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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350 10.1002/mrc.5350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/23/2024]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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2
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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3
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Luo X, Li L. Effects of Solvent Evaporation Methods and Short-Term Room Temperature Storage on High-Coverage Cellular Metabolome Analysis. Metabolites 2023; 13:1052. [PMID: 37887377 PMCID: PMC10609186 DOI: 10.3390/metabo13101052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 09/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
Cellular metabolomics provides insights into the metabolic processes occurring within cells and can help researchers understand how these processes are regulated and how they relate to cellular function, health, and disease. In this technical note, we investigated the effects of solvent evaporation equipment and storage condition on high-coverage cellular metabolomics. We previously introduced a robust CIL LC-MS-based cellular metabolomics workflow that encompasses various steps, including cell harvest, metabolic quenching, cell lysis, metabolite extraction, differential chemical isotope labeling, and LC-MS analysis. This workflow has consistently served as the cornerstone of our collaborative research and service projects. As a core facility catering to users with diverse research needs and financial resources, we have encountered scenarios requiring short-term sample storage. For example, the need often arises to transport samples at room temperature from user sites to our core facility. Herein, we present a study in which we compared different solvent evaporation methods (specifically, the nitrogen blowdown evaporator, SpeedVac concentrator, and lyophilizer) and diverse storage conditions (including dried samples stored in a freezer, samples stored in a freezer with methanol, dried samples stored at room temperature, and samples stored at room temperature with methanol). Our findings indicate that the choice of solvent evaporation equipment did not significantly impact the cellular metabolome. However, we observed a noteworthy change in the metabolome after 7 days of storage when cells were stored with methanol, regardless of whether they were kept at -80 °C or room temperature, in contrast to cells that were dried and frozen. Importantly, we detected no significant alterations in cells that were dried and stored at room temperature. In conclusion, to ensure the production of high-quality CIL LC-MS metabolomics results, we strongly recommend that, in situations where low-temperature storage is not feasible, cell samples should be thoroughly dried before storage or shipment at room temperature.
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Affiliation(s)
- Xian Luo
- The Metabolomics Innovation Centre, Edmonton, AB T6G 2G2, Canada;
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada
| | - Liang Li
- The Metabolomics Innovation Centre, Edmonton, AB T6G 2G2, Canada;
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2, Canada
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4
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Navarro SL, Nagana Gowda GA, Bettcher LF, Pepin R, Nguyen N, Ellenberger M, Zheng C, Tinker LF, Prentice RL, Huang Y, Yang T, Tabung FK, Chan Q, Loo RL, Liu S, Wactawski-Wende J, Lampe JW, Neuhouser ML, Raftery D. Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women. Metabolites 2023; 13:metabo13040514. [PMID: 37110172 PMCID: PMC10143141 DOI: 10.3390/metabo13040514] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023] Open
Abstract
Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.
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Affiliation(s)
- Sandi L. Navarro
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Lisa F. Bettcher
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Robert Pepin
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Natalie Nguyen
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Mathew Ellenberger
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Lesley F. Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ross L. Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ying Huang
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Tao Yang
- School of Public Health, Xinjiang Medical University, Urumqi 830011, China
| | - Fred K. Tabung
- Department of Internal Medicine, Division of Medical Oncology, College of Medicine and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Queenie Chan
- School of Public Health, Imperial College of London, London SW7 2AZ, UK
| | - Ruey Leng Loo
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, School of Public Health, Providence, RI 02912, USA
- Department of Medicine and Surgery, Alpert School of Medicine, Brown University, Providence, RI 02903, USA
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA
| | - Johanna W. Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Marian L. Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Daniel Raftery
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
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5
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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6
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Sakallioglu IT, Barletta RG, Dussault PH, Powers R. Deciphering the mechanism of action of antitubercular compounds with metabolomics. Comput Struct Biotechnol J 2021; 19:4284-4299. [PMID: 34429848 PMCID: PMC8358470 DOI: 10.1016/j.csbj.2021.07.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 01/08/2023] Open
Abstract
Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.
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Affiliation(s)
- Isin T. Sakallioglu
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Raúl G. Barletta
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska Lincoln, Lincoln, NE 68583-0905, USA
| | - Patrick H. Dussault
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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7
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Santacruz L, Hurtado DX, Doohan R, Thomas OP, Puyana M, Tello E. Metabolomic study of soft corals from the Colombian Caribbean: PSYCHE and 1H-NMR comparative analysis. Sci Rep 2020; 10:5417. [PMID: 32214197 PMCID: PMC7096504 DOI: 10.1038/s41598-020-62413-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 02/24/2020] [Indexed: 12/12/2022] Open
Abstract
Marine organisms have evolved to survive against predators in complex marine ecosystems via the production of chemical compounds. Soft corals (Cnidaria, Anthozoa, Octocorallia) are an important source of chemically diverse metabolites with a broad spectrum of biological activities. Herein, we perform a comparative study between high-resolution proton nuclear magnetic resonance (1H-NMR) and pure shift yielded by chirp excitation (PSYCHE) experiments to analyze the metabolic profile of 24 soft corals from the Colombian Caribbean to correlate chemical fingerprints with their cytotoxic activity against three cancer cell lines (human cervical carcinoma (SiHa), human prostatic carcinoma (PC3) and human lung adenocarcinoma (A549)). All data obtained were explored using multivariate analysis using principal components analysis (PCA) and orthogonal partial least squares (OPLS) analysis. The results did not show a significant correlation between clusters using 1H-NMR data in the PCA and OPLS-DA models and therefore did not provide conclusive evidence; on the other hand, a metabolomic analysis of PSYCHE data obtained under the same parameters revealed that when a decoupled experiment is performed, it was possible to establish a statistically valid correlation between the chemical composition of soft corals and their cytotoxic activity against the PC3 cancer cell line, where the asperdiol and plexaurolone markers were putatively identified and related to the cytotoxic activity presented by extracts of Plexaurella sp. and Plexaura kukenthali, respectively. These results increase the speed, effectiveness and reliability of analyses for the study of this type of complex matrices.
