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Liu X, Fang Y, Ma H, Zhang N, Li C. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis. Open Life Sci 2023; 18:20220556. [PMID: 36998512 PMCID: PMC10044292 DOI: 10.1515/biol-2022-0556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/15/2022] [Accepted: 01/02/2023] [Indexed: 03/29/2023] Open
Abstract
Unit variance (UV) scaling, mean centering (CTR) scaling, and Pareto (Par) scaling are three commonly used algorithms in the preprocessing of metabolomics data. Based on our NMR-based metabolomics studies, we found that the clustering identification performances of these three scaling methods were dramatically different as tested by the spectra data of 48 young athletes’ urine samples, spleen tissue (from mice), serum (from mice), and cell (from Staphylococcus aureus) samples. Our data suggested that for the extraction of clustering information, UV scaling could serve as a robust approach for NMR metabolomics data for the identification of clustering analysis even with the existence of technical errors. However, for the purpose of discriminative metabolite identification, UV scaling, CTR scaling, and Par scaling could equally extract discriminative metabolites efficiently based on the coefficient values. Based on the data presented in this study, we propose an optimal working pipeline for the selection of scaling algorithms in NMR-based metabolomics analysis, which has the potential to serve as guidance for junior researchers working in the NMR-based metabolomics research field.
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Affiliation(s)
- Xia Liu
- Department of Diving and Hyperbaric Medicine, Navy Medical Center, Naval Medical University (Second Military Medical University), Shanghai, 200433, China
| | - Yiqun Fang
- Department of Diving and Hyperbaric Medicine, Navy Medical Center, Naval Medical University (Second Military Medical University), Shanghai, 200433, China
| | - Haifeng Ma
- Shanghai University of Sport, Shanghai200438, China
| | - Naixia Zhang
- CAS Key Laboratory of Receptor Research, Department of Analytical Chemistry, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai201203, P. R. China
| | - Ci Li
- Department of Diving and Hyperbaric Medicine, Navy Medical Center, Naval Medical University (Second Military Medical University), Shanghai, 200433, China
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2
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Decker C, Krapf R, Kuballa T, Bunzel M. Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis. Front Nutr 2022; 9:985797. [PMID: 36245505 PMCID: PMC9566576 DOI: 10.3389/fnut.2022.985797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.
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Affiliation(s)
- Christina Decker
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Karlsruhe, Germany
- Chemisches und Veterinäruntersuchungsamt Karlsruhe, Karlsruhe, Germany
| | - Reiner Krapf
- Bosch Power Tools, Leinfelden-Echterdingen, Germany
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt Karlsruhe, Karlsruhe, Germany
| | - Mirko Bunzel
- Karlsruhe Institute of Technology (KIT), Department of Food Chemistry and Phytochemistry, Karlsruhe, Germany
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3
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Simić K, Todorović N, Trifunović S, Miladinović Z, Gavrilović A, Jovanović S, Avramović N, Gođevac D, Vujisić L, Tešević V, Tasić L, Mandić B. NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort. Metabolites 2022; 12:707. [PMID: 36005580 PMCID: PMC9416612 DOI: 10.3390/metabo12080707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 02/04/2023] Open
Abstract
Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.
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Affiliation(s)
- Katarina Simić
- Institute of Chemistry, Technology and Metallurgy, National Institute, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia; (K.S.); (N.T.); (D.G.)
| | - Nina Todorović
- Institute of Chemistry, Technology and Metallurgy, National Institute, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia; (K.S.); (N.T.); (D.G.)
| | - Snežana Trifunović
- University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11000 Belgrade, Serbia; (S.T.); (L.V.); (V.T.)
| | - Zoran Miladinović
- Institute of General and Physical Chemistry, Studentski trg 12-16, 11158 Belgrade, Serbia;
| | - Aleksandra Gavrilović
- Special Hospital for Psychiatric Diseases “Kovin”, Cara Lazara 253, 26220 Kovin, Serbia; (A.G.); (S.J.)
| | - Silvana Jovanović
- Special Hospital for Psychiatric Diseases “Kovin”, Cara Lazara 253, 26220 Kovin, Serbia; (A.G.); (S.J.)
| | - Nataša Avramović
- Institute of Medical Chemistry, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia;
| | - Dejan Gođevac
- Institute of Chemistry, Technology and Metallurgy, National Institute, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia; (K.S.); (N.T.); (D.G.)
| | - Ljubodrag Vujisić
- University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11000 Belgrade, Serbia; (S.T.); (L.V.); (V.T.)
| | - Vele Tešević
- University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11000 Belgrade, Serbia; (S.T.); (L.V.); (V.T.)
| | - Ljubica Tasić
- Institute of Chemistry, Organic Chemistry Department, State University of Campinas, Campinas 13083-970, SP, Brazil;
| | - Boris Mandić
- University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11000 Belgrade, Serbia; (S.T.); (L.V.); (V.T.)
