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Habra H, Meijer JL, Shen T, Fiehn O, Gaul DA, Fernández FM, Rempfert KR, Metz TO, Peterson KE, Evans CR, Karnovsky A. metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics. Metabolites 2024; 14:125. [PMID: 38393017 PMCID: PMC10891690 DOI: 10.3390/metabo14020125] [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: 01/15/2024] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.
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Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Jennifer L. Meijer
- Department of Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA;
| | - Tong Shen
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (T.S.); (O.F.)
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA 95616, USA; (T.S.); (O.F.)
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USA; (D.A.G.); (F.M.F.)
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive, Atlanta, GA 30332, USA; (D.A.G.); (F.M.F.)
| | - Kaitlin R. Rempfert
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA; (K.R.R.); (T.O.M.)
| | - Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA; (K.R.R.); (T.O.M.)
| | - Karen E. Peterson
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA;
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Charles R. Evans
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA;
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2
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Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
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Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
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3
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Năstase AM, Barrett MP, Cárdenas WB, Cordeiro FB, Zambrano M, Andrade J, Chang J, Regato M, Carrillo E, Botana L, Moreno J, Regnault C, Milne K, Spence PJ, Rowe JA, Rogers S. Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites. PLoS Negl Trop Dis 2023; 17:e0011133. [PMID: 37486920 PMCID: PMC10399774 DOI: 10.1371/journal.pntd.0011133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 02/01/2023] [Indexed: 07/26/2023] Open
Abstract
Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.
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Affiliation(s)
- Ana-Maria Năstase
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Michael P Barrett
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Washington B Cárdenas
- Laboratorio para Investigaciones Biomedicas, Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Fernanda Bertuccez Cordeiro
- Laboratorio para Investigaciones Biomedicas, Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Mildred Zambrano
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Joyce Andrade
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Juan Chang
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Mary Regato
- Instituto Nacional de Investigación en Salud Pública (INSPI), Guayaquil, Ecuador
| | - Eugenia Carrillo
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Laura Botana
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Moreno
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Clément Regnault
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Kathryn Milne
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Philip J Spence
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - J Alexandra Rowe
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, Scotland, United Kingdom
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4
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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5
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Habra H, Kachman M, Padmanabhan V, Burant C, Karnovsky A, Meijer J. Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples. J Proteome Res 2022; 21:2936-2946. [PMID: 36367990 DOI: 10.1021/acs.jproteome.2c00371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.
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Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Vasantha Padmanabhan
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, United States
- Department of Obstetrics & Gynecology, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Charles Burant
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Jennifer Meijer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Medicine, Geisel School of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03756, United States
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6
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Skoraczyński G, Gambin A, Miasojedow B. Alignstein: Optimal transport for improved LC-MS retention time alignment. Gigascience 2022; 11:6795291. [PMID: 36329619 PMCID: PMC9633278 DOI: 10.1093/gigascience/giac101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/24/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022] Open
Abstract
Background Reproducibility of liquid chromatography separation is limited by retention time drift. As a result, measured signals lack correspondence over replicates of the liquid chromatography–mass spectrometry (LC-MS) experiments. Correction of these errors is named retention time alignment and needs to be performed before further quantitative analysis. Despite the availability of numerous alignment algorithms, their accuracy is limited (e.g., for retention time drift that swaps analytes’ elution order). Results We present the Alignstein, an algorithm for LC-MS retention time alignment. It correctly finds correspondence even for swapped signals. To achieve this, we implemented the generalization of the Wasserstein distance to compare multidimensional features without any reduction of the information or dimension of the analyzed data. Moreover, Alignstein by design requires neither a reference sample nor prior signal identification. We validate the algorithm on publicly available benchmark datasets obtaining competitive results. Finally, we show that it can detect the information contained in the tandem mass spectrum by the spatial properties of chromatograms. Conclusions We show that the use of optimal transport effectively overcomes the limitations of existing algorithms for statistical analysis of mass spectrometry datasets. The algorithm’s source code is available at https://github.com/grzsko/Alignstein.
