Background and Aims: Heart failure with preserved ejection fraction (HFpEF) exhibits profound phenotypic heterogeneity, which likely contributes to variable therapeutic response. We developed a physiology-informed digital twin-AI framework to predict individual hemodynamic and myocardial energetic responses to accelerated atrial pacing and tested whether simulated physiologic response corresponds to responders in the myPACE randomized clinical trial. Methods: Patient-specific digital twins were constructed for 146 HFpEF patients and used to train a variational autoencoder that generated a virtual HFpEF population (n = 25,000). The model simulated pacing-induced changes in left atrial pressure (LAP), systolic blood pressure (SBP), cardiac output (CO), and cardiac efficiency (CE; derived from myocardial oxygen-demand estimates). These simulations served as labels to train classifiers based on clinical variables available in myPACE, allowing us to examine associations with clinical end points and test a hypothesized relationship between CE and treatment response. Results: Simulations revealed heterogeneous physiological responses, with 95.6% of virtual patients showing reduced LAP, 47.0% an SBP reduction greater than 8.5 mmHg, 93.8% increased CO, and 36.1% improved CE. Classifiers reproduced these patterns with high fidelity. In the myPACE trial, patients classified as having CE improvement or a larger SBP reduction experienced significantly greater 1-month improvements in quality-of-life scores and larger NT-proBNP reductions. Conclusions: A physiology-informed digital twin-AI framework can predict hemodynamic and energetic responses corresponding to clinical benefit in HFpEF patients receiving accelerated atrial pacing. CE improvement functioned as a mechanistic indicator, while SBP reduction served as an accessible clinical correlate, offering mechanistically grounded guidance for patient-specific pacing and motivating prospective validation. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT04721314 ### Funding Statement This study was funded by NIH grant HL173346 and the Michigan Medicine-PKUHSC Joint Institute for Translational and Clinical Research. We also acknowledge support from the Michigan Biology of Cardiovascular Aging Postdoctoral Fellowship, which provided funding for the training and career development of Feng Gu during this study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB committees of the University of Michigan, the University of Wisconsin-Madison, and the University of Vermont gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The virtual cohort generated by the GenAI model is available in the corresponding Git repository (https://github.com/beards-lab/PhysicalAI.git). Data from the myPACE randomized clinical trial is available from the original corresponding investigators upon reasonable request.

Coronary autoregulation maintains relatively constant myocardial blood flow over a wide range of perfusion pressures through myogenic, shear-dependent and metabolic control mechanisms. Understanding this phenomenon is challenging due to the coupled nature of these mechanisms and their heterogeneous effects throughout the coronary tree. In this study, we developed a framework to study coronary autoregulation based on constrained mixture theory. The framework simulates autoregulation at three myocardial depths (subepicardium, midwall and subendocardium), calibrated using extensive literature data. Coronary trees at each myocardial depth are constructed via a homeostatic optimization approach to determine morphological and haemodynamic characteristics. Each vessel is endowed with passive and active mechanical properties, governed by a microstructurally motivated wall model. Autoregulatory stimuli from myogenic, metabolic and shear-dependent mechanisms modulate vascular smooth muscle tone, enabling the framework to reproduce autoregulatory responses, experimentally measured transmural flow ratios and vessel diameter changes with variations in perfusion pressure. The framework also incorporates phasic dynamics by extending Womersley's theory to account for time-varying intramyocardial pressures, successfully capturing key features of coronary flow waveforms. Sensitivity analysis highlights metabolic mechanisms as primary contributors to autoregulatory function, with the myogenic response playing an important role and shear-dependent control having minimal contribution. Additionally, the framework demonstrates how changes in vessel microstructure (e.g. collagen stiffening or impaired smooth muscle contractility) affect autoregulatory capacity, providing mechanistic insight into pathophysiological states. This microstructurally motivated framework offers a novel approach for hypothesis testing in coronary autoregulation while providing a unified platform for describing processes spanning short-term tone regulation and long-term vascular remodelling. KEY POINTS: Coronary autoregulation is defined as the capability of the coronary circulation to maintain the blood supply to the heart over a range of perfusion pressures. This phenomenon is facilitated through intrinsic mechanisms that control the vascular resistance by regulating the function of smooth muscle cells. This paper presents a microstructurally motivated coronary autoregulation framework that uses a non-linear continuum mechanics approach to account for the morphometry and vessel wall composition in three coronary trees in the subepicardial, midwall and subendocardial layers of the myocardium. The model is calibrated against diverse experimental data from the literature and is used to study heterogeneous autoregulatory response in the coronary trees. This model drastically differs from previous models and is suited to the study of long-term pathophysiological growth and remodelling phenomena in coronary vessels.

