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Bracamonte JH, Watkins L, Betty P, Dell’Italia LJ, Saucerman JJ, Holmes JW. Contributions of mechanical loading and hormonal changes to eccentric hypertrophy during volume overload: a Bayesian analysis using logic-based network models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.12.612768. [PMID: 39345523 PMCID: PMC11429691 DOI: 10.1101/2024.09.12.612768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Primary mitral regurgitation (MR) is a pathology that alters mechanical loading on the left ventricle and induces a distinctive ventricular remodeling response known as eccentric hypertrophy. Drug therapies may alleviate symptoms, but only mitral valve repair can provide significant recovery of cardiac function and dimensions. However, 20% of patients still develop systolic dysfunction post-operatively despite being treated according to the current guidelines. Thus, better understanding of the hypertrophic process in the setting of ventricular volume overload (VO) is needed to improve and better personalize the management of MR. To address this knowledge gap, we employ a Bayesian approach to combine data from 70 studies on experimental volume overload in dogs and rats and use it to calibrate a logic-based network model of hypertrophic signaling in myocytes. The calibrated model suggests that growth in experimental VO is mostly driven by the neurohormonal response, with an initial increase in myocardial tissue stretch being compensated by subsequent remodeling fairly early in the time course of VO. This observation contrasts with a common perception that volume-overload hypertrophy is driven primarily by increased myocyte strain. The model suggests that Endothelin1 receptor activity plays a central role in driving hypertrophic responses and the activation of the fetal gene program. The model reproduces a number of responses to drug therapy not used in its calibration, and predicts that a combination of endothelin receptor antagonist and angiotensin receptor blockers would have the greatest potential to dampen cardiomyocyte hypertrophy and dysfunction in VO.
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Affiliation(s)
- Johane H. Bracamonte
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Lionel Watkins
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Pat Betty
- Birmingham Veterans Affairs Health Care System, Birmingham, Alabama, United States of America
- Division of Cardiovascular Disease, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Louis J. Dell’Italia
- Birmingham Veterans Affairs Health Care System, Birmingham, Alabama, United States of America
- Division of Cardiovascular Disease, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jeffrey W. Holmes
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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Jones CE, Oomen PJ. Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603959. [PMID: 39091737 PMCID: PMC11291058 DOI: 10.1101/2024.07.17.603959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability. We utilized a biophysics model to rapidly simulate cardiac growth following mitral valve regurgitation (MVR). Here we developed a two-tiered Bayesian History Matching approach augmented with Gaussian process emulators for efficient calibration of model parameters to align with growth outcomes within a 95% confidence interval. We first generated a synthetic data set to assess the accuracy of our framework, and the effect of changes in data uncertainty on growth predictions. We then calibrated our model to match baseline and chronic canine MVR data and used an independent data set to successfully validate the ability of our calibrated model to accurately predict cardiac growth probability. The combined biophysics and machine learning modeling framework we proposed in this study can be easily translated to predict patient-specific cardiac growth.
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Affiliation(s)
- Clara E. Jones
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- Edwards Lifesciences Foundation Cardiovascular, University of California, Irvine, CA 92697, USA
| | - Pim J.A. Oomen
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- Edwards Lifesciences Foundation Cardiovascular, University of California, Irvine, CA 92697, USA
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Kaissar MS, Yoshida K. Computational model captures cardiac growth in hypertensive pregnancies and in the postpartum period. Am J Physiol Heart Circ Physiol 2024; 326:H1491-H1497. [PMID: 38668702 PMCID: PMC11380950 DOI: 10.1152/ajpheart.00104.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 05/30/2024]
Abstract
Heart growth in the pregnant patient helps maintain cardiovascular function while supporting the growing fetus. However, in some cases, the cardiovascular demand of pregnancy can trigger life-threatening conditions, including hypertensive disorders of pregnancy and peripartum cardiomyopathy. The mechanisms that control heart growth throughout pregnancy are unclear, and treating these diseases remains elusive. We previously developed a computational model that accounts for hormonal and hemodynamic interactions throughout pregnancy and demonstrated its ability to capture realistic cardiac growth in normal rat pregnancy. In this study, we evaluated whether this model could capture heart growth beyond normal pregnancy. After further validation of our normal pregnancy predictions, we tested our model predictions of three rat studies of hypertensive pregnancies. We next simulated the postpartum period and examined the impact of lactation on cardiac growth in rats. We demonstrate that our multiscale model can capture cardiac growth associated with new-onset hypertension during pregnancy and lactation status in the postpartum period. We conclude by elaborating on the potential clinical utility of our model in the future.NEW & NOTEWORTHY Our multiscale model predicts appropriate heart growth beyond normal pregnancy, including elevated heart weights in rats with induced hypertension during pregnancy and in lactating mice and decreased heart weight in nonlactating mice. Our model captures distinct mechanisms that result in similar organ-level growth, highlighting its potential to distinguish healthy from diseased pregnancy-induced growth.
