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Niu L, Wang H, Luo G, Zhou J, Hu Z, Yan B. Advances in understanding immune homeostasis in latent tuberculosis infection. WIREs Mech Dis 2024; 16:e1643. [PMID: 38351551 DOI: 10.1002/wsbm.1643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/19/2024] [Accepted: 01/19/2024] [Indexed: 07/13/2024]
Abstract
Nearly one-fourth of the global population is infected by Mycobacterium tuberculosis (Mtb), and approximately 90%-95% remain asymptomatic as latent tuberculosis infection (LTBI), an estimated 5%-10% of those with latent infections will eventually progress to active tuberculosis (ATB). Although it is widely accepted that LTBI transitioning to ATB results from a disruption of host immune balance and a weakening of protective immune responses, the exact underlying immunological mechanisms that promote this conversion are not well characterized. Thus, it is difficult to accurately predict tuberculosis (TB) progression in advance, leaving the LTBI population as a significant threat to TB prevention and control. This article systematically explores three aspects related to the immunoregulatory mechanisms and translational research about LTBI: (1) the distinct immunocytological characteristics of LTBI and ATB, (2) LTBI diagnostic markers discovery related to host anti-TB immunity and metabolic pathways, and (3) vaccine development focus on LTBI. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology Infectious Diseases > Genetics/Genomics/Epigenetics Immune System Diseases > Genetics/Genomics/Epigenetics.
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Affiliation(s)
- Liangfei Niu
- Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
| | - Hao Wang
- Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, China
| | - Geyang Luo
- Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
| | - Jing Zhou
- Department of Pathology, Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
| | - Zhidong Hu
- Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
| | - Bo Yan
- Center for Tuberculosis Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, People's Republic of China
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2
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McCullagh J, Probert F. New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics. Curr Opin Chem Biol 2024; 80:102466. [PMID: 38772215 DOI: 10.1016/j.cbpa.2024.102466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/19/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024]
Abstract
Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.
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Affiliation(s)
- James McCullagh
- Department of Chemistry, University of Oxford, Mansfield Road, Oxford, OX1 3TA, UK.
| | - Fay Probert
- Department of Chemistry, University of Oxford, Mansfield Road, Oxford, OX1 3TA, UK
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3
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Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
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Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
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Luo D, Yang BY, Qin K, Shi CY, Wei NS, Li H, Qin YX, Liu G, Qin XL, Chen SY, Guo XJ, Gan L, Xu RL, Dong BQ, Li J. Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification. Infect Drug Resist 2023; 16:6121-6138. [PMID: 37719654 PMCID: PMC10505020 DOI: 10.2147/idr.s422363] [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: 06/15/2023] [Accepted: 08/31/2023] [Indexed: 09/19/2023] Open
Abstract
Purpose Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is important to control the prevalence of tuberculosis; however, there is currently no effective method. The aim of this study was to discover specific metabolites through fecal untargeted metabolomics to discriminate ATB, individuals with LTBI, and healthy controls (HC) and to probe the metabolic perturbation associated with the progression of tuberculosis. Patients and Methods Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to comprehensively detect compounds in fecal samples from HC, LTBI, and ATB patients. Differential metabolites between the two groups were screened, and their underlying biological functions were explored. Candidate metabolites were selected and enrolled in LASSO regression analysis to construct diagnostic signatures for discriminating between HC, LTBI, and ATB. A receiver operating characteristic (ROC) curve was applied to evaluate diagnostic value. A nomogram was constructed to predict the risk of progression of LTBI. Results A total of 35 metabolites were found to exist differentially in HC, LTBI, and ATB, and eight biomarkers were selected. Three diagnostic signatures based on the eight biomarkers were constructed to distinguish between HC, LTBI, and ATB, demonstrating excellent discrimination performance in ROC analysis. A nomogram was successfully constructed to evaluate the risk of progression of LTBI to ATB. Moreover, 3,4-dimethylbenzoic acid has been shown to distinguish ATB patients with different responses to etiological tests. Conclusion This study constructed diagnostic signatures based on fecal metabolic biomarkers that effectively discriminated HC, LTBI, and ATB, and established a predictive model to evaluate the risk of progression of LTBI to ATB. The results provide scientific evidence for establishing an accurate, sensitive, and noninvasive differential diagnosis scheme for tuberculosis.
