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Tan, P. M.

Publications and source records attributed to Tan, P. M..

3 recordsLinked to original sources

Systems analysis reveals neuregulin-1 control of cardiomyocyte size and shape mediated by distinct PI3K and p38 pathways

Pathological and physiological stresses induce diverse forms of cardiac hypertrophy, with distinct manifestations in cardiomyocyte size and shape regulated by still poorly understood signaling networks. Here, we combined high-content morphological profiling, phospho-protein arrays, and systems modeling to characterize the diverse forms of hypertrophy induced by angiotensin II, endothelin-1, insulin growth factor-1, and neuregulin-1. Reverse-phase protein array profiling of neonatal rat cardiomyocytes and partial least squares regression modeling revealed that Akt, GSK3, and MAPK signaling are differentially regulated by hypertrophic agonists and are predictive of distinct phenotypic outcomes. Among these agonists, neuregulin-1 uniquely induced cardiomyocyte elongation in both neonatal rat and human iPSC-derived cardiomyocytes, in addition to increasing cell area. Pharmacological perturbations in neonatal rat cardiomyocytes demonstrated that neuregulin1-induced elongation and area expansion both require PI3K activity, whereas p38 selectively mediates cell area. A logic-based network model incorporating dual-specificity phosphatases were sufficient to capture the amplifying PI3K and transient p38 signaling dynamics driving phenotypic changes. Together, these results identify distinct signaling cascades by which neuregulin-1 coordinates cardiomyocyte size and shape, providing mechanistic insight into how hypertrophic remodeling can be differentially regulated. This systems approach provides new insight into the pathways that drive distinct forms of cardiomyocyte hypertrophy, highlighting opportunities to selectively target maladaptive remodeling in heart failure.

systems biology↗

Myocardial Endoglin Regulates Cardiomyocyte Proliferation and Cardiac Regeneration

The mammalian heart loses almost all its regenerative potential in the first week of life due to the cessation of the ability of cardiomyocytes to proliferate. In recent years, a number of regulators of cardiomyocyte proliferation have been identified. Despite this, a clear understanding of the regulatory pathways that control cardiomyocyte proliferation and cardiac regeneration is lacking, and there are likely additional regulators to be discovered. Here, we performed a genome-wide screen on fetal murine cardiomyocytes to identify potential novel regulators of cardiomyocyte proliferation. Endoglin was identified as an inhibitor of cardiomyocyte proliferation in vitro. Endoglin knock-down resulted in enhanced DNA synthesis, cardiomyocyte mitosis and cytokinesis in mouse, rat and human cardiomyocytes. Using gene-targeted mice, we confirmed myocardial Endoglin to be important in cardiomyocyte proliferation and cardiac regeneration using gene-targeted mice. Mechanistically, we show that Smad signaling is required for the endoglin-mediated anti-proliferative effects. Our results identify the TGF-{beta} coreceptor Endoglin as a regulator of cardiac regeneration and cardiomyocyte proliferation. SummaryHigh-content function screening is used to identify a novel inhibitor of cardiomyocyte proliferation which can promote mammalian cardiac regeneration.

genetics↗

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↗