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4
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Decker C, Krapf R, Kuballa T, Bunzel M. Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:7230-7239. [PMID: 35648805 DOI: 10.1021/acs.jafc.2c01871] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The animal species of raw meat and processed meat products was determined by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on comprehensive lipid extraction to capture nonpolar and polar (amphiphilic) fat components of meat. A nontargeted approach was used to analyze the 1H NMR data, followed by a principal component analysis, linear discrimination analysis, and cross-validation embedded in a Monte Carlo re-sampling approach. A total of 437 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, and poultry) were collected to build and/or test the classification model. On average, 98% of the analyzed raw meat samples and 97% of the processed meat products were correctly classified with respect to meat species. Furthermore, relevant spectral regions to identify potential chemical markers such as linoleic acids, trans-fatty acids, and cholesterol for the meat species classification were described.
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Affiliation(s)
- Christina Decker
- Department of Food Chemistry and Phytochemistry, Karlsruhe Institute of Technology (KIT), Adenauerring 20A, D-76131 Karlsruhe, Germany
- Bosch Power Tools, Max-Lang-Straße 40-46, D-70771 Leinfelden-Echterdingen, Germany
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weißenburger Straße 3, D-76187 Karlsruhe, Germany
| | - Reiner Krapf
- Bosch Power Tools, Max-Lang-Straße 40-46, D-70771 Leinfelden-Echterdingen, Germany
| | - Thomas Kuballa
- Chemisches und Veterinäruntersuchungsamt (CVUA) Karlsruhe, Weißenburger Straße 3, D-76187 Karlsruhe, Germany
| | - Mirko Bunzel
- Department of Food Chemistry and Phytochemistry, Karlsruhe Institute of Technology (KIT), Adenauerring 20A, D-76131 Karlsruhe, Germany
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Debik J, Sangermani M, Wang F, Madssen TS, Giskeødegård GF. Multivariate analysis of NMR-based metabolomic data. NMR IN BIOMEDICINE 2022; 35:e4638. [PMID: 34738674 DOI: 10.1002/nbm.4638] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/08/2021] [Accepted: 09/29/2021] [Indexed: 06/13/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.
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Affiliation(s)
- Julia Debik
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Matteo Sangermani
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Feng Wang
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
| | - Torfinn S Madssen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
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6
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Nandy D, Craig SJC, Cai J, Tian Y, Paul IM, Savage JS, Marini ME, Hohman EE, Reimherr ML, Patterson AD, Makova KD, Chiaromonte F. Metabolomic profiling of stool of two-year old children from the INSIGHT study reveals links between butyrate and child weight outcomes. Pediatr Obes 2022; 17:e12833. [PMID: 34327846 PMCID: PMC8647636 DOI: 10.1111/ijpo.12833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/11/2021] [Accepted: 06/09/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Metabolomic analysis is commonly used to understand the biological underpinning of diseases such as obesity. However, our knowledge of gut metabolites related to weight outcomes in young children is currently limited. OBJECTIVES To (1) explore the relationships between metabolites and child weight outcomes, (2) determine the potential effect of covariates (e.g., child's diet, maternal health/habits during pregnancy, etc.) in the relationship between metabolites and child weight outcomes, and (3) explore the relationship between selected gut metabolites and gut microbiota abundance. METHODS Using 1 H-NMR, we quantified 30 metabolites from stool samples of 170 two-year-old children. To identify metabolites and covariates associated with children's weight outcomes (BMI [weight/height2 ], BMI z-score [BMI adjusted for age and sex], and growth index [weight/height]), we analysed the 1 H-NMR data, along with 20 covariates recorded on children and mothers, using LASSO and best subset selection regression techniques. Previously characterized microbiota community information from the same stool samples was used to determine associations between selected gut metabolites and gut microbiota. RESULTS At age 2 years, stool butyrate concentration had a significant positive association with child BMI (p-value = 3.58 × 10-4 ), BMI z-score (p-value = 3.47 × 10-4 ), and growth index (p-value = 7.73 × 10-4 ). Covariates such as maternal smoking during pregnancy are important to consider. Butyrate concentration was positively associated with the abundance of the bacterial genus Faecalibacterium (p-value = 9.61 × 10-3 ). CONCLUSIONS Stool butyrate concentration is positively associated with increased child weight outcomes and should be investigated further as a factor affecting childhood obesity.
