A Topological Data Analysis Network Model of Asthma Based on Blood Gene Expression Profiles

Topological Data Analysis (TDA) network models can represent continuous variation in the shape of disease pathology. We generated a TDA network model of asthma using 498 gene expression profiles of peripheral blood from asthma and healthy participants. The TDA network model was characterised by a core region with increased prevalence of healthy participants and connected routes to increased prevalence of severe asthma associated with increases in circulating inflammatory cells and modulated expression of inflammatory genes. However, stratified medicine requires discretisation of disease populations for targeted treatments. Therefore, a discrete Morse theory algorithm was developed and applied, identifying nine clusters, BC1-9, representing molecular phenotypes with discrete profiles of immune cell populations and activation of Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model was also characterised by differential activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNA9s 39 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.