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Affiliation(s)
- Grzegorz Skoraczyński
- Correspondence address. Grzegorz Skoraczyński, Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097 Warsaw, Poland. E-mail:
| | - Anna Gambin
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097 Warsaw, Poland
| | - Błażej Miasojedow
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097 Warsaw, Poland
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7
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Climaco Pinto R, Karaman I, Lewis MR, Hällqvist J, Kaluarachchi M, Graça G, Chekmeneva E, Durainayagam B, Ghanbari M, Ikram MA, Zetterberg H, Griffin J, Elliott P, Tzoulaki I, Dehghan A, Herrington D, Ebbels T. Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets. Anal Chem 2022; 94:5493-5503. [PMID: 35360896 PMCID: PMC9008693 DOI: 10.1021/acs.analchem.1c03592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
![]()
Integration
of multiple datasets can greatly enhance bioanalytical
studies, for example, by increasing power to discover and validate
biomarkers. In liquid chromatography–mass spectrometry (LC–MS)
metabolomics, it is especially hard to combine untargeted datasets
since the majority of metabolomic features are not annotated and thus
cannot be matched by chemical identity. Typically, the information
available for each feature is retention time (RT), mass-to-charge
ratio (m/z), and feature intensity
(FI). Pairs of features from the same metabolite in separate datasets
can exhibit small but significant differences, making matching very
challenging. Current methods to address this issue are too simple
or rely on assumptions that cannot be met in all cases. We present
a method to find feature correspondence between two similar LC–MS
metabolomics experiments or batches using only the features’
RT, m/z, and FI. We demonstrate
the method on both real and synthetic datasets, using six orthogonal
validation strategies to gauge the matching quality. In our main example,
4953 features were uniquely matched, of which 585 (96.8%) of 604 manually
annotated features were correct. In a second example, 2324 features
could be uniquely matched, with 79 (90.8%) out of 87 annotated features
correctly matched. Most of the missed annotated matches are between
features that behave very differently from modeled inter-dataset shifts
of RT, MZ, and FI. In a third example with simulated data with 4755
features per dataset, 99.6% of the matches were correct. Finally,
the results of matching three other dataset pairs using our method
are compared with a published alternative method, metabCombiner, showing
the advantages of our approach. The method can be applied using M2S
(Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
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Affiliation(s)
- Rui Climaco Pinto
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Matthew R Lewis
- MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Jenny Hällqvist
- Centre for Translational Omics, Great Ormond Street Hospital, University College London, London WC1N 1EH, U.K.,Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K
| | - Manuja Kaluarachchi
- UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Gonçalo Graça
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Elena Chekmeneva
- MRC-NIHR National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K.,Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Brenan Durainayagam
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, 431 41 Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 413 45 Mölndal, Sweden.,Department of Neurodegenerative Disease, University College London, Queen Square, London WC1N 3BG, U.K.,UK Dementia Research Institute, University College London, London WC1N 3BG, U.K
| | - Julian Griffin
- UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 451 10 Ioannina, Greece
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W12 0BZ, U.K.,UK Dementia Research Institute, Imperial College London, London W12 0BZ, U.K.,Department of Epidemiology, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27101, United States
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
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8
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Gupta P, Verma A, Rai N, Singh AK, Singh SK, Kumar B, Kumar R, Gautam V. Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds. ACS Chem Biol 2021; 16:2068-2086. [PMID: 34724607 DOI: 10.1021/acschembio.1c00581] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.
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Affiliation(s)
- Priyamvada Gupta
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Ashish Verma
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Nilesh Rai
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Anurag Kumar Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Santosh Kumar Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Brijesh Kumar
- Department of Pharmacology, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Rajiv Kumar
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
| | - Vibhav Gautam
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221005, India
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9
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Abstract
BACKGROUND Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. AIM OF THE REVIEW In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. KEY SCIENTIFIC CONCEPTS OF REVIEW In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.
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