Abstract Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.

This investigation was designed to test the hypothesis that heart failure (HF) attenuates coronary vasodilation and autoregulation and that deficits in contractile function are proportionally related to reductions in the volume of myocardial perfusion and oxygen delivered per beat. Utilizing a pacing-induced model of HF in Ossabaw swine, we determined that chronic pacing at 180 beats/min for 4 weeks significantly reduced baseline coronary flow by 45

Obesity is associated with cardiac metabolic inflexibility including increased fatty acid uptake/oxidation and reduced glucose oxidation. Inhibition of mineralocorticoid receptor (MR) signaling improves cardiac outcomes in many co-morbid conditions, including obesity, and whether MR signaling contributes to altered cardiac metabolism in obesity is unclear. Indeed, excessive MR signaling has been linked to impaired tissue glucose utilization in non-cardiac tissues. Thus, we investigated the hypothesis that chronic MR blockade with spironolactone (Spiro) will improve cardiac glucose uptake and cardiac metabolism in obese Ossabaw swine. Lean swine, obese swine fed a high-fat, high-fructose diet, and obese swine treated with Spiro (25 mg/d) were used in this study. During hypoxemic coronary vasodilation, using an extracorporeal perfusion system, myocardial glucose delivery was directly and similarly related to coronary blood flow in all groups. In obese swine, however, the slope of the relationship between myocardial glucose uptake and glucose delivery was reduced compared to lean swine. Chronic MR blockade with Spiro prevented this reduction in myocardial glucose uptake. Myocardial metabolomic analysis revealed pronounced metabolic remodeling in obese hearts enriched, in part, for altered ‘phenylalanine, tyrosine, and tryptophan biosynthesis’ and ‘TCA cycle’ versus lean hearts that was largely prevented by Spiro in obese hearts. Cardiac RNA sequencing further revealed gene signatures associated with reduced cholesterol biosynthesis and glycation signaling in obese, compared to lean, hearts. Hearts from obese swine with MR blockade were not enriched for these pathways, compared to lean hearts, and exhibited transcriptomic changes associated with reduced cardiomyopathy compared to obese untreated hearts. Together, these data suggest that cardiac metabolic inflexibility in obesity is MR-dependent and that prevention of cardiac metabolic alterations may contribute to cardioprotection resulting from spironolactone treatment. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

The fluorescent ATP analog mant-ATP has become a valuable tool for quantifying occupancy of the myosin super-relaxed (SRX) state, a biochemically inactive state of myosin in striated muscle. Interpretation of mant-ATP fluorescence decay kinetics is confounded by inconsistencies in state definitions and kinetic assumptions. Here, we develop a mass-action kinetic model of myosin cross-bridge cycling and mant-ATP turnover to reconcile these discrepancies and provide a mechanistic framework for interpreting SRX measurements. Our model simulates ATP label-chase experiments and demonstrates that conventional double-exponential fitting methods do not directly quantify SRX occupancy. Instead, we show that slow and fast decay phases of mant-ATP fluorescence arise from label redistribution among kinetically distinct states, not state populations in equilibrium. The model resolves several apparent paradoxes identified in recent studies by reproducing experimental observations without requiring SRX and DRX kinetic isolation or implausible equilibrium constants. Simulations further quantify the impact of experimental factors—such as ADP accumulation, photobleaching, and initial rigor state occupancy—on fluorescence kinetics and SRX estimates. These results support a revised framework for SRX quantification and suggest that label-chase experiments must be interpreted using mechanistic models to accurately assess myosin state distributions and transition kinetics.

