scholarly journals Differential protein-protein interactions of LRRK1 and LRRK2 indicate roles in distinct cellular signaling pathways

2014 ◽  
Vol 131 (2) ◽  
pp. 239-250 ◽  
Author(s):  
Lauran Reyniers ◽  
Maria Grazia Del Giudice ◽  
Laura Civiero ◽  
Elisa Belluzzi ◽  
Evy Lobbestael ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Protein Interactions ◽  
Cellular Signaling ◽  
Protein Protein Interactions ◽  
Differential Protein

scholarly journals Targeted disruption of PKC from AKAP Signaling Complexes

2021 ◽  
Author(s):  
Ameya J. Limaye ◽  
George N. Bendzunas ◽  
Eileen Kennedy
Keyword(s):  
Protein Kinase C ◽  
Protein Kinase ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Targeted Disruption ◽  
Physiological Processes ◽  
Kinase C ◽  
P Protein

Protein Kinase C (PKC) is a member of the AGC subfamily of kinases and regulates a wide array of signaling pathways and physiological processes. Protein-protein interactions involving PKC and its...

scholarly journals Pan-cancer mapping of differential protein-protein interactions

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gizem Gulfidan ◽  
Beste Turanli ◽  
Hande Beklen ◽  
Raghu Sinha ◽  
Kazim Yalcin Arga
Keyword(s):  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Differential Protein ◽  
Pan Cancer

scholarly journals Chimeric GPCRs mimic distinct signaling pathways and modulate microglia responses

2021 ◽  
Author(s):  
Rouven Schulz ◽  
Medina Korkut-Demirbaş ◽  
Gloria Colombo ◽  
Sandra Siegert
Keyword(s):  
Signaling Pathways ◽  
Protein Interactions ◽  
Immune Cell ◽  
Signal Transmission ◽  
The Body ◽  
Cell Type ◽  
Protein Protein Interactions ◽  
Β2 Adrenergic Receptor ◽  
Neuronal Signal ◽  
And Function

G protein-coupled receptors (GPCRs) regulate multiple processes ranging from cell growth and immune responses to neuronal signal transmission. However, ligands for many GPCRs remain unknown, suffer from off-target effects or have poor bioavailability. Additional challenges exist to dissect cell type-specific responses when the same GPCR is expressed on different cells within the body. Here, we overcome these limitations by engineering DREADD-based GPCR chimeras that selectively bind their agonist clozapine-N-oxide (CNO) and mimic a GPCR-of-interest. We show that the chimeric DREADD-β2-adrenergic receptor (β2AR/ADRB2) triggers comparable responses to levalbuterol on second messenger and kinase activity, post-translational modifications, and protein-protein interactions. Moreover, we successfully recapitulate β2AR-mediated filopodia formation in microglia, a β2AR-expressing immune cell that can drive inflammation in the nervous system. To further dissect microglial inflammation, we compared DREADD-β2AR with two additionally designed DREADD-based chimeras mimicking GPR65 and GPR109A/HCAR2, both enriched in microglia. DREADD-β2AR and DREADD-GPR65 modulate the inflammatory response with a similar profile as endogenously expressed β2AR, while DREADD-GPR109A had no impact. Our DREADD-based approach allows investigation of cell type-dependent signaling pathways and function without known endogenous ligands.

scholarly journals Signaling interaction link prediction using deep graph neural networks integrating protein-protein interactions and omics data

2020 ◽  
Author(s):  
Jiarui Feng ◽  
Amanda Zeng ◽  
Yixin Chen ◽  
Philip Payne ◽  
Fuhai Li
Keyword(s):  
Deep Learning ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Computational Models ◽  
Tumor Development ◽  
Learning Model ◽  
Signaling Cascades ◽  
Large Set ◽  
Protein Protein Interactions ◽  
Deep Learning Model

