1
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Wang Y, Chen L, Huang X, Xia B, Zhou Y. Chain electrospray ionization mass spectrometry for ultra-low volume sample analysis. Talanta 2024; 277:126410. [PMID: 38876033 DOI: 10.1016/j.talanta.2024.126410] [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: 02/20/2024] [Revised: 04/30/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
In this work, chain electrospray ionization (chain-ESI) was developed to efficiently ionize trace samples for mass spectrometry analysis. The primary ion source was found to have the ability to induce secondary electrospray ionization with an extraordinarily low sample consumption rate in the picoliters per minute (pLs/min). This allows low volume sample to generate substantial tandem mass spectrum (MS2) data for metabolite annotations. Notably, chain-ESI can effectively prevent the electro-redox reaction in the process of electrospray, so as to reflect the native state of the analytes. Furthermore, from a single Broussonetia papyrifera (B. papyrifera) trichome and a single A549 cancer cell, 1426 and 617 metabolites were detected respectively. All of those observations demonstrated that chain-ESI offers the advantages of direct, rapid analysis with extreme-low volumes and high coverage, enabling the measurement of bio-information in low volume samples.
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Affiliation(s)
- Yu Wang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Lu Chen
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Xia Huang
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Bing Xia
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
| | - Yan Zhou
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, 610041, China.
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2
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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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3
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Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman A, MacDonald P, Wishart D, Li S, Xia J. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 2024; 52:W398-W406. [PMID: 38587201 PMCID: PMC11223798 DOI: 10.1093/nar/gkae253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/14/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Fiona Hui
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Lei Xu
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Charles Viau
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
| | - Aliya F Spigelman
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Patrick E MacDonald
- Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- University of Connecticut School of Medicine, Farmington, CT, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University,Sainte-Anne-de-Bellevue, Quebec, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
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4
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Pang Z, Xu L, Viau C, Lu Y, Salavati R, Basu N, Xia J. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 2024; 15:3675. [PMID: 38693118 PMCID: PMC11063062 DOI: 10.1038/s41467-024-48009-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
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Affiliation(s)
- Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Xu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Charles Viau
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Reza Salavati
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
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5
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Ewald S, Nasuhidehnavi A, Feng TY, Lesani M, McCall LI. The intersection of host in vivo metabolism and immune responses to infection with kinetoplastid and apicomplexan parasites. Microbiol Mol Biol Rev 2024; 88:e0016422. [PMID: 38299836 PMCID: PMC10966954 DOI: 10.1128/mmbr.00164-22] [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] [Indexed: 02/02/2024] Open
Abstract
SUMMARYProtozoan parasite infection dramatically alters host metabolism, driven by immunological demand and parasite manipulation strategies. Immunometabolic checkpoints are often exploited by kinetoplastid and protozoan parasites to establish chronic infection, which can significantly impair host metabolic homeostasis. The recent growth of tools to analyze metabolism is expanding our understanding of these questions. Here, we review and contrast host metabolic alterations that occur in vivo during infection with Leishmania, trypanosomes, Toxoplasma, Plasmodium, and Cryptosporidium. Although genetically divergent, there are commonalities among these pathogens in terms of metabolic needs, induction of the type I immune responses required for clearance, and the potential for sustained host metabolic dysbiosis. Comparing these pathogens provides an opportunity to explore how transmission strategy, nutritional demand, and host cell and tissue tropism drive similarities and unique aspects in host response and infection outcome and to design new strategies to treat disease.
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Affiliation(s)
- Sarah Ewald
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Azadeh Nasuhidehnavi
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, USA
| | - Tzu-Yu Feng
- Department of Microbiology, Immunology, and Cancer Biology at the Carter Immunology Center, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Mahbobeh Lesani
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, USA
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, USA
- Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, Oklahoma, USA
- Department of Chemistry and Biochemistry, San Diego State University, San Diego, California, USA
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6
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Alves MF, Katchborian-Neto A, Bueno PCP, Carnevale-Neto F, Casoti R, Ferreira MS, Murgu M, de Paula ACC, Dias DF, Soares MG, Chagas-Paula DA. LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case. RSC Adv 2024; 14:10481-10498. [PMID: 38567345 PMCID: PMC10985591 DOI: 10.1039/d4ra01384k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.