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Affiliation(s)
- Dan Luo
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Translational Medicine for Treating High-Incidence Infectious Diseases with Integrative Medicine, Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Bo-Yi Yang
- The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Kai Qin
- The Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Chong-Yu Shi
- The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Nian-Sa Wei
- The Second Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Hai Li
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Yi-Xiang Qin
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Gang Liu
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Xiao-Ling Qin
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Shi-Yi Chen
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Xiao-Jing Guo
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Li Gan
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Ruo-Lan Xu
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Bai-Qing Dong
- Department of Biostatistics, School of Public Health and Management of Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Jing Li
- Deparment of Physiology, School of Basic Medical Sciences of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
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Galvan D, de Aguiar LM, Bona E, Marini F, Killner MHM. Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review. Anal Chim Acta 2023; 1273:341495. [PMID: 37423658 DOI: 10.1016/j.aca.2023.341495] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/20/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.
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Affiliation(s)
- Diego Galvan
- Chemistry Institute, Universidade Federal de Mato Grosso do Sul (UFMS), 79070-900, Campo Grande, MS, Brazil; Chemistry Departament, Universidade Estadual de Londrina (UEL), 86.057-970, Londrina, PR, Brazil.
| | | | - Evandro Bona
- Post-Graduation Program of Food Technology (PPGTA), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Campo Mourão, 87301-899, Campo Mourão, PR, Brazil; Post-Graduation Program of Chemistry (PPGQ), Universidade Tecnológica Federal do Paraná (UTFPR), Campus Curitiba, 80230-901, Curitiba, PR, Brazil
| | - Federico Marini
- Department of Chemistry, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Mário Henrique M Killner
- Chemistry Departament, Universidade Estadual de Londrina (UEL), 86.057-970, Londrina, PR, Brazil
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6
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Alonso-Moreno P, Rodriguez I, Izquierdo-Garcia JL. Benchtop NMR-Based Metabolomics: First Steps for Biomedical Application. Metabolites 2023; 13:metabo13050614. [PMID: 37233655 DOI: 10.3390/metabo13050614] [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: 12/30/2022] [Revised: 04/19/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Nuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.
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Affiliation(s)
- Pilar Alonso-Moreno
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Ignacio Rodriguez
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040 Madrid, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Jose Luis Izquierdo-Garcia
- NMR and Imaging in Biomedicine Group, Instituto Pluridisciplinar, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Chemistry in Pharmaceutical Sciences, Pharmacy School, Universidad Complutense de Madrid, 28040 Madrid, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
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7
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Song Z, Ohnishi Y, Osada S, Gan L, Jiang J, Hu Z, Kumeta H, Kumaki Y, Yokoi Y, Nakamura K, Ayabe T, Yamauchi K, Aizawa T. Application of Benchtop NMR for Metabolomics Study Using Feces of Mice with DSS-Induced Colitis. Metabolites 2023; 13:metabo13050611. [PMID: 37233652 DOI: 10.3390/metabo13050611] [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: 03/24/2023] [Revised: 04/22/2023] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
Nuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz 1H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.
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Affiliation(s)
- Zihao Song
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Yuki Ohnishi
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | | | - Li Gan
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Jiaxi Jiang
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Zhiyan Hu
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Hiroyuki Kumeta
- Advanced NMR Facility, Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Yasuhiro Kumaki
- High-Resolution NMR Laboratory, Graduate School of Science, Hokkaido University, Sapporo 060-0810, Japan
| | - Yuki Yokoi
- Innate Immunity Laboratory, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Kiminori Nakamura
- Innate Immunity Laboratory, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Tokiyoshi Ayabe
- Innate Immunity Laboratory, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
| | - Kazuo Yamauchi
- Instrumental Analysis Section, Okinawa Institute of Science and Technology, Onna 904-0495, Japan
| | - Tomoyasu Aizawa
- Laboratory of Protein Science, Graduate School of Life Science, Hokkaido University, Sapporo 060-0808, Japan
- Advanced NMR Facility, Faculty of Advanced Life Science, Hokkaido University, Sapporo 060-0808, Japan
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8
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 112] [Impact Index Per Article: 112.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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9
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109
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10
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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11
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Yu Y, Jiang XX, Li JC. Biomarker discovery for tuberculosis using metabolomics. Front Mol Biosci 2023; 10:1099654. [PMID: 36891238 PMCID: PMC9986447 DOI: 10.3389/fmolb.2023.1099654] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.
