1
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Xu S, Xu T, Yang Y, Chen X. Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network. mSystems 2024; 9:e0069724. [PMID: 39057922 PMCID: PMC11334518 DOI: 10.1128/msystems.00697-24] [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: 05/21/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.
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Affiliation(s)
- Shaohua Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
| | - Ting Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuping Yang
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xin Chen
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
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2
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Vitale GA, Geibel C, Minda V, Wang M, Aron AT, Petras D. Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products. Nat Prod Rep 2024; 41:885-904. [PMID: 38351834 PMCID: PMC11186733 DOI: 10.1039/d3np00050h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Indexed: 06/20/2024]
Abstract
Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.
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Affiliation(s)
- Giovanni Andrea Vitale
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
| | - Christian Geibel
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
| | - Vidit Minda
- Division of Pharmacology and Pharmaceutical Sciences, University of Missouri - Kansas City, Kansas City, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, USA.
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, USA.
| | - Allegra T Aron
- Department of Chemistry and Biochemistry, University of Denver, Denver, USA.
| | - Daniel Petras
- CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Medicine, University of Tuebingen, Tuebingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, USA.
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3
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Peng Y, Zhang Z, He L, Li C, Liu M. NMR spectroscopy for metabolomics in the living system: recent progress and future challenges. Anal Bioanal Chem 2024; 416:2319-2334. [PMID: 38240793 PMCID: PMC10950998 DOI: 10.1007/s00216-024-05137-8] [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: 09/24/2023] [Revised: 12/08/2023] [Accepted: 01/10/2024] [Indexed: 03/21/2024]
Abstract
Metabolism is a fundamental process that underlies human health and diseases. Nuclear magnetic resonance (NMR) techniques offer a powerful approach to identify metabolic processes and track the flux of metabolites at the molecular level in living systems. An in vitro study through in-cell NMR tracks metabolites in real time and investigates protein structures and dynamics in a state close to their most natural environment. This technique characterizes metabolites and proteins involved in metabolic pathways in prokaryotic and eukaryotic cells. In vivo magnetic resonance spectroscopy (MRS) enables whole-organism metabolic monitoring by visualizing the spatial distribution of metabolites and targeted proteins. One limitation of these NMR techniques is the sensitivity, for which a possible improved approach is through isotopic enrichment or hyperpolarization methods, including dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP). DNP involves the transfer of high polarization from electronic spins of radicals to surrounding nuclear spins for signal enhancements, allowing the detection of low-abundance metabolites and real-time monitoring of metabolic activities. PHIP enables the transfer of nuclear spin polarization from parahydrogen to other nuclei for signal enhancements, particularly in proton NMR, and has been applied in studies of enzymatic reactions and cell signaling. This review provides an overview of in-cell NMR, in vivo MRS, and hyperpolarization techniques, highlighting their applications in metabolic studies and discussing challenges and future perspectives.
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Affiliation(s)
- Yun Peng
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Zeting Zhang
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Lichun He
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Conggang Li
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance in Wuhan, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China.
- Optics Valley Laboratory, Wuhan, 430074, Hubei, China.
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4
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Wu Y, Sanati O, Uchimiya M, Krishnamurthy K, Wedell J, Hoch JC, Edison AS, Delaglio F. SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification. Anal Chem 2024; 96:1843-1851. [PMID: 38273718 PMCID: PMC10896553 DOI: 10.1021/acs.analchem.3c03078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/27/2024]
Abstract
Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.
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Affiliation(s)
- Yue Wu
- Institute
of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Omid Sanati
- School
of Electrical and Computer Engineering, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | - Mario Uchimiya
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
| | | | - Jonathan Wedell
- National
Magnetic Resonance Facility, University
of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Jeffrey C. Hoch
- Department
of Molecular Biology and Biophysics, University
of Connecticut, Farmington, Connecticut 06030-3305, United States
| | - Arthur S. Edison
- Institute
of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
- Complex
Carbohydrate Research Center, University
of Georgia, Athens, Georgia 30602, United States
- Department
of Biochemistry and Molecular Biology, University
of Georgia, Athens, Georgia 30602, United States
| | - Frank Delaglio
- Institute
for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University
of Maryland, Rockville, Maryland 20850, United States
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5
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Charris-Molina A, Burdisso P, Hoijemberg PA. Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics. J Proteome Res 2024; 23:430-448. [PMID: 38127799 DOI: 10.1021/acs.jproteome.3c00638] [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: 12/23/2023]
Abstract
NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.
