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Xu Y, Banerjee R, Kasibhatla S, McFadden J, Joshi R, Borah Slater K. Differential producibility analysis reveals drug-associated carbon and nitrogen metabolite expressions in Mycobacterium tuberculosis. J Biol Chem 2025; 301:108288. [PMID: 39929299 PMCID: PMC11986224 DOI: 10.1016/j.jbc.2025.108288] [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] [Received: 12/09/2024] [Revised: 01/21/2025] [Accepted: 02/04/2025] [Indexed: 03/28/2025] Open
Abstract
Mycobacterium tuberculosis (Mtb) is one of the world's successful pathogens that flexibly adapts its metabolic nature during infection of the host, and in response to drugs. Here we used genome scale metabolic modelling coupled with differential producibility analysis (DPA) to translate RNA-seq datasets into metabolite signals and identified drug-associated metabolic response profiles. We tested four tuberculosis (TB) drugs bedaquiline (BDQ), isoniazid (INH), rifampicin (RIF), and clarithromycin (CLA); conducted RNA-seq experiments of Mtb exposed to the individual drugs at subinhibitory concentrations, followed by DPA of gene expression data to map up and downregulated metabolites. Here we highlight those metabolic pathways that were flexibly used by Mtb to tolerate stress generated upon drug exposure. BDQ and INH upregulated maximum number of central carbon metabolites in glycolysis, pentose phosphate pathway and tri-carboxylic acid cycle with concomitant downregulation of lipid and amino acid metabolite classes. Oxaloacetate was significantly upregulated in all four drug-treated Mtb cells highlighting it as an important metabolite in Mtb's metabolism. Amino acid metabolism was selectively induced by different drugs. We have enhanced our knowledge on Mtb's carbon and nitrogen metabolic adaptations in the presence of drugs and identify metabolic nodes for therapeutic development against TB. Our work also provides DPA omics platform to interrogate RNA-seq datasets of any organism that can be reconstructed as a genome scale metabolic network.
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Affiliation(s)
- Ye Xu
- Department of Clinical Laboratory, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Biosciences, University of Surrey, Guildford, United Kingdom
| | - Ruma Banerjee
- High Performance Computing: Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Pune, India
| | - Sunitha Kasibhatla
- High Performance Computing: Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Pune, India
| | - Johnjoe McFadden
- School of Biosciences, University of Surrey, Guildford, United Kingdom
| | - Rajendra Joshi
- High Performance Computing: Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing (C-DAC), Pune, India
| | - Khushboo Borah Slater
- Faculty of Health and Life Sciences, Department of Biosciences, University of Exeter, Exeter, United Kingdom.
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2
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Mardinoglu A, Palsson BØ. Genome-scale models in human metabologenomics. Nat Rev Genet 2025; 26:123-140. [PMID: 39300314 DOI: 10.1038/s41576-024-00768-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 09/22/2024]
Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
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Affiliation(s)
- Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK.
| | - Bernhard Ø Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
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3
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Chowdhury NB, Pokorzynski N, Rucks EA, Ouellette SP, Carabeo RA, Saha R. Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence. mSystems 2024; 9:e0071724. [PMID: 38940523 PMCID: PMC11323709 DOI: 10.1128/msystems.00717-24] [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: 05/24/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024] Open
Abstract
Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence. IMPORTANCE This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Nick Pokorzynski
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Elizabeth A. Rucks
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Scot P. Ouellette
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Rey A. Carabeo
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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4
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Chowdhury NB, Pokorzynski N, Rucks EA, Ouellette SP, Carabeo RA, Saha R. Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572198. [PMID: 38187683 PMCID: PMC10769294 DOI: 10.1101/2023.12.18.572198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrient-sufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL's stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.
