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Pathogenicity is a complex multifactorial process confounded by the concerted activity of genetic regions
that are associated with virulence or resistance determinants. Pathogenicity islands (PAIs) and antimicrobial
resistance islands (REIs) are key to the evolution of pathogens and they appear to play complimentary roles
in the process of bacterial infection in a manner that PAIs promote disease development and REIs give
a fitness advantage to the host over multiple antimicrobial agents.
Previously, we had developed an algorithm reflecting the evolutionary process of PAIs (Yoon et al. 2005). In 2007, we had established a comprehensive database and a search engine specialized for PAIs, and released PAIDB that contained 112 types of PAIs and 889 GenBank accessions for complete or partial PAI loci previously described in 497 pathogenic bacterial strains (Yoon et al. 2007). Compared with most of the PAI-related databases which focus on predicting PAIs in the aspect of horizontal gene transfer, PAIDB has been the only database that dedicated to provide comprehensive information on all annotated PAIs and predicted ones in prokaryotic genomes. PAIDB is a web-based user-friendly resource and has been widely used for detecting PAIs in newly sequenced genomes and mining virulence genes from metagenome.
In addition to PAIs, PAIDB v2.0 becomes a centralized resource of REIs described so far in academic literatures. PAIDB v2.0 contains 223 types of PAIs with 1331 accessions and 88 types of REIs with 108 accessions. With the improved detection scheme, 2673 prokaryotic genomes were analyzed to locate potential PAIs and REIs. The update encompasses dramatic increase in database contents of genomes analyzed, accuracy improvement of detection of candidate regions, and functionality update of web application.
|Pathogenicity islands (PAIs) are distinct genetic elements of pathogens encoding various virulence factors, and are a subset of genomic islands (GIs) which mediate the horizontal transfer of genes encoding numerous virulence factors such as type III secretion system. They include type III secretion system and host invasion (LEE PAI in pathogenic Escherichia coli, Hrp PAI in Pseudomonas syringae), superantigen (SaPI1 and SaPI2 in Staphylococcus aureus), colonization factor (VPI in Vibrio cholerae), iron uptake system (SHI-2 in Shigella flexneri), and enterotoxin (espC PAI in Escherichia coli, she PAI in Shigella flexneri). Although PAI is loosely defined concept, several criteria were suggested to identify PAI: i) Encoding for virulence factors such as toxins, adhesins and invasins, ii) Presence in pathogenic strains, and absence in non-pathogenic strains of one species or a related species, iii) Different G+C content and codon usage from the rest of the chromosome, iv) Large genomic regions (often > 30 kb), v) Association with tRNA genes and/or insertion sequence (IS) elements at their boundaries, vi) Instability. PAIs are a subset of genomic islands (GIs) which have been transferred by horizontal gene transfer (HGT) event and confer virulence upon the recipient. Identification of PAIs is essential in understanding the development of disease and the evolution of bacterial pathogenesis.|
|Antimicrobial resistance islands (REIs) are another class of GIs, and are linked to pathogenesis by conferring simultaneous resistance to multiple antibiotics and facilitating the emergence of multidrug-resistant pathogens. Acquisition of staphylococcal cassette chromosome mec (SCCmec) resulted in emergence of methicillin-resistant Staphylococcus aureus. Salmonella genomic island 1 (SGI1) is associated with the multiple-drug-resistant region of Salmonella typhimurim. Pseudomonas aeruginosa genomic island 1 (PAGI-1) is found in the majority of the clinical isolates. AbaR1 was reported to contain over 85% of resistance genes of Acinetobacter baumannii AYE, explaining a remarkable ability of this emerging opportunistic pathogen to rapidly acquire multidrug resistance within a few decades.|
|- Yoon SH*, Park YK, and Kim JF. 2015. PAIDB v2.0: exploration and analysis of pathogenicity and resistance islands Nucleic Acids Res. 43:D624-D630 (doi: 10.1093/nar/gku985, Published in advance, October 21, 2014)|
- Yoon SH, Hur CG, Kang HY, Kim YH, Oh TK, and Kim JF. 2005.
A computational approach for identifying pathogenicity islands in prokaryotic genomes.
BMC Bioinformatics 6: 184.|
- Yoon SH, Park YK, Lee S, Choi D, Oh TK, Hur CG, and Kim JF. 2007. Towards Pathogenomics: A Web-Based Resource for Pathogenicity Islands. Nucleic Acids Res. 35:D395-D400.
|Questions, corrections and comments can be emailed to Dr. Sung Ho Yoon (Lab: http://www.sybirg.re.kr).|