Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

Evaluation and optimization of sample pretreatment for GC/MS-based metabolomics in embryonic zebrafish

Metabolomics tactics have been applied in the research associated with embryonic zebrafish. However, the report regarding the evaluation of impacts of sample pretreatment on metabolomics results from zebrafish embryos is limited. In the present study, different data normalization approaches, extraction solvents, and extraction strategies for off-line derivatization gas chromatography coupled with mass spectrometry-based metabolomics analysis of zebrafish eleutheroembryos were evaluated and optimized. The results showed that, when 4-chlorophenylalanine normalization, sample homogenization and pure methanol combined with ultrasonic extraction were conducted, better repeatabilities, higher signals and broader coverages of detected metabolites can be achieved. The recovery and standard deviation of most standards were in the range of 82%-121% and 6.6%-12%, respectively, while the relative standard deviation of major detected metabolites ranged from 5.4% to 19%, indicating good extraction efficiencies and method precision. Under the developed method, 87 important endogenous metabolites such as citric acid and hypoxanthine were identified by universal databases or standards among 270 extracted metabolites, which consisted of sugars, amines, amino acids, nucleotides, fatty acids, and sterols. Therefore, the results could provide a proper pretreatment protocol for the analysis of wide-coverage metabolome in embryonic zebrafish. In addition, this study highlights the impact of normalization and extraction methods on the data quality of metabolomics analysis.

Resistance against Sclerotinia sclerotiorum in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis

Sclerotinia sclerotiorum, a predominately necrotrophic fungal pathogen with a broad host range, causes a significant yield-limiting disease of soybean called Sclerotinia stem rot. Resistance mechanisms against this pathogen in soybean are poorly understood, thus hindering the commercial deployment of resistant varieties. We used a multiomic approach utilizing RNA-sequencing, gas chromatography-mass spectrometry-based metabolomics and chemical genomics in yeast to decipher the molecular mechanisms governing resistance to S. sclerotiorum in soybean. Transcripts and metabolites of two soybean recombinant inbred lines, one resistant and one susceptible to S. sclerotiorum were analysed in a time course experiment. The combined results show that resistance to S. sclerotiorum in soybean is associated in part with an early accumulation of JA-Ile ((+)-7-iso-jasmonoyl-L-isoleucine), a bioactive jasmonate, increased ability to scavenge reactive oxygen species, and importantly, a reprogramming of the phenylpropanoid pathway leading to increased antifungal activities. Indeed, we noted that phenylpropanoid pathway intermediates, such as 4-hydroxybenzoate, cinnamic acid, ferulic acid and caffeic acid, were highly accumulated in the resistant line. In vitro assays show that these metabolites and total stem extracts from the resistant line clearly affect S. sclerotiorum growth and development. Using chemical genomics in yeast, we further show that this antifungal activity targets ergosterol biosynthesis in the fungus, by disrupting enzymes involved in lipid and sterol biosynthesis. Overall, our results are consistent with a model where resistance to S. sclerotiorum in soybean coincides with an early recognition of the pathogen, leading to the modulation of the redox capacity of the host and the production of antifungal metabolites.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

Changes in the Serum Metabolome of Acute Myocardial Ischemia Rat Pretreatment with Electroacupuncture

Myocardial infarction (MI), the most common symptom is chest pain, occurs when blood flow decreases or stops to a part of the heart, causing damage to the heart muscle. Electroacupuncture pretreatment (EP) is a recent observation which has been shown to induce ischemic tolerance like the ischemia preconditioning, suggesting that EP may be a promising preventive strategy for individual susceptibility to MI. This study investigated mechanisms that underlie the effect of EP on MI through the use of gas chromatography-mass spectrometry (GC-MS)-based metabolic profiling. Male Sprague-Dawley rats were randomly divided to receive or not receive three days of EP at PC6 (Neiguan). Then on the fourth day, each group was further divided to undergo mock surgery or MI, induced by ligation of the left anterior descending coronary artery. After 24h, the blood samples and hearts were collected for the follow-up research. The results showed that treatment by EP significantly reduced the levels of CK-MB, cTnT, AST, and MDH in serum and decreased myocardial infarction area. According to GC-MS-based serum metabolic profiling and analysis, a total of 636 characteristic peaks were identified, including 158 known and 478 unknown metabolites. MI caused comprehensive metabolic changes in glycolysis-related metabolites, malate-aspartate shuttle (MAS) metabolites, and purine metabolites with anti-oxidant functions, while EP reversed more than half of the differential metabolic changes, mainly affecting amino acid and energy metabolism, especially the glutamate metabolism and MAS. In a word, our findings suggest that EP exerts its cardioprotective effect on MI by regulating amino acid and energy metabolisms. Meanwhile, GC-MS-based metabolomics provided a powerful way to characterize the metabolic features of MI, with and without EP, and thereby improved our understanding of the effect and mechanisms of EP.

Pharmacometabolomics of Respiratory Phenotypic Response to Dexamethasone in Preterm Infants at Risk for Bronchopulmonary Dysplasia

A prospective cohort study was performed in preterm infants less than 32 weeks gestation at birth who were treated with dexamethasone for developing or established bronchopulmonary dysplasia (BPD). Respiratory phenotype (Respiratory Severity Score (RSS)), serum, and urine metabolomics were assessed before and after treatment. Ten infants provided nine matched serum and nine matched urine samples. There was a significant decrease in RSS with steroid treatment. Serum gluconic acid had the largest median fold change (140 times decreased, P = 0.008). In metabolite set enrichment analysis, in both serum and urine, the urea cycle, ammonia recycling, and malate-aspartate shuttle pathways were most significantly enriched when comparing pretreatment and post-treatment (P value < 0.05). In regression analyses, 6 serum and 28 urine metabolites were significantly associated with change in RSS. Urine gluconic acid lactone was the most significantly correlated with clinical response (correlational coefficient 0.915). Pharmacometabolomic discovery of drug response biomarkers in preterm infants may allow precision therapeutics in BPD treatment.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer

