Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease.

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.

Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy.

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 TGFß- 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 TGFß, 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.

Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models.

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.

Redistribution of lamina-associated domains reshapes binding of pioneer factor FOXA2 in development of nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in type 2 diabetes mellitus and the elderly, impacting 40% of individuals over 70. Regulation of heterochromatin at the nuclear lamina has been associated with aging and age-dependent metabolic changes. We previously showed that changes at the lamina in aged hepatocytes and laminopathy models lead to redistribution of lamina-associated domains (LADs), opening of repressed chromatin, and up-regulation of genes regulating lipid synthesis and storage, culminating in fatty liver. Here, we test the hypothesis that change in the expression of lamina-associated proteins and nuclear shape leads to redistribution of LADs, followed by altered binding of pioneer factor FOXA2 and by up-regulation of lipid synthesis and storage, culminating in steatosis in younger NAFLD patients (aged 21-51). Changes in nuclear morphology alter LAD partitioning and reduced lamin B1 signal correlate with increased FOXA2 binding before severe steatosis in young mice placed on a western diet. Nuclear shape is also changed in younger NAFLD patients. LADs are redistrubted and lamin B1 signal decreases similarly in mild and severe steatosis. In contrast, FOXA2 binding is similar in normal and NAFLD patients with moderate steatosis and is repositioned only in NAFLD patients with more severe lipid accumulation. Hence, changes at the nuclear lamina reshape FOXA2 binding with progression of the disease. Our results suggest a role for nuclear lamina in etiology of NAFLD, irrespective of aging, with potential for improved stratification of patients and novel treatments aimed at restoring nuclear lamina function.