Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.

Exercise-induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise-induced phenotypes. Using this network, we formulated a logic-based ordinary differential equation model predicting time-dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP-activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross-talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes.

To identify how cardiomyocyte mechanosensitive signaling pathways are regulated by anisotropic stretch, micropatterned mouse neonatal cardiomyocytes were stretched primarily longitudinally or transversely to the myofiber axis. Four hours of static, longitudinal stretch induced differential expression of 557 genes, compared with 30 induced by transverse stretch, measured using RNA-seq. A logic-based ordinary differential equation model of the cardiac myocyte mechanosignaling network, extended to include the transcriptional regulation and expression of 784 genes, correctly predicted measured expression changes due to anisotropic stretch with 69% accuracy. The model also predicted published transcriptional responses to mechanical load in vitro or in vivo with 63-91% accuracy. The observed differences between transverse and longitudinal stretch responses were not explained by differential activation of specific pathways but rather by an approximately twofold greater sensitivity to longitudinal stretch than transverse stretch. In vitro experiments confirmed model predictions that stretch-induced gene expression is more sensitive to angiotensin II and endothelin-1, via RhoA and MAP kinases, than to the three membrane ion channels upstream of calcium signaling in the network. Quantitative cardiomyocyte gene expression differs substantially with the axis of maximum principal stretch relative to the myofilament axis, but this difference is due primarily to differences in stretch sensitivity rather than to selective activation of mechanosignaling pathways.NEW & NOTEWORTHY Anisotropic stretch applied to micropatterned neonatal mouse ventricular myocytes induced markedly greater acute transcriptional responses when the major axis of stretch was parallel to the myofilament axis than when it was transverse. Analysis with a novel quantitative network model of mechanoregulated cardiomyocyte gene expression suggests that this difference is explained by higher cell sensitivity to longitudinal loading than transverse loading than by the activation of differential signaling pathways.

Impact of Mass Media on HIV/AIDS Stigma Reduction: A Systematic Review and Meta-analysis.

HIV-related stigma is a major barrier to HIV testing and care engagement. Despite efforts to use mass media to address HIV-related stigma, their impact on reducing HIV-related stigma remains unclear. Thus, we conducted a systematic review and meta-analysis of peer-reviewed publications quantitatively examining the impact of mass media exposure on HIV-related stigma reduction and published from January 1990 to December 2020. Of 388 articles found in the initial screening from scientific databases, 19 met the inclusion criteria and were included in the systematic review. Sixteen articles reported the quantitative effect of mass media exposure on HIV-related stigma and were included in the meta-analysis. Systematic review results showed considerable heterogeneity in studied populations with a few interventions and longitudinal studies. Results suggested a higher interest in utilizing mass media by health policymakers in developing countries with greater HIV prevalence to reduce HIV-related stigma. Meta-analysis results showed a modest impact of mass media use on HIV-related stigma reduction. Despite heterogeneity in the impact of mass media on HIV-related stigma, Egger's regression test and funnel graph indicated no evidence for publication bias. Results demonstrated an increase in the impact of mass media on reducing HIV-related stigma over time and no correlation between the HIV prevalence in countries and the impact of mass media. In summary, mass media exposure has a modest and context-specific impact on HIV-related stigma reduction. More large-scale mass media interventions and studies addressing the impact of mass media on different forms of stigma are required to inform policies.

Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy.

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g., murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy.

Brahma safeguards canalization of cardiac mesoderm differentiation.

Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm-/- cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm-/- cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle-node bifurcations2. In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.

Context-specific network modeling identifies new crosstalk in ß-adrenergic cardiac hypertrophy.

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 ß-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-Gßγ and CaN-Gßγ 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 ß-adrenergic cardiac hypertrophy.

Future Balloon-Expandable Stents: High or Low-Strength Materials?