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Affiliation(s)
- Yi Yu
- Center for Analyses and Measurements, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xin-Xin Jiang
- Clinical Research Laboratory, Shaoxing Seventh People's Hospital, Shaoxing, China
| | - Ji-Cheng Li
- Clinical Research Laboratory, Shaoxing Seventh People's Hospital, Shaoxing, China.,Institute of Cell Biology, Zhejiang University Medical School, Hangzhou, China
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12
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Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research. Metabolites 2022; 12:metabo12100963. [PMID: 36295865 PMCID: PMC9609461 DOI: 10.3390/metabo12100963] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.
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Stojanovic Z, Gonçalves-Carvalho F, Marín A, Abad Capa J, Domínguez J, Latorre I, Lacoma A, Prat-Aymerich C. Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms? ERJ Open Res 2022; 8:00113-2022. [PMID: 36101788 PMCID: PMC9235056 DOI: 10.1183/23120541.00113-2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/31/2022] [Indexed: 11/05/2022] Open
Abstract
Respiratory tract infections (RTIs) are one of the most common reasons for seeking healthcare, but are amongst the most challenging diseases in terms of clinical decision-making. Proper and timely diagnosis is critical in order to optimise management and prevent further emergence of antimicrobial resistance by misuse or overuse of antibiotics. Diagnostic tools for RTIs include those involving syndromic and aetiological diagnosis: from clinical and radiological features to laboratory methods targeting both pathogen detection and host biomarkers, as well as their combinations in terms of clinical algorithms. They also include tools for predicting severity and monitoring treatment response. Unprecedented milestones have been achieved in the context of the COVID-19 pandemic, involving the most recent applications of diagnostic technologies both at genotypic and phenotypic level, which have changed paradigms in infectious respiratory diseases in terms of why, how and where diagnostics are performed. The aim of this review is to discuss advances in diagnostic tools that impact clinical decision-making, surveillance and follow-up of RTIs and tuberculosis. If properly harnessed, recent advances in diagnostic technologies, including omics and digital transformation, emerge as an unprecedented opportunity to tackle ongoing and future epidemics while handling antimicrobial resistance from a One Health perspective.
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Affiliation(s)
- Zoran Stojanovic
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Co-first authors
| | - Filipe Gonçalves-Carvalho
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Co-first authors
| | - Alicia Marín
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Jorge Abad Capa
- Pneumology Dept, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Jose Domínguez
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
| | - Irene Latorre
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
| | - Alicia Lacoma
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
- Co-senior authors
| | - Cristina Prat-Aymerich
- Ciber Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Microbiology Department, Institut d'Investigació Germans Trias i Pujol, Badalona, Spain
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Co-senior authors
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First-Void Urine Microbiome in Women with Chlamydia trachomatis Infection. Int J Mol Sci 2022; 23:ijms23105625. [PMID: 35628436 PMCID: PMC9143427 DOI: 10.3390/ijms23105625] [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: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/16/2022] [Indexed: 11/30/2022] Open
Abstract
Background: Chlamydia trachomatis (CT) is the agent of the most common bacterial sexually transmitted infection worldwide. Until now, little information is available about the microbial composition of urine samples during CT urethritis. Therefore, in this study, we characterized the microbiome and metabolome profiles of first-void urines in a cohort of women with CT urethral infection attending an STI clinic. Methods: Based on CT positivity by nucleic acid amplification techniques on urine samples, the enrolled women were divided into two groups, i.e., “CT-negative” (n = 21) and “CT-positive” (n = 11). Urine samples were employed for (i) the microbiome profile analysis by means of 16s rRNA gene sequencing and (ii) the metabolome analysis by 1H-NMR. Results: Irrespective of CT infection, the microbiome of first-void urines was mainly dominated by Lactobacillus, L. iners and L. crispatus being the most represented species. CT-positive samples were characterized by reduced microbial biodiversity compared to the controls. Moreover, a significant reduction of the Mycoplasmataceae family—in particular, of the Ureaplasma parvum species—was observed during CT infection. The Chlamydia genus was positively correlated with urine hippurate and lactulose. Conclusions: These data can help elucidate the pathogenesis of chlamydial urogenital infections, as well as to set up innovative diagnostic and therapeutic approaches.