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Affiliation(s)
- Niaz Bahar Chowdhury
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68508, USA
| | - Nick Pokorzynski
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Elizabeth A. Rucks
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Scot P. Ouellette
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Rey A. Carabeo
- Department of Pathology, Microbiology, and Immunology, University of Nebraska Medical Center, Omaha, Nebraska, 68198, USA
| | - Rajib Saha
- Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68508, USA
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5
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Borah Slater K, Beyß M, Xu Y, Barber J, Costa C, Newcombe J, Theorell A, Bailey MJ, Beste DJV, McFadden J, Nöh K. One-shot 13 C 15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux. Mol Syst Biol 2023; 19:e11099. [PMID: 36705093 PMCID: PMC9996240 DOI: 10.15252/msb.202211099] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/28/2023] Open
Abstract
Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed 13 C15 N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based 13 C15 N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.
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Affiliation(s)
| | - Martin Beyß
- Forschungszentrum Jülich GmbH, Institute of Bio‐ and Geosciences, IBG‐1: BiotechnologyJülichGermany
- Computational Systems BiotechnologyRWTH Aachen UniversityAachenGermany
| | - Ye Xu
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Jim Barber
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Catia Costa
- Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordUK
| | - Jane Newcombe
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Axel Theorell
- Forschungszentrum Jülich GmbH, Institute of Bio‐ and Geosciences, IBG‐1: BiotechnologyJülichGermany
- Present address:
Computational Systems BiologyETH ZürichBaselSwitzerland
| | - Melanie J Bailey
- Faculty of Engineering and Physical SciencesUniversity of SurreyGuildfordUK
| | - Dany J V Beste
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Johnjoe McFadden
- Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Katharina Nöh
- Forschungszentrum Jülich GmbH, Institute of Bio‐ and Geosciences, IBG‐1: BiotechnologyJülichGermany
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6
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Sarmah DT, Parveen R, Kundu J, Chatterjee S. Latent tuberculosis and computational biology: A less-talked affair. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 178:17-31. [PMID: 36781150 DOI: 10.1016/j.pbiomolbio.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 02/13/2023]
Abstract
Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.
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Affiliation(s)
- Dipanka Tanu Sarmah
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Rubi Parveen
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Jayendrajyoti Kundu
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India.
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7
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Romano GE, Silva-Pereira TT, de Melo FM, Sisco MC, Banari AC, Zimpel CK, Soler-Camargo NC, Guimarães AMDS. Unraveling the metabolism of Mycobacterium caprae using comparative genomics. Tuberculosis (Edinb) 2022; 136:102254. [PMID: 36126496 DOI: 10.1016/j.tube.2022.102254] [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] [Received: 02/27/2022] [Revised: 08/01/2022] [Accepted: 08/25/2022] [Indexed: 11/19/2022]
Abstract
In our laboratory, Mycobacterium caprae has poor growth in standard medium (SM) 7H9-OADC supplemented with pyruvate and Tween-80. Our objectives were to identify mutations affecting M. caprae metabolism and use this information to design a culture medium to improve its growth. We selected 77 M. caprae genomes and sequenced M. caprae NLA000201913 used in our experiments. Mutations present in >95% of the strains compared to Mycobacterium tuberculosis H37Rv were analyzed in silico for their deleterious effects on proteins of metabolic pathways. Apart from the known defect in the pyruvate kinase, M. caprae has important lesions in enzymes of the TCA cycle, methylmalonyl cycle, B12 metabolism, and electron-transport chain. We provide evidence of enzymatic redundancy elimination and epistatic mutations, and possible production of toxic metabolites hindering M. caprae growth in vitro. A newly designed SM supplemented with l-glutamate allowed faster growth and increased final microbial mass of M. caprae. However, possible accumulation of metabolic waste-products and/or nutritional limitations halted M. caprae growth prior to a M. tuberculosis-like stationary phase. Our findings suggest that M. caprae relies on GABA and/or glyoxylate shunts for in vitro growth in routine media. The newly developed medium will improve experiments with this bacterium by allowing faster growth in vitro.
