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Lin P, Sledziona J, Akkaya-Colak KB, Mihaylova MM, Lane AN. Determination of fatty acid uptake and desaturase activity in mammalian cells by NMR-based stable isotope tracing. Anal Chim Acta 2024; 1303:342511. [PMID: 38609261 PMCID: PMC11016156 DOI: 10.1016/j.aca.2024.342511] [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: 10/20/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Mammalian cells both import exogenous fatty acids and synthesize them de novo. Palmitate, the end product of fatty acid synthase (FASN) is a substrate for stearoyl-CoA desaturases (Δ-9 desaturases) that introduce a single double bond into fatty acyl-CoA substrates such as palmitoyl-CoA and stearoyl-CoA. This process is particularly upregulated in lipogenic tissues and cancer cells. Tracer methodology is needed to determine uptake versus de novo synthesis of lipids and subsequent chain elongation and desaturation. Here we describe an NMR method to determine the uptake of 13C-palmitate from the medium into HCT116 human colorectal cancer cells, and the subsequent desaturation and incorporation into complex lipids. RESULTS Exogenous 13C16-palmitate was absorbed from the medium by HCT116 cells and incorporated primarily into complex glycerol lipids. Desaturase activity was determined from the quantification of double bonds in acyl chains, which was greatly reduced by ablation of the major desaturase SCD1. SIGNIFICANCE The NMR approach requires minimal sample preparation, is non-destructive, and provides direct information about the level of saturation and incorporation of fatty acids into complex lipids.
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Affiliation(s)
- Penghui Lin
- Center for Environmental and Systems Biochemistry, Dept. of Toxicology and Cancer Biology, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - James Sledziona
- Department of Biological Chemistry and Pharmacology, The Ohio State University, 1060 Carmack Rd, Columbus, OH, 43210, USA; The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Kubra B Akkaya-Colak
- Department of Biological Chemistry and Pharmacology, The Ohio State University, 1060 Carmack Rd, Columbus, OH, 43210, USA; The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Maria M Mihaylova
- Department of Biological Chemistry and Pharmacology, The Ohio State University, 1060 Carmack Rd, Columbus, OH, 43210, USA; The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Andrew N Lane
- Center for Environmental and Systems Biochemistry, Dept. of Toxicology and Cancer Biology, Markey Cancer Center, University of Kentucky, Lexington, KY, USA.
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Boguszewicz Ł, Heyda A, Ciszek M, Bieleń A, Skorupa A, Mrochem-Kwarciak J, Składowski K, Sokół M. Metabolite Biomarkers of Prolonged and Intensified Pain and Distress in Head and Neck Cancer Patients Undergoing Radio- or Chemoradiotherapy by Means of NMR-Based Metabolomics-A Preliminary Study. Metabolites 2024; 14:60. [PMID: 38248863 PMCID: PMC10819132 DOI: 10.3390/metabo14010060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/09/2024] [Accepted: 01/13/2024] [Indexed: 01/23/2024] Open
Abstract
Treatment of head and neck squamous cell carcinoma (HNSCC) has a detrimental impact on patient quality of life. The rate of recognized distress/depression among HNSCC patients ranges from 9.8% to 83.8%, and the estimated prevalence of depression among patients receiving radiotherapy is 63%. Shorter overall survival also occurs in preexisting depression or depressive conditions. The present study analyzes the nuclear magnetic resonance (NMR) blood serum metabolic profiles during radio-/chemoradiotherapy and correlates the detected alterations with pain and/or distress accumulated with the disease and its treatment. NMR spectra were acquired on a Bruker 400 MHz spectrometer and analyzed using multivariate methods. The results indicate that distress and/or pain primarily affect the serum lipids and metabolites of energy (glutamine, glucose, lactate, acetate) and one-carbon (glycine, choline, betaine, methanol, threonine, serine, histidine, formate) metabolism. Sparse disturbances in the branched-chain amino acids (BCAA) and in the metabolites involved in protein metabolism (lysine, tyrosine, phenylalanine) are also observed. Depending on the treatment modality-radiotherapy or concurrent chemoradiotherapy-there are some differences in the altered metabolites.
