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

Abstract

Listeria monocytogenes (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.