1
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Beck A, Muhoberac M, Randolph CE, Beveridge CH, Wijewardhane PR, Kenttämaa HI, Chopra G. Recent Developments in Machine Learning for Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:233-246. [PMID: 38910862 PMCID: PMC11191731 DOI: 10.1021/acsmeasuresciau.3c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 06/25/2024]
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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Affiliation(s)
- Armen
G. Beck
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Matthew Muhoberac
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Caitlin E. Randolph
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Connor H. Beveridge
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Prageeth R. Wijewardhane
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Hilkka I. Kenttämaa
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Gaurav Chopra
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
- Department
of Computer Science (by courtesy), Purdue University, West Lafayette, Indiana 47907, United States
- Purdue
Institute for Drug Discovery, Purdue Institute for Cancer Research,
Regenstrief Center for Healthcare Engineering, Purdue Institute for
Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907 United States
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2
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Safari Yazd H, Bazargani SF, Fitzpatrick G, Yost RA, Kresak J, Garrett TJ. Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2187-2198. [PMID: 37708056 DOI: 10.1021/jasms.3c00158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.
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Affiliation(s)
- Hoda Safari Yazd
- Department of Chemistry, University of Florida, Gainesville, Florida 32610, United States
| | | | - Garrett Fitzpatrick
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Richard A Yost
- Department of Chemistry, University of Florida, Gainesville, Florida 32610, United States
| | - Jesse Kresak
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida 32610, United States
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida 32610, United States
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3
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Joshi RP, Kumar N. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Molecules 2021; 26:6761. [PMID: 34833853 PMCID: PMC8619999 DOI: 10.3390/molecules26226761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/29/2021] [Indexed: 11/23/2022] Open
Abstract
Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.
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Affiliation(s)
| | - Neeraj Kumar
- Computational Biology Group, Biological Science Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USA;
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4
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Miethke M, Pieroni M, Weber T, Brönstrup M, Hammann P, Halby L, Arimondo PB, Glaser P, Aigle B, Bode HB, Moreira R, Li Y, Luzhetskyy A, Medema MH, Pernodet JL, Stadler M, Tormo JR, Genilloud O, Truman AW, Weissman KJ, Takano E, Sabatini S, Stegmann E, Brötz-Oesterhelt H, Wohlleben W, Seemann M, Empting M, Hirsch AKH, Loretz B, Lehr CM, Titz A, Herrmann J, Jaeger T, Alt S, Hesterkamp T, Winterhalter M, Schiefer A, Pfarr K, Hoerauf A, Graz H, Graz M, Lindvall M, Ramurthy S, Karlén A, van Dongen M, Petkovic H, Keller A, Peyrane F, Donadio S, Fraisse L, Piddock LJV, Gilbert IH, Moser HE, Müller R. Towards the sustainable discovery and development of new antibiotics. Nat Rev Chem 2021; 5:726-749. [PMID: 37118182 PMCID: PMC8374425 DOI: 10.1038/s41570-021-00313-1] [Citation(s) in RCA: 369] [Impact Index Per Article: 123.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2021] [Indexed: 02/08/2023]
Abstract
An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations. ![]()
Antimicrobial resistance is an increasing threat to public health and encouraging the development of new antimicrobials is one of the most important ways to address the problem. This Roadmap article aims to bring together industrial, academic and political partners, and proposes both short-term and long-term solutions to this challenge.
