PubMed

Related Articles Plasma N-acetylputrescine, cadaverine and 1,3-diaminopropane: potential biomarkers of lung cancer used to evaluate the efficacy of anticancer drugs. Oncotarget. 2017 Jul 17;: Authors: Liu R, Li P, Bi CW, Ma R, Yin Y, Bi K, Li Q Abstract Polyamines have been widely investigated as potential biomarkers for various types of cancers, including lung cancer, which is one of the most common causes of death from cancer worldwide. This study was carried out to evaluate the value of polyamines that serve as early diagnostic and cancer progression markers as well as drug evaluation for lung cancer (squamous cell carcinoma of lung, SCCL). SCCL was induced in Wistar rats by intratracheal instillation of 3-methylcholanthrene and treated with three different anti-cancer drugs, Aidi injections, fluorouracil, and a combination of them. After carcinogenesis for 28, 70 and 98 days and therapy for 28 and 56 days, the polyamine levels in plasma of SCCL, healthy and treated rats were determined using a UHPLC-MS/MS assay base on the means of targeted metabolomics. Results showed that increased N-acetylputrescine, cadaverine and 1,3-diaminopropane levels were associated with progression of SCCL. The levels of cadaverine and 1,3-diaminopropane returned to normal after administration of the three different kinds of anticancer drug. In addition, the suitability of using N-acetylputrescine, cadaverine and 1,3-diaminopropane as biomarkers was confirmed by PLS-DA and ROC analysis. It can provide an innovative and effective way for the clinical diagnosis, prevention and treatment of lung cancer, and stimulate a theoretical basis for the design and development of new anticancer drugs. At the same time, this increased the clinical options for polyamines as cancer biomarkers. PMID: 28740003 [PubMed - as supplied by publisher]

Related Articles MetaboQC: A tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls. Talanta. 2017 Nov 01;174:29-37 Authors: Calderón-Santiago M, López-Bascón MA, Peralbo-Molina Á, Priego-Capote F Abstract Nowadays most metabolomic studies involve the analysis of large sets of samples to find a representative metabolite pattern associated to the factor under study. During a sequence of analyses the instrument signals can be subjected to the influence of experimental variability sources. Implementation of quality control (QC) samples to check the contribution of experimental variability is the most common approach in metabolomics. This practice is based on the filtration of molecular entities experiencing a variation coefficient higher than that measured in the QC data set. Although other robust correction algorithms have been proposed, none of them has provided an easy-to-use and easy-to-install tool capable of correcting experimental variability sources. In this research an R-package -the MetaboQC- has been developed to correct intra-day and inter-days variability using QCs analyzed within a pre-set sequence of experiments. MetaboQC has been tested in two data sets to assess the correction effects by comparing the metabolites variability before and after application of the proposed tool. As a result, the number of entities in QCs significantly different between days was reduced from 86% to 19% in the negative ionization mode and from 100% to 13% in the positive ionization mode. Furthermore, principal component analysis allowed detecting the filtration of instrumental variability associated to the injection order. PMID: 28738582 [PubMed - in process]

Related Articles Characterization of carbon dioxide concentrating chemolithotrophic bacterium Serratia sp. ISTD04 for production of biodiesel. Bioresour Technol. 2017 Jul 14;243:893-897 Authors: Kumar M, Morya R, Gnansounou E, Larroche C, Thakur IS Abstract Proteomics and metabolomics analysis has become a powerful tool for characterization of microbial ability for fixation of Carbon dioxide. Bacterial community of palaeoproterozoic metasediments was enriched in the shake flask culture in the presence of NaHCO3. One of the isolate showed resistance to NaHCO3 (100mM) and was identified as Serratia sp. ISTD04 by 16S rRNA sequence analysis. Carbon dioxide fixing ability of the bacterium was established by carbonic anhydrase enzyme assay along with proteomic analysis by LC-MS/MS. In proteomic analysis 96 proteins were identified out of these 6 protein involved in carbon dioxide fixation, 11 in fatty acid metabolism, indicating the carbon dioxide fixing potency of bacterium along with production of biofuel. GC-MS analysis revealed that hydrocarbons and FAMEs produced by bacteria within the range of C13-C24 and C11-C19 respectively. Presence of 59% saturated and 41% unsaturated organic compounds, make it a better fuel composition. PMID: 28738515 [PubMed - as supplied by publisher]

