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

Abstract

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.

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

“…The screening of putative drug targets in K. pneumoniae was studied by iYL1228 with the improved biomass reaction (Cesur et al, 2020 ). So as to mimic the host microenvironment, the growth simulations were performed by using FBA in three different conditions including human body fluid, sputum-macrophage, and generic host media.…”

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

“…Cesur and colleagues investigated predicted drugs targeted against Klebsiella pneumoniae , part of the E nterococcus faecium , S taphylococcus aureus , K lebsiella pneumoniae , A cinetobacter baumannii , P seudomonas aeruginosa , and E nterobacter species (ESKAPE) multidrug resistant group, for which cephalosporin and carbapenem resistance have been identified in part due to widespread acquisition β-lactamase-coding and carbapenemase-coding genes ( Cesur et al, 2020 ; De Oliveira et al, 2020 ). Klebsiella species are capable of causing pneumonia and are associated with increased morbidity and mortality ( Bengoechea and Sa Pessoa, 2019 ).…”

Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation

“…Despite the numerous efforts mentioned above, we are still lacking compounds that can be used in the aquaculture industry to tackle saprolegniasis. Bioinformatic approaches have allowed the identification of inhibitors of bacterial pathogens of humans (Solanki and Tiwari, 2018;Bhardwaj et al, 2019;Cesur et al, 2020). Inspired by these successful strategies, here, we aim to identify specific protein targets and potential lead inhibitors of S. parasitica using combined in silico subtractive proteomics with a sequence-based approach.…”