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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [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: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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2
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Jamal QMS, Ahmad V. Bacterial metabolomics: current applications for human welfare and future aspects. JOURNAL OF ASIAN NATURAL PRODUCTS RESEARCH 2024:1-24. [PMID: 39078342 DOI: 10.1080/10286020.2024.2385365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.
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Affiliation(s)
- Qazi Mohammad Sajid Jamal
- Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Varish Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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3
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Gonçalves AAM, Ribeiro AJ, Resende CAA, Couto CAP, Gandra IB, Dos Santos Barcelos IC, da Silva JO, Machado JM, Silva KA, Silva LS, Dos Santos M, da Silva Lopes L, de Faria MT, Pereira SP, Xavier SR, Aragão MM, Candida-Puma MA, de Oliveira ICM, Souza AA, Nogueira LM, da Paz MC, Coelho EAF, Giunchetti RC, de Freitas SM, Chávez-Fumagalli MA, Nagem RAP, Galdino AS. Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis. Microb Cell Fact 2024; 23:145. [PMID: 38778337 PMCID: PMC11110257 DOI: 10.1186/s12934-024-02418-w] [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: 01/31/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.
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Affiliation(s)
- Ana Alice Maia Gonçalves
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Anna Julia Ribeiro
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carlos Ananias Aparecido Resende
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Carolina Alves Petit Couto
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isadora Braga Gandra
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Isabelle Caroline Dos Santos Barcelos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Jonatas Oliveira da Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Juliana Martins Machado
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Kamila Alves Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Líria Souza Silva
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Michelli Dos Santos
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Lucas da Silva Lopes
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Teixeira de Faria
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sabrina Paula Pereira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Sandra Rodrigues Xavier
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Matheus Motta Aragão
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Mayron Antonio Candida-Puma
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | | | - Amanda Araujo Souza
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Lais Moreira Nogueira
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Mariana Campos da Paz
- Bioactives and Nanobiotechnology Laboratory, Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil
| | - Eduardo Antônio Ferraz Coelho
- Postgraduate Program in Health Sciences, Infectious Diseases and Tropical Medicine, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, 30130-100, Brazil
| | - Rodolfo Cordeiro Giunchetti
- Laboratory of Biology of Cell Interactions, National Institute of Science and Technology on Tropical Diseases (INCT-DT), Department of Morphology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sonia Maria de Freitas
- Biophysics Laboratory, Institute of Biological Sciences, Department of Cell Biology, University of Brasilia, Brasília, 70910-900, Brazil
| | - Miguel Angel Chávez-Fumagalli
- Computational Biology and Chemistry Research Group, Vicerrectorado de Investigación, Universidad Católica de Santa María, Arequipa, 04000, Peru
| | - Ronaldo Alves Pinto Nagem
- Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexsandro Sobreira Galdino
- Microorganism Biotechnology Laboratory, National Institute of Science and Technology on Industrial Biotechnology (INCT-BI), Federal University of São João Del-Rei, Midwest Campus, Divinópolis, 35501-296, Brazil.
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Qian J, Ye C. Development and applications of genome-scale metabolic network models. ADVANCES IN APPLIED MICROBIOLOGY 2024; 126:1-26. [PMID: 38637105 DOI: 10.1016/bs.aambs.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.
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Affiliation(s)
- Jinyi Qian
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China
| | - Chao Ye
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, PR China.
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Batista RS, Chaves GL, Oliveira DB, Pantaleão VL, Neves JDDS, da Silva AJ. Glycerol as substrate and NADP +-dependent glyceraldehyde-3-phosphate dehydrogenase enable higher production of 3-hydroxypropionic acid through the β-alanine pathway in E. coli. BIORESOURCE TECHNOLOGY 2024; 393:130142. [PMID: 38049020 DOI: 10.1016/j.biortech.2023.130142] [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: 11/01/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
Microbial engineering is a promising way to produce3-HP using biorenewable substrates such as glycerol. However, theglycerol pathway to obtain 3-HPrequires vitamin B-12, which hinders its economic viability. The present work showed that 3-HP can be efficiently produced from glycerol through the β-alanine pathway. To develop a cell factory for this purpose, glycerol was evaluated as a substrate and showed more than two-fold improved 3-HP production compared to glucose. Next, the reducing power was modulated by overexpression of an NADP+ -dependent glyceraldehyde-3-phosphate dehydrogenase coupled with CRISPR-based repression of the endogenous gapA gene, resulting in a 91 % increase in 3-HP titer. Finally, the toxicity of 3-HP accumulation was addressed by overexpressing a putative exporter (YohJK). Fed-batch cultivation of the final strain yielded 72.2 g/L of 3-HP and a productivity of 1.64 g/L/h, which are the best results for the β-alanine pathway and are similar to those found for other pathways.
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Affiliation(s)
- Raquel Salgado Batista
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Gabriel Luz Chaves
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Davi Benedito Oliveira
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Vitor Leonel Pantaleão
- Department of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - José Davi Dos Santos Neves
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil
| | - Adilson José da Silva
- Graduate Program of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil; Department of Chemical Engineering, Federal University of São Carlos, Rod. Washington Luís, km 235, São Carlos, São Paulo 13565-905, Brazil.
