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Recent developments in the analysis of amino acid covariation are leading to breakthroughs in protein structure prediction, protein design, and prediction of the interactome. It is assumed that observed patterns of covariation are caused by molecular coevolution, where substitutions at one site affect the evolutionary forces acting at neighboring sites. Our theoretical and empirical results cast doubt on this assumption. We demonstrate that the strongest coevolutionary signal is a decrease in evolutionary rate and that unfeasibly long times are required to produce coordinated substitutions. We find that covarying substitutions are mostly found on different branches of the phylogenetic tree, indicating that they are independent events that may or may not be attributable to coevolution. These observations undermine the hypothesis that molecular coevolution is the primary cause of the covariation signal. In contrast, we find that the pairs of residues with the strongest covariation signal tend to have low evolutionary rates, and that it is this low rate that gives rise to the covariation signal. Slowly evolving residue pairs are disproportionately located in the protein’s core, which explains covariation methods’ ability to detect pairs of residues that are close in three dimensions. These observations lead us to propose the “coevolution paradox”: The strength of coevolution required to cause coordinated changes means the evolutionary rate is so low that such changes are highly unlikely to occur. As modern covariation methods may lead to breakthroughs in structural genomics, it is critical to recognize their biases and limitations.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein–protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Gross chromosomal rearrangements have the potential to be evolutionarily advantageous to an adapting organism. The generation of a hybrid species increases opportunity for recombination by bringing together two homologous genomes. We sought to define the location of genomic rearrangements in three strains of Saccharomyces pastorianus, a natural lager-brewing yeast hybrid of Saccharomyces cerevisiae and Saccharomyces eubayanus, using whole genome shotgun sequencing. Each strain of S. pastorianus has lost species-specific portions of its genome and has undergone extensive recombination, producing chimeric chromosomes. We predicted 30 breakpoints that we confirmed at the single nucleotide level by designing species-specific primers that flank each breakpoint, and then sequencing the PCR product. These rearrangements are the result of recombination between areas of homology between the two subgenomes, rather than repetitive elements such as transposons or tRNAs. Interestingly, 28/30 S. cerevisiae-S. eubayanus recombination breakpoints are located within genic regions, generating chimeric genes. Furthermore we show evidence for the reuse of two breakpoints, located in HSP82 and KEM1, in strains of proposed independent origin.

Duplication of genes or genomes provides the raw material for evolutionary innovation. After duplication a gene may be lost, recombine with another gene, have its function modified or be retained in an unaltered state. The fate of duplication is usually studied by comparing extant genomes and reconstructing the most likely ancestral states. Valuable as this approach is, it may miss the most rapid evolutionary events. Here, we engineered strains of Saccharomyces cerevisiae carrying tandem and non-tandem duplications of the singleton gene IFA38 to monitor (i) the fate of the duplicates in different conditions, including time scale and asymmetry of gene loss, and (ii) the changes in fitness and transcriptome of the strains immediately after duplication and after experimental evolution. We found that the duplication brings widespread transcriptional changes, but a fitness advantage is only present in fermentable media. In respiratory conditions, the yeast strains consistently lose the non-tandem IFA38 gene copy in a surprisingly short time, within only a few generations. This gene loss appears to be asymmetric and dependent on genome location, since the original IFA38 copy and the tandem duplicate are retained. Overall, this work shows for the first time that gene loss can be extremely rapid and context dependent.

Analysis of protein-protein interaction networks is an increasingly popular means to infer biological insight, but is close enough attention being paid to data handling protocols and the degree of bias in the data?

