used with an ACQ of 1.92 s, and a total of 64 transients were collected into 64K data points. Resonance assignments of metabolites were performed by using the Chenomx NMR Suite software (version 8.2, Chenomx Inc., Canada) based on the recorded 1D 1H spectra. Confirmation of metabolite assignments was accomplished by an integration of 2D 1H-13C heteronuclear single quantum coherence (HSQC) spectra and the Human Metabolome Data Base (HMDB), as well as relevant 4


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published literatures.20-22 The HSQC spectra were recorded using the default pulse sequence provided by the NMR spectrometer. NMR data processing NMR spectra were imported into the MestReNova software (version 9.0.1, Mestrelab Research S.L, Spain) for data processing. Prior to Fourier transformation, the free induction delays (FIDs) were multiplied by an exponential function with a 0.3 Hz line-broadening factor. Both spectra phasing and baseline correction were manually conducted. Peaks related to the CH3 groups of TSP and lactate were used to provide the chemical shift references for tissue extract samples (δ 0.00) and serum samples (δ 1.33), respectively. The spectral regions of water resonance δ 5.1-4.7 were excluded. Spectral regions of δ 10.0-0.6 were segmented into bins by a 0.003 ppm width. The peak integrals of the segments were normalized by the sum of peak integrals to compensate for potential variations in the concentrations of samples. The sum of the peak integrals was set to 100 for each spectrum. The normalized integrals were used to represent the relative abundances of assigned metabolites. For pair-wise comparison between two groups, singlet or non-overlapped resonances in each NMR spectrum were selected for computing metabolite integrals. Metabolomic analysis Multivariate statistical analysis was separately performed on 1D 1H NMR spectral data of gastric tissue and gastrocnemius extracts and sera by using the SIMCA-P+ software (version 12.0.1, Umetrics, Sweden). We applied Pareto scaling to the normalized NMR spectral data to enhance the significances of low-level metabolites without noise enlargement. Then, we conducted principal component analysis (PCA) to examine grouping trends and reveal metabolic differences among groups. Moreover, we used partial least-squares discriminant analysis (PLS-DA) to check grouping trends and improve group separation. The cross validation was used to measure the robustness of the PLS-DA model with a response permutation test (200 times). Parameters R2 and Q2 were computed to denote the interpretability and predictability of the model, respectively. The reliability of the model was raised as the R2 and Q2 approach to 1.23,

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The OPLS-DA loading plots were used to identify differential

metabolites that were significantly responsible for the metabolic separations of gastric tissues, sera and gastrocnemius between the two groups of mice.23, 25 The variable importance in projection (VIP) was used to assess the contribution of a given metabolite to the metabolic separation. The metabolites with VIP > 1.0 and P < 0.05 were identified to be differential metabolites. The pair-wise comparison of metabolite levels between the two groups of mice were conducted by using Student’s t-test. RNA-Seq analysis Total RNA was extracted from gastrocnemius muscles of mice in BGC823 and sham groups (five replicates in each group). The Agilent 2100 Bioanalyzer system was used to evaluate the quality of RNA. To construct the RNA library, samples with a RNA Integrity Number (RIN) greater than 7 were selected. The library sequencing was conducted by the BGISEQ-500RS of Beijing Genomics Institute (BGI, Wuhan, China). The Hierarchical Indexing for Spliced Alignment of Transcripts (HISAT) was used to filter and map the raw RNA-Seq data. The gene quantification was performed using the RNA-Seq with Expectation Maximization (RSEM) quantification tool. The gene expression level was computed in fragments per kilobase of exon per million fragments mapped (FPKM). The NOISeq 5



