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Titkare N, Chaturvedi S, Borah S, Sharma N. Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery. Biomed Chromatogr 2024:e6019. [PMID: 39370857 DOI: 10.1002/bmc.6019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2024] [Revised: 08/25/2024] [Accepted: 09/03/2024] [Indexed: 10/08/2024]
Abstract
Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.
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Affiliation(s)
- Nikhil Titkare
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Sachin Chaturvedi
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Sapan Borah
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
| | - Nitish Sharma
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India
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2
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Heuckeroth S, Damiani T, Smirnov A, Mokshyna O, Brungs C, Korf A, Smith JD, Stincone P, Dreolin N, Nothias LF, Hyötyläinen T, Orešič M, Karst U, Dorrestein PC, Petras D, Du X, van der Hooft JJJ, Schmid R, Pluskal T. Reproducible mass spectrometry data processing and compound annotation in MZmine 3. Nat Protoc 2024; 19:2597-2641. [PMID: 38769143 DOI: 10.1038/s41596-024-00996-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 02/26/2024] [Indexed: 05/22/2024]
Abstract
Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.
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Affiliation(s)
| | - Tito Damiani
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | | | - Olena Mokshyna
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Corinna Brungs
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Ansgar Korf
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
| | - Joshua David Smith
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
- First Faculty of Medicine, Charles University, Prague, Czech Republic
| | | | | | - Louis-Félix Nothias
- University of Geneva, Geneva, Switzerland
- Université Côte d'Azur, CNRS, ICN, Nice, France
| | | | - Matej Orešič
- Örebro University, Örebro, Sweden
- University of Turku and Åbo Akademi University, Turku, Finland
| | - Uwe Karst
- University of Münster, Münster, Germany
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Daniel Petras
- University of Tuebingen, Tuebingen, Germany
- University of California Riverside, Riverside, CA, USA
| | - Xiuxia Du
- University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Justin J J van der Hooft
- Wageningen University & Research, Wageningen, the Netherlands
- University of Johannesburg, Johannesburg, South Africa
| | - Robin Schmid
- University of Münster, Münster, Germany.
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
| | - Tomáš Pluskal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
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Belete GT, Zhou L, Li KK, So PK, Do CW, Lam TC. Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia. Front Mol Biosci 2024; 11:1403844. [PMID: 39193222 PMCID: PMC11347317 DOI: 10.3389/fmolb.2024.1403844] [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: 03/20/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024] Open
Abstract
Introduction Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment. Methods The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites. Results Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism. Conclusion This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.
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Affiliation(s)
- Gizachew Tilahun Belete
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lei Zhou
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Centre for Eye and Vision Research (CEVR), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - King-Kit Li
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Pui-Kin So
- University Research Facility in Life Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Chi-Wai Do
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Centre for Eye and Vision Research (CEVR), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for Chinese Medicine Innovation (RCMI), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Thomas Chuen Lam
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Centre for Eye and Vision Research (CEVR), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Centre for Chinese Medicine Innovation (RCMI), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Afiaz A, Ivanov AA, Chamberlin J, Hanauer D, Savonen CL, Goldman MJ, Morgan M, Reich M, Getka A, Holmes A, Pati S, Knight D, Boutros PC, Bakas S, Caporaso JG, Del Fiol G, Hochheiser H, Haas B, Schloss PD, Eddy JA, Albrecht J, Fedorov A, Waldron L, Hoffman AM, Bradshaw RL, Leek JT, Wright C. Best practices to evaluate the impact of biomedical research software-metric collection beyond citations. Bioinformatics 2024; 40:btae469. [PMID: 39067017 PMCID: PMC11297485 DOI: 10.1093/bioinformatics/btae469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/28/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024] Open
Abstract
MOTIVATION Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. RESULTS To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. AVAILABILITY AND IMPLEMENTATION More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.
