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Mamani-Huanca M, Martínez S, López-López Á, López-Gonzálvez Á, Albóniga OE, Gradillas A, Barbas C, González-Ruiz V. CE-MS-Based Clinical Metabolomics of Human Plasma. Methods Mol Biol 2025; 2855:389-423. [PMID: 39354320 DOI: 10.1007/978-1-0716-4116-3_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024]
Abstract
Capillary electrophoresis coupled to mass spectrometry (CE-MS) has emerged as a powerful analytical technique with significant implications for clinical research and diagnostics. The integration of information from CE and MS strengthens confidence in the identification of compounds present in clinical samples. The ability of CE to separate molecules based on their electrophoretic mobility coupled to MS enables the accurate identification and quantification of analytes, even in complex biological matrices such as human plasma.Here, we present a detailed protocol for an untargeted metabolomics study using CE-MS and its application in a study on human plasma from patients suffering Long COVID syndrome. The protocol ranges from sample preparation to biological interpretation, detailing a workflow enabling the analysis of cationic and anionic compounds, metabolite identification, and data processing.
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Affiliation(s)
- Maricruz Mamani-Huanca
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Sara Martínez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Ángeles López-López
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Oihane E Albóniga
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Ana Gradillas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain
| | - Víctor González-Ruiz
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain.
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2
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Pakkir Shah AK, Walter A, Ottosson F, Russo F, Navarro-Diaz M, Boldt J, Kalinski JCJ, Kontou EE, Elofson J, Polyzois A, González-Marín C, Farrell S, Aggerbeck MR, Pruksatrakul T, Chan N, Wang Y, Pöchhacker M, Brungs C, Cámara B, Caraballo-Rodríguez AM, Cumsille A, de Oliveira F, Dührkop K, El Abiead Y, Geibel C, Graves LG, Hansen M, Heuckeroth S, Knoblauch S, Kostenko A, Kuijpers MCM, Mildau K, Papadopoulos Lambidis S, Portal Gomes PW, Schramm T, Steuer-Lodd K, Stincone P, Tayyab S, Vitale GA, Wagner BC, Xing S, Yazzie MT, Zuffa S, de Kruijff M, Beemelmanns C, Link H, Mayer C, van der Hooft JJJ, Damiani T, Pluskal T, Dorrestein P, Stanstrup J, Schmid R, Wang M, Aron A, Ernst M, Petras D. Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data. Nat Protoc 2024:10.1038/s41596-024-01046-3. [PMID: 39304763 DOI: 10.1038/s41596-024-01046-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/02/2024] [Indexed: 09/22/2024]
Abstract
Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.
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Affiliation(s)
- Abzer K Pakkir Shah
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Axel Walter
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Filip Ottosson
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark
| | - Francesco Russo
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark
| | - Marcelo Navarro-Diaz
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Judith Boldt
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
- German Center for Infection Research, Partner Site Braunschweig-Hannover, Braunschweig, Germany
| | - Jarmo-Charles J Kalinski
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Eftychia Eva Kontou
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- The Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - James Elofson
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA
| | - Alexandros Polyzois
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Carolina González-Marín
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Universidad EAFIT, Medellín, Antioquia, Colombia
| | - Shane Farrell
- Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA
- School of Marine Sciences, Darling Marine Center, University of Maine, Walpole, ME, USA
| | - Marie R Aggerbeck
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Thapanee Pruksatrakul
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand
| | - Nathan Chan
- Department of Computer Science, University of California Riverside, Riverside, CA, USA
| | - Yunshu Wang
- Department of Computer Science, University of California Riverside, Riverside, CA, USA
| | - Magdalena Pöchhacker
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Food Chemistry and Toxicology, University of Vienna, Vienna, Austria
| | - Corinna Brungs
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Beatriz Cámara
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile
| | | | - Andres Cumsille
- Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile
| | - Fernanda de Oliveira
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
- Department of Biotechnology, Engineering School of Lorena, University of São Paulo, Lorena, São Paulo, Brazil
| | - Kai Dührkop
- Department of Bioinformatics, University of Jena, Jena, Germany
| | - Yasin El Abiead
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Christian Geibel
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Lana G Graves
- Department of Environmental Systems Analysis, University of Tübingen, Tübingen, Germany
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Martin Hansen
- Department of Environmental Science, Aarhus University, Roskilde, Denmark
| | - Steffen Heuckeroth
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Simon Knoblauch
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Anastasiia Kostenko
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA
| | - Mirte C M Kuijpers
- Department of Ecology, Behavior and Evolution, University of California San Diego, San Diego, CA, USA
| | - Kevin Mildau
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Analytical Chemistry, University of Vienna, Vienna, Austria
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands
| | | | - Paulo Wender Portal Gomes
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Tilman Schramm
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, CA, USA
| | - Karoline Steuer-Lodd
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
- Department of Biochemistry, University of California Riverside, Riverside, CA, USA
| | - Paolo Stincone
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Sibgha Tayyab
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Giovanni Andrea Vitale
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Berenike C Wagner
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Shipei Xing
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Marquis T Yazzie
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA
| | - Simone Zuffa
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Martinus de Kruijff
- Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany
| | - Christine Beemelmanns
- Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany
- Saarland University, Saarbrücken, Germany
| | - Hannes Link
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Christoph Mayer
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany
| | - Justin J J van der Hooft
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
| | - Tito Damiani
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Tomáš Pluskal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Pieter Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Jan Stanstrup
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg C, Denmark
| | - Robin Schmid
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Mingxun Wang
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Computer Science, University of California Riverside, Riverside, CA, USA
| | - Allegra Aron
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA
| | - Madeleine Ernst
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark.
| | - Daniel Petras
- Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
- University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
- Department of Biochemistry, University of California Riverside, Riverside, CA, USA.
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Kong F, Shen T, Li Y, Bashar A, Bird SS, Fiehn O. Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer. RESEARCH SQUARE 2024:rs.3.rs-4758843. [PMID: 39108483 PMCID: PMC11302682 DOI: 10.21203/rs.3.rs-4758843/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Chemical exposures may impact human metabolism and contribute to the etiology of neurodegenerative disorders like Alzheimer's Disease (AD). Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can significantly degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with >150-fold higher intensity of noise ions than true fragment ions. For human plasma samples of AD patients that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.3-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides. This combination of advanced instrumentation with a superior denoising algorithm opens the door for precision medicine in exposome research.
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Affiliation(s)
- Fanzhou Kong
- Chemistry Department, One Shields Avenue, University of California Davis, Davis, CA, 95616, USA
- West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
| | - Tong Shen
- West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
| | - Yuanyue Li
- West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
| | - Amer Bashar
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, CA 95134, USA
| | - Susan S Bird
- Thermo Fisher Scientific, 355 River Oaks Pkwy, San Jose, CA 95134, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA
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Terry J, Dyer RA. Aberrant colon metabolome and the sudden infant death syndrome. Pediatr Res 2024; 95:634-640. [PMID: 37833530 DOI: 10.1038/s41390-023-02847-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND The Sudden Infant Death Syndrome (SIDS) has been associated with increased peripheral serotonin and an abnormal colonic microbiome, suggesting the colonic metabolome may also be abnormal. This study addresses this potential correlation by comparing colonic autopsy tissue from SIDS to age-matched non-SIDS controls. METHODS Untargeted metabolomic analysis by mass spectrometry is used to assess human colonic metabolomic differences including serotonin. Expression of genes associated with colonic serotonin synthesis and transport (TPH1, TPH2, DDC, SCL6A4) is measured by qRT-PCR. Microbiome analysis is performed to compare the SIDS and non-SIDS colonic microbiome. RESULTS Unsupervised hierarchical cluster and principal component analyses of metabolomic data shows increased variability in the SIDS cohort and separation of SIDS cases from the non-SIDS controls. There is a trend toward increased serotonin in the SIDS cohort but there is no significant difference in expression of the serotonin synthesis and transport genes between SIDS and non-SIDS control cohorts. Microbiome analysis shows no significant difference between the SIDS and non-SIDS control cohorts. CONCLUSIONS This study demonstrates increased variability in the colonic metabolome and a trend towards increased colonic serotonin in SIDS. The underlying cause of colon metabolomic variability, and its potential role in SIDS pathogenesis, warrants further investigation. IMPACT STATEMENT The key message of this article is that SIDS is associated with an aberrant colonic metabolome. This is a novel observation suggesting another component in the pathophysiology underlying SIDS. Investigation of why the colonic metabolome is aberrant may offer new insights to SIDS pathogenesis and new strategies to reduce risk.