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Affiliation(s)
- Matheus Fernandes Alves
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Albert Katchborian-Neto
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Paula Carolina Pires Bueno
- Leibniz Institute of Vegetable and Ornamental Crops (IGZ) Theodor-Echtermeyer-Weg 1 14979 Großbeeren Germany
| | - Fausto Carnevale-Neto
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington 850 Republican Street Seattle Washington 98109 USA
| | - Rosana Casoti
- Antibiotics Department, Federal University of Pernambuco 50670-901 Recife Pernambuco Brazil
| | - Miller Santos Ferreira
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Michael Murgu
- Waters Corporation Alameda Tocantins 125, Alphaville 06455-020 São Paulo Brazil
| | | | - Danielle Ferreira Dias
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Marisi Gomes Soares
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
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7
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Calabrese V, Brunet TA, Degli-Esposti D, Chaumot A, Geffard O, Salvador A, Clément Y, Ayciriex S. Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum. Anal Bioanal Chem 2024:10.1007/s00216-024-05232-w. [PMID: 38492024 DOI: 10.1007/s00216-024-05232-w] [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: 12/12/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/18/2024]
Abstract
The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.
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Affiliation(s)
- Valentina Calabrese
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
| | - Thomas Alexandre Brunet
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | | | - Arnaud Chaumot
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Olivier Geffard
- Laboratoire d'écotoxicologie, INRAE, UR RiverLy, 69625, Villeurbanne, France
| | - Arnaud Salvador
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Yohann Clément
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France
| | - Sophie Ayciriex
- Universite Claude Bernard Lyon1, ISA, UMR 5280, CNRS, 5 Rue de La Doua, 69100, Villeurbanne, France.
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8
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Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [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: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
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Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
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9
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Adair LR, Jones I, Cramer R. Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis. Anal Chem 2024; 96:985-990. [PMID: 38193749 PMCID: PMC10809226 DOI: 10.1021/acs.analchem.3c03061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024]
Abstract
Tandem mass spectrometry (MS/MS) has become a key method for the structural analysis of biomolecules such as peptides and proteins. A pervasive problem in MS/MS analyses, especially for top-down proteomics, is the occurrence of chimeric spectra, when two or more precursor ions are co-isolated and fragmented, thus leading to complex MS/MS spectra that are populated with fragment ions originating from different precursor ions. This type of convoluted data typically results in low sequence database search scores due to the vast number of mixed-source fragment ions, of which only a fraction originates from a specific precursor ion. Herein, we present a novel workflow that deconvolutes the data of chimeric MS/MS spectra, improving the protein search scores and sequence coverages in database searching and thus providing a more confident peptide and protein identification. Previously misidentified proteins or proteins with insignificant search scores can be correctly and significantly identified following the presented data acquisition and analysis workflow with search scores increasing by a factor of 3-4 for smaller precursor ions (peptides) and >6 for larger precursor ions such as intact ubiquitin and cytochrome C.
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Affiliation(s)
- Lily R. Adair
- Department
of Chemistry, University of Reading, Whiteknights, Reading RG6 6DX, United Kingdom
| | - Ian Jones
- School
of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AJ, United Kingdom
| | - Rainer Cramer
- Department
of Chemistry, University of Reading, Whiteknights, Reading RG6 6DX, United Kingdom
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10
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Katchborian-Neto A, Alves MF, Bueno PCP, de Jesus Nicácio K, Ferreira MS, Oliveira TB, Barbosa H, Murgu M, de Paula Ladvocat ACC, Dias DF, Soares MG, Lago JHG, Chagas-Paula DA. Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data. Brief Bioinform 2024; 25:bbae013. [PMID: 38324622 PMCID: PMC10849173 DOI: 10.1093/bib/bbae013] [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: 10/02/2023] [Revised: 12/20/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024] Open
Abstract
Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.