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Artemisinin Alleviates Intestinal Inflammation and Metabolic Disturbance in Ulcerative Colitis Rats Induced by DSS. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:6211215. [PMID: 35497913 PMCID: PMC9042626 DOI: 10.1155/2022/6211215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/08/2022] [Indexed: 12/11/2022]
Abstract
Objective This study is aimed to reveal the possible mechanisms of artemisinin in the treatment of ulcerative colitis (UC) through bioinformatics analysis and experimental verification in UC model rats. Methods Firstly, we searched two microarray data of the Gene Expression Omnibus (GEO) database to explore the differentially expressed genes (DEGs) between UC samples and normal samples. Then, we selected DEGs for gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The acute UC model of rats was established by using 3.5% dextran sulfate sodium (DSS) for 10 days to verify the core pathway. Finally, we evaluated the therapeutic effect of artemisinin at the molecular level and used metabonomics to study the endogenous metabolites in the rat serum. Results We screened in the GEO database and selected two eligible microarray datasets, GSE36807 and GSE9452. We performed GO function and KEGG pathway enrichment analyses of DEGs and found that these DEGs were mainly enriched in the inflammatory response, immune response, and IL-17 and NF-κB signaling pathways. Finally, we verified the IL-17 signaling pathway and key cytokines, and ELISA and immunohistochemical results showed that artemisinin could downregulate the expression of proinflammatory cytokines such as IL-1β and IL-17 in the IL-17 signaling pathway and upregulate the expression of the anti-inflammatory cytokine PPAR-γ. Metabolomics analysis showed that 33 differential metabolites were identified in the artemisinin group (AG) compared to the model group (MG). Differential metabolites were mainly involved in alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies. Conclusion In this study, we found that artemisinin can significantly inhibit the inflammatory response in UC rats and regulate metabolites and related metabolic pathways. This study provides a foundation for further research on the mechanism of artemisinin in the treatment of UC.
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NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062824] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.
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Wasito H, Causon T, Hann S. Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics. Talanta 2022; 236:122828. [PMID: 34635218 DOI: 10.1016/j.talanta.2021.122828] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 02/07/2023]
Abstract
Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.
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Affiliation(s)
- Hendri Wasito
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria; Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia
| | - Tim Causon
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria.
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18
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Ruiz-Cabello J, Sevilla IA, Olaizola E, Bezos J, Miguel-Coello AB, Muñoz-Mendoza M, Beraza M, Garrido JM, Izquierdo-García JL. Benchtop nuclear magnetic resonance-based metabolomic approach for the diagnosis of bovine tuberculosis. Transbound Emerg Dis 2021; 69:e859-e870. [PMID: 34717039 DOI: 10.1111/tbed.14365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/23/2021] [Accepted: 10/18/2021] [Indexed: 11/28/2022]
Abstract
Even though enormous efforts and control strategies have been implemented, bovine tuberculosis (TB) remains a significant source of health and socioeconomic concern. The standard method used in TB eradication programs for in vivo detection is the tuberculin skin test. However, the specificity of the tuberculin skin test is affected by infection with non-tuberculous mycobacteria or by vaccination. Thus, some animals are not correctly diagnosed. This study aimed first to identify a plasma metabolic TB profile by high-field (HF) nuclear magnetic resonance (NMR) spectroscopy and second measure this characteristic TB metabolic profile using low-field benchtop (LF) NMR as an affordable molecular technology for TB diagnosis. Plasma samples from cattle diagnosed with TB (derivation set, n = 11), diagnosed with paratuberculosis (PTB, n = 10), PTB-vaccinated healthy control (n = 10) and healthy PTB-unvaccinated control (n = 10) were analyzed by NMR. Unsupervised Principal Component Analysis (PCA) was used to identify metabolic differences between groups. We identified 14 metabolites significantly different between TB and control animals. The second group of TB animals was used to validate the results (validation set, n = 14). Predictive models based on metabolic fingerprint acquired by both HF and LF NMR spectroscopy successfully identified TB versus control subjects (Area under the curve of Receiver Operating Characteristic over 0.92, in both models; Confidence Interval 0.77-1). In summary, plasma fingerprinting using HF and LF-NMR differentiated TB subjects from uninfected animals, and PTB and PTB-vaccinated subjects who may provide a TB-false positive, highlighting the use of LF-NMR-based metabolomics as a complementary or alternative diagnostic tool to the current diagnostic methods.