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Affiliation(s)
- Molly S Kaissar
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States
| | - Kyoko Yoshida
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States
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Eggertsen TG, Saucerman JJ. Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy. Br J Pharmacol 2023; 180:2721-2735. [PMID: 37302817 PMCID: PMC10592153 DOI: 10.1111/bph.16163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/10/2023] [Accepted: 06/04/2023] [Indexed: 06/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Pathological cardiomyocyte hypertrophy is a response to cardiac stress that typically leads to heart failure. Despite being a primary contributor to pathological cardiac remodelling, the therapeutic space that targets hypertrophy is limited. Here, we apply a network model to virtually screen for FDA-approved drugs that induce or suppress cardiomyocyte hypertrophy. EXPERIMENTAL APPROACH A logic-based differential equation model of cardiomyocyte signalling was used to predict drugs that modulate hypertrophy. These predictions were validated against curated experiments from the prior literature. The actions of midostaurin were validated in new experiments using TGFβ- and noradrenaline (NE)-induced hypertrophy in neonatal rat cardiomyocytes. KEY RESULTS Model predictions were validated in 60 out of 70 independent experiments from the literature and identify 38 inhibitors of hypertrophy. We additionally predict that the efficacy of drugs that inhibit cardiomyocyte hypertrophy is often context dependent. We predicted that midostaurin inhibits cardiomyocyte hypertrophy induced by TGFβ, but not noradrenaline, exhibiting context dependence. We further validated this prediction by cellular experiments. Network analysis predicted critical roles for the PI3K and RAS pathways in the activity of celecoxib and midostaurin, respectively. We further investigated the polypharmacology and combinatorial pharmacology of drugs. Brigatinib and irbesartan in combination were predicted to synergistically inhibit cardiomyocyte hypertrophy. CONCLUSION AND IMPLICATIONS This study provides a well-validated platform for investigating the efficacy of drugs on cardiomyocyte hypertrophy and identifies midostaurin for consideration as an antihypertrophic drug.
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Affiliation(s)
- Taylor G. Eggertsen
- Department of Biomedical Engineering, University of Virginia
- Robert M. Berne Cardiovascular Research Center, University of Virginia
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia
- Robert M. Berne Cardiovascular Research Center, University of Virginia
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5
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Bazgir F, Nau J, Nakhaei-Rad S, Amin E, Wolf MJ, Saucerman JJ, Lorenz K, Ahmadian MR. The Microenvironment of the Pathogenesis of Cardiac Hypertrophy. Cells 2023; 12:1780. [PMID: 37443814 PMCID: PMC10341218 DOI: 10.3390/cells12131780] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
Pathological cardiac hypertrophy is a key risk factor for the development of heart failure and predisposes individuals to cardiac arrhythmia and sudden death. While physiological cardiac hypertrophy is adaptive, hypertrophy resulting from conditions comprising hypertension, aortic stenosis, or genetic mutations, such as hypertrophic cardiomyopathy, is maladaptive. Here, we highlight the essential role and reciprocal interactions involving both cardiomyocytes and non-myocardial cells in response to pathological conditions. Prolonged cardiovascular stress causes cardiomyocytes and non-myocardial cells to enter an activated state releasing numerous pro-hypertrophic, pro-fibrotic, and pro-inflammatory mediators such as vasoactive hormones, growth factors, and cytokines, i.e., commencing signaling events that collectively cause cardiac hypertrophy. Fibrotic remodeling is mediated by cardiac fibroblasts as the central players, but also endothelial cells and resident and infiltrating immune cells enhance these processes. Many of these hypertrophic mediators are now being integrated into computational models that provide system-level insights and will help to translate our knowledge into new pharmacological targets. This perspective article summarizes the last decades' advances in cardiac hypertrophy research and discusses the herein-involved complex myocardial microenvironment and signaling components.