We used blood serum samples collected from 31 lung cancer (LC) patients and 29 healthy volunteers in this study. Levels of serum metabolites were qualitative quantified with gas chromatography-mass spectrometry (GC-MS), and the data were analyzed by partial least-squares discrimination analysis (PLS-DA). Based on the Kyoto Encyclopedia of Genes and Genomes database, we performed pathway-based analysis utilizing metabolites presented at differential abundance between the LC serum samples and the normal healthy serum samples for systematical investigation on the metabolic alterations associated with LC pathogenesis. Finally, we analyzed the significantly enriched pathways as well as their relevant differentially expressed messenger RNAs, and drawn a correlation network plot to identify the serum metabolic biomarkers and the significantly altered metabolic pathways for LC. GC-MS analysis showed that 23 of the 169 metabolites identified were significantly different. PLS-DA model revealed that 13 of these metabolites were with variable importance > 1, and particularly five were with area under curve > 0.9. Pathway-based analysis demonstrated that five of eight enriched metabolic pathways were statistically significant with false discovery rate < 0.05. Lastly, the correlation networks between these pathways and their related genes suggested that 29 genes had correlation degree > 10, which were mainly engaged in the purine metabolism. In conclusion, we identified indole-3-lactate, erythritol, adenosine-5-phosphate, paracetamol and threitol as serum metabolic biomarkers for LC through metabolomics analysis. Besides, we identified the purine metabolism as the significantly altered metabolic pathway in LC with the help of transcriptomics analysis.

An untargeted fecal and urine metabolomics analysis of the interplay between the gut microbiome, diet and human metabolism in Indian and Chinese adults

Gut microbiome plays a vital role in human health. Human fecal and urine metabolome could provide a functional readout of gut microbial metabolism as well as its interaction with host and diet. However, this relationship still needs to be fully characterized. We established an untargeted GC-MS metabolomics method which enabled the detection of 122 and 86 metabolites including amino acids, phenolics, indoles, carbohydrates, sugars and metabolites of microbial origin from fecal and urine samples respectively. 41 compounds were confirmed using external standards. Next, we compared the fecal and urine metabolome of 16 healthy Indian and Chinese adults, ages 22–35 years, using a combined GC-MS and LC-MS approach. We showed dietary habit or ethnicity wise grouping of urine and fecal metabolite profiles of Indian and Chinese adults. Our analysis revealed 53 differentiating metabolites including higher abundance of amino acids and phenolics in Chinese and higher abundance of fatty acids, glycocholic acid, metabolites related to tryptophan metabolism in Indian adults. Correlation analysis showed a strong association of metabolites with gut bacterial profiles of the same subjects in the genus and species level. Thus, our results suggest that gut bacterial compositional changes could be eventually monitored and probed using a metabolomics approach.

Metabolic modulation predicts heart failure tests performance

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO₂), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO₂ slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO₂ and Peak VO₂ were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.

Myricetin Ameliorates Ethanol-Induced Lipid Accumulation in Liver Cells by Reducing Fatty Acid Biosynthesis

SCOPE

Alcoholic liver disease is a serious threat to human health. The development of drug candidates from complementary and alternative medicines is an attractive approach. Myricetin can be found in fruit, vegetables, and herbs. This study investigates the protective effect of myricetin on ethanol-induced injury in mouse liver cells.

METHODS AND RESULTS

Oil-red O staining, assays of oxidative stress and measurements of inflammatory markers in mouse AML12 liver cells collectively demonstrate that myricetin elicits a curative effect on ethanol-induced injury. Next, the role of myricetin in the metabolic regulation of ethanol pathology in liver cells is assessed by gas chromatography coupled with mass spectrometry. Myricetin inhibits ethanol-stimulated fatty acid biosynthesis. Additionally, dodecanoic acid may be proposed as a potential biomarker related to ethanol pathology or myricetin therapy. It is also observed that myricetin enhances ethanol-induced inhibition of the mitochondrial electron transport chain. Moreover, fumaric acid is found to be a candidate biomarker related to ethanol toxicity or myricetin therapy. Quantitative reverse-transcription-PCR shows that ethanol-induced fatty acid synthase and sterol regulatory element-binding protein-1c mRNA levels are alleviated by myricetin. Finally, myricetin increases ethanol-induced inhibition of phosphorylation of AMP-activated protein kinase.

CONCLUSION

These results elucidate the pharmacological mechanism of myricetin on ethanol-induced lipid accumulation.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Application of Ion Chromatography Coupled with Mass Spectrometry for Human Serum and Urine Metabolomics

Biomarkers that indicate the presence or severity of organ damage caused by diseases and toxicities are useful diagnostic tools. Metabolomics platforms using chromatography coupled with mass spectrometry (MS) have been widely used for biomarker screening. In this study, we aimed to establish a novel metabolomics platform using ion chromatography coupled with MS (IC-MS) for human biofluids. We found that ethylenediaminetetraacetic acid (EDTA) plasma is not suitable for IC-MS metabolomics platforms because of the desensitization of MS. IC-MS enabled detection of 131 polar metabolites in human serum and urine from healthy volunteers. Pathway analysis demonstrated that the metabolites detectable using our platform were composed of a broad spectrum of organic acids with carboxylic moieties. These metabolites were significantly associated with pathways such as the tricarboxylic acid (TCA) cycle; glyoxylate and dicarboxylate metabolism; alanine, aspartate, and glutamate metabolism; butanoate metabolism; and the pentose phosphate pathway. Moreover, comparison of serum and urine samples showed that four metabolites (4-hydroxybutyric acid, aspartic acid, lactic acid, and γ-glutamyl glutamine) were abundant in serum, whereas 62 metabolites, including phosphoric acid, vanillylmandelic acid, and N-tiglylglycine, were abundant in urine. In addition, allantoin and uric acid were abundant in male serum, whereas no gender-associated differences were found for polar metabolites in urine. Our results demonstrate that the present established IC-MS metabolomics platform can be applied for analysis of human serum and urine as well as detection of a broad spectrum of polar metabolites in human biofluids.

