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Microbes are highly diverse and widely distributed organisms. They account for ~60% of Earth’s biomass and new predictions point for the existence of 1011 to 1012 species, which are constantly sharing genes through several different mechanisms. Genomic Islands (GI) are critical in this context, as they are large regions acquired through horizontal gene transfer. Also, they present common features like genomic signature deviation, transposase genes, flanking tRNAs and insertion sequences. GIs carry large numbers of genes related to specific lifestyle and are commonly classified in Pathogenicity, Resistance, Metabolic or Symbiotic Islands. With the advent of the next-generation sequencing technologies and the deluge of genomic data, many software tools have been developed that aim to tackle the problem of GI prediction and they are all based on the prediction of GI common features. However, there is still room for the development of new software tools that implements new approaches, such as, machine learning and pangenomics based analyses. Finally, GIs will always hold a potential application in every newly invented genomic approach as they are directly responsible for much of the genomic plasticity of bacteria.

Bacteria are highly diverse organisms that are able to adapt to a broad range of environments and hosts due to their high genomic plasticity. Horizontal gene transfer plays a pivotal role in this genome plasticity and in evolution by leaps through the incorporation of large blocks of genome sequences, ordinarily known as genomic islands (GEIs). GEIs may harbor genes encoding virulence, metabolism, antibiotic resistance and symbiosis-related functions, namely pathogenicity islands (PAIs), metabolic islands (MIs), resistance islands (RIs) and symbiotic islands (SIs). Although many software for the prediction of GEIs exist, they only focus on PAI prediction and present other limitations, such as complicated installation and inconvenient user interfaces. Here, we present GIPSy, the genomic island prediction software, a standalone and user-friendly software for the prediction of GEIs, built on our previously developed pathogenicity island prediction software (PIPS). We also present four application cases in which we crosslink data from literature to PAIs, MIs, RIs and SIs predicted by GIPSy. Briefly, GIPSy correctly predicted the following previously described GEIs: 13 PAIs larger than 30kb in Escherichia coli CFT073; 1 MI for Burkholderia pseudomallei K96243, which seems to be a miscellaneous island; 1 RI of Acinetobacter baumannii AYE, named AbaR1; and, 1 SI of Mesorhizobium loti MAFF303099 presenting a mosaic structure. GIPSy is the first life-style-specific genomic island prediction software to perform analyses of PAIs, MIs, RIs and SIs, opening a door for a better understanding of bacterial genome plasticity and the adaptation to new traits., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Helicobacter pylori is a human gastric pathogen implicated as the major cause of peptic ulcer and second leading cause of gastric cancer (~70%) around the world. Conversely, an increased resistance to antibiotics and hindrances in the development of vaccines against H. pylori are observed. Pan-genome analyses of the global representative H. pylori isolates consisting of 39 complete genomes are presented in this paper. Phylogenetic analyses have revealed close relationships among geographically diverse strains of H. pylori. The conservation among these genomes was further analyzed by pan-genome approach; the predicted conserved gene families (1,193) constitute ~77% of the average H. pylori genome and 45% of the global gene repertoire of the species. Reverse vaccinology strategies have been adopted to identify and narrow down the potential core-immunogenic candidates. Total of 28 nonhost homolog proteins were characterized as universal therapeutic targets against H. pylori based on their functional annotation and protein-protein interaction. Finally, pathogenomics and genome plasticity analysis revealed 3 highly conserved and 2 highly variable putative pathogenicity islands in all of the H. pylori genomes been analyzed.

The genus Campylobacter contains pathogens causing a wide range of diseases, targeting both humans and animals. Among them, the Campylobacter fetus subspecies fetus and venerealis deserve special attention, as they are the etiological agents of human bacterial gastroenteritis and bovine genital campylobacteriosis, respectively. We compare the whole genomes of both subspecies to get insights into genomic architecture, phylogenetic relationships, genome conservation and core virulence factors. Pan-genomic approach was applied to identify the core- and pan-genome for both C. fetus subspecies and members of the genus. The C. fetus subspecies conserved (76%) proteome were then analyzed for their subcellular localization and protein functions in biological processes. Furthermore, with pathogenomic strategies, unique candidate regions in the genomes and several potential core-virulence factors were identified. The potential candidate factors identified for attenuation and/or subunit vaccine development against C. fetus subspecies contain: nucleoside diphosphate kinase (Ndk), type IV secretion systems (T4SS), outer membrane proteins (OMP), substrate binding proteins CjaA and CjaC, surface array proteins, sap gene, and cytolethal distending toxin (CDT). Significantly, many of those genes were found in genomic regions with signals of horizontal gene transfer and, therefore, predicted as putative pathogenicity islands. We found CRISPR loci and dam genes in an island specific for C. fetus subsp. fetus, and T4SS and sap genes in an island specific for C. fetus subsp. venerealis. The genomic variations and potential core and unique virulence factors characterized in this study would lead to better insight into the species virulence and to more efficient use of the candidates for antibiotic, drug and vaccine development., (Copyright © 2012 Elsevier B.V. All rights reserved.)

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.