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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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2
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Hsieh YE, Tandon K, Verbruggen H, Nikoloski Z. Comparative analysis of metabolic models of microbial communities reconstructed from automated tools and consensus approaches. NPJ Syst Biol Appl 2024; 10:54. [PMID: 38783065 PMCID: PMC11116368 DOI: 10.1038/s41540-024-00384-y] [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: 01/06/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Genome-scale metabolic models (GEMs) of microbial communities offer valuable insights into the functional capabilities of their members and facilitate the exploration of microbial interactions. These models are generated using different automated reconstruction tools, each relying on different biochemical databases that may affect the conclusions drawn from the in silico analysis. One way to address this problem is to employ a consensus reconstruction method that combines the outcomes of different reconstruction tools. Here, we conducted a comparative analysis of community models reconstructed from three automated tools, i.e. CarveMe, gapseq, and KBase, alongside a consensus approach, utilizing metagenomics data from two marine bacterial communities. Our analysis revealed that these reconstruction approaches, while based on the same genomes, resulted in GEMs with varying numbers of genes and reactions as well as metabolic functionalities, attributed to the different databases employed. Further, our results indicated that the set of exchanged metabolites was more influenced by the reconstruction approach rather than the specific bacterial community investigated. This observation suggests a potential bias in predicting metabolite interactions using community GEMs. We also showed that consensus models encompassed a larger number of reactions and metabolites while concurrently reducing the presence of dead-end metabolites. Therefore, the usage of consensus models allows making full and unbiased use from aggregating genes from the different reconstructions in assessing the functional potential of microbial communities.
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Affiliation(s)
- Yunli Eric Hsieh
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Kshitij Tandon
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Heroen Verbruggen
- School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
| | - Zoran Nikoloski
- Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
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3
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Hari A, Zarrabi A, Lobo D. mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers. NAR Genom Bioinform 2024; 6:lqae010. [PMID: 38312936 PMCID: PMC10836943 DOI: 10.1093/nargab/lqae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/15/2023] [Accepted: 01/16/2024] [Indexed: 02/06/2024] Open
Abstract
Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.
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Affiliation(s)
- Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Arveen Zarrabi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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4
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Wang X, Mohsin A, Sun Y, Li C, Zhuang Y, Wang G. From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology. Bioengineering (Basel) 2023; 10:744. [PMID: 37370675 DOI: 10.3390/bioengineering10060744] [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: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
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Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yifei Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
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5
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Wendering P, Nikoloski Z. COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions. PLoS Comput Biol 2022; 18:e1009906. [PMID: 35320266 PMCID: PMC8942231 DOI: 10.1371/journal.pcbi.1009906] [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: 09/01/2021] [Accepted: 02/09/2022] [Indexed: 11/18/2022] Open
Abstract
Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- * E-mail:
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López-Agudelo VA, Mendum TA, Laing E, Wu H, Baena A, Barrera LF, Beste DJV, Rios-Estepa R. A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks. PLoS Comput Biol 2020; 16:e1007533. [PMID: 32542021 PMCID: PMC7316355 DOI: 10.1371/journal.pcbi.1007533] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 06/25/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023] Open
Abstract
Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.
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Affiliation(s)
- Víctor A. López-Agudelo
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín, Colombia
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Tom A. Mendum
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Emma Laing
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - HuiHai Wu
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Andres Baena
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
- Departamento de Microbiología y Parasitología, Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Luis F. Barrera
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
- Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia
| | - Dany J. V. Beste
- Department of Microbial Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Rigoberto Rios-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín, Colombia
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Gilbert D, Heiner M, Jayaweera Y, Rohr C. Towards dynamic genome-scale models. Brief Bioinform 2020; 20:1167-1180. [PMID: 29040409 PMCID: PMC6781584 DOI: 10.1093/bib/bbx096] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 07/10/2017] [Indexed: 01/14/2023] Open
Abstract
The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large-up to whole genome in size-and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.
