ELUCIDATING MECHANISMS OF THORACIC AORTOPATHY VIA COMPUTATIONAL MODELING

PROJECT SUMMARY Thoracic aortopathy – aneurysm, dissection, and rupture – is increasingly responsible for morbidity and mortality in younger (having genetically predisposed lesions) and older (having sporadic lesions) individuals alike, female and male. Despite many seminal discoveries since 1991, when the genetic basis of Marfan syndrome (MFS) was uncovered, advances in clinical care have largely remained limited to improvements in surgical methods and devices as well as better identification of patients who warrant monitoring of aortic diameter until surgery; patients otherwise continue to be treated with anti-hypertensive medications mainly to reduce hemodynamic loads on the vulnerable aorta. There is clearly a need for increased understanding. Although such aortopathy can arise from a predisposing monogenic mutation, we will test the compelling hypothesis that secondary changes in cell signaling and gene expression represent either potentially protective compensations or further pathological consequences, and macrophages play key roles in this regard. We submit that, in the absence of gene editing to correct the predisposing mutation, there is a need to preserve / promote compensatory gene products while preventing pathological ones. Noting further that hypertension is a key risk factor for aortic disease, we hypothesize that detailed associations of the transcriptional profile and biomechanical phenotype will better elucidate mechanisms by which hypertension exacerbates thoracic aortopathy. Toward this end, we will use consistent methods to quantify the transcriptional profile that drives the bio- mechanical phenotype of the aorta in 3 mouse models (2 novel) of Marfan syndrome. In this way, we will quantify, for the first time, specific roles of hypertension as well as two sub-populations of macrophages (resident vs. recruited) to determine regulatory pathways and cell-cell interactions that superimpose on those associated with the underlying predisposing mutation and drive changes in aortic structure and function. We submit that consistent “transcript-to-tissue” level data across these mouse models, as a function of sex as a biological variable, will provide the large data sets needed to develop 2 new classes of multifidelity, multiscale, data- informed computational models that will enable unprecedented integrative understanding of molecular, cellular, and biomechanical mechanisms that drive thoracic aortopathy while providing unique insight into possible new targets for treatment. This project is significant given the increasing morbidity and mortality associated with thoracic aortopathy; it is innovative in its consistent quantification of the transcriptionally driven biomechanical phenotype across diverse mouse models, its use of two new double-mutant mouse models, and its use of novel advanced (multi-cell, multiscale) computational models to integrate findings across predisposing mutation, cell- type, and risk factors to delineate protective versus pathologic factors that dictate disease progression.