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Affiliation(s)
- Jefferson Terry
- Department of Pathology, British Columbia Children's and Women's Hospitals, Vancouver, BC, Canada.
| | - Roger A Dyer
- Analytical Core for Metabolomics and Nutrition, British Columbia Children's Hospital Research Institute, Vancouver, BC, Canada
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5
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Martinez-Morata I, Wu H, Galvez-Fernandez M, Ilievski V, Bottiglieri T, Niedzwiecki MM, Goldsmith J, Jones DP, Kioumourtzoglou MA, Pierce B, Walker DI, Gamble MV. Metabolomic Effects of Folic Acid Supplementation in Adults: Evidence from the FACT Trial. J Nutr 2024; 154:670-679. [PMID: 38092151 PMCID: PMC10900167 DOI: 10.1016/j.tjnut.2023.12.010] [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: 10/05/2023] [Revised: 12/04/2023] [Accepted: 12/08/2023] [Indexed: 12/31/2023] Open
Abstract
BACKGROUND Folic acid (FA) is the oxidized form of folate found in supplements and FA-fortified foods. Most FA is reduced by dihydrofolate reductase to 5-methyltetrahydrofolate (5mTHF); the latter is the form of folate naturally found in foods. Ingestion of FA increases the plasma levels of both 5mTHF and unmetabolized FA (UMFA). Limited information is available on the downstream metabolic effects of FA supplementation, including potential effects associated with UMFA. OBJECTIVE We aimed to assess the metabolic effects of FA-supplementation, and the associations of plasma 5mTHF and UMFA with the metabolome in FA-naïve Bangladeshi adults. METHODS Sixty participants were selected from the Folic Acid and Creatine Trial; half received 800 μg FA/day for 12 weeks and half placebo. Plasma metabolome profiles were measured by high-resolution mass spectrometry, including 170 identified metabolites and 26,541 metabolic features. Penalized regression methods were used to assess the associations of targeted metabolites with FA-supplementation, plasma 5mTHF, and plasma UMFA. Pathway analyses were conducted using Mummichog. RESULTS In penalized models of identified metabolites, FA-supplementation was associated with higher choline. Changes in 5mTHF concentrations were positively associated with metabolites involved in amino acid metabolism (5-hydroxyindoleacetic acid, acetylmethionine, creatinine, guanidinoacetate, hydroxyproline/n-acetylalanine) and 2 fatty acids (docosahexaenoic acid and linoleic acid). Changes in 5mTHF concentrations were negatively associated with acetylglutamate, acetyllysine, carnitine, propionyl carnitine, cinnamic acid, homogentisate, arachidonic acid, and nicotine. UMFA concentrations were associated with lower levels of arachidonic acid. Together, metabolites selected across all models were related to lipids, aromatic amino acid metabolism, and the urea cycle. Analyses of nontargeted metabolic features identified additional pathways associated with FA supplementation. CONCLUSION In addition to the recapitulation of several expected metabolic changes associated with 5mTHF, we observed additional metabolites/pathways associated with FA-supplementation and UMFA. Further studies are needed to confirm these associations and assess their potential implications for human health. TRIAL REGISTRATION NUMBER This trial was registered at https://clinicaltrials.gov as NCT01050556.
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Affiliation(s)
- Irene Martinez-Morata
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Haotian Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Marta Galvez-Fernandez
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Vesna Ilievski
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Teodoro Bottiglieri
- Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, Dallas, TX, United States
| | - Megan M Niedzwiecki
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jeff Goldsmith
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Dean P Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States; Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, United States
| | - Marianthi-Anna Kioumourtzoglou
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States
| | - Brandon Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL, United States; Department of Human Genetics, University of Chicago, Chicago, IL, United States; Comprehensive Cancer Center, University of Chicago, Chicago, IL, United States
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Mary V Gamble
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States.
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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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7
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Xu R, Zhang H, Crowder MW, Zhu J. Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis. Brief Bioinform 2024; 25:bbae046. [PMID: 38385875 PMCID: PMC10883140 DOI: 10.1093/bib/bbae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student's t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.
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Affiliation(s)
- Rui Xu
- Human Nutrition Program, Department of Human Sciences, The Ohio State University, Columbus, Ohio, USA 43210
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 43210
| | - Huan Zhang
- Human Nutrition Program, Department of Human Sciences, The Ohio State University, Columbus, Ohio, USA 43210
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 43210
| | - Michael W Crowder
- Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio, USA 45056
| | - Jiangjiang Zhu
- Human Nutrition Program, Department of Human Sciences, The Ohio State University, Columbus, Ohio, USA 43210
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA 43210
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8
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Liang S, Cao X, Wang Y, Leng P, Wen X, Xie G, Luo H, Yu R. Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types. Int J Med Sci 2024; 21:234-252. [PMID: 38169594 PMCID: PMC10758149 DOI: 10.7150/ijms.85704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 10/14/2023] [Indexed: 01/05/2024] Open
Abstract
Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.
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Affiliation(s)
- Simin Liang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Xiujun Cao
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yingshuang Wang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Ping Leng
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Xiaoxia Wen
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Guojing Xie
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Rong Yu
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
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9
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Jiang Y, Wen C, Jiang Y, Wang X, Zhang H. Use of random integration to test equality of high dimensional covariance matrices. Stat Sin 2023; 33:2359-2380. [PMID: 37799490 PMCID: PMC10550010 DOI: 10.5705/ss.202020.0486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.
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Affiliation(s)
- Yunlu Jiang
- Jinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University
| | - Canhong Wen
- Jinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University
| | - Yukang Jiang
- Jinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University
| | - Xueqin Wang
- Jinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University
| | - Heping Zhang
- Jinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University
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10
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Overdahl K, Collier JB, Jetten AM, Jarmusch AK. Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1941-1948. [PMID: 37524076 PMCID: PMC10485927 DOI: 10.1021/jasms.3c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 08/02/2023]
Abstract
Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.
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Affiliation(s)
- Kirsten
E. Overdahl
- Immunity, Inflammation, and Disease
Laboratory, Division of Intramural Research, National Institute of
Environmental Health Sciences, National
Institutes of Health, Research
Triangle Park, North Carolina 27709, United States
| | - Justin B. Collier
- Immunity, Inflammation, and Disease
Laboratory, Division of Intramural Research, National Institute of
Environmental Health Sciences, National
Institutes of Health, Research
Triangle Park, North Carolina 27709, United States
| | - Anton M. Jetten
- Immunity, Inflammation, and Disease
Laboratory, Division of Intramural Research, National Institute of
Environmental Health Sciences, National
Institutes of Health, Research
Triangle Park, North Carolina 27709, United States
| | - Alan K. Jarmusch
- Immunity, Inflammation, and Disease
Laboratory, Division of Intramural Research, National Institute of
Environmental Health Sciences, National
Institutes of Health, Research
Triangle Park, North Carolina 27709, United States
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11
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Goh WWB, Hui HWH, Wong L. How missing value imputation is confounded with batch effects and what you can do about it. Drug Discov Today 2023; 28:103661. [PMID: 37301250 DOI: 10.1016/j.drudis.2023.103661] [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: 02/08/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.
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Affiliation(s)
- Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.
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12
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Seeburger P, Forsman H, Bevilacqua G, Marques TM, Morales LO, Prado SBR, Strid Å, Hyötyläinen T, Castro-Alves V. From farm to fork… and beyond! UV enhances Aryl hydrocarbon receptor-mediated activity of cruciferous vegetables in human intestinal cells upon colonic fermentation. Food Chem 2023; 426:136588. [PMID: 37352713 DOI: 10.1016/j.foodchem.2023.136588] [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: 02/14/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 06/25/2023]
Abstract
While the "farm to fork" strategy ticks many boxes in the sustainability agenda, it does not go far enough in addressing how we can improve crop nutraceutical quality. Here, we explored whether supplementary ultraviolet (UV) radiation exposure during growth of broccoli and Chinese cabbage can induce bioactive tryptophan- and glucosinolate-specific metabolite accumulation thereby enhancing Aryl hydrocarbon receptor (AhR) activation in human intestinal cells. By combining metabolomics analysis of both plant extracts and in vitro human colonic fermentation extracts with AhR reporter cell assay, we reveal that human colonic fermentation of UVB-exposed Chinese cabbage led to enhanced AhR activation in human intestinal cells by 23% compared to plants grown without supplementary UV. Thus, by exploring aspects beyond "from farm to fork", our study highlights a new strategy to enhance nutraceutical quality of Brassicaceae, while also providing new insights into the effects of cruciferous vegetables on human intestinal health.
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Affiliation(s)
- P Seeburger
- Man-Technology-Environment Research Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden
| | - H Forsman
- Man-Technology-Environment Research Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden
| | - G Bevilacqua
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 703 62 Örebro, Sweden; School of Human Health Sciences, University of Florence, 501 34 Florence, Italy
| | - T M Marques
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 703 62 Örebro, Sweden
| | - L O Morales
- Life Science Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden
| | - S B R Prado
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 703 62 Örebro, Sweden
| | - Å Strid
- Life Science Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden
| | - T Hyötyläinen
- Man-Technology-Environment Research Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden
| | - V Castro-Alves
- Man-Technology-Environment Research Centre, School of Science and Technology, Örebro University, 702 81 Örebro, Sweden.
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13
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Favilli L, Griffith CM, Schymanski EL, Linster CL. High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics. Anal Bioanal Chem 2023:10.1007/s00216-023-04724-5. [PMID: 37212869 DOI: 10.1007/s00216-023-04724-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/24/2023] [Accepted: 04/28/2023] [Indexed: 05/23/2023]
Abstract
Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS2 database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.