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Affiliation(s)
- Liliana Santacruz
- Bioprospecting Research Group and Biosciences Doctoral Program, Faculty of Engineering, Campus Puente del Común, Universidad de la Sabana, 250001, Chía, Colombia
| | - Diana X Hurtado
- Bioprospecting Research Group and Biosciences Doctoral Program, Faculty of Engineering, Campus Puente del Común, Universidad de la Sabana, 250001, Chía, Colombia
| | - Roisin Doohan
- Marine Biodiscovery, School of Chemistry and Ryan Institute, National University of Ireland Galway (NUI Galway), University Road, H91 TK33, Galway, Ireland
| | - Olivier P Thomas
- Marine Biodiscovery, School of Chemistry and Ryan Institute, National University of Ireland Galway (NUI Galway), University Road, H91 TK33, Galway, Ireland
| | - Mónica Puyana
- Departamento de Ciencias Biológicas y Ambientales, Universidad Jorge Tadeo Lozano, Carrera 4 # 22-61, 110311, Bogotá, Colombia
| | - Edisson Tello
- Bioprospecting Research Group and Biosciences Doctoral Program, Faculty of Engineering, Campus Puente del Común, Universidad de la Sabana, 250001, Chía, Colombia.
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8
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Stuart KA, Welsh K, Walker MC, Edrada-Ebel R. Metabolomic tools used in marine natural product drug discovery. Expert Opin Drug Discov 2020; 15:499-522. [PMID: 32026730 DOI: 10.1080/17460441.2020.1722636] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.
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Affiliation(s)
- Kevin Andrew Stuart
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Keira Welsh
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Molly Clare Walker
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - RuAngelie Edrada-Ebel
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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9
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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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10
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Abstract
Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile" and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.
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11
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Bhinderwala F, Lei S, Woods J, Rose J, Marshall DD, Riekeberg E, Leite ADL, Morton M, Dodds ED, Franco R, Powers R. Metabolomics Analyses from Tissues in Parkinson's Disease. Methods Mol Biol 2019; 1996:217-257. [PMID: 31127560 DOI: 10.1007/978-1-4939-9488-5_19] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metabolomics has been successfully applied to study neurological and neurodegenerative disorders including Parkinson's disease for (1) the identification of potential biomarkers of onset and disease progression; (2) the identification of novel mechanisms of disease progression; and (3) the assessment of treatment prognosis and outcome. Reproducible and efficient extraction of metabolites is imperative to the success of any metabolomics investigation. Unlike other omics techniques, the composition of the metabolome can be negatively impacted by the preparation, processing, and handling of these samples. The proper choice of data collection, preprocessing, and processing protocols is similarly important to the design of an effective metabolomics experiment. Likewise, the correct application of univariate and multivariate statistical methods is essential for providing biologically relevant insights. In this chapter, we have outlined a detailed metabolomics workflow that addresses all of these issues. A step-by-step protocol from the preparation of neuronal cells and metabolomic tissue samples to their metabolic analyses using nuclear magnetic resonance, mass spectrometry, and chemometrics is presented.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Shulei Lei
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Jade Woods
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Jordan Rose
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA.,Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Darrell D Marshall
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Eli Riekeberg
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Aline De Lima Leite
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.,Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Martha Morton
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.,Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Eric D Dodds
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.,Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rodrigo Franco
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA. .,Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA. .,Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE, USA. .,Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA.
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12
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Bhinderwala F, Wase N, DiRusso C, Powers R. Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation. J Proteome Res 2018; 17:4017-4022. [PMID: 30303385 DOI: 10.1021/acs.jproteome.8b00567] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.