R/Bioc package was used to identify differentially expressed genes (DEGs) between the BGC823 and sham groups with two criteria: fold change (FC) ≥ 2, false discovery rate (FDR) ≤ 0.001.26 Pathway enrichment analysis was implemented by using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The pathways with Q value ≤ 0.01 were defined as statistically significant. Effects of AKG on skeletal muscle cell proliferation and protein turnover Cell proliferation rates were measured using 3-(4,5-dimethylthiazol-2-yl)-2,5-dipheniltetrazolium bromide (MTS) Assay Kit (Promega). Briefly, C2C12 myoblasts were seeded (3×103 cells per well) in 96-well plates. After incubation, cells were cultured with medium containing various concentrations (0.2, 0.5, 1, 2, 5 mM) of AKG (Sigma-Aldrich) for 24 h.27 C2C12 myoblasts were stained with crystal violet, and cell morphologies were visualized by using an inverted microscope. To test the effects of AKG on glucose exhaustion induced protein degradation. C2C12 myotubes were treated with no glucose (NG) DMEM (4 mM L-glutamine, no glucose and no sodium pyruvate) and 2 mM AKG for 24 h. Student’s t-test analysis Experimental results were reported as means ± SD. For quantitative comparison between two groups, data were assessed by Student’s t-test analysis using the GraphPad Prism software (version 6.0, La Jolla, USA). P value < 0.05 was considered statistically significant: P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***).

Results Orthotopic implantation of BGC823 cells induced GCC in mice Relative to sham mice (controls), body weight loss f BGC823 mice was beginning at day 33 (P < 0.05), and lasting along with the pathological progress of gastric tumors (Figure 1A). The BGC823 mice and controls were weighed 22.37±1.61g and 26.81±0.77g at day 50, respectively ， indicating a distinct difference of body weights between the two groups (P < 0.001). At necropsy, no tumor metastasis was observed in BGC823 mice. Representative images showed that the sizes of gastric tissues in BGC823 mice were remarkably larger than those in controls (Figure 1B). The weights of gastric tissues compatible with tumor masses were significantly increased in BGC823 mice relative to controls (P < 0.001) (Figure 1C). Food intake data showed that the average daily diet per mouse in BGC823 mice was slightly less relative to controls. The initial and final food intake data were approximately 3.95 g and 3.86 g per mouse in BGC823 mice at day 10 and day 50, respectively, indicating that BGC823 mice did not exhibit evident anorexia resulting from the tumor space-occupying lesion (Figure 1D). The loss of lean body weight was primarily presented with that of skeletal muscle weight. In particular, the weights of BGC823 gastrocnemius muscles were decreased by approximately 30% at day 50 compared to controls (P < 0.001) (Figure 1E). The tumor-bearing mice showed enhanced expression levels of MuRF1 and Atrogin1, two molecular markers of muscle protein degradation, in gastrocnemius muscles compared with controls (Figure 1F). Overall, the mice implanted orthotopicly with BGC823 cells exhibited obvious cancer cachexia symptoms. It was worth to mention that a number of preclinical murine models were established with subcutaneous transplantation approaches to investigate cancer cachexia. In our previous study, we 6


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conducted the subcutaneous implantation of BGC823 cells in mice at the mean time, which showed progressive tumor growth from tumor injection site. These mice were sacrificed at day 26 due to the large volume of tumor. However, no significant body weights loss were observed in the BGC823 group through the study, even when their tumor volumes reached 1.5 cm3 (Fig S1A, B). Moreover, expression levels of MuRF1 and Atrogin1 in gastrocnemius of the tumor-bearing mice did not display observably different from controls (Fig. S1C). Therefore, the orthotopic murine model of GCC was employed for further investigation.

