Nontargeted Serum Lipid Profiling of Nonalcoholic Steatohepatitis by Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry: A Multiplexed Separation Platform for Resolving Ionic Lipids.

New methods are needed for global lipid profiling due to the complex chemical structures and diverse physicochemical properties of lipids. Herein we introduce a robust data workflow to unambiguously select lipid features from serum ether extracts by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). An iterative three-stage screening strategy is developed for nontargeted lipid analyses when using multiplexed electrophoretic separations coupled to an Orbitrap mass analyzer under negative ion mode. This approach enables the credentialing of 270 serum lipid features annotated based on their accurate mass and relative migration time, including 128 ionic lipids reliably measured (median CV ≈ 13%) in most serum samples (>75%) from nonalcoholic steatohepatitis (NASH) patients (n = 85). A mobility map is introduced to classify charged lipid classes over a wide polarity range with selectivity complementary to chromatographic separations, including lysophosphatidic acids, phosphatidylcholines, phosphatidylinositols, phosphatidylethanolamines, and nonesterified fatty acids (NEFAs). Serum lipidome profiles were also used to differentiate high- from low-risk NASH patients using a k-means clustering algorithm, where elevated circulating NEFAs (e.g., palmitic acid) were associated with increased glucose intolerance, more severe liver fibrosis, and greater disease burden. MSI-NACE-MS greatly expands the metabolome coverage of conventional aqueous-based CE-MS protocols and is a promising platform for large-scale lipidomic studies.

Whole-body insulin resistance and energy expenditure indices, serum lipids, and skeletal muscle metabolome in a state of lipoprotein lipase overexpression.

INTRODUCTION: Overexpression of lipoprotein lipase (LPL) protects against high-fat-diet (HFD)-induced obesity and insulin resistance in transgenic rabbits; however, the molecular mechanisms remain unclear. Skeletal muscle is a major organ responsible for insulin-stimulated glucose uptake and energy expenditure. OBJECTIVES: The main purpose of the current study was to examine the effects of the overexpression of LPL on the skeletal muscle metabolomic profiles to test our hypothesis that the mitochondrial oxidative metabolism would be activated in the skeletal muscle of LPL transgenic rabbits and that the higher mitochondrial oxidative metabolism activity would confer better phenotypic metabolic outcomes. METHODS: Under a HFD, insulin resistance index was measured using the intravenous glucose tolerance test, and total energy expenditure (TEE) was measured by doubly-labeled water in control and LPL transgenic rabbits (n = 12, each group). Serum lipids, such as triglycerides and free fatty acid, were also measured. The skeletal muscle metabolite profile was analyzed using capillary electrophoresis time-of flight mass spectrometry in the two groups (n = 9, each group). A metabolite set enrichment analysis (MSEA) with muscle metabolites and a false discovery rate q < 0.2 was performed to identify significantly different metabolic pathways between the 2 groups. RESULTS: The triglycerides and free fatty acid levels and insulin resistance index were lower, whereas the TEE was higher in the LPL transgenic rabbits than in the control rabbits. Among 165 metabolites detected, the levels of 37 muscle metabolites were significantly different between the 2 groups after false discovery rate correction (q < 0.2). The MSEA revealed that the TCA cycle and proteinogenic amino acid metabolism pathways were significantly different between the 2 groups (P < 0.05). In the MSEA, all four selected metabolites for the TCA cycle (2-oxoglutaric acid, citric acid, malic acid, fumaric acid), as well as eight selected metabolites for proteinogenic amino acid metabolism (asparagine, proline, methionine, phenylalanine, histidine, arginine, leucine, isoleucine) were consistently increased in the transgenic rabbits compared with control rabbits, suggesting that these two metabolic pathways were activated in the transgenic rabbits. Some of the selected metabolites, such as citric acid and methionine, were significantly associated with serum lipids and insulin resistance (P < 0.05). CONCLUSION: The current results suggest that the overexpression of LPL may lead to increased activities of TCA cycle and proteinogenic amino acid metabolism pathways in the skeletal muscle, and these enhancements may play an important role in the biological mechanisms underlying the anti-obesity/anti-diabetes features of LPL overexpression.

SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance.

Cancer cells adapt their metabolic processes to support rapid proliferation, but less is known about how cancer cells alter metabolism to promote cell survival in a poorly vascularized tumour microenvironment. Here we identify a key role for serine and glycine metabolism in the survival of brain cancer cells within the ischaemic zones of gliomas. In human glioblastoma multiforme, mitochondrial serine hydroxymethyltransferase (SHMT2) and glycine decarboxylase (GLDC) are highly expressed in the pseudopalisading cells that surround necrotic foci. We find that SHMT2 activity limits that of pyruvate kinase (PKM2) and reduces oxygen consumption, eliciting a metabolic state that confers a profound survival advantage to cells in poorly vascularized tumour regions. GLDC inhibition impairs cells with high SHMT2 levels as the excess glycine not metabolized by GLDC can be converted to the toxic molecules aminoacetone and methylglyoxal. Thus, SHMT2 is required for cancer cells to adapt to the tumour environment, but also renders these cells sensitive to glycine cleavage system inhibition.

Metabolomic profiling of gastric cancer tissues identified potential biomarkers for predicting peritoneal recurrence.

BACKGROUND: Metabolomics is useful for analyzing the nutrients necessary for cancer progression, as the proliferation is regulated by available nutrients. We studied the metabolomic profile of gastric cancer (GC) tissue to elucidate the associations between metabolism and recurrence. METHODS: Cancer and adjacent non-cancerous tissues were obtained in a pair-wise manner from 140 patients with GC who underwent gastrectomy. Frozen tissues were homogenized and analyzed by capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Metabolites were further assessed based on the presence or absence of recurrence. RESULTS: Ninety-three metabolites were quantified. In cancer tissues, the lactate level was significantly higher and the adenylate energy charge was lower than in non-cancerous tissues. The Asp, ß-Ala, GDP, and Gly levels were significantly lower in patients with recurrence than in those without. Based on ROC analyses to determine the cut-off values of the four metabolites, patients were categorized into groups at high risk and low risk of peritoneal recurrence. Logistic regression and Cox proportional hazard analyses identified ß-Ala as an independent predictor of peritoneal recurrence (hazard ratio [HR] 5.21 [95% confidence interval 1.07-35.89], p = 0.029) and an independent prognostic factor for the overall survival (HR 3.44 [95% CI 1.65-7.14], p < 0.001). CONCLUSIONS: The metabolomic profiles of cancer tissues differed from those of non-cancerous tissues. In addition, four metabolites were significantly associated with recurrence in GC. ß-Ala was both a significant predictor of peritoneal recurrence and a prognostic factor.

Mycoplasma infection and hypoxia initiate succinate accumulation and release in the VM-M3 cancer cells.

Succinate is known to act as an inflammatory signal in classically activated macrophages through stabilization of HIF-1α leading to IL-1ß production. Relevant to this, hypoxia is known to drive succinate accumulation and release into the extracellular milieu. The metabolic alterations associated with succinate release during inflammation and under hypoxia are poorly understood. Data are presented showing that Mycoplasma arginini infection of VM-M3 cancer cells enhances the Warburg effect associated with succinate production in mitochondria and eventual release into the extracellular milieu. We investigated how succinate production and release was related to the changes of other soluble metabolites, including itaconate and 2-HG. Furthermore, we found that hypoxia alone could induce succinate release from the VM-M3 cells and that this could occur in the absence of glucose-driven lactate production. Our results elucidate metabolic pathways responsible for succinate accumulation and release in cancer cells, thus identifying potential targets involved in both inflammation and hypoxia. This article is part of a Special Issue entitled 20th European Bioenergetics Conference, edited by László Zimányi and László Tretter.

Gut Microbiome and Plasma Microbiome-Related Metabolites in Patients With Decompensated and Compensated Heart Failure.