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Affiliation(s)
- Awan Afiaz
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, United States
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Andrey A Ivanov
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, Atlanta , GA, 30322, United States
| | - John Chamberlin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, United States
| | - David Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, 48109, United States
| | - Candace L Savonen
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Mary J Goldman
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060, United States
| | - Martin Morgan
- Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, United States
| | - Michael Reich
- University of California, San Diego, La Jolla, CA, 92093, United States
| | - Alexander Getka
- University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Aaron Holmes
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, 90095, United States
- Institute for Precision Health, University of California, Los Angeles, CA, 90095, United States
- Department of Human Genetics, University of California, Los Angeles, CA, 90095, United States
- Department of Urology, University of California, Los Angeles, CA, 90095, United States
| | - Sarthak Pati
- University of Pennsylvania, Philadelphia, PA, 19104, United States
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Center for Federated Learning, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - Dan Knight
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, 90095, United States
- Institute for Precision Health, University of California, Los Angeles, CA, 90095, United States
- Department of Human Genetics, University of California, Los Angeles, CA, 90095, United States
- Department of Urology, University of California, Los Angeles, CA, 90095, United States
| | - Paul C Boutros
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, 90095, United States
- Institute for Precision Health, University of California, Los Angeles, CA, 90095, United States
- Department of Human Genetics, University of California, Los Angeles, CA, 90095, United States
- Department of Urology, University of California, Los Angeles, CA, 90095, United States
| | - Spyridon Bakas
- University of Pennsylvania, Philadelphia, PA, 19104, United States
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
- Center for Federated Learning, Indiana University School of Medicine, Indianapolis, IN, 46202, United States
| | - J Gregory Caporaso
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, United States
| | - Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, 15206, United States
| | - Brian Haas
- Methods Development Laboratory, Broad Institute, Cambridge, MA, 02141, United States
| | - Patrick D Schloss
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, 48109, United States
| | - James A Eddy
- Sage Bionetworks, Seattle, WA, 98121, United States
| | | | - Andrey Fedorov
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02138, United States
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, City University of New York Graduate School of Public Health and Health Policy, New York, NY, 10027, United States
| | - Ava M Hoffman
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, United States
| | - Jeffrey T Leek
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
| | - Carrie Wright
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, United States
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Sharma V, Khokhar M, Panigrahi P, Gadwal A, Setia P, Purohit P. Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy. Clin Chim Acta 2024; 561:119842. [PMID: 38969086 DOI: 10.1016/j.cca.2024.119842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies. METHODS We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN. RESULTS Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care. CONCLUSION Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.
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Affiliation(s)
- V Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - M Khokhar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Panigrahi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - A Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Setia
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Purohit
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India.
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6
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Cheng H, Miller D, Southwell N, Fischer JL, Taylor I, Salbaum JM, Kappen C, Hu F, Yang C, Gross SS, D'Aurelio M, Chen Q. Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575105. [PMID: 38370710 PMCID: PMC10871215 DOI: 10.1101/2024.01.10.575105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
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Yuan C, Jin Y, Zhang H, Chen S, Yi J, Xie Q, Dong J, Wu C. Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software. Anal Chem 2024; 96:7634-7642. [PMID: 38691624 DOI: 10.1021/acs.analchem.4c00527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.
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Affiliation(s)
- Caixia Yuan
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361005, China
| | - Ying Jin
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Hairong Zhang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361005, China
| | - Simian Chen
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361005, China
| | - Jiajin Yi
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361005, China
| | - Qiang Xie
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Caisheng Wu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen 361005, China
- Xiamen Key Laboratory for Clinical Efficacy and Evidence-Based Research of Traditional Chinese Medicine, Xiamen University, Xiamen 361005, China
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8
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Pang Z, Xu L, Viau C, Lu Y, Salavati R, Basu N, Xia J. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 2024; 15:3675. [PMID: 38693118 PMCID: PMC11063062 DOI: 10.1038/s41467-024-48009-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
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Affiliation(s)
- Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Xu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Charles Viau
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Reza Salavati
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
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Zulfiqar M, Crusoe MR, König-Ries B, Steinbeck C, Peters K, Gadelha L. Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study. Metabolites 2024; 14:118. [PMID: 38393009 PMCID: PMC10891576 DOI: 10.3390/metabo14020118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Michael R. Crusoe
- ELIXIR (The European Life-Sciences Infrastructure for Biological Information) Germany, Institute of Bio- and Geosciences (IBG-5)—Computational Metagenomics, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany;
| | - Birgitta König-Ries
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
| | - Kristian Peters
- iDiv—German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, 04103 Leipzig, Germany;
- Geobotany and Botanical Gardens, Martin-Luther University of Halle-Wittenberg, 06108 Halle, Germany
- Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Luiz Gadelha
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany;
- Institute for Informatics, Friedrich Schiller University Jena, 07743 Jena, Germany
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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10
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Goracci L, Tiberi P, Di Bona S, Bonciarelli S, Passeri GI, Piroddi M, Moretti S, Volpi C, Zamora I, Cruciani G. MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics. Anal Chem 2024; 96:1468-1477. [PMID: 38236168 PMCID: PMC10831794 DOI: 10.1021/acs.analchem.3c03620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/19/2024]
Abstract
Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.
