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Xiao X, Wang Q, Chai X, Zhang X, Jiang B, Liu M. Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment. Commun Chem 2024; 7:167. [PMID: 39079950 PMCID: PMC11289489 DOI: 10.1038/s42004-024-01251-x] [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: 02/07/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered in blood samples. Here we proposed a method called spectral editing neural network, which can effectively edit and separate the spectral signals of small and macromolecules in 1H NMR spectra of serum and plasma based on the linewidth of the peaks. We applied the model to process the 1H NMR spectra of plasma and serum. The extracted small and macromolecular spectra were then compared with experimentally obtained relaxation-edited and diffusion-edited spectra. Correlation analysis demonstrated the quantitative capability of the model in the extracted small molecule signals from 1H NMR spectra. The principal component analysis showed that the spectra extracted by the model and those obtained by NMR spectral editing methods reveal similar group information, demonstrating the effectiveness of the model in signal extraction.
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Affiliation(s)
- Xiongjie Xiao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Qianqian Wang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Xin Chai
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Xu Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
- Optics Valley Laboratory, Wuhan, China
| | - Bin Jiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Optics Valley Laboratory, Wuhan, China.
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan National Laboratory for Optoelectronics, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement of Science and Technology, Chinese Academy of Sciences, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, China.
- Optics Valley Laboratory, Wuhan, China.
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Tsermoula P, Kristensen NB, Mobaraki N, Engelsen SRB, Khakimov B. Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum. Anal Chem 2024; 96:1861-1871. [PMID: 38277502 DOI: 10.1021/acs.analchem.3c03449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.
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Affiliation(s)
- Paraskevi Tsermoula
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
| | | | - Nabiollah Mobaraki
- Institute for Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig, Beethovenstraße 55, Braunschweig 38106, Germany
| | - So Ren B Engelsen
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
| | - Bekzod Khakimov
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
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Alexandersson E, Sandström C, Meijer J, Nestor G, Broberg A, Röhnisch HE. Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates. Metabolomics 2023; 20:11. [PMID: 38141081 DOI: 10.1007/s11306-023-02073-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy. OBJECTIVES To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals. METHODS An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix. RESULTS The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated. CONCLUSIONS The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.
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Affiliation(s)
- Elin Alexandersson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Johan Meijer
- Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Gustav Nestor
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Anders Broberg
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
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Akyol S, Ashrafi N, Yilmaz A, Turkoglu O, Graham SF. Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research. Metabolites 2023; 13:1203. [PMID: 38132886 PMCID: PMC10744751 DOI: 10.3390/metabo13121203] [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: 11/01/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.
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Affiliation(s)
- Sumeyya Akyol
- NX Prenatal Inc., 4350 Brownsboro Road, Louisville KY 40207, USA;
| | - Nadia Ashrafi
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
- Metabolomics Division, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Onur Turkoglu
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, 318 Meadow Brook Road, Rochester, MI 48309, USA; (N.A.); (A.Y.); (O.T.)
- Metabolomics Division, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
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Almalki AH. Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer. Metabolites 2023; 13:1037. [PMID: 37887362 PMCID: PMC10609104 DOI: 10.3390/metabo13101037] [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: 08/24/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
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Affiliation(s)
- Atiah H. Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
- Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Madrid-Gambin F, Oller S, Marco S, Pozo ÓJ, Andres-Lacueva C, Llorach R. Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment. Front Mol Biosci 2023; 10:1125582. [PMID: 37333016 PMCID: PMC10273206 DOI: 10.3389/fmolb.2023.1125582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 05/22/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.