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Affiliation(s)
- David Gilbert
- Corresponding author. David Gilbert, Department of Computer Science, Brunel University London, Uxbridge, UB8 3PH, United Kingdom. E-mail:
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iMet: A graphical user interface software tool to merge metabolic networks. Heliyon 2019; 5:e01766. [PMID: 31286073 PMCID: PMC6587100 DOI: 10.1016/j.heliyon.2019.e01766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 04/10/2019] [Accepted: 05/15/2019] [Indexed: 11/23/2022] Open
Abstract
Nowadays, studying microorganisms has become faster and deeper than the last decades, thanks to the modeling of genome-scale metabolic networks. Completed genome sequencing projects of microorganisms and annotating these sequences have provided a worthwhile platform for reconstructing and modeling genome-scale metabolic networks. The genome-scale metabolic network reconstruction is a laborious and time-consuming task which needs an extensive study and search in different types of databases. Furthermore, it also requires an iterative process of creating and curating the obtained network, particularly with experimental methods. Hence, different types of reconstructions and models of a targeted microorganism can be found with different qualities, as the goal and need of researchers differ. Due to these circumstances, scientists have to continue with only one of the reconstructed metabolic networks of each microorganism and ignore the rest in their in silico works. It is clear that having a tool which merges different metabolic networks of a single organism can be a useful and effective way to study them with minimal cost and time. To meet this need, we have developed iMet, the standalone graphical user interface (GUI) software tool to merge multiple reconstructed metabolic networks of microorganisms. As a case study, we merged three reconstructed metabolic networks of a cyanobacterium using iMet, and then all of them (including the new merged one) became modeled. The results of our evaluations including Flux Balance Analysis (FBA), revealed enhancing metabolic network coverage as well as increasing yield of desired products in the new obtained model.
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9
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Kavvas ES, Seif Y, Yurkovich JT, Norsigian C, Poudel S, Greenwald WW, Ghatak S, Palsson BO, Monk JM. Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. BMC SYSTEMS BIOLOGY 2018; 12:25. [PMID: 29499714 PMCID: PMC5834885 DOI: 10.1186/s12918-018-0557-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 02/21/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND The efficacy of antibiotics against M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M. tuberculosis growth in physiological conditions have described an environment that is different from common in vitro media. Thus, elucidating the interplay between available nutrient sources and antibiotic efficacy has clear medical relevance. While genome-scale reconstructions of M. tuberculosis have enabled the ability to interrogate media differences for the past 10 years, recent reconstructions have diverged from each other without standardization. A unified reconstruction of M. tuberculosis H37Rv would elucidate the impact of different nutrient conditions on antibiotic efficacy and provide new insights for therapeutic intervention. RESULTS We present a new genome-scale model of M. tuberculosis H37Rv, named iEK1011, that unifies and updates previous M. tuberculosis H37Rv genome-scale reconstructions. We functionally assess iEK1011 against previous models and show that the model increases correct gene essentiality predictions on two different experimental datasets by 6% (53% to 60%) and 18% (60% to 71%), respectively. We compared simulations between in vitro and approximated in vivo media conditions to examine the predictive capabilities of iEK1011. The simulated differences recapitulated literature defined characteristics in the rewiring of TCA metabolism including succinate secretion, gluconeogenesis, and activation of both the glyoxylate shunt and the methylcitrate cycle. To assist efforts to elucidate mechanisms of antibiotic resistance development, we curated 16 metabolic genes related to antimicrobial resistance and approximated evolutionary drivers of resistance. Comparing simulations of these antibiotic resistance features between in vivo and in vitro media highlighted condition-dependent differences that may influence the efficacy of antibiotics. CONCLUSIONS iEK1011 provides a computational knowledge base for exploring the impact of different environmental conditions on the metabolic state of M. tuberculosis H37Rv. As more experimental data and knowledge of M. tuberculosis H37Rv become available, a unified and standardized M. tuberculosis model will prove to be a valuable resource to the research community studying the systems biology of M. tuberculosis.