Metabolic Reprogramming in Astrocytes Distinguishes Region-Specific Neuronal Susceptibility in Huntington Mice

The basis for region-specific neuronal toxicity in Huntington disease is unknown. Here, we show that region-specific neuronal vulnerability is a substrate-driven response in astrocytes. Glucose is low in HdhQ(150/150) animals, and astrocytes in each brain region adapt by metabolically reprogramming their mitochondria to use endogenous, non-glycolytic metabolites as an alternative fuel. Each region is characterized by distinct metabolic pools, and astrocytes adapt accordingly. The vulnerable striatum is enriched in fatty acids, and mitochondria reprogram by oxidizing them as an energy source but at the cost of escalating reactive oxygen species (ROS)-induced damage. The cerebellum is replete with amino acids, which are precursors for glucose regeneration through the pentose phosphate shunt or gluconeogenesis pathways. ROS is not elevated, and this region sustains little damage. While mhtt expression imposes disease stress throughout the brain, sensitivity or resistance arises from an adaptive stress response, which is inherently region specific. Metabolic reprogramming may have relevance to other diseases.

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.

A new optimization strategy for MALDI FTICR MS tissue analysis for untargeted metabolomics using experimental design and data modeling

Ultra-high-resolution imaging mass spectrometry using matrix-assisted laser desorption ionization (MALDI) MS coupled to a Fourier transform ion cyclotron resonance (FTICR) mass analyzer is a powerful technique for the visualization of small molecule distribution within biological tissues. The FTICR MS provides ultra-high resolving power and mass accuracy that allows large molecular coverage and molecular formula assignments, both essential for untargeted metabolomics analysis. These performances require fine optimizations of the MALDI FTICR parameters. In this context, this study proposes a new strategy, using experimental design, for the optimization of ion transmission voltages and MALDI parameters, for tissue untargeted metabolomics analysis, in both positive and negative ionization modes. These experiments were conducted by assessing the effects of nine factors for ion transmission voltages and four factors for MALDI on the number of peaks, the weighted resolution, and the mean error within m/z 150-1000 mass range. For this purpose, fractional factorial designs were used with multiple linear regression (MLR) to evaluate factor effects and to optimize parameter values. The optimized values of ion transmission voltages (RF amplitude TOF, RF amplitude octopole, frequency transfer optic, RF frequency octopole, deflector plate, funnel 1, skimmer, funnel RF amplitude, time-of-flight, capillary exit), MALDI parameters (laser fluence, number of laser shots), and detection parameters (data size, number of scans) led to an increase of 32% and 18% of the number of peaks, an increase of 8% and 39% of the resolution, and a decrease of 56% and 34% of the mean error in positive and negative ionization modes, respectively. Graphical abstract.

Temporal Effects on Radiation Responses in Nonhuman Primates: Identification of Biofluid Small Molecule Signatures by Gas Chromatography⁻Mass Spectrometry Metabolomics

Whole body exposure to ionizing radiation damages tissues leading to physical symptoms which contribute to acute radiation syndrome. Radiation biodosimetry aims to determine characteristic early biomarkers indicative of radiation exposure and is necessary for effective triage after an unanticipated radiological incident. Radiation metabolomics can address this aim by assessing metabolic perturbations following exposure. Gas chromatography-mass spectrometry (GC-MS) is a standardized platform ideal for compound identification. We performed GC time-of-flight MS for the global profiling of nonhuman primate urine and serum samples up to 60 d after a single 4 Gy γ-ray total body exposure. Multivariate statistical analysis showed higher group separation in urine vs. serum. We identified biofluid markers involved in amino acid, lipid, purine, and serotonin metabolism, some of which may indicate host microbiome dysbiosis. Sex differences were observed for amino acid fold changes in serum samples. Additionally, we explored mitochondrial dysfunction by tricarboxylic acid intermediate analysis in the first week with a GC tandem quadrupole MS platform. By adding this temporal component to our previous work exploring dose effects at 7 d, we observed the highest fold changes occurring at 3 d, returning closer to basal levels by 7 d. These results emphasize the utility of both MS-based metabolomics for biodosimetry and complementary analytical platforms for increased metabolome coverage.

Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts

High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer1,2, but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. Here we develop a label-free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.

MAGE cancer-testis antigens protect the mammalian germline under environmental stress

Ensuring robust gamete production even in the face of environmental stress is of utmost importance for species survival, especially in mammals that have low reproductive rates. Here, we describe a family of genes called melanoma antigens (MAGEs) that evolved in eutherian mammals and are normally restricted to expression in the testis (http://MAGE.stjude.org) but are often aberrantly activated in cancer. Depletion of Mage-a genes disrupts spermatogonial stem cell maintenance and impairs repopulation efficiency in vivo. Exposure of Mage-a knockout mice to genotoxic stress or long-term starvation that mimics famine in nature causes defects in spermatogenesis, decreased testis weights, diminished sperm production, and reduced fertility. Last, human MAGE-As are activated in many cancers where they promote fuel switching and growth of cells. These results suggest that mammalian-specific MAGE genes have evolved to protect the male germline against environmental stress, ensure reproductive success under non-optimal conditions, and are hijacked by cancer cells.

Multi-Omics Analyses Detail Metabolic Reprogramming in Lipids, Carnitines, and Use of Glycolytic Intermediates between Prostate Small Cell Neuroendocrine Carcinoma and Prostate Adenocarcinoma

As the most common cancer in men, prostate cancer is molecularly heterogeneous. Contributing to this heterogeneity are the poorly understood metabolic adaptations of the two main types of prostate cancer, i.e., adenocarcinoma and small cell neuroendocrine carcinoma (SCNC), the latter being more aggressive and lethal. Using transcriptomics, untargeted metabolomics and lipidomics profiling on LASCPC-01 (prostate SCNC) and LNCAP (prostate adenocarcinoma) cell lines, we found significant differences in the cellular phenotypes of the two cell lines. Gene set enrichment analysis on the transcriptomics data showed 62 gene sets were upregulated in LASCPC-01, while 112 gene sets were upregulated in LNCAP. ChemRICH analysis on metabolomics and lipidomics data revealed a total of 25 metabolite clusters were significantly different. LASCPC-01 exhibited a higher glycolytic activity and lower levels of triglycerides, while the LNCAP cell line showed increases in one-carbon metabolism as an exit route of glycolytic intermediates and a decrease in carnitine, a mitochondrial lipid transporter. Our findings pinpoint differences in prostate neuroendocrine carcinoma versus prostate adenocarcinoma that could lead to new therapeutic targets in each type.

