1
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Larsson P, Holz O, Koster G, Postle A, Olin AC, Hohlfeld JM. Exhaled breath particles as a novel tool to study lipid composition of epithelial lining fluid from the distal lung. BMC Pulm Med 2023; 23:423. [PMID: 37924084 PMCID: PMC10623716 DOI: 10.1186/s12890-023-02718-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/19/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND Surfactant phospholipid (PL) composition plays an important role in lung diseases. We compared the PL composition of non-invasively collected exhaled breath particles (PEx) with bronchoalveolar lavage (BAL) and induced sputum (ISP) at baseline and following endotoxin (LPS) challenges. METHODS PEx and BAL were collected from ten healthy nonsmoking participants before and after segmental LPS challenge. Four weeks later, PEx and ISP were sampled in the week before and after a whole lung LPS inhalation challenge. PL composition was analysed using mass spectrometry. RESULTS The overall PL composition of BAL, ISP and PEx was similar, with PC(32:0) and PC(34:1) representing the largest fractions in all three sample types (baseline PC(32:0) geometric mean mol%: 52.1, 56.9, and 51.7, PC(34:1) mol%: 11.7, 11.9 and 11.4, respectively). Despite this similarity, PEx PL composition was more closely related to BAL than to ISP. For most lipids comparable inter-individual differences in BAL, ISP, and PEx were found. PL composition of PEx was repeatable. The most pronounced increase following segmental LPS challenge was detected for SM(d34:1) in BAL (0.24 to 0.52 mol%) and following inhalation LPS challenge in ISP (0.45 to 0.68 mol%). An increase of SM(d34:1) following segmental LPS challenge was also detectable in PEx (0.099 to 0.103 mol%). The inhalation challenge did not change PL composition of PEx. CONCLUSION Our data supports the peripheral origin of PEx. The lack of PL changes in PEx after inhalation challenge might to be due to the overall weaker response of inhaled LPS which primarily affects the larger airways. Compared with BAL, which always contains lining fluid from both peripheral lung and central airways, PEx analysis might add value as a selective and non-invasive method to investigate peripheral airway PL composition. TRIAL REGISTRATION NCT03044327, first posted 07/02/2017.
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Affiliation(s)
- Per Larsson
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Olaf Holz
- Department of Clinical Airway Research, Fraunhofer Institute for Toxicology and Experimental Medicine, 30625, Hannover, Germany.
- German Center for Lung Research, Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany.
| | - Grielof Koster
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Anthony Postle
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Anna-Carin Olin
- Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jens M Hohlfeld
- Department of Clinical Airway Research, Fraunhofer Institute for Toxicology and Experimental Medicine, 30625, Hannover, Germany
- German Center for Lung Research, Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
- Hannover Medical School, Department of Respiratory Medicine, Hannover, Germany
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2
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Wee HN, Lee LS, Han SHY, Zhou J, Yen PM, Ching J. Lipidomics Workflow for Analyzing Lipid Profiles Using Multiple Reaction Monitoring (MRM) in Liver Homogenate of Mice with Non-alcoholic Steatohepatitis (NASH). Bio Protoc 2023; 13:e4773. [PMID: 37456342 PMCID: PMC10338713 DOI: 10.21769/bioprotoc.4773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/11/2023] [Accepted: 06/05/2023] [Indexed: 07/18/2023] Open
Abstract
Non-alcoholic steatohepatitis (NASH) is a condition characterized by inflammation and hepatic injury/fibrosis caused by the accumulation of ectopic fats in the liver. Recent advances in lipidomics have allowed the identification and characterization of lipid species and have revealed signature patterns of various diseases. Here, we describe a lipidomics workflow to assess the lipid profiles of liver homogenates taken from a NASH mouse model. The protocol described below was used to extract and analyze the metabolites from the livers of mice with NASH by liquid chromatography-mass spectrometry (LC-MS); however, it can be applied to other tissue homogenate samples. Using this method, over 1,000 species of lipids from five classes can be analyzed in a single run on the LC-MS. Also, partial elucidation of the identity of neutral lipid (triacylglycerides and diacylglycerides) aliphatic chains can be performed with this simple LC-MS setup. Key features Over 1,000 lipid species (sphingolipids, cholesteryl esters, neutral lipids, phospholipids, fatty acids) are analyzed in one run. Analysis of liver lipids in non-alcoholic steatohepatitis (NASH) mouse model. Normal-phase chromatography coupled to a triple quadrupole mass spectrometer.
