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Meredith LK, Ledford SM, Riemer K, Geffre P, Graves K, Honeker LK, LeBauer D, Tfaily MM, Krechmer J. Automating methods for estimating metabolite volatility. Front Microbiol 2023; 14:1267234. [PMID: 38163064 PMCID: PMC10755872 DOI: 10.3389/fmicb.2023.1267234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024] Open
Abstract
The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.
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Affiliation(s)
- Laura K. Meredith
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - S. Marshall Ledford
- Genetics Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, United States
| | - Kristina Riemer
- Arizona Experiment Station, University of Arizona, Tucson, AZ, United States
| | - Parker Geffre
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
| | - Kelsey Graves
- Department of Environmental Science, University of Arizona, Tucson, AZ, United States
| | - Linnea K. Honeker
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - David LeBauer
- Arizona Experiment Station, University of Arizona, Tucson, AZ, United States
| | - Malak M. Tfaily
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
- Department of Environmental Science, University of Arizona, Tucson, AZ, United States
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Belcour A, Got J, Aite M, Delage L, Collén J, Frioux C, Leblanc C, Dittami SM, Blanquart S, Markov GV, Siegel A. Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe. Genome Res 2023; 33:972-987. [PMID: 37468308 PMCID: PMC10629481 DOI: 10.1101/gr.277056.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
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Affiliation(s)
- Arnaud Belcour
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
| | - Jeanne Got
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Méziane Aite
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France
| | - Ludovic Delage
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Jonas Collén
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Catherine Leblanc
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Simon M Dittami
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | | | - Gabriel V Markov
- Sorbonne Université, CNRS, Integrative Biology of Marine Models (LBI2M), Station Biologique de Roscoff (SBR), 29680 Roscoff, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France;
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Karlsen E, Gylseth M, Schulz C, Almaas E. A study of a diauxic growth experiment using an expanded dynamic flux balance framework. PLoS One 2023; 18:e0280077. [PMID: 36607958 PMCID: PMC9821518 DOI: 10.1371/journal.pone.0280077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Flux balance analysis (FBA) remains one of the most used methods for modeling the entirety of cellular metabolism, and a range of applications and extensions based on the FBA framework have been generated. Dynamic flux balance analysis (dFBA), the expansion of FBA into the time domain, still has issues regarding accessibility limiting its widespread adoption and application, such as a lack of a consistently rigid formalism and tools that can be applied without expert knowledge. Recent work has combined dFBA with enzyme-constrained flux balance analysis (decFBA), which has been shown to greatly improve accuracy in the comparison of computational simulations and experimental data, but such approaches generally do not take into account the fact that altering the enzyme composition of a cell is not an instantaneous process. Here, we have developed a decFBA method that explicitly takes enzyme change constraints (ecc) into account, decFBAecc. The resulting software is a simple yet flexible framework for using genome-scale metabolic modeling for simulations in the time domain that has full interoperability with the COBRA Toolbox 3.0. To assess the quality of the computational predictions of decFBAecc, we conducted a diauxic growth fermentation experiment with Escherichia coli BW25113 in glucose minimal M9 medium. The comparison of experimental data with dFBA, decFBA and decFBAecc predictions demonstrates how systematic analyses within a fixed constraint-based framework can aid the study of model parameters. Finally, in explaining experimentally observed phenotypes, our computational analysis demonstrates the importance of non-linear dependence of exchange fluxes on medium metabolite concentrations and the non-instantaneous change in enzyme composition, effects of which have not previously been accounted for in constraint-based analysis.
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Affiliation(s)
- Emil Karlsen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Gylseth
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Schulz
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K. G. Jebsen Center for Genetic Epidemiology Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- * E-mail:
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Baloni P, Funk CC, Readhead B, Price ND. Systems modeling of metabolic dysregulation in neurodegenerative diseases. Curr Opin Pharmacol 2021; 60:59-65. [PMID: 34352486 PMCID: PMC8511060 DOI: 10.1016/j.coph.2021.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023]
Abstract
Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA, USA
| | - Ben Readhead
- Onegevity, a Division of Thorne HealthTech, New York, NY, USA; Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA; Onegevity, a Division of Thorne HealthTech, New York, NY, USA.
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