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Bhosle A, Bae S, Zhang Y, Chun E, Avila-Pacheco J, Geistlinger L, Pishchany G, Glickman JN, Michaud M, Waldron L, Clish CB, Xavier RJ, Vlamakis H, Franzosa EA, Garrett WS, Huttenhower C. Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease. Mol Syst Biol 2024; 20:338-361. [PMID: 38467837 PMCID: PMC10987656 DOI: 10.1038/s44320-024-00027-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/13/2024] Open
Abstract
Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.
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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
| | - Sena Bae
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Yancong Zhang
- 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
| | - Eunyoung Chun
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | | | - Ludwig Geistlinger
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
| | - Gleb Pishchany
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan N Glickman
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Monia Michaud
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ramnik J Xavier
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hera Vlamakis
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, 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
| | - Wendy S Garrett
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, 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.
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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2
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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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3
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Jia W, Di C, Zhang R, Shi L. Application of liquid chromatography mass spectrometry-based lipidomics to dairy products research: An emerging modulator of gut microbiota and human metabolic disease risk. Food Res Int 2022; 157:111206. [DOI: 10.1016/j.foodres.2022.111206] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 12/19/2022]
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4
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HRPIF data mining based on data-dependent/independent acquisition for Rhei Radix et Rhizoma metabolite screening in rats. J Chromatogr B Analyt Technol Biomed Life Sci 2022; 1190:123095. [PMID: 35032891 DOI: 10.1016/j.jchromb.2021.123095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 11/19/2021] [Accepted: 12/30/2021] [Indexed: 11/20/2022]
Abstract
In traditional Chinese medicine (TCM), components with identical nuclei often share structural similarity, indicating the possibility of similar second-level mass spectrometry (MS/MS) fragments. High-resolution product-ion filter (HRPIF) technique can be utilized to identify metabolites, with similar fragments, in vivo. In principle, this technique applies to TCM; however, its application has been restricted due to the limitations of traditional MS/MS data acquisition. Therefore, a novel analysis strategy, based on data-dependent acquisition (DDA) and data-independent acquisition (DIA) datasets, has been developed for the determination of template product ions and efficient non-targeted identification of TCM-related components in vivo by HRPIF and background subtraction (BS). This DDA-DIA combination strategy, taking Rhei Radix et Rhizoma as a test case, identified 71 anthraquinone prototype components in vitro (36 of which were discovered for the first time), and 45 related components in vivo, confirming glucuronidation and sulfation as the main reactions. The developed strategy could rapidly identify TCM-related components in vivo with high sensitivity, indicating the immense importance of this novel HRPIF data mining technology in TCM analysis.
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5
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Ye D, Li X, Shen J, Xia X. Microbial metabolomics: From novel technologies to diversified applications. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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6
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Zhou L, Yu D, Zheng S, Ouyang R, Wang Y, Xu G. Gut microbiota-related metabolome analysis based on chromatography-mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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7
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A post processing strategy to score and rank the annotation confidence of saponins in natural products by integrating MS 2 spectral similarity and fragment interpretation. J Pharm Biomed Anal 2021; 204:114291. [PMID: 34365115 DOI: 10.1016/j.jpba.2021.114291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 11/21/2022]
Abstract
Tandem mass spectrometry-spectra-based annotation in natural products challenges a lot because of ambiguous structural characterization. It still lacks an efficiency method to score and rank the annotation confidence. Herein, we develop a novel approach to rank the annotation confidences of saponins. Annotations were accomplished according to fragmentation patterns. The corresponding diagnostic fragments and their abundances were recorded. Average abundances were taken as a reference spectrum, and the cosine similarity score (CSS) was calculated to measure how well the spectral matched. According to CSS values, statistic description for confidence levels can be effectively provided. Next, the fragment interpretation score (FIS) was proposed to investigate the deviators' characteristic fragmentation. FIS offset the effect from the deviators' unique fragments. Suspicious annotations involving low CSS and high FIS, may derived from the MS2 spectral background interferences or co-elution. Annotations with low CSS and FIS rank as low confidences, as these annotations need more attention. Using this method, novel saccharide sequences, specific fragmentation preferences, undistinguished precursors, even new structures can also be well traced. By proposed new scoring system, confidence evaluations can be ranked, resulting in significantly enhanced annotation reliability.
