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Wang Z, Li T, Lin L, Xiong Y, Chen H, Ye J, Ji J, Wang S. Investigating the effects of dexamethasone on pulmonary surfactant lipids based on lipidomics studies. Biomed Chromatogr 2024; 38:e5937. [PMID: 38922717 DOI: 10.1002/bmc.5937] [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: 02/20/2024] [Revised: 04/17/2024] [Accepted: 06/01/2024] [Indexed: 06/28/2024]
Abstract
Dexamethasone, a glucocorticoid commonly used in pediatric patients, has potent anti-inflammatory and immunosuppressive properties. However, it is associated with side effects such as reduced lung function and decreased immunity. Pulmonary surfactant lipids are closely linked to lung disease and play a role in reducing surface tension, immune response and antiviral activity. The dysregulation of lipid metabolism is closely associated with lung disease. Hence, untargeted lipidomics may be instrumental in elucidating the effects of dexamethasone on pulmonary surfactant lipids. We obtained surfactant lipid samples from the bronchoalveolar lavage fluid of young mice injected subcutaneously with dexamethasone and conducted a comprehensive lipidomic analysis, comparing them with a control group. We observed a decrease in lipids, such as phosphatidylcholine, phosphatidylglycerol and phosphatidylethanolamine, and an increase in ceramide, fatty acid, diacylglycerol and monoglyceride, which may impact lung health. This study revealed the influence of dexamethasone on pulmonary surfactant lipids, offering new insights into adverse reactions in clinical settings.
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Affiliation(s)
- Zhao Wang
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Tao Li
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Lili Lin
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yingcai Xiong
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Chen
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jin Ye
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jianjian Ji
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shouchuan Wang
- Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Medical Metabolomics Center, Pediatrics Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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2
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Choi EK, Rajendiran TM, Soni T, Park JH, Aring L, Muraleedharan CK, Garcia-Hernandez V, Kamada N, Samuelson LC, Nusrat A, Iwase S, Seo YA. The manganese transporter SLC39A8 links alkaline ceramidase 1 to inflammatory bowel disease. Nat Commun 2024; 15:4775. [PMID: 38839750 PMCID: PMC11153611 DOI: 10.1038/s41467-024-49049-8] [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: 02/25/2022] [Accepted: 05/17/2024] [Indexed: 06/07/2024] Open
Abstract
The metal ion transporter SLC39A8 is associated with physiological traits and diseases, including blood manganese (Mn) levels and inflammatory bowel diseases (IBD). The mechanisms by which SLC39A8 controls Mn homeostasis and epithelial integrity remain elusive. Here, we generate Slc39a8 intestinal epithelial cell-specific-knockout (Slc39a8-IEC KO) mice, which display markedly decreased Mn levels in blood and most organs. Radiotracer studies reveal impaired intestinal absorption of dietary Mn in Slc39a8-IEC KO mice. SLC39A8 is localized to the apical membrane and mediates 54Mn uptake in intestinal organoid monolayer cultures. Unbiased transcriptomic analysis identifies alkaline ceramidase 1 (ACER1), a key enzyme in sphingolipid metabolism, as a potential therapeutic target for SLC39A8-associated IBDs. Importantly, treatment with an ACER1 inhibitor attenuates colitis in Slc39a8-IEC KO mice by remedying barrier dysfunction. Our results highlight the essential roles of SLC39A8 in intestinal Mn absorption and epithelial integrity and offer a therapeutic target for IBD associated with impaired Mn homeostasis.
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Affiliation(s)
- Eun-Kyung Choi
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Thekkelnaycke M Rajendiran
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Tanu Soni
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jin-Ho Park
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Luisa Aring
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Nobuhiko Kamada
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Linda C Samuelson
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Asma Nusrat
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Shigeki Iwase
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Young Ah Seo
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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Roberts JA, Rosales CA, Wasslen KV, Radnoff AS, Godbout E, Diallo JS, Manthorpe JM, Smith JC. An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry 13C-Trimethylation Enhancement Using Diazomethane ( 13C-TrEnDi)-Modified Lipid Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2722-2730. [PMID: 37929927 DOI: 10.1021/jasms.3c00273] [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: 11/07/2023]
Abstract
13C-Trimethylation enhancement using diazomethane (13C-TrEnDi) is a chemical derivatization technique that uses 13C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, 13C-TrEnDi-modified PC and PE species have altered m/z and fragmentation patterns from their native counterparts. To build a database of 13C-TrEnDi-modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching 13C-TrEnDi-modified headgroups with m/z 199 (PC) and 202 (PE). Identities of 25 PC and 10 PE species were assigned after comparing to predicted masses from the Lipid Maps Structure Database with no false positive identifications observed; neutral lipids could still be annotated after derivatization. Collision energies from 16 to 52 eV were examined, resulting in three additional class-specific fragment ions emerging, as well as a combined sn-1/sn-2 fragment ion, allowing sum-composition level annotations to be assigned. Using the Lipid Blast templates, a NIST-compatible 13C-TrEnDi database was produced based on fragmentation spectra observed at 36 eV and tested on HEK 293T cell lipid extracts, identifying 47 PC and 24 PE species, representing a 1.8-fold and 2.2-fold increase in annotations, respectively. The 13C-TrEnDi database is freely available, MS vendor-independent, and widely compatible with MS data processing pipelines, increasing the throughput and accessibility of TrEnDi for lipidomics applications.
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Affiliation(s)
- Joshua A Roberts
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Christian A Rosales
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Karl V Wasslen
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
- Carleton Mass Spectrometry Centre, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Angela S Radnoff
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Elena Godbout
- Centre for Cancer Therapeutics, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada
| | - Jean-Simon Diallo
- Centre for Cancer Therapeutics, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada
| | - Jeffrey M Manthorpe
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
- Carleton Mass Spectrometry Centre, Carleton University, Ottawa, Ontario K1S 5B6, Canada
| | - Jeffrey C Smith
- Department of Chemistry, Carleton University, Ottawa, Ontario K1S 5B6, Canada
- Carleton Mass Spectrometry Centre, Carleton University, Ottawa, Ontario K1S 5B6, Canada
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Ly R, Torres LC, Ly N, Britz-McKibbin P. Expanding Lipidomic Coverage in Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry via a Convenient and Quantitative Methylation Strategy. Anal Chem 2023; 95:17513-17524. [PMID: 37991882 DOI: 10.1021/acs.analchem.3c02605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Orthogonal separation techniques coupled to high-resolution mass spectrometry are required for characterizing the human lipidome, given its inherent chemical and structural complexity. However, electrophoretic separations remain largely unrecognized in contemporary lipidomics research compared to established chromatographic and ion mobility methods. Herein, we introduce a novel derivatization protocol based on 3-methyl-1-p-tolyltriazene (MTT) as a safer alternative to diazomethane for quantitative phospholipid (PL) methylation (∼90%), which enables their rapid analysis by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). Isobaric interferences and ion suppression effects were minimized by performing an initial reaction using 9-fluorenylmethyoxycarbonyl chloride prior to MTT and a subsequent back extraction in hexane. This charge-switch derivatization strategy expands lipidome coverage when using MSI-NACE-MS under positive ion mode with improved resolution, greater sensitivity, and higher throughput (∼3.5 min/sample), notably for zwitterionic PLs that are analyzed as their cationic phosphate methyl esters. Our method was validated by analyzing methyl-tert-butyl ether extracts of reference human plasma, which enabled a direct comparison of 48 phosphatidylcholine and 27 sphingomyelin species previously reported in an interlaboratory lipidomics harmonization study. The potential for plasma PL quantification by MSI-NACE-MS via a serial dilution of NIST SRM-1950 was also demonstrated based on estimation of relative response factors using their reported consensus concentrations. Moreover, lipid identification was supported by modeling predictable changes in the electrophoretic mobility for cationic PLs in conjunction with MS/MS. Overall, this work offers a practical derivatization protocol to expand lipidome coverage in CE-MS beyond the analysis of hydrophilic/polar metabolites under aqueous buffer conditions.
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Affiliation(s)
- Ritchie Ly
- Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4M1
| | - Lucas Christian Torres
- Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4M1
| | - Nicholas Ly
- Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4M1
| | - Philip Britz-McKibbin
- Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4M1
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Cajka T, Hricko J, Rudl Kulhava L, Paucova M, Novakova M, Fiehn O, Kuda O. Exploring the Impact of Organic Solvent Quality and Unusual Adduct Formation during LC-MS-Based Lipidomic Profiling. Metabolites 2023; 13:966. [PMID: 37755246 PMCID: PMC10536874 DOI: 10.3390/metabo13090966] [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: 08/03/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 09/28/2023] Open
Abstract
Liquid chromatography-mass spectrometry (LC-MS) is the key technique for analyzing complex lipids in biological samples. Various LC-MS modes are used for lipid separation, including different stationary phases, mobile-phase solvents, and modifiers. Quality control in lipidomics analysis is crucial to ensuring the generated data's reliability, reproducibility, and accuracy. While several quality control measures are commonly discussed, the impact of organic solvent quality during LC-MS analysis is often overlooked. Additionally, the annotation of complex lipids remains prone to biases, leading to potential misidentifications and incomplete characterization of lipid species. In this study, we investigate how LC-MS-grade isopropanol from different vendors may influence the quality of the mobile phase used in LC-MS-based untargeted lipidomic profiling of biological samples. Furthermore, we report the occurrence of an unusual, yet highly abundant, ethylamine adduct [M+46.0651]+ that may form for specific lipid subclasses during LC-MS analysis in positive electrospray ionization mode when acetonitrile is part of the mobile phase, potentially leading to lipid misidentification. These findings emphasize the importance of considering solvent quality in LC-MS analysis and highlight challenges in lipid annotation.
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Affiliation(s)
- Tomas Cajka
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
| | - Jiri Hricko
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
| | - Lucie Rudl Kulhava
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
| | - Michaela Paucova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
| | - Michaela Novakova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, 451 Health Sciences Drive, Davis, CA 95616, USA;
| | - Ondrej Kuda
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic; (J.H.); (L.R.K.); (M.P.); (M.N.); (O.K.)
