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Ajoolabady A, Pratico D, Dunn WB, Lip GYH, Ren J. Metabolomics: Implication in cardiovascular research and diseases. Obes Rev 2024:e13825. [PMID: 39370721 DOI: 10.1111/obr.13825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 08/13/2024] [Accepted: 08/18/2024] [Indexed: 10/08/2024]
Abstract
Cellular metabolism influences all aspects of cellular function and is crucial for overall organismal health. Metabolic disorders related to cardiovascular health can lead to cardiovascular diseases (CVDs). Moreover, associated comorbidities may also damage cardiovascular metabolism, exacerbating CVD and perpetuating a vicious cycle. Given the prominent role of metabolic alterations in CVD, metabolomics has emerged as an imperative technique enabling a comprehensive assessment of metabolites and metabolic architecture within the body. Metabolite profile and metabolic pathways help to deepen and broaden our understanding of complex genomic landscape and pathophysiology of CVD. Here in this review, we aim to overview the experimental and clinical applications of metabolomics in pathogenesis, diagnosis, prognosis, and management of various CVD plus future perspectives and limitations.
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Affiliation(s)
- Amir Ajoolabady
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Domenico Pratico
- Alzheimer's Center at Temple, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Warwick B Dunn
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Jun Ren
- Shanghai Institute of Cardiovascular Diseases, Department of Cardiology, Zhongshan Hospital Fudan University, Shanghai, China
- National Clinical Research Center for Interventional Medicine, Shanghai, China
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2
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Ahmed W, Wilkinson M, Fowler SJ. Generating pooled quality control samples of volatile organic compounds. J Breath Res 2024; 18:041004. [PMID: 39260379 DOI: 10.1088/1752-7163/ad7977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Accepted: 09/11/2024] [Indexed: 09/13/2024]
Abstract
Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace are influenced by several confounding factors not represented in reference standards. In this study, we propose a method of generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow with thermal desorption tubes. Sample tubes were desorbed and split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of multiple QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects.
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Affiliation(s)
- Waqar Ahmed
- Division of Immunology, Immunity to Infection and Respiratory Medicine; School of Biological Sciences; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Maxim Wilkinson
- Division of Immunology, Immunity to Infection and Respiratory Medicine; School of Biological Sciences; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Glasgow Caledonian University, Cowcaddens Road, Glasgow, United Kingdom
- Public Health Scotland, Meridian Court, 5 Cadogan Street, Glasgow, United Kingdom
| | - Stephen J Fowler
- Division of Immunology, Immunity to Infection and Respiratory Medicine; School of Biological Sciences; Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- Manchester Academic Health Science Centre and NIHR Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
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3
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Offer S, Di Bucchianico S, Czech H, Pardo M, Pantzke J, Bisig C, Schneider E, Bauer S, Zimmermann EJ, Oeder S, Hartner E, Gröger T, Alsaleh R, Kersch C, Ziehm T, Hohaus T, Rüger CP, Schmitz-Spanke S, Schnelle-Kreis J, Sklorz M, Kiendler-Scharr A, Rudich Y, Zimmermann R. The chemical composition of secondary organic aerosols regulates transcriptomic and metabolomic signaling in an epithelial-endothelial in vitro coculture. Part Fibre Toxicol 2024; 21:38. [PMID: 39300536 DOI: 10.1186/s12989-024-00600-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND The formation of secondary organic aerosols (SOA) by atmospheric oxidation reactions substantially contributes to the burden of fine particulate matter (PM2.5), which has been associated with adverse health effects (e.g., cardiovascular diseases). However, the molecular and cellular effects of atmospheric aging on aerosol toxicity have not been fully elucidated, especially in model systems that enable cell-to-cell signaling. METHODS In this study, we aimed to elucidate the complexity of atmospheric aerosol toxicology by exposing a coculture model system consisting of an alveolar (A549) and an endothelial (EA.hy926) cell line seeded in a 3D orientation at the air‒liquid interface for 4 h to model aerosols. Simulation of atmospheric aging was performed on volatile biogenic (β-pinene) or anthropogenic (naphthalene) precursors of SOA condensing on soot particles. The similar physical properties for both SOA, but distinct differences in chemical composition (e.g., aromatic compounds, oxidation state, unsaturated carbonyls) enabled to determine specifically induced toxic effects of SOA. RESULTS In A549 cells, exposure to naphthalene-derived SOA induced stress-related airway remodeling and an early type I immune response to a greater extent. Transcriptomic analysis of EA.hy926 cells not directly exposed to aerosol and integration with metabolome data indicated generalized systemic effects resulting from the activation of early response genes and the involvement of cardiovascular disease (CVD) -related pathways, such as the intracellular signal transduction pathway (PI3K/AKT) and pathways associated with endothelial dysfunction (iNOS; PDGF). Greater induction following anthropogenic SOA exposure might be causative for the observed secondary genotoxicity. CONCLUSION Our findings revealed that the specific effects of SOA on directly exposed epithelial cells are highly dependent on the chemical identity, whereas non directly exposed endothelial cells exhibit more generalized systemic effects with the activation of early stress response genes and the involvement of CVD-related pathways. However, a greater correlation was made between the exposure to the anthropogenic SOA compared to the biogenic SOA. In summary, our study highlights the importance of chemical aerosol composition and the use of cell systems with cell-to-cell interplay on toxicological outcomes.
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Affiliation(s)
- Svenja Offer
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Sebastiano Di Bucchianico
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany.
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany.
- Department Life, Light & Matter (LLM), University of Rostock, D-18051, Rostock, Germany.
| | - Hendryk Czech
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Michal Pardo
- Department of Earth and Planetary Sciences, Faculty of Chemistry, Weizmann Institute of Science, 234 Herzl Street, POB 26, Rehovot, ISR-7610001, Israel
| | - Jana Pantzke
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Christoph Bisig
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
| | - Eric Schneider
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
- Department Life, Light & Matter (LLM), University of Rostock, D-18051, Rostock, Germany
| | - Stefanie Bauer
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
| | - Elias J Zimmermann
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Sebastian Oeder
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
| | - Elena Hartner
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Thomas Gröger
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
| | - Rasha Alsaleh
- Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Henkestr. 9-11, D-91054, Erlangen, Germany
| | - Christian Kersch
- Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Henkestr. 9-11, D-91054, Erlangen, Germany
| | - Till Ziehm
- Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, Troposphere (IEK-8), Wilhelm- Johen-Str, D-52428, Jülich, Germany
| | - Thorsten Hohaus
- Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, Troposphere (IEK-8), Wilhelm- Johen-Str, D-52428, Jülich, Germany
| | - Christopher P Rüger
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
- Department Life, Light & Matter (LLM), University of Rostock, D-18051, Rostock, Germany
| | - Simone Schmitz-Spanke
- Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander University of Erlangen-Nuremberg, Henkestr. 9-11, D-91054, Erlangen, Germany
| | - Jürgen Schnelle-Kreis
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
| | - Martin Sklorz
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
| | - Astrid Kiendler-Scharr
- Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, Troposphere (IEK-8), Wilhelm- Johen-Str, D-52428, Jülich, Germany
| | - Yinon Rudich
- Department of Earth and Planetary Sciences, Faculty of Chemistry, Weizmann Institute of Science, 234 Herzl Street, POB 26, Rehovot, ISR-7610001, Israel
| | - Ralf Zimmermann
- Joint Mass Spectrometry Center (JMSC) at Comprehensive Molecular Analytics (CMA), Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764, Neuherberg, Germany
- Joint Mass Spectrometry Center (JMSC) at Analytical Chemistry, Institute of Chemistry, University of Rostock, Albert-Einstein-Str. 27, D-18059, Rostock, Germany
- Department Life, Light & Matter (LLM), University of Rostock, D-18051, Rostock, Germany
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4
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Little LD, Barnett SE, Issitt T, Bonsall S, Carolan VA, Allen E, Cole LM, Cross NA, Coulson JM, Haywood-Small SL. Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts. J Breath Res 2024; 18:046010. [PMID: 39163890 PMCID: PMC11388873 DOI: 10.1088/1752-7163/ad7166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 08/20/2024] [Indexed: 08/22/2024]
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.
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Affiliation(s)
- Liam D Little
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Sarah E Barnett
- Egg Facility, Liverpool Shared Research Facilities, Technology Infrastructure and Environment Directorate, University of Liverpool, Liverpool, United Kingdom
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Theo Issitt
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Sam Bonsall
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Vikki A Carolan
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Elizabeth Allen
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Laura M Cole
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Neil A Cross
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Judy M Coulson
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Sarah L Haywood-Small
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
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5
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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6
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Zeng Z, Huo J, Zhang Y, Shi Y, Wu Z, Yang Q, Zhang X. Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1243:124219. [PMID: 38943690 DOI: 10.1016/j.jchromb.2024.124219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/24/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.
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Affiliation(s)
- Zhongda Zeng
- College of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Jinfeng Huo
- College of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Yuxi Zhang
- Dalian ChemDataSolution Information Technology Co. Ltd., Dalian 116023, China
| | - Yingjiao Shi
- College of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China
| | - Zeying Wu
- School of Chemical Engineering and Material Sciences, Changzhou Institute of Technology, Changzhou 213032, China.
| | - Qianxu Yang
- Technology Center of China Tobacco Yunnan Industrial Co. Ltd., Kunming 650231, China.
| | - Xiaodan Zhang
- Key Laboratory of Plant Secondary Metabolism and Regulation of Zhejiang Province, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China.