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Affiliation(s)
- Fatema Bhinderwala
- Department of Chemistry , University of Nebraska , Lincoln , Nebraska 68588-0304 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
| | - Nishikant Wase
- Department of Biochemistry , University of Nebraska , Lincoln , Nebraska 68588-0664 , United States
| | - Concetta DiRusso
- Department of Biochemistry , University of Nebraska , Lincoln , Nebraska 68588-0664 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
| | - Robert Powers
- Department of Chemistry , University of Nebraska , Lincoln , Nebraska 68588-0304 , United States.,Nebraska Center for Integrated Biomolecular Communication , Lincoln , Nebraska 68588-0304 , United States
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Tuyiringire N, Tusubira D, Munyampundu JP, Tolo CU, Muvunyi CM, Ogwang PE. Application of metabolomics to drug discovery and understanding the mechanisms of action of medicinal plants with anti-tuberculosis activity. Clin Transl Med 2018; 7:29. [PMID: 30270413 PMCID: PMC6165828 DOI: 10.1186/s40169-018-0208-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 08/29/2018] [Indexed: 11/10/2022] Open
Abstract
Human tuberculosis (TB) is amongst the oldest and deadliest human bacterial diseases that pose major health, social and economic burden at a global level. Current regimens for TB treatment are lengthy, expensive and ineffective to emerging drug resistant strains. Thus, there is an urgent need for identification and development of novel TB drugs and drug regimens with comprehensive and specific mechanisms of action. Many medicinal plants are traditionally used for TB treatment. While some of their phytochemical composition has been elucidated, their mechanisms of action are not well understood. Insufficient knowledge on Mycobacterium tuberculosis (M.tb) biology and the complex nature of its infection limit the effectiveness of current screening-based methods used for TB drug discovery. Nonetheless, application of metabolomics tools within the 'omics' approaches, could provide an alternative method of elucidating the mechanism of action of medicinal plants. Metabolomics aims at high throughput detection, quantification and identification of metabolites in biological samples. Changes in the concentration of specific metabolites in a biological sample indicate changes in the metabolic pathways. In this paper review and discuss novel methods that involve application of metabolomics to drug discovery and the understanding of mechanisms of action of medicinal plants with anti-TB activity. Current knowledge on TB infection, anti-TB drugs and mechanisms of action are also included. We further highlight metabolism of M. tuberculosis and the potential drug targets, as well as current approaches in the development of anti-TB drugs.
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Affiliation(s)
- Naasson Tuyiringire
- Pharm-BioTechnology and Traditional Medicine Centre (PHARMBIOTRAC), Mbarara University of Science & Technology, P.O. Box, 1410 Mbarara, Uganda
- College of Medicine and Health Sciences, University of Rwanda, University Avenue, P.O. Box 56, Butare, Rwanda
| | - Deusdedit Tusubira
- Pharm-BioTechnology and Traditional Medicine Centre (PHARMBIOTRAC), Mbarara University of Science & Technology, P.O. Box, 1410 Mbarara, Uganda
- Department of Biomedicine, University of Bergen, Jonas Lies Vei 91, 5020 Bergen, Norway
| | - Jean-Pierre Munyampundu
- School of Science, College of Science and Technology, University of Rwanda, Avenue de l’Armée, P.O. Box 3900, Kigali, Rwanda
| | - Casim Umba Tolo
- Pharm-BioTechnology and Traditional Medicine Centre (PHARMBIOTRAC), Mbarara University of Science & Technology, P.O. Box, 1410 Mbarara, Uganda
| | - Claude M. Muvunyi
- College of Medicine and Health Sciences, University of Rwanda, University Avenue, P.O. Box 56, Butare, Rwanda
| | - Patrick Engeu Ogwang
- Pharm-BioTechnology and Traditional Medicine Centre (PHARMBIOTRAC), Mbarara University of Science & Technology, P.O. Box, 1410 Mbarara, Uganda
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14
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Armitage EG, Godzien J, Peña I, López-Gonzálvez Á, Angulo S, Gradillas A, Alonso-Herranz V, Martín J, Fiandor JM, Barrett MP, Gabarro R, Barbas C. Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis. ACS Chem Biol 2018; 13:1361-1369. [PMID: 29671577 DOI: 10.1021/acschembio.8b00204] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.
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Affiliation(s)
- Emily G. Armitage
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
- GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo Ochoa 2, 28760 Tres Cantos, Madrid, Spain
- Wellcome Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, United Kingdom
- Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary & Life Sciences, University of Glasgow, Glasgow G61 1QH, United Kingdom
| | - Joanna Godzien
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Imanol Peña
- GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo Ochoa 2, 28760 Tres Cantos, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Santiago Angulo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Vanesa Alonso-Herranz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Julio Martín
- GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo Ochoa 2, 28760 Tres Cantos, Madrid, Spain
| | - Jose M. Fiandor
- GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo Ochoa 2, 28760 Tres Cantos, Madrid, Spain
| | - Michael P. Barrett
- Wellcome Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, United Kingdom
- Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary & Life Sciences, University of Glasgow, Glasgow G61 1QH, United Kingdom
| | - Raquel Gabarro
- GSK I+D Diseases of the Developing World (DDW), Parque Tecnológico de Madrid, Calle de Severo Ochoa 2, 28760 Tres Cantos, Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, Spain
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15
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Bhinderwala F, Lonergan S, Woods J, Zhou C, Fey PD, Powers R. Expanding the Coverage of the Metabolome with Nitrogen-Based NMR. Anal Chem 2018; 90:4521-4528. [PMID: 29505241 DOI: 10.1021/acs.analchem.7b04922] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. 13C-labeled metabolite tracers or unlabeled 1H-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for 15N-labeled metabolite tracer experiments that may also be combined with routine 13C tracer experiments to simultaneously detect both 15N- and 13C-labeled metabolites in metabolic samples. A database consisting of 2D 1H-15N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of 15N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with 15N1-ammonium chloride, 15N4-arginine, and 13C2-acetate. Efficient 15N and 13C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.