The metabolome: A key measure for exposome research in epidemiology

PURPOSE OF REVIEW

Application of omics to study human health has created a new era of opportunities for epidemiology research. However, approaches to characterize exogenous health triggers have largely not leveraged advances in analytical platforms and big data. In this review, we highlight the exposome, which is defined as the cumulative measure of exposure and biological responses across a lifetime as a cornerstone for new epidemiology approaches to study complex and preventable human diseases.

RECENT FINDINGS

While no universal approach exists to measure the entirety of the exposome, use of high-resolution mass spectrometry methods provide distinct advantages over traditional biomonitoring and have provided key advances necessary for exposome research. Application to different study designs and recommendations for combining exposome data with novel data analytic frameworks to study complex interactions of multiple stressors are also discussed.

SUMMARY

Even though challenges still need to be addressed, advances in methods to characterize the exposome provide exciting new opportunities for epidemiology to support fundamental discoveries to improve public health.

Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry

BACKGROUND

The left internal thoracic artery (LITA) has been used as the first conduit of choice in coronary artery bypass grafting (CABG) because of excellent long-term patency and outcomes. However, no studies have examined substances other than nitric oxide that could be beneficial for the bypass conduit, native coronary artery or ischemic myocardium. This study was conducted to evaluate differences in metabolic profiles between the LITA and ascending aorta using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS).

METHODS

Twenty patients who underwent CABG using the LITA were prospectively enrolled. Plasma samples were collected simultaneously from the LITA and ascending aorta. GC-TOF-MS based untargeted metabolomic analyses were performed and a 2-step volcano plot analysis was used to identify distinguishable markers from two plasma metabolome profiles. Semi-quantitative and quantitative analyses were performed using GC-TOF-MS and enzyme-linked immunosorbent assay, respectively, after selecting target metabolites based on the metabolite set enrichment analysis.

RESULTS

Initial volcano plot analysis demonstrated 5 possible markers among 851 peaks detected. The final analysis demonstrated that the L-cysteine peak was significantly higher in the LITA than in the ascending aorta (fold change = 1.86). The concentrations of intermediate metabolites such as L-cysteine, L-methionine and L-cystine in the 'cysteine and methionine metabolism pathway' were significantly higher in the LITA than in the ascending aorta (2.0-, 1.4- and 1.2-fold, respectively). Quantitative analysis showed that the concentration of hydrogen sulfide (H₂S) was significantly higher in the LITA.

CONCLUSION

The plasma metabolome profiles of the LITA and ascending aorta were different, particularly higher plasma concentrations of L-cysteine and H₂S in the LITA.

Metabolic disturbances in synovial fluid are involved in the onset of synovitis in heifers with acute ruminal acidosis

Acute ruminal acidosis (ARA) is the result of increased intake of highly fermentable carbohydrates, which frequently occurs in dairy cattle and is associated with aseptic polysynovitis. To characterise the metabolic changes in the joints of animals with ARA, we performed an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis of synovial fluid. Seven heifers were challenged with an intraruminal oligofructose overload (13 g/kg of body weight [BW]) dissolved in water. Synovial fluid samples were collected at 0, 9 and 24 h post-overload. Metabolome analysis revealed the presence of 67 metabolites. At 9 h post-overload, glyceric acid, cellobiose, fructose and lactic acid were all increased, whereas at 24 h, sorbitol, lactic acid and fructose levels were all increased >10-fold. At 24 h, citric acid and threonine levels were significantly reduced. We detected increased L- and D-lactate, and the presence of interleukin-6 (IL-6) in synovial fluid. Furthermore, using bovine fibroblast-like synoviocytes, we observed that D-lactate induces IL-6 synthesis. Our results suggest that ARA produces severe metabolomic changes in synovial fluid, including disturbances in starch and sucrose metabolism, and increased lactate levels. These changes were observed prior to the appearance of synovitis, suggesting a potential role in the onset of polysynovitis.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Metabolomics Analyses of 14 Classical Neurotransmitters by GC-TOF with LC-MS Illustrates Secretion of 9 Cell-Cell Signaling Molecules from Sympathoadrenal Chromaffin Cells in the Presence of Lithium

The classical small molecule neurotransmitters are essential for cell-cell signaling in the nervous system for regulation of behaviors and physiological functions. Metabolomics approaches are ideal for quantitative analyses of neurotransmitter profiles but have not yet been achieved for the repertoire of 14 classical neurotransmitters. Therefore, this study developed targeted metabolomics analyses by full scan gas chromatography/time-of-flight mass spectrometry (GC-TOF) and hydrophilic interaction chromatography-QTRAP mass spectrometry (HILIC-MS/MS) operated in positive ionization mode for identification and quantitation of 14 neurotransmitters consisting of acetylcholine, adenosine, anandamide, aspartate, dopamine, epinephrine, GABA, glutamate, glycine, histamine, melatonin, norepinephrine, serine, and serotonin. GC-TOF represents a new metabolomics method for neurotransmitter analyses. Sensitive measurements of 11 neurotransmitters were achieved by GC-TOF, and three neurotransmitters were analyzed by LC-MS/MS (acetylcholine, anandamide, and melatonin). The limits of detection (LOD) and limits of quantitation (LOQ) were assessed for linearity for GC-TOF and LC-MS/MS protocols. In neurotransmitter-containing dense core secretory vesicles of adrenal medulla, known as chromaffin granules (CG), metabolomics measured the concentrations of 9 neurotransmitters consisting of the catecholamines dopamine, norepinephrine, and epinephrine, combined with glutamate, serotonin, adenosine, aspartate, glycine, and serine. The CG neurotransmitters were constitutively secreted from sympathoadrenal chromaffin cells in culture. Nicotine- and KCl-stimulated release of the catecholamines and adenosine. Lithium, a drug used for the treatment of bipolar disorder, decreased the constitutive secretion of dopamine and norepinephrine and decreased nicotine-stimulated secretion of epinephrine. Lithium had no effect on other secreted neurotransmitters. Overall, the newly developed GC-TOF with LC-MS/MS metabolomics methods for analyses of 14 neurotransmitters will benefit investigations of neurotransmitter regulation in biological systems and in human disease conditions related to drug treatments.