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Related Articles Porous Graphitic Carbon Liquid Chromatography-Mass Spectrometry Analysis of Drought Stress-Responsive Raffinose Family Oligosaccharides in Plant Tissues. Methods Mol Biol. 2017;1631:279-293 Authors: Jorge TF, Florêncio MH, António C Abstract Drought is a major limiting factor in agriculture and responsible for dramatic crop yield losses worldwide. The adjustment of the metabolic status via accumulation of drought stress-responsive osmolytes is one of the many strategies that some plants have developed to cope with water deficit conditions. Osmolytes are highly polar compounds, analysis of whcih is difficult with typical reversed-phase chromatography. Porous graphitic carbon (PGC) has shown to be a suitable alternative to reversed-phase stationary phases for the analysis of highly polar compounds typically found in the plant metabolome. In this chapter, we describe the development and validation of a PGC-based liquid chromatography tandem mass spectrometry (LC-MS(n)) method suitable for the target analysis of water-soluble carbohydrates, such as raffinose family oligosaccharides (RFOs). We present detailed information regarding PGC column equilibration, LC-MS(n) system operation, data analysis, and important notes to be considered during the steps of method development and validation. PMID: 28735404 [PubMed - in process]

Related Articles Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer. Pharmacol Res. 2017 Jul 19;: Authors: Fleisher B, Andrews K, Brown AA, Ait-Oudhia S Abstract Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine. PMID: 28735000 [PubMed - as supplied by publisher]

Related Articles Ion mobility spectrometry combined with ultra performance liquid chromatography/mass spectrometry for metabolic phenotyping of urine: Effects of column length, gradient duration and ion mobility spectrometry on metabolite detection. Anal Chim Acta. 2017 Aug 22;982:1-8 Authors: Rainville PD, Wilson ID, Nicholson JK, Issacs G, Mullin L, Langridge JI, Plumb RS Abstract The need for rapid and efficient high throughput metabolic phenotyping (metabotyping) in metabolomic/metabonomic studies often requires compromises to be made between analytical speed and metabolome coverage. Here the effect of column length (150, 75 and 30 mm) and gradient duration (15, 7.5 and 3 min respectively) on the number of features detected when untargeted metabolic profiling of human urine using reversed-phase gradient ultra performance chromatography with, and without, ion mobility spectrometry, has been examined. As would be expected, reducing column length from 150 to 30 mm, and gradient duration, from 15 to 3 min, resulted in a reduction in peak capacity from 311 to 63 and a similar reduction in the number of features detected from over ca. 16,000 to ca. 6500. Under the same chromatographic conditions employing UPLC/IMS/MS to provide an additional orthogonal separation resulted in an increase in the number of MS features detected to nearly 20,000 and ca. 7500 for the 150 mm and the 30 mm columns respectively. Based on this limited study the potential of LC/IMS/MS as a tool for improving throughput and increasing metabolome coverage clearly merits further in depth study. PMID: 28734348 [PubMed - in process]

Related Articles Evaluation of the effect of extraction solvent and organ selection on the chemical profile of Astragalus spinosus using HPTLC- multivariate image analysis. J Chromatogr B Analyt Technol Biomed Life Sci. 2017 Jul 15;1061-1062:134-138 Authors: Shawky E, Selim DA Abstract The evaluation of extraction protocols for untargeted and targeted metabolomics was implemented for root and aerial organs of Astragalus spinosus in this work. The efficiency and complementarity of commonly used extraction solvents, namely petroleum ether, methylene chloride, ethyl acetate and n-butanol were considered for method evaluation using chemometric techniques in conjunction with new, simple, and fast high performance thin layer chromatography (HPTLC) method for fingerprint analysis by extracting information from a digitalized HPTLC plate using ImageJ software. A targeted approach was furtherly implemented by developing and validating an HPTLC method allowing the quantification of three saponin glycosides. The results of untargeted and targeted principle component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that the apparent saponins profile seems to depend on a combined effect of matrix composition and the properties of the selected solvent for extraction, where both the biological matrix of the investigated plant organs, as well as the extraction solvent can influence the precision of metabolite abundances. Although, the aerial part is frequently discarded as waste, it is shown hereby that it has similar chemical profile compared to the medicinal part, roots, yet a different extraction solvents pattern is recognized between the two organs which can be attributed to the differences in the composition, permeability or accessibility of the sample matrix/organ tissues, rather than the chemical structures of the detected metabolites. PMID: 28734161 [PubMed - as supplied by publisher]