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Affiliation(s)
- Francisco Madrid-Gambin
- Applied Metabolomics Research Group, IMIM—Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Sergio Oller
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Electronics and Biomedical Engineering, Faculty of Physics, University of Barcelona, Barcelona, Spain
| | - Santiago Marco
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Electronics and Biomedical Engineering, Faculty of Physics, University of Barcelona, Barcelona, Spain
| | - Óscar J. Pozo
- Applied Metabolomics Research Group, IMIM—Institut Hospital del Mar d’Investigacions Mèdiques, Barcelona, Spain
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Sant Coloma de Gramanet, Spain
- Food Innovation Network (XIA), Santa Coloma de Gramanet, Spain
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Santa Coloma de Gramanet, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Llorach
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Campus Torribera, University of Barcelona, Sant Coloma de Gramanet, Spain
- Food Innovation Network (XIA), Santa Coloma de Gramanet, Spain
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Santa Coloma de Gramanet, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
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Stabile M, Girelli CR, Lacitignola L, Samarelli R, Crovace A, Fanizzi FP, Staffieri F. 1H-NMR metabolomic profile of healthy and osteoarthritic canine synovial fluid before and after UC-II supplementation. Sci Rep 2022; 12:19716. [PMID: 36385297 PMCID: PMC9669020 DOI: 10.1038/s41598-022-23977-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022] Open
Abstract
The aim of the study was to compare the metabolomic synovial fluid (SF) profile of dogs affected by spontaneous osteoarthritis (OA) and supplemented with undenatured type II collagen (UC-II), with that of healthy control dogs. Client-owned dogs were enrolled in the study and randomized in two different groups, based on the presence/absence of OA (OA group and OA-free group). All dogs were clinically evaluated and underwent SF sampling for 1H-Nuclear Magnetic Resonance spectroscopy (1H-NMR) analysis at time of presentation. All dogs included in OA group were supplemented with UC-II orally administered for 30 days. After this period, they were reassessed (OA-T30). The differences in the 1H-NMR metabolic SFs profiles between groups (OA-free, OA-T0 and OA-T30) were studied. The multivariate statistical analysis performed on SFs under different conditions (OA-T0 vs OA-T30 SFs; OA-T0 vs OA-free SFs and OA-T30 vs OA-free SFs) gave models with excellent goodness of fit and predictive parameters, revealed by a marked separation between groups. β-Hydroxybutyrate was identified as a characteristic compound of osteoarthritic joints, showing the important role of fat metabolism during OA. The absence of β-hydroxybutyrate after UC-II supplementation suggests the supplement's effectiveness in rebalancing the metabolism inside the joint. The unexpectedly high level of lactate in the OA-free group suggests that lactate could not be considered a good marker for OA. These results prove that 1H-NMR-based metabolomic analysis is a valid tool to study and monitor OA and that UC-II improves clinical symptoms and the SF metabolic profile in OA dogs.
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Affiliation(s)
- Marzia Stabile
- grid.7644.10000 0001 0120 3326Section of Veterinary Clinics and Animal Production, Department of Emergency and Organ Transplantation, University of Bari, 70123 Bari, Italy
| | - Chiara Roberta Girelli
- grid.9906.60000 0001 2289 7785Department of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, Italy
| | - Luca Lacitignola
- grid.7644.10000 0001 0120 3326Section of Veterinary Clinics and Animal Production, Department of Emergency and Organ Transplantation, University of Bari, 70123 Bari, Italy
| | - Rossella Samarelli
- grid.7644.10000 0001 0120 3326Section of Avian Pathology, Department of Veterinary Medicine, University of Bari, 70123 Bari, Italy
| | - Antonio Crovace
- grid.7644.10000 0001 0120 3326Section of Veterinary Clinics and Animal Production, Department of Emergency and Organ Transplantation, University of Bari, 70123 Bari, Italy