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Affiliation(s)
| | | | | | - Chunyi Zhou
- Center for Staphylococcal Research, Department of Pathology and Microbiology , University of Nebraska Medical Center , Omaha , Nebraska 68198-5900 , United States
| | - Paul D Fey
- Center for Staphylococcal Research, Department of Pathology and Microbiology , University of Nebraska Medical Center , Omaha , Nebraska 68198-5900 , United States
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16
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A Novel Anti-Hepatitis C Virus and Antiproliferative Agent Alters Metabolic Networks in HepG2 and Hep3B Cells. Metabolites 2017; 7:metabo7020023. [PMID: 28574427 PMCID: PMC5487994 DOI: 10.3390/metabo7020023] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 05/18/2017] [Accepted: 05/29/2017] [Indexed: 02/08/2023] Open
Abstract
A series of novel diflunisal hydrazide-hydrazones has been reported together with their anti-hepatitis C virus and antiproliferative activities in a number of human hepatoma cell lines. However, the mechanisms underlying the efficacy of these agents remain unclear. It was chosen to investigate the lead diflunisal hydrazide-hydrazone, 2',4'-difluoro-4-hydroxy-N'- [(pyridin-2-yl)methylidene]biphenyl-3-carbohydrazide (compound 3b), in two cultured human hepatoma cell lines-HepG2 and Hep3B-using a metabolomic protocol aimed at uncovering any effects of this agent on cellular metabolism. One sub-therapeutic concentration (2.5 μM) and one close to the IC50 for antimitotic effect (10 μM), after 72 h in cell culture, were chosen for both compound 3b and its inactive parent compound diflusinal as a control. A GCMS-based metabolomic investigation was performed on cell lysates after culture for 24 h. The intracellular levels of a total of 42 metabolites were found to be statistically significantly altered in either HepG2 or Hep3B cells, only eight of which were affected in both cell lines. It was concluded that compound 3b affected the following pathways-purine and pyrimidine catabolism, the glutathione cycle, and energy metabolism through glycolysis and the pentose phosphate pathway. Although the metabolomic findings occurred after 24 h in culture, significant cytotoxicity of compound 3b to both HepG2 and Hep3B cells at 10 μM were reported not to occur until 72 h in culture. These observations show that metabolomics can provide mechanistic insights into the efficacy of novel drug candidates prior to the appearance of their pharmacological effect.
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Gowda GAN, Raftery D. Whole Blood Metabolomics by 1H NMR Spectroscopy Provides a New Opportunity To Evaluate Coenzymes and Antioxidants. Anal Chem 2017; 89:4620-4627. [PMID: 28318242 PMCID: PMC6245939 DOI: 10.1021/acs.analchem.7b00171] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Conventional human blood metabolomics employs serum or plasma and provides a wealth of metabolic information therein. However, this approach lacks the ability to measure and evaluate important metabolites such as coenzymes and antioxidants that are present at high concentrations in red blood cells. As an important alternative to serum/plasma metabolomics, we show here that a simple 1H NMR experiment can simultaneously measure coenzymes and antioxidants in extracts of whole human blood, in addition to the nearly 70 metabolites that were shown to be quantitated in serum/plasma recently [ Anal. Chem. 2015 , 87 , 706 - 715 ]. Coenzymes of redox reactions: oxidized/reduced nicotinamide adenine dinucleotide (NAD+ and NADH) and nicotinamide adenine dinucleotide phosphate (NADP+ and NADPH); coenzymes of energy including adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP); and antioxidants, the sum of oxidized and reduced glutathione (GSSG and GSH) can be measured with essentially no additional effort. A new method was developed for detecting many of these unstable species without affecting other blood/blood plasma metabolites. The identities of coenzymes and antioxidants in blood NMR spectra were established combining 1D/2D NMR techniques, chemical shift databases, pH measurements and, finally, spiking with authentic compounds. This is the first study to report identification of major coenzymes and antioxidants and quantify them, simultaneously, with the large pool of other metabolites in human blood using NMR spectroscopy. Considering that the levels of coenzymes and antioxidants represent a sensitive measure of cellular functions in health and numerous diseases, the NMR method presented here potentially opens a new chapter in the metabolomics of blood.
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
- Department of Chemistry, University of Washington, Seattle, Washington 98109, United States
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
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18
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine and
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine and
- Department of Chemistry, University of Washington, Seattle, Washington 98109, United States
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
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19
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Markley JL, Brüschweiler R, Edison AS, Eghbalnia HR, Powers R, Raftery D, Wishart DS. The future of NMR-based metabolomics. Curr Opin Biotechnol 2016; 43:34-40. [PMID: 27580257 DOI: 10.1016/j.copbio.2016.08.001] [Citation(s) in RCA: 487] [Impact Index Per Article: 60.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 08/05/2016] [Accepted: 08/08/2016] [Indexed: 12/15/2022]
Abstract
The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.
