1
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Hu G, Cui S, Wang H, Shi Y, Li Z. Mechanism of high sensitivity proton acids doped polypyrrole molecularly imprinted electrochemical sensor and its application in urea detection. Talanta 2024; 278:126514. [PMID: 38986310 DOI: 10.1016/j.talanta.2024.126514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/30/2024] [Accepted: 07/03/2024] [Indexed: 07/12/2024]
Abstract
Molecularly imprinted electrochemical sensor is a kind of convenient, fast, and stable analyzer, but the conductivity of electrode materials and their affinity with the analyte affect its performance. A proton acid (PSS, SA, SSA) doping method was proposed to improve the electrochemical performance of the polypyrrole molecularly imprinted polymer (PPy-MIP), which promoted the electropolymerization of pyrrole, reduced the charge transfer resistance, and increased the electrochemical surface area. In terms of both improving conductivity and affinity, the response of the proton acids doped the polypyrrole molecularly imprinted electrochemical sensors (PPy-MIECS) to urea was improved by 25-fold (PSS), 5-fold (SA), and 3-fold (SSA) over that of PPy-MIECS. In addition, the PSS-PPy-MIECS was validated for the practical application with a linear detection range from 0.1 mM to 100 mM, high selectivity (α = 39.73), reusability (RSD% = 4.54 %), reproducibility (RSD% = 0.93 %), and stability (11 days). The advantage of proton acid doping method in PSS-PEDOT-MIECS to urea and PSS-PPy-MIECS to glucose extended its application in the performance enhancement of MIECS design.
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Affiliation(s)
- Guangxing Hu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China; University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Shuang Cui
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China; University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Hongda Wang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China; University of Science and Technology of China, Hefei, Anhui, 230026, China; Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, Shandong, 266200, China
| | - Yan Shi
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China.
| | - Zhuang Li
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China; University of Science and Technology of China, Hefei, Anhui, 230026, China.
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2
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Olivier C, Allen B, Luies L. Optimising a urinary extraction method for non-targeted GC-MS metabolomics. Sci Rep 2023; 13:17591. [PMID: 37845360 PMCID: PMC10579216 DOI: 10.1038/s41598-023-44690-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/11/2023] [Indexed: 10/18/2023] Open
Abstract
Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.
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Affiliation(s)
- Cara Olivier
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa
| | - Bianca Allen
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa
| | - Laneke Luies
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa.
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3
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Sanko V, Şenocak A, Oğuz Tümay S, Demirbas E. A novel comparative study for electrochemical urea biosensor design: effect of different ferrite nanoparticles (MFe2O4, M: Cu, Co, Ni, Zn) in urease immobilized composite system. Bioelectrochemistry 2022; 149:108324. [DOI: 10.1016/j.bioelechem.2022.108324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
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4
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Beyoğlu D, Park EJ, Quiñones-Lombraña A, Dave A, Parande F, Pezzuto JM, Idle JR. Addition of grapes to both a standard and a high-fat Western pattern diet modifies hepatic and urinary metabolite profiles in the mouse. Food Funct 2022; 13:8489-8499. [PMID: 35876245 DOI: 10.1039/d2fo00961g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The benefits of fruit and vegetable dietary consumption are largely defined in epidemiological terms. Relatively little is known about the discrete effects on metabolic pathways elicited by individual dietary fruits and vegetables. To address this, grape powder was added to both a standard and a high-fat Western pattern diet given to 10-week-old female C57BL/6J mice for a period of 91 days, whereupon 24 h urines were collected and the mice euthanized after a 12 h fast for the collection of liver tissue. Alterations in hepatic and urinary metabolite patterns were determined by gas chromatography-mass spectrometry-based metabolomics. Urinary excretion of the gut microbiota metabolites 4-hydroxyphenylacetic acid, 5-hydroxyindole, glyceric acid, gluconic acid and myo-inositol was attenuated when grape was added to the standard diet but the gut microbiota metabolites gluconic acid, scyllo-inositol, mannitol, xylitol, 5-hydroxyindole and 2-deoxyribonic acid were increased in urine when grape was added to the high-fat diet. Increased hepatic ascorbic acid and 5-oxoproline levels indicated the anti-oxidant effect of grape powder on the liver. Pathway enrichment analysis demonstrated that for both standard and high-fat diets, grape addition significantly upregulated the malate-aspartate shuttle indicating enhanced hepatic utilization of glucose via cytosolic glycolysis for mitochondrial ATP production. It is concluded that a grape diet reprogrammes gut microbiota metabolism, attenuates the hepatic oxidative stress of a high-fat diet and increases the efficiency of glucose utilization by the liver for energy production.
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Affiliation(s)
- Diren Beyoğlu
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA.
| | - Eun-Jung Park
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA.
| | - Adolfo Quiñones-Lombraña
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA. .,Darwin Research Facility, Division of Biosciences, University College London, London, UK
| | - Asim Dave
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA.
| | - Falguni Parande
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA.
| | - John M Pezzuto
- College of Pharmacy and Health Sciences, Western New England University, Springfield, Massachusetts, USA
| | - Jeffrey R Idle
- Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, New York, USA.
