Understanding Antimicrobial Resistance via Metabolic Engineering, Interpretable Machine Learning, and Synthetic Biology Approaches

Jason Yang, PhD
Assistant Professor
Rutgers New Jersey Medical School

Abstract:
Antimicrobial resistance is a global public health crisis that threatens modern medicine. There is an imperative to better understand mechanisms underlying antibiotic efficacy, treatment failure, and resistance evolution. Understanding such mechanisms will enable the rational design of next-generation therapies for treating infectious diseases. We and others have shown that bacterial metabolism is an important regulator of antibiotic efficacy. However, critical knowledge gaps exist in understanding how antibiotic treatment alters bacterial
metabolism and how bacterial metabolism impacts resistance evolution. Here we will discuss our recent efforts in studying antimicrobial resistance using metabolic engineering, interpretable machine learning, and synthetic biology approaches. We discover that antibiotic treatment disrupts cellular energy balance by triggering energetically expensive biosynthetic processes. We further discover that energetic deficits promote resistance evolution and treatment failure. We envision these insights will enable druggable vulnerabilities for treating drug-resistant infections and preventing resistance evolution.