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Affiliation(s)
- John L Markley
- Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI 53706, USA.
| | - Rafael Brüschweiler
- Department of Chemistry & Biochemistry, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA; Department of Biological Chemistry & Pharmacology, The Ohio State University, 151 W. Woodruff Ave., Columbus, OH 43210, USA
| | - Arthur S Edison
- Department of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Rd, Athens, GA 30602, USA
| | - Hamid R Eghbalnia
- Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI 53706, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, 722 Hamilton Hall, Lincoln, NE 68588, USA
| | - Daniel Raftery
- Department of Anesthesiology & Pain Medicine, 850 Republican St, University of Washington, Seattle, WA 98109, USA
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada T6G 2E8; Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
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20
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Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health. Metabolites 2016; 6:metabo6030020. [PMID: 27399792 PMCID: PMC5041119 DOI: 10.3390/metabo6030020] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 06/06/2016] [Accepted: 07/01/2016] [Indexed: 12/28/2022] Open
Abstract
Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.
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21
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Liao S, Li P, Wang J, Zhang Q, Xu D, Yang M, Kong L. Protection of baicalin against lipopolysaccharide induced liver and kidney injuries based on 1H NMR metabolomic profiling. Toxicol Res (Camb) 2016; 5:1148-1159. [PMID: 30090421 PMCID: PMC6060722 DOI: 10.1039/c6tx00082g] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 05/05/2016] [Indexed: 01/14/2023] Open
Abstract
Severe sepsis and septic shock are common and lethal conditions characterized by a systemic inflammatory response that is activated by invasive infection. In this study, a lipopolysaccharide (LPS) induced sepsis mice model was established to investigate the toxicities of LPS and the therapeutic effect of baicalin. Sera for clinical biochemistry and NMR metabolomic investigation, and liver and kidney tissues for histopathological examination, molecular biology measurement and NMR metabolomic profiling were collected. Multivariate analysis of metabolic profiles of the serum, liver and kidney extracts of mice revealed the occurrence of a severe inflammatory response, oxidative stress, and perturbances in energy and amino acid metabolism in LPS induced sepsis mice, which could be greatly ameliorated by baicalin treatment. This integrated 1H NMR based metabolomics approach gave us a new insight into the pathology of LPS induced sepsis, and helped in understanding the therapeutic effects of baicalin in a holistic view.
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Affiliation(s)
- Shanting Liao
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
| | - Pei Li
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
| | - Junsong Wang
- Center for Molecular Metabolism , Nanjing University of Science & Technology , 200 Xiao Ling Wei Street , Nanjing 210094 , PR China . ; Tel: +86-25-8431-5512
| | - Qian Zhang
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
| | - Dingqiao Xu
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
| | - Minghua Yang
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
| | - Lingyi Kong
- State Key Laboratory of Natural Medicines , Department of Natural Medicinal Chemistry , China Pharmaceutical University , 24 Tong Jia Xiang , Nanjing 210009 , PR China . ; ; Tel: +86-25-8327-1405
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Frédérich M, Pirotte B, Fillet M, de Tullio P. Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine. J Med Chem 2016; 59:8649-8666. [PMID: 27295417 DOI: 10.1021/acs.jmedchem.5b01335] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.
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Affiliation(s)
- Michel Frédérich
- Laboratory of Pharmacognosy, Center for Interdisciplinary Research on Medicines (CIRM), University of Liege , Quartier Hôpital, Avenue Hippocrate 15, B-4000 Liege, Belgium
| | - Bernard Pirotte
- Laboratory of Medicinal Chemistry, Center for Interdisciplinary Research on Medicines (CIRM), University of Liege , Quartier Hôpital, Avenue Hippocrate 15, B-4000 Liege, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, Center for Interdisciplinary Research on Medicines (CIRM), University of Liege , Quartier Hôpital, Avenue Hippocrate 15, B-4000 Liege, Belgium
| | - Pascal de Tullio
- Laboratory of Medicinal Chemistry, Center for Interdisciplinary Research on Medicines (CIRM), University of Liege , Quartier Hôpital, Avenue Hippocrate 15, B-4000 Liege, Belgium
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23
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Tan Z, Chaudhai R, Zhang S. Polypharmacology in Drug Development: A Minireview of Current Technologies. ChemMedChem 2016; 11:1211-8. [PMID: 27154144 DOI: 10.1002/cmdc.201600067] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 03/21/2016] [Indexed: 01/09/2023]
Abstract
Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.