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Affiliation(s)
- Hai Ning Wee
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Lye Siang Lee
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Sharon Hong Yu Han
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Jin Zhou
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Paul Michael Yen
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Jianhong Ching
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Medical School, Singapore, Singapore
- KK Research Centre, KK Women’s and Children’s Hospital, Singapore, Singapore
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3
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Neighbors M, Li Q, Zhu SJ, Liu J, Wong WR, Jia G, Sandoval W, Tew GW. Bioactive lipid lysophosphatidic acid species are associated with disease progression in idiopathic pulmonary fibrosis. J Lipid Res 2023; 64:100375. [PMID: 37075981 PMCID: PMC10205439 DOI: 10.1016/j.jlr.2023.100375] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 04/21/2023] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive disease with significant mortality. Prognostic biomarkers to identify rapid progressors are urgently needed to improve patient management. Since the lysophosphatidic acid (LPA) pathway has been implicated in lung fibrosis in preclinical models and identified as a potential therapeutic target, we aimed to investigate if bioactive lipid LPA species could be prognostic biomarkers that predict IPF disease progression. LPAs and lipidomics were measured in baseline placebo plasma of a randomized IPF-controlled trial. The association of lipids with disease progression indices were assessed using statistical models. Compared to healthy, IPF patients had significantly higher levels of five LPAs (LPA16:0, 16:1, 18:1, 18:2, 20:4) and reduced levels of two triglycerides species (TAG48:4-FA12:0, -FA18:2) (false discovery rate < 0.05, fold change > 2). Patients with higher levels of LPAs had greater declines in diffusion capacity of carbon monoxide over 52 weeks (P < 0.01); additionally, LPA20:4-high (≥median) patients had earlier time to exacerbation compared to LPA20:4-low (
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Affiliation(s)
| | - Qingling Li
- Department of Microchemistry, Proteomics & Lipidomics, Genentech Inc., South San Francisco, USA
| | - Sha Joe Zhu
- PD Data Science, F Hoffmann-La Roche, Shanghai, China
| | - Jia Liu
- PD Data Science, F Hoffmann-La Roche, Shanghai, China
| | - Weng Ruh Wong
- Department of Microchemistry, Proteomics & Lipidomics, Genentech Inc., South San Francisco, USA
| | - Guiquan Jia
- Department of Biomarker Discovery OMNI, Genentech Inc., South San Francisco, USA
| | - Wendy Sandoval
- Department of Microchemistry, Proteomics & Lipidomics, Genentech Inc., South San Francisco, USA
| | - Gaik W Tew
- I2O Technology and Translational Research, Genentech Inc., South San Francisco, USA.
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4
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Ryan MJ, Grant-St James A, Lawler NG, Fear MW, Raby E, Wood FM, Maker GL, Wist J, Holmes E, Nicholson JK, Whiley L, Gray N. Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry. J Proteome Res 2023; 22:1419-1433. [PMID: 36828482 PMCID: PMC10167688 DOI: 10.1021/acs.jproteome.2c00682] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.