BACKGROUND: Gut microbiome composition or circulating microbiome-related metabolites in patients with heart failure (HF) have not been investigated at different time points (i.e., in the decompensated (Decomp) and compensated (Comp) phases). Methods and Results: We prospectively enrolled 22 patients admitted for HF and 11 age-, sex-, and comorbidity-matched hospitalized control subjects without a history of HF. Gut flora and plasma microbiome-related metabolites were evaluated by amplicon sequencing of the bacterial 16S ribosomal RNA gene and capillary electrophoresis time-of-flight mass spectrometry, respectively. HF patients were evaluated in both the Decomp and Comp phases during hospitalization. The phylum Actinobacteria was enriched in HF patients compared with control subjects. At the genus level, Bifiodobacterium was abundant while Megamonas was depleted in HF patients. Meanwhile, plasma concentration of trimethylamine N-oxide (TMAO), a gut microbiome-derived metabolite, was increased in HF patients (Decomp HF vs. control, P=0.003; Comp HF vs. control, P=0.004). A correlation analysis revealed positive correlations between the abundance of the genus Escherichia/Shigella and levels of TMAO and indoxyl sulfate (IS, a microbe-dependent uremic toxin) in Comp HF (TMAO: r=0.62, P=0.002; IS: r=0.63, P=0.002). Escherichia/Shigella was more abundant in Decomp than in Comp HF (P=0.030). CONCLUSIONS: Our results suggest that gut microbiome composition and microbiome-related metabolites are altered in HF patients.

Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis.

BACKGROUND: Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g., the top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences are made for these metabolites. However, this approach may lead to biased biological inferences because these metabolites are not objectively selected with statistical criteria. RESULTS: We propose a statistical procedure that selects metabolites with statistical hypothesis testing of the factor loading in PCA and makes biological inferences about these significant metabolites with a metabolite set enrichment analysis (MSEA). This procedure depends on the fact that the eigenvector in PCA for autoscaled data is proportional to the correlation coefficient between the PC score and each metabolite level. We applied this approach to two sets of metabolomic data from mouse liver samples: 136 of 282 metabolites in the first case study and 66 of 275 metabolites in the second case study were statistically significant. This result suggests that to set the number of metabolites before the analysis is inappropriate because the number of significant metabolites differs in each study when factor loading is used in PCA. Moreover, when an MSEA of these significant metabolites was performed, significant metabolic pathways were detected, which were acceptable in terms of previous biological knowledge. CONCLUSIONS: It is essential to select metabolites statistically to make unbiased biological inferences from metabolomic data when using factor loading in PCA. We propose a statistical procedure to select metabolites with statistical hypothesis testing of the factor loading in PCA, and to draw biological inferences about these significant metabolites with MSEA. We have developed an R package "mseapca" to facilitate this approach. The "mseapca" package is publicly available at the CRAN website.

Determination of the Minimum Sample Amount for Capillary Electrophoresis-Fourier Transform Mass Spectrometry (CE-FTMS)-Based Metabolomics of Colorectal Cancer Biopsies.

The minimum sample volume for capillary electrophoresis-Fourier transform mass spectrometry (CE-FTMS) useful for analyzing hydrophilic metabolites was investigated using samples obtained from colorectal cancer patients. One, two, five, and ten biopsies were collected from tumor and nontumor parts of the surgically removed specimens from each of the five patients who had undergone colorectal cancer surgery. Metabolomics was performed on the collected samples using CE-FTMS. To determine the minimum number of specimens based on data volume and biological interpretability, we compared the number of annotated metabolites in each sample with different numbers of biopsies and conducted principal component analysis (PCA), hierarchical cluster analysis (HCA), quantitative enrichment analysis (QEA), and random forest analysis (RFA). The number of metabolites detected in one biopsy was significantly lower than those in 2, 5, and 10 biopsies, whereas those detected among 2, 5, and 10 pieces were not significantly different. Moreover, a binary classification model developed by RFA based on 2-biopsy data perfectly distinguished tumor and nontumor samples with 5- and 10-biopsy data. Taken together, two biopsies would be sufficient for CE-FTMS-based metabolomics from a data content and biological interpretability viewpoint, which opens the gate of biopsy metabolomics for practical clinical applications.

Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: a cross-sectional study.

BACKGROUND: Osteoarthritis (OA) is one of the costliest and most disabling forms of arthritis, and it poses a major public health burden; however, its detailed etiology, pathophysiology, and metabolism remain unclear. Therefore, the purpose of this study was to investigate the key plasma metabolites and metabolic pathways, especially focusing on radiographic OA severity and synovitis, from a large sample cohort study. METHODS: We recruited 596 female volunteers who participated in the Iwaki Health Promotion Project in 2017. Standing anterior-posterior radiographs of the knee were classified by the Kellgren-Lawrence (KL) grade. Radiographic OA was defined as a KL grade of ≥ 2. Individual effusion-synovitis was scored according to the Whole-Organ Magnetic Resonance Imaging Scoring System. Blood samples were collected, and metabolites were extracted from the plasma. Metabolome analysis was performed using capillary electrophoresis time-of-flight mass spectrometry. To investigate the relationships among metabolites, the KL grade, and effusion-synovitis scores, partial least squares with rank order of groups (PLS-ROG) analyses were performed. RESULTS: Among the 82 metabolites examined in this assay, PLS-ROG analysis identified 42 metabolites that correlated with OA severity. A subsequent metabolite set enrichment analysis using the significant metabolites showed the urea cycle and tricarboxylic acid cycle as key metabolic pathways. Moreover, further PLS-ROG analysis identified cystine (p = 0.009), uric acid (p = 0.024), and tyrosine (p = 0.048) as common metabolites associated with both OA severity and effusion-synovitis. Receiver operating characteristic analyses showed that cystine levels were moderately associated with radiographic OA (p < 0.001, area under the curve 0.714, odds ratio 3.7). CONCLUSION: Large sample metabolome analyses revealed that cystine, an amino acid associated with antioxidant activity and glutamate homeostasis, might be a potential metabolic biomarker for radiographic osteoarthritis and early phase synovitis.

Capillary Electrophoresis Mass Spectrometry-Based Metabolomics of Plasma Samples from Healthy Subjects in a Cross-Sectional Japanese Population Study.

For large-scale metabolomics, such as in cohort studies, normalization protocols using quality control (QC) samples have been established when using data from gas chromatography and liquid chromatography coupled to mass spectrometry. However, normalization protocols have not been established for capillary electrophoresis-mass spectrometry metabolomics. In this study, we performed metabolome analysis of 314 human plasma samples using capillary electrophoresis-mass spectrometry. QC samples were analyzed every 10 samples. The results of principal component analysis for the metabolome data from only the QC samples showed variations caused by capillary replacement in the first principal component score and linear variation with continuous measurement in the second principal component score. Correlation analysis between diagnostic blood tests and plasma metabolites normalized by the QC samples was performed for samples from 188 healthy subjects who participated in a Japanese population study. Five highly correlated pairs were identified, including two previously unidentified pairs in normal healthy subjects of blood urea nitrogen and guanidinosuccinic acid, and gamma-glutamyl transferase and cysteine glutathione disulfide. These results confirmed the validity of normalization protocols in capillary electrophoresis-mass spectrometry using large-scale metabolomics and comprehensive analysis.

Metagenomic analysis of gut microbiota reveals its role in trimethylamine metabolism in heart failure.