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Affiliation(s)
- Zhi Tan
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Rajan Chaudhai
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Shuxing Zhang
- Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. .,The University of Texas Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
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24
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Gunda V, Yu F, Singh PK. Validation of Metabolic Alterations in Microscale Cell Culture Lysates Using Hydrophilic Interaction Liquid Chromatography (HILIC)-Tandem Mass Spectrometry-Based Metabolomics. PLoS One 2016; 11:e0154416. [PMID: 27120458 PMCID: PMC4847783 DOI: 10.1371/journal.pone.0154416] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 04/12/2016] [Indexed: 02/04/2023] Open
Abstract
By standard convention, in order to increase the efficacy of metabolite detection from cell culture lysates, metabolite extracts from a large quantity of cells are utilized for multiple reaction monitoring-based metabolomic studies. Metabolomics from a small number of cell extracts offers a potential economical alternative to increased cell numbers, in turn increasing the utility of cell culture-based metabolomics. However, the effect of reduced cell numbers on targeted metabolomic profiling is relatively unstudied. Considering the limited knowledge available of the feasibility and accuracy of microscale cell culture metabolomics, the present study analyzes differences in metabolomic profiles of different cell numbers of three pancreatic cancer cell lines. Specifically, it examines the effects of reduced cell numbers on metabolite profiles by obtaining extracts either directly from microscale culture plates or through serial dilution of increased numbers of cellular metabolite extracts. Our results indicate reduced cell numbers only modestly affect the number of metabolites detected (93% of metabolites detected in cell numbers as low as 104 cells and 97% for 105 cells), independent of the method used to obtain the cells. However, metabolite peak intensities were differentially affected by the reduced cell numbers, with some peak intensities inversely proportional to the cell numbers. To help eliminate such potential inverse relationships, peak intensities for increased cell numbers were excluded from the comparative analysis. Overall, metabolite profiles from microscale culture plates were observed to differ from the serial dilution samples, which may be attributable to the medium-to-cell-number ratios. Finally, findings identify perturbations in metabolomic profiling for cellular extracts from reduced cell numbers, which offer future applications in microscale metabolomic evaluations.
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Affiliation(s)
- Venugopal Gunda
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Fang Yu
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Pankaj K. Singh
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
- * E-mail:
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25
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Abstract
This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.
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26
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Castejón D, Fricke P, Cambero MI, Herrera A. Automatic ¹H-NMR Screening of Fatty Acid Composition in Edible Oils. Nutrients 2016; 8:93. [PMID: 26891323 PMCID: PMC4772056 DOI: 10.3390/nu8020093] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 01/14/2016] [Accepted: 01/29/2016] [Indexed: 11/30/2022] Open
Abstract
In this work, we introduce an NMR-based screening method for the fatty acid composition analysis of edible oils. We describe the evaluation and optimization needed for the automated analysis of vegetable oils by low-field NMR to obtain the fatty acid composition (FAC). To achieve this, two scripts, which automatically analyze and interpret the spectral data, were developed. The objective of this work was to drive forward the automated analysis of the FAC by NMR. Due to the fact that this protocol can be carried out at low field and that the complete process from sample preparation to printing the report only takes about 3 min, this approach is promising to become a fundamental technique for high-throughput screening. To demonstrate the applicability of this method, the fatty acid composition of extra virgin olive oils from various Spanish olive varieties (arbequina, cornicabra, hojiblanca, manzanilla, and picual) was determined by 1H-NMR spectroscopy according to this protocol.
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Affiliation(s)
- David Castejón
- Centro de Asistencia a la Investigación de Resonancia Magnética Nuclear y de Resonancia de Spin Electrónico (CAI de RMN y RSE), Universidad Complutense de Madrid, 28040 Madrid, Spain.
| | - Pascal Fricke
- Department of Molecular Biophysics, Leibniz-Institut für Molekulare Pharmakologie, 13125 Berlin, Germany.
| | - María Isabel Cambero
- Department of Nutrition, Bromatology and Food Technology, Veterinary Faculty, Universidad Complutense de Madrid, 28040 Madrid, Spain.
| | - Antonio Herrera
- Department of Organic Chemistry, Chemistry Faculty, Universidad Complutense de Madrid, 28040 Madrid, Spain.
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Shiokawa Y, Misawa T, Date Y, Kikuchi J. Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements. Anal Chem 2016; 88:2714-9. [PMID: 26824632 DOI: 10.1021/acs.analchem.5b04182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle.
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Affiliation(s)
- Yuka Shiokawa
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Takuma Misawa
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yasuhiro Date
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- Graduate School of Medical Life Science, Yokohama City University , 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Graduate School of Bioagricultural Sciences, Nagoya University , 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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28
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Gebregiworgis T, Nielsen HH, Massilamany C, Gangaplara A, Reddy J, Illes Z, Powers R. A Urinary Metabolic Signature for Multiple Sclerosis and Neuromyelitis Optica. J Proteome Res 2016; 15:659-66. [PMID: 26759122 DOI: 10.1021/acs.jproteome.5b01111] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Urine is a metabolite-rich biofluid that reflects the body's effort to maintain chemical and osmotic homeostasis. Clinical diagnosis routinely relies on urine samples because the collection process is easy and noninvasive. Despite these advantages, urine is an under-investigated source of biomarkers for multiple sclerosis (MS). Nuclear magnetic resonance spectroscopy (NMR) has become a common approach for analyzing urinary metabolites for disease diagnosis and biomarker discovery. For illustration of the potential of urinary metabolites for diagnosing and treating MS patients, and for differentiating between MS and other illnesses, 38 urine samples were collected from healthy controls, MS patients, and neuromyelitis optica-spectrum disorder (NMO-SD) patients and analyzed with NMR, multivariate statistics, one-way ANOVA, and univariate statistics. Urine from MS patients exhibited a statistically distinct metabolic signature from healthy and NMO-SD controls. A total of 27 metabolites were differentially altered in the urine from MS and NMO-SD patients and were associated with synthesis and degradation of ketone bodies, amino acids, propionate and pyruvate metabolism, tricarboxylic acid cycle, and glycolysis. Metabolites altered in urine from MS patients were shown to be related to known pathogenic processes relevant to MS, including alterations in energy and fatty acid metabolism, mitochondrial activity, and the gut microbiota.