Multivariate analysis of gastric tissues, sera and gastrocnemius in cachectic mice To obtain a comprehensive comparison of metabolic profiles between the two groups of mice, we conducted multivariate statistical analysis on the NMR data. PCA scores plot was shown with the first two principal components for gastric tissues (Figure 2A), sera (Figure 2D) and gastrocnemius (Figure 2G), respectively. BGC823 mice displayed distinctly different metabolic profiles from sham mice. Furthermore, the PLS-DA scores plots illustrate distinct metabolic separations between the two groups of mice for gastric tissues (Figure 2B), sera (Figure 2E) and gastrocnemius (Figure 2H). The validation plots of the corresponding RPTs confirm that these PLS-DA models are valid for gastric tissues (Figure 2C), sera (Figure 2F) and gastrocnemius (Figure 2I). Summarily, both PCA and PLS-DA scores plots demonstrate that the metabolic profiles of BGC823 mice are clearly distinguished from those of controls. These results revealed that GCC significantly changed metabolic phenotypes of gastric tissues, sera and gastrocnemius in cachectic mice. Gastric cancer cachexia induced metabolic alterations in gastric tissues Resonances of 49 metabolites were assigned with a combination of the 1D (Figure 3A) and 2D NMR spectra (Figure S2). Moreover, 20 significant metabolites (VIP > 1.0 and P < 0.05) were identified from the OPLS-DA loading plot of BGC823 gastric tissues vs. controls, which were significantly responsible for the metabolic distinction (Figure 4A). These significant metabolites were consistent with the identified differential metabolites by using the Student’s t-test (Figure 5A, 5B and Table S1). BGC823 gastric tissues showed significantly increased levels of glycolytic metabolites (pyruvate and lactate), acetate, glycine, glutamate, valine, threonine, aspartate, UDPG and tryptophan, and decreased levels of taurine, GPC, creatine, myoinositol, GSH, metabolites of nucleic acids (inosine, inosinate and hypoxanthine) (Figure 4A, 5A, 5B and Table S1). These differential metabolites were mostly involved in glycolysis, energy metabolism and nucleic acid metabolism, indicating obvious metabolic phenotypes of malignant tumor tissues. Serum metabolic analysis showed typical metabolic phenotypes of cancer cachexia A total of 26 metabolites were identified from the 1D NMR spectra (Figure 3B) and confirmed by the 2D NMR spectra (Figure S3). The OPLS-DA loading plot shows 13 significant metabolites (VIP > 1.0 and P < 0.05) significantly contributing to the metabolic separation between BGC823 sera and controls (Figure 4B). These significant metabolites were consistent with the identified differential metabolites by using the Student’s t-test (Figure 5C, 5D and Table S2). The BGC823 sera displayed down-regulated levels of carbohydrates (glucose), lipids (PUFA and LDL/VLDL) and alanine, and up-regulated levels of lactate, acetate, creatine, amino acids (lysine, glutamate, glutamine, glycine and 7



leucine) (Figure 4B, 5C, 5D and Table S2). These metabolites changes in BGC823 sera were indicative of typical metabolic phenotypes of cancer cachexia characterized by decreased glucose and increased lactate,13 and elevated free amino acids in blood. Gastric cancer cachexia changed metabolomic and transcriptomic profiles of gastrocnemius We identified 27 metabolites in gastrocnemius samples based on a combination of the 1D (Figure 3C) and 2D NMR spectra (Figure S4). Moreover, we identified 14 significant metabolites (VIP > 1.0 and P < 0.05) from the OPLS-DA loading plot, which were significantly responsible for the metabolic difference of BGC823 gastrocnemius and controls showed (Figure 4C). BGC823 gastrocnemius exhibited significantly declined levels of glucose, lactate, glycine and succinate, and raised levels of acetate, glutamate, glutamine, arginine, BCAAs (isoleucine, leucine and valine), IMP and lysine (Figure 4C). We then performed comparisons of metabolite levels for the two groups of mice by Student’s t-test analysis (Figure 5E，5F and Table S3), and identified 17 significant metabolites (P < 0.05, 4 decreased and 13 increased). These significant metabolites were almost consistent with the identified differential metabolites. There were only three inconsistencies potentially due to the different biological significances between significant metabolites and differential metabolites. Three metabolites (tyrosine, phenylalanine and 3-methylhistidine) displayed relative stable levels in the OPLS-DA analysis, while they showed significantly increased levels in Student’s t-test analysis. These differential metabolites were mostly involved in metabolisms of amino acids and carbohydrates, indicative of a prominent metabolic phenotype of cancer cachexia featured by promoted muscle proteolysis and disturbed amino acid metabolism. To gain more comprehensive profiles of skeletal muscle atrophy at the transcriptome level, we also performed RNA-Seq analysis for transcriptomic profiling. Based on the enriched KEGG pathways and their identified DEGs numbers, 290 metabolic-related DEGs were identified in a large number of KEGG pathways including 79 DEGs in carbohydrate metabolism and 66 DEGs in amino acid metabolism (Figure S5). Among these DEGs, the distinctly changed mRNAs associated with the significantly altered metabolic pathways identified in BGC823 gastrocnemius are shown in Table 1. Compared to controls, BGC823 gastrocnemius muscles showed several significantly changed mRNAs, including down-regulated hexokinase3 (Hk3), phosphofructokinase (Pfk), phosphoglycerate mutase 1 (Pgam1) and isocitrate dehydrogenase (Idh1), as well as up-regulated AKG dehydrogenase (Ogdh1). Overall, our transcriptomic and metabolomic data revealed that GCC induced the impaired metabolic pathways mainly concentrated on glycolysis, TCA cycle and amino acid metabolism. AKG promoted C2C12 myoblasts proliferation and reduced glucose deprivation-induced myotubes atrophy To assess the effects of AKG on skeletal muscle in vitro, C2C12 myoblasts were treated with various concentrations of AKG (0.2-5 mM) for 24 h, and MTS assay was conducted. As shown in Figure 6A, the proliferations of C2C12 myoblasts treated with 1.0 and 2.0 mM AKG were significantly promoted compared with that of controls, respectively. MTS assay well supported these results. Relative to controls, C2C12 myoblasts treated with 1.0 (P < 0.01) and 2.0 mM (P < 0.001) AKG displayed distinctly promoted cell proliferations, respectively (Figure 6B). Furthermore, the expression levels of myod1 in C2C12 myoblasts with the treatments of 1.0 mM and 2.0 mM AKG were significantly increased (Figure 6C). The 0.2, 0.5 and 5.0 mM AKG-treated groups did not display significant 8