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Affiliation(s)
- Debmalya Nandy
- Department of StatisticsPenn State UniversityUniversity ParkPAUSA,Present address:
Department of Biostatistics and Informatics, Colorado School of Public HealthUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Sarah J. C. Craig
- Department of BiologyPenn State UniversityUniversity ParkPAUSA,Center for Medical GenomicsPenn State UniversityUniversity ParkPAUSA
| | - Jingwei Cai
- Department of Molecular ToxicologyPenn State UniversityUniversity ParkPAUSA,Present address:
Department of Drug Metabolism and PharmacokineticsGenentech Inc.South San FranciscoCaliforniaUSA
| | - Yuan Tian
- Department of Molecular ToxicologyPenn State UniversityUniversity ParkPAUSA
| | - Ian M. Paul
- Center for Medical GenomicsPenn State UniversityUniversity ParkPAUSA,Department of PediatricsPenn State College of MedicineHersheyPAUSA
| | - Jennifer S. Savage
- Department of Nutritional SciencesPenn State UniversityUniversity ParkPAUSA,Center for Childhood Obesity ResearchPenn State UniversityUniversity ParkPAUSA
| | - Michele E. Marini
- Center for Childhood Obesity ResearchPenn State UniversityUniversity ParkPAUSA
| | - Emily E. Hohman
- Center for Childhood Obesity ResearchPenn State UniversityUniversity ParkPAUSA
| | - Matthew L. Reimherr
- Department of StatisticsPenn State UniversityUniversity ParkPAUSA,Center for Medical GenomicsPenn State UniversityUniversity ParkPAUSA
| | - Andrew D. Patterson
- Department of Molecular ToxicologyPenn State UniversityUniversity ParkPAUSA,Department of Biochemistry & Molecular BiologyPenn State UniversityUniversity ParkPAUSA
| | - Kateryna D. Makova
- Department of BiologyPenn State UniversityUniversity ParkPAUSA,Center for Medical GenomicsPenn State UniversityUniversity ParkPAUSA
| | - Francesca Chiaromonte
- Department of StatisticsPenn State UniversityUniversity ParkPAUSA,Center for Medical GenomicsPenn State UniversityUniversity ParkPAUSA,Institute of EconomicsEMbeDS, Sant'Anna School of Advanced StudiesPisaItaly
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7
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Metabolic Fluctuations in the Human Stool Obtained from Blastocystis Carriers and Non-Carriers. Metabolites 2021; 11:metabo11120883. [PMID: 34940641 PMCID: PMC8708241 DOI: 10.3390/metabo11120883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/26/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Blastocystis is an obligate anaerobic microbial eukaryote that frequently inhabits the gastrointestinal tract. Despite this prevalence, very little is known about the extent of its genetic diversity, pathogenicity, and interaction with the rest of the microbiome and its host. Although the organism is morphologically static, it has no less than 28 genetically distinct subtypes (STs). Reports on the pathogenicity of Blastocystis are conflicting. The association between Blastocystis and intestinal bacterial communities is being increasingly explored. Nonetheless, similar investigations extending to the metabolome are non-existent.Using established NMR metabolomics protocols in 149 faecal samples from individuals from South Korea (n = 38), Thailand (n = 44) and Turkey (n = 69), we have provided a snapshot of the core metabolic compounds present in human stools with (B+) and without (B−) Blastocystis. Samples included hosts with gastrointestinal symptoms and asymptomatics. A total of nine, 62 and 98 significant metabolites were associated with Blastocystis carriage in the South Korean, Thai and Turkish sample sets respectively, with a number of metabolites increased in colonised groups. The metabolic profiles of B+ and B− samples from all countries were distinct and grouped separately in the partial least squares-discriminant analysis (PLS-DA). Typical inflammation-related metabolites negatively associated with Blastocystis positive samples. This data will assist in directing future studies underlying the involvement of Blastocystis in physiological processes of both the gut microbiome and the host. Future studies using metabolome and microbiome data along with host physiology and immune responses information will contribute significantly towards elucidating the role of Blastocystis in health and disease.
