A computational approach for identifying pathogenicity islands in prokaryotic genomes

BACKGROUND: Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs. RESULTS: We present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs. CONCLUSION: Supporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis

Towards pathogenomics: a web-based resource for pathogenicity islands

Pathogenicity islands (PAIs) are genetic elements whose products are essential to the process of disease development. They have been horizontally (laterally) transferred from other microbes and are important in evolution of pathogenesis. In this study, a comprehensive database and search engines specialized for PAIs were established. The pathogenicity island database (PAIDB) is a comprehensive relational database of all the reported PAIs and potential PAI regions which were predicted by a method that combines feature-based analysis and similarity-based analysis. Also, using the PAI Finder search application, a multi-sequence query can be analyzed onsite for the presence of potential PAIs. As of April 2006, PAIDB contains 112 types of PAIs and 889 GenBank accessions containing either partial or all PAI loci previously reported in the literature, which are present in 497 strains of pathogenic bacteria. The database also offers 310 candidate PAIs predicted from 118 sequenced prokaryotic genomes. With the increasing number of prokaryotic genomes without functional inference and sequenced genetic regions of suspected involvement in diseases, this web-based, user-friendly resource has the potential to be of significant use in pathogenomics. PAIDB is freely accessible at

Evolution of ribosomal DNA-derived satellite repeat in tomato genome

<p>Abstract</p> <p>Background</p> <p>Tandemly repeated DNA, also called as satellite DNA, is a common feature of eukaryotic genomes. Satellite repeats can expand and contract dramatically, which may cause genome size variation among genetically-related species. However, the origin and expansion mechanism are not clear yet and needed to be elucidated.</p> <p>Results</p> <p>FISH analysis revealed that the satellite repeat showing homology with intergenic spacer (IGS) of rDNA present in the tomato genome. By comparing the sequences representing distinct stages in the divergence of rDNA repeat with those of canonical rDNA arrays, the molecular mechanism of the evolution of satellite repeat is described. Comprehensive sequence analysis and phylogenetic analysis demonstrated that a long terminal repeat retrotransposon was interrupted into each copy of the 18S rDNA and polymerized by recombination rather than transposition via an RNA intermediate. The repeat was expanded through doubling the number of IGS into the 25S rRNA gene, and also greatly increasing the copy number of type I subrepeat in the IGS of 25-18S rDNA by segmental duplication. Homogenization to a single type of subrepeat in the satellite repeat was achieved as the result of amplifying copy number of the type I subrepeat but eliminating neighboring sequences including the type II subrepeat and rRNA coding sequence from the array. FISH analysis revealed that the satellite repeats are commonly present in closely-related <it>Solanum </it>species, but vary in their distribution and abundance among species.</p> <p>Conclusion</p> <p>These results represent that the dynamic satellite repeats were originated from intergenic spacer of rDNA unit in the tomato genome. This result could serve as an example towards understanding the initiation and the expansion of the satellite repeats in complex eukaryotic genome.</p

Bioinformatics services for analyzing massive genomic datasets

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating down-stream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/. ?? 2020, Korea Genome Organization

Identification and Functional Analysis of Antifungal Immune Response Genes in Drosophila

Essential aspects of the innate immune response to microbial infection appear to be conserved between insects and mammals. Although signaling pathways that activate NF-κB during innate immune responses to various microorganisms have been studied in detail, regulatory mechanisms that control other immune responses to fungal infection require further investigation. To identify new Drosophila genes involved in antifungal immune responses, we selected genes known to be differentially regulated in SL2 cells by microbial cell wall components and tested their roles in antifungal defense using mutant flies. From 130 mutant lines, sixteen mutants exhibited increased sensitivity to fungal infection. Examination of their effects on defense against various types of bacteria and fungi revealed nine genes that are involved specifically in defense against fungal infection. All of these mutants displayed defects in phagocytosis or activation of antimicrobial peptide genes following infection. In some mutants, these immune deficiencies were attributed to defects in hemocyte development and differentiation, while other mutants showed specific defects in immune signaling required for humoral or cellular immune responses. Our results identify a new class of genes involved in antifungal immune responses in Drosophila

In Vitro Amplification of Misfolded Prion Protein Using Lysate of Cultured Cells

Protein misfolding cyclic amplification (PMCA) recapitulates the prion protein (PrP) conversion process under cell-free conditions. PMCA was initially established with brain material and then with further simplified constituents such as partially purified and recombinant PrP. However, availability of brain material from some species or brain material from animals with certain mutations or polymorphisms within the PrP gene is often limited. Moreover, preparation of native PrP from mammalian cells and tissues, as well as recombinant PrP from bacterial cells, involves time-consuming purification steps. To establish a convenient and versatile PMCA procedure unrestricted to the availability of substrate sources, we attempted to conduct PMCA with the lysate of cells that express cellular PrP (PrPC). PrPSc was efficiently amplified with lysate of rabbit kidney epithelial RK13 cells stably transfected with the mouse or Syrian hamster PrP gene. Furthermore, PMCA was also successful with lysate of other established cell lines of neuronal or non-neuronal origins. Together with the data showing that the abundance of PrPC in cell lysate was a critical factor to drive efficient PrPSc amplification, our results demonstrate that cell lysate in which PrPC is present abundantly serves as an excellent substrate source for PMCA

The genome sequence of E. coli W (ATCC 9637): comparative genome analysis and an improved genome-scale reconstruction of E. coli

Background: Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637), one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses