Gene duplication is an important source of evolutionary novelty. Most duplications are of just a single gene, but Ohno proposed that whole-genome duplication (polyploidy) is an important evolutionary mechanism. Many duplicate genes have been found in Saccharomyces cerevisiae, and these often seem to be phenotypically redundant. Here we show that the arrangement of duplicated genes in the S. cerevisiae genome is consistent with Ohno's hypothesis. We propose a model in which this species is a degenerate tetraploid resulting from a whole-genome duplication that occurred after the divergence of Saccharomyces from Kluyveromyces. Only a small fraction of the genes were subsequently retained in duplicate (most were deleted), and gene order was rearranged by many reciprocal translocations between chromosomes. Protein pairs derived from this duplication event make up 13% of all yeast proteins, and include pairs of transcription factors, protein kinases, myosins, cyclins and pheromones. Tetraploidy may have facilitated the evolution of anaerobic fermentation in Saccharomyces.

The genetic code is degenerate, but alternative synonymous codons are generally not used with equal frequency. Since the pioneering work of Grantham's group it has been apparent that genes from one species often share similarities in codon frequency; under the "genome hypothesis" there is a species-specific pattern to codon usage. However, it has become clear that in most species there are also considerable differences among genes. Multivariate analyses have revealed that in each species so far examined there is a single major trend in codon usage among genes, usually from highly biased to more nearly even usage of synonymous codons. Thus, to represent the codon usage pattern of an organism it is not sufficient to sum over all genes as this conceals the underlying heterogeneity. Rather, it is necessary to describe the trend among genes seen in that species. We illustrate these trends for six species where codon usage has been examined in detail, by presenting the pooled codon usage for the 10% of genes at either end of the major trend. Closely-related organisms have similar patterns of codon usage, and so the six species in Table 1 are representative of wider groups. For example, with respect to codon usage, Salmonella typhimurium closely resembles E. coli, while all mammalian species so far examined (principally mouse, rat and cow) largely resemble humans.

Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate–increasing and heart rate–decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.

The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides.We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure.We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive.

BackgroundShort linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many biological systems. SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as two sites may be important for activity, making identification of novel SLiMs extremely difficult. In particular, it can be very difficult to distinguish a randomly recurring “motif” from a truly over-represented one. Incorporating ambiguous amino acid positions and/or variable-length wildcard spacers between defined residues further complicates the matter.Methodology/Principal FindingsIn this paper we present two algorithms. SLiMBuild identifies convergently evolved, short motifs in a dataset of proteins. Motifs are built by combining dimers into longer patterns, retaining only those motifs occurring in a sufficient number of unrelated proteins. Motifs with fixed amino acid positions are identified and then combined to incorporate amino acid ambiguity and variable-length wildcard spacers. The algorithm is computationally efficient compared to alternatives, particularly when datasets include homologous proteins, and provides great flexibility in the nature of motifs returned. The SLiMChance algorithm estimates the probability of returned motifs arising by chance, correcting for the size and composition of the dataset, and assigns a significance value to each motif. These algorithms are implemented in a software package, SLiMFinder. SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery rate on random test data.Conclusions/SignificanceThe efficiency of SLiMBuild and low false discovery rate of SLiMChance make SLiMFinder highly suited to high throughput motif discovery and individual high quality analyses alike. Examples of such analyses on real biological data, and how SLiMFinder results can help direct future discoveries, are provided. SLiMFinder is freely available for download under a GNU license from http://bioinformatics.ucd.ie/shields/software/slimfinder/.

Codon usage data for 56 Bacillus subtilis genes show that synonymous codon usage in B. subtilis is less biased than in Escherichia coli, or in Saccharomyces cerevisiae. Nevertheless, certain genes with a high codon bias can be identified by correspondence analysis, and also by various indices of codon bias. These genes are very highly expressed, and a general trend (a decrease) in codon bias across genes seems to correspond to decreasing expression level. This, then, may be a general phenomenon in unicellular organisms. The unusually small effect of translational selection on the pattern of codon usage in lowly expressed genes in B. subtilis yields similar dinucleotide frequencies among different codon positions, and on complementary strands. These patterns could arise through selection on DNA structure, but more probably are largely determined by mutation. This prevalence of mutational bias could lead to difficulties in assessing whether open reading frames encode proteins.

Summary.  Background: Aspirin (acetylsalicylic acid) irreversibly inhibits platelet cyclooxygenase (COX)‐1, the enzyme that converts arachidonic acid (AA) to the potent platelet agonist thromboxane (TX) A2. Despite clear benefit from aspirin in patients with cardiovascular disease (CAD), evidence of heterogeneity in the way individuals respond has given rise to the concept of ‘aspirin resistance.’Aims: To evaluate the hypothesis that incomplete suppression of platelet COX as a consequence of variation in the COX‐1 gene may affect aspirin response and thus contribute to aspirin resistance. Patients and methods: Aspirin response, determined by serum TXB2 levels and AA‐induced platelet aggregation, was prospectively studied in patients (n = 144) with stable CAD taking aspirin (75–300 mg). Patients were genotyped for five single nucleotide polymorphisms in COX‐1 [A‐842G, C22T (R8W), G128A (Q41Q), C644A (G213G) and C714A (L237M)]. Haplotype frequencies and effect of haplotype on two platelet phenotypes were estimated by maximum likelihood. The four most common haplotypes were considered separately and less common haplotypes pooled. Results: COX‐1 haplotype was significantly associated with aspirin response determined by AA‐induced platelet aggregation (P = 0.004; 4 d.f.). Serum TXB2 generation was also related to genotype (P = 0.02; 4 d.f.). Conclusion: Genetic variability in COX‐1 appears to modulate both AA‐induced platelet aggregation and thromboxane generation. Heterogeneity in the way patients respond to aspirin may in part reflect variation in COX‐1 genotype.

Many important interactions of proteins are facilitated by short, linear motifs (SLiMs) within a protein's primary sequence. Our aim was to establish robust methods for discovering putative functional motifs. The strongest evidence for such motifs is obtained when the same motifs occur in unrelated proteins, evolving by convergence. In practise, searches for such motifs are often swamped by motifs shared in related proteins that are identical by descent. Prediction of motifs among sets of biologically related proteins, including those both with and without detectable similarity, were made using the TEIRESIAS algorithm. The number of motif occurrences arising through common evolutionary descent were normalized based on treatment of BLAST local alignments. Motifs were ranked according to a score derived from the product of the normalized number of occurrences and the information content. The method was shown to significantly outperform methods that do not discount evolutionary relatedness, when applied to known SLiMs from a subset of the eukaryotic linear motif (ELM) database. An implementation of Multiple Spanning Tree weighting outperformed two other weighting schemes, in a variety of settings.