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Affiliation(s)
- Marcus Miethke
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Marco Pieroni
- Food and Drug Department, University of Parma, Parma, Italy
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Mark Brönstrup
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Chemical Biology (CBIO), Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | - Peter Hammann
- Infectious Diseases & Natural Product Research at EVOTEC, and Justus Liebig University Giessen, Giessen, Germany
| | - Ludovic Halby
- Epigenetic Chemical Biology, Department of Structural Biology and Chemistry, Institut Pasteur, UMR n°3523, CNRS, Paris, France
| | - Paola B Arimondo
- Epigenetic Chemical Biology, Department of Structural Biology and Chemistry, Institut Pasteur, UMR n°3523, CNRS, Paris, France
| | - Philippe Glaser
- Ecology and Evolution of Antibiotic Resistance Unit, Microbiology Department, Institut Pasteur, CNRS UMR3525, Paris, France
| | | | - Helge B Bode
- Department of Biosciences, Goethe University Frankfurt, Frankfurt, Germany.,Max Planck Institute for Terrestrial Microbiology, Department of Natural Products in Organismic Interactions, Marburg, Germany
| | - Rui Moreira
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal
| | - Yanyan Li
- Unit MCAM, CNRS, National Museum of Natural History (MNHN), Paris, France
| | - Andriy Luzhetskyy
- Pharmaceutical Biotechnology, Saarland University, Saarbrücken, Germany
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University and Research, Wageningen, Netherlands
| | - Jean-Luc Pernodet
- Institute for Integrative Biology of the Cell (I2BC) & Microbiology Department, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Marc Stadler
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Microbial Drugs (MWIS), Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | | | | | - Andrew W Truman
- Department of Molecular Microbiology, John Innes Centre, Norwich, United Kingdom
| | - Kira J Weissman
- Molecular and Structural Enzymology Group, Université de Lorraine, CNRS, IMoPA, Nancy, France
| | - Eriko Takano
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Stefano Sabatini
- Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy
| | - Evi Stegmann
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbial Bioactive Compounds, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Heike Brötz-Oesterhelt
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbial Bioactive Compounds, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Wolfgang Wohlleben
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Department of Microbiology/Biotechnology, Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany
| | - Myriam Seemann
- Institute for Chemistry UMR 7177, University of Strasbourg/CNRS, ITI InnoVec, Strasbourg, France
| | - Martin Empting
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Brigitta Loretz
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany
| | - Claus-Michael Lehr
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany
| | - Alexander Titz
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Jennifer Herrmann
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Timo Jaeger
- German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Silke Alt
- German Center for Infection Research (DZIF), Braunschweig, Germany
| | | | | | - Andrea Schiefer
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Kenneth Pfarr
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Achim Hoerauf
- German Center for Infection Research (DZIF), Braunschweig, Germany.,Institute of Medical Microbiology, Immunology and Parasitology (IMMIP), University Hospital Bonn, Bonn, Germany
| | - Heather Graz
- Biophys Ltd., Usk, Monmouthshire, United Kingdom
| | - Michael Graz
- School of Law, University of Bristol, Bristol, United Kingdom
| | | | | | - Anders Karlén
- Department of Medicinal Chemistry, Uppsala University, Uppsala, Sweden
| | | | - Hrvoje Petkovic
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, University Hospital, Saarbrücken, Germany
| | | | | | - Laurent Fraisse
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Laura J V Piddock
- The Global Antibiotic Research and Development Partnership (GARDP), Geneva, Switzerland
| | - Ian H Gilbert
- Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, United Kingdom
| | - Heinz E Moser
- Novartis Institutes for BioMedical Research (NIBR), Emeryville, CA USA
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI), and Department of Pharmacy, Saarland University Campus E8.1, Saarbrücken, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
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5
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Li Y, Kuhn M, Zukowska-Kasprzyk J, Hennrich ML, Kastritis PL, O’Reilly FJ, Phapale P, Beck M, Gavin AC, Bork P. Coupling proteomics and metabolomics for the unsupervised identification of protein-metabolite interactions in Chaetomium thermophilum. PLoS One 2021; 16:e0254429. [PMID: 34242379 PMCID: PMC8270407 DOI: 10.1371/journal.pone.0254429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 06/26/2021] [Indexed: 11/18/2022] Open
Abstract
Protein-metabolite interactions play an important role in the cell's metabolism and many methods have been developed to screen them in vitro. However, few methods can be applied at a large scale and not alter biological state. Here we describe a proteometabolomic approach, using chromatography to generate cell fractions which are then analyzed with mass spectrometry for both protein and metabolite identification. Integrating the proteomic and metabolomic analyses makes it possible to identify protein-bound metabolites. Applying the concept to the thermophilic fungus Chaetomium thermophilum, we predict 461 likely protein-metabolite interactions, most of them novel. As a proof of principle, we experimentally validate a predicted interaction between the ribosome and isopentenyl adenine.