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Affiliation(s)
- Monique J Ryan
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Alanah Grant-St James
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Nathan G Lawler
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Mark W Fear
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Edward Raby
- Department of Microbiology, PathWest Laboratory Medicine, Perth, Western Australia 6009, Australia.,Department of Infectious Diseases, Fiona Stanley Hospital, Perth, Western Australia 6150, Australia
| | - Fiona M Wood
- Burn Injury Research Unit, University of Western Australia, Perth, Western Australia 6009, Australia.,WA Department of Health, Burns Service WA, Perth, Western Australia 6009, Australia.,Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Garth L Maker
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Perron Institute for Neurological and Translational Science, Nedlands, Western Australia 6009, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia.,Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
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5
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Medina J, Borreggine R, Teav T, Gao L, Ji S, Carrard J, Jones C, Blomberg N, Jech M, Atkins A, Martins C, Schmidt-Trucksass A, Giera M, Cazenave-Gassiot A, Gallart-Ayala H, Ivanisevic J. Omic-Scale High-Throughput Quantitative LC-MS/MS Approach for Circulatory Lipid Phenotyping in Clinical Research. Anal Chem 2023; 95:3168-3179. [PMID: 36716250 DOI: 10.1021/acs.analchem.2c02598] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Lipid analysis at the molecular species level represents a valuable opportunity for clinical applications due to the essential roles that lipids play in metabolic health. However, a comprehensive and high-throughput lipid profiling remains challenging given the lipid structural complexity and exceptional diversity. Herein, we present an 'omic-scale targeted LC-MS/MS approach for the straightforward and high-throughput quantification of a broad panel of complex lipid species across 26 lipid (sub)classes. The workflow involves an automated single-step extraction with 2-propanol, followed by lipid analysis using hydrophilic interaction liquid chromatography in a dual-column setup coupled to tandem mass spectrometry with data acquisition in the timed-selective reaction monitoring mode (12 min total run time). The analysis pipeline consists of an initial screen of 1903 lipid species, followed by high-throughput quantification of robustly detected species. Lipid quantification is achieved by a single-point calibration with 75 isotopically labeled standards representative of different lipid classes, covering lipid species with diverse acyl/alkyl chain lengths and unsaturation degrees. When applied to human plasma, 795 lipid species were measured with median intra- and inter-day precisions of 8.5 and 10.9%, respectively, evaluated within a single and across multiple batches. The concentration ranges measured in NIST plasma were in accordance with the consensus intervals determined in previous ring-trials. Finally, to benchmark our workflow, we characterized NIST plasma materials with different clinical and ethnic backgrounds and analyzed a sub-set of sera (n = 81) from a clinically healthy elderly population. Our quantitative lipidomic platform allowed for a clear distinction between different NIST materials and revealed the sex-specificity of the serum lipidome, highlighting numerous statistically significant sex differences.
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Affiliation(s)
- Jessica Medina
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Rebecca Borreggine
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Tony Teav
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Liang Gao
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Shanshan Ji
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Justin Carrard
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Christina Jones
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Niek Blomberg
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Martin Jech
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Alan Atkins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Claudia Martins
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, California 95134, United States
| | - Arno Schmidt-Trucksass
- Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Birsstrasse 320B, Basel CH-4052, Switzerland
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden 2333ZA, Netherlands
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine TRP, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore.,Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore 117456, Singapore
| | - Hector Gallart-Ayala
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
| | - Julijana Ivanisevic
- Metabolomics Platform, Faculty of Biology and Medicine, University of Lausanne, Quartier UNIL-CHUV, Rue du Bugnon 19, Lausanne CH-1005, Switzerland
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6
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Alabed HBR, Gorello P, Pellegrino RM, Lancioni H, La Starza R, Taddei AA, Urbanelli L, Buratta S, Fernandez AGL, Matteucci C, Caniglia M, Arcioni F, Mecucci C, Emiliani C. Comparison between Sickle Cell Disease Patients and Healthy Donors: Untargeted Lipidomic Study of Erythrocytes. Int J Mol Sci 2023; 24:ijms24032529. [PMID: 36768849 PMCID: PMC9917006 DOI: 10.3390/ijms24032529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/19/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Sickle cell disease (SCD) is one of the most common severe monogenic disorders in the world caused by a mutation on HBB gene and characterized by hemoglobin polymerization, erythrocyte rigidity, vaso-occlusion, chronic anemia, hemolysis, and vasculopathy. Recently, the scientific community has focused on the multiple genetic and clinical profiles of SCD. However, the lipid composition of sickle cells has received little attention in the literature. According to recent studies, changes in the lipid profile are strongly linked to several disorders. Therefore, the aim of this study is to dig deeper into lipidomic analysis of erythrocytes in order to highlight any variations between healthy and patient subjects. 241 lipid molecular species divided into 17 classes have been annotated and quantified. Lipidomic profiling of SCD patients showed that over 24% of total lipids were altered most of which are phospholipids. In-depth study of significant changes in lipid metabolism can give an indication of the enzymes and genes involved. In a systems biology scenario, these variations can be useful to improve the understanding of the biochemical basis of SCD and to try to make a score system that could be predictive for the severity of clinical manifestations.
