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Pavao A, Girinathan B, Peltier J, Altamirano Silva P, Dupuy B, Muti IH, Malloy C, Cheng LL, Bry L. Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling. Nat Chem Biol 2023; 19:556-564. [PMID: 36894723 PMCID: PMC10154198 DOI: 10.1038/s41589-023-01275-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/30/2023] [Indexed: 03/11/2023]
Abstract
Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.
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Affiliation(s)
- Aidan Pavao
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Brintha Girinathan
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Ginkgo Bioworks, The Innovation and Design Building, Boston, MA, USA
| | - Johann Peltier
- Laboratoire Pathogenèse des Bactéries Anaérobies, F-75015, Institut Pasteur, Université Paris-Cité, UMR-CNRS 6047, Paris, France
- Institute for Integrative Biology of the Cell (I2BC), 91198, University of Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, France
| | - Pamela Altamirano Silva
- Centre for Investigations in Tropical Diseases, Faculty of Microbiology, University of Costa Rica, San José, Costa Rica
| | - Bruno Dupuy
- Laboratoire Pathogenèse des Bactéries Anaérobies, F-75015, Institut Pasteur, Université Paris-Cité, UMR-CNRS 6047, Paris, France
| | - Isabella H Muti
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig Malloy
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Leo L Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lynn Bry
- Massachusetts Host-Microbiome Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Clinical Microbiology Laboratory, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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2
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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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3
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McKay RT. Metabolomics and NMR. Handb Exp Pharmacol 2023; 277:73-116. [PMID: 36355220 DOI: 10.1007/164_2022_616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The purpose of this manuscript will be to convince the reader to dive deeper into NMR spectroscopy and prevent the technique from being just another "black-box" in the lab. We will try to concisely highlight interesting topics and supply additional references for further exploration at each stage. The advantages of delving into the technique will be shown. The secondary objective, i.e., avoiding common problems before starting, will hopefully then become clear. Lastly, we will emphasize the spectrometer information needed for manuscript reporting to allow reproduction of results and confirm findings.
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Affiliation(s)
- Ryan T McKay
- Department Chemistry, College of Natural and Applied Sciences, University of Alberta, Edmonton, AB, Canada.
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4
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Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two major analytical platforms in the field of metabolomics, the other being mass spectrometry (MS). NMR is less sensitive than MS and hence it detects a relatively small number of metabolites. However, NMR exhibits numerous unique characteristics including its high reproducibility and non-destructive nature, its ability to identify unknown metabolites definitively, and its capabilities to obtain absolute concentrations of all detected metabolites, sometimes even without an internal standard. These characteristics outweigh the relatively low sensitivity and resolution of NMR in metabolomics applications. Since biological mixtures are highly complex, increased demand for new methods to improve detection, better identify unknown metabolites, and provide more accurate quantitation continues unabated. Technological and methodological advances to date have helped to improve the resolution and sensitivity and detection of a larger number of metabolite signals. Efforts focused on measuring unknown metabolite signals have resulted in the identification and quantitation of an expanded pool of metabolites including labile metabolites such as cellular redox coenzymes, energy coenzymes, and antioxidants. This chapter describes quantitative NMR methods in metabolomics with an emphasis on recent methodological developments, while highlighting the benefits and challenges of NMR-based metabolomics.
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Affiliation(s)
- G A Nagana Gowda
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA, USA.
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA, USA.
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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Ex Vivo High-Resolution Magic Angle Spinning (HRMAS) 1H NMR Spectroscopy for Early Prostate Cancer Detection. Cancers (Basel) 2022; 14:cancers14092162. [PMID: 35565290 PMCID: PMC9103328 DOI: 10.3390/cancers14092162] [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: 03/25/2022] [Revised: 04/17/2022] [Accepted: 04/22/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Prostate cancer is the second leading cancer diagnosed in men worldwide. Current diagnostic standards lack sufficient reliability in detecting and characterizing prostate cancer. Due to the cancer’s multifocality, prostate biopsies are associated with high numbers of false negatives. Whereas several studies have already shown the potential of metabolomic information for PCa detection and characterization, in this study, we focused on evaluating its predictive power for future PCa diagnosis. In our study, metabolomic information differed substantially between histobenign patients based on their risk for receiving a future PCa diagnosis, making metabolomic information highly valuable for the individualization of active surveillance strategies. Abstract The aim of our study was to assess ex vivo HRMAS (high-resolution magic angle spinning) 1H NMR spectroscopy as a diagnostic tool for early PCa detection by testing whether metabolomic alterations in prostate biopsy samples can predict future PCa diagnosis. In a primary prospective study (04/2006–10/2018), fresh biopsy samples of 351 prostate biopsy patients were NMR spectroscopically analyzed (Bruker 14.1 Tesla, Billerica, MA, USA) and histopathologically evaluated. Three groups of 16 patients were compared: group 1 and 2 represented patients whose NMR scanned biopsy was histobenign, but patients in group 1 were diagnosed with cancer before the end of the study period, whereas patients in group 2 remained histobenign. Group 3 included cancer patients. Single-metabolite concentrations and metabolomic profiles were not only able to separate histobenign and malignant prostate tissue but also to differentiate between samples of histobenign patients who received a PCa diagnosis in the following years and those who remained histobenign. Our results support the hypothesis that metabolomic alterations significantly precede histologically visible changes, making metabolomic information highly beneficial for early PCa detection. Thanks to its predictive power, metabolomic information can be very valuable for the individualization of PCa active surveillance strategies.