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7
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O'Sullivan D, Arora T, Durif C, Uriot O, Brun M, Riu M, Foguet-Romero E, Samarra I, Domingo-Almenara X, Gahan CGM, Etienne-Mesmin L, Blanquet-Diot S. Impact of Western Diet on Enterohemorrhagic Escherichia coli Colonization in the Human In Vitro Mucosal Artificial Colon as Mediated by Gut Microbiota. Nutrients 2024; 16:2046. [PMID: 38999794 PMCID: PMC11243482 DOI: 10.3390/nu16132046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/14/2024] Open
Abstract
Enterohemorrhagic Escherichia coli (EHEC) is a major food-borne pathogen that causes human disease ranging from diarrhea to life-threatening complications. Accumulating evidence demonstrates that the Western diet enhances the susceptibility to enteric infection in mice, but the effect of diet on EHEC colonization and the role of human gut microbiota remains unknown. Our research aimed to investigate the effects of a Standard versus a Western diet on EHEC colonization in the human in vitro Mucosal ARtificial COLon (M-ARCOL) and the associated changes in the gut microbiota composition and activities. After donor selection using simplified fecal batch experiments, two M-ARCOL bioreactors were inoculated with a human fecal sample (n = 4) and were run in parallel, one receiving a Standard diet, the other a Western diet and infected with EHEC O157:H7 strain EDL933. EHEC colonization was dependent on the donor and diet in the luminal samples, but was maintained in the mucosal compartment without elimination, suggesting a favorable niche for the pathogen, and may act as a reservoir. The Western diet also impacted the bacterial short-chain fatty acid and bile acid profiles, with a possible link between high butyrate concentrations and prolonged EHEC colonization. The work demonstrates the application of a complex in vitro model to provide insights into diet, microbiota, and pathogen interactions in the human gut.
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Affiliation(s)
- Deborah O'Sullivan
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
| | - Trisha Arora
- Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, 43204 Reus, Spain
- Department of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Computational Metabolomics for Systems Biology Lab, Eurecat-Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Claude Durif
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
| | - Ophélie Uriot
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
| | - Morgane Brun
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
| | - Marc Riu
- Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, 43204 Reus, Spain
| | - Elisabet Foguet-Romero
- Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, 43204 Reus, Spain
| | - Iris Samarra
- Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, 43204 Reus, Spain
| | - Xavier Domingo-Almenara
- Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat-Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, 43204 Reus, Spain
- Department of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Computational Metabolomics for Systems Biology Lab, Eurecat-Technology Centre of Catalonia, 08005 Barcelona, Spain
| | - Cormac G M Gahan
- APC Microbiome Ireland, University College Cork, T12 YT20 Cork, Ireland
- School of Microbiology, University College Cork, T12 K8AF Cork, Ireland
- School of Pharmacy, University College Cork, T12 K8AF Cork, Ireland
| | - Lucie Etienne-Mesmin
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
| | - Stéphanie Blanquet-Diot
- UMR 454 INRAe, Microbiology, Digestive Environment and Health (MEDIS), Université Clermont Auvergne, 28 Place Henri Dunant, F-63000 Clermont-Ferrand, France
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8
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Ma GM, Wang JN, Wang XC, Ma FL, Wang WX, Li SF, Liu PP, Lv Y, Yu YJ, Fu HY, She Y. AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem. Anal Chem 2024; 96:9379-9389. [PMID: 38805056 DOI: 10.1021/acs.analchem.4c00100] [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: 05/29/2024]
Abstract
Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.
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Affiliation(s)
- Gui-Mei Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Jia-Nan Wang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Xing-Cai Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Wen-Xin Wang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Shu-Fang Li
- Institute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Science, Zhengzhou 450002, China
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Yi Lv
- Key Laboratory of Quality and Safety of Wolfberry and Wine for State Administration for Market Regulation, Ningxia Food Testing and Research Institute, Yinchuan 750004, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
- Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
| | - Yuanbin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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9
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Doose C, Hubas C. The metabolites of light: Untargeted metabolomic approaches bring new clues to understand light-driven acclimation of intertidal mudflat biofilm. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168692. [PMID: 38008320 DOI: 10.1016/j.scitotenv.2023.168692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/06/2023] [Accepted: 11/16/2023] [Indexed: 11/28/2023]
Abstract
The microphytobenthos (MPB), a microbial community of primary producers, play a key role in coastal ecosystem functioning, particularly in intertidal mudflats. These mudflats experience challenging variations of irradiance, forcing the micro-organisms to develop photoprotective mechanisms to survive and thrive in this dynamic environment. Two major adaptations to light are well described in literature: the excess of light energy dissipation through non-photochemical quenching (NPQ), and the vertical migration in the sediment. These mechanisms trigger considerable scientific interest, but the biological processes and metabolic mechanisms involved in light-driven vertical migration remain largely unknown. To our knowledge, this study investigates for the first time metabolomic responses of a migrational mudflat biofilm exposed for 30 min to a light gradient of photosynthetically active radiation (PAR) from 50 to 1000 μmol photons m-2 s-1. The untargeted metabolomic analysis allowed to identify metabolites involved in two types of responses to light irradiance levels. On the one hand, the production of SFAs and MUFAs, primarily derived from bacteria, indicates a healthy photosynthetic state of MPB under low light (LL; 50 and 100 PAR) and medium light (ML; 250 PAR) conditions. Conversely, when exposed to high light (HL; 500, 750 and 1000 PAR), the MPB experienced light-induced stress, triggering the production of alka(e)nes and fatty alcohols. The physiological and ecological roles of these compounds are poorly described in literature. This study sheds new light on the topic, as it suggests that these compounds may play a crucial and previously unexplored role in light-induced stress acclimation of migrational MPB biofilms. Since alka(e)nes are produced from FAs decarboxylation, these results thus emphasize for the first time the importance of FAs pathways in microphytobenthic biofilms acclimation to light.
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Affiliation(s)
- Caroline Doose
- Muséum National d'Histoire Naturelle, UMR BOREA, MNHN-CNRS-UCN-UPMC-IRD-UA, Station Marine de Concarneau, Concarneau, France.
| | - Cédric Hubas
- Muséum National d'Histoire Naturelle, UMR BOREA, MNHN-CNRS-UCN-UPMC-IRD-UA, Station Marine de Concarneau, Concarneau, France.
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10
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Kuo PH, Jhong YC, Kuo TC, Hsu YT, Kuo CH, Tseng YJ. A Clinical Breathomics Dataset. Sci Data 2024; 11:203. [PMID: 38355591 PMCID: PMC10866892 DOI: 10.1038/s41597-024-03052-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024] Open
Abstract
This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient's breath, thereby augmenting future diagnostic and therapeutic initiatives.
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Affiliation(s)
- Ping-Hung Kuo
- National Taiwan University Hospital, No. 1, Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan
| | - Yue-Chen Jhong
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
| | - Tien-Chueh Kuo
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
- The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
| | - Yu-Ting Hsu
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
| | - Ching-Hua Kuo
- The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
- Drug Research Center, College of Pharmacy, College of Medicine, National Taiwan University, No. 33, Linsen S. Road, Taipei, 10055, Taiwan
- Department of Pharmacy, School of Pharmacy, College of Medicine, National Taiwan University, No. 33, Linsen S. Road, Taipei, 10055, Taiwan
| | - Yufeng Jane Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan.
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11
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Ma FL, Liu JN, Wang XC, Ma GM, Song JJ, Chai GB, Lv Y, Zhang QD, Yu YJ, She Y. A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant. J Chromatogr A 2024; 1716:464653. [PMID: 38232638 DOI: 10.1016/j.chroma.2024.464653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/27/2023] [Accepted: 01/11/2024] [Indexed: 01/19/2024]
Abstract
The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.
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Affiliation(s)
- Feng-Lian Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Jia-Nan Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Xing-Cai Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
| | - Gui-Mei Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Jing-Jing Song
- Ningxia Institute of Cultural Relics and Archeology, Yinchuan 750001, China
| | - Guo-Bi Chai
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Yi Lv
- Key Laboratory of Quality and Safety of Wolfberry and Wine for State Administration for Market Regulation, Ningxia Food Testing and Research Institute, Yinchuan 750004, China
| | - Qi-Dong Zhang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China.
| | - Yuanbin She
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, China
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12
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Thong A, Basri N, Chew W. Comparison of untargeted gas chromatography-mass spectrometry analysis algorithms with implications to the interpretation and putative identification of volatile aroma compositions. J Chromatogr A 2024; 1713:464519. [PMID: 38039625 DOI: 10.1016/j.chroma.2023.464519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023]
Abstract
The aroma profiling process requires the identification of the volatile compounds in a sample or its headspace. Typically, the identification of compounds relies on automated feature finding and matching algorithms to (putatively) identify and report compounds based on retention index and mass spectra matching against a compound library. We investigated the use of five different workflows and proposed three metrics (target accuracy A, identification percentage I, uniqueness U) to quantify their impact on generated aroma profiles of a mixture of fragrance standards and a commercial grade essential oil. All workflows accurately identified target compounds (100% in standards, >90% in samples) and reported similar compound identities for major GC-MS features, but beyond that could differ by up to 40-50%. Despite the variances, different workflows did not report conflicting compound identities. Aroma compositions primarily contained unreported or extra (putatively) identified compounds due to variations in mass spectral elucidations within the various workflows. Considering these differences, we show how the proposed metrics, I and U, could be modified to help the analyst interpret and evaluate reported volatile aroma compositions of unknown materials.