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Affiliation(s)
- Albert Katchborian-Neto
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
| | - Matheus F Alves
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
| | - Paula C P Bueno
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
- Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Theodor-Echtermeyer-Weg 1, 14979, Großbeeren, Germany
| | - Karen de Jesus Nicácio
- Department of Chemistry, Federal University of Mato Grosso, 14040-901, Cuiabá, Mato Grosso, Brazil
| | - Miller S Ferreira
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
| | - Tiago B Oliveira
- Department of Pharmacy, Federal University of Sergipe, 49100-000, São Cristóvão, Sergipe, Brazil
| | - Henrique Barbosa
- Center of Natural Sciences and Humanities, Federal University of ABC, 09210-180, Santo Andre, São Paulo, Brazil
| | - Michael Murgu
- Waters Corporation, Alameda Tocantins 125, Alphaville, 06455-020, São Paulo, São Paulo, Brazil
| | - Ana C C de Paula Ladvocat
- Department of Pharmaceutical Sciences, Federal University of Juiz de Fora, 36036-900, Juiz de Fora, Minas Gerais, Brazil
| | - Danielle F Dias
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
| | - Marisi G Soares
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
| | - João H G Lago
- Center of Natural Sciences and Humanities, Federal University of ABC, 09210-180, Santo Andre, São Paulo, Brazil
| | - Daniela A Chagas-Paula
- Chemistry Institute, Federal University of Alfenas, 37130-001, Alfenas, Minas Gerais, Brazil
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11
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Cai J, Auster A, Cho S, Lai Z. Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage. J Adv Res 2023; 52:171-201. [PMID: 37419381 PMCID: PMC10555929 DOI: 10.1016/j.jare.2023.07.002] [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: 01/20/2023] [Revised: 05/25/2023] [Accepted: 07/03/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten "metabolic organ" is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome. AIM OF REVIEW From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research- including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk. KEY SCIENTIFIC CONCEPTS OF REVIEW We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies.
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Affiliation(s)
- Jingwei Cai
- Drug Metabolism & Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080, USA.
| | - Alexis Auster
- Drug Metabolism & Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080, USA
| | - Sungjoon Cho
- Drug Metabolism & Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080, USA
| | - Zijuan Lai
- Drug Metabolism & Pharmacokinetics, Genentech Inc., South San Francisco, CA 94080, USA
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12
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Rohatgi N, Zou W, Li Y, Cho K, Collins PL, Tycksen E, Pandey G, DeSelm CJ, Patti GJ, Dey A, Teitelbaum SL. BAP1 promotes osteoclast function by metabolic reprogramming. Nat Commun 2023; 14:5923. [PMID: 37740028 PMCID: PMC10516877 DOI: 10.1038/s41467-023-41629-4] [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: 12/03/2021] [Accepted: 09/12/2023] [Indexed: 09/24/2023] Open
Abstract
Treatment of osteoporosis commonly diminishes osteoclast number which suppresses bone formation thus compromising fracture prevention. Bone formation is not suppressed, however, when bone degradation is reduced by retarding osteoclast functional resorptive capacity, rather than differentiation. We find deletion of deubiquitinase, BRCA1-associated protein 1 (Bap1), in myeloid cells (Bap1∆LysM), arrests osteoclast function but not formation. Bap1∆LysM osteoclasts fail to organize their cytoskeleton which is essential for bone degradation consequently increasing bone mass in both male and female mice. The deubiquitinase activity of BAP1 modifies osteoclast function by metabolic reprogramming. Bap1 deficient osteoclast upregulate the cystine transporter, Slc7a11, by enhanced H2Aub occupancy of its promoter. SLC7A11 controls cellular reactive oxygen species levels and redirects the mitochondrial metabolites away from the tricarboxylic acid cycle, both being necessary for osteoclast function. Thus, in osteoclasts BAP1 appears to regulate the epigenetic-metabolic axis and is a potential target to reduce bone degradation while maintaining osteogenesis in osteoporotic patients.