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Tasic L, Avramović N, Quintero M, Stanisic D, Martins LG, da Costa TBBC, Jadranin M, de Souza Accioly MT, Faria P, de Camargo B, de Sá Pereira BM, Maschietto M. A Metabonomic View on Wilms Tumor by High-Resolution Magic-Angle Spinning Nuclear Magnetic Resonance Spectroscopy. Diagnostics (Basel) 2022; 12:diagnostics12010157. [PMID: 35054324 PMCID: PMC8775120 DOI: 10.3390/diagnostics12010157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/13/2021] [Accepted: 01/01/2022] [Indexed: 11/16/2022] Open
Abstract
Pediatric cancer NMR-metabonomics might be a powerful tool to discover modified biochemical pathways in tumor development, improve cancer diagnosis, and, consequently, treatment. Wilms tumor (WT) is the most common kidney tumor in young children whose genetic and epigenetic abnormalities lead to cell metabolism alterations, but, so far, investigation of metabolic pathways in WT is scarce. We aimed to explore the high-resolution magic-angle spinning nuclear magnetic resonance (HR-MAS NMR) metabonomics of WT and normal kidney (NK) samples. For this study, 14 WT and 7 NK tissue samples were obtained from the same patients and analyzed. One-dimensional and two-dimensional HR-MAS NMR spectra were processed, and the one-dimensional NMR data were analyzed using chemometrics. Chemometrics enabled us to elucidate the most significant differences between the tumor and normal tissues and to discover intrinsic metabolite alterations in WT. The metabolic differences in WT tissues were revealed by a validated PLS-DA applied on HR-MAS T2-edited 1H-NMR and were assigned to 16 metabolites, such as lipids, glucose, and branched-chain amino acids (BCAAs), among others. The WT compared to NK samples showed 13 metabolites with increased concentrations and 3 metabolites with decreased concentrations. The relative BCAA concentrations were decreased in the WT while lipids, lactate, and glutamine/glutamate showed increased levels. Sixteen tissue metabolites distinguish the analyzed WT samples and point to altered glycolysis, glutaminolysis, TCA cycle, and lipid and BCAA metabolism in WT. Significant variation in the concentrations of metabolites, such as glutamine/glutamate, lipids, lactate, and BCAAs, was observed in WT and opened up a perspective for their further study and clinical validation.
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Affiliation(s)
- Ljubica Tasic
- Laboratory of Chemical Biology, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, Sao Paulo 13083-970, Brazil; (M.Q.); (D.S.); (L.G.M.); (T.B.B.C.d.C.)
- Correspondence:
| | - Nataša Avramović
- Faculty of Medicine, Institute of Medical Chemistry, University of Belgrade, Višegradska 26, 11000 Belgrade, Serbia;
| | - Melissa Quintero
- Laboratory of Chemical Biology, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, Sao Paulo 13083-970, Brazil; (M.Q.); (D.S.); (L.G.M.); (T.B.B.C.d.C.)
| | - Danijela Stanisic
- Laboratory of Chemical Biology, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, Sao Paulo 13083-970, Brazil; (M.Q.); (D.S.); (L.G.M.); (T.B.B.C.d.C.)
| | - Lucas G. Martins
- Laboratory of Chemical Biology, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, Sao Paulo 13083-970, Brazil; (M.Q.); (D.S.); (L.G.M.); (T.B.B.C.d.C.)
| | - Tassia Brena Barroso Carneiro da Costa
- Laboratory of Chemical Biology, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, Sao Paulo 13083-970, Brazil; (M.Q.); (D.S.); (L.G.M.); (T.B.B.C.d.C.)
| | - Milka Jadranin
- Institute of Chemistry, Technology and Metallurgy, Department of Chemistry, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia;
| | | | - Paulo Faria
- Department of Pathology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-901, Brazil;
| | - Beatriz de Camargo
- Clinical Research Department, National Cancer Institute (INCA), Rio de Janeiro 20231-091, Brazil; (B.d.C.); (B.M.d.S.P.)
| | - Bruna M. de Sá Pereira
- Clinical Research Department, National Cancer Institute (INCA), Rio de Janeiro 20231-091, Brazil; (B.d.C.); (B.M.d.S.P.)
| | - Mariana Maschietto
- National Laboratory of Biosciences (LNBio), National Center for Research in Energy and Materials (CNPEM), Campinas, Sao Paulo 13083-100, Brazil;
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MUMCU A. A simple and feasible quantification of metabolites in the human follicular fluid using 1H HR-MAS NMR spectroscopy. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2021. [DOI: 10.18596/jotcsa.986523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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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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Firdous S, Abid R, Nawaz Z, Bukhari F, Anwer A, Cheng LL, Sadaf S. Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data. Metabolites 2021; 11:metabo11080507. [PMID: 34436448 PMCID: PMC8402070 DOI: 10.3390/metabo11080507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/22/2021] [Accepted: 07/28/2021] [Indexed: 01/04/2023] Open
Abstract
Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.
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Affiliation(s)
- Safia Firdous
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
- Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Lahore 54770, Pakistan
| | - Rizwan Abid
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
| | - Zubair Nawaz
- Department of Data Science, Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan; (Z.N.); (F.B.)
| | - Faisal Bukhari
- Department of Data Science, Punjab University College of Information Technology, University of the Punjab, Lahore 54590, Pakistan; (Z.N.); (F.B.)
| | - Ammar Anwer
- Punjab Institute of Neurosciences (PINS), Lahore General Hospital, Lahore 54000, Pakistan;
| | - Leo L. Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA;
| | - Saima Sadaf
- School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54590, Pakistan; (S.F.); (R.A.)
- Correspondence:
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