[Show abstract][Hide abstract] ABSTRACT: One hundred twenty Burkholderia cepacia complex isolates collected during 2004-2010 from 66 patients in two cystic fibrosis reference centers in Argentina were analyzed. B. contaminans was the species most frequently recovered (57.6%) followed by B. cenocepacia (15%), a species distribution not reported so far. The recA-PCR-based techniques applied to the B. contaminans isolates revealed that 85% of the population carried the recA-ST-71 allele. Our results showed the utility of BOX-PCR genotyping in analyzing B. contaminans diversity. This approach allowed us to address a clonal transmission during an outbreak and the genetic changes occurring in infecting bacteria over the course of chronic infection.
Journal of clinical microbiology 11/2012; DOI:10.1128/JCM.02500-12 · 3.99 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Survival of Pseudomonas aeruginosa in cystic fibrosis (CF) chronic infections is based on a genetic adaptation process consisting of mutations in specific genes, which can produce advantageous phenotypic switches and ensure its persistence in the lung. Among these, mutations inactivating the regulators MucA (alginate biosynthesis), LasR (quorum sensing) and MexZ (multidrug-efflux pump MexXY) are the most frequently observed, with those inactivating the DNA mismatch repair system (MRS) being also highly prevalent in P. aeruginosa CF isolates, leading to hypermutator phenotypes that could contribute to this adaptive mutagenesis by virtue of an increased mutation rate. Here, we characterized the mutations found in the mucA, lasR, mexZ and MRS genes in P. aeruginosa isolates obtained from Argentinean CF patients, and analyzed the potential association of mucA, lasR and mexZ mutagenesis with MRS-deficiency and antibiotic resistance. Thus, 38 isolates from 26 chronically infected CF patients were characterized for their phenotypic traits, PFGE genotypic patterns, mutations in the mucA, lasR, mexZ, mutS and mutL gene coding sequences and antibiotic resistance profiles. The most frequently mutated gene was mexZ (79%), followed by mucA (63%) and lasR (39%) as well as a high prevalence (42%) of hypermutators being observed due to loss-of-function mutations in mutL (60%) followed by mutS (40%). Interestingly, mutational spectra were particular to each gene, suggesting that several mechanisms are responsible for mutations during chronic infection. However, no link could be established between hypermutability and mutagenesis in mucA, lasR and mexZ, indicating that MRS-deficiency was not involved in the acquisition of these mutations. Finally, although inactivation of mucA, lasR and mexZ has been previously shown to confer resistance/tolerance to antibiotics, only mutations in MRS genes could be related to an antibiotic resistance increase. These results help to unravel the mutational dynamics that lead to the adaptation of P. aeruginosa to the CF lung.
PLoS ONE 09/2010; 5(9). DOI:10.1371/journal.pone.0012669 · 3.23 Impact Factor
[Show abstract][Hide abstract] 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 Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as Burkholderia cepacia complex (BCC) bacteria, Stenotrophomonas maltophilia, Ralstonia pickettii, Acinetobacter spp., and Achromobacter 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 recA-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by recA 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 P. aeruginosa, S. maltophilia, Achromobacter xylosoxidans, Acinetobacter 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, B. cepacia, B. multivorans, B. cenocepacia, and B. stabilis (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.