Computational Systems Biology of Metabolism in Infection

Cesur, Müberra Fatma; Abdik, Ecehan; Guven-Gulhan, Unzile; Durmuş, Saliha; Çakır, Tunahan

doi:10.1007/978-3-319-74932-7_6

Cited by 8 publications

(11 citation statements)

References 174 publications

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“…Flux Balance Analysis (FBA) is the most widely used in silico analysis method to predict intracellular flux distributions from GMNs at steady-state, which solves an optimization problem satisfying a predefined objective function (e.g., maximal growth rate; Varma and Palsson, 1994;Edwards et al, 2002;Orth et al, 2010). When FBA is used to simulate gene deletion phenotypes, it provides significant quantitative insights about the bacterial metabolism, pathway activities, and potential drug targets (Cesur et al, 2018). 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).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…When applied to pathogenic organisms, it is possible to identify potential biomarkers and drug targets with this approach (Kim et al, 2012;Dunphy and Papin, 2018). There are several studies that report the reconstruction of genome-scale metabolic networks (GMNs) for pathogens, which are reviewed elsewhere (Cesur et al, 2018). The generation of dual-omics data makes it possible to extend the genome-scale metabolic network modeling of pathogens to PHI systems, by simultaneously considering pathogen and host metabolisms.…”

Section: Introductionmentioning

confidence: 99%

Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review

Çakır

Panagiotou

Uddin

et al. 2020

Front. Cell. Infect. Microbiol.

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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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“…Here, we took a systems biology approach and modelled the joint metabolism of tomato and P. infestans to generate hypotheses about their relationship. Our metabolic model of the P. infestans-tomato interaction represents one of the few integrated pathogen-host metabolic models published to date (Cesur et al, 2018). Regarding pathogen and host as one entity can yield hypotheses about the combined metabolism at a single metabolic equilibrium.…”

Section: Discussionmentioning

confidence: 99%

“…Systems biology offers a powerful toolbox to study pathogens and their relation with their hosts (Cesur et al, 2018;Dix et al, 2016;Durmus et al, 2015;Horn et al, 2012;Peyraud et al, 2017). Pathogens and hosts often interact extensively on all molecular levels, i.e.…”

Section: Systems Biology On Pathogensmentioning

confidence: 99%

“…A GEM can be used to predict putative nutrients and essential enzymes/reactions in the cell, thereby generating hypotheses about the responses of the cell metabolism to perturbations (Chavali et al, 2012). Therefore, metabolic models have great potential to aid in the development of novel control strategies against pathogens (Cesur et al, 2018;Peyraud et al, 2017). The metabolism of a pathogen is tightly interconnected with that of its host, and can be regarded as a single system (Olive and Sassetti, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Uncovering oomycete metabolism using systems biology

Rodenburg

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Metabolism is essential for life. The metabolism of an organism defines its capabilities to take up nutrients from the environment and to convert these into its essential building blocks, such as nucleic acids and amino acids (Lazar and Birnbaum, 2012). Cellular metabolism can be described as a system of biochemical conversions (reactions), most of which are catalyzed by metabolic enzymes. Typically, a genome encodes a few thousand metabolic enzymes (Yilmaz and Walhout, 2017). Each enzyme acts on a selection of substrates and converts these into products, typically by adding or removing reactive groups. Reactions that share substrates or products can be considered functionally connected. Consequently, the collection of biochemical reactions within a cell forms a large interconnected network, representing the routes by which the organism converts simple nutrients into complex metabolites and vice-versa. This network is distributed over different subcellular compartments (organelles). Transporter proteins and channels facilitate the transport of metabolites across the lipid bilayers that surround the cell and the organelles (Sahoo et al., 2014). The overall system is subject to many parameters, such as variability in substrates, temperature, or the pH, not only between cells and the extracellular space but also within cells. Cells regulate this system to maintain homeostasis, i.e. the ability to perform important cellular functions despite variations (perturbations), and this provides robustness (Eberl, 2018; Nijhout et al., 2019). The ability to sense environmental variations and metabolic cues and to adapt metabolism accordingly, depends on a tightly interlinked regulatory system (Watson et al., 2015). This involves regulatory feedback loops embedded in interaction networks crossing metabolic, protein, transcript, and (epi)genetic levels (Figure 1). Pathogen G e n o m e T ra n s c ri p to m e P ro te o m e M e ta b o lo m e Host FIGURE 1 | The molecular layers of a cell are all interconnected, and form a complex and integrated system. In the symbiosis of a pathogen and a host, their systems are connected, and interactions occur between all molecular layers. As such, the phenotype of the cell is an emergent property of the system's complexity (Aderem, 2005). The rates of individual metabolic reactions are a direct consequence of the overall state General introduction

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Computational Systems Biology of Metabolism in Infection

Cited by 8 publications

References 174 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

Novel Approaches for Systems Biology of Metabolism-Oriented Pathogen-Human Interactions: A Mini-Review

Uncovering oomycete metabolism using systems biology

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