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Affiliation(s)
- Yuanyue Li
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- * E-mail: (MK); (A-CG); (PB)
| | - Joanna Zukowska-Kasprzyk
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Marco L. Hennrich
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Panagiotis L. Kastritis
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Francis J. O’Reilly
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Prasad Phapale
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Martin Beck
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Anne-Claude Gavin
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- * E-mail: (MK); (A-CG); (PB)
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
- Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
- * E-mail: (MK); (A-CG); (PB)
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6
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Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
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Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
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7
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 2021; 93:7763-7773. [PMID: 34029068 PMCID: PMC8465926 DOI: 10.1021/acs.analchem.0c04850] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - David M Reif
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27606, United States
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8
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Phapale P, Palmer A, Gathungu RM, Kale D, Brügger B, Alexandrov T. Public LC-Orbitrap Tandem Mass Spectral Library for Metabolite Identification. J Proteome Res 2021; 20:2089-2097. [PMID: 33529026 DOI: 10.1021/acs.jproteome.0c00930] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies require high-quality spectral libraries for reliable metabolite identification. We have constructed EMBL-MCF (European Molecular Biology Laboratory-Metabolomics Core Facility), an open LC-MS/MS spectral library that currently contains over 1600 fragmentation spectra from 435 authentic standards of endogenous metabolites and lipids. The unique features of the library include the presence of chromatographic profiles acquired with different LC-MS methods and coverage of different adduct ions. The library covers many biologically important metabolites with some unique metabolites and lipids as compared with other public libraries. The EMBL-MCF spectral library is created and shared using an in-house-developed web application at https://curatr.mcf.embl.de/. The library is freely available online and also integrated with other mass spectral repositories.
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Affiliation(s)
- Prasad Phapale
- Metabolomics Core Facility, EMBL, Heidelberg 69117, Germany
| | - Andrew Palmer
- Metabolomics Core Facility, EMBL, Heidelberg 69117, Germany
| | | | - Dipali Kale
- Heidelberg University Biochemistry Center (BZH), Heidelberg 69120, Germany
| | - Britta Brügger
- Heidelberg University Biochemistry Center (BZH), Heidelberg 69120, Germany
| | - Theodore Alexandrov
- Metabolomics Core Facility, EMBL, Heidelberg 69117, Germany.,Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg 69117, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
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9
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Chemically informed analyses of metabolomics mass spectrometry data with Qemistree. Nat Chem Biol 2021; 17:146-151. [PMID: 33199911 PMCID: PMC8189545 DOI: 10.1038/s41589-020-00677-3] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023]
Abstract
Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.
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10
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He M, Zhou Y. How to identify “Material basis–Quality markers” more accurately in Chinese herbal medicines from modern chromatography-mass spectrometry data-sets: Opportunities and challenges of chemometric tools. CHINESE HERBAL MEDICINES 2021; 13:2-16. [PMID: 36117762 PMCID: PMC9476807 DOI: 10.1016/j.chmed.2020.05.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/26/2020] [Accepted: 05/25/2020] [Indexed: 12/20/2022] Open
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11
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Dias-Audibert FL, Navarro LC, de Oliveira DN, Delafiori J, Melo CFOR, Guerreiro TM, Rosa FT, Petenuci DL, Watanabe MAE, Velloso LA, Rocha AR, Catharino RR. Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers. Front Bioeng Biotechnol 2020; 8:6. [PMID: 32039191 PMCID: PMC6993102 DOI: 10.3389/fbioe.2020.00006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 01/06/2020] [Indexed: 12/20/2022] Open
Abstract
Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.
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Affiliation(s)
- Flávia Luísa Dias-Audibert
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil
| | - Luiz Claudio Navarro
- RECOD Laboratory, Institute of Computing (IC), University of Campinas, Campinas, Brazil
| | - Diogo Noin de Oliveira
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil
| | - Jeany Delafiori
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil
| | | | - Tatiane Melina Guerreiro
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil
| | | | - Diego Lima Petenuci
- Laboratory of Studies and Applications of DNA Polymorphisms, Biological Sciences Center, Londrina State University, Londrina, Brazil
| | - Maria Angelica Ehara Watanabe
- Laboratory of Studies and Applications of DNA Polymorphisms, Biological Sciences Center, Londrina State University, Londrina, Brazil
| | - Licio Augusto Velloso
- Department of Internal Medicine, School of Medical Sciences, University of Campinas, Campinas, Brazil
| | | | - Rodrigo Ramos Catharino
- Innovare Biomarkers Laboratory, School of Pharmaceutical Sciences, University of Campinas, Campinas, Brazil
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