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Affiliation(s)
- Lorenzo Favilli
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg.
| | - Corey M Griffith
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
| | - Carole L Linster
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Avenue du Swing 6, Belvaux, L-4367, Luxembourg
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14
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Thangaraj SV, Kachman M, Halloran KM, Sinclair KD, Lea R, Bellingham M, Evans NP, Padmanabhan V. Developmental programming: Preconceptional and gestational exposure of sheep to a real-life environmental chemical mixture alters maternal metabolome in a fetal sex-specific manner. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 864:161054. [PMID: 36565874 PMCID: PMC10322214 DOI: 10.1016/j.scitotenv.2022.161054] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/12/2022] [Accepted: 12/15/2022] [Indexed: 05/21/2023]
Abstract
BACKGROUND Everyday, humans are exposed to a mixture of environmental chemicals some of which have endocrine and/or metabolism disrupting actions which may contribute to non-communicable diseases. The adverse health impacts of real-world chemical exposure, characterized by chronic low doses of a mixture of chemicals, are only recently emerging. Biosolids derived from human waste represent the environmental chemical mixtures humans are exposed to in real life. Prior studies in sheep have shown aberrant reproductive and metabolic phenotypes in offspring after maternal biosolids exposure. OBJECTIVE To determine if exposure to biosolids perturbs the maternal metabolic milieu of pregnant ewes, in a fetal sex-specific manner. METHODS Ewes were grazed on inorganic fertilizer (Control) or biosolids-treated pastures (BTP) from before mating and throughout gestation. Plasma from pregnant ewes (Control n = 15, BTP n = 15) obtained mid-gestation were analyzed by untargeted metabolomics. Metabolites were identified using Agilent MassHunter. Multivariate analyses were done using MetaboAnalyst 5.0 and confirmed using SIMCA. RESULTS Univariate and multivariate analysis of 2301 annotated metabolites identified 193 differentially abundant metabolites (DM) between control and BTP sheep. The DM primarily belonged to the super-class of lipids and organic acids. 15-HeTrE, oleamide, methionine, CAR(3:0(OH)) and pyroglutamic acid were the top DM and have been implicated in the regulation of fetal growth and development. Fetal sex further exacerbated differences in metabolite profiles in the BTP group. The organic acids class of metabolites was abundant in animals with male fetuses. Prenol lipid, sphingolipid, glycerolipid, alkaloid, polyketide and benzenoid classes showed fetal sex-specific responses to biosolids. DISCUSSION Our study illustrates that exposure to biosolids significantly alters the maternal metabolome in a fetal sex-specific manner. The altered metabolite profile indicates perturbations to fatty acid, arginine, branched chain amino acid and one‑carbon metabolism. These factors are consistent with, and likely contribute to, the adverse phenotypic outcomes reported in the offspring.
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Affiliation(s)
- S V Thangaraj
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - M Kachman
- MM BRCF Metabolomics Core, University of Michigan, Ann Arbor, MI, USA
| | - K M Halloran
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - K D Sinclair
- University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - R Lea
- University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - M Bellingham
- School of Biodiversity One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK
| | - N P Evans
- School of Biodiversity One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, UK
| | - V Padmanabhan
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
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15
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Guan F, You Y, Fay S, Adreance MA, McGoldrick LK, Robinson MA. Factors affecting untargeted detection of doping agents in biological samples. Talanta 2023; 258:124446. [PMID: 36940570 DOI: 10.1016/j.talanta.2023.124446] [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: 12/02/2022] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/12/2023]
Abstract
Doping control is essential for sports, and untargeted detection of doping agents (UDDA) is the holy grail for anti-doping strategies. The present study examined major factors impacting UDDA with metabolomic data processing, including the use of blank samples, signal-to-noise ratio thresholds, and the minimum chromatographic peak intensity. Contrary to data processing in metabolomics studies, both blank sample use (either blank solvent or plasma) and marking of background compounds were found to be unnecessary for UDDA in biological samples, the first such report to the authors' knowledge. The minimum peak intensity required to detect chromatographic peaks affected the limit of detection (LOD) and data processing time for untargeted detection of 57 drugs spiked into equine plasma. The ratio of the mean (ROM) of the extracted ion chromatographic peak area of a compound in the sample group (SG) to that in the control group (CG) impacted its LOD, and a small ROM value such as 2 is recommended for UDDA. Mathematical modeling of the required signal-to-noise ratio (S/N) for UDDA provided insights into the effect of the number of samples in the SG, the number of positive samples, and the ROM on the required S/N, highlighting the power of mathematics in addressing issues in analytical chemistry. The UDDA method was validated by its successful identification of untargeted doping agents in real-world post-competition equine plasma samples. This advancement in UDDA methodology will be a useful addition to the arsenal of approaches used to combat doping in sports.
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Affiliation(s)
- Fuyu Guan
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA.
| | - Youwen You
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA
| | - Savannah Fay
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA
| | - Matthew A Adreance
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA
| | - Leif K McGoldrick
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA
| | - Mary A Robinson
- Department of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, New Bolton Center Campus, 382 West Street Road, Kennett Square, PA, 19348, USA; Pennsylvania Equine Toxicology and Research Laboratory, 220 East Rosedale Avenue, West Chester, PA, 19382, USA
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16
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Seeburger P, Herdenstam A, Kurtser P, Arunachalam A, Castro-Alves V, Hyötyläinen T, Andreasson H. Controlled mechanical stimuli reveal novel associations between basil metabolism and sensory quality. Food Chem 2023; 404:134545. [DOI: 10.1016/j.foodchem.2022.134545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/13/2022] [Accepted: 10/05/2022] [Indexed: 11/22/2022]
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17
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Metayer C, Imani P, Dudoit S, Morimoto L, Ma X, Wiemels JL, Petrick LM. One-Carbon (Folate) Metabolism Pathway at Birth and Risk of Childhood Acute Lymphoblastic Leukemia: A Biomarker Study in Newborns. Cancers (Basel) 2023; 15:1011. [PMID: 36831356 PMCID: PMC9953980 DOI: 10.3390/cancers15041011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 01/25/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Leukemia is the most common cancer in children in industrialized countries, and its initiation often occurs prenatally. Folic acid is a key vitamin in the production and modification of DNA, and prenatal folic acid intake is known to reduce the risk of childhood leukemia. We characterized the one-carbon (folate) metabolism nutrients that may influence risk of childhood acute lymphoblastic leukemia (ALL) among 122 cases diagnosed at age 0-14 years during 1988-2011 and 122 controls matched on sex, age, and race/ethnicity. Using hydrophilic interaction chromatography (HILIC) applied to neonatal dried blood spots, we evaluated 11 folate pathway metabolites, overall and by sex, race/ethnicity, and age at diagnosis. To conduct the prediction analyses, the 244 samples were separated into learning (75%) and test (25%) sets, maintaining the matched pairings. The learning set was used to train classification methods which were evaluated on the test set. High classification error rates indicate that the folate pathway metabolites measured have little predictive capacity for pediatric ALL. In conclusion, the one-carbon metabolism nutrients measured at birth were unable to predict subsequent leukemia in children. These negative findings are reflective of the last weeks of pregnancy and our study does not address the impact of these nutrients at the time of conception or during the first trimester of pregnancy that are critical for the embryo's DNA methylation programming.
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Affiliation(s)
- Catherine Metayer
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA 94704, USA
| | - Partow Imani
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94704, USA
| | - Sandrine Dudoit
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94704, USA
- Department of Statistics, University of California, Berkeley, CA 94720, USA
| | - Libby Morimoto
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA 94704, USA
| | - Xiaomei Ma
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT 06510, USA
| | - Joseph L. Wiemels
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Lauren M. Petrick
- Department of Environmental Medicine and Public Health, Icahn School of Medicine, Mount Sinai, New York, NY 10029, USA
- The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Ramat Gan 5211401, Israel
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18
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Zhang H, Xu Z, Fan X, Wang Y, Yang Q, Sun J, Wen M, Kang X, Zhang Z, Lu H. Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS. Anal Chem 2023; 95:612-620. [PMID: 36597722 DOI: 10.1021/acs.analchem.2c01398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC-MS datasets and a gas chromatography-mass spectrometry (GC-MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC-MS dataset analysis.
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Affiliation(s)
- Hailiang Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Zhenbo Xu
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Yue Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Qiong Yang
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Jinyu Sun
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Ming Wen
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Xiao Kang
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha410083, China.,National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha410083, China
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19
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Ahmad F, Nadeem H. Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery. Methods Mol Biol 2023; 2575:77-103. [PMID: 36301472 DOI: 10.1007/978-1-0716-2716-7_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.
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Affiliation(s)
- Faheem Ahmad
- Department of Botany, Aligarh Muslim University, Aligarh, Uttar Pradesh, India.
| | - Hera Nadeem
- Department of Botany, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
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20
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Iravani S, Conrad TOF. An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:151-161. [PMID: 35007196 DOI: 10.1109/tcbb.2022.3141656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability: The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMS.
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21
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Bhosle A, Wang Y, Franzosa EA, Huttenhower C. Progress and opportunities in microbial community metabolomics. Curr Opin Microbiol 2022; 70:102195. [PMID: 36063685 DOI: 10.1016/j.mib.2022.102195] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 01/25/2023]
Abstract
The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.
