Genetic Linkage and Cotransfer of a Novel, vanB-Containing Transposon (Tn5382) and a Low-Affinity Penicillin-Binding Protein 5 Gene in a Clinical Vancomycin-Resistant Enterococcus faecium Isolate

Mechanisms for the intercellular transfer of VanB-type vancomycin resistance determinants and for the almost universal association of these determinants with those for high-level ampicillin resistance remain poorly defined. We report the discovery of Tn5382, a ca. 27-kb putative transposon encoding VanB-type glycopeptide resistance in Enterococcus faecium. Open reading frames internal to the right end of Tn5382 and downstream of the vanX_B dipeptidase gene exhibit significant homology to genes encoding the excisase and integrase of conjugative transposon Tn916. The ends of Tn5382 are also homologous to the ends of Tn916, especially in regions bound by the integrase enzyme. PCR amplification experiments indicate that Tn5382 excises to form a circular intermediate in E. faecium. Integration of Tn5382 in the chromosome of E. faecium C68 has occurred 113 bp downstream of the stop codon for the pbp5 gene, which encodes high-level ampicillin resistance in this clinical isolate. Transfer of vancomycin, ampicillin, and tetracycline resistance from C68 to an E. faecium recipient strain occurs at low frequency in vitro and is associated with acquisition of a 130- to 160-kb segment of DNA that contains Tn5382, the pbp5 gene, and its putative repressor gene, psr. The interenterococcal transfer of this large chromosomal element appears to be the primary mechanism for vanB operon spread in northeast Ohio. These results expand the known family of Tn916-related transposons, suggest a mechanism for vanB operon entry into and dissemination among enterococci, and provide an explanation for the nearly universal association of vancomycin and high-level ampicillin resistance in clinical E. faecium strains.     

Characterization of Tn5386, a Tn916-related mobile element

In recent work, we described excision of a large genomic region from Enterococcus faecium D344R resulting from the interaction of Tn916 and a related transposon designated Tn5386. In the present study, we present and analyze the complete sequence of Tn5386. Tn5386 is 29,451 bp in length. Fifteen of its 30 open reading frames are analogous to ORFs found in Tn916. Significant differences include a series of ORFs with homology to lantibiotic immunity genes in the same location where tetM is found in Tn916, insertion of a Group II intron and an ORF with similarities to previously described surface exposed collagen adhesion proteins. Our results indicate that Tn5386 falls within the Tn916 family of transposons, and in place of tetM encodes a novel region that may confer resistance to lantibiotics.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.