Publications by year
In Press
Ames RM, Talavera D, Williams SG, Robertson DL, Lovell SC (In Press). Binding interface change and cryptic variation in the evolution of protein-protein interactions.
BMC Evolutionary BiologyAbstract:
Binding interface change and cryptic variation in the evolution of protein-protein interactions
Background:Physical interactions between proteins are essential for almost all biological functions and systems. To understand the evolution of function it is therefore important to understand the evolution of molecular interactions. of key importance is the evolution of binding specificity, the set of interactions made by a protein, since change in specificity can lead to “rewiring” of interaction networks. Unfortunately, the interfaces through which proteins interact are complex, typically containing many amino-acid residues that collectively must contribute to binding specificity as well as binding affinity, structural integrity of the interface and solubility in the unbound state.
Results: in order to study the relationship between interface composition and binding specificity, we make use of paralogous pairs of yeast proteins. Immediately after duplication these paralogues will have identical sequences and protein products that make an identical set of interactions. As the sequences diverge, we can correlate amino-acid change in the interface with any change in the specificity of binding. We show that change in interface regions correlates only weakly with change in specificity, and many variants in interfaces are functionally equivalent. We show that many of the residue replacements within interfaces are silent with respect to their contribution to binding specificity.
Conclusions: We conclude that such functionally-equivalent change has the potential to contribute to evolutionary plasticity in interfaces by creating cryptic variation, which in turn may provide the raw material for functional innovation and coevolution.
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Stoney RA, Ames RM, Nenadic G, Robertson DL, Schwartz JM (In Press). Disentangling the multigenic and pleiotropic nature of molecular function.
BMC Systems Biology,
9(Suppl 6).
Abstract:
Disentangling the multigenic and pleiotropic nature of molecular function
Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity.
We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant.
Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes.
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Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, Tuke MA, Ruth KS, Freathy RM, Davey Smith G, et al (In Press). Gene-obesogenic environment interactions in the UK Biobank study.
Int J Epidemiol,
46(2), 559-575.
Abstract:
Gene-obesogenic environment interactions in the UK Biobank study.
Background: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI). Methods: We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment. Results: We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely. Conclusions: Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.
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2017
Naseeb S, Ames RM, Delneri D, Lovell SC (2017). Rapid functional and evolutionary changes follow gene duplication in yeast.
Proceedings of the Royal Society B: Biological Sciences,
284(1861).
Abstract:
Rapid functional and evolutionary changes follow gene duplication in yeast
© 2017 the Authors. 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.
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Ames R (2017). Using Network Extracted Ontologies to Identify Novel Genes with Roles in Appressorium Development in the Rice Blast Fungus Magnaporthe oryzae.
Microorganisms,
5(1), 3-3.
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2016
Tyrrell J, Yaghootkar H, Beaumont R, Jones SE, Ames RM, Tuke MA, Ruth KS, Kutalik Z, Freathy RM, Murray A, et al (2016). Gene-obesogenic environment interactions in the UK Biobank study.
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Tyrrell J, Yaghootkar H, Beaumont R, Jones SE, Ames R, Tuke MA, Ruth KS, Kutalik Z, Freathy RM, Murray A, et al (2016). High Risk Obesogenic Environments Accentuate Genetic Susceptibility to Obesity.
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2014
Ames RM, Lovell SC (2014). DupliPHY-Web: a web server for DupliPHY and DupliPHY-ML. Bioinformatics, 31(3), 416-417.
Ames RM, Money D, Lovell SC (2014). Inferring Gene Family Histories in Yeast Identifies Lineage Specific Expansions.
PLoS ONE,
9(6), e99480-e99480.
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2013
Ames RM, MacPherson JI, Pinney JW, Lovell SC, Robertson DL (2013). Modular Biological Function is Most Effectively Captured by Combining Molecular Interaction Data Types.
PLoS ONE,
8(5), e62670-e62670.
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2011
Ames RM, Money D, Ghatge VP, Whelan S, Lovell SC (2011). Determining the evolutionary history of gene families.
Bioinformatics,
28(1), 48-55.
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Ames RM, Lovell SC (2011). Diversification at Transcription Factor Binding Sites within a Species and the Implications for Environmental Adaptation.
Molecular Biology and Evolution,
28(12), 3331-3344.
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2010
Ames RM, Rash BM, Hentges KE, Robertson DL, Delneri D, Lovell SC (2010). Gene Duplication and Environmental Adaptation within Yeast Populations.
Genome Biology and Evolution,
2, 591-601.
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