Comparative metabolic modeling and analysis of human pathogens

Abstract

Infectious diseases continue to be major health concerns worldwide. Although major advances have led to accumulation of genomic data about human pathogens, there clearly exists a gap between genome information and studies aiming at identifying potential drug targets. Here, constraint-based modeling (CBM) was deployed to integrate disparate data types with genome-scale metabolic models (GEMs) to advance our understanding of the pathogenesis of infectious agents with respect to identifying and prioritizing drug targets. Specifically, genome-scale metabolic modeling of multiple stages and species of Plasmodium, the causative agent of malaria, was used to prioritize potential drug targets that could be used to simultaneously treat (anti-malarials) and block transmission of the parasite. In addition, species-specific metabolic models were used to guide translation of findings from non-human experimental disease models to human-infecting species. Further, comparative analysis of the essentiality of metabolic genes for V. cholerae, the causative agent of cholera, growth and survival in single and co-infections with other enteric pathogens led to prioritizing conditionally independent essential genes that would be potential drug targets in both single and co-infection scenarios. Taken together, our findings highlight the utility of using genome-scale metabolic models to prioritize druggable targets that would be of broader spectrum against human pathogens.