BACKGROUND: We had previously reported an increase in trimethylamine N-oxide (TMAO) levels in patients with both compensated and decompensated heart failure (HF) and alteration in gut microbiota composition using 16S rRNA gene amplicon analysis. Although a metagenome-wide analysis showed that choline-TMA lyase levels increased in HF patients, which TMA generation pathway from choline, carnitine, or betaine contributes to the increase in TMAO levels in HF needs to be elucidated. METHODS: We conducted a metagenome-wide shotgun sequencing analysis of gut microbiota and measured the TMAO levels in plasma of 22 HF patients during the compensated phase and 11 age-, sex-, and comorbidity-matched control subjects, whose gut microbiota compositions were reported in a previous 16S rRNA-based analysis. RESULTS: The abundance of cntA/B was positively correlated with TMAO, especially in HF patients, whereas that of cutC/D or betaine reductase was not correlated either in controls or HF patients. The abundance of cntA/B was mainly derived from the genera Escherichia and Klebsiella either in controls or HF patients. CONCLUSION: TMAO levels in plasma depend on the abundance of cntA/B in HF. Although it is difficult to exclude the involvement of confounding factors, microbial dysbiosis connecting the abundance of cntA/B in the gut and the increase of TMAO in plasma can be a therapeutic target for HF.

Uncovering the Role of Gut Microbiota in Amino Acid Metabolic Disturbances in Heart Failure Through Metagenomic Analysis.

Aims: Circulating amino acid (AA) abnormalities serve as predictors of adverse outcomes in patients with heart failure (HF). However, the role of the gut microbiota in AA disturbances remains unknown. Thus, we investigated gut microbial functions and their associations with AA metabolic alterations in patients with HF. Methods and Results: We performed whole-genome shotgun sequencing of fecal samples and mass spectrometry-based profiling of AAs in patients with compensated HF. Plasma levels of total essential AAs (EAAs) and histidine were significantly lower in patients with HF than in control subjects. HF patients also displayed increased and decreased abundance of gut microbial genes involved in the degradation and biosynthesis, respectively, of EAAs, including branched-chain AAs (BCAAs) and histidine. Importantly, a significant positive correlation was observed between the abundance of microbial genes involved in BCAA biosynthesis and plasma BCAA levels in patients with HF, but not in controls. Moreover, network analysis revealed that the depletion of Eubacterium and Prevotella, which harbor genes for BCAA and histidine biosynthesis, contributed to decreased abundance of microbial genes involved in the biosynthesis of those EAAs in patients with HF. Conclusions: The present study demonstrated the relationship between gut microbiota and AA metabolic disturbances in patients with HF.

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry.

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but also to identify biomarkers for early-stage cancer detection and prediction of chemotherapy response in cancer patients. Preparation of uniform samples for metabolomic analysis is a critical issue that remains to be addressed. Here, we present an easy and reliable protocol for extracting aqueous metabolites from cultured adherent cells for metabolomic analysis using capillary electrophoresis-mass spectrometry (CE-MS). Aqueous metabolites from cultured cells are analyzed by culturing and washing cells, treating cells with methanol, extracting metabolites, and removing proteins and macromolecules with spin columns for CE-MS analysis. Representative results using lung cancer cell lines treated with diamide, an oxidative reagent, illustrate the clearly observable metabolic shift of cells under oxidative stress. This article would be especially valuable to students and investigators involved in metabolomics research, who are new to harvesting metabolites from cell lines for analysis by CE-MS.

Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry.

Reports of the metabolomic characteristics of esophageal cancer are limited. In the present study, we thus conducted metabolome analysis of paired tumor tissues (Ts) and non-tumor esophageal tissues (NTs) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). The Ts and surrounding NTs were surgically excised pair-wise from 35 patients with esophageal cancer. Following tissue homogenization and metabolite extraction, a total of 110 compounds were absolutely quantified by CE-TOFMS. We compared the concentrations of the metabolites between Ts and NTs, between pT1 or pT2 (pT1-2) and pT3 or pT4 (pT3-4) stage, and between node-negative (pN-) and node-positive (pN+) samples. Principal component analysis and hierarchical clustering analysis revealed clear metabolomic differences between Ts and NTs. Lactate and citrate levels in Ts were significantly higher (P=0.001) and lower (P<0.001), respectively, than those in NTs, which corroborated with the Warburg effect in Ts. The concentrations of most amino acids apart from glutamine were higher in Ts than in NTs, presumably due to hyperactive glutaminolysis in Ts. The concentrations of malic acid (P=0.015) and citric acid (P=0.008) were significantly lower in pT3-4 than in pT1-2, suggesting the downregulation of tricarboxylic acid (TCA) cycle activity in pT3-4. On the whole, in this study, we demonstrate significantly different metabolomic characteristics between tumor and non-tumor tissues and identified a novel set of metabolites that were strongly associated with the degree of tumor progression. A further understanding of cancer metabolomics may enable the selection of more appropriate treatment strategies, thereby contributing to individualized medicine.