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Affiliation(s)
- Laura Goracci
- Department
of Chemistry, Biology and Biotechnology, Universita degli Studi di Perugia, via Elce di Sotto 8, Perugia 06123, Italy
| | - Paolo Tiberi
- Molecular
Discovery Ltd., Centennial
Park, Borehamwood, Hertfordshire WD6 4PJ, U.K.
| | - Stefano Di Bona
- Molecular
Horizon, Via Montelino,
30, Bettona (PG) 06084, Italy
| | - Stefano Bonciarelli
- Molecular
Discovery Ltd., Centennial
Park, Borehamwood, Hertfordshire WD6 4PJ, U.K.
| | | | - Marta Piroddi
- Molecular
Horizon, Via Montelino,
30, Bettona (PG) 06084, Italy
| | - Simone Moretti
- Molecular
Horizon, Via Montelino,
30, Bettona (PG) 06084, Italy
| | - Claudia Volpi
- Department
of Medicine and Surgery, P.le Gambuli 1, Perugia 06129, Italy
| | - Ismael Zamora
- Mass
Analytica, Rambla de
celler 113, Sant Cugat del Vallés 08173, Spain
| | - Gabriele Cruciani
- Department
of Chemistry, Biology and Biotechnology, Universita degli Studi di Perugia, via Elce di Sotto 8, Perugia 06123, Italy
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11
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Spreitzer I, Keife J, Strasser T, Kalaba P, Lubec J, Neuhaus W, Lubec G, Langer T, Wackerlig J, Loryan I. Pharmacokinetics of Novel Dopamine Transporter Inhibitor CE-123 and Modafinil with a Focus on Central Nervous System Distribution. Int J Mol Sci 2023; 24:16956. [PMID: 38069277 PMCID: PMC10707468 DOI: 10.3390/ijms242316956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
S-CE-123, a novel dopamine transporter inhibitor, has emerged as a potential candidate for cognitive enhancement. The objective of this study was to compare the tissue distribution profiles, with a specific focus on central nervous system distribution and metabolism, of S-CE-123 and R-modafinil. To address this objective, a precise liquid chromatography-high resolution mass spectrometry method was developed and partially validated. Neuropharmacokinetic parameters were assessed using the Combinatory Mapping Approach. Our findings reveal distinct differences between the two compounds. Notably, S-CE-123 demonstrates a significantly superior extent of transport across the blood-brain barrier (BBB), with an unbound brain-to-plasma concentration ratio (Kp,uu,brain) of 0.5, compared to R-modafinil's Kp,uu,brain of 0.1. A similar pattern was observed for the transport across the blood-spinal cord barrier. Concerning the drug transport across cellular membranes, we observed that S-CE-123 primarily localizes in the brain interstitial space, whereas R-modafinil distributes more evenly across both sides of the plasma membrane of the brain's parenchymal cells (Kp,uu,cell). Furthermore, our study highlights the substantial differences in hepatic metabolic stability, with S-CE-123 having a 9.3-fold faster metabolism compared to R-modafinil. In summary, the combination of improved BBB transport and higher affinity of S-CE-123 to dopamine transporters in comparison to R-modafinil makes S-CE-123 a promising candidate for further testing for the treatment of cognitive decline.
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Affiliation(s)
- Iva Spreitzer
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria; (I.S.); (T.L.)
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, 1090 Vienna, Austria
| | - Josefin Keife
- Translational Pharmacokinetics/Pharmacodynamics Group, Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden
| | - Tobias Strasser
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria; (I.S.); (T.L.)
| | - Predrag Kalaba
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria; (I.S.); (T.L.)
| | - Jana Lubec
- Programme for Proteomics, Paracelsus Medical University, 5020 Salzburg, Austria (G.L.)
| | - Winfried Neuhaus
- Competence Unit Molecular Diagnostics, Center Health and Bioresources, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria;
- Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems, Austria
| | - Gert Lubec
- Programme for Proteomics, Paracelsus Medical University, 5020 Salzburg, Austria (G.L.)
| | - Thierry Langer
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria; (I.S.); (T.L.)
| | - Judith Wackerlig
- Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria; (I.S.); (T.L.)
| | - Irena Loryan
- Translational Pharmacokinetics/Pharmacodynamics Group, Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden
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12
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McAtamney A, Heaney C, Lizama-Chamu I, Sanchez LM. Reducing Mass Confusion over the Microbiome. Anal Chem 2023; 95:16775-16785. [PMID: 37934885 PMCID: PMC10841885 DOI: 10.1021/acs.analchem.3c02408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.
