Analysis of the pathogenic potential of nosocomial Pseudomonas putida strains

The adherence of coagulase-negative staphylococci to smooth surfaces was assayed by measuring the optical densities of stained bacterial films adherent to the floors of plastic tissue culture plates. The optical densities correlated with the weight of the adherent bacterial film (r = 0.906; P less than 0.01). The measurements also agreed with visual assessments of bacterial adherence to culture tubes, microtiter plates, and tissue culture plates. Selected clinical strains were passed through a mouse model for foreign body infections and a rat model for catheter-induced endocarditis. The adherence measurements of animal passed strains remained the same as those of the laboratory-maintained parent strain. Spectrophotometric classification of coagulase-negative staphylococci into nonadherent and adherent categories according to these measurements had a sensitivity, specificity, and accuracy of 90.6, 80.8, and 88.4%, respectively. We examined a previously described collection of 127 strains of coagulase-negative staphylococci isolated from an outbreak of intravascular catheter-associated sepsis; strains associated with sepsis were more adherent than blood culture contaminants and cutaneous strains (P less than 0.001). We also examined a collection of 84 strains isolated from pediatric patients with cerebrospinal fluid (CSF) shunts; once again, pathogenic strains were more adherent than were CSF contaminants (P less than 0.01). Finally, we measured the adherence of seven endocarditis strains. As opposed to strains associated with intravascular catheters and CSF shunts, endocarditis strains were less adherent than were saprophytic strains of coagulase-negative staphylococci. The optical densities of bacterial films adherent to plastic tissue culture plates serve as a quantitative model for the study of the adherence of coagulase-negative staphylococci to medical devices, a process which may be important in the pathogenesis of foreign body infections.

The persistence of chronic Pseudomonas aeruginosa lung infections in cystic fibrosis (CF) patients is due to biofilm-growing mucoid (alginate-producing) strains. A biofilm is a structured consortium of bacteria, embedded in a self-produced polymer matrix consisting of polysaccharide, protein and DNA. In CF lungs, the polysaccharide alginate is the major part of the P. aeruginosa biofilm matrix. Bacterial biofilms cause chronic infections because they show increased tolerance to antibiotics and resist phagocytosis, as well as other components of the innate and the adaptive immune system. As a consequence, a pronounced antibody response develops, leading to immune complex-mediated chronic inflammation, dominated by polymorphonuclear leukocytes. The chronic inflammation is the major cause of the lung tissue damage in CF. Biofilm growth in CF lungs is associated with an increased frequency of mutations, slow growth and adaptation of the bacteria to the conditions in the lungs, and to antibiotic therapy. Low bacterial metabolic activity and increase of doubling times of the bacterial cells in CF lungs are responsible for some of the tolerance to antibiotics. Conventional resistance mechanisms, such as chromosomal β-lactamase, upregulated efflux pumps, and mutations of antibiotic target molecules in the bacteria, also contribute to the survival of P. aeruginosa biofilms. Biofilms can be prevented by early aggressive antibiotic prophylaxis or therapy, and they can be treated by chronic suppressive therapy.

The recent introduction of massively parallel pyrosequencers allows rapid, inexpensive analysis of microbial community composition using 16S ribosomal RNA (rRNA) sequences. However, a major challenge is to design a workflow so that taxonomic information can be accurately and rapidly assigned to each read, so that the composition of each community can be linked back to likely ecological roles played by members of each species, genus, family or phylum. Here, we use three large 16S rRNA datasets to test whether taxonomic information based on the full-length sequences can be recaptured by short reads that simulate the pyrosequencer outputs. We find that different taxonomic assignment methods vary radically in their ability to recapture the taxonomic information in full-length 16S rRNA sequences: most methods are sensitive to the region of the 16S rRNA gene that is targeted for sequencing, but many combinations of methods and rRNA regions produce consistent and accurate results. To process large datasets of partial 16S rRNA sequences obtained from surveys of various microbial communities, including those from human body habitats, we recommend the use of Greengenes or RDP classifier with fragments of at least 250 bases, starting from one of the primers R357, R534, R798, F343 or F517.