Metabolic modeling predicts unique drug targets in Borrelia burgdorferi

• Published in: mSystems Vol. 8; no. 6; p. e0083523
• Main Authors: Gwynne, Peter J, Stocks, Kee-Lee K, Karozichian, Elysse S, Pandit, Aarya, Hu, Linden T
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 21.12.2023
• Subjects: Anti-Bacterial Agents - pharmacology; Antibiotics; Antimicrobial agents; Antimicrobial resistance; Bacteriology; Biomass; Borrelia burgdorferi; Drug delivery; Drug Discovery; Drug resistance; E coli; Enzyme inhibitors; Enzymes; Genomes; host-pathogen interactions; Humans; Lyme disease; Lyme Disease - drug therapy; metabolic modeling; Metabolism; Metabolites; Nosocomial infections; Plasmids; Proteins; Pyridoxal kinase; Research Article; Sodium; Staphylococcus infections; Therapeutic targets; Lyme disease; host-pathogen interactions; antimicrobial agents; drug discovery; metabolic modeling
• ISSN: 2379-5077; 2379-5077
• DOI: 10.1128/msystems.00835-23

Lyme disease is often treated using long courses of antibiotics, which can cause side effects for patients and risks the evolution of antimicrobial resistance. Narrow-spectrum antimicrobials would reduce these risks, but their development has been slow because the Lyme disease bacterium, , is difficult to work with in the laboratory. To accelerate the drug discovery pipeline, we developed a computational model of 's metabolism and used it to predict essential enzymatic reactions whose inhibition prevented growth . These predictions were validated using small-molecule enzyme inhibitors, several of which were shown to have specific activity against . Although the specific compounds used are not suitable for clinical use, we aim to use them as lead compounds to develop optimized drugs targeting the pathways discovered here.