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Affiliation(s)
- Andrés Charris-Molina
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires C1428EGA, Argentina
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), NMR Group, Godoy Cruz 2390, Ciudad Autónoma de Buenos Aires C1425FQD, Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario (IBR-CONICET), Facultad de Ciencias Bioquimicas y Farmacéuticas, Universidad Nacional de Rosario and Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM), Rosario, Santa Fe S2002LRK, Argentina
| | - Pablo A Hoijemberg
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), NMR Group, Godoy Cruz 2390, Ciudad Autónoma de Buenos Aires C1425FQD, Argentina
- ECyT-UNSAM, San Martín, Buenos Aires B1650HMP, Argentina
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6
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Launay H, Avilan L, Gérard C, Parsiegla G, Receveur-Brechot V, Gontero B, Carriere F. Location of the photosynthetic carbon metabolism in microcompartments and separated phases in microalgal cells. FEBS Lett 2023; 597:2853-2878. [PMID: 37827572 DOI: 10.1002/1873-3468.14754] [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: 06/16/2023] [Revised: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 10/14/2023]
Abstract
Carbon acquisition, assimilation and storage in eukaryotic microalgae and cyanobacteria occur in multiple compartments that have been characterised by the location of the enzymes involved in these functions. These compartments can be delimited by bilayer membranes, such as the chloroplast, the lumen, the peroxisome, the mitochondria or monolayer membranes, such as lipid droplets or plastoglobules. They can also originate from liquid-liquid phase separation such as the pyrenoid. Multiple exchanges exist between the intracellular microcompartments, and these are reviewed for the CO2 concentration mechanism, the Calvin-Benson-Bassham cycle, the lipid metabolism and the cellular energetic balance. Progress in microscopy and spectroscopic methods opens new perspectives to characterise the molecular consequences of the location of the proteins involved, including intrinsically disordered proteins.
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Affiliation(s)
- Hélène Launay
- Aix Marseille Univ, CNRS, BIP, UMR7281, Marseille, France
| | - Luisana Avilan
- Aix Marseille Univ, CNRS, BIP, UMR7281, Marseille, France
| | - Cassy Gérard
- Aix Marseille Univ, CNRS, BIP, UMR7281, Marseille, France
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7
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Tambay V, Raymond VA, Goossens C, Rousseau L, Turcotte S, Bilodeau M. Metabolomics-Guided Identification of a Distinctive Hepatocellular Carcinoma Signature. Cancers (Basel) 2023; 15:3232. [PMID: 37370840 DOI: 10.3390/cancers15123232] [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: 04/27/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming. METHODS HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS). RESULTS Principal component analysis (PCA) within our 1H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS. CONCLUSIONS Together, 1H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.
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Affiliation(s)
- Vincent Tambay
- Laboratoire d'Hépatologie Cellulaire, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0A9, Canada
| | - Valérie-Ann Raymond
- Laboratoire d'Hépatologie Cellulaire, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0A9, Canada
| | - Corentine Goossens
- Laboratoire d'Hépatologie Cellulaire, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0A9, Canada
| | - Louise Rousseau
- Biobanque et Base de Données Hépatobiliaire et Pancréatique, Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0C1, Canada
| | - Simon Turcotte
- Biobanque et Base de Données Hépatobiliaire et Pancréatique, Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0C1, Canada
- Département de Chirurgie, Service de Transplantation Hépatique et de Chirurgie Hépatobiliaire et Pancréatique, Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0C1, Canada
| | - Marc Bilodeau
- Laboratoire d'Hépatologie Cellulaire, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC H2X0A9, Canada
- Département de Médecine, Université de Montréal, Montréal, QC H3T1J4, Canada
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8
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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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9
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Muti IH, Gonzalez Sanchez-Dahl M, Zhong AB, Weng J, Füzesi MV, Kivisäkk P, Hyman BT, Arnold SE, Feldman AS, Mercaldo ND, Cheng LL. Designing a quality assurance process for quality control of nuclear magnetic resonance metabolomics studies of human blood. NMR IN BIOMEDICINE 2023; 36:e4868. [PMID: 36330660 PMCID: PMC9992016 DOI: 10.1002/nbm.4868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR)-based metabolomics has demonstrated its utility in studies of biofluids for various diseases. HRMAS NMR spectroscopy is uniquely well suited for analyzing human blood samples because of the small quantity of samples and minimal preparation required. To develop this methodology into standardized clinical protocols, establishment of the method's quality assurance (QA) and evaluations of its quality control (QC) are critical. This study aims to assess the QA/QC measured from human blood specimens in the form of serum and plasma through within-subject and between-subject comparisons, as well as stability and consistency comparisons over several freezing-thawing cycles of sample storage conditions, and most importantly, the agreement of pooled control samples against individual samples.
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Affiliation(s)
- Isabella H Muti
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Anya B Zhong
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - JianXiang Weng
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark V Füzesi
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Pia Kivisäkk
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bradley T Hyman
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Steven E Arnold
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Adam S Feldman
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathaniel D Mercaldo
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo L Cheng
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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10
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Cheng LL. High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions. NMR IN BIOMEDICINE 2023; 36:e4684. [PMID: 34962004 DOI: 10.1002/nbm.4684] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/15/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.
