SPEED: a molecular-evolution-based database of mammalian orthologous groups

Extinction of chromosomes due to specialization is a universal occurrence

The human X and Y chromosomes evolved from a pair of autosomes approximately 180 million years ago. Despite their shared evolutionary origin, extensive genetic decay has resulted in the human Y chromosome losing 97% of its ancestral genes while gene content and order remain highly conserved on the X chromosome. Five 'stratification' events, most likely inversions, reduced the Y chromosome's ability to recombine with the X chromosome across the majority of its length and subjected its genes to the erosive forces associated with reduced recombination. The remaining functional genes are ubiquitously expressed, functionally coherent, dosage-sensitive genes, or have evolved male-specific functionality. It is unknown, however, whether functional specialization is a degenerative phenomenon unique to sex chromosomes, or if it conveys a potential selective advantage aside from sexual antagonism. We examined the evolution of mammalian orthologs to determine if the selective forces that led to the degeneration of the Y chromosome are unique in the genome. The results of our study suggest these forces are not exclusive to the Y chromosome, and chromosomal degeneration may have occurred throughout our evolutionary history. The reduction of recombination could additionally result in rapid fixation through isolation of specialized functions resulting in a cost-benefit relationship during times of intense selective pressure.

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).

Conclusion

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.

Murasaki: a fast, parallelizable algorithm to find anchors from multiple genomes

Background

With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows.

Methodology/Principal Findings

Our algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1) adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds) and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2) parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow) in 21 hours CPU time (42 minutes wall time). This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy.

Conclusions/Significance

Murasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with computational efficiency significantly greater than existing methods. Murasaki is available under GPL at http://murasaki.sourceforge.net.

Sperm proteomics reveals intensified selection on mouse sperm membrane and acrosome genes

Spermatozoa are a focal point for the impact of sexual selection due to sperm competition and sperm-female interactions in a wide range of sexually reproducing organisms. In-depth molecular investigation of the ramifications of these selective regimes has been limited due to a lack of information concerning the molecular composition of sperm. In this study, we utilize three previously published proteomic data sets in conjunction with our whole mouse sperm proteomic analysis to delineate cellular regions of sperm most impacted by positive selection. Interspecific analysis reveals robust evolutionary acceleration of sperm cell membrane genes (which include genes encoding acrosomal and sperm cell surface proteins) relative to other sperm genes, and evidence for positive selection in approximately 22% of sperm cell membrane components was obtained using maximum likelihood models. The selective forces driving the accelerated evolution of these membrane proteins may occur at a number of locations during sperm development, maturation, and transit through the female reproductive tract where the sperm cell membrane and eventually the acrosome are exposed to the extracellular milieu and available for direct cell-cell interactions.

Reverse polarization in amino acid and nucleotide substitution patterns between human-mouse orthologs of two compositional extrema

Genome-wide analysis of sequence divergence patterns in 12,024 human-mouse orthologous pairs reveals, for the first time, that the trends in nucleotide and amino acid substitutions in orthologs of high and low GC composition are highly asymmetric and polarized to opposite directions. The entire dataset has been divided into three groups on the basis of the GC content at third codon sites of human genes: high, medium, and low. High-GC orthologs exhibit significant bias in favor of the replacements, Thr --> Ala, Ser --> Ala, Val --> Ala, Lys --> Arg, Asn --> Ser, Ile --> Val etc., from mouse to human, whereas in low-GC orthologs, the reverse trends prevail. In general, in the high-GC group, residues encoded by A/U-rich codons of mouse proteins tend to be replaced by the residues encoded by relatively G/C-rich codons in their human orthologs, whereas the opposite trend is observed among the low-GC orthologous pairs. The medium-GC group shares some trends with high-GC group and some with low-GC group. The only significant trend common in all groups of orthologs, irrespective of their GC bias, is (Asp)(Mouse) --> (Glu)(Human) replacement. At the nucleotide level, high-GC orthologs have undergone a large excess of (A/T)(Mouse) --> (G/C)(Human) substitutions over (G/C)(Mouse) --> (A/T)(Human) at each codon position, whereas for low-GC orthologs, the reverse is true.

Uncovering the mutation-fixation correlation in short lineages

BACKGROUND

We recently reported a highly unexpected positive correlation between the fixation probability of nonsynonymous mutations (estimated by omega) and neutral mutation rate (estimated by Ks) in mammalian lineages. However, this positive correlation was observed for lineages with relatively long divergence time such as the human-mouse lineage, and was not found for very short lineages such as the human-chimpanzee lineage. It was previously unclear how to interpret this discrepancy. It may indicate that the positive correlation between omega and Ks in long lineages is a false finding. Alternatively, it may reflect a biologically meaningful difference between various lineages. Finally, the lack of positive correlation in short lineages may be the result of methodological artifacts.

RESULTS

Here we show that a strong positive correlation can indeed be seen in short lineages when a method was introduced to correct for the inherently high levels of stochastic noise in the use of Ks as an estimator of neutral mutation rate. Thus, the previously noted lack of positive correlation between omega and Ks in short lineages is due to stochastic noise in Ks that makes it a far less reliable estimator of neutral mutation rate in short lineages as compared to long lineages.

CONCLUSION

A positive correlation between omega and Ks can be observed in all mammalian lineages for which large amounts of sequence data are available, including very short lineages. It confirms the authenticity of this highly unexpected correlation, and argues that the correction likely applies broadly across all mammals and perhaps even non-mammalian species.