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Affiliation(s)
- Jesús Ruiz-Cabello
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain.,Departamento de Química en Ciencias Farmacéuticas, Universidad Complutense de Madrid. Facultad de Farmacia, Madrid, Spain.,Instituto de Salud Carlos III, CIBER de enfermedades respiratorias (CIBERES), Madrid, Spain.,Basque Foundation for Science, IKERBASQUE, Bilbao, Vizcaya, Spain
| | - Iker A Sevilla
- Basque Research and Technology Alliance (BRTA), Animal Health Department, NEIKER-Basque Institute for Agricultural Research and Development, Derio, Bizkaia, Spain
| | - Ekine Olaizola
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Javier Bezos
- Departamento de Sanidad Animal y Centro de Vigilancia Sanitaria Veterinaria (VISAVET), Universidad Complutense de Madrid. Facultad de Veterinaria, Madrid, Spain
| | - Ana B Miguel-Coello
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Marta Muñoz-Mendoza
- Servicio de Sanidad Animal, Xunta de Galicia, Consellería de Medio Rural, Santiago de Compostela, Spain
| | - Marta Beraza
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Joseba M Garrido
- Basque Research and Technology Alliance (BRTA), Animal Health Department, NEIKER-Basque Institute for Agricultural Research and Development, Derio, Bizkaia, Spain
| | - Jose L Izquierdo-García
- Departamento de Química en Ciencias Farmacéuticas, Universidad Complutense de Madrid. Facultad de Farmacia, Madrid, Spain.,Instituto de Salud Carlos III, CIBER de enfermedades respiratorias (CIBERES), Madrid, Spain.,Instituto Pluridisciplinar, Universidad Complutense de Madrid, Madrid, Spain
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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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Palma M, Bledsoe JW, Tavares LC, Romano N, Small BC, Viegas I, Overturf K. Digesta and Plasma Metabolomics of Rainbow Trout Strains with Varied Tolerance of Plant-Based Diets Highlights Potential for Non-Lethal Assessments of Enteritis Development. Metabolites 2021; 11:metabo11090590. [PMID: 34564406 PMCID: PMC8470503 DOI: 10.3390/metabo11090590] [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: 07/26/2021] [Revised: 08/26/2021] [Accepted: 08/28/2021] [Indexed: 01/12/2023] Open
Abstract
The replacement of fishmeal in aquafeeds is essential to the sustainability of aquaculture. Besides the procurement of alternative protein sources, fish can also be selected for better performance on plant-based alternative diets. Rainbow trout (Oncorhynchus mykiss) is one such species in which the strain ARS-Sel has been selected for higher growth and enhanced utilization when fed soy-based diets. The aim of this study was to compare fish growth and plasma and digesta metabolomes between ARS-Sel and two commercial strains (CS-1 and CS-2), when fed plant-protein (PM) and fishmeal-based (FM) diets, and to correlate them with the onset of enteritis. An NMR-metabolomics approach was taken to assess plasma and digesta metabolite profiles. Diet and strain showed significant effects on fish growth, with the ARS-Sel fish receiving the PM diet reaching the highest final weight at sampling. Multivariate analysis revealed differences between plasma and digesta metabolite profiles of ARS-Sel and CS (CS-1 considered together with CS-2) PM-fed groups in the early stages of enteritis development, which was confirmed by intestinal histology. As reported in previous studies, the ARS-Sel strain performed better than the commercial strains when fed the PM diet. Our findings also suggest that metabolomic profiles of plasma and digesta, samples of which can be obtained through non-lethal methods, offer valuable insight in monitoring the occurrence of enteritis in carnivorous aquaculture species due to plant-based diets.