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Dhariwal S, Maan K, Baghel R, Sharma A, Malakar D, Rana P. Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum. Metabolomics 2023; 19:24. [PMID: 36971892 DOI: 10.1007/s11306-023-01983-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 03/04/2023] [Indexed: 03/28/2023]
Abstract
INTRODUCTION AND OBJECTIVE Taking into consideration the challenges of lipid analytics, present study aims to design the best high-throughput workflow for detection and annotation of lipids. MATERIAL AND METHODS Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software. RESULT AND DISCUSSION Better detection of features was observed in CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin). CONCLUSION The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipid profiling (CSH-C18 column) and confirmatory annotation (LipidBlast).
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Affiliation(s)
- Seema Dhariwal
- Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India
| | - Kiran Maan
- Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India
| | - Ruchi Baghel
- Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India
| | - Apoorva Sharma
- Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India
| | | | - Poonam Rana
- Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India.
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Shen Y, Wang H, Fang J, Liu K, Xu X. Novel insights into the mechanisms of hard exudate in diabetic retinopathy: Findings of serum lipidomic and metabolomics profiling. Heliyon 2023; 9:e15123. [PMID: 37089301 PMCID: PMC10119565 DOI: 10.1016/j.heliyon.2023.e15123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Objective Retinal hard exudates (HEs) result from lipoproteins leaking from capillaries into extracellular retinal space, and are related to decreased visual acuity in diabetic retinopathy (DR). This study aims to identify differential serum lipids and metabolites associated with HEs. Materials and methods A cross-sectional study was conducted Jul 2017 ∼ Mar 2021. We assessed the amount of HEs using standard ETDRS photographs for comparison. HEs severity was rated as "no or questionable", "moderate" or "severe". Serum samples were processed via high coverage pseudotargeted lipidomics analysis, and untargeted liquid chromatography coupled with time-of-flight mass spectrometry for metabolomics study, respectively. Weighted gene co-expression network analyses, partial least squares-discriminant analysis, and multi-receiver operating characteristic analysis were applied. Results A total of 167 patients were included. Discovery group: 116 eyes (116 patients). Validation group: 51 eyes (51 patients). 888 lipids were detected and divided into 18 modules (MEs), ME1 ∼ ME18. Lipids in ME1 significantly increased in patients with HEs in DR (NPDR and PDR combined), NPDR, and PDR, respectively. ME1 enriched to triglycerides (29%), ceramides (17%), and N-acylethanolamines (15%). A combined model of 20 lipids was the best to discriminate HEs, area under curve = 0.804, 95% confidence interval = 0.674-0.916. For metabolomics analysis, 19 metabolites and 13 pathways associated with HEs were identified. Taurine and hypotaurine metabolism, cysteine and methionine metabolism were closely related to HEs (P < 0.01). Conclusions The lipids and metabolites identified may serve as prediction biomarkers in the early stage of HEs in DR.
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MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform. Metabolites 2022; 12:metabo12080684. [PMID: 35893250 PMCID: PMC9330773 DOI: 10.3390/metabo12080684] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022] Open
Abstract
MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.
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Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022; 12:metabo12070584. [PMID: 35888710 PMCID: PMC9319858 DOI: 10.3390/metabo12070584] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/17/2022] [Accepted: 06/19/2022] [Indexed: 12/13/2022] Open
Abstract
Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.
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Affiliation(s)
- Nils Hoffmann
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), 52425 Jülich, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Canan Has
- Biological Mass Spectrometry, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
- University Hospital Carl Gustav Carus, 01307 Dresden, Germany
- CENTOGENE GmbH, 18055 Rostock, Germany
| | - Dominik Kopczynski
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Fadi Al Machot
- Faculty of Science and Technology, Norwegian University for Life Science (NMBU), 1433 Ås, Norway;
| | - Dominik Schwudke
- Bioanalytical Chemistry, Forschungszentrum Borstel, Leibniz Lung Center, 23845 Borstel, Germany;
- Airway Research Center North, German Center for Lung Research (DZL), 23845 Borstel, Germany
- German Center for Infection Research (DZIF), TTU Tuberculosis, 23845 Borstel, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Katrin Marcus
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
| | - Martin Eisenacher
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Turewicz
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, 85764 Neuherberg, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
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An Amish founder population reveals rare-population genetic determinants of the human lipidome. Commun Biol 2022; 5:334. [PMID: 35393526 PMCID: PMC8989972 DOI: 10.1038/s42003-022-03291-2] [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: 08/27/2021] [Accepted: 03/17/2022] [Indexed: 12/02/2022] Open
Abstract
Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and additional insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of previously unknown biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a genome-wide association scan of 355 lipid species in 650 individuals from the Amish founder population including 127 lipid species not previously tested. To the best of our knowledge, we report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLTPD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus and estriol with SLC22A8/A24 locus. Our results show the double power of founder populations and detailed lipidome to discover novel trait-associated variants. A GWAS of 355 lipid species in the Old Order Amish founder population reveals associations between Amish-enriched loci and several sphingolipids.
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Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity. Nutrients 2022; 14:nu14010214. [PMID: 35011090 PMCID: PMC8747180 DOI: 10.3390/nu14010214] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 12/29/2021] [Accepted: 12/31/2021] [Indexed: 02/04/2023] Open
Abstract
Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.
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12
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Irvin MR, Montasser ME, Kind T, Fan S, Barupal DK, Patki A, Tanner RM, Armstrong ND, Ryan KA, Claas SA, O’Connell JR, Tiwari HK, Arnett DK. Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study. Nutrients 2021; 13:4000. [PMID: 34836252 PMCID: PMC8617762 DOI: 10.3390/nu13114000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/25/2022] Open
Abstract
Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.
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Affiliation(s)
- Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (R.M.T.); (N.D.A.)
| | - May E. Montasser
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (M.E.M.); (K.A.R.); (J.R.O.)
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Tobias Kind
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA; (T.K.); (S.F.)
| | - Sili Fan
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA; (T.K.); (S.F.)
| | - Dinesh K. Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.P.); (H.K.T.)
| | - Rikki M. Tanner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (R.M.T.); (N.D.A.)
| | - Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (R.M.T.); (N.D.A.)
| | - Kathleen A. Ryan
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (M.E.M.); (K.A.R.); (J.R.O.)
| | - Steven A. Claas
- College of Public Health, University of Kentucky, Lexington, KY 40536, USA; (S.A.C.); (D.K.A.)
| | - Jeffrey R. O’Connell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (M.E.M.); (K.A.R.); (J.R.O.)
| | - Hemant K. Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (A.P.); (H.K.T.)
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY 40536, USA; (S.A.C.); (D.K.A.)
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13
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Cabruja M, Priotti J, Domizi P, Papsdorf K, Kroetz DL, Brunet A, Contrepois K, Snyder MP. In-depth triacylglycerol profiling using MS 3 Q-Trap mass spectrometry. Anal Chim Acta 2021; 1184:339023. [PMID: 34625255 DOI: 10.1016/j.aca.2021.339023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/07/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022]
Abstract
Total triacylglycerol (TAG) level is a key clinical marker of metabolic and cardiovascular diseases. However, the roles of individual TAGs have not been thoroughly explored in part due to their extreme structural complexity. We present a targeted mass spectrometry-based method combining multiple reaction monitoring (MRM) and multiple stage mass spectrometry (MS3) for the comprehensive qualitative and semiquantitative profiling of TAGs. This method referred as TriP-MS3 - triacylglycerol profiling using MS3 - screens for more than 6,700 TAG species in a fully automated fashion. TriP-MS3 demonstrated excellent reproducibility (median interday CV ∼ 0.15) and linearity (median R2 = 0.978) and detected 285 individual TAG species in human plasma. The semiquantitative accuracy of the method was validated by comparison with a state-of-the-art reverse phase liquid chromatography (RPLC)-MS (R2 = 0.83), which is the most commonly used approach for TAGs profiling. Finally, we demonstrate the utility and the versatility of the method by characterizing the effects of a fatty acid desaturase inhibitor on TAG profiles in vitro and by profiling TAGs in Caenorhabditis elegans.
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Affiliation(s)
- Matias Cabruja
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Josefina Priotti
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Pablo Domizi
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Deanna L Kroetz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Anne Brunet
- Department of Genetics, Stanford University, Stanford, CA, USA
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14
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Qiao Y, Choi JE, Tien JC, Simko SA, Rajendiran T, Vo JN, Delekta AD, Wang L, Xiao L, Hodge NB, Desai P, Mendoza S, Juckette K, Xu A, Soni T, Su F, Wang R, Cao X, Yu J, Kryczek I, Wang XM, Wang X, Siddiqui J, Wang Z, Bernard A, Fernandez-Salas E, Navone NM, Ellison SJ, Ding K, Eskelinen EL, Heath EI, Klionsky DJ, Zou W, Chinnaiyan AM. Autophagy Inhibition by Targeting PIKfyve Potentiates Response to Immune Checkpoint Blockade in Prostate Cancer. NATURE CANCER 2021; 2:978-993. [PMID: 34738088 PMCID: PMC8562569 DOI: 10.1038/s43018-021-00237-1] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Multi-tyrosine kinase inhibitors (MTKIs) have thus far had limited success in the treatment of castration-resistant prostate cancer (CRPC). Here, we report a phase I-cleared orally bioavailable MTKI, ESK981, with a novel autophagy inhibitory property that decreased tumor growth in diverse preclinical models of CRPC. The anti-tumor activity of ESK981 was maximized in immunocompetent tumor environments where it upregulated CXCL10 expression through the interferon gamma pathway and promoted functional T cell infiltration, which resulted in enhanced therapeutic response to immune checkpoint blockade. Mechanistically, we identify the lipid kinase PIKfyve as the direct target of ESK981. PIKfyve-knockdown recapitulated ESK981's anti-tumor activity and enhanced the therapeutic benefit of immune checkpoint blockade. Our study reveals that targeting PIKfyve via ESK981 turns tumors from cold into hot through inhibition of autophagy, which may prime the tumor immune microenvironment in advanced prostate cancer patients and be an effective treatment strategy alone or in combination with immunotherapies.