AbstractUncovering signaling links or cascades among proteins that potentially regulate tumor development and drug response is one of the most critical and challenging tasks in cancer molecular biology. Inhibition of the targets on the core signaling cascades can be effective as novel cancer treatment regimens. However, signaling cascades inference remains an open problem, and there is a lack of effective computational models. The widely used gene co-expression network (no-direct signaling cascades) and shortest-path based protein-protein interaction (PPI) network analysis (with too many interactions, and did not consider the sparsity of signaling cascades) were not specifically designed to predict the direct and sparse signaling cascades. To resolve the challenges, we proposed a novel deep learning model, deepSignalingLinkNet, to predict signaling cascades by integrating transcriptomics data and copy number data of a large set of cancer samples with the protein-protein interactions (PPIs) via a novel deep graph neural network model. Different from the existing models, the proposed deep learning model was trained using the curated KEGG signaling pathways to identify the informative omics and PPI topology features in the data-driven manner to predict the potential signaling cascades. The validation results indicated the feasibility of signaling cascade prediction using the proposed deep learning models. Moreover, the trained model can potentially predict the signaling cascades among the new proteins by transferring the learned patterns on the curated signaling pathways. The code was available at: https://github.com/fuhaililab/deepSignalingPathwayPrediction.

scholarly journals MiRNA-Regulated Pathways for Hypertrophic Cardiomyopathy: Network-Based Approach to Insight into Pathogenesis

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 2016
Author(s):  
German Osmak ◽  
Natalia Baulina ◽  
Ivan Kiselev ◽  
Olga Favorova
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Signaling Pathways ◽  
Protein Interactions ◽  
Target Gene ◽  
High Throughput Sequencing ◽  
Point Of View ◽  
Specific Gene ◽  
Protein Protein Interactions ◽  
Insight Into ◽  
Sarcomeric Genes

Hypertrophic cardiomyopathy (HCM) is the most common hereditary heart disease. The wide spread of high-throughput sequencing casts doubt on its monogenic nature, suggesting the presence of mechanisms of HCM development independent from mutations in sarcomeric genes. From this point of view, HCM may arise from the interactions of several HCM-associated genes, and from disturbance of regulation of their expression. We developed a bioinformatic workflow to study the involvement of signaling pathways in HCM development through analyzing data on human heart-specific gene expression, miRNA-target gene interactions, and protein–protein interactions, available in open databases. Genes regulated by a pool of miRNAs contributing to human cardiac hypertrophy, namely hsa-miR-1-3p, hsa-miR-19b-3p, hsa-miR-21-5p, hsa-miR-29a-3p, hsa-miR-93-5p, hsa-miR-133a-3p, hsa-miR-155-5p, hsa-miR-199a-3p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-451a, and hsa-miR-497-5p, were considered. As a result, we pinpointed a module of TGFβ-mediated SMAD signaling pathways, enriched by targets of the selected miRNAs, that may contribute to the cardiac remodeling in HCM. We suggest that the developed network-based approach could be useful in providing a more accurate glimpse on pathological processes in the disease pathogenesis.

Uncovering the diversity of redox proteoforms and their significance in cellular signaling and protein-protein interactions

2018 ◽  
Vol 120 ◽  
pp. S67 ◽  
Author(s):  
Eva Griesser ◽  
Uladzimir Barayeu ◽  
Jörg Flemmig ◽  
Dolores Perez-Sala ◽  
Maria Fedorova
Keyword(s):  
Protein Interactions ◽  
Cellular Signaling ◽  
Protein Protein Interactions

scholarly journals APPL1 is a multifunctional endosomal signaling adaptor protein

2017 ◽  
Vol 45 (3) ◽  
pp. 771-779 ◽  
Author(s):  
Nicole L. Diggins ◽  
Donna J. Webb
Keyword(s):  
Signaling Pathways ◽  
Protein Interactions ◽  
Leucine Zipper ◽  
Adaptor Protein ◽  
Adaptor Proteins ◽  
Ph Domain ◽  
Protein Protein Interactions ◽  
Multiple Protein ◽  
Signaling Receptors ◽  
Ptb Domain

Endosomal adaptor proteins are important regulators of signaling pathways underlying many biological processes. These adaptors can integrate signals from multiple pathways via localization to specific endosomal compartments, as well as through multiple protein–protein interactions. One such adaptor protein that has been implicated in regulating signaling pathways is the adaptor protein containing a pleckstrin homology (PH) domain, phosphotyrosine-binding (PTB) domain, and leucine zipper motif 1 (APPL1). APPL1 localizes to a subset of Rab5-positive endosomes through its Bin–Amphiphysin–Rvs and PH domains, and it coordinates signaling pathways through its interaction with many signaling receptors and proteins through its PTB domain. This review discusses our current understanding of the role of APPL1 in signaling and trafficking, as well as highlights recent work into the function of APPL1 in cell migration and adhesion.

Sign in / Sign up

Export Citation Format

Share Document