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1
Wu, Yali, Lulu Pan, Zhendong Chen, Yuandong Zheng, Xingxing Diao, and Dafang Zhong. "Metabolite Identification in the Preclinical and Clinical Phase of Drug Development." Current Drug Metabolism 22, no. 11 (September 2021): 838–57. http://dx.doi.org/10.2174/1389200222666211006104502.
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Metabolite identification plays a critical role in the phases during drug development. Drug metabolites can contribute to efficacy, toxicity, and drug-drug interaction. Thus, the correct identification of metabolites is essential to understand the behavior of drugs in humans. Drug administration authorities (e.g., FDA, EMA, and NMPA) emphasize evaluating the safety of human metabolites with exposure higher than 10% of the total drugrelated components. Many previous reviews have summarized the various methods, tools, and strategies for the appropriate and comprehensive identification of metabolites. In this review, we focus on summarizing the importance of identifying metabolites in the preclinical and clinical phases of drug development. Summarized scenarios include the role of metabolites in pharmacokinetics/pharmacodynamics (PK/PD) analysis, disproportional exposure of metabolites that contribute to drug toxicity, changes in metabolite exposure in renal-impaired patients, covalent tyrosine kinase inhibitors (anticancer drugs), and metabolite identification of drug candidates from natural medicines. This review is aimed to provide meaningful insight into the significant role of metabolite identification in drug development.
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Tabrez, Shams, Mohammed Razeeth Shait Mohammed, Nasimudeen R. Jabir, and Mohammad Imran Khan. "Identification of novel cardiovascular disease associated metabolites using untargeted metabolomics." Biological Chemistry 402, no. 6 (January 20, 2021): 749–57. http://dx.doi.org/10.1515/hsz-2020-0331.
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Abstract Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality around the world. Early diagnosis of CVD could provide the opportunity for sensible management and better clinical outcome along with the prevention of further progression of the disease. In the current study, we used an untargeted metabolomic approach to identify possible metabolite(s) that associate well with the CVD and could serve either as therapeutic target or disease-associated metabolite. We identified 26 rationally adjusted unique metabolites that were differentially present in the serum of CVD patients compared with healthy individuals, among them 15 were found to be statistically significant. Out of these metabolites, we identified some novel metabolites like UDP-l-rhamnose and N1-acetylspermidine that have not been reported to be linked with CVD directly. Further, we also found that some metabolites like ethanolamide, solanidine, dimethylarginine, N-acetyl-l-tyrosine, can act as a discriminator of CVD. Metabolites integrating pathway enrichment analysis showed enrichment of various important metabolic pathways like histidine metabolism, methyl histidine metabolism, carnitine synthesis, along with arginine and proline metabolism in CVD patients. Our study provides a great opportunity to understand the pathophysiological role and impact of the identified unique metabolites and can be extrapolated as specific CVD specific metabolites.
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Lake, Juniper A., Yiren Yan, Jack C. M. Dekkers, Jing Qiu, Erin M. Brannick, and Behnam Abasht. "Identification of circulating metabolites associated with wooden breast and white striping." PLOS ONE 17, no. 9 (September 26, 2022): e0274208. http://dx.doi.org/10.1371/journal.pone.0274208.
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Current diagnostic methods for wooden breast and white striping, common breast muscle myopathies of modern commercial broiler chickens, rely on subjective examinations of the pectoralis major muscle, time-consuming microscopy, or expensive imaging technologies. Further research on these disorders would benefit from more quantitative and objective measures of disease severity that can be used in live birds. To this end, we utilized untargeted metabolomics alongside two statistical approaches to evaluate plasma metabolites associated with wooden breast and white striping in 250 male commercial broiler chickens. First, mixed linear modeling was employed to identify metabolites with a significant association with these muscle disorders and found 98 metabolites associated with wooden breast and 44 metabolites associated with white striping (q-value < 0.05). Second, a support vector machine was constructed using stepwise feature selection to determine the smallest subset of metabolites with the highest categorization accuracy for wooden breast. The final support vector machine achieved 94% accuracy using only 6 metabolites. The metabolite 3-methylhistidine, which is often used as an index of myofibrillar breakdown in skeletal muscle, was the top metabolite for both wooden breast and white striping in our mixed linear model and was also the metabolite with highest marginal prediction accuracy (82%) for wooden breast in our support vector machine. Overall, this study identified a candidate set of metabolites for an objective measure of wooden breast or white striping severity in live birds and expanded our understanding of these muscle disorders.
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Otter, Don, Mingshu Cao, Hui-Ming Lin, Karl Fraser, Shelley Edmunds, Geoff Lane, and Daryl Rowan. "Identification of Urinary Biomarkers of Colon Inflammation in IL10-/-Mice Using Short-Column LCMS Metabolomics." Journal of Biomedicine and Biotechnology 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/974701.
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The interleukin-10-deficient (IL10-/-) mouse develops colon inflammation in response to normal intestinal microflora and has been used as a model of Crohn's disease. Short-Column LCMS metabolite profiling of urine from IL10-/-and wild-type (WT) mice was used, in two independent experiments, to identify mass spectral ions differing in intensity between these two genotypes. Three differential metabolites were identified as xanthurenic acid and as the glucuronides of xanthurenic acid and of α-CEHC (2,5,7,8-tetramethyl-2-(2′-carboxyethyl)-6-hydroxychroman). The significance of several differential metabolites as potential biomarkers of colon inflammation was evaluated in an experiment which compared metabolite concentrations in IL10-/-and WT mice housed, either under conventional conditions and dosed with intestinal microflora, or maintained under specific pathogen-free (SPF) conditions. Concentrations of xanthurenic acid, α-CEHC glucuronide, and an unidentified metabolitem/z495-/497+were associated with the degree of inflammation in IL10-/-mice and may prove useful as biomarkers of colon inflammation.
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Sun, Jiahong, Min Zhao, Liu Yang, Xue Liu, Lucia Pacifico, Claudio Chiesa, and Bo Xi. "Identification of Potential Metabolic Markers of Hypertension in Chinese Children." International Journal of Hypertension 2021 (August 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/6691734.
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Background. Studies in adults have shown that several metabolites across multiple pathways are strongly associated with hypertension. However, as yet, to our knowledge, no study has investigated such association in childhood. We, therefore, compared the serum metabolite profile of children with normal and elevated blood pressure (BP) to identify potential metabolic markers and pathways that could be useful for the assessment of pediatric hypertension. Methods. The study included 26 hypertensive children (age range, 6–11 years) and 26 age- and sex-matched ones with normal BP, who were recruited from the baseline survey of the Huantai Childhood Cardiovascular Health Cohort Study. Ultrahigh-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry was performed to assess the serum metabolite profile. Logistic regression analysis was used to select significant metabolites associated with hypertension after adjustment for body mass index, waist circumference, and lipid profile. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were utilized to search for the potential pathways of metabolites. Results. A total of 45 and 34 metabolites were preliminarily screened in positive and negative modes, respectively (variable importance in the projection (VIP) > 1.0 and P < 0.05 ). After adjustment for the false discovery rate, 7 and 1 differential metabolites in the positive and negative modes, respectively, remained significant (VIP > 1.0 and q < 0.05). These metabolites were mainly involved in amino acid metabolism and glycerophospholipid metabolism. Among these, two significant metabolites including ethanolamine and 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate displayed an area under the curve value of 0.820 (95% confidence interval, 0.688–0.951), with a sensitivity of 0.846 and a specificity of 0.769. Conclusion. The untargeted metabolomics approach effectively identified the differential serum metabolite profile in children with and without hypertension. Notably, two metabolites including ethanolamine and 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate exhibited a good discriminative ability to identify children with hypertension, providing new insights into potential mechanisms of pediatric hypertension.
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Liu, Rui, Zheng-Xue Bao, Guo-Hong Li, Chun-Qiang Li, Shao-Lin Wang, Xue-Rong Pan, Ke-Qin Zhang, and Pei-Ji Zhao. "Identification of Nematicidal Metabolites from Purpureocillium lavendulum." Microorganisms 10, no. 7 (July 2, 2022): 1343. http://dx.doi.org/10.3390/microorganisms10071343.