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Affiliation(s)
- Mariana Palma
- Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal;
| | - Jacob W. Bledsoe
- ARS-USDA, Hagerman Fish Culture Experiment Station, Hagerman, ID 83332, USA; (J.W.B.); (K.O.)
| | - Ludgero C. Tavares
- CIVG—Vasco da Gama Research Center, University School Vasco da Gama—EUVG, 3020-210 Coimbra, Portugal;
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-517 Coimbra, Portugal
| | - Nicholas Romano
- Center of Excellence in Aquaculture & Fisheries Center, University of Arkansas at Pine Bluff, Pine Bluff, AR 71601, USA;
| | - Brian C. Small
- Aquaculture Research Institute, Hagerman Fish Culture Experiment Station, University of Idaho, Hagerman, ID 83332, USA;
| | - Ivan Viegas
- Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal;
- Correspondence:
| | - Ken Overturf
- ARS-USDA, Hagerman Fish Culture Experiment Station, Hagerman, ID 83332, USA; (J.W.B.); (K.O.)
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21
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Comella-del-Barrio P, Izquierdo-Garcia JL, Gautier J, Doresca MJC, Campos-Olivas R, Santiveri CM, Muriel-Moreno B, Prat-Aymerich C, Abellana R, Pérez-Porcuna TM, Cuevas LE, Ruiz-Cabello J, Domínguez J. Urine NMR-based TB metabolic fingerprinting for the diagnosis of TB in children. Sci Rep 2021; 11:12006. [PMID: 34099838 PMCID: PMC8184981 DOI: 10.1038/s41598-021-91545-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/25/2021] [Indexed: 02/07/2023] Open
Abstract
Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.
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Affiliation(s)
- Patricia Comella-del-Barrio
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Izquierdo-Garcia
- grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.4795.f0000 0001 2157 7667Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain ,grid.424269.f0000 0004 1808 1283Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia, Spain
| | - Jacqueline Gautier
- Department of Pediatrics, Division of Tuberculosis, Hôpital Saint-Damien, Nos Petits-Frères Et Sœurs, Tabarre, Haiti
| | - Mariette Jean Coute Doresca
- Department of Pediatrics, Division of Tuberculosis, Hôpital Saint-Damien, Nos Petits-Frères Et Sœurs, Tabarre, Haiti
| | - Ramón Campos-Olivas
- grid.7719.80000 0000 8700 1153Spectroscopy and Nuclear Magnetic Resonance Unit, CNIO Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
| | - Clara M. Santiveri
- grid.7719.80000 0000 8700 1153Spectroscopy and Nuclear Magnetic Resonance Unit, CNIO Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
| | - Beatriz Muriel-Moreno
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain
| | - Cristina Prat-Aymerich
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.7692.a0000000090126352Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rosa Abellana
- grid.5841.80000 0004 1937 0247Department of Basic Clinical Practice, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Tomas M. Pérez-Porcuna
- grid.414875.b0000 0004 1794 4956Servei de Pediatria, Atenció Primària, Unitat de Investigació Fundació Mútua Terrassa, Hospital Universitari Mútua Terrassa, Terrassa, Spain
| | - Luis E. Cuevas
- grid.48004.380000 0004 1936 9764Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jesús Ruiz-Cabello
- grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.4795.f0000 0001 2157 7667Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain ,grid.424269.f0000 0004 1808 1283Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia, Spain ,grid.424810.b0000 0004 0467 2314IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - José Domínguez
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
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22
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Abstract
Nuclear magnetic resonance at low field strength is an insensitive spectroscopic technique, precluding portable applications with small sample volumes, such as needed for biomarker detection in body fluids. Here we report a compact double resonant chip stack system that implements in situ dynamic nuclear polarisation of a 130 nL sample volume, achieving signal enhancements of up to - 60 w.r.t. the thermal equilibrium level at a microwave power level of 0.5 W. This work overcomes instrumental barriers to the use of NMR detection for point-of-care applications.
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