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

Abstract

Listeriamonocytogenes (Lm) is a Gram-positive bacillus responsible for listeriosis in humans. Listeriosis has become a major foodborne illness in recent years. This illness is mainly associated with the consumption of contaminated food and ready-to-eat products. Recently, Lm has developed resistances to a broad range of antimicrobials, including those used as the first choice of therapy. Moreover, multidrug-resistant strains have been detected in clinical isolates and settings associated with food processing. This scenario punctuates the need for novel antimicrobials against Lm. On the other hand, increasingly available omics data for diverse pathogens has created new opportunities for rational drug discovery. Identification of an appropriate molecular target is currently accepted as a critical step of this process. In this work, we generated multiple layers of omics data related to Lm, aiming to prioritize proteins that could serve as attractive targets for antimicrobials against L. monocytogenes. We generated genomic, transcriptomic, metabolic, and protein structural information, and this data compendium was integrated onto a freely available web server (Target Pathogen). Thirty targets with desirable features from a drug development point of view were shortlisted. This set of target proteins participates in key metabolic processes such as fatty acid, pentose, rhamnose, and amino acids metabolism. Collectively, our results point towards novel targets for the control of Lm and related bacteria. We invite researchers working in the field of drug discovery to follow up experimentally on our revealed targets.

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.…”

Bacterial cytochrome P450s: a bioinformatics odyssey of substrate discovery

“…The emergence of high-throughput structural biology techniques and computational methods offers new opportunities to bridge this gap ( Sirim et al, 2010 ; Bustamante et al, 2016 ; Darabi et al, 2017 ). Previous studies from our group and others emphasize the effectiveness of these techniques in target identification ( Ramos et al, 2018 ; Sosa et al, 2018 ; Palumbo et al, 2022 ) and predicting interactions between proteins and ligands ( Serral et al, 2021 ). This includes the development of new tools tailored for these approaches, encompassing both sequence (and functional) similarity and physical interactions at the structural level to provide accurate predictions ( Arcon et al, 2017 ; Schottlender et al, 2022 ).…”

Identifying Potential Molecular Targets in Fungi Based on (Dis)Similarities in Binding Site Architecture with Proteins of the Human Pharmacolome

“…Among the filters applied in this search system is the so-called “off-target criterion”, which discards proteins with close homologues in humans. Similarly, in more recent works, Mukherjee and coworkers applied a “subtractive genomics” approach to filter out homologous proteins in the search for targets in Candida species [ 9 ], while Palumbo and coworkers applied the same off-target criterion in a search for potential targets in Listeria monocytogenes , following a multilayer omics strategy [ 10 ].…”