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8
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Peters K, Balcke G, Kleinenkuhnen N, Treutler H, Neumann S. Untargeted In Silico Compound Classification-A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes. Int J Mol Sci 2021; 22:ijms22063251. [PMID: 33806786 PMCID: PMC8005083 DOI: 10.3390/ijms22063251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 12/29/2022] Open
Abstract
In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.
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Affiliation(s)
- Kristian Peters
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, 06108 Halle (Saale), Germany
- Correspondence: ; Tel.: +49-345-5582-1475
| | - Gerd Balcke
- Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany;
| | - Niklas Kleinenkuhnen
- Max Planck Research Group Chromatin and Ageing, Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Str. 9b, 50931 Cologne, Germany;
- MS-Platform, Cluster of Excellence on Plant Sciences, Botanical Institute (CEPLAS), University of Cologne, 50931 Cologne, Germany
| | - Hendrik Treutler
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- Datameer GmbH, Magdeburger Straße 23, 06112 Halle (Saale), Germany
| | - Steffen Neumann
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
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9
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Abstract
Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.
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Affiliation(s)
- Masahiro Sugimoto
- Research and Development Centre for Minimally Invasive Therapies, Medical Research Institute, Tokyo Medical University , Tokyo, Japan.,Institute for Advanced Biosciences, Keio University , Yamagata, Japan
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10
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Fraisier-Vannier O, Chervin J, Cabanac G, Puech V, Fournier S, Durand V, Amiel A, André O, Benamar OA, Dumas B, Tsugawa H, Marti G. MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics. Anal Chem 2020; 92:9971-9981. [PMID: 32589017 DOI: 10.1021/acs.analchem.0c01594] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked according to the database chosen by the user, which enhance identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches. A group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at https://github.com/eMetaboHUB/MS-CleanR).
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Affiliation(s)
- Ophélie Fraisier-Vannier
- Pharma Dev, Université de Toulouse, IRD, UPS, 31400 Toulouse, France.,Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
| | - Justine Chervin
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Guillaume Cabanac
- Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
| | - Virginie Puech
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Sylvie Fournier
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Virginie Durand
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Aurélien Amiel
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Olivier André
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Omar Abdelaziz Benamar
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,De Sangosse, Bonnel, 47480 Pont-Du-Casse, France
| | - Bernard Dumas
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama 230-0045, Japan.,RIKEN Center for Integrative Medical Science, Yokohama 230-0045, Japan
| | - Guillaume Marti
- Pharma Dev, Université de Toulouse, IRD, UPS, 31400 Toulouse, France.,Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Metatoul-AgromiX Platform, MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, LRSV, Université de Toulouse, CNRS, UPS, 31400 Toulouse, France.,Institut de Recherche en Informatique de Toulouse, Université de Toulouse, UPS, Toulouse 31400, France
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11
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Senan O, Aguilar-Mogas A, Navarro M, Capellades J, Noon L, Burks D, Yanes O, Guimerà R, Sales-Pardo M. CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics 2020; 35:4089-4097. [PMID: 30903689 PMCID: PMC6792096 DOI: 10.1093/bioinformatics/btz207] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 01/30/2019] [Accepted: 03/21/2019] [Indexed: 11/26/2022] Open
Abstract
Motivation The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. Availability and implementation https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oriol Senan
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Antoni Aguilar-Mogas
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Miriam Navarro
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Jordi Capellades
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Luke Noon
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Deborah Burks
- CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain.,Centro de Investigación Príncipe Felipe, Valencia, Spain
| | - Oscar Yanes
- Department of Electronic Engineering, Metabolomics Platform, IISPV, Universitat Rovira i Virgili, Tarragona, Spain.,CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Madrid, Spain
| | - Roger Guimerà
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain.,ICREA, Barcelona, Spain
| | - Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
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12
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools. Metabolites 2019; 9:metabo9070144. [PMID: 31315242 PMCID: PMC6680503 DOI: 10.3390/metabo9070144] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 07/10/2019] [Accepted: 07/11/2019] [Indexed: 12/17/2022] Open
Abstract
Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.