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Affiliation(s)
- Erol S. Kavvas
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Yara Seif
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - James T. Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA
| | - Charles Norsigian
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Saugat Poudel
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - William W. Greenwald
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA
| | - Sankha Ghatak
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA
- Department of Pediatrics, University of California, San Diego, La Jolla, CA USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens Lyngby, Denmark
| | - Jonathan M. Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA USA
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Xu N, Ye C, Liu L. Genome-scale biological models for industrial microbial systems. Appl Microbiol Biotechnol 2018; 102:3439-3451. [PMID: 29497793 DOI: 10.1007/s00253-018-8803-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 01/08/2023]
Abstract
The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
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Affiliation(s)
- Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, Jiangsu, 225009, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China.
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11
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López-Agudelo VA, Baena A, Ramirez-Malule H, Ochoa S, Barrera LF, Ríos-Estepa R. Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection. BMC SYSTEMS BIOLOGY 2017; 11:107. [PMID: 29157227 PMCID: PMC5697012 DOI: 10.1186/s12918-017-0496-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 11/13/2017] [Indexed: 12/16/2022]
Abstract
BACKGROUND Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb's vulnerability and metabolic adaptation have been carried out. RESULTS A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports. CONCLUSIONS This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.
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Affiliation(s)
- Víctor A. López-Agudelo
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Andres Baena
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
- Departamento de Microbiología y Parasitología, Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | | | - Silvia Ochoa
- Grupo de investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP), Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Luis F. Barrera
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
- Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Rigoberto Ríos-Estepa
- Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
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12
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van Heck RGA, Ganter M, Martins dos Santos VAP, Stelling J. Efficient Reconstruction of Predictive Consensus Metabolic Network Models. PLoS Comput Biol 2016; 12:e1005085. [PMID: 27563720 PMCID: PMC5001716 DOI: 10.1371/journal.pcbi.1005085] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 07/29/2016] [Indexed: 01/08/2023] Open
Abstract
Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.
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Affiliation(s)
- Ruben G. A. van Heck
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
| | - Mathias Ganter
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
- LifeGlimmer GmbH, Berlin, Germany
- * E-mail: (VAPMdS); (JS)
| | - Joerg Stelling
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- * E-mail: (VAPMdS); (JS)
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Mohammadi R, Fallah-Mehrabadi J, Bidkhori G, Zahiri J, Javad Niroomand M, Masoudi-Nejad A. A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production. MOLECULAR BIOSYSTEMS 2016; 12:2552-61. [DOI: 10.1039/c6mb00119j] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Metabolic network models can be optimized for the production of desired materials like biofuels.
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Affiliation(s)
- Reza Mohammadi
- Laboratory of Systems Biology and Bioinformatics (LBB)
- Institute of Biochemistry and Biophysics
- University of Tehran
- Tehran
- Iran
| | | | | | - Javad Zahiri
- Bioinformatics and Computational Omics Lab (BioCOOL)
- Department of Biophysics
- Faculty of Biological Sciences
- Tarbiat Modares University
- Tehran
| | - Mohammad Javad Niroomand
- Learning Intelligent Systems Lab
- School of Electrical and Computer Engineering
- University of Tehran
- Tehran
- Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB)
- Institute of Biochemistry and Biophysics
- University of Tehran
- Tehran
- Iran
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14
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Licona-Cassani C, Cruz-Morales P, Manteca A, Barona-Gomez F, Nielsen LK, Marcellin E. Systems Biology Approaches to Understand Natural Products Biosynthesis. Front Bioeng Biotechnol 2015; 3:199. [PMID: 26697425 PMCID: PMC4673338 DOI: 10.3389/fbioe.2015.00199] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2015] [Accepted: 11/24/2015] [Indexed: 11/24/2022] Open
Abstract
Actinomycetes populate soils and aquatic sediments that impose biotic and abiotic challenges for their survival. As a result, actinomycetes metabolism and genomes have evolved to produce an overwhelming diversity of specialized molecules. Polyketides, non-ribosomal peptides, post-translationally modified peptides, lactams, and terpenes are well-known bioactive natural products with enormous industrial potential. Accessing such biological diversity has proven difficult due to the complex regulation of cellular metabolism in actinomycetes and to the sparse knowledge of their physiology. The past decade, however, has seen the development of omics technologies that have significantly contributed to our better understanding of their biology. Key observations have contributed toward a shift in the exploitation of actinomycete’s biology, such as using their full genomic potential, activating entire pathways through key metabolic elicitors and pathway engineering to improve biosynthesis. Here, we review recent efforts devoted to achieving enhanced discovery, activation, and manipulation of natural product biosynthetic pathways in model actinomycetes using genome-scale biological datasets.
