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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.

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.

Plasmodium vivax malaria control and elimination is complicated by the presence of dormant liver stages, known as hypnozoites, that can reactivate weeks, months or years after infection giving rise to clinical and transmissible vivax malaria, without exposure to new infectious mosquito bites. Hypnozoite infection remains without symptoms and there are no diagnostic tools available to identify hypnozoite carriers. Such diagnostic tools are invaluable for precise mapping of the scale of the infection problem and for identifying individuals that would qualify for targeted drug treatment, to wipe out this hidden reservoir of malaria parasites. Targeted treatment would have considerable benefits as it would prevent the exposure of individuals without hypnozoites to the considerable side-effects of drugs such as Primaquine, which has a relatively high toxicity to people deficient in glucose-6-phosphate dehydrogenase. Here we present a Proof-of-Concept study aimed at identifying diagnostic markers for malaria hypnozoite infection, by combining in vitro Plasmodium cynomolgi hypnozoite cultures (an accessible proxy to P. vivax with nearly identical biology) with sensitive metabolomics. Specific hypnozoite-related metabolites have been identified in the supernatant of hypnozoite-enriched in vitro liver stage cultures. This suggests that, following in vivo validation of such metabolites in the P. cynomolgi /rhesus monkey model and subsequent in P. vivax -infected individuals, a rapid diagnostic test for hypnozoite infection may be developed.

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.

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 pairwise 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.

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.

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.

Background/Objective: A dysregulated metabolism has been studied as a key aspect of the COVID-19 pathophysiology, but its longitudinal progression in severe cases remains unclear. In this study, we aimed to investigate metabolic dysregulation over time in patients with severe COVID-19 requiring mechanical ventilation (MV). Methods: In this single-center, prospective, observational study, we obtained 236 serum samples from 118 adult patients on MV in an ICU. The metabolite measurements were performed using capillary electrophoresis Fourier transform mass spectrometry, and we categorized the sampling time points into three time zones to align them with the disease progression: time zone 1 (T1) (the hyperacute phase, days 1–3 post-MV initiation), T2 (the acute phase, days 4–14), and T3 (the chronic phase, days 15–30). Using volcano plots and enrichment pathway analyses, we identified the differential metabolites (DMs) and enriched pathways (EPs) between the survivors and non-survivors for each time zone. The DMs and EPs were further grouped into early-stage, late-stage, and consistent groups based on the time zones in which they were detected. Results: With the 566 annotated metabolites, we identified 38 DMs and 17 EPs as the early-stage group, which indicated enhanced energy production in glucose, amino acid, and fatty acid metabolisms in non-survivors. As the late-stage group, 84 DMs and 10 EPs showed upregulated sphingolipid, taurine, and tryptophan–kynurenine metabolisms with downregulated steroid hormone synthesis in non-survivors. Three DMs and 23 EPs in the consistent group showed more pronounced dysregulation in the dopamine and arachidonic acid metabolisms across all three time zones in non-survivors. Conclusions: This study elucidated the temporal differences in metabolic dysregulation between survivors and non-survivors of severe COVID-19, offering insights into its longitudinal progression and disease mechanisms.

Constructing metabolic profiles of 60 cancer cell lines by integrating metabolomics and systems biology revealed increased glycine consumption in highly proliferative cancer cells. Recent breakthroughs in cancer metabolism include the identification of an alternative glycolytic pathway in proliferative cells ( 1 ) and an essential role for the serine synthesis pathway in breast cancer ( 2 ). With a data-driven approach, as opposed to the conventional hypothesis-driven approach, in this issue, on page 1040, Jain et al. ( 3 ) determined that rapidly proliferating cancer cells require large amounts of the nonessential amino acid glycine, which has clear and direct implications for cancer therapy.

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.