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Affiliation(s)
- Aaron Thong
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore
| | - Nurhidayah Basri
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore
| | - Wee Chew
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Nanos, 31 Biopolis Way, #01-02, 138669, Singapore.
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13
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He X, Han B, Wang R, Guo Y, Kao RYT, Li H, Sun H, Xia W. Dual-action gallium-flavonoid compounds for combating Pseudomonas aeruginosa infection. RSC Chem Biol 2023; 4:774-784. [PMID: 37799578 PMCID: PMC10549236 DOI: 10.1039/d3cb00033h] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/10/2023] [Indexed: 10/07/2023] Open
Abstract
The opportunistic pathogen Pseudomonas aeruginosa (P. aeruginosa) causes infections that are difficult to treat, which is due to the bacterial natural resistance to antibiotics. The bacterium is also able to form a biofilm that protects the bacterium from clearance by the human immune system and leads to chronic infection. Herein, we synthesized and characterized a novel gallium compound that interferes with both the iron metabolism and quorum sensing system of P. aeruginosa to achieve a significant bactericidal activity. The compound could substantially reduce the secretion of bacterial virulence factors as well as eliminate biofilm formation. Integrative omics analysis indicates that this compound can significantly disturb the gene transcription and metabolism of P. aeruginosa. The effectiveness of the gallium compound was further validated in mammalian cell and murine skin infection models. Our study offers a new strategy to design new gallium-based antimicrobials to combat P. aeruginosa infection.
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Affiliation(s)
- Xiaojun He
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-sen University Guangzhou 510275 China
- School of Ophthalmology & Optometry, School of Biomedical Engineering, Wenzhou Medical University Wenzhou Zhejiang 325035 China
| | - Bingjie Han
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-sen University Guangzhou 510275 China
| | - Runming Wang
- Department of Chemistry, The University of Hong Kong Pokfulam Road Hong Kong P. R. China
- Department of Microbiology and State Key Laboratory for Emerging Infectious Diseases, The University of Hong Kong Hong Kong P. R. China
| | - Yu Guo
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-sen University Guangzhou 510275 China
| | - Richard Y T Kao
- Department of Microbiology and State Key Laboratory for Emerging Infectious Diseases, The University of Hong Kong Hong Kong P. R. China
| | - Hongyan Li
- Department of Chemistry, The University of Hong Kong Pokfulam Road Hong Kong P. R. China
| | - Hongzhe Sun
- Department of Chemistry, The University of Hong Kong Pokfulam Road Hong Kong P. R. China
| | - Wei Xia
- MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-sen University Guangzhou 510275 China
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14
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Hayton C, Ahmed W, Cunningham P, Piper-Hanley K, Pearmain L, Chaudhuri N, Leonard C, Blaikley JF, Fowler SJ. Changes in lung epithelial cell volatile metabolite profile induced by pro-fibrotic stimulation with TGF- β1. J Breath Res 2023; 17:046012. [PMID: 37619557 DOI: 10.1088/1752-7163/acf391] [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: 04/17/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Volatile organic compounds (VOCs) have shown promise as potential biomarkers in idiopathic pulmonary fibrosis. Measuring VOCs in the headspace ofin vitromodels of lung fibrosis may offer a method of determining the origin of those detected in exhaled breath. The aim of this study was to determine the VOCs associated with two lung cell lines (A549 and MRC-5 cells) and changes associated with stimulation of cells with the pro-fibrotic cytokine, transforming growth factor (TGF)-β1. A dynamic headspace sampling method was used to sample the headspace of A549 cells and MRC-5 cells. These were compared to media control samples and to each other to identify VOCs which discriminated between cell lines. Cells were then stimulated with the TGF-β1 and samples were compared between stimulated and unstimulated cells. Samples were analysed using thermal desorption-gas chromatography-mass spectrometry and supervised analysis was performed using sparse partial least squares-discriminant analysis (sPLS-DA). Supervised analysis revealed differential VOC profiles unique to each of the cell lines and from the media control samples. Significant changes in VOC profiles were induced by stimulation of cell lines with TGF-β1. In particular, several terpenoids (isopinocarveol, sativene and 3-carene) were increased in stimulated cells compared to unstimulated cells. VOC profiles differ between lung cell lines and alter in response to pro-fibrotic stimulation. Increased abundance of terpenoids in the headspace of stimulated cells may reflect TGF-β1 cell signalling activity and metabolic reprogramming. This may offer a potential biomarker target in exhaled breath in IPF.
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Affiliation(s)
- Conal Hayton
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- NIHR-Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Waqar Ahmed
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Peter Cunningham
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Karen Piper-Hanley
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Laurence Pearmain
- NIHR-Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Nazia Chaudhuri
- School of Medicine, The University of Ulster, Magee Campus, Londonderry, United Kingdom
| | - Colm Leonard
- NIHR-Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - John F Blaikley
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- NIHR-Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Stephen J Fowler
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- NIHR-Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom
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15
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Mansour E, Saliba W, Broza YY, Frankfurt O, Zuri L, Ginat K, Palzur E, Shamir A, Haick H. Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics. ACS Sens 2023; 8:3215-3224. [PMID: 37494456 DOI: 10.1021/acssensors.3c00945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.
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Affiliation(s)
- Elias Mansour
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Walaa Saliba
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Yoav Y Broza
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Ora Frankfurt
- Maale Hacarmel Mental Health Center, Tirat Carmel 3911917, Israel
| | - Liat Zuri
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Karen Ginat
- Mazor Mental Health Center, Akko 2423314, Israel
| | - Eilam Palzur
- Eliachar Research Laboratory, Galilee Medical Center, P.O. Box 21, Nahariya 2210001, Israel
| | - Alon Shamir
- Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- Mazor Mental Health Center, Akko 2423314, Israel
| | - Hossam Haick
- The Department of Chemical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel
- The Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel
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16
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Valle A, de la Calle ME, Muhamadali H, Hollywood KA, Xu Y, Lloyd JR, Goodacre R, Cantero D, Bolivar J. Metabolomics of Escherichia coli for Disclosing Novel Metabolic Engineering Strategies for Enhancing Hydrogen and Ethanol Production. Int J Mol Sci 2023; 24:11619. [PMID: 37511377 PMCID: PMC10380867 DOI: 10.3390/ijms241411619] [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: 05/14/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
The biological production of hydrogen is an appealing approach to mitigating the environmental problems caused by the diminishing supply of fossil fuels and the need for greener energy. Escherichia coli is one of the best-characterized microorganisms capable of consuming glycerol-a waste product of the biodiesel industry-and producing H2 and ethanol. However, the natural capacity of E. coli to generate these compounds is insufficient for commercial or industrial purposes. Metabolic engineering allows for the rewiring of the carbon source towards H2 production, although the strategies for achieving this aim are difficult to foresee. In this work, we use metabolomics platforms through GC-MS and FT-IR techniques to detect metabolic bottlenecks in the engineered ΔldhΔgndΔfrdBC::kan (M4) and ΔldhΔgndΔfrdBCΔtdcE::kan (M5) E. coli strains, previously reported as improved H2 and ethanol producers. In the M5 strain, increased intracellular citrate and malate were detected by GC-MS. These metabolites can be redirected towards acetyl-CoA and formate by the overexpression of the citrate lyase (CIT) enzyme and by co-overexpressing the anaplerotic human phosphoenol pyruvate carboxykinase (hPEPCK) or malic (MaeA) enzymes using inducible promoter vectors. These strategies enhanced specific H2 production by up to 1.25- and 1.49-fold, respectively, compared to the reference strains. Other parameters, such as ethanol and H2 yields, were also enhanced. However, these vectors may provoke metabolic burden in anaerobic conditions. Therefore, alternative strategies for a tighter control of protein expression should be addressed in order to avoid undesirable effects in the metabolic network.