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6
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Morrissey J, Strain B, Kontoravdi C. Flux Balance Analysis of Mammalian Cell Systems. Methods Mol Biol 2024; 2774:119-134. [PMID: 38441762 DOI: 10.1007/978-1-0716-3718-0_9] [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] [Indexed: 03/07/2024]
Abstract
Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.
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Affiliation(s)
- James Morrissey
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Benjamin Strain
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London, UK.
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7
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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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8
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Carter EL, Constantinidou C, Alam MT. Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. Brief Bioinform 2023; 25:bbad439. [PMID: 38048080 PMCID: PMC10694557 DOI: 10.1093/bib/bbad439] [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: 07/24/2023] [Revised: 09/21/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.
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Affiliation(s)
- Elena Lucy Carter
- Warwick Medical School, University of Warwick, Coventry, CV4 7HL, UK
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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10
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Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [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: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
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11
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Kostiuk B, Becker ME, Churaman CN, Black JJ, Payne SM, Pukatzki S, Koestler BJ. Vibrio cholerae Alkalizes Its Environment via Citrate Metabolism to Inhibit Enteric Growth In Vitro. Microbiol Spectr 2023; 11:e0491722. [PMID: 36916917 PMCID: PMC10100763 DOI: 10.1128/spectrum.04917-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/22/2023] [Indexed: 03/16/2023] Open
Abstract
Vibrio cholerae is a Gram-negative pathogen, living in constant competition with other bacteria in marine environments and during human infection. One competitive advantage of V. cholerae is the ability to metabolize diverse carbon sources, such as chitin and citrate. We observed that when some V. cholerae strains were grown on a medium with citrate, the medium's chemical composition turned into a hostile alkaline environment for Gram-negative bacteria, such as Escherichia coli and Shigella flexneri. We found that although the ability to exclude competing bacteria was not contingent on exogenous citrate, V. cholerae C6706 citrate metabolism mutants ΔoadA-1, ΔcitE, and ΔcitF were not able to inhibit S. flexneri or E. coli growth. Lastly, we demonstrated that while the V. cholerae C6706-mediated increased medium pH was necessary for the enteric exclusion phenotype, secondary metabolites, such as bicarbonate (protonated to carbonate in the raised pH) from the metabolism of citrate, enhanced the ability to inhibit the growth of E. coli. These data provide a novel example of how V. cholerae outcompetes other Gram-negative bacteria. IMPORTANCE Vibrio cholerae must compete with other bacteria in order to cause disease. Here, we show that V. cholerae creates an alkaline environment, which is able to inhibit the growth of other enteric bacteria. We demonstrate that V. cholerae environmental alkalization is linked to the capacity of the bacteria to metabolize citrate. This behavior could potentially contribute to V. cholerae's ability to colonize the human intestine.
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Affiliation(s)
- Benjamin Kostiuk
- Department of Medical Microbiology and Immunology, 6-020 Katz Group Centre, University of Alberta, Edmonton, Alberta, Canada
| | - Mark E. Becker
- Department of Cell and Developmental Biology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Candice N. Churaman
- Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, USA
| | - Joshua J. Black
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shelley M. Payne
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA
| | - Stefan Pukatzki
- Department of Biology, The City College of New York, New York, New York, USA
| | - Benjamin J. Koestler
- Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, USA
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12
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Tamura T. Trimming Gene Deletion Strategies for Growth-Coupled Production in Constraint-Based Metabolic Networks: TrimGdel. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1540-1549. [PMID: 35731759 DOI: 10.1109/tcbb.2022.3185221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
When simulating genome-scale metabolite production using constraint-based metabolic networks, it is often necessary to find gene deletion strategies which lead to growth-coupled production, which means that target metabolites are produced when cell growth is maximized. Existing methods are effective when the number of gene deletions is relatively small, but when the number of required gene deletions exceeds approximately 1% of whole genes, the time required for the calculation is often unfeasible. Therefore, a complementing algorithm that is effective even when the required number of gene deletions is approximately 1% to 5% of whole genes would be helpful because the number of deletable genes in a strain is increasing with advances in genetic engineering technology. In this study, the author developed an algorithm, TrimGdel, which first computes a strategy with many gene deletions that results in growth-coupled production and then gradually reduces the number of gene deletions while ensuring the original production rate and growth rate. The results of the computer experiments showed that TrimGdel can calculate stoichiometrically feasible gene deletion strategies, especially those whose sizes are 1 to 5% of whole genes, which lead to growth-coupled production of many target metabolites, which include useful vitamins such as biotin and pantothenate, for which existing methods could not.
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13
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Tamura T, Muto-Fujita A, Tohsato Y, Kosaka T. Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN. J Comput Biol 2023; 30:553-568. [PMID: 36809057 DOI: 10.1089/cmb.2022.0352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.