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Affiliation(s)
- Leo L Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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11
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Borges RM, Gouveia GJ, das Chagas FO. Advances in Microbial NMR Metabolomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:123-147. [PMID: 37843808 DOI: 10.1007/978-3-031-41741-2_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.
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Affiliation(s)
- Ricardo Moreira Borges
- Instituto de Pesquisas de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gonçalo Jorge Gouveia
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA
| | - Fernanda Oliveira das Chagas
- Instituto de Pesquisas de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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12
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Abstract
Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.
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Affiliation(s)
- Zuhal Eraslan
- Department of Dermatology, Weill Cornell Medicine, New York, NY, USA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of University of Barcelona (IBUB), University of Barcelona, Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Ulrich L Günther
- Institute of Chemistry and Metabolomics, University of Lübeck, Lübeck, Germany.
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13
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Iman MN, Herawati E, Fukusaki E, Putri SP. Metabolomics-driven strain improvement: A mini review. Front Mol Biosci 2022; 9:1057709. [PMID: 36438656 PMCID: PMC9681786 DOI: 10.3389/fmolb.2022.1057709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/28/2022] [Indexed: 07/22/2023] Open
Abstract
In recent years, mass spectrometry-based metabolomics has been established as a powerful and versatile technique for studying cellular metabolism by comprehensive analysis of metabolites in the cell. Although there are many scientific reports on the use of metabolomics for the elucidation of mechanism and physiological changes occurring in the cell, there are surprisingly very few reports on its use for the identification of rate-limiting steps in a synthetic biological system that can lead to the actual improvement of the host organism. In this mini review, we discuss different strategies for improving strain performance using metabolomics data and compare the application of metabolomics-driven strain improvement techniques in different host microorganisms. Finally, we highlight several success stories on the use of metabolomics-driven strain improvement strategies, which led to significant bioproductivity improvements.
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Affiliation(s)
- Marvin Nathanael Iman
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan
| | - Elisa Herawati
- Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Surakarta, Indonesia
| | - Eiichiro Fukusaki
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan
| | - Sastia Prama Putri
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan
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14
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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15
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Theillet FX, Luchinat E. In-cell NMR: Why and how? PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2022; 132-133:1-112. [PMID: 36496255 DOI: 10.1016/j.pnmrs.2022.04.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/19/2022] [Accepted: 04/27/2022] [Indexed: 06/17/2023]
Abstract
NMR spectroscopy has been applied to cells and tissues analysis since its beginnings, as early as 1950. We have attempted to gather here in a didactic fashion the broad diversity of data and ideas that emerged from NMR investigations on living cells. Covering a large proportion of the periodic table, NMR spectroscopy permits scrutiny of a great variety of atomic nuclei in all living organisms non-invasively. It has thus provided quantitative information on cellular atoms and their chemical environment, dynamics, or interactions. We will show that NMR studies have generated valuable knowledge on a vast array of cellular molecules and events, from water, salts, metabolites, cell walls, proteins, nucleic acids, drugs and drug targets, to pH, redox equilibria and chemical reactions. The characterization of such a multitude of objects at the atomic scale has thus shaped our mental representation of cellular life at multiple levels, together with major techniques like mass-spectrometry or microscopies. NMR studies on cells has accompanied the developments of MRI and metabolomics, and various subfields have flourished, coined with appealing names: fluxomics, foodomics, MRI and MRS (i.e. imaging and localized spectroscopy of living tissues, respectively), whole-cell NMR, on-cell ligand-based NMR, systems NMR, cellular structural biology, in-cell NMR… All these have not grown separately, but rather by reinforcing each other like a braided trunk. Hence, we try here to provide an analytical account of a large ensemble of intricately linked approaches, whose integration has been and will be key to their success. We present extensive overviews, firstly on the various types of information provided by NMR in a cellular environment (the "why", oriented towards a broad readership), and secondly on the employed NMR techniques and setups (the "how", where we discuss the past, current and future methods). Each subsection is constructed as a historical anthology, showing how the intrinsic properties of NMR spectroscopy and its developments structured the accessible knowledge on cellular phenomena. Using this systematic approach, we sought i) to make this review accessible to the broadest audience and ii) to highlight some early techniques that may find renewed interest. Finally, we present a brief discussion on what may be potential and desirable developments in the context of integrative studies in biology.
