Pathway Driven Target Selection in Klebsiella pneumoniae: Insights Into Carbapenem Exposure

Abstract

Carbapenem-resistant Klebsiella pneumoniae (CR-KP) represents an emerging threat to public health. CR-KP infections result in elevated morbidity and mortality. This fact, coupled with their global dissemination and increasingly limited number of therapeutic options, highlights the urgency of novel antimicrobials. Innovative strategies linking genome-wide interrogation with multi-layered metabolic data integration can accelerate the early steps of drug development, particularly target selection. Using the BioCyc ontology, we generated and manually refined a metabolic network for a CR-KP, K. pneumoniae Kp13. Converted into a reaction graph, we conducted topological-based analyses in this network to prioritize pathways exhibiting druggable features and fragile metabolic points likely exploitable to develop novel antimicrobials. Our results point to the aptness of previously recognized pathways, such as lipopolysaccharide and peptidoglycan synthesis, and casts light on the possibility of targeting less explored cellular functions. These functions include the production of lipoate, trehalose, glycine betaine, and flavin, as well as the salvaging of methionine. Energy metabolism pathways emerged as attractive targets in the context of carbapenem exposure, targeted either alone or in conjunction with current therapeutic options. These results prompt further experimental investigation aimed at controlling this highly relevant pathogen.

Omics data integration facilitates target selection for new antiparasitic drugs against TriTryp infections

“…Target Pathogen is our own web server developed for drug target prioritization. This webserver was previously used to obtain attractive targets for drug development projects in other relevant pathogens ( Defelipe et al, 2016 ; Ramos et al, 2018 ; Farfán-López et al, 2020 ; Palumbo et al, 2022 ; Serral et al, 2022 ). The integrated data was used to obtain a list of drug target candidates.…”

Integrating diverse layers of omic data to identify novel drug targets in Listeria monocytogenes

“…In this sense, Target-Pathogen (http://target.sbg.qb.fcen.uba.ar) is a unique resource that combines structural druggability datasets, essentiality analysis, metabolic context, genomic and expression data to rank proteins according to their potential to be used as novel targets. Previous reports from our and other groups have selected and prioritized molecular targets of several relevant pathogens such as Mycobacterium tuberculosis (Defelipe et al, 2016), Klebsiella pneumoniae (Ramos et al, 2018;Serral et al, 2021, Serral et al, 2022, Bartonella bacilliformis (Farfán-López et al, 2020), Trypanosoma cruzi (Osorio-Méndez andCevallos, 2018;Coutinho et al, 2021) and Schistosoma mansoni (Lobo-Silva et al, 2020) using Target-Pathogen.…”

From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale

“…qb.fcen.uba.ar) (Sosa et al, 2018) is a unique resource that helps to achieve this task, combining structural druggability datasets, essentiality analysis, metabolic context, genomic, and expression data to rank gene/proteins according to their potential as novel antimicrobial targets. Previous reports, from our and other groups, used this tool to select and prioritize molecular targets of several relevant pathogens such as M. tuberculosis (Defelipe et al, 2016), K. pneumoniae (Ramos et al, 2018;Serral et al, 2022), Bartonella bacilliformis (Farfán-López et al, 2020;Serral et al, 2021), Trypanosoma cruzi (Osorio-Méndez et al, 2016;Coutinho et al, 2021) and Schistosoma mansoni (Lobo-Silva et al , 2020). Once the target is selected, the challenge moves to the identification of a small molecule that can inhibit the protein target's function, allows further pharmacological validation of the target, and ultimately drives the development of a new antibiotic.…”

“…The process, however, is costly and time-consuming and can fail for several reasons, such as the impossibility of obtaining a resistance strain, or the difficulty in correctly identifying the relevant resistance causing mutation (Farha and Brown, 2016). In this context, several bioinformatic approaches have been proposed and applied in order to aid researchers in the identification of an active compound's target (Hasan et al, 2006;Shanmugam and Natarajan, 2010;Lee et al, 2011;Rahman et al, 2014;Mondal et al, 2015;Neelapu et al, 2015;Cloete et al, 2016;Defelipe et al, 2016;Kaur et al, 2017;Mohana and Venugopal, 2017;Wadood et al, 2017;Oany et al, 2018;Ramos et al, 2018;Uddin and Jamil, 2018;Shuvo et al, 2019;Farfán-López et al, 2020;Karim et al, 2020;Lobo-Silva et al, 2020;Aslam et al, 2021;Chakkyarath et al, 2021;Serral et al, 2022).…”