• Electrochemically Detecting DNA Methylation in the EN1 gene Promoter: Implications for understanding Ageing and Disease

      Morgan, Amy; Acutt, Katie; Mc Auley, Mark; University of Chester
      There is a growing need for biomarkers which predict age-onset pathology. Although this is challenging, the methylome offers significant potential. Cancer is associated with the hypermethylation of many gene promoters, among which are developmental genes. Evolutionary theory suggests developmental genes arbitrate early-late life trade-offs, causing epimutations that increase disease vulnerability. Such genes could predict age related disease. The aim of this work was to optimise an electrochemical procedure for the future investigation of a broad range of ageing related pathologies. An electrochemical approach, which adopted three analytical techniques, was used to investigate DNA methylation in the EN1 gene promoter. Using synthetic single stranded DNA, one technique was able to detect DNA at concentrations as low as 10nM, with methylation status distinguishable at concentrations >25nM. A negative correlation could be observed between % methylation of heterogeneous solution and the key electrochemical parameter, Rct (r = -0.982, p < 0.01). The technique was applied to the breast cancer cell line MCF-7, where a similar correlation was observed (r = -0.965, p < 0.01). These results suggest electrochemistry can effectively measure DNA methylation at low concentrations of DNA. This has implications for the future detection of age-related disease.
    • Modelling the molecular mechanisms of ageing

      Mc Auley, Mark T.; Martinez Guimera, Alvaro; Hodgson, David; McDonald, Neil; Mooney, Kathleen M.; Morgan, Amy; Proctor, Carole; University of Chester; Edgehill University; Newcastle University (Portland Press, 2017-02-23)
      The ageing process is driven at the cellular level by random molecular damage which slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the ageing process. The complexity of the ageing process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards, and discusses many specific examples of models which have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.