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differences in morphology, cell proliferation and myod1 expressions from the control (0 mM AKG). These results indicated that AKG could significantly promote the proliferation of murine C2C12 myoblasts and distinctly increase the level of the myod1 protein. Furthermore, C2C12 myotubes were cultured in glucose deprivation condition to mimic the physiological status of glucose deficiency in vivo animal experiments. C2C12 myotubes displayed evident atrophy including reductions in myotube diameter, area (Figure 6D), and upregulation in ubiquitin E3 MuRF1 when culturing in no-glucose (NG) media (Figure 6E). Interestingly, the atrophy of myotubes induced by glucose deprivation was reversed by AKG in some degree (Fig. 6D), which was also confirmed by the inhibition of expression of ubiquitin E3 MuRF1 (Fig. 6E). These results suggested that AKG are capable of preventing skeletal muscle atrophy and protein degradation during glucose deficiency. Overall metabolomics analysis of gastric tissues, sera and gastrocnemius muscles From the OPLS-DA analysis of BGC823 mice vs. sham mice, we identified 20, 13 and 14 differential metabolites in gastric tumors, sera and gastrocnemius, respectively. The Venn diagram illustrates the overlap of differential metabolites among three kinds of samples (Figure 7). Gastric tumors and sera shared 5 differential metabolites. Creatine was decreased in tumors but increased in sera, while glutamate, glycine, lactate and acetate were both increased in tumors and sera (Figure 7A). Moreover, sera and gastrocnemius shared 8 differential metabolites. Glucose was decreased, while glutamate, glutamine, lysine, leucine and acetate were increased both in sera and gastrocnemius. Lactate and glycine were increased in sera but decreased in gastrocnemius (Figure 7B). Furthermore, gastric tumors and gastrocnemius shared 6 differential metabolites. Glutamate, valine, inosinate and acetate were increased both in tumors and gastrocnemius. Lactate and glycine were increased in tumors but decreased in gastrocnemius (Figure 7C). Interestingly, gastric tumors, sera and gastrocnemius shared 4 differential metabolites (glutamate, acetate, lactate and glycine). Glutamate and acetate were increased in the three kinds of samples. Lactate and glycine were decreased in gastrocnemius but increased in tumors and sera (Figure 7D). Overview of significantly altered metabolic profiles closely related to BGC823 cachectic gastric tissues, sera and gastrocnemius muscles is illustrated in Figure 8. This plot displays the up-regulated and down-regulated metabolites and mRNAs identified from the integrated metabolomic and transcriptomic analyses of BGC823 mice vs. sham mice.