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Affiliation(s)
- Farhad Bazgir
- Institute of Biochemistry and Molecular Biology II, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; (F.B.); (J.N.)
| | - Julia Nau
- Institute of Biochemistry and Molecular Biology II, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; (F.B.); (J.N.)
| | - Saeideh Nakhaei-Rad
- Stem Cell Biology, and Regenerative Medicine Research Group, Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad 91779-48974, Iran;
| | - Ehsan Amin
- Institute of Neural and Sensory Physiology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany;
| | - Matthew J. Wolf
- Department of Medicine and Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22908, USA;
| | - Jeffry J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA;
| | - Kristina Lorenz
- Institute of Pharmacology and Toxicology, University of Würzburg, Leibniz Institute for Analytical Sciences, 97078 Würzburg, Germany;
| | - Mohammad Reza Ahmadian
- Institute of Biochemistry and Molecular Biology II, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; (F.B.); (J.N.)
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Yoshida K, Saucerman JJ, Holmes JW. Multiscale model of heart growth during pregnancy: integrating mechanical and hormonal signaling. Biomech Model Mechanobiol 2022; 21:1267-1283. [PMID: 35668305 DOI: 10.1007/s10237-022-01589-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/01/2022] [Indexed: 12/01/2022]
Abstract
Pregnancy stands at the interface of mechanics and biology. The growing fetus continuously loads the maternal organs as circulating hormone levels surge, leading to significant changes in mechanical and hormonal cues during pregnancy. In response, maternal soft tissues undergo remarkable growth and remodeling to support the mother and baby for a healthy pregnancy. We focus on the maternal left ventricle, which increases its cardiac output and mass during pregnancy. This study develops a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive this growth process. We coupled a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes. We calibrated this multiscale model to data from experimental volume overload and hormonal infusions of angiotensin 2 (AngII), estrogen (E2), and progesterone (P4). We then validated the model's ability to capture interactions between inputs by comparing model predictions against published observations for the combinations of VO + E2 and AngII + E2. Finally, we simulated pregnancy-induced changes in hormones and hemodynamics to predict heart growth during pregnancy. Our model produced growth consistent with experimental data. Overall, our analysis suggests that the rise in P4 during the first half of gestation is an important contributor to heart growth during pregnancy. We conclude with suggestions for future experimental studies that will provide a better understanding of how hormonal and mechanical cues interact to drive pregnancy-induced heart growth.
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Affiliation(s)
- Kyoko Yoshida
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering and Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Jeffrey W Holmes
- School of Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
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Harlev I, Holmes JW, Cohen N. The influence of boundary conditions and protein availability on the remodeling of cardiomyocytes. Biomech Model Mechanobiol 2022; 21:189-201. [PMID: 34661804 DOI: 10.1007/s10237-021-01526-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/03/2021] [Indexed: 11/27/2022]
Abstract
The heart muscle is capable of growing and remodeling in response to changes in its mechanical and hormonal environment. While this capability is essential to the healthy function of the heart, under extreme conditions it may also lead to heart failure. In this work, we derive a thermodynamically based and microscopically motivated model that highlights the influence of mechanical boundary conditions and hormonal changes on the remodeling process in cardiomyocytes. We begin with a description of the kinematics associated with the remodeling process. Specifically, we derive relations between the macroscopic deformation, the number of sarcomeres, the sarcomere stretch, and the number of myofibrils in the cell. We follow with the derivation of evolution equations that describe the production and the degradation of protein in the cytosol. Next, we postulate a dissipation-based formulation that characterizes the remodeling process. We show that this process stems from a competition between the internal energy, the entropy, the energy supplied to the system by ATP and other sources, and dissipation mechanisms. To illustrate the merit of this framework, we study four initial and boundary conditions: (1) a myocyte undergoing isometric contractions in the presence of either an infinite or a limited supply of proteins and (2) a myocyte that is free to dilate along the radial direction with an infinite and a limited supply of proteins. This work underscores the importance of boundary conditions on the overall remodeling response of cardiomyocytes, suggesting a plausible mechanism that might play a role in distinguishing eccentric vs. concentric hypertrophy.