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Affiliation(s)
- Francois-Xavier Theillet
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France.
| | - Enrico Luchinat
- Dipartimento di Scienze e Tecnologie Agro-Alimentari, Alma Mater Studiorum - Università di Bologna, Piazza Goidanich 60, 47521 Cesena, Italy; CERM - Magnetic Resonance Center, and Neurofarba Department, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Italy
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16
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Achreja A, Yu T, Mittal A, Choppara S, Animasahun O, Nenwani M, Wuchu F, Meurs N, Mohan A, Jeon JH, Sarangi I, Jayaraman A, Owen S, Kulkarni R, Cusato M, Weinberg F, Kweon HK, Subramanian C, Wicha MS, Merajver SD, Nagrath S, Cho KR, DiFeo A, Lu X, Nagrath D. Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer. Nat Metab 2022; 4:1119-1137. [PMID: 36131208 DOI: 10.1038/s42255-022-00636-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2022] [Indexed: 11/08/2022]
Abstract
Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11-MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM's machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours.
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Affiliation(s)
- Abhinav Achreja
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Tao Yu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Anjali Mittal
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Srinadh Choppara
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Olamide Animasahun
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Minal Nenwani
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Fulei Wuchu
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Noah Meurs
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Aradhana Mohan
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Jin Heon Jeon
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Itisam Sarangi
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Anusha Jayaraman
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Sarah Owen
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Reva Kulkarni
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA
- Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Michele Cusato
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Frank Weinberg
- Hematology and Oncology, University of Illinois, Chicago, IL, USA
| | - Hye Kyong Kweon
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Chitra Subramanian
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Max S Wicha
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sofia D Merajver
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Sunitha Nagrath
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Kathleen R Cho
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
| | - Analisa DiFeo
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
| | - Xiongbin Lu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
- Melvin & Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Deepak Nagrath
- Laboratory for Systems Biology of Human Diseases, University of Michigan, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
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17
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Wishart DS, Cheng LL, Copié V, Edison AS, Eghbalnia HR, Hoch JC, Gouveia GJ, Pathmasiri W, Powers R, Schock TB, Sumner LW, Uchimiya M. NMR and Metabolomics-A Roadmap for the Future. Metabolites 2022; 12:678. [PMID: 35893244 PMCID: PMC9394421 DOI: 10.3390/metabo12080678] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 12/03/2022] Open
Abstract
Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.
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Affiliation(s)
- David S. Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Leo L. Cheng
- Department of Pathology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Valérie Copié
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59715, USA;
| | - Arthur S. Edison
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Hamid R. Eghbalnia
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Jeffrey C. Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030-3305, USA; (H.R.E.); (J.C.H.)
| | - Goncalo J. Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602-0001, USA
| | - Wimal Pathmasiri
- Nutrition Research Institute, Department of Nutrition, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, 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
| | - Tracey B. Schock
- National Institute of Standards and Technology (NIST), Chemical Sciences Division, Charleston, SC 29412, USA;
| | - Lloyd W. Sumner
- Interdisciplinary Plant Group, MU Metabolomics Center, Bond Life Sciences Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Mario Uchimiya
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; (A.S.E.); (G.J.G.); (M.U.)
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18
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Lutz NW, Bernard M. Methodological Developments for Metabolic NMR Spectroscopy from Cultured Cells to Tissue Extracts: Achievements, Progress and Pitfalls. Molecules 2022; 27:molecules27134214. [PMID: 35807461 PMCID: PMC9268249 DOI: 10.3390/molecules27134214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/08/2022] [Accepted: 06/20/2022] [Indexed: 12/04/2022] Open
Abstract
This is a broad overview and critical review of a particular group of closely related ex vivo and in vitro metabolic NMR spectroscopic methods. The scope of interest comprises studies of cultured cells and excised tissue, either intact or after physicochemical extraction of metabolites. Our detailed discussion includes pitfalls that have led to erroneous statements in the published literature, some of which may cause serious problems in metabolic and biological interpretation of results. To cover a wide range of work from relevant research areas, we consider not only the most recent achievements in the field, but also techniques that proved to be valid and successful in the past, although they may not have generated a very significant number of papers more recently. Thus, this comparative review also aims at providing background information useful for judiciously choosing between the metabolic ex vivo/in vitro NMR methods presented. Finally, the methods of interest are discussed in the context of, and in relation to, other metabolic analysis protocols such as HR-MAS and cell perfusion NMR, as well as the mass spectrometry approach.