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Affiliation(s)
- Yuanyuan Qiao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Jae Eun Choi
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,School of Medicine, University of California, San Diego, California 92093, USA
| | - Jean C. Tien
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Stephanie A. Simko
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Thekkelnaycke Rajendiran
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, Michigan 48109, USA
| | - Josh N. Vo
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Andrew D. Delekta
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Lisha Wang
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Lanbo Xiao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Nathan B. Hodge
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Parth Desai
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Sergio Mendoza
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Kristin Juckette
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Alice Xu
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Tanu Soni
- Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, Michigan 48109, USA
| | - Fengyun Su
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Rui Wang
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xuhong Cao
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Jiali Yu
- Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Ilona Kryczek
- Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Xiao-Ming Wang
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xiaoju Wang
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Javed Siddiqui
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Zhen Wang
- School of Pharmacy, Jinan University, Guangzhou 510632, China
| | - Amélie Bernard
- CNRS, Laboratoire de Biogenèse Membranaire, UMR5200; Université de Bordeaux, Laboratoire de Biogenèse Membranaire, UMR5200, 33000 Bordeaux, France.,Life Sciences Institute and Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Ester Fernandez-Salas
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Nora M. Navone
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Stephanie J. Ellison
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Ke Ding
- School of Pharmacy, Jinan University, Guangzhou 510632, China
| | | | - Elisabeth I. Heath
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan 48201, USA
| | - Daniel J. Klionsky
- Life Sciences Institute and Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Weiping Zou
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - Arul M. Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, USA.,Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Urology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Correspondence to: Arul M. Chinnaiyan, Michigan Center for Translational Pathology, Rogel Cancer Center, 1500 East Medical Center Drive, Ann Arbor, Michigan 48109, USA. Phone: 734-615-4062; Fax: 734-615-4498;
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15
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Abstract
Lipids are natural substances found in all living organisms and involved in many biological functions. Imbalances in the lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species making them a challenge to analyze because of their great structural diversity.Thanks to the technological improvements in the fields of chromatography, high-resolution mass spectrometry, and bioinformatics over the last years, it is now possible to perform global lipidomics analyses, allowing the concomitant detection, identification, and relative quantification of hundreds of lipid species. This review shall provide an insight into a general lipidomics workflow and its application in metabolic biomarker research.
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16
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Gallart-Ayala H, Teav T, Ivanisevic J. Metabolomics meets lipidomics: Assessing the small molecule component of metabolism. Bioessays 2021; 42:e2000052. [PMID: 33230910 DOI: 10.1002/bies.202000052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 09/11/2020] [Indexed: 12/16/2022]
Abstract
Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.
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Affiliation(s)
- Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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17
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Narayanaswamy P, Teo G, Ow JR, Lau A, Kaldis P, Tate S, Choi H. MetaboKit: a comprehensive data extraction tool for untargeted metabolomics. Mol Omics 2021; 16:436-447. [PMID: 32519713 DOI: 10.1039/d0mo00030b] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.
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Affiliation(s)
| | - Guoshou Teo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Jin Rong Ow
- Institute of Molecular and Cell Biology, A*STAR, Singapore
| | | | - Philipp Kaldis
- Institute of Molecular and Cell Biology, A*STAR, Singapore and Department of Clinical Sciences, Lund University, Sweden
| | | | - Hyungwon Choi
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. and Institute of Molecular and Cell Biology, A*STAR, Singapore
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18
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Tao Y, Acket S, Beaumont E, Galez H, Duma L, Rossez Y. Colistin Treatment Affects Lipid Composition of Acinetobacter baumannii. Antibiotics (Basel) 2021; 10:antibiotics10050528. [PMID: 34063718 PMCID: PMC8147793 DOI: 10.3390/antibiotics10050528] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 04/30/2021] [Accepted: 05/01/2021] [Indexed: 12/15/2022] Open
Abstract
Multidrug-resistant Acinetobacter baumannii (A. baumannii) causes severe and often fatal healthcare-associated infections due partly to antibiotic resistance. There are no studies on A. baumannii lipidomics of susceptible and resistant strains grown at lethal and sublethal concentrations. Therefore, we analyzed the impact of colistin resistance on glycerolipids’ content by using untargeted lipidomics on clinical isolate. Nine lipid sub-classes were annotated, including phosphatidylcholine, rarely detected in the bacterial membrane among 130 different lipid species. The other lipid sub-classes detected are phosphatidylethanolamine (PE), phosphatidylglycerol (PG), lysophosphatidylethanolamine, hemibismonoacylglycerophosphate, cardiolipin, monolysocardiolipin, diacylglycerol, and triacylglycerol. Under lethal and sublethal concentrations of colistin, significant reduction of PE was observed on the resistant and susceptible strain, respectively. Palmitic acid percentage was higher at colistin at low concentration but only for the susceptible strain. When looking at individual lipid species, the most abundant PE and PG species (PE 34:1 and PG 34:1) are significantly upregulated when the susceptible and the resistant strains are cultivated with colistin. This is, to date, the most exhaustive lipidomics data compilation of A. baumannii cultivated in the presence of colistin. This work is highlighting the plasma membrane plasticity used by this gram-negative bacterium to survive colistin treatment.
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19
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Züllig T, Köfeler HC. HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS. MASS SPECTROMETRY REVIEWS 2021; 40:162-176. [PMID: 32233039 PMCID: PMC8049033 DOI: 10.1002/mas.21627] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 03/06/2020] [Indexed: 05/04/2023]
Abstract
The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.
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Affiliation(s)
- Thomas Züllig
- Core Facility Mass SpectrometryMedical University of Graz, ZMFGrazAustria
| | - Harald C. Köfeler
- Core Facility Mass SpectrometryMedical University of Graz, ZMFGrazAustria
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20
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Carrard J, Gallart-Ayala H, Infanger D, Teav T, Wagner J, Knaier R, Colledge F, Streese L, Königstein K, Hinrichs T, Hanssen H, Ivanisevic J, Schmidt-Trucksäss A. Metabolic View on Human Healthspan: A Lipidome-Wide Association Study. Metabolites 2021; 11:metabo11050287. [PMID: 33946321 PMCID: PMC8146132 DOI: 10.3390/metabo11050287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 12/22/2022] Open
Abstract
As ageing is a major risk factor for the development of non-communicable diseases, extending healthspan has become a medical and societal necessity. Precise lipid phenotyping that captures metabolic individuality could support healthspan extension strategies. This study applied ‘omic-scale lipid profiling to characterise sex-specific age-related differences in the serum lipidome composition of healthy humans. A subset of the COmPLETE-Health study, composed of 73 young (25.2 ± 2.6 years, 43% female) and 77 aged (73.5 ± 2.3 years, 48% female) clinically healthy individuals, was investigated, using an untargeted liquid chromatography high-resolution mass spectrometry approach. Compared to their younger counterparts, aged females and males exhibited significant higher levels in 138 and 107 lipid species representing 15 and 13 distinct subclasses, respectively. Percentage of difference ranged from 5.8% to 61.7% (females) and from 5.3% to 46.0% (males), with sphingolipid and glycerophophospholipid species displaying the greatest amplitudes. Remarkably, specific sphingolipid and glycerophospholipid species, previously described as cardiometabolically favourable, were found elevated in aged individuals. Furthermore, specific ether-glycerophospholipid and lyso-glycerophosphocholine species displayed higher levels in aged females only, revealing a more favourable lipidome evolution in females. Altogether, age determined the circulating lipidome composition, while lipid species analysis revealed additional findings that were not observed at the subclass level.
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Affiliation(s)
- Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Denis Infanger
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
| | - Jonathan Wagner
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Raphael Knaier
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Flora Colledge
- Division of Sports Science, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland;
| | - Lukas Streese
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Karsten Königstein
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Timo Hinrichs
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Henner Hanssen
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, CH-1005 Lausanne, Switzerland; (H.G.-A.); (T.T.)
- Correspondence: (J.I.); (A.S.-T.)
| | - Arno Schmidt-Trucksäss
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, CH-4052 Basel, Switzerland; (J.C.); (D.I.); (J.W.); (R.K.); (L.S.); (K.K.); (T.H.); (H.H.)
- Correspondence: (J.I.); (A.S.-T.)
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21
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Cai F, Ren F, Zhang Y, Ding X, Fu G, Ren D, Yang L, Chen N, Shang Y, Hu Y, Yi L, Zhang H. Screening of lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1169:122603. [PMID: 33690078 DOI: 10.1016/j.jchromb.2021.122603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 02/09/2021] [Accepted: 02/13/2021] [Indexed: 12/27/2022]
Abstract
Coronary heart disease (CHD) has a high mortality worldwide. This study aimed to screen lipid metabolism biomarkers in patients with coronary heart disease via ultra-performance liquid chromatography-high resolution mass spectrometry. Extraction and reconstitution solvents, liquid chromatographic and mass spectrometry conditions were optimized to detect more plasma lipid metabolites. In this study, the chromatographic and mass spectra characteristics of lipid metabolites were summarized. A total of 316 lipid metabolites were annotated via diagnostic fragment ion filtration, nitrogen rule filtration, and neutral loss filtration. Glycerophospholipid metabolism and sphingolipid metabolism were revealed as the main lipid disorders of CHD. This study provides a novel insight for high-throughput detection of lipid metabolites in plasma and provides a further understanding of the occurrence of CHD, which can provide valuable suggestions for the prevention of CHD.
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Affiliation(s)
- Fang Cai
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Fandong Ren
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Yunmei Zhang
- Department of Cardiology, Yunnan First People's Hospital, Kunming 650032, China
| | - Xiaoxue Ding
- Department of Cardiology, Yunnan First People's Hospital, Kunming 650032, China
| | - Guanghui Fu
- School of Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Dabing Ren
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Lijuan Yang
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Ning Chen
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Ying Shang
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Yongdan Hu
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China
| | - Lunzhao Yi
- Faculty of Agriculture and Food Science, Kunming University of Science and Technology, Yunnan, Kunming 650500, China.
| | - Hong Zhang
- Department of Cardiology, Yunnan First People's Hospital, Kunming 650032, China.