Discussion In this work, we established an orthotopic preclinical model of GCC by implanting human stomach cancer cell line BGC823 into nude mice. Previously, a number of preclinical murine models (such as the robust cachexia models C26 and LLC) were established with subcutaneous transplantation approaches to investigate cancer cachexia28-31. In the present work, however, we did not find cachexia symptoms in the subcutaneous models during the study period. These results emphasized the significant advantages of the orthotopic models over heterotopic xenograft models to mimic the clinical characterizations. On the whole, orthotopic implantation approach can be used to establish a suitable and reproducible model for further studying gastric cancer associated-cachexia. The cachexia symptoms caused by orthotopic BGC823 tumors were confirmed by the significant loss of body weights, marked decrease of muscle weights, and the enhanced levels of MuRF1 and Atrogin1 in 9



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gastrocnemius muscles of BGC823 mice relative to controls. As well known, both Atrogin1 and MuRF1 are skeletal muscle-specific ubiquitin ligases involved in the ubiquitin proteasome pathway, acting as molecular markers highly relevant to skeletal muscle atrophy.32 To obtain comprehensive understanding of the unique GCC-related metabolic features, we performed NMR-based metabolomic analysis to systematically address impaired metabolic profiles and disturbed metabolic pathways induced by GCC. The GCC-induced significant alterations in nucleic acid metabolism of BGC823 gastric tissues were reflected by remarkably declined levels of several metabolites, including hypoxanthine, inosine and inosinate. These changes probably resulted from the greatly increased demands of nucleotides for biosynthesis of DNA and RNA to ensure cancer cell proliferation. The promoted nucleotide biosynthesis might utilize glutamine and glucose as raw materials to meet the demands during tumor growth.33 As reported previously, enhanced glucose metabolism in cancer is closely associated with glycolysis aerobically, resulting in a massive release of lactate into the host’s circulation. The so called Warburg effect has been generally accepted as a metabolic hallmark of cancer.34 Consistently, increased levels of pyruvate and lactate were detected in BGC823 gastric tissues. Interestingly, BGC823 gastric tissues did not show significant difference of glucose and glutamine levels from controls. As well known, tumors tend to make use of large amounts of energy sources mostly including glucose and glutamine, which would be applied either for glycolysis and energy generation, or for protein and nucleic acid synthesis. Simultaneously, the greatly used energy sources would also generate the activation of gluconeogenesis, resulting in a higher glucose production, and developing a dynamic balancing state to meet the demands during tumor growth.35 The down-regulated alanine in BGC823 sera might indicate an enhanced liver gluconeogenesis. Furthermore, the cachectic mice exhibited increased glutamate in gastric tissues, and increased glutamate and glutamine in both sera and gastrocnemius. As known, glutamine could be converted into glutamate through glutaminolysis. These results suggest that the increased serum glutamate might be primarily from the cachectic muscle proteolysis, and then taken up by gastric tumors, to sustain the energy, nitrogen demands and nucleic acid metabolism during tumor growth.33, 36 Previous studies have demonstrated that blood hyperlipidemia and hypoglycemia are primary metabolic changes closely associated with cancer cachexia.37,

38

BGC823 mice displayed decreased

glucose and increased lactate in sera, indicative of promoted glycolysis. Note that gastric tissues also showed

promoted

glucose

metabolism.

Furthermore,

previous

reports

have

revealed

cachexia-associated hypoglycemia as well. On the other hand, BGC823 mice exhibited increased serum glycerol. It has been suggested that multiple cytokines could mobilize fatty acids from adipose tissues,13,

39

which would theoretically result in increased blood levels of glycerol. Therefore, our

results confirm that both hyperlipidemia and hypoglycemia play central roles in cancer cachexia. Free amino acids in blood pool are supplied by either dietary proteins or proteolysis.40 Given that the food intake of BGC823 mice was not significantly decreased compared to that of controls, the enhanced serum levels of glutamine, glutamate, lysine, leucine and glycine might reflect the promoted host hypercatabolism in cachectic mice. Skeletal muscle is a major site of metabolic activity and the most abundant tissue in human body accounting for almost 50% of total body mass. Being the largest protein reservoir, skeletal muscle serves as a source of amino acids used for energy production in diseases such as cancer, heart and renal failure.41 Our work demonstrates that muscle catabolism is triggered primarily by up-regulated E3 ligases. As a result of promoted muscle proteolysis, free amino acids were largely released. A previous 10