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Affiliation(s)
- Łukasz Boguszewicz
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (M.C.); (A.S.); (M.S.)
| | - Alicja Heyda
- 1st Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (A.H.); (A.B.)
| | - Mateusz Ciszek
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (M.C.); (A.S.); (M.S.)
| | - Agata Bieleń
- 1st Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (A.H.); (A.B.)
| | - Agnieszka Skorupa
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (M.C.); (A.S.); (M.S.)
| | - Jolanta Mrochem-Kwarciak
- Analytics and Clinical Biochemistry Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland;
| | - Krzysztof Składowski
- 1st Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (A.H.); (A.B.)
| | - Maria Sokół
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (M.C.); (A.S.); (M.S.)
- 1st Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland; (A.H.); (A.B.)
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Zhang X, Su Y, Lane AN, Stromberg AJ, Fan TWM, Wang C. Bayesian kinetic modeling for tracer-based metabolomic data. BMC Bioinformatics 2023; 24:108. [PMID: 36949395 PMCID: PMC10035190 DOI: 10.1186/s12859-023-05211-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/24/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. RESULTS We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux . CONCLUSIONS Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.
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Affiliation(s)
- Xu Zhang
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA.
| | - Ya Su
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, 23220, USA
| | - Andrew N Lane
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, 40536, USA
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, 40536, USA
| | - Arnold J Stromberg
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA
| | - Teresa W M Fan
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, 40536, USA
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, 40536, USA
| | - Chi Wang
- Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, 40536, USA.
- Markey Cancer Center, University of Kentucky, Lexington, 40536, USA.
- Division of Cancer Biostatistics, Department of Internal Medicine, University of Kentucky, Lexington, 40536, USA.
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Lipidomics analysis in drug discovery and development. Curr Opin Chem Biol 2023; 72:102256. [PMID: 36586190 DOI: 10.1016/j.cbpa.2022.102256] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/08/2022] [Accepted: 11/28/2022] [Indexed: 12/30/2022]
Abstract
Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics', stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology and Pain Medicine, University of Washington
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109
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Lin P, W-M Fan T, Lane AN. NMR-based isotope editing, chemoselection and isotopomer distribution analysis in stable isotope resolved metabolomics. Methods 2022; 206:8-17. [PMID: 35908585 PMCID: PMC9539636 DOI: 10.1016/j.ymeth.2022.07.014] [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: 05/24/2022] [Revised: 07/18/2022] [Accepted: 07/25/2022] [Indexed: 11/20/2022] Open
Abstract
NMR is a very powerful tool for identifying and quantifying compounds within complex mixtures without the need for individual standards or chromatographic separation. Stable Isotope Resolved Metabolomics (or SIRM) is an approach to following the fate of individual atoms from precursors through metabolic transformation, producing an atom-resolved metabolic fate map. However, extracts of cells or tissue give rise to very complex NMR spectra. While multidimensional NMR experiments may partially overcome the spectral overlap problem, additional tools may be needed to determine site-specific isotopomer distributions. NMR is especially powerful by virtue of its isotope editing capabilities using NMR active nuclei such as 13C, 15N, 19F and 31P to select molecules containing just these atoms in a complex mixture, and provide direct information about which atoms are present in identified compounds and their relative abundances. The isotope-editing capability of NMR can also be employed to select for those compounds that have been selectively derivatized with an NMR-active stable isotope at particular functional groups, leading to considerable spectral simplification. Here we review isotope analysis by NMR, and methods of chemoselection both for spectral simplification, and for enhanced isotopomer analysis.
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Affiliation(s)
- Penghui Lin
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY 40536, USA
| | - Teresa W-M Fan
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY 40536, USA; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA
| | - Andrew N Lane
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY 40536, USA; Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40536, USA.
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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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Moco S. Studying Metabolism by NMR-Based Metabolomics. Front Mol Biosci 2022; 9:882487. [PMID: 35573745 PMCID: PMC9094115 DOI: 10.3389/fmolb.2022.882487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/24/2022] [Indexed: 12/12/2022] Open
Abstract
During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.
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