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Affiliation(s)
- Allyson McAtamney
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, United States
| | - Casey Heaney
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, United States
| | - Itzel Lizama-Chamu
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, United States
| | - Laura M Sanchez
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064, United States
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13
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Hu C, Wang J, Qi F, Liu Y, Zhao F, Wang J, Sun B. Untargeted metabolite profiling of serum in rats exposed to pyrraline. Food Sci Biotechnol 2023; 32:1541-1549. [PMID: 37637845 PMCID: PMC10449741 DOI: 10.1007/s10068-023-01256-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Pyrraline, one of advanced glycation end-products, is formed in advanced Maillard reactions. It was reported that the presence of pyrraline was tested to be associated with nephropathy and diabetes. Pyrraline might result in potential health risks because many modern diets are heat processed. In the study, an integrated metabolomics by ultra-high-performance liquid chromatography with mass spectrometry was used to evaluate the effects of pyrraline on metabolism in rats. Thirty-two metabolites were identified as differential metabolites. Linolenic acid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, tyrosine metabolism and glycerophospholipid metabolism were the main perturbed networks in this pathological process. Differential metabolites and metabolic pathways we found give new insights into studying the toxic molecular mechanisms of pyrraline. Supplementary Information The online version contains supplementary material available at 10.1007/s10068-023-01256-7.
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Affiliation(s)
- Chuanqin Hu
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Jiahui Wang
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Fangyuan Qi
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Yingli Liu
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Fen Zhao
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Jing Wang
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
| | - Baoguo Sun
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Engineering and Technology Research Center of Food Additives, Beijing Laboratory for Food Quality and Safety, Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University (BTBU), 11 Fucheng Road, Beijing, 100048 China
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14
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Jin Y, Chi J, LoMonaco K, Boon A, Gu H. Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome. Trends Analyt Chem 2023; 166:117155. [PMID: 37484879 PMCID: PMC10361410 DOI: 10.1016/j.trac.2023.117155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.
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Affiliation(s)
- Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jinhua Chi
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Kaelene LoMonaco
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Haiwei Gu
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
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15
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Chang L, Zhou G, Xia J. mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations. Metabolites 2023; 13:826. [PMID: 37512533 PMCID: PMC10384390 DOI: 10.3390/metabo13070826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.
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Affiliation(s)
- Le Chang
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, QC H9X 3V9, Canada
| | - Jianguo Xia
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Institute of Parasitology, McGill University, Montreal, QC H9X 3V9, Canada
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16
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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17
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Li S, Zheng S. Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data. Anal Chem 2023; 95:6212-6217. [PMID: 37018697 PMCID: PMC10117393 DOI: 10.1021/acs.analchem.2c05810] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/21/2023] [Indexed: 04/07/2023]
Abstract
In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.
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Affiliation(s)
- Shuzhao Li
- Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, United States
| | - Shujian Zheng
- Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, United States
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18
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Parker EJ, Billane KC, Austen N, Cotton A, George RM, Hopkins D, Lake JA, Pitman JK, Prout JN, Walker HJ, Williams A, Cameron DD. Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools. Metabolites 2023; 13:metabo13040463. [PMID: 37110122 PMCID: PMC10142740 DOI: 10.3390/metabo13040463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.