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Affiliation(s)
- Husam B. R. Alabed
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Paolo Gorello
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Roberto Maria Pellegrino
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
- Correspondence:
| | - Hovirag Lancioni
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Roberta La Starza
- Hematology and Bone Marrow Transplantation Unit, Laboratory of Molecular Medicine (CREO), Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Anna Aurora Taddei
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Lorena Urbanelli
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Sandra Buratta
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
| | - Anair Graciela Lema Fernandez
- Hematology and Bone Marrow Transplantation Unit, Laboratory of Molecular Medicine (CREO), Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Caterina Matteucci
- Hematology and Bone Marrow Transplantation Unit, Laboratory of Molecular Medicine (CREO), Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Maurizio Caniglia
- Pediatric Oncology-Hematology, Azienda Ospedaliera di Perugia, 06100 Perugia, Italy
| | - Francesco Arcioni
- Pediatric Oncology-Hematology, Azienda Ospedaliera di Perugia, 06100 Perugia, Italy
| | - Cristina Mecucci
- Hematology and Bone Marrow Transplantation Unit, Laboratory of Molecular Medicine (CREO), Department of Medicine and Surgery, University of Perugia, 06132 Perugia, Italy
| | - Carla Emiliani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, 06100 Perugia, Italy
- Centro di Eccellenza sui Materiali Innovativi Nanostrutturati (CEMIN), University of Perugia, Via del Giochetto, 06123 Perugia, Italy
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7
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Ghorasaini M, Tsezou KI, Verhoeven A, Mohammed Y, Vlachoyiannopoulos P, Mikros E, Giera M. Congruence and Complementarity of Differential Mobility Spectrometry and NMR Spectroscopy for Plasma Lipidomics. Metabolites 2022; 12:1030. [PMID: 36355113 PMCID: PMC9699282 DOI: 10.3390/metabo12111030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/17/2022] Open
Abstract
The lipid composition of lipoprotein particles is determinative of their respective formation and function. In turn, the combination and correlation of nuclear magnetic resonance (NMR)-based lipoprotein measurements with mass spectrometry (MS)-based lipidomics is an appealing technological combination for a better understanding of lipid metabolism in health and disease. Here, we developed a combined workflow for subsequent NMR- and MS-based analysis on single sample aliquots of human plasma. We evaluated the quantitative agreement of the two platforms for lipid quantification and benchmarked our combined workflow. We investigated the congruence and complementarity between the platforms in order to facilitate a better understanding of patho-physiological lipoprotein and lipid alterations. We evaluated the correlation and agreement between the platforms. Next, we compared lipid class concentrations between healthy controls and rheumatoid arthritis patient samples to investigate the consensus among the platforms on differentiating the two groups. Finally, we performed correlation analysis between all measured lipoprotein particles and lipid species. We found excellent agreement and correlation (r > 0.8) between the platforms and their respective diagnostic performance. Additionally, we generated correlation maps detailing lipoprotein/lipid interactions and describe disease-relevant correlations.
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Affiliation(s)
- Mohan Ghorasaini
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Konstantina Ismini Tsezou
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, 157 71 Athens, Greece
- Pharmagnose S.A., 320 11 Inofyta, Greece
| | - Aswin Verhoeven
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Genome BC Proteomics Centre, University of Victoria, Victoria, BC V8Z 5N3, Canada
| | - Panayiotis Vlachoyiannopoulos
- Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, 157 71 Athens, Greece
- Institute for Autoimmune Systemic and Neurologic Diseases, 104 31 Athens, Greece
| | - Emmanuel Mikros
- Pharmagnose S.A., 320 11 Inofyta, Greece
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistiomiopolis, Zografou, 157 71 Athens, Greece
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
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8
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Naudin S, Sampson JN, Moore SC, Stolzenberg-Solomon R. Sources of Variability in Serum Lipidomic Measurements and Implications for Epidemiologic Studies. Am J Epidemiol 2022; 191:1926-1935. [PMID: 35699209 PMCID: PMC10144665 DOI: 10.1093/aje/kwac106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 04/04/2022] [Accepted: 06/09/2022] [Indexed: 02/01/2023] Open
Abstract
Epidemiological studies using lipidomic approaches can identify lipids associated with exposures and diseases. We evaluated the sources of variability of lipidomic profiles measured in blood samples and the implications when designing epidemiologic studies. We measured 918 lipid species in nonfasting baseline serum from 693 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, with 570 participants having serial blood samples separated by 1-5 years and 72 blinded replicate quality control samples. Blood samples were collected during 1993-2006. For each lipid species, we calculated the between-individual, within-individual, and technical variances, and we estimated the statistical power to detect associations in case-control studies. The technical variability was moderate, with a median intraclass correlation coefficient of 0.79. The combination of technical and within-individual variances accounted for most of the variability in 74% of the lipid species. For an average true relative risk of 3 (comparing upper and lower quartiles) after correction for multiple comparisons at the Bonferroni significance threshold (α = 0.05/918 = 5.45 ×10-5), we estimated that a study with 500, 1,000, and 5,000 total participants (1:1 case-control ratio) would have 19%, 57%, and 99% power, respectively. Epidemiologic studies examining associations between lipidomic profiles and disease require large samples sizes to detect moderate effect sizes associations.