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Affiliation(s)
- Antonio Valle
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real, University of Cadiz, 11510 Puerto Real, Spain
- Institute of Viticulture and Agri-Food Research (IVAGRO)-International Campus of Excellence (ceiA3), University of Cadiz, 11510 Puerto Real, Spain
| | - Maria Elena de la Calle
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real, University of Cadiz, 11510 Puerto Real, Spain
- Department of Chemical Engineering and Food Technology, Campus Universitario de Puerto Real, University of Cadiz, 11510 Puerto Real, Spain
| | - Howbeer Muhamadali
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown Street, Liverpool L69 7ZB, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Katherine A Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
- Department of Chemistry, Faculty of Science and Engineering, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Yun Xu
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown Street, Liverpool L69 7ZB, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Jonathan R Lloyd
- Williamson Research Centre, School of Earth & Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, BioSciences Building, Crown Street, Liverpool L69 7ZB, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Domingo Cantero
- Institute of Viticulture and Agri-Food Research (IVAGRO)-International Campus of Excellence (ceiA3), University of Cadiz, 11510 Puerto Real, Spain
- Department of Chemical Engineering and Food Technology, Campus Universitario de Puerto Real, University of Cadiz, 11510 Puerto Real, Spain
| | - Jorge Bolivar
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real, University of Cadiz, 11510 Puerto Real, Spain
- Institute of Biomolecules (INBIO), University of Cadiz, 11510 Puerto Real, Spain
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17
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Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 2023; 8:137. [PMID: 36949046 PMCID: PMC10033890 DOI: 10.1038/s41392-023-01380-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/24/2023] Open
Abstract
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
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Affiliation(s)
- Federica Danzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Raffaella Pacchiana
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maria T Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
| | - Alessandra Fiore
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
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18
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Sola-García A, Cáliz-Molina MÁ, Espadas I, Petr M, Panadero-Morón C, González-Morán D, Martín-Vázquez ME, Narbona-Pérez ÁJ, López-Noriega L, Martínez-Corrales G, López-Fernández-Sobrino R, Carmona-Marin LM, Martínez-Force E, Yanes O, Vinaixa M, López-López D, Reyes JC, Dopazo J, Martín F, Gauthier BR, Scheibye-Knudsen M, Capilla-González V, Martín-Montalvo A. Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging. Commun Biol 2023; 6:250. [PMID: 36890357 PMCID: PMC9995519 DOI: 10.1038/s42003-023-04625-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 02/23/2023] [Indexed: 03/10/2023] Open
Abstract
ATP-citrate lyase is a central integrator of cellular metabolism in the interface of protein, carbohydrate, and lipid metabolism. The physiological consequences as well as the molecular mechanisms orchestrating the response to long-term pharmacologically induced Acly inhibition are unknown. We report here that the Acly inhibitor SB-204990 improves metabolic health and physical strength in wild-type mice when fed with a high-fat diet, while in mice fed with healthy diet results in metabolic imbalance and moderated insulin resistance. By applying a multiomic approach using untargeted metabolomics, transcriptomics, and proteomics, we determined that, in vivo, SB-204990 plays a role in the regulation of molecular mechanisms associated with aging, such as energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, while global alterations on histone acetylation are absent. Our findings indicate a mechanism for regulating molecular pathways of aging that prevents the development of metabolic abnormalities associated with unhealthy dieting. This strategy might be explored for devising therapeutic approaches to prevent metabolic diseases.
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Affiliation(s)
- Alejandro Sola-García
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - María Ángeles Cáliz-Molina
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Isabel Espadas
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Michael Petr
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
- Tracked.bio, Copenhagen, Denmark
| | - Concepción Panadero-Morón
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Daniel González-Morán
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - María Eugenia Martín-Vázquez
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Álvaro Jesús Narbona-Pérez
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Livia López-Noriega
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Guillermo Martínez-Corrales
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Raúl López-Fernández-Sobrino
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Lina M Carmona-Marin
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | | | - Oscar Yanes
- Universitat Rovira i Virgili, Department of electronic Engineering & IISPV, Tarragona, Spain
- CIBER de Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Vinaixa
- Universitat Rovira i Virgili, Department of electronic Engineering & IISPV, Tarragona, Spain
- CIBER de Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Daniel López-López
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, Sevilla, 41013, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, Sevilla, 41013, Spain
| | - José Carlos Reyes
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Joaquín Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, c/Manuel Siurot s/n, 41013, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, Sevilla, 41013, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocío, Sevilla, 41013, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Sevilla, 42013, Spain
| | - Franz Martín
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
- CIBER de Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Benoit R Gauthier
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
- CIBER de Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Morten Scheibye-Knudsen
- Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
- Tracked.bio, Copenhagen, Denmark
| | - Vivian Capilla-González
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain
| | - Alejandro Martín-Montalvo
- Andalusian Molecular Biology and Regenerative Medicine Centre-CABIMER, Universidad de Sevilla-CSIC-Universidad Pablo de Olavide, Seville, 41092, Spain.
- CIBER de Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.
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19
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Fan Y, Yu C, Lu H, Chen Y, Hu B, Zhang X, Su J, Zhang Z. Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples. J Chromatogr A 2023; 1690:463768. [PMID: 36641940 DOI: 10.1016/j.chroma.2022.463768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/21/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to distill chemical information from raw GC-MS data efficiently. In this study, we proposed an Automatic Resolution method (AutoRes) based on pseudo-Siamese convolutional neural networks (pSCNN) to extract the meaningful features swamped by the noises, baseline drifts, retention time shifts, and overlapped peaks. Two pSCNN models were trained with 400,000 augmented spectral pairs, respectively. They can predict the selective region (pSCNN1) and elution region (pSCNN2) of compounds in an untargeted manner. The accuracies of the pSCNN1 model and the pSCNN2 model on their test sets are 99.9% and 92.6%, respectively. Then, the chromatographic profile of each component was automatically resolved by full rank resolution (FRR) based on the predicted regions by these models. The performance of AutoRes was evaluated on the simulated and plant essential oil datasets. Compared to AMDIS and MZmine, AutoRes resolves more reasonable mass spectra, chromatograms, and peak areas to identify and quantify compounds. The average match scores of AutoRes (925 and 936) outperformed AMDIS (909 and 925) and MZmine (888 and 916) when resolving mass spectra from overlapped peaks on the Set Ⅰ and Set Ⅱ of plant essential oil dataset and matching them against the NIST17 library. It extracted peak areas and mass spectra automatically from 10 GC-MS files of plant essential oils, and the entire process was completed in 8 min without any prior information or manual intervention. It is implemented in Python and is available as an open-source package at https://github.com/dyjfan/AutoRes.
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Affiliation(s)
- Yingjie Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Chuanxiu Yu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China
| | - Yi Chen
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China
| | - Binbin Hu
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China
| | - Xingren Zhang
- Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, Yunnan, China; Baoshan City Branch of Yunnan Tobacco Company, Baoshan 678000, Yunnan, China
| | - Jiaen Su
- Dali Prefecture Branch of Yunnan Tobacco Company, Dali 671000, Yunnan, China.
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, Hunan, China.
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20
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Balcázar-Zumaeta CR, Castro-Alayo EM, Cayo-Colca IS, Idrogo-Vásquez G, Muñoz-Astecker LD. Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review. Food Res Int 2023; 163:112190. [PMID: 36596129 DOI: 10.1016/j.foodres.2022.112190] [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] [Received: 09/05/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022]
Abstract
Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.
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Affiliation(s)
- César R Balcázar-Zumaeta
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Efraín M Castro-Alayo
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Ilse S Cayo-Colca
- Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Guillermo Idrogo-Vásquez
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Lucas D Muñoz-Astecker
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
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21
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González-Plaza JJ, Furlan C, Rijavec T, Lapanje A, Barros R, Tamayo-Ramos JA, Suarez-Diez M. Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels. Front Microbiol 2022; 13:1006946. [PMID: 36519168 PMCID: PMC9744117 DOI: 10.3389/fmicb.2022.1006946] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/02/2022] [Indexed: 08/31/2023] Open
Abstract
The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.
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Affiliation(s)
- Juan José González-Plaza
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | - Cristina Furlan
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
| | - Tomaž Rijavec
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Aleš Lapanje
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Rocío Barros
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | | | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
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22
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Pillai MS, Paritala ST, Shah RP, Sharma N, Sengupta P. Cutting-edge strategies and critical advancements in characterization and quantification of metabolites concerning translational metabolomics. Drug Metab Rev 2022; 54:401-426. [PMID: 36351878 DOI: 10.1080/03602532.2022.2125987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Despite remarkable progress in drug discovery strategies, significant challenges are still remaining in translating new insights into clinical applications. Scientists are devising creative approaches to bridge the gap between scientific and translational research. Metabolomics is a unique field among other omics techniques for identifying novel metabolites and biomarkers. Fortunately, characterization and quantification of metabolites are becoming faster due to the progress in the field of orthogonal analytical techniques. This review detailed the advancement in the progress of sample preparation, and data processing techniques including data mining tools, database, and their quality control (QC). Advances in data processing tools make it easier to acquire unbiased data that includes a diverse set of metabolites. In addition, novel breakthroughs including, miniaturization as well as their integration with other devices, metabolite array technology, and crystalline sponge-based method have led to faster, more efficient, cost-effective, and holistic metabolomic analysis. The use of cutting-edge techniques to identify the human metabolite, including biomarkers has proven to be advantageous in terms of early disease identification, tracking the progression of illness, and possibility of personalized treatments. This review addressed the constraints of current metabolomics research, which are impeding the facilitation of translation of research from bench to bedside. Nevertheless, the possible way out from such constraints and future direction of translational metabolomics has been conferred.
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Affiliation(s)
- Megha Sajakumar Pillai
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Sree Teja Paritala
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Ravi P Shah
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Nitish Sharma
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
| | - Pinaki Sengupta
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, India
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23
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Eisen KE, Powers JM, Raguso RA, Campbell DR. An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.1006416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet, which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.