Related Articles Metabolomic analysis reveals the composition differences in 13 Chinese tea cultivars of different manufacturing suitabilities. J Sci Food Agric. 2017 Jul 22;: Authors: Li P, Dai W, Lu M, Xie D, Tan J, Yang C, Zhu Y, Lv H, Peng Q, Zhang Y, Guo L, Ni D, Lin Z Abstract BACKGROUND: Green tea and black tea are manufactured using appropriate tea cultivars in China. However, the metabolite differences relating to the manufacturing suitability of tea cultivars are unclear. In this study, we performed a non-targeted metabolomic analysis on 13 Chinese tea cultivars using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) to investigate comprehensively the metabolite differences between cultivars suitable for manufacturing green tea (GT cultivars) and cultivars suitable for manufacturing both green tea and black tea (G&BT cultivars). RESULTS: Multivariate statistical analysis and cluster analysis divided the 13 cultivars into two groups, namely, GT cultivars and G&BT cultivars, which correlated with their manufacturing suitability. The GT cultivars contained higher levels of flavonoid glycosides, whereas the G&BT cultivars contained higher levels of catechins, dimeric catechins, phenolic acids, and alkaloids. CONCLUSION: Metabolic pathway analysis revealed that the flavonoid pathway inclined toward the synthesis of flavonoid glycosides in GT cultivars, whereas it inclined toward the synthesis of catechins and phenolic acids in G&BT cultivars. This study will be helpful for the manufacturing suitability discrimination and the breeding investigation of tea cultivars. PMID: 28734044 [PubMed - as supplied by publisher]

Related Articles Metabolic profiles of flooding-tolerant mechanism in early-stage soybean responding to initial stress. Plant Mol Biol. 2017 Jul 21;: Authors: Wang X, Zhu W, Hashiguchi A, Nishimura M, Tian J, Komatsu S Abstract KEY MESSAGE: Metabolomic analysis of flooding-tolerant mutant and abscisic acid-treated soybeans suggests that accumulated fructose might play a role in initial flooding tolerance through regulation of hexokinase and phosphofructokinase. Soybean is sensitive to flooding stress, which markedly reduces plant growth. To explore the mechanism underlying initial-flooding tolerance in soybean, mass spectrometry-based metabolomic analysis was performed using flooding-tolerant mutant and abscisic-acid treated soybeans. Among the commonly-identified metabolites in both flooding-tolerant materials, metabolites involved in carbohydrate and organic acid displayed same profile at initial-flooding stress. Sugar metabolism was highlighted in both flooding-tolerant materials with the decreased and increased accumulation of sucrose and fructose, respectively, compared to flooded soybeans. Gene expression of hexokinase 1 was upregulated in flooded soybean; however, it was downregulated in both flooding-tolerant materials. Metabolites involved in carbohydrate/organic acid and proteins related to glycolysis/tricarboxylic acid cycle were integrated. Increased protein abundance of phosphofructokinase was identified in both flooding-tolerant materials, which was in agreement with its enzyme activity. Furthermore, sugar metabolism was pointed out as the tolerant-responsive process at initial-flooding stress with the integration of metabolomics, proteomics, and transcriptomics. Moreover, application of fructose declined the increased fresh weight of plant induced by flooding stress. These results suggest that fructose might be the critical metabolite through regulation of hexokinase and phosphofructokinase to confer initial-flooding stress in soybean. PMID: 28733872 [PubMed - as supplied by publisher]

Related Articles Metabolomic characteristics of cholesterol-induced non-obese nonalcoholic fatty liver disease in mice. Sci Rep. 2017 Jul 21;7(1):6120 Authors: Tu LN, Showalter MR, Cajka T, Fan S, Pillai VV, Fiehn O, Selvaraj V Abstract Nonalcoholic fatty liver disease (NAFLD) in non-obese patients remains a clinical condition with unclear etiology and pathogenesis. Using a metabolomics approach in a mouse model that recapitulates almost all the characteristic features of non-obese NAFLD, we aimed to advance mechanistic understanding of this disorder. Mice fed high fat, high cholesterol, cholate (HFHCC) diet for three weeks consistently developed hepatic pathology similar to NAFLD and nonalcoholic steatohepatitis (NASH) without changes to body weight or fat pad weights. Gas- and liquid chromatography/mass spectrometry-based profiling of lipidomic and primary metabolism changes in the liver and plasma revealed that systemic mechanisms leading to steatosis and hepatitis in this non-obese NAFLD model were driven by a combination of effects directed by elevated free cholesterol, cholesterol esters and cholic acid, and associated changes to metabolism of sphingomyelins and phosphatidylcholines. These results demonstrate that mechanisms underlying cholesterol-induced non-obese NAFLD are distinct from NAFLD occurring as a consequence of metabolic syndrome. In addition, this investigation provides one of the first metabolite reference profiles for interpreting effects of dietary and hepatic cholesterol in human non-obese NAFLD/NASH patients. PMID: 28733574 [PubMed - in process]

