ABSTRACT
Pathogens pose a major risk to human health globally, causing 44% of deaths in low-resource countries. Currently, there are over 500 known bacterial pathobionts, covering a wide range of functional capabilities. Some well-known pathobionts are well characterized computationally and experimentally. However, to gain a deeper understanding of how pathobionts are evolutionarily related to the principles that govern their different functions and ultimately identify possible targeted antimicrobials, we must consider both well-known and lesser-known pathobionts. Here, we developed a database of genome-scale metabolic network reconstructions (GENREs) called PATHGENN, which contains 914 models of pathobiont metabolism to address these questions related to functional metabolic evolution and adaptation. We determined the metabolic phenotypes across all known pathobionts and the role of isolate environment in functional metabolic adaptation. We also predicted novel antimicrobial targets for bacteria specific to their physiological niche. Understanding the functional metabolic similarities between pathobionts is the first step to ultimately developing a precision medicine framework for addressing all infections.
Competing Interest Statement
The authors have declared no competing interest.