| | - Francesco Paolo Fanizzi
- grid.9906.60000 0001 2289 7785Department of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, Italy
| | - Francesco Staffieri
- grid.7644.10000 0001 0120 3326Section of Veterinary Clinics and Animal Production, Department of Emergency and Organ Transplantation, University of Bari, 70123 Bari, Italy
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Blaurock J, Baumann S, Grunewald S, Schiller J, Engel KM. Metabolomics of Human Semen: A Review of Different Analytical Methods to Unravel Biomarkers for Male Fertility Disorders. Int J Mol Sci 2022; 23:ijms23169031. [PMID: 36012302 PMCID: PMC9409482 DOI: 10.3390/ijms23169031] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 12/01/2022] Open
Abstract
Background: Human life without sperm is not possible. Therefore, it is alarming that the fertilizing ability of human spermatozoa is continuously decreasing. The reasons for that are widely unknown, but there is hope that metabolomics-based investigations may be able to contribute to overcoming this problem. This review summarizes the attempts made so far. Methods: We will discuss liquid chromatography–mass spectrometry (LC-MS), gas chromatography (GC), infrared (IR) and Raman as well as nuclear magnetic resonance (NMR) spectroscopy. Almost all available studies apply one of these methods. Results: Depending on the methodology used, different compounds can be detected, which is (in combination with sophisticated methods of bioinformatics) helpful to estimate the state of the sperm. Often, but not in all cases, there is a correlation with clinical parameters such as the sperm mobility. Conclusions: LC-MS detects the highest number of metabolites and can be considered as the method of choice. Unfortunately, the reproducibility of some studies is poor, and, thus, further improvements of the study designs are needed to overcome this problem. Additionally, a stronger focus on the biochemical consequences of the altered metabolite concentrations is also required.
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Affiliation(s)
- Janet Blaurock
- Training Center of the European Academy of Andrology (EAA), Dermatology, Venerology and Allergology Clinic, University Hospital Leipzig, 04103 Leipzig, Germany
| | - Sven Baumann
- Faculty of Medicine, Institute of Legal Medicine, Leipzig University, 04103 Leipzig, Germany
| | - Sonja Grunewald
- Training Center of the European Academy of Andrology (EAA), Dermatology, Venerology and Allergology Clinic, University Hospital Leipzig, 04103 Leipzig, Germany
| | - Jürgen Schiller
- Faculty of Medicine, Institute for Medical Physics and Biophysics, Leipzig University, 04107 Leipzig, Germany
| | - Kathrin M. Engel
- Training Center of the European Academy of Andrology (EAA), Dermatology, Venerology and Allergology Clinic, University Hospital Leipzig, 04103 Leipzig, Germany
- Faculty of Medicine, Institute for Medical Physics and Biophysics, Leipzig University, 04107 Leipzig, Germany
- Correspondence:
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Combined 1H NMR fecal metabolomics and 16 S rRNA gene sequencing to reveal the protective effects of Gushudan on Kidney-yang deficiency syndrome rats via gut-kidney axis. J Pharm Biomed Anal 2022; 217:114843. [DOI: 10.1016/j.jpba.2022.114843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/11/2022] [Accepted: 05/16/2022] [Indexed: 02/02/2023]
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Bliziotis NG, Kluijtmans LAJ, Soto S, Tinnevelt GH, Langton K, Robledo M, Pamporaki C, Engelke UFH, Erlic Z, Engel J, Deutschbein T, Nölting S, Prejbisz A, Richter S, Prehn C, Adamski J, Januszewicz A, Reincke M, Fassnacht M, Eisenhofer G, Beuschlein F, Kroiss M, Wevers RA, Jansen JJ, Deinum J, Timmers HJLM. Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma. Endocrine 2022; 75:254-265. [PMID: 34536194 PMCID: PMC8763816 DOI: 10.1007/s12020-021-02858-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/24/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. DESIGN A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. METHODS Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. RESULTS Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. CONCLUSIONS Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.
