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The Histidine Triad Proteins (HTPs), also known as Pht proteins in Streptococcus pneumoniae, constitute a family of surface-exposed proteins that exist in many pathogenic streptococcal species. Although many studies have revealed the importance of HTPs in streptococcal physiology and pathogenicity, little is known about their origin and evolution. In this study, after identifying all htp homologs from 105 streptococcal genomes representing 38 different species/subspecies, we analyzed their domain structures, positions in genome, and most importantly, their evolutionary histories. By further projecting this information onto the streptococcal phylogeny, we made several major findings. First, htp genes originated earlier than the Streptococcus genus and gene-loss events have occurred among three streptococcal groups, resulting in the absence of the htp gene in the Bovis, Mutans and Salivarius groups. Second, the copy number of htp genes in other groups of Streptococcus is variable, ranging from one to four functional copies. Third, both phylogenetic evidence and domain structure analyses support the division of two htp subfamilies, designated as htp I and htp II. Although present mainly in the pyogenic group and in Streptococcus suis, htp II members are distinct from htp I due to the presence of an additional leucine-rich-repeat domain at the C-terminus. Finally, htp genes exhibit a faster nucleotide substitution rate than do housekeeping genes. Specifically, the regions outside the HTP domains are under strong positive selection. This distinct evolutionary pattern likely helped Streptococcus to easily escape from recognition by host immunity.

Background: In yeast coding sequences, once a particular codon has been used, subsequent occurrence of the same amino acid tends to use codons sharing the same tRNA. Such a phenomenon of co-tRNA codons pairing bias (CTCPB) is also found in some other eukaryotes but it is not known whether it occurs in prokaryotes., Methodology/principal Findings: In this study, we focused on a total of 773 bacterial genomes to investigate their synonymous codon pairing preferences. After calculating the actual frequencies of synonymous codon pairs and comparing them with their expected values, we detected an obvious pairing bias towards identical codon pairs. This seems consistent with the previously reported CTCPB phenomenon, since identical codons are certainly read by the same tRNA. However, among co-tRNA but non-identical codon pairs, only 22 were often found overrepresented, suggesting that many co-tRNA codons actually do not preferentially pair together in prokaryotes. Therefore, the previously reported co-tRNA codons pairing rule needs to be more rigorously defined. The affinity differences between a tRNA anticodon and its readable codons should be taken into account. Moreover, both within-gene-shuffling tests and phylogenetic analyses support the idea that translational selection played an important role in shaping the observed synonymous codon pairing pattern in prokaryotes., Conclusions: Overall, a high level of synonymous codon pairing bias was detected in 73% investigated bacterial species, suggesting the synonymous codon ordering strategy has been prevalently adopted by prokaryotes to improve their translational efficiencies. The findings in this study also provide important clues to better understand the complex dynamics of translational process.

Human tissue kallikreins (KLKs) are members of a multigene family of serine proteases aberrantly expressed in many cancer types. In ovarian cancer, 12 KLKs are upregulated, and of those KLK5, 6 and 10 have been the focus of investigations into new diagnostic and prognostic biomarkers. However, little is known about the contributions of KLK5, 6 and 10 to ovarian cancer pathophysiology.In this study, a panel of 13 human ovarian cancer cell lines was screened by ELISA for secretion of KLK5, 6, 8, 10, 13, and 14. The ES-2 cell line, devoid of these kallikreins, was transfected with expression vectors of KLK5, 6 and 10 individually or in pairs. Co-expression of KLK5, 6 and 10 was correlated with lessened aggressivity of ovarian cancer cell lines as defined by reduced colony formation in soft agar and tumorigenicity in nude mice. ES-2 clones overexpressing KLK5, 10/5, 10/6, 5/6 made significantly fewer colonies in soft agar. When compared to control mice, survival of mice injected with ES-2 clones overexpressing KLK10, 10/5, 10/6, 5/6 was significantly longer, while KLK6 was shorter. All groups displaying a survival advantage also differed quantitatively and qualitatively in their presentation of ascites, with both a reduced incidence of ascites and an absence of cellular aggregates within those ascites. The survival advantage conferred by KLK10 overexpression could be recapitulated with the exogenous administration of a recombinant KLK10. In conclusion, these findings indicate that KLK5, 6 and 10 may modulate the progression of ovarian cancer, and interact together to alter tumour pathophysiology. Furthermore, results support the putative role of KLK10 as a tumour suppressor and suggest it may hold therapeutic potential in ovarian cancer.

A correlation method was recently adopted to identify selection-favored 'optimal' codons from 675 bacterial genomes. Surprisingly, the identities of these optimal codons were found to track the bacterial GC content, leading to a conclusion that selection would generally shape the codon usages to the same direction as the overall mutation does. Raising several concerns, here we report a thorough comparative study on 203 well-selected bacterial species, which strongly suggest that the previous conclusion is likely an illusion. Firstly, the previous study did not preclude species that are suffering weak or no selection pressures on their codon usages. For these species, as showed in this study, the optimal codon identities are prone to be incorrect and follow GC content. Secondly, the previous study only adopted the correlation method, without considering another method to test the reliability of inferred optimal codons. Actually by definition, optimal codons can also be identified by simply comparing codon usages between high- and low-expression genes. After using both methods to identify optimal codons for the selected species, we obtained highly conflicting results, suggesting at least one method is misleading. Further we found a critical problem of correlation method at the step of calculating gene bias level. Due to a failure of accurately defining the background mutation, the problem would result in wrong optimal codon identities. In other words, partial mutational effects on codon choices were mistakenly regarded as selective influences, leading to incorrect and biased optimal codon identities. Finally, considering the translational dynamics, optimal codons identified by comparison method can be well-explained by tRNA compositions, whereas optimal codons identified by correlation method can not be. For all above reasons, we conclude that real optimal codons actually do not track the genomic GC content, and correlation method is misleading in identifying optimal codons and better be avoided.