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Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets. PLoS One 2022; 17:e0268889. [PMID: 35609089 PMCID: PMC9129043 DOI: 10.1371/journal.pone.0268889] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.
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2
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Marrazzo P, Fischer N, Nastasi C, Cricca M, Fusco D. Host–Pathogen Interactions: Organotypic Cultures to Unravel the Mysteries of the Primordial Hostility among Organisms. Pathogens 2022; 11:pathogens11030362. [PMID: 35335685 PMCID: PMC8951007 DOI: 10.3390/pathogens11030362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 03/14/2022] [Indexed: 12/10/2022] Open
Affiliation(s)
- Pasquale Marrazzo
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40126 Bologna, Italy;
- Correspondence: (P.M.); (D.F.)
| | - Natalie Fischer
- Department of Infectious Diseases Epidemiology, Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany;
| | - Claudia Nastasi
- Laboratory of Cancer Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy;
| | - Monica Cricca
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40126 Bologna, Italy;
| | - Daniela Fusco
- Department of Infectious Diseases Epidemiology, Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany;
- German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck-Riems, 20246 Hamburg, Germany
- Correspondence: (P.M.); (D.F.)
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López-Agudelo VA, Baena A, Barrera V, Cabarcas F, Alzate JF, Beste DJV, Ríos-Estepa R, Barrera LF. Dual RNA Sequencing of Mycobacterium tuberculosis-Infected Human Splenic Macrophages Reveals a Strain-Dependent Host-Pathogen Response to Infection. Int J Mol Sci 2022; 23:ijms23031803. [PMID: 35163725 PMCID: PMC8836425 DOI: 10.3390/ijms23031803] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/26/2021] [Accepted: 12/28/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb), leading to pulmonary and extrapulmonary TB, whereby Mtb is disseminated to many other organs and tissues. Dissemination occurs early during the disease, and bacteria can be found first in the lymph nodes adjacent to the lungs and then later in the extrapulmonary organs, including the spleen. The early global gene expression response of human tissue macrophages and intracellular clinical isolates of Mtb has been poorly studied. Using dual RNA-seq, we have explored the mRNA profiles of two closely related clinical strains of the Latin American and Mediterranean (LAM) family of Mtb in infected human splenic macrophages (hSMs). This work shows that these pathogens mediate a distinct host response despite their genetic similarity. Using a genome-scale host–pathogen metabolic reconstruction to analyze the data further, we highlight that the infecting Mtb strain also determines the metabolic response of both the host and pathogen. Thus, macrophage ontogeny and the genetic-derived program of Mtb direct the host–pathogen interaction.
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Affiliation(s)
- Víctor A. López-Agudelo
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
- Grupo de Bioprocesos, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Andres Baena
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
| | - Vianey Barrera
- Programa de Ingeniería Biológica, Universidad Nacional de Colombia, Sede Medellín, Medellín 050010, Colombia;
| | - Felipe Cabarcas
- Grupo Sistemas Embebidos e Inteligencia Computacional (SISTEMIC), Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Juan F. Alzate
- Centro Nacional de Secuenciación Genómica (CNSG), Sede de Investigación Universitaria (SIU), Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Dany J. V. Beste
- Department of Microbial Sciences, Faculty of Health and Medical Science, University of Surrey, Guildford GU2 7XH, UK;
| | - Rigoberto Ríos-Estepa
- Grupo de Bioprocesos, Facultad de Ingeniería, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Luis F. Barrera
- Grupo de Inmunología Celular e Inmunogenética (GICIG), Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia; (V.A.L.-A.); (A.B.)
- Correspondence:
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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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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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Çakır T, Panagiotou G, Uddin R, Durmuş S. Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review. Front Cell Infect Microbiol 2020; 10:52. [PMID: 32117818 PMCID: PMC7031156 DOI: 10.3389/fcimb.2020.00052] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 01/27/2020] [Indexed: 12/23/2022] Open
Abstract
Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases.
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Affiliation(s)
- Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Gianni Panagiotou
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knoll Institute, Jena, Germany
| | - Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Saliha Durmuş
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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Dunphy LJ, Papin JA. Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. Curr Opin Biotechnol 2018; 51:70-79. [PMID: 29223465 PMCID: PMC5991985 DOI: 10.1016/j.copbio.2017.11.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/22/2017] [Accepted: 11/24/2017] [Indexed: 12/14/2022]
Abstract
The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22903, USA.
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Abstract
The science and art of Genome scale metabolic network reconstructions have been explicitly documented in the literature for organisms across all the three kingdoms of life. Constraints-based models derived from such reconstructions have been used to assess metabolic phenotypes of their complex connections to genotype accurately. The problem of infectious disease is complex due to the multifactorial response of the host to the pathogen. Systems biology approaches and modeling allow one to study, understand, and predict emergent properties of such complex responses. The integration of the host and pathogen metabolic networks and the subsequent merger of their stoichiometric matrices is nontrivial and requires understanding of both pathogen and host metabolism and physiologies. The protocol here describes the detailed process of network and stoichiometric matrix merger using a salmonella-mouse macrophage model. The protocol also discusses the interfacial and objective functions required to actually embark on the analysis of host-pathogen interaction models.
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Abstract
Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.
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Affiliation(s)
- Marco Fondi
- Dep. of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Florence, Italy.
| | - Renato Fani
- Dep. of Biology, University of Florence, Via Madonna del Piano 6, 50019, Sesto Fiorentino, Florence, Italy
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Schleicher J, Conrad T, Gustafsson M, Cedersund G, Guthke R, Linde J. Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions. Brief Funct Genomics 2017; 16:57-69. [PMID: 26857943 PMCID: PMC5439285 DOI: 10.1093/bfgp/elv064] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.
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Affiliation(s)
| | | | | | | | | | - Jörg Linde
- Corresponding author: Jörg Linde, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Jena, Germany. Tel.: +49-3641-532-1290; E-mail:
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