Exploring the metabolic profiling ofA. baumanniifor antimicrobial development using genome-scale modeling

Abstract

ABSTRACTWith the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms in 2017 for which new antibiotics are urgently needed. Within this list, the carbapenem-resistant pathogen Acinetobacter baumannii, belonging to the ESKAPE group, has been granted the “critical” status. Over the years, such isolates have been detected within healthcare units, posing a global threat to upcoming pandemics. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics based on environmental and genetic alterations is to apply constraint-based modeling on metabolic networks. We developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a novel metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our modeliACB23LX could recapitulate cellular metabolic phenotypes observed duringin vitroexperiments with an accuracy of over 80%, while positive biomass production rates were observed in growth media relevant toA. baumannii. Additionally, we identified putative essential genes with no human counterparts, which could serve as novel antibiotic candidates for the development of future antimicrobial strategies. Finally, we have assembled the first curated collection of available reconstructions for distinctA. baumanniistrains and analyzed their growth characteristics. The presented models herein are in a standardized and well-curated format, facilitating their usability, while they can be used to guide the reconstruction of multi-strain networks. Ultimately, they serve as a knowledge base for reliable predictions under various perturbations and the development of effective drugs.