Project Summary/Abstract: Therapeutic management of lung disorders triggered by the loss-of-function of the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) function in response to leading to CF are challenged by genetic and epigenetic diversity found in the CF population. The highly effective modulator therapy (HEMT) Trikafta has a pronounced but incomplete and variable impact on the pathology of disease in the clinic. We now need to discover new approaches to further improve clinical outcome. CF is not a simple monogenic disease but rather a complex disease impacted by membrane trafficking and channel function of CFTR- as well as diverse clinical features including inflammation, mucociliary clearance, and bacterial infection. These environmental features of disease lead to lung dysfunction as well as multi-organ symptoms including pancreatic and intestinal dysfunction. New approaches that capture the link between the genotype and cellular dysfunctional phenotypes as a collective of covariant events in the individual will require a deeper understanding of the fundamental principles dictating disease influenced by genetic and epigenetic diversity of the population. This proposal is about understanding the role of the epigenetic environment in management of CF in response to the histone deacetylase (HDAC) program controlling gene expression during development, aging and in response to cellular stress in disease. During the previous funding period, we have shown that the collective of variation found in the CF population can be used to define sequence-to-function-to-structure relationships responsive to HDAC inhibitors (HDACi). We will study the interlinked roles of genetic and epigenetic diversity using novel machine learning computational approaches we developed during the previous funding period that can be integrated with experimental/clinical features to discover therapeutics that could considerably improve patient well-being in response to the HEMT Trikafta. To understand the impact of complex epigenetic pathways in CF to improve Trikafta performance, we will apply our new Gaussian process (GP) based platform, referred to as variation spatial profiling (VSP) based variation capture (VarC) mapping, to profile at a residue-residue basis at atomic resolution a map of hidden spatial covariant (SCV) interactions that can resolve complex phenotypic relationships in response to genetic/epigenetic diversity. During the previous funding period, VSP/VarC mapping revealed a hidden ‘YKDAD’ energetic core in the CFTR fold that is the foundational basis for disease in the majority of the CF population that is not corrected by the HEMT Trikafta- limiting its impact in the clinic. In Aim 1 we will use VSP/VarC mapping to inform us of the complex disease states disrupted by CFTR misfolding, trafficking and function affecting inflammation, mucociliary clearance and infection to predict how to more effectively treat the patient through use of HDA...