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Affiliation(s)
- Giovanni Emiddio Romano
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil.
| | - Taiana Tainá Silva-Pereira
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil.
| | - Filipe Menegatti de Melo
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil.
| | - Maria Carolina Sisco
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil.
| | - Alexandre Campos Banari
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil; Department of Preventive Veterinary Medicine and Animal Health, College of Veterinary Medicine, University of São Paulo, 87 Prof Dr Orlando Marques de Paiva Avenue, São Paulo, SP, 05508-270, Brazil.
| | - Cristina Kraemer Zimpel
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil; Department of Preventive Veterinary Medicine and Animal Health, College of Veterinary Medicine, University of São Paulo, 87 Prof Dr Orlando Marques de Paiva Avenue, São Paulo, SP, 05508-270, Brazil.
| | - Naila Cristina Soler-Camargo
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil; Department of Preventive Veterinary Medicine and Animal Health, College of Veterinary Medicine, University of São Paulo, 87 Prof Dr Orlando Marques de Paiva Avenue, São Paulo, SP, 05508-270, Brazil.
| | - Ana Marcia de Sá Guimarães
- Laboratory of Applied Research in Mycobacteria (LaPAM), Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, 1374 Prof Lineu Prestes Avenue, Room 229, São Paulo, SP, 05508-000, Brazil; Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University. 625 Harrison Street, West Lafayette, IN, 47907, USA.
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8
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Wang Y, Shi Q, Chen Q, Zhou X, Yuan H, Jia X, Liu S, Li Q, Ge L. Emerging advances in identifying signal transmission molecules involved in the interaction between Mycobacterium tuberculosis and the host. Front Cell Infect Microbiol 2022; 12:956311. [PMID: 35959378 PMCID: PMC9359464 DOI: 10.3389/fcimb.2022.956311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 11/21/2022] Open
Abstract
Tuberculosis caused by Mycobacterium tuberculosis (MTB) is an ancient chronic infectious disease and is still the leading cause of death worldwide due to a single infectious disease. MTB can achieve immune escape by interacting with host cells through its special cell structure and secreting a variety of effector proteins. Innate immunity-related pattern recognition receptors (PPR receptors) play a key role in the regulation of signaling pathways. In this review, we focus on the latest research progress on related signal transduction molecules in the interaction between MTB and the host. In addition, we provide new research ideas for the development of new anti-tuberculosis drug targets and lead compounds and provide an overview of information useful for approaching future tuberculosis host-oriented treatment research approaches and strategies, which has crucial scientific guiding significance and research value.
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Affiliation(s)
- Yue Wang
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qiyuan Shi
- School of Pharmacy, Hangzhou Medical College, Hangzhou, China
| | - Qi Chen
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuebin Zhou
- School of Pharmacy, Hangzhou Medical College, Hangzhou, China
| | - Huiling Yuan
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiwen Jia
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuyuan Liu
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qin Li
- School of Pharmacy, Hangzhou Medical College, Hangzhou, China
- *Correspondence: Qin Li, ; Lijun Ge,
| | - Lijun Ge
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China
- *Correspondence: Qin Li, ; Lijun Ge,
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9
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Santamaria G, Ruiz-Rodriguez P, Renau-Mínguez C, Pinto FR, Coscollá M. In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes. Biomolecules 2022; 12:376. [PMID: 35327567 PMCID: PMC8945471 DOI: 10.3390/biom12030376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/29/2022] [Accepted: 02/22/2022] [Indexed: 02/04/2023] Open
Abstract
Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection.