Most proteins attain their biological functions through specific interactions with other proteins. Thus, the study of protein-protein interactions and the interfaces that mediate these interactions is of prime importance for the understanding of biological function. In particular the precise determinants of binding specificity and their contributions to binding energy within protein interfaces are not well understood. In order to better understand these determinants an appropriate description of the interaction surface is needed. Available data from the yeast Saccharomyces cerevisiae allow us to focus on a single species and to use all the available structures, correcting for redundancy, instead of using structural representatives. This allows us to control for potentially confounding factors that may affect sequence propensities. We find a significant contribution of main-chain atoms to protein-protein interactions. These include interactions both with other main-chain and side-chain atoms on the interacting chain. We find that the type of interaction depends on both amino acid and secondary structure type involved in the contact. For example, residues in α-helices and large amino acids are the most likely to be involved in interactions through their side-chain atoms. We find an intriguing homogeneity when calculating the average solvation energy of different areas of the protein surface. Unexpectedly, homo- and hetero-complexes have quite similar results for all analyses. Our findings demonstrate that the manner in which protein-protein interactions are formed is determined by the residue type and the secondary structure found in the interface. However the homogeneity of the desolvation energy despite heterogeneity of interface properties suggests a complex relationship between interface composition and binding energy.

The coronary vasculature is an essential vessel network providing the blood supply to the heart. Disruptions in coronary blood flow contribute to cardiac disease, a major cause of premature death worldwide. The generation of treatments for cardiovascular disease will be aided by a deeper understanding of the developmental processes that underpin coronary vessel formation. From an ENU mutagenesis screen, we have isolated a mouse mutant displaying embryonic hydrocephalus and cardiac defects (EHC). Positional cloning and candidate gene analysis revealed that the EHC phenotype results from a point mutation in a splice donor site of the Myh10 gene, which encodes NMHC IIB. Complementation testing confirmed that the Myh10 mutation causes the EHC phenotype. Characterisation of the EHC cardiac defects revealed abnormalities in myocardial development, consistent with observations from previously generated NMHC IIB null mouse lines. Analysis of the EHC mutant hearts also identified defects in the formation of the coronary vasculature. We attribute the coronary vessel abnormalities to defective epicardial cell function, as the EHC epicardium displays an abnormal cell morphology, reduced capacity to undergo epithelial-mesenchymal transition (EMT), and impaired migration of epicardial-derived cells (EPDCs) into the myocardium. Our studies on the EHC mutant demonstrate a requirement for NMHC IIB in epicardial function and coronary vessel formation, highlighting the importance of this protein in cardiac development and ultimately, embryonic survival.

Studies of interacting proteins have found correlated evolution of the sequences of binding partners, apparently as a result of compensating mutations to maintain specificity (i.e., molecular coevolution). Here, we analyze the coevolution of interacting proteins in yeast and demonstrate correlated evolution of binding partners in eukaryotes. Detailed investigation of this apparent coevolution, focusing on the proteins' surface and binding interface, surprisingly leads to no improvement in the correlation. We conclude that true coevolution, as characterized by compensatory mutations between binding partners, is unlikely to be chiefly responsible for the apparent correlated evolution. We postulate that the correlation between sequence alignments is simply due to interacting proteins being subject to similar constraints on their evolutionary rate. Because gene expression has a strong influence on evolutionary rate, and interacting proteins will tend to have similar levels of expression, we investigated this particular constraint. We found that the absolute expression level outperformed correlated evolution for predicting interacting protein partners. A correlation between sequence alignments could also be identified not only between pairs of proteins that physically interact but also between those that are merely functionally related (i.e., within the same protein complex). This indicates that the observed correlated evolution of interacting proteins is due to similar constraints on evolutionary rate and not coevolution.

The complement of genes found in the genome is a balance between gene gain and gene loss. Knowledge of the specific genes that are gained and lost over evolutionary time allows an understanding of the evolution of biological functions. Here we use new evolutionary models to infer gene family histories across complete yeast genomes; these models allow us to estimate the relative genome-wide rates of gene birth, death, innovation and extinction (loss of an entire family) for the first time. We show that the rates of gene family evolution vary both between gene families and between species. We are also able to identify those families that have experienced rapid lineage specific expansion/contraction and show that these families are enriched for specific functions. Moreover, we find that families with specific functions are repeatedly expanded in multiple species, suggesting the presence of common adaptations and that these family expansions/contractions are not random. Additionally, we identify potential specialisations, unique to specific species, in the functions of lineage specific expanded families. These results suggest that an important mechanism in the evolution of genome content is the presence of lineage-specific gene family changes.

Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these \"essential genes\" play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as \"essential pairs\" of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.