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Purpureocillium lavendulum is a fungus with promising biocontrol applications. Here, transcriptome data acquired during the infection of Caenorhabditis elegans by Purpureocillium lavendulum showed that the transcription of metabolite synthesis genes was significantly up-regulated after 24 and 48 h of the fungus-nematode interaction. Then, the up-regulated transcription level of lipoxygenase was confirmed by RT-qPCR. The ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis of differential metabolites revealed that this interaction resulted in the emergence of new metabolites or enhanced the production of metabolites. The results of the UPLC-MS analysis and the nematicidal assay were used to establish optimal culturing conditions under which 12 metabolites, including 3 hydroxylated C18 fatty acids and 9 steroids, were isolated and identified. Among them, hydroxylated fatty acids showed pronounced nematicidal activity against Meloidogyne incognita, and two degradative sterols showed chemotaxis activity to M. incognita. This study lays a foundation for the function of lipoxygenase and its products during the infection of Purpureocillium lavendulum.
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Srivastava, Nidhi, Vishal Dubey, Madhumita Sengar, and Rastogi Sameer. "Identification and Characterization of Metabolites of Irinotecan in-vivo and in-vitro matrixes by HPLC/LC-MS." Research in Pharmacy and Health Sciences 2, no. 3 (August 15, 2016): 211–18. http://dx.doi.org/10.32463/rphs.2016.v02i03.42.
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In the present study metabolite identification and characterization has done by using HPLC and LC-MS. During method development various mobile phases have tried for identification of metabolites. The matrixes selected for in- vivo study were urine because nearly all the metabolites of irinotecan were obtained in it. The extraction mixtures have selected to retain maximum amount of analyte with less effort. During experiment four extraction solvents were used in six different concentrations out of which TBME suit our method. In-vitro study done by Human Liver microsomes by using Phosphate buffer (pH 7.4) and NADPH as co-factors for initiation of enzymatic reaction. Irinotecan is a prodrug that is converted in the liver to an active metabolite, SN-38. It is eliminate in Bile and Faeces and thus its dose reduced in Hepatic Failure. Irinotecan act by inhibiting Topoisomerase-1.It is the enzyme which nicks, introduces negative supercoils and reseals the DNA strand. Conventionally, drug metabolite identification in the past has usually been based on the comparison of ultraviolet (UV) spectral data and high-performance liquid chromatography (HPLC) retention times of isolated ‘unknown’ metabolites with those of synthesised standards. Such a method of detecting and characterising drug metabolites is an uncertain, time-consuming and expensive process, as well as affording very limited structural information. Furthermore, Phase I metabolism of a drug candidate often results in only minor structural modification of the parent compound; these minor changes can make it particularly difficult to determine suitable chromatographic conditions to effect HPLC separation of metabolites. This study describes contemporary approach to identification and characterization of xenobiotic metabolites in complex biological fluids derived from drug metabolism studies.
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Vikingsson, Svante, Tobias Rautio, Jakob Wallgren, Anna Åstrand, Shimpei Watanabe, Johan Dahlén, Ariane Wohlfarth, et al. "LC-QTOF-MS Identification of Major Urinary Cyclopropylfentanyl Metabolites Using Synthesized Standards." Journal of Analytical Toxicology 43, no. 8 (August 23, 2019): 607–14. http://dx.doi.org/10.1093/jat/bkz057.
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Abstract Cyclopropylfentanyl is a fentanyl analog implicated in 78 deaths in Europe and over 100 deaths in the United States, but toxicological information including metabolism data about this drug is scarce. The aim of this study was to provide the exact structure of abundant and unique metabolites of cyclopropylfentanyl along with synthesis routes. In this study, metabolites were identified in 13 post-mortem urine samples using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were analyzed with and without enzymatic hydrolysis, and seven potential metabolites were synthesized in-house to provide the identity of major metabolites. Cyclopropylfentanyl was detected in all samples, and the most abundant metabolite was norcyclopropylfentanyl (M1) that was detected in 12 out of 13 samples. Reference materials were synthesized (synthesis routes provided) to identify the exact structure of the major metabolites 4-hydroxyphenethyl cyclopropylfentanyl (M8), 3,4-dihydroxyphenethyl cyclopropylfentanyl (M5) and 4-hydroxy-3-methoxyphenethyl cyclopropylfentanyl (M9). These metabolites are suitable urinary markers of cyclopropylfentanyl intake as they are unique and detected in a majority of hydrolyzed urine samples. Minor metabolites included two quinone metabolites (M6 and M7), not previously reported for fentanyl analogs. Interestingly, with the exception of norcyclopropylfentanyl (M1), the metabolites appeared to be between 40% and 90% conjugated in urine. In total, 11 metabolites of cyclopropylfentanyl were identified, including most metabolites previously reported after hepatocyte incubation.
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Peng, Hongquan, Xun Liu, Chiwa Aoieong, Tou Tou, Tsungyang Tsai, Kamleong Ngai, Hao I. Cheang, Zhi Liu, Peijia Liu, and Haibin Zhu. "Identification of Metabolite Markers Associated with Kidney Function." Journal of Immunology Research 2022 (July 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/6190333.
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Background. Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function. Methods. Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory. Results. From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study. Conclusions. Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.
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Karlsson, Therese, Anna Winkvist, Millie Rådjursöga, Lars Ellegård, Anders Pedersen, and Helen M. Lindqvist. "Identification of Single and Combined Serum Metabolites Associated with Food Intake." Metabolites 12, no. 10 (September 27, 2022): 908. http://dx.doi.org/10.3390/metabo12100908.
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Assessment of dietary intake is challenging. Traditional methods suffer from both random and systematic errors; thus objective measures are important complements in monitoring dietary exposure. The study presented here aims to identify serum metabolites associated with reported food intake and to explore whether combinations of metabolites may improve predictive models. Fasting blood samples and a 4-day weighed food diary were collected from healthy Swedish subjects (n = 119) self-defined as having habitual vegan, vegetarian, vegetarian + fish, or omnivore diets. Serum was analyzed for metabolites by 1H-nuclear magnetic resonance spectroscopy. Associations between single and combined metabolites and 39 foods and food groups were explored. Area under the curve (AUC) was calculated for prediction models. In total, 24 foods or food groups associated with serum metabolites using the criteria of rho > 0.2, p < 0.01 and AUC ≥ 0.7 were identified. For the consumption of soybeans, citrus fruits and marmalade, nuts and almonds, green tea, red meat, poultry, total fish and shellfish, dairy, fermented dairy, cheese, eggs, and beer the final models included two or more metabolites. Our results indicate that a combination of metabolites improve the possibilities to use metabolites to identify several foods included in the current diet. Combined metabolite models should be confirmed in dose–response intervention studies.
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Kokil, Sachin, and Manish Bhatia. "Antifungal Azole Metabolites: Significance in Pharmaceutical and Biomedical Analysis." Journal of Medical Biochemistry 28, no. 1 (January 1, 2009): 1–10. http://dx.doi.org/10.2478/v10011-008-0040-1.
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Antifungal Azole Metabolites: Significance in Pharmaceutical and Biomedical Analysis Individualised therapy and factors determining such variability among patients are confusing to both physicians and their patients because of the observed therapeutic, metabolic and toxic response. The same is true about antifungal azoles. They are under the influence and become targets of metabolic drug-drug interactions where more than one active form of the drug may be involved. The clinical relevance of these interactions may vary upon the azole involved and upon the intention of drug administration. The pharmacodynamics and pharmacokinetics of azole drugs as indicated by the reviewed data make the need for characterization of all their metabolites even more evident. The health care systems also emphasize the identification and quantitation of the metabolites for a comprehensive understanding of the biological safety of individual metabolites, thus, revealing the need and scope of bioanalytical research in metabolite and toxicity profiling of drugs. Availability of protocols for qualitative and quantitative characterization of all metabolites will have many applications for therapeutic drug monitoring, bioequivalence, toxicological and all related studies. Identification of metabolites may be done by a variety of chromatographic and spectroscopic techniques, either alone or in combination with other techniques. Conventional liquid chromatography has been exploited widely in the field of metabolite profiling. The arrival of hyphenated techniques has revolutionized metabolite profiling, by not only separating but also generating data for the structural identification of metabolites as well. Among all techniques, the most exploited are Liquid Chromatography-Mass Spectroscopy, Nuclear Magnetic Resonance spectroscopy, Liquid Chromatography-Nuclear Magnetic Resonance spectroscopy, Liquid Chromatography-Nuclear Magnetic Resonance spectroscopy-Mass Spectroscopy and Extraction-Nuclear Magnetic Resonance spectroscopy. This compilation provides a tool for the metabolic, bioanalytical and biomedical understanding of antifungal azole metabolites.