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Affiliation(s)
- Ido Harlev
- Department of Materials Science and Engineering, Technion - Israel Institute of Technology, 3200003, Haifa, Israel
| | - Jeffrey W Holmes
- Division of Cardiovascular Disease, Division of Cardiothoracic Surgery, Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Noy Cohen
- Department of Materials Science and Engineering, Technion - Israel Institute of Technology, 3200003, Haifa, Israel.
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8
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Winkle AJ, Nassal DM, Shaheen R, Thomas E, Mohta S, Gratz D, Weinberg SH, Hund TJ. Emerging therapeutic targets for cardiac hypertrophy. Expert Opin Ther Targets 2022; 26:29-40. [PMID: 35076342 PMCID: PMC8885901 DOI: 10.1080/14728222.2022.2031974] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
INTRODUCTION Cardiac hypertrophy is associated with adverse outcomes across cardiovascular disease states. Despite strides over the last three decades in identifying molecular and cellular mechanisms driving hypertrophy, the link between pathophysiological stress stimuli and specific myocyte/heart growth profiles remains unclear. Moreover, the optimal strategy for preventing pathology in the setting of hypertrophy remains controversial. AREAS COVERED This review discusses molecular mechanisms underlying cardiac hypertrophy with a focus on factors driving the orientation of myocyte growth and the impact on heart function. We highlight recent work showing a novel role for the spectrin-based cytoskeleton, emphasizing regulation of myocyte dimensions but not hypertrophy per se. Finally, we consider opportunities for directing the orientation of myocyte growth in response to hypertrophic stimuli as an alternative therapeutic approach. Relevant publications on the topic were identified through Pubmed with open-ended search dates. EXPERT OPINION To define new therapeutic avenues, more precision is required when describing changes in myocyte and heart structure/function in response to hypertrophic stimuli. Recent developments in computational modeling of hypertrophic networks, in concert with more refined experimental approaches will catalyze translational discovery to advance the field and further our understanding of cardiac hypertrophy and its relationship with heart disease.
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Affiliation(s)
- Alexander J Winkle
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Drew M Nassal
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Rebecca Shaheen
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Evelyn Thomas
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Shivangi Mohta
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Daniel Gratz
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Seth H Weinberg
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA
| | - Thomas J Hund
- The Frick Center for Heart Failure and Arrhythmia, The Dorothy M. Davis Heart and Lung Research Institute, the Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Biomedical Engineering, College of Engineering, the Ohio State University, Columbus, OH, USA.,Department of Internal Medicine, College of Medicine, the Ohio State University Wexner Medical Center, Columbus, OH, USA
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9
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Computational modeling in pregnancy biomechanics research. J Mech Behav Biomed Mater 2022; 128:105099. [DOI: 10.1016/j.jmbbm.2022.105099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/11/2022] [Accepted: 01/18/2022] [Indexed: 11/24/2022]
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10
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Han T, Lee T, Ledwon J, Vaca E, Turin S, Kearney A, Gosain AK, Tepole AB. Bayesian calibration of a computational model of tissue expansion based on a porcine animal model. Acta Biomater 2022; 137:136-146. [PMID: 34634507 PMCID: PMC8678288 DOI: 10.1016/j.actbio.2021.10.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 01/03/2023]
Abstract
Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, lack of fundamental knowledge of skin adaptation to mechanical cues, and lack of predictive computational models limit the broader adoption and efficacy of tissue expansion. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by our porcine animal model. Here we use the data from a new set of experiments to calibrate the computational model within a Bayesian framework. Four 10×10cm2 patches were tattooed in the dorsal skin of four 12 weeks-old minipigs and a total of six patches underwent successful tissue expander placement and inflation to 60cc for expansion times ranging from 1 h to 7 days. Six patches that did not have expanders implanted served as controls for the analysis. We find that growth can be explained based on the elastic deformation. The predicted area growth rate is k∈[0.02,0.08] [h-1]. Growth is anisotropic and reflects the anisotropic mechanical behavior of porcine dorsal skin. The rostral-caudal axis shows greater deformation than the transverse axis, and the time scale of growth in the rostral-caudal direction is given by rate parameters k1∈[0.04,0.1] [h-1] compared to k2∈[0.01,0.05] [h-1] in the transverse direction. Moreover, the calibration results underscore the high variability in biological systems, and the need to create probabilistic computational models to predict tissue adaptation in realistic settings. STATEMENT OF SIGNIFICANCE: Tissue expansion is a widely used technique in reconstructive surgery because it triggers growth of skin for the correction of large skin lesions and for breast reconstruction after mastectomy. Despite of its potential, complications and undesired outcomes persist due to our incomplete understanding of skin mechanobiology. Here we quantify the deformation and growth fields induced by an expander over 7 days in a porcine animal model and use these data to calibrate a computational model of skin growth using finite element simulations and a Bayesian framework. The calibrated model is a leap forward in our understanding skin growth, we now have quantitative understanding of this process: area growth is anisotropic and it is proportional to stretch with a characteristic rate constant of k∈[0.02,0.08] [h-1].