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Affiliation(s)
- Takeyuki Tamura
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
| | - Ai Muto-Fujita
- RIKEN Center for Biosystems Dynamics Research, Suita, Osaka, Japan
| | - Yukako Tohsato
- Faculty of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Tomoyuki Kosaka
- Research Center for Thermotolerant Microbial Resources (RCTMR), Yamaguchi University, Yoshida, Yamaguchi, Japan.,Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yoshida, Yamaguchi, Japan
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14
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Kang CK, Shin J, Cha Y, Kim MS, Choi MS, Kim T, Park YK, Choi YJ. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene. BIORESOURCE TECHNOLOGY 2023; 369:128455. [PMID: 36503092 DOI: 10.1016/j.biortech.2022.128455] [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: 10/27/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The process of designing streamlined workflows for developing microbial strains using classical methods from vast amounts of biological big data has reached its limits. With the continuous increase in the amount of biological big data, data-driven machine learning approaches are being used to overcome the limits of classical approaches for strain development. Here, machine learning-guided engineering of Deinococcus radiodurans R1 for high-yield production of lycopene was demonstrated. The multilayer perceptron models were first trained using the mRNA expression levels of the key genes along with lycopene titers and yields obtained from 17 strains. Then, the potential overexpression targets from 2,047 possible combinations were predicted by the multilayer perceptron combined with a genetic algorithm. Through the machine learning-aided fine-tuning of the predicted genes, the final-engineered LY04 strain resulted in an 8-fold increase in the lycopene production, up to 1.25 g/L from glycerol, and a 6-fold increase in the lycopene yield.
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Affiliation(s)
- Chang Keun Kang
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Jihoon Shin
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - YoonKyung Cha
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Min Sun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - TaeHo Kim
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Young-Kwon Park
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea
| | - Yong Jun Choi
- School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
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15
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Vidya Muthulakshmi M, Srinivasan A, Srivastava S. Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures. ACS OMEGA 2023; 8:3586-3605. [PMID: 36743063 PMCID: PMC9893489 DOI: 10.1021/acsomega.2c05819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.
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Affiliation(s)
- M. Vidya Muthulakshmi
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Aparajitha Srinivasan
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Smita Srivastava
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
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16
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Viana R, Carreiro T, Couceiro D, Dias O, Rocha I, Teixeira MC. Metabolic reconstruction of the human pathogen Candida auris: using a cross-species approach for drug target prediction. FEMS Yeast Res 2023; 23:foad045. [PMID: 37852663 DOI: 10.1093/femsyr/foad045] [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: 05/26/2023] [Revised: 09/05/2023] [Accepted: 10/17/2023] [Indexed: 10/20/2023] Open
Abstract
Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, including some known targets of antifungal agents used in clinical practice, but also new potential drug targets without any human homolog or drug association in Candida species.
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Affiliation(s)
- Romeu Viana
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
- iBB - Institute for Bioengineering and Biosciences, Associate Laboratory Institute for Health and Bioeconomy - i4HB, 1049-001 Lisboa, Portugal
| | - Tiago Carreiro
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
- iBB - Institute for Bioengineering and Biosciences, Associate Laboratory Institute for Health and Bioeconomy - i4HB, 1049-001 Lisboa, Portugal
| | - Diogo Couceiro
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
- iBB - Institute for Bioengineering and Biosciences, Associate Laboratory Institute for Health and Bioeconomy - i4HB, 1049-001 Lisboa, Portugal
| | - Oscar Dias
- CEB - Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal
| | - Isabel Rocha
- ITQB Nova - Instituto de Tecnologia Química e Biológica António Xavier, 2780-157 Oeiras, Portugal
| | - Miguel Cacho Teixeira
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal
- iBB - Institute for Bioengineering and Biosciences, Associate Laboratory Institute for Health and Bioeconomy - i4HB, 1049-001 Lisboa, Portugal
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17
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Systems biology's role in leveraging microalgal biomass potential: Current status and future perspectives. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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18
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Schneider P, Bekiaris PS, von Kamp A, Klamt S. StrainDesign: a comprehensive Python package for computational design of metabolic networks. Bioinformatics 2022; 38:4981-4983. [PMID: 36111857 PMCID: PMC9620819 DOI: 10.1093/bioinformatics/btac632] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/17/2022] [Accepted: 09/15/2022] [Indexed: 10/05/2023] Open
Abstract
SUMMARY Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. AVAILABILITY AND IMPLEMENTATION The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.
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Affiliation(s)
- Philipp Schneider
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Pavlos Stephanos Bekiaris
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Axel von Kamp
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
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19
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Mitra S, Dhar R, Sen R. Designer bacterial cell factories for improved production of commercially valuable non-ribosomal peptides. Biotechnol Adv 2022; 60:108023. [PMID: 35872292 DOI: 10.1016/j.biotechadv.2022.108023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 11/27/2022]
Abstract
Non-ribosomal peptides have gained significant attention as secondary metabolites of high commercial importance. This group houses a diverse range of bioactive compounds, ranging from biosurfactants to antimicrobial and cytotoxic agents. However, low yield of synthesis by bacteria and excessive losses during purification hinders the industrial-scale production of non-ribosomal peptides, and subsequently limits their widespread applicability. While isolation of efficient producer strains and optimization of bioprocesses have been extensively used to enhance yield, further improvement can be made by optimization of the microbial strain using the tools and techniques of metabolic engineering, synthetic biology, systems biology, and adaptive laboratory evolution. These techniques, which directly target the genome of producer strains, aim to redirect carbon and nitrogen fluxes of the metabolic network towards the desired product, bypass the feedback inhibition and repression mechanisms that limit the maximum productivity of the strain, and even extend the substrate range of the cell for synthesis of the target product. The present review takes a comprehensive look into the biosynthesis of bacterial NRPs, how the same is regulated by the cell, and dives deep into the strategies that have been undertaken for enhancing the yield of NRPs, while also providing a perspective on other potential strategies that can allow for further yield improvement. Furthermore, this review provides the reader with a holistic perspective on the design of cellular factories of NRP production, starting from general techniques performed in the laboratory to the computational techniques that help a biochemical engineer model and subsequently strategize the architectural plan.