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Affiliation(s)
- Teklab Gebregiworgis
- Department of Chemistry, University of Nebraska-Lincoln , Lincoln, Nebraska 68588-0304, United States
| | - Helle H Nielsen
- Department of Neurology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark , Odense, Denmark
| | - Chandirasegaran Massilamany
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln , Lincoln, Nebraska 68583-0905, United States
| | - Arunakumar Gangaplara
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln , Lincoln, Nebraska 68583-0905, United States
| | - Jay Reddy
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln , Lincoln, Nebraska 68583-0905, United States
| | - Zsolt Illes
- Department of Neurology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark , Odense, Denmark
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln , Lincoln, Nebraska 68588-0304, United States
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Palomino-Schätzlein M, Pineda-Lucena A. Metabolomic Applications to the Characterization of the Mode-of-Action of CDK Inhibitors. Methods Mol Biol 2016; 1336:211-23. [PMID: 26231718 DOI: 10.1007/978-1-4939-2926-9_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cyclin-dependent kinases (CDKs) regulate cell cycle progression, and some of them are also involved in the control of cellular transcription. Dysregulation of these critical cellular processes, due to the aberrant expression of some of these proteins, is common in many neoplastic malignancies. Consequently, the development of chemical compounds capable of inhibiting the biological activity of CDKs represents an attractive strategy in the anticancer area. CDK inhibition can trigger apoptosis and could be particularly useful in hematological malignancies, which are more sensitive to inhibition of cell cycle and apoptosis induction. Over the last few years, a number of pharmacological inhibitors of CDKs (CDKIs) belonging to different chemical families have been developed, and some of them have been tested in clinical trials. Given the complexity of the role of CDKs in cell functioning, it would be desirable to develop new tools that could facilitate a better understanding of the new insights into CDK functions and the mode-of-actions of CDKIs. In this context, this chapter describes an experimental approach to evaluate the metabolic consequences of CDKIs at the cellular level based on metabolomics by NMR. More specifically, a description of a strategy to characterize the biochemical effects of CDKIs acting on mammalian cells is provided, including protocols for the extraction of hydrophilic and lipophilic metabolites, the acquisition of 1D and 2D metabolomic Nuclear Magnetic Resonance (NMR) experiments, the identification and quantification of metabolites, and the annotation of the results in the context of biochemical pathways.
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Affiliation(s)
- Martina Palomino-Schätzlein
- Structural Biochemistry Laboratory, Advanced Therapies Program, Centro de Investigación Príncipe Felipe, Eduardo Primo Yúfera, 3, Valencia, 46012, Spain
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30
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Zhang B, Xie M, Bruschweiler-Li L, Brüschweiler R. Nanoparticle-Assisted Removal of Protein in Human Serum for Metabolomics Studies. Anal Chem 2015; 88:1003-7. [PMID: 26605638 DOI: 10.1021/acs.analchem.5b03889] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Among human body fluids, serum plays a key role for diagnostic tests and, increasingly, for metabolomics analysis. However, the high protein content of serum poses significant challenges for nuclear magnetic resonance (NMR)-based metabolomics studies because it can strongly interfere with metabolite signal detection and quantitation. Although several methods for protein removal have been proposed, including ultrafiltration and organic-solvent-induced protein precipitation, there is currently no standard operating procedure for the elimination of protein from human serum samples. Here, we introduce novel procedures for the removal of protein from serum by the addition of nanoparticles. It is demonstrated how serum protein can be efficiently, cost-effectively, and environmentally friendly removed at physiological pH (pH 7.4) through attractive interactions with silica nanoparticles. It is further shown how serum can be processed with nanoparticles prior to ultrafiltration or organic-solvent-induced protein precipitation for optimal protein removal. After examination of all of the procedures, the combination of nanoparticle treatment and ultrafiltration is found to have a minimal effect on the metabolite content, leading to remarkably clean homo- and heteronuclear NMR spectra of the serum metabolome that compare favorably to other methods for protein removal.