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22
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Meister I, Zhang P, Sinha A, Sköld CM, Wheelock ÅM, Izumi T, Chaleckis R, Wheelock CE. High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology. Anal Chem 2021; 93:5248-5258. [PMID: 33739820 PMCID: PMC8041248 DOI: 10.1021/acs.analchem.1c00203] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 02/26/2021] [Indexed: 12/15/2022]
Abstract
Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.
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Affiliation(s)
- Isabel Meister
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Biomedicum Quartier 9A, Stockholm 171-77, Sweden
| | - Pei Zhang
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Biomedicum Quartier 9A, Stockholm 171-77, Sweden
| | - Anirban Sinha
- Department
of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands
- Department
of Experimental Immunology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands
- Computational
Physiology and Biostatistics, University
Children’s Hospital, Spitalstrasse 33, Basel 4056, Switzerland
| | - C. Magnus Sköld
- Respiratory
Medicine Unit, K2 Department of Medicine Solna and Center for Molecular
Medicine, Karolinska Institutet, Stockholm 141-86, Sweden
- Department
of Respiratory Medicine and Allergy, Karolinska
University Hospital, Stockholm 141-86, Sweden
| | - Åsa M. Wheelock
- Respiratory
Medicine Unit, K2 Department of Medicine Solna and Center for Molecular
Medicine, Karolinska Institutet, Stockholm 141-86, Sweden
- Department
of Respiratory Medicine and Allergy, Karolinska
University Hospital, Stockholm 141-86, Sweden
| | - Takashi Izumi
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan
- Department
of Biochemistry, Gunma University Graduate
School of Medicine, 3-39-22
Showa-machi, Maebashi, Gunma 371-8511, Japan
| | - Romanas Chaleckis
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Biomedicum Quartier 9A, Stockholm 171-77, Sweden
| | - Craig E. Wheelock
- Gunma
University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan
- Division
of Physiological Chemistry 2, Department of Medical Biochemistry and
Biophysics, Karolinska Institutet, Biomedicum Quartier 9A, Stockholm 171-77, Sweden
- Department
of Respiratory Medicine and Allergy, Karolinska
University Hospital, Stockholm 141-86, Sweden
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23
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Krettler CA, Thallinger GG. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Brief Bioinform 2021; 22:6184408. [PMID: 33758925 DOI: 10.1093/bib/bbab073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.
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Affiliation(s)
- Christoph A Krettler
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
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24
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Hyötyläinen T. Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics. J Sep Sci 2021; 44:1769-1787. [PMID: 33650238 DOI: 10.1002/jssc.202001263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/23/2021] [Accepted: 02/23/2021] [Indexed: 12/19/2022]
Abstract
Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.
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Affiliation(s)
- Tuulia Hyötyläinen
- MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden
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25
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Arsenate-Induced Changes in Bacterial Metabolite and Lipid Pools during Phosphate Stress. Appl Environ Microbiol 2021; 87:AEM.02261-20. [PMID: 33361371 DOI: 10.1128/aem.02261-20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/06/2020] [Indexed: 11/20/2022] Open
Abstract
Agrobacterium tumefaciens GW4 is a heterotrophic arsenite-oxidizing bacterium with a high resistance to arsenic toxicity. It is now a model organism for studying the processes of arsenic detoxification and utilization. Previously, we demonstrated that under low-phosphate conditions, arsenate [As(V)] could enhance bacterial growth and be incorporated into biomolecules, including lipids. While the basic microbial As(V) resistance mechanisms have been characterized, global metabolic responses under low phosphate remain largely unknown. In the present work, the impacts of As(V) and low phosphate on intracellular metabolite and lipid profiles of GW4 were quantified using liquid chromatography-mass spectroscopy (LC-MS) in combination with transcriptional assays and the analysis of intracellular ATP and NADH levels. Metabolite profiling revealed that oxidative stress response pathways were altered and suggested an increase in DNA repair. Changes in metabolite levels in the tricarboxylic acid (TCA) cycle along with increased ATP are consistent with As(V)-enhanced growth of A. tumefaciens GW4. Lipidomics analysis revealed that most glycerophospholipids decreased in abundance when As(V) was available. However, several glycerolipid classes increased, an outcome that is consistent with maximizing growth via a phosphate-sparing phenotype. Differentially regulated lipids included phosphotidylcholine and lysophospholipids, which have not been previously reported in A. tumefaciens The metabolites and lipids identified in this study deepen our understanding of the interplay between phosphate and arsenate on chemical and metabolic levels.IMPORTANCE Arsenic is widespread in the environment and is one of the most ubiquitous environmental pollutants. Parodoxically, the growth of certain bacteria is enhanced by arsenic when phosphate is limited. Arsenate and phosphate are chemically similar, and this behavior is believed to represent a phosphate-sparing phenotype in which arsenate is used in place of phosphate in certain biomolecules. The research presented here uses a global approach to track metabolic changes in an environmentally relevant bacterium during exposure to arsenate when phosphate is low. Our findings are relevant for understanding the environmental fate of arsenic as well as how human-associated microbiomes respond to this common toxin.
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26
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Rawls KD, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model. Toxicol Appl Pharmacol 2021; 412:115390. [PMID: 33387578 PMCID: PMC7859602 DOI: 10.1016/j.taap.2020.115390] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/02/2020] [Accepted: 12/26/2020] [Indexed: 12/12/2022]
Abstract
The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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27
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Beaufrère H, Gardhouse SM, Wood RD, Stark KD. The plasma lipidome of the Quaker parrot (Myiopsitta monachus). PLoS One 2020; 15:e0240449. [PMID: 33259543 PMCID: PMC7707497 DOI: 10.1371/journal.pone.0240449] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 09/25/2020] [Indexed: 11/19/2022] Open
Abstract
Dyslipidemias and lipid-accumulation disorders are common in captive parrots, in particular in Quaker parrots. Currently available diagnostic tests only measure a fraction of blood lipids and have overall problematic cross-species applicability. Comprehensively analyzing lipids in the plasma of parrots is the first step to better understand their lipid metabolism in health and disease, as well as to explore new lipid biomarkers. The plasma lipidome of 12 Quaker parrots was investigated using UHPLC-MS/MS with both targeted and untargeted methods. Targeted methods on 6 replicates measured 432 lipids comprised of sterol, cholesterol ester, bile acid, fatty acid, acylcarnitine, glycerolipid, glycerophospholipid, and sphingolipid panels. For untargeted lipidomics, precursor ion mass-to-charge ratios were matched to corresponding lipids using the LIPIDMAPS structure database and LipidBlast at the sum composition or acyl species level of information. Sterol lipids and glycerophospholipids constituted the majority of plasma lipids on a molar basis. The most common lipids detected with the targeted methods included free cholesterol, CE(18:2), CE(20:4) for sterol lipids; PC(36:2), PC(34:2), PC(34:1) for glycerophospholipids; TG(52:3), TG(54:4), TG(54:5), TG(52:2) for glycerolipids; SM(d18:1/16:0) for sphingolipids; and palmitic acid for fatty acyls. Over a thousand different lipid species were detected by untargeted lipidomics. Sex differences in the plasma lipidome were observed using heatmaps, principal component analysis, and discriminant analysis. This report presents the first comprehensive database of plasma lipid species in psittacine birds and paves the way for further research into blood lipid diagnostics and the impact of diet, diseases, and drugs on the parrot plasma lipidome.
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Affiliation(s)
- Hugues Beaufrère
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- * E-mail:
| | - Sara M. Gardhouse
- Health Sciences Centre, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - R. Darren Wood
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Ken D. Stark
- Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada
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28
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Borkowski K, Newman JW, Aghaeepour N, Mayo JA, Blazenović I, Fiehn O, Stevenson DK, Shaw GM, Carmichael SL. Mid-gestation serum lipidomic profile associations with spontaneous preterm birth are influenced by body mass index. PLoS One 2020; 15:e0239115. [PMID: 33201881 PMCID: PMC7671555 DOI: 10.1371/journal.pone.0239115] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/31/2020] [Indexed: 01/11/2023] Open
Abstract
Spontaneous preterm birth (sPTB) is a major cause of infant morbidity and mortality. While metabolic changes leading to preterm birth are unknown, several factors including dyslipidemia and inflammation have been implicated and paradoxically both low (<18.5 kg/m2) and high (>30 kg/m2) body mass indices (BMIs) are risk factors for this condition. The objective of the study was to identify BMI-associated metabolic perturbations and potential mid-gestation serum biomarkers of preterm birth in a cohort of underweight, normal weight and obese women experiencing either sPTB or full-term deliveries (n = 102; n = 17/group). For this purpose, we combined untargeted metabolomics and lipidomics with targeted metabolic profiling of major regulators of inflammation and metabolism, including oxylipins, endocannabinoids, bile acids and ceramides. Women who were obese and had sPTB showed elevated oxidative stress and dyslipidemia characterized by elevated serum free fatty acids. Women who were underweight-associated sPTB also showed evidence of dyslipidemia characterized by elevated phospholipids, unsaturated triglycerides, sphingomyelins, cholesteryl esters and long-chain acylcarnitines. In normal weight women experiencing sPTB, the relative abundance of 14(15)-epoxyeicosatrienoic acid and 14,15-dihydroxyeicosatrienoic acids to other regioisomers were altered at mid-pregnancy. This phenomenon is not yet associated with any biological process, but may be linked to estrogen metabolism. These changes were differentially modulated across BMI groups. In conclusion, using metabolomics we observed distinct BMI-dependent metabolic manifestations among women who had sPTB. These observations suggest the potential to predict sPTB mid-gestation using a new set of metabolomic markers and BMI stratification. This study opens the door to further investigate the role of cytochrome P450/epoxide hydrolase metabolism in sPTB.