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Affiliation(s)
- Guillem Santamaria
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
- BioISI—Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal
| | - Paula Ruiz-Rodriguez
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
| | - Chantal Renau-Mínguez
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
| | - Francisco R. Pinto
- BioISI—Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal
| | - Mireia Coscollá
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
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10
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López-Agudelo VA, Baena A, Barrera V, Cabarcas F, Alzate JF, Beste DJV, Ríos-Estepa R, Barrera LF. Dual RNA Sequencing of Mycobacterium tuberculosis-Infected Human Splenic Macrophages Reveals a Strain-Dependent Host-Pathogen Response to Infection. Int J Mol Sci 2022; 23:ijms23031803. [PMID: 35163725 PMCID: PMC8836425 DOI: 10.3390/ijms23031803] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/26/2021] [Accepted: 12/28/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb), leading to pulmonary and extrapulmonary TB, whereby Mtb is disseminated to many other organs and tissues. Dissemination occurs early during the disease, and bacteria can be found first in the lymph nodes adjacent to the lungs and then later in the extrapulmonary organs, including the spleen. The early global gene expression response of human tissue macrophages and intracellular clinical isolates of Mtb has been poorly studied. Using dual RNA-seq, we have explored the mRNA profiles of two closely related clinical strains of the Latin American and Mediterranean (LAM) family of Mtb in infected human splenic macrophages (hSMs). This work shows that these pathogens mediate a distinct host response despite their genetic similarity. Using a genome-scale host–pathogen metabolic reconstruction to analyze the data further, we highlight that the infecting Mtb strain also determines the metabolic response of both the host and pathogen. Thus, macrophage ontogeny and the genetic-derived program of Mtb direct the host–pathogen interaction.
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Affiliation(s)
- Víctor A. López-Agudelo
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
- Grupo de Bioprocesos, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Andres Baena
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
| | - Vianey Barrera
- Programa de Ingeniería Biológica, Universidad Nacional de Colombia, Sede Medellín, Medellín 050010, Colombia;
| | - Felipe Cabarcas
- Grupo Sistemas Embebidos e Inteligencia Computacional (SISTEMIC), Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Juan F. Alzate
- Centro Nacional de Secuenciación Genómica (CNSG), Sede de Investigación Universitaria (SIU), Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Dany J. V. Beste
- Department of Microbial Sciences, Faculty of Health and Medical Science, University of Surrey, Guildford GU2 7XH, UK;
| | - Rigoberto Ríos-Estepa
- Grupo de Bioprocesos, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Luis F. Barrera
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
- Correspondence:
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11
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Borah K, Xu Y, McFadden J. Dissecting Host-Pathogen Interactions in TB Using Systems-Based Omic Approaches. Front Immunol 2021; 12:762315. [PMID: 34795672 PMCID: PMC8593131 DOI: 10.3389/fimmu.2021.762315] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 10/18/2021] [Indexed: 01/10/2023] Open
Abstract
Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.
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Affiliation(s)
- Khushboo Borah
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | | | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
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12
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Ramirez-Malule H, López-Agudelo VA, Gómez-Ríos D, Ochoa S, Ríos-Estepa R, Junne S, Neubauer P. TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus. Bioengineering (Basel) 2021; 8:103. [PMID: 34436106 PMCID: PMC8389198 DOI: 10.3390/bioengineering8080103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/04/2021] [Accepted: 07/16/2021] [Indexed: 11/26/2022] Open
Abstract
Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of β-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.
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Affiliation(s)
| | | | - David Gómez-Ríos
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Silvia Ochoa
- Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia; (D.G.-R.); (S.O.)
| | - Rigoberto Ríos-Estepa
- Escuela de Biociencias, Universidad Nacional de Colombia sede Medellín, Medellín 050010, Colombia;
| | - Stefan Junne
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
| | - Peter Neubauer
- Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin, Germany; (S.J.); (P.N.)
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13
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Borah K, Mendum TA, Hawkins ND, Ward JL, Beale MH, Larrouy‐Maumus G, Bhatt A, Moulin M, Haertlein M, Strohmeier G, Pichler H, Forsyth VT, Noack S, Goulding CW, McFadden J, Beste DJV. Metabolic fluxes for nutritional flexibility of Mycobacterium tuberculosis. Mol Syst Biol 2021; 17:e10280. [PMID: 33943004 PMCID: PMC8094261 DOI: 10.15252/msb.202110280] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/25/2022] Open
Abstract
The co-catabolism of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady-state chemostat system. We demonstrate that Mtb efficiently co-metabolises either cholesterol or glycerol, in combination with two-carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt.
