Using systems biology to improve neonatal sepsis diagnosis and treat antimicrobial-resistant infections

Several infectious diseases are among the top causes of death worldwide, including ~7% of deaths in Canada. Bacterial infections are often treatable; however, chronic misuse of antibiotics has created a critical global health threat by increasing antimicrobial resistance (AMR). In addition, bacterial infection can lead to sepsis, which is particularly dangerous for newborns and kills three million babies per year. Avoiding further infant deaths will require (1) methods to predict and detect sepsis early, enabling treatment when the chance of survival is greatest, and (2) knowledge of how pathogens like Klebsiella pneumoniae cause disease in newborns, guiding the development of targeted treatments that overcome AMR. Using hundreds of newborn blood samples, we are using cutting-edge genomic, bioinformatic, and machine learning approaches to identify molecular changes induced by sepsis that are generalizable to infants worldwide. This research is critical for our long-term goal of developing rapid tests and precision treatments that neutralize sepsis—the most common cause of newborn death.