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Affiliation(s)
- Sayak Mitra
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
| | - Ramkrishna Sen
- Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India.
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20
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Domenzain I, Sánchez B, Anton M, Kerkhoven EJ, Millán-Oropeza A, Henry C, Siewers V, Morrissey JP, Sonnenschein N, Nielsen J. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat Commun 2022; 13:3766. [PMID: 35773252 PMCID: PMC9246944 DOI: 10.1038/s41467-022-31421-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/16/2022] [Indexed: 01/08/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.
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Affiliation(s)
- Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Benjamín Sánchez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Mihail Anton
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-412 58, Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Aarón Millán-Oropeza
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Céline Henry
- Plateforme d'analyse protéomique Paris Sud-Ouest (PAPPSO), INRAE, MICALIS Institute, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Verena Siewers
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - John P Morrissey
- School of Microbiology, Environmental Research Institute and APC Microbiome Ireland, University College Cork, T12 K8AF, Cork, Ireland
| | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.
- BioInnovation Institute, Ole Maaløes Vej 3, 2200, Copenhagen, Denmark.
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21
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Vieira V, Ferreira J, Rocha M. A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale. PLoS Comput Biol 2022; 18:e1009294. [PMID: 35749559 PMCID: PMC9278738 DOI: 10.1371/journal.pcbi.1009294] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 07/13/2022] [Accepted: 04/15/2022] [Indexed: 11/18/2022] Open
Abstract
Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction. Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software. We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications.
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Affiliation(s)
- Vítor Vieira
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
| | - Jorge Ferreira
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
- * E-mail:
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22
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Shirai T, Kondo A. In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems. Biomolecules 2022; 12:620. [PMID: 35625545 PMCID: PMC9138359 DOI: 10.3390/biom12050620] [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: 03/05/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 02/05/2023] Open
Abstract
The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.
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Affiliation(s)
- Tomokazu Shirai
- Cell Factory Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan;
| | - Akihiko Kondo
- Cell Factory Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan;
- Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan
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23
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Jiang S, Otero-Muras I, Banga JR, Wang Y, Kaiser M, Krasnogor N. OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production. ACS Synth Biol 2022; 11:1531-1541. [PMID: 35389631 PMCID: PMC9016760 DOI: 10.1021/acssynbio.1c00610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Computational
tools have been widely adopted for strain optimization
in metabolic engineering, contributing to numerous success stories
of producing industrially relevant biochemicals. However, most of
these tools focus on single metabolic intervention strategies (either
gene/reaction knockout or amplification alone) and rely on hypothetical
optimality principles (e.g., maximization of growth) and precise gene
expression (e.g., fold changes) for phenotype prediction. This paper
introduces OptDesign, a new two-step strain design strategy. In the
first step, OptDesign selects regulation candidates that have a noticeable
flux difference between the wild type and production strains. In the
second step, it computes optimal design strategies with limited manipulations
(combining regulation and knockout), leading to high biochemical production.
The usefulness and capabilities of OptDesign are demonstrated for
the production of three biochemicals in Escherichia
coli using the latest genome-scale metabolic model
iML1515, showing highly consistent results with previous studies while
suggesting new manipulations to boost strain performance. The source
code is available at https://github.com/chang88ye/OptDesign.
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Affiliation(s)
- Shouyong Jiang
- Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, U.K
| | - Irene Otero-Muras
- Institute for Integrative Systems Biology, UV-CSIC, Valencia 46980, Spain
| | - Julio R. Banga
- Computational Biology Lab, MBG-CSIC, Pontevedra 36143, Spain
| | - Yong Wang
- School of Automation, Central South University, Changsha 410083, China
| | - Marcus Kaiser
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, U.K
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24
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Viana R, Couceiro D, Carreiro T, Dias O, Rocha I, Teixeira MC. A Genome-Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets. Genes (Basel) 2022; 13:genes13020303. [PMID: 35205348 PMCID: PMC8871546 DOI: 10.3390/genes13020303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/02/2022] [Indexed: 11/29/2022] Open
Abstract
Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis—iDC1003—comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies.
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Affiliation(s)
- Romeu Viana
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal; (R.V.); (D.C.); (T.C.)
- iBB—Institute for Bioengineering and Biosciences, 1049-001 Lisboa, Portugal
- Associate Laboratory Institute for Health and Bioeconomy—i4HB, 1049-001 Lisboa, Portugal
| | - Diogo Couceiro
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal; (R.V.); (D.C.); (T.C.)
- iBB—Institute for Bioengineering and Biosciences, 1049-001 Lisboa, Portugal
- Associate Laboratory Institute for Health and Bioeconomy—i4HB, 1049-001 Lisboa, Portugal
| | - Tiago Carreiro
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal; (R.V.); (D.C.); (T.C.)
