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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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2
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Suyama H, Luu LDW, Zhong L, Raftery MJ, Lan R. Integrating proteomic data with metabolic modeling provides insight into key pathways of Bordetella pertussis biofilms. Front Microbiol 2023; 14:1169870. [PMID: 37601354 PMCID: PMC10435875 DOI: 10.3389/fmicb.2023.1169870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Pertussis, commonly known as whooping cough is a severe respiratory disease caused by the bacterium, Bordetella pertussis. Despite widespread vaccination, pertussis resurgence has been observed globally. The development of the current acellular vaccine (ACV) has been based on planktonic studies. However, recent studies have shown that B. pertussis readily forms biofilms. A better understanding of B. pertussis biofilms is important for developing novel vaccines that can target all aspects of B. pertussis infection. This study compared the proteomic expression of biofilm and planktonic B. pertussis cells to identify key changes between the conditions. Major differences were identified in virulence factors including an upregulation of toxins (adenylate cyclase toxin and dermonecrotic toxin) and downregulation of pertactin and type III secretion system proteins in biofilm cells. To further dissect metabolic pathways that are altered during the biofilm lifestyle, the proteomic data was then incorporated into a genome scale metabolic model using the Integrative Metabolic Analysis Tool (iMAT). The generated models predicted that planktonic cells utilised the glyoxylate shunt while biofilm cells completed the full tricarboxylic acid cycle. Differences in processing aspartate, arginine and alanine were identified as well as unique export of valine out of biofilm cells which may have a role in inter-bacterial communication and regulation. Finally, increased polyhydroxybutyrate accumulation and superoxide dismutase activity in biofilm cells may contribute to increased persistence during infection. Taken together, this study modeled major proteomic and metabolic changes that occur in biofilm cells which helps lay the groundwork for further understanding B. pertussis pathogenesis.
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Affiliation(s)
- Hiroki Suyama
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Laurence Don Wai Luu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Ling Zhong
- Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia
| | - Mark J. Raftery
- Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia
| | - Ruiting Lan
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
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3
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Santamaria G, Ruiz-Rodriguez P, Renau-Mínguez C, Pinto FR, Coscollá M. In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes. Biomolecules 2022; 12:376. [PMID: 35327567 PMCID: PMC8945471 DOI: 10.3390/biom12030376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/29/2022] [Accepted: 02/22/2022] [Indexed: 02/04/2023] Open
Abstract
Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection.
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Affiliation(s)
- Guillem Santamaria
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
- BioISI—Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal
| | - Paula Ruiz-Rodriguez
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
| | - Chantal Renau-Mínguez
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
| | - Francisco R. Pinto
- BioISI—Biosciences & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1749-016 Lisboa, Portugal
| | - Mireia Coscollá
- ISysBio, University of Valencia-FISABIO Joint Unit, 46980 Paterna, Spain; (G.S.); (P.R.-R.); (C.R.-M.)
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4
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Immanuel SRC, Arrieta-Ortiz ML, Ruiz RA, Pan M, Lopez Garcia de Lomana A, Peterson EJR, Baliga NS. Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME. NPJ Syst Biol Appl 2021; 7:43. [PMID: 34873198 PMCID: PMC8648758 DOI: 10.1038/s41540-021-00205-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 11/02/2021] [Indexed: 12/04/2022] Open
Abstract
The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.
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Affiliation(s)
| | | | - Rene A Ruiz
- Institute for Systems Biology, Seattle, WA, USA
| | - Min Pan
- Institute for Systems Biology, Seattle, WA, USA
| | - Adrian Lopez Garcia de Lomana
- Institute for Systems Biology, Seattle, WA, USA
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | | | - Nitin S Baliga
- Institute for Systems Biology, Seattle, WA, USA.
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA.
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
- Lawrence Berkeley National Lab, Berkeley, CA, USA.
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5
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Sinha N, van Schothorst EM, Hooiveld GJEJ, Keijer J, Martins Dos Santos VAP, Suarez-Diez M. Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism. BMC Bioinformatics 2021; 22:574. [PMID: 34839828 PMCID: PMC8628452 DOI: 10.1186/s12859-021-04488-8] [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: 04/07/2021] [Accepted: 11/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. RESULTS Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. CONCLUSION We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.
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Affiliation(s)
- Neeraj Sinha
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.,Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands.,Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Evert M van Schothorst
- Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands
| | - Guido J E J Hooiveld
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands
| | - Jaap Keijer
- Human and Animal Physiology, Wageningen University & Research, De Elst 1, 6708 WD, Wageningen, The Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.,LifeGlimmer GmbH., Markelstrasse 38, 12163, Berlin, Germany.,Bioprocess Engineering Group, Wageningen University & Research, PO Box 16, 6700 AA, Wageningen, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.