Moderate to extreme preterm birth (< 32 weeks gestation) affects cardiopulmonary structure and function and is associated with increased risk of heart failure through adulthood. The rat hyperoxia (Hx) model (term born; postnatal Hx exposure) captures biventricular changes, including at the cell and organ scale, and pulmonary vascular remodeling seen in preterm humans. However, synthesizing these measures across scales and organ systems is challenging. We hypothesized that in silico modeling of biventricular mitochondrial, myofiber, and organ-scale function plus circulatory function could capture key features of cardiopulmonary abnormalities due to preterm birth. Therefore, we calibrated a multiscale model to subject-specific biventricular pressure-volume data previously obtained from Hx rats alongside normoxic (Nx) controls to investigate the abnormalities in cardiopulmonary function at multiple scales in this animal model of human preterm birth. The calibrated model demonstrates excellent agreement with the data and captures the expected pulmonary vascular changes and right ventricular dilation seen in preterm-born children. Our multiscale modeling approach captures cardiopulmonary abnormalities across spatial scales and provides an innovative approach to explore the consequences of preterm birth beyond preclinical experimental data alone. This is a foundational step in understanding the impact of preterm birth on cardiopulmonary disease in childhood as well as adulthood.

In order to investigate the mechanisms governing energy and redox balance in skeletal muscle, we developed a computational model describing the coupled biochemical reaction network of glycolysis and mitochondrial oxidative phosphorylation (OxPhos) in fast-twitch oxidative glycolytic (FOG) muscle fibers. The model was identified against dynamic in vivo recordings of Phosphocreatine (PCr), inorganic Phosphate (Pi), and pH in rodent hindlimb muscle and verified against independent data from in vivo experiments and muscle biopsies. Step response testing revealed that mass action kinetics in combination with feedback control were sufficient to accomplish myoplasmic ATP homeostasis over a 100-fold range of ATP turnover rates. This vital emergent property of the metabolic model was associated with dynamic behaviour of intermediary metabolite concentrations similar to a second-order underdamped system that remains to be verified. The simulations additionally predicted that the lactate dehydrogenase (LDH) reaction makes substantial contributions to redox balance across the physiological range of ATP demands in this myofiber phenotype, while its role in slowing cellular acidification is minimal. Yet, LDH knock-out simulations revealed that oxidative recycling of myoplasmic NADH in and by itself sufficed to maintain redox balance over ATP turnover rates in the range of mitochondrial ATP synthesis. We conclude that aerobic lactate production in working muscles is a byproduct of the metabolic flexibility of FOG myofibers afforded by expression of high levels of LDH and OxPhos enzymes to support continual myoplasmic redox balance and ATP synthesis under conditions of high-intensity mechanical work. In the future, the presented simulation framework may be used to further enhance the understanding of how experimental observations in muscle emerge from the integrative behaviour of the metabolic network for carbohydrate metabolism in FOG myofibers.