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24
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Liu J, Clarke JA, McCann S, Hillier NK, Tahlan K. Analysis of Streptomyces Volatilomes Using Global Molecular Networking Reveals the Presence of Metabolites with Diverse Biological Activities. Microbiol Spectr 2022; 10:e0055222. [PMID: 35900081 PMCID: PMC9431705 DOI: 10.1128/spectrum.00552-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 07/09/2022] [Indexed: 12/20/2022] Open
Abstract
Streptomyces species produce a wide variety of specialized metabolites, some of which are used for communication or competition for resources in their natural environments. In addition, many natural products used in medicine and industry are derived from Streptomyces, and there has been interest in their capacity to produce volatile organic compounds (VOCs) for different industrial and agricultural applications. Recently, a machine-learning workflow called MSHub/GNPS was developed, which enables auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data, molecular networking, and library search capabilities, but it has not been applied to Streptomyces volatilomes. In this study, 131 Streptomyces isolates from the island of Newfoundland were phylogenetically typed, and 37 were selected based on their phylogeny and growth characteristics for VOC analysis using both a user-guided (conventional) and an MSHub/GNPS-based approach. More VOCs were annotated by MSHub/GNPS than by the conventional method. The number of unknown VOCs detected by the two methods was higher than those annotated, suggesting that many novel compounds remain to be identified. The molecular network generated by GNPS can be used to guide the annotation of such unknown VOCs in future studies. However, the number of overlapping VOCs annotated by the two methods is relatively small, suggesting that a combination of analysis methods might be required for robust volatilome analysis. More than half of the VOCs annotated with high confidence by the two approaches are plant-associated, many with reported bioactivities such as insect behavior modulation. Details regarding the properties and reported functions of such VOCs are described. IMPORTANCE This study represents the first detailed analysis of Streptomyces volatilomes using MSHub/GNPS, which in combination with a routinely used conventional method led to many annotations. More VOCs could be annotated using MSHub/GNPS as compared to the conventional method, many of which have known antimicrobial, anticancer, and insect behavior-modulating activities. The identification of numerous plant-associated VOCs by both approaches in the current study suggests that their production could be a more widespread phenomenon by members of the genus, highlighting opportunities for their large-scale production using Streptomyces. Plant-associated VOCs with antimicrobial activities, such as 1-octen-3-ol, octanol, and phenylethyl alcohol, have potential applications as fumigants. Furthermore, many of the annotated VOCs are reported to influence insect behavior, alluding to a possible explanation for their production based on the functions of other recently described Streptomyces VOCs in dispersal and nutrient acquisition.
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Affiliation(s)
- Jingyu Liu
- Department of Biology, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada
| | - Jody-Ann Clarke
- Department of Biology, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada
| | - Sean McCann
- Department of Biology, Acadia University, Wolfville, Nova Scotia, Canada
| | - N. Kirk Hillier
- Department of Biology, Acadia University, Wolfville, Nova Scotia, Canada
| | - Kapil Tahlan
- Department of Biology, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada
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25
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Valle A, Soto Z, Muhamadali H, Hollywood KA, Xu Y, Lloyd JR, Goodacre R, Cantero D, Cabrera G, Bolivar J. Metabolomics for the design of new metabolic engineering strategies for improving aerobic succinic acid production in Escherichia coli. Metabolomics 2022; 18:56. [PMID: 35857216 PMCID: PMC9300530 DOI: 10.1007/s11306-022-01912-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 06/17/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Glycerol is a byproduct from the biodiesel industry that can be biotransformed by Escherichia coli to high added-value products such as succinate under aerobic conditions. The main genetic engineering strategies to achieve this aim involve the mutation of succinate dehydrogenase (sdhA) gene and also those responsible for acetate synthesis including acetate kinase, phosphate acetyl transferase and pyruvate oxidase encoded by ackA, pta and pox genes respectively in the ΔsdhAΔack-ptaΔpox (M4) mutant. Other genetic manipulations to rewire the metabolism toward succinate consist on the activation of the glyoxylate shunt or blockage the pentose phosphate pathway (PPP) by deletion of isocitrate lyase repressor (iclR) or gluconate dehydrogenase (gnd) genes on M4-ΔiclR and M4-Δgnd mutants respectively. OBJECTIVE To deeply understand the effect of the blocking of the pentose phosphate pathway (PPP) or the activation of the glyoxylate shunt, metabolite profiles were analyzed on M4-Δgnd, M4-ΔiclR and M4 mutants. METHODS Metabolomics was performed by FT-IR and GC-MS for metabolite fingerprinting and HPLC for quantification of succinate and glycerol. RESULTS Most of the 65 identified metabolites showed lower relative levels in the M4-ΔiclR and M4-Δgnd mutants than those of the M4. However, fructose 1,6-biphosphate, trehalose, isovaleric acid and mannitol relative concentrations were increased in M4-ΔiclR and M4-Δgnd mutants. To further improve succinate production, the synthesis of mannitol was suppressed by deletion of mannitol dehydrogenase (mtlD) on M4-ΔgndΔmtlD mutant that increase ~ 20% respect to M4-Δgnd. CONCLUSION Metabolomics can serve as a holistic tool to identify bottlenecks in metabolic pathways by a non-rational design. Genetic manipulation to release these restrictions could increase the production of succinate.
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Affiliation(s)
- Antonio Valle
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain.
- Institute of Viticulture and Agri-Food Research (IVAGRO) - International Campus of Excellence (ceiA3), University of Cadiz, 11510, Puerto Real, Cádiz, Spain.
| | - Zamira Soto
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain
- Department of Chemical Engineering and Food Technology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain
- Faculty of Basic and Biomedical Sciences, Universidad Simón Bolívar, 080020, Barranquilla, Colombia
| | - Howbeer Muhamadali
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
- Department of Biochemistry and Systems Biology, Institute of Integrative Systems, Molecular and Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK
| | - Katherine A Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK
| | - Yun Xu
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
- Department of Biochemistry and Systems Biology, Institute of Integrative Systems, Molecular and Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK
| | - Jonathan R Lloyd
- Williamson Research Centre, School of Earth & Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK
| | - Royston Goodacre
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK
- Department of Biochemistry and Systems Biology, Institute of Integrative Systems, Molecular and Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7ZB, UK
| | - Domingo Cantero
- Department of Chemical Engineering and Food Technology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain
- Institute of Viticulture and Agri-Food Research (IVAGRO) - International Campus of Excellence (ceiA3), University of Cadiz, 11510, Puerto Real, Cádiz, Spain
| | - Gema Cabrera
- Department of Chemical Engineering and Food Technology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain
- Institute of Viticulture and Agri-Food Research (IVAGRO) - International Campus of Excellence (ceiA3), University of Cadiz, 11510, Puerto Real, Cádiz, Spain
| | - Jorge Bolivar
- Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, University of Cadiz, Campus Universitario de Puerto Real, 11510, Puerto Real, Cádiz, Spain.
- Institute of Biomolecules (INBIO), University of Cadiz, 11510, Puerto Real, Cádiz, Spain.
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Tzanakis K, Nattkemper TW, Niehaus K, Albaum SP. MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data. BMC Bioinformatics 2022; 23:267. [PMID: 35804309 PMCID: PMC9270834 DOI: 10.1186/s12859-022-04793-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 06/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows. RESULTS Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study. CONCLUSIONS MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.
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Affiliation(s)
- Konstantinos Tzanakis
- International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Faculty of Technology, Bielefeld University, Bielefeld, Germany.
| | - Tim W Nattkemper
- Biodata Mining Group, Center for Biotechnology (CeBiTec), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Karsten Niehaus
- Proteome and Metabolome Research, Center for Biotechnology (CeBiTec), Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | - Stefan P Albaum
- Bioinformatics Resource Facility, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
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Influence of Home Indoor Dampness Exposure on Volatile Organic Compounds in Exhaled Breath of Mothers and Their Infants: The NELA Birth Cohort. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Currently, the effect of exposure to indoor air contaminants and the presence of dampness at home on respiratory/atopic health is of particular concern to physicians. The measurement of volatile organic compounds (VOCs) in exhaled breath is a useful approach for monitoring environmental exposures. A great advantage of this strategy is that it allows the study of the impact of pollutants on the metabolism through a non-invasive method. In this paper, the levels of nine VOCs (acetone, isoprene, toluene, p/m-xylene, o-xylene, styrene, benzaldehyde, naphthalene, and 2-ethyl-1-hexanol) in the exhaled breath of subjects exposed and not exposed to home dampness were assessed. Exhaled breath samples were collected from 337 mother–child pairs of a birth cohort and analysed by gas-chromatography–mass-spectrometry. It was observed that the levels of 2-ethyl-1-hexanol in the exhaled breath of the mothers were significantly influenced by exposure to household humidity. In the case of the infants, differences in some of the VOC levels related to home dampness exposure; however, they did not reach statistical significance. In addition, it was also found that the eosinophil counts of the mothers exposed to home dampness were significantly elevated compared to those of the non-exposed mothers. To our knowledge, these findings show, for the first time, that exposure to home dampness may influence VOC patterns in exhaled breath.
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Wang H, He S, Sun Z, Wang R, Zou X, Lu F. Targeted Profiling of Rodent Unconjugated Bile Acids by GC-MS to Reveal the Influence of High-Fat Diet. Biomed Chromatogr 2022; 36:e5428. [PMID: 35708903 DOI: 10.1002/bmc.5428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 11/10/2022]
Abstract
Unconjugated bile acids (BAs) have gained more attention than conjugated BAs in the association studies among diet, gut microbiota and diseases. Gas chromatography-mass spectrometry (GC-MS) is probably a good choice for specialized analysis of unconjugated BAs due to high separation capacity and identification convenience. However, few reports have focused on the rodent unconjugated BAs by GC-MS, and the main library for identification has not included rodent-specific BAs. We developed a GC-MS method for targeted profiling of eight main unconjugated BAs in rodent models, which showed excellent performance on sensitivity, reproducibility and accuracy. Quantitative reproducibility in terms of relative standard deviation (RSD) was in the range of 2.05%-2.91%, with detection limits of 3-55 ng/mL, quantitation limits of 9-182 ng/mL and the recovery rate of 72%-115%. All the calibration curves displayed good linearity with correlation coefficient values (R2 ) more than 0.99. Using the established method, the influence of high-fat diet on the metabolism of unconjugated BAs were revealed. Significant increasing of fecal unconjugated BAs induced by high-fat diet, would be a potential risk to gut inflammation and cancer. The study provides a convenient and targeted GC-MS method for specialized profiling of rodent unconjugated BAs in physiological and pathological studies.