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Affiliation(s)
- Amrisha Bhosle
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ya Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Eric A Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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22
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Jongedijk E, Fifeik M, Arrizabalaga-Larrañaga A, Polzer J, Blokland M, Sterk S. Use of high-resolution mass spectrometry for veterinary drug multi-residue analysis. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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23
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Mildau K, van der Hooft JJJ, Flasch M, Warth B, El Abiead Y, Koellensperger G, Zanghellini J, Büschl C. Homologue series detection and management in LC-MS data with homologueDiscoverer. Bioinformatics 2022; 38:5139-5140. [PMID: 36165687 PMCID: PMC9665864 DOI: 10.1093/bioinformatics/btac647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AVAILABILITY AND IMPLEMENTATION homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kevin Mildau
- To whom correspondence should be addressed. or or
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen 6708PB, the Netherlands,Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Mira Flasch
- Department of Food Chemistry and Toxicology, University of Vienna, Vienna A-1090, Austria
| | - Benedikt Warth
- Department of Food Chemistry and Toxicology, University of Vienna, Vienna A-1090, Austria
| | - Yasin El Abiead
- Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria
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24
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Glutamine Is Required for M1-like Polarization of Macrophages in Response to Mycobacterium tuberculosis Infection. mBio 2022; 13:e0127422. [PMID: 35762591 PMCID: PMC9426538 DOI: 10.1128/mbio.01274-22] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
In response to Mycobacterium tuberculosis infection, macrophages mount proinflammatory and antimicrobial responses similar to those observed in M1 macrophages activated by lipopolysaccharide (LPS) and interferon gamma (IFN-γ). A metabolic reprogramming to hypoxia-inducible-factor 1 (HIF-1)-mediated uptake of glucose and its metabolism by glycolysis is required for M1-like polarization, but little is known about other metabolic programs driving the M1-like polarization during infection. We report that glutamine serves as a carbon and nitrogen source for the metabolic reprogramming to M1-like macrophages. Widely targeted metabolite screening identified an association of glutamine and/or glutamate with highly affected metabolic pathways of M1-like macrophages. Moreover, stable isotope-assisted metabolomics of U13C glutamine and U13C glucose revealed that glutamine, rather than glucose, is catabolized in both the oxidative and reductive tricarboxylic acid (TCA) cycles of M1-like macrophages, thereby generating signaling molecules that include succinate, biosynthetic precursors such as aspartate, and itaconate. U15N glutamine-tracing metabolomics further revealed participation of glutamine nitrogen in synthesis of intermediates of purine and pyrimidine metabolism plus amino acids, including aspartate. These findings were corroborated by diminished M1 polarization from chemical inhibition of glutaminase (GLS), the key enzyme in the glutaminolysis pathway, and by genetic deletion of GLS in infected macrophages. Thus, the catabolism of glutamine is an integral component of metabolic reprogramming in activating macrophages and it coordinates with elevated cytosolic glycolysis to satisfy the cellular demand for bioenergetic and biosynthetic precursors of M1-like macrophages. Knowledge of these new immunometabolic features of M1-like macrophages should advance the development of host-directed therapies for tuberculosis.
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25
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Cohen CC, Dabelea D, Michelotti G, Tang L, Shankar K, Goran MI, Perng W. Metabolome Alterations Linking Sugar-Sweetened Beverage Intake with Dyslipidemia in Youth: The Exploring Perinatal Outcomes among CHildren (EPOCH) Study. Metabolites 2022; 12:metabo12060559. [PMID: 35736491 PMCID: PMC9228193 DOI: 10.3390/metabo12060559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/17/2022] Open
Abstract
The objective of this study was to assess intermediary metabolic alterations that link sugar-sweetened beverage (SSB) intake to cardiometabolic (CM) risk factors in youth. A total of 597 participants from the multi-ethnic, longitudinal Exploring Perinatal Outcomes among CHildren (EPOCH) Study were followed in childhood (median 10 yrs) and adolescence (median 16 yrs). We used a multi-step approach: first, mixed models were used to examine the associations of SSB intake in childhood with CM measures across childhood and adolescence, which revealed a positive association between SSB intake and fasting triglycerides (β (95% CI) for the highest vs. lowest SSB quartile: 8.1 (−0.9,17.0); p-trend = 0.057). Second, least absolute shrinkage and selection operator (LASSO) regression was used to select 180 metabolite features (out of 767 features assessed by untargeted metabolomics) that were associated with SSB intake in childhood. Finally, 13 of these SSB-associated metabolites (from step two) were also prospectively associated with triglycerides across follow-up (from step one) in the same direction as with SSB intake (Bonferroni-adj. p < 0.0003). All annotated compounds were lipids, particularly dicarboxylated fatty acids, mono- and diacylglycerols, and phospholipids. In this diverse cohort, we identified a panel of lipid metabolites that may serve as intermediary biomarkers, linking SSB intake to dyslipidemia risk in youth.
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Affiliation(s)
- Catherine C. Cohen
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.D.); (K.S.)
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA;
- Correspondence:
| | - Dana Dabelea
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.D.); (K.S.)
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA;
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Lu Tang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA;
| | - Kartik Shankar
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (D.D.); (K.S.)
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Michael I. Goran
- Department of Pediatrics, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA;
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA;
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
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26
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Climaco Pinto R, Karaman I, Lewis MR, Hällqvist J, Kaluarachchi M, Graça G, Chekmeneva E, Durainayagam B, Ghanbari M, Ikram MA, Zetterberg H, Griffin J, Elliott P, Tzoulaki I, Dehghan A, Herrington D, Ebbels T. Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets. Anal Chem 2022; 94:5493-5503. [PMID: 35360896 PMCID: PMC9008693 DOI: 10.1021/acs.analchem.1c03592] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
![]()
Integration
of multiple datasets can greatly enhance bioanalytical
studies, for example, by increasing power to discover and validate
biomarkers. In liquid chromatography–mass spectrometry (LC–MS)
metabolomics, it is especially hard to combine untargeted datasets
since the majority of metabolomic features are not annotated and thus
cannot be matched by chemical identity. Typically, the information
available for each feature is retention time (RT), mass-to-charge
ratio (m/z), and feature intensity
(FI). Pairs of features from the same metabolite in separate datasets
can exhibit small but significant differences, making matching very
challenging. Current methods to address this issue are too simple
or rely on assumptions that cannot be met in all cases. We present
a method to find feature correspondence between two similar LC–MS
metabolomics experiments or batches using only the features’
RT, m/z, and FI. We demonstrate
the method on both real and synthetic datasets, using six orthogonal
validation strategies to gauge the matching quality. In our main example,
4953 features were uniquely matched, of which 585 (96.8%) of 604 manually
annotated features were correct. In a second example, 2324 features
could be uniquely matched, with 79 (90.8%) out of 87 annotated features
correctly matched. Most of the missed annotated matches are between
features that behave very differently from modeled inter-dataset shifts
of RT, MZ, and FI. In a third example with simulated data with 4755
features per dataset, 99.6% of the matches were correct. Finally,
the results of matching three other dataset pairs using our method
are compared with a published alternative method, metabCombiner, showing
the advantages of our approach. The method can be applied using M2S
(Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
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Affiliation(s)
- Rui Climaco Pinto
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Matthew R Lewis
- MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Jenny Hällqvist
- Centre for Translational Omics, Great Ormond Street Hospital, University College London, London WC1N 1EH, U.K.,Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K
| | - Manuja Kaluarachchi
- UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Gonçalo Graça
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Elena Chekmeneva
- MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Brenan Durainayagam
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, 431 41 Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden.,Department of Neurodegenerative Disease, University College London, Queen Square, London WC1N 3BG, U.K.,UK Dementia Research Institute, University College London, London WC1N 3BG, U.K
| | - Julian Griffin
- UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 451 10 Ioannina, Greece
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27101, United States
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
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27
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Evaluation of polarity switching for untargeted lipidomics using liquid chromatography coupled to high resolution mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1195:123200. [DOI: 10.1016/j.jchromb.2022.123200] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 01/30/2023]
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28
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Artati A, Prehn C, Lutter D, Dyar KA. Untargeted and Targeted Circadian Metabolomics Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and Flow Injection-Electrospray Ionization-Tandem Mass Spectrometry (FIA-ESI-MS/MS). Methods Mol Biol 2022; 2482:311-327. [PMID: 35610436 DOI: 10.1007/978-1-0716-2249-0_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A diverse array of 24-h oscillating hormones and metabolites direct and reflect circadian clock function. Circadian metabolomics uses advanced high-throughput analytical chemistry techniques to comprehensively profile these small molecules (<1.5 kDa) across 24 h in cells, media, body fluids, breath, tissues, and subcellular compartments. The goals of circadian metabolomics experiments are often multifaceted. These include identifying and tracking rhythmic metabolic inputs and outputs of central and peripheral circadian clocks, quantifying endogenous free-running period, monitoring relative phase alignment between clocks, and mapping pathophysiological consequences of clock disruption or misalignment. Depending on the particular experimental question, samples are collected under free-running or entrained conditions. Here we describe both untargeted and targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) and flow injection-electrospray ionization-tandem mass spectrometry (FIA-ESI-MS/MS) based assays we have used for circadian metabolomics studies. We discuss tissue homogenization, chemical derivatization, measurement, and tips for data processing, normalization, scaling, how to handle outliers, and imputation of missing values.