Background:Heart failure with preserved ejection fraction (HFpEF) accounts for over half of heart failure cases, yet effective treatments remain limited due to its clinical heterogeneity. This study aimed to identify distinct HFpEF phenotypes using machine-learning based algorithm and to compare treatment responses across different phenogroups. Methods:Our training cohort included 2147 hospitalized patients with HF with left ventricular ejection fraction (LVEF) ≥50% at Peking University Third Hospital (2014-2023). A two-stage DeepCluster model with a fully connected neural network was used to identify HFpEF phenogroups based on 107 demographic and clinical variables from electronic medical record (EMR). Cox proportional hazard models were used to assess patients' prognosis and treatment responses. The phenotyping model was validated internally using leave-one-out cross-validation method and externally with data from the TOPCAT clinical trial (n = 1696) and a well-characterized HFpEF patient cohort from the University of Michigan Health System (UMHS, n = 128). Findings:Three distinct HFpEF phenogroups were identified. Phenogroup 1 (n = 815) had the highest burden of metabolic comorbidities, along with left ventricular hypertrophy, and both systolic and diastolic dysfunction. Phenogroup 2 (n = 608) comprised predominantly females with atrial fibrillation and structural abnormalities in the atria and right ventricle, with mainly diastolic dysfunction. Phenogroup 3 (n = 724) included younger men with unhealthy lifestyles, higher burdens of hyperlipidemia, liver dysfunction, and relatively normal cardiac morphology and function. Overall, phenogroup 1 had the highest risk of all-cause mortality. We didn't observe significant survival benefits from major HF therapies overall. However, in phenogroup 1, post-diagnostic use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) was associated with a 55% reduced risk of HF rehospitalization (HR = 0.45, 95% CI 0.20-0.99), while angiotensin receptor-neprilysin inhibitors (ARNIs) were associated with a 66% lower risk of all-cause mortality (HR = 0.34, 95% CI 0.14-0.79). The effect of ARNI on all-cause mortality differed significantly across the three phenogroups (p for interaction = 0.048). In phenogroup 2, calcium channel blockers were associated with a lower risk of all-cause mortality (HR = 0.62, 95% CI 0.38-0.99) and HF rehospitalization (HR = 0.58, 95% CI 0.38-0.88). Furthermore, our DeepCluster model was internally validated with 96.0% consistency. In external validation in the TOPCAT and UMHS cohort, we observed consistent clinical and pathophysiologic characteristics across the three identified phenogroups. Interpretation:Machine learning-based algorithm identifies HFpEF phenogroups with distinct clinical features and treatment responses. This study suggests that SGLT2i and ARNI have a role in improving the outcomes in patients with metabolic comorbidities and both systolic and diastolic impairment, while calcium channel blockers were most likely to benefit HFpEF patients with atrial fibrillation. Future prospective studies are required to further validate these findings. Funding:This study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0539000), National Natural Science Foundation of China (82300291), Beijing Nova Program (20230484275), University of Michigan Medical School (UMMS) and Peking University Health Science Center (PKUHSC) Joint Institute for Translational and Clinical Research (BMU2023JI004), Clinical Cohort Construction Program of Peking University Third Hospital (BYSYDL2023009), CAMS Innovation Fund for Medical Sciences (2021-I2M-5-003), Peking University Shi-Ji Jin-Yuan Medical Foundation (48014Y0243), and Beijing Excellent Clinical Research Program (BRWEP2024W014090203).

This study tested the hypothesis that K+ serves as an in vivo signal coupling coronary blood flow with the oxidative requirements of the myocardium. Experiments were performed in swine in which coronary parameters and arterial and coronary venous [K+] were measured under baseline conditions, during exogenous administration of K+ (1–5 mM; n = 4), during increases in myocardial oxygen consumption (MVO2) to dobutamine (n = 7) and exercise (n = 6), alterations in coronary perfusion pressure (CPP; n = 8), and systemic hypoxemia (PaO2 to 30 mmHg; n = 7). Exogenous intracoronary K+ increased blood flow ( 20

Heart failure (HF) is a highly heterogeneous condition, and current methods struggle to synthesize extensive clinical data for personalized care. Using data from 343 HF patients, we developed mechanistic computational models of the cardiovascular system to create digital twins. These twins, consisting of optimized measurable and unmeasurable parameters alongside simulations of cardiovascular function, provided comprehensive representations of individual disease states. Unsupervised machine learning applied to digital twin-derived features identified interpretable phenogroups and mechanistic drivers of cardiovascular death risk. Incorporating these features into prognostic AI models improved performance, transferability, and interpretability compared to models using only clinical variables. This framework demonstrates potential to enhance prognosis and guide therapy, paving the way for more precise, individualized HF management.