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Du, Yang, Ji-Hui Dong, Lei Chen, Hua Liu, Guang-En Zheng, Guang-Yang Chen, and Yong Cheng. "Metabolomic Identification of Serum Exosome-Derived Biomarkers for Bipolar Disorder." Oxidative Medicine and Cellular Longevity 2022 (January 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5717445.
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Background. Exosomes are extracellular vesicles that play important roles in various physiological and pathological functions. Previous studies have demonstrated that exosome-derived contents are promising biomarkers to inform the pathogenesis and diagnosis of major depressive disorder and schizophrenia. Methods. We used ultraperformance liquid chromatography-tandem mass spectrometry to analyze the differentially expressed metabolites in serum exosomes of patients with bipolar disorder (BD) and evaluated the potential of exosomal metabolites as biomarkers for BD. Results. Our results showed 26 differentially expressed serum exosomal metabolites in patients with BD ( n = 32 ) when compared with healthy control (HC) subjects ( n = 40 ), and these differentially expressed metabolites were enriched in pathways related to sugar metabolism. We then utilized random forest classifier and identified 15 exosomal metabolites that can be used to classify samples from patients with BD and HC subjects with 0.838 accuracy (95% CI, 0.604–1.00) in the training set of participants. These 15 metabolites showed excellent performance in differentiating between patients with BD and HC subjects in the testing set of participants, with 0.971 accuracy (95% CI, 0.865–1.00). Importantly, the 15 exosomal metabolites also showed good to excellent performance in differentiating between BD patients and other major psychiatric diseases (major depressive disorder and schizophrenia). Conclusion. Collectively, our findings for the first time revealed a potential role of exosomal metabolite dysregulations in the onset and/or development of BD and suggested that blood exosomal metabolites are strong candidates to inform the diagnosis of BD.
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Nguyen, Dai Hai, Canh Hao Nguyen, and Hiroshi Mamitsuka. "Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches." Briefings in Bioinformatics 20, no. 6 (August 6, 2018): 2028–43. http://dx.doi.org/10.1093/bib/bby066.
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Abstract Motivation: Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.
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Su, Qiao, Fuyou Bi, Shu Yang, Huiming Yan, Xiaoxiao Sun, Jiayue Wang, Yuying Qiu, Meijuan Li, Shen Li, and Jie Li. "Identification of Plasma Biomarkers in Drug-Naïve Schizophrenia Using Targeted Metabolomics." Psychiatry Investigation 20, no. 9 (September 25, 2023): 818–25. http://dx.doi.org/10.30773/pi.2023.0121.
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Objective Schizophrenia (SCZ) is a severe psychiatric disorder with unknown etiology and lacking specific biomarkers. Herein, we aimed to explore plasma biomarkers relevant to SCZ using targeted metabolomics.Methods Sixty drug-naïve SCZ patients and 36 healthy controls were recruited. Psychotic symptoms were assessed using the Positive and Negative Syndrome Scale. We analyzed the levels of 271 metabolites in plasma samples from all subjects using targeted metabolomics, and identified metabolites that differed significantly between the two groups. Then we evaluated the diagnostic power of the metabolites based on receiver operating characteristic curves, and explored metabolites associated with the psychotic symptoms in SCZ patients.Results Twenty-six metabolites showed significant differences between SCZ patients and healthy controls. Among them, 12 metabolites were phosphatidylcholines and cortisol, ceramide (d18:1/22:0), acetylcarnitine, and γ-aminobutyric acid, which could significantly distinguish SCZ from healthy controls with the area under the curve (AUC) above 0.7. Further, a panel consisting of the above 4 metabolites had an excellent performance with an AUC of 0.867. In SCZ patients, phosphatidylcholines were positively related with positive symptoms, and cholic acid was positively associated with negative symptoms.Conclusion Our study provides insights into the metabolite alterations associated with SCZ and potential biomarkers for its diagnosis and symptom severity assessment.
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Liang, Yaoyue, Wenjing Zhao, Chenxiao Wang, Zijian Wang, Zhibin Wang, and Jiayu Zhang. "A Comprehensive Screening and Identification of Genistin Metabolites in Rats Based on Multiple Metabolite Templates Combined with UHPLC-HRMS Analysis." Molecules 23, no. 8 (July 26, 2018): 1862. http://dx.doi.org/10.3390/molecules23081862.
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Genistin, an isoflavone belonging to the phytoestrogen family, has been reported to possess various therapeutic effects. In the present study, the genistin metabolites in rats were investigated by UHPLC-LTQ-Orbitrap mass spectrometer in both positive and negative ion modes. Firstly, the data sets were obtained based on data-dependent acquisition method and then 10 metabolite templates were established based on the previous reports. Then diagnostic product ions (DPIs) and neutral loss fragments (NLFs) were proposed to efficiently screen and ascertain the major-to-trace genistin metabolites. Meanwhile, the calculated Clog P values were used to identify the positional isomers with different retention times. Consequently, a total of 64 metabolites, including prototype drug, were positively or putatively characterized. Among them, 40 metabolites were found according to the templates of genistin and genistein, which was the same as the previous research method. After using other metabolite templates, 24 metabolites were added. The results demonstrated that genistin mainly underwent methylation, hydrogenation, hydroxylation, glucosylation, glucuronidation, sulfonation, acetylation, ring-cleavage and their composite reactions in vivo biotransformation. In conclusion, the research not only revealed the genistein metabolites and metabolic pathways in vivo comprehensively, but also proposed a method based on multiple metabolite templates to screen and identify metabolites of other natural compounds.
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Malik, Vasanti S., Marta Guasch-Ferre, Frank B. Hu, Mary K. Townsend, Oana A. Zeleznik, A. Heather Eliassen, Shelley S. Tworoger, et al. "Identification of Plasma Lipid Metabolites Associated with Nut Consumption in US Men and Women." Journal of Nutrition 149, no. 7 (May 16, 2019): 1215–21. http://dx.doi.org/10.1093/jn/nxz048.
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ABSTRACT Background Intake of nuts has been inversely associated with risk of type 2 diabetes and cardiovascular disease, partly through inducing a healthy lipid profile. How nut intake may affect lipid metabolites remains unclear. Objective The aim of this study was to identify the plasma lipid metabolites associated with habitual nut consumption in US men and women. Methods We analyzed cross-sectional data from 1099 participants in the Nurses’ Health Study (NHS), NHS II, and Health Professionals Follow-up Study. Metabolic profiling was conducted on plasma by LC–mass spectrometry. Nut intake was estimated from food-frequency questionnaires. We included 144 known lipid metabolites that had CVs ≤25%. Multivariate linear regression was used to assess the associations of nut consumption with individual plasma lipid metabolites. Results We identified 17 lipid metabolites that were significantly associated with nut intake, based on a 1 serving (28 g)/d increment in multivariate models [false discovery rate (FDR) P value <0.05]. Among these species, 8 were positively associated with nut intake [C24:0 sphingomyelin (SM), C36:3 phosphatidylcholine (PC) plasmalogen-A, C36:2 PC plasmalogen, C24:0 ceramide, C36:1 PC plasmalogen, C22:0 SM, C34:1 PC plasmalogen, and C36:2 phosphatidylethanolamine plasmalogen], with changes in relative metabolite level (expressed in number of SDs on the log scale) ranging from 0.36 to 0.46 for 1 serving/d of nuts. The other 9 metabolites were inversely associated with nut intake with changes in relative metabolite level ranging from −0.34 to −0.44. In stratified analysis, 3 metabolites were positively associated with both peanuts and peanut butter (C24:0 SM, C24:0 ceramide, and C22:0 SM), whereas 6 metabolites were inversely associated with other nuts (FDR P value <0.05). Conclusions A panel of lipid metabolites was associated with intake of nuts, which may provide insight into biological mechanisms underlying associations between nuts and cardiometabolic health. Metabolites that were positively associated with intake of nuts may be helpful in identifying potential biomarkers of nut intake.
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Ding, Ying, Sitan Chen, Honglin Wang, Shanlei Li, Changyang Ma, Jinmei Wang, and Lili Cui. "Identification of Secondary Metabolites in Flammulina velutipes by UPLC-Q-Exactive-Orbitrap MS." Journal of Food Quality 2021 (July 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/4103952.