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Affiliation(s)
- Tianhong Han
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Taeksang Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Joanna Ledwon
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Elbert Vaca
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Sergey Turin
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Aaron Kearney
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Arun K Gosain
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Adrian B Tepole
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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11
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Sharifi H, Mann CK, Rockward AL, Mehri M, Mojumder J, Lee LC, Campbell KS, Wenk JF. Multiscale simulations of left ventricular growth and remodeling. Biophys Rev 2021; 13:729-746. [PMID: 34777616 PMCID: PMC8555068 DOI: 10.1007/s12551-021-00826-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 08/05/2021] [Indexed: 02/07/2023] Open
Abstract
Cardiomyocytes can adapt their size, shape, and orientation in response to altered biomechanical or biochemical stimuli. The process by which the heart undergoes structural changes-affecting both geometry and material properties-in response to altered ventricular loading, altered hormonal levels, or mutant sarcomeric proteins is broadly known as cardiac growth and remodeling (G&R). Although it is likely that cardiac G&R initially occurs as an adaptive response of the heart to the underlying stimuli, prolonged pathological changes can lead to increased risk of atrial fibrillation, heart failure, and sudden death. During the past few decades, computational models have been extensively used to investigate the mechanisms of cardiac G&R, as a complement to experimental measurements. These models have provided an opportunity to quantitatively study the relationships between the underlying stimuli (primarily mechanical) and the adverse outcomes of cardiac G&R, i.e., alterations in ventricular size and function. State-of-the-art computational models have shown promise in predicting the progression of cardiac G&R. However, there are still limitations that need to be addressed in future works to advance the field. In this review, we first outline the current state of computational models of cardiac growth and myofiber remodeling. Then, we discuss the potential limitations of current models of cardiac G&R that need to be addressed before they can be utilized in clinical care. Finally, we briefly discuss the next feasible steps and future directions that could advance the field of cardiac G&R.
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Affiliation(s)
- Hossein Sharifi
- Department of Mechanical Engineering, University of Kentucky, 269 Ralph G. Anderson Building, Lexington, KY 40506-0503 USA
| | - Charles K. Mann
- Department of Mechanical Engineering, University of Kentucky, 269 Ralph G. Anderson Building, Lexington, KY 40506-0503 USA
| | - Alexus L. Rockward
- Department of Mechanical Engineering, University of Kentucky, 269 Ralph G. Anderson Building, Lexington, KY 40506-0503 USA
| | - Mohammad Mehri
- Department of Mechanical Engineering, University of Kentucky, 269 Ralph G. Anderson Building, Lexington, KY 40506-0503 USA
| | - Joy Mojumder
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI USA
| | - Lik-Chuan Lee
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI USA
| | - Kenneth S. Campbell
- Department of Physiology & Division of Cardiovascular Medicine, University of Kentucky, Lexington, KY USA
| | - Jonathan F. Wenk
- Department of Mechanical Engineering, University of Kentucky, 269 Ralph G. Anderson Building, Lexington, KY 40506-0503 USA
- Department of Surgery, University of Kentucky, Lexington, KY USA
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12
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Zeigler AC, Chandrabhatla AS, Christiansen SL, Nelson AR, Holmes JW, Saucerman JJ. Network model-based screen for FDA-approved drugs affecting cardiac fibrosis. CPT Pharmacometrics Syst Pharmacol 2021; 10:377-388. [PMID: 33571402 PMCID: PMC8099443 DOI: 10.1002/psp4.12599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/08/2020] [Accepted: 01/14/2021] [Indexed: 12/30/2022] Open
Abstract
Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.