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Affiliation(s)
- Kamil Borkowski
- West Coast Metabolomic Center, Genome Center, University of California-Davis, Davis, CA, United States of America
- * E-mail:
| | - John W. Newman
- West Coast Metabolomic Center, Genome Center, University of California-Davis, Davis, CA, United States of America
- United States Department of Agriculture-Agriculture Research Service-Western Human Nutrition Research Center, Davis, CA, United States of America
- Department of Nutrition, University of California-Davis, Davis, CA, United States of America
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Jonathan A. Mayo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Ivana Blazenović
- West Coast Metabolomic Center, Genome Center, University of California-Davis, Davis, CA, United States of America
| | - Oliver Fiehn
- West Coast Metabolomic Center, Genome Center, University of California-Davis, Davis, CA, United States of America
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Suzan L. Carmichael
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States of America
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29
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Showalter MR, Berg AL, Nagourney A, Heil H, Carraway KL, Fiehn O. The Emerging and Diverse Roles of Bis(monoacylglycero) Phosphate Lipids in Cellular Physiology and Disease. Int J Mol Sci 2020; 21:ijms21218067. [PMID: 33137979 PMCID: PMC7663174 DOI: 10.3390/ijms21218067] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 02/06/2023] Open
Abstract
Although understudied relative to many phospholipids, accumulating evidence suggests that bis(monoacylglycero)phosphate (BMP) is an important class of regulatory lipid that plays key roles in lysosomal integrity and function. BMPs are rare in most mammalian tissues, comprising only a few percent of total cellular lipid content, but are elevated in cell types such as macrophages that rely heavily on lysosomal function. BMPs are markedly enriched in endosomal and lysosomal vesicles compared to other organelles and membranous structures, and their unique sn-1:sn-1′ stereoconfiguration may confer stability within the hydrolytic lysosomal environment. BMP-enriched vesicles serve in endosomal-lysosomal trafficking and function as docking structures for the activation of lysosomal hydrolytic enzymes, notably those involved in the catabolic breakdown of sphingolipids. BMP levels are dysregulated in lysosomal storage disorders, phospholipidosis, metabolic diseases, liver and kidney diseases and neurodegenerative disorders. However, whether BMP alteration is a mediator or simply a marker of pathological states is unclear. Likewise, although BMP acyl chain composition may be altered with disease states, the functional significance of specific BMP species remains to be resolved. Newly developed tools for untargeted lipidomic analysis, together with a deeper understanding of enzymes mediating BMP synthesis and degradation, will help shed further light on the functional significance of BMPs in cellular physiology and pathology.
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Affiliation(s)
- Megan R. Showalter
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA; (M.R.S.); (A.N.); (H.H.)
| | - Anastasia L. Berg
- Department of Biochemistry and Molecular Medicine, UC Davis School of Medicine, Sacramento, CA 95817, USA; (A.L.B.); (K.L.C.III)
| | - Alexander Nagourney
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA; (M.R.S.); (A.N.); (H.H.)
| | - Hailey Heil
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA; (M.R.S.); (A.N.); (H.H.)
| | - Kermit L. Carraway
- Department of Biochemistry and Molecular Medicine, UC Davis School of Medicine, Sacramento, CA 95817, USA; (A.L.B.); (K.L.C.III)
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA; (M.R.S.); (A.N.); (H.H.)
- Correspondence:
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30
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Lipidomic UPLC-MS/MS Profiles of Normal-Appearing White Matter Differentiate Primary and Secondary Progressive Multiple Sclerosis. Metabolites 2020; 10:metabo10090366. [PMID: 32911763 PMCID: PMC7569864 DOI: 10.3390/metabo10090366] [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: 08/28/2020] [Accepted: 09/07/2020] [Indexed: 01/20/2023] Open
Abstract
Multiple sclerosis (MS) is a neurodegenerative inflammatory disease where an autoimmune response to components of the central nervous system leads to a loss of myelin and subsequent neurological deterioration. People with MS can develop primary or secondary progressive disease (PPMS, SPMS) and differentiation of the specific differences in the pathogenesis of these two courses, at the molecular level, is currently unclear. Recently, lipidomics studies using human biofluids, mainly plasma and cerebrospinal fluid, have highlighted a possible role for lipids in the initiation and progression of MS. However, there is a lack of lipidomics studies in MS on CNS tissues, such as normal-appearing white matter (NAWM), where local inflammation initially occurs. Herein, we developed an untargeted reverse phase ultra-performance liquid chromatography time of flight tandem mass spectrometry (RP-UPLC-TOF MSE)-based workflow, in combination with multivariate and univariate statistical analysis, to assess significant differences in lipid profiles in brain NAWM from post-mortem cases of PPMS, SPMS and controls. Groups of eight control, nine PPMS and seven SPMS NAWM samples were used. Correlation analysis of the identified lipids by RP-UPLC-TOF MSE was undertaken to remove those lipids that correlated with age, gender and post-mortem interval as confounding factors. We demonstrate that there is a significantly altered lipid profile of control cases compared with MS cases and that progressive disease, PPMS and SPMS, can be differentiated on the basis of the lipidome of NAWM with good sensitivity, specificity and prediction accuracy based on receiver operating characteristic (ROC) curve analysis. Metabolic pathway analysis revealed that the most altered lipid pathways between PPMS and SPMS were glycerophospholipid metabolism, glycerophosphatidyl inositol (GPI) anchor synthesis and linoleic acid metabolism. Further understanding of the impact of these lipid alterations described herein associated with progression will provide an increased understanding of the mechanisms underpinning progression and highlight possible new therapeutic targets.
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Züllig T, Zandl-Lang M, Trötzmüller M, Hartler J, Plecko B, Köfeler HC. A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches. Metabolites 2020; 10:E342. [PMID: 32854199 PMCID: PMC7570008 DOI: 10.3390/metabo10090342] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/20/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.
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Affiliation(s)
- Thomas Züllig
- Core Facility Mass Spectrometry, Medical University of Graz, 8036 Graz, Austria; (T.Z.); (M.T.)
| | - Martina Zandl-Lang
- Department of Paediatrics and Adolescent Medicine, Division of General Paediatrics, University Childrens’ Hospital Graz, Medical University of Graz, 8036 Graz, Austria; (M.Z.-L.); (B.P.)
| | - Martin Trötzmüller
- Core Facility Mass Spectrometry, Medical University of Graz, 8036 Graz, Austria; (T.Z.); (M.T.)
| | - Jürgen Hartler
- Institute of Pharmaceutical Sciences, University of Graz, 8036 Graz, Austria;
| | - Barbara Plecko
- Department of Paediatrics and Adolescent Medicine, Division of General Paediatrics, University Childrens’ Hospital Graz, Medical University of Graz, 8036 Graz, Austria; (M.Z.-L.); (B.P.)
| | - Harald C. Köfeler
- Core Facility Mass Spectrometry, Medical University of Graz, 8036 Graz, Austria; (T.Z.); (M.T.)
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Hou Y, He D, Ye L, Wang G, Zheng Q, Hao H. An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. J Pharm Biomed Anal 2020; 191:113531. [PMID: 32889345 DOI: 10.1016/j.jpba.2020.113531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022]
Abstract
Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.
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Affiliation(s)
- Yuanlong Hou
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China
| | - Dandan He
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China
| | - Ling Ye
- Guangdong Provincial Key Laboratory of New Drug Screening, Biopharmaceutics, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Guangji Wang
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
| | - Qiuling Zheng
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China; Department of Pharmaceutical Analysis, College of Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
| | - Haiping Hao
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
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Yu Z, Wang S, Hou H, Ma L, Zhu Y. Lipidomic Profiling Reveals the Effect of Egg Components on Nonalcoholic Steatosis in HepG2 Cells and Its Involved Mechanisms. EUR J LIPID SCI TECH 2020. [DOI: 10.1002/ejlt.201900451] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Zhihui Yu
- College of Food Science and Engineering Shanxi Agricultural University Taigu Shanxi 030801 China
| | - Shiyao Wang
- College of Food Science and Engineering Shanxi Agricultural University Taigu Shanxi 030801 China
| | - Huaming Hou
- College of Food Science and Engineering Shanxi Agricultural University Taigu Shanxi 030801 China
| | - Ling Ma
- College of Food Science and Engineering Shanxi Agricultural University Taigu Shanxi 030801 China
| | - Yingchun Zhu
- College of Food Science and Engineering Shanxi Agricultural University Taigu Shanxi 030801 China
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Bagheri M, Tiwari HK, Murillo AL, Al-Tobasei R, Arnett DK, Kind T, Barupal DK, Fan S, Fiehn O, O'connell J, Montasser M, Aslibekyan S, Irvin MR. A lipidome-wide association study of the lipoprotein insulin resistance index. Lipids Health Dis 2020; 19:153. [PMID: 32586392 PMCID: PMC7318473 DOI: 10.1186/s12944-020-01321-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 06/10/2020] [Indexed: 12/30/2022] Open
Abstract
Background The lipoprotein insulin resistance (LPIR) score was shown to predict insulin resistance (IR) and type 2 diabetes (T2D) in healthy adults. However, the molecular basis underlying the LPIR utility for classification remains unclear. Objective To identify small molecule lipids associated with variation in the LPIR score, a weighted index of lipoproteins measured by nuclear magnetic resonance, in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (n = 980). Methods Linear mixed effects models were used to test the association between the LPIR score and 413 lipid species and their principal component analysis-derived groups. Significant associations were tested for replication with homeostatic model assessment-IR (HOMA-IR), a phenotype correlated with the LPIR score (r = 0.48, p < 0.001), in the Heredity and Phenotype Intervention (HAPI) Heart Study (n = 590). Results In GOLDN, 319 lipids were associated with the LPIR score (false discovery rate-adjusted p-values ranging from 4.59 × 10− 161 to 49.50 × 10− 3). Factors 1 (triglycerides and diglycerides/storage lipids) and 3 (mixed lipids) were positively (β = 0.025, p = 4.52 × 10− 71 and β = 0.021, p = 5.84 × 10− 41, respectively) and factor 2 (phospholipids/non-storage lipids) was inversely (β = − 0.013, p = 2.28 × 10− 18) associated with the LPIR score. These findings were replicated for HOMA-IR in the HAPI Heart Study (β = 0.10, p = 1.21 × 10− 02 for storage, β = − 0.13, p = 3.14 × 10− 04 for non-storage, and β = 0.19, p = 8.40 × 10− 07 for mixed lipids). Conclusions Non-storage lipidomics species show a significant inverse association with the LPIR metabolic dysfunction score and present a promising focus for future therapeutic and prevention studies.
