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Leonurine affected homocysteine-methionine metabolism based on metabolomics and gut microbiota studies of clinical trial samples.
Liao, J, Suguro, R, Zhao, X, Yu, Y, Cui, Y, Zhu, YZ
Clinical and translational medicine. 2021;(10):e535
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Analysis of biomarkers and metabolic pathways in patients with unstable angina based on ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry.
Liu, Y, Li, Y, Zhang, T, Zhao, H, Fan, S, Cai, X, Liu, Y, Li, Z, Gao, S, Li, Y, et al
Molecular medicine reports. 2020;(5):3862-3872
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Abstract
Unstable angina (UA) is a coronary disease with a high mortality and morbidity worldwide. The present study aimed to use non‑invasive techniques to identify urine biomarkers in patients with UA, so as to provide more information for the early diagnosis and treatment of the disease. Based on metabolomics, urine samples from 28 patients with UA and 28 healthy controls (HCs) were analyzed using ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry (UPLC‑Q‑TOF/MS). A total of 16 significant biomarkers that could distinguish between patients with UA and HCs, including D‑glucuronic acid, creatinine, succinic acid and N‑acetylneuraminic acid, were identified. The major metabolic pathways associated with UA were subsequently analyzed by non‑targeted metabolomics. The results demonstrated that amino acid and energy metabolism, fatty acid metabolism, purine metabolism and steroid hormone biosynthetic metabolism may serve important roles in UA. The results of the current study may provide a theoretical basis for the early diagnosis of UA and novel treatment strategies for clinicians. The trial was registered with the Chinese Clinical Trial Registration Center (registration no. ChiCTR‑ROC‑17013957) at Tianjin University of Traditional Chinese Medicine.
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A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space.
Michelini, S, Balakrishnan, B, Parolo, S, Matone, A, Mullaney, JA, Young, W, Gasser, O, Wall, C, Priami, C, Lombardo, R, et al
Microbiome. 2018;(1):171
Abstract
BACKGROUND Weaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space. RESULTS Using public domain literature mining and an in silico reverse metabolic approach, we constructed probiotic-prebiotic-food associations, which can guide targeted feeding of immune health-beneficial microbes by weaning food; analyzed competition and synergy for (prebiotic) nutrients between selected microbes; and translated this information into designing an experimental complementary feed for infants enrolled in a pilot clinical trial ( http://www.nourishtoflourish.auckland.ac.nz/ ). CONCLUSIONS In this study, we applied a benefit-oriented microbiome research strategy for enhanced early-life immune health. We extended from "classical" to molecular nutrition aiming to identify nutrients, bacteria, and mechanisms that point towards targeted feeding to improve immune health in infants around weaning. Here, we present the systems biology-based approach we used to inform us on the most promising prebiotic combinations known to support growth of beneficial gut bacteria ("probiotics") in the infant gut, thereby favorably promoting development of the immune system.
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A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression.
Blasco, H, Patin, F, Descat, A, Garçon, G, Corcia, P, Gelé, P, Lenglet, T, Bede, P, Meininger, V, Devos, D, et al
PloS one. 2018;(6):e0198116
Abstract
There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10-6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.
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Changes in Plasma Acylcarnitine and Lysophosphatidylcholine Levels Following a High-Fructose Diet: A Targeted Metabolomics Study in Healthy Women.
Gonzalez-Granda, A, Damms-Machado, A, Basrai, M, Bischoff, SC
Nutrients. 2018;(9)
Abstract
BACKGROUND The consumption of high amounts of fructose is associated with metabolic diseases. However, the underlying mechanisms are largely unknown. OBJECTIVE To determine the effects of high fructose intake on plasma metabolomics. STUDY DESIGN We enrolled 12 healthy volunteers (six lean and six obese women, age 24⁻35 years) in a crossover intervention study. All participants carried out three diets: (1) low fructose (<10 g/day); (2) high fructose (100 g/day) from natural food sources (fruit); and (3) high fructose (100 g/day) from high fructose syrup (HFS). OUTCOME MEASURES The primary outcome was changes in plasma metabolites measured by targeted metabolomics. RESULTS High compared to low fructose diets caused a marked metabolite class separation, especially because of changes in acylcarnitine and lysophosphatidylcholine levels. Both high fructose diets resulted in a decrease in mean acylcarnitine levels in all subjects, and an increase in mean lysophosphatidylcholine and diacyl-phosphatidylcholine levels in obese individuals. Medium chain acylcarnitines were negatively correlated with serum levels of liver enzymes and with the fatty liver index. DISCUSSION The metabolic shifts induced by high fructose consumption suggest an inhibition of mitochondrial β-oxidation and an increase in lipid peroxidation. The effects tended to be more pronounced following the HFS than the fruit diet.