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Flammulina velutipes is the fourth largest edible fungus in China with high nutritional value. In this paper, ultrahigh-performance liquid chromatography tandem hybrid quadrupole-Orbitrap mass spectrometry (UPLC-Q-Exactive-Orbitrap MS) was used to identify the secondary metabolites of F. velutipes. The metabolites were identified by comparing the retention time, accurate molecular weight, and MS2 data with standard databases of mzVault and mzCloud (compound: 17,000+) and BGI high-resolution accurate mass plant metabolome database (plant metabolite: 2500+). Finally, 26 secondary metabolites were preliminarily identified, including flavonoids, phenylpropanoids, organic acids, and steroids.
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Kim, Seonghye, Yuri Song, Seyeon Kim, Siyeong Kim, Heesam Na, Sujin Lee, Jin Chung, and Suhkmann Kim. "Identification of a Biomarker Panel for Diagnosis of Early Childhood Caries Using Salivary Metabolic Profile." Metabolites 13, no. 3 (February 27, 2023): 356. http://dx.doi.org/10.3390/metabo13030356.
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Several studies have demonstrated that nuclear magnetic resonance (NMR) metabolic profiles can differentiate patients with caries from healthy individuals; however, these studies only identified individual metabolites. The present study aimed to identify a salivary metabolite biomarker panel for the diagnosis of early childhood caries (ECC). Saliva samples from children with and without caries were analyzed using NMR spectroscopy. Multivariate and univariate analyses were performed to identify the discriminating metabolites. Selected metabolites were further evaluated and used to detect ECC. The saliva samples of children with ECC were characterized based on the increased levels of formate, glycerophosphocholine, and lactate and reduced levels of alanine, glycine, isoleucine, lysine, proline, and tyrosine. The levels of these metabolites were significantly different from those in the control in the ECC subgroup according to caries severity and correlated with the number of decayed and filled teeth or surfaces. Subsequently, an optimal salivary metabolite biomarker panel comprising formate, lactate, proline, and glycine was developed. This panel exhibited a better diagnostic performance for ECC than a single metabolite. These results demonstrate that salivary metabolic signatures can reflect oral conditions associated with dental caries, thereby emphasizing the importance of distinct salivary metabolic profiles as potential biomarkers of ECC.
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Aboubechara, John Paul, Yin Allison Liu, Oliver Fiehn, Ruben Fragoso, Han Lee, Jonathan Riess, Rawad Hodeify, Orin Bloch, and Orwa Aboud. "TMET-18. METABOLOMIC APPROACH TO IDENTIFICATION OF PROGNOSTIC BIOMARKERS OF GLIOBLASTOMA." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v276. http://dx.doi.org/10.1093/neuonc/noad179.1062.
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Abstract Glioblastoma, the most common malignant primary brain tumor, is associated with poor prognosis despite aggressive treatment. Alterations in cellular metabolism driven by acquired mutations contribute to tumor progression and recurrence. Metabolic changes in plasma can serve as potential biomarkers, offering insights into treatment response and prognostic value. In this prospective study, we enrolled 36 patients with isocitrate dehydrogenase (IDH) wild type glioblastoma (18 MGMT methylated, 16 MGMT unmethylated, 2 MGMT unknown) and performed untargeted metabolomics on patient plasma before and after surgery, as well as before and after concurrent chemoradiation. Our analysis focused on examining changes in 157 metabolites throughout each stage of treatment. We conducted correlations between metabolite levels and treatment stages, as well as associations between metabolite levels and disease progression or overall survival to identify clinically relevant biomarkers. Preliminary results demonstrate positive correlation between specific metabolite levels at the time of diagnosis and eventual overall survival. Notably, metabolites such as trans-4-hydroxyproline, ribose, pipecolinic acid, phenol, kynurenine, inositol-4-monophosphate, indole-3-propionic acid, glucose, arachidonic acid, 5-methoxytryptamine, and 3-aminopiperidine-2,6-dione show significant positive correlations (p< 0.05) with overall survival. However, no metabolites exhibited negative associations with overall survival. Ongoing analysis will further explore the relationship between metabolite levels at different treatment stages and survival outcomes. By future planned machine learning techniques integration to our study, we aim to enhance our understanding of the metabolomic changes associated with treatment response and prognosis. This study represents a step toward unraveling the metabolomic alterations in glioblastoma patients throughout treatment. Although the underlying metabolic pathways and potential shared mechanisms are not yet fully understood, the identified metabolites associated with overall survival hold promise as potential biomarkers for prognosis. Furthermore, the integration of machine learning techniques will provide a more comprehensive understanding of the complex metabolic changes in glioblastoma.
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Li, Feng, Yanjun Xu, Desi Shang, Haixiu Yang, Wei Liu, Junwei Han, Zeguo Sun, et al. "MPINet: Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile." BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/325697.
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High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet’s effectiveness and reliability for analyzing metabolomic data across multiple different application fields.
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Wiebe, V. J., C. K. Osborne, W. L. McGuire, and M. W. DeGregorio. "Identification of estrogenic tamoxifen metabolite(s) in tamoxifen-resistant human breast tumors." Journal of Clinical Oncology 10, no. 6 (June 1992): 990–94. http://dx.doi.org/10.1200/jco.1992.10.6.990.
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PURPOSE We have shown previously that acquired tamoxifen resistance in an in vivo experimental model is associated with reduced tamoxifen accumulation, isomerization of trans-4-hydroxytamoxifen, and tamoxifen-stimulated tumor growth. The purpose of this study is to isolate and verify the presence of estrogenic tamoxifen metabolites in human breast tumors using high-performance liquid chromatography (HPLC) and mass-spectrometry (MS) techniques. PATIENTS AND METHODS In the present study, we used HPLC and MS to identify the presence of estrogenic metabolites in tumor samples excised from athymic nude mice and in human breast tumors isolated from patients receiving adjuvant tamoxifen therapy. RESULTS We identified the presence of metabolite E, a known estrogenic metabolite of tamoxifen, in tamoxifen-resistant MCF-7 human breast tumors implanted in athymic nude mice, as well as in tumors from patients with clinical resistance. Additionally, we separated another estrogenic metabolite, bisphenol, by HPLC, and this was also tentatively confirmed by MS analysis. CONCLUSION These data suggest that cellular tamoxifen metabolism to estrogenic metabolites may in part contribute to stimulating the growth of hormone-responsive breast tumors following prolonged exposure to tamoxifen. Further evaluation of the relationship between cellular metabolism and acquired tamoxifen resistance is warranted.
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Maya Dian Rakhmawatie, Maya Dian Rakhmawatie, Mustofa Mustofa, Puspita Lisdiyanti Puspita Lisdiyanti, Woro Rukmi Pratiwi Woro Rukmi Pratiwi, and Tri Wibawa Tri Wibawa. "Identification of Antimycobacterial from Actinobacteria (INACC A758) Secondary Metabolites using Metabolomics Data." Sains Malaysiana 51, no. 5 (May 31, 2022): 1465–73. http://dx.doi.org/10.17576/jsm-2022-5105-16.
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Actinobacteria produce active secondary metabolite with medicinal properties, such as antibacterial or anticancer. However, there are some reports about the difficulties in discovering novel secondary metabolites. Therefore, the need for a new approach is obvious. Several factors such as types of nutrients in the culture media or different solvents used for extraction have been proven to influence the Actinobacteria secondary metabolite production. In this study, a combination of culture media optimization and metabolites fingerprint analysis were applied to identify antimycobacterial active compounds from Actinobacteria (InaCC A758). Five culture media were used in the secondary metabolite production of the Actinobacteria. The metabolite fingerprinting was carried out by analyzing the secondary metabolite profile extracted from culture media optimization using UPLC-MS. Multivariate analysis, i.e. cluster analysis and principal component analysis (PCA) was applied. The result showed that a unique antimycobacterial compound candidate against Mycobacterium smegmatis was produced by SYP media cultured InaCC A758 (MIC 6.25 µg/mL).
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Rincon Nigro, Maria Eugenia, Ting Du, Song Gao, Manvir Kaur, Huan Xie, Omonike Arike Olaleye, and Dong Liang. "Metabolite Identification of a Novel Anti-Leishmanial Agent OJT007 in Rat Liver Microsomes Using LC-MS/MS." Molecules 27, no. 9 (April 30, 2022): 2854. http://dx.doi.org/10.3390/molecules27092854.