PURPOSE: Recent progress in material science allows researchers to use novel materials with enhanced capabilities like optimum biodegradability, higher strength, and flexibility in the design of coronary stents. Considering the wide range of mechanical properties of existing and newfangled materials, finding the influence of variations in mechanical properties of stent materials is critical for developing a practical design. METHODS: The sensitivity of stent functional characteristics to variations in its material plastic properties is obtained through FEM modeling. Balloon-expandable coronary stent designs: Absorb BVS, and Xience are examined for artificial and commercial polymeric, and metallic materials, respectively. Standard tests including (1) the crimping process followed by stent implantation in an atherosclerotic artery and (2) the three-point bending test, have been simulated according to ASTM standards. RESULTS: In Absorb BVS, materials with higher yield stress than PLLA have similar residual deflection and maximum bending force to PLLA, which is not the case for Xience stent and Co-Cr. Moreover, elevated yield stress significantly reduces stent flexibility only in Xience stent. For both stents, with different degree of influence, an increase in yield or ultimate stress improves stent radial strength and stiffness and reduces arterial stress and plastic strain of stent, which consequently enhances the stent mechanical performance. Contrarily, yield or ultimate stress elevation increases stent recoil which adversely affects stent performance. CONCLUSION: Using high-strength materials has a double-edged sword effect on the stent performance and existing uncertainty in the precise estimate of stent mechanical properties adversely affects the reliability of numerical models' predictions.

Ca2+-dependent calcineurin/NFAT signaling in ß-adrenergic-induced cardiac hypertrophy.

Ca2+ is an important mediator in the ß-adrenergic-induced cardiac hypertrophy. The ß-adrenergic stimulation alters the Ca2+ transient characteristics including its oscillation frequency, diastolic and systolic levels which lead to the CaN activation and subsequent NFAT-dependent hypertrophic genes transcription. Moreover, ß-adrenergic-induced alterations in PKA and GSK3ß kinase activities in both the cytosol and the nucleus regulate NFAT nuclear translocation and contribute in its hypertrophic response. Due to the complex nature of CaN/NFAT signaling in cardiac cells, we use a computational approach to investigate the ß-adrenergic-induced CaN/NFAT activation in the cardiac myocytes. The presented model predicts well the main physiological characteristics of CaN/NFAT signaling in accordance with the experimental observations. The presented model establishes the previous experimental and mathematical results on the principal role of Ca2+ oscillation frequency in the CaN/NFAT signaling and shows that increase in Ca2+ oscillation frequency enhances CaN activity and its sensitivity to low ISO concentrations. The model illustrates that in addition to the known ISO effect on Ca2+ transient amplitude, ISO-induced alterations in Ca2+ oscillation frequency, PKA and GSK3ß kinase activities also greatly affect the ß-adrenergic-induced NFAT activity. We also found that PKA has both pro-hypertrophic and anti-hypertrophic effects on NFAT activation and is the main kinase in ISO-induced NFAT activation.

Investigating ß-adrenergic-induced cardiac hypertrophy through computational approach: classical and non-classical pathways.

The chronic stimulation of ß-adrenergic receptors plays a crucial role in cardiac hypertrophy and its progression to heart failure. In ß-adrenergic signaling, in addition to the well-established classical pathway, Gs/AC/cAMP/PKA, activation of non-classical pathways such as Gi/PI3K/Akt/GSK3ß and Gi/Ras/Raf/MEK/ERK contribute in cardiac hypertrophy. The signaling network of ß-adrenergic-induced hypertrophy is very complex and not fully understood. So, we use a computational approach to investigate the dynamic response and contribution of ß-adrenergic mediators in cardiac hypertrophy. The proposed computational model provides insights into the effects of ß-adrenergic classical and non-classical pathways on the activity of hypertrophic transcription factors CREB and GATA4. The results illustrate that the model captures the dynamics of the main signaling mediators and reproduces the experimental observations well. The results also show that despite the low portion of ß2 receptors out of total cardiac ß-adrenergic receptors, their contribution in the activation of hypertrophic mediators and regulation of ß-adrenergic-induced hypertrophy is noticeable and variations in ß1/ß2 receptors ratio greatly affect the ISO-induced hypertrophic response. The model results illustrate that GSK3ß deactivation after ß-adrenergic receptor stimulation has a major influence on CREB and GATA4 activation and consequent cardiac hypertrophy. Also, it is found through sensitivity analysis that PKB (Akt) activation has both pro-hypertrophic and anti-hypertrophic effects in ß-adrenergic signaling.