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Zehetner L, Széliová D, Kraus B, Hernandez Bort JA, Zanghellini J. Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways. PLoS Comput Biol 2024; 20:e1012236. [PMID: 38913731 PMCID: PMC11226097 DOI: 10.1371/journal.pcbi.1012236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.
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Affiliation(s)
- Leopold Zehetner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
- Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Diana Széliová
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
| | - Barbara Kraus
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Juan A. Hernandez Bort
- Gene Therapy Process Development, Baxalta Innovations GmbH, a Part of Takeda Companies, Orth an der Donau, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria
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Vezina B, Watts SC, Hawkey J, Cooper HB, Judd LM, Jenney AWJ, Monk JM, Holt KE, Wyres KL. Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models. eLife 2023; 12:RP87406. [PMID: 37815531 PMCID: PMC10564454 DOI: 10.7554/elife.87406] [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: 10/11/2023] Open
Abstract
Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.
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Affiliation(s)
- Ben Vezina
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Stephen C Watts
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Jane Hawkey
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Helena B Cooper
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Louise M Judd
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | | | - Jonathan M Monk
- Department of Bioengineering, University of California, San DiegoSan DiegoUnited States
| | - Kathryn E Holt
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
- Department of Infection Biology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Kelly L Wyres
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
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Murali A, Sarkar RR. Mechano-immunology in microgravity. LIFE SCIENCES IN SPACE RESEARCH 2023; 37:50-64. [PMID: 37087179 DOI: 10.1016/j.lssr.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/16/2023] [Accepted: 03/05/2023] [Indexed: 05/03/2023]
Abstract
Life on Earth has evolved to thrive in the Earth's natural gravitational field; however, as space technology advances, we must revisit and investigate the effects of unnatural conditions on human health, such as gravitational change. Studies have shown that microgravity has a negative impact on various systemic parts of humans, with the effects being more severe in the human immune system. Increasing costs, limited experimental time, and sample handling issues hampered our understanding of this field. To address the existing knowledge gap and provide confidence in modelling the phenomena, in this review, we highlight experimental works in mechano-immunology under microgravity and different computational modelling approaches that can be used to address the existing problems.
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Affiliation(s)
- Anirudh Murali
- Chemical Engineering and Process Development, CSIR - National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development, CSIR - National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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Blázquez B, San León D, Rojas A, Tortajada M, Nogales J. New Insights on Metabolic Features of Bacillus subtilis Based on Multistrain Genome-Scale Metabolic Modeling. Int J Mol Sci 2023; 24:ijms24087091. [PMID: 37108252 PMCID: PMC10138676 DOI: 10.3390/ijms24087091] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/01/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
Bacillus subtilis is an effective workhorse for the production of many industrial products. The high interest aroused by B. subtilis has guided a large metabolic modeling effort of this species. Genome-scale metabolic models (GEMs) are powerful tools for predicting the metabolic capabilities of a given organism. However, high-quality GEMs are required in order to provide accurate predictions. In this work, we construct a high-quality, mostly manually curated genome-scale model for B. subtilis (iBB1018). The model was validated by means of growth performance and carbon flux distribution and provided significantly more accurate predictions than previous models. iBB1018 was able to predict carbon source utilization with great accuracy while identifying up to 28 metabolites as potential novel carbon sources. The constructed model was further used as a tool for the construction of the panphenome of B. subtilis as a species, by means of multistrain genome-scale reconstruction. The panphenome space was defined in the context of 183 GEMs representative of 183 B. subtilis strains and the array of carbon sources sustaining growth. Our analysis highlights the large metabolic versatility of the species and the important role of the accessory metabolism as a driver of the panphenome, at a species level.
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Affiliation(s)
- Blas Blázquez
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
| | - David San León
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
| | - Antonia Rojas
- Archer Daniels Midland, Nutrition, Biopolis S.L. Parc Científic Universitat de València, Carrer del Catedrático Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
| | - Marta Tortajada
- Archer Daniels Midland, Nutrition, Biopolis S.L. Parc Científic Universitat de València, Carrer del Catedrático Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, 28049 Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), 28040 Madrid, Spain
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Holt KE, Aanensen DM, Achtman M. Genomic population structures of microbial pathogens. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210230. [PMID: 35989608 PMCID: PMC9393556 DOI: 10.1098/rstb.2021.0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Kathryn E. Holt
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | | | - Mark Achtman
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
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