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The purpose of this study was to identify potential metabolic pathways and metabolites of OJT007, a methionine aminopeptidase 1 (MetAP1) inhibitor. OJT007 is a novel drug with potent antiproliferative effects against Leishmania Major. We conducted in vitro Phase I oxidation and Phase II glucuronidation assays on OJT007 using rat liver microsomes. Four unknown metabolites were initially identified using a UPLC-UV system from microsomal incubated samples. LC-MS/MS analysis was then used to identify the structural characteristics of these metabolites via precursor ion scan, neutral loss scan, and product ion scan. A glucuronide metabolite was further confirmed by β-glucuronidase hydrolysis. The kinetic parameters of OJT007 glucuronidation demonstrated that OJT007 undergoes rapid metabolism. These results demonstrate the liver’s microsomal ability to mediate three mono-oxidated metabolites and one mono-glucuronide metabolite. This suggests hepatic glucuronidation metabolism of OJT007 may be the cause of its poor oral bioavailability.
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Silva, Catarina L., Ana Olival, Rosa Perestrelo, Pedro Silva, Helena Tomás, and José S. Câmara. "Untargeted Urinary 1H NMR-Based Metabolomic Pattern as a Potential Platform in Breast Cancer Detection." Metabolites 9, no. 11 (November 7, 2019): 269. http://dx.doi.org/10.3390/metabo9110269.
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Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.
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Lin, Chunsheng, Qianqian Tian, Sifan Guo, Dandan Xie, Ying Cai, Zhibo Wang, Hang Chu, Shi Qiu, Songqi Tang, and Aihua Zhang. "Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification." Molecules 29, no. 10 (May 8, 2024): 2198. http://dx.doi.org/10.3390/molecules29102198.
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As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.
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Nedderman, Angus N. R. "Metabolites in safety testing: metabolite identification strategies in discovery and development." Biopharmaceutics & Drug Disposition 30, no. 4 (May 2009): 153–62. http://dx.doi.org/10.1002/bdd.660.
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Tambay, Vincent, Valérie-Ann Raymond, Corentine Goossens, Louise Rousseau, Simon Turcotte, and Marc Bilodeau. "Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature." Cancers 15, no. 12 (June 18, 2023): 3232. http://dx.doi.org/10.3390/cancers15123232.
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Background: Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming. Methods: HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography–mass spectrometry (LC/MS). Results: Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS–DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS. Conclusions: Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.
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Tadić, Đorđe, Michal Gramblicka, Robert Mistrik, and Josep Maria Bayona. "Systematic identification of trimethoprim metabolites in lettuce." Analytical and Bioanalytical Chemistry 414, no. 9 (February 9, 2022): 3121–35. http://dx.doi.org/10.1007/s00216-022-03943-6.
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AbstractAntibiotics are some of the most widely used drugs. Their release in the environment is of great concern since their consumption is a major factor for antibiotic resistance, one of the most important threats to human health. Their occurrence and fate in agricultural systems have been extensively investigated in recent years. Yet whilst their biotic and abiotic degradation pathways have been thoroughly researched, their biotransformation pathways in plants are less understood, such as in case of trimethoprim. Although trimethoprim has been reported in the environment, its fate in higher plants still remains unknown. A bench-scale experiment was performed and 30 trimethoprim metabolites were identified in lettuce (Lactuca sativa L.), of which 5 belong to phase I and 25 to phase II. Data mining yielded a list of 1018 ions as possible metabolite candidates, which was filtered to a final list of 87 candidates. Molecular structures were assigned for 19 compounds, including 14 TMP metabolites reported for the first time. Alongside well-known biotransformation pathways in plants, additional novel pathways were suggested, namely, conjugation with sesquiterpene lactones, and abscisic acid as a part of phase II of plant metabolism. The results obtained offer insight into the variety of phase II conjugates and may serve as a guideline for studying the metabolization of other chemicals that share a similar molecular structure or functional groups with trimethoprim. Finally, the toxicity and potential contribution of the identified metabolites to the selective pressure on antibiotic resistance genes and bacterial communities via residual antimicrobial activity were evaluated.
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Huang, Dan, Yu Long Chu, Zhi Chao Shang, Yu Long Li, and Zhong Ming Lu. "Isolation and Identification of a Mold Strain Producing Esterifying Synthetase and Study on its Metabolies." Advanced Materials Research 236-238 (May 2011): 2460–63. http://dx.doi.org/10.4028/www.scientific.net/amr.236-238.2460.
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The metabolies characterization of mold producing esterifying synthetase of different culture conditions were studied. A mold strain producing esterifying enzyme was isolated from Luzhou-flavor DaQu and it was identified by Biolog Microbes automated identification System. Culture conditions such as culture temperature, fermentation time and carbon source were changed. The fermentatiom broth was extracted by ethanol and analyzed by GC-MS. The results showed that the strain was Aspergillus flavus. When the culture temperature increased, the molecular weight of its metabolites increased, the synthesis of high alcohol increased too, and their carbon chain lengthened gradually. When incubation time was prolonged, the varying law of the synthesis of its metabolites was alcohols-acid-ester, the metabolites with longer carbon chain produced earlier than the metabolites with shorter carbon chain. When the carbon source were starch, sucrose and bran, n-butyl alcohol and isoamyl alcohol were the common metabolites. When the carbon source was sucrose, most of the metabolites were alcohol and acid with shorter carbon chain. When the carbon source was starch, diethyl succinate was produced. When the carbon source was bran, the metabolites were less.
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Jie, Yu, Tianyu Shi, Zhongjei Zhang, and Qiaojuan Yan. "Identification of Key Volatiles Differentiating Aromatic Rice Cultivars Using an Untargeted Metabolomics Approach." Metabolites 11, no. 8 (August 9, 2021): 528. http://dx.doi.org/10.3390/metabo11080528.
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Non-aromatic rice is often sold at the price of aromatic rice to increase profits, seriously impairing consumer experience and brand credibility. The assessment of rice varieties origins in terms of their aroma traits is of great interest to protect consumers from fraud. To address this issue, the study identified differentially abundant metabolites between non-aromatic rice varieties and each of the three most popular aromatic rice varieties in the market using an untargeted metabolomics approach. The 656 metabolites of five rice grain varieties were determined by headspace solid-phase extraction gas chromatography-mass spectrometry, and the multivariate analyses were used to identify differences in metabolites among rice varieties. The metabolites most differentially abundant between Daohuaxiang 2 and non-aromatic rice included 2-acetyl-1-pyrroline and acetoin; the metabolites most differentially abundant between Meixiangzhan 2 and non-aromatic rice included acetoin and 2-methyloctylbenzene,; and the metabolites most differentially abundant between Yexiangyoulisi and non-aromatic rice included bicyclo[4.4.0]dec,1-ene-2-isopropyl-5-methyl-9-methylene and 2-methylfuran. Overall, acetoin was the metabolite that was most differentially abundant between the aromatic and non-aromatic rice. This study provides direct evidence of the outstanding advantages of aromatic rice and acts a reference for future rice authentication processes in the marketplace.
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Hailemariam, Dagnachew, Guanshi Zhang, Rupasri Mandal, David S. Wishart, and Burim N. Ametaj. "Identification of serum metabolites associated with the risk of metritis in transition dairy cows." Canadian Journal of Animal Science 98, no. 3 (September 1, 2018): 525–37. http://dx.doi.org/10.1139/cjas-2017-0069.
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In this study, we aimed to identify metabolite signatures that characterize metritis prior to, during, and after the disease incidence. Blood samples were collected from 100 Holstein cows at five time points before and after parturition. Six cows that developed metritis and 20 controls were selected for metabolomics analysis in a nested case-control study. Twenty nine serum metabolites were quantified using gas chromatography–mass spectroscopy. Results showed that similar panels of metabolites differentiated pre-metritic and control cows at 8 and 4 wk prepartum. The top most important metabolites that differentiated the two groups of cows at 8 wk prepartum were oxalate, ornithine, pyroglutamic acid, d-mannose, and glutamic acid, and at 4 wk prepartum were ornithine, pyroglutamic acid, d-mannose, glutamic acid, and phosphoric acid, suggesting their potential use as risk biomarkers for metritis. Area under the curve with values of 1.0 and 0.969 at 8 and 4 wk, respectively, indicated that those panels of metabolites have a very high sensitivity and specificity to be used as risk biomarkers for metritis. Overall, results showed that specific serum metabolite signatures can be used to screen cows for susceptibility to metritis during the dry off period, and to better understand the etiopathobiology of the disease.