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work reported reduced levels of 3-carbon glycolytic and TCA intermediates in C26 cachectic muscles,42 which was defined as a Warburg-like effect metabolism. In our work, BGC823 cachectic muscles showed decreased levels of glucose (P < 0.05) and lactate (P < 0.001) and 6 increased levels of amino acids in (isoleucine, leucine, valine, arginine, glutamate and glutamine). Furthermore, the glucose level in BGC823 mice was significantly declined in sera (P < 0.01) and was relative stable in gastric tissues (P > 0.05) relative to controls. These results might suggest that glucose was predominantly utilized by gastric tumors for the demands of tumor growth in cachectic mice. As well known, the glycolysis converts glucose to pyruvate by via a series of intermediate metabolites, and 3 rate-limiting enzymes including hexokinase (Hk), phosphofructokinase (Pfk) and pyruvate kinase (Pk). The final metabolite pyruvate can be converted to acetyl-CoA and then enter into the TCA cycle, or can be alternatively converted to lactate by lactate dehydrogenase. Relative to controls, BGC823 gastrocnemius exhibited elevated expressions of Hk3, Pfkp, and Pgam1 genes and a declined level of lactate. We thus speculate that cachectic skeletal muscles might prefer to convert glucose to pyruvate, which then enters into the TCA cycle to join in oxidative metabolism for energy production. The largely released amino acids are mostly involved in glycolysis and TCA cycle anaplerosis. BCAAs can be converted to acetyl-CoA and then enter into the TCA cycle. Arginine, glutamine and glutamate can be converted into TCA intermediates α-ketoglutarate (AKG).43 Our work showed that these metabolites were up-regulated in BGC823 gastrocnemius, suggesting that more active glycolysis and TCA cycle anaplerotic flux might occur to meet the energy demands during severe muscle wasting in cachectic mice. Besides, the levels of TCA intermediates AKG, succinate, and fumarate exhibited discrepant alterations in cachectic skeletal muscles. In the TCA cycle, AKG is generated from isocitrate by oxidative decarboxylation catalyzed by isocitrate dehydrogenase (Idh1), thereafter it is converted to succinyl-CoA and then to succinate via decarboxylation by AKG dehydrogenase (Ogdh1). Therefore, AKG plays crucial roles in skeletal muscle metabolism as a key control point of the TCA cycle.44 The BGC823 gastrocnemius exhibited increased AKG and decreased succinate, as well as up-regulated Idh1 and down-regulated Ogdh1, might indicate that cachectic gastrocnemius are prone to enrich AKG. The question is whether the increased levels of AKG do harm to the muscle function or as a metabolic modulator to protect muscle degradation from the cancer cachexia. Previous works have demonstrated that AKG could enhance protein synthesis in intestinal porcine epithelial cells,27 and prompt skeletal muscle growth in piglets.45 Consistently, we find that supplementation with AKG could significantly promote the proliferation of murine C2C12 myoblasts, and distinctly enhance the level of myod1. It is known that myod1 codes myoblast determination protein 1, which acts a transcriptional activator promoting transcription of muscle-specific target genes and plays a role in myoblast proliferation and skeletal muscle differentiation. These results indicated that AKG could enhance the skeletal muscle cell proliferations under normal conditions. On the other hand, decreased levels of glucose were observed in cachectic skeletal muscle in vivo. Consistently, glucose deficiency could induce C2C12 myotubes atrophy and protein degradation in vitro. Moreover, AKG is capable of reversing the atrophy under this condition. These results suggest that AKG tends to antagonize the protein turnover in atrophic muscles in some degree, which is ultimately not effective probably due to the severe muscle atrophy during cancer cachexia. Bearing all this in mind, these AKG-relevant results might be beneficial to comprehensively understand the molecular mechanisms underlying skeletal muscle atrophy, and to explore potential clinical implications of AKG supplements in prevention and therapy of cancer cachexia. The potentially 11



significant roles of AKG in interventions of cancer cachexia-induced skeletal muscle atrophy are worth to be detailedly addressed in future. In addition, BGC823 mice showed increased levels of acetate in gastric tumors, sera and skeletal muscles. Recent acetate-related reports have reignited extensive interests in acetate metabolism in cancer. The conversion of acetate to acetyl-CoA has been implicated in the growth of hepatocellular carcinoma, glioblastoma, breast cancer and prostate cancer.46 Acetate could become a substantial source for energy production via the TCA cycle when other carbon sources (for example, glucose and glutamine) are limited.46 The detailed molecular mechanisms underlying acetate metabolism should be elucidated in future as well.