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Affiliation(s)
- Angela C. Zeigler
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
| | | | | | - Anders R. Nelson
- Department of PharmacologyUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey W. Holmes
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Jeffrey J. Saucerman
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Division of Cardiovascular MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA
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13
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Grabowska ME, Chun B, Moya R, Saucerman JJ. Computational model of cardiomyocyte apoptosis identifies mechanisms of tyrosine kinase inhibitor-induced cardiotoxicity. J Mol Cell Cardiol 2021; 155:66-77. [PMID: 33667419 DOI: 10.1016/j.yjmcc.2021.02.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 01/21/2021] [Accepted: 02/25/2021] [Indexed: 12/16/2022]
Abstract
Despite clinical observations of cardiotoxicity among cancer patients treated with tyrosine kinase inhibitors (TKIs), the molecular mechanisms by which these drugs affect the heart remain largely unknown. Mechanistic understanding of TKI-induced cardiotoxicity has been limited in part due to the complexity of tyrosine kinase signaling pathways and the multi-targeted nature of many of these drugs. TKI treatment has been associated with reactive oxygen species generation, mitochondrial dysfunction, and apoptosis in cardiomyocytes. To gain insight into the mechanisms mediating TKI-induced cardiotoxicity, this study constructs and validates a computational model of cardiomyocyte apoptosis, integrating intrinsic apoptotic and tyrosine kinase signaling pathways. The model predicts high levels of apoptosis in response to sorafenib, sunitinib, ponatinib, trastuzumab, and gefitinib, and lower levels of apoptosis in response to nilotinib and erlotinib, with the highest level of apoptosis induced by sorafenib. Knockdown simulations identified AP1, ASK1, JNK, MEK47, p53, and ROS as positive functional regulators of sorafenib-induced apoptosis of cardiomyocytes. Overexpression simulations identified Akt, IGF1, PDK1, and PI3K among the negative functional regulators of sorafenib-induced cardiomyocyte apoptosis. A combinatorial screen of the positive and negative regulators of sorafenib-induced apoptosis revealed ROS knockdown coupled with overexpression of FLT3, FGFR, PDGFR, VEGFR, or KIT as a particularly potent combination in reducing sorafenib-induced apoptosis. Network simulations of combinatorial treatment with sorafenib and the antioxidant N-acetyl cysteine (NAC) suggest that NAC may protect cardiomyocytes from sorafenib-induced apoptosis.
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Affiliation(s)
- Monika E Grabowska
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Bryan Chun
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Raquel Moya
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia; Charlottesville, Virginia 22908, USA.
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14
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Estrada AC, Yoshida K, Saucerman JJ, Holmes JW. A multiscale model of cardiac concentric hypertrophy incorporating both mechanical and hormonal drivers of growth. Biomech Model Mechanobiol 2021; 20:293-307. [PMID: 32970240 PMCID: PMC7897221 DOI: 10.1007/s10237-020-01385-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/08/2020] [Indexed: 01/19/2023]
Abstract
Growth and remodeling in the heart is driven by a combination of mechanical and hormonal signals that produce different patterns of growth in response to exercise, pregnancy, and various pathologies. In particular, increases in afterload lead to concentric hypertrophy, a thickening of the walls that increases the contractile ability of the heart while reducing wall stress. In the current study, we constructed a multiscale model of cardiac hypertrophy that connects a finite-element model representing the mechanics of the growing left ventricle to a cell-level network model of hypertrophic signaling pathways that accounts for changes in both mechanics and hormones. We first tuned our model to capture published in vivo growth trends for isoproterenol infusion, which stimulates β-adrenergic signaling pathways without altering mechanics, and for transverse aortic constriction (TAC), which involves both elevated mechanics and altered hormone levels. We then predicted the attenuation of TAC-induced hypertrophy by two distinct genetic interventions (transgenic Gq-coupled receptor inhibitor overexpression and norepinephrine knock-out) and by two pharmacologic interventions (angiotensin receptor blocker losartan and β-blocker propranolol) and compared our predictions to published in vivo data for each intervention. Our multiscale model captured the experimental data trends reasonably well for all conditions simulated. We also found that when prescribing realistic changes in mechanics and hormones associated with TAC, the hormonal inputs were responsible for the majority of the growth predicted by the multiscale model and were necessary in order to capture the effect of the interventions for TAC.