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Affiliation(s)
- Hongbin Wang
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
| | - Shi He
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
| | - Zepeng Sun
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
| | - Ruijia Wang
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
| | - Xiaotong Zou
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
| | - Fuping Lu
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, The College of Biotechnology, Tianjin University of Science and Technology, Tianjin, P. R. China
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Rey-Stolle F, Dudzik D, Gonzalez-Riano C, Fernández-García M, Alonso-Herranz V, Rojo D, Barbas C, García A. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: A tutorial. Anal Chim Acta 2022; 1210:339043. [PMID: 35595356 DOI: 10.1016/j.aca.2021.339043] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/04/2021] [Accepted: 09/06/2021] [Indexed: 12/28/2022]
Abstract
GC-MS for untargeted metabolomics is a well-established technique. Small molecules and molecules made volatile by derivatization can be measured and those compounds are key players in main biological pathways. This tutorial provides ready-to-use protocols for GC-MS-based metabolomics, using either the well-known low-resolution approach (GC-Q-MS) with nominal mass or the more recent high-resolution approach (GC-QTOF-MS) with accurate mass, discussing their corresponding strengths and limitations. Analytical procedures are covered for different types of biofluids (plasma/serum, bronchoalveolar lavage, urine, amniotic fluid) tissue samples (brain/hippocampus, optic nerve, lung, kidney, liver, pancreas) and samples obtained from cell cultures (adipocytes, macrophages, Leishmania promastigotes, mitochondria, culture media). Together with the sample preparation and data acquisition, data processing strategies are described specially focused on Agilent equipments, including deconvolution software and database annotation using spectral libraries. Manual curation strategies and quality control are also deemed. Finally, considerations to obtain a semiquantitative value for the metabolites are also described. As a case study, an illustrative example from one of our experiments at CEMBIO Research Centre, is described and findings discussed.
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Affiliation(s)
- Fernanda Rey-Stolle
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Danuta Dudzik
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain; Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Poland
| | - Carolina Gonzalez-Riano
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Miguel Fernández-García
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Vanesa Alonso-Herranz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - David Rojo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Antonia García
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain.
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Sola-Martínez RA, Sanchez-Solis M, Lozano-Terol G, Gallego-Jara J, García-Marcos L, Cánovas Díaz M, de Diego Puente T. Relationship between lung function and exhaled volatile organic compounds in healthy infants. Pediatr Pulmonol 2022; 57:1282-1292. [PMID: 35092361 PMCID: PMC9304127 DOI: 10.1002/ppul.25849] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE The aim of this study is to assess, for the first time, the relationship between the volatilome and lung function in healthy infants, which may be of help for the early detection of certain respiratory diseases. Lung function tests are crucial in chronic respiratory diseases diagnosis. Moreover, volatile organic compounds (VOCs) analysis in exhaled breath is a noninvasive technique that enables the monitorization of oxidative stress, typical of some forms of airway inflammation. METHODS Lung function was studied in 50 healthy infants of 3-8 months of age and the following parameters were obtained: forced vital capacity (FVC), forced expiratory volume at 0.5 s (FEV0.5 ), forced expiratory flow at 75% of FVC (FEF75 ), forced expiratory flow at 25%-75% of FVC (FEF25-75 ), and FEV0.5 /FVC. Lung function was measured according to the raised volume rapid thoracoabdominal compression technique. In addition, a targeted analysis of six endogenous VOCs (acetone, isoprene, decane, undecane, tetradecane, and pentadecane) in the exhaled breath of the children was carried out by means of thermal desorption coupled gas chromatography-single quadrupole mass spectrometry system. RESULTS A negatively significant relationship has been observed between levels of acetone, tetradecane, and pentadecane in exhaled breath and several of the lung function parameters. Levels of acetone (feature m/z = 58) were significantly negatively associated with FVC and FVE0.5 , levels of tetradecane (feature m/z = 71) with FEV0.5, and levels of pentadecane (feature m/z = 71) with FEV0.5 and FEF25-75 . CONCLUSION The findings of this study highlight a significant association between VOCs related to oxidative stress and lung function in healthy infants.
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Affiliation(s)
- Rosa A Sola-Martínez
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.,Group of Molecular Systems Biology, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Manuel Sanchez-Solis
- Group of Pediatric Research, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain.,Respiratory and Allergy Units, Arrixaca Children's University Hospital, University of Murcia, Murcia, Spain.,Network of Asthma and Adverse and Allergy Reactions (ARADyAL), Health Institute Carlos III, Madrid, Spain
| | - Gema Lozano-Terol
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.,Group of Molecular Systems Biology, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Julia Gallego-Jara
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.,Group of Molecular Systems Biology, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Luis García-Marcos
- Group of Pediatric Research, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain.,Respiratory and Allergy Units, Arrixaca Children's University Hospital, University of Murcia, Murcia, Spain.,Network of Asthma and Adverse and Allergy Reactions (ARADyAL), Health Institute Carlos III, Madrid, Spain
| | - Manuel Cánovas Díaz
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.,Group of Molecular Systems Biology, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Teresa de Diego Puente
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.,Group of Molecular Systems Biology, Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
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Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites. Biomedicines 2022; 10:biomedicines10040879. [PMID: 35453629 PMCID: PMC9024754 DOI: 10.3390/biomedicines10040879] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/04/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022] Open
Abstract
In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.
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Multiomics implicate gut microbiota in altered lipid and energy metabolism in Parkinson's disease. NPJ Parkinsons Dis 2022; 8:39. [PMID: 35411052 PMCID: PMC9001728 DOI: 10.1038/s41531-022-00300-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 03/04/2022] [Indexed: 12/19/2022] Open
Abstract
We aimed to investigate the link between serum metabolites, gut bacterial community composition, and clinical variables in Parkinson’s disease (PD) and healthy control subjects (HC). A total of 124 subjects were part of the study (63 PD patients and 61 HC subjects). 139 metabolite features were found to be predictive between the PD and Control groups. No associations were found between metabolite features and within-PD clinical variables. The results suggest alterations in serum metabolite profiles in PD, and the results of correlation analysis between metabolite features and microbiota suggest that several bacterial taxa are associated with altered lipid and energy metabolism in PD.
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Fukushima A, Takahashi M, Nagasaki H, Aono Y, Kobayashi M, Kusano M, Saito K, Kobayashi N, Arita M. Development of RIKEN Plant Metabolome MetaDatabase. PLANT & CELL PHYSIOLOGY 2022; 63:433-440. [PMID: 34918130 PMCID: PMC8917833 DOI: 10.1093/pcp/pcab173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/15/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.
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Affiliation(s)
- Atsushi Fukushima
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Mikiko Takahashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Hideki Nagasaki
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Yusuke Aono
- Degree Programs in Life and Earth Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Makoto Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Miyako Kusano
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Kazuki Saito
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Norio Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Data Knowledge Organization Unit, RIKEN Information R&D and Strategy Headquarters, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Masanori Arita
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Bioinformation and DDBJ Center, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 411-8540, Japan
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Giera M, Yanes O, Siuzdak G. Metabolite discovery: Biochemistry's scientific driver. Cell Metab 2022; 34:21-34. [PMID: 34986335 PMCID: PMC10131248 DOI: 10.1016/j.cmet.2021.11.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/26/2021] [Accepted: 11/09/2021] [Indexed: 01/19/2023]
Abstract
Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.
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Affiliation(s)
- Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, Leiden 2333 ZA, the Netherlands
| | - Oscar Yanes
- Universitat Rovira i Virgili, Department of Electronic Engineering, IISPV, Tarragona, Spain; CIBER on Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
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Zhao JJ, Guo XM, Wang XC, Zhang Y, Ma XL, Ma MH, Zhang JN, Liu JN, Yu YJ, Lv Y, She YB. A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics. J Chromatogr A 2022; 1664:462801. [PMID: 35007865 DOI: 10.1016/j.chroma.2021.462801] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 12/29/2021] [Accepted: 12/31/2021] [Indexed: 12/27/2022]
Abstract
The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.
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Affiliation(s)
- Juan-Juan Zhao
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Xiao-Meng Guo
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Xing-Cai Wang
- Zhejiang University of Technology, Hangzhou 310014, China
| | - Yang Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Xing-Ling Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Meng-Han Ma
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Jia-Ni Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Jia-Nan Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China
| | - Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Yinchuan 750004, China.
| | - Yi Lv
- Ningxia Inspection and Research Institution of Food Control, Yinchuan 750000, China.
| | - Yuan-Bin She
- Zhejiang University of Technology, Hangzhou 310014, China
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Mohammad S, Bhattacharjee J, Vasanthan T, Harris CS, Bainbridge SA, Adamo KB. Metabolomics to understand placental biology: Where are we now? Tissue Cell 2021; 73:101663. [PMID: 34653888 DOI: 10.1016/j.tice.2021.101663] [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: 06/30/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/16/2022]
Abstract
Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.