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Affiliation(s)
- Minoo Bagheri
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL, 35294, USA.,Department of Cardiovascular Medicine, Vanderbilt University Medical center, Nashville, TN, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anarina L Murillo
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rafet Al-Tobasei
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Donna K Arnett
- Dean's Office, School of Public Health, University of Kentucky, Lexington, KY, USA
| | - Tobias Kind
- West coast metabolomics center, Davis, CA, USA
| | | | - Sili Fan
- West coast metabolomics center, Davis, CA, USA
| | | | - Jeff O'connell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - May Montasser
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL, 35294, USA.
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL, 35294, USA
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Magny R, Regazzetti A, Kessal K, Genta-Jouve G, Baudouin C, Mélik-Parsadaniantz S, Brignole-Baudouin F, Laprévote O, Auzeil N. Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease. Metabolites 2020; 10:metabo10060225. [PMID: 32486009 PMCID: PMC7345884 DOI: 10.3390/metabo10060225] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/07/2020] [Accepted: 05/25/2020] [Indexed: 12/28/2022] Open
Abstract
Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (tR) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental tR values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to tR prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.
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Affiliation(s)
- Romain Magny
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
| | - Anne Regazzetti
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
| | - Karima Kessal
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
| | - Gregory Genta-Jouve
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 97300 Cayenne, French Guiana, France
| | - Christophe Baudouin
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
- Hôpital Ambroise Paré, AP-HP, Université Versailles Saint-Quentin-en-Yvelines, 92100 Boulogne-Billancourt, France
| | - Stéphane Mélik-Parsadaniantz
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
| | - Françoise Brignole-Baudouin
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
| | - Olivier Laprévote
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Hôpital Européen Georges Pompidou, AP-HP, Service de Biochimie, 75006 Paris, France
| | - Nicolas Auzeil
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Correspondence:
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Bernath MM, Bhattacharyya S, Nho K, Barupal DK, Fiehn O, Baillie R, Risacher SL, Arnold M, Jacobson T, Trojanowski JQ, Shaw LM, Weiner MW, Doraiswamy PM, Kaddurah-Daouk R, Saykin AJ. Serum triglycerides in Alzheimer disease: Relation to neuroimaging and CSF biomarkers. Neurology 2020; 94:e2088-e2098. [PMID: 32358220 DOI: 10.1212/wnl.0000000000009436] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 11/19/2019] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To investigate the association of triglyceride (TG) principal component scores with Alzheimer disease (AD) and the amyloid, tau, neurodegeneration, and cerebrovascular disease (A/T/N/V) biomarkers for AD. METHODS Serum levels of 84 TG species were measured with untargeted lipid profiling of 689 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, including 190 cognitively normal older adults (CN), 339 with mild cognitive impairment (MCI), and 160 with AD. Principal component analysis with factor rotation was used for dimension reduction of TG species. Differences in principal components between diagnostic groups and associations between principal components and AD biomarkers (including CSF, MRI and [18F]fluorodeoxyglucose-PET) were assessed with a generalized linear model approach. In both cases, the Bonferroni method of adjustment was used to correct for multiple comparisons. RESULTS The 84 TGs yielded 9 principal components, 2 of which, consisting of long-chain, polyunsaturated fatty acid-containing TGs (PUTGs), were significantly associated with MCI and AD. Lower levels of PUTGs were observed in MCI and AD compared to CN. PUTG principal component scores were also significantly associated with hippocampal volume and entorhinal cortical thickness. In participants carrying the APOE ε4 allele, these principal components were significantly associated with CSF β-amyloid1-42 values and entorhinal cortical thickness. CONCLUSION This study shows that PUTG component scores were significantly associated with diagnostic group and AD biomarkers, a finding that was more pronounced in APOE ε4 carriers. Replication in independent larger studies and longitudinal follow-up are warranted.
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Affiliation(s)
- Megan M Bernath
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Sudeepa Bhattacharyya
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Kwangsik Nho
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Dinesh Kumar Barupal
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Oliver Fiehn
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Rebecca Baillie
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Shannon L Risacher
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Matthias Arnold
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Tanner Jacobson
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - John Q Trojanowski
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Leslie M Shaw
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Michael W Weiner
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - P Murali Doraiswamy
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Rima Kaddurah-Daouk
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC
| | - Andrew J Saykin
- From the Department of Radiology and Imaging Sciences (M.M.B., K.N., S.L.R., T.J., A.J.S.), Center for Neuroimaging, Indiana Alzheimer Disease Center (M.M.B., K.N., S.L.R., T.J., A.J.S.), Medical and Molecular Genetics Department (M.M.B., T.J., A.J.S.), and Medical Scientist Training Program (M.M.B.), Indiana University School of Medicine, Indianapolis; Department of Pediatrics (S.B.), University of Arkansas for Medical Sciences, Little Rock; Department of Environmental Medicine and Public Health (D.K.B.), Icahn School of Medicine at Mt Sinai, New York; NIH-West Coast Metabolomics Center (D.K.B., O.F.), University of California, Davis; Rosa & Co LLC (R.B.), San Carlos, CA; Institute of Bioinformatics and Systems Biology (M.A.), Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Pathology & Laboratory Medicine (J.Q.T., L.M.S.), University of Pennsylvania, Philadelphia; Department of Radiology (M.W.W.), Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco; and Department of Psychiatry and Behavioral Sciences (M.A., P.M.D., R.K.-D.), Duke Institute of Brain Sciences (R.K.-D.), and Department of Medicine (R.K.-D.), Duke University, Durham, NC.
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Abstract
Oleaginous microbes, which contain over 20% intracellular lipid, predominantly triacylglycerols (TG), by dry weight, have been discovered to have high oil content by many different protocols, ranging from simple staining to more complex chromatographic methods. In our laboratory, a methodical process was implemented to identify high oil yeasts, designed to minimize labor while optimizing success in identifying high oil strains among thousands of candidates. First, criteria were developed to select candidate yeast strains for analysis. These included observation of buoyancy of the yeast cell mass in 20% glycerol, and phylogenetic placement near known oleaginous species. A low-labor, semiquantitative Nile red staining protocol was implemented to screen numerous yeast cultures for high oil content in 96-well plates. Then, promising candidates were selected for more quantitative analysis. A more labor-intensive and quantitative gravimetric assay was implemented that gave consistent values for intracellular oil content for a broad range of yeast species. Finally, an LC-MS protocol was utilized to quantify and identify yeast triacylglycerols. This progressive approach was successful in identifying 30 new oleaginous yeast species, out of over 1000 species represented in the Phaff Yeast Culture Collection.
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van Geenen FAMG, Claassen FW, Franssen MCR, Zuilhof H, Nielen MWF. Laser Ablation Electrospray Ionization Hydrogen/Deuterium Exchange Ambient Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:249-256. [PMID: 32031404 PMCID: PMC7053432 DOI: 10.1021/jasms.9b00082] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Identification and confirmation of known as well as unknown (bio)chemical entities in ambient mass spectrometry (MS) and MS imaging (MSI) mostly involve accurate mass determination, often in combination with MS/MS or MSn work flows. To further improve structural assignment, additional molecular information is required. Here we present an ambient hydrogen/deuterium exchange (HDX) laser ablation electrospray ionization (LAESI) MS method in which, apart from the accurate mass and MS/MS data, the number of exchangeable protons in (un)known molecules is obtained. While eventually presenting ambient HDX-LAESI-MSI, samples were not preincubated with deuterated solvents, but instead HDX occurred following fusion of ablated sample material with microdroplets generated by ESI of deuterated solvents. Therefore, the degree of HDX was first studied following ablation of nondeuterated sample solutions of melamine and monosaccharides. From these experiments, it was concluded that the set-up used could provide meaningful HDX data in support of molecular structure elucidation by significantly reducing the number of structure options from a measured elemental composition. This reduction was demonstrated with an unknown accurate m/z value obtained in the analysis of an orange slice, reducing the possible number of molecular structures having the same elemental composition by 87% due to the number of H/D exchanges observed. Next, deuterated and nondeuterated MS/MS experiments showed the number of exchangeable protons in the substructures from deuterated neutral losses in the product ion spectra, confirming the compound to be arginine. Finally, the potential of ambient HDX-LAESI-MSI was demonstrated by the imaging of (secondary) plant metabolites in a Phalaenopsis petal.
