Biology subjects

Eggertsen, T. G.

Publications and source records attributed to Eggertsen, T. G..

3 recordsLinked to original sources

bioRxiv2024

Multi-Scale Computational Model of Microvascular Remodeling in Idiopathic Pulmonary Fibrosis

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.

bioengineering↗

bioRxiv2022

Benchmarking of protein interaction databases for integration with manually reconstructed signaling 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-signaling, and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signaling (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 PKC autophosphorylation and CaMKII 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 signaling.

systems biology↗

bioRxiv2022

Virtual drug screen reveals context-dependent inhibition of cardiomyocyte hypertrophy

Background and PurposePathological cardiomyocyte hypertrophy is a response to cardiac stress that typically leads to heart failure. Despite being a primary contributor to pathological cardiac remodeling, 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 ApproachA logic-based differential equation model of cardiomyocyte signaling 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 TGF{beta}- and NE-induced hypertrophy in neonatal rat cardiomyocytes. Key ResultsModel 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 TGF{beta}, but not NE, exhibiting context dependence. We further validated this prediction by in vitro experimentation. 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 ImplicationsThis study provides a well-validated platform for investigating the efficacy of drugs on cardiomyocyte hypertrophy, and identifies midostaurin for consideration as an antihypertrophic drug. What is already known- Cardiac hypertrophy is a leading predictor of heart failure. - Cardiomyocyte hypertrophy is driven by intracellular signaling pathways that are not targeted by current drugs What this study adds- Computational model integrates 69 unique drugs to predict cardiomyocyte hypertrophy - Drug-induced inhibition of cardiomyocyte hypertrophy is context-dependent - Midostaurin inhibits TGF{beta}-induced cardiomyocyte hypertrophy Clinical significance- Midostaurin is identified as a candidate antihypertrophic drug - Several FDA approved drugs are predicted to inhibit cardiomyocyte hypertrophy either individually or in combination.

pharmacology and toxicology↗