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Affiliation(s)
- S Mohammad
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - J Bhattacharjee
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - T Vasanthan
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
| | - C S Harris
- Department of Biology & Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada
| | - S A Bainbridge
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, ON, Canada; Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, ON, Canada
| | - K B Adamo
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
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Ma A, Qi X. Mining plant metabolomes: Methods, applications, and perspectives. PLANT COMMUNICATIONS 2021; 2:100238. [PMID: 34746766 PMCID: PMC8554038 DOI: 10.1016/j.xplc.2021.100238] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/31/2021] [Accepted: 09/02/2021] [Indexed: 06/13/2023]
Abstract
Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.
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Affiliation(s)
- Aimin Ma
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoquan Qi
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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Sola-Martínez RA, Lozano-Terol G, Gallego-Jara J, Morales E, Cantero-Cano E, Sanchez-Solis M, García-Marcos L, Jiménez-Guerrero P, Noguera-Velasco JA, Cánovas Díaz M, de Diego Puente T. Exhaled volatilome analysis as a useful tool to discriminate asthma with other coexisting atopic diseases in women of childbearing age. Sci Rep 2021; 11:13823. [PMID: 34226570 PMCID: PMC8257728 DOI: 10.1038/s41598-021-92933-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/14/2021] [Indexed: 02/06/2023] Open
Abstract
The prevalence of asthma is considerably high among women of childbearing age. Most asthmatic women also often have other atopic disorders. Therefore, the differentiation between patients with atopic diseases without asthma and asthmatics with coexisting diseases is essential to avoid underdiagnosis of asthma and to design strategies to reduce symptom severity and improve quality of life of patients. Hence, we aimed for the first time to conduct an analysis of volatile organic compounds in exhaled breath of women of childbearing age as a new approach to discriminate between asthmatics with other coexisting atopic diseases and non-asthmatics (with or without atopic diseases), which could be a helpful tool for more accurate asthma detection and monitoring using a noninvasive technique in the near future. In this study, exhaled air samples of 336 women (training set (n = 211) and validation set (n = 125)) were collected and analyzed by thermal desorption coupled with gas chromatography-mass spectrometry. ASCA (ANOVA (analysis of variance) simultaneous component analysis) and LASSO + LS (least absolute shrinkage and selection operator + logistic regression) were employed for data analysis. Fifteen statistically significant models (p-value < 0.05 in permutation tests) that discriminated asthma with other coexisting atopic diseases in women of childbearing age were generated. Acetone, 2-ethyl-1-hexanol and a tetrahydroisoquinoline derivative were selected as discriminants of asthma with other coexisting atopic diseases. In addition, carbon disulfide, a tetrahydroisoquinoline derivative, 2-ethyl-1-hexanol and decane discriminated asthma disease among patients with other atopic disorders. Results of this study indicate that refined metabolomic analysis of exhaled breath allows asthma with other coexisting atopic diseases discrimination in women of reproductive age.
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Affiliation(s)
- Rosa A Sola-Martínez
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Gema Lozano-Terol
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Julia Gallego-Jara
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Eva Morales
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
- Department of Public Health Sciences, University of Murcia, Murcia, Spain
| | | | - Manuel Sanchez-Solis
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
- Respiratory and Allergy Units, Arrixaca Children's University Hospital, University of Murcia, Murcia, Spain
- Department of Paediatrics, University of Murcia, Murcia, Spain
- Network of Asthma and Adverse and Allergy Reactions (ARADyAL), Health Institute Carlos III, Madrid, Spain
| | - Luis García-Marcos
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
- Respiratory and Allergy Units, Arrixaca Children's University Hospital, University of Murcia, Murcia, Spain
- Department of Paediatrics, University of Murcia, Murcia, Spain
- Network of Asthma and Adverse and Allergy Reactions (ARADyAL), Health Institute Carlos III, Madrid, Spain
| | - Pedro Jiménez-Guerrero
- Regional Atmospheric Modelling Group, Department of Physics, University of Murcia, Murcia, Spain
| | - José A Noguera-Velasco
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
- Molecular Therapy and Biomarkers Research Group, Clinical Analysis Service, University Clinical Hospital "Virgen de la Arrixaca", University of Murcia, Murcia, Spain
| | - Manuel Cánovas Díaz
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain
| | - Teresa de Diego Puente
- Department of Biochemistry and Molecular Biology B and Immunology, University of Murcia, Murcia, Spain.
- Biomedical Research Institute of Murcia, IMIB-Arrixaca, Murcia, Spain.
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40
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Li D, Gaquerel E. Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:867-891. [PMID: 33781077 DOI: 10.1146/annurev-arplant-071720-114836] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.
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Affiliation(s)
- Dapeng Li
- Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, 07745 Jena, Germany;
| | - Emmanuel Gaquerel
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, 67084 Strasbourg, France;
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41
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Misra BB. Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:2265-2282. [PMID: 33987631 DOI: 10.1039/d1ay00173f] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.
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Affiliation(s)
- Biswapriya B Misra
- Independent Researcher, Pine-211, Raintree Park Dwaraka Krishna, Namburu, AP-522508, India.
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42
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Müller J, Bertsch T, Volke J, Schmid A, Klingbeil R, Metodiev Y, Karaca B, Kim SH, Lindner S, Schupp T, Kittel M, Poschet G, Akin I, Behnes M. Narrative review of metabolomics in cardiovascular disease. J Thorac Dis 2021; 13:2532-2550. [PMID: 34012599 PMCID: PMC8107570 DOI: 10.21037/jtd-21-22] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.
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Affiliation(s)
- Julian Müller
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Thomas Bertsch
- Institute of Clinical Chemistry, Laboratory Medicine and Transfusion Medicine, Nuremburg General Hospital, Paracelsus Medical University, Nuremberg, Germany
| | - Justus Volke
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Alexander Schmid
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Rebecca Klingbeil
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Yulian Metodiev
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Bican Karaca
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Seung-Hyun Kim
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Simon Lindner
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Tobias Schupp
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Faculty of Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | - Gernot Poschet
- Centre for Organismal Studies (COS), University of Heidelberg, Heidelberg, Germany
| | - Ibrahim Akin
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Behnes
- First Department of Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
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Perez-Sanz F, Ruiz-Hernández V, Terry MI, Arce-Gallego S, Weiss J, Navarro PJ, Egea-Cortines M. gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites. Metabolites 2021; 11:metabo11040211. [PMID: 33807334 PMCID: PMC8065537 DOI: 10.3390/metabo11040211] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 11/16/2022] Open
Abstract
Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.
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Affiliation(s)
- Fernando Perez-Sanz
- Instituto Murciano de Investigaciones Biomédicas El Palmar, 30120 Murcia, Spain;
| | | | - Marta I. Terry
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (M.I.T.); (J.W.)
| | | | - Julia Weiss
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (M.I.T.); (J.W.)
| | - Pedro J. Navarro
- DSIE Cuartel de Antiguones, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain;
| | - Marcos Egea-Cortines
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain; (M.I.T.); (J.W.)
- Correspondence: ; Tel.: +34-868071078
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44
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Sinclair E, Walton-Doyle C, Sarkar D, Hollywood KA, Milne J, Lim SH, Kunath T, Rijs AM, de Bie RMA, Silverdale M, Trivedi DK, Barran P. Validating Differential Volatilome Profiles in Parkinson's Disease. ACS CENTRAL SCIENCE 2021; 7:300-306. [PMID: 33655068 PMCID: PMC7908024 DOI: 10.1021/acscentsci.0c01028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Indexed: 05/10/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder that does not currently have a robust clinical diagnostic test. Nonmotor symptoms such as skin disorders have long since been associated with the disease, and more recently a characteristic odor emanating from the skin of people with Parkinson's has been identified. Here, dynamic head space (DHS) thermal desorption (TD) gas chromatography-mass spectrometry (GC-MS) is implemented to directly measure the volatile components of sebum on swabs sampled from people with Parkinson's-both drug naïve and those on PD medications (n = 100) and control subjects (n = 29). Supervised multivariate analyses of data showed 84.4% correct classification of PD cases using all detected volatile compounds. Variable importance in projection (VIP) scores were generated from these data, which revealed eight features with VIP > 1 and p < 0.05 which all presented a downregulation within the control cohorts. Purified standards based on previously annotated analytes of interest eicosane and octadecanal did not match to patient sample data, although multiple metabolite features are annotated with these compounds all with high spectral matches indicating the presence of a series of similar structured species. DHS-TD-GC-MS analysis of a range of lipid standards has revealed the presence of common hydrocarbon species rather than differentiated intact compounds which are hypothesized to be breakdown products of lipids. This replication study validates that a differential volatile profile between control and PD cohorts can be measured using an analytical method that measures volatile compounds directly from skin swabs.