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15
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Yoshida K, Holmes JW. Computational models of cardiac hypertrophy. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 159:75-85. [PMID: 32702352 PMCID: PMC7855157 DOI: 10.1016/j.pbiomolbio.2020.07.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 06/05/2020] [Accepted: 07/02/2020] [Indexed: 02/07/2023]
Abstract
Cardiac hypertrophy, defined as an increase in mass of the heart, is a complex process driven by simultaneous changes in hemodynamics, mechanical stimuli, and hormonal inputs. It occurs not only during pre- and post-natal development but also in adults in response to exercise, pregnancy, and a range of cardiovascular diseases. One of the most exciting recent developments in the field of cardiac biomechanics is the advent of computational models that are able to accurately predict patterns of heart growth in many of these settings, particularly in cases where changes in mechanical loading of the heart play an import role. These emerging models may soon be capable of making patient-specific growth predictions that can be used to guide clinical interventions. Here, we review the history and current state of cardiac growth models and highlight three main limitations of current approaches with regard to future clinical application: their inability to predict the regression of heart growth after removal of a mechanical overload, inability to account for evolving hemodynamics, and inability to incorporate known growth effects of drugs and hormones on heart growth. Next, we outline growth mechanics approaches used in other fields of biomechanics and highlight some potential lessons for cardiac growth modeling. Finally, we propose a multiscale modeling approach for future studies that blends tissue-level growth models with cell-level signaling models to incorporate the effects of hormones in the context of pregnancy-induced heart growth.
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Affiliation(s)
- Kyoko Yoshida
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, VA, 22908, USA.
| | - Jeffrey W Holmes
- Department of Biomedical Engineering, Robert M. Berne Cardiovascular Research Center, University of Virginia, Box 800759, Health System, Charlottesville, VA, 22908, USA.
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16
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Khalilimeybodi A, Paap AM, Christiansen SLM, Saucerman JJ. Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy. PLoS Comput Biol 2020; 16:e1008490. [PMID: 33338038 PMCID: PMC7781532 DOI: 10.1371/journal.pcbi.1008490] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 01/04/2021] [Accepted: 11/05/2020] [Indexed: 11/25/2022] Open
Abstract
Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model’s predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy. Pathological cardiac hypertrophy is a disease in which the heart grows abnormally in response to different motivators such as high blood pressure or variations in hormones and growth factors. The shape of the heart after its growth depends on the context in which it grows. Since cell signaling in the cardiac cells plays a key role in the determination of heart shape, a thorough understanding of cardiac cells signaling in each context enlightens the mechanisms which control response of cardiac cells. However, cell signaling in cardiac hypertrophy comprises a complex web of pathways with numerous interactions, and predicting how these interactions control the hypertrophic signal in each context is not achievable by only experiments or general computational models. To address this need, we developed an approach to bring together the experimental data of each context with a signaling network curated from literature to identify the main players of cardiac cells response in each context and attain the context-specific models of cardiac hypertrophy. By utilizing our approach, we identified the main regulators of cardiac hypertrophy in four important contexts. We developed a network model of β-adrenergic cardiac hypertrophy, and predicted and validated new interactions that regulate cardiac cells response in this context.
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Affiliation(s)
- Ali Khalilimeybodi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Alexander M. Paap
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Steven L. M. Christiansen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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17
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Sree VD, Tepole AB. Computational systems mechanobiology of growth and remodeling: Integration of tissue mechanics and cell regulatory network dynamics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2020.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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18
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Zeigler AC, Nelson AR, Chandrabhatla AS, Brazhkina O, Holmes JW, Saucerman JJ. Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction. Matrix Biol 2020; 91-92:136-151. [PMID: 32209358 PMCID: PMC7434705 DOI: 10.1016/j.matbio.2020.03.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 02/14/2020] [Accepted: 03/16/2020] [Indexed: 01/09/2023]
Abstract
The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFβ and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFβ positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.
