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Sci Rep. 2018 Oct 26;8(1):15857. doi: 10.1038/s41598-018-33681-8.

Pathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipids.

Author information

1
Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
2
Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA.
3
Toxicology and Pharmacology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
4
Department of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, 21201, USA.
5
Divisions of Microbiology and Molecular Biology, Laboratories Administration, Maryland Department of Health, Baltimore, Maryland, 21205, USA.
6
Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
7
Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
8
Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA. dgoodlett@rx.umaryland.edu.

Abstract

With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.

PMID:
30367087
PMCID:
PMC6203844
DOI:
10.1038/s41598-018-33681-8
[Indexed for MEDLINE]
Free PMC Article

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