scholarly journals Convergent, RIC-8-Dependent Gα Signaling Pathways in the Caenorhabditis elegans Synaptic Signaling Network

Genetics ◽  
2004 ◽  
Vol 169 (2) ◽  
pp. 651-670 ◽  
Author(s):  
Nicole K. Reynolds ◽  
Michael A. Schade ◽  
Kenneth G. Miller
Genetics ◽  
2005 ◽  
Vol 172 (2) ◽  
pp. 943-961 ◽  
Author(s):  
Nicole K. Charlie ◽  
Michael A. Schade ◽  
Angela M. Thomure ◽  
Kenneth G. Miller

Author(s):  
Piera Tocci ◽  
Giovanni Blandino ◽  
Anna Bagnato

AbstractThe rational making the G protein-coupled receptors (GPCR) the centerpiece of targeted therapies is fueled by the awareness that GPCR-initiated signaling acts as pivotal driver of the early stages of progression in a broad landscape of human malignancies. The endothelin-1 (ET-1) receptors (ET-1R), known as ETA receptor (ETAR) and ETB receptor (ETBR) that belong to the GPCR superfamily, affect both cancer initiation and progression in a variety of cancer types. By the cross-talking with multiple signaling pathways mainly through the scaffold protein β-arrestin1 (β-arr1), ET-1R axis cooperates with an array of molecular determinants, including transcription factors and co-factors, strongly affecting tumor cell fate and behavior. In this scenario, recent findings shed light on the interplay between ET-1 and the Hippo pathway. In ETAR highly expressing tumors ET-1 axis induces the de-phosphorylation and nuclear accumulation of the Hippo pathway downstream effectors, the paralogous transcriptional cofactors Yes-associated protein (YAP) and Transcriptional coactivator with PDZ-binding motif (TAZ). Recent evidence have discovered that ET-1R/β-arr1 axis instigates a transcriptional interplay involving YAP and mutant p53 proteins, which share a common gene signature and cooperate in a oncogenic signaling network. Mechanistically, YAP and mutp53 are enrolled in nuclear complexes that turn on a highly selective YAP/mutp53-dependent transcriptional response. Notably, ET-1R blockade by the FDA approved dual ET-1 receptor antagonist macitentan interferes with ET-1R/YAP/mutp53 signaling interplay, through the simultaneous suppression of YAP and mutp53 functions, hampering metastasis and therapy resistance. Based on these evidences, we aim to review the recent findings linking the GPCR signaling, as for ET-1R, to YAP/TAZ signaling, underlining the clinical relevance of the blockade of such signaling network in the tumor and microenvironmental contexts. In particular, we debate the clinical implications regarding the use of dual ET-1R antagonists to blunt gain of function activity of mutant p53 proteins and thereby considering them as a potential therapeutic option for mutant p53 cancers. The identification of ET-1R/β-arr1-intertwined and bi-directional signaling pathways as targetable vulnerabilities, may open new therapeutic approaches able to disable the ET-1R-orchestrated YAP/mutp53 signaling network in both tumor and stromal cells and concurrently sensitizes to high-efficacy combined therapeutics.


Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.


2007 ◽  
Vol 102 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Shunchang Wang ◽  
Minli Tang ◽  
Bei Pei ◽  
Xiang Xiao ◽  
Jun Wang ◽  
...  

2017 ◽  
Vol 13 (5) ◽  
pp. 830-840 ◽  
Author(s):  
Rahul Rao Padala ◽  
Rishabh Karnawat ◽  
Satish Bharathwaj Viswanathan ◽  
Abhishek Vijay Thakkar ◽  
Asim Bikas Das

Perturbations in molecular signaling pathways result in a constitutively activated state, leading to malignant transformation of cells.


2021 ◽  
Author(s):  
Heming Zhang ◽  
Yixin Chen ◽  
Philip R Payne ◽  
Fuhai Li

Complex signaling pathways/networks are believed to be responsible for drug resistance in cancer therapy. Drug combinations inhibiting multiple signaling targets within cancer-related signaling networks have the potential to reduce drug resistance. Deep learning models have been reported to predict drug combinations. However, these models are hard to be interpreted in terms of mechanism of synergy (MoS), and thus cannot well support the human-AI based clinical decision making. Herein, we proposed a novel computational model, DeepSignalingFlow, which seeks to address the preceding two challenges. Specifically, a graph convolutional network (GCN) was developed based on a core cancer signaling network consisting of 1584 genes, with gene expression and copy number data derived from 46 core cancer signaling pathways. The novel up-stream signaling-flow (from up-stream signaling to drug targets), and the down-stream signaling-flow (from drug targets to down-stream signaling), were designed using trainable weights of network edges. The numerical features (accumulated information due to the signaling-flows of the signaling network) of drug nodes that link to drug targets were then used to predict the synergy scores of such drug combinations. The model was evaluated using the NCI ALMANAC drug combination screening data. The evaluation results showed that the proposed DeepSignalingFlow model can not only predict drug combination synergy score, but also interpret potentially interpretable MoS of drug combinations.


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