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Mohammad S, Bhattacharjee J, Vasanthan T, Harris CS, Bainbridge SA, Adamo KB. Metabolomics to understand placental biology: Where are we now? Tissue Cell 2021; 73:101663. [PMID: 34653888 DOI: 10.1016/j.tice.2021.101663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/16/2022]
Abstract
Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.
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Affiliation(s)
- S Mohammad
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - J Bhattacharjee
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - T Vasanthan
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - C S Harris
- Department of Biology & Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada
| | - S A Bainbridge
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, ON, Canada; Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, ON, Canada
| | - K B Adamo
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
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9
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Zamora Obando HR, Duarte GHB, Simionato AVC. Metabolomics Data Treatment: Basic Directions of the Full Process. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:243-264. [PMID: 34628635 DOI: 10.1007/978-3-030-77252-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.
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Affiliation(s)
- Hans Rolando Zamora Obando
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
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10
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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11
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Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis. Metabolites 2021; 11:metabo11050285. [PMID: 33947160 PMCID: PMC8145719 DOI: 10.3390/metabo11050285] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 02/06/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
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12
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Martins JG, Oliveira LES, Weingaertner D, Barison A, Oliveira GAR, Lião LM. A database for automatic classification of gender in Araucaria angustifolia plants. Soft comput 2021. [DOI: 10.1007/s00500-020-05551-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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13
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A Metabolomic Approach to Beer Characterization. Molecules 2021; 26:molecules26051472. [PMID: 33800512 PMCID: PMC7962951 DOI: 10.3390/molecules26051472] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/04/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022] Open
Abstract
The consumers’ interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of “beeromics” has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. 1H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.
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Augustijn D, de Groot HJM, Alia A. HR-MAS NMR Applications in Plant Metabolomics. Molecules 2021; 26:molecules26040931. [PMID: 33578691 PMCID: PMC7916392 DOI: 10.3390/molecules26040931] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 12/24/2022] Open
Abstract
Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.
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Affiliation(s)
- Dieuwertje Augustijn
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Correspondence: (D.A.); (A.A.)
| | - Huub J. M. de Groot
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
| | - A. Alia
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Institute of Medical Physics and Biophysics, University of Leipzig, Härtelstr. 16–17, D-04107 Leipzig, Germany
- Correspondence: (D.A.); (A.A.)
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15
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Ragone R, Todisco S, Triggiani M, Pontrelli S, Latronico M, Mastrorilli P, Intini N, Ferroni C, Musio B, Gallo V. Development of a food class-discrimination system by non-targeted NMR analyses using different magnetic field strengths. Food Chem 2020; 332:127339. [DOI: 10.1016/j.foodchem.2020.127339] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/11/2020] [Accepted: 06/12/2020] [Indexed: 12/13/2022]
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16
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Gauthier L, Tison-Rosebery J, Morin S, Mazzella N. Metabolome response to anthropogenic contamination on microalgae: a review. Metabolomics 2019; 16:8. [PMID: 31863210 DOI: 10.1007/s11306-019-1628-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 12/08/2019] [Indexed: 01/22/2023]
Abstract
BACKGROUND Microalgae play a key role in ecosystems and are widely used in ecological status assessment. Research focusing on such organisms is then well developed and essential. Anyway, approaches for a better comprehension of their metabolome's response towards anthropogenic stressors are only emerging. AIM OF REVIEW This review presents the biochemical responses of various microalgae species towards several contaminants including metals and chemicals as pesticides or industrial compounds. We aim to provide a comprehensive and up-to-date overview of analytical approaches deciphering anthropogenic contaminants impact on microalgae metabolome dynamics, in order to bring out relevant biochemical markers that could be used for risk assessment. KEY SCIENTIFIC CONCEPTS OF REVIEW Studies to date on ecotoxicological metabolomics on microalgae are highly heterogeneous in both analytical techniques and resulting metabolite identification. There is a real need for studies using complementary approaches to determine biomarkers usable for ecological risk assessment.