Conclusions We have clarified unique metabolic signatures of gastric tumors, sera and skeletal muscles of cachectic mice based on a well-established orthotopic preclinical model of GCC. Our results provide significant insights into the complex pathophysiology of GCC, and also expand the mechanistic understanding of metabolic alterations associated with GCC-induced skeletal muscle atrophy. Furthermore, the identified differential metabolites are primarily involved in impaired metabolic pathways, including glucose and lipid metabolism, purine metabolism, energy metabolism, TCA cycle anaplerotic, carbohydrate metabolism and amino acid metabolism. Expectedly, these significantly disturbed metabolic pathways potentially contribute to metabolic mechanisms underlying the GCC progression. Results highlighted the role of AKG in skeletal muscle protein turnover. Further therapeutic interventions on cachexia patients can be designed to exploit nutritional supplementations of crucial metabolites such as AKG against skeletal muscle atrophy.

Supporting Information The following supporting information is available free of charge at ACS website http://pubs.acs.org Figure S1. The subcutaneous injection murine model of BGC823 gastric cancer. Figure S2. Typical 2D 1H-13C HSQC spectrum of the gastric tissue derived from a sham mouse. Figure S3. Typical 2D 1H-13C HSQC spectrum of the serum derived from a sham mouse. Figure S4. Typical 2D 1H-13C HSQC spectrum of the gastrocnemius derived from a sham mouse Figure S5. Gastric cancer cachexia changed metabolic-related transcriptional profiles of gastrocnemius muscles in BGC823 mice relative to sham mice. Table S1. Comparison of metabolite levels between two groups of mice based on relative integrals of metabolites calculated from 1D 1H NMR spectra of aqueous extracts derived from gastric tissues. Table S2. Comparison of metabolite levels between two groups of mice based on relative integrals of metabolites calculated from 1D 1H NMR spectra of sera. Table S3. Comparison of metabolite levels between two groups of mice based on relative integrals of metabolites calculated from 1D 1H NMR spectra of aqueous extracts derived from gastrocnemius.

12


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Competing interests The authors declare that they have no competing financial interests.

Funding & Acknowledgements DHL and PFC received research support from the National Natural Science Foundation of China (No. 91129713 & 81574080). HQZ received research support from Natural Science Foundation of Fujian Province (grant number 2016-ZQN-88). JQG and QWW received research support from National Found for Fostering Talents of Basic Science (No. J1310024).

ORCID Donghai Lin : 0000-0002-2318-2033

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Figure 1. The orthotopic murine model of gastric cancer cachexia exhibited significant body weight loss, gastric tumor formation and gastrocnemius atrophy. The model was established by implanting the human cell line BGC823 into greater curvature of the glandular portion of the stomach. Mice inoculated with PBS served as the control group (sham). (A) Body weights of the mice over the course of the study; (n=8). (B) Photographs of representative gastric tissues of the mice sacrificed at day 50; (n=3). (C) Weights of gastric tissues of the sacrificed mice; (n=8) (D) Average daily food consumption per mouse over the course of the study; (n=8). (E) Weights of gastrocnemius muscles of the sacrificed mice; (n=8). (F) Expressions of two E3 ubiquitin ligases Atrogin1 and MuRF1 in gastrocnemius of the sacrificed mice analyzed by western blot; (n=3). The pair-wise comparison between BGC823 and sham mice was conducted by using Student’s t-test analysis. Data are presented as mean ± SD (*P < 0 .05; **P < 0.01; ***P < 0.001). Figure 2. PCA, PLS-DA scores plots and validation plots of 1D 1H NMR data obtained from gastric tissues, sera and gastrocnemius of BGC823 and sham mice. (A), (B) and (C) Aqueous extracts derived from gastric tissues; (n=8); (D), (E) and (F) Sera; (n=7); (G), (H) and (I) Aqueous extracts derived from gastrocnemius; (n=8). The PLS-DA models were cross-validated to evaluate the robustness by a random permutation test (200 cycles). Figure 3. Average 1D 1H NMR spectra of gastric tissues, sera and gastrocnemius of BGC823 and sham mice. (A) NOESY spectra of aqueous extracts derived from gastric tissues; (n=8). (B) CPMG spectra of sera; (n=7). (C) NOESY spectra of aqueous extracts derived from gastrocnemius; (n=8). The regions of water resonance were removed from the spectra. Figure 4. OPLS-DA loading plots of 1H NMR data obtained from gastric tissues, sera and gastrocnemius of BGC823 and sham mice. (A) Aqueous extracts derived from gastric tissues; (n=8). (B) Sera; (n=7). (C) Aqueous extracts derived from gastrocnemius; (n=8). Red, orange and blue colors indicate very significant variables (P< 0.01), significant variables (P< 0.05), and insignificant variables (NS), respectively.Differential metabolites were identified with two criteria (VIP > 1.0 and P< 0.05). Figure 5. Relative metabolite levels in gastric tissues, sera and gastrocnemius of BGC823 and sham mice. (A), (B) Aqueous extracts derived from gastric tissues; (n=8). (C), (D) Sera; (n=7). (E), (F) Aqueous extracts derived from gastrocnemius; (n=8). Data are presented as means ± SD. The pair-wise 16