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19
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Carrillo AJ, Halilovic L, Hur M, Kirkwood JS, Borkovich KA. Targeted Metabolomics Using LC-MS in Neurospora crassa. Curr Protoc 2022; 2:e454. [PMID: 35616476 DOI: 10.1002/cpz1.454] [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: 11/07/2022]
Abstract
The filamentous fungus Neurospora crassa has historically been a model for understanding the relationship between genes and metabolism-auxotrophic mutants of N. crassa were used by Beadle and Tatum to develop the one-gene-one-enzyme hypothesis for which they earned the Nobel Prize in 1958. In the ensuing decades, several techniques have been developed for the systematic analysis of metabolites in N. crassa and other fungi. Untargeted and targeted approaches have been used, with a focus on secondary metabolites over primary metabolism. Here, we describe a pipeline for sample preparation, metabolite extraction, Liquid Chromatography-Mass Spectrometry (LC-MS), and data analysis that can be used for targeted metabolomics of primary metabolites in N. crassa. Liquid cultures are grown with shaking in a defined minimal medium and then collected using filtration. Samples are lyophilized for 2 days at -80°C, pulverized, and mixed with a solution to extract polar metabolites. The metabolites are separated and identified using LC-MS, with downstream analysis using Skyline interpretive software. Relative levels of hundreds of metabolites can be detected and compared across strains. © 2022 Wiley Periodicals LLC. Basic Protocol: Metabolite extraction and detection from Neurospora crassa cell cultures using Liquid Chromatography-Mass Spectrometry.
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Affiliation(s)
- Alexander J Carrillo
- Department of Microbiology and Plant Pathology, University of California, Riverside, California
| | - Lida Halilovic
- Department of Microbiology and Plant Pathology, University of California, Riverside, California
| | - Manhoi Hur
- Institute for Integrative Genome Biology Metabolomics Core, University of California, Riverside, California
| | - Jay S Kirkwood
- Institute for Integrative Genome Biology Metabolomics Core, University of California, Riverside, California
| | - Katherine A Borkovich
- Department of Microbiology and Plant Pathology, University of California, Riverside, California
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20
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Cheong JH, Qiu X, Liu Y, Al-Omari A, Griffith J, Schüttler HB, Mao L, Arnold J. The macroscopic limit to synchronization of cellular clocks in single cells of Neurospora crassa. Sci Rep 2022; 12:6750. [PMID: 35468928 PMCID: PMC9039089 DOI: 10.1038/s41598-022-10612-2] [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: 08/13/2021] [Accepted: 03/29/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractWe determined the macroscopic limit for phase synchronization of cellular clocks in an artificial tissue created by a “big chamber” microfluidic device to be about 150,000 cells or less. The dimensions of the microfluidic chamber allowed us to calculate an upper limit on the radius of a hypothesized quorum sensing signal molecule of 13.05 nm using a diffusion approximation for signal travel within the device. The use of a second microwell microfluidic device allowed the refinement of the macroscopic limit to a cell density of 2166 cells per fixed area of the device for phase synchronization. The measurement of averages over single cell trajectories in the microwell device supported a deterministic quorum sensing model identified by ensemble methods for clock phase synchronization. A strong inference framework was used to test the communication mechanism in phase synchronization of quorum sensing versus cell-to-cell contact, suggesting support for quorum sensing. Further evidence came from showing phase synchronization was density-dependent.
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21
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Moran MA, Kujawinski EB, Schroer WF, Amin SA, Bates NR, Bertrand EM, Braakman R, Brown CT, Covert MW, Doney SC, Dyhrman ST, Edison AS, Eren AM, Levine NM, Li L, Ross AC, Saito MA, Santoro AE, Segrè D, Shade A, Sullivan MB, Vardi A. Microbial metabolites in the marine carbon cycle. Nat Microbiol 2022; 7:508-523. [PMID: 35365785 DOI: 10.1038/s41564-022-01090-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 02/23/2022] [Indexed: 01/08/2023]
Abstract
One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.
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Affiliation(s)
- Mary Ann Moran
- Department of Marine Sciences, University of Georgia, Athens, GA, USA.
| | - Elizabeth B Kujawinski
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USA.
