Fourier transform infrared spectroscopy for rapid identification of nonfermenting gram-negative bacteria isolated from sputum samples from cystic fibrosis patients

Abstract

The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are <i>Pseudomonas aeruginosa</i>, <i>Staphylococcus aureus</i>, and <i>Haemophilus influenzae</i>, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as <i>Burkholderia cepacia</i> complex (BCC) bacteria, <i>Stenotrophomonas maltophilia</i>, Ralstonia pickettii, <i>Acinetobacter</i> spp., and <i>Achromobacter</i> spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by <i>recA</i>-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by <i>recA</i> species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of <i>P. aeruginosa</i>, <i>S. maltophilia</i>, <i>Achromobacter xylosoxidans</i>, <i>Acinetobacter</i> spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, <i>B. cepacia</i>, <i>B. multivorans</i>, <i>B. cenocepacia</i>, and <i>B. stabilis</i> (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.