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Affiliation(s)
- Nidhi Rohatgi
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Wei Zou
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yongjia Li
- Department of Pharmacology, Jiangsu University School of Medicine, Zhenjiang, Jiangsu Province, 212013, PR China
| | - Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Patrick L Collins
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, 43210, USA
| | - Eric Tycksen
- Genome Technology Access Center, McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Gaurav Pandey
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Carl J DeSelm
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, 63130, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Anwesha Dey
- Department of Discovery Oncology, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Steven L Teitelbaum
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Division of Bone and Mineral Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
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13
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Zhao T, Xing S, Yu H, Huan T. De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics. Anal Chem 2023; 95:13018-13028. [PMID: 37603462 DOI: 10.1021/acs.analchem.3c00736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.
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Affiliation(s)
- Tingting Zhao
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Shipei Xing
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Huaxu Yu
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
| | - Tao Huan
- Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada
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14
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Peng Z, Zhang W, Zhang X, Mao J, Zhang Q, Zhao W, Zhang S, Xie J. Recent advances in analysis of capsaicin and its effects on metabolic pathways by mass spectrometry. Front Nutr 2023; 10:1227517. [PMID: 37575327 PMCID: PMC10419207 DOI: 10.3389/fnut.2023.1227517] [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: 05/23/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
Capsaicin is the main food active component in Capsicum that has gained considerable attention due to its broad biological activities, including antioxidation, anti-inflammation, anti-tumor, weight regulation, cardiac protection, anti-calculi, and diurnal-circadian regulation. The potent biological effects of capsaicin are intimately related to metabolic pathways such as lipid metabolism, energy metabolism, and antioxidant stress. Mass spectrometry (MS) has emerged as an effective tool for deciphering the mechanisms underlying capsaicin metabolism and its biological impacts. However, it remains challenging to accurately identify and quantify capsaicin and its self-metabolites in complex food and biological samples, and to integrate multi-omics data generated from MS. In this work, we summarized recent advances in the detection of capsaicin and its self-metabolites using MS and discussed the relevant MS-based studies of metabolic pathways. Furthermore, we discussed current issues and future directions in this field. In-depth studies of capsaicin metabolism and its physiological functions based on MS is anticipated to yield new insights and methods for preventing and treating a wide range of diseases.
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Affiliation(s)
- Zifang Peng
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
| | - Wenfen Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
| | - Xu Zhang
- School of Ecology and Environment, Zhengzhou University, Zhengzhou, Henan, China
| | - Jian Mao
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Qidong Zhang
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Wuduo Zhao
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
| | - Shusheng Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
| | - Jianping Xie
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, Henan, China
- Food Laboratory of Zhongyuan, Flavor Science Research Center of Zhengzhou University, Luohe, Henan, China
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15
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Jiang Y, Salladay-Perez I, Momenzadeh A, Covarrubias AJ, Meyer JG. Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546628. [PMID: 37425781 PMCID: PMC10326973 DOI: 10.1101/2023.06.26.546628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Combined multi-omics analysis of proteomics, polar metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS) platforms for each omics layer. This requirement for different platforms limits throughput and increases costs, preventing the application of mass spectrometry-based multi-omics to large scale drug discovery or clinical cohorts. Here, we present an innovative strategy for simultaneous multi-omics analysis by direct infusion (SMAD) using one single injection without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of this method and then present two practical applications: mouse macrophage M1/M2 polarization and high throughput drug screening in human 293T cells. Finally, we demonstrate relationships between proteomic and metabolomic data are discovered by machine learning.