| | - William F Schroer
- Department of Marine Sciences, University of Georgia, Athens, GA, USA
| | - Shady A Amin
- Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Nicholas R Bates
- Bermuda Institute of Ocean Sciences, St George's, Bermuda.,School of Ocean and Earth Sciences, University of Southampton, Southampton, UK
| | - Erin M Bertrand
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Rogier Braakman
- Departments of Earth, Atmospheric and Planetary Sciences, and Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - C Titus Brown
- Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Scott C Doney
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA
| | - Sonya T Dyhrman
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA.,Department of Earth and Environmental Science, Columbia University, Palisades, NY, USA
| | - Arthur S Edison
- Departments of Biochemistry and Genetics, Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - A Murat Eren
- Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA, USA.,Helmholtz-Institute for Functional Marine Biodiversity (HIFMB), University of Oldenburg, Oldenburg, Germany
| | - Naomi M Levine
- Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
| | - Avena C Ross
- Department of Chemistry, Queen's University, Kingston, Ontario, Canada
| | - Mak A Saito
- Department of Marine Sciences, University of Georgia, Athens, GA, USA
| | - Alyson E Santoro
- Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA
| | - Daniel Segrè
- Department of Biology and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Ashley Shade
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA
| | - Matthew B Sullivan
- Departments of Microbiology and Civil, Environmental, and Geodetic Engineering, and Center of Microbiome Science, The Ohio State University, Columbus, OH, USA
| | - Assaf Vardi
- Department of Plant and Environmental Sciences, The Weizmann Institute of Science, Rehovot, Israel
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22
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Nami F, Ferraz MJ, Bakkum T, Aerts JMFG, Pandit A. Real‐Time NMR Recording of Fermentation and Lipid Metabolism Processes in Live Microalgae Cells. Angew Chem Int Ed Engl 2022; 61:e202117521. [PMID: 35103372 PMCID: PMC9305762 DOI: 10.1002/anie.202117521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Indexed: 11/10/2022]
Abstract
Non‐invasive and real‐time recording of processes in living cells has been limited to detection of small cellular components such as soluble proteins and metabolites. Here we report a multiphase NMR approach using magic‐angle spinning NMR to synchronously follow microbial processes of fermentation, lipid metabolism and structural dynamic changes in live microalgae cells. Chlamydomonas reinhardtii green algae were highly concentrated, introducing dark fermentation and anoxia conditions. Single‐pulse NMR experiments were applied to obtain temperature‐dependent kinetic profiles of the formed fermentation products. Through dynamics‐based spectral editing NMR, simultaneous conversion of galactolipids into TAG and free fatty acids was observed and rapid loss of rigid lipid structures. This suggests that lipolysis under dark and anoxia conditions finally results in the breakdown of cell and organelle membranes, which could be beneficial for recovery of intracellular microbial useful products.
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Affiliation(s)
- Faezeh Nami
- Dept. of Solid-State NMR Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Maria Joao Ferraz
- Dept. of Medicinal Biochemistry Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Thomas Bakkum
- Dept. of Bio Organic Synthesis Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Johannes M. F. G. Aerts
- Dept. of Medicinal Biochemistry Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Anjali Pandit
- Dept. of Solid-State NMR Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
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23
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Nami F, Ferraz MJ, Bakkum T, Aerts JMFG, Pandit A. Real‐Time NMR Recording of Fermentation and Lipid Metabolism Processes in Live Microalgae Cells. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202117521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Faezeh Nami
- Dept. of Solid-State NMR Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Maria Joao Ferraz
- Dept. of Medicinal Biochemistry Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Thomas Bakkum
- Dept. of Bio Organic Synthesis Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Johannes M. F. G. Aerts
- Dept. of Medicinal Biochemistry Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
| | - Anjali Pandit
- Dept. of Solid-State NMR Leiden Institute of Chemistry Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands
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24
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Rahim M, Ragavan M, Deja S, Merritt ME, Burgess SC, Young JD. INCA 2.0: A tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis. Metab Eng 2022; 69:275-285. [PMID: 34965470 PMCID: PMC8789327 DOI: 10.1016/j.ymben.2021.12.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 01/03/2023]
Abstract
Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.
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Affiliation(s)
- Mohsin Rahim
- Department of Chemical and Biomolecular, Nashville, TN, 37212, USA
| | - Mukundan Ragavan
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Stanislaw Deja
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matthew E Merritt
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Shawn C Burgess
- Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jamey D Young
- Department of Chemical and Biomolecular, Nashville, TN, 37212, USA; Department of Molecular Physiology and Biophysics, Vanderbilt University, School of Engineering, Nashville, TN, 37212, USA.
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25
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Ning P, Lane D, Ghosh Biswas R, Soong R, Schmidig D, Frei T, De Castro P, Kovacevic I, Graf S, Wegner S, Busse F, Kuehn T, Struppe J, Fey M, Stronks HJ, Monette M, Simpson MJ, Simpson AJ. Comprehensive Multiphase NMR Probehead with Reduced Radiofrequency Heating Improves the Analysis of Living Organisms and Heat-Sensitive Samples. Anal Chem 2021; 93:10326-10333. [PMID: 34259008 DOI: 10.1021/acs.analchem.1c01932] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Comprehensive multiphase (CMP) NMR, first described in 2012, combines all of the hardware components necessary to analyze all phases (solid, gel, and solution) in samples in their natural state. In combination with spectral editing experiments, it can fully differentiate phases and study the transfer of chemical species across and between phases, providing unprecedented molecular-level information in unaltered natural systems. However, many natural samples, such as swollen soils, plants, and small organisms, contain water, salts, and ionic compounds, making them electrically lossy and susceptible to RF heating, especially when using high-strength RF fields required to select the solid domains. While dedicated reduced-heating probes have been developed for solid-state NMR, to date, all CMP-NMR probes have been based on solenoid designs, which can lead to problematic sample heating. Here, a new prototype CMP probe was developed, incorporating a loop gap resonator (LGR) for decoupling. Temperature increases are monitored in salt solutions analogous to those in small aquatic organisms and then tested in vivo on Hyalella azteca (freshwater shrimp). In the standard CMP probe (solenoid), 80% of organisms died within 4 h under high-power decoupling, while in the LGR design, all organisms survived the entire test period of 12 h. The LGR design reduced heating by a factor of ∼3, which allowed 100 kHz decoupling to be applied to salty samples with generally ≤10 °C sample heating. In addition to expanding the potential for in vivo research, the ability to apply uncompromised high-power decoupling could be beneficial for multiphase samples containing true crystalline solids that require the strongest possible decoupling fields for optimal detection.