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Affiliation(s)
| | - Kathryn C Billane
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Nichola Austen
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Anne Cotton
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Rachel M George
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - David Hopkins
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Janice A Lake
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - James K Pitman
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - James N Prout
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Heather J Walker
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Williams
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Duncan D Cameron
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
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19
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Quantitative challenges and their bioinformatic solutions in mass spectrometry-based metabolomics. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.117009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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20
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Damiani T, Bonciarelli S, Thallinger GG, Koehler N, Krettler CA, Salihoğlu AK, Korf A, Pauling JK, Pluskal T, Ni Z, Goracci L. Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions. Anal Chem 2023; 95:287-303. [PMID: 36625108 PMCID: PMC9835057 DOI: 10.1021/acs.analchem.2c04406] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tito Damiani
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Stefano Bonciarelli
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Gerhard G. Thallinger
- Institute
of Biomedical Informatics, Graz University
of Technology, 8010 Graz, Austria,
| | - Nikolai Koehler
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | | | - Arif K. Salihoğlu
- Department
of Physiology, Faculty of Medicine and Institute of Health Sciences, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Ansgar Korf
- Bruker Daltonics
GmbH & Co. KG, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Josch K. Pauling
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | - Tomáš Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Zhixu Ni
- Center of
Membrane Biochemistry and Lipid Research, University Hospital and Faculty of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
| | - Laura Goracci
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
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21
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Li S, Zheng S. Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522722. [PMID: 36711587 PMCID: PMC9881955 DOI: 10.1101/2023.01.04.522722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.
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Affiliation(s)
- Shuzhao Li
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Shujian Zheng
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
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22
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Quick tips for re-using metabolomics data. Nat Cell Biol 2022; 24:1560-1562. [PMID: 36280705 DOI: 10.1038/s41556-022-01019-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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23
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Avalon NE, Murray AE, Baker BJ. Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery. Anal Chem 2022; 94:11959-11966. [PMID: 35994737 PMCID: PMC9453739 DOI: 10.1021/acs.analchem.2c02245] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.
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Affiliation(s)
- Nicole E Avalon
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Alison E Murray
- Division of Earth and Ecosystem Sciences, Desert Research Institute, Reno, Nevada 89512, United States
| | - Bill J Baker
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
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24
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Pang Z, Zhou G, Ewald J, Chang L, Hacariz O, Basu N, Xia J. Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat Protoc 2022; 17:1735-1761. [PMID: 35715522 DOI: 10.1038/s41596-022-00710-w] [Citation(s) in RCA: 613] [Impact Index Per Article: 306.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 05/18/2022] [Indexed: 01/01/2023]
Abstract
Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jessica Ewald
- Department of Natural Resources Sciences, McGill University, Montreal, Quebec, Canada
| | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Orcun Hacariz
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Niladri Basu
- Department of Natural Resources Sciences, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
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25
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Zhou G, Pang Z, Lu Y, Ewald J, Xia J. OmicsNet 2.0: a web-based platform for multi-omics integration and network visual analytics. Nucleic Acids Res 2022; 50:W527-W533. [PMID: 35639733 PMCID: PMC9252810 DOI: 10.1093/nar/gkac376] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/21/2022] [Accepted: 05/05/2022] [Indexed: 12/17/2022] Open
Abstract
Researchers are increasingly seeking to interpret molecular data within a multi-omics context to gain a more comprehensive picture of their study system. OmicsNet (www.omicsnet.ca) is a web-based tool developed to allow users to easily build, visualize, and analyze multi-omics networks to study rich relationships among lists of ‘omics features of interest. Three major improvements have been introduced in OmicsNet 2.0, which include: (i) enhanced network visual analytics with eleven 2D graph layout options and a novel 3D module layout; (ii) support for three new ‘omics types: single nucleotide polymorphism (SNP) list from genetic variation studies; taxon list from microbiome profiling studies, as well as liquid chromatography–mass spectrometry (LC–MS) peaks from untargeted metabolomics; and (iii) measures to improve research reproducibility by coupling R command history with the release of the companion OmicsNetR package, and generation of persistent links to share interactive network views. We performed a case study using the multi-omics data obtained from a recent large-scale investigation on inflammatory bowel disease (IBD) and demonstrated that OmicsNet was able to quickly create meaningful multi-omics context to facilitate hypothesis generation and mechanistic insights.
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Affiliation(s)
- Guangyan Zhou
- Institute of Parasitology, McGill University, Quebec, Canada
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Quebec, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
| | - Jessica Ewald
- Department of Natural Resource Sciences, McGill University, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Quebec, Canada.,Department of Microbiology and Immunology, McGill University, Quebec, Canada
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26
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Fakouri Baygi S, Kumar Y, Barupal DK. IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets. J Proteome Res 2022; 21:1485-1494. [PMID: 35579321 DOI: 10.1021/acs.jproteome.2c00120] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12C and 13C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.