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Affiliation(s)
- Nikolaos G Bliziotis
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - Leo A J Kluijtmans
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Sebastian Soto
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Gerjen H Tinnevelt
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Katharina Langton
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Christina Pamporaki
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Udo F H Engelke
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Zoran Erlic
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich, Zürich, Switzerland
| | - Jasper Engel
- Biometris, Wageningen UR, Wageningen, The Netherlands
| | - Timo Deutschbein
- Schwerpunkt Endokrinologie/Diabetologie, Medizinische Klinik und Poliklinik I, Universitätsklinikum Würzburg, Zürich, Germany
- Medicover Oldenburg MVZ, Oldenburg, Germany
| | - Svenja Nölting
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität, München, Munich, Germany
| | | | - Susan Richter
- Institut für Klinische Chemie und Labormedizin, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| | - Cornelia Prehn
- Helmholtz Zentrum München, Research Unit Molecular Endocrinology and Metabolism, Neuherberg, Germany
| | - Jerzy Adamski
- Helmholtz Zentrum München, Research Unit Molecular Endocrinology and Metabolism, Neuherberg, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Martin Reincke
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität, München, Munich, Germany
| | - Martin Fassnacht
- Schwerpunkt Endokrinologie/Diabetologie, Medizinische Klinik und Poliklinik I, Universitätsklinikum Würzburg, Zürich, Germany
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Universität Würzburg, Würzburg, Germany
| | - Graeme Eisenhofer
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Institut für Klinische Chemie und Labormedizin, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| | - Felix Beuschlein
- Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich, Zürich, Switzerland
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität, München, Munich, Germany
| | - Matthias Kroiss
- Schwerpunkt Endokrinologie/Diabetologie, Medizinische Klinik und Poliklinik I, Universitätsklinikum Würzburg, Zürich, Germany
- Medizinische Klinik und Poliklinik IV, Klinikum der Universität, Ludwig-Maximilians-Universität, München, Munich, Germany
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Universität Würzburg, Würzburg, Germany
| | - Ron A Wevers
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen J Jansen
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, Nijmegen, the Netherlands
| | - Jaap Deinum
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Henri J L M Timmers
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands.
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11
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Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor. Metabolites 2021; 11:metabo11080537. [PMID: 34436478 PMCID: PMC8401204 DOI: 10.3390/metabo11080537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 01/03/2023] Open
Abstract
Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).
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12
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Cognitive analysis of metabolomics data for systems biology. Nat Protoc 2021; 16:1376-1418. [PMID: 33483720 DOI: 10.1038/s41596-020-00455-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/27/2020] [Indexed: 01/30/2023]
Abstract
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
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13
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Nagana Gowda GA, Hong NN, Raftery D. Evaluation of Fumaric Acid and Maleic Acid as Internal Standards for NMR Analysis of Protein Precipitated Plasma, Serum, and Whole Blood. Anal Chem 2021; 93:3233-3240. [PMID: 33538164 DOI: 10.1021/acs.analchem.0c04766] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Significant advances have been made in unknown metabolite identification and expansion of the number of quantifiable metabolites in human plasma, serum, and whole blood using NMR spectroscopy. However, reliable quantitation of metabolites is still a challenge. A major bottleneck is the lack of a suitable internal standard that does not interact with the complex blood sample matrix and also does not overlap with metabolite peaks apart from exhibiting other favorable characteristics. With the goal of addressing this challenge, a comprehensive investigation of fumaric and maleic acids as potential internal standards was made along with a comparison with the conventional standards, TSP (trimethylsilylpropionic acid) and DSS (trimethylsilylpropanesulfonic acid). Both fumaric acid and maleic acid exhibited a surprisingly high performance with a quantitation error <1%, while the TSP and DSS caused an average error of up to 35% in plasma, serum, and whole blood. Further, the results indicate that while fumaric acid is a robust standard for all three biospecimens, maleic acid is suitable for only plasma and serum. Maleic acid is not suited for the analysis of whole blood due to its overlap with coenzyme peaks. These findings provide new opportunities for improved and accurate quantitation of metabolites in human plasma, serum, and whole blood using NMR spectroscopy. Moreover, the use of protein precipitation prior to NMR analysis mirrors the sample preparation commonly used for mass spectrometry based metabolomics, such that these findings further strengthen efforts to combine and compare NMR and MS based metabolite data of human plasma, serum, and whole blood for metabolomics based research.
