Chris Cox (Presenter)
Colorado School of Mines
Bio: I received my Bachelors degree in Microbiology and Biochemistry from the University of Oklahoma in 1999. From there I studied under Dr. Michael Gilmore, first at the University of Oklahoma Health Sciences Center where I received my Master’s in 2002, then at Harvard University where I conducted my dissertation research and completed my Ph.D. in 2006. Following the completion of my Ph.D., I went to the Colorado School of Mines in a post-doctoral capacity to expand my expertise into bacterial and viral biochemistry, and investigate the use of phages and various forms of mass spectrometry and analytical chemistry for biomarker measurement and bacterial identification. At the completion of my post doc training, I joined the research faculty at CSM where my main research interests at have been the design, development, and evaluation of novel, rapid bacterial diagnostic technologies.
Authorship: Christopher R. Cox, Kirk R. Jensen, Nicholas R. Saichek and Kent J. Voorhees
Advanced Biodetection Technology Laboratory Department of Chemistry Colorado School of Mines, Golden, CO U.S.A.
| Short Abstract MALDI-TOF MS has emerged as a rapid approach for clinical bacterial identification. Current protein-based methods, while widely accepted, fall short when differentiating closely related phylotypes. To address this, we employed in situ CeO2-catalyzed lipid fragmentation into taxonomically viable fatty acids using the energy inherent to the MALDI laser as an alternative to protein profiling. We observed consistent strain-level ID of a diverse collection of Acinetobacter, Enterobacteriaceae, and Listeria, which were difficult or impossible to differentiate with the Bruker Biotyper. In comparison, protein profiling resulted in significantly lower accuracy and was unable to ID any bacteria at the strain level. |
Long Abstract
MALDI-TOF MS has emerged as a rapid approach for bacterial diagnostics. However, current protein-based commercial bacterial ID methods, while reasonably tractable and widely accepted for clinical use, fall short when trying to differentiate closely related phylotypes. To address this shortcoming, we employed CeO2-catalyzed fragmentation of bacterial lipids into taxonomically viable fatty acids using the energy inherent to the MALDI laser as a novel alternative to diagnostic protein profiling.1 With a CeO2 catalyst in place of a traditional matrix, we observed strain-level ID of a diverse collection of Acinetobacter, Enterobacteriaceae, and Listeria clinical and laboratory strains, which were difficult to differentiate with the Bruker Biotyper.
Five separate identical colonies of 6 Enterobacteriaceae, 10 Acinetobacter, and 10 Listeria were analyzed using the Bruker Biotyper according to the manufacturer’s protocols. Results were compared to analysis of Bligh-Dyer extracts of the same cultures by CeO2-catalyzed MALDI-MS, allowing for a direct assessment of the accuracy of each ID method. Both datasets were subjected to principal component analysis for graphical representation of assay readouts. All bacterial IDs were confirmed by 16s rRNA sequencing.
The Biotyper was unable to ID any of 130 samples analyzed at the strain level. It correctly identified 30-33% of Enterobacteriaceae, 22% of Acinetobacter, and 66-68% of Listeria at the species level, with 67%, 69-78%, and 100% accuracy at the genus level, respectively. It misidentified 33% of Enterobacteriaceae, 49-60% of Acinetobacter, and 28-32% of Listeria as the incorrect species. It failed to ID 7-18% of Acinetobacter, and 4% of Listeria. In comparison, fatty acid analysis gave 100% strain level Enterobacteriaceae and Listeria ID, and 98% strain level Acinetobacter ID (2% mis-ID at strain level).
Novel CeO2-catalyzed lipid fragmentation readily produced taxonomically tractable fatty acid profiles by MALDI-TOF MS. Results from a 130-sample study encompassing 26 different but closely related strains resulted in consistent strain-level ID. In comparison, protein-based analysis of the same strains using the Bruker Biotyper resulted in significantly lower accuracy and was unable to ID any bacteria at the strain level.
References & Acknowledgements:
1. Cox, C.R., Jensen, K.R., Saichek, N.R. and Voorhees, K.J. 2015. Strain-level bacterial ID by CeO2-catalyzed MALDI-TOF MS fatty acid analysis and comparison to commercial protein-based methods. Nature Scientific Reports. 5:10470.
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