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Affiliation(s)
- Angela C Zeigler
- Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908-0759, USA
| | - Anders R Nelson
- Department of Pharmacology, University of Virginia, Charlottesville, VA, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Anirudha S Chandrabhatla
- Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908-0759, USA
| | - Olga Brazhkina
- Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908-0759, USA; Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
| | - Jeffrey W Holmes
- Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908-0759, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA; Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, PO Box 800759, Charlottesville, VA 22908-0759, USA; Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA.
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19
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Saucerman JJ, Tan PM, Buchholz KS, McCulloch AD, Omens JH. Mechanical regulation of gene expression in cardiac myocytes and fibroblasts. Nat Rev Cardiol 2019; 16:361-378. [PMID: 30683889 PMCID: PMC6525041 DOI: 10.1038/s41569-019-0155-8] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The intact heart undergoes complex and multiscale remodelling processes in response to altered mechanical cues. Remodelling of the myocardium is regulated by a combination of myocyte and non-myocyte responses to mechanosensitive pathways, which can alter gene expression and therefore function in these cells. Cellular mechanotransduction and its downstream effects on gene expression are initially compensatory mechanisms during adaptations to the altered mechanical environment, but under prolonged and abnormal loading conditions, they can become maladaptive, leading to impaired function and cardiac pathologies. In this Review, we summarize mechanoregulated pathways in cardiac myocytes and fibroblasts that lead to altered gene expression and cell remodelling under physiological and pathophysiological conditions. Developments in systems modelling of the networks that regulate gene expression in response to mechanical stimuli should improve integrative understanding of their roles in vivo and help to discover new combinations of drugs and device therapies targeting mechanosignalling in heart disease.
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Affiliation(s)
- Jeffrey J Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Philip M Tan
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kyle S Buchholz
- Departments of Bioengineering and Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew D McCulloch
- Departments of Bioengineering and Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Jeffrey H Omens
- Departments of Bioengineering and Medicine, University of California San Diego, La Jolla, CA, USA
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20
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Witzenburg C, Holmes JW. The Impact of Hemodynamic Reflex Compensation Following Myocardial Infarction on Subsequent Ventricular Remodeling. J Biomech Eng 2019; 141:2735313. [PMID: 31141599 DOI: 10.1115/1.4043867] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Indexed: 01/05/2023]
Abstract
Patients who survive a myocardial infarction (MI) are at risk for ventricular dilation and heart failure. While infarct size is an important determinant of post-MI remodeling, different patients with the same size infarct often display different levels of left ventricular (LV) dilation. The acute physiologic response to MI involves reflex compensation, whereby increases in heart rate, arterial resistance, venoconstriction, and contractility of the surviving myocardium act to maintain mean arterial pressure. We hypothesized that variability in compensation might underlie some of the reported variability in post-MI remodeling, a hypothesis that is difficult to test using experimental data alone because some responses are difficult or impossible to measure directly. We therefore employed a computational model to estimate the balance of compensatory mechanisms from experimentally reported hemodynamic data. We found a strikingly wide range of compensatory reflex profiles in response to MI in dogs and verified that pharmacologic blockade of sympathetic and parasympathetic reflexes nearly abolished this variability. Then, using a previously published model of post-infarction remodeling, we showed that observed variability in compensation translated to variability in predicted LV dilation consistent with published data. Treatment with a vasodilator shifted the compensatory response away from arterial and venous vasoconstriction and towards increased heart rate and myocardial contractility. Importantly, this shift reduced predicted dilation, a prediction that matched prior experimental studies. Thus, post-infarction reflex compensation could represent both a source of individual variability in the extent of LV remodeling and a target for therapies aimed at reducing that remodeling.
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Affiliation(s)
| | - Jeffrey W Holmes
- Biomedical Engineering, Medicine, Robert M. Berne Cardiovascular Research Center, and Center for Engineering in Medicine, University of Virginia, Charlottesville, VA, USA
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21
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Administration of ubiquitin-activating enzyme UBA1 inhibitor PYR-41 attenuates angiotensin II-induced cardiac remodeling in mice. Biochem Biophys Res Commun 2018; 505:317-324. [DOI: 10.1016/j.bbrc.2018.09.100] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 09/16/2018] [Indexed: 11/19/2022]
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