Metabolomics biomarkers and the risk of overall mortality and ESRD in CKD: Results from the Progredir Cohort

INTRODUCTION

Studies on metabolomics and CKD have primarily addressed CKD incidence defined as a decline on eGFR or appearance of albuminuria in the general population, with very few evaluating hard outcomes. In the present study, we investigated the association between metabolites and mortality and ESRD in a CKD cohort.

SETTING AND METHODS

Data on 454 participants of the Progredir Cohort Study, Sao Paulo, Brazil were used. Metabolomics was performed by GC-MS (Agilent MassHunter) and metabolites were identified using Agilent Fiehn GC/MS and NIST libraries. After excluding metabolites present in <50% of participants, 293 metabolites were analyzed. An FDR q value <0.05 criteria was applied in Cox models on the composite outcome (mortality or incident renal replacement therapy) adjusted for batch effect, resulting in 34 metabolites associated with the outcome. Multivariable-adjusted Cox models were then built for the composite outcome, death, and ESRD incident events. Competing risk analysis was also performed for ESRD.

RESULTS

Mean age was 68±12y, mean eGFR-CKDEPI was 38.4±14.6 ml/min/1.73m2 and 57% were diabetic. After adjustments (GC-MS batch, sex, age, DM and eGFR), 18 metabolites remained significantly associated with the composite outcome. Nine metabolites were independently associated with death: D-malic acid (HR 1.84, 95%CI 1.32-2.56, p = 0.0003), acetohydroxamic acid (HR 1.90, 95%CI 1.30-2.78, p = 0.0008), butanoic acid (HR 1.59, 95%CI 1.17-2.15, p = 0.003), and docosahexaenoic acid (HR 0.58, 95%CI 0.39-0.88, p = 0.009), among the top associations. Lactose (SHR 1.49, 95%CI 1.04-2.12, p = 0.03), 2-O-glycerol-α-D-galactopyranoside (SHR 1.76, 95%CI 1.06-2.92, p = 0.03), and tyrosine (SHR 0.52, 95%CI 0.31-0.88, p = 0.02) were associated to ESRD risk, while D-threitol, mannitol and myo-inositol presented strong borderline associations.

CONCLUSION

Our results identify specific metabolites related to hard outcomes in a CKD population. These findings point to the need of further exploration of these metabolites as biomarkers in CKD and the understanding of the underlying biological mechanisms related to the observed associations.

Cystic Fibrosis-Associated Stenotrophomonas maltophilia Strain-Specific Adaptations and Responses to pH

Understanding bacterial responses to physiological conditions is an important priority for combating opportunistic infections. The majority of CF patients succumb to inflammation and necrosis in the airways, arising from chronic infection due to ineffective mucociliary clearance. Steep pH gradients characterize the CF airways but are not often incorporated in standard microbiology culture conditions. Stenotrophomonas maltophilia is a prevalent CF opportunistic pathogen also found in many disparate environments, yet this bacterium’s contribution to CF lung damage and its response to changing environmental factors remain largely understudied. Here, we show that pH impacts the physiology and antibiotic susceptibility of S. maltophilia, with implications for the development of relevant in vitro models and assessment of antibiotic sensitivity.

The airway fluids of cystic fibrosis (CF) patients contain local pH gradients and are more acidic than those of healthy individuals. pH is a critical factor that is often overlooked in studies seeking to recapitulate the infection microenvironment. We sought to determine the impact of pH on the physiology of a ubiqituous yet understudied microbe, Stenotrophomonas maltophilia. Phylogenomics was first used to reconstruct evolutionary relationships between 74 strains of S. maltophilia (59 from CF patients). Neither the core genome (2,158 genes) nor the accessory genome (11,978 genes) distinguish the CF and non-CF isolates; however, strains from similar isolation sources grouped into the same subclades. We grew two human and six CF S. maltophilia isolates from different subclades at a range of pH values and observed impaired growth and altered antibiotic tolerances at pH 5. Transcriptomes revealed increased expression of both antibiotic resistance and DNA repair genes in acidic conditions. Although the gene expression profiles of S. maltophilia in lab cultures and CF sputum were distinct, we found that the same genes associated with low pH were also expressed during infection, and the higher pH cultures were more similar to sputum metatranscriptomes. Our findings suggest that S. maltophilia is not well adapted to acidity and may cope with low pH by expressing stress response genes and colonizing less acidic microenvironments. As a whole, our study underlines the impact of microenvironments on bacterial colonization and adaptation in CF infections.

IMPORTANCE Understanding bacterial responses to physiological conditions is an important priority for combating opportunistic infections. The majority of CF patients succumb to inflammation and necrosis in the airways, arising from chronic infection due to ineffective mucociliary clearance. Steep pH gradients characterize the CF airways but are not often incorporated in standard microbiology culture conditions. Stenotrophomonas maltophilia is a prevalent CF opportunistic pathogen also found in many disparate environments, yet this bacterium’s contribution to CF lung damage and its response to changing environmental factors remain largely understudied. Here, we show that pH impacts the physiology and antibiotic susceptibility of S. maltophilia, with implications for the development of relevant in vitro models and assessment of antibiotic sensitivity.

