Rationale G protein-coupled receptors (GPCRs) respond to diversified extracellular stimuli to modulate cellular function. Despite extensive studies investigating the regulation of single GPCR signaling cascades, the effects of concomitant GPCR activation on downstream signaling and cellular function remain unclear. Objective We aim to characterize the cellular mechanism by which GPCR cross-talk regulates MAPK activation. Methods and Results Adrenergic receptors on cardiac fibroblasts were manipulated to examine the role of arrestin in the spatiotemporal regulation of ERK1/2 MAPK signaling. We show a general mechanism in which arrestin activation by one GPCR is capable of regulating signaling originating from another GPCR. Activation of Gq-coupled receptor signaling leads to prolonged ERK1/2 MAPK phosphorylation, nuclear accumulation and cellular proliferation. Interestingly, co-activation of these receptors with the β adrenergic receptors (ARs) induced transient ERK signaling localized within the cytosol, which attenuated cell proliferation. Further studies revealed that recruitment of arrestin3 to the β2AR orchestrates the sequestration of Gq-coupled receptor-induced ERK to the cytosol through direct binding of ERK to arrestin. Conclusion This is the first evidence showing that arrestin3 acts as a coordinator to integrate signals from multiple GPCRs. Our studies not only provide a novel mechanism explaining the integration of mitogenic signaling elicited by different GPCRs, but also underscore the critical role of signaling cross-talk among GPCRs in vivo.

The counter-regulatory effects of insulin and catecholamines on carbohydrate and lipid metabolism are well studied, whereas the details of insulin regulation of β adrenergic receptor (βAR) signaling pathway in heart remain unknown. Here, we characterize a novel signaling pathway of insulin receptor (IR) to G protein-coupled receptor kinase 2 (GRK2) in the heart. Insulin stimulates the recruitment of GRK2 to β2AR, which induces β2AR phosphorylation at the GRK sites of serine 355/356 and subsequently β2AR internalization. Insulin thereby suppresses βAR-induced cAMP-PKA activities and contractile response in neonatal and adult mouse cardiomyocytes. Deletion of Insulin receptor substrate 2 (IRS2) disrupts the complex of IR and GRK2, which attenuates insulin-mediated β2AR phosphorylation at GRK sites and β2AR internalization, and the counter-regulation effects of insulin on βAR signaling. These data indicates the requirements of IRS2 and GRK2 for insulin to stimulate counter-regulation of βAR via β2AR phosphorylation and internalization in cardiomyocytes.

BackgroundThe Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project.MethodsWe develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs.ResultsA computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R2 of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer.ConclusionsWe developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies.Electronic supplementary materialThe online version of this article (10.1186/s12976-018-0095-8) contains supplementary material, which is available to authorized users.

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Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume, length and width of the heart, while the output was the digoxin concentration in dilution necessary to obtain a desired physiological response. ANNs were trained, validated and tested with the dataset of the in vivo experiment results. To select the optimal network, predictions for all the dataset for each configuration of ANNs were made, a maximum 5% relative error for the digoxin concentration was set and the diagnostic accuracy of the predictions made was evaluated. The double-layer perceptron had a barely higher performance than the single-layer perceptron; therefore, both had a good predictive ability. The double-layer perceptron was able to obtain the most accurate predictions of digoxin concentration required in the hearts of T. stultorum using MLP-ANNs.