Research Article

Prediction of genomic islands in seven human pathogens using the Z-Island method

Published: October 05, 2011
Genet.Mol.Res. 10 (4) : 2307-2315 DOI: 10.4238/2011.October.5.1

Abstract

We adopted the method of Zhang and Zhang (the Z-Island method) to identify genomic islands in seven human pathogens, analyzing their chromosomal DNA sequences. The Z-Island method is a theoretical method for predicting genomic islands in bacterial genomes; it consists of determination of the cumulative GC profile and computation of codon usage bias. Thirty-one genomic islands were found in seven pathogens using this method. Further analysis demonstrated that most have the known conserved features; this increases the probability that they are real genomic islands. Eleven genomic islands were found to code for products involved in causing disease (virulence factors) or in resistance to antibiotics (resistance factors). This finding could be useful for research on the pathogenicity of these bacteria and helpful in the treatment of the diseases that they cause. In a comparison of the distribution of mobility elements in genomic islands predicted by different methods, the Z-Island method gave lower false-positive rates. The Z-Island method was found to detect more known genomic islands than the two methods that we compared it with, SIGI-HMM and IslandPick. Furthermore, it maintained a better balance between specificity and sensitivity. The only inconvenience is that the steps for finding genomic islands by the Z-Island method are semi-automatic.

We adopted the method of Zhang and Zhang (the Z-Island method) to identify genomic islands in seven human pathogens, analyzing their chromosomal DNA sequences. The Z-Island method is a theoretical method for predicting genomic islands in bacterial genomes; it consists of determination of the cumulative GC profile and computation of codon usage bias. Thirty-one genomic islands were found in seven pathogens using this method. Further analysis demonstrated that most have the known conserved features; this increases the probability that they are real genomic islands. Eleven genomic islands were found to code for products involved in causing disease (virulence factors) or in resistance to antibiotics (resistance factors). This finding could be useful for research on the pathogenicity of these bacteria and helpful in the treatment of the diseases that they cause. In a comparison of the distribution of mobility elements in genomic islands predicted by different methods, the Z-Island method gave lower false-positive rates. The Z-Island method was found to detect more known genomic islands than the two methods that we compared it with, SIGI-HMM and IslandPick. Furthermore, it maintained a better balance between specificity and sensitivity. The only inconvenience is that the steps for finding genomic islands by the Z-Island method are semi-automatic.

About the Authors

Charkowski AO (2004). Making sense of an alphabet soup: the use of a new bioinformatics tool for identification of novel gene islands. Focus on “identification of genomic islands in the genome of Bacillus cereus by comparative analysis with Bacillus anthracis”. Physiol. Genomics 16: 180-181.
http://dx.doi.org/10.1152/physiolgenomics.00199.2003
PMid:14726601

Do JH and Miyano S (2008). The GC and window-averaged DNA curvature profile of secondary metabolite gene cluster in Aspergillus fumigatus genome. Appl. Microbiol. Biotechnol. 80: 841-847.
http://dx.doi.org/10.1007/s00253-008-1638-4
PMid:18719902

Gogarten JP, Doolittle WF and Lawrence JG (2002). Prokaryotic evolution in light of gene transfer. Mol. Biol. Evol. 19: 2226-2238.
http://dx.doi.org/10.1093/oxfordjournals.molbev.a004046
PMid:12446813

Greub G, Collyn F, Guy L and Roten CA (2004). A genomic island present along the bacterial chromosome of the Parachlamydiaceae UWE25, an obligate amoebal endosymbiont, encodes a potentially functional F-like conjugative DNA transfer system. BMC Microbiol. 4: 48.
http://dx.doi.org/10.1186/1471-2180-4-48
PMid:15615594    PMCid:548262

Hentschel U and Hacker J (2001). Pathogenicity islands: the tip of the iceberg. Microb. Infect. 3: 545-548.
http://dx.doi.org/10.1016/S1286-4579(01)01410-1