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Affiliation(s)
- Anna Artati
- Metabolomics and Proteomics Core Facility, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core Facility, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Dominik Lutter
- Computational Discovery Research, Institute for Diabetes and Obesity (IDO), Helmholtz Diabetes Center, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Kenneth Allen Dyar
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Metabolic Physiology, Institute for Diabetes and Cancer (IDC), Helmholtz Diabetes Center, Helmholtz Zentrum München, Neuherberg, Germany.
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29
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Sun Y, Saito K, Saito Y. Lipidomic Analysis of Extracellular Vesicles Isolated from Human Plasma and Serum. Methods Mol Biol 2022; 2504:157-173. [PMID: 35467286 DOI: 10.1007/978-1-0716-2341-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Lipidomics is an omics approach to comprehensively study lipid profiles in biological samples, such as plasma, serum, urine, and tissue specimens. Moreover, lipidomic analyses are useful for identifying novel lipid biomarkers, especially for various metabolic and malignant diseases in humans. Extracellular vesicles (EVs) are lipid bilayer-encapsulated nanoparticles secreted from various cells into the extracellular space. In particular, circulating EVs in the blood stream have attracted considerable research interest as they are considered the fingerprint of the cells from which they are secreted and are a promising source for less-invasive biomarker screening. Here, we describe the entire workflow for the lipidomic analysis of circulating EVs, including the methods for their purification from human plasma and serum, liquid chromatography coupled with high-resolution mass spectrometry-based lipid measurement, and data analyses for profiling EV lipids. Using this methodological workflow, over 260 lipid molecules belonging to the glycerophospholipid and sphingolipid groups can be detected.
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Affiliation(s)
- Yuchen Sun
- Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan
| | - Kosuke Saito
- Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan
| | - Yoshiro Saito
- Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki City, Kanagawa, Japan.
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30
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Optimization of metabolomic data processing using NOREVA. Nat Protoc 2022; 17:129-151. [PMID: 34952956 DOI: 10.1038/s41596-021-00636-9] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022]
Abstract
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.
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31
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Jibrin MO, Liu Q, Guingab-Cagmat J, Jones JB, Garrett TJ, Zhang S. Metabolomics Insights into Chemical Convergence in Xanthomonas perforans and Metabolic Changes Following Treatment with the Small Molecule Carvacrol. Metabolites 2021; 11:879. [PMID: 34940636 PMCID: PMC8706651 DOI: 10.3390/metabo11120879] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 01/20/2023] Open
Abstract
Microbes are natural chemical factories and their metabolome comprise diverse arrays of chemicals. The genus Xanthomonas comprises some of the most important plant pathogens causing devastating yield losses globally and previous studies suggested that species in the genus are untapped chemical minefields. In this study, we applied an untargeted metabolomics approach to study the metabolome of a globally spread important xanthomonad, X. perforans. The pathogen is difficult to manage, but recent studies suggest that the small molecule carvacrol was efficient in disease control. Bacterial strains were treated with carvacrol, and samples were taken at time intervals (1 and 6 h). An untreated control was also included. There were five replicates for each sample and samples were prepared for metabolomics profiling using the standard procedure. Metabolomics profiling was carried out using a thermo Q-Exactive orbitrap mass spectrometer with Dionex ultra high-performance liquid chromatography (UHPLC) and an autosampler. Annotation of significant metabolites using the Metabolomics Standards Initiative level 2 identified an array of novel metabolites that were previously not reported in Xanthomonas perforans. These metabolites include methoxybrassinin and cyclobrassinone, which are known metabolites of brassicas; sarmentosin, a metabolite of the Passiflora-heliconiine butterfly system; and monatin, a naturally occurring sweetener found in Sclerochiton ilicifolius. To our knowledge, this is the first report of these metabolites in a microbial system. Other significant metabolites previously identified in non-Xanthomonas systems but reported in this study include maculosin; piperidine; β-carboline alkaloids, such as harman and derivatives; and several important medically relevant metabolites, such as valsartan, metharbital, pirbuterol, and ozagrel. This finding is consistent with convergent evolution found in reported biological systems. Analyses of the effect of carvacrol in time-series and associated pathways suggest that carvacrol has a global effect on the metabolome of X. perforans, showing marked changes in metabolites that are critical in energy biosynthesis and degradation pathways, amino acid pathways, nucleic acid pathways, as well as the newly identified metabolites whose pathways are unknown. This study provides the first insight into the X. perforans metabolome and additionally lays a metabolomics-guided foundation for characterization of novel metabolites and pathways in xanthomonad systems.
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Affiliation(s)
- Mustafa Ojonuba Jibrin
- Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL 33031, USA; (M.O.J.); (Q.L.)
- Department of Crop Protection, Ahmadu Bello University, Zaria 810103, Nigeria
| | - Qingchun Liu
- Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL 33031, USA; (M.O.J.); (Q.L.)
| | - Joy Guingab-Cagmat
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (J.G.-C.); (T.J.G.)
| | - Jeffrey B. Jones
- Plant Pathology Department, University of Florida, Gainesville, FL 32611, USA;
| | - Timothy J. Garrett
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL 32610, USA; (J.G.-C.); (T.J.G.)
| | - Shouan Zhang
- Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL 33031, USA; (M.O.J.); (Q.L.)
- Plant Pathology Department, University of Florida, Gainesville, FL 32611, USA;
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Yu M, Tu P, Dolios G, Dassanayake PS, Volk H, Newschaffer C, Fallin MD, Croen L, Lyall K, Schmidt R, Hertz-Piccioto I, Austin C, Arora M, Petrick LM. Tooth biomarkers to characterize the temporal dynamics of the fetal and early-life exposome. ENVIRONMENT INTERNATIONAL 2021; 157:106849. [PMID: 34482270 PMCID: PMC8800489 DOI: 10.1016/j.envint.2021.106849] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/03/2021] [Accepted: 08/22/2021] [Indexed: 05/31/2023]
Abstract
BACKGROUND Teeth have unique histology that make this biomatrix a time-capsule for retrospective exposure analysis of fetal and early life. However, most analytic methods require pulverizing the whole tooth, which eliminates exposure timing information. Further, the range of chemicals and endogenous exposures that can be measured in teeth has yet to be fully characterized. METHODS We performed untargeted metabolomics on micro-dissected layers from naturally shed deciduous teeth. Using four liquid-chromatography high-resolution mass spectrometry analytical modes, we profiled small molecules (<1000 Da) from prenatal and postnatal tooth fractions. In addition, we employed linear regression on the tooth fraction pairs from 31 children to identify metabolites that discriminate between prenatal and postnatal exposures. RESULTS Of over 10,000 features measured in teeth dentin, 390 unique compounds were annotated from 62 chemical classes. The class with the largest number of compounds was carboxylic acids and their derivatives (36%). Of the annotated exogenous metabolites (phthalates, parabens, perfluoroalkyl compounds, and cotinine) and endogenous metabolites (fatty acids, steroids, carnitines, amino acids, and others), 91 are linked to 256 health conditions through published literature. Differential analysis revealed 267 metabolites significantly different between the prenatal and the postnatal tooth fractions (adj. p-value < 0.05, Bonferroni correction), and 21 metabolites exclusive to the prenatal fraction. CONCLUSIONS The prenatal and early postnatal exposome revealed from dental biomarkers represents a broad range of endogenous and exogenous metabolites for a comprehensive characterization in environmental health research. Most importantly, this technology provides a direct window into fetal exposures that is not possible by maternal biomarkers. Indeed, we identified several metabolites exclusively in the prenatal fraction, suggesting unique fetal exposures that are markedly different to postnatal exposures. Expansion of databases that include tooth matrix metabolites will strengthen biological interpretation and shed light on exposures during gestation and early life that may be causally linked with later health conditions.
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Affiliation(s)
- Miao Yu
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Peijun Tu
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georgia Dolios
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Priyanthi S Dassanayake
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Heather Volk
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Craig Newschaffer
- College of Health and Human Development, The Pennsylvania State University, University Park, PA 16802, USA
| | - M Daniele Fallin
- Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Lisa Croen
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94611, USA
| | - Kristen Lyall
- A.J. Drexel Autism Institute, Drexel University, Philadelphia, PA 19104, USA
| | - Rebecca Schmidt
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Irva Hertz-Piccioto
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Christine Austin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Lauren M Petrick
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines. Metabolites 2021; 11:metabo11110713. [PMID: 34822371 PMCID: PMC8624948 DOI: 10.3390/metabo11110713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/13/2021] [Accepted: 10/16/2021] [Indexed: 11/30/2022] Open
Abstract
Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.
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Lassen J, Nielsen KL, Johannsen M, Villesen P. Assessment of XCMS Optimization Methods with Machine-Learning Performance. Anal Chem 2021; 93:13459-13466. [PMID: 34585906 DOI: 10.1021/acs.analchem.1c02000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.