Plasma metabolites and lipids associate with kidney function and kidney volume in hypertensive ADPKD patients early in the disease course

Background

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and is characterized by gradual cyst growth and expansion, increase in kidney volume with an ultimate decline in kidney function leading to end stage renal disease (ESRD). Given the decades long period of stable kidney function while cyst growth occurs, it is important to identify those patients who will progress to ESRD. Recent data from our and other laboratories have demonstrated that metabolic reprogramming may play a key role in cystic epithelial proliferation resulting in cyst growth in ADPKD. Height corrected total kidney volume (ht-TKV) accurately reflects cyst burden and predicts future loss of kidney function. We hypothesize that specific plasma metabolites will correlate with eGFR and ht-TKV early in ADPKD, both predictors of disease progression, potentially indicative of early physiologic derangements of renal disease severity.

Methods

To investigate the predictive role of plasma metabolites on eGFR and/or ht-TKV, we used a non-targeted GC-TOF/MS-based metabolomics approach on hypertensive ADPKD patients in the early course of their disease. Patient data was obtained from the HALT-A randomized clinical trial at baseline including estimated glomerular filtration rate (eGFR) and measured ht-TKV. To identify individual metabolites whose intensities are significantly correlated with eGFR and ht-TKV, association analyses were performed using linear regression with each metabolite signal level as the primary predictor variable and baseline eGFR and ht-TKV as the continuous outcomes of interest, while adjusting for covariates. Significance was determined by Storey’s false discovery rate (FDR) q-values to correct for multiple testing.

Results

Twelve metabolites significantly correlated with eGFR and two triglycerides significantly correlated with baseline ht-TKV at FDR q-value < 0.05. Specific significant metabolites, including pseudo-uridine, indole-3-lactate, uric acid, isothreonic acid, and creatinine, have been previously shown to accumulate in plasma and/or urine in both diabetic and cystic renal diseases with advanced renal insufficiency.

Conclusions

This study identifies metabolic derangements in early ADPKD which may be prognostic for ADPKD disease progression.

Clinical trial

HALT Progression of Polycystic Kidney Disease (HALT PKD) Study A; Clinical www.clinicaltrials.gov identifier: NCT00283686; first posted January 30, 2006, last update posted March 19, 2015.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1249-6) contains supplementary material, which is available to authorized users.

Metabolomic analysis of serum and myocardium in compensated heart failure after myocardial infarction

AIMS

To determine the metabolic adaptations to compensated heart failure using a reproducible model of myocardial infarction and an unbiased metabolic screen. To address the limitations in sample availability and model variability observed in preclinical and clinical metabolic investigations of heart failure.

MAIN METHODS

Metabolomic analysis was performed on serum and myocardial tissue from rabbits after myocardial infarction (MI) was induced by cryo-injury of the left ventricular free wall. Rabbits followed for 12 weeks after MI exhibited left ventricular dilation and depressed systolic function as determined by echocardiography. Serum and tissue from the viable left ventricular free wall, interventricular septum and right ventricle were analyzed using a gas chromatography time of flight mass spectrometry-based untargeted metabolomics assay for primary metabolites.

KEY FINDINGS

Unique results included: a two- three-fold increase in taurine levels in all three ventricular regions of MI rabbits and similarly, the three regions had increased inosine levels compared to sham controls. Reduced myocardial levels of myo-inositol in the myocardium of MI animals point to altered phospholipid metabolism and membrane receptor function in heart failure. Metabolite profiles also provide evidence for responses to oxidative stress and an impairment in TCA cycle energy production in the failing heart.

SIGNIFICANCE

Our results revealed metabolic changes during compensated cardiac dysfunction and suggest potential targets for altering the progression of heart failure.

Molecular profiling of an oleaginous trebouxiophycean alga Parachlorella kessleri subjected to nutrient deprivation for enhanced biofuel production

BACKGROUND

Decreasing fossil fuels and its impact on global warming have led to an increasing demand for its replacement by sustainable renewable biofuels. Microalgae may offer a potential feedstock for renewable biofuels capable of converting atmospheric CO₂ to substantial biomass and valuable biofuels, which is of great importance for the food and energy industries. Parachlorella kessleri, a marine unicellular green alga belonging to class Trebouxiophyceae, accumulates large amount of lipids under nutrient-deprived conditions. The present study aims to understand the metabolic imprints in order to elucidate the physiological mechanisms of lipid accumulations in this microalga under nutrient deprivation.

RESULTS

Molecular profiles were obtained using gas chromatography-mass spectrometry (GC-MS) of P. kessleri subjected to nutrient deprivation. Relative quantities of more than 60 metabolites were systematically compared in all the three starvation conditions. Our results demonstrate that in lipid metabolism, the quantities of neutral lipids increased significantly followed by the decrease in other metabolites involved in photosynthesis, and nitrogen assimilation. Nitrogen starvation seems to trigger the triacylglycerol (TAG) accumulation rapidly, while the microalga seems to tolerate phosphorous limitation, hence increasing both biomass and lipid content. The metabolomic and lipidomic profiles have identified a few common metabolites such as citric acid and 2-ketoglutaric acid which play significant role in diverting flux towards acetyl-CoA leading to accumulation of neutral lipids, whereas other molecules such as trehalose involve in cell growth regulation, when subjected to nutrient deprivation.

CONCLUSIONS

Understanding the entire system through qualitative (untargeted) metabolome approach in P. kessleri has led to identification of relevant metabolites involved in the biosynthesis and degradation of precursor molecules that may have potential for biofuel production, aiming towards the vision of tomorrow's bioenergy needs.

Identification of Putative Biomarkers Specific to Foodborne Pathogens Using Metabolomics

Metabolomics is one of the more recently developed "omics" that measures low molecular weight (typically < 1500 Da) compounds in biological samples. Metabolomics has been widely explored in environmental, clinical, and industrial biotechnology applications. However, its application to the area of food safety has been limited but preliminary work has demonstrated its value. This chapter describes an untargeted (nontargeted) metabolomics workflow using gas chromatography coupled to mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for extraction of polar metabolites from media, analyzing the extracts using GC-MS and, finally, chemometric data analysis using the software "SIMCA" to identify potential pathogen-specific biomarkers.

