Publications by year
2020
Thomas G, Bain JM, Budge S, Brown AJP, Ames RM (2020). Identifying Candida albicans Gene Networks Involved in Pathogenicity.
Front Genet,
11Abstract:
Identifying Candida albicans Gene Networks Involved in Pathogenicity.
Candida albicans is a normal member of the human microbiome. It is also an opportunistic pathogen, which can cause life-threatening systemic infections in severely immunocompromized individuals. Despite the availability of antifungal drugs, mortality rates of systemic infections are high and new drugs are needed to overcome therapeutic challenges including the emergence of drug resistance. Targeting known disease pathways has been suggested as a promising avenue for the development of new antifungals. However,
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Haque S, Ames RM, Moore K, Lee BP, Jeffery N, Harries LW (2020). Islet-expressed circular RNAs are associated with type 2 diabetes status in human primary islets and in peripheral blood.
BMC Med Genomics,
13(1).
Abstract:
Islet-expressed circular RNAs are associated with type 2 diabetes status in human primary islets and in peripheral blood.
BACKGROUND: Circular RNAs are non-coding RNA molecules with gene regulatory potential that have been associated with several human diseases. They are stable and present in the circulation, making them excellent candidates for biomarkers of disease. Despite their promise as biomarkers or future therapeutic targets, information on their expression and functionality in human pancreatic islets is a relatively unexplored subject. METHODS: Here we aimed to produce an enriched circRNAome profile for human pancreatic islets by CircleSeq, and to explore the relationship between circRNA expression, diabetes status, genotype at T2D risk loci and measures of glycaemia (insulin secretory index; SI and HbA1c) in human islet preparations from healthy control donors and donors with type 2 diabetes using ANOVA or linear regression as appropriate. We also assessed the effect of elevated glucose, cytokine and lipid and hypoxia on circRNA expression in the human beta cell line EndoC-βH1. RESULTS: We identified over 2600 circRNAs present in human islets. of the five most abundant circRNAs in human islets, four (circCIRBP, circZKSCAN, circRPH3AL and circCAMSAP1) demonstrated marked associations with diabetes status. CircCIRBP demonstrated an association with insulin secretory index in isolated human islets and circCIRBP and circRPH3AL displayed altered expression with elevated fatty acid in treated EndoC-βH1 cells. CircCAMSAP1 was also noted to be associated with T2D status in human peripheral blood. No associations between circRNA expression and genotype at T2D risk loci were identified in our samples. CONCLUSIONS: Our data suggest that circRNAs are abundantly expressed in human islets, and that some are differentially regulated in the islets of donors with type 2 diabetes. Some islet circRNAs are also expressed in peripheral blood and the expression of one, circCAMSAP1, correlates with diabetes status. These findings highlight the potential of circRNAs as biomarkers for T2D.
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Willis CRG, Ames RM, Deane CS, Phillips BE, Boereboom CL, Abdulla H, Bukhari SSI, Lund JN, Williams JP, Wilkinson DJ, et al (2020). Network analysis of human muscle adaptation to aging and contraction.
Aging (Albany NY),
12(1), 740-755.
Abstract:
Network analysis of human muscle adaptation to aging and contraction.
Resistance exercise (RE) remains a primary approach for minimising aging muscle decline. Understanding muscle adaptation to individual contractile components of RE (eccentric, concentric) might optimise RE-based intervention strategies. Herein, we employed a network-driven pipeline to identify putative molecular drivers of muscle aging and contraction mode responses. RNA-sequencing data was generated from young (21±1 y) and older (70±1 y) human skeletal muscle before and following acute unilateral concentric and contralateral eccentric contractions. Application of weighted gene co-expression network analysis identified 33 distinct gene clusters ('modules') with an expression profile regulated by aging, contraction and/or linked to muscle strength. These included two contraction 'responsive' modules (related to 'cell adhesion' and 'transcription factor' processes) that also correlated with the magnitude of post-exercise muscle strength decline. Module searches for 'hub' genes and enriched transcription factor binding sites established a refined set of candidate module-regulatory molecules (536 hub genes and 60 transcription factors) as possible contributors to muscle aging and/or contraction responses. Thus, network-driven analysis can identify new molecular candidates of functional relevance to muscle aging and contraction mode adaptations.
