IDENTIFICACIÓN OF RESPIRATORY TRACT BACTERIA ISOLATED FROM SPUTUM OF CYSTIC FIBROSIS PATIENTS BY FT-IR SPECTROSCOPY.

ALEJANDRO MIÑÁN; ALEJANDRA BOSCH (EXPOSITOR); CECILIA VESCINA; JOSÉ DEGROSSI; JUERGEN SCHMITT; BLANCA GATTI; MIRTA FRANCO; DIETER NAUMANN; OSVALDO YANTORNO

German Cancer Research Center, Heidelberg, Germany

Conferencia; Spec 2006. Shedding Light on Disease: Optical Diagnosis for the New Millennium; 2006

Robert Koch Institute, Berlin, Germany

Lung disease in cystic fibrosis (CF) is characterized by a vicious cycle of inflammation and infection, and a chronic bacterial colonization, which is the major cause of morbidity and mortality in these patients. Although the most common organisms isolated from sputum samples are Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, during the last decades an increasing fraction of CF patients has been colonized by other non-fermenting gram-negative bacilli as Burkholderia spp, Stenotrophomonas maltophilia, Ralstonia, Acinetobacter spp and Achromobacter spp(1,2,3). Identification of these bacteria at genus and species level in hospitals and CF centres are normally performed by time-consuming, laborious and sometimes unreliable biochemical methods. Furthermore, the taxonomic complexity in some of these genera, as bacteria belonging to B. cepacia complex, also contributes to misidentifications. At least 9 distinct species of closely related bacteria (genomovars), nearly indistinguishable by commercially available systems, comprise this complex. Accurate identification of multiply antibiotic-resistant bacilli isolated from CF patients is critical in epidemiological studies, intra-hospital outbreaks, patient treatment, and therapeutic options.     In this work we develop a rapid, sensitive and specific method based on FT-IR spectroscopy and artificial neural network (ANNs) for the discrimination and identification of nonfermenting and multi-resistant gram-negative bacilli isolated from respiratory samples of CF patients up to genus level, and in the case of B. cepacia complex up to subtype level.    A total of 12 references strains and 100 clinical isolates from 90 patients attended at La Plata Children’s Hospital and Buenos Aires Clinical Hospital were used in this study. Bacteria were isolated by routine clinical procedures, and phenotypic identification of Pseudomonas, Burkholderia, Stenotrophomonas, Ralstonia, Acinetobacter, and Achromobacter, was carried out by API 20NE system. Bacteria of B. cepacia complex were also identified by PCR-RLFP technique.  FT-IR macro-spectroscopy measurements were performed with cells grown on ATS medium. As some of these genus produce different quantities of cell-bond-pili and reserve substances as poly-hydroxy-butyrate acid (PHB), culture conditions and sample preparation were previously optimised to eliminate the corresponding spectral signals that interfere in FT-IR differentiation and identification. First derivatives of original spectra, vector normalized in the whole range(4), were processed by statistical methods based on pattern recognition using cluster analysis (CA) and ANNs,(5).     A hierarchical database consisting of a top level and two subsequent sub-classifications levels was created. The top-level of the network architecture comprise the differentiation and identification of the six above-mentioned genus. Then, a more dedicated network was developed to differentiate the 9 genomovars (I - IX) belonging to B. cepacia complex. Finally, for B. cenocepacia (genomovar III), which is recognised as the specie with the highest virulence spreading capacity of the complex, the hierarchy was further extended and a third level in the classification scheme was created to differentiate the two subtypes IIIa and IIIb.    Our results demonstrate a high reliability and a strong potential of ANN-based FT-IR spectrum analysis for accurate differentiation and identification of isolates from sputum of CF patients in a short time (less than 10 h after bacterial isolation), and without complicated sample handling.