CD44 variant 9 expression as a predictor for gastric cancer recurrence: immunohistochemical and metabolomic analysis of surgically resected tissues.

CD44 variant 9 (CD44v9) and the heavy chain of 4F2 cell-surface antigen (CD98hc) appear important for regulation of reactive oxygen species defence and tumor growth in gastric cancer. This study examined the roles of CD44v9 and CD98hc as markers of gastric cancer recurrence, and investigated associations with energy metabolism. We applied capillary electrophoresis time-of-flight mass spectrometry to metabolome profiling of gastric cancer specimens from 103 patients who underwent resection with no residual tumor or microscopic residual tumor, and compared metabolite levels to immunohistochemical staining for CD44v9 and CD98hc. Positive expression rates were 40.7% for CD44v9 and 42.7% for CD98hc. Various tumor characteristics were significantly associated with CD44v9 expression. Five-year recurrence-free survival rate was significantly lower for CD44v9-positive tumors (39.1%) than for CD44v9-negative tumors (73.5%; P < 0.0001), but no significant differences in recurrence-free survival were seen according to CD98hc expression. Uni- and multivariate analyses identified positive CD44v9 expression as an independent predictor of poorer recurrence-free survival. Metabolome analysis of 110 metabolites found that levels of glutathione disulfide were significantly lower and reduced glutathione (GSH)/ glutathione disulfide (GSSG) ratio was significantly higher in CD44v9-positive tumors than in CD44v9-negative tumors, suggesting that CD44v9 may enhance pentose phosphate pathway flux and maintain GSH levels in cancer cells.

Arginine Deprivation Inhibits the Warburg Effect and Upregulates Glutamine Anaplerosis and Serine Biosynthesis in ASS1-Deficient Cancers.

Targeting defects in metabolism is an underutilized strategy for the treatment of cancer. Arginine auxotrophy resulting from the silencing of argininosuccinate synthetase 1 (ASS1) is a common metabolic alteration reported in a broad range of aggressive cancers. To assess the metabolic effects that arise from acute and chronic arginine starvation in ASS1-deficient cell lines, we performed metabolite profiling. We found that pharmacologically induced arginine depletion causes increased serine biosynthesis, glutamine anaplerosis, oxidative phosphorylation, and decreased aerobic glycolysis, effectively inhibiting the Warburg effect. The reduction of glycolysis in cells otherwise dependent on aerobic glycolysis is correlated with reduced PKM2 expression and phosphorylation and upregulation of PHGDH. Concurrent arginine deprivation and glutaminase inhibition was found to be synthetic lethal across a spectrum of ASS1-deficient tumor cell lines and is sufficient to cause in vivo tumor regression in mice. These results identify two synthetic lethal therapeutic strategies exploiting metabolic vulnerabilities of ASS1-negative cancers.

Differential Glutamate Metabolism in Proliferating and Quiescent Mammary Epithelial Cells.

Mammary epithelial cells transition between periods of proliferation and quiescence during development, menstrual cycles, and pregnancy, and as a result of oncogenic transformation. Utilizing an organotypic 3D tissue culture model coupled with quantitative metabolomics and proteomics, we identified significant differences in glutamate utilization between proliferating and quiescent cells. Relative to quiescent cells, proliferating cells catabolized more glutamate via transaminases to couple non-essential amino acid (NEAA) synthesis to α-ketoglutarate generation and tricarboxylic acid (TCA) cycle anaplerosis. As cells transitioned to quiescence, glutamine consumption and transaminase expression were reduced, while glutamate dehydrogenase (GLUD) was induced, leading to decreased NEAA synthesis. Highly proliferative human tumors display high transaminase and low GLUD expression, suggesting that proliferating cancer cells couple glutamine consumption to NEAA synthesis to promote biosynthesis. These findings describe a competitive and partially redundant relationship between transaminases and GLUD, and they reveal how coupling of glutamate-derived carbon and nitrogen metabolism can be regulated to support cell proliferation.

Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry.

Metabolic microenvironment of tumor cells is influenced by oncogenic signaling and tissue-specific metabolic demands, blood supply, and enzyme expression. To elucidate tumor-specific metabolism, we compared the metabolomics of normal and tumor tissues surgically resected pairwise from nine lung and seven prostate cancer patients, using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Phosphorylation levels of enzymes involved in central carbon metabolism were also quantified. Metabolomic profiles of lung and prostate tissues comprised 114 and 86 metabolites, respectively, and the profiles not only well distinguished tumor from normal tissues, but also squamous cell carcinoma from the other tumor types in lung cancer and poorly differentiated tumors from moderately differentiated tumors in prostate cancer. Concentrations of most amino acids, especially branched-chain amino acids, were significantly higher in tumor tissues, independent of organ type, but of essential amino acids were particularly higher in poorly differentiated than moderately differentiated prostate cancers. Organ-dependent differences were prominent at the levels of glycolytic and tricarboxylic acid cycle intermediates and associated energy status. Significantly high lactate concentrations and elevated activating phosphorylation levels of phosphofructokinase and pyruvate kinase in lung tumors confirmed hyperactive glycolysis. We highlighted the potential of CE-TOFMS-based metabolomics combined with phosphorylated enzyme analysis for understanding tissue-specific tumor microenvironments, which may lead to the development of more effective and specific anticancer therapeutics.

Metabolomic profiling rationalized pyruvate efficacy in cybrid cells harboring MELAS mitochondrial DNA mutations.

Pyruvate treatment was found to alleviate clinical symptoms of mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome and is highly promising therapeutic. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we measured time-changes of 161 intracellular and 85 medium metabolites to elucidate metabolic effects of pyruvate treatment on cybrid human 143B osteosarcoma cells harboring normal (2SA) and MELAS mutant (2SD) mitochondria. The results demonstrated dramatic and sustainable effects of pyruvate administration on the energy metabolism of 2SD cells, corroborating pyruvate as a metabolically rational treatment regimen for improving symptoms associated with MELAS and possibly other mitochondrial diseases.

Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry.

Most cancer cells predominantly produce energy by glycolysis rather than oxidative phosphorylation via the tricarboxylic acid (TCA) cycle, even in the presence of an adequate oxygen supply (Warburg effect). However, little has been reported regarding the direct measurements of global metabolites in clinical tumor tissues. Here, we applied capillary electrophoresis time-of-flight mass spectrometry, which enables comprehensive and quantitative analysis of charged metabolites, to simultaneously measure their levels in tumor and grossly normal tissues obtained from 16 colon and 12 stomach cancer patients. Quantification of 94 metabolites in colon and 95 metabolites in stomach involved in glycolysis, the pentose phosphate pathway, the TCA and urea cycles, and amino acid and nucleotide metabolisms resulted in the identification of several cancer-specific metabolic traits. Extremely low glucose and high lactate and glycolytic intermediate concentrations were found in both colon and stomach tumor tissues, which indicated enhanced glycolysis and thus confirmed the Warburg effect. Significant accumulation of all amino acids except glutamine in the tumors implied autophagic degradation of proteins and active glutamine breakdown for energy production, i.e., glutaminolysis. In addition, significant organ-specific differences were found in the levels of TCA cycle intermediates, which reflected the dependency of each tissue on aerobic respiration according to oxygen availability. The results uncovered unexpectedly poor nutritional conditions in the actual tumor microenvironment and showed that capillary electrophoresis coupled to mass spectrometry-based metabolomics, which is capable of quantifying the levels of energy metabolites in tissues, could be a powerful tool for the development of novel anticancer agents that target cancer-specific metabolism.