Bacterial phylogenetic tree construction based on genomic translation stop signals.
BACKGROUND
The efficiencies of the stop codons TAA, TAG, and TGA in protein synthesis termination are not the same. These variations could allow many genes to be regulated. There are many similar nucleotide trimers found on the second and third reading-frames of a gene. They are called premature stop codons (PSC). Like stop codons, the PSC in bacterial genomes are also highly bias in terms of their quantities and qualities on the genes. Phylogenetically related species often share a similar PSC profile. We want to know whether the selective forces that influence the stop codons and the PSC usage biases in a genome are related. We also wish to know how strong these trimers in a genome are related to the natural history of the bacterium. Knowing these relations may provide better knowledge in the phylogeny of bacteria


RESULTS
A 16SrRNA-alignment tree of 19 well-studied α-, β- and γ-Proteobacteria Type species is used as standard reference for bacterial phylogeny. The genomes of sixty-one bacteria, belonging to the α-, β- and γ-Proteobacteria subphyla, are used for this study. The stop codons and PSC are collectively termed "Translation Stop Signals" (TSS). A gene is represented by nine scalars corresponding to the numbers of counts of TAA, TAG, and TGA on each of the three reading-frames of that gene. "Translation Stop Signals Ratio" (TSSR) is the ratio between the TSS counts. Four types of TSSR are investigated. The TSSR-1, TSSR-2 and TSSR-3 are each a 3-scalar series corresponding respectively to the average ratio of TAA: TAG: TGA on the first, second, and third reading-frames of all genes in a genome. The Genomic-TSSR is a 9-scalar series representing the ratio of distribution of all TSS on the three reading-frames of all genes in a genome. Results show that bacteria grouped by their similarities based on TSSR-1, TSSR-2, or TSSR-3 values could only partially resolve the phylogeny of the species. However, grouping bacteria based on thier Genomic-TSSR values resulted in clusters of bacteria identical to those bacterial clusters of the reference tree. Unlike the 16SrRNA method, the Genomic-TSSR tree is also able to separate closely related species/strains at high resolution. Species and strains separated by the Genomic-TSSR grouping method are often in good agreement with those classified by other taxonomic methods. Correspondence analysis of individual genes shows that most genes in a bacterial genome share a similar TSSR value. However, within a chromosome, the Genic-TSSR values of genes near the replication origin region (Ori) are more similar to each other than those genes near the terminus region (Ter).


CONCLUSIONS
The translation stop signals on the three reading-frames of the genes on a bacterial genome are interrelated, possibly due to frequent off-frame recombination facilitated by translational-associated recombination (TSR). However, TSR may not occur randomly in a bacterial chromosome. Genes near the Ori region are often highly expressed and a bacterium always maintains multiple copies of Ori. Frequent collisions between DNA- polymerase and RNA-polymerase would create many DNA strand-breaks on the genes; whereas DNA strand-break induced homologues-recombination is more likely to take place between genes with similar sequence. Thus, localized recombination could explain why the TSSR of genes near the Ori region are more similar to each other. The quantity and quality of these TSS in a genome strongly reflect the natural history of a bacterium. We propose that the Genomic- TSSR can be used as a subjective biomarker to represent the phyletic status of a bacterium.
Department of Biological Sciences, Bioinformatics Program, The University of Memphis, Memphis, TN, USA.
Lijing
L
Xu
Department of Planning and Research, National Museum of Marine Biology and Aquarium, Pingtung, Taiwan.
Jimmy
J
Kuo
Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan.
Jong-Kang
JK
Liu
Department of Biological Sciences, Bioinformatics Program, The University of Memphis, Memphis, TN, USA.
Tit-Yee
TY
Wong