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Effect of masticatory stimulation on the quantity and quality of saliva and the salivary metabolomic profile.
Okuma, N, Saita, M, Hoshi, N, Soga, T, Tomita, M, Sugimoto, M, Kimoto, K
PloS one. 2017;(8):e0183109
Abstract
BACKGROUND This study characterized the changes in quality and quantity of saliva, and changes in the salivary metabolomic profile, to understand the effects of masticatory stimulation. METHODS Stimulated and unstimulated saliva samples were collected from 55 subjects and salivary hydrophilic metabolites were comprehensively quantified using capillary electrophoresis-time-of-flight mass spectrometry. RESULTS In total, 137 metabolites were identified and quantified. The concentrations of 44 metabolites in stimulated saliva were significantly higher than those in unstimulated saliva. Pathway analysis identified the upregulation of the urea cycle and synthesis and degradation pathways of glycine, serine, cysteine and threonine in stimulated saliva. A principal component analysis revealed that the effect of masticatory stimulation on salivary metabolomic profiles was less dependent on sample population sex, age, and smoking. The concentrations of only 1 metabolite in unstimulated saliva, and of 3 metabolites stimulated saliva, showed significant correlation with salivary secretion volume, indicating that the salivary metabolomic profile and salivary secretion volume were independent factors. CONCLUSIONS Masticatory stimulation affected not only salivary secretion volume, but also metabolite concentration patterns. A low correlation between the secretion volume and these patterns supports the conclusion that the salivary metabolomic profile may be a new indicator to characterize masticatory stimulation.
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Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock.
Puskarich, MA, Finkel, MA, Karnovsky, A, Jones, AE, Trexel, J, Harris, BN, Stringer, KA
Annals of the American Thoracic Society. 2015;(1):46-56
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Abstract
RATIONALE Sepsis therapeutics have a poor history of success in clinical trials, due in part to the heterogeneity of enrolled patients. Pharmacometabolomics could differentiate drug response phenotypes and permit a precision medicine approach to sepsis. OBJECTIVES To use existing serum samples from the phase 1 clinical trial of l-carnitine treatment for severe sepsis to metabolically phenotype l-carnitine responders and nonresponders. METHODS Serum samples collected before (T0) and after completion of the infusion (T24, T48) from patients randomized to either l-carnitine (12 g) or placebo for the treatment of vasopressor-dependent septic shock were assayed by untargeted (1)H-nuclear magnetic resonance metabolomics. The normalized, quantified metabolite data sets of l-carnitine- and placebo-treated patients at each time point were compared by analysis of variance with post-hoc testing for multiple comparisons. Pathway analysis was performed to statistically rank metabolic networks. MEASUREMENTS AND MAIN RESULTS Thirty-eight metabolites were identified in all samples. Concentrations of 3-hydroxybutyrate, acetoacetate, and 3-hydroxyisovalerate were different at T0 and over time in l-carnitine-treated survivors versus nonsurvivors. Pathway analysis of pretreatment metabolites revealed that synthesis and degradation of ketone bodies had the greatest impact in differentiating l-carnitine treatment response. Analysis of all patients based on pretreatment 3-hydroxybutyrate concentration yielded distinct phenotypes. Using the T0 median 3-hydroxybutyrate level (153 μM), patients were categorized as either high or low ketone. l-Carnitine-treated low-ketone patients had greater use of carnitine as evidenced by lower post-treatment l-carnitine levels. The l-carnitine responders also had faster resolution of vasopressor requirement and a trend toward a greater improvement in mortality at 1 year (P = 0.038) compared with patients with higher 3-hydroxybutyrate. CONCLUSIONS The results of this preliminary study, which were not readily apparent from the parent clinical trial, show a unique metabolite profile of l-carnitine responders and introduce pharmacometabolomics as a viable strategy for informing l-carnitine responsiveness. The approach taken in this study represents a concrete example for the application of precision medicine to sepsis therapeutics that warrants further study.
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LC-MS metabolomics of psoriasis patients reveals disease severity-dependent increases in circulating amino acids that are ameliorated by anti-TNFα treatment.