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Affiliation(s)
- Cuauhtemoc Licona-Cassani
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland , Brisbane, QLD , Australia ; National Laboratory of Genomics for Biodiversity (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN) , Irapuato , México
| | - Pablo Cruz-Morales
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN) , Irapuato , México
| | - Angel Manteca
- Departamento de Biología Funcional and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Facultad de Medicina, Universidad de Oviedo , Oviedo , Spain
| | - Francisco Barona-Gomez
- National Laboratory of Genomics for Biodiversity (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN) , Irapuato , México
| | - Lars K Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland , Brisbane, QLD , Australia
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland , Brisbane, QLD , Australia
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15
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Garay CD, Dreyfuss JM, Galagan JE. Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis. BMC SYSTEMS BIOLOGY 2015; 9:57. [PMID: 26377923 PMCID: PMC4574064 DOI: 10.1186/s12918-015-0206-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 09/03/2015] [Indexed: 12/20/2022]
Abstract
BACKGROUND Mycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive experimental work could significantly accelerate research. RESULTS We have developed E-Flux-MFC, an enhancement of our original E-Flux method that enables the prediction of changes in the production of external and internal metabolites corresponding to gene expression measurements. We have used this method to simulate the changes in the metabolic state of Mycobacterium tuberculosis (MTB). We have validated the accuracy of E-Flux-MFC for predicting changes in lipids and metabolites during a hypoxia time course using previously published metabolomics and transcriptomics data. We have further validated the accuracy of the method for predicting changes in MTB lipids following the deletion and induction of two well-studied transcription factors (TFs). We have applied the method to predict the metabolic impact of the induction of each of the approximately 180 MTB TFs using a previously generated and publically available expression data set. CONCLUSIONS E-flux-MFC can be used to study global changes in MTB metabolites from gene expression data associated with environmental and genetic perturbations. The application of this method to a data set of MTB TF perturbations provides a resource for studying the large number of TFs whose functions remain unknown. Most TFs impact metabolites indirectly through the propagation of gene expression changes through the regulatory network rather than through their direct regulons. E-Flux-MFC is also applicable to any organism for which accurate metabolic models are available.
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Affiliation(s)
- Christopher D Garay
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
| | - Jonathan M Dreyfuss
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. .,Joslin Diabetes Center, Boston, MA, 02215, USA.
| | - James E Galagan
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. .,Graduate Program in Bioinformatics, Boston University, Boston, MA, 02215, USA. .,National Emerging Infectious Diseases Laboratories, Boston, MA, 02118, USA.
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16
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Ravikrishnan A, Raman K. Critical assessment of genome-scale metabolic networks: the need for a unified standard. Brief Bioinform 2015; 16:1057-68. [PMID: 25725218 DOI: 10.1093/bib/bbv003] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Indexed: 12/17/2022] Open
Abstract
Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.
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17
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Rienksma RA, Suarez-Diez M, Spina L, Schaap PJ, Martins dos Santos VAP. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets. Semin Immunol 2014; 26:610-22. [PMID: 25453232 DOI: 10.1016/j.smim.2014.09.013] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 09/26/2014] [Accepted: 09/29/2014] [Indexed: 12/28/2022]
Abstract
Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.
