Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids
- So Young Ryu*So Young Ryu*E-mail: [email protected]. Tel.: +1 775-682-7116. Fax: +1 775-784-1340.School of Community Health Sciences, University of Nevada Reno, Reno, Nevada 89557, United StatesMore by So Young Ryu,
- George A. WendtGeorge A. WendtSchool of Community Health Sciences, University of Nevada Reno, Reno, Nevada 89557, United StatesDepartment of Epidemiology, School of Public Health, University of California Berkeley, Berkeley, California 94720, United StatesMore by George A. Wendt,
- Courtney E. ChandlerCourtney E. ChandlerDepartment of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United StatesMore by Courtney E. Chandler,
- Robert K. ErnstRobert K. ErnstDepartment of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United StatesMore by Robert K. Ernst, and
- David R. GoodlettDavid R. GoodlettDepartment of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, Maryland 21201, United StatesInternational Centre for Cancer Vaccine Science, University of Gdansk, 80-308 Gdansk, PolandMore by David R. Goodlett
Abstract
By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.
Cited By
This article is cited by 2 publications.
- Keerthi Appala, Kingsley Bimpeh, Christian Freeman, Kelly M. Hines. Recent applications of mass spectrometry in bacterial lipidomics. Analytical and Bioanalytical Chemistry 2020, 412 (24) , 5935-5943. https://doi.org/10.1007/s00216-020-02541-8
- So Young Ryu, George A. Wendt, Robert K. Ernst, David R. Goodlett. MGMS2: Membrane glycolipid mass spectrum simulator for polymicrobial samples. Rapid Communications in Mass Spectrometry 2020, 34 (16) https://doi.org/10.1002/rcm.8824