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Affiliation(s)
| | | | | | - Rachael Stolzenberg-Solomon
- Correspondence to Dr. Rachael Stolzenberg-Solomon, Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute – Shady Grove, 9609 Medical Center Drive, Room 6E420, Rockville, MD 20850 (e-mail: )
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9
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Goerdten J, Yuan L, Huybrechts I, Neveu V, Nöthlings U, Ahrens W, Scalbert A, Floegel A. Reproducibility of the Blood and Urine Exposome: A Systematic Literature Review and Meta-Analysis. Cancer Epidemiol Biomarkers Prev 2022; 31:1683-1692. [PMID: 35732488 DOI: 10.1158/1055-9965.epi-22-0090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/28/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
Endogenous and exogenous metabolite concentrations may be susceptible to variation over time. This variability can lead to misclassification of exposure levels and in turn to biased results. To assess the reproducibility of metabolites, the intraclass correlation coefficient (ICC) is computed. A literature search in three databases from 2000 to May 2021 was conducted to identify studies reporting ICCs for blood and urine metabolites. This review includes 192 studies, of which 31 studies are included in the meta-analyses. The ICCs of 359 single metabolites are reported, and the ICCs of 10 metabolites were meta-analyzed. The reproducibility of the single metabolites ranges from poor to excellent and is highly compound-dependent. The reproducibility of bisphenol A (BPA), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-2-ethylhexyl phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), methylparaben, and propylparaben is poor to moderate (ICC median: 0.32; range: 0.15-0.49), and for 25-hydroxyvitamin D [25(OH)D], it is excellent (ICC: 0.95; 95% CI, 0.90-0.99). Pharmacokinetics, mainly the half-life of elimination and exposure patterns, can explain reproducibility. This review describes the reproducibility of the blood and urine exposome, provides a vast dataset of ICC estimates, and hence constitutes a valuable resource for future reproducibility and clinical epidemiologic studies.
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Affiliation(s)
- Jantje Goerdten
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Li Yuan
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Inge Huybrechts
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Vanessa Neveu
- International Agency for Research on Cancer (IARC), Lyon, France
| | - Ute Nöthlings
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms - University Bonn, Bonn, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | | | - Anna Floegel
- Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
- Section of Dietetics, Faculty of Agriculture and Food Sciences, Hochschule Neubrandenburg - University of Applied Sciences, Neubrandenburg, Germany
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10
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Gart E, Salic K, Morrison MC, Giera M, Attema J, de Ruiter C, Caspers M, Schuren F, Bobeldijk-Pastorova I, Heer M, Qin Y, Kleemann R. The Human Milk Oligosaccharide 2′-Fucosyllactose Alleviates Liver Steatosis, ER Stress and Insulin Resistance by Reducing Hepatic Diacylglycerols and Improved Gut Permeability in Obese Ldlr-/-.Leiden Mice. Front Nutr 2022; 9:904740. [PMID: 35782914 PMCID: PMC9248376 DOI: 10.3389/fnut.2022.904740] [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: 03/25/2022] [Accepted: 05/17/2022] [Indexed: 12/19/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a complex multifactorial disorder that is associated with gut dysbiosis, enhanced gut permeability, adiposity and insulin resistance. Prebiotics such as human milk oligosaccharide 2′-fucosyllactose are thought to primarily improve gut health and it is uncertain whether they would affect more distant organs. This study investigates whether 2′-fucosyllactose can alleviate NAFLD development in manifest obesity. Obese hyperinsulinemic Ldlr-/-.Leiden mice, after an 8 week run-in on a high-fat diet (HFD), were treated with 2′-fucosyllactose by oral gavage until week 28 and compared to HFD-vehicle controls. 