- iBB—Institute for Bioengineering and Biosciences, 1049-001 Lisboa, Portugal
- Associate Laboratory Institute for Health and Bioeconomy—i4HB, 1049-001 Lisboa, Portugal
| | - Oscar Dias
- CEB—Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal;
| | - Isabel Rocha
- ITQB Nova—Instituto de Tecnologia Química e Biológica António Xavier, 1049-001 Lisboa, Portugal;
| | - Miguel Cacho Teixeira
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisboa, Portugal; (R.V.); (D.C.); (T.C.)
- iBB—Institute for Bioengineering and Biosciences, 1049-001 Lisboa, Portugal
- Associate Laboratory Institute for Health and Bioeconomy—i4HB, 1049-001 Lisboa, Portugal
- Correspondence:
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25
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Narad P, Naresh G, Sengupta A. Metabolomics and flux balance analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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26
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Tryptophan Production Maximization in a Fed-Batch Bioreactor with Modified E. coli Cells, by Optimizing Its Operating Policy Based on an Extended Structured Cell Kinetic Model. Bioengineering (Basel) 2021; 8:bioengineering8120210. [PMID: 34940363 PMCID: PMC8698263 DOI: 10.3390/bioengineering8120210] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 11/17/2022] Open
Abstract
Hybrid kinetic models, linking structured cell metabolic processes to the dynamics of macroscopic variables of the bioreactor, are more and more used in engineering evaluations to derive more precise predictions of the process dynamics under variable operating conditions. Depending on the cell model complexity, such a math tool can be used to evaluate the metabolic fluxes in relation to the bioreactor operating conditions, thus suggesting ways to genetically modify the microorganism for certain purposes. Even if development of such an extended dynamic model requires more experimental and computational efforts, its use is advantageous. The approached probative example refers to a model simulating the dynamics of nanoscale variables from several pathways of the central carbon metabolism (CCM) of Escherichia coli cells, linked to the macroscopic state variables of a fed-batch bioreactor (FBR) used for the tryptophan (TRP) production. The used E. coli strain was modified to replace the PTS system for glucose (GLC) uptake with a more efficient one. The study presents multiple elements of novelty: (i) the experimentally validated modular model itself, and (ii) its efficiency in computationally deriving an optimal operation policy of the FBR.
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27
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Thiele S, von Kamp A, Bekiaris PS, Schneider P, Klamt S. CNApy: a CellNetAnalyzer GUI in Python for analyzing and designing metabolic networks. Bioinformatics 2021; 38:1467-1469. [PMID: 34878104 PMCID: PMC8826044 DOI: 10.1093/bioinformatics/btab828] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 01/06/2023] Open
Abstract
SUMMARY Constraint-based reconstruction and analysis (COBRA) is a widely used modeling framework for analyzing and designing metabolic networks. Here, we present CNApy, an open-source cross-platform desktop application written in Python, which offers a state-of-the-art graphical front-end for the intuitive analysis of metabolic networks with COBRA methods. While the basic look-and-feel of CNApy is similar to the user interface of the MATLAB toolbox CellNetAnalyzer, it provides various enhanced features by using components of the powerful Qt library. CNApy supports a number of standard and advanced COBRA techniques and further functionalities can be easily embedded in its GUI facilitating modular extension in the future. AVAILABILITY AND IMPLEMENTATION CNApy can be installed via conda and its source code is freely available at https://github.com/cnapy-org/CNApy under the Apache 2 license.
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Affiliation(s)
- Sven Thiele
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Axel von Kamp
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Pavlos Stephanos Bekiaris
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Philipp Schneider
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany
| | - Steffen Klamt
- Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg 39106, Germany,To whom correspondence should be addressed.
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28
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MARIA G. A CCM-based modular and hybrid kinetic model to simulate the tryptophan synthesis in a fed-batch bioreactor using modified E. coli cells. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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29
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Frades I, Foguet C, Cascante M, Araúzo-Bravo MJ. Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment. Cancers (Basel) 2021; 13:4609. [PMID: 34572839 PMCID: PMC8470216 DOI: 10.3390/cancers13184609] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/31/2022] Open
Abstract
The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.
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Affiliation(s)
- Itziar Frades
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
- Max Planck Institute of Molecular Biomedicine, 48167 Münster, Germany
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERfes), 28015 Madrid, Spain
- Translational Bioinformatics Network (TransBioNet), 8001 Barcelona, Spain
- Ikerbasque, Basque Foundation for Science, 48012 Bilbao, Spain
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30
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Razaghi-Moghadam Z, Nikoloski Z. GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics 2021; 37:1717-1723. [PMID: 33245091 PMCID: PMC8289378 DOI: 10.1093/bioinformatics/btaa996] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/24/2020] [Accepted: 11/17/2020] [Indexed: 11/18/2022] Open
Abstract
Motivation Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies. Results Here, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies. Availability and implementation https://github.com/MonaRazaghi/GeneReg Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zahra Razaghi-Moghadam
- Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Department of Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Department of Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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31
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Pereira F, Lopes H, Maia P, Meyer B, Nocon J, Jouhten P, Konstantinidis D, Kafkia E, Rocha M, Kötter P, Rocha I, Patil KR. Model-guided development of an evolutionarily stable yeast chassis. Mol Syst Biol 2021; 17:e10253. [PMID: 34292675 PMCID: PMC8297383 DOI: 10.15252/msb.202110253] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/14/2023] Open
Abstract
First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.
