Glucosylceramide (GlcCer) is a major membrane lipid and the precursor of gangliosides. GlcCer is mainly degraded by two enzymes, lysosomal acid β-glucosidase (GBA) and nonlysosomal β-glucosidase (GBA2), which may have different isoforms because of alternative splicing. To understand which GBA2 isoforms are active and how they affect glycosphingolipid levels in cells, we expressed nine human GBA2 isoforms in COS-7 cells, confirmed their expression by qRT-PCR and Western blotting, and assayed their activity to hydrolyze 4-methylumbelliferyl-β-D-glucopyranoside (4MUG) in cell extracts. Human GBA2 isoform 1 showed high activity, while the other isoforms had activity similar to the background. Comparison of sphingolipid levels by ultra-high resolution/accurate mass spectrometry (UHRAMS) analysis showed that isoform 1 overexpression increased ceramide and decreased hexosylceramide levels. Comparison of ratios of glucosylceramides to the corresponding ceramides in the extracts indicated that GBA2 isoform 1 has broad specificity for the lipid component of glucosylceramide, suggesting that only one GBA2 isoform 1 is active and affects sphingolipid levels in the cell. Our study provides new insights into how increased breakdown of GlcCer affects cellular lipid metabolic networks. Full article

Colorectal cancer is one of the most prevalent cancers in Korea and globally. In this study, we aimed to characterize the differential serum metabolomic profiles between pre-operative and post-operative patients with colorectal cancer. To investigate the significant metabolites and metabolic pathways associated with colorectal cancer, we analyzed serum samples from 68 patients (aged 20–71, mean 57.57 years). Untargeted and targeted metabolomics profiling in patients with colorectal cancer were performed using liquid chromatography-mass spectrometry. Untargeted analysis identified differences in sphingolipid metabolism, steroid biosynthesis, and arginine and proline metabolism in pre- and post-operative patients with colorectal cancer. We then performed quantitative target profiling of polyamines, synthesized from arginine and proline metabolism, to identify potential polyamines that may serve as effective biomarkers for colorectal cancer. Results indicate a significantly reduced serum concentration of putrescine in post-operative patients compared to pre-operative patients. Our metabolomics approach provided insights into the physiological alterations in patients with colorectal cancer after surgery. Full article

Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provided a publicly available, user-friendly R script to streamline the imputation of missing endogenous, unannotated, and xenobiotic metabolites. We evaluated the multivariate imputation by chained equations (MICE) and k-nearest neighbors (kNN) analyses implemented in our script by simulations using measured metabolites data from the Netherlands Epidemiology of Obesity (NEO) study (n = 599). We simulated missing values in four unique metabolites from different pathways with different correlation structures in three sample sizes (599, 150, 50) with three missing percentages (15%, 30%, 60%), and using two missing mechanisms (completely at random and not at random). Based on the simulations, we found that for MICE, larger sample size was the primary factor decreasing bias and error. For kNN, the primary factor reducing bias and error was the metabolite correlation with its predictor metabolites. MICE provided consistently higher performance measures particularly for larger datasets (n > 50). In conclusion, we presented an imputation workflow in a publicly available R script to impute untargeted metabolomics data. Our simulations provided insight into the effects of sample size, percentage missing, and correlation structure on the accuracy of the two imputation methods. Full article

Loss of functional β-cell mass is a hallmark of Type 1 and Type 2 Diabetes. Macrophages play an integral role in the maintenance or destruction of pancreatic β-cells. The effect of the macrophage β-cell interaction is dependent on the activation state of the macrophage. Macrophages can be activated across a spectrum, from pro-inflammatory to anti-inflammatory and tissue remodeling. The factors secreted by these differentially activated macrophages and their effect on β-cells define the effect on functional β-cell mass. In this review, the spectrum of macrophage activation is discussed, as are the positive and negative effects on β-cell survival, expansion, and function as well as the defined factors released from macrophages that impinge on functional β-cell mass. Full article

Mice lacking the GABA_B(1) subunit of gamma-aminobutyric acid (GABA) type B receptors exhibit spontaneous seizures, hyperalgesia, hyperlocomotor activity, and memory impairment. Although mice lacking the GABA_B(1) subunit are viable, they are sterile, and to generate knockout (KO) mice, it is necessary to cross heterozygous (HZ) mice. The aim of our study was to detect the metabolic differences between the three genotypes of GABA_B(1) KO mice in order to further characterize this experimental animal model. Plasma samples were collected from wild-type (WT), HZ, and KO mice. Samples were analyzed by means of a gas chromatography-mass spectrometry (GC-MS) platform. Univariate t-test, and partial least square discriminant analysis (PLS-DA) were performed to compare the metabolic pattern of different genotypes. The metabolomic analysis highlighted differences between the three genotypes and identified some metabolites less abundant in KO mice, namely elaidic acid and other fatty acids, and chiro-inositol. Full article

Despite the fact that glucose is the main fuel of the brain, hyperglycemia at hospital admission is generally associated with a poor functional outcome in stroke patients. This paradox may be explained by the lack of information about the blood glucose level at stroke onset. Here, we analyzed the metabolome of blood cells entrapped in cerebral thrombi to gain insight into their metabolism at stroke onset. Fourty-one consecutive stroke patients completely recanalized by mechanical thrombectomy within 6 h were included. The metabolome of retrieved thrombi was analyzed by liquid chromatography tandem with mass spectrometry. Discriminant Analysis (sparse Partial Least Squares Discriminant Analysis (sPLS-DA)) was performed to identify classification models and significant associated features of favorable clinical outcome at 3 months (modified Rankin Scale (mRS) < 2). sPLS-DA of the metabolomes of cerebral thrombi discriminated between stroke patients with a favorable or poor clinical outcome (Area Under the Curve (AUC) = 0.992 (0.931–1)). In addition, our results revealed that high sorbitol and glucose levels in the thrombi positively correlated with favorable clinical outcomes. Sorbitol, a short-term glycemic index reflecting a high blood glucose level at stroke onset, was found to be an independent predictor of good outcome (AUC = 0.908 (0.807–0.995)). This study demonstrates that a high blood glucose level at stroke onset is beneficial to the clinical outcome of the patient. Full article

Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H₂SO₄, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H₂SO₄ is more effective at optimising detection of VOCs than HCl or NaOH. H₂SO₄ resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H₂SO₄ to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification. Full article

Life expectancy has risen in the past decades, resulting in an increase in the number of aged individuals. Exercise remains one of the most cost-effective treatments against disease and the physical consequences of aging. The purpose of this research was to investigate the effects of aging, long-term and lifelong exercise on the rat urinary metabolome. Thirty-six male Wistar rats were divided into four equal groups: exercise from 3 to 12 months of age (A), lifelong exercise from 3 to 21 months of age (B), no exercise (C), and exercise from 12 to 21 months of age (D). Exercise consisted in swimming for 20 min/day, 5 days/week. Urine samples collection was performed at 3, 12 and 21 months of life and their analysis was conducted by liquid chromatography-mass spectrometry. Multivariate analysis of the metabolite data did not show any discrimination between groups at any of the three aforementioned ages. However, multivariate analysis discriminated the three ages clearly when the groups were treated as one. Univariate analysis showed that training increased the levels of urinary amino acids and possibly protected against sarcopenia, as evidenced by the higher levels of creatine in the exercising groups. Aging was accompanied by decreased levels of urinary amino acids and signs of increased glycolysis. Concluding, both aging and, to a lesser degree, exercise affected the rat urinary metabolome, including metabolites related to energy metabolism, with exercise showing a potential to mitigate the consequences of aging. Full article

Compelling evidence from earlier studies suggests that the pancreatic beta cell is inherently weak in its antioxidant defense mechanisms to face the burden of protecting itself against the increased intracellular oxidative stress following exposure to proinflammatory cytokines. Recent evidence implicates novel roles for nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (Noxs) as contributors to the excessive intracellular oxidative stress and damage under metabolic stress conditions. This review highlights the existing evidence on the regulatory roles of at least three forms of Noxs, namely Nox1, Nox2, and Nox4, in the cascade of events leading to islet beta cell dysfunction, specifically under the duress of chronic exposure to cytokines. Potential crosstalk between key signaling pathways (e.g., inducible nitric oxide synthase [iNOS] and Noxs) in the generation and propagation of reactive molecules and metabolites leading to mitochondrial damage and cell apoptosis is discussed. Available data accrued in investigations involving small-molecule inhibitors and antioxidant protein expression methods as tools toward the prevention of cytokine-induced oxidative damage are reviewed. Lastly, current knowledge gaps in this field, and possible avenues for future research are highlighted. Full article

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy. Full article

Gliomas are the most aggressive phenotypes of brain tumors and are classified into four grades according to the malignancy degree by the World Health Organization. Metabolic profiling can provide an overview of metabolic reprogramming at a specific stage of tumor initiation and development. Studies about metabolic alterations related to different grades of gliomas are helpful to understand the molecular mechanism for progression of glioma. In the current study, metabolomics and lipidomics analyses based on chromatography-mass spectrometry were performed on different grades of glioma tissues. Differential metabolites between glioma and para-tumor tissues were studied and used as the basis to explore metabolic alterations related to glioma grading. It was found that short-chain acylcarnitines were elevated, whereas lysophosphatidylethanolamines (LPEs) were decreased in high-grade gliomas. Furthermore, the gene expression of short/branched-chain acyl-coenzyme dehydrogenase (ACADSB), which is involved in fatty acid oxidation, was found down-regulated with glioma progression by analyzing related genes and pathways. In addition, LPE metabolism showed a significant difference among different grades of gliomas. These important metabolic pathways related to glioma progression may provide potential clues for further study on the mechanisms and treatment of glioma. Full article

The citrus industry at present is severely affected by huanglongbing disease (HLB). HLB is caused by the supposed bacterial pathogen “Candidatus Liberibacter asiaticus” and is transmitted by the insect vector, the Asian citrus psyllid, Diaphorina citri Kuwayama. Developing new citrus hybrids to improve HLB management is much needed. In this study, we investigated the metabolomic profiles of three new hybrids produced from the cross of C2-5-12 Pummelo (Citrus maxima (L.) Osbeck) × pollen from Citrus latipes. The hybrids were selected based on leaf morphology and seedling vigor. The selected hybrids exhibited compact and upright tree architecture as seen in C. latipes. Hybrids were verified by simple sequence repeat markers, and were subjected to metabolomic analysis using gas chromatography-mass spectrometry. The volatile organic compounds (VOCs) and polar metabolites profiling also showed that the new hybrids were different from their parents. Interestingly, the levels of stored VOCs in hybrid II were higher than those observed in its parents and other hybrids. The level of most VOCs released by hybrid II was also higher than that released from its parents. Additionally, the preference assay showed that hybrid II was more attractive to D. citri than its parents and other hybrids. The leaf morphology, compact and upright architecture of hybrid II, and its attraction to D. citri suggest that it could be used as a windbreak and trap tree for D. citri (double duty), once its tolerance to HLB disease is confirmed. Our results showed that metabolomic analysis could be successfully used to understand the biochemical mechanisms controlling the interaction of D. citri with its host plants. Full article