Discrimination of the Acinetobacter calcoaceticus-Acinetobacter baumannii complex species by Fourier transform infrared spectroscopy

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Sousa, C (Author) Other; Silva, L (Author) Other; Grosso, F (Author) Other; Nemec, A (Author) Other; Lopes, J (Author) Other; Peixe, L (Author) FFUP

Journal

Title: European Journal of Clinical Microbiology and Infectious Diseases

Vol. 33 No. 12

Pages: 1345-1353

ISSN: 0934-9723

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 15 Citations

Scientific classification

FOS: Medical and Health sciences > Health sciences

Other information

Authenticus ID: P-009-MRE

DOI: 10.1007/s10096-014-2078-y

Abstract (EN): The main goal of this work was to assess the ability of Fourier transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) to discriminate between the species of the Acinetobacter calcoaceticus-Acinetobacter baumannii (Acb) complex, i.e. A. baumannii, A. nosocomialis, A. pittii, A. calcoaceticus, genomic species "Between 1 and 3" and genomic species "Close to 13TU". A total of 122 clinical isolates of the Acb complex previously identified by rpoB sequencing were studied. FTIR-ATR spectra was analysed by partial least squares discriminant analysis (PLSDA) and the model scores were presented in a dendrogram form. This spectroscopic technique proved to be effective in the discrimination of the Acb complex species, with sensitivities from 90 to 100 %. Moreover, a flowchart aiming to help with species identification was developed and tested with 100 % correct predictions for A. baumannii, A. nosocomialis and A. pittii test isolates. This rapid, low cost and environmentally friendly technique proved to be a reliable alternative for the identification of these closely related Acinetobacter species that share many clinical and epidemiological characteristics and are often difficult to distinguish. Its validation towards application on a routine basis could revolutionise high-throughput bacterial identification.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9