3rd International Top-Down Proteomics Symposium

Deuterium-Induced Mass Shifts in MALDI Protein Fingerprints for Enhanced Antibiotic Susceptibility Prediction

Not scheduled

20m

VMP 6 / Philturm (Lecture Hall D)

Poster Poster Session 1

Speaker

Jia Yi (Minhang Hospital, Fudan University)

Description

Bacterial infections are among the leading causes of morbidity and mortality worldwide, posing a significant threat to public health. The rapid emergence of antimicrobial resistance has further exacerbated the challenges in treating infections, making timely and accurate diagnosis crucial for effective patient management. Current methods for bacterial infection diagnosis typically involve two key processes: bacterial identification and antibiotic susceptibility testing (AST). Bacterial identification is usually performed using MALDI-TOF MS, but AST remains limited by the time-consuming bacterial culture process, which can delay results by 6–24 hours. The need for faster, more efficient AST methods is urgent to guide the appropriate use of antibiotics, minimize the spread of resistance, and improve patient outcomes.
To address the limitations of traditional AST methods, we developed a rapid, cost-effective approach that integrates deuterium labeling, MALDI-TOF MS, and machine learning. This method leverages deuterium incorporation into newly synthesized bacterial proteins during antibiotic exposure. The resulting deuterium-induced mass shifts in protein profiles create unique patterns that can be used to distinguish resistant from sensitive strains. These mass shift features are extracted and analyzed using machine learning algorithms, enhancing the accuracy of antibiotic susceptibility predictions. By eliminating the need for extended bacterial culture and sample preparation, this method reduces AST time to just 0.5 to 1 hour after bacterial identification. This streamlined approach not only accelerates AST but also integrates both bacterial identification and susceptibility testing into a single mass spectrometer platform, making it highly compatible with existing MALDI-TOF MS systems used in clinical settings. This innovation offers a scalable solution for rapid AST, improving diagnostic efficiency and enabling faster, more accurate patient care while reducing healthcare costs.