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Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Yashwant Kumar
- Non-communicable Diseases Division, Translational Health Science and Technology Institute, Faridabad, Haryana 121001, India
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
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27
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Colby SM, Chang CH, Bade JL, Nunez JR, Blumer MR, Orton DJ, Bloodsworth KJ, Nakayasu ES, Smith RD, Ibrahim YM, Renslow RS, Metz TO. DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data. Anal Chem 2022; 94:6130-6138. [PMID: 35430813 PMCID: PMC9047447 DOI: 10.1021/acs.analchem.1c05017] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/05/2022] [Indexed: 01/06/2023]
Abstract
We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.
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Affiliation(s)
- Sean M. Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Christine H. Chang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Jessica L. Bade
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Jamie R. Nunez
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Madison R. Blumer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Daniel J. Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Kent J. Bloodsworth
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Richard D. Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Yehia M. Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Ryan S. Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
| | - Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352 United States
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28
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Carriot N, Barry-Martinet R, Briand JF, Ortalo-Magné A, Culioli G. Impact of phosphate concentration on the metabolome of biofilms of the marine bacterium Pseudoalteromonas lipolytica. Metabolomics 2022; 18:18. [PMID: 35290545 DOI: 10.1007/s11306-022-01875-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 02/22/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Marine biofilms are the most widely distributed mode of life on Earth and drive biogeochemical cycling processes of most elements. Phosphorus (P) is essential for many biological processes such as energy transfer mechanisms, biological information storage and membrane integrity. OBJECTIVES Our aim was to analyze the effect of a gradient of ecologically relevant phosphate concentrations on the biofilm-forming capacity and the metabolome of the marine bacterium Pseudoalteromonas lipolytica TC8. METHODS In addition to the evaluation of the effect of different phosphate concentration on the biomass, structure and gross biochemical composition of biofilms of P. lipolytica TC8, untargeted metabolomics based on liquid chromatography-mass spectrometry (LC-MS) analysis was used to determine the main metabolites impacted by P-limiting conditions. Annotation of the most discriminating and statistically robust metabolites was performed through the concomitant use of molecular networking and MS/MS fragmentation pattern interpretation. RESULTS At the lowest phosphate concentration, biomass, carbohydrate content and three-dimensional structures of biofilms tended to decrease. Furthermore, untargeted metabolomics allowed for the discrimination of the biofilm samples obtained at the five phosphate concentrations and the highlighting of a panel of metabolites mainly implied in such a discrimination. A large part of the metabolites of the resulting dataset were then putatively annotated. Ornithine lipids were found in increasing quantity when the phosphate concentration decreased, while the opposite trend was observed for oxidized phosphatidylethanolamines (PEs). CONCLUSION This study demonstrated the suitability of LC-MS-based untargeted metabolomics for evaluating the effect of culture conditions on marine bacterial biofilms. More precisely, these results supported the high plasticity of the membrane of P. lipolytica TC8, while the role of the oxidized PEs remains to be clarified.
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Affiliation(s)
- Nathan Carriot
- Laboratoire MAPIEM, Université de Toulon, EA 4323, La Garde, France
| | | | | | | | - Gérald Culioli
- Laboratoire MAPIEM, Université de Toulon, EA 4323, La Garde, France.
- Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale (IMBE), UMR CNRS-IRD-Avignon, Université-Aix-Marseille Université, Avignon, France.
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29
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Beniddir MA, Kang KB, Genta-Jouve G, Huber F, Rogers S, van der Hooft JJJ. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 2021; 38:1967-1993. [PMID: 34821250 PMCID: PMC8597898 DOI: 10.1039/d1np00023c] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
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Affiliation(s)
- Mehdi A Beniddir
- Université Paris-Saclay, CNRS, BioCIS, 5 rue J.-B Clément, 92290 Châtenay-Malabry, France
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Grégory Genta-Jouve
- Laboratoire de Chimie-Toxicologie Analytique et Cellulaire (C-TAC), UMR CNRS 8038, CiTCoM, Université de Paris, 4, Avenue de l'Observatoire, 75006, Paris, France
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 275 Route de Montabo, 97334 Cayenne, French Guiana, France
| | - Florian Huber
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
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30
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools. Metabolites 2021; 11:metabo11100678. [PMID: 34677393 PMCID: PMC8540572 DOI: 10.3390/metabo11100678] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/18/2021] [Accepted: 09/22/2021] [Indexed: 01/06/2023] Open
Abstract
The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.
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