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MESH Headings
- Antitubercular Agents/therapeutic use
- Bacterial Proteins/genetics
- Bacterial Proteins/metabolism
- Carbon Isotopes
- Drug Resistance, Multiple, Bacterial/genetics
- Gene Expression Regulation, Bacterial
- Gene Regulatory Networks
- Genome, Bacterial
- Host-Pathogen Interactions
- Humans
- Metabolic Networks and Pathways/genetics
- Models, Statistical
- Molecular Targeted Therapy
- Mycobacterium tuberculosis/drug effects
- Mycobacterium tuberculosis/genetics
- Mycobacterium tuberculosis/metabolism
- Systems Biology
- Tuberculosis, Multidrug-Resistant/drug therapy
- Tuberculosis, Multidrug-Resistant/metabolism
- Tuberculosis, Multidrug-Resistant/microbiology
- Tuberculosis, Multidrug-Resistant/pathology
- Tuberculosis, Pulmonary/drug therapy
- Tuberculosis, Pulmonary/metabolism
- Tuberculosis, Pulmonary/microbiology
- Tuberculosis, Pulmonary/pathology
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Affiliation(s)
- Rienk A Rienksma
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Lucie Spina
- Centre National de la Rescherche Scientifique (CNRS), Institut de Pharmacologie et de Biologie Structurale (UMR 5089), Department of Tuberculosis and Infection Biology and Université de Toulouse (Université Paul Sabatier, Toulouse III), IPBS, 205 Route de Narbonne, BP 64182, F-31077 Toulouse, France
| | - Peter J Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands
| | - Vitor A P Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research Centre, Dreijenplein 10, Wageningen 6703 HB, The Netherlands; Lifeglimmer GmbH, Markelstrasse 38, 12163 Berlin, Germany.
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18
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Lofthouse EK, Wheeler PR, Beste DJV, Khatri BL, Wu H, Mendum T, Kierzek AM, McFadden J. Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex. PLoS One 2013; 8:e75913. [PMID: 24098743 PMCID: PMC3783153 DOI: 10.1371/journal.pone.0075913] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2013] [Accepted: 08/16/2013] [Indexed: 01/11/2023] Open
Abstract
The Mycobacterium tuberculosis complex includes bovine and human strains of the tuberculosis bacillus, including Mycobacterium tuberculosis, Mycobacterium bovis and the Mycobacterium bovis BCG vaccine strain. M. bovis has evolved from a M. tuberculosis-like ancestor and is the ancestor of the BCG vaccine. The pathogens demonstrate distinct differences in virulence, host range and metabolism, but the role of metabolic differences in pathogenicity is poorly understood. Systems biology approaches have been used to investigate the metabolism of M. tuberculosis, but not to probe differences between tuberculosis strains. In this study genome scale metabolic networks of M. bovis and M. bovis BCG were constructed and interrogated, along with a M. tuberculosis network, to predict substrate utilisation, gene essentiality and growth rates. The models correctly predicted 87-88% of high-throughput phenotype data, 75-76% of gene essentiality data and in silico-predicted growth rates matched measured rates. However, analysis of the metabolic networks identified discrepancies between in silico predictions and in vitro data, highlighting areas of incomplete metabolic knowledge. Additional experimental studies carried out to probe these inconsistencies revealed novel insights into the metabolism of these strains. For instance, that the reduction in metabolic capability observed in bovine tuberculosis strains, as compared to M. tuberculosis, is not reflected by current genetic or enzymatic knowledge. Hence, the in silico networks not only successfully simulate many aspects of the growth and physiology of these mycobacteria, but also provide an invaluable tool for future metabolic studies.
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Affiliation(s)
- Emma K. Lofthouse
- Animal Health and Veterinary Laboratories Agency (Weybridge), Department for Bovine Tuberculosis, New Haw, Surrey, United Kingdom
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
| | - Paul R. Wheeler
- Animal Health and Veterinary Laboratories Agency (Weybridge), Department for Bovine Tuberculosis, New Haw, Surrey, United Kingdom
| | - Dany J. V. Beste
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
| | - Bhagwati L. Khatri
- Animal Health and Veterinary Laboratories Agency (Weybridge), Department for Bovine Tuberculosis, New Haw, Surrey, United Kingdom
| | - Huihai Wu
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
| | - Tom A. Mendum
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
| | - Andrzej M. Kierzek
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
| | - Johnjoe McFadden
- Department of Microbial and Cellular Sciences, Faculty of Health and Medical Sciences, University of Surrey, Stag Hill, Guildford, Surrey, United Kingdom
- * E-mail:
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19
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Stobbe MD, Swertz MA, Thiele I, Rengaw T, van Kampen AHC, Moerland PD. Consensus and conflict cards for metabolic pathway databases. BMC SYSTEMS BIOLOGY 2013; 7:50. [PMID: 23803311 PMCID: PMC3703255 DOI: 10.1186/1752-0509-7-50] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 06/20/2013] [Indexed: 01/04/2023]
Abstract
Background The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. Results We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. Conclusions C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.