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6
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Borah K, Xu Y, McFadden J. Dissecting Host-Pathogen Interactions in TB Using Systems-Based Omic Approaches. Front Immunol 2021; 12:762315. [PMID: 34795672 PMCID: PMC8593131 DOI: 10.3389/fimmu.2021.762315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 10/18/2021] [Indexed: 01/10/2023] Open
Abstract
Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.
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Affiliation(s)
- Khushboo Borah
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | | | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
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7
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Rodenburg SYA, Seidl MF, de Ridder D, Govers F. Uncovering the Role of Metabolism in Oomycete-Host Interactions Using Genome-Scale Metabolic Models. Front Microbiol 2021; 12:748178. [PMID: 34707596 PMCID: PMC8543037 DOI: 10.3389/fmicb.2021.748178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the "mass-flow" through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host-pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.
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Affiliation(s)
- Sander Y. A. Rodenburg
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
- Bioinformatics Group, Wageningen University & Research, Wageningen, Netherlands
| | - Michael F. Seidl
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
- Theoretical Biology & Bioinformatics group, Department of Biology, Utrecht University, Wageningen, Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University & Research, Wageningen, Netherlands
| | - Francine Govers
- Laboratory of Phytopathology, Wageningen University & Research, Wageningen, Netherlands
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8
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Styles KM, Brown AT, Sagona AP. A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions. Front Microbiol 2021; 12:724767. [PMID: 34621252 PMCID: PMC8490754 DOI: 10.3389/fmicb.2021.724767] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/27/2021] [Indexed: 12/27/2022] Open
Abstract
Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.
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Affiliation(s)
- Kathryn M Styles
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Aidan T Brown
- School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
| | - Antonia P Sagona
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
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9
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Mukherjee S, Kundu I, Askari M, Barai RS, Venkatesh KV, Idicula-Thomas S. Exploring the druggable proteome of Candida species through comprehensive computational analysis. Genomics 2021; 113:728-739. [PMID: 33484798 DOI: 10.1016/j.ygeno.2020.12.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/30/2020] [Accepted: 12/14/2020] [Indexed: 11/30/2022]
Abstract
Candida albicans and non-albicans Candida spp. are major cause of systemic mycoses. Antifungal drugs such as azoles and polyenes are not efficient to successfully eradicate Candida infection owing to their fungistatic nature or low bioavailability. Here, we have adopted a comprehensive computational workflow for identification, prioritization and validation of targets from proteomes of Candida albicans and Candida tropicalis. The protocol involves identification of essential drug-target candidates using subtractive genomics, protein-protein interaction network properties and systems biology based methods. The essentiality of the novel metabolic and non-metabolic targets was established by performing in silico gene knockouts, under aerobic as well as anaerobic conditions, and in vitro drug inhibition assays respectively. Deletion of twelve genes that are involved in amino acid, secondary metabolite, and carbon metabolism showed zero growth in metabolic model under simulated conditions. The algorithm, used in this study, can be downloaded from http://pbit.bicnirrh.res.in/offline.php and executed locally.
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Affiliation(s)
- Shuvechha Mukherjee
- Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive Health, Mumbai 400012, Maharashtra, India
| | - Indra Kundu
- Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive Health, Mumbai 400012, Maharashtra, India
| | - Mehdi Askari
- Department of Bioinformatics, Guru Nanak Khalsa College, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India
| | - Ram Shankar Barai
- Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive Health, Mumbai 400012, Maharashtra, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, Maharashtra, India.
| | - Susan Idicula-Thomas
- Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive Health, Mumbai 400012, Maharashtra, India.
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10
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Sertbas M, Ulgen KO. Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens. Front Cell Dev Biol 2020; 8:566702. [PMID: 33251208 PMCID: PMC7673413 DOI: 10.3389/fcell.2020.566702] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.
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Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey.,Department of Chemical Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Kutlu O Ulgen
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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11
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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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12
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Karakitsou E, Foguet C, de Atauri P, Kultima K, Khoonsari PE, Martins dos Santos VA, Saccenti E, Rosato A, Cascante M. Metabolomics in systems medicine: an overview of methods and applications. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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13
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Rienksma RA, Schaap PJ, Martins dos Santos VAP, Suarez-Diez M. Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis. Front Cell Infect Microbiol 2019; 9:144. [PMID: 31139575 PMCID: PMC6519342 DOI: 10.3389/fcimb.2019.00144] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 04/17/2019] [Indexed: 12/19/2022] Open
Abstract
Little is known about the metabolic state of Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.
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Affiliation(s)
- Rienk A. Rienksma
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, Netherlands
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, Netherlands
- LifeGlimmer GmbH, Berlin, Germany
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, Wageningen, Netherlands
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