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Affiliation(s)
- Paris Ning
- Environmental NMR Centre, University of Toronto, Toronto, Ontario M1C 1A4, Canada
| | - Daniel Lane
- Environmental NMR Centre, University of Toronto, Toronto, Ontario M1C 1A4, Canada
| | | | - Ronald Soong
- Environmental NMR Centre, University of Toronto, Toronto, Ontario M1C 1A4, Canada
| | - Daniel Schmidig
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Thomas Frei
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Peter De Castro
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Ivan Kovacevic
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Stephan Graf
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Sebastian Wegner
- Bruker BioSpin GmbH, Silberstreifen 4, 76287 Rheinstetten, Germany
| | - Falko Busse
- Bruker BioSpin GmbH, Silberstreifen 4, 76287 Rheinstetten, Germany
| | - Till Kuehn
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Jochem Struppe
- Bruker BioSpin Corp., 15 Fortune Drive, Billerica, Massachusetts 01821-3991, United States
| | - Michael Fey
- Bruker BioSpin Corp., 15 Fortune Drive, Billerica, Massachusetts 01821-3991, United States
| | - Henry J Stronks
- Bruker Ltd., 2800 High Point Drive, Milton, Ontario L9T 6P4, Canada
| | - Martine Monette
- Bruker Ltd., 2800 High Point Drive, Milton, Ontario L9T 6P4, Canada
| | - Myrna J Simpson
- Environmental NMR Centre, University of Toronto, Toronto, Ontario M1C 1A4, Canada
| | - André J Simpson
- Environmental NMR Centre, University of Toronto, Toronto, Ontario M1C 1A4, Canada
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26
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Wu Y, Judge MT, Arnold J, Bhandarkar SM, Edison AS. RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking. Bioinformatics 2021; 36:5068-5075. [PMID: 32653900 PMCID: PMC7755419 DOI: 10.1093/bioinformatics/btaa631] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 05/28/2020] [Accepted: 07/06/2020] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. RESULTS We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. AVAILABILITY AND IMPLEMENTATION Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yue Wu
- Institute of Bioinformatics
| | | | | | | | - Arthur S Edison
- Institute of Bioinformatics.,Department of Genetics.,Complex Carbohydrate Research Center.,Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
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27
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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28
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Wild Isolates of Neurospora crassa Reveal Three Conidiophore Architectural Phenotypes. Microorganisms 2020; 8:microorganisms8111760. [PMID: 33182369 PMCID: PMC7695285 DOI: 10.3390/microorganisms8111760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 12/25/2022] Open
Abstract
The vegetative life cycle in the model filamentous fungus, Neurospora crassa, relies on the development of conidiophores to produce new spores. Environmental, temporal, and genetic components of conidiophore development have been well characterized; however, little is known about their morphological variation. We explored conidiophore architectural variation in a natural population using a wild population collection of 21 strains from Louisiana, United States of America (USA). Our work reveals three novel architectural phenotypes, Wild Type, Bulky, and Wrap, and shows their maintenance throughout the duration of conidiophore development. Furthermore, we present a novel image-classifier using a convolutional neural network specifically developed to assign conidiophore architectural phenotypes in a high-throughput manner. To estimate an inheritance model for this discrete complex trait, crosses between strains of each phenotype were conducted, and conidiophores of subsequent progeny were characterized using the trained classifier. Our model suggests that conidiophore architecture is controlled by at least two genes and has a heritability of 0.23. Additionally, we quantified the number of conidia produced by each conidiophore type and their dispersion distance, suggesting that conidiophore architectural phenotype may impact N. crassa colonization capacity.
