Merilahti, J. A. M. | bioRxiv Science

bioRxiv2022

STAT5b is a key effector of NRG-1/ERBB4-mediated cardiomyocyte growth

The growth factor neuregulin-1 (NRG-1) regulates hypertrophic and hyperplastic myocardial growth and is currently under clinical investigation as a treatment for heart failure. We have previously demonstrated that an isoform of the NRG-1 receptor ERBB4 (ERBB4 JM-b) expressed in cardiomyocytes selectively regulates the activation of STAT5b. To explore the role of STAT5b in NRG-1/EBBB4 mediated cardiomyocyte growth, several in vitro and in vivo models were utilized. The downregulation of NRG-1/ERBB4 signaling consistently reduced STAT5b activation and transcription of STAT5b target genes Igf1, Myc and Cdkn1a in murine in vitro and in vivo models of myocardial growth. Stat5b knock-down in primary cardiomyocytes ablated NRG-1-induced cardiomyocyte hypertrophy. Stat5b was activated during NRG-1-induced hyperplastic myocardial growth and chemical inhibition of the Nrg-1/Erbb4 pathway led to the loss of myocardial growth and Stat5 activation in zebrafish embryos. Moreover, CRISPR/Cas9-mediated knock-down of stat5b in zebrafish embryos resulted in reduced myocardial growth and heart failure as indicated by reduced ventricular ejection fraction. Dynamin-2 was discovered to control the cell surface localization of ERBB4 and the chemical inhibition of dynamin-2 downregulated NRG-1/ERBB4/STAT5b signaling in models of hypertrophic and hyperplastic myocardial growth. Finally, the activation of the NRG-1/ERBB4/STAT5b signaling pathway was explored in clinical samples representing pathological cardiac hypertrophy. The NRG-1/ERBB4/STAT5b signaling pathway was differentially regulated both at the mRNA and protein levels in the myocardium of patients with pathological cardiac hypertrophy as compared to myocardium of control subjects. These results establish the role for STAT5b, and dynamin-2 in NRG-1/ERBB4-mediated myocardial growth.

cell biology↗

bioRxiv2022

An unbiased pathway analysis (UPA) designed for multi-omics inference of cell signaling pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into regulatory complexes and pathways. DMPA was validated with publicly available omics data and was found accurate in discovering protein-protein interactions, kinase substrate phosphosite relationships, transcription factor target gene relationships, metabolic reactions, epigenetic trait associations and signaling pathways. DMPA was benchmarked against existing module and network discovery and multi-omics integration methods and outperformed previous methods in module and signaling pathway discovery especially when applied to datasets with low sample sizes and zero-inflated data. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome regulatory complexes and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected both the predicted function and the direction of the predicted function in validation experiments.

systems biology↗