Introduction/Background: Despite numerous medical advances, 30% of patients with Fontan circulation require heart transplantation by 21. Current risk stratification relies on clinical metrics that lack strong correlations to patient outcomes, leading to a high waitlist mortality. Hypothesis: Integration of physics-based hemodynamic modeling with interpretable machine learning will improve prediction of patient outcomes (listing for heart transplant and/or cardiac death) by quantifying physiologic mechanisms inaccessible through clinical measures alone. Goals/Aims: 1. Develop a lumped-parameter mechanistic model (LPMM) of Fontan circulation 2. Identify patient clusters and predictors of patient outcomes 3. Validate model-informed classifiers against clinical-only approaches Methods/Approach: A LPMM incorporating ventricular sarcomere mechanics, atrial hemodynamics, and vascular viscoelastic effects was optimized to cardiac catheterization and echocardiography data from 51 patients (age 25±8 years). Thirty-eight parameters (ventricular inotropy, atrial stiffness, pulmonary elastance, etc.) were calibrated via surrogate optimization, minimizing least-squares error versus 11 clinical targets. Partial least-squares regression with leave-one-out cross-validation compared clinical-only versus model-informed classifiers of outcomes. Results/Data: The LPMM achieved 2% mean error across hemodynamic targets, with CVP and pulmonary pressures matching clinical measurements within 1%. Model-derived minimum atrial pressure (4.46±1.78 vs 2.68±0.87 mmHg, p<0.05) outperformed all clinical variables in discriminating patient outcomes. Integration of LPMM parameters increased AUROC from 0.67 to 0.78 for patient outcome prediction. Conclusions: Physics-based modeling identifies ventricular passive stiffness, atrial end-diastolic volume, and minimum atrial pressure, along with patient age (a clinical value) as critical determinants of Fontan failure. Combined mechanistic-machine learning approaches enable phenotype-specific risk stratification, supporting earlier referral for advanced therapies. This methodology establishes a framework for precision management of complex congenital circulations.

Background: Drug-induced cardiotoxicity is a major cause of clinical trial failures and post-market drug withdrawals. Current screening methods rely primarily on cell assays and transcriptomic profiling, but metabolic perturbations may provide additional predictive signals for cardiotoxic liability in drugs not yet tested in humans. Hypothesis: We hypothesize that predicted metabolic flux changes derived from gene expression data would outperform transcriptomic features for predicting drug cardiotoxicity in a machine learning framework. Methods: We developed a novel computational pipeline integrating a modified iCardio genome-scale metabolic model with transcriptomic data from 5 genetically distinct hiPSC-derived cardiomyocyte cell lines treated with a variety of 31 antineoplastic and immunomodulating drugs (accessed from DToxS Center). Our mathematical framework converts gene expression changes to enzyme activity, then to relative metabolic reaction flux change (4122 reactions) using a novel constrained quadratic approach. Ensemble classifiers were trained to predict cardiotoxicity using FDA Adverse Event Reporting System Reporting Odds Ratio (ROR) as a reference, with drugs above median ROR classified as cardiotoxic. Predictive models were generated using 5-fold cross validation for hyperparameter optimization with 25% hold out for quality metric calculation (reported as mean ± SEM, P values calculated from t-test) over 100 independent iterations. Results: The metabolic flux-based classifiers demonstrated fair predictive performance of drug cardiotoxicity with an AUROC of 0.70±0.02. The flux approach produced equivalent accuracy (+0.02, P = 0.56), and significantly higher F1 (+0.10, P = 0.01), AUROC (+0.08, P = 0.01), and AUPRC (+0.07, P = 0.03) than the gene expression approach. Further analysis revealed that perturbations to fatty acid metabolism were most predictive of cardiotoxic liability, with 42 out of top 100 predictive reactions belonging to fatty acid related subsystems ( P < 1e-5 by binomial test). Conclusions: Metabolic flux prediction from transcriptomic data provides enhanced discrimination of drug cardiotoxicity compared to gene expression analysis alone. This approach enables more informative pre-clinical screening of drug candidates before human exposure, potentially reducing late-stage clinical failures and improving drug safety assessment protocols.