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Affiliation(s)
- Johan Lassen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark
| | - Kirstine Lykke Nielsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Mogens Johannsen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus, Denmark
| | - Palle Villesen
- Bioinformatics Research Center, Aarhus University, CF Moellers Alle 8, DK-8000 Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, DK-8200 Aarhus, Denmark
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35
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da Silva KM, Iturrospe E, Bars C, Knapen D, Van Cruchten S, Covaci A, van Nuijs ALN. Mass Spectrometry-Based Zebrafish Toxicometabolomics: A Review of Analytical and Data Quality Challenges. Metabolites 2021; 11:metabo11090635. [PMID: 34564451 PMCID: PMC8467701 DOI: 10.3390/metabo11090635] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/17/2022] Open
Abstract
Metabolomics has achieved great progress over the last 20 years, and it is currently considered a mature research field. As a result, the number of applications in toxicology, biomarker, and drug discovery has also increased. Toxicometabolomics has emerged as a powerful strategy to provide complementary information to study molecular-level toxic effects, which can be combined with a wide range of toxicological assessments and models. The zebrafish model has gained importance in recent decades as a bridging tool between in vitro assays and mammalian in vivo studies in the field of toxicology. Furthermore, as this vertebrate model is a low-cost system and features highly conserved metabolic pathways found in humans and mammalian models, it is a promising tool for toxicometabolomics. This short review aims to introduce zebrafish researchers interested in understanding the effects of chemical exposure using metabolomics to the challenges and possibilities of the field, with a special focus on toxicometabolomics-based mass spectrometry. The overall goal is to provide insights into analytical strategies to generate and identify high-quality metabolomic experiments focusing on quality management systems (QMS) and the importance of data reporting and sharing.
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Affiliation(s)
- Katyeny Manuela da Silva
- Toxicological Center, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (E.I.); (A.C.)
- Correspondence: (K.M.d.S.); (A.L.N.v.N.)
| | - Elias Iturrospe
- Toxicological Center, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (E.I.); (A.C.)
- Department of In Vitro Toxicology and Dermato-Cosmetology, Faculty of Medicine and Pharmacy, Campus Jette, Free University of Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium
| | - Chloe Bars
- Comparative Perinatal Development, Department of Veterinary Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (C.B.); (S.V.C.)
| | - Dries Knapen
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium;
| | - Steven Van Cruchten
- Comparative Perinatal Development, Department of Veterinary Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (C.B.); (S.V.C.)
| | - Adrian Covaci
- Toxicological Center, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (E.I.); (A.C.)
| | - Alexander L. N. van Nuijs
- Toxicological Center, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium; (E.I.); (A.C.)
- Correspondence: (K.M.d.S.); (A.L.N.v.N.)
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Viallon V, His M, Rinaldi S, Breeur M, Gicquiau A, Hemon B, Overvad K, Tjønneland A, Rostgaard-Hansen AL, Rothwell JA, Lecuyer L, Severi G, Kaaks R, Johnson T, Schulze MB, Palli D, Agnoli C, Panico S, Tumino R, Ricceri F, Verschuren WMM, Engelfriet P, Onland-Moret C, Vermeulen R, Nøst TH, Urbarova I, Zamora-Ros R, Rodriguez-Barranco M, Amiano P, Huerta JM, Ardanaz E, Melander O, Ottoson F, Vidman L, Rentoft M, Schmidt JA, Travis RC, Weiderpass E, Johansson M, Dossus L, Jenab M, Gunter MJ, Lorenzo Bermejo J, Scherer D, Salek RM, Keski-Rahkonen P, Ferrari P. A New Pipeline for the Normalization and Pooling of Metabolomics Data. Metabolites 2021; 11:631. [PMID: 34564446 PMCID: PMC8467830 DOI: 10.3390/metabo11090631] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 01/10/2023] Open
Abstract
Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
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Affiliation(s)
- Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Audrey Gicquiau
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Bertrand Hemon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Kim Overvad
- Department of Public Health, Aarhus University Bartholins Alle 2, DK-8000 Aarhus, Denmark;
| | - Anne Tjønneland
- Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark; (A.T.); (A.L.R.-H.)
| | | | - Joseph A. Rothwell
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Lucie Lecuyer
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Gianluca Severi
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
- Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany;
- Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
| | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy;
| | - Claudia Agnoli
- Epidemiology and Prevention Unit Department of Research, Fondazione IRCCS—Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131 Naples, Italy;
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), 97100 Ragusa, Italy;
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, Italy;
- Unit of Epidemiology, Regional Health Service ASL TO3, 10095 Grugliasco, Italy
| | - W. M. Monique Verschuren
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Peter Engelfriet
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
| | - Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Roel Vermeulen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Ilona Urbarova
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L’Hospitalet de Llobregat, Spain;
| | - Miguel Rodriguez-Barranco
- Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain;
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
| | - Pilar Amiano
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastián, Spain
- Biodonostia Health Research Institute, Group of Epidemiology of Chronic and Communicable Diseases, 20014 San Sebastián, Spain
| | - José Maria Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, 30007 Murcia, Spain
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Navarra Public Health Institute, 31003 Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31008 Pamplona, Spain
| | - Olle Melander
- Department of Clincal Sciences, Lund University, SE-21 428 Malmö, Sweden;
- Department of Emergency and Internal Medicine, Skåne University Hospital, SE-20 502 Malmö, Sweden
| | - Filip Ottoson
- Department of Immunotechnology, Lund University, SE-22 100 Lund, Sweden;
| | - Linda Vidman
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Matilda Rentoft
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France;
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France;
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mazda Jenab
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Justo Lorenzo Bermejo
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Dominique Scherer
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Reza M. Salek
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
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Wang J, Zheng W, Zhang S, Yan K, Jin M, Hu H, Ma Z, Gong F, Lu G, Ren Y, Lin L, Lin G, Hu L, Liu S. An increase of phosphatidylcholines in follicular fluid implies attenuation of embryo quality on day 3 post-fertilization. BMC Biol 2021; 19:200. [PMID: 34503495 PMCID: PMC8428131 DOI: 10.1186/s12915-021-01118-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 08/03/2021] [Indexed: 01/02/2023] Open
Abstract
Background Although oocyte quality is the dominant factor determining embryo quality, few studies have been conducted to evaluate embryo quality based on the metabolites related to the oocyte. With quantification of the follicular fluid (FF) metabolites, in assisted reproductive technology (ART), this study sought to evaluate the embryo or oocyte quality through an informative approach. Results An evaluation model consisting of 17 features was generated to distinguish the embryo quality on day 3 post-fertilization, and phosphatidylcholines (PCs) were the key contributors to the evaluation. The model was extended to the patients under different ages and hyperstimulations, and the features were further enriched to facilitate the evaluation of the embryo quality. The metabolites were clustered through pathway analysis, leading to a hypothesis that accumulation of arachidonic acid induced by PCs might weaken embryo quality on day 3 post-fertilization. Conclusions A discriminating model with metabolic features elicited from follicular fluid was established, which enabled the evaluation of the embryo or oocyte quality even under certain clinical conditions, and the increase of PCs in follicular fluid implies the attenuation of embryo quality on day 3 post-fertilization. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01118-w.
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Affiliation(s)
- Ju Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.,BGI-Shenzhen, Shenzhen, 518083, China
| | - Wei Zheng
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China
| | - Shuoping Zhang
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China
| | - Keqiang Yan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.,BGI-Shenzhen, Shenzhen, 518083, China
| | - Miao Jin
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China
| | - Huiling Hu
- Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Key Laboratory of National Health and Family Planning Commission, Central South University, Changsha, 410008, Hunan, China
| | - Zhen Ma
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.,BGI-Shenzhen, Shenzhen, 518083, China
| | - Fei Gong
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China.,Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Key Laboratory of National Health and Family Planning Commission, Central South University, Changsha, 410008, Hunan, China
| | - Guangxiu Lu
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China.,Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Key Laboratory of National Health and Family Planning Commission, Central South University, Changsha, 410008, Hunan, China
| | - Yan Ren
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.,BGI-Shenzhen, Shenzhen, 518083, China
| | - Liang Lin
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Ge Lin
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China.,Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Key Laboratory of National Health and Family Planning Commission, Central South University, Changsha, 410008, Hunan, China
| | - Liang Hu
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410008, China. .,Institute of Reproductive and Stem Cell Engineering, School of Basic Medical Science, Key Laboratory of National Health and Family Planning Commission, Central South University, Changsha, 410008, Hunan, China.
| | - Siqi Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China. .,BGI-Shenzhen, Shenzhen, 518083, China.
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38
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Ghosh T, Philtron D, Zhang W, Kechris K, Ghosh D. Reproducibility of mass spectrometry based metabolomics data. BMC Bioinformatics 2021; 22:423. [PMID: 34493210 PMCID: PMC8424977 DOI: 10.1186/s12859-021-04336-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 08/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Assessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets. RESULTS We apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well. CONCLUSIONS This research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .
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Affiliation(s)
- Tusharkanti Ghosh
- Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Daisy Philtron
- Eberly College of Science, Penn State University, State College, USA
| | | | - Katerina Kechris
- Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Debashis Ghosh
- Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, Aurora, USA
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39
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Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021; 23:6361045. [PMID: 34471925 PMCID: PMC8769701 DOI: 10.1093/bib/bbab343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.
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Affiliation(s)
| | | | | | - Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
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40
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Birer-Williams CMC, Chu RK, Anderton CR, Wright ES. SubTap, a Versatile 3D Printed Platform for Eavesdropping on Extracellular Interactions. mSystems 2021; 6:e0090221. [PMID: 34427520 PMCID: PMC8422993 DOI: 10.1128/msystems.00902-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/09/2021] [Indexed: 11/20/2022] Open
Abstract
Communication within the microbiome occurs through an immense diversity of small molecules. Capturing these microbial interactions is a significant challenge due to the complexity of the exometabolome and its sensitivity to environmental stimuli. Traditional methods for acquiring exometabolomic data from interacting microorganisms are limited by their low throughput or lack of sampling depth. To address this challenge, we introduce subtapping (short for substrate tapping), a technique for tapping into extracellular metabolites that are being transferred through the growth substrate during coculture. High-throughput subtapping is made possible by a new coculturing platform, named SubTap, that we engineered to resemble a 96-well plate. The three-dimensional (3D) printed SubTap platform captures the exometabolome in an agar compartment that connects physically separated growth chambers, which permits cell growth without competition for space. We show how SubTap facilitates replicable and quick detection of exometabolites via direct infusion mass spectrometry analysis. Using bacterial isolates from the soil, we apply SubTap to characterize the effects of growth medium, growth duration, and mixed versus unmixed coculturing on the exometabolome. Finally, we demonstrate SubTap's versatility by interrogating microbial interactions in multicultures with up to four strains. IMPORTANCE Improvements in experimental techniques and instrumentation have led to the discovery that the microbiome plays an essential role in human and environmental health. Nevertheless, there remain major impediments to conducting large-scale interrogations of the microbiome in a high-throughput manner, particularly in the field of exometabolomics. Existing methods to coculture microorganisms and interrogate their interactions are labor-intensive and low throughput. This inspired us to develop a solution for coculturing that was (i) open source, (ii) inexpensive, (iii) scalable, (iv) customizable, and (v) compatible with existing mass spectrometry instrumentation. Here, we present SubTap-a 3D printed coculturing platform that permits tapping directly into the growth substrate between physically separated, but interconnected, growth compartments. SubTap allows multiculture (with up to four distinct growth compartments) in spatially mixed or unmixed configurations and enables repeatable results with mass spectrometry, as shown by our validation with known compounds and cultures of one to four organisms.
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Affiliation(s)
- Caroline M. C. Birer-Williams
- Biomolécules et Biotechnologies Végétales (BBV) EA 2106, Université de Tours, Tours, France
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Rosalie K. Chu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Christopher R. Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Erik S. Wright
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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41
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Amante E, Alladio E, Rizzo R, Di Corcia D, Negri P, Visintin L, Guglielmotto M, Tamagno E, Vincenti M, Salomone A. Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples. Molecules 2021; 26:4990. [PMID: 34443578 PMCID: PMC8398448 DOI: 10.3390/molecules26164990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/29/2022] Open
Abstract
The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive "fingerprint" of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or "traditional" drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl.
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Affiliation(s)
- Eleonora Amante
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
| | - Eugenio Alladio
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
- Centro Regionale Antidoping e di Tossicologia, 10043 Orbassano, Italy;
| | - Rebecca Rizzo
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
| | - Daniele Di Corcia
- Centro Regionale Antidoping e di Tossicologia, 10043 Orbassano, Italy;
| | | | - Lia Visintin
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
- Centre of Excellence in Mycotoxicology and Public Health, Faculty of Pharmaceutical Sciences, Ghent University, B-9000 Ghent, Belgium
| | - Michela Guglielmotto
- Dipartimento di Neuroscienze Rita Levi Montalcini, Università di Torino, 10126 Torino, Italy; (M.G.); (E.T.)
- Neuroscience Institute Cavalieri-Ottolenghi (NICO), 10043 Orbassano, Italy
| | - Elena Tamagno
- Dipartimento di Neuroscienze Rita Levi Montalcini, Università di Torino, 10126 Torino, Italy; (M.G.); (E.T.)
- Neuroscience Institute Cavalieri-Ottolenghi (NICO), 10043 Orbassano, Italy
| | - Marco Vincenti
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
- Centro Regionale Antidoping e di Tossicologia, 10043 Orbassano, Italy;
| | - Alberto Salomone
- Dipartimento di Chimica, Università di Torino, 10125 Torino, Italy; (E.A.); (E.A.); (R.R.); (L.V.); (A.S.)
- Centro Regionale Antidoping e di Tossicologia, 10043 Orbassano, Italy;
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42
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Alseekh S, Aharoni A, Brotman Y, Contrepois K, D'Auria J, Ewald J, C Ewald J, Fraser PD, Giavalisco P, Hall RD, Heinemann M, Link H, Luo J, Neumann S, Nielsen J, Perez de Souza L, Saito K, Sauer U, Schroeder FC, Schuster S, Siuzdak G, Skirycz A, Sumner LW, Snyder MP, Tang H, Tohge T, Wang Y, Wen W, Wu S, Xu G, Zamboni N, Fernie AR. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods 2021; 18:747-756. [PMID: 34239102 PMCID: PMC8592384 DOI: 10.1038/s41592-021-01197-1] [Citation(s) in RCA: 405] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 05/27/2021] [Indexed: 02/06/2023]
Abstract
Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.
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Affiliation(s)
- Saleh Alseekh
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
- Institute of Plants Systems Biology and Biotechnology, Plovdiv, Bulgaria.
| | - Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yariv Brotman
- Department of Life Sciences, Ben Gurion University of the Negev, Beersheva, Israel
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - John D'Auria
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Jan Ewald
- Department of Bioinformatics, University of Jena, Jena, Germany
| | - Jennifer C Ewald
- Interfaculty Institute of Cell Biology, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Paul D Fraser
- Biological Sciences, Royal Holloway University of London, Egham, UK
| | | | - Robert D Hall
- BU Bioscience, Wageningen Research, Wageningen, the Netherlands
- Laboratory of Plant Physiology, Wageningen University, Wageningen, the Netherlands
| | - Matthias Heinemann
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Jie Luo
- College of Tropical Crops, Hainan University, Haikou, China
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute for Plant Biochemistry, Halle, Germany
| | - Jens Nielsen
- BioInnovation Institute, Copenhagen, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Kazuki Saito
- Plant Molecular Science Center, Chiba University, Chiba, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Uwe Sauer
- Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Frank C Schroeder
- Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Stefan Schuster
- Department of Bioinformatics, University of Jena, Jena, Germany
| | - Gary Siuzdak
- Center for Metabolomics and Mass Spectrometry, Scripps Research Institute, La Jolla, CA, USA
| | - Aleksandra Skirycz
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Lloyd W Sumner
- Department of Biochemistry and MU Metabolomics Center, University of Missouri, Columbia, MO, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, Shanghai, China
| | - Takayuki Tohge
- Department of Biological Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yulan Wang
- Singapore Phenome Center, Lee Kong Chian School of Medicine, School of Biological Sciences, Nanyang Technological University, Nanyang, Singapore
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Nicola Zamboni
- Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
- Institute of Plants Systems Biology and Biotechnology, Plovdiv, Bulgaria.
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Gadara D, Coufalikova K, Bosak J, Smajs D, Spacil Z. Systematic Feature Filtering in Exploratory Metabolomics: Application toward Biomarker Discovery. Anal Chem 2021; 93:9103-9110. [PMID: 34156818 DOI: 10.1021/acs.analchem.1c00816] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Exploratory mass spectrometry-based metabolomics generates a plethora of features in a single analysis. However, >85% of detected features are typically false positives due to inefficient elimination of chimeric signals and chemical noise not relevant for biological and clinical data interpretation. The data processing is considered a bottleneck to unravel the translational potential in metabolomics. Here, we describe a systematic workflow to refine exploratory metabolomics data and reduce reported false positives. We applied the feature filtering workflow in a case/control study exploring common variable immunodeficiency (CVID). In the first stage, features were detected from raw liquid chromatography-mass spectrometry data by XCMS Online processing, blank subtraction, and reproducibility assessment. Detected features were annotated in metabolomics databases to produce a list of tentative identifications. We scrutinized tentative identifications' physicochemical properties, comparing predicted and experimental reversed-phase liquid chromatography (LC) retention time. A prediction model used a linear regression of 42 retention indices with the cLogP ranging from -6 to 11. The LC retention time probes the physicochemical properties and effectively reduces the number of tentatively identified metabolites, which are further submitted to statistical analysis. We applied the retention time-based analytical feature filtering workflow to datasets from the Metabolomics Workbench (www.metabolomicsworkbench.org), demonstrating the broad applicability. A subset of tentatively identified metabolites significantly different in CVID patients was validated by MS/MS acquisition to confirm potential CVID biomarkers' structures and virtually eliminate false positives. Our exploratory metabolomics data processing workflow effectively removes false positives caused by the chemical background and chimeric signals inherent to the analytical technique. It reduced the number of tentatively identified metabolites by 88%, from initially detected 6940 features in XCMS to 839 tentative identifications and streamlined consequent statistical analysis and data interpretation.
