Primers for fourteen protein-coding genes and the deep phylogeny of the true yeasts.

The Saccharomycetales or 'true yeasts' consist of more than 800 described species, including many of scientific, medical and commercial importance. Considerable progress has been made in determining the phylogenetic relationships of these species, largely based on rDNA sequences, but many nodes for early-diverging lineages cannot be resolved with rDNA alone. rDNA is also not ideal for delineating recently diverged species. From published full-genome sequence data, we have identified 14 regions of protein-coding genes that can be PCR-amplified in a large proportion of a diverse collection of 25 yeast species using degenerate primers. Phylogenetic analysis of the sequences thus obtained reveals a well-resolved phylogeny of the Saccharomycetales with many branches having high bootstrap support. Analysis of published sequences from the Saccharomyces paradoxus species complex shows that these protein-coding gene fragments are also informative about genealogical relationships amongst closely related strains. Our set of protein-coding gene fragments is therefore suitable for analysing both ancient and recent evolutionary relationships amongst yeasts.

A metabolic signature of long life in Caenorhabditis elegans.

BACKGROUND: Many Caenorhabditis elegans mutations increase longevity and much evidence suggests that they do so at least partly via changes in metabolism. However, up until now there has been no systematic investigation of how the metabolic networks of long-lived mutants differ from those of normal worms. Metabolomic technologies, that permit the analysis of many untargeted metabolites in parallel, now make this possible. Here we use one of these, 1H nuclear magnetic resonance spectroscopy, to investigate what makes long-lived worms metabolically distinctive. RESULTS: We examined three classes of long-lived worms: dauer larvae, adult Insulin/IGF-1 signalling (IIS)-defective mutants, and a translation-defective mutant. Surprisingly, these ostensibly different long-lived worms share a common metabolic signature, dominated by shifts in carbohydrate and amino acid metabolism. In addition the dauer larvae, uniquely, had elevated levels of modified amino acids (hydroxyproline and phosphoserine). We interrogated existing gene expression data in order to integrate functional (metabolite-level) changes with transcriptional changes at a pathway level. CONCLUSIONS: The observed metabolic responses could be explained to a large degree by upregulation of gluconeogenesis and the glyoxylate shunt as well as changes in amino acid catabolism. These responses point to new possible mechanisms of longevity assurance in worms. The metabolic changes observed in dauer larvae can be explained by the existence of high levels of autophagy leading to recycling of cellular components.See associated minireview: http://jbiol.com/content/9/1/7.

The cellular geometry of growth drives the amino acid economy of Caenorhabditis elegans.

The nematode Caenorhabditis elegans grows largely by increases in cell size. As a consequence of this, the surface: volume ratio of its cells must decline in the course of postembryonic growth. Here we use transcriptomic and metabolomic data to show that this change in geometry can explain a variety of phenomena during growth, including: (i) changes in the relative expression levels of cytoplasmic and membrane proteins; (ii) changes in the relative usage of the twenty amino acids in expressed proteins, as estimated by changes in the transcriptome; and (iii) changes in metabolite pools of free amino acids. We expect these relations to be universal in single cells and in whole multicellular organisms that grow largely by increases in cell size, but not those that grow by cell proliferation.

Selection on synthesis cost affects interprotein amino acid usage in all three domains of life.

Most investigations of the forces shaping protein evolution have focused on protein function. However, cells are typically 50%-75% protein by dry weight, with protein expression levels distributed over five orders of magnitude. Cells may, therefore, be under considerable selection pressure to incorporate amino acids that are cheap to synthesize into proteins that are highly expressed. Such selection pressure has been demonstrated to alter amino acid usage in a few organisms, but whether "cost selection" is a general phenomenon remains unknown. One reason for this is that reliable protein expression level data is not available for most organisms. Accordingly, I have developed a new method for detecting cost selection. This method depends solely on interprotein gradients in amino acid usage. Applying it to an analysis of 43 whole genomes from all three domains of life, I show that selection on the synthesis cost of amino acids is a pervasive force in shaping the composition of proteins. Moreover, some amino acids have different price tags for different organisms--the cost of amino acids is changed for organisms living in hydrothermal vents compared with those living at the sea surface or for organisms that have difficulty acquiring elements such as nitrogen compared with those that do not--so I also investigated whether differences between organisms in amino acid usage might reflect differences in synthesis or acquisition costs. The results suggest that organisms evolve to alter amino acid usage in response to environmental conditions.

The effects of incomplete protein interaction data on structural and evolutionary inferences.

BACKGROUND: Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis. RESULTS: Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. CONCLUSION: Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

Comparative analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans protein interaction networks.

BACKGROUND: Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence. RESULTS: We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive. CONCLUSION: It appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate.

Mitochondrial genetic codes evolve to match amino acid requirements of proteins.

Mitochondria often use genetic codes different from the standard genetic code. Now that many mitochondrial genomes have been sequenced, these variant codes provide the first opportunity to examine empirically the processes that produce new genetic codes. The key question is: Are codon reassignments the sole result of mutation and genetic drift? Or are they the result of natural selection? Here we present an analysis of 24 phylogenetically independent codon reassignments in mitochondria. Although the mutation-drift hypothesis can explain reassignments from stop to an amino acid, we found that it cannot explain reassignments from one amino acid to another. In particular--and contrary to the predictions of the mutation-drift hypothesis--the codon involved in such a reassignment was not rare in the ancestral genome. Instead, such reassignments appear to take place while the codon is in use at an appreciable frequency. Moreover, the comparison of inferred amino acid usage in the ancestral genome with the neutral expectation shows that the amino acid gaining the codon was selectively favored over the amino acid losing the codon. These results are consistent with a simple model of weak selection on the amino acid composition of proteins in which codon reassignments are selected because they compensate for multiple slightly deleterious mutations throughout the mitochondrial genome. We propose that the selection pressure is for reduced protein synthesis cost: most reassignments give amino acids that are less expensive to synthesize. Taken together, our results strongly suggest that mitochondrial genetic codes evolve to match the amino acid requirements of proteins.