Rapid and accurate pathogen detection and identification is needed to allow physicians to react and respond appropriately to potentially life threatening infections. With an increased need to treat life-threatening infections more rapidly, in hours instead of days, improved assays using state-of-the-art technologies that decrease time, diagnosis sample size, and cost per assay are needed. Current Food and Drug Administration (FDA)-approved methods for pathogen detection in a clinical laboratory include biological culture, nucleic acid amplification, ribosomal protein sequence characterization, or genome sequencing. Collectively, these methods suffer from being time intensive, requiring amplification of clinically obtained material, and are often significantly costly and burdensome for diagnostic laboratory support staff. Recent advances in matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI- TOF-MS) have enabled development of an accurate, and precise method for pathogen identification. The method uses extracts containing high abundance proteins, picked directly from colonies on culture plates as a chemical bar code for individual species to detect differences in the composition of ribosomal proteins with ~90% accuracy. To increase the ability to rapidly diagnose bacterial infections using MALDI-TOF-MS, this proposal will develop, refine, and utilize ultra-small scale lipid purification methodologies for the extraction of high abundance lipids from Gram-positive and -negative bacteria, as well as fungi. Essential, high abundance lipids are found in all membranes of these microbes and are a highly diverse set of molecules. This diversity forms the basis of our hypothesis that "essential bacterial and fungal lipids constitute a chemical barcode that can be used to identify pathogens by mass spectrometry profiling". Our preliminary data show that these lipid structures are unique and can be used as novel chemical barcodes for speciation and/or sub-speciation of bacterial and fungal infections and resistance patterns to a subset of antibiotic and antimicrobial peptides. Following a rapid extraction method, lipids will be analyzed by mass spectrometry and the resulting spectra will be used to generate a mass spectral signature library of lipid "fingerprints" from a wide variety of clinically relevant pathogen backgrounds. When combined, the analysis of the protein and lipid phenotype will provide > 99% accuracy in pathogen identification from a variety of samples, such as solid medium, serum, bronchoalveolar lavage fluid and/or wound effluent. Ultimately, treatment plans tailored to specific infections should translate to improved outcomes, such as faster recovery times, decreased complications, and decreased morbidity and mortality infected patients.

Public Health Relevance

To increase the ability to rapidly and accurately diagnose bacterial infections using matrix-assisted laser desorption ionization, this application will develop, refine, and utilize ultra-small scale purification methodologies for the extraction of essential, high abundance lipids from Gram-positive and -negative bacterial, as well as fungal cell walls. When combined with existing protein mass spectral phenotypes, lipid phenotype will provide increased accuracy and sensitivity in bacterial identification from a variety of samples, such as solid medium plates, and clinically obtained patient samples. Ultimately, treatment plans tailored to specific infection profiles should translate to improved outcomes, such as faster recovery times and decreased complications and decreased morbidity and mortality infected patients.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01GM111066-01
Application #
8722128
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Chin, Jean
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Maryland Baltimore
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
City
Baltimore
State
MD
Country
United States
Zip Code
21201