Historically, the detection and identification of bacteria, mycobacteria, yeasts, and molds have relied primarily on their morphologic and phenotypic properties. This approach is imprecise and slow for many clinically significant microbes. We have explored alternative methods, such as targeted sequencing and mass spectrometry, for the detection and identification of selected organisms. Studies in the current fiscal year have focused on alternative identification methods, specifically MALDI-TOF (matrix-assisted laser desorption-ionization time of flight mass spectrometry) for the identification of Nocardia, mycobacteria, and molds and rapid genomic assays for identification, typing and detection of antibiotic resistance in M. abscessus group. MALDI-TOF MS provides a reproducible spectral pattern based on the mass/charge (m/z) ratio of ionized proteins. Preparation of samples is rapid and inexpensive. Well-characterized reference strains of mycobacteria, Nocardia and molds allowed us to build a NIH database that was then challenged with clinical isolates. Following a demonstration of excellent performance, MALDI-TOF MS has been recently selected as the method of choice of the NIH Microbiology Service for identification of both rapid and slowly growing mycobacteria as well as filamentous molds. This approach has been recently validated for the identification of Nocardia at the NIH and more recently also at two other academic centers. A multi center evaluations of the performance of the NIH mold database is ongoing. Finally, we are exploring the use of proteomics and in particular Maldi-Tof MS for direct detection of microbial antigens in clinical samples. Preliminary findings support the validity of this approach for detection of circulating antigens of Mycobacterium tuberculosis.

Agency
National Institute of Health (NIH)
Institute
Clinical Center (CLC)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIACL080012-08
Application #
9154133
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
8
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Clinical Center
Department
Type
DUNS #
City
State
Country
Zip Code
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