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Relative Entropy Differences in Bacterial Chromosomes, Plasmids, Phages and Genomic Islands

Overview
Journal BMC Genomics
Publisher Biomed Central
Specialty Genetics
Date 2012 Feb 14
PMID 22325062
Citations 12
Authors
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Abstract

Background: We sought to assess whether the concept of relative entropy (information capacity), could aid our understanding of the process of horizontal gene transfer in microbes. We analyzed the differences in information capacity between prokaryotic chromosomes, genomic islands (GI), phages, and plasmids. Relative entropy was estimated using the Kullback-Leibler measure.

Results: Relative entropy was highest in bacterial chromosomes and had the sequence chromosomes > GI > phage > plasmid. There was an association between relative entropy and AT content in chromosomes, phages, plasmids and GIs with the strongest association being in phages. Relative entropy was also found to be lower in the obligate intracellular Mycobacterium leprae than in the related M. tuberculosis when measured on a shared set of highly conserved genes.

Conclusions: We argue that relative entropy differences reflect how plasmids, phages and GIs interact with microbial host chromosomes and that all these biological entities are, or have been, subjected to different selective pressures. The rate at which amelioration of horizontally acquired DNA occurs within the chromosome is likely to account for the small differences between chromosomes and stably incorporated GIs compared to the transient or independent replicons such as phages and plasmids.

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References
1.
Moran N . Microbial minimalism: genome reduction in bacterial pathogens. Cell. 2002; 108(5):583-6. DOI: 10.1016/s0092-8674(02)00665-7. View

2.
Bohlin J, Skjerve E, Ussery D . Investigations of oligonucleotide usage variance within and between prokaryotes. PLoS Comput Biol. 2008; 4(4):e1000057. PMC: 2289840. DOI: 10.1371/journal.pcbi.1000057. View

3.
Musto H, Naya H, Zavala A, Romero H, Alvarez-Valin F, Bernardi G . Genomic GC level, optimal growth temperature, and genome size in prokaryotes. Biochem Biophys Res Commun. 2006; 347(1):1-3. DOI: 10.1016/j.bbrc.2006.06.054. View

4.
Paul J, Sullivan M, Segall A, Rohwer F . Marine phage genomics. Comp Biochem Physiol B Biochem Mol Biol. 2002; 133(4):463-76. DOI: 10.1016/s1096-4959(02)00168-9. View

5.
Halary S, Leigh J, Cheaib B, Lopez P, Bapteste E . Network analyses structure genetic diversity in independent genetic worlds. Proc Natl Acad Sci U S A. 2009; 107(1):127-32. PMC: 2806761. DOI: 10.1073/pnas.0908978107. View