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Affiliation(s)
- Léa Gauthier
- IRSTEA, UR EABX, 50 Avenue de Verdun, 33612, Cestas Cedex, France.
| | | | - Soizic Morin
- IRSTEA, UR EABX, 50 Avenue de Verdun, 33612, Cestas Cedex, France
| | - Nicolas Mazzella
- IRSTEA, UR EABX, 50 Avenue de Verdun, 33612, Cestas Cedex, France
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17
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Zhao X, Niu L, Clerici C, Russo R, Byrd M, Setchell KD. Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods. CLINICAL MASS SPECTROMETRY (DEL MAR, CALIF.) 2019; 13:5-17. [PMID: 34841080 PMCID: PMC8620525 DOI: 10.1016/j.clinms.2019.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 05/15/2019] [Accepted: 05/15/2019] [Indexed: 11/28/2022]
Abstract
Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.
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Affiliation(s)
- Xueheng Zhao
- Division of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Liang Niu
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Carlo Clerici
- Clinica Di Gastroenterologia – Endoscopia – Epatologia, Policlinico, S. Maria Della Misericordia Azienda Ospedaliera Di Perugia, Italy
| | - Roberta Russo
- Clinica Di Gastroenterologia – Endoscopia – Epatologia, Policlinico, S. Maria Della Misericordia Azienda Ospedaliera Di Perugia, Italy
| | - Melissa Byrd
- Division of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Kenneth D.R. Setchell
- Division of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
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18
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Stocchero M, Locci E, d'Aloja E, Nioi M, Baraldi E, Giordano G. PLS2 in Metabolomics. Metabolites 2019; 9:E51. [PMID: 30884746 PMCID: PMC6468483 DOI: 10.3390/metabo9030051] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 03/04/2019] [Accepted: 03/06/2019] [Indexed: 12/12/2022] Open
Abstract
Metabolomics is the systematic study of the small-molecule profiles of biological samples produced by specific cellular processes. The high-throughput technologies used in metabolomic investigations generate datasets where variables are strongly correlated and redundancy is present in the data. Discovering the hidden information is a challenge, and suitable approaches for data analysis must be employed. Projection to latent structures regression (PLS) has successfully solved a large number of problems, from multivariate calibration to classification, becoming a basic tool of metabolomics. PLS2 is the most used implementation of PLS. Despite its success, PLS2 showed some limitations when the so called 'structured noise' affects the data. Suitable methods have been recently introduced to patch up these limitations. In this study, a comprehensive and up-to-date presentation of PLS2 focused on metabolomics is provided. After a brief discussion of the mathematical framework of PLS2, the post-transformation procedure is introduced as a basic tool for model interpretation. Orthogonally-constrained PLS2 is presented as strategy to include constraints in the model according to the experimental design. Two experimental datasets are investigated to show how PLS2 and its improvements work in practice.
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Affiliation(s)
- Matteo Stocchero
- Department of Women's and Children's Health, University of Padova, 35128 Padova (PD), Italy.
- Fondazione Istituto di Ricerca Pediatrica Città della Speranza, 35129 Padova (PD), Italy.
| | - Emanuela Locci
- Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato (CA), Italy.
| | - Ernesto d'Aloja
- Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato (CA), Italy.
| | - Matteo Nioi
- Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato (CA), Italy.
| | - Eugenio Baraldi
- Department of Women's and Children's Health, University of Padova, 35128 Padova (PD), Italy.
- Fondazione Istituto di Ricerca Pediatrica Città della Speranza, 35129 Padova (PD), Italy.
| | - Giuseppe Giordano
- Department of Women's and Children's Health, University of Padova, 35128 Padova (PD), Italy.
- Fondazione Istituto di Ricerca Pediatrica Città della Speranza, 35129 Padova (PD), Italy.
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19
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Abstract
Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (1H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that 1H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, 1H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for 1H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for 1H NMR experiments, spectral acquisition and processing, and quantitative profiling of 1H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.