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Affiliation(s)
- Yuming Jiang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ivan Salladay-Perez
- Department of Molecular Biology, Immunology, and Molecular Genetics, University of California, Los Angeles, 90095, USA
| | - Amanda Momenzadeh
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Anthony J. Covarrubias
- Department of Molecular Biology, Immunology, and Molecular Genetics, University of California, Los Angeles, 90095, USA
| | - Jesse G. Meyer
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
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16
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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17
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Bilbao A, Munoz N, Kim J, Orton DJ, Gao Y, Poorey K, Pomraning KR, Weitz K, Burnet M, Nicora CD, Wilton R, Deng S, Dai Z, Oksen E, Gee A, Fasani RA, Tsalenko A, Tanjore D, Gardner J, Smith RD, Michener JK, Gladden JM, Baker ES, Petzold CJ, Kim YM, Apffel A, Magnuson JK, Burnum-Johnson KE. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun 2023; 14:2461. [PMID: 37117207 PMCID: PMC10147702 DOI: 10.1038/s41467-023-37031-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/24/2023] [Indexed: 04/30/2023] Open
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | | | - Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Karl Weitz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan Burnet
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Rosemarie Wilton
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Argonne National Laboratory, Lemont, IL, USA
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ethan Oksen
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aaron Gee
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Rick A Fasani
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Anya Tsalenko
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Deepti Tanjore
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - James Gardner
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Joshua K Michener
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - John M Gladden
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Sandia National Laboratory, Livermore, CA, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher J Petzold
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Alex Apffel
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Kristin E Burnum-Johnson
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
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18
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [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: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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19
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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20
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Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development. Molecules 2023; 28:molecules28062712. [PMID: 36985684 PMCID: PMC10057629 DOI: 10.3390/molecules28062712] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/14/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Mass Spectrometry Imaging (MSI) has emerged as a powerful imaging technique for the analysis of biological samples, providing valuable insights into the spatial distribution and structural characterization of lipids. The advancements in high-resolution MSI have made it an indispensable tool for single-cell or subcellular lipidomics. By preserving both intracellular and intercellular information, MSI enables a comprehensive analysis of lipidomics in individual cells and organelles. This enables researchers to delve deeper into the diversity of lipids within cells and to understand the role of lipids in shaping cell behavior. In this review, we aim to provide a comprehensive overview of recent advancements and future prospects of MSI for cellular/subcellular lipidomics. By keeping abreast of the cutting-edge studies in this field, we will continue to push the boundaries of the understanding of lipid metabolism and the impact of lipids on cellular behavior.
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21
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Yang Y, Yang L, Zheng M, Cao D, Liu G. Data acquisition methods for non-targeted screening in environmental analysis. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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22
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Stancliffe E, Schwaiger-Haber M, Sindelar M, Murphy MJ, Soerensen M, Patti GJ. An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies. Anal Chem 2022; 94:17370-17378. [PMID: 36475608 PMCID: PMC11018270 DOI: 10.1021/acs.analchem.2c01270] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.
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Affiliation(s)
- Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Matthew J. Murphy
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Mette Soerensen
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing at Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri 63130, United States
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23
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Sponagel J, Jones JK, Frankfater C, Zhang S, Tung O, Cho K, Tinkum KL, Gass H, Nunez E, Spitz DR, Chinnaiyan P, Schaefer J, Patti GJ, Graham MS, Mauguen A, Grkovski M, Dunphy MP, Krebs S, Luo J, Rubin JB, Ippolito JE. Sex differences in brain tumor glutamine metabolism reveal sex-specific vulnerabilities to treatment. MED 2022; 3:792-811.e12. [PMID: 36108629 PMCID: PMC9669217 DOI: 10.1016/j.medj.2022.08.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND Brain cancer incidence and mortality rates are greater in males. Understanding the molecular mechanisms that underlie those sex differences could improve treatment strategies. Although sex differences in normal metabolism are well described, it is currently unknown whether they persist in cancerous tissue. METHODS Using positron emission tomography (PET) imaging and mass spectrometry, we assessed sex differences in glioma metabolism in samples from affected individuals. We assessed the role of glutamine metabolism in male and female murine transformed astrocytes using isotope labeling, metabolic rescue experiments, and pharmacological and genetic perturbations to modulate pathway activity. FINDINGS We found that male glioblastoma surgical specimens are enriched for amino acid metabolites, including glutamine. Fluoroglutamine PET imaging analyses showed that gliomas in affected male individuals exhibit significantly higher glutamine uptake. These sex differences were well modeled in murine transformed astrocytes, in which male cells imported and metabolized more glutamine and were more sensitive to glutaminase 1 (GLS1) inhibition. The sensitivity to GLS1 inhibition in males was driven by their dependence on glutamine-derived glutamate for α-ketoglutarate synthesis and tricarboxylic acid (TCA) cycle replenishment. Females were resistant to GLS1 inhibition through greater pyruvate carboxylase (PC)-mediated TCA cycle replenishment, and knockdown of PC sensitized females to GLS1 inhibition. CONCLUSION Our results show that clinically important sex differences exist in targetable elements of metabolism. Recognition of sex-biased metabolism may improve treatments through further laboratory and clinical research. FUNDING This work was supported by NIH grants, Joshua's Great Things, the Siteman Investment Program, and the Barnard Research Fund.
