Linking gene dynamics to vascular hyperplasia – Toward a predictive model of vein graft adaptation

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Abstract
Reductionist approaches, where individual pieces of a process are examined in isolation, have been the mainstay of biomedical research. While these methods are effective in highly compartmentalized systems, they fail to account for the inherent plasticity and non-linearity within the signaling structure. In the current manuscript, we present the computational architecture for tracking an acute perturbation in a biologic system through a multiscale model that links gene dynamics to cell kinetics, with the overall goal of predicting tissue adaptation. Given the complexity of the genome, the problem is made tractable by clustering temporal changes in gene expression into unique patterns. These cluster elements form the core of an integrated network that serves as the driving force for the response of the biologic system. This modeling approach is illustrated using the clinical scenario of vein bypass graft adaptation. Vein segments placed in the arterial circulation for treatment of advanced occlusive disease can develop an aggressive hyperplastic response that narrows the lumen, reduces blood flow, and induces in situ thrombosis. Reducing this hyperplastic response has been a long-standing but unrealized goal of biologic researchers in the field. With repeated failures of single target therapies, the redundant response pathways are thought to be a fundamental issue preventing progress towards a solution. Using the current framework, we demonstrate how theoretical genomic manipulations can be introduced into the system to shift the adaptation to a more beneficial phenotype, where the hyperplastic response is mitigated and the risk of thrombosis reduced. Utilizing our previously published rabbit vein graft genomic data, where grafts were harvested at time points ranging from 2 hours to 28 days and under differential flow conditions, and a customized clustering algorithm, five gene clusters that differentiated the low flow (i.e., pro-hyperplastic) from high flow (i.e., anti-hyperplastic) response were identified. The current analysis advances these general associations to create a model that identifies those genes sets most likely to be of therapeutic benefit. Using this approach, we examine the range of potential opportunities for intervention via gene cluster over-expression or inhibition, delivered in isolation or combination, at the time of vein graft implantation.
Funding Information
  • National Institutes of Health (U01HL119178-01)

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