2′-fucosyllactose did not affect food intake, body weight, total fat mass or plasma lipids. 2′-fucosyllactose altered the fecal microbiota composition which was paralleled by a suppression of HFD-induced gut permeability at t = 12 weeks. 2′-fucosyllactose significantly attenuated the development of NAFLD by reducing microvesicular steatosis. These hepatoprotective effects were supported by upstream regulator analyses showing that 2′-fucosyllactose activated ACOX1 (involved in lipid catabolism), while deactivating SREBF1 (involved in lipogenesis). Furthermore, 2′-fucosyllactose suppressed ATF4, ATF6, ERN1, and NUPR1 all of which participate in endoplasmic reticulum stress. 2′-fucosyllactose reduced fasting insulin concentrations and HOMA-IR, which was corroborated by decreased intrahepatic diacylglycerols. In conclusion, long-term supplementation with 2′-fucosyllactose can counteract the detrimental effects of HFD on gut dysbiosis and gut permeability and attenuates the development of liver steatosis. The observed reduction in intrahepatic diacylglycerols provides a mechanistic rationale for the improvement of hyperinsulinemia and supports the use of 2′-fucosyllactose to correct dysmetabolism and insulin resistance.
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Affiliation(s)
- Eveline Gart
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
- *Correspondence: Eveline Gart,
| | - Kanita Salic
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Martine C. Morrison
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, Netherlands
| | - Joline Attema
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Christa de Ruiter
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
| | - Martien Caspers
- Department of Microbiology and Systems Biology, Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Frank Schuren
- Department of Microbiology and Systems Biology, Netherlands Organisation for Applied Scientific Research (TNO), Zeist, Netherlands
| | - Ivana Bobeldijk-Pastorova
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
| | | | - Yan Qin
- Human Nutrition, BASF Pte Ltd., Singapore, Singapore
| | - Robert Kleemann
- Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), Leiden, Netherlands
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11
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Spix B, Butz ES, Chen CC, Rosato AS, Tang R, Jeridi A, Kudrina V, Plesch E, Wartenberg P, Arlt E, Briukhovetska D, Ansari M, Günsel GG, Conlon TM, Wyatt A, Wetzel S, Teupser D, Holdt LM, Ectors F, Boekhoff I, Boehm U, García-Añoveros J, Saftig P, Giera M, Kobold S, Schiller HB, Zierler S, Gudermann T, Wahl-Schott C, Bracher F, Yildirim AÖ, Biel M, Grimm C. Lung emphysema and impaired macrophage elastase clearance in mucolipin 3 deficient mice. Nat Commun 2022; 13:318. [PMID: 35031603 PMCID: PMC8760276 DOI: 10.1038/s41467-021-27860-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 12/16/2021] [Indexed: 02/06/2023] Open
Abstract
Lung emphysema and chronic bronchitis are the two most common causes of chronic obstructive pulmonary disease. Excess macrophage elastase MMP-12, which is predominantly secreted from alveolar macrophages, is known to mediate the development of lung injury and emphysema. Here, we discovered the endolysosomal cation channel mucolipin 3 (TRPML3) as a regulator of MMP-12 reuptake from broncho-alveolar fluid, driving in two independently generated Trpml3-/- mouse models enlarged lung injury, which is further exacerbated after elastase or tobacco smoke treatment. Mechanistically, using a Trpml3IRES-Cre/eR26-τGFP reporter mouse model, transcriptomics, and endolysosomal patch-clamp experiments, we show that in the lung TRPML3 is almost exclusively expressed in alveolar macrophages, where its loss leads to defects in early endosomal trafficking and endocytosis of MMP-12. Our findings suggest that TRPML3 represents a key regulator of MMP-12 clearance by alveolar macrophages and may serve as therapeutic target for emphysema and chronic obstructive pulmonary disease.