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Affiliation(s)
- Bo Zhang
- Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, and §Department of Biological Chemistry and Pharmacology, The Ohio State University , Columbus, Ohio 43210, United States
| | - Mouzhe Xie
- Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, and §Department of Biological Chemistry and Pharmacology, The Ohio State University , Columbus, Ohio 43210, United States
| | - Lei Bruschweiler-Li
- Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, and §Department of Biological Chemistry and Pharmacology, The Ohio State University , Columbus, Ohio 43210, United States
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry, ‡Campus Chemical Instrument Center, and §Department of Biological Chemistry and Pharmacology, The Ohio State University , Columbus, Ohio 43210, United States
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31
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Puchades-Carrasco L, Pineda-Lucena A. Metabolomics in pharmaceutical research and development. Curr Opin Biotechnol 2015; 35:73-7. [DOI: 10.1016/j.copbio.2015.04.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 04/06/2015] [Accepted: 04/07/2015] [Indexed: 12/26/2022]
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32
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Nagana Gowda GA, Raftery D. Can NMR solve some significant challenges in metabolomics? JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 260:144-60. [PMID: 26476597 PMCID: PMC4646661 DOI: 10.1016/j.jmr.2015.07.014] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Revised: 07/17/2015] [Accepted: 07/18/2015] [Indexed: 05/04/2023]
Abstract
The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.
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Affiliation(s)
- G A Nagana Gowda
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, United States; Department of Chemistry, University of Washington, Seattle, WA 98195, United States; Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States.
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33
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Sokolenko S, Aucoin MG. A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation. BMC SYSTEMS BIOLOGY 2015; 9:51. [PMID: 26335002 PMCID: PMC4558828 DOI: 10.1186/s12918-015-0197-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 08/13/2015] [Indexed: 01/24/2023]
Abstract
Background The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Results Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. Conclusion Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in 1H-NMR methodology and the more general application of quantitative metabolomics. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0197-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Stanislav Sokolenko
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, N2L 3G1, ON, Canada
| | - Marc G Aucoin
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, N2L 3G1, ON, Canada.
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Nuclear magnetic resonance: a key metabolomics platform in the drug discovery process. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 13:39-46. [PMID: 26190682 DOI: 10.1016/j.ddtec.2015.06.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 06/02/2015] [Accepted: 06/17/2015] [Indexed: 12/11/2022]
Abstract
Metabolomics is an innovative tool that is now emerging in the drug discovery process. Indeed, its ability to follow the dynamic perturbations in the metabolome resulting from pathologies but also from drug treatment and or/toxicity is of value for the development of new therapeutic approaches. Nuclear magnetic resonance (NMR) spectroscopy, which is an important analytical technique for several steps of the lead discovery, validation and optimization processes, has been described, together with mass spectrometry (MS) as one of the major platform that could be used for metabolomics studies. This review highlights why NMR could be considered a key tool for the application of metabolomics in drug discovery.
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35
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Fillet M, Frédérich M. The emergence of metabolomics as a key discipline in the drug discovery process. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 13:19-24. [PMID: 26190679 DOI: 10.1016/j.ddtec.2015.01.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 01/21/2015] [Accepted: 01/27/2015] [Indexed: 12/15/2022]
Abstract
Metabolomics is a recent science that could be defined as the comprehensive qualitative and quantitative analysis of all small molecular weight compounds present in a cell, organ (including biofluids) or organism at a specific time point. More and more applications have been found these past years to metabolomics in the pharmaceutical field. Specifically in the drug discovery process, metabolomics open new perspectives, in new targets identification, in toxicological studies and in bioactive natural products discovery. The challenge in metabolomics is to find a technological approach allowing the reproducible identification and quantitation of as much metabolites as possible. In this context, mass spectrometry and NMR are emerging as key and complementary technologies.
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Affiliation(s)
- Marianne Fillet
- Laboratory for the Analysis of Medicines, Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Liège, Belgium
| | - Michel Frédérich
- Laboratory of Pharmacognosy, Center for Interdisciplinary Research on Medicines (CIRM), University of Liège, Liège, Belgium.
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Yang J, Zhao X, Lu X, Lin X, Xu G. A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Front Mol Biosci 2015; 2:4. [PMID: 25988172 PMCID: PMC4428451 DOI: 10.3389/fmolb.2015.00004] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 01/09/2015] [Indexed: 01/18/2023] Open
Abstract
HighlightsDeveloped a data preprocessing strategy to cope with missing values and mask effects in data analysis from high variation of abundant metabolites. A new method- ‘x-VAST’ was developed to amend the measurement deviation enlargement. Applying the above strategy, several low abundant masked differential metabolites were rescued.
Metabolomics is a booming research field. Its success highly relies on the discovery of differential metabolites by comparing different data sets (for example, patients vs. controls). One of the challenges is that differences of the low abundant metabolites between groups are often masked by the high variation of abundant metabolites. In order to solve this challenge, a novel data preprocessing strategy consisting of three steps was proposed in this study. In step 1, a ‘modified 80%’ rule was used to reduce effect of missing values; in step 2, unit-variance and Pareto scaling methods were used to reduce the mask effect from the abundant metabolites. In step 3, in order to fix the adverse effect of scaling, stability information of the variables deduced from intensity information and the class information, was used to assign suitable weights to the variables. When applying to an LC/MS based metabolomics dataset from chronic hepatitis B patients study and two simulated datasets, the mask effect was found to be partially eliminated and several new low abundant differential metabolites were rescued.
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Affiliation(s)
- Jun Yang
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China ; Department of Entomology and Nematology, University of California, Davis Davis, CA, USA
| | - Xinjie Zhao
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China
| | - Xin Lu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology Dalian, China
| | - Guowang Xu
- Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences Dalian, China
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