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5
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Cognitive analysis of metabolomics data for systems biology. Nat Protoc 2021; 16:1376-1418. [PMID: 33483720 DOI: 10.1038/s41596-020-00455-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/27/2020] [Indexed: 01/30/2023]
Abstract
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
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6
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Mussap M, Siracusano M, Noto A, Fattuoni C, Riccioni A, Rajula HSR, Fanos V, Curatolo P, Barberini L, Mazzone L. The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity. Metabolites 2020; 10:metabo10110476. [PMID: 33238400 PMCID: PMC7700197 DOI: 10.3390/metabo10110476] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/10/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.
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Affiliation(s)
- Michele Mussap
- Department of Surgical Sciences, School of Medicine, University of Cagliari, 09042 Monserrato, Italy; (H.S.R.R.); (V.F.)
- Correspondence: ; Tel.: +39-070-51093403
| | - Martina Siracusano
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, 00133 Rome, Italy;
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Antonio Noto
- Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato, Italy; (A.N.); (L.B.)
| | - Claudia Fattuoni
- Department of Chemical and Geological Sciences, University of Cagliari, 09042 Monserrato, Italy;
| | - Assia Riccioni
- Child Neurology and Psychiatry Unit, System Medicine Department, Tor Vergata University Hospital of Rome, 00133 Rome, Italy; (A.R.); (P.C.); (L.M.)
| | - Hema Sekhar Reddy Rajula
- Department of Surgical Sciences, School of Medicine, University of Cagliari, 09042 Monserrato, Italy; (H.S.R.R.); (V.F.)
| | - Vassilios Fanos
- Department of Surgical Sciences, School of Medicine, University of Cagliari, 09042 Monserrato, Italy; (H.S.R.R.); (V.F.)
| | - Paolo Curatolo
- Child Neurology and Psychiatry Unit, System Medicine Department, Tor Vergata University Hospital of Rome, 00133 Rome, Italy; (A.R.); (P.C.); (L.M.)
| | - Luigi Barberini
- Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato, Italy; (A.N.); (L.B.)
| | - Luigi Mazzone
- Child Neurology and Psychiatry Unit, System Medicine Department, Tor Vergata University Hospital of Rome, 00133 Rome, Italy; (A.R.); (P.C.); (L.M.)
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Bi H, Guo Z, Jia X, Liu H, Ma L, Xue L. The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies. Metabolomics 2020; 16:68. [PMID: 32451742 DOI: 10.1007/s11306-020-01666-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 03/14/2020] [Indexed: 10/25/2022]
Abstract
BACKGROUND Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results. AIM OF REVIEW In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing. KEY SCIENTIFIC CONCEPTS OF REVIEW Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.
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Affiliation(s)
- Hai Bi
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China
| | - Zhengyang Guo
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
| | - Xiao Jia
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China
| | - Huiying Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, People's Republic of China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, People's Republic of China.
| | - Lixiang Xue
- Medical Research Center, Peking University Third Hospital, Haidian District, 49 Huayuan North Road, Beijing, People's Republic of China.
- Biobank, Peking University Third Hospital, Beijing, People's Republic of China.
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, People's Republic of China.
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8
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Khodadadi M, Pourfarzam M. A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry. Metabolomics 2020; 16:66. [PMID: 32419109 DOI: 10.1007/s11306-020-01687-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR. AIM OF REVIEW This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented. KEY SCIENTIFIC CONCEPTS OF REVIEW The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.
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Affiliation(s)
- Mohammad Khodadadi
- Department of Clinical Biochemistry, School of Pharmacy & Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Morteza Pourfarzam
- Department of Clinical Biochemistry, School of Pharmacy & Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran.
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9
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Khattab TA, Fouda MM, Rehan M, Okla MK, Alamri SA, Alaraidh IA, AL-ghamdi AA, Soufan WH, Abdelsalam EM, Allam AA. Novel halochromic cellulose nanowhiskers from rice straw: Visual detection of urea. Carbohydr Polym 2020; 231:115740. [DOI: 10.1016/j.carbpol.2019.115740] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 12/26/2022]
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10
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Mussap M, Loddo C, Fanni C, Fanos V. Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics. Expert Rev Clin Pharmacol 2020; 13:115-134. [PMID: 31958027 DOI: 10.1080/17512433.2020.1713750] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.