Quantitative analysis of chemoresistance-inducing fatty acid in food supplements using UHPLC-ESI-MS/MS

Polyunsaturated fatty acids are important signaling molecules. A recent study reported hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid, 12-oxo-5Z,8E,10E-heptadecatrienoic acid, and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid as chemotherapy resistance-inducing factors when tumor cells were treated with cisplatin. Marine-based food supplements like fish oil or algae extracts are rich in polyunsaturated fatty acids and can contain large amounts of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid. Thus, it was concluded that oral uptake of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid might induce chemoresistance as shown in a mouse model. Cancer patients tend to consume food supplements containing polyunsaturated fatty acids on a regular basis. The uptake of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid should be controlled, because even low concentrations of 0.5 ng mL^-1 showed chemoresistance-inducing effects in animal experiments. For accurate analysis of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid a validated method was developed by using ultrahigh-performance liquid chromatography hyphenated to quadrupole time of flight mass spectrometry via electrospray ionization and sample preparation by solid-phase extraction (SPE) with 3-aminopropyl silica. A combined targeted/untargeted approach was utilized using MS/MS by data-independent acquisition with SWATH and applied to commercial food supplements (refined fish oil, fish oil capsules, algae oil capsules, and flaxseed capsules). Accurate quantification of hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid on the MS/MS level with simultaneous untargeted fatty acid screening revealed additional information. The LODs for hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic acid were 0.036 ng mL^-1 and 0.054 ng mL^-1, respectively. Since hexadeca-4Z,7Z,10Z,13Z-tetraenoic acid was present in the samples in large amounts and (12S)-hydroxy-5Z,8E,10E-heptadecatrienoic was not expected to be present in high concentrations, two calibration ranges, namely, 0.5-20 ng mL^-1 and 5-200 ng mL^-1, were validated. An untargeted screening identified 18-39 free fatty acids being present in the lipid extracts of the food supplement samples. Graphical abstract ᅟ.

Improving liquid chromatography-tandem mass spectrometry determination of polycarboxylic acids in human urine by chemical derivatization. Comparison of o-benzyl hydroxylamine and 2-picolyl amine

Due to its high sensitivity and specificity, liquid chromatography-electrospray tandem mass spectrometry (LC-MS/MS) could be considered as the gold-standard in targeted metabolomics. Although LC-MS/MS allows for the direct detection of a large number of molecules, the proper quantification of highly polar compounds such as poly-carboxylic acids in complex matrices like urine is still a challenge. Chemical derivatization offers a suitable way to improve chromatographic behavior and sensitivity for these compounds. Several derivatizing agents have been proposed for the LC-MS/MS determination of carboxylic acids but studies dealing with their comparison in challenging scenarios are scarce. Here we present the evaluation of two different derivatization agents; o-benzylhydroxyl amine (oBHA) and 2-picolyl amine (2-PA); for the quantification of the (poly)-carboxylic acids belonging to the tricarboxylic acid cycle in urine. The suitability of both derivatizating agents was compared by validation of the two approaches. Derivatization with oBHA showed important advantages against 2-PA derivatization such as (i) providing better sensitivity, (ii) more stable derivatives and (iii) allowing for the proper validation of a larger number of analytes. Moreover, while 2-PA derivatization failed in the determination of the target analytes in some stored urine samples, oBHA derivatization successfully allowed for their appropriate determination in the same samples. A comparison between the concentrations obtained using oBHA derivatization and those provided by an external laboratory using UV and GC-MS detection revealed a satisfactory agreement between both results. Additionally, the concentrations obtained by the oBHA method for a set of 38 urines are in agreement with those previously reported in the literature. As a conclusion, our results show that the use of oBHA is preferred against 2-PA for the detection and quantification of (poly)-carboxylic acids in urine.

Mass isotopomer-guided decluttering of metabolomic data to visualize endogenous biomarkers of drug toxicity

Metabolomics offers the opportunity to uncover endogenous biomarkers that can lead to metabolic pathways and networks and that underpin drug toxicity mechanisms. A novel protocol is presented and discussed that is applicable to drugs which generate urinary metabolites when administered to mice sensitive to its toxicity. The protocol would not apply to drugs that are not metabolized or eliminated by a different route. Separate stable isotope-labeled and unlabeled drug administration to mice is made together with collection of urines from control animals. Untargeted mass spectrometry-based metabolomic analysis of these three urine groups is conducted in addition to principal components analysis (PCA). In the case of unlabeled acetaminophen and [acetyl-²H₃]acetaminophen, each given at a hepatotoxic dose (400 mg/kg i.p.) to the sensitive mouse strain (wild-type 129), the PCA loadings plot showed a distribution of ions in the shape of a "fallen-Y" with the deuterated metabolites in one arm and the paired nondeuterated metabolites in the other arm of the fallen-Y. Ions corresponding to the endogenous toxicity biomarkers sat in the mouth of the fallen-Y. This protocol represents an innovative means to separate endogenous biomarkers from drug metabolites, thereby aiding the identification of biomarkers of drug toxicity. For acetaminophen, increased hepatic oxidative stress, mitochondrial damage, Ca²⁺ signaling, heme catabolism, and saturation of glucuronidation, together with decreased fatty acid β-oxidation and cellular energy dysregulation were all implied from the discovered biomarkers. The protocol can be applied to other drugs and may now be translated to clinical studies.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

