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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.

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.

Yes-associated protein (YAP) and its homologue TAZ are transducers of several biochemical and biomechanical signals, integrating multiplexed inputs from the microenvironment into higher level cellular functions such as proliferation, differentiation and migration. Emerging evidence suggests that Ca2+ is a key second messenger that connects microenvironmental input signals and YAP/TAZ regulation. However, studies that directly modulate Ca2+ have reported contradictory YAP/TAZ responses: in some studies, a reduction in Ca2+ influx increases the activity of YAP/TAZ, while in others, an increase in Ca2+ influx activates YAP/TAZ. Importantly, Ca2+ and YAP/TAZ exhibit distinct spatiotemporal dynamics, making it difficult to unravel their connections from a purely experimental approach. In this study, we developed a network model of Ca2+ -mediated YAP/TAZ signalling to investigate how temporal dynamics and crosstalk of signalling pathways interacting with Ca2+ can alter the YAP/TAZ response, as observed in experiments. By including six signalling modules (e.g. GPCR, IP3-Ca2+ , kinases, RhoA, F-actin and Hippo-YAP/TAZ) that interact with Ca2+ , we investigated both transient and steady-state cell response to angiotensin II and thapsigargin stimuli. The model predicts that stimuli, Ca2+ transients and frequency-dependent relationships between Ca2+ and YAP/TAZ are primarily mediated by cPKC, DAG, CaMKII and F-actin. Simulation results illustrate the role of Ca2+ dynamics and CaMKII bistable response in switching the direction of changes in Ca2+ -induced YAP/TAZ activity. A frequency-dependent YAP/TAZ response revealed the competition between upstream regulators of LATS1/2, leading to the YAP/TAZ non-monotonic response to periodic GPCR stimulation. This study provides new insights into underlying mechanisms responsible for the controversial Ca2+ -YAP/TAZ relationship observed in experiments. KEY POINTS: YAP/TAZ integrates biochemical and biomechanical inputs to regulate cellular functions, and Ca2+ acts as a key second messenger linking cellular inputs to YAP/TAZ. Studies have reported contradictory Ca2+ -YAP/TAZ relationships for different cell types and stimuli. A network model of Ca2+ -mediated YAP/TAZ signalling was developed to investigate the underlying mechanisms of divergent Ca2+ -YAP/TAZ relationships. The model predicts context-dependent Ca2+ transient, CaMKII bistable response and frequency-dependent activation of LATS1/2 upstream regulators as mechanisms governing the Ca2+ -YAP/TAZ relationship. This study provides new insights into the underlying mechanisms of the controversial Ca2+ -YAP/TAZ relationship to better understand the dynamics of cellular functions controlled by YAP/TAZ activity.

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.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding interaction mechanisms within cells, tissues, and organisms is crucial for driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating such biological systems. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks. The development of sequencing techniques and computational tools has recently enabled multiscale models. Combining such larger scale network modeling with mechanistic modeling provides us with an opportunity to reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from mechanistic models to multiscale models that integrate multiple layers of cellular networks and discuss how they can be used to shed light on disease states and even wellness-related states. Additionally, we introduce several methods that increase the certainty and accuracy of model predictions. Thus, combining mechanistic models with emerging mathematical and computational techniques can provide us with increasingly powerful tools to understand disease states and inspire drug discoveries.