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Petrick LM, Schiffman C, Edmands WMB, Yano Y, Perttula K, Whitehead T, Metayer C, Wheelock CE, Arora M, Grigoryan H, Carlsson H, Dudoit S, Rappaport SM. Metabolomics of neonatal blood spots reveal distinct phenotypes of pediatric acute lymphoblastic leukemia and potential effects of early-life nutrition. Cancer Lett 2019; 452:71-78. [PMID: 30904619 PMCID: PMC6499387 DOI: 10.1016/j.canlet.2019.03.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 03/01/2019] [Accepted: 03/10/2019] [Indexed: 02/02/2023]
Abstract
Early-life exposures are believed to influence the incidence of pediatric acute lymphoblastic leukemia (ALL). Archived neonatal blood spots (NBS), collected within the first days of life, offer a means to investigate small molecules that reflect early-life exposures. Using untargeted metabolomics, we compared abundances of small-molecule features in extracts of NBS punches from 332 children that later developed ALL and 324 healthy controls. Subjects were stratified by early (1-5 y) and late (6-14 y) diagnosis. Mutually-exclusive sets of metabolic features - representing putative lipids and fatty acids - were associated with ALL, including 9 and 19 metabolites in the early- and late-diagnosis groups, respectively. In the late-diagnosis group, a prominent cluster of features with apparent 18:2 fatty-acid chains suggested that newborn exposure to the essential nutrient, linoleic acid, increased ALL risk. Interestingly, abundances of these putative 18:2 lipids were greater in infants who were fed formula rather than breast milk (colostrum) and increased with the mother's pre-pregnancy body mass index. These results suggest possible etiologic roles of newborn nutrition in late-diagnosis ALL.
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Affiliation(s)
- Lauren M Petrick
- The Senator Frank R. Lautenberg Environmental Health Science Laboratory, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA
| | - Courtney Schiffman
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA
| | - William M B Edmands
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
| | - Yukiko Yano
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA
| | - Kelsi Perttula
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
| | - Todd Whitehead
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA
| | - Catherine Metayer
- Division of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Manish Arora
- The Senator Frank R. Lautenberg Environmental Health Science Laboratory, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hasmik Grigoryan
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
| | - Henrik Carlsson
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA
| | - Sandrine Dudoit
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA; Department of Statistics, University of California, Berkeley, CA, USA
| | - Stephen M Rappaport
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA; Center for Integrative Research on Childhood Leukemia and the Environment, University of California, Berkeley, CA, USA.
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15
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Perttula K, Schiffman C, Edmands WMB, Petrick L, Grigoryan H, Cai X, Gunter MJ, Naccarati A, Polidoro S, Dudoit S, Vineis P, Rappaport SM. Untargeted lipidomic features associated with colorectal cancer in a prospective cohort. BMC Cancer 2018; 18:996. [PMID: 30340609 PMCID: PMC6194742 DOI: 10.1186/s12885-018-4894-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 10/03/2018] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. METHODS Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. RESULTS Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). CONCLUSIONS Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.
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Affiliation(s)
- Kelsi Perttula
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
| | - Courtney Schiffman
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
| | - William M B Edmands
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
| | - Lauren Petrick
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, 10029 USA
| | - Hasmik Grigoryan
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
| | - Xiaoming Cai
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
| | - Marc J Gunter
- International Agency for Research on Cancer, Lyon, France
| | | | - Silvia Polidoro
- Italian Institute for Genomic Medicine (IIGM), Torino, Italy
| | - Sandrine Dudoit
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA 94720 USA
- Department of Statistics, University of California, Berkeley, CA 94720 USA
| | - Paolo Vineis
- Italian Institute for Genomic Medicine (IIGM), Torino, Italy
- MRC-PHE Centre for Environment and Health, Imperial College, Place London W2 1PG, Norfolk, UK
| | - Stephen M Rappaport
- School of Public Health, University of California, Berkeley, CA 94720-7356 USA
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Sobus JR, Wambaugh JF, Isaacs KK, Williams AJ, McEachran AD, Richard AM, Grulke CM, Ulrich EM, Rager JE, Strynar MJ, Newton SR. Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2018; 28:411-426. [PMID: 29288256 PMCID: PMC6661898 DOI: 10.1038/s41370-017-0012-y] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 08/04/2017] [Accepted: 08/25/2017] [Indexed: 05/18/2023]
Abstract
Tens-of-thousands of chemicals are registered in the U.S. for use in countless processes and products. Recent evidence suggests that many of these chemicals are measureable in environmental and/or biological systems, indicating the potential for widespread exposures. Traditional public health research tools, including in vivo studies and targeted analytical chemistry methods, have been unable to meet the needs of screening programs designed to evaluate chemical safety. As such, new tools have been developed to enable rapid assessment of potentially harmful chemical exposures and their attendant biological responses. One group of tools, known as "non-targeted analysis" (NTA) methods, allows the rapid characterization of thousands of never-before-studied compounds in a wide variety of environmental, residential, and biological media. This article discusses current applications of NTA methods, challenges to their effective use in chemical screening studies, and ways in which shared resources (e.g., chemical standards, databases, model predictions, and media measurements) can advance their use in risk-based chemical prioritization. A brief review is provided of resources and projects within EPA's Office of Research and Development (ORD) that provide benefit to, and receive benefits from, NTA research endeavors. A summary of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) is also given, which makes direct use of ORD resources to benefit the global NTA research community. Finally, a research framework is described that shows how NTA methods will bridge chemical prioritization efforts within ORD. This framework exists as a guide for institutions seeking to understand the complexity of chemical exposures, and the impact of these exposures on living systems.