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Affiliation(s)
- Khushboo Borah
- Department of Microbial and Cellular SciencesFaculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Tom A Mendum
- Department of Microbial and Cellular SciencesFaculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Nathaniel D Hawkins
- Department of Computational and Analytical SciencesRothamsted ResearchHarpendenUK
| | - Jane L Ward
- Department of Computational and Analytical SciencesRothamsted ResearchHarpendenUK
| | - Michael H Beale
- Department of Computational and Analytical SciencesRothamsted ResearchHarpendenUK
| | - Gerald Larrouy‐Maumus
- MRC Centre for Molecular Bacteriology and InfectionDepartment of Life SciencesFaculty of Natural SciencesImperial College LondonLondonUK
| | - Apoorva Bhatt
- School of BiosciencesUniversity of BirminghamEdgbastonUK
| | - Martine Moulin
- Life Sciences GroupInstitut Laue‐LangevinGrenoble Cedex 9France
- Partnership for Structural BiologyGrenoble Cedex 9France
| | - Michael Haertlein
- Life Sciences GroupInstitut Laue‐LangevinGrenoble Cedex 9France
- Partnership for Structural BiologyGrenoble Cedex 9France
| | - Gernot Strohmeier
- Austrian Centre of Industrial BiotechnologyGrazAustria
- Institute of Organic ChemistryNAWI GrazGraz University of TechnologyGrazAustria
| | - Harald Pichler
- Austrian Centre of Industrial BiotechnologyGrazAustria
- Institute of Organic ChemistryNAWI GrazGraz University of TechnologyGrazAustria
- Institute of Molecular BiotechnologyNAWI GrazBioTechMed GrazGraz University of TechnologyGrazAustria
| | - V Trevor Forsyth
- Life Sciences GroupInstitut Laue‐LangevinGrenoble Cedex 9France
- Partnership for Structural BiologyGrenoble Cedex 9France
- Faculty of Natural SciencesKeele UniversityStaffordshireUK
| | - Stephan Noack
- Institute of Bio‐ and Geosciences 1: Biotechnology 2Forschungszentrum Jülich GmbHJülichGermany
| | - Celia W Goulding
- Department of Pharmaceutical Sciences & Molecular Biology & BiochemistryUniversity of California IrvineIrvineCAUSA
| | - Johnjoe McFadden
- Department of Microbial and Cellular SciencesFaculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Dany J V Beste
- Department of Microbial and Cellular SciencesFaculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
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14
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Jean-Pierre F, Henson MA, O’Toole GA. Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists. Front Mol Biosci 2021; 8:634479. [PMID: 33681294 PMCID: PMC7930556 DOI: 10.3389/fmolb.2021.634479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.
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Affiliation(s)
- Fabrice Jean-Pierre
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Michael A. Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States
| | - George A. O’Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
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15
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Sertbas M, Ulgen KO. Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens. Front Cell Dev Biol 2020; 8:566702. [PMID: 33251208 PMCID: PMC7673413 DOI: 10.3389/fcell.2020.566702] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.
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Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.,Department of Chemical Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Kutlu O Ulgen
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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16
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Kulyashov M, Peltek SE, Akberdin IR. A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus. Microorganisms 2020; 8:E1002. [PMID: 32635563 PMCID: PMC7409357 DOI: 10.3390/microorganisms8071002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 11/16/2022] Open
Abstract
The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.
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Affiliation(s)
- Mikhail Kulyashov
- Biosoft.ru, 630058 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Department of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
| | - Sergey E. Peltek
- Department of Molecular Biotechnology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia;
| | - Ilya R. Akberdin
- Biosoft.ru, 630058 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Department of Molecular Biotechnology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia;
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