Kamleh, MA, Snowden, SG, Grapov, D, Blackburn, GJ, Watson, DG, Xu, N, Ståhle, M, Wheelock, CE
Journal of proteome research. 2015;(1):557-66
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Abstract
Psoriasis is an immune-mediated highly heterogeneous skin disease in which genetic as well as environmental factors play important roles. In spite of the local manifestations of the disease, psoriasis may progress to affect organs deeper than the skin. These effects are documented by epidemiological studies, but they are not yet mechanistically understood. In order to provide insight into the systemic effects of psoriasis, we performed a nontargeted high-resolution LC-MS metabolomics analysis to measure plasma metabolites from individuals with mild or severe psoriasis as well as healthy controls. Additionally, the effects of the anti-TNFα drug Etanercept on metabolic profiles were investigated in patients with severe psoriasis. Our analyses identified significant psoriasis-associated perturbations in three metabolic pathways: (1) arginine and proline, (2) glycine, serine and threonine, and (3) alanine, aspartate, and glutamate. Etanercept treatment reversed the majority of psoriasis-associated trends in circulating metabolites, shifting the metabolic phenotypes of severe psoriasis toward that of healthy controls. Circulating metabolite levels pre- and post-Etanercept treatment correlated with psoriasis area and severity index (PASI) clinical scoring (R(2) = 0.80; p < 0.0001). Although the responsible mechanism(s) are unclear, these results suggest that psoriasis severity-associated metabolic perturbations may stem from increased demand for collagen synthesis and keratinocyte hyperproliferation or potentially the incidence of cachexia. Data suggest that levels of circulating amino acids are useful for monitoring both the severity of disease as well as therapeutic response to anti-TNFα treatment.
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Use of 1H-nuclear magnetic resonance to screen a set of biomarkers for monitoring metabolic disturbances in severe burn patients.
Zhang, Y, Cai, B, Jiang, H, Yan, H, Yang, H, Peng, J, Wang, W, Ma, S, Wu, X, Peng, X
Critical care (London, England). 2014;(4):R159
Abstract
INTRODUCTION To establish a plasma metabolomics fingerprint spectrum for severe burn patients and to use it to identify a set of biomarkers that could be used for clinical monitoring. METHODS Twenty-one severe burn patients and three healthy control individuals were enrolled in this study, and the plasma samples from patients and healthy individuals were collected for nuclear magnetic resonance (NMR) measurements. The NMR spectra were analyzed using principal component analysis (PCA) and partial least squares (PLS) in order to establish the metabolomics fingerprint representing the changes in metabolism and to select the major biomarkers. RESULTS NMR spectra of the plasma samples showed significant differences between burn patients and healthy individuals. Using metabolomics techniques, we found an Eigen-metabolome that consists of 12 metabolites, which are regulated by 103 enzymes in a global metabolic network. Among these metabolites, α-ketoisovaleric acid, 3-methylhistidine, and β-hydroxybutyric acid were the most important biomarkers that were significantly increased during the early stage of burn injury. These results suggest that the mitochondrial damage and carbohydrate, protein and fatty acid metabolism disturbances occur after burn injury. Our analysis also show that histone deacetylases, which are protein transcription suppressors, were remarkably increased and indicate that protein transcription was inhibited and anabolism was restrained during the early stage of burn injury. CONCLUSIONS Metabolomics techniques based on NMR can be used to monitor metabolism in severe burn patients. Our study demonstrates that integrated 1H-NMR metabolome and global metabolic network analysis is useful for visualizing complex metabolic disturbances after severe burn injury and may provide a new quantitative injury severity evaluation for future clinical use. TRIAL REGISTRATION Chinese Clinical Trial Registry ChiCTR-OCC-12002145. Registered 25 April 2012.
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Obesity-related metabolomic analysis of human subjects in black soybean peptide intervention study by ultraperformance liquid chromatography and quadrupole-time-of-flight mass spectrometry.
Kim, MJ, Yang, HJ, Kim, JH, Ahn, CW, Lee, JH, Kim, KS, Kwon, DY
Journal of obesity. 2013;:874981
Abstract
The present study aimed to identify key metabolites related to weight reduction in humans by studying the metabolic profiles of sera obtained from 34 participants who underwent dietary intervention with black soybean peptides (BSP) for 12 weeks. This research is a sequel to our previous work in which the effects of BSP on BMI and blood composition of lipid were investigated. Sera of the study were subjected to ultra performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and the data were analyzed using partial least-squares discriminate analysis (PLS-DA) score plots. Body mass index and percent body fat of the test group were reduced. Levels of betaine, benzoic acid, pyroglutamic acid, pipecolic acid, N-phenylacetamide, uric acid, l-aspartyl-l-phenylalanine, and lysophosphatidyl cholines (lysoPCs) (C18:1, C18:2, C20:1, and C20:4) showed significant increases. Levels of l-proline, valine, l-leucine/isoleucine, hypoxanthine, glutamine, l-methionine, phenylpyruvic acid, several carnitine derivatives, and lysoPCs (C14:0, PC16:0, C15:0, C16:0, C17:1, C18:0, and C22:0) were significantly decreased. In particular, lysoPC 16:0 with a VIP value of 12.02 is esteemed to be the most important metabolite for evaluating the differences between the 2 serum samples. Our result confirmed weight-lowering effects of BSP, accompanied by favorable changes in metabolites in the subjects' blood. Therefore, this research enables us to better understand obesity and increases the predictability of the obesity-related risk by studying metabolites present in the blood.