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Feng, Wei, Ling-Yu Zhou, Rui-Feng Mu, Le Gao, Bing-Yuan Xu, Ming-Liang Liu, Li-Ying Niu, and Xin-Guo Wang. "Screening and Identification of the Main Metabolites of Schisantherin a In Vivo and In Vitro by Using UHPLC-Q-TOF-MS/MS." Molecules 25, no. 2 (January 8, 2020): 258. http://dx.doi.org/10.3390/molecules25020258.
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Schisantherin A is an active ingredient originating from Schisandra chinensis (Turcz.) which has hepatoprotective and anti-oxidation activities. In this study, in vitro metabolisms investigated on rat liver microsomes (RLMs) and in vivo metabolisms explored on male Sprague Dawley rats of Schisantherin A were tested, respectively. The metabolites of Schisantherin A were identified using ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). Based on the method, 60 metabolites were successfully identified and structurally characterized including 48 phase-I and 12 phase-II metabolites. Among the metabolites, 45 metabolites were reported for the first time. Moreover, 56 and eight metabolites were detected in urine and bile and 19 metabolites were identified in rats’ plasma. It demonstrated that hepatic and extra-hepatic metabolic pathways were both involved in Schisantherin A biotransformation in rats. Five in vitro metabolites were structurally characterized for the first time. The results indicated that the metabolic pathways mainly include oxidation, reduction, methylation, and conjugation with glucuronide, taurine, glucose, and glutathione groups. This study provides a practical strategy for rapidly screening and identifying metabolites, and the results provide basic data for future pharmacological and toxicology studies of Schisantherin A and other lignin ingredients.
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Roni, M. S. Rashid, Nicolas M. Zahn, Brandon N. Mikulsky, Daniel A. Webb, Md Yeunus Mian, Daniel E. Knutson, Margaret L. Guthrie, James M. Cook, Douglas C. Stafford, and Leggy A. Arnold. "Identification and Quantification of MIDD0301 Metabolites." Current Drug Metabolism 22, no. 14 (December 2021): 1114–23. http://dx.doi.org/10.2174/1389200222666211202093841.
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Background: MIDD0301 is an oral asthma drug candidate that binds GABAA receptors on airway smooth muscle and immune cells. Objective: The objective of this study is to identify and quantify MIDD0301 metabolites in vitro and in vivo and determine the pharmacokinetics of oral, IP, and IV administered MIDD0301. Methods: In vitro conversion of MIDD0301 was performed using liver and kidney microsomes/S9 fractions followed by quantification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A LC-MS/MS method was developed using synthesized standards to quantify MIDD0301 and its metabolites in urine and feces. Blood, lung, and brain were harvested from animals that received MIDD0301 by oral, IP, and IV administration, followed by LCMS/ MS quantification. Imaging mass spectrometry was used to demonstrate the presence of MIDD0301 in the lung after oral administration. Results: MIDD0301 is stable in the presence of liver and kidney microsomes and S9 fractions for at least two hours. MIDD0301 undergoes conversion to the corresponding glucuronide and glucoside in the presence of conjugating cofactors. For IP and IV administration, unconjugated MIDD0301 together with significant amounts of MIDD0301 glucoside and MIDD0301 taurine were found in urine and feces. Less conjugation was observed following oral administration, with MIDD0301 glucuronide being the main metabolite. Pharmacokinetic quantification of MIDD0301 in blood, lung, and brain showed very low levels of MIDD0301 in the brain after oral, IV, or IP administration. The drug half-life in these tissues ranged between 4-6 hours for IP and oral and 1-2 hours for IV administration. Imaging mass spectrometry demonstrated that orally administered MIDD0301 distributes uniformly in the lung parenchyma. Conclusion: MIDD0301 undergoes no phase I and moderate phase II metabolism.
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Manjarin, Rodrigo, Magdalena A. Maj, Michael R. La Frano, and Hunter Glanz. "%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data." PLOS ONE 15, no. 12 (December 15, 2020): e0244013. http://dx.doi.org/10.1371/journal.pone.0244013.
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The generation of large metabolomic data sets has created a high demand for software that can fit statistical models to one-metabolite-at-a-time on hundreds of metabolites. We provide the %polynova_2way macro in SAS to identify metabolites differentially expressed in study designs with a two-way factorial treatment and hierarchical design structure. For each metabolite, the macro calculates the least squares means using a linear mixed model with fixed and random effects, runs a 2-way ANOVA, corrects the P-values for the number of metabolites using the false discovery rate or Bonferroni procedure, and calculate the P-value for the least squares mean differences for each metabolite. Finally, the %polynova_2way macro outputs a table in excel format that combines all the results to facilitate the identification of significant metabolites for each factor. The macro code is freely available in the Supporting Information.
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Zhu, Yanlin, Guiying Chen, Kerong Zhang, Chang Chen, Weiqing Chen, Mingshe Zhu, and Hongliang Jiang. "High-Throughput Metabolic Soft-Spot Identification in Liver Microsomes by LC/UV/MS: Application of a Single Variable Incubation Time Approach." Molecules 27, no. 22 (November 20, 2022): 8058. http://dx.doi.org/10.3390/molecules27228058.
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CYP-mediated fast metabolism may lead to poor bioavailability, fast drug clearance and significant drug interaction. Thus, metabolic stability screening in human liver microsomes (HLM) followed by metabolic soft-spot identification (MSSID) is routinely conducted in drug discovery. Liver microsomal incubations of testing compounds with fixed single or multiple incubation time(s) and quantitative and qualitative analysis of metabolites using high-resolution mass spectrometry are routinely employed in MSSID assays. The major objective of this study was to develop and validate a simple, effective, and high-throughput assay for determining metabolic soft-spots of testing compounds in liver microsomes using a single variable incubation time and LC/UV/MS. Model compounds (verapamil, dextromethorphan, buspirone, mirtazapine, saquinavir, midazolam, amodiaquine) were incubated at 3 or 5 µM with HLM for a single variable incubation time between 1 and 60 min based on predetermined metabolic stability data. As a result, disappearances of the parents were around 20–40%, and only one or a few primary metabolites were generated as major metabolite(s) without notable formation of secondary metabolites. The unique metabolite profiles generated from the optimal incubation conditions enabled LC/UV to perform direct quantitative estimation for identifying major metabolites. Consequently, structural characterization by LC/MS focused on one or a few major primary metabolite(s) rather than many metabolites including secondary metabolites. Furthermore, generic data-dependent acquisition methods were utilized to enable Q-TOF and Qtrap to continuously record full MS and MS/MS spectral data of major metabolites for post-acquisition data-mining and interpretation. Results from analyzing metabolic soft-spots of the seven model compounds demonstrated that the novel MSSID assay can substantially simplify metabolic soft-spot identification and is well suited for high-throughput analysis in lead optimization.
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Cardoso, Ana S., Alison Whitby, Martin J. Green, Dong-Hyun Kim, and Laura V. Randall. "Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows." Animals 14, no. 14 (July 10, 2024): 2030. http://dx.doi.org/10.3390/ani14142030.
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The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography–tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.
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Bowers, L. D., D. D. Norman, X. X. Yan, D. Scheeler, and K. L. Carlson. "Isolation and structural identification of 9hydroxy-9desmethyl-cyclosporine." Clinical Chemistry 36, no. 11 (November 1, 1990): 1875–79. http://dx.doi.org/10.1093/clinchem/36.11.1875.
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Abstract A metabolite of cyclosporine has been isolated and its structure identified through use of HPLC and tandem mass spectroscopy. Fast atom bombardment mass spectrometry of an HPLC fraction co-eluting with 1 eta hydroxy-cyclosporine (M17) indicated that the mass of this metabolite was 2 Da greater than that of cyclosporine. Further isolation by HPLC yielded a pure fraction, which we analyzed with tandem mass spectrometry. Linear acyl fragment ions originating from the metabolite under collision-induced dissociation were consistent with the difference in mass being associated with amino acid 9 in the cyclosporine backbone. We propose a nomenclature system for future discussion of cyclosporine metabolites.
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Yuan, Xueyan, Xiaoping Zhang, Jiaquan Xu, Jianhua Ye, Zhendong Yu, and Xinglei Zhang. "Metabolite Fingerprinting for Identification of Panax ginseng Metabolites Using Internal Extractive Electrospray Ionization Mass Spectrometry." Foods 12, no. 6 (March 9, 2023): 1152. http://dx.doi.org/10.3390/foods12061152.