Ho Sui SJ, Fedynak A, Hsiao WW, Langille MG, et al. (2009). The association of virulence factors with genomic islands. PLoS One 4: e8094.
http://dx.doi.org/10.1371/journal.pone.0008094
PMid:19956607    PMCid:2779486

Hsiao WW, Ung K, Aeschliman D, Bryan J, et al. (2005). Evidence of a large novel gene pool associated with prokaryotic genomic islands. PLoS Genet. 1: e62.
http://dx.doi.org/10.1371/journal.pgen.0010062
PMid:16299586    PMCid:1285063

Kanhere A and Vingron M (2009): Horizontal gene transfers in prokaryotes show differential preferences for metabolic and translational genes. BMC Evol. Biol. 9: 9.
http://dx.doi.org/10.1186/1471-2148-9-9
PMid:19134215    PMCid:2651853

Langille MG, Hsiao WW and Brinkman FS (2008). Evaluation of genomic island predictors using a comparative genomics approach. BMC Bioinformatics 9: 329.
http://dx.doi.org/10.1186/1471-2105-9-329
PMid:18680607    PMCid:2518932

Langille MG, Hsiao WW and Brinkman FS (2010). Detecting genomic islands using bioinformatics approaches. Nat. Rev. Microbiol. 8: 373-382.
http://dx.doi.org/10.1038/nrmicro2350
PMid:20395967

Monier A, Pagarete A, de Vargas C, Allen MJ, et al. (2009). Horizontal gene transfer of an entire metabolic pathway between a eukaryotic alga and its DNA virus. Genome Res. 19: 1441-1449.
http://dx.doi.org/10.1101/gr.091686.109
PMid:19451591    PMCid:2720186

Ochman H, Lawrence JG and Groisman EA (2000). Lateral gene transfer and the nature of bacterial innovation. Nature 405: 299-304.
http://dx.doi.org/10.1038/35012500
PMid:10830951

Ou HY, Chen LL, Lonnen J, Chaudhuri RR, et al. (2006). A novel strategy for the identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites in closely related bacteria. Nucleic Acids Res. 34: e3.
http://dx.doi.org/10.1093/nar/gnj005
PMid:16414954    PMCid:1326021

Sridhar J and Rafi ZA (2007). Identification of novel genomic islands associated with small RNAs. In Silico Biol. 7: 601- 611.
PMid:18467773

Vernikos GS and Parkhill J (2008). Resolving the structural features of genomic islands: a machine learning approach. Genome Res. 18: 331-342.
http://dx.doi.org/10.1101/gr.7004508
PMid:18071028    PMCid:2203631

Waack S, Keller O, Asper R, Brodag T, et al. (2006). Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models. BMC Bioinformatics 7: 142.
http://dx.doi.org/10.1186/1471-2105-7-142
PMid:16542435    PMCid:1489950

Yang J, Chen LH, Sun LL, Yu J, et al. (2008). VFDB 2008 release: an enhanced web-based resource for comparative pathogenomics. Nucleic Acids Res. 36: D539-D542.
http://dx.doi.org/10.1093/nar/gkm951
PMid:17984080    PMCid:2238871

Yoon SH, Park YK, Lee S, Choi D, et al. (2007). Towards pathogenomics: a web-based resource for pathogenicity islands. Nucleic Acids Res. 35: D395-D400.
http://dx.doi.org/10.1093/nar/gkl790
PMid:17090594    PMCid:1669727

Zhang R and Zhang CT (2004). A systematic method to identify genomic islands and its applications in analyzing the genomes of Corynebacterium glutamicum and Vibrio vulnificus CMCP6 chromosome I. Bioinformatics 20: 612-622.
http://dx.doi.org/10.1093/bioinformatics/btg453
PMid:15033867

Zhang R and Zhang CT (2005). Genomic islands in the Corynebacterium efficiens genome. Appl. Environ. Microbiol. 71: 3126-3130.
http://dx.doi.org/10.1128/AEM.71.6.3126-3130.2005
PMid:15933011    PMCid:1151870