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Affiliation(s)
- Filipa Pereira
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Life Science InstituteUniversity of MichiganAnn ArborUSA
| | - Helder Lopes
- CEB‐Centre of Biological EngineeringUniversity of MinhoCampus de GualtarBragaPortugal
| | - Paulo Maia
- Silicolife ‐ Computational Biology Solutions for the Life SciencesBragaPortugal
| | - Britta Meyer
- Johann Wolfgang Goethe‐UniversitätFrankfurt am MainGermany
| | - Justyna Nocon
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
| | - Paula Jouhten
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
| | | | - Eleni Kafkia
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- The Medical Research Council Toxicology UnitUniversity of CambridgeCambridgeUK
| | - Miguel Rocha
- CEB‐Centre of Biological EngineeringUniversity of MinhoCampus de GualtarBragaPortugal
| | - Peter Kötter
- Johann Wolfgang Goethe‐UniversitätFrankfurt am MainGermany
| | - Isabel Rocha
- CEB‐Centre of Biological EngineeringUniversity of MinhoCampus de GualtarBragaPortugal
- Instituto de Tecnologia Química e Biológica António XavierUniversidade Nova de Lisboa (ITQB‐NOVA)OeirasPortugal
| | - Kiran R Patil
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- The Medical Research Council Toxicology UnitUniversity of CambridgeCambridgeUK
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32
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Nguyen AD, Pham DN, Chau THT, Lee EY. Enhancing Sesquiterpenoid Production from Methane via Synergy of the Methylerythritol Phosphate Pathway and a Short-Cut Route to 1-Deoxy-D-xylulose 5-Phosphate in Methanotrophic Bacteria. Microorganisms 2021; 9:microorganisms9061236. [PMID: 34200225 PMCID: PMC8227265 DOI: 10.3390/microorganisms9061236] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 12/29/2022] Open
Abstract
Sesquiterpenoids are one of the most diverse classes of isoprenoids which exhibit numerous potentials in industrial biotechnology. The methanotrophs-based methane bioconversion is a promising approach for sustainable production of chemicals and fuels from methane. With intrinsic high carbon flux though the ribulose monophosphate cycle in Methylotuvimicrobium alcaliphilum 20Z, we demonstrated here that employing a short-cut route from ribulose 5-phosphate to 1-deoxy-d-xylulose 5-phosphate (DXP) could enable a more efficient isoprenoid production via the methylerythritol 4-phosphate (MEP) pathway, using α-humulene as a model compound. An additional 2.8-fold increase in α-humulene production yield was achieved by the fusion of the nDXP enzyme and DXP reductase. Additionally, we utilized these engineering strategies for the production of another sesquiterpenoid, α-bisabolene. The synergy of the nDXP and MEP pathways improved the α-bisabolene titer up to 12.24 ± 0.43 mg/gDCW, twofold greater than that of the initial strain. This study expanded the suite of sesquiterpenoids that can be produced from methane and demonstrated the synergistic uses of the nDXP and MEP pathways for improving sesquiterpenoid production in methanotrophic bacteria.
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Affiliation(s)
| | | | | | - Eun Yeol Lee
- Correspondence: ; Tel.: +82-31-201-3839; Fax: +82-31-204-8114
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33
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Saini DK, Rai A, Devi A, Pabbi S, Chhabra D, Chang JS, Shukla P. A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403. BIORESOURCE TECHNOLOGY 2021; 329:124908. [PMID: 33690058 DOI: 10.1016/j.biortech.2021.124908] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
The cyanobacterial phycobiliproteins (PBPs) are an important natural colorant for nutraceutical industries. Here, a multi-objective hybrid machine learning-based optimization approach was used for enhanced cell biomass and PBPs production simultaneously in Nostoc sp. CCC-403. A central composite design (CCD) was employed to design an experimental setup for four input parameters, including three BG-11 medium components and pH. We achieved a 61.76% increase in total PBPs production and an almost 90% increase in cell biomass by our prediction model. We also established a test genome-scale metabolic network (GSMN) for Nostoc sp. and identified potential metabolic fluxes contributing to PBPs enhanced production. This study highlights the advantage of the hybrid machine learning approach and GSMN to achieve optimization for more than one objective and serves as the foundation for future efforts to convert cyanobacteria as an economically viable source for biofuels and natural products.
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Affiliation(s)
- Dinesh Kumar Saini
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India; Centre for Conservation and Utilisation of Blue-Green Algae (CCUBGA), Division of Microbiology, ICAR - Indian Agricultural Research Institute, New Delhi 110 012, India
| | - Amit Rai
- Plant Molecular Science Center, Chiba University, Chiba 260-8675, Japan; RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Alka Devi
- Centre for Conservation and Utilisation of Blue-Green Algae (CCUBGA), Division of Microbiology, ICAR - Indian Agricultural Research Institute, New Delhi 110 012, India
| | - Sunil Pabbi
- Centre for Conservation and Utilisation of Blue-Green Algae (CCUBGA), Division of Microbiology, ICAR - Indian Agricultural Research Institute, New Delhi 110 012, India
| | - Deepak Chhabra
- Department of Mechanical Engineering, University Institute of Engineering & Technology, Maharshi Dayanand University, Rohtak 124001, Haryana, India
| | - Jo-Shu Chang
- Department of Chemical and Materials Engineering, Tunghai University, Taichung 407, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India; School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, India.