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Affiliation(s)
- Jasmin Sponagel
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jill K Jones
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Cheryl Frankfater
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Biomedical Mass Spectrometry Resource, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Shanshan Zhang
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Olivia Tung
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kevin Cho
- Department of Chemistry, Washington University, St. Louis, MO 63130, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kelsey L Tinkum
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hannah Gass
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Elena Nunez
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Douglas R Spitz
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52246, USA; Holden Comprehensive Cancer Center, Department of Pathology, University of Iowa, Iowa City, IA 52246, USA
| | - Prakash Chinnaiyan
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI 48073, USA; Oakland University William Beaumont School of Medicine, Rochester, MI 48073, USA
| | - Jacob Schaefer
- Department of Chemistry, Washington University, St. Louis, MO 63130, USA
| | - Gary J Patti
- Department of Chemistry, Washington University, St. Louis, MO 63130, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maya S Graham
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Audrey Mauguen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Milan Grkovski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mark P Dunphy
- Department of Radiology, Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Simone Krebs
- Department of Radiology, Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Jingqin Luo
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joshua B Rubin
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Joseph E Ippolito
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA.
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24
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Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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25
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Liu Z, Zhang M, Chen P, Harnly JM, Sun J. Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:11138-11153. [PMID: 35998657 DOI: 10.1021/acs.jafc.2c01878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.
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Affiliation(s)
- Zhihao Liu
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Mengliang Zhang
- Department of Chemistry, Middle Tennessee State University, Murfreesboro, Tennessee 37132, United States
| | - Pei Chen
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - James M Harnly
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - Jianghao Sun
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
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26
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Sebastiani P, Song Z, Ellis D, Tian Q, Schwaiger-Haber M, Stancliffe E, Lustgarten MS, Funk CC, Baloni P, Yao CH, Joshi S, Marron MM, Gurinovich A, Li M, Leshchyk A, Xiang Q, Andersen SL, Feitosa MF, Ukraintseva S, Soerensen M, Fiehn O, Ordovas JM, Haigis M, Monti S, Barzilai N, Milman S, Ferrucci L, Rappaport N, Patti GJ, Perls TT. A metabolomic signature of the APOE2 allele. GeroScience 2022; 45:415-426. [PMID: 35997888 PMCID: PMC9886693 DOI: 10.1007/s11357-022-00646-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/15/2022] [Indexed: 02/03/2023] Open
Abstract
With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.
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Affiliation(s)
- Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Boston, MA, 02111, USA.