Related Articles Atherogenic Lipoprotein Determinants of Cardiovascular Disease and Residual Risk Among Individuals With Low Low-Density Lipoprotein Cholesterol. J Am Heart Assoc. 2017 Jul 21;6(7): Authors: Lawler PR, Akinkuolie AO, Chu AY, Shah SH, Kraus WE, Craig D, Padmanabhan L, Glynn RJ, Ridker PM, Chasman DI, Mora S Abstract BACKGROUND: Levels of LDL (low-density lipoprotein) cholesterol in the population are declining, and increasing attention is being focused on residual lipid-related pathways of atherosclerotic cardiovascular disease risk beyond LDL cholesterol. Among individuals with low (<130 mg/dL) LDL cholesterol, we undertook detailed profiling of circulating atherogenic lipoproteins in relation to incident cardiovascular disease in 2 populations. METHODS AND RESULTS: We performed proton nuclear magnetic resonance spectroscopy to quantify concentrations of LDL and VLDL (very low-density lipoprotein) particle subclasses in 11 984 JUPITER trial participants (NCT00239681). Adjusted Cox models examined cardiovascular disease risk associated with lipoprotein measures according to treatment allocation. Risk (adjusted hazard ratio [95%CI] per SD increment) among placebo-allocated participants was associated with total LDL particles (1.19 [1.02, 1.38]) and total VLDL particles (1.21 [1.04, 1.41]), as well as apolipoprotein B, non-high-density lipoprotein cholesterol, and triglycerides, but not LDL-c. Rosuvastatin reduced LDL measures but had variable effects on triglyceride and VLDL measures. On-statin levels of the smallest VLDL particle subclass were associated with a 68% per-SD (adjusted hazard ratio 1.68 [1.28, 2.22]) increase in residual risk-this risk was related to VLDL cholesterol and not triglyceride or larger VLDL particles. There was evidence that residual risk prediction during statin therapy could be significantly improved through the inclusion of key VLDL measures (Harrell C-index 0.780 versus 0.712; P<0.0001). In an independent, prospective cohort of 4721 individuals referred for cardiac catheterization (CATHGEN), similar patterns of lipoprotein-related risk were observed. CONCLUSIONS: Atherogenic lipoprotein particle concentrations were associated with cardiovascular disease risk when LDL cholesterol was low. VLDL lipoproteins, particularly the smallest remnant subclass, may represent unused targets for risk prediction and potential therapeutic intervention for reducing residual risk. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00239681. PMID: 28733430 [PubMed - in process]

Related Articles SG2-type R2R3-MYB transcription factor MYB15 controls defense-induced lignification and basal immunity in Arabidopsis. Plant Cell. 2017 Jul 21;: Authors: Chezem WR, Memon A, Li FS, Weng JK, Clay NK Abstract Lignification of cell wall appositions is a conserved basal defense mechanism in the plant innate immune response. However, the genetic pathway controlling defense-induced lignification remains unknown. Here, we demonstrate the Arabidopsis thaliana SG2-type R2R3-MYB transcription factor MYB15 as a regulator of defense-induced lignification and basal immunity. Loss of MYB15 reduces the content but not the composition of defense-induced lignin, whereas constitutive expression of MYB15 increases lignin content independently of immune activation. Comparative transcriptional and metabolomics analyses implicate MYB15 as necessary for the defense-induced synthesis of guaiacyl lignin and the basal synthesis of the coumarin metabolite scopoletin. MYB15 directly binds to the secondary wall MYB-responsive element consensus sequence, which encompasses the AC elements, to drive lignification. The myb15 and lignin biosynthetic mutants show increased susceptibility to the bacterial pathogen Pseudomonas syringae, consistent with defense-induced lignin having a major role in basal immunity. A scopoletin biosynthetic mutant also shows increased susceptibility independently of immune activation, consistent with a role in preformed defense. Our results support a role for phenylalanine-derived small molecules in preformed and inducible Arabidopsis defense, a role previously dominated by tryptophan-derived small molecules. Understanding the regulatory network linking lignin biosynthesis to plant growth and defense will help lignin engineering efforts to improve the production of biofuels and aromatic industrial products as well as increase disease resistance in energy and agricultural crops. PMID: 28733420 [PubMed - as supplied by publisher]

