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OBJECTIVE - Type 2 diabetes mellitus (T2DM) reduces exercise capacity, but the mechanisms are incompletely understood. We probed the impact of ischemic stress on skeletal muscle metabolite signatures and T2DM-related vascular dysfunction.
METHODS - we recruited 38 subjects (18 healthy, 20 T2DM), placed an antecubital intravenous catheter, and performed ipsilateral brachial artery reactivity testing. Blood samples for plasma metabolite profiling were obtained at baseline and immediately upon cuff release after 5 min of ischemia. Brachial artery diameter was measured at baseline and 1 min after cuff release.
RESULTS - as expected, flow-mediated vasodilation was attenuated in subjects with T2DM (P<0.01). We confirmed known T2DM-associated baseline differences in plasma metabolites, including homocysteine, dimethylguanidino valeric acid and β-alanine (all P<0.05). Ischemia-induced metabolite changes that differed between groups included 5-hydroxyindoleacetic acid (healthy: -27%; DM +14%), orotic acid (healthy: +5%; DM -7%), trimethylamine-N-oxide (healthy: -51%; DM +0.2%), and glyoxylic acid (healthy: +19%; DM -6%) (all P<0.05). Levels of serine, betaine, β-aminoisobutyric acid and anthranilic acid were associated with vessel diameter at baseline, but only in T2DM (all P<0.05). Metabolite responses to ischemia were significantly associated with vasodilation extent, but primarily observed in T2DM, and included enrichment in phospholipid metabolism (P<0.05).
CONCLUSIONS - our study highlights impairments in muscle and vascular signaling at rest and during ischemic stress in T2DM. While metabolites change in both healthy and T2DM subjects in response to ischemia, the relationship between muscle metabolism and vascular function is modified in T2DM, suggesting that dysregulated muscle metabolism in T2DM may have direct effects on vascular function.
© 2020 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.
Identifying early indicators of toxicant-induced organ damage is critical to provide effective treatment. To discover such indicators and the underlying mechanisms of toxicity, we used gentamicin as an exemplar kidney toxicant and performed systematic perturbation studies in Sprague Dawley rats. We obtained high-throughput data 7 and 13 h after administration of a single dose of gentamicin (0.5 g/kg) and identified global changes in genes in the liver and kidneys, metabolites in the plasma and urine, and absolute fluxes in central carbon metabolism. We used these measured changes in genes in the liver and kidney as constraints to a rat multitissue genome-scale metabolic network model to investigate the mechanism of gentamicin-induced kidney toxicity and identify metabolites associated with changes in tissue gene expression. Our experimental analysis revealed that gentamicin-induced metabolic perturbations could be detected as early as 7 h postexposure. Our integrated systems-level analyses suggest that changes in kidney gene expression drive most of the significant metabolite alterations in the urine. The analyses thus allowed us to identify several significantly enriched injury-specific pathways in the kidney underlying gentamicin-induced toxicity, as well as metabolites in these pathways that could serve as potential early indicators of kidney damage.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: email@example.com.
Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.
© 2019 by the American Diabetes Association.
Imaging mass spectrometry is a powerful technology that combines the molecular measurements of mass spectrometry with the spatial information inherent to microscopy. This unique combination of capabilities is ideally suited for the analysis of metabolites and lipids from single cells. This chapter describes a methodology for the sample preparation and analysis of single cells using high performance MALDI FTICR MS. Using this approach, we are able to generate profiles of lipid and metabolite expression from single cells that characterize cellular heterogeneity. This approach also enables the detection of variations in the expression profiles of lipids and metabolites induced by chemical stimulation of the cells. These results demonstrate that MALDI IMS provides an insightful view of lipid and metabolite expression useful in the characterization of a number of biological systems at the single cell level.
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
© The Author(s) 2019. Published by Oxford University Press.
In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations.
Few prospective studies, and none in Asians, have systematically evaluated the relationship between blood metabolites and colorectal cancer risk. We conducted a nested case-control study to search for risk-associated metabolite biomarkers for colorectal cancer in an Asian population using blood samples collected prior to cancer diagnosis. Conditional logistic regression was performed to assess associations of metabolites with cancer risk. In this study, we included 250 incident cases with colorectal cancer and individually matched controls nested within two prospective Shanghai cohorts. We found 35 metabolites associated with risk of colorectal cancer after adjusting for multiple comparisons. Among them, 12 metabolites were glycerophospholipids including nine associated with reduced risk of colorectal cancer and three with increased risk [odds ratios per standard deviation increase of transformed metabolites: 0.31-1.98; p values: 0.002-1.25 × 10 ]. The other 23 metabolites associated with colorectal cancer risk included nine lipids other than glycerophospholipid, seven aromatic compounds, five organic acids and four other organic compounds. After mutual adjustment, nine metabolites remained statistically significant for colorectal cancer. Together, these independently associated metabolites can separate cancer cases from controls with an area under the curve of 0.76 for colorectal cancer. We have identified that dysregulation of glycerophospholipids may contribute to risk of colorectal cancer.
© 2018 UICC.
Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations.
1. Pomalidomide has been shown to be potentially teratogenic in thalidomide-sensitive animal species such as rabbits. Screening for thalidomide analogs devoid of teratogenicity/toxicity - attributable to metabolites formed by cytochrome P450 enzymes - but having immunomodulatory properties is a strategic pathway towards development of new anticancer drugs. 2. In this study, plasma concentrations of pomalidomide, its primary 5-hydroxylated metabolite, and its glucuronide conjugate(s) were investigated in control and humanized-liver mice. Following oral administration of pomalidomide (100 mg/kg), plasma concentrations of 7-hydroxypomalidomide and 5-hydroxypomalidomide glucuronide were slightly higher in humanized-liver mice than in control mice. 3. Simulations of human plasma concentrations of pomalidomide were achieved with simplified physiologically-based pharmacokinetic models in both groups of mice in accordance with reported pomalidomide concentrations after low dose administration in humans. 4. The results indicate that pharmacokinetic profiles of pomalidomide were roughly similar between control mice and humanized-liver mice and that control and humanized-liver mice mediated pomalidomide 5-hydroxylation in vivo. Introducing one aromatic amino group into thalidomide resulted in less species differences in in vivo pharmacokinetics in control and humanized-liver mice.
Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies-specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstract ᅟ.