Publikationsdatenbank

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species (2019)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Moawad, Amira A.

Silge, Anja

Bocklitz, Thomas

Fischer, Katja

Rösch, Petra

Roesler, Uwe (WE 10)

Elschner, Mandy C.

Popp, Jürgen

Neubauer, Heinrich

Quelle

Molecules

Bandzählung: 24

Seiten: Article 4516

ISSN: 1420-3049

Sprache

Englisch

Verweise

URL (Volltext): https://www.mdpi.com/1420-3049/24/24/4516

DOI: 10.3390/molecules24244516

Pubmed: 31835527

Kontakt

Institut für Tier- und Umwelthygiene

Robert-von-Ostertag-Str. 7-13
14169 Berlin
+49 30 838 51845
tierhygiene@vetmed.fu-berlin.de

Abstract / Zusammenfassung

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.