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20
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Augustijn D, van Tol N, van der Zaal BJ, de Groot HJM, Alia A. High-resolution magic angle spinning NMR studies for metabolic characterization of Arabidopsis thaliana mutants with enhanced growth characteristics. PLoS One 2018; 13:e0209695. [PMID: 30596736 PMCID: PMC6312362 DOI: 10.1371/journal.pone.0209695] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 12/10/2018] [Indexed: 02/07/2023] Open
Abstract
Developing smart crops which yield more biomass to meet the increasing demand for plant biomass has been an active area of research in last few decades. We investigated metabolic alterations in two Arabidopsis thaliana mutants with enhanced growth characteristics that were previously obtained from a collection of plant lines expressing artificial transcription factors. The metabolic profiles were obtained directly from intact Arabidopsis leaves using high-resolution magic angle spinning (HR-MAS) NMR. Multivariate analysis showed significant alteration of metabolite levels between the mutants and the wild-type Col-0. Interestingly, most of the metabolites that were reduced in the faster-growing mutants are generally involved in the defence against stress. These results suggest a growth-defence trade-off in the phenotypically engineered mutants. Our results further corroborate the idea that plant growth can be enhanced by suppressing defence pathways.
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Affiliation(s)
| | - Niels van Tol
- Institute of Biology Leiden, Leiden University, BE, Leiden, The Netherlands
| | | | - Huub J. M. de Groot
- Leiden Institute of Chemistry, Leiden University, RA Leiden, The Netherlands
| | - A. Alia
- Leiden Institute of Chemistry, Leiden University, RA Leiden, The Netherlands
- Institute of Medical Physics and Biophysics, University of Leipzig, Leipzig, Germany
- * E-mail:
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21
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Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. J Pharm Biomed Anal 2018; 161:313-325. [PMID: 30195171 DOI: 10.1016/j.jpba.2018.08.046] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/22/2018] [Accepted: 08/23/2018] [Indexed: 12/16/2022]
Abstract
Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.
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22
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Martin M, Legat B, Leenders J, Vanwinsberghe J, Rousseau R, Boulanger B, Eilers PH, De Tullio P, Govaerts B. PepsNMR for 1H NMR metabolomic data pre-processing. Anal Chim Acta 2018; 1019:1-13. [DOI: 10.1016/j.aca.2018.02.067] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 02/15/2018] [Accepted: 02/27/2018] [Indexed: 11/29/2022]
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23
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An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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24
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Euceda LR, Haukaas TH, Bathen TF, Giskeødegård GF. Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles. Methods Mol Biol 2018; 1711:167-189. [PMID: 29344890 DOI: 10.1007/978-1-4939-7493-1_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.
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Affiliation(s)
- Leslie R Euceda
- Department of Circulation and Medical Imaging - MR Center, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Postboks 8905, MTFS, 7489, Trondheim, Norway
| | - Tonje H Haukaas
- Department of Circulation and Medical Imaging - MR Center, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Postboks 8905, MTFS, 7489, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging - MR Center, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Postboks 8905, MTFS, 7489, Trondheim, Norway
| | - Guro F Giskeødegård
- Department of Circulation and Medical Imaging - MR Center, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Postboks 8905, MTFS, 7489, Trondheim, Norway.
- St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
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25
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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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26
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Abstract
NMR-based metabolomics is an established technique for characterizing the metabolite profile of biological fluids and investigating how metabolite profiles change in response to biological and/or clinical stimuli. Thus, NMR-based metabolomics has the potential to discover biomarkers for diagnosis, prognosis, and/or therapy of clinical conditions, as well as to unravel the physiology underlying clinical conditions. Here, we describe a detailed protocol for NMR-based metabolomics of oral biofluids, including sample collection, sample handling, NMR data acquisition, and processing. In addition, we give a general overview of the statistical analysis of the resulting metabolomic data.
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Affiliation(s)
- Horst Joachim Schirra
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Pauline J Ford
- School of Dentistry, Oral Health Centre, The University of Queensland, 288 Herston Road, Herston, QLD, 4006, Australia
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27
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Sethi S, Brietzke E. Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders. Int J Neuropsychopharmacol 2015; 19:pyv096. [PMID: 26453695 PMCID: PMC4815467 DOI: 10.1093/ijnp/pyv096] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 08/17/2015] [Indexed: 12/22/2022] Open
Abstract
One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.
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Affiliation(s)
| | - Elisa Brietzke
- Interdisciplinary Laboratory for Clinical Neuroscience (LiNC), Department of Psychiatry, Universidade Federal de São Paulo - UNIFESP, São Paulo, Brazil.
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