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34
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Nazem-Bokaee H, Hom EFY, Warden AC, Mathews S, Gueidan C. Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling. Front Microbiol 2021; 12:667864. [PMID: 34012428 PMCID: PMC8126723 DOI: 10.3389/fmicb.2021.667864] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022] Open
Abstract
Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.
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Affiliation(s)
- Hadi Nazem-Bokaee
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia.,CSIRO Land and Water, Canberra, ACT, Australia.,CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Erik F Y Hom
- Department of Biology and Center for Biodiversity and Conservation Research, The University of Mississippi, University City, MS, United States
| | | | - Sarah Mathews
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Cécile Gueidan
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia
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35
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The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes. Essays Biochem 2021; 65:261-275. [PMID: 33956071 DOI: 10.1042/ebc20200132] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 02/08/2023]
Abstract
Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.
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36
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Understanding FBA Solutions under Multiple Nutrient Limitations. Metabolites 2021; 11:metabo11050257. [PMID: 33919383 PMCID: PMC8143296 DOI: 10.3390/metabo11050257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 11/27/2022] Open
Abstract
Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.
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37
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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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Mesquita TJB, Campani G, Giordano RC, Zangirolami TC, Horta ACL. Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors. Biotechnol Bioeng 2021; 118:2076-2091. [PMID: 33615444 DOI: 10.1002/bit.27721] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/11/2021] [Accepted: 02/18/2021] [Indexed: 12/22/2022]
Abstract
Various bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L-1 ·h-1 and a yield of 0.48 gethanol .gsubstrate -1 , which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L-1 ·h-1 and 0.35-0.40 gethanol ·gsubstrate -1 , respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.
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Affiliation(s)
- Thiago J B Mesquita
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), São Carlos, São Paulo, Brazil
| | - Gilson Campani
- Department of Engineering, Federal University of Lavras, Lavras, Minas Gerais, Brazil
| | - Roberto C Giordano
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), São Carlos, São Paulo, Brazil
| | - Teresa C Zangirolami
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), São Carlos, São Paulo, Brazil
| | - Antonio C L Horta
- Graduate Program of Chemical Engineering, Federal University of São Carlos (PPGEQ-UFSCar), São Carlos, São Paulo, Brazil
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Abstract
It is generally recognized that proteins constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. To account for the growth condition-dependent proteome in the constraint-based metabolic modeling of Escherichia coli, we consolidated a coarse-grained protein allocation approach with the explicit consideration of enzymatic constraints on reaction fluxes. Besides representing physiologically relevant wild-type phenotypes and flux distributions, the resulting protein allocation model (PAM) advances the predictability of the metabolic responses to genetic perturbations. A main driver of mutant phenotypes was ascribed to inherited regulation patterns in protein distribution among metabolic enzymes. Moreover, the PAM correctly reflected metabolic responses to an augmented protein burden imposed by the heterologous expression of green fluorescent protein. In summary, we were able to model the effects of important and frequently applied metabolic engineering approaches on microbial metabolism. Therefore, we want to promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and engineering purposes. IMPORTANCE Predictive metabolic models are important, e.g., for generating biological knowledge and designing microbes with superior performance for target compound production. Yet today’s whole-cell models either show insufficient predictive capabilities or are computationally too expensive to be applied to metabolic engineering purposes. By linking the inherent genotype-phenotype relationship to a complete representation of the proteome, the PAM advances the accuracy of simulated phenotypes and intracellular flux distributions of E. coli. Being equally computationally lightweight as classical stoichiometric models and allowing for the application of established in silico tools, the PAM and related simulation approaches will foster the use of a model-driven metabolic research. Applications range from the investigation of mechanisms of microbial evolution to the determination of optimal strain design strategies in metabolic engineering, thus supporting basic scientists and engineers alike.
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40
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Johnson ZJ, Krutkin DD, Bohutskyi P, Kalyuzhnaya MG. Metals and methylotrophy: Via global gene expression studies. Methods Enzymol 2021; 650:185-213. [PMID: 33867021 DOI: 10.1016/bs.mie.2021.01.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A number of minerals, such as copper, cobalt, and rare earth elements (REE), are essential modulators of microbial one-carbon metabolism. This chapter provides an overview of the gene expression study design and analysis protocols for uncovering REE-induced changes in methylotrophic bacteria. By interrogating relationships and differences in total gene expression induced by mineral micronutrients, a deeper understanding of gene regulation at a systems scale can be gained. With careful design and execution of RNA-sequencing experiments, thorough processing and assessment of read quality can be utilized to assess and adjust for possible biases. By ensuring only quality data are utilized in downstream processes, differential gene expression, overrepresented analyses, and gene-set enrichment analyses provide reliable and reproducible representation of pathways and functions which are being affected by changes in environmental conditions.
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Affiliation(s)
- Zachary J Johnson
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Dennis D Krutkin
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Pavlo Bohutskyi
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Marina G Kalyuzhnaya
- Department of Biology, San Diego State University, San Diego, CA, United States.
