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Mol V, Bennett M, Sánchez BJ, Lisowska BK, Herrgård MJ, Nielsen AT, Leak DJ, Sonnenschein N. Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism. Metab Eng 2021; 65:123-134. [PMID: 33753231 DOI: 10.1016/j.ymben.2021.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 03/01/2021] [Indexed: 12/27/2022]
Abstract
Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.
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Affiliation(s)
- Viviënne Mol
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martyn Bennett
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Benjamín J Sánchez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Beata K Lisowska
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
| | - Markus J Herrgård
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; BioInnovation Institute, Copenhagen N, Denmark
| | - Alex Toftgaard Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
| | - David J Leak
- The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 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, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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Efficient System Wide Metabolic Pathway Comparisons in Multiple Microbes Using Genome to KEGG Orthology (G2KO) Pipeline Tool. Interdiscip Sci 2020; 12:311-322. [PMID: 32632821 DOI: 10.1007/s12539-020-00375-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 05/17/2020] [Accepted: 05/25/2020] [Indexed: 12/29/2022]
Abstract
Comparison of system-wide metabolic pathways among microbes provides valuable insights of organisms' metabolic capabilities that can further assist in rationally screening organisms in silico for various applications. In this work, we present a much needed, efficient and user-friendly Genome to KEGG Orthology (G2KO) pipeline tool that facilitates efficient comparison of system wide metabolic networks of multiple organisms simultaneously. The optimized strategy primarily involves automatic retrieval of the KEGG Orthology (KO) identifiers of user defined organisms from the KEGG database followed by overlaying and visualization of the metabolic genes using the KEGG Mapper reconstruct pathway tool. We demonstrate the applicability of G2KO via two case studies in which we processed 24,314 genes across 15 organisms, mapped on to 530 reference pathways in KEGG, while focusing on pathways of interest. First, an in-silico designing of synthetic microbial consortia towards bioprocessing of cellulose to valuable products by comparing the cellulose degradation and fermentative pathways of microbes was undertaken. Second, we comprehensively compared the amino acid biosynthetic pathways of multiple microbes and demonstrated the potential of G2KO as an efficient tool for metabolic studies. We envisage the tool will find immensely useful to the metabolic engineers as well as systems biologists. The tool's web-server, along with tutorial is publicly available at https://faculty.iitmandi.ac.in/~shyam/tools/g2ko/g2ko.cgi . Also, standalone tool can be downloaded freely from https://sourceforge.net/projects/g2ko/ , and from the supplementary.
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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Ahmad A, Hartman HB, Krishnakumar S, Fell DA, Poolman MG, Srivastava S. A Genome Scale Model of Geobacillus thermoglucosidasius (C56-YS93) reveals its biotechnological potential on rice straw hydrolysate. J Biotechnol 2017; 251:30-37. [DOI: 10.1016/j.jbiotec.2017.03.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 03/27/2017] [Accepted: 03/27/2017] [Indexed: 01/29/2023]
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