In addition to activation of muscle contraction by Ca2+, previous studies suggest that Ca2+ also affects muscle passive mechanical properties. The goal of this study was to determine if Ca2+ regulates the stiffness of cardiac muscle, independent of active contraction. The mechanical response to stretch for mouse demembranated cardiac trabeculae was probed at different Ca2+ levels after eliminating active contraction using a combination of two myosin ATPase inhibitors: para-nitroblebbistatin (PNB; 50 μM) plus mavacamten (Mava; 50 μM). Myocardial force level was assessed during large stretches (≈20% initial muscle length) with a range of stretch velocities. For relaxed muscle, in response to stretch, muscle force rose to a peak and then decayed toward a lower steady-state level. Peak force was higher with faster stretch velocity, consistent with the presence of a viscoelastic element. However, the steady-state force was independent of stretch velocity, consistent with the presence of an elastic component. In the presence of the inhibitors PNB plus Mava, when the Ca2+ level was increased, active contraction was completely prevented. However, the viscoelastic force response to stretch was markedly increased by high Ca2+ and was >sixfold higher than at the low Ca2+ level. The relationship of viscoelastic force to Ca2+ level had a similar form to the relationship of active force to Ca2+ (measured in the absence of inhibitors), suggesting that a common regulatory mechanism is involved. As expected, Ca2+-activated contraction was inhibited by lowering the temperature from 21°C to 10°C. In contrast, the Ca2+-activated viscoelastic property was not inhibited at lower temperatures, further suggesting that active contraction and the viscoelastic property involve distinct mechanisms. This study demonstrates that in addition to triggering activation of contraction, Ca2+ also increases the apparent viscoelastic property of cardiac muscle.

This study investigates the passive viscoelastic properties of cardiac muscle by introducing a theoretical model that explains the observed power-law kinetics of murine cardiac trabeculae passive stress decay. The model accounts for two parallel processes contributing to passive mechanics: an elastic component and a viscoelastic component designed to simulate stress/strain-mediated unfolding of serial domains in the titin molecule. Under stress, serial globular domains within the elastic region of the titin molecule reversibly unfold. This unfolding phenomenon contributes to both hysteresis (a lag in stress between loading and unloading) and preconditioning effects in simulated mechanics. Experimental evidence indicates that stress relaxation in cardiac muscle follows a power law and that the muscle's non-linear stress-strain relationship and hysteresis behaviour are calcium-dependent. To analyse these phenomena, we simulate the apparent viscous element as a mesoscopic-scale ensemble of chains, each composed of serial globular domains that unfold in a stress-dependent manner. The observed increase in passive tension with increased Ca2+ concentration is attributed to Ca2+-mediated: (1) PEVK attachment to actin; (2) stiffening of the proximal element; and (3) stabilization of folded conformations of serial domains in the titin chain. Although the model was developed to represent the behaviour of titin, it equivalently represents any contributing process involving a linked series of domains that undergo stress-mediated unfolding. By providing a unified basis for the observed viscoelastic and preconditioning effects, calcium dependency, and power-law stress relaxation phenomena, this study offers a novel theoretical basis for understanding and simulating the role of titin in striated muscle mechanics. KEY POINTS: Passive stress relaxation of cardiac muscle follows a power-law decay, a phenomenon that is explained using a theoretical model of dynamic unfolding of globular domains along polymer chain. The theoretical model simulates the behaviour of titin, a giant sarcomere protein linking myosin thick filaments to the Z disk and providing passive restoring force during muscle stretch. The theoretical model is able to account the observed effects of Ca2+ on the effective viscoelastic passive mechanics of cardiac muscle. This model provides a theoretical basis for understanding passive viscocelastic properties and titin's role in striated muscle mechanics.