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Affiliation(s)
- Fred A. M. G. van Geenen
- Laboratory of Organic Chemistry,
Wageningen University, Stippeneng 4, 6708 WE Wageningen,
The Netherlands
- TI-COAST, Science Park 904,
1098 XH Amsterdam, The Netherlands
- Institute for Molecules and Materials, FELIX Laboratory,
Radboud University, Toernooiveld 7c, 6525 ED Nijmegen,
The Netherlands
| | - Frank W. Claassen
- Laboratory of Organic Chemistry,
Wageningen University, Stippeneng 4, 6708 WE Wageningen,
The Netherlands
| | - Maurice C. R. Franssen
- Laboratory of Organic Chemistry,
Wageningen University, Stippeneng 4, 6708 WE Wageningen,
The Netherlands
| | - Han Zuilhof
- Laboratory of Organic Chemistry,
Wageningen University, Stippeneng 4, 6708 WE Wageningen,
The Netherlands
- School of Pharmaceutical Sciences and Technology,
Tianjin University, 92 Weijin Road, Tianjin 300072,
P.R. China
| | - Michel W. F. Nielen
- Laboratory of Organic Chemistry,
Wageningen University, Stippeneng 4, 6708 WE Wageningen,
The Netherlands
- Wageningen Food Safety Research (WFSR),
Wageningen University & Research, P.O. Box 230, 6700 AE
Wageningen, The Netherlands
- E-mail:
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Sorensen MJ, Miller KE, Jorgenson JW, Kennedy RT. Ultrahigh-Performance capillary liquid chromatography-mass spectrometry at 35 kpsi for separation of lipids. J Chromatogr A 2020; 1611:460575. [PMID: 31607445 PMCID: PMC6980658 DOI: 10.1016/j.chroma.2019.460575] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/25/2019] [Accepted: 09/26/2019] [Indexed: 12/24/2022]
Abstract
Improvements in sample preparation, separation, and mass spectrometry continue to expand the coverage in LC-MS based lipidomics. While longer columns packed with smaller particles in theory give higher separation performance compared to shorter columns, the implementation of this technology above commercial limits has been sparse due to difficulties in packing long columns and successfully operating instruments at ultrahigh pressures. In this work, a liquid chromatograph that operates up to 35 kpsi was investigated for the separation and identification of lipid species from human plasma. Capillary columns between 15-50 cm long were packed with 1.7 µm BEH C18 particles and evaluated for their ability to separate lipid isomers and complex lipid extracts from human plasma. Putative lipid class identifications were assigned using accurate mass and relative retention time data of the eluting peaks. Our findings indicate that longer columns packed and operated at 35 kpsi outperform shorter columns packed and run at lower pressures in terms of peak capacity and numbers of features identified. Packing columns with relatively high concentration slurries (200 mg/mL) while sonicating the column resulted in 6-34% increase in peak capacity for 50 cm columns compared to lower slurry concentrations and no sonication. For a given analysis time, 50 cm long columns operated at 35 kpsi provided a 20-95% increase in chromatographic peak capacity compared with 15 cm columns operated at 15 kpsi. Analysis times up to 4 h were evaluated, generating peak capacities up to 410 ± 5 (n = 3, measured at 4σ) and identifying 480 ± 85 lipids (n = 2). Importantly, the results also show a correlation between the peak capacity and the number of lipids identified from a human plasma extract. This correlation indicates that ionization suppression is a limiting factor in obtaining sufficient signal for identification by mass spectrometry. The result also shows that the higher resolution obtained by shallow gradients overcomes possible signal reduction due to broader, more dilute peaks in long gradients for improving detection of lipids in LC-MS. Lastly, longer columns operated at shallow gradients allowed for the best separation of both regional and geometrical isomers. These results demonstrate a system that enables the advantages of using longer columns packed and run at ultrahigh pressure for improving lipid separations and lipidome coverage.
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Affiliation(s)
- Matthew J Sorensen
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kelsey E Miller
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - James W Jorgenson
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Robert T Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA; Department of Pharmacology, University of Michigan, Ann Arbor, MI 48109, USA.
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Liakh I, Pakiet A, Sledzinski T, Mika A. Methods of the Analysis of Oxylipins in Biological Samples. Molecules 2020; 25:E349. [PMID: 31952163 PMCID: PMC7024226 DOI: 10.3390/molecules25020349] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 12/11/2022] Open
Abstract
Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.
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Affiliation(s)
- Ivan Liakh
- Department of Pharmaceutical Biochemistry, Medical University of Gdansk, Debinki 1, 80-211 Gdansk, Poland; (I.L.); (T.S.)
| | - Alicja Pakiet
- Department of Environmental Analysis, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland;
| | - Tomasz Sledzinski
- Department of Pharmaceutical Biochemistry, Medical University of Gdansk, Debinki 1, 80-211 Gdansk, Poland; (I.L.); (T.S.)
| | - Adriana Mika
- Department of Pharmaceutical Biochemistry, Medical University of Gdansk, Debinki 1, 80-211 Gdansk, Poland; (I.L.); (T.S.)
- Department of Environmental Analysis, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland;
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41
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Mohamed A, Collins J, Jiang H, Molendijk J, Stoll T, Torta F, Wenk MR, Bird RJ, Marlton P, Mollee P, Markey KA, Hill MM. Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome. PLoS One 2020; 15:e0227455. [PMID: 31914155 PMCID: PMC6948732 DOI: 10.1371/journal.pone.0227455] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 12/18/2019] [Indexed: 12/31/2022] Open
Abstract
Background Multiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of malignant plasma cells. Though durable remissions are possible, MM is considered incurable, with relapse occurring in almost all patients. There has been limited data reported on the lipid metabolism changes in plasma cells during MM progression. Here, we evaluated the feasibility of concurrent lipidomics and proteomics analyses from patient plasma cells, and report these data on a limited number of patient samples, demonstrating the feasibility of the method, and establishing hypotheses to be evaluated in the future. Methods Plasma cells were purified from fresh bone marrow aspirates using CD138 microbeads. Proteins and lipids were extracted using a bi-phasic solvent system with methanol, methyl tert-butyl ether, and water. Untargeted proteomics, untargeted and targeted lipidomics were performed on 7 patient samples using liquid chromatography-mass spectrometry. Two comparisons were conducted: high versus low risk; relapse versus newly diagnosed. Proteins and pathways enriched in the relapsed group was compared to a public transcriptomic dataset from Multiple Myeloma Research Consortium reference collection (n = 222) at gene and pathways level. Results From one million purified plasma cells, we were able to extract material and complete untargeted (~6000 and ~3600 features in positive and negative mode respectively) and targeted lipidomics (313 lipids), as well as untargeted proteomics analysis (~4100 reviewed proteins). Comparative analyses revealed limited differences between high and low risk groups (according to the standard clinical criteria), hence we focused on drawing comparisons between the relapsed and newly diagnosed patients. Untargeted and targeted lipidomics indicated significant down-regulation of phosphatidylcholines (PCs) in relapsed MM. Although there was limited overlap of the differential proteins/transcripts, 76 significantly enriched pathways in relapsed MM were common between proteomics and transcriptomics data. Further evaluation of transcriptomics data for lipid metabolism network revealed enriched correlation of PC, ceramide, cardiolipin, arachidonic acid and cholesterol metabolism pathways to be exclusively correlated among relapsed but not in newly-diagnosed patients. Conclusions This study establishes the feasibility and workflow to conduct integrated lipidomics and proteomics analyses on patient-derived plasma cells. Potential lipid metabolism changes associated with MM relapse warrant further investigation.
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Affiliation(s)
- Ahmed Mohamed
- The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, Australia
| | - Joel Collins
- Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
- Toowoomba Hospital, Cancer Care Services, Toowoomba, Australia
- The University of Queensland Faculty of Medicine, Brisbane, Australia
| | - Hui Jiang
- The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
| | - Jeffrey Molendijk
- The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, Australia
| | - Thomas Stoll
- The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, Australia
| | - Federico Torta
- Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Markus R. Wenk
- Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Robert J. Bird
- Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
| | - Paula Marlton
- Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
- The University of Queensland Faculty of Medicine, Brisbane, Australia
| | - Peter Mollee
- Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
- The University of Queensland Faculty of Medicine, Brisbane, Australia
| | - Kate A. Markey
- Princess Alexandra Hospital, Division of Cancer Care Services, Department of Haematology, Woolloongabba, Brisbane, Australia
- The University of Queensland Faculty of Medicine, Brisbane, Australia
- SLING, Department of Biochemistry, National University of Singapore, Singapore
| | - Michelle M. Hill
- The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, Brisbane, Australia
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, Australia
- * E-mail:
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Li WX, Zhang AH, Zhou XH, Nan Y, Liu Q, Sun H, Fang H, Wang XJ. High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill. RSC Adv 2020; 10:2347-2358. [PMID: 35494613 PMCID: PMC9048437 DOI: 10.1039/c9ra07621b] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/30/2019] [Indexed: 11/21/2022] Open
Abstract
Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy.
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Affiliation(s)
- Wen-xiu Li
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Ai-hua Zhang
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Xiao-hang Zhou
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Yang Nan
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Qi Liu
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Hui Sun
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Heng Fang
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
| | - Xi-jun Wang
- Engineering Research Center of Efficacy Evaluation and Industrial Development of TCM Classic Formulae of the Ministry of Education
- National Chinmedomics Research Center
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Metabolomics Laboratory
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Reisdorph NA, Walmsley S, Reisdorph R. A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics. Metabolites 2019; 10:metabo10010008. [PMID: 31877765 PMCID: PMC7023092 DOI: 10.3390/metabo10010008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/10/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023] Open
Abstract
Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.
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Affiliation(s)
- Nichole A. Reisdorph
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, USA;
- Correspondence: ; Tel.: +1-303-724-9234
| | - Scott Walmsley
- Masonic Cancer Center, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA;
- Institute for Health Informatics, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA
| | - Rick Reisdorph
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, USA;
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44
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Chen T, Yuan H, Sun YB, Song YC, Lu M, Ni X, Han X. Metabolomics study of the prefrontal cortex in a rat model of attention deficit hyperactivity disorder reveals the association between cholesterol metabolism disorder and hyperactive behavior. Biochem Biophys Res Commun 2019; 523:315-321. [PMID: 31864712 DOI: 10.1016/j.bbrc.2019.12.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/03/2019] [Indexed: 10/25/2022]
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disease for which specific biomarkers and pathological mechanisms have yet to be identified. Methylphenidate (MPH) is commonly used to treat ADHD, but its therapeutic mechanisms and its impact on brain metabolites remain unclear. Metabolomics can help to discover biomarkers and identify pathophysiological mechanisms. We adopted an untargeted metabolomics approach based on gas chromatography-mass spectrometry to investigate the potential biomarkers and pathogenesis of ADHD. Ten Wistar-Kyoto (WKY) rats were chosen as healthy controls (vehicle, i.g.). Twenty young spontaneously hypertensive rats (SHR) were randomly allocated to the SHR group (vehicle, i.g.) and MPH group (2 mg/kg/day, i.g.). We identified 103 metabolites from the prefrontal cortex (PFC). Orthogonal partial least square-discriminate analysis showed the differential expression of these metabolites between the groups. Multivariate and univariate statistical analyses isolated 12 metabolites that differed significantly between the WKY and SHR groups: 3-hydroxymethylglutaric acid, 3-phosphoglyceric acid, adenosine monophosphate, cholesterol, lanosterol, and o-phosphoethanolamine; 3-hydroxymethylglutaric acid and cholesterol were reversed with MPH treatment. Pathway and enrichment analyses revealed that the altered metabolites belonged to the cholesterol metabolism pathways. ELISA and western blotting showed that the activity of 3-hydroxy-3-methyl-glutaryl-CoA reductase and the expression of sterol regulatory element-binding protein-2 and ATP-binding cassette transporter A1 were reduced in the PFC of the SHR; the latter two proteins were upregulated by MPH. In conclusion, metabolomics analysis identified potential biomarkers that influence cholesterol metabolism and may be implicated in the development of ADHD-like behavior. MPH can regulate cholesterol metabolism in the PFC of ADHD models. This study uncovered potential biomarkers and pathways involved in ADHD, providing new insight into its pathogenesis.