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Affiliation(s)
- Miranda D Stobbe
- Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, Amsterdam 1100 DE, the Netherlands
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20
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Tymoshenko S, Oppenheim RD, Soldati-Favre D, Hatzimanikatis V. Functional genomics of Plasmodium falciparum using metabolic modelling and analysis. Brief Funct Genomics 2013; 12:316-27. [PMID: 23793264 PMCID: PMC3743259 DOI: 10.1093/bfgp/elt017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Plasmodium falciparum is an obligate intracellular parasite and the leading cause of severe malaria responsible for tremendous morbidity and mortality particularly in sub-Saharan Africa. Successful completion of the P. falciparum genome sequencing project in 2002 provided a comprehensive foundation for functional genomic studies on this pathogen in the following decade. Over this period, a large spectrum of experimental approaches has been deployed to improve and expand the scope of functionally annotated genes. Meanwhile, rapidly evolving methods of systems biology have also begun to contribute to a more global understanding of various aspects of the biology and pathogenesis of malaria. Herein we provide an overview on metabolic modelling, which has the capability to integrate information from functional genomics studies in P. falciparum and guide future malaria research efforts towards the identification of novel candidate drug targets.
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Affiliation(s)
- Stepan Tymoshenko
- Institute of Chemical Engineering, Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH-1015, Switzerland.
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21
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Ngo LT, Okogun JI, Folk WR. 21st century natural product research and drug development and traditional medicines. Nat Prod Rep 2013; 30:584-92. [PMID: 23450245 PMCID: PMC3652390 DOI: 10.1039/c3np20120a] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Natural products and related structures are essential sources of new pharmaceuticals, because of the immense variety of functionally relevant secondary metabolites of microbial and plant species. Furthermore, the development of powerful analytical tools based upon genomics, proteomics, metabolomics, bioinformatics and other 21st century technologies are greatly expediting identification and characterization of these natural products. Here we discuss the synergistic and reciprocal benefits of linking these 'omics technologies with robust ethnobotanical and ethnomedical studies of traditional medicines, to provide critically needed improved medicines and treatments that are inexpensive, accessible, safe and reliable. However, careless application of modern technologies can challenge traditional knowledge and biodiversity that are the foundation of traditional medicines. To address such challenges while fulfilling the need for improved (and new) medicines, we encourage the development of Regional Centres of 'omics Technologies functionally linked with Regional Centres of Genetic Resources, especially in regions of the world where use of traditional medicines is prevalent and essential for health.
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Affiliation(s)
- Linh T Ngo
- Genetics Area Program, University of Missouri, Columbia, MO 65211, USA
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22
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Altman T, Travers M, Kothari A, Caspi R, Karp PD. A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics 2013; 14:112. [PMID: 23530693 PMCID: PMC3665663 DOI: 10.1186/1471-2105-14-112] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Accepted: 03/04/2013] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND The MetaCyc and KEGG projects have developed large metabolic pathway databases that are used for a variety of applications including genome analysis and metabolic engineering. We present a comparison of the compound, reaction, and pathway content of MetaCyc version 16.0 and a KEGG version downloaded on Feb-27-2012 to increase understanding of their relative sizes, their degree of overlap, and their scope. To assess their overlap, we must know the correspondences between compounds, reactions, and pathways in MetaCyc, and those in KEGG. We devoted significant effort to computational and manual matching of these entities, and we evaluated the accuracy of the correspondences. RESULTS KEGG contains 179 module pathways versus 1,846 base pathways in MetaCyc; KEGG contains 237 map pathways versus 296 super pathways in MetaCyc. KEGG pathways contain 3.3 times as many reactions on average as do MetaCyc pathways, and the databases employ different conceptualizations of metabolic pathways. KEGG contains 8,692 reactions versus 10,262 for MetaCyc. 6,174 KEGG reactions are components of KEGG pathways versus 6,348 for MetaCyc. KEGG contains 16,586 compounds versus 11,991 for MetaCyc. 6,912 KEGG compounds act as substrates in KEGG reactions versus 8,891 for MetaCyc. MetaCyc contains a broader set of database attributes than does KEGG, such as relationships from a compound to enzymes that it regulates, identification of spontaneous reactions, and the expected taxonomic range of metabolic pathways. MetaCyc contains many pathways not found in KEGG, from plants, fungi, metazoa, and actinobacteria; KEGG contains pathways not found in MetaCyc, for xenobiotic degradation, glycan metabolism, and metabolism of terpenoids and polyketides. MetaCyc contains fewer unbalanced reactions, which facilitates metabolic modeling such as using flux-balance analysis. MetaCyc includes generic reactions that may be instantiated computationally. CONCLUSIONS KEGG contains significantly more compounds than does MetaCyc, whereas MetaCyc contains significantly more reactions and pathways than does KEGG, in particular KEGG modules are quite incomplete. The number of reactions occurring in pathways in the two DBs are quite similar.