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Affiliation(s)
| | | | - Daniel Raftery
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
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14
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Locci E, Bazzano G, Chighine A, Locco F, Ferraro E, Demontis R, d'Aloja E. Forensic NMR metabolomics: one more arrow in the quiver. Metabolomics 2020; 16:118. [PMID: 33159593 PMCID: PMC7648736 DOI: 10.1007/s11306-020-01743-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/29/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION NMR metabolomics is increasingly used in forensics, due to the possibility of investigating both endogenous metabolic profiles and exogenous molecules that may help to describe metabolic patterns and their modifications associated to specific conditions of forensic interest. OBJECTIVES The aim of this work was to review the recent literature and depict the information provided by NMR metabolomics. Attention has been devoted to the identification of peculiar metabolic signatures and specific ante-mortem and post-mortem profiles or biomarkers related to different conditions of forensic concern, such as the identification of biological traces, the estimation of the time since death, and the exposure to drugs of abuse. RESULTS AND CONCLUSION The results of the described studies highlight how forensics can benefit from NMR metabolomics by gaining additional information that may help to shed light in several forensic issues that still deserve to be further elucidated.
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Affiliation(s)
- Emanuela Locci
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy.
- Department of Medical Sciences and Public Health, Legal Medicine Section, University of Cagliari, Cittadella Universitaria di Monserrato, 09042, Monserrato, CA, Italy.
| | - Giovanni Bazzano
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
| | - Alberto Chighine
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
| | - Francesco Locco
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
| | - Ernesto Ferraro
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
| | - Roberto Demontis
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
| | - Ernesto d'Aloja
- Department of Medical Sciences and Public Health, Section of Legal Medicine, University of Cagliari, Cagliari, Italy
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15
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Crook AA, Powers R. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications. Molecules 2020; 25:E5128. [PMID: 33158172 PMCID: PMC7662776 DOI: 10.3390/molecules25215128] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.
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Affiliation(s)
- Alexandra A. Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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16
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Tang J, Xiong K, Zhang T, Han Han. Application of Metabolomics in Diagnosis and Treatment of Chronic Liver Diseases. Crit Rev Anal Chem 2020; 52:906-916. [PMID: 33146026 DOI: 10.1080/10408347.2020.1842172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Chronic liver disease represents stepwise destruction of the liver parenchyma after chronic liver injury, which is often difficult to be diagnosed accurately. Thus, the development of specific biomarkers of chronic liver disease is important. Metabolomics is a powerful tool for biomarker exploration, which enables the exploration of disease pathogenesis or drug action mechanisms at the global metabolic level. The metabolomics workflow generally includes collection, preparation, and analysis of samples, and data processing and bioinformatics. A metabolomics study can simultaneously detect the dysfunctions in the glucose, lipid, amino-acid, and nucleotide metabolisms. Hence, it facilitates the obtaining of a better understanding of the pathogenesis of chronic liver disease and its diagnosis. Many effective drugs could reverse the change of comprehensive biochemical phenotypes induced by chronic liver disease. They can even potentially restore the normal metabolic signatures of patients. Increasingly more researchers have begun to apply metabolomics technologies to diagnose chronic liver disease and investigate the mechanism of action of effective drugs or the variations in drug responses. We are convinced that deepening the understanding of the metabolic alterations could extend their use as powerful biomarkers, promoting the more effective clinical diagnosis and treatment of chronic liver disease in the future.
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Affiliation(s)
- Jie Tang
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Kai Xiong
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tong Zhang
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Han Han
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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17
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Labordiagnostik bei angeborenen Stoffwechselstörungen. Monatsschr Kinderheilkd 2020. [DOI: 10.1007/s00112-020-00938-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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