Related Articles Metabolomics reveal physiological changes in mayfly larvae (Neocloeon triangulifer) at ecological upper thermal limits. J Insect Physiol. 2017 Jul 18;: Authors: Chou H, Pathmasiri W, Deese-Spruill J, Sumner S, Buchwalter DB Abstract Aquatic insects play critical roles in freshwater ecosystems and temperature is a fundamental driver of species performance and distributions. However, the physiological mechanisms that determine the thermal performance of species remain unclear. Here we used a metabolomics approach to gain insights into physiological changes associated with a short-term, sublethal thermal challenge in the mayfly Neocloeon triangulifer (Ephemeroptera: Baetidae). Larvae were subjected to a thermal ramp (from 22 to 30 °C at a rate of 1°C/h) and metabolomics analysis (both Nuclear Magnetic Resonance (NMR) Spectroscopy and Gas Chromatography coupled Time-of-Flight Mass Spectrometry (GC-TOF-MS)) indicated that processes related to energetics (sugar metabolism) and membrane stabilization primarily differentiated heat treated larvae from controls. Limited evidence of anaerobic metabolism was observed in the heat treated larvae at 30°C, a temperature that is chronically lethal to larvae. PMID: 28733240 [PubMed - as supplied by publisher]

Related Articles Lipoprotein insulin resistance score and risk of incident diabetes during extended follow-up of 20 years: The Women's Health Study. J Clin Lipidol. 2017 Jun 21;: Authors: Harada PHN, Demler OV, Dugani SB, Akinkuolie AO, Moorthy MV, Ridker PM, Cook NR, Pradhan AD, Mora S Abstract BACKGROUND: Type II diabetes (T2D) is preceded by prolonged insulin resistance and relative insulin deficiency incompletely captured by glucose metabolism parameters, high-density lipoprotein (HDL) cholesterol and triglycerides. OBJECTIVE: Whether lipoprotein insulin resistance (LPIR) score, a metabolomic marker, is associated with incident diabetes and improves risk reclassification over traditional markers on extended follow-up. METHODS: Among 25,925 nondiabetic women aged 45 years or older, LPIR was measured by nuclear magnetic resonance spectroscopy as a weighted score of very low density lipoprotein, low-density lipoprotein, and HDL particle sizes, and their subsets concentrations. We run adjusted cox regression models for LPIR with incident T2D (20.4 years median follow-up). RESULTS: Adjusting for demographics, body mass index, life style factors, blood pressure, and T2D family history, the LPIR hazard ratio for T2D (hazard ratio [HR] per standard deviation, 95% confidence interval) was 1.95 (1.85, 2.06). Further adjusting for HbA1c, C-reactive protein, triglycerides, HDL and low-density lipoprotein cholesterol, LPIR HR was attenuated to 1.41 (1.31, 1.53) and had the strongest association with T2D after HbA1C in mutually adjusted models. The association persisted even in those with optimal clinical profiles, adjusted HR per standard deviation 1.91 (1.17, 3.13). In participants deemed at intermediate T2D risk by the Framingham Offspring T2D score, LPIR led to a net reclassification of 0.145 (0.117, 0.175). CONCLUSION: In middle-aged or older healthy women followed prospectively for over 20 years, LPIR was robustly associated with incident T2D, including among those with an optimal clinical metabolic profile. LPIR improved T2D risk classification and may guide early and targeted prevention strategies. PMID: 28733174 [PubMed - as supplied by publisher]

