Novel Applications of Mass Spectrometry and Proteomics to Infectious Disease Diagnostics

Clinical Center

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Abstract

Rapid molecular methods for tracking drug-resistant bacteria are essential to modern hospital infection control strategy. Classical strain-typing techniques, such as pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) have been used extensively in epidemiologic investigations, but they are time-consuming in the microbiology laboratory and may prove inadequate for the construction of outbreak transmission maps that involve the movement of mobile resistance elements among genetically unrelated organisms. PCR- and sequencing-based methods can be used in these scenarios but have traditionally been difficult to integrate into the daily diagnostic workflow of a clinical microbiology laboratory. Rapid methods for epidemiologic challenges such as tracking individual resistance plasmids as part of routine data analysis could greatly benefit infection control efforts. To this end, we aim to develop novel applications of mass spectrometry to epidemiology and infectious disease diagnostics in the clinical microbiology laboratory. This work involves both the identification and characterization of new bacterial protein biomarkers and test development based on these biomarkers. Work done during the current fiscal year involved a proof-of-principle demonstration that real-time, direct tracking of a single resistance plasmid associated with a hospital outbreak of carbapenem-resistant organisms in the NIH Clinical Center could be achieved with a commercial matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry system. This was accomplished by identifying and characterizing the spectral fingerprint of a plasmid-encoded protein with a combination of proteomics and molecular techniques. This method is presently undergoing additional validation for clinical use. Further work will aim to identify spectral features of other plasmids encoding resistance elements, as well as spectral features that may be used for epidemiologic tracking of other bacterial pathogens.