Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Norsigian, Charles J.; Attia, Heba; Szubin, Richard; Yassin, Aymen S.; Palsson, Bernhard Ø.; Aziz, Ramy K.; Monk, Jonathan M.

doi:10.3389/fcimb.2019.00161

Cited by 33 publications

(41 citation statements)

References 28 publications

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“…Genome-scale metabolic networks of different K. pneumoniae strains have been developed so far (Liao et al, 2011;Henry et al, 2017;Ramos et al, 2018;Norsigian et al, 2019), but to our knowledge they were not used for drug target discovery via the constraint-based analysis coupled to bioinformatic prioritization steps. This prompted us to investigate candidate drug targets for K. pneumoniae comprehensively via a network-based metabolism-centered approach.…”

Section: Resultsmentioning

confidence: 99%

“…To date, this approach has been commonly used in drug target discovery process at systems-level for different pathogens (Raman et al, 2008;Plata et al, 2010;Perumal et al, 2011;Ahn et al, 2014;Larocque et al, 2014;Presta et al, 2017). GMN models are available for different K. pneumoniae strains (Liao et al, 2011;Henry et al, 2017;Ramos et al, 2018;Norsigian et al, 2019). The first K. pneumoniae model at the genome level, called iYL1228, appeared in 2011 for the MGH 78578 strain (Liao et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Rather, they used the network topology of the reconstructed GMN to prioritize drug targets in combination with genomic, transcriptomic and structure-based information. More recently, the GMN models of diverse K. pneumoniae strains with different levels of antibiotic resistance were reconstructed (Norsigian et al, 2019). They were used to predict the catabolic capabilities of these strains.…”

Section: Introductionmentioning

confidence: 99%

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Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Cesur

Siraj

Uddin

et al. 2020

Front. Cell. Infect. Microbiol.

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Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene-and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Cesur

Siraj

Uddin

et al. 2020

Front. Cell. Infect. Microbiol.

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show abstract

“…Hence, GEMs can also give remarkable clues toward uncovering adaptive evolution mechanism. In another study, the in silico iYL1228 was used as a platform to reconstruct GEMs for 22 K. pneumoniae strains (Norsigian et al, 2019 ). These models were manipulated for the investigation of growth capabilities on carbon, nitrogen, sulfur and phosphorus sources.…”

Section: Genome-scale Metabolic Models Of Pathogenic Microorganisms Amentioning

confidence: 99%

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Sertbaş

Ülgen

2020

Front. Cell Dev. Biol.

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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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“…Reconstructing GEMs for multiple strains across a single species has enabled a systems-level approach to study and characterize the pan-metabolic capabilities of the species. Pangenomic studies have been accomplished for a wide range of species from Staphylococcus aureus to Klebsiella pneumoniae [ 58 , 59 , 60 , 61 , 62 , 63 ]. Integration of genomics, phenomics, transcriptomics, and genome-scale modeling for seven commonly used E. coli strains linked molecular features to strain-specific phenotypes.…”

Section: Frontier 1: Constraint-based Reconstruction and Modelingmentioning

confidence: 99%

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

2020

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Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

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Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Cited by 33 publications

References 28 publications

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

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