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Ginseng, a kind of functional food and medicine with high nutritional value, contains various pharmacological metabolites that influence human metabolic functions. Therefore, it is very important to analyze the composition and metabolites of ginseng. However, the analysis of active metabolites in ginseng samples usually involves various experimental steps, such as extraction, chromatographic separation, and characterization, which may be time-consuming and laborious. In this study, an internal extractive electrospray ionization mass spectrometry (iEESI-MS) method was developed to analyze active metabolites in ginseng samples with sequential sampling and no pretreatment. A total of 44 metabolites, with 32 ginsenosides, 6 sugars, and 6 organic acids, were identified in the ginseng samples. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot showed a clear separation of ginseng samples from different origins, indicating that metabolic changes occurred under different growing conditions. This study demonstrated that different cultivation conditions of ginseng can be successfully discriminated when using iEESI-MS-based metabolite fingerprints, which provide an alternative solution for the quality identification of plant drugs.
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Colyer, Alison, Matthew S. Gilham, Beate Kamlage, Dietrich Rein, and David Allaway. "Identification of intra- and inter-individual metabolite variation in plasma metabolite profiles of cats and dogs." British Journal of Nutrition 106, S1 (October 12, 2011): S146—S149. http://dx.doi.org/10.1017/s000711451100081x.
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The purpose of the present study was first to identify drivers of variance in plasma metabolite profiles of cats and dogs that may affect the interpretation of nutritional metabolomic studies. A total of fourteen cats and fourteen dogs housed in environmentally enriched accommodation were fed a single batch of diet to maintain body weight. Fasting blood samples were taken on days 14, 16 and 18 of the study. Gas chromatography–mass spectrometry (GC–MS), liquid chromatography (LC)–MS/MS and solid-phase extraction–LC–MS/MS analyses were used for metabolite profiling. Principal component (PC) analysis that indicated 31 and 27 % of the variance was explained in PC1 and PC2 for cats and dogs, respectively, with most individuals occupying a unique space. As the individual was a major driver of variance in the plasma metabolome, the second objective was to identify metabolites associated with the individual variation observed. The proportion of intra- and inter-individual variance was calculated for 109 cat and 101 dog metabolites with a low intra-individual variance (sd< 0·05). Of these, fifteen cat and six dog metabolites had inter-individual variance accounting for at least 90 % of the total variance. There were four metabolites common to both species (campesterol, DHA, a cholestenol and a sphingosine moiety). Many of the metabolites with >75 % inter-individual variance were common to both species and to similar areas of metabolism. In summary, the individual is an important driver of variance in the fasted plasma metabolome, and specific metabolites and areas of metabolism may be differentially regulated by individuals in two companion animal species.
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Lin, Weida, Yueling Li, Qiuwei Lu, Hongfei Lu, and Junmin Li. "Combined Analysis of the Metabolome and Transcriptome Identified Candidate Genes Involved in Phenolic Acid Biosynthesis in the Leaves of Cyclocarya paliurus." International Journal of Molecular Sciences 21, no. 4 (February 17, 2020): 1337. http://dx.doi.org/10.3390/ijms21041337.
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To assess changes of metabolite content and regulation mechanism of the phenolic acid biosynthesis pathway at different developmental stages of leaves, this study performed a combined metabolome and transcriptome analysis of Cyclocarya paliurus leaves at different developmental stages. Metabolite and transcript profiling were conducted by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. Transcriptome identification showed that 58 genes were involved in the biosynthesis of phenolic acid. Among them, 10 differentially expressed genes were detected between every two developmental stages. Identification and quantification of metabolites indicated that 14 metabolites were located in the phenolic acid biosynthetic pathway. Among them, eight differentially accumulated metabolites were detected between every two developmental stages. Association analysis between metabolome and transcriptome showed that six differentially expressed structural genes were significantly positively correlated with metabolite accumulation and showed similar expression trends. A total of 128 transcription factors were identified that may be involved in the regulation of phenolic acid biosynthesis; these include 12 MYBs and 10 basic helix–loop–helix (bHLH) transcription factors. A regulatory network of the phenolic acid biosynthesis was established to visualize differentially expressed candidate genes that are involved in the accumulation of metabolites with significant differences. The results of this study contribute to the further understanding of phenolic acid biosynthesis during the development of leaves of C. paliurus.
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Heinzmann, Silke S., Melanie Waldenberger, Annette Peters, and Philippe Schmitt-Kopplin. "Cluster Analysis Statistical Spectroscopy for the Identification of Metabolites in 1H NMR Metabolomics." Metabolites 12, no. 10 (October 19, 2022): 992. http://dx.doi.org/10.3390/metabo12100992.
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Metabolite identification in non-targeted NMR-based metabolomics remains a challenge. While many peaks of frequently occurring metabolites are assigned, there is a high number of unknowns in high-resolution NMR spectra, hampering biological conclusions for biomarker analysis. Here, we use a cluster analysis approach to guide peak assignment via statistical correlations, which gives important information on possible structural and/or biological correlations from the NMR spectrum. Unknown peaks that cluster in close proximity to known peaks form hypotheses for their metabolite identities, thus, facilitating metabolite annotation. Subsequently, metabolite identification based on a database search, 2D NMR analysis and standard spiking is performed, whereas without a hypothesis, a full structural elucidation approach would be required. The approach allows a higher identification yield in NMR spectra, especially once pathway-related subclusters are identified.
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Ujlaki, Gyula, Tünde Kovács, András Vida, Endre Kókai, Boglára Rauch, Szandra Schwarcz, Edit Mikó, et al. "Identification of Bacterial Metabolites Modulating Breast Cancer Cell Proliferation and Epithelial-Mesenchymal Transition." Molecules 28, no. 15 (August 5, 2023): 5898. http://dx.doi.org/10.3390/molecules28155898.
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Breast cancer patients are characterized by the oncobiotic transformation of multiple microbiome communities, including the gut microbiome. Oncobiotic transformation of the gut microbiome impairs the production of antineoplastic bacterial metabolites. The goal of this study was to identify bacterial metabolites with antineoplastic properties. We constructed a 30-member bacterial metabolite library and screened the library compounds for effects on cell proliferation and epithelial-mesenchymal transition. The metabolites were applied to 4T1 murine breast cancer cells in concentrations corresponding to the reference serum concentrations. However, yric acid, glycolic acid, d-mannitol, 2,3-butanediol, and trans-ferulic acid exerted cytostatic effects, and 3-hydroxyphenylacetic acid, 4-hydroxybenzoic acid, and vanillic acid exerted hyperproliferative effects. Furthermore, 3-hydroxyphenylacetic acid, 4-hydroxybenzoic acid, 2,3-butanediol, and hydrocinnamic acid inhibited epithelial-to-mesenchymal (EMT) transition. We identified redox sets among the metabolites (d-mannitol—d-mannose, 1-butanol—butyric acid, ethylene glycol—glycolic acid—oxalic acid), wherein only one partner within the set (d-mannitol, butyric acid, glycolic acid) possessed bioactivity in our system, suggesting that changes to the local redox potential may affect the bacterial secretome. Of the nine bioactive metabolites, 2,3-butanediol was the only compound with both cytostatic and anti-EMT properties.
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Ludányi, Krisztina, Károly Vékey, József Szúnyog, Emil Mincsovics, Tamás Karancsi, Kálmán Újszászy, Katalin Balogh Nemes, and Imre Klebovich. "Application of Overpressured Layer Chromatography Combined with Digital Autoradiography and Mass Spectrometry in the Study of Deramciclane Metabolism." Journal of AOAC INTERNATIONAL 82, no. 2 (March 1, 1999): 231–38. http://dx.doi.org/10.1093/jaoac/82.2.231.
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Abstract Overpressured layer chromatography was combined with the highly sensitive and rapid digital autoradiography (DAR) and mass spectrometry to separate, detect, and identify 3H-and 14C-labeled deramciclane metabolites in different biological matrixes. Several minor and major metabolites were separated from plasma and urine samples. The radioactive metabolites localized by DAR were scraped from the thin-layer chromatographic plate and transferred to a mass spectrometer for structure identification. Several metabolites were isolated and characterized, including hydroxy-/V-des- methyl deramciclane, which is described in detail. The combination of techniques is efficient and has good sensitivity: about 2 μg metabolite from a biological matrix was isolated and identified this way.
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Jia, Xiao, Jiayi Song, Yijian Wu, Sai Feng, Zeao Sun, Yan Hu, Mengxue Yu, Rui Han, and Bin Zeng. "Strategies for the Enhancement of Secondary Metabolite Production via Biosynthesis Gene Cluster Regulation in Aspergillus oryzae." Journal of Fungi 10, no. 5 (April 25, 2024): 312. http://dx.doi.org/10.3390/jof10050312.