| | - Zeyuan Song
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Dylan Ellis
- Institute for Systems Biology, Seattle, WA, USA
| | - Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Department of Medicine, Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, USA
| | - Ethan Stancliffe
- Department of Chemistry, Department of Medicine, Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, USA
| | - Michael S Lustgarten
- Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center On Aging at Tufts University, Boston, MA, USA
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA, USA
| | | | - Cong-Hui Yao
- Department of Cell Biology at Harvard Medical School, Boston, MA, USA
| | - Shakchhi Joshi
- Department of Cell Biology at Harvard Medical School, Boston, MA, USA
| | - Megan M Marron
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Anastasia Gurinovich
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Boston, MA, 02111, USA
| | - Mengze Li
- Bioinformatics Program, Boston University, Boston, MA, USA
| | | | - Qingyan Xiang
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Stacy L Andersen
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MI, USA
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research, Duke University, Durham, NC, USA
| | - Mette Soerensen
- Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA, USA
| | - Jose M Ordovas
- Nutrition and Genomics Team, Jean Mayer USDA Human Nutrition Research Center On Aging and Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MB, USA
| | - Marcia Haigis
- Department of Cell Biology at Harvard Medical School, Boston, MA, USA
| | - Stefano Monti
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Sofiya Milman
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA
| | | | - Gary J Patti
- Department of Chemistry, Department of Medicine, Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, USA
| | - Thomas T Perls
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
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27
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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28
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Jackstadt MM, Chamberlain CA, Doonan SR, Shriver LP, Patti GJ. A multidimensional metabolomics workflow to image biodistribution and evaluate pharmacodynamics in adult zebrafish. Dis Model Mech 2022; 15:dmm049550. [PMID: 35972155 PMCID: PMC9411795 DOI: 10.1242/dmm.049550] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 07/13/2022] [Indexed: 12/16/2022] Open
Abstract
An integrated evaluation of the tissue distribution and pharmacodynamic properties of a therapeutic is essential for successful translation to the clinic. To date, however, cost-effective methods to measure these parameters at the systems level in model organisms are lacking. Here, we introduce a multidimensional workflow to evaluate drug activity that combines mass spectrometry-based imaging, absolute drug quantitation across different biological matrices, in vivo isotope tracing and global metabolome analysis in the adult zebrafish. As a proof of concept, we quantitatively determined the whole-body distribution of the anti-rheumatic agent hydroxychloroquine sulfate (HCQ) and measured the systemic metabolic impacts of drug treatment. We found that HCQ distributed to most organs in the adult zebrafish 24 h after addition of the drug to water, with the highest accumulation of both the drug and its metabolites being in the liver, intestine and kidney. Interestingly, HCQ treatment induced organ-specific alterations in metabolism. In the brain, for example, HCQ uniquely elevated pyruvate carboxylase activity to support increased synthesis of the neuronal metabolite, N-acetylaspartate. Taken together, this work validates a multidimensional metabolomics platform for evaluating the mode of action of a drug and its potential off-target effects in the adult zebrafish. This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Madelyn M. Jackstadt
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Casey A. Chamberlain
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Steven R. Doonan
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Leah P. Shriver
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA
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29
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Tian H, Ni Z, Lam SM, Jiang W, Li F, Du J, Wang Y, Shui G. Precise Metabolomics Reveals a Diversity of Aging-Associated Metabolic Features. SMALL METHODS 2022; 6:e2200130. [PMID: 35527334 DOI: 10.1002/smtd.202200130] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/24/2022] [Indexed: 06/14/2023]
Abstract
Mass spectrometry-based metabolomics has emerged as a powerful technique for biomedical research, although technical issues with its analytical precision and structural characterization remain. Herein, a robust non-targeted strategy for accurate quantitation and precise profiling of metabolomes is developed and applied to investigate plasma metabolic features associated with human aging. A comprehensive set of isotope-labeled standards (ISs) covering major metabolic pathways is incorporated to quantify polar metabolites. Matching rules to select ISs for calibration follow a primary criterion of minimal coefficients of variations (COVs). If minimal COVs between specific ISs for a particular metabolite fall within 5% window, a further selection of ISs is conducted based on structural similarities and proximity in retention time. The introduction and refined selection of appropriate ISs for quantitation reduces the COVs of 480 identified metabolites in quality control samples from 14.3% to 9.8% and facilitates identification of additional metabolite. Finally, the precise metabolomics approach reveals perturbations in a diverse array of metabolic pathways across aging that principally implicate steroid metabolism, amino acid metabolism, lipid metabolism, and purine metabolism, which allows the authors to draw correlates to the pathology of various age-related diseases. These findings provide clues for the prevention and treatment of these age-related diseases.
