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Gebauer AM, Pfaller MR, Braeu FA, Cyron CJ, Wall WA. A homogenized constrained mixture model of cardiac growth and remodeling: analyzing mechanobiological stability and reversal. Biomech Model Mechanobiol 2023; 22:1983-2002. [PMID: 37482576 PMCID: PMC10613155 DOI: 10.1007/s10237-023-01747-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/06/2023] [Indexed: 07/25/2023]
Abstract
Cardiac growth and remodeling (G&R) patterns change ventricular size, shape, and function both globally and locally. Biomechanical, neurohormonal, and genetic stimuli drive these patterns through changes in myocyte dimension and fibrosis. We propose a novel microstructure-motivated model that predicts organ-scale G&R in the heart based on the homogenized constrained mixture theory. Previous models, based on the kinematic growth theory, reproduced consequences of G&R in bulk myocardial tissue by prescribing the direction and extent of growth but neglected underlying cellular mechanisms. In our model, the direction and extent of G&R emerge naturally from intra- and extracellular turnover processes in myocardial tissue constituents and their preferred homeostatic stretch state. We additionally propose a method to obtain a mechanobiologically equilibrated reference configuration. We test our model on an idealized 3D left ventricular geometry and demonstrate that our model aims to maintain tensional homeostasis in hypertension conditions. In a stability map, we identify regions of stable and unstable G&R from an identical parameter set with varying systolic pressures and growth factors. Furthermore, we show the extent of G&R reversal after returning the systolic pressure to baseline following stage 1 and 2 hypertension. A realistic model of organ-scale cardiac G&R has the potential to identify patients at risk of heart failure, enable personalized cardiac therapies, and facilitate the optimal design of medical devices.
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Affiliation(s)
- Amadeus M Gebauer
- Institute for Computational Mechanics, Technical University of Munich, 85748, Garching, Germany.
| | - Martin R Pfaller
- Pediatric Cardiology, Stanford Maternal & Child Health Research Institute, and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, USA
| | - Fabian A Braeu
- Ophthalmic Engineering & Innovation Laboratory, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Christian J Cyron
- Institute of Continuum and Material Mechanics, Hamburg University of Technology, 21073, Hamburg, Germany
- Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany
| | - Wolfgang A Wall
- Institute for Computational Mechanics, Technical University of Munich, 85748, Garching, Germany
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2
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Khalilimeybodi A, Riaz M, Campbell SG, Omens JH, McCulloch AD, Qyang Y, Saucerman JJ. Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy. J Mol Cell Cardiol 2023; 174:1-14. [PMID: 36370475 PMCID: PMC10230857 DOI: 10.1016/j.yjmcc.2022.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 08/26/2022] [Accepted: 10/20/2022] [Indexed: 11/11/2022]
Abstract
Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.
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Affiliation(s)
- Ali Khalilimeybodi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Muhammad Riaz
- Yale Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Stuart G Campbell
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Jeffrey H Omens
- Departments of Bioengineering and Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Andrew D McCulloch
- Departments of Bioengineering and Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Yibing Qyang
- Yale Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Yale Stem Cell Center, New Haven, CT, United States of America; Department of Pathology, Yale University, New Haven, CT, United States of America; Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, United States of America
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, United States of America.
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Fan Y, Coll-Font J, van den Boomen M, Kim JH, Chen S, Eder RA, Roche ET, Nguyen CT. Characterization of Exercise-Induced Myocardium Growth Using Finite Element Modeling and Bayesian Optimization. Front Physiol 2021; 12:694940. [PMID: 34434115 PMCID: PMC8381603 DOI: 10.3389/fphys.2021.694940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023] Open
Abstract
Cardiomyocyte growth can occur in both physiological (exercised-induced) and pathological (e.g., volume overload and pressure overload) conditions leading to left ventricular (LV) hypertrophy. Studies using animal models and histology have demonstrated the growth and remodeling process at the organ level and tissue-cellular level, respectively. However, the driving factors of growth and the mechanistic link between organ, tissue, and cellular growth remains poorly understood. Computational models have the potential to bridge this gap by using constitutive models that describe the growth and remodeling process of the myocardium coupled with finite element (FE) analysis to model the biomechanics of the heart at the organ level. Using subject-specific imaging data of the LV geometry at two different time points, an FE model can be created with the inverse method to characterize the growth parameters of each subject. In this study, we developed a framework that takes in vivo cardiac magnetic resonance (CMR) imaging data of exercised porcine model and uses FE and Bayesian optimization to characterize myocardium growth in the transverse and longitudinal directions. The efficacy of this framework was demonstrated by successfully predicting growth parameters of 18 synthetic LV targeted masks which were generated from three LV porcine geometries. The framework was further used to characterize growth parameters in 4 swine subjects that had been exercised. The study suggested that exercise-induced growth in swine is prone to longitudinal cardiomyocyte growth (58.0 ± 19.6% after 6 weeks and 79.3 ± 15.6% after 12 weeks) compared to transverse growth (4.0 ± 8.0% after 6 weeks and 7.8 ± 9.4% after 12 weeks). This framework can be used to characterize myocardial growth in different phenotypes of LV hypertrophy and can be incorporated with other growth constitutive models to study different hypothetical growth mechanisms.
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Affiliation(s)
- Yiling Fan
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jaume Coll-Font
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Maaike van den Boomen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Joan H. Kim
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Shi Chen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Robert Alan Eder
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ellen T. Roche
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States,Harvard Medical School, Boston, MA, United States,*Correspondence: Ellen T. Roche,
| | - Christopher T. Nguyen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States,Christopher T. Nguyen,
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McCabe KJ, Rangamani P. Computational modeling approaches to cAMP/PKA signaling in cardiomyocytes. J Mol Cell Cardiol 2021; 154:32-40. [PMID: 33548239 DOI: 10.1016/j.yjmcc.2021.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/11/2021] [Accepted: 01/15/2021] [Indexed: 12/12/2022]
Abstract
The cAMP/PKA pathway is a fundamental regulator of excitation-contraction coupling in cardiomyocytes. Activation of cAMP has a variety of downstream effects on cardiac function including enhanced contraction, accelerated relaxation, adaptive stress response, mitochondrial regulation, and gene transcription. Experimental advances have shed light on the compartmentation of cAMP and PKA, which allow for control over the varied targets of these second messengers and is disrupted in heart failure conditions. Computational modeling is an important tool for understanding the spatial and temporal complexities of this system. In this review article, we outline the advances in computational modeling that have allowed for deeper understanding of cAMP/PKA dynamics in the cardiomyocyte in health and disease, and explore new modeling frameworks that may bring us closer to a more complete understanding of this system. We outline various compartmental and spatial signaling models that have been used to understand how β-adrenergic signaling pathways function in a variety of simulation conditions. We also discuss newer subcellular models of cardiovascular function that may be used as templates for the next phase of computational study of cAMP and PKA in the heart, and outline open challenges which are important to consider in future models.
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Affiliation(s)
- Kimberly J McCabe
- Simula Research Laboratory, Department of Computational Physiology, PO Box 134, 1325 Lysaker, Norway.
| | - Padmini Rangamani
- University of California San Diego, Department of Mechanical and Aerospace Engineering, 9500 Gilman Drive MC 0411, La Jolla, CA 92093, United States of America
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