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The filamentous fungus Aspergillus oryzae (A. oryzae) has been extensively used for the biosynthesis of numerous secondary metabolites with significant applications in agriculture and food and medical industries, among others. However, the identification and functional prediction of metabolites through genome mining in A. oryzae are hindered by the complex regulatory mechanisms of secondary metabolite biosynthesis and the inactivity of most of the biosynthetic gene clusters involved. The global regulatory factors, pathway-specific regulatory factors, epigenetics, and environmental signals significantly impact the production of secondary metabolites, indicating that appropriate gene-level modulations are expected to promote the biosynthesis of secondary metabolites in A. oryzae. This review mainly focuses on illuminating the molecular regulatory mechanisms for the activation of potentially unexpressed pathways, possibly revealing the effects of transcriptional, epigenetic, and environmental signal regulation. By gaining a comprehensive understanding of the regulatory mechanisms of secondary metabolite biosynthesis, strategies can be developed to enhance the production and utilization of these metabolites, and potential functions can be fully exploited.
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Ifediora, R. G., C. O. Anyamene, M. O. Ikele, and C. U. Ezebialu. "GC-MS Based Metabolomic Profiling of Streptomyces clavuligerus Isolated from Ocimum gratissimum Rhizosphere." Journal of Advances in Microbiology 23, no. 8 (July 24, 2023): 28–35. http://dx.doi.org/10.9734/jamb/2023/v23i8741.
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Streptomyces clavuligerus is a member of the Actinobacteria family primarily known for its production of clavulanic acid antibiotic. The need for identification of new antimicrobials led to the identification of volatile components of S. clavuligerus metabolites using GC-MS. The isolate was obtained from Ocimum gratissimum rhizosphere using starch casein agar, and identified using molecular typing. The preliminary antibacterial screening of the isolate was carried out using some indicator bacteria from wound sites and urinary tract infection. Its bioactive metabolites were obtained using sub-merged fermentation over a four-day period, and the volatile compounds identified using GC-MS. The organism showed significant (p<0.05) inhibition on Pseudomonas aeruginosa and Escherichia coli. Metabolomics study revealed the presence of compounds of alkanone and alkene functional groups. Eicosene was the major antimicrobial compound identified. Likewise, a non-antimicrobial, steroidal metabolite – pregnelonone, was also dominant in the metabolite mixture produced by the organism. Identifying volatile constituents of microbial metabolites may be a route for obtaining new antimicrobials and the GC-MS is a useful tool for achieving such aim.
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Upadyshev, Mikhail, Bojidarka Ivanova, and Svetlana Motyleva. "Mass Spectrometric Identification of Metabolites after Magnetic-Pulse Treatment of Infected Pyrus communis L. Microplants." International Journal of Molecular Sciences 24, no. 23 (November 26, 2023): 16776. http://dx.doi.org/10.3390/ijms242316776.
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The major goal of this study is to create a venue for further work on the effect of pulsed magnetic fields on plant metabolism. It deals with metabolite synthesis in the aforementioned conditions in microplants of Pyrus communis L. So far, there have been glimpses into the governing factors of plant biochemistry in vivo, and low-frequency pulsed magnestatic fields have been shown to induce additional electric currents in plant tissues, thus perturbing the value of cell membrane potential and causing the biosynthesis of new metabolites. In this study, sixty-seven metabolites synthesized in microplants within 3–72 h after treatment were identified and annotated. In total, thirty-one metabolites were produced. Magnetic-pulse treatment caused an 8.75-fold increase in the concentration of chlorogenic acid (RT = 8.33 ± 0.0197 min) in tissues and the perturbation of phenolic composition. Aucubin, which has antiviral and antistress biological activity, was identified as well. This study sheds light on the effect of magnetic fields on the biochemistry of low-molecular-weight metabolites of pear plants in vitro, thus providing in-depth metabolite analysis under optimized synthetic conditions. This study utilized high-resolution gas chromatography-mass spectrometry, metabolomics methods, stochastic dynamics mass spectrometry, quantum chemistry, and chemometrics, respectively. Stochastic dynamics uses the relationships between measurands and molecular structures of silylated carbohydrates, showing virtually identical mass spectra and comparable chemometrics parameters.
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Schroeder, Mark, Sven W. Meyer, Heino M. Heyman, Aiko Barsch, and Lloyd W. Sumner. "Generation of a Collision Cross Section Library for Multi-Dimensional Plant Metabolomics Using UHPLC-Trapped Ion Mobility-MS/MS." Metabolites 10, no. 1 (December 24, 2019): 13. http://dx.doi.org/10.3390/metabo10010013.
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The utility of metabolomics is well documented; however, its full scientific promise has not yet been realized due to multiple technical challenges. These grand challenges include accurate chemical identification of all observable metabolites and the limiting depth-of-coverage of current metabolomics methods. Here, we report a combinatorial solution to aid in both grand challenges using UHPLC-trapped ion mobility spectrometry coupled to tandem mass spectrometry (UHPLC-TIMS-TOF-MS). TIMS offers additional depth-of-coverage through increased peak capacities realized with the multi-dimensional UHPLC-TIMS separations. Metabolite identification confidence is simultaneously enhanced by incorporating orthogonal collision cross section (CCS) data matching. To facilitate metabolite identifications, we created a CCS library of 146 plant natural products. This library was generated using TIMS with N2 drift gas to record the TIMSCCSN2 of plant natural products with a high degree of reproducibility; i.e., average RSD = 0.10%. The robustness of TIMSCCSN2 data matching was tested using authentic standards spiked into complex plant extracts, and the precision of CCS measurements were determined to be independent of matrix affects. The utility of the UHPLC-TIMS-TOF-MS/MS in metabolomics was then demonstrated using extracts from the model legume Medicago truncatula and metabolites were confidently identified based on retention time, accurate mass, molecular formula, and CCS.
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Wohlgemuth, Roland. "Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems." Metabolites 13, no. 10 (October 19, 2023): 1097. http://dx.doi.org/10.3390/metabo13101097.
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Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.
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Oyama, Masayoshi, Zhihong Xu, Kuo-Hsiung Lee, Timothy Spitzer, Peter Kitrinos, Octerloney McDonald, Rosie Jones, and Edward Garvey. "Fungal Metabolites as Potent Protein Kinase Inhibitors: Identification of a Novel Metabolite and Novel Activities of Known Metabolites." Letters in Drug Design & Discovery 1, no. 1 (January 1, 2004): 24–29. http://dx.doi.org/10.2174/1570180043485626.
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Zheng, Hui-Hua, Chong-Tao Du, Yu-Zhu Zhang, Chao Yu, Rong-Lei Huang, Xin-Yue Tang, and Guang-Hong Xie. "Identification of Canine Pyometra-Associated Metabolites Using Untargeted Metabolomics." International Journal of Molecular Sciences 23, no. 22 (November 16, 2022): 14161. http://dx.doi.org/10.3390/ijms232214161.
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Canine pyometra frequently occurs in middle-aged to older intact bitches, which seriously affects the life of dogs and brings an economic loss to their owners. Hence, finding a key metabolite is very important for the diagnosis and development of a new safe and effective therapy for the disease. In this study, dogs with pyometra were identified by blood examinations, laboratory analyses and diagnostic imaging, and fifteen endometrium tissues of sick dogs with pyometra and fifteen controls were collected and their metabolites were identified utilizing a UHPLC-qTOF-MS-based untargeted metabolomics approach. The results indicated that the elevated inflammatory cells were observed in dogs with pyometra, suggesting that sick dogs suffered systemic inflammation. In the untargeted metabolic profile, 705 ion features in the positive polarity mode and 414 ion features in the negative polarity mode were obtained in endometrium tissues of sick dogs with pyometra, with a total of 275 differential metabolites (173 in positive and 102 in negative polarity modes). Moreover, the multivariate statistical analyses such as PCA and PLS-DA also showed that the metabolites were significantly different between the two groups. Then, these differential metabolites were subjected to pathway analysis using Metaboanalyst 4.0, and Galactose metabolism, cAMP signaling pathway and Glycerophospholipid metabolism were enriched, proving some insights into the metabolic changes during pyometra. Moreover, the receiver operating characteristic curves further confirmed kynurenic acid was expected to be a candidate biomarker of canine pyometra. In conclusion, this study provided a new idea for exploring early diagnosis methods and a safe and effective therapy for canine pyometra.