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Affiliation(s)
- He Tian
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Zhen Ni
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Sin Man Lam
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- LipidALL Technologies Company Limited, Changzhou, Jiangsu Province, 213022, China
| | - Wenxi Jiang
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Fengjuan Li
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Jie Du
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Yuan Wang
- Beijing Institute of Heart, Lung, and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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30
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Kaeslin J, Zenobi R. Resolving isobaric interferences in direct infusion tandem mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2022; 36:e9266. [PMID: 35124854 PMCID: PMC9286799 DOI: 10.1002/rcm.9266] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
RATIONALE The co-fragmentation of precursors in direct infusion (DI) tandem high-resolution mass spectrometry (HRMS) can complicate the fragment spectra and consequently lead to false hits during compound identification. METHODS The method herein described, termed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra), modulates the intensities of precursors and fragments by stepwise movement of the quadrupole isolation window over the mass-to-charge (m/z) range of the precursors. The modulated signals are then deconvoluted by a linear regression model to reconstruct the fragment spectra with less interference. The hardware to demonstrate the use of IQAROS was an orbitrap with electrospray ionization (ESI) or secondary electrospray ionization (SESI), although the method can also be applied to other ionization techniques or mass analyzers. RESULTS Assessing the performance of IQAROS with isobaric standards revealed that the reconstructed fragment spectra match with spectra acquired from the pure standards and that more compounds were correctly identified compared with the classical approach with the quadrupole centered at the m/z value of the precursor of interest. Moreover, the strength of IQAROS is exemplified by the identification of two isobaric biomarkers directly from a breath sample with SESI-HRMS. CONCLUSIONS With IQAROS, cleaner fragment spectra of co-fragmenting isobars during DI-HRMS analysis can be obtained. IQAROS can easily be set up by the standard graphical user interface of the instrument. Therefore, it facilitates the characterization of features of interest in samples analyzed by DI-HRMS, for example, in high-throughput or real-time metabolomics.
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Affiliation(s)
- Jérôme Kaeslin
- Department of Chemistry and Applied BiosciencesETH ZürichZürichSwitzerland
| | - Renato Zenobi
- Department of Chemistry and Applied BiosciencesETH ZürichZürichSwitzerland
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31
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Nelson AB, Chow LS, Stagg DB, Gillingham JR, Evans MD, Pan M, Hughey CC, Myers CL, Han X, Crawford PA, Puchalska P. Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners. JCI Insight 2022; 7:158037. [PMID: 35192550 PMCID: PMC9057596 DOI: 10.1172/jci.insight.158037] [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] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/18/2022] [Indexed: 11/23/2022] Open
Abstract
Background Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO2max) and longer-term outcomes, including the development of diabetes and its complications. Methods Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO2max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome. Results NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO2max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO2max. Conclusion These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO2max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance. Trial registration ClinicalTrials.gov, NCT02150889. Funding NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.
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Affiliation(s)
- Alisa B Nelson
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - David B Stagg
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Jacob R Gillingham
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, United States of America
| | - Meixia Pan
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Curtis C Hughey
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, United States of America
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Peter A Crawford
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Patrycja Puchalska
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
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New Advances in Tissue Metabolomics: A Review. Metabolites 2021; 11:metabo11100672. [PMID: 34677387 PMCID: PMC8541552 DOI: 10.3390/metabo11100672] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 12/20/2022] Open
Abstract
Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.
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Schwaiger-Haber M, Stancliffe E, Arends V, Thyagarajan B, Sindelar M, Patti GJ. A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer. ACS MEASUREMENT SCIENCE AU 2021; 1:35-45. [PMID: 34476422 PMCID: PMC8377714 DOI: 10.1021/acsmeasuresciau.1c00007] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Indexed: 05/25/2023]
Abstract
The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for "unknowns" that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.
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Affiliation(s)
- Michaela Schwaiger-Haber
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Ethan Stancliffe
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Valerie Arends
- Department
of Laboratory Medicine and Pathology, University
of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Bharat Thyagarajan
- Department
of Laboratory Medicine and Pathology, University
of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Miriam Sindelar
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
| | - Gary J. Patti
- Department
of Chemistry, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
- Department
of Medicine, Washington University in St.
Louis, St. Louis, Missouri 63130, United States
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