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Affiliation(s)
- Eleanor Sinclair
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Caitlin Walton-Doyle
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Depanjan Sarkar
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Katherine A. Hollywood
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
- Manchester
Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM),
Manchester Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Joy Milne
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Sze Hway Lim
- Department
of Neurology, Salford Royal Foundation Trust, Manchester Academic
Health Science Centre, The University of
Manchester, Manchester M13 9PL, United Kingdom
| | - Tilo Kunath
- Institute
for Stem Cell Research, School of Biological Sciences, The University of Edinburgh, Edinburgh EH16 4UU, United Kingdom
| | - Anouk M. Rijs
- Division
of BioAnalytical Chemistry, AIMMS Amsterdam Institute of Molecular
and Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Rob M. A. de Bie
- Department
of Neurology, Amsterdam Neuroscience, Amsterdam
University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105
AZ Amsterdam, The Netherlands
| | - Monty Silverdale
- Department
of Neurology, Salford Royal Foundation Trust, Manchester Academic
Health Science Centre, The University of
Manchester, Manchester M13 9PL, United Kingdom
| | - Drupad K. Trivedi
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Perdita Barran
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
- E-mail:
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45
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Meregalli C, Bonomo R, Cavaletti G, Carozzi VA. Blood molecular biomarkers for chemotherapy-induced peripheral neuropathy: From preclinical models to clinical practice. Neurosci Lett 2021; 749:135739. [PMID: 33600907 DOI: 10.1016/j.neulet.2021.135739] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 02/04/2021] [Accepted: 02/09/2021] [Indexed: 12/26/2022]
Abstract
Chemotherapy-induced peripheral neuropathy (CIPN) has long been recognized as a clinically significant issue in patients treated with antineoplastic drugs. This common long-term toxic side-effect which negatively impacts the outcome of the disease can lead to disability and have detrimental effects on patients' quality of life. Since axonal injury is a prominent feature of CIPN, responsible for several sensory symptoms, including pain, sensory loss and hypersensitivity to mechanical and/or cold stimuli in the hands and feet, neurophysiological assessments remain the gold standard for clinical diagnosis of CIPN. Given the large impact of CIPN on cancer patients, there is increasing emphasis on biomarkers of adverse outcomes in safety assessment and translational research, to prevent permanent neuroaxonal damage. Since the results on reliable blood molecular markers for axonal degeneration are still controversial, here we provide a brief overview of blood molecular biomarkers used for assessing and/or predicting CIPN in preclinical and clinical settings.
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Affiliation(s)
- C Meregalli
- Experimental Neurology Unit, School of Medicine and Surgery, NeuroMI (Milan Center for Neuroscience), University of Milan Bicocca, Monza, Italy
| | - R Bonomo
- Experimental Neurology Unit, School of Medicine and Surgery, NeuroMI (Milan Center for Neuroscience), University of Milan Bicocca, Monza, Italy; PhD Program in Neuroscience, University of Milan Bicocca, Monza, Italy
| | - G Cavaletti
- Experimental Neurology Unit, School of Medicine and Surgery, NeuroMI (Milan Center for Neuroscience), University of Milan Bicocca, Monza, Italy
| | - V A Carozzi
- Experimental Neurology Unit, School of Medicine and Surgery, NeuroMI (Milan Center for Neuroscience), University of Milan Bicocca, Monza, Italy; Young Against Pain Group, Italy.
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46
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Zushi Y. NMF-Based Spectral Deconvolution with a Web Platform GC Mixture Touch. ACS OMEGA 2021; 6:2742-2748. [PMID: 33553892 PMCID: PMC7860082 DOI: 10.1021/acsomega.0c04982] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/17/2020] [Indexed: 06/12/2023]
Abstract
Complete separation of chemicals in a complex mixture is far from being achieved even with the current high-performance separation technology, such as gas chromatography-mass spectrometry (GC-MS). Several deconvolution techniques based on multivariate curve resolution (MCR), or model peak methods, which are represented by AMDIS, have been developed to address the above-mentioned issue. The model peak methods have been developed to provide easy-to-use tools, including AMDIS, but are limited for MCR with approximation methods. The objective of this study was to provide an easy-to-use deconvolution tool based on the MCR approach for GC-MS data. The spectral deconvolution tool based on non-negative matrix factorization (NMF), which calculates outputs using an approximation method, was implemented as a free web platform, namely, GC Mixture Touch, clarifying the effects of the parameters required for the deconvolution. The GC Mixture Touch was applied to the actual mixture sample of road dust spiked with chemical standards. The recommended parameter settings for smoothing of the chromatogram, the number of ranks, and the NMF algorithm for the deconvolution were clarified through the study. The performance with the suggested parameters was evaluated with respect to compound identification for the actual sample. All of the test compounds in the sample were correctly identified with the GC Mixture Touch, outperforming AMDIS with respect to the identification. The GC Mixture Touch is easy to use on the web even for users without programming skills. This is expected to enhance the application of the NMF-based deconvolution, and it should prove helpful in finding the compounds hidden in complex mixtures that are difficult to find using conventional approaches.
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Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, Bittremieux W, Nothias LF, Nothias-Esposito M, Maloney KN, Misra BB, Melnik AV, Smirnov A, Du X, Jones KL, Dorrestein K, Panitchpakdi M, Ernst M, van der Hooft JJJ, Gonzalez M, Carazzone C, Amézquita A, Callewaert C, Morton JT, Quinn RA, Bouslimani A, Orio AA, Petras D, Smania AM, Couvillion SP, Burnet MC, Nicora CD, Zink E, Metz TO, Artaev V, Humston-Fulmer E, Gregor R, Meijler MM, Mizrahi I, Eyal S, Anderson B, Dutton R, Lugan R, Boulch PL, Guitton Y, Prevost S, Poirier A, Dervilly G, Le Bizec B, Fait A, Persi NS, Song C, Gashu K, Coras R, Guma M, Manasson J, Scher JU, Barupal DK, Alseekh S, Fernie AR, Mirnezami R, Vasiliou V, Schmid R, Borisov RS, Kulikova LN, Knight R, Wang M, Hanna GB, Dorrestein PC, Veselkov K. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nat Biotechnol 2021; 39:169-173. [PMID: 33169034 PMCID: PMC7971188 DOI: 10.1038/s41587-020-0700-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 08/26/2020] [Accepted: 09/09/2020] [Indexed: 12/23/2022]
Abstract
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.
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Affiliation(s)
- Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Ivan Laponogov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Zheng Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Sophie L F Doran
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Ilaria Belluomo
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Dennis Veselkov
- Intelligify Limited, London, UK
- Department of Computing, Imperial College, South Kensington Campus, London, UK
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Louis Felix Nothias
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Mélissa Nothias-Esposito
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Katherine N Maloney
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Chemistry, Point Loma Nazarene University, San Diego, CA, USA
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Alexey V Melnik
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Aleksandr Smirnov
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Kenneth L Jones
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Kathleen Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Madeleine Ernst
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Justin J J van der Hooft
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Mabel Gonzalez
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Chiara Carazzone
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Adolfo Amézquita
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Chris Callewaert
- Center for Microbial Ecology and Technology, Ghent, Belgium
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Robert A Quinn
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Amina Bouslimani
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea Albarracín Orio
- IRNASUS, Universidad Católica de Córdoba, CONICET, Facultad de Ciencias Agropecuarias, Córdoba, Argentina
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea M Smania
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Biológica Ranwel Caputto, Córdoba, Argentina
- CONICET, Universidad Nacional de Córdoba, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan C Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Erika Zink
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | - Rachel Gregor
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael M Meijler
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Itzhak Mizrahi
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Stav Eyal
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Brooke Anderson
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rachel Dutton
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Raphaël Lugan
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Pauline Le Boulch
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Yann Guitton
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Stephanie Prevost
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Audrey Poirier
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Gaud Dervilly
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Bruno Le Bizec
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Aaron Fait
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Noga Sikron Persi
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Chao Song
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Kelem Gashu
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Roxana Coras
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Monica Guma
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Julia Manasson
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Jose U Scher
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saleh Alseekh
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Reza Mirnezami
- Department of Colorectal Surgery, Royal Free Hospital NHS Foundation Trust, Hampstead, London, UK
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Robin Schmid
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Roman S Borisov
- A.V. Topchiev Institute of Petrochemical Synthesis RAS, Moscow, Russian Federation
| | - Larisa N Kulikova
- Рeoples' Friendship University of Russia (RUDN University), Moscow, Russian Federation
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.
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Zhang Q, He Z, Liu Z, Gong L. Integrated plasma and liver gas chromatography mass spectrometry and liquid chromatography mass spectrometry metabolomics to reveal physiological functions of sodium taurocholate cotransporting polypeptide (NTCP) with an Ntcp knockout mouse model. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1165:122531. [DOI: 10.1016/j.jchromb.2021.122531] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 12/10/2020] [Accepted: 01/05/2021] [Indexed: 12/12/2022]
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Capellades J, Junza A, Samino S, Brunner JS, Schabbauer G, Vinaixa M, Yanes O. Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics. Anal Chem 2021; 93:1242-1248. [PMID: 33369389 DOI: 10.1021/acs.analchem.0c02998] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.
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Affiliation(s)
- Jordi Capellades
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Alexandra Junza
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Samino
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain
| | - Julia S Brunner
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Gernot Schabbauer
- Institute for Vascular Biology, Centre for Physiology and Pharmacology, Medical University Vienna, 1090 Vienna, Austria.,Christian Doppler Laboratory for Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis, 1090 Vienna, Austria
| | - Maria Vinaixa
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Metabolomics Platform, Reus, Spain
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50
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Lebanov L, Ghiasvand A, Paull B. Data handling and data analysis in metabolomic studies of essential oils using GC-MS. J Chromatogr A 2021; 1640:461896. [PMID: 33548825 DOI: 10.1016/j.chroma.2021.461896] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/08/2021] [Indexed: 12/26/2022]
Abstract
Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.
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Affiliation(s)
- Leo Lebanov
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia; ARC Industrial Transformation Research Hub for Processing Advanced Lignocellulosics (PALS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
| | - Alireza Ghiasvand
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
| | - Brett Paull
- Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia; ARC Industrial Transformation Research Hub for Processing Advanced Lignocellulosics (PALS), School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia.
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