The metabolic hallmarks of heart failure (HF) include diminished ATP hydrolysis potential and alterations in myocardial energy substrate metabolism, such as a switch in substrate utilization away from fatty acid (FA) to carbohydrate oxidation and reduced metabolic flexibility. However, the mechanisms underlying these phenomena and their potential contributions to impaired exercise tolerance are poorly understood. We developed a comprehensive quantitative systems pharmacology (QSP) model of mitochondrial metabolism to interrogate specific pathways hypothesized to contribute to reductions in reserve cardiac power output in heart failure. The aim of this work was to understand how changes in mitochondrial function and cardiac energetics associated with heart failure may affect exercise capacity. To accomplish this task, we coupled published in silico models of oxidative phosphorylation and the tricarboxylic acid cycle with a model of β-oxidation and extended the model to incorporate an updated representation of the enzyme pyruvate dehydrogenase (PDH) to account for the role of PDH in substrate selection. We tested several hypotheses to determine how metabolic dysfunction, such as a decrease in PDH activity or altered mitochondrial volume, could lead to marked changes in energetic biomarkers, such as myocardial phosphocreatine-ATP ratio (PCr/ATP). The model predicts expected changes in fuel selection and also demonstrates PDH activity is responsible for substrate-dependent switch driven by feedback from NAD, NADH, ATP, ADP, CoASH, Acetyl-CoA and pyruvate in healthy and simulated HF conditions. Through simulations, we also found elevated malonyl-coA may contribute to lower PCr/ATP ratio during exercise conditions as observed in some HF patients.

While exercise-mediated vasoregulation in the myocardium is understood to be governed by autonomic, myogenic, and metabolic-mediated mechanisms, we do not yet understand the spatial heterogeneity of vasodilation or its effects on microvascular flow patterns and oxygen delivery. This study uses a simulation and modeling approach to explore the mechanisms underlying the recruitment of myocardial perfusion and oxygen delivery in exercise. The simulation approach integrates model components representing: whole-body cardiovascular hemodynamics, cardiac mechanics and myocardial work; myocardial perfusion; and myocardial oxygen transport. Integrating these systems together, model simulations reveal: (1.) To match expected flow and transmural flow ratios at increasing levels of exercise, a greater degree of vasodilation must occur in the subendocardium compared to the subepicardium. (2.) Oxygen extraction and venous oxygenation are predicted to substantially decrease with increasing exercise level preferentially in the subendocardium, suggesting that an oxygen-dependent error signal driving metabolic mediated recruitment of flow would be operative only in the subendocardium. (3.) Under baseline physiological conditions approximately 4% of the oxygen delivered to the subendocardium may be supplied via retrograde flow from coronary veins.

