Heart disease, stroke, and limb loss continue to be a leading cause of U.S. mortality and morbidity. Despite advances in the surgical treatments for these pathologies, the long-term success of these interventions remains limited. Prior strategies aimed at improving the durability of vein grafts have focused largely on reductionist strategies and used linear models to describe the physical and biologic components of vascular disease progression. In order to advance our understanding of such complex phenomena, it is necessary to integrate different types of data and use quantitative models to predict behavior and outcomes. OVERALL HYPOTHESIS: A specific, finite set of shear-regulated genes are critical to controlling pathologic vein graft adaptation. Using the integration of multiscale modeling and experimental techniques, these genes can be identified and manipulated in vivo to improve vein graft durability.
SPECIFIC AIM 1 : Identify those model parameters that are most critical for accelerated loss of the vein graft lumen. Approach: Using a stochastic optimization algorithm to evaluate our in silico model of vein graft adaptation, a large-scale simulation is use to define the minimum parameter set, and correspondingly the core biologic processes, that lead to reduced intimal thickening and enhanced outward remodeling.
SPECIFIC AIM 2 : Create a dynamic gene regulatory network, which when integrated with an agent-based model of vascular adaptation, identifies the subset of genes that have the most significant impact on reducing intimal hyperplasia and preserving vein graft lumen. Approach: Transcriptional profiling of vein graft samples is used to create a gene regulatory network. Through a systematic evaluation of this network, highly interconnected genes are identified, specifically identifying those genes that when deleted lead to substantial changes in global gene expression. This subset of genes is explored using an agent-based model of vein graft adaptation to identify a set of key hub genes that when deleted result in significant improvements in lumen preservation.
SPECIFIC AIM 3 : Validate the model prediction and explore combinations of key hub genes that provide the most critical impact on the biologic processes that are central to pathologic vein graft adaptation. Approach: Employing a high throughput, cell culture system and an siRNA inhibition strategy, the effect of key hub gene deletion on cell proliferation, apoptosis, matrix production, protease activation and cell motility will be evaluated. The most promising genes, either alone or in combination, will move into Aim 4 for in vivo testing.
SPECIFIC AIM 4 : Identify the optimum combination of genes that will move forward into a large animal validation model and translated into a therapeutic tool to improve vein graft survival. Approach: Packaging a lentivirus delivery vehicle with a siRNA construct, we will utilize a rabbit vein bypass graft mode to evaluate the in vivo performance of the most promising gene sets that have been vetted through Aim 1 through 3, assessing their impact on reducing intimal hyperplasia and preserving vein graft lumen.
Heart attack and stroke continue to be the leading cause of mortality and morbidity in the United States. Vein bypass grafting currently provides only short-term improvements, and are prone to failure due to aggressive scar formation. The current proposal uses systems biology approaches to study the complex mechanisms of the vascular response to injury, providing a predictive model with application both as a research tool and in the care of patients. Using this model, a set of genes will be identified that when inhibited lead o substantial improvement in vein graft durability.
|Garbey, Marc; Casarin, Stefano; Berceli, Scott A (2017) Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System. J Theor Biol 429:149-163|
|Klein, Benjamin; Destephens, Anthony; Dumeny, Leanne et al. (2017) Hemodynamic Influence on Smooth Muscle Cell Kinetics and Phenotype During Early Vein Graft Adaptation. Ann Biomed Eng 45:644-655|
|Amensag, Salma; Goldberg, Leslie; O'Malley, Kerri A et al. (2017) Pilot assessment of a human extracellular matrix-based vascular graft in a rabbit model. J Vasc Surg 65:839-847.e1|
|Zhang, Yanhang; Barocas, Victor H; Berceli, Scott A et al. (2016) Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention. Ann Biomed Eng 44:2642-60|
|Wang, Ningtao; Gosik, Kirk; Li, Runze et al. (2016) A block mixture model to map eQTLs for gene clustering and networking. Sci Rep 6:21193|
|Sun, Lidan; Wu, Rongling (2015) Mapping complex traits as a dynamic system. Phys Life Rev 13:155-85|
|Garbey, Marc; Rahman, Mahbubur; Berceli, Scott A (2015) A Multiscale Computational Framework to Understand Vascular Adaptation. J Comput Sci 8:32-47|
|Longchamp, Alban; Allagnat, Florent; Alonso, Florian et al. (2015) Connexin43 Inhibition Prevents Human Vein Grafts Intimal Hyperplasia. PLoS One 10:e0138847|
|DeSart, Kenneth M; Butler, Khayree; O'Malley, Kerri A et al. (2015) Time and flow-dependent changes in the p27(kip1) gene network drive maladaptive vascular remodeling. J Vasc Surg 62:1296-302.e2|
|Wu, Rongling (2014) Genomic applications to various biological questions. Curr Genomics 15:325|
Showing the most recent 10 out of 14 publications