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Affiliation(s)
- Darshak Gadara
- RECETOX Centre, Faculty of Science, Masaryk University, Brno 62500, Czech Republic
| | - Katerina Coufalikova
- RECETOX Centre, Faculty of Science, Masaryk University, Brno 62500, Czech Republic
| | - Juraj Bosak
- Department of Biology, Faculty of Medicine, Masaryk University, Brno 62500, Czech Republic
| | - David Smajs
- Department of Biology, Faculty of Medicine, Masaryk University, Brno 62500, Czech Republic
| | - Zdenek Spacil
- RECETOX Centre, Faculty of Science, Masaryk University, Brno 62500, Czech Republic
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Barupal DK, Baygi SF, Wright RO, Arora M. Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics. Front Public Health 2021; 9:653599. [PMID: 34178917 PMCID: PMC8222544 DOI: 10.3389/fpubh.2021.653599] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 01/27/2023] Open
Abstract
Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.
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Abstract
Phytoplankton transform inorganic carbon into thousands of biomolecules that represent an important pool of fixed carbon, nitrogen, and sulfur in the surface ocean. Metabolite production differs between phytoplankton, and the flux of these molecules through the microbial food web depends on compound-specific bioavailability to members of a wider microbial community. Yet relatively little is known about the diversity or concentration of metabolites within marine plankton. Here, we compare 313 polar metabolites in 21 cultured phytoplankton species and in natural planktonic communities across environmental gradients to show that bulk community metabolomes reflect the chemical composition of the phytoplankton community. We also show that groups of compounds have similar patterns across space and taxonomy, suggesting that the concentrations of these compounds in the environment are controlled by similar sources and sinks. We quantify several compounds in the surface ocean that represent substantial understudied pools of labile carbon. For example, the N-containing metabolite homarine was up to 3% of particulate carbon and is produced in high concentrations by cultured Synechococcus, and S-containing gonyol accumulated up to 2.5 nM in surface particles and likely originates from dinoflagellates or haptophytes. Our results show that phytoplankton composition directly shapes the carbon composition of the surface ocean. Our findings suggest that in order to access these pools of bioavailable carbon, the wider microbial community must be adapted to phytoplankton community composition. IMPORTANCE Microscopic phytoplankton transform 100 million tons of inorganic carbon into thousands of different organic compounds each day. The structure of each chemical is critical to its biological and ecosystem function, yet the diversity of biomolecules produced by marine microbial communities remained mainly unexplored, especially small polar molecules which are often considered the currency of the microbial loop. Here, we explore the abundance and diversity of small biomolecules in planktonic communities across ecological gradients in the North Pacific and within 21 cultured phytoplankton species. Our work demonstrates that phytoplankton diversity is an important determinant of the chemical composition of the highly bioavailable pool of organic carbon in the ocean, and we highlight understudied yet abundant compounds in both the environment and cultured organisms. These findings add to understanding of how the chemical makeup of phytoplankton shapes marine microbial communities where the ability to sense and use biomolecules depends on the chemical structure.
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Petrick L, Imani P, Perttula K, Yano Y, Whitehead T, Metayer C, Schiffman C, Dolios G, Dudoit S, Rappaport S. Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia. Leuk Res 2021; 106:106585. [PMID: 33971561 PMCID: PMC8275155 DOI: 10.1016/j.leukres.2021.106585] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 02/07/2023]
Abstract
The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS). Because sex disparities are suggested by differences in AML incidence rates, metabolite features associated with AML were identified in analyses stratified by sex. There was no overlap between the 16 predictors of AML in females and 15 predictors in males, suggesting that neonatal metabolomic profiles of pediatric AML risk are sex-specific. In females, four predictors of AML were putatively annotated as ceramides, a class of metabolites that has been linked with cancer cell proliferation. In females, two metabolite predictors of AML were strongly correlated with breastfeeding duration, indicating a possible biological link between this putative protective risk factor and childhood leukemia. In males, a heterogeneous metabolite profile of AML predictors was observed. Replication with larger participant numbers is required to validate findings.
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Affiliation(s)
- Lauren Petrick
- The Institute of Exposomics Research, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, 94720, USA.
| | - Partow Imani
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Kelsi Perttula
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, 94720, USA; Department of Health Sciences, California State University East Bay, Hayward, CA, 94542, USA
| | - Yukiko Yano
- Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, 94720, USA; Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Todd Whitehead
- Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, 94720, USA; Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Catherine Metayer
- Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, 94720, USA; Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Courtney Schiffman
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Georgia Dolios
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sandrine Dudoit
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA; Department of Statistics, University of California, Berkeley, CA, 94720, USA
| | - Stephen Rappaport
- Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, 94720, USA; Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, 94720, USA
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Fu J, Zhang Y, Liu J, Lian X, Tang J, Zhu F. Pharmacometabonomics: data processing and statistical analysis. Brief Bioinform 2021; 22:6236068. [PMID: 33866355 DOI: 10.1093/bib/bbab138] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/09/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jin Liu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Xichen Lian
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jing Tang
- Department of Bioinformatics in Chongqing Medical University, China
| | - Feng Zhu
- College of Pharmaceutical Sciences in Zhejiang University, China
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48
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Clark TN, Houriet J, Vidar WS, Kellogg JJ, Todd DA, Cech NB, Linington RG. Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability. JOURNAL OF NATURAL PRODUCTS 2021; 84:824-835. [PMID: 33666420 PMCID: PMC8326878 DOI: 10.1021/acs.jnatprod.0c01376] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Despite the value of mass spectrometry in modern natural products discovery workflows, it remains very difficult to compare data sets between laboratories. In this study we compared mass spectrometry data for the same sample set from two different laboratories (quadrupole time-of-flight and quadrupole-Orbitrap) and evaluated the similarity between these two data sets in terms of both mass spectrometry features and their ability to describe the chemical composition of the sample set. Somewhat surprisingly, the two data sets, collected with appropriate controls and replication, had very low feature overlap (25.7% of Laboratory A features overlapping 21.8% of Laboratory B features). Our data clearly demonstrate that differences in fragmentation, charge state, and adduct formation in the ionization source are a major underlying cause for these differences. Consistent with other recent literature, these findings challenge the conventional wisdom that electrospray ionization mass spectrometry (ESI-MS) yields a simple one-to-one correspondence between analytes in solution and features in the data set. Importantly, despite low overlap in feature lists, principal component analysis (PCA) generated qualitatively similar PCA plots. Overall, our findings demonstrate that comparing untargeted metabolomics data between laboratories is challenging, but that data sets with low feature overlap can yield the same qualitative description of a sample set using PCA.
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Affiliation(s)
- Trevor N. Clark
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Joëlle Houriet
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, North Carolina 27402, United States
| | - Warren S. Vidar
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, North Carolina 27402, United States
| | - Joshua J. Kellogg
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, North Carolina 27402, United States
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
| | - Daniel A. Todd
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, North Carolina 27402, United States
| | - Nadja B. Cech
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, North Carolina 27402, United States
| | - Roger G. Linington
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
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49
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Aftabi Y, Soleymani J, Jouyban A. Efficacy of Analytical Technologies in Metabolomics Studies of the Gastrointestinal Cancers. Crit Rev Anal Chem 2021; 52:1593-1605. [PMID: 33757389 DOI: 10.1080/10408347.2021.1901646] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
According to the reports of the World Health Organization and the International Agency for Research on Cancer, cancer is the second leading cause of human death worldwide. However, early-stage detection of cancers can efficiently enhance the chance of therapy and saving lives. Metabolomics strategies apply a variety of approaches to discover new potential diagnoses, prognoses, and/or therapeutic biomarkers of various diseases. Metabolomics aims to identify and measure different low-molecular-weight biomolecules in physiological environments. In these studies, special metabolites are extracted from biological samples and identified using analytical techniques. Afterward, using data processing programs discovering significantly associated biomarkers is pursued. In the present review, we aimed to discuss recently reported analytical approaches on the metabolomics studies of gastrointestinal cancers including gastric, colorectal, and esophageal cancers. The gas- and liquid-chromatography with different detectors have been shown that are the main analytical techniques and for metabolites quantification, nuclear magnetic resonance has been utilized as a master method.
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Affiliation(s)
- Younes Aftabi
- Tuberculosis and Lung Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Soleymani
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Abolghasem Jouyban
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
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Bettenhausen HM, Barr L, Omerigic H, Yao L, Heuberger AL. Mass Spectrometry Metabolomics of Hot Steep Malt Extracts and Association to Sensory Traits. JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS 2021. [DOI: 10.1080/03610470.2020.1869499] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Harmonie M. Bettenhausen
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, U.S.A.
| | | | - Heather Omerigic
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, U.S.A.
| | - Linxing Yao
- Analytical Resources Core-Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, U.S.A.
| | - Adam L. Heuberger
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, U.S.A.
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, U.S.A
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