Background: Parkin-mediated mitophagy eliminates dysfunctional mitochondria to reduce inflammatory responses caused by mtDAMPs. Given that reduced mitophagy is a hallmark of aging and AAA is an aging-associated disease, we sought to investigate the role of Parkin-mediated mitophagy in the development of AAAs. Methods: Groups of young (8-10 wks, n= 6/group) and aged (18-20 months, n=6/group) male Mito-QC mice either without intervention or underwent AAV.mPCSK9 D377Y infection and western diet feeding followed by Angiotensin II (AngII) or saline pump infusion for 28 days. Separately, young male Myh11-creER T2 Parkin fl/fl mice underwent tamoxifen (Parkin SMCKO) or vehicle injections (n=35-39/group), AAV.mPCSK9 D377Y infection, western diet feeding and AngII infusion. In vitro , MOVAs were treated with AngII and the USP30 inhibitor, a mitophagy enhancer, or vehicle followed by assessment of Parkin and mtROS. Results: VSMCs in aged Mito-QC mice, identified by a CD45 - CD90 - α-actin + profile, exhibit diminished mitophagy activity (p=0.017) and decreased Parkin expression (p=0.029). In young AAAs, VSMCs characterized as CD45 - CD90 - α-actin high and α-actin low also demonstrated decreased mitophagy (p=0.0017, p=0.0020), decreased Parkin(p=0.029), and a decreased trend in SDHB in Complex II (p=0.103) and NDUFB8 in Complex I (p=0.040) of the electron transport chain. Parkin SMCKO mice demonstrated increased AAA rupture (p=0.038), greater aneurysm size (p=0.044), reduced mtDNA copy numbers (p=0.026) and elevated mtROS compared to their controls. Parkin SMCKO mice demonstrated trends of diminished expression of respiratory complexes (p=0.070) and an exacerbated decrease in the maximal OCR involving both coupled and uncoupled activities of Complex I plus II (p=0.045, 0.028). In vitro experiments demonstrated treatment with the USP30 inhibitor reversed the reduction in mitophagy activity and mtROS increasement induced by AngII in MOVAS. Conclusions: In conclusion, mitophagy activity in aortic VSMCs and Parkin decreased with aging and separately, in AAAs and Parkin SMCKO mice, associated with exacerbation of AAA progression. These studies suggest stabilization of Parkin-dependent mitophagy could represent a novel AAA therapeutic target.

The coronary circulation has the inherent ability to maintain myocardial perfusion constant over a wide range of perfusion pressures. The phenomenon of pressure-flow autoregulation is crucial in response to flow-limiting atherosclerotic lesions which diminish coronary driving pressure and increase risk of myocardial ischemia and infarction. Despite well over half a century of devoted research, understanding of the mechanisms responsible for autoregulation remains one of the most fundamental and contested questions in the field today. The purpose of this review is to highlight current knowledge regarding the complex interrelationship between the pathways and mechanisms proposed to dictate the degree of coronary pressure-flow autoregulation. Our group recently likened the intertwined nature of the essential determinants of coronary flow control to the symbolically unsolvable “Gordian knot”. To further efforts to unravel the autoregulatory “knot”, we consider recent challenges to the local metabolic and myogenic hypotheses and the complicated dynamic structural and functional heterogeneity unique to the heart and coronary circulation. Additional consideration is given to interrogation of putative mediators, role of K+ and Ca2+ channels, and recent insights from computational modeling studies. Improved understanding of how specific vasoactive mediators, pathways, and underlying disease states influence coronary pressure-flow relations stands to significantly reduce morbidity and mortality for what remains the leading cause of death worldwide.

Abdominal aortic aneurysms (AAAs) are a degenerative aortic disease and associated with hallmarks of aging, such as mitophagy. Despite this, the exact associations among mitophagy, aging, and AAA progression remain unknown. In our study, gene expression analysis of human AAA tissue revealed downregulation of mitophagy pathways, mitochondrial structure, and function-related proteins. Human proteomic analyses identified decreased levels of mitophagy mediators PINK1 and Parkin. Aged mice and, separately, a murine AAA model showed reduced mitophagy in aortic vascular smooth muscle cells (VSMCs) and PINK1 and Parkin expression. Parkin knockdown in VSMCs aggravated AAA dilation in murine models, with elevated mitochondrial ROS and impaired mitochondrial function. Importantly, inhibiting USP30, an antagonist of the PINK1/Parkin pathway, increased mitophagy in VSMCs, improved mitochondrial function, and reduced AAA incidence and growth. Our study elucidates a critical mechanism that proposes AAAs as an age-associated disease with altered mitophagy, introducing new potential therapeutic approaches.