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29
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Affiliation(s)
- Philipp Brandt
- Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology–Hans Knöll Institute, Jena, Germany
| | - Enrico Garbe
- Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology–Hans Knöll Institute, Jena, Germany
| | - Slavena Vylkova
- Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology–Hans Knöll Institute, Jena, Germany
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30
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Kumar R, Ghosh M, Kumar S, Prasad M. Single Cell Metabolomics: A Future Tool to Unmask Cellular Heterogeneity and Virus-Host Interaction in Context of Emerging Viral Diseases. Front Microbiol 2020; 11:1152. [PMID: 32582094 PMCID: PMC7286130 DOI: 10.3389/fmicb.2020.01152] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/06/2020] [Indexed: 12/15/2022] Open
Abstract
Viral emergence is an unpredictable but obvious event, particularly in the era of climate change and globalization. Efficient management of viral outbreaks depends on pre-existing knowledge and alertness. The potential hotspots of viral emergence often remain neglected and the information related to them is insufficient, particularly for emerging viruses. Viral replication and transmission rely upon usurping the host metabolic machineries. So altered host metabolic pathways can be exploited for containment of these viruses. Metabolomics provides the insight for tracing out such checkpoints. Consequently introspection of metabolic alteration at virus-host interface has evolved as prime area in current virology research. Chromatographic separation followed by mass spectrometry has been used as the predominant analytical platform in bulk of the analyses followed by nuclear magnetic resonance (NMR) and fluorescence based techniques. Although valuable information regarding viral replication and modulation of host metabolic pathways have been extracted but ambiguity often superseded the real events due to population effect over the infected cells. Exploration of cellular heterogeneity and differentiation of infected cells from the nearby healthy ones has become essential. Single cell metabolomics (SCM) emerges as necessity to explore such minute details. Mass spectrometry imaging (MSI) coupled with several soft ionization techniques such as electrospray ionization (ESI), laser ablation electrospray ionization (LAESI), matrix assisted laser desorption/ionization (MALDI), matrix-free laser desorption ionization (LDI) have evolved as the best suited platforms for SCM analyses. The potential of SCM has already been exploited to resolve several biological conundrums. Thus SCM is knocking at the door of virus-host interface.
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Affiliation(s)
- Rajesh Kumar
- Department of Veterinary Physiology and Biochemistry, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, India
| | - Mayukh Ghosh
- Department of Veterinary Physiology and Biochemistry, RGSC, Banaras Hindu University, Mirzapur, India
| | - Sandeep Kumar
- Department of Veterinary Surgery and Radiology, College of Veterinary Sciences, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, India
| | - Minakshi Prasad
- Department of Animal Biotechnology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, India
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31
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Nguyen TTM, An YJ, Cha JW, Ko YJ, Lee H, Chung CH, Jeon SM, Lee J, Park S. Real-Time In-Organism NMR Metabolomics Reveals Different Roles of AMP-Activated Protein Kinase Catalytic Subunits. Anal Chem 2020; 92:7382-7387. [PMID: 32392040 DOI: 10.1021/acs.analchem.9b05670] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
AMP-activated protein kinase (AMPK in human and AAK in C. elegans) is a master regulator of metabolism. It has many isotypes, but its isotype-dependent functions are largely unknown. By developing real-time in-organism NMR metabolomics for C. elegans, we were able to study different roles of the isotypic catalytic subunits of AAK/AMPK, AAK-1, and AAK-2 in live worms at the whole organism level. The aak-1 knockout animals exhibited enhanced glucose production under starvation, strikingly opposite to aak-2 knockout animals. Unusually high compensatory expression of the reciprocal isotypes in each KO strain and the results for the double KO animals suggested an unconventional phenotype-genotype relationship and the dominance of aak-2 in glucose production. The gene expression patterns showed that the differential phenotypes of aak-1 KO strain are due to reduced TCA and glycolysis and enhanced gluconeogenesis compared to the aak-2 KO strain. Subsequent 13C-isotope incorporation experiment showed that the glucose production in aak-1 KO occurs through the activation of fatty acid oxidation and glyoxylate shunt. Revealing differential roles of the isotypes of AAK/AMPK, our convenient approach is readily applicable to many C. elegans models for human metabolic diseases.
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Affiliation(s)
- Tin Tin Manh Nguyen
- Natural Product Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Korea
| | - Yong Jin An
- Natural Product Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Korea
| | - Jin Wook Cha
- Natural Product Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Korea.,Natural Product Informatics Research Center, KIST Gangneung Institute of Natural Products, Gangneung 25451, Korea
| | - Yoon-Joo Ko
- National Centre for Inter-University Research Facilities (NCIRF), Seoul National University, Seoul 08826, Korea
| | - Hanee Lee
- Department of Biological Sciences, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Korea
| | - Christine H Chung
- Department of Biological Sciences, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Korea
| | - Sang-Min Jeon
- College of Pharmacy and Institute of Pharmaceutical Science and Technology, Ajou University, Gyeonggi-do 16499, Korea
| | - Junho Lee
- Department of Biological Sciences, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Korea
| | - Sunghyouk Park
- Natural Product Research Institute, College of Pharmacy, Seoul National University, Seoul 08826, Korea
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