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Affiliation(s)
- Tianyi Chen
- First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China; Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Medical Metabolomics Center, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Haixia Yuan
- First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China; Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Nanjing University of Chinese Medicine, Nanjing, 210023, China; Medical Metabolomics Center, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Yu-Bo Sun
- School of Basic Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Yu-Chen Song
- First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China; Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Manqi Lu
- First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China; Jiangsu Key Laboratory of Pediatric Respiratory Disease, Institute of Pediatrics, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| | - Xinqiang Ni
- Pediatrics of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518038, Guangdong Province, China.
| | - Xinmin Han
- First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China.
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Ivanisevic J, Want EJ. From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data. Metabolites 2019; 9:metabo9120308. [PMID: 31861212 PMCID: PMC6950334 DOI: 10.3390/metabo9120308] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.
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Affiliation(s)
- Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Rue du Bugnon 19, 1005 Lausanne, Switzerland
- Correspondence: (J.I.); (E.J.W.)
| | - Elizabeth J. Want
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
- Correspondence: (J.I.); (E.J.W.)
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46
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Rawls KD, Blais EM, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes. Toxicol Sci 2019; 172:279-291. [PMID: 31501904 PMCID: PMC6876259 DOI: 10.1093/toxsci/kfz197] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Context-specific GEnome-scale metabolic Network REconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically-exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to four toxicants (acetaminophen, carbon tetrachloride, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hour. TIMBR predictions were compared with paired fresh and spent media metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure. Here, we identified changes in the TCA metabolites citrate and alpha-ketoglutarate along with changes in carbohydrate metabolism and interruptions in ATP production and the TCA Cycle. Where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Edik M Blais
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
| | - Kalyan C Vinnakota
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Venkat R Pannala
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland 20817
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, Maryland 21702
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908
- Department of Medicine, Division of Infectious Diseases and International Health
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia 22908
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47
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Wei F, Lamichhane S, Orešič M, Hyötyläinen T. Lipidomes in health and disease: Analytical strategies and considerations. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.115664] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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48
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Calderón C, Rubarth L, Cebo M, Merfort I, Lämmerhofer M. Lipid Atlas of Keratinocytes and Betulin Effects on its Lipidome Profiled by Comprehensive UHPLC–MS/MS with Data Independent Acquisition Using Targeted Data Processing. Proteomics 2019; 20:e1900113. [DOI: 10.1002/pmic.201900113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 07/24/2019] [Indexed: 01/11/2023]
Affiliation(s)
- Carlos Calderón
- Institute of Pharmaceutical Sciences Pharmaceutical (Bio‐)Analysis University of Tübingen Auf der Morgenstelle 8 72076 Tübingen Germany
| | - Lara Rubarth
- Institute of Pharmaceutical Sciences Department of Pharmaceutical Biology and Biotechnology University of Freiburg 79104 Freiburg Germany
| | - Malgorzata Cebo
- Institute of Pharmaceutical Sciences Pharmaceutical (Bio‐)Analysis University of Tübingen Auf der Morgenstelle 8 72076 Tübingen Germany
| | - Irmgard Merfort
- Institute of Pharmaceutical Sciences Department of Pharmaceutical Biology and Biotechnology University of Freiburg 79104 Freiburg Germany
| | - Michael Lämmerhofer
- Institute of Pharmaceutical Sciences Pharmaceutical (Bio‐)Analysis University of Tübingen Auf der Morgenstelle 8 72076 Tübingen Germany
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49
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Eckert MA, Coscia F, Chryplewicz A, Chang JW, Hernandez KM, Pan S, Tienda SM, Nahotko DA, Li G, Blaženović I, Lastra RR, Curtis M, Yamada SD, Perets R, McGregor SM, Andrade J, Fiehn O, Moellering RE, Mann M, Lengyel E. Proteomics reveals NNMT as a master metabolic regulator of cancer-associated fibroblasts. Nature 2019; 569:723-728. [PMID: 31043742 PMCID: PMC6690743 DOI: 10.1038/s41586-019-1173-8] [Citation(s) in RCA: 277] [Impact Index Per Article: 55.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 03/27/2019] [Indexed: 12/23/2022]
Abstract
High-grade serous carcinoma has a poor prognosis, owing primarily to its early dissemination throughout the abdominal cavity. Genomic and proteomic approaches have provided snapshots of the proteogenomics of ovarian cancer1,2, but a systematic examination of both the tumour and stromal compartments is critical in understanding ovarian cancer metastasis. Here we develop a label-free proteomic workflow to analyse as few as 5,000 formalin-fixed, paraffin-embedded cells microdissected from each compartment. The tumour proteome was stable during progression from in situ lesions to metastatic disease; however, the metastasis-associated stroma was characterized by a highly conserved proteomic signature, prominently including the methyltransferase nicotinamide N-methyltransferase (NNMT) and several of the proteins that it regulates. Stromal NNMT expression was necessary and sufficient for functional aspects of the cancer-associated fibroblast (CAF) phenotype, including the expression of CAF markers and the secretion of cytokines and oncogenic extracellular matrix. Stromal NNMT expression supported ovarian cancer migration, proliferation and in vivo growth and metastasis. Expression of NNMT in CAFs led to depletion of S-adenosyl methionine and reduction in histone methylation associated with widespread gene expression changes in the tumour stroma. This work supports the use of ultra-low-input proteomics to identify candidate drivers of disease phenotypes. NNMT is a central, metabolic regulator of CAF differentiation and cancer progression in the stroma that may be therapeutically targeted.
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Affiliation(s)
- Mark A Eckert
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Fabian Coscia
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Clinical Proteomics Group, Proteomics Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Agnieszka Chryplewicz
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Jae Won Chang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
| | - Kyle M Hernandez
- Center for Research Informatics, University of Chicago, Chicago, IL, USA
| | - Shawn Pan
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Samantha M Tienda
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Dominik A Nahotko
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Gang Li
- Department of Chemistry, University of Chicago, Chicago, IL, USA
| | - Ivana Blaženović
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA, USA
| | - Ricardo R Lastra
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | - Marion Curtis
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - S Diane Yamada
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA
| | - Ruth Perets
- Division of Oncology, Clinical Research Institute at Rambam, Rambam Health Care Campus, Haifa, Israel
| | | | - Jorge Andrade
- Center for Research Informatics, University of Chicago, Chicago, IL, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA, USA
| | | | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- Clinical Proteomics Group, Proteomics Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Ernst Lengyel
- Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, University of Chicago, Chicago, IL, USA.
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50
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Showalter MR, Wancewicz B, Fiehn O, Archard JA, Clayton S, Wagner J, Deng P, Halmai J, Fink KD, Bauer G, Fury B, Perotti NH, Apperson M, Butters J, Belafsky P, Farwell G, Kuhn M, Nolta JA, Anderson JD. Primed mesenchymal stem cells package exosomes with metabolites associated with immunomodulation. Biochem Biophys Res Commun 2019; 512:729-735. [PMID: 30926165 DOI: 10.1016/j.bbrc.2019.03.119] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 03/18/2019] [Indexed: 12/14/2022]
Abstract
Mesenchymal stem cell (MSC) based therapies are currently being evaluated as a putative therapeutic in numerous human clinical trials. Recent reports have established that exosomes mediate much of the therapeutic properties of MSCs. Exosomes are nanovesicles which mediate intercellular communication, transmitting signals between cells which regulate a diverse range of biological processes. MSC-derived exosomes are packaged with numerous types of proteins and RNAs, however, their metabolomic and lipidomic profiles to date have not been well characterized. We previously reported that MSCs, in response to priming culture conditions that mimic the in vivo microenvironmental niche, substantially modulate cellular signaling and significantly increase the secretion of exosomes. Here we report that MSCs exposed to such priming conditions undergo glycolytic reprogramming, which homogenizes MSCs' metabolomic profile. In addition, we establish that exosomes derive from primed MSCs are packaged with numerous metabolites that have been directly associated with immunomodulation, including M2 macrophage polarization and regulatory T lymphocyte induction.
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Affiliation(s)
| | | | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, USA
| | | | - Shannon Clayton
- Department of Otolaryngology, University of California Davis, USA
| | - Joseph Wagner
- Drug Discovery Consortium, University of California, USA
| | - Peter Deng
- Department of Neurology, University of California Davis, USA
| | - Julian Halmai
- Department of Neurology, University of California Davis, USA
| | - Kyle D Fink
- Department of Neurology, University of California Davis, USA
| | - Gerhard Bauer
- Good Manufacturing Practice Facility, University of California Davis, USA
| | - Brian Fury
- Good Manufacturing Practice Facility, University of California Davis, USA
| | - Nicholas H Perotti
- Good Manufacturing Practice Facility, University of California Davis, USA
| | | | - Janelle Butters
- Department of Neurology, University of California Davis, USA
| | - Peter Belafsky
- Department of Otolaryngology, University of California Davis, USA
| | - Gregory Farwell
- Department of Otolaryngology, University of California Davis, USA
| | - Maggie Kuhn
- Department of Otolaryngology, University of California Davis, USA
| | - Jan A Nolta
- Stem Cell Program, University of California Davis, USA
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