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Haque S, Ames RM, Moore K, Pilling LC, Peters LL, Bandinelli S, Ferrucci L, Harries LW (2020). circRNAs expressed in human peripheral blood are associated with human aging phenotypes, cellular senescence and mouse lifespan.
Geroscience,
42(1), 183-199.
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circRNAs expressed in human peripheral blood are associated with human aging phenotypes, cellular senescence and mouse lifespan.
Circular RNAs (circRNAs) are an emerging class of non-coding RNA molecules that are thought to regulate gene expression and human disease. Despite the observation that circRNAs are known to accumulate in older organisms and have been reported in cellular senescence, their role in aging remains relatively unexplored. Here, we have assessed circRNA expression in aging human blood and followed up age-associated circRNA in relation to human aging phenotypes, mammalian longevity as measured by mouse median strain lifespan and cellular senescence in four different primary human cell types. We found that circRNAs circDEF6, circEP300, circFOXO3 and circFNDC3B demonstrate associations with parental longevity or hand grip strength in 306 subjects from the InCHIANTI study of aging, and furthermore, circFOXO3 and circEP300 also demonstrate differential expression in one or more human senescent cell types. Finally, four circRNAs tested showed evidence of conservation in mouse. Expression levels of one of these, circPlekhm1, was nominally associated with lifespan. These data suggest that circRNA may represent a novel class of regulatory RNA involved in the determination of aging phenotypes, which may show future promise as both biomarkers and future therapeutic targets for age-related disease.
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2019
Namboori SC, Thomas P, Ames R, Garrett LO, Willis CRG, Stanton LW, Bhinge A (2019). Single cell transcriptomics identifies master regulators of dysfunctional pathways in SOD1 ALS motor neurons. https://www.biorxiv.org/content/10.1101/593129v1.abstract
Deane CS, Ames RM, Phillips BE, Weedon MN, Willis CRG, Boereboom C, Abdulla H, Bukhari SSI, Lund JN, Williams JP, et al (2019). The acute transcriptional response to resistance exercise: impact of age and contraction mode.
Aging (Albany NY),
11(7), 2111-2126.
Abstract:
The acute transcriptional response to resistance exercise: impact of age and contraction mode.
Optimization of resistance exercise (RE) remains a hotbed of research for muscle building and maintenance. However, the interactions between the contractile components of RE (i.e. concentric (CON) and eccentric (ECC)) and age, are poorly defined. We used transcriptomics to compare age-related molecular responses to acute CON and ECC exercise. Eight young (21±1 y) and eight older (70±1 y) exercise-naïve male volunteers had vastus lateralis biopsies collected at baseline and 5 h post unilateral CON and contralateral ECC exercise. RNA was subjected to next-generation sequencing and differentially expressed (DE) genes tested for pathway enrichment using Gene Ontology (GO). The young transcriptional response to CON and ECC was highly similar and older adults displayed moderate contraction-specific profiles, with no GO enrichment. Age-specific responses to ECC revealed 104 DE genes unique to young, and 170 DE genes in older muscle, with no GO enrichment. Following CON, 15 DE genes were young muscle-specific, whereas older muscle uniquely expressed 147 up-regulated genes enriched for cell adhesion and blood vessel development, and 28 down-regulated genes involved in mitochondrial respiration, amino acid and lipid metabolism. Thus, older age is associated with contraction-specific regulation often without clear functional relevance, perhaps reflecting a degree of stochastic age-related dysregulation.
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Meakin JR, Ames RM, Jeynes JCG, Welsman J, Gundry M, Knapp K, Everson R (2019). The feasibility of using citizens to segment anatomy from medical images: Accuracy and motivation.
PLOS ONE,
14(10), e0222523-e0222523.
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2017
Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, Tuke MA, Ruth KS, Freathy RM, Davey Smith G, et al (2017). 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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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
Ames RM, Talavera D, Williams SG, Robertson DL, Lovell SC (2016). Binding interface change and cryptic variation in the evolution of protein-protein interactions.
BMC Evolutionary Biology,
16, 40-40.
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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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2015
Stoney RA, Ames RM, Nenadic G, Robertson DL, Schwartz J-M (2015). Disentangling the multigenic and pleiotropic nature of molecular function.
BMC Systems Biology,
9, S3-S3.
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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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