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Affiliation(s)
- Michele Mussap
- Laboratory Unit, Department of Surgical Sciences, University of Cagliari, Cagliari, Italy
| | | | - Claudia Fanni
- Division of Pediatrics, Rovigo Hospital, Rovigo, Italy
| | - Vassilios Fanos
- Neonatal Intensive Care Unit, Neonatal Pathology and Neonatal Section, Department of Surgical Sciences, University of Cagliari, Cagliari, Italy
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11
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Jáčová J, Jořenek M, Pospíšková K, Najdekr L, Zajoncová L, Friedecký D, Adam T. Urease-immobilized magnetic microparticles in urine sample preparation for metabolomic analysis by gas chromatography-mass spectrometry. J Chromatogr A 2019; 1605:360355. [DOI: 10.1016/j.chroma.2019.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/04/2019] [Accepted: 07/01/2019] [Indexed: 11/16/2022]
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12
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González-Riano C, Dudzik D, Garcia A, Gil-de-la-Fuente A, Gradillas A, Godzien J, López-Gonzálvez Á, Rey-Stolle F, Rojo D, Ruperez FJ, Saiz J, Barbas C. Recent Developments along the Analytical Process for Metabolomics Workflows. Anal Chem 2019; 92:203-226. [PMID: 31625723 DOI: 10.1021/acs.analchem.9b04553] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Carolina González-Riano
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Danuta Dudzik
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy , Medical University of Gdańsk , 80-210 Gdańsk , Poland
| | - Antonia Garcia
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Alberto Gil-de-la-Fuente
- Department of Information Technology, Escuela Politécnica Superior , Universidad San Pablo-CEU , 28003 Madrid , Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain.,Clinical Research Centre , Medical University of Bialystok , 15-089 Bialystok , Poland
| | - Ángeles López-Gonzálvez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Fernanda Rey-Stolle
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - David Rojo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Francisco J Ruperez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Jorge Saiz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Chemistry and Biochemistry Department, Pharmacy Faculty , Universidad San Pablo-CEU , Boadilla del Monte , 28668 Madrid , Spain
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13
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Co-encapsulation of enzyme and tricyanofuran hydrazone into alginate microcapsules incorporated onto cotton fabric as a biosensor for colorimetric recognition of urea. REACT FUNCT POLYM 2019. [DOI: 10.1016/j.reactfunctpolym.2019.06.016] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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14
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Beyoğlu D, Zhou Y, Chen C, Idle JR. Mass isotopomer-guided decluttering of metabolomic data to visualize endogenous biomarkers of drug toxicity. Biochem Pharmacol 2018; 156:491-500. [PMID: 30243960 DOI: 10.1016/j.bcp.2018.09.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 09/17/2018] [Indexed: 02/06/2023]
Abstract
Metabolomics offers the opportunity to uncover endogenous biomarkers that can lead to metabolic pathways and networks and that underpin drug toxicity mechanisms. A novel protocol is presented and discussed that is applicable to drugs which generate urinary metabolites when administered to mice sensitive to its toxicity. The protocol would not apply to drugs that are not metabolized or eliminated by a different route. Separate stable isotope-labeled and unlabeled drug administration to mice is made together with collection of urines from control animals. Untargeted mass spectrometry-based metabolomic analysis of these three urine groups is conducted in addition to principal components analysis (PCA). In the case of unlabeled acetaminophen and [acetyl-2H3]acetaminophen, each given at a hepatotoxic dose (400 mg/kg i.p.) to the sensitive mouse strain (wild-type 129), the PCA loadings plot showed a distribution of ions in the shape of a "fallen-Y" with the deuterated metabolites in one arm and the paired nondeuterated metabolites in the other arm of the fallen-Y. Ions corresponding to the endogenous toxicity biomarkers sat in the mouth of the fallen-Y. This protocol represents an innovative means to separate endogenous biomarkers from drug metabolites, thereby aiding the identification of biomarkers of drug toxicity. For acetaminophen, increased hepatic oxidative stress, mitochondrial damage, Ca2+ signaling, heme catabolism, and saturation of glucuronidation, together with decreased fatty acid β-oxidation and cellular energy dysregulation were all implied from the discovered biomarkers. The protocol can be applied to other drugs and may now be translated to clinical studies.
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Affiliation(s)
- Diren Beyoğlu
- Arthur G. Zupko's Systems Pharmacology and Pharmacogenomics, Samuel J. and Joan B. Williamson Institute, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY 11201, United States
| | - Yuyin Zhou
- Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, United States
| | - Chi Chen
- Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108, United States
| | - Jeffrey R Idle
- Arthur G. Zupko's Systems Pharmacology and Pharmacogenomics, Samuel J. and Joan B. Williamson Institute, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY 11201, United States.
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15
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Mussap M, Zaffanello M, Fanos V. Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism. ANNALS OF TRANSLATIONAL MEDICINE 2018; 6:338. [PMID: 30306077 DOI: 10.21037/atm.2018.09.18] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.
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Affiliation(s)
- Michele Mussap
- Laboratory Medicine, Department of Surgical Sciences, University of Cagliari, Cagliari, Italy
| | - Marco Zaffanello
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy
| | - Vassilios Fanos
- Department of Surgical Sciences, Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Cagliari, Italy
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