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Affiliation(s)
- Barbara Spix
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Elisabeth S Butz
- Department of Pharmacy, Ludwig-Maximilians-University, Munich, Germany
- Institute for Neurophysiology, Hannover Medical School, Hannover, Germany
| | - Cheng-Chang Chen
- Department of Pharmacy, Ludwig-Maximilians-University, Munich, Germany
- Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Anna Scotto Rosato
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Rachel Tang
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Aicha Jeridi
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Veronika Kudrina
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Eva Plesch
- Department of Pharmacy, Ludwig-Maximilians-University, Munich, Germany
| | - Philipp Wartenberg
- Saarland University, Center for Molecular Signaling (PZMS), Experimental Pharmacology, Homburg, Germany
| | - Elisabeth Arlt
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Daria Briukhovetska
- Division of Clinical Pharmacology, Department of Medicine IV, University Hospital Munich, Munich, Germany
| | - Meshal Ansari
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Gizem Günes Günsel
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Thomas M Conlon
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Amanda Wyatt
- Saarland University, Center for Molecular Signaling (PZMS), Experimental Pharmacology, Homburg, Germany
| | - Sandra Wetzel
- Institute of Biochemistry, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Daniel Teupser
- Institute of Laboratory Medicine, University Hospital Munich, Munich, Germany
| | - Lesca M Holdt
- Institute of Laboratory Medicine, University Hospital Munich, Munich, Germany
| | - Fabien Ectors
- FARAH Mammalian Transgenics Platform, Liège University, Liège, Belgium
| | - Ingrid Boekhoff
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
| | - Ulrich Boehm
- Saarland University, Center for Molecular Signaling (PZMS), Experimental Pharmacology, Homburg, Germany
| | - Jaime García-Añoveros
- Departments of Anesthesiology, Physiology and Neurology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Paul Saftig
- Institute of Biochemistry, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333ZA, Leiden, The Netherlands
| | - Sebastian Kobold
- Division of Clinical Pharmacology, Department of Medicine IV, University Hospital Munich, Munich, Germany
- German Center for Translational Cancer Research (DKTK), partner site Munich, Munich, Germany
| | - Herbert B Schiller
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Susanna Zierler
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
- Institute of Pharmacology, Johannes-Keppler-University, Linz, Australia
| | - Thomas Gudermann
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany
- German Center of Lung Research (DZL), Munich, Germany
| | | | - Franz Bracher
- Department of Pharmacy, Ludwig-Maximilians-University, Munich, Germany
| | - Ali Önder Yildirim
- Comprehensive Pneumology Center, Institute of Lung Biology and Disease, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany.
| | - Martin Biel
- Department of Pharmacy, Ludwig-Maximilians-University, Munich, Germany.
| | - Christian Grimm
- Walther Straub Institute of Pharmacology and Toxicology, Faculty of Medicine, Ludwig-Maximilians-University, Munich, Germany.
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12
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Ghorasaini M, Mohammed Y, Adamski J, Bettcher L, Bowden JA, Cabruja M, Contrepois K, Ellenberger M, Gajera B, Haid M, Hornburg D, Hunter C, Jones CM, Klein T, Mayboroda O, Mirzaian M, Moaddel R, Ferrucci L, Lovett J, Nazir K, Pearson M, Ubhi BK, Raftery D, Riols F, Sayers R, Sijbrands EJG, Snyder MP, Su B, Velagapudi V, Williams KJ, de Rijke YB, Giera M. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring. Anal Chem 2021; 93:16369-16378. [PMID: 34859676 PMCID: PMC8674878 DOI: 10.1021/acs.analchem.1c02826] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/18/2021] [Indexed: 12/15/2022]
Abstract
Modern biomarker and translational research as well as personalized health care studies rely heavily on powerful omics' technologies, including metabolomics and lipidomics. However, to translate metabolomics and lipidomics discoveries into a high-throughput clinical setting, standardization is of utmost importance. Here, we compared and benchmarked a quantitative lipidomics platform. The employed Lipidyzer platform is based on lipid class separation by means of differential mobility spectrometry with subsequent multiple reaction monitoring. Quantitation is achieved by the use of 54 deuterated internal standards and an automated informatics approach. We investigated the platform performance across nine laboratories using NIST SRM 1950-Metabolites in Frozen Human Plasma, and three NIST Candidate Reference Materials 8231-Frozen Human Plasma Suite for Metabolomics (high triglyceride, diabetic, and African-American plasma). In addition, we comparatively analyzed 59 plasma samples from individuals with familial hypercholesterolemia from a clinical cohort study. We provide evidence that the more practical methyl-tert-butyl ether extraction outperforms the classic Bligh and Dyer approach and compare our results with two previously published ring trials. In summary, we present standardized lipidomics protocols, allowing for the highly reproducible analysis of several hundred human plasma lipids, and present detailed molecular information for potentially disease relevant and ethnicity-related materials.
