Dr Sari Pennings, Sari.Pennings@ed.ac.uk
Dr Robert Illingworth, robert.illingworth@ed.ac.uk
Project Description
Air pollution and its toxic effects are detrimental to the lungs upon inhalation, as well as to more distant tissues of the cardiovascular system, brain, and reproduction via the systemic circulation.
It is important to know how environmental exposures during the life course can increase disease risk in the population and to identify mechanistic pathways and biomarkers that would permit more targeted treatments and lead to preventive policies. Exposures are associated with changes in characteristic chemical modifications on genomic DNA, termed epigenetic marks, as well as persistent cell trait changes due to changes in gene expression. Many of the epigenetic molecular mechanisms that maintain gene expression patterns during normal development have continuing roles in human health and disease. Accumulating evidence indicates that atmospheric particulates (e.g. PM2.5) can aggravate chronic conditions including cardiovascular disease and neurodegenerative disorders, through a combination of direct toxicity and inflammation. This PhD project aims to build AI models to integrate our multi-omic analyses of the brain and other tissues from mice models (transgenic reporters and models for Alzheimer’s disease) subjected to controlled exposures to pollutants and extend these to human datasets from collaborative and public data sets. The PhD study’s objectives are to (i) perform an multimodal bioinformatics analysis of DNA methylation changes induced by aerosol exposures in association with stress signalling, gene expression and other data sets, which are relevant to human models. The study will (ii) screen our environmental exposure genomic data, against oxidative stress models, Alzheimer’s disease models, epigenetic age acceleration models and controls. The study will translate the mouse model knowledge database to facilitate feature engineering and apply AI algorithms in human data sets to (iii) develop exposure-specific epigenetic biomarker profiles.