Multi-approach metabolomics analysis and artificial simplified phytocomplexes reveal cultivar-dependent synergy between polyphenols and ascorbic acid in fruits of the sweet cherry (Prunus avium L.). PLoS One. 2017;12(7):e0180889 Authors: Commisso M, Bianconi M, Di Carlo F, Poletti S, Bulgarini A, Munari F, Negri S, Stocchero M, Ceoldo S, Avesani L, Assfalg M, Zoccatelli G, Guzzo F Abstract Fruits of the sweet cherry (Prunus avium L.) accumulate a range of antioxidants that can help to prevent cardiovascular disease, inflammation and cancer. We tested the in vitro antioxidant activity of 18 sweet cherry cultivars collected from 12 farms in the protected geographical indication region of Marostica (Vicenza, Italy) during two growing seasons. Multiple targeted and untargeted metabolomics approaches (NMR, LC-MS, HPLC-DAD, HPLC-UV) as well as artificial simplified phytocomplexes representing the cultivars Sandra Tardiva, Sandra and Grace Star were then used to determine whether the total antioxidant activity reflected the additive effects of each compound or resulted from synergistic interactions. We found that the composition of each cultivar depended more on genetic variability than environmental factors. Furthermore, phenolic compounds were the principal source of antioxidant activity and experiments with artificial simplified phytocomplexes indicated strong synergy between the anthocyanins and quercetins/ascorbic acid specifically in the cultivar Sandra Tardiva. Our data therefore indicate that the total antioxidant activity of sweet cherry fruits may originate from cultivar-dependent interactions among different classes of metabolite. PMID: 28732012 [PubMed - in process]

Related Articles Deconstructing the Metabolic Networks of Oncogenic Signaling Using Targeted Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). Methods Mol Biol. 2017;1636:405-414 Authors: Poulogiannis G Abstract Metabolic reprogramming is recognized as an emerging hallmark of oncogenic signaling and cancer development. Hence the need to identify novel quantitative analytical platforms for studying metabolism in vitro and in vivo has dramatically increased. Here, we describe the experimental workflow for a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach involving positive/negative ion switching to analyze >250 metabolites of central carbon metabolism, nucleotides, and amino acids. PMID: 28730494 [PubMed - in process]

Related Articles Advances in Applications of Metabolomics in Pluripotent Stem Cell Research. Curr Opin Chem Eng. 2017 Feb;15:36-43 Authors: Bhute VJ, Bao X, Palecek SP Abstract Stem cells undergo extensive metabolic rewiring during reprogramming, proliferation and differentiation, and numerous studies have demonstrated a significant role of metabolism in controlling stem cell fates. Recent applications of metabolomics, the study of concentrations and fluxes of small molecules in cells, have advanced efforts to characterize and maturate stem cell fates, assess drug toxicity in stem cell tissue models, identify biomarkers, and study the effects of environment on metabolic pathways in stem cells and their progeny. Looking to the future, combining metabolomics with other -omics approaches will provide a deeper understanding of the complex regulatory mechanisms of stem cells. PMID: 28729963 [PubMed]

Related Articles Serum and Plasma Metabolomic Biomarkers for Lung Cancer. Bioinformation. 2017;13(6):202-208 Authors: Kumar N, Shahjaman M, Mollah MNH, Islam SMS, Hoque MA Abstract In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker. PMID: 28729763 [PubMed]

Related Articles Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes. Sci Rep. 2017 Jul 20;7(1):6037 Authors: Jäger S, Wahl S, Kröger J, Sharma S, Hoffmann P, Floegel A, Pischon T, Prehn C, Adamski J, Müller-Nurasyid M, Waldenberger M, Strauch K, Peters A, Gieger C, Suhre K, Grallert H, Boeing H, Schulze MB, Meidtner K Abstract Diabetes-associated metabolites may aid the identification of new risk variants for type 2 diabetes. Using targeted metabolomics within a subsample of the German EPIC-Potsdam study (n = 2500), we tested previously published SNPs for their association with diabetes-associated metabolites and conducted an additional exploratory analysis using data from the exome chip including replication within 2,692 individuals from the German KORA F4 study. We identified a total of 16 loci associated with diabetes-related metabolite traits, including one novel association between rs499974 (MOGAT2) and a diacyl-phosphatidylcholine ratio (PC aa C40:5/PC aa C38:5). Gene-based tests on all exome chip variants revealed associations between GFRAL and PC aa C42:1/PC aa C42:0, BIN1 and SM (OH) C22:2/SM C18:0 and TFRC and SM (OH) C22:2/SM C16:1). Selecting variants for gene-based tests based on functional annotation identified one additional association between OR51Q1 and hexoses. Among single genetic variants consistently associated with diabetes-related metabolites, two (rs174550 (FADS1), rs3204953 (REV3L)) were significantly associated with type 2 diabetes in large-scale meta-analysis for type 2 diabetes. In conclusion, we identified a novel metabolite locus in single variant analyses and four genes within gene-based tests and confirmed two previously known mGWAS loci which might be relevant for the risk of type 2 diabetes. PMID: 28729637 [PubMed - in process]