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Affiliation(s)
- Mohan Ghorasaini
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
| | - Yassene Mohammed
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
- Genome
BC Proteomics Centre, University of Victoria, Victoria, British Columbia V8Z 7X8, Canada
| | - Jerzy Adamski
- Institute
of Experimental Genetics, German Research Center for Environmental
Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
- Department
of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
- Institute
of Biochemistry, Faculty of Medicine, University
of Ljubljana, Vrazov
Trg 2, Ljubljana 1000, Slovenia
| | - Lisa Bettcher
- Northwest
Metabolomics Research Center, Department of Anesthesiology, University of Washington, Seattle, Washington 98109, United States
| | - John A. Bowden
- Department
of Physiological Sciences, College of Veterinary Medicine, University of Florida, 1333 Center Drive, Gainesville, Florida 32610, United States
| | - Matias Cabruja
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Kévin Contrepois
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Mathew Ellenberger
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Bharat Gajera
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | - Mark Haid
- Metabolomics
and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
| | - Daniel Hornburg
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | | | - Christina M. Jones
- Material Measurement Laboratory, National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Theo Klein
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Oleg Mayboroda
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
| | - Mina Mirzaian
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Ruin Moaddel
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Luigi Ferrucci
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Jacqueline Lovett
- National Institute on Aging, National Institutes of
Health, Baltimore, Maryland 21224, United
States
| | - Kenneth Nazir
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | | | | | - Daniel Raftery
- Northwest
Metabolomics Research Center, Department of Anesthesiology, University of Washington, Seattle, Washington 98109, United States
| | - Fabien Riols
- Metabolomics
and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Ingolstaedter Landstr. 1, Neuherberg 85764, Germany
| | | | - Eric J. G. Sijbrands
- Department of Internal Medicine, University
Medical Center, Erasmus MC, Rotterdam 3000CA, The Netherlands
| | - Michael P. Snyder
- Department
of Genetics, School of Medicine, Stanford
University, 300 Pasteur Drive, Stanford, California 94305, United States
| | - Baolong Su
- Department of Biological
Chemistry, University
of California, Los Angeles, California 90095, United States
| | - Vidya Velagapudi
- Metabolomics
Unit, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Biomedicum 2U, Helsinki 00014, Finland
| | - Kevin J. Williams
- Department of Biological
Chemistry, University
of California, Los Angeles, California 90095, United States
| | - Yolanda B. de Rijke
- Department
of Clinical Chemistry, University Medical Center, Erasmus MC, Rotterdam, 3000CA, The Netherlands
| | - Martin Giera
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands
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13
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Loef M, Faquih TO, von Hegedus JH, Ghorasaini M, Ioan-Facsinay A, Kroon FP, Giera M, Kloppenburg M. The lipid profile for the prediction of prednisolone treatment response in patients with inflammatory hand osteoarthritis: The HOPE study. OSTEOARTHRITIS AND CARTILAGE OPEN 2021; 3:100167. [DOI: 10.1016/j.ocarto.2021.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 10/21/2022] Open
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14
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Analytical considerations for reducing the matrix effect for the sphingolipidome quantification in whole blood. Bioanalysis 2021; 13:1037-1049. [PMID: 34110924 PMCID: PMC8240607 DOI: 10.4155/bio-2021-0098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Aim: Plasma and serum are widely used blood-derived biofluids for metabolomics and lipidomics assays, but analytes that are present in high concentrations in blood cells cannot be evaluated in those samples and isolating serum or plasma could introduce additional variability in the data. Materials & methods: In this study, we provide a comprehensive method for quantification of the whole blood (WB) sphingolipidome, combining a single-phase extraction method with LC-high-resolution mass spectrometry. Results: We were able to quantify more than 150 sphingolipids, and when compared with paired plasma, WB contained higher concentration of most sphingolipids and individual variations were lower. These findings suggest that WB could be a better alternative to plasma, and potentially guide the evaluation of the sphingolipidome for biomarker discovery.
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