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Affiliation(s)
- Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Andrew D McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Christopher M Grulke
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Julia E Rager
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
- ToxStrategies, Inc., 9390 Research Blvd., Suite 100, Austin, TX, 78759, USA
| | - Mark J Strynar
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Seth R Newton
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
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Redefining environmental exposure for disease etiology. NPJ Syst Biol Appl 2018; 4:30. [PMID: 30181901 PMCID: PMC6119193 DOI: 10.1038/s41540-018-0065-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 05/14/2018] [Accepted: 05/23/2018] [Indexed: 12/16/2022] Open
Abstract
Etiological studies of human exposures to environmental factors typically rely on low-throughput methods that target only a few hundred chemicals or mixtures. In this Perspectives article, I outline how environmental exposure can be defined by the blood exposome—the totality of chemicals circulating in blood. The blood exposome consists of chemicals derived from both endogenous and exogenous sources. Endogenous chemicals are represented by the human proteome and metabolome, which establish homeostatic networks of functional molecules. Exogenous chemicals arise from diet, vitamins, drugs, pathogens, microbiota, pollution, and lifestyle factors, and can be measured in blood as subsets of the proteome, metabolome, metals, macromolecular adducts, and foreign DNA and RNA. To conduct ‘exposome-wide association studies’, blood samples should be obtained prospectively from subjects—preferably at critical stages of life—and then analyzed in incident disease cases and matched controls to find discriminating exposures. Results from recent metabolomic investigations of archived blood illustrate our ability to discover potentially causal exposures with current technologies.
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18
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Wang A, Gerona RR, Schwartz JM, Lin T, Sirota M, Morello-Frosch R, Woodruff TJ. A Suspect Screening Method for Characterizing Multiple Chemical Exposures among a Demographically Diverse Population of Pregnant Women in San Francisco. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:077009. [PMID: 30044231 PMCID: PMC6108847 DOI: 10.1289/ehp2920] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 06/11/2018] [Accepted: 06/15/2018] [Indexed: 05/18/2023]
Abstract
BACKGROUND In utero exposure to environmental chemicals can adversely impact pregnancy outcomes and childhood health, but minimal biomonitoring data exist on the majority of chemicals used in commerce. OBJECTIVES We aimed to profile exposure to multiple environmental organic acids (EOAs) and identify novel chemicals that have not been previously biomonitored in a diverse population of pregnant women. METHODS We used liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) to perform a suspect screen for 696 EOAs, (e.g., phenols and phthalate metabolites) on the maternal serum collected at delivery from 75 pregnant women delivering at two large San Francisco Hospitals. We examined demographic differences in peak areas and detection frequency (DF) of suspect EOAs using a Kruskal-Wallis Rank Sum test or Fisher's exact test. We confirmed selected suspects by comparison with their respective reference standards. RESULTS We detected, on average, 56 [standard deviation (SD)]: 8) suspect EOAs in each sample (range: 32-73). Twelve suspect EOAs with DF≥60 were matched to 21 candidate compounds in our EOA database, two-thirds of which are novel chemicals. We found demographic differences in DF for 13 suspect EOAs and confirmed the presence of 6 priority novel chemicals: 2,4-Di-tert-butylphenol, Pyrocatechol, 2,4-Dinitrophenol, 3,5-Di-tert-butylsalicylic acid, 4-Hydroxycoumarin, and 2'-Hydroxyacetophenone (or 3'-Hydroxyacetophenone). The first two are high-production-volume chemicals in the United States. CONCLUSION Suspect screening in human biomonitoring provides a viable method to characterize a broad spectrum of environmental chemicals to prioritize for targeted method development and quantification. https://doi.org/10.1289/EHP2920.
