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. Physiological and pathological cardiac structural remodelling are commonly associated with chronic alterations in haemodynamics, chamber shape and myocardial mechanics that can initially compensate for, but ultimately exacerbate, the physical triggers of cardiac remodelling 1,2. Cell-mediated mechanotrans-duction responses are important regulators of adaptive and maladaptive myocyte and matrix remodelling 3. Mechanical loading also induces the release of factors such as angiotensin II, endothelin 1 and transforming growth

Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Direct reprogramming of fibroblasts into cardiomyocytes is a promising approach for cardiac regeneration but still faces challenges in efficiently generating mature cardiomyocytes. Systematic optimization of reprogramming protocols requires scalable, objective methods to assess cellular phenotype beyond what is captured by transcriptional signatures alone. To address this question, we automatically segmented reprogrammed cardiomyocytes from immunofluorescence images and analyzed cell morphology. We also introduce a method to quantify sarcomere structure using Haralick texture features, called SarcOmere Texture Analysis (SOTA). We show that induced cardiac-like myocytes (iCLMs) are highly variable in expression of cardiomyocyte markers, producing subtypes that are not typically seen in vivo. Compared to neonatal mouse cardiomyocytes, iCLMs have more variable cell size and shape, have less organized sarcomere structure, and demonstrate reduced sarcomere length. Taken together, these results indicate that traditional methods of assessing cardiomyocyte reprogramming by quantifying induction of cardiomyocyte marker proteins may not be sufficient to predict functionality. The automated image analysis methods described in this study may enable more systematic approaches for improving reprogramming techniques above and beyond existing algorithms that rely heavily on transcriptome profiling.

Structuring classroom activities around games has been shown to increase student motivation and enjoyment. Less work has been done evaluating whether gamification benefits students in the particular context of a student response system (SRS). This evidence-based practice paper compares two SRSs, SurveyMonkey and Kahoot, to quantify the added value of gamification in enhancing student engagement during in-class problem sessions in a numerical methods course for biomedical engineering undergraduates. Students reported that both the traditional and gamified systems encouraged collaboration and made them more likely to complete the problems and to achieve the correct answer than if there had been no SRS. The gamified response system, however, resulted in significantly higher student motivation, enjoyment, and encouragement to collaborate than the non-gamified version. Students also indicated that gamification helped increase learning during the problem session, although it did not make them significantly more likely to complete the problems and achieve the correct answers. Our results suggest that by enhancing aesthetics and letting students compete as teams, gamification can boost the appeal and efficacy of SRSs.

No abstract