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41
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The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases. Metabolites 2021; 11:metabo11020113. [PMID: 33671140 PMCID: PMC7923039 DOI: 10.3390/metabo11020113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 11/17/2022] Open
Abstract
Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.
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Pereira V, Cruz F, Rocha M. MEWpy: A Computational Strain Optimization Workbench in Python. Bioinformatics 2021; 37:2494-2496. [PMID: 33459757 PMCID: PMC8388025 DOI: 10.1093/bioinformatics/btab013] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 12/29/2020] [Accepted: 01/05/2021] [Indexed: 11/16/2022] Open
Abstract
Summary Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. Availability and implementation MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.
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Affiliation(s)
- Vítor Pereira
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Fernando Cruz
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
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43
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Cheung FKM, Qin J. The Methods and Tools for Molecular Network Construction. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11464-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Anand S, Mukherjee K, Padmanabhan P. An insight to flux-balance analysis for biochemical networks. Biotechnol Genet Eng Rev 2020; 36:32-55. [PMID: 33292061 DOI: 10.1080/02648725.2020.1847440] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.
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Affiliation(s)
- Shreya Anand
- Department of Bio-Engineering, Birla Institute of Technology , Ranchi, JH, India
| | - Koel Mukherjee
- Department of Bio-Engineering, Birla Institute of Technology , Ranchi, JH, India
| | - Padmini Padmanabhan
- Department of Bio-Engineering, Birla Institute of Technology , Ranchi, JH, India
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Jiang S, Wang Y, Kaiser M, Krasnogor N. NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 2020; 36:3482-3492. [PMID: 32167529 PMCID: PMC7267835 DOI: 10.1093/bioinformatics/btaa163] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/28/2020] [Accepted: 03/10/2020] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shouyong Jiang
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Yong Wang
- School of Automation, Central South University, Changsha 410083, China
| | - Marcus Kaiser
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Natalio Krasnogor
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
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Viana R, Dias O, Lagoa D, Galocha M, Rocha I, Teixeira MC. Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction. J Fungi (Basel) 2020; 6:E171. [PMID: 32932905 PMCID: PMC7559133 DOI: 10.3390/jof6030171] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022] Open
Abstract
Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis.
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Affiliation(s)
- Romeu Viana
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
| | - Davide Lagoa
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
| | - Mónica Galocha
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
| | - Isabel Rocha
- Centre of Biological Engineering, Universidade do Minho, 4710-057 Braga, Portugal; (O.D.); (D.L.)
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB-NOVA), 2780-157 Oeiras, Portugal
| | - Miguel Cacho Teixeira
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; (R.V.); (M.G.)
- Institute for Bioengineering and Biosciences, Biological Sciences Research Group, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
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Nguyen AD, Kim D, Lee EY. Unlocking the biosynthesis of sesquiterpenoids from methane via the methylerythritol phosphate pathway in methanotrophic bacteria, using α-humulene as a model compound. Metab Eng 2020; 61:69-78. [PMID: 32387228 DOI: 10.1016/j.ymben.2020.04.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/21/2020] [Accepted: 04/25/2020] [Indexed: 11/22/2022]
Abstract
Isoprenoids are an abundant and diverse class of natural products with various applications in the pharmaceutical, cosmetics and biofuel industries. A methanotroph-based biorefinery is an attractive scenario for the production of a variety of value-added compounds from methane, because methane is a promising alternative feedstock for industrial biomanufacturing. In this study, we metabolically engineered Methylotuvimicrobium alcaliphilum 20Z for de novo synthesis of a sesquiterpenoid from methane, using α-humulene as a model compound, via optimization of the native methylerythritol phosphate (MEP) pathway. Expression of codon-optimized α-humulene synthase from Zingiber zerumbet in M. alcaliphilum 20Z resulted in an initial yield of 0.04 mg/g dry cell weight. Overexpressing key enzymes (IspA, IspG, and Dxs) for debottlenecking of the MEP pathway increased α-humulene production 5.2-fold compared with the initial strain. Subsequently, redirecting the carbon flux through the Embden-Meyerhof-Parnas pathway resulted in an additional 3-fold increase in α-humulene production. Additionally, a genome-scale model using flux scanning based on enforced objective flux method was used to identify potential overexpression targets to increase flux towards isoprenoid production. Several target reactions from cofactor synthesis pathways were probed and evaluated for their effects on α-humulene synthesis, resulting in α-humulene yield up to 0.75 mg/g DCW with 18.8-fold enhancement from initial yield. This study first demonstrates production of a sesquiterpenoid from methane using methanotrophs as the biocatalyst and proposes potential strategies to enhance production of sesquiterpenoid and related isoprenoid products in engineered methanotrophic bacteria.
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Affiliation(s)
- Anh Duc Nguyen
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea
| | - Donghyuk Kim
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, South Korea
| | - Eun Yeol Lee
- Department of Chemical Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, 17104, South Korea.
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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: 1.8] [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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Abstract
Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.
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50
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Blanco-Míguez A, Fdez-Riverola F, Sánchez B, Lourenço A. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Brief Bioinform 2020; 20:1032-1056. [PMID: 29186315 DOI: 10.1093/bib/bbx156] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 10/23/2017] [Indexed: 12/18/2022] Open
Abstract
The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.
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Affiliation(s)
| | | | | | - Anália Lourenço
- Dpto. de Informática - Universidade de Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
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