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Metabolites
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Computational Strategies in Metabolite Research
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Computational Strategies in Metabolite Research

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".

Deadline for manuscript submissions: closed (19 September 2021) | Viewed by 15537

Special Issue Editors

Dr. Josch K. Pauling

E-Mail Website
Guest Editor
TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
Interests: lipidomics; metabolomics; precision medicine; mass spectrometry; biomarker discovery; network medicine; molecular disease classification; AI in healthcare
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Baumbach

E-Mail Website
Guest Editor
Institute for Computational Systems Biology, University of Hamburg, D-22607 Hamburg, Germany
Interests: bioinformatics; computational biology; systems medicine; network medicine; metabolomics; multi-omics integration
Special Issues, Collections and Topics in MDPI journals
Dr. Richard Röttger

E-Mail Website
Guest Editor
Practical Computer Science and Bioinformatics, Department of Mathematics and Computer Science (IMADA), University of Southern Denmark, Compusvej 55, DK-5230 Odense M, Denmark
Interests: machine learning; classification; clustering; systems biology; biological network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Bioinformatics and computational biology have been crucial to the success of the -omics era where large amounts of biological data are generated that require innovative analysis solutions. With steadily increasing computational capacities and the introduction of next-generation sequencing and high-resolution mass spectrometry, great successes have been achieved in the fields of genomics, transcriptomics, and proteomics, and experimental as well as computational standards could be established. This is not yet the case in the fields of metabolite research, where researchers do not have the same amount and quality of computational tools and databases available to conduct their own large-scale data mining via publicly available and freely accessible web interfaces. However, with cardiovascular diseases, obesity, diabetes, fatty liver diseases, and other manifestations around the metabolic syndrome, we are today facing an increasing necessity to unravel the underlying molecular mechanisms of metabolic disorders.

Therefore, the aim of this Special Issue is to highlight state-of-the-art computational technologies in the fields of metabolite research, i.e., (computational) metabolomics and lipidomics. Hence, it focuses on the demonstration and application of innovative computational approaches in the fields of metabolomics and lipidomics that lead to insights into molecular disease mechanisms, options for treatment and therapy, marker identification, and molecular patient stratification and disease classification. Methods include statistical analyses, biological network analyses and network medicine, pathway analyses and computational models, machine learning and AI, and big data analyses. Integrated multi-omics approaches with a strong contribution of metabolomics/lipidomics are also invited. Manuscripts will be reviewed considering their innovative value in creating solutions to common current problems in computational metabolite research and their effort to formulate and establish standards as well as tools that focus on applicability by a broad range of scientists.

Dr. Josch K. Pauling
Prof. Dr. Jan Baumbach
Dr. Richard Röttger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • lipidomics
  • mass spectrometry
  • ion mobility spectrometry
  • metabolic disorders
  • metabotyping and lipotyping
  • clinical metabolomics
  • data science
  • bioinformatics
  • computational biology
  • data mining in metabolomics
  • web applications

Published Papers (5 papers)

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Research

8 pages, 1613 KiB  
Article
Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics
by Felicity F. Nielson, Bill Kay, Stephen J. Young, Sean M. Colby, Ryan S. Renslow and Thomas O. Metz
Metabolites 2023, 13(1), 105; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo13010105 - 09 Jan 2023
Cited by 1 | Viewed by 1304 | Correction
Abstract
Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers [...] Read more.
Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution. Full article
(This article belongs to the Special Issue Computational Strategies in Metabolite Research)
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19 pages, 5828 KiB  
Article
Investigating Global Lipidome Alterations with the Lipid Network Explorer
by Nikolai Köhler, Tim Daniel Rose, Lisa Falk and Josch Konstantin Pauling
Metabolites 2021, 11(8), 488; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11080488 - 28 Jul 2021
Cited by 16 | Viewed by 4859
Abstract
Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway [...] Read more.
Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container. Full article
(This article belongs to the Special Issue Computational Strategies in Metabolite Research)
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19 pages, 3154 KiB  
Article
Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors
by Noah R. Flynn, Michael D. Ward, Mary A. Schleiff, Corentine M. C. Laurin, Rohit Farmer, Stuart J. Conway, Gunnar Boysen, S. Joshua Swamidass and Grover P. Miller
Metabolites 2021, 11(6), 390; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11060390 - 15 Jun 2021
Cited by 3 | Viewed by 2612
Abstract
The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, [...] Read more.
The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads. Full article
(This article belongs to the Special Issue Computational Strategies in Metabolite Research)
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20 pages, 2928 KiB  
Article
gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites
by Fernando Perez-Sanz, Victoria Ruiz-Hernández, Marta I. Terry, Sara Arce-Gallego, Julia Weiss, Pedro J. Navarro and Marcos Egea-Cortines
Metabolites 2021, 11(4), 211; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo11040211 - 31 Mar 2021
Cited by 3 | Viewed by 2643
Abstract
Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS [...] Read more.
Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites. Full article
(This article belongs to the Special Issue Computational Strategies in Metabolite Research)
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16 pages, 8692 KiB  
Article
BALSAM—An Interactive Online Platform for Breath Analysis, Visualization and Classification
by Philipp Weber, Josch Konstantin Pauling, Markus List and Jan Baumbach
Metabolites 2020, 10(10), 393; https://0-doi-org.brum.beds.ac.uk/10.3390/metabo10100393 - 02 Oct 2020
Cited by 6 | Viewed by 2885
Abstract
The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM—a comprehensive web-platform to simplify and automate this process, [...] Read more.
The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM—a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container. Full article
(This article belongs to the Special Issue Computational Strategies in Metabolite Research)
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