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Affiliation(s)
- Aolin Wang
- Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, California, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California, USA
| | - Roy R Gerona
- Clinical Toxicology and Environmental Biomonitoring Lab, University of California, San Francisco, California, USA
| | - Jackie M Schwartz
- Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, California, USA
| | - Thomas Lin
- Clinical Toxicology and Environmental Biomonitoring Lab, University of California, San Francisco, California, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, California, USA
- Department of Pediatrics, University of California, San Francisco, California, USA
| | - Rachel Morello-Frosch
- School of Public Health and Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA
| | - Tracey J Woodruff
- Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, California, USA
- Philip R Lee Institute for Health Policy Studies, University of California, San Francisco, California, USA
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19
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Misra BB. New tools and resources in metabolomics: 2016-2017. Electrophoresis 2018; 39:909-923. [PMID: 29292835 DOI: 10.1002/elps.201700441] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 12/17/2017] [Accepted: 12/18/2017] [Indexed: 01/07/2023]
Abstract
Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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20
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Fenaille F, Barbier Saint-Hilaire P, Rousseau K, Junot C. Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand? J Chromatogr A 2017; 1526:1-12. [PMID: 29074071 DOI: 10.1016/j.chroma.2017.10.043] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/15/2017] [Accepted: 10/16/2017] [Indexed: 01/08/2023]
Abstract
Typical mass spectrometry (MS) based untargeted metabolomics protocols are tedious as well as time- and sample-consuming. In particular, they often rely on "full-scan-only" analyses using liquid chromatography (LC) coupled to high resolution mass spectrometry (HRMS) from which metabolites of interest are first highlighted, and then tentatively identified by using targeted MS/MS experiments. However, this situation is evolving with the emergence of integrated HRMS based-data acquisition protocols able to perform multi-event acquisitions. Most of these protocols, referring to as data dependent and data independent acquisition (DDA and DIA, respectively), have been initially developed for proteomic applications and have recently demonstrated their applicability to biomedical studies. In this context, the aim of this article is to take stock of the progress made in the field of DDA- and DIA-based protocols, and evaluate their ability to change conventional metabolomic and lipidomic data acquisition workflows, through a review of HRMS instrumentation, DDA and DIA workflows, and also associated informatics tools.
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Affiliation(s)
- François Fenaille
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Pierre Barbier Saint-Hilaire
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Kathleen Rousseau
- Service de Pharmacologie et Immuno-Analyse (SPI), Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France
| | - Christophe Junot
- Service de Pharmacologie et Immuno-Analyse (SPI), CEA, INRA, Université Paris Saclay, MetaboHUB, F-91191 Gif-sur-Yvette, France.
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21
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Rapid identification of herbal compounds derived metabolites using zebrafish larvae as the biotransformation system. J Chromatogr A 2017; 1515:100-108. [DOI: 10.1016/j.chroma.2017.07.076] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Revised: 07/17/2017] [Accepted: 07/24/2017] [Indexed: 11/24/2022]
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22
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Naz S, Gallart-Ayala H, Reinke SN, Mathon C, Blankley R, Chaleckis R, Wheelock CE. Development of a Liquid Chromatography-High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition. Anal Chem 2017. [PMID: 28641411 DOI: 10.1021/acs.analchem.7b00925] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.
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Affiliation(s)
- Shama Naz
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Hector Gallart-Ayala
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Stacey N Reinke
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | - Caroline Mathon
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden
| | | | - Romanas Chaleckis
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm SE 17177, Sweden.,Gunma University Initiative for Advanced Research (GIAR), Gunma University , Gunma, Japan
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