Metabolomic changes in vertebrate host during malaria disease progression

Metabolomics refers to top-down systems biological analysis of metabolites in biological specimens. Phenotypic proximity of metabolites makes them interesting candidates for studying biomarkers of environmental stressors such as parasitic infections. Moreover, the host-parasite interaction directly impinges upon metabolic pathways since the parasite uses the host metabolite pool as a biosynthetic resource. Malarial infection, although not recognized as a classic metabolic disorder, often leads to severe metabolic changes such as hypoglycemia and lactic acidosis. Thus, metabolomic analysis of the infection has become an invaluable tool for promoting a better understanding of the host-parasite interaction and for the development of novel therapeutics. In this review, we summarize the current knowledge obtained from metabolomic studies of malarial infection in rodent models and human patients. Metabolomic analysis of experimental rodent malaria has provided significant insights into the mechanisms of disease progression including utilization of host resources by the parasite, sexual dimorphism in metabolic phenotypes, and cellular changes in host metabolism. Moreover, these studies also provide proof of concept for prediction of cerebral malaria. On the other hand, metabolite analysis of patient biofluids generates extensive data that could be of use in identifying biomarkers of infection severity and in monitoring disease progression. Through the use of metabolomic datasets one hopes to assess crucial infection-specific issues such as clinical severity, drug resistance, therapeutic targets, and biomarkers. Also discussed are nascent or newly emerging areas of metabolomics such as pre-erythrocytic stages of the infection and the host immune response. This review is organized in four broad sections-methodologies for metabolomic analysis, rodent infection models, studies of human clinical specimens, and potential of immunometabolomics. Data summarized in this review should serve as a springboard for novel hypothesis testing and lead to a better understanding of malarial infection and parasite biology.

The serum metabolomics signature of type 2 diabetes is obscured in Alzheimer's disease

There is evidence for systemic metabolic impairment in Alzheimer's disease (AD), and type 2 diabetes (T2D) increases AD risk. Although studies analyzing blood metabolomics signatures have shown differences between cognitively healthy (CH) and AD subjects, these signatures have not been compared with individuals with T2D. We utilized untargeted analysis platforms (primary metabolism and complex lipids) to characterize the serum metabolome of 126 overnight-fasted elderly subjects classified into four groups based upon AD status (CH or AD) and T2D status [nondiabetic (ND) or T2D]. Cognitive diagnosis groups were a priori weighted equally with T2D subjects. We hypothesized that AD subjects would display a metabolic profile similar to cognitively normal elderly individuals with T2D. However, partial least squares-discriminant analysis (PLS-DA) modeling resulted in poor classification across the four groups (<50% classification accuracy of test subjects). Binary classification of AD vs. CH was poor, but binary classification of T2D vs. ND was good, providing >79.5% and >76.9% classification accuracy for held-out samples using primary metabolism and complex lipids, respectively. When modeling was limited to CH subjects, T2D discrimination improved for the primary metabolism platform (>89.5%) and remained accurate for complex lipids (>73% accuracy). Greater abundances of glucose, fatty acids (C20:2), and phosphatidylcholines and lower abundances of glycine, maleimide, octanol, and tryptophan, cholesterol esters, phosphatidylcholines, and sphingomyelins were identified in CH subjects with T2D relative to those without T2D. In contrast, T2D was not accurately discriminated within AD subjects. Results herein suggest that AD may obscure the typical metabolic phenotype of T2D.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites

INTRODUCTION

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

Challenges and Opportunities for Biological Mass Spectrometry Core Facilities in the Developing World

The developing world is seeing rapid growth in the availability of biological mass spectrometry (MS), particularly through core facilities. As proteomics and metabolomics becomes locally feasible for investigators in these nations, application areas associated with high burden in these nations, such as infectious disease, will see greatly increased research output. This article evaluates the rapid growth of MS in South Africa (currently approaching 20 laboratories) as a model for establishing MS core facilities in other nations of the developing world. Facilities should emphasize new services rather than new instruments. The reduction of the delays associated with reagent and other supply acquisition would benefit both facilities and the users who make use of their services. Instrument maintenance and repair, often mediated by an in-country business for an international vendor, is also likely to operate on a slower schedule than in the wealthiest nations. A key challenge to facilities in the developing world is educating potential facility users in how best to design experiments for proteomics and metabolomics, what reagents are most likely to introduce problematic artifacts, and how to interpret results from the facility. Here, we summarize the experience of 6 different institutions to raise the level of biological MS available to researchers in South Africa.

1,5-Anhydroglucitol predicts CKD progression in macroalbuminuric diabetic kidney disease: results from non-targeted metabolomics

INTRODUCTION

Metabolomics allows exploration of novel biomarkers and provides insights on metabolic pathways associated with disease. To date, metabolomics studies on CKD have been largely limited to Caucasian populations and have mostly examined surrogate end points.

OBJECTIVE

In this study, we evaluated the role of metabolites in predicting a primary outcome defined as dialysis need, doubling of serum creatinine or death in Brazilian macroalbuminuric DKD patients.

METHODS

Non-targeted metabolomics was performed on plasma from 56 DKD patients. Technical triplicates were done. Metabolites were identified using Agilent Fiehn GC/MS Metabolomics and NIST libraries (Agilent MassHunter Work-station Quantitative Analysis, version B.06.00). After data cleaning, 186 metabolites were left for analyses.

RESULTS

During a median follow-up time of 2.5 years, the PO occurred in 17 patients (30.3%). In non-parametric testing, 13 metabolites were associated with the PO. In univariate Cox regression, only 1,5-anhydroglucitol (HR 0.10; 95% CI 0.01-0.63, p = .01), norvaline and L-aspartic acid were associated with the PO. After adjustment for baseline renal function, 1,5-anhydroglucitol (HR 0.10; 95% CI 0.02-0.63, p = .01), norvaline (HR 0.01; 95% CI 0.001-0.4, p = .01) and aspartic acid (HR 0.12; 95% CI 0.02-0.64, p = .01) remained significantly and inversely associated with the PO.

CONCLUSION

Our results show that lower levels of 1,5-anhydroglucitol, norvaline and L-aspartic acid are associated with progression of macroalbuminuric DKD. While norvaline and L-aspartic acid point to interesting metabolic pathways, 1,5-anhydroglucitol is of particular interest since it has been previously shown to be associated with incident CKD. This inverse biomarker of hyperglycemia should be further explored as a new tool in DKD.