PIPS: Pathogenicity Island Prediction Software

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands

Frequencies of PAI features within the PICPs and in the full genomes of <i>C. pseudotuberculosis</i> strains 1002 and C231.

<p>Y-axis: frequency in percentage; X-axis: PICPs and genomes of <i>C. pseudotuberculosis</i> strains 1002 and C231. The frequencies of the features in each PICP and in the whole genomes of the two strains are represented in the following colors: blue for codon usage deviation; red for GC content deviation; green for virulence factors; and purple for hypothetical proteins.</p

Percentage of PAI features along the genome and the pathogenicity islands of <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i>.

<p>Percentage of PAI features along the genome and the pathogenicity islands of <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i>.</p

Comparison between the software used to identify pathogenicity islands in the <i>C. diphtheriae</i> strain <i>NCTC 13129</i>.

<p>Comparison between the software used to identify pathogenicity islands in the <i>C. diphtheriae</i> strain <i>NCTC 13129</i>.</p

PICP3 and PICD3 (top and bottom, respectively) in the <i>C. pseudotuberculosis</i> and <i>C. diphtheriae</i> genomes.

<p>Cp1002 and <i>C. diphtheriae</i> NCTC 13129 are shown at the top and bottom, respectively. Regions of similarity between the two genomes are marked in pink. Regions of similarity between two PAIs are marked in yellow, showing the presence of PICD3 in <i>C. pseudotuberculosis</i> with an insertion. Image generated by ACT (the Artemis Comparison Tool).</p