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comparison of relative metabolite levels between BGC823 and sham mice were conducted by using Student’s t-test analysis. Statistical significances: *, P< 0.05; **, P< 0.01; ***, P< 0.001 for BGC823 mice vs. sham mice. GPC, glycerophosphocholine; Tau, taurine; IMP, inosinate; Hyp, hypoxanthine; 3MH, 3-methylhistidine; AKG, α-ketoglutarate. Figure 6. AKG prompt myoblasts proliferation and reduce glucose deficiency-induced myotubes atrophy. Proliferation assay of C2C12 myoblasts treated with various concentrations of AKG (A)

Cell morphology. (B) Cell viability. (C) Expressions of myod1 analyzed by western blot. (D) C2C12 myotubes cultured in no-glucose (NG) media, treated with or without AKG (2 mM) for 24 h, compared with control (Ctrl). (E) Expressions of MuRF1 analyzed by western blot. Statistical significances: *,

P< 0.05; **, P< 0.01; ***, P< 0.001 for AKG-treated C2C12 cells vs. controls. Figure 7. Venn diagram shows the overlap among the differential metabolites of BGC823 gastric tissues, sera and gastrocnemius, relative to controls. G: gastric tissues; S: sera; M: gastrocnemius. The number of differential metabolites shared between G and S samples is 5 (A), between S and M samples is 8 (B), between G and M samples is 6 (C), among G, S and M samples is 4 (D). Red and green colors represent up-regulated and down-regulated metabolites in BGC823 mice relative to controls, respectively. Figure 8. Overview of significantly altered metabolic profiles involved in BGC823 cachectic gastric tissues (top), sera (middle) and gastrocnemius (bottom). Red and green colors represent up-regulated and down-regulated metabolites/mRNAs in BGC823 mice relative to sham mice, respectively. Table 1. Metabolism-associated differentially expressed genes (DEGs) identified from the transcriptomic analysis of BGC823 gastrocnemius relative to controls. Gene ID

Gene name

Description

Log2FCa

Q-value

Associated metabolism

212032

Hk3

hexokinase-3

3.5080

3.16E-143

glycolysis

56421

Pfkp

phosphofructokinase

1.6606

1.19E-171

glycolysis

18648

Pgam1

phosphoglycerate mutase 1

1.0876

7.75E-236

glycolysis

15926

Idh1

isocitrate dehydrogenase

1.2281

9.25E-118

tricarboxylic acid cycle

239017

Ogdh1

-1.3473

7.41E-22

tricarboxylic acid cycle

α-ketoglutarate dehydrogenase

Note:aFC = fold change of the DEG expression level in BGC823 mice relative to sham mice; n=(5).

17



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Figure 1



Figure 2 143x128mm (300 x 300 DPI)


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Figure 3



Figure 4 190x267mm (300 x 300 DPI)


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Figure 5 296x199mm (300 x 300 DPI)



Figure 6


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Figure 7 145x106mm (300 x 300 DPI)



Figure 8


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Metabolic profiling of tumors, sera and skeletal muscles from an

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