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Affiliation(s)
- Tomer Altman
- Bioinformatics Research Group, SRI International, Menlo Park, USA
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23
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Ma CY, Lin SH, Lee CC, Tang CY, Berger B, Liao CS. Reconstruction of phyletic trees by global alignment of multiple metabolic networks. BMC Bioinformatics 2013; 14 Suppl 2:S12. [PMID: 23368411 PMCID: PMC3549807 DOI: 10.1186/1471-2105-14-s2-s12] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. Results We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. Conclusions We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.
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Affiliation(s)
- Cheng-Yu Ma
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
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24
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Stobbe MD, Jansen GA, Moerland PD, van Kampen AHC. Knowledge representation in metabolic pathway databases. Brief Bioinform 2012. [PMID: 23202525 DOI: 10.1093/bib/bbs060] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.
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Affiliation(s)
- Miranda D Stobbe
- Bioinformatics Laboratory, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, the Netherlands. Tel.: ++31 20 5667096;
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25
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Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M. Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform 2012; 15:123-35. [PMID: 23172809 PMCID: PMC3896926 DOI: 10.1093/bib/bbs058] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of metabolite and reaction discrepancies, these resources aim to accelerate the development and application of high-quality genome-scale metabolic network reconstructions and models.
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Reconstruction of the Saccharopolyspora erythraea genome-scale model and its use for enhancing erythromycin production. Antonie van Leeuwenhoek 2012; 102:493-502. [DOI: 10.1007/s10482-012-9783-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2012] [Accepted: 07/21/2012] [Indexed: 10/28/2022]
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Stobbe MD, Houten SM, Kampen AHC, Wanders RJA, Moerland PD. Improving the description of metabolic networks: the TCA cycle as example. FASEB J 2012; 26:3625-36. [DOI: 10.1096/fj.11-203091] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Miranda D. Stobbe
- Bioinformatics LaboratoryUniversity of AmsterdamAmsterdamThe Netherlands
- Netherlands Bioinformatics CentreNijmegenThe Netherlands
| | - Sander M. Houten
- Laboratory Genetic Metabolic DiseasesAcademic Medical CenterUniversity of AmsterdamAmsterdamThe Netherlands
| | - Antoine H. C. Kampen
- Bioinformatics LaboratoryUniversity of AmsterdamAmsterdamThe Netherlands
- Biosystems Data AnalysisSwammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
- Netherlands Consortium for Systems BiologyUniversity of AmsterdamAmsterdamThe Netherlands
- Netherlands Bioinformatics CentreNijmegenThe Netherlands
| | - Ronald J. A. Wanders
- Laboratory Genetic Metabolic DiseasesAcademic Medical CenterUniversity of AmsterdamAmsterdamThe Netherlands
| | - Perry D. Moerland
- Bioinformatics LaboratoryUniversity of AmsterdamAmsterdamThe Netherlands
- Netherlands Bioinformatics CentreNijmegenThe Netherlands
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