Comparison of virulence gene profiles of Escherichia coli strains isolated from healthy and diarrheic swine

A combination of uni- and multiplex PCR assays targeting 58 virulence genes (VGs) associated with Escherichia coli strains causing intestinal and extraintestinal disease in humans and other mammals was used to analyze the VG repertoire of 23 commensal E. coli isolates from healthy pigs and 52 clinical isolates associated with porcine neonatal diarrhea (ND) and postweaning diarrhea (PWD). The relationship between the presence and absence of VGs was interrogated using three statistical methods. According to the generalized linear model, 17 of 58 VGs were found to be significant (P < 0.05) in distinguishing between commensal and clinical isolates. Nine of the 17 genes represented by iha, hlyA, aidA, east1, aah, fimH, iroN(E).(coli), traT, and saa have not been previously identified as important VGs in clinical porcine isolates in Australia. The remaining eight VGs code for fimbriae (F4, F5, F18, and F41) and toxins (STa, STh, LT, and Stx2), normally associated with porcine enterotoxigenic E. coli. Agglomerative hierarchical algorithm analysis grouped E. coli strains into subclusters based primarily on their serogroup. Multivariate analyses of clonal relationships based on the 17 VGs were collapsed into two-dimensional space by principal coordinate analysis. PWD clones were distributed in two quadrants, separated from ND and commensal clones, which tended to cluster within one quadrant. Clonal subclusters within quadrants were highly correlated with serogroups. These methods of analysis provide different perspectives in our attempts to understand how commensal and clinical porcine enterotoxigenic E. coli strains have evolved and are engaged in the dynamic process of losing or acquiring VGs within the pig population.

A DHA14 drug efflux gene from Xanthomonas albilineans confers high-level albicidin antibiotic resistance in Escherichia coli

Aims: Identification of a gene for self-protection from the antibiotic-producing plant pathogen Xanthomonas albilineans, and functional testing by heterologous expression. Methods and Results: Albicidin antibiotics and phytotoxins are potent inhibitors of prokaryote DNA replication. A resistance gene (albF) isolated by shotgun cloning from the X. albilineans albicidin-biosynthesis region encodes a protein with typical features of DHA14 drug efflux pumps. Low-level expression of albF in Escherichia coli increased the MIC of albicidin 3000-fold, without affecting tsx-mediated albicidin uptake into the periplasm or resistance to other tested antibiotics. Bioinformatic analysis indicates more similarity to proteins involved in self-protection in polyketide-antibiotic-producing actinomycetes than to multi-drug resistance pumps in other Gram-negative bacteria. A complex promoter region may co-regulate albF with genes for hydrolases likely to be involved in albicidin activation or self-protection. Conclusions: AlbF is the first apparent single-component antibiotic-specific efflux pump from a Gram-negative antibiotic producer. It shows extraordinary efficiency as measured by resistance level conferred upon heterologous expression. Significance and Impact of the Study: Development of the clinical potential of albicidins as potent bactericidial antibiotics against diverse bacteria has been limited because of low yields in culture. Expression of albF with recently described albicidin-biosynthesis genes may enable large-scale production. Because albicidins are X. albilineans pathogenicity factors, interference with AlbF function is also an opportunity for control of the associated plant disease.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

Cluster analysis of the p53 genetic regulatory network: topology and biology

We describe a network module detection approach which combines a rapid and robust clustering algorithm with an objective measure of the coherence of the modules identified. The approach is applied to the network of genetic regulatory interactions surrounding the tumor suppressor gene p53. This algorithm identifies ten clusters in the p53 network, which are visually coherent and biologically plausible.