Genomic function: Rate of evolution and gene dispensability

Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast

Under laboratory conditions 80% of yeast genes seem not to be essential for viability. This raises the question of what the mechanistic basis for dispensability is, and whether it is the result of selection for buffering or an incidental side product. Here we analyse these issues using an in silico flux model of the yeast metabolic network. The model correctly predicts the knockout fitness effects in 88% of the genes studied and in vivo fluxes. Dispensable genes might be important, but under conditions not yet examined in the laboratory. Our model indicates that this is the dominant explanation for apparent dispensability, accounting for 37-68% of dispensable genes, whereas 15-28% of them are compensated by a duplicate, and only 4-17% are buffered by metabolic network flux reorganization. For over one-half of those not important under nutrient-rich conditions, we can predict conditions when they will be important. As expected, such condition-specific genes have a more restricted phylogenetic distribution. Gene duplicates catalysing the same reaction are not more common for indispensable reactions, suggesting that the reason for their retention is not to provide compensation. Instead their presence is better explained by selection for high enzymatic flux.

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level: response

A response to Fraser HB, Hirsh AE: Evolutionary rate depends on number of protein-protein interactions independently of gene expression level. BMC Evol Biol 2004, 4:13

Noise minimization in eukaryotic gene expression

All organisms have elaborate mechanisms to control rates of protein production. However, protein production is also subject to stochastic fluctuations, or “noise.” Several recent studies in Saccharomyces cerevisiae and Escherichia coli have investigated the relationship between transcription and translation rates and stochastic fluctuations in protein levels, or more generally, how such randomness is a function of intrinsic and extrinsic factors. However, the fundamental question of whether stochasticity in protein expression is generally biologically relevant has not been addressed, and it remains unknown whether random noise in the protein production rate of most genes significantly affects the fitness of any organism. We propose that organisms should be particularly sensitive to variation in the protein levels of two classes of genes: genes whose deletion is lethal to the organism and genes that encode subunits of multiprotein complexes. Using an experimentally verified model of stochastic gene expression in S. cerevisiae, we estimate the noise in protein production for nearly every yeast gene, and confirm our prediction that the production of essential and complex-forming proteins involves lower levels of noise than does the production of most other genes. Our results support the hypothesis that noise in gene expression is a biologically important variable, is generally detrimental to organismal fitness, and is subject to natural selection.

Analysis of gene expression data for nearly every gene in yeast provides evidence that random variation in the production rate of proteins could significantly affect the fitness of an organism

Approaching the quantum limit of a nanomechanical resonator

By coupling a single-electron transistor to a high-quality factor, 19.7-megahertz nanomechanical resonator, we demonstrate position detection approaching that set by the Heisenberg uncertainty principle limit. At millikelvin temperatures, position resolution a factor of 4.3 above the quantum limit is achieved and demonstrates the near-ideal performance of the single-electron transistor as a linear amplifier. We have observed the resonator's thermal motion at temperatures as low as 56 millikelvin, with quantum occupation factors of NTH = 58. The implications of this experiment reach from the ultimate limits of force microscopy to qubit readout for quantum information devices.

Duplicate genes and robustness to transient gene knock-downs in Caenorhabditis elegans

We examine robustness to mutations in the nematode worm Caenorhabditis elegans and the role of single-copy and duplicate genes in it. We do so by integrating complete genome sequence and microarray gene expression data with results from a genome-scale study using RNA interference (RNAi) to temporarily eliminate the functions of more than 16000 worm genes. We found that 89% of single-copy and 96% of duplicate genes show no detectable phenotypic effect in an RNAi knock-down experiment. We find that mutational robustness is greatest for closely related gene duplicates, large gene families and similarly expressed genes. We discuss the different causes of mutational robustness in single-copy and duplicate genes, as well as its evolutionary origin.

Bioinformatical assay of human gene morbidity

Only a fraction of eukaryotic genes affect the phenotype drastically. We compared 18 parameters in 1273 human morbid genes, known to cause diseases, and in the remaining 16 580 unambiguous human genes. Morbid genes evolve more slowly, have wider phylogenetic distributions, are more similar to essential genes of Drosophila melanogaster, code for longer proteins containing more alanine and glycine and less histidine, lysine and methionine, possess larger numbers of longer introns with more accurate splicing signals and have higher and broader expressions. These differences make it possible to classify as non-morbid 34% of human genes with unknown morbidity, when only 5% of known morbid genes are incorrectly classified as non-morbid. This classification can help to identify disease-causing genes among multiple candidates.

Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution

Lineage-specific gene loss, to a large extent, accounts for the differences in gene repertoires between genomes, particularly among eukaryotes. We derived a parsimonious scenario of gene losses for eukaryotic orthologous groups (KOGs) from seven complete eukaryotic genomes. The scenario involves substantial gene loss in fungi, nematodes, and insects. Based on this evolutionary scenario and estimates of the divergence times between major eukaryotic phyla, we introduce a numerical measure, the propensity for gene loss (PGL). We explore the connection among the propensity of a gene to be lost in evolution (PGL value), protein sequence divergence, the effect of gene knockout on fitness, the number of protein-protein interactions, and expression level for the genes in KOGs. Significant correlations between PGL and each of these variables were detected. Genes that have a lower propensity to be lost in eukaryotic evolution accumulate fewer substitutions in their protein sequences and tend to be essential for the organism viability, tend to be highly expressed, and have many interaction partners. The dependence between PGL and gene dispensability and interactivity is much stronger than that for sequence evolution rate. Thus, propensity of a gene to be lost during evolution seems to be a direct reflection of its biological importance.

Gene essentiality determines chromosome organisation in bacteria

In Escherichia coli and Bacillus subtilis, essentiality, not expressivity, drives the distribution of genes between the two replicating strands. Although essential genes tend to be coded in the leading replicating strand, the underlying selective constraints and the evolutionary extent of these findings have still not been subject to comparative studies. Here, we extend our previous analysis to the genomes of low G + C firmicutes and gamma-proteobacteria, and in a second step to all sequenced bacterial genomes. The inference of essentiality by homology allows us to show that essential genes are much more frequent in the leading strand than other genes, even when compared with non- essential highly expressed genes. Smaller biases were found in the genomes of obligatory intracellular bacteria, for which the assignment of essentiality by homology from fast growing free-living bacteria is most problematic. Cross-comparisons used to assess potential errors in the assignment of essentiality by homology revealed that, in most cases, variations in the assignment criteria have little influence on the overall results. Essential genes tend to be more conserved in the leading strand than average genes, which is consistent with selection for this positioning and may impose a strong constraint on chromosomal rearrangements. These results indicate that essentiality plays a fundamental role in the distribution of genes in most bacterial genomes.

An analysis of determinants of amino acids substitution rates in bacterial proteins

The variation of amino acid substitution rates in proteins depends on several variables. Among these, the protein's expression level, functional category, essentiality, or metabolic costs of its amino acid residues may play an important role. However, the relative importance of each variable has not yet been evaluated in comparative analyses. To this aim, we made regression analyses combining data available on these variables and on evolutionary rates, in two well-documented model bacteria, Escherichia coli and Bacillus subtilis. In both bacteria, the level of expression of the protein in the cell was by far the most important driving force constraining the amino acids substitution rate. Subsequent inclusion in the analysis of the other variables added little further information. Furthermore, when the rates of synonymous substitutions were included in the analysis of the E. coli data, only the variable expression levels remained statistically significant. The rate of nonsynonymous substitution was shown to correlate with expression levels independently of the rate of synonymous substitution. These results suggest an important direct influence of expression levels, or at least codon usage bias for translation optimization, on the rates of nonsynonymous substitutions in bacteria. They also indicate that when a control for this variable is included, essentiality plays no significant role in the rate of protein evolution in bacteria, as is the case in eukaryotes.

Apparent dependence of protein evolutionary rate on number of interactions is linked to biases in protein-protein interactions data sets

Background

Several studies have suggested that proteins that interact with more partners evolve more slowly. The strength and validity of this association has been called into question. Here we investigate how biases in high-throughput protein–protein interaction studies could lead to a spurious correlation.

Results

We examined the correlation between evolutionary rate and the number of protein–protein interactions for sets of interactions determined by seven different high-throughput methods in Saccharomyces cerevisiae. Some methods have been shown to be biased towards counting more interactions for abundant proteins, a fact that could be important since abundant proteins are known to evolve more slowly. We show that the apparent tendency for interactive proteins to evolve more slowly varies directly with the bias towards counting more interactions for abundant proteins. Interactions studies with no bias show no correlation between evolutionary rate and the number of interactions, and the one study biased towards counting fewer interactions for abundant proteins actually suggests that interactive proteins evolve more rapidly. In all cases, controlling for protein abundance significantly decreases the observed correlation between interactions and evolutionary rate. Finally, we disprove the hypothesis that small data set size accounts for the failure of some interactions studies to show a correlation between evolutionary rate and the number of interactions.

Conclusions

The only correlation supported by a careful analysis of the data is between evolutionary rate and protein abundance. The reported correlation between evolutionary rate and protein–protein interactions cannot be separated from the biases of some protein–protein interactions studies to count more interactions for abundant proteins.

Evolutionary conservation of motif constituents in the yeast protein interaction network

Understanding why some cellular components are conserved across species but others evolve rapidly is a key question of modern biology. Here we show that in Saccharomyces cerevisiae, proteins organized in cohesive patterns of interactions are conserved to a substantially higher degree than those that do not participate in such motifs. We find that the conservation of proteins in distinct topological motifs correlates with the interconnectedness and function of that motif and also depends on the structure of the overall interactome topology. These findings indicate that motifs may represent evolutionary conserved topological units of cellular networks molded in accordance with the specific biological function in which they participate.